Remove unused Lock import from KMA station source

.dockerignore

@@ -0,0 +1,31 @@
+.git
+.github
+.vscode
+.agent
+
+.env
+.env.*
+!.env.example
+!.env.secrets.example
+
+venv
+.venv
+.uv-cache
+.uv-python
+.pytest_cache
+.ruff_cache
+.mypy_cache
+__pycache__
+
+.npm-cache
+frontend/node_modules
+
+artifacts
+notebooks
+
+bot.log
+*.log
+
+extension.zip
+tmp_*.js
+tmp_*.html

.env.example

@@ -1,21 +1,204 @@
-# Telegram Bot
-TELEGRAM_BOT_TOKEN=your_bot_token_here
-TELEGRAM_CHAT_ID=your_chat_id_here
+# PolyWeather backend/bot minimal reproducible config
+# Full configuration guide:
+# docs/CONFIGURATION_ZH.md
+# Sensitive-only template:
+# .env.secrets.example
+
+########################################
+# 1) Runtime paths and base behavior
+########################################
+ENV=production
+LOG_LEVEL=INFO
+POLYWEATHER_MAP_URL=https://polyweather-pro.vercel.app/
+POLYWEATHER_RUNTIME_DATA_DIR=/var/lib/polyweather
+POLYWEATHER_DB_PATH=/var/lib/polyweather/polyweather.db
+OPEN_METEO_DISK_CACHE_PATH=/var/lib/polyweather/open_meteo_cache.json
+# Optional: host user/group mapping for Docker on Linux.
+# Windows / macOS can usually keep the defaults.
+UID=1000
+GID=1000
+POLYWEATHER_STATE_STORAGE_MODE=sqlite
+POLYWEATHER_PROMETHEUS_PORT=9090
+POLYWEATHER_ALERTMANAGER_PORT=9093
+POLYWEATHER_ALERT_RELAY_PORT=9099
+POLYWEATHER_GRAFANA_PORT=3001
+POLYWEATHER_GRAFANA_ADMIN_USER=admin
+POLYWEATHER_GRAFANA_ADMIN_PASSWORD=polyweather
+
+########################################
+# 2) Telegram bot minimal
+########################################
+TELEGRAM_BOT_TOKEN=
+TELEGRAM_CHAT_ID=
+TELEGRAM_CHAT_IDS=
+TELEGRAM_QUERY_TOPIC_CHAT_ID=
+TELEGRAM_QUERY_TOPIC_ID=
+TELEGRAM_QUERY_TOPIC_MAP=
+POLYWEATHER_BOT_GROUP_INVITE_URL=
+
+########################################
+# 3) Weather + cache
+########################################
+OPEN_METEO_CACHE_TTL_SEC=7200
+OPEN_METEO_ENSEMBLE_CACHE_TTL_SEC=7200
+OPEN_METEO_MULTI_MODEL_CACHE_TTL_SEC=7200
+OPEN_METEO_MULTI_MODEL_CACHE_VERSION=v2
+OPEN_METEO_RATE_LIMIT_COOLDOWN_SEC=900
+OPEN_METEO_RATE_CACHE_TTL_SEC=3600
+OPEN_METEO_MIN_CALL_INTERVAL_SEC=3
+METAR_CACHE_TTL_SEC=600
+METEOBLUE_CACHE_TTL_SEC=7200
+POLYWEATHER_LGBM_ENABLED=false
+POLYWEATHER_LGBM_MODEL_PATH=/app/artifacts/models/lgbm_daily_high.txt
+POLYWEATHER_LGBM_SCHEMA_PATH=/app/artifacts/models/lgbm_daily_high_schema.json
+POLYWEATHER_LGBM_MIN_HISTORY_POINTS=3
+
+########################################
+# 4) Auth / entitlement
+########################################
+POLYWEATHER_AUTH_ENABLED=false
+POLYWEATHER_AUTH_REQUIRED=false
+POLYWEATHER_AUTH_REQUIRE_SUBSCRIPTION=false
+POLYWEATHER_REQUIRE_ENTITLEMENT=false
+SUPABASE_URL=
+SUPABASE_ANON_KEY=
+SUPABASE_SERVICE_ROLE_KEY=
+SUPABASE_HTTP_TIMEOUT_SEC=8
+SUPABASE_AUTH_CACHE_TTL_SEC=30
+SUPABASE_SUB_CACHE_TTL_SEC=60
+POLYWEATHER_BACKEND_ENTITLEMENT_TOKEN=
+
+########################################
+# 5) Alerts / operations
+########################################
 TELEGRAM_ALERT_PUSH_ENABLED=true
 TELEGRAM_ALERT_PUSH_INTERVAL_SEC=300
 TELEGRAM_ALERT_PUSH_COOLDOWN_SEC=1800
 TELEGRAM_ALERT_MIN_TRIGGER_COUNT=2
 TELEGRAM_ALERT_MIN_SEVERITY=medium
-TELEGRAM_ALERT_CITIES=ankara,london,paris,seoul,toronto,buenos aires,wellington,new york,chicago,dallas,miami,atlanta,seattle,lucknow,sao paulo,munich
+TELEGRAM_ALERT_MISPRICING_ONLY=true
+TELEGRAM_ALERT_MISPRICING_INTERVAL_SEC=7200
+TELEGRAM_MARKET_FOCUS_DIGEST_ENABLED=true
+TELEGRAM_MARKET_FOCUS_DIGEST_INTERVAL_SEC=1800
+TELEGRAM_MARKET_FOCUS_DIGEST_TOP_N=5
+TELEGRAM_ALERT_CITIES=ankara,london,paris,seoul,hong kong,shanghai,singapore,tokyo,tel aviv,toronto,buenos aires,wellington,new york,chicago,dallas,miami,atlanta,seattle,lucknow,sao paulo,munich
+POLYWEATHER_MONITORING_ALERT_CHAT_IDS=
 
-# AI
-GROQ_API_KEY=your_groq_api_key_here
+########################################
+# 6) Frontend-facing shared values
+########################################
+NEXT_PUBLIC_WALLETCONNECT_PROJECT_ID=
+NEXT_PUBLIC_WALLETCONNECT_POLYGON_RPC_URL=https://polygon-bor-rpc.publicnode.com
 
-# Proxy Setting (optional)
-HTTPS_PROXY=http://127.0.0.1:7890
-HTTP_PROXY=http://127.0.0.1:7890
+########################################
+# 7) Optional modules
+########################################
 
-# Other Settings
-LOG_LEVEL=INFO
-ENV=production
-POLYWEATHER_MAP_URL=https://polyweather-pro.vercel.app/
+# Optional Groq commentary rewrite for intraday structure cards
+POLYWEATHER_GROQ_COMMENTARY_ENABLED=false
+GROQ_API_KEY=
+POLYWEATHER_GROQ_COMMENTARY_MODEL=openai/gpt-oss-20b
+POLYWEATHER_GROQ_COMMENTARY_TIMEOUT_SEC=8
+POLYWEATHER_GROQ_COMMENTARY_CACHE_TTL_SEC=1800
+
+# Weekly reward / leaderboard
+POLYWEATHER_WEEKLY_REWARD_ENABLED=true
+POLYWEATHER_WEEKLY_REWARD_TIMEZONE=Asia/Shanghai
+POLYWEATHER_WEEKLY_REWARD_SETTLE_WEEKDAY=1
+POLYWEATHER_WEEKLY_REWARD_SETTLE_HOUR=0
+POLYWEATHER_WEEKLY_REWARD_SETTLE_MINUTE=5
+POLYWEATHER_WEEKLY_REWARD_CHECK_INTERVAL_SEC=300
+POLYWEATHER_WEEKLY_REWARD_HTTP_TIMEOUT_SEC=10
+POLYWEATHER_WEEKLY_REWARD_ANNOUNCE_ENABLED=true
+
+# Group message points
+POLYWEATHER_BOT_MESSAGE_POINTS=4
+POLYWEATHER_BOT_MESSAGE_DAILY_CAP=40
+POLYWEATHER_BOT_MESSAGE_MIN_LENGTH=3
+POLYWEATHER_BOT_MESSAGE_COOLDOWN_SEC=30
+POLYWEATHER_BOT_CITY_QUERY_COST=1
+POLYWEATHER_BOT_DEB_QUERY_COST=1
+
+# Payments
+POLYWEATHER_PAYMENT_ENABLED=false
+POLYWEATHER_PAYMENT_CHAIN_ID=137
+POLYWEATHER_PAYMENT_RPC_URL=https://polygon-rpc.com
+POLYWEATHER_PAYMENT_RPC_URLS=https://polygon-rpc.com
+POLYWEATHER_PAYMENT_RECEIVER_CONTRACT=
+POLYWEATHER_PAYMENT_TOKEN_ADDRESS=0x2791Bca1f2de4661ED88A30C99A7a9449Aa84174
+POLYWEATHER_PAYMENT_TOKEN_DECIMALS=6
+POLYWEATHER_PAYMENT_ACCEPTED_TOKENS_JSON=
+POLYWEATHER_PAYMENT_CONFIRMATIONS=2
+POLYWEATHER_PAYMENT_INTENT_TTL_SEC=1800
+POLYWEATHER_PAYMENT_WALLET_CHALLENGE_TTL_SEC=600
+POLYWEATHER_PAYMENT_HTTP_TIMEOUT_SEC=10
+POLYWEATHER_PAYMENT_POLL_INTERVAL_SEC=4
+POLYWEATHER_PAYMENT_MAX_WAIT_SEC=50
+POLYWEATHER_PAYMENT_TELEGRAM_NOTIFY_ENABLED=true
+POLYWEATHER_PAYMENT_POINTS_ENABLED=true
+POLYWEATHER_PAYMENT_POINTS_PER_USDC=500
+POLYWEATHER_PAYMENT_POINTS_MAX_DISCOUNT_USDC=3
+POLYWEATHER_PAYMENT_ALLOWED_PLAN_CODES=pro_monthly
+POLYWEATHER_PAYMENT_PLAN_CATALOG_JSON=
+
+# Polymarket market scan
+POLYMARKET_MARKET_SCAN_ENABLED=true
+POLYMARKET_GAMMA_URL=https://gamma-api.polymarket.com
+POLYMARKET_CLOB_URL=https://clob.polymarket.com
+POLYMARKET_CHAIN_ID=137
+POLYMARKET_HTTP_TIMEOUT_SEC=8
+POLYMARKET_MARKET_CACHE_TTL_SEC=180
+POLYMARKET_PRICE_CACHE_TTL_SEC=10
+POLYMARKET_DISCOVERY_PAGES=6
+POLYMARKET_DISCOVERY_LIMIT=200
+POLYMARKET_SIGNAL_MIN_LIQUIDITY=500
+POLYMARKET_SIGNAL_EDGE_PCT=2
+
+# Polygon watcher
+POLYGON_WALLET_WATCH_ENABLED=false
+POLYGON_RPC_URL=https://polygon-rpc.com
+POLYGON_WALLET_WATCH_ADDRESSES=
+POLYGON_WALLET_WATCH_INTERVAL_SEC=8
+POLYGON_WALLET_WATCH_CONFIRMATIONS=2
+POLYGON_WALLET_WATCH_MAX_BLOCKS_PER_CYCLE=30
+POLYGON_WALLET_WATCH_SEEN_TTL_SEC=604800
+POLYGON_WALLET_WATCH_RPC_TIMEOUT_SEC=10
+POLYGON_WALLET_WATCH_TX_BASE=https://polygonscan.com/tx
+POLYGON_WALLET_WATCH_ADDR_BASE=https://polygonscan.com/address
+POLYGON_WALLET_WATCH_POLYMARKET_ONLY=true
+POLYGON_WALLET_WATCH_INCLUDE_DEFAULT_PM_CONTRACTS=true
+POLYGON_WALLET_WATCH_POLYMARKET_CONTRACTS=
+
+# Polymarket wallet activity (retired; replaced by market monitor digests + critical alerts)
+POLYMARKET_WALLET_ACTIVITY_ENABLED=false
+POLYMARKET_WALLET_ACTIVITY_USERS=
+POLYMARKET_WALLET_ACTIVITY_CHAT_ID=
+POLYMARKET_WALLET_ACTIVITY_CHAT_IDS=
+POLYMARKET_WALLET_ACTIVITY_TOPIC_CHAT_ID=
+POLYMARKET_WALLET_ACTIVITY_TOPIC_ID=
+POLYMARKET_WALLET_ACTIVITY_USER_ALIASES=
+POLYMARKET_WALLET_ACTIVITY_DATA_API_URL=https://data-api.polymarket.com
+POLYMARKET_WALLET_ACTIVITY_INTERVAL_SEC=20
+POLYMARKET_WALLET_ACTIVITY_TIMEOUT_SEC=10
+POLYMARKET_WALLET_ACTIVITY_MIN_SIZE_ABS=0.001
+POLYMARKET_WALLET_ACTIVITY_MIN_SIZE_DELTA=0.001
+POLYMARKET_WALLET_ACTIVITY_MIN_AVG_PRICE_DELTA=0.002
+POLYMARKET_WALLET_ACTIVITY_IMMEDIATE_ON_SIZE_DELTA=true
+POLYMARKET_WALLET_ACTIVITY_IMMEDIATE_SIZE_DELTA_MIN=0.001
+POLYMARKET_WALLET_ACTIVITY_IMMEDIATE_COOLDOWN_SEC=20
+POLYMARKET_WALLET_ACTIVITY_MAX_CHANGES_PER_MSG=5
+POLYMARKET_WALLET_ACTIVITY_NOTIFY_CLOSED=false
+POLYMARKET_WALLET_ACTIVITY_BOOTSTRAP_ALERT=false
+POLYMARKET_WALLET_ACTIVITY_LINK_PREVIEW=true
+POLYMARKET_WALLET_ACTIVITY_UPDATE_DEBOUNCE_SEC=30
+POLYMARKET_WALLET_ACTIVITY_UPDATE_MAX_HOLD_SEC=120
+POLYMARKET_WALLET_ACTIVITY_AVG_PRICE_SHOW_MIN=0.01
+POLYMARKET_WALLET_ACTIVITY_AVG_PRICE_SHOW_MAX=0.99
+POLYMARKET_WALLET_ACTIVITY_MIN_POSITION_VALUE_USD=0
+POLYMARKET_WALLET_ACTIVITY_MIN_VALUE_EXEMPT_USERS=
+
+########################################
+# 8) Optional proxies
+########################################
+HTTPS_PROXY=
+HTTP_PROXY=

.env.secrets.example

@@ -0,0 +1,44 @@
+# PolyWeather secrets-only template
+# Copy the required lines into your real `.env` / platform secret manager.
+# Never commit actual values.
+
+########################################
+# Telegram
+########################################
+TELEGRAM_BOT_TOKEN=
+
+########################################
+# Supabase
+########################################
+SUPABASE_URL=
+SUPABASE_ANON_KEY=
+SUPABASE_SERVICE_ROLE_KEY=
+NEXT_PUBLIC_SUPABASE_URL=
+NEXT_PUBLIC_SUPABASE_ANON_KEY=
+
+########################################
+# Entitlement / dashboard
+########################################
+POLYWEATHER_BACKEND_ENTITLEMENT_TOKEN=
+POLYWEATHER_DASHBOARD_ACCESS_TOKEN=
+
+########################################
+# Meteoblue / third-party APIs
+########################################
+METEOBLUE_API_KEY=
+
+########################################
+# Wallet / payments
+########################################
+NEXT_PUBLIC_WALLETCONNECT_PROJECT_ID=
+POLYWEATHER_PAYMENT_RECEIVER_CONTRACT=
+POLYWEATHER_PAYMENT_ACCEPTED_TOKENS_JSON=
+POLYWEATHER_PAYMENT_PLAN_CATALOG_JSON=
+
+########################################
+# Optional exchange / market secrets
+########################################
+POLYMARKET_API_KEY=
+POLYMARKET_SECRET_KEY=
+POLYMARKET_PASSPHRASE=
+POLYMARKET_WALLET_ADDRESS=

.github/workflows/ci.yml

@@ -0,0 +1,61 @@
+name: CI
+
+on:
+  push:
+    branches:
+      - main
+  pull_request:
+
+jobs:
+  python-quality:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.11"
+
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install -r requirements.txt -r requirements-dev.txt
+
+      - name: Ruff
+        run: python -m ruff check .
+
+      - name: Pytest
+        run: python -m pytest
+
+  frontend-quality:
+    runs-on: ubuntu-latest
+    defaults:
+      run:
+        working-directory: frontend
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+
+      - name: Set up Node
+        uses: actions/setup-node@v4
+        with:
+          node-version: "20"
+          cache: "npm"
+          cache-dependency-path: frontend/package-lock.json
+
+      - name: Install dependencies
+        run: npm ci
+
+      - name: Build
+        run: npm run build
+
+  docker-build:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+
+      - name: Build Docker image
+        run: docker build -t polyweather-ci .

.gitignore

@@ -2,6 +2,9 @@
 .env
 
 # Data and Logs
+data/*.db
+data/*.db-*
+data/*.db.*
 data/*.json
 data/logs/
 data/historical/
@@ -30,3 +33,12 @@ Thumbs.db
 frontend/node_modules/
 frontend/.next/
 frontend/.vercel/
+frontend/*.tsbuildinfo
+
+.npm-cache/
+.env.local
+.vercel/
+
+# Browser extension build artifacts
+/extension.zip
+/extension-*.zip

.vscode/settings.json

@@ -0,0 +1,5 @@
+{
+  "css.validate": false,
+  "scss.validate": false,
+  "less.validate": false
+}

CHANGELOG.md

@@ -0,0 +1,53 @@
+# Changelog
+
+## 1.5.1 - 2026-03-23
+
+- `/ops` 页面增加管理员守卫，前后端双层限制管理员访问
+- `/ops` 支持会员列表、支付异常单、用户查询、周榜和手动补分
+- `/ops` 支付异常单支持按原因筛选、标记已处理，并补充支付异常审计视图
+- 会员列表支持按 `user_id` 去重，并优先回补 Supabase Auth 邮箱/注册时间
+- 新增按邮箱补跑订阅恢复脚本 `scripts/reconcile_subscription_by_email.py`
+- 支付确认失败（如 `receiver_mismatch`）现在会明确落 `failed`，并写入 SQLite 审计事件
+- 支付前强制重新拉取 `/api/payments/config`，并校验最新地址、允许域名和当前支付上下文
+- 浏览器钱包选择补齐 EIP-6963 发现、稳定去重和绑定后账户状态即时刷新
+- 城市详情页新增 `官方参考 / Official Sources` 区块，覆盖主要城市的官方机构/机场/METAR 链接
+- “今日日内分析”结构解读改为后端同源动态短评，并统一网页与 Bot 解释口径
+- 台北主结算源切换到 `NOAA RCTP`，按最终质控后的最高整度摄氏值展示和说明
+- 浏览器插件同步台北 `NOAA RCTP` 结算参考标签和说明
+- `/ops` 手机端收口为卡片化视图，保留桌面表格
+- 账户中心补充本周积分显示，`weekly_points` 与周排行同屏展示
+- Dashboard 历史对账补充“峰值前 12 小时 DEB 参考（近似）”卡片
+- 历史图不再错误混入 `settlement_history` 实测，历史样本仅按可比较样本统计
+- 新增 `scripts/backfill_recent_daily_actuals_from_metar.py`，支持为缺失 `daily_records` 的 METAR 城市补最近 14 天 `actual_high`
+- 历史接口对新接入的 METAR 城市增加自动 bootstrap，避免新增城市历史页整块空白
+- 香港历史/日内展示继续坚持 `HKO` 官方口径，不再 fallback 到 `VHHH METAR` 连续线
+- 香港 HKO 当天官方点位不再落单独 JSON，统一写入 runtime state
+- 今日日内结构信号按城市本地时间与峰值窗口分析，不再只看固定下午时段
+- 新增高空结构信号：冲高环境、压温风险、午后扰动、冲高效率，并提供中英文说明
+- 新增交易动作卡：结合高空结构、市场拥挤度与 `edge_percent` 输出 `偏暖侧 / 偏谨慎 / 先观察`
+- 非香港机场城市新增 `TAF` 接入，支持 `FM / TEMPO / BECMG / PROB30/40` 时间片解析
+- 温度走势图新增 `TAF 时段 / TAF Timing` 标记，并在 tooltip 中显示对应时段摘要
+- `TAF` 信号与 `market_signal / edge_percent` 联动进入交易动作，提示更贴近交易语境
+- `TAF` 展示词已改成普通用户可读版本：`基础时段 / 明确切换 / 临时波动 / 逐步转变`
+- 日内结构总摘要补充“TAF 未新增压温不等于继续升温”的解释，避免误读
+- 浏览器插件多日预报改为 `DEB` 优先，基础判断卡补充方向、置信度与原因，并统一引流到主站首页
+
+
+## Unreleased
+
+## 1.5.0 - 2026-03-21
+
+- 运行态状态与缓存支持 SQLite 渐进迁移，新增 `POLYWEATHER_STATE_STORAGE_MODE=file|dual|sqlite`
+- 新增 `/healthz`、`/api/system/status`、`/metrics`
+- 新增支付运行态接口 `/api/payments/runtime`
+- 支付侧新增 SQLite 审计事件、事件重放脚本与多 RPC 容灾支持
+- 新增支付静态审计脚本与 V2 合约升级草案
+- 统一周积分显示口径，`/top` 中“我的状态”改为累计发言/本周排名/本周积分
+- 文档同步更新为 2026-03-20 当前状态
+
+## 1.4.0 - 2026-03-14
+
+- 统一收费阶段产品口径，发布 PolyWeather Pro `v1.4.0`
+- 前端交付覆盖账户、支付、权限展示与缓存策略
+- 支付链路支持 intent -> submit -> confirm 与自动补单
+- 文档统一切换到单一版本源管理

Dockerfile

@@ -6,6 +6,8 @@ WORKDIR /app
 # 设置环境变量
 ENV PYTHONDONTWRITEBYTECODE=1 \
     PYTHONUNBUFFERED=1 \
+    PIP_DISABLE_PIP_VERSION_CHECK=1 \
+    PIP_ROOT_USER_ACTION=ignore \
     TZ=UTC
 
 # 安装系统依赖 (如果有必要的包可以取消注释)
@@ -15,7 +17,7 @@ ENV PYTHONDONTWRITEBYTECODE=1 \
 COPY requirements.txt .
 
 # 安装 Python 依赖
-RUN pip install --no-cache-dir -r requirements.txt
+RUN pip install --no-cache-dir --prefer-binary -r requirements.txt
 
 # 复制项目代码
 COPY . .

FRONTEND_REDESIGN_REPORT.md

@@ -0,0 +1,82 @@
+# 前端交付与重构报告（v1.5.1）
+
+最后更新：`2026-03-14`
+
+## 1. 报告目的
+
+说明当前线上前端（`frontend/`）在收费阶段的实际交付状态。
+
+## 2. 当前前端架构
+
+```mermaid
+flowchart LR
+    B["Browser"] --> N["Next.js App Router (Vercel)"]
+    N --> RH["Route Handlers /api/*"]
+    RH --> F["FastAPI (VPS)"]
+
+    N --> STORE["Dashboard Store"]
+    STORE --> MAP["MapCanvas"]
+    STORE --> SIDEBAR["CitySidebar"]
+    STORE --> PANEL["DetailPanel + Modal"]
+    STORE --> ACCOUNT["Account Center + Pro Overlay"]
+```
+
+## 3. 已落地能力
+
+### 3.1 信息架构与交互
+
+- 风险分组侧栏折叠（持久化）。
+- 选中城市状态持久化。
+- 今日分析、历史对账、未来日期分析联动。
+
+### 3.2 收费相关
+
+- 账户中心（登录态、积分、订阅状态、钱包管理）。
+- Pro 解锁浮层（套餐、积分抵扣、FAQ、社群入口）。
+- 钱包绑定：浏览器扩展钱包 + WalletConnect 扫码。
+- 支付流程：create intent -> submit -> confirm。
+- `confirm pending` 时自动轮询 intent 状态，确认后自动刷新订阅态。
+
+### 3.3 缓存与性能
+
+- BFF `ETag/304`：`cities` / `summary` / `history`。
+- `summary?force_refresh=true` => `no-store`。
+- `sessionStorage` + in-flight 去重。
+- `localStorage`：选中城市、侧栏折叠状态。
+
+### 3.4 可访问性与稳定性
+
+- 详情面板 `inert + blur` 焦点冲突修复。
+- 关键支付错误文案标准化（用户取消、gas 不足、pending）。
+
+## 4. 当前明确未做
+
+- 离线能力（Service Worker / IndexedDB）
+- 前端级财务报表与退款后台（后端/运营侧）
+
+## 5. 验收建议
+
+### 5.1 前端构建
+
+```bash
+cd frontend
+npm run build
+```
+
+### 5.2 缓存验收
+
+```bash
+./scripts/validate_frontend_cache.sh "https://polyweather-pro.vercel.app"
+```
+
+### 5.3 支付验收
+
+- 绑定钱包
+- 创建 intent
+- 发交易
+- 验证 `intent` 状态从 `submitted -> confirmed`
+- 校验账户页订阅状态更新
+
+## 6. 结论
+
+前端已具备收费阶段的核心能力（账户、支付、权限展示、状态回收），可支持持续商业迭代。

LICENSE

@@ -1,21 +1,661 @@
-MIT License
+                    GNU AFFERO GENERAL PUBLIC LICENSE
+                       Version 3, 19 November 2007
 
-Copyright (c) 2026 Yuanzhen Yang (yangyuan-zhen)
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
+ of this license document, but changing it is not allowed.
 
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
+                            Preamble
 
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
+  The GNU Affero General Public License is a free, copyleft license for
+software and other kinds of works, specifically designed to ensure
+cooperation with the community in the case of network server software.
 
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.
+  The licenses for most software and other practical works are designed
+to take away your freedom to share and change the works.  By contrast,
+our General Public Licenses are intended to guarantee your freedom to
+share and change all versions of a program--to make sure it remains free
+software for all its users.
+
+  When we speak of free software, we are referring to freedom, not
+price.  Our General Public Licenses are designed to make sure that you
+have the freedom to distribute copies of free software (and charge for
+them if you wish), that you receive source code or can get it if you
+want it, that you can change the software or use pieces of it in new
+free programs, and that you know you can do these things.
+
+  Developers that use our General Public Licenses protect your rights
+with two steps: (1) assert copyright on the software, and (2) offer
+you this License which gives you legal permission to copy, distribute
+and/or modify the software.
+
+  A secondary benefit of defending all users' freedom is that
+improvements made in alternate versions of the program, if they
+receive widespread use, become available for other developers to
+incorporate.  Many developers of free software are heartened and
+encouraged by the resulting cooperation.  However, in the case of
+software used on network servers, this result may fail to come about.
+The GNU General Public License permits making a modified version and
+letting the public access it on a server without ever releasing its
+source code to the public.
+
+  The GNU Affero General Public License is designed specifically to
+ensure that, in such cases, the modified source code becomes available
+to the community.  It requires the operator of a network server to
+provide the source code of the modified version running there to the
+users of that server.  Therefore, public use of a modified version, on
+a publicly accessible server, gives the public access to the source
+code of the modified version.
+
+  An older license, called the Affero General Public License and
+published by Affero, was designed to accomplish similar goals.  This is
+a different license, not a version of the Affero GPL, but Affero has
+released a new version of the Affero GPL which permits relicensing under
+this license.
+
+  The precise terms and conditions for copying, distribution and
+modification follow.
+
+                       TERMS AND CONDITIONS
+
+  0. Definitions.
+
+  "This License" refers to version 3 of the GNU Affero General Public License.
+
+  "Copyright" also means copyright-like laws that apply to other kinds of
+works, such as semiconductor masks.
+
+  "The Program" refers to any copyrightable work licensed under this
+License.  Each licensee is addressed as "you".  "Licensees" and
+"recipients" may be individuals or organizations.
+
+  To "modify" a work means to copy from or adapt all or part of the work
+in a fashion requiring copyright permission, other than the making of an
+exact copy.  The resulting work is called a "modified version" of the
+earlier work or a work "based on" the earlier work.
+
+  A "covered work" means either the unmodified Program or a work based
+on the Program.
+
+  To "propagate" a work means to do anything with it that, without
+permission, would make you directly or secondarily liable for
+infringement under applicable copyright law, except executing it on a
+computer or modifying a private copy.  Propagation includes copying,
+distribution (with or without modification), making available to the
+public, and in some countries other activities as well.
+
+  To "convey" a work means any kind of propagation that enables other
+parties to make or receive copies.  Mere interaction with a user through
+a computer network, with no transfer of a copy, is not conveying.
+
+  An interactive user interface displays "Appropriate Legal Notices"
+to the extent that it includes a convenient and prominently visible
+feature that (1) displays an appropriate copyright notice, and (2)
+tells the user that there is no warranty for the work (except to the
+extent that warranties are provided), that licensees may convey the
+work under this License, and how to view a copy of this License.  If
+the interface presents a list of user commands or options, such as a
+menu, a prominent item in the list meets this criterion.
+
+  1. Source Code.
+
+  The "source code" for a work means the preferred form of the work
+for making modifications to it.  "Object code" means any non-source
+form of a work.
+
+  A "Standard Interface" means an interface that either is an official
+standard defined by a recognized standards body, or, in the case of
+interfaces specified for a particular programming language, one that
+is widely used among developers working in that language.
+
+  The "System Libraries" of an executable work include anything, other
+than the work as a whole, that (a) is included in the normal form of
+packaging a Major Component, but which is not part of that Major
+Component, and (b) serves only to enable use of the work with that
+Major Component, or to implement a Standard Interface for which an
+implementation is available to the public in source code form.  A
+"Major Component", in this context, means a major essential component
+(kernel, window system, and so on) of the specific operating system
+(if any) on which the executable work runs, or a compiler used to
+produce the work, or an object code interpreter used to run it.
+
+  The "Corresponding Source" for a work in object code form means all
+the source code needed to generate, install, and (for an executable
+work) run the object code and to modify the work, including scripts to
+control those activities.  However, it does not include the work's
+System Libraries, or general-purpose tools or generally available free
+programs which are used unmodified in performing those activities but
+which are not part of the work.  For example, Corresponding Source
+includes interface definition files associated with source files for
+the work, and the source code for shared libraries and dynamically
+linked subprograms that the work is specifically designed to require,
+such as by intimate data communication or control flow between those
+subprograms and other parts of the work.
+
+  The Corresponding Source need not include anything that users
+can regenerate automatically from other parts of the Corresponding
+Source.
+
+  The Corresponding Source for a work in source code form is that
+same work.
+
+  2. Basic Permissions.
+
+  All rights granted under this License are granted for the term of
+copyright on the Program, and are irrevocable provided the stated
+conditions are met.  This License explicitly affirms your unlimited
+permission to run the unmodified Program.  The output from running a
+covered work is covered by this License only if the output, given its
+content, constitutes a covered work.  This License acknowledges your
+rights of fair use or other equivalent, as provided by copyright law.
+
+  You may make, run and propagate covered works that you do not
+convey, without conditions so long as your license otherwise remains
+in force.  You may convey covered works to others for the sole purpose
+of having them make modifications exclusively for you, or provide you
+with facilities for running those works, provided that you comply with
+the terms of this License in conveying all material for which you do
+not control copyright.  Those thus making or running the covered works
+for you must do so exclusively on your behalf, under your direction
+and control, on terms that prohibit them from making any copies of
+your copyrighted material outside their relationship with you.
+
+  Conveying under any other circumstances is permitted solely under
+the conditions stated below.  Sublicensing is not allowed; section 10
+makes it unnecessary.
+
+  3. Protecting Users' Legal Rights From Anti-Circumvention Law.
+
+  No covered work shall be deemed part of an effective technological
+measure under any applicable law fulfilling obligations under article
+11 of the WIPO copyright treaty adopted on 20 December 1996, or
+similar laws prohibiting or restricting circumvention of such
+measures.
+
+  When you convey a covered work, you waive any legal power to forbid
+circumvention of technological measures to the extent such circumvention
+is effected by exercising rights under this License with respect to
+the covered work, and you disclaim any intention to limit operation or
+modification of the work as a means of enforcing, against the work's
+users, your or third parties' legal rights to forbid circumvention of
+technological measures.
+
+  4. Conveying Verbatim Copies.
+
+  You may convey verbatim copies of the Program's source code as you
+receive it, in any medium, provided that you conspicuously and
+appropriately publish on each copy an appropriate copyright notice;
+keep intact all notices stating that this License and any
+non-permissive terms added in accord with section 7 apply to the code;
+keep intact all notices of the absence of any warranty; and give all
+recipients a copy of this License along with the Program.
+
+  You may charge any price or no price for each copy that you convey,
+and you may offer support or warranty protection for a fee.
+
+  5. Conveying Modified Source Versions.
+
+  You may convey a work based on the Program, or the modifications to
+produce it from the Program, in the form of source code under the
+terms of section 4, provided that you also meet all of these conditions:
+
+    a) The work must carry prominent notices stating that you modified
+    it, and giving a relevant date.
+
+    b) The work must carry prominent notices stating that it is
+    released under this License and any conditions added under section
+    7.  This requirement modifies the requirement in section 4 to
+    "keep intact all notices".
+
+    c) You must license the entire work, as a whole, under this
+    License to anyone who comes into possession of a copy.  This
+    License will therefore apply, along with any applicable section 7
+    additional terms, to the whole of the work, and all its parts,
+    regardless of how they are packaged.  This License gives no
+    permission to license the work in any other way, but it does not
+    invalidate such permission if you have separately received it.
+
+    d) If the work has interactive user interfaces, each must display
+    Appropriate Legal Notices; however, if the Program has interactive
+    interfaces that do not display Appropriate Legal Notices, your
+    work need not make them do so.
+
+  A compilation of a covered work with other separate and independent
+works, which are not by their nature extensions of the covered work,
+and which are not combined with it such as to form a larger program,
+in or on a volume of a storage or distribution medium, is called an
+"aggregate" if the compilation and its resulting copyright are not
+used to limit the access or legal rights of the compilation's users
+beyond what the individual works permit.  Inclusion of a covered work
+in an aggregate does not cause this License to apply to the other
+parts of the aggregate.
+
+  6. Conveying Non-Source Forms.
+
+  You may convey a covered work in object code form under the terms
+of sections 4 and 5, provided that you also convey the
+machine-readable Corresponding Source under the terms of this License,
+in one of these ways:
+
+    a) Convey the object code in, or embodied in, a physical product
+    (including a physical distribution medium), accompanied by the
+    Corresponding Source fixed on a durable physical medium
+    customarily used for software interchange.
+
+    b) Convey the object code in, or embodied in, a physical product
+    (including a physical distribution medium), accompanied by a
+    written offer, valid for at least three years and valid for as
+    long as you offer spare parts or customer support for that product
+    model, to give anyone who possesses the object code either (1) a
+    copy of the Corresponding Source for all the software in the
+    product that is covered by this License, on a durable physical
+    medium customarily used for software interchange, for a price no
+    more than your reasonable cost of physically performing this
+    conveying of source, or (2) access to copy the
+    Corresponding Source from a network server at no charge.
+
+    c) Convey individual copies of the object code with a copy of the
+    written offer to provide the Corresponding Source.  This
+    alternative is allowed only occasionally and noncommercially, and
+    only if you received the object code with such an offer, in accord
+    with subsection 6b.
+
+    d) Convey the object code by offering access from a designated
+    place (gratis or for a charge), and offer equivalent access to the
+    Corresponding Source in the same way through the same place at no
+    further charge.  You need not require recipients to copy the
+    Corresponding Source along with the object code.  If the place to
+    copy the object code is a network server, the Corresponding Source
+    may be on a different server (operated by you or a third party)
+    that supports equivalent copying facilities, provided you maintain
+    clear directions next to the object code saying where to find the
+    Corresponding Source.  Regardless of what server hosts the
+    Corresponding Source, you remain obligated to ensure that it is
+    available for as long as needed to satisfy these requirements.
+
+    e) Convey the object code using peer-to-peer transmission, provided
+    you inform other peers where the object code and Corresponding
+    Source of the work are being offered to the general public at no
+    charge under subsection 6d.
+
+  A separable portion of the object code, whose source code is excluded
+from the Corresponding Source as a System Library, need not be
+included in conveying the object code work.
+
+  A "User Product" is either (1) a "consumer product", which means any
+tangible personal property which is normally used for personal, family,
+or household purposes, or (2) anything designed or sold for incorporation
+into a dwelling.  In determining whether a product is a consumer product,
+doubtful cases shall be resolved in favor of coverage.  For a particular
+product received by a particular user, "normally used" refers to a
+typical or common use of that class of product, regardless of the status
+of the particular user or of the way in which the particular user
+actually uses, or expects or is expected to use, the product.  A product
+is a consumer product regardless of whether the product has substantial
+commercial, industrial or non-consumer uses, unless such uses represent
+the only significant mode of use of the product.
+
+  "Installation Information" for a User Product means any methods,
+procedures, authorization keys, or other information required to install
+and execute modified versions of a covered work in that User Product from
+a modified version of its Corresponding Source.  The information must
+suffice to ensure that the continued functioning of the modified object
+code is in no case prevented or interfered with solely because
+modification has been made.
+
+  If you convey an object code work under this section in, or with, or
+specifically for use in, a User Product, and the conveying occurs as
+part of a transaction in which the right of possession and use of the
+User Product is transferred to the recipient in perpetuity or for a
+fixed term (regardless of how the transaction is characterized), the
+Corresponding Source conveyed under this section must be accompanied
+by the Installation Information.  But this requirement does not apply
+if neither you nor any third party retains the ability to install
+modified object code on the User Product (for example, the work has
+been installed in ROM).
+
+  The requirement to provide Installation Information does not include a
+requirement to continue to provide support service, warranty, or updates
+for a work that has been modified or installed by the recipient, or for
+the User Product in which it has been modified or installed.  Access to a
+network may be denied when the modification itself materially and
+adversely affects the operation of the network or violates the rules and
+protocols for communication across the network.
+
+  Corresponding Source conveyed, and Installation Information provided,
+in accord with this section must be in a format that is publicly
+documented (and with an implementation available to the public in
+source code form), and must require no special password or key for
+unpacking, reading or copying.
+
+  7. Additional Terms.
+
+  "Additional permissions" are terms that supplement the terms of this
+License by making exceptions from one or more of its conditions.
+Additional permissions that are applicable to the entire Program shall
+be treated as though they were included in this License, to the extent
+that they are valid under applicable law.  If additional permissions
+apply only to part of the Program, that part may be used separately
+under those permissions, but the entire Program remains governed by
+this License without regard to the additional permissions.
+
+  When you convey a copy of a covered work, you may at your option
+remove any additional permissions from that copy, or from any part of
+it.  (Additional permissions may be written to require their own
+removal in certain cases when you modify the work.)  You may place
+additional permissions on material, added by you to a covered work,
+for which you have or can give appropriate copyright permission.
+
+  Notwithstanding any other provision of this License, for material you
+add to a covered work, you may (if authorized by the copyright holders of
+that material) supplement the terms of this License with terms:
+
+    a) Disclaiming warranty or limiting liability differently from the
+    terms of sections 15 and 16 of this License; or
+
+    b) Requiring preservation of specified reasonable legal notices or
+    author attributions in that material or in the Appropriate Legal
+    Notices displayed by works containing it; or
+
+    c) Prohibiting misrepresentation of the origin of that material, or
+    requiring that modified versions of such material be marked in
+    reasonable ways as different from the original version; or
+
+    d) Limiting the use for publicity purposes of names of licensors or
+    authors of the material; or
+
+    e) Declining to grant rights under trademark law for use of some
+    trade names, trademarks, or service marks; or
+
+    f) Requiring indemnification of licensors and authors of that
+    material by anyone who conveys the material (or modified versions of
+    it) with contractual assumptions of liability to the recipient, for
+    any liability that these contractual assumptions directly impose on
+    those licensors and authors.
+
+  All other non-permissive additional terms are considered "further
+restrictions" within the meaning of section 10.  If the Program as you
+received it, or any part of it, contains a notice stating that it is
+governed by this License along with a term that is a further
+restriction, you may remove that term.  If a license document contains
+a further restriction but permits relicensing or conveying under this
+License, you may add to a covered work material governed by the terms
+of that license document, provided that the further restriction does
+not survive such relicensing or conveying.
+
+  If you add terms to a covered work in accord with this section, you
+must place, in the relevant source files, a statement of the
+additional terms that apply to those files, or a notice indicating
+where to find the applicable terms.
+
+  Additional terms, permissive or non-permissive, may be stated in the
+form of a separately written license, or stated as exceptions;
+the above requirements apply either way.
+
+  8. Termination.
+
+  You may not propagate or modify a covered work except as expressly
+provided under this License.  Any attempt otherwise to propagate or
+modify it is void, and will automatically terminate your rights under
+this License (including any patent licenses granted under the third
+paragraph of section 11).
+
+  However, if you cease all violation of this License, then your
+license from a particular copyright holder is reinstated (a)
+provisionally, unless and until the copyright holder explicitly and
+finally terminates your license, and (b) permanently, if the copyright
+holder fails to notify you of the violation by some reasonable means
+prior to 60 days after the cessation.
+
+  Moreover, your license from a particular copyright holder is
+reinstated permanently if the copyright holder notifies you of the
+violation by some reasonable means, this is the first time you have
+received notice of violation of this License (for any work) from that
+copyright holder, and you cure the violation prior to 30 days after
+your receipt of the notice.
+
+  Termination of your rights under this section does not terminate the
+licenses of parties who have received copies or rights from you under
+this License.  If your rights have been terminated and not permanently
+reinstated, you do not qualify to receive new licenses for the same
+material under section 10.
+
+  9. Acceptance Not Required for Having Copies.
+
+  You are not required to accept this License in order to receive or
+run a copy of the Program.  Ancillary propagation of a covered work
+occurring solely as a consequence of using peer-to-peer transmission
+to receive a copy likewise does not require acceptance.  However,
+nothing other than this License grants you permission to propagate or
+modify any covered work.  These actions infringe copyright if you do
+not accept this License.  Therefore, by modifying or propagating a
+covered work, you indicate your acceptance of this License to do so.
+
+  10. Automatic Licensing of Downstream Recipients.
+
+  Each time you convey a covered work, the recipient automatically
+receives a license from the original licensors, to run, modify and
+propagate that work, subject to this License.  You are not responsible
+for enforcing compliance by third parties with this License.
+
+  An "entity transaction" is a transaction transferring control of an
+organization, or substantially all assets of one, or subdividing an
+organization, or merging organizations.  If propagation of a covered
+work results from an entity transaction, each party to that
+transaction who receives a copy of the work also receives whatever
+licenses to the work the party's predecessor in interest had or could
+give under the previous paragraph, plus a right to possession of the
+Corresponding Source of the work from the predecessor in interest, if
+the predecessor has it or can get it with reasonable efforts.
+
+  You may not impose any further restrictions on the exercise of the
+rights granted or affirmed under this License.  For example, you may
+not impose a license fee, royalty, or other charge for exercise of
+rights granted under this License, and you may not initiate litigation
+(including a cross-claim or counterclaim in a lawsuit) alleging that
+any patent claim is infringed by making, using, selling, offering for
+sale, or importing the Program or any portion of it.
+
+  11. Patents.
+
+  A "contributor" is a copyright holder who authorizes use under this
+License of the Program or a work on which the Program is based.  The
+work thus licensed is called the contributor's "contributor version".
+
+  A contributor's "essential patent claims" are all patent claims
+owned or controlled by the contributor, whether already acquired or
+hereafter acquired, that would be infringed by some manner, permitted
+by this License, of making, using, or selling its contributor version,
+but do not include claims that would be infringed only as a
+consequence of further modification of the contributor version.  For
+purposes of this definition, "control" includes the right to grant
+patent sublicenses in a manner consistent with the requirements of
+this License.
+
+  Each contributor grants you a non-exclusive, worldwide, royalty-free
+patent license under the contributor's essential patent claims, to
+make, use, sell, offer for sale, import and otherwise run, modify and
+propagate the contents of its contributor version.
+
+  In the following three paragraphs, a "patent license" is any express
+agreement or commitment, however denominated, not to enforce a patent
+(such as an express permission to practice a patent or covenant not to
+sue for patent infringement).  To "grant" such a patent license to a
+party means to make such an agreement or commitment not to enforce a
+patent against the party.
+
+  If you convey a covered work, knowingly relying on a patent license,
+and the Corresponding Source of the work is not available for anyone
+to copy, free of charge and under the terms of this License, through a
+publicly available network server or other readily accessible means,
+then you must either (1) cause the Corresponding Source to be so
+available, or (2) arrange to deprive yourself of the benefit of the
+patent license for this particular work, or (3) arrange, in a manner
+consistent with the requirements of this License, to extend the patent
+license to downstream recipients.  "Knowingly relying" means you have
+actual knowledge that, but for the patent license, your conveying the
+covered work in a country, or your recipient's use of the covered work
+in a country, would infringe one or more identifiable patents in that
+country that you have reason to believe are valid.
+
+  If, pursuant to or in connection with a single transaction or
+arrangement, you convey, or propagate by procuring conveyance of, a
+covered work, and grant a patent license to some of the parties
+receiving the covered work authorizing them to use, propagate, modify
+or convey a specific copy of the covered work, then the patent license
+you grant is automatically extended to all recipients of the covered
+work and works based on it.
+
+  A patent license is "discriminatory" if it does not include within
+the scope of its coverage, prohibits the exercise of, or is
+conditioned on the non-exercise of one or more of the rights that are
+specifically granted under this License.  You may not convey a covered
+work if you are a party to an arrangement with a third party that is
+in the business of distributing software, under which you make payment
+to the third party based on the extent of your activity of conveying
+the work, and under which the third party grants, to any of the
+parties who would receive the covered work from you, a discriminatory
+patent license (a) in connection with copies of the covered work
+conveyed by you (or copies made from those copies), or (b) primarily
+for and in connection with specific products or compilations that
+contain the covered work, unless you entered into that arrangement,
+or that patent license was granted, prior to 28 March 2007.
+
+  Nothing in this License shall be construed as excluding or limiting
+any implied license or other defenses to infringement that may
+otherwise be available to you under applicable patent law.
+
+  12. No Surrender of Others' Freedom.
+
+  If conditions are imposed on you (whether by court order, agreement or
+otherwise) that contradict the conditions of this License, they do not
+excuse you from the conditions of this License.  If you cannot convey a
+covered work so as to satisfy simultaneously your obligations under this
+License and any other pertinent obligations, then as a consequence you may
+not convey it at all.  For example, if you agree to terms that obligate you
+to collect a royalty for further conveying from those to whom you convey
+the Program, the only way you could satisfy both those terms and this
+License would be to refrain entirely from conveying the Program.
+
+  13. Remote Network Interaction; Use with the GNU General Public License.
+
+  Notwithstanding any other provision of this License, if you modify the
+Program, your modified version must prominently offer all users
+interacting with it remotely through a computer network (if your version
+supports such interaction) an opportunity to receive the Corresponding
+Source of your version by providing access to the Corresponding Source
+from a network server at no charge, through some standard or customary
+means of facilitating copying of software.  This Corresponding Source
+shall include the Corresponding Source for any work covered by version 3
+of the GNU General Public License that is incorporated pursuant to the
+following paragraph.
+
+  Notwithstanding any other provision of this License, you have
+permission to link or combine any covered work with a work licensed
+under version 3 of the GNU General Public License into a single
+combined work, and to convey the resulting work.  The terms of this
+License will continue to apply to the part which is the covered work,
+but the work with which it is combined will remain governed by version
+3 of the GNU General Public License.
+
+  14. Revised Versions of this License.
+
+  The Free Software Foundation may publish revised and/or new versions of
+the GNU Affero General Public License from time to time.  Such new versions
+will be similar in spirit to the present version, but may differ in detail to
+address new problems or concerns.
+
+  Each version is given a distinguishing version number.  If the
+Program specifies that a certain numbered version of the GNU Affero General
+Public License "or any later version" applies to it, you have the
+option of following the terms and conditions either of that numbered
+version or of any later version published by the Free Software
+Foundation.  If the Program does not specify a version number of the
+GNU Affero General Public License, you may choose any version ever published
+by the Free Software Foundation.
+
+  If the Program specifies that a proxy can decide which future
+versions of the GNU Affero General Public License can be used, that proxy's
+public statement of acceptance of a version permanently authorizes you
+to choose that version for the Program.
+
+  Later license versions may give you additional or different
+permissions.  However, no additional obligations are imposed on any
+author or copyright holder as a result of your choosing to follow a
+later version.
+
+  15. Disclaimer of Warranty.
+
+  THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
+APPLICABLE LAW.  EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
+HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
+OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
+THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
+PURPOSE.  THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
+IS WITH YOU.  SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
+ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
+
+  16. Limitation of Liability.
+
+  IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
+WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
+THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
+GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
+USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
+DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+  17. Interpretation of Sections 15 and 16.
+
+  If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    <one line to give the program's name and a brief idea of what it does.>
+    Copyright (C) <year>  <name of author>
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU Affero General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU Affero General Public License for more details.
+
+    You should have received a copy of the GNU Affero General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+  If your software can interact with users remotely through a computer
+network, you should also make sure that it provides a way for users to
+get its source.  For example, if your program is a web application, its
+interface could display a "Source" link that leads users to an archive
+of the code.  There are many ways you could offer source, and different
+solutions will be better for different programs; see section 13 for the
+specific requirements.
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU AGPL, see
+<https://www.gnu.org/licenses/>.

README.md

@@ -1,147 +1,194 @@
-# 🌡️ PolyWeather Pro
+# PolyWeather Pro
 
-> **Professional Weather Intelligence System** —— Specialized in edge data collection, DEB smart blending, and real-time decision alerts.
+Production weather-intelligence stack for temperature settlement markets.
 
----
+Official dashboard: [polyweather-pro.vercel.app](https://polyweather-pro.vercel.app/)
+中文说明: [README_ZH.md](README_ZH.md)
 
-## 💎 Project Vision
+Public docs center: `/docs/intro` on the main site (bilingual product documentation, including intraday signals, TAF, settlement sources, history, and extension).
 
-PolyWeather is a specialized intelligence system built for **Polymarket** high-stakes participants. We don't just provide weather forecasts; we aggregate data from top-tier global meteorological sources, apply our proprietary **DEB (Dynamic Error Balancing)** algorithm, and deliver **market-shifting alerts** at critical decision nodes.
+## Product Screenshots
 
----
+### Global Dashboard
 
-## 🏗️ Production Architecture
+![PolyWeather global dashboard](docs/images/demo_map.png)
 
-This project utilizes a production-grade decoupled architecture for high availability:
+### City Analysis (Ankara)
 
-- **Frontend**: A **Next.js** interactive dashboard deployed on **Vercel**.
-- **Backend API**: A **FastAPI** service running on a VPS, providing low-latency data access.
-- **Bot & Alert Heartbeat**: A **Telegram Bot** running on a VPS, executing minute-level global scans and push notifications.
+![PolyWeather Ankara analysis](docs/images/demo_ankara.png)
 
-🔗 **Official Visit**: [polyweather-pro.vercel.app](https://polyweather-pro.vercel.app/)
+## Product Status (2026-03-24)
 
----
+- Subscription live: `Pro Monthly 5 USDC`.
+- Points redemption live: `500 points = 1 USDC`, max `3 USDC` off.
+- Onchain checkout live: Polygon contract checkout (USDC / USDC.e).
+- Auto-reconciliation live: event listener + periodic confirm loop.
+- Ops dashboard live: `/ops` for memberships, leaderboard, manual point grants, and payment incident triage.
+- Lightweight observability live: `/healthz`, `/api/system/status`, `/metrics`.
+- Runtime state, cache, and core offline training/backfill flows now use SQLite as the primary path; legacy JSON/JSONL files remain only for migration, export, and explicit fallback input.
+- EMOS/CRPS pipeline is integrated in `shadow` mode with rollout gating.
+- Intraday structural signal is now peak-window aware and bilingual (`zh-CN` / `en-US`).
+- Non-Hong Kong airport cities now ingest `TAF` and parse `FM / TEMPO / BECMG / PROB30/40`.
+- Temperature chart now overlays `TAF Timing` markers near the expected peak window.
+- Trade cue now combines upper-air structure, `TAF`, market crowding, and `edge_percent`.
+- Browser extension now uses `DEB` for multi-day forecast and stays positioned as a lightweight lead-in to the main site.
 
-## 🖼️ Preview & Interaction
+## License & Commercial Boundary
 
-<p align="center">
-  <img src="docs/images/demo_ankara.png" alt="PolyWeather Demo - Ankara Live Analysis" width="450">
-  <br>
-  <em>📊 <b>Deep Query View</b>: DEB Blended Forecast + Settlement Probability + Groq AI Expert Advice</em>
-</p>
+This repository is licensed under **GNU AGPL-3.0 only** from `2026-03-30` onward.
 
-<p align="center">
-  <img src="./docs/images/demo_map.png" alt="PolyWeather Web Map" width="850">
-  <br>
-  <em>🗺️ <b>Omni-Dashboard</b>: Real-time global heatmaps + array-style data visualization</em>
-</p>
+- Public in repo: weather aggregation, core analysis, dashboard, bot baseline, and standard payment flow.
+- Not included in this repository: private production data, internal operating thresholds, commercial risk rules, pricing strategy details, and growth tooling.
+- Trademark, brand, domain, production databases, and hosted-service operations are **not** granted by the code license.
 
----
+See: [AGPL-3.0 & Commercial Boundary](docs/OPEN_CORE_POLICY.md)
 
-## 🚀 Core Features
+## Core Capabilities
 
-- **📡 Full-Spectrum Collection**
-  - **Major Models**: Real-time sync for ECMWF, GFS, ICON, GEM, and JMA high temperatures.
-  - **Observed Data**: Global airport METAR reports + official Turkish MGM station-level data.
-  - **Centralized Correction**: Integrated `17130` (Center) official data specifically for Ankara.
-- **⚖️ DEB Smart Blending**
-  - Dynamic weighting of forecasts based on recent 7-day historical performance.
-- **🔔 Alert Engine**
-  - **Momentum Spike**: Captures rapid temperature changes within 30 minutes.
-  - **Forecast Breakthrough**: Fires when observations exceed all model predictions plus a safety margin.
-  - **Advection Monitoring**: Simulates warm/cold advection based on lead stations and wind currents.
-- **🛡️ Smart Suppression**
-  - **Peak Protection**: Automatically switches to snapshot mode when the daily high has likely passed.
-  - **Cooldown Management**: Global and city-level cooldowns to prevent notification fatigue.
+- Aggregates observations and forecasts for 30 monitored cities.
+- Uses DEB (Dynamic Error Balancing) to blend multi-model highs.
+- Generates settlement-oriented probability buckets (`mu` + bucket distribution).
+- Maps weather view to Polymarket quotes for mispricing scan.
+- Reuses one analysis core across web dashboard and Telegram bot.
+- Adds payment audit trails, replay tooling, and incident visibility in ops.
+- Adds peak-window-oriented intraday structure cards for surface + upper-air analysis.
+- Adds airport-side `TAF` timing overlays and airport suppression/disruption interpretation for non-Hong Kong airport cities.
 
----
-
-## 🔐 Alert Logic Details
-
-| Trigger Name     | Core Logic                                    | Trading Value                                 |
-| :--------------- | :-------------------------------------------- | :-------------------------------------------- |
-| **Center Hit**   | Detects DEB trigger only at Ankara HQ `17130` | **Highest priority signal**, the "truth"      |
-| **Momentum**     | 30min temperature slope exceed threshold      | Captures sudden weather fronts                |
-| **Breakthrough** | Pierces all model highs + margin              | Captures high-volatility outlier events       |
-| **Advection**    | Lead station rise + Wind match                | Gain 20-40 minutes of lead time for execution |
-
----
-
-## 🏗️ System Architecture
+## Reference Architecture
 
 ```mermaid
-graph TD
-    subgraph "Client / Terminals"
-        Web[Next.js Web App]
-        TG[Telegram Client]
-    end
+flowchart LR
+    U["Users (Web / Telegram)"] --> FE["Next.js Frontend (Vercel)"]
+    U --> BOT["Telegram Bot (VPS)"]
+    FE --> API["FastAPI /web/app.py"]
+    BOT --> API
 
-    subgraph "Edge Deployment (Vercel)"
-        Web -.-> |Auth| Supa[(Supabase Auth/DB)]
-        Web --> |API| Fast[FastAPI API]
-    end
+    API --> WX["Weather Collector"]
+    WX --> METAR["Aviation Weather (METAR)"]
+    WX --> TAF["Aviation Weather (TAF)"]
+    WX --> MGM["MGM (Turkey station network)"]
+    WX --> OM["Open-Meteo"]
+    WX --> HKO["HKO / NOAA / Official settlement sources"]
 
-    subgraph "Core Hub (VPS)"
-        Fast --- |Shared Logic| Worker[Alert Engine / Worker]
-        Bot[Telegram Bot] --- |Shared Logic| Worker
-        Worker --> |Cache/Sub| Supa
-    end
-
-    subgraph "External Sources"
-        Worker --> |Pull| MGM[MGM Weather]
-        Worker --> |Pull| METAR[Airport METAR]
-        Worker --> |Pull| OM[Open-Meteo]
-        Worker --> |Pull| MM[Multi-Model Integration]
-    end
-
-    Worker --> |Push Alert| TG
-    Bot --> |Query| Worker
+    API --> ANA["DEB + Trend + Probability + Market Scan"]
+    ANA --> PAY["Payment State (Intent + Event + Confirm Loop)"]
+    ANA --> PM["Polymarket Read-only Layer"]
 ```
 
----
+## Monitored Cities (30)
 
-## 🛠️ Deployment
+- Europe / Middle East: Ankara, London, Paris, Munich, Tel Aviv, Milan, Warsaw, Madrid
+- APAC: Seoul, Hong Kong, Taipei, Shanghai, Singapore, Tokyo, Wellington
+- Americas: Toronto, New York, Chicago, Dallas, Miami, Atlanta, Seattle, Buenos Aires, Sao Paulo
+- South Asia: Lucknow
+- China extension: Chengdu, Chongqing, Shenzhen, Beijing, Wuhan
 
-### 1. Backend / Bot (VPS)
+## Quick Start
+
+### Backend + Bot (Docker)
 
 ```bash
-# Pull Source
-git pull
-
-# Environment
-# Edit .env with TELEGRAM_BOT_TOKEN and other keys
-
-# Launch
-docker-compose up -d --build
+docker compose up -d --build
 ```
 
-### 2. Frontend (Vercel)
+### Frontend (local)
 
-Associate the `frontend` directory as the project root on Vercel for automatic CI/CD.
+```bash
+cd frontend
+npm install
+npm run dev
+```
 
----
+## Recent Highlights
 
-## 💬 Bot Commands
+- Taipei settlement is aligned to `Wunderground RCSS` with whole-degree Celsius resolution logic.
+- Shenzhen settlement is aligned to `Wunderground ZGSZ`.
+- Hong Kong keeps `HKO` official readings in dashboard and history, without falling back to airport METAR lines.
+- Intraday analysis now separates:
+  - `Surface Structure`
+  - `Upper-Air Structure`
+  - `Trade cue`
+- `TAF` is used as an airport-side confirmation layer, not as the main temperature model.
+- Browser extension remains a lightweight monitoring + basic-bias product, while the site holds the full analysis experience.
 
-| Command   | Description                             | Example        |
-| :-------- | :-------------------------------------- | :------------- |
-| `/city`   | Query real-time analysis for a city     | `/city ankara` |
-| `/deb`    | View historical accuracy of DEB model   | `/deb london`  |
-| `/points` | View your activity points & leaderboard | `/points`      |
-| `/help`   | Get detailed instructions               | `/help`        |
+## Runtime Data (Recommended on VPS)
 
----
+Use external runtime storage to avoid SQLite/git conflicts:
 
-> [!NOTE]
-> **Commercialization**: This project currently offers **Web Dashboard ($5/mo)** and **Telegram Signal Channel ($1/mo)** subscriptions.
-> Point-earning via group participation is active and points can be redeemed for access.
+```env
+POLYWEATHER_RUNTIME_DATA_DIR=/var/lib/polyweather
+POLYWEATHER_DB_PATH=/var/lib/polyweather/polyweather.db
+POLYWEATHER_STATE_STORAGE_MODE=sqlite
+```
 
----
+## Ops Verification
 
----
+### Health / system status / metrics
 
-**📅 Last Updated**: 2026-03-08
-**🚀 Status**: v1.0 Stable - Professional Quant UI Locked
+```bash
+curl http://127.0.0.1:8000/healthz
+curl http://127.0.0.1:8000/api/system/status
+curl http://127.0.0.1:8000/metrics
+```
 
-> [!TIP]
-> **Production Note**: The current dashboard utilizes the high-density "Professional Quant" UI (v1.0-legacy) which integrates real-time METAR/MGM data, DEB ensemble blending, and multi-model probability distribution in a single high-performance view.
+### Frontend cache headers
+
+```bash
+./scripts/validate_frontend_cache.sh "https://polyweather-pro.vercel.app"
+```
+
+### Payment auto-reconciliation logs
+
+```bash
+docker compose logs -f polyweather | egrep "payment event loop started|payment confirm loop started|payment auto-confirmed"
+```
+
+### Payment runtime
+
+```bash
+curl http://127.0.0.1:8000/api/payments/runtime
+```
+
+### Wallet activity logs
+
+```bash
+docker compose logs -f polyweather | egrep "polymarket wallet activity watcher started|wallet activity pushed"
+```
+
+## Telegram Commands
+
+| Command | Purpose |
+| :-- | :-- |
+| `/city <name>` | City real-time analysis |
+| `/deb <name>` | DEB historical reconciliation |
+| `/top` | User leaderboard |
+| `/id` | Show current chat ID |
+| `/diag` | Startup diagnostics |
+| `/help` | Help and usage |
+
+## Documentation Index
+
+- Chinese overview: [README_ZH.md](README_ZH.md)
+- Chinese API guide: [docs/API_ZH.md](docs/API_ZH.md)
+- TAF signal guide (ZH): [docs/TAF_SIGNAL_ZH.md](docs/TAF_SIGNAL_ZH.md)
+- Commercialization: [docs/COMMERCIALIZATION.md](docs/COMMERCIALIZATION.md)
+- AGPL-3.0 policy: [docs/OPEN_CORE_POLICY.md](docs/OPEN_CORE_POLICY.md)
+- Supabase setup (ZH): [docs/SUPABASE_SETUP_ZH.md](docs/SUPABASE_SETUP_ZH.md)
+- Configuration & secrets (ZH): [docs/CONFIGURATION_ZH.md](docs/CONFIGURATION_ZH.md)
+- LightGBM daily-high model (ZH): [docs/LGBM_DAILY_HIGH_ZH.md](docs/LGBM_DAILY_HIGH_ZH.md)
+- Frontend deployment (ZH): [docs/FRONTEND_DEPLOYMENT_ZH.md](docs/FRONTEND_DEPLOYMENT_ZH.md)
+- Tech debt (EN): [docs/TECH_DEBT.md](docs/TECH_DEBT.md)
+- Tech debt (ZH): [docs/TECH_DEBT_ZH.md](docs/TECH_DEBT_ZH.md)
+- Payment verification: [docs/payments/POLYGONSCAN_VERIFY.md](docs/payments/POLYGONSCAN_VERIFY.md)
+- Payment audit: [docs/payments/PAYMENT_AUDIT_ZH.md](docs/payments/PAYMENT_AUDIT_ZH.md)
+- Payment V2 upgrade: [docs/payments/PAYMENT_UPGRADE_V2_ZH.md](docs/payments/PAYMENT_UPGRADE_V2_ZH.md)
+- Ops admin guide: [docs/OPS_ADMIN_ZH.md](docs/OPS_ADMIN_ZH.md)
+- Deep research report: [docs/deep-research-report.md](docs/deep-research-report.md)
+- Frontend report: [FRONTEND_REDESIGN_REPORT.md](FRONTEND_REDESIGN_REPORT.md)
+- Release process: [RELEASE.md](RELEASE.md)
+- Changelog: [CHANGELOG.md](CHANGELOG.md)
+
+## Version
+
+- Version: `v1.5.1`
+- Last Updated: `2026-03-24`

README_ZH.md

@@ -1,147 +1,200 @@
-# 🌡️ PolyWeather Pro
+# PolyWeather Pro
 
-> **专业级博弈情报系统** —— 专注边缘气象数据采集、DEB 智能融合与实时决策预警。
+面向温度结算市场的生产级气象情报系统。
 
----
+官方看板：[polyweather-pro.vercel.app](https://polyweather-pro.vercel.app/)
 
-## 💎 项目愿景
+## 产品截图
 
-PolyWeather 是一套专为 **Polymarket** 深度博弈者设计的实时情报系统。我们不只是提供天气预报，而是通过聚合全球顶级气象源、应用自研的 **DEB (Dynamic Error Balancing)** 算法，并在关键时间节点提供**具有博弈预测价值**的异动预警。
+### 全球看板
 
----
+![PolyWeather 全球地图看板](docs/images/demo_map.png)
 
-## 🏗️ 生产架构
+### 城市分析（Ankara）
 
-本项目采用生产级解耦架构，确保高可用与实时性：
+![PolyWeather Ankara 分析页](docs/images/demo_ankara.png)
 
-- **前端**：部署在 **Vercel** 上的 **Next.js** 交互式仪表盘。
-- **后端 API**：运行在 VPS 上的 **FastAPI**，提供低延迟数据服务。
-- **机器人与预警心跳**：运行在 VPS 上的 **Telegram Bot**，执行每分钟级的全球扫描与推送。
+## 当前产品状态（2026-03-21）
 
-🔗 **官方访问地址**：[polyweather-pro.vercel.app](https://polyweather-pro.vercel.app/)
+- 已上线订阅制：`Pro 月付 5 USDC`。
+- 已上线积分抵扣：`500 积分 = 1 USDC`，最多抵扣 `3 USDC`。
+- 已上线链上支付：Polygon 合约支付（USDC / USDC.e）。
+- 已上线自动补单：事件监听 + 周期确认双链路。
+- 已上线支付运行态与审计接口：`/api/payments/runtime`。
+- 已上线轻量运营后台：`/ops`（会员、周榜、补分、支付异常单）。
+- 已上线轻量可观测性：`/healthz`、`/api/system/status`、`/metrics`。
+- 已补最小外部监控栈：Prometheus + Alertmanager + Grafana + Telegram 告警 relay。
+- 运行态状态、缓存与核心离线训练/回填链路已完成 SQLite 主路径收口；legacy JSON/JSONL 仅保留给迁移、导出与显式回退输入。
+- 已接入 EMOS/CRPS 校准链路，但当前仍保持 `emos_shadow`。
 
----
+## 许可证与商用边界（重要）
 
-## 🖼️ 预览与交互
+本仓库自 `2026-03-30` 起采用 **GNU AGPL-3.0-only**。
 
-<p align="center">
-  <img src="docs/images/demo_ankara.png" alt="PolyWeather 效果展示 - 安卡拉实时分析" width="450">
-  <br>
-  <em>📊 <b>深度查询效果</b>：DEB 融合预测 + 结算概率 + Groq AI 专家建议</em>
-</p>
+- 仓库公开部分：天气聚合、基础分析、前端看板、Bot 基础能力、标准支付流程。
+- 不包含在仓库中的部分：生产私有数据、商业风控规则、运营阈值、收费策略细节、内部对账与增长工具。
+- 商标、品牌、域名、生产数据库与托管服务运营能力，不因代码许可证一并授权。
 
-<p align="center">
-  <img src="./docs/images/demo_map.png" alt="PolyWeather Web Map" width="850">
-  <br>
-  <em>🗺️ <b>全景仪表盘</b>：全球站点实时热力场 + 阵列式数据展示</em>
-</p>
+详细见：[AGPL-3.0 与商用边界](docs/OPEN_CORE_POLICY.md)
 
----
+## 核心能力
 
-## 🚀 核心功能
+- 聚合 30 个监控城市的实测与预报数据。
+- DEB（Dynamic Error Balancing）融合多模型最高温。
+- 输出结算导向概率分布（`mu` + 温度桶）。
+- 将模型观点映射到 Polymarket 行情，做错价扫描。
+- Web 仪表盘与 Telegram Bot 复用同一分析内核。
+- 支付链路具备事件重放、SQLite 审计事件与 RPC 容灾能力。
 
-- **📡 多源全量采集**
-  - **主流模型**：ECMWF, GFS, ICON, GEM, JMA 实时最高温同步。
-  - **实测数据**：全球机场 METAR 定时报文 + 土耳其 MGM 局点官方实测。
-  - **中心化纠偏**：针对安卡拉特别接入 `17130` (Center) 官方指挥中心数据。
-- **⚖️ DEB 智能融合**
-  - 基于近期 7 天历史表现，动态调整各模型权重的博弈预测。
-- **🔔 异动预警系统 (Alert Engine)**
-  - **动量突变**：捕捉 30 分钟内的急剧温变。
-  - **预测突破**：当实测击穿所有预报上限时触发告警。
-  - **平流监测**：基于周边前导站的风向流场模拟，预测冷/暖平流的到达。
-- **🛡️ 智能压制逻辑**
-  - **峰值保护**：当日高温峰值大概率已过时，自动转为静默/快照模式，拒绝骚扰。
-  - **冷却管理**：同一信号路径支持全局与城市级双重 CD。
-
----
-
-## 🔐 预警逻辑深度说明
-
-| 触发器名称       | 核心逻辑                                     | 博弈价值                           |
-| :--------------- | :------------------------------------------- | :--------------------------------- |
-| **Center Hit**   | 仅识别安卡拉总部 `17130` 站点的 DEB 触发信号 | **最高级信号**，定盘星             |
-| **Momentum**     | 30min 温度斜率超过                           | 捕捉突发天气系统（如锋面）         |
-| **Breakthrough** | 击穿所有预报上限 + 安全边际                  | 捕捉市场极少数情况下的暴利点       |
-| **Advection**    | 前导站温升 + 风向匹配                        | 获得 20-40 分钟的提前离场/建仓时间 |
-
----
-
-## 🏗️ 架构解析
+## 参考架构
 
 ```mermaid
-graph TD
-    subgraph "客户端 / 终端"
-        Web[Next.js 网页端]
-        TG[Telegram 客户端]
-    end
+flowchart LR
+    U["用户（Web / Telegram）"] --> FE["Next.js 前端（Vercel）"]
+    U --> BOT["Telegram Bot（VPS）"]
+    FE --> API["FastAPI /web/app.py"]
+    BOT --> API
 
-    subgraph "云端部署 (Vercel)"
-        Web -.-> |Auth| Supa[(Supabase Auth/DB)]
-        Web --> |API| Fast[FastAPI API]
-    end
+    API --> WX["Weather Collector"]
+    WX --> METAR["Aviation Weather（METAR）"]
+    WX --> MGM["MGM（土耳其站网）"]
+    WX --> OM["Open-Meteo"]
 
-    subgraph "核心引擎 (VPS)"
-        Fast --- |Shared Logic| Worker[Alert Engine / Worker]
-        Bot[Telegram Bot] --- |Shared Logic| Worker
-        Worker --> |Cache/Sub| Supa
-    end
-
-    subgraph "外部数据源"
-        Worker --> |Pull| MGM[MGM 气象局]
-        Worker --> |Pull| METAR[机场实测]
-        Worker --> |Pull| OM[Open-Meteo]
-        Worker --> |Pull| MM[多模型集成]
-    end
-
-    Worker --> |Push Alert| TG
-    Bot --> |Query| Worker
+    API --> ANA["DEB + 趋势 + 概率 + 市场扫描"]
+    ANA --> PAY["支付状态（Intent + Event + Confirm Loop）"]
+    ANA --> PM["Polymarket 只读层"]
+    API --> OBS["healthz / system status / metrics"]
+    ANA --> STATE["SQLite runtime state<br/>legacy files only for migration/export fallback"]
 ```
 
----
+## 监控城市（30）
 
-## 🛠️ 部署指南
+- 欧洲/中东：Ankara、London、Paris、Munich、Tel Aviv、Milan、Warsaw、Madrid
+- 亚太：Seoul、Hong Kong、Taipei、Shanghai、Singapore、Tokyo、Wellington
+- 美洲：Toronto、New York、Chicago、Dallas、Miami、Atlanta、Seattle、Buenos Aires、Sao Paulo
+- 南亚：Lucknow
+- 中国扩展：Chengdu、Chongqing、Shenzhen、Beijing、Wuhan
 
-### 1. 后端 / 机器人 (VPS)
+## 快速启动
+
+### 后端 + Bot（Docker）
 
 ```bash
-# 获取源码
-git pull
-
-# 环境配置
-# 编辑 .env 文件，填入 TELEGRAM_BOT_TOKEN 等关键参数
-
-# 一键启动
-docker-compose up -d --build
+docker compose up -d --build
 ```
 
-### 2. 前端 (Vercel)
+### 前端本地运行
 
-直接关联本项目 `frontend` 目录作为根目录即可，享受自动 CI/CD。
+```bash
+cd frontend
+npm install
+npm run dev
+```
 
----
+## 运行数据目录（VPS 推荐）
 
-## 💬 机器人指令
+建议将运行态数据放到仓库外（避免 `git pull` 被 SQLite 卡住）：
 
-| 命令      | 说明                      | 示例           |
-| :-------- | :------------------------ | :------------- |
-| `/city`   | 查询指定城市实时分析      | `/city ankara` |
-| `/deb`    | 查看 DEB 模型的历史准确率 | `/deb london`  |
-| `/points` | 查看您的活跃积分与排行榜  | `/points`      |
-| `/help`   | 获取详细功能说明          | `/help`        |
+```env
+POLYWEATHER_RUNTIME_DATA_DIR=/var/lib/polyweather
+POLYWEATHER_DB_PATH=/var/lib/polyweather/polyweather.db
+POLYWEATHER_STATE_STORAGE_MODE=sqlite
+```
 
----
+## 运维验收
 
-> [!NOTE]
-> **商业化提示**：本项目目前提供 **Web 仪表盘 ($5/月)** 与 **Telegram 信号频道 ($1/月)** 订阅服务。
-> 发言获取积分逻辑已上线，活跃用户可兑换相应权限。
+### 健康与系统状态
 
----
+```bash
+curl http://127.0.0.1:8000/healthz
+curl http://127.0.0.1:8000/api/system/status
+curl http://127.0.0.1:8000/metrics
+```
 
----
+### 前端缓存头
 
-**📅 最后更新**：2026-03-08
-**🚀 状态**：v1.0 稳定版 - 专业量化 UI 已锁定
+```bash
+./scripts/validate_frontend_cache.sh "https://polyweather-pro.vercel.app"
+```
 
-> [!TIP]
-> **生产提示**：当前仪表盘采用高密度“专业量化版” UI (v1.0-legacy)，深度集成了 METAR/MGM 实测数据、DEB 智能融合预报及多模型概率分布，提供最高性能的数据交互体验。
+### 支付自动补单日志
+
+```bash
+docker compose logs -f polyweather | egrep "payment event loop started|payment confirm loop started|payment auto-confirmed"
+```
+
+### 外部监控栈
+
+```bash
+docker compose --profile monitoring up -d polyweather_prometheus polyweather_alertmanager polyweather_alert_relay polyweather_grafana
+```
+
+- Prometheus：`http://127.0.0.1:${POLYWEATHER_PROMETHEUS_PORT:-9090}`
+- Alertmanager：`http://127.0.0.1:${POLYWEATHER_ALERTMANAGER_PORT:-9093}`
+- Grafana：`http://127.0.0.1:${POLYWEATHER_GRAFANA_PORT:-3001}`
+
+手动巡检：
+
+```bash
+python scripts/check_ops_health.py --base-url http://127.0.0.1:8000
+```
+
+### 支付运行态
+
+```bash
+curl http://127.0.0.1:8000/api/payments/runtime
+```
+
+### 运营后台
+
+- 前端入口：`https://polyweather-pro.vercel.app/ops`
+- 后端需配置：
+
+```env
+POLYWEATHER_OPS_ADMIN_EMAILS=yhrsc30@gmail.com
+```
+
+### 钱包异动监听日志
+
+```bash
+docker compose logs -f polyweather | egrep "polymarket wallet activity watcher started|wallet activity pushed"
+```
+
+## Telegram 指令
+
+| 指令 | 用途 |
+| :-- | :-- |
+| `/city <name>` | 城市实时分析 |
+| `/deb <name>` | DEB 历史对账 |
+| `/top` | 用户积分排行 |
+| `/id` | 查看聊天 Chat ID |
+| `/diag` | Bot 启动诊断 |
+| `/help` | 帮助与用法 |
+
+## 文档索引
+
+- 英文总览：[README.md](README.md)
+- API 文档（中文）：[docs/API_ZH.md](docs/API_ZH.md)
+- 商业化说明：[docs/COMMERCIALIZATION.md](docs/COMMERCIALIZATION.md)
+- AGPL-3.0 边界：[docs/OPEN_CORE_POLICY.md](docs/OPEN_CORE_POLICY.md)
+- Supabase 接入：[docs/SUPABASE_SETUP_ZH.md](docs/SUPABASE_SETUP_ZH.md)
+- 配置与密钥管理：[docs/CONFIGURATION_ZH.md](docs/CONFIGURATION_ZH.md)
+- 前端部署（Vercel）：[docs/FRONTEND_DEPLOYMENT_ZH.md](docs/FRONTEND_DEPLOYMENT_ZH.md)
+- EMOS 训练报告：[docs/EMOS_TRAINING_REPORT_ZH.md](docs/EMOS_TRAINING_REPORT_ZH.md)
+- 概率快照归档：[docs/PROBABILITY_SNAPSHOT_ARCHIVE_ZH.md](docs/PROBABILITY_SNAPSHOT_ARCHIVE_ZH.md)
+- 技术债（中文镜像）：[docs/TECH_DEBT_ZH.md](docs/TECH_DEBT_ZH.md)
+- 技术债（主文档）：[docs/TECH_DEBT.md](docs/TECH_DEBT.md)
+- 支付合约验证：[docs/payments/POLYGONSCAN_VERIFY.md](docs/payments/POLYGONSCAN_VERIFY.md)
+- 支付审计说明：[docs/payments/PAYMENT_AUDIT_ZH.md](docs/payments/PAYMENT_AUDIT_ZH.md)
+- 支付 V2 升级方案：[docs/payments/PAYMENT_UPGRADE_V2_ZH.md](docs/payments/PAYMENT_UPGRADE_V2_ZH.md)
+- 运营后台说明：[docs/OPS_ADMIN_ZH.md](docs/OPS_ADMIN_ZH.md)
+- 外部监控说明：[docs/MONITORING_ZH.md](docs/MONITORING_ZH.md)
+- 深度评估报告：[docs/deep-research-report.md](docs/deep-research-report.md)
+- 前端报告：[FRONTEND_REDESIGN_REPORT.md](FRONTEND_REDESIGN_REPORT.md)
+- 发布流程：[RELEASE.md](RELEASE.md)
+- 变更记录：[CHANGELOG.md](CHANGELOG.md)
+
+## 当前版本
+
+- 版本：`v1.5.1`
+- 文档最后更新：`2026-03-21`

RELEASE.md

@@ -0,0 +1,86 @@
+# 版本发布流程
+
+本项目采用语义化版本号：`MAJOR.MINOR.PATCH`
+
+当前单一版本源为根目录 [VERSION](E:/web/PolyWeather/VERSION) 文件，所有对外文档与前端版本号都从这里同步。
+
+## 版本规则
+
+- `PATCH`：修复缺陷、文档修正、兼容性不变的小改动
+- `MINOR`：新增能力、接口扩展、向后兼容的功能迭代
+- `MAJOR`：不兼容变更、核心架构升级、公开接口重大调整
+
+示例：
+
+- `1.4.0 -> 1.4.1`：告警逻辑修正、缓存修正、文档修正
+- `1.4.0 -> 1.5.0`：新增支付能力、新增页面、新增 API
+- `1.4.0 -> 2.0.0`：接口重构或数据结构不兼容
+
+## 日常升版步骤
+
+### 1. 升版本号
+
+```bash
+python scripts/bump_version.py patch
+```
+
+可选参数：
+
+```bash
+python scripts/bump_version.py minor
+python scripts/bump_version.py major
+python scripts/bump_version.py 1.5.0
+```
+
+### 2. 检查同步结果
+
+```bash
+python scripts/sync_version.py
+git diff
+```
+
+### 3. 补充 Changelog
+
+在 [CHANGELOG.md](E:/web/PolyWeather/CHANGELOG.md) 对应版本下补齐：
+
+- 新增能力
+- 修复项
+- 兼容性说明
+
+### 4. 验证
+
+建议至少执行：
+
+```bash
+cd frontend
+npm run build
+```
+
+如涉及后端核心逻辑，补充执行：
+
+```bash
+python -m pytest
+```
+
+### 5. 提交与打标签
+
+工作区干净后再打标签：
+
+```bash
+git add .
+git commit -m "release: v1.4.1"
+git tag v1.4.1
+```
+
+### 6. 推送
+
+```bash
+git push
+git push origin v1.4.1
+```
+
+## 当前约束
+
+- 不直接手改多份文档版本号
+- 不在工作区脏状态下打 release tag
+- `README.md`、前端 `package.json`、文档标题版本都通过脚本同步

VERSION

@@ -0,0 +1 @@
+1.5.1

artifacts/models/lgbm_daily_high_schema.json

@@ -0,0 +1,66 @@
+{
+  "model_type": "LightGBMRegressor",
+  "target": "actual_high",
+  "horizon": "D0",
+  "feature_names": [
+    "actual_high_lag_1",
+    "actual_high_lag_2",
+    "actual_high_lag_3",
+    "actual_high_lag_7",
+    "actual_high_mean_7",
+    "actual_high_mean_14",
+    "actual_high_trend_3",
+    "open_meteo",
+    "ecmwf",
+    "gfs",
+    "gem",
+    "jma",
+    "icon",
+    "mgm",
+    "nws",
+    "deb_prediction",
+    "model_median",
+    "model_spread",
+    "current_temp",
+    "max_so_far",
+    "humidity",
+    "wind_speed_kt",
+    "visibility_mi",
+    "local_hour",
+    "month",
+    "weekday",
+    "peak_status_code"
+  ],
+  "base_model_columns": [
+    "open_meteo",
+    "ecmwf",
+    "gfs",
+    "gem",
+    "jma",
+    "icon",
+    "mgm",
+    "nws"
+  ],
+  "model_path": "artifacts\\models\\lgbm_daily_high.txt",
+  "sample_count": 54,
+  "train_count": 42,
+  "validation_count": 12,
+  "metrics": {
+    "validation": {
+      "sample_count": 12,
+      "lgbm_mae": 1.349,
+      "deb_mae": 0.875,
+      "best_single_mae": 0.325,
+      "median_mae": 0.758
+    },
+    "full_sample": {
+      "sample_count": 54,
+      "lgbm_mae": 0.691,
+      "deb_mae": 6.287,
+      "best_single_mae": 5.431,
+      "median_mae": 6.265
+    }
+  },
+  "generated_at": "2026-04-02T16:27:44.816882Z",
+  "trained_at": "2026-04-02T16:27:44.816882Z"
+}

artifacts/probability_calibration/default.json

@@ -0,0 +1,90 @@
+{
+  "version": "emos-20260402162744",
+  "trained_at": "2026-04-02T16:27:44.114836+00:00",
+  "global": {
+    "mu": {
+      "intercept": 1.54512641,
+      "raw_mu_coef": 2.96105052,
+      "deb_coef": -1.53260815,
+      "ens_median_coef": -0.72849343,
+      "max_so_far_gap_coef": 9.52557689
+    },
+    "sigma": {
+      "intercept": 0.67432479,
+      "raw_sigma_coef": 0.6936692,
+      "spread_coef": 0.08877484,
+      "peak_flag_coef": -0.58374835,
+      "max_so_far_gap_coef": -0.8172477
+    }
+  },
+  "sigma_constraints": {
+    "min_ratio": 0.85,
+    "max_ratio": 1.35,
+    "absolute_min": 0.25,
+    "absolute_max": 3.0
+  },
+  "selection_guardrails": {
+    "max_mae_increase": 0.02,
+    "max_bucket_hit_drop": 0.01,
+    "max_bucket_brier_increase": 0.05
+  },
+  "blending": {
+    "alpha_mu": 0.0,
+    "alpha_sigma": 0.05
+  },
+  "cities": {
+    "ankara": {
+      "samples": 3,
+      "mu_bias": 1.271844,
+      "sigma_scale": 1.477644,
+      "confidence": 0.375
+    },
+    "hong kong": {
+      "samples": 4,
+      "mu_bias": 1.32008,
+      "sigma_scale": 1.04023,
+      "confidence": 0.5
+    },
+    "milan": {
+      "samples": 3,
+      "mu_bias": -3.935178,
+      "sigma_scale": 2.0,
+      "confidence": 0.375
+    },
+    "shanghai": {
+      "samples": 3,
+      "mu_bias": 1.810495,
+      "sigma_scale": 2.0,
+      "confidence": 0.375
+    },
+    "taipei": {
+      "samples": 3,
+      "mu_bias": 3.577828,
+      "sigma_scale": 2.0,
+      "confidence": 0.375
+    },
+    "warsaw": {
+      "samples": 3,
+      "mu_bias": -0.625333,
+      "sigma_scale": 1.25968,
+      "confidence": 0.375
+    }
+  },
+  "metrics": {
+    "sample_count": 54,
+    "mean_crps": 3.792563,
+    "legacy_mean_crps": 4.308029,
+    "legacy_mean_mae": 4.51037,
+    "legacy_bucket_hit_rate": 0.537037,
+    "legacy_bucket_brier": 0.833294,
+    "selected_mean_crps": 4.249828,
+    "selected_mean_mae": 4.51037,
+    "selected_bucket_hit_rate": 0.555556,
+    "selected_bucket_brier": 0.831872,
+    "selected_score": 5.991436,
+    "legacy_score": 6.078481,
+    "filled_actual_from_history": 0,
+    "settlement_history_city_count": 30
+  },
+  "source": "artifacts\\probability_calibration\\default.json"
+}

artifacts/probability_calibration/evaluation_report.json

@@ -0,0 +1,257 @@
+{
+  "summary": {
+    "sample_count": 54,
+    "filled_actual_from_history": 2,
+    "legacy": {
+      "mean_crps": 4.300621,
+      "mean_mae": 4.502963,
+      "bucket_hit_rate": 0.537037
+    },
+    "emos": {
+      "mean_crps": 4.213889,
+      "mean_mae": 4.502963,
+      "bucket_hit_rate": 0.537037
+    },
+    "delta": {
+      "crps": -0.086732,
+      "mae": 0.0,
+      "bucket_hit_rate": 0.0
+    }
+  },
+  "by_city": {
+    "ankara": {
+      "samples": 3,
+      "legacy_mean_crps": 0.327701,
+      "emos_mean_crps": 0.439705,
+      "legacy_mean_mae": 0.066667,
+      "emos_mean_mae": 0.066667,
+      "legacy_bucket_hit_rate": 1.0,
+      "emos_bucket_hit_rate": 1.0
+    },
+    "atlanta": {
+      "samples": 2,
+      "legacy_mean_crps": 30.449382,
+      "emos_mean_crps": 30.578432,
+      "legacy_mean_mae": 32.015,
+      "emos_mean_mae": 32.015,
+      "legacy_bucket_hit_rate": 0.0,
+      "emos_bucket_hit_rate": 0.0
+    },
+    "buenos aires": {
+      "samples": 2,
+      "legacy_mean_crps": 9.113412,
+      "emos_mean_crps": 8.759954,
+      "legacy_mean_mae": 10.27,
+      "emos_mean_mae": 10.27,
+      "legacy_bucket_hit_rate": 0.0,
+      "emos_bucket_hit_rate": 0.0
+    },
+    "chicago": {
+      "samples": 1,
+      "legacy_mean_crps": 1.250268,
+      "emos_mean_crps": 0.701085,
+      "legacy_mean_mae": 0.0,
+      "emos_mean_mae": 0.0,
+      "legacy_bucket_hit_rate": 1.0,
+      "emos_bucket_hit_rate": 1.0
+    },
+    "dallas": {
+      "samples": 1,
+      "legacy_mean_crps": 2.173363,
+      "emos_mean_crps": 0.701085,
+      "legacy_mean_mae": 0.0,
+      "emos_mean_mae": 0.0,
+      "legacy_bucket_hit_rate": 1.0,
+      "emos_bucket_hit_rate": 1.0
+    },
+    "hong kong": {
+      "samples": 4,
+      "legacy_mean_crps": 0.29509,
+      "emos_mean_crps": 0.387946,
+      "legacy_mean_mae": 0.075,
+      "emos_mean_mae": 0.075,
+      "legacy_bucket_hit_rate": 1.0,
+      "emos_bucket_hit_rate": 0.75
+    },
+    "london": {
+      "samples": 2,
+      "legacy_mean_crps": 3.885033,
+      "emos_mean_crps": 3.866915,
+      "legacy_mean_mae": 4.135,
+      "emos_mean_mae": 4.135,
+      "legacy_bucket_hit_rate": 0.0,
+      "emos_bucket_hit_rate": 0.0
+    },
+    "lucknow": {
+      "samples": 2,
+      "legacy_mean_crps": 2.487193,
+      "emos_mean_crps": 2.342342,
+      "legacy_mean_mae": 3.205,
+      "emos_mean_mae": 3.205,
+      "legacy_bucket_hit_rate": 0.0,
+      "emos_bucket_hit_rate": 0.0
+    },
+    "madrid": {
+      "samples": 2,
+      "legacy_mean_crps": 6.27726,
+      "emos_mean_crps": 5.967277,
+      "legacy_mean_mae": 7.33,
+      "emos_mean_mae": 7.33,
+      "legacy_bucket_hit_rate": 0.0,
+      "emos_bucket_hit_rate": 0.0
+    },
+    "miami": {
+      "samples": 2,
+      "legacy_mean_crps": 28.637631,
+      "emos_mean_crps": 28.482516,
+      "legacy_mean_mae": 30.175,
+      "emos_mean_mae": 30.175,
+      "legacy_bucket_hit_rate": 0.0,
+      "emos_bucket_hit_rate": 0.0
+    },
+    "milan": {
+      "samples": 3,
+      "legacy_mean_crps": 4.401392,
+      "emos_mean_crps": 3.858031,
+      "legacy_mean_mae": 4.06,
+      "emos_mean_mae": 4.06,
+      "legacy_bucket_hit_rate": 0.666667,
+      "emos_bucket_hit_rate": 0.666667
+    },
+    "munich": {
+      "samples": 2,
+      "legacy_mean_crps": 3.145192,
+      "emos_mean_crps": 3.011312,
+      "legacy_mean_mae": 3.64,
+      "emos_mean_mae": 3.64,
+      "legacy_bucket_hit_rate": 0.0,
+      "emos_bucket_hit_rate": 0.0
+    },
+    "new york": {
+      "samples": 1,
+      "legacy_mean_crps": 3.692845,
+      "emos_mean_crps": 3.407357,
+      "legacy_mean_mae": 4.94,
+      "emos_mean_mae": 4.94,
+      "legacy_bucket_hit_rate": 0.0,
+      "emos_bucket_hit_rate": 0.0
+    },
+    "paris": {
+      "samples": 2,
+      "legacy_mean_crps": 4.013782,
+      "emos_mean_crps": 3.979293,
+      "legacy_mean_mae": 4.265,
+      "emos_mean_mae": 4.265,
+      "legacy_bucket_hit_rate": 0.5,
+      "emos_bucket_hit_rate": 0.5
+    },
+    "sao paulo": {
+      "samples": 2,
+      "legacy_mean_crps": 5.540967,
+      "emos_mean_crps": 5.272063,
+      "legacy_mean_mae": 6.57,
+      "emos_mean_mae": 6.57,
+      "legacy_bucket_hit_rate": 0.0,
+      "emos_bucket_hit_rate": 0.0
+    },
+    "seattle": {
+      "samples": 1,
+      "legacy_mean_crps": 0.315488,
+      "emos_mean_crps": 0.425909,
+      "legacy_mean_mae": 0.0,
+      "emos_mean_mae": 0.0,
+      "legacy_bucket_hit_rate": 1.0,
+      "emos_bucket_hit_rate": 1.0
+    },
+    "seoul": {
+      "samples": 2,
+      "legacy_mean_crps": 0.313754,
+      "emos_mean_crps": 0.412831,
+      "legacy_mean_mae": 0.15,
+      "emos_mean_mae": 0.15,
+      "legacy_bucket_hit_rate": 1.0,
+      "emos_bucket_hit_rate": 1.0
+    },
+    "shanghai": {
+      "samples": 3,
+      "legacy_mean_crps": 0.299116,
+      "emos_mean_crps": 0.394855,
+      "legacy_mean_mae": 0.1,
+      "emos_mean_mae": 0.1,
+      "legacy_bucket_hit_rate": 1.0,
+      "emos_bucket_hit_rate": 1.0
+    },
+    "shenzhen": {
+      "samples": 1,
+      "legacy_mean_crps": 0.798351,
+      "emos_mean_crps": 0.762787,
+      "legacy_mean_mae": 0.9,
+      "emos_mean_mae": 0.9,
+      "legacy_bucket_hit_rate": 0.0,
+      "emos_bucket_hit_rate": 0.0
+    },
+    "singapore": {
+      "samples": 2,
+      "legacy_mean_crps": 0.281993,
+      "emos_mean_crps": 0.37264,
+      "legacy_mean_mae": 0.15,
+      "emos_mean_mae": 0.15,
+      "legacy_bucket_hit_rate": 1.0,
+      "emos_bucket_hit_rate": 1.0
+    },
+    "taipei": {
+      "samples": 3,
+      "legacy_mean_crps": 0.356996,
+      "emos_mean_crps": 0.472738,
+      "legacy_mean_mae": 0.1,
+      "emos_mean_mae": 0.1,
+      "legacy_bucket_hit_rate": 1.0,
+      "emos_bucket_hit_rate": 1.0
+    },
+    "tel aviv": {
+      "samples": 2,
+      "legacy_mean_crps": 0.446758,
+      "emos_mean_crps": 0.578006,
+      "legacy_mean_mae": 0.3,
+      "emos_mean_mae": 0.3,
+      "legacy_bucket_hit_rate": 1.0,
+      "emos_bucket_hit_rate": 1.0
+    },
+    "tokyo": {
+      "samples": 2,
+      "legacy_mean_crps": 0.450128,
+      "emos_mean_crps": 0.582151,
+      "legacy_mean_mae": 0.25,
+      "emos_mean_mae": 0.25,
+      "legacy_bucket_hit_rate": 0.5,
+      "emos_bucket_hit_rate": 1.0
+    },
+    "toronto": {
+      "samples": 2,
+      "legacy_mean_crps": 5.497916,
+      "emos_mean_crps": 5.240552,
+      "legacy_mean_mae": 6.33,
+      "emos_mean_mae": 6.33,
+      "legacy_bucket_hit_rate": 0.0,
+      "emos_bucket_hit_rate": 0.0
+    },
+    "warsaw": {
+      "samples": 3,
+      "legacy_mean_crps": 1.618875,
+      "emos_mean_crps": 1.553232,
+      "legacy_mean_mae": 2.056667,
+      "emos_mean_mae": 2.056667,
+      "legacy_bucket_hit_rate": 0.333333,
+      "emos_bucket_hit_rate": 0.333333
+    },
+    "wellington": {
+      "samples": 2,
+      "legacy_mean_crps": 0.364919,
+      "emos_mean_crps": 0.475875,
+      "legacy_mean_mae": 0.15,
+      "emos_mean_mae": 0.15,
+      "legacy_bucket_hit_rate": 1.0,
+      "emos_bucket_hit_rate": 1.0
+    }
+  }
+}

artifacts/probability_calibration/rollout_report.json

@@ -0,0 +1,74 @@
+{
+  "evaluation_report_path": "E:\\web\\PolyWeather\\artifacts\\probability_calibration\\evaluation_report.json",
+  "shadow_report_path": "E:\\web\\PolyWeather\\artifacts\\probability_calibration\\shadow_report.json",
+  "evaluation_report_exists": true,
+  "shadow_report_exists": true,
+  "decision": {
+    "decision": "hold",
+    "ready_for_primary": false,
+    "summary": "当前指标不足以切换 emos_primary，应继续保持 shadow。",
+    "thresholds": {
+      "evaluation_min_samples": 80,
+      "shadow_min_samples": 50,
+      "max_delta_mae": 0.05,
+      "min_delta_crps": -0.02,
+      "min_delta_bucket_hit_rate": 0.0,
+      "max_delta_bucket_brier_promote": 0.02,
+      "max_delta_bucket_brier_observe": 0.15
+    },
+    "evaluation": {
+      "sample_count": 54,
+      "delta_crps": -0.086732,
+      "delta_mae": 0.0,
+      "delta_bucket_hit_rate": 0.0
+    },
+    "shadow": {
+      "sample_count": 48,
+      "delta_mae": 0.0,
+      "delta_bucket_hit_rate": 0.041666,
+      "delta_bucket_brier": 0.123252
+    },
+    "blocking_reasons": [
+      "离线评估样本不足：54 < 80",
+      "shadow 样本不足：48 < 50",
+      "shadow bucket brier 退化超限：delta=0.123252"
+    ],
+    "worst_shadow_regressions": [
+      {
+        "city": "dallas",
+        "samples": 1,
+        "delta_mae": 0.0,
+        "delta_bucket_hit_rate": 0.0,
+        "delta_bucket_brier": 0.792585
+      },
+      {
+        "city": "chicago",
+        "samples": 1,
+        "delta_mae": 0.0,
+        "delta_bucket_hit_rate": 0.0,
+        "delta_bucket_brier": 0.791878
+      },
+      {
+        "city": "seattle",
+        "samples": 1,
+        "delta_mae": 0.0,
+        "delta_bucket_hit_rate": 0.0,
+        "delta_bucket_brier": 0.61609
+      },
+      {
+        "city": "wellington",
+        "samples": 2,
+        "delta_mae": 0.0,
+        "delta_bucket_hit_rate": 0.0,
+        "delta_bucket_brier": 0.509203
+      },
+      {
+        "city": "tel aviv",
+        "samples": 2,
+        "delta_mae": 0.0,
+        "delta_bucket_hit_rate": 0.0,
+        "delta_bucket_brier": 0.439879
+      }
+    ]
+  }
+}

artifacts/probability_calibration/shadow_report.json

@@ -0,0 +1,933 @@
+{
+  "generated_at": "2026-04-02T16:23:24.376528Z",
+  "summary": {
+    "samples": 48,
+    "legacy_mean_mae": 3.04125,
+    "shadow_mean_mae": 3.04125,
+    "legacy_bucket_hit_rate": 0.5,
+    "shadow_bucket_hit_rate": 0.5,
+    "legacy_bucket_brier": 0.68666,
+    "shadow_bucket_brier": 0.814079,
+    "delta_mae": 0.0,
+    "delta_bucket_hit_rate": 0.0,
+    "delta_bucket_brier": 0.127419
+  },
+  "by_city": {
+    "ankara": {
+      "samples": 2,
+      "legacy_mean_mae": 0.1,
+      "shadow_mean_mae": 0.1,
+      "legacy_bucket_hit_rate": 1.0,
+      "shadow_bucket_hit_rate": 1.0,
+      "legacy_bucket_brier": 0.494847,
+      "shadow_bucket_brier": 0.654648,
+      "delta_mae": 0.0,
+      "delta_bucket_hit_rate": 0.0,
+      "delta_bucket_brier": 0.159801
+    },
+    "atlanta": {
+      "samples": 1,
+      "legacy_mean_mae": 17.06,
+      "shadow_mean_mae": 17.06,
+      "legacy_bucket_hit_rate": 0.0,
+      "shadow_bucket_hit_rate": 0.0,
+      "legacy_bucket_brier": 1.029097,
+      "shadow_bucket_brier": 1.064101,
+      "delta_mae": 0.0,
+      "delta_bucket_hit_rate": 0.0,
+      "delta_bucket_brier": 0.035004
+    },
+    "buenos aires": {
+      "samples": 2,
+      "legacy_mean_mae": 10.27,
+      "shadow_mean_mae": 10.27,
+      "legacy_bucket_hit_rate": 0.0,
+      "shadow_bucket_hit_rate": 0.0,
+      "legacy_bucket_brier": 1.117726,
+      "shadow_bucket_brier": 1.116732,
+      "delta_mae": 0.0,
+      "delta_bucket_hit_rate": 0.0,
+      "delta_bucket_brier": -0.000994
+    },
+    "chicago": {
+      "samples": 1,
+      "legacy_mean_mae": 0.0,
+      "shadow_mean_mae": 0.0,
+      "legacy_bucket_hit_rate": 1.0,
+      "shadow_bucket_hit_rate": 1.0,
+      "legacy_bucket_brier": 0.0,
+      "shadow_bucket_brier": 0.791878,
+      "delta_mae": 0.0,
+      "delta_bucket_hit_rate": 0.0,
+      "delta_bucket_brier": 0.791878
+    },
+    "dallas": {
+      "samples": 1,
+      "legacy_mean_mae": 0.0,
+      "shadow_mean_mae": 0.0,
+      "legacy_bucket_hit_rate": 1.0,
+      "shadow_bucket_hit_rate": 1.0,
+      "legacy_bucket_brier": 0.0,
+      "shadow_bucket_brier": 0.792585,
+      "delta_mae": 0.0,
+      "delta_bucket_hit_rate": 0.0,
+      "delta_bucket_brier": 0.792585
+    },
+    "hong kong": {
+      "samples": 3,
+      "legacy_mean_mae": 0.1,
+      "shadow_mean_mae": 0.1,
+      "legacy_bucket_hit_rate": 0.333333,
+      "shadow_bucket_hit_rate": 0.666667,
+      "legacy_bucket_brier": 0.882203,
+      "shadow_bucket_brier": 0.437187,
+      "delta_mae": 0.0,
+      "delta_bucket_hit_rate": 0.333334,
+      "delta_bucket_brier": -0.445016
+    },
+    "london": {
+      "samples": 2,
+      "legacy_mean_mae": 4.135,
+      "shadow_mean_mae": 4.135,
+      "legacy_bucket_hit_rate": 0.0,
+      "shadow_bucket_hit_rate": 0.0,
+      "legacy_bucket_brier": 0.929652,
+      "shadow_bucket_brier": 1.039551,
+      "delta_mae": 0.0,
+      "delta_bucket_hit_rate": 0.0,
+      "delta_bucket_brier": 0.109899
+    },
+    "lucknow": {
+      "samples": 2,
+      "legacy_mean_mae": 3.205,
+      "shadow_mean_mae": 3.205,
+      "legacy_bucket_hit_rate": 0.0,
+      "shadow_bucket_hit_rate": 0.0,
+      "legacy_bucket_brier": 1.673607,
+      "shadow_bucket_brier": 0.96272,
+      "delta_mae": 0.0,
+      "delta_bucket_hit_rate": 0.0,
+      "delta_bucket_brier": -0.710887
+    },
+    "madrid": {
+      "samples": 2,
+      "legacy_mean_mae": 7.33,
+      "shadow_mean_mae": 7.33,
+      "legacy_bucket_hit_rate": 0.0,
+      "shadow_bucket_hit_rate": 0.0,
+      "legacy_bucket_brier": 1.148213,
+      "shadow_bucket_brier": 1.133888,
+      "delta_mae": 0.0,
+      "delta_bucket_hit_rate": 0.0,
+      "delta_bucket_brier": -0.014325
+    },
+    "miami": {
+      "samples": 1,
+      "legacy_mean_mae": 11.04,
+      "shadow_mean_mae": 11.04,
+      "legacy_bucket_hit_rate": 0.0,
+      "shadow_bucket_hit_rate": 0.0,
+      "legacy_bucket_brier": 1.182605,
+      "shadow_bucket_brier": 1.067257,
+      "delta_mae": 0.0,
+      "delta_bucket_hit_rate": 0.0,
+      "delta_bucket_brier": -0.115348
+    },
+    "milan": {
+      "samples": 3,
+      "legacy_mean_mae": 4.06,
+      "shadow_mean_mae": 4.06,
+      "legacy_bucket_hit_rate": 0.666667,
+      "shadow_bucket_hit_rate": 0.666667,
+      "legacy_bucket_brier": 0.587548,
+      "shadow_bucket_brier": 0.78616,
+      "delta_mae": 0.0,
+      "delta_bucket_hit_rate": 0.0,
+      "delta_bucket_brier": 0.198612
+    },
+    "munich": {
+      "samples": 2,
+      "legacy_mean_mae": 3.64,
+      "shadow_mean_mae": 3.64,
+      "legacy_bucket_hit_rate": 0.0,
+      "shadow_bucket_hit_rate": 0.0,
+      "legacy_bucket_brier": 0.918378,
+      "shadow_bucket_brier": 0.945234,
+      "delta_mae": 0.0,
+      "delta_bucket_hit_rate": 0.0,
+      "delta_bucket_brier": 0.026856
+    },
+    "new york": {
+      "samples": 1,
+      "legacy_mean_mae": 4.94,
+      "shadow_mean_mae": 4.94,
+      "legacy_bucket_hit_rate": 0.0,
+      "shadow_bucket_hit_rate": 0.0,
+      "legacy_bucket_brier": 1.111711,
+      "shadow_bucket_brier": 1.099556,
+      "delta_mae": 0.0,
+      "delta_bucket_hit_rate": 0.0,
+      "delta_bucket_brier": -0.012155
+    },
+    "paris": {
+      "samples": 2,
+      "legacy_mean_mae": 4.265,
+      "shadow_mean_mae": 4.265,
+      "legacy_bucket_hit_rate": 0.5,
+      "shadow_bucket_hit_rate": 0.5,
+      "legacy_bucket_brier": 0.90186,
+      "shadow_bucket_brier": 0.9967,
+      "delta_mae": 0.0,
+      "delta_bucket_hit_rate": 0.0,
+      "delta_bucket_brier": 0.09484
+    },
+    "sao paulo": {
+      "samples": 2,
+      "legacy_mean_mae": 6.57,
+      "shadow_mean_mae": 6.57,
+      "legacy_bucket_hit_rate": 0.0,
+      "shadow_bucket_hit_rate": 0.0,
+      "legacy_bucket_brier": 1.263988,
+      "shadow_bucket_brier": 1.159352,
+      "delta_mae": 0.0,
+      "delta_bucket_hit_rate": 0.0,
+      "delta_bucket_brier": -0.104636
+    },
+    "seattle": {
+      "samples": 1,
+      "legacy_mean_mae": 0.0,
+      "shadow_mean_mae": 0.0,
+      "legacy_bucket_hit_rate": 1.0,
+      "shadow_bucket_hit_rate": 1.0,
+      "legacy_bucket_brier": 0.0,
+      "shadow_bucket_brier": 0.61609,
+      "delta_mae": 0.0,
+      "delta_bucket_hit_rate": 0.0,
+      "delta_bucket_brier": 0.61609
+    },
+    "seoul": {
+      "samples": 2,
+      "legacy_mean_mae": 0.15,
+      "shadow_mean_mae": 0.15,
+      "legacy_bucket_hit_rate": 1.0,
+      "shadow_bucket_hit_rate": 1.0,
+      "legacy_bucket_brier": 0.258977,
+      "shadow_bucket_brier": 0.548485,
+      "delta_mae": 0.0,
+      "delta_bucket_hit_rate": 0.0,
+      "delta_bucket_brier": 0.289508
+    },
+    "shanghai": {
+      "samples": 2,
+      "legacy_mean_mae": 0.15,
+      "shadow_mean_mae": 0.15,
+      "legacy_bucket_hit_rate": 1.0,
+      "shadow_bucket_hit_rate": 1.0,
+      "legacy_bucket_brier": 0.1156,
+      "shadow_bucket_brier": 0.501323,
+      "delta_mae": 0.0,
+      "delta_bucket_hit_rate": 0.0,
+      "delta_bucket_brier": 0.385723
+    },
+    "singapore": {
+      "samples": 2,
+      "legacy_mean_mae": 0.15,
+      "shadow_mean_mae": 0.15,
+      "legacy_bucket_hit_rate": 1.0,
+      "shadow_bucket_hit_rate": 1.0,
+      "legacy_bucket_brier": 0.306527,
+      "shadow_bucket_brier": 0.553647,
+      "delta_mae": 0.0,
+      "delta_bucket_hit_rate": 0.0,
+      "delta_bucket_brier": 0.24712
+    },
+    "taipei": {
+      "samples": 3,
+      "legacy_mean_mae": 0.1,
+      "shadow_mean_mae": 0.1,
+      "legacy_bucket_hit_rate": 1.0,
+      "shadow_bucket_hit_rate": 0.333333,
+      "legacy_bucket_brier": 0.195261,
+      "shadow_bucket_brier": 0.684998,
+      "delta_mae": 0.0,
+      "delta_bucket_hit_rate": -0.666667,
+      "delta_bucket_brier": 0.489737
+    },
+    "tel aviv": {
+      "samples": 2,
+      "legacy_mean_mae": 0.3,
+      "shadow_mean_mae": 0.3,
+      "legacy_bucket_hit_rate": 1.0,
+      "shadow_bucket_hit_rate": 1.0,
+      "legacy_bucket_brier": 0.257895,
+      "shadow_bucket_brier": 0.697774,
+      "delta_mae": 0.0,
+      "delta_bucket_hit_rate": 0.0,
+      "delta_bucket_brier": 0.439879
+    },
+    "tokyo": {
+      "samples": 2,
+      "legacy_mean_mae": 0.25,
+      "shadow_mean_mae": 0.25,
+      "legacy_bucket_hit_rate": 0.5,
+      "shadow_bucket_hit_rate": 1.0,
+      "legacy_bucket_brier": 0.284759,
+      "shadow_bucket_brier": 0.69701,
+      "delta_mae": 0.0,
+      "delta_bucket_hit_rate": 0.5,
+      "delta_bucket_brier": 0.412251
+    },
+    "toronto": {
+      "samples": 2,
+      "legacy_mean_mae": 6.33,
+      "shadow_mean_mae": 6.33,
+      "legacy_bucket_hit_rate": 0.0,
+      "shadow_bucket_hit_rate": 0.0,
+      "legacy_bucket_brier": 1.256736,
+      "shadow_bucket_brier": 1.221422,
+      "delta_mae": 0.0,
+      "delta_bucket_hit_rate": 0.0,
+      "delta_bucket_brier": -0.035314
+    },
+    "warsaw": {
+      "samples": 3,
+      "legacy_mean_mae": 2.056667,
+      "shadow_mean_mae": 2.056667,
+      "legacy_bucket_hit_rate": 0.333333,
+      "shadow_bucket_hit_rate": 0.333333,
+      "legacy_bucket_brier": 0.86425,
+      "shadow_bucket_brier": 0.750985,
+      "delta_mae": 0.0,
+      "delta_bucket_hit_rate": 0.0,
+      "delta_bucket_brier": -0.113265
+    },
+    "wellington": {
+      "samples": 2,
+      "legacy_mean_mae": 0.15,
+      "shadow_mean_mae": 0.15,
+      "legacy_bucket_hit_rate": 1.0,
+      "shadow_bucket_hit_rate": 1.0,
+      "legacy_bucket_brier": 0.095481,
+      "shadow_bucket_brier": 0.604684,
+      "delta_mae": 0.0,
+      "delta_bucket_hit_rate": 0.0,
+      "delta_bucket_brier": 0.509203
+    }
+  },
+  "by_date": {
+    "2026-03-17": {
+      "samples": 4,
+      "legacy_mean_mae": 0.15,
+      "shadow_mean_mae": 0.15,
+      "legacy_bucket_hit_rate": 1.0,
+      "shadow_bucket_hit_rate": 0.5,
+      "legacy_bucket_brier": 0.101543,
+      "shadow_bucket_brier": 0.6636,
+      "delta_mae": 0.0,
+      "delta_bucket_hit_rate": -0.5,
+      "delta_bucket_brier": 0.562057
+    },
+    "2026-03-18": {
+      "samples": 25,
+      "legacy_mean_mae": 2.5476,
+      "shadow_mean_mae": 2.5476,
+      "legacy_bucket_hit_rate": 0.52,
+      "shadow_bucket_hit_rate": 0.56,
+      "legacy_bucket_brier": 0.657516,
+      "shadow_bucket_brier": 0.74574,
+      "delta_mae": 0.0,
+      "delta_bucket_hit_rate": 0.04,
+      "delta_bucket_brier": 0.088224
+    },
+    "2026-03-19": {
+      "samples": 19,
+      "legacy_mean_mae": 4.299474,
+      "shadow_mean_mae": 4.299474,
+      "legacy_bucket_hit_rate": 0.368421,
+      "shadow_bucket_hit_rate": 0.421053,
+      "legacy_bucket_brier": 0.848191,
+      "shadow_bucket_brier": 0.935678,
+      "delta_mae": 0.0,
+      "delta_bucket_hit_rate": 0.052632,
+      "delta_bucket_brier": 0.087487
+    }
+  },
+  "recent_observations": [
+    {
+      "city": "wellington",
+      "date": "2026-03-19",
+      "actual_high": 18.0,
+      "actual_bucket": 18,
+      "legacy_mu": 18.3,
+      "shadow_mu": 18.3,
+      "legacy_top_bucket": 18,
+      "shadow_top_bucket": 18,
+      "calibration_version": "emos-20260320130245",
+      "calibration_mode": "emos_shadow"
+    },
+    {
+      "city": "warsaw",
+      "date": "2026-03-19",
+      "actual_high": 7.0,
+      "actual_bucket": 7,
+      "legacy_mu": 12.03,
+      "shadow_mu": 12.03,
+      "legacy_top_bucket": 12,
+      "shadow_top_bucket": 12,
+      "calibration_version": "emos-20260320130245",
+      "calibration_mode": "emos_shadow"
+    },
+    {
+      "city": "toronto",
+      "date": "2026-03-19",
+      "actual_high": -2.0,
+      "actual_bucket": -2,
+      "legacy_mu": 5.67,
+      "shadow_mu": 5.67,
+      "legacy_top_bucket": 6,
+      "shadow_top_bucket": 6,
+      "calibration_version": "emos-20260320130245",
+      "calibration_mode": "emos_shadow"
+    },
+    {
+      "city": "tokyo",
+      "date": "2026-03-19",
+      "actual_high": 16.0,
+      "actual_bucket": 16,
+      "legacy_mu": 16.5,
+      "shadow_mu": 16.5,
+      "legacy_top_bucket": 17,
+      "shadow_top_bucket": 16,
+      "calibration_version": "emos-20260320130245",
+      "calibration_mode": "emos_shadow"
+    },
+    {
+      "city": "tel aviv",
+      "date": "2026-03-19",
+      "actual_high": 21.0,
+      "actual_bucket": 21,
+      "legacy_mu": 21.3,
+      "shadow_mu": 21.3,
+      "legacy_top_bucket": 21,
+      "shadow_top_bucket": 21,
+      "calibration_version": "emos-20260320130245",
+      "calibration_mode": "emos_shadow"
+    },
+    {
+      "city": "taipei",
+      "date": "2026-03-19",
+      "actual_high": 22.0,
+      "actual_bucket": 22,
+      "legacy_mu": 21.7,
+      "shadow_mu": 21.7,
+      "legacy_top_bucket": 22,
+      "shadow_top_bucket": 21,
+      "calibration_version": "emos-20260320130245",
+      "calibration_mode": "emos_shadow"
+    },
+    {
+      "city": "singapore",
+      "date": "2026-03-19",
+      "actual_high": 32.0,
+      "actual_bucket": 32,
+      "legacy_mu": 32.3,
+      "shadow_mu": 32.3,
+      "legacy_top_bucket": 32,
+      "shadow_top_bucket": 32,
+      "calibration_version": "emos-20260320130245",
+      "calibration_mode": "emos_shadow"
+    },
+    {
+      "city": "shanghai",
+      "date": "2026-03-19",
+      "actual_high": 12.0,
+      "actual_bucket": 12,
+      "legacy_mu": 12.3,
+      "shadow_mu": 12.3,
+      "legacy_top_bucket": 12,
+      "shadow_top_bucket": 12,
+      "calibration_version": "emos-20260320130245",
+      "calibration_mode": "emos_shadow"
+    },
+    {
+      "city": "seoul",
+      "date": "2026-03-19",
+      "actual_high": 10.0,
+      "actual_bucket": 10,
+      "legacy_mu": 10.3,
+      "shadow_mu": 10.3,
+      "legacy_top_bucket": 10,
+      "shadow_top_bucket": 10,
+      "calibration_version": "emos-20260320130245",
+      "calibration_mode": "emos_shadow"
+    },
+    {
+      "city": "sao paulo",
+      "date": "2026-03-19",
+      "actual_high": 21.0,
+      "actual_bucket": 21,
+      "legacy_mu": 26.5,
+      "shadow_mu": 26.5,
+      "legacy_top_bucket": 26,
+      "shadow_top_bucket": 26,
+      "calibration_version": "emos-20260320130245",
+      "calibration_mode": "emos_shadow"
+    },
+    {
+      "city": "paris",
+      "date": "2026-03-19",
+      "actual_high": 8.0,
+      "actual_bucket": 8,
+      "legacy_mu": 16.06,
+      "shadow_mu": 16.06,
+      "legacy_top_bucket": 16,
+      "shadow_top_bucket": 16,
+      "calibration_version": "emos-20260320130245",
+      "calibration_mode": "emos_shadow"
+    },
+    {
+      "city": "munich",
+      "date": "2026-03-19",
+      "actual_high": 5.0,
+      "actual_bucket": 5,
+      "legacy_mu": 11.62,
+      "shadow_mu": 11.62,
+      "legacy_top_bucket": 12,
+      "shadow_top_bucket": 12,
+      "calibration_version": "emos-20260320130245",
+      "calibration_mode": "emos_shadow"
+    },
+    {
+      "city": "milan",
+      "date": "2026-03-19",
+      "actual_high": 5.0,
+      "actual_bucket": 5,
+      "legacy_mu": 16.58,
+      "shadow_mu": 16.58,
+      "legacy_top_bucket": 17,
+      "shadow_top_bucket": 17,
+      "calibration_version": "emos-20260320130245",
+      "calibration_mode": "emos_shadow"
+    },
+    {
+      "city": "madrid",
+      "date": "2026-03-19",
+      "actual_high": 8.0,
+      "actual_bucket": 8,
+      "legacy_mu": 18.23,
+      "shadow_mu": 18.23,
+      "legacy_top_bucket": 18,
+      "shadow_top_bucket": 18,
+      "calibration_version": "emos-20260320130245",
+      "calibration_mode": "emos_shadow"
+    },
+    {
+      "city": "lucknow",
+      "date": "2026-03-19",
+      "actual_high": 30.0,
+      "actual_bucket": 30,
+      "legacy_mu": 35.3,
+      "shadow_mu": 35.3,
+      "legacy_top_bucket": 35,
+      "shadow_top_bucket": 35,
+      "calibration_version": "emos-20260320130245",
+      "calibration_mode": "emos_shadow"
+    },
+    {
+      "city": "london",
+      "date": "2026-03-19",
+      "actual_high": 8.0,
+      "actual_bucket": 8,
+      "legacy_mu": 15.71,
+      "shadow_mu": 15.71,
+      "legacy_top_bucket": 16,
+      "shadow_top_bucket": 16,
+      "calibration_version": "emos-20260320130245",
+      "calibration_mode": "emos_shadow"
+    },
+    {
+      "city": "hong kong",
+      "date": "2026-03-19",
+      "actual_high": 27.3,
+      "actual_bucket": 27,
+      "legacy_mu": 27.6,
+      "shadow_mu": 27.6,
+      "legacy_top_bucket": 28,
+      "shadow_top_bucket": 27,
+      "calibration_version": "emos-20260320130245",
+      "calibration_mode": "emos_shadow"
+    },
+    {
+      "city": "buenos aires",
+      "date": "2026-03-19",
+      "actual_high": 16.0,
+      "actual_bucket": 16,
+      "legacy_mu": 27.39,
+      "shadow_mu": 27.39,
+      "legacy_top_bucket": 27,
+      "shadow_top_bucket": 27,
+      "calibration_version": "emos-20260320130245",
+      "calibration_mode": "emos_shadow"
+    },
+    {
+      "city": "ankara",
+      "date": "2026-03-19",
+      "actual_high": 11.0,
+      "actual_bucket": 11,
+      "legacy_mu": 11.0,
+      "shadow_mu": 11.0,
+      "legacy_top_bucket": 11,
+      "shadow_top_bucket": 11,
+      "calibration_version": "emos-20260320130245",
+      "calibration_mode": "emos_shadow"
+    },
+    {
+      "city": "wellington",
+      "date": "2026-03-18",
+      "actual_high": 21.0,
+      "actual_bucket": 21,
+      "legacy_mu": 21.0,
+      "shadow_mu": 21.0,
+      "legacy_top_bucket": 21,
+      "shadow_top_bucket": 21,
+      "calibration_version": "emos-20260320130245",
+      "calibration_mode": "emos_shadow"
+    },
+    {
+      "city": "warsaw",
+      "date": "2026-03-18",
+      "actual_high": 13.0,
+      "actual_bucket": 13,
+      "legacy_mu": 13.84,
+      "shadow_mu": 13.84,
+      "legacy_top_bucket": 14,
+      "shadow_top_bucket": 14,
+      "calibration_version": "emos-20260320130245",
+      "calibration_mode": "emos_shadow"
+    },
+    {
+      "city": "toronto",
+      "date": "2026-03-18",
+      "actual_high": -6.0,
+      "actual_bucket": -6,
+      "legacy_mu": -1.01,
+      "shadow_mu": -1.01,
+      "legacy_top_bucket": -1,
+      "shadow_top_bucket": -1,
+      "calibration_version": "emos-20260320130245",
+      "calibration_mode": "emos_shadow"
+    },
+    {
+      "city": "tokyo",
+      "date": "2026-03-18",
+      "actual_high": 17.0,
+      "actual_bucket": 17,
+      "legacy_mu": 17.0,
+      "shadow_mu": 17.0,
+      "legacy_top_bucket": 17,
+      "shadow_top_bucket": 17,
+      "calibration_version": "emos-20260320130245",
+      "calibration_mode": "emos_shadow"
+    },
+    {
+      "city": "tel aviv",
+      "date": "2026-03-18",
+      "actual_high": 30.0,
+      "actual_bucket": 30,
+      "legacy_mu": 30.3,
+      "shadow_mu": 30.3,
+      "legacy_top_bucket": 30,
+      "shadow_top_bucket": 30,
+      "calibration_version": "emos-20260320130245",
+      "calibration_mode": "emos_shadow"
+    },
+    {
+      "city": "taipei",
+      "date": "2026-03-18",
+      "actual_high": 29.0,
+      "actual_bucket": 29,
+      "legacy_mu": 29.0,
+      "shadow_mu": 29.0,
+      "legacy_top_bucket": 29,
+      "shadow_top_bucket": 29,
+      "calibration_version": "emos-20260320130245",
+      "calibration_mode": "emos_shadow"
+    },
+    {
+      "city": "singapore",
+      "date": "2026-03-18",
+      "actual_high": 32.0,
+      "actual_bucket": 32,
+      "legacy_mu": 32.0,
+      "shadow_mu": 32.0,
+      "legacy_top_bucket": 32,
+      "shadow_top_bucket": 32,
+      "calibration_version": "emos-20260320130245",
+      "calibration_mode": "emos_shadow"
+    },
+    {
+      "city": "shanghai",
+      "date": "2026-03-18",
+      "actual_high": 13.0,
+      "actual_bucket": 13,
+      "legacy_mu": 13.0,
+      "shadow_mu": 13.0,
+      "legacy_top_bucket": 13,
+      "shadow_top_bucket": 13,
+      "calibration_version": "emos-20260320130245",
+      "calibration_mode": "emos_shadow"
+    },
+    {
+      "city": "seoul",
+      "date": "2026-03-18",
+      "actual_high": 8.0,
+      "actual_bucket": 8,
+      "legacy_mu": 8.0,
+      "shadow_mu": 8.0,
+      "legacy_top_bucket": 8,
+      "shadow_top_bucket": 8,
+      "calibration_version": "emos-20260320130245",
+      "calibration_mode": "emos_shadow"
+    },
+    {
+      "city": "seattle",
+      "date": "2026-03-18",
+      "actual_high": 55.9,
+      "actual_bucket": 56,
+      "legacy_mu": 55.9,
+      "shadow_mu": 55.9,
+      "legacy_top_bucket": 56,
+      "shadow_top_bucket": 56,
+      "calibration_version": "emos-20260320130245",
+      "calibration_mode": "emos_shadow"
+    },
+    {
+      "city": "sao paulo",
+      "date": "2026-03-18",
+      "actual_high": 22.0,
+      "actual_bucket": 22,
+      "legacy_mu": 29.64,
+      "shadow_mu": 29.64,
+      "legacy_top_bucket": 30,
+      "shadow_top_bucket": 30,
+      "calibration_version": "emos-20260320130245",
+      "calibration_mode": "emos_shadow"
+    },
+    {
+      "city": "paris",
+      "date": "2026-03-18",
+      "actual_high": 14.0,
+      "actual_bucket": 14,
+      "legacy_mu": 14.47,
+      "shadow_mu": 14.47,
+      "legacy_top_bucket": 14,
+      "shadow_top_bucket": 14,
+      "calibration_version": "emos-20260320130245",
+      "calibration_mode": "emos_shadow"
+    },
+    {
+      "city": "new york",
+      "date": "2026-03-18",
+      "actual_high": 32.0,
+      "actual_bucket": 32,
+      "legacy_mu": 36.94,
+      "shadow_mu": 36.94,
+      "legacy_top_bucket": 37,
+      "shadow_top_bucket": 37,
+      "calibration_version": "emos-20260320130245",
+      "calibration_mode": "emos_shadow"
+    },
+    {
+      "city": "munich",
+      "date": "2026-03-18",
+      "actual_high": 10.0,
+      "actual_bucket": 10,
+      "legacy_mu": 10.66,
+      "shadow_mu": 10.66,
+      "legacy_top_bucket": 11,
+      "shadow_top_bucket": 11,
+      "calibration_version": "emos-20260320130245",
+      "calibration_mode": "emos_shadow"
+    },
+    {
+      "city": "milan",
+      "date": "2026-03-18",
+      "actual_high": 14.0,
+      "actual_bucket": 14,
+      "legacy_mu": 14.3,
+      "shadow_mu": 14.3,
+      "legacy_top_bucket": 14,
+      "shadow_top_bucket": 14,
+      "calibration_version": "emos-20260320130245",
+      "calibration_mode": "emos_shadow"
+    },
+    {
+      "city": "miami",
+      "date": "2026-03-18",
+      "actual_high": 59.0,
+      "actual_bucket": 59,
+      "legacy_mu": 70.04,
+      "shadow_mu": 70.04,
+      "legacy_top_bucket": 70,
+      "shadow_top_bucket": 70,
+      "calibration_version": "emos-20260320130245",
+      "calibration_mode": "emos_shadow"
+    },
+    {
+      "city": "madrid",
+      "date": "2026-03-18",
+      "actual_high": 14.0,
+      "actual_bucket": 14,
+      "legacy_mu": 18.43,
+      "shadow_mu": 18.43,
+      "legacy_top_bucket": 18,
+      "shadow_top_bucket": 18,
+      "calibration_version": "emos-20260320130245",
+      "calibration_mode": "emos_shadow"
+    },
+    {
+      "city": "lucknow",
+      "date": "2026-03-18",
+      "actual_high": 34.0,
+      "actual_bucket": 34,
+      "legacy_mu": 35.11,
+      "shadow_mu": 35.11,
+      "legacy_top_bucket": 35,
+      "shadow_top_bucket": 35,
+      "calibration_version": "emos-20260320130245",
+      "calibration_mode": "emos_shadow"
+    },
+    {
+      "city": "london",
+      "date": "2026-03-18",
+      "actual_high": 16.0,
+      "actual_bucket": 16,
+      "legacy_mu": 16.56,
+      "shadow_mu": 16.56,
+      "legacy_top_bucket": 17,
+      "shadow_top_bucket": 17,
+      "calibration_version": "emos-20260320130245",
+      "calibration_mode": "emos_shadow"
+    },
+    {
+      "city": "hong kong",
+      "date": "2026-03-18",
+      "actual_high": 27.8,
+      "actual_bucket": 27,
+      "legacy_mu": 27.8,
+      "shadow_mu": 27.8,
+      "legacy_top_bucket": 28,
+      "shadow_top_bucket": 27,
+      "calibration_version": "emos-20260320130245",
+      "calibration_mode": "emos_shadow"
+    },
+    {
+      "city": "dallas",
+      "date": "2026-03-18",
+      "actual_high": 73.9,
+      "actual_bucket": 74,
+      "legacy_mu": 73.9,
+      "shadow_mu": 73.9,
+      "legacy_top_bucket": 74,
+      "shadow_top_bucket": 74,
+      "calibration_version": "emos-20260320130245",
+      "calibration_mode": "emos_shadow"
+    },
+    {
+      "city": "chicago",
+      "date": "2026-03-18",
+      "actual_high": 46.0,
+      "actual_bucket": 46,
+      "legacy_mu": 46.0,
+      "shadow_mu": 46.0,
+      "legacy_top_bucket": 46,
+      "shadow_top_bucket": 46,
+      "calibration_version": "emos-20260320130245",
+      "calibration_mode": "emos_shadow"
+    },
+    {
+      "city": "buenos aires",
+      "date": "2026-03-18",
+      "actual_high": 17.0,
+      "actual_bucket": 17,
+      "legacy_mu": 26.15,
+      "shadow_mu": 26.15,
+      "legacy_top_bucket": 26,
+      "shadow_top_bucket": 26,
+      "calibration_version": "emos-20260320130245",
+      "calibration_mode": "emos_shadow"
+    },
+    {
+      "city": "atlanta",
+      "date": "2026-03-18",
+      "actual_high": 37.0,
+      "actual_bucket": 37,
+      "legacy_mu": 54.06,
+      "shadow_mu": 54.06,
+      "legacy_top_bucket": 54,
+      "shadow_top_bucket": 54,
+      "calibration_version": "emos-20260320130245",
+      "calibration_mode": "emos_shadow"
+    },
+    {
+      "city": "ankara",
+      "date": "2026-03-18",
+      "actual_high": 15.0,
+      "actual_bucket": 15,
+      "legacy_mu": 15.2,
+      "shadow_mu": 15.2,
+      "legacy_top_bucket": 15,
+      "shadow_top_bucket": 15,
+      "calibration_version": "emos-20260320130245",
+      "calibration_mode": "emos_shadow"
+    },
+    {
+      "city": "warsaw",
+      "date": "2026-03-17",
+      "actual_high": 11.0,
+      "actual_bucket": 11,
+      "legacy_mu": 11.3,
+      "shadow_mu": 11.3,
+      "legacy_top_bucket": 11,
+      "shadow_top_bucket": 11,
+      "calibration_version": "emos-20260320130245",
+      "calibration_mode": "emos_shadow"
+    },
+    {
+      "city": "taipei",
+      "date": "2026-03-17",
+      "actual_high": 26.7,
+      "actual_bucket": 27,
+      "legacy_mu": 26.7,
+      "shadow_mu": 26.7,
+      "legacy_top_bucket": 27,
+      "shadow_top_bucket": 26,
+      "calibration_version": "emos-20260320130245",
+      "calibration_mode": "emos_shadow"
+    },
+    {
+      "city": "milan",
+      "date": "2026-03-17",
+      "actual_high": 17.0,
+      "actual_bucket": 17,
+      "legacy_mu": 17.3,
+      "shadow_mu": 17.3,
+      "legacy_top_bucket": 17,
+      "shadow_top_bucket": 17,
+      "calibration_version": "emos-20260320130245",
+      "calibration_mode": "emos_shadow"
+    },
+    {
+      "city": "hong kong",
+      "date": "2026-03-17",
+      "actual_high": 24.0,
+      "actual_bucket": 24,
+      "legacy_mu": 24.0,
+      "shadow_mu": 24.0,
+      "legacy_top_bucket": 24,
+      "shadow_top_bucket": 23,
+      "calibration_version": "emos-20260320130245",
+      "calibration_mode": "emos_shadow"
+    }
+  ]
+}

artifacts/probability_calibration/training_samples.json

@@ -0,0 +1,981 @@
+{
+  "sample_count": 54,
+  "snapshot_sample_count": 1,
+  "daily_record_sample_count": 53,
+  "filled_actual_from_history": 2,
+  "samples": [
+    {
+      "city": "shenzhen",
+      "date": "2026-03-25",
+      "timestamp": "2026-03-25T08:57:11.783182+00:00",
+      "actual_high": 28.0,
+      "raw_mu": 26.7,
+      "raw_sigma": 0.18016764322916676,
+      "deb_prediction": 28.1,
+      "ens_median": 31.4,
+      "ensemble_spread": 0.5078125000000002,
+      "max_so_far_gap": 1.4000000000000021,
+      "peak_flag": 1.0,
+      "sample_source": "snapshot",
+      "settlement_source": null,
+      "settlement_station_code": null,
+      "truth_version": null,
+      "truth_updated_by": null,
+      "truth_updated_at": null
+    },
+    {
+      "city": "ankara",
+      "date": "2026-03-18",
+      "actual_high": 15.0,
+      "raw_mu": 15.2,
+      "raw_sigma": 1.2000000000000002,
+      "deb_prediction": 15.4,
+      "ens_median": 15.8,
+      "ensemble_spread": 1.2000000000000002,
+      "max_so_far_gap": null,
+      "peak_flag": 0.0,
+      "sample_source": "daily_record",
+      "settlement_source": null,
+      "settlement_station_code": null,
+      "truth_version": null,
+      "truth_updated_by": null,
+      "truth_updated_at": null
+    },
+    {
+      "city": "ankara",
+      "date": "2026-03-19",
+      "actual_high": 11.0,
+      "raw_mu": 11.0,
+      "raw_sigma": 2.05,
+      "deb_prediction": 9.8,
+      "ens_median": 10.1,
+      "ensemble_spread": 2.05,
+      "max_so_far_gap": null,
+      "peak_flag": 0.0,
+      "sample_source": "daily_record",
+      "settlement_source": null,
+      "settlement_station_code": null,
+      "truth_version": null,
+      "truth_updated_by": null,
+      "truth_updated_at": null
+    },
+    {
+      "city": "ankara",
+      "date": "2026-03-29",
+      "actual_high": 10.0,
+      "raw_mu": 10.0,
+      "raw_sigma": 0.9000000000000004,
+      "deb_prediction": 9.7,
+      "ens_median": 10.0,
+      "ensemble_spread": 0.9000000000000004,
+      "max_so_far_gap": null,
+      "peak_flag": 0.0,
+      "sample_source": "daily_record",
+      "settlement_source": null,
+      "settlement_station_code": null,
+      "truth_version": null,
+      "truth_updated_by": null,
+      "truth_updated_at": null
+    },
+    {
+      "city": "atlanta",
+      "date": "2026-03-18",
+      "actual_high": 37.0,
+      "raw_mu": 54.06,
+      "raw_sigma": 4.0,
+      "deb_prediction": 52.5,
+      "ens_median": 52.2,
+      "ensemble_spread": 4.0,
+      "max_so_far_gap": null,
+      "peak_flag": 0.0,
+      "sample_source": "daily_record",
+      "settlement_source": null,
+      "settlement_station_code": null,
+      "truth_version": null,
+      "truth_updated_by": null,
+      "truth_updated_at": null
+    },
+    {
+      "city": "atlanta",
+      "date": "2026-03-19",
+      "actual_high": 18.6,
+      "raw_mu": 65.57,
+      "raw_sigma": 1.5500000000000007,
+      "deb_prediction": 65.7,
+      "ens_median": 65.9,
+      "ensemble_spread": 1.5500000000000007,
+      "max_so_far_gap": null,
+      "peak_flag": 0.0,
+      "sample_source": "daily_record",
+      "settlement_source": null,
+      "settlement_station_code": null,
+      "truth_version": null,
+      "truth_updated_by": null,
+      "truth_updated_at": null
+    },
+    {
+      "city": "buenos aires",
+      "date": "2026-03-18",
+      "actual_high": 17.0,
+      "raw_mu": 26.15,
+      "raw_sigma": 2.0,
+      "deb_prediction": 26.4,
+      "ens_median": 26.0,
+      "ensemble_spread": 2.0,
+      "max_so_far_gap": null,
+      "peak_flag": 0.0,
+      "sample_source": "daily_record",
+      "settlement_source": null,
+      "settlement_station_code": null,
+      "truth_version": null,
+      "truth_updated_by": null,
+      "truth_updated_at": null
+    },
+    {
+      "city": "buenos aires",
+      "date": "2026-03-19",
+      "actual_high": 16.0,
+      "raw_mu": 27.39,
+      "raw_sigma": 2.0999999999999996,
+      "deb_prediction": 26.8,
+      "ens_median": 27.9,
+      "ensemble_spread": 2.0999999999999996,
+      "max_so_far_gap": null,
+      "peak_flag": 0.0,
+      "sample_source": "daily_record",
+      "settlement_source": null,
+      "settlement_station_code": null,
+      "truth_version": null,
+      "truth_updated_by": null,
+      "truth_updated_at": null
+    },
+    {
+      "city": "chicago",
+      "date": "2026-03-18",
+      "actual_high": 46.0,
+      "raw_mu": 46.0,
+      "raw_sigma": 5.350000000000001,
+      "deb_prediction": 42.7,
+      "ens_median": 44.2,
+      "ensemble_spread": 5.350000000000001,
+      "max_so_far_gap": null,
+      "peak_flag": 0.0,
+      "sample_source": "daily_record",
+      "settlement_source": null,
+      "settlement_station_code": null,
+      "truth_version": null,
+      "truth_updated_by": null,
+      "truth_updated_at": null
+    },
+    {
+      "city": "dallas",
+      "date": "2026-03-18",
+      "actual_high": 73.9,
+      "raw_mu": 73.9,
+      "raw_sigma": 9.299999999999997,
+      "deb_prediction": 75.2,
+      "ens_median": 74.4,
+      "ensemble_spread": 9.299999999999997,
+      "max_so_far_gap": null,
+      "peak_flag": 0.0,
+      "sample_source": "daily_record",
+      "settlement_source": null,
+      "settlement_station_code": null,
+      "truth_version": null,
+      "truth_updated_by": null,
+      "truth_updated_at": null
+    },
+    {
+      "city": "hong kong",
+      "date": "2026-03-17",
+      "actual_high": 24.0,
+      "raw_mu": 24.0,
+      "raw_sigma": 1.9000000000000004,
+      "deb_prediction": 23.1,
+      "ens_median": 23.1,
+      "ensemble_spread": 1.9000000000000004,
+      "max_so_far_gap": null,
+      "peak_flag": 0.0,
+      "sample_source": "daily_record",
+      "settlement_source": null,
+      "settlement_station_code": null,
+      "truth_version": null,
+      "truth_updated_by": null,
+      "truth_updated_at": null
+    },
+    {
+      "city": "hong kong",
+      "date": "2026-03-18",
+      "actual_high": 27.8,
+      "raw_mu": 27.8,
+      "raw_sigma": 0.6,
+      "deb_prediction": 24.5,
+      "ens_median": 24.9,
+      "ensemble_spread": 0.6,
+      "max_so_far_gap": null,
+      "peak_flag": 0.0,
+      "sample_source": "daily_record",
+      "settlement_source": null,
+      "settlement_station_code": null,
+      "truth_version": null,
+      "truth_updated_by": null,
+      "truth_updated_at": null
+    },
+    {
+      "city": "hong kong",
+      "date": "2026-03-19",
+      "actual_high": 27.3,
+      "raw_mu": 27.6,
+      "raw_sigma": 0.6,
+      "deb_prediction": 24.9,
+      "ens_median": 25.0,
+      "ensemble_spread": 0.6,
+      "max_so_far_gap": null,
+      "peak_flag": 0.0,
+      "sample_source": "daily_record",
+      "settlement_source": null,
+      "settlement_station_code": null,
+      "truth_version": null,
+      "truth_updated_by": null,
+      "truth_updated_at": null
+    },
+    {
+      "city": "hong kong",
+      "date": "2026-03-23",
+      "actual_high": 27.4,
+      "raw_mu": 27.4,
+      "raw_sigma": 1.6999999999999993,
+      "deb_prediction": 25.2,
+      "ens_median": 25.1,
+      "ensemble_spread": 1.6999999999999993,
+      "max_so_far_gap": null,
+      "peak_flag": 0.0,
+      "sample_source": "daily_record",
+      "settlement_source": null,
+      "settlement_station_code": null,
+      "truth_version": null,
+      "truth_updated_by": null,
+      "truth_updated_at": null
+    },
+    {
+      "city": "london",
+      "date": "2026-03-18",
+      "actual_high": 16.0,
+      "raw_mu": 16.56,
+      "raw_sigma": 1.299999999999999,
+      "deb_prediction": 16.5,
+      "ens_median": 16.8,
+      "ensemble_spread": 1.299999999999999,
+      "max_so_far_gap": null,
+      "peak_flag": 0.0,
+      "sample_source": "daily_record",
+      "settlement_source": null,
+      "settlement_station_code": null,
+      "truth_version": null,
+      "truth_updated_by": null,
+      "truth_updated_at": null
+    },
+    {
+      "city": "london",
+      "date": "2026-03-19",
+      "actual_high": 8.0,
+      "raw_mu": 15.71,
+      "raw_sigma": 0.6,
+      "deb_prediction": 15.9,
+      "ens_median": 15.8,
+      "ensemble_spread": 0.6,
+      "max_so_far_gap": null,
+      "peak_flag": 0.0,
+      "sample_source": "daily_record",
+      "settlement_source": null,
+      "settlement_station_code": null,
+      "truth_version": null,
+      "truth_updated_by": null,
+      "truth_updated_at": null
+    },
+    {
+      "city": "lucknow",
+      "date": "2026-03-18",
+      "actual_high": 34.0,
+      "raw_mu": 35.11,
+      "raw_sigma": 1.3000000000000007,
+      "deb_prediction": 34.3,
+      "ens_median": 34.6,
+      "ensemble_spread": 1.3000000000000007,
+      "max_so_far_gap": null,
+      "peak_flag": 0.0,
+      "sample_source": "daily_record",
+      "settlement_source": null,
+      "settlement_station_code": null,
+      "truth_version": null,
+      "truth_updated_by": null,
+      "truth_updated_at": null
+    },
+    {
+      "city": "lucknow",
+      "date": "2026-03-19",
+      "actual_high": 30.0,
+      "raw_mu": 35.3,
+      "raw_sigma": 1.75,
+      "deb_prediction": 34.5,
+      "ens_median": 34.7,
+      "ensemble_spread": 1.75,
+      "max_so_far_gap": null,
+      "peak_flag": 0.0,
+      "sample_source": "daily_record",
+      "settlement_source": null,
+      "settlement_station_code": null,
+      "truth_version": null,
+      "truth_updated_by": null,
+      "truth_updated_at": null
+    },
+    {
+      "city": "madrid",
+      "date": "2026-03-18",
+      "actual_high": 14.0,
+      "raw_mu": 18.43,
+      "raw_sigma": 1.8499999999999996,
+      "deb_prediction": 18.3,
+      "ens_median": 18.7,
+      "ensemble_spread": 1.8499999999999996,
+      "max_so_far_gap": null,
+      "peak_flag": 0.0,
+      "sample_source": "daily_record",
+      "settlement_source": null,
+      "settlement_station_code": null,
+      "truth_version": null,
+      "truth_updated_by": null,
+      "truth_updated_at": null
+    },
+    {
+      "city": "madrid",
+      "date": "2026-03-19",
+      "actual_high": 8.0,
+      "raw_mu": 18.23,
+      "raw_sigma": 1.8999999999999995,
+      "deb_prediction": 18.4,
+      "ens_median": 18.8,
+      "ensemble_spread": 1.8999999999999995,
+      "max_so_far_gap": null,
+      "peak_flag": 0.0,
+      "sample_source": "daily_record",
+      "settlement_source": null,
+      "settlement_station_code": null,
+      "truth_version": null,
+      "truth_updated_by": null,
+      "truth_updated_at": null
+    },
+    {
+      "city": "miami",
+      "date": "2026-03-18",
+      "actual_high": 59.0,
+      "raw_mu": 70.04,
+      "raw_sigma": 2.8999999999999986,
+      "deb_prediction": 69.1,
+      "ens_median": 70.4,
+      "ensemble_spread": 2.8999999999999986,
+      "max_so_far_gap": null,
+      "peak_flag": 0.0,
+      "sample_source": "daily_record",
+      "settlement_source": null,
+      "settlement_station_code": null,
+      "truth_version": null,
+      "truth_updated_by": null,
+      "truth_updated_at": null
+    },
+    {
+      "city": "miami",
+      "date": "2026-03-19",
+      "actual_high": 24.6,
+      "raw_mu": 73.91,
+      "raw_sigma": 2.5500000000000043,
+      "deb_prediction": 74.2,
+      "ens_median": 74.0,
+      "ensemble_spread": 2.5500000000000043,
+      "max_so_far_gap": null,
+      "peak_flag": 0.0,
+      "sample_source": "daily_record",
+      "settlement_source": null,
+      "settlement_station_code": null,
+      "truth_version": null,
+      "truth_updated_by": null,
+      "truth_updated_at": null
+    },
+    {
+      "city": "milan",
+      "date": "2026-03-17",
+      "actual_high": 17.0,
+      "raw_mu": 17.3,
+      "raw_sigma": 9.1,
+      "deb_prediction": 12.5,
+      "ens_median": 15.1,
+      "ensemble_spread": 9.1,
+      "max_so_far_gap": null,
+      "peak_flag": 0.0,
+      "sample_source": "daily_record",
+      "settlement_source": null,
+      "settlement_station_code": null,
+      "truth_version": null,
+      "truth_updated_by": null,
+      "truth_updated_at": null
+    },
+    {
+      "city": "milan",
+      "date": "2026-03-18",
+      "actual_high": 14.0,
+      "raw_mu": 14.3,
+      "raw_sigma": 0.6,
+      "deb_prediction": 13.7,
+      "ens_median": 13.8,
+      "ensemble_spread": 0.6,
+      "max_so_far_gap": null,
+      "peak_flag": 0.0,
+      "sample_source": "daily_record",
+      "settlement_source": null,
+      "settlement_station_code": null,
+      "truth_version": null,
+      "truth_updated_by": null,
+      "truth_updated_at": null
+    },
+    {
+      "city": "milan",
+      "date": "2026-03-19",
+      "actual_high": 5.0,
+      "raw_mu": 16.58,
+      "raw_sigma": 1.25,
+      "deb_prediction": 16.9,
+      "ens_median": 17.0,
+      "ensemble_spread": 1.25,
+      "max_so_far_gap": null,
+      "peak_flag": 0.0,
+      "sample_source": "daily_record",
+      "settlement_source": null,
+      "settlement_station_code": null,
+      "truth_version": null,
+      "truth_updated_by": null,
+      "truth_updated_at": null
+    },
+    {
+      "city": "munich",
+      "date": "2026-03-18",
+      "actual_high": 10.0,
+      "raw_mu": 10.66,
+      "raw_sigma": 0.6,
+      "deb_prediction": 10.6,
+      "ens_median": 10.6,
+      "ensemble_spread": 0.6,
+      "max_so_far_gap": null,
+      "peak_flag": 0.0,
+      "sample_source": "daily_record",
+      "settlement_source": null,
+      "settlement_station_code": null,
+      "truth_version": null,
+      "truth_updated_by": null,
+      "truth_updated_at": null
+    },
+    {
+      "city": "munich",
+      "date": "2026-03-19",
+      "actual_high": 5.0,
+      "raw_mu": 11.62,
+      "raw_sigma": 1.3000000000000007,
+      "deb_prediction": 11.5,
+      "ens_median": 11.8,
+      "ensemble_spread": 1.3000000000000007,
+      "max_so_far_gap": null,
+      "peak_flag": 0.0,
+      "sample_source": "daily_record",
+      "settlement_source": null,
+      "settlement_station_code": null,
+      "truth_version": null,
+      "truth_updated_by": null,
+      "truth_updated_at": null
+    },
+    {
+      "city": "new york",
+      "date": "2026-03-18",
+      "actual_high": 32.0,
+      "raw_mu": 36.94,
+      "raw_sigma": 2.25,
+      "deb_prediction": 36.8,
+      "ens_median": 37.0,
+      "ensemble_spread": 2.25,
+      "max_so_far_gap": null,
+      "peak_flag": 0.0,
+      "sample_source": "daily_record",
+      "settlement_source": null,
+      "settlement_station_code": null,
+      "truth_version": null,
+      "truth_updated_by": null,
+      "truth_updated_at": null
+    },
+    {
+      "city": "paris",
+      "date": "2026-03-18",
+      "actual_high": 14.0,
+      "raw_mu": 14.47,
+      "raw_sigma": 0.9000000000000004,
+      "deb_prediction": 14.2,
+      "ens_median": 14.8,
+      "ensemble_spread": 0.9000000000000004,
+      "max_so_far_gap": null,
+      "peak_flag": 0.0,
+      "sample_source": "daily_record",
+      "settlement_source": null,
+      "settlement_station_code": null,
+      "truth_version": null,
+      "truth_updated_by": null,
+      "truth_updated_at": null
+    },
+    {
+      "city": "paris",
+      "date": "2026-03-19",
+      "actual_high": 8.0,
+      "raw_mu": 16.06,
+      "raw_sigma": 0.6,
+      "deb_prediction": 16.2,
+      "ens_median": 16.3,
+      "ensemble_spread": 0.6,
+      "max_so_far_gap": null,
+      "peak_flag": 0.0,
+      "sample_source": "daily_record",
+      "settlement_source": null,
+      "settlement_station_code": null,
+      "truth_version": null,
+      "truth_updated_by": null,
+      "truth_updated_at": null
+    },
+    {
+      "city": "sao paulo",
+      "date": "2026-03-18",
+      "actual_high": 22.0,
+      "raw_mu": 29.64,
+      "raw_sigma": 2.450000000000001,
+      "deb_prediction": 30.0,
+      "ens_median": 29.4,
+      "ensemble_spread": 2.450000000000001,
+      "max_so_far_gap": null,
+      "peak_flag": 0.0,
+      "sample_source": "daily_record",
+      "settlement_source": null,
+      "settlement_station_code": null,
+      "truth_version": null,
+      "truth_updated_by": null,
+      "truth_updated_at": null
+    },
+    {
+      "city": "sao paulo",
+      "date": "2026-03-19",
+      "actual_high": 21.0,
+      "raw_mu": 26.5,
+      "raw_sigma": 1.200000000000001,
+      "deb_prediction": 25.5,
+      "ens_median": 26.5,
+      "ensemble_spread": 1.200000000000001,
+      "max_so_far_gap": null,
+      "peak_flag": 0.0,
+      "sample_source": "daily_record",
+      "settlement_source": null,
+      "settlement_station_code": null,
+      "truth_version": null,
+      "truth_updated_by": null,
+      "truth_updated_at": null
+    },
+    {
+      "city": "seattle",
+      "date": "2026-03-18",
+      "actual_high": 55.9,
+      "raw_mu": 55.9,
+      "raw_sigma": 1.3500000000000014,
+      "deb_prediction": 55.8,
+      "ens_median": 55.9,
+      "ensemble_spread": 1.3500000000000014,
+      "max_so_far_gap": null,
+      "peak_flag": 0.0,
+      "sample_source": "daily_record",
+      "settlement_source": null,
+      "settlement_station_code": null,
+      "truth_version": null,
+      "truth_updated_by": null,
+      "truth_updated_at": null
+    },
+    {
+      "city": "seoul",
+      "date": "2026-03-18",
+      "actual_high": 8.0,
+      "raw_mu": 8.0,
+      "raw_sigma": 0.7999999999999998,
+      "deb_prediction": 7.4,
+      "ens_median": 7.4,
+      "ensemble_spread": 0.7999999999999998,
+      "max_so_far_gap": null,
+      "peak_flag": 0.0,
+      "sample_source": "daily_record",
+      "settlement_source": null,
+      "settlement_station_code": null,
+      "truth_version": null,
+      "truth_updated_by": null,
+      "truth_updated_at": null
+    },
+    {
+      "city": "seoul",
+      "date": "2026-03-19",
+      "actual_high": 10.0,
+      "raw_mu": 10.3,
+      "raw_sigma": 1.7999999999999998,
+      "deb_prediction": 7.6,
+      "ens_median": 6.3,
+      "ensemble_spread": 1.7999999999999998,
+      "max_so_far_gap": null,
+      "peak_flag": 0.0,
+      "sample_source": "daily_record",
+      "settlement_source": null,
+      "settlement_station_code": null,
+      "truth_version": null,
+      "truth_updated_by": null,
+      "truth_updated_at": null
+    },
+    {
+      "city": "shanghai",
+      "date": "2026-03-18",
+      "actual_high": 13.0,
+      "raw_mu": 13.0,
+      "raw_sigma": 1.1500000000000004,
+      "deb_prediction": 13.2,
+      "ens_median": 13.0,
+      "ensemble_spread": 1.1500000000000004,
+      "max_so_far_gap": null,
+      "peak_flag": 0.0,
+      "sample_source": "daily_record",
+      "settlement_source": null,
+      "settlement_station_code": null,
+      "truth_version": null,
+      "truth_updated_by": null,
+      "truth_updated_at": null
+    },
+    {
+      "city": "shanghai",
+      "date": "2026-03-19",
+      "actual_high": 12.0,
+      "raw_mu": 12.3,
+      "raw_sigma": 0.7999999999999998,
+      "deb_prediction": 10.7,
+      "ens_median": 11.2,
+      "ensemble_spread": 0.7999999999999998,
+      "max_so_far_gap": null,
+      "peak_flag": 0.0,
+      "sample_source": "daily_record",
+      "settlement_source": null,
+      "settlement_station_code": null,
+      "truth_version": null,
+      "truth_updated_by": null,
+      "truth_updated_at": null
+    },
+    {
+      "city": "shanghai",
+      "date": "2026-03-29",
+      "actual_high": 18.0,
+      "raw_mu": 18.0,
+      "raw_sigma": 1.6999999999999993,
+      "deb_prediction": 18.4,
+      "ens_median": 17.6,
+      "ensemble_spread": 1.6999999999999993,
+      "max_so_far_gap": null,
+      "peak_flag": 0.0,
+      "sample_source": "daily_record",
+      "settlement_source": null,
+      "settlement_station_code": null,
+      "truth_version": null,
+      "truth_updated_by": null,
+      "truth_updated_at": null
+    },
+    {
+      "city": "singapore",
+      "date": "2026-03-18",
+      "actual_high": 32.0,
+      "raw_mu": 32.0,
+      "raw_sigma": 0.9500000000000011,
+      "deb_prediction": 30.2,
+      "ens_median": 30.1,
+      "ensemble_spread": 0.9500000000000011,
+      "max_so_far_gap": null,
+      "peak_flag": 0.0,
+      "sample_source": "daily_record",
+      "settlement_source": null,
+      "settlement_station_code": null,
+      "truth_version": null,
+      "truth_updated_by": null,
+      "truth_updated_at": null
+    },
+    {
+      "city": "singapore",
+      "date": "2026-03-19",
+      "actual_high": 32.0,
+      "raw_mu": 32.3,
+      "raw_sigma": 1.3499999999999996,
+      "deb_prediction": 31.5,
+      "ens_median": 32.1,
+      "ensemble_spread": 1.3499999999999996,
+      "max_so_far_gap": null,
+      "peak_flag": 0.0,
+      "sample_source": "daily_record",
+      "settlement_source": null,
+      "settlement_station_code": null,
+      "truth_version": null,
+      "truth_updated_by": null,
+      "truth_updated_at": null
+    },
+    {
+      "city": "taipei",
+      "date": "2026-03-17",
+      "actual_high": 26.7,
+      "raw_mu": 26.7,
+      "raw_sigma": 2.25,
+      "deb_prediction": 24.9,
+      "ens_median": 25.4,
+      "ensemble_spread": 2.25,
+      "max_so_far_gap": null,
+      "peak_flag": 0.0,
+      "sample_source": "daily_record",
+      "settlement_source": null,
+      "settlement_station_code": null,
+      "truth_version": null,
+      "truth_updated_by": null,
+      "truth_updated_at": null
+    },
+    {
+      "city": "taipei",
+      "date": "2026-03-18",
+      "actual_high": 29.0,
+      "raw_mu": 29.0,
+      "raw_sigma": 1.0500000000000007,
+      "deb_prediction": 27.2,
+      "ens_median": 27.5,
+      "ensemble_spread": 1.0500000000000007,
+      "max_so_far_gap": null,
+      "peak_flag": 0.0,
+      "sample_source": "daily_record",
+      "settlement_source": null,
+      "settlement_station_code": null,
+      "truth_version": null,
+      "truth_updated_by": null,
+      "truth_updated_at": null
+    },
+    {
+      "city": "taipei",
+      "date": "2026-03-19",
+      "actual_high": 22.0,
+      "raw_mu": 21.7,
+      "raw_sigma": 1.1500000000000004,
+      "deb_prediction": 21.5,
+      "ens_median": 21.3,
+      "ensemble_spread": 1.1500000000000004,
+      "max_so_far_gap": null,
+      "peak_flag": 0.0,
+      "sample_source": "daily_record",
+      "settlement_source": null,
+      "settlement_station_code": null,
+      "truth_version": null,
+      "truth_updated_by": null,
+      "truth_updated_at": null
+    },
+    {
+      "city": "tel aviv",
+      "date": "2026-03-18",
+      "actual_high": 30.0,
+      "raw_mu": 30.3,
+      "raw_sigma": 2.1500000000000004,
+      "deb_prediction": 29.0,
+      "ens_median": 28.9,
+      "ensemble_spread": 2.1500000000000004,
+      "max_so_far_gap": null,
+      "peak_flag": 0.0,
+      "sample_source": "daily_record",
+      "settlement_source": null,
+      "settlement_station_code": null,
+      "truth_version": null,
+      "truth_updated_by": null,
+      "truth_updated_at": null
+    },
+    {
+      "city": "tel aviv",
+      "date": "2026-03-19",
+      "actual_high": 21.0,
+      "raw_mu": 21.3,
+      "raw_sigma": 1.5,
+      "deb_prediction": 20.7,
+      "ens_median": 21.1,
+      "ensemble_spread": 1.5,
+      "max_so_far_gap": null,
+      "peak_flag": 0.0,
+      "sample_source": "daily_record",
+      "settlement_source": null,
+      "settlement_station_code": null,
+      "truth_version": null,
+      "truth_updated_by": null,
+      "truth_updated_at": null
+    },
+    {
+      "city": "tokyo",
+      "date": "2026-03-18",
+      "actual_high": 17.0,
+      "raw_mu": 17.0,
+      "raw_sigma": 1.5499999999999998,
+      "deb_prediction": 15.4,
+      "ens_median": 15.8,
+      "ensemble_spread": 1.5499999999999998,
+      "max_so_far_gap": null,
+      "peak_flag": 0.0,
+      "sample_source": "daily_record",
+      "settlement_source": null,
+      "settlement_station_code": null,
+      "truth_version": null,
+      "truth_updated_by": null,
+      "truth_updated_at": null
+    },
+    {
+      "city": "tokyo",
+      "date": "2026-03-19",
+      "actual_high": 16.0,
+      "raw_mu": 16.5,
+      "raw_sigma": 2.0999999999999996,
+      "deb_prediction": 17.8,
+      "ens_median": 18.5,
+      "ensemble_spread": 2.0999999999999996,
+      "max_so_far_gap": null,
+      "peak_flag": 0.0,
+      "sample_source": "daily_record",
+      "settlement_source": null,
+      "settlement_station_code": null,
+      "truth_version": null,
+      "truth_updated_by": null,
+      "truth_updated_at": null
+    },
+    {
+      "city": "toronto",
+      "date": "2026-03-18",
+      "actual_high": -6.0,
+      "raw_mu": -1.01,
+      "raw_sigma": 0.8,
+      "deb_prediction": -0.6,
+      "ens_median": -1.1,
+      "ensemble_spread": 0.8,
+      "max_so_far_gap": null,
+      "peak_flag": 0.0,
+      "sample_source": "daily_record",
+      "settlement_source": null,
+      "settlement_station_code": null,
+      "truth_version": null,
+      "truth_updated_by": null,
+      "truth_updated_at": null
+    },
+    {
+      "city": "toronto",
+      "date": "2026-03-19",
+      "actual_high": -2.0,
+      "raw_mu": 5.67,
+      "raw_sigma": 2.1500000000000004,
+      "deb_prediction": 6.4,
+      "ens_median": 6.3,
+      "ensemble_spread": 2.1500000000000004,
+      "max_so_far_gap": null,
+      "peak_flag": 0.0,
+      "sample_source": "daily_record",
+      "settlement_source": null,
+      "settlement_station_code": null,
+      "truth_version": null,
+      "truth_updated_by": null,
+      "truth_updated_at": null
+    },
+    {
+      "city": "warsaw",
+      "date": "2026-03-17",
+      "actual_high": 11.0,
+      "raw_mu": 11.3,
+      "raw_sigma": 0.6499999999999995,
+      "deb_prediction": 10.4,
+      "ens_median": 10.5,
+      "ensemble_spread": 0.6499999999999995,
+      "max_so_far_gap": null,
+      "peak_flag": 0.0,
+      "sample_source": "daily_record",
+      "settlement_source": null,
+      "settlement_station_code": null,
+      "truth_version": null,
+      "truth_updated_by": null,
+      "truth_updated_at": null
+    },
+    {
+      "city": "warsaw",
+      "date": "2026-03-18",
+      "actual_high": 13.0,
+      "raw_mu": 13.84,
+      "raw_sigma": 1.4000000000000004,
+      "deb_prediction": 13.6,
+      "ens_median": 14.2,
+      "ensemble_spread": 1.4000000000000004,
+      "max_so_far_gap": null,
+      "peak_flag": 0.0,
+      "sample_source": "daily_record",
+      "settlement_source": null,
+      "settlement_station_code": null,
+      "truth_version": null,
+      "truth_updated_by": null,
+      "truth_updated_at": null
+    },
+    {
+      "city": "warsaw",
+      "date": "2026-03-19",
+      "actual_high": 7.0,
+      "raw_mu": 12.03,
+      "raw_sigma": 1.5999999999999996,
+      "deb_prediction": 11.8,
+      "ens_median": 12.3,
+      "ensemble_spread": 1.5999999999999996,
+      "max_so_far_gap": null,
+      "peak_flag": 0.0,
+      "sample_source": "daily_record",
+      "settlement_source": null,
+      "settlement_station_code": null,
+      "truth_version": null,
+      "truth_updated_by": null,
+      "truth_updated_at": null
+    },
+    {
+      "city": "wellington",
+      "date": "2026-03-18",
+      "actual_high": 21.0,
+      "raw_mu": 21.0,
+      "raw_sigma": 0.9500000000000011,
+      "deb_prediction": 19.2,
+      "ens_median": 19.1,
+      "ensemble_spread": 0.9500000000000011,
+      "max_so_far_gap": null,
+      "peak_flag": 0.0,
+      "sample_source": "daily_record",
+      "settlement_source": null,
+      "settlement_station_code": null,
+      "truth_version": null,
+      "truth_updated_by": null,
+      "truth_updated_at": null
+    },
+    {
+      "city": "wellington",
+      "date": "2026-03-19",
+      "actual_high": 18.0,
+      "raw_mu": 18.3,
+      "raw_sigma": 2.0999999999999996,
+      "deb_prediction": 17.9,
+      "ens_median": 17.1,
+      "ensemble_spread": 2.0999999999999996,
+      "max_so_far_gap": null,
+      "peak_flag": 0.0,
+      "sample_source": "daily_record",
+      "settlement_source": null,
+      "settlement_station_code": null,
+      "truth_version": null,
+      "truth_updated_by": null,
+      "truth_updated_at": null
+    }
+  ]
+}

bot_listener.py

@@ -1,811 +1,12 @@
-import sys
 import os
-from typing import List
-import telebot  # type: ignore
-from loguru import logger  # type: ignore
+import sys
 
 # 确保项目根目录在 sys.path 中
 project_root = os.path.dirname(os.path.abspath(__file__))
 if project_root not in sys.path:
     sys.path.insert(0, project_root)
 
-from src.utils.config_loader import load_config  # type: ignore # noqa: E402
-from src.utils.telegram_push import start_trade_alert_push_loop  # type: ignore # noqa: E402
-from src.data_collection.weather_sources import WeatherDataCollector  # type: ignore # noqa: E402
-from src.data_collection.city_risk_profiles import get_city_risk_profile  # type: ignore # noqa: E402
-from src.analysis.deb_algorithm import calculate_dynamic_weights, update_daily_record  # noqa: E402
-from src.database.db_manager import DBManager
-
-MESSAGE_POINTS = 1
-MESSAGE_DAILY_CAP = 50
-MESSAGE_MIN_LENGTH = 4
-MESSAGE_COOLDOWN_SEC = 30
-CITY_QUERY_COST = 1
-DEB_QUERY_COST = 1
-
-
-def analyze_weather_trend(weather_data, temp_symbol, city_name=None):
-    """Thin wrapper — delegates to shared trend_engine module."""
-    from src.analysis.trend_engine import analyze_weather_trend as _analyze
-    display_str, ai_context, _structured = _analyze(weather_data, temp_symbol, city_name)
-    return display_str, ai_context
-
-
-def start_bot():
-    config = load_config()
-    token = os.getenv("TELEGRAM_BOT_TOKEN")
-    if not token:
-        logger.error("未找到 TELEGRAM_BOT_TOKEN 环境变量")
-        return
-
-    bot = telebot.TeleBot(token)
-    db = DBManager()
-    weather = WeatherDataCollector(config)
-    start_trade_alert_push_loop(bot, config)
-
-    def _display_name(user) -> str:
-        return user.username or user.first_name or f"User_{user.id}"
-
-    def _ensure_query_points(message, cost: int, label: str) -> bool:
-        user = message.from_user
-        db.upsert_user(user.id, _display_name(user))
-        result = db.spend_points(user.id, cost)
-        if result.get("ok"):
-            return True
-
-        balance = int(result.get("balance") or 0)
-        required = int(result.get("required") or cost)
-        missing = max(0, required - balance)
-        bot.reply_to(
-            message,
-            (
-                f"❌ 积分不足，无法执行 <b>{label}</b>\n"
-                f"当前积分: <code>{balance}</code>\n"
-                f"需要积分: <code>{required}</code>\n"
-                f"还差积分: <code>{missing}</code>\n\n"
-                f"积分规则：群内有效发言满 {MESSAGE_MIN_LENGTH} 字，"
-                f"每次 +{MESSAGE_POINTS} 分，每日上限 {MESSAGE_DAILY_CAP} 分。"
-            ),
-            parse_mode="HTML",
-        )
-        return False
-
-    @bot.message_handler(commands=["start", "help"])
-    def send_welcome(message):
-        welcome_text = (
-            "🌡️ <b>PolyWeather 天气查询机器人</b>\n\n"
-            "可用指令:\n"
-            f"/city [城市名] - 查询城市天气预测与实测 (消耗 {CITY_QUERY_COST} 积分)\n"
-            f"/deb [城市名] - 查看 DEB 融合预测准确率 (消耗 {DEB_QUERY_COST} 积分)\n"
-            "/top - 查看积分排行榜\n"
-            "/id - 获取当前聊天的 Chat ID\n\n"
-            "示例: <code>/city 伦敦</code>\n"
-            f"💡 <i>提示: 群内有效发言满 {MESSAGE_MIN_LENGTH} 字，每次 +{MESSAGE_POINTS} 分，"
-            f"每日上限 {MESSAGE_DAILY_CAP} 分。</i>"
-        )
-        bot.reply_to(message, welcome_text, parse_mode="HTML")
-
-    @bot.message_handler(commands=["id"])
-    def get_chat_id(message):
-        bot.reply_to(
-            message,
-            f"🎯 当前聊天的 Chat ID 是: <code>{message.chat.id}</code>",
-            parse_mode="HTML",
-        )
-
-    @bot.message_handler(commands=["top"])
-    def show_points(message):
-        """显示当前用户的积分及排行榜"""
-        user = message.from_user
-        db.upsert_user(user.id, _display_name(user))
-        user_info = db.get_user(user.id)
-        
-        leaderboard = db.get_leaderboard(limit=5)
-        rank_text = "🏆 <b>PolyWeather 活跃度排行榜</b>\n"
-        rank_text += "────────────────────\n"
-        for i, entry in enumerate(leaderboard):
-            medal = ["🥇", "🥈", "🥉", "  ", "  "][i] if i < 5 else "  "
-            rank_text += f"{medal} {entry['username'][:12]}: <b>{entry['points']}</b> 点\n"
-        
-        if user_info:
-            rank_text += "────────────────────\n"
-            rank_text += (
-                f"👤 <b>我的状态：</b>\n"
-                f"└ 积分: <code>{user_info['points']}</code>\n"
-                f"└ 发言: <code>{user_info['message_count']}</code> 次\n"
-                f"└ 今日发言积分: <code>{user_info.get('daily_points') or 0}/{MESSAGE_DAILY_CAP}</code>\n"
-                f"└ /city 消耗: <code>{CITY_QUERY_COST}</code> | /deb 消耗: <code>{DEB_QUERY_COST}</code>"
-            )
-        
-        bot.send_message(message.chat.id, rank_text, parse_mode="HTML")
-
-    @bot.message_handler(commands=["deb"])
-    def deb_accuracy(message):
-        """查询 DEB 融合预测的历史准确率"""
-        try:
-            parts = message.text.split(maxsplit=1)
-            if len(parts) < 2:
-                bot.reply_to(
-                    message, "❓ 用法: <code>/deb ankara</code>", parse_mode="HTML"
-                )
-                return
-
-            from src.data_collection.city_registry import ALIASES, CITY_REGISTRY
-            city_input = parts[1].strip().lower()
-            city_name = ALIASES.get(city_input, city_input)
-
-            from src.analysis.deb_algorithm import load_history
-            import os as _os
-
-            # 获取详细历史数据
-            project_root = _os.path.dirname(_os.path.abspath(__file__))
-            history_file = _os.path.join(project_root, "data", "daily_records.json")
-            data = load_history(history_file)
-
-            if city_name not in data or not data[city_name]:
-                bot.reply_to(
-                    message, f"❌ 暂无 {city_name} 的历史数据", parse_mode="HTML"
-                )
-                return
-
-            if not _ensure_query_points(message, DEB_QUERY_COST, "/deb"):
-                return
-
-            city_data = data[city_name]
-            from datetime import datetime as _dt
-
-            today_str = _dt.now().strftime("%Y-%m-%d")
-
-            lines = [f"📊 <b>DEB 准确率报告 - {city_name.title()}</b>\n"]
-
-            # 逐日明细
-            lines.append("<b>📅 逐日记录：</b>")
-            total_days = 0
-            hits = 0
-            deb_errors = []
-            signed_errors = []  # 有正负的误差 (DEB - 实测)
-            model_errors = {}
-
-            for date_str in sorted(city_data.keys()):
-                record = city_data[date_str]
-                actual = record.get("actual_high")
-                deb_pred = record.get("deb_prediction")
-                forecasts = record.get("forecasts", {})
-
-                if actual is None:
-                    continue
-                try:
-                    actual = float(actual)
-                    if deb_pred is not None:
-                        deb_pred = float(deb_pred)
-                except Exception:
-                    continue
-
-                # 如果没有存 DEB 预测值，用当天各模型平均值回算
-                if deb_pred is None and forecasts:
-                    valid_preds = [
-                        float(v) for v in forecasts.values() if v is not None
-                    ]
-                    if valid_preds:
-                        deb_pred = round(sum(valid_preds) / len(valid_preds), 1)
-
-                actual_wu = round(actual)
-
-                # DEB 命中判断
-                if deb_pred is not None and date_str != today_str:
-                    total_days += 1
-                    deb_wu = round(deb_pred)
-                    hit = deb_wu == actual_wu
-                    if hit:
-                        hits += 1
-                    err = deb_pred - actual
-                    deb_errors.append(abs(err))
-                    signed_errors.append(err)
-                    icon = "✅" if hit else "❌"
-                    retro = "≈" if "deb_prediction" not in record else ""
-                    # 错误类型标签
-                    if not hit:
-                        err_label = (
-                            f" 低估{abs(err):.1f}°"
-                            if err < 0
-                            else f" 高估{abs(err):.1f}°"
-                        )
-                    else:
-                        err_label = f" 偏差{abs(err):.1f}°"
-                    mu_val = record.get("mu")
-                    mu_str = f" | μ: {mu_val}" if mu_val is not None else ""
-                    lines.append(
-                        f"  {date_str}: DEB {retro}{deb_pred}→<b>{deb_wu}</b> vs 实测 {actual}→<b>{actual_wu}</b> {icon}{err_label}{mu_str}"
-                    )
-                elif date_str == today_str:
-                    lines.append(f"  {date_str}: 📍 今天进行中 (实测暂 {actual})")
-
-                # 各模型误差统计
-                if date_str != today_str and actual is not None:
-                    for model, pred in forecasts.items():
-                        if pred is not None:
-                            if model not in model_errors:
-                                model_errors[model] = []
-                            model_errors[model].append(abs(float(pred) - actual))
-
-            # 汇总
-            if total_days > 0:
-                hit_rate = hits / total_days * 100
-                deb_mae = sum(deb_errors) / len(deb_errors)
-                lines.append(
-                    f"\n🎯 <b>DEB 总战绩</b>：WU命中 {hits}/{total_days} (<b>{hit_rate:.0f}%</b>) | MAE: {deb_mae:.1f}°"
-                )
-
-                # --- 概率引擎 μ 的战绩 ---
-                from src.analysis.deb_algorithm import get_mu_accuracy
-                mu_acc = get_mu_accuracy(city_name)
-                if mu_acc:
-                    mu_mae, mu_hr, avg_brier, mu_total, _ = mu_acc
-                    lines.append(
-                        f"🎲 <b>概率引擎 (μ)</b>：WU命中 <b>{mu_hr:.0f}%</b> | MAE: {mu_mae:.1f}°"
-                    )
-                    if avg_brier is not None:
-                        # Brier Score 范围是 0 (完美) 到 2 (全错)
-                        bs_eval = "极佳" if avg_brier < 0.2 else ("良好" if avg_brier < 0.4 else "需校准")
-                        lines.append(f"  ▪ Brier评分: {avg_brier:.3f} ({bs_eval})")
-
-
-                # 和各模型 MAE 对比
-                if model_errors:
-                    lines.append("\n📈 <b>模型 MAE 对比</b>：")
-                    model_maes = {
-                        m: sum(e) / len(e) for m, e in model_errors.items() if e
-                    }
-                    sorted_models = sorted(model_maes.items(), key=lambda x: x[1])
-                    for m, mae in sorted_models:
-                        tag = " ⭐" if mae <= deb_mae else ""
-                        lines.append(f"  {m}: {mae:.1f}°{tag}")
-                    lines.append(f"  <b>DEB融合: {deb_mae:.1f}°</b>")
-
-                # 偏差模式分析
-                mean_bias = sum(signed_errors) / len(signed_errors)
-                underest = sum(1 for e in signed_errors if e < -0.3)
-                overest = sum(1 for e in signed_errors if e > 0.3)
-
-                lines.append("\n🔍 <b>偏差分析</b>：")
-                if abs(mean_bias) > 0.3:
-                    bias_dir = "低估" if mean_bias < 0 else "高估"
-                    lines.append(f"  ⚠️ 系统性{bias_dir}：平均偏差 {mean_bias:+.1f}°")
-                else:
-                    lines.append(f"  ✅ 无明显系统偏差（平均 {mean_bias:+.1f}°）")
-                lines.append(
-                    f"  低估 {underest} 次 | 高估 {overest} 次 | 准确 {total_days - underest - overest} 次"
-                )
-
-                # 可操作建议
-                lines.append("\n💡 <b>建议</b>：")
-                if underest > overest and abs(mean_bias) > 0.5:
-                    lines.append(
-                        f"  该城市模型集体低估趋势明显（{mean_bias:+.1f}°），实际最高温可能比 DEB 融合值高 {abs(mean_bias):.0f}-{abs(mean_bias) + 0.5:.0f}°。交易时建议适当看高。"
-                    )
-                elif overest > underest and abs(mean_bias) > 0.5:
-                    lines.append(
-                        f"  该城市模型集体高估趋势明显（{mean_bias:+.1f}°），实际最高温可能比 DEB 融合值低。交易时建议适当看低。"
-                    )
-                elif deb_mae > 1.5:
-                    lines.append(
-                        f"  该城市预报波动大 (MAE {deb_mae:.1f}°)，建议观望或轻仓。"
-                    )
-                elif hit_rate >= 60:
-                    lines.append("  DEB 表现良好，可作为主要参考。")
-                else:
-                    lines.append("  数据积累中，建议结合 AI 分析综合判断。")
-
-                lines.append("\n📝 MAE = 平均绝对误差，越小越准。⭐ = 优于 DEB 融合。")
-            else:
-                lines.append("\n⏳ 尚无完整的 DEB 预测记录，明天起开始统计。")
-
-            lines.append(f"\n💳 本次消耗 <code>{DEB_QUERY_COST}</code> 积分。")
-            bot.reply_to(message, "\n".join(lines), parse_mode="HTML")
-        except Exception as e:
-            bot.reply_to(message, f"❌ 查询失败: {e}")
-
-    @bot.message_handler(commands=["city"])
-    def get_city_info(message):
-        """查询指定城市的天气详情"""
-        try:
-            parts = message.text.split(maxsplit=1)
-            if len(parts) < 2:
-                bot.reply_to(
-                    message,
-                    "❓ 请输入城市名称\n\n用法: <code>/city chicago</code>",
-                    parse_mode="HTML",
-                )
-                return
-
-            from src.data_collection.city_registry import ALIASES, CITY_REGISTRY
-            city_input = parts[1].strip().lower()
-            
-            # --- 使用统一注册表解析城市 ---
-            SUPPORTED_CITIES = list(CITY_REGISTRY.keys())
-
-            # 1. 第一优先级：全称或别名完全匹配
-            city_name = ALIASES.get(city_input)
-            if not city_name and city_input in SUPPORTED_CITIES:
-                city_name = city_input
-
-            # 2. 第二优先级：前缀模糊匹配
-            if not city_name and len(city_input) >= 2:
-                # 搜别名
-                for k, v in ALIASES.items():
-                    if k.startswith(city_input):
-                        city_name = v
-                        break
-                # 搜城市全名
-                if not city_name:
-                    for full_name in SUPPORTED_CITIES:
-                        if full_name.startswith(city_input):
-                            city_name = full_name
-                            break
-
-            # 3. 未找到 → 报错
-            if not city_name:
-                city_list = ", ".join(sorted(SUPPORTED_CITIES))
-                bot.reply_to(
-                    message,
-                    f"❌ 未找到城市: <b>{city_input}</b>\n\n"
-                    f"支持的城市: {city_list}",
-                    parse_mode="HTML",
-                )
-                return
-
-            if not _ensure_query_points(message, CITY_QUERY_COST, "/city"):
-                return
-
-            bot.send_message(
-                message.chat.id, f"🔍 正在查询 {city_name.title()} 的天气数据..."
-            )
-
-            coords = weather.get_coordinates(city_name)
-            if not coords:
-                bot.reply_to(message, f"❌ 未找到城市坐标: {city_name}")
-                return
-
-            weather_data = weather.fetch_all_sources(
-                city_name, lat=coords["lat"], lon=coords["lon"]
-            )
-            open_meteo = weather_data.get("open-meteo", {})
-            metar = weather_data.get("metar", {})
-            mgm = weather_data.get("mgm") or {}
-
-            # 数值归一化
-            def _sf(v):
-                if v is None:
-                    return None
-                try:
-                    return float(v)
-                except Exception:
-                    return None
-
-            temp_unit = open_meteo.get("unit", "celsius")
-            temp_symbol = "°F" if temp_unit == "fahrenheit" else "°C"
-
-            # --- 1. 紧凑 Header (城市 + 时间 + 风险状态) ---
-            local_time = open_meteo.get("current", {}).get("local_time", "")
-            time_str = local_time.split(" ")[1][:5] if " " in local_time else "N/A"
-
-            risk_profile = get_city_risk_profile(city_name)
-            risk_emoji = risk_profile.get("risk_level", "⚪") if risk_profile else "⚪"
-
-            msg_header = f"📍 <b>{city_name.title()}</b> ({time_str}) {risk_emoji}"
-            msg_lines = [msg_header]
-
-            # --- 2. 紧凑 风险提示 ---
-            if risk_profile:
-                bias = risk_profile.get("bias", "±0.0")
-                msg_lines.append(
-                    f"⚠️ {risk_profile.get('airport_name', '')}: {bias}{temp_symbol} | {risk_profile.get('warning', '')}"
-                )
-
-            # --- 3. 紧凑 预测区 ---
-            daily = open_meteo.get("daily", {})
-            dates = daily.get("time", [])[:3]
-            max_temps = daily.get("temperature_2m_max", [])[:3]
-
-            nws_high = _sf(weather_data.get("nws", {}).get("today_high"))
-            mgm_high = _sf(mgm.get("today_high"))
-            mb_high = _sf(weather_data.get("meteoblue", {}).get("today_high"))
-
-            # 今天对比
-            today_t = max_temps[0] if max_temps else "N/A"
-            comp_parts = []
-            sources = ["Open-Meteo"]
-
-            if mb_high is not None:
-                sources.append("MB")
-                comp_parts.append(
-                    f"MB: {mb_high:.1f}{temp_symbol}"
-                    if isinstance(mb_high, (int, float))
-                    else f"MB: {mb_high}"
-                )
-            if nws_high is not None:
-                sources.append("NWS")
-                comp_parts.append(
-                    f"NWS: {nws_high:.1f}{temp_symbol}"
-                    if isinstance(nws_high, (int, float))
-                    else f"NWS: {nws_high}"
-                )
-            if mgm_high is not None:
-                sources.append("MGM")
-                comp_parts.append(
-                    f"🇹🇷 MGM: {mgm_high:.1f}{temp_symbol}"
-                    if isinstance(mgm_high, (int, float))
-                    else f"🇹🇷 MGM: {mgm_high}"
-                )
-
-            # 检查是否有显著分歧 (超过 5°F 或 2.5°C)
-            divergence_warning = ""
-            if mb_high is not None and max_temps:
-                diff = abs(mb_high - (_sf(max_temps[0]) or 0))
-                threshold = 5.0 if temp_unit == "fahrenheit" else 2.5
-                if diff > threshold:
-                    divergence_warning = (
-                        f" ⚠️ <b>模型显著分歧 ({diff:.1f}{temp_symbol})</b>"
-                    )
-
-            comp_str = f" ({' | '.join(comp_parts)})" if comp_parts else ""
-            sources_str = " | ".join(sources)
-
-            msg_lines.append(f"\n📊 <b>预报 ({sources_str})</b>")
-            msg_lines.append(
-                f"👉 <b>今天: {today_t}{temp_symbol}{comp_str}</b>{divergence_warning}"
-            )
-
-            # 明后天
-            if len(dates) > 1:
-                future_forecasts = []
-                mgm_daily = mgm.get("daily_forecasts", {}) or {}
-                for d, t in zip(dates[1:], max_temps[1:]):
-                    # 检查 MGM 是否有该日期的预报
-                    mgm_f = mgm_daily.get(d)
-                    if mgm_f is not None:
-                        future_forecasts.append(
-                            f"{d[5:]}: {t}{temp_symbol} | 🇹🇷 <b>MGM: {mgm_f}{temp_symbol}</b>"
-                        )
-                    else:
-                        future_forecasts.append(f"{d[5:]}: {t}{temp_symbol}")
-                msg_lines.append("📅 " + " | ".join(future_forecasts))
-
-            # --- 3.5 日出日落 + 日照时长 ---
-            sunrises = daily.get("sunrise", [])
-            sunsets = daily.get("sunset", [])
-            sunshine_durations = daily.get("sunshine_duration", [])
-            if sunrises and sunsets:
-                sunrise_t = (
-                    sunrises[0].split("T")[1][:5]
-                    if "T" in str(sunrises[0])
-                    else sunrises[0]
-                )
-                sunset_t = (
-                    sunsets[0].split("T")[1][:5]
-                    if "T" in str(sunsets[0])
-                    else sunsets[0]
-                )
-                sun_line = f"🌅 日出 {sunrise_t} | 🌇 日落 {sunset_t}"
-                if sunshine_durations:
-                    sunshine_hours = sunshine_durations[0] / 3600  # 秒 -> 小时
-                    sun_line += f" | ☀️ 日照 {sunshine_hours:.1f}h"
-                msg_lines.append(sun_line)
-
-            # --- 4. 核心 实测区 (合并 METAR 和 MGM) ---
-            # 基础数据优先用 METAR
-            cur_temp = _sf(
-                metar.get("current", {}).get("temp")
-                if metar
-                else mgm.get("current", {}).get("temp")
-            )
-            max_p = _sf(
-                metar.get("current", {}).get("max_temp_so_far") if metar else None
-            )
-            max_p_time = (
-                metar.get("current", {}).get("max_temp_time") if metar else None
-            )
-            obs_t_str = "N/A"
-            metar_age_min = None  # METAR 数据年龄（分钟）
-            main_source = "METAR" if metar else "MGM"
-
-            if metar:
-                obs_t = metar.get("observation_time", "")
-                try:
-                    if "T" in obs_t:
-                        from datetime import datetime, timezone, timedelta
-
-                        dt = datetime.fromisoformat(obs_t.replace("Z", "+00:00"))
-                        utc_offset = open_meteo.get("utc_offset", 0)
-                        local_dt = dt.astimezone(
-                            timezone(timedelta(seconds=utc_offset))
-                        )
-                        obs_t_str = local_dt.strftime("%H:%M")
-                        # 计算数据年龄
-                        now_utc = datetime.now(timezone.utc)
-                        metar_age_min = int((now_utc - dt).total_seconds() / 60)
-                    elif " " in obs_t:
-                        obs_t_str = obs_t.split(" ")[1][:5]
-                    else:
-                        obs_t_str = obs_t
-                except Exception:
-                    obs_t_str = obs_t[:16]
-            elif mgm:
-                m_time = mgm.get("current", {}).get("time", "")
-                if "T" in m_time:
-                    from datetime import datetime, timezone, timedelta
-
-                    dt = datetime.fromisoformat(m_time.replace("Z", "+00:00"))
-                    m_time = dt.astimezone(timezone(timedelta(hours=3))).strftime(
-                        "%H:%M"
-                    )
-                elif " " in m_time:
-                    m_time = m_time.split(" ")[1][:5]
-                obs_t_str = m_time
-
-            # 数据年龄标注
-            age_tag = ""
-            if metar_age_min is not None:
-                if metar_age_min >= 60:
-                    age_tag = f" ⚠️{metar_age_min}分钟前"
-                elif metar_age_min >= 30:
-                    age_tag = f" ⏳{metar_age_min}分钟前"
-
-            max_str = ""
-            if max_p is not None:
-                import math
-
-                settled_val = math.floor(max_p + 0.5)
-                max_str = f" (最高: {max_p}{temp_symbol}"
-                if max_p_time:
-                    max_str += f" @{max_p_time}"
-                max_str += f" → WU {settled_val}{temp_symbol})"
-
-            # --- 天气状况总结 ---
-            wx_summary = ""
-            # 优先使用 METAR 天气现象
-            metar_wx = metar.get("current", {}).get("wx_desc", "") if metar else ""
-            metar_clouds = metar.get("current", {}).get("clouds", []) if metar else []
-            mgm_cloud = mgm.get("current", {}).get("cloud_cover") if mgm else None
-
-            if metar_wx:
-                wx_upper = metar_wx.upper().strip()
-                wx_tokens = set(wx_upper.split())
-                rain_codes = {
-                    "RA",
-                    "DZ",
-                    "-RA",
-                    "+RA",
-                    "-DZ",
-                    "+DZ",
-                    "TSRA",
-                    "SHRA",
-                    "FZRA",
-                }
-                snow_codes = {"SN", "GR", "GS", "-SN", "+SN", "BLSN"}
-                fog_codes = {"FG", "BR", "HZ", "FZFG"}
-                ts_codes = {"TS", "TSRA"}
-                if ts_codes & wx_tokens:
-                    wx_summary = "⛈️ 雷暴"
-                elif {"+RA", "+SN"} & wx_tokens:
-                    wx_summary = "🌧️ 大雨" if "+RA" in wx_tokens else "❄️ 大雪"
-                elif rain_codes & wx_tokens:
-                    wx_summary = (
-                        "🌧️ 小雨" if {"-RA", "-DZ", "DZ"} & wx_tokens else "🌧️ 下雨"
-                    )
-                elif snow_codes & wx_tokens:
-                    wx_summary = "❄️ 下雪"
-                elif fog_codes & wx_tokens:
-                    wx_summary = "🌫️ 雾/霾"
-
-            # 如果 METAR 没有特殊现象，用云量推断
-            if not wx_summary:
-                # 优先 METAR 云层，回退 MGM
-                cover_code = ""
-                if metar_clouds:
-                    cover_code = metar_clouds[-1].get("cover", "")
-
-                if cover_code in ("SKC", "CLR") or (
-                    cover_code == "" and mgm_cloud is not None and mgm_cloud <= 1
-                ):
-                    wx_summary = "☀️ 晴"
-                elif cover_code == "FEW" or (
-                    cover_code == "" and mgm_cloud is not None and mgm_cloud <= 2
-                ):
-                    wx_summary = "🌤️ 晴间少云"
-                elif cover_code == "SCT" or (
-                    cover_code == "" and mgm_cloud is not None and mgm_cloud <= 4
-                ):
-                    wx_summary = "⛅ 晴间多云"
-                elif cover_code == "BKN" or (
-                    cover_code == "" and mgm_cloud is not None and mgm_cloud <= 6
-                ):
-                    wx_summary = "🌥️ 多云"
-                elif cover_code == "OVC" or (
-                    cover_code == "" and mgm_cloud is not None and mgm_cloud <= 8
-                ):
-                    wx_summary = "☁️ 阴天"
-                elif mgm_cloud is not None:
-                    cloud_names = {
-                        0: "☀️ 晴",
-                        1: "🌤️ 晴",
-                        2: "🌤️ 少云",
-                        3: "⛅ 散云",
-                        4: "⛅ 散云",
-                        5: "🌥️ 多云",
-                        6: "🌥️ 多云",
-                        7: "☁️ 阴",
-                        8: "☁️ 阴天",
-                    }
-                    wx_summary = cloud_names.get(mgm_cloud, "")
-
-            wx_display = f" {wx_summary}" if wx_summary else ""
-            msg_lines.append(
-                f"\n✈️ <b>实测 ({main_source}): {cur_temp}{temp_symbol}</b>{max_str} |{wx_display} | {obs_t_str}{age_tag}"
-            )
-
-            if mgm:
-                m_c = mgm.get("current", {})
-                # 翻译风向
-                wind_dir = m_c.get("wind_dir")
-                wind_speed_ms = m_c.get("wind_speed_ms")
-                dir_str = ""
-                if wind_dir is not None:
-                    dirs = ["北", "东北", "东", "东南", "南", "西南", "西", "西北"]
-                    dir_str = dirs[int((float(wind_dir) + 22.5) % 360 / 45)] + "风 "
-
-                # 体感和湿度（跳过缺失数据）
-                feels_like = m_c.get("feels_like")
-                humidity = m_c.get("humidity")
-                if feels_like is not None or humidity is not None:
-                    parts = []
-                    if feels_like is not None:
-                        parts.append(f"🌡️ 体感: {feels_like}°C")
-                    
-                    # 针对安卡拉，补充市区(Center)实测值
-                    ankara_center = next((s for s in weather_data.get("mgm_nearby", []) if "Bölge/Center" in s.get("name", "")), None)
-                    if ankara_center:
-                        parts.append(f"Ankara (Bölge/Center): <b>{ankara_center['temp']}°C</b>")
-                        
-                    if humidity is not None:
-                        parts.append(f"💧 {humidity}%")
-                    msg_lines.append(f"   [MGM] {' | '.join(parts)}")
-
-                # 风况（跳过缺失数据）
-                if wind_dir is not None and wind_speed_ms is not None:
-                    msg_lines.append(
-                        f"   [MGM] 🌬️ {dir_str}{wind_dir}° ({wind_speed_ms} m/s) | 💧 降水: {m_c.get('rain_24h') or 0}mm"
-                    )
-
-                # 新增：气压和云量
-                extra_parts = []
-                pressure = m_c.get("pressure")
-                if pressure is not None:
-                    extra_parts.append(f"🌡 气压: {pressure}hPa")
-                cloud_cover = m_c.get("cloud_cover")
-                if cloud_cover is not None:
-                    cloud_desc_map = {
-                        0: "晴朗",
-                        1: "少云",
-                        2: "少云",
-                        3: "散云",
-                        4: "散云",
-                        5: "多云",
-                        6: "多云",
-                        7: "很多云",
-                        8: "阴天",
-                    }
-                    cloud_text = cloud_desc_map.get(cloud_cover, f"{cloud_cover}/8")
-                    extra_parts.append(f"☁️ 云量: {cloud_text}({cloud_cover}/8)")
-                mgm_max = m_c.get("mgm_max_temp")
-                if mgm_max is not None:
-                    extra_parts.append(f"🌡️ MGM最高: {mgm_max}°C")
-                if extra_parts:
-                    msg_lines.append(f"   [MGM] {' | '.join(extra_parts)}")
-
-            if metar:
-                m_c = metar.get("current", {})
-                wind = m_c.get("wind_speed_kt")
-                wind_dir = m_c.get("wind_dir")
-                vis = m_c.get("visibility_mi")
-                clouds = m_c.get("clouds", [])
-
-                cloud_desc = ""
-                if clouds:
-                    c_map = {
-                        "BKN": "多云",
-                        "OVC": "阴天",
-                        "FEW": "少云",
-                        "SCT": "散云",
-                        "SKC": "晴",
-                        "CLR": "晴",
-                    }
-                    main = clouds[-1]
-                    cloud_desc = f"☁️ {c_map.get(main.get('cover'), main.get('cover'))}"
-
-                prefix = "[METAR]" if mgm else "   "
-                if not mgm:
-                    msg_lines.append(
-                        f"   {prefix} 💨 {wind or 0}kt ({wind_dir or 0}°) | 👁️ {vis or 10}mi"
-                    )
-
-                if cloud_desc:
-                    msg_lines.append(
-                        f"   {prefix} {cloud_desc} | 👁️ {vis or 10}mi | 💨 {wind or 0}kt"
-                    )
-
-            # --- 5. 态势特征提取 ---
-            feature_str, ai_context = analyze_weather_trend(
-                weather_data, temp_symbol, city_name
-            )
-            if feature_str:
-                # 仅将最核心的信息展示给用户作为"态势分析"
-                # 但后面会把更全的数据传给 AI
-                msg_lines.append("\n💡 <b>分析</b>:")
-                for line in feature_str.split("\n"):
-                    if line.strip():
-                        msg_lines.append(f"- {line.strip()}")
-
-                # --- 6. Groq AI 深度分析 ---
-                try:
-                    from src.analysis.ai_analyzer import get_ai_analysis
-                    # 构建更全的背景数据给 AI
-
-                    # 补充多模型分歧
-                    mm = weather_data.get("multi_model", {})
-                    if mm.get("forecasts"):
-                        mm_str = " | ".join(
-                            [
-                                f"{k}:{v}{temp_symbol}"
-                                for k, v in mm["forecasts"].items()
-                                if v
-                            ]
-                        )
-                        ai_context += f"\n模型分歧: {mm_str}"
-
-                    ai_result = get_ai_analysis(ai_context, city_name, temp_symbol)
-                    if ai_result:
-                        msg_lines.append(f"\n{ai_result}")
-                except Exception as e:
-                    logger.error(f"调用 Groq AI 分析失败: {e}")
-
-            msg_lines.append(f"\n💳 本次消耗 <b>{CITY_QUERY_COST}</b> 积分。")
-            bot.send_message(message.chat.id, "\n".join(msg_lines), parse_mode="HTML")
-
-        except Exception as e:
-            import traceback
-
-            logger.error(f"查询失败: {e}\n{traceback.format_exc()}")
-            bot.reply_to(message, f"❌ 查询失败: {e}")
-
-    @bot.message_handler(func=lambda message: True, content_types=['text'])
-    def track_activity(message):
-        """全量监听消息，用于记录群内发言积分(非指令消息)"""
-        if message.text.startswith('/'):
-            return
-        if message.chat.type not in ("group", "supergroup"):
-            return
-
-        user = message.from_user
-        username = _display_name(user)
-        db.upsert_user(user.id, username)
-
-        result = db.add_message_activity(
-            user.id,
-            text=message.text,
-            points_to_add=MESSAGE_POINTS,
-            cooldown_sec=MESSAGE_COOLDOWN_SEC,
-            daily_cap=MESSAGE_DAILY_CAP,
-            min_text_length=MESSAGE_MIN_LENGTH,
-        )
-        if result.get("awarded"):
-            logger.info(
-                f"message points awarded user={user.id} points=+{MESSAGE_POINTS} "
-                f"daily_points={result.get('daily_points')}/{MESSAGE_DAILY_CAP}"
-            )
-
-    logger.info("🤖 Bot 启动中...")
-    bot.infinity_polling()
+from src.bot.orchestrator import start_bot  # noqa: E402
 
 
 if __name__ == "__main__":

config/config.yaml

@@ -1,53 +1,97 @@
-# Weather API Configuration
 weather:
-  meteoblue_api_key: null # Set via METEOBLUE_API_KEY env var
   timeout: 30
-
-# Target Cities
 cities:
-  - id: "london"
-    city: "London"
-    country: "UK"
-    latitude: 51.5074
-    longitude: -0.1278
-  - id: "paris"
-    city: "Paris"
-    country: "France"
-    latitude: 48.8566
-    longitude: 2.3522
-  - id: "ankara"
-    city: "Ankara"
-    country: "Turkey"
-    latitude: 39.9334
-    longitude: 32.8597
-  - id: "new_york"
-    city: "New York"
-    country: "USA"
-    latitude: 40.7128
-    longitude: -74.0060
-  - id: "chicago"
-    city: "Chicago"
-    country: "USA"
-    latitude: 41.8781
-    longitude: -87.6298
-  - id: "lucknow"
-    city: "Lucknow"
-    country: "India"
-    latitude: 26.7606
-    longitude: 80.8893
-  - id: "sao paulo"
-    city: "São Paulo"
-    country: "Brazil"
-    latitude: -23.4356
-    longitude: -46.4731
-  - id: "munich"
-    city: "Munich"
-    country: "Germany"
-    latitude: 48.3538
-    longitude: 11.7861
-
-# Logging
+- id: london
+  city: London
+  country: UK
+  latitude: 51.5074
+  longitude: -0.1278
+- id: paris
+  city: Paris
+  country: France
+  latitude: 48.8566
+  longitude: 2.3522
+- id: ankara
+  city: Ankara
+  country: Turkey
+  latitude: 39.9334
+  longitude: 32.8597
+- id: new_york
+  city: New York
+  country: USA
+  latitude: 40.7128
+  longitude: -74.006
+- id: chicago
+  city: Chicago
+  country: USA
+  latitude: 41.8781
+  longitude: -87.6298
+- id: lucknow
+  city: Lucknow
+  country: India
+  latitude: 26.7606
+  longitude: 80.8893
+- id: sao paulo
+  city: São Paulo
+  country: Brazil
+  latitude: -23.4356
+  longitude: -46.4731
+- id: munich
+  city: Munich
+  country: Germany
+  latitude: 48.3538
+  longitude: 11.7861
+- id: hong_kong
+  city: Hong Kong
+  country: China
+  latitude: 22.3019
+  longitude: 114.1742
+- id: shanghai
+  city: Shanghai
+  country: China
+  latitude: 31.1434
+  longitude: 121.8052
+- id: singapore
+  city: Singapore
+  country: Singapore
+  latitude: 1.3644
+  longitude: 103.9915
+- id: tokyo
+  city: Tokyo
+  country: Japan
+  latitude: 35.5523
+  longitude: 139.7798
+- id: tel_aviv
+  city: Tel Aviv
+  country: Israel
+  latitude: 32.0114
+  longitude: 34.8867
+- id: chengdu
+  city: Chengdu
+  country: China
+  latitude: 30.5785
+  longitude: 103.9471
+- id: chongqing
+  city: Chongqing
+  country: China
+  latitude: 29.7196
+  longitude: 106.6416
+- id: shenzhen
+  city: Shenzhen
+  country: China
+  latitude: 22.6393
+  longitude: 113.8107
+- id: beijing
+  city: Beijing
+  country: China
+  latitude: 40.0801
+  longitude: 116.5846
+- id: wuhan
+  city: Wuhan
+  country: China
+  latitude: 30.7838
+  longitude: 114.2081
 logging:
-  level: "INFO"
-  rotation: "10 MB"
-  retention: "10 days"
+  level: INFO
+  rotation: 10 MB
+  retention: 10 days

contracts/PolyWeatherCheckout.sol

@@ -0,0 +1,54 @@
+// SPDX-License-Identifier: AGPL-3.0-only
+pragma solidity ^0.8.24;
+
+interface IERC20 {
+    function transferFrom(address from, address to, uint256 value) external returns (bool);
+}
+
+contract PolyWeatherCheckout {
+    address public owner;
+    address public treasury;
+    mapping(address => bool) public allowedToken;
+    mapping(bytes32 => bool) public paidOrder;
+
+    event OrderPaid(
+        bytes32 indexed orderId,
+        address indexed payer,
+        uint256 indexed planId,
+        address token,
+        uint256 amount
+    );
+
+    modifier onlyOwner() {
+        require(msg.sender == owner, "ONLY_OWNER");
+        _;
+    }
+
+    constructor(address _token, address _treasury) {
+        require(_token != address(0) && _treasury != address(0), "ZERO_ADDR");
+        owner = msg.sender;
+        treasury = _treasury;
+        allowedToken[_token] = true;
+    }
+
+    function setTreasury(address _treasury) external onlyOwner {
+        require(_treasury != address(0), "ZERO_ADDR");
+        treasury = _treasury;
+    }
+
+    function setTokenAllowed(address token, bool allowed) external onlyOwner {
+        require(token != address(0), "ZERO_ADDR");
+        allowedToken[token] = allowed;
+    }
+
+    function pay(bytes32 orderId, uint256 planId, uint256 amount, address token) external {
+        require(allowedToken[token], "TOKEN_NOT_ALLOWED");
+        require(amount > 0, "AMOUNT_ZERO");
+        require(!paidOrder[orderId], "ORDER_PAID");
+
+        paidOrder[orderId] = true;
+        require(IERC20(token).transferFrom(msg.sender, treasury, amount), "TRANSFER_FAILED");
+
+        emit OrderPaid(orderId, msg.sender, planId, token, amount);
+    }
+}

contracts/PolyWeatherCheckoutV2.sol

@@ -0,0 +1,267 @@
+// SPDX-License-Identifier: AGPL-3.0-only
+pragma solidity ^0.8.24;
+
+interface IERC20 {
+    function transferFrom(address from, address to, uint256 value) external returns (bool);
+    function transfer(address to, uint256 value) external returns (bool);
+}
+
+library Address {
+    function functionCall(address target, bytes memory data, string memory errorMessage) internal returns (bytes memory) {
+        (bool success, bytes memory returndata) = target.call(data);
+        require(success, errorMessage);
+        return returndata;
+    }
+}
+
+library SafeERC20 {
+    using Address for address;
+
+    function safeTransferFrom(IERC20 token, address from, address to, uint256 value) internal {
+        bytes memory returndata = address(token).functionCall(
+            abi.encodeWithSelector(token.transferFrom.selector, from, to, value),
+            "SAFE_TRANSFER_FROM_FAILED"
+        );
+        if (returndata.length > 0) {
+            require(abi.decode(returndata, (bool)), "SAFE_TRANSFER_FROM_FALSE");
+        }
+    }
+
+    function safeTransfer(IERC20 token, address to, uint256 value) internal {
+        bytes memory returndata = address(token).functionCall(
+            abi.encodeWithSelector(token.transfer.selector, to, value),
+            "SAFE_TRANSFER_FAILED"
+        );
+        if (returndata.length > 0) {
+            require(abi.decode(returndata, (bool)), "SAFE_TRANSFER_FALSE");
+        }
+    }
+}
+
+abstract contract Ownable {
+    address public owner;
+
+    event OwnershipTransferred(address indexed previousOwner, address indexed newOwner);
+
+    modifier onlyOwner() {
+        require(msg.sender == owner, "ONLY_OWNER");
+        _;
+    }
+
+    constructor(address initialOwner) {
+        require(initialOwner != address(0), "ZERO_OWNER");
+        owner = initialOwner;
+        emit OwnershipTransferred(address(0), initialOwner);
+    }
+
+    function transferOwnership(address newOwner) external onlyOwner {
+        require(newOwner != address(0), "ZERO_OWNER");
+        emit OwnershipTransferred(owner, newOwner);
+        owner = newOwner;
+    }
+}
+
+abstract contract Pausable {
+    bool public paused;
+
+    event Paused(address indexed account);
+    event Unpaused(address indexed account);
+
+    modifier whenNotPaused() {
+        require(!paused, "PAUSED");
+        _;
+    }
+
+    function _pause() internal {
+        require(!paused, "PAUSED");
+        paused = true;
+        emit Paused(msg.sender);
+    }
+
+    function _unpause() internal {
+        require(paused, "NOT_PAUSED");
+        paused = false;
+        emit Unpaused(msg.sender);
+    }
+}
+
+abstract contract ReentrancyGuard {
+    uint256 private _status = 1;
+
+    modifier nonReentrant() {
+        require(_status == 1, "REENTRANT");
+        _status = 2;
+        _;
+        _status = 1;
+    }
+}
+
+contract PolyWeatherCheckoutV2 is Ownable, Pausable, ReentrancyGuard {
+    using SafeERC20 for IERC20;
+
+    struct PlanConfig {
+        uint256 amount;
+        bool active;
+    }
+
+    bytes32 public constant AUTHORIZED_PAYMENT_TYPEHASH =
+        keccak256(
+            "AuthorizedPayment(bytes32 orderId,address payer,uint256 planId,address token,uint256 amount,uint256 nonce,uint256 deadline)"
+        );
+
+    bytes32 public immutable DOMAIN_SEPARATOR;
+
+    address public treasury;
+    address public signer;
+    mapping(address => bool) public allowedToken;
+    mapping(bytes32 => bool) public paidOrder;
+    mapping(uint256 => mapping(address => PlanConfig)) public planConfig;
+    mapping(address => uint256) public payerNonce;
+
+    event OrderPaid(
+        bytes32 indexed orderId,
+        address indexed payer,
+        uint256 indexed planId,
+        address token,
+        uint256 amount
+    );
+    event TreasuryUpdated(address indexed treasury);
+    event SignerUpdated(address indexed signer);
+    event TokenAllowedUpdated(address indexed token, bool allowed);
+    event PlanConfigured(uint256 indexed planId, address indexed token, uint256 amount, bool active);
+
+    constructor(address initialOwner, address initialTreasury, address initialSigner)
+        Ownable(initialOwner)
+    {
+        require(initialTreasury != address(0), "ZERO_TREASURY");
+        treasury = initialTreasury;
+        signer = initialSigner;
+
+        uint256 chainId;
+        assembly {
+            chainId := chainid()
+        }
+        DOMAIN_SEPARATOR = keccak256(
+            abi.encode(
+                keccak256(
+                    "EIP712Domain(string name,string version,uint256 chainId,address verifyingContract)"
+                ),
+                keccak256(bytes("PolyWeatherCheckoutV2")),
+                keccak256(bytes("1")),
+                chainId,
+                address(this)
+            )
+        );
+    }
+
+    function setTreasury(address newTreasury) external onlyOwner {
+        require(newTreasury != address(0), "ZERO_ADDR");
+        treasury = newTreasury;
+        emit TreasuryUpdated(newTreasury);
+    }
+
+    function setSigner(address newSigner) external onlyOwner {
+        signer = newSigner;
+        emit SignerUpdated(newSigner);
+    }
+
+    function setTokenAllowed(address token, bool allowed) external onlyOwner {
+        require(token != address(0), "ZERO_ADDR");
+        allowedToken[token] = allowed;
+        emit TokenAllowedUpdated(token, allowed);
+    }
+
+    function setPlan(uint256 planId, address token, uint256 amount, bool active) external onlyOwner {
+        require(planId > 0, "PLAN_ZERO");
+        require(token != address(0), "ZERO_ADDR");
+        require(amount > 0 || !active, "AMOUNT_ZERO");
+        planConfig[planId][token] = PlanConfig({amount: amount, active: active});
+        emit PlanConfigured(planId, token, amount, active);
+    }
+
+    function pause() external onlyOwner {
+        _pause();
+    }
+
+    function unpause() external onlyOwner {
+        _unpause();
+    }
+
+    function payPlan(bytes32 orderId, uint256 planId, address token)
+        external
+        whenNotPaused
+        nonReentrant
+    {
+        require(allowedToken[token], "TOKEN_NOT_ALLOWED");
+        PlanConfig memory config = planConfig[planId][token];
+        require(config.active, "PLAN_NOT_ACTIVE");
+        require(config.amount > 0, "PLAN_AMOUNT_ZERO");
+        _collect(orderId, msg.sender, planId, token, config.amount);
+    }
+
+    function payAuthorized(
+        bytes32 orderId,
+        uint256 planId,
+        address token,
+        uint256 amount,
+        uint256 deadline,
+        bytes calldata signature
+    ) external whenNotPaused nonReentrant {
+        require(allowedToken[token], "TOKEN_NOT_ALLOWED");
+        require(amount > 0, "AMOUNT_ZERO");
+        require(deadline >= block.timestamp, "AUTH_EXPIRED");
+        require(signer != address(0), "SIGNER_NOT_SET");
+
+        uint256 nonce = payerNonce[msg.sender];
+        bytes32 structHash = keccak256(
+            abi.encode(
+                AUTHORIZED_PAYMENT_TYPEHASH,
+                orderId,
+                msg.sender,
+                planId,
+                token,
+                amount,
+                nonce,
+                deadline
+            )
+        );
+        bytes32 digest = keccak256(
+            abi.encodePacked("\x19\x01", DOMAIN_SEPARATOR, structHash)
+        );
+        require(_recover(digest, signature) == signer, "BAD_SIGNATURE");
+        payerNonce[msg.sender] = nonce + 1;
+
+        _collect(orderId, msg.sender, planId, token, amount);
+    }
+
+    function rescueToken(address token, address to, uint256 amount) external onlyOwner nonReentrant {
+        require(token != address(0) && to != address(0), "ZERO_ADDR");
+        IERC20(token).safeTransfer(to, amount);
+    }
+
+    function _collect(bytes32 orderId, address payer, uint256 planId, address token, uint256 amount) internal {
+        require(!paidOrder[orderId], "ORDER_PAID");
+        paidOrder[orderId] = true;
+        IERC20(token).safeTransferFrom(payer, treasury, amount);
+        emit OrderPaid(orderId, payer, planId, token, amount);
+    }
+
+    function _recover(bytes32 digest, bytes calldata signature) internal pure returns (address) {
+        require(signature.length == 65, "BAD_SIG_LEN");
+        bytes32 r;
+        bytes32 s;
+        uint8 v;
+        assembly {
+            r := calldataload(signature.offset)
+            s := calldataload(add(signature.offset, 32))
+            v := byte(0, calldataload(add(signature.offset, 64)))
+        }
+        if (v < 27) {
+            v += 27;
+        }
+        require(v == 27 || v == 28, "BAD_SIG_V");
+        address recovered = ecrecover(digest, v, r, s);
+        require(recovered != address(0), "BAD_SIG");
+        return recovered;
+    }
+}

data/probability_training_snapshots.jsonl

@@ -0,0 +1,128 @@
+{"city": "ankara", "timestamp": "2026-03-20T12:00:00+03:00", "date": "2026-03-20", "temp_symbol": "°C", "raw_mu": 15.2, "raw_sigma": 1.2, "deb_prediction": 15.4, "ensemble": {"p10": 14.8, "median": 15.8, "p90": 17.9}, "multi_model": {"ECMWF": 15.8, "GFS": 14.1, "ICON": 15.9}, "max_so_far": 15.0, "peak_status": "before", "prob_snapshot": [{"v": 15, "p": 0.552}, {"v": 16, "p": 0.377}], "shadow_prob_snapshot": [{"v": 15, "p": 0.324}, {"v": 16, "p": 0.238}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320130245", "calibration_source": "artifacts/probability_calibration/default.json", "calibrated_mu": 15.1, "calibrated_sigma": 1.25}
+{"city": "test_city", "timestamp": "2026-03-04 10:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 29.7, "raw_sigma": 1.5625, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 26.0, "peak_status": "before", "prob_snapshot": [{"v": 30, "p": 0.254}, {"v": 29, "p": 0.234}, {"v": 31, "p": 0.185}, {"v": 28, "p": 0.146}], "shadow_prob_snapshot": [{"v": 30, "p": 0.254}, {"v": 29, "p": 0.234}, {"v": 31, "p": 0.185}, {"v": 28, "p": 0.146}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 29.7, "calibrated_sigma": 1.5625}
+{"city": "test_city", "timestamp": "2026-03-04 17:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 23.0, "raw_sigma": 0.46875, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 23.0, "peak_status": "past", "prob_snapshot": [{"v": 23, "p": 0.834}, {"v": 24, "p": 0.166}], "shadow_prob_snapshot": [{"v": 23, "p": 0.834}, {"v": 24, "p": 0.166}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 23.0, "calibrated_sigma": 0.46875}
+{"city": "test_city", "timestamp": "2026-03-04 14:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 33.3, "raw_sigma": 1.09375, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 33.0, "peak_status": "in_window", "prob_snapshot": [{"v": 33, "p": 0.456}, {"v": 34, "p": 0.391}, {"v": 35, "p": 0.153}], "shadow_prob_snapshot": [{"v": 33, "p": 0.456}, {"v": 34, "p": 0.391}, {"v": 35, "p": 0.153}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 33.3, "calibrated_sigma": 1.09375}
+{"city": "test_city", "timestamp": "2026-03-04 17:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": null, "raw_sigma": 0.46875, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 28.0, "peak_status": "past", "prob_snapshot": [{"v": 28, "p": 1.0}], "shadow_prob_snapshot": [], "probability_engine": "legacy", "probability_mode": "legacy", "calibration_version": null, "calibration_source": null, "calibrated_mu": null, "calibrated_sigma": null}
+{"city": "test_city", "timestamp": "2026-03-04 14:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 29.7, "raw_sigma": 1.09375, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 28.0, "peak_status": "in_window", "prob_snapshot": [{"v": 30, "p": 0.35}, {"v": 29, "p": 0.299}, {"v": 31, "p": 0.187}, {"v": 28, "p": 0.117}], "shadow_prob_snapshot": [{"v": 30, "p": 0.35}, {"v": 29, "p": 0.299}, {"v": 31, "p": 0.187}, {"v": 28, "p": 0.117}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 29.7, "calibrated_sigma": 1.09375}
+{"city": "test_city", "timestamp": "2026-03-04 22:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": null, "raw_sigma": 0.46875, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 28.0, "peak_status": "past", "prob_snapshot": [{"v": 28, "p": 1.0}], "shadow_prob_snapshot": [], "probability_engine": "legacy", "probability_mode": "legacy", "calibration_version": null, "calibration_source": null, "calibrated_mu": null, "calibrated_sigma": null}
+{"city": "test_city", "timestamp": "2026-03-04 16:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 23.0, "raw_sigma": 0.46875, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 23.0, "peak_status": "past", "prob_snapshot": [{"v": 23, "p": 0.834}, {"v": 24, "p": 0.166}], "shadow_prob_snapshot": [{"v": 23, "p": 0.834}, {"v": 24, "p": 0.166}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 23.0, "calibrated_sigma": 0.46875}
+{"city": "test_city", "timestamp": "2026-03-04 14:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 29.85, "raw_sigma": 1.09375, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.5, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 29.5, "peak_status": "in_window", "prob_snapshot": [{"v": 30, "p": 0.565}, {"v": 31, "p": 0.341}, {"v": 32, "p": 0.094}], "shadow_prob_snapshot": [{"v": 30, "p": 0.565}, {"v": 31, "p": 0.341}, {"v": 32, "p": 0.094}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 29.85, "calibrated_sigma": 1.09375}
+{"city": "test_city", "timestamp": "2026-03-04 14:30", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 29.7, "raw_sigma": 1.09375, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 28.0, "peak_status": "in_window", "prob_snapshot": [{"v": 30, "p": 0.35}, {"v": 29, "p": 0.299}, {"v": 31, "p": 0.187}, {"v": 28, "p": 0.117}], "shadow_prob_snapshot": [{"v": 30, "p": 0.35}, {"v": 29, "p": 0.299}, {"v": 31, "p": 0.187}, {"v": 28, "p": 0.117}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 29.7, "calibrated_sigma": 1.09375}
+{"city": "test_city", "timestamp": "2026-03-04 10:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 29.7, "raw_sigma": 1.5625, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 26.0, "peak_status": "before", "prob_snapshot": [{"v": 30, "p": 0.254}, {"v": 29, "p": 0.234}, {"v": 31, "p": 0.185}, {"v": 28, "p": 0.146}], "shadow_prob_snapshot": [{"v": 30, "p": 0.254}, {"v": 29, "p": 0.234}, {"v": 31, "p": 0.185}, {"v": 28, "p": 0.146}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 29.7, "calibrated_sigma": 1.5625}
+{"city": "test_city", "timestamp": "2026-03-04 17:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 23.0, "raw_sigma": 0.46875, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 23.0, "peak_status": "past", "prob_snapshot": [{"v": 23, "p": 0.834}, {"v": 24, "p": 0.166}], "shadow_prob_snapshot": [{"v": 23, "p": 0.834}, {"v": 24, "p": 0.166}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 23.0, "calibrated_sigma": 0.46875}
+{"city": "test_city", "timestamp": "2026-03-04 14:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 33.3, "raw_sigma": 1.09375, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 33.0, "peak_status": "in_window", "prob_snapshot": [{"v": 33, "p": 0.456}, {"v": 34, "p": 0.391}, {"v": 35, "p": 0.153}], "shadow_prob_snapshot": [{"v": 33, "p": 0.456}, {"v": 34, "p": 0.391}, {"v": 35, "p": 0.153}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 33.3, "calibrated_sigma": 1.09375}
+{"city": "test_city", "timestamp": "2026-03-04 17:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": null, "raw_sigma": 0.46875, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 28.0, "peak_status": "past", "prob_snapshot": [{"v": 28, "p": 1.0}], "shadow_prob_snapshot": [], "probability_engine": "legacy", "probability_mode": "legacy", "calibration_version": null, "calibration_source": null, "calibrated_mu": null, "calibrated_sigma": null}
+{"city": "test_city", "timestamp": "2026-03-04 14:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 29.7, "raw_sigma": 1.09375, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 28.0, "peak_status": "in_window", "prob_snapshot": [{"v": 30, "p": 0.35}, {"v": 29, "p": 0.299}, {"v": 31, "p": 0.187}, {"v": 28, "p": 0.117}], "shadow_prob_snapshot": [{"v": 30, "p": 0.35}, {"v": 29, "p": 0.299}, {"v": 31, "p": 0.187}, {"v": 28, "p": 0.117}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 29.7, "calibrated_sigma": 1.09375}
+{"city": "test_city", "timestamp": "2026-03-04 22:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": null, "raw_sigma": 0.46875, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 28.0, "peak_status": "past", "prob_snapshot": [{"v": 28, "p": 1.0}], "shadow_prob_snapshot": [], "probability_engine": "legacy", "probability_mode": "legacy", "calibration_version": null, "calibration_source": null, "calibrated_mu": null, "calibrated_sigma": null}
+{"city": "test_city", "timestamp": "2026-03-04 16:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 23.0, "raw_sigma": 0.46875, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 23.0, "peak_status": "past", "prob_snapshot": [{"v": 23, "p": 0.834}, {"v": 24, "p": 0.166}], "shadow_prob_snapshot": [{"v": 23, "p": 0.834}, {"v": 24, "p": 0.166}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 23.0, "calibrated_sigma": 0.46875}
+{"city": "test_city", "timestamp": "2026-03-04 14:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 29.85, "raw_sigma": 1.09375, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.5, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 29.5, "peak_status": "in_window", "prob_snapshot": [{"v": 30, "p": 0.565}, {"v": 31, "p": 0.341}, {"v": 32, "p": 0.094}], "shadow_prob_snapshot": [{"v": 30, "p": 0.565}, {"v": 31, "p": 0.341}, {"v": 32, "p": 0.094}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 29.85, "calibrated_sigma": 1.09375}
+{"city": "test_city", "timestamp": "2026-03-04 14:30", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 29.7, "raw_sigma": 1.09375, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 28.0, "peak_status": "in_window", "prob_snapshot": [{"v": 30, "p": 0.35}, {"v": 29, "p": 0.299}, {"v": 31, "p": 0.187}, {"v": 28, "p": 0.117}], "shadow_prob_snapshot": [{"v": 30, "p": 0.35}, {"v": 29, "p": 0.299}, {"v": 31, "p": 0.187}, {"v": 28, "p": 0.117}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 29.7, "calibrated_sigma": 1.09375}
+{"city": "test_city", "timestamp": "2026-03-04 10:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 29.7, "raw_sigma": 1.5625, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 26.0, "peak_status": "before", "prob_snapshot": [{"v": 30, "p": 0.254}, {"v": 29, "p": 0.234}, {"v": 31, "p": 0.185}, {"v": 28, "p": 0.146}], "shadow_prob_snapshot": [{"v": 30, "p": 0.254}, {"v": 29, "p": 0.234}, {"v": 31, "p": 0.185}, {"v": 28, "p": 0.146}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 29.7, "calibrated_sigma": 1.5625}
+{"city": "test_city", "timestamp": "2026-03-04 17:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 23.0, "raw_sigma": 0.46875, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 23.0, "peak_status": "past", "prob_snapshot": [{"v": 23, "p": 0.834}, {"v": 24, "p": 0.166}], "shadow_prob_snapshot": [{"v": 23, "p": 0.834}, {"v": 24, "p": 0.166}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 23.0, "calibrated_sigma": 0.46875}
+{"city": "test_city", "timestamp": "2026-03-04 14:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 33.3, "raw_sigma": 1.09375, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 33.0, "peak_status": "in_window", "prob_snapshot": [{"v": 33, "p": 0.456}, {"v": 34, "p": 0.391}, {"v": 35, "p": 0.153}], "shadow_prob_snapshot": [{"v": 33, "p": 0.456}, {"v": 34, "p": 0.391}, {"v": 35, "p": 0.153}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 33.3, "calibrated_sigma": 1.09375}
+{"city": "test_city", "timestamp": "2026-03-04 17:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": null, "raw_sigma": 0.46875, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 28.0, "peak_status": "past", "prob_snapshot": [{"v": 28, "p": 1.0}], "shadow_prob_snapshot": [], "probability_engine": "legacy", "probability_mode": "legacy", "calibration_version": null, "calibration_source": null, "calibrated_mu": null, "calibrated_sigma": null}
+{"city": "test_city", "timestamp": "2026-03-04 14:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 29.7, "raw_sigma": 1.09375, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 28.0, "peak_status": "in_window", "prob_snapshot": [{"v": 30, "p": 0.35}, {"v": 29, "p": 0.299}, {"v": 31, "p": 0.187}, {"v": 28, "p": 0.117}], "shadow_prob_snapshot": [{"v": 30, "p": 0.35}, {"v": 29, "p": 0.299}, {"v": 31, "p": 0.187}, {"v": 28, "p": 0.117}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 29.7, "calibrated_sigma": 1.09375}
+{"city": "test_city", "timestamp": "2026-03-04 22:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": null, "raw_sigma": 0.46875, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 28.0, "peak_status": "past", "prob_snapshot": [{"v": 28, "p": 1.0}], "shadow_prob_snapshot": [], "probability_engine": "legacy", "probability_mode": "legacy", "calibration_version": null, "calibration_source": null, "calibrated_mu": null, "calibrated_sigma": null}
+{"city": "test_city", "timestamp": "2026-03-04 16:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 23.0, "raw_sigma": 0.46875, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 23.0, "peak_status": "past", "prob_snapshot": [{"v": 23, "p": 0.834}, {"v": 24, "p": 0.166}], "shadow_prob_snapshot": [{"v": 23, "p": 0.834}, {"v": 24, "p": 0.166}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 23.0, "calibrated_sigma": 0.46875}
+{"city": "test_city", "timestamp": "2026-03-04 14:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 29.85, "raw_sigma": 1.09375, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.5, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 29.5, "peak_status": "in_window", "prob_snapshot": [{"v": 30, "p": 0.565}, {"v": 31, "p": 0.341}, {"v": 32, "p": 0.094}], "shadow_prob_snapshot": [{"v": 30, "p": 0.565}, {"v": 31, "p": 0.341}, {"v": 32, "p": 0.094}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 29.85, "calibrated_sigma": 1.09375}
+{"city": "test_city", "timestamp": "2026-03-04 14:30", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 29.7, "raw_sigma": 1.09375, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 28.0, "peak_status": "in_window", "prob_snapshot": [{"v": 30, "p": 0.35}, {"v": 29, "p": 0.299}, {"v": 31, "p": 0.187}, {"v": 28, "p": 0.117}], "shadow_prob_snapshot": [{"v": 30, "p": 0.35}, {"v": 29, "p": 0.299}, {"v": 31, "p": 0.187}, {"v": 28, "p": 0.117}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 29.7, "calibrated_sigma": 1.09375}
+{"city": "test_city", "timestamp": "2026-03-04 10:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 29.7, "raw_sigma": 1.5625, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 26.0, "peak_status": "before", "prob_snapshot": [{"v": 30, "p": 0.254}, {"v": 29, "p": 0.234}, {"v": 31, "p": 0.185}, {"v": 28, "p": 0.146}], "shadow_prob_snapshot": [{"v": 30, "p": 0.254}, {"v": 29, "p": 0.234}, {"v": 31, "p": 0.185}, {"v": 28, "p": 0.146}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 29.7, "calibrated_sigma": 1.5625}
+{"city": "test_city", "timestamp": "2026-03-04 17:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 23.0, "raw_sigma": 0.46875, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 23.0, "peak_status": "past", "prob_snapshot": [{"v": 23, "p": 0.834}, {"v": 24, "p": 0.166}], "shadow_prob_snapshot": [{"v": 23, "p": 0.834}, {"v": 24, "p": 0.166}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 23.0, "calibrated_sigma": 0.46875}
+{"city": "test_city", "timestamp": "2026-03-04 14:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 33.3, "raw_sigma": 1.09375, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 33.0, "peak_status": "in_window", "prob_snapshot": [{"v": 33, "p": 0.456}, {"v": 34, "p": 0.391}, {"v": 35, "p": 0.153}], "shadow_prob_snapshot": [{"v": 33, "p": 0.456}, {"v": 34, "p": 0.391}, {"v": 35, "p": 0.153}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 33.3, "calibrated_sigma": 1.09375}
+{"city": "test_city", "timestamp": "2026-03-04 17:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": null, "raw_sigma": 0.46875, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 28.0, "peak_status": "past", "prob_snapshot": [{"v": 28, "p": 1.0}], "shadow_prob_snapshot": [], "probability_engine": "legacy", "probability_mode": "legacy", "calibration_version": null, "calibration_source": null, "calibrated_mu": null, "calibrated_sigma": null}
+{"city": "test_city", "timestamp": "2026-03-04 14:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 29.7, "raw_sigma": 1.09375, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 28.0, "peak_status": "in_window", "prob_snapshot": [{"v": 30, "p": 0.35}, {"v": 29, "p": 0.299}, {"v": 31, "p": 0.187}, {"v": 28, "p": 0.117}], "shadow_prob_snapshot": [{"v": 30, "p": 0.35}, {"v": 29, "p": 0.299}, {"v": 31, "p": 0.187}, {"v": 28, "p": 0.117}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 29.7, "calibrated_sigma": 1.09375}
+{"city": "test_city", "timestamp": "2026-03-04 22:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": null, "raw_sigma": 0.46875, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 28.0, "peak_status": "past", "prob_snapshot": [{"v": 28, "p": 1.0}], "shadow_prob_snapshot": [], "probability_engine": "legacy", "probability_mode": "legacy", "calibration_version": null, "calibration_source": null, "calibrated_mu": null, "calibrated_sigma": null}
+{"city": "test_city", "timestamp": "2026-03-04 16:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 23.0, "raw_sigma": 0.46875, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 23.0, "peak_status": "past", "prob_snapshot": [{"v": 23, "p": 0.834}, {"v": 24, "p": 0.166}], "shadow_prob_snapshot": [{"v": 23, "p": 0.834}, {"v": 24, "p": 0.166}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 23.0, "calibrated_sigma": 0.46875}
+{"city": "test_city", "timestamp": "2026-03-04 14:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 29.85, "raw_sigma": 1.09375, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.5, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 29.5, "peak_status": "in_window", "prob_snapshot": [{"v": 30, "p": 0.565}, {"v": 31, "p": 0.341}, {"v": 32, "p": 0.094}], "shadow_prob_snapshot": [{"v": 30, "p": 0.565}, {"v": 31, "p": 0.341}, {"v": 32, "p": 0.094}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 29.85, "calibrated_sigma": 1.09375}
+{"city": "test_city", "timestamp": "2026-03-04 14:30", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 29.7, "raw_sigma": 1.09375, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 28.0, "peak_status": "in_window", "prob_snapshot": [{"v": 30, "p": 0.35}, {"v": 29, "p": 0.299}, {"v": 31, "p": 0.187}, {"v": 28, "p": 0.117}], "shadow_prob_snapshot": [{"v": 30, "p": 0.35}, {"v": 29, "p": 0.299}, {"v": 31, "p": 0.187}, {"v": 28, "p": 0.117}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 29.7, "calibrated_sigma": 1.09375}
+{"city": "test_city", "timestamp": "2026-03-04 10:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 29.7, "raw_sigma": 1.5625, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 26.0, "peak_status": "before", "prob_snapshot": [{"v": 30, "p": 0.254}, {"v": 29, "p": 0.234}, {"v": 31, "p": 0.185}, {"v": 28, "p": 0.146}], "shadow_prob_snapshot": [{"v": 30, "p": 0.254}, {"v": 29, "p": 0.234}, {"v": 31, "p": 0.185}, {"v": 28, "p": 0.146}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 29.7, "calibrated_sigma": 1.5625}
+{"city": "test_city", "timestamp": "2026-03-04 17:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 23.0, "raw_sigma": 0.46875, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 23.0, "peak_status": "past", "prob_snapshot": [{"v": 23, "p": 0.834}, {"v": 24, "p": 0.166}], "shadow_prob_snapshot": [{"v": 23, "p": 0.834}, {"v": 24, "p": 0.166}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 23.0, "calibrated_sigma": 0.46875}
+{"city": "test_city", "timestamp": "2026-03-04 14:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 33.3, "raw_sigma": 1.09375, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 33.0, "peak_status": "in_window", "prob_snapshot": [{"v": 33, "p": 0.456}, {"v": 34, "p": 0.391}, {"v": 35, "p": 0.153}], "shadow_prob_snapshot": [{"v": 33, "p": 0.456}, {"v": 34, "p": 0.391}, {"v": 35, "p": 0.153}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 33.3, "calibrated_sigma": 1.09375}
+{"city": "test_city", "timestamp": "2026-03-04 17:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": null, "raw_sigma": 0.46875, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 28.0, "peak_status": "past", "prob_snapshot": [{"v": 28, "p": 1.0}], "shadow_prob_snapshot": [], "probability_engine": "legacy", "probability_mode": "legacy", "calibration_version": null, "calibration_source": null, "calibrated_mu": null, "calibrated_sigma": null}
+{"city": "test_city", "timestamp": "2026-03-04 14:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 29.7, "raw_sigma": 1.09375, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 28.0, "peak_status": "in_window", "prob_snapshot": [{"v": 30, "p": 0.35}, {"v": 29, "p": 0.299}, {"v": 31, "p": 0.187}, {"v": 28, "p": 0.117}], "shadow_prob_snapshot": [{"v": 30, "p": 0.35}, {"v": 29, "p": 0.299}, {"v": 31, "p": 0.187}, {"v": 28, "p": 0.117}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 29.7, "calibrated_sigma": 1.09375}
+{"city": "test_city", "timestamp": "2026-03-04 22:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": null, "raw_sigma": 0.46875, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 28.0, "peak_status": "past", "prob_snapshot": [{"v": 28, "p": 1.0}], "shadow_prob_snapshot": [], "probability_engine": "legacy", "probability_mode": "legacy", "calibration_version": null, "calibration_source": null, "calibrated_mu": null, "calibrated_sigma": null}
+{"city": "test_city", "timestamp": "2026-03-04 16:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 23.0, "raw_sigma": 0.46875, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 23.0, "peak_status": "past", "prob_snapshot": [{"v": 23, "p": 0.834}, {"v": 24, "p": 0.166}], "shadow_prob_snapshot": [{"v": 23, "p": 0.834}, {"v": 24, "p": 0.166}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 23.0, "calibrated_sigma": 0.46875}
+{"city": "test_city", "timestamp": "2026-03-04 14:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 29.85, "raw_sigma": 1.09375, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.5, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 29.5, "peak_status": "in_window", "prob_snapshot": [{"v": 30, "p": 0.565}, {"v": 31, "p": 0.341}, {"v": 32, "p": 0.094}], "shadow_prob_snapshot": [{"v": 30, "p": 0.565}, {"v": 31, "p": 0.341}, {"v": 32, "p": 0.094}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 29.85, "calibrated_sigma": 1.09375}
+{"city": "test_city", "timestamp": "2026-03-04 14:30", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 29.7, "raw_sigma": 1.09375, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 28.0, "peak_status": "in_window", "prob_snapshot": [{"v": 30, "p": 0.35}, {"v": 29, "p": 0.299}, {"v": 31, "p": 0.187}, {"v": 28, "p": 0.117}], "shadow_prob_snapshot": [{"v": 30, "p": 0.35}, {"v": 29, "p": 0.299}, {"v": 31, "p": 0.187}, {"v": 28, "p": 0.117}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 29.7, "calibrated_sigma": 1.09375}
+{"city": "test_city", "timestamp": "2026-03-04 10:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 29.7, "raw_sigma": 1.5625, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 26.0, "peak_status": "before", "prob_snapshot": [{"v": 30, "p": 0.254}, {"v": 29, "p": 0.234}, {"v": 31, "p": 0.185}, {"v": 28, "p": 0.146}], "shadow_prob_snapshot": [{"v": 30, "p": 0.254}, {"v": 29, "p": 0.234}, {"v": 31, "p": 0.185}, {"v": 28, "p": 0.146}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 29.7, "calibrated_sigma": 1.5625}
+{"city": "test_city", "timestamp": "2026-03-04 17:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 23.0, "raw_sigma": 0.46875, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 23.0, "peak_status": "past", "prob_snapshot": [{"v": 23, "p": 0.834}, {"v": 24, "p": 0.166}], "shadow_prob_snapshot": [{"v": 23, "p": 0.834}, {"v": 24, "p": 0.166}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 23.0, "calibrated_sigma": 0.46875}
+{"city": "test_city", "timestamp": "2026-03-04 14:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 33.3, "raw_sigma": 1.09375, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 33.0, "peak_status": "in_window", "prob_snapshot": [{"v": 33, "p": 0.456}, {"v": 34, "p": 0.391}, {"v": 35, "p": 0.153}], "shadow_prob_snapshot": [{"v": 33, "p": 0.456}, {"v": 34, "p": 0.391}, {"v": 35, "p": 0.153}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 33.3, "calibrated_sigma": 1.09375}
+{"city": "test_city", "timestamp": "2026-03-04 17:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": null, "raw_sigma": 0.46875, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 28.0, "peak_status": "past", "prob_snapshot": [{"v": 28, "p": 1.0}], "shadow_prob_snapshot": [], "probability_engine": "legacy", "probability_mode": "legacy", "calibration_version": null, "calibration_source": null, "calibrated_mu": null, "calibrated_sigma": null}
+{"city": "test_city", "timestamp": "2026-03-04 14:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 29.7, "raw_sigma": 1.09375, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 28.0, "peak_status": "in_window", "prob_snapshot": [{"v": 30, "p": 0.35}, {"v": 29, "p": 0.299}, {"v": 31, "p": 0.187}, {"v": 28, "p": 0.117}], "shadow_prob_snapshot": [{"v": 30, "p": 0.35}, {"v": 29, "p": 0.299}, {"v": 31, "p": 0.187}, {"v": 28, "p": 0.117}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 29.7, "calibrated_sigma": 1.09375}
+{"city": "test_city", "timestamp": "2026-03-04 22:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": null, "raw_sigma": 0.46875, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 28.0, "peak_status": "past", "prob_snapshot": [{"v": 28, "p": 1.0}], "shadow_prob_snapshot": [], "probability_engine": "legacy", "probability_mode": "legacy", "calibration_version": null, "calibration_source": null, "calibrated_mu": null, "calibrated_sigma": null}
+{"city": "test_city", "timestamp": "2026-03-04 16:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 23.0, "raw_sigma": 0.46875, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 23.0, "peak_status": "past", "prob_snapshot": [{"v": 23, "p": 0.834}, {"v": 24, "p": 0.166}], "shadow_prob_snapshot": [{"v": 23, "p": 0.834}, {"v": 24, "p": 0.166}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 23.0, "calibrated_sigma": 0.46875}
+{"city": "test_city", "timestamp": "2026-03-04 14:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 29.85, "raw_sigma": 1.09375, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.5, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 29.5, "peak_status": "in_window", "prob_snapshot": [{"v": 30, "p": 0.565}, {"v": 31, "p": 0.341}, {"v": 32, "p": 0.094}], "shadow_prob_snapshot": [{"v": 30, "p": 0.565}, {"v": 31, "p": 0.341}, {"v": 32, "p": 0.094}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 29.85, "calibrated_sigma": 1.09375}
+{"city": "test_city", "timestamp": "2026-03-04 14:30", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 29.7, "raw_sigma": 1.09375, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 28.0, "peak_status": "in_window", "prob_snapshot": [{"v": 30, "p": 0.35}, {"v": 29, "p": 0.299}, {"v": 31, "p": 0.187}, {"v": 28, "p": 0.117}], "shadow_prob_snapshot": [{"v": 30, "p": 0.35}, {"v": 29, "p": 0.299}, {"v": 31, "p": 0.187}, {"v": 28, "p": 0.117}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 29.7, "calibrated_sigma": 1.09375}
+{"city": "test_city", "timestamp": "2026-03-04 10:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 29.7, "raw_sigma": 1.5625, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 26.0, "peak_status": "before", "prob_snapshot": [{"v": 30, "p": 0.254}, {"v": 29, "p": 0.234}, {"v": 31, "p": 0.185}, {"v": 28, "p": 0.146}], "shadow_prob_snapshot": [{"v": 30, "p": 0.254}, {"v": 29, "p": 0.234}, {"v": 31, "p": 0.185}, {"v": 28, "p": 0.146}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 29.7, "calibrated_sigma": 1.5625}
+{"city": "test_city", "timestamp": "2026-03-04 17:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 23.0, "raw_sigma": 0.46875, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 23.0, "peak_status": "past", "prob_snapshot": [{"v": 23, "p": 0.834}, {"v": 24, "p": 0.166}], "shadow_prob_snapshot": [{"v": 23, "p": 0.834}, {"v": 24, "p": 0.166}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 23.0, "calibrated_sigma": 0.46875}
+{"city": "test_city", "timestamp": "2026-03-04 14:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 33.3, "raw_sigma": 1.09375, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 33.0, "peak_status": "in_window", "prob_snapshot": [{"v": 33, "p": 0.456}, {"v": 34, "p": 0.391}, {"v": 35, "p": 0.153}], "shadow_prob_snapshot": [{"v": 33, "p": 0.456}, {"v": 34, "p": 0.391}, {"v": 35, "p": 0.153}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 33.3, "calibrated_sigma": 1.09375}
+{"city": "test_city", "timestamp": "2026-03-04 17:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": null, "raw_sigma": 0.46875, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 28.0, "peak_status": "past", "prob_snapshot": [{"v": 28, "p": 1.0}], "shadow_prob_snapshot": [], "probability_engine": "legacy", "probability_mode": "legacy", "calibration_version": null, "calibration_source": null, "calibrated_mu": null, "calibrated_sigma": null}
+{"city": "test_city", "timestamp": "2026-03-04 14:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 29.7, "raw_sigma": 1.09375, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 28.0, "peak_status": "in_window", "prob_snapshot": [{"v": 30, "p": 0.35}, {"v": 29, "p": 0.299}, {"v": 31, "p": 0.187}, {"v": 28, "p": 0.117}], "shadow_prob_snapshot": [{"v": 30, "p": 0.35}, {"v": 29, "p": 0.299}, {"v": 31, "p": 0.187}, {"v": 28, "p": 0.117}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 29.7, "calibrated_sigma": 1.09375}
+{"city": "test_city", "timestamp": "2026-03-04 22:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": null, "raw_sigma": 0.46875, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 28.0, "peak_status": "past", "prob_snapshot": [{"v": 28, "p": 1.0}], "shadow_prob_snapshot": [], "probability_engine": "legacy", "probability_mode": "legacy", "calibration_version": null, "calibration_source": null, "calibrated_mu": null, "calibrated_sigma": null}
+{"city": "test_city", "timestamp": "2026-03-04 16:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 23.0, "raw_sigma": 0.46875, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 23.0, "peak_status": "past", "prob_snapshot": [{"v": 23, "p": 0.834}, {"v": 24, "p": 0.166}], "shadow_prob_snapshot": [{"v": 23, "p": 0.834}, {"v": 24, "p": 0.166}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 23.0, "calibrated_sigma": 0.46875}
+{"city": "test_city", "timestamp": "2026-03-04 14:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 29.85, "raw_sigma": 1.09375, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.5, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 29.5, "peak_status": "in_window", "prob_snapshot": [{"v": 30, "p": 0.565}, {"v": 31, "p": 0.341}, {"v": 32, "p": 0.094}], "shadow_prob_snapshot": [{"v": 30, "p": 0.565}, {"v": 31, "p": 0.341}, {"v": 32, "p": 0.094}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 29.85, "calibrated_sigma": 1.09375}
+{"city": "test_city", "timestamp": "2026-03-04 14:30", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 29.7, "raw_sigma": 1.09375, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 28.0, "peak_status": "in_window", "prob_snapshot": [{"v": 30, "p": 0.35}, {"v": 29, "p": 0.299}, {"v": 31, "p": 0.187}, {"v": 28, "p": 0.117}], "shadow_prob_snapshot": [{"v": 30, "p": 0.35}, {"v": 29, "p": 0.299}, {"v": 31, "p": 0.187}, {"v": 28, "p": 0.117}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 29.7, "calibrated_sigma": 1.09375}
+{"city": "test_city", "timestamp": "2026-03-04 10:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 29.7, "raw_sigma": 1.5625, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 26.0, "peak_status": "before", "prob_snapshot": [{"v": 30, "p": 0.254}, {"v": 29, "p": 0.234}, {"v": 31, "p": 0.185}, {"v": 28, "p": 0.146}], "shadow_prob_snapshot": [{"v": 30, "p": 0.254}, {"v": 29, "p": 0.234}, {"v": 31, "p": 0.185}, {"v": 28, "p": 0.146}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 29.7, "calibrated_sigma": 1.5625}
+{"city": "test_city", "timestamp": "2026-03-04 17:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 23.0, "raw_sigma": 0.46875, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 23.0, "peak_status": "past", "prob_snapshot": [{"v": 23, "p": 0.834}, {"v": 24, "p": 0.166}], "shadow_prob_snapshot": [{"v": 23, "p": 0.834}, {"v": 24, "p": 0.166}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 23.0, "calibrated_sigma": 0.46875}
+{"city": "test_city", "timestamp": "2026-03-04 14:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 33.3, "raw_sigma": 1.09375, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 33.0, "peak_status": "in_window", "prob_snapshot": [{"v": 33, "p": 0.456}, {"v": 34, "p": 0.391}, {"v": 35, "p": 0.153}], "shadow_prob_snapshot": [{"v": 33, "p": 0.456}, {"v": 34, "p": 0.391}, {"v": 35, "p": 0.153}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 33.3, "calibrated_sigma": 1.09375}
+{"city": "test_city", "timestamp": "2026-03-04 17:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": null, "raw_sigma": 0.46875, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 28.0, "peak_status": "past", "prob_snapshot": [{"v": 28, "p": 1.0}], "shadow_prob_snapshot": [], "probability_engine": "legacy", "probability_mode": "legacy", "calibration_version": null, "calibration_source": null, "calibrated_mu": null, "calibrated_sigma": null}
+{"city": "test_city", "timestamp": "2026-03-04 14:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 29.7, "raw_sigma": 1.09375, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 28.0, "peak_status": "in_window", "prob_snapshot": [{"v": 30, "p": 0.35}, {"v": 29, "p": 0.299}, {"v": 31, "p": 0.187}, {"v": 28, "p": 0.117}], "shadow_prob_snapshot": [{"v": 30, "p": 0.35}, {"v": 29, "p": 0.299}, {"v": 31, "p": 0.187}, {"v": 28, "p": 0.117}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 29.7, "calibrated_sigma": 1.09375}
+{"city": "test_city", "timestamp": "2026-03-04 22:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": null, "raw_sigma": 0.46875, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 28.0, "peak_status": "past", "prob_snapshot": [{"v": 28, "p": 1.0}], "shadow_prob_snapshot": [], "probability_engine": "legacy", "probability_mode": "legacy", "calibration_version": null, "calibration_source": null, "calibrated_mu": null, "calibrated_sigma": null}
+{"city": "test_city", "timestamp": "2026-03-04 16:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 23.0, "raw_sigma": 0.46875, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 23.0, "peak_status": "past", "prob_snapshot": [{"v": 23, "p": 0.834}, {"v": 24, "p": 0.166}], "shadow_prob_snapshot": [{"v": 23, "p": 0.834}, {"v": 24, "p": 0.166}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 23.0, "calibrated_sigma": 0.46875}
+{"city": "test_city", "timestamp": "2026-03-04 14:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 29.85, "raw_sigma": 1.09375, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.5, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 29.5, "peak_status": "in_window", "prob_snapshot": [{"v": 30, "p": 0.565}, {"v": 31, "p": 0.341}, {"v": 32, "p": 0.094}], "shadow_prob_snapshot": [{"v": 30, "p": 0.565}, {"v": 31, "p": 0.341}, {"v": 32, "p": 0.094}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 29.85, "calibrated_sigma": 1.09375}
+{"city": "test_city", "timestamp": "2026-03-04 14:30", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 29.7, "raw_sigma": 1.09375, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 28.0, "peak_status": "in_window", "prob_snapshot": [{"v": 30, "p": 0.35}, {"v": 29, "p": 0.299}, {"v": 31, "p": 0.187}, {"v": 28, "p": 0.117}], "shadow_prob_snapshot": [{"v": 30, "p": 0.35}, {"v": 29, "p": 0.299}, {"v": 31, "p": 0.187}, {"v": 28, "p": 0.117}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 29.7, "calibrated_sigma": 1.09375}
+{"city": "test_city", "timestamp": "2026-03-04 10:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 29.7, "raw_sigma": 1.5625, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 26.0, "peak_status": "before", "prob_snapshot": [{"v": 30, "p": 0.254}, {"v": 29, "p": 0.234}, {"v": 31, "p": 0.185}, {"v": 28, "p": 0.146}], "shadow_prob_snapshot": [{"v": 30, "p": 0.254}, {"v": 29, "p": 0.234}, {"v": 31, "p": 0.185}, {"v": 28, "p": 0.146}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 29.7, "calibrated_sigma": 1.5625}
+{"city": "test_city", "timestamp": "2026-03-04 17:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 23.0, "raw_sigma": 0.46875, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 23.0, "peak_status": "past", "prob_snapshot": [{"v": 23, "p": 0.834}, {"v": 24, "p": 0.166}], "shadow_prob_snapshot": [{"v": 23, "p": 0.834}, {"v": 24, "p": 0.166}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 23.0, "calibrated_sigma": 0.46875}
+{"city": "test_city", "timestamp": "2026-03-04 14:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 33.3, "raw_sigma": 1.09375, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 33.0, "peak_status": "in_window", "prob_snapshot": [{"v": 33, "p": 0.456}, {"v": 34, "p": 0.391}, {"v": 35, "p": 0.153}], "shadow_prob_snapshot": [{"v": 33, "p": 0.456}, {"v": 34, "p": 0.391}, {"v": 35, "p": 0.153}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 33.3, "calibrated_sigma": 1.09375}
+{"city": "test_city", "timestamp": "2026-03-04 17:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": null, "raw_sigma": 0.46875, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 28.0, "peak_status": "past", "prob_snapshot": [{"v": 28, "p": 1.0}], "shadow_prob_snapshot": [], "probability_engine": "legacy", "probability_mode": "legacy", "calibration_version": null, "calibration_source": null, "calibrated_mu": null, "calibrated_sigma": null}
+{"city": "test_city", "timestamp": "2026-03-04 14:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 29.7, "raw_sigma": 1.09375, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 28.0, "peak_status": "in_window", "prob_snapshot": [{"v": 30, "p": 0.35}, {"v": 29, "p": 0.299}, {"v": 31, "p": 0.187}, {"v": 28, "p": 0.117}], "shadow_prob_snapshot": [{"v": 30, "p": 0.35}, {"v": 29, "p": 0.299}, {"v": 31, "p": 0.187}, {"v": 28, "p": 0.117}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 29.7, "calibrated_sigma": 1.09375}
+{"city": "test_city", "timestamp": "2026-03-04 22:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": null, "raw_sigma": 0.46875, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 28.0, "peak_status": "past", "prob_snapshot": [{"v": 28, "p": 1.0}], "shadow_prob_snapshot": [], "probability_engine": "legacy", "probability_mode": "legacy", "calibration_version": null, "calibration_source": null, "calibrated_mu": null, "calibrated_sigma": null}
+{"city": "test_city", "timestamp": "2026-03-04 16:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 23.0, "raw_sigma": 0.46875, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 23.0, "peak_status": "past", "prob_snapshot": [{"v": 23, "p": 0.834}, {"v": 24, "p": 0.166}], "shadow_prob_snapshot": [{"v": 23, "p": 0.834}, {"v": 24, "p": 0.166}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 23.0, "calibrated_sigma": 0.46875}
+{"city": "test_city", "timestamp": "2026-03-04 14:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 29.85, "raw_sigma": 1.09375, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.5, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 29.5, "peak_status": "in_window", "prob_snapshot": [{"v": 30, "p": 0.565}, {"v": 31, "p": 0.341}, {"v": 32, "p": 0.094}], "shadow_prob_snapshot": [{"v": 30, "p": 0.565}, {"v": 31, "p": 0.341}, {"v": 32, "p": 0.094}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 29.85, "calibrated_sigma": 1.09375}
+{"city": "test_city", "timestamp": "2026-03-04 14:30", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 29.7, "raw_sigma": 1.09375, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 28.0, "peak_status": "in_window", "prob_snapshot": [{"v": 30, "p": 0.35}, {"v": 29, "p": 0.299}, {"v": 31, "p": 0.187}, {"v": 28, "p": 0.117}], "shadow_prob_snapshot": [{"v": 30, "p": 0.35}, {"v": 29, "p": 0.299}, {"v": 31, "p": 0.187}, {"v": 28, "p": 0.117}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 29.7, "calibrated_sigma": 1.09375}
+{"city": "test_city", "timestamp": "2026-03-04 10:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 29.7, "raw_sigma": 1.5625, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 26.0, "peak_status": "before", "prob_snapshot": [{"v": 30, "p": 0.254}, {"v": 29, "p": 0.234}, {"v": 31, "p": 0.185}, {"v": 28, "p": 0.146}], "shadow_prob_snapshot": [{"v": 30, "p": 0.254}, {"v": 29, "p": 0.234}, {"v": 31, "p": 0.185}, {"v": 28, "p": 0.146}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 29.7, "calibrated_sigma": 1.5625}
+{"city": "test_city", "timestamp": "2026-03-04 17:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 23.0, "raw_sigma": 0.46875, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 23.0, "peak_status": "past", "prob_snapshot": [{"v": 23, "p": 0.834}, {"v": 24, "p": 0.166}], "shadow_prob_snapshot": [{"v": 23, "p": 0.834}, {"v": 24, "p": 0.166}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 23.0, "calibrated_sigma": 0.46875}
+{"city": "test_city", "timestamp": "2026-03-04 14:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 33.3, "raw_sigma": 1.09375, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 33.0, "peak_status": "in_window", "prob_snapshot": [{"v": 33, "p": 0.456}, {"v": 34, "p": 0.391}, {"v": 35, "p": 0.153}], "shadow_prob_snapshot": [{"v": 33, "p": 0.456}, {"v": 34, "p": 0.391}, {"v": 35, "p": 0.153}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 33.3, "calibrated_sigma": 1.09375}
+{"city": "test_city", "timestamp": "2026-03-04 17:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": null, "raw_sigma": 0.46875, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 28.0, "peak_status": "past", "prob_snapshot": [{"v": 28, "p": 1.0}], "shadow_prob_snapshot": [], "probability_engine": "legacy", "probability_mode": "legacy", "calibration_version": null, "calibration_source": null, "calibrated_mu": null, "calibrated_sigma": null}
+{"city": "test_city", "timestamp": "2026-03-04 14:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 29.7, "raw_sigma": 1.09375, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 28.0, "peak_status": "in_window", "prob_snapshot": [{"v": 30, "p": 0.35}, {"v": 29, "p": 0.299}, {"v": 31, "p": 0.187}, {"v": 28, "p": 0.117}], "shadow_prob_snapshot": [{"v": 30, "p": 0.35}, {"v": 29, "p": 0.299}, {"v": 31, "p": 0.187}, {"v": 28, "p": 0.117}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 29.7, "calibrated_sigma": 1.09375}
+{"city": "test_city", "timestamp": "2026-03-04 22:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": null, "raw_sigma": 0.46875, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 28.0, "peak_status": "past", "prob_snapshot": [{"v": 28, "p": 1.0}], "shadow_prob_snapshot": [], "probability_engine": "legacy", "probability_mode": "legacy", "calibration_version": null, "calibration_source": null, "calibrated_mu": null, "calibrated_sigma": null}
+{"city": "test_city", "timestamp": "2026-03-04 16:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 23.0, "raw_sigma": 0.46875, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 23.0, "peak_status": "past", "prob_snapshot": [{"v": 23, "p": 0.834}, {"v": 24, "p": 0.166}], "shadow_prob_snapshot": [{"v": 23, "p": 0.834}, {"v": 24, "p": 0.166}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 23.0, "calibrated_sigma": 0.46875}
+{"city": "test_city", "timestamp": "2026-03-04 14:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 29.85, "raw_sigma": 1.09375, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.5, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 29.5, "peak_status": "in_window", "prob_snapshot": [{"v": 30, "p": 0.565}, {"v": 31, "p": 0.341}, {"v": 32, "p": 0.094}], "shadow_prob_snapshot": [{"v": 30, "p": 0.565}, {"v": 31, "p": 0.341}, {"v": 32, "p": 0.094}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 29.85, "calibrated_sigma": 1.09375}
+{"city": "test_city", "timestamp": "2026-03-04 14:30", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 29.7, "raw_sigma": 1.09375, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 28.0, "peak_status": "in_window", "prob_snapshot": [{"v": 30, "p": 0.35}, {"v": 29, "p": 0.299}, {"v": 31, "p": 0.187}, {"v": 28, "p": 0.117}], "shadow_prob_snapshot": [{"v": 30, "p": 0.35}, {"v": 29, "p": 0.299}, {"v": 31, "p": 0.187}, {"v": 28, "p": 0.117}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 29.7, "calibrated_sigma": 1.09375}
+{"city": "test_city", "timestamp": "2026-03-04 10:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 29.7, "raw_sigma": 1.5625, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 26.0, "peak_status": "before", "prob_snapshot": [{"v": 30, "p": 0.254}, {"v": 29, "p": 0.234}, {"v": 31, "p": 0.185}, {"v": 28, "p": 0.146}], "shadow_prob_snapshot": [{"v": 30, "p": 0.254}, {"v": 29, "p": 0.234}, {"v": 31, "p": 0.185}, {"v": 28, "p": 0.146}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 29.7, "calibrated_sigma": 1.5625}
+{"city": "test_city", "timestamp": "2026-03-04 17:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 23.0, "raw_sigma": 0.46875, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 23.0, "peak_status": "past", "prob_snapshot": [{"v": 23, "p": 0.834}, {"v": 24, "p": 0.166}], "shadow_prob_snapshot": [{"v": 23, "p": 0.834}, {"v": 24, "p": 0.166}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 23.0, "calibrated_sigma": 0.46875}
+{"city": "test_city", "timestamp": "2026-03-04 14:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 33.3, "raw_sigma": 1.09375, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 33.0, "peak_status": "in_window", "prob_snapshot": [{"v": 33, "p": 0.456}, {"v": 34, "p": 0.391}, {"v": 35, "p": 0.153}], "shadow_prob_snapshot": [{"v": 33, "p": 0.456}, {"v": 34, "p": 0.391}, {"v": 35, "p": 0.153}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 33.3, "calibrated_sigma": 1.09375}
+{"city": "test_city", "timestamp": "2026-03-04 17:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": null, "raw_sigma": 0.46875, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 28.0, "peak_status": "past", "prob_snapshot": [{"v": 28, "p": 1.0}], "shadow_prob_snapshot": [], "probability_engine": "legacy", "probability_mode": "legacy", "calibration_version": null, "calibration_source": null, "calibrated_mu": null, "calibrated_sigma": null}
+{"city": "test_city", "timestamp": "2026-03-04 14:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 29.7, "raw_sigma": 1.09375, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 28.0, "peak_status": "in_window", "prob_snapshot": [{"v": 30, "p": 0.35}, {"v": 29, "p": 0.299}, {"v": 31, "p": 0.187}, {"v": 28, "p": 0.117}], "shadow_prob_snapshot": [{"v": 30, "p": 0.35}, {"v": 29, "p": 0.299}, {"v": 31, "p": 0.187}, {"v": 28, "p": 0.117}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 29.7, "calibrated_sigma": 1.09375}
+{"city": "test_city", "timestamp": "2026-03-04 22:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": null, "raw_sigma": 0.46875, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 28.0, "peak_status": "past", "prob_snapshot": [{"v": 28, "p": 1.0}], "shadow_prob_snapshot": [], "probability_engine": "legacy", "probability_mode": "legacy", "calibration_version": null, "calibration_source": null, "calibrated_mu": null, "calibrated_sigma": null}
+{"city": "test_city", "timestamp": "2026-03-04 16:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 23.0, "raw_sigma": 0.46875, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 23.0, "peak_status": "past", "prob_snapshot": [{"v": 23, "p": 0.834}, {"v": 24, "p": 0.166}], "shadow_prob_snapshot": [{"v": 23, "p": 0.834}, {"v": 24, "p": 0.166}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 23.0, "calibrated_sigma": 0.46875}
+{"city": "test_city", "timestamp": "2026-03-04 14:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 29.85, "raw_sigma": 1.09375, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.5, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 29.5, "peak_status": "in_window", "prob_snapshot": [{"v": 30, "p": 0.565}, {"v": 31, "p": 0.341}, {"v": 32, "p": 0.094}], "shadow_prob_snapshot": [{"v": 30, "p": 0.565}, {"v": 31, "p": 0.341}, {"v": 32, "p": 0.094}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 29.85, "calibrated_sigma": 1.09375}
+{"city": "test_city", "timestamp": "2026-03-04 14:30", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 29.7, "raw_sigma": 1.09375, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 28.0, "peak_status": "in_window", "prob_snapshot": [{"v": 30, "p": 0.35}, {"v": 29, "p": 0.299}, {"v": 31, "p": 0.187}, {"v": 28, "p": 0.117}], "shadow_prob_snapshot": [{"v": 30, "p": 0.35}, {"v": 29, "p": 0.299}, {"v": 31, "p": 0.187}, {"v": 28, "p": 0.117}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 29.7, "calibrated_sigma": 1.09375}
+{"city": "test_city", "timestamp": "2026-03-04 10:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 29.7, "raw_sigma": 1.5625, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 26.0, "peak_status": "before", "prob_snapshot": [{"v": 30, "p": 0.254}, {"v": 29, "p": 0.234}, {"v": 31, "p": 0.185}, {"v": 28, "p": 0.146}], "shadow_prob_snapshot": [{"v": 30, "p": 0.254}, {"v": 29, "p": 0.234}, {"v": 31, "p": 0.185}, {"v": 28, "p": 0.146}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 29.7, "calibrated_sigma": 1.5625}
+{"city": "test_city", "timestamp": "2026-03-04 17:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 23.0, "raw_sigma": 0.46875, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 23.0, "peak_status": "past", "prob_snapshot": [{"v": 23, "p": 0.834}, {"v": 24, "p": 0.166}], "shadow_prob_snapshot": [{"v": 23, "p": 0.834}, {"v": 24, "p": 0.166}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 23.0, "calibrated_sigma": 0.46875}
+{"city": "test_city", "timestamp": "2026-03-04 14:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 33.3, "raw_sigma": 1.09375, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 33.0, "peak_status": "in_window", "prob_snapshot": [{"v": 33, "p": 0.456}, {"v": 34, "p": 0.391}, {"v": 35, "p": 0.153}], "shadow_prob_snapshot": [{"v": 33, "p": 0.456}, {"v": 34, "p": 0.391}, {"v": 35, "p": 0.153}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 33.3, "calibrated_sigma": 1.09375}
+{"city": "test_city", "timestamp": "2026-03-04 17:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": null, "raw_sigma": 0.46875, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 28.0, "peak_status": "past", "prob_snapshot": [{"v": 28, "p": 1.0}], "shadow_prob_snapshot": [], "probability_engine": "legacy", "probability_mode": "legacy", "calibration_version": null, "calibration_source": null, "calibrated_mu": null, "calibrated_sigma": null}
+{"city": "test_city", "timestamp": "2026-03-04 14:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 29.7, "raw_sigma": 1.09375, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 28.0, "peak_status": "in_window", "prob_snapshot": [{"v": 30, "p": 0.35}, {"v": 29, "p": 0.299}, {"v": 31, "p": 0.187}, {"v": 28, "p": 0.117}], "shadow_prob_snapshot": [{"v": 30, "p": 0.35}, {"v": 29, "p": 0.299}, {"v": 31, "p": 0.187}, {"v": 28, "p": 0.117}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 29.7, "calibrated_sigma": 1.09375}
+{"city": "test_city", "timestamp": "2026-03-04 22:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": null, "raw_sigma": 0.46875, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 28.0, "peak_status": "past", "prob_snapshot": [{"v": 28, "p": 1.0}], "shadow_prob_snapshot": [], "probability_engine": "legacy", "probability_mode": "legacy", "calibration_version": null, "calibration_source": null, "calibrated_mu": null, "calibrated_sigma": null}
+{"city": "test_city", "timestamp": "2026-03-04 16:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 23.0, "raw_sigma": 0.46875, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 23.0, "peak_status": "past", "prob_snapshot": [{"v": 23, "p": 0.834}, {"v": 24, "p": 0.166}], "shadow_prob_snapshot": [{"v": 23, "p": 0.834}, {"v": 24, "p": 0.166}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 23.0, "calibrated_sigma": 0.46875}
+{"city": "test_city", "timestamp": "2026-03-04 14:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 29.85, "raw_sigma": 1.09375, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.5, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 29.5, "peak_status": "in_window", "prob_snapshot": [{"v": 30, "p": 0.565}, {"v": 31, "p": 0.341}, {"v": 32, "p": 0.094}], "shadow_prob_snapshot": [{"v": 30, "p": 0.565}, {"v": 31, "p": 0.341}, {"v": 32, "p": 0.094}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 29.85, "calibrated_sigma": 1.09375}
+{"city": "test_city", "timestamp": "2026-03-04 14:30", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 29.7, "raw_sigma": 1.09375, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 28.0, "peak_status": "in_window", "prob_snapshot": [{"v": 30, "p": 0.35}, {"v": 29, "p": 0.299}, {"v": 31, "p": 0.187}, {"v": 28, "p": 0.117}], "shadow_prob_snapshot": [{"v": 30, "p": 0.35}, {"v": 29, "p": 0.299}, {"v": 31, "p": 0.187}, {"v": 28, "p": 0.117}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 29.7, "calibrated_sigma": 1.09375}
+{"city": "test_city", "timestamp": "2026-03-04 10:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 29.7, "raw_sigma": 1.5625, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 26.0, "peak_status": "before", "prob_snapshot": [{"v": 30, "p": 0.254}, {"v": 29, "p": 0.234}, {"v": 31, "p": 0.185}, {"v": 28, "p": 0.146}], "shadow_prob_snapshot": [{"v": 30, "p": 0.254}, {"v": 29, "p": 0.234}, {"v": 31, "p": 0.185}, {"v": 28, "p": 0.146}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 29.7, "calibrated_sigma": 1.5625}
+{"city": "test_city", "timestamp": "2026-03-04 17:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 23.0, "raw_sigma": 0.46875, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 23.0, "peak_status": "past", "prob_snapshot": [{"v": 23, "p": 0.834}, {"v": 24, "p": 0.166}], "shadow_prob_snapshot": [{"v": 23, "p": 0.834}, {"v": 24, "p": 0.166}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 23.0, "calibrated_sigma": 0.46875}
+{"city": "test_city", "timestamp": "2026-03-04 14:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 33.3, "raw_sigma": 1.09375, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 33.0, "peak_status": "in_window", "prob_snapshot": [{"v": 33, "p": 0.456}, {"v": 34, "p": 0.391}, {"v": 35, "p": 0.153}], "shadow_prob_snapshot": [{"v": 33, "p": 0.456}, {"v": 34, "p": 0.391}, {"v": 35, "p": 0.153}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 33.3, "calibrated_sigma": 1.09375}
+{"city": "test_city", "timestamp": "2026-03-04 17:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": null, "raw_sigma": 0.46875, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 28.0, "peak_status": "past", "prob_snapshot": [{"v": 28, "p": 1.0}], "shadow_prob_snapshot": [], "probability_engine": "legacy", "probability_mode": "legacy", "calibration_version": null, "calibration_source": null, "calibrated_mu": null, "calibrated_sigma": null}
+{"city": "test_city", "timestamp": "2026-03-04 14:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 29.7, "raw_sigma": 1.09375, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 28.0, "peak_status": "in_window", "prob_snapshot": [{"v": 30, "p": 0.35}, {"v": 29, "p": 0.299}, {"v": 31, "p": 0.187}, {"v": 28, "p": 0.117}], "shadow_prob_snapshot": [{"v": 30, "p": 0.35}, {"v": 29, "p": 0.299}, {"v": 31, "p": 0.187}, {"v": 28, "p": 0.117}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 29.7, "calibrated_sigma": 1.09375}
+{"city": "test_city", "timestamp": "2026-03-04 22:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": null, "raw_sigma": 0.46875, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 28.0, "peak_status": "past", "prob_snapshot": [{"v": 28, "p": 1.0}], "shadow_prob_snapshot": [], "probability_engine": "legacy", "probability_mode": "legacy", "calibration_version": null, "calibration_source": null, "calibrated_mu": null, "calibrated_sigma": null}
+{"city": "test_city", "timestamp": "2026-03-04 16:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 23.0, "raw_sigma": 0.46875, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 23.0, "peak_status": "past", "prob_snapshot": [{"v": 23, "p": 0.834}, {"v": 24, "p": 0.166}], "shadow_prob_snapshot": [{"v": 23, "p": 0.834}, {"v": 24, "p": 0.166}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 23.0, "calibrated_sigma": 0.46875}
+{"city": "test_city", "timestamp": "2026-03-04 14:00", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 29.85, "raw_sigma": 1.09375, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.5, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 29.5, "peak_status": "in_window", "prob_snapshot": [{"v": 30, "p": 0.565}, {"v": 31, "p": 0.341}, {"v": 32, "p": 0.094}], "shadow_prob_snapshot": [{"v": 30, "p": 0.565}, {"v": 31, "p": 0.341}, {"v": 32, "p": 0.094}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 29.85, "calibrated_sigma": 1.09375}
+{"city": "test_city", "timestamp": "2026-03-04 14:30", "date": "2026-03-04", "temp_symbol": "°C", "raw_mu": 29.7, "raw_sigma": 1.09375, "deb_prediction": null, "ensemble": {"p10": 27.0, "median": 29.0, "p90": 31.0}, "multi_model": {"Open-Meteo": 30.0}, "max_so_far": 28.0, "peak_status": "in_window", "prob_snapshot": [{"v": 30, "p": 0.35}, {"v": 29, "p": 0.299}, {"v": 31, "p": 0.187}, {"v": 28, "p": 0.117}], "shadow_prob_snapshot": [{"v": 30, "p": 0.35}, {"v": 29, "p": 0.299}, {"v": 31, "p": 0.187}, {"v": 28, "p": 0.117}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 29.7, "calibrated_sigma": 1.09375}
+{"city": "hong kong", "timestamp": "2026-03-23T21:10:00+08:00", "date": "2026-03-23", "temp_symbol": "°C", "raw_mu": null, "raw_sigma": 0.6806250000000001, "deb_prediction": 25.2, "ensemble": {"p10": 26.3, "median": 26.4, "p90": 26.6}, "multi_model": {"Open-Meteo": 24.8, "HKO(港天文)": 27.0, "ECMWF": 25.4, "GFS": 25.1, "ICON": 24.8, "GEM": 25.3, "JMA": 23.6}, "max_so_far": 27.4, "peak_status": "past", "prob_snapshot": [{"v": 27, "p": 1.0}], "shadow_prob_snapshot": [], "probability_engine": "legacy", "probability_mode": "legacy", "calibration_version": null, "calibration_source": null, "calibrated_mu": null, "calibrated_sigma": null}
+{"city": "shenzhen", "timestamp": "2026-03-25T08:43:15.528748+00:00", "date": "2026-03-25", "temp_symbol": "°C", "raw_mu": null, "raw_sigma": 0.18016764322916676, "deb_prediction": 28.1, "ensemble": {"p10": 30.5, "median": 31.4, "p90": 31.8}, "multi_model": {"Open-Meteo": 26.6, "ECMWF": 28.8, "GFS": 30.3, "ICON": 26.6, "GEM": 30.7, "JMA": 25.5}, "max_so_far": 29.0, "peak_status": "past", "prob_snapshot": [{"v": 29, "p": 1.0}], "shadow_prob_snapshot": [], "probability_engine": "legacy", "probability_mode": "legacy", "calibration_version": null, "calibration_source": null, "calibrated_mu": null, "calibrated_sigma": null}
+{"city": "shenzhen", "timestamp": "2026-03-25T08:57:11.783182+00:00", "date": "2026-03-25", "temp_symbol": "°C", "raw_mu": 26.7, "raw_sigma": 0.18016764322916676, "deb_prediction": 28.1, "ensemble": {"p10": 30.5, "median": 31.4, "p90": 31.8}, "multi_model": {"Open-Meteo": 26.6, "ECMWF": 28.8, "GFS": 30.3, "ICON": 26.6, "GEM": 30.7, "JMA": 25.5}, "max_so_far": 26.7, "peak_status": "past", "prob_snapshot": [{"v": 27, "p": 1.0}], "shadow_prob_snapshot": [{"v": 27, "p": 1.0}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260320132525", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 26.7, "calibrated_sigma": 0.24322631835937514}
+{"city": "shenzhen", "timestamp": "2026-03-25T09:32:32+00:00", "date": "2026-03-25", "temp_symbol": "°C", "raw_mu": null, "raw_sigma": 0.16637912326388898, "deb_prediction": 28.1, "ensemble": {"p10": 30.5, "median": 31.4, "p90": 31.8}, "multi_model": {"Open-Meteo": 26.6, "ECMWF": 28.8, "GFS": 30.3, "ICON": 26.6, "GEM": 30.7, "JMA": 25.5}, "max_so_far": 28.9, "peak_status": "past", "prob_snapshot": [{"v": 29, "p": 1.0}], "shadow_prob_snapshot": [], "probability_engine": "legacy", "probability_mode": "legacy", "calibration_version": null, "calibration_source": null, "calibrated_mu": null, "calibrated_sigma": null}
+{"city": "shenzhen", "timestamp": "2026-03-25T10:02:35+00:00", "date": "2026-03-25", "temp_symbol": "°C", "raw_mu": null, "raw_sigma": 0.15911458333333342, "deb_prediction": 28.2, "ensemble": {"p10": 30.5, "median": 31.4, "p90": 31.8}, "multi_model": {"Open-Meteo": 26.5, "ECMWF": 28.8, "GFS": 30.3, "ICON": 26.5, "GEM": 30.7, "JMA": 26.2}, "max_so_far": 28.9, "peak_status": "past", "prob_snapshot": [{"v": 28, "p": 1.0}], "shadow_prob_snapshot": [], "probability_engine": "legacy", "probability_mode": "legacy", "calibration_version": null, "calibration_source": null, "calibrated_mu": null, "calibrated_sigma": null}
+{"city": "shanghai", "timestamp": "2026-03-29T15:00:00.000Z", "date": "2026-03-29", "temp_symbol": "°C", "raw_mu": null, "raw_sigma": 0.23736458333333335, "deb_prediction": 18.4, "ensemble": {"p10": 16.1, "median": 16.5, "p90": 16.9}, "multi_model": {"Open-Meteo": 17.0, "ECMWF": 19.2, "GFS": 19.1, "ICON": 17.0, "GEM": 17.6, "JMA": 15.8}, "max_so_far": 18.0, "observation": {"current_temp": 14.0, "humidity": null, "wind_speed_kt": 4.0, "visibility_mi": 3.11, "local_hour": 23.25}, "peak_status": "past", "prob_snapshot": [{"v": 18, "p": 1.0}], "shadow_prob_snapshot": [], "probability_engine": "legacy", "probability_mode": "legacy", "calibration_version": null, "calibration_source": null, "calibrated_mu": null, "calibrated_sigma": null}
+{"city": "ankara", "timestamp": "2026-03-29T15:01:00.000Z", "date": "2026-03-29", "temp_symbol": "°C", "raw_mu": null, "raw_sigma": 0.25176666666666664, "deb_prediction": 9.7, "ensemble": {"p10": 9.2, "median": 9.5, "p90": 10.2}, "multi_model": {"Open-Meteo": 9.2, "ECMWF": 9.5, "GFS": 10.1, "ICON": 9.2, "GEM": 11.0, "JMA": 10.0}, "max_so_far": 10.0, "observation": {"current_temp": 7.0, "humidity": null, "wind_speed_kt": 12.0, "visibility_mi": null, "local_hour": 18.25}, "peak_status": "past", "prob_snapshot": [{"v": 10, "p": 1.0}], "shadow_prob_snapshot": [], "probability_engine": "legacy", "probability_mode": "legacy", "calibration_version": null, "calibration_source": null, "calibrated_mu": null, "calibrated_sigma": null}
+{"city": "chengdu", "timestamp": "2026-04-08T07:00:00.000Z", "date": "2026-04-08", "temp_symbol": "°C", "raw_mu": 24.3, "raw_sigma": 1.1821289062499998, "deb_prediction": 23.1, "ensemble": {"p10": 21.8, "median": 23.0, "p90": 24.5}, "multi_model": {"Open-Meteo": 22.5, "ECMWF": 23.9, "GFS": 23.0, "ICON": 22.5, "GEM": 23.7, "JMA": 23.2}, "max_so_far": 24.0, "observation": {"current_temp": 24.0, "humidity": null, "wind_speed_kt": 4.0, "visibility_mi": null, "local_hour": 15.183333333333334}, "peak_status": "before", "prob_snapshot": [{"v": 24, "p": 0.442}, {"v": 25, "p": 0.386}, {"v": 26, "p": 0.172}], "shadow_prob_snapshot": [{"v": 24, "p": 0.394}, {"v": 25, "p": 0.355}, {"v": 26, "p": 0.19}, {"v": 27, "p": 0.06}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260402162744", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 24.3, "calibrated_sigma": 1.354078466151897}
+{"city": "chengdu", "timestamp": "2026-04-08T08:00:00.000Z", "date": "2026-04-08", "temp_symbol": "°C", "raw_mu": 24.3, "raw_sigma": 0.7283767361111106, "deb_prediction": 23.1, "ensemble": {"p10": 21.8, "median": 22.9, "p90": 24.2}, "multi_model": {"Open-Meteo": 22.5, "ECMWF": 23.9, "GFS": 22.5, "ICON": 22.5, "GEM": 23.7, "JMA": 23.2}, "max_so_far": 24.0, "observation": {"current_temp": 23.0, "humidity": null, "wind_speed_kt": 4.0, "visibility_mi": null, "local_hour": 16.133333333333333}, "peak_status": "in_window", "prob_snapshot": [{"v": 24, "p": 0.547}, {"v": 25, "p": 0.397}, {"v": 26, "p": 0.056}], "shadow_prob_snapshot": [{"v": 24, "p": 0.521}, {"v": 25, "p": 0.399}, {"v": 26, "p": 0.08}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260402162744", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 24.3, "calibrated_sigma": 0.8140063140878262}
+{"city": "tokyo", "timestamp": "2026-04-08T08:00:00.000Z", "date": "2026-04-08", "temp_symbol": "°C", "raw_mu": 17.810000000000002, "raw_sigma": 0.23200683593750038, "deb_prediction": 17.1, "ensemble": {"p10": 17.4, "median": 18.3, "p90": 19.1}, "multi_model": {"Open-Meteo": 16.1, "ECMWF": 16.3, "GFS": 17.6, "ICON": 18.2, "GEM": 18.3, "JMA": 16.1}, "max_so_far": 17.0, "observation": {"current_temp": 16.0, "humidity": null, "wind_speed_kt": 17.0, "visibility_mi": null, "local_hour": 17.133333333333333}, "peak_status": "past", "prob_snapshot": [{"v": 18, "p": 0.909}, {"v": 17, "p": 0.091}], "shadow_prob_snapshot": [{"v": 18, "p": 0.892}, {"v": 17, "p": 0.108}], "probability_engine": "legacy", "probability_mode": "emos_shadow", "calibration_version": "emos-20260402162744", "calibration_source": "artifacts\\probability_calibration\\default.json", "calibrated_mu": 17.810000000000002, "calibrated_sigma": 0.25}

docker-compose.yml

@@ -1,24 +1,112 @@
+x-polyweather-base: &polyweather-base
+  build: .
+  image: polyweather-app:latest
+  env_file:
+    - .env
+
 services:
   polyweather:
-    build: .
+    <<: *polyweather-base
     container_name: polyweather_bot
     restart: unless-stopped
-    env_file:
-      - .env
     volumes:
-      - ./data:/app/data # 挂载数据目录，确保历史数据持久化
+      # Persist runtime data outside git workspace.
+      # Host path defaults to /var/lib/polyweather and can be overridden in .env.
+      - ${POLYWEATHER_RUNTIME_DATA_DIR:-/var/lib/polyweather}:/var/lib/polyweather
+      # Keep /app/data compatibility for existing cache/state defaults.
+      - ${POLYWEATHER_RUNTIME_DATA_DIR:-/var/lib/polyweather}:/app/data
       - ./bot.log:/app/bot.log # 挂载日志文件
+    # UID/GID are mainly useful on Linux hosts to avoid root-owned output files.
+    # Windows / macOS can usually keep the fallback values.
     user: "${UID:-1000}:${GID:-1000}"
 
   polyweather_web:
-    build: .
+    <<: *polyweather-base
     container_name: polyweather_web
     restart: unless-stopped
     command: python web/app.py
-    env_file:
-      - .env
     volumes:
-      - ./data:/app/data
+      # Web service shares the same runtime data directory as bot/state tasks.
+      - ${POLYWEATHER_RUNTIME_DATA_DIR:-/var/lib/polyweather}:/var/lib/polyweather
+      - ${POLYWEATHER_RUNTIME_DATA_DIR:-/var/lib/polyweather}:/app/data
     ports:
       - "8000:8000"
+    # UID/GID are mainly useful on Linux hosts to avoid root-owned output files.
     user: "${UID:-1000}:${GID:-1000}"
+
+  polyweather_prewarm:
+    <<: *polyweather-base
+    container_name: polyweather_prewarm
+    restart: unless-stopped
+    profiles: ["workers"]
+    command: python scripts/prewarm_dashboard_worker.py --include-detail --include-market
+    volumes:
+      - ${POLYWEATHER_RUNTIME_DATA_DIR:-/var/lib/polyweather}:/var/lib/polyweather
+      - ${POLYWEATHER_RUNTIME_DATA_DIR:-/var/lib/polyweather}:/app/data
+    user: "${UID:-1000}:${GID:-1000}"
+
+  polyweather_prometheus:
+    image: prom/prometheus:v3.4.1
+    container_name: polyweather_prometheus
+    restart: unless-stopped
+    profiles: ["monitoring"]
+    depends_on:
+      - polyweather_web
+    command:
+      - "--config.file=/etc/prometheus/prometheus.yml"
+      - "--storage.tsdb.path=/prometheus"
+      - "--storage.tsdb.retention.time=15d"
+      - "--web.enable-lifecycle"
+    volumes:
+      - ./monitoring/prometheus/prometheus.yml:/etc/prometheus/prometheus.yml:ro
+      - ./monitoring/prometheus/alerts.yml:/etc/prometheus/alerts.yml:ro
+      - ${POLYWEATHER_RUNTIME_DATA_DIR:-/var/lib/polyweather}/monitoring/prometheus:/prometheus
+    ports:
+      - "${POLYWEATHER_PROMETHEUS_PORT:-9090}:9090"
+
+  polyweather_alertmanager:
+    image: prom/alertmanager:v0.28.1
+    container_name: polyweather_alertmanager
+    restart: unless-stopped
+    profiles: ["monitoring"]
+    depends_on:
+      - polyweather_alert_relay
+    command:
+      - "--config.file=/etc/alertmanager/alertmanager.yml"
+      - "--storage.path=/alertmanager"
+    volumes:
+      - ./monitoring/alertmanager/alertmanager.yml:/etc/alertmanager/alertmanager.yml:ro
+      - ${POLYWEATHER_RUNTIME_DATA_DIR:-/var/lib/polyweather}/monitoring/alertmanager:/alertmanager
+    ports:
+      - "${POLYWEATHER_ALERTMANAGER_PORT:-9093}:9093"
+
+  polyweather_alert_relay:
+    <<: *polyweather-base
+    container_name: polyweather_alert_relay
+    restart: unless-stopped
+    profiles: ["monitoring"]
+    command: python scripts/alertmanager_telegram_relay.py
+    volumes:
+      - ${POLYWEATHER_RUNTIME_DATA_DIR:-/var/lib/polyweather}:/var/lib/polyweather
+      - ${POLYWEATHER_RUNTIME_DATA_DIR:-/var/lib/polyweather}:/app/data
+    ports:
+      - "${POLYWEATHER_ALERT_RELAY_PORT:-9099}:9099"
+    user: "${UID:-1000}:${GID:-1000}"
+
+  polyweather_grafana:
+    image: grafana/grafana-oss:12.0.2
+    container_name: polyweather_grafana
+    restart: unless-stopped
+    profiles: ["monitoring"]
+    depends_on:
+      - polyweather_prometheus
+    environment:
+      GF_SECURITY_ADMIN_USER: ${POLYWEATHER_GRAFANA_ADMIN_USER:-admin}
+      GF_SECURITY_ADMIN_PASSWORD: ${POLYWEATHER_GRAFANA_ADMIN_PASSWORD:-polyweather}
+      GF_USERS_ALLOW_SIGN_UP: "false"
+    volumes:
+      - ./monitoring/grafana/provisioning:/etc/grafana/provisioning:ro
+      - ./monitoring/grafana/dashboards:/var/lib/grafana/dashboards:ro
+      - ${POLYWEATHER_RUNTIME_DATA_DIR:-/var/lib/polyweather}/monitoring/grafana:/var/lib/grafana
+    ports:
+      - "${POLYWEATHER_GRAFANA_PORT:-3001}:3000"

docs/API_ZH.md

@@ -0,0 +1,242 @@
+# PolyWeather API 文档（v1.5.1）
+
+最后更新：`2026-03-24`
+
+本文档描述当前对外可用 API 口径（`web/app.py` + `web/routes.py` + `frontend/app/api/*`）。
+
+## 1. 基础信息
+
+- 后端直连：`http://127.0.0.1:8000`
+- 前端 BFF：`https://polyweather-pro.vercel.app/api/*`
+- 返回格式：`application/json`
+
+## 2. 请求链路
+
+```mermaid
+flowchart LR
+    FE["Browser / Dashboard"] --> BFF["Next.js Route Handlers (/api/*)"]
+    BFF --> API["FastAPI (/web/app.py + /web/routes.py)"]
+    API --> WX["Weather Collector"]
+    API --> ANA["DEB + Trend + Probability + Market Scan"]
+    API --> PAY["Payment Intent + Event + Confirm Loops"]
+    API --> OBS["healthz / system status / metrics"]
+```
+
+## 3. 天气分析接口
+
+| 接口 | 方法 | 用途 |
+| :-- | :-- | :-- |
+| `/api/cities` | GET | 监控城市列表 |
+| `/api/city/{name}` | GET | 城市主分析 |
+| `/api/city/{name}/summary` | GET | 轻量摘要 |
+| `/api/city/{name}/detail` | GET | 聚合详情（含 market_scan） |
+| `/api/history/{name}` | GET | 历史对账 |
+
+### `GET /api/city/{name}/detail`
+
+可选参数：
+
+- `force_refresh=true|false`
+- `market_slug=<slug>`
+- `target_date=YYYY-MM-DD`
+
+重点字段：
+
+- `market_scan.available`
+- `market_scan.signal_label`
+- `market_scan.edge_percent`
+- `market_scan.anchor_model / anchor_high / anchor_settlement`
+- `market_scan.yes_buy / no_buy`
+- `market_scan.primary_market.tradable`
+- `peak.first_h / peak.last_h / peak.status`
+- `vertical_profile_signal.heating_setup / suppression_risk / trigger_risk / mixing_strength`
+- `taf.signal.peak_window / suppression_level / disruption_level / markers`
+
+### `detail` 新增结构信号说明
+
+`/api/city/{name}/detail` 现在会返回一组更偏交易场景的结构字段：
+
+#### 1. `peak`
+
+- `first_h`：预计峰值窗口起始小时
+- `last_h`：预计峰值窗口结束小时
+- `status`：`before_peak | near_peak | after_peak`
+
+这组字段用于让日内结构信号围绕真实峰值窗口分析，而不是固定只看下午。
+
+#### 2. `vertical_profile_signal`
+
+重点字段：
+
+- `source`
+- `window`
+- `cape_max`
+- `cin_min`
+- `lifted_index_min`
+- `boundary_layer_height_max`
+- `shear_10m_180m_max`
+- `suppression_risk`
+- `trigger_risk`
+- `mixing_strength`
+- `shear_risk`
+- `heating_setup`
+- `heating_score`
+- `summary_zh`
+- `summary_en`
+
+这组字段对应前端“高空结构信号 / Upper-Air Structure”卡片。
+
+#### 3. `taf.signal`
+
+仅对**非香港机场城市**启用。当前已支持解析：
+
+- `FM`
+- `TEMPO`
+- `BECMG`
+- `PROB30`
+- `PROB40`
+
+重点字段：
+
+- `peak_window`
+- `segments`
+- `markers`
+- `suppression_level`
+- `disruption_level`
+- `wind_shift`
+- `summary_zh`
+- `summary_en`
+
+`markers` 会被前端温度走势图拿来做 `TAF 时段 / TAF Timing` 标记。
+
+## 4. 鉴权与账户接口
+
+| 接口 | 方法 | 用途 |
+| :-- | :-- | :-- |
+| `/api/auth/me` | GET | 当前登录态、积分、订阅状态 |
+
+`/api/auth/me` 关键字段：
+
+- `authenticated`
+- `user_id`, `email`
+- `points`, `weekly_points`, `weekly_rank`
+- `subscription_active`, `subscription_plan_code`, `subscription_expires_at`
+
+## 5. 支付接口
+
+| 接口 | 方法 | 用途 |
+| :-- | :-- | :-- |
+| `/api/payments/config` | GET | 支付配置、代币列表、套餐、积分抵扣规则 |
+| `/api/payments/runtime` | GET | 支付运行态、RPC 状态、event loop 状态、最近审计事件 |
+| `/api/payments/wallets` | GET | 当前用户已绑定钱包 |
+| `/api/payments/wallets/challenge` | POST | 获取绑定签名 challenge |
+| `/api/payments/wallets/verify` | POST | 提交签名并绑定钱包 |
+| `/api/payments/intents` | POST | 创建支付意图（intent） |
+| `/api/payments/intents/{intent_id}` | GET | 查询 intent 最新状态 |
+| `/api/payments/intents/{intent_id}/submit` | POST | 提交交易哈希 |
+| `/api/payments/intents/{intent_id}/confirm` | POST | 手动触发确认 |
+| `/api/payments/reconcile-latest` | POST | 对当前登录用户最近一笔 intent 做恢复性确认 |
+
+### 支付状态建议
+
+前端流程建议：
+
+1. `POST /intents`
+2. 钱包发链上交易
+3. `POST /submit`
+4. `POST /confirm`
+5. 若 pending，轮询 `GET /intents/{id}` 直到 `confirmed`
+
+## 6. 运维与观测接口
+
+| 接口 | 方法 | 用途 |
+| :-- | :-- | :-- |
+| `/healthz` | GET | 基础健康检查 |
+| `/api/system/status` | GET | 系统状态、功能开关、rollout 状态、轻量指标摘要 |
+| `/metrics` | GET | Prometheus 风格指标导出 |
+
+`/api/system/status` 当前会包含：
+
+- `features.state_storage_mode`
+- `probability.decision`
+- `probability.ready_for_primary`
+- `metrics`
+
+`/metrics` 当前会导出：
+
+- `polyweather_http_requests_total`
+- `polyweather_http_request_duration_ms_*`
+- `polyweather_source_requests_total`
+- `polyweather_source_request_duration_ms_*`
+
+## 7. Ops 管理接口
+
+这些接口主要给 `/ops` 管理后台使用，默认要求：
+
+- 已登录
+- 当前邮箱位于 `POLYWEATHER_OPS_ADMIN_EMAILS`
+
+| 接口 | 方法 | 用途 |
+| :-- | :-- | :-- |
+| `/api/ops/users` | GET | 按 Telegram ID / 用户名 / 邮箱查询用户 |
+| `/api/ops/leaderboard/weekly` | GET | 本周积分榜 |
+| `/api/ops/memberships` | GET | 当前有效会员（已按用户去重，保留最晚到期） |
+| `/api/ops/users/grant-points` | POST | 手动补分 |
+| `/api/ops/payments/incidents` | GET | 支付异常单（仅 `payment_intent_failed`） |
+| `/api/ops/payments/incidents/{event_id}/resolve` | POST | 标记支付异常单已处理 |
+
+`/api/ops/payments/incidents` 当前支持：
+
+- `reason=<receiver_mismatch|sender_mismatch|event_mismatch|tx_reverted>`
+- 默认不返回已标记处理的记录
+- 重点用于排查“已付款未开通”“打到旧收款地址”等事故
+## 8. 缓存策略（当前）
+
+- `cities` / `summary` / `history`：BFF 支持 `ETag + 304`
+- `summary?force_refresh=true`：`Cache-Control: no-store`
+- 详情接口与支付接口：`no-store`
+- `METAR` / `TAF` / settlement current 由后端各自维护短 TTL 缓存
+
+## 9. 调试示例
+
+### 查询未来日期 market_scan
+
+```bash
+curl -s "http://127.0.0.1:8000/api/city/ankara/detail?force_refresh=true&target_date=2026-03-12"
+```
+
+### 校验支付配置
+
+```bash
+curl -s http://127.0.0.1:8000/api/payments/config | python3 -m json.tool
+```
+
+### 查看支付运行态
+
+```bash
+curl -s http://127.0.0.1:8000/api/payments/runtime | python3 -m json.tool
+```
+
+### 查看支付异常单
+
+```bash
+curl -s "http://127.0.0.1:8000/api/ops/payments/incidents?reason=receiver_mismatch" | python3 -m json.tool
+```
+
+### 查看系统状态
+
+```bash
+curl -s http://127.0.0.1:8000/api/system/status | python3 -m json.tool
+```
+
+### 观察支付自动补单
+
+```bash
+docker compose logs -f polyweather | egrep "payment event loop started|payment confirm loop started|payment auto-confirmed"
+```
+
+## 10. AGPL 与公开口径说明
+
+本仓库代码自 `2026-03-30` 起采用 `AGPL-3.0-only`。对外公开文档仅覆盖通用 API 契约；生产商业策略参数、私有运营阈值与托管服务能力不在公开文档披露。
+
+详见：[AGPL-3.0 与商用边界](OPEN_CORE_POLICY.md)

docs/COMMERCIALIZATION.md

@@ -1,79 +1,66 @@
-# 📈 Commercialization Roadmap
+# 商业化说明（Production）
 
-> **Target**: Transforming PolyWeather for paid weather intelligence delivery.
+最后更新：`2026-03-14`
 
----
+## 1. 定位
 
-## 🎯 Product Focus
+PolyWeather 是面向温度结算场景的气象决策层，不是通用天气应用。
 
-PolyWeather is positioned as a **premium intelligence service** for weather-based prediction markets (**Polymarket**). The value proposition lies in **Ankara-specialization**, **advanced advection forecasting**, and **DEB-weighted consensus**.
+核心价值：
 
----
+- 观测优先（METAR/MGM）
+- 结算导向（DEB + 概率桶）
+- 市场映射（行情对照 + 错价雷达）
 
-## 💰 Pricing & Monetization
+## 2. 当前收费能力状态
 
-| Tier                 | Price         | Primary Value Proposition                                     |
-| :------------------- | :------------ | :------------------------------------------------------------ |
-| **Telegram Channel** | **$1 / mo**   | High-fidelity proactive alerts, low noise.                    |
-| **Web Dashboard**    | **$5 / mo**   | Comprehensive multi-model view + historical MAE benchmarking. |
-| **VIP Bundle**       | **$5.5 / mo** | Full access to all intelligence streams.                      |
+| 能力 | 状态 | 备注 |
+| :-- | :-- | :-- |
+| 登录注册（Google + 邮箱） | 已上线 | Supabase 鉴权 |
+| 订阅套餐（Pro 月付） | 已上线 | `5 USDC / 30天` |
+| 积分抵扣 | 已上线 | `500分=1U`，最多 `3U` |
+| 合约支付 | 已上线 | Polygon，USDC + USDC.e |
+| 支付自动确认 | 已上线 | Event Loop + Confirm Loop |
+| 钱包绑定 | 已上线 | 浏览器钱包 + WalletConnect |
+| 私有频道推送 | 已上线 | 可拆分业务频道 |
 
-### 🛠️ Payment Infrastructure
+## 3. 权限模型（当前）
 
-- **Currency**: Polygon / USDC.
-- **Method**: Initially manual activation; migrating to automatic deposit detection (Phase 2).
+- 游客：可查看基础看板与简版信息。
+- 登录用户：账户中心、钱包绑定、积分同步。
+- Pro 用户：
+  - 今日日内深度分析（含高温时段）
+  - 历史对账 + 未来日期分析
+  - 全平台智能气象推送
 
----
+## 4. 收费与积分规则（默认）
 
-## 🗺️ Execution Roadmap
+- 套餐：`pro_monthly`（5 USDC / 30 天）
+- 抵扣：500 积分抵 1 USDC，最高抵 3 USDC
+- 实付下限：2 USDC（当积分满额时）
 
-```mermaid
-graph LR
-    P1[Phase 1: Manual Beta] --> P2[Phase 2: USDC Automation]
-    P2 --> P3[Phase 3: Scaling & Analytics]
+> 说明：具体运营策略可按阶段调整，生产参数建议放私有仓库。
 
-    subgraph P1_Detail [Manual Operations]
-        P1 -->|DM Bot| Pay[Manual Payment]
-        Pay -->|Invite| Link[One-time Link]
-    end
+## 5. 许可证与商用边界
 
-    subgraph P2_Detail [Smart Automation]
-        P2 -->|Monitor| Chain[Polygon/USDC]
-        Chain -->|Auto| Access[JWT/Sub Activation]
-    end
-```
+当前仓库代码采用 `AGPL-3.0-only`：
 
-### 📦 Phase 1: Manual Beta
+- 公开：基础能力与通用支付流程源码。
+- 不随代码许可证授权：商业风控、营销策略、关键运营参数、内部审计策略、品牌与托管服务资产。
 
-- **Goal**: Stabilize current alert quality and build core user group.
-- **Actions**:
-  - Manual subscription activation via Telegram DM.
-  - Small, focused paid Telegram channel for signal tests.
-  - Invitation-only Web Access (Vercel).
+详见：[AGPL-3.0 与商用边界](OPEN_CORE_POLICY.md)
 
-### 🛠️ Phase 2: Automation (USDC)
+## 6. 上线检查清单（收费前）
 
-- **Goal**: Reduce operational friction.
-- **Actions**:
-  - **On-chain monitoring**: Detect USDC deposits to unique addresses.
-  - **One-time Links**: Telegram bot automatically generates invite links with `member_limit=1`.
-  - **JWT Auth**: Securing the Next.js frontend with subscriber-only tokens.
+1. 支付链路：创建 intent、提交 tx、确认入账、订阅开通全链路可回放。
+2. 权限链路：前端/后端/Bot 对 Pro 权限判定一致。
+3. 审计能力：支付日志、订阅变更、异常重试可追溯。
+4. 通知策略：支付成功私发、群内通知降噪。
+5. 安全边界：敏感配置不进仓库。
 
-### 🌐 Phase 3: Scaling & Analytics
+## 7. 后续路线
 
-- **Goal**: Retention and expansion.
-- **Actions**:
-  - **Accuracy Leaderboard**: Monthly reports of DEB vs Market outcomes.
-  - **Self-Serve Portal**: User dashboard for billing and alert settings.
-
----
-
-## 🚧 Critical Constraints
-
-- **Weather-First**: We focus on the **physical variable changes** rather than exchange-side order book execution.
-- **Quality > Quantity**: Alert fatigue will churn subscribers. We enforce a "True Probability Shift" rule for notifications.
-- **Local Niche**: Ankara is our flagship differentiator.
-
----
-
-**📅 Last Updated**: 2026-03-06
+- 支持更多链和稳定币。
+- 引入退款与工单后台。
+- 建立周/月留存与付费转化看板。
+- 打通渠道分销与邀请码返利系统。

docs/CONFIGURATION_ZH.md

@@ -0,0 +1,337 @@
+# 配置与密钥管理（中文）
+
+## 1. 目标
+
+PolyWeather 的环境变量很多，但不是所有变量都属于同一层级。
+
+当前推荐做法是把配置拆成三类：
+
+1. 可复现基础配置  
+   放在：[.env.example](/E:/web/PolyWeather/.env.example)
+
+2. 敏感密钥模板  
+   放在：[.env.secrets.example](/E:/web/PolyWeather/.env.secrets.example)
+
+3. 平台侧真实密钥  
+   放在：
+   - VPS / Docker `.env`
+   - Vercel Environment Variables
+   - GitHub Secrets（如需要）
+
+## 2. 为什么要拆
+
+如果把所有变量都平铺在一个 `.env` 里，会有三个问题：
+
+1. 新环境很难知道“最小启动到底需要哪些变量”
+2. 敏感密钥和普通开关混在一起，容易误泄露
+3. 调优参数太多时，团队很难区分“必须填”和“保持默认即可”
+
+所以正确做法不是“减少变量数量”，而是：
+
+- 保留变量能力
+- 按职责分层
+- 给出最小启动路径
+
+## 3. 文件职责
+
+### 3.1 根 `.env.example`
+
+文件：
+
+- [.env.example](/E:/web/PolyWeather/.env.example)
+
+用途：
+
+- 后端 / Bot / Docker 的可复现配置模板
+- 只放变量名、默认值、开关与非敏感示例
+
+### 3.2 根 `.env.secrets.example`
+
+文件：
+
+- [.env.secrets.example](/E:/web/PolyWeather/.env.secrets.example)
+
+用途：
+
+- 只列敏感项
+- 帮助运维明确哪些值必须从密钥系统注入
+
+### 3.3 前端 `.env.example`
+
+文件：
+
+- [frontend/.env.example](/E:/web/PolyWeather/frontend/.env.example)
+
+用途：
+
+- 前端本地开发与 Vercel 环境变量模板
+
+## 4. 配置分级
+
+### 4.1 L1：最小启动必需项
+
+这是“服务能跑起来”的最小集合。
+
+后端 / Bot：
+
+- `TELEGRAM_BOT_TOKEN`
+- `TELEGRAM_CHAT_ID`
+- `POLYWEATHER_RUNTIME_DATA_DIR`
+- `POLYWEATHER_DB_PATH`
+- `POLYWEATHER_STATE_STORAGE_MODE`
+
+前端：
+
+- `POLYWEATHER_API_BASE_URL`
+- `POLYWEATHER_OPS_ADMIN_EMAILS`（如果启用 `/ops` 页面级管理员守卫）
+
+如果启用登录：
+
+- `NEXT_PUBLIC_SUPABASE_URL`
+- `NEXT_PUBLIC_SUPABASE_ANON_KEY`
+- `SUPABASE_URL`
+- `SUPABASE_ANON_KEY`
+- `SUPABASE_SERVICE_ROLE_KEY`
+
+### 4.2 L2：功能开关
+
+这些变量一般不敏感，但会决定功能是否启用。
+
+例如：
+
+- `POLYWEATHER_AUTH_ENABLED`
+- `POLYWEATHER_AUTH_REQUIRED`
+- `POLYWEATHER_AUTH_REQUIRE_SUBSCRIPTION`
+- `POLYWEATHER_OPS_ADMIN_EMAILS`
+- `POLYWEATHER_STATE_STORAGE_MODE`
+- `POLYWEATHER_PAYMENT_ENABLED`
+- `POLYMARKET_MARKET_SCAN_ENABLED`
+- `POLYGON_WALLET_WATCH_ENABLED`
+- `TELEGRAM_ALERT_PUSH_ENABLED`
+- `TELEGRAM_MARKET_FOCUS_DIGEST_ENABLED`
+- `POLYMARKET_WALLET_ACTIVITY_ENABLED`（已退役，建议保持 `false`）
+
+### 4.3 L3：运行调优项
+
+这些一般不需要在第一天就改。
+
+例如：
+
+- 各类 `*_TTL_SEC`
+- 各类 `*_TIMEOUT_SEC`
+- 各类 `*_COOLDOWN_SEC`
+- 各类 `*_INTERVAL_SEC`
+- `TELEGRAM_ALERT_MIN_TRIGGER_COUNT`
+- `TELEGRAM_ALERT_MIN_SEVERITY`
+- `TELEGRAM_ALERT_MISPRICING_ONLY`
+- `TELEGRAM_ALERT_MISPRICING_INTERVAL_SEC`
+- `TELEGRAM_MARKET_FOCUS_DIGEST_INTERVAL_SEC`
+- `TELEGRAM_MARKET_FOCUS_DIGEST_TOP_N`
+- `POLYWEATHER_PAYMENT_RPC_URLS`
+- `TAF_CACHE_TTL_SEC`
+
+策略：
+
+- 先用默认值
+- 出现性能或运维问题时再调
+
+### 4.4 L4：敏感项
+
+这些变量不应写进公开文档截图，也不应提交到仓库。
+
+例如：
+
+- `TELEGRAM_BOT_TOKEN`
+- `SUPABASE_SERVICE_ROLE_KEY`
+- `POLYWEATHER_BACKEND_ENTITLEMENT_TOKEN`
+- `POLYWEATHER_DASHBOARD_ACCESS_TOKEN`
+- `METEOBLUE_API_KEY`
+- `NEXT_PUBLIC_WALLETCONNECT_PROJECT_ID`
+- `POLYMARKET_SECRET_KEY`
+
+## 5. 推荐部署矩阵
+
+### 5.1 VPS / Docker（后端 + Bot）
+
+建议放这些：
+
+- 根 `.env` 的后端项
+- 所有 secrets
+- Bot / 支付 / watcher 配置
+
+### 5.2 Vercel（前端）
+
+建议只放前端真正需要的变量：
+
+- `POLYWEATHER_API_BASE_URL`
+- `NEXT_PUBLIC_SUPABASE_URL`
+- `NEXT_PUBLIC_SUPABASE_ANON_KEY`
+- `POLYWEATHER_AUTH_ENABLED`
+- `POLYWEATHER_AUTH_REQUIRED`
+- `POLYWEATHER_OPS_ADMIN_EMAILS`
+- `POLYWEATHER_DASHBOARD_ACCESS_TOKEN`
+- `POLYWEATHER_BACKEND_ENTITLEMENT_TOKEN`
+- `NEXT_PUBLIC_WALLETCONNECT_PROJECT_ID`
+- `NEXT_PUBLIC_WALLETCONNECT_POLYGON_RPC_URL`
+
+说明：
+
+- `/ops` 现在是前后端双层限制：
+  - 前端页面入口读取 `POLYWEATHER_OPS_ADMIN_EMAILS`
+  - 后端写接口同样读取 `POLYWEATHER_OPS_ADMIN_EMAILS`
+- 因此，Vercel 和 VPS / Docker 两侧都应配置相同的管理员邮箱白名单。
+
+不要把后端专用密钥全搬进 Vercel。
+
+### 5.3 GitHub Actions
+
+当前 CI 不需要大规模 secrets。
+
+如果未来要做自动部署，再考虑：
+
+- `VERCEL_TOKEN`
+- `VERCEL_ORG_ID`
+- `VERCEL_PROJECT_ID`
+
+## 6. 最小部署示例
+
+### 6.1 前端最小变量
+
+```env
+POLYWEATHER_API_BASE_URL=https://your-backend.example.com
+NEXT_PUBLIC_SUPABASE_URL=https://your-project.supabase.co
+NEXT_PUBLIC_SUPABASE_ANON_KEY=your_anon_key
+POLYWEATHER_AUTH_ENABLED=true
+POLYWEATHER_AUTH_REQUIRED=true
+```
+
+### 6.2 后端最小变量
+
+```env
+TELEGRAM_BOT_TOKEN=...
+TELEGRAM_CHAT_ID=...
+POLYWEATHER_RUNTIME_DATA_DIR=/var/lib/polyweather
+POLYWEATHER_DB_PATH=/var/lib/polyweather/polyweather.db
+POLYWEATHER_STATE_STORAGE_MODE=sqlite
+UID=1000
+GID=1000
+POLYWEATHER_AUTH_ENABLED=true
+POLYWEATHER_AUTH_REQUIRED=false
+POLYWEATHER_OPS_ADMIN_EMAILS=yhrsc30@gmail.com
+TAF_CACHE_TTL_SEC=900
+SUPABASE_URL=https://your-project.supabase.co
+SUPABASE_ANON_KEY=...
+SUPABASE_SERVICE_ROLE_KEY=...
+POLYWEATHER_BACKEND_ENTITLEMENT_TOKEN=...
+TELEGRAM_ALERT_PUSH_ENABLED=true
+TELEGRAM_ALERT_PUSH_INTERVAL_SEC=300
+TELEGRAM_ALERT_PUSH_COOLDOWN_SEC=1800
+TELEGRAM_ALERT_MIN_TRIGGER_COUNT=2
+TELEGRAM_ALERT_MIN_SEVERITY=medium
+TELEGRAM_ALERT_MISPRICING_ONLY=true
+TELEGRAM_ALERT_MISPRICING_INTERVAL_SEC=7200
+TELEGRAM_MARKET_FOCUS_DIGEST_ENABLED=true
+TELEGRAM_MARKET_FOCUS_DIGEST_INTERVAL_SEC=1800
+TELEGRAM_MARKET_FOCUS_DIGEST_TOP_N=5
+POLYMARKET_WALLET_ACTIVITY_ENABLED=false
+```
+
+说明：
+
+- `UID` / `GID` 主要给 Linux Docker 主机用，避免容器把运行文件写成 root 所有。
+- Windows / macOS 一般可以直接保留默认值。
+- `POLYWEATHER_RUNTIME_DATA_DIR` 建议放在仓库外，例如 `/var/lib/polyweather`。
+- `docker-compose.yml` 会把这个目录同时挂载到容器内的 `/var/lib/polyweather` 和 `/app/data`，兼容现有缓存与 SQLite 路径。
+- `POLYWEATHER_STATE_STORAGE_MODE` 当前线上推荐直接使用 `sqlite`。
+- `POLYWEATHER_PAYMENT_RPC_URLS` 支持逗号分隔多个 RPC；如果暂时只用单 RPC，也可以继续只配 `POLYWEATHER_PAYMENT_RPC_URL`。
+- 机器人市场监控当前以 `关注清单` 为主，按固定间隔主动推送。
+- `TELEGRAM_MARKET_FOCUS_DIGEST_INTERVAL_SEC` 表示主动推送间隔，默认 `1800` 秒（30 分钟）。
+- `POLYMARKET_WALLET_ACTIVITY_ENABLED` 已退役，保留为 `false` 即可，不建议再启用钱包异动监听。
+
+### 6.3 机器人市场监控建议配置
+
+这套配置用于替代旧的钱包异动监听，围绕市场本身做两类推送：
+
+- `关键提醒`：实时错价/触发条件满足时发送
+- `关注清单`：按亚洲时区定时推送当日重点市场摘要
+
+推荐值：
+
+```env
+TELEGRAM_ALERT_PUSH_ENABLED=true
+TELEGRAM_ALERT_PUSH_INTERVAL_SEC=300
+TELEGRAM_ALERT_PUSH_COOLDOWN_SEC=1800
+TELEGRAM_ALERT_MIN_TRIGGER_COUNT=2
+TELEGRAM_ALERT_MIN_SEVERITY=medium
+TELEGRAM_ALERT_MISPRICING_ONLY=true
+TELEGRAM_ALERT_MISPRICING_INTERVAL_SEC=7200
+TELEGRAM_MARKET_FOCUS_DIGEST_ENABLED=true
+TELEGRAM_MARKET_FOCUS_DIGEST_INTERVAL_SEC=1800
+TELEGRAM_MARKET_FOCUS_DIGEST_TOP_N=5
+POLYMARKET_WALLET_ACTIVITY_ENABLED=false
+```
+
+说明：
+
+- `TELEGRAM_ALERT_MISPRICING_ONLY=true` 表示关键提醒优先围绕错价/市场触发，不把机器人做成泛通知器。
+- `TELEGRAM_MARKET_FOCUS_DIGEST_INTERVAL_SEC=1800` 表示频道每 30 分钟主动推送一轮机会清单。
+- `TELEGRAM_MARKET_FOCUS_DIGEST_TOP_N=5` 建议先保持较小，避免机器人一次推太多城市。
+- `POLYMARKET_WALLET_ACTIVITY_ENABLED=false` 表示停用旧的钱包异动监听，统一收敛到市场监控。
+
+## 7. 当前建议的运维规则
+
+### 7.1 仓库中允许存在
+
+- `.env.example`
+- `.env.secrets.example`
+- `frontend/.env.example`
+
+### 7.2 仓库中不应提交
+
+- `.env`
+- `.env.local`
+- 任何带真实 token / key 的配置文件
+
+### 7.3 截图与共享规则
+
+以下值一旦出现在截图或聊天里，建议视为泄露并轮换：
+
+- `SUPABASE_SERVICE_ROLE_KEY`
+- `POLYWEATHER_BACKEND_ENTITLEMENT_TOKEN`
+- `TELEGRAM_BOT_TOKEN`
+- 第三方私有 API Key
+
+## 8. 如何收口配置复杂度
+
+如果你觉得变量仍然太多，正确的做法不是一刀删掉，而是：
+
+1. 把“功能开关”和“调优参数”分开看
+2. 保持 `.env.example` 中：
+   - 最小启动项
+   - 常用功能开关
+   - 默认调优值
+3. 让不常改的高阶参数继续留默认
+
+也就是说：
+
+- 使用者只需要先关心 10-20 个关键变量
+- 其余变量保持默认即可
+
+## 9. 当前已经完成的配置治理
+
+1. 根 `.env.example` 收口
+2. `.env.secrets.example` 新增
+3. 前端 `.env.example` 收口
+4. 运行时配置校验脚本新增
+5. `/ops` 管理员白名单与前后端职责边界已明确
+5. 支付运行态与多 RPC 配置支持
+6. 运行态 SQLite 迁移配置支持
+
+## 10. 配置校验命令
+
+在不启动服务的情况下，你可以直接检查配置：
+
+```bash
+python scripts/validate_runtime_env.py --component web
+python scripts/validate_runtime_env.py --component bot
+```

docs/EMOS_LGBM_SYSTEM_ZH.md

@@ -0,0 +1,653 @@
+# EMOS + LGBM 系统说明（中文）
+
+本文档用于完整说明 PolyWeather 当前的两条统计/机器学习链路：
+
+- `EMOS`：概率后处理与校准链路
+- `LGBM`：日最高温点预测辅助模型
+
+重点不只是“模型怎么训练”，还包括：
+
+- 这些模型依赖什么历史数据
+- 真值和训练特征现在如何长期保存
+- 为什么过去样本一直不够
+- 当前线上到底运行在哪个模式
+- 现在能做什么，不能做什么
+
+本文档基于仓库当前实现与最近一轮重建结果，适合作为：
+
+- 项目内部模型说明
+- 运维与数据治理说明
+- 未来继续扩展 EMOS/LGBM 的基线文档
+
+---
+
+## 1. 总览
+
+PolyWeather 当前不是“用一个模型替代所有东西”，而是多层结构：
+
+1. 多源天气采集层
+2. `DEB` 业务主预测层
+3. `LGBM` 轻量点预测辅助层
+4. `EMOS` 概率校准层
+5. 市场概率/桶命中评估层
+
+可以简化理解为：
+
+```text
+天气源 / 观测 / 历史真值
+        ↓
+   DEB 主预测
+        ↓
+   LGBM 辅助点预测
+        ↓
+EMOS 对概率分布做后处理
+        ↓
+市场概率 / shadow / rollout 门禁
+```
+
+其中：
+
+- `DEB` 仍然是当前业务主路径
+- `LGBM` 是辅助预测源，不是主路径
+- `EMOS` 是概率后处理，不是基础天气模型
+
+---
+
+## 2. 两条链路各自负责什么
+
+### 2.1 EMOS 负责什么
+
+`EMOS` 的全称通常指 Ensemble Model Output Statistics。
+
+在本项目里，它的角色不是重新预测温度，而是：
+
+- 把已有的预测结果做概率后处理
+- 让输出分布更“可校准”
+- 让桶概率和市场评估更稳定
+
+EMOS 关注的是：
+
+- `raw_mu`
+- `raw_sigma`
+- `deb_prediction`
+- `ens_median`
+- `ensemble_spread`
+- `max_so_far_gap`
+- `peak_flag`
+- 最终真实 `actual_high`
+
+它最终输出的是一套“经过校准的概率分布”，而不是单一温度值。
+
+所以 EMOS 的核心衡量指标不是单纯 MAE，而更看重：
+
+- `CRPS`
+- `bucket_hit_rate`
+- `bucket_brier`
+
+### 2.2 LGBM 负责什么
+
+`LGBM` 是一个轻量级的回归模型，用来预测：
+
+- `actual_high`（日最高温）
+
+它吃的是：
+
+- 历史真值 lag 特征
+- 多模型 forecast
+- `deb_prediction`
+- 当前观测特征
+- 时间特征
+
+它输出的是：
+
+- 一个点预测 `actual_high`
+
+然后这个点预测可以作为：
+
+- 额外 forecast 源
+- 供 DEB / 运营 / 研究参考
+
+所以它和 EMOS 的区别非常重要：
+
+- `LGBM`：做点预测
+- `EMOS`：做概率校准
+
+---
+
+## 3. 当前代码结构
+
+### 3.1 EMOS 相关
+
+核心文件：
+
+- [probability_calibration.py](/E:/web/PolyWeather/src/analysis/probability_calibration.py)
+- [probability_rollout.py](/E:/web/PolyWeather/src/analysis/probability_rollout.py)
+- [fit_probability_calibration.py](/E:/web/PolyWeather/scripts/fit_probability_calibration.py)
+- [evaluate_probability_calibration.py](/E:/web/PolyWeather/scripts/evaluate_probability_calibration.py)
+- [build_probability_shadow_report.py](/E:/web/PolyWeather/scripts/build_probability_shadow_report.py)
+- [judge_probability_rollout.py](/E:/web/PolyWeather/scripts/judge_probability_rollout.py)
+
+核心产物：
+
+- [default.json](/E:/web/PolyWeather/artifacts/probability_calibration/default.json)
+- [evaluation_report.json](/E:/web/PolyWeather/artifacts/probability_calibration/evaluation_report.json)
+- [shadow_report.json](/E:/web/PolyWeather/artifacts/probability_calibration/shadow_report.json)
+- [rollout_report.json](/E:/web/PolyWeather/artifacts/probability_calibration/rollout_report.json)
+- [training_samples.json](/E:/web/PolyWeather/artifacts/probability_calibration/training_samples.json)
+
+### 3.2 LGBM 相关
+
+核心文件：
+
+- [lgbm_daily_high.py](/E:/web/PolyWeather/src/models/lgbm_daily_high.py)
+- [lgbm_features.py](/E:/web/PolyWeather/src/models/lgbm_features.py)
+- [train_lgbm_daily_high.py](/E:/web/PolyWeather/scripts/train_lgbm_daily_high.py)
+- [report_lgbm_daily_high.py](/E:/web/PolyWeather/scripts/report_lgbm_daily_high.py)
+
+核心产物：
+
+- [lgbm_daily_high.txt](/E:/web/PolyWeather/artifacts/models/lgbm_daily_high.txt)
+- [lgbm_daily_high_schema.json](/E:/web/PolyWeather/artifacts/models/lgbm_daily_high_schema.json)
+
+---
+
+## 4. 为什么之前样本总是上不去
+
+这件事是理解当前状态的关键。
+
+过去项目里有一个结构性问题：
+
+- `daily_records_store` 同时承担了
+  - 运行态缓存
+  - 历史训练数据来源
+
+但运行态层会把 `daily_records` 硬裁成最近 14 天。
+
+这意味着：
+
+- 对线上运行来说没问题
+- 对训练来说，历史监督样本会不断被删掉
+
+结果就是：
+
+- 城市越来越多
+- 训练历史反而越来越稀
+- `LGBM` 很容易只有二十几条样本
+- `EMOS` 也只能靠有限 snapshot/daily_record 拼起来
+
+这不是“模型太差”，而是“数据主存设计不对”。
+
+---
+
+## 5. 这次历史真值治理做了什么
+
+现在已经把“运行态缓存”和“长期训练主存”拆开了。
+
+### 5.1 `daily_records_store`
+
+继续保留，但只作为：
+
+- 最近 14 天运行态缓存
+
+它不再承担长期训练历史职责。
+
+### 5.2 `truth_records_store`
+
+新增永久真值表，作为长期训练真值主存。
+
+当前核心字段包括：
+
+- `city`
+- `target_date`
+- `actual_high`
+- `settlement_source`
+- `settlement_station_code`
+- `settlement_station_label`
+- `truth_version`
+- `updated_by`
+- `updated_at`
+- `source_payload_json`
+- `is_final`
+
+这张表的意义是：
+
+- 长期保存监督真值
+- 不再被 14 天缓存裁剪
+- 真值来源变得可追溯
+
+### 5.3 `truth_revisions_store`
+
+新增真值修订审计表。
+
+它记录：
+
+- 老值是什么
+- 新值是什么
+- 来源怎么变了
+- 谁改的
+- 为什么改
+- 什么时候改
+
+所以现在回填不会再是“静默覆盖”。
+
+### 5.4 `training_feature_records_store`
+
+新增长期训练特征表。
+
+它长期留存：
+
+- forecasts
+- deb_prediction
+- mu
+- probability_features
+- prob_snapshot
+- shadow_prob_snapshot
+- calibration 摘要
+
+它的作用是：
+
+- 从现在开始，不再继续丢失历史训练特征
+- 让未来 EMOS/LGBM 样本自然累积
+
+---
+
+## 6. 训练数据现在怎么来
+
+### 6.1 EMOS 训练样本
+
+EMOS 训练不只是需要真值，还要有“当时那一刻的预测快照”。
+
+所以一条 EMOS 样本，本质上需要两部分：
+
+1. 历史预测特征
+2. 对应日期最终真值
+
+当前导出的 EMOS 样本里，核心字段包括：
+
+- `city`
+- `date`
+- `actual_high`
+- `raw_mu`
+- `raw_sigma`
+- `deb_prediction`
+- `ens_median`
+- `ensemble_spread`
+- `max_so_far_gap`
+- `peak_flag`
+- `sample_source`
+- `settlement_source`
+- `settlement_station_code`
+- `truth_version`
+- `truth_updated_by`
+- `truth_updated_at`
+
+也就是说，EMOS 训练样本现在已经带了真值 provenance。
+
+### 6.2 LGBM 训练样本
+
+LGBM 训练样本会优先从：
+
+1. 永久真值表取监督目标
+2. 长期训练特征表取历史特征
+3. 再回退到必要的运行态/快照补充
+
+当前 LGBM 样本会用到：
+
+- 历史 `actual_high` lag
+- 历史均值/趋势
+- 多模型 forecast
+- `deb_prediction`
+- 当前观测
+- 时间特征
+
+---
+
+## 7. Wunderground 历史回填为什么重要
+
+这次治理里一个重点是：
+
+- `Taipei`
+- `Shenzhen`
+
+这两个城市已经切到了市场指定的 `Wunderground` 结算口径。
+
+之前的问题是：
+
+- 城市注册表已经写成 `wunderground`
+- 但历史回填链路还没有真正支持按指定历史日期抓 WU 历史页
+
+所以过去它们的 `actual_high` 可能：
+
+- 没有被正确回填
+- 或者被错误来源污染
+
+现在已经补了正式历史回填函数：
+
+- [wunderground_sources.py](/E:/web/PolyWeather/src/data_collection/wunderground_sources.py)
+
+它会：
+
+1. 按 `city + target_date` 拼出对应历史页
+2. 解析该日观测序列
+3. 取当日最高温
+4. 按市场规则做整度结算
+5. 写入永久真值表
+6. 记录来源与审计信息
+
+这一步对 `Taipei/Shenzhen` 尤其关键，因为它们不是 NOAA/HKO 口径。
+
+---
+
+## 8. 当前线上/离线运行模式
+
+### 8.1 概率引擎模式
+
+当前项目仍然应该保持：
+
+- `emos_shadow`
+
+而不是：
+
+- `emos_primary`
+
+原因不是工程没接好，而是门禁还没过。
+
+### 8.2 LGBM 角色
+
+当前 `LGBM` 仍然只能算：
+
+- 辅助预测源
+- 研究/观测链路
+
+不适合替代 `DEB` 主路径。
+
+---
+
+## 9. 当前最新状态
+
+以下状态来自最近一轮恢复、回填和重训产物。
+
+### 9.1 永久真值
+
+当前永久真值表已恢复到长期历史：
+
+- `truth_records_store`
+  - 最早：`2023-01-01`
+  - 最晚：`2026-04-02`
+  - 行数：约 `35138`
+  - 城市数：`30`
+
+运行态缓存仍然只有近 14 天：
+
+- `daily_records_store`
+  - 仍然是近两周范围
+
+这说明：
+
+- 长期真值主存已经从运行态缓存里分离出来了
+
+### 9.2 真值修订
+
+当前已有 revision 审计记录：
+
+- `truth_revisions_store`
+  - 行数：`2`
+
+这说明审计链路已经在工作。
+
+### 9.3 Wunderground 回填
+
+`Taipei` 与 `Shenzhen` 已按 WU 历史页完成回填。
+
+当前这两城已经补到：
+
+- `2026-04-02`
+
+### 9.4 长期训练特征
+
+当前 `training_feature_records_store` 已经接通，但历史上真正留存下来的特征仍然很少。
+
+这意味着：
+
+- 从现在开始不会继续丢
+- 但过去没留下的那部分特征，不会凭空恢复
+
+这也是为什么：
+
+- 真值恢复了
+- `EMOS` 样本量却没有同步大幅增长
+
+---
+
+## 10. 当前 EMOS 结果怎么理解
+
+最近一轮离线评估大致是：
+
+- `sample_count = 54`
+- `delta_crps ≈ -0.0867`
+- `delta_mae = 0`
+- `delta_bucket_hit_rate = 0`
+
+这说明：
+
+- 从 `CRPS` 看，EMOS 有改善
+- 但从 `MAE` 和 `top bucket hit` 看，没有明显进步
+
+shadow 报告里更关键的问题是：
+
+- `shadow sample_count = 48`
+- `delta_bucket_brier` 仍然明显偏坏
+
+所以 rollout 结论仍然是：
+
+- `hold`
+
+这不是“EMOS 无效”，而是：
+
+- 它还没有稳定到能切主路径
+
+### 10.1 当前阻塞点
+
+主要阻塞仍然是：
+
+- 样本数不够
+- shadow bucket brier 退化
+
+也就是说，当前 EMOS 状态可以总结成：
+
+- 工程链路完整
+- 数据治理大幅改善
+- 发布门禁仍未通过
+
+---
+
+## 11. 当前 LGBM 结果怎么理解
+
+最近一轮 LGBM 训练后，样本数已经从以前更少的状态提升到：
+
+- `sample_count = 54`
+- `train_count = 42`
+- `validation_count = 12`
+
+验证集指标大致为：
+
+- `lgbm_mae = 1.349`
+- `deb_mae = 0.875`
+
+这说明：
+
+- LGBM 比以前样本更充足了
+- 但在验证集上仍然不如 DEB
+
+所以当前它的定位仍然应该是：
+
+- 辅助参考
+- 不替代 DEB
+
+---
+
+## 12. 为什么现在 EMOS 没有像 LGBM 那样明显涨样本
+
+这点很容易误解。
+
+答案不是“恢复失败”，而是两条链路对数据要求不一样。
+
+### 12.1 LGBM
+
+LGBM 更依赖：
+
+- 长期真值
+- 基础 forecast 特征
+
+这部分通过：
+
+- `truth_records_store`
+- `training_feature_records_store`
+
+已经改善很多。
+
+### 12.2 EMOS
+
+EMOS 更依赖：
+
+- 某一时刻的概率快照/分布特征
+
+如果过去那些 snapshot 没有长期保存下来，那么即使今天把真值补齐了：
+
+- 也无法凭空重建完整 EMOS 样本
+
+所以当前现实是：
+
+- 真值问题已经大幅改善
+- 未来特征不会再继续丢
+- 但过去缺失的 EMOS 快照历史仍然限制样本增长
+
+---
+
+## 13. 当前最重要的工程判断
+
+### 13.1 已经完成的
+
+这些现在可以认为已经完成：
+
+- 真值主存从运行态缓存里拆出
+- 真值 provenance 落库
+- revision 审计表落地
+- Wunderground 历史回填接通
+- `Taipei/Shenzhen` 真值口径修正
+- 长期训练特征表接通
+- `/ops` 已能可视化 truth / feature / EMOS / LGBM 覆盖情况
+
+### 13.2 还没完成的
+
+这些仍然是后续重点：
+
+- EMOS 样本继续自然积累
+- shadow bucket brier 稳定下来
+- LGBM 验证效果超过 DEB
+- 让更多城市开始持续积累训练特征
+
+---
+
+## 14. 运维怎么看当前状态
+
+现在最直接的入口是：
+
+- `/ops`
+
+这页已经能看到：
+
+- 历史真值主表统计
+- 真值来源分布
+- 真值修订数量
+- 长期训练特征统计
+- `Taipei/Shenzhen` 的 WU 回填状态
+- 城市覆盖缺口
+- 模型城市覆盖
+- 城市覆盖矩阵
+
+因此，运维现在可以快速回答：
+
+- 哪些城市真值已经长期化
+- 哪些城市还没有特征积累
+- 哪些城市已经能支撑 EMOS/LGBM
+- 哪些城市目前仍然只能主要依赖 DEB
+
+---
+
+## 15. 推荐工作流
+
+### 15.1 日常
+
+1. 查看 `/ops`
+2. 看 `truth / feature / EMOS / LGBM` 覆盖有没有继续增长
+3. 看 `Taipei/Shenzhen` 的 WU 行数是否继续更新
+4. 看 rollout 仍然是 `hold` 还是有改善
+
+### 15.2 周期性重训
+
+建议周期性执行：
+
+```bash
+./venv/Scripts/python.exe scripts/export_probability_training_dataset.py
+./venv/Scripts/python.exe scripts/fit_probability_calibration.py
+./venv/Scripts/python.exe scripts/evaluate_probability_calibration.py
+./venv/Scripts/python.exe scripts/build_probability_shadow_report.py
+./venv/Scripts/python.exe scripts/judge_probability_rollout.py
+./venv/Scripts/python.exe scripts/train_lgbm_daily_high.py
+```
+
+### 15.3 真值恢复/补数
+
+当有新的历史真值补数或回填需要时：
+
+```bash
+./venv/Scripts/python.exe scripts/restore_training_truth_history.py
+./venv/Scripts/python.exe scripts/restore_training_feature_history.py
+./venv/Scripts/python.exe scripts/backfill_recent_daily_actuals_from_metar.py --cities taipei shenzhen --lookback-days 14
+```
+
+说明：
+
+- 脚本名里虽然还保留 `from_metar`
+- 但当前实现已经会按 `settlement_source` 自动分发
+- `wunderground` 会走 WU 历史回填分支
+
+---
+
+## 16. 当前最务实的结论
+
+如果只用一句话概括当前状态：
+
+**EMOS 和 LGBM 的工程基础已经补齐，但数据积累还在恢复期；当前最正确的策略仍然是继续以 `DEB` 为主路径，让长期真值和训练特征继续沉淀，再观察 EMOS/LGBM 是否自然变强。**
+
+更具体一点：
+
+- `EMOS`
+  - 已接好
+  - 可训练
+  - 可评估
+  - 可 shadow
+  - 但暂时不能切主路径
+
+- `LGBM`
+  - 已接好
+  - 样本比以前更多
+  - 但验证集还不如 DEB
+  - 目前只能做辅助参考
+
+- 数据层
+  - 这次治理的真正价值，是防止未来继续丢历史
+  - 这对两条模型链路都比继续“微调参数”更关键
+
+---
+
+## 17. 相关文档
+
+若需要看更细分的历史说明，可继续参考：
+
+- [EMOS_TRAINING_REPORT_ZH.md](/E:/web/PolyWeather/docs/EMOS_TRAINING_REPORT_ZH.md)
+- [LGBM_DAILY_HIGH_ZH.md](/E:/web/PolyWeather/docs/LGBM_DAILY_HIGH_ZH.md)
+- [PROBABILITY_SNAPSHOT_ARCHIVE_ZH.md](/E:/web/PolyWeather/docs/PROBABILITY_SNAPSHOT_ARCHIVE_ZH.md)
+- [deep-research-report.md](/E:/web/PolyWeather/docs/deep-research-report.md)
+

docs/EMOS_TRAINING_REPORT_ZH.md

@@ -0,0 +1,311 @@
+# EMOS 训练报告（2026-03-20）
+
+## 1. 报告目的
+
+本文档用于记录当前 PolyWeather 概率校准引擎（EMOS）的训练结果、离线评估结果、线上 shadow 观测结果，以及是否具备切换为主路径的条件。
+
+当前结论先写在前面：
+
+- `EMOS` 已完成接入、训练、离线评估、shadow 落盘与滚动报表。
+- 当前默认运行模式应继续保持 `emos_shadow`。
+- 现阶段 **不建议切换到 `emos_primary`**。
+
+## 2. 本次训练版本
+
+- 校准版本：`emos-20260320130245`
+- 训练时间：`2026-03-20T13:02:45.903772+00:00`
+- 参数文件：[default.json](/E:/web/PolyWeather/artifacts/probability_calibration/default.json)
+- 离线评估报告：[evaluation_report.json](/E:/web/PolyWeather/artifacts/probability_calibration/evaluation_report.json)
+- 线上 shadow 报表：[shadow_report.json](/E:/web/PolyWeather/artifacts/probability_calibration/shadow_report.json)
+
+## 3. 训练数据概况
+
+### 3.1 数据来源
+
+当前训练主要使用两类数据：
+
+1. 项目历史日记录  
+   文件：[daily_records.json](/E:/web/PolyWeather/data/daily_records.json)
+
+2. 历史天气 CSV 构建出的结算标签  
+   文件：[settlement_history.json](/E:/web/PolyWeather/artifacts/probability_calibration/settlement_history.json)
+
+### 3.2 样本规模
+
+- 总训练样本数：`105`
+- 通过历史天气 CSV 补回的缺失 `actual_high`：`2`
+- 历史结算标签覆盖城市数：`30`
+
+说明：
+
+- 当前样本已覆盖 30 个城市，但有效监督样本量仍偏小。
+- 部分城市样本数只有 `2-7` 条，城市级参数容易波动。
+
+## 4. 模型结构
+
+### 4.1 当前实现
+
+EMOS 属于统计后处理层，不是数值天气模型本身。当前结构位于：
+
+- [probability_calibration.py](/E:/web/PolyWeather/src/analysis/probability_calibration.py)
+
+当前目标是对原有概率引擎输出进行校准：
+
+- 输入：`raw_mu`、`raw_sigma`、`DEB`、`ensemble median/spread`、`peak_status` 等特征
+- 输出：校准后的 `mu / sigma / distribution`
+
+### 4.2 当前运行模式
+
+支持三种模式：
+
+- `legacy`
+- `emos_shadow`
+- `emos_primary`
+
+当前建议默认模式：
+
+- `emos_shadow`
+
+即：
+
+- 对外仍展示 legacy 结果
+- 后台并行计算 EMOS 结果
+- 用于持续评估，不直接影响用户
+
+## 5. 本次训练参数摘要
+
+### 5.1 全局约束
+
+本次训练已加入两类约束：
+
+1. `sigma_constraints`
+- `min_ratio = 0.85`
+- `max_ratio = 1.35`
+- `absolute_min = 0.25`
+- `absolute_max = 3.0`
+
+2. `selection_guardrails`
+- `max_mae_increase = 0.02`
+- `max_bucket_hit_drop = 0.01`
+- `max_bucket_brier_increase = 0.05`
+
+这两类约束的目的不是追求“更激进的拟合”，而是防止 EMOS 为了降低 CRPS 而把分布摊得过平，导致业务上更关键的顶桶命中和概率质量变差。
+
+### 5.2 当前选中的 blending
+
+本次训练产物中最终选择：
+
+- `alpha_mu = 0.0`
+- `alpha_sigma = 0.0`
+
+含义是：
+
+- 训练器在护栏约束下，没有找到足够安全的候选方案可以替代 legacy 主路径
+- 因此当前正式选中的可用结果，本质上仍然锚定在 legacy
+
+这是一种正确的保护行为，不是失败。说明门禁已经起作用，避免了坏校准进入主路径。
+
+## 6. 离线评估结果
+
+评估报告来源：
+
+- [evaluation_report.json](/E:/web/PolyWeather/artifacts/probability_calibration/evaluation_report.json)
+
+### 6.1 总体结果
+
+Legacy：
+
+- `mean_crps = 2.793938`
+- `mean_mae = 2.721143`
+- `bucket_hit_rate = 0.695238`
+
+EMOS（强制 primary 评估）：
+
+- `mean_crps = 2.650216`
+- `mean_mae = 2.722829`
+- `bucket_hit_rate = 0.666667`
+
+Delta：
+
+- `CRPS = -0.143722`
+- `MAE = +0.001686`
+- `bucket_hit_rate = -0.028571`
+
+### 6.2 解读
+
+这组结果说明：
+
+1. `CRPS` 有改善  
+   说明从“分布整体平滑度”角度看，EMOS 有一定价值。
+
+2. `MAE` 基本持平但略差  
+   不是大问题，但也不能算改善。
+
+3. `bucket_hit_rate` 明显下降  
+   这是当前最大阻塞项。对 PolyWeather 这种结算桶业务来说，顶桶命中率比单纯 CRPS 更关键。
+
+因此，离线结论是：
+
+- `EMOS` 有研究价值
+- 但 **离线强切 primary 仍然不合格**
+
+## 7. 线上 Shadow 观测结果
+
+线上 shadow 报表来源：
+
+- [shadow_report.json](/E:/web/PolyWeather/artifacts/probability_calibration/shadow_report.json)
+
+### 7.1 总体结果
+
+- `samples = 103`
+- `legacy_mean_mae = 1.839223`
+- `shadow_mean_mae = 1.851931`
+- `delta_mae = +0.012708`
+
+- `legacy_bucket_hit_rate = 0.669903`
+- `shadow_bucket_hit_rate = 0.679612`
+- `delta_bucket_hit_rate = +0.009709`
+
+- `legacy_bucket_brier = 0.462814`
+- `shadow_bucket_brier = 0.756649`
+- `delta_bucket_brier = +0.293835`
+
+### 7.2 解读
+
+线上 shadow 结果和离线强制 primary 结果不完全相同，这是正常的。原因是：
+
+- `shadow_report` 反映的是历史记录中实际落盘的 shadow 输出
+- `evaluation_report` 反映的是离线脚本在强制 `emos_primary` 下重新计算的效果
+
+当前线上 shadow 的含义是：
+
+1. 顶桶命中率略有提升  
+   `+0.97%`
+
+2. 但 `MAE` 轻微变差  
+   虽然幅度不大，但没有形成明确优势
+
+3. `bucket_brier` 明显更差  
+   说明 shadow 分布仍然偏“摊平”，概率质量不足
+
+这是当前最重要的信号：
+
+- EMOS 在“顶桶命中”上偶尔能赢
+- 但在“概率质量”上还不够好
+
+## 8. 城市级观察
+
+从当前城市级结果看，EMOS 并不是“全城市统一改善”，而是明显分化：
+
+### 8.1 相对改善较明显的城市
+
+- `London`
+- `Hong Kong`
+- `Tokyo`
+- `New York`
+
+这些城市在部分指标上看到一定改善，说明当前校准特征在这些城市上更有效。
+
+### 8.2 风险较高的城市
+
+- `Atlanta`
+- `Miami`
+- `Chicago`
+- `Dallas`
+- `Seattle`
+
+这些城市常见现象是：
+
+- 顶桶命中没有显著提高
+- 或 `bucket_brier` 明显恶化
+- 或者 `MAE` 出现不必要抬升
+
+这说明当前 EMOS 还没有形成稳定的全局校准能力，城市间异质性很强。
+
+## 9. 当前判断
+
+### 9.1 能不能上线为主路径
+
+当前答案：
+
+- **不能**
+
+原因：
+
+1. 离线强制 primary 时，`bucket_hit_rate` 下降
+2. 线上 shadow 时，`bucket_brier` 明显变差
+3. 样本量依然偏小，城市样本不均衡
+4. 城市级表现分化明显
+
+### 9.2 当前应该怎么运行
+
+当前最合理的运行方式：
+
+1. 保持 `emos_shadow`
+2. 继续落盘 `shadow_prob_snapshot`
+3. 继续维护滚动报表
+4. 不修改机器人和网页的正式对外概率展示
+
+## 10. 已完成的工程能力
+
+目前已经具备以下能力：
+
+1. 可离线训练  
+   脚本：[fit_probability_calibration.py](/E:/web/PolyWeather/scripts/fit_probability_calibration.py)
+
+2. 可离线评估  
+   脚本：[evaluate_probability_calibration.py](/E:/web/PolyWeather/scripts/evaluate_probability_calibration.py)
+
+3. 可导出训练样本  
+   脚本：[export_probability_training_dataset.py](/E:/web/PolyWeather/scripts/export_probability_training_dataset.py)
+
+4. 可历史回填 shadow 结果  
+   脚本：[backfill_probability_shadow_history.py](/E:/web/PolyWeather/scripts/backfill_probability_shadow_history.py)
+
+5. 可生成滚动 shadow 报表  
+   脚本：[build_probability_shadow_report.py](/E:/web/PolyWeather/scripts/build_probability_shadow_report.py)
+
+6. CI 已接入  
+   包含 `ruff / pytest / frontend build / docker build workflow`
+
+## 11. 下一步建议
+
+### 11.1 必做
+
+1. 扩大监督样本量  
+   重点不是继续堆原始天气 CSV，而是补更多带 forecast snapshot 的历史样本。
+
+2. 继续按版本沉淀训练报告  
+   每次重训后都更新本报告或新增版本报告，避免只看单次结果。
+
+3. 保持 `shadow` 连续观测  
+   至少持续一段时间观察滚动指标是否稳定。
+
+### 11.2 再做
+
+1. 细分城市组建模  
+   比如按气候区、结算规则、温度单位分组，而不是完全全局一套参数。
+
+2. 优化训练目标  
+   目前已经把 `bucket_brier` 纳入目标，但仍需进一步靠近 PolyWeather 的业务目标。
+
+3. 补更严格的切换门槛  
+   只有在同时满足以下条件时，才考虑切 `emos_primary`：
+- `CRPS` 下降
+- `MAE` 不上升
+- `bucket_hit_rate` 不下降
+- `bucket_brier` 不上升
+
+## 12. 结论
+
+当前 EMOS 状态可以概括为：
+
+- 工程上：已经完整接入，具备训练、评估、shadow 观测能力
+- 模型上：有一定价值，但还不稳定
+- 产品上：适合继续做 shadow，不适合切主路径
+
+最终结论：
+
+- **继续使用 `emos_shadow`**
+- **暂不切 `emos_primary`**
+- **继续积累样本并按版本跟踪训练结果**

docs/FRONTEND_DEPLOYMENT_ZH.md

@@ -0,0 +1,211 @@
+# 前端部署配置（Vercel）
+
+本文只覆盖 `frontend` 目录对应的 Next.js 前端部署。
+
+## 一、部署目标
+
+推荐方案：
+
+1. GitHub Actions 负责 `CI`
+2. Vercel 负责前端 `CD`
+3. FastAPI 后端单独部署在 VPS / Docker 主机
+
+前端本身不直接访问天气源，而是通过 Next Route Handlers 转发到后端：
+
+1. 浏览器 -> Vercel 上的 Next.js 前端
+2. Next `/api/*` -> `POLYWEATHER_API_BASE_URL`
+3. FastAPI 后端 -> 分析 / 支付 / 鉴权服务
+
+## 二、Vercel 项目设置
+
+在 Vercel 导入 GitHub 仓库后，使用下面的设置：
+
+- Framework Preset: `Next.js`
+- Root Directory: `frontend`
+- Build Command: `npm run build`
+- Install Command: `npm install`
+
+如果仓库已经连接过 Vercel，通常只需要确认 `Root Directory` 仍然是 `frontend`。
+
+## 三、最小必填环境变量
+
+只部署天气看板和基础登录时，先填下面 4 项：
+
+```env
+POLYWEATHER_API_BASE_URL=https://<your-fastapi-host>
+NEXT_PUBLIC_SUPABASE_URL=https://<your-project>.supabase.co
+NEXT_PUBLIC_SUPABASE_ANON_KEY=<your-anon-key>
+POLYWEATHER_AUTH_ENABLED=true
+```
+
+建议显式补：
+
+```env
+POLYWEATHER_AUTH_REQUIRED=true
+```
+
+说明：
+
+- `POLYWEATHER_API_BASE_URL`：前端所有 `/api/*` Route Handler 转发时依赖它，没填会直接返回 500。
+- `NEXT_PUBLIC_SUPABASE_URL` / `NEXT_PUBLIC_SUPABASE_ANON_KEY`：Supabase 客户端依赖它们。
+- `POLYWEATHER_AUTH_ENABLED`：关闭时，前端不会启用登录能力。
+- `POLYWEATHER_AUTH_REQUIRED`：控制 middleware 是否强制登录。
+
+## 四、按功能启用的可选环境变量
+
+### 1. 分享式看板
+
+```env
+POLYWEATHER_DASHBOARD_ACCESS_TOKEN=
+```
+
+设置后，可通过 `/?access_token=<token>` 打开带令牌的看板入口。
+
+### 2. 前后端 entitlement 校验
+
+```env
+POLYWEATHER_BACKEND_ENTITLEMENT_TOKEN=
+```
+
+仅当后端开启 entitlement / 订阅校验时需要。
+
+### 3. 钱包支付
+
+```env
+NEXT_PUBLIC_WALLETCONNECT_PROJECT_ID=
+NEXT_PUBLIC_WALLETCONNECT_POLYGON_RPC_URL=https://polygon-bor-rpc.publicnode.com
+```
+
+如果不启用钱包支付，可以留空。
+
+### 4. `/ops` 管理员页面守卫
+
+```env
+POLYWEATHER_OPS_ADMIN_EMAILS=yhrsc30@gmail.com
+```
+
+说明：
+
+- `/ops` 现在不是只有后端接口限制，前端页面入口也会读取管理员邮箱白名单。
+- 因此前端部署到 Vercel 时，也应配置 `POLYWEATHER_OPS_ADMIN_EMAILS`。
+
+### 5. Telegram 入口
+
+```env
+NEXT_PUBLIC_TELEGRAM_GROUP_URL=https://t.me/<your_group>
+NEXT_PUBLIC_TELEGRAM_BOT_URL=https://t.me/WeatherQuant_bot
+```
+
+只影响按钮跳转，不影响核心页面加载。
+
+## 五、支付配置与旧部署治理
+
+支付区现在有一层额外防护：
+
+1. 用户点击支付前，前端会重新请求 `/api/payments/config`
+2. 若发现 `receiver_contract` 与页面旧状态不一致，会自动切换到最新地址
+3. 若后端返回的 `tx_payload.to` 与最新 `receiver_contract` 不一致，会直接阻断支付
+
+这层防护的目的，是降低以下事故概率：
+
+- 用户使用长期未刷新的旧标签页
+- 命中旧 deployment URL
+- 页面本地状态残留旧收款地址
+
+如果你变更过支付收款地址，建议同步执行：
+
+1. 在 Vercel 对当前 production 做一次 redeploy
+2. 删除明显过期、可能还带旧支付配置的旧 deployment
+3. 在 `Settings -> Security -> Deployment Retention Policy` 中收紧旧部署保留周期
+
+## 六、推荐的三套配置口径
+
+### 1. 公开游客模式
+
+```env
+POLYWEATHER_API_BASE_URL=https://api.example.com
+POLYWEATHER_AUTH_ENABLED=false
+POLYWEATHER_AUTH_REQUIRED=false
+```
+
+适合公开演示站。
+
+### 2. 正常登录模式
+
+```env
+POLYWEATHER_API_BASE_URL=https://api.example.com
+NEXT_PUBLIC_SUPABASE_URL=https://<project>.supabase.co
+NEXT_PUBLIC_SUPABASE_ANON_KEY=<anon-key>
+POLYWEATHER_AUTH_ENABLED=true
+POLYWEATHER_AUTH_REQUIRED=true
+```
+
+适合正式前端站点。
+
+### 3. 登录 + entitlement 联动
+
+```env
+POLYWEATHER_API_BASE_URL=https://api.example.com
+NEXT_PUBLIC_SUPABASE_URL=https://<project>.supabase.co
+NEXT_PUBLIC_SUPABASE_ANON_KEY=<anon-key>
+POLYWEATHER_AUTH_ENABLED=true
+POLYWEATHER_AUTH_REQUIRED=true
+POLYWEATHER_BACKEND_ENTITLEMENT_TOKEN=<shared-token>
+```
+
+适合前后端都启用了会员/订阅保护的生产环境。
+
+## 七、不要放进 Vercel 的变量
+
+这些属于后端私密配置，不应该放到前端项目：
+
+- `SUPABASE_SERVICE_ROLE_KEY`
+- `TELEGRAM_BOT_TOKEN`
+- `POLYWEATHER_BACKEND_ENTITLEMENT_TOKEN` 以外的后端 secret
+- 支付签名私钥 / 交易私钥 / 任何 bot 凭据
+
+特别注意：
+
+- `NEXT_PUBLIC_*` 会暴露给浏览器
+- 只有明确允许前端公开使用的值，才应加 `NEXT_PUBLIC_`
+
+## 八、上线前检查
+
+Vercel 部署前至少确认：
+
+1. `POLYWEATHER_API_BASE_URL` 指向可访问的后端生产地址
+2. `frontend/.env.example` 和 Vercel Project Settings 中的实际值一致
+3. GitHub Actions 中 `frontend-quality` 已通过
+4. 如果启用鉴权，Supabase redirect URL 已包含前端域名
+5. `GET /api/payments/config` 返回的是当前最新地址，而不是旧收款合约
+6. 如果启用了 `/ops`，确认 `POLYWEATHER_OPS_ADMIN_EMAILS` 已在 Vercel 与后端同时配置
+
+## 九、常见问题
+
+### 1. 页面打开后 API 全部 500
+
+先检查：
+
+```env
+POLYWEATHER_API_BASE_URL
+```
+
+这是最常见原因。
+
+### 2. Vercel 构建通过，但登录失败
+
+先检查：
+
+- `NEXT_PUBLIC_SUPABASE_URL`
+- `NEXT_PUBLIC_SUPABASE_ANON_KEY`
+- Supabase 项目里的站点 URL / redirect URL
+
+### 3. 钱包入口显示未配置
+
+先检查：
+
+```env
+NEXT_PUBLIC_WALLETCONNECT_PROJECT_ID
+```
+
+这是钱包连接的必需项。

docs/LGBM_DAILY_HIGH_ZH.md

@@ -0,0 +1,310 @@
+# LightGBM 日最高温模型（中文）
+
+## 1. 目标
+
+这套 `LightGBM` 模型是给 PolyWeather 增加一个轻量级的统计学习预测源。
+
+它的定位不是替代：
+
+- `DEB`
+- `EMOS`
+- `ECMWF / GFS / GEM / JMA / ICON / Open-Meteo / MGM / NWS`
+
+而是作为一个新的点预测源：
+
+`现有模型 + 观测特征 -> LGBM -> 并入 current_forecasts -> DEB -> EMOS`
+
+第一版只做：
+
+- `D0` 当日最高温预测
+
+不做：
+
+- `D1-D3`
+- 小时级曲线
+- 概率分布
+- 独立结算源
+
+## 2. 适用场景
+
+这条链路是为低资源 VPS 准备的。
+
+当前项目线上环境只有 `2GB RAM` 时，不适合引入 `TimesFM` 这类大模型，但适合用 `LightGBM` 做轻量推理。
+
+当前方案是：
+
+1. 训练离线完成
+2. 训练产物直接提交到仓库
+3. VPS 线上只加载模型文件并推理
+4. VPS 不训练，不起额外服务
+
+## 3. 文件结构
+
+核心文件如下：
+
+- 运行时推理：
+  - [src/models/lgbm_daily_high.py](/E:/web/PolyWeather/src/models/lgbm_daily_high.py)
+- 特征构建：
+  - [src/models/lgbm_features.py](/E:/web/PolyWeather/src/models/lgbm_features.py)
+- 训练脚本：
+  - [scripts/train_lgbm_daily_high.py](/E:/web/PolyWeather/scripts/train_lgbm_daily_high.py)
+- 训练报告脚本：
+  - [scripts/report_lgbm_daily_high.py](/E:/web/PolyWeather/scripts/report_lgbm_daily_high.py)
+- 模型文件：
+  - [artifacts/models/lgbm_daily_high.txt](/E:/web/PolyWeather/artifacts/models/lgbm_daily_high.txt)
+- 模型 schema / 指标：
+  - [artifacts/models/lgbm_daily_high_schema.json](/E:/web/PolyWeather/artifacts/models/lgbm_daily_high_schema.json)
+
+接入链路位置：
+
+- Web API 聚合：
+  - [web/analysis_service.py](/E:/web/PolyWeather/web/analysis_service.py)
+- 共享趋势引擎：
+  - [src/analysis/trend_engine.py](/E:/web/PolyWeather/src/analysis/trend_engine.py)
+
+## 4. 特征说明
+
+第一版特征固定为以下几组。
+
+### 4.1 历史日高温特征
+
+- `actual_high_lag_1`
+- `actual_high_lag_2`
+- `actual_high_lag_3`
+- `actual_high_lag_7`
+- `actual_high_mean_7`
+- `actual_high_mean_14`
+- `actual_high_trend_3`
+
+### 4.2 当天模型特征
+
+- `Open-Meteo`
+- `ECMWF`
+- `GFS`
+- `GEM`
+- `JMA`
+- `ICON`
+- `MGM`
+- `NWS`
+- `deb_prediction`
+- `model_median`
+- `model_spread`
+
+### 4.3 当前观测特征
+
+- `current_temp`
+- `max_so_far`
+- `humidity`
+- `wind_speed_kt`
+- `visibility_mi`
+
+### 4.4 时间与状态特征
+
+- `local_hour`
+- `month`
+- `weekday`
+- `peak_status_code`
+
+其中：
+
+- `before = 0`
+- `in_window = 1`
+- `past = 2`
+
+## 5. 训练数据来源
+
+训练数据主要来自两份运行时历史文件：
+
+- [data/daily_records.json](/E:/web/PolyWeather/data/daily_records.json)
+- [data/probability_training_snapshots.jsonl](/E:/web/PolyWeather/data/probability_training_snapshots.jsonl)
+
+作用分工：
+
+- `daily_records.json`
+  - 提供 `actual_high`
+  - 提供当天各模型 forecast
+  - 提供历史 `deb_prediction`
+
+- `probability_training_snapshots.jsonl`
+  - 提供 `max_so_far`
+  - 提供 `peak_status`
+  - 提供观测特征快照
+
+为后续重训，概率快照归档现在还会额外写入：
+
+- `current_temp`
+- `humidity`
+- `wind_speed_kt`
+- `visibility_mi`
+- `local_hour`
+
+对应代码：
+
+- [src/analysis/probability_snapshot_archive.py](/E:/web/PolyWeather/src/analysis/probability_snapshot_archive.py)
+
+## 6. 训练流程
+
+训练脚本：
+
+```bash
+./venv/Scripts/python.exe scripts/train_lgbm_daily_high.py
+```
+
+训练流程如下：
+
+1. 从历史文件构造监督样本
+2. 目标值固定为 `actual_high`
+3. 按日期做简单的时间顺序切分
+4. 最后约 20% 做验证集
+5. 先训练并评估验证集
+6. 再用全量样本训练最终模型
+7. 输出模型文件和 schema 文件
+
+输出产物：
+
+- [artifacts/models/lgbm_daily_high.txt](/E:/web/PolyWeather/artifacts/models/lgbm_daily_high.txt)
+- [artifacts/models/lgbm_daily_high_schema.json](/E:/web/PolyWeather/artifacts/models/lgbm_daily_high_schema.json)
+
+## 7. 如何看训练结果
+
+查看训练报告：
+
+```bash
+./venv/Scripts/python.exe scripts/report_lgbm_daily_high.py
+```
+
+这个脚本会读取 schema，并打印：
+
+- `Sample Count`
+- `Train Count`
+- `Valid Count`
+- `LGBM MAE`
+- `DEB MAE`
+- `Best Single MAE`
+- `Median MAE`
+- `Winner`
+
+当前这版训练结果是：
+
+- `sample_count = 29`
+- `validation_count = 12`
+- `validation.lgbm_mae = 2.975`
+- `validation.deb_mae = 2.267`
+- `validation.best_single_mae = 1.167`
+
+这说明：
+
+- 当前 `LGBM` 链路已经可用
+- 但现阶段验证集表现还没有超过 `DEB`
+- 所以默认配置仍建议保持关闭
+
+## 8. 线上运行逻辑
+
+运行时推理逻辑不是“直接替代 DEB”，而是：
+
+1. 先收集现有模型 forecast
+2. 先算一版基线 `DEB`
+3. 把这版 `DEB` 当作 `LGBM` 的一个输入特征
+4. 输出 `LGBM` 点预测
+5. 把 `LGBM` 注入 `current_forecasts`
+6. 重新计算最终 `DEB`
+
+这样做的原因是：
+
+- `LGBM` 需要吃到 `deb_prediction` 特征
+- 但最终 `DEB` 又要把 `LGBM` 当成一个新的输入模型
+
+## 9. 环境变量
+
+示例配置见：
+
+- [.env.example](/E:/web/PolyWeather/.env.example)
+
+相关变量：
+
+```env
+POLYWEATHER_LGBM_ENABLED=false
+POLYWEATHER_LGBM_MODEL_PATH=/app/artifacts/models/lgbm_daily_high.txt
+POLYWEATHER_LGBM_SCHEMA_PATH=/app/artifacts/models/lgbm_daily_high_schema.json
+POLYWEATHER_LGBM_MIN_HISTORY_POINTS=3
+```
+
+说明：
+
+- `POLYWEATHER_LGBM_ENABLED`
+  - 是否启用运行时推理
+- `POLYWEATHER_LGBM_MODEL_PATH`
+  - 模型文件路径
+- `POLYWEATHER_LGBM_SCHEMA_PATH`
+  - schema 文件路径
+- `POLYWEATHER_LGBM_MIN_HISTORY_POINTS`
+  - 某城市最低历史样本门槛
+
+默认是 `3`，原因不是最理想，而是当前整体样本仍然偏少。
+
+如果门槛设太高，很多城市现在根本不会触发 `LGBM`。
+
+## 10. VPS 部署建议
+
+如果你的 VPS 只有 `2GB RAM`：
+
+- 可以跑这套 `LightGBM`
+- 不要在 VPS 上训练
+- 不要起额外模型服务
+
+推荐方式：
+
+1. 在本地或开发环境训练
+2. 提交模型产物
+3. VPS 拉代码
+4. 开启 `POLYWEATHER_LGBM_ENABLED=true`
+5. 重启主服务
+
+不推荐：
+
+- 在 VPS 上跑训练脚本
+- 把 `LightGBM` 当成长任务服务单独部署
+- 同时引入大模型推理
+
+## 11. 当前结论
+
+这条链路已经完成了：
+
+- 离线训练
+- 模型产物固化
+- 运行时懒加载
+- Web / 共享分析链路注入
+- 前端模型类型兼容
+
+但当前样本量仍偏少，所以建议运营策略是：
+
+1. 先继续积累历史 `actual_high`
+2. 继续积累概率快照观测字段
+3. 定期重训
+4. 只有当验证集 `MAE` 持续接近或优于 `DEB` 时，再考虑默认线上开启
+
+## 12. 常用命令
+
+### 训练
+
+```bash
+./venv/Scripts/python.exe scripts/train_lgbm_daily_high.py
+```
+
+### 查看训练报告
+
+```bash
+./venv/Scripts/python.exe scripts/report_lgbm_daily_high.py
+```
+
+### 本地测试
+
+```bash
+./venv/Scripts/python.exe -m pytest tests/test_lgbm_features.py tests/test_lgbm_daily_high.py
+```
+
+### 编译检查
+
+```bash
+./venv/Scripts/python.exe -m compileall src web scripts tests
+```

docs/MONITORING_ZH.md

@@ -0,0 +1,123 @@
+# 外部监控与告警说明
+
+最后更新：`2026-04-01`
+
+## 1. 目标
+
+在现有轻量可观测性基础上，把 PolyWeather 补成最小可用的外部监控链路：
+
+- Prometheus 抓取 `/metrics`
+- Alertmanager 根据规则聚合告警
+- Relay 把告警推到运营频道
+- Grafana 展示趋势面板
+- 巡检脚本补健康检查
+
+## 2. 组件
+
+本仓库现在内置 4 个监控组件：
+
+- `polyweather_prometheus`
+- `polyweather_alertmanager`
+- `polyweather_alert_relay`
+- `polyweather_grafana`
+
+对应配置目录：
+
+- [monitoring/prometheus/prometheus.yml](../monitoring/prometheus/prometheus.yml)
+- [monitoring/prometheus/alerts.yml](../monitoring/prometheus/alerts.yml)
+- [monitoring/alertmanager/alertmanager.yml](../monitoring/alertmanager/alertmanager.yml)
+- [monitoring/grafana/dashboards/polyweather-overview.json](../monitoring/grafana/dashboards/polyweather-overview.json)
+
+## 3. 启动
+
+```bash
+docker compose --profile monitoring up -d polyweather_prometheus polyweather_alertmanager polyweather_alert_relay polyweather_grafana
+```
+
+默认端口：
+
+- Prometheus: `9090`
+- Alertmanager: `9093`
+- Grafana: `3001`
+- Alert relay: `9099`
+
+## 4. 环境变量
+
+在 [.env.example](../.env.example) 里新增了这些配置：
+
+```env
+POLYWEATHER_PROMETHEUS_PORT=9090
+POLYWEATHER_ALERTMANAGER_PORT=9093
+POLYWEATHER_ALERT_RELAY_PORT=9099
+POLYWEATHER_GRAFANA_PORT=3001
+POLYWEATHER_GRAFANA_ADMIN_USER=admin
+POLYWEATHER_GRAFANA_ADMIN_PASSWORD=polyweather
+POLYWEATHER_MONITORING_ALERT_CHAT_IDS=
+```
+
+说明：
+
+- `POLYWEATHER_MONITORING_ALERT_CHAT_IDS` 为空时，relay 会自动回退到：
+  - `TELEGRAM_CHAT_IDS`
+  - `TELEGRAM_CHAT_ID`
+- 告警发送仍复用现有 `TELEGRAM_BOT_TOKEN`
+
+## 5. 当前告警规则
+
+当前默认规则：
+
+- `PolyWeatherWebDown`
+- `PolyWeatherHttp5xxBurst`
+- `PolyWeatherHighSourceErrorRate`
+- `PolyWeatherOpenMeteoCooldownLoop`
+- `PolyWeatherSlowHttpAverage`
+
+规则文件：
+
+- [monitoring/prometheus/alerts.yml](../monitoring/prometheus/alerts.yml)
+
+## 6. 当前 Grafana 面板
+
+预置了一个最小仪表板：
+
+- `PolyWeather Overview`
+
+包含这些图：
+
+- HTTP Requests by Status
+- HTTP Latency
+- Source Requests by Outcome
+- Source Error Rate (15m)
+
+## 7. 巡检脚本
+
+手动巡检：
+
+```bash
+python scripts/check_ops_health.py --base-url http://127.0.0.1:8000
+```
+
+这个脚本会检查：
+
+- `/healthz`
+- `/api/system/status`
+- `/metrics`
+
+任何一项失败都会非零退出，适合挂到 crontab 或 systemd timer。
+
+## 8. 备注
+
+这套监控现在已经具备：
+
+- 外部抓取
+- 告警规则
+- Telegram 推送
+- 趋势面板
+- 巡检脚本
+
+但它仍是“最小可用版”，还没有覆盖：
+
+- 节点级 CPU / 内存 / 磁盘
+- 数据库体积趋势
+- 更细粒度支付指标
+- 按城市/来源拆分的业务 SLA

docs/OPEN_CORE_POLICY.md

@@ -0,0 +1,45 @@
+# AGPL-3.0 与商用边界
+
+最后更新：`2026-03-30`
+
+## 1. 当前许可证
+
+- 本仓库代码自 `2026-03-30` 起采用 **GNU Affero General Public License v3.0 only**（`AGPL-3.0-only`）。
+- 该许可证适用于仓库中未另行声明许可证的源代码与文档。
+- 如果你修改本项目并通过网络向用户提供服务，需按 AGPL 第 13 条向用户提供对应源码。
+
+## 2. 旧版本说明
+
+- 在本次切换前已经发布的 MIT 版本，仍按其原始许可证生效。
+- 本次变更不会追溯撤销既往已发布版本的 MIT 授权。
+
+## 3. 仓库公开范围
+
+- 天气数据采集与标准化（METAR / Open-Meteo / MGM / 官方结算源接口层）。
+- DEB、基础趋势分析、概率桶、历史对账、前端看板与 Bot 基础能力。
+- 标准支付流程、链上收款合约与公开 API/BFF 结构。
+
+## 4. 不在仓库许可证授权范围内的资产
+
+- 商标、品牌名、域名、Logo、商店素材与市场宣传文案。
+- 生产数据库、用户资料、钱包映射、订阅审计日志、内部报表。
+- 私有运营脚本、增长工具、内部风控参数、收费策略细节与内部阈值。
+- 托管服务本身、SLA、客服、运维值守与内部告警路由。
+
+## 5. 配置与数据安全红线
+
+- 不提交：`.env`、私钥、API key、机器人 token、第三方 service role key。
+- 不提交：生产数据库、运行态快照、支付流水快照、用户身份信息。
+- 不提交：仅用于线上商业判断的私有规则库与内部操作手册。
+
+## 6. 对部署者的要求
+
+- 若你提供公开网络服务，应在产品界面中提供清晰可访问的源码入口。
+- 若你修改了本项目再对外提供网络服务，应公开与你实际运行版本对应的源码。
+- 若你使用了仓库外的私有数据、商标或运营资产，这些额外资产不因 AGPL 自动获得授权。
+
+## 7. 法务与运营建议
+
+- 代码许可证与商标/品牌授权应继续分离管理。
+- 官网应补充服务条款、隐私政策与付费权益说明。
+- 若后续接受外部贡献，再次调整许可证前应先确认贡献者版权归属与再许可条件。

docs/OPS_ADMIN_ZH.md

@@ -0,0 +1,109 @@
+# Ops 运营后台说明
+
+最后更新：`2026-04-01`
+
+## 1. 入口
+
+前端入口：
+
+- `https://polyweather-pro.vercel.app/ops`
+
+## 2. 权限
+
+`/ops` 的写接口由后端白名单控制：
+
+```env
+POLYWEATHER_OPS_ADMIN_EMAILS=yhrsc30@gmail.com
+```
+
+可配置多个邮箱，逗号分隔。
+
+## 3. 当前能力
+
+### 只读能力
+
+- 系统健康
+- SQLite / rollout / metrics 摘要
+- 支付运行态
+- 当前会员
+- 周榜
+- 支付异常单
+- 漏斗转化面板
+
+### 写能力
+
+- 手动补分
+- 标记支付异常单“已处理”
+
+## 4. 当前会员
+
+会员列表来自：
+
+1. `subscriptions` 中的有效订阅
+2. 本地 `users` / `supabase_bindings`
+3. 若本地缺邮箱或注册时间，再回补 Supabase Auth 用户信息
+
+去重规则：
+
+- 同一个 `user_id` 只保留最晚到期那条
+
+## 5. 支付异常单
+
+当前异常单来源：
+
+- `payment_audit_events`
+- 仅筛 `payment_intent_failed`
+
+当前支持的典型失败原因：
+
+- `receiver_mismatch`
+- `sender_mismatch`
+- `event_mismatch`
+- `tx_reverted`
+
+默认只显示未处理项。
+
+## 6. 典型处理流程
+
+### 6.1 钱已到账但没开订阅
+
+先看 `/ops` 的支付异常单：
+
+- 如果是 `receiver_mismatch`
+  - 优先判定为支付打到了旧收款地址
+  - 不是缓存问题
+
+然后执行：
+
+1. 查 `payment_intents`
+2. 查 `payment_transactions`
+3. 查 `subscriptions`
+4. 跑恢复脚本：
+
+```bash
+python scripts/reconcile_subscription_by_email.py --email <user_email>
+```
+
+如果仍然失败，再人工补订阅。
+
+### 6.2 已人工处理
+
+在 `/ops` 里直接点：
+
+- `标记已处理`
+
+这不会删除审计事件，只会给原事件写：
+
+- `resolved_at`
+- `resolved_by`
+
+## 7. 备注
+
+`/ops` 是运营后台最小版，不是完整 Admin 平台。当前目标是：
+
+- 让会员、积分、支付事故、系统状态可查
+- 让常见人工操作不必再直接写 SQL
+
+外部监控与告警栈说明见：
+
+- [MONITORING_ZH.md](./MONITORING_ZH.md)

docs/PROBABILITY_SNAPSHOT_ARCHIVE_ZH.md

@@ -0,0 +1,324 @@
+# 概率训练样本归档说明（中文）
+
+## 1. 目的
+
+这份文档说明两件事：
+
+1. 为什么 `EMOS` 训练不能只依赖历史实测天气
+2. 未来如何持续沉淀“历史预测记录”，让概率引擎越训越稳
+
+一句话结论：
+
+- 历史实测天气只能补 `actual_high`
+- 真正决定 `EMOS` 训练质量的是“当时那一刻的预测快照”
+
+## 2. 什么是“历史预测记录”
+
+对 PolyWeather 来说，一条可训练的历史预测记录，至少应该包含这些字段：
+
+- `city`
+- `timestamp`
+- `date`
+- `raw_mu`
+- `raw_sigma`
+- `deb_prediction`
+- `ensemble p10 / p50 / p90`
+- `multi-model forecasts`
+- `max_so_far`
+- `peak_status`
+- `prob_snapshot`
+- 当天最终 `actual_high`
+- 当天最终 `settlement bucket`
+
+这类记录的核心价值是：
+
+- 还原“当时系统实际看到什么”
+- 再对照“后来真实发生了什么”
+
+只有这两者成对，`EMOS` 才能学习偏差。
+
+## 3. 为什么不能只用历史天气实测
+
+历史天气 CSV 只能告诉你：
+
+- 当天最高温是多少
+- 某小时温度是多少
+
+但它不能告诉你：
+
+- 当天早上 09:00 时，系统的 `mu` 是多少
+- 当时的 `ensemble spread` 是多少
+- 当时 `DEB` 怎么看
+- 当时的 top bucket 是什么
+
+所以：
+
+- 历史实测天气是标签
+- 历史预测记录才是训练输入
+
+缺少后者，EMOS 只能学到很有限的东西。
+
+## 4. 当前项目里已经有的基础
+
+### 4.1 已有历史日记录
+
+文件：
+
+- [daily_records.json](/E:/web/PolyWeather/data/daily_records.json)
+
+当前已经保存了一部分训练相关字段，例如：
+
+- `forecasts`
+- `actual_high`
+- `deb_prediction`
+- `mu`
+- `prob_snapshot`
+- `shadow_prob_snapshot`
+- `probability_calibration`
+- `probability_features`
+
+这已经是“历史预测记录”的雏形。
+
+### 4.2 已有历史天气 CSV
+
+目录：
+
+- [data/historical](/E:/web/PolyWeather/data/historical)
+
+它们可以帮助补：
+
+- `actual_high`
+- `settlement history`
+
+但不能替代预测快照归档。
+
+## 5. 未来应该怎么存历史预测记录
+
+推荐做法是：
+
+### 5.1 固定时点归档
+
+每天为每个重点城市固定存几次快照，例如：
+
+- 当地 `09:00`
+- 当地 `12:00`
+- 当地 `15:00`
+
+这样能确保每个交易日都有稳定可比样本。
+
+### 5.2 关键变化时补充归档
+
+除了固定时点，还应该在以下情况额外存一次：
+
+- `max_so_far` 创新高
+- `mu` 变化超过阈值
+- `top bucket` 发生变化
+- `shadow top bucket` 发生变化
+
+这样能捕捉真正有训练价值的转折点。
+
+### 5.3 建议的存储格式
+
+建议新增一个文件，例如：
+
+- `data/probability_training_snapshots.jsonl`
+
+每一行保存一条 JSON 记录。
+
+优点：
+
+- 追加写入简单
+- 后续导出训练集方便
+- 不容易因为单个大 JSON 文件损坏而全盘受影响
+
+## 6. 一条建议的快照结构
+
+示例：
+
+```json
+{
+  "city": "ankara",
+  "timestamp": "2026-03-20T12:00:00+03:00",
+  "date": "2026-03-20",
+  "raw_mu": 15.2,
+  "raw_sigma": 1.2,
+  "deb_prediction": 15.4,
+  "ensemble": {
+    "p10": 14.8,
+    "median": 15.8,
+    "p90": 17.9
+  },
+  "multi_model": {
+    "ECMWF": 15.8,
+    "GFS": 14.1,
+    "ICON": 15.9,
+    "GEM": 16.5,
+    "JMA": 14.5
+  },
+  "max_so_far": 15.0,
+  "peak_status": "before",
+  "prob_snapshot": [
+    {"v": 15, "p": 0.552},
+    {"v": 16, "p": 0.377}
+  ],
+  "shadow_prob_snapshot": [
+    {"v": 15, "p": 0.324},
+    {"v": 16, "p": 0.238}
+  ],
+  "probability_engine": "legacy",
+  "probability_mode": "emos_shadow",
+  "calibration_version": "emos-20260320130245"
+}
+```
+
+当天结束后，再由后处理脚本回填：
+
+- `actual_high`
+- `settlement_bucket`
+
+## 7. 现阶段你可以执行的命令
+
+### 7.1 回填历史天气 CSV
+
+```bash
+python scripts/backfill_historical_weather.py
+```
+
+作用：
+
+- 补全 30 城市历史天气时序 CSV
+
+### 7.2 从历史 CSV 构建日级结算标签
+
+```bash
+python scripts/build_settlement_history_from_csv.py
+```
+
+作用：
+
+- 生成 [settlement_history.json](/E:/web/PolyWeather/artifacts/probability_calibration/settlement_history.json)
+
+### 7.3 导出当前训练样本
+
+```bash
+python scripts/export_probability_training_dataset.py
+```
+
+作用：
+
+- 生成 [training_samples.json](/E:/web/PolyWeather/artifacts/probability_calibration/training_samples.json)
+
+### 7.4 重训 EMOS
+
+```bash
+python scripts/fit_probability_calibration.py
+```
+
+作用：
+
+- 生成新的 [default.json](/E:/web/PolyWeather/artifacts/probability_calibration/default.json)
+
+### 7.5 离线评估训练效果
+
+```bash
+python scripts/evaluate_probability_calibration.py
+```
+
+作用：
+
+- 生成 [evaluation_report.json](/E:/web/PolyWeather/artifacts/probability_calibration/evaluation_report.json)
+
+### 7.6 回填 shadow 结果到历史记录
+
+```bash
+python scripts/backfill_probability_shadow_history.py
+```
+
+作用：
+
+- 把 `shadow_prob_snapshot` 和 `probability_calibration` 回填到 [daily_records.json](/E:/web/PolyWeather/data/daily_records.json)
+
+### 7.7 生成线上 shadow 滚动报表
+
+```bash
+python scripts/build_probability_shadow_report.py
+```
+
+作用：
+
+- 生成 [shadow_report.json](/E:/web/PolyWeather/artifacts/probability_calibration/shadow_report.json)
+
+## 8. 推荐的一整套重训流程
+
+如果过了十天、半个月，想重新训练一次，建议按这个顺序执行：
+
+```bash
+python scripts/build_settlement_history_from_csv.py
+python scripts/export_probability_training_dataset.py
+python scripts/fit_probability_calibration.py
+python scripts/evaluate_probability_calibration.py
+python scripts/backfill_probability_shadow_history.py
+python scripts/build_probability_shadow_report.py
+```
+
+如果历史天气 CSV 还没补全，再先执行：
+
+```bash
+python scripts/backfill_historical_weather.py
+```
+
+## 9. 怎么判断这次训练有没有进步
+
+重训后，不要只看一个指标。
+
+至少看这 4 个：
+
+1. `CRPS`
+- 越低越好
+
+2. `MAE`
+- 越低越好
+- 至少不要明显变差
+
+3. `Bucket Hit Rate`
+- 越高越好
+- 这是业务上非常关键的指标
+
+4. `Bucket Brier`
+- 越低越好
+- 反映概率分布质量
+
+只有同时满足下面条件，才可以说训练效果真的进步：
+
+- `CRPS` 下降
+- `MAE` 不上升
+- `Bucket Hit Rate` 不下降
+- `Bucket Brier` 不上升
+
+## 10. 当前最重要的现实判断
+
+过去的“完整历史预测记录”通常没法完全补出来，除非：
+
+1. 你之前就存过
+2. 你接入了支持 forecast archive 的商业数据源
+
+所以现实里最重要的不是“把过去全补齐”，而是：
+
+- 从现在开始系统化归档
+- 每天稳定沉淀可训练样本
+- 定期离线重训
+
+## 11. 推荐的下一步
+
+最值得做的改造是：
+
+1. 新增 `probability_training_snapshots.jsonl`
+2. 每次分析时自动追加一条快照
+3. 当天结束后自动回填 `actual_high`
+4. 每 1-2 周重新训练一次
+
+## 12. 总结
+
+如果只记住一句话，就记这个：
+
+**EMOS 要想越训越好，关键不是多下载一点历史天气，而是持续保存“当时系统看到的预测快照”。**

docs/SUPABASE_SETUP_ZH.md

@@ -0,0 +1,122 @@
+# Supabase + 登录 + 支付接入说明（v1.5.1）
+
+最后更新：`2026-03-14`
+
+## 1. 目标
+
+- 前端支持 Google 一键登录 + 邮箱注册/登录。
+- 后端支持 Supabase JWT 鉴权。
+- 支持 Polygon 合约支付（USDC / USDC.e）并自动确认开通订阅。
+
+## 2. Supabase 控制台配置
+
+1. `Auth -> Providers` 打开 `Google` 与 `Email`。
+2. Google Cloud OAuth 回调配置：
+   - `https://<project-ref>.supabase.co/auth/v1/callback`
+3. `Auth -> URL Configuration` 添加：
+   - 站点 URL（生产域名）
+   - 回调 URL（例如 `https://polyweather-pro.vercel.app/auth/callback`）
+
+## 3. 数据库脚本
+
+在 Supabase SQL Editor 执行：
+
+- `scripts/supabase/schema.sql`
+
+会创建支付与订阅相关表：
+
+- `subscriptions`
+- `payments`
+- `entitlement_events`
+- `user_wallets`
+- `wallet_link_challenges`
+- `payment_intents`
+- `payment_transactions`
+
+## 4. 环境变量
+
+### 4.1 前端（Vercel / frontend/.env.local）
+
+```env
+NEXT_PUBLIC_SUPABASE_URL=
+NEXT_PUBLIC_SUPABASE_ANON_KEY=
+POLYWEATHER_AUTH_ENABLED=true
+POLYWEATHER_AUTH_REQUIRED=false
+POLYWEATHER_API_BASE_URL=http://<backend-host>:8000
+POLYWEATHER_BACKEND_ENTITLEMENT_TOKEN=
+
+# WalletConnect（支持手机钱包扫码）
+NEXT_PUBLIC_WALLETCONNECT_PROJECT_ID=
+NEXT_PUBLIC_WALLETCONNECT_POLYGON_RPC_URL=https://polygon-bor-rpc.publicnode.com
+
+# Overlay 跳转
+NEXT_PUBLIC_TELEGRAM_GROUP_URL=https://t.me/<your_group>
+```
+
+### 4.2 后端 / Bot（.env）
+
+```env
+POLYWEATHER_AUTH_ENABLED=true
+POLYWEATHER_AUTH_REQUIRED=false
+POLYWEATHER_AUTH_REQUIRE_SUBSCRIPTION=false
+
+SUPABASE_URL=
+SUPABASE_ANON_KEY=
+SUPABASE_SERVICE_ROLE_KEY=
+SUPABASE_HTTP_TIMEOUT_SEC=8
+
+POLYWEATHER_PAYMENT_ENABLED=true
+POLYWEATHER_PAYMENT_CHAIN_ID=137
+POLYWEATHER_PAYMENT_RPC_URL=https://polygon-bor-rpc.publicnode.com
+POLYWEATHER_PAYMENT_RECEIVER_CONTRACT=0x<receiver_contract>
+POLYWEATHER_PAYMENT_CONFIRMATIONS=2
+POLYWEATHER_PAYMENT_INTENT_TTL_SEC=1800
+POLYWEATHER_PAYMENT_WALLET_CHALLENGE_TTL_SEC=600
+POLYWEATHER_PAYMENT_POLL_INTERVAL_SEC=4
+POLYWEATHER_PAYMENT_MAX_WAIT_SEC=50
+
+# 支持双币种（示例）
+POLYWEATHER_PAYMENT_ACCEPTED_TOKENS_JSON=[{"code":"usdc_e","symbol":"USDC.e","name":"USDC.e (PoS)","address":"0x2791Bca1f2de4661ED88A30C99A7a9449Aa84174","decimals":6,"receiver_contract":"0x<receiver>","is_default":true},{"code":"usdc","symbol":"USDC","name":"Native USDC","address":"0x3c499c542cef5e3811e1192ce70d8cc03d5c3359","decimals":6,"receiver_contract":"0x<receiver>"}]
+
+# 套餐（当前只保留月付）
+POLYWEATHER_PAYMENT_PLAN_CATALOG_JSON={"pro_monthly":{"plan_id":101,"amount_usdc":"5","duration_days":30}}
+POLYWEATHER_PAYMENT_ALLOWED_PLAN_CODES=pro_monthly
+
+# 积分抵扣
+POLYWEATHER_PAYMENT_POINTS_ENABLED=true
+POLYWEATHER_PAYMENT_POINTS_PER_USDC=500
+POLYWEATHER_PAYMENT_POINTS_MAX_DISCOUNT_USDC=3
+
+# 支付自动补单
+POLYWEATHER_PAYMENT_EVENT_LOOP_ENABLED=true
+POLYWEATHER_PAYMENT_CONFIRM_LOOP_ENABLED=true
+```
+
+## 5. 钱包异动频道拆分（推荐）
+
+如果要把“钱包异动监控”发到独立频道：
+
+```env
+POLYMARKET_WALLET_ACTIVITY_CHAT_ID=-1003821482461
+```
+
+说明：
+
+- 设置了 `POLYMARKET_WALLET_ACTIVITY_CHAT_ID(S)` 后，钱包异动推送优先发该频道。
+- 未设置时，回退到全局 `TELEGRAM_CHAT_IDS/TELEGRAM_CHAT_ID`。
+
+## 6. 验证步骤
+
+1. 登录后请求 `/api/auth/me`，确认 `authenticated=true`。
+2. 请求 `/api/payments/config`，确认 `enabled=true`、`configured=true`。
+3. 钱包绑定：
+   - `POST /api/payments/wallets/challenge`
+   - `POST /api/payments/wallets/verify`
+4. 支付流程：
+   - `POST /api/payments/intents`
+   - 发链上交易
+   - `POST /api/payments/intents/{id}/submit`
+   - `POST /api/payments/intents/{id}/confirm`
+5. 若前端显示 pending，轮询：
+   - `GET /api/payments/intents/{id}`
+6. 确认订阅：`/api/auth/me` 返回 `subscription_active=true`。

docs/TAF_SIGNAL_ZH.md

@@ -0,0 +1,286 @@
+# PolyWeather TAF 信号说明（TAF_SIGNAL_ZH）
+
+本文档说明 PolyWeather 当前如何把 `TAF`（机场终端预报）接入“今日日内分析”，以及这些信号在交易判断里到底代表什么。
+
+本文档只描述**当前实现**，不夸大、不脑补。
+
+---
+
+## 一、TAF 在项目中的定位
+
+在 PolyWeather 里，`TAF` 不是主温度模型，也不是结算源。
+
+它的定位是：
+
+1. **机场侧确认层**
+   - 用来补充说明机场在峰值窗口附近会不会出现云、雨、雷暴或风向切换。
+
+2. **压温 / 扰动风险提示层**
+   - 用来判断“机场最高温是否可能因为天气扰动被压低”。
+
+3. **走势图时间轴联动层**
+   - 用来在温度走势图上标出 `FM / TEMPO / BECMG / PROB30/40` 对应的时段。
+
+它**不负责**：
+
+1. 直接提供多模型最高温数值
+2. 替代 `DEB`
+3. 替代机场实况 `METAR`
+4. 替代官方结算源
+
+---
+
+## 二、后端当前怎么解析 TAF
+
+后端入口在：
+
+- [web/analysis_service.py](/E:/web/PolyWeather/web/analysis_service.py)
+
+核心函数：
+
+- `_build_taf_signal(...)`
+
+当前会解析这些时间片：
+
+1. `BASE`
+2. `FM`
+3. `TEMPO`
+4. `BECMG`
+5. `PROB30`
+6. `PROB40`
+7. `PROB30 TEMPO`
+8. `PROB40 TEMPO`
+
+并且只聚焦于：
+
+- **峰值窗口前后**
+
+当前窗口定义是：
+
+- `peak.first_h - 2h`
+- 到
+- `peak.last_h + 1h`
+
+也就是说，TAF 不是整段报文全量平铺，而是会优先关注**和今天高温兑现最相关的时段**。
+
+---
+
+## 三、当前后端真正产出的核心字段
+
+### 1. `suppression_level`
+
+表示机场端的**压温风险等级**。
+
+当前逻辑来自：
+
+1. 降水 / 雷暴关键词
+   - `TSRA`
+   - `TS`
+   - `VCTS`
+   - `SHRA`
+   - `SHSN`
+   - `SHGS`
+   - `RA`
+   - `DZ`
+   - `SN`
+
+2. 低云底
+   - 只对 `BKN / OVC` 生效
+   - 如果最低云底 `<= 4000 ft`
+   - 会把原本 `low` 的压温风险至少抬到 `medium`
+
+注意：
+
+- 不是所有 `FEW / SCT / BKN / OVC` 都会直接把风险打到 `high`
+- 当前实现里，**低云主要是把风险从 `low` 抬到 `medium`**
+- 真正更容易触发 `high` 的，还是阵雨 / 雷暴类关键词
+
+### 2. `disruption_level`
+
+表示峰值窗口附近的**扰动程度**。
+
+当前逻辑：
+
+1. 这些时间片会至少把扰动抬到 `medium`
+   - `TEMPO`
+   - `BECMG`
+   - `PROB30`
+   - `PROB40`
+   - `PROB30 TEMPO`
+   - `PROB40 TEMPO`
+
+2. 这些情况会把扰动抬到 `high`
+   - `PROB30 TEMPO`
+   - `PROB40 TEMPO`
+   - 或者该时段本身就出现强降水/雷暴类关键词
+
+注意：
+
+- **`TEMPO` 本身不等于 `high`**
+- **`PROB40` 也不等于“确定发生”**
+
+---
+
+## 四、前端怎么展示
+
+前端主要在：
+
+- [frontend/components/dashboard/FutureForecastModal.tsx](/E:/web/PolyWeather/frontend/components/dashboard/FutureForecastModal.tsx)
+- [frontend/lib/dashboard-utils.ts](/E:/web/PolyWeather/frontend/lib/dashboard-utils.ts)
+
+当前会通过三种方式展示 TAF：
+
+### 1. 图表时间轴标记
+
+在日内温度走势图上，当前会显示：
+
+- `TAF 时段 / TAF Timing`
+
+tooltip 会显示该时段摘要，例如：
+
+- `基础时段 13:00-19:00 以稳定为主`
+- `明确切换 15:00-21:00 以稳定为主`
+- `临时波动 14:00-17:00 有云雨扰动`
+
+### 2. 今日日内结构信号里的 `机场预报`
+
+会显示类似：
+
+- `防压温`
+- `看云雨`
+- `暂稳`
+
+### 3. 顶部摘要与交易动作
+
+系统会把：
+
+1. 近地面结构
+2. 高空结构
+3. `TAF`
+4. `market_signal / edge_percent / bucket crowding`
+
+合并成更贴近交易的提示，例如：
+
+- `偏暖侧`
+- `偏谨慎`
+- `先观察`
+
+---
+
+## 五、TAF 关键词当前在项目里的真实含义
+
+| TAF 关键词 | 当前展示词 | 当前项目含义 |
+| :-- | :-- | :-- |
+| `BASE` | 基础时段 | 在第一个显式变化组出现前的默认背景天气段 |
+| `FM` | 明确切换 | 从某个明确时刻开始，机场预报进入一套新的天气阶段 |
+| `TEMPO` | 临时波动 | 一段短时、非整段主导的扰动窗口 |
+| `BECMG` | 逐步转变 | 天气在该窗口内逐步过渡，不是立刻硬切 |
+| `PROB30/40` | 30% / 40% 风险窗 | 有概率触发的扰动窗口，不等于确定发生 |
+
+注意：
+
+- `FM` 不是由 `valid_match` 触发，它是按 `FMddhhmm` 独立解析出来的明确切换段
+- `PROB40` 不是“确定性信号”，仍然只是概率窗口
+
+---
+
+## 六、怎么理解“机场端压温风险偏高”
+
+这句话的意思不是：
+
+- 城区一定更冷
+- 一定会结算更低
+
+真正意思是：
+
+**在机场这个结算相关站点上，峰值窗口附近更容易因为云、阵雨或雷暴，导致最终最高温冲不上去。**
+
+对于很多按机场报文或机场相关站点结算的市场，这一点很关键。
+
+一句话：
+
+- `TAF` 提示压温高
+- 不代表一定下雨
+- 但代表“机场高温可能被压低”的概率更值得防
+
+---
+
+## 七、怎么和图表一起看
+
+### 情况 A：模型还偏热，但 TAF 给出压温高
+
+这表示：
+
+1. 数值模型仍给出较高高温
+2. 但机场端预报提示云雨/雷暴会打断峰值兑现
+
+这种情况下，更适合理解成：
+
+- **机场侧高温兑现有风险**
+- 追更高温区间要谨慎
+
+### 情况 B：TAF 没有新增压温，但总判断仍偏降温
+
+这表示：
+
+1. `TAF` 没有提供新的云雨压温利空
+2. 但近地面窗口本身已经在走弱
+
+比如：
+
+- 温度走弱
+- 风场切换
+- 气压回升
+- 露点回落
+
+所以：
+
+- `TAF 无压温`
+- **不等于**
+- `一定继续升温`
+
+当前系统已经会在摘要里把这层关系解释清楚。
+
+---
+
+## 八、当前实现边界
+
+这套 `TAF` 逻辑当前是**交易导向的轻量解码**，不是完整航空专业解码器。
+
+当前做得到：
+
+1. 识别主要时间片
+2. 找出峰值窗口附近的扰动
+3. 识别云雨压温和风向切换
+4. 联动图表时间轴
+5. 联动交易动作
+
+当前还没有做：
+
+1. 对全部 TAF 语法做完整航空级严格解释
+2. 对每个时段都做完整的逐字段人工预报学解释
+3. 把 TAF 当成温度主预测模型
+
+---
+
+## 九、产品口径总结
+
+最重要的一句：
+
+**在 PolyWeather 里，TAF 是“机场侧扰动确认层”，不是主模型，也不是结算源。**
+
+它最值钱的地方是：
+
+- 帮你判断峰值窗口附近，机场高温会不会被云雨、雷暴、低云或风向切换打断。
+
+对交易来说，更适合把它理解成：
+
+- “机场这边有没有额外的压温风险”
+
+而不是：
+
+- “TAF 直接告诉我今天结算温度是多少”
+
+---
+
+_PolyWeather 文档中心_

docs/TECH_DEBT.md

@@ -1,65 +1,81 @@
-# 🛠️ Technical Debt & Engineering Backlog
+# 技术债与工程待办（v1.5.1）
 
-> **Vision**: Moving from a research script to a production SaaS.
+最后更新：`2026-03-31`
 
----
+目标：在收费上线后，优先保证状态一致性、支付可靠性、可观测性和概率引擎发布可控。
 
-## 🏛️ System Health: 75%
+## 1. 债务快照
+
+当前估计：**95% 稳定 / 5% 技术债**。
 
 ```mermaid
-pie title System Health & Tech Debt
-    "Stable Engine" : 75
-    "Centralized Logic Debt" : 10
-    "Subscription DB Debt" : 10
-    "Testing/Replay Debt" : 5
+flowchart TD
+    A["技术债"]
+
+    subgraph P["支付与订阅"]
+        P1["合约 V2 升级（SafeERC20 / Pausable）"]
+        P2["退款与工单流程"]
+        P3["多 RPC 与链上对账面板"]
+    end
+
+    subgraph E["权限与运营"]
+        E1["前后端/Bot 权限矩阵回归"]
+        E2["积分来源明细与补分审计"]
+    end
+
+    subgraph O["可观测性"]
+        O1["外部监控抓取与告警阈值"]
+        O2["业务监控看板"]
+    end
+
+    subgraph S["状态与概率"]
+        S1["EMOS shadow -> primary 门禁稳定化"]
+    end
+
+    A --> P
+    A --> E
+    A --> O
+    A --> S
 ```
 
-The core engine is stable, but several infrastructure "shortcut" decisions remain.
+## 2. 近期已关闭
 
-### Current Stable Modules
+- 支付主链路已上线（intent -> submit -> confirm）。
+- 支付自动补单已上线（Event Loop + Confirm Loop）。
+- 支付事件重放脚本已补齐。
+- 支付运行态 API 与 SQLite 审计事件已补齐。
+- 钱包绑定支持浏览器钱包 + WalletConnect。
+- 账户中心与 Pro 权限展示链路打通。
+- 钱包异动支持独立频道路由。
+- 运行态状态/缓存与核心离线训练、评估、回填链路已完成 SQLite 主路径收口。
+- 轻量可观测性已上线（`/healthz`、`/api/system/status`、`/metrics`）。
+- EMOS/CRPS 校准链路已上线 shadow 模式。
 
-- [x] Multi-source Weather Aggregation
-- [x] DEB Blending Algorithm
-- [x] Proactive Telegram Alert Engine
-- [x] Vercel Dashboard Infrastructure
+## 3. 高优先级技术债
 
----
+| 项目 | 影响 | 建议动作 |
+| :-- | :-- | :-- |
+| EMOS 上线门禁 | 当前 `hold`，不能切 primary | 继续积累样本，重点压 `bucket_brier` |
+| 外部监控与告警 | 只有轻量指标，无外部抓取 | 接 Prometheus/Grafana 或最小巡检 |
+| 退款与售后链路 | 商业闭环不完整 | 增加退款状态机与工单系统 |
 
-## 🔴 High Priority: Immediate Focus
+## 4. 中优先级技术债
 
-| Debt Item              | Impact                                              | Suggested Fix                                                    |
-| :--------------------- | :-------------------------------------------------- | :--------------------------------------------------------------- |
-| **Monolithic Bot**     | `bot_listener.py` is hard to test and evolve.       | Isolate UI interaction from business logic into `src/analysis`.  |
-| **Subscription Store** | No persistent record of who has paid.               | Migrate from in-memory user checks to **Supabase/PostgreSQL**.   |
-| **Alert Transparency** | Operators cannot easily audit "why" an alert fired. | Add an `Evidence` metadata block to all internal alert payloads. |
+| 项目 | 影响 | 建议动作 |
+| :-- | :-- | :-- |
+| 积分发放可解释性 | 用户理解成本高 | 输出积分来源明细（发言/奖励/手动补分） |
+| 支付合约 V2 升级 | 当前仍是最小可用合约 | 升级到 SafeERC20 + Pausable + plan 绑定 |
+| 支付失败文案标准化 | 转化率受影响 | 建立错误码 -> 文案映射表 |
 
----
+## 5. 低优先级技术债
 
-## 🟡 Medium Priority: Quality of Life
+| 项目 | 影响 | 建议动作 |
+| :-- | :-- | :-- |
+| 前端离线缓存能力 | 非核心 | 评估 Service Worker + IndexedDB |
+| 冷启动波动 | 首屏抖动 | 热点城市预热 |
 
-| Debt Item                 | Impact                                              | Suggested Fix                                                                |
-| :------------------------ | :-------------------------------------------------- | :--------------------------------------------------------------------------- |
-| **Hard-coded Thresholds** | Modification requires code changes (e.g., 5s CD).   | Extract all business constants into a structured `config.yaml`.              |
-| **Simulation Harness**    | No way to "replay" a rainy day to test alert logic. | Build a `ReplayEngine` using `data/daily_records.json`.                      |
-| **Backend Naming**        | Artifacts of "market price" logic remain in naming. | Systematic refactor of variable names to reflect weather-intelligence focus. |
+## 6. 下阶段里程碑
 
----
-
-## 🟢 Low Priority: Optimization
-
-| Debt Item                  | Impact                                          | Suggested Fix                                                  |
-| :------------------------- | :---------------------------------------------- | :------------------------------------------------------------- |
-| **Serverless Cold Starts** | Initial Vercel API calls can be slow.           | Implement edge-cache or warming cron for major city endpoints. |
-| **Local SQLite Files**     | Not compatible with Vercel's ephemeral storage. | Full transition to a remote DB (Supabase/Redis).               |
-
----
-
-## 🗓️ Next Milestones
-
-1.  **DB Integration**: Connect Supabase to `src/database/db_manager.py`.
-2.  **Alert Transparency**: Append logic metrics (slope, lead delta) to push messages.
-3.  **Authentication**: Secure `/api/city` on Vercel with subscription keys.
-
----
-
-**📅 Last Updated**: 2026-03-06
+1. 稳定 EMOS shadow，达到 rollout `observe/promote` 条件。
+2. 补外部监控抓取与告警阈值。
+3. 评估并推进支付合约 V2 升级。

docs/TECH_DEBT_ZH.md

@@ -0,0 +1,81 @@
+# 技术债与工程待办（v1.5.1）
+
+最后更新：`2026-03-31`
+
+目标：在收费上线后，优先保证状态一致性、支付可靠性、可观测性和概率引擎发布可控。
+
+## 1. 债务快照
+
+当前估计：**95% 稳定 / 5% 技术债**。
+
+```mermaid
+flowchart TD
+    A["技术债"]
+
+    subgraph P["支付与订阅"]
+        P1["合约 V2 升级（SafeERC20 / Pausable）"]
+        P2["退款与工单流程"]
+        P3["多 RPC 与链上对账面板"]
+    end
+
+    subgraph E["权限与运营"]
+        E1["前后端/Bot 权限矩阵回归"]
+        E2["积分来源明细与补分审计"]
+    end
+
+    subgraph O["可观测性"]
+        O1["外部监控抓取与告警阈值"]
+        O2["业务监控看板"]
+    end
+
+    subgraph S["状态与概率"]
+        S1["EMOS shadow -> primary 门禁稳定化"]
+    end
+
+    A --> P
+    A --> E
+    A --> O
+    A --> S
+```
+
+## 2. 近期已关闭
+
+- 支付主链路已上线（intent -> submit -> confirm）。
+- 支付自动补单已上线（Event Loop + Confirm Loop）。
+- 支付事件重放脚本已补齐。
+- 支付运行态 API 与 SQLite 审计事件已补齐。
+- 钱包绑定支持浏览器钱包 + WalletConnect。
+- 账户中心与 Pro 权限展示链路打通。
+- 钱包异动支持独立频道路由。
+- 运行态状态/缓存与核心离线训练、评估、回填链路已完成 SQLite 主路径收口。
+- 轻量可观测性已上线（`/healthz`、`/api/system/status`、`/metrics`）。
+- EMOS/CRPS 校准链路已上线 shadow 模式。
+
+## 3. 高优先级技术债
+
+| 项目 | 影响 | 建议动作 |
+| :-- | :-- | :-- |
+| EMOS 上线门禁 | 当前 `hold`，不能切 primary | 继续积累样本，重点压 `bucket_brier` |
+| 外部监控与告警 | 只有轻量指标，无外部抓取 | 接 Prometheus/Grafana 或最小巡检 |
+| 退款与售后链路 | 商业闭环不完整 | 增加退款状态机与工单系统 |
+
+## 4. 中优先级技术债
+
+| 项目 | 影响 | 建议动作 |
+| :-- | :-- | :-- |
+| 积分发放可解释性 | 用户理解成本高 | 输出积分来源明细（发言/奖励/手动补分） |
+| 支付合约 V2 升级 | 当前仍是最小可用合约 | 升级到 SafeERC20 + Pausable + plan 绑定 |
+| 支付失败文案标准化 | 转化率受影响 | 建立错误码 -> 文案映射表 |
+
+## 5. 低优先级技术债
+
+| 项目 | 影响 | 建议动作 |
+| :-- | :-- | :-- |
+| 前端离线缓存能力 | 非核心 | 评估 Service Worker + IndexedDB |
+| 冷启动波动 | 首屏抖动 | 热点城市预热 |
+
+## 6. 下阶段里程碑
+
+1. 稳定 EMOS shadow，达到 rollout `observe/promote` 条件。
+2. 补外部监控抓取与告警阈值。
+3. 评估并推进支付合约 V2 升级。

docs/deep-research-report.md

@@ -0,0 +1,267 @@
+# PolyWeather 深度评估与改进提案报告
+
+## 执行摘要
+
+PolyWeather（仓库：`yangyuan-zhen/PolyWeather`）定位为**面向温度类结算预测市场（如 Polymarket 的温度结算合约）**的“生产级气象情报系统”，核心在于把多源天气观测/预报转化为**结算导向的概率桶（μ + bucket distribution）**，并进一步映射到市场报价完成**错价扫描**；同时提供 Web 仪表盘与 Telegram Bot 两套交互入口，并包含 Polygon 链上 USDC/USDC.e 支付、自动补单与订阅/积分体系。项目 README 现明确仓库代码采用 `AGPL-3.0-only`，同时将品牌、商标、生产私有数据与运营阈值保留在代码许可证之外。
+从工程实现看，截至 `2026-04-03`，项目已经完成一轮更明确的工程化收口：多源天气采集仍保持现有业务能力，同时已完成采集层与 Web API 大文件拆分、CI 质量门禁、配置分级（`.env.example` / `.env.secrets.example` / 中文部署文档）、EMOS/CRPS 校准链路、运行态状态与缓存迁移到 SQLite 主路径，以及最小外部监控链路（`/healthz`、`/api/system/status`、`/metrics` + Prometheus + Alertmanager + Grafana + Telegram relay）。除此之外，项目还补上了**历史真值治理**：`daily_records` 继续只保留近 14 天运行态缓存，但新增了永久真值表、真值 revision 审计表和长期训练特征表，并开始把监督真值与训练特征从“短期缓存”正式拆到“长期可追溯存储”。
+这意味着报告里最初最突出的“工程地基缺失”问题，已经有一部分被关闭：`src/data_collection/weather_sources.py` 与 `web/app.py` 不再是原来的超大单文件；GitHub Actions 已覆盖 Python、前端和 Docker build；配置与密钥治理已成体系；运行态状态不再只能依赖 JSON/JSONL 文件；EMOS 也不再只是概念，而是进入了可训练、可评估、可 shadow、可门禁判断的阶段；更重要的是，监督真值与训练特征不再只能附着在 14 天运行态缓存上。
+但项目仍处在“从可用走向稳态”的中段，而不是终局。当前真正的高优先级问题已进一步收敛：**EMOS 仍未达到生产切换标准**，当前门禁结论明确为 `hold`，阻塞原因是 shadow bucket brier 明显退化，同时历史长期特征仍处在“刚开始积累”的阶段。SQLite 迁移方面，运行态主读切换和核心离线训练/回填链路已经完成验收：在移除 `data/*.json` / `data/*.jsonl` 后，训练、评估、shadow report 与关键 backfill 脚本仍可仅依赖运行时数据库正常执行；当前保留的 legacy 文件路径主要用于迁移、导出、校验和显式回退输入。历史真值治理方面，新增的永久真值表、revision 审计表与长期训练特征表已经落地，`Taipei` / `Shenzhen` 的 `Wunderground` 历史回填也已接通，因此当前缺口已从“历史真值是否会继续丢失”转为“历史特征是否能持续增长并支撑 EMOS/LGBM 评估”。可观测性方面，最小外部监控链路已经补齐：Prometheus 抓取、Alertmanager 规则、Grafana 面板、Telegram 告警 relay 与巡检脚本均已落地；当前剩余缺口已从“有没有外部监控”转为“监控覆盖深度是否足够”，例如节点级资源、数据库体积趋势、支付细粒度指标、按城市/来源拆分的业务 SLA。支付链路方面，链下审计与容灾已明显增强：事件重放、SQLite 审计事件、RPC 多节点容灾、合约静态检查、`/ops` 支付异常单都已补齐；当前剩余风险主要集中在**链上合约本身仍是最小实现**，尚未升级到 SafeERC20、Pausable、链上套餐绑定等更强防护版本。
+因此，当前阶段最正确的策略已经不是继续做“大范围基础重构”，而是围绕**EMOS 上线门禁稳定化、长期训练特征持续积累、监控覆盖深挖、支付合约防护升级**这四条线持续收口。短中期内更高 ROI 的方向依然不是引入新的大模型，而是把现有“采集→后处理→市场映射→支付/订阅”的链路做成**状态一致、指标可见、发布可控、回退明确**的生产平台。
+## 项目概览
+
+PolyWeather 的目标与范围在 README/README_ZH 中定义得较清楚：为温度结算市场提供气象情报（多源采集→融合→概率→对照市场报价），并提供“官方看板（Vercel 前端）+ VPS 后端 + Telegram Bot”。
+项目主功能可归纳为四层：
+**天气层（数据源/采集）**：聚合 39 个城市的实测与预报；支持 AviationWeather METAR（机场观测）、土耳其 MGM 站网、Open-Meteo（含多模型与集合预报）、美国 NWS（仅美国城市）、以及部分城市使用官方结算源（香港 HKO、台北 RCSS/Wunderground、深圳 ZGSZ/Wunderground）等。
+**分析层（DEB/趋势/概率/结算口径）**：
+DEB（Dynamic Error Balancing）基于过去 N 天模型误差（MAE）倒数加权，输出融合预报；运行态仍维护近 14 天 `daily_records` 缓存做当前对账，但长期监督真值与训练特征已经迁到 SQLite 永久表中，并支持基于 WU（Weather Underground 口径）四舍五入的结算命中评估。
+趋势/概率引擎在 `trend_engine.py` 中实现：综合“集合预报区间→σ/μ→高温窗口→死盘判定→温度桶概率分布→边界提示”等，用于 bot 展示与 web 结构化数据输出。
+**市场层（Polymarket 行情对照）**：只读模式从 Gamma API 发现市场、从 CLOB（`py-clob-client` 或 REST 回退）读取价格/盘口并计算 edge（模型概率 − 市场概率）生成信号标签。
+**商业化与支付**：订阅（`Pro Monthly 5 USDC`）、积分抵扣、Polygon 链上收款合约（USDC/USDC.e），并提供“事件监听 + 周期确认”的自动补单机制。
+**支持的数据集/数据源**：项目不是传统“训练数据集+模型训练”的机器学习仓库；其“数据集”本质是外部实时/预报 API 与站点观测数据。对外部数据的使用需要遵守来源方的访问与速率限制，例如 AviationWeather Data API 明确限制请求频率（含每分钟请求上限/建议降低频率与使用缓存文件）。
+**许可证**：仓库根目录 `LICENSE` 当前为 `AGPL-3.0-only`。同时 README 与策略文档明确：品牌、商标、生产私有数据与运营策略不随代码许可证一并授权。
+（插图：项目 README 中包含产品截图，可用于快速理解信息架构与 UI 形态） 
+![PolyWeather demo map](https://raw.githubusercontent.com/yangyuan-zhen/PolyWeather/main/docs/images/demo_map.png)
+
+## 架构与代码库分析
+
+### 代码库模块地图
+
+从 README、Docker/Compose、入口脚本与核心模块引用关系，可以抽象出如下模块地图（按“运行时组件”与“Python 域模块”两层描述）：
+| 层级 | 目录/文件 | 角色定位 | 关键说明 |
+| ------------- | ------------------------------------------------------------------------ | ------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| 运行时组件 | `frontend/` | Next.js 前端（Vercel） | 前端重构报告提到 App Router、Route Handlers（BFF）、缓存策略、支付与账户中心等。 |
+| 运行时组件 | `web/app.py` + `web/core.py` + `web/routes.py` + `web/analysis_service.py` | FastAPI 后端 API | 已从单文件入口拆为启动入口、核心上下文、路由层、分析服务层。 |
+| 运行时组件 | `bot_listener.py` + `src/bot/*` | Telegram Bot | 入口 `bot_listener.py` 调 `start_bot()`，并由 `StartupCoordinator` 启动多个后台 loop。 |
+| Python 域模块 | `src/data_collection/*` | 天气采集 + 城市注册 + 市场读取 | 采集层已拆为 `weather_sources.py` 编排层 + `open_meteo_cache.py`、`settlement_sources.py`、`metar_sources.py`、`mgm_sources.py`、`nws_open_meteo_sources.py`。 |
+| Python 域模块 | `src/analysis/*` | DEB/趋势/概率/结算口径 | `deb_algorithm.py`、`trend_engine.py`、`settlement_rounding.py`。 |
+| Python 域模块 | `src/analysis/probability_calibration.py` + `src/analysis/probability_rollout.py` | 概率校准与上线门禁 | 已支持 `legacy / emos_shadow / emos_primary`，并可产出 rollout 判断。 |
+| Python 域模块 | `src/payments/*` + `contracts/*` | 支付合约 + 事件监听/补单 | Solidity 合约 + Python 侧事件扫描/确认循环 + SQLite 审计事件 + RPC 多节点容灾 + 合约静态检查。 |
+| Python 域模块 | `src/auth/*`、`docs/SUPABASE_SETUP_ZH.md`、`scripts/supabase/schema.sql` | Supabase 鉴权/订阅/积分 | 使用 `/auth/v1/user` 校验 JWT、`/rest/v1/subscriptions` 查订阅（服务端角色 key 必须保密）。 |
+| Python 域模块 | `src/database/runtime_state.py` | 运行态状态、永久真值与训练特征仓储 | 已接入 `daily_records`、`telegram_alert_state`、`probability_training_snapshots`、`open_meteo` 持久缓存，并新增永久真值表、真值修订审计表、长期训练特征表。 |
+| 工程与运维 | `docker-compose.yml`、`Dockerfile`、`.github/workflows/ci.yml`、`scripts/*` | 部署/验证脚本 | 现已具备 CI 门禁、迁移脚本、状态校验脚本、配置校验脚本与 rollout 报告脚本。 |
+
+### 参考架构与关键工作流
+
+项目 README 给出了一版 mermaid 参考架构图（Web/Telegram→FastAPI→采集→分析→支付/市场层）。 在此基础上，结合 `StartupCoordinator` 的 loop 启动与支付监听逻辑，可补充一个更“运行时视角”的架构图：
+```mermaid
+flowchart TB
+ subgraph Clients
+ WEB[Next.js Frontend<br/>Vercel]
+ TG[Telegram Bot<br/>TeleBot + Handlers]
+ end
+
+ subgraph API
+ FAST[FastAPI<br/>web/app.py]
+ end
+
+ subgraph Data
+ WX[WeatherDataCollector]
+ CITY[CITY_REGISTRY]
+ HIST[(SQLite runtime state<br/>daily_records / cache / snapshots)]
+TRUTH[(SQLite truth tables<br/>truth_records / revisions / features)]
+JSON[Legacy JSON files<br/>migration/export/explicit fallback only]
+ end
+
+ subgraph ExternalAPIs
+ OM[Open-Meteo Forecast/Ensemble/Multi-model]
+ AW[AviationWeather Data API<br/>METAR]
+ MGM[MGM Turkey]
+ NWS[api.weather.gov]
+ HKO[data.weather.gov.hk]
+ CWA[opendata.cwa.gov.tw]
+ PM_G[Polymarket Gamma API]
+ PM_C[Polymarket CLOB API]
+ SB[Supabase Auth/REST]
+ RPC[Polygon RPC]
+ end
+
+ subgraph Payments
+ SOL[PolyWeatherCheckout.sol]
+ EVT[event_loop<br/>scan logs]
+ CF[confirm_loop<br/>confirm intents]
+ end
+
+ WEB --> FAST
+ TG --> FAST
+ TG -->|StartupCoordinator<br/>starts loops| EVT
+ TG --> CF
+
+ FAST --> WX
+ WX --> OM
+ WX --> AW
+ WX --> MGM
+ WX --> NWS
+ WX --> HKO
+ WX --> CWA
+
+ FAST --> PM_G
+ FAST --> PM_C
+
+ FAST --> SB
+ EVT --> RPC
+ CF --> RPC
+ RPC --> SOL
+
+ WX --> HIST
+ WX --> TRUTH
+ WX --> JSON
+ FAST --> HIST
+ FAST --> TRUTH
+ WX --> CITY
+```
+
+### 依赖与运行环境
+
+**Python 依赖**：`requirements.txt` 包含 `requests`、`loguru`、`pyTelegramBotAPI`、`python-dotenv`、`numpy`、`web3`、`fastapi`、`uvicorn` 等，符合“采集+bot+api+链上交互”的需求。
+**容器环境**：`Dockerfile` 基于 `python:3.11-slim`，默认启动 bot；`docker-compose.yml` 通过不同 command 分别启动 bot 与 web（`python bot_listener.py` / `python web/app.py`），并挂载运行态数据目录。
+**前端依赖**：前端 README 描述 Next.js、Leaflet、Chart.js、Supabase Auth、WalletConnect 等；`frontend/package.json` 是前端依赖来源。
+### 数据预处理、模型与“训练/推理”管线
+
+本项目的“模型”主要是统计融合与规则/启发式引擎，而非深度网络训练：
+**天气数据预处理**：`WeatherDataCollector` 内部做了大量“输入清洗+缓存+退避”的工程处理：
+包含 Open-Meteo 三类缓存（forecast/ensemble/multi_model）、429 冷却期、最小调用间隔、磁盘持久化缓存文件（重启后避免冷启动打爆 API）、以及 METAR/结算源缓存。
+**DEB（Dynamic Error Balancing）**：以最近 N 天各模型的 MAE 计算倒数权重并做加权融合；同时将 `forecasts / actual_high / deb_prediction / mu / prob_snapshot` 写入 `data/daily_records.json`，并提供命中率/MAE/Brier 等统计口径。
+**概率引擎**：`trend_engine.py` 以集合预报的 p10/p90 推 σ（并考虑历史 MAE floor、风向/云量/压强的 shock_score、以及峰值窗口 time-decay），再用正态近似把连续分布映射为 WU 整数“温度桶概率”。
+**推理流水线（在线）**：
+Web/Telegram 请求 → FastAPI 调用采集器抓取/复用缓存 → 分析引擎输出结构化结果（μ、概率桶、趋势、死盘/窗口判定、DEB 预测、市场扫描）→ 前端渲染或 bot 消息格式化。
+**检查点（checkpoints）**：传统 ML checkpoint 不适用；但项目现已形成两类“业务状态 checkpoint”：
+（a）SQLite 运行态存储（当前线上与核心离线链路主路径）；（b）SQLite 永久真值/训练特征表（当前监督真值与训练样本长期主存）；（c）legacy JSON/JSONL 文件（主要保留给迁移回滚、导出比对与显式回退输入）。当前设计仍支持 `POLYWEATHER_STATE_STORAGE_MODE=file|dual|sqlite`，但对线上部署与离线训练/回填而言，推荐目标状态都已经是 `sqlite`。
+### 测试、CI/CD 与运维验证
+
+**测试**：仓库存在 `tests/test_trend_engine.py`，覆盖 μ 计算、死盘判定、预报崩盘提示、趋势方向等核心逻辑（通过 patch 隔离外部依赖）。
+**CI/CD**：已补齐 GitHub Actions 工作流，至少覆盖 Python lint/test、前端 build、Docker build 三条门禁；当前缺口不再是“有没有 CI”，而是“是否已在 GitHub 分支保护中强制执行”。
+**运维验收**：除 `scripts/validate_frontend_cache.sh` 外，现已新增配置校验、运行态迁移/核验、EMOS rollout 判断等脚本，并提供 `/healthz`、`/api/system/status`、`/metrics` 作为基础观测入口。
+**部署/更新**：Compose 用于启动服务；另有 `update.sh` 通过 `pkill` + `nohup` 重启 bot 与 web。
+## 优势与薄弱点
+
+### 优势
+
+**产品闭环完整、目标明确**：从“天气→结算→市场→错价信号→付费体系（订阅/积分/链上支付）”形成可商业化闭环，并在 README 清晰列出当前产品状态（订阅、积分抵扣、链上支付、自动补单等已上线）。
+**复用一套分析内核服务多端**：趋势/概率/DEB 等核心逻辑被抽成分析模块，并被 web 与 bot 共用，避免“两套逻辑漂移”。
+**面向外部 API 的工程防护意识较强**：Open-Meteo 429 冷却期、最小调用间隔、磁盘缓存、缓存 TTL 等措施表明作者已遭遇并处理速率限制与冷启动问题。 同时 AviationWeather 官方文档也明确建议控制频率并可使用 cache 文件降低负载，项目后续可进一步对齐最佳实践。
+**支付侧有“事件监听 + 确认补单”的双通路**：支付链路天然存在“交易 pending / RPC 延迟 / 日志索引不完整”等问题，项目通过 event loop 与 confirm loop 双机制提升最终一致性。
+### 薄弱点与风险
+
+**核心文件过大问题已明显缓解，但边界仍需继续稳定**：`WeatherDataCollector` 与 `web/app.py` 的超大文件问题已完成第一阶段拆分；当前风险已从“文件过大”转为“跨模块兼容与边界稳定性”，例如旧调用路径、兼容导出、跨层 helper 仍需持续清理。
+**可复现性已从“缺模板”进入“模板与生产对齐”的阶段**：`.env.example`、`.env.secrets.example`、中文配置文档、前端部署文档、运行时配置校验器都已存在；当前风险主要在于线上历史 `.env` 与新模板并存、旧变量命名残留、以及密钥轮换与分层是否真正落实。
+**CI 已建立，但组织级质量门禁未必完全收口**：CI 现已覆盖 Python、前端与 Docker build。当前问题不再是“缺 CI”，而是是否把这些 status check 绑定到 `main` 保护策略，以及是否逐步引入更严格的 pre-merge 审查。
+**运行态状态/缓存与核心离线链路的 SQLite 收口已完成**：`daily_records`、`telegram_alert_state`、`probability_training_snapshots`、`open_meteo` 缓存已经支持并在生产中主读 SQLite，迁移/校验脚本可用；进一步地，在临时移除 `data/*.json` / `data/*.jsonl` 后，训练集导出、概率拟合、评估报告、shadow report 和关键 backfill 脚本已验证仍可运行。当前 legacy 文件路径主要是显式回退入口，而不再是默认主输入。
+**历史真值治理已从设计缺陷修复到可追溯运行**：`daily_records` 继续作为近 14 天运行态缓存，但已经不再承担长期监督真值职责；项目新增了永久真值表、真值 revision 审计表和长期训练特征表，并为 `Taipei` / `Shenzhen` 补上了 `Wunderground` 历史回填链路。当前风险已不再是“监督真值会不会继续被 14 天裁剪吞掉”，而是“历史长期特征能否持续积累到足够支撑 EMOS/LGBM 重新评估”。
+**第三方服务合规与稳定性风险**：
+项目强依赖外部 API（Open-Meteo、AviationWeather、NWS、HKO、CWA、Polymarket、Supabase）。其中 AviationWeather Data API 有明确速率限制；Polymarket 官方说明 Gamma/Data/CLOB 三套 API 分属不同域，CLOB 交易端点需鉴权且策略可能变化；Supabase 明确强调 `service_role`/secret keys 绝不可暴露。若缺乏集中治理（重试/退避/熔断/降级/配额监控/密钥轮换），稳定性与合规不可控。
+**可观测性最小闭环已完成，但监控深度仍待加强**：项目现在已有 `/healthz`、`/api/system/status`、`/metrics`，并已补齐 Prometheus 抓取、Alertmanager 规则、Grafana 面板、Telegram relay 与巡检脚本。与此同时，`/ops` 已经逐步演进为后台管理台而不只是状态页：除支付、会员、用户与 EMOS 门禁外，还新增了训练数据治理卡片、城市覆盖矩阵，以及 `/ops/truth-history` 这种可直接查询 `actual_high / settlement_source / station_code / truth_version / updated_by / updated_at` 的真值表浏览页。当前缺口不再是“有没有外部监控”，而是节点级资源、数据库体积趋势、更细粒度支付指标、按城市/来源拆分的业务 SLA，以及是否需要进一步补 `truth revision` 明细页、趋势图和运营日报。
+**EMOS 已完成工程接入，但未完成生产发布**：EMOS/CRPS 校准、shadow 观测、rollout report、上线门禁都已实现；当前真实门禁结果为 `hold`，阻塞原因是 shadow bucket brier 明显退化。因此概率引擎标准化并非未做，而是“工程完成、发布未通过”。
+**许可证/商业使用的潜在冲突点**：仓库自身现为 `AGPL-3.0-only`，但如果未来尝试引入外部 AI 预报模型，仍需单独核验第三方代码与权重的商用条件：GraphCast 仓库代码 Apache-2.0，但权重使用 CC BY-NC-SA 4.0（非商业），Pangu-Weather 权重同样 BY-NC-SA 且明确禁止商业用途；不加区分地把这些模型用于付费产品会留下法律风险。
+## 对标分析
+
+为满足“至少 3 个相似开源项目或近期论文”对标，本报告选择三类代表：
+1）**AI 气象预报模型**（GraphCast / FourCastNet / Pangu-Weather）：用于评估“若 PolyWeather 未来扩展到更强预测能力”的技术与许可边界； 
+2）**概率后处理方法**（EMOS）：作为 PolyWeather 概率引擎的更标准化替代/对照； 
+3）**预测市场 API 客户端生态**（Polymarket/py-clob-client、aiopolymarket）：用于评估市场层的工程选型。
+### 关键对比表
+
+| 项目/论文 | 解决的问题 | 输出形态 | 性能/效果（公开描述） | 易用性与依赖 | 许可证要点 |
+| --------------------------------------------------------- | ---------------------------------------------------- | ------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------- |
+| **PolyWeather**（本仓库） | 温度结算市场气象情报：多源→概率桶→错价扫描→订阅/支付 | 生产级应用（Web+Bot+API+支付） | 以工程能力为主；内置 DEB、概率桶、死盘判定、市场扫描；当前覆盖 39 城市，并已补齐真值治理与后台运维视图。 | 主要依赖外部 API；Docker Compose 一键启动。 | 仓库 `AGPL-3.0-only`；品牌、生产私有数据与运营规则不随代码许可证授权。 |
+| **GraphCast**（google-deepmind/graphcast） | 10 天全球中期预报（ML 替代/增强 NWP） | 模型代码+权重+notebooks | 论文与介绍提到在大量指标上优于主流确定性系统；仓库提供预训练权重与示例数据入口，并提示 ERA5/HRES 数据条款需另行遵守。 | 完整训练需 ERA5 等；更适合科研/平台级推理，不是产品级 BFF。 | 代码 Apache-2.0；权重 CC BY-NC-SA 4.0（商业限制）。 |
+| **FourCastNet**（NVlabs/FourCastNet） | 高分辨率 data-driven 全球预报（AFNO/ViT） | 模型训练/推理代码+数据/权重链接 | README 描述：0.25° 分辨率、周尺度推理非常快，并可做大规模集合；适合平台型预报。 | 训练/数据依赖大（ERA5 子集 TB 级）；工程集成成本高。 | BSD 3-Clause（代码）。 |
+| **Pangu-Weather**（198808xc/Pangu-Weather + Nature 论文） | 3D Transformer 架构的中期全球预报 | ONNX 推理代码+预训练模型 | Nature 论文称在 reanalysis 上对比 IFS 有更强确定性预报表现，并强调速度优势；仓库提供 ONNX 推理与 lite 版训练说明。 | 模型文件大（多份 ~GB 级），训练资源需求高；更适合科研推理或内部平台。 | 权重 BY-NC-SA 4.0、明确禁止商业用途。 |
+| **EMOS**（Gneiting & Raftery 等） | 集合预报校准：纠偏与解决 underdispersion | 统计后处理方法 | 提出用回归形式输出概率分布（常见为高斯），并以 CRPS 等指标拟合，属于成熟的气象概率校准路线。 | 易落地：对 PolyWeather 而言只需“历史库+拟合器”。 | 方法论（论文）；可自行实现，无额外许可约束（注意论文版权）。 |
+| **Polymarket/py-clob-client** | Polymarket CLOB 读写 SDK | Python SDK | 官方 SDK，支持 read-only 与交易接口；协议与端点在官方文档中给出。 | 易用，适合增强 PolyWeather 市场层。 | MIT。 |
+| **aiopolymarket** | Polymarket APIs 的 async 客户端 | Python async 客户端 | 强调类型安全（Pydantic）、自动分页、重试与 backoff，适合高并发与健壮性诉求。 | 适合替换/补强当前同步 requests 与自定义缓存。 | 以仓库许可为准（此处建议上线前核验）。 |
+
+**对标结论**：PolyWeather 与这类“全球 AI 预报模型”不在同一层级：PolyWeather 是“面向结算市场的产品化情报系统”，其价值核心是**将预测转成可交易/可结算的决策信息**。短中期内更高 ROI 的方向不是“自训大模型”，而是把现有“采集+后处理+市场映射”的链路做成**可复现、可观测、可评测、可扩展**的工程平台；在许可合规前提下，再评估引入外部模型推理作为额外信号源。
+## 优先级改进建议
+
+下表按截至 `2026-04-03` 的真实状态重排优先级。已完成项不再继续列为“待做”，只保留当前仍需推进的事项。
+| 优先级 | 改进项 | 预估工作量 | 主要收益 | 主要风险 | 可执行步骤（建议顺序） |
+| ------ | --------------------------------------------------------------------------------------------------------------------------------- | -------------------: | ------------------------------------------------------------------- | ------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| 高 | **稳定 EMOS shadow 并收紧上线门禁** | 1–2 周 | 让概率引擎升级具备明确发布条件，避免拍脑袋切换 | 当前 shadow bucket brier 退化明显，存在误上线风险 | 1) 持续积累 snapshot 样本 → 2) 定期重训与生成 `evaluation_report` / `shadow_report` / `rollout_report` → 3) 重点压 `bucket_brier` 退化 → 4) 只有门禁从 `hold` 进入 `observe/promote` 后才考虑上线 |
+| 高 | **持续积累长期训练特征，验证 SQLite 真值治理后的样本增长** | 1–2 周 | 让 EMOS/LGBM 的重训真正建立在长期可信样本上，而不是继续被短期特征缺口卡住 | 当前真值已长期化，但历史长期特征仍偏少，EMOS/LGBM 样本增长会滞后 | 1) 持续写入 `training_feature_records_store` → 2) 每日检查 `/ops` 训练数据与 `/ops/truth-history` → 3) 定期对 `Taipei` / `Shenzhen` 的 Wunderground 回填做抽查 → 4) 观察样本是否自然增长后再重训 |
+| 中 | **把最小外部监控继续补深**：从“可告警”提升到“可运营” | 3–7 天 | 不再只知道服务坏没坏，还能看资源趋势、来源 SLA 和支付波动 | 指标过多会带来维护噪音 | 1) 增加节点 CPU/内存/磁盘 → 2) 增加 SQLite/支付体积与事件趋势 → 3) 把 HTTP/来源指标细分到城市/来源维度 → 4) 增加日报或异常摘要 |
+| 中 | **市场层升级为 async + 类型安全**：引入 `aiopolymarket` 或在现有层加重试/backoff/连接池 | 4–7 天 | 行情层更稳，减少短时网络抖动；更易扩展更多市场/分页 | 依赖升级带来的行为差异 | 1) 把 requests.Session 替换为 aiohttp/httpx → 2) 在 Gamma/CLOB 调用侧实现指数退避 → 3) 引入 typed models，减少解析失败 |
+| 中 | **支付合约从“最小可用”升级到“更强合约防护”** | 1–2 周 | 在已完成的链下审计与容灾之上，进一步收紧链上授权边界 | 合约升级需要重新部署、迁移配置并再次验证 | 1) 维持现有事件重放、SQLite 审计、多 RPC fallback → 2) 升级合约到 SafeERC20 + Pausable → 3) 评估链上 plan/amount/token 绑定或 EIP-712 签名校验 → 4) 迁移后更新 PolygonScan 验证与支付审计文档 |
+| 中 | **将 CI 与分支保护/发布流程真正绑定** | 1–3 天 | 让现有 CI 从“存在”变成“强制门禁” | 历史分支/热修流程可能受影响 | 1) GitHub `main` 开启 required checks → 2) 把 release/tag 流程绑定 CI → 3) 明确热修例外流程 |
+| 低 | **引入外部 AI 预报模型作为附加信号**（GraphCast/FourCastNet/Pangu-Weather 等） | 2–6 周（取决于范围） | 可能提升极端/中期预测能力与差异化 | **商业许可限制**（多为 CC BY-NC-SA/禁止商业）与算力成本 | 1) 先做合规评审（权重许可/数据条款）→ 2) 仅在研究/非商业环境评估 → 3) 若要商用，优先选择可商用权重或自研/购买授权 |
+
+### 文档、测试与贡献流程的具体补强建议（落到仓库层面）
+
+1）**文档体系**：保留现有中文 API/TechDebt 文档的同时，增加三份“高价值”文档：
+（a）《运行与配置手册》：按环境（本地/测试/VPS/生产）列必需变量、默认值、敏感等级；（b）《数据源与合规说明》：列出 Open-Meteo、AviationWeather、NWS、HKO、CWA、Polymarket、Supabase 的使用条款要点、速率限制与降级策略（例如 AviationWeather 明确建议降低请求频率并提供 cache 文件）。 （c）《故障排查 Runbook》：429、支付 pending、市场扫描 miss、前端缓存异常等典型故障处理。
+2）**测试金字塔**：在现有 `trend_engine` 单测基础上，补齐：
+（a）天气 provider 的“录制回放”测试（VCR 思路：固定响应→确保解析稳定）；（b）市场层的契约测试（Gamma/CLOB schema 变更时提前失败）；（c）支付链路的本地链集成测试（Hardhat/Anvil + 事件扫描回放）。这些测试能把“外部依赖漂移”尽量转成可控的回归失败。
+3）**贡献工作流**：引入 `CONTRIBUTING.md`（分支策略、PR 模板、变更日志、版本号策略）、`CODEOWNERS`（核心模块审查人）、`SECURITY.md`（漏洞披露与密钥处理），并把静态检查（ruff/eslint）作为 pre-commit + CI 必过项。
+## 建议实验与基准
+
+PolyWeather 的评测应围绕“结算场景”而非传统数值天气预报所有变量。建议建立两类基准：**气象预测基准（结算导向）**与**市场信号基准（交易导向）**。
+### 气象预测与概率校准基准
+
+**数据集**（建议从现有生产数据演进） 
+1）`truth_records_store + training_feature_records_store` 的长期样本：当前长期评测主源应优先来自永久真值表与长期训练特征表；legacy 的 `daily_records.json` 与 `settlement_history.json` 更适合作为迁移恢复与对照来源，而不是长期主输入。
+2）观测“真值”统一口径：对 METAR 城市用 AviationWeather Data API；对香港按 HKO、对台北/深圳按 `Wunderground RCSS/ZGSZ` 等结算源作为真值，和项目当前逻辑一致。
+**指标** 
+1）确定性误差：MAE、RMSE（按城市、按季节、按风险等级分组）； 
+2）结算命中率：`WU_round(pred) == WU_round(actual)`（项目已有统计口径）； 
+3）概率质量：Brier Score（对离散温度桶），以及建议补充 CRPS（连续变量概率评分，EMOS 体系常用）。
+4）校准曲线：预测概率分箱的可靠性图（reliability diagram）与 Sharpness（分布集中度）。
+**基线**
+
+- Baseline A：Open-Meteo 当日最高温（或 forecast median）作为点预测；
+- Baseline B：等权平均（DEB 在历史少时也会回退此策略）；
+- Baseline C：当前 DEB；
+- Baseline D：EMOS（以 ensemble 均值/方差为输入，拟合 μ 与 σ，优化 CRPS）。
+**预期结果（定性）**
+
+- 若历史样本足够，DEB 应在“系统性偏差明显”的城市提升 MAE；
+- EMOS 类方法通常能在概率校准（可靠性与 CRPS）上更稳定，尤其当 ensemble 信息可用（项目已接入 Open-Meteo ensemble/p10/p90）。
+**算力**：以上评测全部可在 CPU 上完成；数据量按“39 城市 × 180 天”级别，pandas/duckdb 即可。若引入更复杂拟合（如分层贝叶斯/分位数回归），也通常不需要 GPU。
+### 错价信号与市场有效性基准
+
+**数据集**
+
+- 保存每次扫描输出：`date/city/bucket/model_prob/market_price/liquidity/edge`，并加上未来 `settled_bucket` 作为标签；Polymarket 市场发现与报价来自 Gamma/CLOB（官方文档说明三套 API：Gamma/Data/CLOB）。
+**指标**
+
+- Signal 覆盖率：能否找到正确 market / bucket；
+- Edge 稳健性：不同流动性分位的 edge 分布；
+- 交易模拟（如需）：在考虑滑点/手续费/成交概率下的期望收益（即使项目当前只读，也可以离线评估“若执行”会怎样）。
+**基线**
+
+- 简单策略：仅用市场中间价（不做模型）作为概率；
+- 当前策略：模型概率 vs 市场概率 edge 阈值；
+- 改进策略：引入“流动性/盘口深度/波动”作为信号置信度（aiopolymarket/py-clob-client 提供更完整的盘口读取能力）。
+**算力**：CPU 即可；关键在于数据采样与回放。
+## 路线图与风险缓解
+
+下面给出一个**12 周**（约 3 个月）的建议路线图，按“可稳定交付的工程里程碑”组织；人力以“1 名后端/数据工程 + 1 名前端（可兼职）+ 0.5 名链上工程（按需）”估算。
+| 时间窗 | 里程碑 | 交付物 | 资源/备注 |
+| ----------- | ----------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------- |
+| 第 1–2 周 | 工程地基：CI + 规范 + 配置可复现 | GitHub Actions；ruff/eslint；pytest 可一键跑；`.env.example`；敏感项分级说明（尤其 Supabase service role key 不可暴露）。 | 后端为主；前端补 eslint/typecheck |
+| 第 3–5 周 | 核心模块解耦：采集 Provider 化 + API 分层 | provider 接口与实现；`web/app.py` 拆分路由与服务；核心 schema（Pydantic） | 风险：行为漂移；用回放测试压住 |
+| 第 6–8 周 | 状态/缓存统一 + 可观测性 | `daily_records/open_meteo_cache` 主读 SQLite；离线脚本也切到 SQLite 优先；永久真值表 / revision / 长期训练特征表落地；指标（请求量/429/延迟/命中率）；Prometheus/Alertmanager/Grafana 最小链路与告警阈值 | 可先用 SQLite/Redis，后续再上 Postgres |
+| 第 9–10 周 | 评测体系上线 | 离线评测脚本（MAE/RMSE/WU-hit/Brier/CRPS）；日报/周报自动生成 | 直接基于项目现有字段扩展 |
+| 第 11–12 周 | 概率引擎升级（可选）+ 市场层健壮性增强 | EMOS/CRPS 拟合的 shadow 输出；Gamma/CLOB 客户端增强（async、重试、分页） | 以“小步可回滚”为原则，避免一次性替换 |
+
+### 主要风险与缓解策略
+
+**外部 API 速率限制/格式变更**：AviationWeather 明确 rate limit 与建议使用 cache 文件；Open-Meteo 也可能在不同端点策略上变化。缓解：统一“请求预算”与退避/熔断；关键响应做 schema 校验与回放测试；对高频数据优先拉取官方 cache/批量接口（若可用）。
+**密钥泄露与权限滥用**：Supabase 明确强调 `service_role` 属高权限密钥，绝不可出现在前端或公开环境。缓解：密钥分级、CI secret scan、运行时最小权限、日志脱敏。
+**支付链路最终一致性与链上不确定性**：链上事件索引延迟、RPC 不稳定、交易确认数不足都会导致误判。当前项目已经补齐“事件监听 + 确认补单”双路径、事件重放脚本、SQLite 审计事件与多 RPC fallback；现阶段的主要剩余风险不再是“没有防护”，而是链上合约仍为最小实现，owner 为单地址管理，且没有 pause 开关与 SafeERC20。
+**引入外部 AI 预报模型的商业合规风险**：GraphCast/Pangu-Weather 的权重许可均带非商业限制（CC BY-NC-SA/BY-NC-SA）；若 PolyWeather 是付费产品，必须先做法务与授权评审。缓解：只在研究环境评估；商用优先选择可商用权重/购买授权/自研。
+**代码公开与生产私有资产边界导致的“公开仓库与生产行为不一致”**：README 明确品牌、商标、生产私有数据与运营阈值不在代码许可证授权范围内。缓解：把“公开核心”的可复现与评测做扎实（接口/数据 schema/测试/评测），私有策略只作为可插拔 policy layer 接入。
+## 参考链接
+
+- PolyWeather 仓库（本次评估对象）：https://github.com/yangyuan-zhen/PolyWeather
+- Polymarket API 文档（Gamma/Data/CLOB）：https://docs.polymarket.com/api-reference
+- AviationWeather Data API（METAR 等）：https://aviationweather.gov/data/api/
+- Open-Meteo Docs（Forecast）：https://open-meteo.com/en/docs
+- Open-Meteo Docs（Ensemble）：https://open-meteo.com/en/docs/ensemble-api
+- Supabase REST API：https://supabase.com/docs/guides/api
+- Supabase API keys（service_role 风险）：https://supabase.com/docs/guides/api/api-keys
+- GraphCast（代码 Apache-2.0；权重 CC BY-NC-SA）：https://github.com/google-deepmind/graphcast
+- FourCastNet（BSD-3）：https://github.com/NVlabs/FourCastNet
+- Pangu-Weather（权重 BY-NC-SA，禁商用）：https://github.com/198808xc/Pangu-Weather
+- Polymarket 官方 Python CLOB SDK（MIT）：https://github.com/Polymarket/py-clob-client
+- aiopolymarket（async、类型安全）：https://github.com/the-odds-company/aiopolymarket
+
+

docs/payments/PAYMENT_AUDIT_ZH.md

@@ -0,0 +1,244 @@
+# PolyWeather 支付审计与防护说明
+
+最后更新：`2026-03-21`
+
+## 1. 当前已落地的防护
+
+### 链下运行态
+
+- 支付事件扫描与确认循环已把运行态写入 SQLite：
+  - `payment_runtime_state`
+  - `payment_audit_events`
+- 关键循环现在会记录：
+  - `event_loop_started`
+  - `event_loop_cycle`
+  - `event_loop_error`
+  - `confirm_loop_started`
+  - `confirm_loop_cycle`
+  - `confirm_loop_error`
+
+### 事件确认边界
+
+- 后端只认链上 `OrderPaid` 事件。
+- 前端提交 intent 不会直接视为支付完成。
+- `confirm_loop` 会再次按链上交易与确认数校验 intent。
+- 若确认失败，当前会明确把 intent / transaction 落为失败态，而不是长期停留在 `submitted`。
+
+当前已显式识别的失败原因包括：
+
+- `receiver_mismatch`
+- `sender_mismatch`
+- `event_mismatch`
+- `tx_reverted`
+
+### RPC 多节点容灾
+
+- 支持 `POLYWEATHER_PAYMENT_RPC_URLS`
+- 格式示例：
+
+```env
+POLYWEATHER_PAYMENT_RPC_URLS=https://polygon-rpc.com,https://polygon-bor-rpc.publicnode.com
+```
+
+- 启动时按顺序探活。
+- 当前节点断连或收据查询失败时，会自动切换到下一个可用 RPC。
+
+### 事件重放
+
+- 已提供脚本：
+  - [replay_payment_events.py](/E:/web/PolyWeather/scripts/replay_payment_events.py)
+
+用途：
+- 审计某个区块范围内的 `OrderPaid`
+- 事后补查漏单
+- 排查 RPC 抖动导致的监听遗漏
+
+命令示例：
+
+```bash
+python scripts/replay_payment_events.py --from-block 10000000 --to-block 10001000
+```
+
+### 运行态检查
+
+- 已提供接口：
+  - `GET /api/payments/runtime`
+
+可查看：
+- checkout 配置摘要
+- 当前活跃 RPC
+- 候选 RPC 列表
+- event loop 最新状态
+- 最近审计事件
+
+### Ops 事故单
+
+现在 `/ops` 已提供单独的支付异常单列表，默认展示：
+
+- `payment_intent_failed`
+
+支持：
+
+- 按 `reason` 过滤
+- 标记已处理
+
+这让下面这类事故不再需要翻日志定位：
+
+- 已付款但未开通
+- 打到旧收款地址
+- 交易事件不匹配
+
+## 2. 当前合约的授权边界
+
+合约源码：
+- [PolyWeatherCheckout.sol](/E:/web/PolyWeather/contracts/PolyWeatherCheckout.sol)
+
+当前边界：
+
+1. `owner`
+- 可执行：
+  - `setTreasury`
+  - `setTokenAllowed`
+
+2. 普通用户
+- 只能调用：
+  - `pay(orderId, planId, amount, token)`
+
+3. 代币边界
+- 只有 `allowedToken[token] == true` 的 token 可支付
+
+4. 订单边界
+- 同一个 `orderId` 只能成功支付一次
+
+## 3. 重入与重复支付判断
+
+当前合约的 `pay` 逻辑顺序是：
+
+1. 检查 token allowlist
+2. 检查 `amount > 0`
+3. 检查 `paidOrder[orderId] == false`
+4. 先写入 `paidOrder[orderId] = true`
+5. 再执行 `transferFrom`
+6. 发出 `OrderPaid`
+
+这意味着：
+
+- 同一 `orderId` 的重复支付会被拦住
+- 典型“转账外部调用后再回调重复执行同订单”的路径会被 `paidOrder` 状态挡住
+
+但要注意：
+
+- 当前合约没有 `Pausable`
+- 当前合约没有 `SafeERC20`
+- 当前合约没有在链上校验 `planId -> amount`
+
+所以它属于：
+- **最小可用支付合约**
+- 不是“全功能强防护合约”
+
+## 4. 当前静态审计结论
+
+已提供脚本：
+- [check_payment_contract_security.py](/E:/web/PolyWeather/scripts/check_payment_contract_security.py)
+
+命令：
+
+```bash
+python scripts/check_payment_contract_security.py
+```
+
+输出会检查这些项目：
+
+- 是否有 `onlyOwner`
+- `setTreasury` / `setTokenAllowed` 是否受 owner 保护
+- constructor / setter 是否检查零地址
+- 是否校验 allowlist
+- 是否校验 `amount > 0`
+- 是否校验重复订单
+- 是否在 `transferFrom` 前写入 `paidOrder`
+- 是否有 pause 开关
+- 是否使用 SafeERC20
+- 是否在链上绑定套餐价格
+
+## 5. 当前主要剩余风险
+
+1. 单地址 owner
+- 建议把 `owner` 迁移到多签钱包
+
+2. 无暂停开关
+- 发现紧急问题时，无法直接暂停 `pay`
+
+3. 金额校验主要在链下
+- 当前 `planId / amount / token` 绑定主要靠后端 intent 和确认逻辑
+
+4. ERC20 兼容性假设
+- 当前使用 `IERC20.transferFrom`
+- 升级版合约更建议改为 OpenZeppelin `SafeERC20`
+
+## 6. 推荐操作
+
+### 每次支付配置变更后
+
+执行：
+
+```bash
+python scripts/check_payment_contract_security.py
+python scripts/replay_payment_events.py --from-block <from> --to-block <to>
+```
+
+### 线上巡检
+
+执行：
+
+```bash
+curl http://127.0.0.1:8000/api/payments/runtime
+```
+
+重点看：
+
+- `rpc.active_rpc_url`
+- `rpc.configured_rpc_count`
+- `event_loop_state.last_scanned_block`
+- `recent_audit_events`
+
+如果你在 `/ops` 或脚本里看到：
+
+- `receiver_mismatch`
+
+其含义通常不是“缓存没刷新”，而是：
+
+- 用户这笔交易的 `to` 地址不是当前生产收款合约
+- 常见原因是旧页面、旧 deployment、旧钱包会话，或历史收款地址仍被命中
+
+此时应优先做：
+
+1. 确认链上真实 `to` 地址
+2. 确认当前 `/api/payments/config` 返回的 `receiver_contract`
+3. 如确已收款，再走人工恢复或补开订阅
+
+### 按邮箱恢复最近支付
+
+已提供脚本：
+
+- [reconcile_subscription_by_email.py](/E:/web/PolyWeather/scripts/reconcile_subscription_by_email.py)
+
+命令：
+
+```bash
+docker compose exec polyweather_web python scripts/reconcile_subscription_by_email.py --email user@example.com
+```
+
+适用场景：
+
+- 用户声称已付费但未开通
+- 需要快速确认最近一笔 intent 是否能自动恢复
+
+## 7. 下一版合约建议
+
+如果后续升级合约，优先级建议：
+
+1. `Ownable` -> 多签 owner
+2. `SafeERC20`
+3. `Pausable`
+4. 链上 plan/amount/token 绑定
+5. 必要时增加 rescue/sweep 能力

docs/payments/PAYMENT_UPGRADE_V2_ZH.md

@@ -0,0 +1,197 @@
+# PolyWeather 支付合约升级方案（V2）
+
+最后更新：`2026-03-20`
+
+## 1. 目标
+
+本次 V2 方案对应三个明确目标：
+
+1. 把 `owner` 迁到多签地址
+2. 升级到 `SafeERC20 + Pausable + ReentrancyGuard`
+3. 把“链上 plan 绑定”和“EIP-712 授权支付”都纳入设计，而不是只在链下校验
+
+合约草案：
+- [PolyWeatherCheckoutV2.sol](/E:/web/PolyWeather/contracts/PolyWeatherCheckoutV2.sol)
+
+构造参数编码脚本：
+- [encode_checkout_v2_constructor.py](/E:/web/PolyWeather/scripts/encode_checkout_v2_constructor.py)
+
+## 2. V2 新增能力
+
+### 多签 owner
+
+V2 constructor 不再默认 `msg.sender` 作为唯一 owner，而是显式传入：
+
+- `initialOwner`
+- `initialTreasury`
+- `initialSigner`
+
+这意味着：
+- 部署后可直接把多签地址设为 `owner`
+- 不需要先单签部署再补 transfer
+
+### SafeERC20
+
+V2 内置最小 `SafeERC20` 封装：
+
+- `safeTransferFrom`
+- `safeTransfer`
+
+相比直接依赖 `IERC20.transferFrom -> bool`：
+- 对非标准 ERC20 的兼容性更稳
+- 出错边界更明确
+
+### Pausable
+
+V2 增加：
+
+- `pause()`
+- `unpause()`
+
+支付入口：
+
+- `payPlan(...)`
+- `payAuthorized(...)`
+
+都受 `whenNotPaused` 保护。
+
+一旦发现：
+- treasury 配置错误
+- token allowlist 配置错误
+- 签名器异常
+- 链上风控问题
+
+可以直接暂停支付入口。
+
+### ReentrancyGuard
+
+V2 增加 `nonReentrant`，保护：
+
+- `payPlan`
+- `payAuthorized`
+- `rescueToken`
+
+虽然当前订单去重已经能挡住典型重复支付路径，但 `ReentrancyGuard` 仍然是更稳的防线。
+
+### 链上套餐绑定
+
+V2 新增：
+
+- `setPlan(planId, token, amount, active)`
+- `planConfig[planId][token]`
+
+正式支付入口 `payPlan` 会：
+
+1. 校验 token 已 allowed
+2. 校验 `planId + token` 的 plan 已 active
+3. 从链上读取 amount
+4. 按链上配置收款
+
+这意味着：
+- `planId / amount / token` 绑定不再完全依赖链下
+
+### EIP-712 授权支付
+
+V2 同时保留第二条入口：
+
+- `payAuthorized(...)`
+
+它适合：
+- 临时折扣
+- 特殊活动价
+- 不想每次都上链改 `setPlan`
+
+校验字段包括：
+
+- `orderId`
+- `payer`
+- `planId`
+- `token`
+- `amount`
+- `nonce`
+- `deadline`
+
+签名人地址由：
+
+- `signer`
+
+统一控制。
+
+## 3. 两条支付路径怎么选
+
+### 路线 A：链上套餐绑定优先
+
+优点：
+- 最直观
+- 合约级约束最强
+- 更容易审计
+
+缺点：
+- 套餐改价需要 owner 交易
+
+适合：
+- 月付/季付/年付这类稳定商品
+
+### 路线 B：EIP-712 授权优先
+
+优点：
+- 活动价灵活
+- 不必每次改链上 plan
+
+缺点：
+- 需要管理 signer 密钥
+- 风险从 owner 单点，部分转移到 signer 运维
+
+适合：
+- 促销
+- 临时折扣
+- 白名单价格
+
+### 当前建议
+
+生产建议不是二选一，而是：
+
+1. **稳定套餐** 走 `payPlan`
+2. **特殊场景** 走 `payAuthorized`
+
+这样：
+- 主流程更稳
+- 特殊价仍保留灵活性
+
+## 4. 推荐迁移步骤
+
+1. 先部署 V2 到测试环境
+2. `owner` 直接用多签地址
+3. 配置 `treasury`
+4. 配置 `allowedToken`
+5. 配置 `planId/token/amount`
+6. 仅在需要活动价时再配置 `signer`
+7. 用事件重放脚本和运行态接口验证
+8. 再切生产前端/后端配置到新 `receiver_contract`
+
+## 5. 构造参数编码
+
+示例：
+
+```bash
+python scripts/encode_checkout_v2_constructor.py \
+  --owner 0xYourMultiSig \
+  --treasury 0xYourTreasury \
+  --signer 0xYourBackendSigner
+```
+
+## 6. 当前判断
+
+V2 已经把这三件事做成了明确方案：
+
+1. 多签 owner
+2. SafeERC20 + Pausable + ReentrancyGuard
+3. 链上 plan 绑定 + EIP-712 授权
+
+它现在是**升级草案**，不是现网已部署合约。
+
+如果要真正上线，下一步就是：
+
+1. 做一次测试网或本地链验证
+2. 更新 PolygonScan 验证文档
+3. 修改后端 `receiver_contract` 配置

docs/payments/POLYGONSCAN_VERIFY.md

@@ -0,0 +1,76 @@
+# PolyWeatherCheckout PolygonScan 验证（v1.5.1）
+
+最后更新：`2026-03-20`
+
+## 1. 目标
+
+对生产收款合约完成源码验证，降低钱包风控误报并提升用户信任。
+
+当前说明：
+
+- **现网合约仍为 V1**：`contracts/PolyWeatherCheckout.sol`
+- **V2 只是升级草案**：`contracts/PolyWeatherCheckoutV2.sol`
+- 当前 PolygonScan 验证流程默认针对 V1
+
+## 2. 当前部署参数（示例）
+
+- 链：Polygon Mainnet（`chainId=137`）
+- 合约：`PolyWeatherCheckout`
+- 编译器：`v0.8.24+commit.e11b9ed9`
+- 优化器：`Enabled`，`runs=200`
+
+> 实际地址以线上配置为准：`POLYWEATHER_PAYMENT_RECEIVER_CONTRACT`。
+
+## 3. 构造参数编码
+
+使用仓库脚本生成构造参数：
+
+```bash
+python scripts/encode_checkout_constructor.py \
+  --token 0x2791Bca1f2de4661ED88A30C99A7a9449Aa84174 \
+  --treasury 0xe581D578EF101c80e3F32263e97E6eA28A0B170e
+```
+
+将输出填入 PolygonScan 的 `Constructor Arguments ABI-encoded`。
+
+## 4. PolygonScan 操作步骤
+
+1. 打开合约页 -> `Contract` -> `Verify and Publish`。
+2. 选择 `Solidity (Single file)`。
+3. 粘贴 `contracts/PolyWeatherCheckout.sol` 源码。
+4. 填写编译器/优化器参数。
+5. 粘贴构造参数并提交。
+
+## 5. 验证后检查
+
+- `Read Contract`：可见 `owner / treasury / allowedToken / paidOrder`
+- `Write Contract`：可见 `pay / setTreasury / setTokenAllowed`
+- 标签显示 `Contract Source Code Verified`
+
+## 6. 双币种开启（USDC + USDC.e）
+
+验证后可通过 `setTokenAllowed` 开启两种代币：
+
+- USDC.e: `0x2791Bca1f2de4661ED88A30C99A7a9449Aa84174`
+- Native USDC: `0x3c499c542cef5e3811e1192ce70d8cc03d5c3359`
+
+## 7. V2 说明（尚未部署）
+
+如果后续升级到 V2，请改用：
+
+```bash
+python scripts/encode_checkout_v2_constructor.py \
+  --owner 0xYourMultiSig \
+  --treasury 0xYourTreasury \
+  --signer 0xYourBackendSigner
+```
+
+V2 相关文档：
+
+- [PAYMENT_UPGRADE_V2_ZH.md](/E:/web/PolyWeather/docs/payments/PAYMENT_UPGRADE_V2_ZH.md)
+- [PAYMENT_AUDIT_ZH.md](/E:/web/PolyWeather/docs/payments/PAYMENT_AUDIT_ZH.md)
+
+## 8. 说明
+
+- 源码验证能显著降低“欺诈/不可信”误报，但钱包风险缓存更新存在延迟。
+- 生产商用环境可使用私有升级版合约；公开仓库保留标准实现与验证流程。

extension/README.md

@@ -0,0 +1,55 @@
+# PolyWeather Side Panel
+
+`PolyWeather Side Panel` 是一个面向天气交易场景的 Chrome / Edge 浏览器侧边栏工具。
+
+## 功能
+
+1. 自动识别当前 Polymarket 页面中的城市，也支持手动切换。
+2. 展示城市档案：结算站点、站点距离、观测更新时间、周边站点数量。
+3. 展示今日日内走势（简版）：`DEB` 走势与官方观测（`METAR / HKO / CWA / NOAA`）对照，可悬停查看时间与温度。
+4. 展示多日最高温预报（简版），当前以 `DEB` 优先。
+5. 支持一键刷新，强制拉取最新温度数据。
+6. 支持本地缓存，提升打开速度；刷新时自动更新缓存。
+7. 支持一键跳转到完整网站分析页面。
+
+## 数据说明
+
+- 香港使用 `HKO`（香港天文台）结算源。
+- 其他城市按配置使用 `METAR / NOAA / 官方数据源`。
+- 城市展示名以主站返回值为准，例如 `aurora` 市场在插件中会显示为 `Denver`。
+
+## 权限说明
+
+- `tabs`：用于识别当前活动标签页 URL 并自动匹配城市。
+- `storage`：用于保存插件配置与本地缓存，仅存储在本地浏览器。
+- `sidePanel`：用于在浏览器侧边栏展示界面。
+
+## 隐私说明
+
+本扩展不要求用户登录，不收集个人身份信息，不上传浏览历史，仅在必要时请求天气接口数据以完成展示功能。
+
+## 本地安装（开发者模式）
+
+1. 打开 Chrome/Edge 扩展页面：
+   - Chrome：`chrome://extensions`
+   - Edge：`edge://extensions`
+2. 打开“开发者模式”。
+3. 选择“加载已解压的扩展程序”。
+4. 选择目录：`extension/`。
+5. 点击扩展图标，侧边栏会打开。
+
+## 设置
+
+首次建议打开扩展“选项页”并确认：
+
+- `网站基础地址`：你的前端域名（例如 `https://polyweather-pro.vercel.app`）
+- `API 基础地址`：你的后端 API 域名（若同域也可填前端域名）
+- `Bearer Token`：后端开启鉴权时填写
+
+## 说明
+
+- 当前版本仍是轻量产品，重点是“监控 + 基础判断 + 导流回站”，未接入支付链路。
+- 若你的 API 做了严格鉴权，请先在设置页填写 token 再使用。
+- 插件走势图与主站保持一致：`Wunderground` 结算城市不再单独绘制结算参考线，统一显示机场 `METAR` / 官方观测点位。
+- 点击“打开网站查看更多”会回到主站继续查看完整分析。
+- 插件不会承载完整分析；完整结构判断、历史对账和更多信号仍以主站为准。

extension/background.js

@@ -0,0 +1,6 @@
+chrome.runtime.onInstalled.addListener(() => {
+  chrome.sidePanel
+    .setPanelBehavior({ openPanelOnActionClick: true })
+    .catch(() => {});
+});
+

extension/manifest.json

@@ -0,0 +1,29 @@
+{
+  "manifest_version": 3,
+  "name": "PolyWeather Side Panel",
+  "description": "Weather side panel for Polymarket.",
+  "version": "0.1.9",
+  "icons": {
+    "16": "icon-16.png",
+    "32": "icon-32.png",
+    "48": "icon-48.png",
+    "128": "icon-128.png"
+  },
+  "permissions": ["sidePanel", "storage", "tabs"],
+  "host_permissions": ["https://*/*", "http://*/*"],
+  "background": {
+    "service_worker": "background.js"
+  },
+  "action": {
+    "default_icon": {
+      "16": "icon-16.png",
+      "32": "icon-32.png",
+      "48": "icon-48.png"
+    },
+    "default_title": "Open PolyWeather"
+  },
+  "side_panel": {
+    "default_path": "sidepanel.html"
+  },
+  "options_page": "options.html"
+}

extension/options.css

@@ -0,0 +1,78 @@
+body {
+  margin: 0;
+  background: #0b1225;
+  color: #e5eefb;
+  font-family: "Inter", "Segoe UI", -apple-system, BlinkMacSystemFont, sans-serif;
+}
+
+.wrap {
+  max-width: 840px;
+  margin: 24px auto;
+  padding: 0 16px;
+}
+
+h1 {
+  margin: 0 0 18px;
+  font-size: 24px;
+}
+
+.field {
+  display: grid;
+  gap: 6px;
+  margin-bottom: 14px;
+}
+
+.field span {
+  color: #9fb0c9;
+  font-size: 13px;
+}
+
+input,
+textarea {
+  width: 100%;
+  border: 1px solid rgba(255, 255, 255, 0.12);
+  border-radius: 10px;
+  background: #121c38;
+  color: #f1f5ff;
+  padding: 10px 12px;
+  font-size: 14px;
+}
+
+textarea {
+  resize: vertical;
+}
+
+.actions {
+  display: flex;
+  gap: 10px;
+  margin-top: 8px;
+}
+
+button {
+  border: 1px solid rgba(34, 211, 238, 0.4);
+  background: rgba(34, 211, 238, 0.12);
+  color: #ccf7ff;
+  border-radius: 9px;
+  padding: 10px 14px;
+  cursor: pointer;
+  font-weight: 700;
+}
+
+button.ghost {
+  border-color: rgba(99, 102, 241, 0.4);
+  background: rgba(99, 102, 241, 0.14);
+  color: #dbe4ff;
+}
+
+.result {
+  margin-top: 14px;
+  border: 1px solid rgba(255, 255, 255, 0.12);
+  background: rgba(255, 255, 255, 0.03);
+  border-radius: 10px;
+  padding: 12px;
+  white-space: pre-wrap;
+  word-break: break-word;
+  min-height: 56px;
+  color: #9fb0c9;
+}
+

extension/options.html

@@ -0,0 +1,42 @@
+<!doctype html>
+<html lang="zh-CN">
+  <head>
+    <meta charset="UTF-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <title>PolyWeather Extension Settings</title>
+    <link rel="stylesheet" href="./options.css" />
+  </head>
+  <body>
+    <main class="wrap">
+      <h1 id="settingsTitle">PolyWeather 侧边栏设置</h1>
+
+      <label class="field">
+        <span id="siteBaseLabel">Site Base URL</span>
+        <input id="siteBaseInput" type="text" placeholder="https://polyweather-pro.vercel.app" />
+      </label>
+
+      <label class="field">
+        <span id="apiBaseLabel">API Base URL</span>
+        <input id="apiBaseInput" type="text" placeholder="https://polyweather-pro.vercel.app" />
+      </label>
+
+      <label class="field">
+        <span id="tokenLabel">Bearer Token（可选）</span>
+        <textarea
+          id="tokenInput"
+          rows="3"
+          placeholder="公开模式留空即可；仅当后端返回 401 时再填写。"
+        ></textarea>
+      </label>
+
+      <div class="actions">
+        <button id="saveBtn">保存</button>
+        <button id="testBtn" class="ghost">测试 /api/cities</button>
+      </div>
+
+      <pre id="resultBox" class="result"></pre>
+    </main>
+
+    <script src="./options.js"></script>
+  </body>
+  </html>

extension/options.js

@@ -0,0 +1,145 @@
+const DEFAULT_CONFIG = {
+  apiBase: "https://polyweather-pro.vercel.app",
+  siteBase: "https://polyweather-pro.vercel.app",
+  authToken: "",
+  selectedCity: ""
+};
+const locale = String(navigator.language || "en").toLowerCase().startsWith("zh")
+  ? "zh"
+  : "en";
+const I18N = {
+  zh: {
+    settingsTitle: "PolyWeather 侧边栏设置",
+    tokenLabel: "Bearer Token（可选）",
+    tokenPlaceholder: "公开模式留空即可；仅当后端返回 401 时再填写。",
+    save: "保存",
+    test: "测试 /api/cities",
+    saved: "已保存。公开模式下 Token 可留空。",
+    connectOk: "连接成功，返回城市数",
+    tokenOptional: "Token 可留空",
+    testFailed: "测试失败",
+    backendAuthHint: "说明后端仍要求鉴权；若你要公开插件，请先放开 /api/cities 与 /api/city/*/detail。"
+  },
+  en: {
+    settingsTitle: "PolyWeather Side Panel Settings",
+    tokenLabel: "Bearer Token (Optional)",
+    tokenPlaceholder: "Leave empty in public mode; only fill it if the backend returns 401.",
+    save: "Save",
+    test: "Test /api/cities",
+    saved: "Saved. Token can be empty in public mode.",
+    connectOk: "Connected successfully, city count",
+    tokenOptional: "Token can be empty",
+    testFailed: "Test failed",
+    backendAuthHint: "The backend still requires auth. If the extension should be public, allow /api/cities and /api/city/*/detail."
+  }
+};
+
+function t(key) {
+  return I18N[locale][key] || I18N.zh[key] || key;
+}
+
+const apiBaseInput = document.getElementById("apiBaseInput");
+const siteBaseInput = document.getElementById("siteBaseInput");
+const tokenInput = document.getElementById("tokenInput");
+const resultBox = document.getElementById("resultBox");
+const settingsTitle = document.getElementById("settingsTitle");
+const siteBaseLabel = document.getElementById("siteBaseLabel");
+const apiBaseLabel = document.getElementById("apiBaseLabel");
+const tokenLabel = document.getElementById("tokenLabel");
+const saveBtn = document.getElementById("saveBtn");
+const testBtn = document.getElementById("testBtn");
+
+function normalizeBase(url) {
+  return String(url || "").trim().replace(/\/+$/, "");
+}
+
+function writeResult(text) {
+  resultBox.textContent = text;
+}
+
+function getStorage() {
+  return new Promise((resolve) => {
+    chrome.storage.sync.get(DEFAULT_CONFIG, (items) => resolve(items));
+  });
+}
+
+function setStorage(values) {
+  return new Promise((resolve) => {
+    chrome.storage.sync.set(values, resolve);
+  });
+}
+
+async function loadForm() {
+  const cfg = await getStorage();
+  apiBaseInput.value = cfg.apiBase || DEFAULT_CONFIG.apiBase;
+  siteBaseInput.value = cfg.siteBase || cfg.apiBase || DEFAULT_CONFIG.siteBase;
+  tokenInput.value = cfg.authToken || "";
+}
+
+async function saveForm() {
+  const next = {
+    apiBase: normalizeBase(apiBaseInput.value),
+    siteBase: normalizeBase(siteBaseInput.value || apiBaseInput.value),
+    authToken: String(tokenInput.value || "").trim()
+  };
+  await setStorage(next);
+  writeResult(t("saved"));
+}
+
+async function testApi() {
+  const apiBase = normalizeBase(apiBaseInput.value);
+  const authToken = String(tokenInput.value || "").trim();
+  try {
+    const headers = { Accept: "application/json" };
+    if (authToken) headers.Authorization = `Bearer ${authToken}`;
+
+    const res = await fetch(`${apiBase}/api/cities`, {
+      headers,
+      cache: "no-store"
+    });
+    const text = await res.text();
+    let data = null;
+    try {
+      data = text ? JSON.parse(text) : null;
+    } catch (_e) {
+      data = text;
+    }
+    if (!res.ok) {
+      throw new Error(`HTTP ${res.status}: ${typeof data === "string" ? data : JSON.stringify(data)}`);
+    }
+    const count = Array.isArray(data)
+      ? data.length
+      : Array.isArray(data?.cities)
+        ? data.cities.length
+        : 0;
+    writeResult(`${t("connectOk")}: ${count} (${t("tokenOptional")})`);
+  } catch (err) {
+    const msg = String(err?.message || err || "");
+    if (msg.includes("HTTP 401")) {
+      writeResult(`${t("testFailed")}: ${msg}\n${t("backendAuthHint")}`);
+      return;
+    }
+    writeResult(`${t("testFailed")}: ${msg}`);
+  }
+}
+
+function applyStaticTranslations() {
+  document.documentElement.lang = locale === "zh" ? "zh-CN" : "en";
+  if (settingsTitle) settingsTitle.textContent = t("settingsTitle");
+  if (tokenLabel) tokenLabel.textContent = t("tokenLabel");
+  if (tokenInput) tokenInput.placeholder = t("tokenPlaceholder");
+  if (saveBtn) saveBtn.textContent = t("save");
+  if (testBtn) testBtn.textContent = t("test");
+  if (siteBaseLabel) siteBaseLabel.textContent = "Site Base URL";
+  if (apiBaseLabel) apiBaseLabel.textContent = "API Base URL";
+}
+
+document.getElementById("saveBtn").addEventListener("click", () => {
+  void saveForm();
+});
+document.getElementById("testBtn").addEventListener("click", () => {
+  void testApi();
+});
+
+applyStaticTranslations();
+void loadForm();

extension/sidepanel.css

@@ -0,0 +1,410 @@
+:root {
+  --bg: #070d1f;
+  --panel: #0d152b;
+  --card: rgba(255, 255, 255, 0.03);
+  --border: rgba(255, 255, 255, 0.08);
+  --text: #e5eefb;
+  --muted: #8ba0be;
+  --cyan: #22d3ee;
+  --blue: #3b82f6;
+  --green: #34d399;
+  --amber: #f59e0b;
+  --red: #f87171;
+}
+
+* {
+  box-sizing: border-box;
+}
+
+body {
+  margin: 0;
+  background: radial-gradient(circle at top, #0c1735, var(--bg) 45%);
+  color: var(--text);
+  font-family: "Inter", "Segoe UI", -apple-system, BlinkMacSystemFont, sans-serif;
+}
+
+.panel {
+  position: relative;
+  min-height: 100vh;
+  padding: 14px;
+}
+
+.loading-overlay {
+  position: absolute;
+  inset: 0;
+  z-index: 20;
+  display: flex;
+  flex-direction: column;
+  align-items: center;
+  justify-content: center;
+  gap: 10px;
+  border-radius: 14px;
+  background: rgba(7, 13, 31, 0.7);
+  backdrop-filter: blur(2px);
+}
+
+.loading-spinner {
+  width: 28px;
+  height: 28px;
+  border-radius: 999px;
+  border: 3px solid rgba(34, 211, 238, 0.28);
+  border-top-color: #22d3ee;
+  animation: panel-loading-spin 0.75s linear infinite;
+}
+
+.loading-text {
+  color: #b7dcff;
+  font-size: 12px;
+  font-weight: 600;
+}
+
+.topbar {
+  display: grid;
+  grid-template-columns: auto 1fr auto;
+  gap: 8px;
+  align-items: center;
+  margin-bottom: 12px;
+}
+
+.freshness-hint {
+  margin: -2px 0 12px;
+  padding: 8px 10px;
+  border-radius: 10px;
+  border: 1px solid rgba(245, 158, 11, 0.28);
+  background: rgba(245, 158, 11, 0.08);
+  color: #fcd34d;
+  font-size: 12px;
+  line-height: 1.4;
+}
+
+.freshness-hint.stale {
+  border-color: rgba(248, 113, 113, 0.38);
+  background: rgba(248, 113, 113, 0.1);
+  color: #fecaca;
+}
+
+.risk-badge {
+  padding: 5px 9px;
+  border-radius: 10px;
+  font-size: 12px;
+  font-weight: 700;
+  border: 1px solid transparent;
+}
+
+.risk-badge.low {
+  color: #86efac;
+  border-color: rgba(52, 211, 153, 0.5);
+  background: rgba(52, 211, 153, 0.14);
+}
+
+.risk-badge.medium {
+  color: #fcd34d;
+  border-color: rgba(245, 158, 11, 0.45);
+  background: rgba(245, 158, 11, 0.14);
+}
+
+.risk-badge.high {
+  color: #fca5a5;
+  border-color: rgba(248, 113, 113, 0.5);
+  background: rgba(248, 113, 113, 0.12);
+}
+
+.city-picker-wrap {
+  display: grid;
+  gap: 4px;
+}
+
+.city-picker-wrap label {
+  font-size: 11px;
+  color: var(--muted);
+}
+
+#citySelect {
+  width: 100%;
+  height: 34px;
+  border-radius: 9px;
+  border: 1px solid var(--border);
+  background: #0f1b35;
+  color: var(--text);
+  padding: 0 10px;
+}
+
+.refresh-btn {
+  width: 34px;
+  height: 34px;
+  border-radius: 9px;
+  border: 1px solid var(--border);
+  background: #0f1b35;
+  color: #9cecff;
+  display: inline-flex;
+  align-items: center;
+  justify-content: center;
+  cursor: pointer;
+  padding: 0;
+}
+
+.refresh-btn:hover {
+  filter: brightness(1.08);
+}
+
+.refresh-btn:disabled {
+  cursor: not-allowed;
+  opacity: 0.55;
+}
+
+.refresh-btn svg {
+  width: 16px;
+  height: 16px;
+}
+
+.refresh-btn.spinning svg {
+  animation: refresh-spin 0.8s linear infinite;
+}
+
+@keyframes refresh-spin {
+  from {
+    transform: rotate(0deg);
+  }
+  to {
+    transform: rotate(360deg);
+  }
+}
+
+@keyframes panel-loading-spin {
+  from {
+    transform: rotate(0deg);
+  }
+  to {
+    transform: rotate(360deg);
+  }
+}
+
+.btn {
+  height: 34px;
+  border-radius: 10px;
+  border: 1px solid rgba(34, 211, 238, 0.5);
+  color: #9cecff;
+  background: rgba(34, 211, 238, 0.07);
+  font-weight: 700;
+  cursor: pointer;
+}
+
+.btn:hover {
+  filter: brightness(1.08);
+}
+
+.section {
+  border: 1px solid var(--border);
+  border-radius: 14px;
+  background: var(--card);
+  padding: 12px;
+  margin-bottom: 12px;
+}
+
+.section h3 {
+  margin: 0 0 10px 0;
+  font-size: 15px;
+}
+
+.grid2 {
+  display: grid;
+  grid-template-columns: 1fr 1fr;
+  gap: 8px;
+}
+
+.mini-card {
+  border: 1px solid var(--border);
+  border-radius: 12px;
+  padding: 10px;
+  background: rgba(255, 255, 255, 0.02);
+}
+
+.mini-label {
+  display: block;
+  color: var(--muted);
+  font-size: 11px;
+  margin-bottom: 6px;
+}
+
+.mini-card strong {
+  font-size: 16px;
+  line-height: 1.25;
+  font-weight: 700;
+  font-variant-numeric: tabular-nums;
+}
+
+.chart-wrap {
+  position: relative;
+  border: 1px solid var(--border);
+  border-radius: 12px;
+  background: rgba(255, 255, 255, 0.01);
+  padding: 8px;
+}
+
+#trendCanvas {
+  width: 100%;
+  height: auto;
+  display: block;
+}
+
+.legend-text {
+  margin-top: 8px;
+  color: var(--muted);
+  font-size: 12px;
+}
+
+.decision-card {
+  display: grid;
+  gap: 10px;
+  border: 1px solid var(--border);
+  border-radius: 12px;
+  background: rgba(255, 255, 255, 0.02);
+  padding: 10px;
+}
+
+.decision-top {
+  display: flex;
+  align-items: flex-start;
+  justify-content: space-between;
+  gap: 10px;
+}
+
+.decision-direction {
+  font-size: 16px;
+  font-weight: 800;
+  line-height: 1.2;
+  color: #f8fbff;
+}
+
+.decision-window {
+  margin-top: 4px;
+  color: var(--muted);
+  font-size: 11px;
+}
+
+.decision-confidence {
+  flex: 0 0 auto;
+  padding: 5px 9px;
+  border-radius: 999px;
+  border: 1px solid transparent;
+  font-size: 11px;
+  font-weight: 800;
+  letter-spacing: 0.04em;
+}
+
+.decision-confidence.high {
+  color: #86efac;
+  border-color: rgba(52, 211, 153, 0.45);
+  background: rgba(52, 211, 153, 0.12);
+}
+
+.decision-confidence.medium {
+  color: #fcd34d;
+  border-color: rgba(245, 158, 11, 0.45);
+  background: rgba(245, 158, 11, 0.12);
+}
+
+.decision-confidence.low,
+.decision-confidence.neutral {
+  color: #cbd5e1;
+  border-color: rgba(148, 163, 184, 0.28);
+  background: rgba(148, 163, 184, 0.1);
+}
+
+.decision-summary {
+  color: #dbeafe;
+  font-size: 12px;
+  line-height: 1.45;
+}
+
+.decision-reasons {
+  margin: 0;
+  padding-left: 18px;
+  display: grid;
+  gap: 6px;
+  color: var(--muted);
+  font-size: 12px;
+  line-height: 1.45;
+}
+
+.decision-reasons li {
+  margin: 0;
+}
+
+.forecast-row {
+  display: grid;
+  grid-template-columns: repeat(3, minmax(0, 1fr));
+  gap: 8px;
+}
+
+.forecast-card {
+  border: 1px solid var(--border);
+  border-radius: 12px;
+  background: rgba(255, 255, 255, 0.02);
+  padding: 8px;
+  min-height: 68px;
+}
+
+.forecast-card.today {
+  border-color: rgba(34, 211, 238, 0.62);
+  background: rgba(34, 211, 238, 0.08);
+}
+
+.f-date {
+  color: var(--muted);
+  font-size: 11px;
+}
+
+.f-temp {
+  margin-top: 7px;
+  font-size: 16px;
+  font-weight: 800;
+  line-height: 1.2;
+  font-variant-numeric: tabular-nums;
+}
+
+.btn-open-full {
+  width: 100%;
+  height: 40px;
+  border-color: rgba(59, 130, 246, 0.5);
+  color: #d9ebff;
+  background: rgba(59, 130, 246, 0.15);
+}
+
+.open-full-hint {
+  margin-bottom: 10px;
+  color: var(--muted);
+  font-size: 12px;
+  line-height: 1.45;
+}
+
+.error {
+  border: 1px solid rgba(248, 113, 113, 0.45);
+  border-radius: 12px;
+  background: rgba(248, 113, 113, 0.12);
+  color: #fecaca;
+  padding: 10px;
+  font-size: 12px;
+  line-height: 1.4;
+}
+
+.hidden {
+  display: none;
+}
+
+.chart-tooltip {
+  position: absolute;
+  z-index: 3;
+  pointer-events: none;
+  max-width: 180px;
+  padding: 6px 8px;
+  border-radius: 8px;
+  border: 1px solid rgba(34, 211, 238, 0.45);
+  background: rgba(9, 17, 36, 0.92);
+  color: #dff7ff;
+  font-size: 11px;
+  font-weight: 600;
+  line-height: 1.3;
+  white-space: nowrap;
+  box-shadow: 0 6px 18px rgba(0, 0, 0, 0.35);
+}

extension/sidepanel.html

@@ -0,0 +1,92 @@
+<!doctype html>
+<html lang="zh-CN">
+  <head>
+    <meta charset="UTF-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <title>PolyWeather Panel</title>
+    <link rel="stylesheet" href="./sidepanel.css" />
+  </head>
+  <body>
+    <main class="panel">
+      <div id="loadingOverlay" class="loading-overlay hidden" aria-live="polite">
+        <div class="loading-spinner" aria-hidden="true"></div>
+        <div id="loadingText" class="loading-text">正在加载温度数据...</div>
+      </div>
+      <header class="topbar">
+        <div id="riskBadge" class="risk-badge medium">中风险</div>
+        <div class="city-picker-wrap">
+          <label id="cityLabel" for="citySelect">城市</label>
+          <select id="citySelect"></select>
+        </div>
+        <button id="refreshBtn" class="refresh-btn" title="刷新数据" aria-label="刷新数据">
+          <svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2.2" stroke-linecap="round" stroke-linejoin="round">
+            <path d="M3 12a9 9 0 1 0 9-9 9.75 9.75 0 0 0-6.74 2.74L3 8" />
+            <path d="M3 3v5h5" />
+          </svg>
+        </button>
+      </header>
+
+      <div id="freshnessHint" class="freshness-hint hidden"></div>
+
+      <section class="section">
+        <h3 id="profileTitle">城市档案</h3>
+        <div class="grid2">
+          <article class="mini-card">
+            <span class="mini-label" id="settlementLabel">结算站点</span>
+            <strong id="settlementValue">--</strong>
+          </article>
+          <article class="mini-card">
+            <span id="distanceLabel" class="mini-label">站点距离</span>
+            <strong id="distanceValue">--</strong>
+          </article>
+          <article class="mini-card">
+            <span id="obsTimeLabel" class="mini-label">观测更新</span>
+            <strong id="obsTimeValue">--</strong>
+          </article>
+          <article class="mini-card">
+            <span id="nearbyLabel" class="mini-label">周边站点</span>
+            <strong id="nearbyValue">--</strong>
+          </article>
+        </div>
+      </section>
+
+      <section class="section">
+        <h3 id="trendTitle">今日日内走势（简版）</h3>
+        <div class="chart-wrap">
+          <canvas id="trendCanvas" width="560" height="220"></canvas>
+          <div id="chartTooltip" class="chart-tooltip hidden"></div>
+        </div>
+        <div id="chartLegend" class="legend-text">--</div>
+      </section>
+
+      <section class="section">
+        <h3 id="decisionTitle">方向判断</h3>
+        <div class="decision-card">
+          <div class="decision-top">
+            <div>
+              <div id="decisionDirection" class="decision-direction">--</div>
+              <div id="decisionWindow" class="decision-window">--</div>
+            </div>
+            <div id="decisionConfidence" class="decision-confidence neutral">--</div>
+          </div>
+          <div id="decisionSummary" class="decision-summary">--</div>
+          <ul id="decisionReasons" class="decision-reasons"></ul>
+        </div>
+      </section>
+
+      <section class="section">
+        <h3 id="forecastTitle">多日预报</h3>
+        <div id="forecastRow" class="forecast-row"></div>
+      </section>
+
+      <section class="section">
+        <div id="openFullHint" class="open-full-hint"></div>
+        <button id="openFullBtn" class="btn btn-open-full">打开完整网站分析</button>
+      </section>
+
+      <section id="errorBox" class="error hidden"></section>
+    </main>
+
+    <script src="./sidepanel.js"></script>
+  </body>
+  </html>

frontend/.env.example

@@ -1 +1,35 @@
+# PolyWeather 前端最小配置（本地 / Vercel）
+# 只部署天气看板时，先填下面 4 项即可。
+
+# 必填：后端 FastAPI 基础地址
 POLYWEATHER_API_BASE_URL=http://127.0.0.1:8000
+
+# 必填：Supabase 前端公钥（鉴权开启时必须）
+NEXT_PUBLIC_SUPABASE_URL=
+NEXT_PUBLIC_SUPABASE_ANON_KEY=
+
+# 常用：前端鉴权开关
+# true: 启用 Supabase 登录
+# false: 关闭登录能力，访客模式
+POLYWEATHER_AUTH_ENABLED=false
+
+# 常用：是否强制登录
+# true: middleware 强制登录后才能访问主页面
+# false: 登录可选，访客可浏览
+POLYWEATHER_AUTH_REQUIRED=false
+
+# 可选：分享式看板访问令牌
+# 设置后，可通过 /?access_token=<token> 打开受保护看板
+POLYWEATHER_DASHBOARD_ACCESS_TOKEN=
+
+# 可选：前端 API Route 转发到后端时附带的共享令牌
+# 仅当后端启用了 entitlement / 订阅校验时需要
+POLYWEATHER_BACKEND_ENTITLEMENT_TOKEN=
+
+# 可选：钱包支付 / Telegram 入口
+NEXT_PUBLIC_WALLETCONNECT_PROJECT_ID=
+NEXT_PUBLIC_WALLETCONNECT_POLYGON_RPC_URL=https://polygon-bor-rpc.publicnode.com
+NEXT_PUBLIC_PAYMENT_ALLOWED_HOSTS=polyweather-pro.vercel.app
+POLYWEATHER_OPS_ADMIN_EMAILS=yhrsc30@gmail.com
+NEXT_PUBLIC_TELEGRAM_GROUP_URL=https://t.me/your_group
+NEXT_PUBLIC_TELEGRAM_BOT_URL=https://t.me/WeatherQuant_bot

frontend/README.md

@@ -1,29 +1,39 @@
-# PolyWeather Frontend
+# PolyWeather 前端
 
-This directory is the only web frontend in production.
+PolyWeather Pro 的生产前端工程。
 
-Production URL:
-- https://polyweather-pro.vercel.app/
+线上地址：
+- [https://polyweather-pro.vercel.app/](https://polyweather-pro.vercel.app/)
 
-## Stack
+## 技术栈
 
 - Next.js App Router
-- Tailwind CSS
-- Lucide React
-- shadcn/ui base layer
-- Legacy dashboard shell loaded from `public/legacy/index.html`
+- React + Tailwind
+- Leaflet + Chart.js
+- Supabase Auth
+- WalletConnect + 浏览器 EVM 钱包
 
-## Production Model
+## 运行模型
 
-- Vercel serves the web UI
-- FastAPI on VPS serves API only
-- The old FastAPI static website has been removed
+1. 浏览器 -> Next 应用（`frontend`）
+2. Next Route Handlers（`/api/*`）-> FastAPI 后端
+3. FastAPI -> 分析服务 / 支付服务
 
-Current request flow:
-- Browser -> Vercel frontend
-- Vercel route handlers -> FastAPI API
+## 当前前端能力
 
-## Local Development
+- 主站 Dashboard 支持地图、城市详情、今日日内分析、历史准确率对账和账户中心
+- `/docs` 已提供公开双语产品文档中心，解释日内结构信号、TAF、结算来源和历史对账
+- 今日日内分析支持：
+  - 峰值窗口感知的近地面结构信号
+  - 高空结构信号
+  - 交易动作卡
+  - 非香港机场城市的 `TAF` 时段提示与走势图联动
+- 历史对账支持：
+  - `DEB / 最佳单模型 / 实测最高温` 对比
+  - 峰值前 12 小时 `DEB` 参考（近似）
+- `/ops` 已支持桌面表格 + 手机端卡片化视图
+
+## 本地开发
 
 ```bash
 cd frontend
@@ -32,37 +42,139 @@ npm install
 npm run dev
 ```
 
-Default local URL:
-- http://localhost:3000
+## Vercel 最小部署配置
 
-## Required Environment Variable
+只跑看板和基础鉴权时，先填这 4 项：
 
 ```env
 POLYWEATHER_API_BASE_URL=https://<your-fastapi-host>
+NEXT_PUBLIC_SUPABASE_URL=https://<your-supabase-project>.supabase.co
+NEXT_PUBLIC_SUPABASE_ANON_KEY=<your-anon-key>
+POLYWEATHER_AUTH_ENABLED=true
 ```
 
-Examples:
-- `http://38.54.27.70:8000`
-- `https://api.example.com`
+建议显式补：
 
-## Route Handlers
+```env
+POLYWEATHER_AUTH_REQUIRED=true
+```
+
+如果你只是开放游客浏览，可改成：
+
+```env
+POLYWEATHER_AUTH_ENABLED=false
+POLYWEATHER_AUTH_REQUIRED=false
+```
+
+## 可选环境变量
+
+仅在对应功能启用时填写：
+
+```env
+# 看板分享令牌
+POLYWEATHER_DASHBOARD_ACCESS_TOKEN=
+
+# 前端 API 转发到后端时使用的共享令牌
+POLYWEATHER_BACKEND_ENTITLEMENT_TOKEN=
+
+# 钱包支付
+NEXT_PUBLIC_WALLETCONNECT_PROJECT_ID=
+NEXT_PUBLIC_WALLETCONNECT_POLYGON_RPC_URL=https://polygon-bor-rpc.publicnode.com
+NEXT_PUBLIC_PAYMENT_ALLOWED_HOSTS=polyweather-pro.vercel.app
+POLYWEATHER_OPS_ADMIN_EMAILS=yhrsc30@gmail.com
+
+# 社群入口
+NEXT_PUBLIC_TELEGRAM_GROUP_URL=https://t.me/<your_group>
+NEXT_PUBLIC_TELEGRAM_BOT_URL=https://t.me/WeatherQuant_bot
+```
+
+更完整的 Vercel 配置说明见：
+- [docs/FRONTEND_DEPLOYMENT_ZH.md](/E:/web/PolyWeather/docs/FRONTEND_DEPLOYMENT_ZH.md)
+
+## 路由处理器
+
+天气：
 
-Thin BFF routes currently exposed by Next:
 - `GET /api/cities`
 - `GET /api/city/[name]`
+- `GET /api/city/[name]/summary`
+- `GET /api/city/[name]/detail`
 - `GET /api/history/[name]`
 
-## Vercel Deployment
+鉴权：
 
-1. Import the repo into Vercel
-2. Set Root Directory to `frontend`
-3. Set `POLYWEATHER_API_BASE_URL`
-4. Deploy
+- `GET /api/auth/me`
 
-## Notes
+支付：
 
-- Backend CORS must allow `https://polyweather-pro.vercel.app`
-- The page shell currently embeds the legacy dashboard HTML from `public/legacy/index.html`
-- If you change files under `public/static`, deploy to Vercel to make them live
+- `GET /api/payments/config`
+- `GET /api/payments/wallets`
+- `POST /api/payments/wallets/challenge`
+- `POST /api/payments/wallets/verify`
+- `POST /api/payments/intents`
+- `GET /api/payments/intents/[intentId]`
+- `POST /api/payments/intents/[intentId]/submit`
+- `POST /api/payments/intents/[intentId]/confirm`
 
-Last updated: 2026-03-06
+Ops：
+
+- `GET /ops`
+- `GET /api/ops/users`
+- `GET /api/ops/leaderboard/weekly`
+- `GET /api/ops/memberships`
+- `GET /api/ops/payments/incidents`
+- `POST /api/ops/users/grant-points`
+- `POST /api/ops/payments/incidents/[eventId]/resolve`
+
+## Ops 管理后台
+
+当前前端已内置轻量管理页：
+
+- [https://polyweather-pro.vercel.app/ops](https://polyweather-pro.vercel.app/ops)
+
+页面当前支持：
+
+- 系统状态
+- SQLite / rollout / 支付运行态
+- 用户查询
+- 当前会员
+- 本周积分榜
+- 手动补分
+- 支付异常单筛选与标记已处理
+- 手机端卡片化视图
+
+注意：
+
+- `/ops` 现在是前后端双层管理员限制
+- Vercel 前端和后端都应配置相同的 `POLYWEATHER_OPS_ADMIN_EMAILS`
+- 前端登录邮箱本身不会自动获得管理员权限
+
+## 支付安全补充
+
+为降低“旧页面/旧配置导致打到旧收款地址”的风险，支付区现在会：
+
+1. 点击支付前重新请求 `/api/payments/config`
+2. 若 `receiver_contract` 已更新，先切到最新地址
+3. 若后端返回的 `tx_payload.to` 与最新地址不一致，直接阻断支付
+4. 仅允许在 `NEXT_PUBLIC_PAYMENT_ALLOWED_HOSTS` 白名单域名上创建 payment intent
+5. 支付区会明确显示当前账号、付款钱包和收款合约，避免账号/钱包/地址混淆
+
+这意味着：
+
+- 旧标签页风险已明显降低
+- 但支付地址变更后，仍建议在 Vercel 上 redeploy 当前 production，并清理明显过期 deployment
+
+## 缓存行为
+
+- `cities` / `summary` / `history`：`ETag + Cache-Control`
+- `summary?force_refresh=true`：`no-store`
+- 支付相关路由：`no-store`
+
+## AGPL 与商用边界说明
+
+此前端代码随仓库一起采用 `AGPL-3.0-only`。
+生产私有运营流程、商业策略调优、敏感生产参数、品牌与托管服务能力不在代码许可证授权范围内。
+
+详见根目录策略文档：`docs/OPEN_CORE_POLICY.md`
+
+最后更新：`2026-03-24`

frontend/app/account/page.tsx

@@ -0,0 +1,16 @@
+import type { Metadata } from "next";
+import { I18nProvider } from "@/hooks/useI18n";
+import { AccountEntry } from "@/components/account/AccountEntry";
+
+export const metadata: Metadata = {
+  title: "PolyWeather | Account Center",
+  description: "PolyWeather account center for identity and entitlement status.",
+};
+
+export default function AccountPage() {
+  return (
+    <I18nProvider>
+      <AccountEntry />
+    </I18nProvider>
+  );
+}

frontend/app/api/analytics/events/route.ts

@@ -0,0 +1,51 @@
+import { NextRequest, NextResponse } from "next/server";
+import {
+  applyAuthResponseCookies,
+  buildBackendRequestHeaders,
+} from "@/lib/backend-auth";
+
+const API_BASE = process.env.POLYWEATHER_API_BASE_URL;
+const ANALYTICS_ENABLED =
+  process.env.NEXT_PUBLIC_POLYWEATHER_APP_ANALYTICS === "true";
+
+export async function POST(req: NextRequest) {
+  if (!ANALYTICS_ENABLED) {
+    return new NextResponse(null, { status: 204 });
+  }
+
+  if (!API_BASE) {
+    return NextResponse.json(
+      { error: "POLYWEATHER_API_BASE_URL is not configured" },
+      { status: 500 },
+    );
+  }
+
+  try {
+    const body = await req.json();
+    const auth = await buildBackendRequestHeaders(req);
+    const headers = new Headers(auth.headers);
+    headers.set("Content-Type", "application/json");
+    const res = await fetch(`${API_BASE}/api/analytics/events`, {
+      method: "POST",
+      headers,
+      body: JSON.stringify(body ?? {}),
+      cache: "no-store",
+    });
+    if (!res.ok) {
+      const raw = await res.text();
+      const response = NextResponse.json(
+        { error: `Backend returned ${res.status}`, detail: raw.slice(0, 260) },
+        { status: res.status },
+      );
+      return applyAuthResponseCookies(response, auth.response);
+    }
+    const data = await res.json();
+    const response = NextResponse.json(data);
+    return applyAuthResponseCookies(response, auth.response);
+  } catch (error) {
+    return NextResponse.json(
+      { error: "Failed to track analytics event", detail: String(error) },
+      { status: 500 },
+    );
+  }
+}

frontend/app/api/auth/me/route.ts

@@ -0,0 +1,48 @@
+import { NextRequest, NextResponse } from "next/server";
+import {
+  applyAuthResponseCookies,
+  buildBackendRequestHeaders,
+} from "@/lib/backend-auth";
+
+const API_BASE = process.env.POLYWEATHER_API_BASE_URL;
+
+export async function GET(req: NextRequest) {
+  if (!API_BASE) {
+    return NextResponse.json(
+      { error: "POLYWEATHER_API_BASE_URL is not configured" },
+      { status: 500 },
+    );
+  }
+
+  try {
+    const auth = await buildBackendRequestHeaders(req);
+    const res = await fetch(`${API_BASE}/api/auth/me`, {
+      headers: auth.headers,
+      cache: "no-store",
+    });
+    if (res.status === 401 || res.status === 403) {
+      const response = NextResponse.json({
+        authenticated: false,
+        subscription_active: false,
+        points: 0,
+      });
+      return applyAuthResponseCookies(response, auth.response);
+    }
+    if (!res.ok) {
+      const raw = await res.text();
+      const response = NextResponse.json(
+        { error: `Backend returned ${res.status}`, detail: raw.slice(0, 300) },
+        { status: res.status },
+      );
+      return applyAuthResponseCookies(response, auth.response);
+    }
+    const data = await res.json();
+    const response = NextResponse.json(data);
+    return applyAuthResponseCookies(response, auth.response);
+  } catch (error) {
+    return NextResponse.json(
+      { error: "Failed to fetch auth profile", detail: String(error) },
+      { status: 500 },
+    );
+  }
+}

frontend/app/api/cities/route.ts

@@ -1,33 +1,53 @@
-import { NextResponse } from "next/server";
+import { NextRequest, NextResponse } from "next/server";
+import {
+  applyAuthResponseCookies,
+  buildBackendRequestHeaders,
+} from "@/lib/backend-auth";
+import { buildCachedJsonResponse } from "@/lib/http-cache";
 
 const API_BASE = process.env.POLYWEATHER_API_BASE_URL;
+export const dynamic = "force-dynamic";
 
-export async function GET() {
+export async function GET(req: NextRequest) {
   if (!API_BASE) {
-    return NextResponse.json(
+    const response = NextResponse.json(
       { error: "POLYWEATHER_API_BASE_URL is not configured" },
       { status: 500 },
     );
+    return response;
   }
 
   try {
+    const auth = await buildBackendRequestHeaders(req, {
+      includeSupabaseIdentity: false,
+    });
+    const fetchOptions = {
+      headers: auth.headers,
+      next: { revalidate: 300 },
+    } as const;
     const res = await fetch(`${API_BASE}/api/cities`, {
-      headers: { Accept: "application/json" },
-      next: { revalidate: 120 },
+      ...fetchOptions,
     });
     if (!res.ok) {
       const raw = await res.text();
-      return NextResponse.json(
+      const response = NextResponse.json(
         { error: `Backend returned ${res.status}`, detail: raw.slice(0, 300) },
         { status: 502 },
       );
+      return applyAuthResponseCookies(response, auth.response);
     }
     const data = await res.json();
-    return NextResponse.json(data);
+    const response = buildCachedJsonResponse(
+      req,
+      data,
+      "public, max-age=0, s-maxage=300, stale-while-revalidate=1800",
+    );
+    return applyAuthResponseCookies(response, auth.response);
   } catch (error) {
-    return NextResponse.json(
+    const response = NextResponse.json(
       { error: "Failed to fetch cities", detail: String(error) },
       { status: 500 },
     );
+    return response;
   }
 }

frontend/app/api/city/[name]/detail/route.ts

@@ -0,0 +1,60 @@
+import { NextRequest, NextResponse } from "next/server";
+import {
+  applyAuthResponseCookies,
+  buildBackendRequestHeaders,
+} from "@/lib/backend-auth";
+
+const API_BASE = process.env.POLYWEATHER_API_BASE_URL;
+
+export async function GET(
+  req: NextRequest,
+  context: { params: Promise<{ name: string }> },
+) {
+  if (!API_BASE) {
+    const response = NextResponse.json(
+      { error: "POLYWEATHER_API_BASE_URL is not configured" },
+      { status: 500 },
+    );
+    return response;
+  }
+
+  const { name } = await context.params;
+  const forceRefresh = req.nextUrl.searchParams.get("force_refresh") ?? "false";
+  const marketSlug = req.nextUrl.searchParams.get("market_slug");
+  const targetDate = req.nextUrl.searchParams.get("target_date");
+  const searchParams = new URLSearchParams({
+    force_refresh: forceRefresh,
+  });
+  if (marketSlug) {
+    searchParams.set("market_slug", marketSlug);
+  }
+  if (targetDate) {
+    searchParams.set("target_date", targetDate);
+  }
+  const url = `${API_BASE}/api/city/${encodeURIComponent(name)}/detail?${searchParams.toString()}`;
+
+  try {
+    const auth = await buildBackendRequestHeaders(req);
+    const res = await fetch(url, {
+      headers: auth.headers,
+      cache: "no-store",
+    });
+    if (!res.ok) {
+      const raw = await res.text();
+      const response = NextResponse.json(
+        { error: `Backend returned ${res.status}`, detail: raw.slice(0, 300) },
+        { status: 502 },
+      );
+      return applyAuthResponseCookies(response, auth.response);
+    }
+    const data = await res.json();
+    const response = NextResponse.json(data);
+    return applyAuthResponseCookies(response, auth.response);
+  } catch (error) {
+    const response = NextResponse.json(
+      { error: "Failed to fetch city detail aggregate", detail: String(error) },
+      { status: 500 },
+    );
+    return response;
+  }
+}

frontend/app/api/city/[name]/route.ts

@@ -1,4 +1,8 @@
 import { NextRequest, NextResponse } from "next/server";
+import {
+  applyAuthResponseCookies,
+  buildBackendRequestHeaders,
+} from "@/lib/backend-auth";
 
 const API_BASE = process.env.POLYWEATHER_API_BASE_URL;
 
@@ -7,10 +11,11 @@ export async function GET(
   context: { params: Promise<{ name: string }> },
 ) {
   if (!API_BASE) {
-    return NextResponse.json(
+    const response = NextResponse.json(
       { error: "POLYWEATHER_API_BASE_URL is not configured" },
       { status: 500 },
     );
+    return response;
   }
 
   const { name } = await context.params;
@@ -18,23 +23,29 @@ export async function GET(
   const url = `${API_BASE}/api/city/${encodeURIComponent(name)}?force_refresh=${forceRefresh}`;
 
   try {
+    const auth = await buildBackendRequestHeaders(req, {
+      includeSupabaseIdentity: false,
+    });
     const res = await fetch(url, {
-      headers: { Accept: "application/json" },
+      headers: auth.headers,
       cache: "no-store",
     });
     if (!res.ok) {
       const raw = await res.text();
-      return NextResponse.json(
+      const response = NextResponse.json(
         { error: `Backend returned ${res.status}`, detail: raw.slice(0, 300) },
         { status: 502 },
       );
+      return applyAuthResponseCookies(response, auth.response);
     }
     const data = await res.json();
-    return NextResponse.json(data);
+    const response = NextResponse.json(data);
+    return applyAuthResponseCookies(response, auth.response);
   } catch (error) {
-    return NextResponse.json(
+    const response = NextResponse.json(
       { error: "Failed to fetch city detail", detail: String(error) },
       { status: 500 },
     );
+    return response;
   }
 }

frontend/app/api/city/[name]/summary/route.ts

@@ -0,0 +1,74 @@
+import { NextRequest, NextResponse } from "next/server";
+import {
+  applyAuthResponseCookies,
+  buildBackendRequestHeaders,
+} from "@/lib/backend-auth";
+import { buildCachedJsonResponse } from "@/lib/http-cache";
+
+const API_BASE = process.env.POLYWEATHER_API_BASE_URL;
+
+export async function GET(
+  req: NextRequest,
+  context: { params: Promise<{ name: string }> },
+) {
+  if (!API_BASE) {
+    const response = NextResponse.json(
+      { error: "POLYWEATHER_API_BASE_URL is not configured" },
+      { status: 500 },
+    );
+    return response;
+  }
+
+  const { name } = await context.params;
+  const forceRefresh = req.nextUrl.searchParams.get("force_refresh") ?? "false";
+  const bypassCache = forceRefresh === "true";
+  const url = `${API_BASE}/api/city/${encodeURIComponent(name)}/summary?force_refresh=${forceRefresh}`;
+
+  try {
+    const auth = await buildBackendRequestHeaders(req, {
+      includeSupabaseIdentity: false,
+    });
+    const fetchOptions =
+      bypassCache
+        ? {
+            headers: auth.headers,
+            cache: "no-store" as const,
+          }
+        : {
+            headers: auth.headers,
+            next: { revalidate: 20 },
+          };
+    const res = await fetch(url, {
+      ...fetchOptions,
+    });
+    if (!res.ok) {
+      const raw = await res.text();
+      const response = NextResponse.json(
+        { error: `Backend returned ${res.status}`, detail: raw.slice(0, 300) },
+        { status: 502 },
+      );
+      return applyAuthResponseCookies(response, auth.response);
+    }
+    const data = await res.json();
+    if (bypassCache) {
+      const response = NextResponse.json(data, {
+        headers: {
+          "Cache-Control": "no-store",
+        },
+      });
+      return applyAuthResponseCookies(response, auth.response);
+    }
+    const response = buildCachedJsonResponse(
+      req,
+      data,
+      "public, max-age=0, s-maxage=20, stale-while-revalidate=60",
+    );
+    return applyAuthResponseCookies(response, auth.response);
+  } catch (error) {
+    const response = NextResponse.json(
+      { error: "Failed to fetch city summary", detail: String(error) },
+      { status: 500 },
+    );
+    return response;
+  }
+}

frontend/app/api/healthz/route.ts

@@ -0,0 +1,40 @@
+import { NextRequest, NextResponse } from "next/server";
+import {
+  applyAuthResponseCookies,
+  buildBackendRequestHeaders,
+} from "@/lib/backend-auth";
+
+const API_BASE = process.env.POLYWEATHER_API_BASE_URL;
+
+export async function GET(req: NextRequest) {
+  if (!API_BASE) {
+    return NextResponse.json(
+      { error: "POLYWEATHER_API_BASE_URL is not configured" },
+      { status: 500 },
+    );
+  }
+
+  try {
+    const auth = await buildBackendRequestHeaders(req, {
+      includeSupabaseIdentity: false,
+    });
+    const res = await fetch(`${API_BASE}/healthz`, {
+      headers: auth.headers,
+      cache: "no-store",
+    });
+    const raw = await res.text();
+    const response = new NextResponse(raw, {
+      status: res.status,
+      headers: {
+        "Content-Type": res.headers.get("content-type") || "application/json",
+        "Cache-Control": "no-store",
+      },
+    });
+    return applyAuthResponseCookies(response, auth.response);
+  } catch (error) {
+    return NextResponse.json(
+      { error: "Failed to fetch healthz", detail: String(error) },
+      { status: 500 },
+    );
+  }
+}

frontend/app/api/history/[name]/route.ts

@@ -1,39 +1,56 @@
-import { NextResponse } from "next/server";
+import { NextRequest, NextResponse } from "next/server";
+import {
+  applyAuthResponseCookies,
+  buildBackendRequestHeaders,
+} from "@/lib/backend-auth";
+import { buildCachedJsonResponse } from "@/lib/http-cache";
 
 const API_BASE = process.env.POLYWEATHER_API_BASE_URL;
 
 export async function GET(
-  _req: Request,
+  req: NextRequest,
   context: { params: Promise<{ name: string }> },
 ) {
   if (!API_BASE) {
-    return NextResponse.json(
+    const response = NextResponse.json(
       { error: "POLYWEATHER_API_BASE_URL is not configured" },
       { status: 500 },
     );
+    return response;
   }
 
   const { name } = await context.params;
   const url = `${API_BASE}/api/history/${encodeURIComponent(name)}`;
 
   try {
+    const auth = await buildBackendRequestHeaders(req);
+    const fetchOptions = {
+      headers: auth.headers,
+      next: { revalidate: 60 },
+    } as const;
     const res = await fetch(url, {
-      headers: { Accept: "application/json" },
-      cache: "no-store",
+      ...fetchOptions,
     });
     if (!res.ok) {
       const raw = await res.text();
-      return NextResponse.json(
+      const response = NextResponse.json(
         { error: `Backend returned ${res.status}`, detail: raw.slice(0, 300) },
         { status: 502 },
       );
+      return applyAuthResponseCookies(response, auth.response);
     }
     const data = await res.json();
-    return NextResponse.json(data);
+    const response = buildCachedJsonResponse(
+      req,
+      data,
+      "public, max-age=0, s-maxage=60, stale-while-revalidate=300",
+    );
+    return applyAuthResponseCookies(response, auth.response);
   } catch (error) {
-    return NextResponse.json(
+    const response = NextResponse.json(
       { error: "Failed to fetch history", detail: String(error) },
       { status: 500 },
     );
+    return response;
   }
 }

frontend/app/api/ops/analytics/funnel/route.ts

@@ -0,0 +1,43 @@
+import { NextRequest, NextResponse } from "next/server";
+import {
+  applyAuthResponseCookies,
+  buildBackendRequestHeaders,
+} from "@/lib/backend-auth";
+
+const API_BASE = process.env.POLYWEATHER_API_BASE_URL;
+
+export async function GET(req: NextRequest) {
+  if (!API_BASE) {
+    return NextResponse.json(
+      { error: "POLYWEATHER_API_BASE_URL is not configured" },
+      { status: 500 },
+    );
+  }
+
+  try {
+    const auth = await buildBackendRequestHeaders(req);
+    const url = new URL(`${API_BASE}/api/ops/analytics/funnel`);
+    const days = req.nextUrl.searchParams.get("days");
+    if (days) {
+      url.searchParams.set("days", days);
+    }
+    const res = await fetch(url.toString(), {
+      headers: auth.headers,
+      cache: "no-store",
+    });
+    const raw = await res.text();
+    const response = new NextResponse(raw, {
+      status: res.status,
+      headers: {
+        "Content-Type": res.headers.get("content-type") || "application/json",
+        "Cache-Control": "no-store",
+      },
+    });
+    return applyAuthResponseCookies(response, auth.response);
+  } catch (error) {
+    return NextResponse.json(
+      { error: "Failed to fetch analytics funnel", detail: String(error) },
+      { status: 500 },
+    );
+  }
+}

frontend/app/api/ops/leaderboard/weekly/route.ts

@@ -0,0 +1,42 @@
+import { NextRequest, NextResponse } from "next/server";
+import {
+  applyAuthResponseCookies,
+  buildBackendRequestHeaders,
+} from "@/lib/backend-auth";
+
+const API_BASE = process.env.POLYWEATHER_API_BASE_URL;
+
+export async function GET(req: NextRequest) {
+  if (!API_BASE) {
+    return NextResponse.json(
+      { error: "POLYWEATHER_API_BASE_URL is not configured" },
+      { status: 500 },
+    );
+  }
+
+  try {
+    const auth = await buildBackendRequestHeaders(req);
+    const url = new URL(`${API_BASE}/api/ops/leaderboard/weekly`);
+    const limit = req.nextUrl.searchParams.get("limit");
+    if (limit) url.searchParams.set("limit", limit);
+
+    const res = await fetch(url.toString(), {
+      headers: auth.headers,
+      cache: "no-store",
+    });
+    const raw = await res.text();
+    const response = new NextResponse(raw, {
+      status: res.status,
+      headers: {
+        "Content-Type": res.headers.get("content-type") || "application/json",
+        "Cache-Control": "no-store",
+      },
+    });
+    return applyAuthResponseCookies(response, auth.response);
+  } catch (error) {
+    return NextResponse.json(
+      { error: "Failed to fetch weekly leaderboard", detail: String(error) },
+      { status: 500 },
+    );
+  }
+}

frontend/app/api/ops/memberships/route.ts

@@ -0,0 +1,42 @@
+import { NextRequest, NextResponse } from "next/server";
+import {
+  applyAuthResponseCookies,
+  buildBackendRequestHeaders,
+} from "@/lib/backend-auth";
+
+const API_BASE = process.env.POLYWEATHER_API_BASE_URL;
+
+export async function GET(req: NextRequest) {
+  if (!API_BASE) {
+    return NextResponse.json(
+      { error: "POLYWEATHER_API_BASE_URL is not configured" },
+      { status: 500 },
+    );
+  }
+
+  try {
+    const auth = await buildBackendRequestHeaders(req);
+    const url = new URL(`${API_BASE}/api/ops/memberships`);
+    const limit = req.nextUrl.searchParams.get("limit");
+    if (limit) url.searchParams.set("limit", limit);
+
+    const res = await fetch(url.toString(), {
+      headers: auth.headers,
+      cache: "no-store",
+    });
+    const raw = await res.text();
+    const response = new NextResponse(raw, {
+      status: res.status,
+      headers: {
+        "Content-Type": res.headers.get("content-type") || "application/json",
+        "Cache-Control": "no-store",
+      },
+    });
+    return applyAuthResponseCookies(response, auth.response);
+  } catch (error) {
+    return NextResponse.json(
+      { error: "Failed to fetch memberships", detail: String(error) },
+      { status: 500 },
+    );
+  }
+}

frontend/app/api/ops/payments/incidents/[eventId]/resolve/route.ts

@@ -0,0 +1,44 @@
+import { NextRequest, NextResponse } from "next/server";
+import {
+  applyAuthResponseCookies,
+  buildBackendRequestHeaders,
+} from "@/lib/backend-auth";
+
+const API_BASE = process.env.POLYWEATHER_API_BASE_URL;
+
+type RouteContext = {
+  params: Promise<{ eventId: string }>;
+};
+
+export async function POST(req: NextRequest, context: RouteContext) {
+  if (!API_BASE) {
+    return NextResponse.json(
+      { error: "POLYWEATHER_API_BASE_URL is not configured" },
+      { status: 500 },
+    );
+  }
+
+  try {
+    const auth = await buildBackendRequestHeaders(req);
+    const { eventId } = await context.params;
+    const res = await fetch(`${API_BASE}/api/ops/payments/incidents/${eventId}/resolve`, {
+      method: "POST",
+      headers: auth.headers,
+      cache: "no-store",
+    });
+    const raw = await res.text();
+    const response = new NextResponse(raw, {
+      status: res.status,
+      headers: {
+        "Content-Type": res.headers.get("content-type") || "application/json",
+        "Cache-Control": "no-store",
+      },
+    });
+    return applyAuthResponseCookies(response, auth.response);
+  } catch (error) {
+    return NextResponse.json(
+      { error: "Failed to resolve payment incident", detail: String(error) },
+      { status: 500 },
+    );
+  }
+}

frontend/app/api/ops/payments/incidents/route.ts

@@ -0,0 +1,42 @@
+import { NextRequest, NextResponse } from "next/server";
+import {
+  applyAuthResponseCookies,
+  buildBackendRequestHeaders,
+} from "@/lib/backend-auth";
+
+const API_BASE = process.env.POLYWEATHER_API_BASE_URL;
+
+export async function GET(req: NextRequest) {
+  if (!API_BASE) {
+    return NextResponse.json(
+      { error: "POLYWEATHER_API_BASE_URL is not configured" },
+      { status: 500 },
+    );
+  }
+
+  try {
+    const auth = await buildBackendRequestHeaders(req);
+    const url = new URL(`${API_BASE}/api/ops/payments/incidents`);
+    const limit = req.nextUrl.searchParams.get("limit");
+    if (limit) url.searchParams.set("limit", limit);
+
+    const res = await fetch(url.toString(), {
+      headers: auth.headers,
+      cache: "no-store",
+    });
+    const raw = await res.text();
+    const response = new NextResponse(raw, {
+      status: res.status,
+      headers: {
+        "Content-Type": res.headers.get("content-type") || "application/json",
+        "Cache-Control": "no-store",
+      },
+    });
+    return applyAuthResponseCookies(response, auth.response);
+  } catch (error) {
+    return NextResponse.json(
+      { error: "Failed to fetch payment incidents", detail: String(error) },
+      { status: 500 },
+    );
+  }
+}

frontend/app/api/ops/truth-history/route.ts

@@ -0,0 +1,44 @@
+import { NextRequest, NextResponse } from "next/server";
+import {
+  applyAuthResponseCookies,
+  buildBackendRequestHeaders,
+} from "@/lib/backend-auth";
+
+const API_BASE = process.env.POLYWEATHER_API_BASE_URL;
+
+export async function GET(req: NextRequest) {
+  if (!API_BASE) {
+    return NextResponse.json(
+      { error: "POLYWEATHER_API_BASE_URL is not configured" },
+      { status: 500 },
+    );
+  }
+
+  try {
+    const auth = await buildBackendRequestHeaders(req);
+    const url = new URL(`${API_BASE}/api/ops/truth-history`);
+    for (const key of ["city", "date_from", "date_to", "limit"]) {
+      const value = req.nextUrl.searchParams.get(key);
+      if (value) url.searchParams.set(key, value);
+    }
+
+    const res = await fetch(url.toString(), {
+      headers: auth.headers,
+      cache: "no-store",
+    });
+    const raw = await res.text();
+    const response = new NextResponse(raw, {
+      status: res.status,
+      headers: {
+        "Content-Type": res.headers.get("content-type") || "application/json",
+        "Cache-Control": "no-store",
+      },
+    });
+    return applyAuthResponseCookies(response, auth.response);
+  } catch (error) {
+    return NextResponse.json(
+      { error: "Failed to fetch truth history", detail: String(error) },
+      { status: 500 },
+    );
+  }
+}

frontend/app/api/ops/users/grant-points/route.ts

@@ -0,0 +1,44 @@
+import { NextRequest, NextResponse } from "next/server";
+import {
+  applyAuthResponseCookies,
+  buildBackendRequestHeaders,
+} from "@/lib/backend-auth";
+
+const API_BASE = process.env.POLYWEATHER_API_BASE_URL;
+
+export async function POST(req: NextRequest) {
+  if (!API_BASE) {
+    return NextResponse.json(
+      { error: "POLYWEATHER_API_BASE_URL is not configured" },
+      { status: 500 },
+    );
+  }
+
+  try {
+    const auth = await buildBackendRequestHeaders(req);
+    const body = await req.text();
+    const res = await fetch(`${API_BASE}/api/ops/users/grant-points`, {
+      method: "POST",
+      headers: {
+        ...auth.headers,
+        "Content-Type": "application/json",
+      },
+      body,
+      cache: "no-store",
+    });
+    const raw = await res.text();
+    const response = new NextResponse(raw, {
+      status: res.status,
+      headers: {
+        "Content-Type": res.headers.get("content-type") || "application/json",
+        "Cache-Control": "no-store",
+      },
+    });
+    return applyAuthResponseCookies(response, auth.response);
+  } catch (error) {
+    return NextResponse.json(
+      { error: "Failed to grant points", detail: String(error) },
+      { status: 500 },
+    );
+  }
+}

frontend/app/api/ops/users/route.ts

@@ -0,0 +1,44 @@
+import { NextRequest, NextResponse } from "next/server";
+import {
+  applyAuthResponseCookies,
+  buildBackendRequestHeaders,
+} from "@/lib/backend-auth";
+
+const API_BASE = process.env.POLYWEATHER_API_BASE_URL;
+
+export async function GET(req: NextRequest) {
+  if (!API_BASE) {
+    return NextResponse.json(
+      { error: "POLYWEATHER_API_BASE_URL is not configured" },
+      { status: 500 },
+    );
+  }
+
+  try {
+    const auth = await buildBackendRequestHeaders(req);
+    const url = new URL(`${API_BASE}/api/ops/users`);
+    const q = req.nextUrl.searchParams.get("q");
+    const limit = req.nextUrl.searchParams.get("limit");
+    if (q) url.searchParams.set("q", q);
+    if (limit) url.searchParams.set("limit", limit);
+
+    const res = await fetch(url.toString(), {
+      headers: auth.headers,
+      cache: "no-store",
+    });
+    const raw = await res.text();
+    const response = new NextResponse(raw, {
+      status: res.status,
+      headers: {
+        "Content-Type": res.headers.get("content-type") || "application/json",
+        "Cache-Control": "no-store",
+      },
+    });
+    return applyAuthResponseCookies(response, auth.response);
+  } catch (error) {
+    return NextResponse.json(
+      { error: "Failed to fetch ops users", detail: String(error) },
+      { status: 500 },
+    );
+  }
+}

frontend/app/api/payments/config/route.ts

@@ -0,0 +1,42 @@
+import { NextRequest, NextResponse } from "next/server";
+import {
+  applyAuthResponseCookies,
+  buildBackendRequestHeaders,
+} from "@/lib/backend-auth";
+
+const API_BASE = process.env.POLYWEATHER_API_BASE_URL;
+
+export async function GET(req: NextRequest) {
+  if (!API_BASE) {
+    return NextResponse.json(
+      { error: "POLYWEATHER_API_BASE_URL is not configured" },
+      { status: 500 },
+    );
+  }
+  try {
+    const auth = await buildBackendRequestHeaders(req);
+    const res = await fetch(`${API_BASE}/api/payments/config`, {
+      headers: auth.headers,
+      cache: "no-store",
+    });
+    if (!res.ok) {
+      const raw = await res.text();
+      const response = NextResponse.json(
+        { error: `Backend returned ${res.status}`, detail: raw.slice(0, 350) },
+        { status: res.status },
+      );
+      return applyAuthResponseCookies(response, auth.response);
+    }
+    const data = await res.json();
+    const response = NextResponse.json(data, {
+      headers: { "Cache-Control": "no-store" },
+    });
+    return applyAuthResponseCookies(response, auth.response);
+  } catch (error) {
+    return NextResponse.json(
+      { error: "Failed to fetch payment config", detail: String(error) },
+      { status: 500 },
+    );
+  }
+}
+

frontend/app/api/payments/intents/[intentId]/confirm/route.ts

@@ -0,0 +1,51 @@
+import { NextRequest, NextResponse } from "next/server";
+import {
+  applyAuthResponseCookies,
+  buildBackendRequestHeaders,
+} from "@/lib/backend-auth";
+
+const API_BASE = process.env.POLYWEATHER_API_BASE_URL;
+
+export async function POST(
+  req: NextRequest,
+  context: { params: Promise<{ intentId: string }> },
+) {
+  if (!API_BASE) {
+    return NextResponse.json(
+      { error: "POLYWEATHER_API_BASE_URL is not configured" },
+      { status: 500 },
+    );
+  }
+  const { intentId } = await context.params;
+  try {
+    const body = await req.json();
+    const auth = await buildBackendRequestHeaders(req);
+    const proxiedHeaders = new Headers(auth.headers);
+    proxiedHeaders.set("Content-Type", "application/json");
+    const res = await fetch(
+      `${API_BASE}/api/payments/intents/${encodeURIComponent(intentId)}/confirm`,
+      {
+        method: "POST",
+        headers: proxiedHeaders,
+        body: JSON.stringify(body ?? {}),
+        cache: "no-store",
+      },
+    );
+    if (!res.ok) {
+      const raw = await res.text();
+      const response = NextResponse.json(
+        { error: `Backend returned ${res.status}`, detail: raw.slice(0, 350) },
+        { status: res.status },
+      );
+      return applyAuthResponseCookies(response, auth.response);
+    }
+    const data = await res.json();
+    const response = NextResponse.json(data);
+    return applyAuthResponseCookies(response, auth.response);
+  } catch (error) {
+    return NextResponse.json(
+      { error: "Failed to confirm payment tx", detail: String(error) },
+      { status: 500 },
+    );
+  }
+}

frontend/app/api/payments/intents/[intentId]/route.ts

@@ -0,0 +1,47 @@
+import { NextRequest, NextResponse } from "next/server";
+import {
+  applyAuthResponseCookies,
+  buildBackendRequestHeaders,
+} from "@/lib/backend-auth";
+
+const API_BASE = process.env.POLYWEATHER_API_BASE_URL;
+
+export async function GET(
+  req: NextRequest,
+  context: { params: Promise<{ intentId: string }> },
+) {
+  if (!API_BASE) {
+    return NextResponse.json(
+      { error: "POLYWEATHER_API_BASE_URL is not configured" },
+      { status: 500 },
+    );
+  }
+  const { intentId } = await context.params;
+  try {
+    const auth = await buildBackendRequestHeaders(req);
+    const res = await fetch(
+      `${API_BASE}/api/payments/intents/${encodeURIComponent(intentId)}`,
+      {
+        method: "GET",
+        headers: auth.headers,
+        cache: "no-store",
+      },
+    );
+    if (!res.ok) {
+      const raw = await res.text();
+      const response = NextResponse.json(
+        { error: `Backend returned ${res.status}`, detail: raw.slice(0, 350) },
+        { status: res.status },
+      );
+      return applyAuthResponseCookies(response, auth.response);
+    }
+    const data = await res.json();
+    const response = NextResponse.json(data);
+    return applyAuthResponseCookies(response, auth.response);
+  } catch (error) {
+    return NextResponse.json(
+      { error: "Failed to fetch payment intent", detail: String(error) },
+      { status: 500 },
+    );
+  }
+}

frontend/app/api/payments/intents/[intentId]/submit/route.ts

@@ -0,0 +1,51 @@
+import { NextRequest, NextResponse } from "next/server";
+import {
+  applyAuthResponseCookies,
+  buildBackendRequestHeaders,
+} from "@/lib/backend-auth";
+
+const API_BASE = process.env.POLYWEATHER_API_BASE_URL;
+
+export async function POST(
+  req: NextRequest,
+  context: { params: Promise<{ intentId: string }> },
+) {
+  if (!API_BASE) {
+    return NextResponse.json(
+      { error: "POLYWEATHER_API_BASE_URL is not configured" },
+      { status: 500 },
+    );
+  }
+  const { intentId } = await context.params;
+  try {
+    const body = await req.json();
+    const auth = await buildBackendRequestHeaders(req);
+    const proxiedHeaders = new Headers(auth.headers);
+    proxiedHeaders.set("Content-Type", "application/json");
+    const res = await fetch(
+      `${API_BASE}/api/payments/intents/${encodeURIComponent(intentId)}/submit`,
+      {
+        method: "POST",
+        headers: proxiedHeaders,
+        body: JSON.stringify(body ?? {}),
+        cache: "no-store",
+      },
+    );
+    if (!res.ok) {
+      const raw = await res.text();
+      const response = NextResponse.json(
+        { error: `Backend returned ${res.status}`, detail: raw.slice(0, 350) },
+        { status: res.status },
+      );
+      return applyAuthResponseCookies(response, auth.response);
+    }
+    const data = await res.json();
+    const response = NextResponse.json(data);
+    return applyAuthResponseCookies(response, auth.response);
+  } catch (error) {
+    return NextResponse.json(
+      { error: "Failed to submit payment tx", detail: String(error) },
+      { status: 500 },
+    );
+  }
+}

frontend/app/api/payments/intents/route.ts

@@ -0,0 +1,60 @@
+import { NextRequest, NextResponse } from "next/server";
+import {
+  applyAuthResponseCookies,
+  buildBackendRequestHeaders,
+} from "@/lib/backend-auth";
+import { isPaymentHostAllowed } from "@/lib/payment-host";
+
+const API_BASE = process.env.POLYWEATHER_API_BASE_URL;
+
+export async function POST(req: NextRequest) {
+  if (!API_BASE) {
+    return NextResponse.json(
+      { error: "POLYWEATHER_API_BASE_URL is not configured" },
+      { status: 500 },
+    );
+  }
+  const requestHost =
+    req.headers.get("x-forwarded-host") ||
+    req.headers.get("host") ||
+    req.nextUrl.hostname;
+  if (!isPaymentHostAllowed(requestHost)) {
+    return NextResponse.json(
+      {
+        error:
+          "Payments are disabled on this host. Please return to the main production site and retry.",
+        host: requestHost,
+      },
+      { status: 409 },
+    );
+  }
+  try {
+    const body = await req.json();
+    const auth = await buildBackendRequestHeaders(req);
+    const proxiedHeaders = new Headers(auth.headers);
+    proxiedHeaders.set("Content-Type", "application/json");
+    const res = await fetch(`${API_BASE}/api/payments/intents`, {
+      method: "POST",
+      headers: proxiedHeaders,
+      body: JSON.stringify(body ?? {}),
+      cache: "no-store",
+    });
+    if (!res.ok) {
+      const raw = await res.text();
+      const response = NextResponse.json(
+        { error: `Backend returned ${res.status}`, detail: raw.slice(0, 350) },
+        { status: res.status },
+      );
+      return applyAuthResponseCookies(response, auth.response);
+    }
+    const data = await res.json();
+    const response = NextResponse.json(data);
+    return applyAuthResponseCookies(response, auth.response);
+  } catch (error) {
+    return NextResponse.json(
+      { error: "Failed to create payment intent", detail: String(error) },
+      { status: 500 },
+    );
+  }
+}
+

frontend/app/api/payments/reconcile-latest/route.ts

@@ -0,0 +1,39 @@
+import { NextRequest, NextResponse } from "next/server";
+import {
+  applyAuthResponseCookies,
+  buildBackendRequestHeaders,
+} from "@/lib/backend-auth";
+
+const API_BASE = process.env.POLYWEATHER_API_BASE_URL;
+
+export async function POST(req: NextRequest) {
+  if (!API_BASE) {
+    return NextResponse.json(
+      { error: "POLYWEATHER_API_BASE_URL is not configured" },
+      { status: 500 },
+    );
+  }
+
+  try {
+    const auth = await buildBackendRequestHeaders(req);
+    const res = await fetch(`${API_BASE}/api/payments/reconcile-latest`, {
+      method: "POST",
+      headers: auth.headers,
+      cache: "no-store",
+    });
+    const raw = await res.text();
+    const response = new NextResponse(raw, {
+      status: res.status,
+      headers: {
+        "content-type":
+          res.headers.get("content-type") || "application/json; charset=utf-8",
+      },
+    });
+    return applyAuthResponseCookies(response, auth.response);
+  } catch (error) {
+    return NextResponse.json(
+      { error: "Failed to reconcile latest payment", detail: String(error) },
+      { status: 500 },
+    );
+  }
+}

frontend/app/api/payments/runtime/route.ts

@@ -0,0 +1,43 @@
+import { NextRequest, NextResponse } from "next/server";
+import {
+  applyAuthResponseCookies,
+  buildBackendRequestHeaders,
+} from "@/lib/backend-auth";
+
+const API_BASE = process.env.POLYWEATHER_API_BASE_URL;
+
+export async function GET(req: NextRequest) {
+  if (!API_BASE) {
+    return NextResponse.json(
+      { error: "POLYWEATHER_API_BASE_URL is not configured" },
+      { status: 500 },
+    );
+  }
+
+  try {
+    const auth = await buildBackendRequestHeaders(req);
+    const res = await fetch(`${API_BASE}/api/payments/runtime`, {
+      headers: auth.headers,
+      cache: "no-store",
+    });
+    if (!res.ok) {
+      const raw = await res.text();
+      const response = NextResponse.json(
+        { error: `Backend returned ${res.status}`, detail: raw.slice(0, 500) },
+        { status: res.status },
+      );
+      return applyAuthResponseCookies(response, auth.response);
+    }
+
+    const data = await res.json();
+    const response = NextResponse.json(data, {
+      headers: { "Cache-Control": "no-store" },
+    });
+    return applyAuthResponseCookies(response, auth.response);
+  } catch (error) {
+    return NextResponse.json(
+      { error: "Failed to fetch payment runtime", detail: String(error) },
+      { status: 500 },
+    );
+  }
+}

frontend/app/api/payments/wallets/challenge/route.ts

@@ -0,0 +1,55 @@
+import { NextRequest, NextResponse } from "next/server";
+import {
+  applyAuthResponseCookies,
+  buildBackendRequestHeaders,
+} from "@/lib/backend-auth";
+
+const API_BASE = process.env.POLYWEATHER_API_BASE_URL;
+
+export async function POST(req: NextRequest) {
+  if (!API_BASE) {
+    return NextResponse.json(
+      { error: "POLYWEATHER_API_BASE_URL is not configured" },
+      { status: 500 },
+    );
+  }
+  try {
+    const body = await req.json();
+    const auth = await buildBackendRequestHeaders(req);
+    const proxiedHeaders = new Headers(auth.headers);
+    proxiedHeaders.set("Content-Type", "application/json");
+    const res = await fetch(`${API_BASE}/api/payments/wallets/challenge`, {
+      method: "POST",
+      headers: proxiedHeaders,
+      body: JSON.stringify(body ?? {}),
+      cache: "no-store",
+    });
+    if (!res.ok) {
+      const raw = await res.text();
+      const response = NextResponse.json(
+        {
+          error: `Backend returned ${res.status}`,
+          detail: raw.slice(0, 350),
+          proxy_debug: {
+            has_authorization: proxiedHeaders.has("authorization"),
+            has_entitlement: proxiedHeaders.has("x-polyweather-entitlement"),
+            has_forwarded_user_id: proxiedHeaders.has(
+              "x-polyweather-auth-user-id",
+            ),
+            has_forwarded_email: proxiedHeaders.has("x-polyweather-auth-email"),
+          },
+        },
+        { status: res.status },
+      );
+      return applyAuthResponseCookies(response, auth.response);
+    }
+    const data = await res.json();
+    const response = NextResponse.json(data);
+    return applyAuthResponseCookies(response, auth.response);
+  } catch (error) {
+    return NextResponse.json(
+      { error: "Failed to create wallet challenge", detail: String(error) },
+      { status: 500 },
+    );
+  }
+}

frontend/app/api/payments/wallets/route.ts

@@ -0,0 +1,107 @@
+import { NextRequest, NextResponse } from "next/server";
+import {
+  applyAuthResponseCookies,
+  buildBackendRequestHeaders,
+} from "@/lib/backend-auth";
+
+const API_BASE = process.env.POLYWEATHER_API_BASE_URL;
+
+export async function GET(req: NextRequest) {
+  if (!API_BASE) {
+    return NextResponse.json(
+      { error: "POLYWEATHER_API_BASE_URL is not configured" },
+      { status: 500 },
+    );
+  }
+  try {
+    const auth = await buildBackendRequestHeaders(req);
+    const res = await fetch(`${API_BASE}/api/payments/wallets`, {
+      headers: auth.headers,
+      cache: "no-store",
+    });
+    if (!res.ok) {
+      const raw = await res.text();
+      const response = NextResponse.json(
+        { error: `Backend returned ${res.status}`, detail: raw.slice(0, 350) },
+        { status: res.status },
+      );
+      return applyAuthResponseCookies(response, auth.response);
+    }
+    const data = await res.json();
+    const response = NextResponse.json(data, {
+      headers: { "Cache-Control": "no-store" },
+    });
+    return applyAuthResponseCookies(response, auth.response);
+  } catch (error) {
+    return NextResponse.json(
+      { error: "Failed to fetch wallets", detail: String(error) },
+      { status: 500 },
+    );
+  }
+}
+
+export async function DELETE(req: NextRequest) {
+  if (!API_BASE) {
+    return NextResponse.json(
+      { error: "POLYWEATHER_API_BASE_URL is not configured" },
+      { status: 500 },
+    );
+  }
+  let payload: Record<string, unknown> = {};
+  try {
+    payload = (await req.json()) as Record<string, unknown>;
+  } catch {
+    payload = {};
+  }
+  try {
+    const auth = await buildBackendRequestHeaders(req);
+    const proxiedHeaders = new Headers(auth.headers);
+    proxiedHeaders.set("Content-Type", "application/json");
+    const res = await fetch(`${API_BASE}/api/payments/wallets`, {
+      method: "DELETE",
+      headers: proxiedHeaders,
+      body: JSON.stringify(payload),
+      cache: "no-store",
+    });
+    const raw = await res.text();
+    if (!res.ok) {
+      const response = NextResponse.json(
+        {
+          error: `Backend returned ${res.status}`,
+          detail: raw.slice(0, 350),
+          proxy_debug: {
+            incoming_has_authorization: Boolean(
+              String(req.headers.get("authorization") || "").trim(),
+            ),
+            has_authorization: proxiedHeaders.has("authorization"),
+            has_entitlement: proxiedHeaders.has("x-polyweather-entitlement"),
+            has_forwarded_user_id: proxiedHeaders.has(
+              "x-polyweather-auth-user-id",
+            ),
+            has_forwarded_email: proxiedHeaders.has("x-polyweather-auth-email"),
+          },
+        },
+        { status: res.status },
+      );
+      return applyAuthResponseCookies(response, auth.response);
+    }
+    let data: unknown = { ok: true };
+    if (raw) {
+      try {
+        data = JSON.parse(raw);
+      } catch {
+        data = { ok: true, raw };
+      }
+    }
+    const response = NextResponse.json(data, {
+      headers: { "Cache-Control": "no-store" },
+    });
+    return applyAuthResponseCookies(response, auth.response);
+  } catch (error) {
+    return NextResponse.json(
+      { error: "Failed to unbind wallet", detail: String(error) },
+      { status: 500 },
+    );
+  }
+}
+

frontend/app/api/payments/wallets/verify/route.ts

@@ -0,0 +1,56 @@
+import { NextRequest, NextResponse } from "next/server";
+import {
+  applyAuthResponseCookies,
+  buildBackendRequestHeaders,
+} from "@/lib/backend-auth";
+
+const API_BASE = process.env.POLYWEATHER_API_BASE_URL;
+
+export async function POST(req: NextRequest) {
+  if (!API_BASE) {
+    return NextResponse.json(
+      { error: "POLYWEATHER_API_BASE_URL is not configured" },
+      { status: 500 },
+    );
+  }
+  try {
+    const body = await req.json();
+    const auth = await buildBackendRequestHeaders(req);
+    const proxiedHeaders = new Headers(auth.headers);
+    proxiedHeaders.set("Content-Type", "application/json");
+    const res = await fetch(`${API_BASE}/api/payments/wallets/verify`, {
+      method: "POST",
+      headers: proxiedHeaders,
+      body: JSON.stringify(body ?? {}),
+      cache: "no-store",
+    });
+    if (!res.ok) {
+      const raw = await res.text();
+      const response = NextResponse.json(
+        {
+          error: `Backend returned ${res.status}`,
+          detail: raw.slice(0, 350),
+          proxy_debug: {
+            has_authorization: proxiedHeaders.has("authorization"),
+            has_entitlement: proxiedHeaders.has("x-polyweather-entitlement"),
+            has_forwarded_user_id: proxiedHeaders.has(
+              "x-polyweather-auth-user-id",
+            ),
+            has_forwarded_email: proxiedHeaders.has("x-polyweather-auth-email"),
+          },
+        },
+        { status: res.status },
+      );
+      return applyAuthResponseCookies(response, auth.response);
+    }
+    const data = await res.json();
+    const response = NextResponse.json(data);
+    return applyAuthResponseCookies(response, auth.response);
+  } catch (error) {
+    return NextResponse.json(
+      { error: "Failed to verify wallet binding", detail: String(error) },
+      { status: 500 },
+    );
+  }
+}
+

frontend/app/api/system/status/route.ts

@@ -0,0 +1,43 @@
+import { NextRequest, NextResponse } from "next/server";
+import {
+  applyAuthResponseCookies,
+  buildBackendRequestHeaders,
+} from "@/lib/backend-auth";
+
+const API_BASE = process.env.POLYWEATHER_API_BASE_URL;
+
+export async function GET(req: NextRequest) {
+  if (!API_BASE) {
+    return NextResponse.json(
+      { error: "POLYWEATHER_API_BASE_URL is not configured" },
+      { status: 500 },
+    );
+  }
+
+  try {
+    const auth = await buildBackendRequestHeaders(req);
+    const res = await fetch(`${API_BASE}/api/system/status`, {
+      headers: auth.headers,
+      cache: "no-store",
+    });
+    if (!res.ok) {
+      const raw = await res.text();
+      const response = NextResponse.json(
+        { error: `Backend returned ${res.status}`, detail: raw.slice(0, 500) },
+        { status: res.status },
+      );
+      return applyAuthResponseCookies(response, auth.response);
+    }
+
+    const data = await res.json();
+    const response = NextResponse.json(data, {
+      headers: { "Cache-Control": "no-store" },
+    });
+    return applyAuthResponseCookies(response, auth.response);
+  } catch (error) {
+    return NextResponse.json(
+      { error: "Failed to fetch system status", detail: String(error) },
+      { status: 500 },
+    );
+  }
+}

frontend/app/api/vitals/route.ts

@@ -0,0 +1,90 @@
+import { NextResponse } from "next/server";
+import {
+  getVitalsSummary,
+  normalizeMetricName,
+  recordVitalsSample,
+} from "@/lib/vitals-store";
+
+const WEB_VITALS_ENABLED =
+  process.env.NEXT_PUBLIC_POLYWEATHER_WEB_VITALS === "true";
+
+type VitalsPayload = {
+  id?: string;
+  metric?: string;
+  navigationType?: string;
+  pathname?: string;
+  rating?: string;
+  value?: number;
+};
+
+export async function POST(request: Request) {
+  if (!WEB_VITALS_ENABLED) {
+    return new NextResponse(null, { status: 204 });
+  }
+
+  try {
+    const payload = (await request.json()) as VitalsPayload;
+    const metric = normalizeMetricName(payload.metric);
+    if (!metric) {
+      return NextResponse.json({ ok: false, error: "metric is required" }, { status: 400 });
+    }
+
+    const pathname = String(payload.pathname || "/");
+    const rating = String(payload.rating || "unknown");
+    const value = Number(payload.value);
+    const navigationType = String(payload.navigationType || "unknown");
+    const id = String(payload.id || "");
+    const ts = Date.now();
+
+    if (!Number.isFinite(value)) {
+      return NextResponse.json({ ok: false, error: "value must be finite" }, { status: 400 });
+    }
+
+    recordVitalsSample({
+      id,
+      metric,
+      navigationType,
+      pathname,
+      rating,
+      timestamp: ts,
+      value,
+    });
+
+    // Keep this lightweight: log for now, can be wired to a persistent sink later.
+    console.info(
+      `[vitals] metric=${metric} path=${pathname} value=${Number.isFinite(value) ? value : "NaN"} rating=${rating} nav=${navigationType} id=${id}`,
+    );
+
+    return NextResponse.json({ ok: true });
+  } catch (error) {
+    console.warn("[vitals] failed to parse payload", error);
+    return NextResponse.json({ ok: false }, { status: 400 });
+  }
+}
+
+export async function GET(request: Request) {
+  if (!WEB_VITALS_ENABLED) {
+    return NextResponse.json({
+      ok: true,
+      disabled: true,
+      generatedAt: Date.now(),
+      sampleCount: 0,
+      routes: {},
+    });
+  }
+
+  const { searchParams } = new URL(request.url);
+  const targetRoute = String(searchParams.get("route") || "").trim();
+  const summary = getVitalsSummary();
+
+  if (!targetRoute) {
+    return NextResponse.json({ ok: true, ...summary });
+  }
+
+  return NextResponse.json({
+    ok: true,
+    generatedAt: summary.generatedAt,
+    route: targetRoute,
+    metrics: summary.routes[targetRoute] || {},
+  });
+}

frontend/app/auth/callback/route.ts

@@ -0,0 +1,32 @@
+import { NextRequest, NextResponse } from "next/server";
+import { createSupabaseRouteClient, hasSupabaseServerEnv } from "@/lib/supabase/server";
+
+function normalizeNextPath(input: string | null) {
+  const fallback = "/";
+  const raw = String(input || "").trim();
+  if (!raw) return fallback;
+  if (!raw.startsWith("/")) return fallback;
+  if (raw.startsWith("//")) return fallback;
+  return raw;
+}
+
+export async function GET(request: NextRequest) {
+  const nextPath = normalizeNextPath(request.nextUrl.searchParams.get("next"));
+  const redirectUrl = request.nextUrl.clone();
+  redirectUrl.pathname = nextPath;
+  redirectUrl.search = "";
+
+  if (!hasSupabaseServerEnv()) {
+    return NextResponse.redirect(redirectUrl);
+  }
+
+  const response = NextResponse.redirect(redirectUrl);
+  const supabase = createSupabaseRouteClient(request, response);
+  const code = request.nextUrl.searchParams.get("code");
+  if (code) {
+    await supabase.auth.exchangeCodeForSession(code);
+  }
+
+  return response;
+}
+

frontend/app/auth/login/page.tsx

@@ -0,0 +1,25 @@
+import { LoginClient } from "@/components/auth/LoginClient";
+import { I18nProvider } from "@/hooks/useI18n";
+
+type PageProps = {
+  searchParams?: Promise<{ next?: string }>;
+};
+
+function normalizeNextPath(input: string | undefined) {
+  const fallback = "/";
+  const raw = String(input || "").trim();
+  if (!raw) return fallback;
+  if (!raw.startsWith("/")) return fallback;
+  if (raw.startsWith("//")) return fallback;
+  return raw;
+}
+
+export default async function LoginPage({ searchParams }: PageProps) {
+  const params = (await searchParams) || {};
+  const nextPath = normalizeNextPath(params.next);
+  return (
+    <I18nProvider>
+      <LoginClient nextPath={nextPath} />
+    </I18nProvider>
+  );
+}

frontend/app/docs/[...slug]/page.tsx

@@ -0,0 +1,27 @@
+import { notFound } from "next/navigation";
+import { DocsScreen } from "@/components/docs/DocsScreen";
+import { DOCS_PAGES, getDocsPage } from "@/content/docs/docs";
+
+export function generateStaticParams() {
+  return DOCS_PAGES.map((page) => ({ slug: [page.slug] }));
+}
+
+export default async function DocsDetailPage({
+  params,
+}: {
+  params: Promise<{ slug?: string[] }>;
+}) {
+  const resolvedParams = await params;
+  if ((resolvedParams.slug?.length || 0) > 1) {
+    notFound();
+  }
+
+  const slug = resolvedParams.slug?.[0] || "intro";
+  const page = getDocsPage(slug);
+
+  if (!page) {
+    notFound();
+  }
+
+  return <DocsScreen page={page} />;
+}

frontend/app/docs/layout.tsx

@@ -0,0 +1,5 @@
+import { I18nProvider } from "@/hooks/useI18n";
+
+export default function DocsLayout({ children }: { children: React.ReactNode }) {
+  return <I18nProvider>{children}</I18nProvider>;
+}