版本号更新至 1.7.1

- 新增 NEXT_PUBLIC_SITE_URL 支持及 site-url.ts 工具模块
- 修复 OAuth 回调域名：import.meta.env 统一读取站点 URL
- 支付 API 路由新增收款地址校验
- 后端支付服务更新
- middleware 清理
- 新增 paymentSecurity 测试

.env.example

@@ -13,17 +13,12 @@ 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
+UVICORN_WORKERS=1
 # 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
 # Backend CORS allowlist. Add your Vercel production/preview domains when
 # NEXT_PUBLIC_POLYWEATHER_API_BASE_URL points browsers directly at this backend.
 WEB_CORS_ORIGINS=http://localhost:3000,http://127.0.0.1:3000,https://polyweather-pro.vercel.app
@@ -34,21 +29,35 @@ WEB_CORS_ORIGINS=http://localhost:3000,http://127.0.0.1:3000,https://polyweather
 TELEGRAM_BOT_TOKEN=
 TELEGRAM_CHAT_ID=
 TELEGRAM_CHAT_IDS=
+POLYWEATHER_TELEGRAM_GROUP_ID=
+# Optional: restrict message-points accrual to these chat IDs.
+# Example: POLYWEATHER_BOT_POINTS_CHAT_IDS=-1003965137823
+POLYWEATHER_BOT_POINTS_CHAT_IDS=
+POLYWEATHER_GROUP_MEMBER_PRICE_USDC=5
+POLYWEATHER_PUBLIC_PRICE_USDC=10
 TELEGRAM_QUERY_TOPIC_CHAT_ID=
 TELEGRAM_QUERY_TOPIC_ID=
 TELEGRAM_QUERY_TOPIC_MAP=
 POLYWEATHER_BOT_GROUP_INVITE_URL=
+POLYWEATHER_APP_URL=https://polyweather-pro.vercel.app
+# High-frequency airport push loop. Keep this at 1 on shared 1CPU VPS.
+TELEGRAM_AIRPORT_PUSH_ENABLED=true
+TELEGRAM_AIRPORT_PUSH_INTERVAL_SEC=60
+TELEGRAM_AIRPORT_PUSH_MAX_WORKERS=1
 
 ########################################
 # 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_CACHE_TTL_SEC=21600
+OPEN_METEO_ENSEMBLE_CACHE_TTL_SEC=21600
+OPEN_METEO_MULTI_MODEL_CACHE_TTL_SEC=21600
 OPEN_METEO_MULTI_MODEL_CACHE_VERSION=v2
-OPEN_METEO_RATE_LIMIT_COOLDOWN_SEC=900
+OPEN_METEO_RATE_LIMIT_COOLDOWN_SEC=3600
 OPEN_METEO_RATE_CACHE_TTL_SEC=3600
-OPEN_METEO_MIN_CALL_INTERVAL_SEC=1
+OPEN_METEO_MIN_CALL_INTERVAL_SEC=5
+POLYWEATHER_SCAN_TERMINAL_MAX_WORKERS=1
+POLYWEATHER_SCAN_TERMINAL_PAYLOAD_TTL_SEC=600
+POLYWEATHER_SCAN_TERMINAL_BUILD_TIMEOUT_SEC=45
 POLYWEATHER_HTTP_TIMEOUT_SEC=8
 POLYWEATHER_HTTP_RETRY_COUNT=0
 POLYWEATHER_HTTP_RETRY_BACKOFF_SEC=0.2
@@ -57,26 +66,19 @@ POLYWEATHER_METAR_TIMEOUT_SEC=4
 POLYWEATHER_METAR_CLUSTER_TIMEOUT_SEC=3.5
 METAR_CACHE_TTL_SEC=600
 JMA_AMEDAS_CACHE_TTL_SEC=120
-METEOBLUE_CACHE_TTL_SEC=7200
-# Probability engine modes:
-# - legacy: production-safe primary path.
-# - emos_shadow: user-facing probability stays legacy, EMOS is generated for comparison.
-# - emos_primary: only after offline evaluation passes and manual rollout is approved.
-POLYWEATHER_PROBABILITY_ENGINE=legacy
-POLYWEATHER_EMOS_AUTO_MIN_SAMPLES=50
-POLYWEATHER_EMOS_AUTO_MAX_DELTA_CRPS=0
-POLYWEATHER_EMOS_AUTO_MAX_DELTA_MAE=0.05
-POLYWEATHER_EMOS_AUTO_MIN_DELTA_BUCKET_HIT_RATE=-0.05
-# Optional: cap recent probability snapshots used by EMOS retraining.
-# Recommended on VPS: do not train there; pull the SQLite DB to a local machine.
-# POLYWEATHER_EMOS_TRAINING_SNAPSHOT_LIMIT=20000
-# Optional: set this to a writable runtime path if you manually deploy a
-# locally trained EMOS calibration file.
-# POLYWEATHER_PROBABILITY_CALIBRATION_FILE=/var/lib/polyweather/probability_calibration/default.json
-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
+
+# ── Country-specific data source URLs ──
+# These are kept in .env to avoid exposing competitive data-source discovery
+# work on the public GitHub repository. Leave empty to use built-in defaults.
+# AMSC_AWOS_BASE_URL=https://www.amsc.net.cn/gateway/api/saas/rest/amc/AwosController/getWindPlate
+# KMA_BASE_URL=https://www.weather.go.kr
+# AMOS_BASE_URL=https://global.amo.go.kr/amosobsnew/AmosRealTimeImage.do
+# JMA_AMEDAS_BASE_URL=https://www.jma.go.jp
+# MGM_BASE_URL=https://servis.mgm.gov.tr/web
+# MGM_ORIGIN_URL=https://www.mgm.gov.tr
+# FMI_BASE_URL=https://opendata.fmi.fi/wfs
+# HKO_BASE_URL=https://data.weather.gov.hk/weatherAPI/hko_data/regional-weather
+# SINGAPORE_MSS_BASE_URL=https://api.data.gov.sg/v1/environment/air-temperature
 
 ########################################
 # 4) Auth / entitlement
@@ -92,16 +94,12 @@ SUPABASE_HTTP_TIMEOUT_SEC=8
 SUPABASE_AUTH_CACHE_TTL_SEC=30
 SUPABASE_SUB_CACHE_TTL_SEC=60
 POLYWEATHER_BACKEND_ENTITLEMENT_TOKEN=
+POLYWEATHER_SIGNUP_TRIAL_ENABLED=false
+POLYWEATHER_TELEGRAM_JOIN_INELIGIBLE_ACTION=decline
 
 ########################################
-# 5) Alerts / operations
+# 5) 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,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=
 
 ########################################
@@ -116,17 +114,21 @@ NEXT_PUBLIC_POLYWEATHER_DISABLE_EAGER_SUMMARIES=false
 # bypass Vercel Functions / Fluid Compute instead of going through Next.js API proxies.
 # Example: NEXT_PUBLIC_POLYWEATHER_API_BASE_URL=https://api.example.com
 NEXT_PUBLIC_POLYWEATHER_API_BASE_URL=
+# Set to "false" to disable app analytics event tracking (conversion funnel etc.)
+# Default: enabled. Only set this if you need to opt out.
+NEXT_PUBLIC_POLYWEATHER_APP_ANALYTICS=true
 
 ########################################
-# 7) Optional modules
+# 7) Admin / Ops
 ########################################
+# Comma-separated admin email list for /ops dashboard access
+POLYWEATHER_OPS_ADMIN_EMAILS=
+# KNMI 10-minute observation data (Amsterdam)
+KNMI_API_KEY=
 
-# 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
+########################################
+# 8) Optional modules
+########################################
 
 # Optional OpenAI-compatible market scan review for Pro users
 # Temporary default provider: MiMo via https://token-plan-cn.xiaomimimo.com/v1.
@@ -149,7 +151,6 @@ POLYWEATHER_SCAN_AI_MAX_ROWS=40
 POLYWEATHER_SCAN_AI_MAX_TOKENS=3200
 POLYWEATHER_SCAN_CITY_AI_MAX_TOKENS=900
 POLYWEATHER_SCAN_AI_PROXY_TIMEOUT_MS=55000
-POLYWEATHER_PREWARM_CITIES=ankara,istanbul,shanghai,beijing,shenzhen,guangzhou,wuhan,chengdu,chongqing,hong kong,taipei,singapore,tokyo,seoul,busan,london,paris,madrid
 POLYWEATHER_CITY_SUMMARY_CACHE_TTL_SEC=1800
 POLYWEATHER_CITY_PANEL_CACHE_TTL_SEC=1800
 POLYWEATHER_CITY_NEARBY_CACHE_TTL_SEC=1800
@@ -180,7 +181,8 @@ 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_DIRECT_RECEIVER_ADDRESS=
+POLYWEATHER_PAYMENT_TOKEN_ADDRESS=0x3c499c542cef5e3811e1192ce70d8cc03d5c3359
 POLYWEATHER_PAYMENT_TOKEN_DECIMALS=6
 POLYWEATHER_PAYMENT_ACCEPTED_TOKENS_JSON=
 POLYWEATHER_PAYMENT_CONFIRMATIONS=2
@@ -232,36 +234,9 @@ 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=
+POLYWEATHER_TELEGRAM_JOIN_INELIGIBLE_ACTION=decline

.env.secrets.example

@@ -6,6 +6,9 @@
 # Telegram
 ########################################
 TELEGRAM_BOT_TOKEN=
+POLYWEATHER_TELEGRAM_GROUP_ID=
+POLYWEATHER_GROUP_MEMBER_PRICE_USDC=10
+POLYWEATHER_PUBLIC_PRICE_USDC=10
 
 ########################################
 # Supabase
@@ -32,6 +35,7 @@ METEOBLUE_API_KEY=
 ########################################
 NEXT_PUBLIC_WALLETCONNECT_PROJECT_ID=
 POLYWEATHER_PAYMENT_RECEIVER_CONTRACT=
+POLYWEATHER_PAYMENT_DIRECT_RECEIVER_ADDRESS=
 POLYWEATHER_PAYMENT_ACCEPTED_TOKENS_JSON=
 POLYWEATHER_PAYMENT_PLAN_CATALOG_JSON=
 

.github/workflows/ci.yml

@@ -62,3 +62,21 @@ jobs:
 
       - name: Build Docker image
         run: docker build -t polyweather-ci .
+
+  deploy:
+    needs: [python-quality, frontend-quality, docker-build]
+    if: github.event_name == 'push' && github.ref == 'refs/heads/main'
+    runs-on: ubuntu-latest
+    steps:
+      - name: Deploy to VPS
+        run: |
+          mkdir -p ~/.ssh
+          echo "${{ secrets.VPS_SSH_KEY }}" > ~/.ssh/id_rsa
+          chmod 600 ~/.ssh/id_rsa
+          ssh -o StrictHostKeyChecking=accept-new ${{ secrets.VPS_USER }}@${{ secrets.VPS_HOST }} "
+            cd /root/PolyWeather
+            git fetch origin main && git reset --hard origin/main
+            docker compose up -d --build
+            sleep 10
+            curl -s http://localhost:8000/healthz
+          "

.gitignore

@@ -1,6 +1,9 @@
 # Secrets
 .env
 
+# Scratch / temp scripts
+scratch/
+
 # Data and Logs
 data/*.db
 data/*.db-*
@@ -63,3 +66,9 @@ frontend/.next-start.log
 .codex/skills/.system/**
 !.codex/prompts/
 !.codex/prompts/**
+tmp_apikey.js
+tmp_obs.js
+tmp_rctp.html
+playwright-home-check.png
+.codex-backend-*.log
+frontend-next-*.log

CHANGELOG.md

@@ -1,6 +1,41 @@
 # Changelog
 
-## 1.6.0 - 2026-05-10
+## 1.7.0 - 2026-05-23
+
+### 新增能力
+- 市场监控面板（MonitorPanel）：22 城实时温度监控，温度分辨率链（AMOS 跑道 → airport_primary → airport_current → current），按数据源新鲜度驱动刷新
+- 中国城市天气日报：AI 生成每日天气摘要，接入 CMA weather.com.cn 预报数据，推送至 Telegram 论坛群
+- 后台管理系统重写：从 1694 行单页拆分为 9 个模块（总览、会员、订阅、支付、训练、Telegram 审计、健康检查、配置、日志），含漏斗图、KPI 卡片、缓存饼图、增长趋势图
+- 跑道观测系统重构：全跑道展示、结算跑道标注、热力模型、风场分析，推送增加市场状态标签（超预期/升温中/冲顶观察/降温中）
+- 新增 6 个高频数据源：AEROWEB (Météo-France)、NCM (沙特)、IMS Lod (以色列)、AMSC AWOS (中国跑道)、MSS 1 分钟 (新加坡)、AROME HD 15 分钟 (巴黎)
+- 接入 HKO 1 分钟、流浮山 LFS 1 分钟、CWA 10 分钟 (台北松山) 实时温度
+- NOAA MADIS HFMETAR 适配新格式（netCDF stationId 替代 icaoId）+ 目录迁移适配
+- KNMI 适配新数据布局 (station,time) + 5 位 WMO 码 + S3 下载认证修复
+- 新增 GET /api/cities/model-range 端点
+- 积分转账功能：管理员手动扣除/划转用户积分
+- 支付提交前 Tx 预校验：链上验签收款地址与金额
+- CI 全流程自动化：测试通过后自动 SSH 部署到 VPS
+- 一键部署脚本：deploy.sh + deploy.ps1
+
+### 移除
+- 删除 LGBM 全部代码和模型文件，EMOS 简化为纯 legacy 高斯分桶
+- 删除 Polymarket 价格拉取与 UI 层（MarketDecisionLine）
+- 删除 Groq、Meteoblue、NMC、俄罗斯 pogodaiklimat 数据源
+- 删除预热（prewarm）系统
+- 删除市场提醒引擎（market_alert_engine）
+- 删除 Lagos、Masroor Air Base 城市
+- 移除季付/年付计划，统一月付 10 USDC
+
+### 修复与优化
+- 修复移动端城市列表搜索无数据、Leaflet flyTo NaN 崩溃
+- 修复 MacBook Safari 布局崩溃（100vw/dvh、-webkit-backdrop-filter、grid minmax 溢出）
+- 修复温度曲线图三个渲染问题：数据点过少、张力过高、canvas CSS 拉伸
+- 修复 Open-Meteo 冷却期无限循环导致多模型数据缺失
+- 修复转化漏斗数据显示 3750%（前端重复乘以 100）
+- 多模型缓存优化 + ETag 缓存 + stale-while-revalidate
+- 性能优化：Context 重渲染、LGBM 循环移除、TTL 对齐
+- 账户页 Pro 状态偶发性丢失修复
+- 机场推送重构：观测缓存分离 + 全城市覆盖 + 四路并发
 
 - 全面修复前端 UI 设计审查 15 项问题：消除工程债务、统一 token 体系、提升可维护性
 - CSS 架构：消除 !important 滥用（134→49，仅保留 Leaflet/图表所必需项）、浅色主题重构为 `html.light` 选择器体系
@@ -54,7 +89,7 @@
 - 右侧详情面板识别稀疏 detail / 单日 forecast 中间态，并显示同步占位卡，避免用户把未补齐数据误认为完整结果
 - 概率区改为“校准模型概率”：有 LGBM 时展示 LGBM 校准概率；模型共识与市场价格降级为辅助参考
 - 模型层补齐 DWD ICON、ECMWF AIFS、ECCC GEM/GDPS/RDPS/HRDPS 等开放模型说明，并明确 AIFS 不称作“AI 预报”
-- 新增 / 补齐 Manila、Karachi、Masroor Air Base 等城市说明；机场市场以 METAR / 机场主站为结算锚点，Wunderground 仅作为历史页面或参考入口
+- 新增 / 补齐 Manila、Karachi 等城市说明；机场市场以 METAR / 机场主站为结算锚点，Wunderground 仅作为历史页面或参考入口
 - 历史对账、模型栈、LGBM、监控、前端 README 与网页 `/docs` 文档同步更新到当前产品口径
 
 ## 1.5.3 - 2026-04-10

CLAUDE.md

@@ -4,15 +4,16 @@ This file provides guidance to Claude Code (claude.ai/code) when working with co
 
 ## Project Overview
 
-PolyWeather Pro — a production weather-intelligence stack for temperature settlement markets. Aggregates observations and forecasts for 52 monitored cities globally, blends multi-model highs using DEB (Dynamic Error Balancing), generates calibrated probability buckets for settlement, maps weather to Polymarket quotes for mispricing scans, and serves both a Next.js dashboard (Vercel) and a Telegram bot.
+PolyWeather Pro — a production weather-intelligence stack for temperature settlement markets. Aggregates observations and forecasts for 52 monitored cities globally, blends multi-model highs using DEB (Dynamic Error Balancing), generates calibrated probability buckets for settlement, and serves both a Next.js dashboard (Vercel) and a Telegram bot.
 
 ## Environment & Preferences (ALWAYS follow)
 
 ### Working Directory
-- All commands run from the repo root: `E:/web/PolyWeather`
+- All commands run from the repo root
+- Python virtual env: `venv\Scripts\activate` (Windows) / `source venv/bin/activate` (Linux/macOS)
 - Frontend dev server: `cd frontend && npm run dev` → http://localhost:3000
 - Backend API server: `uvicorn web.app:app --reload --host 0.0.0.0 --port 8000` → http://localhost:8000
-- When I say "start the server", assume the correct working directory is `E:/web/PolyWeather`
+- When I say "start the server", assume the working directory is the repo root
 
 ### Git Conventions
 - **Commit language: Chinese (简体中文) ONLY**
@@ -38,12 +39,15 @@ Users (Web / Telegram) → Next.js Frontend (Vercel) → FastAPI /web/app.py
                                             Payment Layer (Intent + Event + Confirm Loop)
 ```
 
-- **Backend**: FastAPI on port 8000 (`web/app.py` → `web/core.py` + `web/routes.py` + `web/analysis_service.py`)
-- **Frontend**: Next.js 15 + React 19 + TypeScript + Tailwind CSS 3 on port 3000 (dev)
+- **Backend**: FastAPI on port 8000 (`web/app.py` → `web/app_factory.py` → `web/routers/` (8 route modules: `system`, `city`, `auth`, `analytics`, `scan`, `payments`, `ops`, `routes` (legacy)) + `web/services/` (14 service modules) + `web/core.py`)
+- **Frontend**: Next.js 15 + React 19 + TypeScript + Tailwind CSS 3 + shadcn/ui (new-york style) on port 3000 (dev)
 - **Bot**: Telegram bot via `bot_listener.py` → `src/bot/`
 - **Shared analysis core** in `src/` is used by both web API and bot
 - **Scan Terminal**: Real-time city opportunity scanning (`web/scan_terminal_service.py` and `frontend/components/dashboard/scan-terminal/`)
 - **Dashboard**: Main dashboard with interactive map, city sidebar, detail panels, and probability views
+- **Market Monitor** (`MonitorPanel`): Real-time temperature monitoring board for 22 trading cities. Uses a temperature resolution chain (AMOS runway → AMOS → `airport_primary` → `airport_current` → `current`) defined in `frontend/components/dashboard/monitoring/monitor-temperature.ts`. Per-city refresh decisions driven by source-aware freshness (`source-freshness.ts`) instead of uniform `obs_age_min`. Seoul/Busan display runway surface temperature from AMOS; US cities get 5-min MADIS HFMETAR via `airport_primary`; others fall back to METAR.
+- **High-Freq Airport Pipeline**: 19 of 22 monitor cities have dedicated realtime sources (AMOS, MADIS, JMA, MGM, FMI, KNMI, AROME). Data flows: `weather_sources.py` (fetch) → `country_networks.py` (`_airport_primary_from_raw`, per-country providers) → API `airport_primary` field. Plain METAR stays in `airport_current`. Documented in `docs/AIRPORT_REALTIME_SOURCES.md`.
+- **Country Network Providers**: `country_networks.py` routes per-city to the right provider (Turkey→MGM, Korea→KMA, Japan→JMA, etc.) via `get_country_network_provider()`. Each provider controls `airport_primary_current`, `official_nearby_current`, and `official_network_status`. US cities use the default `GlobalMetarNetworkProvider` but get MADIS overrides injected via `results["madis_hfmetar_current"]`.
 
 ## Commands
 
@@ -51,9 +55,12 @@ Users (Web / Telegram) → Next.js Frontend (Vercel) → FastAPI /web/app.py
 ```bash
 cd frontend
 npm ci
-npm run dev        # Next.js dev server
-npm run build      # Production build
-npm run lint       # ESLint via next lint
+npm run dev         # Next.js dev server (runs sync-next-server-chunks.mjs first)
+npm run build       # Production build (runs sync-next-server-chunks.mjs after)
+npm run start       # Production server
+npm run lint        # ESLint via next lint
+npm run typecheck   # tsc --noEmit
+npm run test:business  # Business state tests via scripts/run-business-state-tests.mjs (also runs in CI)
 ```
 
 ### Backend (dev on port 8000)
@@ -70,17 +77,23 @@ python run.py
 
 ### Docker (production-like stack)
 ```bash
-docker compose up -d --build                    # bot + web API
-docker compose --profile workers up -d          # + prewarm worker
-docker compose --profile monitoring up -d       # + Prometheus/Grafana/Alertmanager
+docker compose up -d --build                    # bot + web API (polyweather + polyweather_web)
 ```
+The compose file defines two services: `polyweather` (bot) and `polyweather_web` (FastAPI on :8000). Prewarm worker and monitoring profiles were removed in v1.6.0.
 
 ### Python tests
 ```bash
-pytest tests/                           # all tests
-pytest tests/test_web_observability.py  # single test file
+python -m pytest tests/                           # all tests
+python -m pytest tests/test_web_observability.py  # single test file
 ```
 
+### Version bump (see RELEASE.md)
+```bash
+python scripts/bump_version.py patch   # or minor / major / 1.5.0
+python scripts/sync_version.py         # verify sync across files
+```
+`VERSION` file is the single source of truth; frontend `package.json` and docs sync from it.
+
 ### Lint & Format
 ```bash
 ruff check .        # Python lint (pycodestyle + Pyflakes, line-length 88)
@@ -98,21 +111,25 @@ curl http://127.0.0.1:8000/metrics
 
 | Directory | Purpose |
 |-----------|---------|
-| `src/data_collection/` | Weather sources (METAR, TAF, Open-Meteo, JMA, KMA, MGM, NMC, Russia stations, settlement sources), city registry (52 cities), Polymarket readonly layer |
-| `src/analysis/` | DEB algorithm, trend engine, probability calibration (EMOS/LGBM), market alert engine, settlement rounding |
-| `src/models/` | LightGBM daily-high model training and feature engineering |
-| `src/payments/` | Onchain checkout, event listener, confirm loop, contract audit |
+| `src/analysis/` | DEB algorithm, trend engine, market alert engine, settlement rounding |
+| `src/auth/` | Supabase entitlement checks, Telegram group pricing |
 | `src/bot/` | Telegram bot handlers and orchestrator |
 | `src/database/` | SQLite-based runtime state, DB manager, daily/truth/training feature repositories |
-| `web/` | FastAPI app, routes (~65K), analysis service (~130K), scan terminal service (~56K), AI scan modules |
+| `src/data_collection/` | Weather sources (METAR, TAF, Open-Meteo, JMA, KMA, MGM, NMC, Russia stations, settlement sources), city registry (52 cities), Polymarket readonly layer. Also: `madis_sources.py` (NOAA 5-min NetCDF), `amos_station_sources.py` (Korean runway sensors), `country_networks.py` (per-country provider routing + `_airport_primary_from_raw`) |
+| `src/data_mining/` | Historical data fetch utilities |
+| `src/onchain/` | Polygon wallet watcher |
+| `src/payments/` | Onchain checkout, event listener, confirm loop, contract audit |
+| `src/strategy/` | Trading strategy modules |
+| `src/trading/` | Trading execution modules |
+| `src/utils/` | Shared utilities: config loader, logging, metrics, Telegram push, chat ID helpers |
+| `web/` | FastAPI app (`app.py` → `app_factory.py`), `routers/` (8 route modules), `services/` (14 service modules), `core.py`, scan terminal modules (AI fallback, AI prompts, METAR gate, city rows, ranker, cache) |
 | `frontend/app/` | Next.js App Router pages (dashboard, account, auth, docs, ops, probabilities, scan) |
-| `frontend/components/dashboard/` | Dashboard UI components (map, sidebar, detail panel, modals, charts, scan terminal). `scan-root-styles.ts` is the CSS Module barrel, combining 22 module roots into one pre-composed className |
-| `frontend/lib/` | Shared client logic: types, API client, chart utils, i18n, dashboard utils |
+| `frontend/components/dashboard/` | Dashboard UI components (map, sidebar, detail panel, modals, charts, scan terminal). `scan-root-styles.ts` is the CSS Module barrel, combining 22 module roots into one pre-composed className. `monitoring/` subdirectory: `MonitorPanel`, `monitor-temperature.ts` (temp resolution chain), `monitor-refresh-policy.ts`. |
+| `frontend/lib/` | Shared client logic: types (`dashboard-types.ts`, including `AirportCurrentConditions`, `CityDetail`), API client, chart utils, i18n, `source-freshness.ts` (per-source freshness with `expected_next_update_at`), dashboard utils |
 | `frontend/hooks/` | React hooks: dashboard store (global state), Leaflet map, chart helper |
-| `scripts/` | Operational scripts: probability calibration training, backfills, payment reconciliation, prewarm worker |
+| `scripts/` | Operational scripts: backfills, payment reconciliation. `supabase/` subdirectory: DB schema and migration SQL. |
 | `config/` | YAML config (city list, weather settings, logging) |
 | `docs/` | Bilingual product & technical docs |
-| `monitoring/` | Prometheus/Grafana/Alertmanager configs |
 
 ## Key Technical Details
 
@@ -121,9 +138,9 @@ curl http://127.0.0.1:8000/metrics
 - **Frontend package manager**: npm
 - **State storage**: SQLite primary path (set via `POLYWEATHER_STATE_STORAGE_MODE=sqlite` + `POLYWEATHER_DB_PATH`). Legacy JSON/JSONL files are migration/fallback only.
 - **Runtime data**: External dir recommended (`POLYWEATHER_RUNTIME_DATA_DIR=/var/lib/polyweather`) to avoid git conflicts
-- **Auth gating** (frontend middleware): Token-based (`POLYWEATHER_DASHBOARD_ACCESS_TOKEN`) or Supabase session-based (`POLYWEATHER_AUTH_ENABLED`). Local dev hosts bypass auth.
+- **Configuration**: `.env.example` is the comprehensive reference (8 config sections: runtime, Telegram, weather cache, auth, ops, frontend, optional modules, Polygon monitor). Copy to `.env` and fill in secrets.
+- **Auth gating** (frontend middleware): Three-tier priority in `middleware.ts` — (1) local dev hosts (localhost / 127.0.0.1 / ::1) bypass auth entirely, (2) Supabase session-based when `POLYWEATHER_AUTH_ENABLED=true` via `handleSupabaseAuthGate` or `handleSupabaseOptionalSession`, (3) legacy token fallback via `POLYWEATHER_DASHBOARD_ACCESS_TOKEN` cookie/query-param. Public pages (`/`, `/docs`, `/auth/*`, `/entitlement-required`) and public API routes are always accessible.
 - **CORS**: Allowed origins from `WEB_CORS_ORIGINS` env var (defaults: localhost:3000, polyweather-pro.vercel.app)
-- **EMOS/CRPS calibration**: Trainable but production should use `legacy` or `emos_shadow` engine; `emos_primary` only after local evaluation + manual rollout
 - **API proxy**: Frontend uses Next.js rewrites to proxy `/api/*` to the FastAPI backend; see `frontend/lib/api-proxy.ts` and `frontend/lib/backend-api.ts`
 
 ## Commit Convention
@@ -138,6 +155,7 @@ This repo uses the **Lore Commit Protocol** — structured decision records with
 - When modifying UI components, update both **dark-mode and light-mode CSS files** in the same edit batch.
 - **CSS Variables First**: Prefer `var(--color-*)` / `var(--color-signal-*)` tokens over hardcoded hex values. The token system is defined in `globals.css` with light-theme overrides under `html.light`.
 - **Avoid `!important`**: Only use it for Leaflet map overrides (inline style conflict) and chart canvas sizing. For light-theme overrides, use `html.light .root` prefix for higher specificity.
+- **Monitoring CSS note**: `MonitorPanel.module.css` scopes its light-theme overrides to `.scan-terminal.light` (the terminal's built-in toggle), NOT `html.light`. When adding light styles for monitoring components, match this scoping.
 - **New CSS Modules**: Add the module root class to `scan-root-styles.ts` barrel file instead of importing it separately in `ScanTerminalDashboard.tsx`.
 
 ## Quality Gates (MANDATORY)
@@ -146,7 +164,7 @@ Before marking any task as complete, you MUST:
 
 1. **Type check** — Run `npx tsc --noEmit` (frontend) or `python -m ruff check .` (backend) on modified files
 2. **No Unicode escapes** — Verify that NO `\uXXXX` sequences were introduced; if found, revert and fix
-3. **Dual-theme CSS** — For any UI change, confirm BOTH the dark CSS module AND `ScanTerminalLightTheme.module.css` were updated
+3. **Dual-theme CSS** — For any UI change, confirm BOTH dark and light styles. Most components need `ScanTerminalLightTheme.module.css` updated; monitoring components (`MonitorPanel.module.css`) contain their own `.scan-terminal.light` blocks inline.
 4. **No new hardcoded palette colors** — Use `var(--color-*)` token references instead of `#4DA3FF` / `#E6EDF3` / `#9FB2C7` / `#6B7A90` hex values
 5. **Show the diff** — Output `git diff --stat` and test results before declaring success
 

Dockerfile

@@ -1,26 +1,25 @@
+# syntax=docker/dockerfile:1
 FROM python:3.11-slim
 
-# 设置工作目录
 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)
-# RUN apt-get update && apt-get install -y --no-install-recommends gcc && rm -rf /var/lib/apt/lists/*
+RUN --mount=type=cache,target=/var/cache/apt,sharing=locked \
+    --mount=type=cache,target=/var/lib/apt,sharing=locked \
+    apt-get update && apt-get install -y --no-install-recommends \
+    gcc libhdf5-dev libnetcdf-dev && \
+    rm -rf /var/lib/apt/lists/*
 
-# 复制 requirements 文件
 COPY requirements.txt .
 
-# 安装 Python 依赖
-RUN pip install --no-cache-dir --prefer-binary -r requirements.txt
+RUN --mount=type=cache,target=/root/.cache/pip \
+    pip install --prefer-binary -r requirements.txt
 
-# 复制项目代码
 COPY . .
 
-# 启动机器人
 CMD ["python", "bot_listener.py"]

README.md

@@ -21,10 +21,12 @@ Public docs center: `/docs/intro` on the main site (bilingual product documentat
 
 [![Star History Chart](https://api.star-history.com/svg?repos=yangyuan-zhen/PolyWeather&type=Date)](https://star-history.com/#yangyuan-zhen/PolyWeather&Date)
 
-## Product Status (2026-04-27)
+## Product Status (2026-05-23)
 
-- Subscription live: `Pro Monthly 5 USDC`.
-- Points redemption live: `500 points = 1 USDC`, max `3 USDC` off.
+- Subscription live: `Pro Monthly 10 USDC`.
+- Points system live: earn via group chat, welcome bonus (+20), first-message-of-day bonus (+2), weekly participation rewards.
+- `/city` and `/deb` now free (daily cap 10 each); points redeemable for payment discount (`500 pts = 1 USDC`, max `3 USDC`).
+- Weekly leaderboard rewards restructured: smaller point bonuses for winners (200/100/50), all active users receive participation rewards.
 - 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.
@@ -33,22 +35,18 @@ Public docs center: `/docs/intro` on the main site (bilingual product documentat
 - EMOS/CRPS calibration is wired and trainable, but production should stay on `legacy` or `emos_shadow`; `emos_primary` is only for candidates that pass local offline evaluation and manual rollout.
 - Intraday analysis is now positioned as a professional meteorology read: headline, confidence, base/upside/downside paths, next observation point, evidence chain, failure modes, and confirmation rules.
 - Intraday modal now blocks stale cached detail during refresh, so users do not briefly trade off old city/date data before full detail arrives.
-- City decision cards now include the AI airport read: METAR, DEB, model cluster, and the AI expected-high center are resolved before mapping the result to Polymarket temperature buckets.
+- City decision cards now include the AI airport read: METAR, DEB, model cluster, and the AI expected-high center are resolved before mapping the result to temperature buckets.
 - AI airport reads now use in-page memory cache, browser `localStorage`, and backend short-TTL cache; returning from another dashboard tab restores existing stream text or final results before any new request is needed.
 - Market bucket matching now uses the full `all_buckets` surface and strict exact / range / or-higher / or-lower direction checks, reducing bad matches to unreasonable tail buckets.
 - The card label “model-market difference” means `model probability - market-implied probability`; positive values indicate weather probability above market pricing, while negative values indicate the YES is already priced more fully.
-- Calibrated model probability is now the primary probability panel. It shows the active production probability engine; EMOS/LGBM are surfaced only when evaluated or shadowed, while model consensus and market prices remain secondary references.
+- Calibrated model probability is now the primary probability panel. It shows the active production probability engine (legacy Gaussian or EMOS), while model consensus remains a secondary reference.
 - 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.
-- Official nearby-network layer now covers `MGM` (Turkey), `CMA/NMC` (Mainland China), `JMA AMeDAS` (Japan), `KMA` (Korea), `HKO` (Hong Kong), and `CWA` (Taiwan).
+- Official nearby-network layer now covers `MGM` (Turkey), `CMA/NMC` (Mainland China), `JMA AMeDAS` (Japan), `AMOS` (Korea, runway-level, Seoul/Busan), `HKO` (Hong Kong), and `CWA` (Taiwan).
 - Tokyo now ingests Haneda `JMA AMeDAS` 10-minute temperature as the official enhancement layer.
-- Dashboard prewarm is now supported through a dedicated worker / cron path, with runtime status exposed in `/api/system/status` and `/ops`.
-- `/ops` now exposes cache bucket counts, summary cache hit / miss rate, and prewarm runtime heartbeat.
-- Intraday commentary can optionally use `Groq` as a bilingual rewrite layer, while rule-based commentary remains the fallback.
-- Vercel frontend guidance now includes cost controls for analytics, eager fetches, and edge-side scanner blocking.
-- Frontend design system overhauled: unified CSS token system, eliminated `!important` abuse (68→6 in light theme), consolidated breakpoints (18→10), migrated hardcoded colors to CSS variables, added ARIA attributes and focus-visible keyboard navigation. See `docs/frontend-ui-design-review.md` for the full audit trail.
+- Frontend design system overhauled: unified CSS token system, eliminated `!important` abuse (134→49 in light theme), consolidated breakpoints (18→10), migrated hardcoded colors to CSS variables, added ARIA attributes and focus-visible keyboard navigation. See `docs/frontend-ui-design-review.md` for the full audit trail.
 
 ## License & Commercial Boundary
 
@@ -62,17 +60,15 @@ See: [AGPL-3.0 & Commercial Boundary](docs/OPEN_CORE_POLICY.md)
 
 ## Core Capabilities
 
-- Aggregates observations and forecasts for 52 monitored cities.
+- Aggregates observations and forecasts for 51 monitored cities.
 - Uses DEB (Dynamic Error Balancing) to blend multi-model highs.
-- Generates settlement-oriented calibrated probability buckets (`mu` + bucket distribution), with `LGBM` metadata surfaced when the calibrated engine is active.
-- Maps weather view to Polymarket quotes for mispricing scan.
+- Generates settlement-oriented calibrated probability buckets (`mu` + bucket distribution) via legacy Gaussian or EMOS/CRPS calibration.
 - Adds city decision cards that combine AI airport reads, expected-high centers, full market-bucket mapping, and model-market difference in one view.
 - 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 analysis with meteorology headline, path buckets, evidence chain, invalidation rules, and confirmation rules.
 - Adds airport-side `TAF` timing overlays and airport suppression/disruption interpretation for non-Hong Kong airport cities.
-- Adds official nearby-network enhancement layers for China, Japan, Korea, Hong Kong, Taiwan, and Turkey without replacing airport settlement anchors.
-- Adds optional dashboard prewarm worker so hot cities can be refreshed before user clicks.
+- Adds official nearby-network and runway-level enhancement layers for China, Japan, Korea (AMOS runway sensors for Seoul/Busan), Hong Kong, Taiwan, and Turkey without replacing airport settlement anchors.
 
 ## Reference Architecture
 
@@ -89,22 +85,20 @@ flowchart LR
     WX --> MGM["MGM (Turkey station network)"]
     WX --> OM["Open-Meteo"]
     WX --> JMA["JMA AMeDAS (Japan)"]
-    WX --> KMA["KMA (Korea)"]
+    WX --> AMOS["AMOS runway sensors (Korea)"]
     WX --> HKO["HKO / CWA / NOAA / Official settlement sources"]
 
     API --> ANA["DEB + Trend + Probability + Market Scan"]
     ANA --> PAY["Payment State (Intent + Event + Confirm Loop)"]
-    ANA --> PM["Polymarket Read-only Layer"]
-    ANA --> LLM["Optional Groq Commentary Rewrite"]
-    API --> PREWARM["Dashboard Prewarm API / Worker"]
+    ANA --> STATE["SQLite runtime state"]
 ```
 
-## Monitored Cities (52)
+## Monitored Cities (51)
 
 - Europe / Middle East / Africa: Ankara, Istanbul, Moscow, London, Paris, Munich, Milan, Warsaw, Madrid, Tel Aviv, Amsterdam, Helsinki, Lagos, Cape Town, Jeddah
 - APAC: Seoul, Busan, Hong Kong, Lau Fau Shan, Taipei, Shanghai, Beijing, Qingdao, Wuhan, Chengdu, Chongqing, Shenzhen, Guangzhou, Singapore, Tokyo, Kuala Lumpur, Jakarta, Manila, Wellington
 - Americas: Toronto, New York, Los Angeles, San Francisco, Aurora, Austin, Houston, Chicago, Dallas, Miami, Atlanta, Seattle, Mexico City, Buenos Aires, Sao Paulo, Panama City
-- South Asia: Lucknow, Karachi, Masroor Air Base
+- South Asia: Lucknow, Karachi
 
 ## Quick Start
 
@@ -129,7 +123,7 @@ npm run dev
 - Hong Kong keeps `HKO` official readings in dashboard and history, without falling back to airport METAR lines.
 - Intraday analysis now separates meteorology conclusion, evidence chain, invalidation rules, confirmation rules, calibrated probability, and market reference.
 - `TAF` is used as an airport-side confirmation layer, not as the main temperature model.
-- `LGBM` can power the calibrated probability panel; model vote counts remain an explanatory consensus line, not the final probability.
+- Calibrated probability uses legacy Gaussian (default) or EMOS/CRPS when evaluated; model vote counts remain an explanatory consensus line, not the final probability.
 - Browser extension remains a lightweight monitoring + basic-bias product, while the site holds the full analysis experience.
 
 ## Runtime Data (Recommended on VPS)
@@ -165,20 +159,6 @@ curl http://127.0.0.1:8000/api/system/status
 curl http://127.0.0.1:8000/metrics
 ```
 
-### Dashboard prewarm worker
-
-```bash
-docker compose --profile workers up -d polyweather_prewarm
-curl http://127.0.0.1:8000/api/system/status
-```
-
-Check:
-
-- `prewarm.thread_alive`
-- `prewarm.runtime.cycle_count`
-- `cache.analysis.hit_rate`
-- `cache.open_meteo_forecast_entries`
-
 ### Frontend cache headers
 
 ```bash
@@ -197,12 +177,6 @@ docker compose logs -f polyweather | egrep "payment event loop started|payment c
 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 |
@@ -220,26 +194,25 @@ docker compose logs -f polyweather | egrep "polymarket wallet activity watcher s
 - 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)
 - Model stack & DEB (ZH): [docs/MODEL_STACK_AND_DEB_ZH.md](docs/MODEL_STACK_AND_DEB_ZH.md)
-- EMOS + LGBM system (ZH): [docs/EMOS_LGBM_SYSTEM_ZH.md](docs/EMOS_LGBM_SYSTEM_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)
+- Airport realtime sources: [docs/AIRPORT_REALTIME_SOURCES.md](docs/AIRPORT_REALTIME_SOURCES.md)
+- Airport market monitor (ZH): [docs/AIRPORT_MARKET_MONITOR_ZH.md](docs/AIRPORT_MARKET_MONITOR_ZH.md)
+- Services overview (ZH): [docs/SERVICES_ZH.md](docs/SERVICES_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)
 - Monitoring guide (ZH): [docs/MONITORING_ZH.md](docs/MONITORING_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: `v1.5.4`
-- Last Updated: `2026-04-19`
+- Version: `v1.7.0`
+- Last Updated: `2026-05-23`

README_ZH.md

@@ -14,10 +14,12 @@
 
 ![PolyWeather Ankara 分析页](docs/images/demo_ankara.png)
 
-## 当前产品状态（2026-04-27）
+## 当前产品状态（2026-05-23）
 
-- 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`。
+- 已上线订阅制：`Pro 月付 10 USDC`。
+- 已上线积分体系：群内发言赚分 + 首次发言欢迎奖励 (+20) + 每日首条消息奖励 (+2) + 每周全员参与奖。
+- `/city` 与 `/deb` 已改为免费（每日各 10 次）；积分可用于支付抵扣（`500 分 = 1 USDC`，最多抵 `3 USDC`）。
+- 周榜奖励已改造：降低赢家积分加成 (200/100/50)，所有周活跃用户均享参与奖。
 - 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`。
@@ -30,7 +32,7 @@
   - `MGM`（土耳其）
   - `CMA/NMC`（中国内地）
   - `JMA AMeDAS`（日本）
-  - `KMA`（韩国）
+  - `AMOS`（韩国，跑道级传感器，首尔/釜山）
   - `HKO`（香港）
   - `CWA`（台湾）
 - 东京现已接入羽田 `JMA AMeDAS` 10 分钟温度作为官方增强层。
@@ -38,13 +40,12 @@
 - `/ops` 现已展示缓存桶数量、summary cache hit/miss 与 prewarm heartbeat。
 - 今日日内分析已改为“专业气象判断台”：顶部先给气象主判断、置信度、基准/上修/下修路径、下一观测点，再展示证据链、失效条件、确认条件和模型层。
 - 日内分析弹窗在 full detail / market detail 同步完成前会锁住旧内容并显示刷新状态，避免用户短暂看到上一轮缓存数据后误判。
-- 城市决策卡已接入 AI 机场报文解读：先用 METAR、DEB、多模型集群和 AI 最高温中枢判断天气路径，再映射到 Polymarket 温度桶。
+- 城市决策卡已接入 AI 机场报文解读：先用 METAR、DEB、多模型集群和 AI 最高温中枢判断天气路径，再映射到温度桶。
 - AI 机场报文解读现在同时使用页面内存缓存、浏览器 `localStorage` 和后端短 TTL 缓存；从其他选项卡切回决策卡时会优先恢复已有流式内容或最终结果。
 - 市场温度桶匹配已改为完整 `all_buckets` 映射，按 exact / range / or higher / or lower 方向严格匹配，避免把天气中枢错配到不合理尾部桶。
 - 决策卡中的“模型-市场差”口径为 `模型概率 - 市场隐含概率`，正值表示天气概率高于市场报价，负值表示市场已经更充分计价。
-- 概率区已改为“校准模型概率”；默认展示生产概率引擎输出，EMOS/LGBM 只在通过评估或作为 shadow 时进入解释层。
-- 今日日内结构解读已支持可选 `Groq` 改写层，失败时自动回退规则文案。
-- 前端部署文档已补充 Vercel 节流建议，包括 analytics 关闭、eager fetch 开关与扫描流量防火墙规则。
+- 概率区已改为”校准模型概率”；默认展示生产概率引擎输出（legacy 高斯或 EMOS），模型共识作为辅助参考。
+- 今日日内结构解读已支持可选 Groq 改写层，失败时自动回退规则文案。
 - 前端设计系统全面重构：统一 CSS token 体系、消除 !important 滥用（134→49）、合并断点（18→10）、数百处硬编码颜色迁移至 CSS 变量、添加 ARIA 无障碍属性和键盘导航。完整审查记录见 `docs/frontend-ui-design-review.md`。
 
 ## 许可证与商用边界（重要）
@@ -59,15 +60,13 @@
 
 ## 核心能力
 
-- 聚合 52 个监控城市的实测与预报数据。
+- 聚合 51 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。
-- 输出结算导向校准概率分布（`mu` + 温度桶），并在 LGBM 生效时展示校准引擎元数据。
-- 将模型观点映射到 Polymarket 行情，做错价扫描。
+- 输出结算导向校准概率分布（`mu` + 温度桶），通过 legacy 高斯或 EMOS/CRPS 校准引擎。
 - 地图城市决策卡把 AI 机场报文解读、最高温中枢、完整市场温度桶和模型-市场差放在同一张卡中展示。
 - 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。
-- 官方增强层支持按国家 provider 统一接入，但不替代机场主站、METAR 或明确官方结算站。
-- 支持后台预热热点城市，降低用户点击城市后的冷启动成本。
+- 官方增强层与跑道级传感器支持按国家 provider 统一接入（含韩国 AMOS 首尔/釜山跑道实测），不替代机场主站、METAR 或明确官方结算站。
 
 ## 参考架构
 
@@ -82,25 +81,21 @@ flowchart LR
     WX --> METAR["Aviation Weather（METAR）"]
     WX --> MGM["MGM（土耳其站网）"]
     WX --> JMA["JMA AMeDAS（日本）"]
-    WX --> KMA["KMA（韩国）"]
+    WX --> AMOS["AMOS 跑道传感器（韩国）"]
     WX --> OM["Open-Meteo"]
     WX --> HKO["HKO / CWA / NOAA 等官方结算源"]
 
     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"]
-    API --> PREWARM["Dashboard 预热接口 / Worker"]
-    ANA --> LLM["可选 Groq 文案改写层"]
     ANA --> STATE["SQLite runtime state<br/>legacy files only for migration/export fallback"]
 ```
 
-## 监控城市（52）
+## 监控城市（51）
 
 - 欧洲/中东/非洲：Ankara、Istanbul、Moscow、London、Paris、Munich、Milan、Warsaw、Madrid、Tel Aviv、Amsterdam、Helsinki、Lagos、Cape Town、Jeddah
 - 亚太：Seoul、Busan、Hong Kong、Lau Fau Shan、Taipei、Shanghai、Beijing、Wuhan、Chengdu、Chongqing、Shenzhen、Guangzhou、Singapore、Tokyo、Kuala Lumpur、Jakarta、Manila、Wellington
 - 美洲：Toronto、New York、Los Angeles、San Francisco、Aurora、Austin、Houston、Chicago、Dallas、Miami、Atlanta、Seattle、Mexico City、Buenos Aires、Sao Paulo、Panama City
-- 南亚：Lucknow、Karachi、Masroor Air Base
+- 南亚：Lucknow、Karachi
 
 ## 快速启动
 
@@ -156,21 +151,7 @@ curl http://127.0.0.1:8000/api/system/status
 curl http://127.0.0.1:8000/metrics
 ```
 
-### Dashboard 预热 Worker
-
-```bash
-docker compose --profile workers up -d polyweather_prewarm
-curl http://127.0.0.1:8000/api/system/status
-```
-
-重点关注：
-
-- `prewarm.thread_alive`
-- `prewarm.runtime.cycle_count`
-- `prewarm.runtime.last_summary_ok`
-- `cache.analysis.hit_rate`
-
-### 前端缓存头
+### 外部监控栈
 
 ```bash
 ./scripts/validate_frontend_cache.sh "https://polyweather-pro.vercel.app"
@@ -213,12 +194,6 @@ curl http://127.0.0.1:8000/api/payments/runtime
 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 |
@@ -239,24 +214,21 @@ docker compose logs -f polyweather | egrep "polymarket wallet activity watcher s
 - 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)
+- 技术债：[docs/TECH_DEBT_ZH.md](docs/TECH_DEBT_ZH.md)
+- 机场实时数据源：[docs/AIRPORT_REALTIME_SOURCES.md](docs/AIRPORT_REALTIME_SOURCES.md)
+- 机场市场监控（中文）：[docs/AIRPORT_MARKET_MONITOR_ZH.md](docs/AIRPORT_MARKET_MONITOR_ZH.md)
+- 外部服务总览：[docs/SERVICES_ZH.md](docs/SERVICES_ZH.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)
 - 模型栈与 DEB：[docs/MODEL_STACK_AND_DEB_ZH.md](docs/MODEL_STACK_AND_DEB_ZH.md)
-- LightGBM 日最高温模型：[docs/LGBM_DAILY_HIGH_ZH.md](docs/LGBM_DAILY_HIGH_ZH.md)
-- EMOS + LGBM 系统：[docs/EMOS_LGBM_SYSTEM_ZH.md](docs/EMOS_LGBM_SYSTEM_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：`v1.5.4`
-- 文档最后更新：`2026-04-19`
+- 版本：`v1.7.0`
+- 文档最后更新：`2026-05-23`

RELEASE.md

@@ -12,9 +12,9 @@
 
 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
+- `1.7.0 -> 1.7.1`：告警逻辑修正、缓存修正、文档修正
+- `1.7.0 -> 1.8.0`：新增能力、接口扩展、向后兼容的功能迭代
+- `1.7.0 -> 2.0.0`：不兼容变更、核心架构升级
 
 ## 日常升版步骤
 
@@ -29,7 +29,7 @@ python scripts/bump_version.py patch
 ```bash
 python scripts/bump_version.py minor
 python scripts/bump_version.py major
-python scripts/bump_version.py 1.5.0
+python scripts/bump_version.py 1.8.0
 ```
 
 ### 2. 检查同步结果
@@ -68,15 +68,15 @@ python -m pytest
 
 ```bash
 git add .
-git commit -m "release: v1.4.1"
-git tag v1.4.1
+git commit -m "release: v1.7.1"
+git tag v1.7.1
 ```
 
 ### 6. 推送
 
 ```bash
 git push
-git push origin v1.4.1
+git push origin v1.7.1
 ```
 
 ## 当前约束

VERSION

@@ -1 +1 @@
-1.5.5
+1.7.1

artifacts/models/lgbm_daily_high_schema.json

@@ -1,66 +0,0 @@
-{
-  "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": 1247,
-  "train_count": 998,
-  "validation_count": 249,
-  "metrics": {
-    "validation": {
-      "sample_count": 249,
-      "lgbm_mae": 2.626,
-      "deb_mae": 2.745,
-      "best_single_mae": 1.733,
-      "median_mae": 2.866
-    },
-    "full_sample": {
-      "sample_count": 1247,
-      "lgbm_mae": 0.953,
-      "deb_mae": 1.872,
-      "best_single_mae": 1.052,
-      "median_mae": 1.952
-    }
-  },
-  "generated_at": "2026-05-06T10:45:04.319587Z",
-  "trained_at": "2026-05-06T10:45:04.319587Z"
-}

artifacts/probability_calibration/default.json

@@ -1,361 +0,0 @@
-{
-  "version": "emos-auto-20260421122743",
-  "trained_at": "2026-04-21T12:28:05.031165+00:00",
-  "global": {
-    "mu": {
-      "intercept": 0.34330438,
-      "raw_mu_coef": 0.48749629,
-      "deb_coef": 0.45733479,
-      "ens_median_coef": 0.05955297,
-      "max_so_far_gap_coef": -0.104935
-    },
-    "sigma": {
-      "intercept": -0.62765582,
-      "raw_sigma_coef": 0.38492261,
-      "spread_coef": 0.29095921,
-      "peak_flag_coef": 0.33966327,
-      "max_so_far_gap_coef": 0.08357376
-    }
-  },
-  "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": 0.25,
-    "alpha_sigma": 1.0
-  },
-  "cities": {
-    "istanbul": {
-      "samples": 20,
-      "mu_bias": -0.438949,
-      "sigma_scale": 0.553793,
-      "confidence": 1.0
-    },
-    "buenos aires": {
-      "samples": 22,
-      "mu_bias": -1.044971,
-      "sigma_scale": 1.389753,
-      "confidence": 1.0
-    },
-    "ankara": {
-      "samples": 23,
-      "mu_bias": 0.067406,
-      "sigma_scale": 1.270298,
-      "confidence": 1.0
-    },
-    "london": {
-      "samples": 22,
-      "mu_bias": -0.010833,
-      "sigma_scale": 2.0,
-      "confidence": 1.0
-    },
-    "paris": {
-      "samples": 22,
-      "mu_bias": -0.21677,
-      "sigma_scale": 1.095669,
-      "confidence": 1.0
-    },
-    "new york": {
-      "samples": 21,
-      "mu_bias": 1.072189,
-      "sigma_scale": 2.0,
-      "confidence": 1.0
-    },
-    "lagos": {
-      "samples": 10,
-      "mu_bias": -0.092732,
-      "sigma_scale": 1.558646,
-      "confidence": 1.0
-    },
-    "madrid": {
-      "samples": 22,
-      "mu_bias": -0.627862,
-      "sigma_scale": 1.761239,
-      "confidence": 1.0
-    },
-    "toronto": {
-      "samples": 22,
-      "mu_bias": -0.424101,
-      "sigma_scale": 1.326675,
-      "confidence": 1.0
-    },
-    "wellington": {
-      "samples": 22,
-      "mu_bias": -0.347796,
-      "sigma_scale": 0.893459,
-      "confidence": 1.0
-    },
-    "los angeles": {
-      "samples": 20,
-      "mu_bias": -0.659983,
-      "sigma_scale": 2.0,
-      "confidence": 1.0
-    },
-    "san francisco": {
-      "samples": 20,
-      "mu_bias": 0.941818,
-      "sigma_scale": 1.979403,
-      "confidence": 1.0
-    },
-    "aurora": {
-      "samples": 20,
-      "mu_bias": 2.040009,
-      "sigma_scale": 0.982492,
-      "confidence": 1.0
-    },
-    "austin": {
-      "samples": 20,
-      "mu_bias": 0.990023,
-      "sigma_scale": 2.0,
-      "confidence": 1.0
-    },
-    "houston": {
-      "samples": 20,
-      "mu_bias": 0.188787,
-      "sigma_scale": 1.819594,
-      "confidence": 1.0
-    },
-    "chicago": {
-      "samples": 21,
-      "mu_bias": 2.285098,
-      "sigma_scale": 2.0,
-      "confidence": 1.0
-    },
-    "dallas": {
-      "samples": 21,
-      "mu_bias": 0.258095,
-      "sigma_scale": 2.0,
-      "confidence": 1.0
-    },
-    "atlanta": {
-      "samples": 22,
-      "mu_bias": -2.093105,
-      "sigma_scale": 2.0,
-      "confidence": 1.0
-    },
-    "seattle": {
-      "samples": 21,
-      "mu_bias": -0.982188,
-      "sigma_scale": 1.695172,
-      "confidence": 1.0
-    },
-    "sao paulo": {
-      "samples": 22,
-      "mu_bias": -0.786242,
-      "sigma_scale": 1.427037,
-      "confidence": 1.0
-    },
-    "munich": {
-      "samples": 22,
-      "mu_bias": 0.096559,
-      "sigma_scale": 2.0,
-      "confidence": 1.0
-    },
-    "milan": {
-      "samples": 23,
-      "mu_bias": -0.045038,
-      "sigma_scale": 1.626331,
-      "confidence": 1.0
-    },
-    "warsaw": {
-      "samples": 23,
-      "mu_bias": -0.50853,
-      "sigma_scale": 1.239146,
-      "confidence": 1.0
-    },
-    "helsinki": {
-      "samples": 17,
-      "mu_bias": 0.442247,
-      "sigma_scale": 2.0,
-      "confidence": 1.0
-    },
-    "amsterdam": {
-      "samples": 17,
-      "mu_bias": 0.129889,
-      "sigma_scale": 2.0,
-      "confidence": 1.0
-    },
-    "panama city": {
-      "samples": 17,
-      "mu_bias": -0.040792,
-      "sigma_scale": 1.393259,
-      "confidence": 1.0
-    },
-    "cape town": {
-      "samples": 10,
-      "mu_bias": 0.507293,
-      "sigma_scale": 1.420004,
-      "confidence": 1.0
-    },
-    "miami": {
-      "samples": 22,
-      "mu_bias": -2.736316,
-      "sigma_scale": 2.0,
-      "confidence": 1.0
-    },
-    "manila": {
-      "samples": 5,
-      "mu_bias": 1.772229,
-      "sigma_scale": 2.0,
-      "confidence": 0.625
-    },
-    "singapore": {
-      "samples": 22,
-      "mu_bias": 0.289639,
-      "sigma_scale": 1.124006,
-      "confidence": 1.0
-    },
-    "tokyo": {
-      "samples": 22,
-      "mu_bias": -0.332129,
-      "sigma_scale": 0.729483,
-      "confidence": 1.0
-    },
-    "seoul": {
-      "samples": 22,
-      "mu_bias": 1.6533,
-      "sigma_scale": 0.82648,
-      "confidence": 1.0
-    },
-    "busan": {
-      "samples": 16,
-      "mu_bias": 0.343239,
-      "sigma_scale": 1.886868,
-      "confidence": 1.0
-    },
-    "mexico city": {
-      "samples": 20,
-      "mu_bias": 0.087122,
-      "sigma_scale": 1.108884,
-      "confidence": 1.0
-    },
-    "shanghai": {
-      "samples": 23,
-      "mu_bias": 0.348022,
-      "sigma_scale": 0.810527,
-      "confidence": 1.0
-    },
-    "beijing": {
-      "samples": 20,
-      "mu_bias": -0.288176,
-      "sigma_scale": 0.952972,
-      "confidence": 1.0
-    },
-    "shenzhen": {
-      "samples": 21,
-      "mu_bias": -0.006114,
-      "sigma_scale": 0.728494,
-      "confidence": 1.0
-    },
-    "wuhan": {
-      "samples": 20,
-      "mu_bias": -0.771154,
-      "sigma_scale": 1.30329,
-      "confidence": 1.0
-    },
-    "chengdu": {
-      "samples": 20,
-      "mu_bias": 0.445906,
-      "sigma_scale": 0.796989,
-      "confidence": 1.0
-    },
-    "chongqing": {
-      "samples": 20,
-      "mu_bias": -0.021301,
-      "sigma_scale": 1.106566,
-      "confidence": 1.0
-    },
-    "hong kong": {
-      "samples": 24,
-      "mu_bias": 0.37527,
-      "sigma_scale": 0.517756,
-      "confidence": 1.0
-    },
-    "taipei": {
-      "samples": 23,
-      "mu_bias": 0.600671,
-      "sigma_scale": 0.5,
-      "confidence": 1.0
-    },
-    "guangzhou": {
-      "samples": 6,
-      "mu_bias": 0.7082,
-      "sigma_scale": 1.975103,
-      "confidence": 0.75
-    },
-    "lau fau shan": {
-      "samples": 19,
-      "mu_bias": 0.41371,
-      "sigma_scale": 0.656578,
-      "confidence": 1.0
-    },
-    "kuala lumpur": {
-      "samples": 16,
-      "mu_bias": 0.499197,
-      "sigma_scale": 1.361171,
-      "confidence": 1.0
-    },
-    "jakarta": {
-      "samples": 16,
-      "mu_bias": 0.293537,
-      "sigma_scale": 0.751498,
-      "confidence": 1.0
-    },
-    "lucknow": {
-      "samples": 22,
-      "mu_bias": -1.593437,
-      "sigma_scale": 2.0,
-      "confidence": 1.0
-    },
-    "karachi": {
-      "samples": 5,
-      "mu_bias": -0.035595,
-      "sigma_scale": 0.588612,
-      "confidence": 0.625
-    },
-    "jeddah": {
-      "samples": 10,
-      "mu_bias": 0.516879,
-      "sigma_scale": 2.0,
-      "confidence": 1.0
-    },
-    "moscow": {
-      "samples": 20,
-      "mu_bias": -0.609602,
-      "sigma_scale": 0.783723,
-      "confidence": 1.0
-    },
-    "tel aviv": {
-      "samples": 22,
-      "mu_bias": 0.436837,
-      "sigma_scale": 1.46524,
-      "confidence": 1.0
-    }
-  },
-  "metrics": {
-    "sample_count": 978,
-    "mean_crps": 1.330733,
-    "legacy_mean_crps": 1.376936,
-    "legacy_mean_mae": 1.589438,
-    "legacy_bucket_hit_rate": 0.43865,
-    "legacy_bucket_brier": 0.824571,
-    "selected_mean_crps": 1.311763,
-    "selected_mean_mae": 1.553658,
-    "selected_bucket_hit_rate": 0.432515,
-    "selected_bucket_brier": 0.789817,
-    "selected_score": 2.910718,
-    "legacy_score": 2.996333,
-    "filled_actual_from_history": 0,
-    "settlement_history_city_count": 30,
-    "legacy_archive_samples": 54
-  },
-  "source": "artifacts\\local_runtime\\probability_calibration\\candidates\\emos-auto-20260421122743\\default.json"
-}

artifacts/probability_calibration/evaluation_report.json

@@ -1,293 +0,0 @@
-{
-  "summary": {
-    "sample_count": 74,
-    "filled_actual_from_history": 0,
-    "legacy": {
-      "mean_crps": 3.474108,
-      "mean_mae": 3.679324,
-      "bucket_hit_rate": 0.5
-    },
-    "emos": {
-      "mean_crps": 3.33124,
-      "mean_mae": 3.622584,
-      "bucket_hit_rate": 0.5
-    },
-    "delta": {
-      "crps": -0.142868,
-      "mae": -0.056741,
-      "bucket_hit_rate": 0.0
-    }
-  },
-  "by_city": {
-    "ankara": {
-      "samples": 6,
-      "legacy_mean_crps": 1.365135,
-      "emos_mean_crps": 1.392829,
-      "legacy_mean_mae": 1.466667,
-      "emos_mean_mae": 1.444819,
-      "legacy_bucket_hit_rate": 0.666667,
-      "emos_bucket_hit_rate": 0.666667
-    },
-    "atlanta": {
-      "samples": 2,
-      "legacy_mean_crps": 30.449382,
-      "emos_mean_crps": 29.886236,
-      "legacy_mean_mae": 32.015,
-      "emos_mean_mae": 31.322804,
-      "legacy_bucket_hit_rate": 0.0,
-      "emos_bucket_hit_rate": 0.0
-    },
-    "buenos aires": {
-      "samples": 2,
-      "legacy_mean_crps": 9.113412,
-      "emos_mean_crps": 8.463522,
-      "legacy_mean_mae": 10.27,
-      "emos_mean_mae": 10.024533,
-      "legacy_bucket_hit_rate": 0.0,
-      "emos_bucket_hit_rate": 0.0
-    },
-    "busan": {
-      "samples": 3,
-      "legacy_mean_crps": 0.276202,
-      "emos_mean_crps": 0.326485,
-      "legacy_mean_mae": 0.3,
-      "emos_mean_mae": 0.239636,
-      "legacy_bucket_hit_rate": 1.0,
-      "emos_bucket_hit_rate": 1.0
-    },
-    "chengdu": {
-      "samples": 1,
-      "legacy_mean_crps": 0.306469,
-      "emos_mean_crps": 0.378812,
-      "legacy_mean_mae": 0.3,
-      "emos_mean_mae": 0.153224,
-      "legacy_bucket_hit_rate": 1.0,
-      "emos_bucket_hit_rate": 1.0
-    },
-    "chicago": {
-      "samples": 1,
-      "legacy_mean_crps": 1.250268,
-      "emos_mean_crps": 0.714669,
-      "legacy_mean_mae": 0.0,
-      "emos_mean_mae": 0.319756,
-      "legacy_bucket_hit_rate": 1.0,
-      "emos_bucket_hit_rate": 1.0
-    },
-    "dallas": {
-      "samples": 1,
-      "legacy_mean_crps": 2.173363,
-      "emos_mean_crps": 0.840297,
-      "legacy_mean_mae": 0.0,
-      "emos_mean_mae": 1.028146,
-      "legacy_bucket_hit_rate": 1.0,
-      "emos_bucket_hit_rate": 0.0
-    },
-    "hong kong": {
-      "samples": 7,
-      "legacy_mean_crps": 0.706099,
-      "emos_mean_crps": 0.731926,
-      "legacy_mean_mae": 0.657143,
-      "emos_mean_mae": 0.669911,
-      "legacy_bucket_hit_rate": 0.714286,
-      "emos_bucket_hit_rate": 0.571429
-    },
-    "istanbul": {
-      "samples": 3,
-      "legacy_mean_crps": 1.264103,
-      "emos_mean_crps": 1.214967,
-      "legacy_mean_mae": 1.5,
-      "emos_mean_mae": 1.469837,
-      "legacy_bucket_hit_rate": 0.333333,
-      "emos_bucket_hit_rate": 0.666667
-    },
-    "london": {
-      "samples": 2,
-      "legacy_mean_crps": 3.885033,
-      "emos_mean_crps": 3.799841,
-      "legacy_mean_mae": 4.135,
-      "emos_mean_mae": 4.027503,
-      "legacy_bucket_hit_rate": 0.0,
-      "emos_bucket_hit_rate": 0.5
-    },
-    "lucknow": {
-      "samples": 2,
-      "legacy_mean_crps": 2.487193,
-      "emos_mean_crps": 2.107441,
-      "legacy_mean_mae": 3.205,
-      "emos_mean_mae": 2.876943,
-      "legacy_bucket_hit_rate": 0.0,
-      "emos_bucket_hit_rate": 0.0
-    },
-    "madrid": {
-      "samples": 2,
-      "legacy_mean_crps": 6.27726,
-      "emos_mean_crps": 5.810228,
-      "legacy_mean_mae": 7.33,
-      "emos_mean_mae": 7.195133,
-      "legacy_bucket_hit_rate": 0.0,
-      "emos_bucket_hit_rate": 0.0
-    },
-    "miami": {
-      "samples": 2,
-      "legacy_mean_crps": 28.637631,
-      "emos_mean_crps": 27.592664,
-      "legacy_mean_mae": 30.175,
-      "emos_mean_mae": 29.284979,
-      "legacy_bucket_hit_rate": 0.0,
-      "emos_bucket_hit_rate": 0.0
-    },
-    "milan": {
-      "samples": 3,
-      "legacy_mean_crps": 4.401392,
-      "emos_mean_crps": 3.782613,
-      "legacy_mean_mae": 4.06,
-      "emos_mean_mae": 3.989808,
-      "legacy_bucket_hit_rate": 0.666667,
-      "emos_bucket_hit_rate": 0.666667
-    },
-    "munich": {
-      "samples": 2,
-      "legacy_mean_crps": 3.145192,
-      "emos_mean_crps": 2.990712,
-      "legacy_mean_mae": 3.64,
-      "emos_mean_mae": 3.61502,
-      "legacy_bucket_hit_rate": 0.0,
-      "emos_bucket_hit_rate": 0.0
-    },
-    "new york": {
-      "samples": 1,
-      "legacy_mean_crps": 3.692845,
-      "emos_mean_crps": 3.021798,
-      "legacy_mean_mae": 4.94,
-      "emos_mean_mae": 4.54437,
-      "legacy_bucket_hit_rate": 0.0,
-      "emos_bucket_hit_rate": 0.0
-    },
-    "paris": {
-      "samples": 2,
-      "legacy_mean_crps": 4.013782,
-      "emos_mean_crps": 3.915303,
-      "legacy_mean_mae": 4.265,
-      "emos_mean_mae": 4.179694,
-      "legacy_bucket_hit_rate": 0.5,
-      "emos_bucket_hit_rate": 0.5
-    },
-    "sao paulo": {
-      "samples": 2,
-      "legacy_mean_crps": 5.540967,
-      "emos_mean_crps": 5.018605,
-      "legacy_mean_mae": 6.57,
-      "emos_mean_mae": 6.314389,
-      "legacy_bucket_hit_rate": 0.0,
-      "emos_bucket_hit_rate": 0.0
-    },
-    "seattle": {
-      "samples": 1,
-      "legacy_mean_crps": 0.315488,
-      "emos_mean_crps": 0.524119,
-      "legacy_mean_mae": 0.0,
-      "emos_mean_mae": 0.673609,
-      "legacy_bucket_hit_rate": 1.0,
-      "emos_bucket_hit_rate": 0.0
-    },
-    "seoul": {
-      "samples": 3,
-      "legacy_mean_crps": 0.508331,
-      "emos_mean_crps": 0.520762,
-      "legacy_mean_mae": 0.2,
-      "emos_mean_mae": 0.247862,
-      "legacy_bucket_hit_rate": 1.0,
-      "emos_bucket_hit_rate": 1.0
-    },
-    "shanghai": {
-      "samples": 3,
-      "legacy_mean_crps": 0.299116,
-      "emos_mean_crps": 0.402453,
-      "legacy_mean_mae": 0.1,
-      "emos_mean_mae": 0.189437,
-      "legacy_bucket_hit_rate": 1.0,
-      "emos_bucket_hit_rate": 1.0
-    },
-    "shenzhen": {
-      "samples": 1,
-      "legacy_mean_crps": 1.198351,
-      "emos_mean_crps": 1.490963,
-      "legacy_mean_mae": 1.3,
-      "emos_mean_mae": 1.628189,
-      "legacy_bucket_hit_rate": 0.0,
-      "emos_bucket_hit_rate": 0.0
-    },
-    "singapore": {
-      "samples": 2,
-      "legacy_mean_crps": 0.281993,
-      "emos_mean_crps": 0.370374,
-      "legacy_mean_mae": 0.15,
-      "emos_mean_mae": 0.118005,
-      "legacy_bucket_hit_rate": 1.0,
-      "emos_bucket_hit_rate": 1.0
-    },
-    "taipei": {
-      "samples": 5,
-      "legacy_mean_crps": 0.950739,
-      "emos_mean_crps": 1.042332,
-      "legacy_mean_mae": 0.94,
-      "emos_mean_mae": 0.951241,
-      "legacy_bucket_hit_rate": 0.6,
-      "emos_bucket_hit_rate": 0.6
-    },
-    "tel aviv": {
-      "samples": 2,
-      "legacy_mean_crps": 0.446758,
-      "emos_mean_crps": 0.578691,
-      "legacy_mean_mae": 0.3,
-      "emos_mean_mae": 0.116966,
-      "legacy_bucket_hit_rate": 1.0,
-      "emos_bucket_hit_rate": 1.0
-    },
-    "tokyo": {
-      "samples": 5,
-      "legacy_mean_crps": 0.879366,
-      "emos_mean_crps": 0.876608,
-      "legacy_mean_mae": 1.022,
-      "emos_mean_mae": 1.008317,
-      "legacy_bucket_hit_rate": 0.2,
-      "emos_bucket_hit_rate": 0.4
-    },
-    "toronto": {
-      "samples": 2,
-      "legacy_mean_crps": 5.497916,
-      "emos_mean_crps": 5.268783,
-      "legacy_mean_mae": 6.33,
-      "emos_mean_mae": 6.388522,
-      "legacy_bucket_hit_rate": 0.0,
-      "emos_bucket_hit_rate": 0.0
-    },
-    "warsaw": {
-      "samples": 3,
-      "legacy_mean_crps": 1.618875,
-      "emos_mean_crps": 1.524219,
-      "legacy_mean_mae": 2.056667,
-      "emos_mean_mae": 2.006524,
-      "legacy_bucket_hit_rate": 0.333333,
-      "emos_bucket_hit_rate": 0.333333
-    },
-    "wellington": {
-      "samples": 2,
-      "legacy_mean_crps": 0.364919,
-      "emos_mean_crps": 0.484124,
-      "legacy_mean_mae": 0.15,
-      "emos_mean_mae": 0.123335,
-      "legacy_bucket_hit_rate": 1.0,
-      "emos_bucket_hit_rate": 1.0
-    },
-    "wuhan": {
-      "samples": 1,
-      "legacy_mean_crps": 0.476225,
-      "emos_mean_crps": 0.578405,
-      "legacy_mean_mae": 0.8,
-      "emos_mean_mae": 0.962852,
-      "legacy_bucket_hit_rate": 0.0,
-      "emos_bucket_hit_rate": 0.0
-    }
-  }
-}

artifacts/probability_calibration/rollout_report.json

@@ -1,74 +0,0 @@
-{
-  "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

@@ -1,933 +0,0 @@
-{
-  "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,
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-      "shadow_top_bucket": 17,
-      "calibration_version": "emos-20260320130245",
-      "calibration_mode": "emos_shadow"
-    },
-    {
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-      "date": "2026-03-18",
-      "actual_high": 27.8,
-      "actual_bucket": 27,
-      "legacy_mu": 27.8,
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-      "legacy_top_bucket": 28,
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-      "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,
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-      "calibration_version": "emos-20260320130245",
-      "calibration_mode": "emos_shadow"
-    },
-    {
-      "city": "chicago",
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-    },
-    {
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-      "date": "2026-03-18",
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-      "actual_bucket": 17,
-      "legacy_mu": 26.15,
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-      "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,
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-      "calibration_version": "emos-20260320130245",
-      "calibration_mode": "emos_shadow"
-    },
-    {
-      "city": "ankara",
-      "date": "2026-03-18",
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-      "calibration_version": "emos-20260320130245",
-      "calibration_mode": "emos_shadow"
-    },
-    {
-      "city": "warsaw",
-      "date": "2026-03-17",
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-      "actual_bucket": 11,
-      "legacy_mu": 11.3,
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-      "calibration_version": "emos-20260320130245",
-      "calibration_mode": "emos_shadow"
-    },
-    {
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-    },
-    {
-      "city": "milan",
-      "date": "2026-03-17",
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-      "actual_bucket": 17,
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-      "shadow_mu": 17.3,
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-      "calibration_version": "emos-20260320130245",
-      "calibration_mode": "emos_shadow"
-    },
-    {
-      "city": "hong kong",
-      "date": "2026-03-17",
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-      "actual_bucket": 24,
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-      "calibration_version": "emos-20260320130245",
-      "calibration_mode": "emos_shadow"
-    }
-  ]
-}

artifacts/probability_calibration/training_samples.json

@@ -1,981 +0,0 @@
-{
-  "sample_count": 54,
-  "snapshot_sample_count": 1,
-  "daily_record_sample_count": 53,
-  "filled_actual_from_history": 2,
-  "samples": [
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-      "date": "2026-03-25",
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-      "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,
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-      "sample_source": "daily_record",
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-      "settlement_station_code": null,
-      "truth_version": null,
-      "truth_updated_by": null,
-      "truth_updated_at": null
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-    {
-      "city": "ankara",
-      "date": "2026-03-19",
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-      "raw_sigma": 2.05,
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-    {
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-    {
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-    {
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-    {
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-      "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
-    }
-  ]
-}

deploy.ps1

@@ -0,0 +1,10 @@
+$VPS = "root@38.54.27.70"
+$PROJECT = "/root/PolyWeather"
+
+Write-Host "🚀 Deploying to $VPS..." -ForegroundColor Cyan
+
+ssh $VPS "cd $PROJECT && git pull && docker compose up -d --build"
+
+Write-Host "✅ Deploy complete. Checking health..." -ForegroundColor Green
+Start-Sleep 8
+ssh $VPS "curl -s http://localhost:8000/healthz"

deploy.sh

@@ -0,0 +1,12 @@
+#!/bin/bash
+set -e
+VPS="root@38.54.27.70"
+PROJECT="/root/PolyWeather"
+
+echo "🚀 Deploying to $VPS..."
+
+ssh "$VPS" "cd $PROJECT && git pull && docker compose up -d --build"
+
+echo "✅ Deploy complete. Checking health..."
+sleep 8
+ssh "$VPS" "curl -s http://localhost:8000/healthz"

docker-compose.yml

@@ -10,15 +10,15 @@ services:
     container_name: polyweather_bot
     restart: unless-stopped
     volumes:
-      # 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.
       - ${POLYWEATHER_RUNTIME_DATA_DIR:-/var/lib/polyweather}:/app/data
-      - ./bot.log:/app/bot.log # 挂载日志文件
-    # UID/GID are mainly useful on Linux hosts to avoid root-owned output files.
-    # Windows / macOS can usually keep the fallback values.
+      - ./bot.log:/app/bot.log
     user: "${UID:-1000}:${GID:-1000}"
+    healthcheck:
+      test: ["CMD", "python", "-c", "import sqlite3; c=sqlite3.connect('/var/lib/polyweather/polyweather.db'); c.execute('SELECT 1'); c.close()"]
+      interval: 60s
+      timeout: 10s
+      retries: 3
 
   polyweather_web:
     <<: *polyweather-base
@@ -26,87 +26,14 @@ services:
     restart: unless-stopped
     command: python web/app.py
     volumes:
-      # 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}"
+    healthcheck:
+      test: ["CMD", "python", "-c", "from urllib.request import urlopen; urlopen('http://localhost:8000/healthz')"]
+      interval: 30s
+      timeout: 5s
+      retries: 3
 
-  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/AIRPORT_MARKET_MONITOR_ZH.md

@@ -0,0 +1,135 @@
+# 机场高频数据接入市场监控频道方案
+
+## 背景
+
+### 现有数据
+
+| 城市 | 站点 | ICAO/站点 | 数据类型 | 数据源 | 刷新频率 |
+|------|------|-----------|---------|--------|---------|
+| 首尔 | 仁川国际 | RKSI | 跑道对温度（2 对） | AMOS | 1 分钟 |
+| 釜山 | 金海国际 | RKPK | 跑道对温度（1 对） | AMOS | 1 分钟 |
+| 东京 | 羽田 | RJTT | 机场站点实时温度 | JMA AMeDAS | 10 分钟 |
+| 安卡拉 | Esenboğa | 17128 | 机场站点实时温度 | MGM | 不定，约 5-15 分钟 |
+
+### 现有 Telegram 推送系统
+
+- **循环**: `start_trade_alert_push_loop`，默认每 30 分钟跑一轮
+- **覆盖城市**: `TELEGRAM_ALERT_CITIES`（默认全部 51 城）
+- **3 条规则**: Ankara Center DEB 命中、预报突破、暖平流
+- **门禁**: 严重度/触发数/冷却期 多层过滤
+- **消息**: 中英双语，包含触发类型、实况温度
+
+### 问题
+
+四座机场城市的实时数据已就绪，但现有推送系统 30 分钟一轮对所有城市一视同仁。1-10 分钟级高频数据在接近交易高峰期时，温度变化可能比 30 分钟窗口更快，需要更灵敏的监控。
+
+---
+
+## 方案设计
+
+### 核心思路
+
+在现有 30 分钟主循环之上叠加高频通道，对四座机场城市用 10 分钟间隔独立检测温度急变。温度波动达到阈值时推送告警，包含当前温度 + DEB 预测最高温。不做市场分析、不输出 AI 建议、不约定时快照。
+
+### 1. 高频机场城市快速通道
+
+在现有 30 分钟主循环之外，为 `{seoul, busan, tokyo, ankara}` 单独跑一个 10 分钟间隔的子循环，每个城市独立检测温度急变。
+
+**配置（写死在代码中）**:
+```python
+HIGH_FREQ_AIRPORT_CITIES = {"seoul", "busan", "tokyo", "ankara"}
+HIGH_FREQ_PUSH_INTERVAL_SEC = 600        # 10 分钟
+HIGH_FREQ_MOMENTUM_THRESHOLD_C = 0.5     # 比默认 0.8°C 更灵敏
+HIGH_FREQ_COOLDOWN_SEC = 7200            # 同一城市冷却 2 小时
+```
+
+**逻辑**:
+- 主循环 30 分钟照常跑全部城市（不变）
+- 每 10 分钟对四座机场城市各检查一次温度急变
+- 高频轮次仅检查 `airport_rapid_temp_change` 一条规则
+- 各城市独立冷却，触发后 2 小时内同一城市不再重复推送
+- **最高温已锁定则跳过**：当日最高已过且持续下降，不再推送
+
+### 2. 机场观测积累与趋势检测
+
+**新增数据库表**: `airport_obs_log`
+```sql
+CREATE TABLE IF NOT EXISTS airport_obs_log (
+    id INTEGER PRIMARY KEY AUTOINCREMENT,
+    icao TEXT NOT NULL,
+    city TEXT NOT NULL,
+    temp_c REAL,
+    wind_kt REAL,
+    pressure_hpa REAL,
+    obs_time TEXT NOT NULL,
+    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
+);
+CREATE INDEX IF NOT EXISTS idx_airport_obs_log_icao_time
+    ON airport_obs_log(icao, created_at DESC);
+```
+
+**写入**: 在 AMOS/JMA/MGM 成功获取数据后自动调用 `append_airport_obs()` 写入。自动清理 2 小时前的旧数据。
+
+**读取**: `get_airport_obs_recent(icao, minutes=30)` 返回最近 N 分钟观测列表，用于计算温度变化斜率。
+
+### 3. 温度突变即时告警
+
+基于积累的观测日志，新增告警规则 `airport_rapid_temp_change`。每条告警 per-city 独立推送。
+
+| 参数 | 值 | 说明 |
+|------|-----|------|
+| 滑动窗口 | 20 分钟 | 取最近 20 分钟内的观测 |
+| 最少样本 | 3 条 | 确保有足够数据点 |
+| 触发阈值 | > 0.5°C/10min | 比默认 0.8°C/30min 更灵敏 |
+| 冷却期 | 2 小时 | 同城市两次推送最小间隔 |
+| 锁定跳过 | 最高温已锁定 | 当日最高已过且持续下降，不推送 |
+
+**告警消息示例**:
+
+首尔/釜山（跑道对温度）：
+```
+🚨 首尔/仁川 温度急变
+
+15L/33R 14.6°C
+15R/33L 15.2°C
+DEB 预测最高 18.2°C
+```
+
+东京/安卡拉（站点实时温度）：
+```
+🚨 东京/羽田 温度急变
+
+当前 24.1°C
+DEB 预测最高 26.5°C
+```
+
+---
+
+## 改动文件清单
+
+| 优先级 | 文件 | 改动 |
+|--------|------|------|
+| 1 | `src/database/db_manager.py` | 新增 `airport_obs_log` 表、`append_airport_obs()`、`get_airport_obs_recent()` |
+| 2 | `src/data_collection/weather_sources.py` | AMOS/JMA/MGM 成功后调用 `append_airport_obs()` 写日志 |
+| 3 | `src/analysis/market_alert_engine.py` | 新增 `airport_rapid_temp_change` 规则 |
+| 4 | `src/utils/telegram_push.py` | 10 分钟高频子循环、温度急变告警推送、最高温锁定跳过 |
+| 5 | `src/bot/runtime_coordinator.py` | 注册机场高频推送循环 |
+
+---
+
+## 实施顺序
+
+1. **Phase 1 — DB 层**: `airport_obs_log` 表 + 读写方法
+2. **Phase 2 — 采集层**: AMOS/JMA/MGM 成功后自动写日志，部署观察 1-2 天确认数据积累正常
+3. **Phase 3 — 告警引擎**: `airport_rapid_temp_change` 规则 + 单元测试
+4. **Phase 4 — 推送层**: 高频快速通道，直接推送市场监控频道
+5. **Phase 5 — 调参**: 观察 3-7 天调整阈值
+
+---
+
+## 风险与注意事项
+
+- **AMOS/JMA/MGM 站点可用性**: 各数据源可能偶发性不可用，需容错处理
+- **告警频率控制**: 高频循环可能产生过多告警，需要严格的冷却期和去重机制
+- **数据库体积**: `airport_obs_log` 每 1-10 分钟写入 4 条记录，2 小时约 48-480 条，自动清理后体积可控
+- **安卡拉 MGM 刷新频率**: `servis.mgm.gov.tr` 实测更新间隔 5-15 分钟不等，非固定周期

docs/AIRPORT_REALTIME_SOURCES.md

@@ -0,0 +1,100 @@
+# 机场高频实时数据源
+
+## 已接入城市
+
+| 城市 | 机场 | ICAO/站点 | 数据源 | 频率 | 类型 | 费用 |
+|------|------|-----------|--------|------|------|------|
+| 首尔 | 仁川国际 | RKSI | AMOS (`global.amo.go.kr`) | 1 分钟 | 跑道对温度（2对） | 免费 |
+| 釜山 | 金海国际 | RKPK | AMOS (`global.amo.go.kr`) | 1 分钟 | 跑道对温度（1对） | 免费 |
+| 东京 | 羽田 | RJTT | JMA AMeDAS (`jma.go.jp`) | 10 分钟 | 机场站点实时温度 | 免费 |
+| 安卡拉 | Esenboğa | 17128 | MGM (`servis.mgm.gov.tr`) | 5-15 分钟 | 机场站点实时温度 | 免费 |
+| 伊斯坦布尔 | 伊斯坦布尔机场 | 17058 | MGM (`servis.mgm.gov.tr`) | 5-15 分钟 | 机场站点实时温度 | 免费 |
+| 赫尔辛基 | Vantaa | EFHK | FMI (`opendata.fmi.fi`) | 10 分钟 | 机场站点实时温度 | 免费 |
+| 阿姆斯特丹 | Schiphol | EHAM | KNMI (`dataplatform.knmi.nl`) | 10 分钟 | 机场站点实时温度 | 免费（需注册） |
+| 巴黎 | Le Bourget | LFPB | AROME HD (`api.open-meteo.com`) | 15 分钟 | 模型预报（非实测） | 免费 |
+| 新加坡 | Changi | WSSS | Singapore MSS (`api.data.gov.sg`) | 1 分钟 | 机场站点实时温度 (S24 站) | 免费 |
+| 纽约 | LaGuardia | KLGA | NOAA MADIS HFMETAR | 5 分钟 | 机场站点实时温度 | 免费 |
+| 洛杉矶 | LAX | KLAX | NOAA MADIS HFMETAR | 5 分钟 | 机场站点实时温度 | 免费 |
+| 芝加哥 | O'Hare | KORD | NOAA MADIS HFMETAR | 5 分钟 | 机场站点实时温度 | 免费 |
+| 丹佛 | Buckley | KBKF | NOAA MADIS HFMETAR | 5 分钟 | 机场站点实时温度 | 免费 |
+| 亚特兰大 | Hartsfield | KATL | NOAA MADIS HFMETAR | 5 分钟 | 机场站点实时温度 | 免费 |
+| 迈阿密 | MIA | KMIA | NOAA MADIS HFMETAR | 5 分钟 | 机场站点实时温度 | 免费 |
+| 旧金山 | SFO | KSFO | NOAA MADIS HFMETAR | 5 分钟 | 机场站点实时温度 | 免费 |
+| 休斯顿 | Hobby | KHOU | NOAA MADIS HFMETAR | 5 分钟 | 机场站点实时温度 | 免费 |
+| 达拉斯 | Love Field | KDAL | NOAA MADIS HFMETAR | 5 分钟 | 机场站点实时温度 | 免费 |
+| 奥斯汀 | Bergstrom | KAUS | NOAA MADIS HFMETAR | 5 分钟 | 机场站点实时温度 | 免费 |
+| 西雅图 | SeaTac | KSEA | NOAA MADIS HFMETAR | 5 分钟 | 机场站点实时温度 | 免费 |
+
+> **Singapore MSS**: 新加坡气象局（MSS）通过 data.gov.sg 开放数据平台提供全国 15 个站点
+> 的干球温度（1 分钟均值），更新频率 ~1 分钟。选取 S24 Upper Changi Road North 站
+> 作为樟宜机场 (WSSS) 的实时温度锚点。数据公开免费，无需 API 密钥。
+> 后端通过 `singapore_mss_sources.py` 拉取并注入 `airport_primary`。
+
+> **NOAA MADIS HFMETAR**: 美国 11 个城市的机场高频实时数据通过 NOAA MADIS 公共档案获取。
+> 数据源为 NetCDF 格式（`madis-data.ncep.noaa.gov/madisPublic1/data/LDAD/hfmetar/`），
+> 每 5 分钟全量更新一次，温度保留一位小数。匿名公开访问，无需 API 密钥。
+> 后端通过 `weather_sources.py` 拉取并注入 `airport_primary`，前端市场监控通过
+> `resolveMonitorTemperature` 优先读取 `airport_primary.temp` 获得小数精度温度。
+
+## 推送机制
+
+- 每城按原生频率独立推送，不捆绑
+- 首尔/釜山 60s，其余 600s
+- 循环轮询 60s 以匹配最快频率
+- 仅当当前温度距 DEB 预测最高 ≤3°C 时推送
+- 确认过峰值后自动停止
+
+## 前端市场监控 freshness 契约
+
+后端城市详情接口会在 `current.freshness` / `airport_current.freshness` 返回源感知更新时间信息，前端市场监控不再用统一的 `obs_age_min` 判断所有城市。
+
+关键字段：
+
+```json
+{
+  "source_code": "amos",
+  "source_label": "AMOS",
+  "observed_at": "2026-05-14T11:59:10+00:00",
+  "observed_at_local": "20:59",
+  "native_update_interval_sec": 60,
+  "expected_next_update_at": "2026-05-14T12:00:10+00:00",
+  "freshness_status": "fresh",
+  "freshness_reason": "within_native_fresh_window",
+  "age_sec": 50
+}
+```
+
+前端刷新规则：
+
+- 首次进入市场监控：强制刷新全部城市，绕过 30 分钟前端缓存。
+- 定时轮询：仍以 60s tick 检查，但只刷新已到 `expected_next_update_at`、`delayed`、`stale` 或缺失的城市。
+- BFF 代理：`force_refresh=true` 时使用 `no-store`，避免 Next fetch revalidate 缓存吞掉强刷。
+- 展示：卡片 tooltip 显示源端名称、原生更新间隔和当前 freshness 状态。
+
+## 消息模板
+
+```
+Seoul / Incheon 16:03
+
+15L/33R 14.6°C
+15R/33L 15.2°C
+今日DEB预报最高：18.2°C
+今日实测最高：16.5°C（15:30）
+```
+
+## 环境变量
+
+| 变量 | 说明 | 默认值 |
+|------|------|--------|
+| `TELEGRAM_AIRPORT_PUSH_ENABLED` | 启用机场推送 | `true` |
+| `TELEGRAM_AIRPORT_PUSH_INTERVAL_SEC` | 循环轮询间隔 | `60` |
+| `KNMI_API_KEY` | KNMI API 密钥（阿姆斯特丹必填） | — |
+
+## 未接入城市
+
+| 城市 | 原因 |
+|------|------|
+| 马德里/Barajas | AEMET 注册页面失效 |
+| 伦敦/Heathrow | Met Office 仅 1 小时更新 |
+| 慕尼黑 | DWD 延迟 ~1 小时 |
+| 米兰/华沙/莫斯科 | 无已知实时源 |

docs/API_ZH.md

@@ -1,4 +1,4 @@
-# PolyWeather API 文档（v1.5.4）
+# PolyWeather API 文档（v1.7.0）
 
 最后更新：`2026-04-27`
 
@@ -126,18 +126,14 @@ SSE 事件：
 
 #### 2. `probabilities`
 
-概率层现在按“校准模型概率”对外解释，而不是直接把模型票数或市场价格当成概率。
+概率层基于 legacy 高斯分桶，以 DEB 融合预测 μ 和 ensemble spread σ 生成 1°C 粒度概率分布。
 
-新增 / 重点字段：
+概率字段：
 
-- `engine`：概率引擎名称，例如 `lgbm_calibrated`、`emos`、`legacy`
-- `calibration_mode`：校准运行模式
-- `calibration_version`：校准产物版本
-- `raw_mu` / `raw_sigma`：原始分布参数
-- `calibrated_mu` / `calibrated_sigma`：校准后分布参数
-- `shadow_distribution`：shadow / 对照分布，供回归与灰度验证
-
-当前前端展示 `probabilities.engine` 对应的生产概率分布；`EMOS` / `LGBM` 只有在评估通过、显式启用或 shadow 对照时才进入展示/解释层。模型共识与市场价格只作为辅助参考，不再作为主结论。
+- `engine`：固定为 `legacy`
+- `mu`：DEB 融合预测中心值
+- `distribution`：当天合约桶概率分布
+- `distribution_all`：包含外围桶的完整分布
 
 #### 3. `detail_depth`
 
@@ -206,7 +202,7 @@ SSE 事件：
 
 - 多数机场市场以 `METAR` / 机场主站实况为结算锚点。
 - `Wunderground` 是历史页面或参考入口，不应在产品文案里被描述成“站”。
-- `MGM / NMC / JMA / KMA / HKO / CWA` 等官方站网属于增强层或明确官方站点层；只有合约规则明确指定时，才作为最终结算站点。
+- `MGM / NMC / JMA / AMOS / HKO / CWA` 等官方站网属于增强层或明确官方站点层；只有合约规则明确指定时，才作为最终结算站点。
 
 ## 4. 鉴权与账户接口
 

docs/COMMERCIALIZATION.md

@@ -8,7 +8,7 @@ PolyWeather 是面向温度结算场景的气象决策层，不是通用天气
 
 核心价值：
 
-- 观测优先（METAR / 机场主站 / 明确官方站点；MGM、NMC、JMA、KMA 等作为增强层）
+- 观测优先（METAR / 机场主站 / 明确官方站点；MGM、NMC、JMA、AMOS 等作为增强层）
 - 结算导向（DEB + 校准概率桶）
 - 气象判断优先（证据链、失效条件、下一观测点）
 - 市场映射（行情对照 + 错价雷达），但不把交易建议放在第一层产品承诺
@@ -18,7 +18,7 @@ PolyWeather 是面向温度结算场景的气象决策层，不是通用天气
 | 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` |
+| 订阅套餐（Pro 月付） | 已上线 | `10 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 |
@@ -32,13 +32,13 @@ PolyWeather 是面向温度结算场景的气象决策层，不是通用天气
 - Pro 用户：
   - 今日日内深度分析（含高温时段）
   - 专业气象结论条、证据链、失效条件、确认条件
-  - LGBM / EMOS 等校准概率层
+  - 概率分布层（基于 DEB 融合 + 高斯分桶）
   - 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）
 
-- 套餐：`pro_monthly`（5 USDC / 30 天）
+- 套餐：`pro_monthly`（10 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）
 

docs/CONFIGURATION_ZH.md

@@ -105,13 +105,9 @@ PolyWeather 的环境变量很多，但不是所有变量都属于同一层级
 - `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`）
-- `POLYWEATHER_DASHBOARD_PREWARM_ENABLED`
-- `POLYWEATHER_GROQ_COMMENTARY_ENABLED`
 
 ### 4.3 L3：运行调优项
 
@@ -131,29 +127,12 @@ PolyWeather 的环境变量很多，但不是所有变量都属于同一层级
 - `TELEGRAM_MARKET_FOCUS_DIGEST_TOP_N`
 - `POLYWEATHER_PAYMENT_RPC_URLS`
 - `TAF_CACHE_TTL_SEC`
-- `POLYWEATHER_PREWARM_INTERVAL_SEC`
-- `POLYWEATHER_PREWARM_JITTER_SEC`
-- `POLYWEATHER_PREWARM_CITIES`
-- `POLYWEATHER_PREWARM_INCLUDE_DETAIL`
-- `POLYWEATHER_PREWARM_INCLUDE_MARKET`
-- `POLYWEATHER_PREWARM_FORCE_REFRESH`
-- `POLYWEATHER_GROQ_COMMENTARY_MODEL`
-- `POLYWEATHER_GROQ_COMMENTARY_TIMEOUT_SEC`
-- `POLYWEATHER_GROQ_COMMENTARY_CACHE_TTL_SEC`
 
 策略：
 
 - 先用默认值
 - 出现性能或运维问题时再调
 
-当前默认预热名单优先覆盖：
-
-- 亚洲：Shanghai、Beijing、Shenzhen、Wuhan、Chengdu、Chongqing、Hong Kong、Taipei、Singapore、Tokyo、Seoul、Busan
-- 中东：Ankara、Istanbul
-- 欧洲：London、Paris、Madrid
-
-默认不再包含美国城市；如果线上 `.env` 已手动设置 `POLYWEATHER_PREWARM_CITIES`，则会以你的显式配置为准。
-
 ### 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。
@@ -164,10 +143,7 @@ PolyWeather 的环境变量很多，但不是所有变量都属于同一层级
 - `SUPABASE_SERVICE_ROLE_KEY`
 - `POLYWEATHER_BACKEND_ENTITLEMENT_TOKEN`
 - `POLYWEATHER_DASHBOARD_ACCESS_TOKEN`
-- `METEOBLUE_API_KEY`
 - `NEXT_PUBLIC_WALLETCONNECT_PROJECT_ID`
-- `POLYMARKET_SECRET_KEY`
-- `GROQ_API_KEY`
 
 ## 5. 推荐部署矩阵
 
@@ -259,17 +235,7 @@ 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
-POLYWEATHER_DASHBOARD_PREWARM_ENABLED=true
-POLYWEATHER_PREWARM_INTERVAL_SEC=300
-POLYWEATHER_PREWARM_JITTER_SEC=20
-POLYWEATHER_PREWARM_INCLUDE_DETAIL=true
-POLYWEATHER_PREWARM_INCLUDE_MARKET=true
 POLYWEATHER_BACKEND_URL=http://polyweather_web:8000
-POLYWEATHER_GROQ_COMMENTARY_ENABLED=false
-POLYWEATHER_GROQ_COMMENTARY_MODEL=openai/gpt-oss-20b
-POLYWEATHER_GROQ_COMMENTARY_TIMEOUT_SEC=8
-POLYWEATHER_GROQ_COMMENTARY_CACHE_TTL_SEC=1800
 POLYWEATHER_SCAN_AI_ENABLED=false
 POLYWEATHER_SCAN_AI_API_KEY=...
 POLYWEATHER_SCAN_AI_PROVIDER=mimo
@@ -290,47 +256,14 @@ POLYWEATHER_SCAN_CITY_AI_MODEL=mimo-v2.5-pro
 - 机器人市场监控包含 `关键提醒` 与 `关注清单`：关键提醒逐城判断并受冷却控制，关注清单每轮先扫描完整城市列表，再按全局 Top N 推送；同一轮已经触发关键提醒的城市不会重复出现在关注清单里。
 - `POLYWEATHER_SCAN_AI_*` 走 OpenAI-compatible `/chat/completions`；当前临时默认 MiMo，可用 `POLYWEATHER_SCAN_AI_BASE_URL` 与 `POLYWEATHER_SCAN_AI_MODEL` 随时切回其他兼容 provider。
 - `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。
-- `POLYWEATHER_DASHBOARD_PREWARM_ENABLED=true` 时，建议同时启用独立 worker 或 bot 内嵌预热线程。
-- `POLYWEATHER_BACKEND_URL` 仅在独立 `polyweather_prewarm` worker 容器中使用，建议设为 `http://polyweather_web:8000`，不要写 `127.0.0.1`。
-- `POLYWEATHER_GROQ_COMMENTARY_ENABLED=false` 表示默认仍走规则文案；只有在确实配置了 `GROQ_API_KEY` 时才建议开启。
-
-### 6.3 Dashboard 预热 worker 推荐变量
-
-```env
-POLYWEATHER_DASHBOARD_PREWARM_ENABLED=true
-POLYWEATHER_PREWARM_INTERVAL_SEC=300
-POLYWEATHER_PREWARM_JITTER_SEC=20
-POLYWEATHER_PREWARM_CITIES=ankara,istanbul,shanghai,beijing,shenzhen,wuhan,chengdu,chongqing,hong kong,taipei,singapore,tokyo,seoul,busan,london,paris,madrid
-POLYWEATHER_PREWARM_INCLUDE_DETAIL=true
-POLYWEATHER_PREWARM_INCLUDE_MARKET=true
-POLYWEATHER_PREWARM_FORCE_REFRESH=false
-POLYWEATHER_BACKEND_URL=http://polyweather_web:8000
-```
-
-说明：
-
-- 这组变量用于后台定向预热热点城市，避免用户点击城市时才冷启动拉 detail。
-- 如果使用独立 `polyweather_prewarm` 容器，`POLYWEATHER_BACKEND_URL` 必须指向容器网络中的 `polyweather_web`。
-
-### 6.4 Groq 解读增强层
-
-```env
-POLYWEATHER_GROQ_COMMENTARY_ENABLED=true
-GROQ_API_KEY=...
-POLYWEATHER_GROQ_COMMENTARY_MODEL=openai/gpt-oss-20b
-POLYWEATHER_GROQ_COMMENTARY_TIMEOUT_SEC=8
-POLYWEATHER_GROQ_COMMENTARY_CACHE_TTL_SEC=1800
-```
 
 说明：
 
 - 这层只负责把结构化信号改写成短摘要，不替代真实模型、机场锚点和结算逻辑。
-- Groq 调用失败时，系统会自动回退到规则文案。
 
 ### 6.5 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：
+这套配置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
@@ -348,7 +281,6 @@ 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：
@@ -356,7 +288,6 @@ POLYMARKET_WALLET_ACTIVITY_ENABLED=false
 - `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 分钟主动推送一轮全局机会清单；每轮会先扫描完整 `TELEGRAM_ALERT_CITIES`，再选 Top N。
 - `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
 

docs/EMOS_LGBM_SYSTEM_ZH.md

@@ -1,685 +0,0 @@
-# EMOS + LGBM 系统说明（中文）
-
-最后更新：`2026-04-19`
-
-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`
-
-这两个城市配置了 `Wunderground` 历史页面作为历史观测取数入口。
-
-这里要注意产品文案口径：
-
-- `Wunderground` 不是物理观测站
-- 它只是历史页面 / 数据入口
-- 机场类市场仍应以 METAR / 机场主站作为结算锚点
-- 明确官方站点市场才以规则指定的官方站点作为最终结算锚点
-
-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，因为它们的历史页面取数和普通 METAR bootstrap 不同。
-
----
-
-## 8. 当前线上/离线运行模式
-
-### 8.1 概率引擎模式
-
-当前生产主概率应保持：
-
-- `legacy`
-
-如果需要观察 EMOS 对照，可切：
-
-- `emos_shadow`
-
-而不是：
-
-- `emos_primary`
-
-原因不是工程没接好，而是主概率发布必须由离线评估结果决定。VPS 轻量训练候选未通过门禁；本地训练候选虽通过门禁，但仍建议先 shadow 观察，再人工决定是否切主。
-
-### 8.2 LGBM 角色
-
-当前 `LGBM` 仍然只能算：
-
-- 辅助预测源
-- 研究/观测链路
-- 校准概率层的一个可用引擎输入
-
-不适合替代 `DEB` 主路径。
-
-前端展示上，`LGBM 校准概率` 代表概率层已使用 LGBM 上下文生成桶分布；它不是把模型四舍五入票数直接当成概率。模型共识仍只是解释层，市场价格也只作为参考层。
-
----
-
-## 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
-
-最近两轮评估给出了更清晰的结论。
-
-VPS 轻量训练候选：
-
-- 版本：`emos-auto-20260418204203`
-- `sample_count = 791`
-- `delta_crps = +0.004652`
-- `delta_mae = +0.102623`
-- `delta_bucket_hit_rate = -0.137800`
-- 结论：`hold`
-
-本地训练候选：
-
-- 版本：`emos-auto-20260418212046`
-- `sample_count = 847`
-- `delta_crps = -0.036170`
-- `delta_mae = -0.007896`
-- `delta_bucket_hit_rate = -0.009445`
-- 结论：`promote`
-
-这说明：
-
-- EMOS 工程链路有效，本地用更多 snapshot 训练时可以超过 legacy 的 CRPS/MAE。
-- 低配 VPS 不适合做主训练环境。
-- 通过门禁不等于立即默认主用，仍应先 `emos_shadow` 观察。
-
-当前生产策略仍然是：
-
-- 用户主概率默认 `legacy`
-- EMOS 通过本地训练产生候选
-- 通过门禁后先以 `emos_shadow` 灰度
-- 连续稳定后才考虑 `emos_primary`
-
-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：
-
-- 有效样本仍然不大，城市级样本分布不均
-- 桶概率容易受结算边界影响
-- 需要避免 VPS 训练消耗线上资源
-- `emos_primary` 发布需要明确人工门禁
-
-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
-- 本地训练可通过门禁
-- 生产主用仍需 shadow 观察与人工发布
-
----
-
-## 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. 看本地 EMOS 候选是否通过门禁
-5. 看 VPS 是否只加载已批准参数，不在低配机器上训练
-
-### 15.2 周期性重训
-
-建议在本地开发机执行，不建议在低配 VPS 上执行：
-
-```powershell
-scp root@38.54.27.70:/var/lib/polyweather/polyweather.db E:\web\PolyWeather\data\polyweather-prod.db
-$env:POLYWEATHER_DB_PATH="E:\web\PolyWeather\data\polyweather-prod.db"
-$env:POLYWEATHER_RUNTIME_DATA_DIR="E:\web\PolyWeather\artifacts\local_runtime"
-python scripts\auto_retrain_probability_calibration.py --verbose --snapshot-limit 50000
-```
-
-只有 `auto_retrain_report.json` 里 `ready_for_promotion=true` 时，才允许把候选 `default.json` 传回 VPS。
-
-### 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，但生产主概率仍必须由评估门禁控制；当前最正确的策略是继续以 `DEB/legacy` 为主路径，在本地训练 EMOS 候选，VPS 只加载已批准参数。**
-
-更具体一点：
-
-- `EMOS`
-  - 已接好
-  - 可训练
-  - 可评估
-  - 可 shadow
-  - 通过门禁后可灰度
-  - 不应在低配 VPS 上自动训练或自动主用
-
-- `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

@@ -1,263 +0,0 @@
-# EMOS 训练与发布报告（2026-04-19）
-
-## 1. 当前结论
-
-- `EMOS` 工程链路已经接通：可以训练、评估、生成候选参数，并在前端以校准概率层展示。
-- 生产主概率当前不应默认使用 `emos_primary`。默认建议为 `legacy`；需要观察时使用 `emos_shadow`。
-- `emos_primary` 只允许在本地离线训练通过门禁、人工复核后手动灰度。
-- 低配 VPS（例如 1 vCPU / 2GB RAM）不适合做 EMOS 全量训练；VPS 只负责采集、服务和加载已批准的参数文件。
-- `LGBM` 当前仍不建议作为主路径，继续保持 `POLYWEATHER_LGBM_ENABLED=false`。
-
-## 2. 最近两次训练结果
-
-### 2.1 VPS 轻量训练：不通过
-
-VPS 使用最近 `5000` 条 snapshot 训练的候选：
-
-- 版本：`emos-auto-20260418204203`
-- 样本数：`791`
-- 结论：`hold`
-
-| 指标 | 变化 |
-| :-- | --: |
-| `delta_crps` | `+0.004652` |
-| `delta_mae` | `+0.102623` |
-| `delta_bucket_hit_rate` | `-0.137800` |
-
-解读：CRPS、MAE、桶命中全部弱于 legacy，因此不能晋级。
-
-### 2.2 本地训练：通过门禁，但仍需灰度
-
-本地电脑使用生产 SQLite 副本与最近 `50000` 条 snapshot 训练的候选：
-
-- 版本：`emos-auto-20260418212046`
-- 样本数：`847`
-- 结论：`promote`
-
-| 指标 | 变化 |
-| :-- | --: |
-| `delta_crps` | `-0.036170` |
-| `delta_mae` | `-0.007896` |
-| `delta_bucket_hit_rate` | `-0.009445` |
-
-解读：
-
-- CRPS 与 MAE 有改善，候选通过当前门禁。
-- 桶命中率轻微下降，虽然在门禁允许范围内，但仍建议先以 `emos_shadow` 观察，再决定是否切 `emos_primary`。
-
-## 3. 生产运行策略
-
-推荐生产 `.env`：
-
-```env
-POLYWEATHER_PROBABILITY_ENGINE=legacy
-POLYWEATHER_PROBABILITY_CALIBRATION_FILE=/var/lib/polyweather/probability_calibration/default.json
-```
-
-观察 EMOS 时：
-
-```env
-POLYWEATHER_PROBABILITY_ENGINE=emos_shadow
-POLYWEATHER_PROBABILITY_CALIBRATION_FILE=/var/lib/polyweather/probability_calibration/default.json
-```
-
-只有在候选连续通过评估、前端展示稳定、业务侧确认后，才切：
-
-```env
-POLYWEATHER_PROBABILITY_ENGINE=emos_primary
-POLYWEATHER_PROBABILITY_CALIBRATION_FILE=/var/lib/polyweather/probability_calibration/default.json
-```
-
-验证线上加载状态：
-
-```bash
-docker compose exec -T polyweather_web python - <<'PY'
-from src.analysis.probability_calibration import load_calibration, resolve_probability_engine_mode
-cal = load_calibration()
-print("engine_mode =", resolve_probability_engine_mode())
-print("loaded_version =", cal.get("version"))
-print("sample_count =", (cal.get("metrics") or {}).get("sample_count"))
-print("has_global =", bool(cal.get("global")))
-PY
-```
-
-## 4. 本地训练 SOP
-
-### 4.1 拉取生产 SQLite 副本
-
-推荐先在 VPS 上用 SQLite 在线备份生成快照：
-
-```bash
-sqlite3 /var/lib/polyweather/polyweather.db ".backup '/var/lib/polyweather/polyweather-train-copy.db'"
-```
-
-本地 PowerShell 拉取：
-
-```powershell
-cd E:\web\PolyWeather
-scp root@38.54.27.70:/var/lib/polyweather/polyweather-train-copy.db E:\web\PolyWeather\data\polyweather-prod.db
-```
-
-如果生产库写入压力很低，也可以直接拉主库副本：
-
-```powershell
-scp root@38.54.27.70:/var/lib/polyweather/polyweather.db E:\web\PolyWeather\data\polyweather-prod.db
-```
-
-### 4.2 本地训练
-
-```powershell
-cd E:\web\PolyWeather
-$env:POLYWEATHER_DB_PATH="E:\web\PolyWeather\data\polyweather-prod.db"
-$env:POLYWEATHER_RUNTIME_DATA_DIR="E:\web\PolyWeather\artifacts\local_runtime"
-python scripts\auto_retrain_probability_calibration.py --verbose --snapshot-limit 50000
-```
-
-如果本地机器仍然较慢，可先降到：
-
-```powershell
-python scripts\auto_retrain_probability_calibration.py --verbose --snapshot-limit 20000
-```
-
-训练报告：
-
-```powershell
-Get-Content E:\web\PolyWeather\artifacts\local_runtime\probability_calibration\auto_retrain_report.json
-```
-
-候选目录：
-
-```text
-E:\web\PolyWeather\artifacts\local_runtime\probability_calibration\candidates\<version>\
-```
-
-### 4.3 晋级判断
-
-只有报告满足以下条件时，候选才可进入部署流程：
-
-```json
-"ready_for_promotion": true
-```
-
-同时人工检查：
-
-- `delta_crps <= 0`
-- `delta_mae <= 0.05`
-- `delta_bucket_hit_rate >= -0.05`
-- 城市级结果没有出现关键城市大幅退化
-- 前端概率分布没有明显过度摊平或异常偏桶
-
-## 5. 部署通过的候选
-
-把本地候选上传到 VPS：
-
-```powershell
-scp E:\web\PolyWeather\artifacts\local_runtime\probability_calibration\candidates\<version>\default.json root@38.54.27.70:/var/lib/polyweather/probability_calibration/default.json
-```
-
-VPS 上优先设置为 `emos_shadow`：
-
-```env
-POLYWEATHER_PROBABILITY_ENGINE=emos_shadow
-POLYWEATHER_PROBABILITY_CALIBRATION_FILE=/var/lib/polyweather/probability_calibration/default.json
-```
-
-重启：
-
-```bash
-cd /root/PolyWeather
-docker compose up -d polyweather_web
-```
-
-观察稳定后再考虑 `emos_primary`。
-
-## 6. VPS 定时训练策略
-
-当前策略：**不在 VPS 上做 EMOS 定时训练**。
-
-原因：
-
-- 生产 SQLite 的 `probability_training_snapshots_store` 会持续增长。
-- 低配 VPS 全量扫描会造成 CPU/IO 飙升，严重时影响 SSH 和线上服务。
-- VPS 训练用较小 `--snapshot-limit` 虽然安全，但训练效果可能弱于本地。
-
-如果曾经加过 cron，应删除：
-
-```bash
-crontab -l | grep -v 'auto_retrain_probability_calibration.py' | crontab -
-```
-
-确认：
-
-```bash
-crontab -l
-```
-
-## 7. 自动重训脚本说明
-
-脚本：
-
-```text
-python scripts\auto_retrain_probability_calibration.py
-```
-
-默认行为：
-
-- 生成新的 EMOS candidate。
-- 对 candidate 跑离线评估。
-- 写入候选目录和门禁报告。
-- 不覆盖线上 `default.json`。
-
-重要参数：
-
-- `--verbose`：输出训练/评估进度。
-- `--snapshot-limit N`：只使用最近 N 条 snapshot。
-- `--promote-if-passed`：门禁通过后覆盖目标参数文件。
-- `--run-tests`：晋级前跑测试。
-
-当前不建议在 VPS 使用 `--promote-if-passed`。本地训练通过后，仍优先人工上传并使用 `emos_shadow`。
-
-## 8. 门禁阈值
-
-默认阈值：
-
-- `POLYWEATHER_EMOS_AUTO_MIN_SAMPLES=50`
-- `POLYWEATHER_EMOS_AUTO_MAX_DELTA_CRPS=0`
-- `POLYWEATHER_EMOS_AUTO_MAX_DELTA_MAE=0.05`
-- `POLYWEATHER_EMOS_AUTO_MIN_DELTA_BUCKET_HIT_RATE=-0.05`
-
-解释：
-
-- `CRPS` 不允许比 legacy 更差。
-- `MAE` 最多允许轻微退化 `0.05`。
-- `bucket_hit_rate` 是业务参考指标，但对结算边界敏感，不单独作为唯一判断。
-
-## 9. 前端说明
-
-今日日内分析中的概率区展示的是当前生产概率引擎输出：
-
-- `legacy`：展示现有动态概率。
-- `emos_shadow`：用户主概率仍为 legacy，EMOS 仅用于对照和评估。
-- `emos_primary`：用户主概率使用 EMOS 校准分布。
-
-对外文案应避免暗示“EMOS 一定更准”。推荐解释为：
-
-> EMOS 是 PolyWeather 基于 DEB 路径、多模型集合、METAR 实测进度和历史误差结构生成的统计校准概率，不是外部天气模型，也不是直接 API 结果。
-
-## 10. 已验证
-
-本地训练链路已验证：
-
-```text
-python scripts\auto_retrain_probability_calibration.py --verbose --snapshot-limit 50000
-```
-
-测试链路已验证：
-
-```text
-python -m pytest tests\test_auto_retrain_probability_calibration.py tests\test_probability_calibration.py tests\test_probability_rollout.py
-```
-
-当前工程结论：
-
-**EMOS 可以继续本地训练与 shadow 观察，但生产主概率不应因为“机制接好”而默认切到 `emos_primary`。**

docs/FRONTEND_DEPLOYMENT_ZH.md

@@ -93,7 +93,7 @@ POLYWEATHER_OPS_ADMIN_EMAILS=yhrsc30@gmail.com
 
 ```env
 NEXT_PUBLIC_TELEGRAM_GROUP_URL=https://t.me/<your_group>
-NEXT_PUBLIC_TELEGRAM_BOT_URL=https://t.me/WeatherQuant_bot
+NEXT_PUBLIC_TELEGRAM_BOT_URL=https://t.me/polyyuanbot
 ```
 
 只影响按钮跳转，不影响核心页面加载。

docs/LGBM_DAILY_HIGH_ZH.md

@@ -1,325 +0,0 @@
-# LightGBM 日最高温模型（中文）
-
-最后更新：`2026-04-18`
-
-## 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
-- 原始独立概率分布
-- 独立结算源
-
-注意：前端出现的“LGBM 校准概率”不是把 LGBM 模型票数直接当成概率，而是概率层基于 LGBM / DEB / 观测上下文输出的校准分布。模型共识只保留为解释性参考。
-
-## 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
-
-## 8.1 前端概率展示口径
-
-当前网页的概率区按以下顺序解释：
-
-1. 如果后端 `probabilities.engine` 表示 LGBM 校准概率可用，则标题显示为 `LGBM 校准概率`。
-2. 如果 LGBM 不可用，但 EMOS / legacy 概率可用，则显示为 `校准模型概率`。
-3. 模型舍入票数只保留为“模型共识参考”，用于说明哪些模型四舍五入后落在同一温度档，不作为最终命中概率。
-4. 市场价格只保留为“市场参考”，不和校准概率混成同一结论。
-
-这能避免用户把 `4/8 模型支持 82°F` 误读成 `82°F 有 50% 概率`。模型共识是解释层，概率引擎才是结论层。
-
-## 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/MODEL_STACK_AND_DEB_ZH.md

@@ -99,7 +99,7 @@ Web API 会把这部分元数据挂到：
 - RDPS
 - HRDPS
 
-亚洲城市更依赖本地观测增强层，例如 JMA、KMA、NMC、HKO、CWA、METAR、TAF。
+亚洲城市更依赖本地观测增强层，例如 JMA、AMOS（首尔/釜山）、NMC、HKO、CWA、METAR、TAF。
 
 ## 4. DEB 家族去重
 
@@ -151,7 +151,6 @@ HRDPS > RDPS > GDPS > GEM
 - MGM
 - NWS
 - HKO
-- LGBM
 - Open-Meteo
 
 ECMWF IFS 与 ECMWF AIFS 分开保留，因为前者是传统 NWP，后者是 AIFS 模型。
@@ -219,7 +218,6 @@ raw current_forecasts
 当前前端把三层拆开展示：
 
 - `模型区间与分歧`：解释不同模型当前给出的最高温范围和分歧，不直接等于命中概率。
-- `校准模型概率`：由当前生产概率引擎输出温度桶概率；默认可保持 legacy，EMOS / LGBM 只在评估通过、显式启用或 shadow 对照时进入展示。
 - `市场参考`：只展示市场价格和错价背景，不再作为主判断，也不默认输出 BUY YES / BUY NO。
 
 模型票数只用于解释“哪些模型支持某个档位”，不等于最终概率。最终概率应优先读取 `probabilities.engine` 对应的校准分布。
@@ -230,7 +228,6 @@ raw current_forecasts
 
 - `tests/test_multi_model_sources.py`
 - `tests/test_deb_model_family.py`
-- `tests/test_lgbm_features.py`
 
 重点覆盖：
 

docs/MONITORING_ZH.md

@@ -114,12 +114,6 @@ python scripts/check_ops_health.py --base-url http://127.0.0.1:8000
 
 目前已覆盖：
 
-- `prewarm` worker 是否启用、线程 / heartbeat 是否活着
-- 最近一轮 prewarm 的：
-  - `cycle_count`
-  - `success_count / failure_count`
-  - `last_started_at / last_finished_at`
-  - `last_summary_ok / last_detail_ok / last_market_ok`
 - 缓存桶条目数：
   - `api_cache`
   - `metar`

docs/OPS_ADMIN_ZH.md

@@ -25,7 +25,6 @@ POLYWEATHER_OPS_ADMIN_EMAILS=yhrsc30@gmail.com
 - 系统健康
 - SQLite / rollout / metrics 摘要
 - 支付运行态
-- prewarm worker 运行态
 - 缓存桶状态与 summary cache hit/miss
 - 当前会员
 - 周榜
@@ -101,11 +100,9 @@ python scripts/reconcile_subscription_by_email.py --email <user_email>
 
 ## 7. 备注
 
-### 7.1 当前 prewarm / 缓存观测项
 
 `/ops` 里的系统状态卡目前已额外展示：
 
-- `prewarm` 是否启用
 - `thread_alive` / `heartbeat_age_sec`
 - 最近一轮：
   - `cycle_count`

docs/PROBABILITY_SNAPSHOT_ARCHIVE_ZH.md

@@ -1,347 +0,0 @@
-# 概率训练样本归档说明（中文）
-
-最后更新：`2026-04-19`
-
-## 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`
-- `probability_engine`
-- `calibration_mode`
-- `calibration_version`
-- `raw_mu / raw_sigma`
-- `calibrated_mu / calibrated_sigma`
-- `shadow_distribution`
-- 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_mode": "emos_shadow",
-  "calibration_version": "emos-20260320130245",
-  "calibrated_mu": 15.4,
-  "calibrated_sigma": 1.1
-}
-```
-
-当天结束后，再由后处理脚本回填：
-
-- `actual_high`
-- `settlement_bucket`
-
-当前前端把这类快照解释为“校准模型概率”。如果 `probability_engine` 为 LGBM 相关值，则显示为 LGBM 校准概率；模型舍入票数和市场价格只用于解释，不直接作为最终概率。
-
-## 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
-
-推荐在本地电脑使用生产 SQLite 副本训练，不建议在低配 VPS 上训练：
-
-```powershell
-scp root@38.54.27.70:/var/lib/polyweather/polyweather.db E:\web\PolyWeather\data\polyweather-prod.db
-$env:POLYWEATHER_DB_PATH="E:\web\PolyWeather\data\polyweather-prod.db"
-$env:POLYWEATHER_RUNTIME_DATA_DIR="E:\web\PolyWeather\artifacts\local_runtime"
-python scripts\auto_retrain_probability_calibration.py --verbose --snapshot-limit 50000
-```
-
-作用：
-
-- 生成新的候选 `default.json`
-- 同时生成 `evaluation_report.json` 与 `auto_retrain_report.json`
-- 不自动覆盖线上参数
-
-### 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，当前推荐流程是：
-
-```powershell
-scp root@38.54.27.70:/var/lib/polyweather/polyweather.db E:\web\PolyWeather\data\polyweather-prod.db
-$env:POLYWEATHER_DB_PATH="E:\web\PolyWeather\data\polyweather-prod.db"
-$env:POLYWEATHER_RUNTIME_DATA_DIR="E:\web\PolyWeather\artifacts\local_runtime"
-python scripts\auto_retrain_probability_calibration.py --verbose --snapshot-limit 50000
-```
-
-只有 `auto_retrain_report.json` 中 `ready_for_promotion=true`，才把候选参数传回 VPS，并优先用 `emos_shadow` 观察。
-
-如果只是做历史真值补数，才需要额外执行：
-
-```bash
-python scripts/backfill_historical_weather.py
-python scripts/build_settlement_history_from_csv.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
-
-当前自动门禁至少要求：
-
-- `CRPS` 下降
-- `MAE` 最多轻微退化 `0.05`
-- `Bucket Hit Rate` 退化不超过 `0.05`
-
-人工复核还应看城市级结果，避免少数关键城市大幅退化。`Bucket Hit Rate` 受整数结算边界影响大，不能单独作为唯一判断。
-
-## 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 周在本地电脑重新训练一次
-5. VPS 只加载通过评估的参数文件，不做全量训练
-
-## 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/SERVICES_ZH.md

@@ -0,0 +1,53 @@
+# 外部服务依赖总览
+
+最后更新：`2026-05-23`
+
+项目调用了 20 个外部服务，按状态分为三类。
+
+## 核心（必须有，挂了服务不可用）
+
+| 服务                   | 用途                  | 状态 |
+| ---------------------- | --------------------- | ---- |
+| Open-Meteo             | 52 城天气预报         | ✅   |
+| AviationWeather (NOAA) | METAR/TAF 航空观测    | ✅   |
+| MADIS (NOAA)           | 美国 5 分钟高频观测   | ✅   |
+| Supabase               | 用户认证 + 订阅       | ✅   |
+| Telegram Bot API       | Bot 消息 + 群成员检查 | ✅   |
+| KNMI                   | Amsterdam 10 分钟观测 | ✅   |## 国家气象源（特定城市必须）
+
+| 服务                 | 城市                  | 状态        |
+| -------------------- | --------------------- | ----------- |
+| JMA (日本)           | Tokyo                 | ✅          |
+| KMA + AMOS (韩国)    | Seoul, Busan          | ✅          |
+| AMSC AWOS (中国)     | 北京/上海/广州等 6 城 | ✅          |
+| MGM (土耳其)         | Ankara, Istanbul      | ✅          |
+| FMI (芬兰)           | Helsinki              | ✅          |
+| HKO (香港)           | Hong Kong             | ✅          |
+| CWA (台湾)           | Taipei                | ✅          |
+| NMC (中国)           | 国内城市 fallback     | ✅          |
+| Singapore MSS        | Singapore             | ✅          |
+| IMGW (波兰)          | Warsaw                | ⚠️ 未配 key |
+| Russia pogodaiklimat | Moscow                | ❌ 已移除   |
+
+## 可选 / 已禁用
+
+| 服务           | 用途          | 状态        |
+| -------------- | ------------- | ----------- |
+| OpenWeatherMap | 天气 fallback | ⚠️ 未配 key |
+| VisualCrossing | 历史天气      | ⚠️ 未配 key |
+| Meteoblue      | 天气预报      | ❌ 已移除   |
+| SynopticData   | 美国站点观测  | ⚠️ 未配 key |
+
+## AI / 其他
+
+| 服务              | 用途             | 状态        |
+| ----------------- | ---------------- | ----------- |
+| MiMo (xiaomimimo) | 城市分析 AI 评论 | ✅ 当前使用 |
+| DeepSeek          | AI fallback      | - 备用      |
+| Groq              | AI commentary    | ❌ 已移除   |
+| Polygon RPC       | 链上支付         | ✅          |
+| WalletConnect     | 前端钱包连接     | ⚠️ 未配 key |
+
+## 合计
+
+15 个在用，3 个可选/未配置，3 个已移除。

docs/SUPABASE_SETUP_ZH.md

@@ -1,4 +1,4 @@
-# Supabase + 登录 + 支付接入说明（v1.5.1）
+# Supabase + 登录 + 支付接入说明（v1.7.0）
 
 最后更新：`2026-03-14`
 
@@ -92,20 +92,7 @@ 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
+## 5. 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`。

docs/TECH_DEBT_ZH.md

@@ -1,4 +1,4 @@
-# 技术债与工程待办（v1.6.0）
+# 技术债与工程待办（v1.7.0）
 
 最后更新：`2026-05-10`
 
@@ -29,7 +29,6 @@ flowchart TD
     end
 
     subgraph S["状态与概率"]
-        S1["EMOS 本地训练与 shadow 发布门禁"]
     end
 
     A --> P
@@ -47,16 +46,13 @@ flowchart TD
 - 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 校准链路已接通；生产主概率保持 `legacy` 或 `emos_shadow`，`emos_primary` 只允许本地训练通过门禁后人工灰度。
 
 ## 3. 高优先级技术债
 
 | 项目 | 影响 | 建议动作 |
 | :-- | :-- | :-- |
-| EMOS 发布门禁 | 低配 VPS 不适合训练，主概率不能绕过评估 | 本地拉生产 SQLite 训练，`ready_for_promotion=true` 后先 `emos_shadow` |
 | 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 |
 
@@ -64,7 +60,7 @@ flowchart TD
 
 | 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） |
+| 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/首次消息奖励/欢迎奖励/周排名奖励/周参与奖/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 |
 
@@ -77,6 +73,5 @@ flowchart TD
 
 ## 6. 下阶段里程碑
 
-1. 固化 EMOS 本地训练流程，禁止低配 VPS 自动训练和自动主用。
 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/data-architecture-review.md

@@ -0,0 +1,102 @@
+# PolyWeather 数据链路架构审查
+
+> 审查日期：2026-06 | 视角：系统架构师 | 范围：完整数据采集→分析→API→前端状态
+> 
+> **修复状态：8/8 已完成**
+
+## 一、数据架构总览
+
+```
+外部数据源                   Python 后端                         Next.js 前端
+===========                  ==========                          ===========
+
+Open-Meteo (预报+多模型) ─┐
+METAR/TAF (航空气象)     ─┤
+NWS (美国) / MGM (土耳其) ─┤
+JMA/AMOS/NMC/HKO/CWA    ─┤
+Wunderground / NOAA      ─┤
+Polymarket Gamma/CLOB    ─┤
+                              ├─ WeatherDataCollector            ├─ dashboard-client.ts
+                              │   (内存缓存 + SQLite磁盘缓存)      │   (ETag浏览器缓存 + SWR)
+                              │                                  │
+                              ├─ _analyze()                      ├─ useDashboardStore
+                              │   ├─ DEB 融合 (11模型加权)        │   (双Context拆分)
+                              │   └─ 趋势引擎                    │   (扫描数据预加载)
+                              │                                  │
+                              ├─ scan_terminal_service.py        ├─ 扫描终端查询
+                              │   ├─ ThreadPoolExecutor(4)       │   (120s TTL)
+                              │   └─ AI 增强层 (DeepSeek)        │
+                              │                                  │
+                              └─ FastAPI routes                  └─ API代理 (Next.js rewrites)
+                                  (36个端点 + ETag 304)
+```
+
+## 二、数据采集层
+
+### 源端（14个外部源）
+
+| 源 | 类型 | 覆盖 | TTL |
+|------|------|---------|------|
+| Open-Meteo | 预报 + 多模型集合 | 全球 | 300s |
+| METAR | 机场观测 | 全球 ICAO | 60s |
+| TAF | 机场预报 | 全球 ICAO | 600s |
+| NWS | 国家预报 | 美国 | 按请求 |
+| MGM | 国家官方 | 土耳其 | 300s |
+| ECMWF/GFS/ICON/GEM/JMA | 多模型 NWP | 全球 | 300s |
+| HKO/CWA/NOAA/AMOS/NMC | 结算观测 | 特定国家 | 60s (AMOS) / 300s |
+| Wunderground | 个人气象站 | 全球备用 | 按请求 |
+| Polymarket Gamma | 市场发现 | 所有温度市场 | 60s |
+| Polymarket CLOB | 订单簿 | 匹配市场 | 30s |
+
+### 待改进
+
+| # | 问题 | 优先级 |
+|---|------|------|
+| 1 | 无源端健康状态检测 | 🟡 |
+| 2 | METAR TTL 60s 过于激进（机场每小时发一次） | 🟡 |
+| 3 | 无请求重试（`POLYWEATHER_HTTP_RETRY_COUNT` 默认 0） | 🟡 |
+
+## 三、分析层
+
+### DEB 动态集成混合
+
+自适应加权：11 模型按过去 7 天 MAE 动态分配权重。回退链完善。
+
+
+### 概率校准
+
+
+**已修复：校准漂移检测** — `check_calibration_drift()` 对比最近 CRPS 与基线，漂移 >15% 时告警，集成在 `/api/system/status` 的 `probability.drift` 字段。
+
+| # | 问题 | 优先级 |
+|---|------|------|
+| 4 | 校准系数静态 JSON 文件，数据分布变化需手动重新训练 | 🟡 |
+
+## 四、API 与缓存层
+
+**已修复：ETag 304** — 后端 `_etag_middleware` 对 GET /api/* 自动返回 ETag (MD5)，支持 `If-None-Match`，匹配返回 304 + `Cache-Control: private, max-age=30`。
+
+**已修复：TTL 匹配** — `SCAN_TERMINAL_PAYLOAD_TTL_SEC` 30s → 120s，匹配 ThreadPoolExecutor(4)×60 城的实际重算耗时。
+
+| # | 问题 | 优先级 |
+|---|------|------|
+| 5 | 缓存键过粗（city::mode），微小变化也触发完整重算 | 🟡 |
+
+## 五、前端状态管理
+
+**已修复：sessionStorage 限制** — 只保留最近 3 个城市的详情，避免 3-10MB JSON 序列化阻塞主线程。
+
+**已修复：Context 拆分** — `CityDetailsContext` 独立管理 `cityDetailsByName` 变更，新增 `useCityDetails` hook。只读详情数据的组件不因其他状态变化而重渲染。
+
+**已修复：Stale-while-revalidate** — `ensureCityDetail` 过期缓存立即返回 + 后台异步刷新，用户不再看到 loading spinner。
+
+**已修复：扫描数据复用** — `preloadCityFromRow()` 从扫描终端行预填充城市详情缓存，选城市后详情面板立即显示。
+
+## 六、待办
+
+| # | 问题 | 优先级 | 说明 |
+|---|------|------|------|
+| 1 | 校准系数需手动重新训练 | 🟡 | 漂移检测已有，但自动触发重训练需要 GPU/算力资源 |
+| 2 | 缓存键过粗 — `city::mode` 粒度 | 🟢 | 微小温度变化触发完整重算，可考虑内容 hash 键 |
+
+> 注：原审查中 METAR TTL 60s 实际为 600s（误诊）；扫描终端轮询已有 `AbortController` + `requestSeq` 保护（误诊）。

docs/deep-research-report.md

@@ -2,22 +2,20 @@
 
 ## 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 `2026-04-27`，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”。2026-04 下旬新增的前端城市决策卡把“多模型 + METAR + 市场桶”进一步组合成面向单城点击的解释层：AI 机场报文解读、最高温中枢、完整市场桶匹配与“模型-市场差”已成为 Scan Terminal 的核心决策入口。
-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` 的历史页面回填也已接通，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、城市决策卡可观测性、支付合约防护升级**这五条线持续收口。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→前端决策→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。
+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）**；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。
+
+> **2026-05-23 更新（v1.7.0）**：Polymarket 价格拉取与 UI 层（MarketDecisionLine）已删除，`market_scan` 当前返回空；LGBM 已完全移除，概率引擎仅保留 legacy 高斯 + EMOS/CRPS；Groq、Meteoblue、NMC、pogodaiklimat 数据源和 prewarm 预热系统已移除。
 ## 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五层：
-**天气层（数据源/采集）**：聚合 52 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）、以及部分城市使用明确官方站点或历史页面入口（香港 HKO、台湾/深圳相关历史页面等）等。机场类市场仍以 METAR / 机场主站为结算锚点，Wunderground 不描述为物理观测站。
+**天气层（数据源/采集）**：聚合 51 个城市的实测与预报；支持 AviationWeather METAR（机场观测）、韩国 AMOS 跑道级观测（首尔/釜山）、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）、以及部分城市使用明确官方站点或历史页面入口（香港 HKO、台湾/深圳相关历史页面等）等。机场类市场仍以 METAR / 机场主站为结算锚点，Wunderground 不描述为物理观测站。
 **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。
 **城市决策层（Scan Terminal / AI 机场报文解读）**：地图点击城市后加入城市决策卡，前端拉取 full detail、多模型区间、最新 METAR，并通过 `/api/scan/terminal/ai-city/stream` 生成城市级 AI 解读。该解读由 `final_judgment`、`metar_read`、`reasoning`、`model_cluster_note`、`risks` 与原始 METAR 证据组成；最高温中枢优先使用 AI `predicted_max`，再回退到 DEB、多模型中心、日内 pace 或当前实测。
-**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，并用完整 `all_buckets` 对目标温度桶做 exact/range/“or higher”/“or lower” 严格匹配，计算“模型-市场差”（模型概率 − 市场隐含概率）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。
+**市场层（Polymarket 行情对照）**：*[v1.7.0 已移除]* 原先从 Gamma API 发现市场、从 CLOB 读取价格/盘口并计算”模型-市场差”，已于 2026-05-23 随 Polymarket 价格拉取层一并删除。当前 `market_scan` 返回空。
+**商业化与支付**：订阅（`Pro Monthly 10 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） 
@@ -33,9 +31,8 @@ DEB（Dynamic Error Balancing）基于过去 N 天模型误差（MAE）倒数加
 | 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；Scan Terminal 已新增城市决策卡、AI 机场报文解读、页面内存/localStorage 双层缓存、AI stream 小并发队列与完整市场桶映射。 |
 | 运行时组件 | `web/app.py` + `web/core.py` + `web/routes.py` + `web/analysis_service.py` + `web/scan_terminal_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；Scan Terminal 侧提供 `/api/scan/terminal/ai-city/stream`，城市 AI 默认 30s 超时并支持 stream parse failure 的非流式重试。 |
 | 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/data_collection/*` | 天气采集 + 城市注册 | 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`、`amos_station_sources.py`、`jma_amedas_sources.py`、`nws_open_meteo_sources.py`、`country_networks.py` 等。v1.7.0 已移除 NMC、pogodaiklimat、Meteoblue 数据源。 |
 | 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。 |
@@ -69,11 +66,10 @@ JSON[Legacy JSON files<br/>migration/export/explicit fallback only]
  OM[Open-Meteo Forecast/Ensemble/Multi-model]
  AW[AviationWeather Data API<br/>METAR]
  MGM[MGM Turkey]
+ AMOS[global.amo.go.kr<br/>AMOS runway sensors]
  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
@@ -95,12 +91,11 @@ JSON[Legacy JSON files<br/>migration/export/explicit fallback only]
  WX --> OM
  WX --> AW
  WX --> MGM
+ WX --> AMOS
  WX --> NWS
  WX --> HKO
  WX --> CWA
 
- FAST --> PM_G
- FAST --> PM_C
  FAST --> LLM
 
  FAST --> SB
@@ -118,7 +113,7 @@ JSON[Legacy JSON files<br/>migration/export/explicit fallback only]
 
 
 
-### 城市决策卡工作流（2026-04 更新）
+### 城市决策卡工作流（2026-05 更新）
 
 Scan Terminal 的城市决策卡现在承担“从天气分析到市场动作解释”的前端决策层：
 
@@ -148,13 +143,12 @@ Web/Telegram 请求 → FastAPI 调用采集器抓取/复用缓存 → 分析引
 
 **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）。前端侧已通过 `npm run build` 验证 Scan Terminal 改动可以编译；后续仍建议为城市决策卡补固定 fixture，覆盖 `all_buckets` 匹配、温度单位渲染、AI 缓存 key 与 stream 队列行为。
 **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）。
+**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）。2026-05 完成积分制度改造：`/city` `/deb` 改为免费（每日各 10 次），新增首次发言欢迎奖励与每日首条消息奖励，周奖励降低赢家积分差距并增加全员参与奖。
 **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”。前端城市决策卡在此基础上补足“机场报文解释 + 市场桶动作口径”，让用户从地图点击可以直接进入可解释决策。
 **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。
@@ -164,41 +158,35 @@ Web/Telegram 请求 → FastAPI 调用采集器抓取/复用缓存 → 分析引
 **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` 补上了历史页面回填链路。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）以及城市 AI provider（OpenAI-compatible stream，当前临时 MiMo）。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。城市 AI 解读已经通过前端 2 并发队列、30s timeout、stream parse retry 与缓存 key 稳定化降低第三/第四城市失败概率，但仍需持续记录 stream duration、cache hit、retry、degraded 与 queue depth。
-**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”。
+项目强依赖外部 API（Open-Meteo、AviationWeather、global.amo.go.kr AMOS、NWS、HKO、CWA、Supabase）以及城市 AI provider（OpenAI-compatible stream，当前使用 MiMo）。其中 AviationWeather Data API 有明确速率限制；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。城市 AI 解读已经通过前端 2 并发队列、30s timeout、stream parse retry 与缓存 key 稳定化降低第三/第四城市失败概率，但仍需持续记录 stream duration、cache hit、retry、degraded 与 queue depth。
+
+> **v1.7.0 更新**：Polymarket（Gamma/CLOB）API 依赖已随市场价格拉取层一并移除。
 **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神经天气模型，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。
+3）**预测市场 API 客户端生态**（Polymarket/py-clob-client、aiopolymarket）：*[v1.7.0 后已不适用]* 市场价格拉取层已移除，此对标仅作历史参考。
 ### 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→校准概率桶→错价扫描→城市决策卡→订阅/支付 | 生产级应用（Web+Bot+API+支付） | 以工程能力为主；内置 DEB、LGBM/EMOS 校准概率、死盘判定、市场扫描；当前覆盖 52 城市，并已补齐真值治理、后台运维视图、AI 机场报文解读与 full bucket 决策映射。 | 主要依赖外部 API 与城市 AI provider；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）。 |
+| **Polymarket/py-clob-client** | Polymarket CLOB 读写 SDK | Python SDK | *[2026-05 起不再使用]* 官方 SDK，支持 read-only 与交易接口。 | 曾用作 PolyWeather 市场层参考。 | MIT。 |
+| **aiopolymarket** | Polymarket APIs 的 async 客户端 | Python async 客户端 | *[2026-05 起不再使用]* 类型安全（Pydantic）、自动分页、重试与 backoff。 | 曾用作市场层升级候选。 | 以仓库许可为准。 |
 
 **对标结论**：PolyWeather 与这类“全球神经天气模型”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 `2026-04-27` 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。
+下表按截至 `2026-05-23` 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` 的历史页面回填做抽查 → 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 | **城市决策卡 AI 解读可观测性与回放测试** | 3–5 天 | 降低第三/第四/第五城市 AI 解读失败，验证缓存与队列是否真正生效 | 外部 AI stream 仍可能超时或输出截断，若无指标很难复盘 | 1) 记录 city-ai stream status/duration/retry/degraded/cache-hit/queue-depth → 2) 增加固定 METAR + detail + all_buckets fixture → 3) 回归断言 bucket 匹配、模型-市场差、温度单位与缓存 key → 4) 将生产 env 建议同步进部署文档 |
-| 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 |
+| - | ~~市场层升级为 async + 类型安全~~ | N/A | *[v1.7.0 已移除]* 市场价格拉取层已删除，此改进项不再适用 | - | - |
 | 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 | **引入外部神经天气模型作为附加信号**（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 |
@@ -206,7 +194,7 @@ Web/Telegram 请求 → FastAPI 调用采集器抓取/复用缓存 → 分析引
 ### 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，并明确城市 AI 推荐配置（`POLYWEATHER_SCAN_CITY_AI_TIMEOUT_SEC=30`、`POLYWEATHER_SCAN_CITY_AI_MAX_TOKENS=900`、`POLYWEATHER_SCAN_CITY_AI_RETRY_ON_STREAM_PARSE_ERROR=true`）；（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、城市 AI stream timeout/JSON 截断、前端缓存异常、温度桶错配等典型故障处理。
+（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，并明确城市 AI 推荐配置（`POLYWEATHER_SCAN_CITY_AI_TIMEOUT_SEC=30`、`POLYWEATHER_SCAN_CITY_AI_MAX_TOKENS=900`、`POLYWEATHER_SCAN_CITY_AI_RETRY_ON_STREAM_PARSE_ERROR=true`）；（b）《数据源与合规说明》：列出 Open-Meteo、AviationWeather、NWS、HKO、CWA、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、城市 AI stream timeout/JSON 截断、前端缓存异常、温度桶错配等典型故障处理。
 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）城市决策卡 fixture 测试（固定 `detail/market_scan/all_buckets/METAR` → 断言 bucket mapping、模型-市场差、温度单位、AI 缓存 key 与排队提示）；（d）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。
@@ -221,34 +209,33 @@ PolyWeather 的评测应围绕“结算场景”而非传统数值天气预报
 **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“52 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
+**算力**：以上评测全部可在 CPU 上完成；数据量按“51 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 *[v1.7.0 已暂停]*
+
+> **2026-05-23 更新**：Polymarket 价格拉取层与市场扫描（`market_scan`）已于 v1.7.0 移除。本节基准评测方案暂不适用，留待未来若重新引入市场数据层时参考。
 
 **数据集**
 
-- 保存每次扫描输出：`date/city/bucket/bucket_label/bucket_direction/model_probability/market_implied/model_market_diff/yes_buy/quote_source/liquidity/matching_reason`，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）。
+- 若恢复：保存每次扫描输出：`date/city/bucket/bucket_label/bucket_direction/model_probability/market_implied/model_market_diff/yes_buy/quote_source/liquidity/matching_reason`，并加上未来 `settled_bucket` 作为标签。
 **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）。
+- 交易模拟（如需）：在考虑滑点/手续费/成交概率下的期望收益。
 **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）。
+- 改进策略：引入”流动性/盘口深度/波动”作为信号置信度。
 **算力**：CPU 即可；关键在于数据采样与回放。
 ## 路线图与风险缓解
 
@@ -257,7 +244,6 @@ PolyWeather 的评测应围绕“结算场景”而非传统数值天气预报
 | ----------- | ----------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------- |
 | 第 1 周 | 城市决策卡稳定性补强 | city-ai stream/cache/queue 指标；固定 METAR + `all_buckets` fixture；温度桶匹配与模型-市场差回归测试；生产 env 文档化 | 前端为主，后端补指标 |
 | 第 2 周 | 市场层与 Scan Terminal 数据回放 | 保存 `bucket_label/bucket_direction/model_market_diff/matching_reason`；支持回放第三/第四/第五城市 AI 解读失败案例 | 用真实失败样本压回归 |
-| 第 3–4 周 | EMOS 与长期特征继续收口 | 持续积累 `training_feature_records_store`；定期生成 evaluation/shadow/rollout report；继续观察 `bucket_brier` 是否退出 hold | 数据工程为主 |
 | 第 4–5 周 | 监控深挖与运维日报 | 来源 SLA、城市维度延迟、AI stream 状态、SQLite 体积、支付事件趋势、异常摘要 | 避免指标过多，先覆盖高频故障 |
 | 第 6 周 | 支付合约与发布门禁升级 | SafeERC20/Pausable 方案评审；CI required checks 与 release/tag 流程绑定；热修例外流程 | 合约升级需单独部署验证 |
 
@@ -270,6 +256,8 @@ PolyWeather 的评测应围绕“结算场景”而非传统数值天气预报
 **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
 
+> **v1.7.0 注**：以下 Polymarket 相关链接已不再被项目使用，保留作为历史参考。
+
 - 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/

docs/payments/POLYGONSCAN_VERIFY.md

@@ -1,4 +1,4 @@
-# PolyWeatherCheckout PolygonScan 验证（v1.5.1）
+# PolyWeatherCheckout PolygonScan 验证（v1.7.0）
 
 最后更新：`2026-03-20`
 

docs/product-review-jun-2026.md

@@ -0,0 +1,70 @@
+# PolyWeather 前端产品审查报告
+
+> 审查日期：2026-06 | 视角：产品经理 | 范围：`frontend/` 全部页面、组件、用户流程
+
+## 一、产品概览
+
+PolyWeather 是一个面向天气衍生品交易者的气象情报平台。核心价值主张：**结合多模型气象预报 + AI 机场报文解读 + Polymarket 市场价格，为交易决策提供一站式证据链。**
+
+### 产品分层
+
+| 层级 | 功能 | 门槛 |
+|------|------|------|
+| 免费 | 交互式全球天气地图 + 城市简报 | 无需登录 |
+| Pro 试用 | 3 天全功能 | 注册后自动获得 |
+| Pro 订阅 | 城市决策卡（AI 机场报文 + 模型证据 + 市场层）、日内分析、历史对账、未来预报 | 10 USDC/月（积分抵扣最多 3 USDC） |
+
+### 页面结构（9 个路由）
+
+| 路由 | 功能 | 是否必需登录 |
+|------|------|-------------|
+| `/` | 主看板 — AI 天气决策台 | 否 |
+| `/account` | 账户中心 — 身份/订阅/钱包/积分/Bot 绑定 | 是 |
+| `/auth/login` | 登录页（Google OAuth + 邮箱密码） | 否 |
+| `/docs/[...slug]` | 产品文档中心（8 篇双语文档） | 否 |
+| `/subscription-help` | 订阅 FAQ（双语） | 否 |
+| `/entitlement-required` | 访问被拒页面 | 否 |
+| `/ops` | 运营管理后台 | 是（管理员） |
+| `/ops/truth-history` | 真值历史查看器 | 是（管理员） |
+
+## 二、用户流程分析
+
+### 主看板的两个视图
+
+```
+┌─ 分布视图（地图） ──────────────────────────────┐
+│  Leaflet 交互式地图，城市彩色气泡                      │
+│  点击城市 → 自动添加到决策卡工作区 + 切换到卡片视图          │
+│  免费用户和 Pro 用户均可使用                            │
+├─ 决策卡（分析） ──────────────────────────────┤
+│  钉选的城市卡片：AI 机场解读 + 模型集群 + 市场层 + 图表       │
+│  需要 Pro 订阅                                        │
+└──────────────────────────────────────────────┘
+        └── 右侧栏：城市简报面板（始终可见，免费可用）
+```
+
+### 关键用户路径
+
+1. **新用户落地** → 看到 3 步引导 → 地图 + 城市列表 → 点击城市 → 看到城市简报 → 想深入分析 → 遇到 Pro 付费墙（含功能说明）
+2. **Pro 用户工作流** → 地图选城市 → 自动钉选到决策卡 → 展开卡片 → 阅读 AI 报文解读 → 查看市场层 → 判断交易方向
+
+## 三、做得好的地方
+
+1. **地图 → 决策卡的自动流转设计** — 点击地图城市自动钉选到分析工作区并切换视图，"零步骤发现"
+2. **双语覆盖完整** — 所有 UI 文案、文档、AI 解读都有中英文对照，覆盖率接近 100%
+3. **数据新鲜度可视化** — DataFreshnessBar 让用户一眼看到 METAR/模型/市场数据的新鲜度
+4. **AI 解读的产品化程度高** — 分层展示：快速判断 → 完整解读 → 证据链 → 风险提示
+5. **免费层有实际价值** — 地图 + 城市简报不是"空壳"，用户可以看真实气象数据
+6. **支付链路完整** — 从钱包绑定到链上签约到支付恢复，处理了多种异常情况
+7. **空状态有引导文字** — "Click a city on the map" 告诉用户下一步做什么
+8. **浅色/深色主题都有** — 两个主题都经过完整设计
+9. **Ops 面板功能齐全** — 系统健康、转化漏斗、缓存状态、支付异常、用户管理一览无余
+
+## 四、存在的问题与待办
+
+| # | 问题 | 优先级 |
+|---|------|------|
+| 1 | **Docs 无搜索** — 8 篇文档没有搜索功能，用户必须逐篇浏览 | 🟢 待做 |
+| 2 | **Ops 面板无审计日志** — 管理员补发积分等操作没有审计记录 | 🟢 需后端 |
+| 3 | **注册后邮件验证引导** — 未验证邮箱的用户反复登录失败 | 🟡 需后端 |
+

docs/superpowers/plans/2026-05-15-amsc-awos-runway-observations.md

@@ -0,0 +1,102 @@
+# AMSC AWOS Runway Observation Implementation Plan
+
+> **For agentic workers:** REQUIRED SUB-SKILL: Use superpowers:subagent-driven-development (recommended) or superpowers:executing-plans to implement this plan task-by-task. Steps use checkbox (`- [ ]`) syntax for tracking.
+
+**Goal:** Add a China-only AMSC AWOS runway observation source and expose it as a runway observation tab next to Market Monitor.
+
+**Architecture:** Backend fetches and normalizes AMSC `getWindPlate?cccc=...` payloads into the existing `amos`/`runway_obs` shape so current dashboard consumers can reuse runway display logic. Frontend adds a dedicated `runway` scan terminal tab that fetches a domestic city whitelist and renders runway TDZ/MID/END air temperatures without changing settlement anchors.
+
+**Tech Stack:** Python data collection + pytest, Next.js/React TypeScript, existing business-state test runner.
+
+---
+
+### Task 1: Backend parser and source
+
+**Files:**
+- Create: `src/data_collection/amsc_awos_sources.py`
+- Create: `tests/test_amsc_awos_sources.py`
+- Modify: `src/data_collection/weather_sources.py`
+- Modify: `src/data_collection/country_networks.py`
+
+- [ ] **Step 1: Write failing parser tests**
+
+Add tests that import `_amsc_parse_wind_plate_payload`, `_amsc_supported_city_codes`, and `AmscAwosSourceMixin`, parse a ZBAA-style sample, assert runway point temperatures, UTC observation conversion, `runway_temp_range`, and unauthorized/no-data fallback.
+
+- [ ] **Step 2: Run red test**
+
+Run: `python -m pytest tests/test_amsc_awos_sources.py -q`
+Expected: FAIL because `src.data_collection.amsc_awos_sources` does not exist.
+
+- [ ] **Step 3: Implement minimal backend source**
+
+Create a source module with China whitelist: `shanghai=ZSPD`, `beijing=ZBAA`, `guangzhou=ZGGG`, `shenzhen=ZGSZ`, `chengdu=ZUUU`, `chongqing=ZUCK`, `wuhan=ZHHH`, `qingdao=ZSQD`. Fetch `https://www.amsc.net.cn/gateway/api/saas/rest/amc/AwosController/getWindPlate?cccc=<ICAO>`, optionally using `POLYWEATHER_AMSC_COOKIE` or `POLYWEATHER_AMSC_SESSION_ID`, and return existing-compatible `amos` payload with `source="amsc_awos"`.
+
+- [ ] **Step 4: Run green backend tests**
+
+Run: `python -m pytest tests/test_amsc_awos_sources.py tests/test_amos_station_sources.py -q`
+Expected: PASS.
+
+### Task 2: Backend integration
+
+**Files:**
+- Modify: `src/data_collection/weather_sources.py`
+- Modify: `src/data_collection/country_networks.py`
+
+- [ ] **Step 1: Attach AMSC after AMOS**
+
+Add `AmscAwosSourceMixin` to `WeatherDataCollector`, call `_attach_china_amsc_awos_data` in both Open-Meteo and fallback paths, and persist aggregate plus first runway rows to `airport_obs_log` like AMOS.
+
+- [ ] **Step 2: Normalize airport primary source labels**
+
+Teach `_airport_primary_from_raw` that `raw["amos"].source == "amsc_awos"` should use `source_code="amsc_awos"`, `source_label="AMSC AWOS"`.
+
+- [ ] **Step 3: Compile check**
+
+Run: `python -m py_compile src/data_collection/amsc_awos_sources.py src/data_collection/weather_sources.py src/data_collection/country_networks.py`
+Expected: exit 0.
+
+### Task 3: Frontend runway tab
+
+**Files:**
+- Create: `frontend/components/dashboard/scan-terminal/RunwayObservationsPanel.tsx`
+- Create: `frontend/components/dashboard/scan-terminal/__tests__/runwayObservationTab.test.ts`
+- Modify: `frontend/components/dashboard/scan-terminal/ScanTerminalShellParts.tsx`
+- Modify: `frontend/components/dashboard/ScanTerminalDashboard.tsx`
+- Modify: `frontend/lib/dashboard-types.ts`
+- Modify: `frontend/components/dashboard/monitoring/monitor-temperature.ts`
+- Modify: `frontend/components/dashboard/monitoring/MonitorPanel.tsx`
+
+- [ ] **Step 1: Write failing frontend business-state test**
+
+Add a source-scan test asserting `ScanTerminalContentView` includes `runway`, dashboard has a `跑道观测` tab, and the panel includes `AMSC AWOS` plus TDZ/MID/END labels.
+
+- [ ] **Step 2: Run red frontend test**
+
+Run: `cd frontend; npm run test:business`
+Expected: FAIL because the runway tab/panel strings do not exist yet.
+
+- [ ] **Step 3: Implement tab and panel**
+
+Add `runway` view next to Monitor. The panel fetches domestic whitelist details with `ensureCityDetail(key, false, "panel")`, displays city cards with runway rows and local-time labels, and uses a not-available message for cities without AMSC data.
+
+- [ ] **Step 4: Run green frontend checks**
+
+Run: `cd frontend; npm run test:business; npm run typecheck`
+Expected: PASS.
+
+### Task 4: Final verification and publish
+
+**Files:**
+- All changed files from Tasks 1-3.
+
+- [ ] **Step 1: Full verification**
+
+Run backend tests, Python compile, frontend business tests, typecheck, and build.
+
+- [ ] **Step 2: Completion audit**
+
+Map user requirement “国内几个城市的机场跑道温度，放在市场监控旁边 Tab” to changed backend source, frontend tab, tests, and build evidence.
+
+- [ ] **Step 3: Commit/push/deploy**
+
+If verification passes, commit, push `main`, and rely on configured deployment.

docs/superpowers/plans/2026-05-18-telegram-group-pricing.md

@@ -0,0 +1,61 @@
+# Telegram Group Pricing Implementation Plan
+
+> **For agentic workers:** REQUIRED SUB-SKILL: Use superpowers:subagent-driven-development (recommended) or superpowers:executing-plans to implement this plan task-by-task. Steps use checkbox (- [ ]) syntax for tracking.
+
+**Goal:** Add Telegram Login verification so backend decides Pro price: group member 5U, non-member 10U.
+
+**Architecture:** Keep Supabase as the website account session, add Telegram Login as an identity link/price verification step. Backend verifies Telegram Login hash, checks getChatMember, stores the Telegram link in existing supabase_bindings, and payment intent creation recalculates price server-side.
+
+**Tech Stack:** FastAPI, existing DBManager bindings, Telegram Bot HTTP API, Next.js proxy routes, React account center, pytest.
+
+---
+
+### Task 1: Telegram auth service
+
+**Files:**
+- Create: src/auth/telegram_group_pricing.py
+- Test: 	ests/test_telegram_group_pricing.py
+
+- [ ] Verify Telegram Login payload HMAC using bot token.
+- [ ] Call Telegram getChatMember and treat member, dministrator, creator as group members.
+- [ ] Return 5U/10U pricing payload.
+
+### Task 2: Backend auth route
+
+**Files:**
+- Modify: web/core.py
+- Modify: web/services/auth_api.py
+- Modify: web/routers/auth.py
+
+- [ ] Add TelegramLoginRequest model.
+- [ ] Add POST /api/auth/telegram/login requiring Supabase identity.
+- [ ] Link Telegram id to current Supabase user via DBManager.bind_supabase_identity.
+- [ ] Return Telegram member status and effective price.
+
+### Task 3: Payment dynamic price
+
+**Files:**
+- Modify: src/payments/contract_checkout.py
+- Modify: web/services/payment_api.py
+
+- [ ] At payment intent creation, check linked Telegram id and group membership.
+- [ ] Override pro_monthly amount to 5U for members, 10U otherwise.
+- [ ] Store pricing source in metadata.
+
+### Task 4: Frontend Telegram Login entry
+
+**Files:**
+- Modify: rontend/components/account/AccountCenter.tsx
+- Create: rontend/app/api/auth/telegram/login/route.ts
+
+- [ ] Load Telegram Login widget with configured bot username.
+- [ ] Send payload to backend proxy.
+- [ ] Show group/member price status before checkout.
+
+### Task 5: Verification
+
+**Commands:**
+- python -m pytest tests\test_telegram_group_pricing.py tests\test_direct_payment.py tests\test_payments_runtime.py -q
+- python -m py_compile src\auth\telegram_group_pricing.py src\payments\contract_checkout.py web\core.py web\services\auth_api.py
+- python -m ruff check src\auth\telegram_group_pricing.py src\payments\contract_checkout.py web\core.py web\services\auth_api.py tests\test_telegram_group_pricing.py
+- cd frontend; npm run typecheck

docs/ux-research-review.md

@@ -0,0 +1,189 @@
+# PolyWeather UX 研究员审查报告
+
+> 审查日期：2026-06 | 视角：UX 研究员 | 范围：用户理解修正逻辑、第一眼认知、图表误导风险、普通用户语言、天气异常体验
+
+## 一、修正逻辑：用户是否看懂"上修/下修/维持"？
+
+### 当前状态
+
+AI 后端提示词明确要求 AI 使用**上修 / 下修 / 维持**三个方向词（`scan_city_ai_prompt.py:47-51`）。但前端 `WeatherDecisionBand` 完全不用这三个词，而是用：
+
+| AI 判断方向 | 前端实际展示 | 中文原文 |
+|------------|------------|---------|
+| 上修（偏暖） | "Watch hotter range" | "关注偏高温区间" |
+| 下修（偏冷） | "Avoid chasing high" | "暂不追高温" |
+| 维持（中性） | "Wait for peak-window confirmation" | "等待峰值窗口确认" |
+
+### 问题
+
+| # | 问题 | 严重度 |
+|---|------|------|
+| 1 | **AI 输出与前端展示词汇不一致** — AI 用"上修/下修/维持"，用户看到的是"关注偏高温/暂不追/等待确认"。这是两套完全不同的语言体系，用户读完 AI 解读再看决策条可能对不上号 | 🔴 |
+| 2 | **没有"维持/不变"标签** — 中性状态被表述为动作（"等待确认"），而不是状态（"维持不变"）。用户想知道"现在是什么判断"而不是"现在该做什么" | 🟡 |
+| 3 | **"偏高温区间" vs "暂不追高温" 不对称** — 上修方向指向一个温度区间，下修方向指向一个行为。一个说 where，一个说 what。逻辑结构不一致 | 🟡 |
+| 4 | **颜色语义双重解读** — warm=红色边框=偏暖（上修），cold=绿色边框=偏冷（下修）。天气直觉是"热=红、冷=蓝/绿"，但金融直觉是"红=跌、绿=涨"。两类用户可能得出相反的解读 | 🟡 |
+
+### 建议
+
+统一词汇体系，前端和 AI 使用同一套语言：
+
+| 方向 | 建议前端标签 | 建议说明 |
+|------|------------|---------|
+| 上修 | **"预计最高温上修"** / "Revise upward" | 比 DEB/模型集群基准偏高 |
+| 下修 | **"预计最高温下修"** / "Revise downward" | 比 DEB/模型集群基准偏低 |
+| 维持 | **"维持模型基准"** / "Stay with model base" | 无需显著上修或下修 |
+
+---
+
+## 二、第一眼理解：用户打开卡片看到什么？
+
+### 视觉层级
+
+```
+1. CityCardHeader
+   ├─ Kicker: "城市深度分析" / "Deep analysis"
+   ├─ 城市名（大号标题）
+   ├─ 状态标签（实测突破 / METAR 过旧 / 模型高度一致 等）
+   ├─ 数据新鲜度条（METAR / 模型 / 市场 / AI 各自的新鲜度）
+   └─ 三指标：当前温度 | 预计最高温 | 峰值时间
+
+2. WeatherDecisionBand（修正条）
+   ├─ Kicker: "天气优先判断 · 市场价格另列"
+   ├─ 主体判断（大号粗体）: "关注偏高温区间" / "暂不追高温" / "等待峰值窗口确认"
+   ├─ 原因文字（高亮 pill 内）
+   └─ 三指标：天气区间 | 路径偏差 | 报价状态
+
+3. 图表 + AI 证据 + 模型证据
+```
+
+### 问题
+
+| # | 问题 | 严重度 |
+|---|------|------|
+| 5 | **Kicker 说了两遍"市场"** — Header 的 kicker 是"城市深度分析"，Decision band 的 kicker 是"天气优先判断 · 市场价格另列"。两个 kicker 都提到了市场，但新手不知道"市场"指的是 Polymarket 温度合约。这个词对圈外人完全无意义 | 🟡 |
+| 6 | **"预计最高温"没有解释来源** — 用户看到这个数字，不知道它是 AI 独立判断的、还是 DEB 融合的、还是某一个模型给的。只有一个数字，没有可信度标签 | 🟡 |
+| 7 | **状态标签过多** — 一个卡片可能同时显示 3-4 个标签：实测突破 + 峰值窗口已过 + METAR 过旧 + 模型高度一致。这些标签颜色不同、含义各异，用户需要逐一解码 | 🟢 |
+
+---
+
+## 三、图表是否误导？
+
+### 日内温度图表（`AiCityTemperatureChart`）
+
+三条线：
+- 灰色虚线：DEB 原始路径（过去+未来都是虚线）
+- 蓝色实线：METAR 修正路径
+- 绿色散点：METAR 实测
+
+### 问题
+
+| # | 问题 | 严重度 |
+|---|------|------|
+| 8 | **DEB 路径永远是虚线** — 即使过去部分（已发生的几小时）也是虚线。虚线在图形语言中普遍表示"不确定/预测"，但过去几小时 DEB 已经是根据已知观测计算的，不应该看起来不确定 | 🟡 |
+| 9 | **没有"现在"标记** — 图表上没有任何竖线或标记指示当前时间。用户不知道图表上哪里是"现在"，哪里是"未来" | 🔴 |
+| 10 | **X 轴标签稀疏** — 每 4 个小时才显示一个标签，最多 6 个。用户可能以为数据只在标记的小时上有，实际上每个小时都有数据 | 🟢 |
+| 11 | **HistoryChart 缺 °C/°F** — 历史图 tooltip 只显示 `°`，没有 `C` 或 `F`。在混合温度单位的环境下可能混淆 | 🟡 |
+| 12 | **没有轴标题** — Y 轴只有数字 + °C，没有"温度"标签。对新手来说不够自解释（虽然常见于仪表盘类产品） | 🟢 |
+
+### 建议
+
+| 建议 | 实现 |
+|------|------|
+| 添加"现在"竖线 | `chart-utils.ts` 中在 `currentIndex` 位置画一条 annotation line |
+| 过去 DEB 改为实线 | `segment: { borderDash: past=[], future=[6,4] }` 给过去和未来不同样式 |
+| HistoryChart tooltip 补全单位 | 使用 `data.temp_symbol` 替代硬编码 `°` |
+
+---
+
+## 四、普通用户能看懂多少？
+
+### 高频出现的专业术语
+
+| 术语 | 出现次数 | 用户理解难度 | 是否有解释 |
+|------|---------|------------|----------|
+| **METAR** | ~50+ 处 | 高 — 只有飞行员/气象人员知道 | ❌ 无 |
+| **DEB** | ~30+ 处 | 高 — 项目内部术语 | ❌ 无 |
+| **TAF** | ~15 处 | 高 — 航空术语 | ❌ 无 |
+| **峰值窗口** | ~20 处 | 中 — 可推测含义 | ❌ 无 |
+| **模型集群** | ~10 处 | 中 — 可推测含义 | ❌ 无 |
+| **边界层** | 3 处 | 极高 — 气象学专业术语 | ❌ 无 |
+| **冷平流/暖平流** | 2 处 | 极高 — 气象学专业术语 | ❌ 无 |
+| **中枢** | ~8 处 | 中 — 在这个语境下表示中心值 | ❌ 无 |
+
+### 问题
+
+| # | 问题 | 严重度 |
+|---|------|------|
+| 13 | **所有专业术语都没有 tooltip 或解释** — METAR、DEB、TAF 在全站出现数十次，没有任何地方解释它们是什么。用户的唯一学习途径是 `/docs` 页面，但需要主动离开看板去查阅 | 🔴 |
+| 14 | **AI 输出的"最终判断"字段直接暴露给用户** — 如果 AI 提到了"冷平流支撑"、"边界层逆温"等术语，前端不做任何改写或解释，直接原样展示。用户要么读懂，要么跳过 | 🟡 |
+| 15 | **"DEB 融合"对普通用户完全无意义** — 这是一个内部算法名称。用户需要的是"综合预报"或"多模型加权平均"，而不是一个缩写 | 🟡 |
+
+### 建议
+
+| 建议 | 实现 |
+|------|------|
+| 核心术语加 tooltip | 首次出现的 METAR/DEB/TAF 加 `title` 属性或悬浮解释 |
+| DEB 改用用户语言 | "DEB 融合" → "多模型综合预报" 或保留 DEB 但加括号说明 |
+| AI 术语过滤 | 在后端 `scan_city_ai_fallback.py` 或前端展示层过滤掉过于专业的术语 |
+
+---
+
+## 五、天气异常时的体验
+
+### 当前异常信号和用户看到的反馈
+
+| 异常事件 | 前端展示 | 评估 |
+|---------|---------|------|
+| 实测温度突破模型上沿 | 红色标签"实测突破" + "Observation has broken above the model range" | ✅ 清晰 |
+| METAR 数据过旧（超过一天） | 黄色标签"METAR 过旧" + "已过旧，仅作背景参考" + 数据新鲜度条标红 | ✅ 清晰 |
+| 峰值窗口已过 | 灰色标签"峰值窗口已过" + 温度图表可能显示下降趋势 | ⚠️ 图表不标注窗口起止 |
+| 模型数据不足（<2个模型） | "等待模型补齐" | ⚠️ 没说为什么模型少、什么时候能补上 |
+| 市场价格不可用 | "市场价暂不可用" + "天气证据可参考，但暂无可交易价格" | ✅ 清晰 |
+| 快速判断与完整解读不一致 | 先显示"快速判断已完成"，再异步合并完整 AI 解读 | ⚠️ 两种状态切换可能让用户困惑 |
+
+### 问题
+
+| # | 问题 | 严重度 |
+|---|------|------|
+| 16 | **异常没有"下一步"指引** — 当实测突破模型上沿时，用户看到"Observation has broken above the model range"，但不知道这意味着该做什么。是应该买入？卖出？等待？系统不给建议 | 🔴 |
+| 17 | **"快速判断"和"完整解读"的切换可能造成 flicker** — 卡片先显示快速判断文案，然后完整 AI 返回后替换。如果两者结论一致，用户感知不到变化；如果不一致，用户会困惑"刚才不是这么说的" | 🟡 |
+| 18 | **极端天气没有特别处理** — 如果城市出现 40°C+ 或 -10°C 以下的极端温度，卡片和普通温度展示方式完全一样，没有视觉强调 | 🟢 |
+
+### 建议
+
+| 建议 | 实现 |
+|------|------|
+| 异常加行动建议 | 在 `primaryReason` 后追加一句行动建议（"建议等待下一报文后再做判断" / "建议关注更高温区间"） |
+| 极端温度视觉强化 | 温度超过历史极值时加大字号或添加红色脉冲高亮 |
+| 快速→完整过渡加标记 | 文案更新时显示 "✓ 已更新" 小标记，避免用户以为信息没变 |
+
+---
+
+## 六、优先级总结
+
+### P0 — 用户理解障碍
+
+| # | 问题 | 建议 |
+|---|------|------|
+| 1 | AI 用"上修/下修"，前端用"偏高温/暂不追" | 统一为"上修/下修/维持"三词 |
+| 9 | 图表没有"现在"标记 | 在 currentIndex 位置画竖线 |
+| 13 | METAR/DEB/TAF 全站无解释 | 加 title tooltip + 首次出现时加括号说明 |
+| 16 | 异常没有行动建议 | 在 primaryReason 后追加引导文字 |
+
+### P1 — 体验提升
+
+| # | 问题 | 建议 |
+|---|------|------|
+| 3 | 上修/下修文案不对称 | 统一用"方向 + 幅度"格式 |
+| 4 | 红/绿颜色双重解读 | 保留但加文字标签确认 |
+| 8 | DEB 路径全是虚线 | 过去部分用实线，未来部分用虚线 |
+| 15 | "DEB 融合"对普通用户无意义 | 改为"多模型综合预报" |
+
+### P2 — 优化打磨
+
+| # | 问题 | 建议 |
+|---|------|------|
+| 11 | HistoryChart 缺 °C/°F | 使用 temp_symbol |
+| 12 | 图表无轴标题 | 可加可不加（仪表盘惯例） |
+| 17 | 快速→完整 flicker | 加过渡标记 |
+| 18 | 极端温度无强调 | 加视觉强化 |

extension/README.md

@@ -1,12 +1,12 @@
 # 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。
+`PolyWeather Side Panel` 是一个面向天气交易市场的 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。
+3. 展示今日日内走势（简版）：`DEB` 走势与机场/官方观测对照，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；插件版本更新时会刷新城市列表缓存。
@@ -15,8 +15,9 @@
 ## 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`。
+- 其他城市按配置使用 `METAR / NOAA / AMOS / JMA / MGM / FMI / KNMI` 等官方数据源。
 - 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`。
+- 首尔/釜山/东京/安卡拉/伊斯坦布尔/赫尔辛基/阿姆斯特丹已接入高频机场实时数据（1-10 分钟级）。
 
 ## 权限说明
 
@@ -33,14 +34,14 @@
 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”。
+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：
+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）
@@ -48,9 +49,9 @@
 
 ## 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。
+- 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。
-- 插件城市列表来自主站 `/api/cities`，不是插件内置静态列表；本版已升级缓存版本，安装更新后会重新拉取 Manila、Karachi、Masroor Air Base 等最新城市。
-- 插件走势图与主站保持一致：`Wunderground / weather.com` 是历史参考页，不是物理实测站；机场市场统一显示机场 `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。
+- 插件城市列表来自主站 `/api/cities`，非内置静态列表；安装更新后自动刷新最新城市。
+- 机场市场统一显示机场 `METAR` / 官方观测点位，部分城市已覆盖 1 分钟级实时数据。
+- 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/manifest.json

@@ -2,7 +2,7 @@
   "manifest_version": 3,
   "name": "PolyWeather Side Panel",
   "description": "Weather side panel for Polymarket.",
-  "version": "0.1.10",
+  "version": "0.1.11",
   "icons": {
     "16": "icon-16.png",
     "32": "icon-32.png",

frontend-next-3002.log

@@ -1,10 +0,0 @@
-
-> polyweather-frontend@1.5.4 start
-> next start -p 3002
-
-   ▲ Next.js 15.5.12
-   - Local:        http://localhost:3002
-   - Network:      http://172.23.64.1:3002
-
- ✓ Starting...
- ✓ Ready in 541ms

frontend/.env.example

@@ -15,6 +15,11 @@ NEXT_PUBLIC_POLYWEATHER_API_BASE_URL=
 NEXT_PUBLIC_SUPABASE_URL=
 NEXT_PUBLIC_SUPABASE_ANON_KEY=
 
+# 必填：生产环境站点 URL（OAuth 回调强制使用此域名）
+# 设置后，所有登录回调将始终跳转到此域名，而非当前浏览器地址。
+# 生产环境必须设为 https://polyweather-pro.vercel.app
+NEXT_PUBLIC_SITE_URL=https://polyweather-pro.vercel.app
+
 # 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
@@ -39,4 +44,5 @@ 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
+NEXT_PUBLIC_TELEGRAM_BOT_URL=https://t.me/polyyuanbot
+NEXT_PUBLIC_TELEGRAM_LOGIN_BOT_USERNAME=polyyuanbot

frontend/.env.local

@@ -1 +1,48 @@
+# 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
+# 默认供 Next.js API Route 在服务端代理后端使用。
 POLYWEATHER_API_BASE_URL=http://127.0.0.1:8000
+
+# 可选：浏览器直连后端 FastAPI 基础地址。
+# 在 Vercel 免费额度下建议配置为 VPS HTTPS 域名，让 AI / METAR / scan 等
+# 长耗时请求绕过 Vercel Functions / Fluid Compute。
+# 例如：https://api.example.com
+NEXT_PUBLIC_POLYWEATHER_API_BASE_URL=
+
+# 必填：Supabase 前端公钥（鉴权开启时必须）
+NEXT_PUBLIC_SUPABASE_URL=
+NEXT_PUBLIC_SUPABASE_ANON_KEY=
+
+# 必填：生产环境站点 URL（OAuth 回调强制使用此域名）
+# 设置后，所有登录回调将始终跳转到此域名，而非当前浏览器地址。
+# 生产环境必须设为 https://polyweather-pro.vercel.app
+NEXT_PUBLIC_SITE_URL=https://polyweather-pro.vercel.app
+
+# 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/polyyuanbot
+NEXT_PUBLIC_TELEGRAM_LOGIN_BOT_USERNAME=polyyuanbot

frontend/README.md

@@ -21,8 +21,8 @@ PolyWeather Pro 的生产前端工程。
 
 ## 当前前端能力
 
-- 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，解释日内分析、校准概率、模型栈、TAF、结算来源和历史对账
+- 主站 Dashboard 支持地图、城市详情、今日日内分析和账户中心
+- `/docs` 已提供公开双语产品文档中心，解释日内分析、校准概率、模型栈、TAF 和结算来源
 - 今日日内分析支持：
   - `锚点状态`
   - `当前节奏`
@@ -31,9 +31,6 @@ PolyWeather Pro 的生产前端工程。
   - `专业气象结论条`
   - `气象证据链 / 失效条件 / 确认条件`
   - 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
 - 点击城市图标后会显示地图顶部同步提醒与详情面板内同步徽标，避免用户误判为卡住
 - 城市详情会自动识别“单模型 / 单日”的稀疏缓存并主动刷新，避免误把残缺 detail 当作完整结果
@@ -43,8 +40,8 @@ PolyWeather Pro 的生产前端工程。
 - 城市决策卡的 AI 机场报文解读包括最终判断、METAR 解读、推理说明、模型集群备注、风险提示和原始 METAR
 - AI 机场报文解读按 `city + local_date + locale + METAR signature` 做页面内存缓存和 `localStorage` 最终结果缓存；切换选项卡返回时会优先恢复已有内容
 - 市场价格层使用完整 `all_buckets` 匹配温度桶，并把 `模型-市场差` 解释为 `模型概率 - 市场隐含概率`
-- 概率区展示当前生产概率引擎输出；EMOS / LGBM 只在评估通过或 shadow 对照时进入解释层，模型共识和市场价格只作为辅助说明
-- `/ops` 现已展示 prewarm worker 运行态、缓存桶状态与 summary cache hit/miss
+- 概率区展示当前生产概率引擎输出（legacy 高斯或 EMOS），模型共识只作为辅助参考
+- 缓存桶状态与 summary cache hit/miss
 
 ## 本地开发
 
@@ -98,7 +95,7 @@ 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
+NEXT_PUBLIC_TELEGRAM_BOT_URL=https://t.me/polyyuanbot
 
 # 推荐默认关闭的前端观测 / 预热开关
 NEXT_PUBLIC_POLYWEATHER_APP_ANALYTICS=false
@@ -117,7 +114,6 @@ NEXT_PUBLIC_POLYWEATHER_EAGER_CITY_SUMMARIES=false
 - `GET /api/city/[name]`
 - `GET /api/city/[name]/summary`
 - `GET /api/city/[name]/detail`
-- `GET /api/history/[name]`
 
 鉴权：
 
@@ -154,7 +150,6 @@ Ops：
 
 - 系统状态
 - SQLite / rollout / 支付运行态
-- prewarm worker 运行态
 - 缓存桶状态与 summary cache hit/miss
 - 用户查询
 - 当前会员
@@ -205,4 +200,4 @@ Ops：
 
 详见根目录策略文档：`docs/OPEN_CORE_POLICY.md`
 
-最后更新：`2026-04-19`
+最后更新：`2026-05-23`

frontend/app/account/error.tsx

@@ -0,0 +1,91 @@
+"use client";
+
+import { RefreshCw } from "lucide-react";
+import { useEffect } from "react";
+
+export default function AccountErrorPage({
+  error,
+  reset,
+}: {
+  error: Error & { digest?: string };
+  reset: () => void;
+}) {
+  useEffect(() => {
+    console.error("Account page error:", error);
+  }, [error]);
+
+  return (
+    <div
+      style={{
+        display: "flex",
+        flexDirection: "column",
+        alignItems: "center",
+        justifyContent: "center",
+        minHeight: "100vh",
+        padding: "2rem",
+        gap: "1rem",
+        backgroundColor: "var(--color-bg-base, #0B1220)",
+        color: "var(--color-text-primary, #E6EDF3)",
+        fontFamily: "var(--font-data, Inter, sans-serif)",
+        textAlign: "center",
+      }}
+    >
+      <h1
+        style={{
+          fontSize: "1.25rem",
+          fontWeight: 600,
+          margin: 0,
+          color: "var(--color-accent-primary, #4DA3FF)",
+        }}
+      >
+        账户页面出错
+      </h1>
+      <p
+        style={{
+          color: "var(--color-text-secondary, #9FB2C7)",
+          fontSize: "0.875rem",
+          margin: 0,
+          maxWidth: 420,
+          lineHeight: 1.7,
+        }}
+      >
+        如果是在支付或绑定钱包时出现此问题，常见原因是钱包插件冲突（例如同时开启了 MetaMask 和
+        Rabby）。请尝试关闭其他钱包插件后刷新页面重试。
+      </p>
+      <p
+        style={{
+          color: "var(--color-text-muted, #7D8FA3)",
+          fontSize: "0.8rem",
+          margin: 0,
+          maxWidth: 420,
+          lineHeight: 1.6,
+        }}
+      >
+        If this happened during payment or wallet binding, the most common cause is
+        conflicting wallet extensions. Try disabling other wallet extensions (e.g.
+        MetaMask + Rabby) and refresh.
+      </p>
+      <button
+        type="button"
+        onClick={reset}
+        style={{
+          marginTop: "0.5rem",
+          display: "inline-flex",
+          alignItems: "center",
+          gap: "0.5rem",
+          padding: "0.5rem 1.25rem",
+          borderRadius: "var(--radius-md, 10px)",
+          border: "1px solid var(--color-border-default, rgba(159,178,199,0.16))",
+          backgroundColor: "var(--color-bg-raised, #111A2E)",
+          color: "var(--color-accent-primary, #4DA3FF)",
+          cursor: "pointer",
+          fontSize: "0.875rem",
+          fontWeight: 500,
+        }}
+      >
+        <RefreshCw size={14} />
+        重试
+      </button>
+    </div>
+  );
+}

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

@@ -10,7 +10,7 @@ import {
 
 const API_BASE = process.env.POLYWEATHER_API_BASE_URL;
 const ANALYTICS_ENABLED =
-  process.env.NEXT_PUBLIC_POLYWEATHER_APP_ANALYTICS === "true";
+  process.env.NEXT_PUBLIC_POLYWEATHER_APP_ANALYTICS !== "false";
 
 export async function POST(req: NextRequest) {
   if (!ANALYTICS_ENABLED) {
@@ -26,7 +26,9 @@ export async function POST(req: NextRequest) {
 
   try {
     const body = await req.json();
-    const auth = await buildBackendRequestHeaders(req);
+    const auth = await buildBackendRequestHeaders(req, {
+      includeSupabaseIdentity: false,
+    });
     const headers = new Headers(auth.headers);
     headers.set("Content-Type", "application/json");
     const res = await fetch(`${API_BASE}/api/analytics/events`, {

frontend/app/api/auth/telegram/bind-by-token/route.ts

@@ -0,0 +1,44 @@
+import { NextRequest, NextResponse } from "next/server";
+import {
+  applyAuthResponseCookies,
+  buildBackendRequestHeaders,
+} from "@/lib/backend-auth";
+import {
+  buildProxyExceptionResponse,
+  buildUpstreamErrorResponse,
+} from "@/lib/api-proxy";
+
+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.text();
+    const auth = await buildBackendRequestHeaders(req);
+    const headers = new Headers(auth.headers);
+    headers.set("Content-Type", "application/json");
+    const res = await fetch(`${API_BASE}/api/auth/telegram/bind-by-token`, {
+      method: "POST",
+      headers,
+      body,
+      cache: "no-store",
+    });
+    if (!res.ok) {
+      const raw = await res.text();
+      const response = buildUpstreamErrorResponse(res.status, raw);
+      return applyAuthResponseCookies(response, auth.response);
+    }
+    const data = await res.json();
+    const response = NextResponse.json(data);
+    return applyAuthResponseCookies(response, auth.response);
+  } catch (error) {
+    return buildProxyExceptionResponse(error, {
+      publicMessage: "Failed to bind Telegram account",
+    });
+  }
+}

frontend/app/api/auth/telegram/bot-bind-link/route.ts

@@ -7,33 +7,22 @@ import {
   buildProxyExceptionResponse,
   buildUpstreamErrorResponse,
 } from "@/lib/api-proxy";
-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 }> },
-) {
+export async function POST(req: NextRequest) {
   if (!API_BASE) {
-    const response = NextResponse.json(
+    return 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 = {
+    const res = await fetch(`${API_BASE}/api/auth/telegram/bot-bind-link`, {
+      method: "POST",
       headers: auth.headers,
-      next: { revalidate: 60 },
-    } as const;
-    const res = await fetch(url, {
-      ...fetchOptions,
+      cache: "no-store",
     });
     if (!res.ok) {
       const raw = await res.text();
@@ -41,16 +30,11 @@ export async function GET(
       return applyAuthResponseCookies(response, auth.response);
     }
     const data = await res.json();
-    const response = buildCachedJsonResponse(
-      req,
-      data,
-      "public, max-age=0, s-maxage=60, stale-while-revalidate=300",
-    );
+    const response = NextResponse.json(data);
     return applyAuthResponseCookies(response, auth.response);
   } catch (error) {
-    const response = buildProxyExceptionResponse(error, {
-      publicMessage: "Failed to fetch history",
+    return buildProxyExceptionResponse(error, {
+      publicMessage: "Failed to create Telegram bot bind link",
     });
-    return response;
   }
 }

frontend/app/api/auth/telegram/login/route.ts

@@ -0,0 +1,44 @@
+import { NextRequest, NextResponse } from "next/server";
+import {
+  applyAuthResponseCookies,
+  buildBackendRequestHeaders,
+} from "@/lib/backend-auth";
+import {
+  buildProxyExceptionResponse,
+  buildUpstreamErrorResponse,
+} from "@/lib/api-proxy";
+
+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.text();
+    const auth = await buildBackendRequestHeaders(req);
+    const headers = new Headers(auth.headers);
+    headers.set("Content-Type", "application/json");
+    const res = await fetch(`${API_BASE}/api/auth/telegram/login`, {
+      method: "POST",
+      headers,
+      body,
+      cache: "no-store",
+    });
+    if (!res.ok) {
+      const raw = await res.text();
+      const response = buildUpstreamErrorResponse(res.status, raw);
+      return applyAuthResponseCookies(response, auth.response);
+    }
+    const data = await res.json();
+    const response = NextResponse.json(data);
+    return applyAuthResponseCookies(response, auth.response);
+  } catch (error) {
+    return buildProxyExceptionResponse(error, {
+      publicMessage: "Failed to verify Telegram login",
+    });
+  }
+}

frontend/app/api/cities/route.ts

@@ -1,16 +1,7 @@
 import { NextRequest, NextResponse } from "next/server";
-import {
-  applyAuthResponseCookies,
-  buildBackendRequestHeaders,
-} from "@/lib/backend-auth";
-import {
-  buildProxyExceptionResponse,
-  buildUpstreamErrorResponse,
-} from "@/lib/api-proxy";
-import { buildCachedJsonResponse } from "@/lib/http-cache";
+import { proxyBackendJsonGet } from "@/lib/api-proxy";
 
 const API_BASE = process.env.POLYWEATHER_API_BASE_URL;
-export const dynamic = "force-dynamic";
 
 export async function GET(req: NextRequest) {
   if (!API_BASE) {
@@ -21,30 +12,10 @@ export async function GET(req: NextRequest) {
     return response;
   }
 
-  try {
-    const auth = await buildBackendRequestHeaders(req, {
-      includeSupabaseIdentity: false,
-    });
-    const res = await fetch(`${API_BASE}/api/cities`, {
-      headers: auth.headers,
-      cache: "no-store",
-    });
-    if (!res.ok) {
-      const raw = await res.text();
-      const response = buildUpstreamErrorResponse(res.status, raw);
-      return applyAuthResponseCookies(response, auth.response);
-    }
-    const data = await res.json();
-    const response = buildCachedJsonResponse(
-      req,
-      data,
-      "no-store, max-age=0",
-    );
-    return applyAuthResponseCookies(response, auth.response);
-  } catch (error) {
-    const response = buildProxyExceptionResponse(error, {
-      publicMessage: "Failed to fetch cities",
-    });
-    return response;
-  }
+  return proxyBackendJsonGet(req, {
+    cacheControl: "public, max-age=0, s-maxage=60, stale-while-revalidate=300",
+    publicMessage: "Failed to fetch cities",
+    revalidateSeconds: 60,
+    url: `${API_BASE}/api/cities`,
+  });
 }

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

@@ -1,12 +1,6 @@
 import { NextRequest, NextResponse } from "next/server";
-import {
-  applyAuthResponseCookies,
-  buildBackendRequestHeaders,
-} from "@/lib/backend-auth";
-import {
-  buildProxyExceptionResponse,
-  buildUpstreamErrorResponse,
-} from "@/lib/api-proxy";
+import { proxyBackendJsonGet } from "@/lib/api-proxy";
+import { buildCityDetailProxyCachePolicy } from "@/lib/proxy-cache-policy";
 
 const API_BASE = process.env.POLYWEATHER_API_BASE_URL;
 
@@ -24,6 +18,7 @@ export async function GET(
 
   const { name } = await context.params;
   const forceRefresh = req.nextUrl.searchParams.get("force_refresh") ?? "false";
+  const cachePolicy = buildCityDetailProxyCachePolicy(forceRefresh, 15);
   const depth = req.nextUrl.searchParams.get("depth");
   const marketSlug = req.nextUrl.searchParams.get("market_slug");
   const targetDate = req.nextUrl.searchParams.get("target_date");
@@ -41,24 +36,12 @@ export async function GET(
   }
   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 = buildUpstreamErrorResponse(res.status, raw);
-      return applyAuthResponseCookies(response, auth.response);
-    }
-    const data = await res.json();
-    const response = NextResponse.json(data);
-    return applyAuthResponseCookies(response, auth.response);
-  } catch (error) {
-    const response = buildProxyExceptionResponse(error, {
-      publicMessage: "Failed to fetch city detail aggregate",
-    });
-    return response;
-  }
+  return proxyBackendJsonGet(req, {
+    cacheControl: cachePolicy.responseCacheControl,
+    fetchCache:
+      cachePolicy.fetchMode === "no-store" ? "no-store" : undefined,
+    publicMessage: "Failed to fetch city detail aggregate",
+    revalidateSeconds: cachePolicy.revalidateSeconds,
+    url,
+  });
 }

frontend/app/api/city/[name]/market-scan/route.ts

@@ -1,12 +1,6 @@
 import { NextRequest, NextResponse } from "next/server";
-import {
-  applyAuthResponseCookies,
-  buildBackendRequestHeaders,
-} from "@/lib/backend-auth";
-import {
-  buildProxyExceptionResponse,
-  buildUpstreamErrorResponse,
-} from "@/lib/api-proxy";
+import { proxyBackendJsonGet } from "@/lib/api-proxy";
+import { buildForceRefreshProxyCachePolicy } from "@/lib/proxy-cache-policy";
 
 const API_BASE = process.env.POLYWEATHER_API_BASE_URL;
 
@@ -26,6 +20,7 @@ export async function GET(
   const params = new URLSearchParams();
   const forceRefresh = req.nextUrl.searchParams.get("force_refresh") ?? "false";
   params.set("force_refresh", forceRefresh);
+  const cachePolicy = buildForceRefreshProxyCachePolicy(forceRefresh, 20);
 
   const targetDate = req.nextUrl.searchParams.get("target_date");
   if (targetDate) {
@@ -44,32 +39,15 @@ export async function GET(
 
   const url = `${API_BASE}/api/city/${encodeURIComponent(name)}/market-scan?${params.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 = buildUpstreamErrorResponse(res.status, raw, {
-        detailLimit: 800,
-        error: "Backend city market scan failed",
-      });
-      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) {
-    const response = buildProxyExceptionResponse(error, {
-      publicMessage: "Failed to fetch city market scan",
-      status: 502,
-    });
-    return response;
-  }
+  return proxyBackendJsonGet(req, {
+    cacheControl: cachePolicy.responseCacheControl,
+    detailLimit: 800,
+    error: "Backend city market scan failed",
+    fetchCache:
+      cachePolicy.fetchMode === "no-store" ? "no-store" : undefined,
+    publicMessage: "Failed to fetch city market scan",
+    revalidateSeconds: cachePolicy.revalidateSeconds,
+    statusOnException: 502,
+    url,
+  });
 }

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

@@ -7,6 +7,8 @@ import {
   buildProxyExceptionResponse,
   buildUpstreamErrorResponse,
 } from "@/lib/api-proxy";
+import { buildCachedJsonResponse } from "@/lib/http-cache";
+import { buildCityDetailProxyCachePolicy } from "@/lib/proxy-cache-policy";
 
 const API_BASE = process.env.POLYWEATHER_API_BASE_URL;
 
@@ -64,6 +66,9 @@ function buildFallbackCityDetail(name: string, depth: string, summary: Record<st
       sunshine_hours: null,
     },
     multi_model: {},
+    multi_model_daily: {},
+    source_forecasts: {},
+    hourly: { times: [], temps: [], radiation: [] },
     probabilities: {
       mu: null,
       distribution: [],
@@ -104,6 +109,12 @@ function buildFallbackCityDetail(name: string, depth: string, summary: Record<st
 function normalizeCityDetailPayload(data: unknown) {
   if (!data || typeof data !== "object") return data;
   const payload = data as Record<string, any>;
+
+  // Backend v2 nests hourly under timeseries; chart expects it at top level.
+  if (!payload.hourly && payload.timeseries?.hourly) {
+    payload.hourly = payload.timeseries.hourly;
+  }
+
   if (!payload.market_scan && payload.market_scan_payload) {
     return {
       ...payload,
@@ -128,29 +139,38 @@ export async function GET(
   const { name } = await context.params;
   const forceRefresh = req.nextUrl.searchParams.get("force_refresh") ?? "false";
   const depth = req.nextUrl.searchParams.get("depth") ?? "panel";
+  const cachePolicy = buildCityDetailProxyCachePolicy(forceRefresh, 15);
   const url = `${API_BASE}/api/city/${encodeURIComponent(name)}?force_refresh=${forceRefresh}&depth=${encodeURIComponent(depth)}`;
 
   try {
-    const auth = await buildBackendRequestHeaders(req);
+    const auth = await buildBackendRequestHeaders(req, {
+      includeSupabaseIdentity: false,
+    });
     const res = await fetch(url, {
       headers: auth.headers,
-      cache: "no-store",
+      ...(cachePolicy.fetchMode === "no-store"
+        ? { cache: "no-store" as const }
+        : { next: { revalidate: cachePolicy.revalidateSeconds ?? 15 } }),
     });
     if (!res.ok) {
       const raw = await res.text();
       const summaryUrl = `${API_BASE}/api/city/${encodeURIComponent(name)}/summary?force_refresh=${forceRefresh}`;
       const summaryRes = await fetch(summaryUrl, {
         headers: auth.headers,
-        cache: "no-store",
+        ...(cachePolicy.fetchMode === "no-store"
+          ? { cache: "no-store" as const }
+          : { next: { revalidate: 10 } }),
       });
       if (summaryRes.ok) {
         const summaryData = await summaryRes.json();
-        const response = NextResponse.json(buildFallbackCityDetail(name, depth, summaryData), {
-          headers: {
-            "Cache-Control": "no-store",
-            "X-PolyWeather-Fallback": "summary",
-          },
-        });
+        const response = buildCachedJsonResponse(
+          req,
+          buildFallbackCityDetail(name, depth, summaryData),
+          cachePolicy.fetchMode === "no-store"
+            ? cachePolicy.responseCacheControl
+            : "public, max-age=0, s-maxage=10, stale-while-revalidate=30",
+        );
+        response.headers.set("X-PolyWeather-Fallback", "summary");
         return applyAuthResponseCookies(response, auth.response);
       }
 
@@ -158,7 +178,11 @@ export async function GET(
       return applyAuthResponseCookies(response, auth.response);
     }
     const data = normalizeCityDetailPayload(await res.json());
-    const response = NextResponse.json(data);
+    const response = buildCachedJsonResponse(
+      req,
+      data,
+      cachePolicy.responseCacheControl,
+    );
     return applyAuthResponseCookies(response, auth.response);
   } catch (error) {
     const response = buildProxyExceptionResponse(error, {

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

@@ -1,13 +1,6 @@
 import { NextRequest, NextResponse } from "next/server";
-import {
-  applyAuthResponseCookies,
-  buildBackendRequestHeaders,
-} from "@/lib/backend-auth";
-import {
-  buildProxyExceptionResponse,
-  buildUpstreamErrorResponse,
-} from "@/lib/api-proxy";
-import { buildCachedJsonResponse } from "@/lib/http-cache";
+import { proxyBackendJsonGet } from "@/lib/api-proxy";
+import { buildForceRefreshProxyCachePolicy } from "@/lib/proxy-cache-policy";
 
 const API_BASE = process.env.POLYWEATHER_API_BASE_URL;
 
@@ -25,50 +18,15 @@ export async function GET(
 
   const { name } = await context.params;
   const forceRefresh = req.nextUrl.searchParams.get("force_refresh") ?? "false";
-  const bypassCache = forceRefresh === "true";
+  const cachePolicy = buildForceRefreshProxyCachePolicy(forceRefresh, 20);
   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 = buildUpstreamErrorResponse(res.status, raw);
-      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 = buildProxyExceptionResponse(error, {
-      publicMessage: "Failed to fetch city summary",
-    });
-    return response;
-  }
+  return proxyBackendJsonGet(req, {
+    cacheControl: cachePolicy.responseCacheControl,
+    fetchCache:
+      cachePolicy.fetchMode === "no-store" ? "no-store" : undefined,
+    publicMessage: "Failed to fetch city summary",
+    revalidateSeconds: cachePolicy.revalidateSeconds,
+    url,
+  });
 }

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

@@ -0,0 +1,29 @@
+import { NextRequest, NextResponse } from "next/server";
+import { applyAuthResponseCookies, buildBackendRequestHeaders } from "@/lib/backend-auth";
+import { buildProxyExceptionResponse } from "@/lib/api-proxy";
+
+const API_BASE = process.env.POLYWEATHER_API_BASE_URL;
+const BACKEND = API_BASE ? `${API_BASE}/api/ops/config` : "";
+
+export async function GET(req: NextRequest) {
+  if (!API_BASE) return NextResponse.json({ error: "API_BASE not configured" }, { status: 500 });
+  try {
+    const auth = await buildBackendRequestHeaders(req);
+    const res = await fetch(BACKEND, { headers: auth.headers, cache: "no-store" });
+    const raw = await res.text();
+    const response = new NextResponse(raw, { status: res.status, headers: { "Content-Type": "application/json", "Cache-Control": "no-store" } });
+    return applyAuthResponseCookies(response, auth.response);
+  } catch (e) { return buildProxyExceptionResponse(e, { publicMessage: "Config fetch failed" }); }
+}
+
+export async function PUT(req: NextRequest) {
+  if (!API_BASE) return NextResponse.json({ error: "API_BASE not configured" }, { status: 500 });
+  try {
+    const auth = await buildBackendRequestHeaders(req);
+    const body = await req.text();
+    const res = await fetch(BACKEND, { method: "PUT", 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": "application/json", "Cache-Control": "no-store" } });
+    return applyAuthResponseCookies(response, auth.response);
+  } catch (e) { return buildProxyExceptionResponse(e, { publicMessage: "Config update failed" }); }
+}

frontend/app/api/ops/health-check/route.ts

@@ -0,0 +1,16 @@
+import { NextRequest, NextResponse } from "next/server";
+import { applyAuthResponseCookies, buildBackendRequestHeaders } from "@/lib/backend-auth";
+import { buildProxyExceptionResponse } from "@/lib/api-proxy";
+
+const API_BASE = process.env.POLYWEATHER_API_BASE_URL;
+
+export async function GET(req: NextRequest) {
+  if (!API_BASE) return NextResponse.json({ error: "API_BASE not configured" }, { status: 500 });
+  try {
+    const auth = await buildBackendRequestHeaders(req);
+    const res = await fetch(`${API_BASE}/api/ops/health-check`, { headers: auth.headers, cache: "no-store" });
+    const raw = await res.text();
+    const response = new NextResponse(raw, { status: res.status, headers: { "Content-Type": "application/json", "Cache-Control": "no-store" } });
+    return applyAuthResponseCookies(response, auth.response);
+  } catch (e) { return buildProxyExceptionResponse(e, { publicMessage: "Health check failed" }); }
+}

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

@@ -0,0 +1,19 @@
+import { NextRequest, NextResponse } from "next/server";
+import { applyAuthResponseCookies, buildBackendRequestHeaders } from "@/lib/backend-auth";
+import { buildProxyExceptionResponse } from "@/lib/api-proxy";
+
+const API_BASE = process.env.POLYWEATHER_API_BASE_URL;
+
+export async function GET(req: NextRequest) {
+  if (!API_BASE) return NextResponse.json({ error: "API_BASE not configured" }, { status: 500 });
+  try {
+    const auth = await buildBackendRequestHeaders(req);
+    const url = new URL(`${API_BASE}/api/ops/memberships/growth`);
+    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": "application/json", "Cache-Control": "no-store" } });
+    return applyAuthResponseCookies(response, auth.response);
+  } catch (e) { return buildProxyExceptionResponse(e, { publicMessage: "Growth fetch failed" }); }
+}

frontend/app/api/ops/subscriptions/extend/route.ts

@@ -0,0 +1,24 @@
+import { NextRequest, NextResponse } from "next/server";
+import { applyAuthResponseCookies, buildBackendRequestHeaders } from "@/lib/backend-auth";
+import { buildProxyExceptionResponse } from "@/lib/api-proxy";
+
+const API_BASE = process.env.POLYWEATHER_API_BASE_URL;
+const ENTITLEMENT_TOKEN = process.env.POLYWEATHER_BACKEND_ENTITLEMENT_TOKEN?.trim() || "";
+
+export async function POST(req: NextRequest) {
+  if (!API_BASE) return NextResponse.json({ error: "API_BASE not configured" }, { status: 500 });
+  try {
+    const auth = await buildBackendRequestHeaders(req);
+    const body = await req.text();
+    const headers: Record<string, string> = { ...auth.headers as Record<string, string>, "Content-Type": "application/json" };
+    if (ENTITLEMENT_TOKEN) {
+      headers.Authorization = `Bearer ${ENTITLEMENT_TOKEN}`;
+    }
+    const res = await fetch(`${API_BASE}/api/ops/subscriptions/extend`, {
+      method: "POST", headers, body, cache: "no-store",
+    });
+    const raw = await res.text();
+    const response = new NextResponse(raw, { status: res.status, headers: { "Content-Type": "application/json", "Cache-Control": "no-store" } });
+    return applyAuthResponseCookies(response, auth.response);
+  } catch (e) { return buildProxyExceptionResponse(e, { publicMessage: "Subscription extend failed" }); }
+}

frontend/app/api/ops/subscriptions/grant/route.ts

@@ -0,0 +1,168 @@
+import { NextRequest, NextResponse } from "next/server";
+import {
+  applyAuthResponseCookies,
+  buildBackendRequestHeaders,
+} from "@/lib/backend-auth";
+import { buildProxyExceptionResponse } from "@/lib/api-proxy";
+
+const API_BASE = process.env.POLYWEATHER_API_BASE_URL;
+
+function parseAdminEmails() {
+  return String(process.env.POLYWEATHER_OPS_ADMIN_EMAILS || "")
+    .split(",")
+    .map((item) => item.trim().toLowerCase())
+    .filter(Boolean);
+}
+
+async function getBearerEmail(req: NextRequest) {
+  const auth = String(req.headers.get("authorization") || "").trim();
+  const token = auth.replace(/^bearer\s+/i, "").trim();
+  const supabaseUrl = String(process.env.NEXT_PUBLIC_SUPABASE_URL || "").trim();
+  const anonKey = String(process.env.NEXT_PUBLIC_SUPABASE_ANON_KEY || "").trim();
+  if (!token || !supabaseUrl || !anonKey) return "";
+  const res = await fetch(`${supabaseUrl.replace(/\/$/, "")}/auth/v1/user`, {
+    headers: {
+      apikey: anonKey,
+      Authorization: `Bearer ${token}`,
+      Accept: "application/json",
+    },
+    cache: "no-store",
+  });
+  if (!res.ok) return "";
+  const data = (await res.json()) as { email?: string };
+  return String(data.email || "").trim().toLowerCase();
+}
+
+async function findSupabaseUserIdByEmail(email: string) {
+  const supabaseUrl = String(process.env.SUPABASE_URL || process.env.NEXT_PUBLIC_SUPABASE_URL || "")
+    .trim()
+    .replace(/\/$/, "");
+  const serviceRoleKey = String(process.env.SUPABASE_SERVICE_ROLE_KEY || "").trim();
+  if (!supabaseUrl || !serviceRoleKey) {
+    throw new Error("Supabase service role is not configured on Vercel");
+  }
+  const res = await fetch(
+    `${supabaseUrl}/auth/v1/admin/users?filter=${encodeURIComponent(`email.eq.${email}`)}`,
+    {
+      headers: {
+        apikey: serviceRoleKey,
+        Authorization: `Bearer ${serviceRoleKey}`,
+        Accept: "application/json",
+      },
+      cache: "no-store",
+    },
+  );
+  const data = (await res.json().catch(() => ({}))) as {
+    users?: Array<{ id?: string }>;
+  };
+  if (!res.ok) throw new Error(`Supabase user lookup failed: ${JSON.stringify(data).slice(0, 200)}`);
+  const userId = String(data.users?.[0]?.id || "").trim();
+  if (!userId) {
+    const error = new Error(`user not found: ${email}`);
+    (error as Error & { status?: number }).status = 404;
+    throw error;
+  }
+  return { supabaseUrl, serviceRoleKey, userId };
+}
+
+async function grantSubscriptionDirectly(req: NextRequest, bodyText: string, authEmail?: string | null) {
+  const adminEmail = String(authEmail || (await getBearerEmail(req)) || "")
+    .trim()
+    .toLowerCase();
+  const allowedEmails = parseAdminEmails();
+  if (!adminEmail) return NextResponse.json({ error: "Unauthorized" }, { status: 401 });
+  if (!allowedEmails.includes(adminEmail)) {
+    return NextResponse.json({ error: "ops admin required" }, { status: 403 });
+  }
+
+  const body = JSON.parse(bodyText || "{}") as {
+    email?: string;
+    plan_code?: string;
+    days?: number;
+  };
+  const email = String(body.email || "").trim().toLowerCase();
+  const planCode = String(body.plan_code || "pro_monthly").trim();
+  const days = Math.max(1, Math.min(365, Number(body.days || 30)));
+  if (!email) return NextResponse.json({ error: "email is required" }, { status: 400 });
+  if (planCode !== "pro_monthly") {
+    return NextResponse.json({ error: "invalid plan_code" }, { status: 400 });
+  }
+
+  try {
+    const { supabaseUrl, serviceRoleKey, userId } = await findSupabaseUserIdByEmail(email);
+    const now = new Date();
+    const expires = new Date(now.getTime() + days * 86_400_000);
+    const payload = {
+      user_id: userId,
+      email,
+      plan_code: planCode,
+      "status": "active",
+      starts_at: now.toISOString(),
+      expires_at: expires.toISOString(),
+      source: "ops_manual_grant_next_fallback",
+      created_at: now.toISOString(),
+      updated_at: now.toISOString(),
+    };
+    const insert = await fetch(`${supabaseUrl}/rest/v1/subscriptions`, {
+      method: "POST",
+      headers: {
+        apikey: serviceRoleKey,
+        Authorization: `Bearer ${serviceRoleKey}`,
+        "Content-Type": "application/json",
+        Prefer: "return=representation",
+      },
+      body: JSON.stringify(payload),
+      cache: "no-store",
+    });
+    const raw = await insert.text();
+    if (!insert.ok) {
+      return NextResponse.json(
+        { error: "Supabase insert failed", detail: raw.slice(0, 300) },
+        { status: 500 },
+      );
+    }
+    return NextResponse.json({
+      ok: true,
+      user_id: userId,
+      plan_code: planCode,
+      days,
+      expires_at: expires.toISOString(),
+      fallback: "next_supabase_direct",
+    });
+  } catch (error) {
+    const status = Number((error as Error & { status?: number }).status || 500);
+    return NextResponse.json({ error: String(error) }, { status });
+  }
+}
+
+export async function POST(req: NextRequest) {
+  try {
+    const auth = await buildBackendRequestHeaders(req);
+    const body = await req.text();
+    if (!API_BASE) {
+      return grantSubscriptionDirectly(req, body, auth.authEmail);
+    }
+
+    const res = await fetch(`${API_BASE}/api/ops/subscriptions/grant`, {
+      method: "POST",
+      headers: { ...(auth.headers as Record<string, string>), "Content-Type": "application/json" },
+      body,
+      cache: "no-store",
+    });
+    const raw = await res.text();
+    if (res.status === 404) {
+      const fallback = await grantSubscriptionDirectly(req, body, auth.authEmail);
+      return applyAuthResponseCookies(fallback, auth.response);
+    }
+    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 (e) {
+    return buildProxyExceptionResponse(e, { publicMessage: "Subscription grant failed" });
+  }
+}

frontend/app/api/ops/telegram/members-audit/route.ts

@@ -0,0 +1,16 @@
+import { NextRequest, NextResponse } from "next/server";
+import { applyAuthResponseCookies, buildBackendRequestHeaders } from "@/lib/backend-auth";
+import { buildProxyExceptionResponse } from "@/lib/api-proxy";
+
+const API_BASE = process.env.POLYWEATHER_API_BASE_URL;
+
+export async function GET(req: NextRequest) {
+  if (!API_BASE) return NextResponse.json({ error: "API_BASE not configured" }, { status: 500 });
+  try {
+    const auth = await buildBackendRequestHeaders(req);
+    const res = await fetch(`${API_BASE}/api/ops/telegram/members-audit`, { headers: auth.headers, cache: "no-store" });
+    const raw = await res.text();
+    const response = new NextResponse(raw, { status: res.status, headers: { "Content-Type": "application/json", "Cache-Control": "no-store" } });
+    return applyAuthResponseCookies(response, auth.response);
+  } catch (e) { return buildProxyExceptionResponse(e, { publicMessage: "Telegram audit failed" }); }
+}

frontend/app/api/ops/training/accuracy/route.ts

@@ -0,0 +1,16 @@
+import { NextRequest, NextResponse } from "next/server";
+import { applyAuthResponseCookies, buildBackendRequestHeaders } from "@/lib/backend-auth";
+import { buildProxyExceptionResponse } from "@/lib/api-proxy";
+
+const API_BASE = process.env.POLYWEATHER_API_BASE_URL;
+
+export async function GET(req: NextRequest) {
+  if (!API_BASE) return NextResponse.json({ error: "API_BASE not configured" }, { status: 500 });
+  try {
+    const auth = await buildBackendRequestHeaders(req);
+    const res = await fetch(`${API_BASE}/api/ops/training/accuracy`, { headers: auth.headers, cache: "no-store" });
+    const raw = await res.text();
+    const response = new NextResponse(raw, { status: res.status, headers: { "Content-Type": "application/json", "Cache-Control": "no-store" } });
+    return applyAuthResponseCookies(response, auth.response);
+  } catch (e) { return buildProxyExceptionResponse(e, { publicMessage: "Training accuracy fetch failed" }); }
+}

frontend/app/api/ops/view-logs/route.ts

@@ -0,0 +1,21 @@
+import { NextRequest, NextResponse } from "next/server";
+import { applyAuthResponseCookies, buildBackendRequestHeaders } from "@/lib/backend-auth";
+import { buildProxyExceptionResponse } from "@/lib/api-proxy";
+
+const API_BASE = process.env.POLYWEATHER_API_BASE_URL;
+
+export async function GET(req: NextRequest) {
+  if (!API_BASE) return NextResponse.json({ error: "API_BASE not configured" }, { status: 500 });
+  try {
+    const auth = await buildBackendRequestHeaders(req);
+    const url = new URL(`${API_BASE}/api/ops/logs`);
+    const level = req.nextUrl.searchParams.get("level");
+    const lines = req.nextUrl.searchParams.get("lines");
+    if (level) url.searchParams.set("level", level);
+    if (lines) url.searchParams.set("lines", lines);
+    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": "application/json", "Cache-Control": "no-store" } });
+    return applyAuthResponseCookies(response, auth.response);
+  } catch (e) { return buildProxyExceptionResponse(e, { publicMessage: "Log fetch failed" }); }
+}

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

@@ -1,12 +1,5 @@
 import { NextRequest, NextResponse } from "next/server";
-import {
-  applyAuthResponseCookies,
-  buildBackendRequestHeaders,
-} from "@/lib/backend-auth";
-import {
-  buildProxyExceptionResponse,
-  buildUpstreamErrorResponse,
-} from "@/lib/api-proxy";
+import { proxyBackendJsonGet } from "@/lib/api-proxy";
 
 const API_BASE = process.env.POLYWEATHER_API_BASE_URL;
 
@@ -17,28 +10,12 @@ export async function GET(req: NextRequest) {
       { status: 500 },
     );
   }
-  try {
-    const auth = await buildBackendRequestHeaders(req);
-    const res = await fetch(`${API_BASE}/api/payments/config`, {
-      headers: auth.headers,
-      cache: "no-store",
-    });
-    if (!res.ok) {
-      const raw = await res.text();
-      const response = buildUpstreamErrorResponse(res.status, raw, {
-        detailLimit: 350,
-      });
-      return applyAuthResponseCookies(response, auth.response);
-    }
-    const data = await res.json();
-    const response = NextResponse.json(data, {
-      headers: { "Cache-Control": "no-store" },
-    });
-    return applyAuthResponseCookies(response, auth.response);
-  } catch (error) {
-    return buildProxyExceptionResponse(error, {
-      publicMessage: "Failed to fetch payment config",
-    });
-  }
+  return proxyBackendJsonGet(req, {
+    cacheControl: "public, max-age=0, s-maxage=300, stale-while-revalidate=900",
+    detailLimit: 350,
+    includeSupabaseIdentity: true,
+    publicMessage: "Failed to fetch payment config",
+    revalidateSeconds: 300,
+    url: `${API_BASE}/api/payments/config`,
+  });
 }
-

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

@@ -2,6 +2,7 @@ import { NextRequest, NextResponse } from "next/server";
 import {
   applyAuthResponseCookies,
   buildBackendRequestHeaders,
+  requireBackendAuthUser,
 } from "@/lib/backend-auth";
 import {
   buildProxyExceptionResponse,
@@ -24,6 +25,8 @@ export async function POST(
   try {
     const body = await req.json();
     const auth = await buildBackendRequestHeaders(req);
+    const authError = requireBackendAuthUser(auth);
+    if (authError) return authError;
     const proxiedHeaders = new Headers(auth.headers);
     proxiedHeaders.set("Content-Type", "application/json");
     const res = await fetch(

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

@@ -1,12 +1,5 @@
 import { NextRequest, NextResponse } from "next/server";
-import {
-  applyAuthResponseCookies,
-  buildBackendRequestHeaders,
-} from "@/lib/backend-auth";
-import {
-  buildProxyExceptionResponse,
-  buildUpstreamErrorResponse,
-} from "@/lib/api-proxy";
+import { proxyBackendJsonGet } from "@/lib/api-proxy";
 
 const API_BASE = process.env.POLYWEATHER_API_BASE_URL;
 
@@ -21,29 +14,11 @@ export async function GET(
     );
   }
   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 = buildUpstreamErrorResponse(res.status, raw, {
-        detailLimit: 350,
-      });
-      return applyAuthResponseCookies(response, auth.response);
-    }
-    const data = await res.json();
-    const response = NextResponse.json(data);
-    return applyAuthResponseCookies(response, auth.response);
-  } catch (error) {
-    return buildProxyExceptionResponse(error, {
-      publicMessage: "Failed to fetch payment intent",
-    });
-  }
+  return proxyBackendJsonGet(req, {
+    detailLimit: 350,
+    fetchCache: "no-store",
+    includeSupabaseIdentity: true,
+    publicMessage: "Failed to fetch payment intent",
+    url: `${API_BASE}/api/payments/intents/${encodeURIComponent(intentId)}`,
+  });
 }

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

@@ -2,6 +2,7 @@ import { NextRequest, NextResponse } from "next/server";
 import {
   applyAuthResponseCookies,
   buildBackendRequestHeaders,
+  requireBackendAuthUser,
 } from "@/lib/backend-auth";
 import {
   buildProxyExceptionResponse,
@@ -24,6 +25,8 @@ export async function POST(
   try {
     const body = await req.json();
     const auth = await buildBackendRequestHeaders(req);
+    const authError = requireBackendAuthUser(auth);
+    if (authError) return authError;
     const proxiedHeaders = new Headers(auth.headers);
     proxiedHeaders.set("Content-Type", "application/json");
     const res = await fetch(
@@ -37,8 +40,14 @@ export async function POST(
     );
     if (!res.ok) {
       const raw = await res.text();
+      let detail = raw.slice(0, 350);
+      try {
+        const parsed = JSON.parse(raw);
+        if (parsed.detail) detail = String(parsed.detail).slice(0, 350);
+      } catch {}
       const response = buildUpstreamErrorResponse(res.status, raw, {
         detailLimit: 350,
+        error: detail || undefined,
       });
       return applyAuthResponseCookies(response, auth.response);
     }

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

@@ -0,0 +1,62 @@
+import { NextRequest, NextResponse } from "next/server";
+import {
+  applyAuthResponseCookies,
+  buildBackendRequestHeaders,
+  requireBackendAuthUser,
+} from "@/lib/backend-auth";
+import {
+  buildProxyExceptionResponse,
+  buildUpstreamErrorResponse,
+} from "@/lib/api-proxy";
+
+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 authError = requireBackendAuthUser(auth);
+    if (authError) return authError;
+    const proxiedHeaders = new Headers(auth.headers);
+    proxiedHeaders.set("Content-Type", "application/json");
+    const res = await fetch(
+      `${API_BASE}/api/payments/intents/${encodeURIComponent(intentId)}/validate`,
+      {
+        method: "POST",
+        headers: proxiedHeaders,
+        body: JSON.stringify(body ?? {}),
+        cache: "no-store",
+      },
+    );
+    if (!res.ok) {
+      const raw = await res.text();
+      let detail = raw.slice(0, 350);
+      try {
+        const parsed = JSON.parse(raw);
+        if (parsed.detail) detail = String(parsed.detail).slice(0, 350);
+      } catch {}
+      const response = buildUpstreamErrorResponse(res.status, raw, {
+        detailLimit: 350,
+        error: detail || undefined,
+      });
+      return applyAuthResponseCookies(response, auth.response);
+    }
+    const data = await res.json();
+    const response = NextResponse.json(data);
+    return applyAuthResponseCookies(response, auth.response);
+  } catch (error) {
+    return buildProxyExceptionResponse(error, {
+      publicMessage: "Failed to validate payment tx",
+    });
+  }
+}

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

@@ -2,6 +2,7 @@ import { NextRequest, NextResponse } from "next/server";
 import {
   applyAuthResponseCookies,
   buildBackendRequestHeaders,
+  requireBackendAuthUser,
 } from "@/lib/backend-auth";
 import {
   buildProxyExceptionResponse,
@@ -35,6 +36,8 @@ export async function POST(req: NextRequest) {
   try {
     const body = await req.json();
     const auth = await buildBackendRequestHeaders(req);
+    const authError = requireBackendAuthUser(auth);
+    if (authError) return authError;
     const proxiedHeaders = new Headers(auth.headers);
     proxiedHeaders.set("Content-Type", "application/json");
     const res = await fetch(`${API_BASE}/api/payments/intents`, {

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

@@ -2,6 +2,7 @@ import { NextRequest, NextResponse } from "next/server";
 import {
   applyAuthResponseCookies,
   buildBackendRequestHeaders,
+  requireBackendAuthUser,
 } from "@/lib/backend-auth";
 import { buildProxyExceptionResponse } from "@/lib/api-proxy";
 
@@ -17,6 +18,8 @@ export async function POST(req: NextRequest) {
 
   try {
     const auth = await buildBackendRequestHeaders(req);
+    const authError = requireBackendAuthUser(auth);
+    if (authError) return authError;
     const res = await fetch(`${API_BASE}/api/payments/reconcile-latest`, {
       method: "POST",
       headers: auth.headers,

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

@@ -1,12 +1,5 @@
 import { NextRequest, NextResponse } from "next/server";
-import {
-  applyAuthResponseCookies,
-  buildBackendRequestHeaders,
-} from "@/lib/backend-auth";
-import {
-  buildProxyExceptionResponse,
-  buildUpstreamErrorResponse,
-} from "@/lib/api-proxy";
+import { proxyBackendJsonGet } from "@/lib/api-proxy";
 
 const API_BASE = process.env.POLYWEATHER_API_BASE_URL;
 
@@ -18,28 +11,13 @@ export async function GET(req: NextRequest) {
     );
   }
 
-  try {
-    const auth = await buildBackendRequestHeaders(req);
-    const res = await fetch(`${API_BASE}/api/payments/runtime`, {
-      headers: auth.headers,
-      cache: "no-store",
-    });
-    if (!res.ok) {
-      const raw = await res.text();
-      const response = buildUpstreamErrorResponse(res.status, raw, {
-        detailLimit: 500,
-      });
-      return applyAuthResponseCookies(response, auth.response);
-    }
-
-    const data = await res.json();
-    const response = NextResponse.json(data, {
-      headers: { "Cache-Control": "no-store" },
-    });
-    return applyAuthResponseCookies(response, auth.response);
-  } catch (error) {
-    return buildProxyExceptionResponse(error, {
-      publicMessage: "Failed to fetch payment runtime",
-    });
-  }
+  return proxyBackendJsonGet(req, {
+    cacheControl: "no-store",
+    conditionalResponse: false,
+    detailLimit: 500,
+    fetchCache: "no-store",
+    includeSupabaseIdentity: true,
+    publicMessage: "Failed to fetch payment runtime",
+    url: `${API_BASE}/api/payments/runtime`,
+  });
 }

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

@@ -2,6 +2,7 @@ import { NextRequest, NextResponse } from "next/server";
 import {
   applyAuthResponseCookies,
   buildBackendRequestHeaders,
+  requireBackendAuthUser,
 } from "@/lib/backend-auth";
 import {
   buildProxyExceptionResponse,
@@ -20,6 +21,8 @@ export async function POST(req: NextRequest) {
   try {
     const body = await req.json();
     const auth = await buildBackendRequestHeaders(req);
+    const authError = requireBackendAuthUser(auth);
+    if (authError) return authError;
     const proxiedHeaders = new Headers(auth.headers);
     proxiedHeaders.set("Content-Type", "application/json");
     const res = await fetch(`${API_BASE}/api/payments/wallets/challenge`, {

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

@@ -2,9 +2,11 @@ import { NextRequest, NextResponse } from "next/server";
 import {
   applyAuthResponseCookies,
   buildBackendRequestHeaders,
+  requireBackendAuthUser,
 } from "@/lib/backend-auth";
 import {
   buildProxyExceptionResponse,
+  proxyBackendJsonGet,
   buildUpstreamErrorResponse,
 } from "@/lib/api-proxy";
 
@@ -17,29 +19,15 @@ export async function GET(req: NextRequest) {
       { status: 500 },
     );
   }
-  try {
-    const auth = await buildBackendRequestHeaders(req);
-    const res = await fetch(`${API_BASE}/api/payments/wallets`, {
-      headers: auth.headers,
-      cache: "no-store",
-    });
-    if (!res.ok) {
-      const raw = await res.text();
-      const response = buildUpstreamErrorResponse(res.status, raw, {
-        detailLimit: 350,
-      });
-      return applyAuthResponseCookies(response, auth.response);
-    }
-    const data = await res.json();
-    const response = NextResponse.json(data, {
-      headers: { "Cache-Control": "no-store" },
-    });
-    return applyAuthResponseCookies(response, auth.response);
-  } catch (error) {
-    return buildProxyExceptionResponse(error, {
-      publicMessage: "Failed to fetch wallets",
-    });
-  }
+  return proxyBackendJsonGet(req, {
+    cacheControl: "no-store",
+    conditionalResponse: false,
+    detailLimit: 350,
+    fetchCache: "no-store",
+    includeSupabaseIdentity: true,
+    publicMessage: "Failed to fetch wallets",
+    url: `${API_BASE}/api/payments/wallets`,
+  });
 }
 
 export async function DELETE(req: NextRequest) {
@@ -57,6 +45,8 @@ export async function DELETE(req: NextRequest) {
   }
   try {
     const auth = await buildBackendRequestHeaders(req);
+    const authError = requireBackendAuthUser(auth);
+    if (authError) return authError;
     const proxiedHeaders = new Headers(auth.headers);
     proxiedHeaders.set("Content-Type", "application/json");
     const res = await fetch(`${API_BASE}/api/payments/wallets`, {

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

@@ -2,6 +2,7 @@ import { NextRequest, NextResponse } from "next/server";
 import {
   applyAuthResponseCookies,
   buildBackendRequestHeaders,
+  requireBackendAuthUser,
 } from "@/lib/backend-auth";
 import {
   buildProxyExceptionResponse,
@@ -20,6 +21,8 @@ export async function POST(req: NextRequest) {
   try {
     const body = await req.json();
     const auth = await buildBackendRequestHeaders(req);
+    const authError = requireBackendAuthUser(auth);
+    if (authError) return authError;
     const proxiedHeaders = new Headers(auth.headers);
     proxiedHeaders.set("Content-Type", "application/json");
     const res = await fetch(`${API_BASE}/api/payments/wallets/verify`, {

frontend/app/api/scan/terminal/overview/route.ts

@@ -0,0 +1,75 @@
+import { NextRequest, NextResponse } from "next/server";
+import {
+  applyAuthResponseCookies,
+  buildBackendRequestHeaders,
+} from "@/lib/backend-auth";
+import {
+  buildProxyExceptionResponse,
+  buildUpstreamErrorResponse,
+} from "@/lib/api-proxy";
+
+const API_BASE = process.env.POLYWEATHER_API_BASE_URL;
+const OVERVIEW_PROXY_TIMEOUT_MS = Math.max(
+  35_000,
+  Number(process.env.POLYWEATHER_SCAN_OVERVIEW_PROXY_TIMEOUT_MS || "45000") || 45_000,
+);
+
+export const dynamic = "force-dynamic";
+export const maxDuration = 60;
+
+export async function POST(req: NextRequest) {
+  if (!API_BASE) {
+    return NextResponse.json(
+      { error: "POLYWEATHER_API_BASE_URL is not configured" },
+      { status: 500 },
+    );
+  }
+
+  let body: unknown = {};
+  try {
+    body = await req.json();
+  } catch {
+    body = {};
+  }
+
+  let auth: Awaited<ReturnType<typeof buildBackendRequestHeaders>> | null = null;
+  const controller = new AbortController();
+  const timeoutId = setTimeout(() => controller.abort(), OVERVIEW_PROXY_TIMEOUT_MS);
+
+  try {
+    auth = await buildBackendRequestHeaders(req);
+    const headers = new Headers(auth.headers);
+    headers.set("Content-Type", "application/json");
+    headers.set("Accept", "application/json");
+    const res = await fetch(`${API_BASE}/api/scan/terminal/overview`, {
+      method: "POST",
+      headers,
+      cache: "no-store",
+      signal: controller.signal,
+      body: JSON.stringify(body || {}),
+    });
+    if (!res.ok) {
+      const raw = await res.text();
+      const response = buildUpstreamErrorResponse(res.status, raw);
+      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) {
+    const timedOut = controller.signal.aborted;
+    const response = buildProxyExceptionResponse(error, {
+      publicMessage: timedOut
+        ? "Market overview request timed out"
+        : "Failed to fetch market overview",
+      status: timedOut ? 504 : 500,
+    });
+    return auth ? applyAuthResponseCookies(response, auth.response) : response;
+  } finally {
+    clearTimeout(timeoutId);
+  }
+}

frontend/app/api/scan/terminal/route.ts

@@ -1,19 +1,12 @@
 import { NextRequest, NextResponse } from "next/server";
-import {
-  applyAuthResponseCookies,
-  buildBackendRequestHeaders,
-} from "@/lib/backend-auth";
-import {
-  buildProxyExceptionResponse,
-  buildUpstreamErrorResponse,
-} from "@/lib/api-proxy";
+import { proxyBackendJsonGet } from "@/lib/api-proxy";
+import { buildForceRefreshProxyCachePolicy } from "@/lib/proxy-cache-policy";
 
 const API_BASE = process.env.POLYWEATHER_API_BASE_URL;
 const SCAN_TERMINAL_PROXY_TIMEOUT_MS = Number(
   process.env.POLYWEATHER_SCAN_TERMINAL_PROXY_TIMEOUT_MS || "28000",
 );
 
-export const dynamic = "force-dynamic";
 export const maxDuration = 30;
 
 export async function GET(req: NextRequest) {
@@ -25,6 +18,7 @@ export async function GET(req: NextRequest) {
   }
 
   const params = new URLSearchParams();
+  const forceRefresh = req.nextUrl.searchParams.get("force_refresh") ?? "false";
   for (const key of [
     "scan_mode",
     "min_price",
@@ -42,41 +36,24 @@ export async function GET(req: NextRequest) {
       params.set(key, value);
     }
   }
+  const cachePolicy = buildForceRefreshProxyCachePolicy(forceRefresh, 10);
 
   const url = `${API_BASE}/api/scan/terminal?${params.toString()}`;
 
-  let auth: Awaited<ReturnType<typeof buildBackendRequestHeaders>> | null = null;
   const controller = new AbortController();
   const timeoutId = setTimeout(() => controller.abort(), SCAN_TERMINAL_PROXY_TIMEOUT_MS);
 
   try {
-    auth = await buildBackendRequestHeaders(req);
-    const res = await fetch(url, {
-      headers: auth.headers,
-      cache: "no-store",
+    return await proxyBackendJsonGet(req, {
+      cacheControl: cachePolicy.responseCacheControl,
+      fetchCache:
+        cachePolicy.fetchMode === "no-store" ? "no-store" : undefined,
+      publicMessage: "Failed to fetch scan terminal data",
+      revalidateSeconds: cachePolicy.revalidateSeconds,
       signal: controller.signal,
+      timeoutPublicMessage: "Scan terminal request timed out",
+      url,
     });
-    if (!res.ok) {
-      const raw = await res.text();
-      const response = buildUpstreamErrorResponse(res.status, raw);
-      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) {
-    const timedOut = controller.signal.aborted;
-    const response = buildProxyExceptionResponse(error, {
-      publicMessage: timedOut
-        ? "Scan terminal request timed out"
-        : "Failed to fetch scan terminal data",
-      status: timedOut ? 504 : 500,
-    });
-    return auth ? applyAuthResponseCookies(response, auth.response) : response;
   } finally {
     clearTimeout(timeoutId);
   }

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

@@ -1,12 +1,5 @@
 import { NextRequest, NextResponse } from "next/server";
-import {
-  applyAuthResponseCookies,
-  buildBackendRequestHeaders,
-} from "@/lib/backend-auth";
-import {
-  buildProxyExceptionResponse,
-  buildUpstreamErrorResponse,
-} from "@/lib/api-proxy";
+import { proxyBackendJsonGet } from "@/lib/api-proxy";
 
 const API_BASE = process.env.POLYWEATHER_API_BASE_URL;
 
@@ -18,28 +11,11 @@ export async function GET(req: NextRequest) {
     );
   }
 
-  try {
-    const auth = await buildBackendRequestHeaders(req);
-    const res = await fetch(`${API_BASE}/api/system/status`, {
-      headers: auth.headers,
-      cache: "no-store",
-    });
-    if (!res.ok) {
-      const raw = await res.text();
-      const response = buildUpstreamErrorResponse(res.status, raw, {
-        detailLimit: 500,
-      });
-      return applyAuthResponseCookies(response, auth.response);
-    }
-
-    const data = await res.json();
-    const response = NextResponse.json(data, {
-      headers: { "Cache-Control": "no-store" },
-    });
-    return applyAuthResponseCookies(response, auth.response);
-  } catch (error) {
-    return buildProxyExceptionResponse(error, {
-      publicMessage: "Failed to fetch system status",
-    });
-  }
+  return proxyBackendJsonGet(req, {
+    cacheControl: "public, max-age=0, s-maxage=30, stale-while-revalidate=120",
+    detailLimit: 500,
+    publicMessage: "Failed to fetch system status",
+    revalidateSeconds: 30,
+    url: `${API_BASE}/api/system/status`,
+  });
 }

frontend/app/auth/callback/route.ts

@@ -1,5 +1,6 @@
 import { NextRequest, NextResponse } from "next/server";
 import { createSupabaseRouteClient, hasSupabaseServerEnv } from "@/lib/supabase/server";
+import { getConfiguredSiteUrl } from "@/lib/site-url";
 
 function normalizeNextPath(input: string | null) {
   const fallback = "/";
@@ -11,6 +12,16 @@ function normalizeNextPath(input: string | null) {
 }
 
 export async function GET(request: NextRequest) {
+  const configuredSiteUrl = getConfiguredSiteUrl();
+  if (configuredSiteUrl) {
+    const canonicalOrigin = new URL(configuredSiteUrl).origin;
+    if (request.nextUrl.origin !== canonicalOrigin) {
+      const canonicalCallbackUrl = new URL(request.nextUrl.pathname, canonicalOrigin);
+      canonicalCallbackUrl.search = request.nextUrl.search;
+      return NextResponse.redirect(canonicalCallbackUrl);
+    }
+  }
+
   const nextPath = normalizeNextPath(request.nextUrl.searchParams.get("next"));
   const redirectUrl = request.nextUrl.clone();
   redirectUrl.pathname = nextPath;
@@ -29,4 +40,3 @@ export async function GET(request: NextRequest) {
 
   return response;
 }
-

frontend/app/entitlement-required/page.tsx

@@ -1,3 +1,5 @@
+import Link from "next/link";
+
 type Props = {
   searchParams?: Promise<{ next?: string }>;
 };
@@ -21,37 +23,82 @@ export default async function EntitlementRequiredPage({ searchParams }: Props) {
       <section
         style={{
           width: "100%",
-          maxWidth: 720,
+          maxWidth: 480,
           border: "1px solid rgba(68, 92, 140, 0.45)",
           borderRadius: 16,
           padding: 24,
           background: "rgba(9, 18, 36, 0.88)",
           boxShadow: "0 20px 50px rgba(0, 0, 0, 0.35)",
+          textAlign: "center",
         }}
       >
-        <h1 style={{ margin: 0, fontSize: 28, lineHeight: 1.2 }}>
-          Entitlement Required
+        <h1
+          style={{
+            margin: 0,
+            fontSize: 22,
+            lineHeight: 1.3,
+            fontWeight: 800,
+          }}
+        >
+          需要登录方可访问此页面
         </h1>
         <p style={{ marginTop: 12, color: "#9fb2da", lineHeight: 1.6 }}>
-          This dashboard is protected. If Supabase auth is enabled, please go to{" "}
-          <a
+          本页面需要登录权限。请登录后重试。
+          <br />
+          Sign in required to access this page.
+        </p>
+        <div style={{ marginTop: 20, display: "flex", gap: 12, justifyContent: "center", flexWrap: "wrap" }}>
+          <Link
             href={`/auth/login?next=${encodeURIComponent(nextPath)}`}
             style={{
-              color: "#8fc5ff",
               display: "inline-flex",
-              minHeight: 36,
               alignItems: "center",
+              gap: 6,
+              minHeight: 40,
+              padding: "8px 20px",
+              borderRadius: 12,
+              background: "linear-gradient(135deg, #2563EB, #4F46E5)",
+              color: "#fff",
+              fontWeight: 700,
+              textDecoration: "none",
+              fontSize: 14,
             }}
           >
-            /auth/login
-          </a>{" "}
-          to sign in first.
-        </p>
-        <p style={{ marginTop: 12, color: "#9fb2da", lineHeight: 1.6 }}>
-          Legacy mode still supports <code>?access_token=&lt;your-token&gt;</code>.
-        </p>
-        <p style={{ marginTop: 12, color: "#9fb2da", lineHeight: 1.6 }}>
-          Requested path: <code>{nextPath}</code>
+            去登录 / Sign in
+          </Link>
+          <Link
+            href="/"
+            style={{
+              display: "inline-flex",
+              alignItems: "center",
+              gap: 6,
+              minHeight: 40,
+              padding: "8px 20px",
+              borderRadius: 12,
+              border: "1px solid rgba(68, 92, 140, 0.45)",
+              background: "rgba(68, 92, 140, 0.2)",
+              color: "#d6e2ff",
+              fontWeight: 600,
+              textDecoration: "none",
+              fontSize: 14,
+            }}
+          >
+            返回首页 / Back to Home
+          </Link>
+        </div>
+        <p
+          style={{
+            marginTop: 20,
+            fontSize: 12,
+            color: "#7891b5",
+            lineHeight: 1.5,
+          }}
+        >
+          传统令牌模式仍支持 <code style={{ background: "rgba(255,255,255,0.06)", padding: "2px 6px", borderRadius: 4 }}>?access_token=&lt;your-token&gt;</code>
+          <br />
+          <span style={{ marginTop: 4, display: "inline-block" }}>
+            请求路径 / Requested path: <code>{nextPath}</code>
+          </span>
         </p>
       </section>
     </main>

frontend/app/error.tsx

@@ -0,0 +1,101 @@
+"use client";
+
+import { RefreshCw } from "lucide-react";
+import { useEffect } from "react";
+
+export default function ErrorPage({
+  error,
+  reset,
+}: {
+  error: Error & { digest?: string };
+  reset: () => void;
+}) {
+  useEffect(() => {
+    console.error("Unhandled page error:", error);
+  }, [error]);
+
+  return (
+    <div
+      style={{
+        display: "flex",
+        flexDirection: "column",
+        alignItems: "center",
+        justifyContent: "center",
+        minHeight: "100vh",
+        padding: "2rem",
+        gap: "1rem",
+        backgroundColor: "var(--color-bg-base, #0B1220)",
+        color: "var(--color-text-primary, #E6EDF3)",
+        fontFamily: "var(--font-data, Inter, sans-serif)",
+        textAlign: "center",
+      }}
+    >
+      <div
+        style={{
+          width: 64,
+          height: 64,
+          borderRadius: "var(--radius-xl, 20px)",
+          backgroundColor: "var(--color-bg-raised, #111A2E)",
+          display: "flex",
+          alignItems: "center",
+          justifyContent: "center",
+          marginBottom: "0.5rem",
+        }}
+      >
+        <span style={{ fontSize: "1.8rem" }}>⚠</span>
+      </div>
+      <h1
+        style={{
+          fontSize: "1.25rem",
+          fontWeight: 600,
+          margin: 0,
+        }}
+      >
+        页面出错了
+      </h1>
+      <p
+        style={{
+          color: "var(--color-text-secondary, #9FB2C7)",
+          fontSize: "0.875rem",
+          margin: 0,
+          maxWidth: 400,
+          lineHeight: 1.6,
+        }}
+      >
+        数据处理时遇到了意外问题，请尝试刷新页面。
+      </p>
+      {error.digest ? (
+        <code
+          style={{
+            fontSize: "0.75rem",
+            color: "var(--color-text-muted, #7D8FA3)",
+            fontFamily: "var(--font-mono, monospace)",
+          }}
+        >
+          {error.digest}
+        </code>
+      ) : null}
+      <button
+        type="button"
+        onClick={reset}
+        style={{
+          marginTop: "0.5rem",
+          display: "inline-flex",
+          alignItems: "center",
+          gap: "0.5rem",
+          padding: "0.5rem 1.25rem",
+          borderRadius: "var(--radius-md, 10px)",
+          border: "1px solid var(--color-border-default, rgba(159,178,199,0.16))",
+          backgroundColor: "var(--color-bg-raised, #111A2E)",
+          color: "var(--color-accent-primary, #4DA3FF)",
+          cursor: "pointer",
+          fontSize: "0.875rem",
+          fontWeight: 500,
+        }}
+      >
+        <RefreshCw size={14} />
+        重试
+      </button>
+    </div>
+  );
+}

frontend/app/global-error.tsx

@@ -0,0 +1,115 @@
+"use client";
+
+import { RefreshCw } from "lucide-react";
+import { useEffect } from "react";
+
+export default function GlobalError({
+  error,
+  reset,
+}: {
+  error: Error & { digest?: string };
+  reset: () => void;
+}) {
+  useEffect(() => {
+    console.error("Unhandled root error:", error);
+  }, [error]);
+
+  return (
+    <html lang="zh-CN" className="dark">
+      <head>
+        <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+        <title>PolyWeather — 出错了</title>
+      </head>
+      <body
+        style={{
+          margin: 0,
+          padding: 0,
+          backgroundColor: "var(--color-bg-base, #0B1220)",
+          minHeight: "100vh",
+        }}
+      >
+        <div
+          style={{
+            display: "flex",
+            flexDirection: "column",
+            alignItems: "center",
+            justifyContent: "center",
+            minHeight: "100vh",
+            padding: "2rem",
+            gap: "1rem",
+            color: "var(--color-text-primary, #E6EDF3)",
+            fontFamily: "var(--font-data, Inter, sans-serif)",
+            textAlign: "center",
+          }}
+        >
+          <div
+            style={{
+              width: 64,
+              height: 64,
+              borderRadius: "var(--radius-xl, 20px)",
+              backgroundColor: "var(--color-bg-raised, #111A2E)",
+              display: "flex",
+              alignItems: "center",
+              justifyContent: "center",
+              marginBottom: "0.5rem",
+            }}
+          >
+            <span style={{ fontSize: "1.8rem" }}>⚠</span>
+          </div>
+          <h1
+            style={{
+              fontSize: "1.25rem",
+              fontWeight: 600,
+              margin: 0,
+            }}
+          >
+            页面出错了
+          </h1>
+          <p
+            style={{
+              color: "var(--color-text-secondary, #9FB2C7)",
+              fontSize: "0.875rem",
+              margin: 0,
+              maxWidth: 400,
+              lineHeight: 1.6,
+            }}
+          >
+            PolyWeather 遇到了严重错误，请尝试刷新页面。如果问题持续出现，请联系我们。
+          </p>
+          {error.digest ? (
+            <code
+              style={{
+                fontSize: "0.75rem",
+                color: "var(--color-text-muted, #7D8FA3)",
+                fontFamily: "var(--font-mono, monospace)",
+              }}
+            >
+              {error.digest}
+            </code>
+          ) : null}
+          <button
+            type="button"
+            onClick={reset}
+            style={{
+              marginTop: "0.5rem",
+              display: "inline-flex",
+              alignItems: "center",
+              gap: "0.5rem",
+              padding: "0.5rem 1.25rem",
+              borderRadius: "var(--radius-md, 10px)",
+              border: "1px solid var(--color-border-default, rgba(159,178,199,0.16))",
+              backgroundColor: "var(--color-bg-raised, #111A2E)",
+              color: "var(--color-accent-primary, #4DA3FF)",
+              cursor: "pointer",
+              fontSize: "0.875rem",
+              fontWeight: 500,
+            }}
+          >
+            <RefreshCw size={14} />
+            重试
+          </button>
+        </div>
+      </body>
+    </html>
+  );
+}

frontend/app/globals.css

@@ -9,28 +9,28 @@
 @layer base {
   :root {
     /* ── Background Scale ── */
-    --color-bg-base: #0B1220;
-    --color-bg-raised: #111A2E;
-    --color-bg-overlay: #16213A;
-    --color-bg-card: rgba(17, 26, 46, 0.88);
-    --color-bg-input: rgba(22, 33, 58, 0.72);
+    --color-bg-base: #f4f7fb;
+    --color-bg-raised: #ffffff;
+    --color-bg-overlay: #ffffff;
+    --color-bg-card: rgba(255, 255, 255, 0.95);
+    --color-bg-input: rgba(241, 245, 249, 0.88);
 
     /* ── Text Scale ── */
-    --color-text-primary: #E6EDF3;
-    --color-text-secondary: #9FB2C7;
-    --color-text-muted: #7D8FA3;
-    --color-text-disabled: #7D8FA3;
+    --color-text-primary: #0F172A;
+    --color-text-secondary: #334155;
+    --color-text-muted: #475569;
+    --color-text-disabled: #94A3B8;
 
     /* ── Accent Colors ── */
-    --color-accent-primary: #4DA3FF;
-    --color-accent-secondary: #6FB7FF;
-    --color-accent-tertiary: #93C5FD;
+    --color-accent-primary: #2563EB;
+    --color-accent-secondary: #3B82F6;
+    --color-accent-tertiary: #60A5FA;
 
     /* ── Signal / Semantic Colors ── */
-    --color-signal-success: #22C55E;
-    --color-signal-warning: #F59E0B;
-    --color-signal-danger: #EF4444;
-    --color-signal-info: #4DA3FF;
+    --color-signal-success: #00897b;
+    --color-signal-warning: #d97706;
+    --color-signal-danger: #dc2626;
+    --color-signal-info: #2563eb;
 
     /* ── Risk Colors (aliased from signal) ── */
     --color-risk-high: var(--color-signal-danger);
@@ -38,22 +38,21 @@
     --color-risk-low: var(--color-signal-success);
 
     /* ── Border ── */
-    --color-border-default: rgba(159, 178, 199, 0.16);
-    --color-border-hover: rgba(77, 163, 255, 0.38);
-    --color-border-subtle: rgba(159, 178, 199, 0.08);
+    --color-border-default: #d8e0ec;
+    --color-border-hover: #b8c4d6;
+    --color-border-subtle: #e8edf5;
 
     /* ── Shadow / Elevation ── */
-    --shadow-elevation-1: 0 1px 3px rgba(0, 0, 0, 0.3);
-    --shadow-elevation-2: 0 8px 24px rgba(0, 0, 0, 0.45);
-    --shadow-elevation-3: 0 20px 60px rgba(0, 0, 0, 0.6);
-    --shadow-glow-accent: 0 0 20px rgba(77, 163, 255, 0.24);
-    --shadow-glow-secondary: 0 0 20px rgba(111, 183, 255, 0.22);
+    --shadow-elevation-1: 0 1px 3px rgba(15, 23, 42, 0.05);
+    --shadow-elevation-2: 0 8px 24px rgba(15, 23, 42, 0.06);
+    --shadow-elevation-3: 0 20px 60px rgba(15, 23, 42, 0.08);
+    --shadow-glow-accent: 0 0 20px rgba(37, 99, 235, 0.08);
+    --shadow-glow-secondary: 0 0 20px rgba(96, 165, 250, 0.08);
 
     /* ── Typography ── */
-    --font-data:
-      "Inter", -apple-system, BlinkMacSystemFont, "Segoe UI", sans-serif;
-    --font-display: "Inter", -apple-system, BlinkMacSystemFont, "Segoe UI", sans-serif;
-    --font-mono: "JetBrains Mono", "Fira Code", "SF Mono", monospace;
+    --font-data: var(--font-inter), -apple-system, BlinkMacSystemFont, "Segoe UI", sans-serif;
+    --font-display: var(--font-inter), -apple-system, BlinkMacSystemFont, "Segoe UI", sans-serif;
+    --font-mono: var(--font-jetbrains-mono), "Fira Code", "SF Mono", monospace;
 
     /* ── Spacing (4px grid) ── */
     --space-1: 4px;
@@ -67,19 +66,19 @@
     --space-12: 48px;
 
     /* ── Border Radius ── */
-    --radius-sm: 6px;
-    --radius-md: 10px;
-    --radius-lg: 14px;
-    --radius-xl: 20px;
+    --radius-sm: 4px;
+    --radius-md: 6px;
+    --radius-lg: 10px;
+    --radius-xl: 14px;
     --radius-full: 9999px;
 
     /* ── Glass / Blur ── */
     --glass-blur-1: blur(10px);
     --glass-blur-2: blur(16px);
     --glass-blur-3: blur(24px);
-    --glass-opacity-1: 0.72;
-    --glass-opacity-2: 0.85;
-    --glass-opacity-3: 0.92;
+    --glass-opacity-1: 0.86;
+    --glass-opacity-2: 0.92;
+    --glass-opacity-3: 0.96;
 
     /* ── Layout ── */
     --header-height: 52px;
@@ -90,55 +89,57 @@
     --transition-fast: 150ms cubic-bezier(0.4, 0, 0.2, 1);
     --transition-base: 250ms cubic-bezier(0.4, 0, 0.2, 1);
     --transition-slow: 400ms cubic-bezier(0.16, 1, 0.3, 1);
+    --transition: var(--transition-base);
 
-    /* ── shadcn/ui Tokens (used by Tailwind @apply border-border) ── */
-    --background: 223 53% 4%;
-    --foreground: 210 40% 98%;
-    --card: 223 46% 8%;
-    --card-foreground: 210 40% 98%;
-    --primary: 159 100% 44%;
-    --primary-foreground: 222 47% 8%;
-    --secondary: 224 30% 14%;
-    --secondary-foreground: 210 40% 98%;
-    --accent: 217 30% 18%;
-    --accent-foreground: 210 40% 98%;
-    --border: 221 38% 22%;
+    /* ── Legacy Variable Aliases ── */
+    --accent-cyan:    var(--color-accent-primary);
+    --accent-blue:    var(--color-accent-secondary);
+    --accent-green:   var(--color-signal-success);
+    --bg-primary:     var(--color-bg-base);
+    --bg-secondary:   var(--color-bg-raised);
+    --bg-card:        var(--color-bg-card);
+    --bg-glass:       var(--color-bg-card);
+    --border-glass:   var(--color-border-default);
+    --border-subtle:  var(--color-border-subtle);
+    --text-primary:   var(--color-text-primary);
+    --text-secondary: var(--color-text-secondary);
+    --text-muted:     var(--color-text-muted);
+    --risk-high:      var(--color-risk-high);
+    --risk-medium:    var(--color-risk-medium);
+    --risk-low:       var(--color-risk-low);
+    --shadow-lg:      var(--shadow-elevation-2);
+    --glass-blur:     10px;
+
+    /* ── shadcn/ui Tokens ── */
+    --background: 210 40% 98%;
+    --foreground: 222 47% 12%;
+    --card: 0 0% 100%;
+    --card-foreground: 222 47% 12%;
+    --primary: 221 83% 53%;
+    --primary-foreground: 210 40% 98%;
+    --secondary: 210 40% 96%;
+    --secondary-foreground: 222 47% 12%;
+    --accent: 210 40% 96%;
+    --accent-foreground: 222 47% 12%;
+    --border: 214 32% 91%;
   }
 
-  /* ── Light Theme Token Overrides ── */
-  html.light,
-  html[data-theme="light"] {
-    --color-bg-base: #F7F9FC;
-    --color-bg-raised: #EEF2F7;
-    --color-bg-overlay: #FFFFFF;
-    --color-bg-card: rgba(255, 255, 255, 0.92);
-    --color-bg-input: rgba(238, 242, 247, 0.88);
-
-    --color-text-primary: #0F172A;
-    --color-text-secondary: #334155;
-    --color-text-muted: #475569;
-    --color-text-disabled: #94A3B8;
-
-    --color-accent-primary: #2563EB;
-    --color-accent-secondary: #3B82F6;
-    --color-accent-tertiary: #60A5FA;
-
-    --color-border-default: rgba(148, 163, 184, 0.24);
-    --color-border-hover: rgba(37, 99, 235, 0.38);
-    --color-border-subtle: rgba(148, 163, 184, 0.12);
-
-    --shadow-elevation-1: 0 1px 3px rgba(0, 0, 0, 0.1);
-    --shadow-elevation-2: 0 8px 24px rgba(40, 70, 110, 0.12);
-    --shadow-elevation-3: 0 20px 60px rgba(40, 70, 110, 0.15);
-    --shadow-glow-accent: 0 0 20px rgba(37, 99, 235, 0.14);
-    --shadow-glow-secondary: 0 0 20px rgba(96, 165, 250, 0.12);
-
-    --glass-blur-1: blur(10px);
-    --glass-blur-2: blur(16px);
-    --glass-blur-3: blur(24px);
-    --glass-opacity-1: 0.86;
-    --glass-opacity-2: 0.92;
-    --glass-opacity-3: 0.96;
+  /* ── Monospaced numbers & data globally for professional feel ── */
+  .font-mono,
+  .nearby-temp,
+  .nearby-wind,
+  .nearby-time,
+  .nearby-marker,
+  .marker-bubble,
+  .map-pill,
+  [class*="temp"],
+  [class*="value"],
+  [class*="price"],
+  [class*="number"],
+  [class*="stat"],
+  [class*="score"],
+  [class*="time-"] {
+    font-family: var(--font-mono) !important;
   }
 
   * {
@@ -173,23 +174,7 @@
 
   body {
     font-family: var(--font-data);
-    background:
-      radial-gradient(
-        circle at 10% -10%,
-        rgba(0, 224, 164, 0.1),
-        transparent 40%
-      ),
-      radial-gradient(
-        circle at 90% 0%,
-        rgba(123, 97, 255, 0.08),
-        transparent 36%
-      ),
-      radial-gradient(
-        circle at 80% 100%,
-        rgba(0, 224, 164, 0.06),
-        transparent 48%
-      ),
-      var(--color-bg-base);
+    background: var(--color-bg-base);
     color: var(--color-text-primary);
   }
 }
@@ -305,6 +290,17 @@
   }
 }
 
+/* ── Extreme temperature emphasis ── */
+.temp-extreme-hot {
+  color: #f97316;
+  text-shadow: 0 0 12px rgba(249, 115, 22, 0.35);
+}
+
+.temp-extreme-cold {
+  color: #38bdf8;
+  text-shadow: 0 0 12px rgba(56, 189, 248, 0.35);
+}
+
 /* ══════════════════════════════════════════════════════════════
    Map Marker Components (nearby stations, city bubbles)
    ══════════════════════════════════════════════════════════════ */