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61 Commits

Author SHA1 Message Date
2569718930@qq.com 4cc579ccb3 feat: add AMSC runway observations 2026-05-15 01:41:49 +08:00
2569718930@qq.com 6c08a68413 @ 
性能与用户体验全面优化

    前端性能:
    - 移除 Three.js 依赖（~600KB），天气粒子改为纯 CSS 动画 + Canvas 2D
    - Google Fonts 切换为 next/font 自托管，消除跨域字体请求
    - 合并 ScanTerminalLightTheme.module.css (37KB) 到主 CSS，亮/暗主题统一用 CSS 变量
    - 新增 /api/dashboard/init 聚合端点，首次加载 4 次往返 → 1 次
    - 添加 Service Worker 静态资源缓存，修复 PWA manifest 配置

    用户体验:
    - 新增全局错误边界 error.tsx / global-error.tsx，崩溃不再白屏
    - 决策卡和城市详情的更新时间改为相对时间（"15秒前"），每秒自动刷新
    - 数据陈旧时状态标签从青色切换为琥珀色提示

    DEB 算法增强:
    - 市场扫描路径接入 Open-Meteo 多模型数据（ECMWF/GFS/ICON/JMA/HRDPS 等）
    - MAE 计算加入时间衰减（decay_factor=0.85），近期模型误差权重更高

    Scope-risk: MEDIUM — 全量 170 测试通过，前端 TypeScript/build 通过，ruff 零告警
    Tested: python -m pytest -q (170 passed), npx tsc --noEmit (0 errors), npm run build (success), ruff check .
@
 2026-05-14 21:31:05 +08:00
2569718930@qq.com 37494a7192 @ 
将 web/routes.py 拆分为模块化 router + service 架构

    - 新增 web/app_factory.py 集中注册 7 个域名 router
    - 新增 web/routers/ 薄壳路由层（auth/city/system/scan/ops/payments/analytics）
    - 新增 web/services/ 业务函数下沉（每域独立 service 文件）
    - web/routes.py 缩减为 city_runtime 的兼容重导出 facade
    - analysis_service.py/app.py 适配新入口并清理冗余导入

    Scope-risk: LOW — 全量 170 测试通过，router 注册顺序与原路由一致
    Tested: python -m pytest -q (170 passed), ruff check . (All checks passed)
@
 2026-05-14 20:01:26 +08:00
2569718930@qq.com 02add6d994 feat: implement city weather detail API routing, dashboard data models, and monitoring infrastructure 2026-05-14 14:21:28 +08:00
AmandaloveYang e344fae75f 首尔/釜山 AMOS 数据缓存 TTL 从 300s 降至 60s，对齐官网 1 分钟刷新频率 
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
 2026-05-11 17:28:53 +08:00
2569718930@qq.com 8075fb66b7 AMOS 增加 info 级别日志追踪数据流 
- weather_sources.py: _attach_korean_amos_data 记录 fetch 开始、成功/失败、温度/跑道数据
- amos_station_sources.py: _amos_get_page 记录页面匹配/不匹配
- amos_station_sources.py: fetch_amos_official_current 记录 HTML 获取和解析
- analysis_service.py: _analyze 记录 AMOS 数据是否到达分析层

重启后端后在日志中搜索 "AMOS" 可追踪完整数据流：
  AMOS: fetching for city=seoul
  AMOS page matched icao=RKSI length=...
  AMOS fetch_amos_official_current: got HTML for RKSI...
  AMOS: got data for city=seoul temp_c=... source=...
  AMOS _analyze: found amos data for city=seoul temp_c=...
 2026-05-10 19:46:26 +08:00
2569718930@qq.com 40cd8fc2a6 修复机场观测面板不显示 + 放宽 AMOS 展示条件 2026-05-10 19:06:22 +08:00
2569718930@qq.com e23a90a961 修复釜山 AMOS 数据获取 + 跑道面板容错 
AMOS 页面用 JS 切换机场，GET 参数无效。新增策略：
- 先 GET 建立 session，再 POST form data 切换机场
- 尝试多种 form data 组合（icao/stn/airport/code）
- 回退到 GET 参数尝试

跑道面板容错：
- 无温度数据时仍展示跑道风/能见度/RVR
- 温度缺失显示 "--" 而非隐藏整行
- analysis_service 输出条件放宽：有 temp_c 或 runway_obs 即可

Tested: python -m ruff check ., npx tsc --noEmit
 2026-05-10 18:39:37 +08:00
2569718930@qq.com ba352feb34 首尔/釜山机场报文优先使用 AMOS（更新更快） 
- analysis_service.py：AMOS 数据覆盖 airport_current 的 raw_metar、wind、pressure
- source_label 设为 "AMOS"（区别于 aviationweather.gov METAR）
- stale_for_today 强制 false（AMOS 数据本身证明了时效性）
- observation_time 优先使用 AMOS 时间戳
- AI 读取的 city_snapshot.current.raw_metar 来自 AMOS 跑道传感器

优先级：AMOS raw_metar > aviationweather.gov METAR（首尔/釜山）

Reason: AMOS 直连韩国机场系统，延迟更低，更新更快
 2026-05-10 18:18:40 +08:00
2569718930@qq.com 04b2f0e4a3 AMOS 数据接入分析层和 AI 预测判定 
问题：AMOS 数据在采集层取出后从未被 _analyze() 读取，AI 无法看到跑道级数据

修复：
- analysis_service.py：_analyze() 新增 AMOS 温度读取优先级
  观测来源顺序：结算源 > AMOS跑道传感器 > METAR > MGM > NMC
- AMOS 数据自动覆盖 current.wind_speed_kt、current.pressure_hpa、current.raw_metar
- 输出新增 "amos" 字段携带完整跑道数据（temp_source、runway_temps）
- scan_terminal_service.py：AI prompt 的 current 区块新增 pressure_hpa 和 observation_source
- AI 现在知晓数据来自 AMOS 跑道传感器（observation_source="amos"），可据此判断数据权威性

数据流：
  AMOS fetch -> raw["amos"] -> _analyze() -> current.observation_source="amos"
  -> result["amos"] -> _build_city_ai_prompt -> city_snapshot.current
  -> AI reads runway sensor temp/wind/pressure as authoritative observation

Tested: python -m ruff check ., npx tsc --noEmit
 2026-05-10 18:16:10 +08:00
2569718930@qq.com b3ea8dcfa7 数据链路 P1 修复：ETag 缓存 + 校准漂移检测 
P1-5 ETag 支持：
- 后端新增 _etag_middleware：GET /api/* 自动返回 ETag (MD5)
- 支持 If-None-Match 请求头，匹配时返回 304 + 30s Cache-Control
- 前端 cache: no-store → default，浏览器自动处理 ETag/304 节省带宽

P1-6 校准漂移检测：
- probability_calibration.py 新增 check_calibration_drift()
- 对比最近 200 条 daily_records 的 CRPS 与校准基线
- 漂移 >15% 时返回 warning 提示重新训练
- 集成到 /api/system/status 的 probability.drift 字段

Tested: python -m ruff check ., npx tsc --noEmit
 2026-05-10 16:54:48 +08:00
2569718930@qq.com c0bb2acf78 修复数据链路 P0 瓶颈：缓存炸弹、Context 重渲染、LGBM 循环、TTL 不匹配 
P0-1 sessionStorage 限制：
- writeCityDetailCacheBundle 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

P0-2 Context 拆分：
- 新增 CityDetailsContext 独立管理 cityDetailsByName 变更
- 新增 useCityDetails hook，只订阅详情的组件不再因 cities/proAccess 变化重渲染
- DashboardStoreContext 保持不变，向后兼容

P0-3 扫描终端 TTL：
- SCAN_TERMINAL_PAYLOAD_TTL_SEC 30s → 120s
- 匹配 ThreadPoolExecutor(4) x 60 城的实际重算耗时

P0-4 LGBM 循环依赖：
- LGBM 预测值不再作为 DEB 输入参与权重计算
- 保留为独立参考字段 lgbm.prediction 输出给前端展示
- 消除 DEB → LGBM 训练 → DEB 的循环

新增 docs/data-architecture-review.md 完整数据链路审查报告

Tested: npx tsc --noEmit, python -m ruff check .
 2026-05-10 16:48:28 +08:00
2569718930@qq.com 7b82ba68df 回退 _analyze_summary 中未启用的 LGBM 增强步骤 
LGBM 当前默认禁用 (POLYWEATHER_LGBM_ENABLED=false)，predict_lgbm_daily_high
直接返回 None，在 _analyze 中也未实际参与 DEB 计算，_analyze_summary 无需重复。

Rejected: 在 LGBM 正式启用前，_analyze_summary 不应包含会被跳过的 LGBM 调用
 2026-05-06 18:30:47 +08:00
2569718930@qq.com a684586cb4 统一 DEB 数据源为单一计算路径 
getModelView 优先使用根级 detail.deb.prediction 保证与 AI 证据面板一致，
_build_city_detail_payload 补上缺失的 deb 和 multi_model_daily 字段，
_analyze_summary 补上 LGBM 增强步骤使其与 _analyze 产出相同的 DEB 值。

Constraint: 三个入口 (panel/full/summary) 的 DEB 必须经过同一套 calculate_dynamic_weights + LGBM 重算流程
Tested: ruff + tsc 全过
 2026-05-06 18:24:15 +08:00
2569718930@qq.com 6c203bee60 This version of Antigravity is no longer supported. Please upgrade to receive the latest features. 2026-05-01 11:27:16 +08:00
2569718930@qq.com d54050ceb0 feat: implement AI-driven city weather analysis and market decision dashboard components 2026-04-26 10:02:54 +08:00
2569718930@qq.com 1595f4a92e feat: implement ScanTerminalDashboard with AI-driven city forecast streaming and opportunity tracking 2026-04-26 08:12:31 +08:00
2569718930@qq.com 99ffa717ef Use model cluster for tail no scan signals 2026-04-24 15:12:53 +08:00
2569718930@qq.com ddc0180ae6 Implement EMOS scan terminal with REST-only market data 2026-04-23 23:49:52 +08:00
2569718930@qq.com ca81fda287 feat: implement analysis service, dashboard components, and data collection utilities for PolyWeather 2026-04-23 21:10:22 +08:00
2569718930@qq.com 54b326ff42 feat: implement comprehensive Polymarket weather analysis service with frontend dashboard and market scanning capabilities 2026-04-23 20:45:32 +08:00
2569718930@qq.com 974b55e34f Add full probability distributions to dashboard 2026-04-22 01:43:13 +08:00
2569718930@qq.com 582ded8cfb 改进METAR当日状态判断与展示 2026-04-21 19:13:33 +08:00
2569718930@qq.com d6cc3b1b90 Fix city-local time display for nearby stations 2026-04-19 15:37:13 +08:00
2569718930@qq.com 7e2209e928 Include UTC offsets in city and nearby observation times 2026-04-19 14:47:18 +08:00
2569718930@qq.com b2f15ae7d8 Clarify intraday suppression headlines 2026-04-19 14:15:46 +08:00
2569718930@qq.com fbbd13b1c3 Add nearby station timing sync labels 2026-04-19 02:38:34 +08:00
2569718930@qq.com 7909a16002 Add open multi-model forecast sources 2026-04-17 00:27:57 +08:00
2569718930@qq.com 9ed31b95a4 Fix METAR refresh timing and local observation display 2026-04-16 18:21:51 +08:00
2569718930@qq.com 9823e3961f Add localized meteorology text and filter implausible METAR temps 2026-04-16 17:34:39 +08:00
2569718930@qq.com e2cb0cfe5e Add professional intraday meteorology analysis 2026-04-16 17:13:53 +08:00
2569718930@qq.com 25424700ce Restore observed temperatures on map and trend charts 2026-04-16 15:29:26 +08:00
2569718930@qq.com baaccc6636 feat: implement multi-source weather data collection system and dashboard frontend with integrated analysis services. 2026-04-13 18:42:27 +08:00
2569718930@qq.com a27ae564fb Load nearby stations before opening intraday analysis 2026-04-12 20:49:51 +08:00
2569718930@qq.com 627bb8aa06 Prioritize homepage summary preloading for Asia and Europe 2026-04-12 10:42:00 +08:00
2569718930@qq.com 77b4d7b341 feat: implement METAR data collection service and dashboard infrastructure 2026-04-11 20:11:01 +08:00
2569718930@qq.com f0904dbcc5 Add Groq commentary config and simplify forecast modal 2026-04-08 11:41:34 +08:00
2569718930@qq.com c3da29c09c Add dashboard prewarm worker and cache visibility 2026-04-08 06:53:49 +08:00
2569718930@qq.com bdc300bdd2 Expose station network coverage and settlement station details 2026-04-06 07:21:59 +08:00
2569718930@qq.com a5b5711863 Remove AI analysis and rank cities by recent DEB performance 2026-04-05 07:13:00 +08:00
2569718930@qq.com 3e265d2764 Refactor weather app logic and simplify related components 2026-04-03 01:47:03 +08:00
2569718930@qq.com 2ee30ce551 Add Moscow and broaden Turkish MGM support 2026-03-30 19:03:13 +08:00
2569718930@qq.com 0cc4f4f0d3 Add LightGBM daily high forecasting pipeline 2026-03-29 23:29:17 +08:00
2569718930@qq.com 7237278f5a Generalize NOAA settlement handling across dashboard and docs 2026-03-27 20:58:38 +08:00
2569718930@qq.com 7de31ec429 Parse Wunderground history observations for settlement highs 2026-03-25 18:38:34 +08:00
2569718930@qq.com 7589fd74bf feat: Implement Wunderground data integration, enhance dashboard weather data processing, and add future forecast modal. 2026-03-25 18:03:56 +08:00
2569718930@qq.com 2b0e6a6f67 Add Wunderground as Shenzhen settlement source 2026-03-25 17:00:36 +08:00
2569718930@qq.com 69ea686750 Refine TAF summaries and trim peak-window marker overlaps 2026-03-24 19:21:28 +08:00
2569718930@qq.com 54acfc05aa Clarify TAF timing labels and suppression wording 2026-03-24 03:44:12 +08:00
2569718930@qq.com 29f6542a72 Handle TAF hour rollover in period parsing 2026-03-24 03:27:50 +08:00