akiba/CA

Files

Akiba So fc468464b2 feat: Initial CBPOA commit — 武汉儿童呼吸疾病风险评估系统

Context: Build a spatial risk assessment system correlating air quality
data with children's respiratory disease incidence across Wuhan.

Approach: FastAPI backend serving PostGIS spatial queries, React
frontend with Deck.gl maps, and a PyTorch SpatialTemporalGCN pipeline
for multi-day (1d/3d/7d) risk prediction.

Changes:
- backend/ — FastAPI API with auth (JWT), alerts, risk analysis,
  geocoded case data, grid statistics, and report endpoints
- frontend/ — React dashboard with interactive risk maps, alert
  monitoring, district comparison charts, and timeline player
- models/ — SpatialTemporalGCN model with trained weights and ONNX
  export for inference
- scripts/ — ETL pipeline for weather + medical data, grid generation,
  feature engineering, training, and daily inference
- deploy/ — Docker Compose configs for backend, frontend, and MLflow
- docs/ — API docs, deployment guide, user guide, and code review

Impact: Enables spatial risk visualization, alert monitoring, and
ML-driven health risk forecasting for environmental health teams.

2026-06-05 02:13:49 +08:00

1.9 KiB

Raw Blame History

CBPOA — 武汉儿童呼吸疾病风险评估系统

FastAPI + React + PyTorch GCN pipeline. 预测空气质量对儿童健康的空间风险。

Development

# Frontend (pnpm)
cd frontend && pnpm dev          # localhost:5173 → proxies /api to :8000

# Backend (Python venv)
cd backend && uvicorn main:app --reload   # localhost:8000

# ML pipeline
cd scripts && python train_model.py       # PyTorch + MLflow

Where to Look

Task	Location
API endpoint	`backend/routers/`
Database / PostGIS	`backend/database.py`
UI component	`frontend/src/components/`
Page view	`frontend/src/pages/`
API client / cache	`frontend/src/services/api.ts`
State management	`frontend/src/stores/`
TypeScript types	`frontend/src/types/`
ETL / data processing	`scripts/`
ML model architecture	`models/spatiotemporal_gcn/`
Trained weights	`models/spatiotemporal_gcn/best_model.pt`
Processed features	`processed/`
Raw data sources	`Datas/`
Docker / deploy	`deploy/`

Data Sources

Data	Path	Notes
气象+空气	`Datas/气象+空气/站点_*.csv`	3yr, 2192 files, ~2.37M rows
门诊	`Datas/view_门诊.xlsx`	107,579 rows
住院	`Datas/view_住院.xlsx`	5,822 rows
DEM高程	`Datas/DEM/CJJJD_DEM.TIF`	3.1GB raster
人口密度	`Datas/landscan-hd-china-v1-assets/*.tif`	284MB
行政边界	`Datas/武汉市.geojson`	Wuhan boundary

ML Pipeline

气象(时间序列) + 站点坐标 + DEM高程 + 人口密度 → SpatialTemporalGCN → 风险预测 [1d, 3d, 7d]

Agent Workflow

Explore finds → Librarian reads → You plan → Worker implements → Validator checks

Context-specific guidance lives in nested CLAUDE.md files — they load automatically when you work in those directories. Closest CLAUDE.md to the file being edited takes precedence.

1.9 KiB Raw Blame History