Akiba So
fc468464b2
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.
388 lines
13 KiB
Python
388 lines
13 KiB
Python
"""
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Router for CBPOA reports endpoints
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Generates and manages risk assessment reports
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"""
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from fastapi import APIRouter, HTTPException, Query
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from datetime import datetime, timedelta
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from typing import List, Literal, Dict
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import re
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from config import DATA_DIR, REPORTS_DIR, RISK_HIGH
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from models import (
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ReportResponse,
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ReportListResponse,
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ReportMetadata,
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ReportSummary,
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ReportSection,
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ReportRecommendation,
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)
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from utils.date_helpers import get_latest_date, get_available_dates
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from utils.geojson import parse_geojson_file
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router = APIRouter(prefix="/api/reports", tags=["reports"])
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def calculate_report_summary(grids: List[dict], period_days: int) -> ReportSummary:
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"""Calculate summary statistics for report"""
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if not grids:
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return ReportSummary(
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total_cases=0,
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avg_risk=0.0,
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peak_risk_date="",
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peak_risk_value=0.0,
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high_risk_areas=0,
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trend_direction="stable"
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)
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risk_values = [g["risk_value"] for g in grids]
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avg_risk = sum(risk_values) / len(risk_values)
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high_risk_count = sum(1 for v in risk_values if v >= RISK_HIGH)
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peak_risk_value = max(risk_values)
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peak_grid = next(g for g in grids if g["risk_value"] == peak_risk_value)
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latest_date = get_latest_date()
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peak_risk_date = latest_date
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trend_direction = "stable"
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if len(grids) > 0:
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avg_3d = sum(g.get("risk_3d", g["risk_value"]) for g in grids) / len(grids)
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if avg_risk > avg_3d * 1.05:
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trend_direction = "worsening"
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elif avg_risk < avg_3d * 0.95:
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trend_direction = "improving"
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total_cases = int(len(grids) * avg_risk * 0.1 * period_days)
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return ReportSummary(
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total_cases=total_cases,
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avg_risk=round(avg_risk, 4),
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peak_risk_date=peak_risk_date,
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peak_risk_value=round(peak_risk_value, 4),
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high_risk_areas=high_risk_count,
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trend_direction=trend_direction
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)
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def generate_report_sections(summary: ReportSummary, grids: List[Dict], period_days: int) -> List[ReportSection]:
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"""Generate report sections"""
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sections = [
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ReportSection(
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title="执行摘要",
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content=(
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f"本期报告覆盖{period_days}天的监测数据。全市平均风险指数为{summary.avg_risk:.4f}，"
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f"共识别出{summary.high_risk_areas}个高风险区域。"
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f"总体趋势{summary.trend_direction}，"
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f"峰值风险出现在{summary.peak_risk_date}，风险值为{summary.peak_risk_value:.4f}。"
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),
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charts=["overview_chart", "trend_line"]
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),
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ReportSection(
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title="风险空间分布",
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content=(
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f"高风险区域主要集中在人口密集区域。"
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f"平均风险值{summary.avg_risk:.4f}，表明整体风险处于可控范围。"
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f"建议加强对高风险网格的监测和干预措施。"
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),
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charts=["risk_map", "heatmap"]
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),
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ReportSection(
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title="时间趋势分析",
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content=(
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f"过去{period_days}天内，风险水平呈现{summary.trend_direction}趋势。"
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f"累计报告病例约{summary.total_cases}例。"
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f"需要持续关注风险变化趋势，及时调整防控策略。"
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),
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charts=["time_series", "daily_comparison"]
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),
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ReportSection(
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title="重点区域识别",
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content=(
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f"识别出{summary.high_risk_areas}个高风险网格，需要优先关注。"
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f"建议对这些区域实施精准防控措施，加强监测频率。"
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),
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charts=["hotspot_map", "district_ranking"]
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),
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]
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return sections
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def generate_recommendations(summary: ReportSummary, grids: List[Dict]) -> List[ReportRecommendation]:
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"""Generate report recommendations"""
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recommendations = []
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if summary.high_risk_areas > 0:
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high_risk_grids = [g["grid_id"] for g in grids if g["risk_value"] >= RISK_HIGH][:5]
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recommendations.append(
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ReportRecommendation(
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priority="high",
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category="intervention",
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title="加强高风险区域干预",
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description=f"对{summary.high_risk_areas}个高风险区域实施精准干预措施，包括增加监测频次、加强防控力度。",
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target_areas=high_risk_grids
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)
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)
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if summary.trend_direction == "worsening":
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recommendations.append(
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ReportRecommendation(
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priority="high",
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category="monitoring",
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title="提升监测预警级别",
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description="风险趋势恶化，建议提升监测预警级别，增加数据采集频率，密切跟踪风险变化。",
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target_areas=[]
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)
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)
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recommendations.append(
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ReportRecommendation(
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priority="medium",
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category="prevention",
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title="加强健康宣教",
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description="在人口密集区域加强健康宣教，提高公众防护意识，减少暴露风险。",
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target_areas=[]
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)
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)
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recommendations.append(
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ReportRecommendation(
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priority="medium",
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category="resource_allocation",
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title="优化资源配置",
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description="根据风险分布优化医疗资源配置，确保高风险区域有充足的医疗资源储备。",
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target_areas=[]
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)
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)
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if summary.avg_risk < 0.3:
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recommendations.append(
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ReportRecommendation(
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priority="low",
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category="monitoring",
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title="维持常规监测",
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description="当前风险水平较低，建议维持常规监测，保持防控力度不放松。",
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target_areas=[]
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)
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)
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return recommendations
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def generate_report_id(report_type: str, date_str: str) -> str:
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"""Generate unique report ID"""
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return f"RPT-{report_type.upper()}-{date_str}"
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@router.get("/list", response_model=ReportListResponse)
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async def get_reports_list(
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report_type: Literal["daily", "weekly", "monthly", "all"] = Query(
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default="all",
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description="Filter by report type"
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),
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limit: int = Query(default=20, ge=1, le=100, description="Maximum reports to return"),
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):
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"""
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Get list of available reports
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Args:
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report_type: Filter by report type (daily, weekly, monthly, or all)
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limit: Maximum number of reports to return (1-100)
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Returns:
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List of report metadata
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"""
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available_dates = get_available_dates(90)
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reports = []
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for date_str in available_dates[:limit]:
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report_date = datetime.strptime(date_str, "%Y%m%d")
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if report_type != "all":
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if report_type == "daily":
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pass
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elif report_type == "weekly" and report_date.weekday() != 6:
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continue
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elif report_type == "monthly" and report_date.day != 1:
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continue
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reports.append(
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ReportMetadata(
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report_id=generate_report_id(report_type, date_str),
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title=f"武汉市健康风险评估报告 ({date_str})",
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type=report_type if report_type != "all" else "daily",
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generated_at=datetime.now().isoformat(),
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period_start=(report_date - timedelta(days=6)).strftime("%Y%m%d"),
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period_end=date_str
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)
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)
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return ReportListResponse(
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reports=reports,
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total=len(reports),
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timestamp=datetime.now().isoformat()
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)
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@router.get("/{report_id}", response_model=ReportResponse)
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async def get_report(report_id: str):
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"""
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Get full report by ID
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Args:
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report_id: Report identifier (e.g., RPT-DAILY-20240115)
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Returns:
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Full report with sections and recommendations
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"""
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match = re.search(r"RPT-\w+-([0-9]{8})", report_id)
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if not match:
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raise HTTPException(status_code=400, detail="Invalid report ID format")
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date_str = match.group(1)
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filepath = DATA_DIR / f"risk_{date_str}.geojson"
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if not filepath.exists():
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raise HTTPException(status_code=404, detail=f"No data found for date {date_str}")
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grids = parse_geojson_file(filepath)
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if not grids:
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raise HTTPException(status_code=404, detail="No grid data found")
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report_date = datetime.strptime(date_str, "%Y%m%d")
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report_type = "daily"
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if report_date.weekday() == 6:
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report_type = "weekly"
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if report_date.day == 1:
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report_type = "monthly"
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period_days = 1 if report_type == "daily" else 7 if report_type == "weekly" else 30
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summary = calculate_report_summary(grids, period_days)
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sections = generate_report_sections(summary, grids, period_days)
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recommendations = generate_recommendations(summary, grids)
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metadata = ReportMetadata(
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report_id=report_id,
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title=f"武汉市健康风险评估报告 ({date_str})",
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type=report_type,
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generated_at=datetime.now().isoformat(),
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period_start=(report_date - timedelta(days=period_days-1)).strftime("%Y%m%d"),
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period_end=date_str,
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author="CBPOA System"
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)
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attachments = [
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f"/reports/{date_str}/summary.pdf",
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f"/reports/{date_str}/maps.zip",
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f"/reports/{date_str}/data.csv"
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]
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return ReportResponse(
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metadata=metadata,
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summary=summary,
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sections=sections,
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recommendations=recommendations,
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attachments=attachments,
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timestamp=datetime.now().isoformat()
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)
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@router.get("/generate/{report_type}", response_model=ReportResponse)
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async def generate_new_report(
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report_type: Literal["daily", "weekly", "monthly"],
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date: str | None = Query(default=None, description="Date in YYYYMMDD format"),
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):
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"""
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Generate a new report
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Args:
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report_type: Type of report to generate (daily, weekly, monthly)
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date: Optional date in YYYYMMDD format. Defaults to latest.
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Returns:
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Newly generated report
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"""
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if date is None:
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date = get_latest_date()
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try:
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report_date = datetime.strptime(date, "%Y%m%d")
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except ValueError:
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raise HTTPException(status_code=400, detail="Invalid date format. Use YYYYMMDD.")
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if report_type == "weekly" and report_date.weekday() != 6:
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raise HTTPException(
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status_code=400,
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detail="Weekly reports can only be generated for Sundays (weekday 6)"
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)
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if report_type == "monthly" and report_date.day != 1:
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raise HTTPException(
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status_code=400,
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detail="Monthly reports can only be generated for the 1st of the month"
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)
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filepath = DATA_DIR / f"risk_{date}.geojson"
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if not filepath.exists():
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raise HTTPException(status_code=404, detail=f"No data found for date {date}")
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grids = parse_geojson_file(filepath)
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if not grids:
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raise HTTPException(status_code=404, detail="No grid data found")
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report_id = generate_report_id(report_type, date)
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period_days = 1 if report_type == "daily" else 7 if report_type == "weekly" else 30
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summary = calculate_report_summary(grids, period_days)
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sections = generate_report_sections(summary, grids, period_days)
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recommendations = generate_recommendations(summary, grids)
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metadata = ReportMetadata(
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report_id=report_id,
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title=f"武汉市健康风险评估报告 ({date})",
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type=report_type,
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generated_at=datetime.now().isoformat(),
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period_start=(report_date - timedelta(days=period_days-1)).strftime("%Y%m%d"),
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period_end=date,
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author="CBPOA System"
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)
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attachments = [
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f"/reports/{date}/summary.pdf",
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f"/reports/{date}/maps.zip",
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f"/reports/{date}/data.csv"
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]
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return ReportResponse(
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metadata=metadata,
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summary=summary,
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sections=sections,
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recommendations=recommendations,
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attachments=attachments,
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timestamp=datetime.now().isoformat()
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)
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@router.get("/summary/latest", response_model=ReportSummary)
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async def get_latest_summary():
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"""
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Get latest risk summary
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Returns:
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Current risk summary statistics
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"""
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latest_date = get_latest_date()
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filepath = DATA_DIR / f"risk_{latest_date}.geojson"
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if not filepath.exists():
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raise HTTPException(status_code=404, detail=f"No data found for date {latest_date}")
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grids = parse_geojson_file(filepath)
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if not grids:
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raise HTTPException(status_code=404, detail="No grid data found")
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return calculate_report_summary(grids, 1)
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