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.

backend/utils/risk.py

@@ -0,0 +1,56 @@
+"""
+Risk level classification and trend calculation utilities.
+"""
+from typing import Literal
+
+from config import (
+    RISK_HIGH,
+    RISK_MEDIUM_HIGH,
+    RISK_MEDIUM,
+    RISK_MEDIUM_LOW,
+    TREND_SLOPE_THRESHOLD,
+)
+
+
+def risk_value_to_level(risk_value: float) -> str:
+    """Convert risk value (0-1) to risk level string."""
+    if risk_value >= RISK_HIGH:
+        return "high"
+    elif risk_value >= RISK_MEDIUM_HIGH:
+        return "medium_high"
+    elif risk_value >= RISK_MEDIUM:
+        return "medium"
+    elif risk_value >= RISK_MEDIUM_LOW:
+        return "medium_low"
+    else:
+        return "low"
+
+
+def calculate_trend(values: list[float]) -> Literal["up", "down", "stable"]:
+    """Calculate trend direction from a series of values using linear regression slope."""
+    if len(values) < 2:
+        return "stable"
+
+    n = len(values)
+    x_mean = (n - 1) / 2
+    y_mean = sum(values) / n
+
+    numerator = sum((i - x_mean) * (values[i] - y_mean) for i in range(n))
+    denominator = sum((i - x_mean) ** 2 for i in range(n))
+
+    if denominator == 0:
+        return "stable"
+
+    slope = numerator / denominator
+
+    if y_mean == 0:
+        return "stable"
+
+    relative_slope = slope / y_mean
+
+    if relative_slope > TREND_SLOPE_THRESHOLD:
+        return "up"
+    elif relative_slope < -TREND_SLOPE_THRESHOLD:
+        return "down"
+    else:
+        return "stable"