feat: GeoScene frontend POC + Docker deploy for remote host

Migrate maps to @geoscene/core, polish monitoring/alerts UX, fix timeline
basemap flicker and district alert regions, and ship compose/nginx Docker
deploy assets with CBPOA_ROOT data mounts.

Co-authored-by: Cursor <cursoragent@cursor.com>

backend/routers/alerts.py

@@ -13,6 +13,7 @@ from config import DATA_DIR, ALERT_P1_RISK, ALERT_P2_RISK, WUHAN_BOUNDS, LAT_STE
 from models import Alert, AlertResponse
 from utils.date_helpers import get_latest_date, validate_date_format
 from utils.risk import risk_value_to_level
+from utils.district_lookup import district_for_grid
 
 router = APIRouter(prefix="/api/alerts", tags=["alerts"])
 
@@ -95,13 +96,14 @@ def _generate_alerts_cached(date: str) -> List[Alert]:
 
         lat, lon = grid_id_to_center(grid_id)
         risk_level = risk_value_to_level(max_risk)
+        district = district_for_grid(grid_id)
 
         alerts.append(
             Alert(
                 alert_id=f"alert_{date}_{grid_id}",
                 grid_id=grid_id,
-                region="武汉市",
-                street=f"Grid {grid_id}",
+                region=district,
+                street=grid_id,
                 latitude=lat,
                 longitude=lon,
                 risk_value=max_risk,

backend/routers/analysis.py

@@ -17,6 +17,8 @@ from utils.date_helpers import get_latest_date
 from utils.geojson import parse_geojson_file, load_districts
 from utils.geo import point_in_polygon
 from utils.risk import calculate_trend
+from utils.daily_risk_avg import daily_avg_risk
+from utils.district_lookup import grid_district_lookup
 
 router = APIRouter(prefix="/api/analysis", tags=["analysis"])
 
@@ -83,22 +85,10 @@ async def get_trend(days: int = Query(default=7, ge=1, le=30)):
     for i in range(days):
         date = base_date - timedelta(days=days - 1 - i)
         date_str = date.strftime("%Y%m%d")
-        filepath = DATA_DIR / f"risk_{date_str}.geojson"
-
-        if filepath.exists():
-            grids = parse_geojson_file(filepath)
-            if grids:
-                avg_risk = sum(g["risk_value"] for g in grids) / len(grids)
-                values.append(round(avg_risk, 4))
-            else:
-                values.append(0)
-        else:
-            values.append(0)
+        # Disk+memory cached mean — avoids re-parsing ~45MB GeoJSON every request
+        values.append(daily_avg_risk(date_str))
         dates.append(date.strftime("%Y-%m-%d"))
 
-    # Preserve the full requested date range: a "7天" request must return 7
-    # contiguous points. Days with no geojson (or empty grids) stay 0 rather
-    # than being dropped, which previously produced fewer, non-contiguous points.
     trend_direction = calculate_trend(values)
 
     return TrendResponse(
@@ -110,15 +100,8 @@ async def get_trend(days: int = Query(default=7, ge=1, le=30)):
 
 @lru_cache(maxsize=1)
 def _grid_district_lookup() -> dict:
-    """Map precomputed r{row}_c{col} grid id -> district name (loaded once)."""
-    path = PROJECT_ROOT / "processed" / "grid_district_mapping.parquet"
-    if not path.exists():
-        return {}
-    df = pd.read_parquet(path)
-    # Some grids have a null district_name; drop them so the lookup only ever
-    # returns valid strings (missing keys fall back to "其他").
-    df = df.dropna(subset=["district_name"])
-    return dict(zip(df["grid_id"].astype(str), df["district_name"].astype(str)))
+    """Backward-compatible alias — prefer utils.district_lookup."""
+    return grid_district_lookup()
 
 
 @lru_cache(maxsize=1)