fix: analysis 500s, caching, alert page perf

P0: Fix KeyError in 3 analysis endpoints. geojson.py stores 1d risk
as "risk_value" but analysis.py accessed "risk_1d" — always crashed.

Backend: Add lru_cache to GeoJSON/CSV/Parquet loaders, date helpers,
and district loader. Add try/except and FileNotFoundError guards.

Frontend: Debounce riskRange, merge counts into useMemo, stabilize
handleGridClick with ref, memoize nearest-grid scan, wrap AlertMap
in React.memo, switch useLodGrid from fetch to cachedGet.

backend/routers/analysis.py

@@ -83,7 +83,7 @@ async def get_trend(days: int = Query(default=7, ge=1, le=30)):
         if filepath.exists():
             grids = parse_geojson_file(filepath)
             if grids:
-                avg_risk = sum(g["risk_1d"] for g in grids) / len(grids)
+                avg_risk = sum(g["risk_value"] for g in grids) / len(grids)
                 values.append(round(avg_risk, 4))
             else:
                 values.append(0)
@@ -125,8 +125,8 @@ async def get_districts():
 
     if not districts:
         # Fallback: return city-wide aggregation
-        avg_risk = sum(g["risk_1d"] for g in grids) / len(grids) if grids else 0
+        avg_risk = sum(g["risk_value"] for g in grids) / len(grids) if grids else 0
-        high_risk_count = sum(1 for g in grids if g["risk_1d"] >= RISK_HIGH)
+        high_risk_count = sum(1 for g in grids if g["risk_value"] >= RISK_HIGH)
 
         return DistrictsResponse(
             districts=[
@@ -150,14 +150,14 @@ async def get_districts():
         for district in districts:
             if point_in_polygon(grid["latitude"], grid["longitude"], district["coordinates"]):
                 district_data[district["name"]]["grids"].append(grid)
-                if grid["risk_1d"] >= RISK_HIGH:
+                if grid["risk_value"] >= RISK_HIGH:
                     district_data[district["name"]]["high_risk"] += 1
                 assigned = True
                 break
 
         if not assigned:
             unassigned["grids"].append(grid)
-            if grid["risk_1d"] >= RISK_HIGH:
+            if grid["risk_value"] >= RISK_HIGH:
                 unassigned["high_risk"] += 1
 
     # Build response
@@ -169,7 +169,7 @@ async def get_districts():
         if not grids_in_district:
             continue
 
-        avg_risk = sum(g["risk_1d"] for g in grids_in_district) / len(grids_in_district)
+        avg_risk = sum(g["risk_value"] for g in grids_in_district) / len(grids_in_district)
         high_risk_count = district_data[name]["high_risk"]
 
         # Mock total cases based on risk and grid count
@@ -187,7 +187,7 @@ async def get_districts():
 
     # Add unassigned as "其他" if significant
     if unassigned["grids"]:
-        avg_risk = sum(g["risk_1d"] for g in unassigned["grids"]) / len(unassigned["grids"])
+        avg_risk = sum(g["risk_value"] for g in unassigned["grids"]) / len(unassigned["grids"])
         result.append(
             DistrictRisk(
                 name="其他",
@@ -224,8 +224,8 @@ async def get_correlations():
 
     # Calculate mock correlations based on risk patterns
     # In production, this would use actual weather and health data
-    avg_risk = sum(g["risk_1d"] for g in grids) / len(grids)
+    avg_risk = sum(g["risk_value"] for g in grids) / len(grids)
-    risk_variance = sum((g["risk_1d"] - avg_risk) ** 2 for g in grids) / len(grids)
+    risk_variance = sum((g["risk_value"] - avg_risk) ** 2 for g in grids) / len(grids)
 
     # Generate realistic correlation coefficients
     correlations = [

backend/routers/geocoded.py

@@ -5,6 +5,7 @@ from fastapi import APIRouter, HTTPException
 from pydantic import BaseModel
 from typing import List, Optional
 import logging
+from functools import lru_cache
 import pandas as pd
 from pathlib import Path
 
@@ -15,6 +16,11 @@ router = APIRouter(prefix="/api/geocoded", tags=["geocoded"])
 PROJECT_ROOT = Path(__file__).parent.parent.parent
 DATA_DIR = PROJECT_ROOT / "outputs"
 
+
+@lru_cache(maxsize=1)
+def _load_csv(path: Path) -> pd.DataFrame:
+    return pd.read_csv(path)
+
 class GridCaseData(BaseModel):
     """Grid case data for visualization"""
     grid_id: int
@@ -60,7 +66,7 @@ async def get_grid_cases():
         raise HTTPException(status_code=404, detail="Grid data not found")
     
     try:
-        df = pd.read_csv(grid_file)
+        df = _load_csv(grid_file)
         
         grids = []
         for _, row in df.iterrows():
@@ -105,7 +111,7 @@ async def get_geocoded_cases(
         raise HTTPException(status_code=404, detail="Geocoded data not found")
     
     try:
-        df = pd.read_csv(cases_file)
+        df = _load_csv(cases_file)
 
         # Drop rows with missing coordinates
         df = df.dropna(subset=['latitude', 'longitude'])
@@ -122,7 +128,7 @@ async def get_geocoded_cases(
         df = df.head(limit)
 
         cases = []
-        for _, row in df.iterrows():
+        for row in df.to_dict('records'):
             street_val = row.get('street')
             if pd.isna(street_val):
                 street_val = None
@@ -157,7 +163,7 @@ async def get_geocoded_count():
         raise HTTPException(status_code=404, detail="Geocoded data not found")
     
     try:
-        df = pd.read_csv(cases_file)
+        df = _load_csv(cases_file)
         street_matched = len(df[df['geocode_method'] == 'street'])
         district_fallback = len(df[df['geocode_method'] == 'district'])
         

backend/routers/grid.py

@@ -1,5 +1,6 @@
 from fastapi import APIRouter, HTTPException, Query
 from datetime import datetime, timedelta
+from functools import lru_cache
 from pathlib import Path
 from typing import Optional
 import logging
@@ -22,6 +23,12 @@ from models import (
 router = APIRouter(prefix="/api", tags=["grid"])
 
 
+@lru_cache(maxsize=1)
+def _load_parquet(path: Path) -> "pd.DataFrame":
+    import pandas as pd
+    return pd.read_parquet(path)
+
+
 @router.get("/history/aggregated", response_model=HistoricalAggregationResponse)
 async def get_historical_aggregated(
     start_date: str = Query(..., description="Start date (YYYY-MM-DD)"),
@@ -45,7 +52,13 @@ async def get_historical_aggregated(
 
     import pandas as pd
 
-    cases_df = pd.read_parquet(PROJECT_ROOT / "processed" / "cases_by_district_daily.parquet")
+    try:
+        cases_df = _load_parquet(PROJECT_ROOT / "processed" / "cases_by_district_daily.parquet")
+    except FileNotFoundError:
+        return HistoricalAggregationResponse(
+            aggregations=[], total_records=0,
+            date_range=(start_date, end_date), timestamp=datetime.now().isoformat(),
+        )
     cases_df['date'] = pd.to_datetime(cases_df['date'])
 
     filtered_cases = cases_df[
@@ -78,7 +91,10 @@ async def get_historical_aggregated(
         grouped = filtered_cases.copy()
         grouped['date'] = grouped['date'].dt.strftime('%Y-%m-%d')
 
-    weather_df = pd.read_parquet(PROJECT_ROOT / "processed" / "weather" / "station_daily_2022.parquet")
+    try:
+        weather_df = _load_parquet(PROJECT_ROOT / "processed" / "weather" / "station_daily_2022.parquet")
+    except FileNotFoundError:
+        weather_df = pd.DataFrame(columns=['date', 'AQI', 'PM25', 'PM10'])
     weather_df['date'] = pd.to_datetime(weather_df['date']).dt.strftime('%Y-%m-%d')
 
     # Weather data doesn't have district - aggregate by date only
@@ -124,12 +140,12 @@ async def get_grids_geojson(
     import pandas as pd
 
     try:
-        grid_df = pd.read_parquet(PROJECT_ROOT / "processed" / "grid_100m_index.parquet")
+        grid_df = _load_parquet(PROJECT_ROOT / "processed" / "grid_100m_index.parquet")
     except FileNotFoundError:
         return GridGeoJSONResponse(type="FeatureCollection", features=[], timestamp=datetime.now().isoformat())
 
     try:
-        district_map = pd.read_parquet(PROJECT_ROOT / "processed" / "grid_district_mapping.parquet")
+        district_map = _load_parquet(PROJECT_ROOT / "processed" / "grid_district_mapping.parquet")
     except FileNotFoundError:
         return GridGeoJSONResponse(type="FeatureCollection", features=[], timestamp=datetime.now().isoformat())
 
@@ -139,7 +155,7 @@ async def get_grids_geojson(
         merged = merged[merged['district_name'].str.contains(district.replace('区', ''), na=False, regex=False)]
 
     try:
-        cases_df = pd.read_parquet(PROJECT_ROOT / "processed" / "cases_by_district_daily.parquet")
+        cases_df = _load_parquet(PROJECT_ROOT / "processed" / "cases_by_district_daily.parquet")
     except FileNotFoundError:
         return GridGeoJSONResponse(type="FeatureCollection", features=[], timestamp=datetime.now().isoformat())
     cases_df['date'] = pd.to_datetime(cases_df['date']).dt.strftime('%Y-%m-%d')
@@ -313,7 +329,7 @@ async def get_grid_history(
     """
     import pandas as pd
 
-    district_map = pd.read_parquet(PROJECT_ROOT / "processed" / "grid_district_mapping.parquet")
+    district_map = _load_parquet(PROJECT_ROOT / "processed" / "grid_district_mapping.parquet")
     grid_info = district_map[district_map['grid_id'] == grid_id]
 
     if len(grid_info) == 0:
@@ -321,7 +337,7 @@ async def get_grid_history(
 
     district = grid_info.iloc[0]['district_name']
 
-    cases_df = pd.read_parquet(PROJECT_ROOT / "processed" / "cases_by_district_daily.parquet")
+    cases_df = _load_parquet(PROJECT_ROOT / "processed" / "cases_by_district_daily.parquet")
     cases_df['date'] = pd.to_datetime(cases_df['date'])
 
     end_date = datetime.now()

backend/utils/date_helpers.py

@@ -3,6 +3,7 @@ Date utilities: finding latest dates from GeoJSON files, parsing date strings.
 """
 import glob
 import re
+from functools import lru_cache
 from pathlib import Path
 
 from fastapi import HTTPException
@@ -10,6 +11,7 @@ from fastapi import HTTPException
 from config import DATA_DIR, DATE_FORMAT_GEOJSON
 
 
+@lru_cache(maxsize=1)
 def get_latest_date() -> str:
     """Get latest available date from GeoJSON files in DATA_DIR."""
     pattern = str(DATA_DIR / "risk_*.geojson")
@@ -29,6 +31,7 @@ def get_latest_date() -> str:
     return max(dates)
 
 
+@lru_cache(maxsize=1)
 def get_available_dates(days: int = 30) -> list[str]:
     """Get list of available dates, most recent first."""
     pattern = str(DATA_DIR / "risk_*.geojson")

backend/utils/geojson.py

@@ -2,17 +2,26 @@
 GeoJSON file parsing utilities.
 """
 import json
+import logging
+from functools import lru_cache
 from pathlib import Path
 from typing import Any
 
 from config import WUHAN_BOUNDARY_PATH
 from utils.risk import risk_value_to_level
 
+logger = logging.getLogger(__name__)
 
+
+@lru_cache(maxsize=8)
 def parse_geojson_file(filepath: Path) -> list[dict[str, Any]]:
     """Parse GeoJSON file and extract grid data with standard fields."""
+    try:
         with open(filepath, "r", encoding="utf-8") as f:
             geojson = json.load(f)
+    except (json.JSONDecodeError, OSError) as e:
+        logger.warning("Failed to parse GeoJSON file %s: %s", filepath, e)
+        return []
 
     grids: list[dict[str, Any]] = []
     for feature in geojson.get("features", []):
@@ -33,6 +42,7 @@ def parse_geojson_file(filepath: Path) -> list[dict[str, Any]]:
     return grids
 
 
+@lru_cache(maxsize=1)
 def load_districts() -> list[dict[str, Any]]:
     """Load Wuhan district boundaries from GeoJSON."""
     if not WUHAN_BOUNDARY_PATH.exists():

frontend/src/components/AlertMap.tsx

@@ -1,10 +1,10 @@
-import { useEffect, useRef, useState, useCallback } from 'react';
+import { useEffect, useRef, useState, useCallback, memo } from 'react';
 import L from 'leaflet';
 import { useRiskStore } from '@/stores';
 import { LodGridLayer } from '@/components/LodGridLayer';
 import { GridStatsOverlay } from '@/components/GridStatsOverlay';
 import { useLodGrid } from '@/hooks/useLodGrid';
-import type { Alert } from '@/types';
+import type { Alert, GridRisk } from '@/types';
 
 export interface CellInfo {
   lat: number;
@@ -44,6 +44,8 @@ function getRiskLabel(value: number): string {
   return '低风险';
 }
 
+const EMPTY_GRIDS: GridRisk[] = [];
+
 function AlertMapComponent({
   selectedGridId,
   onGridClick,
@@ -62,7 +64,7 @@ function AlertMapComponent({
   const clickHandlerRef = useRef(onGridClick);
   const [currentZoom, setCurrentZoom] = useState(10);
 
-  const grids = useRiskStore((s) => s.grids ?? []);
+  const grids = useRiskStore((s) => s.grids ?? EMPTY_GRIDS);
 
   // LOD grid data for stats overlay
   const { count, avgRisk, maxRisk, loading } = useLodGrid(currentZoom, forecastDay);
@@ -299,4 +301,4 @@ function AlertMapComponent({
   );
 }
 
-export const AlertMap = AlertMapComponent;
+export const AlertMap = memo(AlertMapComponent);

frontend/src/hooks/useLodGrid.ts

@@ -1,4 +1,5 @@
 import { useState, useEffect, useRef, useCallback } from 'react';
+import { cachedGet } from '../services/api';
 
 export interface LodGridResult {
   grids: number[][];
@@ -30,23 +31,17 @@ export function useLodGrid(zoom: number, forecastDay: 1 | 3 | 7, bounds?: MapBou
   const abortRef = useRef<AbortController>();
 
   const fetchData = useCallback(async (z: number, day: 1 | 3 | 7, b?: MapBounds) => {
-    abortRef.current?.abort();
-    const controller = new AbortController();
-    abortRef.current = controller;
-
     setResult((prev) => ({ ...prev, loading: true }));
 
     try {
-      let url = `/api/risk/lod-grid?zoom=${z}&forecast_day=${day}`;
+      const params: Record<string, any> = { zoom: z, forecast_day: day };
       if (b && z >= 10) {
-        url += `&min_lat=${b.min_lat}&max_lat=${b.max_lat}&min_lon=${b.min_lon}&max_lon=${b.max_lon}`;
+        params.min_lat = b.min_lat;
+        params.max_lat = b.max_lat;
+        params.min_lon = b.min_lon;
+        params.max_lon = b.max_lon;
       }
-      const resp = await fetch(url, {
+      const data = await cachedGet<any>('/risk/lod-grid', params);
-        signal: controller.signal,
-      });
-      if (!resp.ok) throw new Error(`HTTP ${resp.status}`);
-
-      const data = await resp.json();
       const grids: number[][] = data.grids || [];
       const count = data.total_count || grids.length;
 

frontend/src/pages/AlertsDashboard.tsx

@@ -1,4 +1,5 @@
-import { useState, useMemo, useCallback, useEffect } from 'react';
+import { useState, useMemo, useCallback, useEffect, useRef } from 'react';
+import React from 'react';
 import { useRiskStore } from '@/stores';
 import { useLodGrid } from '@/hooks/useLodGrid';
 import { AlertMap } from '@/components/AlertMap';
@@ -36,6 +37,7 @@ export function AlertsDashboard() {
   const [showAlertMarkers, setShowAlertMarkers] = useState(true);
   const [selectedAlert, setSelectedAlert] = useState<string | null>(null);
   const [riskRange, setRiskRange] = useState<[number, number]>([0.6, 1.0]);
+  const [debouncedRiskRange, setDebouncedRiskRange] = useState<[number, number]>([0.6, 1.0]);
   const [forecastDay, setForecastDay] = useState<1 | 3 | 7>(1);
   const [isFullscreen, setIsFullscreen] = useState(false);
   const [showGrid, setShowGrid] = useState(true);
@@ -44,6 +46,12 @@ export function AlertsDashboard() {
   // LOD grid data for cell info lookup (1d/3d/7d risk values)
   const { grids: lodGrids } = useLodGrid(10, forecastDay);
 
+  // Debounce riskRange for filteredAlerts computation
+  useEffect(() => {
+    const timer = setTimeout(() => setDebouncedRiskRange(riskRange), 300);
+    return () => clearTimeout(timer);
+  }, [riskRange]);
+
   // Fetch grids (for map) and alerts (for side panel) on mount
   useEffect(() => {
     fetchRiskMap();
@@ -71,7 +79,7 @@ export function AlertsDashboard() {
       .filter((alert) => {
         const horizonMatch = selectedHorizon === 'all' || alert.forecast_horizon === selectedHorizon;
         const priorityMatch = selectedPriority === 'all' || alert.priority === selectedPriority;
-        const riskMatch = alert.risk_value >= riskRange[0] && alert.risk_value <= riskRange[1];
+        const riskMatch = alert.risk_value >= debouncedRiskRange[0] && alert.risk_value <= debouncedRiskRange[1];
         return horizonMatch && priorityMatch && riskMatch;
       })
       .sort((a, b) => {
@@ -80,13 +88,12 @@ export function AlertsDashboard() {
         }
         return new Date(b.forecast_time).getTime() - new Date(a.forecast_time).getTime();
       });
-  }, [extendedAlerts, selectedHorizon, selectedPriority, sortBy, riskRange]);
+  }, [extendedAlerts, selectedHorizon, selectedPriority, sortBy, debouncedRiskRange]);
 
-  const p1Count = extendedAlerts.filter((a) => a.priority === 'P1').length;
+  // Risk distribution stats (includes p1/p2 counts)
-  const p2Count = extendedAlerts.filter((a) => a.priority === 'P2').length;
-
-  // Risk distribution stats
   const riskStats = useMemo(() => {
+    const p1 = extendedAlerts.filter(a => a.priority === 'P1').length;
+    const p2 = extendedAlerts.filter(a => a.priority === 'P2').length;
     const high = filteredAlerts.filter(a => a.risk_value >= 0.8).length;
     const mediumHigh = filteredAlerts.filter(a => a.risk_value >= 0.6 && a.risk_value < 0.8).length;
     const medium = filteredAlerts.filter(a => a.risk_value >= 0.4 && a.risk_value < 0.6).length;
@@ -103,8 +110,8 @@ export function AlertsDashboard() {
       .sort((a, b) => b[1] - a[1])
       .slice(0, 5);
 
-    return { high, mediumHigh, medium, avgRisk, topDistricts };
+    return { p1, p2, high, mediumHigh, medium, avgRisk, topDistricts };
-  }, [filteredAlerts]);
+  }, [extendedAlerts, filteredAlerts]);
 
   const selectedAlertData = useMemo(() => {
     return filteredAlerts.find(a => a.alert_id === selectedAlert);
@@ -116,12 +123,15 @@ export function AlertsDashboard() {
     return alert?.grid_id ?? null;
   }, [filteredAlerts, selectedAlert]);
 
+  const filteredAlertsRef = useRef(filteredAlerts);
+  useEffect(() => { filteredAlertsRef.current = filteredAlerts; }, [filteredAlerts]);
+
   const handleGridClick = useCallback((gridId: string) => {
-    const alertForGrid = filteredAlerts.find(a => a.grid_id === gridId);
+    const alertForGrid = filteredAlertsRef.current.find(a => a.grid_id === gridId);
     if (alertForGrid) {
       setSelectedAlert(alertForGrid.alert_id);
     }
-  }, [filteredAlerts]);
+  }, []);
 
   const handleAlertCardClick = useCallback((id: string) => {
     setSelectedAlert(id);
@@ -169,6 +179,20 @@ export function AlertsDashboard() {
     URL.revokeObjectURL(url);
   }, [filteredAlerts]);
 
+  const nearestGrid = useMemo(() => {
+    if (!cellInfo || !lodGrids.length) return null;
+    let best: { lat: number; lon: number; risk_1d: number; risk_3d: number; risk_7d: number } | null = null;
+    let bestDist = Infinity;
+    for (const g of lodGrids) {
+      const d = Math.sqrt((g[0] - cellInfo.lat) ** 2 + (g[1] - cellInfo.lon) ** 2);
+      if (d < bestDist) {
+        bestDist = d;
+        best = { lat: g[0], lon: g[1], risk_1d: g[2] ?? 0, risk_3d: g[3] ?? 0, risk_7d: g[4] ?? 0 };
+      }
+    }
+    return best;
+  }, [cellInfo, lodGrids]);
+
   return (
     <div className={isFullscreen ? 'fixed inset-0 z-40 bg-bg-page pt-[52px] p-5' : 'p-5'}>
       {error && (
@@ -189,8 +213,8 @@ export function AlertsDashboard() {
         </div>
         <div className="flex items-center gap-3 text-[11px]">
           <span className="text-text-muted">共 <span className="font-semibold text-text-primary">{filteredAlerts.length}</span> 条预警</span>
-          <span className="px-2 py-1 bg-danger/10 border border-danger/20 rounded text-danger font-semibold">P1: {p1Count}</span>
+          <span className="px-2 py-1 bg-danger/10 border border-danger/20 rounded text-danger font-semibold">P1: {riskStats.p1}</span>
-          <span className="px-2 py-1 bg-warning/10 border border-warning/20 rounded text-warning font-semibold">P2: {p2Count}</span>
+          <span className="px-2 py-1 bg-warning/10 border border-warning/20 rounded text-warning font-semibold">P2: {riskStats.p2}</span>
         </div>
       </div>
 
@@ -464,20 +488,7 @@ export function AlertsDashboard() {
       )}
 
       {/* Cell info panel - shown when clicking grid cell without alert */}
-      {cellInfo && !selectedAlertData && (() => {
+      {cellInfo && !selectedAlertData && nearestGrid && (
-        // Find nearest LOD grid cell for multi-day risk display
-        // grids are [lat, lon, risk_1d, risk_3d, risk_7d]
-        let nearest: { lat: number; lon: number; risk_1d: number; risk_3d: number; risk_7d: number } | null = null;
-        let minDist = Infinity;
-        for (const g of lodGrids) {
-          const d = Math.sqrt((g[0] - cellInfo.lat) ** 2 + (g[1] - cellInfo.lon) ** 2);
-          if (d < minDist) {
-            minDist = d;
-            nearest = { lat: g[0], lon: g[1], risk_1d: g[2] ?? 0, risk_3d: g[3] ?? 0, risk_7d: g[4] ?? 0 };
-          }
-        }
-
-        return (
         <div className="fixed bottom-5 left-1/2 -translate-x-1/2 bg-bg-card rounded-lg border border-border-light shadow-lg z-50 px-5 py-4 min-w-[320px]">
           <div className="flex items-center justify-between mb-3">
             <span className="text-[14px] font-semibold text-text-primary">网格详情</span>
@@ -494,22 +505,20 @@ export function AlertsDashboard() {
                 {(cellInfo.risk * 100).toFixed(1)}%
               </span>
             </div>
-              {nearest && (
             <div className="flex gap-3 pt-1">
               <div className="flex-1 text-center p-1.5 rounded bg-bg-page">
                 <div className="text-[10px] text-text-muted">1天</div>
-                    <div className="font-bold text-[13px]">{(nearest.risk_1d * 100).toFixed(0)}%</div>
+                <div className="font-bold text-[13px]">{(nearestGrid.risk_1d * 100).toFixed(0)}%</div>
               </div>
               <div className="flex-1 text-center p-1.5 rounded bg-bg-page">
                 <div className="text-[10px] text-text-muted">3天</div>
-                    <div className="font-bold text-[13px]">{(nearest.risk_3d * 100).toFixed(0)}%</div>
+                <div className="font-bold text-[13px]">{(nearestGrid.risk_3d * 100).toFixed(0)}%</div>
               </div>
               <div className="flex-1 text-center p-1.5 rounded bg-bg-page">
                 <div className="text-[10px] text-text-muted">7天</div>
-                    <div className="font-bold text-[13px]">{(nearest.risk_7d * 100).toFixed(0)}%</div>
+                <div className="font-bold text-[13px]">{(nearestGrid.risk_7d * 100).toFixed(0)}%</div>
               </div>
             </div>
-              )}
             {cellInfo.nearestAlertId && (
               <div className="flex justify-between">
                 <span className="text-text-muted">最近预警距离</span>
@@ -523,8 +532,7 @@ export function AlertsDashboard() {
             )}
           </div>
         </div>
-        );
+      )}
-      })()}
 
       {/* Alert detail modal */}
       {selectedAlertData && (
@@ -574,7 +582,7 @@ interface AlertCardProps {
   onClick?: () => void;
 }
 
-function AlertCard({ alert, isSelected, onClick }: AlertCardProps) {
+const AlertCard = React.memo(function AlertCard({ alert, isSelected, onClick }: AlertCardProps) {
   const isP1 = alert.priority === 'P1';
   const riskPercent = Math.round(alert.risk_value * 100);
 
@@ -625,4 +633,4 @@ function AlertCard({ alert, isSelected, onClick }: AlertCardProps) {
       </div>
     </div>
   );
-}
+});

frontend/src/services/api.ts

@@ -87,7 +87,7 @@ api.interceptors.response.use(
   }
 );
 
-async function cachedGet<T>(url: string, params?: Record<string, any>): Promise<T> {
+export async function cachedGet<T>(url: string, params?: Record<string, any>): Promise<T> {
   const key = getCacheKey(url, params);
   const cached = getCached<T>(key);
   if (cached !== undefined) return cached;