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fix: analysis 500s, caching, alert page perf

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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.
This commit is contained in:
Akiba So
2026-06-05 02:27:10 +08:00
parent fc468464b2
commit e64ca3b4f5
9 changed files with 154 additions and 114 deletions
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18
backend/routers/analysis.py
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@@ -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
high_risk_count = sum(1 for g in grids if g["risk_1d"] >= RISK_HIGH)
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_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)
risk_variance = sum((g["risk_1d"] - avg_risk) ** 2 for g in grids) / len(grids)
avg_risk = sum(g["risk_value"] 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 = [