Phase 3 of the UX modernization. Frontend-only role view-presets (D2 — no
backend, no JWT role claim, honestly labeled 视角 not 权限). Three conflict-free lanes.
Role infrastructure (worker-session):
- sessionStore with single swappable getRoleSource() seam (localStorage today,
one-line swap to /api/auth/me for future RBAC); Role = official|community|doctor|admin
- 视角 switcher in TopNav (replaces hardcoded "admin"); on change persists role +
navigates to that perspective's default landing
- roleViews.ts: ROLE_LABELS + roleDefaultPath (official→/overview?granularity=district,
community→/monitoring?granularity=street, doctor→/alerts?view=cluster, admin→/monitoring)
- RoleRedirect index route → current role's default; * fallback unchanged
URL-driven granularity (worker-monitoring):
- granularity (city|district|street) query param is the source of truth; drilldownStore
DERIVES from it via a one-way effect; deep-linkable + reload-safe
- reconciled the imperative desync — DistrictBreakdown no longer calls
useDrilldownStore.getState(); MonitoringDashboard owns useSearchParams, writes URL
- granularity-control Segmented (全市/区域/街道); district-rollup testid
Role-aware alerts + privacy (worker-alerts):
- doctor/cluster view: individual alert markers hard-locked off (effective flag is
single source of truth; toggle not rendered) → aggregated density only; DiseaseFilter
mounted; privacy invariant testable via hidden patient-point DOM mirror (count=0)
- 官员: 100m grid hidden (grid-layer-wrapper unrendered); admin/community unchanged
Gates: tsc 0 · vitest 75 · e2e 30/30 (incl 10 new P3 tests) · build ok
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
- Add vitest config and unit tests for components, api, stores
- Add Playwright e2e test for user flows
- Add backend test files
- Update risk.py with LOD grid KDTree optimization
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.
