feat: add reports center, admin drill-down, disease filter + bug fixes + perf optimization
Frontend features:
- 报表中心 (ReportsCenter): list/detail views, diagnosis breakdown chart, CSV export
- 多级行政下钻 (AdminBreadcrumb): 湖北省→武汉市→区→街道 hierarchical drill-down
- 按病种筛选 (DiseaseFilter): multi-select diagnosis filter on monitoring + reports pages
Backend:
- Add /forecast/{days} endpoint, diagnosis filter params on cases endpoints
- Add /streets aggregation endpoint, enrich reports with real case data
- Extract shared case_loader module
Bug fixes (14):
- Fix missing /risk/forecast route (404), historyApi pointing to non-existent router
- Fix min_risk filter silently ignored in insights/hotspots
- Fix type mismatches: CaseTrendResponse, CaseStatsResponse shapes
- Fix silent .catch(() => {}) swallowing errors, fetchAlerts not clearing stale state
- Fix lru_cache caching exceptions, generateReport used cachedGet for write op
- Fix missing useEffect deps in Insights, DistrictComparison, ReportsCenter
Performance (9):
- Zustand selectors across 9 components (eliminate re-render cascades)
- Fix districtCases.sort() mutating store state, inline IIFE → memo'd component
- CaseLocationMap: React.memo, race protection, correct deps
- AlertCard: stable callbacks, TimelinePlayer: useMemo, TopNav: clock isolation
- SideNav: modules array to module scope, DiseaseFilter: memoized filter
This commit is contained in:
@@ -9,94 +9,12 @@ from pydantic import BaseModel
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from typing import Optional
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from datetime import datetime, date
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import pandas as pd
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import re
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from pathlib import Path
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import json
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DATE_PATTERN = re.compile(r"^\d{4}-\d{2}-\d{2}$")
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from data.case_loader import load_data, get_combined_data, get_outpatient_data, get_inpatient_data, WUHAN_DISTRICTS, DATE_PATTERN
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router = APIRouter(prefix="/api/cases", tags=["cases"])
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# 数据缓存
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_cache = {
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"outpatient": None,
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"inpatient": None,
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"loaded_at": None,
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}
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# 武汉市区映射
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WUHAN_DISTRICTS = {
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'江岸区': ['江岸'],
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'江汉区': ['江汉'],
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'武昌区': ['武昌'],
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'洪山区': ['洪山'],
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'汉阳区': ['汉阳'],
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'东西湖区': ['东西湖'],
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'黄陂区': ['黄陂'],
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'硚口区': ['硚口'],
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'江夏区': ['江夏'],
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'青山区': ['青山'],
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'新洲区': ['新洲'],
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'蔡甸区': ['蔡甸'],
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'东湖新技术开发区': ['东湖新技术开发区', '光谷'],
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'经开（汉南）区': ['经开', '汉南', '经济开发区'],
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'东湖生态旅游风景区': ['东湖生态旅游风景区']
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}
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PROJECT_ROOT = Path(__file__).parent.parent.parent
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DATA_DIR = PROJECT_ROOT / "Datas"
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def _extract_district(addr: str) -> str:
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"""从地址提取武汉市区名"""
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if pd.isna(addr):
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return '未知'
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addr = str(addr)
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for district, keywords in WUHAN_DISTRICTS.items():
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for kw in keywords:
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if kw in addr:
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return district
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return '其他'
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def _load_data():
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"""加载并缓存数据"""
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if _cache["loaded_at"] is not None:
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return
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try:
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# 加载门诊数据
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df_out = pd.read_excel(DATA_DIR / "view_门诊.xlsx")
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df_out['date'] = pd.to_datetime(df_out['门诊日期_re'])
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df_out['district'] = df_out['现住址区'].fillna('未知')
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_cache["outpatient"] = df_out
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# 加载住院数据
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df_in = pd.read_excel(DATA_DIR / "view_住院.xlsx")
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df_in['date'] = pd.to_datetime(df_in['入院日期_re'])
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df_in['district'] = df_in['现住址_脱敏'].apply(_extract_district)
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_cache["inpatient"] = df_in
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_cache["loaded_at"] = datetime.now()
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except Exception as e:
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raise RuntimeError(f"数据加载失败：{str(e)}")
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def _get_combined_data():
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"""获取合并的病例数据"""
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_load_data()
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df_out = _cache["outpatient"][['date', 'district', '初诊', '主诉']].copy()
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df_out['type'] = 'outpatient'
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df_out['diagnosis'] = df_out['初诊']
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df_in = _cache["inpatient"][['date', 'district', '诊断名称']].copy()
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df_in['type'] = 'inpatient'
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df_in['diagnosis'] = df_in['诊断名称']
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df_in['主诉'] = None
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return pd.concat([df_out, df_in], ignore_index=True)
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# ============== Response Models ==============
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@@ -152,19 +70,26 @@ class RealtimeData(BaseModel):
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# ============== API Endpoints ==============
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@router.get("/stats", response_model=StatsResponse, summary="获取病例统计数据")
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async def get_cases_stats():
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async def get_cases_stats(
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diagnosis: Optional[str] = Query(None, description="Filter to single disease stats"),
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):
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"""
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获取病例总体统计信息
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- 总门诊量、总住院量
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- 数据日期范围
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- 就诊量前 10 的区域
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- 最常见诊断前 10
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"""
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_load_data()
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df_out = _cache["outpatient"]
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df_in = _cache["inpatient"]
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load_data()
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df_out = get_outpatient_data()
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df_in = get_inpatient_data()
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# 诊断过滤
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if diagnosis:
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df_out = df_out[df_out['初诊'].str.contains(diagnosis, na=False, case=False)]
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df_in = df_in[df_in['诊断名称'].str.contains(diagnosis, na=False, case=False)]
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# 计算统计
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total_outpatient = len(df_out)
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@@ -207,10 +132,11 @@ async def get_cases_trend(
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start_date: Optional[str] = Query(None, description="开始日期 (YYYY-MM-DD)"),
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end_date: Optional[str] = Query(None, description="结束日期 (YYYY-MM-DD)"),
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group_by: str = Query("day", description="分组粒度：day, week, month"),
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diagnosis: Optional[str] = Query(None, description="Filter by diagnosis name"),
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):
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"""
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获取病例时间趋势数据
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- 支持按日、周、月分组
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- 可指定日期范围
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- 返回门诊、住院、总计趋势
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@@ -220,13 +146,17 @@ async def get_cases_trend(
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if end_date and not DATE_PATTERN.match(end_date):
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raise HTTPException(status_code=400, detail="Invalid end_date format. Use YYYY-MM-DD")
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df = _get_combined_data()
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df = get_combined_data()
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# 日期过滤
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if start_date:
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df = df[df['date'] >= pd.to_datetime(start_date)]
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if end_date:
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df = df[df['date'] <= pd.to_datetime(end_date)]
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# 诊断过滤
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if diagnosis:
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df = df[df['diagnosis'].str.contains(diagnosis, na=False, case=False)]
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# 分组
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if group_by == "week":
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@@ -272,15 +202,20 @@ async def get_cases_trend(
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async def get_cases_districts(
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case_type: Optional[str] = Query(None, description="病例类型：outpatient, inpatient, all"),
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min_count: int = Query(10, description="最小病例数过滤"),
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diagnosis: Optional[str] = Query(None, description="Filter by diagnosis name"),
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):
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"""
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获取病例区域分布数据
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- 支持按病例类型筛选
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- 可设置最小病例数过滤
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- 返回各区门诊、住院量及占比
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"""
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df = _get_combined_data()
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df = get_combined_data()
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# 诊断过滤
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if diagnosis:
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df = df[df['diagnosis'].str.contains(diagnosis, na=False, case=False)]
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# 类型过滤
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if case_type == "outpatient":
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@@ -331,8 +266,8 @@ async def get_cases_realtime():
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- 变化率
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- 状态评估 (正常/偏高/偏低)
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"""
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df = _get_combined_data()
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df = get_combined_data()
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today = pd.Timestamp.today().normalize()
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last_7d = today - pd.Timedelta(days=7)
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@@ -368,3 +303,16 @@ async def get_cases_realtime():
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change_ratio=change_ratio,
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status=status
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)
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class DiagnosesResponse(BaseModel):
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"""诊断列表响应"""
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diagnoses: list[str]
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@router.get("/diagnoses", response_model=DiagnosesResponse, summary="获取所有诊断名称列表")
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async def get_diagnoses():
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"""Returns deduplicated, sorted list of unique diagnosis names"""
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df = get_combined_data()
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diagnoses = sorted(df['diagnosis'].dropna().unique().tolist())
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return DiagnosesResponse(diagnoses=diagnoses)
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