selfrelease
301 lines
11 KiB
Python
301 lines
11 KiB
Python
"""健康度评分 + 仪表盘路由。"""
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from fastapi import APIRouter, Depends, Query
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from sqlalchemy import func, select
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from sqlalchemy.ext.asyncio import AsyncSession
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from app.core.database import get_db
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from app.core.dependencies import get_current_user
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from app.models.company import Company
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from app.models.health_score import HealthScore
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from app.models.report import MonthlyReport
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from app.models.risk import RiskEvent
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from app.models.user import User
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from app.schemas.common import ApiResponse, success
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from app.schemas.health_score import DashboardSummary, HealthScoreResponse
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from app.services.predictor import predict_trend, detect_anomalies
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router = APIRouter(prefix="/dashboard", tags=["dashboard"])
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# 14 维度 key 列表
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_DIMENSION_KEYS = [
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"financial_score", "operational_score", "ai_commercial_score", "ai_cost_score",
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"org_talent_score", "product_tech_score", "market_compete_score", "governance_score",
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"financing_score", "synergy_score", "ai_model_product_score",
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"data_compliance_score", "team_tech_score", "customer_success_score",
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]
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@router.get("/summary", response_model=ApiResponse[DashboardSummary])
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async def get_dashboard_summary(
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company_id: str | None = Query(default=None, description="指定企业 ID，不传则汇总全租户"),
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db: AsyncSession = Depends(get_db),
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user: User = Depends(get_current_user),
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):
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"""获取仪表盘汇总数据。支持按企业过滤。"""
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tenant_id = user.tenant_id
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# 企业总数（单企业视角时为 1）
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if company_id:
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total_companies = 1
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else:
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companies_result = await db.execute(
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select(func.count()).select_from(Company).where(Company.tenant_id == tenant_id)
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)
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total_companies = companies_result.scalar_one()
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# 平均健康度（按企业过滤时只算该企业）
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avg_query = (
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select(func.avg(HealthScore.total_score))
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.join(Company, HealthScore.company_id == Company.id)
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.where(Company.tenant_id == tenant_id)
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)
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if company_id:
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avg_query = avg_query.where(HealthScore.company_id == company_id)
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avg_result = await db.execute(avg_query)
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avg_score = avg_result.scalar_one()
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avg_health_score = float(avg_score) if avg_score else 0.0
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# 高风险事件数
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risk_query = (
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select(func.count())
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.select_from(RiskEvent)
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.join(Company, RiskEvent.company_id == Company.id)
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.where(Company.tenant_id == tenant_id, RiskEvent.status.in_(["open", "assigned", "in_progress"]))
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)
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if company_id:
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risk_query = risk_query.where(RiskEvent.company_id == company_id)
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risk_result = await db.execute(risk_query)
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high_risk_count = risk_result.scalar_one()
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# 待审阅月报数
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pending_query = (
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select(func.count())
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.select_from(MonthlyReport)
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.join(Company, MonthlyReport.company_id == Company.id)
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.where(Company.tenant_id == tenant_id, MonthlyReport.status.in_(["submitted", "ai_parsed"]))
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)
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if company_id:
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pending_query = pending_query.where(MonthlyReport.company_id == company_id)
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pending_result = await db.execute(pending_query)
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pending_reports = pending_result.scalar_one()
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# 最近评分 — 每家企业只取最新一条（避免历史数据导致企业重复）
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subq = (
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select(
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HealthScore.company_id,
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func.max(HealthScore.calculated_at).label("max_at"),
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)
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.join(Company, HealthScore.company_id == Company.id)
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.where(Company.tenant_id == tenant_id)
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.group_by(HealthScore.company_id)
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)
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if company_id:
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subq = subq.where(HealthScore.company_id == company_id)
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subq = subq.subquery()
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recent_query = (
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select(HealthScore, Company.name.label("company_name"))
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.join(Company, HealthScore.company_id == Company.id)
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.join(subq, (HealthScore.company_id == subq.c.company_id) & (HealthScore.calculated_at == subq.c.max_at))
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.order_by(HealthScore.calculated_at.desc())
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.limit(10)
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)
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recent_result = await db.execute(recent_query)
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recent_rows = recent_result.all()
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recent_scores = []
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for row in recent_rows:
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score = row[0]
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company_name = row[1]
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resp = HealthScoreResponse.model_validate(score, from_attributes=True)
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resp.company_name = company_name
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recent_scores.append(resp)
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return success(
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data=DashboardSummary(
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total_companies=total_companies,
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avg_health_score=round(avg_health_score, 1),
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high_risk_count=high_risk_count,
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pending_reports=pending_reports,
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recent_scores=recent_scores,
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)
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)
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@router.get("/scores", response_model=ApiResponse[list[HealthScoreResponse]])
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async def list_health_scores(
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company_id: str | None = Query(default=None),
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limit: int = Query(default=20, ge=1, le=100),
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db: AsyncSession = Depends(get_db),
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user: User = Depends(get_current_user),
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):
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"""获取健康度评分列表 — 每家企业只返回最新一条。"""
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subq = (
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select(
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HealthScore.company_id,
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func.max(HealthScore.calculated_at).label("max_at"),
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)
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.join(Company, HealthScore.company_id == Company.id)
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.where(Company.tenant_id == user.tenant_id)
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.group_by(HealthScore.company_id)
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)
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if company_id:
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subq = subq.where(HealthScore.company_id == company_id)
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subq = subq.subquery()
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query = (
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select(HealthScore, Company.name.label("company_name"))
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.join(Company, HealthScore.company_id == Company.id)
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.join(subq, (HealthScore.company_id == subq.c.company_id) & (HealthScore.calculated_at == subq.c.max_at))
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.order_by(HealthScore.calculated_at.desc())
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.limit(limit)
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)
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result = await db.execute(query)
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scores = []
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for row in result.all():
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score = row[0]
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company_name = row[1]
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resp = HealthScoreResponse.model_validate(score, from_attributes=True)
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resp.company_name = company_name
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scores.append(resp)
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return success(data=scores)
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@router.get("/heatmap", response_model=ApiResponse[list[dict]])
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async def get_health_heatmap(
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db: AsyncSession = Depends(get_db),
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user: User = Depends(get_current_user),
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):
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"""获取健康度热力图数据 — 企业 × 维度评分矩阵。
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返回格式：[{ company_id, company_name, scores: { dimension: score } }]
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"""
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# 获取租户下所有企业
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companies_result = await db.execute(
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select(Company).where(Company.tenant_id == user.tenant_id).order_by(Company.name)
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)
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companies = companies_result.scalars().all()
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# 获取每个企业最新评分
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heatmap = []
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for company in companies:
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score_result = await db.execute(
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select(HealthScore)
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.where(HealthScore.company_id == company.id)
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.order_by(HealthScore.calculated_at.desc())
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.limit(1)
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)
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score = score_result.scalar_one_or_none()
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scores_dict = {}
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if score:
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for dim_key in _DIMENSION_KEYS:
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val = getattr(score, dim_key, None)
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if val is not None:
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scores_dict[dim_key] = val
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heatmap.append({
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"company_id": company.id,
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"company_name": company.name,
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"total_score": score.total_score if score else None,
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"scores": scores_dict,
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})
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return success(data=heatmap)
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@router.get("/trends", response_model=ApiResponse[list[dict]])
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async def get_health_trends(
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company_id: str | None = Query(default=None, description="指定企业 ID，不传则返回全部企业"),
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months: int = Query(default=6, ge=1, le=24, description="趋势月数"),
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db: AsyncSession = Depends(get_db),
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user: User = Depends(get_current_user),
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):
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"""获取健康度趋势数据 — 按企业分组，每家企业一条折线。
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返回格式：[{ company_id, company_name, data: [{ period, score }] }]
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"""
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query = (
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select(HealthScore, Company.name.label("company_name"))
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.join(Company, HealthScore.company_id == Company.id)
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.where(Company.tenant_id == user.tenant_id)
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)
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if company_id:
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query = query.where(HealthScore.company_id == company_id)
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query = query.order_by(HealthScore.calculated_at.asc()).limit(months * 100)
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result = await db.execute(query)
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# 按企业分组
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by_company: dict[str, dict[str, list]] = {}
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for row in result.all():
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score = row[0]
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cname = row[1]
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cid = str(score.company_id)
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if cid not in by_company:
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by_company[cid] = {"company_name": cname, "scores": []}
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by_company[cid]["scores"].append(score)
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trends = []
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for cid, info in by_company.items():
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# 每月取最新一条
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monthly: dict[str, float] = {}
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for s in info["scores"]:
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period = s.calculated_at.strftime("%Y-%m")
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monthly[period] = s.total_score
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data_points = [
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{"period": p, "score": round(v, 1)}
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for p, v in sorted(monthly.items())
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]
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trends.append({
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"company_id": cid,
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"company_name": info["company_name"],
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"data": data_points,
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})
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return success(data=trends)
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@router.get("/forecasts", response_model=ApiResponse[dict])
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async def get_health_forecasts(
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company_id: str | None = Query(default=None, description="指定企业 ID，不传则汇总全租户"),
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months_ahead: int = Query(default=3, ge=1, le=6, description="预测月数"),
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db: AsyncSession = Depends(get_db),
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user: User = Depends(get_current_user),
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):
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"""获取健康度预测数据 — 基于历史评分预测未来趋势 + 异常检测。
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返回格式：{ predictions: [...], trend_direction, confidence, anomalies: [...] }
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"""
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query = (
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select(HealthScore)
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.join(Company, HealthScore.company_id == Company.id)
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.where(Company.tenant_id == user.tenant_id)
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)
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if company_id:
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query = query.where(HealthScore.company_id == company_id)
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query = query.order_by(HealthScore.calculated_at.asc()).limit(24)
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result = await db.execute(query)
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scores = result.scalars().all()
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historical = [s.total_score for s in scores]
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forecast = predict_trend(historical, months_ahead)
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# 异常检测 — 各维度
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anomalies_by_dim: dict[str, list[int]] = {}
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for dim_key in _DIMENSION_KEYS:
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dim_values = [getattr(s, dim_key) for s in scores if getattr(s, dim_key) is not None]
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if len(dim_values) >= 3:
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dim_anomalies = detect_anomalies(dim_values)
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if dim_anomalies:
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anomalies_by_dim[dim_key] = dim_anomalies
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return success(data={
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"predictions": forecast.get("predicted", []),
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"slope": forecast.get("slope", 0),
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"confidence": forecast.get("confidence", 0),
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"trend_direction": "up" if forecast.get("slope", 0) > 1 else "down" if forecast.get("slope", 0) < -1 else "stable",
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"anomalies": anomalies_by_dim,
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"historical_count": len(historical),
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})
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