docs(uiux): UIUX 设计方案大改 + 5 份作业指导书对齐 + 开发任务文档

- UIUX 文档：填充 19 个缺口（多主体画像/健康度/AI+看板/增长域/洞察域/创始人端/OODA/助推/商密）
- UIUX 文档：插入 6 个新章节（十四~十九），旧章节重编号为二十~三十一，更新目录和交叉引用
- 作业指导书 x5：导航改为 6 域分组，新增 Context Bar/工作模式/Insight Rail/决策线程/多工作区等 UI 概念
- 新建 docs/2-task-uiux.md：50 个代码落地开发任务，按 P0-P6 分优先级 + 8 Sprint 规划
- 后端/前端：大量新增模型、路由、组件（来自之前 Phase 开发）

backend/app/routers/dashboard.py

@@ -13,9 +13,18 @@ from app.models.risk import RiskEvent
 from app.models.user import User
 from app.schemas.common import ApiResponse, success
 from app.schemas.health_score import DashboardSummary, HealthScoreResponse
+from app.services.predictor import predict_trend, detect_anomalies
 
 router = APIRouter(prefix="/dashboard", tags=["dashboard"])
 
+# 14 维度 key 列表
+_DIMENSION_KEYS = [
+    "financial_score", "operational_score", "ai_commercial_score", "ai_cost_score",
+    "org_talent_score", "product_tech_score", "market_compete_score", "governance_score",
+    "financing_score", "synergy_score", "ai_model_product_score",
+    "data_compliance_score", "team_tech_score", "customer_success_score",
+]
+
 
 @router.get("/summary", response_model=ApiResponse[DashboardSummary])
 async def get_dashboard_summary(
@@ -105,3 +114,140 @@ async def list_health_scores(
         for s in result.scalars().all()
     ]
     return success(data=scores)
+
+
+@router.get("/heatmap", response_model=ApiResponse[list[dict]])
+async def get_health_heatmap(
+    db: AsyncSession = Depends(get_db),
+    user: User = Depends(get_current_user),
+):
+    """获取健康度热力图数据 — 企业 × 维度评分矩阵。
+
+    返回格式：[{ company_id, company_name, scores: { dimension: score } }]
+    """
+    # 获取租户下所有企业
+    companies_result = await db.execute(
+        select(Company).where(Company.tenant_id == user.tenant_id).order_by(Company.name)
+    )
+    companies = companies_result.scalars().all()
+
+    # 获取每个企业最新评分
+    heatmap = []
+    for company in companies:
+        score_result = await db.execute(
+            select(HealthScore)
+            .where(HealthScore.company_id == company.id)
+            .order_by(HealthScore.calculated_at.desc())
+            .limit(1)
+        )
+        score = score_result.scalar_one_or_none()
+        scores_dict = {}
+        if score:
+            for dim_key in _DIMENSION_KEYS:
+                val = getattr(score, dim_key, None)
+                if val is not None:
+                    scores_dict[dim_key] = val
+        heatmap.append({
+            "company_id": company.id,
+            "company_name": company.name,
+            "total_score": score.total_score if score else None,
+            "scores": scores_dict,
+        })
+
+    return success(data=heatmap)
+
+
+@router.get("/trends", response_model=ApiResponse[list[dict]])
+async def get_health_trends(
+    company_id: str | None = Query(default=None, description="指定企业 ID，不传则汇总"),
+    months: int = Query(default=6, ge=1, le=24, description="趋势月数"),
+    db: AsyncSession = Depends(get_db),
+    user: User = Depends(get_current_user),
+):
+    """获取健康度趋势对比数据 — 按月汇总评分变化。
+
+    返回格式：[{ period, avg_score, company_count, dimension_avgs: { dimension: avg } }]
+    """
+    query = (
+        select(HealthScore)
+        .join(Company, HealthScore.company_id == Company.id)
+        .where(Company.tenant_id == user.tenant_id)
+    )
+    if company_id:
+        query = query.where(HealthScore.company_id == company_id)
+
+    query = query.order_by(HealthScore.calculated_at.desc()).limit(months * 50)
+    result = await db.execute(query)
+    scores = result.scalars().all()
+
+    # 按月分组
+    monthly: dict[str, list[HealthScore]] = {}
+    for s in scores:
+        period = s.calculated_at.strftime("%Y-%m")
+        monthly.setdefault(period, []).append(s)
+
+    trends = []
+    for period in sorted(monthly.keys()):
+        month_scores = monthly[period]
+        count = len(month_scores)
+        avg_total = sum(s.total_score for s in month_scores) / count if count else 0
+
+        dim_avgs = {}
+        for dim_key in _DIMENSION_KEYS:
+            vals = [getattr(s, dim_key) for s in month_scores if getattr(s, dim_key) is not None]
+            if vals:
+                dim_avgs[dim_key] = round(sum(vals) / len(vals), 1)
+
+        trends.append({
+            "period": period,
+            "avg_score": round(avg_total, 1),
+            "company_count": count,
+            "dimension_avgs": dim_avgs,
+        })
+
+    return success(data=trends)
+
+
+@router.get("/forecasts", response_model=ApiResponse[dict])
+async def get_health_forecasts(
+    company_id: str | None = Query(default=None, description="指定企业 ID，不传则汇总全租户"),
+    months_ahead: int = Query(default=3, ge=1, le=6, description="预测月数"),
+    db: AsyncSession = Depends(get_db),
+    user: User = Depends(get_current_user),
+):
+    """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。
+
+    返回格式：{ predictions: [...], trend_direction, confidence, anomalies: [...] }
+    """
+    query = (
+        select(HealthScore)
+        .join(Company, HealthScore.company_id == Company.id)
+        .where(Company.tenant_id == user.tenant_id)
+    )
+    if company_id:
+        query = query.where(HealthScore.company_id == company_id)
+
+    query = query.order_by(HealthScore.calculated_at.asc()).limit(24)
+    result = await db.execute(query)
+    scores = result.scalars().all()
+
+    historical = [s.total_score for s in scores]
+    forecast = predict_trend(historical, months_ahead)
+
+    # 异常检测 — 各维度
+    anomalies_by_dim: dict[str, list[int]] = {}
+    for dim_key in _DIMENSION_KEYS:
+        dim_values = [getattr(s, dim_key) for s in scores if getattr(s, dim_key) is not None]
+        if len(dim_values) >= 3:
+            dim_anomalies = detect_anomalies(dim_values)
+            if dim_anomalies:
+                anomalies_by_dim[dim_key] = dim_anomalies
+
+    return success(data={
+        "predictions": forecast.get("predicted", []),
+        "slope": forecast.get("slope", 0),
+        "confidence": forecast.get("confidence", 0),
+        "trend_direction": "up" if forecast.get("slope", 0) > 1 else "down" if forecast.get("slope", 0) < -1 else "stable",
+        "anomalies": anomalies_by_dim,
+        "historical_count": len(historical),
+    })