diff --git a/backend/app/api/kpis.py b/backend/app/api/kpis.py
index 24608357..58303c18 100644
--- a/backend/app/api/kpis.py
+++ b/backend/app/api/kpis.py
@@ -8,7 +8,7 @@ import json
from app.database import get_db
from app.auth_middleware import require_auth, require_role, filter_kpis_by_role, kpi_visible_dims
-from app.models import StrategicMap, MapObjective, KPIDefinition, KPIValue, KPIAlert, OperationLog, Entity
+from app.models import StrategicMap, MapObjective, KPIDefinition, KPIValue, KPIAlert, OperationLog, Entity, KPICausality, KPIHierarchy
router = APIRouter(prefix="/api/cma/kpis", tags=["KPI字典"],
dependencies=[Depends(require_role("ceo", "finance", "business", "it"))],
@@ -97,6 +97,290 @@ def get_kpi_categories(current_user = Depends(require_auth), db: Session = Depen
return {"tree": tree, "total": sum(dim_counts.values())}
+# ============================================================
+# KPI-5: 五档评分引擎（静态路由必须在动态/{kpi_id}之前）
+# ============================================================
+
+def _calc_five_tier_score(current_value, target_value):
+ """五档评分：1-5分"""
+ if current_value is None or target_value is None or target_value == 0:
+ return None, "info"
+ ratio = current_value / target_value
+ if ratio >= 1.2:
+ return 5, "success" # 卓越
+ elif ratio >= 1.0:
+ return 4, "success" # 达标
+ elif ratio >= 0.8:
+ return 3, "warning" # 预警
+ elif ratio >= 0.5:
+ return 2, "danger" # 危险
+ else:
+ return 1, "danger" # 失效
+
+
+@router.get("/score")
+def get_kpi_score(
+ entity_id: int = Query(1, ge=1),
+ period: Optional[str] = None,
+ db: Session = Depends(get_db),
+ current_user = Depends(require_auth),
+):
+ """五档评分引擎 - 返回各KPI评分和BSC四层汇总
+ 评分: 5卓越(≥1.2×目标) 4达标(≥目标) 3预警(≥0.8×目标) 2危险(≥0.5×目标) 1失效(<0.5×目标)
+ """
+ # 获取该企业所有活跃KPI
+ kpis = db.query(KPIDefinition).filter(
+ KPIDefinition.status == "active",
+ KPIDefinition.entity_id == entity_id,
+ ).all()
+
+ if not kpis:
+ return {"entity_id": entity_id, "kpis": [], "layers": {}, "overall": None}
+
+ # 获取企业信息
+ ent = db.query(Entity).filter(Entity.id == entity_id).first()
+ entity_info = {"id": ent.id, "name": ent.name, "short_name": ent.short_name} if ent else {"id": entity_id}
+
+ # 单个KPI评分
+ kpi_scores = []
+ for k in kpis:
+ # 取最新实际值
+ val_query = db.query(KPIValue).filter(
+ KPIValue.kpi_id == k.id,
+ KPIValue.actual_value.isnot(None),
+ )
+ if period:
+ val_query = val_query.filter(KPIValue.period == period)
+ latest_val = val_query.order_by(KPIValue.period.desc()).first()
+
+ current_val = latest_val.actual_value if latest_val else None
+ score, status = _calc_five_tier_score(current_val, k.target_value)
+
+ kpi_scores.append({
+ "kpi_id": k.id,
+ "kpi_code": k.kpi_code,
+ "kpi_name": k.kpi_name,
+ "dimension": k.dimension,
+ "target_value": k.target_value,
+ "current_value": current_val,
+ "score": score,
+ "status": status,
+ "unit": k.unit,
+ "weight": 10,
+ "period": latest_val.period if latest_val else None,
+ })
+
+ # BSC四层汇总
+ layer_map = {
+ "finance": {"label": "财务", "order": 0},
+ "customer": {"label": "客户", "order": 1},
+ "process": {"label": "流程", "order": 2},
+ "learning": {"label": "学习成长", "order": 3},
+ }
+ layers = {}
+ total_weighted_score = 0
+ total_weight = 0
+
+ for dim_key, dim_info in layer_map.items():
+ layer_kpis = [s for s in kpi_scores if s["dimension"] == dim_key and s["score"] is not None]
+ if not layer_kpis:
+ layers[dim_key] = {"label": dim_info["label"], "score": None, "status": "info", "kpi_count": 0, "weighted_score": None}
+ continue
+
+ w = sum(k["weight"] for k in layer_kpis)
+ ws = sum(k["score"] * k["weight"] for k in layer_kpis)
+ avg_score = ws / w if w > 0 else None
+ avg_status = "success" if avg_score and avg_score >= 4 else ("warning" if avg_score and avg_score >= 3 else "danger") if avg_score else "info"
+
+ layers[dim_key] = {
+ "label": dim_info["label"],
+ "score": round(avg_score, 2) if avg_score else None,
+ "status": avg_status,
+ "kpi_count": len(layer_kpis),
+ "weighted_score": round(avg_score, 2) if avg_score else None,
+ }
+
+ if avg_score:
+ total_weighted_score += avg_score * len(layer_kpis)
+ total_weight += len(layer_kpis)
+
+ # 综合得分
+ overall_score = round(total_weighted_score / total_weight, 2) if total_weight > 0 else None
+ overall_status = "success" if overall_score and overall_score >= 4 else ("warning" if overall_score and overall_score >= 3 else "danger") if overall_score else "info"
+
+ return {
+ "entity": entity_info,
+ "kpis": kpi_scores,
+ "layers": layers,
+ "overall": {"score": overall_score, "status": overall_status},
+ }
+
+
+# ============================================================
+# KPI-6: KPI三级分解树
+# ============================================================
+
+@router.get("/hierarchy")
+def get_kpi_hierarchy(
+ entity_id: int = Query(1, ge=1),
+ kpi_id: Optional[int] = None,
+ db: Session = Depends(get_db),
+ current_user = Depends(require_auth),
+):
+ """KPI三级分解树：公司→部门→个人"""
+ query = db.query(KPIHierarchy).filter(KPIHierarchy.entity_id == entity_id)
+ if kpi_id is not None:
+ query = query.filter(
+ (KPIHierarchy.parent_kpi_id == kpi_id) | (KPIHierarchy.child_kpi_id == kpi_id)
+ )
+ relations = query.order_by(KPIHierarchy.level).all()
+
+ if not relations:
+ # 无层级数据，返回公司级KPI作为根节点
+ kpis = db.query(KPIDefinition).filter(
+ KPIDefinition.status == "active",
+ KPIDefinition.entity_id == entity_id,
+ ).limit(20).all()
+ return {
+ "entity_id": entity_id,
+ "tree": [{"id": k.id, "kpi_code": k.kpi_code, "kpi_name": k.kpi_name,
+ "dimension": k.dimension, "level": 1, "children": []} for k in kpis],
+ "total": len(kpis),
+ }
+
+ # 构建树
+ kpi_ids = set()
+ for r in relations:
+ kpi_ids.add(r.parent_kpi_id)
+ kpi_ids.add(r.child_kpi_id)
+
+ kpi_map = {}
+ for kid in kpi_ids:
+ k = db.query(KPIDefinition).filter(KPIDefinition.id == kid).first()
+ if k:
+ kpi_map[kid] = {"id": k.id, "kpi_code": k.kpi_code, "kpi_name": k.kpi_name,
+ "dimension": k.dimension, "level": None, "children": []}
+
+ # 分配层级
+ for r in relations:
+ if r.parent_kpi_id in kpi_map:
+ kpi_map[r.parent_kpi_id]["level"] = 1 # 公司级
+ if r.child_kpi_id in kpi_map:
+ current_level = kpi_map[r.child_kpi_id].get("level")
+ new_level = r.level or 2
+ if current_level is None or current_level > new_level:
+ kpi_map[r.child_kpi_id]["level"] = new_level
+
+ # 构造父子关系
+ tree = []
+ added = set()
+ for r in relations:
+ parent = kpi_map.get(r.parent_kpi_id)
+ child = kpi_map.get(r.child_kpi_id)
+ if parent and child:
+ child_node = dict(child)
+ child_node["weight"] = r.weight
+ child_node["child_name"] = r.child_name
+ # 避免重复添加
+ child_key = r.child_kpi_id
+ existing_child = next(
+ (c for c in parent["children"] if c["id"] == child_key), None
+ )
+ if not existing_child:
+ parent["children"].append(child_node)
+
+ # 收集顶级节点（有子节点且未被引用的parent）
+ all_child_ids = {r.child_kpi_id for r in relations}
+ for r in relations:
+ pid = r.parent_kpi_id
+ if pid not in all_child_ids or pid == (kpi_id if kpi_id else -1):
+ if pid not in added and pid in kpi_map:
+ tree.append(kpi_map[pid])
+ added.add(pid)
+
+ # 如果kpi_id指定，返回该节点为根的子树
+ if kpi_id is not None and kpi_id in kpi_map:
+ root = kpi_map[kpi_id]
+ return {"entity_id": entity_id, "tree": [root], "total": len(tree)}
+
+ # 否则按level排序
+ tree.sort(key=lambda n: (n.get("level") or 99, n["kpi_code"]))
+
+ return {"entity_id": entity_id, "tree": tree, "total": len(tree)}
+
+
+# ============================================================
+# KPI-8: KPI因果链追踪
+# ============================================================
+
+@router.get("/{kpi_id}/causality-chain")
+def get_kpi_causality_chain(
+ kpi_id: int,
+ db: Session = Depends(get_db),
+ current_user = Depends(require_auth),
+):
+ """KPI因果链追踪 — 返回单个KPI的上下游因果链"""
+ kpi = db.query(KPIDefinition).filter(KPIDefinition.id == kpi_id).first()
+ if not kpi:
+ raise HTTPException(404, "KPI不存在")
+
+ # 上游（驱动当前KPI的因子）
+ upstream = db.query(KPICausality).filter(KPICausality.target_kpi_id == kpi_id).all()
+ upstream_list = []
+ for c in upstream:
+ src = db.query(KPIDefinition).filter(KPIDefinition.id == c.source_kpi_id).first()
+ if src:
+ upstream_list.append({
+ "causality_id": c.id,
+ "kpi_id": src.id,
+ "kpi_code": src.kpi_code,
+ "kpi_name": src.kpi_name,
+ "dimension": src.dimension,
+ "layer": src.dimension,
+ "strength": c.strength,
+ "lag_months": c.lag_months,
+ "direction": c.direction,
+ "formula": c.formula,
+ })
+
+ # 下游（当前KPI影响的指标）
+ downstream = db.query(KPICausality).filter(KPICausality.source_kpi_id == kpi_id).all()
+ downstream_list = []
+ for c in downstream:
+ tgt = db.query(KPIDefinition).filter(KPIDefinition.id == c.target_kpi_id).first()
+ if tgt:
+ downstream_list.append({
+ "causality_id": c.id,
+ "kpi_id": tgt.id,
+ "kpi_code": tgt.kpi_code,
+ "kpi_name": tgt.kpi_name,
+ "dimension": tgt.dimension,
+ "layer": tgt.dimension,
+ "strength": c.strength,
+ "lag_months": c.lag_months,
+ "direction": c.direction,
+ "formula": c.formula,
+ })
+
+ return {
+ "kpi": {
+ "id": kpi.id,
+ "kpi_code": kpi.kpi_code,
+ "kpi_name": kpi.kpi_name,
+ "dimension": kpi.dimension,
+ "layer": kpi.dimension,
+ },
+ "drives": downstream_list,
+ "driven_by": upstream_list,
+ "total_upstream": len(upstream_list),
+ "total_downstream": len(downstream_list),
+ }
+
+
+# ============================================================
+# 动态路由（必须在静态路由之后）
+# ============================================================
+
@router.get("/{kpi_id}")
def get_kpi(kpi_id: int, db: Session = Depends(get_db)):
kpi = db.query(KPIDefinition).filter(KPIDefinition.id == kpi_id).first()
diff --git a/backend/app/models/__init__.py b/backend/app/models/__init__.py
index e7a00c4d..1953d779 100644
--- a/backend/app/models/__init__.py
+++ b/backend/app/models/__init__.py
@@ -293,6 +293,19 @@ class BscLayerConfig(Base):
kpi_count_max = Column(Integer, default=5, comment="最多KPI数")
+class KPIHierarchy(Base):
+ """KPI层级关系 — 公司→部门→个人三级分解"""
+ __tablename__ = "kpi_hierarchy"
+ id = Column(Integer, primary_key=True, index=True)
+ entity_id = Column(Integer, ForeignKey("entities.id"), default=1, comment="企业ID")
+ parent_kpi_id = Column(Integer, ForeignKey("kpi_definitions.id"), nullable=False, comment="上级KPI")
+ child_kpi_id = Column(Integer, ForeignKey("kpi_definitions.id"), nullable=False, comment="下级KPI")
+ level = Column(Integer, default=1, comment="1=公司级 2=部门级 3=个人级")
+ weight = Column(Float, default=1.0, comment="下级对上级的贡献权重（%）")
+ child_name = Column(String(200), nullable=True, comment="下级节点名称（个人或部门名）")
+ created_at = Column(DateTime, server_default=func.now())
+
+
# 兼容性: P2开发新增的模板API需要的模型
# KPIDefinition 已存在，KPITemplate映射到同一定义
KPITemplate = KPIDefinition
diff --git a/frontend/src/api/index.ts b/frontend/src/api/index.ts
index 2ed17258..44e42646 100644
--- a/frontend/src/api/index.ts
+++ b/frontend/src/api/index.ts
@@ -36,6 +36,12 @@ export const kpiApi = {
delete: (id: number) => api.delete(`/kpis/${id}`),
listCategories: () => api.get('/kpis/categories'),
associateMap: (id: number, data: any) => api.put(`/kpis/${id}/associate-map`, data),
+ // KPI-5: 五档评分
+ score: (params?: any) => api.get('/kpis/score', { params }),
+ // KPI-6: 三级分解树
+ hierarchy: (params?: any) => api.get('/kpis/hierarchy', { params }),
+ // KPI-8: 因果链追踪
+ causalityChain: (id: number) => api.get(`/kpis/${id}/causality-chain`),
}
export const mapApi = {
diff --git a/frontend/src/views/KPIDetail.vue b/frontend/src/views/KPIDetail.vue
index 1ed9b759..ccfdb9e1 100644
--- a/frontend/src/views/KPIDetail.vue
+++ b/frontend/src/views/KPIDetail.vue
@@ -93,6 +93,29 @@
+
+
+
+
暂无层级分解数据
+
+
+
+
+ {{ data.level === 1 ? '公司级' : data.level === 2 ? '部门级' : '个人级' }}
+
+ {{ data.kpi_name }}
+ {{ data.kpi_code }}
+
+ {{ hierarchyDimLabel(data.dimension) }}
+
+ · {{ data.child_name }}
+ 权重:{{ data.weight }}%
+
+
+
+
@@ -237,6 +260,32 @@ const fullNetworkNodes = ref([])
const fullNetworkEdges = ref([])
const fullNetworkLoading = ref(false)
+// 层级分解
+const hierarchyTree = ref([])
+const hierarchyLoading = ref(false)
+const hierarchyProps = { children: 'children', label: 'kpi_name' }
+
+function hierarchyLevelTag(level: number) {
+ return level === 1 ? 'success' : level === 2 ? 'warning' : 'info'
+}
+function hierarchyDimTag(d: string) {
+ return ({ finance: '', customer: 'success', process: 'warning', learning: 'info' } as any)[d] || ''
+}
+function hierarchyDimLabel(d: string) {
+ return ({ finance: '财务', customer: '客户', process: '流程', learning: '学习' } as any)[d] || d
+}
+
+async function loadHierarchy() {
+ hierarchyLoading.value = true
+ try {
+ const r: any = await kpiApi.hierarchy({ entity_id: 1, kpi_id: kpi.value?.id })
+ hierarchyTree.value = r.tree || []
+ } catch (e) {
+ console.error('层级分解加载失败', e)
+ }
+ hierarchyLoading.value = false
+}
+
const CAT_MAP: Record = {
revenue_growth: '收入增长', profitability: '盈利水平', cost_control: '成本费用',
asset_efficiency: '资产效率', cash_risk: '现金流风控',
@@ -513,6 +562,8 @@ onMounted(async () => {
} catch(e) {}
// 加载因果链
await loadCausality(Number(route.params.id))
+ // 加载层级分解
+ await loadHierarchy()
// 加载KPI列表（用于模拟推演选择器）
try {
const kr: any = await kpiApi.list({ page_size: 100 })