init: 管理会计OS初始代码
包含前后端完整代码： - 前端：Vue3+Vite+ElementPlus - 后端：FastAPI+SQLAlchemy - 模块：驾驶舱/KPI/战略地图/预警/预算/成本/预测/改善行动 - 当前版本：v1.0.0
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Hermes CI Fix
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"""KPI目标对齐管理 API — 管理会计OS
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支持三种对齐模式：纵向分解 / 横向支撑 / BSC瀑布链
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管理员可初始化选择，后续按模式运作"""
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from fastapi import APIRouter, Depends, HTTPException, Query
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from sqlalchemy.orm import Session
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from sqlalchemy import func
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from typing import Optional, List
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from datetime import datetime
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import json
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from app.database import get_db
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from app.auth_middleware import require_auth, require_role
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from app.models import KPIDefinition, OperationLog, RolePermission
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router = APIRouter(prefix="/api/cma/alignment", tags=["KPI目标对齐"],
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# 不设全局权限，每个接口单独控制
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)
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# 三种对齐模式定义
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ALIGNMENT_MODES = [
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{
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"key": "vertical_decomposition",
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"name": "纵向分解",
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"description": "上级KPI直接拆分为多个下级KPI，目标值汇总等于上级目标。适用于营收、成本等可量化指标。",
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"example": "公司销售总额2000万 → 区域A 800万 + 区域B 700万 + 区域C 500万",
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},
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{
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"key": "horizontal_support",
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"name": "横向支撑",
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"description": "下级KPI是上级KPI的驱动因子，下级目标达成支撑上级结果。适用于复合型指标。",
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"example": "销售毛利率30% ← 销售总额↑ + 成本控制↓ + 高毛利产品占比↑",
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},
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{
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"key": "bsc_chain",
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"name": "BSC瀑布链",
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"description": "按平衡计分卡因果链层层传导：学习成长→内部流程→客户→财务。",
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"example": "培训完成率↑ → 订单交付及时率↑ → 客户满意度↑ → 销售总额↑",
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},
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]
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@router.get("/modes")
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def list_modes():
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"""返回三种对齐模式的定义（公开接口）"""
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return {"modes": ALIGNMENT_MODES}
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@router.get("/config")
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def get_alignment_config(db: Session = Depends(get_db)):
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"""获取当前系统对齐模式配置（公开接口，无需认证）"""
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perm = db.query(RolePermission).filter(RolePermission.key == "alignment_config").first()
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if not perm:
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return {
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"mode": None,
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"configured": False,
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"modes": ALIGNMENT_MODES,
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}
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return {
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"mode": perm.value,
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"configured": True,
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"modes": ALIGNMENT_MODES,
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}
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@router.post("/config")
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def set_alignment_config(
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data: dict,
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db: Session = Depends(get_db),
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user = Depends(require_role("ceo", "it")),
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):
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"""初始化/修改系统对齐模式（CEO/IT权限）"""
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mode_key = data.get("mode")
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if mode_key not in [m["key"] for m in ALIGNMENT_MODES]:
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raise HTTPException(400, f"无效的对齐模式: {mode_key}")
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perm = db.query(RolePermission).filter(RolePermission.key == "alignment_config").first()
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if perm:
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perm.value = {"mode": mode_key, "set_at": datetime.now().isoformat()}
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else:
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perm = RolePermission(key="alignment_config", value={"mode": mode_key, "set_at": datetime.now().isoformat()})
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db.add(perm)
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db.commit()
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return {"message": f"对齐模式已设置为: {mode_key}", "mode": mode_key}
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@router.get("/tree")
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def get_alignment_tree(
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kpi_id: Optional[int] = Query(None),
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db: Session = Depends(get_db),
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user = Depends(require_auth),
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):
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"""获取KPI对齐关系树
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根据当前系统配置的对齐模式，返回KPI的父子层级关系。
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如果指定kpi_id，返回该KPI及其下级树；
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如果不指定，返回整个对齐树。
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"""
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# 获取当前模式
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config_perm = db.query(RolePermission).filter(RolePermission.key == "alignment_config").first()
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mode = config_perm.value.get("mode") if config_perm else None
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if not mode:
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raise HTTPException(400, "系统未配置对齐模式，请先在系统设置中初始化")
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# 获取所有KPI
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kpis = db.query(KPIDefinition).filter(KPIDefinition.status == "active").order_by(KPIDefinition.kpi_code).all()
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kpi_map = {k.id: k for k in kpis}
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# 构建父子关系
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if mode == "vertical_decomposition":
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# 纵向分解：BSC编码前缀相同=同一系列
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return _build_vertical_tree(kpis, kpi_id)
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elif mode == "horizontal_support":
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# 横向支撑：按BSC维度+类别的因果关系
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return _build_horizontal_tree(kpis, kpi_id)
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elif mode == "bsc_chain":
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# BSC瀑布链：按维度层级传导
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return _build_bsc_chain(kpis, kpi_id)
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else:
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raise HTTPException(400, f"未知的对齐模式: {mode}")
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def _build_vertical_tree(kpis, kpi_id=None):
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"""纵向分解树：按编码前缀分组，同一前缀=同一系列"""
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from collections import defaultdict
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# 提取前缀（如 F_REVENUE_001 → F_REVENUE）
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groups = defaultdict(list)
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for k in kpis:
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parts = k.kpi_code.rsplit("_", 1)
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prefix = parts[0] if len(parts) > 1 else k.kpi_code
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groups[prefix].append(k)
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def make_node(kpi):
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return {
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"id": kpi.id, "kpi_code": kpi.kpi_code, "kpi_name": kpi.kpi_name,
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"dimension": kpi.dimension, "category": kpi.category,
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"target_value": kpi.target_value, "unit": kpi.unit,
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"children": [],
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}
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trees = []
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# 每个前缀组中，按序号升序，第一个为父级
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for prefix, group in sorted(groups.items()):
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sorted_group = sorted(group, key=lambda k: k.kpi_code)
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if len(sorted_group) > 1:
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parent = make_node(sorted_group[0])
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parent["children"] = [make_node(c) for c in sorted_group[1:]]
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for c in parent["children"]:
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c["alignment_type"] = "vertical_split"
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c["parent_code"] = parent["kpi_code"]
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parent["child_count"] = len(parent["children"])
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trees.append(parent)
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else:
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trees.append(make_node(sorted_group[0]))
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if kpi_id:
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# 只返回指定KPI的子树
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return _filter_tree(trees, kpi_id)
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return {"mode": "vertical_decomposition", "mode_name": "纵向分解", "tree": trees, "total": len(kpis)}
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def _build_horizontal_tree(kpis, kpi_id=None):
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"""横向支撑树：按BSC维度因果关联"""
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# 因果顺序：learning → process → customer → finance
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dim_order = {"learning": 0, "process": 1, "customer": 2, "finance": 3}
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dim_name = {"finance": "财务", "customer": "客户", "process": "内部流程", "learning": "学习成长"}
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# 按维度分组
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groups = {"finance": [], "customer": [], "process": [], "learning": []}
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for k in kpis:
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if k.dimension in groups:
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groups[k.dimension].append(k)
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def make_node(kpi):
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return {
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"id": kpi.id, "kpi_code": kpi.kpi_code, "kpi_name": kpi.kpi_name,
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"dimension": kpi.dimension, "category": kpi.category,
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"target_value": kpi.target_value, "unit": kpi.unit,
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"children": [],
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}
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# 构建层级：一个维度节点包含该维度所有KPI
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trees = []
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for dim, ks in sorted(groups.items(), key=lambda x: dim_order.get(x[0], 9)):
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if not ks:
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continue
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dim_node = {
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"id": None,
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"dimension": dim,
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"kpi_name": dim_name.get(dim, dim),
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"is_dimension_group": True,
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"children": [make_node(k) for k in sorted(ks, key=lambda x: x.kpi_code)],
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"child_count": len(ks),
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}
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# 建立因果关联说明
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if dim == "learning":
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dim_node["description"] = "驱动因素：人才培养与创新"
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for c in dim_node["children"]:
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c["drives"] = "internal_process"
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elif dim == "process":
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dim_node["description"] = "过程保障：效率与质量提升"
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for c in dim_node["children"]:
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c["drives"] = "customer"
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elif dim == "customer":
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dim_node["description"] = "市场反馈：客户规模与满意度"
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for c in dim_node["children"]:
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c["drives"] = "finance"
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elif dim == "finance":
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dim_node["description"] = "结果指标：收入与盈利"
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for c in dim_node["children"]:
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c["drives"] = None
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trees.append(dim_node)
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if kpi_id:
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return _filter_tree(trees, kpi_id)
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return {
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"mode": "horizontal_support",
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"mode_name": "横向支撑",
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"tree": trees,
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"total": len(kpis),
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"causal_chain": [
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{"from": "学习成长", "to": "内部流程", "logic": "培训与创新→流程效率提升"},
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{"from": "内部流程", "to": "客户", "logic": "流程效率→客户满意度提升"},
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{"from": "客户", "to": "财务", "logic": "客户规模→财务结果达成"},
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],
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}
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def _build_bsc_chain(kpis, kpi_id=None):
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"""BSC瀑布链：按category类别间的因果传导"""
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from collections import defaultdict
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# 每个维度的KPI按category分组
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cat_kpis = defaultdict(list)
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for k in kpis:
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if k.category:
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cat_kpis[k.category].append(k)
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# BSC瀑布链的传导关系
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chain = [
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{"cat": "talent_pipeline", "label": "人才梯队", "dim": "learning", "feeds": ["supply_chain", "delivery_quality"]},
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{"cat": "employee_engagement", "label": "员工敬业", "dim": "learning", "feeds": ["supply_chain"]},
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{"cat": "innovation", "label": "创新改善", "dim": "learning", "feeds": ["delivery_quality"]},
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{"cat": "supply_chain", "label": "供应链效率", "dim": "process", "feeds": ["delivery_quality"]},
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{"cat": "delivery_quality", "label": "交付质量", "dim": "process", "feeds": ["customer_scale", "customer_satisfaction"]},
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{"cat": "customer_scale", "label": "客户规模", "dim": "customer", "feeds": ["revenue_growth"]},
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{"cat": "customer_concentration", "label": "客户集中度", "dim": "customer", "feeds": ["profitability"]},
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{"cat": "customer_satisfaction", "label": "客户满意", "dim": "customer", "feeds": ["revenue_growth", "profitability"]},
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{"cat": "revenue_growth", "label": "收入增长", "dim": "finance", "feeds": ["profitability"]},
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{"cat": "profitability", "label": "盈利水平", "dim": "finance", "feeds": None},
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{"cat": "cost_control", "label": "成本费用", "dim": "finance", "feeds": ["profitability"]},
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{"cat": "asset_efficiency", "label": "资产效率", "dim": "finance", "feeds": ["profitability"]},
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{"cat": "cash_risk", "label": "现金流风控", "dim": "finance", "feeds": ["profitability"]},
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]
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def make_node(kpi):
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return {
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"id": kpi.id, "kpi_code": kpi.kpi_code, "kpi_name": kpi.kpi_name,
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"dimension": kpi.dimension, "category": kpi.category,
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"target_value": kpi.target_value, "unit": kpi.unit,
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}
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# 构建瀑布链
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trees = []
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for link in chain:
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cat = link["cat"]
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if cat not in cat_kpis:
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continue
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cat_node = {
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"id": None,
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"category": cat,
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"category_label": link["label"],
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"dimension": link["dim"],
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"is_category_group": True,
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"feeds": link["feeds"],
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"children": [make_node(k) for k in sorted(cat_kpis[cat], key=lambda x: x.kpi_code)],
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"child_count": len(cat_kpis[cat]),
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}
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trees.append(cat_node)
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if kpi_id:
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return _filter_tree(trees, kpi_id)
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return {
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"mode": "bsc_chain",
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"mode_name": "BSC瀑布链",
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"tree": trees,
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"total": len(kpis),
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"chain": chain,
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}
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def _filter_tree(nodes, target_id):
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"""在树中查找包含指定KPI的子树"""
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for node in nodes:
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if node.get("id") == target_id:
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return node
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if node.get("children"):
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for child in node["children"]:
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if child.get("id") == target_id:
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return child
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# 递归查找
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found = _filter_tree(node["children"], target_id)
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if found:
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return found
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return None
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