841 lines
32 KiB
Plaintext
841 lines
32 KiB
Plaintext
"""驾驶舱 API v2 — 支持时间区间"""
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from fastapi import APIRouter, Depends, Query, Request, HTTPException
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from sqlalchemy.orm import Session
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from sqlalchemy import func, or_
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from datetime import datetime, timedelta
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from typing import Optional
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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, KPIValue, KPIAlert, User
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from app.utils.cache import get as cache_get, set as cache_set
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import json
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import logging
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logger = logging.getLogger("cma.dashboard")
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router = APIRouter(prefix="/api/cma/dashboard", tags=["驾驶舱"],
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dependencies=[Depends(require_role("ceo", "finance", "business", "it"))],
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)
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def parse_period(period_type: str, start_date: str = None, end_date: str = None):
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"""解析时间区间"""
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today = datetime.now()
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if period_type == "month":
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start = today.replace(day=1, hour=0, minute=0, second=0, microsecond=0)
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end = today
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elif period_type == "quarter":
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q = (today.month - 1) // 3
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start = today.replace(month=q*3+1, day=1, hour=0, minute=0, second=0, microsecond=0)
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end = today
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elif period_type == "year":
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start = today.replace(month=1, day=1, hour=0, minute=0, second=0, microsecond=0)
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end = today
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elif period_type == "custom" and start_date and end_date:
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start = datetime.strptime(start_date, "%Y-%m-%d")
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end = datetime.strptime(end_date, "%Y-%m-%d") + timedelta(days=1)
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else:
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start = today.replace(day=1, hour=0, minute=0, second=0, microsecond=0)
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end = today
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return start, end
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def period_prefix(period_type: str):
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"""生成SQL期间前缀匹配"""
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if period_type == "month":
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return datetime.now().strftime("%Y-%m")
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elif period_type == "quarter":
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now = datetime.now()
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q = (now.month - 1) // 3
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months = [f"{now.year}-{m:02d}" for m in range(q*3+1, q*3+4)]
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return months
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elif period_type == "year":
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return str(datetime.now().year)
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return None
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@router.get("/summary")
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def get_dashboard_summary(role: str = Query("ceo"), period: str = Query("month"), db: Session = Depends(get_db)):
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cache_key = f"summary:{role}:{period}"
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cached = cache_get("dashboard", cache_key)
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if cached:
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return cached
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kpi_total = db.query(func.count(KPIDefinition.id)).filter(KPIDefinition.status == "active").scalar()
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alert_count = db.query(func.count(KPIAlert.id)).filter(KPIAlert.status == "pending").scalar()
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dims = db.query(KPIDefinition.dimension, func.count(KPIDefinition.id)).filter(
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KPIDefinition.status == "active").group_by(KPIDefinition.dimension).all()
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# 读取最近一次同步状态（从日志文件最后一行）
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sync_status = {"last_sync": None, "status": "unknown", "detail": ""}
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try:
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with open("/var/log/cma-daily-sync.log", "r") as f:
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lines = f.readlines()
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# 从最后往前找包含 "完成" 或 "失败" 的行
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for line in reversed(lines[-50:]):
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if "全部完成" in line:
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sync_status["status"] = "success"
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sync_status["last_sync"] = line.strip()
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break
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elif "失败" in line or "ERROR" in line:
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sync_status["status"] = "failed"
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sync_status["last_sync"] = line.strip()
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break
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else:
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# 没找到完成/失败标记，取最后一行
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sync_status["last_sync"] = lines[-1].strip() if lines else None
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except Exception as e:
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sync_status["detail"] = str(e)
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result = {
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"kpi_total": kpi_total or 0, "alert_count": alert_count or 0,
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"dimension_stats": [{"dimension": d[0], "count": d[1]} for d in dims],
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"sync_status": sync_status,
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}
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cache_set("dashboard", cache_key, result, ttl_seconds=30)
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return result
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@router.get("/kpis")
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def get_dashboard_kpis(role: str = Query("ceo"), period: str = Query("month"),
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start_date: str = Query(None), end_date: str = Query(None),
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db: Session = Depends(get_db)):
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start, end = parse_period(period, start_date, end_date)
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period_str = start.strftime("%Y-%m")
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kpis = db.query(KPIDefinition).filter(KPIDefinition.status == "active").all()
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result = []
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for k in kpis:
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base_query = db.query(KPIValue).filter(KPIValue.kpi_id == k.id)
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if period == "month":
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latest = base_query.filter(KPIValue.period == period_str).order_by(KPIValue.id.desc()).first()
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elif period == "quarter":
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months = period_prefix("quarter")
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values = base_query.filter(KPIValue.period.in_(months)).all()
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latest_val = sum(v.actual_value for v in values if v.actual_value) if values else None
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latest = type('obj', (object,), {"actual_value": latest_val, "period": f"{months[0]}~{months[-1]}"})() if latest_val else None
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elif period == "year":
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values = base_query.filter(KPIValue.period.like(f"{period_str[:4]}%")).all()
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latest_val = sum(v.actual_value for v in values if v.actual_value) if values else None
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latest = type('obj', (object,), {"actual_value": latest_val, "period": period_str[:4]})() if latest_val else None
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elif period == "custom" and start_date and end_date:
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periods = []
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d = start
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while d <= end:
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periods.append(d.strftime("%Y-%m"))
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d += timedelta(days=32)
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d = d.replace(day=1)
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values = base_query.filter(KPIValue.period.in_(set(periods))).all()
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latest_val = sum(v.actual_value for v in values if v.actual_value) if values else None
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latest = type('obj', (object,), {"actual_value": latest_val, "period": f"{start_date}~{end_date}"})() if latest_val else None
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else:
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latest = base_query.order_by(KPIValue.period.desc()).first()
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alert = db.query(KPIAlert).filter(
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KPIAlert.kpi_id == k.id,
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KPIAlert.status == "pending",
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).order_by(KPIAlert.id.desc()).first()
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result.append({
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"id": k.id, "kpi_code": k.kpi_code, "kpi_name": k.kpi_name,
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"dimension": k.dimension, "unit": k.unit, "target_value": k.target_value,
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"actual_value": latest.actual_value if latest else None,
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"period": latest.period if latest else None,
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"alert_level": alert.alert_level if alert else "none",
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"alert_message": alert.alert_message if alert else None,
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"frequency": k.frequency,
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"responsible_dept": k.responsible_dept,
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})
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return {"data": result, "period": period, "range": {"start": start.strftime("%Y-%m-%d"), "end": end.strftime("%Y-%m-%d")}}
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@router.get("/my-kpis")
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def get_my_kpis(
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current_user: User = Depends(require_auth),
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period: str = Query("month"),
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db: Session = Depends(get_db),
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):
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"""获取当前用户负责的KPI
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- business角色：只看自己负责的KPI
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- 其他角色：看所有有预警的KPI
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"""
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role = current_user.role
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username = current_user.username
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name = current_user.name
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period_str = datetime.now().strftime("%Y-%m")
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kpis = db.query(KPIDefinition).filter(KPIDefinition.status == "active").all()
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result = []
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for k in kpis:
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# business角色筛选
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if role == "business":
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responsible = (k.responsible_user or "").strip()
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if responsible and responsible != username and responsible != name:
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continue
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latest = db.query(KPIValue).filter(
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KPIValue.kpi_id == k.id,
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KPIValue.period == period_str,
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).order_by(KPIValue.id.desc()).first()
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alert = db.query(KPIAlert).filter(
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KPIAlert.kpi_id == k.id,
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KPIAlert.status == "pending",
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).order_by(KPIAlert.id.desc()).first()
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trend_values = db.query(KPIValue).filter(
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KPIValue.kpi_id == k.id,
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).order_by(KPIValue.period.desc()).limit(6).all()
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trend = [{"period": v.period, "value": v.actual_value} for v in reversed(trend_values)]
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result.append({
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"id": k.id, "kpi_code": k.kpi_code, "kpi_name": k.kpi_name,
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"dimension": k.dimension, "unit": k.unit,
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"target_value": k.target_value,
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"actual_value": latest.actual_value if latest else None,
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"period": latest.period if latest else period_str,
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"alert_level": alert.alert_level if alert else "none",
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"alert_message": alert.alert_message if alert else None,
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"alert_id": alert.id if alert else None,
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"frequency": k.frequency,
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"responsible_dept": k.responsible_dept,
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"responsible_user": k.responsible_user,
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"trend": trend,
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"threshold_green": k.threshold_green,
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"threshold_yellow": k.threshold_yellow,
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"threshold_red": k.threshold_red,
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})
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return {"data": result, "user_role": role, "user_name": name, "period": period_str}
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@router.get("/finance-analysis")
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def get_finance_analysis(
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current_user: User = Depends(require_auth),
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period: str = Query("month"),
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db: Session = Depends(get_db),
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):
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"""财务工作台分析数据"""
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period_str = datetime.now().strftime("%Y-%m")
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finance_kpis = db.query(KPIDefinition).filter(
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KPIDefinition.status == "active",
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KPIDefinition.dimension == "finance",
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).all()
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kpi_data = []
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for k in finance_kpis:
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latest = db.query(KPIValue).filter(
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KPIValue.kpi_id == k.id,
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KPIValue.period == period_str,
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).order_by(KPIValue.id.desc()).first()
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trend_values = db.query(KPIValue).filter(
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KPIValue.kpi_id == k.id,
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).order_by(KPIValue.period.desc()).limit(6).all()
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trend = [{"period": v.period, "value": v.actual_value} for v in reversed(trend_values)]
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alert = db.query(KPIAlert).filter(
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KPIAlert.kpi_id == k.id,
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KPIAlert.status == "pending",
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).order_by(KPIAlert.id.desc()).first()
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kpi_data.append({
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"id": k.id, "kpi_code": k.kpi_code, "kpi_name": k.kpi_name,
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"unit": k.unit, "target_value": k.target_value,
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"actual_value": latest.actual_value if latest else None,
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"threshold_green": k.threshold_green,
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"threshold_yellow": k.threshold_yellow,
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"threshold_red": k.threshold_red,
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"trend": trend,
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"alert_level": alert.alert_level if alert else "none",
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"frequency": k.frequency,
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})
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total_sales = next((k for k in kpi_data if k["kpi_code"] == "SALES_TOTAL"), None)
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gross_profit = next((k for k in kpi_data if k["kpi_code"] == "SALES_PROFIT_RATE"), None)
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cost_control = next((k for k in kpi_data if k["kpi_code"] == "COST_CONTROL_RATE"), None)
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receivable = next((k for k in kpi_data if k["kpi_code"] == "RECEIVABLE_TURNOVER"), None)
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return {
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"period": period_str,
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"kpis": kpi_data,
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"summary": {
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"total_sales": total_sales["actual_value"] if total_sales else None,
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"gross_profit_rate": gross_profit["actual_value"] if gross_profit else None,
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"cost_control_rate": cost_control["actual_value"] if cost_control else None,
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"receivable_turnover": receivable["actual_value"] if receivable else None,
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}
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}
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@router.get("/predict")
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def predict_kpis(db: Session = Depends(get_db)):
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"""基于历史趋势预测下月KPI值（简单线性回归）"""
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from datetime import datetime, timedelta
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period_str = datetime.now().strftime("%Y-%m")
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next_month = int(period_str[5:7]) + 1
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next_year = int(period_str[:4])
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if next_month > 12:
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next_month = 1
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next_year += 1
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next_period = f"{next_year}-{next_month:02d}"
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kpis = db.query(KPIDefinition).filter(KPIDefinition.status == "active").all()
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predictions = []
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for k in kpis:
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values = db.query(KPIValue).filter(
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KPIValue.kpi_id == k.id,
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).order_by(KPIValue.period.asc()).all()
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# 需要至少3个数据点才能做预测
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if len(values) < 3:
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continue
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# 简单线性回归: y = a + bx
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points = [(i, v.actual_value) for i, v in enumerate(values) if v.actual_value is not None]
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if len(points) < 3:
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continue
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n = len(points)
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sum_x = sum(p[0] for p in points)
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sum_y = sum(p[1] for p in points)
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sum_xy = sum(p[0] * p[1] for p in points)
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sum_xx = sum(p[0] ** 2 for p in points)
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# 斜率 b = (n*sum_xy - sum_x*sum_y) / (n*sum_xx - sum_x*sum_x)
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denom = n * sum_xx - sum_x * sum_x
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if denom == 0:
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continue
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b = (n * sum_xy - sum_x * sum_y) / denom
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a = (sum_y - b * sum_x) / n
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# 预测下个月（x = n，因为最后一个索引是 n-1）
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predicted_value = a + b * n
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# 检查预测值是否触发阈值
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alert_level = "none"
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if k.threshold_red:
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try:
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op = k.threshold_red[:2] if len(k.threshold_red) > 1 and k.threshold_red[1] in "=<>" else k.threshold_red[0]
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val_str = k.threshold_red.replace(op, "").strip()
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val = float(val_str)
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if (op in (">=", ">") and predicted_value >= val) or (op in ("<=", "<") and predicted_value <= val):
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alert_level = "red"
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except (ValueError, IndexError):
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pass
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if alert_level == "none" and k.threshold_yellow:
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try:
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op = k.threshold_yellow[:2] if len(k.threshold_yellow) > 1 and k.threshold_yellow[1] in "=<>" else k.threshold_yellow[0]
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val_str = k.threshold_yellow.replace(op, "").strip()
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val = float(val_str)
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if (op in (">=", ">") and predicted_value >= val) or (op in ("<=", "<") and predicted_value <= val):
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alert_level = "yellow"
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except (ValueError, IndexError):
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pass
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predictions.append({
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"kpi_id": k.id,
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"kpi_code": k.kpi_code,
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"kpi_name": k.kpi_name,
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"target_value": k.target_value,
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"last_value": points[-1][1] if points else None,
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"predicted_value": round(predicted_value, 2),
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"predicted_period": next_period,
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"alert_level": alert_level,
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"trend": "up" if b > 0 else ("down" if b < 0 else "stable"),
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"confidence": "high" if len(points) >= 6 else ("medium" if len(points) >= 4 else "low"),
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"data_points": len(points),
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})
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return {
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"current_period": period_str,
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"next_period": next_period,
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"predictions": predictions,
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"kpi_count": len(kpis),
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"predictable_count": len(predictions),
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}
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# ── 个人工作台 ──────────────────────────────
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@router.get("/my-dashboard")
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def my_dashboard(
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current_user: User = Depends(require_auth),
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db: Session = Depends(get_db),
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):
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"""个人工作台：返回我的KPI、改善行动、待办提醒"""
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username = current_user.username
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name = current_user.name
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role = current_user.role
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# 角色 → 维度映射（从已发布战略地图中按角色筛选对应维度的KPI）
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ROLE_DIMENSIONS = {
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"ceo": ["finance", "customer", "process", "learning"], # CEO看全部维度
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"finance": ["finance"], # 财务看财务维度
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"business": ["customer", "process"], # 业务看客户+流程维度
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"it": ["process", "learning"], # IT看流程+学习成长
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}
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role_dims = ROLE_DIMENSIONS.get(role, ["finance", "customer"])
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# 获取所有已发布战略地图的KPI code集合（dimensions中引用的）
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from app.models import StrategicMap
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published_maps = db.query(StrategicMap).filter(StrategicMap.status == "published").all()
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map_kpi_codes = set()
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for sm in published_maps:
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dims = sm.dimensions
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if isinstance(dims, str):
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try:
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dims = json.loads(dims)
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except Exception:
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continue
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for dim in dims:
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for obj in dim.get("objectives", []):
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for code in obj.get("kpis", []):
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map_kpi_codes.add(code)
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# 1. 按角色维度筛选（从已发布地图的KPI中取符合角色维度的）
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map_kpis = []
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if map_kpi_codes:
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map_kpis = db.query(KPIDefinition).filter(
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KPIDefinition.kpi_code.in_(map_kpi_codes),
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KPIDefinition.dimension.in_(role_dims),
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KPIDefinition.status == "active",
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).all()
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# 2. 补充负责的KPI（responsible_user匹配）
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assigned_kpis = db.query(KPIDefinition).filter(
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or_(
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KPIDefinition.responsible_user == username,
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KPIDefinition.responsible_user == name,
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),
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KPIDefinition.status == "active",
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).all()
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assigned_ids = {k.id for k in assigned_kpis}
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# 去重合并
|
||
all_kpis = map_kpis + [k for k in assigned_kpis if k.id not in {mk.id for mk in map_kpis}]
|
||
|
||
kpi_list = []
|
||
for k in all_kpis:
|
||
latest_v = db.query(KPIValue).filter(
|
||
KPIValue.kpi_id == k.id
|
||
).order_by(KPIValue.calculated_at.desc()).first()
|
||
|
||
actual = latest_v.actual_value if latest_v else None
|
||
target = k.target_value
|
||
level = "gray"
|
||
if actual is not None and target:
|
||
ratio = actual / target
|
||
level = "green" if ratio >= 0.9 else ("yellow" if ratio >= 0.7 else "red")
|
||
|
||
kpi_list.append({
|
||
"id": k.id,
|
||
"kpi_code": k.kpi_code,
|
||
"kpi_name": k.kpi_name,
|
||
"dimension": k.dimension,
|
||
"category": k.category,
|
||
"target_value": target,
|
||
"actual_value": actual,
|
||
"unit": k.unit,
|
||
"level": level,
|
||
"period": latest_v.period if latest_v else None,
|
||
})
|
||
|
||
# 2. 我的改善行动（assignee匹配）
|
||
from app.models import ActionPlan
|
||
my_plans = db.query(ActionPlan).filter(
|
||
or_(
|
||
ActionPlan.assignee == username,
|
||
ActionPlan.assignee == name,
|
||
)
|
||
).order_by(ActionPlan.updated_at.desc()).all()
|
||
|
||
plan_list = []
|
||
for p in my_plans:
|
||
overdue = False
|
||
if p.due_date and p.status not in ("completed", "cancelled"):
|
||
overdue = p.due_date < datetime.now()
|
||
kpi_name = ""
|
||
kpi = db.query(KPIDefinition).filter(KPIDefinition.id == p.kpi_id).first()
|
||
if kpi:
|
||
kpi_name = kpi.kpi_name
|
||
|
||
plan_list.append({
|
||
"id": p.id,
|
||
"kpi_id": p.kpi_id,
|
||
"kpi_name": kpi_name,
|
||
"title": p.title,
|
||
"assignee": p.assignee,
|
||
"priority": p.priority,
|
||
"status": p.status,
|
||
"progress": p.progress or 0,
|
||
"due_date": p.due_date.isoformat() if p.due_date else None,
|
||
"overdue": overdue,
|
||
"created_at": p.created_at.isoformat() if p.created_at else None,
|
||
})
|
||
|
||
# 3. 待办提醒
|
||
reminders = []
|
||
|
||
# 逾期行动
|
||
for p in plan_list:
|
||
if p["overdue"]:
|
||
reminders.append({
|
||
"type": "overdue_plan",
|
||
"severity": "danger",
|
||
"message": f"你负责的「{p['title']}」已逾期",
|
||
"related_id": p["id"],
|
||
"related_type": "action_plan",
|
||
})
|
||
|
||
# 红色预警KPI
|
||
for k in kpi_list:
|
||
if k["level"] == "red":
|
||
reminders.append({
|
||
"type": "red_kpi",
|
||
"severity": "danger",
|
||
"message": f"你负责的KPI「{k['kpi_name']}」处于红色预警",
|
||
"related_id": k["id"],
|
||
"related_type": "kpi",
|
||
})
|
||
|
||
# 黄色预警KPI
|
||
for k in kpi_list:
|
||
if k["level"] == "yellow":
|
||
reminders.append({
|
||
"type": "yellow_kpi",
|
||
"severity": "warning",
|
||
"message": f"你负责的KPI「{k['kpi_name']}」处于黄色预警",
|
||
"related_id": k["id"],
|
||
"related_type": "kpi",
|
||
})
|
||
|
||
return {
|
||
"kpis": kpi_list,
|
||
"action_plans": plan_list,
|
||
"reminders": reminders,
|
||
}
|
||
|
||
|
||
@router.get("/erp-trends")
|
||
def get_erp_trends(
|
||
current_user: User = Depends(require_auth),
|
||
months: int = Query(12, ge=3, le=36),
|
||
db: Session = Depends(get_db),
|
||
):
|
||
"""获取ERP关键指标趋势数据（驾驶舱趋势分析用）"""
|
||
codes = [
|
||
"F_REVENUE",
|
||
"F_PROFIT_RATE",
|
||
"F_NET_PROFIT_RATE",
|
||
"F_COST_RATIO",
|
||
"F_CASH_FLOW",
|
||
"F_AR_TURNOVER",
|
||
"F_ROE",
|
||
"F_ASSET_TURNOVER",
|
||
"F_DEBT_RATIO",
|
||
"C_CUSTOMER_COUNT",
|
||
"C_CUSTOMER_SATISFACTION",
|
||
"C_CUSTOMER_CONCENTRATION",
|
||
"P_DELIVERY_ON_TIME",
|
||
"P_DEFECT_RATE",
|
||
"P_SUPPLY_CYCLE",
|
||
"L_TRAINING_HOURS",
|
||
"L_EMPLOYEE_TURNOVER",
|
||
"L_INNOVATION_COUNT",
|
||
"L_TECH_COVERAGE",
|
||
]
|
||
result = {}
|
||
|
||
for code in codes:
|
||
kpi = db.query(KPIDefinition).filter(KPIDefinition.kpi_code == code).first()
|
||
if not kpi:
|
||
continue
|
||
values = db.query(KPIValue).filter(
|
||
KPIValue.kpi_id == kpi.id,
|
||
).order_by(KPIValue.period.desc()).limit(months).all()
|
||
|
||
trend = [{"period": v.period, "value": v.actual_value} for v in reversed(values)]
|
||
if trend:
|
||
vals = [v["value"] for v in trend if v["value"] is not None]
|
||
latest = vals[-1] if vals else 0
|
||
first = vals[0] if vals else 0
|
||
if latest > first * 1.05:
|
||
trend_dir = "up"
|
||
elif latest < first * 0.95:
|
||
trend_dir = "down"
|
||
else:
|
||
trend_dir = "stable"
|
||
|
||
mom_val = vals[-2] if len(vals) >= 2 else None
|
||
yoy_val = vals[-12] if len(vals) >= 12 else (vals[0] if len(vals) >= 1 else None)
|
||
|
||
result[code] = {
|
||
"name": kpi.kpi_name,
|
||
"unit": kpi.unit or "",
|
||
"target": kpi.target_value,
|
||
"trend": trend,
|
||
"trend_dir": trend_dir,
|
||
"latest": latest,
|
||
"mom": mom_val,
|
||
"mom_rate": round((latest - mom_val) / abs(mom_val) * 100, 1) if mom_val and mom_val != 0 else None,
|
||
"yoy": yoy_val,
|
||
"yoy_rate": round((latest - yoy_val) / abs(yoy_val) * 100, 1) if yoy_val and yoy_val != 0 else None,
|
||
}
|
||
|
||
return {"data": result}
|
||
|
||
|
||
@router.get("/dupont")
|
||
async def dupont_analysis(
|
||
db: Session = Depends(get_db),
|
||
current_user: User = Depends(require_auth),
|
||
):
|
||
"""杜邦分析 — ROE分解
|
||
ROE = 净利率 × 资产周转率 × 权益乘数
|
||
"""
|
||
cache_key = f"dupont:{current_user.role}"
|
||
cached = cache_get("dashboard", cache_key)
|
||
if cached:
|
||
return cached
|
||
|
||
# 获取底层数据KPI
|
||
def get_kpi_value(code: str) -> tuple:
|
||
kpi = db.query(KPIDefinition).filter(KPIDefinition.kpi_code == code).first()
|
||
if not kpi:
|
||
return None, None, None
|
||
latest = db.query(KPIValue).filter(KPIValue.kpi_id == kpi.id).order_by(KPIValue.period.desc()).first()
|
||
prev = db.query(KPIValue).filter(KPIValue.kpi_id == kpi.id).order_by(KPIValue.period.desc()).offset(1).first()
|
||
val = latest.actual_value if latest else None
|
||
pval = prev.actual_value if prev else None
|
||
return val, pval, kpi.unit
|
||
|
||
# 营收、利润、总资产、净资产
|
||
revenue, prev_revenue, _ = get_kpi_value("F_REVENUE")
|
||
# 用营收×净利润率估算净利润（数据库没有净利润绝对值）
|
||
profit_net = None
|
||
prev_profit_net = None
|
||
if revenue:
|
||
net_profit_rate, prev_npr, _ = get_kpi_value("F_NET_PROFIT_RATE")
|
||
if net_profit_rate:
|
||
profit_net = revenue * (net_profit_rate / 100)
|
||
if prev_revenue and prev_npr:
|
||
prev_profit_net = prev_revenue * (prev_npr / 100)
|
||
# 如果还是算不出来，用毛利率做替代估算
|
||
if profit_net is None and revenue:
|
||
gross_profit, _, _ = get_kpi_value("F_PROFIT_RATE")
|
||
profit_net = revenue * (gross_profit / 100) * 0.7 if gross_profit else None # 粗略估算净利润=毛利*0.7
|
||
|
||
asset_total, prev_asset, _ = get_kpi_value("F_ASSET_TOTAL")
|
||
equity_total, prev_equity, _ = get_kpi_value("F_EQUITY_TOTAL")
|
||
|
||
# 计算杜邦因子
|
||
result = {"roe": None, "factors": {}, "raw_data": {}, "history": {}}
|
||
|
||
if revenue and profit_net and asset_total and equity_total and all(v > 0 for v in [revenue, asset_total, equity_total]):
|
||
net_profit_margin = round(profit_net / revenue, 4) # 净利率
|
||
asset_turnover = round(revenue / asset_total, 4) # 资产周转率
|
||
equity_multiplier = round(asset_total / equity_total, 4) # 权益乘数
|
||
roe = round(net_profit_margin * asset_turnover * equity_multiplier * 100, 2)
|
||
|
||
result["roe"] = roe
|
||
result["factors"] = {
|
||
"net_profit_margin": {"value": net_profit_margin, "label": "净利率", "desc": f"净利润/{'营收' if revenue else '-'} = {net_profit_margin*100:.2f}%"},
|
||
"asset_turnover": {"value": asset_turnover, "label": "资产周转率", "desc": f"营收/总资产 = {asset_turnover:.4f}次"},
|
||
"equity_multiplier": {"value": equity_multiplier, "label": "权益乘数", "desc": f"总资产/净资产 = {equity_multiplier:.4f}"},
|
||
}
|
||
result["raw_data"] = {
|
||
"revenue": revenue,
|
||
"profit_net": profit_net,
|
||
"asset_total": asset_total,
|
||
"equity_total": equity_total,
|
||
}
|
||
|
||
# 环比计算
|
||
if prev_revenue and prev_profit_net and prev_asset and prev_equity and all(v > 0 for v in [prev_revenue, prev_asset, prev_equity]):
|
||
prev_npm = round(prev_profit_net / prev_revenue, 4)
|
||
prev_at = round(prev_revenue / prev_asset, 4)
|
||
prev_em = round(prev_asset / prev_equity, 4)
|
||
prev_roe = round(prev_npm * prev_at * prev_em * 100, 2)
|
||
result["history"]["prev"] = {
|
||
"roe": prev_roe,
|
||
"net_profit_margin": prev_npm,
|
||
"asset_turnover": prev_at,
|
||
"equity_multiplier": prev_em,
|
||
}
|
||
# 同比变化
|
||
change = round(roe - prev_roe, 2)
|
||
npm_change = round((net_profit_margin - prev_npm) * 10000, 2) # 转成BP
|
||
at_change = round(asset_turnover - prev_at, 4)
|
||
em_change = round(equity_multiplier - prev_em, 4)
|
||
result["history"]["change"] = {
|
||
"roe": change,
|
||
"roe_label": f"{'+' if change > 0 else ''}{change}%",
|
||
"net_profit_margin_bp": npm_change,
|
||
"asset_turnover": at_change,
|
||
"equity_multiplier": em_change,
|
||
}
|
||
result["history"]["trend"] = "up" if change > 0 else ("down" if change < 0 else "stable")
|
||
|
||
# 补上原始数据（即使计算不全也返回给前端展示）
|
||
if not result.get("raw_data"):
|
||
result["raw_data"] = {
|
||
"revenue": revenue,
|
||
"profit_net": profit_net,
|
||
"asset_total": asset_total,
|
||
"equity_total": equity_total,
|
||
}
|
||
|
||
cache_set("dashboard", cache_key, result, ttl_seconds=300)
|
||
return result
|
||
|
||
|
||
def _get_kpi_trend(kpi_id: int, db: Session) -> dict:
|
||
"""计算KPI的环比和同比趋势"""
|
||
from datetime import datetime
|
||
now = datetime.now()
|
||
cur_period = now.strftime("%Y-%m")
|
||
|
||
# 上月
|
||
if now.month == 1:
|
||
prev_month = f"{now.year-1}-12"
|
||
else:
|
||
prev_month = f"{now.year}-{now.month-1:02d}"
|
||
|
||
# 去年同期
|
||
last_year = f"{now.year-1}-{now.month:02d}"
|
||
|
||
cur_val = db.query(KPIValue).filter(
|
||
KPIValue.kpi_id == kpi_id,
|
||
KPIValue.period == cur_period
|
||
).order_by(KPIValue.id.desc()).first()
|
||
|
||
prev_val = db.query(KPIValue).filter(
|
||
KPIValue.kpi_id == kpi_id,
|
||
KPIValue.period == prev_month
|
||
).order_by(KPIValue.id.desc()).first()
|
||
|
||
yoy_val = db.query(KPIValue).filter(
|
||
KPIValue.kpi_id == kpi_id,
|
||
KPIValue.period == last_year
|
||
).order_by(KPIValue.id.desc()).first()
|
||
|
||
def calc_rate(curr, prev):
|
||
if curr and prev and prev.actual_value and prev.actual_value != 0:
|
||
return round((curr.actual_value - prev.actual_value) / prev.actual_value * 100, 2)
|
||
return None
|
||
|
||
return {
|
||
"current_value": cur_val.actual_value if cur_val else None,
|
||
"current_period": cur_period,
|
||
"mom_value": prev_val.actual_value if prev_val else None,
|
||
"mom_rate": calc_rate(cur_val, prev_val),
|
||
"yoy_value": yoy_val.actual_value if yoy_val else None,
|
||
"yoy_rate": None if not yoy_val else calc_rate(cur_val, yoy_val),
|
||
}
|
||
|
||
|
||
@router.get("/kpis/enhanced")
|
||
def get_kpis_enhanced(role: str = Query("ceo"), period: str = Query("month"),
|
||
start_date: str = None, end_date: str = None,
|
||
db: Session = Depends(get_db)):
|
||
"""增强版KPI列表（带趋势）"""
|
||
result = get_dashboard_kpis(role=role, period=period, start_date=start_date, end_date=end_date, db=db)
|
||
if "data" in result and result["data"]:
|
||
for kpi in result["data"]:
|
||
if kpi.get("id"):
|
||
trend = _get_kpi_trend(kpi["id"], db)
|
||
kpi["trend"] = trend
|
||
return result
|
||
|
||
|
||
@router.get("/trend-analysis")
|
||
def get_trend_analysis(kpi_ids: str = Query(""), period: str = Query("month"),
|
||
db: Session = Depends(get_db)):
|
||
"""多KPI趋势对比（折线图数据）"""
|
||
ids = [int(x) for x in kpi_ids.split(",") if x.strip().isdigit()]
|
||
if not ids:
|
||
return {"data": []}
|
||
|
||
result = []
|
||
for kpi_id in ids:
|
||
kpi = db.query(KPIDefinition).filter(KPIDefinition.id == kpi_id).first()
|
||
if not kpi:
|
||
continue
|
||
|
||
values = db.query(KPIValue).filter(
|
||
KPIValue.kpi_id == kpi_id
|
||
).order_by(KPIValue.period).all()
|
||
|
||
series = []
|
||
for v in values:
|
||
if v.actual_value is not None:
|
||
series.append({
|
||
"period": v.period,
|
||
"value": v.actual_value,
|
||
})
|
||
|
||
result.append({
|
||
"kpi_id": kpi.id,
|
||
"kpi_code": kpi.kpi_code,
|
||
"kpi_name": kpi.kpi_name,
|
||
"unit": kpi.unit,
|
||
"target": kpi.target_value,
|
||
"data": series,
|
||
})
|
||
|
||
return {"data": result}
|
||
|
||
|
||
@router.get("/alert-stats")
|
||
def get_alert_stats(period: str = Query("month"), db: Session = Depends(get_db)):
|
||
"""预警统计（按等级和维度）"""
|
||
from sqlalchemy import func
|
||
|
||
# 按等级统计
|
||
by_level = db.query(
|
||
KPIAlert.alert_level,
|
||
func.count(KPIAlert.id)
|
||
).group_by(KPIAlert.alert_level).all()
|
||
|
||
level_stats = {row[0]: row[1] for row in by_level}
|
||
|
||
# 按维度统计
|
||
by_dim = db.query(
|
||
KPIDefinition.dimension,
|
||
func.count(KPIAlert.id)
|
||
).join(KPIAlert, KPIDefinition.id == KPIAlert.kpi_id
|
||
).group_by(KPIDefinition.dimension).all()
|
||
|
||
dim_stats = {row[0]: row[1] for row in by_dim}
|
||
|
||
return {
|
||
"by_level": level_stats,
|
||
"by_dimension": dim_stats,
|
||
"total": sum(level_stats.values()) if level_stats else 0,
|
||
}
|
||
|
||
|
||
@router.get("/export")
|
||
def export_kpi_data(kpi_ids: str = "", db: Session = Depends(get_db)):
|
||
"""导出KPI数据为CSV格式"""
|
||
from fastapi.responses import PlainTextResponse
|
||
|
||
ids = [int(x) for x in kpi_ids.split(",") if x.strip().isdigit()]
|
||
query = db.query(KPIValue).join(KPIDefinition, KPIValue.kpi_id == KPIDefinition.id)
|
||
if ids:
|
||
query = query.filter(KPIValue.kpi_id.in_(ids))
|
||
|
||
rows = query.order_by(KPIDefinition.kpi_code, KPIValue.period).all()
|
||
|
||
csv_lines = ["KPI编码,KPI名称,期间,实际值,目标值,来源,状态"]
|
||
for r in rows:
|
||
kpi = db.query(KPIDefinition).filter(KPIDefinition.id == r.kpi_id).first()
|
||
csv_lines.append(f"{kpi.kpi_code},{kpi.kpi_name},{r.period},{r.actual_value},{kpi.target_value},{r.source_type},{r.data_status}")
|
||
|
||
return PlainTextResponse("\n".join(csv_lines), media_type="text/csv",
|
||
headers={"Content-Disposition": "attachment; filename=kpi_export.csv"})
|