feat: 波士顿产品矩阵 — 四象限分析(明星/现金牛/问题/瘦狗)
- 数据层: product_sales表+导入脚本(93条, 酣客1-8月商品销售排行榜) - 后端: GET /api/cma/products/matrix?entity_id&months 横轴=销售趋势(线性回归), 纵轴=加权毛利率, 气泡=销售额 - 前端: ProductMatrix.vue ECharts散点图+象限卡片+明细表, 支持酣客/博海切换 - 验证: API四象限分类正确, 数据联动, 前端路由200, 全部通过
This commit is contained in:
Hermes CI Fix
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"""波士顿产品矩阵 API — 四象限分析"""
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from fastapi import APIRouter, Depends, Query
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from sqlalchemy.orm import Session
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from app.database import get_db
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from app.models import ProductSales
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from collections import defaultdict
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router = APIRouter(prefix="/api/cma/products", tags=["产品矩阵"])
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def _calc_quadrant(trend: float, margin: float) -> str:
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"""四象限分类：
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横轴=近3月销售趋势（正=增长），纵轴=毛利率
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明星(Star) = 高趋势+高毛利
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现金牛(CashCow) = 低趋势+高毛利
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问题(QuestionMark) = 高趋势+低毛利
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瘦狗(Dog) = 低趋势+低毛利
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"""
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trend_high = trend >= 0
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margin_high = margin >= 0
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if trend_high and margin_high:
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return "star"
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if not trend_high and margin_high:
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return "cash_cow"
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if trend_high and not margin_high:
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return "question_mark"
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return "dog"
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@router.get("/matrix")
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def get_product_matrix(
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entity_id: int = Query(1, description="1=酣客 2=博海"),
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months: int = Query(3, ge=1, le=6, description="趋势计算月数"),
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db: Session = Depends(get_db),
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):
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"""产品矩阵：横轴=销售趋势，纵轴=毛利率，气泡=销售额"""
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# 取最近 months+1 个月（多取1个月用于计算趋势）
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periods = db.query(ProductSales.period_month).filter(
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ProductSales.entity_id == entity_id
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).distinct().order_by(ProductSales.period_month.desc()).limit(months + 1).all()
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periods = sorted([p[0] for p in periods])
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if len(periods) < 2:
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return {
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"entity_id": entity_id,
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"has_data": False,
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"message": "数据不足，至少需要2个月数据",
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"quadrants": [],
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"products": [],
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}
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trend_periods = periods[-months:] # 最近 months 个月
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prev_periods = periods[:-months] if len(periods) > months else periods[:1]
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# 加载数据
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rows = db.query(ProductSales).filter(
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ProductSales.entity_id == entity_id,
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ProductSales.period_month.in_(periods),
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).all()
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# 按商品聚合
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products = defaultdict(lambda: {
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"code": "", "name": "", "months": {},
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"total_sales": 0, "total_qty": 0, "total_gross": 0,
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})
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for row in rows:
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p = products[row.product_code]
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p["code"] = row.product_code
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p["name"] = row.product_name
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p["months"][row.period_month] = {
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"sales": float(row.sales_amount or 0),
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"margin": float(row.gross_margin_rate or 0),
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"gross": float(row.gross_profit or 0),
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"qty": int(row.sales_qty or 0),
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}
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p["total_sales"] += float(row.sales_amount or 0)
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p["total_qty"] += int(row.sales_qty or 0)
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p["total_gross"] += float(row.gross_profit or 0)
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# 计算每个商品的趋势和毛利率
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result_products = []
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for code, p in products.items():
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# 趋势 = 最近月份 vs 前一月的环比（取趋势期间的平均环比增速）
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# 用最近3个月的销售序列做简单线性趋势
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trend_sales = []
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for pp in periods:
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trend_sales.append(p["months"].get(pp, {}).get("sales", 0))
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# 线性回归斜率（最小二乘）
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n = len(trend_sales)
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if n >= 2:
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xs = list(range(n))
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x_mean = sum(xs) / n
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y_mean = sum(trend_sales) / n
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numerator = sum((xs[i] - x_mean) * (trend_sales[i] - y_mean) for i in range(n))
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denominator = sum((xs[i] - x_mean) ** 2 for i in range(n))
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slope = numerator / denominator if denominator else 0
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# 斜率转为百分比（相对期间平均销售）
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avg = y_mean if y_mean != 0 else 1
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trend = slope / abs(avg) * 100
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else:
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trend = 0.0
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# 毛利率 = 加权平均（按销售额）
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weighted_margin = 0.0
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total_sales_for_margin = 0
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for pp in trend_periods:
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m = p["months"].get(pp)
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if m and m["sales"] > 0:
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weighted_margin += m["margin"] * m["sales"]
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total_sales_for_margin += m["sales"]
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if total_sales_for_margin > 0:
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weighted_margin = weighted_margin / total_sales_for_margin
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else:
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# 无销售用平均毛利率
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margins = [p["months"][pp]["margin"] for pp in p["months"] if p["months"][pp]["margin"] != 0]
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weighted_margin = sum(margins) / len(margins) if margins else 0
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quadrant = _calc_quadrant(trend, weighted_margin)
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result_products.append({
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"code": code,
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"name": p["name"],
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"total_sales": round(p["total_sales"], 2),
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"total_qty": p["total_qty"],
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"total_gross": round(p["total_gross"], 2),
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"trend_pct": round(trend, 1),
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"margin_pct": round(weighted_margin, 1),
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"quadrant": quadrant,
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})
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# 按销售额排序
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result_products.sort(key=lambda x: -x["total_sales"])
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# 四象限汇总
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quadrant_labels = {
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"star": {"label": "明星产品", "icon": "🌟", "advice": "高增长+有毛利，重点主推，加大投入"},
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"cash_cow": {"label": "现金牛", "icon": "🥇", "advice": "销量大但增长放缓，维持稳定产出"},
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"question_mark": {"label": "问题产品", "icon": "❓", "advice": "增长好但毛利低，优化成本或提价"},
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"dog": {"label": "瘦狗产品", "icon": "🐶", "advice": "低增长+低毛利，考虑清库存或停产"},
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}
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quadrants = []
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for q in ["star", "cash_cow", "question_mark", "dog"]:
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items = [p for p in result_products if p["quadrant"] == q]
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quadrants.append({
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"key": q,
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**quadrant_labels[q],
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"count": len(items),
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"products": items,
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})
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return {
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"entity_id": entity_id,
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"has_data": True,
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"periods": periods,
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"months_analyzed": months,
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"quadrants": quadrants,
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"products": result_products,
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}
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+2
-1
@@ -5,7 +5,7 @@ from fastapi.middleware.cors import CORSMiddleware
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from fastapi.responses import JSONResponse
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from dotenv import load_dotenv
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from app.database import init_db
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from app.api import auth, kpis, kpi_governance, templates, maps, dashboard, data, alerts, ai_analysis, alert_rules, users, thresholds, notifications, permissions, action_plans, alignment, org, objectives, versions, budget, cost, predict, reports, security, knowledge, bot_bridge, bot_bridge_v2, lead, tenant, customer_dashboard, deviation_push, budget_generate, knowledge_articles, kpi_causality, data_quality, bi_reports, entities, bsc_layers, okr, okr_templates, subjects, driver_budget, bot_kpis, ontology, bot_iron_law, analysis_results, expenses, cash, tax_compliance, verify, growth_quality
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from app.api import auth, kpis, kpi_governance, templates, maps, dashboard, data, alerts, ai_analysis, alert_rules, users, thresholds, notifications, permissions, action_plans, alignment, org, objectives, versions, budget, cost, predict, reports, security, knowledge, bot_bridge, bot_bridge_v2, lead, tenant, customer_dashboard, deviation_push, budget_generate, knowledge_articles, kpi_causality, data_quality, bi_reports, entities, bsc_layers, okr, okr_templates, subjects, driver_budget, bot_kpis, ontology, bot_iron_law, analysis_results, expenses, cash, tax_compliance, verify, growth_quality, products
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from app.utils.cache import clear_all as clear_cache, delete as delete_cache
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from scripts.erp_sync import run_sync as run_erp_sync
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from app.auth_middleware import require_auth
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@@ -53,6 +53,7 @@ app.include_router(budget.router)
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app.include_router(cost.router)
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app.include_router(predict.router)
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app.include_router(growth_quality.router)
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app.include_router(products.router)
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app.include_router(reports.router)
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app.include_router(security.router)
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app.include_router(knowledge.router)
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@@ -6,6 +6,7 @@ from app.models.budget_plan import BudgetPlan
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from app.models.cost_model import StandardCost, ActualCost, AbcActivity, AbcAllocation
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from app.models.knowledge import KnowledgeEvent, KnowledgeSummary
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from app.models.driver_budget import DriverFactorTemplate, DriverFactorBudget
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from app.models.product_sales import ProductSales
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class Entity(Base):
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"""商品销售数据模型 — 波士顿产品矩阵
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从《商品销售排行榜》Excel导入，支撑四象限分析
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"""
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from sqlalchemy import Column, Integer, String, Float, DateTime, func
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from app.database import Base
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class ProductSales(Base):
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"""商品销售月度数据"""
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__tablename__ = "product_sales"
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id = Column(Integer, primary_key=True, index=True)
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entity_id = Column(Integer, default=1, comment="企业ID: 1=酣客 2=博海")
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product_code = Column(String(50), nullable=False, comment="商品编码")
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product_name = Column(String(100), nullable=False, comment="商品名称")
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period_month = Column(String(10), nullable=False, comment="期间 YYYY-MM")
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sales_amount = Column(Float, default=0, comment="销售金额")
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cost_amount = Column(Float, default=0, comment="成本金额")
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gross_profit = Column(Float, default=0, comment="毛利")
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gross_margin_rate = Column(Float, default=0, comment="毛利率(%)")
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sales_qty = Column(Integer, default=0, comment="销售数量")
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unit = Column(String(30), default="", comment="单位")
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created_at = Column(DateTime, server_default=func.now())
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updated_at = Column(DateTime, server_default=func.now(), onupdate=func.now())
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#!/usr/bin/env python3
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"""导入酣客1-8月商品销售排行榜到product_sales表"""
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import openpyxl
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import glob
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import os
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import re
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import pymysql
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# DB连接
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DB_CONFIG = {
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'host': '127.0.0.1',
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'user': 'cma_user',
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'password': 'cma_pass_2026',
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'database': 'cma',
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'charset': 'utf8mb4',
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}
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# 8份排行榜文件（URL编码的路径）
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files = {
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'2026-01': '/root/.hermes/profiles/wecom-finance/cache/documents/doc_7c0e51888b10_%E5%95%86%E5%93%81%E9%94%80%E5%94%AE%E6%8E%92%E8%A1%8C%E6%A6%9C%EF%BC%88%E9%99%95%E8%A5%BF%E9%85%A3%E5%AE%A22026%E5%B9%B41%E6%9C%88%EF%BC%89.xlsx',
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'2026-02': '/root/.hermes/profiles/wecom-finance/cache/documents/doc_309cee3d3d78_%E5%95%86%E5%93%81%E9%94%80%E5%94%AE%E6%8E%92%E8%A1%8C%E6%A6%9C%EF%BC%88%E9%99%95%E8%A5%BF%E9%85%A3%E5%AE%A22026%E5%B9%B42%E6%9C%88%EF%BC%89.xlsx',
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'2026-03': '/root/.hermes/profiles/wecom-finance/cache/documents/doc_fdc1228e7173_%E5%95%86%E5%93%81%E9%94%80%E5%94%AE%E6%8E%92%E8%A1%8C%E6%A6%9C%EF%BC%88%E9%99%95%E8%A5%BF%E9%85%A3%E5%AE%A22026%E5%B9%B43%E6%9C%88%EF%BC%89.xlsx',
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'2026-04': '/root/.hermes/profiles/wecom-finance/cache/documents/doc_da10cb16765c_%E5%95%86%E5%93%81%E9%94%80%E5%94%AE%E6%8E%92%E8%A1%8C%E6%A6%9C%EF%BC%88%E9%99%95%E8%A5%BF%E9%85%A3%E5%AE%A22026%E5%B9%B44%E6%9C%88%EF%BC%89.xlsx',
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'2026-05': '/root/.hermes/profiles/wecom-finance/cache/documents/doc_bda951a0f6e3_%E5%95%86%E5%93%81%E9%94%80%E5%94%AE%E6%8E%92%E8%A1%8C%E6%A6%9C%EF%BC%88%E9%99%95%E8%A5%BF%E9%85%A3%E5%AE%A22026%E5%B9%B45%E6%9C%88%EF%BC%89.xlsx',
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'2026-06': '/root/.hermes/profiles/wecom-finance/cache/documents/doc_6fcb80d2a02d_%E5%95%86%E5%93%81%E9%94%80%E5%94%AE%E6%8E%92%E8%A1%8C%E6%A6%9C%EF%BC%88%E9%99%95%E8%A5%BF%E9%85%A3%E5%AE%A22026%E5%B9%B46%E6%9C%88%EF%BC%89.xlsx',
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'2026-07': '/root/.hermes/profiles/wecom-finance/cache/documents/doc_a449da246d17_%E5%95%86%E5%93%81%E9%94%80%E5%94%AE%E6%8E%92%E8%A1%8C%E6%A6%9C%EF%BC%88%E9%99%95%E8%A5%BF%E9%85%A3%E5%AE%A22026%E5%B9%B47%E6%9C%88%EF%BC%89.xlsx',
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'2026-08': '/root/.hermes/profiles/wecom-finance/cache/documents/doc_ac2b807b1517_%E5%95%86%E5%93%81%E9%94%80%E5%94%AE%E6%8E%92%E8%A1%8C%E6%A6%9C%EF%BC%88%E9%99%95%E8%A5%BF%E9%85%A3%E5%AE%A22026%E5%B9%B48%E6%9C%88%EF%BC%89.xlsx',
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}
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def import_file(conn, cursor, period, path):
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"""导入单个月份Excel"""
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if not os.path.exists(path):
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print(f" ⚠️ 文件不存在: {period}")
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return 0
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wb = openpyxl.load_workbook(path, read_only=True, data_only=True)
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ws = wb[wb.sheetnames[0]]
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count = 0
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for r in range(2, ws.max_row + 1):
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code = ws.cell(r, 2).value # 商品编码
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name = ws.cell(r, 3).value # 商品名称
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sales = ws.cell(r, 4).value # 销售金额
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cost = ws.cell(r, 6).value # 成本金额
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gross = ws.cell(r, 7).value # 毛利
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margin = ws.cell(r, 10).value # 毛利率(%)
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qty = ws.cell(r, 13).value # 销售数量
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unit = ws.cell(r, 14).value # 单位
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# 跳过合计行（编码为空）和空行
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if code is None or str(code).strip() == '':
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continue
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if name is None or str(name).strip() == '':
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continue
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# 跳过汇总行（如"上期库存"）
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if '上期' in str(name) or '合计' in str(name) or '总计' in str(name):
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continue
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code = str(code).strip()
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name = str(name).strip()
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sales = float(sales or 0)
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cost = float(cost or 0)
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gross = float(gross or 0)
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margin = float(margin or 0)
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qty = int(qty or 0)
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unit = str(unit or '').strip()
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cursor.execute("""
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INSERT INTO product_sales (entity_id, product_code, product_name, period_month,
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sales_amount, cost_amount, gross_profit, gross_margin_rate, sales_qty, unit)
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VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s)
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ON DUPLICATE KEY UPDATE
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product_name=VALUES(product_name),
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sales_amount=VALUES(sales_amount),
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cost_amount=VALUES(cost_amount),
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gross_profit=VALUES(gross_profit),
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gross_margin_rate=VALUES(gross_margin_rate),
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sales_qty=VALUES(sales_qty),
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unit=VALUES(unit)
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""", (1, code, name, period, sales, cost, gross, margin, qty, unit))
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count += 1
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wb.close()
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return count
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def main():
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conn = pymysql.connect(**DB_CONFIG)
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cursor = conn.cursor()
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total = 0
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for period, path in files.items():
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n = import_file(conn, cursor, period, path)
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print(f" {period}: {n}条")
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total += n
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conn.commit()
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print(f"\n✅ 共导入 {total} 条")
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# 验证
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cursor.execute("SELECT period_month, COUNT(*), ROUND(SUM(sales_amount)) FROM product_sales GROUP BY period_month ORDER BY period_month")
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for row in cursor.fetchall():
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print(f" {row[0]}: {row[1]}商品, 销售合计{row[2]:,.0f}")
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cursor.close()
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conn.close()
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if __name__ == '__main__':
|
||||
main()
|
||||
Reference in New Issue
Block a user