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

backend/app/api/products.py

@@ -0,0 +1,158 @@
+"""波士顿产品矩阵 API — 四象限分析"""
+from fastapi import APIRouter, Depends, Query
+from sqlalchemy.orm import Session
+from app.database import get_db
+from app.models import ProductSales
+from collections import defaultdict
+
+router = APIRouter(prefix="/api/cma/products", tags=["产品矩阵"])
+
+
+def _calc_quadrant(trend: float, margin: float) -> str:
+    """四象限分类：
+    横轴=近3月销售趋势（正=增长），纵轴=毛利率
+    明星(Star) = 高趋势+高毛利
+    现金牛(CashCow) = 低趋势+高毛利
+    问题(QuestionMark) = 高趋势+低毛利
+    瘦狗(Dog) = 低趋势+低毛利
+    """
+    trend_high = trend >= 0
+    margin_high = margin >= 0
+    if trend_high and margin_high:
+        return "star"
+    if not trend_high and margin_high:
+        return "cash_cow"
+    if trend_high and not margin_high:
+        return "question_mark"
+    return "dog"
+
+
+@router.get("/matrix")
+def get_product_matrix(
+    entity_id: int = Query(1, description="1=酣客 2=博海"),
+    months: int = Query(3, ge=1, le=6, description="趋势计算月数"),
+    db: Session = Depends(get_db),
+):
+    """产品矩阵：横轴=销售趋势，纵轴=毛利率，气泡=销售额"""
+    # 取最近 months+1 个月（多取1个月用于计算趋势）
+    periods = db.query(ProductSales.period_month).filter(
+        ProductSales.entity_id == entity_id
+    ).distinct().order_by(ProductSales.period_month.desc()).limit(months + 1).all()
+    periods = sorted([p[0] for p in periods])
+
+    if len(periods) < 2:
+        return {
+            "entity_id": entity_id,
+            "has_data": False,
+            "message": "数据不足，至少需要2个月数据",
+            "quadrants": [],
+            "products": [],
+        }
+
+    trend_periods = periods[-months:]  # 最近 months 个月
+    prev_periods = periods[:-months] if len(periods) > months else periods[:1]
+
+    # 加载数据
+    rows = db.query(ProductSales).filter(
+        ProductSales.entity_id == entity_id,
+        ProductSales.period_month.in_(periods),
+    ).all()
+
+    # 按商品聚合
+    products = defaultdict(lambda: {
+        "code": "", "name": "", "months": {},
+        "total_sales": 0, "total_qty": 0, "total_gross": 0,
+    })
+    for row in rows:
+        p = products[row.product_code]
+        p["code"] = row.product_code
+        p["name"] = row.product_name
+        p["months"][row.period_month] = {
+            "sales": float(row.sales_amount or 0),
+            "margin": float(row.gross_margin_rate or 0),
+            "gross": float(row.gross_profit or 0),
+            "qty": int(row.sales_qty or 0),
+        }
+        p["total_sales"] += float(row.sales_amount or 0)
+        p["total_qty"] += int(row.sales_qty or 0)
+        p["total_gross"] += float(row.gross_profit or 0)
+
+    # 计算每个商品的趋势和毛利率
+    result_products = []
+    for code, p in products.items():
+        # 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）
+        # 用最近3个月的销售序列做简单线性趋势
+        trend_sales = []
+        for pp in periods:
+            trend_sales.append(p["months"].get(pp, {}).get("sales", 0))
+        # 线性回归斜率（最小二乘）
+        n = len(trend_sales)
+        if n >= 2:
+            xs = list(range(n))
+            x_mean = sum(xs) / n
+            y_mean = sum(trend_sales) / n
+            numerator = sum((xs[i] - x_mean) * (trend_sales[i] - y_mean) for i in range(n))
+            denominator = sum((xs[i] - x_mean) ** 2 for i in range(n))
+            slope = numerator / denominator if denominator else 0
+            # 斜率转为百分比（相对期间平均销售）
+            avg = y_mean if y_mean != 0 else 1
+            trend = slope / abs(avg) * 100
+        else:
+            trend = 0.0
+
+        # 毛利率 = 加权平均（按销售额）
+        weighted_margin = 0.0
+        total_sales_for_margin = 0
+        for pp in trend_periods:
+            m = p["months"].get(pp)
+            if m and m["sales"] > 0:
+                weighted_margin += m["margin"] * m["sales"]
+                total_sales_for_margin += m["sales"]
+        if total_sales_for_margin > 0:
+            weighted_margin = weighted_margin / total_sales_for_margin
+        else:
+            # 无销售用平均毛利率
+            margins = [p["months"][pp]["margin"] for pp in p["months"] if p["months"][pp]["margin"] != 0]
+            weighted_margin = sum(margins) / len(margins) if margins else 0
+
+        quadrant = _calc_quadrant(trend, weighted_margin)
+
+        result_products.append({
+            "code": code,
+            "name": p["name"],
+            "total_sales": round(p["total_sales"], 2),
+            "total_qty": p["total_qty"],
+            "total_gross": round(p["total_gross"], 2),
+            "trend_pct": round(trend, 1),
+            "margin_pct": round(weighted_margin, 1),
+            "quadrant": quadrant,
+        })
+
+    # 按销售额排序
+    result_products.sort(key=lambda x: -x["total_sales"])
+
+    # 四象限汇总
+    quadrant_labels = {
+        "star": {"label": "明星产品", "icon": "🌟", "advice": "高增长+有毛利，重点主推，加大投入"},
+        "cash_cow": {"label": "现金牛", "icon": "🥇", "advice": "销量大但增长放缓，维持稳定产出"},
+        "question_mark": {"label": "问题产品", "icon": "❓", "advice": "增长好但毛利低，优化成本或提价"},
+        "dog": {"label": "瘦狗产品", "icon": "🐶", "advice": "低增长+低毛利，考虑清库存或停产"},
+    }
+    quadrants = []
+    for q in ["star", "cash_cow", "question_mark", "dog"]:
+        items = [p for p in result_products if p["quadrant"] == q]
+        quadrants.append({
+            "key": q,
+            **quadrant_labels[q],
+            "count": len(items),
+            "products": items,
+        })
+
+    return {
+        "entity_id": entity_id,
+        "has_data": True,
+        "periods": periods,
+        "months_analyzed": months,
+        "quadrants": quadrants,
+        "products": result_products,
+    }

backend/app/main.py

@@ -5,7 +5,7 @@ from fastapi.middleware.cors import CORSMiddleware
 from fastapi.responses import JSONResponse
 from dotenv import load_dotenv
 from app.database import init_db
-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
+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
 from app.utils.cache import clear_all as clear_cache, delete as delete_cache
 from scripts.erp_sync import run_sync as run_erp_sync
 from app.auth_middleware import require_auth
@@ -53,6 +53,7 @@ app.include_router(budget.router)
 app.include_router(cost.router)
 app.include_router(predict.router)
 app.include_router(growth_quality.router)
+app.include_router(products.router)
 app.include_router(reports.router)
 app.include_router(security.router)
 app.include_router(knowledge.router)

backend/app/models/__init__.py

@@ -6,6 +6,7 @@ from app.models.budget_plan import BudgetPlan
 from app.models.cost_model import StandardCost, ActualCost, AbcActivity, AbcAllocation
 from app.models.knowledge import KnowledgeEvent, KnowledgeSummary
 from app.models.driver_budget import DriverFactorTemplate, DriverFactorBudget
+from app.models.product_sales import ProductSales
 
 
 class Entity(Base):

backend/app/models/product_sales.py

@@ -0,0 +1,24 @@
+"""商品销售数据模型 — 波士顿产品矩阵
+从《商品销售排行榜》Excel导入，支撑四象限分析
+"""
+from sqlalchemy import Column, Integer, String, Float, DateTime, func
+from app.database import Base
+
+
+class ProductSales(Base):
+    """商品销售月度数据"""
+    __tablename__ = "product_sales"
+
+    id = Column(Integer, primary_key=True, index=True)
+    entity_id = Column(Integer, default=1, comment="企业ID: 1=酣客 2=博海")
+    product_code = Column(String(50), nullable=False, comment="商品编码")
+    product_name = Column(String(100), nullable=False, comment="商品名称")
+    period_month = Column(String(10), nullable=False, comment="期间 YYYY-MM")
+    sales_amount = Column(Float, default=0, comment="销售金额")
+    cost_amount = Column(Float, default=0, comment="成本金额")
+    gross_profit = Column(Float, default=0, comment="毛利")
+    gross_margin_rate = Column(Float, default=0, comment="毛利率(%)")
+    sales_qty = Column(Integer, default=0, comment="销售数量")
+    unit = Column(String(30), default="", comment="单位")
+    created_at = Column(DateTime, server_default=func.now())
+    updated_at = Column(DateTime, server_default=func.now(), onupdate=func.now())