kx_guo
5c7163d79a
统一对话页与供应商画像的采购订单、异常数、基础分、频次系数、供应商得分和层级计算；补充供应商维度计算结果字段，保留未归类品类标记并优化供应商列表聚合查询；增加响应压缩并完善供应商统计测试。
299 lines
12 KiB
Python
299 lines
12 KiB
Python
"""Live supplier profile aggregation from the approved anomaly case data."""
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from __future__ import annotations
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import re
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from collections import Counter, defaultdict
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from datetime import UTC, date, datetime, timedelta
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from sqlalchemy import func, select
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from sqlalchemy.ext.asyncio import AsyncSession
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from app.core.exceptions import AppException
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from app.models.entities import AnomalyCase
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from app.schemas.suppliers import (
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SupplierCategoryBreakdown,
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SupplierListData,
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SupplierMetric,
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SupplierOption,
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SupplierProfileData,
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SupplierRecentRecord,
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)
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from app.services.database_risk_scoring import DatabaseRiskScoringService, normalize_category
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from app.services.purchase_orders import supplier_order_counts
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class SupplierProfileService:
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history_days = 365 * 3
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recent_months = 6
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max_categories = 5
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max_recent_records = 10
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def __init__(self, session: AsyncSession, *, as_of: date | None = None) -> None:
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self.session = session
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self.as_of = as_of or datetime.now(UTC).date()
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async def list(self, *, keyword: str | None = None, limit: int = 100) -> SupplierListData:
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grouped = await self._load_supplier_summary(keyword=keyword)
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items = [
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SupplierOption(
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name=name,
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anomaly_count=summary["total"],
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tier=self.tier_for(
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summary["total"],
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summary["a"],
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summary["b"],
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),
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)
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for name, summary in sorted(
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grouped.items(), key=lambda item: (-item[1]["total"], item[0])
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)[:limit]
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]
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return SupplierListData(
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items=items,
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default_supplier=items[0].name if items else None,
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)
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async def detail(self, supplier_name: str) -> SupplierProfileData:
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all_rows = await self._load_rows()
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rows = [row for row in all_rows if self.matches(row.get("supplier"), supplier_name)]
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if not rows:
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raise AppException("供应商不存在或暂无近3年异常记录", code=404, status_code=404)
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anomaly_total = len(rows)
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order_count = await supplier_order_counts(
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self.session,
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supplier_name,
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self.as_of - timedelta(days=self.history_days),
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self.as_of,
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)
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a_count = sum(self.defect_code(row.get("defect_level")) == "A" for row in rows)
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b_count = sum(self.defect_code(row.get("defect_level")) == "B" for row in rows)
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tier = self.tier_for(anomaly_total, a_count, b_count)
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categories = self._categories(rows)
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scoring = DatabaseRiskScoringService(self.session, as_of=self.as_of)
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score = await scoring.score(
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supplier=supplier_name,
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rows=all_rows,
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cooperation_count=order_count,
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)
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supplier_dimension = next(
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(item for item in score.dimensions if item.name == "供应商表现"),
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None,
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)
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cooperation_display = order_count if order_count > 0 else None
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display_tier = {
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"S级": "S级 · 战略",
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"A级": "A级 · 优质",
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"B级": "B级 · 关注",
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"C级": "C级 · 高风险",
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}.get(tier, tier)
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return SupplierProfileData(
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supplier_name=supplier_name,
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tier=display_tier,
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tier_condition=self.tier_condition(tier),
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metric=SupplierMetric(
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cooperation_total=cooperation_display,
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anomaly_total=anomaly_total,
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anomaly_rate=None,
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frequency_alpha=(
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supplier_dimension.frequency_alpha
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if supplier_dimension and supplier_dimension.frequency_alpha is not None
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else self.alpha(
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cooperation_display or anomaly_total,
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coefficient=scoring.frequency_coefficient,
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cap=scoring.frequency_cap,
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)
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),
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supplier_base_score=(
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supplier_dimension.base_score if supplier_dimension else None
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),
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supplier_score=supplier_dimension.score if supplier_dimension else None,
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risk_score=score.score,
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main_category_count=len(categories),
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),
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category_breakdown=self._display_categories(categories),
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monthly_trend=self._monthly_trend(rows),
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recent_records=self._recent_records(rows),
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factory_recommendations=[],
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recommendation_note="合作次数按近3年采购订单去重单据编号统计；异常数和供应商层级仍按异常案例库统计。",
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)
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async def _load_supplier_summary(
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self, *, keyword: str | None = None
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) -> dict[str, dict[str, int]]:
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"""Load only supplier aggregates for the selector.
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The selector used to fetch every anomaly row and group it in Python.
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This grouped query returns a handful of rows even when the anomaly
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table grows, which materially reduces first-navigation latency over
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the remote PostgreSQL connection.
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"""
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start = self.as_of - timedelta(days=self.history_days)
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statement = (
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select(AnomalyCase.supplier, AnomalyCase.defect_level, func.count())
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.where(
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AnomalyCase.supplier.is_not(None),
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AnomalyCase.date.is_not(None),
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AnomalyCase.date >= start,
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AnomalyCase.date <= self.as_of,
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)
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.group_by(AnomalyCase.supplier, AnomalyCase.defect_level)
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)
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if keyword and keyword.strip():
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statement = statement.where(AnomalyCase.supplier.ilike(f"%{keyword.strip()}%"))
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grouped: dict[str, dict[str, int]] = defaultdict(
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lambda: {"total": 0, "a": 0, "b": 0}
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)
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for supplier, defect_level, count in (await self.session.execute(statement)).all():
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name = (supplier or "").strip()
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if not name:
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continue
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value = grouped[name]
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amount = int(count or 0)
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value["total"] += amount
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code = self.defect_code(defect_level)
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if code == "A":
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value["a"] += amount
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elif code == "B":
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value["b"] += amount
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return grouped
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async def _load_rows(self, *, supplier_name: str | None = None) -> list[dict]:
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start = self.as_of - timedelta(days=self.history_days)
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statement = select(
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AnomalyCase.id,
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AnomalyCase.date,
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AnomalyCase.product_name,
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AnomalyCase.product_category,
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AnomalyCase.material_type,
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AnomalyCase.supplier,
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AnomalyCase.factory,
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AnomalyCase.anomaly_type,
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AnomalyCase.defect_level,
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).where(
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AnomalyCase.date.is_not(None),
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AnomalyCase.date >= start,
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AnomalyCase.date <= self.as_of,
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)
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if supplier_name:
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statement = statement.where(AnomalyCase.supplier.ilike(f"%{supplier_name}%"))
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return [dict(row) for row in (await self.session.execute(statement)).mappings().all()]
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@staticmethod
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def matches(value: str | None, target: str) -> bool:
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return bool(value and target.strip().lower() in value.lower())
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@classmethod
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def _group_suppliers(cls, rows: list[dict], keyword: str | None) -> dict[str, list[dict]]:
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grouped: dict[str, list[dict]] = defaultdict(list)
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for row in rows:
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name = (row.get("supplier") or "").strip()
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if name and (not keyword or cls.matches(name, keyword)):
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grouped[name].append(row)
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return grouped
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@staticmethod
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def defect_code(value: str | None) -> str | None:
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match = re.search(r"[ABCD]", (value or "").upper())
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return match.group(0) if match else None
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@staticmethod
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def alpha(anomaly_count: int, *, coefficient: float = 0.3, cap: float = 1.5) -> float:
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return round(min(cap, 1 + anomaly_count / 100 * coefficient), 2)
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@classmethod
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def tier_for(cls, anomaly_count: int, a_count: int, b_count: int) -> str:
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if anomaly_count == 0:
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return "新供应商"
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ab_ratio = (a_count + b_count) / anomaly_count
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if anomaly_count > 120 or a_count > 0 or ab_ratio > 0.5:
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return "C级"
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if anomaly_count > 60 or ab_ratio >= 0.3:
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return "B级"
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if anomaly_count >= 20:
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return "A级"
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return "S级"
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@staticmethod
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def tier_condition(tier: str) -> str:
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return {
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"S级": "异常记录 < 20 次，且无 A 级缺陷",
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"A级": "异常记录 20~60 次，且 A/B 级缺陷占比 < 30%",
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"B级": "异常记录 60~120 次，或 A/B 级缺陷占比 30%~50%",
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"C级": "异常记录 > 120 次，或存在 A 级缺陷，或 A/B 级缺陷占比 > 50%",
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"新供应商": "近3年异常记录为 0",
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}[tier]
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@classmethod
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def _categories(cls, rows: list[dict]) -> list[SupplierCategoryBreakdown]:
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grouped: dict[str, list[dict]] = defaultdict(list)
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for row in rows:
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category = normalize_category(row.get("product_category")) or normalize_category(row.get("product_name"))
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grouped[category or "待补标品类"].append(row)
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result = []
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for category, items in grouped.items():
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is_unclassified = category == "待补标品类"
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materials = Counter(row.get("material_type") for row in items if row.get("material_type"))
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problems = Counter(row.get("anomaly_type") for row in items if row.get("anomaly_type"))
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result.append(
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SupplierCategoryBreakdown(
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category=category,
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is_unclassified=is_unclassified,
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anomaly_count=len(items),
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anomaly_rate=None,
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top_material=materials.most_common(1)[0][0] if materials else None,
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top_problem=problems.most_common(1)[0][0] if problems else None,
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)
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)
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return sorted(result, key=lambda item: (item.is_unclassified, -item.anomaly_count, item.category))
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@classmethod
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def _display_categories(
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cls, categories: list[SupplierCategoryBreakdown]
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) -> list[SupplierCategoryBreakdown]:
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"""Keep up to five real categories and always retain the unclassified row."""
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real = [item for item in categories if not item.is_unclassified][: cls.max_categories]
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unclassified = next((item for item in categories if item.is_unclassified), None)
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return real + ([unclassified] if unclassified else [])
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def _monthly_trend(self, rows: list[dict]) -> list[dict[str, int | str]]:
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start = self._month_offset(self.as_of.replace(day=1), -5)
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counts = Counter(
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row["date"].strftime("%Y-%m")
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for row in rows
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if row.get("date") and row["date"] >= start
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)
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output = []
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cursor = start
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for _ in range(self.recent_months):
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key = cursor.strftime("%Y-%m")
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output.append({"month": key, "count": counts.get(key, 0)})
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cursor = date(cursor.year + (cursor.month == 12), 1 if cursor.month == 12 else cursor.month + 1, 1)
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return output
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@staticmethod
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def _month_offset(value: date, months: int) -> date:
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index = value.year * 12 + value.month - 1 + months
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return date(index // 12, index % 12 + 1, 1)
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def _recent_records(self, rows: list[dict]) -> list[SupplierRecentRecord]:
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start = self._month_offset(self.as_of.replace(day=1), -5)
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recent = sorted(
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(row for row in rows if row.get("date") and row["date"] >= start),
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key=lambda row: row["date"],
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reverse=True,
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)
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return [
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SupplierRecentRecord(
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case_id=int(row["id"]),
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month=row["date"].strftime("%Y-%m") if row.get("date") else None,
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product=row.get("product_name"),
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anomaly=row.get("anomaly_type"),
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defect_level=self.defect_code(row.get("defect_level")),
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)
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for row in recent[: self.max_recent_records]
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]
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