kx_guo
1d58bb8913
- 统一供应商异常统计为近3年数据口径 - 以近3年异常记录数作为合作频次代理值并明确提示 - 实现 A/B 缺陷占比与 S/A/B/C 层级判定 - 按供应商层级输出正常合作、加强巡检、驻厂及备选供应商等管理建议 - 完善风险卡片和供应商卡片的频次、层级、建议展示 - 补充供应商画像接口、测试和历史会话卡片兼容处理
386 lines
15 KiB
Python
386 lines
15 KiB
Python
"""Database-backed dashboard aggregation."""
|
||
|
||
from collections import Counter, defaultdict
|
||
from datetime import date, timedelta
|
||
from typing import Literal
|
||
|
||
from sqlalchemy import func, select
|
||
|
||
from app.models.entities import AnomalyCase, StandardRule
|
||
from app.schemas.dashboard import (
|
||
DashboardCategory,
|
||
DashboardData,
|
||
DashboardDefect,
|
||
DashboardMatrixRow,
|
||
DashboardMetric,
|
||
DashboardStage,
|
||
DashboardSupplierRank,
|
||
DashboardTrendPoint,
|
||
)
|
||
|
||
|
||
class DashboardService:
|
||
_stages = (
|
||
("需求发现", "第一阶段"),
|
||
("产品立项", "第二阶段"),
|
||
("产品开发", "第三阶段"),
|
||
("量产上市", "第四阶段"),
|
||
("产品生命管理", "第五阶段"),
|
||
)
|
||
_category_order = (
|
||
"面霜/护肤类",
|
||
"洗发水/洗护类",
|
||
"沐浴露类",
|
||
"洁面/卸妆类",
|
||
"身体护理类",
|
||
"头发护理类",
|
||
"脱毛类",
|
||
"牙膏/口腔护理类",
|
||
"彩妆/唇部类",
|
||
"香氛/止汗类",
|
||
"清洁剂类",
|
||
"包材/物料专项",
|
||
"待补标品类",
|
||
)
|
||
|
||
def __init__(self, session) -> None: # noqa: ANN001
|
||
self.session = session
|
||
|
||
async def overview(self, year: Literal["all", "2024", "2025", "2026"]) -> DashboardData:
|
||
rows = list(
|
||
(
|
||
await self.session.execute(
|
||
select(
|
||
AnomalyCase.date,
|
||
AnomalyCase.product_name,
|
||
AnomalyCase.stage_new,
|
||
AnomalyCase.anomaly_type,
|
||
AnomalyCase.material_type,
|
||
AnomalyCase.supplier,
|
||
AnomalyCase.factory,
|
||
AnomalyCase.defect_level,
|
||
)
|
||
)
|
||
).mappings()
|
||
)
|
||
filtered = [row for row in rows if self._in_year(row["date"], year)]
|
||
previous = self._previous_period(rows, year, filtered)
|
||
total = len(filtered)
|
||
previous_total = len(previous)
|
||
standards_count = int(
|
||
await self.session.scalar(
|
||
select(func.count()).select_from(StandardRule).where(StandardRule.status == "启用")
|
||
)
|
||
or 0
|
||
)
|
||
defects = self._defects(filtered, total)
|
||
actors = self._actors(filtered)
|
||
high_risk_count = sum(1 for item in actors.values() if item["tier"] in {"B", "C"})
|
||
ab_count = sum(item.count for item in defects if item.level in {"A", "B"})
|
||
metrics = [
|
||
DashboardMetric(
|
||
value=total,
|
||
label="异常案例总数",
|
||
change_percent=self._change(total, previous_total),
|
||
),
|
||
DashboardMetric(
|
||
value=ab_count,
|
||
label="A+B级缺陷",
|
||
change_percent=self._change(
|
||
ab_count,
|
||
sum(1 for row in previous if row["defect_level"] in {"A", "B"}),
|
||
),
|
||
),
|
||
DashboardMetric(
|
||
value=high_risk_count,
|
||
label="高风险供应商/工厂",
|
||
change_percent=self._change(
|
||
high_risk_count,
|
||
sum(1 for item in self._actors(previous).values() if item["tier"] in {"B", "C"}),
|
||
),
|
||
),
|
||
DashboardMetric(value=None, label="预测准确率", change_percent=None),
|
||
DashboardMetric(value=standards_count, label="标准库覆盖", change_percent=None),
|
||
]
|
||
stages = self._stages_data(filtered, total)
|
||
categories = self._categories_data(filtered, total)
|
||
matrix_categories = [item.name for item in categories]
|
||
matrix = self._matrix(filtered, matrix_categories)
|
||
ranking = self._ranking(actors)
|
||
trend = self._trend(filtered, year)
|
||
insight = self._insight(trend, stages, categories)
|
||
warnings = self._warnings(trend, categories, ranking)
|
||
return DashboardData(
|
||
year=year,
|
||
total_cases=total,
|
||
metrics=metrics,
|
||
stages=stages,
|
||
categories=categories,
|
||
matrix_categories=matrix_categories,
|
||
matrix=matrix,
|
||
defects=defects,
|
||
supplier_ranking=ranking,
|
||
trend=trend,
|
||
insight=insight,
|
||
warnings=warnings,
|
||
)
|
||
|
||
@staticmethod
|
||
def _in_year(value: date | None, year: str) -> bool:
|
||
return year == "all" or (value is not None and value.year == int(year))
|
||
|
||
@staticmethod
|
||
def _change(current: int, previous: int) -> float | None:
|
||
if not previous:
|
||
return None
|
||
return round((current - previous) / previous * 100, 1)
|
||
|
||
@staticmethod
|
||
def _previous_period(rows, year: str, filtered) -> list: # noqa: ANN001
|
||
if year != "all":
|
||
return [
|
||
row
|
||
for row in rows
|
||
if row["date"] is not None and row["date"].year == int(year) - 1
|
||
]
|
||
dates = [row["date"] for row in filtered if row["date"] is not None]
|
||
if not dates:
|
||
return []
|
||
current_end = max(dates)
|
||
current_start = current_end - timedelta(days=365)
|
||
previous_start = current_start - timedelta(days=365)
|
||
return [
|
||
row
|
||
for row in rows
|
||
if row["date"] is not None
|
||
and previous_start <= row["date"] < current_start
|
||
]
|
||
|
||
@classmethod
|
||
def _category(cls, product_name: str | None, material_type: str | None = None) -> str:
|
||
value = (product_name or "").lower()
|
||
if "脱毛" in value:
|
||
return "脱毛类"
|
||
if any(word in value for word in ("牙膏", "牙粉", "漱口", "口腔")):
|
||
return "牙膏/口腔护理类"
|
||
if any(word in value for word in ("洁面", "洁颜", "洗面", "卸妆")):
|
||
return "洁面/卸妆类"
|
||
if any(word in value for word in ("护发", "发膜", "护发素", "染发")):
|
||
return "头发护理类"
|
||
if any(word in value for word in ("唇", "口红", "唇膏", "气垫", "粉底", "彩妆", "腮红")):
|
||
return "彩妆/唇部类"
|
||
if any(word in value for word in ("香氛", "香水", "香体", "走珠", "狐臭", "止汗")):
|
||
return "香氛/止汗类"
|
||
if any(word in value for word in ("身体乳", "护手霜", "磨砂", "搓泥", "防晒")):
|
||
return "身体护理类"
|
||
if any(word in value for word in ("面霜", "乳液", "精华", "面膜", "素颜霜", "爽肤水", "化妆水", "眼霜")):
|
||
return "面霜/护肤类"
|
||
if any(word in value for word in ("洗发", "洗发水", "洗发露")):
|
||
return "洗发水/洗护类"
|
||
if "沐浴" in value:
|
||
return "沐浴露类"
|
||
if "牙膏" in value:
|
||
return "牙膏类"
|
||
if any(word in value for word in ("清洁剂", "消毒", "洁厕", "洗衣")):
|
||
return "清洁剂类"
|
||
if material_type and material_type.strip() not in {"--", "-"}:
|
||
return "包材/物料专项"
|
||
return "待补标品类"
|
||
|
||
@staticmethod
|
||
def _risk_level_by_ratio(ratio: float) -> str:
|
||
"""Use the four visual bands defined by the dashboard prototype."""
|
||
|
||
if ratio >= 0.8:
|
||
return "高风险"
|
||
if ratio >= 0.6:
|
||
return "中高"
|
||
if ratio >= 0.35:
|
||
return "中等"
|
||
return "较低"
|
||
|
||
@classmethod
|
||
def _stages_data(cls, rows, total: int) -> list[DashboardStage]: # noqa: ANN001
|
||
counts = Counter()
|
||
for row in rows:
|
||
raw = row["stage_new"] or ""
|
||
for label, marker in cls._stages:
|
||
if label in raw or marker in raw:
|
||
counts[label] += 1
|
||
break
|
||
maximum = max(counts.values(), default=0)
|
||
return [
|
||
DashboardStage(
|
||
name=label,
|
||
count=counts[label],
|
||
share=round(counts[label] / total * 100, 1) if total else 0,
|
||
risk_level=cls._risk_level_by_ratio(counts[label] / maximum) if maximum else "较低",
|
||
)
|
||
for label, _ in cls._stages
|
||
]
|
||
|
||
@classmethod
|
||
def _categories_data(cls, rows, total: int) -> list[DashboardCategory]: # noqa: ANN001
|
||
grouped: dict[str, list] = defaultdict(list)
|
||
for row in rows:
|
||
grouped[cls._category(row["product_name"], row["material_type"])].append(row)
|
||
output = []
|
||
for name in cls._category_order:
|
||
group = grouped.get(name, [])
|
||
if not group:
|
||
continue
|
||
materials = Counter(row["material_type"] for row in group if row["material_type"])
|
||
output.append(
|
||
DashboardCategory(
|
||
name=name,
|
||
count=len(group),
|
||
share=round(len(group) / total * 100, 1) if total else 0,
|
||
risk_level="较低",
|
||
top_material=materials.most_common(1)[0][0] if materials else None,
|
||
)
|
||
)
|
||
output = [item for item in output if item.count]
|
||
maximum = max((item.count for item in output), default=0)
|
||
return [
|
||
item.model_copy(
|
||
update={
|
||
"risk_level": cls._risk_level_by_ratio(item.count / maximum)
|
||
if maximum
|
||
else "较低"
|
||
}
|
||
)
|
||
for item in sorted(output, key=lambda item: (-item.count, cls._category_order.index(item.name)))
|
||
]
|
||
|
||
@staticmethod
|
||
def _tier(count: int, has_a: bool = False) -> str:
|
||
if count > 120 or has_a:
|
||
return "C"
|
||
if count > 60:
|
||
return "B"
|
||
if count >= 20:
|
||
return "A"
|
||
return "S"
|
||
|
||
@classmethod
|
||
def _actors(cls, rows) -> dict[tuple[str, str], dict]: # noqa: ANN001
|
||
grouped: dict[tuple[str, str], list] = defaultdict(list)
|
||
for row in rows:
|
||
for field, party_type in (("supplier", "供应商"), ("factory", "加工厂")):
|
||
name = (row[field] or "").strip()
|
||
if name and name != "--":
|
||
grouped[(name, party_type)].append(row)
|
||
return {
|
||
key: {
|
||
"count": len(items),
|
||
"has_a": any(row["defect_level"] == "A" for row in items),
|
||
"items": items,
|
||
"tier": cls._tier(len(items), any(row["defect_level"] == "A" for row in items)),
|
||
}
|
||
for key, items in grouped.items()
|
||
}
|
||
|
||
@classmethod
|
||
def _matrix(cls, rows, categories: list[str]) -> list[DashboardMatrixRow]: # noqa: ANN001
|
||
actors = cls._actors(rows)
|
||
result = []
|
||
for (name, party_type), item in sorted(actors.items(), key=lambda entry: -entry[1]["count"]):
|
||
anomaly_count = item["count"]
|
||
# No order or production denominator exists in anomaly_cases.
|
||
cooperation_total = None
|
||
values = {category: 0 for category in categories}
|
||
for row in item["items"]:
|
||
category = cls._category(row["product_name"], row["material_type"])
|
||
if category in values:
|
||
values[category] += 1
|
||
result.append(
|
||
DashboardMatrixRow(
|
||
name=name,
|
||
party_type=party_type,
|
||
categories=values,
|
||
cooperation_total=cooperation_total,
|
||
anomaly_count=anomaly_count,
|
||
anomaly_rate=None,
|
||
tier=item["tier"],
|
||
)
|
||
)
|
||
return result
|
||
|
||
@classmethod
|
||
def _ranking(cls, actors: dict[tuple[str, str], dict]) -> list[DashboardSupplierRank]:
|
||
ranking = []
|
||
for (name, party_type), item in sorted(actors.items(), key=lambda entry: -entry[1]["count"]):
|
||
problems = Counter(
|
||
row["anomaly_type"] for row in item["items"] if row["anomaly_type"]
|
||
)
|
||
ranking.append(
|
||
DashboardSupplierRank(
|
||
name=name,
|
||
party_type=party_type,
|
||
count=item["count"],
|
||
tier=item["tier"],
|
||
top_problem=problems.most_common(1)[0][0] if problems else None,
|
||
)
|
||
)
|
||
return ranking
|
||
|
||
@staticmethod
|
||
def _defects(rows, total: int) -> list[DashboardDefect]: # noqa: ANN001
|
||
labels = (("A", "A级缺陷"), ("B", "B级缺陷"), ("C", "C级缺陷"), ("D", "D级/未标注"))
|
||
counts = Counter(row["defect_level"] if row["defect_level"] in {"A", "B", "C"} else "D" for row in rows)
|
||
return [
|
||
DashboardDefect(
|
||
level=level,
|
||
label=label,
|
||
count=counts[level],
|
||
share=round(counts[level] / total * 100, 1) if total else 0,
|
||
)
|
||
for level, label in labels
|
||
]
|
||
|
||
@classmethod
|
||
def _trend(cls, rows, year: str) -> list[DashboardTrendPoint]: # noqa: ANN001
|
||
dated = [row["date"] for row in rows if row["date"] is not None]
|
||
if not dated:
|
||
return []
|
||
end = max(dated)
|
||
if year != "all":
|
||
end = date(int(year), 12, 1)
|
||
months = []
|
||
current_year, current_month = end.year, end.month
|
||
for _ in range(12):
|
||
key = f"{current_year:04d}-{current_month:02d}"
|
||
count = sum(1 for value in dated if value.year == current_year and value.month == current_month)
|
||
months.append(DashboardTrendPoint(month=key, label=f"{current_month}月", count=count))
|
||
current_month -= 1
|
||
if current_month == 0:
|
||
current_year -= 1
|
||
current_month = 12
|
||
return list(reversed(months))
|
||
|
||
@staticmethod
|
||
def _insight(trend, stages, categories) -> str: # noqa: ANN001
|
||
if len([item for item in trend if item.count]) < 3:
|
||
return "数据量不足，暂无法形成稳定的趋势洞察。"
|
||
recent = sum(item.count for item in trend[-3:])
|
||
earlier = sum(item.count for item in trend[:3])
|
||
direction = "上升" if recent > earlier * 1.1 else "下降" if recent < earlier * 0.9 else "基本平稳"
|
||
stage = max(stages, key=lambda item: item.count, default=None)
|
||
category = max(categories, key=lambda item: item.count, default=None)
|
||
stage_name = stage.name if stage else "产品开发"
|
||
category_name = category.name if category else "品类"
|
||
return f"近期开案趋势{direction}；{stage_name}阶段异常最多，{category_name}是当前主要风险来源。"
|
||
|
||
@staticmethod
|
||
def _warnings(trend, categories, ranking) -> list[str]: # noqa: ANN001
|
||
warnings = []
|
||
if trend and trend[-1].count > 10:
|
||
warnings.append(f"最近月份异常 {trend[-1].count} 条，已超过供应商月异常预警参考阈值。")
|
||
for item in ranking:
|
||
if item.count > 60:
|
||
warnings.append(f"{item.name}{item.party_type}累计异常 {item.count} 条，层级为 {item.tier} 级。")
|
||
for item in categories:
|
||
if item.count > 50:
|
||
warnings.append(f"{item.name}累计异常 {item.count} 条，建议重点关注{item.top_material or '高频物料'}。")
|
||
return warnings[:5]
|