feat: 管理层数据大屏（后端聚合接口 + 前端全屏页面）
PC 管理端新增独立全屏数据大屏，供管理层查看直通率/产量趋势/不良分布， 替代原 AdminDashboard 上零散的手工下钻。 后端： - endpoints/screen.py + services/screen_service.py: 大屏聚合接口 （复用 lifecycle 的售后工序归一，保证统计口径与展示一致） - router.py: 注册 screen_router 前端： - pages/admin/ScreenDashboard.tsx: 全屏大屏页（自带鉴权守卫，无侧边栏） - services/screenApi.ts: 大屏数据接口封装 - components/admin/UserOperationDetailDrawer.tsx: 人员操作明细抽屉， 由 AdminDashboard 的原生 state 抽成独立组件（含命令式 handle） - AdminDashboard.tsx: 改为使用该抽屉组件，移除内联的下钻状态 - MatrixBoard.tsx / App.tsx / AdminLayout.tsx: 挂载路由与导航入口 - BaseEChart.tsx: 注册 GaugeChart 与 GraphicComponent （graphic 需显式注册，否则饼图中心文字静默不渲染）
This commit is contained in:
52
backend/app/api/v1/endpoints/screen.py
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52
backend/app/api/v1/endpoints/screen.py
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"""大屏 API — 面向管理层**日常运营与督导**的轻量聚合接口
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视角：当月吞吐 / 当前卡点 / 系统活跃度。
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与 /dashboard 的区别：/dashboard 面向 PC 后台明细下钻（返回大列表），
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/screen 只返回图表直接可用的扁平聚合数据，字段少、无分页、供高频轮询。
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"""
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from fastapi import APIRouter, Depends, Query
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from sqlalchemy.ext.asyncio import AsyncSession
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from app.core.database import get_db
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from app.services.screen_service import (
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get_monthly_metrics, MonthlyMetrics,
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get_wip_distribution, WipDistributionResponse,
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get_active_users, ActiveUsersResponse,
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)
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router = APIRouter(prefix="/screen", tags=["大屏统计"])
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@router.get("/monthly-metrics", response_model=MonthlyMetrics)
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async def monthly_metrics(db: AsyncSession = Depends(get_db)):
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"""
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当月吞吐 — 大屏顶部四张数字卡。
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返回：本月生产流转 / 本月已入库 / 本月已出库 / 本月返厂回流。
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统计区间为北京时间当月 1 日 00:00 至此刻。
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"""
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return await get_monthly_metrics(db)
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@router.get("/wip-distribution", response_model=WipDistributionResponse)
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async def wip_distribution(db: AsyncSession = Depends(get_db)):
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"""
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工序积压分布 — 当前未完结设备按 overall_status 聚合的**纯数量**。
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返回固定阶段列表（含 0 值），保证柱状图类目稳定、不因缺数据而塌陷。
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"""
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return await get_wip_distribution(db)
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@router.get("/active-users", response_model=ActiveUsersResponse)
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async def active_users(
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top_n: int = Query(5, ge=1, le=20, description="返回的活跃人员数量"),
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db: AsyncSession = Depends(get_db),
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):
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"""
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本月系统使用活跃度排行 — 接收 / 转交 / 上传备注次数。
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桥接 /dashboard/user-operations 的统计口径，仅返回本月确实有操作的人员。
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"""
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return await get_active_users(db, top_n=top_n)
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@ -16,6 +16,7 @@ from app.api.v1.endpoints.analytics import router as analytics_router
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from app.api.v1.endpoints.holidays import router as holidays_router
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from app.api.v1.endpoints.webhooks import router as webhooks_router
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from app.api.v1.endpoints.external_products import router as external_products_router
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from app.api.v1.endpoints.screen import router as screen_router
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api_router = APIRouter()
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@ -35,3 +36,4 @@ api_router.include_router(analytics_router)
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api_router.include_router(holidays_router)
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api_router.include_router(webhooks_router)
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api_router.include_router(external_products_router)
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api_router.include_router(screen_router)
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254
backend/app/services/screen_service.py
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254
backend/app/services/screen_service.py
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"""大屏统计服务 — 面向管理层**日常运营与督导**的轻量只读聚合
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设计约定：
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- 只读，无写操作，字段扁平，便于大屏高频轮询。
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- 时间一律按**北京时间**判定；DB 列为 timestamptz（实存 UTC），
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比较前统一把边界换算成 UTC，避免月初/凌晨的边界漂移。
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- 口径与 dashboard_service.get_dashboard_stats 保持一致：
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未完结 = overall_status 不属于 {待仓库收货, 已入库, 在库, 已出库}。
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视角说明：
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管理层每天要看的是「这个月干得怎么样、现在卡在哪、系统有没有在跑」，
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而不是历史品质排名。故本模块聚焦三件事 —— 当月吞吐、当前卡点、使用活跃度。
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"""
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from __future__ import annotations
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from datetime import datetime, timezone
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from pydantic import BaseModel
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from sqlalchemy import func, or_, select
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from sqlalchemy.ext.asyncio import AsyncSession
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from app.core.lifecycle import (
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AFTER_SALES_ONLY_STEPS,
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LIFECYCLE_AFTER_SALES,
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LIFECYCLE_PRODUCTION,
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)
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from app.core.time_utils import get_beijing_time
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from app.models.product import Product
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from app.models.task import Task
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from app.models.task_log import TaskLog
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# 已完结的宏观状态 —— 与 dashboard_service.get_dashboard_stats 同口径
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FINISHED_OVERALL = ("待仓库收货", "已入库", "在库", "已出库")
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# 仓储动作日志类型（由 webhooks / product_finalize_service 写入）
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LOG_WAREHOUSE_INBOUND = "warehouse_inbound"
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LOG_WAREHOUSE_OUTBOUND = "warehouse_outbound"
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def _not_finished():
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"""未完结条件 —— overall_status 为 NULL 或不在已完结集合内。
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注意 PostgreSQL 中 `NULL NOT IN (...)` 结果为 NULL 而非 TRUE，
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必须显式带上 IS NULL 分支，否则建单后未流转的设备会被整批漏掉。
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"""
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return or_(
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Product.overall_status.is_(None),
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~Product.overall_status.in_(FINISHED_OVERALL),
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)
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def _month_bounds() -> tuple[datetime, datetime, str]:
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"""返回 (本月起点 UTC, 当前时刻 UTC, 'YYYY-MM')。
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本月起点 = 北京时间当月 1 日 00:00 —— 直接换算成 UTC 参与 timestamptz 比较，
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不依赖数据库会话时区设置。
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"""
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now_bj = get_beijing_time()
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month_start_bj = now_bj.replace(day=1, hour=0, minute=0, second=0, microsecond=0)
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return (
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month_start_bj.astimezone(timezone.utc),
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now_bj.astimezone(timezone.utc),
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month_start_bj.strftime("%Y-%m"),
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)
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# ============================================================
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# 1. 当月吞吐指标
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# ============================================================
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class MonthlyMetrics(BaseModel):
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"""大屏顶部四张数字卡（当月视角）"""
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month: str # "2026-09"
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month_start: str # "2026-09-01"（北京时间）
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month_production: int # 本月有流转记录或新建的生产态设备数
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month_inbound: int # 本月扫码入库数
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month_outbound: int # 本月扫码出库数
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month_returned: int # 本月进入售后/回流状态的设备数
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async def get_monthly_metrics(db: AsyncSession) -> MonthlyMetrics:
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"""当月吞吐四联指标。
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口径：
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- month_production：lifecycle_phase = PRODUCTION，且「本月新建」或
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「本月产生过任意 TaskLog 流转记录」的设备数（按设备去重）。
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反映这个月实际被推着走的机器有多少。
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- month_inbound / month_outbound：task_logs 中 action_type =
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warehouse_inbound / warehouse_outbound 的记录数（MOM 扫码回调写入）。
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- month_returned：本月创建过售后专属工序任务（发货测试 / 售后维修）的
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设备数（去重）。Product 表无 updated_at，无法直接查「转为 AFTER_SALES
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的时刻」，故以售后工序任务的创建时间作为进入售后阶段的时间锚点。
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"""
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month_start_utc, now_utc, month_label = _month_bounds()
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# 本月该设备产生过任意流转日志
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has_activity = (
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select(TaskLog.id)
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.join(Task, Task.id == TaskLog.task_id)
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.where(
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Task.product_id == Product.id,
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TaskLog.created_at >= month_start_utc,
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)
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.exists()
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)
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month_production = await db.scalar(
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select(func.count(func.distinct(Product.id))).where(
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Product.lifecycle_phase == LIFECYCLE_PRODUCTION,
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or_(Product.created_at >= month_start_utc, has_activity),
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)
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) or 0
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async def _count_log(action_type: str) -> int:
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return await db.scalar(
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select(func.count(TaskLog.id)).where(
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TaskLog.action_type == action_type,
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TaskLog.created_at >= month_start_utc,
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)
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) or 0
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month_inbound = await _count_log(LOG_WAREHOUSE_INBOUND)
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month_outbound = await _count_log(LOG_WAREHOUSE_OUTBOUND)
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month_returned = await db.scalar(
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select(func.count(func.distinct(Task.product_id)))
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.join(Product, Task.product_id == Product.id)
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.where(
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Product.lifecycle_phase == LIFECYCLE_AFTER_SALES,
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Task.task_name.in_(tuple(AFTER_SALES_ONLY_STEPS)),
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Task.created_at >= month_start_utc,
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)
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) or 0
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return MonthlyMetrics(
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month=month_label,
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month_start=f"{month_label}-01",
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month_production=int(month_production),
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month_inbound=int(month_inbound),
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month_outbound=int(month_outbound),
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month_returned=int(month_returned),
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)
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# ============================================================
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# 2. 工序积压分布（柱状图）
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# ============================================================
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class WipStage(BaseModel):
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stage: str # 工序名（中文，直接作为图表类目）
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phase: str # PRODUCTION / AFTER_SALES，供前端分区着色
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count: int
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class WipDistributionResponse(BaseModel):
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total: int # 未完结设备总数
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items: list[WipStage]
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# 阶段与流转顺序 —— 顺序即「工序先后」，前端据此排布柱子
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WIP_STAGES: tuple[tuple[str, str], ...] = (
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("待启动", LIFECYCLE_PRODUCTION), # overall_status 为空：建单后尚未流转
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("备货", LIFECYCLE_PRODUCTION),
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("生产", LIFECYCLE_PRODUCTION),
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("测试", LIFECYCLE_PRODUCTION),
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("维修", LIFECYCLE_PRODUCTION),
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("发货测试", LIFECYCLE_AFTER_SALES),
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("售后维修", LIFECYCLE_AFTER_SALES),
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)
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# 「待启动」对应的真实分组键（overall_status IS NULL / 空串）
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_UNSTARTED_KEY = ""
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async def get_wip_distribution(db: AsyncSession) -> WipDistributionResponse:
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"""工序积压分布 —— 当前未完结设备按 overall_status 聚合的数量。
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返回**固定阶段列表**（含 0 值），保证大屏布局稳定、柱子不因某天缺数据而
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整根消失；同时把数据里出现但不在词表内的状态追加在末尾，确保 items 的
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count 之和恒等于 total（避免统计悄悄漏数）。
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"""
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rows = await db.execute(
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select(Product.overall_status, func.count(Product.id))
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.where(_not_finished())
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.group_by(Product.overall_status)
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)
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counts: dict[str, int] = {}
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for raw_status, cnt in rows.all():
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key = (raw_status or "").strip()
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counts[key] = counts.get(key, 0) + cnt
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total = sum(counts.values())
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items: list[WipStage] = []
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for stage, phase in WIP_STAGES:
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key = _UNSTARTED_KEY if stage == "待启动" else stage
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items.append(WipStage(stage=stage, phase=phase, count=counts.get(key, 0)))
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known_keys = {_UNSTARTED_KEY if s == "待启动" else s for s, _ in WIP_STAGES}
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for key, cnt in counts.items():
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if key not in known_keys and cnt:
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items.append(WipStage(
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stage=key or "待启动",
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phase=LIFECYCLE_PRODUCTION,
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count=cnt,
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))
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return WipDistributionResponse(total=total, items=items)
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# ============================================================
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# 3. 系统使用活跃度（本月人员排行）
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# ============================================================
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class ActiveUser(BaseModel):
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user_id: str
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user_name: str
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receive_count: int # 接收
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transfer_count: int # 转交
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record_count: int # 上传备注
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total: int
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class ActiveUsersResponse(BaseModel):
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month: str
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items: list[ActiveUser]
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async def get_active_users(db: AsyncSession, top_n: int = 5) -> ActiveUsersResponse:
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"""本月系统活跃度排行 —— 直接桥接 /dashboard/user-operations 的口径。
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不重复实现聚合逻辑：转交/接收取 task_logs（action_type = receive / complete），
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上传备注取 task_records（排除系统自动备注）。仅返回本月**确实有操作**的人员，
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避免排行榜被一串 0 稀释 —— 领导要看到的是"系统真的有人在用"。
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"""
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from app.services.dashboard_service import get_user_operations
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month_start_utc, _now, month_label = _month_bounds()
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operations = await get_user_operations(db, since=month_start_utc, until=None)
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active = [op for op in operations if op.total > 0][:top_n]
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return ActiveUsersResponse(
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month=month_label,
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items=[
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ActiveUser(
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user_id=op.user_id,
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user_name=op.user_name,
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receive_count=op.receive_count,
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transfer_count=op.transfer_count,
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record_count=op.record_count,
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total=op.total,
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)
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for op in active
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],
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)
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Block a user