90707005ed
- 项目详情页三阶段里程碑管理(前期/制作/后期) - 制作卡片改用180px ECharts圆环进度图+右侧数据列表 - 修复损耗率双重计算bug(测试秒数不再重复计入超产) - 新增飞书推送服务、豆包AI风险分析、APScheduler定时报告 - 项目列表页增强(筛选/排序/批量操作/废弃功能) - 成员详情页产出时间轴+效率对比 - 成本页固定开支管理 Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
508 lines
19 KiB
Python
508 lines
19 KiB
Python
"""报告生成服务 —— 汇总数据库数据 + 调用 AI 生成摘要"""
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import logging
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from datetime import date, timedelta
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from sqlalchemy.orm import Session
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from sqlalchemy import func as sa_func
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from models import (
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User, Project, Submission, AIToolCost,
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ProjectStatus, WorkType,
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)
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from calculations import (
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calc_waste_for_project,
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calc_labor_cost_for_project,
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calc_ai_tool_cost_for_project,
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calc_outsource_cost_for_project,
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calc_overhead_cost_for_project,
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calc_team_efficiency,
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calc_project_settlement,
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)
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from services.ai_service import generate_report_summary
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logger = logging.getLogger(__name__)
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def _fmt_seconds(secs: float) -> str:
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"""秒数格式化为 Xm Xs"""
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if secs <= 0:
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return "0s"
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m = int(secs) // 60
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s = int(secs) % 60
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if m > 0:
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return f"{m}m {s}s" if s > 0 else f"{m}m"
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return f"{s}s"
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def _fmt_money(amount: float) -> str:
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"""金额格式化"""
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if amount >= 10000:
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return f"¥{amount/10000:.1f}万"
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return f"¥{amount:,.0f}"
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# ──────────────────────────── 日报 ────────────────────────────
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def generate_daily_report(db: Session) -> dict:
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"""
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生成日报
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返回 {"title": str, "content": str, "data": dict}
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"""
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today = date.today()
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# 今日提交
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today_subs = db.query(Submission).filter(
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Submission.submit_date == today
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).all()
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today_submitter_ids = set(s.user_id for s in today_subs)
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today_total_secs = sum(s.total_seconds for s in today_subs if s.total_seconds > 0)
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# 所有活跃用户（有提交记录的）
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all_active_user_ids = set(
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uid for (uid,) in db.query(Submission.user_id).distinct().all()
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)
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not_submitted = []
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for uid in all_active_user_ids:
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if uid not in today_submitter_ids:
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user = db.query(User).filter(User.id == uid).first()
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if user and user.is_active:
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not_submitted.append(user.name)
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# 进行中项目
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active_projects = db.query(Project).filter(
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Project.status == ProjectStatus.IN_PROGRESS
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).all()
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project_lines = []
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risk_lines = []
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for p in active_projects:
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waste = calc_waste_for_project(p.id, db)
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total_secs = waste.get("total_submitted_seconds", 0)
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target = p.target_total_seconds
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progress = round(total_secs / target * 100, 1) if target > 0 else 0
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# 今日该项目产出
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proj_today_secs = sum(
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s.total_seconds for s in today_subs
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if s.project_id == p.id and s.total_seconds > 0
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)
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project_lines.append(
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f"- **{p.name}**：进度 {progress}%，今日产出 {_fmt_seconds(proj_today_secs)}"
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)
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# 风险检测
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if p.estimated_completion_date:
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days_left = (p.estimated_completion_date - today).days
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if days_left < 0:
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risk_lines.append(f"- **{p.name}**：已超期 {-days_left} 天，进度 {progress}%")
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elif days_left <= 7 and progress < 80:
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risk_lines.append(f"- **{p.name}**：距截止仅剩 {days_left} 天，进度仅 {progress}%")
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# 组装数据上下文（供 AI 使用）
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data_context = (
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f"日期：{today}\n"
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f"进行中项目：{len(active_projects)} 个\n"
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f"今日提交人次：{len(today_submitter_ids)}\n"
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f"今日总产出：{_fmt_seconds(today_total_secs)}\n"
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f"今日未提交人员：{', '.join(not_submitted) if not_submitted else '无'}\n"
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f"各项目情况：\n" + "\n".join(project_lines) + "\n"
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f"风险项目：\n" + ("\n".join(risk_lines) if risk_lines else "无")
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)
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# 调用 AI 生成摘要
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ai_summary = generate_report_summary(data_context, "daily")
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# 组装飞书 markdown 内容
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title = f"AirLabs 日报 — {today}"
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lines = [
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f"**【今日概览】**",
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f"- 进行中项目：{len(active_projects)} 个",
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f"- 今日提交：{len(today_submitter_ids)} 人次，总产出 {_fmt_seconds(today_total_secs)}",
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]
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if not_submitted:
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lines.append(f"- 今日未提交：{', '.join(not_submitted)}")
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lines.append("")
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lines.append("**【各项目进展】**")
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lines.extend(project_lines if project_lines else ["- 暂无进行中项目"])
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if risk_lines:
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lines.append("")
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lines.append("**【风险提醒】**")
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lines.extend(risk_lines)
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if ai_summary:
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lines.append("")
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lines.append("**【AI 点评】**")
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lines.append(ai_summary)
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content = "\n".join(lines)
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return {"title": title, "content": content, "data": {"date": str(today)}}
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# ──────────────────────────── 周报 ────────────────────────────
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def generate_weekly_report(db: Session) -> dict:
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"""生成周报（本周一到当天的数据）"""
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today = date.today()
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# 本周一
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monday = today - timedelta(days=today.weekday())
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# 本周提交
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week_subs = db.query(Submission).filter(
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Submission.submit_date >= monday,
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Submission.submit_date <= today,
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).all()
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week_submitter_ids = set(s.user_id for s in week_subs)
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week_total_secs = sum(s.total_seconds for s in week_subs if s.total_seconds > 0)
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working_days = min((today - monday).days + 1, 5)
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avg_daily = round(week_total_secs / max(1, len(week_submitter_ids)) / max(1, working_days), 1)
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# 进行中项目
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active_projects = db.query(Project).filter(
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Project.status == ProjectStatus.IN_PROGRESS
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).all()
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# 各项目周报数据
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project_lines = []
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for p in active_projects:
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waste = calc_waste_for_project(p.id, db)
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total_secs = waste.get("total_submitted_seconds", 0)
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target = p.target_total_seconds
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progress = round(total_secs / target * 100, 1) if target > 0 else 0
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proj_week_secs = sum(
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s.total_seconds for s in week_subs
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if s.project_id == p.id and s.total_seconds > 0
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)
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project_lines.append(
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f"- **{p.name}**：当前进度 {progress}%，本周产出 {_fmt_seconds(proj_week_secs)}"
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)
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# 本周成本（简化：统计提交人的日成本）
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week_labor = 0.0
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processed = set()
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for s in week_subs:
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key = (s.user_id, s.submit_date)
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if key not in processed:
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processed.add(key)
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user = db.query(User).filter(User.id == s.user_id).first()
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if user:
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week_labor += user.daily_cost
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week_ai_cost = db.query(sa_func.sum(AIToolCost.amount)).filter(
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AIToolCost.record_date >= monday,
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AIToolCost.record_date <= today,
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).scalar() or 0
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# 损耗排行
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waste_ranking = []
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for p in active_projects:
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w = calc_waste_for_project(p.id, db)
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if w.get("total_waste_seconds", 0) > 0:
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waste_ranking.append({
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"name": p.name,
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"rate": w["waste_rate"],
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})
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waste_ranking.sort(key=lambda x: x["rate"], reverse=True)
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# 效率排行（找产出最高的人）
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user_week_secs = {}
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for s in week_subs:
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if s.total_seconds > 0:
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user_week_secs[s.user_id] = user_week_secs.get(s.user_id, 0) + s.total_seconds
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top_producer = None
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if user_week_secs:
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top_uid = max(user_week_secs, key=user_week_secs.get)
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top_user = db.query(User).filter(User.id == top_uid).first()
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if top_user:
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top_daily = round(user_week_secs[top_uid] / max(1, working_days), 1)
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top_producer = f"{top_user.name}（日均 {_fmt_seconds(top_daily)}）"
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# AI 数据上下文
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data_context = (
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f"周期：{monday} ~ {today}\n"
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f"进行中项目：{len(active_projects)} 个\n"
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f"本周总产出：{_fmt_seconds(week_total_secs)}\n"
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f"人均日产出：{_fmt_seconds(avg_daily)}\n"
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f"效率最高：{top_producer or '暂无'}\n"
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f"本周人力成本：{_fmt_money(week_labor)}\n"
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f"本周AI工具成本：{_fmt_money(week_ai_cost)}\n"
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f"各项目：\n" + "\n".join(project_lines) + "\n"
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f"损耗排行：\n" + "\n".join(
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f"- {w['name']}：{w['rate']}%" for w in waste_ranking[:5]
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) if waste_ranking else "损耗排行：无"
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)
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ai_summary = generate_report_summary(data_context, "weekly")
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# 组装内容
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title = f"AirLabs 周报 — 第{today.isocalendar()[1]}周（{monday} ~ {today}）"
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lines = [
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"**【项目进展】**",
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]
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lines.extend(project_lines if project_lines else ["- 暂无进行中项目"])
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lines.append("")
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lines.append("**【团队产出】**")
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lines.append(f"- 本周总产出：{_fmt_seconds(week_total_secs)}")
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lines.append(f"- 人均日产出：{_fmt_seconds(avg_daily)}")
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if top_producer:
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lines.append(f"- 效率最高：{top_producer}")
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lines.append("")
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lines.append("**【成本概览】**")
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lines.append(f"- 本周人力成本：{_fmt_money(week_labor)}")
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lines.append(f"- 本周 AI 工具支出：{_fmt_money(week_ai_cost)}")
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if waste_ranking:
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lines.append("")
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lines.append("**【损耗排行】**")
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for w in waste_ranking[:5]:
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lines.append(f"- {w['name']}：损耗率 {w['rate']}%")
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if ai_summary:
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lines.append("")
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lines.append("**【AI 分析与建议】**")
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lines.append(ai_summary)
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content = "\n".join(lines)
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return {"title": title, "content": content, "data": {"week_start": str(monday), "week_end": str(today)}}
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# ──────────────────────────── 月报 ────────────────────────────
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def generate_monthly_report(db: Session) -> dict:
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"""生成月报（上月完整数据，在每月1号调用）"""
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today = date.today()
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# 上月日期范围
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first_of_this_month = today.replace(day=1)
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last_of_prev_month = first_of_this_month - timedelta(days=1)
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first_of_prev_month = last_of_prev_month.replace(day=1)
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month_label = f"{last_of_prev_month.year}年{last_of_prev_month.month}月"
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# 上月提交
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month_subs = db.query(Submission).filter(
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Submission.submit_date >= first_of_prev_month,
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Submission.submit_date <= last_of_prev_month,
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).all()
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month_total_secs = sum(s.total_seconds for s in month_subs if s.total_seconds > 0)
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month_submitters = set(s.user_id for s in month_subs)
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# 所有项目（含进行中和上月完成的）
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all_projects = db.query(Project).filter(
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Project.status.in_([ProjectStatus.IN_PROGRESS, ProjectStatus.COMPLETED])
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).all()
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# 上月完成的项目
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completed_this_month = [
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p for p in all_projects
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if p.status == ProjectStatus.COMPLETED
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and p.actual_completion_date
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and first_of_prev_month <= p.actual_completion_date <= last_of_prev_month
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]
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active_projects = [p for p in all_projects if p.status == ProjectStatus.IN_PROGRESS]
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# 各项目成本
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project_cost_lines = []
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total_all_cost = 0.0
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for p in active_projects + completed_this_month:
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labor = calc_labor_cost_for_project(p.id, db)
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ai_tool = calc_ai_tool_cost_for_project(p.id, db)
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outsource = calc_outsource_cost_for_project(p.id, db)
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overhead = calc_overhead_cost_for_project(p.id, db)
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total = labor + ai_tool + outsource + overhead
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total_all_cost += total
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project_cost_lines.append(
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f"- **{p.name}**：人力 {_fmt_money(labor)} / AI工具 {_fmt_money(ai_tool)} / "
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f"外包 {_fmt_money(outsource)} / 固定 {_fmt_money(overhead)} → 总计 {_fmt_money(total)}"
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)
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# 盈亏概览（已结算的客户正式项目）
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profit_lines = []
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total_profit = 0.0
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total_contract = 0.0
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for p in completed_this_month:
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settlement = calc_project_settlement(p.id, db)
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if settlement.get("contract_amount"):
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pl = settlement.get("profit_loss", 0)
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total_profit += pl
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total_contract += settlement["contract_amount"]
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sign = "+" if pl >= 0 else ""
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profit_lines.append(
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f"- **{p.name}**：回款 {_fmt_money(settlement['contract_amount'])}，"
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f"成本 {_fmt_money(settlement['total_cost'])}，利润 {sign}{_fmt_money(pl)}"
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)
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# 损耗汇总
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total_waste_secs = 0.0
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total_target_secs = 0.0
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for p in active_projects + completed_this_month:
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w = calc_waste_for_project(p.id, db)
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total_waste_secs += w.get("total_waste_seconds", 0)
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total_target_secs += p.target_total_seconds or 0
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waste_rate = round(total_waste_secs / total_target_secs * 100, 1) if total_target_secs > 0 else 0
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# 人均产出
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working_days_month = 22
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avg_per_person = round(month_total_secs / max(1, len(month_submitters)), 1)
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# AI 数据上下文
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data_context = (
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f"月份：{month_label}\n"
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f"进行中项目：{len(active_projects)} 个\n"
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f"本月完成项目：{len(completed_this_month)} 个\n"
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f"月度总产出：{_fmt_seconds(month_total_secs)}\n"
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f"月度总成本：{_fmt_money(total_all_cost)}\n"
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f"总损耗率：{waste_rate}%\n"
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f"参与人数：{len(month_submitters)}\n"
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f"人均产出：{_fmt_seconds(avg_per_person)}\n"
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f"各项目成本：\n" + "\n".join(project_cost_lines) + "\n"
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f"盈亏：\n" + ("\n".join(profit_lines) if profit_lines else "本月无结算项目")
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)
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ai_summary = generate_report_summary(data_context, "monthly")
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# 组装内容
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title = f"AirLabs 月报 — {month_label}"
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lines = [
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"**【月度总览】**",
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f"- 进行中项目：{len(active_projects)} 个",
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f"- 本月完成项目：{len(completed_this_month)} 个",
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f"- 月度总产出：{_fmt_seconds(month_total_secs)}",
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f"- 月度总成本：{_fmt_money(total_all_cost)}",
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]
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if project_cost_lines:
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lines.append("")
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lines.append("**【各项目成本明细】**")
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lines.extend(project_cost_lines)
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if profit_lines:
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lines.append("")
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lines.append("**【盈亏概览】**")
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lines.extend(profit_lines)
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if total_contract > 0:
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profit_rate = round(total_profit / total_contract * 100, 1)
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lines.append(f"- 总利润率：{profit_rate}%")
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lines.append("")
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lines.append("**【月度损耗】**")
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lines.append(f"- 总损耗：{_fmt_seconds(total_waste_secs)}（损耗率 {waste_rate}%）")
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lines.append("")
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lines.append("**【人均产出】**")
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lines.append(f"- 参与人数：{len(month_submitters)} 人")
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lines.append(f"- 月度人均产出：{_fmt_seconds(avg_per_person)}")
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if ai_summary:
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lines.append("")
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lines.append("**【AI 深度分析】**")
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lines.append(ai_summary)
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content = "\n".join(lines)
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return {
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"title": title,
|
||
"content": content,
|
||
"data": {"month": month_label, "start": str(first_of_prev_month), "end": str(last_of_prev_month)},
|
||
}
|
||
|
||
|
||
# ──────────────────────────── 风险预警 ────────────────────────────
|
||
|
||
def analyze_project_risks(db: Session) -> list:
|
||
"""
|
||
分析所有进行中项目的风险，返回风险列表
|
||
纯规则引擎判断，不依赖 AI
|
||
"""
|
||
today = date.today()
|
||
active_projects = db.query(Project).filter(
|
||
Project.status == ProjectStatus.IN_PROGRESS
|
||
).all()
|
||
|
||
risks = []
|
||
for p in active_projects:
|
||
waste = calc_waste_for_project(p.id, db)
|
||
total_secs = waste.get("total_submitted_seconds", 0)
|
||
target = p.target_total_seconds
|
||
progress = round(total_secs / target * 100, 1) if target > 0 else 0
|
||
|
||
risk_factors = []
|
||
risk_level = "low"
|
||
|
||
# 1. 超期检测
|
||
if p.estimated_completion_date:
|
||
days_left = (p.estimated_completion_date - today).days
|
||
if days_left < 0:
|
||
risk_factors.append(f"已超期 {-days_left} 天")
|
||
risk_level = "high"
|
||
elif days_left <= 7 and progress < 80:
|
||
risk_factors.append(f"距截止仅剩 {days_left} 天，进度仅 {progress}%")
|
||
risk_level = "high"
|
||
elif days_left <= 14 and progress < 60:
|
||
risk_factors.append(f"距截止 {days_left} 天，进度 {progress}% 偏低")
|
||
risk_level = "medium"
|
||
|
||
# 进度落后于时间线
|
||
if p.estimated_completion_date and days_left > 0:
|
||
# 估算已用天数
|
||
if hasattr(p, 'created_at') and p.created_at:
|
||
created = p.created_at.date() if hasattr(p.created_at, 'date') else p.created_at
|
||
total_days = (p.estimated_completion_date - created).days
|
||
elapsed_days = (today - created).days
|
||
if total_days > 0:
|
||
expected_progress = round(elapsed_days / total_days * 100, 1)
|
||
if progress < expected_progress * 0.7:
|
||
risk_factors.append(
|
||
f"预期进度 {expected_progress}%，实际 {progress}%，严重落后"
|
||
)
|
||
risk_level = "high"
|
||
elif progress < expected_progress * 0.85:
|
||
risk_factors.append(
|
||
f"预期进度 {expected_progress}%，实际 {progress}%"
|
||
)
|
||
if risk_level != "high":
|
||
risk_level = "medium"
|
||
|
||
# 2. 损耗率检测
|
||
waste_rate = waste.get("waste_rate", 0)
|
||
if waste_rate > 80:
|
||
risk_factors.append(f"损耗率 {waste_rate}%，严重偏高")
|
||
risk_level = "high"
|
||
elif waste_rate > 50:
|
||
risk_factors.append(f"损耗率 {waste_rate}%，偏高")
|
||
if risk_level != "high":
|
||
risk_level = "medium"
|
||
|
||
# 3. 近7天无提交
|
||
week_ago = today - timedelta(days=7)
|
||
recent_subs = db.query(Submission).filter(
|
||
Submission.project_id == p.id,
|
||
Submission.submit_date >= week_ago,
|
||
).count()
|
||
if recent_subs == 0 and total_secs > 0:
|
||
risk_factors.append("近 7 天无提交，产出停滞")
|
||
if risk_level != "high":
|
||
risk_level = "medium"
|
||
|
||
if risk_factors:
|
||
risks.append({
|
||
"project_id": p.id,
|
||
"project_name": p.name,
|
||
"risk_level": risk_level,
|
||
"progress": progress,
|
||
"risk_factors": risk_factors,
|
||
})
|
||
|
||
# 高风险排前面
|
||
level_order = {"high": 0, "medium": 1, "low": 2}
|
||
risks.sort(key=lambda x: level_order.get(x["risk_level"], 99))
|
||
return risks
|