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#!/usr/bin/env python3
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"""
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考勤报表导出 — 每日统计粒度
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⛔ 【AI Agent 强制门禁】调用本脚本前必须先阅读：
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references/attendance-report.md
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本脚本仅是"考勤报表导出工作流"的执行末端，工作流完整定义在 attendance-report.md，
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包含但不限于：
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- 阶段 0：报表类型判断（默认月度汇总）
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- 阶段 1：人员列表获取（aisearch person / contact dept list-members）
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- 阶段 2：列选择（是否传 --column-keywords）
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- 阶段 3：调用本脚本
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- 阶段 4：结果回传给用户的标准格式
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- 错误处理（403 权限、HSF_ILLEGALPARAMS、空数据等）
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❌ 严禁仅凭本脚本 docstring 或 --help 输出就直接拼命令执行，会导致：
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- 报表数据不全 / 列错位 / 人员遗漏
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- 错误处理缺失，把环境错误当业务错误反馈给用户
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- 输出格式不规范，用户体验差
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按 (userId, workDate) 分组，每人每天一行。
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聚合策略：
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- 通过启发式识别每条记录的"工作日期"：依次尝试字段名
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workDate / work_date / date / userCheckTime / day / 工作日期
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- 同一 (userId, workDate) 下的多条记录按字段聚合：
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* 数值字段 → sum
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* 非数值字段 → 取首个非空值（因为同一天同一字段通常只有一个值）
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- 缺少 workDate 的记录会归入 "_no_date"，并 warn
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用法:
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python attendance_report_daily.py \
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--users userId1,userId2,... \
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--start "2026-03-01 00:00:00" \
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--end "2026-03-31 23:59:59" \
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[--columns 1001,1002]
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[--column-keywords "工作日期,出勤状态,迟到时长"]
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[--out attendance_report_2026-03-01_2026-03-31_daily.xlsx]
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[--inspect]
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"""
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from __future__ import annotations
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import argparse
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import sys
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from collections import defaultdict
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from datetime import datetime
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from typing import Any
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import attendance_report_common as cmn
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# 默认关注字段 — 与 SKILL.md「每日统计预定义列集合」严格对齐（共 33 个）
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# 字段名必须和 `dws attendance report columns` 返回的 name 精确匹配
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DEFAULT_KEYWORDS = [
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"班次",
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"上班1打卡时间",
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"上班1打卡结果",
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"下班1打卡时间",
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"下班1打卡结果",
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"上班2打卡时间",
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"上班2打卡结果",
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"下班2打卡时间",
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"下班2打卡结果",
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"上班3打卡时间",
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"上班3打卡结果",
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"下班3打卡时间",
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"下班3打卡结果",
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"关联的审批单",
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"出勤天数",
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"休息天数",
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"工作时长",
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"迟到次数",
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"迟到时长",
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"严重迟到次数",
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"严重迟到时长",
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"旷工迟到次数",
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"早退次数",
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"早退时长",
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"上班缺卡次数",
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"下班缺卡次数",
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"旷工天数",
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"出差时长",
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"外出时长",
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"请假",
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"加班-审批单统计",
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]
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# 工作日期字段的候选 key（按优先级试探）
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DATE_KEY_CANDIDATES = (
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"workDate", "work_date", "userCheckDate", "checkDate",
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"date", "day", "工作日期",
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)
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# 请假字段 — 触发"按假期类型展开"的字段名
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# 不参与 query-data 查询，单独走 query-leave 接口，按 4 类假期展开为多列
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# 注意：钉钉接口实际返回的字段名可能是 "请假"、"请假分类"、"请假时长" 等，
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# 凡以 "请假" 开头的都视为请假字段，统一替换为 4 列假期类型展开。
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LEAVE_FIELD_NAME = "请假"
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LEAVE_TYPES: tuple[str, ...] = ("事假", "调休", "病假", "年假")
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def _is_leave_field(name: str) -> bool:
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"""判断一个字段名是否属于"请假"系列（如 请假 / 请假分类 / 请假时长）。"""
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return isinstance(name, str) and name.startswith(LEAVE_FIELD_NAME)
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# ─────────────────────────────────────────────────────────────────────────────
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# 参数解析
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# ─────────────────────────────────────────────────────────────────────────────
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def parse_args() -> argparse.Namespace:
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p = argparse.ArgumentParser(
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description=(
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"导出考勤报表 — 每日统计粒度。"
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"⛔ AI Agent 必须先读 references/attendance-report.md 再调用本脚本，"
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"禁止凭 --help 或脚本路径自行拼命令。"
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),
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)
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p.add_argument("--users", required=True,
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help="userId 列表，逗号分隔（必填）")
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p.add_argument("--start", required=True,
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help='开始时间，YYYY-MM-DD 或 "YYYY-MM-DD HH:mm:ss"（必填）')
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p.add_argument("--end", required=True,
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help='结束时间，YYYY-MM-DD 或 "YYYY-MM-DD HH:mm:ss"（必填）')
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p.add_argument("--columns", default="",
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help="字段 ID 列表，逗号分隔；与 --column-keywords 二选一")
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p.add_argument("--column-keywords", default="",
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help="字段名关键词，逗号分隔；不传则走默认字段集")
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p.add_argument("--out", default="",
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help="输出 xlsx 文件名；不传则按规范自动生成")
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p.add_argument("--inspect", action="store_true",
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help="首次跑时打印首条记录原始结构（用于核对真实字段）")
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return p.parse_args()
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# ─────────────────────────────────────────────────────────────────────────────
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# 字段解析（与 detail / monthly 一致）
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# ─────────────────────────────────────────────────────────────────────────────
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def resolve_columns(args: argparse.Namespace) -> list[dict]:
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if args.columns.strip():
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cids = [c.strip() for c in args.columns.split(",") if c.strip()]
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all_cols_payload = cmn.run_dws(["attendance", "report", "columns"])
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all_cols = cmn.extract_records(all_cols_payload)
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id_to_name: dict[str, str] = {}
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for col in all_cols:
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cid = cmn._first_nonempty(col, ("id", "columnId", "code", "key"))
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name = cmn._first_nonempty(col, ("name", "columnName", "title", "label"))
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if cid is not None:
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id_to_name[str(cid)] = str(name) if name else str(cid)
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return [{"_column_id": cid, "_column_name": id_to_name.get(cid, cid)}
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for cid in cids]
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keywords = (
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[k.strip() for k in args.column_keywords.split(",") if k.strip()]
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if args.column_keywords.strip()
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else DEFAULT_KEYWORDS
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)
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cmn.log(f"[columns] 使用关键词匹配字段：{keywords}")
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all_cols_payload = cmn.run_dws(["attendance", "report", "columns"])
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all_cols = cmn.extract_records(all_cols_payload)
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cmn.log(f"[columns] dws 返回 {len(all_cols)} 个字段")
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matched = cmn.match_columns_by_keywords(all_cols, keywords)
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if not matched:
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raise RuntimeError(
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f"未匹配到任何字段。可用字段示例："
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f"{[cmn._first_nonempty(c, ('name','columnName','title','label')) for c in all_cols[:10]]}"
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)
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cmn.log(f"[columns] 匹配到 {len(matched)} 个字段：{[c['_column_name'] for c in matched]}")
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return matched
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# ─────────────────────────────────────────────────────────────────────────────
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# 接口调用（与 detail / monthly 一致）
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# ─────────────────────────────────────────────────────────────────────────────
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def query_one_batch(
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user_batch: list[str],
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column_ids: list[str],
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date_slice: cmn.DateSlice,
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stats: cmn.CallStats,
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*,
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column_id_to_name: dict[str, str] | None = None,
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inspect: bool = False,
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inspected_flag: list[bool] = None,
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) -> list[dict]:
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cmn.log(
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f"[query] users={len(user_batch)} cols={len(column_ids)} "
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f"slice={date_slice.label}"
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)
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try:
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payload = cmn.run_dws([
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"attendance", "report", "query-data",
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"--users", ",".join(user_batch),
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"--columns", ",".join(column_ids),
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"--start", date_slice.start_str,
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"--end", date_slice.end_str,
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])
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stats.total_dws_calls += 1
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except cmn.DwsCallError as e:
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stats.total_dws_calls += 1
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stats.failed_calls += 1
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if e.is_permission_error:
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cmn.error(
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"权限错误：当前账号无管理员权限，无法导出考勤报表。"
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"请联系考勤管理员或换号重试。"
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)
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raise SystemExit(2) from e
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stats.add_warning(f"[query failed] {date_slice.label}: {e}")
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return []
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records = cmn.extract_records(payload)
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# 展平 report query-data 返回的嵌套 values 结构
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records = cmn.flatten_query_data_records(records, column_id_to_name)
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if inspect and records and inspected_flag is not None and not inspected_flag[0]:
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cmn.dump_first_record_for_inspection(records, "query-data (flattened)")
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inspected_flag[0] = True
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return records
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# ─────────────────────────────────────────────────────────────────────────────
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# 每日聚合
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# ─────────────────────────────────────────────────────────────────────────────
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def _value_for_column(record: dict, col: dict) -> Any:
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cname, cid = col["_column_name"], col["_column_id"]
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for key in (cname, cid, f"col_{cid}", f"column_{cid}"):
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if key in record:
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return record[key]
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return None
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def _try_number(value: Any) -> float | None:
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if value is None or value == "":
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return None
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if isinstance(value, bool):
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return None
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if isinstance(value, (int, float)):
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return float(value)
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if isinstance(value, str):
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try:
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return float(value.strip())
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except ValueError:
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return None
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return None
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def _user_id_of(record: dict) -> str | None:
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uid = cmn._first_nonempty(record, ("userId", "userid", "user_id", "targetUserId"))
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return str(uid) if uid is not None else None
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def _extract_work_date(record: dict, columns: list[dict]) -> str | None:
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"""
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从一条记录里提取"工作日期"（YYYY-MM-DD 格式）。
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试探顺序：
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1. record 里的 DATE_KEY_CANDIDATES
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2. columns 里 _column_name 含"日期"的字段
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3. 13 位毫秒时间戳 → 转 YYYY-MM-DD
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4. ISO 字符串 → 截前 10 位
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都没找到返回 None。
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"""
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candidates: list[Any] = []
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# 1) 直接 key
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for key in DATE_KEY_CANDIDATES:
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if key in record and record[key] not in (None, ""):
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candidates.append(record[key])
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# 2) 字段名含"日期"
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for col in columns:
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if "日期" in col["_column_name"] or "date" in col["_column_name"].lower():
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v = _value_for_column(record, col)
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if v not in (None, ""):
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candidates.append(v)
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for raw in candidates:
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date_str = _normalize_date(raw)
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if date_str:
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return date_str
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return None
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def _normalize_date(raw: Any) -> str | None:
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"""把任意形态的日期值归一化为 YYYY-MM-DD 字符串。"""
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if raw is None:
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return None
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# 毫秒时间戳
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if isinstance(raw, (int, float)) and 1_000_000_000_000 <= raw <= 9_999_999_999_999:
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try:
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return datetime.fromtimestamp(raw / 1000).strftime(cmn.DATE_FMT)
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except (OSError, ValueError, OverflowError):
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return None
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# 秒级时间戳
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if isinstance(raw, (int, float)) and 1_000_000_000 <= raw <= 9_999_999_999:
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try:
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return datetime.fromtimestamp(raw).strftime(cmn.DATE_FMT)
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except (OSError, ValueError, OverflowError):
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return None
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s = str(raw).strip()
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if not s:
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return None
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# 已经是 YYYY-MM-DD
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if len(s) >= 10 and s[4] == "-" and s[7] == "-":
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head = s[:10]
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try:
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datetime.strptime(head, cmn.DATE_FMT)
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return head
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except ValueError:
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return None
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return None
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def aggregate_daily(
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all_records: list[dict],
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columns: list[dict],
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user_ids: list[str],
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user_name_map: dict[str, str],
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stats: cmn.CallStats,
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) -> list[dict[str, Any]]:
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"""
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按 (userId, workDate) 聚合：
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- 数值字段：sum
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- 非数值字段：取首个非空值（同一天同字段通常只有一个值）
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返回每人每天一行的 dict 列表，按 userId、workDate 排序。
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"""
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# bucket: (userId, date) → column_name → {sum: float, count_num: int, first_nonnum: Any}
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buckets: dict[tuple[str, str], dict[str, dict]] = defaultdict(
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lambda: {col["_column_name"]: {"sum": 0.0, "count_num": 0, "first_nonnum": None}
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for col in columns}
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)
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no_date_count = 0
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for record in all_records:
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uid = _user_id_of(record)
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if uid is None:
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continue
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date_str = _extract_work_date(record, columns)
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if date_str is None:
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no_date_count += 1
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date_str = "_no_date"
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for col in columns:
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cname = col["_column_name"]
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raw = _value_for_column(record, col)
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num = _try_number(raw)
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cell = buckets[(uid, date_str)][cname]
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if num is not None:
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cell["sum"] += num
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cell["count_num"] += 1
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elif raw not in (None, "") and cell["first_nonnum"] is None:
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cell["first_nonnum"] = raw
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if no_date_count > 0:
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stats.add_warning(
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f"{no_date_count} 条记录无法识别工作日期，已归入 '_no_date'。"
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"请用 --inspect 查看真实字段名"
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)
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# 输出：按 (uid, date) 排序
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rows: list[dict[str, Any]] = []
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for (uid, date_str) in sorted(buckets.keys(), key=lambda x: (x[0], x[1])):
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row: dict[str, Any] = {
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"userId": uid,
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"userName": user_name_map.get(uid, uid),
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"workDate": date_str,
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}
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bucket = buckets[(uid, date_str)]
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for col in columns:
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cname = col["_column_name"]
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cell = bucket[cname]
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if cell["count_num"] > 0:
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total = cell["sum"]
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row[cname] = int(total) if total == int(total) else round(total, 2)
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elif cell["first_nonnum"] is not None:
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row[cname] = cell["first_nonnum"]
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else:
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row[cname] = ""
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rows.append(row)
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return rows
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# ─────────────────────────────────────────────────────────────────────────────
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# main
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# ─────────────────────────────────────────────────────────────────────────────
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def main() -> int:
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args = parse_args()
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||||
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||||
raw_ids = [u.strip() for u in args.users.split(",") if u.strip()]
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||||
if not raw_ids:
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cmn.error("--users 不能为空")
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return 2
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||||
# 自动识别部门ID并展开为员工userId
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||||
user_ids = cmn.resolve_users_from_input(raw_ids)
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||||
if not user_ids:
|
||||
cmn.error("未能解析出任何有效的员工userId")
|
||||
return 2
|
||||
cmn.log(f"[users] 最终用户列表：{len(user_ids)} 人")
|
||||
|
||||
try:
|
||||
start = cmn.parse_datetime_arg(args.start, end_of_day=False)
|
||||
end = cmn.parse_datetime_arg(args.end, end_of_day=True)
|
||||
except ValueError as e:
|
||||
cmn.error(str(e))
|
||||
return 2
|
||||
|
||||
if end < start:
|
||||
cmn.error(f"--end ({end}) 早于 --start ({start})")
|
||||
return 2
|
||||
|
||||
try:
|
||||
columns = resolve_columns(args)
|
||||
except cmn.DwsCallError as e:
|
||||
if e.is_permission_error:
|
||||
cmn.error("权限错误：当前账号无管理员权限，无法获取考勤字段列表。")
|
||||
return 2
|
||||
cmn.error(f"获取字段列表失败：{e}")
|
||||
return 1
|
||||
except RuntimeError as e:
|
||||
cmn.error(str(e))
|
||||
return 1
|
||||
column_ids = [c["_column_id"] for c in columns]
|
||||
column_names = [c["_column_name"] for c in columns]
|
||||
column_id_to_name = {c["_column_id"]: c["_column_name"] for c in columns}
|
||||
|
||||
cmn.log(f"[users] 获取 {len(user_ids)} 个用户基础信息")
|
||||
user_info_map = cmn.resolve_user_info(user_ids)
|
||||
user_name_map = {uid: info.name or uid for uid, info in user_info_map.items()}
|
||||
|
||||
user_batches = cmn.chunk_users(user_ids)
|
||||
date_slices = cmn.slice_date_range(start, end)
|
||||
stats = cmn.CallStats(
|
||||
user_batches=len(user_batches),
|
||||
date_slices=len(date_slices),
|
||||
)
|
||||
cmn.log(
|
||||
f"[plan] 共 {len(user_batches)} 批 × {len(date_slices)} 个时间片 "
|
||||
f"= {len(user_batches) * len(date_slices)} 次接口调用"
|
||||
)
|
||||
|
||||
inspected_flag = [False]
|
||||
all_records: list[dict] = []
|
||||
for bi, batch in enumerate(user_batches, start=1):
|
||||
for si, dslice in enumerate(date_slices, start=1):
|
||||
cmn.log(f"[batch {bi}/{len(user_batches)}] [slice {si}/{len(date_slices)}]")
|
||||
records = query_one_batch(
|
||||
batch, column_ids, dslice, stats,
|
||||
column_id_to_name=column_id_to_name,
|
||||
inspect=args.inspect,
|
||||
inspected_flag=inspected_flag,
|
||||
)
|
||||
all_records.extend(records)
|
||||
|
||||
if not all_records:
|
||||
stats.add_warning("查询完成，但未得到任何记录")
|
||||
|
||||
# 从原始记录中提取每个用户的考勤组名称
|
||||
group_name_map = cmn.extract_group_names_from_records(all_records, user_ids)
|
||||
|
||||
rows_dict = aggregate_daily(all_records, columns, user_ids, user_name_map, stats)
|
||||
|
||||
# 请假数据特殊处理：通过 query-leave 单独查询，按 4 类假期按天展开
|
||||
# 凡是 "请假" 开头的字段（请假 / 请假分类 / 请假时长 等）都视为请假列
|
||||
leave_in_columns = any(_is_leave_field(name) for name in column_names)
|
||||
leave_data: dict[str, dict[str, dict[str, float]]] = {}
|
||||
if leave_in_columns:
|
||||
try:
|
||||
leave_data = cmn.query_leave_data(
|
||||
user_ids, start, end,
|
||||
leave_names=LEAVE_TYPES,
|
||||
stats=stats,
|
||||
)
|
||||
except cmn.DwsCallError as e:
|
||||
stats.add_warning(f"[leave] 查询请假数据失败：{e}")
|
||||
|
||||
# 表头对齐 SKILL.md 每日统计预定义列集合：姓名 | 考勤组 | 部门 | 日期 | 考勤字段...
|
||||
# 请假按假期类型展开为多列（如 "请假-事假", "请假-调休", ...），其余字段保持顺序
|
||||
# 多个 "请假*" 字段（如 "请假分类" + "请假时长"）只展开 1 次，避免重复
|
||||
base_headers = ["姓名", "考勤组", "部门", "日期"]
|
||||
data_headers: list[str] = []
|
||||
leave_expanded = False
|
||||
for cname in column_names:
|
||||
if cname == "工作日期":
|
||||
continue
|
||||
if _is_leave_field(cname):
|
||||
if not leave_expanded:
|
||||
data_headers.extend(f"{LEAVE_FIELD_NAME}-{lt}" for lt in LEAVE_TYPES)
|
||||
leave_expanded = True
|
||||
continue
|
||||
data_headers.append(cname)
|
||||
headers = base_headers + data_headers
|
||||
|
||||
rows_2d = []
|
||||
for row in rows_dict:
|
||||
uid = row.get("userId", "")
|
||||
info = user_info_map.get(uid, cmn.UserInfo(name=uid))
|
||||
group_name = group_name_map.get(uid, "")
|
||||
work_date = row.get("workDate", "")
|
||||
base = [info.name or uid, group_name, info.dept_name, work_date]
|
||||
# 当天该用户的请假数据
|
||||
day_leave = leave_data.get(uid, {}).get(work_date, {}) if leave_in_columns else {}
|
||||
data: list[Any] = []
|
||||
leave_filled = False
|
||||
for cname in column_names:
|
||||
if cname == "工作日期":
|
||||
continue
|
||||
if _is_leave_field(cname):
|
||||
if not leave_filled:
|
||||
for lt in LEAVE_TYPES:
|
||||
val = day_leave.get(lt, 0.0)
|
||||
if val == 0.0:
|
||||
data.append("")
|
||||
elif val == int(val):
|
||||
data.append(int(val))
|
||||
else:
|
||||
data.append(round(val, 2))
|
||||
leave_filled = True
|
||||
continue
|
||||
data.append(row.get(cname, ""))
|
||||
rows_2d.append(base + data)
|
||||
|
||||
out_name = args.out or cmn.build_output_filename(start, end, suffix="daily")
|
||||
title = (
|
||||
f"每日统计展示 统计日期：{start.strftime(cmn.DATE_FMT)} "
|
||||
f"至 {end.strftime(cmn.DATE_FMT)}"
|
||||
)
|
||||
subtitle = f"报表生成时间：{datetime.now().strftime('%Y-%m-%d %H:%M')}"
|
||||
try:
|
||||
cmn.write_excel(
|
||||
out_name, headers, rows_2d,
|
||||
sheet_name="每日统计",
|
||||
title=title,
|
||||
subtitle=subtitle,
|
||||
)
|
||||
except RuntimeError as e:
|
||||
cmn.error(str(e))
|
||||
return 1
|
||||
|
||||
cmn.print_summary(
|
||||
granularity_label="每日统计",
|
||||
out_path=out_name,
|
||||
user_count=len(user_ids),
|
||||
column_names=column_names,
|
||||
start=start,
|
||||
end=end,
|
||||
rows_count=len(rows_2d),
|
||||
stats=stats,
|
||||
extra_tail="ℹ️ 同一 (用户, 日期) 下数值字段已求和、非数值字段取首个值。",
|
||||
)
|
||||
return 0
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
sys.exit(main())
|
||||
Reference in New Issue
Block a user