zwf
469 lines
17 KiB
Python
469 lines
17 KiB
Python
import datetime
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import random
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from typing import Union
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import pandas as pd
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from sqlalchemy import select, delete
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from tornado_swagger.model import register_swagger_model
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from wtforms import StringField, IntegerField
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from wtforms.validators import Length
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import models
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from models.common_model import CommonModel
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from models.db_models import TD3iGovcTaskHistory
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from paste.core.logging import echo_log
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from paste.rbac.rbac_user import RbacUser
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from paste.util.pagination import Pagination
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from paste.web.form import ModelForm
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class GovcTaskHistoryForm(ModelForm):
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"""
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历史工单表单验证类（完全映射 TD3iGovcTaskHistory 字段）。
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用于验证和处理市12345历史工单的创建/修改表单数据。
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字段完全映射数据库表 t_d3i_govc_task_history 的字段结构。
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"""
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# 基础信息
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id = IntegerField('记录ID')
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task_id = IntegerField('关联工单主表ID', validators=[], message='关联工单主表ID必须为整数')
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history_date = StringField('日期', validators=[Length(max=32, message='日期长度不能超过32字符')])
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serial_num = StringField('历史工单号', validators=[Length(max=64, message='历史工单号长度不能超过64字符')])
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detail_url = StringField('详情页URL', validators=[Length(max=65535, message='详情页URL长度不能超过65535字符')])
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rqst_title = StringField('工单标题', validators=[Length(max=500, message='工单标题长度不能超过500字符')])
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state = StringField('状态', validators=[Length(max=64, message='状态长度不能超过64字符')])
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def process(self, formdata=None, obj=None, **kwargs):
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"""
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处理表单数据，在数据绑定前进行预处理。
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主要功能：
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- 遍历所有表单字段
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- 对字符串类型的值去除两端空白字符
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- 调用父类的process方法继续处理
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"""
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if formdata:
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for name, values in formdata.items():
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if isinstance(values, list) and values:
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formdata[name] = [v.strip() if isinstance(v, str) else v for v in values]
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elif isinstance(values, str):
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formdata[name] = values.strip()
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super().process(formdata, obj, **kwargs)
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class GovcTaskHistoryBase(TD3iGovcTaskHistory, CommonModel):
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"""
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历史工单基础类（完全映射 TD3iGovcTaskHistory 字段）。
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继承自数据库模型 TD3iGovcTaskHistory 和通用模型 CommonModel。
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封装所有与历史工单相关的通用操作方法。
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"""
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FieldMapping = {
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'id': 'id',
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'task_id': 'task_id',
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'history_date': 'history_date',
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'serial_num': 'serial_num',
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'detail_url': 'detail_url',
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'rqst_title': 'rqst_title',
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'state': 'state',
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'created_at': 'created_at',
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'created_by': 'created_by',
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'updated_at': 'updated_at',
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'updated_by': 'updated_by',
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}
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"""
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历史工单数据映射
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"""
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@classmethod
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async def is_exist(cls, serial_num: str):
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"""
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检查历史工单记录是否已存在（根据历史工单号）。
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:param serial_num: 历史工单号
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:return: 存在返回对象，不存在返回None
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"""
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_query = select(cls).where(cls.serial_num == serial_num)
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_history: cls = await cls.query_first(_query)
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return _history
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@classmethod
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async def search_base(cls, is_paging=True, **kwargs):
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"""
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按参数搜索历史工单数据的基础方法。
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支持字段：
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- task_id, serial_num, state, history_date
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- 支持模糊匹配：rqst_title, detail_url
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- 支持精确匹配：task_id, state
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:param is_paging: 是否分页
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:param kwargs: 查询参数
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:key int page_number: 页码（缺省随机1~100）
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:key int page_size: 每页数量（缺省20）
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:key dict sort_clause: 排序配置，如 {'serial_num': 'asc'}
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:key int task_id: 精确匹配关联工单主表ID
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:key str serial_num: 精确匹配历史工单号
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:key str history_date: 精确匹配日期
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:key str rqst_title: 模糊匹配工单标题
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:key str detail_url: 模糊匹配详情页URL
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:key str state: 精确匹配状态
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:return: (DataFrame, Pagination)
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"""
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page_number = kwargs.get('page_number', random.randint(1, 100))
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page_size = kwargs.get('page_size', 20)
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kwargs.update({'page_number': page_number, 'page_size': page_size})
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# 模糊查询字段
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_name_likes = {
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cls.rqst_title.key: '%{}%',
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cls.detail_url.key: '%{}%',
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}
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_query = select(cls).where(
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*cls.search_wheres(likes=_name_likes, **kwargs)
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).group_by(cls.id)
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_paging = None
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if is_paging:
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_row_count = await cls.query_count(_query)
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_paging = Pagination(_row_count).paging(page_number, page_size)
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_data_query = _query.limit(page_size).offset(_paging.offset_size)
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else:
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_data_query = _query.where()
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_sort_clause = cls.sort_clauses(kwargs.get('sort_clause', {}))
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if _sort_clause:
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_data_query = _data_query.order_by(*_sort_clause)
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else:
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_data_query = _data_query.order_by(cls.serial_num, cls.task_id)
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_history_df = await cls.query_as_df(_data_query)
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if not _history_df.empty:
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_history_df.replace(models.EmptyInDF + models.EmptyDatetimeInDF, '', inplace=True)
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_history_df[cls.id.key] = _history_df[cls.id.key].astype(str)
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_history_df[cls.task_id.key] = _history_df[cls.task_id.key].astype(str)
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return _history_df, _paging
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@classmethod
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async def search(cls, **kwargs):
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"""
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按参数搜索历史工单数据，返回分页格式数据。
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"""
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_history_df, _paging = await cls.search_base(** kwargs)
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return {
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'total': _paging.row_count,
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'rows': _history_df.to_dict('records'),
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'pagination': {
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'page_number': _paging.page_number,
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'page_count': _paging.page_count,
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'page_size': _paging.page_size,
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},
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}
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@classmethod
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async def exists_serial_num(cls, data_df: pd.DataFrame):
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"""
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查找 data_df 中在数据库中已存在和不存在的记录。根据 serial_num 字段判断。
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:param data_df: 输入的数据框架，必须包含 serial_num 列
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:return: (exists_df: pd.DataFrame, latest_df: pd.DataFrame)
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- exists_df: 在数据库中存在的记录
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- latest_df: 在数据库中不存在的记录
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"""
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if data_df.empty:
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return pd.DataFrame(), pd.DataFrame()
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# 获取待查询的 serial_num 列表（去重）
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serial_nums = data_df[cls.serial_num.key].unique().tolist()
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if not serial_nums:
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return pd.DataFrame(), data_df.copy()
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# 查询数据库中已存在的 serial_num
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_query = select(cls.id, cls.serial_num).where(cls.serial_num.in_(serial_nums))
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serial_nums_df = await cls.query_as_df(_query)
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if serial_nums_df.empty:
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return pd.DataFrame(), data_df.copy()
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# 构建 serial_num -> id 的映射字典
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serial_num_to_id_map = dict(zip(serial_nums_df[cls.serial_num.key], serial_nums_df[cls.id.key]))
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# 根据 serial_num 是否在数据库中，划分数据
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mask_exists = data_df[cls.serial_num.key].isin(serial_nums_df[cls.serial_num.key])
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# 数据库已经有的记录
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exists_df = data_df[mask_exists].copy()
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# 自动补充从数据库查到的 id 字段
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exists_df[cls.id.key] = exists_df[cls.serial_num.key].map(serial_num_to_id_map)
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# 新的数据
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latest_df = data_df[~mask_exists].copy()
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return exists_df, latest_df
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@register_swagger_model
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class GovcTaskHistory(GovcTaskHistoryBase):
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"""
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历史工单模型类（主业务类，完全继承 TD3iGovcTaskHistory 字段）。
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---
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description: 市12345历史工单接口
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type: object
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properties:
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id:
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description: 主键ID
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type: integer
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example: 1001
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readOnly: true
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task_id:
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description: 关联工单主表ID
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type: integer
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example: 5001
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maxLength: 20
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history_date:
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description: 日期
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type: string
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example: "2024-05-01"
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maxLength: 32
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serial_num:
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description: 历史工单号
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type: string
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example: "HIST20240501001"
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maxLength: 64
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detail_url:
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description: 详情页URL
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type: string
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example: "http://12345.gov.cn/detail/1001"
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maxLength: 65535
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rqst_title:
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description: 工单标题
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type: string
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example: "市民反映小区垃圾分类问题"
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maxLength: 500
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state:
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description: 状态
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type: string
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example: "已办结"
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maxLength: 64
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created_at:
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description: 创建时间，ISO格式的日期时间字符串
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type: string
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format: date-time
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example: "2024-01-15 10:30:00"
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readOnly: true
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created_by:
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description: 创建者用户名
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type: string
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example: "admin"
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readOnly: true
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updated_at:
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description: 修改时间，ISO格式的日期时间字符串
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type: string
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format: date-time
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example: "2024-01-16 14:25:00"
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readOnly: true
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updated_by:
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description: 修改者用户名
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type: string
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example: "editor"
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readOnly: true
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"""
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@classmethod
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async def create(cls, user: RbacUser = None, **kwargs):
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"""
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创建新的历史工单记录。
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业务流程：
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1. 使用 GovcTaskHistoryForm 验证表单数据完整性
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2. 检查是否已存在相同 serial_num 的记录（避免重复提交）
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3. 创建新历史工单对象
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4. 设置创建者和更新者为当前用户
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5. 保存到数据库
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6. 返回创建的对象
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:param RbacUser user: 操作用户对象
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:param kwargs: 历史工单参数字典
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:return: 新建历史工单对象
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:rtype: GovcTaskHistory
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:raises AssertionError: 当记录已存在时抛出
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:raises ValidationError: 当表单验证失败时抛出
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"""
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# 处理字符串字段去除空格
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for _k, _v in kwargs.items():
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if isinstance(_v, str):
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kwargs[_k] = _v.strip()
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_form = GovcTaskHistoryForm(formdata=kwargs)
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_form.validate_form()
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# 检查是否已存在相同 serial_num 的记录
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_existing = await cls.is_exist(_form.serial_num.data)
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assert _existing is None, "该历史工单已存在，不能重复提交。"
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# 创建对象
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_history = cls().copy_from_dict(_form.data, skip_none=True).before_save()
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if user:
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_history.created_by = user.username
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_history.updated_by = user.username
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await _history.async_save()
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return _history
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@classmethod
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async def delete(cls, history_id: Union[str, int]):
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"""
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删除历史工单记录。
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业务流程：
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1. 根据ID查找记录
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2. 验证存在性
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3. 执行删除
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:param history_id: 要删除的历史工单记录ID
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:return: 删除的记录对象
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:rtype: GovcTaskHistory
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:raises AssertionError: 当记录不存在时抛出
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"""
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_history: cls = await cls.async_find_by_id(history_id)
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assert _history, f"根据 ID {history_id} 未找到历史工单记录。"
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_del_query = delete(cls).where(cls.id == _history.id)
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_del_count = (await cls.raw_execute(_del_query)).rowcount
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echo_log(f'已删除历史工单记录（历史工单号：{_history.serial_num}，ID：{_history.id}）.')
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return _history
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@classmethod
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async def modify(cls, history_id: Union[str, int], user: RbacUser = None, **kwargs):
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"""
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修改已有历史工单记录。
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业务流程：
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1. 将 history_id 添加到参数中
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2. 处理字符串字段去除首尾空格
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3. 使用 GovcTaskHistoryForm 验证表单数据
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4. 查询原记录
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5. 验证存在性
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6. 更新字段并设置更新者
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7. 保存到数据库
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8. 返回更新后的对象
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:param history_id: 要修改的历史工单记录ID
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:param RbacUser user: 操作用户对象
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:param kwargs: 需要更新的字段
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:return: 修改后的历史工单对象
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:rtype: GovcTaskHistory
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:raises AssertionError: 当记录不存在时抛出
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:raises ValidationError: 当表单验证失败时抛出
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"""
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# 处理字符串字段去除空格
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for _k, _v in kwargs.items():
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if isinstance(_v, str):
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kwargs[_k] = _v.strip()
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# 表单验证
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_form = GovcTaskHistoryForm(formdata=kwargs)
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_form.validate_form()
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# 查询原记录
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_history: cls = await cls.async_find_by_id(history_id)
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assert _history, f'查无此历史工单信息。'
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# 更新字段
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_history.copy_from_dict(_form.data, skip_none=True).before_save()
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_history.updated_by = user.username
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await _history.async_save()
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return _history
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@classmethod
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async def create_batch(cls, data_df: pd.DataFrame, user: RbacUser = None):
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"""
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批量创建历史工单记录（传入数据应为全新记录）。
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:param data_df: 包含历史工单数据的 DataFrame
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:param user: 操作用户对象，用于设置 created_by / updated_by
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:return: 成功创建的数量
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:rtype: int
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"""
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if data_df.empty:
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return 0
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if user:
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data_df['created_by'] = user.username
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data_df['updated_by'] = user.username
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records = data_df.to_dict('records')
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histories = [cls().copy_from_dict(record, skip_none=True).before_save() for record in records]
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session = cls.get_aio_session()
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try:
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session.add_all(histories)
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await session.commit()
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except Exception as e:
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await session.rollback()
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raise e
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finally:
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await session.close()
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echo_log(f"批量创建成功：创建 {len(histories)} 条历史工单记录。")
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return len(histories)
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@classmethod
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async def modify_batch(cls, data_df: pd.DataFrame, user: RbacUser = None):
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"""
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批量修改已有历史工单记录。
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:param data_df: 包含历史工单数据的 DataFrame（必须包含 id 列）
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:param user: 操作用户对象，用于设置 updated_by
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:return: 成功更新的数量
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:rtype: int
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"""
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if data_df.empty:
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return 0
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# 必须包含 id 列
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if 'id' not in data_df.columns:
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echo_log(f"错误：modify_batch 要求输入数据必须包含 '{cls.id.key}' 列（主键）")
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return 0
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# 手动添加更新时间戳
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data_df['updated_at'] = datetime.datetime.now()
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# 添加更新者信息
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if user:
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data_df['updated_by'] = user.username
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# 转换为字典列表
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update_data = data_df.to_dict('records')
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# 使用 bulk_update_mappings
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session = cls.get_aio_session()
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try:
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await session.run_sync(
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lambda sync_session: sync_session.bulk_update_mappings(cls, update_data)
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)
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await session.commit()
|
||
updated_count = len(update_data)
|
||
except Exception as e:
|
||
await session.rollback()
|
||
raise e
|
||
finally:
|
||
await session.close()
|
||
|
||
echo_log(f"批量修改成功：更新 {updated_count} 条历史工单记录。")
|
||
return updated_count
|
||
|
||
@classmethod
|
||
async def save_batch(cls, data_df: pd.DataFrame, user: RbacUser = None):
|
||
"""
|
||
批量保存历史工单数据，自动处理新建和更新。
|
||
|
||
:param data_df: 要保存的数据框架
|
||
:param user: 用户
|
||
:return: 新建和更新的数量
|
||
"""
|
||
# 筛选数据状态
|
||
_exists_df, _latest_df = await GovcTaskHistory.exists_serial_num(data_df)
|
||
# 保存到数据库
|
||
_created_count = await GovcTaskHistory.create_batch(_latest_df, user)
|
||
_updated_count = await GovcTaskHistory.modify_batch(_exists_df, user)
|
||
return _created_count, _updated_count |