290 lines
10 KiB
Python
290 lines
10 KiB
Python
from api import API
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import pandas as pd
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from tqdm import tqdm
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import hashlib
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from datetime import datetime
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# 初始化 API 实例和 Token
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api_instance = API()
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TOKEN = api_instance.generateToken()
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FORMID = "FORM-XHA66881FHMAR0F07GT4Y59GGA972DD6B5OHLB"
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appType = "APP_RTPWHV37ENXPQUZHTL25"
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systemToken = "IA766O61SHFZT6UB0WNOB58GI5RW2K58KCU1LL6"
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def fetch_process_data():
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"""获取所有流程实例"""
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today_midnight = datetime.now().replace(hour=0, minute=0, second=0, microsecond=0) # 当天0点
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form_data_two = api_instance.read_processes_instances(
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token=TOKEN, formUuid=FORMID, page=1, n=100,
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appType=appType, systemToken=systemToken, instanceStatus=""
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# createFromTimeGMT=today_midnight.timestamp() * 1000
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) # 之后添加增量更新
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all_process_list = []
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PAGES_two = form_data_two.get('totalCount') // 100 + 1
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# 手动控制小于3w
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PAGES_two = 290
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for a in tqdm(range(1, PAGES_two + 1)):
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try:
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form_data_two = api_instance.read_processes_instances(
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token=TOKEN, formUuid=FORMID, page=a, n=100,
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appType=appType, systemToken=systemToken, instanceStatus=""
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)
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all_process_list = all_process_list + form_data_two.get("data")
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except Exception as e:
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print(f"Error fetching page {a}: {e}")
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continue
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# break
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return all_process_list
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def extract_approval_records(process_instances):
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"""提取每条流程的审批记录"""
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all_data_list = []
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for data in tqdm(process_instances, desc="处理流程实例"):
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processInstanceId = data.get("processInstanceId")
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version = data.get("version")
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res_new = api_instance.get_approval_records(
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token=TOKEN, processInstanceId=processInstanceId,
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appType=appType, systemToken=systemToken
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)
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records_new = res_new.get('result', [])
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for record in records_new:
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operateTimeGMT = record.get('operateTimeGMT')
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# if operateTimeGMT is not None:
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# operateTime = datetime.fromtimestamp(operateTimeGMT / 1000).strftime('%Y-%m-%d %H:%M:%S')
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# else:
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# operateTime = operateTimeGMT
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showName = record.get('showName')
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operatorName = record.get('operatorName')
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action = record.get('action')
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# data_id = record.get('dataId')
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activity_id = record.get('activityId')
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all_data_list.append(
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[operateTimeGMT, showName, operatorName, action, processInstanceId, version, activity_id])
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df = pd.DataFrame(all_data_list)
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df.to_csv("审批记录.csv", index=False)
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return all_data_list
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def group_by_process(all_data_list):
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"""按 '提交申请' 分组，一个流程为一组"""
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result_groups = []
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current_group = []
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j = 1
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for record in all_data_list:
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showName = record[1]
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if showName == "提交申请":
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record.append(0)
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j = 1
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if current_group:
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result_groups.append(current_group)
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current_group = []
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current_group.append(record)
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else:
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record.append(j)
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j += 1
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current_group.append(record)
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if current_group:
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result_groups.append(current_group)
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return result_groups
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def transform_to_wide_table(result_groups):
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"""将审批记录从长表转为宽表"""
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flattened_rows = []
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for group in result_groups:
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row_data = {}
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# 遍历其余审批节点
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for i, item in enumerate(group, start=1):
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operateTimeGMT, showName, operatorName, action, dataId, version, activity_id, index, = item
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if action == "已撤销":
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showName = "该节点已撤销"
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row_data.update({
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f'审批{i}时间': operateTimeGMT,
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f'审批{i}节点名': showName,
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f'审批{i}人': operatorName,
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f'审批{i}动作': action,
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f'序号{i}': index,
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f'审批{i}数据id': dataId,
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f'审批{i}流程版本': version,
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f'审批{i}流程节点id': activity_id
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})
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flattened_rows.append(row_data)
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# 转换为DataFrame
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df_final = pd.DataFrame(flattened_rows)
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# 计算最大审批步骤
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max_steps = max(len(group) - 1 for group in result_groups) # 减去提交节点
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# 构建所有列名
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all_columns = [
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'审批时间', '审批节点名', '审批人', '审批动作', '序号', '数据id', '流程版本', '流程节点id'
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]
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for i in range(1, max_steps + 1):
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all_columns.extend([
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f'审批{i}时间',
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f'审批{i}节点名',
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f'审批{i}人',
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f'审批{i}动作',
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f'序号{i}',
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f'审批{i}数据id',
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f'审批{i}流程版本',
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f'审批{i}流程节点id'
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])
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# 统一列结构并填充缺失值
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df_final = df_final.reindex(columns=all_columns)
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df_final.fillna("-", inplace=True)
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df_final = df_final[df_final['审批时间'].notna()]
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# 导出CSV
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df_final.to_csv("审批流程行转列结果_with_node_name.csv", index=False)
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return df_final, max_steps
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def classify_flows(df_final, max_steps):
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"""根据审批节点名 + 动作组合进行流程分组"""
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def extract_signature(row):
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signature = []
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i = 1
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while f'审批{i}节点名' in row:
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node_name = row[f'审批{i}节点名']
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action = row[f'审批{i}动作']
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if node_name == "-":
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break
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signature.append((node_name, action))
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i += 1
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return signature
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def has_special_action(signature):
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special_actions = {"已撤销", "已转交", "已退回", "已拒绝"}
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for _, action in signature:
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if action in special_actions:
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return True
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return False
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def get_hash(signature):
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sig_str = str(signature)
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return hashlib.md5(sig_str.encode('utf-8')).hexdigest()
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df_final['signature'] = df_final.apply(extract_signature, axis=1)
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group_map = {}
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current_group_id = 1
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df_final['group_id'] = 0
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for idx, row in df_final.iterrows():
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sig = row['signature']
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if has_special_action(sig):
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df_final.at[idx, 'group_id'] = 5000
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# current_group_id += 1
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else:
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sig_hash = get_hash(sig)
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if sig_hash not in group_map:
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group_map[sig_hash] = current_group_id
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current_group_id += 1
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df_final.at[idx, 'group_id'] = group_map[sig_hash]
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df_final.to_csv("审批流程分类结果.csv", index=False)
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print("✅ 分组完成，已保存至 '审批流程分类结果.csv'")
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result_rows = []
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for index, row in df_final.iterrows():
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base_info = {'group_id': row["group_id"]}
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process_id_list = []
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process_list = []
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for i in range(1, max_steps):
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prefix = f'审批{i}'
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if row[f'{prefix}流程节点id'] != "-":
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process_id = row[f'{prefix}流程节点id']
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process_id_list.append(process_id)
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if row[f'{prefix}节点名'] != "-":
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process = row[f'{prefix}节点名']
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process_list.append(process)
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for i in range(1, max_steps): # 审批1到审批n
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prefix = f'审批{i}'
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approval_data = {}
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if f'{prefix}时间' in df_final.columns and pd.notna(row[f'{prefix}时间']) and row[f'{prefix}时间'] != '-':
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approval_data = {
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'审批时间': row[f'{prefix}时间'],
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'审批节点名': row[f'{prefix}节点名'],
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'审批人': row[f'{prefix}人'],
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'审批动作': row[f'{prefix}动作'],
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'序号': row[f'序号{i}'] if f'序号{i}' in df_final.columns else '-',
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f'审批数据id': row[f'审批{i}数据id'] if f'审批{i}数据id' in df_final.columns else '-',
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f'审批流程版本': row[f'审批{i}流程版本'] if f'审批{i}流程版本' in df_final.columns else '-',
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f'审批流程节点id': row[f'审批{i}流程节点id'] if f'审批{i}流程节点id' in df_final.columns else '-',
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f'审批节点id合并': process_id_list,
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f'审批节点名合并': process_list
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}
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# 合并基础数据和审批数据
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result_row = {**base_info, **approval_data}
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result_rows.append(result_row)
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dfn = pd.DataFrame(result_rows)
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dfn.to_csv("审批流程分类结果_with_node_name.csv", index=False)
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return dfn
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def time_calculate(df_final):
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"""计算每个审批步骤之间的耗时（秒），并设置“提交申请”的耗时为0"""
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# 确保审批时间为 datetime 类型
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df_final["审批时间"] = pd.to_datetime(df_final["审批时间"])
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# 按照流程 ID 分组计算耗时（如果存在流程 ID 列）
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if '流程ID' in df_final.columns:
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df_final['耗时'] = df_final.groupby('流程ID')['审批时间'].diff().dt.total_seconds().div(60)
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else:
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df_final['耗时'] = df_final['审批时间'].diff().dt.total_seconds().div(60)
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# 将“提交申请”行的耗时设置为0
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df_final.loc[df_final['审批动作'] == '提交申请', '耗时'] = 0
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# 处理首行 NaN（或者非同一流程导致的 NaN）
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df_final['耗时'] = df_final['耗时'].fillna(0)
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df_final = df_final[df_final['审批时间'].notna()]
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# 保存结果
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df_final.to_csv("最终结果.csv", index=False)
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return df_final
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if __name__ == "__main__":
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# Step 1: 获取流程实例
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process_instances = fetch_process_data()
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# Step 2: 提取审批记录
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all_data_list = extract_approval_records(process_instances)
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# Step 3: 按 '提交申请' 分组
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result_groups = group_by_process(all_data_list)
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# Step 4: 转换为宽表
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df_final, max_steps = transform_to_wide_table(result_groups)
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# Step 5: 对流程进行分类并保存结果
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df_final1 = classify_flows(df_final, max_steps)
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# Step 6: 耗时计算
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df_final2 = time_calculate(df_final1)
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