200 lines
7.1 KiB
Python
200 lines
7.1 KiB
Python
# -*- coding: utf-8 -*-
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"""
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票据理赔自动化最小化实现
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功能清单
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https://liubiren.feishu.cn/docx/WFjTdBpzroUjQvxxrNIcKvGnneh?from=from_copylink
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"""
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from datetime import datetime
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from pathlib import Path
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from typing import Any, Dict, List
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import pandas
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from jinja2 import Environment, FileSystemLoader
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from common import dossier, rule_engine
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from image import image_classify
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from image import image_recognize
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if __name__ == "__main__":
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# 初始化工作目录路径
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workplace_path = Path("directory")
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workplace_path.mkdir(parents=True, exist_ok=True) # 若工作目录不存在则创建
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# 实例化JINJA2环境
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environment = Environment(loader=FileSystemLoader("."))
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# 添加DATE过滤器
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environment.filters["date"] = lambda date: (
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date.strftime("%Y-%m-%d") if date else "长期"
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)
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# 加载赔案档案模版
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template = environment.get_template("template.html")
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# -------------------------
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# 自定义方法
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# -------------------------
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# noinspection PyShadowingNames
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def case_adjust() -> None:
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"""
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理算赔案并整合至赔案档案
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:return: 无
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"""
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def receipt_adjust(row: pandas.Series) -> List[Dict[str, Any]]:
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"""
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票据理算
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:param row: 票据
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:return: 理算记录
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"""
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date = row["date"]
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current_type = row["就诊类型"]
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current_amount = row["合理金额"]
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remaining_claim = current_amount
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claim_details = []
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if current_amount <= 0:
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return []
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# 筛选有效保单并排序
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valid_rules = sorted(
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[
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r
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for r in policy_rules
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if current_type in r["就诊类型"]
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and r["生效日期"] <= current_date <= r["失效日期"]
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and r["剩余额度"] > 0.0
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],
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key=lambda x: x["剩余额度"],
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reverse=True,
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)
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# 循环分摊赔付，生成分明细列表
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for rule in valid_rules:
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if remaining_claim <= 0.0:
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break
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pay_ratio = rule["赔付比例"]
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rule_name = rule["责任名称"]
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remaining_quota = rule["剩余额度"]
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max_payable = remaining_claim * pay_ratio
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actual_pay = min(remaining_quota, max_payable)
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if actual_pay > 0.0:
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corresponding_actual_amount = actual_pay / pay_ratio
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# 构建明细字典（字段与后续DataFrame列对应）
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detail = {
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"就诊类型": current_type,
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"就诊合理金额": current_amount,
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"保单责任名称": rule_name,
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"保单赔付比例": pay_ratio,
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"保单本次赔付金额": round(actual_pay, 2),
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"本次对应合理金额部分": round(corresponding_actual_amount, 2),
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"保单赔付后剩余额度": round(remaining_quota - actual_pay, 2),
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}
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claim_details.append(detail)
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# 更新保单额度和剩余待赔付金额
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rule["剩余额度"] -= actual_pay
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remaining_claim -= corresponding_actual_amount
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return claim_details
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# 基于据拒付规则评估
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if not (result := rule_engine.evaluate(decision="拒付", inputs=dossier)):
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# TODO: 若评估结果为空值（保险分公司未配置拒付规则）则流转至人工处理
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raise
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dossier["adjustment_layer"].update(
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{
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"conclusion": result["conclusion"], # 理赔结论
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"explanation": result["explanation"], # 结论说明
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}
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)
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if result["conclusion"] == "拒付":
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return
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adjustments = (
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pandas.DataFrame(dossier["receipts_layer"]).assing(
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adjustments=lambda dataframe: dataframe.apply(
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receipt_adjust, axis="columns"
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)
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)
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).explode("adjustments", ignore_index=True)
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print(adjustments)
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# 遍历工作目录中赔案目录并创建赔案档案（模拟自动化域就待自动化任务创建理赔档案）
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for case_path in [x for x in workplace_path.iterdir() if x.is_dir()]:
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# 初始化赔案档案（保险公司将提供投保公司、保险分公司和报案时间等，TPA作业系统签收后生成赔案号）
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dossier["report_layer"].update(
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{
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"report_time": datetime(2025, 7, 25, 12, 0, 0), # 指定报案时间
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"case_number": case_path.stem, # 设定：赔案目录名称为赔案号
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"insurer_company": (
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insurer_company := "中银保险有限公司苏州分公司"
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), # 指定保险分公司
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}
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)
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# 遍历赔案目录中影像件
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for image_index, image_path in enumerate(
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sorted(
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[
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x
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for x in case_path.glob(pattern="*")
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if x.is_file() and x.suffix.lower() in [".jpg", ".jpeg", ".png"]
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], # 实际作业亦仅支持JPG、JPEG或PNG
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key=lambda x: x.stat().st_ctime, # 根据影像件创建时间顺序排序
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),
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1,
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):
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# 分类影像件并旋正（较初审自动化无使能检查）
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image_classify(image_index, image_path)
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# 就影像件层按照影像件类型指定排序
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dossier["images_layer"].sort(
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key=lambda x: [
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"居民户口簿",
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"居民身份证(国徽面)",
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"居民身份证(头像面)",
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"居民身份证(国徽、头像面)",
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"中国港澳台地区及境外护照",
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"理赔申请书",
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"增值税发票",
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"医疗门诊收费票据",
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"医疗住院收费票据",
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"医疗费用清单",
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"银行卡",
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"其它",
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].index(x["image_type"])
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)
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# 遍历影像件层中影像件
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for image in dossier["images_layer"]:
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# 识别影像件并整合至赔案档案
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image_recognize(
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image,
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insurer_company,
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)
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# 就票据层按照开票日期和票据号顺序排序
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dossier["receipts_layer"].sort(key=lambda x: (x["date"], x["number"]))
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print(dossier["insured_persons_layer"])
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exit()
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# 理算
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case_adjust()
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print(dossier["adjustment_layer"])
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for receipt in dossier["receipts_layer"]:
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print(receipt)
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print(dossier["report_layer"])
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print(dossier["insured_person_layer"])
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print(dossier["insured_persons_layer"])
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dossier.pop("images_layer")
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dossier.pop("receipts_layer")
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