parent
0a92a848d3
commit
9a06479491
340
票据理赔自动化/main.py
340
票据理赔自动化/main.py
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@ -16,6 +16,7 @@ from typing import Any, Dict, List, Optional, Tuple
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import cv2
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import numpy
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import pandas
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from fuzzywuzzy import fuzz
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from jinja2 import Environment, FileSystemLoader
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from jionlp import parse_location
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@ -813,14 +814,13 @@ if __name__ == "__main__":
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)
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candidates = []
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# 基于加权补偿的莱文斯坦距离算法计算所有内容的键名和指定键名的相似度
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for content in contents:
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candidates.append(
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(
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content["word"]["value"],
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fuzz.WRatio(
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content["name"], key, force_ascii=False
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),
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), # 基于加权补偿的莱文斯坦距离算法计算所有内容的键名和指定键名的相似度
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)
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)
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@ -831,63 +831,6 @@ if __name__ == "__main__":
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else None
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)
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def parse_items(contents):
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"""
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就识别结果，解析明细项
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:param contents: 识别结果
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:return 解析后的明细项
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"""
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# noinspection PyInconsistentReturns
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match contents[0].keys():
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case _ if "desc" in contents[0].keys():
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# noinspection PyTypeChecker
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return [
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{
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"名称": name,
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"数量": Decimal(quantity).quantize(
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Decimal("0.00"),
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rounding=ROUND_HALF_UP,
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),
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"金额": (Decimal(amount) + Decimal(tax)).quantize(
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Decimal("0.00"), rounding=ROUND_HALF_UP
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), # 深圳快瞳票据识别接口中明细的金额和税额由字符串转为Decimal，保留两位小数，并求和
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}
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for name, quantity, amount, tax in zip(
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[
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x["value"]
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for x in contents
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if re.match(
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r"^项目名称(\d+)?$",
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x["desc"],
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)
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],
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[
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x["value"]
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for x in contents
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if re.match(
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r"^数量(\d+)?$",
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x["desc"],
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)
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],
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[
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x["value"]
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for x in contents
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if re.match(
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r"^金额(\d+)?$",
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x["desc"],
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)
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],
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[
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x["value"]
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for x in contents
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if re.match(
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r"^税额(\d+)?$",
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x["desc"],
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)
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],
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)
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]
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# 初始化票据数据
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receipt = {"影像件编号": image["影像件编号"]}
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# 请求深圳快瞳票据查验接口（兼容增值税发票、医疗门诊/住院收费票据）
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@ -1115,7 +1058,54 @@ if __name__ == "__main__":
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"购药及就医机构": query_value(
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response["data"], "销售方名称"
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),
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"明细项": parse_items(response["data"]),
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"明细项": [
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{
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"名称": name,
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"数量": Decimal(quantity).quantize(
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Decimal("0.00"),
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rounding=ROUND_HALF_UP,
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),
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"金额": (
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Decimal(amount) + Decimal(tax)
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).quantize(
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Decimal("0.00"), rounding=ROUND_HALF_UP
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), # 深圳快瞳票据识别接口中明细的金额和税额由字符串转为Decimal，保留两位小数，并求和
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}
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for name, quantity, amount, tax in zip(
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[
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x["value"]
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for x in response["data"]
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if re.match(
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r"^项目名称(\d+)?$",
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x["desc"],
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)
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],
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[
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x["value"]
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for x in response["data"]
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if re.match(
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r"^数量(\d+)?$",
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x["desc"],
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)
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],
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[
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x["value"]
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for x in response["data"]
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if re.match(
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r"^金额(\d+)?$",
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x["desc"],
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)
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],
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[
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x["value"]
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for x in response["data"]
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if re.match(
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r"^税额(\d+)?$",
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x["desc"],
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)
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],
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)
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],
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"备注": query_value(response["data"], "备注"),
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}
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)
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@ -1168,6 +1158,20 @@ if __name__ == "__main__":
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receipt["global_detail"]["invoice_date"]["value"],
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"%Y-%m-%d",
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),
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"入院日期": (
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datetime.strptime(
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receipt["starttime"]["value"], "%Y-%m-%d"
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)
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if isinstance(receipt["starttime"], dict)
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else None
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),
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"出院日期": (
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datetime.strptime(
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receipt["endtime"]["value"], "%Y-%m-%d"
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)
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if isinstance(receipt["endtime"], dict)
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else None
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),
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"校验码": query_value(
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receipt["global_detail"]["region_specific"],
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"校验码",
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@ -1182,142 +1186,114 @@ if __name__ == "__main__":
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"购药及就医机构": receipt["hospital_name"]["value"],
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"明细项": [
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{
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"名称": field["value"],
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"数量": Decimal(field["value"]).quantize(
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Decimal("0.00"),
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rounding=(
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ROUND_HALF_UP
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if isinstance(
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(field := item["number"]), dict
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)
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else None
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),
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),
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"金额": (
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format(
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Decimal(field["value"]).quantize(
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Decimal("0.00"),
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rounding=ROUND_HALF_UP,
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),
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".2f",
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)
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if isinstance(
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(field := item["total_amount"]), dict
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)
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"名称": (
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item["item_name"]["value"]
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if isinstance(item["item_name"], dict)
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else None
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),
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"数量": Decimal(
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item["number"]["value"]
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if isinstance(item["number"], dict)
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else Decimal("1.00")
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).quantize(
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Decimal("0.00"),
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rounding=ROUND_HALF_UP,
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),
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"金额": Decimal(
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item["total_amount"]["value"]
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if isinstance(item["total_amount"], dict)
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else Decimal("1.00")
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).quantize(
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Decimal("0.00"),
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rounding=ROUND_HALF_UP,
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),
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}
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for item in receipt["feeitems"]
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if isinstance(item, dict)
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],
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"医保支付": (
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format(
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Decimal(field["value"]).quantize(
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Decimal("0.00"),
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rounding=ROUND_HALF_UP,
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),
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".2f",
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"个人自费": (
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Decimal(receipt["self_cost"]["value"]).quantize(
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Decimal("0.00"),
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rounding=ROUND_HALF_UP,
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)
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if isinstance(
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(field := receipt.get("medicare_pay")), dict
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)
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else None
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),
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"其它支付": format(
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(
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Decimal(value).quantize(
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Decimal("0.00"),
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rounding=ROUND_HALF_UP,
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)
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if (
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value := fuzzy_match(
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target="其它支付",
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components=receipt.get(
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"global_detail", {}
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).get("pay_list", []),
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specify_key="name",
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return_key="word.value",
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)
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)
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else None
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),
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".2f",
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),
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"个人自付": (
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format(
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Decimal(field["value"]).quantize(
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Decimal("0.00"),
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rounding=ROUND_HALF_UP,
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),
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".2f",
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Decimal(receipt["self_pay"]["value"]).quantize(
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Decimal("0.00"),
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rounding=ROUND_HALF_UP,
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)
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if isinstance(
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(field := receipt.get("self_pay")), dict
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)
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else None
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),
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"自付一": (
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format(
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Decimal(field["value"]).quantize(
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Decimal("0.00"),
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rounding=ROUND_HALF_UP,
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),
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".2f",
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)
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if isinstance(
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field := (receipt.get("self_pay_one")), dict
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)
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else None
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"医保支付": (
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Decimal(
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receipt["medicare_pay"]["value"]
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) # 医保基金统筹支付
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+ (
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Decimal(receipt["addition_pay"]["value"])
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if isinstance(receipt["addition_pay"], dict)
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else Decimal("0.00")
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) # 附加支付
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+ (
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Decimal(receipt["third_pay"]["value"])
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if isinstance(receipt["third_pay"], dict)
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else Decimal("0.00")
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) # 第三方支付
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).quantize(
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Decimal("0.00"),
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rounding=ROUND_HALF_UP,
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),
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"自付二": (
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format(
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Decimal(field["value"]).quantize(
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Decimal("0.00"),
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rounding=ROUND_HALF_UP,
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),
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".2f",
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)
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if isinstance(
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field := (receipt.get("self_pay_two")), dict
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)
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else None
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),
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"个人自费": (
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format(
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Decimal(field["value"]).quantize(
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Decimal("0.00"),
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rounding=ROUND_HALF_UP,
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),
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".2f",
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)
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if isinstance(
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field := (receipt.get("self_cost")), dict
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)
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else None
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),
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"住院日期": (
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datetime.strptime(
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field["value"], "%Y%m%d"
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).strftime("%Y-%m-%d")
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if isinstance(
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field := (receipt.get("starttime")), dict
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)
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else None
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),
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"出院日期": (
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datetime.strptime(
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field["value"], "%Y%m%d"
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).strftime("%Y-%m-%d")
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if isinstance(
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field := (receipt.get("endtime")), dict
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)
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else None
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),
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"医疗机构类型": receipt["others"][
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"medical_institution_type"
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]["value"],
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}
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)
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# 根据购药及就医机构查询购药及就医机构类型
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receipt["购药及就医机构类型"] = master_data.query_institution_type(
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receipt["购药及就医机构"]
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)
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# 根据影像件类型和购药及就医机构类型匹配购药及就医类型，就增值税发票且药店扣除不合理费用、增值税发票且私立医院解析个人自费、个人自付和医保支付
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match (image["影像件类型"], receipt["购药及就医机构类型"]):
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case ("增值税发票", "药店"):
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receipt["购药及就医类型"] = "药店购药"
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# 就相同明细项合并其数量和金额
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items = (
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pandas.DataFrame(receipt["明细项"])
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.groupby("名称")
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.agg(数量=("数量", "sum"), 金额=("金额", "sum"))
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.loc[
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lambda dataframe: dataframe["金额"] != 0
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] # 仅保留金额非0的明细项
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.reset_index()
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.to_dict("records")
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)
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for item in items:
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# 解析明细项大类名称和具体名称
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if match := re.match(
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r"^\*(?P<category_name>.*?)\*(?P<specific_name>.*)$",
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item["名称"],
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):
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category_name = match.group(
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"category_name"
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) # 明细项大类名称
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specific_name = match.group(
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"specific_name"
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) # 明细项具体名称
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else:
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pass
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print(specific_name)
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exit()
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case ("增值税发票", "私立医院"):
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receipt["购药及就医类型"] = "门诊就医"
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case ("医疗门诊收费票据", "公立医院"):
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receipt["购药及就医类型"] = "门诊就医"
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case ("医疗住院收费票据", "公立医院"):
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receipt["购药及就医类型"] = "住院治疗"
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case _:
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# TODO: 若匹配购药及就医类型发生异常则流转至人工处理
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raise RuntimeError("匹配购药及就医类型发生异常")
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# noinspection PyShadowingNames
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def bank_card_recognize(image) -> None:
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"""
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Reference in New Issue