日常更新

from NUC

票据理赔自动化/image.py

@@ -24,15 +24,6 @@ authenticator = Authenticator()
 http_client = HTTPClient(timeout=300, cache_enabled=True)  # 使用缓存
 
 
-# noinspection PyShadowingNames
-def image_classify(image_index: int, image_path: Path) -> Optional[Tuple[str, str]]:
-    """
-    分类影像件并旋正
-    :param image_index: 影像件编号
-    :param image_path: 影像件路径（path对象）
-    :return: 无
-    """
-
 # noinspection PyShadowingNames
 def image_read(
     image_path: Path,
@@ -52,6 +43,7 @@ def image_classify(image_index: int, image_path: Path) -> Optional[Tuple[str, st
     except Exception as exception:
         raise RuntimeError(f"打开并读取影像件发生异常：{str(exception)}")
 
+
 # noinspection PyShadowingNames
 def image_serialize(image_format: str, image_ndarray: numpy.ndarray) -> str:
     """
@@ -68,6 +60,7 @@ def image_classify(image_index: int, image_path: Path) -> Optional[Tuple[str, st
     image_guid = md5(image_ndarray_encoded.tobytes()).hexdigest().upper()
     return image_guid
 
+
 # noinspection PyShadowingNames
 def image_compress(
     image_format: str,
@@ -101,9 +94,7 @@ def image_classify(image_index: int, image_path: Path) -> Optional[Tuple[str, st
                 break
 
             # 影像件BASE64编码
-                image_base64 = b64encode(image_ndarray_encoded.tobytes()).decode(
+            image_base64 = b64encode(image_ndarray_encoded.tobytes()).decode("utf-8")
-                    "utf-8"
-                )
             if len(image_base64) <= image_size_specified:
                 return image_base64
 
@@ -121,6 +112,124 @@ def image_classify(image_index: int, image_path: Path) -> Optional[Tuple[str, st
 
     return None
 
+
+# noinspection PyShadowingNames
+def calculate_age(report_time: datetime, birth_date: datetime) -> int:
+    """
+    根据报案时间计算周岁
+    :param report_time: 报案时间
+    :param birth_date: 出生日期
+    :return 周岁
+    """
+    age = report_time.year - birth_date.year
+
+    return (
+        age - 1
+        if (report_time.month, report_time.day)
+        < (
+            birth_date.month,
+            birth_date.day,
+        )
+        else age
+    )  # 若报案时间的月日小于生成日期的月日则前推一年
+
+
+# TODO: 后续添加居民身份证(国徽面)和居民身份证(头像面)合并
+# noinspection PyShadowingNames
+def identity_card_recognize(image, insurer_company) -> None:
+    """
+    识别居民身份证并整合至赔案档案
+    :param image: 影像件
+    :param insurer_company: 保险分公司
+    :return: 无
+    """
+    # 请求深圳快瞳居民身份证识别接口
+    response = http_client.post(
+        url=(url := "https://ai.inspirvision.cn/s/api/ocr/identityCard"),
+        headers={
+            "X-RequestId-Header": image["image_guid"]
+        },  # 以影像件唯一标识作为请求唯一标识，用于双方联查
+        data={
+            "token": authenticator.get_token(servicer="szkt"),  # 获取深圳快瞳访问令牌
+            "imgBase64": f"data:image/{image["image_format"].lstrip(".")};base64,{image["image_base64"]}",  # 影像件BASE64编码嵌入数据统一资源标识符
+        },  # 深圳快瞳支持同时识别居民国徽面和头像面
+        guid=md5((url + image["image_guid"]).encode("utf-8")).hexdigest().upper(),
+    )
+    # TODO: 若请求深圳快瞳居民身份证识别接口发生异常则流转至人工处理
+    if not (response.get("status") == 200 and response.get("code") == 0):
+        raise
+
+    if image["image_type"] in [
+        "居民身份证(国徽、头像面)",
+        "居民身份证(头像面)",
+    ]:
+        # noinspection PyTypeChecker
+        dossier["insured_person_layer"].update(
+            {
+                "insured_person": (
+                    insured_person := response["data"]["name"]
+                ),  # 被保险人
+                "identity_type": (identity_type := "居民身份证"),  # 证件类型
+                "identity_number": (
+                    indentity_number := response["data"]["idNo"]
+                ),  # 证件号码
+                "gender": response["data"]["sex"],  # 性别
+                "birth_date": (
+                    birth_date := datetime.strptime(
+                        response["data"]["birthday"], "%Y-%m-%d"
+                    )
+                ),  # 出生日期，转为日期时间（datetime对象），格式默认为%Y-%m-%d
+                "age": calculate_age(
+                    dossier["report_layer"]["report_time"], birth_date
+                ),  # 年龄
+                "province": (
+                    residential_address := parse_location(response["data"]["address"])
+                ).get(
+                    "province"
+                ),  # 就住址解析为所在省、市、区和详细地址
+                "city": residential_address.get("city"),
+                "district": residential_address.get("county"),
+                "detailed_address": residential_address.get("detail"),
+            }
+        )
+
+        # 根据保险分公司、被保险人、证件类型、证件号码和出险时间查询个单
+        dossier["insured_persons_layer"] = master_data.query_liabilities(
+            insurer_company,
+            insured_person,
+            identity_type,
+            indentity_number,
+            dossier["report_layer"]["report_time"].strftime("%Y-%m-%d"),
+        )
+
+    if image["image_type"] in [
+        "居民身份证(国徽、头像面)",
+        "居民身份证(国徽面)",
+    ]:
+        # noinspection PyTypeChecker
+        dossier["insured_person_layer"].update(
+            {
+                "commencement_date": datetime.strptime(
+                    (period := response["data"]["validDate"].split("-"))[0],
+                    "%Y.%m.%d",
+                ),  # 就有效期限解析为有效起期和有效止期。其中，若有效止期为长期则默认为9999-12-31
+                "termination_date": (
+                    datetime(9999, 12, 31)
+                    if period[1] == "长期"
+                    else datetime.strptime(period[1], "%Y.%m.%d")
+                ),
+            }
+        )
+
+
+# noinspection PyShadowingNames
+def image_classify(image_index: int, image_path: Path) -> Optional[Tuple[str, str]]:
+    """
+    分类影像件并旋正
+    :param image_index: 影像件编号
+    :param image_path: 影像件路径（path对象）
+    :return: 无
+    """
     # 打开并读取影像件
     image_ndarray = image_read(image_path)
     image_index = f"{image_index:02d}"
@@ -221,139 +330,6 @@ def image_classify(image_index: int, image_path: Path) -> Optional[Tuple[str, st
     )
 
 
-# noinspection PyShadowingNames
-def image_recognize(
-    image,
-    insurer_company,
-) -> None:
-    """
-    识别影像件并整合至赔案档案
-    :param image: 影像件
-    :param insurer_company: 保险分公司
-    :return: 无
-    """
-
-    # TODO: 后续添加居民身份证(国徽面)和居民身份证(头像面)合并
-    # noinspection PyShadowingNames
-    def identity_card_recognize(image, insurer_company) -> None:
-        """
-        识别居民身份证并整合至赔案档案
-        :param image: 影像件
-        :param insurer_company: 保险分公司
-        :return: 无
-        """
-
-        # noinspection PyShadowingNames
-        def calculate_age(report_time: datetime, birth_date: datetime) -> int:
-            """
-            根据报案时间计算周岁
-            :param report_time: 报案时间
-            :param birth_date: 出生日期
-            :return 周岁
-            """
-            age = report_time.year - birth_date.year
-
-            return (
-                age - 1
-                if (report_time.month, report_time.day)
-                < (
-                    birth_date.month,
-                    birth_date.day,
-                )
-                else age
-            )  # 若报案时间的月日小于生成日期的月日则前推一年
-
-        # 请求深圳快瞳居民身份证识别接口
-        response = http_client.post(
-            url=(url := "https://ai.inspirvision.cn/s/api/ocr/identityCard"),
-            headers={
-                "X-RequestId-Header": image["image_guid"]
-            },  # 以影像件唯一标识作为请求唯一标识，用于双方联查
-            data={
-                "token": authenticator.get_token(
-                    servicer="szkt"
-                ),  # 获取深圳快瞳访问令牌
-                "imgBase64": f"data:image/{image["image_format"].lstrip(".")};base64,{image["image_base64"]}",  # 影像件BASE64编码嵌入数据统一资源标识符
-            },  # 深圳快瞳支持同时识别居民国徽面和头像面
-            guid=md5((url + image["image_guid"]).encode("utf-8")).hexdigest().upper(),
-        )
-        # TODO: 若请求深圳快瞳居民身份证识别接口发生异常则流转至人工处理
-        if not (response.get("status") == 200 and response.get("code") == 0):
-            raise
-
-        if image["image_type"] in [
-            "居民身份证(国徽、头像面)",
-            "居民身份证(头像面)",
-        ]:
-            # noinspection PyTypeChecker
-            dossier["insured_person_layer"].update(
-                {
-                    "insured_person": (
-                        insured_person := response["data"]["name"]
-                    ),  # 被保险人
-                    "identity_type": (identity_type := "居民身份证"),  # 证件类型
-                    "identity_number": (
-                        indentity_number := response["data"]["idNo"]
-                    ),  # 证件号码
-                    "gender": response["data"]["sex"],  # 性别
-                    "birth_date": (
-                        birth_date := datetime.strptime(
-                            response["data"]["birthday"], "%Y-%m-%d"
-                        )
-                    ),  # 出生日期，转为日期时间（datetime对象），格式默认为%Y-%m-%d
-                    "age": calculate_age(
-                        dossier["report_layer"]["report_time"], birth_date
-                    ),  # 年龄
-                    "province": (
-                        residential_address := parse_location(
-                            response["data"]["address"]
-                        )
-                    ).get(
-                        "province"
-                    ),  # 就住址解析为所在省、市、区和详细地址
-                    "city": residential_address.get("city"),
-                    "district": residential_address.get("county"),
-                    "detailed_address": residential_address.get("detail"),
-                }
-            )
-
-            # 根据保险分公司、被保险人、证件类型、证件号码和出险时间查询个单
-            dossier["insured_persons_layer"] = master_data.query_liabilities(
-                insurer_company,
-                insured_person,
-                identity_type,
-                indentity_number,
-                dossier["report_layer"]["report_time"].strftime("%Y-%m-%d"),
-            )
-
-        if image["image_type"] in [
-            "居民身份证(国徽、头像面)",
-            "居民身份证(国徽面)",
-        ]:
-            # noinspection PyTypeChecker
-            dossier["insured_person_layer"].update(
-                {
-                    "commencement_date": datetime.strptime(
-                        (period := response["data"]["validDate"].split("-"))[0],
-                        "%Y.%m.%d",
-                    ),  # 就有效期限解析为有效起期和有效止期。其中，若有效止期为长期则默认为9999-12-31
-                    "termination_date": (
-                        datetime(9999, 12, 31)
-                        if period[1] == "长期"
-                        else datetime.strptime(period[1], "%Y.%m.%d")
-                    ),
-                }
-            )
-
-    # noinspection PyShadowingNames
-    def application_recognize(image, insurer_company) -> None:
-        """
-        识别理赔申请书并整合至赔案档案
-        :param image: 影像件
-        :param insurer_company: 保险分公司
-        :return: 无
-        """
-
 # noinspection PyShadowingNames
 def mlm_recognize(image, schema) -> Optional[Dict[str, Any]]:
     """
@@ -422,6 +398,7 @@ def image_recognize(
     except:
         return None
 
+
 # noinspection PyShadowingNames
 def boc_application_recognize(image: str) -> None:
     """
@@ -541,20 +518,22 @@ def image_recognize(
         }
     )
 
+
+# noinspection PyShadowingNames
+def application_recognize(image, insurer_company) -> None:
+    """
+    识别理赔申请书并整合至赔案档案
+    :param image: 影像件
+    :param insurer_company: 保险分公司
+    :return: 无
+    """
+
     # 根据保险分公司匹配处理方法
     match insurer_company:
         # 中银保险有限公司
         case _ if insurer_company.startswith("中银保险有限公司"):
             boc_application_recognize(image)
 
-    # noinspection PyShadowingNames
-    def receipt_recognize(image, insurer_company) -> None:
-        """
-        识别票据并整合至赔案档案
-        :param image: 影像件
-        :param insurer_company: 保险分公司
-        :return: 空
-        """
 
 # noinspection PyShadowingNames
 def fuzzy_match(contents: list, key: str) -> Optional[str]:
@@ -598,9 +577,7 @@ def image_recognize(
             for content in contents:
                 if content["name"] == key:
                     return (
-                                content["word"]["value"]
+                        content["word"]["value"] if content["word"]["value"] else None
-                                if content["word"]["value"]
-                                else None
                     )
 
             candidates = []
@@ -620,6 +597,7 @@ def image_recognize(
                 else None
             )  # 返回>=80且最大的相似度的值
 
+
 def parse_item(item: str) -> Tuple[str, Optional[str]]:
     """
     根据明细项解析明细项类别和具体内容，并根据具体内容查询药品/医疗服务
@@ -637,6 +615,15 @@ def image_recognize(
     else:
         return item, None
 
+
+# noinspection PyShadowingNames
+def receipt_recognize(image, insurer_company) -> None:
+    """
+    识别票据并整合至赔案档案
+    :param image: 影像件
+    :param insurer_company: 保险分公司
+    :return: 空
+    """
     # 初始化票据数据
     receipt = {"image_index": image["image_index"]}
     # 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）
@@ -646,9 +633,7 @@ def image_recognize(
             "X-RequestId-Header": image["image_guid"]
         },  # 以影像件唯一标识作为请求唯一标识，用于双方联查
         data={
-                "token": authenticator.get_token(
+            "token": authenticator.get_token(servicer="szkt"),  # 获取深圳快瞳访问令牌
-                    servicer="szkt"
-                ),  # 获取深圳快瞳访问令牌
             "imgBase64": f"data:image/{image["image_format"].lstrip(".")};base64,{image["image_base64"]}",  # 影像件BASE64编码嵌入数据统一资源标识符
         },
         guid=md5((url + image["image_guid"]).encode("utf-8")).hexdigest().upper(),
@@ -676,9 +661,7 @@ def image_recognize(
                         "date": datetime.strptime(
                             response["data"]["details"]["date"], "%Y年%m月%d日"
                         ),  # 转为日期时间（datetime对象）
-                            "verification_code": response["data"]["details"][
+                        "verification_code": response["data"]["details"]["check_code"],
-                                "check_code"
-                            ],
                         "amount": Decimal(
                             response["data"]["details"]["total"]
                         ).quantize(
@@ -731,9 +714,7 @@ def image_recognize(
                         ),  # 转为日期时间（datetime对象）
                         "admission_date": (
                             datetime.strptime(
-                                    response["data"]["hospitalizationDate"].split("-")[
+                                response["data"]["hospitalizationDate"].split("-")[0],
-                                        0
-                                    ],
                                 "%Y%m%d",
                             )
                             if response["data"]["hospitalizationDate"]
@@ -741,9 +722,7 @@ def image_recognize(
                         ),  # 深圳快瞳票据查验接口中住院日期解析为入院日期和出院日期
                         "discharge_date": (
                             datetime.strptime(
-                                    response["data"]["hospitalizationDate"].split("-")[
+                                response["data"]["hospitalizationDate"].split("-")[1],
-                                        1
-                                    ],
                                 "%Y%m%d",
                             )
                             if response["data"]["hospitalizationDate"]
@@ -829,9 +808,7 @@ def image_recognize(
                     .upper(),
                 )
                 # TODO: 若请求深圳快瞳增值税发票识别接口发生异常则流转至人工处理
-                    if not (
+                if not (response.get("status") == 200 and response.get("code") == 0):
-                        response.get("status") == 200 and response.get("code") == 0
-                    ):
                     raise
 
                 match fuzzy_match(response["data"], "发票类型"):
@@ -849,16 +826,14 @@ def image_recognize(
                                     response["data"], "校验码"
                                 ),
                                 "amount": Decimal(
-                                        fuzzy_match(
+                                    fuzzy_match(response["data"], "小写金额").replace(
-                                            response["data"], "小写金额"
+                                        "¥", ""
-                                        ).replace("¥", "")
+                                    )
                                 ).quantize(
                                     Decimal("0.00"),
                                     rounding=ROUND_HALF_UP,
                                 ),
-                                    "payer": fuzzy_match(
+                                "payer": fuzzy_match(response["data"], "购买方名称"),
-                                        response["data"], "购买方名称"
-                                    ),
                                 "institution": fuzzy_match(
                                     response["data"], "销售方名称"
                                 ),
@@ -935,9 +910,7 @@ def image_recognize(
                                     Decimal("0.00"),
                                     rounding=ROUND_HALF_UP,
                                 ),
-                                    "payer": fuzzy_match(
+                                "payer": fuzzy_match(response["data"], "购买方名称"),
-                                        response["data"], "购买方名称"
-                                    ),
                                 "institution": fuzzy_match(
                                     response["data"], "销售方名称"
                                 ),
@@ -1001,9 +974,7 @@ def image_recognize(
                     .upper(),
                 )
                 # TODO: 若请求深圳快瞳医疗收费票据识别接口发生异常则流转至人工处理
-                    if not (
+                if not (response.get("status") == 200 and response.get("code") == 0):
-                        response.get("status") == 200 and response.get("code") == 0
-                    ):
                     raise
 
                 # noinspection PyTypeChecker
@@ -1028,16 +999,12 @@ def image_recognize(
                             "%Y-%m-%d",
                         ),
                         "admission_date": (
-                                datetime.strptime(
+                            datetime.strptime(receipt["starttime"]["value"], "%Y-%m-%d")
-                                    receipt["starttime"]["value"], "%Y-%m-%d"
-                                )
                             if isinstance(receipt["starttime"], dict)
                             else None
                         ),
                         "discharge_date": (
-                                datetime.strptime(
+                            datetime.strptime(receipt["endtime"]["value"], "%Y-%m-%d")
-                                    receipt["endtime"]["value"], "%Y-%m-%d"
-                                )
                             if isinstance(receipt["endtime"], dict)
                             else None
                         ),
@@ -1045,9 +1012,7 @@ def image_recognize(
                             receipt["global_detail"]["region_specific"],
                             "校验码",
                         ),
-                            "amount": Decimal(
+                        "amount": Decimal(receipt["total_amount"]["value"]).quantize(
-                                receipt["total_amount"]["value"]
-                            ).quantize(
                             Decimal("0.00"),
                             rounding=ROUND_HALF_UP,
                         ),
@@ -1207,6 +1172,7 @@ def image_recognize(
 
     dossier["receipts_layer"].append(receipt)
 
+
 # noinspection PyShadowingNames
 def bank_card_recognize(image) -> None:
     """
@@ -1221,9 +1187,7 @@ def image_recognize(
             "X-RequestId-Header": image["image_guid"]
         },  # 以影像件唯一标识作为请求唯一标识，用于双方联查
         data={
-                "token": authenticator.get_token(
+            "token": authenticator.get_token(servicer="szkt"),  # 获取深圳快瞳访问令牌
-                    servicer="szkt"
-                ),  # 获取深圳快瞳访问令牌
             "imgBase64": f"data:image/{image["image_format"].lstrip(".")};base64,{image["image_base64"]}",  # 影像件BASE64编码嵌入数据统一资源标识符
         },
         guid=md5((url + image["image_guid"]).encode("utf-8")).hexdigest().upper(),
@@ -1246,6 +1210,18 @@ def image_recognize(
         }
     )
 
+
+# noinspection PyShadowingNames
+def image_recognize(
+    image,
+    insurer_company,
+) -> None:
+    """
+    识别影像件并整合至赔案档案
+    :param image: 影像件
+    :param insurer_company: 保险分公司
+    :return: 无
+    """
     # 基于影像件识别使能规则评估影像件是否识别
     if not rule_engine.evaluate(
         decision="影像件识别使能",

票据理赔自动化/main.py

@@ -15,18 +15,11 @@ from jinja2 import Environment, FileSystemLoader
 from common import dossier, rule_engine
 from image import image_classify
 from image import image_recognize
-from utils.client import Authenticator, HTTPClient
 
 # -------------------------
 # 主逻辑
 # -------------------------
 if __name__ == "__main__":
-
-    # 实例化认证器
-    authenticator = Authenticator()
-    # 实例化请求客户端
-    http_client = HTTPClient(timeout=300, cache_enabled=True)  # 使用缓存
-
     # 初始化工作目录路径
     workplace_path = Path("directory")
     workplace_path.mkdir(parents=True, exist_ok=True)  # 若工作目录不存在则创建