日常更新

from NUC

rfm/main.py

@@ -14,7 +14,7 @@ from decimal import Decimal, ROUND_HALF_UP
 import pandas
 from jinja2 import Environment, FileSystemLoader
 
-from utils.client import MySQLClient
+from utils.mysql import MySQLClient
 from utils.pandas_extension import DrawAsHTML
 
 

utils/__init__.py

@@ -0,0 +1,5 @@
+from mysql import MySQL
+from sqlite import SQLite
+from request import restrict, Authenticator, Request
+from feishu import Feishu
+from rules_engine import RulesEngine

utils/feishu.py

@@ -0,0 +1,242 @@
+# -*- coding: utf-8 -*-
+
+# 导入模块
+
+import re
+import time
+from email.parser import BytesParser
+from email.policy import default
+from email.utils import parsedate_to_datetime
+from imaplib import IMAP4_SSL
+
+import pandas
+
+
+class Feishu:
+    """飞书客户端"""
+
+    def __init__(self):
+        self.authenticator = Authenticator()
+        self.http_client = HTTPClient()
+
+    def _headers(self):
+        """请求头"""
+        # 装配飞书访问凭证
+        return {
+            "Authorization": f"Bearer {self.authenticator.get_token(servicer='feishu')}",
+        }
+
+    @staticmethod
+    def get_verification_code():
+        try:
+            # 执行时间戳
+            execute_timestamp = time.time()
+            # 超时时间戳
+            timeout_timestamp = execute_timestamp + 65
+            # 建立加密IMAP连接
+            server = IMAP4_SSL("imap.feishu.cn", 993)
+            # 登录
+            server.login("mars@liubiren.cloud", "a2SfPUgbKDmrjPV2")
+
+            while True:
+                # 若当前时间戳大于超时时间戳则返回NONE
+                if time.time() <= timeout_timestamp:
+                    # 等待10秒
+                    time.sleep(10)
+                    # 选择文件夹（邮箱验证码）
+                    server.select("&kK57sZqMi8F4AQ-")
+                    # noinspection PyBroadException
+                    try:
+                        # 获取最后一封邮件索引，server.search()返回数据类型为元组，第一个元素为查询状态，第二个元素为查询结果（邮件索引字节串的列表）；然后，从列表获取字节串并分割取最后一个，作为最后一封邮件索引
+                        index = server.search(None, "ALL")[1][0].split()[-1]
+                        # 获取最后一封邮件内容并解析，server.fetch()返回数据类型为元组，第一个元素为查询状态，第二个元素为查询结果（邮件内容字节串的列表）；然后，从列表获取字节串并解析正文
+                        # noinspection PyUnresolvedReferences
+                        contents = BytesParser(policy=default).parsebytes(
+                            server.fetch(index, "(RFC822)")[1][0][1]
+                        )
+                        # 遍历邮件内容，若正文内容类型为纯文本或HTML则解析发送时间和验证码
+                        for content in contents.walk():
+                            if (
+                                content.get_content_type() == "text/plain"
+                                or content.get_content_type() == "text/html"
+                            ):
+                                # 邮件发送时间戳
+                                # noinspection PyUnresolvedReferences
+                                send_timestamp = parsedate_to_datetime(
+                                    content["Date"]
+                                ).timestamp()
+                                # 若邮件发送时间戳大于执行时间戳则解析验证码并返回
+                                if (
+                                    execute_timestamp
+                                    > send_timestamp
+                                    >= execute_timestamp - 35
+                                ):
+                                    # 登出
+                                    server.logout()
+                                    # 解析验证码
+                                    return re.search(
+                                        r"【普康健康】您的验证码是：(\d+)",
+                                        content.get_payload(decode=True).decode(),
+                                    ).group(1)
+
+                    # 若文件夹无邮件则继续
+                    except:
+                        pass
+
+                # 若超时则登出
+                else:
+                    server.logout()
+                    return None
+
+        except Exception:
+            raise RuntimeError("获取邮箱验证码发生其它异常")
+
+    # 查询多维表格记录，单次最多查询500条记录
+    @restrict(refill_rate=5, max_tokens=5)
+    def query_bitable_records(
+        self,
+        bitable: str,
+        table_id: str,
+        field_names: Optional[list[str]] = None,
+        filter_conditions: Optional[dict] = None,
+    ) -> pandas.DataFrame:
+        # 先查询多维表格记录，在根据字段解析记录
+        # 装配多维表格查询记录地址
+        url = f"https://open.feishu.cn/open-apis/bitable/v1/apps/{bitable}/tables/{table_id}/records/search?page_size=20"
+
+        response = self.http_client.post(
+            url=url,
+            headers=self._headers(),
+            json={"field_names": field_names, "filter": filter_conditions},
+        )
+
+        # 响应业务码为0则定义为响应成功
+        assert response.get("code") == 0, "查询多维表格记录发生异常"
+
+        # 多维表格记录
+        records = response.get("data").get("items")
+
+        # 检查响应中是否包含还有下一页标识，若有则继续请求下一页
+        while response.get("data").get("has_more"):
+
+            url_next = url + "&page_token={}".format(
+                response.get("data").get("page_token")
+            )
+
+            response = self.http_client.post(
+                url=url_next,
+                headers=self._headers(),
+                json={"field_names": field_names, "filter": filter_conditions},
+            )
+
+            assert response.get("code") == 0, "查询多维表格记录发生异常"
+
+            # 合并记录
+            records.append(response.get("data").get("items"))
+
+        # 装配多维表格列出字段地址
+        url = f"https://open.feishu.cn/open-apis/bitable/v1/apps/{bitable}/tables/{table_id}/fields?page_size=20"
+
+        response = self.http_client.get(
+            url=url,
+            headers=self._headers(),
+        )
+
+        assert response.get("code") == 0, "列出多维表格字段发生异常"
+
+        # 多维表格字段
+        fields = response.get("data").get("items")
+
+        while response.get("data").get("has_more"):
+
+            url_next = url + "&page_token={}".format(
+                response.get("data").get("page_token")
+            )
+
+            response = self.http_client.get(
+                url=url_next,
+                headers=self._headers(),
+            )
+
+            assert response.get("code") == 0, "列出多维表格字段发生异常"
+
+            fields.append(response.get("data").get("items"))
+
+        # 字段映射
+        field_mappings = {}
+
+        for field in fields:
+
+            # 字段名
+            field_name = field["field_name"]
+
+            # 根据字段类型匹配
+            match field["type"]:
+
+                case 1005:
+
+                    field_type = "主键"
+
+                case 1:
+
+                    field_type = "文本"
+
+                case 3:
+
+                    field_type = "单选"
+
+                case 2:
+
+                    # 数字、公式字段的显示格式
+                    match field["property"]["formatter"]:
+
+                        case "0":
+
+                            field_type = "整数"
+
+                        case _:
+
+                            raise ValueError("未设置数字、公式字段的显示格式")
+
+                case _:
+
+                    raise ValueError("未设置字段类型")
+
+            # noinspection PyUnboundLocalVariable
+            field_mappings.update({field_name: field_type})
+
+        # 记录数据体
+        records_data = []
+
+        # 解析记录
+        for record in records:
+
+            # 单条记录数据体
+            record_data = {}
+
+            for field_name, content in record["fields"].items():
+
+                match field_mappings[field_name]:
+
+                    case "主键" | "单选" | "整数":
+
+                        record_data.update({field_name: content})
+
+                    case "文本":
+
+                        # 若存在多行文本则拼接
+                        fragments_content = ""
+
+                        for fragment_content in content:
+
+                            fragments_content += fragment_content["text"]
+
+                        record_data.update({field_name: fragments_content})
+
+                    case _:
+
+                        raise ValueError("未设置字段解析方法")
+
+            records_data.append(record_data)
+
+        return pandas.DataFrame(records_data)

utils/mysql.py

@@ -0,0 +1,66 @@
+# -*- coding: utf-8 -*-
+
+# 导入模块
+from urllib.parse import quote_plus
+
+import pandas
+from sqlalchemy import create_engine, text
+
+
+class MySQL:
+    """
+    MySQL客户端
+    """
+
+    def __init__(
+        self,
+        database: str,
+        host: str = "cdb-7z9lzx4y.cd.tencentcdb.com",  # 默认为刘弼仁的腾讯云MySQL数据库
+        port: int = 10039,
+        username: str = "root",
+        password: str = "Te198752",
+    ) -> None:
+        """
+        初始化
+        :param database: 数据库名称
+        :param host: 主机
+        :param port: 端口
+        :param username: 用户名
+        :param password: 登录密码
+        """
+        # 就登录密码编码
+        password = quote_plus(password)
+        # 构建数据库连接
+        connection_url = f"mysql+pymysql://{username}:{password}@{host}:{port}/{database}?charset=utf8"
+
+        # 创建MySQL引擎并连接数据库
+        self.engine = create_engine(
+            connection_url,
+            pool_size=5,
+            max_overflow=10,
+            pool_recycle=3600,
+            pool_pre_ping=True,
+        )  # 连接池中保持打开连接的数量为5，额外连接数为10，连接1小时后重新回收重连，检查连接有效性
+
+    def execute_query(self, sql: str) -> pandas.DataFrame:
+        """执行SQL查询并返回DATAFRAME"""
+
+        if not hasattr(self, "engine") or not self.engine:
+            raise ConnectionError("未创建数据库连接")
+
+        try:
+            with self.engine.connect() as connection:
+                dataframe = pandas.read_sql_query(
+                    text(sql), connection, coerce_float=False
+                )  # 不尝试将非整数数值转为浮点（维持DECIMAL）
+            return dataframe
+
+        except:
+            connection.rollback()
+            raise RuntimeError("执行SQL查询并返回DATAFRAME发生其它异常")
+
+    def __del__(self):
+        """析构时自动关闭连接"""
+
+        if hasattr(self, "engine") and self.engine:
+            self.engine.dispose()

utils/request.py

@@ -5,264 +5,18 @@
 import hashlib
 import hmac
 import json
-import re
-import sqlite3
 import threading
 import time
-from email.parser import BytesParser
-from email.policy import default
-from email.utils import parsedate_to_datetime
 from functools import wraps
-from imaplib import IMAP4_SSL
 from pathlib import Path
-from typing import Any, Callable, Dict, Generator, List, Literal, Optional, Tuple, Union
-from urllib.parse import quote_plus
+from typing import Any, Callable, Dict, Generator, Literal, Optional, Tuple, Union
 from xml.etree import ElementTree
 
-import pandas
 from pydantic import BaseModel, Field, HttpUrl, model_validator
 from requests import Response, Session
 from requests.adapters import HTTPAdapter
-from sqlalchemy import create_engine, text
 from urllib3.util.retry import Retry
 
-"""
-封装sqlalchemy，实现按照SQL查询
-类和函数的区别
-类作为对象的模板，定义了对象的结构和行为；而函数则作为实现特定功能或操作的代码块，提高了代码的可读性和可维护性。
-使用方法：
-with MySQLClient(database='data_analysis') as client:
-    dataframe = client.execute_query(sql='select * from {{}}')')
-"""
-
-
-class MySQLClient:
-    """MySQL客户端"""
-
-    def __init__(
-        self,
-        database,
-        host: str = "cdb-7z9lzx4y.cd.tencentcdb.com",
-        port: int = 10039,
-        username: str = "root",
-        password: str = "Te198752",
-    ):  # 默认为刘弼仁的腾讯云MySQL数据库
-
-        # 数据库登录密码安全编码
-        password = quote_plus(password)
-
-        # 构建数据库连接字符
-        connect_config = f"mysql+pymysql://{username}:{password}@{host}:{port}/{database}?charset=utf8"
-
-        # 创建MySQL引擎并连接数据库
-        self.engine = create_engine(
-            connect_config,
-            pool_size=5,
-            max_overflow=10,
-            pool_recycle=3600,
-            pool_pre_ping=True,
-        )  # 连接池中保持打开连接的数量为5，额外连接数为10，连接1小时后重新回收重连，检查连接有效性
-
-    def __del__(self):
-        """析构时自动关闭连接"""
-
-        if hasattr(self, "engine") and self.engine:
-            self.engine.dispose()
-
-    def execute_query(self, sql: str) -> pandas.DataFrame:
-        """执行SQL查询并返回DATAFRAME"""
-
-        if not hasattr(self, "engine") or not self.engine:
-            raise ConnectionError("未创建数据库连接")
-
-        try:
-            with self.engine.connect() as connection:
-                dataframe = pandas.read_sql_query(
-                    text(sql), connection, coerce_float=False
-                )  # 不尝试将非整数数值转为浮点（维持DECIMAL）
-            return dataframe
-
-        except:
-            connection.rollback()
-            raise RuntimeError("执行SQL查询并返回DATAFRAME发生其它异常")
-
-
-class SQLiteClient:
-    """SQLite客户端"""
-
-    def __init__(self, database: Union[str, Path]):
-        """
-        初始化SQLite客户端
-        :param database: 数据库
-        """
-        self.database = database
-        # 初始化本地线程存储
-        self.threads = threading.local()
-
-    def _connect(self):
-        """为当前线程创建数据库连接和游标"""
-        # 检查当前线程有数据库连接，若有则继续否则创建数据库连接
-        if hasattr(self.threads, "connection") and self.threads.connection is not None:
-            return
-        # 为当前线程关闭数据库连接和游标
-        self._disconnect()
-
-        # noinspection PyBroadException
-        try:
-            # 为当前线程创建数据库连接
-            self.threads.connection = sqlite3.connect(
-                database=self.database,
-                check_same_thread=True,
-                timeout=30,  # 数据库锁超时时间（单位：秒），默认为30秒，避免并发锁死
-            )
-            # 开启行映射，支持按照字段名取值
-            self.threads.connection.row_factory = sqlite3.Row
-            # 为当前线程创建游标
-            self.threads.cursor = self.threads.connection.cursor()
-        except Exception as exception:
-            self.threads.connection = None
-            self.threads.cursor = None
-            raise RuntimeError(
-                f"为当前线程创建数据库连接和游标发生异常，{str(exception)}"
-            ) from exception
-
-    def _disconnect(self) -> None:
-        """为当前线程关闭数据库连接和游标"""
-        # 检查当前线程有游标，若有则关闭游标
-        if hasattr(self.threads, "cursor") and self.threads.cursor is not None:
-            # noinspection PyBroadException
-            try:
-                # 为当前线程关闭游标
-                self.threads.cursor.close()
-                self.threads.cursor = None
-            except Exception as exception:
-                raise RuntimeError(
-                    f"为当前线程关闭游标发生异常，{str(exception)}"
-                ) from exception
-
-        # 检查当前线程有数据库连接，若有则关闭数据库连接
-        if hasattr(self.threads, "connection") and self.threads.connection is not None:
-            # noinspection PyBroadException
-            try:
-                # 为当前线程提交事务
-                self.threads.connection.commit()
-                # 为当前线程关闭数据库连接
-                self.threads.connection.close()
-                self.threads.connection = None
-            except Exception as exception:
-                raise RuntimeError(
-                    f"为当前线程关闭数据库连接发生异常，{str(exception)}"
-                ) from exception
-
-    def _query_one(
-        self, sql: str, parameters: Tuple[Any, ...] = ()
-    ) -> Optional[Dict[str, Any]]:
-        """
-        为当前线程查询并获取单行数据
-        :param sql: 查询SQL语句
-        :param parameters: SQL参数
-        :return: 单行数据
-        """
-        # noinspection PyBroadException
-        try:
-            # 为当前线程创建数据库连接和游标
-            self._connect()
-            # 检查当前线程无游标，若无则抛出异常
-            if not hasattr(self.threads, "cursor") or self.threads.cursor is None:
-                raise RuntimeError("为当前线程创建游标发生异常")
-
-            # 为当前线程执行SQL
-            self.threads.cursor.execute(sql, parameters)
-            return (
-                None
-                if (result := self.threads.cursor.fetchone()) is None
-                else dict(result)
-            )
-        # 若发生异常则回滚事务并抛出异常
-        except Exception as exception:
-            # 检查当前线程有数据库连接，若有则回滚
-            if (
-                hasattr(self.threads, "connection")
-                and self.threads.connection is not None
-            ):
-                self.threads.connection.rollback()
-            raise RuntimeError("为当前线程查询并获取单行数据发生异常") from exception
-
-    def _query_all(
-        self, sql: str, parameters: Tuple[Any, ...] = ()
-    ) -> List[Dict[str, Any]]:
-        """
-        为当前线程查询并获取多行数据
-        :param sql: 查询SQL语句
-        :param parameters: SQL参数
-        :return: 多行数据
-        """
-        # noinspection PyBroadException
-        try:
-            # 为当前线程创建数据库连接和游标
-            self._connect()
-            # 检查当前线程无游标，若无则抛出异常
-            if not hasattr(self.threads, "cursor") or self.threads.cursor is None:
-                raise RuntimeError("为当前线程创建游标发生异常")
-
-            # 为当前线程执行SQL
-            self.threads.cursor.execute(sql, parameters)
-            result = []
-            while batch := self.threads.cursor.fetchmany(1000):
-                result.extend([dict(row) for row in batch])
-            return result
-        # 若发生异常则回滚事务并抛出异常
-        except Exception as exception:
-            # 检查当前线程有数据库连接，若有则回滚
-            if (
-                hasattr(self.threads, "connection")
-                and self.threads.connection is not None
-            ):
-                self.threads.connection.rollback()
-            raise RuntimeError("为当前线程查询并获取单行数据发生异常") from exception
-
-    def _execute(self, sql: str, parameters: Tuple[Any, ...] = ()) -> bool:
-        """
-        为当前线程执行SQL
-        :param sql: 新增、删除和修改SQL语句
-        :param parameters: SQL参数
-        :return: 执行结果
-        """
-        try:
-            self._connect()
-            # 检查当前线程无游标，若无则抛出异常
-            if not hasattr(self.threads, "cursor") or self.threads.cursor is None:
-                raise RuntimeError("为当前线程创建游标发生异常")
-
-            # 为当前线程执行SQL
-            self.threads.cursor.execute(sql, parameters)
-            # 为当前线程提交事务
-            self.threads.connection.commit()
-            return True
-        # 若发生异常则回滚事务并抛出异常
-        except Exception as exception:
-            # 检查当前线程有数据库连接，若有则回滚
-            if (
-                hasattr(self.threads, "connection")
-                and self.threads.connection is not None
-            ):
-                self.threads.connection.rollback()
-            raise RuntimeError("为当前线程执行SQL发生异常") from exception
-
-    def __enter__(self):
-        """进入上下文管理时为当前线程创建数据库连接和游标"""
-        self._connect()
-        return self
-
-    def __exit__(self, exc_type, exc_val, exc_tb):
-        """退出上下文管理时为当前线程关闭数据库连接和游标"""
-        self._disconnect()
-        return False
-
-    def __del__(self):
-        """析构时为当前线程关闭数据库连接和游标"""
-        self._disconnect()
-
 
 # 基于令牌桶限流算法的装饰器
 def restrict(refill_rate: float = 5.0, max_tokens: int = 5):
@@ -357,7 +111,7 @@ def restrict(refill_rate: float = 5.0, max_tokens: int = 5):
     return decorator
 
 
-class HTTPClient:
+class Request:
     """请求客户端"""
 
     class RequestException(Exception):
@@ -915,233 +669,3 @@ class Authenticator:
                     )
 
             return token
-
-
-class FeishuClient:
-    """飞书客户端"""
-
-    def __init__(self):
-        self.authenticator = Authenticator()
-        self.http_client = HTTPClient()
-
-    def _headers(self):
-        """请求头"""
-        # 装配飞书访问凭证
-        return {
-            "Authorization": f"Bearer {self.authenticator.get_token(servicer='feishu')}",
-        }
-
-    @staticmethod
-    def get_verification_code():
-        try:
-            # 执行时间戳
-            execute_timestamp = time.time()
-            # 超时时间戳
-            timeout_timestamp = execute_timestamp + 65
-            # 建立加密IMAP连接
-            server = IMAP4_SSL("imap.feishu.cn", 993)
-            # 登录
-            server.login("mars@liubiren.cloud", "a2SfPUgbKDmrjPV2")
-
-            while True:
-                # 若当前时间戳大于超时时间戳则返回NONE
-                if time.time() <= timeout_timestamp:
-                    # 等待10秒
-                    time.sleep(10)
-                    # 选择文件夹（邮箱验证码）
-                    server.select("&kK57sZqMi8F4AQ-")
-                    # noinspection PyBroadException
-                    try:
-                        # 获取最后一封邮件索引，server.search()返回数据类型为元组，第一个元素为查询状态，第二个元素为查询结果（邮件索引字节串的列表）；然后，从列表获取字节串并分割取最后一个，作为最后一封邮件索引
-                        index = server.search(None, "ALL")[1][0].split()[-1]
-                        # 获取最后一封邮件内容并解析，server.fetch()返回数据类型为元组，第一个元素为查询状态，第二个元素为查询结果（邮件内容字节串的列表）；然后，从列表获取字节串并解析正文
-                        # noinspection PyUnresolvedReferences
-                        contents = BytesParser(policy=default).parsebytes(
-                            server.fetch(index, "(RFC822)")[1][0][1]
-                        )
-                        # 遍历邮件内容，若正文内容类型为纯文本或HTML则解析发送时间和验证码
-                        for content in contents.walk():
-                            if (
-                                content.get_content_type() == "text/plain"
-                                or content.get_content_type() == "text/html"
-                            ):
-                                # 邮件发送时间戳
-                                # noinspection PyUnresolvedReferences
-                                send_timestamp = parsedate_to_datetime(
-                                    content["Date"]
-                                ).timestamp()
-                                # 若邮件发送时间戳大于执行时间戳则解析验证码并返回
-                                if (
-                                    execute_timestamp
-                                    > send_timestamp
-                                    >= execute_timestamp - 35
-                                ):
-                                    # 登出
-                                    server.logout()
-                                    # 解析验证码
-                                    return re.search(
-                                        r"【普康健康】您的验证码是：(\d+)",
-                                        content.get_payload(decode=True).decode(),
-                                    ).group(1)
-
-                    # 若文件夹无邮件则继续
-                    except:
-                        pass
-
-                # 若超时则登出
-                else:
-                    server.logout()
-                    return None
-
-        except Exception:
-            raise RuntimeError("获取邮箱验证码发生其它异常")
-
-    # 查询多维表格记录，单次最多查询500条记录
-    @restrict(refill_rate=5, max_tokens=5)
-    def query_bitable_records(
-        self,
-        bitable: str,
-        table_id: str,
-        field_names: Optional[list[str]] = None,
-        filter_conditions: Optional[dict] = None,
-    ) -> pandas.DataFrame:
-        # 先查询多维表格记录，在根据字段解析记录
-        # 装配多维表格查询记录地址
-        url = f"https://open.feishu.cn/open-apis/bitable/v1/apps/{bitable}/tables/{table_id}/records/search?page_size=20"
-
-        response = self.http_client.post(
-            url=url,
-            headers=self._headers(),
-            json={"field_names": field_names, "filter": filter_conditions},
-        )
-
-        # 响应业务码为0则定义为响应成功
-        assert response.get("code") == 0, "查询多维表格记录发生异常"
-
-        # 多维表格记录
-        records = response.get("data").get("items")
-
-        # 检查响应中是否包含还有下一页标识，若有则继续请求下一页
-        while response.get("data").get("has_more"):
-
-            url_next = url + "&page_token={}".format(
-                response.get("data").get("page_token")
-            )
-
-            response = self.http_client.post(
-                url=url_next,
-                headers=self._headers(),
-                json={"field_names": field_names, "filter": filter_conditions},
-            )
-
-            assert response.get("code") == 0, "查询多维表格记录发生异常"
-
-            # 合并记录
-            records.append(response.get("data").get("items"))
-
-        # 装配多维表格列出字段地址
-        url = f"https://open.feishu.cn/open-apis/bitable/v1/apps/{bitable}/tables/{table_id}/fields?page_size=20"
-
-        response = self.http_client.get(
-            url=url,
-            headers=self._headers(),
-        )
-
-        assert response.get("code") == 0, "列出多维表格字段发生异常"
-
-        # 多维表格字段
-        fields = response.get("data").get("items")
-
-        while response.get("data").get("has_more"):
-
-            url_next = url + "&page_token={}".format(
-                response.get("data").get("page_token")
-            )
-
-            response = self.http_client.get(
-                url=url_next,
-                headers=self._headers(),
-            )
-
-            assert response.get("code") == 0, "列出多维表格字段发生异常"
-
-            fields.append(response.get("data").get("items"))
-
-        # 字段映射
-        field_mappings = {}
-
-        for field in fields:
-
-            # 字段名
-            field_name = field["field_name"]
-
-            # 根据字段类型匹配
-            match field["type"]:
-
-                case 1005:
-
-                    field_type = "主键"
-
-                case 1:
-
-                    field_type = "文本"
-
-                case 3:
-
-                    field_type = "单选"
-
-                case 2:
-
-                    # 数字、公式字段的显示格式
-                    match field["property"]["formatter"]:
-
-                        case "0":
-
-                            field_type = "整数"
-
-                        case _:
-
-                            raise ValueError("未设置数字、公式字段的显示格式")
-
-                case _:
-
-                    raise ValueError("未设置字段类型")
-
-            # noinspection PyUnboundLocalVariable
-            field_mappings.update({field_name: field_type})
-
-        # 记录数据体
-        records_data = []
-
-        # 解析记录
-        for record in records:
-
-            # 单条记录数据体
-            record_data = {}
-
-            for field_name, content in record["fields"].items():
-
-                match field_mappings[field_name]:
-
-                    case "主键" | "单选" | "整数":
-
-                        record_data.update({field_name: content})
-
-                    case "文本":
-
-                        # 若存在多行文本则拼接
-                        fragments_content = ""
-
-                        for fragment_content in content:
-
-                            fragments_content += fragment_content["text"]
-
-                        record_data.update({field_name: fragments_content})
-
-                    case _:
-
-                        raise ValueError("未设置字段解析方法")
-
-            records_data.append(record_data)
-
-        return pandas.DataFrame(records_data)

utils/rules_engine.py

@@ -1,9 +1,5 @@
 # -*- coding: utf-8 -*-
 
-"""
-封装ZenEngine
-"""
-
 # 导入模块
 
 from datetime import datetime
@@ -14,7 +10,7 @@ from typing import Any, Dict
 from zen import ZenDecision, ZenEngine
 
 
-class RuleEngine:
+class RulesEngine:
     """
     规则引擎，实现打开并读取规则，根据规则和输入评估并输出
     """

utils/sqlite.py

@@ -0,0 +1,187 @@
+# -*- coding: utf-8 -*-
+
+# 导入模块
+
+import sqlite3
+import threading
+from pathlib import Path
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+
+class SQLite:
+    """
+    SQLite客户端
+    """
+
+    def __init__(self, database: Union[str, Path]):
+        """
+        初始化
+        :param database: 数据库地址
+        """
+        self.database = database
+        # 初始化本地线程存储
+        self.threads = threading.local()
+
+    def _connect(self):
+        """为当前线程创建数据库连接和游标"""
+        # 检查当前线程有数据库连接，若有则继续否则创建数据库连接
+        if hasattr(self.threads, "connection") and self.threads.connection is not None:
+            return
+        # 为当前线程关闭数据库连接和游标
+        self._disconnect()
+
+        # noinspection PyBroadException
+        try:
+            # 为当前线程创建数据库连接
+            self.threads.connection = sqlite3.connect(
+                database=self.database,
+                check_same_thread=True,
+                timeout=30,  # 数据库锁超时时间（单位：秒），默认为30秒，避免并发锁死
+            )
+            # 开启行映射，支持按照字段名取值
+            self.threads.connection.row_factory = sqlite3.Row
+            # 为当前线程创建游标
+            self.threads.cursor = self.threads.connection.cursor()
+        except Exception as exception:
+            self.threads.connection = None
+            self.threads.cursor = None
+            raise RuntimeError(
+                f"为当前线程创建数据库连接和游标发生异常，{str(exception)}"
+            ) from exception
+
+    def _disconnect(self) -> None:
+        """为当前线程关闭数据库连接和游标"""
+        # 检查当前线程有游标，若有则关闭游标
+        if hasattr(self.threads, "cursor") and self.threads.cursor is not None:
+            # noinspection PyBroadException
+            try:
+                # 为当前线程关闭游标
+                self.threads.cursor.close()
+                self.threads.cursor = None
+            except Exception as exception:
+                raise RuntimeError(
+                    f"为当前线程关闭游标发生异常，{str(exception)}"
+                ) from exception
+
+        # 检查当前线程有数据库连接，若有则关闭数据库连接
+        if hasattr(self.threads, "connection") and self.threads.connection is not None:
+            # noinspection PyBroadException
+            try:
+                # 为当前线程提交事务
+                self.threads.connection.commit()
+                # 为当前线程关闭数据库连接
+                self.threads.connection.close()
+                self.threads.connection = None
+            except Exception as exception:
+                raise RuntimeError(
+                    f"为当前线程关闭数据库连接发生异常，{str(exception)}"
+                ) from exception
+
+    def _query_one(
+        self, sql: str, parameters: Tuple[Any, ...] = ()
+    ) -> Optional[Dict[str, Any]]:
+        """
+        为当前线程查询并获取单行数据
+        :param sql: 查询SQL语句
+        :param parameters: SQL参数
+        :return: 单行数据
+        """
+        # noinspection PyBroadException
+        try:
+            # 为当前线程创建数据库连接和游标
+            self._connect()
+            # 检查当前线程无游标，若无则抛出异常
+            if not hasattr(self.threads, "cursor") or self.threads.cursor is None:
+                raise RuntimeError("为当前线程创建游标发生异常")
+
+            # 为当前线程执行SQL
+            self.threads.cursor.execute(sql, parameters)
+            return (
+                None
+                if (result := self.threads.cursor.fetchone()) is None
+                else dict(result)
+            )
+        # 若发生异常则回滚事务并抛出异常
+        except Exception as exception:
+            # 检查当前线程有数据库连接，若有则回滚
+            if (
+                hasattr(self.threads, "connection")
+                and self.threads.connection is not None
+            ):
+                self.threads.connection.rollback()
+            raise RuntimeError("为当前线程查询并获取单行数据发生异常") from exception
+
+    def _query_all(
+        self, sql: str, parameters: Tuple[Any, ...] = ()
+    ) -> List[Dict[str, Any]]:
+        """
+        为当前线程查询并获取多行数据
+        :param sql: 查询SQL语句
+        :param parameters: SQL参数
+        :return: 多行数据
+        """
+        # noinspection PyBroadException
+        try:
+            # 为当前线程创建数据库连接和游标
+            self._connect()
+            # 检查当前线程无游标，若无则抛出异常
+            if not hasattr(self.threads, "cursor") or self.threads.cursor is None:
+                raise RuntimeError("为当前线程创建游标发生异常")
+
+            # 为当前线程执行SQL
+            self.threads.cursor.execute(sql, parameters)
+            result = []
+            while batch := self.threads.cursor.fetchmany(1000):
+                result.extend([dict(row) for row in batch])
+            return result
+        # 若发生异常则回滚事务并抛出异常
+        except Exception as exception:
+            # 检查当前线程有数据库连接，若有则回滚
+            if (
+                hasattr(self.threads, "connection")
+                and self.threads.connection is not None
+            ):
+                self.threads.connection.rollback()
+            raise RuntimeError("为当前线程查询并获取单行数据发生异常") from exception
+
+    def _execute(self, sql: str, parameters: Tuple[Any, ...] = ()) -> bool:
+        """
+        为当前线程执行SQL
+        :param sql: 新增、删除和修改SQL语句
+        :param parameters: SQL参数
+        :return: 执行结果
+        """
+        try:
+            self._connect()
+            # 检查当前线程无游标，若无则抛出异常
+            if not hasattr(self.threads, "cursor") or self.threads.cursor is None:
+                raise RuntimeError("为当前线程创建游标发生异常")
+
+            # 为当前线程执行SQL
+            self.threads.cursor.execute(sql, parameters)
+            # 为当前线程提交事务
+            self.threads.connection.commit()
+            return True
+        # 若发生异常则回滚事务并抛出异常
+        except Exception as exception:
+            # 检查当前线程有数据库连接，若有则回滚
+            if (
+                hasattr(self.threads, "connection")
+                and self.threads.connection is not None
+            ):
+                self.threads.connection.rollback()
+            raise RuntimeError("为当前线程执行SQL发生异常") from exception
+
+    def __enter__(self):
+        """进入上下文管理时为当前线程创建数据库连接和游标"""
+        self._connect()
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        """退出上下文管理时为当前线程关闭数据库连接和游标"""
+        self._disconnect()
+        return False
+
+    def __del__(self):
+        """析构时为当前线程关闭数据库连接和游标"""
+        self._disconnect()

普康健康发票查验/main.py

@@ -18,7 +18,7 @@ import cv2
 import numpy
 import pandas
 
-from utils.client import Authenticator, HTTPClient, RequestException, restrict
+from utils.mysql import Authenticator, HTTPClient, RequestException, restrict
 from utils.pandas_extension import open_csv, save_as_workbook, traverse_directory
 
 

普康健康发票识别/main.py

@@ -22,7 +22,7 @@ from 普康健康发票查验.main import image_compression
 
 from utils.pandas_extension import traverse_directory, save_as_workbook
 
-from utils.client import restrict, HTTPClient, RequestException, Authenticator
+from utils.mysql import restrict, HTTPClient, RequestException, Authenticator
 
 
 if __name__ == "__main__":

普康健康审核机器人/pageobject.py

@@ -38,7 +38,7 @@ sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
 
 from utils.logger import Logger
 
-from utils.rule_engine import FeishuMail
+from utils.rules_engine import FeishuMail
 
 # 创建日志记录器
 logger = Logger(logger_name="pageobject").get_logger()

普康健康客服会话记录整合/main.py

@@ -13,7 +13,7 @@ from pathlib import Path
 import pandas
 from jinja2 import Environment, FileSystemLoader
 
-from utils.client import Authenticator, HTTPClient
+from utils.mysql import Authenticator, HTTPClient
 
 # 创建目录地址对象
 directory_path = Path("客服会话记录")

短视频合成自动化/@AutomationLog.txt

@@ -1,2 +0,0 @@
-2026-01-05 15:44:22.377 export.py[409] export -> Find Control Timeout(10s): {NameContains: '删除', VisibleOnly: True, ControlType: MenuItemControl}
-2026-01-05 15:46:56.070 export.py[409] export -> Find Control Timeout(10s): {NameContains: '删除', VisibleOnly: True, ControlType: MenuItemControl}

短视频合成自动化/main.py

@@ -10,7 +10,7 @@ if __name__ == "__main__":
     # 实例化 JianYingExport
     jianying_export = JianYingExport(
         materials_folder_path=r"E:\jianying\materials\260104",
-        draft_counts=2,
+        draft_counts=1,
     )
 
     # 导出草稿

票据理赔自动化/a.py

@@ -0,0 +1,2 @@
+a = 1
+print(a)

票据理赔自动化/common.py

@@ -3,7 +3,7 @@
 from pathlib import Path
 
 from masterdata import MasterData
-from utils.rule_engine import RuleEngine
+# from ..utils import RuleEngine
 
 # 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）
 dossier = {

票据理赔自动化/image.py

@@ -16,12 +16,15 @@ from fuzzywuzzy import fuzz
 from jionlp import parse_location
 
 from common import dossier, master_data, rule_engine
-from utils.client import Authenticator, HTTPClient
+
+print(1)
+exit()
+from utils import Authenticator, Request
 
 # 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
+http_client = Request(timeout=300, cache_enabled=True)  # 使用缓存
 
 
 # noinspection PyShadowingNames

票据理赔自动化/main.py

@@ -1,7 +1,7 @@
 # -*- coding: utf-8 -*-
 
 """
-票据理赔自动化最小化实现
+票据理赔自动化
 功能清单
 https://liubiren.feishu.cn/docx/WFjTdBpzroUjQvxxrNIcKvGnneh?from=from_copylink
 """

票据理赔自动化/masterdata.py

@@ -4,10 +4,10 @@ from datetime import datetime
 from decimal import Decimal, ROUND_HALF_UP
 from typing import Any, Dict, List, Optional
 
-from utils.client import SQLiteClient
+from utils import SQLite
 
 
-class MasterData(SQLiteClient):
+class MasterData(SQLite):
     """主数据"""
 
     def __init__(self):