tijiao

tools/分数字段结果分析/result_export.py

@@ -1,4 +1,4 @@
-# 导入所需库
+# 瀵煎叆鎵€闇€搴�
 from datetime import datetime
 
 import pandas as pd
@@ -7,7 +7,7 @@ from openpyxl import load_workbook
 from sympy.physics.continuum_mechanics.beam import numpy
 
 
-# 将这个函数定义放在你的脚本顶部或其他函数定义附近
+# 灏嗚繖涓�嚱鏁板畾涔夋斁鍦ㄤ綘鐨勮剼鏈�《閮ㄦ垨鍏朵粬鍑芥暟瀹氫箟闄勮繎
 def convert_numpy_int(obj):
     if isinstance(obj, numpy.int64):
         return int(obj)
@@ -18,73 +18,73 @@ def convert_numpy_int(obj):
     else:
         return obj
 
-# MongoDB连接配置
-host = '192.168.3.149'  # MongoDB主机地址
-port = 27180  # MongoDB端口
-dbname = 'data_quality'  # 数据库名称
-collection_name = 'bidding_20241033'  # 集合名称
+# MongoDB杩炴帴閰嶇疆
+host = '192.168.3.149'  # MongoDB涓绘満鍦板潃
+port = 27180  # MongoDB绔�彛
+dbname = 'data_quality'  # 鏁版嵁搴撳悕绉�
+collection_name = 'bidding_20241033'  # 闆嗗悎鍚嶇О
 
-# 创建MongoDB连接
+# 鍒涘缓MongoDB杩炴帴
 client = MongoClient(host, port)
 db = client[dbname]
 collection = db[collection_name]
 
-# 从MongoDB读取数据
+# 浠嶮ongoDB璇诲彇鏁版嵁
 data = pd.DataFrame(list(collection.find()))
 
-# 定义字段中英文映射
+# 瀹氫箟瀛楁�涓�嫳鏂囨槧灏�
 column_name_mapping = {
-    "area_qa": "省份",
-    "bidamount_qa": "中标金额",
-    "budget_qa": "预算",
-    "buyer_qa": "采购单位",
-    "multipackage_qa": "分包",
-    "projectcode_qa": "项目编号",
-    "projectname_qa": "项目名称",
-    "title_qa": "标题",
-    "winner_qa": "中标单位",
-    "score": "标讯总分数",
-    "bidopentime_qa": "开标时间",
-    "publishtime_qa": "发布时间",
-    "toptype_qa": "信息一级分类",
-    "subtype_qa": "信息二级分类"
+    "area_qa": "鐪佷唤",
+    "bidamount_qa": "涓�爣閲戦�",
+    "budget_qa": "棰勭畻",
+    "buyer_qa": "閲囪喘鍗曚綅",
+    "multipackage_qa": "鍒嗗寘",
+    "projectcode_qa": "椤圭洰缂栧彿",
+    "projectname_qa": "椤圭洰鍚嶇О",
+    "title_qa": "鏍囬�",
+    "winner_qa": "涓�爣鍗曚綅",
+    "score": "鏍囪�鎬诲垎鏁�",
+    "bidopentime_qa": "寮€鏍囨椂闂�",
+    "publishtime_qa": "鍙戝竷鏃堕棿",
+    "toptype_qa": "淇℃伅涓€绾у垎绫�",
+    "subtype_qa": "淇℃伅浜岀骇鍒嗙被"
 }
 
-# 将字段名称更改为中文
+# 灏嗗瓧娈靛悕绉版洿鏀逛负涓�枃
 data.rename(columns=column_name_mapping, inplace=True)
-# 检查列是否存在并输出列名
-print("当前的列名：")
+# 妫€鏌ュ垪鏄�惁瀛樺湪骞惰緭鍑哄垪鍚�
+print("褰撳墠鐨勫垪鍚嶏細")
 print(data.columns)
 
-# 定义你期望检查的列
-expected_columns = ["信息一级分类", "信息二级分类"]
+# 瀹氫箟浣犳湡鏈涙�鏌ョ殑鍒�
+expected_columns = ["淇℃伅涓€绾у垎绫�", "淇℃伅浜岀骇鍒嗙被"]
 
-# 循环检查每一列
+# 寰�幆妫€鏌ユ瘡涓€鍒�
 for col in expected_columns:
     if col in data.columns:
-        print(f"列 '{col}' 存在于数据框架中。")
-        # 列存在时，打印这一列的值分布
-        print(f"{col} 字段值分布：")
+        print(f"鍒� '{col}' 瀛樺湪浜庢暟鎹��鏋朵腑銆�")
+        # 鍒楀瓨鍦ㄦ椂锛屾墦鍗拌繖涓€鍒楃殑鍊煎垎甯�
+        print(f"{col} 瀛楁�鍊煎垎甯冿細")
         print(data[col].value_counts(dropna=False))
     else:
-        print(f"警告：列 '{col}' 不在数据框架中。")
-
-# 打印语句来检查 '一级分类' 和 '二级分类' 字段的值
-# print("一级分类字段值分布：")
-# print(data['信息一级分类'].value_counts(dropna=False))
-# print("\n二级分类字段值分布：")
-# print(data['信息二级分类'].value_counts(dropna=False))
-# 关闭MongoDB连接
+        print(f"璀﹀憡锛氬垪 '{col}' 涓嶅湪鏁版嵁妗嗘灦涓�€�")
+
+# 鎵撳嵃璇�彞鏉ユ�鏌� '涓€绾у垎绫�' 鍜� '浜岀骇鍒嗙被' 瀛楁�鐨勫€�
+# print("涓€绾у垎绫诲瓧娈靛€煎垎甯冿細")
+# print(data['淇℃伅涓€绾у垎绫�'].value_counts(dropna=False))
+# print("\n浜岀骇鍒嗙被瀛楁�鍊煎垎甯冿細")
+# print(data['淇℃伅浜岀骇鍒嗙被'].value_counts(dropna=False))
+# 鍏抽棴MongoDB杩炴帴
 client.close()
-#  analyze_column 函数，处理 NaN 值
+#  analyze_column 鍑芥暟锛屽�鐞� NaN 鍊�
 def analyze_column(dataframe, column_name):
     if column_name not in dataframe.columns:
-        # 字段不存在时，认为所有记录都是正确的
+        # 瀛楁�涓嶅瓨鍦ㄦ椂锛岃�涓烘墍鏈夎�褰曢兘鏄��纭�殑
         total = len(dataframe)
         correct = total
         error = 0
     else:
-        # 对于存在的字段，NaN 和空字典 {} 视为正确，其他视为错误
+        # 瀵逛簬瀛樺湪鐨勫瓧娈碉紝NaN 鍜岀┖瀛楀吀 {} 瑙嗕负姝ｇ‘锛屽叾浠栬�涓洪敊璇�
         total = len(dataframe[column_name])
         correct = dataframe[column_name].apply(lambda x: pd.isna(x) or x == {}).sum()
         error = total - correct
@@ -92,62 +92,62 @@ def analyze_column(dataframe, column_name):
     accuracy = correct / total if total > 0 else 0
     error_rate = error / total if total > 0 else 0
 
-    # 收集错误原因
+    # 鏀堕泦閿欒�鍘熷洜
     error_reasons = dataframe[column_name].apply(
         lambda x: x if x != {} and not pd.isna(x) else None).dropna().value_counts()
 
     return total, correct, error, accuracy, error_rate, error_reasons
 
-# 重新格式化错误原因的数据结构
+# 閲嶆柊鏍煎紡鍖栭敊璇�師鍥犵殑鏁版嵁缁撴瀯
 def reformat_error_reasons_safe(error_reasons_series):
-    # 初始化一个空字典，用于存储重新格式化的错误原因
+    # 鍒濆�鍖栦竴涓�┖瀛楀吀锛岀敤浜庡瓨鍌ㄩ噸鏂版牸寮忓寲鐨勯敊璇�師鍥�
     reformatted_reasons = {}
 
-    # 遍历错误原因字典及其对应的次数
+    # 閬嶅巻閿欒�鍘熷洜瀛楀吀鍙婂叾瀵瑰簲鐨勬�鏁�
     for error_dict, count in error_reasons_series.items():
-        if isinstance(error_dict, dict):  # 如果是字典类型的错误原因
+        if isinstance(error_dict, dict):  # 濡傛灉鏄�瓧鍏哥被鍨嬬殑閿欒�鍘熷洜
             for error_code, reason in error_dict.items():
-                # 检查原因字符串是否包含逗号
+                # 妫€鏌ュ師鍥犲瓧绗︿覆鏄�惁鍖呭惈閫楀彿
                 if ',' in reason:
                     parts = reason.split(',')
                     formatted_reason = parts[1].strip()
                 else:
                     formatted_reason = reason.strip()
 
-                # 如果格式化后的原因非空，则构建键值对并更新字典
+                # 濡傛灉鏍煎紡鍖栧悗鐨勫師鍥犻潪绌猴紝鍒欐瀯寤洪敭鍊煎�骞舵洿鏂板瓧鍏�
                 if formatted_reason:
                     key = (formatted_reason,)
                     if key not in reformatted_reasons:
                         reformatted_reasons[key] = count
                     else:
                         reformatted_reasons[key] += count
-        elif isinstance(error_dict, list):  # 如果是列表类型的错误原因
+        elif isinstance(error_dict, list):  # 濡傛灉鏄�垪琛ㄧ被鍨嬬殑閿欒�鍘熷洜
             key = (tuple(error_dict),) if error_dict else None
             if key not in reformatted_reasons:
                 reformatted_reasons[key] = count
             else:
                 reformatted_reasons[key] += count
-        else:  # 其他类型的错误原因
+        else:  # 鍏朵粬绫诲瀷鐨勯敊璇�師鍥�
             key = (error_dict,) if error_dict else None
             if key not in reformatted_reasons:
                 reformatted_reasons[key] = count
             else:
                 reformatted_reasons[key] += count
 
-    # 构建最终格式化后的结果字典，去除空键和空字符串键
+    # 鏋勫缓鏈€缁堟牸寮忓寲鍚庣殑缁撴灉瀛楀吀锛屽幓闄ょ┖閿�拰绌哄瓧绗︿覆閿�
     formatted_results = {
         str(key[0]): value for key, value in reformatted_reasons.items() if key and key[0] != ''
     }
     return formatted_results
 
 
-# 对每个字段进行分析
-fields_to_analyze = ["省份", "中标金额", "预算", "采购单位", "分包", "项目编号", "项目名称", "标题", "中标单位",
-                     "开标时间", "发布时间", "信息一级分类", "信息二级分类"]
+# 瀵规瘡涓�瓧娈佃繘琛屽垎鏋�
+fields_to_analyze = ["鐪佷唤", "涓�爣閲戦�", "棰勭畻", "閲囪喘鍗曚綅", "鍒嗗寘", "椤圭洰缂栧彿", "椤圭洰鍚嶇О", "鏍囬�", "涓�爣鍗曚綅",
+                     "寮€鏍囨椂闂�", "鍙戝竷鏃堕棿", "淇℃伅涓€绾у垎绫�", "淇℃伅浜岀骇鍒嗙被"]
 expanded_analysis_results = []
 
 for col in fields_to_analyze:
-    if col in data.columns:  # 在尝试分析之前检查字段是否存在
+    if col in data.columns:  # 鍦ㄥ皾璇曞垎鏋愪箣鍓嶆�鏌ュ瓧娈垫槸鍚﹀瓨鍦�
         total, correct, error, accuracy, error_rate, error_reasons = analyze_column(data, col)
         reformatted_error_reasons = reformat_error_reasons_safe(error_reasons)
 
@@ -156,63 +156,63 @@ for col in fields_to_analyze:
             if error > 0:
                 single_reason_error_rate = count / error
             else:
-                single_reason_error_rate = 0  # 防止除以零的情况
+                single_reason_error_rate = 0  # 闃叉�闄や互闆剁殑鎯呭喌
 
             expanded_analysis_results.append({
-                '字段': col,
-                '总量': total,
-                '正确数量': correct,
-                '错误数量': error,
-                '正确率': f'{accuracy:.2%}',
-                '错误率': f'{error_rate:.2%}',
-                '错误原因': reason,
-                '错误次数': count,
-                '单个原因错误率': f'{single_reason_error_rate:.2%}'
+                '瀛楁�': col,
+                '鎬婚噺': total,
+                '姝ｇ‘鏁伴噺': correct,
+                '閿欒�鏁伴噺': error,
+                '姝ｇ‘鐜�': f'{accuracy:.2%}',
+                '閿欒�鐜�': f'{error_rate:.2%}',
+                '閿欒�鍘熷洜': reason,
+                '閿欒�娆℃暟': count,
+                '鍗曚釜鍘熷洜閿欒�鐜�': f'{single_reason_error_rate:.2%}'
             })
     else:
-        print(f"警告：列 '{col}' 不在数据框架中，将跳过此字段。")
+        print(f"璀﹀憡锛氬垪 '{col}' 涓嶅湪鏁版嵁妗嗘灦涓�紝灏嗚烦杩囨�瀛楁�銆�")
 
-# 创建DataFrame并可能进行后续操作
+# 鍒涘缓DataFrame骞跺彲鑳借繘琛屽悗缁�搷浣�
 expanded_analysis_results_df = pd.DataFrame(expanded_analysis_results)
-# "标讯总分数" 字段的分布
-if "标讯总分数" in data.columns:
-    # 转换为浮点数
-    data['标讯总分数'] = data['标讯总分数'].astype(float)
-    score_counts = data['标讯总分数'].value_counts().sort_index()
-    total_scores = len(data['标讯总分数'])
+# "鏍囪�鎬诲垎鏁�" 瀛楁�鐨勫垎甯�
+if "鏍囪�鎬诲垎鏁�" in data.columns:
+    # 杞�崲涓烘诞鐐规暟
+    data['鏍囪�鎬诲垎鏁�'] = data['鏍囪�鎬诲垎鏁�'].astype(float)
+    score_counts = data['鏍囪�鎬诲垎鏁�'].value_counts().sort_index()
+    total_scores = len(data['鏍囪�鎬诲垎鏁�'])
     score_percentages = (score_counts / total_scores) * 100
     score_distribution_df = pd.DataFrame({
-        '标讯总分数': score_counts.index,
-        '数量': score_counts.values,
-        '百分比': score_percentages.values
+        '鏍囪�鎬诲垎鏁�': score_counts.index,
+        '鏁伴噺': score_counts.values,
+        '鐧惧垎姣�': score_percentages.values
     })
-    # 确保得分正确转换为浮点数
-    data['标讯总分数'] = data['标讯总分数'].apply(float)
-    # 计算得分为100的数量，确保类型匹配
+    # 纭�繚寰楀垎姝ｇ‘杞�崲涓烘诞鐐规暟
+    data['鏍囪�鎬诲垎鏁�'] = data['鏍囪�鎬诲垎鏁�'].apply(float)
+    # 璁＄畻寰楀垎涓�100鐨勬暟閲忥紝纭�繚绫诲瀷鍖归厤
     score_100_count = score_counts.get(100) if 100 in score_counts else 0
-    # 创建MongoDB连接
-    client = MongoClient('192.168.3.149', 27180)  # 使用指定的地址和端口
-    db = client['data_quality']  # 选择 'data_quality' 数据库
-    score_collection = db['score']  # 选择 'score' 集合
+    # 鍒涘缓MongoDB杩炴帴
+    client = MongoClient('192.168.3.149', 27180)  # 浣跨敤鎸囧畾鐨勫湴鍧€鍜岀�鍙�
+    db = client['data_quality']  # 閫夋嫨 'data_quality' 鏁版嵁搴�
+    score_collection = db['score']  # 閫夋嫨 'score' 闆嗗悎
 
-    # 构建要存储到MongoDB的数据
+    # 鏋勫缓瑕佸瓨鍌ㄥ埌MongoDB鐨勬暟鎹�
     data_to_store = {
         'score': 100,
         'score_number': score_100_count,
-        'timestamp': datetime.now()  # 添加当前时间戳
+        'timestamp': datetime.now()  # 娣诲姞褰撳墠鏃堕棿鎴�
     }
-    # 使用 convert_numpy_int 函数确保所有数据都是 MongoDB 兼容的格式
+    # 浣跨敤 convert_numpy_int 鍑芥暟纭�繚鎵€鏈夋暟鎹�兘鏄� MongoDB 鍏煎�鐨勬牸寮�
     data_to_store_converted = convert_numpy_int(data_to_store)
-    # 存储转换后的数据到新指定的MongoDB集合中
+    # 瀛樺偍杞�崲鍚庣殑鏁版嵁鍒版柊鎸囧畾鐨凪ongoDB闆嗗悎涓�
     score_collection.insert_one(data_to_store_converted)
 
-    # 百分比格式化为字符串，并附加百分号
-    score_distribution_df['百分比'] = score_distribution_df['百分比'].apply(lambda x: f'{x:.2f}%')
+    # 鐧惧垎姣旀牸寮忓寲涓哄瓧绗︿覆锛屽苟闄勫姞鐧惧垎鍙�
+    score_distribution_df['鐧惧垎姣�'] = score_distribution_df['鐧惧垎姣�'].apply(lambda x: f'{x:.2f}%')
 
 
-# "purchasinglist" 下的 "score" 字段的分布
+# "purchasinglist" 涓嬬殑 "score" 瀛楁�鐨勫垎甯�
 if 'purchasinglist' in data.columns:
-    # 提取 "score" 并转换为浮点数
+    # 鎻愬彇 "score" 骞惰浆鎹�负娴�偣鏁�
     purchasinglist_scores = data['purchasinglist'].map(
         lambda x: float(x[0]['score']) if isinstance(x, list) and x and isinstance(x[0], dict) and 'score' in x[
             0] else 0
@@ -222,83 +222,83 @@ if 'purchasinglist' in data.columns:
     purchasinglist_total_scores = purchasinglist_scores.notnull().sum()
     purchasinglist_score_percentages = (purchasinglist_score_counts / purchasinglist_total_scores) * 100
     purchasinglist_score_distribution_df = pd.DataFrame({
-        '标的物分数': purchasinglist_score_counts.index,
-        '数量': purchasinglist_score_counts.values,
-        '百分比': purchasinglist_score_percentages.values
+        '鏍囩殑鐗╁垎鏁�': purchasinglist_score_counts.index,
+        '鏁伴噺': purchasinglist_score_counts.values,
+        '鐧惧垎姣�': purchasinglist_score_percentages.values
     })
-    # 百分比格式化为字符串，并附加百分号
-    purchasinglist_score_distribution_df['百分比'] = purchasinglist_score_distribution_df['百分比'].apply(
+    # 鐧惧垎姣旀牸寮忓寲涓哄瓧绗︿覆锛屽苟闄勫姞鐧惧垎鍙�
+    purchasinglist_score_distribution_df['鐧惧垎姣�'] = purchasinglist_score_distribution_df['鐧惧垎姣�'].apply(
         lambda x: f'{x:.2f}%')
 
-# 对错误次数进行倒序排序
-expanded_analysis_results_df = expanded_analysis_results_df.sort_values(by='错误次数', ascending=False)
+# 瀵归敊璇��鏁拌繘琛屽€掑簭鎺掑簭
+expanded_analysis_results_df = expanded_analysis_results_df.sort_values(by='閿欒�娆℃暟', ascending=False)
 
-# MongoDB导出配置
-export_host = '192.168.3.149'  # MongoDB主机地址
-export_port = 27180  # MongoDB端口
-export_dbname = 'data_quality'  # 数据库名称
-export_collection_name = 'export'  # 导出的集合名称
+# MongoDB瀵煎嚭閰嶇疆
+export_host = '192.168.3.149'  # MongoDB涓绘満鍦板潃
+export_port = 27180  # MongoDB绔�彛
+export_dbname = 'data_quality'  # 鏁版嵁搴撳悕绉�
+export_collection_name = 'export'  # 瀵煎嚭鐨勯泦鍚堝悕绉�
 
-# 创建用于导出数据的MongoDB连接
+# 鍒涘缓鐢ㄤ簬瀵煎嚭鏁版嵁鐨凪ongoDB杩炴帴
 export_client = MongoClient(export_host, export_port)
 export_db = export_client[export_dbname]
 export_collection = export_db[export_collection_name]
-# 将分析结果导入MongoDB
+# 灏嗗垎鏋愮粨鏋滃�鍏�ongoDB
 for result in expanded_analysis_results:
-    # 构建导出数据的格式
+    # 鏋勫缓瀵煎嚭鏁版嵁鐨勬牸寮�
     export_entry = {
-        'error_cause': result['错误原因'],
-        'error_count': result['错误次数'],
-        'timestamp': datetime.now()  # 添加当前时间戳
+        'error_cause': result['閿欒�鍘熷洜'],
+        'error_count': result['閿欒�娆℃暟'],
+        'timestamp': datetime.now()  # 娣诲姞褰撳墠鏃堕棿鎴�
     }
-    print(export_entry)  # 查看时间戳是否正确生成
+    print(export_entry)  # 鏌ョ湅鏃堕棿鎴虫槸鍚︽�纭�敓鎴�
 
-    # 在插入之前应用转换
+    # 鍦ㄦ彃鍏ヤ箣鍓嶅簲鐢ㄨ浆鎹�
     export_entry = convert_numpy_int(export_entry)
 
-    # 插入数据到MongoDB集合
+    # 鎻掑叆鏁版嵁鍒癕ongoDB闆嗗悎
     export_collection.insert_one(export_entry)
-# 关闭导出数据用的MongoDB连接
+# 鍏抽棴瀵煎嚭鏁版嵁鐢ㄧ殑MongoDB杩炴帴
 export_client.close()
 
 
-# 使用 pd.ExcelWriter 进行写入操作
-with pd.ExcelWriter('临时文件.xlsx', engine='openpyxl') as writer:
-    # 新建一个工作表 "分数分析结果"
-    writer.sheets['分数分析结果'] = writer.book.create_sheet('分数分析结果')
-    if "标讯总分数" in data.columns:
-        # 添加总量列
-        score_distribution_df['总量'] = total_scores
-        # 对分数进行倒序排序
-        score_distribution_df = score_distribution_df.sort_values(by='标讯总分数', ascending=False)
-        score_distribution_df.to_excel(writer, sheet_name='分数分析结果', index=False)
+# 浣跨敤 pd.ExcelWriter 杩涜�鍐欏叆鎿嶄綔
+with pd.ExcelWriter('涓存椂鏂囦欢.xlsx', engine='openpyxl') as writer:
+    # 鏂板缓涓€涓�伐浣滆〃 "鍒嗘暟鍒嗘瀽缁撴灉"
+    writer.sheets['鍒嗘暟鍒嗘瀽缁撴灉'] = writer.book.create_sheet('鍒嗘暟鍒嗘瀽缁撴灉')
+    if "鏍囪�鎬诲垎鏁�" in data.columns:
+        # 娣诲姞鎬婚噺鍒�
+        score_distribution_df['鎬婚噺'] = total_scores
+        # 瀵瑰垎鏁拌繘琛屽€掑簭鎺掑簭
+        score_distribution_df = score_distribution_df.sort_values(by='鏍囪�鎬诲垎鏁�', ascending=False)
+        score_distribution_df.to_excel(writer, sheet_name='鍒嗘暟鍒嗘瀽缁撴灉', index=False)
 
-    # 新建一列写入 "purchasinglist" 下的 "score" 分布
+    # 鏂板缓涓€鍒楀啓鍏� "purchasinglist" 涓嬬殑 "score" 鍒嗗竷
     if 'purchasinglist' in data.columns and purchasinglist_scores.notnull().any():
-        # 注意这里的startcol参数，它应该基于您的实际数据列数来设置
-        purchasinglist_score_distribution_df = purchasinglist_score_distribution_df.sort_values(by='标的物分数',
+        # 娉ㄦ剰杩欓噷鐨剆tartcol鍙傛暟锛屽畠搴旇�鍩轰簬鎮ㄧ殑瀹為檯鏁版嵁鍒楁暟鏉ヨ�缃�
+        purchasinglist_score_distribution_df = purchasinglist_score_distribution_df.sort_values(by='鏍囩殑鐗╁垎鏁�',
                                                                                                 ascending=False)
-        purchasinglist_score_distribution_df.to_excel(writer, sheet_name='分数分析结果',
+        purchasinglist_score_distribution_df.to_excel(writer, sheet_name='鍒嗘暟鍒嗘瀽缁撴灉',
                                                       startcol=len(score_distribution_df.columns) + 2, index=False)
 
-        # 添加总量列
-        purchasinglist_score_distribution_df['总量'] = purchasinglist_total_scores
-        purchasinglist_score_distribution_df.to_excel(writer, sheet_name='分数分析结果',
+        # 娣诲姞鎬婚噺鍒�
+        purchasinglist_score_distribution_df['鎬婚噺'] = purchasinglist_total_scores
+        purchasinglist_score_distribution_df.to_excel(writer, sheet_name='鍒嗘暟鍒嗘瀽缁撴灉',
                                                       startcol=len(score_distribution_df.columns) + 2, index=False)
 
-    expanded_analysis_results_df.to_excel(writer, sheet_name='字段分析结果', index=False)
+    expanded_analysis_results_df.to_excel(writer, sheet_name='瀛楁�鍒嗘瀽缁撴灉', index=False)
 
-# 假设您的分析结果已经保存在一个临时文件中
-temp_analysis_file = '临时文件.xlsx'  # 临时文件的路径
+# 鍋囪�鎮ㄧ殑鍒嗘瀽缁撴灉宸茬粡淇濆瓨鍦ㄤ竴涓�复鏃舵枃浠朵腑
+temp_analysis_file = '涓存椂鏂囦欢.xlsx'  # 涓存椂鏂囦欢鐨勮矾寰�
 
-# 加载您想要合并结果到的Excel文件
-modified_file_path = 'pin.xlsx'  #拼接文件路径
+# 鍔犺浇鎮ㄦ兂瑕佸悎骞剁粨鏋滃埌鐨凟xcel鏂囦欢
+modified_file_path = 'pin.xlsx'  #鎷兼帴鏂囦欢璺�緞
 wb = load_workbook(modified_file_path)
 
-# 加载包含分析结果的临时Excel文件
+# 鍔犺浇鍖呭惈鍒嗘瀽缁撴灉鐨勪复鏃禘xcel鏂囦欢
 temp_wb = load_workbook(temp_analysis_file)
 
-# 将临时文件中的工作表复制到修改过的文件中
+# 灏嗕复鏃舵枃浠朵腑鐨勫伐浣滆〃澶嶅埗鍒颁慨鏀硅繃鐨勬枃浠朵腑
 for sheet_name in temp_wb.sheetnames:
     source = temp_wb[sheet_name]
     target = wb.create_sheet(sheet_name)
@@ -306,6 +306,6 @@ for sheet_name in temp_wb.sheetnames:
     for row in source.iter_rows(min_row=1, max_col=source.max_column, max_row=source.max_row, values_only=True):
         target.append(row)
 
-# 保存最终的合并文件
-final_merged_file_path = '质量分析报告.xlsx'  # 最终合并文件的路径
-wb.save(final_merged_file_path)
+# 淇濆瓨鏈€缁堢殑鍚堝苟鏂囦欢
+final_merged_file_path = '璐ㄩ噺鍒嗘瀽鎶ュ憡.xlsx'  # 鏈€缁堝悎骞舵枃浠剁殑璺�緞
+wb.save(final_merged_file_path)