feat 🐛:增加计算

app/tools/count_data.py

@@ -63,10 +63,8 @@ def count_outage_sentiment(text):
     # text = "全网监测到涉电力供应类舆情风险信息11条，环比减少2条；"
     # text = "涉电力供应类舆情风险信息22条，环比持平。其中1条为官方媒体发布，其余21条均为个人账号发布。"
     # 使用正则表达式匹配数字和关键词
-    pattern = r"信息(\d+)条，环比(增加|减少)(\d)条"
-    pattern_equal = r"信息(\d+)条，环比持平"
+    pattern = r"信息(\d+)条，环比(增加|减少)(\d+)条"
     match = re.search(pattern, text)
-    match_equal = re.search(pattern_equal, text)
     num1 = ""
     change = ""
     num2 = ""
@@ -93,13 +91,16 @@ def count_outage_sentiment(text):
             print(f"计算结果：{result}")
         else:
             print("变化类型未知，无法计算")
+
+    else:
         # update:2025-07-08 增加持平
-    elif match_equal:
-        num1 = int(match_equal.group(1))
+        pattern = r"信息(\d+)条，环比持平"
+        match = re.search(pattern, text)
+        if match:
+            num1 = int(match.group(1))
             change = "持平"
-        num2 = int(match_equal.group(1))
+            num2 = int(match.group(1))
             result = ""
-        # change = match_equal.group(2)
         else:
             pattern = r"信息(\d+)条，同比(增加|减少)(\d+)条"
             match = re.search(pattern, text)

app/tools/final_doc.py

@@ -283,7 +283,7 @@ def deal_docx(folder_path, save_path=None, time_type=0):
                 top_dod_dict[need_district_statistics[i][0]] = top5_dod_analysis(
                     need_district_statistics[i], district_stat_before
                 )
-        if count == 1:
+        if count == 1 and other_count==0:
             top5_name_list.append(
                 need_district_statistics[len(need_district_statistics) - 1][0]
             )
@@ -476,7 +476,7 @@ def deal_docx(folder_path, save_path=None, time_type=0):
                 over_load_percent = f"{over_load_percent:.2f}%"
                 over_load_type = "增加"
             else:
-                over_load_percent = 0
+                over_load_percent = ""
                 over_load_type = "持平"
         else:
             over_load_before = ""
@@ -680,6 +680,7 @@ def deal_docx(folder_path, save_path=None, time_type=0):
         )
 
         # 放入舆情的数据
+        logger.info("将自行统计的数据插入表格2")
         copy_table(
             table_sentiment,
             electricity_daily.tables[0],
@@ -691,6 +692,7 @@ def deal_docx(folder_path, save_path=None, time_type=0):
         )
 
         # 复制表2的数据
+        logger.info("将自行统计的数据插入表格3")
         copy_table(
             table2,
             electricity_daily.tables[1],
@@ -702,6 +704,7 @@ def deal_docx(folder_path, save_path=None, time_type=0):
         )
 
         # 复制表3的数据
+        logger.info("将自行统计的数据插入表格4")
         copy_table(
             table3,
             electricity_daily.tables[2],
@@ -714,6 +717,7 @@ def deal_docx(folder_path, save_path=None, time_type=0):
 
         # 填充表格4
         # 需要判断是否前五数据不存在
+        logger.info("将自行统计的数据插入表格5")
         if top5_list:
             copy_sta_table(
                 electricity_daily.tables[3],

app/tools/replace_table.py

@@ -3,6 +3,7 @@ import logging
 
 from docx.enum.text import WD_ALIGN_PARAGRAPH
 from docx.oxml import CT_P
+from pandas.core.indexers import length_of_indexer
 
 # 获取日志记录器
 logger = logging.getLogger(__name__)
@@ -41,9 +42,9 @@ def copy_sta_table(
     length: int = None,
 ):
     try:
-        logger.info("开始将自行统计的数据插入word表格中")
+        logger.info(f"开始将自行统计的数据插入word表格中,{target_table}")
         # update:2025-07-04 删除多余的行数
-        if is_dynamics is not None and is_dynamics and len(data) < length:
+        if is_dynamics is not None and is_dynamics and len(data)>1:
             for i in range(len(data) - 1):
                 source_row = target_table.rows[-1]._element
                 new_row_element = copy.deepcopy(source_row)