1

aidso_geo/models/process.py

@@ -50,39 +50,38 @@ def get_keyword_ranks(text, keywords):
     if isinstance(keywords, str):
         keywords = [keywords]
 
-    appearance_order = []
-    start_pos = 0
-    text_length = len(text)
+    matches = []
 
-    while start_pos < text_length:
-        min_position = None
-        found_key = None
-
-        for key in keywords:
-            pos = text.find(key, start_pos)
+    for key in keywords:
+        start = 0
+        while True:
+            pos = text.find(key, start)
             if pos == -1:
-                continue
+                break
 
-            # 1. 更早出现的优先
-            # 2. 如果起始位置相同，优先更短的关键词
-            if (
-                    min_position is None
-                    or pos < min_position
-                    or (pos == min_position and len(key) < len(found_key))
-            ):
-                min_position = pos
-                found_key = key
+            matches.append({
+                "word": key,
+                "pos": pos
+            })
 
-        if found_key is None:
-            break
+            # 允许继续查找后面的相同关键词
+            start = pos + 1
 
-        appearance_order.append(found_key)
-        start_pos = min_position + len(found_key)
+    # 按出现位置排序；同位置时短词优先
+    matches.sort(key=lambda x: (x["pos"], len(x["word"])))
 
     result = []
     for key in keywords:
-        rank_list = [i + 1 for i, k in enumerate(appearance_order) if k == key]
-        result.append({"word": key, "rank_list": rank_list})
+        rank_list = [
+            i + 1
+            for i, item in enumerate(matches)
+            if item["word"] == key
+        ]
+        result.append({
+            "word": key,
+            "rank_list": rank_list
+        })
+
     return result
 
 
@@ -623,6 +622,11 @@ def merge_com_and_ai_by_subset(brand_words_rank, ai_word_list_with_rank):
 
     return brand_list, remain_ai_list
 
+def to_int_trans(v):
+    try:
+        return int(v or 0)
+    except Exception:
+        return 0
 
 def rebuild_count_and_rank(data_list):
     """
@@ -648,8 +652,8 @@ def rebuild_count_and_rank(data_list):
             if not isinstance(vo, dict):
                 continue
 
-            count = vo.get("count", 0) or 0
-            rank = vo.get("rank", 0) or 0
+            count = to_int_trans(vo.get("count", 0))
+            rank = to_int_trans(vo.get("rank", 0))
 
             total_count += count
 
@@ -1399,9 +1403,23 @@ def result_v2(response_content, data):
         product_list = list(set(product_list))
 
         product_list = [str(x).strip() for x in product_list if str(x).strip()]
+        print(response_content)
+        print('----')
+        print('----')
+        print('----')
+        print(product_list)
+
         # 获取所有产品排名
         product_rank_list = get_keyword_ranks(response_content, product_list)
+
+        print(product_rank_list)
+        print('----')
+        print('----')
+        print('----')
+
         all_product_list_with_rank = convert_rank_data(product_rank_list)
+
+
         # 所有产品
         product_all = get_all_words(all_product_list_with_rank)
         # 获取产品的情感倾向以及正面次负面词
@@ -1414,17 +1432,25 @@ def result_v2(response_content, data):
 
         # 获取产品词的排名
         product_map_list_with_rank = build_brand_summary(all_product_list_with_rank, product_map)
+
+
         # 获取所有词的情感倾向
         product_map_list_with_rank = merge_sentiment_to_brand_words(product_map_list_with_rank,
                                                                     all_product_sentiment_and_mentions)
+
         # 删除ai中出现的品牌词
+
         product_map_list_with_rank, all_product_list_with_rank = merge_com_and_ai_by_subset(product_map_list_with_rank,
                                                                                             all_product_list_with_rank)
+
+
         # 获取产品词的分
+
         product_map_list_with_rank = attach_favorability_brand_score(all_product_sentiment_and_mentions_score,
                                                                      product_map_list_with_rank)
 
         product_map_list_with_rank_result = rebuild_count_and_rank(product_map_list_with_rank)
+
         # 获取所有品牌的分
         all_product_list_with_rank = attach_favorability_brand_score(all_product_sentiment_and_mentions_score,
                                                                      all_product_list_with_rank)
@@ -1789,6 +1815,8 @@ def task_send_queue(data, queue):
     redis_client.lpush(f"{data['platform']}:geo:{queue}:list", json.dumps(data))
 
 
+
+
 if __name__ == '__main__':
 
     # data = {
@@ -1808,34 +1836,25 @@ if __name__ == '__main__':
     import json
     from concurrent.futures import ThreadPoolExecutor, as_completed
 
-    #
-    # begin = '2026-05-18'
-    # end = '2026-05-20'
-    #
-    #
-    # req_list = get_req_id(15100000026,begin,end)
-    # req_ids = []
-    # for item in req_list:
-    #     req_id = item.get("req_id")
-    #     req_ids.append(req_id)
-    #
-    # req_id_sql = ",".join([f"'{req_id}'" for req_id in req_ids])
-    # platform = ['DOUBA']
-    # platform_list = ",".join([f"'{p}'" for p in platform])
-    # query_sql = f"select * from geo_commit_task where reqId in ({req_id_sql}) and platform in ({platform_list}) and type ='success'"
-    # data_list = bh_utils.query_data(query_sql)
-    # print(len(data_list))
-    data_list = bh_utils.query_data("select * from  geo_commit_task where  status = 'ING'  and type in ('batch','stream_batch') ")
-    #
 
-    # data_list = bh_utils.query_data(f"select * from geo_commit_task where reqId in ('0bf417a5-79f9-47f8-8f62-41ac63fe1a83','4deb09b0-de4b-4067-9bd3-b34cd29a089e','fd11816c-2255-4d15-ab78-5966effb4651','4deb09b0-de4b-4067-9bd3-b34cd29a089e','39e2b623-ee9f-4057-8a8e-6673d411226e','4deb09b0-de4b-4067-9bd3-b34cd29a089e','f81511cc-1c63-438c-99ce-b603a2340c06','4ce38505-257f-4bc5-a0ef-0b37615ec3a0','31a7119d-97b0-44f9-adb4-b67c957e1ec6','ef9b7c91-a60f-4afd-916f-fe8a5cd6c42d','6a4c7971-f455-427b-9fce-054066feecbf','bfeccabb-3a54-42cf-aacc-e481a486816e','e5decb39-f9ab-4eed-9605-83dc847f9b44','0bf417a5-79f9-47f8-8f62-41ac63fe1a83','fd11816c-2255-4d15-ab78-5966effb4651','ba90889e-db64-4df3-bf42-734bb28f02d6','9c45a2e2-b07c-44f7-a72a-05f338169f79','ecbb8c0c-4857-44ea-9703-5813dd6e4685','23543fee-ee44-48fa-8e23-d5a8657ce651','7927c636-d948-46d2-a7b0-4569ae8b5617','bfeccabb-3a54-42cf-aacc-e481a486816e','39e2b623-ee9f-4057-8a8e-6673d411226e','c068a9d4-277a-4428-9195-8174d1a1ff37','bfeccabb-3a54-42cf-aacc-e481a486816e','1ea51e2d-b069-4713-a5b5-bc75e7e0d6dc','e5decb39-f9ab-4eed-9605-83dc847f9b44','ba90889e-db64-4df3-bf42-734bb28f02d6','eb1d4753-e585-4ba0-ae95-28e938afe6c8','5d18240f-143d-4a97-b687-d98e0e2fdc20','fd7294a4-2c9c-4e92-91d7-ff6b5477ba2c','5495ee25-96fb-4ab7-a576-a43601aa1fb7','23543fee-ee44-48fa-8e23-d5a8657ce651')")
+    begin = '2026-05-22'
+    end = '2026-05-22'
+    req_list = get_req_id(15100000026,begin,end)
+
+    req_ids = []
+    for item in req_list:
+        req_id = item.get("req_id")
+        req_ids.append(req_id)
+    req_id_sql = ",".join([f"'{req_id}'" for req_id in req_ids])
+
+    query_sql = f"select * from geo_commit_task where reqId in ({req_id_sql}) "
+    data_list = bh_utils.query_data(query_sql)
     #
-    # # data_list = bh_utils.query_data(f"-- select * from geo_commit_task where reqId =  'bb32742f-c353-4a80-8855-ee097e4e0c91'")
+    # data_list = bh_utils.query_data(f"select * from geo_commit_task where reqId='92b5611d-8ecf-4dc4-a96e-db41c3f29d05' ")
     # #
     # #
     # # # #
     def handle_item(i):
-        print(i.get('reqId'))
         if i.get('comWordsMap'):
             i['comWordsMap'] = json.loads(i.get('comWordsMap'))
         if i.get('brandWords'):
@@ -1848,7 +1867,9 @@ if __name__ == '__main__':
             i['productWordsMap'] = json.loads(i.get('productWordsMap'))
         type_t = i.get('type')
 
-        return task_send_queue(i,type_t)
+        # return task_send_queue(i,type_t)
+        return platform_process(i)
+
     if data_list:
         with ThreadPoolExecutor(max_workers=30) as executor:
             futures = [executor.submit(handle_item, i) for i in data_list]

aidso_geo/utils/spider_interface.py

@@ -30,9 +30,7 @@ def get_platform_response(
     while retry_count < max_retries:
         try:
             response = requests.get(platform_config["url"], params=params, timeout=600)
-            print(platform_config["url"])
             response_data = response.json()
-
             storage_path = platform_config["storage_path"](data['taskId'])
             if response_data.get("code") == 200:
                 put_string_to_tos(storage_path, response_data.get("data"))