重构管道中心选择逻辑，优化数据处理方式

app/algorithms/burst_location/burst_locator.py

@@ -37,6 +37,8 @@ def _ensure_signatures_for_centers(
     其中 subset 只包含 center_list 的行（顺序与 center_list 保持一致）。
     """
 
+    center_list = _dedupe_preserve_order(center_list)
+
     # 1) 推断传感器列名（与现有数据保持一致）
     sensor_name_all = list(pressure_monitor.columns)
     sensor_f_name_all = (
@@ -193,6 +195,17 @@ def find_list_repeat(candidate_center, target):
     return repeated_list
 
 
+def _dedupe_preserve_order(items):
+    seen = set()
+    output = []
+    for item in items:
+        if item in seen:
+            continue
+        seen.add(item)
+        output.append(item)
+    return output
+
+
 def cal_DtoTop1(
     G0, pipe_leak, located_pipe, pipe_start_node_all, pipe_end_node_all, pipe_length
 ):
@@ -324,6 +337,7 @@ def DN_search_multi_simple_add_flow_count_new(
         dis_f_h = 0
         if_compalsive = 0
         record_center_dataset = []
+        record_center_set = set()
         # iter
         while 1:
             final_area = []
@@ -374,18 +388,21 @@ def DN_search_multi_simple_add_flow_count_new(
             leak_center_dict = dict()
             for i in range(len(candidate_center_list)):
                 houxuan_center = []
+                candidate_group_set = set(candidate_group_list[i])
                 for each_center in record_center_dataset:
                     if (
-                        each_center in candidate_group_list[i]
+                        each_center in candidate_group_set
                         and each_center != candidate_center_list[i]
                     ):
                         houxuan_center.append(each_center)
                 add_center = add_center + houxuan_center
                 houxuan_center.append(candidate_center_list[i])
                 leak_center_dict[candidate_center_list[i]] = houxuan_center
+            add_center = _dedupe_preserve_order(add_center)
             for each_center in candidate_center_list:
-                if each_center not in record_center_dataset:
+                if each_center not in record_center_set:
                     record_center_dataset.append(each_center)
+                    record_center_set.add(each_center)
 
             # --------------------------------------------------------
             # --------------------------------------------------------
@@ -417,7 +434,9 @@ def DN_search_multi_simple_add_flow_count_new(
             if len(candidate_pipe_input) < 1.2 * top_pipe_num_max / top_group_ratio:
                 if_compalsive = 1
             cos_h, dis_h, dis_f_h = adjust_ratio(similarity_mode, cos_h, dis_h, dis_f_h)
-            candidate_center_list_sup = candidate_center_list + add_center
+            candidate_center_list_sup = _dedupe_preserve_order(
+                candidate_center_list + add_center
+            )
             similarity, cos_h, dis_h, dis_f_h, break_flag = (
                 cal_similarity_all_multi_new_sq_improve_double_lzr(
                     candidate_center_list_sup,

app/algorithms/burst_location/network_partitioner.py

@@ -21,25 +21,19 @@ def _to_metis_edge_weight(edge_weight):
 
 
 def pick_center_pipe(node_x, node_y, candidate_pipe, pipe_start_node, pipe_end_node):
-    data_set_t = pd.DataFrame(dtype=object)
+    candidate_pipe_list = list(candidate_pipe)
-    data_set_t["x"] = (
+    start_nodes = pipe_start_node[candidate_pipe_list]
-        node_x[pipe_start_node[candidate_pipe]].values
+    end_nodes = pipe_end_node[candidate_pipe_list]
-        + node_x[pipe_start_node[candidate_pipe]].values
+
-    ) / 2
+    x_vals = (
-    data_set_t["y"] = (
+        node_x[start_nodes].to_numpy() + node_x[start_nodes].to_numpy()
-        node_y[pipe_end_node[candidate_pipe]].values
+    ) / 2.0
-        + node_y[pipe_end_node[candidate_pipe]].values
+    y_vals = (node_y[end_nodes].to_numpy() + node_y[end_nodes].to_numpy()) / 2.0
-    ) / 2
+    mean_x = float(np.mean(x_vals))
-    data_set_t.index = list(candidate_pipe)
+    mean_y = float(np.mean(y_vals))
-    mean_x = data_set_t["x"].mean()
+    distance = np.abs(x_vals - mean_x) + np.abs(y_vals - mean_y)
-    mean_y = data_set_t["y"].mean()
+    center_idx = int(np.argmin(distance))
-    data_set_t["d"] = abs(data_set_t["x"] - mean_x) + abs(data_set_t["y"] - mean_y)
+    return candidate_pipe_list[center_idx]
-    distance_t = data_set_t["d"].sort_values(ascending=True, inplace=False)
-    """if distance_t.index==[]:
-        print(candidate_pipe)
-    else:"""
-    center_t = distance_t.index[0]
-    return center_t
 
 
 def find_new_center_pipe(
@@ -56,11 +50,8 @@ def find_new_center_pipe(
 
 def cal_area_node_linked_pipe(nodeset, node_pipe_dic):
     pipeset = []
-    nodeset = list(nodeset)
+    for temp_node in nodeset:
-    for i in range(len(nodeset)):
+        pipeset.extend(node_pipe_dic[temp_node])
-        temp_node = nodeset[i]
-        pipe = node_pipe_dic[temp_node]
-        pipeset = pipeset + pipe
     return pipeset