From 0f8d33291d87614de2a2d7a1929a515257f9757e Mon Sep 17 00:00:00 2001 From: Jiang Date: Sat, 7 Mar 2026 15:23:05 +0800 Subject: [PATCH] =?UTF-8?q?=E9=87=8D=E6=9E=84=E7=AE=A1=E9=81=93=E4=B8=AD?= =?UTF-8?q?=E5=BF=83=E9=80=89=E6=8B=A9=E9=80=BB=E8=BE=91=EF=BC=8C=E4=BC=98?= =?UTF-8?q?=E5=8C=96=E6=95=B0=E6=8D=AE=E5=A4=84=E7=90=86=E6=96=B9=E5=BC=8F?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../burst_location/burst_locator.py | 25 ++++++++++-- .../burst_location/network_partitioner.py | 39 +++++++------------ 2 files changed, 37 insertions(+), 27 deletions(-) diff --git a/app/algorithms/burst_location/burst_locator.py b/app/algorithms/burst_location/burst_locator.py index 77c4af9..599ba59 100644 --- a/app/algorithms/burst_location/burst_locator.py +++ b/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, diff --git a/app/algorithms/burst_location/network_partitioner.py b/app/algorithms/burst_location/network_partitioner.py index 5038d16..153bd94 100644 --- a/app/algorithms/burst_location/network_partitioner.py +++ b/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) - data_set_t["x"] = ( - node_x[pipe_start_node[candidate_pipe]].values - + node_x[pipe_start_node[candidate_pipe]].values - ) / 2 - data_set_t["y"] = ( - node_y[pipe_end_node[candidate_pipe]].values - + node_y[pipe_end_node[candidate_pipe]].values - ) / 2 - data_set_t.index = list(candidate_pipe) - mean_x = data_set_t["x"].mean() - mean_y = data_set_t["y"].mean() - data_set_t["d"] = abs(data_set_t["x"] - mean_x) + abs(data_set_t["y"] - mean_y) - 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 + candidate_pipe_list = list(candidate_pipe) + start_nodes = pipe_start_node[candidate_pipe_list] + end_nodes = pipe_end_node[candidate_pipe_list] + + x_vals = ( + node_x[start_nodes].to_numpy() + node_x[start_nodes].to_numpy() + ) / 2.0 + y_vals = (node_y[end_nodes].to_numpy() + node_y[end_nodes].to_numpy()) / 2.0 + mean_x = float(np.mean(x_vals)) + mean_y = float(np.mean(y_vals)) + distance = np.abs(x_vals - mean_x) + np.abs(y_vals - mean_y) + center_idx = int(np.argmin(distance)) + return candidate_pipe_list[center_idx] 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 i in range(len(nodeset)): - temp_node = nodeset[i] - pipe = node_pipe_dic[temp_node] - pipeset = pipeset + pipe + for temp_node in nodeset: + pipeset.extend(node_pipe_dic[temp_node]) return pipeset