减少爆管定位代码中引入的不确定性

This commit is contained in:
2026-03-09 11:29:57 +08:00
parent d55e23bc44
commit f9111ab9c1
5 changed files with 145 additions and 31 deletions
@@ -220,6 +220,7 @@ def run_burst_location(
node_y=node_y, node_y=node_y,
pipe_start_node_all=pipe_start_node_all, pipe_start_node_all=pipe_start_node_all,
pipe_end_node_all=pipe_end_node_all, pipe_end_node_all=pipe_end_node_all,
pipe_diameter=pipe_diameter,
couple_node_length=couple_node_length, couple_node_length=couple_node_length,
node_pipe_dic=node_pipe_dic, node_pipe_dic=node_pipe_dic,
all_node_series=all_node_series, all_node_series=all_node_series,
+24 -6
View File
@@ -345,6 +345,7 @@ def DN_search_multi_simple_add_flow_count_new(
node_y, node_y,
pipe_start_node_all, pipe_start_node_all,
pipe_end_node_all, pipe_end_node_all,
pipe_diameter,
couple_node_length, couple_node_length,
node_pipe_dic, node_pipe_dic,
all_node_series, all_node_series,
@@ -415,7 +416,12 @@ def DN_search_multi_simple_add_flow_count_new(
# group 分组,得出候选漏损中心 # group 分组,得出候选漏损中心
stage_start = perf_counter() stage_start = perf_counter()
candidate_center_list, candidate_group_list, new_all_node = ( (
candidate_center_list,
candidate_group_list,
new_all_node,
candidate_center_candidates,
) = (
metis_grouping_pipe_weight( metis_grouping_pipe_weight(
G0, G0,
wn, wn,
@@ -429,6 +435,7 @@ def DN_search_multi_simple_add_flow_count_new(
node_pipe_dic, node_pipe_dic,
all_node_series, all_node_series,
couple_node_length, couple_node_length,
pipe_diameter,
) )
) )
_accumulate_stage(stage_timing, "group_partitioning", stage_start) _accumulate_stage(stage_timing, "group_partitioning", stage_start)
@@ -479,19 +486,30 @@ def DN_search_multi_simple_add_flow_count_new(
add_center = [] add_center = []
leak_center_dict = dict() leak_center_dict = dict()
for i in range(len(candidate_center_list)): for i in range(len(candidate_center_list)):
houxuan_center = [] primary_center = candidate_center_list[i]
houxuan_center = [
center
for center in candidate_center_candidates[i]
if center != primary_center
]
candidate_group_set = set(candidate_group_list[i]) candidate_group_set = set(candidate_group_list[i])
for each_center in record_center_dataset: for each_center in record_center_dataset:
if ( if (
each_center in candidate_group_set each_center in candidate_group_set
and each_center != candidate_center_list[i] and each_center != primary_center
): ):
houxuan_center.append(each_center) houxuan_center.append(each_center)
add_center = add_center + houxuan_center add_center = add_center + houxuan_center
houxuan_center.append(candidate_center_list[i]) leak_center_dict[primary_center] = _dedupe_preserve_order(
leak_center_dict[candidate_center_list[i]] = houxuan_center houxuan_center + [primary_center]
)
add_center = _dedupe_preserve_order(add_center) add_center = _dedupe_preserve_order(add_center)
for each_center in candidate_center_list: for each_group_centers in candidate_center_candidates:
for each_center in each_group_centers:
if each_center not in record_center_set:
record_center_dataset.append(each_center)
record_center_set.add(each_center)
for each_center in add_center:
if each_center not in record_center_set: if each_center not in record_center_set:
record_center_dataset.append(each_center) record_center_dataset.append(each_center)
record_center_set.add(each_center) record_center_set.add(each_center)
@@ -474,6 +474,7 @@ def cal_signature_pipe_multi_pf(
float(leak_mag), float(leak_mag),
list(sensor_name), list(sensor_name),
option_values, option_values,
list(candidate_center),
), ),
) as pool: ) as pool:
for i, (center_name, pressure_array) in enumerate( for i, (center_name, pressure_array) in enumerate(
@@ -482,6 +483,9 @@ def cal_signature_pipe_multi_pf(
pressure_leak.loc[(center_name, slice(None)), :] = pressure_array pressure_leak.loc[(center_name, slice(None)), :] = pressure_array
sys.stdout.write("\r" + "已经完成计算" + str(i + 1) + "个特征中心") sys.stdout.write("\r" + "已经完成计算" + str(i + 1) + "个特征中心")
else: else:
# Pre-insert all mid-nodes so every simulation sees the same topology
for center in candidate_center:
ensure_mid_node(wn, center)
for i in range(candidate_center_num): for i in range(candidate_center_num):
temp_prefix = _make_temp_prefix(f"sig_{i}") temp_prefix = _make_temp_prefix(f"sig_{i}")
wn, pressure_output = leak_simulation_pipe_dd_multi_pf( wn, pressure_output = leak_simulation_pipe_dd_multi_pf(
@@ -500,10 +504,17 @@ def cal_signature_pipe_multi_pf(
return pressure_leak, candidate_center return pressure_leak, candidate_center
def _signature_worker_init(inp_path, leak_mag, sensor_name, option_values): def _signature_worker_init(
inp_path, leak_mag, sensor_name, option_values, candidate_centers=None
):
global _SIGNATURE_WORKER_DATA global _SIGNATURE_WORKER_DATA
wn = wntr.network.WaterNetworkModel(inp_path) wn = wntr.network.WaterNetworkModel(inp_path)
_apply_hydraulic_options(wn, option_values) _apply_hydraulic_options(wn, option_values)
# Pre-insert ALL mid-nodes so every simulation runs on the same topology,
# regardless of which worker handles which task.
if candidate_centers is not None:
for center in candidate_centers:
ensure_mid_node(wn, center)
_SIGNATURE_WORKER_DATA = { _SIGNATURE_WORKER_DATA = {
"wn": wn, "wn": wn,
"leak_mag": leak_mag, "leak_mag": leak_mag,
@@ -20,6 +20,17 @@ def _to_metis_edge_weight(edge_weight):
return max(1, int(round(weight))) return max(1, int(round(weight)))
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 pick_center_pipe(node_x, node_y, candidate_pipe, pipe_start_node, pipe_end_node): def pick_center_pipe(node_x, node_y, candidate_pipe, pipe_start_node, pipe_end_node):
candidate_pipe_list = list(candidate_pipe) candidate_pipe_list = list(candidate_pipe)
start_nodes = pipe_start_node[candidate_pipe_list] start_nodes = pipe_start_node[candidate_pipe_list]
@@ -34,14 +45,52 @@ def pick_center_pipe(node_x, node_y, candidate_pipe, pipe_start_node, pipe_end_n
return candidate_pipe_list[center_idx] return candidate_pipe_list[center_idx]
def pick_max_diameter_pipe(candidate_pipe, pipe_diameter):
candidate_pipe_list = list(candidate_pipe)
diameters = pd.to_numeric(
pipe_diameter.reindex(candidate_pipe_list), errors="coerce"
).dropna()
if len(diameters) != len(candidate_pipe_list):
missing = sorted(set(candidate_pipe_list) - set(diameters.index))
preview = ", ".join(map(str, missing[:10]))
raise ValueError(f"Missing or invalid diameter for pipes: {preview}")
max_diameter = float(diameters.max())
max_diameter_pipes = sorted(
[pipe for pipe, diameter in diameters.items() if float(diameter) == max_diameter],
key=str,
)
return max_diameter_pipes[0]
def pick_dual_center_pipes(
node_x, node_y, candidate_pipe, pipe_start_node, pipe_end_node, pipe_diameter
):
geometric_center = pick_center_pipe(
node_x, node_y, candidate_pipe, pipe_start_node, pipe_end_node
)
diameter_center = pick_max_diameter_pipe(candidate_pipe, pipe_diameter)
return _dedupe_preserve_order([geometric_center, diameter_center])
def find_new_center_pipe( def find_new_center_pipe(
node_x, node_y, candidate_pipe, pipe_start_node, pipe_end_node, record_center node_x,
node_y,
candidate_pipe,
pipe_start_node,
pipe_end_node,
pipe_diameter,
record_center,
): ):
new_candidate_pipe = sorted(set(candidate_pipe) - set(record_center)) new_candidate_pipe = sorted(set(candidate_pipe) - set(record_center))
if new_candidate_pipe == []: if new_candidate_pipe == []:
new_candidate_pipe = candidate_pipe new_candidate_pipe = candidate_pipe
center_t = pick_center_pipe( center_t = pick_center_pipe(
node_x, node_y, new_candidate_pipe, pipe_start_node, pipe_end_node node_x,
node_y,
new_candidate_pipe,
pipe_start_node,
pipe_end_node,
) )
return center_t return center_t
@@ -66,6 +115,7 @@ def metis_grouping_pipe_weight(
node_pipe_dic, node_pipe_dic,
all_node_series, all_node_series,
couple_node_length, couple_node_length,
pipe_diameter,
): ):
all_node_iter_series_new = all_node_series[all_node_iter] all_node_iter_series_new = all_node_series[all_node_iter]
all_node_iter_series_new = all_node_iter_series_new.sort_values(ascending=True) all_node_iter_series_new = all_node_iter_series_new.sort_values(ascending=True)
@@ -82,10 +132,12 @@ def metis_grouping_pipe_weight(
correspond_dic = {} correspond_dic = {}
count_node = 0 count_node = 0
w = [] w = []
for node_name, neighbors in G1.adjacency(): for node_name in all_node_iter_new:
neighbors = G1[node_name]
w_temp = [] w_temp = []
n_t = [node_dict[node_name]] n_t = [node_dict[node_name]]
for neighbor_name, edge_data in neighbors.items(): for neighbor_name in sorted(neighbors.keys()):
edge_data = neighbors[neighbor_name]
edge_key = f"{node_name},{neighbor_name}" edge_key = f"{node_name},{neighbor_name}"
reverse_edge_key = f"{neighbor_name},{node_name}" reverse_edge_key = f"{neighbor_name},{node_name}"
if edge_key in couple_node_length: if edge_key in couple_node_length:
@@ -122,14 +174,14 @@ def metis_grouping_pipe_weight(
w_f = w_f + w_new[i] w_f = w_f + w_new[i]
# (edgecuts, parts) = pymetis.part_graph(nparts=group_num, adjacency=adjacency_list_new) # (edgecuts, parts) = pymetis.part_graph(nparts=group_num, adjacency=adjacency_list_new)
# metis_options = pymetis.Options() metis_options = pymetis.Options()
# metis_options.seed = 42 metis_options.seed = 42
(edgecuts, parts) = pymetis.part_graph( (edgecuts, parts) = pymetis.part_graph(
nparts=group_num, nparts=group_num,
adjncy=final_adjacency_list, adjncy=final_adjacency_list,
xadj=xadj, xadj=xadj,
eweights=w_f, eweights=w_f,
# options=metis_options, options=metis_options,
) )
# (edgecuts, parts) = pymetis.part_graph(nparts=group_num, adjacency=adjacency_list_new) # (edgecuts, parts) = pymetis.part_graph(nparts=group_num, adjacency=adjacency_list_new)
candidate_group_list = [[] * 1 for i in range(group_num)] candidate_group_list = [[] * 1 for i in range(group_num)]
@@ -145,6 +197,7 @@ def metis_grouping_pipe_weight(
new_center = [] new_center = []
new_group = [] new_group = []
new_center_candidates = []
new_all_node = [] new_all_node = []
candidate_pipe_set = set(candidate_pipe_input) candidate_pipe_set = set(candidate_pipe_input)
all_grouped_pipe = [] all_grouped_pipe = []
@@ -158,7 +211,7 @@ def metis_grouping_pipe_weight(
# 求交集 # 求交集
nodeset = G_sub.nodes() nodeset = G_sub.nodes()
pipeset_set = set(cal_area_node_linked_pipe(nodeset, node_pipe_dic)) pipeset_set = set(cal_area_node_linked_pipe(nodeset, node_pipe_dic))
candidate_pipe = list(pipeset_set.intersection(candidate_pipe_set)) candidate_pipe = sorted(pipeset_set.intersection(candidate_pipe_set))
# 判断集合是否保留 # 判断集合是否保留
if len(candidate_pipe) > 0: if len(candidate_pipe) > 0:
@@ -170,21 +223,30 @@ def metis_grouping_pipe_weight(
pipe_start_node_all, pipe_start_node_all,
pipe_end_node_all, pipe_end_node_all,
) )
center_candidates_t = pick_dual_center_pipes(
node_x,
node_y,
candidate_pipe,
pipe_start_node_all,
pipe_end_node_all,
pipe_diameter,
)
# 更新 # 更新
new_center.append(center_t) new_center.append(center_t)
new_center_candidates.append(center_candidates_t)
new_group.append(candidate_pipe) new_group.append(candidate_pipe)
new_all_node.append(nodeset) new_all_node.append(nodeset)
all_grouped_pipe = all_grouped_pipe + candidate_pipe all_grouped_pipe = all_grouped_pipe + candidate_pipe
else: else:
for c in sub_graphs: for c in sorted(sub_graphs, key=lambda c: min(c)):
G_temp = G0.subgraph(c) G_temp = G0.subgraph(c)
nodeset = G_temp.nodes() nodeset = G_temp.nodes()
pipeset = cal_area_node_linked_pipe(nodeset, node_pipe_dic) pipeset = cal_area_node_linked_pipe(nodeset, node_pipe_dic)
pipeset_set = set(pipeset) pipeset_set = set(pipeset)
# 求交集 # 求交集
candidate_pipe = list(pipeset_set.intersection(candidate_pipe_set)) candidate_pipe = sorted(pipeset_set.intersection(candidate_pipe_set))
# print(len(candidate_node)) # print(len(candidate_node))
# 判断集合是否保留 # 判断集合是否保留
if len(candidate_pipe) > 0: if len(candidate_pipe) > 0:
@@ -196,8 +258,17 @@ def metis_grouping_pipe_weight(
pipe_start_node_all, pipe_start_node_all,
pipe_end_node_all, pipe_end_node_all,
) )
center_candidates_t = pick_dual_center_pipes(
node_x,
node_y,
candidate_pipe,
pipe_start_node_all,
pipe_end_node_all,
pipe_diameter,
)
# 更新 # 更新
new_center.append(center_t) new_center.append(center_t)
new_center_candidates.append(center_candidates_t)
new_group.append(candidate_pipe) new_group.append(candidate_pipe)
new_all_node.append(nodeset) new_all_node.append(nodeset)
all_grouped_pipe = all_grouped_pipe + candidate_pipe all_grouped_pipe = all_grouped_pipe + candidate_pipe
@@ -217,8 +288,12 @@ def metis_grouping_pipe_weight(
each_group, each_group,
pipe_start_node_all, pipe_start_node_all,
pipe_end_node_all, pipe_end_node_all,
pipe_diameter,
record_center, record_center,
) )
new_center_candidates[c_g] = _dedupe_preserve_order(
[new_center[c_g]] + list(new_center_candidates[c_g])
)
record_center.append(new_center[c_g]) record_center.append(new_center[c_g])
c_g += 1 c_g += 1
@@ -227,7 +302,7 @@ def metis_grouping_pipe_weight(
# node_x, node_y, # node_x, node_y,
# pipe_start_node_all, pipe_end_node_all # pipe_start_node_all, pipe_end_node_all
# ) # )
return new_center, new_group, new_all_node return new_center, new_group, new_all_node, new_center_candidates
def visualize_metis_partition( def visualize_metis_partition(
@@ -44,7 +44,7 @@ def cal_similarity_simple_return_dd(
similarity_dis = 0 similarity_dis = 0
else:""" else:"""
none_flag = 0 none_flag = 0
sensor_for_cos = list( sensor_for_cos = sorted(
set(dpressure_s.index).intersection(set(act_dpressure.index)) set(dpressure_s.index).intersection(set(act_dpressure.index))
) )
"""if len(dpressure_s) ==0 or len(dpressure) ==0: """if len(dpressure_s) ==0 or len(dpressure) ==0:
@@ -74,7 +74,7 @@ def cal_similarity_simple_return_dd(
none_flag = 1 none_flag = 1
else:""" else:"""
none_flag = 0 none_flag = 0
important_sensor = list( important_sensor = sorted(
set(important_sensor).intersection(set(act_dpressure.index)) set(important_sensor).intersection(set(act_dpressure.index))
) )
part_dpressure = dpressure_s[important_sensor] - dpressure[important_sensor] part_dpressure = dpressure_s[important_sensor] - dpressure[important_sensor]
@@ -107,7 +107,7 @@ def cal_similarity_simple_return_dd(
elif similarity_mode == "CAD_new_gy" or similarity_mode == "CDF": elif similarity_mode == "CAD_new_gy" or similarity_mode == "CDF":
# cos # cos
sensor_for_cos = list( sensor_for_cos = sorted(
set(dpressure_s.index).intersection(set(act_dpressure.index)) set(dpressure_s.index).intersection(set(act_dpressure.index))
) )
if len(sensor_for_cos) == 0 and len(dpressure_s) == 0: if len(sensor_for_cos) == 0 and len(dpressure_s) == 0:
@@ -141,7 +141,7 @@ def cal_similarity_simple_return_dd(
similarity_cos = s1 / s2 similarity_cos = s1 / s2
# DIS # DIS
important_sensor_new = list( important_sensor_new = sorted(
set(important_sensor).intersection(set(act_dpressure.index)) set(important_sensor).intersection(set(act_dpressure.index))
) )
if len(important_sensor_new) == 0: if len(important_sensor_new) == 0:
@@ -172,7 +172,7 @@ def cal_similarity_simple_return_dd(
similarity_cos = 0 similarity_cos = 0
none_flag = 0 none_flag = 0
# DIS # DIS
important_sensor_new = list( important_sensor_new = sorted(
set(important_sensor).intersection(set(act_dpressure.index)) set(important_sensor).intersection(set(act_dpressure.index))
) )
if len(important_sensor_new) == 0: if len(important_sensor_new) == 0:
@@ -762,15 +762,24 @@ def update_similarity(leak_candidate_center, similarity, leak_center_dict):
return similarity_new return similarity_new
def extra_judge(similarity): def extra_judge(
similarity, min_candidates_to_prune: int = 200, std_relax_factor: float = 0.5
):
if len(similarity.index) == 0:
return 1.0, similarity
if len(similarity.index) < int(min_candidates_to_prune):
return 1.0, similarity
mean_similarity = float(similarity.mean()) mean_similarity = float(similarity.mean())
record_sensor = [] std_similarity = float(similarity.std())
record_value = [] if not math.isfinite(std_similarity):
for i in range(len(similarity.index)): std_similarity = 0.0
if similarity.iloc[i] >= mean_similarity - 1e-10:
record_value.append(similarity.iloc[i]) threshold = mean_similarity - float(std_relax_factor) * std_similarity
record_sensor.append(similarity.index[i]) out_put_similarity = similarity[similarity >= threshold - 1e-10]
out_put_similarity = pd.Series(record_value, index=record_sensor, dtype=float) if len(out_put_similarity.index) == 0:
out_put_similarity = similarity.iloc[:1]
cut_ratio = len(out_put_similarity.index) / len(similarity.index) cut_ratio = len(out_put_similarity.index) / len(similarity.index)
return cut_ratio, out_put_similarity return cut_ratio, out_put_similarity