新增爆管位置检测模块及相关API接口

2026-03-06 15:27:59 +08:00
parent 63d3458fb4
commit b83b895e2b
11 changed files with 3084 additions and 0 deletions
@@ -0,0 +1,169 @@
+"""管网模型读取与图构建模块。"""
+
+import copy
+
+import numpy as np
+import networkx as nx
+import pandas as pd
+import wntr
+
+
+def load_inp(inp_name, inp_location, inp_time, driven_mode, require_p, minimum_p):
+    inp_file = inp_location + inp_name
+    wn = wntr.network.WaterNetworkModel(inp_file)
+    if driven_mode == "PDD":
+        wn.options.hydraulic.demand_model = "PDD"
+        wn.options.hydraulic.required_pressure = require_p
+        wn.options.hydraulic.minimum_pressure = minimum_p
+    else:
+        wn.options.hydraulic.demand_model = "DD"
+    return wn
+
+
+def read_inf_inp(wn):
+    all_node = wn.node_name_list
+    node_elevation = wn.query_node_attribute("elevation")
+    node_coordinates = wn.query_node_attribute("coordinates")
+
+    all_pipe = wn.pipe_name_list
+    # 改_wz__________________________________
+    n_pipe = []
+    for p in all_pipe:
+        pipe = wn.get_link(p)
+        if pipe.initial_status == 0:  # 状态为'Closed'
+            n_pipe.append(p)
+    candidate_pipe_init = list(set(all_pipe) - set(n_pipe))
+    pipe_start_node = wn.query_link_attribute(
+        "start_node_name", link_type=wntr.network.model.Pipe
+    )
+    pipe_end_node = wn.query_link_attribute(
+        "end_node_name", link_type=wntr.network.model.Pipe
+    )
+    pipe_length = wn.query_link_attribute("length")
+    pipe_diameter = wn.query_link_attribute("diameter")
+
+    return (
+        all_node,
+        node_elevation,
+        node_coordinates,
+        candidate_pipe_init,
+        pipe_start_node,
+        pipe_end_node,
+        pipe_length,
+        pipe_diameter,
+    )
+
+
+def read_inf_inp_other(wn):
+    all_link = wn.link_name_list
+    pipe_start_node_all = wn.query_link_attribute("start_node_name")
+    pipe_end_node_all = wn.query_link_attribute("end_node_name")
+
+    return all_link, pipe_start_node_all, pipe_end_node_all
+
+
+def construct_graph(wn):
+    length = wn.query_link_attribute("length")
+    G = wn.get_graph(wn, link_weight=length)
+    # 转为无向图
+    G0 = G.to_undirected()
+    # A0 = np.array(nx.adjacency_graph(G0).todense())
+    return G0  # , A0
+
+
+def cal_pipe_coordinate(all_pipe, pipe_start_node, pipe_end_node, node_coordinates):
+    pipe_num = len(all_pipe)
+    pipe_coordinates = np.zeros([pipe_num, 2])
+    pipe_x = copy.deepcopy(pipe_start_node)
+    pipe_y = copy.deepcopy(pipe_start_node)
+    for i in range(pipe_num):
+        temp_pipe = all_pipe[i]
+        pipe_x[temp_pipe] = (
+            node_coordinates[pipe_start_node[temp_pipe]][0]
+            + node_coordinates[pipe_end_node[temp_pipe]][0]
+        ) / 2
+        pipe_y[temp_pipe] = (
+            node_coordinates[pipe_start_node[temp_pipe]][1]
+            + node_coordinates[pipe_end_node[temp_pipe]][1]
+        ) / 2
+    return pipe_x, pipe_y
+
+
+def cal_node_coordinate(all_node, node_coordinates):
+    node_x = copy.deepcopy(node_coordinates)
+    node_y = copy.deepcopy(node_coordinates)
+    for i in range(len(node_x)):
+        temp_node = all_node[i]
+        node_x[temp_node] = node_coordinates[temp_node][0]
+        node_y[temp_node] = node_coordinates[temp_node][1]
+    return node_x, node_y
+
+
+def produce_pattern_value(wn, all_node):
+    wn_o = copy.deepcopy(wn)
+    # 改_wz_____________________________
+    # sample_node = wn_o.get_node(all_node[0])
+    # num_categories = len(sample_node.demand_timeseries_list)
+    num_categories = 1
+    columns = [f"D{i}" for i in range(num_categories)]
+    basic_demand_pd = pd.DataFrame(index=all_node, columns=columns)
+    for each in all_node:
+        node = wn_o.get_node(each)
+        for i in range(num_categories):
+            basic_demand_pd.loc[each, columns[i]] = node.demand_timeseries_list[
+                i
+            ].base_value
+
+    return basic_demand_pd
+
+
+def _build_node_pipe_maps(
+    all_nodes, candidate_pipes, pipe_start_node, pipe_end_node, pipe_length
+):
+    node_pipe_dic = {node: [] for node in all_nodes}
+    couple_node_length = {}
+    for pipe in candidate_pipes:
+        start_node = pipe_start_node[pipe]
+        end_node = pipe_end_node[pipe]
+        if start_node in node_pipe_dic:
+            node_pipe_dic[start_node].append(pipe)
+        if end_node in node_pipe_dic:
+            node_pipe_dic[end_node].append(pipe)
+        length = float(pipe_length[pipe])
+        couple_node_length[f"{start_node},{end_node}"] = length
+        couple_node_length[f"{end_node},{start_node}"] = length
+    return node_pipe_dic, couple_node_length
+
+
+class NetworkModelReader:
+    @staticmethod
+    def load_inp(*args, **kwargs):
+        return load_inp(*args, **kwargs)
+
+    @staticmethod
+    def read_inf_inp(*args, **kwargs):
+        return read_inf_inp(*args, **kwargs)
+
+    @staticmethod
+    def read_inf_inp_other(*args, **kwargs):
+        return read_inf_inp_other(*args, **kwargs)
+
+    @staticmethod
+    def construct_graph(*args, **kwargs):
+        return construct_graph(*args, **kwargs)
+
+    @staticmethod
+    def cal_pipe_coordinate(*args, **kwargs):
+        return cal_pipe_coordinate(*args, **kwargs)
+
+    @staticmethod
+    def cal_node_coordinate(*args, **kwargs):
+        return cal_node_coordinate(*args, **kwargs)
+
+    @staticmethod
+    def produce_pattern_value(*args, **kwargs):
+        return produce_pattern_value(*args, **kwargs)
+
+    @staticmethod
+    def build_node_pipe_maps(*args, **kwargs):
+        return _build_node_pipe_maps(*args, **kwargs)