110 lines
2.5 KiB
Python
110 lines
2.5 KiB
Python
import wntr
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import pandas as pd
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import numpy as np
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import matplotlib.pyplot as plt
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import sklearn.cluster
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import os
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class QD_KMeans(object):
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def __init__(self, wn, num_monitors):
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# self.inp = inp
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self.cluster_num = num_monitors # 聚类中心个数，也即测压点个数
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self.wn=wn
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self.monitor_nodes = []
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self.coords = []
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self.junction_nodes = {} # Added missing initialization
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def get_junctions_coordinates(self):
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for junction_name in self.wn.junction_name_list:
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junction = self.wn.get_node(junction_name)
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self.junction_nodes[junction_name] = junction.coordinates
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self.coords.append(junction.coordinates )
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# print(f"Total junctions: {self.junction_coordinates}")
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def select_monitoring_points(self):
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if not self.coords: # Add check if coordinates are collected
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self.get_junctions_coordinates()
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coords = np.array(self.coords)
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coords_normalized = (coords - coords.min(axis=0)) / (coords.max(axis=0) - coords.min(axis=0))
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kmeans = sklearn.cluster.KMeans(n_clusters= self.cluster_num, random_state=42)
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kmeans.fit(coords_normalized)
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for center in kmeans.cluster_centers_:
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distances = np.sum((coords_normalized - center) ** 2, axis=1)
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nearest_node = self.wn.junction_name_list[np.argmin(distances)]
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self.monitor_nodes.append(nearest_node)
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return self.monitor_nodes
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def visualize_network(self):
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"""Visualize network with monitoring points"""
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ax=wntr.graphics.plot_network(self.wn,
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node_attribute=self.monitor_nodes,
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node_size=30,
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title='Optimal sensor')
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plt.show()
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def kmeans_sensor_placement(name: str, sensor_num: int, min_diameter: int) -> list:
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inp_name = f'./db_inp/{name}.db.inp'
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wn= wntr.network.WaterNetworkModel(inp_name)
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wn_cluster=QD_KMeans(wn, sensor_num)
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# Select monitoring pointse
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sensor_ids= wn_cluster.select_monitoring_points()
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# wn_cluster.visualize_network()
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return sensor_ids
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if __name__ == "__main__":
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#sensorindex = get_ID(name='suzhouhe_2024_cloud_0817', sensor_num=30, min_diameter=500)
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sensorindex = kmeans_sensor_placement(name='szh', sensor_num=50, min_diameter=300)
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print(sensorindex)
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