A Method for Optimal Arrangement of Monitoring Points in Urban Water Supply Network

Abstract

The invention discloses an optimal layout method for urban water supply pipe network monitoring points. The present invention improves the traditional sensitivity analysis and cluster analysis methods respectively, and combines the two methods to obtain an optimized measuring point distribution, so that the measuring point arrangement not only has good sensitivity, but also is relatively dispersed, which is beneficial to demarcate abnormal areas for accurate detection At the same time, the distribution of measuring points is wider and more uniform than the existing measuring points, and the layout of key positions is reasonable, which is conducive to model verification.

Description

Technical field [0001] The invention belongs to the field of urban water supply pipe networks, in particular to a method for optimizing the arrangement of measuring points used for urban water supply pipe network monitoring. Background technique [0002] The measurement point arrangement of the water supply pipe network refers to the distributed installation of a certain number of flow, pressure and other sensors in the water supply pipe network to monitor the operating conditions of the pipe network and provide data support for the pipe network scheduling analysis. Due to the large scale of the urban water supply network in my country and the complex pipelines, the flow and pressure measurement points are not deployed much during the initial construction and expansion of the pipe network, and the lack of system considerations leads to biased measurement and the running state of the water supply network cannot be comprehensive and accurate. Reflect, and then affect the scientific ...

AI Technical Summary

Problems solved by technology

In traditional sensitivity analysis, constructing an objective function by finding the detection point with the highest sensitivity for each abnormal node cannot ensure that the average sensitivity is large enough; and using the average sensitivity as the objective function cannot guarantee that the node with the highest sensitivity can be optimized

And the sensitivity standard deviation method will make the distribution of measuring points uneven, because the node sensitivity is relatively discrete, it cannot guarantee that the actual nodes are relatively dispersed geographically, and cannot well reflect the status of the entire pipe network

In cluster analysis, the weaving network method has problems such as slow efficiency and low reliability of classification results when clustering a large number of nodes.

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