Optimized arrangement method for monitoring points of urban water supply pipe network

Abstract

The invention discloses an optimized arrangement method for monitoring points of an urban water supply pipe network. The invention improves the traditional sensitivity analysis and cluster analysis methods separately and combines the two methods to obtain optimized distribution of monitoring points, so that the arrangement of the monitoring points can have the characteristics of being good in sensitivity, being relatively dispersed and facilitating the delimiting of abnormal regions for accurate detection, and meanwhile, the monitoring points are more widely and uniformly distributed as compared with those in the prior art, and are reasonably arranged at key positions, thereby facilitating 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 layout of measuring points for urban water supply pipe network monitoring. Background technique [0002] The measuring point layout of the water supply pipe network refers to the distribution and installation of a certain number of flow and pressure sensors in the water supply pipe network to monitor the operating conditions of the pipe network and provide data support for pipe network scheduling analysis. Due to the large scale of my country's urban water supply network and complex pipelines, flow measurement and pressure measurement points are not deployed in the initial construction and expansion of the network, and there is a lack of system considerations, resulting in biased measurement, and the operation status of the water supply network cannot be comprehensively and accurately measured. Reflect, and then affect the scientific anal...

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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