Complex pipe network leakage positioning method based on deep belief network

Abstract

The invention relates to the technical field of complex pipe network leakage positioning, in particular to a complex pipe network leakage positioning method based on a deep belief network, and the method is implemented according to the following steps: 1, obtaining a training sample and a test sample comprising a monitoring point pressure value and a leakage position coordinate; 2, constructing a complex pipe network leakage positioning model based on a deep belief network; 3, pre-training the complex pipe network leakage positioning model by adopting a layer-by-layer unsupervised learning algorithm according to the test sample; 4, performing parameter optimization on the pre-trained complex pipe network leakage positioning model by adopting a BP algorithm; step 5, performing leakage positioning on the test sample in the step 1 by using the complex pipe network leakage positioning model obtained in the step 4, and outputting a positioning result; and calculating the diagnosis accuracy of the model. According to the method, monitoring of the whole pipe network can be completed only by establishing the complex pipe network leakage database training network, and the diagnosis efficiency of pipe network leakage is greatly improved.

Description

technical field [0001] The invention relates to the technical field of leakage location of complex pipe networks, in particular to a leakage location method of complex pipe networks based on a deep belief network. Background technique [0002] There are many kinds of leakage location methods in complex pipeline networks, which can be generally divided into hardware-based and software-based location techniques. Most of the hardware-based positioning technologies require a large number of sensors to be installed in the pipe network, which has high installation costs and limited positioning accuracy; although most methods based on software-based positioning technologies can locate leaks through changes in pipe network parameters, they cannot accurately establish pipe network parameters. Nonlinear model between mesh parameter variation and leak location. The artificial neural network method that has been continuously developed in recent years can approximate any nonlinear funct...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to provide a complex pipeline network leakage location method based on a deep belief network, which is used to overcome the problem of not being able to establish a nonlinear model between pipeline network parameter changes and leakage locations

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