Clustering and deep belief network-based leakage initial locating method

Abstract

The invention discloses a clustering and deep belief network-based leakage initial locating method. The method comprises the steps of firstly performing calculation to obtain a pipe network monitoringpoint sensitivity coefficient matrix; secondly dividing a pipe network into multiple leakage regions based on K-means clustering, and generating a leakage sample by utilizing hydraulic simulation software; thirdly building and training a deep belief network-based leakage region identification model; and finally identifying the leakage regions according to actually measured pressure data. The method solves the problem of leakage sample scarcity during modeling, realizes quick locating of the leakage regions in an initial leakage stage, and has relatively high identification precision and relatively strong operability.

Description

technical field [0001] The invention belongs to the field of urban water supply pipe networks, and relates to leakage location of water supply pipe networks, in particular to an initial leakage location method based on clustering and deep belief networks. Background technique [0002] Due to various reasons such as pipeline aging, corrosion, and loose joint seals, there are inevitably open and hidden leaks in the water supply network. If the leakage area is not found in time and monitoring is strengthened, it will not only cause waste of water resources, but also may cause pipe bursts, road damage and related safety issues. Therefore, in order to avoid the occurrence of extraordinarily large squib leakage accidents, it is of great practical significance to efficiently and quickly determine the leakage area. [0003] The topological structure of most domestic water supply pipe networks is very complex, and the layout of pipes is chaotic and disorderly, so it is difficult to ...

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Problems solved by technology

[0004] In view of the irreversible deterioration of water supply pipe network leakage and the shortcomings of existing methods, the present invention proposes an initial leakage location method based on the combination of clustering and deep belief network, aiming at quickly locating the leakage area at the initial stage of leakage. timely warning

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