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A Leakage Initial Location Method Based on Clustering and Deep Belief Network

A technology of deep belief network and initial location, applied in the field of initial location of leaks based on clustering and deep belief network

Active Publication Date: 2022-05-31
HANGZHOU DIANZI UNIV
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  • Description
  • Claims
  • Application Information

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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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  • A Leakage Initial Location Method Based on Clustering and Deep Belief Network
  • A Leakage Initial Location Method Based on Clustering and Deep Belief Network
  • A Leakage Initial Location Method Based on Clustering and Deep Belief Network

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Embodiment Construction

[0058] In order to further clarify the technical innovation point realized by the present invention, below in conjunction with the accompanying drawings and examples, the implementation mode of the present invention is described in detail, and the specific steps are as follows:

[0059] Step 1. Calculate and obtain the sensitivity coefficient matrix of monitoring points in the pipeline network

[0060] (1) Sensitivity coefficient matrix

[0061] The pipe network simulation model simulates a DMA area in YC district of SX city, which has a total of 5377 nodes (excluding water sources). In order to select a representative working condition, the water demand data of the node at the peak of water consumption (11:30 noon) during the maximum work is used as the test data. An example of the water demand of a node is shown in Table 1:

[0062] Table 1 Node water demand

[0063]

[0064] Among them, the node numbers are sorted by index. In EPANET, the diffuser coefficient of each...

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Abstract

The invention discloses an initial leak location method based on clustering and deep belief network. The present invention first calculates and obtains the matrix of the sensitivity coefficient of the monitoring point of the pipe network. Secondly, the pipe network is divided into several leakage areas based on K-means clustering, and the leakage samples are generated by hydraulic simulation software. Then build and train the leakage area identification model based on deep belief network. Finally, the leakage area is identified according to the measured pressure data. The invention overcomes the problem of scarcity of leakage samples during modeling, realizes rapid location of leakage areas at the initial stage of leakage, and has high identification accuracy and strong operability.

Description

technical field [0001] The invention belongs to the field of urban water supply pipe networks, and relates to leak location of water supply pipe networks, in particular to an initial leak location method based on clustering and deep belief network. Background technique [0002] Due to various reasons such as pipeline aging, corrosion, loose interface seals, etc., the water supply pipeline network inevitably has open leaks and hidden leaks. If the leaked area is not discovered in time and monitoring is strengthened, it will not only cause waste of water resources, but may also lead to burst pipes, road damage and related safety problems. Therefore, in order to avoid the occurrence of the leakage accident of extra-large burst pipes, the method of efficiently and quickly determining the leakage area is of great practical significance. [0003] Most of the domestic water supply network topology is very complex, and the pipeline layout is chaotic and disorderly, and it is diffic...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F30/27G06N3/04G06N3/08F17D5/02
CPCG06N3/08F17D5/02G06F30/20G06N3/045Y02A20/15
Inventor 徐哲黄兴李玉全陈晖何必仕
Owner HANGZHOU DIANZI UNIV
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