Multi-point leakage locating method and device for water supply network based on convolution neural network

A technology of convolutional neural network and water supply pipe network, which is applied in the field of multi-point leakage location of water supply pipe network based on convolutional neural network, which can solve the problems of difficult location of multi-point leakage

Active Publication Date: 2019-01-18
ANHUI UNIVERSITY OF ARCHITECTURE
View PDF2 Cites 16 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] The technical problem to be solved by the present invention is how to reduce the noise points in the data through convolution and pooling operations, extract the special features in each type of data, and make the prediction more accurate; at the same time, it also solves the problem of the difficulty in multi-point leakage location. The problem

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Multi-point leakage locating method and device for water supply network based on convolution neural network
  • Multi-point leakage locating method and device for water supply network based on convolution neural network
  • Multi-point leakage locating method and device for water supply network based on convolution neural network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0093] This embodiment discloses a method for locating multi-point leaks in a municipal water supply network based on a convolutional neural network. The specific implementation steps are as follows:

[0094] Step 1. Collect the pressure data of the water supply network by type, including normal data, single-point leakage and multi-point leakage data, and put a unique label on each piece of data according to the type (the data labels of the same type are the same) , divide the labeled data into training samples and test samples, and normalize the training samples and test samples respectively.

[0095] Step 1.1 The water supply data is collected every 5 seconds through the sensors installed on the water supply network around the clock. The data includes one set of normal data, four sets of single-point leakage, two-point leakage, and three-point leakage data. The types of leakage points are shown in Tables 1 and 2, and each data format is {23.421747, 23.721256, 22.024464...21....

Embodiment 2

[0162] This embodiment discloses a multi-point leakage location device for municipal water supply pipe network based on convolutional neural network, including

[0163] The data collection module is used to collect the pressure data of the water supply pipe, and divide the collected data into training samples and test samples;

[0164] A data normalization module, configured to normalize the training samples and the test samples;

[0165] The training module is used to input the normalized training samples into the convolutional neural network model for training to obtain the convolutional neural network model, and use the normalized test samples to test the convolutional neural network model, and Save the trained convolutional neural network model;

[0166] The test module is used to normalize the real-time data collected by the pipeline network and input it into the trained convolutional neural network model, and obtain the prediction result through the trained convolutiona...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a multi-point leakage locating method of a water supply network based on a convolution neural network, comprising the following steps: pressure data of the water supply networkis collected, the collected water supply data is divided into a training sample and a test sample; the smaples are normalized, the normalized samples are input into the convolution neural network model for training, and the convolution neural network model is obtained. The normalized test samples are used to test the convolution neural network model, and the trained convolution neural network model is saved. The real-time data are normalized and input into the trained convolution neural network model, and the prediction results are obtained through the trained convolution neural network model. The prediction results are compared with the tag index to judge the leakage. The invention also discloses a multi-point leakage locating device of a water supply network based on a convolution neural network. The invention reduces noise points in data through convolution and pooling operations, extracts special features in each type of data, and makes prediction more accurate.

Description

technical field [0001] The invention relates to the field of municipal water supply pipe networks, in particular to a multi-point leakage location method and device for water supply pipe networks based on convolutional neural networks. Background technique [0002] The municipal water supply network is an important infrastructure to ensure a city's economic development and living standards, and is the lifeline of a city's survival and development. However, due to the continuous expansion of water consumption and the increase in the service life of the pipe network, there is a lack of a modern, intelligent, and theoretical municipal water supply pipe network management system, and the municipal water supply pipe network has gradually exposed a wide range of leakage. , will cause waste of water resources and economic losses. [0003] With the development of science and technology and the continuous improvement of water supply requirements, it is imminent to establish a water ...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N3/04G06Q50/06
CPCG06Q50/06G06N3/045G06F18/24G06F18/214
Inventor 方潜生谢陈磊杨亚龙张振亚张继鑫张红艳张毅李善寿朱徐来涂畅盛锦壮郭玉涵任守明袁翠艳钟永祥
Owner ANHUI UNIVERSITY OF ARCHITECTURE
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap