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
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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...
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