City water supply network burst detection method based on dynamic neural network prediction

Abstract

The invention discloses a city water supply network burst detection method based on dynamic neural network prediction. The method comprises the steps that 1, normal history flow data of the W durationof city water supply nodes is utilized to serve as the original time sequence; 2, the original time sequence is analyzed to obtain a pretreated time sequence; 3, wavelet analysis is carried out, wherein the wavelet analysis is utilized for carrying out denoising on the pretreated time sequence, and denoised flow data is obtained; 4, a model is set up, wherein the NAR dynamic neural network is utilized for training the denoised flow data, and the burst recognition model is set up; 5, a flow time window of the W duration slides backwards along with time, and the flow data is updated; 6, the flow time window of the W duration slides backwards continuously along with time, the step 5 is repeated, and until the accumulation probability of flow abnormality surpasses a set threshold value, it isjudged that the burst accident occur. According to the method, in combination with the wavelet analysis and the dynamic neural network prediction algorithm, burst accident detection of the city watersupply network based on dynamic neural network prediction is achieved.

Description

technical field [0001] The invention belongs to the measurement category of water supply systems, mainly relates to leakage detection of urban water supply pipe networks, and specifically proposes a detection method for pipe bursts of urban water supply pipe networks based on dynamic neural network prediction. Background technique [0002] my country is a country with a large population and insufficient water resources per capita. However, due to the increase of pipe age and pipeline quality problems in my country's urban water supply network, pipe bursts often occur. The average leakage rate of urban water supply exceeds 15%, while the leakage rate in developed countries is controlled below 8%. The gap is significant. Therefore, the accurate and timely detection of pipe burst events in urban water supply nodes is particularly important. [0003] In recent years, smart water affairs have gradually emerged, but the large amount of data on the pipe network has not been fully ...

AI Technical Summary

Problems solved by technology

[0003] In recent years, smart water affairs have gradually emerged, but the large amount of data on the pipe network has not been fully utilized and analyzed

The current water supply network leakage monitoring system algorithm is relatively simple and traditional. When the system generates an alarm message, manual empirical analysis is required. The detection takes a long time, and the labor intensity of the workers is relatively high, so it cannot detect the sudden leakage accurately and timely. of pipe burst incidents, the false alarm rate is extremely high

Although there have been some theoretical studies and experiments, due to many uncertain factors in the water supply network, there are few examples of successful application in the actual network and the application effect is general

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