Burst Prediction Method Based on Gray Neural Network

A technology of gray neural network and prediction method, which is applied in the field of pipe burst prediction of water supply network based on gray neural network, and can solve the problems that the gray system does not have parallel computing capability and the model accuracy is not high

Active Publication Date: 2016-11-30
HANGZHOU DIANZI UNIV
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Problems solved by technology

However, the gray system does not have parallel computing capability, and the model accuracy is not high.

Method used

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  • Burst Prediction Method Based on Gray Neural Network
  • Burst Prediction Method Based on Gray Neural Network
  • Burst Prediction Method Based on Gray Neural Network

Examples

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Embodiment Construction

[0051] An example is given below, and the specific implementation manner of the present invention is further described in detail. The following examples are only used to illustrate the present invention, but not to limit the scope of the present invention.

[0052] (1) Collecting and sorting out statistics on pipe burst data

[0053] From the pipe burst database in a water supply area, the pipe diameter, pipe age, and pressure data of the pipeline are counted, and the pipe burst rate is calculated (generally, the annual pipe burst rate is calculated).

[0054] The specific statistical methods are:

[0055] All the pipe sections are first arranged according to the pipe diameter (unit ) into Group.

[0056] Then, calculate the total tube length for each group :

[0057]

[0058] in number the pipe segment, for the pipe segment the length of .

[0059] Weighted average tube age based on tube length :

[0060]

[0061] in for the pipe segment tube ag...

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Abstract

The invention discloses a method for predicting burst pipes based on a gray neural network. In the present invention, firstly, for a given pipe burst factor and pipe burst rate data sequence, the pipe burst rate sequence is predicted by static gray modeling. The prediction results are compared with the atomic bomb rate sequence to obtain the residual. Then, a neural network approximation model is established between these residuals and squib factors by using neural network. The repeatedly trained neural network is the mapping relationship between the residual and the selected gray model data. In the final prediction, the predicted value of the gray model is compensated by the compensation value of the neural network. The invention combines a gray modeling method and a neural network model to establish a gray neural network model, overcomes the disadvantage that a large amount of data is required by the traditional squib model, can better solve the problem of small sample prediction, and improve the prediction accuracy.

Description

technical field [0001] The invention belongs to the field of urban water supply, in particular to a gray neural network-based pipe burst prediction method for a water supply pipe network. Background technique [0002] The water supply network is one of the important infrastructures of the city and an important part of the urban lifeline project. The bursting of the pipe network will cause a lot of waste of water resources, threaten the safety of water supply, and affect normal production and life. Analyzing historical leakage data and establishing an effective pipe burst prediction model can control pipeline network leakage from the source, achieve early prevention, early detection, scientific and reasonable maintenance, and realize active control of leakage. [0003] At present, pipe burst prediction models mainly include physical models and statistical models. The physical model generally predicts pipeline accidents by analyzing the load acting on the pipeline, the abili...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06Q10/04
Inventor 徐哲杨洁车栩龙孔亚广薛安克
Owner HANGZHOU DIANZI UNIV
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