Method for predicting pipe explosion risk of water supply pipe network based on Bayesian survival analysis

Abstract

The invention relates to a method for predicting the pipe explosion risk of a water supply pipe network based on Bayesian survival analysis, and the method comprises the following steps: (1), buildinga pipe explosion database through the collected pipe explosion historical data, and extracting key information as a concomitant variable; (2), carrying out the spatial clustering analysis of a pipe explosion point, quantifying the spatial distribution information of the pipe explosion point as a new concomitant variable, and adding the new concomitant variable to the pipe explosion database; (3),constructing a pipe explosion risk prediction model based on the pipe explosion database through employing the Bayesian survival analysis method; (4), predicting the pipe explosion risk of a pipe through the pipe explosion risk prediction model. Compared with the prior art, the method is more accurate and reasonable in prediction result.

Description

technical field [0001] The invention relates to a pipe burst risk prediction method for a water supply pipe network, in particular to a pipe burst risk prediction method for a water supply pipe network based on Bayesian survival analysis. Background technique [0002] As one of the important public infrastructures of the city, the water supply pipe network is the main artery of the whole city and undertakes the important task of delivering water to users. In the past ten years, the speed of urbanization in my country has continued to accelerate, the demand for urban water has increased sharply, and the scale of urban water supply pipe networks has increased day by day. However, there are still a series of problems in my country's water supply network, such as the laying time of some pipelines is too long, the phenomenon of aging is serious, and poor-quality pipes (such as gray cast iron pipes) occupy a large proportion of the pipe network. At the same time, in terms of safe...

AI Technical Summary

Problems solved by technology

But its disadvantages are: (1) The physical mechanism of pipe bursting is relatively complex and there are many factors causing pipe bursting, and the physical prediction model often cannot include all factors, and its application is difficult; (2) The data required for building the model Difficult or expensive to obtain

But its disadvantages are: (1) It needs a large amount of historical data of squibs for training; (2) The prediction results obtained by the purely data-driven model may be inconsistent with the actual observed results

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