Pipeline defect prediction method and device

Abstract

The invention provides a pipeline defect prediction method and a device, wherein, the method comprises the following steps of: acquiring characteristic data of a pipeline to be predicted; inputting the characteristic data of the pipeline to be predicted into a stochastic forest model to predict the defect level of the pipeline to be predicted; the stochastic forest model being generated by training according to a plurality of characteristic data affecting the pipeline defect level. As the stochastic for model is generated by training according to a plurality of characteristic data affecting the pipeline defect level, that is to say, stochastic forest model involves many kinds of characteristic data which affect pipeline defect grade, therefore, a more accurate pipeline defect prediction result is obtained. At the same time, due to the research of pipeline defect prediction classification, relevant personnel can take corresponding measures according to pipeline defect classification fordifferent defect levels, thus realizing effective risk monitoring of pipeline, and improving the safety of pipeline transportation.

Description

technical field [0001] The invention relates to the technical field of pipeline defect detection, in particular to a pipeline defect prediction method and device. Background technique [0002] Pipeline transportation is the most commonly used method of natural gas transportation in my country and even in the world. However, with the gradual increase of pipeline service time, most of the pipeline systems in service in my country have gradually entered the middle-aged and old age. At the same time, due to the existence of various risks (such as partial manufacturing or construction defects, third-party damage, misuse and natural geological disasters, etc.), the pipeline system is prone to defects such as cracks, holes, corrosion, etc., which will cause the pipeline system to bend, Accidents such as breakage and leakage have a serious impact on the safety of people's lives and property, national economic construction and environmental protection. [0003] At present, industri...

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Problems solved by technology

[0004] Therefore, in the current research on pipeline defect prediction models, most researchers use relevant models to predict pipeline defects based on pipeline corrosion data, ignoring the influence of many other factors on the service life of pipelines, which is one-sided and too ideal

[0005] For the above problems, no effective solutions have been proposed so far

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