Intelligent identification method for working condition parameters of transverse landslide buried pipeline

Abstract

The invention discloses an intelligent identification method for working condition parameters of a transverse landslide buried pipeline. The intelligent identification method comprises the following steps: S100, establishing a template database through a large number of template working conditions and axial maximum strain data of the cross section of the pipeline under all the template working conditions; S200, monitoring the maximum axial strain of each monitoring section of the pipeline in the actual working condition to form a monitoring strain time sequence; S300, calculating the similarity between the template database and the monitoring strain time sequence by using a multi-dimensional dynamic time warping algorithm, and identifying actual working condition parameters by comparing the similarity, wherein the more the template working conditions are, the larger the template database is, and the more accurate the identification result is. According to the method, the genetic algorithm is adopted to solve the mechanical model of the buried pipeline under the action of the transverse landslide, the template database is generated, the multi-dimensional dynamic time warping algorithm is adopted, the space-time monitoring information of the actual working condition is fully utilized, the actual working condition parameters are accurately identified, and a large amount of manpower and material resources consumed by field testing are prevented.

Description

technical field [0001] The invention relates to the field of reverse analysis of engineering parameters, in particular to an intelligent identification method for operating condition parameters of buried pipelines in transverse landslides. Background technique [0002] Accurately obtaining the working condition parameters of buried pipelines under the action of lateral landslides is an important basis for early warning of pipeline safety through mechanical analysis methods. The traditional method of measuring working condition parameters through on-site tests is inefficient and low in practicability. With the frequent occurrence of lateral landslides and long-distance oil and gas pipeline safety accidents, the pipeline safety monitoring work has been continuously carried out, thus accumulating a large amount of monitoring information. It has become a technical trend to use the monitoring information of the pipeline to identify the parameters of the pipeline working condition...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to provide an intelligent identification method for the operating condition parameters of buried pipelines in lateral landslides, so as to solve the technical problem that the existing identification technology cannot use continuous monitoring information, resulting in low reliability of parameter identification results

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