Research on the Application of Data Mining Algorithm in the Detection of Gas Pipeline Outside

As one of the important energy sources in China, gas has been widely used. However, with the increasing demand for gas, the continuous extension and use of gas pipelines has made the detection of gas pipelines more and more important. In the past, the detection methods outside the pipeline were carried out manually, mainly relying on empirical judgment and manual operation, which had certain limitations, resulting in low work efficiency and poor hidden danger identification accuracy. Therefore, this paper proposes a data mining algorithm for the analysis of combinatorial optimization detection technology. Firstly, the machine learning theory is used to process the detection data outside the pipeline, and the indicators are divided according to the evaluation requirements of the detection technology to reduce the interference factors in the detection technology. Then, the machine learning theory evaluates the detection technology outside the gas pipeline, forms the detection technology evaluation scheme, and comprehensively analyzes the detection technology results. MATLAB simulation shows that under certain detection standards, the accuracy and detection time of data mining algorithms for gas pipeline external detection technology are better than traditional detection methods.