基于MPSO-KMeans++的长输油气管道泄漏风险分级模型OA

Leakage accidents in long-distance oil and gas pipelines are caused by diverse and complex factors.To improve the efficiency and effectiveness of leakage risk management,a risk classification model based on the modified particle swarm optimization(MPSO)algorithm and KMeans++algorithm was developed.First,a leakage risk evaluation index system was established,including four primary indicators and 17 secondary indicators.Then,based on the risk matrix and a combination of subjective and objective weighting methods,each indicator was scored in terms of accident occurrence probability and consequence severity,providing the data basis for risk classification.To overcome the tendency of the KMeans++clustering algorithm to fall into local optima,an improved particle swarm optimization(PSO)algorithm incorporating dynamic inertia weight adjustment and a synchronous learning factor was introduced to optimize the clustering process.Finally,the proposed model was validated through case studies by taking three typical cases,namely crossing pipelines,permafrost pipelines,and pipelines in densely populated urban areas,as examples.The results show that compared with the standalone KMeans++-based risk classification model,the proposed model improves classification accuracy and stability by averages of 5.9％and 17.61％,respectively.Compared with the PSO-KMeans++-based risk classification model,the proposed model improves classification accuracy and stability by averages of 3.68％and 13.23％,respectively.These findings indicate that the MPSO-KMeans++-based model has good applicability and engineering practicality for leakage risk classification of long-distance oil and gas pipelines,and can provide scientific support for pipeline integrity management and risk prevention and control.