吉林大学学报（信息科学版）2025，Vol.43Issue(5)：937-943,7.
基于AOA-SVM的数字孪生管道泄漏识别模型
Leakage Identification Model of Digital Twin Pipeline Based on AOA-SVM
摘要
Abstract
To address the problem of low accuracy of oil and gas pipeline leakage identification,the digital twin technology is introduced,and a digital twin pipeline leakage identification model is constructed based on arithmetic optimisation AOA-SVM(Arithmetic Optimization Algorithm-Support Vector Machine).Firstly,the 3D ROM(3D Reduced Order Model)pipeline model of oil and gas pipelines is constructed using Ansys software.Secondly,the collected pipeline signals are imported into MySql database through Java interface,and then the data are imported into the 3D ROM pipeline model.Finally,the AOA-SVM algorithm is used to carry out the work recognition of the pipeline signals in Matlab environment,and the recognition effect is shown in its dynamic form by Twin builder software.The recognition effect is shown in its dynamic form.In order to show the superiority of AOA-SVM condition recognition ability,it is compared with other popular SVM(Support Vector Machine)optimisation algorithms on the basis of the same signal.The comparison results show that AOA-SVM has the highest classification accuracy,which can reach 90.5％,i.e.,the recognition model of the proposed digital twin can simulate the leakage of pipelines and has a high monitoring credibility.关键词
支持向量机/数字孪生/数字化管道/3D ROM模型Key words
support vector machine/digital twinning/digital pipeline/3D reduced order model(ROM)model分类
电子信息工程引用本文复制引用
王冬梅,宋南南,张丹,王鹏,路敬祎..基于AOA-SVM的数字孪生管道泄漏识别模型[J].吉林大学学报（信息科学版）,2025,43(5):937-943,7.基金项目
国家自然科学基金资助项目(62103096) （62103096）
海南省科技专项基金资助项目(ZDYF2022SHFZ105) （ZDYF2022SHFZ105）
海南省自然科学基金资助项目(623MS071) （623MS071）
春晖计划基金资助项目(HZKY20220314) （HZKY20220314）
