基于KNN和随机森林算法的腐蚀泄漏风险软检测模型

Soft detection model of corrosion leakage risk based on KNN and random forest algorithms

【目的】城镇燃气管网完整性管理需要有效的风险评价方法,腐蚀泄漏风险评价需要将风险评价因子充分与各项检测业务相结合,然而当前检测数据繁杂且缺失严重,函需一种可预测并评价腐蚀泄漏风险的方法。【方法】通过相关性分析,筛选出与腐蚀泄漏风险相关的关键指标,结合管道本体数据与周围环境数据,采用KNN(K-Nearest Neighbor)与随机森林算法,建立智能软检测模型。【结果】该模型能够对缺失检测数据进行预测,实现关键指标的间接测量,模型预测值与真实测量值的相对误差小于25%,达到合格水平。该模型可在数据缺失情况下有效预测管道腐蚀泄漏风险,为定量评价奠定基础。与前人研究相比,模型在多因素耦合关系提取与算法选择上进行创新,提高了预测的准确性与可靠性。然而,部分异常数据表明该模型在某些条件下的预测能力有限,且模型依赖于数据完整性和准确性。设法提高检测数据数量和质量,优化关键风险指标特征提取方法,可以进一步提高模型精度。【结论】丰富了燃气管道腐蚀泄漏风险预测理论,在提高管道运行安全性与可靠性方面具有实用价值,未来应着重改进数据采集和分析技术,进一步优化模型结构,提升其在不同应用场景下的适应性与准确性。(图10,表6,参25)

[Objective]The integrity management of urban gas pipeline networks demands effective risk assessment methods.Corrosion leakage risk assessment necessitates the comprehensive integration of risk assessment factors with various detection operations.Current detection tasks face challenges due to data complexities and significant data deficiencies.Therefore,it is vital to develop a method for predicting and evaluating corrosion leakage risks.[Methods]Key indicators associated with corrosion leakage risks were selected through a correlation analysis.These identified indicators were then employed to develop an intelligent soft detection model that integrates pipeline and environmental data,based on the K-Nearest Neighbor(KNN)and Random Forest algorithms.[Results]The model conducted predictions on missing detection data and achieved indirect measurements of key indicators,with a relative error between predicted and measured values staying below 25%,meeting acceptable standards.It effectively forecasts pipeline corrosion leakage risks in instances of missing data,paving the way for additional quantitative assessments.In comparison to prior research,the model displayed enhanced prediction accuracy and reliability,attributed to innovations in extracting multi-factor coupling relationships and algorithm choices.Nonetheless,the emergence of some abnormal data suggested constraints on its predictive capacity under specific circumstances and its dependence on complete and precise data.Consequently,enhancing both the quantity and quality of detection data,along with refining the feature extraction approach for key risk indicators,is anticipated to further boost the accuracy of the model.[Conclusion]This research enriches the risk prediction theory concerning corrosion leakage in gas pipelines and offers practical benefits in enhancing pipeline operation safety and reliability.Future research efforts should focus on enhancing data acquisition and analysis techniques,optimizing the model structure,and improving the model adaptability and accuracy across various application scenarios.(10 Figures,6Tables,25References)