基于多源数据融合与模糊聚类的电力工程数据处理方法研究

Research on power engineering data processing method based on multi⁃source data fusion and fuzzy clustering

针对电力工程项目智能化监理管控问题,提出了一种基于FCM-IFA-SVM算法的电力工程数据处理与风险识别模型。该模型利用FCM算法对环境风险、技术风险、经济风险与管理风险等指标数据进行聚类分析,并采用IFA算法优化了SVM模型的惩罚系数及核函数参数。将聚类后的数据集输入至已完成参数优化的SVM模型中,进而得到电力工程项目风险等级的评估结果。通过对某电力工程数据集的仿真测试结果表明,所提算法在风险识别方面具有更高的准确率。同时相比于SVM和FCM-SVM算法,其平均准确率分别提升了7.1%及2.9%,说明所提算法能更准确地识别并评估电力工程项目风险等级。

Aiming at the problem of intelligent supervision and control of power engineering projects,this paper proposes a power engineering data processing and risk identification model based on FCM-IFASVM algorithm.The algorithm uses FCM algorithm to cluster and analyze the index data of environmental risk,technical risk,economic risk and management risk,and then uses IFA algorithm to optimize the penalty parameters and kernel function parameters of SVM model.The clustered data set is input into the SVM algorithm after parameter optimization,and the risk grade evaluation result of power engineering project is obtained.The simulation test results of a power engineering data set show that the proposed algorithm has higher accuracy in risk identification.Compared with SVM and FCM-SVM algorithm,the average accuracy is improved by 7.1%and 2.9%,it shows that the proposed algorithm can more accurately identify and evaluate the risk level of power engineering projects.