基于Hadoop分布式支持向量机球磨机大数据建模

doi:10.3969/j.issn.1000-1565.2017.03.014

摘要/Abstract

摘要： 大数据时代环境下,火电厂大量数据被存储到数据库中而不能被充分利用,由于双进双出钢球磨煤机系统的复杂性,很难建立其准确的机理数学模型,为此提出一种基于大数据挖掘的建模方法.首先分析影响磨煤机料位的因素,提取现场海量的实际运行数据,在Hadoop平台下利用K-Means聚类算法删除离群点,利用主成分分析法(PCA)降维完成属性约简,然后在MapReduce架构上采用分布式支持向量机(D_SVM)建立模型,实现计算并行化.结果表明,采取该方法提高了建模效率,所建立的模型具有很高的精确度,且具有很好的泛化能力,该模型可以用于表征实际料位的特性.

关键词: 双进双出磨煤机, Hadoop平台, 分布式支持向量机, K-Means聚类, 主成分分析

Abstract: In the era of big data environment,a large amount of data in thermal power plant is stored in the database and cannot be fully utilized.Because of the complicated process of the double inlet and double outlet mill system,the mathematical model is difficult to build.A method of modeling based on data mining is presented.The actual operation big data which impact the coal mill material is extracted.First, the K-Means clustering is used to delete outliers,and then the principal component analysis(PCA)is used to complete attribute reduction,at last the distributed support vector machine(D_SVM)is used to build a model on the Hadoop platform in MapReduce framework for the parallel computation.The results show that modeling time is greatly reduced due to the use of the method,and the accuracy and applicability of the model are very high. Therefore the model can be used to represent the actual material properties.

Key words: double inlet and double outlet mill, Hadoop platform, D_SVM, K-Means clustering, principal component analysis

中图分类号:

TP399

高学伟,付忠广,孙力,张刚. 基于Hadoop分布式支持向量机球磨机大数据建模[J]. 河北大学学报(自然科学版), 2017, 37(3): 309-315.

GAO Xuewei,FU Zhongguang,SUN Li,ZHANG Gang. Big data modeling of ball mill based on distributed support vector machine on Hadoop platform[J]. Journal of Hebei University (Natural Science Edition), 2017, 37(3): 309-315.

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