分布式聚类在农场环境数据异常检测中的应用

发布时间：2018-03-30 01:20

  本文选题：Dirichlet过程混合模型　切入点：分布式聚类　出处：《系统仿真学报》2017年12期

【摘要】：为了处理大量分布式存储的农场环境数据,为作物增产提供异常环境参考并制定预防策略,本文结合农场环境数据的特点,在Hadoop平台中实现了对农场环境数据的Dirichlet过程混合模型聚类,并提出了基于聚类分析的农场环境异常检测方法。在Map Reduce框架下,Map阶段完成样本点到模型的分配;Reduce阶段对模型与类簇个数进行更新。通过实验验证了分布式Dirichlet聚类的性能,分析结果表明该方法可以应用于大量农场环境数据的异常检测。
[Abstract]:In order to deal with a large number of distributed farm environmental data, provide abnormal environmental reference for crop yield increase and formulate prevention strategies, this paper combines the characteristics of farm environmental data. The Dirichlet process hybrid model clustering of farm environment data is realized in Hadoop platform. A method of farm environment anomaly detection based on cluster analysis is proposed. The model and cluster number are updated in the phase of distribution from sample points to models in the Map Reduce framework. The performance of distributed Dirichlet clustering is verified by experiments. The analysis results show that this method can be applied to the anomaly detection of a large number of farm environmental data.
【作者单位】： 上海大学机电工程与自动化学院;上海市电站自动化技术重点实验室;
【基金】：上海市科委重点项目(14DZ1206302)
【分类号】：S126;TP311.13
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本文编号：1683723