云计算下物联网密集场景大数据挖掘技术

Big data mining technology in dense scene of internet of things in cloud computing

为了提高对云计算下物联网密集场景大数据的挖掘和检测能力,提出一种基于云计算信息融合和模糊聚类的物联网密集场景大数据挖掘方法,提取物联网密集场景大数据的统计特征量和关联规则特征量,采用分块区域融合和模糊聚类方法实现物联网密集场景大数据的自适应信息融合,采用语义本体模型进行云计算下物联网密集场景大数据的多维尺度分解,构建物联网密集场景大数据的关联规则知识库,根据关联知识的融合结果进行物联网数据的相关性转发控制协议设计,根据提取的特征量实现云计算下物联网密集场景大数据挖掘。仿真结果表明,采用该方法进行物联网密集场景大数据挖掘的抗干扰能力较强,挖掘准确率较高,时间开销较小。

In order to improve that data mining and detection capability of the dense scene of the internet of things under the cloud computing,a large data mining method of the dense scene of the internet of things based on the cloud computing information fusion and the fuzzy clustering is proposed,by adopting a block-area fusion and a fuzzy clustering method to realize the self-adaptive information fusion of the large-scale data of the internet of things,the semantic ontology model is adopted to carry out the multi-dimensional scale decomposition of the large data of the dense scene of the internet of things under the cloud computing,According to the fusion result of the related knowledge,the design of the control protocol is carried out,and the large data mining of the cloud computing under-internet-of-things dense scene is realized according to the extracted feature quantity.The simulation results show that the method has the advantages of strong anti-interference ability,higher mining accuracy and less time cost.