船舶物联网远程监控数据分类处理
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  • 英文篇名:Classification and processing of remote monitoring data in ship internet of things
  • 作者:柳惠秋
  • 英文作者:LIU Hui-qiu;School of Chongqing Youth Vocational and Technical College;
  • 关键词:物联网 ; 远程监控数据 ; 分类处理 ; BP神经网络 ; 数据节点
  • 英文关键词:internet of things;;remote monitoring data;;classified processing;;BP neural network;;data node
  • 中文刊名:JCKX
  • 英文刊名:Ship Science and Technology
  • 机构:重庆青年职业技术学院;
  • 出版日期:2019-03-23
  • 出版单位:舰船科学技术
  • 年:2019
  • 期:v.41
  • 语种:中文;
  • 页:JCKX201906044
  • 页数:3
  • CN:06
  • ISSN:11-1885/U
  • 分类号:131-133
摘要
传统方法在处理船舶远程监控数据分类问题时会倾向于单一处理,导致分类结果过于分散,且处理速度过慢,不利于对船舶远程监控数据的整体分析,为此提出并设计了船舶物联网远程监控数据分类处理方法。利用动态数据的映射反应对多维空间内的远程监控数据进行标记,并确定分类处理的数据范围,引用BP神经网络算法,对远程监控数据进行分类计算,将监控数据执行分类处理逻辑,实现远程监控数据的分类过程。仿真实验结果表明,设计的数据分类方法能够实现远程监控数据的有序、紧密分类,且数据处理速度比传统方法的处理速度高出23.1%,具备极高的有效性。
        Traditional methods tend to deal with the classification of ship remote monitoring data in a single way,which results in the classification results being too scattered and the processing speed too slow, which is not conducive to the overall analysis of ship remote monitoring data. For this reason, a classification method of ship remote monitoring data in the Internet of Things is proposed and designed. The mapping response of dynamic data is used to mark the remote monitoring data in multi-dimensional space, and determine the data range of classification processing. The BP neural network algorithm is used to classify and calculate the remote monitoring data. The classification processing logic of monitoring data is implemented to realize the classification process of remote monitoring data. The simulation results show that the designed data classification method can achieve orderly and compact classification of remote monitoring data, and the data processing speed is 23.1% faster than the traditional method, which has a very high efficiency.
引文
[1]张沛朋,陈永翔.船联网数据分类算法研究[J].舰船科学技术,2016,15(4):133–135.
    [2]裴士新,于贺.海量船舶移动网络流量数据分析处理[J].舰船科学技术,2016,21(9x):118–120.
    [3]杨桦.基于关联规则的船舶故障数据自动分类方法[J].舰船科学技术,2018,39(12):159–162.
    [4]马亚玲.云环境下多载体图书信息自动分类方法仿真[J].计算机仿真,2018,35(11):285–288.

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