云计算环境下船舶实时远程故障数据自动分类方法  

Automatic classification method for real-time remote fault data of ships in cloud computing environment

作　　者：韩英[1] HAN Ying(Jin Zhong Vocational and Technical College,Jinzhong 030600,China)

机构地区：[1]晋中职业技术学院,山西晋中030600

出　　处：《舰船科学技术》2020年第6期160-162,共3页Ship Science and Technology

摘　　要：传统的船舶实时远程故障数据自动分类方法中,对于密集数据存在运算时间较长的问题。为此,设计云计算环境下船舶实时远程故障数据自动分类方法。将云终端作为故障数据的中转站,实时获取船舶远程故障数据,计算历史故障数据的相似度,筛选出合适的数据块,经过训练生成基础分类器,利用KL散度计算权重系数,确定分类器的有效权值,以此为依据,构成一个集成分类器,实现船舶实时远程故障数据自动分类。测试结果表明:与传统的分类方法相比,设计的云计算环境下船舶实时远程故障数据自动分类方法面对密集数据,所需运算时间较短。The traditional automatic classification method of real-time remote fault data of ships has the problem of long calculation time for dense data. To this end, an automatic classification method of real-time remote fault data for ships in a cloud computing environment is designed. Using the cloud terminal as a transit point for fault data, real-time remote fault data of the ship is obtained, the similarity of historical fault data is calculated, a suitable data block is selected, and a basic classifier is trained to generate a weight coefficient using KL divergence to determine the classifier Based on this, an integrated classifier is constructed to realize automatic classification of real-time remote fault data of ships. The test results show that compared with traditional classification methods, the automatic classification method for real-time remote fault data of ships in the cloud computing environment is designed to face dense data and requires shorter calculation time.

关 键 词：云计算 实时 故障数据 自动分类 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]