摘要
针对目前职业健康数据大、技术分析落后的问题,提出了新型的解决方法。该方法构建出包括数据获取层、数据存储层、数据处理层和数据分析层的系统架构,实现了职业健康大数据的采集、存储、计算和数据传递。该研究方法还采用大数据挖掘算法实现职业健康大数据的微观分析,通过AdaBoost算法实现不同健康大数据的关联分析,通过BP神经网络模型实现健康大数据的故障诊断,又采用可视化技术实现数据的集合与映射,提高职业健康大数据的管控,试验表明,该研究的方法数据处理速度快,准确率大于95%。
In response to the current problems of large amount of occupational health data and backward technical analysis,a new management method is proposed.This method constructs a system architecture including data acquisition layer,data storage layer,data process layer and data analysis layer,and realizes the collection,storage,calculation and data transmission of occupational health big data.This research method also uses big data mining algorithm to achieve microscopic analysis of occupational health big data,uses AdaBoost algorithm to achieve correlation analysis of different health big data,uses BP neural network model to achieve fault diagnosis of health big data,and uses visualization technology to achieve data analysis collection and mapping,which improve the management and control of occupational health big data.Experiments show that the method of this study has a fast data processing speed and an accuracy rate is more than 95%.
作者
刘博
邓舒平
杨楠
郑继辉
谢金龙
LIU Bo;DENG Shu-ping;YANG Nan;ZHENG Ji-hui;XIE Jin-long(Yunnan Power Grid Co.,Ltd.,Yuxi Power Supply Bureau,Yuxi 653100,Yunnan Province,China;Yunnan Power Grid Co.,Ltd.,Kunming 650000,China)
出处
《信息技术》
2021年第5期128-134,共7页
Information Technology
