摘要
根据煤矿安全监控和人员定位联网数据内容,提出了一套煤矿联网监测大数据分析架构。该架构包含瓦斯微小波动分析、瓦斯偏离均值分析、瓦斯上升趋势分析、疲劳作业分析和轨迹相似分析等5种数据分析模型,利用大数据分析技术,在海量煤矿联网监测数据中进行数据挖掘分析,找到价值高的数据,并在此基础上设计了一套为煤矿远程监管人员服务的煤矿大数据分析系统。该系统包括综合数据看板、瓦斯专项分析和人员专项分析等多个功能模块,利用散点图、面积图、南丁格尔玫瑰图等多种可视化展现方式,展现了数据挖掘分析结果。
A set of coal mine networking monitoring big data analysis architecture is proposed based on the content of coal mine safety monitoring and personnel positioning networking data. The architecture includes five data analysis models, including gas micro fluctuation analysis, gas deviation mean value analysis, gas rising trend analysis, fatigue operation analysis, and trajectory similarity analysis. It uses big data analysis technology to conduct data mining and analysis from massive coal mine network monitoring data to find high-value data, and on this basis, design a set of coal mine big data analysis system for remote coal mine supervisors. The system includes multiple functional modules such as comprehensive data board, gas special analysis and personnel special analysis, and uses multiple visualization methods such as scatter charts, area charts, and Nightingale rose charts to display the results of data mining analysis.
作者
陈清
CHEN Qing(China Coal Technology and Engineering Group Chongqing Research Institute,Chongqing 400039,China)
出处
《煤矿安全》
CAS
北大核心
2021年第8期162-166,共5页
Safety in Coal Mines
基金
国家重点研发计划资助项目(2018YFC0808306)
中煤科工集团重庆研究院有限公司自立重点研发科研资助项目(2020ZDXM13)。
关键词
煤矿监控系统
联网数据
数据挖掘
趋势分析
疲劳指数
coal mine monitoring system
networked data
data mining
trend analysis
fatigue index