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矿井安全监测预警系统异常数据分析和图表识别

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矿井安全风险监测预警数据中一直存在大量缺失、干扰、错误、隐瞒等数据异常现象,不利于安全管理和救援.该文针对目前在煤矿安全管理中应用最广泛的两个主要专业子系统——安全监控系统、人员位置监测系统,分别统计整理远程在线数据和煤矿企业安全信息,从两个角度总结并相互验证了异常数据的各种类型和数量占比,发现和两个专业系统有关的违规行为或安全隐患中大约70%可以追溯到人为因素,探讨了这些因素的产生原因和应对措施.针对高频率发生的异常数据类型,阐述有助于提高数据分析效率、快速判定或排除违规行为的图表分析方法,尝试系统化、理论化归纳整理矿井安全感知数据预警分析技术,提出减少异常数据、提高监测预警系统应用效果的措施.
Abnormal Data Analysis and Chart Recognition of Mine Safety Monitoring and Early Warning System
There have always been a large number of data anomalies such as missing,interference,error,and concealment in the mine safety risk monitoring and early warning data,which is not conducive to safety management and rescue.In this paper,we summarize and verify the various types and quantity proportions of abnormal data from two perspectives,and find that about 70%of the violations or safety hazards related to the two professional systems related to the two professional systems can be traced back to human factors,and discuss the causes and countermeasures of these factors.In view of the types of abnormal data that occur with high frequency,this paper expounds a chart analysis method that is helpful to improve the efficiency of data analysis and quickly determine or eliminate violations,and tries to systematically and theoretically summarize and sort out the early warning and analysis technology of mine safety perception data.Measures to reduce abnormal data and improve the application effect of monitoring and early warning system are proposed.

big datasecurity-aware datadata analysisrisk early warningmine hidden danger investigation

李澎

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山东煤炭工业信息计算中心,山东 济南 250031

大数据 安全感知数据 数据分析 风险预警 矿井隐患排查

2024

山东煤炭科技
山东省煤炭学会

山东煤炭科技

影响因子:0.185
ISSN:1005-2801
年,卷(期):2024.42(1)