关联分析数据挖掘在煤矿瓦斯安全监测预警中的应用  

Application of correlation analysis on data mining in coal mine gas safety monitoring and early warning

作　　者：石记斌 石记红 

机构地区：[1]山西国化能源有限责任公司,山西太原030600 [2]郑州煤炭工业集团有限责任公司,河南郑州450042

出　　处：《能源与环保》2017年第8期1-5,11,共6页CHINA ENERGY AND ENVIRONMENTAL PROTECTION

基　　金：国家自然科学基金(51174113)

摘　　要：针对目前煤矿安全监控系统对数据综合分析及处理能力欠缺、很多井下瓦斯异常情况无法自动识别的问题,介绍了数据挖掘的概念、关联分析及关联分析数据挖掘的核心算法Apriori算法和FPgrowth算法,随后结合煤矿特点设计了一种基于以上2种算法的煤矿瓦斯监测数据的关联分析模型,利用云模型实现连续型数据到定性数据的转换,从监测数据集中发掘出蕴含的有价值的关联规则。最后对煤矿瓦斯监测监控数据进行了实验分析并得出了相关结论,该方法对提高煤矿瓦斯安全监测预警能力和安全管理水平有一定的指导作用。Aimed at the problems of low comprehensive data analysis and processing capability,and abnormal gas anomalies can not be identified for current coal mine safety monitoring system,this paper introduced the concept of data mining,and analyzed the core algorithm of the data mining correlation analysis Apriori algorithm and FP-growth algorithm.Designed a correlation analysis model of coal mine gas monitoring data,this model is based on the above two kinds of algorithms.By using cloud model to realize the conversion from continuous data to qualitative data,then excavated valuable association rules from the monitoring data.Finally,the coal mine gas monitoring data were analyzed in the experiment,this method has a certain guiding role to improve the capability of coal mine gas safety monitoring and early warning.

关 键 词：数据挖掘 瓦斯监测 APRIORI算法 FP-GROWTH算法 云理论 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]