摘要
为了解决煤矿企业在瓦斯安全管理中存在的不足,根据大数据理论,提出一种安全管理分析方法。首先基于行为安全理论的分析,将瓦斯隐患致因进行分类;然后利用HDFS存储行为观察员发现的不安全行为和不安全物态解决海量的结构化与非结构化数据的存储问题;最后基于MapReduce的并行化FP-growth算法找出日常作业中反复、危险性高的不安全行为,给出基于Hadoop的瓦斯行为安全隐患大数据存储分析模型。实验结果表明,该模型对煤矿企业有针对性地实施瓦斯行为安全管理有一定实际参考价值,对煤矿企业安全可靠生产,减少瓦斯事故的发生具有重要作用。
In order to overcome the shortcomings existing in gas safety management of coal mine enterprises,a safety management analysis method is proposed according to the big data theory.The causes of methane gas hazards are classified on basis of the analysis of behavioral safety theory.Then,the unsafe behavior and unsafe physical state discovered by behavior observers are stored by means of HDFS to solve the storage problem of massive structurized and unstructurrized data.Finally,based on MapReduce The parallelized FP-growth algorithm is used to find out the repetitive and dangerous unsafe behaviors in daily operations,and gives a big data storage analysis model based on Hadoop′s gas behavior security risks.The experimental results show that the model has certain practical reference value for coal mine enterprises to implement gas safety management in a targeted manner.It has important effect on safe and reliable production of coal mine enterprises and reducing the occurrence of gas accidents.
作者
王健
于万钧
WANG Jian;YU Wanjun(Shanghai Institude of Technology,Shanghai 201418,China)
出处
《现代电子技术》
北大核心
2019年第21期154-156,162,共4页
Modern Electronics Technique
