Connotation, characteristics and framework of coal mine safety big data

Abstract

With the continuous development of automation and information technology, large amounts of safety data are produced in the processes of coal production. Most enterprises simply focus on statistics and do not conduct systematic big data analyses. Therefore, it is necessary to study the theory of coal mine safety while using big data systematically. This paper expounds on the changes in coal mine safety that have been driven by big data from three aspects: the connotation, characteristics and research framework. First, the connotation of coal mine safety big data (CMSBD) is redefined by changing the safety entities and methods. Second, the advantages and disadvantages of the big data model are compared from the perspective of feature analysis. Finally, the research paradigm and technical framework of CMSBD are designed. The results show that the management connotation of CMSBD focuses on the role of big data in coal mine safety. Compared with coal mine safety small data (CMSSD), CMSBD has both advantages and disadvantages. Therefore, CMSBD must be combined with a small data method. The research paradigm emphasizes the intersection of the research, the relevance of safety thinking, the importance of safety data analysis, and the fusion of big data with traditional small data models.