Research on the coal mine safety big data features and governance method system

Efficient analysis and utilization of coal mine safety big data is of great significance for improving the safety management level and production efficiency of coal mines. At present, there are some problems in coal mine safety big data governance, such as unclear data features and governance methods. In order to solve the above problems, this paper emphatically analyzes the features of coal mine safety big data. It is concluded that coal mine safety big data has 5V features, namely, large data volume (Volume), multiple data varieties (Variety), fast processing velocity (Velocity), low value density (Value), veracity (Veracity), and also has the features of inconsistent structural degree. This paper introduces the main data governance methods and models that can be applied to coal mine safety management. The methods are divided into five categories: single variable method, multivariate statistical analysis method, intelligent pattern recognition method, system dynamics model and comprehensive integration model. From the perspective of the subject and object, this paper puts forward a big data governance method system for coal mine safety. It is believed that the selection of data governance methods must be consistent with the data governance goals of the subject and object of intelligent mines. Selection of subject-based governance methods: the specific content of data governance is determined according to the needs, levels, tasks and security management objectives of data subjects. Selection of object-based governance methods: the specific content of data governance is determined according to the timeliness of object objects, throughput requirements and security management objectives. Finally, it is concluded that the determination of coal mine safety big data governance method needs to meet the common and individual needs according to the different scopes and objects under the unified goal and standard.