Research On Automatic Classification Method Of Coal Mine Safety Hazard Information Based On HD-MSCNN

In the research report of China’s Sustainable Energy Development Strategy,many academicians of the Chinese Academy of Sciences and the Chinese Academy of Engineering agreed that the proportion of coal’s energy structure will not be lower than 50% until 2050.For a long time to come,coal as a major energy source will play a prominent role in China’s economic development and social improvement.“Safety first,prevention first” is the safety production policy established by China for the coal mine field.However,as the amount of coal mining increases year by year,the development of scientific mining technology and information technology has brought a series of new problems.Safety management work is slightly backward.On the one hand,coal mine safety management is still accustomed to post-event management,lack of pre-existing prevention and safety prediction for hidden dangers,and only takes “prevention first” as a form;on the other hand,standardization of safety hazards is insufficient.The identification and treatment of hidden dangers are not scientific and timely,resulting in hidden danger monitoring and the risk of accidents.In view of the above problems,this paper focuses on the automatic processing and analysis of coal mine safety hazard information,with deep learning as a solution,and gives a short text automatic classification method,and applies it to the automatic classification and classification task of safety hazard information.Firstly,the author builds a professional vocabulary for the information of mine safety hazard information,and then uses the Word2 vec model to train the word vector in the field of coal mine safety hazard information,and converts the language information into computer identifiable vector information.Then,the wording of coal mine safety hazard information is segmented.Pre-processing such as stop words;then applying deep learning technology to realize the classification task of security risk information,using the deep learning development framework Pytorch to build the MSCNN network,and using pre-trained data and Word2 vec word vector model for coarse-grained classification model training and tuning;then the Hierarchical Deep Multi Scale Convolutional Neural Networks(HD-MSCNN)framework is constructed and trained to achieve fine-grained classification of coal mine safety hazard information and risk hazard grading.Finally,the classification and classification model is integrated with the coal mine safety hazard information management system to realize the practical application of automatic classification and classification of coal mine safety hazard information.Through the above stages of work,the task of automatic classification and classification of coal mine safety hazard information can be completed and applied to actual production activities.The automatic classification framework of coal mine safety hazard information realized in this paper has the following advantages: Firstly,compared with other deep learning text classification models,the Hierarchical classification framework HD-MSCNN based on deep learning proposed in this paper has higher precision,Accuracy and F1 value;Secondly,the model adopts a redundancy mechanism and has strong scalability,but the classification model can be improved more quickly when new categories are added without re-training.