Topics mining on potential safety hazard data of coal mine based on deep learning models
To enhance coal mine safety risk identification and supervision capabilities,a model based on Bidirectional Long Short-Term Memory Networks(BiLSTM),Conditional Random Fields(CRF),and Latent Dirichlet Allocation(LDA)is proposed.The BiLSTM-CRF model is trained to split words;the perplexity-var is used to calculate the optimal number of topics for the LDA model;and the BiLSTM-CRF-LDA model is constructed to mine the data of safety hazards in a coal mine in Inner Mongolia.The research findings indicate that the perplexity-variance metric can more accurately determine the number of topics;the word segmentation results of the BiLSTM-CRF model are more precise compared to those of the jieba library;the BiLSTM-CRF-LDA model can accurately identify types of safety hazards,spatial distribution of safety hazards,and the allocation of safety responsibilities in coal mines.These results can provide a reference for coal mine safety risk exam-ination and supervision.
potential safety hazard data of coal minebi-directional long short-term memory(BiLSTM)conditional random field(CRF)latent Dirichlet allocation(LDA)perplexity-topic variance
万方数据
维普
