Paper
10 August 2023 Research on prediction model of coal mine accident hidden dangers based on deep learning
Yulu Cao, Lihui Dong, Meihao Liu, Senjie Zhang, Yanyan Jing
Author Affiliations +
Proceedings Volume 12759, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2023); 127592G (2023) https://doi.org/10.1117/12.2686513
Event: 2023 3rd International Conference on Automation Control, Algorithm and Intelligent Bionics (ACAIB 2023), 2023, Xiamen, China
Abstract
Aiming at the complex mechanism of traditional mining accidents, various influencing factors, and difficult to predict accident risks, this paper uses the text description of the accident occurrence process to extract the unsafe actions of personnel and equipment that lead to the occurrence of accidents, and establishes a prediction model for the level of hidden dangers in coal mine accidents through in-depth learning methods. Select 426 coal mine accident processes as a dataset to train and validate the prediction model, with a ratio of 7:3 for the number of training and validation accidents. The research results show that the accuracy rate of the model in predicting the level of coal mine accidents reaches 83.53%, which can provide a theoretical basis for mining production management and safety decision-making to a certain extent.
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yulu Cao, Lihui Dong, Meihao Liu, Senjie Zhang, and Yanyan Jing "Research on prediction model of coal mine accident hidden dangers based on deep learning", Proc. SPIE 12759, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2023), 127592G (10 August 2023); https://doi.org/10.1117/12.2686513
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KEYWORDS
Mining

Artificial neural networks

Data modeling

Safety

Education and training

Machine learning

Deep learning

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