Research on prediction model of coal mine accident hidden dangers based on deep learning

Yulu Cao; Lihui Dong; Meihao Liu; Senjie Zhang; Yanyan Jing

doi:10.1117/12.2686513

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

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.

Citation Download Citation

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

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available