China Safety Science Journal ›› 2023, Vol. 33 ›› Issue (2): 9-15.doi: 10.16265/j.cnki.issn1003-3033.2023.02.0949

• Safety social science and safety management • Previous Articles     Next Articles

Study on self-organizing mapping distribution of coal mine accident factors

ZHANG Jiangshi(), LI Yongtun**(), QIN Fang, WANG Huichao, PAN Yu, WANG Ziyi   

  1. School of Emergency Management and Safety Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China
  • Received:2022-09-19 Revised:2022-12-15 Online:2023-02-28 Published:2023-08-28

Abstract:

In order to further clarify the complicated coupling mechanism between the coal mine accident causation factor, SOM neural network was introduced to study the coupling relationship between factors. Firstly, 24Model was used to analyze the factors of coal mine accidents and the accident attribute dataset was built. The SOM algorithm was then used to visualize the coupling relationship between factors and accidents. Combined with the K-means clustering algorithm, the mapping characteristics of single-factor and multi-factor coupling to coal mine accident causes were studied, and the correlation degree between the factors was analyzed. The results show that the difference in impact degree of a single factor on the accident is reflected by "accident-single factor" mapping distribution. Factors such as inadequate management, inadequate safety training and imperfect institutional documents contributed more to the accident. Four factors, including illegal command, illegal operation, operation error and unsafe object state, are the strongest coupling effect in the "accident-multifactor coupling" mapping. There is a strong correlation with coupling degree≥0.8 between the factors such as "imperfect system documents, insufficient safety culture--unreasonable personnel organization" and "incomplete supporting facilities--poor habits" in the factor correlation analysis.

Key words: coal mine accident, self-organizing mapping(SOM), cause of accident factor, coupling relationship, visualization