Research on Mine Disaster Risk Monitoring Based on Topic Model Retrieval Technology

Authors

DOI:

https://doi.org/10.31181/jidmgc21202629

Keywords:

Topic model retrieval technology, Mine disasters risk monitoring, LDA model

Abstract

As the main energy source in China, coal holds an irreplaceable position in ensuring the country's energy security and supporting economic and social development. However, as the intensity and depth of mining continue to increase, especially in mines with complex geological conditions, improper mining processes and strength design can easily trigger various geological disasters. In the context of the digital intelligence era, the efficient acquisition, rapid management, and precise retrieval of multimodal information provide key technical support for cross-modal information retrieval and visual analysis applications. Based on this, this manuscript proposes a mining disaster risk monitoring method that integrates topic model retrieval technology. First, analysing the retrieval principles and adaptation mechanisms of the topic model in the context of mining engineering disaster scenarios; Secondly, based on topic model retrieval technology, visualising disaster accidents and conducting heat analysis. Thereby, carrying out dynamic regulation and decision-making for mine disaster risks; Finally, based on the above dual research findings, achieving precise monitoring and reliable prediction of various types of mining disaster risks.

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Published

2026-01-09

Issue

Vol. 2 No. 1 (2026): Journal of Intelligent Decision Making and Granular Computing

Section

Articles

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Copyright (c) 2026 Ziyi Wang, Gang Cheng, Yaxi Wu (Author); Yujie Nie (Translator); Liu Yang (Author); Jingjing Xia (Translator)

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Wang, Z., Gang Cheng, Wu, Y., Nie, Y., Yang, L., & Xia, J. (2026). Research on Mine Disaster Risk Monitoring Based on Topic Model Retrieval Technology. Journal of Intelligent Decision Making and Granular Computing, 2(1), 1-12. https://doi.org/10.31181/jidmgc21202629