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Enhancing the intelligence of coal mines is a core focus of mining engineering development. Fueled by rapid advances in artificial intelligence and Industry 4.0, the application of coal-rock identification and digital twin technologies in mines has shown great potential. In this context, this paper systematically reviews the development of coal-rock identification and digital twin technology in mining. It analyzes representative research cases and technical approaches, and identifies current bottlenecks in data fusion, real-time computation, and system integration. We propose a novel multimodal fusion framework based on the Transformer model, which combines image and sound data for coal-rock identification and integrates them into a digital twin system for dynamic optimization. Finally, future development directions are outlined.

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A Review of the Identification Methods of Digital Twin Coal Rock Driven by Multimodal Model

  • 会议论文

  • 发表日期：

    2025年

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  • Xingyue Guo,
  • Fan Zhang

摘要

Enhancing the intelligence of coal mines is a core focus of mining engineering development. Fueled by rapid advances in artificial intelligence and Industry 4.0, the application of coal-rock identification and digital twin technologies in mines has shown great potential. In this context, this paper systematically reviews the development of coal-rock identification and digital twin technology in mining. It analyzes representative research cases and technical approaches, and identifies current bottlenecks in data fusion, real-time computation, and system integration. We propose a novel multimodal fusion framework based on the Transformer model, which combines image and sound data for coal-rock identification and integrates them into a digital twin system for dynamic optimization. Finally, future development directions are outlined.