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<p>Against the backdrop of the digital era, the mining industry is undergoing a profound transformation from traditional operation methods to intelligent and automated directions. To enhance the reliability of safety monitoring systems and align with the technological imperatives of the Fourth Industrial Revolution, this study develops a distributed soft bus-based microservice architecture for mining IoT applications. The proposed framework integrates an ITransformer-based crack detection model with multi-modal sensing capabilities to establish a comprehensive intelligent monitoring and early warning system, thereby advancing both mine safety and operational intelligence. This architecture specifically addresses the challenges of heterogeneous device integration and real-time data processing in complex mining environments through its service-oriented design and dynamic noise suppression mechanisms. The experiment outcomes indicate that the detection accuracy of the image crack detection model is 99.16%, the CPU utilization is 35.64%, and the average detection time is 28&#xa0;ms. The monitoring coverage of the safety monitoring and early warning system in actual testing is over 98%, and the average fast response time is 26&#xa0;ms. The experiment outcomes indicate that the proposed method can effectively cover the surrounding environment of the mine, conduct real-time intelligent monitoring, accurately detect mine cracks, and quickly warn of abnormal information. The research not only provides a new method for mine safety monitoring, but also offers a new tool for the intelligent construction of mines.</p>

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Reliability of mining safety intelligent monitoring and early warning system based on transformer model and Internet of Things technology

  • 期刊论文

  • 发表日期：

    2026年7月9日

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  • Qi Liu

摘要

Against the backdrop of the digital era, the mining industry is undergoing a profound transformation from traditional operation methods to intelligent and automated directions. To enhance the reliability of safety monitoring systems and align with the technological imperatives of the Fourth Industrial Revolution, this study develops a distributed soft bus-based microservice architecture for mining IoT applications. The proposed framework integrates an ITransformer-based crack detection model with multi-modal sensing capabilities to establish a comprehensive intelligent monitoring and early warning system, thereby advancing both mine safety and operational intelligence. This architecture specifically addresses the challenges of heterogeneous device integration and real-time data processing in complex mining environments through its service-oriented design and dynamic noise suppression mechanisms. The experiment outcomes indicate that the detection accuracy of the image crack detection model is 99.16%, the CPU utilization is 35.64%, and the average detection time is 28 ms. The monitoring coverage of the safety monitoring and early warning system in actual testing is over 98%, and the average fast response time is 26 ms. The experiment outcomes indicate that the proposed method can effectively cover the surrounding environment of the mine, conduct real-time intelligent monitoring, accurately detect mine cracks, and quickly warn of abnormal information. The research not only provides a new method for mine safety monitoring, but also offers a new tool for the intelligent construction of mines.