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With the rapid development of the electric vehicle (EV) industry, ensuring the safe dismantling of waste batteries has emerged as a critical challenge that demands immediate attention. To address the high-risk and complex nature of dismantling operations, this study proposes an intelligent monitoring framework that integrates depth sensing and fuzzy logic. The system employs an Intel Realsense L515 LiDAR camera to collect RGB-D data, combined with advanced 3D skeleton extraction technology, enabling real-time analysis of workers’ operational behaviors. A fuzzy logic-based risk assessment framework is specifically designed to quantify different levels of risk and provide actionable suggestions for operation optimization. Additionally, a spatiotemporal prism modeling approach is introduced to dynamically identify potential errors and unsafe behaviors in the workplace, thereby significantly enhancing the system’s adaptability to highly complex and variable industrial environments. This preliminary study highlights the significant potential of intelligent monitoring technologies in addressing safety challenges in high-risk industrial scenarios. It provides valuable insights and an important reference for improving both the safety and efficiency of dismantling operations. Future work will focus on extending this system to broader applications, such as intelligent assembly and dynamic inspection, ultimately contributing to the intelligent transformation of industrial safety practices.

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Real Time Multi-modal Operation Monitoring for Industrial Disassembly Based on Spatiotemporal Prism Model

  • 会议论文

  • 发表日期：

    2025年

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  • Weilong Niu,
  • Chongyuan Meng,
  • Shunru Chen,
  • Mian Yan,
  • Xiaoguang Sun,
  • Quantao Zhang,
  • Jinglin Mo

摘要

With the rapid development of the electric vehicle (EV) industry, ensuring the safe dismantling of waste batteries has emerged as a critical challenge that demands immediate attention. To address the high-risk and complex nature of dismantling operations, this study proposes an intelligent monitoring framework that integrates depth sensing and fuzzy logic. The system employs an Intel Realsense L515 LiDAR camera to collect RGB-D data, combined with advanced 3D skeleton extraction technology, enabling real-time analysis of workers’ operational behaviors. A fuzzy logic-based risk assessment framework is specifically designed to quantify different levels of risk and provide actionable suggestions for operation optimization. Additionally, a spatiotemporal prism modeling approach is introduced to dynamically identify potential errors and unsafe behaviors in the workplace, thereby significantly enhancing the system’s adaptability to highly complex and variable industrial environments. This preliminary study highlights the significant potential of intelligent monitoring technologies in addressing safety challenges in high-risk industrial scenarios. It provides valuable insights and an important reference for improving both the safety and efficiency of dismantling operations. Future work will focus on extending this system to broader applications, such as intelligent assembly and dynamic inspection, ultimately contributing to the intelligent transformation of industrial safety practices.