Adaptive intelligent agent for cloud edge collaborative industrial inspection driven by multimodal data fusion and deep transformation networks

Jia Hao, Jiawei Sun*, Zhicheng Zhu, Zhaoxin Chen, Yan Yan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Currently, the rapid development of the industrial Internet has led to the creation of a massive number of intelligent agents that are widely and distributively applied in various edge scenarios. The work conditions in these edge scenarios are complex, uncertain, and random. Traditional manual updates or human judgments are used for task decision-making in large-scale distributive intelligent agent edge work scenarios, which lack dynamic perception and autonomous recognition capabilities for edge work conditions. This inevitably leads to low decision-making accuracy, poor reliability, and ultimately, task failure. To address this issue, this study proposes an adaptive task identification strategy based on cloud-edge collaboration. This method utilizes a cloud-edge collaborative industrial intelligent application architecture to achieve cloud-based training and encapsulation of the task model, with online calling at the edge-end. Then, edge-end intelligent agents identify edge work conditions through multi-source data fusion, enabling accurate task decision-making. Finally, the edge-end requests the cloud for task model matching. The effectiveness of the proposed method is validated in an industrial safety situation virtual detection system.

Original languageEnglish
Pages (from-to)753-766
Number of pages14
JournalAlexandria Engineering Journal
Volume106
DOIs
Publication statusPublished - Nov 2024

Keywords

  • Cloud-edge collaboration
  • Data fusion
  • Large-scale edge intelligence
  • Model switching
  • Virtual inspection system

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Hao, J., Sun, J., Zhu, Z., Chen, Z., & Yan, Y. (2024). Adaptive intelligent agent for cloud edge collaborative industrial inspection driven by multimodal data fusion and deep transformation networks. Alexandria Engineering Journal, 106, 753-766. https://doi.org/10.1016/j.aej.2024.08.031