Abnormal evolution identification method of material supply chain node relationship based on deep learning

Yunyan Tan; Yuanyuan Li

doi:10.1117/12.3030271

5 June 2024 Abnormal evolution identification method of material supply chain node relationship based on deep learning

Yunyan Tan, Yuanyuan Li

Proceedings Volume 13163, Fourth International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024); 1316390 (2024) https://doi.org/10.1117/12.3030271
Event: International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024), 2024, Xi'an, China

Abstract

In the actual material supply chain, there are a lot of noise and abnormal data. These abnormal data can be caused by various factors, such as sensor failure, human error, etc. Therefore, it is difficult to identify supply chain node anomalies. Therefore, this paper proposes a deep learning-based method to identify the abnormal evolution of node relations in the material supply chain. In combination with fitness function, the relationship anomaly index of material supply chain nodes is obtained. Deep learning is used to build the abnormal evolution identification model of supply chain node relations, and the forward three-layer topology is adopted as the model connection mode. The abnormal relationship indicators of material supply chain nodes are taken as the network input nodes, and the abnormal recognition results are taken as the output nodes. The experimental results show that the method has higher recognition accuracy and shorter delay.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yunyan Tan and Yuanyuan Li "Abnormal evolution identification method of material supply chain node relationship based on deep learning", Proc. SPIE 13163, Fourth International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024), 1316390 (5 June 2024); https://doi.org/10.1117/12.3030271

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KEYWORDS

Deep learning

Raw materials

Failure analysis

Design

Education and training

Performance modeling

Network security

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