Research on intelligent warehouse digital twin system based on digital twin

Biao Feng; Minghua Jiang; Kunfang Song; Changlong Zhou; Feng Yu; Xiong Wei

doi:10.1117/12.2641102

13 October 2022 Research on intelligent warehouse digital twin system based on digital twin

Biao Feng, Minghua Jiang, Kunfang Song, Changlong Zhou, Feng Yu, Xiong Wei

Proceedings Volume 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022); 1228712 (2022) https://doi.org/10.1117/12.2641102
Event: International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 2022, Wuhan, China

Abstract

Intelligent warehouses greatly improve management efficiency and reduce the management cost of enterprises. Now many enterprises are building their own intelligent warehouses. However, the current intelligent warehouse lacks the communication between digital space and physical space, and the efficiency of warehouse management still has a lot of room for improvement. The digital twin technology is a useful way to realize the fusion of virtual warehouse and physical warehouse and optimizes the activities. In order to realize the communication between digital space and physical space of the intelligent warehouse, eliminate information island and improve the efficiency of warehouse management, this paper proposed the digital twin system architecture of intelligent warehouse based on digital twin technology. At the same time, this paper analyzed the system application requirements, designed and constructed an intelligent warehouse digital twin system. Finally, the prototype system is developed with the warehouse of a garment enterprise as an example to verify the feasibility and effectiveness of the system.

Citation Download Citation

Biao Feng, Minghua Jiang, Kunfang Song, Changlong Zhou, Feng Yu, and Xiong Wei "Research on intelligent warehouse digital twin system based on digital twin", Proc. SPIE 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 1228712 (13 October 2022); https://doi.org/10.1117/12.2641102

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