Author(s)

Ji Botong ¹, Liang Can ¹, Wu Ruihao ¹, Zhu Yining ¹, Peng Yihua ¹

Affiliation(s)

¹ School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan, 114051, Liaoning, China

Corresponding Author

Ji Botong

ABSTRACT

To address issues such as low efficiency and poor real-time performance in traditional warehouse management models, this paper designs a cloud-based warehouse platform solution utilizing AI large models and Internet of Things technology. The system achieves comprehensive intelligent management of warehouse inbound and outbound processes through multi-modal AI shared decision-making (DeepSeek-R1 language model and GLM-4V vision model), AGV high-precision navigation, and 3D visualization technology, forming a closed-loop management system of "perception-decision-action-monitoring." The solution employs modular structural design, supports easy scalability, and features storage and computing design combining edge and cloud modes. It integrates dual-factor authentication and distributed transaction design to ensure the reliability and security of the warehouse management system. This solution enables multi-modal data extraction, dynamic path planning, and real-time data display, providing a relatively complete and extensible technical approach to intelligent warehouse logistics. Future work on this solution may explore the integration of 5G technology and digital twin technology for further applications.

KEYWORDS

Intelligent Warehouse; AI Large Models; AGV; Spring Cloud; Internet of Things

CITE THIS PAPER

Ji Botong, Liang Can, Wu Ruihao, Zhu Yining, Peng Yihua. Design and Implementation of an Express Warehousing Cloud Platform Based on Multimodal AI. Computing, Performance and Communication Systems (2026). Vol. 10, No. 1, 53-60. DOI: http://dx.doi.org/10.23977/cpcs.2026.100106.

REFERENCES

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