Warehouse goods location intelligent allocation method for cross-border transit warehouse

By constructing a coupled evaluation system of information entropy and a greedy entropy pruning strategy, the problem of balancing task timeliness and equipment load balancing in cross-border transit warehouses was solved, achieving rapid response and adaptive scheduling, and improving the system's flexibility and stability.

CN122134257APending Publication Date: 2026-06-02PANGUDHAO (SHENZHEN) TECHNOLOGY DEVELOPMENT CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
PANGUDHAO (SHENZHEN) TECHNOLOGY DEVELOPMENT CO LTD
Filing Date
2026-03-24
Publication Date
2026-06-02

AI Technical Summary

Technical Problem

In scenarios involving high-concurrency tasks and dynamic scheduling of equipment resources in cross-border transit warehouses, existing technologies struggle to balance task timeliness with equipment load balancing, and their slow response speed makes them unable to adapt to real-time business changes, thus limiting the overall responsiveness of the system.

Method used

By constructing a coupled evaluation system based on information entropy, a spatiotemporal coupled entropy value is generated by weighted fusion of task timeliness entropy and equipment load entropy. Combined with a greedy entropy pruning strategy, a cargo location remapping is performed to achieve rapid decision-making and adjustment.

Benefits of technology

It significantly improves the adaptive control capabilities of cross-border transit warehouses, ensuring that the system can quickly identify scheduling conflicts and resource congestion in high-concurrency scenarios, avoid response delays, and improve the flexibility and stability of the system.

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Abstract

This invention relates to an intelligent warehouse location allocation method for cross-border transit warehouses. Addressing the problems of resource allocation imbalance and high scheduling failure rates in existing warehousing systems under high concurrency with multiple tasks, this invention proposes a mathematical modeling approach based on remaining grace period and equipment load intensity. By calculating task timeliness entropy and equipment load entropy, a system spatiotemporal coupling entropy value is dynamically fused and generated, and the weighting coefficient is adaptively adjusted based on historical scheduling failure rates. When the spatiotemporal coupling entropy value exceeds a dynamic threshold, a location remapping instruction is automatically triggered, and an improved greedy pruning strategy is used to select the optimal target location, synchronously updating the path and equipment binding relationships in real time. This method improves the scheduling coordination of the warehousing system under complex operating conditions, effectively reduces task delays and equipment overload risks caused by resource congestion, and improves the overall throughput and operational robustness of automated warehouses.
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