Supply chain logistics efficiency collaborative optimization method and system based on artificial intelligence

By constructing a multi-layered logistics collaboration network and an intelligent logistics collaborative optimization engine, the problems of information silos and insufficient coordination in the supply chain logistics system have been solved, enabling real-time monitoring and dynamic adjustment, and improving the overall operational efficiency and response speed of the supply chain logistics.

CN120851760AActive Publication Date: 2025-10-28湖南工商大学
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Patent Information

Application Number
CN202511017720.3
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-07-23
Publication Date
2025-10-28
Estimated Expiration
2045-07-23

AI Technical Summary

Technical Problem

Existing supply chain logistics optimization systems lack real-time performance, intelligence, and collaboration in scenarios involving multiple nodes and concurrent tasks, making it difficult to effectively cope with dynamic and complex business scenarios. Furthermore, information silos are prevalent, resulting in insufficient overall transparency and responsiveness.

Method used

By acquiring real-time supply chain logistics data streams, performing node coupling density analysis and adaptive hierarchical mapping, a multi-layer logistics collaboration network is constructed. This network performs path node load response back-calculation and nonlinear distortion assessment, identifies sensitive nodes, reconstructs topology associations, predicts future load demands, identifies potential risk links, and performs local resource scheduling and global collaborative processing to build an intelligent logistics collaborative optimization engine.

Benefits of technology

It enables real-time monitoring and dynamic adjustment of supply chain logistics, optimizes the coordination between various links, improves the efficiency of resource allocation and information transmission, identifies resource bottlenecks and potential risks, enhances the smoothness and responsiveness of supply chain logistics operations, and ensures that the supply chain has greater resilience and adaptability in the face of external changes.

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Abstract

The invention relates to the field of supply chain optimization, in particular to a supply chain logistics efficiency collaborative optimization method and system based on artificial intelligence. The method comprises the following steps: obtaining a real-time supply chain logistics data stream, carrying out node coupling density analysis and self-adaptive layered mapping, and constructing a multi-layer logistics cooperation network; performing path node load response backstepping on the multi-layer logistics cooperative network, and performing nonlinear distortion evaluation to obtain a nonlinear distortion degree of a response curve; and carrying out sensitivity quantitative evaluation according to the nonlinear distortion degree of the response curve, carrying out topological association reconstruction among nodes, and constructing a supply chain logistics topological optimization network. And performing future load demand rehearsal according to the real-time supply chain logistics data flow, performing call operation path evolution analysis, and generating call operation path evolution data. According to the invention, the configuration of supply chain logistics resources is optimized, and the overall operation efficiency and response capability of supply chain logistics are improved.
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