Intelligent logistics scheduling method and device based on dynamic weight and medium

Through dynamic environment perception and reinforcement learning-driven weight adjustment, the problems of path rigidity and resource waste in traditional AGV scheduling are solved, efficient and flexible AGV path planning and conflict management are achieved, and the operation efficiency of the smart logistics system is improved.

CN120506948APending Publication Date: 2025-08-19SHANDONG INSPUR DIGITAL SUPPLY CHAIN TECH CO LTD
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Patent Information

Application Number
CN202510560200.0
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-04-30
Publication Date
2025-08-19

AI Technical Summary

Technical Problem

Traditional AGV scheduling methods are difficult to adapt to real-time changing traffic density, task priority and equipment status in complex dynamic environments, resulting in rigid path planning, local congestion and waste of resources, and lack of deep environment perception capabilities, and serious interference with positioning errors and noise.

Method used

By fusion of multi-source sensor data, building a dynamic environmental model, using reinforcement learning model to dynamically adjust weights, predict spatiotemporal trajectories and identify potential conflict areas, global path re-planning and local trajectory optimization generate smooth obstacle avoidance paths, and adjusting model parameters through feedback to achieve adaptive optimization of dynamic weights.

Benefits of technology

Improves conflict detection accuracy and path planning flexibility, reduces equipment mechanical loss and energy waste, enhances response capabilities to complex environments, and ensures trajectory smoothness and multi-objective optimization.

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Abstract

The invention discloses an intelligent logistics scheduling method and device based on a dynamic weight and a medium, and the method comprises the steps: carrying out the fusion processing of the operation environment data of a logistics carrier, so as to construct a dynamic environment model, and carrying out the fusion processing of the operation environment data of the logistics carrier based on a reinforcement learning model according to a real-time environment state and a multi-target optimization demand. Dynamically adjusting the weight vector of each logistics parameter in the logistics carrier; predicting the space-time trajectory of each logistics carrier based on the kinematic model of the logistics carrier, mapping the space-time trajectory to a three-dimensional grid, identifying a potential conflict area between the logistics carriers, and calculating a corresponding conflict severity index; based on the dynamic weight vector and the conflict severity index, generating an obstacle avoidance path of the logistics carrier by adopting a global path re-planning algorithm, and optimizing the obstacle avoidance path through a local track to generate a corresponding smooth track; and executing a scheduling instruction corresponding to the smooth trajectory, and collecting an execution feedback result in real time to update the reinforcement learning model parameters and the dynamic weight strategy.
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