Dynamic scheduling model training and dynamic scheduling scheme generation method for strong coupling operation

By constructing a dynamic scheduling model for maritime aircraft support operations and using deep reinforcement learning technology to train the decision-making agent for aircraft maintenance support and airborne material transportation, the problems of poor dynamic adaptability and difficulty in solving strongly coupled scheduling in maritime aircraft operations were solved, generating a highly adaptive scheduling scheme and improving overall operational efficiency.

CN122134050APending Publication Date: 2026-06-02INST OF AUTOMATION CHINESE ACAD OF SCI
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
INST OF AUTOMATION CHINESE ACAD OF SCI
Filing Date
2026-04-09
Publication Date
2026-06-02

AI Technical Summary

Technical Problem

Existing technologies struggle to achieve real-time, efficient, and convergent collaborative scheduling planning in dynamic, strongly coupled, and multi-constrained maritime aircraft support operations. In particular, they suffer from slow response and solution space expansion when faced with sudden disturbances, resulting in low solution efficiency.

Method used

By constructing a parallel inference model of two coupled decision-making agents—aircraft maintenance support and airborne material transportation—deep reinforcement learning technology is used for training. This model simulates the serial interaction and state influence between the two decision-making links. Based on a unified objective reward that reflects global performance, collaborative training is conducted to generate a dynamic scheduling scheme with adaptability and stability.

Benefits of technology

It has achieved joint scheduling optimization of aircraft maintenance support and airborne material transportation operations, improved the overall efficiency of maritime aircraft support operations, and solved the problems of response lag under dynamic uncertainty and low solution efficiency in large-scale scenarios.

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Abstract

This invention provides a method for training a dynamic scheduling model and generating dynamic scheduling schemes for strongly coupled operations. The method includes: inputting a first state representation into an initial airborne logistics support decision model to obtain a first decision output; updating a simulation environment based on the first decision output, obtaining a second state representation representing the status of airborne material transportation operations from the updated simulation environment, inputting the second state representation into the initial airborne material transportation decision model to obtain a second decision output; using the simulation environment after executing the second decision output as the simulation environment for obtaining the first state representation, obtaining the target reward for executing the first and second decision outputs in the simulation environment, and updating the model parameters based on the target reward. This invention simulates the serial interaction and state influence between two decision-making stages through a shared simulation environment, and performs collaborative training of the two models based on the target reward, enabling joint scheduling optimization of airborne logistics support and airborne material transportation operations.
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