Multi-unmanned aerial vehicle cooperative transportation dynamic decision-making method and system in emergency environment

By combining a dynamic hunter local decision-making model, a multi-objective minimum cost maximum flow model, and an improved non-dominated sorting genetic algorithm, the overall efficiency and robustness issues of UAV scheduling in emergency environments are solved, achieving efficient, fair, and safe collaborative transportation of resources.

CN121836027APending Publication Date: 2026-04-10HUAIYIN INSTITUTE OF TECHNOLOGY
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Patent Information

Application Number
CN202610010051.5
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-01-06
Publication Date
2026-04-10

AI Technical Summary

Technical Problem

Existing drone emergency transport and dispatch technologies struggle to balance individual urgency with overall system efficiency in emergency environments. They neglect node status changes and fail to effectively characterize the complex trade-offs between energy consumption, timeliness, fairness, and risk, resulting in insufficient model feasibility.

Method used

By employing a dynamic hunter local decision-making model, a multi-objective minimum cost maximum flow model, and an improved non-dominated sorting genetic algorithm, and combining the local decisions of hunter and prey nodes, a multi-objective optimization method is constructed. A multi-objective evolutionary algorithm is then used for fine-grained search to generate a cooperative transportation strategy.

Benefits of technology

It improves the overall decision-making performance and robustness of multi-drone emergency transport systems in complex environments, enhances the overall efficiency and fairness of material allocation, shortens response time, and reduces the energy consumption of drones returning empty.

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Abstract

The invention provides a multi-unmanned aerial vehicle cooperative transportation dynamic decision-making method and system in an emergency environment, and the method comprises the steps: 1, obtaining the material state information reported by a disaster region node after an emergency occurs; step 2, constructing a hunter and prey local decision model taking the disaster-affected node as a main body, and forming a local priority and competition relationship between the nodes; 3, establishing a multi-target minimum cost maximum flow (MCMF) model, and solving to obtain a candidate transportation scheme; and step 4, inputting the candidate transportation scheme as an initial population into an improved non-dominated sorting genetic algorithm NSGA-II, performing non-dominated optimization on energy consumption, time, fairness and risk, and generating an optimal collaborative transportation strategy cluster approaching the Pareto frontier. According to the method, through a mode of combining local decision and global optimization, comprehensive improvement of multi-unmanned aerial vehicle collaborative transportation efficiency, fairness and safety is realized in a complex dynamic emergency environment.
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