Automatic control method and system for large-scale dispersed nodes based on unstable scene

By combining situational awareness and neural network models with mobility coefficients to classify people to be rescued, and constructing a cost matrix for optimal matching, the problem of uneven task allocation in large-scale decentralized rescue is solved, and low-cost and efficient rescue path optimization is achieved.

CN122411547APending Publication Date: 2026-07-17THE NAVAL MEDICAL UNIV OF PLA
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
THE NAVAL MEDICAL UNIV OF PLA
Filing Date
2026-04-07
Publication Date
2026-07-17

AI Technical Summary

Technical Problem

In large-scale, decentralized rescue scenarios, existing technologies fail to effectively consider the different states of people awaiting rescue under unstable conditions, resulting in suboptimal allocation of rescue tasks and impacting rescue efficiency.

Method used

By acquiring the status information of people to be rescued through situational awareness, they are divided into three groups: the first group, the second group, and the third group. Different rescue matching is carried out based on these groups. The presence of floating objects is determined by using a neural network model. The movement coefficient is combined for detailed classification, and a cost matrix is ​​constructed for optimal matching.

Benefits of technology

It enables optimized allocation of rescue missions in unstable scenarios, reduces overall rescue costs, and improves the scientific nature and accuracy of rescue efforts.

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Abstract

The application relates to the technical field of unmanned plane control, and particularly discloses an automatic control method and system for large-scale dispersed nodes based on unstable scenes, which comprises the following steps: dividing to-be-rescued personnel into a first crowd, a second crowd and a third crowd based on state information; obtaining the number of available unmanned devices, which is recorded as a first device number, and then performing first rescue matching based on the number of the first crowd and the first device number; obtaining the number of remaining available unmanned devices, which is recorded as a second device number, and then performing second rescue matching based on the number of the second crowd and the third crowd and the second device number; and dividing the to-be-rescued personnel according to the information of the to-be-rescued personnel, then performing the first rescue matching and the second rescue matching based on the divided personnel, and considering the falling state of different personnel to maximize the minimization of the overall rescue cost.
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