Intelligent game decision-making method and system for multi-unmanned aerial vehicle task distribution in countermeasure environment

Abstract

The invention provides an intelligent game decision-making method and system for multi-unmanned aerial vehicle task distribution in a countermeasure environment. The method comprises the steps of determining a plurality of second monitoring strategies of a monitoring party according to the same monitoring area in first monitoring strategies of the monitoring party and attack strategies of an attacker; screening a target attack strategy from the attack strategy on the basis of a non-discovery probability of a strategy pair comprising a first monitoring strategy and an attack strategy; and finally, screening a target monitoring strategy from the second monitoring strategies to monitor the attacker based on the second monitoring strategies, the target attack strategy and the monitoring discovery probability. According to the technical scheme, a stackelberg game model is used for solving, so that the technical problem that an effective patrol strategy cannot be made on the premise that theattacker can observe an implemented patrol strategy to discover the attacker with the maximum probability is solved; and meanwhile, the monitoring strategies of the monitoring party are processed, and the strategies of the attacker are screened, so that the calculation amount is reduced, and the calculation efficiency is improved.

Description

technical field [0001] The invention relates to the field of electric power, in particular to a game intelligent decision-making method and system for multi-UAV task assignment in an adversarial environment. Background technique [0002] UAVs can autonomously complete given tasks in a variety of environments, effectively reducing labor costs. At present, the use of multiple drones to patrol a certain area has gradually become one of the important means of security. Designing an effective patrol strategy to deploy when and where drones patrol will increase the likelihood of detecting attackers in the monitored area. [0003] The patrol strategy of UAV includes the patrol time and patrol location of each UAV in any patrol cycle. Since the patrol strategy needs to be formulated in advance, and the attacker can observe the implemented patrol strategy, the formulation of the patrol strategy needs to consider not only the monitoring accuracy of the UAV-equipped equipment, but al...

AI Technical Summary

Problems solved by technology

[0007] Aiming at the deficiencies of the prior art, the present invention provides a game intelligent decision-making method and system for multi-UAV task assignment in an adversarial environment, which solves the problem that the attacker cannot observe the implemented patrol strategy in the prior art , formulate an effective patrol strategy to find the attacker with the greatest probability, and at the same time, the process of determining the patrol strategy in the prior art is a problem of huge calculation and low efficiency

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