Task unloading intelligent decision-making method based on unmanned aerial vehicle group in edge computing environment

Abstract

The invention discloses a task unloading intelligent decision-making method based on an unmanned aerial vehicle group in an edge computing environment. The method comprises the following steps that (1) environment information is acquired; (2) meta-learning is carried out, and if it is found that the environment of an edge server or a cloud center changes, initial parameters of the model are modified; (3) a retrieval mechanism and reinforcement learning are carried out, the retrieval mechanism is responsible for retrieving whether similar tasks exist before or not, and if yes, a decision resultis directly output; and if not, reinforcement learning is carried out, the reinforcement learning is responsible for training and judging the whole reinforcement learning system, two used modules arenetwork freezing and experience playback, and the action with the maximum value function after judgment is taken as a decision result to be output. According to the scheme, the meta-learning model isadopted to quickly adapt to the environment, and when the environment of the decision-making system is changed, the scheme can be quickly adjusted and a reasonable result can be quickly given. For the similar tasks of the unmanned aerial vehicle group, a memory function is introduced in the scheme, and rapid decisions can be made for the similar tasks.

Description

technical field [0001] The invention relates to the technical field of unmanned aerial vehicle swarm task offloading decision-making, in particular to an intelligent decision-making method for unmanned aerial vehicle swarm-based task offloading in an edge computing environment. Background technique [0002] In recent years, with the maturity of 5G and UAV technology, UAV swarms have been more and more widely used. Through the combination with mobile communication, UAV swarms can provide multiple applications such as image acquisition and information transmission by virtue of its characteristics of not being affected by space and strong responsiveness. However, the limited computing power and UAV cache size hinders its application for mobile applications and results in a large amount of computing processing time. In addition, performing mission calculations on the drone will increase the energy consumption of the mobile device, shorten the battery life, and reduce the usage ...

AI Technical Summary

Problems solved by technology

Among them, traditional systems often use some heuristic algorithms, which are often unable to solve complex problems, and require a large amount of calculation, which consumes a lot of computing resources; on the contrary, intelligent algorithms that have emerged recently can effectively solve such problems, by introducing deep neural networks. Make the decision-making system automatically learn a reasonable and feasible decision-making plan, but there are still problems such as slow learning speed and poor portability

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