Task unloading sequence prediction method, task unloading decision-making method, electronic equipment and storage medium

Abstract

The invention discloses a task unloading sequence prediction method, a task unloading decision-making method, electronic equipment and a storage medium. According to the task unloading sequence prediction method, a cooperative node set is obtained based on position coordinates obtained through prediction, the influence of the vehicle motion state on cooperative node unloading is considered, and the robustness of cooperative node selection in the cooperative unloading process is ensured; multi-cooperative-node concurrent cooperative task unloading is proposed, so that the method can adapt to multi-scene tasks, and channel duration occupation is reduced; the task execution sequence is determined by providing a game theory of Nash equilibrium. According to the task unloading decision-making method, the optimization problem of multiple cooperative nodes is solved through a quadratic programming algorithm, and the complexity and convergence of the solved optimization problem can be reduced. The selection and the execution sequence of the cooperative nodes of the cooperative unloading task and the size of the cooperative unloading task quantity are realized, a complete cooperative unloading process is realized, the purpose of optimizing the execution time delay of the task is achieved, and the duration occupation of a channel can be effectively reduced through multi-node concurrent execution.

Description

technical field [0001] The present invention relates to the technical field of mobile edge computing for Internet of Vehicles, and in particular to a task unloading sequence prediction method, a decision-making method, electronic equipment, and a storage medium. Background technique [0002] In related schemes, in order to consider the impact of vehicle mobility on it, stochastic geometry theory and PPP method are used, and the calculation offloading algorithm based on LBB and the calculation algorithm based on CRI are proposed, and the use of graph theory is also proposed. The method and the heuristic algorithm are two ways to calculate the triage formulation problem. This technology is mainly used in the field of transportation systems. It uses a binary unloading scheme, which can realize cooperative unloading execution between vehicles, thereby greatly reducing the execution delay of tasks and unloading tasks in a shorter time. Execution can intervene in the vehicle and ...

AI Technical Summary

Problems solved by technology

The cooperative unloading model with relatively stationary vehicles only assumes that the vehicle road scene is in a relatively ideal state, and does not consider the topology changes in the communication process between vehicles caused by the vehicle movement process

The binary collaborative offloading model does not consider the channel occupancy during offloading execution, and pure binary offloading cannot meet various complex types of scene service services in the Internet of Vehicles

The joint cooperative unloading model adopts joint cooperative unloading in three parts: local, cooperative vehicle and cooperative RSU, but it cannot achieve adaptive optimization

[0004] In summary, the main flaws in the related schemes are that the impact of vehicle mobility is not considered, and complex tasks cannot be satisfied by only binary offloading, and only the single-process streamline execution process is used to execute the results, resulting in longer time for the total task to occupy the channel. Longer is more likely to fail as a result during cooperative unloading

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