AI unloading optimization method for random tasks in industrial Internet of Things

Abstract

The invention discloses an AI unloading optimization method for a random task in an industrial Internet of Things, and the method employs an unmanned plane as an edge server, builds an edge calculation entity network of a two-layer unmanned plane, is used for unloading and processing the random task, and optimizes an edge calculation model through an artificial intelligence method. An NIO method is provided for specific task processing and energy consumption when a communication fault occurs in a production area in a dangerous operation environment, and a system unloading model is constructed according to equipment information collected by an unmanned aerial vehicle under an edge computing entity network of two layers of unmanned aerial vehicles. Firstly, an unloading decision of nearby unloading of dense equipment and centralized unloading of dispersed equipment is proposed based on the distance, then energy consumption optimization processing is performed through a Lyapunov optimization method, and finally, an optimal scheme of computing resource allocation and unmanned aerial vehicle deployment is obtained based on a DDPG-G algorithm. According to the method, the problem of task unloading failure caused by communication faults in the dangerous operation process is solved, and system energy consumption optimization is achieved.

Description

technical field [0001] The invention relates to the field of UAV-assisted edge computing, in particular to an AI unloading optimization method for random tasks in the Industrial Internet of Things. Background technique [0002] With the popularity of smart mobile devices and 5G, many computing-intensive services have emerged, but at the same time, the development of these computing-intensive services is limited by the computing resources and battery capacity of smart mobile devices. In order to solve the above problems, by offloading the computing tasks to the cloud server on the base station or a closer edge server, the computing tasks can be completed faster, the energy consumption of the equipment can be reduced, and the task processing time can be shortened. In some special cases, such as crowded places or natural disasters, ground base stations cannot provide services for equipment due to network congestion or damage, resulting in a solution for drones to carry edge ser...

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Problems solved by technology

[0004] However, when the Internet of Things technology, cloud computing and edge computing technology are applied to industrial production, they also bring some problems. It will lead to emergencies, but the industrial Internet is different. Communication failures can affect production at least, and cannot guarantee the safety of workers and equipment. Therefore, it is extremely important to ensure the smooth communication of the industrial Internet of things

[0005] At present, the drone-assisted edge computing under the Industrial Internet of Things has the following problems: Although drones are flexible and easy to deploy, they are small in size, battery capacity and computing resources are limited, so that drones cannot handle tasks as long as base stations. Time work, also can not have the computing power of the base station

The calculation and data transmission of drones consume energy, so in order to make drones work as long as possible, it is necessary to reduce the energy consumption of the system as much as possible; in the industrial Internet of things, devices will continuously generate data, so using random The task arrival model is more realistic, but the task arrival is random, the buffer is dynamic, and system problems will be difficult to solve

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