Research on influencing factors of artificial intelligence multi-cloud scheduling applied talent training based on DEMATEL-TAISM
Yijie Bian, Lu Xie, Jingqi Li
Abstract
Abstract With the rapid development of Internet of Things (IoT) technology and the rising popularity of IoT devices, an increasing number of computing intensive IoT applications have been developed. However, due to the limited resources of IoT devices, cloud computing systems are required to compute intensive IoT applications. Furthermore, to be subject to a single cloud computing service provider, multi-cloud computing has become an IoT service cloud computing solution. As a result of the complexity of multi-cloud scheduling, the application of artificial intelligence is an important technology to solve IoT multi-cloud scheduling. The corresponding talent training plays an important role in the development and implementation of the IoT artificial intelligence multi-cloud scheduling. First, this paper studies the key influencing factors of IoT artificial intelligence multi-cloud scheduling applied talent training. Combined with the characteristics of the development of China’s artificial intelligence industry, this paper summarizes the influencing factors from the four-dimensional training path of government departments, universities, enterprises and scientific research institutes. The purpose of artificial intelligence multi-cloud scheduling applied talent training is to build an artificial intelligence multi-cloud scheduling applied talent training influencing factor index system. Then, the DEMATEL method is used to establish multiple correlation matrices according to the direct influence correlation between the factors and calculate the degree of influence, the degree of being influenced, the center degree and the cause degree of the factors. Using the improved AISM method, based on the idea of game confrontation, from the two opposite extraction rules of result priority and cause priority, a group of confrontation level topological maps with comprehensive influence values reflecting the interacting factors are obtained, and relevant suggestions are presented to provide a reference for the training of artificial intelligence multi-cloud scheduling applied talent.