The rapid development of artificial intelligence technology demands higher requirements for employment and talent training. The integration of industry and education is an important way to solve the mismatch between industrial demand and talent supply. Therefore, this study starts from the perspective of the integration of industry and education. We collect recruitment texts from the perspective of “industry” and mine the specific requirements of the artificial intelligence post system through the LDA topic model and the combination of Word2Vec and K-means. We then conduct expert consultations and adjust the selected indicators from the perspective of “education.” Finally, we construct a four-dimensional vocational ability grade evaluation index system, including basic vocational skills of artificial intelligence, database, network skills, algorithm and design skills, and research and practice skills. The intuitionistic fuzzy analytic hierarchy process, which can eliminate the subjective uncertainty of experts in the scoring process, is applied to calculate the index weights. We find that the weight of algorithm and design skill is the highest, which is an important criterion for artificial intelligence professional ability evaluation. Among the second-level indicators, practical indicators such as team spirit, innovation ability, and communication ability are the focus of investigation from the perspective of industry, while in education, the cultivation of knowledge and skills such as programming ability, applied mathematics ability, data structures, and algorithms are more important.

The identification of key influencing factors in the training of applied talents in artificial intelligence includes two stages, namely, the establishment of factor index system and the identification of key influencing factors. Firstly, referring to a large number of literatures on the influencing factors of talent training and the sustainability evaluation index framework at home and abroad, and in combination with the characteristics of the development of China's artificial intelligence industry, this paper summarizes the influencing factors from the multi-dimensional training path of government departments, universities, enterprises and scientific research institutes. According to the training purpose of artificial intelligence application-oriented talent training, this paper constructs the influencing factor index system of artificial intelligence applied talents training. Then, the DEMATEL method is used to establish multiple correlation matrices according to the direct influence correlation between the factors, and the degree of influence, the degree of being influenced, the centrality degree, and the reason degree are calculated; Using the improved AISM method and based on the idea of game confrontation, a set of confrontation level topological maps with comprehensive influence values reflecting the interaction of factors are obtained from the two opposite extraction rules of result priority and cause priority, respectively. Finally, the relevant suggestions are put forward in order to provide reference for promoting the training of applied talents in China's artificial intelligence industry.

With the rapid development of Internet of things (IoT) technology and the increasing popularity of IoT devices, more and more computing intensive IoT applications came into being. However, due to the limited resources of IoT devices, cloud computing systems are required to compute intensive IoT applications. Further, in order to be subject to a single cloud computing service provider, multi-cloud computing will become an IoT service cloud computing solution. In view of the complexity of multi-cloud scheduling, the application of artificial intelligence will be an important technology to solve the multi-cloud scheduling of IoT. The corresponding talent training plays an important role in the development and implementation of the artificial intelligence multi-cloud scheduling of IoT. Firstly, this paper studies the key influencing factors of IoT’s artificial intelligence multi-cloud scheduling applied talents 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. According to the training purpose of artificial intelligence multi-cloud scheduling applied talents training, build an artificial intelligence multi-cloud scheduling applied talents 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 influence degree, affected degree, center degree and 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 respectively, a group of confrontation level topological maps with comprehensive influence values reflecting the interaction of factors are obtained, and relevant suggestions are put forward in order to provide reference for the training of artificial intelligence multi-cloud scheduling applied talents.

In the current urban rail transit systems, nearly 15% of passengers are noncommuter travelers who use single-trip ticket cards (ticket cards). Accordingly, the effective management of ticket cards is of great importance. This article suggests a time series model for use in predicting ticket card storage based on the characteristics of ticket cards collected by an automatic fare collection (AFC) system. The distribution cycle, station types, and distribution volume of each station are also determined. Then, drawing on small package transportation feasibility theory, an unbalanced distribution model between production and demand (unbalanced distribution model), as well as a hybrid distribution model of loading and unloading (hybrid distribution model), is established. Application of these models to the Beijing Subway system is used to verify the efficiency and feasibility of such a hybrid distribution model. The analysis and results offer insights into usage patterns of urban rail transit ticket cards, providing solid evidence for a relative decision-making process.

This paper analyses the insufficiency of the existing teaching system in supporting the teaching collaborative activities, that is, it is difficult to support the teaching supervisory activities, teachers' common courses preparation, the management and storage of students' electronic document assignments, and so on. The idea to utilize private cloud disk to build a collaborative teaching system is suggested. Then it designs the teaching supervision process, course collaboration process and teacher-student collaboration process based on private cloud disk, puts forward the overall framework of Collaborative Teaching System based on Private Cloud Disk (CTS-PCD), and designs its functional structure. Finally, the technical framework and application of CTS-PCD are discussed. The CTS-PCD has good support and reference significance for the current teaching informatization in universities.