Construction of Big Data Technology Training Environment for Vocational Education Based on Edge Computing Technology

Cui, Libiao

doi:10.1155/2022/1060464

Cited by 21 publications

(18 citation statements)

References 22 publications

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“…Big Data Module. Because the data collection of big data is very large, the requirements for data retrieval, storage, control, and analysis are very high [11]. It has four characteristics of large scale, fast flow, diverse types, and low value density.…”

Section: Tourist Attraction Recommendation Systemmentioning

confidence: 99%

Tourist Attraction Recommendation Method Based on Megadata and Artificial Intelligence Algorithm

Yun¹,

Jiao

2022

Wireless Communications and Mobile Computing

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As China’s economy continues to grow of informational technology and mobile Internet industry, the online tourism industry has received more and more extensive attention and use. However, as an emerging industry, users often need to spend a lot of time to choose travel services that match their needs because of the complex amount of relevant information. Under such circumstances, this paper studied the recommendation method in travel platform. First, the big data is used to extract user data. Secondly, the current online travel business recommendation for users has the problem of low accuracy. The reason is that the services provided are still in traditional recommendation algorithm. In this paper, the Bayesian network is used to evaluate the user’s attribute preference and generate a data model, using effective methods in artificial intelligence algorithms to improve collaborative filtering algorithms and finally generate hybrid recommendation algorithms. Compared with the traditional recommendation method, the experimental results showed that the research can improve the recommendation accuracy of tourist attractions by 6.55%, increase the user’s satisfaction for the platform, and enhance the visit rate and retention rate of the tourist attraction recommendation platform.

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Section: Tourist Attraction Recommendation Systemmentioning

confidence: 99%

Tourist Attraction Recommendation Method Based on Megadata and Artificial Intelligence Algorithm

Yun¹,

Jiao

2022

Wireless Communications and Mobile Computing

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“…Jia and others said that these data can simplify student management, help some relevant managers liberate from heavy work, and promote the continuous improvement of work efficiency [5]. Cui and others believe that in the traditional information management mode, managers collect all kinds of information with the help of traditional means, which makes it difficult to obtain effective statistics, and there are problems of missing and error [6]. Ma et al It is believed that with the help of big data technology, administrators can dig deep into the key points in the data, and explore the relationship between student behavior and assistance through analysis, so as to improve awareness and control decision-making.…”

Section: Literature Reviewmentioning

confidence: 99%

Construction and Management Method of University Information Platform Based on Big Data Technology

2022

Mobile Information Systems

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In order to solve the problems faced in the current management of colleges and universities, this topic integrates data mining technology and traditional statistical analysis technology in the process of College data management, and integrates multisource campus raw data. Take corresponding research models for different research objectives, implement the fine, meticulous, and accurate management concept of colleges and universities for students, build a student information management platform and help the construction of smart campus. The research integrates the school’s multi-business system data, opens up the information channel between information systems, establishes a multisystem data center and realizes the whole life cycle management of College Students’ data, including data collection, cleaning, storage and file recycling, and records the data of important nodes during students’ enrollment, graduation, and departure. Through the analysis of the school’s business management needs and the study of the school’s data characteristics, the data collected from the system information has been effectively distributed, and the data linking process is only used to delete data features based on data features. Under the action of big data technology, the information platform for college students can be constructed, which can provide more efficient, safe, and comprehensive decision support for college students’ management, making all work decisions more scientific. It can be said that the integration of big data will support the management of college students to a new level, which is very important for the modern management of students.

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“…Mobile beacons are frequently used by current positioning algorithms to assist in the positioning of unknown nodes because they can decrease energy consumption and increase positioning accuracy [11]. e majority of node deployment techniques use random deployment, and the deployment areas primarily consist of two types of deployment shapes: regular deployment shapes and irregular deployment shapes, or communication blind spots [12]. is helps to make the experimental environment more realistic.…”

Section: Introductionmentioning

confidence: 99%

The Application Trend and Advantages of Mobile Technology in Higher Education

2022

Mobile Information Systems

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The emergence of mobile wireless technology has profoundly altered how people produce goods and live their lives, but even as we take advantage of its speed and convenience, we must also be aware of its drawbacks. A new technology supported by contemporary technology is mobile wireless Internet technology. It is computer-, network-, and new media-based. Education and teaching are becoming more important in society as a result of the social economy’s rapid growth and the reform and development policies’ gradual advancement. It can develop the skills required by society and make teaching more widely known. The semisupervised learning positioning algorithm used in this study lowers the training phase acquisition costs. The proposed algorithm significantly resolves the issue that the DV-Hop algorithm is not suitable for anisotropic network positioning by applying the concept of triple equidistant coverage to anisotropic wireless sensor networks and combining it with multiple mobile beacons. According to the results of the experimental analysis, the following conclusions can be made: information processing can be found to be essentially stable at a level of more than 40%, which will greatly strengthen the use of mobile wireless technology in higher education. The improvement in overall performance will reach 65.7 percent. Following the experiment, it was discovered that the average transmission efficiency for mobile wireless technology is 57.4%, which is extremely high.

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Construction of Big Data Technology Training Environment for Vocational Education Based on Edge Computing Technology

Cited by 21 publications

References 22 publications

Tourist Attraction Recommendation Method Based on Megadata and Artificial Intelligence Algorithm

Tourist Attraction Recommendation Method Based on Megadata and Artificial Intelligence Algorithm

Construction and Management Method of University Information Platform Based on Big Data Technology

The Application Trend and Advantages of Mobile Technology in Higher Education

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