Teacher cluster management system based on big data platform

Abstract

The invention provides a teacher cluster management system based on a big data platform, and the system comprises a cluster configuration unit which is used for customizing the configuration needed bya teacher cluster after a teacher logs in the system; a teacher course arrangement unit used for setting course information, and a course information which comprises course types, course time, teaching classes and teaching places; a course starting unit used for starting a teacher cluster and a student cluster after the teacher identity logs in the system, and demonstrating course contents corresponding to the course information in the teacher cluster; a course concluding unit used for ending course demonstration and releasing cluster resources after teaching is completed; and a cluster hanging unit used for hanging the teacher cluster and/or the student cluster. According to the invention, the flexibility and practicability of teacher cluster management are enhanced, the data of theteacher cluster and the student cluster can be kept for any long time under the condition that the data is suspended, the teaching time of courses is not limited any more, the complexity of operationis reduced, and the error-tolerant rate is improved.

Description

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AI Technical Summary

Benefits of technology

This patented technology improves upon existing systems used for managing students' clusters. It allows them to select configurations based on their needs during training without overwhelming up any computer hardware components like processors or storage devices. Additionally it simplifies operations while maintaining high-performance computing capabilities. Overall, this new method enhances the efficiency and effectiveness of educational institution settings such as school science classes.

Problems solved by technology

This patented technical problem addressed in this patents relates to improving how educational institutions manage their staffing systems efficiently while maintaining consistently accurate results over multiple courses. Existing solutions either require all servers at once or only allow one server per team member, leading to wasted resource allocation when some members take up too much workload.

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