Objective: To address the problems of dispersed educational information resources and low resource sharing in current education platforms, blockchain technology is applied to education cloud platforms to improve the concentration and sharing of educational resources. Methods: This paper built a smart community education cloud platform based on blockchain technology, and specifically designed the service content of the platform. In order to improve the load capacity of the platform, this paper also tested the performance of the platform in combination with the load balancing algorithm. Result: The experimental results show that under the algorithm proposed in this paper, when 600 users access the platform, the CPU (Central Processing Unit) utilization rate reaches 45.86%, and the memory utilization rate reaches 37.97%. Conclusion: From the above data, it can be seen that the algorithm proposed in this paper can effectively reduce the CPU and memory usage of the platform and improve its operational efficiency when facing a large number of user visits.

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Construction of Smart Community Education Cloud Platform Integrating Blockchain Technology

  • Zhongying Yang,
  • Yu Ren

摘要

Objective: To address the problems of dispersed educational information resources and low resource sharing in current education platforms, blockchain technology is applied to education cloud platforms to improve the concentration and sharing of educational resources. Methods: This paper built a smart community education cloud platform based on blockchain technology, and specifically designed the service content of the platform. In order to improve the load capacity of the platform, this paper also tested the performance of the platform in combination with the load balancing algorithm. Result: The experimental results show that under the algorithm proposed in this paper, when 600 users access the platform, the CPU (Central Processing Unit) utilization rate reaches 45.86%, and the memory utilization rate reaches 37.97%. Conclusion: From the above data, it can be seen that the algorithm proposed in this paper can effectively reduce the CPU and memory usage of the platform and improve its operational efficiency when facing a large number of user visits.