Research Article
Research on Fault Intelligent Detection Technology of Dynamic Knowledge Network Learning System
@INPROCEEDINGS{10.1007/978-3-030-51100-5_39, author={Shuang-cheng Jia and Tao Wang}, title={Research on Fault Intelligent Detection Technology of Dynamic Knowledge Network Learning System}, proceedings={Multimedia Technology and Enhanced Learning. Second EAI International Conference, ICMTEL 2020, Leicester, UK, April 10-11, 2020, Proceedings, Part I}, proceedings_a={ICMTEL}, year={2020}, month={7}, keywords={Network learning Fault detection Intelligent detection Technology research}, doi={10.1007/978-3-030-51100-5_39} }- Shuang-cheng Jia
Tao Wang
Year: 2020
Research on Fault Intelligent Detection Technology of Dynamic Knowledge Network Learning System
ICMTEL
Springer
DOI: 10.1007/978-3-030-51100-5_39
Abstract
The rapid development of computers has improved the scope of dynamic knowledge network learning applications. Online learning has brought convenience to people in time and place. At the same time, people began to pay attention to the efficiency and quality of online learning. At present, the network knowledge storage system is distributed storage system. The distributed storage system has great performance in terms of capacity, scalability, and parallelism. However, its storage node is inexpensive, and the reliability is not high, and it is prone to fault. Based on the designed fault detection model detection path, relying on building the knowledge data node fault detection mode, constructing the knowledge data link fault detection mode, completing the fault detection model detection mode, and finally realizing the dynamic knowledge network learning system fault intelligent detection technology research. The experiment proves that the dynamic knowledge network learning system fault intelligent detection technology designed in this paper reduces the fault rate of the network learning system by 37.5%.
