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Research on Fault Intelligent Detection Technology of Dynamic Knowledge Network Learning System

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Multimedia Technology and Enhanced Learning (ICMTEL 2020)

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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%.

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Authors and Affiliations

  1. Alibaba Network Technology Co., Ltd., Beijing, China

    Shuang-cheng Jia & Tao Wang

Authors
  1. Shuang-cheng Jia
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  2. Tao Wang
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Correspondence to Shuang-cheng Jia .

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Editors and Affiliations

  1. School of Informatics, University of Leicester, Leicestershire, UK

    Yu-Dong Zhang

  2. University of Leicester, Leicestershire, UK

    Shui-Hua Wang

  3. Human Normal University, Changsha, China

    Shuai Liu

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© 2020 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Jia, Sc., Wang, T. (2020). Research on Fault Intelligent Detection Technology of Dynamic Knowledge Network Learning System. In: Zhang, YD., Wang, SH., Liu, S. (eds) Multimedia Technology and Enhanced Learning. ICMTEL 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 326. Springer, Cham. https://doi.org/10.1007/978-3-030-51100-5_39

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  • DOI: https://doi.org/10.1007/978-3-030-51100-5_39

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-51099-2

  • Online ISBN: 978-3-030-51100-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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