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Research on Visual Display Method of Virtual Experimental Elements Based on Big Data Technology

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Advanced Hybrid Information Processing (ADHIP 2019)

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Abstract

In order to solve the problem that the visual simulation image of virtual experimental element is missing in the reconstruction module of virtual experimental element, and the reconstruction accuracy of virtual experimental element is not good. A visual display method of virtual experimental element visual simulation image based on big data technology and virtual visual reconstruction is proposed. Firstly, the information transmission model of virtual experimental element visual simulation image is constructed. Then the 5-level wavelet decomposition method is used to decompose and fuse the visual simulation images of virtual experimental elements, and big data fusion technology is used to reconstruct the visual information of virtual experimental elements. The visual simulation image visualization of virtual experimental elements is realized. The simulation results show that this method has good visual display performance and high feature fusion degree in the reconstruction and modeling of virtual experimental element visual simulation image, and has high value in the application of virtual experimental element visual display and digital reconstruction.

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Author information

Authors and Affiliations

  1. Teaching Equipment Department, Xuzhou Institute of Technology, Xuzhou, China

    Wei-wei Xu

  2. Lianyungang Jari Electronics Co., Lianyungang, China

    Chen-guang Bai

Authors
  1. Wei-wei Xu
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  2. Chen-guang Bai
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Corresponding author

Correspondence to Wei-wei Xu .

Editor information

Editors and Affiliations

  1. Nanjing University, Nanjing, China

    Guan Gui

  2. Harbin Engineering University, Harbin, China

    Lin Yun

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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Xu, Ww., Bai, Cg. (2019). Research on Visual Display Method of Virtual Experimental Elements Based on Big Data Technology. In: Gui, G., Yun, L. (eds) Advanced Hybrid Information Processing. ADHIP 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 302. Springer, Cham. https://doi.org/10.1007/978-3-030-36405-2_3

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  • DOI: https://doi.org/10.1007/978-3-030-36405-2_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-36404-5

  • Online ISBN: 978-3-030-36405-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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