Automated Design Method of Environmental Art Design Scheme Based on Big Data Analysis

  • Dongyou WangEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1117)


Customers’ requirements for environmental art design are constantly improving, which undoubtedly brings greater challenges to environmental art designers. There are many excellent environmental design schemes around. If we can develop new design schemes according to existing excellent schemes, it will greatly reduce the workload of designers, and at the same time, it will bring excellent practical experience to customers. In view of the above problems, this paper proposes an automatic design method of environmental art design based on large data analysis. By describing the environmental design works with text, and then according to the input statements (i.e. the description of the desired works), using the generation of confrontation network and sorting algorithm to obtain the best match with the existing environmental design schemes, thus completing the automatic design of environmental art design schemes. Through the simulation of existing environmental design cases such as gardens, residential buildings, entertainment cities and office buildings, the results show that the proportion of the method in this paper meets the requirements of customers reaches 95.1%.


Environmental art design Program automation design Large data analysis Generation of countermeasure network 


  1. 1.
    Li, X.: Problems in environmental art design and their individualization. Rural Sci. Exp. (10), 85 (2017)Google Scholar
  2. 2.
    Yang, Q.: Ecological concept in environmental art design. Tomorrow’s Fashion (9), 44 (2017)Google Scholar
  3. 3.
    Ning, X., Sitong, L.: Analysis of the reference method perception in environmental art design. Furniture Inter. Design (9), 27 (2016)Google Scholar
  4. 4.
    Xiang, W.: The application of Chinese traditional cultural elements in modern environmental art design. Mod. Hortic. (2), 128 (2018)Google Scholar
  5. 5.
    Zhao, W.: The infiltration and application of tea culture in modern environmental art design. Fujian Tea (2), 104 (2018)Google Scholar
  6. 6.
    Kamilaris, A., Kartakoullis, A., Prenafeta-Boldú, F.X.: A review on the practice of big data analysis in agriculture. Comput. Electron. Agric. 143, 23–37 (2017)CrossRefGoogle Scholar
  7. 7.
    Mao, L., Cheng, W.: Research on the construction of smart agriculture big data platform. Agric. Netw. Inf. 264(6), 8–12 (2018)Google Scholar
  8. 8.
    Tang, B., Chen, Z., Hefferman, G., Pei, S., Wei, T., He, H., Yang, Q.: Incorporating intelligence in fog computing for big data analysis in smart cities. IEEE Trans. Ind. Inf. 13(5), 2140–2150 (2017)CrossRefGoogle Scholar
  9. 9.
    Lo’ai, A.T., Mehmood, R., Benkhlifa, E., Song, H.: Mobile cloud computing model and big data analysis for healthcare applications. IEEE Access 4, 6171–6180 (2016)CrossRefGoogle Scholar
  10. 10.
    Lin, W., Wu, Z., Lin, L., Wen, A., Li, J.: An ensemble random forest algorithm for insurance big data analysis. IEEE Access 5, 16568–16575 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Jilin Engineering Normal UniversityChangchunChina

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