Environment optimization design of green building landscape space based on genetic algorithm

Chuifeng Dong

doi:10.1117/12.2646629

14 November 2022 Environment optimization design of green building landscape space based on genetic algorithm

Chuifeng Dong

Proceedings Volume 12299, International Conference on Sustainable Technology and Management (ICSTM 2022); 1229911 (2022) https://doi.org/10.1117/12.2646629
Event: International Conference on Sustainable Technology and Management (ICSTM 2022), 2022, Macao, China

Abstract

In the development of information technology innovation, in order to further promote green building landscape effect of space environment design, scientific research and academic way of analysis of image processing to extract the internal environmental characteristics, and based on visual information processing technology to build the corresponding model structure diagrams, such not only can quickly identify the artistic features of the space environment, can also improve the design of the image. In this paper, we study the clear objectives on the basis of optimization theory and genetic algorithm, according to the image of green landscape space environment design, using the spatial characteristics of block area and matching module distributed test, combined with similarity matching, information fusion model of information fusion and module to obtain fuzzy characteristics of the space environment design image, Finally, the quality is evaluated by interactive genetic algorithm. The experimental results show that the optimization design effect of green building landscape space environment based on genetic algorithm is better, and the accuracy of image recognition is higher, so it has a positive impact on the innovation of modern urban architecture.

Citation Download Citation

Chuifeng Dong "Environment optimization design of green building landscape space based on genetic algorithm", Proc. SPIE 12299, International Conference on Sustainable Technology and Management (ICSTM 2022), 1229911 (14 November 2022); https://doi.org/10.1117/12.2646629

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KEYWORDS

Genetic algorithms

Optimization (mathematics)

Fusion energy

Cements

Image fusion

Lithium

Foam

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