Abstract: In response to the problems of insufficient data management and low construction quality in architectural interior design, high-performance computing technology was proposed to be applied to optimize the overall effect of architectural interior design. This article proposed some solutions to the problems in interior design of buildings, and applied high-performance computing technology to building design materials, building operation data processing, etc. Furthermore, combined with multi-objective genetic algorithm, relevant experimental tests were conducted on the optimization of interior space environment design in buildings. The experimental results showed that under the algorithm proposed in this paper, the average health standard compliance of each scheme reached 92.17%, and the average emotional requirement satisfaction reached 88.42%. The average interface detail attention reaches 84.65%, and the average environmental comfort reaches 92.87%. Under the genetic algorithm, the average compliance of health standards for each scheme was 88.14%, and the average satisfaction of emotional needs was 84.17%. The average attention to interface details was 81.18%, and the average environmental comfort was 90.30%. From the above data, it can be seen that the algorithm proposed in this paper can achieve good optimization results in the design of indoor space environment in buildings.
Keywords: Big data, high performance computing, architectural interval design, space environment design
