Automatic Layout of Indoor Buildings Based on Computer Data

Indoor building layout design has always been a complex and time-consuming process. Traditional manual design makes it difficult to respond quickly to personalized needs and layout optimization problems in complex spaces. To address these issues, this paper proposes an automatic layout method for indoor buildings based on computer data analysis and deep learning technology. First, this paper analyzes a large amount of indoor space data to extract common layout patterns and design rules, and adopts convolutional neural networks (CNNs) to automatically identify spatial features and generate preliminary layout plans. Subsequently, a generative adversarial network (GAN) is used to further generate diversified layout plans that meet aesthetics and functionality to ensure that the layout meets design requirements and customer needs. Finally, in this paper, GA (Genetic Algorithm) and Simulated Annealing Algorithm (SAA) are combined to optimize and adjust the initially generated layout to improve the space utilization and layout rationality. Through simulation tests and actual case verification, the method in this paper has shown good adaptability and design efficiency in different application scenarios such as residential, office space and public buildings. In terms of layout rationality, the rationality of residential space has increased from 85% to 92%, while that of office space has increased from 88% to 95%. The average optimization time is between 2.7 and 2.8 s. This paper can provide technical support for designers and meet personalized needs, and provide a new research direction for the field of automated indoor design.