Implementation of Deep Learning in Environmental Design Automation

In view of the problem that the generation scheme cannot accurately meet the safety, functional and aesthetic requirements due to the fragmentation of cross-modal data and insufficient physical constraints in the current environmental design automation, this paper constructs an intelligent design framework that integrates the diffusion model and multi-modal (text, image, architectural parameter) data. Its progressive generation characteristics can effectively integrate multi-modal data and realize efficient and controllable automatic generation of 2D/3D design schemes through physical simulation constraints. A cross-modal encoder is designed to uniformly map text descriptions, image inputs and architectural parameters to a high-dimensional feature space, and dynamically adjust the weights of each modal data through the attention mechanism to ensure the synergy of text semantics, image details and parameter constraints in the generation process. A generation network based on a diffusion model is constructed. By gradually adding noise and denoising, design solutions are generated progressively from low resolution to high resolution. Multimodal features are introduced as conditional inputs at each stage to ensure the consistency of the generation results in details and overall structure. A physical simulator (structural mechanics simulation, material performance analysis) is embedded in the generation process to evaluate and optimize the generation solutions in real time. The parameters of the generative network are adjusted through gradient back propagation to ensure that the design scheme meets the requirements of safety, functionality and feasibility. Experimental results show that the FID value of the multimodal diffusion model is 28 ± 2 and the SSIM value is 0.93 ± 0.01, which has obvious advantages in terms of the authenticity of the generated scheme, image quality and detail consistency. The comprehensive physical feasibility score of the generative model after embedding physical constraints is 86.4 ± 2.8, which has certain structural stability and practical feasibility.