Research on the Floor Plan Design Method of Exhibition Hall in CGAN-assisted Museum Architecture(Training set for machine learning)

This paper proposes a generative adversarial network (CGAN)-based method for designing a museum exhibition hall floor plan. In the study, the basic concepts and structure of CGAN are first introduced, and then the design and training process of the CGAN model used are described in detail, and the datasets and evaluation metrics adopted are briefly described. In the Results and Analysis section, this paper presents an example of the generated floor plan design of a museum exhibition hall and evaluates and analyzes the floor plan design of a museum exhibition hall gen-erated using the method proposed in this paper. Finally, the paper summarizes the advantages and disadvantages of the proposed method and looks forward to its future development. The research results show that: (1) The method proposed in this paper takes advantage of the CGAN model and can generate a museum exhibition hall floor plan design with certain regularity according to the given conditions rather than pure random generation. (2) This method can automatically generate a variety of floor plan designs of museum exhibition halls in different styles, providing designers with more choices and flexibility. (3) This method can carry out design optimization through hu-man-computer interaction, and iterative improvement can be carried out according to user needs and feedback, which improves the practicability of the design.

本研究提出了一种基于生成对抗网络（CGAN）的博物馆展览厅平面设计方案。首先，文章对CGAN的基本概念和结构进行了介绍，随后详尽地描述了所使用的CGAN模型的设计与训练过程，并对采用的 datasets 和 evaluation metrics 进行了简要概述。在结果与分析部分，本文展示了一个由该方法生成的博物馆展览厅平面设计方案实例，并对该方法生成的博物馆展览厅平面设计进行了评估与分析。最后，文章总结了所提方法的优缺点，并展望了其未来的发展方向。研究结果表明：（1）本文提出的方法充分利用了CGAN模型的优势，能够根据给定条件生成具有一定规律性的博物馆展览厅平面设计方案，而非纯粹随机生成。（2）该方法能够自动生成不同风格的博物馆展览厅平面设计方案，为设计师提供更多选择和灵活性。（3）该方法能够通过人机交互进行设计优化，并根据用户需求和反馈进行迭代改进，从而提升设计的实用性。