School of Landscape Architecture, Nanjing Forestry University, Nanjing 210037, China.
School of Architecture and Civil Engineering, Xiamen University, Xiamen 361005, China.
Comput Intell Neurosci. 2022 Aug 21;2022:7274525. doi: 10.1155/2022/7274525. eCollection 2022.
With the gradual improvement of material living standards, people have higher and higher requirements for the livability of modern cities. As an important component of urban construction, the optimal layout of street public space has gradually received more and more attention. In the development stage of the new era, it is very important to improve the image of the city by transforming the street construction, optimizing the urban public space, and building a place full of vitality. Implementing the people-oriented connotation and improving the green travel components in the city, such as encouraging walking and increasing bicycles, are of great significance for optimizing the street public space. This article studies the relevant content of the optimization design of street public space layout based on the Internet of Things and deep learning and expounds the solutions for the optimization design of street public space layout based on the Internet of Things and deep learning. Design research provides cutting-edge scientific theories and evidence. This paper uses data to prove that based on the Internet of Things and deep learning technology, the optimized design of street public space layout has increased the latter's recognition among residents by an average of 21.7%. The designed model has both space utilization and environmental protection. Very good results have been obtained.
随着物质生活水平的逐步提高，人们对现代城市的宜居性提出了更高的要求。街道公共空间作为城市建设的重要组成部分，其优化布局逐渐受到越来越多的关注。在新时代的发展阶段，通过改造街道建设、优化城市公共空间、打造充满活力的场所来提升城市形象非常重要。实施以人为本的内涵，增加城市绿色出行的组成部分，如鼓励步行和增加自行车，对于优化街道公共空间具有重要意义。本文基于物联网和深度学习研究街道公共空间布局优化设计的相关内容，并阐述基于物联网和深度学习的街道公共空间布局优化设计方案。为设计研究提供前沿的科学理论和依据。本文通过数据证明，基于物联网和深度学习技术，街道公共空间布局的优化设计使后者在居民中的认知度平均提高了 21.7%。设计的模型在空间利用和环境保护方面都取得了非常好的效果。
