基于多模式传感数据融合的城市开放空间的AI驱动的实时响应设计
1School of Design, East China Normal University, Shanghai, 200062, China. 15595768545@163.com.
Scientific reports
|November 21, 2025
概括
本研究介绍了一种基于人工智能的方法,用于城市开放空间的实时响应式设计. 它提高了空间利用率和行人流量,同时降低了智慧城市环境的成本.
科学领域:
- 城市规划和设计.
- 人工智能的人工智能
- 环境科学 环境科学
背景情况:
- 传统的静态设计方法对于动态的城市开放空间和不断变化的用户需求是不够的.
- 当代城市环境需要适应性解决方案来管理不断变化的条件并优化功能.
研究的目的:
- 为城市开放空间实时响应式设计提出人工智能驱动的方法.
- 通过使用多模式传感数据和自适应算法,实现城市开放空间的动态优化.
主要方法:
- 开发了一个层次化的数据融合架构,处理视觉,声学和环境传感器数据.
- 采用深度学习用于空间优化和强化学习用于自适应设计生成.
- 通过优化计算和智能缓存实现了小于100ms的响应时间.
主要成果:
- 空间利用效率上升了34.2%.
- 在行人流量优化方面实现了28.7%的改进.
- 与静态设计相比,运营成本降低了22.3%.
结论:
- 由人工智能驱动的方法学显著提高了城市开放空间的效率,流量和成本效益.
- 在现实世界的部署中验证了有效性,提高了用户满意度和环境质量.
- 建立了开发智能,适应性城市环境的原则.
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