Indoor Space Centralized Design and User Demand Analysis: the Perspective of AI Technology
- DOI
- 10.2991/978-94-6463-453-2_26How to use a DOI?
- Keywords
- Intelligent Design; Indoor Space; User Needs; Artificial Intelligence; Machine Learning
- Abstract
With the rapid development of artificial intelligence technology, intelligent indoor space design has become a new hot spot in the design field. Based on the perspective of AI technology, this article explores how intelligent indoor space design can better meet user needs. Through the collection and analysis of user behavior data, machine learning algorithms are used to cluster user portraits and identify the space usage preferences and characteristics of different user groups. On this basis, a set of intelligent indoor space design framework is proposed, including demand mining, design generation, interaction optimization and other links. Finally, through case analysis, it is proved that this framework can significantly improve the user experience of space design. This research provides new ideas for intelligent interior design and is of great significance for promoting the intelligent process of architectural design.
- Copyright
- © 2024 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Aiwei Liu AU - Zhuying Ran PY - 2024 DA - 2024/07/26 TI - Indoor Space Centralized Design and User Demand Analysis: the Perspective of AI Technology BT - Proceedings of the 2024 International Conference on Urban Planning and Design (UPD 2024) PB - Atlantis Press SP - 337 EP - 346 SN - 2352-5401 UR - https://doi.org/10.2991/978-94-6463-453-2_26 DO - 10.2991/978-94-6463-453-2_26 ID - Liu2024 ER -