A Data-Driven Intelligent System for Assistive Design of Interior Environments

• Published in: Computational intelligence and neuroscience Vol. 2022; pp. 1 - 11
• Main Author: Chen, Guoxing
• Format: Journal Article
• Language: English
• Published: New York Hindawi 25.08.2022; John Wiley & Sons, Inc
• Subjects: Accuracy; Algorithms; Artificial intelligence; Big Data; Binary codes; Collaboration; Computer simulation; Deep learning; Design analysis; Embedding; Feature extraction; Indoor environments; Interior design; Layouts; Neural networks; Physiological effects; Physiological factors; Recommender systems; Segmentation; Segments; Sparsity; User behavior
• ISSN: 1687-5265; 1687-5273; 1687-5273
• DOI: 10.1155/2022/8409495

This paper analyses the design of a healthy interior environment using big data intelligence. The application of big data intelligence in the design of healthy interior environments is necessary because the traditional interior design approaches consume a lot of energy and other problems. Benefited by its strong ability of computation and analytics, artificial intelligence can well improve a series of problems in the field of interior design. The proposal summarizes the sources, classifications, and expressions of behavioral data in interior spaces, carries out analysis and research on behavioral data from two aspects: display space and supermarket space, summarizes the interior methods based on behavioral data, and analyses the visualization application of behavioral data in different interior scenes, to explore the application value of behavioral data in interior design. In contrast to it is the unconscious behavioral response, the biggest characteristic of which is that it is regulated by the behavioral subject’s physiological factors or habits of the behavior issuer. In this paper, we convert the layout recommendation problem of a space into a functional classification problem of segmented segments and household segments on a plane. The scene layout features are extracted by binary coding, the abstraction of the cross features between the vector segments is achieved by using a word embedding algorithm, the feature matrix is reduced in dimensionality, and finally, the segmentation network model and the layout network model are constructed, respectively, by using a bidirectional LSTM. The experiments show that the accuracy of the layout recommendation model in this paper is 98%, which can meet the demand for real-time online layouts.