Comparative Analysis: Cognitive Process of Residential Interior Space

Firstly, an optimization algorithm based on planar design constraints is proposed, which can automatically generate interior space furnishings. The main idea of the algorithm is to represent the room units in interior space furnishings with multiple rectangular combinations of polygons and to translate the constraints and rules in furnishing design into relevant rectangular constraints. In order to improve the applicability of the method, this study also proposes a hierarchical algorithm flow based on the above method. Then, the interactive toilet aid design and the new intelligent wheelchair design, and the intelligent and ecological interior furnishing design are used as examples to explore the practice of interactive furnishing design based on multi-functional needs, which meet the emotional, psychological, and physiological needs of users. Based on the data analysis of furnishing design by hierarchical algorithm, the results show that the average score of ordinary users in the comparison results with Humans is 13.37 the average score of designers is 11.72; the MIQP model can be used for interior space furnishing design of residential buildings and can be extended to apply to space furnishing of large-scale scenes, MIQP method is much higher than existing methods in terms of efficiency The MIQP method is much more efficient than existing methods. This study improves the quality and level of furnishing design and promotes innovation in the design industry.

Section: Introductionmentioning

confidence: 99%

A study of interactive furnishing design in the context of digitalization with a view to functional requirements

Zhu

2023

“…Furniture is the main body in indoor space. People's work, study, and life in indoor spaces are interpreted and developed with furniture as the main body [8][9]. So whether in the living space, workspace, or public space, we should put the supporting design of furniture in indoor space in the first place, and at the same time, furniture is the main thing that constitutes the style of indoor space environment design [10].…”

Section: Introductionmentioning

confidence: 99%

Optimization of furniture combination design and space configuration based on graph theory

Xie

2023

This paper is based on graph theory, combined with a particle swarm optimization algorithm to find the optimal solution in the whole particle swarm through continuous iteration and obtain the sum of squares of each variable in the particle cognitive domain. Searching for the global optimal position calculates the inertia weights in the particle swarm algorithm and changes the velocity and position of the particles based on the motion properties of graph theory to improve the global search ability. The domain competition operator is executed after particle swarm initialization, and a hybrid crossover strategy is used to select a crossover point, which improves the algorithm’s performance by exchanging information with the global optimum. Based on graph theory, the spatial configuration optimization of furniture combinations is scored, in which the average score of furniture space ranges from 3.3 to 6.2, and the average score of spatial configuration optimization ranges from 4.5 to 8. Therefore, based on graph theory, the reasonable furniture form for spatial layout optimization can fully reflect the practical function of the space, comfort, and the creation of a spatial atmosphere.

Research on the Application of Computer Vision Technology and Optimization of Space Design in a Smart Home Environment

Peng,

Kang,

Zhang

et al. 2024

This study aims to explore the application of computer vision technology in smart home environments and its influence on space design optimization. Through the comprehensive application of image recognition and processing technology, the behavior patterns of family members, the placement status of household items, and the real-time changes in the indoor environment in smart home systems are deeply analyzed. The experimental results show that computer vision technology can effectively improve smart home systems’ perception and analysis ability, providing data support for automatically adjusting indoor environments and providing personalized service. At the same time, this study also focuses on the optimization strategy of space design based on computer vision technology and puts forward a series of humanized design schemes through reasonable arrangement of cameras and sensors, real-time analysis of user behavior data and environmental parameters, aiming at improving living comfort and achieving efficient use of energy. This study reveals the internal relationship between computer vision technology and space design, which complement each other and jointly promote the intelligent and humanized development of smart homes.