Research on Evaluation Model of Mobile Residential Space Based on Edge Intelligence

With the rapid rise in the population, the demand for mobile residential space design has also increased accordingly. Residential spatial design has a complex design period, and requires higher labor. At present, the development of artificial intelligence and internet influences the architectural design of mobile residential space and work efficiency of designers in smart city applications. However, the usage of hand-crafted design features and error rate in residential space design is still high with traditional design techniques, which eventually leads to design inefficiency and reduced user satisfaction. The advent of edge-intelligence based on deep learning gave architectural design a new path. In this paper, we proposed Edge-based spatial adaptive graph Convolutional neural network (E-SAGCNN) approach for mobile residential space design prediction. Initially, data regarding Chinese residential building plan were collected. Residential space design features were automatically extracted by Recurrent Neural network-based Auto-Encoder (RNN-AE). The E-SAGCNN approach is trained over the residential space design features to generate the efficient mobile residential space design plan. The E-SAGCNN model is optimized by Butterfly optimization algorithm (BOA) to reduce the design error. The mobile residential space design plan generated using E-SAGCNN is stored in edge server for further applications. The proposed approach is compared with existing methods in residential space design planning. The results showed that the proposed model is effective in mobile residential space design compared to existing methods.