Application of computer vision in optimal design of assembled buildings

Abstract

The shape data of prefabricated building components are closely related to their safety and reliability. To solve the problem of shape energy saving optimization, a radial basis function neural network (RBF) model based on particle swarm optimization (PSO) considering temperature compensation is studied and designed, and BIM (Building Information Modeling Technology) is introduced as an auxiliary technology for effective management of visual information, which finally realizes the energy saving calculation of building shape dimensions. The results show that the maximum expansion deformation measured by the proposed model appears in the 28th minute, the maximum expansion deformation is 0.11 mm, the error between the model and the actual value is only 0.0 2mm, and the difference between the monitoring time point is only 3 min. The total energy consumption of this model is 36.92 kWh/m2, 42.15 kWh/m2, and 33.58 kWh/m2 less than that of the PSO model in three types of buildings. In terms of the total contribution rate of energy conservation, the former is 0.76%, 0.88%, and 2.94% higher than the latter respectively. Therefore, this research has effectively improved monocular machine vision technology. At the same time, the energy-saving model of shape with temperature compensation for innovative design has also been effectively verified.