Quality Detection Method of Phase Change Energy Storage and Thermal Insulation Building Materials Based on Neural Network

Abstract

In order to improve the quality detection ability of thermal insulation building material, a phase change energy storage thermal insulation building material quality detection method based on neural network is put forward. The double broken line model is used to detect and evaluate the quality of phase change energy storage thermal insulation building material. Combined with the material stress characteristics and dissipation characteristics, the seismic evaluation of phase change energy storage thermal insulation building material is carried out, and the self-recovery energy dissipation support model of phase change energy storage thermal insulation building material is established. Under the constraint of quality parameter constraint model and neural network control model, the quality of phase change energy storage thermal insulation building materials is evaluated quantitatively by using network adaptive control method, and the stress distribution characteristic parameters of phase change energy storage thermal insulation building materials are calculated respectively to realize the quality detection of phase change energy storage thermal insulation building materials. The simulation results show that the accuracy of phase change energy storage and thermal insulation building material quality detection is high, the accuracy of parameter evaluation is good, and the quality detection ability is very good.