Optimization of low-energy building interior design based on machine vision: Environmental thermal energy conduction and layout design

This article provides an overview of existing research on low-energy buildings and interior design, pointing out the shortcomings of traditional design methods in terms of thermal comfort and energy consumption. Applications involving machine vision extract user behavior and demand data, and perform filtering processing to accurately analyze the impact of user distribution. To evaluate the thermal comfort of low-energy buildings, this study proposes a complete set of thermal comfort evaluation indicators and solves the indoor heat conduction to ensure that design optimization can effectively improve the thermal comfort of residents. By defining the relationship between thermal comfort indicators and environmental parameters, this study achieved quantitative measurement of indoor thermal comfort. In terms of design optimization strategies, a control strategy combining machine vision was proposed to implement inverse optimization design, ensuring that design decisions can accurately adapt to the laws of thermal energy conduction and user needs. When analyzing the optimization effect, the results showed that the optimized building design significantly improved indoor thermal comfort and effectively reduced energy consumption.