An AI-sensing method for non-invasive human thermal comfort

Abstract

The present invention discloses a non-invasive AI perception method for human body thermal comfort, including data collection and preprocessing, defining and extracting skin sensitivity index, constructing a deep learning network for non-invasive skin perception thermal comfort and training and generating a network model; specifically Computer vision is used to collect image data of human skin, and after data processing, an address mapping table between the image of the domain of interest and skin temperature is constructed; the difference in skin sensitivity of different human bodies to external cold and hot stimuli is introduced as a weight coefficient, and the address mapping table and SSI extracts features separately, further trains after fusion, saves and optimizes to obtain a network model for skin temperature prediction. Applying the AI ​​sensing method of the present invention overcomes the three challenges of micro-variation of skin changes, inter-individual variability and intra-individual time-varying in human thermal comfort detection, realizes energy optimization, energy saving and environmental protection, and has better operability sex.

Description

technical field [0001] The invention belongs to the fields of computer vision, machine learning and building physics, and in particular relates to a non-invasive human body thermal comfort detection method for intelligent buildings or intelligent automatic driving. Background technique [0002] According to the annual statistical report, 21% of the world's annual energy consumption comes from commercial and residential buildings. In some countries and regions with relatively rapid urbanization, energy consumption is increasing at a rate of 32% per year. In building energy consumption, 50% comes from heating, ventilation and air conditioning system (HVAC, hereinafter collectively referred to as central air conditioning system). If it is possible to detect the thermal comfort level of the human body in real time, adjust indoor parameters (temperature, humidity, airflow, etc.) in a targeted manner, or perform local heating / cooling, it can meet the needs of individual thermal co...

AI Technical Summary

Problems solved by technology

This method can reflect the user's thermal comfort feeling, but has obvious disadvantages

On the one hand, the temperature control has a strong hysteresis; on the other hand, it needs continuous intervention from the user, especially when resting at night, it cannot be controlled

For the heating system, currently users cannot know the specific temperature, and can only switch gears based on experience

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