CN-122015241-A - Intelligent optimal regulation and control method and system for thermal environment of living space of multiple people

CN122015241ACN 122015241 ACN122015241 ACN 122015241ACN-122015241-A

Abstract

The invention belongs to the technical field of air conditioning temperature control and intelligent perception, and particularly discloses an intelligent optimal control method and system for thermal environment of a multi-living space, which comprises the steps of obtaining basic metabolic rates of all persons in a living space, dividing all persons into different metabolic categories, and further determining a thermal acceptable interval corresponding to each person; and identifying target individuals with thermal comfort demands deviating from the group unified regulation and control interval, generating personalized compensation instructions for the target individuals, and completing intelligent optimal regulation and control of the thermal environment of the living space of multiple people. The invention solves the problem of differentiation of thermal comfort requirements caused by individual physiological and characteristic differences in a multi-person space.

Inventors

ZHENG WUXING
LIU LU
ZHANG JIAYING
WANG YINGLI
SHI HENG

Assignees

西北工业大学

Dates

Publication Date: 20260512
Application Date: 20260414

Claims (10)

1. The intelligent optimal regulation and control method for the thermal environment of the living space of multiple people is characterized by comprising the following steps: acquiring physiological characteristic data of all people in the living space, calculating basic metabolism rate of each person, and dividing all people into different metabolism categories according to the basic metabolism rate; Determining a thermal acceptable interval corresponding to each person based on the metabolism category and a preset thermal comfort model; Calculating intersections of the thermal acceptable intervals corresponding to all personnel, constructing a group unified regulation and control interval based on the intersections of the thermal acceptable intervals, and generating a control instruction according to the group unified regulation and control interval to control the operation of the environment regulation and control equipment; Acquiring face images of people in real time, and extracting the temperature of key points of the faces; based on the facial key point temperature, identifying target individuals with thermal comfort demands deviating from a uniform group regulation and control interval, generating personalized compensation instructions for the target individuals, and completing intelligent optimal regulation and control of the thermal environment of the living space of multiple people.
2. The intelligent optimal regulation method for the thermal environment of the living space of multiple people according to claim 1, wherein the basic metabolic rate BMR is calculated by adopting a Harris-Beneidick formula, and is specifically as follows: Male BMR= 88.362+ (13.397×weight) + (4.799×height) - (5.677 ×age) Female BMR= 447.593+ (9.247×body weight) + (3.098×height) - (4.330 ×age) The male metabolic categories are specifically: very low BMR <1500kcal, low BMR 1500-1800kcal, normal BMR 1800-2100kcal, high BMR 2100-2400kcal, very high BMR >2400kcal; the female metabolic categories are specifically: Very low BMR <1200 kcal, low BMR 1200-1500 kcal, normal BMR 1500-1800 kcal, high BMR 1800-2200 kcal, very high BMR >2200 kcal.
3. The intelligent optimal regulation and control method for the thermal environment of the living space of multiple people according to claim 1, wherein the preset thermal comfort model predefines ten thermal acceptable intervals of metabolic categories and thermal sensation TSVs; The thermal sensation TSV was quantified using a 7-level scale, specifically: heat tsv=3; Warm tsv=2; slightly warm tsv=1; neutral tsv=0; Slightly cooling TSV= -1; cool tsv= -2; cold tsv= -3.
4. The intelligent optimal regulation and control method for the thermal environment of the living space of multiple people according to claim 3, wherein the method for determining the thermal acceptable interval of each metabolism category is as follows: Calculating the operating temperature of each metabolic class : Wherein, the The temperature of the air is indicated as such, A coefficient representing the temperature of the air, The temperature of the radiation is indicated as such, The temperature of the black ball is indicated, Representing wind speed; carrying out BIN treatment on the operation temperature according to a preset temperature interval to obtain a treated temperature interval; Counting the heat non-acceptable rate in each temperature interval, and determining the heat acceptable interval of each metabolism category by taking the heat non-acceptable rate smaller than or equal to a preset threshold as a standard, wherein a fitting formula of the heat non-acceptable rate is as follows: Wherein, the Indicating an unacceptable rate of heat up, And The regression coefficient is represented as a function of the regression coefficient, Representing constant terms.
5. The intelligent optimal regulation method for thermal environment of living spaces of multiple people according to claim 1, wherein the facial key point temperature comprises forehead temperature, left inner canthus temperature, right inner canthus temperature, tip of nose temperature, left cheek temperature and right cheek temperature.
6. The intelligent optimal regulation and control method for the thermal environment of the living space of multiple people according to claim 5, wherein in summer, the personalized compensation instruction is an air speed regulation instruction for an intelligent fan, and specifically comprises: Starting a fan to aim at a target individual and running at the lowest gear; Monitoring facial key point temperature changes of a target individual at preset time intervals; If the temperature of the key point of the face of the target individual is not in the heat acceptable interval in a plurality of continuous monitoring periods, the speed gear of the fan is gradually increased until the real-time heat sensation state of the target individual returns to the comfortable range or the maximum wind speed of the fan is reached, and if the maximum wind speed is still not satisfied, the number of the fans is increased or the position of the fan is adjusted.
7. The intelligent optimal regulation and control method for the thermal environment of the living space of multiple people according to claim 5, wherein in winter, the personalized compensation instruction is a clothes thermal resistance regulation suggestion, specifically: establishing a comfortable garment fitting curve: Wherein, the The thermal resistance value of the clothing is represented, The regression coefficient is represented as a function of the regression coefficient, Representing constant terms, subscripts γ=0, 1,2,3,4,5,6, corresponding to tsv=0, 1,2,3, 3, 2, 1; Based on the facial key point temperature, a current operating temperature is calculated : Wherein, the The regression coefficient is represented as a function of the regression coefficient, The term of the constant is represented by a term, Representing facial key point temperatures; will present the operating temperature Substituting a comfortable clothing fitting curve corresponding to TSV=0, and calculating a clothing thermal resistance value corresponding to TSV=0; Judging the current operating temperature The temperature interval is located, and then the corresponding thermal sensation state is obtained; will present the operating temperature Substituting the fit curve of the comfortable clothing corresponding to the thermal sensation state obtained by judgment, and calculating the thermal resistance value of the clothing corresponding to the thermal sensation state obtained by judgment; and calculating a difference value between the clothing thermal resistance value corresponding to TSV=0 and the clothing thermal resistance value corresponding to the judged thermal sensation degree, generating a clothing increase and decrease suggestion according to the difference value, and performing voice broadcasting.
8. The intelligent optimal regulation method for thermal environment of multi-living space according to claim 7, wherein the current operation temperature is judged The specific method for obtaining the corresponding thermal sensation state in the temperature interval comprises the following steps: if the current operating temperature is Judging whether the operation temperature is greater than neutral temperature in a temperature interval corresponding to-1 < TSV <1, if so, setting the thermal sensation state as1, otherwise, setting the thermal sensation state as-1; if the current operating temperature is Judging whether the operation temperature is greater than neutral temperature in a temperature interval corresponding to-2 < TSV <2, if so, setting the thermal sensation state as 2, otherwise, setting the thermal sensation state as-2; if the current operating temperature is Judging whether the operation temperature is greater than neutral temperature in a temperature interval corresponding to-3 < TSV <3, if so, setting the thermal sensation state to be 3, otherwise, setting the thermal sensation state to be-3; the temperature interval corresponding to-1 < TSV <1, -2< TSV <2, and-3 < TSV <3 are determined by a linear regression equation of operating temperature and thermal sensation, which is: Wherein, the Indicating the sensation of heat and, The operating temperature is indicated as such, The regression coefficient is represented as a function of the regression coefficient, Representing constant terms.
9. An intelligent optimal regulation and control system for a thermal environment of a space occupied by multiple people is characterized by comprising: the first module is used for acquiring physiological characteristic data of all people in the living space, calculating the basic metabolism rate of each person, and dividing all the people into different metabolism categories according to the basic metabolism rate; the second module is used for determining a thermal acceptable interval corresponding to each person based on the metabolism category and a preset thermal comfort model; The third module is used for calculating the intersection of the heat acceptable intervals corresponding to all people, constructing a group unified regulation and control interval based on the intersection of the heat acceptable intervals, and generating a control instruction according to the group unified regulation and control interval to control the operation of the environment regulation and control equipment; a fourth module, configured to collect a facial image of a person in real time, and extract a temperature of a facial key point; And a fifth module, configured to identify, based on the facial key point temperature, a target individual whose thermal comfort requirement deviates from a uniform regulation and control interval of the group, and generate a personalized compensation instruction for the target individual, so as to complete intelligent optimal regulation and control of the thermal environment of the living space of multiple people.
10. A non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the method of any one of claims 1-8.

Description

Intelligent optimal regulation and control method and system for thermal environment of living space of multiple people Technical Field The invention belongs to the technical field of air conditioning temperature control and intelligent sensing, and particularly relates to an intelligent optimal conditioning method and system for thermal environments of living spaces of multiple people. Background In indoor spaces where multiple people co-live or live (e.g., home residences, student dormitories, senior citizens homes, office spaces, etc.), comfortable regulation of the thermal environment is one of the core demands for improving the living experience and the space use efficiency. However, different individuals naturally have significant differences in physiological and individual characteristics such as body type (body mass index BMI), gender, age, basal Metabolic Rate (BMR), etc., which directly lead to the individual exhibiting significant differentiation in subjective thermal responses to thermal environments (e.g., thermal sensation) and the need for comfort in the wind environment. Specifically, in the age group, the old people have a general higher thermal perception threshold and tend to warm hot environments due to the reduction of metabolism rate and the weakening of body temperature regulation capability, while the young people have vigorous basic metabolism, active body temperature regulation function and stronger tolerance to cool environments. Sex and body type differences also influence the thermal comfort requirement, basic metabolic rate calculation models of men and women are essentially different, larger body types often have more outstanding heat emission requirements due to higher body surface areas and total metabolic amounts, and thinner body types are more sensitive to low-temperature environments due to insufficient heat storage. In addition, dynamic factors such as daily activity, exercise intensity, etc. of an individual can further alter metabolic levels such that thermal comfort requirements exhibit dynamic characteristics over time. On one hand, the manual regulation mode depends on individual subjective feedback, obvious response hysteresis often exists, dynamic comfort requirements of a plurality of bodies in a group cannot be matched in time, on the other hand, the existing intelligent air conditioning system mostly adopts a single fixed temperature setting mode, the pertinence consideration of individual differences is lacking, only the comfort requirements of partial groups can be met, the thermal environment optimization at the group level is difficult to realize, and the phenomena of high energy consumption and large carbon emission of the air conditioning system are easily brought. Based on the temperature identification regulation scheme of the wearable equipment, although individual physiological parameters are collected to realize personalized regulation, the wearable equipment needs to be worn for a long time, is easy to bring invasiveness and wearing burden to a user, has insufficient suitability and user acceptance, and is difficult to meet the convenience requirement of daily living scenes. Meanwhile, the existing regulation and control technology has two key defects that firstly, hierarchical cooperative regulation and control logic between equipment such as an air conditioner and a fan is not established, the energy consumption is easy to be excessively high due to single-dependent air-conditioning temperature regulation or individual discomfort is aggravated due to local temperature unevenness, secondly, the regulation and control priority under the group environment is lost, a dynamic regulation and control mechanism of 'integrally considering both individual compensation' is lacked, and accurate fine adjustment can not be carried out on individuals with special requirements on the premise of guaranteeing the overall comfort of the group. In summary, in the current technical system, aiming at the living space of multiple people, how to cooperatively utilize the unified regulation and control of environmental equipment and the accurate compensation means of individuals, the balance of regulation and control efficiency and energy consumption optimization is realized while the overall thermal comfort and individual differentiation requirements of the group are considered, and the key technical problem which is not solved yet. The influence of individual characteristic differences on thermal comfort demands is not fully considered in the existing scheme, and a scientific thermal perception correlation model and systematic regulation and control logic are lacked, so that the accuracy of thermal environment regulation and control is insufficient, the user experience is poor, and the actual demands of multiple people on intelligent and personalized regulation and control of the thermal environment are difficult to meet. Disclosure of Invention The invention aims to s