An old person inhabits space thermal environment multi-parameter intelligent control method and system

Abstract

This invention belongs to the field of intelligent thermal environment control technology in buildings, specifically disclosing a multi-parameter intelligent control method and system for the thermal environment of elderly living spaces. The method includes: establishing a priority sequence for thermal environment parameter control; collecting infrared temperature data from multiple points on the user's body, calculating comprehensive facial temperature parameters, and then calculating the current real-time operating temperature; determining whether the current operating temperature is within a preset acceptable range; if so, no operation is performed; if not, based on a preset thermal environment parameter comfort range, neutral parameter value, and thermal environment parameter control priority sequence, the thermal environment parameters and their corresponding appliances are adjusted to ensure thermal comfort is within an acceptable range, thus completing multi-parameter intelligent control of the thermal environment in elderly living spaces. This invention solves the problems of existing technologies not being specifically designed for the adaptive thermal comfort characteristics of the elderly and the invasiveness of existing physiological parameter testing equipment.

Description

Technical Field

[0001] This invention belongs to the field of intelligent control technology for building thermal environment, specifically relating to a multi-parameter intelligent control method and system for the thermal environment of elderly living spaces. Background Technology

[0002] When residents try to control uncomfortable indoor thermal environments, they often rely on manual adjustments based on their individual subjective feelings of cold and heat. This often results in a lag in thermal response, leading to discomfort and high energy consumption. Older adults have lower basal metabolic rates, reduced skin blood flow regulation, and lower sensitivity to hot and cold stimuli. Their thermoregulation mechanisms are also slower, making manual adjustments to the thermal environment significantly delayed and increasing health risks. Furthermore, existing residential temperature control systems are mostly based on fixed temperature thresholds or sensory parameters of younger people, which fundamentally conflict with the thermal physiological characteristics of the elderly and fail to meet their individual needs. With the intelligent development of IoT technology, accelerating the development of age-friendly intelligent control technology for residential thermal environments is a crucial requirement for the future of smart elderly care.

[0003] Human physiological parameters such as skin temperature and heart rate are significantly related to thermal comfort, thus monitoring these parameters can be used to predict thermal comfort. However, more precise physiological parameter testing instruments are often used in laboratory research and are not suitable for real-life living environments. Existing methods for collecting physiological parameters in daily life mostly employ contact / semi-contact measurement methods, such as temperature guns, thermometers, or wearable physiological parameter sensors. These methods require frequent cooperation or contact from the user, are somewhat invasive, and can cause significant inconvenience and physical and psychological discomfort. Furthermore, most existing indoor intelligent thermal environment control methods rely on controlling a single temperature index to achieve residential comfort. However, the thermal environment of a living space is composed of parameters such as indoor air temperature, relative humidity, airflow velocity, and ambient radiation temperature, all of which collectively affect occupant comfort. While relying on a single control index can achieve indoor thermal comfort, it may cause discomfort related to humidity and drafts. Moreover, with the development of whole-house intelligence and the diversification of indoor thermal environment control facilities, multi-parameter coordinated control is more conducive to energy conservation and carbon reduction.

[0004] In summary, considering the adaptive thermal comfort characteristics of the elderly in terms of psychological adaptation, physiological adaptation and behavioral adjustment, and combined with the potential needs of the future smart elderly care field, there is an urgent need to develop a multi-parameter intelligent control method and system for the thermal environment of living spaces. Summary of the Invention

[0005] The purpose of this invention is to address the problems of existing technologies not being specifically designed for the adaptive thermal comfort characteristics of the elderly and the invasiveness of existing physiological parameter testing equipment, and to propose a multi-parameter intelligent control method and system for the thermal environment of the living space of the elderly.

[0006] The technical solution of the present invention is as follows: Firstly, a multi-parameter intelligent control method for the thermal environment of an elderly person's living space, comprising the following steps:

[0007] The system obtains the types of home appliances used by users, their power consumption levels, and their ranking of the appliances' ability to adjust thermal environment parameters, thereby establishing a priority sequence for thermal environment parameter control.

[0008] Infrared temperature data from multiple points on the user's face is collected, and comprehensive facial temperature parameters are calculated.

[0009] Calculate the current real-time operating temperature based on comprehensive facial temperature parameters;

[0010] Determine whether the current operating temperature is within the preset acceptable range. If yes, no operation is performed; otherwise, based on the preset thermal environment parameter comfort range, neutral parameter value, and thermal environment parameter control priority sequence, adjust the thermal environment parameters and their corresponding home appliances to ensure that the thermal comfort state is within the acceptable range, thus completing the multi-parameter intelligent control of the thermal environment in the elderly's living space.

[0011] As a preferred method, the priority sequence for thermal environment parameter regulation is determined based on the comprehensive regulation score of household appliances, and specifically includes the following steps:

[0012] Calculate the energy-saving weight of each household appliance :

[0013]

[0014] Calculate the scores of each household appliance for various thermal environment parameters. :

[0015]

[0016] in, This indicates the total number of household appliances. The order of home appliances' ability to adjust to thermal environment parameters is indicated by the subscript. Indicates thermal environment parameters;

[0017] Based on ratings Calculate the comfort weight of each home appliance :

[0018]

[0019]

[0020] in, Indicates the weight of each thermal environment parameter;

[0021] According to the energy-saving weight of each household appliance Comfort weight of each home appliance Calculate the overall control score of home appliances :

[0022]

[0023] In the formula, Assigning weights to users regarding energy conservation. Weighting home appliances for energy conservation Assigning weights to users based on their comfort level. Weighting of home appliance comfort:

[0024] The home appliances are sorted from highest to lowest based on their comprehensive control scores to obtain a home appliance control priority sequence. Then, the thermal comfort parameters of each home appliance with the highest scores are sorted from highest to lowest to obtain a thermal environment parameter control priority sequence.

[0025] As a preferred method, multiple points include the forehead, between the eyebrows, the tip of the nose, the left cheek, and the right cheek.

[0026] As a preferred option, the formula for calculating the comprehensive facial temperature parameters is:

[0027]

[0028] in, Forehead temperature, Temperature between the eyebrows Temperature of the tip of the nose. Temperature of the left cheek Temperature of the right cheek , , , and is the regression coefficient.

[0029] As a preferred option, the current real-time operating temperature The calculation formula is:

[0030]

[0031] in, Indicates thermal sensation TSV and overall facial temperature parameters The regression coefficients of the linear regression equation between them. Indicates thermal sensitivity TSV and operating temperature The regression coefficients of the linear regression equation between them. Indicates thermal sensation TSV and overall facial temperature parameters The constant term of the linear regression equation between them, Indicates thermal sensitivity TSV and operating temperature The constant term of the linear regression equation between them.

[0032] As a preferred option, the method for determining the comfort range and neutral parameter values ​​of the thermal environment is as follows:

[0033] Obtain objective thermal environment parameters and the user's subjective thermal experience; objective thermal environment parameters include air temperature. air velocity Black ball temperature And relative humidity RH, subjective thermal sensation includes thermal sensation TSV, wind sensation WSV and humidity sensation RSV;

[0034] Principal component analysis was used to process the objective thermal environment parameters and obtain the weights of each parameter.

[0035] Establish thermal sensing TSV and air temperature respectively Wind perception WSV and airflow velocity The regression equations of humidity sensation RSV and relative humidity RH are used to determine the corresponding thermal environment parameter comfort range based on the thresholds of thermal sensation TSV, wind sensation WSV, and humidity sensation RSV within the range of ±1. The neutral parameter values ​​are determined based on the thresholds of thermal sensation TSV, wind sensation WSV, and humidity sensation RSV being equal to zero.

[0036] As a preferred option, the thermal environment parameters and their corresponding household appliances are adjusted, specifically as follows:

[0037] Obtain the current objective thermal environment parameters, and determine whether each thermal environment parameter is within the corresponding comfort range according to the thermal environment parameter adjustment priority sequence;

[0038] For the first thermal environment parameter that is not within the comfort range Based on the current values ​​of other thermal environment parameters and the preset neutral temperature, calculate the thermal environment parameters. The target adjustment value;

[0039] If thermal environment parameters The target adjustment value is located in the thermal environment parameter Within the comfort range, parameters for the thermal environment are generated. If the control command is not given, the next thermal environment parameter will be determined. If all thermal environment parameters cannot be adjusted effectively by reverse calculation, the thermal environment parameter with the highest priority in the thermal environment parameter adjustment priority sequence will be directly set to its corresponding neutral parameter value.

[0040] Based on control commands or neutral parameter values, drive the corresponding home appliances to perform adjustment operations.

[0041] The beneficial effects of this invention are:

[0042] This invention uses facial infrared temperature feature recognition of the elderly to intelligently control multiple parameters of the indoor thermal environment, making up for the shortcomings of thermal comfort prediction models and adjustment methods based on the general population that are not specifically designed for the adaptive thermal comfort characteristics of the elderly. It avoids the invasiveness of existing physiological parameter testing equipment, incorporates multi-parameter collaborative control indicators of the thermal environment, and can provide a more comfortable and healthy living thermal environment for the elderly. In addition, by intelligently judging and selecting priority parameters for adjusting the thermal environment, it achieves the optimization of energy resource efficiency.

[0043] Secondly, a multi-parameter intelligent control system for the thermal environment of elderly living spaces includes:

[0044] The first module is used to obtain the types of home appliances used by the user, their power consumption levels, and the ranking of the home appliances' ability to adjust thermal environment parameters, thereby establishing a priority sequence for adjusting thermal environment parameters.

[0045] The second module is used to collect infrared temperature data from multiple points on the user's face and calculate comprehensive facial temperature parameters.

[0046] The third module is used to calculate the current operating temperature based on comprehensive facial temperature parameters;

[0047] The fourth module is used to determine whether the current operating temperature is within the preset acceptable range. If so, no operation is performed; otherwise, based on the preset thermal environment parameter comfort range, neutral parameter value, and thermal environment parameter control priority sequence, the thermal environment parameters and their corresponding home appliances are adjusted to ensure that the thermal comfort state is within the acceptable range, thus completing the multi-parameter intelligent control of the thermal environment in the elderly's living space.

[0048] Thirdly, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the method as described in the first aspect.

[0049] Fourthly, a non-transitory computer-readable storage medium is provided that stores computer instructions for causing a computer to perform the method as described in the first aspect. Attached Figure Description

[0050] Figure 1 The diagram shows a flowchart of a multi-parameter intelligent control method for the thermal environment of an elderly person's living space. Detailed Implementation

[0051] Exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be understood that the embodiments shown and described in the drawings are merely exemplary and are intended to illustrate the principles and spirit of the invention, and are not intended to limit the scope of the invention.

[0052] Example 1:

[0053] like Figure 1 As shown, a multi-parameter intelligent control method for the thermal environment of an elderly person's living space includes the following steps:

[0054] S1. Obtain the types of home appliances used by the user, their power consumption levels, and the ranking of the home appliances' ability to adjust thermal environment parameters, and then establish a priority sequence for adjusting thermal environment parameters;

[0055] In this embodiment, the elderly are divided into six groups according to age: 60-65, 65-70, 70-75, 75-80, 80-85, and 85+. A thermal comfort survey and facial temperature test are conducted on the elderly in different age groups to obtain the required formulas for representing comprehensive facial temperature for each age group, the weights of each thermal environment parameter, the comfort range and neutral parameters of each environmental parameter, the 90% acceptable operating temperature range, and the formula for the relationship between operating temperature and comprehensive facial temperature. The thermal comfort survey includes objective thermal environment parameters and subjective thermal sensations. Objective thermal environment parameters include air temperature. air velocity Black ball temperature And relative humidity RH, subjective thermal perception includes the subject's thermal sensation TSV, wind sensation WSV, and humidity sensation RSV. Among them, air temperature air velocity The relative humidity (RH) is the thermal environment parameter to be controlled.

[0056] The thermal sensation TSV scale includes: +3 very hot, +2 hot, +1 slightly warm, 0 moderate, -1 slightly cool, -2 cold, -3 very cold. The wind sensation WSV scale includes: +3 very strong, +2 strong, +1 somewhat strong, 0 moderate, -1 somewhat weak, -2 weak, -3 very weak. The humidity sensation RSV scale includes: +3 very dry, +2 dry, +1 somewhat dry, 0 moderate, -1 somewhat humid, -2 humid, -3 very humid.

[0057] Establish thermal perception TSV and forehead of elderly people in different age groups Between the eyebrows ,tip of the nose left cheek and right cheek Multiple linear regression model between:

[0058]

[0059] ( For constant terms, , , , and (regression coefficients);

[0060] Pick To take into account facial temperature parameters.

[0061] Principal component analysis was used to analyze air temperature. air velocity The analysis was performed using three variables: relative humidity (RH).

[0062] First, the indoor temperature is divided into several temperature intervals (Bin) with a 0.5℃ interval using the temperature frequency method. Then, the corresponding air temperature in each temperature interval is calculated. air velocity The average relative humidity (RH) forms a new index variable, which is represented by matrix X as follows:

[0063]

[0064] in, This indicates the number of temperature zones (Bin); to avoid the influence of different units on the analysis, the data in matrix X is standardized using the following formula:

[0065]

[0066]

[0067]

[0068] in, Represents the first element in matrix X. Air temperature data The standardized value, Represents the first element in matrix X. Airflow velocity data The standardized value, Represents the first element in matrix X. Relative humidity data The standardized value, This represents the average air temperature. This represents the average air velocity. This represents the average relative humidity. The standard deviation of air temperature The standard deviation of air velocity, The standard deviation of relative humidity.

[0069] Calculate the covariance among the variables:

[0070]

[0071] in, Representing variables and Covariance between , , , , express or or The average value;

[0072] The Cov matrix is ​​represented as:

[0073]

[0074] in, This represents the covariance among the variables;

[0075] Calculate the eigenvalues ​​of the matrix Cov:

[0076]

[0077] in, Represents a 3rd order identity matrix. This represents the eigenvalue to be solved, and the eigenvalue is obtained by solving. Take the maximum. The corresponding principal component Z1 is identified, and the eigenvectors are calculated. The formula for calculating the eigenvectors is as follows:

[0078]

[0079] in, , =1. Solving for 1, we get 1. , , Therefore, the principal component equation is:

[0080]

[0081] Using thermal perception volume (TSV) of elderly people in each age group as the dependent variable and principal component Z1 as the independent variable, a linear regression analysis was performed. If the model variance passed the F-test, it indicates that the expression has high reliability.

[0082]

[0083] in, For constant terms, is the regression coefficient; a matrix composed of the coefficient vectors of the principal component Z1 and the estimated value of the principal component regression coefficient are used to obtain the regression coefficient matrix, and finally the principal component regression equation expressed by the standardized independent variable is obtained:

[0084]

[0085] Let the weights of each thermal environment parameter be .

[0086] In addition, a linear regression equation between the thermal sensation TSV of the elderly in each age group and the air temperature is:

[0087]

[0088] Among them, represents the regression coefficient, represents the constant term;

[0089] The linear regression equation between the wind sensation WSV and the air velocity is as follows:

[0090]

[0091] Among them, represents the regression coefficient, represents the constant term;

[0092] The linear regression equation between the humidity sensation RSV and the relative humidity RH is as follows:

[0093]

[0094] Among them, represents the regression coefficient, represents the constant term;

[0095] Let -1 < TSV < 1, -1 < WSV < 1, -1 < RSV < 1 respectively, and obtain the 80% acceptable regions of the air temperature , the air velocity and the relative humidity RH, which are determined as the comfortable temperature region Com (t a下限 ~t a上限 ), the comfortable air velocity region Com va (v a下限 ~v a上限 ) and the comfortable humidity region Com RH (RH 下限 ~RH 上限 ). Let TSV = 0, WSV = 0, RSV = 0, and obtain the neutral temperature t a0 , v a0and RH0. And calculate the operating temperature :

[0096]

[0097]

[0098] where represents the coefficient, and its value is shown in Table 1, represents the radiant temperature, represents the globe temperature;

[0099] Table 1 Coefficient A values

[0100]

[0101] Establish the linear regression equation between the thermal sensation TSV of the elderly in each age group and the operating temperature :

[0102]

[0103] where represents the regression coefficient, represents the constant term;

[0104] Let -0.5 < TSV < 0.5, and obtain the 90% acceptable range of the operating temperature for each age group. Let TSV = 0, and obtain the neutral temperature of the operating temperature for each age group .

[0105] Establish the linear regression equation between the thermal sensation TSV of the elderly in each age group and the comprehensive facial temperature parameter :

[0106]

[0107] where represents the regression coefficient, represents the constant term;

[0108] According to the conversion of the above two-step equations, establish the correlation equation between the comprehensive facial temperature parameter of the elderly in each age group and the operating temperature :

[0109] .

[0110] Input the types of household appliances used by the user, the power consumption level, and the ranking of the household appliances' ability to adjust the thermal environment parameters, and assign the user's allocation weights for energy conservation and comfort.

[0111] Calculate the comprehensive regulation score of the household appliances :

[0112]

[0113] In the formula, Assigning weights to users regarding energy conservation. Weighting home appliances for energy conservation Assigning weights to users based on their comfort level. Weighting of home appliance comfort.

[0114] Based on this, the home appliances are sorted to obtain the sequence: Home Appliance 1, Home Appliance 2, Home Appliance 3... For each home appliance, the thermal comfort parameter with the highest score is selected to obtain the sequence: Parameter 1, Parameter 2, Parameter 3...

[0115] S2. Collect infrared temperature data from multiple points on the user's face and calculate comprehensive facial temperature parameters:

[0116]

[0117] in, Forehead temperature, Temperature between the eyebrows Temperature of the tip of the nose. Temperature of the left cheek Temperature of the right cheek , , , and is the regression coefficient.

[0118] S3. Calculate the current real-time operating temperature based on comprehensive facial temperature parameters. :

[0119]

[0120] in, Indicates thermal sensation TSV and overall facial temperature parameters The regression coefficients of the linear regression equation between them. Indicates thermal sensitivity TSV and operating temperature The regression coefficients of the linear regression equation between them. Indicates thermal sensation TSV and overall facial temperature parameters The constant term of the linear regression equation between them. Indicates thermal sensitivity TSV and operating temperature The constant term of the linear regression equation between them.

[0121] S4. Determine whether the current operating temperature is within the preset acceptable range. If yes, do not perform the operation; otherwise, adjust the thermal environment parameters and their corresponding home appliances based on the preset thermal environment parameter comfort range, neutral parameter value, and thermal environment parameter control priority sequence to make the thermal comfort state within the acceptable range, thus completing the multi-parameter intelligent control of the thermal environment in the elderly living space.

[0122] Based on pre-established comfort ranges and neutral parameters of thermal environment parameters corresponding to user age, as well as a priority sequence for thermal environment parameter adjustment, the thermal environment parameters and their corresponding home appliances are adjusted, specifically as follows:

[0123] Test the current indoor thermal environment parameters, and then make the following judgments:

[0124] The measured thermal environment parameter i is compared with the corresponding comfort zone Com. i (Comfort Temperature Zone) t (t) a下限 ~t a上限 Comfortable airflow zone Com va (v) a下限 ~v a上限 ) and comfort humidity zone Com RH (RH) 下限 ~RH 上限 If the temperature is within the comfort zone, increment i by 1 and repeat the comparison. If the temperature is outside the comfort zone, substitute the measured thermal environment parameters into the operating temperature. The calculation formula is based on t. op0 Calculate the digital adjustment ad that should be applied to thermal environment parameter i. i . will ad i Comfort zone corresponding to thermal environment parameters Com i The comparison is performed. If the appliance is within the comfort zone, the system outputs the required control to the controller, which then adjusts the appliance and proceeds to step four. If the appliance is outside the comfort zone, i+1 is incremented, and the judgment is repeated.

[0125] If i > n, then directly adjust appliance 1 to adjust parameter 1 to the corresponding neutral number (t). a0 v a0 The gear position or RH0).

[0126] (If the parameter is relative humidity (RH), the judgment becomes: compare the measured relative humidity with the corresponding comfort zone (Com).) RH The comparison is performed. If the result is within the comfort zone, i is incremented by 1, and the judgment is repeated. If the result is outside the comfort zone, the system outputs the required control to the controller, and proceeds to step four.

[0127] After a preset time interval, return to step S2 and repeat the process.

[0128] This invention uses facial infrared temperature feature recognition of the elderly to intelligently control multiple parameters of the indoor thermal environment, making up for the shortcomings of thermal comfort prediction models and adjustment methods based on the general population that are not specifically designed for the adaptive thermal comfort characteristics of the elderly. It avoids the invasiveness of existing physiological parameter testing equipment, incorporates multi-parameter collaborative control indicators of the thermal environment, and can provide a more comfortable and healthy living thermal environment for the elderly. In addition, by intelligently judging and selecting priority parameters for adjusting the thermal environment, it also optimizes energy resource efficiency.

[0129] Example 2:

[0130] Based on Example 1, this embodiment of the invention provides a detailed description of the multi-parameter intelligent control method for the thermal environment of the living space for the elderly proposed in this invention.

[0131] User age 72, γ 节能 =0.6, γ 舒适 =0.4. Use relevant data and formulas for the 70-80 age group. θ ta =0.5, θ RH =0.3, θ va =0.2. t s =0.3t s1 +0.2t s2 -0.05t s3 +0.25t s4 +0.25t s5 Comfort temperature zone Com t 26℃~32℃, neutral operating temperature t op0 29℃, neutral temperature t a0 29℃; Comfortable humidity zone (Com) RH 30%~40%, neutral humidity RH 0:35%; comfortable wind speed Com va : 0m / s~0.3m / s. t op =0.35*t s / 0.3.

[0132] The household appliances include: air conditioner, humidifier, and fan. Their energy efficiency ratings are Level 3, Level 2, and Level 1. The air conditioner's ratings are: temperature 5 points, humidity 3 points, airflow 2 points; the humidifier's ratings are: temperature 0 points, humidity 5 points, airflow 1 point; the fan's ratings are: temperature 1 point, humidity 0 points, airflow 5 points.

[0133] Therefore, the comfort level of the air conditioner = 5*0.5 + 3*0.3 + 2*0.2 = 3.8, the comfort level of the humidifier = 0*0.5 + 5*0.3 + 1*0.2 = 1.7, and the comfort level of the fan = 1*0.5 + 0*0.3 + 5*0.2 = 1.2. E 空调=(3.8-1.2) / (3.8-1.2)=1, E 加湿器 =(1.7-1.2) / (3.8-1.2)≈0.19, E 风扇 =(1.2-1.2) / (3.8-1.2)=0.

[0134] The energy efficiency of the air conditioner is 6 - 3 = 3, the energy efficiency of the humidifier is 6 - 2 = 4, and the energy efficiency of the fan is 6 - 1 = 5. C 空调 =(3-3) / (5-3)=0,C 加湿器 =(4-3) / (5-3)=0.5, C 风扇 =(5-3) / (5-3)=1.

[0135] P 空调 =γ 节能 *C 空调 +γ 舒适 *E 空调 =0.6*0+0.4*1=0.4, P 加湿器 =γ 节能 *C 加湿器 +γ 舒适 *E 加湿器 =0.6*0.5+0.4*0.19=0.376, P 风扇 =γ 节能 *C 风扇 +γ 舒适 *E 风扇 =0.6*1 + 0.4*0 = 0.6

[0136] P 风扇 >P 加湿器 >P 空调 Therefore, the furniture sequence is fan, humidifier, air conditioner, and the parameter sequence is air velocity, humidity, and temperature.

[0137] At a certain moment, the temperature of the user's forehead was measured. s1 =27℃, glabella t s2 =29℃, tip of nose t s3 =27℃, left cheek t s4 =27℃ and right cheek t s5 =27℃.

[0138] t s =0.3t s1 +0.2t s2 -0.05t s3 +0.25t s4 +0.25t s5 =0.3*27+0.2*29-0.05*27+0.25*27+0.25*27=26.05℃

[0139] t oprt =0.35*t s / 0.3=0.35*26.05 / 0.3≈30.39℃

[0140] 26℃<t oprt <32℃, so no operation is performed.

[0141] At a certain moment, the temperature of the user's forehead was measured. s1 =28℃, glabella t s2 =30℃, tip of nose t s3 =28, Left cheek t s4 =30℃ and right cheek t s5 =30℃.

[0142] t s =0.3t s1 +0.2t s2 -0.05t s3 +0.25t s4 +0.25t s5 =0.3*28+0.2*30-0.05*28+0.25*30+0.25*30=28℃

[0143] t oprt =0.35*t s / 0.3=0.35*28 / 0.3≈32.67℃

[0144] t oprt >32℃, start adjusting.

[0145] The instrument measured that no appliances were currently on, and the indoor temperature was t. a =29℃, black ball temperature t g =29.5℃, relative humidity RH=25.9%, air velocity v a =0 m / s. Let t op0 =29℃, substituting into the formula t op =A*t a +(1-A)*t r The calculated parameter 1 is the air velocity ad. va ≈0.17m / s.

[0146] 0 < ad va Since the value is less than 0.3, adjust the fan to the lowest setting (level 1).

[0147] After a fixed period of time, the test result on the user's forehead was measured again. s1 =28℃, glabella t s2 =29℃, tip of nose t s3 =27℃, left cheek t s4=28℃ and right cheek t s5 =28℃.

[0148] t s =0.3t s1 +0.2t s2 -0.05t s3 +0.25t s4 +0.25t s5 =0.3*28+0.2*29-0.05*27+0.25*28+0.25*28=26.85℃

[0149] t oprt =0.35*t s / 0.3=0.35*26.85 / 0.3=31.325℃

[0150] 26℃<t oprt <32℃, so no operation is performed.

[0151] Example 3:

[0152] Based on Example 1, this embodiment of the invention provides a multi-parameter intelligent control system for the thermal environment of elderly living spaces, which can be used to implement the multi-parameter intelligent control method for the thermal environment of elderly living spaces as described in the foregoing embodiments. The system includes:

[0153] The first module is used to obtain the types of home appliances used by the user, their power consumption levels, and the ranking of the home appliances' ability to adjust thermal environment parameters, thereby establishing a priority sequence for adjusting thermal environment parameters.

[0154] The second module is used to collect infrared temperature data from multiple points on the user's face and calculate comprehensive facial temperature parameters.

[0155] The third module is used to calculate the current real-time operating temperature based on comprehensive facial temperature parameters;

[0156] The fourth module is used to determine whether the current operating temperature is within the preset acceptable range. If so, no operation is performed; otherwise, based on the preset thermal environment parameter comfort range, neutral parameter value, and thermal environment parameter control priority sequence, the thermal environment parameters and their corresponding home appliances are adjusted to ensure that the thermal comfort state is within the acceptable range, thus completing the multi-parameter intelligent control of the thermal environment in the elderly's living space.

[0157] According to embodiments of the present invention, the present invention also provides an electronic device, a readable storage medium, and a computer program product.

[0158] In an exemplary embodiment, the electronic device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the multi-parameter intelligent control method for the thermal environment of the elderly living space as described in Embodiment 1 above.

[0159] In an exemplary embodiment, the readable storage medium may be a non-transitory computer-readable storage medium storing computer instructions for causing a computer to execute the multi-parameter intelligent control method for the thermal environment of the elderly living space according to Embodiment 1 above.

[0160] In an exemplary embodiment, the computer program product includes a computer program that, when executed by a processor, implements the multi-parameter intelligent control method for the thermal environment of the elderly living space as described in Embodiment 1 above.

[0161] The program code used to implement the methods of the present invention can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code can be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0162] In the context of this invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0163] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0164] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with embodiments of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0165] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0166] Those skilled in the art will recognize that the embodiments described herein are intended to help the reader understand the principles of the invention, and should be understood that the scope of protection of the invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific modifications and combinations based on the technical teachings disclosed in this invention without departing from the spirit of the invention, and these modifications and combinations are still within the scope of protection of this invention.

Claims

1. A multi-parameter intelligent control method for the thermal environment of elderly living spaces, characterized in that, Includes the following steps: The system obtains the types of home appliances used by users, their power consumption levels, and their ranking of the appliances' ability to adjust thermal environment parameters, thereby establishing a priority sequence for thermal environment parameter control. Infrared temperature data from multiple points on the user's face is collected, and comprehensive facial temperature parameters are calculated. Calculate the current real-time operating temperature based on comprehensive facial temperature parameters; Determine whether the current operating temperature is within the preset acceptable range. If yes, no operation is performed; otherwise, adjust the thermal environment parameters and their corresponding home appliances based on the preset thermal environment parameter comfort range, neutral parameter value, and thermal environment parameter control priority sequence to ensure that the thermal comfort state is within the acceptable range, thus completing the multi-parameter intelligent control of the thermal environment in the elderly living space. The method for adjusting thermal environment parameters and their corresponding home appliances based on preset comfort ranges, neutral parameter values, and thermal environment parameter control priority sequences is as follows: Obtain the current objective thermal environment parameters, and determine whether each thermal environment parameter is within the corresponding comfort range according to the thermal environment parameter adjustment priority sequence; For the first thermal environment parameter that is not within the comfort range Based on the current values ​​of other thermal environment parameters and the preset neutral temperature, calculate the thermal environment parameters. The target adjustment value; If thermal environment parameters The target adjustment value is located in the thermal environment parameter Within the comfort range, parameters for the thermal environment are generated. If the control command is not given, the next thermal environment parameter will be determined. If all thermal environment parameters cannot be adjusted effectively by reverse calculation, the thermal environment parameter with the highest priority in the thermal environment parameter adjustment priority sequence will be directly set to its corresponding neutral parameter value. Based on control commands or neutral parameter values, drive the corresponding home appliances to perform adjustment operations.

2. The multi-parameter intelligent control method for the thermal environment of elderly living spaces according to claim 1, characterized in that, The priority sequence for thermal environment parameter regulation is determined based on the comprehensive regulation score of household appliances, and includes the following steps: Calculate the energy-saving weight of each household appliance : Calculate the scores of each household appliance for various thermal environment parameters. : in, This indicates the total number of household appliances. The order of home appliances' ability to adjust to thermal environment parameters is indicated by the subscript. Indicates thermal environment parameters; Based on ratings Calculate the comfort weight of each home appliance : in, Indicates the weight of each thermal environment parameter; According to the energy-saving weight of each household appliance Comfort weight of each home appliance Calculate the overall control score of home appliances : In the formula, Assigning weights to users regarding energy conservation. Weighting home appliances for energy conservation Assigning weights to users based on their comfort level. Weighting of home appliance comfort: The home appliances are sorted from highest to lowest based on their comprehensive control scores to obtain a home appliance control priority sequence. Then, the thermal comfort parameters of each home appliance with the highest scores are sorted from highest to lowest to obtain a thermal environment parameter control priority sequence.

3. The multi-parameter intelligent control method for the thermal environment of elderly living spaces according to claim 1, characterized in that, Multiple points include the forehead, between the eyebrows, the tip of the nose, the left cheek, and the right cheek.

4. The multi-parameter intelligent control method for the thermal environment of elderly living spaces according to claim 3, characterized in that, The formula for calculating comprehensive facial temperature parameters is as follows: in, Forehead temperature, Temperature between the eyebrows Temperature of the tip of the nose. Temperature of the left cheek Temperature of the right cheek , , , and is the regression coefficient.

5. The multi-parameter intelligent control method for the thermal environment of elderly living spaces according to claim 4, characterized in that, Current real-time operating temperature The calculation formula is: in, Indicates thermal sensation TSV and overall facial temperature parameters The regression coefficients of the linear regression equation between them. Indicates thermal sensitivity TSV and operating temperature The regression coefficients of the linear regression equation between them. Indicates thermal sensation TSV and overall facial temperature parameters The constant term of the linear regression equation between them. Indicates thermal sensitivity TSV and operating temperature The constant term of the linear regression equation between them.

6. The multi-parameter intelligent control method for the thermal environment of elderly living spaces according to claim 1, characterized in that, The specific methods for determining the comfort range and neutral parameter values ​​of the thermal environment are as follows: Obtain objective thermal environment parameters and the user's subjective thermal experience; objective thermal environment parameters include air temperature. air velocity Black ball temperature And relative humidity RH, subjective thermal sensation includes thermal sensation TSV, wind sensation WSV and humidity sensation RSV; Principal component analysis was used to process the objective thermal environment parameters and obtain the weights of each parameter. Establish thermal sensing TSV and air temperature respectively Wind perception WSV and airflow velocity The regression equations of humidity sensation RSV and relative humidity RH are used to determine the corresponding thermal environment parameter comfort range based on the thresholds of thermal sensation TSV, wind sensation WSV, and humidity sensation RSV within the range of ±1. Neutral parameter values ​​are determined based on the thresholds of thermal sensation TSV, wind sensation WSV, and humidity sensation RSV being equal to zero.

7. A multi-parameter intelligent control system for the thermal environment of elderly living spaces based on the multi-parameter intelligent control method for the thermal environment of elderly living spaces according to any one of claims 1-6, characterized in that, include: The first module is used to obtain the types of home appliances used by the user, their power consumption levels, and the ranking of the home appliances' ability to adjust thermal environment parameters, thereby establishing a priority sequence for adjusting thermal environment parameters. The second module is used to collect infrared temperature data from multiple points on the user's face and calculate comprehensive facial temperature parameters. The third module is used to calculate the current real-time operating temperature based on comprehensive facial temperature parameters; The fourth module is used to determine whether the current operating temperature is within the preset acceptable range. If so, no operation is performed; otherwise, based on the preset thermal environment parameter comfort range, neutral parameter value, and thermal environment parameter control priority sequence, the thermal environment parameters and their corresponding home appliances are adjusted to make the thermal comfort state within the acceptable range, thus completing the multi-parameter intelligent control of the thermal environment in the elderly living space. The method for adjusting thermal environment parameters and their corresponding home appliances based on preset comfort ranges, neutral parameter values, and thermal environment parameter control priority sequences is as follows: Obtain the current objective thermal environment parameters, and determine whether each thermal environment parameter is within the corresponding comfort range according to the thermal environment parameter adjustment priority sequence; For the first thermal environment parameter that is not within the comfort range Based on the current values ​​of other thermal environment parameters and the preset neutral temperature, calculate the thermal environment parameters. The target adjustment value; If thermal environment parameters The target adjustment value is located in the thermal environment parameter Within the comfort range, parameters for the thermal environment are generated. If the control command is not given, the next thermal environment parameter will be determined. If all thermal environment parameters cannot be adjusted effectively by reverse calculation, the thermal environment parameter with the highest priority in the thermal environment parameter adjustment priority sequence will be directly set to its corresponding neutral parameter value. Based on control commands or neutral parameter values, drive the corresponding home appliances to perform adjustment operations.

8. An electronic device, characterized in that, include: At least one processor; And a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to cause the at least one processor to perform the method according to any one of claims 1-6.

9. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-6.

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