Multi-person living space thermal environment intelligent optimal regulation and control method and system

Abstract

The invention belongs to the technical field of air-conditioner temperature control and intelligent sensing, and particularly discloses an intelligent optimal control method and system for the thermal environment of a multi-person living space, and the method comprises the steps: obtaining the basal metabolic rate of all persons in the living space, dividing all persons into different metabolic categories, and determining a heat acceptable interval corresponding to each person; building a group unified regulation and control interval based on the intersection of the heat acceptable intervals corresponding to all the personnel, and generating a control instruction according to the group unified regulation and control interval to control the environment regulation and control equipment to operate; and target individuals whose thermal comfort demands deviate from the group unified regulation and control interval are identified, personalized compensation instructions are generated for the target individuals, and intelligent optimal regulation and control of the thermal environment of the multi-person living space are completed. According to the invention, the problem of thermal comfort demand differentiation caused by individual physiology and characteristic differences in a multi-person space is solved.

Description

Technical Field

[0001] This invention belongs to the field of air conditioning temperature control and intelligent sensing technology, specifically relating to an intelligent optimal control method and system for the thermal environment of a multi-person living space. Background Technology

[0002] In indoor spaces where multiple people live or engage in activities together (such as family homes, student dormitories, nursing homes, and offices), comfortable thermal environment control is one of the core requirements for improving the living experience and space utilization efficiency. However, different individuals have different body types (body mass index). BMI ), gender, age, basal metabolic rate ( BMR Significant differences naturally exist in physiological and individual characteristics, such as temperature and humidity. These differences directly lead to a clear differentiation in individuals' subjective thermal response (such as thermal sensation) to the thermal environment and their comfort needs in the wind environment.

[0003] Specifically, in terms of age, older adults generally have a higher heat perception threshold due to a slower metabolic rate and weakened thermoregulation, making them more tolerant of warmer environments. Younger people, on the other hand, have a higher basal metabolic rate and more active thermoregulation, making them more tolerant of cooler environments. Gender and body size differences also influence thermal comfort needs. The basal metabolic rate calculation models for men and women differ fundamentally. Larger individuals often have a higher body surface area and total metabolic rate, resulting in a greater need for heat dissipation; thinner individuals may have insufficient heat reserves, making them more sensitive to colder environments. Furthermore, dynamic factors such as individual daily activity levels and exercise intensity further alter metabolic levels, causing thermal comfort needs to exhibit dynamic changes over time.

[0004] Traditional indoor thermal environment control solutions are difficult to adapt to the differentiated needs of the aforementioned groups: on the one hand, manual adjustment relies on individual subjective feedback, which often has obvious response lag and cannot match the dynamic comfort needs of multiple individuals in the group in a timely manner; on the other hand, existing intelligent air conditioning systems mostly adopt a single fixed temperature setting mode, which lacks targeted consideration of individual differences and can only meet the comfort needs of some people. It is difficult to achieve thermal environment optimization at the group level, and it is also easy to lead to high energy consumption and large carbon emissions in the air conditioning system.

[0005] While some temperature control solutions relying on wearable devices attempt to achieve personalized adjustments by collecting individual physiological parameters, these devices require prolonged wear, which can be invasive and burdensome for users, resulting in insufficient adaptability and user acceptance, and failing to meet the convenience needs of daily living scenarios. Furthermore, existing control technologies suffer from two major drawbacks: First, they lack a hierarchical collaborative control logic between devices such as air conditioners and fans, making reliance solely on air conditioning temperature control prone to excessive energy consumption or exacerbating individual discomfort due to uneven local temperatures; second, they lack prioritization in group settings, lacking a dynamic control mechanism that balances overall comfort with individual compensation, and thus cannot precisely fine-tune adjustments for individuals with specific needs while ensuring overall group comfort.

[0006] In summary, within the current technological system, a key unresolved challenge remains how to collaboratively utilize unified environmental control equipment and precise individual compensation methods to achieve a balance between overall group thermal comfort and individual differences in needs, while simultaneously optimizing control efficiency and energy consumption for multi-person living spaces. Existing solutions fail to adequately consider the impact of individual differences on thermal comfort requirements, lack scientific thermal perception correlation models and systematic control logic, resulting in insufficient precision in thermal environment control, poor user experience, and an inability to meet the actual needs of multi-person spaces for intelligent and personalized thermal environment control. Summary of the Invention

[0007] The purpose of this invention is to solve the problem of differentiated thermal comfort needs in multi-person spaces due to individual physiological and characteristic differences. It proposes an intelligent optimal control method and system for the thermal environment of multi-person living spaces. Through the collaborative logic of "unified group control + precise individual compensation", combined with infrared thermal imaging recognition, individual feature modeling and multi-device collaborative control technology, the group-optimal and individual-adaptive thermal environment control is achieved, taking into account both comfort experience and energy consumption optimization.

[0008] The technical solution of the present invention is as follows: Firstly, a method for intelligent optimal control of the thermal environment in a multi-person living space, comprising the following steps: Acquire physiological characteristic data of all people in the living space, calculate the basal metabolic rate of each person, and classify all people into different metabolic categories based on their basal metabolic rate; The acceptable thermal range for each person is determined based on metabolic category and a pre-set thermal comfort model. Calculate the intersection of the thermally acceptable intervals for all personnel, construct a unified control interval for the group based on the intersection of the thermally acceptable intervals, and generate control commands to control the operation of environmental control equipment based on the unified control interval for the group. Real-time acquisition of facial images of people, extraction of temperature of key facial points; Based on facial key point temperature, the system identifies target individuals whose thermal comfort needs deviate from the group's unified control range, generates personalized compensation instructions for the target individuals, and completes intelligent optimal control of the thermal environment in multi-person living spaces.

[0009] The beneficial effects of this invention are: This invention achieves three core breakthroughs through the integration of multiple technologies and innovative control logic: First, based on infrared thermal imaging recognition and facial temperature modeling, it achieves accurate perception of individual thermal status without the need for wearable devices, adapting to the needs of daily living scenarios without intrusion. Second, it constructs a dynamic mechanism of "unified group control + precise individual compensation," ensuring overall thermal comfort in multi-person spaces while meeting individual needs through differentiated methods of fan and clothing thermal resistance adjustment. Third, it establishes a hierarchical collaborative logic between air conditioners and fans, optimizing set temperatures based on outdoor temperatures to improve comfort while reducing energy consumption, combining practicality and economy. Fourth, it establishes an intelligent clothing suggestion and broadcasting system, considering the needs of the elderly with disabilities or dementia, and people with hemiplegia or other cognitive or physical impairments, achieving more intelligent adjustment while meeting individual thermal comfort requirements.

[0010] As a preferred option, basal metabolic rate BMR The calculation is performed using the Harris-Benedict formula, specifically: male: BMR =88.362 + (13.397 × weight) + (4.799 × height) - (5.677 × age) female: BMR =447.593 + (9.247 × weight) + (3.098 × height) - (4.330 × age) The specific metabolic categories for men are: Extremely low BMR <1500kcal, low BMR 1500-1800kcal, normal BMR 1800-2100kcal, high BMR 2100-2400kcal, extremely high BMR >2400kcal; The specific metabolic categories for women are: Extremely low BMR <1200 kcal, low BMR 1200-1500 kcal, normal BMR 1500-1800 kcal, high BMR 1800-2200 kcal, extremely high BMR >2200 kcal.

[0011] As a preferred option, the preset thermal comfort model predefines the acceptable thermal ranges and thermal sensations for ten metabolic categories. TSV ; heat sensation TSV A 7-level scale is used for quantization, specifically: hot: TSV =3; warm: TSV =2; Slight warmth: TSV =1; neutral: TSV =0; Slightly cool: TSV =-1; cold: TSV =-2; cold: TSV =-3.

[0012] As a preferred method, the thermally acceptable range for each metabolic category is determined as follows: Calculate the operating temperature for each metabolic category. :

[0013]

[0014] in, Indicates air temperature. A coefficient representing air temperature. Indicates radiation temperature. Indicates the temperature of the black ball. Indicates wind speed; The operating temperature is adjusted according to the preset temperature interval. BIN The process yields the processed temperature range. The thermal unacceptability rate within each temperature range was statistically analyzed. Using a thermal unacceptability rate less than or equal to a preset threshold as the standard, the thermally acceptable range for each metabolic category was determined. The fitting formula for the thermal unacceptability rate is:

[0015] in, Indicates the rate of unacceptability of heat. and Represents the regression coefficient. This represents a constant term.

[0016] Preferably, the facial key point temperatures include forehead temperature, left inner canthus temperature, right inner canthus temperature, nose tip temperature, left cheek temperature, and right cheek temperature.

[0017] Preferably, during the summer, the personalized compensation command is a command to adjust the wind speed of the smart fan, specifically: Turn on the fan, aim it at the target individual, and run it at the lowest speed. Monitor the temperature changes of key facial points of the target individual at preset time intervals; If the temperature of key facial points of the target individual is not within the acceptable thermal range for several consecutive monitoring cycles, the fan speed will be gradually increased until the real-time thermal sensation of the target individual returns to a comfortable range or the maximum fan speed is reached; if the maximum fan speed is still not sufficient, the number of fans will be increased or the fan position will be adjusted.

[0018] As a preferred option, in winter, the personalized compensation instruction is a suggestion for adjusting the thermal resistance of clothing, specifically: Establish a fitting curve for comfortable clothing:

[0019] in, Indicates the thermal resistance value of clothing. Represents the regression coefficient. Indicates a constant term, subscript γ =0,1,2,3,4,5,6, respectively correspond to TSV =0,1,2,3, 3, 2, 1; Calculate the current operating temperature based on the temperature of key facial points. :

[0020] in, Represents the regression coefficient. Represents a constant term. Indicates the temperature of key facial points; Current operating temperature Substitution TSV The fitting curve for comfortable clothing corresponding to =0 was calculated. TSV =0 corresponds to the thermal resistance value of the clothing; Determine the current operating temperature The temperature range in which one is located determines the corresponding thermal sensation. Current operating temperature Substitute the fitting curve of the comfortable clothing corresponding to the thermal sensation state obtained from the judgment into the thermal resistance value of the clothing corresponding to the thermal sensation state obtained from the judgment. calculate TSV The difference between the thermal resistance value of the clothing corresponding to =0 and the thermal resistance value of the clothing corresponding to the judged degree of heat sensation is used to generate suggestions for adding or removing clothing and announce them by voice.

[0021] As a preferred option, determine the current operating temperature. The specific method for determining the corresponding thermal sensation state based on the temperature range is as follows: If the current operating temperature At -1< TSV Within the temperature range corresponding to <1, determine whether there is an operating temperature greater than the neutral temperature. If yes, set the thermal sensing state to 1; otherwise, set the thermal sensing state to -1. If the current operating temperature At -2< TSV Within the temperature range corresponding to <2, determine whether there is an operating temperature greater than the neutral temperature. If yes, set the thermal sensing state to 2; otherwise, set the thermal sensing state to -2. If the current operating temperature At -3< TSV Within the temperature range corresponding to <3, determine whether there is an operating temperature greater than the neutral temperature. If yes, set the thermal sensation state to 3; otherwise, set the thermal sensation state to -3. -1< TSV <1 corresponds to the temperature range, -2< TSV <2 corresponds to the temperature range and -3< TSV The temperature range corresponding to <3 is determined by the linear regression equation between operating temperature and thermal sensation. The linear regression equation between operating temperature and thermal sensation is:

[0022] in, Indicates the sensation of heat. Indicates the operating temperature. Represents the regression coefficient. This represents a constant term.

[0023] Secondly, an intelligent optimal control system for the thermal environment of a multi-person living space includes: The first module is used to acquire physiological characteristic data of all people in the living space, calculate the basal metabolic rate of each person, and classify all people into different metabolic categories based on the basal metabolic rate. The second module is used to determine the acceptable thermal range for each person based on metabolic category and a preset thermal comfort model. The third module is used to calculate the intersection of the thermally acceptable intervals for all personnel, construct a unified control interval for the group based on the intersection of the thermally acceptable intervals, and generate control commands to control the operation of environmental control equipment based on the unified control interval for the group. The fourth module is used to acquire facial images of people in real time and extract the temperature of key facial points; The fifth module is used to identify target individuals whose thermal comfort needs deviate from the unified control range of the group based on the temperature of key facial points, generate personalized compensation instructions for the target individuals, and complete the intelligent optimal control of the thermal environment in multi-person living spaces.

[0024] Thirdly, 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

[0025] Figure 1 The diagram shows a flowchart of an intelligent optimal control method for the thermal environment of a multi-person living space.

[0026] Figure 2 The diagram shows the basis for clothing adjustment.

[0027] Figure 3 The image shows individual B in Embodiment 2 of the present invention. TSV Clothing thermal resistance at =0 and TSV Schematic diagram of the thermal resistance relationship of clothing when =-3.

[0028] Figure 4 The image shows individual B in Embodiment 2 of the present invention. TSV Clothing thermal resistance at =0 and TSV Schematic diagram of the thermal resistance relationship of clothing when =3. Detailed Implementation

[0029] 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.

[0030] Example 1: like Figure 1 As shown, an intelligent optimal control method for the thermal environment of a multi-person living space includes the following steps: S1. Obtain physiological characteristic data of all people in the living space, calculate the basal metabolic rate of each person, and classify all people into different metabolic categories based on the basal metabolic rate; S2. Determine the acceptable thermal range for each person based on metabolic category and a preset thermal comfort model; S3. Calculate the intersection of the thermally acceptable intervals for all personnel, construct a unified control interval for the group based on the intersection of the thermally acceptable intervals, and generate control commands to control the operation of environmental control equipment based on the unified control interval for the group. S4. Real-time acquisition of facial images of personnel, extraction of temperature of key facial points; S5. Based on facial key point temperature, identify target individuals whose thermal comfort needs deviate from the unified control range of the group, generate personalized compensation instructions for the target individuals, and complete the intelligent optimal control of the thermal environment of multi-person living space.

[0031] In this embodiment, relevant data were collected from individuals of different ages, genders, heights, and weights at different times throughout the year, including subjective psychological thermal responses, thermal environment parameters, and human facial thermal infrared images. Specifically, a thermal comfort questionnaire and thermal environment parameter tests were conducted on each individual. Detailed information such as age, gender, height, and weight was collected for each individual. The thermal environment questionnaire was used to obtain individual thermal sensation and thermal acceptability range evaluations. Thermal sensation was quantified using a 7-point scale, with values ​​of 3 (hot), 2 (warm), 1 (slightly warm), 0 (neutral), -1 (slightly cool), -2 (cool), and -3 (cold). Thermal acceptability was assessed using a 4-point scale, with values ​​corresponding to "completely unacceptable" (-2), "just unacceptable" (-1), "just acceptable" (1), and "completely acceptable" (2). An infrared thermal imager was used to capture a complete facial infrared image of each individual. A thermal index meter (Delta HD32.3) was used to measure current objective thermal environment parameters, including air temperature, relative humidity, sphere temperature, and wind speed.

[0032] The relevant data are categorized, specifically based on basal metabolic rate (BMR). BMR The division label will be BMR The different levels are calculated according to the Harris-Benedict formula, that is: male: BMR =88.362 + (13.397 × weight (kg)) + (4.799 × height (cm)) - (5.677 × age (years)) female: BMR =447.593 + (9.247 × weight (kg)) + (3.098 × height (cm)) - (4.330 × age (years)) Based on the above formula, according to BMR The standard divides the collected data into 10 categories (5 for men and 5 for women, as shown in Table 1). In subsequent use, personalized presets will be made in advance on the mobile terminal, that is, basic information such as gender, height, weight and age will be entered as the basic information of a person indoors.

[0033] Table 1 is based on BMR Standard Classification

[0034] In this embodiment, the acceptable temperature range and neutral temperature for 10 population groups are calculated. Specifically, the operating temperature is calculated using the collected thermal environment parameters. :

[0035] Where A is a coefficient, and its values ​​are shown in Table 2; Air temperature; The formula for calculating radiation temperature is:

[0036] In the formula, Indicates the temperature of the black ball. Indicates wind speed.

[0037] Table 2. Values ​​of Coefficient A

[0038] For 10 groups of people, separate indoor operating temperatures were established. ) and thermal sensation ( TSV The relationship between )

[0039] in, Represents the regression coefficient. Represents a constant term, when TSV When =0, the neutral temperature for different population groups is obtained. And calculate -1< TSV Operating temperature <1 The range of intervals [ , ], and -2< TSV <2 operating temperature The interval ranges are denoted as [ , ].

[0040] The operating temperature is adjusted in 1℃ intervals. BIN The process involves summing the percentages of "completely unacceptable" and "just unacceptable" conditions within each temperature range, and then fitting this sum to the operating temperature to obtain the thermal unacceptability rate. TPD The thermal comfort zone is determined based on a TPD ≤ 10%. t α , t β ], where α=2n-1, β=2n (where n is a constant).

[0041] The formula for calculating the heat unacceptability rate is:

[0042] in, Represents the regression coefficient. Represents a constant term. This refers to the operating temperature.

[0043] Construct fitting curves for comfort clothing, and apply them to different comfort clothing for different groups. TSV Numerical value ( TSV =-3, TSV =-2, TSV =-1, TSV =0, TSV =1, TSV =2, TSV =3) Establish operating temperatures respectively and clothing thermal resistance The series of relations:

[0044] in, Represents the regression coefficient. These are the constant terms corresponding to δ in different sets of formulas. TSV =0, TSV =1, TSV =2, TSV =3, TSV =-3, TSV =-2, TSV When =-1, The numbers are 0, 1, 2, 3, 4, 5, and 6 respectively. .

[0045] In this embodiment, thermal sensing-related temperature conversion is first performed: model training and iterative optimization are carried out. Based on the YOLO v11 algorithm, training data is input into the initial model to complete the image recognition and precise localization of facial feature points, covering the forehead, left inner canthus, right inner canthus, tip of the nose, left cheek, and right cheek. Subsequently, the initial model is debugged and optimized based on the model output results, and finally an optimized model suitable for facial feature point recognition is obtained.

[0046] Temperature-related point weights were calculated. Based on the extracted temperature values ​​of each facial key point, and combined with the correlation characteristics between temperature and facial regions, the temperature distribution patterns of each key point in the facial region were analyzed, and the forehead was analyzed separately. Left inner canthus , right medial canthus ,tip of the nose left cheek and right cheek The relationship between thermal comfort and facial thermal infrared recognition is determined, and the point with the highest correlation is identified as the sole point for subsequent facial thermal infrared recognition.

[0047] Establish thermal perception for different population groups TSV Facial key point temperature reference The linear regression equation between them:

[0048] in, Represents the regression coefficient. This represents the constant term. The fit of each correlation equation is compared, and the point with the strongest correlation is selected as the point for temperature recognition by the device.

[0049] Establish facial temperature reference points for different population groups Operating temperature The correlation equations between these parameters reveal the relationship between facial temperature, operating temperature, and air conditioning temperature:

[0050] in, Represents the regression coefficient. This represents a constant term.

[0051] The specific methods for constructing the thermal sensation information extraction model and extracting key points are as follows: Prepare and preprocess the facial infrared image dataset. Obtain a training dataset containing facial infrared images, which should include labeled facial key point locations; preprocess the infrared images, including image standardization and data augmentation (such as rotation, scaling, and translation) to improve the model's generalization ability; and simultaneously organize the infrared images and temperature table information generated by the Testo software. YOLO v11 model structure adjustments. In the YOLO v11 network, the output layer has been adjusted to suit the facial keypoint detection task. The output layer contains multiple channels, including the bounding box of the facial region and the coordinates of each keypoint; YOLO v11 model training. Preprocessed facial infrared images and their labeled data are used as input to train the YOLO v11 model. During training, target detection is performed using infrared images containing facial keypoint annotations, and the model learns how to identify facial regions and their corresponding keypoint coordinates. Facial key point information extraction. After training, the new infrared image is input into the trained YOLO v11 model to obtain the detected facial regions and the position coordinates of each key point; The original image temperature data was exported using the testo software of the original thermal infrared detector. At the same time, based on the coordinate data output by the model, the original image temperature data and coordinate data were combined and the temperature information of different facial key points was identified using programming software.

[0052] In this embodiment, the method for determining the thermal comfort zone is as follows: Group-priority control: Considering the physiological differences among individuals in various multi-person scenarios, this invention allows users to manually input information such as height, weight, and age to create information profiles for multiple users, including User 1, User 2, ..., User m; and, based on the Harris-Benedict formula mentioned earlier, converts this information into basal metabolic rate (BMR). BMR The target population is ultimately defined and classified based on this value.

[0053] By classifying the user groups, the neutral temperature and acceptable thermal range corresponding to each group are derived through preliminary calculations. Then, the intersection of the acceptable thermal ranges for each group is calculated, and the result is the overall acceptable thermal range for the room. That is, the acceptable temperature range for user 1 in the air conditioning system is […]. t 1, t 2], User 2's acceptance range is [ t 3, t 4], the user m's acceptance interval is [ t 2m-1 , t 2m ]conduct t 1. t 3... t 2m-1 , t 2. t 4... t 2m By comparing the acceptable temperature ranges of each member, the intersection of the acceptable temperature ranges is taken. The maximum value of the left closed interval of all members is taken as the lower limit of the overall interval, and the minimum value of the right closed interval of all members is taken as the upper limit of the overall interval. This determines the overall acceptable temperature range within a specific space. t min , t max ].

[0054] Individual fine-tuning compensation: Dynamically adjusts the system within the group's acceptable temperature range based on energy consumption standards to minimize energy consumption while ensuring thermal comfort. Air conditioning energy consumption is highly correlated with the indoor-outdoor temperature difference; the greater the temperature difference, the more heat the system needs to transfer, resulting in higher energy consumption. Therefore, properly controlling the air conditioning set temperature can effectively improve its operating efficiency and significantly reduce energy consumption. Thus, when the air conditioner is running, users can manually input the outdoor temperature (based on the real-time weather forecast) into the terminal. t out During temperature regulation, within the acceptable temperature range [ t min , t max Select based on outdoor temperature (t out The closest temperature setting, i.e. t air .

[0055] Due to individual differences, some users are more suited to cooler or warmer environments, with their preferred temperature exceeding the room's overall acceptable thermal range. t min , t max First, such individuals are screened and identified, and then the indoor air temperature is set based on the thermal comfort standards of the remaining users. t air At the same time, personalized temperature control is implemented for this group of individuals, which not only achieves energy-saving operation of the air conditioning system, but also ensures the thermal comfort of all users.

[0056] In this embodiment, a personalized temperature control is also provided. When carrying out personalized temperature control, for energy-saving considerations, clothing adjustment strategy is given priority under the premise of seasonal adaptation: in winter, the overall thermal comfort level of the group can be ensured by adding or removing clothing; while in summer, the group wears less clothing, and the implementation conditions for clothing adjustment are insufficient, so a fan adjustment method is added to meet the thermal comfort needs of the group.

[0057] 1. Summer fan adjustment When an individual does not meet the temperature range, personalized temperature adjustment is used.

[0058] In summer, a combination of air conditioning and fans is used for temperature control. The fans are portable and adjust only according to individual thermal conditions, with no limit on the number that can be used. The principle is that by adjusting the fan speed, the convective heat transfer coefficient of the user's facial surface is changed, thereby controlling the convective heat dissipation power based on Newton's law of cooling, ultimately regulating and stabilizing the facial skin temperature within the individual's thermal comfort range. Individuals use portable fans for personalized temperature adjustment. To balance energy saving and comfort, the system employs a step-by-step adaptive adjustment strategy, with the following operational steps: (1) Initial settings: The fan starts at the lowest speed by default (to ensure the lowest energy consumption). (2) Temperature monitoring: After startup, the system continuously monitors the user's facial temperature once per minute using a thermal infrared thermometer. (3) Steady-state determination: When the facial temperature is detected to be stable (no change in facial temperature for multiple consecutive times), a formal temperature assessment is performed. (The "no change in facial temperature" means that, through continuous monitoring by a thermal infrared thermometer, the average temperature fluctuation of a specific area of ​​the face is less than the set threshold ±0.3℃ within 5 minutes). (4) Comfort Judgment: The system uses a preset facial temperature-operating temperature-thermal sensation correlation model (automatic identification and extraction of temperature at specific points on the face via a thermal infrared system). ) and formula The operating temperature at this time is obtained. Determine whether the current state is within the user's acceptable range for heat; (5) Wind speed regulation decision: ①If the heat acceptability assessment result is "just acceptable", then maintain the current setting. ②If the thermal acceptableness is “just unacceptable”, the fan will automatically increase to the next speed and repeat the monitoring and judgment process from (2) to (4) until it matches the user’s comfort needs or reaches the highest level (if it still does not meet the needs, the number of fans will be increased until the thermal acceptableness is just acceptable).

[0059] 2. Adjusting winter clothing When personalizing temperature settings for users excluded from winter, a combination of air conditioning and clothing thermal resistance adjustment is used to meet thermal comfort requirements through clothing adjustments. TSV =0). (After fitting with the comfort clothing model, if the thermal resistance value of the clothing corresponding to the current thermal sensation is higher than 0). TSV If the thermal resistance of the clothing is 0, then the adjustment measure is to reduce the amount of clothing; otherwise, the adjustment measure is to increase the amount of clothing. Users need to manually configure the current clothing situation on the terminal before use.

[0060] The facial temperature was measured using thermal infrared imaging, and then analyzed using the formula... and The operating temperature at this time was calculated. ; by formula The corresponding neutral temperature was calculated. ;Will and Compare and judge the temperature of the two items. If there is a difference, the thermal resistance of the clothing needs to be adjusted.

[0061] After determining that the thermal resistance of the clothing needs to be adjusted, the formula is... The result of [ , ], [ , ], [ , The operating temperature range is used to determine this. First, determine the range of -1 < TSV <1. Determine the temperature range and the current operating temperature. Is it within the range? , Within ] . If within the temperature range [ , Within the specified range, determine the current operating temperature. With neutral temperature The relationship between them. If > Then the current heat sensation level will be automatically set to TSV =1; if Then the current heat sensation level will be automatically set to TSV =-1.

[0062] If not within the range [ , Within ], then perform -2< TSV Use the interval <2 to determine the current operating temperature. Is it within the range? , Within [ ], if it is within the temperature range [ , Within the specified range, determine the current operating temperature. With neutral temperature The relationship between them. If > Then the current heat sensation level will be automatically set to TSV =2; if The system will automatically set the current temperature of heat to [the specified value]. TSV =-2.

[0063] If it is still not within the above range [ , If ], then it is determined to belong to -3< TSV The temperature range is <3. TSV In terms of value selection, the current operating temperature is also compared. With neutral temperature The relationship between them. If > Then the current heat sensation level will be automatically set to TSV =3; if Then the current heat sensation level will be automatically set to TSV =-3.

[0064] By formula ,Mode Japanese style By combining the operating temperature, facial point temperature, and thermal sensation, the operating temperature of the garment can be determined. .Will TSV The comfort clothing fitting formula when =0, and the comfort clothing fitting formula under the current thermal sensation level (i.e., formula...) ), current thermal sensation level ( TSV (value) andTSV The fitting relationship between operating temperature and clothing thermal resistance at =0 is established. Based on the difference between these two values ​​within the comfortable clothing thermal resistance range, targeted clothing adjustment suggestions are formulated, and these suggestions are intelligently broadcast using a voice system. The clothing adjustment is based on the following... Figure 2 As shown.

[0065] Example 2: The outdoor temperature is 34℃, and there are 5 users indoors. The basic information is shown in Table 3. Samples with large differences were selected.

[0066] Table 3 Basic Information

[0067] The summer thermal comfort ranges for the five individuals are: A: 21.0~24.0℃, B: 24.5~27.0℃, C: 23.5~26.0℃, D: 23.0~25.5℃, and E: 23.0~25.5℃; the wind perception ranges are: A: 0.6~1.0m / s, B: 0.15~0.25m / s, C: 0.2~0.4m / s, D: 0.4~0.7m / s, and E: 0.3~0.6m / s.

[0068] Thermal sensory information extraction: based on the formula By establishing a relationship between point temperature and thermal sensation, and during data processing, the forehead was analyzed separately. Left inner canthus , right medial canthus ,tip of the nose left cheek and right cheek The relationship between thermal comfort and facial comfort is assessed. It is assumed that the forehead is the most relevant facial feature at this time. .

[0069] A thermal comfort study of the above groups revealed that there was no complete overlap among the five individuals. After excluding B, the others […]. t min , t max The value is [23.5, 24]. Since the air conditioner is set to integer values ​​and the outdoor temperature is 36℃, it is set to 24℃.

[0070] Personalized temperature control: 1. Clothing adjustment: Given individual B: The 80% acceptable temperature range is [24.5, 27], and the neutral temperature is 25°C; at this point, it is assumed that individual B is within -1 < TSV <1, -2< TSV <2 cases correspond to [ , ], [ , The values ​​are [24.5, 27] and [23.8, 27.6], respectively.

[0071] If the facial temperature is 18℃, the operating temperature is 19℃, and the neutral temperature is 25℃, then the clothing needs to be adjusted.

[0072] Compare the current operating temperature with -1< TSV <1, -2< TSV We first determine the interval corresponding to <2, and then determine if it is not within -1<. TSV <1, then it was determined that it was not in -2< TSV In <2, the current thermal sensation is TSV =-3 or 3. Finally, compare the current operating temperature. With neutral temperature The relationship between them. In this embodiment, if (19℃) < (25℃), then the system will automatically set the temperature of heat sensation at this time to [the desired temperature]. TSV =-3.

[0073] Through Combined with the individual's current operating temperature of 19°C, the degree of thermal sensation at this time is: TSV =-3, according to the formula If individual B is in TSV The formula for comfortable clothing obtained when =0 , TSV The formula for comfortable clothing obtained when =-3 (The relationship between the two is as follows) Figure 3 (As shown).

[0074] when TSV When =0, =3.85clo when TSV When =-3, =3.256clo≈3.26clo (rounded to two decimal places) The thermal resistance difference of the clothing is 0.59clo, so additional clothing needs to be added based on this thermal resistance difference. To meet the thermal comfort requirements at the current temperature, it is sufficient to add a thin long-sleeved shirt and a thin short robe. The above clothing supplement information will be announced in voice form.

[0075] If individual B is at this time TSV The formula for comfortable clothing obtained when =0 ,exist TSV The formula for comfortable clothing obtained when =3 (The relationship between the two is as follows) Figure 4 (As shown).

[0076] when TSV When =0, =3.85clo when TSV When =3, =4.313clo≈4.31clo (rounded to two decimal places) The thermal resistance difference of the clothing is 0.46clo, and the amount of clothing should be reduced according to this thermal resistance difference. If individual B is wearing a thick long-sleeved garment at this time, it is recommended that they remove the garment, and the above supplementary clothing information will be broadcast as a prompt in voice form.

[0077] 2. Fan adjustment In practical applications, if an individual's temperature exceeds the applicable range for group regulation, individualized regulation needs to be carried out using fans (fans can be moved and added, and are only used for targeted regulation of individuals). This example uses individual B as the research subject for explanation.

[0078] If an acceptable temperature range of [24.5, 27] is required, cooling adjustment is necessary.

[0079] At this point, fan adjustment needs to be considered, if the facial temperature is 29℃.

[0080] After the initial adjustment, if the facial temperature stabilizes at 28℃, the current operating temperature is calculated based on the correlation between facial temperature, operating temperature, and thermal sensation. The temperature was set to 27.6℃, and this temperature was compared with the individual's acceptable temperature range. That is, the individual's current operating temperature is 27.6℃, while the user's acceptable temperature range is [24.5, 27]. Therefore, adjusting the fan to the lowest setting did not bring the operating temperature into the individual's acceptable temperature range.

[0081] The fan automatically adjusts to level two. At this time, the facial temperature is 27.3℃. The operating temperature is calculated from this. The temperature was 26°C, which is within the acceptable range for the individual, so it was decided to keep the fan running at the second speed.

[0082] Example 3: Based on Example 1, this embodiment of the invention provides an intelligent optimal control system for the thermal environment of a multi-person living space, which can be used to implement the intelligent optimal control method for the thermal environment of a multi-person living space as described in the foregoing embodiments. The system includes: The first module is used to acquire physiological characteristic data of all people in the living space, calculate the basal metabolic rate of each person, and classify all people into different metabolic categories based on the basal metabolic rate. The second module is used to determine the acceptable thermal range for each person based on metabolic category and a preset thermal comfort model. The third module is used to calculate the intersection of the thermally acceptable intervals for all personnel, construct a unified control interval for the group based on the intersection of the thermally acceptable intervals, and generate control commands to control the operation of environmental control equipment based on the unified control interval for the group. The fourth module is used to acquire facial images of people in real time and extract the temperature of key facial points; The fifth module is used to identify target individuals whose thermal comfort needs deviate from the unified control range of the group based on the temperature of key facial points, generate personalized compensation instructions for the target individuals, and complete the intelligent optimal control of the thermal environment in multi-person living spaces.

[0083] This system is a multimodal intelligent control system, with its core comprising a perception layer, a data processing layer, a decision control layer, an execution layer, and an information feedback layer. These layers work together to achieve precise control of the thermal environment. Later, voice information is broadcast via a command receiving module and a speech synthesis module. The perception layer includes an infrared thermal imager (used to collect infrared images of an individual's face and temperature data at key points), an indoor environmental parameter sensor (collecting objective parameters such as indoor air temperature, relative humidity, black sphere temperature, and wind speed), and a behavior recognition module (which automatically identifies or obtains an individual's activity status through sensor input or user terminal input), enabling comprehensive perception of an individual's thermal state, environmental state, and behavioral state.

[0084] Data processing layer: Based on an edge computing and cloud-based collaborative architecture, it preprocesses, extracts features, and performs model calculations on multi-dimensional data collected by the perception layer, including individual basal metabolic rate (BMR). BMR ) Calculation, temperature weight analysis of key facial points, thermal comfort zone modeling, etc., provide data support for decision control.

[0085] Decision control layer: Construct a dynamic decision-making model of "group priority + individual fine-tuning", determine the optimal ambient temperature range for the group based on data processing results, and generate unified air conditioning control instructions; at the same time, formulate suggestions for fan speed adjustment or clothing thermal resistance supplementation for individual comfort deviations, and output personalized compensation instructions.

[0086] The execution layer includes environmental control equipment such as air conditioners and smart fans, as well as user interaction terminals (used to input basic individual information and receive control suggestions), which complete the adjustment of environmental parameters according to the instructions of the decision control layer.

[0087] Information feedback layer: This includes voice-guided dressing suggestions for smart and comfortable clothing based on thermal resistance, making instructions more intelligent and addressing cognitive impairment issues for special groups (such as the elderly with disabilities or dementia, and those with hemiplegia).

[0088] This system can be widely used in various multi-person living spaces such as homes, student dormitories, nursing homes, and offices, providing an efficient, accurate, and user-friendly solution for intelligent thermal environment control.

[0089] According to embodiments of the present invention, the present invention also provides an electronic device, a readable storage medium, and a computer program product.

[0090] 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 intelligent optimal control method for thermal environment of multi-person living spaces as described in Embodiment 1 above.

[0091] In an exemplary embodiment, the readable storage medium may be a non-transitory computer-readable storage medium storing computer instructions for causing the computer to execute the intelligent optimal control method for the thermal environment of a multi-person living space as described in Embodiment 1 above.

[0092] In an exemplary embodiment, the computer program product includes a computer program that, when executed by a processor, implements the intelligent optimal control method for the thermal environment of a multi-person living space as described in Embodiment 1 above.

[0093] 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.

[0094] 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.

[0095] 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).

[0096] 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.

[0097] 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.

[0098] 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 method for intelligent optimal control of thermal environment in multi-person living spaces, characterized in that, Includes the following steps: Acquire physiological characteristic data of all people in the living space, calculate the basal metabolic rate of each person, and classify all people into different metabolic categories based on their basal metabolic rate; The acceptable thermal range for each person is determined based on metabolic category and a pre-set thermal comfort model. Calculate the intersection of the thermally acceptable intervals for all personnel, construct a unified control interval for the group based on the intersection of the thermally acceptable intervals, and generate control commands to control the operation of environmental control equipment based on the unified control interval for the group. Real-time acquisition of facial images of people, extraction of temperature of key facial points; Based on facial key point temperature, the system identifies target individuals whose thermal comfort needs deviate from the group's unified control range, generates personalized compensation instructions for the target individuals, and completes intelligent optimal control of the thermal environment in multi-person living spaces.

2. The intelligent optimal control method for thermal environment in multi-person living spaces according to claim 1, characterized in that, Basal metabolic rate BMR The calculation is performed using the Harris-Benedict formula, specifically: male: BMR =88.362 + (13.397 × weight) + (4.799 × height) - (5.677 × age) female: BMR =447.593 + (9.247 × weight) + (3.098 × height) - (4.330 × age) The specific metabolic categories for men are: Extremely low BMR <1500kcal, low BMR 1500-1800kcal, normal BMR 1800-2100kcal, high BMR 2100-2400kcal, extremely high BMR >2400kcal; The specific metabolic categories for women are: Extremely low BMR <1200 kcal, low BMR 1200-1500 kcal, normal BMR 1500-1800 kcal, high BMR 1800-2200 kcal, extremely high BMR >2200 kcal.

3. The intelligent optimal control method for thermal environment in multi-person living spaces according to claim 1, characterized in that, The preset thermal comfort model predefines the acceptable thermal range and thermal sensation for ten metabolic categories. TSV ; heat sensation TSV A 7-level scale is used for quantization, specifically: hot: TSV =3; warm: TSV =2; Slight warmth: TSV =1; neutral ： TSV =0； Slightly cool: TSV =-1; cold: TSV =-2; cold: TSV =-3.

4. The intelligent optimal control method for thermal environment in multi-person living spaces according to claim 3, characterized in that, The method for determining the acceptable thermal range for each metabolic category is as follows: Calculate the operating temperature for each metabolic category. : in, Indicates air temperature. A coefficient representing air temperature. Indicates radiation temperature. Indicates the temperature of the black ball. Indicates wind speed; The operating temperature is adjusted according to the preset temperature interval. BIN The process yields the processed temperature range. The thermal unacceptability rate within each temperature range was statistically analyzed. Using a thermal unacceptability rate less than or equal to a preset threshold as the standard, the thermally acceptable range for each metabolic category was determined. The fitting formula for the thermal unacceptability rate is as follows: in, Indicates the rate of unacceptability of heat. and Represents the regression coefficient. This represents a constant term.

5. The intelligent optimal control method for thermal environment in multi-person living spaces according to claim 1, characterized in that, Facial key point temperatures include forehead temperature, left inner canthus temperature, right inner canthus temperature, nose tip temperature, left cheek temperature, and right cheek temperature.

6. The intelligent optimal control method for thermal environment in multi-person living spaces according to claim 5, characterized in that, In summer, the personalized compensation command is a command to adjust the wind speed of the smart fan, specifically: Turn on the fan, aim it at the target individual, and run it at the lowest speed. Monitor the temperature changes of key facial points of the target individual at preset time intervals; If the temperature of the key facial points of the target individual is not within the acceptable thermal range for several consecutive monitoring cycles, the fan speed will be gradually increased until the real-time thermal sensation of the target individual returns to the comfortable range or reaches the maximum fan speed. If the maximum wind speed is still not sufficient, increase the number of fans or adjust their positions.

7. The intelligent optimal control method for thermal environment in multi-person living spaces according to claim 5, characterized in that, In winter, the personalized compensation instructions are suggestions for adjusting the thermal resistance of clothing, specifically: Establish a fitting curve for comfortable clothing: in, Indicates the thermal resistance value of clothing. Represents the regression coefficient. Indicates a constant term, subscript γ =0,1,2,3,4,5,6, respectively correspond to TSV =0,1,2,3, 3, 2, 1; Calculate the current operating temperature based on the temperature of key facial points. : in, Represents the regression coefficient. Represents a constant term. Indicates the temperature of key facial points; Current operating temperature Substitution TSV The fitting curve for comfortable clothing corresponding to =0 was calculated. TSV =0 corresponds to the thermal resistance value of the clothing; Determine the current operating temperature The temperature range in which one is located determines the corresponding thermal sensation. Current operating temperature Substitute the fitting curve of the comfortable clothing corresponding to the thermal sensation state obtained from the judgment into the thermal resistance value of the clothing corresponding to the thermal sensation state obtained from the judgment. calculate TSV The difference between the thermal resistance value of the clothing corresponding to =0 and the thermal resistance value of the clothing corresponding to the judged degree of heat sensation is used to generate suggestions for adding or removing clothing and announce them by voice.

8. The intelligent optimal control method for thermal environment in multi-person living spaces according to claim 7, characterized in that, Determine the current operating temperature The specific method for determining the corresponding thermal sensation state based on the temperature range is as follows: If the current operating temperature At -1< TSV Within the temperature range corresponding to <1, determine whether there is an operating temperature greater than the neutral temperature. If yes, set the thermal sensing state to 1; otherwise, set the thermal sensing state to -1. If the current operating temperature At -2< TSV Within the temperature range corresponding to <2, determine whether there is an operating temperature greater than the neutral temperature. If yes, set the thermal sensing state to 2; otherwise, set the thermal sensing state to -2. If the current operating temperature At -3< TSV Within the temperature range corresponding to <3, determine whether there is an operating temperature greater than the neutral temperature. If yes, set the thermal sensation state to 3; otherwise, set the thermal sensation state to -3. -1< TSV <1 corresponds to the temperature range, -2< TSV <2 corresponds to the temperature range and -3< TSV The temperature range corresponding to <3 is determined by the linear regression equation between operating temperature and thermal sensation. The linear regression equation between operating temperature and thermal sensation is: in, Indicates the sensation of heat. Indicates the operating temperature. Represents the regression coefficient. This represents a constant term.

9. A smart optimal control system for the thermal environment of a multi-person living space, characterized in that, include: The first module is used to acquire physiological characteristic data of all people in the living space, calculate the basal metabolic rate of each person, and classify all people into different metabolic categories based on the basal metabolic rate. The second module is used to determine the acceptable thermal range for each person based on metabolic category and a preset thermal comfort model. The third module is used to calculate the intersection of the thermally acceptable intervals for all personnel, construct a unified control interval for the group based on the intersection of the thermally acceptable intervals, and generate control commands to control the operation of environmental control equipment based on the unified control interval for the group. The fourth module is used to acquire facial images of people in real time and extract the temperature of key facial points; The fifth module is used to identify target individuals whose thermal comfort needs deviate from the unified control range of the group based on the temperature of key facial points, generate personalized compensation instructions for the target individuals, and complete the intelligent optimal control of the thermal environment in multi-person living spaces.

10. 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 1-8.

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