Air conditioner control method, control device, storage medium and air conditioner

Abstract

The present application provides a control method, control device, storage medium, and air conditioner for an air conditioner. The method includes: obtaining a predicted value of a user's human body sleep heat load, where the predicted value is a predicted value of the human body sleep heat load for the next time period based on the current human body sleep heat load value, where the current human body sleep heat load value is the difference between the user's own heat generation per unit time and the heat dissipated by the user while in a sleeping state; and adjusting the temperature of the air conditioner based on at least the predicted value of the human body sleep heat load, so that the temperature of the air conditioner can maintain the human body sleep heat load value within a predetermined range after a predetermined time period. This method uses relevant parameters to obtain a predicted heat load value and a load prediction curve to predict the user's demand for heating and cooling within a certain future time period, thereby achieving accurate control of the air conditioner temperature. This solves the problem that existing air conditioners cannot accurately adjust the temperature according to the user's real-time changing heating and cooling needs, thereby improving the user experience.

Description

Technical Field

[0001] The present application relates to the field of air conditioning, and in particular to a control method, a control device, a storage medium and an air conditioner. Background Art

[0002] With the continuous development of the economy and technology, users' demand for a more comfortable indoor environment in their homes and workplaces is also increasing, and household appliances (such as air conditioners) are playing an increasingly important role in daily life. Conventional air conditioning systems operate based on user-defined parameters such as temperature, humidity, and wind speed, controlling the various indoor environmental parameters within a range that the user feels comfortable at the moment. However, during the user's sleep phase, existing air conditioners have two problems. Problem 1: During sleep, the user's subjective consciousness will remain relatively weak for a long time. When environmental parameters change, the user cannot issue real-time instructions to the air conditioner to ensure that the indoor environment maintained by the air conditioning system meets the user's comfort requirements at different sleep stages. Problem 2: Most existing technologies can only adjust the air conditioner according to the user's current needs. That is, the control decision can only be executed when the user feels cold or hot, and at this time, the user has already experienced discomfort, resulting in a significantly reduced user experience.

[0003] Currently, most existing "sleep smart control air conditioners" proposed to address the first problem above can only reflect the "smart control" feature by setting a fixed operating mode during sleep. These products have poor user interaction during use, and cannot meet the comfort needs of different groups of people, nor can they adjust according to the user's real-time changing heating and cooling needs. Existing research on "sleep smart control air conditioners" has proposed methods to obtain user and room temperature parameters by installing infrared sensors in the air conditioner unit, or to obtain user local skin temperature parameters through wearable devices such as bracelets, or to assess human thermal comfort through parameters such as snoring, body movement, and sleep stages. However, such methods often have many interference factors, poor accuracy, and cannot reflect the overall changes in the human body's thermal comfort needs for sleep. In addition, most of the control strategies proposed in existing research to address the second problem above are ineffective and have little practical application. Summary of the Invention

[0004] The main purpose of the present application is to provide a control method, a control device, a storage medium and an air conditioner for an air conditioner, so as to solve the problem that the existing air conditioner cannot accurately adjust the temperature according to the user's real-time changing cooling and heating needs.

[0005] To achieve the above-mentioned purpose, according to one aspect of the present application, a method for controlling an air conditioner is provided, the method comprising: obtaining a predicted value of a user's human sleep heat load, the predicted value being a predicted value of the human sleep heat load for a next time period obtained based on a current human sleep heat load value, the current human sleep heat load value being the difference between the heat generated by the user per unit time in a sleeping state and the heat dissipated by the user; and adjusting the temperature of the air conditioner at least according to the predicted value of the human sleep heat load, so that the temperature of the air conditioner can keep the human sleep heat load value within a predetermined range after a predetermined time.

[0006] Optionally, adjusting the temperature of the air conditioner at least according to the human body sleep heat load prediction value includes: obtaining the human body sleep heat load prediction curve of the user, the human body sleep heat load prediction curve is a prediction curve obtained based on the current human body sleep heat load curve, and the human body sleep heat load prediction curve is a curve formed by multiple human body sleep heat load prediction values ​​obtained at different times; adjusting the temperature of the air conditioner according to the human body sleep heat load prediction value and the change trend of the human body sleep heat load prediction curve, so that the temperature of the air conditioner can keep the human body sleep heat load value within a predetermined range after a predetermined time.

[0007] Optionally, the temperature of the air conditioner is adjusted at least according to the human body sleep heat load prediction value, including: when the human body sleep heat load prediction value is less than a first threshold, adjusting the temperature of the air conditioner to a first preset temperature; when the human body sleep heat load prediction value is greater than a second threshold, adjusting the temperature of the air conditioner to a second preset temperature, wherein the first threshold is less than the second threshold, and the first preset temperature is greater than the second preset temperature.

[0008] Optionally, adjusting the temperature of the air conditioner based on the change trends of the human body sleep heat load prediction value and the human body sleep heat load prediction curve includes: maintaining the temperature of the air conditioner unchanged when the human body sleep heat load prediction value is greater than or equal to a first threshold value and less than a third threshold value, and the human body sleep heat load prediction curve shows an increasing trend over time; adjusting the temperature of the air conditioner to a third preset temperature when the human body sleep heat load prediction value is greater than or equal to the first threshold value and less than the third threshold value, and the human body sleep heat load prediction curve shows a decreasing trend over time; adjusting the temperature of the air conditioner to a fourth preset temperature when the human body sleep heat load prediction value is less than or equal to the second threshold value and greater than or equal to the third threshold value, and the human body sleep heat load prediction curve shows an increasing trend over time; maintaining the temperature of the air conditioner unchanged when the human body sleep heat load prediction value is less than or equal to the first threshold value and greater than or equal to the third threshold value, and the human body sleep heat load prediction curve shows a decreasing trend over time; wherein the third threshold value is greater than the first threshold value and less than the second threshold value, and the third preset temperature is greater than the fourth preset temperature.

[0009] Optionally, the method also includes: obtaining relevant parameters, the relevant parameters including at least one of the following: the surface skin temperature of the human body, the indoor environment temperature of the user, the temperature of the bed where the user is located, the saturated partial pressure of water vapor at the current temperature, and the saturated partial pressure of water vapor at the current humidity; determining a first power based on the relevant parameters, wherein the first power is the sum of the power of the user dissipating heat to the outside world through the skin, the power of the user dissipating heat to the outside world through breathing, and the power of the energy stored in the user's body doing work to the outside world; obtaining a second power, wherein the second power is the power of the user doing work to the outside world through metabolism; determining the current human body sleep heat load value based on the first power and the second power, wherein the current human body sleep heat load value is the difference between the first power and the second power.

[0010] Optionally, when the user is in a sleeping state, determining the first power according to the relevant parameters includes: constructing a first formula,

[0011]

[0012] Wherein, P1 is the first power, M is the work done by the human body through metabolism and external work, a is the ratio of the upper surface of the human body to the total surface of the human body, b is the ratio of the lower surface of the human body to the total surface of the human body, c is the sensible heat loss estimation coefficient, d is the latent heat loss estimation coefficient, e is the temperature generated by breathing, and f is the saturated partial pressure of water vapor generated by human breathing. is the surface skin temperature of the human body, t a is the temperature in the room where the user is located, t c is the temperature of the bed, i m is the total water vapor permeability coefficient of the clothing, L R is the Lewis rate, w is the moisture content of the user's skin, and p k s is the saturated partial pressure of water vapor at the current temperature, p a is the saturated partial pressure of water vapor at the current humidity, R t1 is the thermal resistance between the bedding and the air near the upper surface of the human body, R t2 is the thermal resistance of the quilt close to the lower surface of the human body, R t3 is the thermal resistance value of the surface clothing of the human body, wherein the upper surface of the human body is the surface away from the bed, the lower surface of the human body is the surface close to the bed, the bed is the bed on which the user is located, and the human body is the user; the first power is determined according to the relevant parameters and the first formula.

[0013] Optionally, after adjusting the temperature of the air conditioner at least according to the human sleep heat load prediction value, the method further includes: obtaining the indoor humidity value of the user; and controlling the air conditioner to turn on the dehumidification function or the humidification function according to the indoor humidity value.

[0014] According to another aspect of the present application, a control device for an air conditioner is provided, comprising: an acquisition unit for acquiring a predicted value of a user's human body sleep heat load, the predicted value being a predicted value of the human body sleep heat load for a next time period obtained based on a current human body sleep heat load value, the current human body sleep heat load value being the difference between the user's own heat generation per unit time while in a sleeping state and the heat dissipated by the user; and an adjustment unit for adjusting the temperature of the air conditioner based at least on the predicted value of the human body sleep heat load, so that the temperature of the air conditioner can keep the human body sleep heat load value within a predetermined range after a predetermined time.

[0015] According to another aspect of the present application, a computer-readable storage medium is provided, which includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute any one of the air conditioner control methods.

[0016] According to another aspect of the present application, an air conditioner is provided, comprising: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include a control method for executing any one of the air conditioners.

[0017] Applying the technical solution of the present application, the air conditioner control method first obtains a user's predicted human sleep heat load value, which is a predicted human sleep heat load value for the next time period based on the current human sleep heat load value. The current human sleep heat load value is the difference between the user's own heat generation per unit time and the heat dissipated by the user while in a sleeping state. The air conditioner temperature is then adjusted based on at least the predicted human sleep heat load value, such that the air conditioner temperature maintains the human sleep heat load value within a predetermined range after a predetermined time period. This method, using relevant parameters to obtain the predicted heat load value and a load prediction curve, can predict the user's heating and cooling needs within a certain future time period. This method not only regulates the user's current heat demand for the environment during sleep, but also predicts changes in the user's heat load sensation caused by changes in the user's heat load, based on the changing trend of the user's heat demand. This allows for pre-regulation of the sleeping environment, avoiding the drawback of only adjusting the temperature when the user experiences heat or cold. This ensures that indoor environmental parameters are always within the user's most comfortable range, resolving the problem of existing air conditioners being unable to accurately adjust the temperature based on the user's real-time changing heating and cooling needs, thereby improving the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings that constitute part of this application are used to provide a further understanding of this application. The illustrative embodiments of this application and their descriptions are used to explain this application and do not constitute an improper limitation on this application. In the drawings:

[0019] Figure 1 A schematic flow chart of a method for controlling an air conditioner according to an embodiment of the present application is shown;

[0020] Figure 2 shows a skin temperature measurement point diagram according to an embodiment of the present application;

[0021] Figure 3 A general logic diagram of a method for controlling an air conditioner according to an embodiment of the present application is shown;

[0022] Figure 4 Another logic diagram of a method for controlling an air conditioner according to an embodiment of the present application is shown;

[0023] Figure 5 A schematic diagram of an adjustment flow chart of a method for controlling an air conditioner according to an embodiment of the present application is shown;

[0024] Figure 6 A schematic diagram of a control device for an air conditioner according to an embodiment of the present application is shown.

[0025] The above drawings include the following reference numerals:

[0026] A. Left chest; B. Abdomen; C. Left back; D. Left forearm; E. Right upper arm; F. Back of right hand; G. Front of right thigh; H. Front of right calf; I. Instep of right foot. DETAILED DESCRIPTION

[0027] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0028] In order to enable those skilled in the art to better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of this application.

[0029] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0030] It should be understood that when an element (such as a layer, film, region, or substrate) is described as being "on" another element, the element may be directly on the other element or intervening elements may be present. Moreover, in the specification and claims, when it is described that an element is "connected to" another element, the element may be "directly connected to" the other element or "connected to" the other element through a third element.

[0031] For ease of description, some nouns or terms involved in the embodiments of the present application are explained below:

[0032] Human sleep heat load: the difference between the heat generated by the user and the heat dissipated per unit time when the user is in a sleeping state.

[0033] As described in the background technology, existing air conditioning systems in sleep mode mostly use pre-set operating modes to control the air conditioner, which is poorly adapted to the user's dynamically changing heating and cooling needs. Existing methods for assessing the human body's heating and cooling needs during sleep mostly use external factors such as the duration of different sleep stages, body movement, and sound, or use local skin temperature for assessment. These methods often have many interference factors and poor accuracy. Furthermore, existing air conditioners adjust indoor environmental parameters in real time after receiving instructions, but it takes additional time for the room's ambient temperature to reach the target temperature, resulting in a time lag. To address the problem that existing air conditioners cannot accurately adjust the temperature according to the user's real-time heating and cooling needs, embodiments of the present application provide an air conditioner control method, control device, storage medium, and air conditioner.

[0034] According to an embodiment of the present application, a method for controlling an air conditioner is provided. The method can be applied when a user is in a sleeping state.

[0035] During sleep, the human body is in a state of thermal balance, which is an important part of maintaining thermal comfort. When the thermal environment parameters remain stable and the human body is kept in a state of thermal balance, the human body can feel thermally comfortable. On the one hand, the human body will generate a certain amount of heat through metabolism. In this process, due to the temperature difference between the human body and the surrounding thermal environment, the human body must continuously exchange heat with the surrounding thermal environment through convection, radiation, sweat evaporation and other forms of heat dissipation. In order to make the body feel thermally comfortable, the human body needs to be in a basic state of thermal balance. The relationship between the specific human comfort situation and the human body heat load is as follows: Figure 1 shown.

[0036] Table 1 Four types of human comfort

[0037]

[0038] As far as this solution is concerned, the above-mentioned Category 1 and Category 2 situations are the range of human sleep comfort. As long as the human body heat load of the sleeping thermal environment based on the air-conditioning system is within these two situations, it is considered that the human body feels comfortable in this environment, and the user can set the system according to his or her own needs.

[0039] Figure 1 FIG. 1 is a flow chart of a method for controlling an air conditioner according to an embodiment of the present application, as shown in FIG. Figure 1 As shown, the method includes the following steps:

[0040] Step S101: Obtain a predicted value of the user's human body sleep heat load. The predicted value is a predicted value of the human body sleep heat load for the next time period based on the current human body sleep heat load value. The current human body sleep heat load value is the difference between the user's own heat generation per unit time and the heat dissipated by the user in a sleeping state. The next time period may be 1 minute.

[0041] To more comprehensively and accurately assess a person's real-time thermal comfort needs during sleep, this solution considers parameters such as the person's overall skin temperature, mattress system temperature and thermal resistance, and indoor air temperature, humidity, and air flow rate. The specific steps for obtaining the current person's sleeping heat load are as follows:

[0042] Step S201, obtaining relevant parameters, wherein the relevant parameters include at least one of the following: human skin surface temperature, indoor ambient temperature of the user, temperature of the bed where the user is located, saturated partial pressure of water vapor at current temperature, and saturated partial pressure of water vapor at current humidity;

[0043] Step S202: determining a first power based on the relevant parameters, wherein the first power is the sum of the power dissipated by the user to the outside world through the skin, the power dissipated by the user to the outside world through breathing, and the power of the user's body energy performing work on the outside world;

[0044] The specific implementation steps of step S202 are as follows:

[0045] Step S2021, construct the first formula,

[0046]

[0047] Wherein, P1 is the first power, M is the work done by the human body through metabolism and external work, a is the ratio of the upper surface of the human body to the total surface of the human body, b is the ratio of the lower surface of the human body to the total surface of the human body, c is the sensible heat loss estimation coefficient, d is the latent heat loss estimation coefficient, e is the temperature generated by breathing, and f is the saturated partial pressure of water vapor generated by human breathing. is the surface skin temperature of the human body, t a is the temperature in the room where the user is located, t c is the temperature of the bed, i m is the total water vapor permeability coefficient of the clothing, L R is the Lewis rate, w is the moisture content of the user's skin, and p ks is the saturated partial pressure of water vapor at the current temperature, p a is the saturated partial pressure of water vapor at the current humidity, R t1is the thermal resistance between the bedding and the air near the upper surface of the human body, R t2 is the thermal resistance of the quilt near the lower surface of the human body, R t3 is the thermal resistance of the clothing on the surface of the human body, wherein the upper surface of the human body is the surface away from the bed, the lower surface of the human body is the surface close to the bed, the bed is the bed on which the user is located, and the human body is the user. Since the loss caused by breathing has a smaller impact than the other factors, to simplify the formula, the values ​​of the relevant parameters can be obtained through empirical formulas, namely: c = 0.0014, e = 34, d = 0.0173, f = 5.87;

[0048] Step S2022: Determine the first power according to the relevant parameters and the first formula.

[0049] Step S203: obtaining a second power, where the second power is the power of the user performing work on the outside world through metabolism;

[0050] Step S204 : determining the current human body sleep heat load value according to the first power and the second power, where the current human body sleep heat load value is the difference between the first power and the second power.

[0051] To obtain the above formula 1, as shown in formula 2.

[0052] P1=q sk +q res + S=(C+R+E sk )+(C res +E res )+(S sk +S cr )(Formula 2)

[0053] Where q sk is the amount of heat dissipated by the human body through the skin; q res is the amount of heat dissipated by the human body through breathing, S is the energy storage part of the human body. C+R is the sum of the latent heat and sensible heat dissipated by the human skin; S sk With S sr are the energy stored on the human body surface and in the core; E res The latent heat loss caused by human breathing during sleep, E sk is the amount of heat dissipated by evaporation of moisture from the skin. During a complete sleep cycle, the human body can be considered to perform no external work. Since sleep duration is relatively long, the body is able to maintain a basic state of thermal equilibrium, and the energy stored in the skin and core can be considered to be zero. Taking an adult male as an example, the following equations can be used to simplify the above equations, as shown in Formula 3-5:

[0054] S sk +S cr=0 (Formula 3)

[0055] C res =cM(et a ),E res =dM(fp a ) (Formula 4)

[0056] E sk =i m L R w(p k,s -p a ) / R t (Formula 5)

[0057] C in the formula res and E res are the sensible heat and latent heat loss caused by human breathing during sleep, M is the heat production of the human body through metabolism and external work, E sk R is the amount of heat dissipated by evaporation of water from the skin. t is the total thermal resistance of the mattress system, p k ,s is the saturated partial pressure at the current temperature, w is the skin moisture. Under normal circumstances, the skin does not sweat, and the skin moisture is approximately equal to 0.06. L R is the Lewis rate, which is 16.5K / kPa, i m is the total water vapor permeability coefficient of the garment, taking into account the moisture transfer process from the skin to the ambient air. Among them, c can be 0.0014, d can be 0.0173, e can be 34, and f can be 5.87. Taking an adult man as an example, M can be taken as 40W / m 2 .

[0058] During sleep, the heat exchange resistance between the human body and the surrounding environment is different from the thermal resistance of clothing during daytime activities. During sleep, a sleeping microenvironment is formed with the human body as the center. Therefore, the sum of the heat dissipation resistance between the human body and the surrounding environment is the sum of the additional thermal resistance of bedding and other factors on the human body and the thermal resistance of the air layer around the human body. Different sleeping postures and different quilt coverage rates will also result in significant differences in the calculated total thermal resistance. Existing studies have provided very detailed descriptions of thermal resistance calculation methods for different human body coverage rates. They comprehensively consider the effects of parameters such as mattress type, bedding type and thickness, pajamas type, unit area weight of bedding, fabric texture, and coverage rate on the total thermal resistance of the mattress system. A warm manikin was used to test the thermal resistance of a large number of combinations of bedding and clothing types and coverage rates. Based on the experimental test results, a mattress system thermal resistance database was established, and a calculation method for the total thermal resistance of the mattress system based on parameters such as unit area weight of bedding and coverage rate was obtained. This method is suitable for simple calculation of mattress thermal resistance, as shown in Formula 6:

[0059] R t =(0.034×SBSAC)+(0.000032×CBWT×CBBSAC)-1.29 (Formula 6)

[0060] Where R t The total thermal resistance of the mattress system is clo, 1clo = 0.155 (m 2 ·K) / W; SBSAC is the coverage of the mattress system (quilt, mattress, pillow, pajamas) on the human body, %; CBWT is the weight of bedding and clothing per unit area, g / m 2 CBBSAC is the coverage rate of bedding and pajamas on the human body, 1.29 is the modifiable coefficient. The value varies within a certain range according to actual conditions.

[0061] This invention primarily focuses on the heat load on the human body during sleep. The indoor operating environment controlled by the air conditioning system and the sleep microenvironment formed by the mattress differ from the daytime work and living environment. Therefore, the calculation of the human body heat load in this invention can be divided into two parts: the heat exchange between the human body and the relatively low-temperature room air, and the heat exchange between the human body and the relatively high-temperature mattress, as shown in Formula 7:

[0062]

[0063] Among them, a and b are the ratios of the heat exchange area between the human body and the air environment and the bed surface to the total surface area of ​​the human body, respectively. is the human skin temperature, t a is the indoor air temperature, t c is the bed surface temperature (the temperature under the quilt). Integrating the above formula and the above values ​​into formula (2), we can get:

[0064]

[0065] In addition, the second power is calculated as shown in Formula 9:

[0066] P2=MW(Formula 9)

[0067] Where M is the heat generated by the human body through metabolism and external work; W is the external work and muscle work of the human body. During a complete sleep process, the human body can be considered to do no external work, and the sleep time is relatively long. The human body can maintain a basic thermal balance state, so the external work and muscle work of the human body can be considered to be 0, that is, M = 40W / m 2 ,W=0W / m 2 .

[0068] According to the above formula and the above values, we can get P2=40W / m 2 .

[0069] In addition, the values ​​of each parameter in the above formula can be effectively adjusted according to the actual user's age, gender, height or living habits.

[0070] Therefore, by measuring the human skin surface temperature, indoor air temperature and humidity, bed surface temperature, and bedding thermal resistance during use, the human body's heat load during sleep can be calculated. Indoor air temperature and humidity can be measured by the air conditioner's internal unit, while human skin surface temperature and bed surface temperature can be obtained via external sensors. The bedding thermal resistance can be estimated by simply inputting basic information such as mattress type, thickness, and the user's preferred bedding method into the system.

[0071] Skin temperature is a physiological state formed through physiological regulation of the human body's thermal regulation system and physical heat exchange between the human body and the surrounding environment. In a non-uniform thermal environment, skin temperature can be used as an objective physiological indicator to evaluate human thermal comfort.

[0072] like Figure 2 As shown, in the calculation of the average skin temperature, the area weighted average method can be used to select key human skin surface measurement points, and make a weighted average based on the sensitivity of the corresponding parts of the measurement points to temperature and their size relative to the total area of ​​the human skin. Depending on the selected skin temperature measurement points and their weight coefficients, a variety of calculation methods for the average human skin temperature can be obtained. The present invention adopts a highly reliable 9-point method, that is, the skin temperature of 9 points, namely, the left chest A, abdomen B, left back C, left forearm D, right upper arm E, right back E, right thigh G, right calf H, and right foot dorsum I, is used to calculate the average human skin temperature. It has the advantages of being able to sensitively reflect the degree of change of skin temperature to cold and hot stimuli. The weight is calculated and the calculation formula is shown in Formula 10:

[0073] T 皮肤 =0.1277t 胸 +0.1277t 腹 +0.1277t 左背 +0.0851t 右上臂 +0.0638t 左前臂

[0074] +0.0532t 右手背 +0.202t 右大腿前 +0.1383t 右小腿前 +0.745t 右脚面 (Formula 10)

[0075] Among them, T 皮肤 is the average skin temperature of the human body, t 胸 is the temperature of the left chest of the human body, t 腹 is the temperature of the human abdomen, t 左背 is the temperature of the left back of the human body, t右上臂 is the temperature of the right upper arm of the human body, t 左前臂 is the temperature of the left forearm of the human body, t 右手背 is the temperature of the right upper arm of the human body, t 右大腿前 is the temperature of the front of the right thigh of the human body, t 右小腿前 is the temperature of the front of the right calf of the human body, t 右脚面 It is the temperature of the right foot of the human body.

[0076] While sleeping, the user wears pajamas with a built-in patch-type temperature sensor to obtain the overall and real-time surface skin temperature of the body. The coefficients in the above formula can be adjusted according to actual conditions. Furthermore, this solution can also adopt the 6-point method, the 12-point method, and other methods.

[0077] Step S102: Adjust the temperature of the air conditioner based on at least the predicted human sleep heat load value, so that the temperature of the air conditioner can keep the human sleep heat load value within a predetermined range after a predetermined time. The predetermined time can be 1 minute, and the predetermined range can be 0-2.

[0078] Since the human body perceives the environment differently in different states, in order to avoid the discomfort caused by adjusting the environment only when the user feels cold or hot, it is necessary to predict the user's sleeping heat load in advance and adjust the air conditioner based on the prediction results. The specific implementation steps of the above step S102 are as follows:

[0079] Step S1021: Obtaining a human body sleep heat load prediction curve for the user. The human body sleep heat load prediction curve is a prediction curve obtained based on the current human body sleep heat load curve. The human body sleep heat load prediction curve is a curve formed by multiple human body sleep heat load prediction values ​​obtained at different times.

[0080] Step S1022: adjusting the temperature of the air conditioner according to the human body sleep heat load prediction value and the change trend of the human body sleep heat load prediction curve, so that the temperature of the air conditioner can keep the human body sleep heat load value within a predetermined range after a predetermined time.

[0081] The above scheme can predict the human body sleep heat load Q' and its change trend S'(Q)' 1 minute later based on the human body sleep heat load data of the user's initial sleep period (which can be 10 minutes), and pre-regulate the indoor environment based on this result, such as Figure 3As shown in the figure, after the air conditioner is turned on, the user can manually select whether to enter the intelligent sleep adjustment mode using a remote control. If not, the air conditioner will operate according to other rules and record the user's usage habits to the cloud platform. If yes, the user's user information is retrieved. This user information can be set in advance through cloud devices such as mobile phones and computers, or obtained from historical usage records. In the initial stage of entering the intelligent sleep adjustment mode, the air conditioner runs for 10 minutes according to the instructions set by the user when falling asleep. Parameters such as human skin temperature, bed surface temperature, indoor air temperature, humidity, and flow rate are simultaneously collected and substituted into the model to calculate the real-time human sleep heat load value Q and human sleep heat load curve S(Q). The data curve calculated in the first 10 minutes is then uploaded to the cloud platform for fitting and matching. Based on the results, the user's predicted human sleep heat load value Q' and human sleep heat load change trend S'(Q)' for the next time interval (1 minute) are predicted and calculated. The human sleep heat load change trend can be the derivative of the human sleep heat load prediction curve, or it can be obtained based on the human sleep heat load prediction curve using other algorithms. Then, based on the calculated human sleep heat load Q' and the predicted change trend S'(Q)', instructions are issued to the air conditioning system to adjust the ambient temperature, humidity, etc.

[0082] In the calculation model for human sleep heat load, Q, the air conditioning system acquires a set of human and environmental parameters every 10 seconds and calculates the human sleep heat load over time, t. This data is recorded as (Q1, t1), (Q2, t2), (Q3, t3), and so on. The model data is then used through a stepwise method to establish a regression equation, S(Q), representing the time-varying curve of the human sleep heat load. This time-varying curve is then uploaded to the cloud platform. The cloud platform then feeds this data into different algorithms for fitting and prediction, based on the user's usage duration (and the richness of the user's sleep heat load data).

[0083] There are two algorithms set in this prediction model: initial prediction algorithm and internal prediction algorithm.

[0084] (1) In the initial algorithm, the system can match the data uploaded to the cloud by all users based on the environmental parameters during use and the user's race, gender, age, body parameters, and hot and cold preferences. The system can also fit the human body heat load curve detected by the matching results with the user's real-time human body heat load parameters, thereby predicting the trend of human body heat load changes during the user's sleep. Based on this result, the air conditioning system parameters are pre-regulated to ensure that the human body thermal comfort parameters based on the air conditioning system are always maintained within the appropriate standard range. The air conditioning system will record all the user's human body heat load curve data during this stage.

[0085] (2) When the system has recorded sufficient data on the user's human sleep heat load curve covering different environmental characteristics (different seasons), the system will prioritize the internal prediction algorithm and match the user's human heat load curve at the beginning of sleep with its own collected data to predict the real-time trend of changes in the user's human sleep heat load. This algorithm has the characteristics of being highly consistent with the user's own data, and has the advantages of higher accuracy and less data processing.

[0086] Using the two algorithms above, we predict the human sleep heat load curve to obtain the predicted human sleep heat load value Q' and the predicted human sleep heat load curve S(Q)'. Furthermore, we differentiate S(Q)' with respect to time t to derive the predicted human heat load value trend S'(Q)'.

[0087] By solving the two models Q' and S'(Q)', we can know the environmental conditions required by users in real time and in the future, and thus derive the air conditioning operation strategy and its changing trend to maintain the user's sleeping heat load within the comfort parameter range.

[0088] The air conditioner has an internal database where users can save their information, which can be entered and deleted through the app or remote control. This information includes user name, height, weight, and age. The database also records the user's recent usage habits. After turning on the air conditioner, the user can select smart adjustment mode. If the user does not select smart adjustment mode, the air conditioner will operate according to the original logic. If the user selects smart adjustment mode, the user must select the user and access the user information to enter the smart sleep adjustment mode.

[0089] In addition, if Figure 4As shown, the first 10 minutes of the air conditioner entering intelligent sleep adjustment mode are set as the initial stage. During this stage, the system operates according to user-set instructions. Every 10 seconds, the system collects a set of parameters such as human skin temperature, bed surface temperature, indoor air temperature, humidity, and air flow rate. These parameters are then substituted into the human sleep heat load calculation model, resulting in a total of 60 data points at time t: (Q1, t1), (Q2, t2), (Q3, t3), etc. A stepwise method is then used to establish the current human sleep heat load curve S(Q), which shows the change of human sleep heat load over time. At this point, the system queries the user's own human sleep heat load data in the cloud platform to determine a suitable database. This curve is then uploaded to the cloud platform, and the database is called for fitting and matching. Based on the matching results, the human sleep heat load for the next time interval is predicted, resulting in the predicted human sleep heat load value Q' and the human sleep heat load prediction curve S(Q)'. S(Q)' is then differentiated with respect to time t to further derive the change trend of the predicted human heat load value S'(Q)'. This change trend of the predicted human heat load value S'(Q)' is then solved, and the environment is pre-regulated by combining the data of Q' and S'(Q)'. The first time a user uses this feature, the user can call upon the database of all users' sleep heat load curves and select the data appropriate for that user based on their various parameters.

[0090] Specifically, if Figure 5 As shown, corresponding to the data in Table 1, if Q' is less than 3.3, it indicates that the user will experience a cooler thermal sensation within one minute; if Q' is greater than 3.3, it indicates that the user will experience a warmer thermal sensation within one minute. Adjusting the air conditioner temperature based on at least the predicted human sleep heat load value includes: if the predicted human sleep heat load value is less than a first threshold, increasing the air conditioner temperature to a first preset temperature; if the predicted human sleep heat load value is greater than a second threshold, decreasing the air conditioner temperature to a second preset temperature, wherein the first threshold is less than the second threshold, and the first preset temperature is greater than the second preset temperature. The first threshold can be -3.3, the second threshold can be 3.3, the first preset temperature can be 28, and the second preset temperature can be 26. Accordingly, when the air conditioner is in cooling mode, if the predicted human sleep heat load value is less than the first threshold, the system compressor frequency needs to be reduced by 2Hz; if the predicted human sleep heat load value is greater than the second threshold, the system compressor frequency needs to be increased by 2Hz. Each of the above data can be adjusted accordingly based on actual conditions.

[0091] Adjusting the temperature of the air conditioner based on the predicted human sleep heat load value and the changing trends of the predicted human sleep heat load curve includes: maintaining the temperature of the air conditioner unchanged when the predicted human sleep heat load value is greater than or equal to a first threshold value and less than a third threshold value, and the predicted human sleep heat load curve shows an increasing trend over time; increasing the temperature of the air conditioner to a third preset temperature when the predicted human sleep heat load value is greater than or equal to the first threshold value and less than the third threshold value, and the predicted human sleep heat load curve shows a decreasing trend over time; decreasing the temperature of the air conditioner to a fourth preset temperature when the predicted human sleep heat load value is less than or equal to the second threshold value and greater than or equal to the third threshold value, and the predicted human sleep heat load curve shows an increasing trend over time; and maintaining the temperature of the air conditioner unchanged when the predicted human sleep heat load value is less than or equal to the first threshold value and greater than or equal to the third threshold value, and the predicted human sleep heat load curve shows a decreasing trend over time; wherein the third threshold value is greater than the first threshold value and less than the second threshold value, and the third preset temperature is greater than the fourth preset temperature. The third threshold value may be 0, the third preset temperature may be 27°C, and the fourth preset temperature may be 26.5°C.

[0092] For example, using the air conditioner in cooling mode, corresponding to the data in Table 1, if the Q' value is within the range of -3.3 to 3.3, the human body will reach thermal comfort within this time interval. However, it is still necessary to determine whether the Q' value is in the warm range (0 to 3.3) or the cool range (-3.3 to 0). Furthermore, the trend of the predicted human sleep heat load must be predicted, that is, whether the S'(Q)' value is greater than 0. Four possible outcomes can be considered: ① If Q' is in the warm range (0 to 3.3) and S'(Q)' is greater than 0, this indicates that while the human body's thermal sensation is within the required comfort range, it is slightly warmer and is trending towards a warmer state. In this case, the system frequency needs to be increased by 1Hz to eliminate the warming trend. ② If Q' is in the warm range (0-3.3) but S'(Q)' is less than 0, this indicates that while the body's thermal sensation is within the required comfort range, it is slightly warmer. However, the body's thermal sensation is trending towards a cooler state and returning to equilibrium. In this case, the system can continue to operate. If Q' is in the cool range (-3.3-0) but S'(Q)' is greater than 0, this indicates that while the body's thermal sensation is within the required comfort range, it is slightly cooler. However, the body's thermal sensation is trending towards a warmer state and returning to equilibrium. In this case, the system can continue to operate. ④ If Q' is in the cool range (-3.3-0) and S'(Q)' is less than 0, this indicates that while the body's thermal sensation is within the required comfort range, it is slightly cooler and trending towards a cooler state. In this case, the system needs to reduce its frequency by 1Hz to eliminate the cooling trend. After this process is completed, the system will enter a loop and repeat the above process. Since the value of Q' is within the range of -3.3 to 3.3, the human body will reach a thermal comfort state, so the control of the compressor in this range can be appropriately less than the control of the compressor when Q' is less than 3.3 and Q' is greater than 3.3.

[0093] like Figure 5 As shown, in order to fully meet user needs and improve user comfort, after adjusting the temperature of the above-mentioned air conditioner at least according to the above-mentioned human sleep heat load prediction value, the above-mentioned method also includes: obtaining the indoor humidity value where the above-mentioned user is located; according to the above-mentioned indoor humidity value, controlling the above-mentioned air conditioner to turn on the dehumidification function or the humidification function.

[0094] The air conditioning system is adjusted in advance based on the prediction results to avoid user discomfort. At this time, the changing trends of the prediction results are also analyzed, and further adjustments to the air conditioning system are issued in advance to ensure that the human sleep heat load is always within the human comfort range.

[0095] This solution can both assess and predict the user's heating and cooling needs while sleeping in real time, enabling intelligent and proactive control. This air conditioning system uses a skin temperature measurement method that considers all key temperature points on the human body surface to obtain the overall skin temperature. This critical data, along with parameters such as bed surface temperature, indoor air temperature, humidity, and flow rate, and mattress thermal resistance, is then incorporated into a human sleep heat load calculation model to determine the real-time heat load of the human body while sleeping.

[0096] The air conditioning system can then predict the user's human sleep heat load value at the next time node based on the real-time human sleep heat load, and comprehensively consider environmental parameters and the user's own race, age, BMI index and other parameters through the prediction and solution model. Simultaneously, the system can solve the user's human sleep heat load curve formed in the prediction model to obtain the changing trend of the user's cooling and heating needs during sleep. Finally, the system can autonomously adjust the air conditioning operation strategy in advance based on the solved user's human sleep heat load prediction value and its changing trend, avoiding the drawback of adjusting the air conditioning operation strategy only when the user feels cold or hot, so as to meet the dynamically changing thermal comfort needs of the human body, so that the user is always in a more comfortable environment, and further improve the user experience.

[0097] It should be noted that the above values ​​are for illustration only. In actual situations, the above data can be adjusted within a certain range.

[0098] The air conditioner control method of the present application first obtains a user's predicted human sleep heat load value, which is a predicted human sleep heat load value for the next time period based on the current human sleep heat load value. The current human sleep heat load value is the difference between the user's own heat generation per unit time while in a sleeping state and the heat dissipated by the user. The air conditioner temperature is then adjusted based on at least the predicted human sleep heat load value, such that the air conditioner temperature maintains the human sleep heat load value within a predetermined range after a predetermined time period. This method, using relevant parameters to obtain the predicted heat load value and a load prediction curve, can predict the user's heating and cooling needs within a certain future time period. This method not only regulates the user's current heat demand for the environment during sleep, but also predicts changes in the user's heat load sensation caused by changes in the user's heat load, based on the changing trend of the user's heat demand. This allows for pre-regulation of the sleeping environment, avoiding the drawback of only adjusting the temperature when the user experiences heat or cold. This ensures that indoor environmental parameters are always within the user's most comfortable range, resolving the problem of existing air conditioners being unable to accurately adjust the temperature based on the user's real-time changing heating and cooling needs, thereby improving the user experience.

[0099] According to the embodiment of the present application, Figure 6As shown, a control device for an air conditioner is provided, the device comprising an acquisition unit 01 and an adjustment unit 02. The acquisition unit 01 is configured to acquire a predicted value of a user's human body sleep heat load, wherein the predicted value is a predicted value of the human body sleep heat load for a next time period obtained based on a current human body sleep heat load value, wherein the current human body sleep heat load value is the difference between the heat generated by the user per unit time while the user is in a sleeping state; and the adjustment unit 02 is configured to adjust the temperature of the air conditioner based on at least the predicted value of the human body sleep heat load, so that the temperature of the air conditioner can keep the human body sleep heat load value within a predetermined range after a predetermined time period.

[0100] Since the human body perceives the environment differently in different states, in order to avoid the discomfort caused by adjusting the environment only when the user feels cold or hot, it is necessary to predict the user's human sleep heat load in advance and adjust the air conditioner according to the prediction results. The above-mentioned adjustment unit includes a first acquisition module and a first adjustment module. The first acquisition module is used to obtain the user's human sleep heat load prediction curve. The above-mentioned human sleep heat load prediction curve is a prediction curve obtained based on the current human sleep heat load curve. The above-mentioned human sleep heat load prediction curve is a curve formed by multiple human sleep heat load prediction values ​​obtained at different times; the first adjustment module is used to adjust the temperature of the above-mentioned air conditioner according to the above-mentioned human sleep heat load prediction value and the change trend of the above-mentioned human sleep heat load prediction curve, so that the temperature of the above-mentioned air conditioner can keep the human sleep heat load value within a predetermined range after a predetermined time.

[0101] In order to avoid the disadvantage of adjusting the temperature only when the user feels cold or hot, the environment is always in a relatively comfortable state, and energy waste can be avoided while ensuring comfort. The above-mentioned adjustment unit includes a second adjustment module and a third adjustment module. The second adjustment module is used to adjust the temperature of the above-mentioned air conditioner to a first preset temperature when the above-mentioned human body sleep heat load prediction value is less than a first threshold; the third adjustment module is used to adjust the temperature of the above-mentioned air conditioner to a second preset temperature when the above-mentioned human body sleep heat load prediction value is greater than a second threshold, wherein the above-mentioned first threshold is less than the above-mentioned second threshold, and the above-mentioned first preset temperature is greater than the above-mentioned second preset temperature.

[0102] Exemplarily, the first adjustment module includes a first maintaining submodule, a first adjusting submodule, a second adjusting submodule and a second maintaining submodule. The first maintaining submodule is used to keep the temperature of the air conditioner unchanged when the human body sleep heat load prediction value is greater than or equal to the first threshold and less than the third threshold, and the human body sleep heat load prediction curve shows an increasing trend as time increases; the first adjusting submodule is used to adjust the temperature of the air conditioner to a third preset temperature when the human body sleep heat load prediction value is greater than or equal to the first threshold and less than the third threshold, and the human body sleep heat load prediction curve shows a decreasing trend as time increases; the second The adjustment submodule is configured to adjust the temperature of the air conditioner to a fourth preset temperature when the predicted human sleep heat load value is less than or equal to the second threshold and greater than or equal to the third threshold, and the predicted human sleep heat load curve shows an increasing trend over time. The second maintenance submodule is configured to maintain the temperature of the air conditioner unchanged when the predicted human sleep heat load value is less than or equal to the first threshold and greater than or equal to the third threshold, and the predicted human sleep heat load curve shows a decreasing trend over time. The third threshold is greater than the first threshold and less than the second threshold, and the third preset temperature is greater than the fourth preset temperature. This solution not only regulates the heat demand of the human body for the current environment during sleep, but also predicts changes in the heat sensation caused by changes in the heat load of the human body in advance based on the changing trend of the heat demand of the human body, thereby pre-regulating the sleeping environment in advance, avoiding the drawback of adjusting the indoor environment only when the user experiences cold or heat, and ensuring that indoor environmental parameters are always maintained within the range that the user feels most comfortable.

[0103] In order to conduct a more comprehensive and accurate assessment of the real-time thermal comfort needs of the human body during sleep, the above-mentioned device also includes a second acquisition module, a first determination unit, a third acquisition module, and a second determination unit. The second acquisition module is used to acquire relevant parameters, and the above-mentioned relevant parameters include at least one of the following: the surface skin temperature of the human body, the indoor environment temperature of the above-mentioned user, the temperature of the bed where the above-mentioned user is located, the saturated partial pressure of water vapor at the current temperature, and the saturated partial pressure of water vapor at the current humidity; the first determination unit is used to determine a first power based on the above-mentioned relevant parameters, wherein the first power is the sum of the power of the above-mentioned user dissipating heat to the outside world through the skin, the power of the above-mentioned user dissipating heat to the outside world through breathing, and the power of the energy stored in the user's body doing work to the outside world; the third acquisition module is used to acquire a second power, wherein the second power is the power of the above-mentioned user doing work to the outside world through metabolism; the second determination unit determines the above-mentioned current human body sleep heat load value based on the above-mentioned first power and the above-mentioned second power, and the above-mentioned current human body sleep heat load value is the difference between the above-mentioned first power and the above-mentioned second power.

[0104] In some embodiments, when the user is in a sleeping state, the first determining unit includes a constructing unit and a third determining unit, wherein the constructing unit is configured to construct the first formula.

[0105]

[0106] Wherein, P1 is the first power, M is the work done by the human body through metabolism and external work, a is the ratio of the upper surface of the human body to the total surface of the human body, b is the ratio of the lower surface of the human body to the total surface of the human body, c is the sensible heat loss estimation coefficient, d is the latent heat loss estimation coefficient, e is the temperature generated by breathing, and f is the saturated partial pressure of water vapor generated by human breathing. is the surface skin temperature of the human body, t a is the temperature in the room where the user is located, t c is the temperature of the bed, i m is the total water vapor permeability coefficient of the clothing, L R is the Lewis rate, w is the moisture content of the user's skin, and p k s is the saturated partial pressure of water vapor at the current temperature, p a is the saturated partial pressure of water vapor at the current humidity, R t1 is the thermal resistance between the bedding and the air near the upper surface of the human body, R t2 is the thermal resistance of the quilt near the lower surface of the human body, R t3 is the thermal resistance of the clothing on the surface of the human body, where the upper surface of the human body is the surface away from the bed, the lower surface of the human body is the surface close to the bed, the bed is the bed on which the user is seated, and the human body is the user. A third determination unit is configured to determine the first power based on the relevant parameters and the first formula. This method also takes into account parameters such as the overall surface skin temperature of the human body, the temperature and thermal resistance of the mattress system, and the temperature, humidity, and flow rate of the indoor air. This method enables a more comprehensive and accurate assessment of a person's real-time thermal comfort needs during sleep.

[0107] In order to fully meet user needs and improve user comfort, after adjusting the temperature of the above-mentioned air conditioner at least according to the above-mentioned human sleep heat load prediction value, the above-mentioned device also includes a fourth acquisition module and a control module. The fourth acquisition module is used to obtain the indoor humidity value where the above-mentioned user is located; the control module is used to control the above-mentioned air conditioner to turn on the dehumidification function or the humidification function according to the above-mentioned indoor humidity value.

[0108] The air conditioner control device of the present application includes an acquisition unit for acquiring a user's predicted human sleep heat load value, the predicted human sleep heat load value being a predicted human sleep heat load value for a next time period based on a current human sleep heat load value, wherein the current human sleep heat load value is the difference between the user's own heat generation per unit time while in a sleeping state and the heat dissipated by the user; and an adjustment unit for adjusting the air conditioner temperature based on at least the predicted human sleep heat load value, such that the air conditioner temperature maintains the human sleep heat load value within a predetermined range after a predetermined time period. The device obtains the predicted heat load value and a load prediction curve using relevant parameters to predict the user's heating and cooling needs within a predetermined time period. The device not only adjusts the temperature based on the user's current heat demand for the environment during sleep, but also predicts changes in the user's heat load sensation caused by changes in the user's heat load sensation based on the changing trend of the user's heat demand, thereby pre-adjusting the sleeping environment in advance. This avoids the drawback of adjusting the temperature only when the user experiences heat or cold, ensuring that indoor environmental parameters are always maintained within the user's most comfortable range. This solves the problem of existing air conditioners being unable to accurately adjust the temperature based on the user's real-time changing heating and cooling needs, thereby improving the user experience.

[0109] According to an embodiment of the present application, a computer-readable storage medium is provided, which includes a stored program, wherein when the program is run, the device where the computer-readable storage medium is located is controlled to execute any one of the above-mentioned air conditioner control methods.

[0110] According to an embodiment of the present application, an air conditioner is provided, comprising: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include a method for executing any one of the above-mentioned air conditioner control methods.

[0111] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0112] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0113] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0114] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0115] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0116] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0117] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0118] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0119] From the above description, it can be seen that the above embodiments of the present application achieve the following technical effects:

[0120] 1) The air conditioner control method of the present application first obtains a user's predicted human sleep heat load value. The predicted human sleep heat load value is a predicted human sleep heat load value for the next time period based on the current human sleep heat load value. The current human sleep heat load value is the difference between the user's own heat generation per unit time and the heat dissipated by the user while in sleep. The air conditioner temperature is then adjusted based on at least the predicted human sleep heat load value, such that the air conditioner temperature maintains the human sleep heat load value within a predetermined range after a predetermined time period. This method, using relevant parameters to obtain the predicted heat load value and a load prediction curve, can predict the user's heating and cooling needs for a specific time period in the future. This method not only regulates the user's current heat demand for the environment during sleep, but also predicts changes in the user's heat load sensation caused by changes in the user's heat load, based on the changing trend of the user's heat demand. This allows for pre-regulation of the sleeping environment, avoiding the drawback of only adjusting the temperature when the user experiences heat or cold. This ensures that indoor environmental parameters are always within the user's most comfortable range, resolving the problem of existing air conditioners being unable to accurately adjust the temperature based on the user's changing heating and cooling needs in real time, thereby improving the user experience.

[0121] 2) The control device for the air conditioner of the present application includes an acquisition unit for acquiring a user's predicted human sleep heat load value, the predicted human sleep heat load value being a predicted value of the human sleep heat load for a next time period based on a current human sleep heat load value, the current human sleep heat load value being the difference between the user's own heat generation per unit time and the heat dissipated by the user while in a sleeping state; and an adjustment unit for adjusting the temperature of the air conditioner based at least on the predicted human sleep heat load value, such that the air conditioner temperature maintains the human sleep heat load value within a predetermined range after a predetermined time period. The device, by obtaining the predicted heat load value and a load prediction curve using relevant parameters, can predict the user's heating and cooling needs for a specific time period in the future. This device not only adjusts the temperature based on the user's current heat demand for the environment during sleep, but also predicts changes in the user's heat load sensation caused by changes in the user's heat load in advance based on the changing trend of the user's heat demand, thereby pre-adjusting the sleeping environment in advance. This avoids the drawback of adjusting the temperature only when the user experiences heat or cold, ensuring that indoor environmental parameters are always maintained within the user's most comfortable range. This solves the problem of existing air conditioners being unable to accurately adjust the temperature based on the user's real-time changing heating and cooling needs, thereby improving the user experience.

[0122] The above description is merely a preferred embodiment of the present application and is not intended to limit the present application. Various modifications and variations are possible for those skilled in the art. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present application shall be included within the scope of protection of the present application.

Claims

1. A method for controlling an air conditioner, characterized in that: include: Obtaining a predicted human sleep heat load value for the user, where the predicted human sleep heat load value is a predicted human sleep heat load value for the next time period based on a current human sleep heat load value, where the current human sleep heat load value is the difference between the user's own heat generation per unit time while in a sleeping state and the heat dissipated by the user; adjusting the temperature of the air conditioner at least according to the predicted value of the human body sleep heat load so that the temperature of the air conditioner can keep the human body sleep heat load value within a predetermined range after a predetermined time; Adjusting the temperature of the air conditioner at least according to the predicted value of the human body sleep heat load includes: Obtaining a human body sleep heat load prediction curve for the user, where the human body sleep heat load prediction curve is a prediction curve obtained based on a current human body sleep heat load curve, and the human body sleep heat load prediction curve is a curve formed by multiple human body sleep heat load prediction values ​​obtained at different times; When the human body sleep heat load prediction value is greater than or equal to a first threshold value and less than a third threshold value, and the human body sleep heat load prediction curve shows an increasing trend over time, maintaining the temperature of the air conditioner unchanged; When the predicted value of the human body sleep heat load is greater than or equal to the first threshold and less than the third threshold, and the predicted curve of the human body sleep heat load shows a decreasing trend with time, adjusting the temperature of the air conditioner to a third preset temperature; When the predicted value of the human body sleep heat load is less than or equal to the second threshold value and greater than or equal to the third threshold value, and the human body sleep heat load prediction curve shows an increasing trend over time, adjusting the temperature of the air conditioner to a fourth preset temperature; When the human body sleep heat load prediction value is less than or equal to a first threshold value and greater than or equal to a third threshold value, and the human body sleep heat load prediction curve shows a decreasing trend with time, the temperature of the air conditioner is maintained unchanged; wherein the third threshold value is greater than the first threshold value and less than the second threshold value, and the third preset temperature is greater than the fourth preset temperature.

2. The control method according to claim 1, characterized in that: Adjusting the temperature of the air conditioner at least according to the predicted value of the human body sleep heat load includes: When the predicted value of the human body sleep heat load is less than a first threshold, adjusting the temperature of the air conditioner to increase to a first preset temperature; When the predicted value of the human body sleep heat load is greater than a second threshold, the temperature of the air conditioner is adjusted to a second preset temperature, wherein the first threshold is less than the second threshold, and the first preset temperature is greater than the second preset temperature.

3. The control method according to claim 1, wherein: The method further comprises: Obtaining relevant parameters, the relevant parameters including at least one of the following: a human body's surface skin temperature, a room environment temperature where the user is located, a temperature of a bed where the user is located, a saturated partial pressure of water vapor at a current temperature, and a saturated partial pressure of water vapor at a current humidity; Determining a first power based on the relevant parameters, wherein the first power is the sum of power dissipated by the user to the outside world through the skin, power dissipated by the user to the outside world through breathing, and power of energy stored in the user's body doing work on the outside world; Acquiring a second power, where the second power is the power of the user performing work on the outside world through metabolism; The current human body sleep heat load value is determined according to the first power and the second power, where the current human body sleep heat load value is the difference between the first power and the second power.

4. The control method according to claim 3, characterized in that: When the user is in a sleeping state, determining a first power according to the relevant parameters includes: Construct the first formula, Wherein, P1 is the first power, M is the work done by the human body through metabolism and external work, a is the ratio of the upper surface of the human body to the total surface of the human body, b is the ratio of the lower surface of the human body to the total surface of the human body, c is the sensible heat loss estimation coefficient, d is the latent heat loss estimation coefficient, e is the temperature generated by breathing, and f is the saturated partial pressure of water vapor generated by human breathing. is the surface skin temperature of the human body, t a is the temperature in the room where the user is located, t c is the temperature of the bed, i m is the total water vapor permeability coefficient of the clothing, L R is the Lewis rate, w is the moisture content of the user's skin, and p ks is the saturated partial pressure of water vapor at the current temperature, p a is the saturated partial pressure of water vapor at the current humidity, R t1 is the thermal resistance between the bedding and the air near the upper surface of the human body, R t2 is the thermal resistance of the quilt close to the lower surface of the human body, R t3 is the thermal resistance value of the clothing on the surface of the human body, wherein the upper surface of the human body is the surface away from the bed, the lower surface of the human body is the surface close to the bed, the bed is the bed on which the user is located, and the human body is the user; The first power is determined according to the relevant parameters and the first formula.

5. The control method according to any one of claims 1 to 4, characterized in that: After adjusting the temperature of the air conditioner at least according to the predicted value of the human body sleep heat load, the method further includes: Obtain the indoor humidity value of the user; According to the indoor humidity value, the air conditioner is controlled to start a dehumidification function or a humidification function.

6. A control device for an air conditioner, characterized in that: include: an acquisition unit, configured to acquire a predicted value of a user's human body sleep heat load, the predicted value being a predicted value of the human body sleep heat load for a next time period obtained based on a current human body sleep heat load value, the current human body sleep heat load value being the difference between the user's own heat generation per unit time while in a sleeping state and the heat dissipated by the user; an adjusting unit, configured to adjust the temperature of the air conditioner at least according to the predicted value of the human body sleep heat load, so that the temperature of the air conditioner can keep the human body sleep heat load value within a predetermined range after a predetermined time; The adjustment unit includes a first acquisition module, a first maintenance submodule, a first adjustment submodule, a second adjustment submodule, and a second maintenance submodule. The first acquisition module is used to obtain the user's human body sleep heat load prediction curve, where the human body sleep heat load prediction curve is a prediction curve obtained based on the current human body sleep heat load curve. The human body sleep heat load prediction curve is a curve formed by multiple human body sleep heat load prediction values ​​obtained at different times. The first maintaining submodule is configured to maintain the temperature of the air conditioner unchanged when the human body sleep heat load prediction value is greater than or equal to a first threshold and less than a third threshold, and the human body sleep heat load prediction curve shows an increasing trend over time; The first adjustment submodule is configured to adjust the temperature of the air conditioner to a third preset temperature when the predicted value of the human body sleep heat load is greater than or equal to the first threshold and less than the third threshold, and the predicted curve of the human body sleep heat load shows a decreasing trend as time increases; the second adjustment submodule is configured to adjust the temperature of the air conditioner to a fourth preset temperature when the predicted value of the human body sleep heat load is less than or equal to the second threshold and greater than or equal to the third threshold, and the predicted curve of the human body sleep heat load shows an increasing trend as time increases; The second maintaining submodule is configured to maintain the temperature of the air conditioner unchanged when the predicted value of the human body sleep heat load is less than or equal to a first threshold value and greater than or equal to a third threshold value, and a human body sleep heat load prediction curve shows a decreasing trend with time; wherein the third threshold value is greater than the first threshold value and less than the second threshold value, and the third preset temperature is greater than the fourth preset temperature.

7. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the air conditioner control method according to any one of claims 1 to 5.

8. An air conditioner, characterized in that: include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include a method for executing the control method of the air conditioner according to any one of claims 1 to 5.

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