Air conditioner, control method, controller, medium and program product thereof

Abstract

The application provides an air conditioner and a control method, a controller, a medium and a program product thereof, and relates to the technical field of air conditioning, and specifically comprises the following steps: acquiring a current operation parameter of the air conditioner and a health level of a target object, wherein the health level is determined by at least one of health information, endurance information and strength information of the target object; and adjusting an operation parameter of the air conditioner according to the current operation parameter and the health level. Compared with the general control mode of the prior art which does not consider the physiological characteristics of special groups such as silver-haired and weak people, the control method of the application can adapt to different physical grades of the target object, and can adapt to the physiological needs of special groups, improve the indoor environment in time, and enhance the air conditioning use of the special groups.

Description

Technical Field

[0001] This application relates to the field of air conditioner technology, and in particular to an air conditioner and its control method, controller, medium and program products. Background Technology

[0002] In related technologies, most existing air conditioner control technologies for indoor environments rely on a unified recommended temperature, user history habits, and simple population identification as their core control mechanisms. However, they lack systematic consideration for the specific physiological characteristics of special populations, such as the elderly or frail individuals. Therefore, in application scenarios involving special populations, existing air conditioner control solutions are prone to the following problems: uncomfortable or even health-risk environments for these individuals are not promptly corrected, resulting in a poor user experience. Summary of the Invention

[0003] This application aims to address at least one of the technical problems existing in the prior art. To this end, this application proposes an air conditioner and its control method, controller, medium, and program product, designed to consider the special physiological characteristics of specific population groups and reduce their health risks during air conditioner use.

[0004] In a first aspect, embodiments of this application provide a method for controlling an air conditioner, including: The current operating parameters of the air conditioner and the health level of the target object are obtained, wherein the health level is determined by at least one of the target object's health information, endurance information, and strength information; The operating parameters of the air conditioner are adjusted according to the current operating parameters and the health level.

[0005] According to some embodiments of this application, adjusting the operating parameters of the air conditioner based on the current operating parameters and the health level includes: Based on the health level, a target recommended range corresponding to the operating parameters of the air conditioner is determined; Determine thermal comfort deviations and / or health risk indicators based on the current operating parameters and the target recommended range; The operating parameters of the air conditioner are adjusted according to the thermal comfort deviation and / or the health risk indicators.

[0006] According to some embodiments of this application, adjusting the operating parameters of the air conditioner based on the thermal comfort deviation and / or the health risk indicators includes: Obtain the operating power and time window of the air conditioner; The energy consumption index of the air conditioner is determined based on the operating power and the time window. The operating parameters of the air conditioner are adjusted based on the thermal comfort deviation, the energy consumption index, and the health risk index.

[0007] According to some embodiments of this application, adjusting the operating parameters of the air conditioner based on the thermal comfort deviation, the energy consumption index, and the health risk index includes: Determine a first weighting parameter corresponding to the thermal comfort deviation, a second weighting parameter corresponding to the energy consumption index, and a third weighting parameter corresponding to the health risk index; Based on the comprehensive evaluation function, the optimal value of the comprehensive evaluation function is calculated according to the thermal comfort deviation and the first weight parameter, the energy consumption index and the second weight parameter, and the health risk index and the third weight parameter. The operating parameters of the air conditioner are adjusted according to the optimal value of the function.

[0008] According to some embodiments of this application, the thermal comfort deviation is determined through the following steps: A first parameter deviation is determined between the current operating parameters and the target recommended interval. The thermal comfort deviation is determined based on the first target weight parameter and the first parameter deviation. The thermal comfort deviation includes at least one of temperature thermal comfort deviation, wind speed thermal comfort deviation, and wind mode thermal comfort deviation.

[0009] According to some embodiments of this application, determining the first parameter deviation between the current operating parameters and the target recommended interval, and determining the thermal comfort deviation based on the first target weight parameter and the first parameter deviation, includes at least one of the following: When the current operating parameters include the current indoor temperature and the target recommended range includes a temperature recommended range, the temperature deviation between the current indoor temperature and the temperature recommended range is determined, and the temperature thermal comfort deviation is obtained according to the temperature weight parameter and the temperature deviation, wherein the temperature weight parameter is determined by the first weight parameter; When the current operating parameters include the current supply air velocity and the target recommended range includes the wind speed recommended range, the wind speed deviation between the current supply air velocity and the wind speed recommended range is determined, and the wind speed thermal comfort deviation is obtained according to the wind speed weight parameter and the wind speed deviation, wherein the wind speed weight parameter is determined by the first weight parameter; When the current operating parameters include the current rating corresponding to the current wind feel mode and the target recommendation range includes the target rating corresponding to the recommended wind feel mode, the wind feel mode deviation between the current rating and the target rating is determined, and the wind speed thermal comfort deviation is obtained according to the wind feel mode weight parameter and the wind feel mode deviation, wherein the wind feel mode weight parameter is determined by the first weight parameter.

[0010] According to some embodiments of this application, determining the energy consumption index of the air conditioner based on the operating power and the time window includes one of the following: When the operating power includes the current compressor power and fan power, the energy consumption index of the air conditioner is determined based on the compressor power and the cumulative operating time of the compressor in the time window, and the fan power and the cumulative operating time of the fan in the time window. When the operating power includes the equivalent power of the air conditioner, the energy consumption index of the air conditioner is determined based on the equivalent power and the time window.

[0011] According to some embodiments of this application, the health risk indicator includes at least one of a temperature risk component, a temperature change rate risk component, and a wind speed change rate risk component; the health risk indicator is determined through the following steps: Determine the second parameter deviation between the current operating parameters and the target recommendation interval, and determine the health risk indicator based on the second target weight parameter and the second parameter deviation; Determine the third parameter deviation between the current operating parameters and the preset rate parameters, and determine the health risk indicator based on the third target weight parameter and the third parameter deviation.

[0012] According to some embodiments of this application, determining the second parameter deviation between the current operating parameters and the target recommendation interval, and determining the health risk indicator based on the second target weight parameter and the second parameter deviation, includes: If the current operating parameters include the current indoor temperature and the target recommended range includes a temperature recommended range, determine the temperature deviation between the current indoor temperature and the temperature recommended range, and determine the temperature risk component based on the temperature deviation.

[0013] According to some embodiments of this application, determining the third parameter deviation between the current operating parameters and the preset rate parameters, and determining the health risk indicator based on the third target weight parameter and the third parameter deviation, includes one of the following: When the current operating parameters include the current rate of change of indoor temperature, the deviation of the rate of change of temperature between the current rate of change of temperature and the preset rate of change of temperature is determined, and the risk component of the rate of change of temperature is determined according to the deviation of the rate of change of temperature and the first risk weight coefficient, wherein the first risk weight coefficient is determined by the first weight parameter. When the current operating parameters include the current wind speed change rate of the outlet wind speed, the deviation of the wind speed change rate between the current wind speed change rate and the preset wind speed change rate is determined, and the wind speed change rate risk component is determined based on the wind speed change rate deviation and the second risk weight coefficient, wherein the second risk weight coefficient is determined by the first weight parameter.

[0014] According to some embodiments of this application, the health risk indicators are determined through the following steps: Determine the temperature risk weight coefficient corresponding to the temperature risk component, the temperature change rate risk weight coefficient corresponding to the temperature change rate risk component, and the wind speed change rate risk weight coefficient corresponding to the wind speed change rate risk component. The health risk index is calculated based on the temperature risk weight coefficient and the temperature risk component, the temperature change rate risk weight coefficient and the temperature change rate risk component, and the wind speed change rate risk weight coefficient and the wind speed change rate risk component.

[0015] According to some embodiments of this application, the method includes: The health information is used to characterize the physical constitution and health stability of the target subject; The endurance information is used to characterize the endurance level of the target object during the activity; The force information is used to characterize the muscle strength of the target object.

[0016] According to some embodiments of this application, the first weighting parameter is used to characterize the degree of physical weakness of the target object; the first weighting parameter is determined through the following steps: The health information, endurance information, and strength information are quantified to obtain the basic state classification, endurance state classification, and strength state classification of the target object, respectively. The basic condition classification, the endurance condition classification, and the strength condition classification are superimposed to obtain the first weight parameter, wherein the first weight parameter corresponds to the health level.

[0017] According to some embodiments of this application, the second weighting parameter is used to characterize the sensitivity of the target object to the electricity cost of the air conditioner; the second weighting parameter is determined through one of the following steps: Receive input energy-saving preference options, and determine the second weight parameter based on the energy-saving preference options; Obtain historical electricity consumption behavior within a preset observation period, and determine the second weighting parameter based on the historical electricity consumption behavior; The system receives input energy-saving preference options and historical electricity consumption behavior within a preset observation period, and determines the second weighting parameter based on the energy-saving preference options and the historical electricity consumption behavior.

[0018] According to some embodiments of this application, the third weighting parameter is used to characterize the degree of influence of the target object on regulating health risk items in different temperature ranges or different health states. The third weighting parameter is determined through the following steps: The basic health weight is determined based on the first weighting parameter; If the current operating parameters include the current indoor temperature and the target recommended range includes a temperature recommended range, a risk correction coefficient is determined based on the current indoor temperature and the temperature recommended range. The third weight parameter is determined based on the basic health weight and the risk correction coefficient.

[0019] Secondly, embodiments of this application provide an air conditioner, including: The controller is used to acquire the current operating parameters of the air conditioner and the health level of the target object, and adjust the operating parameters of the air conditioner according to the current operating parameters and the health level, wherein the health level is determined by at least one of the target object's health information, endurance information, and strength information; A display device, connected to the controller, is used to display the current operating parameters of the air conditioner and / or the health level of the target object.

[0020] Thirdly, embodiments of this application provide a controller, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the control method for the air conditioner described in the first aspect when running the computer program.

[0021] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions for performing the control method for an air conditioner as described in the first aspect above.

[0022] Fifthly, embodiments of this application provide a computer program product, including a computer program or computer instructions, the computer program or computer instructions being stored in a computer-readable storage medium, a processor of a computer device reading the computer program or computer instructions from the computer-readable storage medium, and the processor executing the computer program or computer instructions to cause the computer device to perform the air conditioner control method as described in the first aspect above.

[0023] According to the technical solution of this application embodiment, it has at least the following beneficial effects: First, this application embodiment obtains the current operating parameters of the air conditioner and the health level determined by at least one of the target object's health information, endurance information, and strength information. Based on the health level, it can determine the degree of physical weakness of the target object, thereby optimizing the air conditioner's operating parameters such as temperature, fan speed, and airflow mode. Compared to the general control methods of the prior art that do not consider the physiological characteristics of special groups such as the elderly and those with weak constitutions, the control method of this application embodiment can adapt to the differentiated control of the air conditioner's operating parameters for target objects with different physical levels. It can adapt to the physiological needs of special groups and promptly improve uncomfortable and health-risk indoor environments, enhancing the air conditioning experience for special groups.

[0024] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0025] The accompanying drawings are used to provide a further understanding of the technical solutions of this application and constitute a part of the specification. They are used together with the embodiments of this application to explain the technical solutions of this application and do not constitute a limitation on the technical solutions of this application.

[0026] Figure 1 This is a flowchart of an air conditioner control method provided in one embodiment of this application; Figure 2 yes Figure 1 A flowchart of a sub-step of step S120; Figure 3 yes Figure 2 A flowchart of a sub-step of step S230; Figure 4 yes Figure 3 A flowchart of a sub-step of step S330; Figure 5 This is a flowchart illustrating the steps for obtaining thermal comfort deviation in one embodiment. Figure 6 This is a flowchart of a sub-step of steps S510 and S520; Figure 7This is a flowchart of another sub-step of steps S510 and S520; Figure 8 This is a flowchart of another sub-step of steps S510 and S520; Figure 9 This is a flowchart illustrating the steps involved in obtaining one embodiment of health risk indicators; Figure 10 This is a flowchart illustrating the steps for obtaining another embodiment of health risk indicators; Figure 11 This is a flowchart of a sub-step of steps S910 and S920; Figure 12 This is a flowchart of a sub-step of steps S1010 and S1020; Figure 13 This is a flowchart of another sub-step of steps S1010 and S1020; Figure 14 This is a flowchart illustrating the steps for obtaining health risk indicators in yet another embodiment. Figure 15 This is a flowchart illustrating the steps for obtaining the first weight parameter in one embodiment. Figure 16 This is a flowchart illustrating the steps for obtaining the second weighting parameter in one embodiment; Figure 17 This is a flowchart illustrating the steps for obtaining the third weighting parameter in one embodiment. Figure 18 This is a schematic diagram of the classification mechanism for the elderly population; Figure 19 This is a schematic diagram illustrating the calculation method for the basic state classification α; Figure 20 This is a schematic diagram illustrating the calculation methods for endurance state classification β and strength state classification γ. Figure 21 This is a schematic diagram illustrating the assignment of values ​​to the endurance state classification β. Figure 22 This is a schematic diagram illustrating the assignment of values ​​to the force state classification γ; Figure 23 This is a schematic diagram illustrating the calculation method for δ, the classification of the silver-haired population. Figure 24 This is a schematic diagram illustrating the usage logic and control methods of air conditioning for the elderly. Figure 25 This is a schematic diagram of a controller for performing a control method for an air conditioner, provided in one embodiment of this application. Detailed Implementation

[0027] The embodiments of this application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain this application, and should not be construed as limiting this application.

[0028] In the description of this application, it should be understood that the orientation descriptions, such as up, down, front, back, left, right, etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.

[0029] In the description of this application, "several" means one or more, "more than" means two or more, "greater than," "less than," and "exceeding" are understood to exclude the stated number, while "above," "below," and "within" are understood to include the stated number. The use of "first" and "second" in the description is merely for distinguishing technical features and should not be construed as indicating or implying relative importance, or implicitly indicating the number of indicated technical features, or implicitly indicating the order of the indicated technical features.

[0030] In the description of this application, unless otherwise expressly defined, terms such as "setup," "installation," and "connection" should be interpreted broadly, and those skilled in the art can reasonably determine the specific meaning of the above terms in this application in conjunction with the specific content of the technical solution.

[0031] In some cases, existing technologies for controlling indoor thermal environments in air conditioners mostly still employ a control approach based on a uniform set temperature and simple sensor-based correction. These include the following typical scenarios: The first scenario: Control approach using fixed or experience-recommended settings: Air conditioner set temperatures are usually based on experience-recommended values, such as 26℃, which are considered a compromise between comfort and energy saving in engineering and product marketing. This setting is considered acceptable for most ordinary adults and in general office and home scenarios, so many users (such as the elderly or those with weak constitutions) habitually use this default value and do not actively adjust it.

[0032] The second scenario involves setting target values ​​based on usage habits or simple intelligent learning: Existing solutions record users' historical usage data to create a profile of preferred temperatures, fan speeds, etc., and automatically set the air conditioner to these preferred values ​​during subsequent use. Alternatively, some smart air conditioners utilize simple intelligent learning algorithms to automatically recommend or restore the user's frequently used temperature settings based on temperature settings and environmental changes over a period of time.

[0033] The third scenario: target value correction based on environmental or crowd detection: existing solutions use smart sensors to identify whether there are people indoors, the distribution of people, activity areas, etc., and make certain corrections to the original set values. For example, when no one is detected indoors, the set temperature is automatically increased to save energy; when someone is detected entering or approaching the air outlet, the set temperature is appropriately reduced or the airflow direction is adjusted; some solutions can also switch the air conditioner's operating mode based on simple status labels such as whether there are people, no one, activity, or stillness.

[0034] In summary, existing air conditioner control technologies are mostly based on uniform recommended temperatures, user history habits, and simple population identification corrections, lacking systematic consideration of the special physiological characteristics and electricity consumption behavior of the elderly or those with weak constitutions.

[0035] Therefore, the fundamental problems with existing technical solutions are as follows: First, the technical focus is biased towards uniform comfort and simple energy saving, without comprehensive modeling of the physiological state (degree of weakness, changes in physical condition), behavioral characteristics (lack of active adjustment, fear of wasting electricity), and sensory characteristics (sensitivity to wind, insensitivity to temperature) of the elderly or those with weak constitutions; Second, crowd recognition and intelligent learning remain at the level of whether someone is present or not, and the memorization of set values, lacking quantitative assessment of multi-dimensional health indicators such as the basic state, endurance state, and strength state of the elderly or those with weak constitutions, and even more so, failing to systematically correlate these assessment results with the temperature, wind speed, wind mode, and energy consumption control of the air conditioner.

[0036] Therefore, in scenarios involving the elderly, existing solutions are prone to the following problems: environmental target settings deviate from the appropriate range for extended periods; uncomfortable or even health-risk environmental conditions are not corrected in a timely manner; and due to concerns about energy consumption, neither true energy saving nor ensuring thermal comfort and health is achieved. To address these issues, it is necessary to propose an air conditioning system specifically designed for the elderly or those with weakened constitutions. This system should conduct multi-dimensional assessments of the user's basic condition, endurance, and strength, and, considering the elderly's cost sensitivity and specific needs regarding airflow, employ a multi-objective optimization control strategy. This would achieve reasonable energy consumption control while ensuring health and comfort, thereby improving the current situation where people either do not use air conditioning or do not know how to adjust it.

[0037] Based on the above, this application proposes an air conditioner and its control method, controller, medium and program product, aiming to take into account the special physiological characteristics of special groups and reduce the health risks of special groups during the use of air conditioners.

[0038] The various embodiments of the air conditioner control method of this application will be further described below with reference to the accompanying drawings.

[0039] like Figure 1 As shown, Figure 1This is a flowchart of a control method for an air conditioner provided in one embodiment of this application. The control method for the air conditioner may include, but is not limited to, steps S110 and S120.

[0040] Step S110: Obtain the current operating parameters of the air conditioner and the health level of the target object, wherein the health level is determined by at least one of the target object's health information, endurance information, and strength information; Step S120: Adjust the operating parameters of the air conditioner according to the current operating parameters and health level.

[0041] Specifically, this application embodiment acquires the current operating parameters of the air conditioner and the health level determined by at least one of the target object's health information, endurance information, and strength information. Based on the health level, it can determine the degree of physical weakness of the target object, thereby optimizing the air conditioner's operating parameters such as temperature, fan speed, and airflow mode. Compared to existing technologies that do not consider the physiological characteristics of special groups such as the elderly and those with weak constitutions, the control method of this application embodiment can adapt to target objects with different physical levels and differentiate the air conditioner's operating parameters. It can meet the physiological needs of special groups, promptly improve uncomfortable and health-risk indoor environments, and enhance the air conditioning experience for special groups.

[0042] It is understood that the aforementioned current operating parameters may be temperature parameters, wind speed parameters, wind sensing mode, or other types of parameters, and this application embodiment does not specifically limit them.

[0043] It should be noted that the target group mentioned above can be a special age group, such as the elderly or young infants, or a person with weak constitution, such as a person recovering from surgery, a patient with chronic disease, or a pregnant woman, or other groups. This application does not specifically limit this.

[0044] Additionally, it should be noted that the aforementioned health information is used to characterize the target individual's physical constitution and health stability. Specifically, this health information reflects the target individual's basic physical condition and level of health stability, and can reflect differences in the user's own basic health level. This information may include, but is not limited to, physical examination information (basic disease information) and physical constitution information. This information can be imported through physical examination data or obtained through a questionnaire.

[0045] Additionally, it should be noted that the aforementioned endurance information is used to characterize the target subject's endurance level during the activity; specifically, the aforementioned endurance information can reflect the target subject's physical endurance during daily activities, and combined with this information, a complete assessment of the user's physical function can be made; specifically, it may include, but is not limited to, the amount of aerobic exercise activity, which can be obtained through smart wearable devices or question-and-answer sessions.

[0046] Additionally, it should be noted that the aforementioned strength information is used to characterize the muscle strength status of the target object; specifically, the aforementioned strength information can reflect the muscle strength status of the target object; it may include, but is not limited to, anaerobic exercise activity, wherein the anaerobic exercise activity can be obtained through smart wearable devices or question-and-answer sessions.

[0047] Additionally, it should be noted that the aforementioned health level can include at least two or more levels. For example, the health level can be divided into healthy, sub-healthy, and weak. Furthermore, this health level can be determined based on one of the aforementioned health information, endurance information, and strength information, or two of the aforementioned three information, or all three of the aforementioned information.

[0048] like Figure 2 As shown, Figure 2 yes Figure 1 A flowchart of a sub-step of step S120. Step S120 may include, but is not limited to, steps S210, S220, and S230.

[0049] Step S210: Determine the target recommended range corresponding to the operating parameters of the air conditioner based on the health level; Step S220: Determine thermal comfort deviation and / or health risk indicators based on current operating parameters and target recommended range; Step S230: Adjust the operating parameters of the air conditioner according to thermal comfort deviation and / or health risk indicators.

[0050] Specifically, firstly, a target recommended range of air conditioner operating parameters suitable for the user's physical condition is matched according to the user's health level. Next, the current operating parameters of the air conditioner are compared with the target recommended range to calculate the corresponding thermal comfort deviation E_comfort and / or health risk index E_health. Finally, the operating parameters of the air conditioner are adjusted based on the calculated thermal comfort deviation E_comfort and / or health risk index E_health. This embodiment of the application can match appropriate parameter ranges according to different users' physical conditions, quantify the physical discomfort and health risks caused by the current environment, correct operating parameters that deviate from the adaptation standard, and prevent the target user from being in an indoor thermal environment that easily causes discomfort and poses health risks for a long time, meeting the usage needs of people with poor physiological tolerance, such as the elderly and those with weak constitutions.

[0051] It should be noted that the aforementioned target recommendation range can be a room temperature recommendation range, a wind speed recommendation range, a wind feel recommendation mode range, or other types of parameter ranges. This application embodiment does not specifically limit this.

[0052] Additionally, it's important to clarify that the aforementioned thermal comfort deviation E_comfort measures the difference between the air conditioner's current operating parameters and the ideal range for temperature, fan speed, and airflow mode suited to the user's physical condition. For example, if the air conditioner's temperature and fan speed are outside the comfort range corresponding to the health level, or if the current airflow mode is not the most suitable for the user, a thermal comfort deviation E_comfort will occur. The larger the deviation value, the stronger the discomfort caused by the current environment to the user. The system will proactively adjust the air conditioner's operating parameters based on this thermal comfort deviation E_comfort, adjusting the indoor environment to a comfortable range that matches the user's physical tolerance.

[0053] Additionally, it's important to note that the aforementioned health risk indicator, E_health, quantifies the level of health risks posed to users by the current indoor environment. This includes factors such as whether the indoor temperature is too cold or too hot, and whether temperature or wind speed changes too rapidly. It also considers the user's individual health level. The weaker the user's constitution, the greater the deviation of the indoor temperature from the safe and comfortable range, and the more drastic the temperature and wind fluctuations, the higher the E_health value, indicating that the environment is more likely to induce problems such as catching a cold, feeling stuffy, or experiencing cardiovascular discomfort. The system will use this E_health indicator to manage hazardous conditions and reduce health hazards caused by high temperatures, low temperatures, and sudden changes in airflow.

[0054] like Figure 3 As shown, Figure 3 yes Figure 2A flowchart of a sub-step of step S230. Step S230 may include, but is not limited to, steps S310, S320, and S330.

[0055] Step S310: Obtain the operating power and time window of the air conditioner; Step S320: Determine the energy consumption index of the air conditioner based on the operating power and time window; Step S330: Adjust the operating parameters of the air conditioner according to thermal comfort deviation, energy consumption index and health risk index.

[0056] Specifically, this embodiment first determines the operating power of the air conditioner and a preset control time window. Then, it calculates the energy consumption index E_energy by combining the operating power and the time window. Finally, it adjusts the air conditioner's operating parameters by comprehensively considering three types of data: thermal comfort deviation E_comfort, energy consumption index E_energy, and health risk index E_health. Therefore, this embodiment not only considers user comfort and environmental health and safety but also simultaneously takes into account the air conditioner's power consumption. It avoids excessive power consumption in pursuit of comfort, and balances electricity costs while adapting to the physical conditions of special groups such as the elderly and those with weak constitutions and reducing environmental health risks, achieving a control effect that balances comfort, health, and energy saving.

[0057] In addition to adjusting the air conditioner's operating parameters based on thermal comfort deviation E_comfort and / or health risk index E_health as described in the above embodiments, and adjusting the air conditioner's operating parameters based on thermal comfort deviation E_comfort, energy consumption index E_energy, and health risk index E_health, the embodiments of this application may also be as follows: adjusting the air conditioner's operating parameters based on at least one of thermal comfort deviation E_comfort, energy consumption index E_energy, and health risk index E_health. For example, adjusting the air conditioner's operating parameters based on health risk index E_health, or adjusting the air conditioner's operating parameters based on thermal comfort deviation E_comfort and health risk index E_health, or adjusting the air conditioner's operating parameters based on energy consumption index E_energy and health risk index E_health.

[0058] It should be noted that the energy consumption index E_energy mentioned above is a quantitative value used to measure the amount of electricity consumed by an air conditioner within a fixed time window. The system collects the operating power of the air conditioner's compressor and fan, the operating power of other loads, and the operating duration during this period. The energy consumption index E_energy is calculated by this data; a higher value indicates that the air conditioner consumes more electricity. When adjusting the air conditioner's operating parameters, the energy consumption index E_energy can be considered together with the thermal comfort deviation E_comfort and the health risk index E_health. This avoids prioritizing comfort or health at the expense of electricity consumption, and can accommodate the needs of special groups who are concerned about high electricity bills, achieving a balance between comfort, health, and energy saving.

[0059] like Figure 4 As shown, Figure 4 yes Figure 3 A flowchart of a sub-step of step S330. Step S330 may include, but is not limited to, steps S410, S420, and S430.

[0060] Step S410: Determine the first weight parameter corresponding to thermal comfort deviation, the second weight parameter corresponding to energy consumption index, and the third weight parameter corresponding to health risk index; Step S420: Based on the comprehensive evaluation function, calculate the optimal value of the comprehensive evaluation function according to the thermal comfort deviation and the first weight parameter, the energy consumption index and the second weight parameter, the health risk index and the third weight parameter; Step S430: Adjust the operating parameters of the air conditioner according to the optimal value of the function.

[0061] Specifically, firstly, this embodiment determines three types of weight parameters: a first weight parameter δ matching the thermal comfort deviation E_comfort, a second weight parameter λ matching the energy consumption index E_energy, and a third weight parameter μ matching the health risk index E_health. Next, the thermal comfort deviation E_comfort, energy consumption index E_energy, and health risk index E_health are input into a comprehensive evaluation function along with their respective weight parameters for calculation. This yields the optimal value of the comprehensive evaluation function, which is a function that weights and fuses multiple evaluation indices (thermal comfort deviation E_comfort, energy consumption index E_energy, and health risk index E_health) into a single evaluation value. This function is used for multi-objective optimization to find the optimal operating parameters that balance thermal comfort, energy consumption, and health risk. Finally, this embodiment adjusts the air conditioner's operating parameters based on the aforementioned optimal function value.

[0062] It should be noted that, by setting multiple adaptively adjustable weight parameters, this application embodiment can allocate the priority of comfort, energy consumption, and health risk in regulation according to the target object's physical condition, energy-saving concern, and the current environmental health hazard level. When the physical condition is weaker, the impact of thermal comfort and health risk is amplified; when the user is more concerned about electricity bills, the reference weight of energy consumption indicators is increased; and in high-temperature and low-temperature dangerous environments, the constraint effect of health risk is strengthened. Ultimately, the air conditioning operation parameters are adjusted to take into account physical comfort, energy saving, and physical health protection.

[0063] In addition to the above embodiments, which calculate the optimal value of the comprehensive evaluation function based on the thermal comfort deviation E_comfort and the first weight parameter δ, the energy consumption index E_energy and the second weight parameter λ, the health risk index E_health and the third weight parameter μ, the embodiments of this application may also be as follows: The optimal value of the comprehensive evaluation function is calculated based on one or two of the following three sets of data: thermal comfort deviation E_comfort and the first weight parameter δ, energy consumption index E_energy and the second weight parameter λ, and health risk index E_health and the third weight parameter μ.

[0064] like Figure 5 As shown, Figure 5 This is a flowchart illustrating the steps for obtaining the thermal comfort deviation E_comfort, as described in one embodiment. The process for obtaining the thermal comfort deviation E_comfort may include, but is not limited to, steps S510 and S520.

[0065] Step S510: Determine the first parameter deviation between the current operating parameters and the target recommended interval; Step S520: Determine the thermal comfort deviation based on the first target weight parameter and the first parameter deviation, wherein the thermal comfort deviation includes at least one of temperature thermal comfort deviation, wind speed thermal comfort deviation, and wind mode thermal comfort deviation.

[0066] Specifically, firstly, this embodiment compares the current operating parameters of the air conditioner with the target recommended range for the user's health level to calculate first parameter deviations such as temperature deviation, wind speed deviation, and wind mode deviation. Then, this embodiment uses the corresponding sub-item weight coefficients and each first parameter deviation to calculate a thermal comfort deviation including at least one of temperature, wind speed, and wind mode.

[0067] It should be noted that the embodiments of this application can quantify the discomfort caused by temperature, airflow speed, and airflow mode, and can also adaptively adjust the proportion of each sub-item weight coefficient according to the user's physical condition, distinguish the physical discomfort caused by different factors, so that the air conditioner can improve the options in temperature, airflow speed, and airflow mode that are not suitable for the user's physical condition, and meet the special physical needs of the elderly and the weak for mild temperature and gentle airflow.

[0068] Additionally, it should be noted that steps S510 and S520 described above may include, but are not limited to, the following: Figures 6 to 8 The three implementation scenarios are as follows: like Figure 6 As shown, Figure 6 This is a flowchart of a sub-step of steps S510 and S520. Steps S510 and S520 may include, but are not limited to, steps S610 and S620.

[0069] Step S610: Given that the current operating parameters include the current indoor temperature and the target recommended range includes the temperature recommended range, determine the temperature deviation between the current indoor temperature and the temperature recommended range; Step S620: Obtain the temperature thermal comfort deviation based on the temperature weighting parameter and the temperature deviation, wherein the temperature weighting parameter is determined by the first weighting parameter.

[0070] Specifically, firstly, after collecting the current indoor temperature T and the recommended temperature range [T_min, T_max] that matches the user's health level, this embodiment calculates the temperature deviation ΔT between the current indoor temperature T and the recommended temperature range [T_min, T_max]. Then, this embodiment calculates the thermal comfort deviation based on the temperature deviation ΔT and the temperature weight parameter w_T determined by the first weight parameter δ.

[0071] Therefore, the embodiments of this application can quantify the physical discomfort caused by temperature deviation from the suitable range. Moreover, the weaker the user's physical condition, the higher the value of the temperature weight parameter w_T, which will amplify the impact of temperature deviation ΔT on the overall comfort evaluation. This allows the air conditioner to prioritize correcting indoor temperatures that deviate from the suitable range during regulation, thus meeting the special room temperature needs of the elderly and the frail.

[0072] It should be noted that the temperature weighting parameter w_T can be determined by the first weighting parameter δ or it can be preset. This application embodiment does not specifically limit this.

[0073] like Figure 7 As shown, Figure 7This is a flowchart of another sub-step of steps S510 and S520. Steps S510 and S520 may include, but are not limited to, steps S710 and S720.

[0074] Step S710: Given that the current operating parameters include the current supply air velocity and the target recommended range includes the wind speed recommended range, determine the wind speed deviation between the current supply air velocity and the wind speed recommended range; Step S720: Obtain the wind speed thermal comfort deviation based on the wind speed weight parameter and the wind speed deviation, wherein the wind speed weight parameter is determined by the first weight parameter.

[0075] Specifically, firstly, after collecting the current supply air velocity V and the recommended air velocity range [V_min, V_max] matching the user's health level, this embodiment of the application calculates the air velocity deviation ΔV between the current supply air velocity V and the recommended air velocity range [V_min, V_max]. Then, this embodiment of the application combines the air velocity deviation ΔV and the air velocity weight parameter w_V determined by the first weight parameter δ to calculate the air velocity thermal comfort deviation.

[0076] Therefore, the embodiments of this application can quantify the discomfort caused by wind speed deviating from the appropriate range. The wind speed weight parameter w_V corresponding to the weaker the user will be adjusted accordingly, amplifying the impact of wind speed deviation ΔV on comfort evaluation, so that the air conditioner avoids strong wind blowing directly, excessively high or low wind speed when adjusting, and meets the special needs of the elderly and the weak for wind speed.

[0077] It should be noted that the wind speed weighting parameter w_V can be determined by the first weighting parameter δ or it can be preset. This application embodiment does not specifically limit this.

[0078] like Figure 8 As shown, Figure 8 This is a flowchart of another sub-step of steps S510 and S520. Steps S510 and S520 may include, but are not limited to, steps S810 and S820.

[0079] Step S810: If the current operating parameters include the current score corresponding to the current wind feel mode and the target recommendation range includes the target score corresponding to the recommended wind feel mode, determine the wind feel mode deviation between the current score and the target score. Step S820: Obtain the wind speed thermal comfort deviation based on the wind mode weight parameter and the wind mode deviation, wherein the wind mode weight parameter is determined by the first weight parameter.

[0080] Specifically, firstly, after collecting the current rating S_current of the current wind mode and the target rating S_best of the recommended wind mode that is adapted to the user's health level, this embodiment of the application calculates the wind mode deviation ΔF between the two. Then, this embodiment of the application combines the wind mode deviation ΔF and the wind mode weight parameter w_F determined by the first weight parameter δ to obtain the wind thermal comfort deviation.

[0081] Therefore, the embodiments of this application can quantify the discomfort caused by an unsuitable wind mode. The weaker the user's physical condition, the higher the corresponding wind mode weight parameter w_F, which will amplify the impact of the wind mode deviation ΔF on the overall comfort. When adjusting, the air conditioner will switch to a gentler and less irritating wind mode to reduce the discomfort caused by direct airflow and strong airflow to the elderly, the weak, and other groups, and meet the special needs of the elderly and the weak for wind.

[0082] Additionally, it should be noted that the wind mode weight parameter w_F can be determined by the first weight parameter δ or it can be preset. This application embodiment does not specifically limit this.

[0083] In one embodiment, the energy consumption index E_energy can be obtained in the following two ways: In one embodiment, step S320 described above may be: when the operating power includes the current compressor power P_comp and fan power P_fan, the energy consumption index E_energy of the air conditioner is determined based on the compressor power P_comp and the cumulative compressor operating time t_comp in the time window Δt, and the fan power P_fan and the cumulative fan operating time t_fan in the time window Δt.

[0084] Specifically, in this embodiment, the compressor power P_comp and fan power P_fan are obtained separately. Combined with the cumulative operating time of the compressor and fan within the corresponding time window Δt, the power consumption of the two components is calculated separately and integrated to obtain the overall energy consumption index E_energy of the air conditioner. Therefore, through this calculation method, this embodiment can reflect the actual power consumption of the air conditioner. When adjusting parameters subsequently, the power consumption of different operating modes can be evaluated. Under the premise of ensuring the comfort and health safety of the elderly and frail, the operating status of the compressor and fan can be reasonably controlled, effectively balancing indoor health and wellness needs with electricity costs.

[0085] In one embodiment, step S320 above can also be: when the operating power includes the equivalent power P_eq of the air conditioner, the energy consumption index E_energy of the air conditioner is determined based on the equivalent power P_eq and the time window Δt.

[0086] Specifically, in addition to the above calculation method, the embodiments of this application can also directly use the overall equivalent power P_eq of the air conditioner and the preset time window Δt to calculate the energy consumption index E_energy, without having to separately calculate the power and running time of the compressor and fan. The calculation logic is simple and the computational burden is lower, enabling the system to quickly obtain the energy consumption information.

[0087] In one embodiment, the health risk indicator E_health may include at least one of the following: temperature risk component E_T_health, temperature change rate risk component E_RT, and wind speed change rate risk component E_RV.

[0088] like Figure 9 As shown, Figure 9 This is a flowchart illustrating the steps for obtaining health risk indicators, as exemplified by one embodiment. The process for obtaining health risk indicators may include, but is not limited to, steps S910 and S920.

[0089] Step S910: Determine the second parameter deviation between the current operating parameters and the target recommended interval; Step S920: Determine the health risk indicators based on the second target weight parameter and the deviation of the second parameter.

[0090] Specifically, this embodiment compares the current operating parameters of the air conditioner with the target recommended range for the user's health level to obtain the corresponding second parameter deviation. Then, this embodiment calculates a health risk index by combining the second target weight parameter and the second parameter deviation. This index may include multiple risk components such as temperature, temperature change rate, and wind speed change rate. This embodiment can quantify the health hazards caused by deviations of various environmental parameters from safe ranges, and then differentiate the degree of risk impact by combining corresponding weights. It can identify working conditions unfavorable to the elderly and frail, such as excessively cold or hot conditions and drastic fluctuations in temperature, allowing air conditioning adjustments to prioritize avoiding high-risk environments.

[0091] like Figure 10 As shown, Figure 10 This is a flowchart illustrating the steps for obtaining health risk indicators, as described in another embodiment. The process for obtaining health risk indicators may include, but is not limited to, steps S1010 and S1020.

[0092] Step S1010: Determine the deviation of the third parameter between the current operating parameters and the preset rate parameters; Step S1020: Determine the health risk indicators based on the third target weight parameter and the third parameter deviation.

[0093] Specifically, this embodiment compares the current operating parameters of the air conditioner with the preset room temperature and fan speed change rates to calculate the third parameter deviation. Then, this embodiment combines the corresponding third target weight parameter with the third parameter deviation to calculate a health risk indicator. This embodiment can quantitatively assess working conditions where rapid temperature and fan speed changes can easily damage the health of the elderly and those with weakened constitutions, and it slows down the rate of temperature and fan speed changes during regulation, further reducing health risks.

[0094] In addition, such as Figure 11 As shown, Figure 11 This is a flowchart of a sub-step of steps S910 and S920. Steps S910 and S920 may include, but are not limited to, steps S1110 and S1120.

[0095] Step S1110: Given that the current operating parameters include the current indoor temperature and the target recommended range includes the temperature recommended range, determine the temperature deviation between the current indoor temperature and the temperature recommended range; Step S1120: Determine the temperature risk component based on the temperature deviation.

[0096] Specifically, firstly, when the current indoor temperature T is collected and the recommended temperature range [T_low, T_high] corresponding to the user's health level is obtained, the temperature deviation between the two is calculated. Then, a temperature risk component E_T_health is generated based on this temperature deviation. This embodiment of the application uses this temperature risk component E_T_health to quantify the health risks caused by room temperature exceeding the suitable range. Considering the low tolerance for cold and heat among the elderly and those with weak constitutions, the air conditioner will primarily refer to this temperature risk component E_T_health when adjusting the temperature, regulating the room temperature to a safe and mild range to avoid discomfort caused by excessively cold or hot environments and reduce temperature-related health risks.

[0097] In one embodiment, if the current indoor temperature T is within the recommended temperature range [T_low, T_high], the temperature risk component E_T_health can take a small constant value or be approximately 0.

[0098] In one embodiment, if the current indoor temperature T is lower than the low temperature threshold T_cold, the temperature risk component E_T_health is determined based on the temperature deviation between the current indoor temperature T and the low temperature threshold T_cold and the low temperature risk coefficient k_cold, wherein the low temperature threshold T_cold is less than or equal to the recommended temperature range [T_low, T_high].

[0099] In one embodiment, if the current indoor temperature T is higher than the high temperature threshold T_hot, the temperature risk component E_T_health is determined based on the temperature deviation between the current indoor temperature T and the high temperature threshold T_hot and the high temperature risk coefficient k_hot, wherein the high temperature threshold T_hot is greater than or equal to the recommended temperature range [T_low, T_high].

[0100] In addition, such as Figure 12 As shown, Figure 12 This is a flowchart of a sub-step of steps S1010 and S1020. Steps S1010 and S1020 may include, but are not limited to, steps S1210 and S1220.

[0101] Step S1210: Given the current operating parameters, including the current rate of temperature change of indoor temperature, determine the temperature change rate deviation between the current rate of temperature change and the preset rate of temperature change. Step S1220: Determine the temperature change rate risk component based on the temperature change rate deviation and the first risk weight coefficient, wherein the first risk weight coefficient is determined by the first weight parameter.

[0102] Specifically, in this embodiment, after obtaining the current rate of change of indoor temperature, it compares it with a preset rate of change of temperature to calculate the deviation ΔR_T of the rate of change. Then, this embodiment combines the deviation ΔR_T of the rate of change of temperature with the first risk weight coefficient k_RT determined by the first weight parameter δ to obtain the risk component E_RT of the rate of change of temperature. This embodiment can quantify the health hazards caused by rapid rises and falls in room temperature. The weaker the user's constitution, the higher the corresponding first risk weight coefficient k_RT, which amplifies the risk proportion corresponding to drastic temperature fluctuations. In response, the air conditioner will slow down the rate of heating and cooling during regulation to reduce the discomfort and health problems caused by rapid temperature changes.

[0103] In addition, such as Figure 13 As shown, Figure 13 This is a flowchart of another sub-step of steps S1010 and S1020. Steps S1010 and S1020 may include, but are not limited to, steps S1310 and S1320.

[0104] Step S1310: Given that the current operating parameters include the current rate of change of the outlet wind speed, determine the deviation of the current rate of change of the wind speed from the preset rate of change of the wind speed. Step S1320: Determine the wind speed change rate risk component based on the wind speed change rate deviation and the second risk weight coefficient, wherein the second risk weight coefficient is determined by the first weight parameter.

[0105] Specifically, in this embodiment, after obtaining the real-time rate of change of the air outlet wind speed, it compares it with a preset rate of change of wind speed to obtain the wind speed change rate deviation ΔR_V. Then, this embodiment combines the wind speed change rate deviation ΔR_V with the second risk weight coefficient k_RV determined by the first weight parameter δ to calculate the wind speed change rate risk component E_RV. This embodiment can quantify the health hazards caused by sudden changes in wind speed. The weaker the user's constitution, the larger the second risk weight coefficient k_RV, which increases the impact of drastic wind speed fluctuations on health assessment. Therefore, the air conditioner will adjust the fan speed smoothly during control to reduce discomfort and health problems caused by drastic changes in airflow.

[0106] In addition, such as Figure 14 As shown, Figure 14 This is a flowchart illustrating the steps for obtaining health risk indicators, as described in another embodiment. The process for obtaining health risk indicators may include, but is not limited to, steps S1410 and S1420.

[0107] Step S1410: Determine the temperature risk weight coefficient corresponding to the temperature risk component, the temperature change rate risk weight coefficient corresponding to the temperature change rate risk component, and the wind speed change rate risk weight coefficient corresponding to the wind speed change rate risk component. Step S1420: Calculate the health risk index based on the temperature risk weight coefficient and temperature risk component, the temperature change rate risk weight coefficient and temperature change rate risk component, and the wind speed change rate risk weight coefficient and wind speed change rate risk component.

[0108] Specifically, in this embodiment, the temperature risk weight coefficient a_T of the temperature risk component E_T_health, the temperature change rate risk weight coefficient a_RT of the temperature change rate risk component E_RT, and the wind speed change rate risk weight coefficient a_RV of the wind speed change rate risk component E_RV are configured respectively, corresponding to the independent weights of the temperature risk component E_T_health, the temperature change rate risk component E_RT, and the wind speed change rate risk component E_RV. Then, this embodiment multiplies each type of risk component with its corresponding weight coefficient and integrates the results to calculate the total health risk index E_health.

[0109] Therefore, the embodiments of this application can distinguish the relative importance of the impact of temperature, sudden temperature changes, and wind speed fluctuations on users. For the elderly and those with weak constitutions, the weights of temperature, temperature fluctuations, and wind speed fluctuations can be increased accordingly to reduce the physical discomfort caused by various environmental stimuli to the elderly and those with weak constitutions.

[0110] In addition, such as Figure 15 As shown, Figure 15 This is a flowchart illustrating the steps for obtaining the first weighting parameter in one embodiment. The first weighting parameter, used to characterize the degree of physical weakness of the target object, can be obtained through steps S1510 and S1520, among others.

[0111] Step S1510: Quantify the health information, endurance information and strength information to obtain the target object's basic status grade, endurance status grade and strength status grade, respectively; Step S1520: The basic condition rating, endurance condition rating, and strength condition rating are superimposed to obtain the first weight parameter, which corresponds to the health level.

[0112] Specifically, for the first weighted parameter δ used to characterize the degree of physical weakness of the target object, this embodiment first collects the target object's health information, endurance information, and strength information, and quantifies and grades them respectively to obtain the basic state grade α, endurance state grade β, and strength state grade γ. Then, this embodiment superimposes the above three grades to generate the first weighted parameter δ corresponding to the user's health level, i.e., δ=α+β+γ. Therefore, this embodiment considers the user's basic health, physical endurance, and muscle strength multi-dimensional physical data to accurately distinguish the strength of physical tolerance of different groups. The weaker the physical condition, the higher the value of the first weighted parameter δ. This amplifies the negative impact of the uncomfortable warm air environment when calculating various comfort deviations and health risk components in the future, making the air conditioning control logic more gentle and more in line with the physical characteristics of the elderly and the frail.

[0113] In one embodiment, the health level can be determined by a first weighting parameter. For example, if the range of the first weighting parameter δ is 0-25, the health level is divided into three categories: healthy, sub-healthy, and weak. The value range of 0-5 is healthy, the value range of 6-15 is sub-healthy, and the value range of 16-25 is weak.

[0114] In addition, such as Figure 16 As shown, Figure 16 This is a flowchart illustrating the steps for obtaining the second weighting parameter in one embodiment. The second weighting parameter, used to characterize the target object's sensitivity to the electricity costs of the air conditioner, can be obtained through steps S1610 and S1620, among others.

[0115] Step S1610: Receive input energy-saving preference options and / or historical electricity consumption behavior within a preset observation period; Step S1620: Determine the second weighting parameter based on energy-saving preference options and / or historical electricity consumption behavior.

[0116] In one embodiment, for the second weighting parameter λ used to determine the user's sensitivity to electricity costs, this embodiment can receive energy-saving preference options manually selected by the user, such as "more comfortable," "balanced," or "more energy-saving," and then obtain the second weighting parameter λ by matching the corresponding value according to the selected level. The higher the energy-saving level selected by the user, the larger the value of the second weighting parameter λ. This will increase the weighting of energy consumption indicators when adjusting the air conditioner's operating parameters, prioritizing the constraint of air conditioner power consumption while ensuring a comfortable and safe indoor environment for the frail and elderly, thus better meeting the user's energy-saving needs.

[0117] In one embodiment, for the second weighting parameter λ used to determine the user's sensitivity to electricity costs, this application embodiment can collect the user's historical electricity consumption behavior of using the air conditioner within a preset observation period, and then calculate and match the corresponding second weighting parameter λ based on past electricity consumption habits such as turning the air conditioner on and off, adjusting temperature and fan speed, and selecting fan mode. If the user habitually lowers the cooling and heating power, shortens the operating time, and frequently selects the energy-saving mode, then it will be determined that their energy-saving demand is high, and the value of the second weighting parameter λ will be larger. In subsequent adjustments to the air conditioner's operating parameters, the weight of energy consumption indicators will be increased. Under the premise of ensuring a comfortable and safe indoor environment for the frail and elderly, the power consumption of the air conditioner will be constrained first, so as to better meet the user's energy-saving needs.

[0118] In one embodiment, for the second weighting parameter λ used to determine the user's sensitivity to electricity costs, this application embodiment can receive the energy-saving preference settings manually selected by the user and the previous electricity usage records of the air conditioner during an observation period, and then combine the user's energy-saving selection and past electricity usage habits to comprehensively determine the magnitude of the second weighting parameter λ.

[0119] In addition, such as Figure 17 As shown, Figure 17 This is a flowchart illustrating the steps for obtaining the third weighting parameter in one embodiment. The third weighting parameter, used to characterize the degree of influence of the target object on regulating health risk items under different temperature ranges or different health states, may be obtained through steps S1710, S1720, and S1730, among others.

[0120] Step S1710: Determine the basic health weight based on the first weight parameter; Step S1720: Given that the current operating parameters include the current indoor temperature and the target recommended range includes the temperature recommended range, determine the risk correction coefficient based on the current indoor temperature and the temperature recommended range; Step S1730: Determine the third weight parameter based on the basic health weight and the risk correction coefficient.

[0121] Specifically, for the third weighting parameter μ used to characterize the impact of health risk items, firstly, this embodiment calculates the basic health weight μ_base based on the first weighting parameter δ, which reflects the user's physical strength. The weaker the physical condition, the higher the value of the basic health weight μ_base. Next, this embodiment compares the current indoor temperature T with the recommended temperature range [T_low, T_high] for the user and calculates the risk correction coefficient k_temp, which is used to correct the risk percentage. The further the room temperature deviates from the safe and suitable range, the larger the value of this correction coefficient. Finally, this embodiment combines the basic health weight μ_base and the risk correction coefficient k_temp to calculate the third weighting parameter μ. Therefore, this embodiment takes into account both the user's physical tolerance and the danger level of the current indoor temperature. When a person with weak physical condition is in an excessively cold or hot environment, the third weighting parameter μ will increase accordingly. When adjusting the air conditioner parameters, extreme room temperatures will be corrected first to reduce the physical stimulation caused by high and low temperatures to the elderly and people with weak physical condition.

[0122] Based on the air conditioner control methods of the above embodiments, the overall embodiments of the air conditioner control methods of this application are presented below.

[0123] like Figures 18 to 24 As shown, Figure 18 This is a diagram illustrating the grading mechanism for the elderly population. Figure 19 This is a schematic diagram illustrating the calculation method for the basic state classification α. Figure 20 This is a schematic diagram illustrating the calculation methods for endurance condition classification β and strength condition classification γ. Figure 21 This is a schematic diagram illustrating the assignment of values ​​to the endurance condition classification β. Figure 22 This is a schematic diagram illustrating the assignment of values ​​to the force state classification γ. Figure 23 This is a schematic diagram illustrating the calculation method for δ, a criterion used to classify and categorize the elderly population. Figure 24 This is a schematic diagram illustrating the usage logic and control methods of air conditioning for the elderly.

[0124] In one embodiment, the silver-haired population refers to middle-aged and elderly users whose age has reached or exceeded a certain threshold (e.g., 60 or 65 years old), who typically have physiological characteristics such as decreased thermoregulation, reduced metabolic level, and increased burden on the cardiovascular system.

[0125] Basic status grading α: This is a quantitative grading indicator for the basic physiological and health status of the elderly population, used to reflect the user's overall physical condition and health stability. In one embodiment of this application, basic status grading α can be obtained based on a comprehensive assessment of one or more of the following indicators: weight and its rate of change (e.g., the percentage of weight loss over a certain period); body mass index (BMI); past medical history or chronic disease status; and the stability of basic physiological parameters such as resting heart rate and blood pressure. Figure 19 In This is the proportionality coefficient.

[0126] Endurance Status Rating β: This is a quantitative rating index for the activity endurance and physical endurance of the elderly, used to reflect the user's endurance level during continuous activities or daily walking.

[0127] In one embodiment of this application, the endurance status rating β can be obtained based on a comprehensive evaluation of one or more of the following indicators: walking speed; long walking distance or similar physical fitness tests; daily activity volume (which can be recorded by wearable devices, such as step count and activity duration); subjective fatigue evaluation, etc. Figure 21 T_B in the figure represents the measured value of the endurance condition.

[0128] Strength status grading γ: This is a quantitative grading index for muscle strength and motor ability in the elderly, used to reflect the user's muscle strength status, such as upper and lower limb muscle strength. In one embodiment of this application, strength status grading γ can be obtained based on a comprehensive evaluation of one or more of the following indicators: grip strength test results; lower limb muscle strength test results (e.g., number of chair-stand tests); and whether assistive tools (such as walking aids) are needed during daily activities. Figure 22 T_C in the figure represents the measured value of the force state.

[0129] Health level: refers to the comprehensive grading result of the overall frailty of the elderly population based on the combination of the above-mentioned basic condition grading α, endurance condition grading β, and strength condition grading γ. In one embodiment of this application, the grading results of the three dimensions α, β, and γ can be integrated into several levels through preset scoring or mapping rules.

[0130] In one embodiment, an air conditioning control system suitable for the elderly includes a user status assessment module, a cost sensitivity determination module, a health risk assessment module, a multi-objective optimization control module, a wind sensing control module, and an air conditioning execution module. The user status assessment module obtains the basic status level α, endurance level β, and strength level γ of the elderly user and determines the weight of the thermal comfort deviation, i.e., the first weight parameter δ. The cost sensitivity determination module determines the cost sensitivity parameter, i.e., the second weight parameter λ, based on user input information and historical electricity consumption behavior. The health risk assessment module determines the health risk coefficient, i.e., the third weight parameter μ, based on the relationship between indoor temperature and recommended temperature range and high / low temperature thresholds. The multi-objective optimization control module constructs a comprehensive evaluation function including thermal comfort, energy consumption, and health risk: J = δ × E_comfort + λ × E_energy + μ × E_health, where E_comfort is the thermal comfort deviation, E_energy is the energy consumption index, and E_health is the health risk index. The first weighting parameter δ of the thermal comfort deviation is determined based on α, β, and γ; the second weighting parameter λ of the energy consumption index is determined based on user input information and historical electricity consumption behavior; and the third weighting parameter μ of the health risk index is determined based on the relative risk of adverse health events (such as heatstroke, cardiovascular events, etc.). The wind control module is used to select at least one wind mode from the regular wind mode, circulating fan mode, anti-direct blowing mode, and no wind mode. The air conditioning execution module is used to jointly adjust the air supply temperature, wind speed, and wind mode of the air conditioner based on the optimal value of the objective function obtained by the multi-objective optimization control module, so as to achieve comprehensive optimization of the comfort and energy consumption of elderly users under the premise of meeting health risk constraints.

[0131] In one embodiment, the level of silver hair can be divided into three categories: healthy, sub-healthy, and weak; the level can be distinguished based on basic status information, strength information, and endurance information.

[0132] In one embodiment, health information includes: physical examination information (basic disease information), physical fitness information, etc. This information can be imported from physical examination data or obtained through a question-and-answer process.

[0133] In one embodiment, endurance information includes aerobic exercise activity. This can be obtained through smart wearable devices or a question-and-answer process.

[0134] In one embodiment, the strength information includes anaerobic exercise activity. This can be obtained through smart wearable devices or a question-and-answer process.

[0135] In one embodiment, the first weighting parameter of thermal comfort deviation, δ = α + β + γ, ranges from 0 to 25. Health levels are divided into three categories: healthy, sub-healthy, and weak. A value range of 0-5 indicates healthy; 6-15 indicates sub-healthy; and 16-25 indicates weak.

[0136] Among them, the larger the value of the first weight parameter δ of thermal comfort deviation E_comfort, the worse the physical condition or the higher the degree of weakness of the elderly user, and the higher the requirements for thermal comfort and health safety. Therefore, E_comfort and E_health are given greater weight in the comprehensive evaluation function.

[0137] In one embodiment, the second weight parameter λ of the energy consumption index E_energy is used to characterize the sensitivity of elderly users to the cost of air conditioning electricity. The larger the value of λ, the more the user cares about energy consumption, and the control strategy assigns a greater weight to E_energy in the comprehensive evaluation function.

[0138] In one embodiment, the third weighting parameter μ of the health risk indicator E_health is used to adjust the degree of influence of the health risk item in different temperature ranges and different health states. For example, when the indoor temperature is in the low temperature range, the value of μ is small, indicating that the health risk item mainly focuses on cold-related risks; when the indoor temperature exceeds the high temperature threshold and the δ value is large (for weak users), the value of μ increases significantly to emphasize the importance of controlling the temperature from rising further in the high temperature range.

[0139] In one embodiment of this application, for the air conditioning control process of elderly users, the control module constructs a comprehensive evaluation function that includes thermal comfort deviation E_comfort, energy consumption index E_energy, and health risk index E_health. To this end, it is first necessary to define the calculation method for thermal comfort deviation E_comfort.

[0140] In one embodiment, the thermal comfort deviation E_comfort is calculated based on the temperature deviation, wind speed deviation, and wind feeling pattern deviation, and can be specifically expressed as: E_comfort = w_T × (ΔT)^2 + w_V × (ΔV)^2 + w_F × ΔF, where ΔT is the temperature deviation, ΔV is the wind speed deviation, ΔF is the wind feeling pattern deviation, the temperature weight parameter w_T, the wind speed weight parameter w_V, and the wind feeling pattern weight parameter w_F are the weight coefficients corresponding to the temperature deviation ΔT, the wind speed deviation ΔV, and the wind feeling pattern deviation ΔF, respectively, and ^2 is a quadratic square operation.

[0141] Among them, the calculation of the temperature deviation ΔT determines the suitable room temperature range, that is, the temperature recommendation range [T_min, T_max], according to the comprehensive weight of the physical condition / weakness of the elderly users, namely the first weight parameter δ. The comprehensive weight δ of the physical condition / weakness is obtained by summing the basic state classification α, the endurance state classification β, and the strength state classification γ, that is: δ = α + β + γ. According to different values of δ, the elderly users can be divided into different health state levels such as healthy, sub-healthy, and weak, and different temperature recommendation ranges [T_min, T_max] are set accordingly. When δ is small, a relatively wide suitable room temperature range can be selected; when δ is large and the user is in a weak state, the suitable room temperature range is set to be narrower and偏向更温和的温度范围.

[0142] When the current indoor temperature is T, the temperature deviation ΔT is defined as the distance between the current temperature and the above temperature recommendation range. When T is within this range, ΔT is 0. When T exceeds the range boundary, the difference from the boundary is used as the deviation, specifically: When T_min ≤ T ≤ T_max, ΔT = 0; When T < T_min, ΔT = T_min - T; When T > T_max, ΔT = T - T_max.

[0143] Through the above definition, when the indoor temperature is within the suitable range of the elderly users, the temperature deviation term does not contribute to E_comfort; when the temperature deviates from the suitable range, the deviation increases in a square form with the degree of deviation, so as to give a higher penalty to a larger deviation.

[0144] In an embodiment, for the calculation of the wind speed deviation ΔV, similarly, according to the comprehensive weight δ of the physical condition / weakness of the elderly users and the wind feeling preference, the suitable wind speed range, that is, the wind speed recommendation range [V_min, V_max], is determined. For users with relatively poor physical conditions and more sensitive to the wind feeling, the upper limit V_max of the suitable wind speed can be set lower, and the range is appropriately narrowed; for users with better physical conditions and higher wind feeling tolerance, the wind speed range can be appropriately widened.

[0145] When the current air supply wind speed is V, the definition method of the wind speed deviation ΔV is similar to that of the temperature deviation, specifically: When V_min ≤ V ≤ V_max, ΔV = 0; When V < V_min, ΔV = V_min - V; When V > V_max, ΔV = V - V_max.

[0146] According to the above definition, when the wind speed is within a suitable range, the wind speed deviation is zero; when the wind speed is too low or too high, the deviation value increases with the degree of deviation, and is reflected in E_comfort through the (ΔV)^2 term.

[0147] In one embodiment, the calculation of the wind mode deviation ΔF considers multiple wind modes, including but not limited to circulating fan mode, anti-direct blowing mode, and no wind mode. To quantify the impact of wind mode on the comfort of elderly users, this embodiment pre-sets a suitability score S_i for each wind mode. This score is determined based on factors such as the elderly user's physical condition / weakness combined weight δ, wind sensitivity, and actual usage feedback.

[0148] The rating for the currently active wind mode is denoted as S_current, and the rating for the recommended wind mode with the highest compatibility among all available wind modes for the current senior user is denoted as S_best. The wind mode deviation ΔF is defined as the absolute value of the difference between the two ratings, i.e.: ΔF = |S_current S_best|; When the current wind mode is the recommended wind mode, ΔF equals 0 and has no additional impact on E_comfort; when there is a difference between the current wind mode and the recommended mode, ΔF increases with the increase of the difference in fit, thus reflecting the impact of wind mode selection on the comfort of elderly users in E_comfort.

[0149] In addition, regarding the setting of weighting coefficients w_T, w_V, and w_F: these coefficients are used to adjust the relative importance of temperature deviation, wind speed deviation, and wind pattern deviation in the thermal comfort deviation E_comfort. The weighting coefficients can be adaptively adjusted based on the combined physical condition / weakness weight δ of elderly users to reflect the greater sensitivity of users with poor physical condition to environmental changes. For example, when δ is large and the user is in a weak state, the values ​​of w_T and w_F can be appropriately increased to increase the weight of temperature deviation and wind pattern deviation in E_comfort; when δ is small, these weights can be appropriately reduced to make the system more tolerant of minor deviations.

[0150] In one embodiment, the energy consumption index E_energy is used to quantify the power consumption level of the air conditioner under a given control strategy, and can be used as the target of the cost sensitivity parameter λ. For example, E_energy can be determined comprehensively based on parameters such as the power of the air conditioner compressor, the power of the air supply fan, and the operating time.

[0151] Operating power acquisition: The control module can acquire the current compressor power P_comp, indoor unit fan power P_fan, and the power P_other of other related electrical components in real time or periodically. For simplicity, P_other can be combined into the compressor power or fan power, or it can be counted separately.

[0152] Estimating energy consumption within a time window: Within a preset control time window Δt (e.g., 5 minutes, 10 minutes, or 1 hour), the energy consumption index E_energy can be defined as the total electrical energy consumption or its estimated value within that time window. Taking simple linear estimation as an example, E_energy can be expressed as: E_energy = P_comp×t_comp + P_fan×t_fan; Where t_comp is the cumulative operating time of the compressor within the time window Δt, and t_fan is the cumulative operating time of the fan within the time window Δt. For further simplification, energy consumption can also be estimated by multiplying the equivalent power P_eq by the time window Δt, for example: E_energy = P_eq×Δt; Among them, P_eq can be pre-calibrated or estimated online based on parameters such as the current operating mode, set temperature, and wind speed.

[0153] Combining Normalization and Cost Sensitivity: To facilitate the calculation of the comprehensive evaluation function in conjunction with E_comfort and E_health, E_energy can be normalized. For example, E_energy can be mapped to the interval [0,1] or [0, E_max], where E_max is the preset maximum reference energy consumption value. When calculating the comprehensive evaluation function, the control module weights E_energy using the cost sensitivity parameter λ. When λ is larger, the system will be more inclined to select the lower energy consumption operating scheme, such as appropriately increasing the set temperature, reducing the compressor start-stop frequency, or prioritizing the use of the fan mode to improve comfort.

[0154] In one embodiment, the cost sensitivity parameter λ is used to characterize the sensitivity of elderly users to air conditioning electricity costs; the higher the value, the more concerned the user is about energy consumption. This application can determine λ through user subjective settings and estimation of historical behavior.

[0155] (a) The basic sensitivity λ_user obtained by the user's subjective settings: The air conditioning terminal or its accompanying mobile terminal interface should provide elderly users with at least three energy-saving preference options, such as "more comfortable", "balanced", and "more energy-saving".

[0156] The system maps the above options to the basic cost sensitivity λ_user, as shown in the example below: When the user selects "more comfortable", λ_user takes a smaller value, for example, λ_user = 1; When the user selects "balanced", λ_user takes a medium value, for example, λ_user = 2; When a user selects "more energy-efficient", λ_user takes a larger value, for example, λ_user = 3.

[0157] (ii) The sensitivity λ_behave based on historical electricity consumption behavior: The system records indoor and outdoor temperatures, air conditioner on / off status, and set temperature within a preset observation period (e.g., 7 or 30 days). When the system detects the following behavioral characteristics, it considers the user to be more cost-sensitive and appropriately increases cost sensitivity: When the indoor temperature is consistently higher than the preset high temperature threshold (e.g., 30°C), the air conditioner is not turned on for a long time or the time it is turned on is significantly less than the average level. When the air conditioner is turned on, the set temperature is kept at a relatively high level for a long time, for example, the set temperature is never lower than 27°C during the cooling season.

[0158] Conversely, when the following behavioral characteristics are detected, it is considered that the user is not sensitive to costs, and the cost sensitivity should be appropriately reduced: Frequent use of air conditioning even when the outdoor temperature is not high or the indoor temperature is close to a comfortable range; During the long-term cooling season, the set temperature is set to a low level, such as 24°C or below.

[0159] Based on the above behavioral characteristics, the system can set λ_behave to 0, +1, or... 1. Discrete correction values ​​are used to fine-tune λ_user. For example: When behavior shows a clear tendency to save energy, λ_behave = +1; When behavior clearly prioritizes comfort, λ_behave = 1; When there is no obvious bias in behavior, λ_behave = 0.

[0160] (III) Calculation of overall cost sensitivity λ: Taking into account both user subjective settings and historical behavior, the cost sensitivity parameter λ can be defined as: λ = λ_user + λ_behave; To avoid λ from taking too large a value or being negative, the range of values ​​for λ can be limited, for example: 1 ≤ λ ≤ 3; Alternatively, it can be set to 1 ≤ λ ≤ 5 based on actual needs.

[0161] In the above manner, λ not only reflects the user's subjective energy-saving preference but also can reflect their long-term real usage behavior, which is more in line with the actual psychological characteristics of the elderly users who are afraid of wasting electricity.

[0162] In one embodiment, the health risk index E_health is used to reflect the relative risk level of the elderly users having adverse health events under the current environmental conditions, especially the additional burdens on the cardiovascular and respiratory systems of the elderly population caused by high or low temperature environments. The calculation of E_health comprehensively considers factors such as the relationship between the indoor temperature and the suitable temperature range and the high / low temperature thresholds, the temperature change rate, the wind speed change rate, and the physical / weak comprehensive weight δ, etc.

[0163] Setting of the temperature range and threshold: According to the existing epidemiological research conclusions and the physical characteristics of the elderly population, the present application divides the environmental temperature into a low temperature range, a suitable range, and a high temperature range. For example, for each physical fitness level (the health status corresponding to δ, such as healthy, sub-healthy, weak), the following can be set respectively: (1) The temperature recommended range [T_low, T_high], within which the temperature-related health risk of the elderly users is the lowest; (2) The low temperature threshold T_cold, when the room temperature T < T_cold, the cold-related risk increases significantly; (3) The high temperature threshold T_hot, when the room temperature T > T_hot, the high temperature-related risk increases significantly.

[0164] The above thresholds can be adjusted according to the change of δ. For example, for weak users, the suitable temperature range is narrower, and the high temperature threshold T_hot and the low temperature threshold T_cold are more stringent.

[0165] In one embodiment, the temperature-related risk component E_T_health: Within different temperature ranges, the temperature-related risk component E_T_health can be defined in the following manner: (1) When T_low ≤ T ≤ T_high, it is considered that the temperature-related risk is low, and E_T_health can be set to a small constant value or approximated to 0; (2) When T_cold > T or T > T_hot, E_T_health increases with the degree of temperature deviation from the threshold. For example: When T < T_cold: E_T_health = k_cold×(T_cold T)^2; When T > T_hot: E_T_health = k_hot×(T T_hot)^2.

[0166] Among them, k_cold and k_hot are the low temperature risk coefficient and high temperature risk coefficient, respectively. They can be preset according to the overall sensitivity of the silver population. When δ is larger, the values ​​of k_cold and k_hot can be appropriately increased to reflect that users with poorer physical condition are more sensitive to cold and high temperature.

[0167] In one embodiment, the risk components of the rate of temperature change and the rate of wind speed change are: Besides absolute temperature levels, the rate of temperature change and the rate of wind speed change also affect the health of elderly users. Therefore, this embodiment further introduces the deviations in temperature change rate ΔR_T and wind speed change rate ΔR_V, and the corresponding risk components can be defined as: E_RT = k_RT×(ΔR_T)^2; E_RV = k_RV×(ΔR_V)^2.

[0168] Among them, the temperature change rate deviation ΔR_T represents the difference between the current rate of change of room temperature per unit time and the preset upper limit of the safe rate of change. When the temperature change rate is lower than the upper limit of the safe rate of change, ΔR_T is 0; when it exceeds the upper limit of the safe rate of change, ΔR_T is the excess portion. The wind speed change rate deviation ΔR_V is defined similarly and is used to measure whether the rate of change of wind speed is too fast. The first risk weight coefficient k_RT and the second risk weight coefficient k_RV are the corresponding risk weight coefficients, which can also be appropriately amplified or reduced according to δ.

[0169] In one embodiment, the comprehensive form of the health risk indicator E_health is: Considering the above factors, the health risk indicator E_health in this embodiment can be expressed as: E_health = a_T×E_T_health + a_RT×E_RT + a_RV×E_RV; Among them, the temperature risk weighting coefficient a_T, the temperature change rate risk weighting coefficient a_RT, and the wind speed change rate risk weighting coefficient a_RV are the weighting coefficients of each risk component, which can be configured according to actual application needs and health management strategies for the elderly. For example, when the overall weight δ of physical condition / weakness is large, the values ​​of a_T and a_RT are increased to make the impact of high temperature, low temperature, and drastic temperature changes on E_health more significant; when δ is small, the above weights are appropriately reduced.

[0170] Normalization and coordination with μ: Similarly, for the convenience of participating in the calculation of the comprehensive evaluation function together with E_comfort and E_energy, E_health can be normalized to match the numerical range of other indicators. The control module weights E_health through the health risk weight μ. When the room temperature is in the high temperature range or the low temperature range and δ is large, the value of μ can be increased to emphasize the importance of controlling the temperature level and the change rate in such dangerous ranges.

[0171] In one embodiment, μ is used to adjust the influence degree of the health risk index E_health in the comprehensive evaluation function. The larger its value, the more the system attaches importance to reducing health risks in the current state. In this application, μ is mainly determined according to the constitution / weakness comprehensive weight δ and the current temperature range (low temperature range, suitable range, high temperature range).

[0172] (I) Division of temperature ranges: According to the temperature-health relationship of the elderly population, a temperature recommendation range [T_low, T_high] and a low temperature threshold T_cold and a high temperature threshold T_hot are preset for each δ level. For example: When T_low ≤ T ≤ T_high, it is considered to be in the suitable temperature range; When T < T_cold, it is considered to be in the low temperature risk range; When T > T_hot, it is considered to be in the high temperature risk range.

[0173] The suitable temperature range and the threshold can be adjusted according to δ. For frail users (δ is large), T_cold and T_hot can be set more strictly.

[0174] (II) Basic health weight μ_base based on δ; First, the health weight is classified according to the constitution / weakness comprehensive weight δ to obtain the basic health weight μ_base. For example: When δ ≤ 5 (healthy), μ_base = 1; When 6 ≤ δ ≤ 15 (sub-healthy), μ_base = 2; When 16 ≤ δ ≤ 25 (frail), μ_base = 3.

[0175] The larger δ is, the larger μ_base is, indicating that the older users with poorer physical constitution should pay more attention to health risks in any temperature range.

[0176] (III) Risk correction coefficient k_temp based on the temperature range: Apply different amplification factors to μ_base at different temperature ranges to obtain the health risk weight, that is, the second weight parameter μ. The example is defined as follows: When the room temperature T is within the suitable temperature range [T_low, T_high], k_temp takes 1; When the room temperature T is within the low temperature range (T < T_cold) or the high temperature range (T > T_hot), k_temp takes a value greater than 1, such as 2 or 3, to reflect that the current is in a high-risk environment.

[0177] (4) Calculation formula for the health risk weight μ: Considering the above factors, the health risk weight μ can be defined as: μ = μ_base × k_temp; Where: μ_base is determined by δ and is used to reflect the influence of physical fitness / weakness on the sensitivity of health risks; k_temp is determined by the temperature range to which the current room temperature belongs and is used to amplify the weight of the health risk term in the low or high temperature risk range.

[0178] In a specific implementation, it can be set that: When δ ≤ 5, μ_base = 1; When 6 ≤ δ ≤ 15, μ_base = 2; When 16 ≤ δ ≤ 25, μ_base = 3.

[0179] When T_low ≤ T ≤ T_high, k_temp = 1; When T < T_cold or T > T_hot, k_temp = 2. Then: μ = μ_base · k_temp ∈ {1, 2, 3, 4, 6}.

[0180] In this way, for the elderly users with weak constitutions and in the high or low temperature risk ranges, the value of μ is the largest, and the influence of the health risk index E_health on the comprehensive evaluation function is the strongest. The system will preferentially adjust the set temperature, reduce the temperature change rate, and optimize the wind feeling mode to reduce the health risk; while for the users with better constitutions and in the suitable temperature range, the value of μ is smaller, and the influence of the health risk term on the comprehensive evaluation function is relatively weakened.

[0181] Based on the control methods of the air conditioners in the above various embodiments, the following respectively present the various embodiments of the air conditioner, controller, computer-readable storage medium, and computer program product of the present application.

[0182] In one embodiment, an embodiment of this application also provides an air conditioner, including a controller and a display device.

[0183] The controller is used to acquire the current operating parameters of the air conditioner and the health level of the target object, and adjust the operating parameters of the air conditioner according to the current operating parameters and the health level. The health level is determined by at least one of the target object's health information, endurance information, and strength information.

[0184] In addition, a display device is connected to the controller, and the display device is used to display the current operating parameters of the air conditioner and / or the health level of the target object.

[0185] like Figure 25 As shown, Figure 25 This is a schematic diagram of a controller for performing a control method for an air conditioner according to an embodiment of this application. The controller 100 implemented in this application includes: a processor 110, a memory 120, and a computer program stored in the memory 120 and executable on the processor 110, wherein... Figure 25 The example uses a processor 110 and a memory 120.

[0186] Processor 110 and memory 120 can be connected via a bus or other means. Figure 25 Taking the example of a connection between China and Israel via a bus.

[0187] Memory 120, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory 120 may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory 120 may optionally include remotely located memories 120 relative to processor 110, which can be connected to controller 100 via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0188] Those skilled in the art will understand that Figure 25 The device structure shown does not constitute a limitation on the controller 100 and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0189] exist Figure 25In the controller 100 shown, the processor 110 can be used to call the control program stored in the memory 120, thereby implementing the air conditioner control method described above. Specifically, the non-transitory software program and instructions required to implement the air conditioner control method of the above embodiment are stored in the memory 120. When executed by the processor 110, the air conditioner control method of the above embodiment is executed.

[0190] It is worth noting that since the controller 100 of this application embodiment can execute the air conditioner control method of any of the above embodiments, the specific implementation method and technical effect of the controller 100 of this application embodiment can refer to the specific implementation method and technical effect of the air conditioner control method of any of the above embodiments.

[0191] Furthermore, one embodiment of this application provides a computer-readable storage medium storing computer-executable instructions for performing the aforementioned air conditioner control method. Exemplarily, the above-described method is executed... Figures 1 to 7 The methods and steps in the text.

[0192] It is worth noting that, since the computer-readable storage medium of this application embodiment can execute the air conditioner control method of any of the above embodiments, the specific implementation and technical effects of the computer-readable storage medium of this application embodiment can be referred to the specific implementation and technical effects of the air conditioner control method of any of the above embodiments.

[0193] Furthermore, one embodiment of this application also provides a computer program product, including a computer program or computer instructions, which are stored in a computer-readable storage medium. A processor of a computer device reads the computer program or computer instructions from the computer-readable storage medium and executes the computer program or computer instructions, causing the computer device to perform the aforementioned air conditioner control method. Exemplarily, the above-described method is executed... Figures 1 to 7 The methods and steps in the text.

[0194] It is worth noting that, since the computer program product of this application embodiment can execute the air conditioner control method of any of the above embodiments, the specific implementation method and technical effect of the computer program product of this application embodiment can refer to the specific implementation method and technical effect of the air conditioner control method of any of the above embodiments.

[0195] It will be understood by those skilled in the art that all or some of the steps and systems in the methods disclosed above can be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components can be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, which can include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, as is known to those skilled in the art, communication media typically include computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0196] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0197] In the several embodiments provided in this application, it should be understood that the disclosed systems, instruments, and methods can be implemented in other ways. For example, the instrument embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the shown or discussed mutual couplings, direct couplings, or communication connections may be through some interfaces; indirect couplings or communication connections between instruments or units may be electrical, mechanical, or other forms. Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, i.e., they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0198] It should also be understood that the various implementation methods provided in this application can be combined arbitrarily to achieve different technical effects.

[0199] The above provides a detailed description of the preferred embodiments of this application. However, this application is not limited to the above-described embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of this application. All such equivalent modifications or substitutions are included within the scope defined by the claims of this application.

Claims

1. A control method for an air conditioner, characterized in that, include: The current operating parameters of the air conditioner and the health level of the target object are obtained, wherein the health level is determined by at least one of the target object's health information, endurance information, and strength information; The operating parameters of the air conditioner are adjusted according to the current operating parameters and the health level.

2. The method according to claim 1, characterized in that, The adjustment of the air conditioner's operating parameters based on the current operating parameters and the health level includes: Based on the health level, a target recommended range corresponding to the operating parameters of the air conditioner is determined; Determine thermal comfort deviations and / or health risk indicators based on the current operating parameters and the target recommended range; The operating parameters of the air conditioner are adjusted according to the thermal comfort deviation and / or the health risk indicators.

3. The method according to claim 2, characterized in that, The adjustment of the operating parameters of the air conditioner based on the thermal comfort deviation and / or the health risk indicators includes: Obtain the operating power and time window of the air conditioner; The energy consumption index of the air conditioner is determined based on the operating power and the time window. The operating parameters of the air conditioner are adjusted based on the thermal comfort deviation, the energy consumption index, and the health risk index.

4. The method according to claim 3, characterized in that, The adjustment of the air conditioner's operating parameters based on the thermal comfort deviation, energy consumption index, and health risk index includes: Determine a first weighting parameter corresponding to the thermal comfort deviation, a second weighting parameter corresponding to the energy consumption index, and a third weighting parameter corresponding to the health risk index; Based on the comprehensive evaluation function, the optimal value of the comprehensive evaluation function is calculated according to the thermal comfort deviation and the first weight parameter, the energy consumption index and the second weight parameter, and the health risk index and the third weight parameter. The operating parameters of the air conditioner are adjusted according to the optimal value of the function.

5. The method according to claim 4, characterized in that, The thermal comfort deviation is determined through the following steps: A first parameter deviation is determined between the current operating parameters and the target recommended interval. The thermal comfort deviation is determined based on the first target weight parameter and the first parameter deviation. The thermal comfort deviation includes at least one of temperature thermal comfort deviation, wind speed thermal comfort deviation, and wind mode thermal comfort deviation.

6. The method according to claim 5, characterized in that, Determining the first parameter deviation between the current operating parameters and the target recommended interval, and determining the thermal comfort deviation based on the first target weight parameter and the first parameter deviation, includes at least one of the following: When the current operating parameters include the current indoor temperature and the target recommended range includes a temperature recommended range, the temperature deviation between the current indoor temperature and the temperature recommended range is determined, and the temperature thermal comfort deviation is obtained according to the temperature weight parameter and the temperature deviation, wherein the temperature weight parameter is determined by the first weight parameter; When the current operating parameters include the current supply air velocity and the target recommended range includes the wind speed recommended range, the wind speed deviation between the current supply air velocity and the wind speed recommended range is determined, and the wind speed thermal comfort deviation is obtained according to the wind speed weight parameter and the wind speed deviation, wherein the wind speed weight parameter is determined by the first weight parameter; When the current operating parameters include the current rating corresponding to the current wind feel mode and the target recommendation range includes the target rating corresponding to the recommended wind feel mode, the wind feel mode deviation between the current rating and the target rating is determined, and the wind speed thermal comfort deviation is obtained according to the wind feel mode weight parameter and the wind feel mode deviation, wherein the wind feel mode weight parameter is determined by the first weight parameter.

7. The method according to claim 3, characterized in that, Determining the energy consumption index of the air conditioner based on the operating power and the time window includes one of the following: When the operating power includes the current compressor power and fan power, the energy consumption index of the air conditioner is determined based on the compressor power and the cumulative operating time of the compressor in the time window, and the fan power and the cumulative operating time of the fan in the time window. When the operating power includes the equivalent power of the air conditioner, the energy consumption index of the air conditioner is determined based on the equivalent power and the time window.

8. The method according to claim 4, characterized in that, The health risk indicator includes at least one of a temperature risk component, a temperature change rate risk component, and a wind speed change rate risk component; the health risk indicator is determined through the following steps: Determine the second parameter deviation between the current operating parameters and the target recommendation interval, and determine the health risk indicator based on the second target weight parameter and the second parameter deviation; Determine the third parameter deviation between the current operating parameters and the preset rate parameters, and determine the health risk indicator based on the third target weight parameter and the third parameter deviation.

9. The method according to claim 8, characterized in that, The step of determining the second parameter deviation between the current operating parameters and the target recommendation interval, and determining the health risk indicator based on the second target weight parameter and the second parameter deviation, includes: If the current operating parameters include the current indoor temperature and the target recommended range includes a temperature recommended range, determine the temperature deviation between the current indoor temperature and the temperature recommended range, and determine the temperature risk component based on the temperature deviation.

10. The method according to claim 9, characterized in that, The step of determining the third parameter deviation between the current operating parameters and the preset rate parameters, and determining the health risk indicator based on the third target weight parameter and the third parameter deviation, includes one of the following: When the current operating parameters include the current rate of change of indoor temperature, the deviation of the rate of change of temperature between the current rate of change of temperature and the preset rate of change of temperature is determined, and the risk component of the rate of change of temperature is determined according to the deviation of the rate of change of temperature and the first risk weight coefficient, wherein the first risk weight coefficient is determined by the first weight parameter. When the current operating parameters include the current wind speed change rate of the outlet wind speed, the deviation of the wind speed change rate between the current wind speed change rate and the preset wind speed change rate is determined, and the wind speed change rate risk component is determined based on the wind speed change rate deviation and the second risk weight coefficient, wherein the second risk weight coefficient is determined by the first weight parameter.

11. The method according to claim 10, characterized in that, The health risk indicators were determined through the following steps: Determine the temperature risk weight coefficient corresponding to the temperature risk component, the temperature change rate risk weight coefficient corresponding to the temperature change rate risk component, and the wind speed change rate risk weight coefficient corresponding to the wind speed change rate risk component. The health risk index is calculated based on the temperature risk weight coefficient and the temperature risk component, the temperature change rate risk weight coefficient and the temperature change rate risk component, and the wind speed change rate risk weight coefficient and the wind speed change rate risk component.

12. The method according to claim 4, characterized in that, The method includes: The health information is used to characterize the physical constitution and health stability of the target subject; The endurance information is used to characterize the endurance level of the target object during the activity; The force information is used to characterize the muscle strength of the target object.

13. The method according to claim 12, characterized in that, The first weighting parameter is used to characterize the degree of physical weakness of the target object; the first weighting parameter is determined through the following steps: The health information, endurance information, and strength information are quantified to obtain the basic state classification, endurance state classification, and strength state classification of the target object, respectively. The basic condition classification, the endurance condition classification, and the strength condition classification are superimposed to obtain the first weight parameter, wherein the first weight parameter corresponds to the health level.

14. The method according to claim 4, characterized in that, The second weighting parameter is used to characterize the sensitivity of the target object to the electricity cost of the air conditioner; the second weighting parameter is determined through one of the following steps: Receive input energy-saving preference options, and determine the second weight parameter based on the energy-saving preference options; Obtain historical electricity consumption behavior within a preset observation period, and determine the second weighting parameter based on the historical electricity consumption behavior; The system receives input energy-saving preference options and historical electricity consumption behavior within a preset observation period, and determines the second weighting parameter based on the energy-saving preference options and the historical electricity consumption behavior.

15. The method according to claim 4, characterized in that, The third weighting parameter is used to characterize the degree of influence of the target object on the health risk item under different temperature ranges or different health states. The third weighting parameter is determined through the following steps: The basic health weight is determined based on the first weighting parameter; If the current operating parameters include the current indoor temperature and the target recommended range includes a temperature recommended range, a risk correction coefficient is determined based on the current indoor temperature and the temperature recommended range. The third weight parameter is determined based on the basic health weight and the risk correction coefficient.

16. An air conditioner, characterized in that, include: The controller is used to acquire the current operating parameters of the air conditioner and the health level of the target object, and adjust the operating parameters of the air conditioner according to the current operating parameters and the health level, wherein the health level is determined by at least one of the target object's health information, endurance information, and strength information; A display device, connected to the controller, is used to display the current operating parameters of the air conditioner and / or the health level of the target object.

17. A controller, characterized in that, include: The device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, performs the control method for an air conditioner as described in any one of claims 1 to 15.

18. A computer-readable storage medium, characterized in that: The device stores computer-executable instructions for performing the control method of the air conditioner as described in any one of claims 1 to 15.

19. A computer program product, characterized in that, The method includes a computer program or computer instructions stored in a computer-readable storage medium, wherein a processor of a computer device reads the computer program or computer instructions from the computer-readable storage medium, and the processor executes the computer program or computer instructions to cause the computer device to perform the control method for an air conditioner as described in any one of claims 1 to 15.

Images