Control implementation method and device of intelligent air conditioning equipment

Abstract

The application discloses a control implementation method and device of an intelligent air conditioning equipment. After detecting that a user's corresponding thermal sensation correction prompt is received, the method automatically determines the user's corresponding parameters, such as the user's thermal sensation feedback, current environmental parameters, the user's gender, age and the like, and further automatically corrects the user's current PMV value based on the determined parameters to obtain a corrected PMV value. The control device of the intelligent air conditioning equipment can intelligently adjust the working parameters of the intelligent air conditioning equipment according to the corrected PMV value, thereby improving the adjustment efficiency of the intelligent air conditioning equipment, meeting the needs of different users for environmental comfort, and providing an accurate comfortable environment for the user without manual control of the intelligent air conditioning equipment, even when the user is in a sleep state, thereby improving the user's use experience.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent air conditioning equipment, and in particular to a control implementation method and device of intelligent air conditioning equipment. BACKGROUND

[0002] With the rapid development of electronic technology, more and more devices are developing towards intelligence, and based on the advantages of convenient and fast operation, time and labor saving, intelligent devices are becoming more and more popular and important in life, work, production and other scenes.

[0003] In practical application, for the intelligent air conditioning equipment, the user can realize the cooling / heating control of the intelligent air conditioning equipment through the corresponding APP or the corresponding remote control device, that is, after the user selects the working mode and the working temperature of the intelligent air conditioning equipment, the intelligent air conditioning equipment works at the working mode and the working temperature selected by the user. However, it is found in practice that when the user feels uncomfortable temperature, the user needs to repeatedly adjust the working temperature of the intelligent air conditioning equipment through the corresponding APP or the corresponding remote control device, and the working temperature adjustment efficiency is not conducive to improving the user experience. It can be seen that how to intelligently adjust the working parameters of the intelligent air conditioning equipment to improve the adjustment efficiency is particularly important. SUMMARY

[0004] The present application provides a control implementation method and device of intelligent air conditioning equipment, which can improve the adjustment efficiency of the working parameters of the intelligent air conditioning equipment.

[0005] In order to solve the above technical problems, the first aspect of the present application discloses a control implementation method of intelligent air conditioning equipment, the method comprising:

[0006] detecting whether a thermal sensation correction instruction corresponding to a target user is received, the target user including at least one user in a current environment where the intelligent air conditioning equipment is located;

[0007] when it is detected that the thermal sensation correction instruction corresponding to the target user is received, determining a parameter corresponding to the target user, and correcting a current PMV value of the target user determined in advance according to the parameter corresponding to the target user to obtain a target PMV value;

[0008] wherein the target PMV value is used for the control device of the intelligent air conditioning equipment to adjust the working parameters of the intelligent air conditioning equipment.

[0009] As an optional implementation manner, in the first aspect of the present application, the parameter corresponding to the target user includes one or more combinations of the environmental parameter of the current environment, the personal parameter of the target user, the rest parameter corresponding to the target user and the thermal sensation feedback corresponding to the target user.

[0010] wherein the target PMV value is obtained by correcting the pre-determined current PMV value of the target user according to the parameter corresponding to the target user, and the method further comprises:

[0011] inputting the parameter corresponding to the target user and the pre-determined current PMV value of the target user into a pre-trained PMV analysis model for analysis, and obtaining an analysis result output by the PMV analysis model as the target PMV value; or

[0012] when the parameter corresponding to the target user comprises the thermal sensation feedback corresponding to the target user, correcting the pre-determined current PMV value of the target user according to the thermal sensation feedback corresponding to the target user and the determined PMV correction coefficient to obtain the target PMV value.

[0013] As an optional implementation, in the first aspect of the present application, after the target PMV value is obtained by correcting the pre-determined current PMV value of the target user according to the parameter corresponding to the target user, the method further comprises:

[0014] calculating the thermal environment preference parameter matched with the target user according to the target PMV value, wherein the thermal environment preference parameter matched with the target user is used to provide the control device of the intelligent air conditioning equipment, so as to trigger the control device of the intelligent air conditioning equipment to adjust the working parameter of the intelligent air conditioning equipment according to the thermal environment preference parameter matched with the target user, and the thermal environment preference parameter matched with the target user comprises the preferred heat load value matched with the target user and / or the preferred temperature matched with the target user.

[0015] wherein the thermal environment preference parameter matched with the target user is calculated according to the target PMV value.

[0016] when the thermal environment preference parameter matched with the target user comprises the preferred heat load value matched with the target user, a heat load conversion coefficient matched with the target user is determined, and the preferred heat load value matched with the target user is calculated according to the target PMV value and the heat load conversion coefficient.

[0017] As an optional implementation, in the first aspect of the present application, the method further comprises:

[0018] when the thermal environment preference parameter matched with the target user comprises the preferred temperature matched with the target user, the preferred temperature matched with the target user is calculated according to the preferred heat load value matched with the target user, or the preferred temperature matched with the target user is calculated according to the preferred heat load value matched with the target user and the parameter corresponding to the target user.

[0019] As an optional implementation, in the first aspect of the invention, determining the heat load conversion coefficient matching the target user includes:

[0020] Determine the target parameters corresponding to the target user, wherein the target parameters corresponding to the target user include the metabolic rate corresponding to the target user;

[0021] Calculate the heat load conversion coefficient that matches the target user based on the metabolic rate corresponding to the target user and the determined heat load correction coefficient;

[0022] The formula for calculating the heat load conversion coefficient matched with the target user is as follows:

[0023] ;

[0024] In the formula, the The heat load conversion coefficient is used to match the target user, where M is the metabolic rate of the target user, and a, b, and α are heat load correction coefficients, respectively.

[0025] As an optional implementation, in the first aspect of the present invention, the method further includes:

[0026] A training set is determined for sample users corresponding to different user tags. The number of sample users corresponding to each user tag is greater than or equal to a determined threshold. The training set for sample users corresponding to each user tag includes user training parameters for each user tag. The user training parameters for each user tag include user training metabolic rate and training PMV value for each user tag.

[0027] Based on the determined coefficient fitting method, a fitting operation is performed on the user training metabolic rate corresponding to the sample user of each user label and the training PMV value corresponding to the sample user of each user label to obtain fitting coefficients, and the fitting coefficients are determined as the heat load correction coefficients.

[0028] As an optional implementation, in the first aspect of the present invention, the method further includes:

[0029] determining a training set corresponding to a sample user of each of the user labels, a quantity of the sample user corresponding to each of the user labels being greater than or equal to a determined quantity threshold, and the training set corresponding to each of the sample user including a user training preference heat load value corresponding to each of the sample user, a user training metabolic rate corresponding to each of the sample user, a training environment parameter corresponding to each of the sample user, and a training personal attribute parameter corresponding to each of the sample user;

[0030] establishing an association between each of the sample user and the training set corresponding to the sample user, and training the pre-determined PMV basic analysis model based on the training set corresponding to the sample user of all of the determined user labels and the association between each of the sample user and the training set corresponding to the sample user, to obtain a trained PMV basic analysis model, and determining the trained PMV basic analysis model as a pre-trained PMV analysis model.

[0031] As an optional implementation, in the first aspect of the present application, after the target PMV value is obtained by correcting the pre-determined current PMV value of the target user according to the parameter corresponding to the target user, the method further includes:

[0032] determining whether the target PMV value meets a pre-determined iteration stop condition;

[0033] when it is determined that the target PMV does not meet the iteration stop condition, determining the target PMV as the current PMV value of the target user, and triggering the operation of detecting whether the thermal sensation correction instruction corresponding to the target user is received.

[0034] As an optional implementation, in the first aspect of the present application, the method further includes: the target PMV value is obtained by correcting the pre-determined current PMV value of the target user according to the thermal sensation feedback corresponding to the target user and the determined PMV correction coefficient, including:

[0035] when the quantity of the target user is equal to 1, the target PMV value is obtained by correcting the pre-determined current PMV value of the target user according to the thermal sensation feedback corresponding to the target user and the determined PMV correction coefficient;

[0036] When the number of the target users is greater than 1, the current PMV value of each of the target users is corrected according to the thermal sensation feedback corresponding to each of the target users and the PMV correction coefficient of each of the target users, to obtain a corrected PMV value of each of the target users, and a target PMV value is determined based on the corrected PMV values of all the target users.

[0037] The target PMV value is determined based on the corrected PMV values of all the target users, including:

[0038] The mean value of the corrected PMV values of all the target users is calculated as the target PMV value; or,

[0039] When the number of the target users is greater than 1 and is an odd number, the PMV value of the target users whose PMV values are in the middle value among the corrected PMV values of all the target users is determined as the target PMV value.

[0040] As an optional implementation, in the first aspect of the present application, the environmental parameters of the current environment include one or more combinations of the air pressure of the current environment, the humidity of the current environment and the temperature of the current environment, the personal parameters of the target user include one or more combinations of the personal attribute parameters of the target user, the health parameters of the target user and the physiological parameters of the target user, the personal attribute parameters of the target user include the gender of the target user and / or the age group to which the target user belongs; the rest parameters corresponding to the target user include the parameters corresponding to the articles used by the target user during the rest and / or the rest posture of the target user, the articles used by the target user during the rest include a quilt and / or a cushion, and the parameters corresponding to the articles used by the target user during the rest include at least one of the contact area between the articles used by the target user during the rest and the target user, the thickness of the articles, the type of the articles, the material of the articles and the air tightness of the articles.

[0041] The second aspect of the present application discloses a control implementation device of an intelligent air conditioning equipment, which comprises:

[0042] A detection module is configured to detect whether a thermal sensation correction instruction corresponding to a target user is received, the target user including at least one user in a current environment where the intelligent air conditioning equipment is located.

[0043] A determination module is configured to determine parameters corresponding to the target user when it is detected that the thermal sensation correction instruction corresponding to the target user is received.

[0044] a correction module, configured to correct the pre-determined current PMV value of the target user according to the parameter corresponding to the target user to obtain a target PMV value;

[0045] The target PMV value is used to adjust the working parameter of the intelligent air conditioning device by the control device of the intelligent air conditioning device.

[0046] As an optional implementation, in the second aspect of the present application, the parameter corresponding to the target user includes one or more of the following: an environmental parameter of the current environment, a personal parameter of the target user, a rest parameter corresponding to the target user, and a thermal sensation feedback corresponding to the target user.

[0047] The correction module corrects the pre-determined current PMV value of the target user according to the parameter corresponding to the target user to obtain a target PMV value.

[0048] The parameter corresponding to the target user and the pre-determined current PMV value of the target user are input into a pre-trained PMV analysis model for analysis, and an analysis result output by the PMV analysis model is obtained as the target PMV value; or,

[0049] When the parameter corresponding to the target user includes the thermal sensation feedback corresponding to the target user, the pre-determined current PMV value of the target user is corrected according to the thermal sensation feedback corresponding to the target user and a PMV correction coefficient to obtain a target PMV value.

[0050] As an optional implementation, in the second aspect of the present application, the device further includes:

[0051] a calculation module, configured to calculate a thermal environment preference parameter matched with the target user according to the target PMV value after the correction module corrects the pre-determined current PMV value of the target user according to the parameter corresponding to the target user to obtain a target PMV value, the thermal environment preference parameter matched with the target user being used to provide the control device of the intelligent air conditioning device to trigger the control device of the intelligent air conditioning device to adjust the working parameter of the intelligent air conditioning device according to the thermal environment preference parameter matched with the target user, the thermal environment preference parameter matched with the target user including a preferred heat load value matched with the target user and / or a preferred temperature matched with the target user.

[0052] The calculation module includes:

[0053] determining a thermal load conversion coefficient matched with the target user when the thermal environment preference parameter matched with the target user comprises a preferred thermal load value matched with the target user;

[0054] calculating a preferred thermal load value matched with the target user according to the target PMV value and the thermal load conversion coefficient.

[0055] As an optional implementation, in the second aspect, the calculating sub-module is further configured to calculate the preferred temperature matched with the target user according to the preferred thermal load value matched with the target user, or calculate the preferred temperature matched with the target user according to the preferred thermal load value matched with the target user and the parameter corresponding to the target user.

[0056] As an optional implementation, in the second aspect, the determining sub-module determines the thermal load conversion coefficient matched with the target user in the following manner:

[0057] determining a target parameter corresponding to the target user, wherein the target parameter corresponding to the target user comprises a metabolic rate corresponding to the target user;

[0058] calculating the thermal load conversion coefficient matched with the target user according to the metabolic rate corresponding to the target user and the determined thermal load correction coefficient;

[0059] wherein the calculation formula of the thermal load conversion coefficient matched with the target user is as follows:

[0060]

[0061] wherein the is the thermal load conversion coefficient matched with the target user, the M is the metabolic rate of the target user, and the a, the b and the α are respectively thermal load correction coefficients.

[0062] As an optional implementation, in the second aspect, the determining module is further configured to determine a training set corresponding to a sample user of each user label, wherein the number of the sample user of each user label is greater than or equal to the determined number threshold, and the training set corresponding to the sample user of each user label comprises a user training parameter corresponding to the sample user of each user label, and the user training parameter corresponding to the sample user of each user label comprises a user training metabolic rate corresponding to the sample user of each user label and a training PMV value corresponding to the sample user of each user label.

[0063] ​The device further comprises:

[0064] a fitting module, configured to perform a fitting operation on the user training metabolic rate corresponding to each sample user of each user label and the training PMV value corresponding to each sample user of each user label based on the determined fitting mode, to obtain a fitting coefficient;

[0065] The determination module is further configured to determine the fitting coefficient as the heat load correction coefficient.

[0066] As an optional implementation, in the second aspect, the determination module is further configured to determine training sets corresponding to sample users of different user labels, the number of sample users corresponding to each user label is greater than or equal to the determined number threshold, and each training set corresponding to each sample user of each user label comprises a user training preferred heat load value corresponding to each sample user of each user label, a user training metabolic rate corresponding to each sample user of each user label, a training environment parameter corresponding to each sample user of each user label, and a training personal attribute parameter corresponding to each sample user of each user label.

[0067] The device further comprises:

[0068] a training module, configured to establish an association between each sample user and the training set corresponding to the sample user, and train the pre-determined PMV basic analysis model based on the determined training sets corresponding to sample users of all user labels and the association between each sample user and the training set corresponding to the sample user, to obtain a trained PMV basic analysis model.

[0069] The determination module is further configured to determine the trained PMV basic analysis model as a pre-trained PMV analysis model.

[0070] As an optional implementation, in the second aspect, the device further comprises:

[0071] a judgment module, configured to, after the correction module corrects the pre-determined current PMV value of the target user according to the parameter corresponding to the target user to obtain a target PMV value, judge whether the target PMV value meets a pre-determined iteration stop condition.

[0072] The determination module is further configured to, when the judgment module judges that the target PMV does not meet the iteration stop condition, determine the target PMV as the current PMV value of the target user, and trigger the detection module to perform the operation of detecting whether the heat sensation correction instruction corresponding to the target user is received.

[0073] As an optional implementation, in the second aspect of the present application, the manner in which the correction module corrects the pre-determined current PMV value of the target user according to the thermal sensation feedback corresponding to the target user and the determined PMV correction coefficient to obtain the target PMV value is specifically as follows:

[0074] When the number of target users is equal to 1, the pre-determined current PMV value of the target user is corrected according to the thermal sensation feedback corresponding to the target user and the determined PMV correction coefficient to obtain the target PMV value.

[0075] When the number of target users is greater than 1, the pre-determined current PMV value of each target user is corrected according to the thermal sensation feedback corresponding to each target user and the determined PMV correction coefficient of each target user to obtain the corrected PMV value of each target user, and the target PMV value is determined based on the corrected PMV values of all target users.

[0076] The manner in which the correction module determines the target PMV value based on the corrected PMV values of all target users is specifically as follows:

[0077] The mean value of the corrected PMV values of all target users is calculated as the target PMV value; or,

[0078] When the number of target users is greater than 1 and is an odd number, the PMV value of the target PMV value is determined as the PMV value in the middle value among the corrected PMV values of all target users.

[0079] As an optional implementation, in the second aspect of the present application, the environmental parameters of the current environment include one or more combinations of the air pressure of the current environment, the humidity of the current environment, and the temperature of the current environment, the personal parameters of the target user include one or more combinations of the personal attribute parameters of the target user, the health parameters of the target user, and the physiological parameters of the target user, the personal attribute parameters of the target user include the gender of the target user and / or the age group to which the target user belongs, the rest parameters corresponding to the target user include the parameters corresponding to the articles used by the target user during the rest process and / or the rest posture of the target user, the articles used by the target user during the rest process include a quilt and / or a cushion, and the parameters corresponding to the articles used by the target user during the rest process include at least one of the contact area between the articles used by the target user during the rest process and the target user, the thickness of the articles, the type of the articles, the material of the articles, and the air tightness of the articles.

[0080] The third aspect of the present application discloses another control implementation device of the intelligent air conditioning equipment, the device comprises:

[0081] a memory storing executable program codes;

[0082] a processor coupled with the memory;

[0083] The processor invokes the executable program codes stored in the memory to execute part or all steps of the control implementation method of the intelligent air conditioning equipment disclosed in the first aspect of the present application.

[0084] The fourth aspect of the present application discloses a computer storage medium storing computer instructions, when the computer instructions are invoked, part or all steps of the control implementation method of the intelligent air conditioning equipment disclosed in the first aspect of the present application are executed.

[0085] Compared with the prior art, the embodiment of the present application has the following beneficial effects:

[0086] In the embodiment of the present application, it is detected whether the thermal sensation correction instruction corresponding to the target user is received, the target user includes at least one user in the current environment where the intelligent air conditioning equipment is located; when it is detected that the thermal sensation correction instruction corresponding to the target user is received, the parameter corresponding to the target user is determined, and the current PMV value of the target user is corrected according to the parameter corresponding to the target user to obtain a target PMV value; wherein the target PMV value is used for the control device of the intelligent air conditioning equipment to adjust the working parameter of the intelligent air conditioning equipment. It can be seen that after it is detected that the thermal sensation correction instruction corresponding to the user is received, the parameter corresponding to the user is automatically determined, such as the thermal sensation feedback of the user, the current environmental parameter, and the like, and the current PMV value of the user is further automatically corrected based on the determined parameter to obtain the corrected PMV value, which is convenient for the control device of the intelligent air conditioning equipment to intelligently adjust the working parameter of the intelligent air conditioning equipment according to the corrected PMV value, thereby improving the adjustment efficiency and the intelligence of the intelligent air conditioning equipment, and further meeting the needs of different users for environmental comfort, without the need for the user to manually control the intelligent air conditioning equipment, even when the user is in a sleep state, an accurate comfortable environment can also be provided for the user, and the use experience of the user is improved. BRIEF DESCRIPTION OF DRAWINGS

[0087] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0088] Figure 1is a flowchart of a control implementation method of a smart air conditioning device according to an embodiment of the present application.

[0089] Figure 2 is a flowchart of another control implementation method of a smart air conditioning device according to an embodiment of the present application.

[0090] Figure 3 is a structural diagram of a control implementation device of a smart air conditioning device according to an embodiment of the present application.

[0091] Figure 4 is a structural diagram of another control implementation device of a smart air conditioning device according to an embodiment of the present application.

[0092] Figure 5 is a structural diagram of still another control implementation device of a smart air conditioning device according to an embodiment of the present application.

[0093] Figure 6 is a scene diagram according to an embodiment of the present application. DETAILED DESCRIPTION

[0094] In order to make the personnel in the art better understand the present application scheme, the technical scheme in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of the present application.

[0095] The terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish different objects, not to describe a specific order. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, device, product or end including a series of steps or units is not limited to the listed steps or units, but optionally also includes steps or units not listed, or optionally also includes other steps or units inherent to the process, method, product or end.

[0096] In this document, reference to "embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearance of the phrase in various places in the specification does not necessarily all refer to the same embodiment, nor is it necessarily mutually exclusive of other embodiments. It is explicitly and implicitly understood that the embodiments described herein can be combined with other embodiments.

[0097] The application discloses a control implementation method and device of an intelligent air conditioning equipment, which can automatically determine parameters corresponding to a user, such as a heat feeling feedback of the user, a current environment parameter and the like, after detecting that a heat feeling correction prompt corresponding to the user is received, and further automatically corrects a current PMV value of the user based on the determined parameters to obtain a corrected PMV value, so that a control device of the intelligent air conditioning equipment can intelligently adjust working parameters of the intelligent air conditioning equipment according to the corrected PMV value, thereby improving the adjustment efficiency and intelligence of the intelligent air conditioning equipment, further meeting the needs of different users for environmental comfort, and providing an accurate comfortable environment for the user without manual control of the intelligent air conditioning equipment, even when the user is in a sleep state, and improving the use experience of the user. The control implementation method and device of the intelligent air conditioning equipment are described in detail as follows.

[0098] Embodiment one

[0099] Please refer to Figure 1 , Figure 1 is a flowchart of the control implementation method of the intelligent air conditioning equipment disclosed by the application. Wherein, Figure 1 The method described can be applied to a control implementation device, wherein the control implementation device includes a control device of the intelligent air conditioning equipment or other control devices (such as a central controller of the intelligent air conditioning equipment in a cell) capable of establishing a communication connection with the control device of the intelligent air conditioning equipment, wherein the control device of the intelligent air conditioning equipment can include the intelligent air conditioning equipment itself or a server corresponding to the intelligent air conditioning equipment, wherein the control implementation device can be in communication connection with a user terminal and / or other intelligent devices (such as an intelligent sweeping device, an intelligent image acquisition device, an intelligent door lock, an intelligent fume machine and the like) of a current environment, wherein the user terminal includes but is not limited to an intelligent wearable device (such as an intelligent sports bracelet and the like) and / or a smart phone (an Android phone, an iOS phone and the like), and the application embodiments are not limited thereto. As shown in Figure 1 The control implementation method of the intelligent air conditioning equipment can include the following operations:

[0100] 101, detecting whether a heat feeling correction instruction corresponding to a target user is received, the target user including at least one user in a current environment where the intelligent air conditioning equipment is located.

[0101] In the embodiment of the present application, the target user corresponding thermal sensation correction indication can be triggered by the target user, or can be triggered by other users with the same label as the target user. The label includes a user label and / or a scene label, wherein the user label includes one or a combination of gender label, age label, physical fitness label, and eating habit label, and the scene label includes a user activity scene label and / or a region scene label, wherein the region scene label includes one or a combination of a living room scene label, a room scene label, a studio scene label, a post-shower scene label, and the user activity scene label includes a post-shower user scene label and / or a post-exercise user scene label. This is beneficial for correcting the PMV value of the user / scene matching with the same label, thereby facilitating the intelligent adjustment of the intelligent air conditioning device in the environment of the user with the same label, and improving the intelligence of the control implementation device.

[0102] 102. When receiving the target user corresponding thermal sensation correction indication, determine the target user corresponding parameter.

[0103] In the embodiment of the present application, the target user corresponding parameter includes one or a combination of the current environment parameter, the target user personal parameter, the target user corresponding rest parameter, and the target user corresponding thermal sensation feedback. The current environment parameter includes one or a combination of the current environment air pressure, the current environment humidity, and the current environment temperature, the target user personal parameter includes one or a combination of the target user personal attribute parameter, the target user health parameter, and the target user physiological parameter, and the target user personal attribute parameter includes one or a combination of the target user gender, the target user age, and the target user physical fitness. The target user corresponding rest parameter includes the target user used article corresponding parameter and / or the target user rest posture during the target user rest process, and the target user used article includes a quilt and / or a cushion. The target user used article corresponding parameter includes at least one of the target user used article contact area with the target user, the article thickness, the article type, the article material, and the article air tightness. The target user corresponding thermal sensation correction indication can carry the target user corresponding thermal sensation feedback. Further, the target user corresponding parameter further includes the relative position between the intelligent air conditioning device and the user rest location (such as a bed), and the target user activity. The more the target user corresponding parameter includes, the more beneficial for improving the correction accuracy of the target user PMV value, thereby facilitating the calculation accuracy of the target user matching thermal environment preference parameter and the adjustment accuracy of the intelligent air conditioning device working parameter, and further facilitating the provision of a comfortable environment for the user.

[0104] In the embodiment of the present application, the thermal sensation feedback of the user can include one or more of the following: temperature feedback information felt during the current rest, humidity feedback information felt during the current rest, wind feedback information felt during the current rest, and feedback information about the size and / or direction of the air outlet of the intelligent air conditioning device during the current rest. The more information included in the user feedback information, the more accurate the correction of the PMV value. It should be noted that the time of the thermal sensation feedback of the user can include any time after the intelligent air conditioning device enters the corresponding working mode, or any time after the intelligent air conditioning device ends the corresponding working mode, or the time when the intelligent air conditioning device has not started to enter the corresponding working mode, which is not limited in the embodiment of the present application. The working mode of the intelligent air conditioning device can include a sleep working mode or a non-sleep working mode, wherein different working modes correspond to different working parameters. The sleep working mode corresponds to the sleep state of the user, and the non-sleep working mode corresponds to the non-sleep state of the user, wherein the non-sleep state includes one of the following: a leisure state, a working state, and a heavy labor state. The leisure state is a state in which a person is in a sitting position and does not need physical strength and slightly uses the brain, such as reading, watching movies, etc. The working state is a state in which a person is in a sitting position and needs light physical strength and strongly uses the brain, such as writing articles, analyzing data, etc. The heavy labor state is a state in which a person is in a standing position and needs a lot of physical strength, such as doing housework, exercising, etc.

[0105] 103. Correct the pre-determined current PMV value of the target user according to the parameters corresponding to the target user to obtain a target PMV value.

[0106] In the embodiments of the present application, the target PMV value is used for the control device of the intelligent air conditioning equipment to adjust the working parameters of the intelligent air conditioning equipment. Specifically, after receiving the target PMV value, the control device of the intelligent air conditioning equipment directly adjusts the working parameters of the intelligent air conditioning equipment according to the target PMV value; or, after receiving the target PMV value, the control device of the intelligent air conditioning equipment calculates the target user-matched thermal environment preference parameter based on the target PMV value, and adjusts the working parameters of the intelligent air conditioning equipment based on the calculated target user-matched thermal environment preference parameter; or, when the control implementation device includes other control devices, the other control devices calculate the target user-matched thermal environment preference parameter based on the target PMV value, and send the thermal environment preference parameter to the control device of the intelligent air conditioning equipment, and the control device of the intelligent air conditioning equipment receives the thermal environment preference parameter and adjusts the working parameters of the intelligent air conditioning equipment based on the thermal environment preference parameter. For the description of the other control devices calculating the target user-matched thermal environment preference parameter based on the target PMV value, please refer to the specific description of the related content below. Further, the thermal environment preference parameter matched with the target user includes the preferred heat load value matched with the target user and / or the preferred temperature matched with the target user. Still further, the thermal environment preference parameter matched with the target user also includes the preferred wind speed matched with the target user, so the more contents included in the thermal environment preference parameter matched with the user, the more conducive to improving the adjustment accuracy and efficiency of the working parameters of the intelligent air conditioning equipment. The working parameters of the intelligent air conditioning equipment include the working output temperature. Further, the working parameters of the intelligent air conditioning equipment also include one or a combination of the working wind speed and the air outlet mode. The air outlet mode of the intelligent air conditioning equipment is the mode formed by the movement of the air deflector of the intelligent air conditioning equipment and / or the movement of the front panel of the intelligent air conditioning equipment. The air outlet mode of the intelligent air conditioning equipment includes one of the horizontal air outlet mode, the up-down air outlet mode, and the full-range air outlet (four-around air outlet) mode. The movement of the air deflector of the intelligent air conditioning equipment and the movement of the front panel of the intelligent air conditioning equipment both include moving and flipping, and the moving of the air deflector of the intelligent air conditioning equipment and the moving of the front panel of the intelligent air conditioning equipment both include one of upward moving, downward moving, forward moving, and backward moving.

[0107] It should be noted that after the control device of the intelligent air conditioning equipment receives the related parameters (such as the target PMV value and the thermal environment preference parameter), it can immediately adjust the working parameters of the intelligent air conditioning equipment according to the related parameters, or it can adjust the working parameters of the intelligent air conditioning equipment according to the related parameters after the intelligent air conditioning equipment is turned on, and the embodiments of the present application are not limited.

[0108] It can be seen that the method described in the embodiment of the present application automatically determines the parameters corresponding to the user, such as the thermal sensation feedback of the user, the current environmental parameters, the gender and age of the user, and the like, after detecting that the user corresponding thermal sensation correction prompt is received, and further automatically corrects the current PMV value of the user based on the determined parameters to obtain a corrected PMV value, so as to facilitate the control device of the intelligent air conditioning equipment to intelligently adjust the working parameters of the intelligent air conditioning equipment according to the corrected PMV value, thereby improving the adjustment efficiency and intelligence of the intelligent air conditioning equipment, and further meeting the needs of different users for environmental comfort, without the need for the user to manually control the intelligent air conditioning equipment, even when the user is in a sleep state, the user can still be provided with an accurate comfortable environment, and the use experience of the user is improved.

[0109] In an optional embodiment, the pre-determined current PMV value of the target user is corrected according to the parameters corresponding to the target user to obtain a target PMV value, including:

[0110] The parameters corresponding to the target user and the pre-determined current PMV value of the target user are input into the pre-trained PMV analysis model for analysis, and an analysis result output by the PMV analysis model is obtained as the target PMV value; and / or,

[0111] When the parameters corresponding to the target user include the thermal sensation feedback corresponding to the target user, the pre-determined current PMV value of the target user is corrected according to the thermal sensation feedback corresponding to the target user and the determined PMV correction coefficient to obtain the target PMV value.

[0112] In the optional embodiment, when the parameters corresponding to the target user include the thermal sensation feedback corresponding to the target user, the calculation formula of the target PMV value is as follows:

[0113]

[0114] In the formula, is the target PMV value, is the current PMV value of the target user, is the PMV correction coefficient, and is any real number, for example, if the user feedback is too cold, then = -0.1; if the user feedback is too hot, then = 0.1.

[0115] It can be seen that the optional embodiment can improve the correction accuracy and efficiency of the PMV value by inputting the parameters corresponding to the user and the current PMV value into the trained PMV analysis model for analysis, or can enrich the correction method of the PMV value by correcting the PMV value through the thermal sensation feedback of the user, which is conducive to improving the adjustment efficiency and accuracy of the working parameters of the intelligent air conditioning equipment.

[0116] In another optional embodiment, after the current PMV value of the target user is corrected according to the parameters corresponding to the target user to obtain a target PMV value, the method can further include the following steps:

[0117] According to the target PMV value, a thermal environment preference parameter matched with the target user is calculated, and the thermal environment preference parameter matched with the target user is used to provide a control device of the intelligent air conditioning equipment, so as to trigger the control device of the intelligent air conditioning equipment to adjust the working parameters of the intelligent air conditioning equipment according to the thermal environment preference parameter matched with the target user, and the thermal environment preference parameter matched with the target user includes a preferred heat load value matched with the target user and / or a preferred temperature matched with the target user.

[0118] In the optional embodiment, optionally, according to the target PMV value, a thermal environment preference parameter matched with the target user is calculated, including:

[0119] When the thermal environment preference parameter matched with the target user includes the preferred heat load value matched with the target user, a heat load conversion coefficient matched with the target user is determined, and according to the target PMV value and the heat load conversion coefficient, the preferred heat load value matched with the target user is calculated.

[0120] In the optional embodiment, the calculation formula of the preferred heat load value matched with the target user is as follows:

[0121] ;

[0122] In the formula, is the preferred heat load value matched with the target user; is the heat load conversion coefficient matched with the target user.

[0123] It can be seen that after the PMV value of the user is corrected, the preferred heat load value matched with the user is further calculated based on the corrected PMV value of the user and the corresponding heat load conversion coefficient in the optional embodiment, which can improve the calculation accuracy and efficiency of the preferred heat load value matched with the user, thereby being conducive to improving the accuracy of the working parameters of the intelligent air conditioning equipment.

[0124] In yet another optional embodiment, the method can further include the following operations:

[0125] When the thermal environment preference parameter matched with the target user includes the preferred temperature matched with the target user, the preferred temperature matched with the target user is calculated according to the preferred thermal load value matched with the target user, or the preferred temperature matched with the target user is calculated according to the preferred thermal load value matched with the target user and the parameter corresponding to the target user.

[0126] In the optional embodiment, the calculation formula of the preferred temperature matched with the target user is as follows:

[0127]

[0128] In the formula, T is the preferred temperature matched with the target user, is the initial thermal resistance value of the article used by the target user during rest, M is the human metabolic rate of the target user, S is the human body surface area of the target user, and m is the rest posture of the target user, is the thermal resistance value error of the article used by the target user during rest, is the air pressure of the current environment, , , , , , , , are constants, for example: = 13.342, = 1.519 , = 0.128, = 34.6, = 0.24, = 35.4, = 3.074 , = 5520. The initial thermal resistance value of the article is estimated according to at least one of the following parameters: the thickness of the article, the type of the article, the material of the article, the air tightness of the article, the contact area (or coverage rate 0-100%, wherein 100% indicates that the whole body of the person is under the article, and 95% indicates that only the head of the person is exposed) between the article used by the person during rest and the person, and the dressing of the target user during rest, or collected based on the thermal sensitive sensor arranged on or around the article.

[0129] It can be seen that the optional embodiment can also automatically calculate the preferred temperature matched with the user according to the calculated preferred thermal load value matched with the user (and the corresponding parameter of the user, such as the humidity of the current environment), which can improve the calculation accuracy and efficiency of the preferred temperature matched with the user, thereby facilitating the adjustment accuracy of the working parameters of the intelligent air conditioning device.

[0130] In yet another optional embodiment, determining the heat load conversion coefficient matched with the target user comprises:

[0131] determining a target parameter corresponding to the target user, the target parameter corresponding to the target user comprising a metabolic rate corresponding to the target user;

[0132] calculating the heat load conversion coefficient matched with the target user according to the metabolic rate corresponding to the target user and the determined heat load correction coefficient;

[0133] In this optional embodiment, the calculation formula of the heat load conversion coefficient matched with the target user is as follows:

[0134]

[0135] wherein, is the heat load conversion coefficient matched with the target user, M is the metabolic rate of the target user, a, b and a are the heat load correction coefficient respectively.

[0136] In this optional embodiment, each different user has a matched heat load conversion coefficient.

[0137] It can be seen that this optional embodiment calculates the heat load conversion coefficient matched with the target user by combining the metabolic rate of the user and the heat load correction coefficient, which can improve the calculation accuracy and reliability of the heat load conversion coefficient, thereby being conducive to improving the calculation accuracy and reliability of the thermal environment preference parameter, and further being conducive to improving the adjustment accuracy and reliability of the working parameter of the intelligent air conditioning equipment.

[0138] In yet another optional embodiment, the method can further comprise the following operations:

[0139] determining a training set corresponding to a sample user of each user label, the number of sample users of each user label being greater than or equal to a determined number threshold (such as 500), and each sample user corresponding to the training set of each sample user of each user label comprising a user training metabolic rate corresponding to each sample user, a training PMV value and a training preferred heat load value;

[0140] performing a fitting operation on the user training metabolic rate corresponding to each sample user of each user label and the training PMV value corresponding to each sample user of each user label based on the determined coefficient fitting mode, obtaining a fitting coefficient, and determining the fitting coefficient as the heat load correction coefficient.

[0141] In this optional embodiment, the source of the training set corresponding to the sample user of each user label can be all over the country, can be the same community, or can be family members.

[0142] In the optional embodiment, specifically, the fitting operation is performed on each sample user of each user label based on the least square method, and the user training metabolic rate corresponding to each sample user of each user label and the training PMV value corresponding to each sample user of each user label are obtained, and the fitting coefficient is obtained, wherein the calculation formula of the fitting coefficient is:

[0143] ;

[0144] In the formula, is the ratio of the training PMV value corresponding to each sample user of each user label and the training preferred heat load value corresponding to each sample user of each user label, is the user training metabolic rate corresponding to each sample user of each user label, , b, are heat load correction coefficients, respectively.

[0145] It can be seen that the optional embodiment can improve the fitting accuracy and reliability of the heat load correction coefficient by automatically fitting the training PMV value, the training preferred heat load value and the training metabolic rate of the users collected from each region, thereby facilitating to improve the accuracy and reliability of the subsequent obtained preferred heat load value of the user.

[0146] In yet another optional embodiment, the method can further include the following operations:

[0147] determining a training set corresponding to the sample users of different user labels, the number of sample users of each user label being greater than or equal to the determined number threshold, and the training set corresponding to each sample user of each user label including the user training preferred heat load value corresponding to each sample user of each user label, the user training metabolic rate corresponding to each sample user of each user label, the training environment parameter corresponding to each sample user of each user label and the training personal attribute parameter corresponding to each sample user of each user label;

[0148] establishing an association relationship between each sample user and the training set corresponding to the sample user, and training the pre-determined PMV basic analysis model based on the training set corresponding to the sample users of all user labels determined and the association relationship between each sample user and the training set corresponding to the sample user, to obtain the trained PMV basic analysis model, and determining the trained PMV basic analysis model as the pre-trained PMV analysis model.

[0149] In the optional embodiment, the description of the training environment parameter corresponding to the sample user of each user label and the training personal attribute parameter corresponding to the sample user of each user label can refer to the description of the related content above, and will not be repeated here. Further, the training set corresponding to the sample user of each user label further includes the parameter of other devices corresponding to the sample user of each user label, such as the opening time of a smart door lock, the opening time of a smart smoke machine, the dining time, etc.

[0150] In the optional embodiment, the PMV basic analysis model includes but is not limited to one or more combinations of Naive Bayes, K-Nearest Neighbor, Decision Tree, Linear SVM, Radial Basis Function (RBF) SVM, Random forest, and Neural Network model.

[0151] In the optional embodiment, after obtaining the trained PMV basic analysis model, the method can further include the following steps:

[0152] The verified PMV basic analysis model is continuously verified based on the verification set corresponding to the verification sample user of the sample user of each user label, and the accuracy of the verified PMV basic analysis model is continuously determined until the accuracy of the PMV basic analysis model is greater than or equal to the determined accuracy threshold (such as 95%), the verification operation on the PMV basic analysis model is stopped, or until the absolute difference between the PMV value obtained by verifying the PMV basic analysis model and the predicted PMV value is less than or equal to the determined absolute difference threshold, and the PMV basic analysis model of the last verification is determined as the determined PMV analysis model.

[0153] In the optional embodiment, the PMV analysis model is corrected in combination with the working parameter (such as the air outlet mode, the working output temperature, etc.) of the smart air conditioning device, which can be beneficial to obtain the PMV analysis model more in line with various scenes and conditions, and can be beneficial to improve the analysis accuracy of the PMV value.

[0154] As can be seen, this optional embodiment can obtain a PMV analysis model suitable for this solution by automatically training the collected training set. This is beneficial to improving the efficiency and accuracy of subsequent use of the PMV analysis model to correct the user's PMV value, thereby improving the accuracy of the calculation of the user's thermal environment preference coefficient and the accuracy of adjusting the working parameters of the intelligent air conditioning equipment. Furthermore, by performing a verification operation on the trained model based on the verification set, and stopping the verification operation on the model only when the accuracy of the model reaches the set accuracy threshold, a more accurate PMV analysis model can be obtained, thereby further improving the accuracy and efficiency of PMV value correction.

[0155] In another optional embodiment, the target PMV value is obtained by correcting the pre-determined current PMV value of the target user based on the thermal feedback corresponding to the target user and the determined PMV correction coefficient, including:

[0156] When the number of target users is equal to 1, the current PMV value of the target user is corrected according to the thermal sensory feedback of the target user and the determined PMV correction coefficient to obtain the target PMV value.

[0157] When the number of target users is greater than 1, the current PMV value of each target user is corrected according to the thermal sensation feedback corresponding to each target user and the PMV correction coefficient determined for each target user, so as to obtain the corrected PMV value of each target user, and the target PMV value is determined based on the corrected PMV values ​​of all target users.

[0158] Specifically, the target PMV value is determined based on the corrected PMV values ​​of all target users, including:

[0159] Calculate the mean of the corrected PMV values ​​for all target users, and use it as the target PMV value; or...

[0160] When the number of target users is greater than 1 and is odd, the PMV value that is the median among all the corrected PMV values ​​of the target users is determined as the target PMV value; or...

[0161] Determine the sensitivity of each target user to the information, and select the PMV value matching the target user (such as children) with the highest sensitivity from all target users as the target PMV value. This information includes temperature and / or dust.

[0162] It can be seen that the optional embodiment selects a suitable PMV value according to different environmental conditions of the user in the current environment, such as determining the average value of the PMV values of multiple users as the final PMV value when there are multiple users, thereby facilitating the adjustment of the working parameters of the intelligent air conditioning device, and further facilitating the provision of a suitable comfortable environment for all users in the current environment, and meeting the comfort requirements of different users.

[0163] Embodiment two

[0164] Please refer to Figure 2 , Figure 2 is another flowchart of the control implementation method of the intelligent air conditioning device disclosed in the embodiment of the application. Wherein, Figure 2 The method described can be applied to a control implementation device, wherein the control implementation device includes a control device of the intelligent air conditioning device or other control devices (such as a central controller of the intelligent air conditioning device in the cell) capable of establishing a communication connection with the control device of the intelligent air conditioning device, wherein the control device of the intelligent air conditioning device can include the intelligent air conditioning device itself or a server corresponding to the intelligent air conditioning device, wherein the control implementation device can be in communication connection with a user terminal and / or other intelligent devices (such as an intelligent sweeping device, an intelligent image acquisition device, an intelligent door lock, an intelligent fume machine, etc.) in the current environment, wherein the user terminal includes but is not limited to an intelligent wearable device (such as an intelligent sports bracelet, etc.) and / or a smart phone (Android phone, iOS phone, etc.), and the embodiment of the application does not make any limitation. As shown in Figure 2 The control implementation method of the intelligent air conditioning device can include the following operations:

[0165] 201, detecting whether a thermal sensation correction instruction corresponding to a target user is received, the target user including at least one user in a current environment where the intelligent air conditioning device is located.

[0166] In the embodiment of the application, the thermal sensation correction instruction corresponding to the target user can be triggered by the target user, or triggered by other users with the same label as the target user. Wherein the label includes one or a combination of gender label, age label, physical condition label and eating habit label, which is beneficial to correct the PMV value matched by the user with the same label, thereby facilitating the intelligent adjustment of the intelligent air conditioning device in the environment of the user with the same label.

[0167] 202, when the thermal sensation correction instruction corresponding to the target user is received, determining the parameter corresponding to the target user.

[0168] 203, correcting the pre-determined current PMV value of the target user according to the parameter corresponding to the target user to obtain a target PMV value.

[0169] 204、determine whether the target PMV value meets a predetermined iteration stop condition; when it is determined that the target PMV does not meet the iteration stop condition, trigger step 205 to be executed; when it is determined that the target PMV meets the iteration stop condition, trigger step 206 to be executed, wherein step 206 and the related detailed description can be referred to the description of the related content in Embodiment One.

[0170] 205、determine the target PMV as the current PMV value of the target user.

[0171] 206、calculate the thermal environment preference parameter matched with the target user according to the target PMV value.

[0172] It is to be noted that the related description of steps 201-203 can be referred to the detailed description of steps 101-103 in Embodiment One, and the detailed description of the present embodiment will not be repeated.

[0173] It can be seen that, after detecting the thermal sensation correction prompt corresponding to the user, the present embodiment automatically determines the parameters corresponding to the user, such as the thermal sensation feedback of the user, the current environment parameter, etc., and further automatically corrects the current PMV value of the user based on the determined parameters to obtain the corrected PMV value, so that the control device of the intelligent air conditioning equipment can intelligently adjust the working parameters of the intelligent air conditioning equipment according to the corrected PMV value, thereby improving the adjustment efficiency and intelligence of the intelligent air conditioning equipment, and further meeting the needs of different users for the environmental comfort, without the need for the user to manually control the intelligent air conditioning equipment, even when the user is in a sleep state, the user can still be provided with an accurate comfortable environment, and the user experience is improved. In addition, after correcting the PMV value of the user, it is determined whether the corrected PMV value meets the determined iteration condition, if not, the correction is continued until it meets, and if so, the subsequent calculation operation of the thermal environment preference parameter is performed, which is beneficial to improve the correction accuracy and reliability of the PMV value, thereby further improving the calculation accuracy and reliability of the thermal environment preference coefficient of the user, and improving the adjustment accuracy and reliability of the working parameters of the intelligent air conditioning equipment, thereby improving the accuracy and reliability of providing a comfortable environment for the user.

[0174] In an optional embodiment, determining whether the target PMV value meets a predetermined iteration stop condition comprises:

[0175] calculating whether the absolute value of the difference between the target PMV value and a preset PMV correction value (such as 0) is within a determined correction value range (such as 0-0.3), when it is determined that the absolute value is within the correction value range, it is determined that the target PMV value meets the predetermined iteration stop condition; or,

[0176] The number of times of correction of the current PMV value of the target user is determined, and it is determined whether the number of times of correction is greater than or equal to the determined number of times of correction threshold (for example, 3 times). When it is determined that the number of times of correction is greater than or equal to the number of times of correction threshold, it is determined that the target PMV value satisfies the pre-determined iteration stop condition.

[0177] It can be seen that the optional embodiment can provide multiple ways to determine that the corrected PMV value satisfies the correction requirement by judging that the size of the corrected PMV value tends to be the preset size or by judging that the number of times of correction of the current PMV value of the user reaches the preset number of times, which can improve the determination accuracy and efficiency of the corrected PMV value satisfying the iteration stop condition.

[0178] In another optional embodiment, before the target PMV value is obtained by correcting the pre-determined current PMV value of the target user according to the parameter corresponding to the target user, the method can further include the following steps:

[0179] The weight value corresponding to each content included in the parameter corresponding to the target user is set according to the personal attribute parameter of the target user.

[0180] In the optional embodiment, the target PMV value is obtained by correcting the pre-determined current PMV value of the target user according to the parameter corresponding to the target user, which includes:

[0181] The pre-determined current PMV value of the target user is corrected to obtain the target PMV value according to all contents included in the parameter corresponding to the target user and the weight value corresponding to each content included in the parameter corresponding to the target user.

[0182] In the optional embodiment, the weight value corresponding to each content included in the parameter corresponding to the target user determined for users with different personal attribute parameters is different, but the sum of the weight values corresponding to each content included in the parameter corresponding to the target user is equal to 1. For example, the weight value corresponding to the environmental temperature for a user with cold constitution is greater than the weight value corresponding to the environmental temperature for a user with non-cold constitution; for users of the same age, the weight value corresponding to the environmental temperature for a female user is greater than the weight value corresponding to the environmental temperature for a male user.

[0183] It can be seen that the optional embodiment can assign a weight value corresponding to the content included in the parameter corresponding to the user according to users with different personal attribute parameters, which can improve the setting accuracy of the weight value corresponding to the attribute, and further improve the correction accuracy and reliability of the PMV value by combining the weight value corresponding to each content included in the parameter corresponding to the user with the determined multiple parameters, which is beneficial to further improve the calculation accuracy and reliability of the thermal environment preference parameter, and further beneficial to provide accurate comfortable environment for users with corresponding attributes, and further improve the user experience.

[0184] In yet another optional embodiment, the method may further include the following steps:

[0185] If the smart air conditioning device in the current environment is in working condition, other parameters of the current environment are collected. These other parameters include one or more combinations of the following: air outlet speed of the smart air conditioning device, working output temperature, size of the current environment, spatial density of the current environment (used to indicate the placement of items in the current environment), and working status of other smart devices in the current environment.

[0186] In this optional embodiment, optionally, the current PMV value of the target user is corrected based on all the content included in the parameters corresponding to the target user and the weight value corresponding to each content included in the parameters corresponding to the target user, to obtain the target PMV value, including:

[0187] Based on all the parameters for the target user, the weight value of each parameter for the target user, and other parameters of the current environment, the current PMV value of the pre-determined target user is corrected to obtain the target PMV value.

[0188] As can be seen, this optional embodiment can further combine other parameters of the current environment, such as the operating parameters of the smart air conditioning equipment, the operating status of other smart devices, and the spatial conditions of the current environment, which can further improve the accuracy and reliability of the PMV value correction, and help to further improve the calculation accuracy and reliability of the thermal environment preference parameters. This will further help to provide a more accurate and comfortable environment for users with corresponding attributes, further enhance the user experience, and thus provide a more appropriate and comfortable environment for people in the current area.

[0189] The following examples use users Xiaoming, Xiaohui, and Xiaowei as illustrations:

[0190] like Figure 6 As shown, Figure 6 This is a schematic diagram of a scenario according to an embodiment of the present invention, such as... Figure 6As shown, it is detected whether the thermal sensation correction instructions corresponding to Xiaoming, Xiaohui and Xiaowei in the room are received, when the thermal sensation correction instructions are detected, the environmental parameters in the room, the personal parameters of Xiaoming, Xiaohui and Xiaowei, the personal rest parameters of Xiaoming, Xiaohui and Xiaowei, and the personal thermal sensation feedback of Xiaoming, Xiaohui and Xiaowei are automatically determined, the current PMV values corresponding to Xiaoming, Xiaohui and Xiaowei are corrected by combining the environmental parameters with the personal parameters, respectively, to obtain the target PMV value of Xiaoming, the target PMV value of Xiaohui and the target PMV value of Xiaowei after correction, then the average value of the target PMV value of Xiaoming, the target PMV value of Xiaohui and the target PMV value of Xiaowei is calculated as the final target PMV value, or if the target PMV value of Xiaoming, the target PMV value of Xiaohui and the target PMV value of Xiaowei decrease in turn, the target PMV value of Xiaohui as the intermediate value is taken as the final target PMV value, or if the intelligent air conditioning device has three independent air supply areas (such as Figure 6 As shown, s1, s2, s3), it is determined that the target PMV value of Xiaoming, the target PMV value of Xiaohui and the target PMV value of Xiaowei are all the final target PMV value, and the corresponding air supply area of Xiaoming, Xiaohui and Xiaowei is determined according to the position of Xiaoming, Xiaohui and Xiaowei and the position of each independent air supply area, that is, the s1 air supply system corresponds to Xiaohui, which is used to output the appropriate temperature, air speed and the like for Xiaohui, the s2 air supply area corresponds to Xiaoming, which is used to output the appropriate temperature, air speed and the like for Xiaoming, and the s3 air supply area corresponds to Xiaowei, which is used to output the appropriate temperature, air speed and the like for Xiaowei, after the final target PMV value is determined, the final target PMV value is further directly sent to the control device of the intelligent air conditioning device in the room, so that the control device of the intelligent air conditioning device adjusts the working parameters of the intelligent air conditioning device, or after the final target PMV value is determined, the preferred heat load value and the preferred temperature that Xiaoming, Xiaohui and Xiaowei all match are calculated based on the final target PMV value and the above determined parameters corresponding to the room, and then the preferred heat load value and the preferred temperature are sent to the control device of the intelligent air conditioning device in the room, so that the control device of the intelligent air conditioning device adjusts the working parameters of the intelligent air conditioning device, thereby improving the adjustment efficiency of the intelligent air conditioning device in the room, and further meeting the demand of Xiaoming, Xiaohui and Xiaowei for environmental comfort, without manual control of the intelligent air conditioning device by Xiaoming, Xiaohui and Xiaowei, even when Xiaoming is in a sleep state, an accurate comfortable environment can still be provided for Xiaoming, and the use experience of Xiaoming, Xiaohui and Xiaowei is improved.

[0191] Embodiment three

[0192] Please refer to Figure 3 , Figure 3 is a structural schematic diagram of a control implementation device of an intelligent air conditioning device disclosed by an embodiment of the present application. Wherein, Figure 3The described device may include a control device for a smart air conditioning unit or other control devices capable of establishing a communication connection with the control device for the smart air conditioning unit (such as a central controller for smart air conditioning units in a residential community). The control device for the smart air conditioning unit may include the smart air conditioning unit itself or a server corresponding to the smart air conditioning unit. The control implementation device can communicate with user terminals and / or other smart devices in the current environment (such as smart vacuum cleaners, smart image acquisition devices, smart door locks, smart range hoods, etc.). User terminals include, but are not limited to, smart wearable devices (such as smart fitness trackers) and / or smartphones (Android phones, iOS phones, etc.). This invention does not limit the scope of the invention. Figure 3 As shown, the control device for this intelligent air conditioning equipment may include:

[0193] The detection module 301 is used to detect whether a thermal sensation correction instruction corresponding to a target user is received, the target user including at least one user in the current environment where the smart air conditioning device is located.

[0194] The determination module 302 is used to determine the parameters corresponding to the target user when a thermal sensation correction instruction corresponding to the target user is received.

[0195] The correction module 303 is used to correct the current PMV value of the target user based on the parameters corresponding to the target user, so as to obtain the target PMV value;

[0196] The target PMV value is used by the control device of the smart air conditioning equipment to adjust the operating parameters of the smart air conditioning equipment.

[0197] It is evident that implementation Figure 3 The described device, upon receiving a user's corresponding thermal sensation correction prompt, automatically determines the user's parameters, such as the user's thermal sensation feedback and current environmental parameters. It then automatically corrects the user's current PMV value based on these determined parameters, obtaining a corrected PMV value. This allows the smart air conditioning device's control unit to intelligently adjust the device's operating parameters according to the corrected PMV value, thereby improving the device's adjustment efficiency and intelligence. Ultimately, this meets the environmental comfort needs of different users without requiring manual control of the smart air conditioning device. Even when the user is asleep, it can still provide an accurate and comfortable environment, enhancing the user experience.

[0198] In an optional embodiment, such as Figure 4 As shown, the parameters corresponding to the target user include one or more combinations of environmental parameters of the current environment, personal parameters of the target user, rest parameters of the target user, and thermal sensory feedback of the target user.

[0199] The target PMV value is obtained by correcting the pre-determined current PMV value of the target user according to the parameter corresponding to the target user by the correction module 303.

[0200] The parameter corresponding to the target user and the pre-determined current PMV value of the target user are input into the pre-trained PMV analysis model for analysis, and an analysis result output by the PMV analysis model is obtained as the target PMV value; and / or,

[0201] When the parameter corresponding to the target user includes the thermal sensation feedback corresponding to the target user, the pre-determined current PMV value of the target user is corrected according to the thermal sensation feedback corresponding to the target user and the determined PMV correction coefficient to obtain the target PMV value.

[0202] It can be seen that the embodiments Figure 4 The described device can improve the correction accuracy and efficiency of the PMV value by inputting the parameter corresponding to the user and the current PMV value into the trained PMV analysis model for analysis, or can enrich the correction method of the PMV value by correcting the PMV value through the thermal sensation feedback of the user, which is conducive to improving the adjustment efficiency and accuracy of the working parameters of the intelligent air conditioning equipment.

[0203] In another optional embodiment, as Figure 4 The device further comprises:

[0204] The calculation module 304 is configured to calculate the thermal environment preference parameter matched with the target user according to the target PMV value after the correction module 303 corrects the pre-determined current PMV value of the target user according to the parameter corresponding to the target user to obtain the target PMV value, and the thermal environment preference parameter matched with the target user is used to provide the control device of the intelligent air conditioning equipment, so as to trigger the control device of the intelligent air conditioning equipment to adjust the working parameters of the intelligent air conditioning equipment according to the thermal environment preference parameter matched with the target user, and the thermal environment preference parameter matched with the target user includes the preferred heat load value matched with the target user and / or the preferred temperature matched with the target user.

[0205] The calculation module 304 includes:

[0206] The determination sub-module 3041 is configured to determine the heat load conversion coefficient matched with the target user when the thermal environment preference parameter matched with the target user includes the preferred heat load value matched with the target user.

[0207] The calculation sub-module 3042 is configured to calculate the preferred heat load value matched with the target user according to the target PMV value and the heat load conversion coefficient.

[0208] It can be seen that the embodiments Figure 4The described device can also automatically calculate the user's preferred heat load value based on the corrected user's PMV value and the corresponding heat load conversion coefficient after correcting the user's PMV value. This can improve the accuracy and efficiency of the calculation of the user's preferred heat load value, thereby helping to improve the accuracy of the operating parameters of the intelligent air conditioning equipment.

[0209] In yet another alternative embodiment, such as Figure 4 As shown, the calculation submodule 3042 is further configured to calculate the preferred temperature matching the target user based on the preferred heat load value matching the target user when the thermal environment preference parameter matching the target user includes the preferred temperature matching the target user, or to calculate the preferred temperature matching the target user based on the preferred heat load value matching the target user and the parameters corresponding to the target user.

[0210] It is evident that implementation Figure 4 The described device can also automatically calculate the user's preferred temperature based on the calculated preferred heat load value (and the user's corresponding parameters, such as the current ambient humidity), which can improve the accuracy and efficiency of calculating the user's preferred temperature, thereby helping to improve the accuracy of adjusting the operating parameters of the intelligent air conditioning equipment.

[0211] In yet another alternative embodiment, such as Figure 4 As shown, the specific method by which submodule 3041 determines the heat load conversion coefficient matching the target user is as follows:

[0212] Determine the target parameters for the target user, including the metabolic rate for the target user;

[0213] Calculate the heat load conversion coefficient that matches the target user based on the metabolic rate corresponding to the target user and the determined heat load correction coefficient;

[0214] In this optional embodiment, the formula for calculating the heat load conversion factor matching the target user is as follows:

[0215]

[0216] In the formula, The heat load conversion factor is used to match the target user, where M is the metabolic rate of the target user, and a, b, and α are heat load correction factors, respectively.

[0217] It is evident that implementation Figure 4The described device can also calculate the heat load conversion coefficient matching the target user by combining the user's metabolic rate and heat load correction coefficient, which can improve the accuracy and reliability of the heat load conversion coefficient calculation, thereby improving the accuracy and reliability of the thermal environment preference parameter calculation, and further improving the accuracy and reliability of the adjustment of the operating parameters of the intelligent air conditioning equipment.

[0218] In yet another alternative embodiment, such as Figure 4 As shown, the determining module 302 is also used to determine the training set corresponding to the sample users of different user labels. The number of sample users corresponding to each user label is greater than or equal to the determined number threshold. The training set corresponding to the sample users of each user label includes the user training parameters corresponding to the sample users of each user label. The user training parameters corresponding to the sample users of each user label include the user training metabolic rate corresponding to the sample users of each user label and the training PMV value corresponding to the sample users of each user label.

[0219] like Figure 4 As shown, the device also includes:

[0220] The fitting module 305 is used to perform a fitting operation on the user training metabolic rate corresponding to the sample user of each user label and the training PMV value corresponding to the sample user of each user label based on the determined coefficient fitting method, so as to obtain the fitting coefficients.

[0221] The determination module 302 is also used to determine the fitting coefficients, which are the heat load correction coefficients.

[0222] It is evident that implementation Figure 4 The described device can also improve the fitting accuracy and reliability of the heat load correction coefficient by automatically fitting the training PMV value, training preference heat load value and training metabolic rate of users in various regions, thereby improving the accuracy and reliability of the subsequently obtained user preference heat load value.

[0223] In yet another alternative embodiment, such as Figure 4 As shown, the determining module 302 is also used to determine the training set corresponding to the sample users of different user labels. The number of sample users corresponding to each user label is greater than or equal to the determined number threshold. The training set corresponding to the sample users of each user label includes the user training preference heat load value, the user training metabolic rate, the training environment parameters, and the training personal attribute parameters.

[0224] like Figure 4 As shown, the device also includes:

[0225] The training module 306 is configured to establish an association between each sample user and the training set corresponding to the sample user, and train the pre-determined PMV basic analysis model based on the training set corresponding to the sample user of all the determined user labels and the association between each sample user and the training set corresponding to the sample user, to obtain the trained PMV basic analysis model.

[0226] The determination module 302 is further configured to determine that the trained PMV basic analysis model is a pre-trained PMV analysis model.

[0227] It can be seen that the apparatus described in the embodiments can automatically train the collected training set, and can obtain the PMV analysis model suitable for the present solution, which is beneficial to improving the efficiency and accuracy of correcting the PMV value of the user using the PMV analysis model, thereby improving the calculation accuracy of the thermal environment preference coefficient of the user and the adjustment accuracy of the working parameters of the intelligent air conditioning equipment. Figure 4 The apparatus described in the embodiments can automatically train the collected training set, and can obtain the PMV analysis model suitable for the present solution, which is beneficial to improving the efficiency and accuracy of correcting the PMV value of the user using the PMV analysis model, thereby improving the calculation accuracy of the thermal environment preference coefficient of the user and the adjustment accuracy of the working parameters of the intelligent air conditioning equipment.

[0228] In another optional embodiment, as shown in Figure 4 The apparatus described in the embodiments can automatically train the collected training set, and can obtain the PMV analysis model suitable for the present solution, which is beneficial to improving the efficiency and accuracy of correcting the PMV value of the user using the PMV analysis model, thereby improving the calculation accuracy of the thermal environment preference coefficient of the user and the adjustment accuracy of the working parameters of the intelligent air conditioning equipment.

[0229] The determination module 302 is further configured to determine that the trained PMV basic analysis model is a pre-trained PMV analysis model.

[0230] The determination module 302 is further configured to determine that the trained PMV basic analysis model is a pre-trained PMV analysis model.

[0231] It can be seen that the apparatus described in the embodiments can automatically train the collected training set, and can obtain the PMV analysis model suitable for the present solution, which is beneficial to improving the efficiency and accuracy of correcting the PMV value of the user using the PMV analysis model, thereby improving the calculation accuracy of the thermal environment preference coefficient of the user and the adjustment accuracy of the working parameters of the intelligent air conditioning equipment. Figure 4 The apparatus described in the embodiments can automatically train the collected training set, and can obtain the PMV analysis model suitable for the present solution, which is beneficial to improving the efficiency and accuracy of correcting the PMV value of the user using the PMV analysis model, thereby improving the calculation accuracy of the thermal environment preference coefficient of the user and the adjustment accuracy of the working parameters of the intelligent air conditioning equipment.

[0232] In another optional embodiment, as shown in Figure 5As shown, the correction module 303 corrects the pre-determined current PMV value of the target user according to the thermal sensation feedback corresponding to the target user and the determined PMV correction coefficient to obtain the target PMV value in the following manner:

[0233] When the number of target users is equal to 1, the pre-determined current PMV value of the target user is corrected according to the thermal sensation feedback corresponding to the target user and the determined PMV correction coefficient to obtain the target PMV value.

[0234] When the number of target users is greater than 1, the pre-determined current PMV value of each target user is corrected according to the thermal sensation feedback corresponding to each target user and the determined PMV correction coefficient of each target user to obtain the corrected PMV value of each target user, and the target PMV value is determined based on the corrected PMV values of all target users.

[0235] The correction module 303 determines the target PMV value based on the corrected PMV values of all target users in the following manner:

[0236] The mean value of the corrected PMV values of all target users is calculated as the target PMV value; or,

[0237] When the number of target users is greater than 1 and is an odd number, the PMV value of the middle value among the corrected PMV values of all target users is determined as the target PMV value.

[0238] It can be seen that the embodiment Figure 5 The described device can also select a suitable PMV value according to different environmental conditions of the users in the current environment, such as determining the average value of the PMV values of multiple users as the final PMV value when there are multiple users, thereby facilitating the adjustment of the working parameters of the intelligent air conditioning device, and further facilitating the provision of a suitable comfortable environment for all users in the current environment to meet the comfort requirements of different users.

[0239] Embodiment Four

[0240] Please refer to Figure 5 , Figure 5 is another structure schematic diagram of the control implementation device of the intelligent air conditioning device disclosed in the embodiment of the present application. In the diagram, ​The described device can include a control device of a smart air conditioning device or other control device (such as a central controller of smart air conditioning devices in a cell) capable of establishing a communication connection with the control device of the smart air conditioning device, wherein the control device of the smart air conditioning device can include the smart air conditioning device itself or a server corresponding to the smart air conditioning device, wherein the control implementation device can be in communication connection with a user terminal and / or other smart devices (such as a smart sweeping device, a smart image acquisition device, a smart door lock, a smart fume machine, etc.) of the current environment, wherein the user terminal includes but is not limited to a smart wearable device (such as a smart sports bracelet, etc.) and / or a smart phone (an Android phone, an iOS phone, etc.), and the present embodiment is not limited. ​ The control implementation device of the smart air conditioning device can include:

[0241] The memory 401 stores executable program codes.

[0242] The processor 402 is coupled with the memory 401.

[0243] The processor 402 invokes the executable program codes stored in the memory 401 to execute part or all of the steps of the control implementation method of the smart air conditioning device disclosed in the first embodiment or the second embodiment.

[0244] Embodiment five

[0245] The computer storage medium disclosed in the present embodiment stores computer instructions, and when the computer instructions are invoked, part or all of the steps of the control implementation method of the smart air conditioning device disclosed in the first embodiment or the second embodiment are executed.

[0246] The device embodiments described above are only schematic, wherein the modules described as separate components can or can not be physically separated, and the components displayed as modules can or can not be physical modules, that is, they can be located in one place or distributed on multiple network modules. According to actual needs, part or all of the modules can be selected to achieve the purpose of the present embodiment. Those skilled in the art can understand and implement without creative labor.

[0247] Those skilled in the art can clearly understand the implementation of the various embodiments by means of software and necessary general hardware platforms through the above specific description of the embodiments, and of course, the embodiments can also be implemented by hardware. Based on such understanding, the above technical solutions, essentially or in other words, the part of the prior art that contributes to the present application can be embodied in the form of a software product. The computer software product can be stored in a computer readable storage medium, including a Read-Only Memory (ROM), a Random Access Memory (RAM), a Programmable Read-only Memory (PROM), an Erasable Programmable Read Only Memory (EPROM), a One-time Programmable Read-Only Memory (OTPROM), an Electrically-Erasable Programmable Read-Only Memory (EEPROM), a Compact Disc Read-Only Memory (CD-ROM) or other optical disk storage, a magnetic disk storage, a magnetic tape storage, or any other medium that can be used to carry or store data in a computer readable manner.

[0248] Finally, it should be noted that: the control implementation method and device of the intelligent air conditioning equipment disclosed by the embodiments of the present application are only the preferred embodiments of the present application, and are used to illustrate the technical solutions of the present application, but not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that; it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A control implementation method for an intelligent air conditioning device, characterized in that, The method includes: Detect whether a thermal sensation correction instruction corresponding to a target user is received, wherein the target user includes at least one user in the current environment in which the smart air conditioning device is located; When a thermal sensation correction instruction corresponding to the target user is received, the parameters corresponding to the target user are determined, and the current PMV value of the target user, which has been predetermined, is corrected according to the parameters corresponding to the target user to obtain the target PMV value; The target PMV value is used by the control device of the smart air conditioning equipment to adjust the operating parameters of the smart air conditioning equipment. After correcting the pre-determined current PMV value of the target user according to the parameters corresponding to the target user to obtain the target PMV value, the method further includes: The thermal environment preference parameters matching the target user are calculated based on the target PMV value. The thermal environment preference parameters matching the target user are provided to the control device of the smart air conditioning equipment to trigger the control device of the smart air conditioning equipment to adjust the operating parameters of the smart air conditioning equipment according to the thermal environment preference parameters matching the target user. The thermal environment preference parameters matching the target user include the preferred heat load value matching the target user and / or the preferred temperature matching the target user. The step of calculating the thermal environment preference parameters matching the target user based on the target PMV value includes: When the thermal environment preference parameters matching the target user include a preferred heat load value matching the target user, a heat load conversion coefficient matching the target user is determined, and a preferred heat load value matching the target user is calculated based on the target PMV value and the heat load conversion coefficient.

2. The control implementation method for the intelligent air conditioning equipment according to claim 1, characterized in that, The parameters corresponding to the target user include one or more of the following combinations: environmental parameters of the current environment, personal parameters of the target user, rest parameters corresponding to the target user, and thermal sensory feedback corresponding to the target user. The step of correcting the pre-determined current PMV value of the target user according to the parameters corresponding to the target user to obtain the target PMV value includes: The parameters corresponding to the target user and the pre-determined current PMV value of the target user are input into a pre-trained PMV analysis model for analysis, and the analysis result output by the PMV analysis model is obtained as the target PMV value; or, When the parameters corresponding to the target user include the thermal feedback corresponding to the target user, the current PMV value of the target user is corrected according to the thermal feedback corresponding to the target user and the determined PMV correction coefficient to obtain the target PMV value.

3. The control implementation method for the intelligent air conditioning equipment according to claim 1 or 2, characterized in that, The method further includes: When the thermal environment preference parameters matching the target user include the preferred temperature matching the target user, the preferred temperature matching the target user is calculated based on the preferred heat load value matching the target user, or the preferred temperature matching the target user is calculated based on the preferred heat load value matching the target user and the parameters corresponding to the target user.

4. The control implementation method for the intelligent air conditioning equipment according to claim 1 or 2, characterized in that, Determining the heat load conversion coefficient that matches the target user includes: Determine the target parameters corresponding to the target user, wherein the target parameters corresponding to the target user include the metabolic rate corresponding to the target user; Calculate the heat load conversion coefficient that matches the target user based on the metabolic rate corresponding to the target user and the determined heat load correction coefficient; The formula for calculating the heat load conversion coefficient matched with the target user is as follows: ; In the formula, the The heat load conversion coefficient is used to match the target user, where M is the metabolic rate of the target user, and a, b, and α are heat load correction coefficients, respectively.

5. The control implementation method for the intelligent air conditioning equipment according to claim 4, characterized in that, The method further includes: A training set is determined for sample users corresponding to different user tags. The number of sample users corresponding to each user tag is greater than or equal to a determined threshold. The training set for sample users corresponding to each user tag includes user training parameters for each user tag. The user training parameters for each user tag include user training metabolic rate and training PMV value for each user tag. Based on the determined coefficient fitting method, a fitting operation is performed on the user training metabolic rate corresponding to the sample user of each user label and the training PMV value corresponding to the sample user of each user label to obtain fitting coefficients, and the fitting coefficients are determined as the heat load correction coefficients.

6. The control implementation method for intelligent air conditioning equipment according to claim 2, characterized in that, The method further includes: The training set corresponding to sample users with different user tags is determined. The number of sample users corresponding to each user tag is greater than or equal to the determined number threshold. The training set corresponding to sample users with each user tag includes the user training preference heat load value, the user training metabolic rate, the training environment parameters, and the training personal attribute parameters. Establish the association between each sample user and the training set corresponding to that sample user, and based on the training sets corresponding to all the user tags and the association between each sample user and the training set corresponding to that sample user, train the pre-determined PMV basic analysis model to obtain the trained PMV basic analysis model, and determine the trained PMV basic analysis model as the pre-trained PMV analysis model.

7. The control implementation method for the intelligent air conditioning equipment according to any one of claims 1, 2, 5 and 6, characterized in that, After correcting the pre-determined current PMV value of the target user according to the parameters corresponding to the target user to obtain the target PMV value, the method further includes: Determine whether the target PMV value meets the predetermined iteration stopping condition; When it is determined that the target PMV does not meet the iteration stop condition, the target PMV is determined as the current PMV value of the target user, and the operation of detecting whether the thermal sensation correction instruction corresponding to the target user is received is triggered. The process continues to determine whether the corrected target PMV value meets the iteration stop condition. When it is determined that the corrected target PMV value does not meet the iteration stop condition, the corrected target PMV value continues to be corrected until the corrected target PMV value meets the iteration stop condition.

8. The control implementation method for the intelligent air conditioning equipment according to claim 2, characterized in that, The step of correcting the pre-determined current PMV value of the target user based on the thermal feedback corresponding to the target user and the determined PMV correction coefficient to obtain the target PMV value includes: When the number of target users is equal to 1, the current PMV value of the target user is corrected according to the thermal sensation feedback corresponding to the target user and the determined PMV correction coefficient, so as to obtain the target PMV value; When the number of target users is greater than 1, the current PMV value of each target user is corrected according to the thermal sensation feedback corresponding to each target user and the PMV correction coefficient of each target user, so as to obtain the corrected PMV value of each target user, and the target PMV value is determined based on the corrected PMV values ​​of all target users. The step of determining the target PMV value based on the corrected PMV values ​​of all the target users includes: Calculate the mean of the corrected PMV values ​​for all the target users, and use it as the target PMV value; or... When the number of target users is greater than 1 and is odd, the PMV value that is the middle value among all the corrected PMV values ​​of the target users is determined as the target PMV value.

9. A control device for an intelligent air conditioning system, characterized in that, The device is used to implement the control method for the intelligent air conditioning equipment as described in any one of claims 1-8, and the device comprises: The detection module is used to detect whether a thermal sensation correction instruction corresponding to a target user is received, wherein the target user includes at least one user in the current environment in which the smart air conditioning device is located; The determination module is used to determine the parameters corresponding to the target user when the thermal sensation correction instruction corresponding to the target user is received by the detection module. The correction module is used to correct the current PMV value of the target user, which has been predetermined, according to the parameters corresponding to the target user, so as to obtain the target PMV value; The target PMV value is used by the control device of the smart air conditioning equipment to adjust the operating parameters of the smart air conditioning equipment.

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