A method for recommending the startup time of an air conditioner and an air conditioner

Abstract

The present invention discloses a method for recommending the turning-on time of an air conditioner and an air conditioner. The method includes: processing the historical data of all users using the air conditioner within a preset time interval based on a clustering algorithm and determining the preferred time periods for each user group to turn on the air conditioner; determining the preferred usage comfort values for each user group according to the target parameters set last by each user group before turning off the air conditioner in the historical data; determining the environmental comfort values corresponding to each air conditioner according to the real-time environmental parameters of the geographical location where each air conditioner is located; if there is a target air conditioner that meets the preset recommendation conditions, sending a recommendation message for turning on the target air conditioner to the user terminal of the target air conditioner, realizing accurate intelligent recommendation of the turning-on time of the air conditioner and improving the user experience.

Description

Technical Field

[0001] The present application relates to the technical field of air conditioners, and more specifically, to a method for recommending the turning-on time of an air conditioner and an air conditioner. Background Art

[0002] With the rapid development of Internet technology and the popularization of smart home appliances in people's daily lives, the concept of intelligence has penetrated into more and more ordinary consumers. More intelligent use and control of smart home appliances can improve the user experience and increase user stickiness.

[0003] In the currently applied recommendation methods, only the scenarios after the air conditioner is turned on are recommended to users, but whether the air conditioner needs to be turned on in different environments has not been intelligently recommended yet.

[0004] Therefore, how to accurately recommend the turning-on time of an air conditioner to improve the user experience is a technical problem to be solved currently. Summary of the Invention

[0005] The present invention provides a method for recommending the turning-on time of an air conditioner to solve the technical problem in the prior art that the turning-on time of an air conditioner cannot be accurately recommended.

[0006] The method includes:

[0007] Processing the historical data of all users using the air conditioner within a preset time interval based on a clustering algorithm and determining the preferred time periods for each user group to turn on the air conditioner;

[0008] Determining the preferred usage comfort values for each user group according to the target parameters set last by each user group before turning off the air conditioner in the historical data;

[0009] Determining the environmental comfort value corresponding to each air conditioner according to the real-time environmental parameters of the geographical location where each air conditioner is located;

[0010] If there is a target air conditioner that meets the preset recommendation conditions, sending a recommendation message to turn on the target air conditioner to the user terminal of the target air conditioner;

[0011] Wherein, the preset recommendation conditions are that the difference between the usage comfort value and the environmental comfort value is greater than a preset threshold and the current time is within the preferred time period, or, the preset recommendation conditions are that the difference is greater than the preset threshold and the current time is within the preferred time period and there is someone in the usage site of the target air conditioner.

[0012] In some embodiments of the present application, the target parameters include a target temperature and a target humidity. A usage comfort value preferred by each user group is determined according to the target parameters set last by each user group in the historical data before turning off the air conditioner. Specifically:

[0013] Determine DI1 = 0.81t1 + 0.99f1t1 - 14.3f1 + 46.3;

[0014] Wherein, DI1 is the usage comfort value, t1 is the target temperature, and f1 is the target humidity.

[0015] In some embodiments of the present application, the real-time environmental parameters include a real-time environmental temperature and a real-time environmental humidity. An environmental comfort value corresponding to each air conditioner is determined according to the real-time environmental parameters of the geographical location where each air conditioner is located. Specifically:

[0016] Determine DI2 = 0.81t2 + 0.99f2t2 - 14.3f2 + 46.3;

[0017] Wherein, DI2 is the environmental comfort value, t2 is the real-time environmental temperature, and f2 is the real-time environmental humidity.

[0018] In some embodiments of the present application, the method further includes:

[0019] If there are new users, the characteristic data of the new users using the air conditioner is processed based on a clustering algorithm, and the target user group to which the new users belong is determined from each user group;

[0020] When sending the recommendation information to the user terminals of the target user group, the recommendation information is simultaneously sent to the user terminals of the new users.

[0021] In some embodiments of the present application, the new users are determined according to the historical data.

[0022] In some embodiments of the present application, the characteristic data includes age group, gender, season, date, weekday, holiday, geographical location, environmental temperature, environmental humidity, set air conditioner mode, set air conditioner temperature, and set air conditioner wind speed.

[0023] In some embodiments of the present application, after sending the recommendation information to turn on the target air conditioner to the user terminal of the target air conditioner, the method further includes:

[0024] If the user accepts the recommendation information, save the recommendation information and incorporate it into the historical data;

[0025] If the user rejects the recommendation information and the number of rejections reaches a preset number, delete the recommendation information.

[0026] In some embodiments of the present application, the clustering characteristics of various user groups include age group, gender, season, date, weekday, holiday, geographical location, environmental temperature, and environmental humidity when using the air conditioner.

[0027] Correspondingly, the present invention further provides an air conditioner, comprising:

[0028] A communication module for communicating with a user terminal and a cloud server;

[0029] A controller configured to:

[0030] Regularly upload historical data of user use of the air conditioner to the cloud server;

[0031] When receiving an opening instruction sent by the user terminal, turn on the air conditioner;

[0032] Wherein, the opening instruction is triggered after the user terminal displays recommended information for turning on the air conditioner to the user and receives an acceptance operation by the user based on the recommended information, and the recommended information is sent by the cloud server to the user terminal according to the method described above.

[0033] In some embodiments of the present application, it further comprises:

[0034] An outdoor temperature sensor for detecting the outdoor environmental temperature;

[0035] An outdoor humidity sensor for detecting the outdoor environmental humidity;

[0036] A human presence sensing module for detecting whether there is anyone in the place where the air conditioner is used;

[0037] The controller is further configured to:

[0038] Regularly upload the outdoor environmental temperature, outdoor environmental humidity, and human detection results to the cloud server.

[0039] By applying the above technical solutions, the historical data of all users' use of the air conditioner within a preset time interval is processed based on a clustering algorithm to determine the preferred time periods for various user groups to turn on the air conditioner; the preferred usage comfort values for various user groups are determined according to the target parameters set last by various user groups before turning off the air conditioner in the historical data; the environmental comfort values corresponding to each air conditioner are determined according to the real-time environmental parameters of the geographical location where each air conditioner is located; if there is a target air conditioner that meets the preset recommendation conditions, recommended information for turning on the target air conditioner is sent to the user terminal of the target air conditioner, realizing accurate intelligent recommendation of the turning-on time of the air conditioner and improving the user experience. Description of the Drawings

[0040] To more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application. For those skilled in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0041] Figure 1 The flowchart shows a method for recommending the start time of an air conditioner according to an embodiment of the present invention.

[0042] Figure 2 The flowchart shows a method for recommending the start time of an air conditioner according to another embodiment of the present invention.

[0043] Figure 3 The schematic diagram shows the structure of an air conditioner according to an embodiment of the present invention.

[0044] Figure 4 The schematic diagram shows the structure of an air conditioner according to another embodiment of the present invention. Detailed implementation manners

[0045] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, rather than all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.

[0046] The terms "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the present application, unless otherwise stated, the meaning of "a plurality" is two or more.

[0047] In the present application, the air conditioner performs a refrigeration cycle by using a compressor, a condenser, an expansion valve, and an evaporator. The refrigeration cycle includes a series of processes involving compression, condensation, expansion, and evaporation, and supplies refrigerant to the air that has been conditioned and heat-exchanged.

[0048] The compressor compresses the refrigerant gas in a high-temperature and high-pressure state and discharges the compressed refrigerant gas. The discharged refrigerant gas flows into the condenser, and the condenser condenses the compressed refrigerant into a liquid phase, and the heat is released to the surrounding environment through the condensation process.

[0049] The expansion valve expands the high-temperature and high-pressure liquid-phase refrigerant condensed in the condenser into a low-pressure liquid-phase refrigerant. The evaporator evaporates the refrigerant expanded in the expansion valve and returns the refrigerant gas in the low-temperature and low-pressure state to the compressor. The evaporator can achieve a refrigeration effect by using the latent heat of evaporation of the refrigerant to exchange heat with the material to be cooled. During the entire cycle, the air conditioner can adjust the temperature of the indoor space.

[0050] The outdoor unit of the air conditioner refers to the part of the refrigeration cycle including the compressor and the outdoor heat exchanger. The indoor unit of the air conditioner includes the indoor heat exchanger, and the expansion valve can be provided in the indoor unit or the outdoor unit.

[0051] The indoor heat exchanger and the outdoor heat exchanger are used as condensers or evaporators. When the indoor heat exchanger is used as a condenser, the air conditioner serves as a heater in the heating mode. When the indoor heat exchanger is used as an evaporator, the air conditioner serves as a cooler in the cooling mode.

[0052] An embodiment of the present application provides a method for recommending the startup time of an air conditioner, which can be applied to a cloud server. The cloud server establishes a communication connection with multiple air conditioners, and each air conditioner regularly uploads historical data when the user uses the air conditioner to the cloud server, such as Figure 1 As shown, the method includes the following steps:

[0053] Step S101: Process the historical data of all users using the air conditioner within a preset time interval based on a clustering algorithm and determine the preferred time periods for each user group to turn on the air conditioner.

[0054] In this embodiment, the clustering algorithm divides the data set into multiple clusters by calculating the similarity between data objects, so that objects in the same cluster have a high similarity, while the differences between objects in different clusters are large. The clustering algorithm can include hierarchical clustering, k-means algorithm, EM algorithm, DBSCAN algorithm, OPTICS algorithm, Mean Shift algorithm, spectral clustering algorithm, and those skilled in the art can flexibly select according to needs.

[0055] Processing the historical data of all users using the air conditioner within a preset time interval based on the clustering algorithm can divide the users using the air conditioner into multiple user groups and determine the preferred time periods for each user group to turn on the air conditioner. It can be understood that the preferred time periods of the same user group may be different under different conditions (such as weekdays and holidays), and each user group corresponds to at least one preferred time period.

[0056] Optionally, the preset time interval is one month.

[0057] It can be understood that all users are the users of all air conditioners connected to the cloud server.

[0058] In some embodiments of the present application, in order to more accurately recommend the turning-on time, the clustering features of various user groups include age group, gender, season, date, weekday, holiday, geographical location, ambient temperature, and ambient humidity when using the air conditioner. The age group and gender can be obtained from the user's registration information, and other clustering features can be obtained through the network.

[0059] Those skilled in the art can flexibly set different clustering features according to actual needs, which does not affect the protection scope of the present application.

[0060] Step S102: Determine the usage comfort values preferred by various user groups according to the target parameters set for the last time by various user groups in the historical data before turning off the air conditioner.

[0061] In this embodiment, the target parameters set for the last time by various user groups before turning off the air conditioner are screened out from the historical data. The target parameters represent the ideal environment that the user expects to achieve by using the air conditioner. According to the target parameters, the usage comfort values preferred by various user groups can be determined.

[0062] Optionally, a first correspondence between different target parameters and usage comfort values can be established in advance, and the usage comfort value corresponding to the target parameter can be determined according to the first correspondence.

[0063] In some embodiments of the present application, in order to accurately determine the usage comfort values preferred by various user groups, the target parameters include target temperature and target humidity. Determining the usage comfort values preferred by various user groups according to the target parameters set for the last time by various user groups in the historical data before turning off the air conditioner is specifically as follows:

[0064] Determine DI1 = 0.81t1 + 0.99f1t1 - 14.3f1 + 46.3;

[0065] Wherein, DI1 is the usage comfort value, t1 is the target temperature, and f1 is the target humidity.

[0066] It should be noted that the solutions of the above embodiments are only a specific implementation solution proposed by the present application, and other ways of determining the usage comfort values preferred by various user groups according to the target parameters belong to the protection scope of the present application.

[0067] Step S103: Determine the environmental comfort values corresponding to each air conditioner according to the real-time environmental parameters of the geographical location where each air conditioner is located.

[0068] In this embodiment, the geographical locations of each air conditioner may be different, and the environmental parameters of different geographical locations may also be different. Preferably, if the air conditioner is equipped with an outdoor temperature sensor for detecting the outdoor environmental temperature and an outdoor humidity sensor for detecting the outdoor environmental humidity, the real-time environmental parameters can be determined according to the outdoor environmental temperature and the outdoor environmental humidity uploaded by the controller of the air conditioner. The real-time environmental parameters determined in this way are more accurate. If the air conditioner is not equipped with an outdoor temperature sensor and an outdoor humidity sensor, the real-time environmental parameters can be determined through the weather data of the geographical location where the air conditioner is located. The weather data can be obtained from the network, and the environmental comfort value can be determined according to the real-time environmental parameters.

[0069] Optionally, a second correspondence between different environmental parameters and environmental comfort values can be established in advance, and the environmental comfort value under the real-time environmental parameters can be determined according to this second correspondence.

[0070] In order to accurately determine the environmental comfort value, in some embodiments of the present application, the real-time environmental parameters include the real-time environmental temperature and the real-time environmental humidity. The environmental comfort value corresponding to each air conditioner is determined according to the real-time environmental parameters of the geographical location where each air conditioner is located. Specifically:

[0071] Determine DI2 = 0.81t2 + 0.99f2t2 - 14.3f2 + 46.3;

[0072] Wherein, DI2 is the environmental comfort value, t2 is the real-time environmental temperature, and f2 is the real-time environmental humidity.

[0073] It should be noted that the solutions of the above embodiments are only a specific implementation solution proposed in the present application. Other methods for determining the environmental comfort value according to the real-time environmental parameters of the geographical location where each air conditioner is located all fall within the protection scope of the present application.

[0074] Step S104, if there is a target air conditioner that meets the preset recommendation conditions, send a recommendation message to the user terminal of the target air conditioner to turn on the target air conditioner.

[0075] In this embodiment, if the air conditioner is not equipped with a human presence sensing module for detecting whether there is someone in the usage site, the preset recommendation condition is that the difference between the usage comfort value and the environmental comfort value is greater than the preset threshold and the current time is within the preferred time period; if the air conditioner is equipped with a human presence sensing module, the preset recommendation condition is that the difference is greater than the preset threshold, the current time is within the preferred time period, and there is someone in the usage site of the target air conditioner.

[0076] The air conditioner can communicate with a user terminal, which can be a mobile phone, a tablet, a smart wearable device, etc. If there is a target air conditioner that meets the preset recommendation conditions, a recommendation message to turn on the target air conditioner is sent to the user terminal of the target air conditioner to prompt the user whether to turn on the target air conditioner now.

[0077] In some embodiments of the present application, in order to accurately send the recommendation message to new users, the method further includes:

[0078] If there are new users, based on the clustering algorithm, the characteristic data of the new users using the air conditioner is processed, and the target user group to which the new users belong is determined from various user groups;

[0079] When sending the recommendation message to the user terminal of the target user group, the recommendation message is simultaneously sent to the user terminal of the new users.

[0080] In this embodiment, the new users are cold start users. The historical data of these new users using the air conditioner is less or there is no historical data, and it is impossible to directly determine the preferred time period for the new users to turn on the air conditioner. First, based on the clustering algorithm, the characteristic data of the new users using the air conditioner is processed, and the target user group to which the new users belong is determined from various user groups. When sending the recommendation message to the user terminal of the target user group, the recommendation message is simultaneously sent to the user terminal of these new users.

[0081] In some embodiments of the present application, in order to reliably determine whether there are new users, the new users are determined according to the historical data.

[0082] In this embodiment, since the historical data of the new users is less or there is no historical data, the new users can be screened out from the historical data of all users using the air conditioner.

[0083] In some embodiments of the present application, in order to more accurately determine the target user group to which the new users belong, the characteristic data includes age group, gender, season, date, weekday, holiday, geographical location, ambient temperature, ambient humidity, set air conditioner mode, set air conditioner temperature, set air conditioner wind speed.

[0084] Those skilled in the art can flexibly set different characteristic data according to actual needs, which does not affect the protection scope of the present application.

[0085] In some embodiments of the present application, in order to further improve the accuracy of the recommended turn-on time, after sending the recommendation message to turn on the target air conditioner to the user terminal of the target air conditioner, the method further includes:

[0086] If the user accepts the recommendation message, save the recommendation message and incorporate it into the historical data;

[0087] If the user rejects the recommended information and the number of rejections reaches a preset number, delete the recommended information.

[0088] In this embodiment, if the user accepts the recommended information, it indicates that the recommended information is accurate, and the recommended information is saved and classified into the historical data; if the user rejects the recommended information and the number of rejections reaches a preset number, it indicates that the recommended information is inaccurate, and the recommended information is deleted, improving the accuracy of the recommended start time.

[0089] It can be understood that the order of step S101, step S102, and step S103 can be interchanged.

[0090] By applying the above technical solution, the historical data of all users using the air conditioner within a preset time interval is processed based on the clustering algorithm to determine the preferred time periods for various user groups to turn on the air conditioner; the preferred usage comfort values for various user groups are determined according to the target parameters set last time before turning off the air conditioner in the historical data; the environmental comfort values corresponding to each air conditioner are determined according to the real-time environmental parameters of the geographical location where each air conditioner is located; if there is a target air conditioner that meets the preset recommendation conditions, a recommended information for turning on the target air conditioner is sent to the user terminal of the target air conditioner, realizing an accurate intelligent recommendation for the start time of the air conditioner and improving the user experience.

[0091] To further elaborate on the technical idea of the present invention, the technical solution of the present invention will be described in combination with a specific application scenario.

[0092] An embodiment of the present application provides a method for recommending the start time of an air conditioner, as Figure 2 shown, including the following steps:

[0093] Step S201, start.

[0094] Step S202, determine whether there are new users according to the historical data of one month. If so, execute step S204; otherwise, execute step S203.

[0095] Step S203, process the historical data based on the clustering algorithm and determine the preferred time periods for various user groups to turn on the air conditioner.

[0096] The clustering features include age group, gender, season, date, weekday, holiday, geographical location, environmental temperature, and environmental humidity when using the air conditioner. Table 1 is an example of the clustering result.

[0097] Table 1

[0098]

[0099] Step S204: Process the characteristic data of the new users' use of the air conditioner based on the clustering algorithm, determine the target user group to which the new users belong, and execute Step S203.

[0100] Table 2

[0101]

[0102]

[0103] As shown in Table 2, user m is a new user without historical air conditioner usage data. Through the clustering algorithm, it is determined that user m belongs to the same user group as the users in group A. Therefore, it is predicted that when it is a non-working day in summer and the ambient temperature is 28°, 9 o'clock belongs to the preferred time period for user m to turn on the air conditioner.

[0104] The characteristic data includes age group, gender, season, date, working day, holiday, geographical location, ambient temperature, ambient humidity, set air conditioner mode, set air conditioner temperature, and set air conditioner wind speed.

[0105] Step S205: Determine the preferred usage comfort values of each user group according to the target parameters set last time before turning off the air conditioner by each user group in the historical data.

[0106] The target parameters include target temperature and target humidity. The calculation formula for usage comfort is:

[0107] DI1 = 0.81t1 + 0.99f1t1 - 14.3f1 + 46.3;

[0108] where DI1 is the usage comfort value, t1 is the target temperature, and f1 is the target humidity. Table 3 is an example of the calculated usage comfort value.

[0109] Table 3

[0110]

[0111] Step S206: Determine the environmental comfort value corresponding to each air conditioner according to the real-time environmental parameters of the geographical location where each air conditioner is located.

[0112] The real-time environmental parameters include real-time environmental temperature and real-time environmental humidity. The calculation formula for the environmental comfort value is:

[0113] DI2 = 0.81t2 + 0.99f2t2 - 14.3f2 + 46.3;

[0114] where DI2 is the environmental comfort value, t2 is the real-time environmental temperature, and f2 is the real-time environmental humidity.

[0115] Table 4 is an example of the calculated environmental comfort value.

[0116] Table 4

[0117] Date Time City Environmental comfort value 2021 / 8 / 9 18 Beijing 78 2021 / 8 / 9 18 Jinan 77 2021 / 8 / 9 18 Qingdao 77 2021 / 8 / 9 18 Weihai 74

[0118] Step S207, if there is a target air conditioner that meets the preset recommendation conditions, execute Step S208; otherwise, execute Step S207.

[0119] Step S208, send a recommendation message to turn on the target air conditioner to the user terminal of the target air conditioner.

[0120] Compare the usage comfort value and the environmental comfort value. If the difference between the usage comfort value and the environmental comfort value is greater than the preset threshold R, and the current time period belongs to the preferred time period for the user to use the air conditioner under conditions such as the current season, holiday, environmental temperature, and environmental humidity, then recommend that the user turn on the air conditioner. (If the air conditioner device has a function of identifying the human body (i.e., is equipped with a human sensing module), then it is recommended only when the air conditioner device identifies that there is someone in the usage site). Table 5 shows an example when R = 5.

[0121] Table 5

[0122]

[0123] Step S209, if the recommendation message is accepted, execute Step S210; otherwise, execute Step S211.

[0124] Step S210, save the recommendation message, turn on the target air conditioner, and execute Step S203.

[0125] Step S211, if the number of rejections reaches the preset number, execute Step S212; otherwise, execute Step S209.

[0126] Step S212, delete the recommendation message and stop making recommendations.

[0127] The embodiment of the present application also provides an air conditioner, as Figure 3 shown, including:

[0128] A communication module 100, configured to communicate with a user terminal and a cloud server;

[0129] A controller 200, configured to:

[0130] Regularly upload historical data of the user using the air conditioner to the cloud server;

[0131] When receiving an opening instruction sent by the user terminal, turn on the air conditioner;

[0132] Among them, the opening instruction is triggered after the user terminal displays the recommended information for turning on the air conditioner to the user and receives the acceptance operation performed by the user based on the recommended information. The recommended information is sent by the cloud server to the user terminal according to the method for recommending the opening time of the air conditioner as described above.

[0133] In this embodiment, the communication module 100 is a device component with functions such as communication and data processing, and can be a WiFi module, a 2 / 3 / 4 / 5G module, or an NB-IoT module. After the cloud server sends the recommended information for turning on the air conditioner to the user terminal, the user terminal displays the recommended information to the user. If the user performs an acceptance operation based on the recommended information, the user terminal sends an opening instruction to the controller 200 of the air conditioner, causing the controller 200 to start the air conditioner.

[0134] In order to accurately recommend the opening time, in some embodiments of the present application, as Figure 4 shown, it further includes:

[0135] An outdoor temperature sensor 300 for detecting the outdoor environmental temperature;

[0136] An outdoor humidity sensor 400 for detecting the outdoor environmental humidity;

[0137] A human presence detection module 500 for detecting whether there is anyone in the place where the air conditioner is used;

[0138] The controller 200 is further configured to:

[0139] Regularly upload the outdoor environmental temperature, the outdoor environmental humidity, and the human detection result to the cloud server.

[0140] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and are not intended to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for recommending the startup time of an air conditioner, characterized in that, The method includes: Processing the historical data of all users using air conditioners within a preset time interval based on a clustering algorithm and determining the preferred time periods for each user group to turn on the air conditioner; Determining the preferred usage comfort values for each user group according to the target parameters set last by each user group in the historical data before turning off the air conditioner; Determining the environmental comfort values corresponding to each air conditioner according to the real-time environmental parameters of the geographical location where each air conditioner is located; If there is a target air conditioner that meets the preset recommendation conditions, sending a recommendation message to turn on the target air conditioner to the user terminal of the target air conditioner; Wherein, the preset recommendation conditions are that the difference between the usage comfort value and the environmental comfort value is greater than a preset threshold and the current time is within the preferred time period, or, the preset recommendation conditions are that the difference is greater than the preset threshold and the current time is within the preferred time period and there are people in the usage site of the target air conditioner; The method further includes: If there is a new user, processing the characteristic data of the new user using the air conditioner based on a clustering algorithm and determining the target user group to which the new user belongs from each user group; When sending the recommendation message to the user terminal of the target user group, simultaneously sending the recommendation message to the user terminal of the new user; The new user is determined according to the historical data; The characteristic data includes age group, gender, season, date, weekday, holiday, geographical location, environmental temperature, environmental humidity, set air conditioner mode, set air conditioner temperature, set air conditioner wind speed; After sending the recommendation message to turn on the target air conditioner to the user terminal of the target air conditioner, the method further includes: If the user accepts the recommendation message, saving the recommendation message and incorporating it into the historical data; If the user rejects the recommendation message and the number of rejections reaches the preset number of times, deleting the recommendation message; The clustering characteristics of each user group include age group, gender, season when using the air conditioner, date, weekday, holiday, geographical location, environmental temperature, environmental humidity.

2. The method according to claim 1, wherein The target parameters include target temperature and target humidity. Determining the preferred usage comfort values for each user group according to the target parameters set last by each user group in the historical data before turning off the air conditioner, specifically: Determining DI1 = 0.81t1 + 0.99f1t1 - 14.3f1 + 46.3; Wherein, DI1 is the usage comfort value, t1 is the target temperature, and f1 is the target humidity.

3. The method according to claim 1, characterized in that The real-time environmental parameters include real-time environmental temperature and real-time environmental humidity. Determining the environmental comfort values corresponding to each air conditioner according to the real-time environmental parameters of the geographical location where each air conditioner is located, specifically: Determining DI2 = 0.81t2 + 0.99f2t2 - 14.3f2 + 46.3; Wherein, DI2 is the environmental comfort value, t2 is the real-time environmental temperature, and f2 is the real-time environmental humidity.

4. An air conditioner, characterized in that, Includes: A communication module for communicating with user terminals and cloud servers; A controller configured to: Regularly upload the historical data of the user's use of the air conditioner to the cloud server; When receiving the start instruction sent by the user terminal, start the air conditioner; Wherein, the start instruction is triggered after the user terminal displays the recommended information for starting the air conditioner to the user and receives the acceptance operation of the user based on the recommended information, and the recommended information is sent by the cloud server to the user terminal according to the method described in any one of claims 1-3.

5. The air conditioner according to claim 4, characterized in that, It further includes: An outdoor temperature sensor for detecting the outdoor ambient temperature; An outdoor humidity sensor for detecting the outdoor ambient humidity; A human presence detection module for detecting whether there is anyone in the place where the air conditioner is used; The controller is further configured to: Regularly upload the outdoor ambient temperature, the outdoor ambient humidity and the human detection result to the cloud server.

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