Air conditioning control method and electronic equipment

Abstract

This invention discloses an air conditioner control method and an electronic device. The method includes: receiving the habitual operating curves of a set model of air conditioner under different associated modes during a set time period, pushed by a cloud server; and displaying the habitual operating curves on a coordinate system with time as the horizontal axis and status parameters as the vertical axis for user reference. The method provided by this invention allows the cloud to recommend suitable habitual operating curves for the air conditioner during a set time period. Users can create custom operating curves for the air conditioner during that time period based on the cloud-recommended curves and their actual needs. This allows the air conditioner to automatically control itself according to the user-planned curves, eliminating the need for frequent changes to the air conditioner's operating status and providing the most suitable user experience.

Description

Technical Field

[0001] This invention relates to the field of air conditioning control, and more particularly to an air conditioning control method and electronic device. Background Technology

[0002] When using an air conditioner, its current state needs to be changed through control. Users often need to adjust the air conditioner's state using different control methods such as remote control, mobile app, and voice commands based on different needs. The air conditioner will not automatically change its current state according to the user's wishes, and each control takes time. Summary of the Invention

[0003] In view of this, the present invention discloses an air conditioning control method and electronic device to solve the problem that existing air conditioners cannot automatically change their air conditioning status.

[0004] To achieve the above objectives, the technical solution adopted by this invention is as follows:

[0005] The first aspect of this invention discloses an air conditioning control method, characterized in that the method includes:

[0006] Receives the custom operating curves of a set model of air conditioner under different associated modes during a set time period, pushed by the cloud server;

[0007] The user's preferred operating curve is displayed on a coordinate system with time as the horizontal axis and status parameters as the vertical axis for reference, in order to help the user create a custom operating curve for a specific model of air conditioner within a specified time period.

[0008] Further, optionally, the method also includes:

[0009] The system receives the typical operating curves of a set model of air conditioner within a set time period from the cloud server and displays them on a coordinate system for user reference.

[0010] The habitual operation curve carries the corresponding operation indicator information.

[0011] Further, optionally, the habitual operating curves include time-varying temperature and / or wind speed curves;

[0012] Operational metrics include energy consumption, ideal temperature, and execution time required to reach the ideal temperature.

[0013] When the set time period is a sleep period, the associated modes include sleep mode and energy-saving mode;

[0014] When the set time period is a non-sleep period, the associated modes include energy-saving mode and rapid cooling / heating mode.

[0015] Further, optionally, the method also includes:

[0016] Get the user-created custom operating curve of the specified air conditioner model over a specified time period;

[0017] The system performs timed control on the user-defined air conditioner model based on a custom operating curve.

[0018] Further, optionally, the method also includes:

[0019] Get the user-created custom operating curve of the specified air conditioner model over a specified time period;

[0020] Based on the similarity of different users' custom running curves and users' geographical locations, different users are classified.

[0021] If at least one user in the same category adjusts their custom running curve and uploads it to the cloud server, the judgment is made based on the number of at least one user and the magnitude of the curve adjustment by at least one user.

[0022] If the number of at least one user exceeds the first set value, and the number of users whose curve adjustment range exceeds the set threshold exceeds the second set value, then the adjusted running curve of at least one user will be recommended to other users in the same category.

[0023] A second aspect of this invention discloses an air conditioning control method for a cloud server, the method comprising:

[0024] Obtain user usage data for each air conditioner model within a specified time period;

[0025] Based on the acquired user data, statistical analysis was performed to identify different user habits.

[0026] Based on usage data from different habits, the system determines the habitual operating curves of a specific air conditioner model under different association modes within a set time period and pushes this data to the client of the specified air conditioner model.

[0027] Further, optionally, the method also includes:

[0028] Determine the typical operating curve of a specified air conditioner model within a specified time period and push it to the client.

[0029] The user's operating curve carries corresponding operating indicator information for reference.

[0030] Further, optionally, the habitual operating curves include time-varying temperature and / or wind speed curves;

[0031] Operational metrics include energy consumption, ideal temperature, and execution time required to reach the ideal temperature.

[0032] When the set time period is a sleep period, the associated modes include sleep mode and energy-saving mode;

[0033] When the set time period is a non-sleep period, the associated modes include energy-saving mode and rapid cooling / heating mode.

[0034] Further, optionally, the method also includes:

[0035] Receive a custom operating curve for a specified model of air conditioner within a specified time period;

[0036] The system performs timed control on the user-defined air conditioner model based on a custom operating curve.

[0037] Further, optionally, timed control of a set model of air conditioner based on a custom operating curve includes:

[0038] Analyze the custom curve to determine the time point when the air conditioner needs to change its state and the corresponding state parameter settings.

[0039] The air conditioner is controlled on a timed basis according to the determined time point and the corresponding status parameter settings.

[0040] Optionally, the custom curve can be analyzed to determine the time point when the air conditioner needs to change its state and the corresponding state parameter settings, including:

[0041] Determine whether the air conditioner needs to change its current state based on the preset state change amount;

[0042] Whenever the change in the status parameter setting value reaches the preset change amount, it is determined that the air conditioner needs to change its current state, and the corresponding status parameter setting value and time point are recorded.

[0043] Further, optionally, the method also includes:

[0044] Obtain data on the operation of a specified air conditioner model based on a custom operating curve;

[0045] The system determines the corresponding performance metrics for the custom performance curve based on the data and pushes them to the client.

[0046] A third aspect of the present invention discloses an electronic device, comprising: a memory for storing computer instructions; and a controller for calling and executing the computer instructions stored in the memory to implement the method of either the first or second aspect.

[0047] Beneficial effects: The cloud can recommend suitable operating curves for air conditioners during set time periods. Users can create custom operating curves for their air conditioners during those time periods based on the cloud-recommended operating curves and their actual needs. In this way, the air conditioner can be automatically controlled according to the curves planned by the user, eliminating the need for users to frequently change the operating status of the air conditioner and providing the most suitable user experience. Attached Figure Description

[0048] The above and other objects, features, and advantages of the present invention will become more apparent from the detailed description of exemplary embodiments with reference to the accompanying drawings. The drawings described below are merely some embodiments disclosed in the present invention; those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0049] Figure 1 An exemplary flowchart of an air conditioning control method according to an embodiment of the present invention is shown;

[0050] Figure 2 A schematic flowchart of an air conditioning control method according to an embodiment of the present invention is shown as an example.

[0051] Figure 3 A schematic flowchart of an air conditioning control method according to an embodiment of the present invention is shown as an example. Detailed Implementation

[0052] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0053] The terminology used in the embodiments of this invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. The singular forms “a,” “the,” and “the” used in the embodiments of this invention and the appended claims are also intended to include the plural forms, unless the context clearly indicates otherwise. “Multiple” generally includes at least two, but does not exclude the inclusion of at least one.

[0054] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0055] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a product or system comprising a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a product or system. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the product or system that includes said element.

[0056] In daily use, air conditioners require manual control to change their status. Users frequently adjust the air conditioner's state using various methods such as remote control, mobile app, and voice commands based on different needs. The air conditioner doesn't automatically change its status based on the user's requests, and each control operation takes time. However, the user's daily needs for the air conditioner's status are generally consistent. Therefore, a technology that automatically changes the air conditioner's status over a long period could be implemented.

[0057] Therefore, the first aspect of this embodiment discloses an air conditioning control method, such as... Figure 1 As shown, the method includes S101 to S102, wherein:

[0058] S101 receives the habitual operating curves of a set model of air conditioner under different associated modes during a set time period, pushed by the cloud server.

[0059] S102 displays the custom operating curve on a coordinate system with time as the horizontal axis and status parameters as the vertical axis for user reference, in order to assist users in creating a custom operating curve for a set model of air conditioner within a set time period.

[0060] The method provided in this embodiment can be used on terminals or air conditioners. Terminals include mobile phones, and air conditioners require a control panel with a display screen. First, the cloud server uses big data to statistically analyze user usage data for each model of air conditioner, identifying different usage habits. For example, it analyzes data showing usage by A% of users during sleep hours and another set of data by B% of users, where the sum of A and B is less than or equal to 100. Based on this data, the cloud server determines the air conditioner's habitual operating curve for sleep hours and the appropriate operating mode, and then pushes it to the client. The client displays the habitual operating curve on a coordinate system with time as the horizontal axis and status parameters as the vertical axis for user reference, recommending energy-saving, rapid cooling / heating, and sleep modes to provide the most professional and suitable user experience. Status parameters include fan speed and temperature. Based on the cloud-recommended curve and actual usage needs, the user can create the air conditioner's operating curve for a specific time period on the app or the air conditioner's display screen, or directly select a cloud-recommended habitual operating curve. In this way, the air conditioner can automatically control itself according to the user's planned curve, eliminating the need for frequent changes to the air conditioner's operating status until it is turned off.

[0061] Preferably, when the set time period is a sleep period, the associated modes include sleep mode and energy-saving mode;

[0062] When the set time period is a non-sleep period, the associated modes include energy-saving mode, rapid cooling or rapid heating mode.

[0063] In other words, it pushes operating curves that are suitable for the air conditioner's operating mode based on a set time period. In the cloud, different set time periods can be pre-associated with suitable operating modes, thereby realizing the habitual operating curves that recommend suitable air conditioner operating modes for different set time periods, giving users the most professional and suitable user experience.

[0064] Furthermore, the method also includes S103:

[0065] S103 receives the habitual operating curve of the set model of air conditioner within a set time period pushed by the cloud server and displays it on the coordinate system for user reference.

[0066] In this embodiment, the cloud pushes the habitual operating curve of each air conditioner and the habitual operating curve of the same model of air conditioner under different association modes within the set time period, based on a set time period and big data statistics, to the client. This allows users to clearly understand their own habits within the set time period and the habits of the majority of people within that time period. This serves as a reference curve for setting a custom operating curve, making the curve set by each user more professional and providing the most suitable user experience.

[0067] Furthermore, the habitual operating curves include temperature curves and / or wind speed curves based on time variations.

[0068] Furthermore, the habitual operation curve carries corresponding operation indicator information; the operation indicator information includes energy consumption information, ideal temperature, and execution time required to reach the ideal temperature;

[0069] In this embodiment, the cloud server recommends suitable air conditioning control methods for the user based on the current time period. Specifically, the cloud recommendation system uses the current indoor and outdoor temperatures, big data analysis of air conditioning usage patterns among most users at that time, and the user's own air conditioning usage habits to recommend several control methods for reference, displayed as curves on a coordinate system. Recommended modes include energy-saving mode and rapid cooling mode. In energy-saving mode, the air conditioner maintains the user's preferred temperature and fan speed, which are derived from the user's usage habits, achieving the most energy-efficient control. Rapid cooling mode quickly lowers the indoor temperature to the ideal temperature and maintains a constant temperature.

[0070] Furthermore, the method also includes S104 to S105, wherein:

[0071] S104, Obtain the user-created custom operating curve of the specified air conditioner model within a specified time period;

[0072] S105 performs timed control on the user-defined air conditioner model based on a user-defined operating curve.

[0073] In this embodiment, the control terminal can be a client or the cloud. Both mobile APP and cloud server can analyze the customized operating curve to determine the state that needs to be changed and the corresponding time point. By controlling the air conditioner of the user's set model in a timed manner, the number of times the user controls the air conditioner can be reduced, so that the user can control the air conditioner until the air conditioner is turned off with one control, thereby improving the user experience. At the same time, based on the recommendation system and big data, it can not only make the indoor temperature reach the user's satisfaction, but also select the most energy-saving operating mode while ensuring a constant temperature, thereby reducing power consumption.

[0074] Further optionally, the method also includes S106 to S109, wherein:

[0075] S106, Obtain the user-created custom operating curve of the specified air conditioner model within a specified time period;

[0076] S107, classify different users based on the similarity of their custom running curves and their geographical location;

[0077] The preferred approach is to first classify users by geographic location, and then classify them based on curve similarity. Geographic location can intuitively reflect the local environment and climate, while curve similarity can intuitively reflect the similarity of user habits, making user classification simpler and more accurate. In addition, compared with traditional user classification based on user profiles containing multi-dimensional data, directly calculating curve similarity has lower data dimensionality, higher computational efficiency, and reduces the pressure on cloud servers.

[0078] S108, if at least one user among similar users adjusts its custom running curve and uploads it to the cloud server, the cloud server makes a judgment based on the number of at least one user and the magnitude of the curve adjustment by at least one user.

[0079] The adjustment range can also be judged by the similarity between the curves before and after the adjustment; if the similarity is low to a certain extent, it indicates that the adjustment range is large.

[0080] S109, if the number of at least one user exceeds the first set value, and the number of users whose curve adjustment range is greater than the set threshold exceeds the second set value, then recommend the adjusted running curve of at least one user to other users in the same category.

[0081] For example, if 20% of users in the same category have made significant adjustments (such as adjusting by half the set range), it may be due to climate change in their corresponding geographical location. Therefore, the adjusted operating curve can be recommended to other users in the same region. This intelligent recommendation of operating curves provides users with a more comfortable user experience.

[0082] The relevant settings in this embodiment can be obtained from experiments or experience, and no specific values ​​are limited.

[0083] The second aspect of this embodiment discloses an air conditioning control method for use on a cloud server, such as... Figure 2 As shown, the method includes S201 to S203, wherein:

[0084] S201, Obtain user usage data for each air conditioner of a specified model within a specified time period;

[0085] S202, Based on the acquired user usage data, statistical analysis is performed to determine different user habits;

[0086] S203: Based on usage data of different habits, determine the habitual operating curve of the set model of air conditioner under different association modes during the set time period, and push it to the client of the set model of air conditioner.

[0087] The method provided in this embodiment first involves a cloud server statistically analyzing user usage data for each air conditioner model based on big data analytics. This analysis identifies different usage habits, such as data on usage by user A% during sleep hours and user B% during sleep hours, with the sum of A and B being less than or equal to 100. Based on this data, the cloud server determines the air conditioner's operating curve for the sleep hours and the appropriate operating mode, and then pushes this curve to the client. Client users can choose a cloud-recommended operating curve based on their actual needs, or draw an operating curve for the air conditioner within a set time period based on the recommended curve and their actual needs. This allows the air conditioner to automatically control itself according to the user-planned curve, eliminating the need for frequent manual adjustments to the air conditioner's operating status until it is turned off.

[0088] Specifically, the cloud server recommends suitable air conditioning control methods for users based on the current time period. The principle is that the cloud recommendation system, based on the current indoor and outdoor temperatures, big data analysis of air conditioning usage patterns among most users at that time, and the user's own air conditioning usage habits, recommends several control methods for reference, displaying them as curves on a coordinate system. Recommended modes include energy-saving mode and rapid cooling mode. In energy-saving mode, the air conditioner maintains the user's preferred temperature and fan speed, which are derived from the user's usage habits, achieving the most energy-efficient control. Rapid cooling mode quickly lowers the indoor temperature to the ideal temperature and maintains a constant temperature.

[0089] Preferably, when the set time period is a sleep period, the associated modes include sleep mode and energy-saving mode;

[0090] When the set time period is a non-sleep period, the associated modes include energy-saving mode, rapid cooling or rapid heating mode.

[0091] In other words, it is a habitual operating curve that recommends the appropriate operating mode for the air conditioner based on a set time period. In the cloud, different set time periods can be pre-associated with suitable operating modes, thereby realizing the habitual operating curve that recommends the appropriate operating mode for the air conditioner in different set time periods, giving users the most professional and suitable user experience.

[0092] Furthermore, the method also includes S204:

[0093] S204, determine the habitual operating curve of the set air conditioner model within the set time period, and push it to the client;

[0094] The user's operating curve carries corresponding operating indicator information for reference.

[0095] In this embodiment, the cloud pushes the habitual operating curve of each air conditioner and the habitual operating curve of the same model of air conditioner under different association modes within the set time period, based on a set time period and big data statistics, to the client. This allows users to clearly understand their own habits within the set time period and the habits of the majority of people within that time period. This serves as a reference curve for setting a custom operating curve, making the curve set by each user more professional and providing the most suitable user experience.

[0096] Furthermore, the habitual operating curves include temperature curves and / or wind speed curves based on time variations;

[0097] The operational metrics include energy consumption, ideal temperature, and the execution time required to reach the ideal temperature.

[0098] Further, optionally, the method further includes steps A1 to A2, wherein:

[0099] A1, Receives a custom operating curve for a specified model of air conditioner within a specified time period;

[0100] A2, performs timed control on the user-defined air conditioner model based on a custom operating curve.

[0101] The cloud server analyzes and sets the time and state for the air conditioner based on the coordinate system and the curves drawn by the user. Once the time point is reached, the cloud sends a command and the air conditioner automatically adjusts to the corresponding state.

[0102] Furthermore, the method also includes steps A3 to A4, wherein:

[0103] A3: Obtain data on the operation of a specified air conditioner model based on a custom operating curve;

[0104] A4 determines the corresponding operating indicator information for the custom operating curve based on the data and pushes it to the client.

[0105] Specifically, once the set curve state is completed, the air conditioner will automatically shut down. At the same time, the analysis system in the system will generate a report and send it to the user, which will include information such as the actual power consumption, execution time, and actual indoor temperature changes during the execution period. This will help the user set up a more energy-efficient and comfortable custom operating curve.

[0106] Furthermore, step A2 includes A21 to A22, wherein:

[0107] A21, Analyze the custom curve to determine the time point when the air conditioner needs to change its state and the corresponding state parameter settings;

[0108] A22, the air conditioner is controlled on a timed basis according to the determined time point and the corresponding status parameter set value.

[0109] In this embodiment, the air conditioner is controlled based on the user-defined operating curve. When the air conditioner runs in a set state at different time periods, it is necessary to determine the time point when the air conditioner needs to change its state and the corresponding state parameter setting value. This can avoid the air conditioner frequently changing its state, thereby obtaining a better comfort experience and energy saving.

[0110] Furthermore, step A21 includes A211 to A212, wherein:

[0111] A211, determine whether the air conditioner needs to change its current state based on a preset state change amount;

[0112] A212, whenever the change in the state parameter setting value reaches the preset change amount, it is determined that the air conditioner needs to change its current state, and the corresponding state parameter setting value and time point are recorded.

[0113] The following is combined with Figure 3 The method of this embodiment will be described in detail.

[0114] The method in this embodiment is divided into three main control systems to realize the autonomous adjustment of the air conditioner's state: a recommendation system, a coordinate system, and a cloud control system.

[0115] The recommendation system aims to suggest suitable air conditioning control methods for users based on the current time period. Specifically, it works by analyzing the current indoor and outdoor temperatures, big data to determine the air conditioning usage patterns of most users at that time, and the user's own air conditioning habits. The system then recommends several control methods for reference, displaying these as curves on a coordinate system. Recommended modes include energy-saving mode and rapid cooling mode. In energy-saving mode, the air conditioner maintains the user's preferred temperature and fan speed, derived from their usage habits, achieving the most energy-efficient control. Rapid cooling mode quickly lowers the indoor temperature to the ideal level and maintains a constant temperature.

[0116] The coordinate system is the core of this method. Users draw time-varying temperature and wind speed curves based on the coordinate system provided, according to their specific needs. Users can refer to curves recommended by the system on the coordinate system when drawing curves. For example, if a user wants to set an energy-saving mode, they can create their own energy-saving mode based on the recommended curve. The user can set their appropriate temperature and wind speed before and during use based on the energy-saving mode curve, without necessarily strictly adhering to the recommended temperature and wind speed settings. After the user draws the temperature and wind speed curves over time, the coordinate system analyzes these two curves, analyzing whether the air conditioner needs to change its current state based on specific intervals. For example, the temperature is set in 0.5-degree intervals; when the temperature changes by 0.5 degrees, the air conditioner will change its state. The analysis results are uploaded to the cloud control system. After the set curve states are executed, the air conditioner will automatically shut down. Simultaneously, the analysis system will generate a report and send it to the user, including information such as the actual power consumption, execution time, and actual indoor temperature changes during the execution period.

[0117] The cloud-based control system receives the analysis results based on the coordinate system, sets the results on a timer, and controls the air conditioner to change the set state on a timer.

[0118] After the curve is set, users can choose to execute it daily during that time period or click to execute the curve mode and start execution from the clicked time, allowing for personalized use. Combining these three systems reduces the number of times users need to control the air conditioner, allowing for a single control to the desired shutdown time, improving the user experience. Furthermore, based on the recommendation system and big data, it not only ensures the indoor temperature reaches the user's satisfaction but also selects the most energy-efficient operating mode while maintaining a constant temperature, reducing power consumption.

[0119] In this embodiment, the user draws lines on the front end, while the analysis and recognition of the curves are preferably performed on the cloud server. The cloud server sends instructions to the air conditioner to change its state.

[0120] The third aspect of this embodiment discloses an electronic device, which includes: a memory for storing computer instructions; and a controller for calling and executing the computer instructions stored in the memory to implement the method provided in either the first or second aspect.

[0121] In the different embodiments provided by this invention, the same parameters, terms, logic, etc. should be understood to have the same meaning, and this application does not intentionally repeat the description in each embodiment.

[0122] Exemplary embodiments of the present disclosure have been specifically shown and described above. It should be understood that the present disclosure is not limited to the detailed structures, arrangements, or implementation methods described herein; rather, the present disclosure is intended to cover various modifications and equivalent arrangements contained within the spirit and scope of the appended claims.

Claims

1. An air conditioning control method, characterized in that, The method comprises: The system receives the habitual operating curves of a specified model of air conditioner under different associated modes within a specified time period, pushed by a cloud server; the habitual operating curves carry the operating indicator information of the specified model of air conditioner. The habitual operating curve is displayed on a coordinate system with time as the horizontal axis and state parameters as the vertical axis for user reference, in order to assist users in creating a custom operating curve for the specified air conditioner model during the specified time period. Obtain the user-created custom operating curve of the specified air conditioner model during the specified time period; The user-defined model of air conditioner is controlled on a timed basis according to the customized operating curve.

2. The method as described in claim 1, characterized in that, The habitual operating curves include temperature curves and / or wind speed curves based on time variations; The operational metrics information includes energy consumption information, ideal temperature, and the execution time required to reach the ideal temperature; When the set time period is a sleep period, the associated mode includes sleep mode and energy-saving mode; When the set time period is a non-sleep period, the associated mode includes the energy-saving mode and the rapid cooling / heating mode.

3. The method according to any one of claims 1-2, characterized in that, The method further includes: Obtain the custom operating curves of the specified air conditioner model created by different users during the specified time period; The different users are classified based on the similarity of their custom running curves and their geographical location. If at least one user among similar users adjusts their custom running curve and uploads it to the cloud server, the judgment is made based on the number of the at least one user and the magnitude of the curve adjustment by the at least one user. If the number of the at least one user exceeds a first preset value, and the number of users whose curve adjustment magnitude exceeds a preset threshold exceeds a second preset value, then the adjusted running curve of the at least one user is recommended to other users in the same category.

4. An air conditioning control method for a cloud server, characterized in that, The method comprises: Obtain user usage data for each air conditioner model within a specified time period; Based on the acquired user data, statistical analysis was performed to identify different user habits. Based on the usage data of the different habits, the habitual operation curve of the specified air conditioner model under different association modes during the specified time period is determined and pushed to the client of the specified air conditioner model; the habitual operation curve carries corresponding operation index information for user reference; The method further includes: Receive the customized operating curve of the specified air conditioner model during the specified time period; The user-defined model of air conditioner is controlled on a timed basis according to the customized operating curve.

5. The method as described in claim 4, characterized in that, The method further includes: Based on user usage data for each air conditioner model within the specified time period, the habitual operating curve of each air conditioner is calculated and pushed to the client.

6. The method as described in claim 5, characterized in that, The habitual operating curves include temperature curves and / or wind speed curves based on time variations; The operational metrics information includes energy consumption information, ideal temperature, and the execution time required to reach the ideal temperature; When the set time period is a sleep period, the associated mode includes sleep mode and energy-saving mode; When the set time period is a non-sleep period, the associated mode includes the energy-saving mode and the rapid cooling / heating mode.

7. The method as described in claim 6, characterized in that, The step of performing timed control of the user's selected air conditioner model based on the customized operating curve includes: The custom operating curve is analyzed to determine the time point when the air conditioner needs to change its state and the corresponding state parameter settings. The air conditioner is controlled on a timed basis according to the determined time point and the corresponding status parameter settings.

8. The method as described in claim 7, characterized in that, The analysis of the custom operating curve to determine the time points when the air conditioner needs to change its state and the corresponding state parameter settings includes: Determine whether the air conditioner needs to change its current state based on a preset state change amount; Whenever the change in the state parameter setting value reaches the preset state change amount, it is determined that the air conditioner needs to change its current state, and the corresponding state parameter setting value and time point are recorded.

9. The method as described in claim 4, characterized in that, The method further includes: Obtain data on the operation of the specified air conditioner model based on the custom operating curve; Based on the data, determine the operating indicator information corresponding to the custom operating curve and push it to the client.

10. An electronic device, characterized in that, The electronic device includes: a memory for storing computer instructions; and a controller for calling and executing the computer instructions stored in the memory to implement the method as described in any one of claims 1-9.

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