Remote controller optimization method and device based on big data, medium and equipment

Abstract

This invention relates to a remote control optimization method, apparatus, medium, and device based on big data. The method includes: acquiring remote control button usage data reported by multiple air conditioners to a big data platform; calculating the adjustment rate of each button based on the remote control button usage data, and determining whether the adjustment rate of each button is greater than a first preset threshold; if so, setting the button as a high-frequency button; otherwise, setting the button as a low-frequency button; generating an optimization scheme based on each of the high-frequency buttons and each of the low-frequency buttons; wherein, in the optimization scheme, each of the high-frequency buttons is set in the high-frequency adjustment area of ​​the remote control, and each of the low-frequency buttons is set in the low-frequency adjustment area of ​​the remote control. This invention can optimize each button on the remote control, making the buttons on the remote control more suitable for the usage needs of most users, thus providing convenience for users.

Description

Technical Field

[0001] This invention relates to the field of remote control optimization technology, and in particular to a remote control optimization method, apparatus, medium, and device based on big data. Background Technology

[0002] With the increasing variety of home appliances, the number of home appliances that families can own is constantly growing. However, due to various reasons, firstly, some home appliances are not yet smart; secondly, the complex initial and reconfiguration processes for network connection lead to most users being unable to use the network; and thirdly, many elderly users are not familiar with operating smartphones. Currently, users still prefer to control home appliances using remote controls. However, the various remote controls on the market vary greatly, and most are designed with aesthetics in mind, without considering the ease of user adjustment. Therefore, it is necessary to optimize remote controls based on big data. Summary of the Invention

[0003] In order to solve the above-mentioned technical problems, or at least partially solve the above-mentioned technical problems, the present invention provides a remote control optimization method, device, medium, and equipment based on big data.

[0004] In a first aspect, embodiments of the present invention provide a remote control optimization method based on big data, comprising:

[0005] Obtain remote control button usage data reported by multiple air conditioners to the big data platform;

[0006] Based on the remote control button usage data, calculate the adjustment rate of each button and determine whether the adjustment rate of each button is greater than a first preset threshold.

[0007] If yes, set the button to a high-frequency button; otherwise, set the button to a low-frequency button.

[0008] An optimization scheme is generated based on each of the high-frequency buttons and each of the low-frequency buttons; wherein, in the optimization scheme, each of the high-frequency buttons is set in the high-frequency adjustment area of ​​the remote control, and each of the low-frequency buttons is set in the low-frequency adjustment area of ​​the remote control.

[0009] Secondly, embodiments of the present invention provide a remote control optimization device based on big data, comprising:

[0010] The data acquisition module is used to acquire remote control button usage data reported by multiple air conditioners to the big data platform;

[0011] The first judgment module is used to calculate the adjustment rate of each button based on the remote control button usage data, and to determine whether the adjustment rate of each button is greater than a first preset threshold.

[0012] The button classification module is used to classify a button as a high-frequency button if the adjustment rate of a button is greater than the first preset threshold; otherwise, it is classified as a low-frequency button.

[0013] The scheme generation module is used to generate an optimized scheme based on each of the high-frequency buttons and each of the low-frequency buttons; wherein, in the optimized scheme, each of the high-frequency buttons is set in the high-frequency adjustment area of ​​the remote control, and each of the low-frequency buttons is set in the low-frequency adjustment area of ​​the remote control.

[0014] Thirdly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to perform the method provided in the first aspect.

[0015] Fourthly, embodiments of the present invention provide a computing device, including a memory and a processor, wherein the memory stores executable code, and when the processor executes the executable code, it implements the method provided in the first aspect.

[0016] The remote control optimization method, apparatus, medium, and device based on big data provided in this invention have the following beneficial effects when combined:

[0017] (1) Obtain remote control button usage data reported by multiple air conditioners to the big data platform. Calculate the adjustment rate of each button based on the usage data. Divide each button into low-frequency and high-frequency buttons according to the adjustment rate. Finally, generate an optimization scheme based on each low-frequency and high-frequency button. In the optimization scheme, low-frequency buttons are placed in the low-frequency adjustment area, and high-frequency buttons are placed in the high-frequency adjustment area, thereby optimizing each button on the remote control. Through optimization, the buttons on the remote control can better meet the usage needs of most users, providing convenience for users.

[0018] (2) In one embodiment, if the usage rate and frequency of a button are both low, it indicates that the button is unnecessary to set up, and therefore the button is removed in the optimization scheme. If at least one of the parameters of the usage rate and frequency of a button is greater than or equal to a third preset threshold, it indicates that the button is necessary to retain, and therefore the button is retained in the optimization scheme. In this way, buttons can be further filtered for retention or removal.

[0019] (3) In one embodiment, if the first maximum value is greater than the fourth preset threshold, the operating mode of the quick-heat button and the quick-cool button is configured according to the first correlation corresponding to the first maximum value, so that the operating mode of the quick-heat button and the quick-cool button can meet the needs of most users and improve the usage rate and frequency of the quick-heat button and the quick-cool button.

[0020] (4) In one embodiment, if the second maximum value is greater than the fifth preset threshold, the low-temperature setting temperature of the quick-cooling button is configured according to the second correlation corresponding to the second maximum value, which can make the low-temperature setting temperature of the quick-cooling button meet the needs of most users and improve the usage rate and frequency of the quick-cooling button. If the third maximum value is greater than the sixth preset threshold, the high-temperature setting temperature of the quick-heating button is configured according to the third correlation corresponding to the third maximum value, which can make the high-temperature setting temperature of the quick-heating button meet the needs of most users and improve the usage rate and frequency of the quick-heating button. Attached Figure Description

[0021] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a flowchart illustrating a remote control optimization method based on big data in one embodiment of the present invention;

[0024] Figure 2 A schematic diagram of a remote control manufactured according to an optimization scheme provided in an embodiment of the present invention. Detailed Implementation

[0025] 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, not all, of the embodiments of the present invention. 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.

[0026] This invention provides a remote control optimization method based on big data.

[0027] See Figure 1 The method includes the following steps S110 to S140:

[0028] S110: Obtain remote control button usage data reported by multiple air conditioners to the big data platform;

[0029] Understandably, smart air conditioners with IoT modules will report the usage data of the air conditioner's remote control buttons to a big data platform and store it on the big data platform, thereby realizing the collection of usage data.

[0030] S120. Based on the remote control button usage data, calculate the adjustment rate of each button and determine whether the adjustment rate of each button is greater than a first preset threshold.

[0031] The adjustment rate of a button represents how the user uses that button.

[0032] In one embodiment, the adjustment rate of each button can be calculated using a first formula, which is:

[0033] P = Time1 / Time2

[0034] Where P is the adjustment rate of a button, Time1 is the number of times the button is used during the detection period, and Time2 is the total number of times all buttons in the air conditioner are used during the detection period.

[0035] Understandably, the ratio of the number of times a button is used within a certain detection period to the total number of times all buttons are used within that detection period can reflect the percentage of usage of that button among all buttons.

[0036] The first preset threshold can be set as needed.

[0037] S130. If the adjustment rate of a button is greater than a first preset threshold, then the button is set as a high-frequency button; otherwise, the button is set as a low-frequency button.

[0038] Understandably, if the adjustment rate of a button is greater than a first preset threshold, it indicates that the button's adjustment frequency is relatively high, and therefore the button can be designated as a high-frequency button. If the adjustment rate of a button is less than or equal to the first preset threshold, it indicates that the button's adjustment frequency is relatively low, and therefore the button can be designated as a low-frequency button.

[0039] In this way, the buttons on the remote control can be divided into two categories: low-frequency buttons and high-frequency buttons.

[0040] S140. An optimization scheme is generated based on each of the high-frequency buttons and each of the low-frequency buttons; wherein, in the optimization scheme, each of the high-frequency buttons is set in the high-frequency adjustment area of ​​the remote control, and each of the low-frequency buttons is set in the low-frequency adjustment area of ​​the remote control.

[0041] Understandably, this optimization scheme involves refining the various buttons on the remote control. In this scheme, high-frequency buttons are placed in the high-frequency adjustment range, and low-frequency buttons are placed in the low-frequency adjustment range. Remote controls manufactured according to this optimization scheme better meet user needs.

[0042] The high-frequency buttons in the high-frequency adjustment area can be larger than the low-frequency buttons in the low-frequency adjustment area, and the high-frequency adjustment area can be positioned on the remote control for easier user operation. Furthermore, the high-frequency and low-frequency buttons can be set to different shapes; for example, the high-frequency buttons can be square, while the low-frequency buttons can be round.

[0043] In one embodiment, the method provided by this invention may further include:

[0044] Determine whether the adjustment rate of each of the high-frequency buttons is greater than a second preset threshold; wherein the second preset threshold is greater than the first preset threshold;

[0045] If so, the high-frequency button in the optimization scheme is set to a first color, which is different from the colors of the other buttons on the remote control.

[0046] In other words, the high-frequency buttons in the high-frequency adjustment area are further distinguished. For example, some high-frequency buttons have a much higher adjustment rate than other high-frequency buttons, so these high-frequency buttons with particularly high adjustment rates are further distinguished by color.

[0047] The second preset threshold is used to further distinguish the various high-frequency buttons in the high-frequency adjustment area, so the second preset threshold is greater than the first preset threshold.

[0048] For example, in the high-frequency adjustment area, there are two high-frequency buttons with a much higher adjustment rate than other high-frequency buttons. Therefore, one of these two high-frequency buttons is set to green, and the other is set to red, while the other high-frequency buttons and low-frequency buttons are all set to gray.

[0049] In one embodiment, in the optimized scheme, the remote control panel is divided into a first remote control panel and a second remote control panel along the center line. Both the first remote control panel and the second remote control panel are provided with the high-frequency adjustment area and the low-frequency button. The button layout of the first remote control panel and the button layout of the second remote control panel are symmetrical along the center line. The first remote control panel is suitable for users who are left-handed, and the second remote control panel is suitable for users who are right-handed.

[0050] In other words, see Figure 2In the optimized design, the remote control panel is divided into two sub-panels: a first remote control panel and a second remote control panel. Both the first and second remote control panels include low-frequency and high-frequency adjustment areas. The low-frequency adjustment buttons on the first and second remote control panels are identical, as are the high-frequency adjustment buttons on the second remote control panel. Furthermore, the layout of the buttons on both panels is symmetrical about the center line. The first remote control panel is suitable for left-handed users, while the second remote control panel is suitable for right-handed users. Therefore, the optimized remote control can meet the needs of users with different hand preferences.

[0051] Among them, Figure 2 The high-frequency operating area refers to the high-frequency adjustment area, while the low-frequency operating area refers to the low-frequency adjustment area.

[0052] In one embodiment, before calculating the adjustment rate of each button based on the remote control button usage data in step S120, the method may further include A1 to A2:

[0053] A1. Calculate the usage rate and usage frequency of each button; wherein, the usage rate is the ratio of the number of users who have used the button to the total number of users, and the usage frequency is the ratio of the usage time of the state corresponding to the button to the total usage time of the air conditioner;

[0054] A2. Determine whether the usage rate and frequency of the button are both lower than a third preset threshold; if so, remove the button in the optimization scheme; otherwise, retain the button in the optimization scheme.

[0055] Understandably, by analyzing how users use each button in terms of usage rate and frequency, it can be determined whether it is necessary to set up a separate button on the remote control.

[0056] Understandably, if a button's usage rate and frequency are both low, below the third preset threshold, then the button is unnecessary and should be removed from the optimization scheme. Conversely, if at least one of the parameters of a button's usage rate and frequency is greater than or equal to the third preset threshold, then the button is worth retaining and should be kept in the optimization scheme.

[0057] Based on this, the step of calculating the adjustment rate of each button according to the remote control button usage data in S120 may include: calculating the adjustment rate corresponding to each button retained in the optimization scheme according to the remote control button usage data.

[0058] In other words, after eliminating rarely used buttons on the remote control based on usage rate and frequency, the remaining buttons are included in the calculation in S120. This reduces the amount of calculation and further optimizes the remote control based on the necessity of setting each button.

[0059] In one embodiment, the method provided by this invention may further include:

[0060] B1. If the removed button is the strong fan button, then add a quick cooling button and a quick heating button in the optimization scheme.

[0061] In other words, if the high-power fan button is used infrequently, it will be removed. However, considering that some users may not have used the high-power fan button because they are unaware of its function, a quick-heat button and a quick-cool button have been added to the remote control in the optimization solution.

[0062] As the name suggests, the quick-heat button is for rapid heating, while the quick-cool button is for rapid cooling. The basic configuration parameters of these two buttons are the fan speed of the strong fan button, meaning that the fan speed of both buttons is the same as that of the strong fan button.

[0063] B2. Calculate the correlation between each operating mode and the strong wind button, and take this correlation as the first correlation; wherein, the first correlation for each operating mode is the ratio between the usage time of the strong wind state corresponding to the strong wind button in that operating mode and the total usage time of that operating mode.

[0064] The air conditioner has several operating modes, such as dehumidification mode, air supply mode, and sleep mode.

[0065] Understandably, the primary relevance of an operating mode reflects its correlation with the strong wind button.

[0066] B3. Select the maximum value from the first relevance of each operating mode, take the maximum value as the first maximum value, and determine whether the first maximum value is greater than the fourth preset threshold.

[0067] For example, the fourth preset threshold is set to 50%. If the first maximum value is greater than 50%, it indicates that the operating mode corresponding to the first maximum value is strongly correlated with the strong wind button. If the first maximum value is less than or equal to 50%, it indicates that the operating mode corresponding to the first maximum value is weakly correlated with the strong wind button.

[0068] B4. If the first maximum value is greater than the fourth preset threshold, the operating mode corresponding to the first maximum value is added to the configuration data of the quick-cooling button and the quick-heating button, so that the user runs the device in the operating mode corresponding to the first maximum value when the quick-cooling button or the quick-heating button is turned on.

[0069] That is, if the operating mode corresponding to the first maximum value is strongly correlated with the strong wind button, then the operating mode corresponding to the first maximum value is added to the configuration data of the quick cooling button and the quick heating button. In this way, when the user activates the quick cooling button or the quick heating button, the air conditioner will operate in the operating mode corresponding to the first maximum value.

[0070] As can be seen, if the first maximum value is greater than the fourth preset threshold, the operating mode of the quick-heating button and the quick-cooling button is configured according to the first relevance corresponding to the first maximum value. This can enable the operating mode of the quick-heating button and the quick-cooling button to meet the needs of most users and improve the usage rate and frequency of the quick-heating button and the quick-cooling button.

[0071] In one embodiment, the method provided by this invention may further include:

[0072] C1. Calculate the correlation between each low temperature setting and the high wind button, and use this correlation as the second correlation; wherein, the second correlation for each low temperature setting is the difference between the first proportion and the second proportion, the first proportion is the ratio between the user's usage time at the low temperature setting and the total usage time of each low temperature setting in the high wind state corresponding to the high wind button, and the second proportion is the ratio between the user's usage time at the low temperature setting and the total usage time of each low temperature setting when not in the high wind state;

[0073] Understandably, users typically set a low temperature setting during the summer to avoid having to adjust the temperature every time they turn the device on. This low temperature setting is relative to the ambient temperature during the summer.

[0074] For example, the typical temperature setting range is 16 degrees Celsius to 32 degrees Celsius. Lower temperature settings can be further selected within this range.

[0075] As can be seen, the second correlation is reflected here by comparing the correlation between the use of the strong wind button and the low temperature setting when the strong wind button is turned on and when the strong wind button is not turned on.

[0076] The first percentage reflects the distribution of user time spent at a low set temperature when the strong fan is on. The second percentage reflects the distribution of user time spent at a low set temperature when the strong fan is off.

[0077] C2. Select the maximum value from the second correlation values ​​corresponding to each low temperature setting temperature, take the maximum value as the second maximum value, and determine whether the second correlation value corresponding to the second maximum value is greater than the fifth preset threshold. If the second correlation value corresponding to the second maximum value is greater than the fifth preset threshold, add the low temperature setting temperature corresponding to the second maximum value to the configuration data of the quick cooling button so that the user runs at the low temperature setting temperature corresponding to the second maximum value when the quick cooling button is turned on.

[0078] The fifth preset threshold can be set as needed, for example, 10%.

[0079] Understandably, if the maximum value among the various second correlation coefficients (i.e., the second maximum value) is greater than 10%, it indicates a strong correlation between the low-temperature setting corresponding to the second maximum value and the high-power fan button; users prefer to turn on the high-power fan button at the low-temperature setting corresponding to the second maximum value. If the second maximum value is less than or equal to 10%, it indicates a weak correlation between the low-temperature setting corresponding to the second maximum value and the high-power fan button; users do not prefer to turn on the high-power fan button at the low-temperature setting corresponding to the second maximum value.

[0080] If the second maximum value is greater than 10%, the low temperature setting temperature corresponding to the second maximum value will be added to the configuration data of the quick-cooling button. In this way, when the user turns on the quick-cooling button, the air conditioner will run at the low temperature setting temperature corresponding to the second maximum value.

[0081] That is, if the second maximum value is greater than the fifth preset threshold, the low temperature setting temperature of the quick-cooling button is configured according to the second correlation corresponding to the second maximum value, which can make the low temperature setting temperature of the quick-cooling button meet the needs of most users and improve the usage rate and frequency of the quick-cooling button.

[0082] C3. Calculate the correlation between each high temperature setting and the strong wind button, and use this correlation as the third correlation; wherein, the third correlation for each high temperature setting is the difference between the third proportion and the fourth proportion, the third proportion is the ratio between the user's usage time at the high temperature setting and the total usage time of all high temperature settings in the strong wind state corresponding to the strong wind button, and the fourth proportion is the ratio between the user's usage time at the high temperature setting and the total usage time of all high temperature settings when not in the strong wind state;

[0083] Understandably, users typically set a high temperature setting in winter to avoid having to adjust the temperature every time they turn the computer on. This high temperature setting is relative to the ambient temperature during winter.

[0084] As can be seen, the third correlation is reflected here by comparing the correlation between the use of the strong wind button and the high temperature setting when the strong wind button is turned on and when it is not turned on.

[0085] The third percentage reflects the distribution of user time spent at high-temperature set temperatures when the strong fan is on. The fourth percentage reflects the distribution of user time spent at high-temperature set temperatures when the strong fan is off.

[0086] C4. Select the maximum value from the third correlation degree corresponding to each high temperature setting temperature, take the maximum value as the third maximum value, and determine whether the third correlation degree corresponding to the third maximum value is greater than the sixth preset threshold; if the third correlation degree corresponding to the third maximum value is greater than the sixth preset threshold, then add the high temperature setting temperature corresponding to the third maximum value to the configuration data of the quick-heating button, so that the user runs at the high temperature setting temperature corresponding to the third maximum value when the quick-heating button is turned on.

[0087] The sixth preset threshold can be set as needed, for example, 10%.

[0088] Understandably, if the maximum value among the various third correlation coefficients (i.e., the third maximum value) is greater than 10%, it indicates a strong correlation between the high-temperature setting corresponding to the third maximum value and the high-power fan button; users tend to activate the high-power fan button at the high-temperature setting temperature corresponding to the third maximum value. If the third maximum value is less than or equal to 10%, it indicates a weak correlation between the high-temperature setting corresponding to the third maximum value and the high-power fan button; users do not tend to activate the high-power fan button at the high-temperature setting temperature corresponding to the third maximum value.

[0089] If the third maximum value is greater than 10%, the high temperature setting temperature corresponding to the third maximum value will be added to the configuration data of the quick-heat button. In this way, when the user turns on the quick-heat button, the air conditioner will run at the high temperature setting temperature corresponding to the third maximum value.

[0090] That is, if the third maximum value is greater than the sixth preset threshold, the high temperature setting temperature of the quick-heating button is configured according to the third correlation corresponding to the third maximum value, which can make the high temperature setting temperature of the quick-heating button meet the needs of most users and improve the usage rate and frequency of the quick-heating button.

[0091] The optimized solution integrates functions such as strong fan speed, low / high temperature setting, and operating mode into the quick-heat and quick-cool buttons. While retaining the strong fan function, multiple configuration data are integrated into the quick-heat and quick-cool buttons. These configuration data are statistically derived from big data and have high reference value, greatly improving convenience.

[0092] Secondly, embodiments of the present invention provide a remote control optimization device based on big data, comprising:

[0093] The data acquisition module is used to acquire remote control button usage data reported by multiple air conditioners to the big data platform;

[0094] The first judgment module is used to calculate the adjustment rate of each button based on the remote control button usage data, and to determine whether the adjustment rate of each button is greater than a first preset threshold.

[0095] The button classification module is used to classify a button as a high-frequency button if the adjustment rate of a button is greater than the first preset threshold; otherwise, it is classified as a low-frequency button.

[0096] The scheme generation module is used to generate an optimized scheme based on each of the high-frequency buttons and each of the low-frequency buttons; wherein, in the optimized scheme, each of the high-frequency buttons is set in the high-frequency adjustment area of ​​the remote control, and each of the low-frequency buttons is set in the low-frequency adjustment area of ​​the remote control.

[0097] In one embodiment, the first judgment module is specifically used to calculate the adjustment rate of each button using a first calculation formula, wherein the first calculation formula is:

[0098] P = Time1 / Time2

[0099] Where P is the adjustment rate of a button, Time1 is the number of times the button is used during the detection period, and Time2 is the total number of times all buttons in the air conditioner are used during the detection period.

[0100] In one embodiment, the apparatus further includes:

[0101] The second judgment module is used to determine whether the adjustment rate of each of the high-frequency buttons is greater than a second preset threshold; wherein the second preset threshold is greater than the first preset threshold; if so, the high-frequency button is set to a first color in the optimization scheme, and the first color is different from the colors of the other buttons on the remote control.

[0102] In one embodiment, in the optimized scheme, the remote control panel is divided into a first remote control panel and a second remote control panel along the center line. Both the first remote control panel and the second remote control panel are provided with the high-frequency adjustment area and the low-frequency button. The button layout of the first remote control panel and the button layout of the second remote control panel are symmetrical along the center line. The first remote control panel is suitable for users who are left-handed, and the second remote control panel is suitable for users who are right-handed.

[0103] In one embodiment, the apparatus further includes:

[0104] The third judgment module is used to calculate the usage rate and usage frequency of each button before the first judgment module calculates the adjustment rate of each button based on the remote control button usage data; wherein, the usage rate is the ratio of the number of users who have used the button to the total number of users, and the usage frequency is the ratio of the usage time of the state corresponding to the button to the total usage time of the air conditioner; it is used to determine whether the usage rate and usage frequency of the button are both lower than a third preset threshold; if so, the button is removed from the optimization scheme; otherwise, the button is retained in the optimization scheme.

[0105] Correspondingly, the first judgment module is specifically used to: calculate the adjustment rate corresponding to each button retained in the optimization scheme based on the remote control button usage data.

[0106] In one embodiment, the apparatus further includes:

[0107] A button addition module is used to add a rapid cooling button and a rapid heating button in the optimization scheme if the removed button is a strong wind button.

[0108] The first calculation module is used to calculate the correlation between each operating mode and the strong wind button, and to use the correlation as the first correlation. The first correlation for each operating mode is the ratio between the usage time of the strong wind state corresponding to the strong wind button in that operating mode and the total usage time of that operating mode.

[0109] The first selection module is used to select the maximum value from the first relevance of each operating mode, take the maximum value as the first maximum value, and determine whether the first maximum value is greater than the fourth preset threshold.

[0110] The first configuration module is used to add the operating mode corresponding to the first maximum value to the configuration data of the quick-cooling button and the quick-heating button if the first maximum value is greater than the fourth preset threshold, so that the user can run the device in the operating mode corresponding to the first maximum value when the quick-cooling button or the quick-heating button is turned on.

[0111] In one embodiment, the apparatus further includes:

[0112] The second calculation module is used to calculate the correlation between each low temperature setting and the strong wind button, and to use this correlation as the second correlation. The second correlation for each low temperature setting is the difference between the first ratio and the second ratio. The first ratio is the ratio between the user's usage time at the low temperature setting and the total usage time of all low temperature settings when the user is in the strong wind state corresponding to the strong wind button. The second ratio is the ratio between the user's usage time at the low temperature setting and the total usage time of all low temperature settings when the user is not in the strong wind state.

[0113] The second selection module is used to select the maximum value from the second correlation values ​​corresponding to each low temperature setting temperature, take the maximum value as the second maximum value, and determine whether the second correlation value corresponding to the second maximum value is greater than the fifth preset threshold. If the second correlation value corresponding to the second maximum value is greater than the fifth preset threshold, the low temperature setting temperature corresponding to the second maximum value is added to the configuration data of the quick cooling button so that the user runs at the low temperature setting temperature corresponding to the second maximum value when the quick cooling button is turned on.

[0114] The third calculation module is used to calculate the correlation between each high temperature setting and the strong wind button, and to use this correlation as the third correlation. The third correlation for each high temperature setting is the difference between the third proportion and the fourth proportion. The third proportion is the ratio between the user's usage time at the high temperature setting and the total usage time of all high temperature settings when the user is not in the strong wind state. The fourth proportion is the ratio between the user's usage time at the high temperature setting and the total usage time of all high temperature settings when the user is not in the strong wind state.

[0115] The third selection module is used to select the maximum value from the third correlation degree corresponding to each high temperature setting temperature, take the maximum value as the third maximum value, and determine whether the third correlation degree corresponding to the third maximum value is greater than the sixth preset threshold. If the third correlation degree corresponding to the third maximum value is greater than the sixth preset threshold, the high temperature setting temperature corresponding to the third maximum value is added to the configuration data of the quick-heating button so that the user runs at the high temperature setting temperature corresponding to the third maximum value when the quick-heating button is turned on.

[0116] It is understood that explanations, examples, and beneficial effects of the relevant content in the apparatus provided in the embodiments of the present invention can be referred to the relevant content in the first aspect, and will not be repeated here.

[0117] Thirdly, embodiments of the present invention provide a computer-readable medium storing computer instructions, which, when executed by a processor, cause the processor to perform the method provided in the first aspect.

[0118] Specifically, a system or apparatus equipped with a storage medium may be provided, on which software program code implementing the functions of any of the embodiments described above is stored, and the computer (or CPU or MPU) of the system or apparatus may read and execute the program code stored in the storage medium.

[0119] In this case, the program code read from the storage medium can itself implement the function of any of the above embodiments, and therefore the program code and the storage medium storing the program code constitute part of the present invention.

[0120] Examples of storage media used to provide program code include floppy disks, hard disks, magneto-optical disks, optical disks (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD+RW), magnetic tapes, non-volatile memory cards, and ROMs. Alternatively, program code can be downloaded from a server computer via a communication network.

[0121] Furthermore, it should be clear that not only can the program code read by the computer be executed, but also the operating system or other components operating on the computer can be instructed based on the program code to perform some or all of the actual operations, thereby realizing the function of any of the embodiments described above.

[0122] Furthermore, it is understood that the program code read from the storage medium is written to the memory set in the expansion board inserted into the computer or to the memory set in the expansion module connected to the computer. Then, based on the instructions of the program code, the CPU or other components installed on the expansion board or expansion module execute some and all of the actual operations, thereby realizing the function of any of the above embodiments.

[0123] It is understood that explanations, specific implementation methods, beneficial effects, examples, etc. of the contents in the computer-readable medium provided in the embodiments of the present invention can be found in the corresponding parts of the method provided in the first aspect, and will not be repeated here.

[0124] Fourthly, one embodiment of this specification provides a computing device including a memory and a processor, wherein the memory stores executable code, and when the processor executes the executable code, it implements the method of any embodiment of the specification.

[0125] It is understood that explanations, specific implementation methods, beneficial effects, examples, etc. of the computing device provided in the embodiments of the present invention can be found in the corresponding parts of the method provided in the first aspect, and will not be repeated here.

[0126] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the apparatus embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0127] Those skilled in the art will recognize that, in one or more of the examples above, the functions described in this invention can be implemented using hardware, software, widgets, or any combination thereof. When implemented in software, these functions can be stored in a computer-readable medium or transmitted as one or more instructions or code on a computer-readable medium.

[0128] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made on the basis of the technical solution of the present invention should be included within the scope of protection of the present invention.

Claims

1. A remote control optimization method based on big data, characterized in that, include: Obtain remote control button usage data reported by multiple air conditioners to the big data platform; Based on the remote control button usage data, calculate the adjustment rate of each button and determine whether the adjustment rate of each button is greater than a first preset threshold. If yes, then set the button to a high-frequency button; otherwise, set the button to a low-frequency button. An optimization scheme is generated based on each of the high-frequency buttons and each of the low-frequency buttons; wherein, in the optimization scheme, each of the high-frequency buttons is set in the high-frequency adjustment area of ​​the remote control, and each of the low-frequency buttons is set in the low-frequency adjustment area of ​​the remote control. Before calculating the adjustment rate of each button based on the remote control button usage data, the method further includes: Calculate the usage rate and usage frequency of each button; wherein, the usage rate is the ratio of the number of users who have used the button to the total number of users, and the usage frequency is the ratio of the usage time of the state corresponding to the button to the total usage time of the air conditioner; Determine whether the usage rate and frequency of use of the button are both lower than a third preset threshold; if so, remove the button in the optimization scheme; otherwise, retain the button in the optimization scheme. The step of calculating the adjustment rate of each button based on the remote control button usage data includes: calculating the adjustment rate corresponding to each button retained in the optimization scheme based on the remote control button usage data; Also includes: If the removed button is the high-power fan button, then a rapid cooling button and a rapid heating button will be added to the optimization scheme. Calculate the correlation between each operating mode and the strong wind button, and use this correlation as the first correlation; wherein, the first correlation for each operating mode is the ratio between the usage time of the strong wind state corresponding to the strong wind button in that operating mode and the total usage time of that operating mode. The maximum value is selected from the first relevance of each operating mode, and this maximum value is taken as the first maximum value. It is then determined whether the first maximum value is greater than the fourth preset threshold. If the first maximum value is greater than the fourth preset threshold, the operating mode corresponding to the first maximum value is added to the configuration data of the quick-cooling button and the quick-heating button, so that the user runs the device in the operating mode corresponding to the first maximum value when the quick-cooling button or the quick-heating button is turned on.

2. The method according to claim 1, characterized in that, The adjustment rate of each button is calculated using a first formula, which is: P = Time1 / Time2 Where P is the adjustment rate of a button, Time1 is the number of times the button is used during the detection period, and Time2 is the total number of times all buttons in the air conditioner are used during the detection period.

3. The method according to claim 1, characterized in that, Also includes: Determine whether the adjustment rate of each of the high-frequency buttons is greater than a second preset threshold; wherein the second preset threshold is greater than the first preset threshold; If so, the high-frequency button in the optimization scheme is set to a first color, which is different from the colors of the other buttons on the remote control.

4. The method according to claim 1, characterized in that, In the optimized scheme, the remote control panel is divided into a first remote control panel and a second remote control panel along the center line. Both the first and second remote control panels are provided with the high-frequency adjustment area and the low-frequency button. The button layout of the first remote control panel and the button layout of the second remote control panel are symmetrical along the center line. The first remote control panel is suitable for users who are left-handed, and the second remote control panel is suitable for users who are right-handed.

5. The method according to claim 1, characterized in that, Also includes: Calculate the correlation between each low temperature setting and the high wind button, and use this correlation as the second correlation. The second correlation for each low temperature setting is the difference between the first ratio and the second ratio. The first ratio is the ratio between the user's usage time at the low temperature setting and the total usage time of all low temperature settings when the user is in the high wind state corresponding to the high wind button. The second ratio is the ratio between the user's usage time at the low temperature setting and the total usage time of all low temperature settings when the user is not in the high wind state. The maximum value is selected from the second correlation values ​​corresponding to each low temperature setting temperature, and this maximum value is used as the second maximum value. It is then determined whether the second correlation value corresponding to the second maximum value is greater than the fifth preset threshold. If the second correlation value corresponding to the second maximum value is greater than the fifth preset threshold, the low temperature setting temperature corresponding to the second maximum value is added to the configuration data of the quick cooling button so that the user can run the quick cooling button at the low temperature setting temperature corresponding to the second maximum value. Calculate the correlation between each high temperature setting and the strong wind button, and use this correlation as the third correlation. The third correlation for each high temperature setting is the difference between the third proportion and the fourth proportion. The third proportion is the ratio between the user's usage time at the high temperature setting and the total usage time of all high temperature settings in the strong wind state corresponding to the strong wind button. The fourth proportion is the ratio between the user's usage time at the high temperature setting and the total usage time of all high temperature settings when not in the strong wind state. The maximum value is selected from the third correlation coefficients corresponding to each high temperature setting temperature. This maximum value is used as the third maximum value. It is then determined whether the third correlation coefficient corresponding to the third maximum value is greater than the sixth preset threshold. If the third correlation coefficient corresponding to the third maximum value is greater than the sixth preset threshold, the high temperature setting temperature corresponding to the third maximum value is added to the configuration data of the quick-heating button so that the user can run the quick-heating button at the high temperature setting temperature corresponding to the third maximum value.

6. A remote control optimization device based on big data, characterized in that, include: The data acquisition module is used to acquire remote control button usage data reported by multiple air conditioners to the big data platform; The first judgment module is used to calculate the adjustment rate of each button based on the remote control button usage data, and to determine whether the adjustment rate of each button is greater than a first preset threshold. The button classification module is used to classify a button as a high-frequency button if the adjustment rate of a button is greater than the first preset threshold; otherwise, it is classified as a low-frequency button. The scheme generation module is used to generate an optimized scheme based on each of the high-frequency buttons and each of the low-frequency buttons; wherein, in the optimized scheme, each of the high-frequency buttons is set in the high-frequency adjustment area of ​​the remote control, and each of the low-frequency buttons is set in the low-frequency adjustment area of ​​the remote control. The device also includes: The third judgment module is used to calculate the usage rate and usage frequency of each button before the first judgment module calculates the adjustment rate of each button based on the remote control button usage data; wherein, the usage rate is the ratio of the number of users who have used the button to the total number of users, and the usage frequency is the ratio of the usage time of the state corresponding to the button to the total usage time of the air conditioner; it is used to determine whether the usage rate and usage frequency of the button are both lower than a third preset threshold; if so, the button is removed from the optimization scheme; otherwise, the button is retained in the optimization scheme. The first judgment module is specifically used to: calculate the adjustment rate corresponding to each button retained in the optimization scheme based on the remote control button usage data; The device also includes: A button addition module is used to add a rapid cooling button and a rapid heating button in the optimization scheme if the removed button is a strong wind button. The first calculation module is used to calculate the correlation between each operating mode and the strong wind button, and to use the correlation as the first correlation. The first correlation for each operating mode is the ratio between the usage time of the strong wind state corresponding to the strong wind button in that operating mode and the total usage time of that operating mode. The first selection module is used to select the maximum value from the first relevance of each operating mode, take the maximum value as the first maximum value, and determine whether the first maximum value is greater than the fourth preset threshold. The first configuration module is used to add the operating mode corresponding to the first maximum value to the configuration data of the quick-cooling button and the quick-heating button if the first maximum value is greater than the fourth preset threshold, so that the user can run the device in the operating mode corresponding to the first maximum value when the quick-cooling button or the quick-heating button is turned on.

7. A computer-readable medium storing computer instructions that, when executed by a processor, cause the processor to perform the method according to any one of claims 1 to 5.

8. A computing device, comprising a memory and a processor, wherein the memory stores executable code, and the processor, when executing the executable code, implements the method according to any one of claims 1 to 5.

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