An electric fan control method and device, an electric fan and a storage medium

Abstract

The application discloses an electric fan control method and device, an electric fan and a storage medium, and relates to the technical field of electric fan control. The method comprises the following steps: collecting the user's felt temperature under the current wind speed, and collecting the current environment temperature and the environment humidity; inputting the user's felt temperature, the environment temperature and the environment humidity into a pre-constructed wind speed prediction model, and outputting a target wind speed from the wind speed prediction model; the wind speed prediction model is obtained by training and learning a linear regression model through historical user's felt temperature, corresponding historical environment temperature and historical environment humidity; and based on the target wind speed, the current wind speed is regulated. The application realizes intelligent control of the electric fan, reduces the frequency of manual adjustment of the user, and enables the user to obtain a comfortable experience under different felt temperatures, thereby improving the user's experience.

Description

Technical Field

[0001] The present invention relates to the technical field of electric fans, and in particular to an electric fan control method and device, an electric fan, and a storage medium. Background Art

[0002] Currently, most electric fans on the market operate at a fixed wind speed after determining the gear and mode. This fixed wind speed operating mode has obvious drawbacks: when the wind speed is too high, the user will feel cold and need to manually adjust the gear to increase the temperature. However, manual adjustment by the user is not only inconvenient, but also unable to adapt to changes in the user's perceived temperature in real time. In particular, when the user's movement state changes, the perceived temperature will also change accordingly. Therefore, even with manual adjustment, the adjusted fixed wind speed cannot meet the user's comfort needs, resulting in a poor user experience. Summary of the Invention

[0003] The embodiments of the present invention provide an electric fan control method, an electric fan device, an electric fan, and a storage medium, aiming to realize intelligent control of the electric fan and improve the user experience.

[0004] In a first aspect, an embodiment of the present invention provides an electric fan control method, comprising:

[0005] When the fan is running, the user's perceived temperature at the current wind speed is collected, as well as the current ambient temperature and humidity.

[0006] Inputting the user's perceived temperature, ambient temperature, and ambient humidity into a pre-built wind speed prediction model, and having the wind speed prediction model output a target wind speed; wherein the wind speed prediction model is obtained by training a linear regression model using historical user perceived temperatures and corresponding historical ambient temperatures and historical ambient humidity;

[0007] Based on the target wind speed, the current wind speed is regulated.

[0008] In a second aspect, an embodiment of the present invention provides an electric fan control device, comprising:

[0009] The collection unit is used to collect the user's body temperature at the current wind speed, as well as the current ambient temperature and humidity during the operation of the electric fan;

[0010] a prediction unit, configured to input the user's perceived temperature, ambient temperature, and ambient humidity into a pre-built wind speed prediction model, and have the wind speed prediction model output a target wind speed; wherein the wind speed prediction model is obtained by training a linear regression model using historical user perceived temperatures and corresponding historical ambient temperatures and historical ambient humidity;

[0011] A control unit is used to control the current wind speed based on the target wind speed.

[0012] In a third aspect, an embodiment of the present invention provides an electric fan, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the electric fan control method as described in the first aspect when executing the computer program.

[0013] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the electric fan control method as described in the first aspect is implemented.

[0014] An embodiment of the present invention provides an electric fan control method, device, electric fan and storage medium, the method comprising: during the operation of the electric fan, collecting the user's perceived temperature at the current wind speed, and collecting the current ambient temperature and ambient humidity; inputting the user's perceived temperature, ambient temperature and ambient humidity into a pre-constructed wind speed prediction model, and having the wind speed prediction model output a target wind speed; wherein the wind speed prediction model is obtained by training and learning a linear regression model through historical user perceived temperatures and corresponding historical ambient temperatures and historical ambient humidity; and regulating the current wind speed based on the target wind speed. Based on the constructed wind speed prediction model, the embodiment of the present invention adjusts the wind speed of the electric fan in real time by collecting the user's perceived temperature, ambient temperature and ambient humidity during the operation of the electric fan. This can achieve intelligent control of the electric fan, reduce the frequency of manual adjustment by the user, and enable the user to have a comfortable experience at different perceived temperatures, thereby improving the user's experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0016] Figure 1 A schematic flow chart of an electric fan control method provided by an embodiment of the present invention;

[0017] Figure 2 Another schematic flow chart of a method for controlling an electric fan provided by an embodiment of the present invention;

[0018] Figure 3 A schematic diagram of a first sub-process of an electric fan control method provided by an embodiment of the present invention;

[0019] Figure 4A schematic diagram of a second sub-process of an electric fan control method provided by an embodiment of the present invention;

[0020] Figure 5 A schematic diagram of a wind speed adjustment process of an electric fan control method provided in an embodiment of the present invention;

[0021] Figure 6 A schematic diagram of another wind speed adjustment process of an electric fan control method provided by an embodiment of the present invention;

[0022] Figure 7 A schematic diagram of a model training process of an electric fan control method provided by an embodiment of the present invention;

[0023] Figure 8 A schematic block diagram of an electric fan control device provided by an embodiment of the present invention;

[0024] Figure 9 Another schematic block diagram of an electric fan control device provided by an embodiment of the present invention;

[0025] Figure 10 A first sub-schematic block diagram of an electric fan control device provided by an embodiment of the present invention;

[0026] Figure 11 This is a second sub-schematic block diagram of an electric fan control device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0027] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0028] It will be understood that when used in this specification and the appended claims, the terms “comprises” and “comprising” indicate the presence of described features, integers, steps, operations, elements and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0029] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the present invention. As used in the specification and appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0030] It should be further understood that the term "and / or" used in the present description and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0031] See below Figure 1 , an embodiment of the present invention provides an electric fan control method, specifically including: steps S101-S103.

[0032] Step S101: During the operation of the electric fan, the user's perceived temperature at the current wind speed is collected, as well as the current ambient temperature and humidity.

[0033] Step S102: inputting the user's perceived temperature, ambient temperature, and ambient humidity into a pre-built wind speed prediction model, and having the wind speed prediction model output a target wind speed; wherein the wind speed prediction model is obtained by training a linear regression model using historical user perceived temperatures and corresponding historical ambient temperatures and historical ambient humidity;

[0034] Step S103: regulating the current wind speed based on the target wind speed.

[0035] In this embodiment, combined with Figure 5 and Figure 6 When the electric fan is running, the current user's perceived temperature and the temperature and humidity of the environment are collected, and the current wind speed is predicted using a pre-built wind speed prediction model, that is, the collected user's perceived temperature and the ambient temperature and humidity are input into the wind speed prediction model, and then the wind speed prediction model outputs the corresponding target wind speed. After obtaining the target wind speed, the current wind speed is adjusted according to the relationship between the target wind speed and the current wind speed. Specifically, the target wind speed can be compared with the current wind speed. If the current wind speed is lower than the target wind speed, it means that the current wind speed is small, so the wind speed of the electric fan can be increased according to the target wind speed. If the current wind speed is greater than the target wind speed, it means that the current wind speed is large, so the wind speed of the electric fan can be reduced according to the target wind speed. Of course, if the current wind speed is consistent with the target wind speed, there is no need to adjust the current wind speed.

[0036] This embodiment, based on a constructed wind speed prediction model, collects user-perceived temperature, ambient temperature, and humidity during fan operation to adjust the fan's wind speed in real time. This enables intelligent fan control, reduces the need for manual adjustments, and allows users to experience comfort at varying perceived temperatures, thereby improving the user experience. For example, as the user's motion state and surrounding environment change, the fan can automatically adjust its wind speed based on the user's motion state, ambient temperature, and humidity, to meet diverse needs.

[0037] In actual application scenarios, an intelligent wind speed adjustment mode can be set for the electric fan. When the intelligent wind speed adjustment mode is turned on, the wind speed of the electric fan is intelligently controlled based on the wind speed prediction model. If the intelligent wind speed adjustment mode is not turned on or is turned off, there is no need to intelligently control the wind speed of the electric fan through the wind speed prediction model.

[0038] In one embodiment, if Figure 2 As shown, the process before step S101 includes steps S201-S203.

[0039] Step S201: determining whether a wind speed adjustment instruction sent by a user is received;

[0040] Step S202: If it is determined that a wind speed adjustment instruction sent by the user is received, the current wind speed is adjusted according to the wind speed adjustment instruction;

[0041] Step S203: If it is determined that no wind speed adjustment instruction sent by the user is received, the process proceeds to the collection step.

[0042] In this embodiment, during the operation of the electric fan, in addition to predicting the wind speed based on the user's perceived temperature, the ambient temperature, and the ambient humidity, thereby adjusting and controlling the current wind speed, the current wind speed can also be adjusted and controlled based on a wind speed adjustment instruction sent by the user. It is understandable that the electric fan control method provided in this embodiment is intended to improve the user experience. Therefore, when a wind speed adjustment instruction sent by the user is received, the electric fan wind speed is preferentially adjusted and controlled based on the wind speed adjustment instruction. If no wind speed adjustment instruction is received, the collection step, i.e., step S101, is entered to collect the input parameters required by the wind speed prediction model, and the wind speed prediction is continued using the wind speed prediction model to adjust and control the current wind speed.

[0043] Furthermore, after step S202, the following steps are included:

[0044] Obtaining an adjusted wind speed corresponding to the wind speed adjustment instruction;

[0045] Feedback adjustment is performed on the model parameters of the wind speed prediction model according to the adjusted wind speed.

[0046] In this embodiment, after the wind speed is adjusted according to the user's wind speed adjustment instruction, the wind speed prediction model can be further optimized according to the user's wind speed adjustment instruction so that the target wind speed subsequently predicted and output by the wind speed prediction model better meets the user's usage requirements. Specifically, based on the wind speed adjustment instruction sent by the user, the wind speed to be adjusted by the user is obtained, and the user's perceived temperature, ambient temperature, and ambient humidity at that time are also obtained. Then, the wind speed prediction model is reversely adjusted based on the adjusted wind speed, user's perceived temperature, ambient temperature, and ambient humidity, thereby optimizing the model parameters of the wind speed prediction model.

[0047] For example, user A considers a wind speed of 1.5m / s to be the most suitable when the ambient temperature is 30°C, the humidity is 50%, and the perceived temperature is 37°C. User B considers a wind speed of 2m / s to be the most suitable when the ambient temperature is 30°C, the humidity is 50%, and the perceived temperature is 37°C. If both user A and user B manually adjust the wind speed while using the fan, the wind speed adjustment commands sent by user A and user B, as well as the corresponding perceived temperature, ambient temperature, and current wind speed of the fan, will be recorded. This data will be used to optimize the parameters of the wind speed prediction model so that the next time the wind speed is adjusted, the wind speed predicted by the optimized model will be adjusted, further improving user comfort.

[0048] Combine Figure 5 When the electric fan is in operation and the intelligent wind speed adjustment mode is turned on, it is first determined whether the user manually adjusts the wind speed, that is, whether the wind speed adjustment instruction sent by the user is received. If the user manually adjusts the wind speed, the electric fan is controlled to run according to the wind speed manually adjusted by the user, and the wind speed prediction model is learned based on the wind speed manually adjusted by the user; if the user does not manually adjust the wind speed, the user's perceived temperature, ambient temperature, and ambient humidity are collected to predict the output target wind speed through the wind speed prediction model, and then the current wind speed is adjusted according to the target wind speed. The electric fan is then controlled to run according to the adjusted wind speed, and the running wind speed is continued to be adjusted according to the user's instructions or collected data until the preset time is reached or the user turns off the current mode.

[0049] In one embodiment, during the operation of the electric fan, collecting the user's perceived temperature at the current wind speed, and collecting the current ambient temperature and humidity, includes:

[0050] Use acoustic wave sensors to capture user's body surface vibration data;

[0051] The user's perceived temperature is obtained according to the body surface vibration data.

[0052] In this embodiment, an acoustic wave sensor can be added to the existing structure of the electric fan to monitor the human body temperature AT°C in real time. At the same time, a temperature and humidity sensor can be installed on the electric fan to collect ambient temperature and humidity.

[0053] When using an acoustic wave sensor to collect the user's perceived temperature (AT°C), the acoustic wave sensor can capture tiny vibrations on the human skin surface to infer the perceived temperature. In actual application scenarios, the acoustic wave sensor is integrated into a wearable device (such as a bracelet, watch, wristband, etc.) to monitor the vibration of the user's skin at close range. The acoustic wave sensor collects vibration data on the skin surface in real time (the data includes information such as the frequency, amplitude, and phase of the vibration) and converts it into an electrical signal. The collected vibration data is processed and the changes in skin temperature can be inferred by analyzing the characteristics of the vibration signal (such as frequency and amplitude).

[0054] In some other embodiments, the user's perceived temperature can be collected through other methods, such as directly attaching a skin temperature sensor to the user's skin to directly measure the skin temperature, and further integrating it into a smart bracelet, patch, or other wearable device to achieve user perceived temperature detection. Another example is collecting the user's perceived temperature through a thermistor, that is, measuring the temperature through the change in resistance. The thermistor can be placed near the user's clothing or integrated into a wearable device to measure the user's perceived temperature.

[0055] In one embodiment, inputting the user's perceived temperature, ambient temperature, and ambient humidity into a pre-built wind speed prediction model, and outputting a target wind speed by the wind speed prediction model, includes:

[0056] The target wind speed is predicted and output according to the following formula:

[0057] S=β0+β1*T+β2*H+β3*U

[0058] Wherein, S represents the target wind speed, T represents the ambient temperature, H represents the ambient humidity, U represents the user perceived temperature, β0 represents the intercept term, β1 represents the coefficient of ambient temperature, β2 represents the coefficient of ambient humidity, and β3 represents the coefficient of user perceived temperature.

[0059] This embodiment uses the above-mentioned wind speed prediction model to predict the wind speed based on the collected user's perceived temperature, ambient temperature, and ambient humidity, so as to output the target wind speed that is most suitable for the user at that time.

[0060] In a specific embodiment, Figure 3 As shown, the linear regression model is trained and learned by using the historical user-perceived temperature and the corresponding historical ambient temperature and historical ambient humidity, including steps S301-S306.

[0061] Step S301: Acquire historical user-perceived temperature, historical ambient temperature, and historical ambient humidity, and perform data preprocessing on the historical user-perceived temperature, historical ambient temperature, and historical ambient humidity;

[0062] Step S302: Construct a training matrix based on the historical user-perceived temperature, historical ambient temperature, and historical ambient humidity after data preprocessing:

[0063] X=[1,T i , H i , U i ];

[0064] Among them, 1 represents the intercept term, T i represents the i-th historical user's body temperature, H i represents the i-th historical ambient temperature, U i represents the i-th historical ambient humidity;

[0065] Step S303: input the training matrix into a linear regression model, and the linear regression model outputs the predicted value of the training matrix:

[0066] S^=Xβ

[0067] Wherein, S^ represents the predicted value, β represents the training parameter of the linear regression model, β={β0; β1; β2; β3};

[0068] Step S304: Calculate the sum of square errors between the predicted value and the true values ​​corresponding to the historical user-perceived temperature, historical ambient temperature, and historical ambient humidity;

[0069] Step S305: Minimize the sum of square errors using the least squares method, and obtain training parameters corresponding to the minimization process;

[0070] Step S306: Using the training parameters corresponding to the minimization process as model parameters of the linear regression model, and constructing the wind speed prediction model based on the model parameters.

[0071] In this embodiment, combined with Figure 7 , by collecting historical data as training data to train the linear regression model, and then construct a wind speed prediction model. Specifically, the mathematical expression of the linear regression model can be expressed as:

[0072] S=β0+β1*T+β2*H+β3*U

[0073] Among them, β0 is the intercept term, β1 is the coefficient of ambient temperature, β2 is the coefficient of ambient humidity, and β3 is the coefficient of user-perceived temperature.

[0074] In order to determine the model parameters β0, β1, β2, and β3, a large amount of training data needs to be collected. The training data contains multiple historical data values, including historical records of ambient temperature, humidity, user-perceived temperature, and corresponding wind speed settings. Assuming there are n collected data values, each value can be expressed as:

[0075] (T i , H i , U i , S i ), where i = 1, 2, ..., n

[0076] Therefore, the training matrix X (a column of 1 containing the intercept term) and the parameter vector β can be designed as follows:

[0077] X={1,T1,H1,U1;

[0078] 1, T2, H2, U2; ......

[0080] 1. T i , H i , U i ; ......

[0082] 1. T n , H n , U n}

[0083] β={β0;β1;β2;β3}

[0084] Combine the above training matrix X and parameter vector β to express the predicted value S^:

[0085] S^=Xβ

[0086] Furthermore, the sum of squared errors E between the predicted value and the true value is calculated:

[0087]

[0088] In this way, the square error and E can be combined to solve the optimal solution for the parameter β. In other words, in order to find the parameter β that minimizes E, it is necessary to solve the equation:

[0089]

[0090] By taking the derivative and setting it to zero, we can get:

[0091] X T (S-Xβ)=0

[0092] Simplified to:

[0093] X T Xβ=X T S

[0094] The final solution is:

[0095] β=(X T X) -1 X T S

[0096] After obtaining the correlation coefficients β0, β1, β2, and β3, a wind speed prediction model can be constructed.

[0097] In actual applications, the wind speed can be predicted through the trained model by measuring the real-time ambient temperature and humidity and the user's perceived temperature. If the user manually adjusts the wind speed during the process, the model will be adjusted and memorized based on the user's manually adjusted wind speed, so that the user can have a better experience during use.

[0098] For example, in actual applications, the measured ambient temperature, ambient humidity, and user perceived temperature are 27, 55, and 28 respectively. The model can predict that the wind speed should be 2m / s. If the user manually adjusts the wind speed to 1.5m / s during the process, the training model will overwrite the predicted wind speed at this time with 1.5m / s, thereby continuously improving the user experience.

[0099] In one embodiment, if Figure 4 As shown, the step S103 includes: steps S401-S403.

[0100] Step S401: Determine whether there is a wind speed adjustment instruction sent by the user;

[0101] Step S402: If it is determined that there is a wind speed adjustment instruction sent by the user, obtaining an adjustment wind speed corresponding to the wind speed adjustment instruction, and adjusting the current wind speed to the adjustment wind speed;

[0102] Step S403: If it is determined that there is no wind speed adjustment instruction sent by the user, the target wind speed output by the wind speed prediction model is obtained, and the current wind speed is adjusted to the target wind speed.

[0103] In this embodiment, when adjusting the current wind speed of the electric fan, if there is a wind speed adjustment instruction, the current wind speed is adjusted according to the wind speed adjustment instruction, that is, the current wind speed is adjusted to the adjusted wind speed; if there is no wind speed adjustment instruction, the current wind speed is adjusted according to the target wind speed, that is, the current wind speed is adjusted to the target wind speed. During adjustment, the wind speed value of the current wind speed can be directly adjusted to the wind speed value of the adjusted wind speed or the target wind speed, or the wind speed difference between the current wind speed and the adjusted wind speed or the target wind speed can be obtained, and then the wind speed value of the current wind speed is adjusted according to the wind speed difference.

[0104] Figure 8 This is a schematic block diagram of an electric fan control device 800 provided in an embodiment of the present invention. The device 800 includes:

[0105] The collecting unit 801 is used to collect the user's body temperature at the current wind speed, as well as the current ambient temperature and humidity during the operation of the electric fan;

[0106] Prediction unit 802, configured to input the user's perceived temperature, ambient temperature, and ambient humidity into a pre-built wind speed prediction model, and have the wind speed prediction model output a target wind speed; wherein the wind speed prediction model is obtained by training a linear regression model using historical user perceived temperatures and corresponding historical ambient temperatures and historical ambient humidity;

[0107] The control unit 803 is configured to control the current wind speed based on the target wind speed.

[0108] In one embodiment, if Figure 9 As shown, the electric fan control device 800 further includes:

[0109] The first judgment unit 901 is used to judge whether a wind speed adjustment instruction sent by the user is received;

[0110] The first determining unit 902 is configured to, if it is determined that a wind speed adjustment instruction sent by the user is received, adjust the current wind speed according to the wind speed adjustment instruction;

[0111] The second determining unit 903 is configured to: if it is determined that the wind speed adjustment instruction sent by the user is not received, the collecting unit 801.

[0112] In one embodiment, the electric fan control device 800 further includes:

[0113] A wind speed acquisition unit, configured to acquire an adjusted wind speed corresponding to the wind speed adjustment instruction;

[0114] A feedback adjustment unit is used to perform feedback adjustment on the model parameters of the wind speed prediction model according to the adjusted wind speed.

[0115] In one embodiment, the acquisition unit 801 includes:

[0116] a data capture unit, configured to capture body surface vibration data of the user using an acoustic wave sensor;

[0117] A temperature acquisition unit is used to acquire the user's body temperature based on the body surface vibration data.

[0118] In one embodiment, the prediction unit 802 includes:

[0119] The wind speed prediction unit is used to predict and output the target wind speed according to the following formula:

[0120] S=β0+β1*T+β2*H+β3*U

[0121] Wherein, S represents the target wind speed, T represents the ambient temperature, H represents the ambient humidity, U represents the user perceived temperature, β0 represents the intercept term, β1 represents the coefficient of ambient temperature, β2 represents the coefficient of ambient humidity, and β3 represents the coefficient of user perceived temperature.

[0122] In one embodiment, if Figure 10 As shown, the prediction unit 802 includes:

[0123] The history acquisition unit 1001 is used to acquire historical user-perceived temperature, historical ambient temperature, and historical ambient humidity, and perform data preprocessing on the historical user-perceived temperature, historical ambient temperature, and historical ambient humidity;

[0124] The matrix construction unit 1002 is configured to construct a training matrix based on the historical user perceived temperature, historical ambient temperature, and historical ambient humidity after data preprocessing:

[0125] X=[1,T i , H i , U i ];

[0126] Among them, 1 represents the intercept term, T i represents the i-th historical user's body temperature, H i represents the i-th historical ambient temperature, U i represents the i-th historical ambient humidity;

[0127] The model prediction unit 1003 is used to input the training matrix into a linear regression model, and the linear regression model outputs the predicted value of the training matrix:

[0128] S^=Xβ

[0129] Wherein, S^ represents the predicted value, β represents the training parameter of the linear regression model, β={β0; β1; β2; β3};

[0130] An error calculation unit 1004 is configured to calculate the sum of square errors between the predicted value and the true values ​​corresponding to the historical user perceived temperature, historical ambient temperature, and historical ambient humidity;

[0131] The minimization processing unit 1005 is used to minimize the sum of square errors using the least squares method and obtain training parameters corresponding to the minimization process;

[0132] The model construction unit 1006 is configured to use the training parameters corresponding to the minimization process as model parameters of the linear regression model, and construct the wind speed prediction model based on the model parameters.

[0133] In one embodiment, if Figure 11 As shown, the control unit 803 includes:

[0134] The second determining unit 1101 is configured to determine whether there is a wind speed adjustment instruction sent by a user;

[0135] The first regulating unit 1102 is configured to, if it is determined that there is a wind speed regulating instruction sent by the user, obtain an adjusted wind speed corresponding to the wind speed regulating instruction, and regulate the current wind speed to the adjusted wind speed;

[0136] The second regulating unit 1103 is configured to obtain the target wind speed output by the wind speed prediction model and regulate the current wind speed to the target wind speed if it is determined that there is no wind speed regulating instruction sent by the user.

[0137] Since the embodiments of the apparatus part correspond to the embodiments of the method part, please refer to the description of the embodiments of the method part for the embodiments of the apparatus part, and they will not be repeated here.

[0138] The present invention also provides a computer-readable storage medium having a computer program stored thereon. When executed, the computer program can implement the steps provided in the above embodiments. The storage medium can include a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, among other media capable of storing program code.

[0139] The present invention also provides an electric fan that may include a memory and a processor. The memory stores a computer program, and when the processor calls the computer program in the memory, the steps provided in the above embodiment can be implemented. Of course, the electric fan may also include various network interfaces, a power supply, and other components.

[0140] The various embodiments in the specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same and similar parts between the various embodiments can be referred to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description. It should be pointed out that for ordinary technicians in this technical field, without departing from the principles of this application, several improvements and modifications can be made to this application, and these improvements and modifications also fall within the scope of protection of the claims of this application.

[0141] It should also be noted that, in this specification, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus comprising the element.

Claims

1. A method for controlling an electric fan, characterized in that: include: When the fan is running, the user's perceived temperature at the current wind speed is collected, as well as the current ambient temperature and humidity. Inputting the user's perceived temperature, ambient temperature, and ambient humidity into a pre-built wind speed prediction model, and having the wind speed prediction model output a target wind speed; wherein the wind speed prediction model is obtained by training a linear regression model using historical user perceived temperatures and corresponding historical ambient temperatures and historical ambient humidity; Based on the target wind speed, regulating the current wind speed; Before collecting the user's perceived temperature at the current wind speed, and the current ambient temperature and humidity, the following steps are performed: Determine whether a wind speed adjustment instruction sent by the user is received; If it is determined that a wind speed adjustment instruction sent by the user is received, the current wind speed is adjusted according to the wind speed adjustment instruction; If it is determined that no wind speed adjustment instruction sent by the user has been received, the collection step is entered; If it is determined that a wind speed adjustment instruction sent by the user is received, after the step of adjusting the current wind speed according to the wind speed adjustment instruction, the method further includes: Obtaining an adjusted wind speed corresponding to the wind speed adjustment instruction; performing feedback adjustment on model parameters of the wind speed prediction model according to the adjusted wind speed; During the operation of the electric fan, collecting the user's perceived temperature at the current wind speed, and collecting the current ambient temperature and humidity, includes: Use acoustic wave sensors to capture user's body surface vibration data; Obtaining the user's perceived temperature based on the body surface vibration data; The step of inputting the user's perceived temperature, ambient temperature, and ambient humidity into a pre-built wind speed prediction model, and outputting a target wind speed from the wind speed prediction model, includes: The target wind speed is predicted and output according to the following formula: S=β0+β1*T+β2*H+β3*U Wherein, S represents the target wind speed, T represents the ambient temperature, H represents the ambient humidity, U represents the user perceived temperature, β0 represents the intercept term, β1 represents the coefficient of ambient temperature, β2 represents the coefficient of ambient humidity, and β3 represents the coefficient of user perceived temperature.

2. The electric fan control method according to claim 1, wherein: The linear regression model is trained and learned using historical user-perceived temperatures and corresponding historical ambient temperatures and historical ambient humidity, including: Acquire historical user-perceived temperature, historical ambient temperature, and historical ambient humidity, and perform data preprocessing on the historical user-perceived temperature, historical ambient temperature, and historical ambient humidity; A training matrix is ​​constructed based on the historical user perceived temperature, historical ambient temperature, and historical ambient humidity after data preprocessing: X=[1,T i ,H i ,U i ]; Among them, 1 represents the intercept term, T i represents the i-th historical user's body temperature, H i represents the i-th historical ambient temperature, U i represents the i-th historical ambient humidity; The training matrix is ​​input into a linear regression model, and the linear regression model outputs the predicted value of the training matrix: S^=Xβ Wherein, S^ represents the predicted value, β represents the training parameter of the linear regression model, β={β0; β1; β2; β3}; Calculating the sum of squared errors between the predicted value and the true values ​​corresponding to the historical user perceived temperature, historical ambient temperature, and historical ambient humidity; Minimizing the sum of square errors using a least squares method, and obtaining training parameters corresponding to the minimization process; The training parameters corresponding to the minimization process are used as model parameters of the linear regression model, and the wind speed prediction model is constructed based on the model parameters.

3. The electric fan control method according to claim 1, wherein: The regulating the current wind speed based on the target wind speed includes: Determine whether there is a wind speed adjustment instruction sent by the user; If it is determined that there is a wind speed adjustment instruction sent by the user, obtaining an adjustment wind speed corresponding to the wind speed adjustment instruction, and adjusting the current wind speed to the adjustment wind speed; If it is determined that there is no wind speed adjustment instruction sent by the user, the target wind speed output by the wind speed prediction model is obtained, and the current wind speed is adjusted to the target wind speed.

4. An electric fan control device using the electric fan control method according to claim 1, characterized in that: include: The collection unit is used to collect the user's body temperature at the current wind speed, as well as the current ambient temperature and humidity during the operation of the electric fan; a prediction unit, configured to input the user's perceived temperature, ambient temperature, and ambient humidity into a pre-built wind speed prediction model, and have the wind speed prediction model output a target wind speed; wherein the wind speed prediction model is obtained by training a linear regression model using historical user perceived temperatures and corresponding historical ambient temperatures and historical ambient humidity; A control unit is used to control the current wind speed based on the target wind speed.

5. An electric fan, characterized in that: The method comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the electric fan control method according to any one of claims 1 to 3 when executing the computer program.

6. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the electric fan control method according to any one of claims 1 to 3 is implemented.

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