Control method of electric toothbrush, electronic equipment and storage medium

Abstract

The embodiment of the invention provides a control method of an electric toothbrush, electronic equipment and a storage medium, and belongs to the technical field of electric toothbrush control. The method comprises the steps of inputting first attitude data into a preset target KNN model, and determining a target tooth brushing area according to an output result of the target KNN model; determining a brush head movement mode according to the first acceleration; determining a first driving parameter of the motor according to the target tooth brushing area, and updating the first driving parameter according to the first pressure data and the brush head movement mode to obtain a second driving parameter; and controlling the driving force provided by the motor to the brush head according to the second driving parameter. According to the embodiment of the invention, the first posture data can be input into the preset target KNN model to determine the target tooth brushing area, and the electric toothbrush is driven to brush teeth based on the target tooth brushing area, the first pressure data and the motion mode, so that the cleaning effect of the electric toothbrush is improved.

Description

Technical Field

[0001] The present application belongs to the technical field of electric toothbrush control, and in particular, relates to a control method, electronic device and storage medium for an electric toothbrush. Background Art

[0002] Electric toothbrushes are increasingly used due to their convenience of not needing to brush teeth manually, and have gradually become a commonly used oral health care tool. At the same time, the requirements for the cleaning effect of electric toothbrushes are also getting higher and higher. Existing electric toothbrushes are provided with a motor and a brush head. The brush head is provided with a pressure sensor. The pressure data of the brush head is obtained through the pressure sensor, and the driving parameters of the motor are adjusted according to the pressure data, so that the teeth can be cleaned while protecting the gums. However, in this way, the cleaning effect of the electric toothbrush is not high. Summary of the invention

[0003] In order to solve the above-mentioned problems, the present application proposes a control method, an electronic device and a storage medium for an electric toothbrush, which can improve the cleaning effect of the electric toothbrush.

[0004] To achieve the above objectives, in a first aspect, an embodiment of the present application provides a control method for an electric toothbrush, wherein the electric toothbrush is provided with a posture sensor, a pressure sensor, a brush head, and a motor, and the control method comprises:

[0005] Acquire first sensor data of the posture sensor, where the first sensor data includes first posture data and first acceleration;

[0006] Acquiring first pressure data of the pressure sensor;

[0007] Inputting the first posture data into a preset target KNN model, and determining a target toothbrushing area according to an output result of the target KNN model;

[0008] Determining the brush head motion mode according to the first acceleration;

[0009] Determine a first driving parameter of the motor according to the target brushing area, and update the first driving parameter according to the first pressure data and the brush head motion mode to obtain a second driving parameter;

[0010] The driving force provided by the motor to the brush head is controlled according to the second driving parameter.

[0011] In a second aspect, an electronic device provided according to an embodiment of the present application includes:

[0012] at least one processor;

[0013] at least one memory for storing at least one program;

[0014] When at least one of the programs is executed by at least one of the processors, the control method of the electric toothbrush described in any one of the first aspects is implemented.

[0015] In a third aspect, a computer-readable storage medium is provided according to an embodiment of the application, storing computer-executable instructions, wherein the computer-executable instructions are used to execute the control method of the electric toothbrush described in any one of the first aspects.

[0016] In summary, the above-mentioned embodiments of the present application can identify the target brushing area through the target KNN model, and determine the driving parameters of the motor corresponding to the target brushing area one by one according to the first pressure data and the brush head movement mode, so that the motor driving parameters can change with the target brushing area, thereby improving the cleaning effect of the electric toothbrush. Compared with the prior art, the embodiments of the present application can use the target KNN model to improve the recognition accuracy of the target brushing area, and determine the motor driving parameters based on different target brushing areas, thereby improving the cleaning effect of the electric toothbrush in different target brushing areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A flow chart of an electric toothbrush control method according to an embodiment of the present application;

[0018] Figure 2 A schematic diagram of brushing area categories according to an embodiment of the present application;

[0019] Figure 3 A schematic diagram showing the relationship between the accuracy and K value of a candidate KNN model of an embodiment provided in the present application;

[0020] Figure 4 A schematic diagram of the hardware structure of an electronic device according to an embodiment of the present application;

[0021] Figure 5 A flow chart of a specific control method for an electric toothbrush according to an embodiment of the present application. DETAILED DESCRIPTION

[0022] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0023] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0024] Electric toothbrushes are increasingly used due to their convenience of not requiring manual brushing, and have gradually become a commonly used oral health care tool. At the same time, users have increasingly higher requirements for the cleaning effect of electric toothbrushes. The existing electric toothbrush is provided with a motor and a brush head, and a pressure sensor is provided on the motor. The electric toothbrush mainly obtains the pressure data at the current moment through the pressure sensor. When the pressure data is greater than the preset pressure threshold, the motor drive parameters are adjusted to weaken the vibration effect of the electric toothbrush and reduce the probability of damaging the gums. On the contrary, when the pressure data is less than the threshold, the electric toothbrush drive parameters are adjusted to increase the vibration effect of the electric toothbrush, so that the teeth can be cleaned on the basis of protecting the gums. However, the existing electric toothbrush often provides a plurality of different brushing modes. In different brushing modes, the brushing angle and strength of the electric toothbrush for the brushing area are different. At this time, when only relying on the pressure data for adjustment, the cleaning effect of the electric toothbrush cannot be ensured. Based on this, the embodiment of the present application provides a control method for an electric toothbrush, which can determine the driving parameters according to different brushing areas when cleaning different brushing areas, thereby improving the cleaning effect of the electric toothbrush.

[0025] The electric toothbrush in the embodiment of the present application is provided with a posture sensor, a pressure sensor, a brush head and a motor.

[0026] Figure 1 This is a flow chart of the electric toothbrush control method of the present application embodiment, refer to Figure 1 As shown, the control method includes steps S101 to S106, which are specifically as follows:

[0027] Step S101, acquiring first sensor data of a posture sensor, where the first sensor data includes first posture data and a first acceleration;

[0028] Step S102, obtaining first pressure data of the pressure sensor;

[0029] Step S103, inputting the first posture data into a preset target KNN model, and determining a target toothbrushing area according to an output result of the target KNN model;

[0030] Step S104, determining a brush head motion mode according to the first acceleration;

[0031] Step S105, determining a first driving parameter of the motor according to the target brushing area, updating the first driving parameter according to the first pressure data and the brush head motion mode, and obtaining a second driving parameter;

[0032] Step S106: Control the driving force provided by the motor to the brush head according to the second driving parameter.

[0033] Therefore, the embodiment of the present application can identify the target brushing area through the KNN model. Based on the target brushing area, the driving parameters of the motor are confirmed according to the first pressure data and the brush head movement pattern. The driving force provided by the motor to the brush head is controlled according to the driving parameters. The driving parameters can be determined for the target brushing area, thereby improving the cleaning effect of the electric toothbrush.

[0034] In the embodiment of the present application, a pressure sensor (Pressure Transducer) is used to obtain a pressure signal of the brush head. The pressure sensor is a device that converts a pressure signal into an available output electrical signal. In the embodiment of the present application, the first pressure data represents the pressure state of the brush head. In some embodiments, the first pressure data can be obtained by preprocessing the electrical signal collected by the pressure sensor after the brush head is started. In other embodiments, the first pressure data can be obtained by preprocessing the electrical signal collected by the pressure sensor. The preprocessing is based on taking the electrical signal collected by the pressure sensor when the brush head is started as a reference electrical signal, and then performing a difference calculation between the electrical signal collected in real time after the brush head is started and the reference signal to obtain the first pressure data.

[0035] The first posture data characterizes the posture and orientation of the brush head; in the embodiment of the present application, the posture sensor may include a three-axis gyroscope, a three-axis accelerometer, and a three-axis electronic compass, so that the first posture data and the first acceleration can be obtained at the same time. Among them, the three-axis accelerometer is used to obtain the first acceleration of the brush head, and the three-axis gyroscope, the three-axis accelerometer, and the three-axis electronic compass are used to obtain three-dimensional posture and orientation data. In some embodiments, the posture sensor is provided with a three-dimensional algorithm and a data fusion algorithm, and the three-dimensional posture and orientation data are calculated by the three-dimensional algorithm and the data fusion algorithm to output the first posture data.

[0036] The target KNN model has the advantages of high classification accuracy and insensitivity to abnormal points. Therefore, the embodiment of the present application can accurately classify the first posture data through the target KNN model, so that the determined target brushing area is closer to the actual brushing area.

[0037] In the embodiment of the present application, the second driving parameter can be achieved by adjusting the frequency and duty cycle of the pulse width modulation signal of the motor to control the driving force provided by the motor to the brush head.

[0038] In some embodiments, the first posture data includes first feature posture data corresponding to a preset plurality of feature dimensions one by one; the first posture data is input into a preset target KNN model, and the target brushing area is determined according to the output result of the target KNN model, including:

[0039] Obtaining a preset K value and a plurality of second posture data in the target KNN model; wherein the second posture data corresponds one-to-one to a preset tooth brushing area, and each second posture data includes a plurality of second feature state data corresponding one-to-one to a feature dimension;

[0040] Calculate the distance between each first characteristic posture data and the second characteristic posture data of the corresponding characteristic dimension in each second posture data respectively, obtain multiple position distances corresponding to each second posture data, and determine the Euclidean distance between the first posture data and each second posture data according to the sum of the multiple position distances of the same second posture data;

[0041] According to the K value, the second posture data corresponding to the smallest K Euclidean distances are all used as the comparison posture data;

[0042] Determine the brushing area distribution probability of the first posture data according to the comparison posture data;

[0043] According to the brushing area distribution probability, the target brushing area is obtained.

[0044] Therefore, the embodiment of the present application calculates the distance between each first feature posture data and each second feature posture data in multiple feature dimensions, so that each Euclidean distance reflects the difference between the first posture data and the corresponding second posture data in multiple feature dimensions, and determines the target brushing area by selecting K comparison posture data. Compared with the method of using a single comparison posture data and a single feature dimension, the accuracy of target brushing area identification is higher.

[0045] In some embodiments, the Euclidean distance may be as shown in formula (1):

[0046]

[0047] Among them, x i is the i-th first posture data, x j is the jth second posture data, x i,k is the kth first feature posture data of the i-th first posture data, x j,k is the kth second feature posture data of the jth second posture data, and N is the number of feature dimensions in the first posture data.

[0048] In some embodiments, Figure 2 As shown, the preset tooth brushing area may include an occlusal surface area and a tooth side surface area. Correspondingly, the occlusal surface area and the tooth side surface area respectively have different second posture data in one-to-one correspondence.

[0049] In some embodiments, the brushing area distribution probability can be obtained by formula (2), which is as follows:

[0050]

[0051] Among them, x i is the first posture data, x j is the jth comparative posture data, K is the number of comparative posture data, c is the region category set by the target KNN model, and I is an indicator function, which is used to indicate whether the jth comparative posture data belongs to the region category c, so as to count the number of comparative posture data belonging to the region category c. Thus, the number of each first posture data belonging to different brushing region categories is counted, and the brushing region distribution probability of the first posture data is obtained, and then the category with the largest brushing region distribution probability can be selected as the category of the first posture data by adopting the classification decision rule of majority voting, so as to minimize the recognition error of the target brushing region.

[0052] For example, the number of comparative posture data is set to 3, and the target KNN model is set to include the occlusal surface c1 and the tooth side surface c2, then K=3. Assume that the three comparative posture data are represented by x j＝1 ,x j＝2 ,x j＝3 At this time, formula (2) can be used to obtain as well as Among them, for Then we can judge x j＝1 ,x j＝2 ,x j＝3 Does it belong to c1? Assume x i＝1 and x j＝2 , belongs to c1, then It can be found that there are 2 comparative posture data belonging to c1. Correspondingly, the distribution probability of the brushing area belonging to c1 is By analogy, assuming that the first posture data belongs to the brushing area distribution probability of c2 is The brushing area distribution probabilities of c1 and c2 can be compared, and the brushing area with a larger distribution probability can be selected as the target brushing area, that is, the target brushing area is the occlusal surface.

[0053] In some embodiments, the first characteristic posture data includes a first yaw angle, a first tilt angle, and a first roll angle; the plurality of second characteristic state data each includes a second yaw angle, a second tilt angle, and a second roll angle; and the distance between each first characteristic posture data and each second characteristic posture data in each second posture data is calculated respectively, including:

[0054] Performing variance calculation on the first yaw angle and the second yaw angle in each second attitude data respectively to obtain the distance between the first yaw angle and each second yaw angle;

[0055] Calculate the variance of the first tilt angle and the second tilt angle in each second posture data to obtain the distance between the first tilt angle and each second tilt angle;

[0056] The first roll angle and the second roll angle in each second posture data are respectively calculated for variance to obtain the distance between the first roll angle and each second roll angle.

[0057] Therefore, the embodiment of the present application calculates the distance between the first posture data and the second posture data through the characteristic dimensions of the yaw angle, the pitch angle and the roll angle, and can obtain the difference in the characteristic dimensions between the first posture data and the second posture data, thereby improving the accuracy of the calculated distance.

[0058] In some embodiments, the K value of the target KNN model is determined by the following steps:

[0059] Obtain the test data set and the preset K value range;

[0060] Determine multiple sample K values ​​from the value range, and construct candidate KNN models corresponding to the sample K values ​​based on the second posture data respectively;

[0061] Input the test data set into the candidate KNN model, and determine the accuracy of each candidate KNN model based on the output results of the candidate KNN model;

[0062] The sample K value corresponding to the candidate KNN model with the highest accuracy is used as the K value of the target KNN model.

[0063] It is understandable that different K values ​​have different effects on the accuracy of the selected KNN model. For example, if the K value is too small, the KNN model will over-rely on the training samples of the nearest neighbors, resulting in overfitting of the KNN model. In addition, if the K value is too large, the KNN model will be over-smoothed and underfitting will occur, that is, if the K value is too large or too small, the accuracy of the KNN model will be reduced. Therefore, the selection of the K value is strongly related to the recognition accuracy of the KNN model. The embodiment of the present application constructs a candidate KNN model corresponding to the sample K value one by one through the second posture data, and can calculate the accuracy of the candidate KNN model corresponding to each sample K value, and select the K value corresponding to the KNN model with the highest accuracy, thereby improving the accuracy of the KNN model in identifying the target brushing area.

[0064] For example, refer to Figure 3As shown, the preset K value ranges from 0 to 50, and the KNN model recognition accuracy corresponding to each K value is calculated by taking values ​​at equal intervals in the range. When the K value ranges from 0 to 20, the accuracy of the KNN model in identifying the target brushing area increases with the increase of the K value. When the K value is 20, the accuracy of the KNN model in identifying the target brushing area reaches a maximum of 98%. When the K value ranges from 20 to 50, the accuracy of the KNN model in identifying the target brushing area decreases with the increase of the K value. Therefore, in order to improve the accuracy of the KNN model, the K value of 20 corresponding to the maximum accuracy of 98% is selected as the preset K value of the KNN model.

[0065] In some embodiments, determining the brush head motion mode according to the first acceleration includes:

[0066] Acquire a second acceleration at a plurality of consecutive second collection moments before a first collection moment corresponding to the first acceleration;

[0067] Obtaining acceleration component variance according to the first acceleration and the plurality of second accelerations;

[0068] When the variance of the acceleration component is less than or equal to a preset motion threshold, determining that the motion mode of the brush head of the electric toothbrush at the first acquisition moment is static;

[0069] When the variance of the acceleration component is greater than a preset motion threshold, it is determined that the motion mode of the brush head of the electric toothbrush at the first collection moment is motion.

[0070] Therefore, the embodiment of the present application determines the acceleration component variance based on the first acceleration and the second acceleration at multiple consecutive second collection moments before the first collection moment corresponding to the first acceleration, and can integrate the acceleration analysis of multiple consecutive moments, reduce the randomness and error caused by judging the motion mode based on the acceleration at a single moment, and determine the motion mode of the brush head based on the acceleration component variance and the motion threshold, which can improve the accuracy of determining the motion mode of the brush head.

[0071] The first acceleration and the second acceleration can both be decomposed into acceleration components along the X / Y / Z directions, and the variances of the acceleration components in the X / Y / Z directions are calculated respectively to obtain the variances of the acceleration components in the X / Y / Z directions that correspond one to one with the first acceleration and the second acceleration.

[0072] In some embodiments, taking the number of the second moment as 9 as an example, assuming that for the first moment, the first acceleration (acc x1 ,acc y1 ,acc z1 ), where acc x1 ,acc y1 ,accz1 Respectively represent the acceleration components of the first acceleration in the X, Y, and Z directions. Similarly, the acceleration (acc x2 ,acc y2 ,acc z2 )~(acc x10 ,acc y10 ,acc z10 ), then for the X direction, we can use acc x1 ~acc x10 Get the acceleration component variance var(acc x ); then for the Y direction, based on acc y1 ~acc y10 Get the acceleration component variance var(acc y ); for the Z direction, based on acc z1 ~acc z10 Get the acceleration component variance var(acc z ). For each acceleration component variance, it is determined whether it is less than or equal to the preset motion threshold. When all acceleration component variances are less than or equal to the motion threshold, it is determined that the motion mode of the brush head at the first acquisition moment is static; otherwise, it is determined that the motion mode of the brush head at the first acquisition moment is motion.

[0073] In some embodiments, the target brushing area includes the occlusal surface and the tooth side surface; determining the first driving parameter of the motor according to the target brushing area includes:

[0074] When the target brushing area is the occlusal surface, the driving mode of the motor is set to the vibration and shaft motion mode;

[0075] When the target brushing area is the tooth side, the driving mode of the motor is set to the vibration and sweeping mode;

[0076] A first driving parameter of the motor is determined according to a driving mode of the motor.

[0077] Therefore, the embodiment of the present application determines the driving mode of the motor according to the target brushing area, and determines the first driving parameter of the motor according to the driving mode. It is possible to design driving parameters corresponding to the driving mode according to different target brushing areas, thereby improving the cleaning effect of the electric toothbrush.

[0078] Since there are many tooth pits and gaps on the occlusal surface, the swing amplitude of the brush head can be adjusted left and right and up and down by increasing the axial motion, making it easier to clean the tooth pits and gaps on the occlusal surface, thereby improving the cleaning effect of the electric toothbrush. Since the tooth side surface is usually a vertical plane, by increasing the sweeping motion and adjusting the sweeping amplitude, the brush head can fit the tooth side surface more closely, thereby achieving a better cleaning effect of the electric toothbrush.

[0079] In some embodiments, the driving mode of the motor can be determined by looking up a table. Referring to Table 1, a mapping table of the driving mode and the target brushing area is constructed. At this time, the driving mode of the target brushing area can be determined by looking up Table 1. Table 1 is as follows:

[0080] Target brushing area Drive Mode Occlusal surface Vibration and shaft movement Tooth side Vibrate and Sweep

[0081] Table 1

[0082] The driving parameters corresponding to the driving mode of the motor can be determined by looking up a table. For example, the driving parameter mapping tables corresponding to the vibration mode, shaft motion mode and sweeping mode shown in Tables 2 to 4 can be constructed respectively. At this time, the driving parameters corresponding to the driving mode can be determined by looking up Tables 2 to 4. The technicians in this field do not impose specific numerical restrictions on the driving parameters, and the technicians in this field can selectively set them according to the actual situation. Among them, Tables 2 to 4 are as follows:

[0083] Vibration frequency (Hz) Vibration duty cycle (%) 185 85

[0084] Table 2

[0085] As shown in Table 2 above, the driving parameters corresponding to the vibration mode include vibration frequency and vibration duty cycle. The vibration frequency is the number of times the motor completes a vibration cycle per unit time, which characterizes the speed of the motor vibration; the vibration duty cycle is the ratio of the time the motor is in a vibration state to the entire cycle time during a vibration cycle, and the vibration duty cycle characterizes the output of the motor vibration energy. The specific parameters of the vibration duty cycle and the vibration frequency can be selectively set according to actual needs. As shown in Table 2, the vibration frequency is set to 185Hz and the corresponding duty cycle is set to 85%.

[0086] Sweep frequency(Hz) Sweep Angle(°) 8 25

[0087] Table 3

[0088] As shown in Table 3 above, the driving parameters corresponding to the sweeping mode include sweeping frequency and sweeping angle. The sweeping frequency is the number of times the motor completes a sweeping cycle per unit time, which represents the up and down sweeping of the brush head; the sweeping angle is the maximum angle of the brush head swing, which represents the sweeping amplitude of the brush head. The sweeping frequency and sweeping angle can be selectively set according to actual needs. For example, setting the sweeping frequency to 8Hz and the sweeping angle to 25° can make the brush head fit the side of the teeth better, thereby achieving a better electric toothbrush cleaning effect.

[0089] Shaft frequency (Hz) Shaft motion duty cycle (%) 65 80

[0090] Table 4

[0091] Among them, as shown in Table 4 above, the driving parameters corresponding to the axial motion mode include the axial motion frequency and the axial motion duty cycle. Among them, the axial motion frequency is the number of times the motor completes the axial motion cycle in a unit time, which represents the swing amplitude of the brush head left and right and up and down; the axial motion duty cycle is the ratio of the time the motor is in the axial motion state in one axial motion cycle to the entire cycle time, which represents the output of the motor axial motion energy. The axial motion frequency and the axial motion duty cycle can be selectively set according to actual conditions. The axial motion frequency is set to 65Hz and the duty cycle is set to 80%, so that the tooth pits and tooth gaps on the occlusal surface are easier to clean, thereby improving the cleaning effect of the electric toothbrush.

[0092] In some embodiments, updating the first driving parameter according to the first pressure data and the brush head motion mode to obtain the second driving parameter includes:

[0093] Obtaining a first motor strength coefficient corresponding to the first pressure data;

[0094] Performing weighted calculation according to the first motor strength coefficient and the first driving parameter to obtain a third driving parameter;

[0095] Obtaining a second motor strength coefficient corresponding to the brush head motion mode;

[0096] A weighted calculation is performed according to the second motor strength coefficient and the third driving parameter to obtain the second driving parameter.

[0097] Therefore, the embodiment of the present application determines the pressure exerted on the brush head through the first pressure data, adjusts and updates the first drive parameter through the first pressure data to obtain the third drive parameter, and then adjusts and updates the third drive parameter through the brush head motion mode to obtain the second drive parameter, which can clean the teeth while protecting the gums.

[0098] In the embodiment of the present application, the first pressure data represents the pressure state of the brush head. When the pressure state is low, in order to ensure the cleaning effect of the electric toothbrush, the motor strength coefficient is increased to clean the teeth while protecting the gums. When the pressure state is high, the motor strength coefficient is reduced to reduce the vibration effect of the motor, thereby protecting the gums. Therefore, the electric toothbrush adjusts and updates the first driving parameter according to the first pressure data, and can take into account the cleaning effect while protecting the gums.

[0099] In some implementations, the pressure data can be set to different force ranges, and different motor strength coefficients can be allocated according to the different force ranges. The first motor strength coefficient can be determined by looking up a table, or can be realized by program logic judgment. Those skilled in the art do not make specific restrictions on the motor strength coefficient, and those skilled in the art can selectively set it according to actual conditions.

[0100] Taking the first motor strength coefficient as an example, the mapping table of the first pressure data and the motor strength coefficient is constructed as shown in Table 5. In Table 5, the pressure data is divided into three force ranges, and each force range is set with a pressure threshold range and a motor strength coefficient. Exemplarily, assuming that the first pressure data belongs to the first pressure threshold range, that is, the force range corresponding to the first pressure data is 0-35g, it indicates that the pressure on the brush head is weak. At this time, the motor strength coefficient corresponding to the first pressure data can be obtained from Table 5 as 10%. Assuming that the first pressure data belongs to the second pressure threshold range, that is, the force range corresponding to the first pressure data is 35-350g, it indicates that the pressure on the brush head is moderate at this time, and the motor strength coefficient can be obtained from Table 5 as 100%. Assuming that the first pressure data belongs to the third pressure threshold range, that is, the force range corresponding to the first pressure data is greater than 350g, it indicates that the pressure at this time is large. At this time, the motor strength coefficient corresponding to the first pressure data can be obtained from Table 5 as 50%.

[0101] Criteria Velocity Range Motor strength factor Δ∈First pressure threshold range 0-35g 10％ Δ∈Second pressure threshold range 35-350g 100％ Δ∈the third pressure threshold range >350g 50％

[0102] Table 5

[0103] In some embodiments, obtaining a second motor strength coefficient corresponding to the brush head motion mode includes:

[0104] When the movement mode of the brush head is movement, the preset movement motor intensity coefficient is used as the second motor intensity coefficient;

[0105] When the movement mode of the brush head is static, the preset static motor strength coefficient is used as the second motor strength coefficient, wherein the static motor strength coefficient is smaller than the moving motor strength coefficient.

[0106] Therefore, the embodiment of the present application determines the second motor strength coefficient according to the motor strength coefficient corresponding to the movement mode of the brush head, wherein the static motor strength coefficient is set to be smaller than the motion motor strength coefficient, which can weaken the vibration effect of the motor in the static state and protect the gums when brushing teeth.

[0107] The technical personnel in this field do not impose any specific restrictions on the specific value of the motor strength coefficient, and the technical personnel in this field can selectively set it according to the actual situation. For example, when the movement mode of the brush head is motion, the corresponding preset motion motor strength coefficient is 100%, and the motion motor strength coefficient is used as the second motor strength coefficient, which can maintain the vibration effect of the motor, thereby ensuring the cleaning effect of the electric toothbrush; when the movement mode of the brush head is static, the corresponding preset static motor strength coefficient is 10%, and the static motor strength coefficient is used as the second motor strength coefficient, which can weaken the vibration effect of the motor, thereby protecting the gums when brushing teeth statically.

[0108] The motor strength coefficient corresponding to the movement mode of the brush head can be determined by looking up a table. For example, a motor strength coefficient mapping table corresponding to the movement mode of the brush head is constructed as shown in Table 6. At this time, the motor strength coefficient corresponding to the movement mode of the brush head can be determined by looking up a table.

[0109] Table 6 determines the motor strength coefficient corresponding to the brush head motion mode.

[0110] Sport Mode Motor strength factor still 10％ sports 100％

[0111] Table 6

[0112] For example, refer to Figure 4 As shown, the electric toothbrush in the embodiment of the present application is controlled by the following steps:

[0113] Step 1: Identification of stress status;

[0114] Among them, the pressure state recognition is used to obtain the first pressure data, such as collecting the electrical signal x of the pressure sensor in real time through a high-precision ADC chip, and collecting the reference signal when the toothbrush is started. Then according to the signal difference First pressure state data is obtained.

[0115] Step 2: Identify the target tooth brushing area, specifically, identify the area corresponding to the category with the highest probability as the target tooth brushing area through the target KNN model.

[0116] Step 3: Brush head motion pattern recognition, as follows: Calculate with 10 accelerations as one cycle, then collect 10 times of data and fill it into the data window ACC x ,ACC y ,ACC z , calculate the variance var(ACC) in the x, y and z dimensions respectivelyx ),var(ACC y ),var(ACC z ); when the variance var(ACC x ),var(ACC y ),var(ACC z ) are all less than a certain threshold, the current mode is considered to be stationary, otherwise it is considered to be moving.

[0117] Step 4: Motor vibration mode control, as follows: When the target brushing area is the chewing surface, the driving mode of the motor is set to vibration and axial movement, and the driving parameters corresponding to the vibration and axial movement modes are obtained as the first driving parameters as shown in Table 2 and Table 4. In addition, when the target brushing area is the tooth side surface, the driving mode of the motor is set to vibration and sweeping, and the driving parameters corresponding to the vibration and sweeping modes are obtained as the first driving parameters as shown in Table 2 and Table 3; after the target brushing area is determined, first, refer to Table 5, multiply the motor strength coefficient corresponding to the pressure state by the first driving parameter, thereby updating the first parameter to obtain the second driving parameter, and then, when it is identified that the brush head motion mode is stationary, refer to Table 6, multiply the second driving parameter by the motor strength coefficient corresponding to the stationary state, thereby updating the second driving parameter, and in addition, when it is identified that the brush head motion mode is stationary, it is not described here one by one, wherein the updated second driving parameter is used to drive the motor.

[0118] The embodiment of the present application also provides an electronic device, the electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the above-mentioned multi-active bridge transformer control method when executing the computer program. The electronic device can be any intelligent terminal including a tablet computer, a car computer, etc.

[0119] See also Figure 5 , Figure 5 The hardware structure of an electronic device of another embodiment is illustrated, and the electronic device includes:

[0120] The processor 201 may be implemented by a general-purpose CPU (central processing unit 201), a microprocessor 201, an application-specific integrated circuit (ASIC), or one or more integrated circuits, etc., and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application;

[0121] The memory 202 can be implemented in the form of a read-only memory 202 (ROM), a static storage device, a dynamic storage device, or a random access memory 202 (RAM). The memory 202 can store an operating system and other application programs. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 202, and the processor 201 calls and executes the electric toothbrush control method of the embodiment of this application;

[0122] Input / output interface 203, used to implement information input and output;

[0123] The communication interface 204 is used to realize the communication interaction between the device and other devices. The communication can be realized through a wired manner (such as USB, network cable, etc.) or a wireless manner (such as mobile network, WIFI, Bluetooth, etc.);

[0124] Bus 205 , which transmits information between various components of the device (e.g., processor 201 , memory 202 , input / output interface 203 , and communication interface 204 );

[0125] The processor 201 , the memory 202 , the input / output interface 203 and the communication interface 204 are connected to each other in communication within the device via the bus 205 .

[0126] In some embodiments, the embodiments of the present application further provide a computer-readable storage medium, which stores a computer program, and the computer program implements the above-mentioned electric toothbrush control method when executed by a processor.

[0127] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0128] The embodiments described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0129] Those skilled in the art will appreciate that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0130] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0131] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0132] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0133] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0134] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0135] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0136] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0137] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including multiple instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store programs.

Claims

1. A control method for an electric toothbrush, characterized in that: The electric toothbrush is provided with a posture sensor, a pressure sensor, a brush head and a motor, and the control method comprises: Acquire first sensor data of the posture sensor, where the first sensor data includes first posture data and first acceleration; Acquiring first pressure data of the pressure sensor; Inputting the first posture data into a preset target KNN model, and determining a target toothbrushing area according to an output result of the target KNN model; Determining the brush head motion mode according to the first acceleration; Determine a first driving parameter of the motor according to the target brushing area, and update the first driving parameter according to the first pressure data and the brush head motion mode to obtain a second driving parameter; The driving force provided by the motor to the brush head is controlled according to the second driving parameter.

2. The control method according to claim 1, characterized in that: The first posture data includes first feature posture data corresponding to a preset plurality of feature dimensions one by one; the first posture data is input into a preset target KNN model, and a target toothbrushing area is determined according to an output result of the target KNN model, including: Acquire a preset K value and a plurality of second posture data in the target KNN model; wherein the second posture data corresponds one-to-one to a preset tooth brushing area, and each second posture data includes a plurality of second feature state data corresponding one-to-one to the feature dimension; Calculate the distance between each of the first characteristic posture data and the second characteristic posture data of the corresponding characteristic dimension in each of the second posture data respectively, obtain a plurality of position distances corresponding to each of the second posture data, and determine the Euclidean distance between the first posture data and each of the second posture data according to the sum of the plurality of position distances of the same second posture data; According to the K value, the second posture data corresponding to the smallest K Euclidean distances are all used as comparison posture data; Determine the brushing area distribution probability of the first posture data according to each of the comparison posture data; According to the brushing area distribution probability, a target brushing area is obtained.

3. The control method according to claim 2, characterized in that: The first characteristic posture data includes a first yaw angle, a first tilt angle, and a first roll angle; each of the second characteristic state data includes a second yaw angle, a second tilt angle, and a second roll angle; and the distance between each of the first characteristic posture data and each of the second characteristic posture data in each of the second posture data is calculated respectively, including: Performing variance calculation on the first yaw angle and the second yaw angle in each of the second posture data respectively to obtain a distance between the first yaw angle and each of the second yaw angles; Calculate the variance of the first inclination angle and the second inclination angle in each of the second posture data to obtain the distance between the first inclination angle and each of the second inclination angles; The first roll angle and the second roll angle in each of the second posture data are respectively calculated for variance to obtain the distance between the first roll angle and each of the second roll angles.

4. The control method according to claim 2, characterized in that: The K value of the target KNN model is determined by the following steps: Obtain the test data set and the preset K value range; Determine a plurality of sample K values ​​from the value range, and construct candidate KNN models corresponding to the sample K values ​​one by one based on the second posture data respectively; Inputting the test data set into the candidate KNN model, and determining the accuracy of each candidate KNN model according to the output result of the candidate KNN model; The sample K value corresponding to the candidate KNN model with the highest accuracy is used as the K value of the target KNN model.

5. The control method according to claim 1, characterized in that: Determining the brush head motion mode according to the first acceleration includes: Acquire a second acceleration at a plurality of consecutive second collection moments before a first collection moment corresponding to the first acceleration; Obtaining an acceleration component variance according to the first acceleration and a plurality of the second accelerations; In the case where the acceleration component variance is less than or equal to a preset motion threshold, determining that the brush head motion mode of the electric toothbrush at the first acquisition moment is stationary; In the case where the variance of the acceleration component is greater than the preset motion threshold, it is determined that the motion mode of the brush head of the electric toothbrush at the first collection moment is motion.

6. The control method according to claim 1, characterized in that: The target brushing area includes an occlusal surface and a tooth side surface; and determining the first driving parameter of the motor according to the target brushing area includes: When the target brushing area is the occlusal surface, the driving mode of the motor is set to a vibration and shaft motion mode; When the target brushing area is the tooth side, the driving mode of the motor is set to a vibration and sweeping mode; A first driving parameter of the motor is determined according to the driving mode.

7. The control method according to claim 1, characterized in that: The updating of the first driving parameter according to the first pressure data and the brush head movement mode to obtain a second driving parameter includes: Acquire a first motor strength coefficient corresponding to the first pressure data; Performing weighted calculation according to the first motor strength coefficient and the first driving parameter to obtain a third driving parameter; Obtaining a second motor strength coefficient corresponding to the brush head motion mode; The second driving parameter is obtained by performing weighted calculation according to the second motor strength coefficient and the third driving parameter.

8. The control method according to claim 7, characterized in that: The obtaining of the second motor strength coefficient corresponding to the brush head motion mode includes: When the brush head movement mode of the brush head is movement, a preset movement motor intensity coefficient is used as a second motor intensity coefficient; When the brush head movement mode of the brush head is static, a preset static motor strength coefficient is used as the second motor strength coefficient, wherein the static motor strength coefficient is smaller than the motion motor strength coefficient.

9. An electronic device, comprising: at least one processor; at least one memory for storing at least one program; When at least one of the programs is executed by at least one of the processors, the method according to any one of claims 1 to 8 is implemented.

10. A computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions are used to execute the method according to any one of claims 1 to 8.

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