Touch control processing method and device, electronic equipment and storage medium

Abstract

The invention relates to a touch control processing method and device, electronic equipment and a storage medium. The touch control processing method comprises the following steps: in response to a detected touch control operation, obtaining a first induction signal corresponding to the touch control operation; acquiring a correction parameter associated with the touch screen to which the first induction signal belongs; wherein the correction parameter is used for filtering noise introduced when the sensing signal of the touch screen is filtered, the correction parameter is obtained by carrying out noise reduction processing on a sample sensing signal, and the sample sensing signal is a signal obtained based on data associated with the touch screen to which the first sensing signal belongs; correcting the first induction signal by using the correction parameter to obtain a second induction signal; and determining a touch position according to the second sensing signal. According to the embodiment of the invention, the noise or redundant information in the first induction signal can be effectively reduced, and the principal component in the first induction signal is reserved, so that the quality of the second induction signal is improved, and the user experience is improved.

Description

Technical Field

[0001] This disclosure relates to the field of touch technology, and more particularly to a touch processing method and apparatus, electronic device and storage medium. Background Technology

[0002] With the continuous development of electronic devices, people have increasingly higher requirements for the touch quality of touchscreen electronic devices such as mobile phones. Currently, for touchscreen electronic devices, the processor determines the coordinate position of the touch point by detecting the touch signal. Taking a capacitive touchscreen as an example, when a finger touches the touchscreen, it causes a change in capacitance. The processor of the electronic device can calculate the coordinate position of the finger's touch point based on the change in capacitance value.

[0003] However, during touch screen operation, the touch signals detected by electronic devices can be affected by noise from the environment, power supply, display, or other sources, resulting in poor touch accuracy. Summary of the Invention

[0004] To overcome the problems existing in related technologies, this disclosure provides a touch processing method and apparatus, electronic device and storage medium, which can effectively reduce noise or redundant information in the first sensing signal, retain the main components in the first sensing signal, so as to improve the quality of the second sensing signal and enhance the user experience.

[0005] According to a first aspect of the present disclosure, a touch processing method is provided, comprising at least:

[0006] In response to detecting a touch operation, a first sensing signal corresponding to the touch operation is acquired;

[0007] Obtain correction parameters associated with the touchscreen to which the first sensing signal belongs; wherein, the correction parameters are used to filter out noise introduced when the touchscreen sensing signal is received, the correction parameters are obtained by denoising the sample sensing signal, and the sample sensing signal is a signal obtained based on data associated with the touchscreen to which the first sensing signal belongs;

[0008] The first sensing signal is corrected using the correction parameters to obtain the second sensing signal;

[0009] The touch position is determined based on the second sensing signal.

[0010] In some embodiments, the method further includes:

[0011] Obtain a sample dataset; wherein, one sample data in the sample dataset corresponds to one sample sensing signal, and each sample sensing signal includes the electrical signal sensed by each touch sensing point of the touch sensing array in the touch screen;

[0012] The sample sensing signals in the sample data set are combined to obtain a first sample matrix; wherein the number of columns in the first sample matrix is ​​the same as the number of columns in the touch sensing array, and the number of rows in the first sample matrix is ​​equal to the product of the number of sample data and the number of rows in the touch sensing array;

[0013] Principal component analysis is performed on the first sample matrix to determine the correction parameters.

[0014] In some embodiments, performing principal component analysis on the first sample matrix to determine the correction parameters includes:

[0015] Based on the first sample matrix, eigenvalue decomposition is performed to obtain multiple eigenvalues ​​and multiple eigenvectors; wherein, one eigenvalue corresponds to one eigenvector.

[0016] A predetermined number of target feature values ​​from the plurality of feature values, and a target feature vector from the plurality of feature vectors corresponding to the target feature values, are determined as the correction parameters; wherein, the target feature value is greater than the feature values ​​other than the target feature value among the plurality of feature values.

[0017] In some embodiments, the eigenvalue decomposition based on the first sample matrix to obtain multiple eigenvalues ​​and multiple eigenvectors includes:

[0018] The first sample matrix is ​​decentralized to obtain the second sample matrix after decentralization;

[0019] Calculate the covariance matrix corresponding to the second sample matrix;

[0020] The covariance matrix is ​​decomposed into eigenvalues ​​to obtain the plurality of eigenvalues ​​and the plurality of eigenvectors.

[0021] In some embodiments, the process of decentralizing the first sample matrix to obtain a decentralized second sample matrix includes:

[0022] Calculate the average value of each column of data in the first sample matrix;

[0023] The second sample matrix is ​​obtained by subtracting the average value of the column corresponding to each data point from each data point in the first sample matrix.

[0024] In some embodiments, the correction parameters include a preset number of feature values ​​and feature vectors corresponding to the feature values, and the first sensing signal includes the electrical signal sensed by each touch sensing point of the touch sensing array in the touch screen.

[0025] The step of correcting the first sensing signal using the correction parameters to obtain the second sensing signal includes:

[0026] The second sensing signal is obtained by multiplying the first matrix formed by the signals sensed by each touch sensing point of the touch sensing array, the second matrix formed by the preset number of feature values, and the third matrix formed by the feature vectors.

[0027] In some embodiments, the second sensing signal is obtained by multiplying the first matrix formed by the signals sensed by each touch sensing point of the touch sensing array, the second matrix formed by the preset number of feature values, and the third matrix formed by the feature vectors, including:

[0028] The third matrix formed by the feature vectors is transposed to obtain a fourth matrix; wherein the fourth matrix has the same number of rows as the first matrix.

[0029] The first matrix is ​​multiplied by the fourth matrix, and the result of the multiplication is multiplied by the second matrix to obtain the second sensing signal.

[0030] According to a second aspect of the present disclosure, a touch processing device is provided, comprising at least:

[0031] The first acquisition module is configured to acquire a first sensing signal corresponding to the touch operation in response to the detection of a touch operation;

[0032] The second acquisition module is configured to acquire correction parameters associated with the touch screen to which the first sensing signal belongs; wherein, the correction parameters are used to filter out noise introduced when the touch screen sensing signal is received, the correction parameters are obtained by performing noise reduction processing on the sample sensing signal, and the sample sensing signal is a signal obtained based on data associated with the touch screen to which the first sensing signal belongs;

[0033] The processing module is configured to correct the first sensing signal using the correction parameters to obtain the second sensing signal;

[0034] The determination module is configured to determine the touch position based on the second sensing signal.

[0035] In some embodiments, the touch processing device further includes: a third acquisition module configured to acquire a sample dataset; wherein, one sample data in the sample dataset corresponds to one sample sensing signal, and each sample sensing signal includes an electrical signal sensed by each touch sensing point of the touch sensing array in the touch screen; the processing module is further configured to: combine the sample sensing signals in the sample dataset to obtain a first sample matrix; wherein, the number of columns of the first sample matrix is ​​the same as the number of columns of the touch sensing array, and the number of rows of the first sample matrix is ​​equal to the product of the number of sample data and the number of rows of the touch sensing array; the determining module is further configured to: perform principal component analysis on the first sample matrix to determine the correction parameters.

[0036] In some embodiments, the determining module is further configured to: perform eigenvalue decomposition based on the first sample matrix to obtain multiple eigenvalues ​​and multiple eigenvectors; wherein, one eigenvalue corresponds to one eigenvector; determine a preset number of target eigenvalues ​​among the multiple eigenvalues, and a target eigenvector among the multiple eigenvectors corresponding to the target eigenvalues, as the correction parameters; wherein, the target eigenvalues ​​are greater than the eigenvalues ​​other than the target eigenvalues ​​among the multiple eigenvalues.

[0037] In some embodiments, the processing module is further configured to: decentralize the first sample matrix to obtain a decentralized second sample matrix; calculate the covariance matrix corresponding to the second sample matrix; and perform eigenvalue decomposition on the covariance matrix to obtain the plurality of eigenvalues ​​and the plurality of eigenvectors.

[0038] In some embodiments, the processing module is further configured to: calculate the average value of each column of data in the first sample matrix; and subtract the average value of the column corresponding to each data in the first sample matrix to obtain the second sample matrix.

[0039] In some embodiments, the correction parameters include a preset number of feature values ​​and feature vectors corresponding to the feature values, and the first sensing signal includes the electrical signal sensed by each touch sensing point of the touch sensing array in the touch screen; the processing module is specifically configured to multiply the first matrix formed by the signal sensed by each touch sensing point of the touch sensing array, the second matrix formed by the preset number of feature values, and the third matrix formed by the feature vector to obtain the second sensing signal.

[0040] In some embodiments, the processing module is further configured to: transpose the third matrix formed by the feature vectors to obtain a fourth matrix; multiply the first matrix by the fourth matrix, and multiply the multiplied value by the second matrix to obtain the second sensing signal.

[0041] According to a third aspect of the present disclosure, an electronic device is provided, comprising at least:

[0042] processor;

[0043] Memory used to store computer programs or instructions;

[0044] The processor executes the computer program or instructions to implement the steps of the method described in the first aspect.

[0045] According to a fourth aspect of the present disclosure, a non-transitory computer-readable storage medium is provided, the storage medium storing a computer program or instructions that, when executed by a processor, implement the steps of the method described in the first aspect.

[0046] The technical solutions provided by the embodiments of this disclosure may include the following beneficial effects:

[0047] This disclosure provides a touch processing method that pre-processes sample sensing signals to obtain correction parameters that filter out noise introduced when the touch screen sensing signals are used. Then, it obtains correction parameters of the touch screen to which the first sensing signal corresponding to the detected touch operation belongs, and uses these correction parameters to correct the first sensing signal to obtain a second sensing signal. The touch position is then determined based on the second sensing signal, thereby effectively reducing noise or redundant information in the first sensing signal, retaining the principal components in the first sensing signal, improving the quality of the second sensing signal, making the determined touch position more accurate, the corresponding touch operation smoother, and enhancing the user experience.

[0048] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0049] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0050] Figure 1 This is a flowchart illustrating a touch processing method according to an exemplary embodiment. Figure 1 .

[0051] Figure 2 This is a flowchart illustrating a touch processing method according to an exemplary embodiment. Figure 2 .

[0052] Figure 3 This is a schematic diagram illustrating the principle of a touch processing method according to an exemplary embodiment.

[0053] Figure 4 This is a schematic diagram illustrating a first sensing signal and a second sensing signal in a touch processing method according to an exemplary embodiment.

[0054] Figure 5 This is a schematic diagram of data of a first sensing signal and a second sensing signal in a touch processing method according to an exemplary embodiment.

[0055] Figure 6a This is a scatter plot of the first sensing signal and the second sensing signal in a touch processing method according to an exemplary embodiment.

[0056] Figure 6b This is a minimum value diagram corresponding to the first sensing signal and the second sensing signal in a touch processing method according to an exemplary embodiment.

[0057] Figure 6c This is a maximum value diagram of the first sensing signal and the second sensing signal in a touch processing method according to an exemplary embodiment.

[0058] Figure 7 This is a structural block diagram of a touch processing device according to an exemplary embodiment.

[0059] Figure 8 This is a schematic diagram of the structure of an electronic device according to an exemplary embodiment. Detailed Implementation

[0060] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0061] The technical solutions provided by the embodiments of this disclosure are described in detail below with reference to the accompanying drawings.

[0062] Figure 1 This is a flowchart illustrating a touch processing method according to an exemplary embodiment. Figure 1 ,like Figure 1 As shown, the touch processing method provided in this embodiment includes at least the following steps:

[0063] S110. In response to detecting a touch operation, acquire the first sensing signal corresponding to the touch operation;

[0064] S120. Obtain the correction parameters of the touch screen associated with the first sensing signal; wherein, the correction parameters are used to filter out the noise introduced when the touch screen sensing signal is received, and the correction parameters are obtained by denoising the sample sensing signal, and the sample sensing signal is a signal obtained based on the data associated with the touch screen associated with the first sensing signal.

[0065] S130. The first sensing signal is corrected using correction parameters to obtain the second sensing signal;

[0066] S140. Determine the touch position based on the second sensing signal.

[0067] In this embodiment of the disclosure, the touch processing method described above can be applied to any electronic device with a touch screen. Here, the electronic device may include: a terminal device, such as a mobile terminal or a fixed terminal. The mobile terminal may include: a mobile phone, tablet computer, laptop computer, wearable electronic device, etc.; the fixed terminal may include: a desktop computer, smart TV, in-vehicle equipment, etc., and this embodiment of the disclosure does not impose any limitations.

[0068] In step S110, when the electronic device detects a touch operation, it can obtain the first sensing signal corresponding to the touch operation through the underlying integrated circuit (IC) scanning.

[0069] Here, the first sensing signal may include the sensing signal of the touchable area of ​​the touchscreen affected by the touch operation. Obtaining the first sensing signal through IC scanning can be understood as follows: the touchscreen of an electronic device contains an array of multiple touch sensors, each capable of detecting signal changes in the touchable area caused by a touch operation. When a user brings their finger or other charged object close to the touchscreen, the electric field at the location of the touch sensor changes, causing a change in the electrical signal corresponding to the touchable area.

[0070] Understandably, since the first sensing signal is sensed by an array of multiple touch sensors, it can be presented in matrix form. For example, an electronic device can obtain a first sensing signal in 18x40 matrix form by scanning the touch screen with an IC.

[0071] It should be noted that the first sensing signal may include, but is not limited to, a sensing capacitance value or a sensing resistance value. When the touch screen of the electronic device is a capacitive touch screen, the corresponding first sensing signal can be a sensing capacitance value; when the touch screen of the electronic device is a resistive touch screen, the corresponding first sensing signal can be a sensing resistance value.

[0072] In steps S120-S140, the electronic device can first perform noise reduction processing on the sample sensing signal to obtain correction parameters that can filter out noise introduced when the touch screen sensing signal is used. Then, it can obtain correction parameters of the touch screen to which the first sensing signal corresponding to the detected touch operation belongs, and use the correction parameters to correct the first sensing signal to obtain the second sensing signal. Then, it can determine the touch position based on the second sensing signal to determine the location where the touch operation occurs, and perform subsequent processing and response.

[0073] Here, the correction parameters can be parameters that can filter out noise introduced when the touch screen sensing signal is received, that is, parameters that can retain the principal components in the first sensing signal; for example, the correction parameters can include eigenvalues ​​and eigenvectors. The principal components in the first sensing signal can refer to the parts of the first sensing signal that carry key information or main features.

[0074] Understandably, the correction parameter can be obtained by denoising the sample sensing signal, for example, by performing Principal Component Analysis (PCA) on the sample sensing signal. PCA can transform the sample sensing signal into new linearly uncorrelated variables (i.e., principal components) through linear transformation. Principal components can reflect key information in the sample sensing signal while reducing data complexity.

[0075] It should be noted that the sample sensing signal can be obtained through data associated with the touch screen to which the first sensing signal belongs, such as simulation data corresponding to the touch screen; or, the sample sensing signal can also be obtained by acting on a sample touch screen associated with the touch screen to which the first sensing signal belongs, and this disclosure does not impose any limitations.

[0076] Here, the sample sensing signal can be obtained by scanning the sample touch screen with the IC of the electronic device; for example, the sample sensing signal may include multiple sensing signals of the touchable area acted upon by the touch operation in any sample touch screen, or multiple sensing signals of the touchable area acted upon by the touch operation in a sample touch screen of the same model as the touch screen of the electronic device.

[0077] It is understood that the sample sensing signal may include, but is not limited to, a sensing capacitance value or a sensing resistance value. When the sample sensing signal is a sensing capacitance value, the first sensing signal may be a sensing capacitance value; when the sample sensing signal is a sensing resistance value, the first sensing signal may be a sensing resistance value.

[0078] It should be noted that the model of the touchscreen corresponding to the sample sensing signal and the first sensing signal can be the same; that is, the number of touch sensing points corresponding to the touch sensing array in each touchscreen is the same. For example, both the sample sensing signal and the first sensing signal can be 18x40 matrices. The correction parameters associated with the touchscreen to which the first sensing signal belongs can be obtained by acquiring the correction parameters obtained from principal component analysis of the sample sensing signal.

[0079] In some embodiments, the model of the touch screen corresponding to the sample sensing signal and the first sensing signal may be different. In this case, the correction parameters obtained by principal component analysis of the sample sensing signal can be obtained first, and then the correction parameters corresponding to the sample sensing signal can be converted according to the correlation between the touch screen corresponding to the sample sensing signal and the touch screen corresponding to the first sensing signal to obtain the correction parameters of the touch screen associated with the first sensing signal. This disclosure does not impose any limitations.

[0080] In other embodiments, if the model of the touch screen of the first sensing signal changes, it is necessary to collect a new sample sensing signal corresponding to a sample touch screen with the same model as the new touch screen to determine new correction parameters, so as to correct the first sensing signal to obtain a new second sensing signal.

[0081] In this embodiment of the disclosure, the correction of the first sensing signal using correction parameters to obtain the second sensing signal may include, but is not limited to: when the correction parameters include a preset number of feature values ​​and the feature vectors corresponding to the feature values, multiplying the first sensing signal by each feature value and the feature vector corresponding to the feature value, and summing the multiplied values ​​to obtain the second sensing signal; or, when the correction parameters include a feature matrix, multiplying the first sensing signal by the feature matrix to obtain the second sensing signal, wherein the feature matrix may be a matrix composed of corresponding feature vectors arranged in the order of a preset number of feature values.

[0082] Here, the second sensing signal can be the induced electrical signal after filtering out the noise introduced when the touch screen sensing signal is used, and retaining the main components of the first sensing signal; compared with the first sensing signal, the second sensing signal reduces the noise or redundant information in the first sensing signal.

[0083] It is understandable that electronic devices can determine the touch location, i.e., the location information where the touch operation occurs, through a second sensing signal, so as to control the processor to execute the corresponding touch operation command based on the touch location. The touch location can be determined by information such as touch coordinates obtained from the second sensing signal; the touch operation command can include, but is not limited to, various functional commands such as: opening a page, taking a screenshot, swiping to browse, zooming in and out of an image, selecting text, performing in-game operations, gesture recognition, multi-finger operation, or two-finger separation.

[0084] It should be noted that, in the embodiments of this disclosure, the touch IC of the electronic device can detect the changes in the induced electrical signal of the touchable area of ​​the touch screen in real time, determine the location of the corresponding touch operation, and convert the location information corresponding to the touch operation, such as touch coordinates, into a recognizable signal and output it to the processor of the electronic device to control the processor to execute the corresponding touch operation instruction.

[0085] In related technologies, the filtering algorithms currently used to address noise in touch signals are typically traditional fitting filtering algorithms. These algorithms can be used to smooth or remove noise from touch signals. The principle of fitting filtering algorithms is based on fitting local data of the signal to predict or estimate the overall characteristics of the signal. Various fitting filtering algorithms first fit the local data of the signal. This process usually involves selecting a mathematical model (such as a polynomial, exponential function, etc.) and then using least squares or other fitting methods to make the model match the local data. By replacing the original data points with the predicted values ​​of the fitted model, the impact of noise on the touch signal can be reduced, thereby achieving smoothing. Fitting filtering algorithms achieve smoothing and noise reduction of touch signals by fitting local data of the touch signal and using the fitted model for filtering.

[0086] However, choosing an appropriate fitting model and parameters has a significant impact on the performance and effectiveness of fitting filtering algorithms. For example, polynomial fitting in second- or third-order fitting filtering algorithms may be affected by outliers, leading to unstable fitting results. Furthermore, fitting filtering algorithms require the selection of an appropriate polynomial order; an excessively high order can lead to overfitting. Additionally, fitting filtering algorithms only consider fitting local data and may not be suitable for analyzing global data.

[0087] In contrast, the touch processing method provided in this disclosure can first perform noise reduction processing on the sample sensing signal to obtain correction parameters that can filter out noise introduced when the touch screen sensing signal is used. Then, it obtains the correction parameters of the touch screen to which the first sensing signal corresponding to the detected touch operation belongs, and uses the correction parameters to correct the first sensing signal to obtain a second sensing signal. The touch position is then determined based on the second sensing signal, thereby effectively reducing noise or redundant information in the first sensing signal, retaining the principal components in the first sensing signal, improving the quality of the second sensing signal, making the determined touch position more accurate, the corresponding touch operation smoother, and improving the user experience. At the same time, since the sample sensing signal is a signal obtained based on data associated with the touch screen to which the first sensing signal belongs, and the correction parameters are obtained by principal component analysis of the sample sensing signal, it can achieve dimensionality reduction of the first sensing signal while retaining the principal components in the first sensing signal, thereby simplifying the calculation process for the touch position and improving the processing speed for the touch position.

[0088] In some embodiments, the above-described touch processing method further includes:

[0089] Obtain a sample dataset; where each sample data in the sample dataset corresponds to a sample sensing signal, and each sample sensing signal includes the electrical signal sensed by each touch sensing point of the touch sensing array in the touch screen;

[0090] The sensor signals of each sample in the sample data set are combined to obtain the first sample matrix; wherein, the number of columns of the first sample matrix is ​​the same as the number of columns of the touch sensing array, and the number of rows of the first sample matrix is ​​equal to the product of the number of sample data and the number of rows of the touch sensing array;

[0091] Principal component analysis was performed on the first sample matrix to determine the correction parameters.

[0092] In this embodiment of the present disclosure, the electronic device can obtain a first sample matrix by combining the sample sensing signals of each sample in the sample dataset, and determine the correction parameters by performing principal component analysis on the first sample matrix, so as to subsequently correct the first sensing signal to obtain a second sensing signal.

[0093] It is understood that the embodiments of this disclosure can sample the electrical signals sensed by each touch sensing point of the touch sensing array in the touch screen to construct a sample dataset; wherein, a sample data in the sample dataset may include a sample sensing signal sensed by each touch sensing point of the touch sensing array in the touch screen.

[0094] It should be noted that the touch sensing array in the above-mentioned touch screen can be a capacitive sensor array or a resistive sensor array in the touch screen of any electronic device; or it can be a capacitive sensor array or a resistive sensor array in the sample touch screen of other electronic devices of the same model as the touch screen of the electronic device, etc., and the embodiments disclosed herein are not limited.

[0095] Here, the first sample matrix can be a matrix obtained by combining the sensing signals of each sample in the sample dataset.

[0096] It is understandable that the number of columns in the first sample matrix can be the same as the number of columns in the touch-sensing array of the touch screen, and the number of rows in the first sample matrix can be equal to the product of the number of sample data in the sample dataset and the number of rows in the touch-sensing array of the touch screen.

[0097] It should be noted that the number of rows and columns of the first sample matrix can be determined based on the number of rows and columns of the touch-sensing array of the touch screen in the actual application scenario, and this embodiment does not impose any limitations. For example, if the number of sample data in the sample dataset is 5, and the order of the touch-sensing array of the touch screen is 18x40, the number of rows of the touch-sensing array can be 18, and the number of columns of the touch-sensing array can be 40; then the number of rows of the first sample matrix obtained by combining the sample sensing signals can be 90, that is, 18x5=90, and the number of columns of the first sample matrix can be 40.

[0098] In one embodiment of this disclosure, determining the correction parameters by performing principal component analysis on the first sample matrix can be understood as follows: first, the first sample matrix is ​​preprocessed by standardization, such as by decentering; then, the covariance matrix corresponding to the preprocessed matrix is ​​calculated; then, the covariance matrix is ​​decomposed by eigenvalues ​​to obtain multiple eigenvalues ​​and multiple eigenvectors that correspond one-to-one; finally, the correction parameters that can characterize the principal components in the sample sensing signal are determined from the multiple eigenvalues ​​and multiple eigenvectors.

[0099] In this embodiment of the present disclosure, a first sample matrix can be obtained by combining the sensing signals of each sample in the sample dataset, and the correction parameters can be determined by performing principal component analysis on the first sample matrix. This prepares for the subsequent correction of the first sensing signal to obtain a second sensing signal, so as to better retain the principal components in the first sensing signal, effectively reduce the noise or redundant information in the first sensing signal, and further improve the quality of the second sensing signal.

[0100] In some embodiments, principal component analysis is performed on the first sample matrix to determine correction parameters, including:

[0101] Based on the first sample matrix, eigenvalue decomposition is performed to obtain multiple eigenvalues ​​and multiple eigenvectors; where each eigenvalue corresponds to one eigenvector.

[0102] A predetermined number of target feature values ​​from multiple feature values, and a target feature vector corresponding to the target feature value from multiple feature vectors, are determined as correction parameters; wherein, the target feature value is greater than the feature value other than the target feature value among the multiple feature values.

[0103] In this embodiment of the present disclosure, the electronic device can perform eigenvalue decomposition based on the first sample matrix to obtain multiple eigenvalues ​​and multiple eigenvectors that correspond one-to-one. A preset number of target eigenvalues ​​among the multiple eigenvalues ​​and the target eigenvectors corresponding to the target eigenvalues ​​among the multiple eigenvectors are determined as correction parameters so that the first sensing signal can be corrected in the subsequent process to obtain the second sensing signal.

[0104] Here, eigenvalue decomposition is a method for decomposing a matrix into eigenvectors and eigenvalues. Eigenvalue decomposition based on the first sample matrix can be understood as first calculating the covariance matrix corresponding to the centered first sample matrix, and then performing eigenvalue decomposition on this covariance matrix to obtain multiple eigenvalues ​​and multiple eigenvectors.

[0105] It should be explained that the eigenvector can be the direction of the principal component in the first sample matrix, that is, the direction in which the data in the first sample matrix changes the most; the eigenvalue can be used to reflect the degree of data change in the direction represented by the corresponding eigenvector. The larger the eigenvalue, the greater the change or variance of the data in the first sample matrix in that direction, that is, the more important that direction is.

[0106] It is understood that when performing principal component analysis on the first sample matrix, the embodiments of this disclosure can select a preset number of target eigenvalues ​​in descending order of eigenvalues, and determine the preset number of target eigenvalues ​​and the target eigenvectors corresponding to the target eigenvalues ​​among multiple eigenvectors as correction parameters. These correction parameters can characterize the principal components in the first sample matrix.

[0107] Here, the target feature value can be a predetermined number of feature values ​​selected sequentially from multiple feature values ​​in descending order; for example, the predetermined number can be 5, then the target feature value can be the first 5 feature values ​​from the multiple feature values ​​in descending order, so the target feature value can be greater than the feature values ​​other than the target feature value among the multiple feature values. The target feature vector can be the feature vector corresponding to the selected target feature value among the multiple feature vectors.

[0108] It should be noted that the number of target feature values ​​selected from multiple feature values, i.e., the preset number, can be set according to the actual application scenario, and this disclosed embodiment does not impose any restrictions.

[0109] In this embodiment of the present disclosure, eigenvalue decomposition can be performed based on the first sample matrix to obtain multiple eigenvalues ​​and multiple eigenvectors that correspond one-to-one. A preset number of target eigenvalues ​​among the multiple eigenvalues ​​and the target eigenvectors corresponding to the target eigenvalues ​​among the multiple eigenvectors are determined as correction parameters. This prepares for subsequent correction of the first sensing signal to obtain the second sensing signal, so as to better retain the principal components in the first sensing signal, effectively reduce noise or redundant information in the first sensing signal, and improve the quality of the second sensing signal.

[0110] In some embodiments, eigenvalue decomposition is performed based on the first sample matrix to obtain multiple eigenvalues ​​and multiple eigenvectors, including:

[0111] The first sample matrix is ​​decentered to obtain the second sample matrix.

[0112] Calculate the covariance matrix corresponding to the second sample matrix;

[0113] Eigenvalue decomposition of the covariance matrix yields multiple eigenvalues ​​and multiple eigenvectors.

[0114] In this embodiment of the disclosure, the electronic device can first decentralize the first sample matrix to obtain the decentralized second sample matrix, then calculate the covariance matrix corresponding to the second sample matrix, and then perform eigenvalue decomposition on the covariance matrix to obtain multiple corresponding eigenvalues ​​and multiple eigenvectors, so as to determine the correction parameters that can correct the first sensing signal to obtain the second sensing signal in the subsequent process.

[0115] Here, the second sample matrix can be obtained by decentering the first sample matrix, i.e., by zero-mean normalizing it. For example, the second sample matrix is ​​obtained by subtracting the average value of the column corresponding to each data point from the first sample matrix.

[0116] It is understandable that after obtaining the decentralized second sample matrix, the covariance matrix corresponding to the second sample matrix can be calculated, and the covariance matrix can be decomposed into eigenvalues ​​to obtain multiple eigenvalues ​​and multiple eigenvectors, with one eigenvalue corresponding to one eigenvector.

[0117] For example, suppose the first sample matrix is ​​X, and the second sample matrix after decentering is X'; then the calculation process of the covariance matrix corresponding to the second sample matrix can be expressed by formula (1):

[0118]

[0119] Where m can be the number of rows in the second sample matrix X', and the order of both the first and second sample matrices can be m x n; C can be the covariance matrix, and the order of the covariance matrix can be n x n.

[0120] The calculation process of eigenvalue decomposition of the covariance matrix C can be expressed by formula (2):

[0121] Cv k =λ k v k (2);

[0122] Where, λ k It can represent an eigenvalue, v k A feature vector can be represented by a feature value. In some embodiments, the feature vector v k It can be a k x n matrix with eigenvalues ​​λ. k It can be understood as a 1xk matrix.

[0123] In this embodiment, the first sample matrix can be first decentered to obtain a second sample matrix, which can eliminate the translation effect of the data in the first sample matrix and make the direction of the principal components in the first sample matrix clearer. Then, the covariance matrix corresponding to the second sample matrix is ​​calculated, and then the covariance matrix is ​​decomposed into eigenvalues ​​to obtain multiple eigenvalues ​​and multiple eigenvectors that correspond one-to-one. This can prepare for the subsequent determination of correction parameters, so that the correction parameters can be better used to correct the first sensing signal to obtain the second sensing signal, so as to better retain the principal components in the first sensing signal, effectively reduce noise or redundant information in the first sensing signal, and improve the quality of the second sensing signal.

[0124] In some embodiments, the first sample matrix is ​​decentralized to obtain a decentralized second sample matrix, including:

[0125] Calculate the average value of each column of data in the first sample matrix;

[0126] Subtract the average value of the column corresponding to each data point from the first sample matrix to obtain the second sample matrix.

[0127] In this embodiment of the disclosure, the electronic device can first calculate the average value of each column of data in the first sample matrix, and then subtract the average value of each column of data from each data in the first sample matrix to obtain the decentralized second sample matrix, so as to determine multiple eigenvalues ​​and eigenvectors in the subsequent process, and then determine the correction parameters that can be used to correct the first sensing signal to obtain the second sensing signal.

[0128] Here, the order of the second sample matrix is ​​the same as that of the first sample matrix; that is, the number of rows in the second sample matrix is ​​equal to the number of rows in the first sample matrix, and the number of columns in the second sample matrix is ​​equal to the number of columns in the first sample matrix.

[0129] It is understood that, by subtracting the average value of the column corresponding to each data in the first sample matrix from each data in the first sample matrix, the mean of each column in the first sample matrix can be zeroed out, thereby eliminating the translation effect of each data in the first sample matrix, making the direction of each principal component in the first sample matrix clearer, simplifying the subsequent calculation process of the covariance matrix, and improving the accuracy of principal component analysis of the first sample matrix.

[0130] For example, suppose the first sample matrix is ​​an m x n matrix, i.e. The calculation process for the average value of each column of data in the first sample matrix can be expressed by formula (3):

[0131]

[0132] in, It can represent the average value of each column of data in the first sample matrix X; m can be the number of rows in the first sample matrix X, and n can be the number of columns in the first sample matrix X.

[0133] The calculation process for obtaining the second sample matrix by subtracting the average value of the column corresponding to each data point from the data in the first sample matrix can be expressed by formula (4):

[0134]

[0135] in, The data x in the m-th row and n-th column of the first sample matrix X can be represented by x. mn Subtract the average value corresponding to the nth column The data after that.

[0136] In this embodiment, the average value of each column of data in the first sample matrix can be calculated first, and then the average value of each column of data in the first sample matrix can be subtracted from each data in the first sample matrix to obtain the decentralized second sample matrix. This can better eliminate the translation effect of the data in the first sample matrix, make the direction of the principal components in the first sample matrix clearer, and thus better prepare for the subsequent determination of multiple eigenvalues ​​and eigenvectors, as well as the determination of the correction parameters that can correct the first sensing signal to obtain the second sensing signal, so as to improve the quality of the second sensing signal.

[0137] In some embodiments, the correction parameters may include a preset number of feature values ​​and feature vectors corresponding to the feature values, and the first sensing signal may include the signal sensed by each touch sensing point of the touch sensing array in the touch screen; step S130 may include:

[0138] The second sensing signal is obtained by multiplying the first matrix formed by the signals sensed by each touch sensing point of the touch sensing array, the second matrix formed by a preset number of feature values, and the third matrix formed by feature vectors.

[0139] In this embodiment of the present disclosure, the electronic device can obtain a second sensing signal by multiplying a first matrix formed by the signals sensed by each touch sensing point of the touch sensing array, a second matrix formed by the preset number of feature values, and a third matrix formed by the feature vectors corresponding to the feature values ​​obtained by principal component analysis of the sample sensing signals. This allows the touch position to be determined based on the second sensing signal, thereby controlling the processor to execute the corresponding touch operation instructions.

[0140] Here, the first sensing signal may include the signal sensed by each touch sensing point of the touch sensing array in the touch screen of the electronic device. Therefore, the first sensing signal can be presented in the form of a matrix, namely the first matrix, which can be multiplied by the second matrix formed by each feature value and the third matrix formed by each feature vector to obtain the second sensing signal.

[0141] It is understandable that the preset number of eigenvalues ​​and their corresponding eigenvectors can be determined by performing principal component analysis on the sample sensing signal. Since the sample sensing signal is the same as or related to the touchscreen model corresponding to the first sensing signal, the first sensing signal can be corrected using the correction parameters obtained from the principal component analysis of the sample sensing signal, namely the preset number of eigenvalues ​​and their corresponding eigenvectors, to obtain the second sensing signal after filtering out the noise introduced when the touchscreen sensing signal is processed.

[0142] In this embodiment, a second sensing signal can be obtained by multiplying the first matrix formed by the signals sensed by each touch sensing point of the touch sensing array, the second matrix formed by the preset number of feature values, and the third matrix formed by the feature vectors corresponding to the feature values ​​obtained by principal component analysis of the sample sensing signal. This multiplication prepares the processor to determine the touch position based on the second sensing signal, so that the processor can execute the corresponding touch operation instruction according to the touch position. This also preserves the principal components in the first sensing signal, effectively reduces noise or redundant information in the first sensing signal, improves the quality of the second sensing signal, makes the subsequently determined touch position more accurate, the corresponding touch operation smoother, and improves the user experience.

[0143] In some embodiments, the second sensing signal is obtained by multiplying the first matrix formed by the signals sensed by each touch sensing point of the touch sensing array, the second matrix formed by a preset number of feature values, and the third matrix formed by feature vectors, including:

[0144] Transpose the third matrix formed by the eigenvectors to obtain the fourth matrix; the fourth matrix has the same number of rows as the first matrix.

[0145] The second induced signal is obtained by multiplying the first matrix by the fourth matrix and then multiplying the result by the second matrix.

[0146] In this embodiment of the present disclosure, the electronic device can first transpose the third matrix formed by the feature vectors to obtain the fourth matrix, then multiply the first matrix formed by the signals sensed by each touch sensing point of the touch sensing array with the fourth matrix, and multiply the multiplied value with the second matrix formed by each feature value to obtain the second sensing signal.

[0147] Here, the fourth matrix can be the matrix obtained by transposing the third matrix formed by the eigenvectors; transposing the third matrix is ​​to make the number of rows of the fourth matrix the same as the number of columns of the first matrix, so that the first matrix and the fourth matrix can be multiplied.

[0148] For example, the first matrix corresponding to the first sensing signal is an 18x40 matrix, and the preset number of eigenvalues ​​is 2. Then, the second matrix formed by each eigenvalue is a 2x1 matrix, and the third matrix formed by the eigenvectors corresponding to each eigenvalue is a 2x40 matrix. At this time, the fourth matrix obtained after transposing the third matrix can be a 40x2 matrix. The second sensing signal obtained after multiplying the first matrix and the fourth matrix and then multiplying it with the second matrix can be an 18x1 matrix.

[0149] In this embodiment, a fourth matrix can be obtained by transposing the third matrix formed by the feature vectors. Then, the first matrix formed by the signals sensed by each touch sensing point of the touch sensing array is multiplied by the fourth matrix, and the multiplied value is multiplied by the second matrix formed by each feature value to obtain the second sensing signal. This can better prepare for determining the touch position based on the second sensing signal, better retain the principal components in the first sensing signal, effectively reduce noise or redundant information in the first sensing signal, further improve the quality of the second sensing signal, make the subsequently determined touch position more accurate, the corresponding touch operation smoother, and improve the user experience.

[0150] In some embodiments, step S130 may further include: multiplying the first sensing signal by each feature value and the feature vector corresponding to the feature value, and summing the multiplied values ​​to obtain the second sensing signal.

[0151] For example, assuming the first sensing signal is Y, the calculation process for the second sensing signal Y' can be expressed by formula (5):

[0152] Y'=Y*λ1*v1+Y*λ2*v2+…+Y*λ k *v k (5).

[0153] Wherein, λ1, λ2, ..., λ k It can represent a preset number of k feature values, v1, v2, ..., v k It can represent the eigenvectors corresponding to k eigenvalues.

[0154] In this embodiment, the second sensing signal can be obtained by multiplying the first sensing signal with each feature value and the feature vector corresponding to the feature value, and then summing the multiplied values. This prepares the processor for determining the touch position based on the second sensing signal, so that the processor can be controlled to execute the corresponding touch operation instruction according to the touch position. This retains the main components in the first sensing signal, effectively reduces noise or redundant information in the first sensing signal, improves the quality of the second sensing signal, makes the subsequently determined touch position more accurate, the corresponding touch operation smoother, and improves the user experience.

[0155] Figure 2 This is a flowchart illustrating a touch processing method according to an exemplary embodiment. Figure 2 .like Figure 2 As shown, the touch processing method provided in this disclosure is merely an example and not a limitation, and is intended to help those skilled in the art better understand the technical solutions of this disclosure. See also Figure 1 and Figure 2 The touch processing method provided in this disclosure can be applied to electronic devices, and may specifically include the following steps:

[0156] S201. Obtain the sample dataset.

[0157] Here, one sample data in the sample dataset corresponds to one sample sensing signal, and each sample sensing signal includes the signal sensed by each touch sensing point in the touch sensing array of the touch screen.

[0158] S202. Combine the inductive signals of each sample in the sample dataset to obtain the first sample matrix.

[0159] The first sample matrix has the same number of columns as the touch sensing array, and the number of rows in the first sample matrix is ​​equal to the product of the number of sample data and the number of rows in the touch sensing array.

[0160] S203. Calculate the average value of each column of data in the first sample matrix.

[0161] S204. Subtract the average value of the column corresponding to each data point from each data point in the first sample matrix to obtain the second sample matrix.

[0162] Here, the order of the second sample matrix is ​​the same as that of the first sample matrix; that is, the number of rows in the second sample matrix is ​​equal to the number of rows in the first sample matrix, and the number of columns in the second sample matrix is ​​equal to the number of columns in the first sample matrix.

[0163] S205. Calculate the covariance matrix corresponding to the second sample matrix.

[0164] S206. Perform eigenvalue decomposition on the covariance matrix to obtain multiple eigenvalues ​​and multiple eigenvectors.

[0165] Here, one eigenvalue corresponds to one eigenvector.

[0166] S207. A preset number of target feature values ​​from multiple feature values, and the target feature vectors corresponding to the target feature values ​​from multiple feature vectors, are determined as correction parameters.

[0167] Among them, the target feature value is greater than the feature value other than the target feature value among multiple feature values.

[0168] In one embodiment of this disclosure, such as Figure 3 As shown, L1 identifies the training process of determining the correction parameters in steps S201-S207, and L2 identifies the application process of using the correction parameters to correct the first sensing signal to obtain the second sensing signal. The electronic device can process the first sample matrix X, which combines the sample sensing signals, through steps S201-S207 to obtain correction parameters T containing a preset number of feature values ​​and the corresponding feature vectors. After obtaining the first sensing signal Y corresponding to the touch operation and the correction parameters T, the electronic device can first transpose the third matrix formed by the feature vectors corresponding to each feature value to obtain a fourth matrix, and then multiply the first matrix corresponding to the first sensing signal Y by the fourth matrix and the second matrix formed by the preset number of feature values ​​to obtain the second sensing signal Y'; alternatively, it can multiply the first sensing signal by each feature value and the corresponding feature vector, and sum the multiplied values ​​to obtain the second sensing signal; then, the touch position is determined based on the second sensing signal to control the processor to execute the corresponding touch operation instruction.

[0169] For example, such as Figure 4 As shown, L3 represents the first induction signal before correction, and L4 represents the second induction signal after correction using the correction parameters. Figure 5As shown, L5 identifies the data of the first sensing signal before correction, and L6 identifies the data of the second sensing signal after correction using the correction parameters; through Figure 4 and Figure 5 The data shown indicates that the signal quality of the second sensing signal is significantly improved, effectively reducing noise or redundant information in the first sensing signal.

[0170] like Figure 6a As shown, L7 represents the standard deviation between the first sensing signals before correction, and L8 represents the standard deviation between the second sensing signals after correction using the correction parameters; from Figure 6a As can be seen, the maximum standard deviation among the first sensing signals before correction was 52, and the minimum was 31; while the maximum standard deviation among the second sensing signals after correction using the correction parameters was 9, and the minimum was 4. Figure 6b and Figure 6c As shown, L9 identifies the first sensing signals before correction, and L10 identifies the second sensing signals after correction using the correction parameters; from Figure 6b and Figure 6c It is evident that the noise of each of the first sensing signals before correction is high, while the noise of each of the second sensing signals after correction using the correction parameters is low. In other words, the second sensing signal obtained by correcting the first sensing signal using the correction parameters can effectively reduce the noise or redundant information in the first sensing signal while retaining the principal components in the first sensing signal, thereby improving the quality of the second sensing signal.

[0171] In related technologies, during the touch screen process, the touch signals detected by electronic devices may be affected by noise from the environment, power supply, display or other interference sources, resulting in poor touch accuracy and easily causing problems such as insensitive touch gestures, game touch interruptions, screen skipping ghost points, accidental touches or touch abnormalities, which affect the user experience.

[0172] In response, the touch processing method provided in this disclosure can obtain correction parameters that can filter out noise introduced when the touch screen sensing signal is used by performing principal component analysis on the sample sensing signal. Then, it obtains the correction parameters of the touch screen to which the first sensing signal corresponding to the detected touch operation belongs, and uses the correction parameters to correct the first sensing signal to obtain the second sensing signal. The touch position is then determined based on the second sensing signal, thereby effectively reducing noise or redundant information in the first sensing signal, retaining the principal components in the first sensing signal, improving the quality of the second sensing signal, making the determined touch position more accurate, the corresponding touch operation smoother, and improving the user experience. At the same time, since the sample sensing signal is a signal obtained based on data associated with the touch screen to which the first sensing signal belongs, and the correction parameters are obtained by performing principal component analysis on the sample sensing signal, it can achieve dimensionality reduction of the first sensing signal while retaining the principal components in the first sensing signal, thereby simplifying the calculation process for the touch position and improving the processing speed for the touch position.

[0173] Figure 7 This is a structural block diagram illustrating a touch processing device according to an exemplary embodiment. See also... Figure 7 The touch processing device 700 provided in this embodiment is applied to an electronic device and may include: a first acquisition module 710, a second acquisition module 720, a processing module 730 and a determination module 740.

[0174] The first acquisition module 710 is configured to acquire a first sensing signal corresponding to the touch operation in response to the detection of a touch operation;

[0175] The second acquisition module 720 is configured to acquire correction parameters of the touch screen associated with the first sensing signal; wherein, the correction parameters are used to filter out noise introduced when the touch screen sensing signal is applied, the correction parameters are obtained by denoising the sample sensing signal, and the sample sensing signal is a signal obtained based on data associated with the touch screen associated with the first sensing signal.

[0176] The processing module 730 is configured to correct the first sensing signal using correction parameters to obtain the second sensing signal;

[0177] The determination module 740 is configured to determine the touch position based on the second sensing signal.

[0178] The touch processing apparatus provided in this embodiment can pre-process the sample sensing signal to obtain correction parameters that can filter out noise introduced when the touch screen sensing signal is used. Then, it obtains the correction parameters of the touch screen to which the first sensing signal corresponding to the detected touch operation belongs, and uses the correction parameters to correct the first sensing signal to obtain a second sensing signal. The touch position is then determined based on the second sensing signal, thereby effectively reducing noise or redundant information in the first sensing signal, retaining the principal components in the first sensing signal, improving the quality of the second sensing signal, making the determined touch position more accurate, the corresponding touch operation smoother, and improving the user experience. At the same time, since the sample sensing signal is a signal obtained based on data associated with the touch screen to which the first sensing signal belongs, and the correction parameters are obtained by principal component analysis of the sample sensing signal, it can achieve dimensionality reduction of the first sensing signal while retaining the principal components in the first sensing signal, thereby simplifying the calculation process for the touch position and improving the processing speed for the touch position.

[0179] for Figure 7 In one possible implementation of the technical solution shown, the touch processing device 700 further includes: a third acquisition module configured to acquire a sample dataset; wherein, one sample data in the sample dataset corresponds to one sample sensing signal, and each sample sensing signal includes an electrical signal sensed by each touch sensing point of the touch sensing array in the touch screen; the processing module 730 is further configured to: combine the sample sensing signals in the sample dataset to obtain a first sample matrix; wherein, the number of columns of the first sample matrix is ​​the same as the number of columns of the touch sensing array, and the number of rows of the first sample matrix is ​​equal to the product of the number of sample data and the number of rows of the touch sensing array; the determination module 740 is further configured to: perform principal component analysis on the first sample matrix to determine correction parameters.

[0180] for Figure 7 In one possible implementation of the technical solution shown, the determining module 740 is further configured to: perform eigenvalue decomposition based on the first sample matrix to obtain multiple eigenvalues ​​and multiple eigenvectors; wherein, one eigenvalue corresponds to one eigenvector; determine a preset number of target eigenvalues ​​among the multiple eigenvalues, and the target eigenvectors among the multiple eigenvectors that correspond to the target eigenvalues, as correction parameters; wherein, the target eigenvalues ​​are greater than the eigenvalues ​​other than the target eigenvalues ​​among the multiple eigenvalues.

[0181] for Figure 7 In one possible implementation of the technical solution shown, the processing module 730 is further configured to: decentralize the first sample matrix to obtain a decentralized second sample matrix; calculate the covariance matrix corresponding to the second sample matrix; and perform eigenvalue decomposition on the covariance matrix to obtain multiple eigenvalues ​​and multiple eigenvectors.

[0182] for Figure 7 In one possible implementation of the technical solution shown, the processing module 730 is further configured to: calculate the average value of each column of data in the first sample matrix; and subtract the average value of the column corresponding to each data from each data in the first sample matrix to obtain the second sample matrix.

[0183] for Figure 7 In one possible implementation of the technical solution shown, the correction parameters include a preset number of feature values ​​and the feature vectors corresponding to the feature values. The first sensing signal includes the signal sensed by each touch sensing point of the touch sensing array in the touch screen. The processing module 730 is specifically configured to multiply the first matrix formed by the signal sensed by each touch sensing point of the touch sensing array, the second matrix formed by the preset number of feature values, and the third matrix formed by the feature vectors to obtain the second sensing signal.

[0184] for Figure 7 In one possible implementation of the technical solution shown, the processing module 730 is further configured to: transpose the third matrix formed by the feature vectors to obtain a fourth matrix; wherein the number of rows in the fourth matrix is ​​the same as the number of columns in the first matrix; multiply the first matrix and the fourth matrix, and multiply the result of the multiplication with the second matrix to obtain a second sensing signal.

[0185] It should be noted that the touch processing device provided in this embodiment corresponds to the touch processing method mentioned above. Related details can be found in the description of the touch processing method above, and will not be repeated here.

[0186] Figure 8 This is a schematic diagram illustrating the structure of an electronic device according to an exemplary embodiment. For example, the electronic device 800 may be a mobile phone, computer, digital broadcasting terminal, messaging device, game console, tablet device, medical device, fitness equipment, personal digital assistant, etc.

[0187] Reference Figure 8 The electronic device 800 may include one or more of the following components: processing component 802, memory 804, power supply component 806, multimedia component 808, audio component 810, input / output (I / O) interface 812, sensor component 814, and communication component 816.

[0188] Processing component 802 typically controls the overall operation of electronic device 800, such as operations associated with at least one of display, telephone call, data communication, camera operation, and recording operation. Processing component 802 may include one or more processors 820 to execute instructions to perform all or part of the steps of the methods described above. Furthermore, processing component 802 may include one or more modules to facilitate interaction between processing component 802 and other components. For example, processing component 802 may include a multimedia module to facilitate interaction between multimedia component 808 and processing component 802.

[0189] Memory 804 is configured to store various types of data to support the operation of electronic device 800. Examples of such data include at least one of the following: instructions for any application or method operating on electronic device 800, contact data, phonebook data, messages, pictures, and videos. Memory 804 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0190] Power supply component 806 provides power to various components of electronic device 800. Power supply component 806 may include at least one of the following: a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to electronic device 800.

[0191] Multimedia component 808 includes a screen that provides an output interface between electronic device 800 and user. In some embodiments, the screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a Touch Panel, the screen may be implemented as a touchscreen to receive input signals from the user. The Touch Panel includes one or more touch sensors to sense touches, swipes, and gestures on the Touch Panel. The touch sensors may sense not only the boundaries of touch or swipe actions but also the duration and pressure associated with the touch or swipe operation. In some embodiments, multimedia component 808 includes a front-facing camera and / or a rear-facing camera. When electronic device 800 is in an operating mode, such as a shooting mode or video mode, the front-facing camera and / or rear-facing camera may receive external multimedia data. Each front-facing camera and rear-facing camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

[0192] Audio component 810 is configured to output and / or input audio signals. For example, audio component 810 includes a microphone (MIC) configured to receive external audio signals when electronic device 800 is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in memory 804 or transmitted via communication component 816. In some embodiments, audio component 810 also includes a speaker for outputting audio signals.

[0193] I / O interface 812 provides an interface between processing component 802 and peripheral interface modules, such as keyboards, click wheels, and buttons. These buttons may include, but are not limited to, home buttons, volume buttons, power buttons, and lock buttons.

[0194] Sensor assembly 814 includes one or more sensors for providing state assessments of various aspects of electronic device 800. For example, sensor assembly 814 may detect the on / off state of electronic device 800, the relative positioning of components such as the display and keypad of electronic device 800, changes in position of electronic device 800 or one of its components, the presence or absence of user contact with electronic device 800, orientation or acceleration / deceleration of electronic device 800, and temperature changes of electronic device 800. Sensor assembly 814 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 814 may also include an optical sensor, such as a complementary metal-oxide-semiconductor (CMOS) or charge-coupled device (CCD) image sensor, for use in imaging applications. In some embodiments, sensor assembly 814 may also include, but is not limited to, at least one of the following: an accelerometer, a gyroscope, a magnetometer, a pressure sensor, and a temperature sensor.

[0195] Communication component 816 is configured to facilitate wired or wireless communication between electronic device 800 and other devices. Electronic device 800 can access wireless networks based on communication standards, such as Wi-Fi, 4G, 5G, or combinations thereof. In one exemplary embodiment, communication component 816 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 816 also includes a Near Field Communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID), Infrared Data Association (IrDA), Ultra Wide Band (UWB), Bluetooth (BT), and other technologies.

[0196] In an exemplary embodiment, the electronic device 800 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components.

[0197] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory including executable instructions or a computer program that can be executed by a processor of an electronic device to perform a touch processing method. For example, the non-transitory computer-readable storage medium may be a ROM, random access memory (RAM), a compact disc read-only memory (CD-ROM), magnetic tape, floppy disk, and optical data storage device, etc.

[0198] A non-transitory computer-readable storage medium, when the instructions in the storage medium are executed by the processor of an electronic device, enables the electronic device to perform any of the touch processing methods described in the above embodiments. For example, the touch processing method includes: in response to detecting a touch operation, acquiring a first sensing signal corresponding to the touch operation; acquiring correction parameters associated with the touch screen to which the first sensing signal belongs; wherein the correction parameters are used to filter out noise introduced when the touch screen sensing signal is received, the correction parameters are obtained by performing noise reduction processing on a sample sensing signal, and the sample sensing signal is a signal obtained based on data associated with the touch screen to which the first sensing signal belongs; correcting the first sensing signal using the correction parameters to obtain a second sensing signal; and determining the touch position based on the second sensing signal.

[0199] This disclosure provides a computer program product comprising a computer program or executable instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer program or executable instructions from the computer-readable storage medium and executes the computer program or executable instructions, causing the computer device to perform any of the touch processing methods described above.

[0200] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the claims.

[0201] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A touch processing method, characterized in that, include: In response to detecting a touch operation, a first sensing signal corresponding to the touch operation is acquired; Obtain correction parameters associated with the touchscreen to which the first sensing signal belongs; wherein, the correction parameters are used to filter out noise introduced when the touchscreen sensing signal is received, the correction parameters are obtained by denoising the sample sensing signal, and the sample sensing signal is a signal obtained based on data associated with the touchscreen to which the first sensing signal belongs; The first sensing signal is corrected using the correction parameters to obtain the second sensing signal; The touch position is determined based on the second sensing signal.

2. The method according to claim 1, characterized in that, The method further includes: Obtain a sample dataset; wherein, one sample data in the sample dataset corresponds to one sample sensing signal, and each sample sensing signal includes the signal sensed by each touch sensing point of the touch sensing array in the touch screen; The sensor signals of each sample in the sample data set are combined to obtain a first sample matrix; wherein the number of columns of the first sample matrix is ​​the same as the number of columns of the touch sensing array, and the number of rows of the first sample matrix is ​​equal to the product of the number of sample data and the number of rows of the touch sensing array; Principal component analysis is performed on the first sample matrix to determine the correction parameters.

3. The method according to claim 2, characterized in that, The step of performing principal component analysis on the first sample matrix to determine the correction parameters includes: Based on the first sample matrix, eigenvalue decomposition is performed to obtain multiple eigenvalues ​​and multiple eigenvectors; wherein, one eigenvalue corresponds to one eigenvector. A predetermined number of target feature values ​​from the plurality of feature values, and a target feature vector from the plurality of feature vectors corresponding to the target feature values, are determined as the correction parameters; wherein, the target feature value is greater than the feature values ​​other than the target feature value among the plurality of feature values.

4. The method according to claim 3, characterized in that, The step of performing eigenvalue decomposition based on the first sample matrix to obtain multiple eigenvalues ​​and multiple eigenvectors includes: The first sample matrix is ​​decentralized to obtain the second sample matrix after decentralization; Calculate the covariance matrix corresponding to the second sample matrix; The covariance matrix is ​​decomposed into eigenvalues ​​to obtain the plurality of eigenvalues ​​and the plurality of eigenvectors.

5. The method according to claim 4, characterized in that, The process of decentralizing the first sample matrix to obtain the decentralized second sample matrix includes: Calculate the average value of each column of data in the first sample matrix; The second sample matrix is ​​obtained by subtracting the average value of the column corresponding to each data point from each data point in the first sample matrix.

6. The method according to any one of claims 1 to 5, characterized in that, The correction parameters include a preset number of feature values ​​and feature vectors corresponding to the feature values, and the first sensing signal includes the signal sensed by each touch sensing point of the touch sensing array in the touch screen; The step of correcting the first sensing signal using the correction parameters to obtain the second sensing signal includes: The second sensing signal is obtained by multiplying the first matrix formed by the signals sensed by each touch sensing point of the touch sensing array, the second matrix formed by the preset number of feature values, and the third matrix formed by the feature vectors.

7. The method according to claim 6, characterized in that, The second sensing signal is obtained by multiplying the first matrix formed by the signals sensed by each touch sensing point of the touch sensing array, the second matrix formed by the preset number of feature values, and the third matrix formed by the feature vectors, including: The third matrix formed by the feature vectors is transposed to obtain a fourth matrix; wherein the fourth matrix has the same number of rows as the first matrix. The first matrix is ​​multiplied by the fourth matrix, and the result of the multiplication is multiplied by the second matrix to obtain the second sensing signal.

8. A touch processing device, characterized in that, include: The first acquisition module is configured to acquire a first sensing signal corresponding to the touch operation in response to the detection of a touch operation; The second acquisition module is configured to acquire correction parameters associated with the touch screen to which the first sensing signal belongs; wherein, the correction parameters are used to filter out noise introduced when the touch screen sensing signal is received, the correction parameters are obtained by performing noise reduction processing on the sample sensing signal, and the sample sensing signal is a signal obtained based on data associated with the touch screen to which the first sensing signal belongs; The processing module is configured to correct the first sensing signal using the correction parameters to obtain the second sensing signal; The determination module is configured to determine the touch position based on the second sensing signal.

9. An electronic device, characterized in that, include: processor; Memory used to store computer programs or instructions; The processor executes the computer program or instructions to implement the steps of the method according to any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium storing a computer program or instructions, characterized in that, When the computer program or instructions in the storage medium are executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

Images