WO2020087335A1 - 降噪方法、触控显示装置和计算机可读存储介质 - Google Patents

降噪方法、触控显示装置和计算机可读存储介质 Download PDF

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WO2020087335A1
WO2020087335A1 PCT/CN2018/112952 CN2018112952W WO2020087335A1 WO 2020087335 A1 WO2020087335 A1 WO 2020087335A1 CN 2018112952 W CN2018112952 W CN 2018112952W WO 2020087335 A1 WO2020087335 A1 WO 2020087335A1
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Prior art keywords
touch detection
noise
data
noise data
node
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PCT/CN2018/112952
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English (en)
French (fr)
Inventor
陈曦
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深圳市汇顶科技股份有限公司
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Priority to EP18917574.8A priority Critical patent/EP3667477A4/en
Priority to CN201880002036.XA priority patent/CN111527472B/zh
Priority to PCT/CN2018/112952 priority patent/WO2020087335A1/zh
Priority to US16/683,087 priority patent/US10963099B2/en
Publication of WO2020087335A1 publication Critical patent/WO2020087335A1/zh

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
    • G06F3/03—Arrangements for converting the position or the displacement of a member into a coded form
    • G06F3/041—Digitisers, e.g. for touch screens or touch pads, characterised by the transducing means
    • G06F3/0416—Control or interface arrangements specially adapted for digitisers
    • G06F3/0418—Control or interface arrangements specially adapted for digitisers for error correction or compensation, e.g. based on parallax, calibration or alignment
    • G06F3/04182—Filtering of noise external to the device and not generated by digitiser components
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
    • G06F3/03—Arrangements for converting the position or the displacement of a member into a coded form
    • G06F3/041—Digitisers, e.g. for touch screens or touch pads, characterised by the transducing means
    • G06F3/0416—Control or interface arrangements specially adapted for digitisers
    • G06F3/0418—Control or interface arrangements specially adapted for digitisers for error correction or compensation, e.g. based on parallax, calibration or alignment
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
    • G06F3/03—Arrangements for converting the position or the displacement of a member into a coded form
    • G06F3/041—Digitisers, e.g. for touch screens or touch pads, characterised by the transducing means
    • G06F3/044—Digitisers, e.g. for touch screens or touch pads, characterised by the transducing means by capacitive means
    • G06F3/0446—Digitisers, e.g. for touch screens or touch pads, characterised by the transducing means by capacitive means using a grid-like structure of electrodes in at least two directions, e.g. using row and column electrodes

Definitions

  • the present application relates to the field of touch display technology, and in particular, to a noise reduction method, a touch display device, and a computer-readable storage medium.
  • the existing technology mainly avoids the noise brought by the LCD through synchronous sampling, but the synchronous processing will bring the following side effects: (1) The real-time requirements of the hardware are very high , You need to increase the cost of hardware. When the synchronization process is not completed, the touch screen will also be subject to strong interference and cannot be used. (2) Because of the need for synchronization signals, the touch screen must use a fixed frequency for data collection, and the sampling frequency cannot be switched. This will greatly reduce the resistance of the touch screen to other external disturbances. (3) In the existing environment, most of the LCD noise interference is DC components. The existing technology is to remove the DC noise on the screen to achieve the purpose of noise reduction. During interference, noise reduction cannot be performed.
  • the purpose of some embodiments of the present application is to provide a noise reduction method for a touch display device and a computer-readable storage medium, so that the hardware cost is reduced without being affected by the limitation of a fixed sampling frequency, and the noise interference of non-DC components Filter out.
  • An embodiment of the present application provides a noise reduction method, which is applied to a touch display device.
  • the touch display device is provided with a plurality of touch detection nodes, including: acquiring noise data of each touch detection node; acquiring characteristic values according to the noise data; The noise data and eigenvalues of the target node are fitted to obtain a fitting function that takes the eigenvalue as the independent variable and the noise data of the target node as the dependent variable; the target node is the touch detection node to be reduced; the eigenvalue is substituted
  • the fitting function obtains the fitting data corresponding to the characteristic values; the difference between the fitting data and the noise data of the target node is used as the data after noise reduction.
  • An embodiment of the present application also provides a touch display device, which is provided with a plurality of touch detection nodes, including: a noise data acquisition module for acquiring noise data of each touch detection node; a feature value acquisition module for In order to obtain characteristic values based on noise data; the fitting processing module is used to perform fitting processing on the noise data of the target node and the characteristic values, to obtain a simulation with the characteristic value as the independent variable and the noise data of the target node as the dependent variable The composite function; the target node is the touch detection node to be noise-reduced; the fitting data acquisition module is used to substitute the feature value into the fitting function to obtain fitting data corresponding to the feature value; the noise reduction module is used to apply the The difference between the combined data and the noise data of the target node is used as the data after noise reduction.
  • An embodiment of the present application further provides a touch display device including: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor. The instructions are executed by the at least one processor to enable the at least one processor to perform the noise reduction method described above.
  • An embodiment of the present application further provides a computer-readable storage medium that stores a computer program, and the computer program is executed by a processor to implement the foregoing noise reduction method.
  • the embodiment of the present application obtains the fitting function according to the characteristic value of the noise data of the touch detection node and the target node to be noise-reduced, and substitutes the characteristic value into the fitting function to obtain the corresponding
  • the difference between the fitting data and the noise data of the target node is used as the data after noise reduction.
  • the noise data brought by the DC component and the non-DC component of the display screen can be directly filtered, thereby improving the signal-to-noise ratio and reducing the hardware cost required for synchronization processing, without being limited by the fixed sampling frequency influences.
  • the data after noise reduction will provide a strong guarantee for accurate and stable touch detection results in subsequent processing.
  • obtaining noise data of each touch detection node includes: sampling each touch detection node to obtain sample data of each touch detection node; calculating the reference data of each touch detection node and the reference value of each touch detection node respectively Difference; use the difference of each touch detection node as the noise data of each touch detection node. Since the touch detection device is in a state of no touch and no interference, it will generate a static background detection data, and the static background detection data is expressed by a reference value. Therefore, taking the difference between the sampled data of each touch detection node and the reference reference value of each touch detection node as the noise data of each touch detection node is beneficial to obtain accurate noise data, thereby improving the accuracy of noise reduction.
  • the noise data before obtaining the feature value according to the noise data, it further includes: if there are touch detection nodes that satisfy the preset condition, then excluding the difference of the touch detection nodes that satisfy the preset condition in the noise data of each touch detection node; where The preset condition is that the difference between the touch detection nodes is greater than the preset threshold. If the difference between the touch detection nodes is greater than the preset threshold, a finger touches the touch detection node, and the data sampled from the touch detection node is removed to eliminate the effect of finger touch during noise reduction, which is beneficial to the accuracy of the fitting process To further ensure the accuracy of noise reduction.
  • the noise data obtained from multiple samplings of each selected touch detection node are separately counted, specifically: according to the noise data obtained from multiple samplings of each selected touch detection node, each selected touch detection is separately counted
  • the maximum value, minimum value or average value of the noise data of the node according to the statistical result, obtaining the characteristic value, specifically: using the maximum value, minimum value or average value as the characteristic value.
  • the noise data obtained by multiple sampling of each selected touch detection node is counted separately: specifically: according to the noise data obtained by multiple sampling of each selected touch detection node, each selected touch detection is counted separately
  • the mode interval of the noise data of the node obtaining the characteristic value according to the statistical result, specifically: obtaining the characteristic value according to the statistical mode interval.
  • the target node is specifically: a touch detection node to be noise-reduced with the same vertical axis coordinate and different horizontal axis coordinates, and the fitting function is specifically the fitting function on the horizontal axis; obtaining the characteristic value according to the noise data includes: extracting the vertical axis Noise data of touch detection nodes with the same axis coordinates and different horizontal axis coordinates; wherein the vertical coordinate of the extracted touch detection node is different from the vertical axis of the target node; the extracted noise data of the touch detection node is used as a feature value.
  • the target node is specifically: a touch detection node to be noise-reduced with the same horizontal axis coordinate and different vertical axis coordinate, and the fitting function is specifically the fitting function on the vertical axis; obtaining the characteristic value according to the noise data includes: Extracting noise data of touch detection nodes with the same horizontal axis coordinate and different vertical axis coordinates; wherein, the extracted horizontal axis coordinate of the touch detection node is different from the horizontal axis coordinate of the target node; the noise data of the touch detection node to be extracted As the characteristic value.
  • Another way of performing fitting processing in the unit of the vertical axis is provided, so that the embodiments of the present application can be flexibly implemented.
  • FIG. 1 is a flowchart of a noise reduction method according to the first embodiment of the present application
  • FIG. 2 is a flowchart of a noise reduction method according to a second embodiment of the present application.
  • FIG. 3 is a flowchart of a noise reduction method according to a third embodiment of the present application.
  • FIG. 4 is a schematic structural diagram of a touch display device according to a fourth embodiment of the present application.
  • FIG. 5 is a schematic structural diagram of a touch display device according to a fifth embodiment of the present application.
  • the first embodiment of the present application relates to a noise reduction method, which includes: acquiring noise data of each touch detection node; acquiring feature values according to the noise data; fitting the noise data and feature values of the target node to obtain the features
  • the value is an independent variable, and the noise data of the target node is a fitting function of the dependent variable; the target node is a touch detection node to be noise-reduced; the characteristic value is substituted into the fitting function to obtain
  • the fitting data corresponding to the eigenvalues; the difference between the fitting data and the noise data of the target node is used as the noise-reduced data, so that the hardware cost is reduced without being affected by the limitation of the fixed sampling frequency, and the Non-DC component noise interference is filtered.
  • the noise reduction method in this embodiment is applied to a touch display device.
  • the touch display device may be a device in which a touch layer is integrated in a display panel.
  • the touch display device may be a Y-OCTA screen.
  • the noise reduction method in this embodiment can be applied not only to the Y-OCTA screen, but can be used in all cases where the LCD noise is large and cannot be fitted by the DC component model.
  • This embodiment is applied to the Y-OCTA screen as an example, but it is not limited to this in practical applications.
  • the Y-OCTA screen is provided with a number of touch detection nodes, and the touch detection nodes are a plurality of projected capacitive nodes distributed in a matrix form on the surface of the Y-OCTA screen, that is, a plurality of touch detection nodes may be arranged in a matrix.
  • Different touch detection nodes have different amounts of interference, and the amount of noise that appears at different times is random. The amount of noise interferes with the processing of the detected finger touch information. Therefore, the noise on the Y-OCTA screen needs to be filtered.
  • the specific process of the noise reduction method of this embodiment is shown in FIG. 1 and includes:
  • Step 101 Acquire noise data of each touch detection node.
  • each touch detection node on the Y-OCTA screen can be sampled in a scanning manner.
  • the required sampling data of each touch detection node can be obtained by grouping or acquiring one by one.
  • the sampling data can be used as LCD noise data, as shown in Table 1.
  • Step 102 Acquire feature values according to noise data.
  • this embodiment mainly enumerates the following two schemes for obtaining feature values:
  • the target nodes to be noise-reduced are: touch detection nodes with the same vertical axis coordinate and different horizontal axis coordinates, that is, touch detection nodes in the same row.
  • the feature value can be obtained from the noise data by: extracting the noise data of the touch detection node with the same vertical coordinate and different horizontal coordinate.
  • the extracted vertical coordinate of the touch detection node is different from the vertical coordinate of the target node.
  • the noise data of the touch detection node is used as the feature value.
  • the target node to be noise-reduced may be the node to which the noise data of the last row in Table 1 belongs, and the noise data extracted as the eigenvalues may be: randomly selected noise different from the sixth row of the last row data.
  • the target nodes to be noise-reduced are: touch detection nodes with the same horizontal axis coordinates and different vertical axis coordinates, that is, touch detection nodes in the same column.
  • the feature value can be obtained from the noise data by: extracting noise data of touch detection nodes with the same horizontal axis coordinate and different vertical axis coordinates; wherein, the extracted horizontal axis coordinate of the touch detection node is different from the horizontal axis coordinate of the target node;
  • the noise data of the touch detection node is used as the feature value.
  • the target node to be noise-reduced may be the node to which the noise data in the last column in Table 1 belongs, and the noise data extracted as the eigenvalues may be: randomly selected noise different from the sixth column in the last column data.
  • the target node to be noise-reduced is a certain touch detection node.
  • the feature value can be obtained according to the noise data by: extracting the noise data of the first type of touch detection node with the same vertical axis coordinate and different horizontal axis coordinates as the first type of feature value; The vertical axis coordinates are different.
  • the noise data of the second type of touch detection nodes with the same horizontal axis coordinates and different vertical axis coordinates are extracted as the second type feature values; the horizontal axis coordinates of the second type touch detection nodes are different from the horizontal axis coordinates of the target node.
  • the target node to be noise-reduced is the node in the third row and third column of Table 1
  • the first type of eigenvalues can be extracted in any row except line 3
  • the noise data in any row or column in Table 1 above can be used as the feature value. It should be noted that this embodiment only lists three schemes that can obtain the feature value, but it is not limited to this in practical applications.
  • Step 103 Perform fitting processing on the noise data and the eigenvalues of the target node to obtain a fitting function that takes the eigenvalue as the independent variable and the noise data of the target node as the dependent variable.
  • the target node is a touch detection node to be noise-reduced.
  • This embodiment specifically provides the following four solutions for obtaining the fitting function:
  • Solution 1 Fitting is performed on the horizontal axis, and the target node is a touch detection node to be noise-reduced with the same vertical coordinate and different horizontal coordinate.
  • the obtained fitting function is specifically the fitting function on the horizontal axis.
  • the process of fitting processing can be as follows:
  • Table 2 The characteristic values in Table 2 are used as independent variables, and the noise data to be reduced in Table 3 are used as dependent variable models for fitting.
  • a linear function model or a non-linear function model can be used for fitting to obtain a fitting function Coefficients, which determines the fitting function on the horizontal axis.
  • Option 2 Fitting with the vertical axis as a unit.
  • the method of fitting on the vertical axis is similar to the method of fitting on the horizontal axis. The difference is that when fitting on the vertical axis, the data on the vertical axis is extracted as the feature value.
  • the characteristic value is the independent variable, and the noise data to be reduced on the vertical axis is used as the dependent variable for fitting to obtain the fitting function on the vertical axis.
  • Option 3 Two-dimensional surface fitting.
  • the target node is a certain node to be denoised. Fitting the first type of eigenvalues to the noise data of the first reference target node to obtain a first fitting sub-function; the first reference target node is a touch detection node in the same row as the target node. Fitting the second type of eigenvalues to the noise data of the second reference target node to obtain a second fitting subfunction; the second reference target node is a touch detection node in the same column as the target node; superimposing the first fit The sub-function and the second fitting sub-function obtain the fitting function.
  • the target node to be noise-reduced is the node in row 3, column 3 of Table 1
  • the first type of feature value extracted is the noise data in row 5
  • Scheme 4 Fitting with part of the data.
  • the difference between the fitting scheme of Scheme 4 and Scheme 1 is that the feature values selected by Scheme 1 and the noise data to be reduced are data on a certain horizontal axis.
  • part of the data in Table 1 can be selected randomly, and not necessarily all data on the same horizontal axis.
  • Step 104 Substitute the feature value into the fitting function to obtain fitting data corresponding to the feature value.
  • the feature values in Table 2 are brought into the fitting function on the obtained horizontal axis to obtain fitting data corresponding to the feature values, as shown in Table 4 below:
  • Step 105 The difference between the fitted data and the noise data of the target node is used as the noise-reduced data.
  • the difference between the fitted data and the noise data of the target node is obtained by subtracting the data in Table 3 from the data in Table 4 to obtain the data after noise reduction as shown in Table 5.
  • This data can be used for subsequent coordinate calculation processing, which means that the detection data used in the subsequent processing and control of the Y-OCTA screen largely excludes noise interference, so that the Y-OCTA screen is interfering Reliable and stable work in the environment.
  • this embodiment will perform the fitting process with the vertical axis as the unit, from the determined feature value, the noise data to be filtered out, the fitted data after fitting, and the data after noise reduction are as shown in Table 6.
  • the specific process is similar to the process of fitting on the horizontal axis. To avoid repetition, it will not be repeated here.
  • this embodiment solves the problem that when the touch screen is subjected to non-DC component LCD noise, the noise reduction cannot be performed and the finger signal cannot be accurately recognized through the method of software fitting.
  • the LCD noise of the non-DC component can be better filtered, and the normal touch effect can be maintained when the LCD noise is relatively large.
  • the signal-to-noise ratio is improved, and the hardware cost required for synchronization processing is reduced without being affected by the limitation of the fixed sampling frequency.
  • the second embodiment of the present application relates to a noise reduction method.
  • the second embodiment is substantially the same as the first embodiment, except that the method of acquiring the characteristic value is different.
  • each touch detection node can be Once sampling, the extracted noise data of the touch detection node on the horizontal axis or vertical axis is used as the feature value.
  • the touch detection node is sampled multiple times, the noise data obtained by the multiple sampling is counted, and the characteristic value is obtained according to the statistical result.
  • FIG. 2 The specific process of the noise reduction method of this embodiment is shown in FIG. 2 and includes:
  • Step 201 Perform multiple samplings on each touch detection node to obtain multiple sampled noise data.
  • each touch detection node may be sampled 10 times, and each touch detection node will record the noise data obtained by sampling 10 times.
  • Step 202 Select multiple touch detection nodes.
  • touch detection nodes can be randomly selected from each touch detection node on the Y-OCTA screen.
  • the selected multiple touch detection nodes may have the same ordinate and different abscissas, or may have different ordinates and the same abscissa, for example, select a row or column of touch detection nodes according to certain rules.
  • 7 touch detection nodes are selected, and the noise data of the 7 touch detection nodes selected is noise data obtained by sampling each touch detection node 10 times, as shown in Table 7 below:
  • Step 203 Count the noise data obtained by multiple sampling of each touch detection node selected.
  • the maximum value, minimum value, or average value of the noise data of each selected touch detection node may be separately counted according to the noise data obtained by multiple sampling of each selected touch detection node.
  • the maximum value, minimum value, or average value of the noise data of the 10 samples of the 7 nodes selected in step 202 may be calculated separately, and the statistical results calculated according to the data in Table 7 are shown in Table 8 below.
  • the mode interval of each node can also be obtained statistically, that is, several data sets that appear more frequently in the multiple sampling data of each node.
  • Step 204 Acquire characteristic values according to the statistical results.
  • the maximum value, minimum value, or average value can be used as the feature value. If the statistic is the mode interval of each node, you can get the feature value according to the mode interval. Value, or average the three data, and use the average of the three data as the characteristic value.
  • Step 205 Perform fitting processing on the noise data and the eigenvalues of the target node to obtain a fitting function with the eigenvalue as the independent variable and the noise data of the target node as the dependent variable.
  • Step 206 Substitute the feature value into the fitting function to obtain fitting data corresponding to the feature value.
  • Step 207 Use the difference between the fitted data and the noise data of the target node as the noise-reduced data.
  • Steps 205 to 207 are substantially the same as steps 103 to 105 in the first embodiment, and will not be repeated here to avoid repetition.
  • the feature values obtained from the statistics of the noise data in this embodiment include, but are not limited to the following statistical methods: find the maximum value, minimum value, average value, and average value of the noise data of the touch detection node sampled multiple times Number interval, etc.
  • the statistical data is used to establish a feature model for noise.
  • the feature model is the feature value obtained by statistical processing. As the basis of the fitting process, it is conducive to a reasonable fitting process and improves the accuracy of noise reduction.
  • the third embodiment of the present application relates to a noise reduction method.
  • the third embodiment is a further improvement of the first embodiment.
  • the main improvement is that: in this embodiment, the touch detection node with a finger touch is eliminated during fitting To make the fitting result more accurate.
  • FIG. 3 The specific process of the noise reduction method of this embodiment is shown in FIG. 3 and includes:
  • Step 301 Sampling each touch detection node to obtain sampling data of each touch detection node.
  • Step 302 Calculate the difference between the sample data of each touch detection node and the reference reference value of each touch detection node, and use the calculated difference as the noise data of each touch detection node.
  • the calculated difference value is the result of the sample data being corrected relative to the reference value, and the calculated difference value is used as the noise data for the next processing.
  • the reference reference value used in the actual application is the static background detection data detected by the touch display device in the state of no touch and interference, the static background detection data is represented by the reference reference value, and the reference reference is deducted from the sampling data Value to get accurate noise data.
  • the benchmark reference value can be maintained while the code is running.
  • Step 303 Determine whether there is a touch detection node that satisfies the preset condition. If yes, go to step 304, otherwise go to step 305.
  • the detection data may contain local touch information components in addition to systemic noise components
  • using the data containing touch components for the next step of fitting processing will produce final noise reduction processing. influences.
  • the preset condition is that the noise data of the touch detection node is greater than a preset threshold, and the preset threshold can be set by those skilled in the art according to actual application needs.
  • the touch detection nodes that meet the preset conditions can be shown in Table 9 below. Table 9 highlights the touch detection nodes that meet the preset conditions. The data of other nodes are not indicated, and the touch detection node to which the displayed data belongs A node touched by a finger is detected.
  • Step 304 In the noise data of each touch detection node, remove the noise data of the touch detection node that meets the preset condition.
  • excluding the noise data of the touch detection node that satisfies the preset condition can be understood as changing the size of the noise data of the touch detection node that satisfies the preset condition to 0, as shown in Table 10 below:
  • Step 305 Obtain the feature value according to the noise data.
  • Step 306 Perform fitting processing on the noise data and the eigenvalues of the target node to obtain a fitting function with the eigenvalue as the independent variable and the noise data of the target node as the dependent variable.
  • Step 307 Substitute the feature value into the fitting function to obtain fitting data corresponding to the feature value.
  • Step 308 The difference between the fitted data and the noise data of the target node is used as the noise-reduced data.
  • Steps 305 to 308 are substantially the same as steps 102 to 105 in the first embodiment, and are not repeated here to avoid repetition.
  • this embodiment samples each touch detection node to obtain sample data of each touch detection node; calculates the difference between the sample data of each touch detection node and the reference reference value of each touch detection node; Using the difference value of each touch detection node as the noise data of each touch detection node is beneficial to obtain accurate noise data and improves the accuracy of noise reduction. If the difference between the touch detection nodes is greater than the preset threshold, a finger touches the touch detection node, and the touch detection node is removed to eliminate the effect of finger touch during noise reduction, so that the fitting result is more accurate, thereby further improving the accuracy of noise reduction Sex.
  • the fourth embodiment of the present application relates to a touch display device. As shown in FIG. 4, it includes: a noise data acquisition module 401 for acquiring noise data of each touch detection node; a characteristic value acquisition module 402 for determining data according to the noise data Obtain characteristic values; fitting processing module 403, which is used to perform fitting processing on the noise data and characteristic values of the target node, to obtain a fitting function with the characteristic value as the independent variable and the noise data of the target node as the dependent variable; It is the touch detection node to be noise-reduced; the fitting data acquisition module 404 is used to substitute the feature value into the fitting function to obtain the fitting data corresponding to the feature value; the noise reduction module 405 is used to match the fitting data and the target node The difference of the noise data is used as the data after noise reduction.
  • the noise data acquisition module may specifically include: a sampling sub-module for sampling each touch detection node to obtain sampling data of each touch detection node; and a difference calculation sub-module for calculating the touch detection node separately The difference between the sampled data and the reference reference value of each touch detection node, and the difference between each touch detection node is used as the noise data of each touch detection node.
  • the sampling sub-module can also be used to sample multiple touch detection nodes multiple times to obtain multiple sampled noise data.
  • the feature value obtaining module 402 may specifically include: selecting a sub-module for selecting a plurality of touch detection nodes; a statistical sub-module for separately counting the noise data obtained by sampling the touch detection nodes for multiple times, According to the statistical result, the characteristic value is obtained.
  • the selection sub-module may be specifically used to randomly select a plurality of touch detection nodes among the touch detection nodes provided on the touch display device.
  • the statistics sub-module may be specifically configured to separately calculate the maximum value and the minimum value of the noise data of each selected touch detection node according to the noise data obtained by multiple sampling of each selected touch detection node Value or average value, and use the maximum value, minimum value or average value as the characteristic value.
  • the statistics sub-module may be specifically configured to separately count the mode of the noise data of each selected touch detection node according to the noise data obtained by multiple sampling of each selected touch detection node Interval, the feature value is obtained according to the statistical mode interval.
  • the feature value acquisition module 402 may be used to extract noise data of touch detection nodes with the same vertical coordinate and different horizontal coordinate; wherein the vertical coordinate of the extracted touch detection node and the target The vertical coordinate of the node is different; the noise data of the touch detection node extracted is used as the feature value.
  • the target node is specifically: a touch detection node to be reduced in noise with the same vertical coordinate and different horizontal coordinate.
  • the fitting function is specifically a fitting function on the horizontal axis.
  • the feature value acquisition module 402 can be used to extract noise data of touch detection nodes with the same horizontal axis coordinates and different vertical axis coordinates; wherein, the vertical axis coordinates of the extracted touch detection nodes are different from the The vertical coordinate of the target node is different; the noise data of the touch detection node extracted is used as the feature value, and the target node is specifically: a touch detection node with the same horizontal axis coordinate and different vertical axis coordinate to be noise-reduced,
  • the fitting function is specifically a fitting function on the vertical axis.
  • the fitting processing module 403 can also be used to extract noise data of the first type of touch detection nodes with the same vertical coordinate and different horizontal coordinate; wherein, the vertical coordinate of the first type of touch detection node , Which is different from the vertical coordinate of the target node; fitting the characteristic value of the noise data of the first type of touch detection node to the noise data of the first reference target node to obtain a first fitting sub-function; wherein , The first reference target node is a touch detection node in the same row as the target node; extract noise data of a second type of touch detection node with the same horizontal axis coordinate and different vertical axis coordinate; wherein, the second type The horizontal coordinate of the touch detection node is different from the horizontal coordinate of the target node; the characteristic values of the noise data of the second type of touch detection node and the noise data of the second reference target node are fitted to obtain the first Two fitting sub-functions; wherein, the second reference target node is a touch detection node in the same column as the target node; the
  • the touch display device may further include a culling sub-module for touching detection nodes that meet preset conditions, and then excluding the noise data of the touch detection nodes from those that meet the preset conditions The noise data of the touch detection node, wherein the preset condition is that the noise data of the touch detection node is greater than a preset threshold.
  • this embodiment can be used as a device embodiment corresponding to any one of the above method embodiments, and this embodiment can be implemented in cooperation with the corresponding method embodiment.
  • the relevant technical details mentioned in the above method embodiments are still valid in this embodiment, and in order to reduce repetition, they will not be repeated here.
  • modules involved in this embodiment are all logical modules.
  • a logical module may be a physical module or a part of a physical module, or multiple physical modules Combination of modules.
  • this embodiment does not introduce modules that are not closely related to solving the technical problems proposed by the present invention, but this does not mean that there are no other modules in this embodiment.
  • a fifth embodiment of the present application relates to a touch display device. As shown in FIG. 5, it includes: at least one processor 501; and at least one processor 501 in communication with a memory 502; wherein, the memory 502 stores at least one An instruction executed by one processor 501, the instruction is executed by the at least one processor 501, so that the at least one processor 501 can execute the above noise reduction method.
  • the bus may include any number of interconnected buses and bridges.
  • the bus connects one or more processors 501 and various circuits of the memory 502 together.
  • the bus can also connect various other circuits such as peripheral devices, voltage regulators, and power management circuits, etc., which are well known in the art, and therefore, they will not be further described in this article.
  • the bus interface provides an interface between the bus and the transceiver.
  • the transceiver can be a single element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices on the transmission medium.
  • the data processed by the processor 501 is transmitted on the wireless medium through the antenna. Further, the antenna also receives the data and transmits the data to the processor 501.
  • the processor 501 is responsible for managing the bus and general processing, and can also provide various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions.
  • the memory 502 may be used to store data used by the processor 501 when performing operations.
  • the sixth embodiment of the present application relates to a computer-readable storage medium that stores a computer program.
  • the computer program is executed by the processor, the above method embodiments are implemented.
  • a program which is stored in a storage medium and includes several instructions to make a device ( It may be a single chip microcomputer, a chip, etc.) or a processor to execute all or part of the steps of the methods described in the embodiments of the present application.
  • the aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disk or optical disk and other media that can store program code

Abstract

一种降噪方法、触控显示装置和计算机可读存储介质。降噪方法，应用于触控显示装置，触控显示装置上设置有若干触摸检测节点，包括：获取各触摸检测节点的噪声数据(101)；根据噪声数据获取特征值(102)；对目标节点的噪声数据和特征值进行拟合处理，得到以特征值为自变量，以目标节点的噪声数据为因变量的拟合函数(103)；目标节点为待降噪的触摸检测节点；将特征值代入拟合函数，得到与特征值对应的拟合数据(104)；将拟合数据和目标节点的噪声数据的差值，作为降噪后的数据(105)。降低了硬件成本，无需受固定采样频率限制的影响，而且可对非直流分量的噪声干扰进行滤除。

Description

降噪方法、触控显示装置和计算机可读存储介质 技术领域
本申请涉及触控显示技术领域，特别涉及一种降噪方法、触控显示装置、和计算机可读存储介质。
背景技术
随着科学技术的发展，新型的触控显示屏的结构不断在市场上出现，其中，以Y-OCTA为代表的柔性屏，在其中占有举足轻重的地位。为了提高在实际应用中的使用效果，通常需要对LCD带来的噪声干扰进行改善处理。
发明人发现现有技术至少存在以下问题：现有的技术，主要是通过同步采样来避开LCD带来的噪声，但同步处理会带来以下副作用：(1)对硬件的实时性要求非常高，需要增加硬件成本。在同步处理未做好时，触控屏同样会受到较强的干扰而无法使用。(2)是因为同步信号的需求，触控屏必须使用固定的频率进行数据采集，无法切换采样频率，这会极大地降低触控屏对外界其他干扰的抵抗能力。(3)在现有的环境下，大部分的LCD噪声干扰都是直流分量，现有的技术，是把屏上的直流噪声去除，来实现降噪的目的，在受非直流分量的LCD噪声干扰时，无法进行降噪。
发明内容
本申请部分实施例的目的在于提供一种降噪方法触控显示装置和计算机可读存储介质，使得降低了硬件成本，无需受固定采样频率限制的影响，而且可对非直流分量的噪声干扰进行滤除。
本申请实施例提供了一种降噪方法，应用于触控显示装置，触控显示装置上设置有若干触摸检测节点，包括：获取各触摸检测节点的噪声数据；根据噪声数据获取特征值；对目标节点的噪声数据和特征值进行拟合处理，得到以特征值为自变量，以目标节点的噪声数据为因变量的拟合函数；目标节点为待降噪的触摸检测节点；将特征值代入拟合函数，得到与特征值对应的拟合数据；将拟合数据和目标节点的噪声数据的差值，作为降噪后的数据。
本申请实施例还提供了一种触控显示装置，触控显示装置上设置有若干触摸检测节点，包括：噪声数据获取模块，用于获取各触摸检测节点的噪声数据；特征值获取模块，用于根据噪声数据获取特征值；拟合处理模块，用于对目标节点的噪声数据和所述特征值进行拟合处理，得到以特征值为自变量，以目标节点的噪声数据为因变量的拟合函数；目标节点为待降噪的触摸检测节点；拟合数据获取模块，用于将特征值代入拟合函数，得到与特征值对应的拟合数据；降噪模块，用于将所述拟合数据和所述目标节点的噪声数据的差值，作为降噪后的数据。
本申请实施例还提供了一种触控显示装置包括：至少一个处理器；以及，与至少一个处理器通信连接的存储器；其中，存储器存储有可被所述至少一个处理器执行的指令，所述指令被所述至少一个处理器执行，以使所述至少一个处理器能够执行上述的降噪方法。
本申请实施例还提供了一种计算机可读存储介质，存储有计算机程序， 计算机程序被处理器执行时实现上述的降噪方法。
本申请实施例相对于现有技术而言，根据触摸检测节点的噪声数据的特征值，以及待降噪的目标节点得到拟合函数，通过将特征值代入拟合函数，得到与特征值对应的拟合数据，将拟合数据和目标节点的噪声数据的差值，作为降噪后的数据。通过软件拟合的方式，可以对显示屏带来直流分量和非直流分量的噪声数据直接进行滤除，从而提升信噪比，降低了同步处理所需的硬件成本，无需受固定采样频率限制的影响。降噪后的数据将为后续的处理中得到精准和稳定的触摸检测结果提供有力的保证。
例如，获取各触摸检测节点的噪声数据，具体包括：对各触摸检测节点进行采样，得到各触摸检测节点的采样数据；分别计算各触摸检测节点的采样数据与各触摸检测节点的基准参照值的差值；将各触摸检测节点的差值作为各触摸检测节点的噪声数据。由于触摸检测装置在无触摸无干扰状态下，自身会产生一个静态背景检测数据，该静态背景检测数据用基准参照值表示。因此，将各触摸检测节点的采样数据与各触摸检测节点的基准参照值的差值作为各触摸检测节点的噪声数据，有利于得到准确的噪声数据，从而提高了降噪的准确性。
例如，在根据噪声数据获取特征值之前，还包括：若存在满足预设条件的触摸检测节点，则在各触摸检测节点的噪声数据中，剔除满足预设条件的触摸检测节点的差值；其中，预设条件为触摸检测节点的差值大于预设阈值。触摸检测节点的差值大于预设阈值即有手指触摸该触摸检测节点，剔除对该触摸检测节点采样得到的数据，以消除在降噪时手指触摸造成的影响，有利于拟合处理的准确性，从而进一步保证降噪的准确性。
例如，分别对选取的每个触摸检测节点的多次采样得到的噪声数据进行统计，具体为：根据选取的每个触摸检测节点的多次采样得到的噪声数据，分别统计选取的每个触摸检测节点的噪声数据的最大值、最小值或平均值；根据统计结果，获取所述特征值，具体为：将最大值、最小值或平均值作为特征值。提供了一种获取特征值的方式。
例如，分别对选取的每个触摸检测节点的多次采样得到的噪声数据进行统计：具体为：根据选取的每个触摸检测节点的多次采样得到的噪声数据，分别统计选取的每个触摸检测节点的噪声数据的众数区间；根据统计结果，获取所述特征值，具体为：根据统计的众数区间获取所述特征值。提供了另一种获取特征值的方式，使得本申请的实施方式可以灵活多变的实现。
例如，目标节点具体为：纵轴坐标相同且横轴坐标不同的待降噪的触摸检测节点，拟合函数具体为横轴上的拟合函数；根据噪声数据获取特征值，具体包括：提取纵轴坐标相同且横轴坐标不同的触摸检测节点的噪声数据；其中，提取的触摸检测节点的纵轴坐标，与目标节点的纵轴坐标不同；将提取的触摸检测节点的噪声数据作为特征值。提供了一种以横轴为单位进行拟合处理的方式。
例如，目标节点具体为：横轴坐标相同且纵轴坐标不同的待降噪的触摸检测节点，拟合函数具体为纵轴上的拟合函数；根据所述噪声数据获取特征值，具体包括：提取横轴坐标相同且纵轴坐标不同的触摸检测节点的噪声数据；其中，提取的触摸检测节点的横轴坐标，与目标节点的横轴坐标不同；将提取的所述触摸检测节点的噪声数据作为所述特征值。提供了另一种以纵轴为单位进行拟合处理的方式，使得本申请的实施方式可以灵活多变的实现。
附图说明
一个或多个实施例通过与之对应的附图中的图片进行示例性说明，这些示例性说明并不构成对实施例的限定，附图中具有相同参考数字标号的元件表示为类似的元件，除非有特别申明，附图中的图不构成比例限制。
图1是根据本申请第一实施例中的降噪方法的流程图；
图2是根据本申请第二实施例中的降噪方法的流程图；
图3是根据本申请第三实施例中的降噪方法的流程图；
图4是根据本申请第四实施例中的触控显示装置的结构示意图；
图5是根据本申请第五实施例中的触控显示装置的结构示意图。
具体实施方式
为了使本申请的目的、技术方案及优点更加清楚明白，以下结合附图及实施例，对本申请部分实施例进行进一步详细说明。应当理解，此处所描述的具体实施例仅仅用以解释本申请，并不用于限定本申请。
本申请第一实施例涉及一种降噪方法，包括：获取各触摸检测节点的噪声数据；根据噪声数据获取特征值；对目标节点的噪声数据和特征值进行拟合处理，得到以所述特征值为自变量，以所述目标节点的噪声数据为因变量的拟合函数；所述目标节点为待降噪的触摸检测节点；将所述特征值代入所述拟合函数，得到与所述特征值对应的拟合数据；将所述拟合数据和所述目标节点的噪声数据的差值，作为降噪后的数据，使得降低硬件成本，无需受固定采样频率限制的影响，而且可对非直流分量的噪声干扰进行滤除。
本实施例中的降噪方法应用于触控显示装置，触控显示装置可以为触控层集成在显示面板中的装置，比如说触控显示装置可以为Y-OCTA屏。在实际应用中，本实施例中的降噪方法不止可以应用在Y-OCTA屏上，在所有受LCD噪声影响较大，且不能用直流分量模型来拟合的情况下，皆可使用。本实施例以应用于Y-OCTA屏为例，但在实际应用中并不以此为限。Y-OCTA屏上设置有若干触摸检测节点，触摸检测节点为纵横分布在Y-OCTA屏表面形成矩阵形式的多个投射式电容节点，即若干触摸检测节点可以呈矩阵状排列。不同的触摸检测节点，受到的干扰量大小不同，在不同时刻出现的噪声大小随机，噪声大小会干扰对检测到的手指触摸信息的处理，因此需要对Y-OCTA屏上的噪声进行滤除。本实施例的降噪方法的具体流程如图1所示，包括：
步骤101：获取各触摸检测节点的噪声数据。
具体的说，可以对Y-OCTA屏上的各触摸检测节点以扫描方式进行采样。在每一个检测采样周期，可以通过分组或逐个获取的方式得到所需的各触摸检测节点的采样数据，采样数据可以作为LCD噪声数据，如表1所示。
表1
-233 -181 -22 0 0 5 0 -330
-264 -178 -22 99 111 4 38 -364
-262 -164 -27 205 209 3 69 -395
-221 -131 -27 327 322 46 135 -362
-255 -148 -41 358 358 18 129 -417
-180 -140 -32 446 402 36 168 -368
-173 -105 1 493 449 15 180 -373
-181 -91 -1 526 469 -17 168 -376
-297 -125 0 411 396 -8 125 -427
步骤102：根据噪声数据获取特征值。
具体的说，本实施例主要列举以下两种可以获取特征值的方案：
方案1：待降噪的目标节点为：纵轴坐标相同且横轴坐标不同的触摸检 测节点，即处于同一行的触摸检测节点。根据噪声数据获取特征值可以通过：提取纵轴坐标相同且横轴坐标不同的触摸检测节点的噪声数据，其中提取的触摸检测节点的纵轴坐标，与目标节点的纵轴坐标不同，将提取的触摸检测节点的噪声数据作为所述特征值。在一个例子中，待降噪的目标节点可以为表1中最后一行的噪声数据所属的节点，提取的可作为特征值的噪声数据可以为：随机选取的不同于最后一行的第六行的噪声数据。
方案2：待降噪的目标节点为：横轴坐标相同且纵轴坐标不同的触摸检测节点，即同一列的触摸检测节点。根据噪声数据获取特征值可以通过：提取横轴坐标相同且纵轴坐标不同的触摸检测节点的噪声数据；其中，提取的触摸检测节点的横轴坐标，与目标节点的横轴坐标不同；将提取的触摸检测节点的噪声数据作为特征值。在一个例子中，待降噪的目标节点可以为表1中最后一列的噪声数据所属的节点，提取的可作为特征值的噪声数据可以为：随机选取的不同于最后一列的第6列的噪声数据。
方案3：待降噪的目标节点为某一个触摸检测节点。根据噪声数据获取特征值可以通过：提取纵轴坐标相同且横轴坐标不同的第一类触摸检测节点的噪声数据作为第一类特征值；第一类触摸检测节点的纵轴坐标，与目标节点的纵轴坐标不同。提取横轴坐标相同且纵轴坐标不同的第二类触摸检测节点的噪声数据作为第二类特征值；第二类触摸检测节点的横轴坐标，与目标节点的横轴坐标不同。举例而言，如果待降噪的目标节点为表1中第3行第3列的节点，那么，第一类特征值可以在除第3行以外的任意一行中提取，第二类特征值可以在除第3列以外的任意一列中提取。
也就是说，可以将上表1中的任意一行或任意一列的噪声数据作为特征 值。需要说明的是，本实施例只是列举了三种可以获取特征值的方案，但在实际应用中并不以此为限。
步骤103：对目标节点的噪声数据和特征值进行拟合处理，得到以特征值为自变量，以目标节点的噪声数据为因变量的拟合函数。
具体的说，目标节点为待降噪的触摸检测节点。本实施例具体提供以下四种得到拟合函数的方案：
方案一：以横轴为单位进行拟合，目标节点为纵轴坐标相同且横轴坐标不同的待降噪的触摸检测节点，得到的拟合函数具体为横轴上的拟合函数。在一个例子中，拟合处理的过程可以如下：
A.提取表1第六行数据直接作为特征值，特征值如下表2所示：
表2
-180 -140 -32 446 402 36 168 -368
B.提取表1最后一行数据作为待降噪的噪声数据，待降噪的噪声数据如下表3所示：
表3
-297 -125 0 411 396 -8 125 -427
C.以表2中的特征值为自变量，以表3中待降噪的噪声数据为因变量机型拟合，具体可以用线性函数模型或非线性函数模型进行拟合，得到拟合函数的系数，从而确定横轴上的拟合函数。
方案二：以纵轴为单位进行拟合。以纵轴为单位进行拟合与以横轴为单位进行拟合的方法类似，不同之处在于以纵轴为单位进行拟合时，提取纵轴上的数据作为特征值，以纵轴上的特征值为自变量，以纵轴上的待降噪的噪声数据为因变量进行拟合，得到纵轴上的拟合函数。
方案三：二维曲面拟合。目标节点为待降噪的某一个节点。对第一类特征值与第一参考目标节点的噪声数据进行拟合处理，得到第一拟合子函数；第一参考目标节点为与所述目标节点处于同一行的触摸检测节点。对第二类特征值与第二参考目标节点的噪声数据进行拟合处理，得到第二拟合子函数；第二参考目标节点为与目标节点处于同一列的触摸检测节点；叠加第一拟合子函数和第二拟合子函数，得到拟合函数。
举例而言，如果待降噪的目标节点为表1中第3行第3列的节点，提取的第一类特征值为第5行的噪声数据，第二类特征值为第5列的噪声数据。那么可以将第3行的噪声数据与第5行的噪声数据进行拟合得到第一拟合子函数f1(x)，将第3列的噪声数据与第5列的噪声数据进行拟合得到第二拟合子函数f2(x)，再将两次拟合效果叠加，得到待降噪的触摸检测节点的拟合函数y＝f2(f1(x))。
方案四：以部分数据进行拟合，方案四的拟合方案与方案一相比的不同之处在于，方案一选择的特征值和待降噪的噪声数据均为某一横轴上的数据，方案四种可随机选取表1中的部分数据，并不一定都是同一横轴上的数据。
需要说明的是，本实施例只是为方便说明列举以上四种拟合处理的方案，但在实际应用中，拟合处理的方案并不以此为限。
步骤104：将特征值代入拟合函数，得到与特征值对应的拟合数据。
具体的说，以步骤103中的方案一为例，将表2中的特征值带入得到的横轴上的拟合函数中，得到与特征值对应的拟合数据，如下表4所示：
表4
-234 -192 -78 425 378 -7 132 -431
上述方案二、三、四中关于本步骤的处理方式与方案一中的处理方式大致相同， 为避免重复，此处不再一一赘述。
步骤105：将拟合数据和目标节点的噪声数据的差值，作为降噪后的数据。
具体的说，以方案一为例，将拟合数据和目标节点的噪声数据作差，即用表4中的数据减去表3中的数据，得到降噪后的数据如表5所示，此数据即可用于后续坐标计算处理，也就意味着Y-OCTA屏在后续的处理和控制中所用到的检测数据在很大程度上是排除了噪声干扰的，从而使得Y-OCTA屏在干扰环境中能够可靠稳定的工作。
表5
63 -67 -78 14 -18 1 7 -4
为方便说明，本实施例将以纵轴为单位进行拟合的过程，从确定的特征值、待滤除的噪声数据、拟合后的拟合数据，直到降噪后的数据以表6的方式呈现，具体过程与以横轴为单位进行拟合的过程类似，为避免重复此处不再重复。
表6
A：特征值 B：待滤除的噪声数据 C：拟合数据 D：降噪后数据
154 -233 -241 -8
135 -264 -246 18
166 -262 -238 24
196 -221 -231 -10
215 -255 -226 29
221 -180 -225 -45
223 -173 -224 -51
228 -181 -223 -42
255 -297 -216 81
本实施例相对于现有技术而言，通过软件拟合的方式，解决了触控显示屏受非直流分量的LCD噪声时，无法进行降噪，无法准确识别手指信号的问题。 本实施例，可以较好地对非直流分量的LCD噪声进行滤除，在受到较大LCD噪声时保持正常的触控效果。而且提升了信噪比，降低了同步处理所需的硬件成本，无需受固定采样频率限制的影响。
本申请第二实施例涉及一种降噪方法，第二实施例与第一实施例大致相同，不同之处在于：获取特征值的方法不同，在第一实施例中对各触摸检测节点可进行一次采样，将提取的横轴或纵轴上的触摸检测节点的噪声数据作为特征值。而本实施例中，对触摸检测节点进行多次采样，对多次采样得到的噪声数据进行统计，根据统计结果得到特征值。
本实施例的降噪方法的具体流程如图2所示，包括：
步骤201：对各触摸检测节点进行多次采样，获取多次采样的噪声数据。
比如说，本实施例可以对各触摸检测节点进行10次采样，每个触摸检测节点都会记录10次采样得到的噪声数据。
步骤202：选取多个触摸检测节点。
具体的说，可以在Y-OCTA屏上的各触摸检测节点中随机选取若干个触摸检测节点。较佳的，选取的多个触摸检测节点可以纵坐标相同横坐标不同，或者，可以纵坐标不同横坐标相同，比如说，根据一定的规则来选取某一行或者某一列的触摸检测节点。为方便说明本实施例选取其中7个触摸检测节点，选取的7个触摸检测节点的噪声数据为对每个触摸检测节点进行10次采样得到的噪声数据，如下表7所示：
表7
Figure PCTCN2018112952-appb-000001
Figure PCTCN2018112952-appb-000002
步骤203：分别对选取的每个触摸检测节点的多次采样得到的噪声数据进行统计。
具体的说，可以根据选取的每个触摸检测节点的多次采样得到的噪声数据，分别统计选取的每个触摸检测节点的噪声数据的最大值、最小值或平均值。比如说，可以分别计算步骤202中选取的7个节点10次采样的噪声数据的最大值、最小值或平均值，根据表7中的数据计算得到的统计结果如下表8所示。在实际应用中还可以统计得到每个节点的众数区间，即每个节点的多次采样数据中出现次数较多的几个数据集合。
表8
统计结果 节点1 节点2 节点3 节点4 节点5 节点6 节点7
最大值 -8 -18 -10 -5 -7 9 8
最小值 -70 -83 -78 -61 -62 -51 -59
平均值 -45.3 -51.9 -44.2 -31.1 -31.4 -20.5 -27.6
步骤204：根据统计结果，获取特征值。
具体的说，如果统计结果为最大值、最小值或平均值，那么可以将最大值、最小值或平均值作为特征值。如果统计的是每个节点的众数区间，可以根据众数区间获取特征值，比如说，如果众数区间为三个数据组成的集合，那么可以将三个数据中最大的或最小的作为特征值，或是对三个数据求平均数，将三个数据的平均数作为特征值。
步骤205：对目标节点的噪声数据和特征值进行拟合处理，得到以特征 值为自变量，以目标节点的噪声数据为因变量的拟合函数。
步骤206：将特征值代入拟合函数，得到与特征值对应的拟合数据。
步骤207：将拟合数据和目标节点的噪声数据的差值，作为降噪后的数据。
步骤205至步骤207与第一实施例中步骤103至步骤105大致相同，为避免重复此处不再一一赘述。
本实施例相对于现有技术而言，由噪声数据统计得到的特征值，包括但不限于以下统计方法：求多次采样的触摸检测节点的噪声数据的最大值、最小值、平均值、众数区间等。由统计数据为噪声建立特征模型，该特征模型即为由统计处理得到的特征值，作为拟合处理的依据，有利于进行合理的拟合处理，提高降噪的准确性。
本申请第三实施例涉及一种降噪方法，第三实施例是第一实施例上位进一步改进，主要改进之处在于：本实施例中，进行拟合时，剔除有手指触摸的触摸检测节点，使得拟合结果更加准确。
本实施例的降噪方法的具体流程如图3所示，包括：
步骤301：对各触摸检测节点进行采样，得到各触摸检测节点的采样数据。
步骤302：分别计算各触摸检测节点的采样数据与各触摸检测节点的基准参照值的差值，将计算的差值作为各触摸检测节点的噪声数据。
具体地说，计算的差值为采样数据相对于基准参照值进行了修正的结果，将计算的差值作为噪声数据进行下一步的处理。实际应用中用到的基准参照值是按照触控显示装置在无触摸无干扰状态下检测到的静态背景检测数据，该静 态背景检测数据用基准参照值表示，在采样数据中进行扣除此基准参照值以得到准确噪声数据。基准参照值可以在代码运行过程中会对其进行维护。
步骤303：判断是否存在满足预设条件的触摸检测节点，如果是，则执行步骤304，否则执行步骤305。
具体地说，由于检测数据中除了包含有系统性的噪声成分还有可能包含有局部的触摸信息成分，而利用包含触摸成分的数据进行下一步的拟合处理，会对最终的降噪处理产生影响。为了有效降噪，就需要将包含有触摸成分的数据排除在接下来的拟合处理所采用的数据之外。更具体地说，也就是需要进行某种统计计算处理，原则上只挑选出有效的噪声数据。预设条件为触摸检测节点的噪声数据大于预设阈值，预设阈值可以由本领域技术人员根据实际应用的需要进行设置。比如说，满足预设条件的触摸检测节点可以如下表9所示，表9中为突出满足预设条件的触摸检测节点，其他节点的数据均未标明，显示的数据所属的触摸检测节点即为检测到有手指触摸的节点。
表9
               
               
    20 200 170 10    
    60 365 256 30    
      50 30      
               
               
               
               
步骤304：在各触摸检测节点的噪声数据中，剔除满足预设条件的触摸检测节点的噪声数据。
具体地说，剔除满足预设条件的触摸检测节点的噪声数据可以理解为将满足预设条件的触摸检测节点的噪声数据的大小改为0，如下表10所示：
表10
-233 0 -22 0 0 5 0 -330
-264 -178 -22 99 111 4 38 -364
-262 -164 0 0 0 0 69 -395
-221 -131 0 0 0 0 135 -362
-255 -148 -41 0 0 18 129 -417
-180 -140 -32 446 402 36 168 -368
-173 -105 1 493 449 15 180 -373
-181 -91 -1 526 469 -17 168 -376
-297 -125 0 411 396 -8 125 -427
步骤305：根据噪声数据获取特征值。
步骤306：对目标节点的噪声数据和特征值进行拟合处理，得到以特征值为自变量，以目标节点的噪声数据为因变量的拟合函数。
步骤307：将特征值代入拟合函数，得到与特征值对应的拟合数据。
步骤308：将拟合数据和目标节点的噪声数据的差值，作为降噪后的数据。
步骤305至步骤308与第一实施方式中步骤102至步骤105大致相同，为避免重复此处不再赘述。
本实施例相对于现有技术而言，对各触摸检测节点进行采样，得到各触摸检测节点的采样数据；分别计算各触摸检测节点的采样数据与各触摸检测节点的基准参照值的差值；将各触摸检测节点的差值作为各触摸检测节点的噪声数据，有利于得到准确的噪声数据，提高了降噪的准确性。触摸检测节点的差值大于预设阈值即有手指触摸该触摸检测节点，剔除该触摸检测节点以消除在降噪时手指触摸造成的影响，使得拟合结果更加准确，从而进一步提高降噪的准确性。
上面各种方法的步骤划分，只是为了描述清楚，实现时可以合并为一个步骤或者对某些步骤进行拆分，分解为多个步骤，只要包括相同的逻辑关系， 都在本专利的保护范围内；对算法中或者流程中添加无关紧要的修改或者引入无关紧要的设计，但不改变其算法和流程的核心设计都在该专利的保护范围内。
本申请第四实施例涉及一种触控显示装置，如图4所示，包括：噪声数据获取模块401，用于获取各触摸检测节点的噪声数据；特征值获取模块402，用于根据噪声数据获取特征值；拟合处理模块403，用于对目标节点的噪声数据和特征值进行拟合处理，得到以特征值为自变量，以目标节点的噪声数据为因变量的拟合函数；目标节点为待降噪的触摸检测节点；拟合数据获取模块404，用于将特征值代入拟合函数，得到与特征值对应的拟合数据；降噪模块405，用于将拟合数据和目标节点的噪声数据的差值，作为降噪后的数据。
其中，噪声数据获取模块可以具体包括：采样子模块，用于对各触摸检测节点进行采样，得到各触摸检测节点的采样数据；以及，差值计算子模块，用于分别计算各触摸检测节点的采样数据与各触摸检测节点的基准参照值的差值，将各触摸检测节点的差值作为各触摸检测节点的噪声数据。
在一个例子中，采样子模块，还可以用于对各触摸检测节点进行多次采样，获取多次采样的噪声数据。特征值获取模块402可以具体包括：选取子模块、用于选取多个触摸检测节点；统计子模块、用于分别对所述选取的每个触摸检测节点的多次采样得到的噪声数据进行统计，根据统计结果，获取所述特征值。
具体的说，选取子模块可以具体用于在所述触控显示装置上设置的若干触摸检测节点中，随机选取多个触摸检测节点。在一个例子中，统计子模块可以具体用于根据所述选取的每个触摸检测节点的多次采样得到的噪声数据，分别统计所述选取的每个触摸检测节点的噪声数据的最大值、最小值或平均值， 将所述最大值、最小值或平均值作为所述特征值。在另一个例子中，统计子模块可以具体用于根据所述选取的每个触摸检测节点的多次采样得到的噪声数据，分别统计所述选取的每个触摸检测节点的噪声数据的的众数区间，根据统计的所述众数区间获取所述特征值。
在一个例子中，特征值获取模块402，可以用于提取纵轴坐标相同且横轴坐标不同的触摸检测节点的噪声数据；其中，所述提取的触摸检测节点的纵轴坐标，与所述目标节点的纵轴坐标不同；将提取的所述触摸检测节点的噪声数据作为所述特征值，所述目标节点具体为：纵轴坐标相同且横轴坐标不同的待降噪的触摸检测节点，所述拟合函数具体为横轴上的拟合函数。
在另一个例子中，特征值获取模块402，可以用于提取横轴坐标相同且纵轴坐标不同的触摸检测节点的噪声数据；其中，所述提取的触摸检测节点的纵轴坐标，与所述目标节点的纵轴坐标不同；将提取的所述触摸检测节点的噪声数据作为所述特征值，所述目标节点具体为：横轴坐标相同且纵轴坐标不同的待降噪的触摸检测节点，所述拟合函数具体为纵轴上的拟合函数。
在实际应用中，拟合处理模块403，还可以用于提取纵轴坐标相同且横轴坐标不同的第一类触摸检测节点的噪声数据；其中，所述第一类触摸检测节点的纵轴坐标，与所述目标节点的纵轴坐标不同；对所述第一类触摸检测节点的噪声数据的特征值与第一参考目标节点的噪声数据进行拟合处理，得到第一拟合子函数；其中，所述第一参考目标节点为与所述目标节点处于同一行的触摸检测节点；提取横轴坐标相同且纵轴坐标不同的第二类触摸检测节点的噪声数据；其中，所述第二类触摸检测节点的横轴坐标，与所述目标节点的横轴坐标不同；对所述第二类触摸检测节点的噪声数据的特征值与第二参考目标节点 的噪声数据进行拟合处理，得到第二拟合子函数；其中，所述第二参考目标节点为与所述目标节点处于同一列的触摸检测节点；叠加所述第一拟合子函数和所述第二拟合子函数，得到所述拟合函数。、
值得一提的是，触控显示装置还可以包括剔除子模块、用于在满足预设条件的触摸检测节点，则在所述各触摸检测节点的噪声数据中，剔除所述满足预设条件的触摸检测节点的噪声数据，其中，所述预设条件为触摸检测节点的噪声数据大于预设阈值。
不难发现，本实施例可作为与上述任意一个方法实施例相对应的装置实施例，本实施例可与对应的方法实施例互相配合实施。上述方法实施例中提到的相关技术细节在本实施例中依然有效，为了减少重复，这里不再赘述。
值得一提的是，本实施例中所涉及到的各模块均为逻辑模块，在实际应用中，一个逻辑模块可以是一个物理模块，也可以是一个物理模块的一部分，还可以以多个物理模块的组合实现。此外，为了突出本发明的创新部分，本实施方式中并没有将与解决本发明所提出的技术问题关系不太密切的模块引入，但这并不表明本实施方式中不存在其它的模块。
本申请第五实施例涉及一种触控显示装置，如图5所示，包括：至少一个处理器501；以及，至少一个处理器501通信连接的存储器502；其中，存储器502存储有可被至少一个处理器501执行的指令，指令被所述至少一个处理器501执行，以使至少一个处理器501能够执行上述的降噪方法。
其中，存储器502和处理器501采用总线方式连接，总线可以包括任意数量的互联的总线和桥，总线将一个或多个处理器501和存储器502的各种电路连接在一起。总线还可以将诸如外围设备、稳压器和功率管理电路等之类的 各种其他电路连接在一起，这些都是本领域所公知的，因此，本文不再对其进行进一步描述。总线接口在总线和收发机之间提供接口。收发机可以是一个元件，也可以是多个元件，比如多个接收器和发送器，提供用于在传输介质上与各种其他装置通信的单元。经处理器501处理的数据通过天线在无线介质上进行传输，进一步，天线还接收数据并将数据传送给处理器501。
处理器501负责管理总线和通常的处理，还可以提供各种功能，包括定时，外围接口，电压调节、电源管理以及其他控制功能。而存储器502可以被用于存储处理器501在执行操作时所使用的数据。
本申请第六实施例涉及一种计算机可读存储介质，存储有计算机程序。计算机程序被处理器执行时实现上述方法实施例。
即，本领域技术人员可以理解，实现上述实施例方法中的全部或部分步骤是可以通过程序来指令相关的硬件来完成，该程序存储在一个存储介质中，包括若干指令用以使得一个设备(可以是单片机，芯片等)或处理器(processor)执行本申请各个实施例所述方法的全部或部分步骤。而前述的存储介质包括：U盘、移动硬盘、只读存储器(ROM，Read-Only Memory)、随机存取存储器(RAM，Random Access Memory)、磁碟或者光盘等各种可以存储程序代码的介质
本领域的普通技术人员可以理解，上述各实施例是实现本申请的具体实施例，而在实际应用中，可以在形式上和细节上对其作各种改变，而不偏离本申请的精神和范围。

Claims (14)

  1. 一种降噪方法，其特征在于，应用于触控显示装置，所述触控显示装置上设置有若干触摸检测节点，所述方法包括：
    获取各触摸检测节点的噪声数据；
    根据所述噪声数据获取特征值；
    对目标节点的噪声数据和所述特征值进行拟合处理，得到以所述特征值为自变量，以所述目标节点的噪声数据为因变量的拟合函数；所述目标节点为待降噪的触摸检测节点；
    将所述特征值代入所述拟合函数，得到与所述特征值对应的拟合数据；
    将所述拟合数据和所述目标节点的噪声数据的差值，作为降噪后的数据。
  2. 如权利要求1所述的降噪方法，其特征在于，所述获取各触摸检测节点的噪声数据，具体包括：
    对所述各触摸检测节点进行采样，得到所述各触摸检测节点的采样数据；
    分别计算所述各触摸检测节点的采样数据与所述各触摸检测节点的基准参照值的差值；
    将所述各触摸检测节点的差值作为所述各触摸检测节点的噪声数据。
  3. 如权利要求2所述的降噪方法，其特征在于，在所述根据所述噪声数据获取特征值之前，还包括：
    若存在满足预设条件的触摸检测节点，则在所述各触摸检测节点的噪声数据中，剔除所述满足预设条件的触摸检测节点的噪声数据；其中，所述预设条件为触摸检测节点的噪声数据大于预设阈值。
  4. 如权利要求1所述的降噪方法，其特征在于，所述获取所述各触摸检测节点的噪声数据，具体为：
    对各触摸检测节点进行多次采样，获取多次采样的噪声数据；
    所述根据所述噪声数据获取特征值，具体包括：
    选取多个触摸检测节点；
    分别对所述选取的每个触摸检测节点的多次采样得到的噪声数据进行统计；
    根据统计结果，获取所述特征值。
  5. 如权利要求4所述的降噪方法，其特征在于，所述选取多个触摸检测节点，具体为：
    在所述触控显示装置上设置的若干触摸检测节点中，随机选取多个触摸检测节点；其中，随机选取的所述多个触摸检测节点纵坐标相同横坐标不同，或者，纵坐标不同横坐标相同。
  6. 如权利要求4所述的降噪方法，其特征在于，所述分别对所述选取的每个触摸检测节点的多次采样得到的噪声数据进行统计，具体为：
    根据所述选取的每个触摸检测节点的多次采样得到的噪声数据，分别统计所述选取的每个触摸检测节点的噪声数据的最大值、最小值或平均值；
    所述根据统计结果，获取所述特征值，具体为：
    将所述最大值、最小值或平均值作为所述特征值。
  7. 如权利要求4所述的降噪方法，其特征在于，分别对所述选取的每个触摸检测节点的多次采样得到的噪声数据进行统计：具体为：
    根据所述选取的每个触摸检测节点的多次采样得到的噪声数据，分别统计所述选取的每个触摸检测节点的噪声数据的众数区间；
    所述根据统计结果，获取所述特征值，具体为：
    根据统计的所述众数区间获取所述特征值。
  8. 如权利要求1所述的降噪方法，其特征在于，所述目标节点具体为：纵轴坐标相同且横轴坐标不同的待降噪的触摸检测节点，所述拟合函数具体为横轴上的拟合函数；
    所述根据所述噪声数据获取特征值，具体包括：
    提取纵轴坐标相同且横轴坐标不同的触摸检测节点的噪声数据；其中，所述提取的触摸检测节点的纵轴坐标，与所述目标节点的纵轴坐标不同；
    将提取的所述触摸检测节点的噪声数据作为所述特征值。
  9. 如权利要求1所述的降噪方法，其特征在于，所述目标节点具体为：横轴坐标相同且纵轴坐标不同的待降噪的触摸检测节点，所述拟合函数具体为纵轴上的拟合函数；
    所述根据所述噪声数据获取特征值，具体包括：
    提取横轴坐标相同且纵轴坐标不同的触摸检测节点的噪声数据；其中，所述提取的触摸检测节点的横轴坐标，与所述目标节点的横轴坐标不同；
    将提取的所述触摸检测节点的噪声数据作为所述特征值。
  10. 如权利要求1所述的降噪方法，其特征在于，所述根据所述噪声数据获取特征值，具体包括：
    提取纵轴坐标相同且横轴坐标不同的第一类触摸检测节点的噪声数据；其中，所述第一类触摸检测节点的纵轴坐标，与所述目标节点的纵轴坐标不同；
    将提取的所述第一类触摸检测节点的噪声数据作为第一类特征值；
    提取横轴坐标相同且纵轴坐标不同的第二类触摸检测节点的噪声数据；其中，所述第二类触摸检测节点的横轴坐标，与所述目标节点的横轴坐标不同；
    将提取的所述第二类触摸检测节点的噪声数据作为第二类特征值；
    所述对目标节点的噪声数据和所述特征值进行拟合处理，得到以所述特征值为自变量，以所述目标节点的噪声数据为因变量的拟合函数，具体包括：
    对所述第一类特征值与第一参考目标节点的噪声数据进行拟合处理，得到第一拟合子函数；其中，所述第一参考目标节点为与所述目标节点处于同一行的触摸检测节点；
    对所述第二类特征值与第二参考目标节点的噪声数据进行拟合处理，得到第二拟合子函数；其中，所述第二参考目标节点为与所述目标节点处于同一列的触摸检测节点；
    叠加所述第一拟合子函数和所述第二拟合子函数，得到所述拟合函数。
  11. 如权利要求1至10任一项所述的降噪方法，其特征在于，所述触控显示装置的触控层集成在显示面板中。
  12. 一种触控显示装置，其特征在于，所述触控显示装置上设置有若干触摸检测节点，包括：
    噪声数据获取模块，用于获取各触摸检测节点的噪声数据；
    特征值获取模块，用于根据所述噪声数据获取特征值；
    拟合处理模块，用于对目标节点的噪声数据和所述特征值进行拟合处理，得到以所述特征值为自变量，以所述目标节点的噪声数据为因变量的拟合函数；所述目标节点为待降噪的触摸检测节点；
    拟合数据获取模块，用于将所述特征值代入所述拟合函数，得到与所述特征值对应的拟合数据；
    降噪模块，用于将所述拟合数据和所述目标节点的噪声数据的差值，作为降噪后的数据。
  13. 一种触控显示装置，其特征在于，包括
    至少一个处理器；以及，
    与所述至少一个处理器通信连接的存储器；其中，
    所述存储器存储有可被所述至少一个处理器执行的指令，所述指令被所述至少一个处理器执行，以使所述至少一个处理器能够执行如权利要求1至11中任一所述的降噪方法。
  14. 一种计算机可读存储介质，存储有计算机程序，其特征在于，所述计算机程序被处理器执行时实现权利要求1至11中任一项所述的降噪方法。
PCT/CN2018/112952 2018-10-31 2018-10-31 降噪方法、触控显示装置和计算机可读存储介质 WO2020087335A1 (zh)

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