A signal calibration method, device and storage medium

Abstract

A signal calibration method, apparatus, and storage medium are disclosed. The method includes: for a panel to be calibrated, determining calibration points and obtaining a calibration point response dataset obtained by marking the calibration points; selecting a calibration template from multiple calibration templates that matches the calibration point response dataset, the calibration template describing the target response data marked on the panel to be calibrated; for any point on the panel to be calibrated, determining an associated calibration point from the calibration points and obtaining the response data of the associated calibration point from the calibration point response dataset; obtaining a transformation relationship matrix describing the trend of response data change based on the response data of the associated calibration point and the target response data of the associated calibration point obtained from the matched calibration template; deriving equivalent derivation response data marked at any point based on the transformation relationship matrix and the target response data of any point obtained from the matched calibration template, and determining the response signal calibration coefficient of any point based on the derivation response data.

Description

Technical Field

[0001] This article relates to touch technology, and more particularly to a signal calibration method, device, and storage medium. Background Technology

[0002] During the mass production of touchpad (also known as TouchPad or TrackPad) force modules, when the machine applies the same force to different locations on the touchpad, the force signals obtained by the sensors on the touchpad will differ, even with the same application force. This is because the relative spatial positions of the application points and the sensors differ. To achieve a consistent force experience across the entire panel, it is often necessary to perform full-surface consistency calibration on the touchpad. Simultaneously, to increase production line productivity, it is desirable to achieve force calibration across the entire surface using as few calibration points as possible.

[0003] In existing technologies, interpolation or fitting methods are often used for force calibration of the entire surface. These methods all assume that the force response data of the touchpad points conforms to certain laws, such as "linear law", "cubic spline law", "nth degree polynomial law", etc. However, in reality, the spatial distribution of the force response data of the touchpad points is difficult to express accurately with a simple mathematical or geometric relationship. Summary of the Invention

[0004] This application provides a signal calibration method, apparatus, and storage medium that can improve the force calibration accuracy of a touchpad.

[0005] This application provides a signal calibration method, comprising:

[0006] For the panel to be calibrated, determine the calibration points and obtain the calibration point response dataset by marking the calibration points.

[0007] Select a calibration template from multiple calibration templates that matches the calibration point response dataset, wherein the calibration template is used to describe the target response data of the panel to be calibrated;

[0008] For any point on the panel to be calibrated, determine the associated calibration point from the calibration points, and obtain the response data of the associated calibration point from the calibration point response dataset;

[0009] Based on the response data of the associated calibration points and the target response data of the associated calibration points obtained from the matched calibration template, a transformation relationship matrix describing the trend of response data change is obtained.

[0010] Based on the transformation relationship matrix and the target response data of any point obtained from the matched calibration template, the equivalent derivation response data for marking at any point is derived, and the response signal calibration coefficient of any point is determined based on the derivation response data.

[0011] As an exemplary embodiment, the generation method of the plurality of calibration templates includes:

[0012] Obtain multiple sample response datasets after N points are marked on multiple sample panels in sequence, where each sample panel corresponds to one sample response dataset, and N is an integer greater than 1;

[0013] By comparing the similarity between multiple sample response datasets, sample response datasets with a similarity greater than a first preset threshold are classified.

[0014] A calibration template is generated for each category based on the sample response dataset contained in each category.

[0015] As an exemplary embodiment, the sample response dataset is a set consisting of the same feature parameters of N response data obtained after N points are marked on a single sample panel;

[0016] Methods for comparing the similarity between multiple sample response datasets include: treating each sample response dataset as a vector; and comparing the similarity between multiple vectors.

[0017] When a category contains multiple sample response datasets, a calibration template corresponding to the category is generated based on the sample response datasets contained in the category, including: taking the average of the multiple sample response datasets contained in the category as the calibration template corresponding to the category.

[0018] As an exemplary embodiment, determining the associated calibration point from the calibration points includes:

[0019] Calculate the distance between each calibration point and the current location;

[0020] The calculated distances are sorted in ascending order, and the calibration points corresponding to the first M configurable distances are selected as the associated calibration points, where M is an integer greater than 0.

[0021] As an exemplary embodiment, the transformation relationship matrix describing the changing trend of the response data includes: a spatial transformation relationship matrix describing the spatial changing trend of the response data;

[0022] Based on the response data of the associated calibration points and the target response data of the associated calibration points obtained from the matched calibration template, a transformation relationship matrix describing the trend of response data change is obtained, including:

[0023] Establish a spatial transformation relationship between the response data of the associated calibration point and the target response data of the associated calibration point obtained from the matched calibration template;

[0024] The spatial transformation relationship matrix is ​​obtained based on the spatial transformation relationship.

[0025] As an exemplary embodiment, establishing a spatial transformation relationship between the response data of the associated calibration point and the target response data of the associated calibration point obtained from the matched calibration template includes:

[0026] Establish the following relation:

[0027]

[0028] Where x represents the associated calibration point; y represents the target response data of the associated calibration point obtained through the calibration template; and y' represents the response data of the associated calibration point obtained by dot notation.

[0029] Represents the spatial transformation relation matrix; t x t y These represent translation transformations along the X-axis and Y-axis, respectively, while a1, a2, a3, and a4 represent rotation and scaling transformations.

[0030] As an exemplary embodiment, selecting a calibration template that matches the calibration point response dataset from a plurality of calibration templates includes:

[0031] Determine the similarity between the response data of each calibration point in the calibration point response dataset and the target response data of the corresponding calibration point location in each calibration template;

[0032] Based on the similarity, a calibration template with a response data similarity greater than a second threshold is selected from the calibration templates as the calibration template that matches the calibration point response dataset.

[0033] As an exemplary embodiment, the method further includes:

[0034] Determining the calibration coefficient of the response signal at any point based on the derived response data includes: taking the reciprocal of the derived response data as the calibration coefficient of the response signal at any point.

[0035] This application provides a computer-readable storage medium storing one or more programs that can be executed by one or more processors to implement the methods described in any of the preceding embodiments.

[0036] This application provides a signal calibration device, including a memory and a processor. The memory stores a program, which, when read and executed by the processor, implements the method described in any of the preceding embodiments.

[0037] Compared with related technologies, this application includes: for a panel to be calibrated, determining calibration points and obtaining a calibration point response dataset obtained by marking the calibration points; selecting a calibration template that matches the calibration point response dataset from multiple calibration templates, wherein the calibration template is used to describe the target response data marked on the panel to be calibrated; for any point on the panel to be calibrated, determining an associated calibration point from the calibration points and obtaining the response data of the associated calibration point from the calibration point response dataset; obtaining a transformation relationship matrix describing the trend of response data change based on the response data of the associated calibration point and the target response data of the associated calibration point obtained from the matched calibration template; deriving equivalent derivation response data marked on the arbitrary point based on the transformation relationship matrix and the target response data of the arbitrary point obtained from the matched calibration template, and determining the response signal calibration coefficient of the arbitrary point based on the derivation response data. In this application, the transformation relationship matrix involved in signal calibration does not depend on the assumption method, but only on the changing trend of the point response data itself. Therefore, based on the transformation relationship matrix, the deduced response data of any point on the panel to be calibrated can be more accurately derived, thereby improving the accuracy of the response signal calibration coefficients obtained from the deduced response data.

[0038] Other features and advantages of this application will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the application. Other advantages of this application can be realized and obtained by means of the solutions described in the description and the accompanying drawings. Attached Figure Description

[0039] The accompanying drawings are used to provide an understanding of the technical solutions of this application and constitute a part of the specification. They are used together with the embodiments of this application to explain the technical solutions of this application and do not constitute a limitation on the technical solutions of this application.

[0040] Figure 1 This is a flowchart of a signal calibration method provided in an embodiment of this application;

[0041] Figure 2 A flowchart illustrating the generation method of multiple calibration templates provided in the embodiments of this application;

[0042] Figure 3 Example diagram illustrating the relationship between the categories and their calibration templates provided in this application embodiment;

[0043] Figure 4Example diagram of a panel to be calibrated, with calibration points and non-calibration points marked, provided for embodiments of this application;

[0044] Figure 5 This is a structural diagram of the signal calibration device provided in an embodiment of this application. Detailed Implementation

[0045] This application describes several embodiments, but these descriptions are exemplary and not restrictive, and it will be apparent to those skilled in the art that many more embodiments and implementations are possible within the scope of the embodiments described herein. Although many possible combinations of features are shown in the drawings and discussed in the detailed description, many other combinations of the disclosed features are also possible. Unless specifically limited, any feature or element of any embodiment may be used in combination with, or may replace, any feature or element of any other embodiment.

[0046] This application includes and contemplates combinations of features and elements known to those skilled in the art. The embodiments, features, and elements disclosed in this application may also be combined with any conventional features or elements to form a unique inventive scheme as defined by the claims. Any feature or element of any embodiment may also be combined with features or elements from other inventive schemes to form another unique inventive scheme as defined by the claims. Therefore, it should be understood that any feature shown and / or discussed in this application may be implemented individually or in any suitable combination. Therefore, the embodiments are not limited except by the limitations imposed by the appended claims and their equivalents. Furthermore, various modifications and changes may be made within the scope of the appended claims.

[0047] Furthermore, in describing representative embodiments, the specification may have presented methods and / or processes as a specific sequence of steps. However, the method or process should not be limited to the specific order of steps described herein, to the extent that it does not depend on such a specific order. As will be understood by those skilled in the art, other sequences of steps are also possible. Therefore, the specific order of steps set forth in the specification should not be construed as a limitation of the claims. Moreover, the claims concerning the method and / or process should not be limited to the steps performed in the written order, and those skilled in the art will readily understand that these orders can be varied and still remain within the spirit and scope of the embodiments of this application.

[0048] This application provides a signal calibration method that can be applied to the force signal calibration of a touchpad, such as... Figure 1 As shown, the method includes:

[0049] Step S101: For the panel to be calibrated, determine the calibration points and obtain the calibration point response dataset obtained by marking the calibration points.

[0050] After marking each calibration point, a calibration point response data will be obtained;

[0051] The calibration point response dataset is composed of multiple calibration point response data obtained after marking multiple calibration points;

[0052] Step S102 selects a calibration template that matches the calibration point response dataset from multiple calibration templates, wherein the calibration template is used to describe the target response data of the panel to be calibrated;

[0053] Step S103: For any point on the panel to be calibrated, determine the associated calibration point from the calibration points, and obtain the response data of the associated calibration point from the calibration point response dataset;

[0054] Step S104: Based on the response data of the associated calibration point and the target response data of the associated calibration point obtained from the matched calibration template, obtain a transformation relationship matrix describing the trend of response data change.

[0055] Step S105: Based on the transformation relationship matrix and the target response data of any point obtained from the matched calibration template, derive the equivalent derivation response data for marking at any point, and determine the response signal calibration coefficient of any point based on the derivation response data.

[0056] In this embodiment, the transformation relationship matrix involved in signal calibration does not depend on the assumption method, but only on the changing trend of the point response data itself. Therefore, based on the transformation relationship matrix, the deduced response data for any point on the panel to be calibrated can be derived more accurately, thereby improving the accuracy of the response signal calibration coefficients obtained from the deduced response data.

[0057] In this embodiment of the application, there are multiple ways to determine the calibration point on the panel to be calibrated, including:

[0058] Select calibration points evenly from the points on the calibration panel;

[0059] Select the calibration point on the row where the force sensor is located on the calibration panel;

[0060] Select the calibration point in the column where the force sensor is located on the calibration panel;

[0061] Use the point with the largest deformation on the calibration panel as the calibration point;

[0062] Select the calibration point in the row containing the point with the largest deformation on the calibration panel;

[0063] Select the calibration point in the column containing the point with the largest deformation on the calibration panel;

[0064] Use the geometric center of the calibration panel as the calibration point;

[0065] Use the center of each physical boundary of the calibration panel as the calibration point, such as the center of each side of a quadrilateral calibration panel.

[0066] Use the points on the calibration panel corresponding to the maximum and minimum values ​​of the force response data as calibration points.

[0067] In practical applications, those skilled in the art can use one or a combination of the above methods to determine the calibration point. In addition to the methods for determining the calibration point described above, any other methods for determining the calibration point can also be applied to the embodiments of this application.

[0068] In one exemplary embodiment, the plurality of calibration templates are generated in the following manner: Figure 2 As shown, it includes:

[0069] Step S201 obtains multiple sample response datasets after N points are marked on multiple sample panels in sequence, where each sample panel corresponds to one sample response dataset, and N is an integer greater than 1.

[0070] As an example, when producing touchpads in small batches, each touchpad can serve as a sample panel;

[0071] The sample response dataset is a set of the same feature parameters of N response data obtained after N points are marked on a single sample panel. The type of the feature parameters can be the integral value of the response data, the energy sum of the response data, or the maximum amplitude of the response data. For example, the sample response dataset is a set of the maximum amplitudes of N response data obtained after N points are marked on a single sample panel.

[0072] Step S202 involves comparing the similarity between multiple sample response datasets and classifying sample response datasets with a similarity greater than a first preset threshold.

[0073] Since each sample response dataset includes N data points, for example, each sample response data point can be regarded as a vector, and the comparison of similarity between multiple sample response datasets can be transformed into the comparison of similarity between multiple vectors; existing methods for comparing the similarity between multiple vectors in the prior art can be applied to the embodiments of this application.

[0074] Step S203 generates a calibration template corresponding to each category based on the sample response dataset contained in each category;

[0075] For example, when a category contains one sample response dataset, that sample response dataset serves as the calibration template for that category; when a category contains multiple sample response datasets, the vector obtained by averaging the multiple sample response datasets contained in that category can be used as the calibration template for that category. Figure 3 An example of the relationship between a category and its calibration template is given.

[0076] In an exemplary embodiment, step S102, selecting a calibration template that matches the calibration point response dataset from a plurality of calibration templates, includes:

[0077] Determine the similarity between the response data of each calibration point in the calibration point response dataset and the target response data of the corresponding calibration point location in each calibration template;

[0078] Based on the similarity, a calibration template with a response data similarity greater than a second threshold is selected from the calibration templates as the calibration template that matches the calibration point response dataset.

[0079] As an example, selecting a calibration template whose response data similarity is greater than a second threshold from the calibration templates based on the similarity as the calibration template matching the calibration point response dataset includes:

[0080] The average similarity obtained by comparing with each calibration template is determined based on the similarity obtained by comparing with each calibration template. The calibration template with an average similarity greater than a second threshold is selected from the calibration templates as the calibration template that matches the calibration point response dataset. For example, suppose there are k calibration points, and the similarity between the k calibration points and the target response data at the corresponding calibration point positions in calibration template 1 is {v11, v21, ..., vij, ..., vk1}, where v represents the similarity, i represents the i-th calibration point, and j represents calibration template j; the similarity between the k calibration points and the target response data at the corresponding calibration point positions in calibration template 2 is {v12, v22, ..., vk2}; the similarity between the k calibration points and the target response data at the corresponding calibration point positions in calibration template 3 is {v13, v23, ..., vk3}; the average similarity of {v11, v21, ..., vij, ..., vk1}, {v12, v22, ..., vk2}, and {v13, v23, ..., vk3} is determined respectively, and the determined average similarity is compared with a second threshold. The calibration template with an average similarity greater than the second threshold is selected as the calibration template that matches the calibration point response dataset.

[0081] In one exemplary embodiment, step S103, for any point on the panel to be calibrated, determines the associated calibration point from the calibration points, including:

[0082] Calculate the distance between each calibration point and the current location;

[0083] The calculated distances are sorted in ascending order, and the calibration points corresponding to the first M configurable distances are selected as the associated calibration points, where M is an integer greater than 0.

[0084] For the same point, when M takes different values, the multiple spatial transformation relationship matrices calculated based on each value of M will have slight differences. The final spatial transformation relationship matrix can be determined by relevant indicators such as the mean method, median method, and index method.

[0085] Specifically, when any point is a calibration point, its associated calibration point is itself, that is, M is set to 1 at this time.

[0086] by Figure 4 Taking the panel to be calibrated shown as an example, the dashed circles in the figure represent calibration points, and the solid circles represent non-calibration points. For example, for Figure 4 For non-calibration point 2 in the graph, calculate the distance between each calibration point and non-calibration point 2, and sort the calculated distances in ascending order, denoted as S. 1,2 -S 14,2 -S 24,2 -S 34,2 ..., select the first three distances corresponding to calibration points 1, 14, and 24 as associated calibration points for non-calibration point 2. For example, for... Figure 4 In the calibration point 1, the distance between itself and the calibration point is 0, and it can be used as the associated calibration point.

[0087] In an exemplary embodiment, the transformation relationship matrix describing the changing trend of the response data includes: a spatial transformation relationship matrix describing the spatial changing trend of the response data;

[0088] Based on the response data of the associated calibration points and the target response data of the associated calibration points obtained from the matched calibration template, a transformation relationship matrix describing the trend of response data change is obtained, including:

[0089] Establish a spatial transformation relationship between the response data of the associated calibration point and the target response data of the associated calibration point obtained from the matched calibration template;

[0090] The spatial transformation relationship matrix is ​​obtained based on the spatial transformation relationship.

[0091] In one exemplary embodiment, establishing a spatial transformation relationship between the response data of the associated calibration point and the target response data of the associated calibration point obtained from the matched calibration template includes:

[0092] Establish the following relation (1):

[0093]

[0094] Where x represents the associated calibration point; y represents the target response data of the associated calibration point obtained through the calibration template; and y' represents the response data of the associated calibration point obtained by dot notation.

[0095] Represents the spatial transformation relation matrix; t x t y These represent translation transformations along the X-axis and Y-axis, respectively, while a1, a2, a3, and a4 represent rotation and scaling transformations.

[0096] Similarly Figure 4 For example, for Figure 4 Non-calibration point 2 in the model has corresponding associated calibration points 1, 14, and 24. The sequence numbers of associated calibration points 1, 14, and 24 can be used to form x (or the horizontal or vertical position coordinates of associated calibration points 1, 14, and 24 can be used to form x). The target response data of associated calibration points 1, 14, and 24 obtained through the calibration template are used to form y. The response data of associated calibration points 1, 14, and 24 obtained through point marking are used to form y'.

[0097] Substituting x, y, and y' into the above relation (1), we can obtain the spatial transformation relation matrix.

[0098] After determining the spatial transformation relation matrix, for any point, based on the above relation (1), since x, y, and the spatial transformation relation matrix are known, y' can be derived.

[0099] In an exemplary embodiment, determining the response signal calibration coefficient of the arbitrary point based on the derived response data includes: taking the reciprocal of the derived response data as the response signal calibration coefficient of the arbitrary point.

[0100] As an exemplary embodiment, the method further includes:

[0101] During signal calibration, the response data obtained after marking the corresponding point is calibrated according to the calibration coefficient of the response signal at any given point, including:

[0102] When performing signal calibration, the response data obtained after marking the corresponding point is multiplied by the response signal calibration coefficient of that point to obtain the calibrated response data of that point.

[0103] This application also provides a computer-readable storage medium storing one or more programs that can be executed by one or more processors to implement the methods described in any of the preceding embodiments.

[0104] This application also provides a signal calibration device, such as... Figure 5 As shown, it includes a memory 501 and a processor 502. The memory 501 stores a program, which, when read and executed by the processor 502, implements the method described in any of the previous embodiments.

[0105] It will be understood by those skilled in the art that all or some of the steps, systems, or apparatuses disclosed above, and their functional modules / units, can be implemented as software, firmware, hardware, or suitable combinations thereof. In hardware implementations, the division between functional modules / units mentioned above does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed collaboratively by several physical components. Some or all components may be implemented as software executed by a processor, such as a digital signal processor or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit (ASIC). Such software may be distributed on a computer-readable medium, which may include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and can be accessed by a computer. Furthermore, it is well known to those skilled in the art that communication media typically contain computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

Claims

1. A signal calibration method, comprising: determining calibration points for a panel to be calibrated, and obtaining calibration point response data sets obtained by dotting the calibration points; selecting a calibration template matching the calibration point response data sets from a plurality of calibration templates, the calibration template being used to describe target response data of dotting a panel to be calibrated; determining an associated calibration point of a point position on the panel to be calibrated from the calibration points, and obtaining response data of the associated calibration point from the calibration point response data sets; obtaining a transformation relationship matrix describing a response data variation trend according to the response data of the associated calibration point and target response data of the associated calibration point obtained from the matching calibration template; and deducing response data of dotting the point position according to the transformation relationship matrix and target response data of the point position obtained from the matching calibration template, and determining a response signal calibration coefficient of the point position according to the deduced response data. 2.The method of claim 1, wherein the plurality of calibration templates are generated in the following manner: obtaining a plurality of sample response data sets obtained by sequentially dotting a plurality of sample panels at N points, wherein each sample panel corresponds to a sample response data set, and N is an integer greater than 1; classifying sample response data sets with a similarity greater than a first preset threshold by comparing the similarity between the plurality of sample response data sets; and generating a calibration template corresponding to each category according to the sample response data sets contained in the category. 3.The method of claim 2, wherein the sample response data set is a set composed of the same characteristic parameters of N response data obtained by dotting a single sample panel at N points; the method of comparing the similarity between the plurality of sample response data sets comprises: taking each sample response data set as a vector; and comparing the similarity between the plurality of vectors; and when a category contains a plurality of sample response data sets, the method of generating a calibration template corresponding to the category according to the sample response data sets contained in the category comprises: taking a vector obtained by averaging the plurality of sample response data sets contained in the category as the calibration template corresponding to the category. 4.The method of claim 1, wherein the method of determining an associated calibration point of a point position from the calibration points comprises: calculating the distance between each calibration point in the calibration points and the point position respectively; and selecting the calibration points corresponding to the M distances with the smallest values in ascending order of the calculated distances as the associated calibration points, wherein M is an integer greater than 0. 5.The method of claim 1, wherein the transformation relationship matrix describing a response data variation trend comprises a spatial transformation relationship matrix describing a spatial variation trend of response data; and the method of obtaining a transformation relationship matrix describing a response data variation trend according to the response data of the associated calibration point and target response data of the associated calibration point obtained from the matching calibration template comprises: ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ establishing a spatial transformation relationship between the response data of the associated calibration point and the target response data of the associated calibration point obtained from the matched calibration template; obtaining the spatial transformation relationship matrix according to the spatial transformation relationship.

6. The method of claim 5, wherein, establishing a spatial transformation relationship between the response data of the associated calibration point and the target response data of the associated calibration point obtained from the matched calibration template comprises: establishing the following relationship: wherein x represents the associated calibration point; y represents the target response data of the associated calibration point obtained from the calibration template; and y' represents the response data of the associated calibration point obtained by dotting; represents a spatial transformation relationship matrix; respectively represent translation transformation along the X-axis, Y-axis, represents a rotation transformation and a scaling transformation.

7. The method of claim 1, wherein, the selecting of the calibration template matched with the calibration point response data set from the plurality of calibration templates comprises: determining the similarity of the response data of each calibration point in the calibration point response data set and the target response data of the corresponding calibration point position in each calibration template; selecting, according to the similarity, the calibration template with the response data similarity greater than a second threshold value from the calibration templates as the calibration template matched with the calibration point response data set.

8. The method of claim 1, wherein, The method further comprises: the determining of the response signal calibration coefficient of the any point position according to the derived response data comprises: taking the inverse of the derived response data as the response signal calibration coefficient of the any point position.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores one or more programs, which can be executed by one or more processors to implement the method of any one of claims 1 to 8.

10. A signal calibration apparatus, characterized by comprising: The computer readable storage medium stores one or more programs, which can be executed by one or more processors to implement the method of any one of claims 1 to 8. The computer readable storage medium stores one or more programs, which can be executed by one or more processors to implement the method of any one of claims 1 to 8.

Images