CN118012288A - Touch screen parameter self-adaptive correction method, system, electronic equipment and storage medium - Google Patents
Touch screen parameter self-adaptive correction method, system, electronic equipment and storage medium Download PDFInfo
- Publication number
- CN118012288A CN118012288A CN202410130444.0A CN202410130444A CN118012288A CN 118012288 A CN118012288 A CN 118012288A CN 202410130444 A CN202410130444 A CN 202410130444A CN 118012288 A CN118012288 A CN 118012288A
- Authority
- CN
- China
- Prior art keywords
- capacitance
- deviation rate
- touch screen
- environment
- parameter
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000012937 correction Methods 0.000 title claims abstract description 102
- 238000000034 method Methods 0.000 title claims abstract description 60
- 238000012549 training Methods 0.000 claims abstract description 59
- 230000007613 environmental effect Effects 0.000 claims abstract description 26
- 238000012360 testing method Methods 0.000 claims description 33
- 230000015654 memory Effects 0.000 claims description 16
- 239000013598 vector Substances 0.000 claims description 15
- 230000003044 adaptive effect Effects 0.000 claims description 12
- 238000013480 data collection Methods 0.000 claims description 12
- 238000004590 computer program Methods 0.000 claims description 8
- 238000004364 calculation method Methods 0.000 claims description 4
- 230000006870 function Effects 0.000 claims description 4
- 238000002474 experimental method Methods 0.000 claims description 3
- 230000000694 effects Effects 0.000 abstract description 5
- 230000008859 change Effects 0.000 description 6
- 238000010586 diagram Methods 0.000 description 6
- 238000005516 engineering process Methods 0.000 description 5
- 230000032683 aging Effects 0.000 description 4
- 239000000306 component Substances 0.000 description 2
- 230000005684 electric field Effects 0.000 description 2
- 230000003993 interaction Effects 0.000 description 2
- 230000002452 interceptive effect Effects 0.000 description 2
- 238000003062 neural network model Methods 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 230000004044 response Effects 0.000 description 2
- 230000003190 augmentative effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 239000003990 capacitor Substances 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 230000006835 compression Effects 0.000 description 1
- 238000007906 compression Methods 0.000 description 1
- 239000004020 conductor Substances 0.000 description 1
- 239000008358 core component Substances 0.000 description 1
- 230000008878 coupling Effects 0.000 description 1
- 238000010168 coupling process Methods 0.000 description 1
- 238000005859 coupling reaction Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 238000005286 illumination Methods 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 238000012805 post-processing Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000011002 quantification Methods 0.000 description 1
- 230000035945 sensitivity Effects 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
Landscapes
- User Interface Of Digital Computer (AREA)
Abstract
The invention discloses a self-adaptive correction method, a system, electronic equipment and a storage medium for touch screen parameters, which relate to the technical field of touch screen correction and are characterized in that a capacitance deviation rate prediction model for predicting the capacitance deviation rate generated by a touch screen after being pressed under different environmental conditions is trained by collecting capacitance reference value, capacitance deviation rate training input data and capacitance deviation rate label data in advance, historical operation statistics data are collected, capacitance deviation rate real-time input data are collected in real time, user touch frequency data of the touch screen to be corrected are collected, whether hardware correction requirements exist or not is judged, and if the hardware correction requirements exist, capacitance reference adjustment parameters are output based on the capacitance deviation rate real-time input data, the capacitance deviation rate prediction model and the hardware correction parameters; and based on the capacitance reference adjustment parameters, the capacitance reference values are corrected to realize more accurate and reliable effective touch judgment, so that better user experience effect is achieved.
Description
Technical Field
The invention relates to the technical field of touch screen correction, in particular to a touch screen parameter self-adaptive correction method, a touch screen parameter self-adaptive correction system, electronic equipment and a storage medium.
Background
Touch screens, one of the core components of modern interactive technology, are widely used in 3D modeling, video post-processing, and various interactive software. With the advent of meta-universe related technologies such as pseudo-reality (VR), augmented Reality (AR), and Mixed Reality (MR), touch screen technology has played an increasingly important role in providing an immersive experience and efficient interaction. In these application scenarios, the user may perform complex operations in the virtual environment or highly interact with the digital information, so that the accuracy and response speed of the touch screen are extremely high.
However, in practical applications, many factors, such as ambient temperature change, ambient humidity change, device aging caused by long-term use, etc., affect the accuracy and response speed of the touch screen. In a metauniverse environment, these challenges may be more complex because more data may need to be processed while maintaining real-time interaction with the user. This places higher demands on the performance stability and adaptability of the touch screen
In addition, in order to prevent a user from touching by mistake, a capacitance reference value is generally set for the capacitive touch screen. However, in a metauniverse application scenario, a user may use a device under more various environmental conditions, and such a fixed capacitance reference value is difficult to flexibly cope with environmental changes, so that erroneous judgment may be caused, and user experience is further affected. Therefore, exploring how to better adapt touch screen technology to meta-universe environment becomes an urgent problem to be solved
Chinese patent with the issued publication number CN103488364B discloses a capacitive touch screen and a self-adaptive correction method and system thereof. The method and the system realize the self-adaptive adjustment of the reference value along with the environmental change to a certain extent, and provide a certain hint for the development of the touch screen technology. However, the method fails to quantitatively analyze the influence of the environment on the accuracy of the touch screen, and does not consider the influence caused by complicated and changeable virtual situations and machine aging in a metauniverse environment
Therefore, the invention provides a touch screen parameter self-adaptive correction method, a system, electronic equipment and a storage medium, which aim to at least solve one of the technical problems existing in the prior art, realize more accurate and reliable effective touch judgment and provide better user experience effect for meta-universe application.
Disclosure of Invention
The present invention aims to solve at least one of the technical problems existing in the prior art. Therefore, the invention provides the self-adaptive correction method, the self-adaptive correction system, the electronic equipment and the storage medium for the touch screen parameters, so that more accurate and reliable effective touch judgment is realized, and a better user experience effect is achieved.
To achieve the above object, embodiment 1 of the present invention proposes a touch screen parameter adaptive correction method, including the steps of:
Step one: collecting capacitance reference values in advance, and collecting capacitance deviation rate training input data and capacitance deviation rate label data in a test environment;
Step two: training input data of the capacitance deviation rate as input and tag data of the capacitance deviation rate as output, and training a capacitance deviation rate prediction model for predicting the capacitance deviation rate generated after the touch screen is pressed under different environmental conditions;
Step three: collecting historical operation statistical data for a touch screen to be corrected, and collecting capacitance deviation rate real-time input data in real time;
Step four: presetting a hardware correction parameter as 1; collecting user touch frequency data of a touch screen to be corrected, judging whether hardware correction requirements exist or not based on the user touch frequency data, if so, turning to the fifth step, and if not, turning to the sixth step;
step five: updating hardware correction parameters based on historical operation statistics;
step six: inputting data, a capacitance deviation rate prediction model and hardware correction parameters in real time based on the capacitance deviation rate, and outputting capacitance reference adjustment parameters; correcting the capacitance reference value based on the capacitance reference adjustment parameter;
The method for collecting capacitance deviation rate training input data and capacitance deviation rate label data in the passing test environment comprises the following steps:
setting a reference environment value for each environment parameter in advance, and representing the environment of all the environment parameters under the corresponding reference environment values as a standard environment;
in a standard environment, when a capacitance value generated by the touch screen is a capacitance reference value, obtaining a contact area of a touch screen object in contact with the touch screen through an experimental method, and representing the contact area as a standard area;
In a test environment, selecting N unused test touch screens, respectively placing each test touch screen in a standard environment and other parameter environments, touching the test touch screen by using a contact object with other contact areas, and respectively collecting standard capacitance values and other parameter capacitance values generated by the test touch screen after each touch; the other parameter environments are environments with different values of at least one environment parameter from the environment parameters of the standard environment, and the other contact areas are area values different from the standard area; n is the number of the selected test touch screens;
For each touch in the other parameter environment, calculating an environment deviation rate of each environmental parameter, an area deviation rate of a contact area and a capacitance deviation rate label of a generated capacitance value;
the area deviation rate group generated by each touch and the environment deviation rate of each environmental parameter form an environment deviation characteristic vector, and all the environment deviation characteristic vectors form capacitance deviation rate training input data; forming capacitance deviation rate labels generated by each touch into capacitance deviation rate label data;
the calculation modes of the environment deviation rate, the area deviation rate and the capacitance deviation rate are as follows:
In other parameter environments, for each environment parameter, dividing each environment parameter value of the environment where each touch is located by a corresponding reference environment value to obtain a corresponding environment deviation rate;
In other parameter environments, dividing other contact areas by a standard area when each touch is performed to obtain an area deviation rate;
In other parameter environments, after each touch, dividing the capacitance value of other parameters generated by the test touch screen by the standard capacitance value generated in the corresponding standard environment to obtain a capacitance deviation rate tag;
The mode of training the capacitance deviation rate prediction model generated after the touch screen is pressed under different environment conditions is as follows:
Taking each group of environment deviation feature vectors in the capacitance deviation rate training input data as input of a capacitance deviation rate prediction model, wherein the capacitance deviation rate prediction model takes a predicted value of the capacitance deviation rate of the group of environment deviation feature vectors as output, takes a capacitance deviation rate label corresponding to the group of environment deviation feature vectors as a prediction target, takes a difference value between the predicted value of the capacitance deviation rate and the capacitance deviation rate label as a prediction error, and takes the sum of minimized prediction errors as a training target; training the capacitance deviation rate prediction model until the sum of prediction errors reaches convergence, and stopping training;
the method for collecting the historical operation statistical data comprises the following steps:
For each touch screen to be corrected, collecting the service time, the pressing average force, the pressing times and the sliding length of the touch screen to be corrected in real time by the equipment background of the touch screen to be corrected;
The mode for acquiring capacitance deviation rate real-time input data in real time is as follows:
When a touch screen object is used for touching the touch screen to be corrected, collecting real-time environment values of various environment parameters of the environment where the touch screen to be corrected is positioned and real-time contact area between the touch screen object and the touch screen to be corrected;
dividing the real-time environment value of each environment parameter by the corresponding reference environment value to obtain the corresponding real-time environment deviation rate;
Dividing the real-time contact area by the standard area to obtain a real-time area deviation rate;
The capacitance deviation rate real-time input data are formed by the real-time environment deviation rate and the real-time area deviation rate;
the method for collecting the touch frequency data of the user of the touch screen to be corrected comprises the following steps:
When a touch screen object is used for touching the touch screen to be corrected, counting the maximum continuous times of continuous identical touch operation of a user, and taking the maximum continuous times as user touch frequency data; the same touch operation includes pressing the same position, sliding with the same sliding route; the maximum continuous times are times of continuous and uninterrupted same touch operation of a user;
The method for judging whether the hardware correction requirement exists or not based on the user touch frequency data is as follows:
presetting a continuous operation frequency threshold, judging that hardware correction requirements exist if the touch frequency data of the user is larger than the continuous operation frequency threshold, and judging that the hardware correction requirements do not exist if the touch frequency data of the user is smaller than or equal to the continuous operation frequency threshold;
The method for updating the hardware correction parameters comprises the following steps:
the using time length is marked as t, the average pressing force is marked as p, the pressing times are marked as m, and the sliding length is marked as c;
and marking the hardware correction parameter as Y, wherein the calculation formula of the hardware correction parameter Y is as follows: ; wherein a1, a2, a3 and a4 are respectively preset proportionality coefficients, e is a natural constant and is used for ensuring that denominator is larger than 1, and ln is a logarithmic function based on e;
the output capacitance reference adjustment parameters are as follows:
Inputting the capacitance deviation rate real-time input data into a capacitance deviation rate prediction model to obtain a predicted value of the capacitance deviation rate output by the capacitance deviation rate prediction model;
Marking the predicted value of the output capacitance deviation rate as Z;
marking the capacitance reference adjustment parameter as B, and calculating the capacitance reference adjustment parameter B according to the following formula: 。
the touch screen parameter self-adaptive correction system comprises a pre-data collection module, a model training module, a real-time data collection module and a self-adaptive correction module; wherein, each module is electrically connected;
The pre-data collection module is used for collecting capacitance reference values in advance, collecting capacitance deviation rate training input data and capacitance deviation rate tag data in a test environment, sending the capacitance reference values to the self-adaptive correction module, and sending the capacitance deviation rate training input data and the capacitance deviation rate tag data to the model training module;
the model training module is used for training a capacitance deviation rate prediction model for predicting the capacitance deviation rate generated by the touch screen after being pressed under different environmental conditions by taking the capacitance deviation rate training input data as input and the capacitance deviation rate label data as output, and sending the capacitance deviation rate prediction model to the self-adaptive correction module;
The real-time data collection module is used for collecting historical operation statistical data of the touch screen to be corrected, collecting capacitance deviation rate real-time input data in real time, and sending the historical operation statistical data and the capacitance deviation rate real-time input data to the self-adaptive correction module;
the self-adaptive correction module is used for presetting a hardware correction parameter to be 1; collecting user touch frequency data of a touch screen to be corrected, judging whether hardware correction requirements exist or not based on the user touch frequency data, and updating hardware correction parameters based on historical operation statistical data if the hardware correction requirements exist; if the hardware correction requirement does not exist, inputting data, a capacitance deviation rate prediction model and hardware correction parameters in real time based on the capacitance deviation rate, and outputting capacitance reference adjustment parameters; and correcting the capacitance reference value based on the capacitance reference adjustment parameter.
An electronic device, comprising: a processor and a memory, wherein the memory stores a computer program for the processor to call;
The processor executes the touch screen parameter self-adaptive correction method by calling the computer program stored in the memory.
A computer readable storage medium having stored thereon a computer program that is erasable;
The computer program, when run on a computer device, causes the computer device to perform the touch screen parameter adaptive correction method described above.
Compared with the prior art, the invention has the beneficial effects that:
According to the method, capacitance reference values are collected in advance, capacitance deviation rate training input data and capacitance deviation rate tag data are collected in a test environment, the capacitance deviation rate training input data are used as input, the capacitance deviation rate tag data are used as output, a capacitance deviation rate prediction model for predicting the capacitance deviation rate generated by a touch screen after being pressed under different environment conditions is trained, historical operation statistics data are collected for the touch screen to be corrected, capacitance deviation rate real-time input data are collected in real time, a hardware correction parameter is preset to be 1, user touch frequency data of the touch screen to be corrected are collected, whether hardware correction requirements exist is judged based on the user touch frequency data, if hardware correction requirements exist, hardware correction parameters are updated based on historical operation statistics data, and if hardware correction requirements do not exist, capacitance reference adjustment parameters are output based on the capacitance deviation rate real-time input data, the capacitance deviation rate prediction model and the hardware correction parameters; correcting the capacitance reference value based on the capacitance reference adjustment parameter; the influence degree of the environment where the touch screen is located on the touch screen sensing sensitivity is analyzed through the design neural network model, and then when the touch screen is judged to be aged, the correction parameters of the hardware layer are dynamically generated when the touch screen is insensitive to the touch sensing of a user, the influence of the environment and the influence of the hardware layer are integrated, the capacitance reference value is dynamically adjusted, more accurate and reliable effective touch judgment is realized, and better user experience effect is achieved.
Drawings
FIG. 1 is a flowchart of a method for adaptively correcting parameters of a touch screen according to embodiment 1 of the present invention;
FIG. 2 is a schematic diagram of a capacitive touch screen;
FIG. 3 is a block diagram illustrating a system for adaptive correction of touch screen parameters according to embodiment 2 of the present invention;
fig. 4 is a schematic structural diagram of an electronic device in embodiment 3 of the present invention;
fig. 5 is a schematic diagram of the structure of a computer-readable storage medium in embodiment 4 of the present invention.
Detailed Description
The technical solutions of the present invention will be clearly and completely described in connection with the embodiments, and it is obvious that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
As shown in fig. 1, the adaptive correction method for the touch screen parameter comprises the following steps:
Step one: collecting capacitance reference values in advance, and collecting capacitance deviation rate training input data and capacitance deviation rate label data in a test environment;
Step two: training input data of the capacitance deviation rate as input and tag data of the capacitance deviation rate as output, and training a capacitance deviation rate prediction model for predicting the capacitance deviation rate generated after the touch screen is pressed under different environmental conditions;
Step three: collecting historical operation statistical data for a touch screen to be corrected, and collecting capacitance deviation rate real-time input data in real time;
Step four: presetting a hardware correction parameter as 1; collecting user touch frequency data of a touch screen to be corrected, judging whether hardware correction requirements exist or not based on the user touch frequency data, if so, turning to the fifth step, and if not, turning to the sixth step;
step five: updating hardware correction parameters based on historical operation statistics;
step six: inputting data, a capacitance deviation rate prediction model and hardware correction parameters in real time based on the capacitance deviation rate, and outputting capacitance reference adjustment parameters; correcting the capacitance reference value based on the capacitance reference adjustment parameter;
As shown in fig. 2, which is a principle of a capacitive touch screen, the capacitive touch screen is coated with elongated electrodes on four sides of the touch screen, and a low-voltage ac electric field is formed in a conductive body. When a user touches the screen, a coupling capacitor is formed between the finger and the conductor layer due to the human body electric field, current emitted by the four-side electrodes flows to the contact, the current intensity is in direct proportion to the distance between the finger and the electrode, and a controller positioned behind the touch screen can calculate the proportion and intensity of the current, so that the position of a touch point can be accurately calculated; in order to prevent the user from touching by mistake, a capacitance reference value is generally set, and only when the actually generated capacitance is larger than the preset capacitance reference value, the effective touch is judged;
However, the capacitive touch screen is inevitably affected by the surrounding environment, such as temperature, humidity, air pressure, illumination intensity and the like around the touch key, and aging phenomenon occurs along with the increase of the use time, so that the capacitive touch screen is not sensitive enough to the change of the capacitance, and the capacitance generated by the touch screen is smaller than a preset capacitance reference value when a user touches the touch screen according to the past habit, so that the touch screen is misjudged as invalid touch;
the method for collecting the capacitance deviation rate training input data and the capacitance deviation rate label data in the test environment comprises the following steps of:
Setting a reference environment value for each environment parameter in advance, and representing the environment of all the environment parameters under the corresponding reference environment values as a standard environment; the environmental parameters include, but are not limited to, temperature, humidity, air pressure, light intensity, etc.; the reference environmental value is set according to actual requirements, for example, the reference environmental value of temperature can be set to 273.15K (0 degrees celsius);
in a standard environment, when a capacitance value generated by the touch screen is a capacitance reference value, obtaining a contact area of a touch screen object in contact with the touch screen through an experimental method, and representing the contact area as a standard area; specifically, the touch screen object may be a finger or other conductive object; for example, the standard area is 0.5cm 2
In a test environment, selecting N unused test touch screens, respectively placing each test touch screen in a standard environment and other parameter environments, touching the test touch screen by using a contact object with other contact areas, and respectively collecting standard capacitance values and other parameter capacitance values generated by the test touch screen after each touch; the other parameter environments are environments with different values of at least one environment parameter from the environment parameters of the standard environment, and the other contact areas are area values different from the standard area; n is the number of the selected test touch screens;
For each touch in the other parameter environment, calculating an environment deviation rate of each environmental parameter, an area deviation rate of a contact area and a capacitance deviation rate label of a generated capacitance value;
the area deviation rate group generated by each touch and the environment deviation rate of each environmental parameter form an environment deviation characteristic vector, and all the environment deviation characteristic vectors form capacitance deviation rate training input data; forming capacitance deviation rate labels generated by each touch into capacitance deviation rate label data;
Specifically, the calculating modes of the environment deviation rate, the area deviation rate and the capacitance deviation rate are as follows:
In other parameter environments, for each environment parameter, dividing each environment parameter value of the environment where each touch is located by a corresponding reference environment value to obtain a corresponding environment deviation rate;
In other parameter environments, dividing other contact areas by a standard area when each touch is performed to obtain an area deviation rate;
In other parameter environments, after each touch, dividing the capacitance value of other parameters generated by the test touch screen by the standard capacitance value generated in the corresponding standard environment to obtain a capacitance deviation rate tag;
For example, when the temperature is 290K (the environmental deviation rate of the temperature is ) The contact area was 0.7cm 2 (the area deviation rate of the contact area was/>) Under the condition, the capacitance deviation rate generated by the test touch screen is 1.1 (the capacitance value of other parameters is 110pF, and the standard capacitance value is 100 pF); namely the capacitance value and standard generated by contact; it can be understood that the capacitance deviation rate is measured by controlling different environmental parameters, and the capacitance value generated by touching the touch screen is compared with the standard value, so that the deviation degree with the standard capacitance is obtained, and the quantification effect on the capacitance deviation degree is achieved;
Further, the method for training the capacitive deviation rate prediction model generated after the touch screen is pressed under different predicted environmental conditions by taking the capacitive deviation rate training input data as input and the capacitive deviation rate tag data as output is as follows:
Taking each group of environment deviation feature vectors in the capacitance deviation rate training input data as input of a capacitance deviation rate prediction model, wherein the capacitance deviation rate prediction model takes a predicted value of the capacitance deviation rate of the group of environment deviation feature vectors as output, takes a capacitance deviation rate label corresponding to the group of environment deviation feature vectors as a prediction target, takes a difference value between the predicted value of the capacitance deviation rate and the capacitance deviation rate label as a prediction error, and takes the sum of minimized prediction errors as a training target; training the capacitance deviation rate prediction model, stopping training until the sum of prediction errors reaches convergence, and training the capacitance deviation rate prediction model for predicting the generated capacitance deviation rate according to the parameter values of all environmental parameters and the contact area of the touch screen; the capacitance deviation rate prediction model is any one of a deep neural network model and a deep belief network model; the sum of the prediction errors may be a mean square error;
Further, the method for collecting the historical operation statistical data is as follows:
For each touch screen to be corrected, collecting the service time, the pressing average force, the pressing times and the sliding length of the touch screen to be corrected in real time by the equipment background of the touch screen to be corrected;
The using time length is the total time length of the user using the touch screen to be corrected;
the average pressing force is an average pressing force value of a user in the process of using the touch screen to be corrected; it will be appreciated that compression force may be collected in real time by using a pressure sensor;
The sliding length is calculated by counting the total length of sliding of a user on the touch screen to be corrected by using the touch screen object; it can be understood that each time a user slides on the touch screen to be corrected, the capacitance value of the sliding position will change, so that the length of each sliding can be counted;
further, the mode of collecting capacitance deviation rate real-time input data in real time is as follows:
When a touch screen object is used for touching the touch screen to be corrected, collecting real-time environment values of various environment parameters of the environment where the touch screen to be corrected is positioned and real-time contact area between the touch screen object and the touch screen to be corrected;
dividing the real-time environment value of each environment parameter by the corresponding reference environment value to obtain the corresponding real-time environment deviation rate;
Dividing the real-time contact area by the standard area to obtain a real-time area deviation rate;
The capacitance deviation rate real-time input data are formed by the real-time environment deviation rate and the real-time area deviation rate;
Further, the method for collecting the touch frequency data of the user of the touch screen to be corrected is as follows:
When a touch screen object is used for touching the touch screen to be corrected, counting the maximum continuous times of continuous identical touch operation of a user, and taking the maximum continuous times as user touch frequency data; the same touch operation includes pressing the same position, sliding with the same sliding route; the maximum continuous times are times of continuous and uninterrupted same touch operations performed by a user, for example, the user continuously clicks the same position on the touch screen to be corrected for 4 times, and it can be understood that when the user continuously performs the same touch operations for several times, it is indicated that the touch screen to be corrected cannot effectively complete the intended operation purpose of the user, which may be caused by aging of the touch screen to be corrected, and the change of the capacitance value of the screen is too small after the touch, so that the capacitance reference value needs to be adjusted to ensure that the screen is more sensitive to the touch;
Further, the method for judging whether the hardware correction requirement exists based on the touch frequency data of the user is as follows:
presetting a continuous operation frequency threshold, judging that hardware correction requirements exist if the touch frequency data of the user is larger than the continuous operation frequency threshold, and judging that the hardware correction requirements do not exist if the touch frequency data of the user is smaller than or equal to the continuous operation frequency threshold;
further, the method for updating the hardware correction parameters based on the historical operation statistical data is as follows:
the using time length is marked as t, the average pressing force is marked as p, the pressing times are marked as m, and the sliding length is marked as c;
and marking the hardware correction parameter as Y, wherein the calculation formula of the hardware correction parameter Y is as follows: ; wherein a1, a2, a3 and a4 are respectively preset proportionality coefficients, e is a natural constant and is used for ensuring that denominator is larger than 1, and ln is a logarithmic function based on e;
Further, the method for outputting the capacitance reference adjustment parameter based on the capacitance deviation rate real-time input data, the capacitance deviation rate prediction model and the hardware correction parameter is as follows:
Inputting the capacitance deviation rate real-time input data into a capacitance deviation rate prediction model to obtain a predicted value of the capacitance deviation rate output by the capacitance deviation rate prediction model;
Marking the predicted value of the output capacitance deviation rate as Z;
marking the capacitance reference adjustment parameter as B, and calculating the capacitance reference adjustment parameter B according to the following formula: ; it will be appreciated that the larger the capacitance deviation rate, for example, the capacitance deviation rate is 1.1, the more sensitive the touch screen to be corrected is in the current environment than in the standard environment, and thus the capacitance reference value can be set smaller;
further, the method for correcting the capacitance reference value based on the capacitance reference adjustment parameter is as follows:
The capacitance reference value is updated to be the capacitance reference value multiplied by the capacitance reference adjustment parameter.
Example 2
As shown in fig. 3, the touch screen parameter adaptive correction system comprises a pre-data collection module, a model training module, a real-time data collection module and an adaptive correction module; wherein, each module is electrically connected;
The pre-data collection module is mainly used for collecting capacitance reference values in advance, collecting capacitance deviation rate training input data and capacitance deviation rate tag data in a test environment, sending the capacitance reference values to the self-adaptive correction module, and sending the capacitance deviation rate training input data and the capacitance deviation rate tag data to the model training module;
The model training module is mainly used for training input data of a capacitance deviation rate, taking tag data of the capacitance deviation rate as output, training a capacitance deviation rate prediction model for predicting the capacitance deviation rate generated after the touch screen is pressed under different environmental conditions, and sending the capacitance deviation rate prediction model to the self-adaptive correction module;
The real-time data collection module is mainly used for collecting historical operation statistical data of the touch screen to be corrected, collecting capacitance deviation rate real-time input data in real time, and sending the historical operation statistical data and the capacitance deviation rate real-time input data to the self-adaptive correction module;
The self-adaptive correction module is mainly used for presetting a hardware correction parameter to be 1; collecting user touch frequency data of a touch screen to be corrected, judging whether hardware correction requirements exist or not based on the user touch frequency data, and updating hardware correction parameters based on historical operation statistical data if the hardware correction requirements exist; if the hardware correction requirement does not exist, inputting data, a capacitance deviation rate prediction model and hardware correction parameters in real time based on the capacitance deviation rate, and outputting capacitance reference adjustment parameters; and correcting the capacitance reference value based on the capacitance reference adjustment parameter.
Example 3
Fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application. As shown in fig. 4, there is also provided an electronic device 100 according to yet another aspect of the present application. The electronic device 100 may include one or more processors, which may include a CPU102 and a GPU109, and one or more memories. Wherein the memory has stored therein computer readable code which, when executed by the one or more processors, can perform the touch screen parameter adaptive correction method as described above.
The method or system according to embodiments of the application may also be implemented by means of the architecture of the electronic device shown in fig. 4. As shown in fig. 4, the electronic device 100 may include a bus 101, one or more CPUs 102, a Read Only Memory (ROM) 103, a Random Access Memory (RAM) 104, a communication port 105 connected to a network, an input/output component 106, a hard disk 107, a GPU109, and the like. A storage device in the electronic device 100, such as the ROM103 or the hard disk 107, may store the touch screen parameter adaptive correction method provided by the present application. The touch screen parameter adaptive correction method may, for example, comprise the steps of: step one: collecting capacitance reference values in advance, and collecting capacitance deviation rate training input data and capacitance deviation rate label data in a test environment; step two: training input data of the capacitance deviation rate as input and tag data of the capacitance deviation rate as output, and training a capacitance deviation rate prediction model for predicting the capacitance deviation rate generated after the touch screen is pressed under different environmental conditions; step three: collecting historical operation statistical data for a touch screen to be corrected, and collecting capacitance deviation rate real-time input data in real time; step four: presetting a hardware correction parameter as 1; collecting user touch frequency data of a touch screen to be corrected, judging whether hardware correction requirements exist or not based on the user touch frequency data, if so, turning to the fifth step, and if not, turning to the sixth step; step five: updating hardware correction parameters based on historical operation statistics; step six: inputting data, a capacitance deviation rate prediction model and hardware correction parameters in real time based on the capacitance deviation rate, and outputting capacitance reference adjustment parameters; and correcting the capacitance reference value based on the capacitance reference adjustment parameter.
Further, the electronic device 100 may also include a user interface 108. Of course, the architecture shown in fig. 4 is merely exemplary, and one or more components of the electronic device shown in fig. 4 may be omitted as may be desired in implementing different devices.
Example 4
Fig. 5 is a schematic diagram of a computer readable storage medium according to an embodiment of the present application. As shown in fig. 5, is a computer-readable storage medium 200 according to one embodiment of the application. The computer-readable storage medium 200 has stored thereon computer-readable instructions. The touch screen parameter adaptive correction method according to the embodiments of the present application described with reference to the above drawings may be performed when computer readable instructions are executed by a processor. Computer-readable storage medium 200 includes, but is not limited to, for example, volatile memory and/or nonvolatile memory. Volatile memory can include, for example, random Access Memory (RAM), cache memory (cache), and the like. The non-volatile memory may include, for example, read Only Memory (ROM), hard disk, flash memory, and the like.
In addition, according to embodiments of the present application, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, the present application provides a non-transitory machine-readable storage medium storing machine-readable instructions executable by a processor to perform instructions corresponding to the method steps provided by the present application, which when executed by a Central Processing Unit (CPU), perform the functions defined above in the method of the present application.
The methods and apparatus, devices of the present application may be implemented in numerous ways. For example, the methods and apparatus, devices of the present application may be implemented by software, hardware, firmware, or any combination of software, hardware, firmware. The above-described sequence of steps for the method is for illustration only, and the steps of the method of the present application are not limited to the sequence specifically described above unless specifically stated otherwise. Furthermore, in some embodiments, the present application may also be embodied as programs recorded in a recording medium, the programs including machine-readable instructions for implementing the methods according to the present application. Thus, the present application also covers a recording medium storing a program for executing the method according to the present application.
The above preset parameters or preset thresholds are set by those skilled in the art according to actual conditions or are obtained by mass data simulation.
The above embodiments are only for illustrating the technical method of the present invention, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical method of the present invention may be modified or substituted with equivalents thereof without departing from the spirit and scope of the technical method of the present invention.
Claims (10)
1. The self-adaptive correction method for the parameters of the touch screen is characterized by comprising the following steps of:
Step one: collecting capacitance reference values in advance, and collecting capacitance deviation rate training input data and capacitance deviation rate label data in a test environment;
Step two: training input data of the capacitance deviation rate as input and tag data of the capacitance deviation rate as output, and training a capacitance deviation rate prediction model for predicting the capacitance deviation rate generated after the touch screen is pressed under different environmental conditions;
Step three: collecting historical operation statistical data for a touch screen to be corrected, and collecting capacitance deviation rate real-time input data in real time;
Step four: presetting a hardware correction parameter as 1; collecting user touch frequency data of a touch screen to be corrected, judging whether hardware correction requirements exist or not based on the user touch frequency data, if so, turning to the fifth step, and if not, turning to the sixth step;
step five: updating hardware correction parameters based on historical operation statistics;
step six: inputting data, a capacitance deviation rate prediction model and hardware correction parameters in real time based on the capacitance deviation rate, and outputting capacitance reference adjustment parameters; and correcting the capacitance reference value based on the capacitance reference adjustment parameter.
2. The method for adaptively correcting parameters of a touch screen according to claim 1, wherein the method for collecting the capacitance deviation rate training input data and the capacitance deviation rate tag data in the test environment is as follows:
setting a reference environment value for each environment parameter in advance, and representing the environment of all the environment parameters under the corresponding reference environment values as a standard environment;
in a standard environment, when a capacitance value generated by the touch screen is a capacitance reference value, obtaining a contact area of a touch screen object in contact with the touch screen through an experimental method, and representing the contact area as a standard area;
In a test environment, selecting N unused test touch screens, respectively placing each test touch screen in a standard environment and other parameter environments, touching the test touch screen by using a contact object with other contact areas, and respectively collecting standard capacitance values and other parameter capacitance values generated by the test touch screen after each touch; the other parameter environments are environments with different values of at least one environment parameter from the environment parameters of the standard environment, and the other contact areas are area values different from the standard area; n is the number of the selected test touch screens;
For each touch in the other parameter environment, calculating an environment deviation rate of each environmental parameter, an area deviation rate of a contact area and a capacitance deviation rate label of a generated capacitance value;
The area deviation rate group generated by each touch and the environment deviation rate of each environmental parameter form an environment deviation characteristic vector, and all the environment deviation characteristic vectors form capacitance deviation rate training input data; and forming the capacitance deviation rate label generated by each touch into capacitance deviation rate label data.
3. The method for adaptively correcting parameters of a touch screen according to claim 2, wherein the environmental deviation rate, the area deviation rate, and the capacitance deviation rate are calculated by:
In other parameter environments, for each environment parameter, dividing each environment parameter value of the environment where each touch is located by a corresponding reference environment value to obtain a corresponding environment deviation rate;
In other parameter environments, dividing other contact areas by a standard area when each touch is performed to obtain an area deviation rate;
And in other parameter environments, dividing the capacitance value of other parameters generated by the test touch screen after each touch by the standard capacitance value generated in the corresponding standard environment to obtain the capacitance deviation rate tag.
4. The method for adaptively correcting parameters of a touch screen according to claim 3, wherein the method for training a prediction model of a capacitance deviation rate generated after the touch screen is pressed under different environmental conditions is as follows:
Taking each group of environment deviation feature vectors in the capacitance deviation rate training input data as input of a capacitance deviation rate prediction model, wherein the capacitance deviation rate prediction model takes a predicted value of the capacitance deviation rate of the group of environment deviation feature vectors as output, takes a capacitance deviation rate label corresponding to the group of environment deviation feature vectors as a prediction target, takes a difference value between the predicted value of the capacitance deviation rate and the capacitance deviation rate label as a prediction error, and takes the sum of minimized prediction errors as a training target; and training the capacitance deviation rate prediction model until the sum of the prediction errors reaches convergence, and stopping training.
5. The method for adaptively correcting parameters of a touch screen according to claim 4, wherein the manner of acquiring capacitance deviation rate real-time input data in real time is as follows:
When a touch screen object is used for touching the touch screen to be corrected, collecting real-time environment values of various environment parameters of the environment where the touch screen to be corrected is positioned and real-time contact area between the touch screen object and the touch screen to be corrected;
dividing the real-time environment value of each environment parameter by the corresponding reference environment value to obtain the corresponding real-time environment deviation rate;
Dividing the real-time contact area by the standard area to obtain a real-time area deviation rate;
and forming capacitance deviation rate real-time input data by each real-time environment deviation rate and real-time area deviation rate.
6. The method for adaptively correcting parameters of a touch screen according to claim 5, wherein the method for collecting the user touch frequency data of the touch screen to be corrected is as follows:
When a touch screen object is used for touching the touch screen to be corrected, counting the maximum continuous times of continuous identical touch operation of a user, and taking the maximum continuous times as user touch frequency data; the same touch operation includes pressing the same position, sliding with the same sliding route; the maximum continuous times are the times of continuous and uninterrupted same touch operation of the user.
7. The method for adaptively correcting parameters of a touch screen according to claim 6, wherein the method for updating the hardware correction parameters is as follows:
the using time length is marked as t, the average pressing force is marked as p, the pressing times are marked as m, and the sliding length is marked as c;
and marking the hardware correction parameter as Y, wherein the calculation formula of the hardware correction parameter Y is as follows: ; wherein a1, a2, a3 and a4 are respectively preset proportionality coefficients, e is a natural constant and is used for ensuring that denominator is larger than 1, and ln is a logarithmic function based on e;
the output capacitance reference adjustment parameters are as follows:
Inputting the capacitance deviation rate real-time input data into a capacitance deviation rate prediction model to obtain a predicted value of the capacitance deviation rate output by the capacitance deviation rate prediction model;
Marking the predicted value of the output capacitance deviation rate as Z;
marking the capacitance reference adjustment parameter as B, and calculating the capacitance reference adjustment parameter B according to the following formula: 。
8. The touch screen parameter self-adaptive correction system is used for realizing the touch screen parameter self-adaptive correction method according to any one of claims 1-7, and is characterized by comprising a pre-data collection module, a model training module, a real-time data collection module and a self-adaptive correction module; wherein, each module is electrically connected;
The pre-data collection module is used for collecting capacitance reference values in advance, collecting capacitance deviation rate training input data and capacitance deviation rate tag data in a test environment, sending the capacitance reference values to the self-adaptive correction module, and sending the capacitance deviation rate training input data and the capacitance deviation rate tag data to the model training module;
the model training module is used for training a capacitance deviation rate prediction model for predicting the capacitance deviation rate generated by the touch screen after being pressed under different environmental conditions by taking the capacitance deviation rate training input data as input and the capacitance deviation rate label data as output, and sending the capacitance deviation rate prediction model to the self-adaptive correction module;
The real-time data collection module is used for collecting historical operation statistical data of the touch screen to be corrected, collecting capacitance deviation rate real-time input data in real time, and sending the historical operation statistical data and the capacitance deviation rate real-time input data to the self-adaptive correction module;
the self-adaptive correction module is used for presetting a hardware correction parameter to be 1; collecting user touch frequency data of a touch screen to be corrected, judging whether hardware correction requirements exist or not based on the user touch frequency data, and updating hardware correction parameters based on historical operation statistical data if the hardware correction requirements exist; if the hardware correction requirement does not exist, inputting data, a capacitance deviation rate prediction model and hardware correction parameters in real time based on the capacitance deviation rate, and outputting capacitance reference adjustment parameters; and correcting the capacitance reference value based on the capacitance reference adjustment parameter.
9. An electronic device, comprising: a processor and a memory, wherein,
The memory stores a computer program which can be called by the processor;
The processor performs the touch screen parameter adaptive correction method of any one of claims 1-7 in the background by invoking a computer program stored in the memory.
10. A computer readable storage medium having stored thereon a computer program that is erasable;
The computer program, when run on a computer device, causes the computer device to perform implementing the touch screen parameter adaptive correction method of any one of claims 1-7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410130444.0A CN118012288A (en) | 2024-01-31 | 2024-01-31 | Touch screen parameter self-adaptive correction method, system, electronic equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410130444.0A CN118012288A (en) | 2024-01-31 | 2024-01-31 | Touch screen parameter self-adaptive correction method, system, electronic equipment and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN118012288A true CN118012288A (en) | 2024-05-10 |
Family
ID=90949447
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410130444.0A Pending CN118012288A (en) | 2024-01-31 | 2024-01-31 | Touch screen parameter self-adaptive correction method, system, electronic equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN118012288A (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103677452A (en) * | 2012-08-30 | 2014-03-26 | 华为终端有限公司 | Capacitive touch screen calibration method and capacitive touch device |
| CN105807998A (en) * | 2016-03-09 | 2016-07-27 | 周奇 | Correction method and device of capacitive touch screen |
| CN109564481A (en) * | 2017-06-22 | 2019-04-02 | 深圳市汇顶科技股份有限公司 | Update method, device, touch screen and the electric terminal of touch screen present reference value |
| WO2021081709A1 (en) * | 2019-10-28 | 2021-05-06 | 深圳市欢太科技有限公司 | Touch screen calibration method and apparatus, electronic device and storage medium |
| CN115079867A (en) * | 2022-06-17 | 2022-09-20 | 东莞市泰宇达光电科技有限公司 | Input accuracy detection and correction method for capacitive touch screen |
-
2024
- 2024-01-31 CN CN202410130444.0A patent/CN118012288A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103677452A (en) * | 2012-08-30 | 2014-03-26 | 华为终端有限公司 | Capacitive touch screen calibration method and capacitive touch device |
| CN105807998A (en) * | 2016-03-09 | 2016-07-27 | 周奇 | Correction method and device of capacitive touch screen |
| CN109564481A (en) * | 2017-06-22 | 2019-04-02 | 深圳市汇顶科技股份有限公司 | Update method, device, touch screen and the electric terminal of touch screen present reference value |
| WO2021081709A1 (en) * | 2019-10-28 | 2021-05-06 | 深圳市欢太科技有限公司 | Touch screen calibration method and apparatus, electronic device and storage medium |
| CN115079867A (en) * | 2022-06-17 | 2022-09-20 | 东莞市泰宇达光电科技有限公司 | Input accuracy detection and correction method for capacitive touch screen |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US10234992B2 (en) | Force-sensitive touch sensor compensation | |
| CN108960163B (en) | Gesture recognition method, device, equipment and storage medium | |
| US9612675B2 (en) | Emulating pressure sensitivity on multi-touch devices | |
| CN112380981B (en) | Face key point detection method and device, storage medium and electronic equipment | |
| CN107748641B (en) | Numerical value adjustment control method and device, electronic equipment and storage medium | |
| JP2017529582A (en) | Touch classification | |
| CN109614016B (en) | Touch identification method and device of capacitive touch screen and electronic equipment | |
| CN108228057B (en) | Touch inflection point correction method and device and touch screen | |
| CN113253878B (en) | Touch screen report point determining method and device, electronic equipment and storage medium | |
| CN107562366A (en) | Gesture identification method, computer installation and computer-readable recording medium | |
| CN109491502B (en) | Haptic rendering method, terminal device and computer-readable storage medium | |
| US9697184B2 (en) | Adjusting layout size of hyperlink | |
| CN111126268A (en) | Key point detection model training method and device, electronic equipment and storage medium | |
| CN110442266B (en) | Object recognition method, object recognition device, electronic equipment and storage medium | |
| CN107450840B (en) | Method and device for determining finger touch connected domain and electronic equipment | |
| CN109298809A (en) | A kind of touch action recognition methods, device and terminal device | |
| CN118012288A (en) | Touch screen parameter self-adaptive correction method, system, electronic equipment and storage medium | |
| CN110248023B (en) | Intelligent terminal control method, device, equipment and medium | |
| CN112990163B (en) | Fingerprint calibration method, electronic device and storage medium | |
| CN116661618A (en) | Touch mode switching method and device for touch screen | |
| US20120299837A1 (en) | Identifying contacts and contact attributes in touch sensor data using spatial and temporal features | |
| CN110471576B (en) | Single-camera near-screen touch method, system, platform and storage medium | |
| CN113190160A (en) | Input error correction method, computing device and medium for analyzing hand tremor false touch | |
| KR101993257B1 (en) | Apparatus of correcting touch input based on compensation hand vibration | |
| CN111338552A (en) | Chalk handwriting writing method and device and electronic equipment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |