Touch calibration method and system for touch display screen

Abstract

The invention relates to the technical field of display screen touch, and discloses a touch calibration method and system for a touch display screen, and the method comprises the steps: obtaining the initial characteristic data of the touch display screen, and obtaining the signal distribution reference value of each region; analyzing the relevance between the difference of each region and the use frequency, and determining the signal attenuation trend of each region; according to the signal attenuation trend, determining an area in which a response signal deviates from a preset response threshold range, and obtaining a list of areas to be adjusted; sorting the to-be-adjusted areas, and performing association matching in combination with the obtained user behavior difference data to obtain preliminary calibration parameters; correcting the preliminary calibration parameters in combination with external environment data to obtain optimized calibration parameters; and issuing the optimized calibration parameter set to each regional control unit, and combining real-time user behavior adjustment to obtain a final calibration scheme. According to the method, the problem that the screen touch response accuracy is reduced due to the fact that an existing touch screen touch technology cannot be dynamically adjusted can be solved.

Description

Technical Field

[0001] This invention relates to the field of display screen touch technology, and in particular to a touch calibration method and system for a touch display screen. Background Technology

[0002] Currently, touch displays, as the core interactive interface of modern electronic devices, directly determine the quality of user experience through their accuracy and reliability, making them an indispensable key technology in the field of human-computer interaction. Whether it's smartphones, tablets, or industrial control equipment, the accurate response of touchscreens profoundly impacts operational efficiency and the realization of equipment functions.

[0003] Current mainstream solutions rely on preset calibration templates or fixed algorithms to achieve touch position correction; this technology collects reference signal values ​​of a limited number of points on the screen, generates a standardized compensation parameter table, and uses a linear compensation algorithm to perform one-dimensional linear mapping of the touch signal; it can meet basic positioning requirements in a static environment. However, existing technologies apply the same set of parameters to the entire screen, ignoring regional differences and failing to make dynamic adjustments. When faced with environmental changes causing screen capacitance characteristics to drift or sensor fatigue in localized areas due to prolonged pressing, the accuracy of screen touch response decreases. Summary of the Invention This invention provides a touch calibration method and system for a touch display screen to solve the problem of reduced screen touch response accuracy caused by existing touch screen technologies.

[0004] In a first aspect, to solve the above-mentioned technical problems, the present invention provides a touch calibration method for a touch display screen, comprising: Acquire and analyze the initial characteristic data of each area of ​​the touch screen to obtain the signal distribution reference value of each area; Based on the signal distribution baseline value, the correlation between the differences in each region and the frequency of use is analyzed to determine the signal attenuation trend of each region at different stages of use. Based on the signal attenuation trend, the regions where the response signal deviates from the preset response threshold range are identified as regions to be adjusted, thus obtaining a list of regions to be adjusted. The list of areas to be adjusted is sorted, and the obtained user behavior difference data is used for correlation matching to obtain preliminary calibration parameters; Collect external environmental data, and correct the preliminary calibration parameters if the external environmental data exceeds the preset fluctuation range to obtain optimized calibration parameters; The optimized calibration parameter set is distributed to each area control unit, and the optimized calibration parameters are adjusted based on real-time user touch behavior to obtain the final calibration scheme.

[0005] In one optional implementation, the step of acquiring and analyzing initial characteristic data of each area of ​​the touch display screen to obtain a signal distribution reference value for each area includes: Divide the screen into multiple independent detection areas; Acquire initial characteristic data for each region to obtain preliminary signal distribution records; The initial characteristic data in the preliminary signal distribution record are analyzed to determine the difference in response signals in each region; The data whose response signal difference value exceeds a preset difference threshold are calibrated to obtain the signal distribution reference value of each region.

[0006] In one optional implementation, the step of analyzing the correlation between the differences in each region and the usage frequency based on the signal distribution reference value, and determining the signal attenuation trend of each region at different usage stages, includes: Acquire usage frequency, response characteristics, and phase change data for each area of ​​the screen; Based on the response characteristic data, the changes in the usage frequency are analyzed to obtain signal change records for each region at different stages; The signal change records are compared and analyzed with the signal distribution benchmark value to determine the correlation between the signal changes in each region and the usage frequency. Based on the regions where the correlation exceeds the preset correlation threshold and the stage change data, a time-series-based signal monitoring archive is constructed to obtain the signal attenuation trend of each region at different usage stages.

[0007] In one optional implementation, the step of determining the region where the response signal deviates from a preset response threshold range as the region to be adjusted based on the signal attenuation trend, and obtaining a list of regions to be adjusted, includes: Based on the signal attenuation trend and the physical characteristics of each region, the historical response signals of each region are obtained; The historical response characteristic signal is compared with a preset signal value anomaly threshold to obtain a set of regions where the signal deviates from the anomaly. The regions in the set of regions where the signal deviates from the abnormal region are continuously exceeding the preset abnormal signal value threshold are classified and processed to obtain the priority order for adjustment. Based on the priority of the adjustment, the signal data of the areas to be adjusted are integrated into a preset database to obtain a list of areas to be adjusted.

[0008] In one optional implementation, the step of sorting the list of regions to be adjusted and performing correlation matching with the acquired user behavior difference data to obtain preliminary calibration parameters includes: Based on the list of areas to be adjusted, obtain area priority data, touch accuracy data, and response lag data; Based on the region priority data, the touch accuracy data, and the response lag data, each region is graded to obtain a priority ranking of each region, and user behavior difference value data is obtained based on the priority ranking. The user behavior difference data, the touch accuracy data, and the sluggishness data are matched to determine the adjustment direction of the area calibration parameters; Based on the adjustment direction of the regional calibration parameters and the regional priority data, the calibration parameter information is integrated to obtain preliminary calibration parameters.

[0009] In one optional implementation, the step of collecting external environmental data and correcting the preliminary calibration parameters where the external environmental data exceeds a preset fluctuation range to obtain optimized calibration parameters includes: Acquire data on environmental fluctuations around the touchscreen display; If the fluctuation of the environmental data exceeds the preset fluctuation range, the preliminary calibration parameters are correlated with the fluctuation of the environmental data to obtain the basis for the secondary correction of the preliminary calibration parameters. Based on the aforementioned secondary correction, the preliminary calibration parameters are updated one by one to obtain optimized calibration parameters.

[0010] In one optional implementation, the step of distributing the optimized calibration parameter set to each area control unit, and adjusting the optimized calibration parameters based on real-time user touch behavior to obtain the final calibration scheme includes: The optimized calibration parameters are distributed to each regional control unit, and distribution status details are obtained. If the distribution status details are fully displayed, then real-time user touch behavior data is obtained; The real-time data of user touch behavior is compared one by one with the pre-established historical archives to obtain the deviation of the real-time data of user touch behavior. Abnormal fluctuation information is obtained from the real-time data deviation of the user's touch behavior and converted into parameter correction amounts; The optimized calibration parameters are adjusted according to the parameter correction amount to obtain the final calibration scheme.

[0011] Secondly, the present invention provides a touch calibration system for a touch display screen, comprising: The data acquisition module is used to acquire and analyze the initial characteristic data of each area of ​​the touch screen to obtain the signal distribution reference value of each area. The attenuation trend acquisition module is used to analyze the correlation between the differences in each region and the usage frequency based on the signal distribution reference value, and to determine the signal attenuation trend of each region at different usage stages. The region list acquisition module is used to determine the regions where the response signal deviates from the preset response threshold range as regions to be adjusted based on the signal attenuation trend, and to obtain a list of regions to be adjusted. The parameter calibration module is used to sort the list of areas to be adjusted and perform correlation matching with the acquired user behavior difference data to obtain preliminary calibration parameters; The calibration parameter correction module is used to collect external environmental data and correct the preliminary calibration parameters when the external environmental data exceeds the preset fluctuation range to obtain optimized calibration parameters. The calibration scheme output module is used to send the optimized calibration parameter set to the control units of each area, and adjust the optimized calibration parameters in combination with real-time user touch behavior to obtain the final calibration scheme.

[0012] Thirdly, the present invention also provides an electronic device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement a touch calibration method for a touch display screen as described in any one of the preceding claims.

[0013] Fourthly, the present invention also provides a computer-readable storage medium comprising a stored computer program, wherein, when the computer program is executed, it controls the device on which the computer-readable storage medium is located to perform a touch calibration method for a touch display screen as described in any one of the preceding claims.

[0014] Compared with the prior art, the present invention has the following beneficial effects: (1) The present invention divides the touch screen and collects initial characteristic data to obtain the signal distribution benchmark value of each area. Then, based on the correlation between the differences in each area and the frequency of use, the signal attenuation trend of each area is determined to obtain the list of areas to be adjusted. Through the partitioning and collection processing of the touch screen, the system can obtain the areas to be processed more efficiently and accurately, reducing resource consumption.

[0015] (2) This invention collects external environmental data, maps the environmental data into correction values, optimizes calibration parameters, and then combines real-time user behavior adjustments to obtain the final calibration scheme. By incorporating environmental data into the optimization scope, the touch accuracy of the touchscreen can be effectively improved in various environments.

[0016] (3) This invention constructs a multi-dimensional data fusion and dynamic feedback calibration system by structurally collecting and modeling historical regional response data, usage frequency data, touch accuracy performance, and touch lag. Based on this, the system introduces environmental data and real-time user feedback data, employs time series analysis, and performs correlation analysis on the multi-dimensional data to derive a set of calibration parameters that dynamically match the environment, user behavior, and device status. These calibration parameters are distributed to each regional control unit through an adaptive adjustment mechanism and are locally fine-tuned based on real-time feedback. By adjusting the control parameters in real-time through feedback on parameters such as touchscreen sensitivity, the dynamic performance of the touchscreen is effectively improved, and the touchscreen accuracy is significantly enhanced. Attached Figure Description

[0017] Figure 1 This is a schematic flowchart of a touch calibration method for a touch display screen provided in the first embodiment of the present invention; Figure 2 This is a schematic diagram of a touch calibration system module for a touch display screen provided in the second embodiment of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Currently, touch displays, as the core interactive interface of modern electronic devices, directly determine the quality of user experience through their accuracy and reliability, making them an indispensable key technology in the field of human-computer interaction. Whether it's smartphones, tablets, or industrial control equipment, the accurate response of touchscreens profoundly impacts operational efficiency and the realization of equipment functions.

[0020] Current mainstream solutions rely on preset calibration templates or fixed algorithms to achieve touch position correction; this technology collects reference signal values ​​of a limited number of points on the screen, generates a standardized compensation parameter table, and uses a linear compensation algorithm to perform one-dimensional linear mapping of the touch signal; it can meet basic positioning requirements in a static environment. However, existing technologies apply the same set of parameters to the entire screen, ignoring regional differences and failing to make dynamic adjustments. When faced with environmental changes causing screen capacitance characteristics to drift or sensor fatigue in localized areas due to prolonged pressing, the accuracy of screen touch response decreases.

[0021] To address the aforementioned issues, the touch calibration method for a touch display screen provided in this application will be described in detail and explained through the following specific embodiments.

[0022] Reference Figure 1 The first embodiment of the present invention provides a touch calibration method for a touch display screen, comprising the following steps: S101, acquire and analyze the initial characteristic data of each area of ​​the touch screen to obtain the signal distribution reference value of each area; S102, Based on the signal distribution reference value, analyze the correlation between the differences in each region and the usage frequency, and determine the signal attenuation trend of each region at different usage stages; S103, Based on the signal attenuation trend, determine the regions where the response signal deviates from the preset response threshold range as the regions to be adjusted, and obtain a list of regions to be adjusted; S104, Sort the list of areas to be adjusted, and perform correlation matching with the acquired user behavior difference data to obtain preliminary calibration parameters; S105, Collect external environmental data, and correct the preliminary calibration parameters for external environmental data that exceed the preset fluctuation range to obtain optimized calibration parameters; S106, the optimized calibration parameter set is sent to each area control unit, and the optimized calibration parameters are adjusted in combination with real-time user touch behavior to obtain the final calibration scheme.

[0023] In step S101, initial characteristic data of each area of ​​the touch screen are acquired and analyzed to obtain the signal distribution reference value of each area, including: S1011 divides the screen into multiple independent detection areas; S1012, Obtain initial characteristic data for each region to obtain preliminary signal distribution records; S1013, Analyze the initial characteristic data in the preliminary signal distribution record to determine the response signal difference value of each region; S1014, calibrate the data whose response signal difference value exceeds the preset difference threshold to obtain the signal distribution reference value of each region.

[0024] In step S1011, the screen is divided into multiple independent detection areas.

[0025] In one implementation, a screen is divided into a 10x10 grid, comprising 100 units, and the touch response signal of each unit is recorded separately.

[0026] Using a grid-based approach allows for more refined unit management and precise location of problem areas, avoiding the waste of resources in full-screen calibration. In step S1012, the initial characteristic data of each region are obtained to obtain a preliminary signal distribution record.

[0027] It should be noted that the "initial characteristic data" refers to the raw touch response signal directly collected from each independent detection unit after the touch display screen has been divided into grids, without any calibration or compensation. Its core indicator is the signal strength value of that area.

[0028] In one implementation, the signal strength value of each area is obtained. This value is typically represented as a discrete value of 0–255 or an ADC quantization value of 0–4095, with a higher value indicating a more sensitive touch response in that area.

[0029] For example, suppose a touch screen is divided into 100 areas, the target measured a signal strength of 80, and its right adjacent cell is 75.

[0030] By recording this data, a preliminary signal distribution record can be created. This preliminary signal distribution record can not only correct current problems but also provide data support for subsequent maintenance, facilitating large-scale production and maintenance.

[0031] In step S1013, the initial characteristic data in the preliminary signal distribution record is analyzed to determine the response signal difference value of each region.

[0032] It should be noted that the response signal difference value is used to measure the degree of deviation between a single detection unit and a preset reference value.

[0033] In one implementation, 100 sets of preliminary signal distribution records for the target area are sampled at equal intervals to obtain the data mean and standard deviation. Outlier data exceeding three times the standard deviation are removed, and the remaining data are averaged again to obtain the sampling data baseline value for that unit. The difference between this baseline value and the initial preset baseline value for the corresponding area is then calculated to obtain the response signal difference value for that area. The initial preset baseline value for the corresponding unit is determined by factory calibration or the full-screen median value.

[0034] Arithmetic sampling effectively avoids extreme value shifts caused by transient noise or occasional interference, providing numerical basis for subsequent threshold determination, local calibration, and attenuation trend modeling.

[0035] In step S1014, the data whose response signal difference value exceeds a preset difference threshold are calibrated to obtain the signal distribution reference value of each region.

[0036] In one implementation, if the initial preset reference value is 78, the obtained response signal difference is 7, and the preset difference threshold is 5, then calibration is required. The calibration process can be performed by adjusting the signal gain in this region or reducing the sensitivity, so that the obtained response signal difference is close to the reference value. Specifically, adjusting the signal gain in this region involves reducing the gain of the digital front-end amplifier to make the output amplitude approach the reference. By comparing the differences between different areas, it can be found that some areas of the screen may have experienced sensor aging due to long-term use, resulting in lower response signals. This calibration process can effectively reduce uneven touch response and improve the user experience.

[0037] In step S102, based on the signal distribution reference value, the correlation between the differences in each region and the usage frequency is analyzed to determine the signal attenuation trend of each region at different usage stages, including: S1021, acquire usage frequency, response characteristic data and stage change data of each area of ​​the screen.

[0038] S1022, Based on the response characteristic data, analyze the changes in the usage frequency to obtain signal change records for each region at different stages.

[0039] S1023, compare and analyze the signal change records with the signal distribution reference value to determine the correlation between the signal changes in each region and the usage frequency.

[0040] S1024, Based on the regions where the correlation exceeds the preset correlation threshold and the stage change data, construct a time series-based signal monitoring archive to obtain the signal attenuation trend of each region at different usage stages.

[0041] In step S1021, the usage frequency, response characteristics data, and stage change data of each area of ​​the screen are obtained.

[0042] It should be noted that the usage frequency of each area of ​​the screen refers to touch behavior counted in units of a single effective press-and-release event; the response characteristic data of each area of ​​the screen refers to the peak signal intensity of the target area under standard finger pressure; and the usage frequency of each area of ​​the screen refers to the incremental sequence of the above two indicators recorded at a fixed time window.

[0043] In one implementation, the system uses a multi-channel sensor array to periodically and with low power scan each detection unit that has completed grid division, simultaneously acquiring usage frequency, response characteristic data, and stage change data for each area of ​​the screen. For example, the response intensities of preset areas in the three time periods (T1, T2, T3) are recorded as 77, 80, and 79, respectively; the usage frequencies of different areas within the same preset time period are 110, 70, and 95, respectively. The three time periods can be T1 08:00-12:00, T2 12:00-18:00, and T3 18:00-22:00, respectively.

[0044] In step S1022, the changes in the usage frequency are analyzed based on the response characteristic data to obtain signal change records for each region at different stages.

[0045] In one implementation, for each region system, the usage frequency (denoted as P) - response characteristic data (denoted as Q) pairs are continuously recorded using the sliding window method, and the stage difference is calculated to form a "stage". "The five-element record obtains the signal change records of each region at different stages. For example, suppose the target region during time period T1 on a certain day..." =80, =180; During time period T2, P=75, Q=220. The period records are as follows: .

[0046] In step S1023, the signal change records are compared and analyzed with the signal distribution reference value to determine the correlation between the signal changes in each region and the usage frequency.

[0047] In one implementation, the signal change record is compared with the signal distribution reference value to obtain the signal difference of each region. Regions whose signal difference exceeds a preset range are marked. The average usage frequency of each region is taken as the reference usage frequency. The difference between the usage frequency of each region and the reference usage frequency is calculated. The percentage of the signal difference exceeding a preset threshold and the percentage of the usage frequency change exceeding the associated threshold are calculated respectively. The correlation between the regional signal change and the usage frequency is obtained by multiplying the percentage of the signal difference exceeding the preset threshold and the percentage of the usage frequency exceeding the preset associated threshold.

[0048] For example, the signal distribution baseline value is 78, the signal change is recorded as the target area decreasing from 80 to 75, the preset threshold range is 2, then the proportion exceeding the preset threshold is 150%; the mean calculation shows that the average usage frequency of the touch screen in all areas within the preset time is 200, the usage frequency of the target area is 220, then the proportion of usage frequency change exceeding the correlation threshold is 110%, multiplying the proportion of signal exceeding the preset threshold of 150% by the relative usage frequency of 110% gives 1.65, thus obtaining the correlation between regional signal change and usage frequency.

[0049] In step S1024, based on the regions where the correlation exceeds the preset correlation threshold and the stage change data, a time-series-based signal monitoring archive is constructed to obtain the signal attenuation trend of each region at different usage stages.

[0050] It should be noted that the time-series-based signal monitoring archive includes corresponding timestamps, usage frequency data, response intensity data, correlation data, and signal change trends.

[0051] In one implementation, a preset correlation threshold of 1.5 is set, and the correlation between signal changes in the target area and usage frequency is 1.65. Then, stage change data of the area is retrieved, and response intensity data and usage frequency data are recorded in different time periods to construct a signal monitoring archive based on time series. The usage frequency data and response intensity data in the archive are retrieved, and a univariate nonlinear fitting is performed to obtain an attenuation trend curve. The signal attenuation trend of the target area in different usage stages is obtained from the attenuation trend curve. The dependent variable of the univariate nonlinear fitting is the usage frequency, and the dependent variable is the corresponding response intensity.

[0052] Through the above process, the system establishes a traceable, predictable, and compensable "time series signal monitoring file" for each highly correlated area, transforming the correlation into an operable attenuation trend curve, providing a data foundation for ensuring touch consistency throughout the entire lifecycle.

[0053] In step S103, based on the signal attenuation trend, regions where the response signal deviates from a preset response threshold range are identified as regions to be adjusted, resulting in a list of regions to be adjusted, including: S1031, Based on the signal attenuation trend and the physical characteristics of each region, obtain the historical response signal of each region.

[0054] S1032, compare the historical response characteristic signal with the preset signal value abnormality threshold to obtain a set of regions where the signal deviates from the abnormality.

[0055] S1033, the regions in the set of regions where the signal continuously exceeds the preset abnormal signal value threshold are classified and processed to obtain the priority order for adjustment.

[0056] S1034, according to the priority of the adjustment, the signal data of the areas to be adjusted are integrated into a preset database to obtain a list of areas to be adjusted.

[0057] In step S1031, the historical response signals of each region are obtained based on the signal attenuation trend and the physical characteristics of each region.

[0058] It should be noted that the physical characteristics of each region, including ITO coating thickness, line resistance, adhesive thickness, and temperature coefficient, are updated in real time by the factory calibration table and online temperature drift sensor. The historical response signal includes timestamps, usage frequency data, response intensity data, correlation data, and signal change trends.

[0059] In one implementation, the system first calls the signal attenuation trend at different usage stages, combines the physical characteristics of each region, assigns weights to each part, and jointly determines whether the "historical response signal" is abnormal; and extracts the historical response information of the region set with abnormal signal attenuation from the pre-established time series signal monitoring archive.

[0060] In step S1032, the historical response characteristic signal is compared with a preset signal value abnormality threshold to obtain a set of regions where the signal deviates from the abnormality.

[0061] It should be noted that the preset signal value anomaly threshold includes two levels of thresholds: an absolute threshold and a relative threshold. The absolute threshold is used to determine whether the average signal strength during the target time period falls within the normal range; the relative threshold is used to quantify the signal drop rate within a preset time period. Only when the regional signal simultaneously triggers both the absolute and relative thresholds is it marked as an anomaly.

[0062] In one implementation, the average signal strength of the target area during the target time period is compared with a preset absolute threshold, and the signal change trend of the target area during the target time period is compared with a preset relative threshold. Areas that exceed both the absolute and relative thresholds are recorded to obtain a set of areas with abnormal signal deviation.

[0063] By comparing historical response characteristic signals with dual thresholds in parallel, the system can accurately locate areas of accelerated aging caused by high-frequency touch, thereby reducing invalid global calibration, lowering maintenance costs, and extending screen life.

[0064] In step S1033, the regions in the set of regions where the signal continuously exceeds the preset abnormal signal value threshold are classified and processed to obtain an adjustment priority ranking.

[0065] It should be noted that the grading process sorts the areas that deviate from the abnormality according to the comprehensive risk score of touch reliability from high to low. The risk score includes three elements: signal deviation depth, duration, and expected deterioration rate.

[0066] In one implementation, the system assigns weights to regions deviating from the anomaly based on three factors: deviation depth, duration, and expected deterioration rate, and calculates a total weight using weighted averages. The adjustment priority of the regions deviating from the anomaly is then ranked according to the magnitude of the total weight. These weights are assigned by the user.

[0067] In step S1034, the signal data of the areas to be adjusted are integrated into a preset database according to the priority of the adjustment to obtain a list of areas to be adjusted.

[0068] It should be noted that the preset database is determined by two parts: priority and resources. Priority determines the writing precision, and resource quota determines the sampling density. For high-priority data, signal strength, usage frequency, correlation, attenuation trend curve, and priority data are written in full. For low-priority data, segmented mean sampling is used to compress the signal strength, usage frequency, correlation, attenuation trend curve, and priority data before adding them to the preset database. Through a tiered write mechanism, high-priority areas receive sufficient data support, while low-priority areas avoid redundant storage; while ensuring real-time calibration of critical areas, storage and transmission overhead are significantly reduced, and a scalable, systematic management closed loop is formed.

[0069] In step S104, the list of regions to be adjusted is sorted, and correlation matching is performed with the acquired user behavior difference data to obtain preliminary calibration parameters, including: S1041, Based on the list of areas to be adjusted, obtain area priority data, touch accuracy data, and response lag data.

[0070] S1042, based on the area priority data, the touch accuracy data, and the response lag data, each area is graded to obtain a priority ranking of each area, and user behavior difference value data is obtained based on the priority ranking.

[0071] S1043, Match the user behavior difference data, the touch accuracy data, and the sluggishness data to determine the adjustment direction of the area calibration parameters.

[0072] S1044, Based on the adjustment direction of the regional calibration parameters and the regional priority data, the calibration parameter information is integrated to obtain preliminary calibration parameters.

[0073] In step S1041, based on the list of areas to be adjusted, area priority data, touch accuracy data, and response lag data are obtained.

[0074] It should be noted that the area priority data refers to the corresponding data in the preset database; the touch accuracy data and response latency data are stored in a pre-established historical archive. The pre-established historical archive refers to the location where preset user touch performance data is stored, including user touch frequency data, response intensity data, touch accuracy data, and touch latency data for each area.

[0075] In step S1042, each region is graded according to the region priority data, the touch accuracy data, and the response lag data to obtain a priority ranking of each region, and user behavior difference value data is obtained according to the priority ranking.

[0076] It should be noted that the user behavior difference data includes differences in user touch frequency, user touch response intensity, user touch accuracy, and user touch response latency.

[0077] In one implementation, the system assigns weights to each region based on three factors: region priority data, touch accuracy data, and response latency data. All regions to be adjusted are then categorized according to their total weight, and corresponding user behavior difference data is extracted for each category. For high-priority data, user touch frequency differences, user touch response intensity differences, and user touch response latency differences are fully extracted. For low-priority data, segmented mean sampling is used to compress these differences before extraction. The weights are assigned by the user.

[0078] In step S1043, the user behavior difference data, the touch accuracy data, and the sluggishness data are matched to determine the adjustment direction of the area calibration parameters.

[0079] In one implementation, the differences in user touch accuracy and user touch response latency are compared with touch accuracy and sluggishness in the user behavior difference values ​​to calculate the user touch accuracy deviation and user touch sluggishness deviation. These deviations are then mapped to sensitivity compensation R and front-end gain compensation Y, respectively, to determine the adjustment direction of the area calibration parameters. .

[0080] In step S1044, the calibration parameter information is integrated according to the adjustment direction of the regional calibration parameters and the regional priority data to obtain preliminary calibration parameters.

[0081] In one implementation, the adjustment direction of the regional calibration parameters is defined. After normalizing the priority data for each region, it is linearly mapped to the interval of 0.8-1.2 to obtain the gain coefficient k, and then the final adjustment range is determined. Integrate the adjustment range information of each region to obtain preliminary calibration parameters. For example, suppose there are a total of 36 regions to be adjusted, and the priority of the target region is 9 (priority range is 1-10). After normalization with the same priority, the result is an interval range. Take the median value of the range instead. If the normalization result is 85%, then the gain coefficient of the region is 1.14.

[0082] Through amplitude-priority linear mapping, high-priority regions receive more aggressive compensation, while low-priority regions maintain conservative adjustments; this integration method ensures the systematic nature and traceability of the adjustment scheme.

[0083] In step S105, external environmental data is collected, and the preliminary calibration parameters for which the external environmental data exceeds a preset fluctuation range are corrected to obtain optimized calibration parameters, including: S1051, acquire environmental data fluctuations around the touch display screen.

[0084] S1052, if the environmental data fluctuation exceeds the preset fluctuation range, the preliminary calibration parameters are correlated with the environmental data fluctuation to obtain the basis for secondary correction of the preliminary calibration parameters.

[0085] S1053, Based on the secondary correction basis, the preliminary calibration parameters are updated one by one to obtain the optimized calibration parameters.

[0086] In step S1051, the environmental data fluctuation around the touch screen is obtained.

[0087] It should be noted that the environmental data includes temperature, humidity, and light. All data is written to a circular buffer at a sampling rate of 1Hz, retaining the original sequence of the most recent 30 minutes for easy sliding window analysis.

[0088] In one implementation, environmental data within 50mm of the touchscreen is monitored in real time to capture changes in key indicators such as temperature, humidity, and light.

[0089] In step S1052, if the environmental data fluctuation exceeds the preset fluctuation range, the preliminary calibration parameters are correlated with the environmental data fluctuation to obtain the basis for secondary correction of the preliminary calibration parameters.

[0090] It should be noted that the secondary correction basis refers to the mapping direction from the initial calibration parameters to the optimized calibration parameters. The preset fluctuation range includes the preset temperature range, the preset humidity range, and the preset light range.

[0091] In one implementation, for the environmental data to be processed, the fluctuation details are compared with a preset range item by item. For areas exceeding the preset fluctuation range, the deviation is calculated to obtain temperature deviation, humidity deviation, and light deviation, respectively. These deviations are then weighted according to preset weights and applied to the R and Y parameters of the initial calibration parameters to obtain the basis for the secondary correction of the initial calibration parameters. The preset weights are given by the user.

[0092] By quantifying and converting environmental fluctuations into calibration parameters through linear mapping, computational delays caused by complex models are avoided, while ensuring touch stability under sudden changes in humidity, temperature, and light. This comparative analysis helps to accurately pinpoint the direction of environmental changes' impact on the device.

[0093] In step S1053, the preliminary calibration parameters are updated one by one according to the secondary correction basis to obtain optimized calibration parameters.

[0094] In one implementation, it is based on a quadratic correction basis. Using Newton's method, based on the initial calibration parameters The correction amount is calculated using the first and second derivative information, and the parameter values ​​are updated one by one to obtain the optimized calibration parameters. .

[0095] In step S106, the optimized calibration parameter set is distributed to each area control unit, and the optimized calibration parameters are adjusted based on real-time user touch behavior to obtain the final calibration scheme, including: S1061, The optimized calibration parameters are sent to each regional control unit to obtain the distribution status details.

[0096] S1062 If the distribution status details display is fully covered, then obtain real-time data on user touch behavior.

[0097] S1063, compare the real-time data of user touch behavior with the pre-established historical archives one by one to obtain the deviation of the real-time data of user touch behavior.

[0098] S1064, Obtain abnormal fluctuation information from the real-time data deviation of the user's touch behavior and convert it into parameter correction amount.

[0099] S1065, Adjust the optimized calibration parameters according to the parameter correction amount to obtain the final calibration scheme.

[0100] In step S1061, the optimized calibration parameters are sent to each regional control unit to obtain distribution status details.

[0101] In one implementation, calibration parameter data is grouped based on the hardware characteristics and location of the regional control units, and then pushed to each regional control unit. For example, suppose a touchscreen system covers 10 regions, where the control units in 5 regions are sensitive to signal latency, and the other 5 regions have higher signal strength requirements. Grouping processing will categorize and push the parameter data as needed, ensuring that each region receives the appropriate parameter set. Distribution status details will record the reception status of each region; if a region is found not to have received data, the system will automatically retry pushing until all regions are covered.

[0102] This approach can effectively improve the targeting and completeness of parameter distribution.

[0103] In step S1062, if the distribution status details display is fully covered, then real-time data of user touch behavior is obtained.

[0104] It should be noted that the real-time user touch behavior data includes the user's real-time touch frequency and the user's real-time touch intensity variation. The user's real-time touch frequency refers to touch behavior counted in units of a single effective press-and-release event; the user's real-time touch intensity variation refers to the peak signal intensity of the target area under standard finger pressure.

[0105] In one implementation, the system monitors whether the distribution status is fully covered, and after full coverage, it obtains the user's real-time touch frequency and the user's real-time touch intensity changes.

[0106] In step S1063, the real-time data of user touch behavior is compared one by one with the pre-established historical archives to obtain the deviation of the real-time data of user touch behavior.

[0107] It should be noted that the pre-established historical archives refer to the location where user touch performance data is stored, including user touch frequency data, response intensity data, touch accuracy data, and touch latency data for each area.

[0108] In one implementation, usage frequency data and response intensity data from a pre-established historical archive are retrieved and subtracted from real-time user touch behavior data to obtain user usage frequency deviation and response intensity deviation, respectively.

[0109] In step S1064, abnormal fluctuation information is obtained from the real-time data deviation of the user's touch behavior and converted into parameter correction amount.

[0110] It should be noted that the abnormal fluctuation information includes the duration of instability. Peak-to-peak fluctuation range .

[0111] In one implementation, the user's frequency deviation and input intensity deviation are compared with preset frequency and intensity thresholds. Signal data from local touch areas where the frequency deviation exceeds the threshold but the intensity deviation is below the threshold is amplified, and the amplified signal waveform is simultaneously acquired to obtain the parameter correction amount. When the instability lasts for a certain period of time Exceeding the duration of the enhancement When it is 5%, calculate the time correction amount. The time correction for the amplitude limit is less than 15%; when the peak-to-peak fluctuation range Exceeding the duration of the enhancement When it is 10%, calculate the strength correction amount. The amplitude limiting intensity correction is less than 20%. It should be noted that the enhancement process can instantaneously increase the front-end drive power of the target area for 300ms; the parameter correction amount... The initial value is 0.

[0112] In step S1065, the optimized calibration parameters are adjusted according to the parameter correction amount to obtain the final calibration scheme.

[0113] In one implementation, the parameter is adjusted accordingly. Adjust and optimize the calibration parameter set The final calibration scheme was calculated. .

[0114] This fine-tuning method can accurately adapt to the actual conditions of different areas, and this verification mechanism can ensure the accuracy of parameter adjustments, providing support for the stable operation of the touch screen in different areas.

[0115] Reference Figure 2 The second embodiment of the present invention provides a touch calibration system for a touch display screen, comprising: The data acquisition module is used to acquire and analyze the initial characteristic data of each area of ​​the touch screen to obtain the signal distribution reference value of each area. The attenuation trend acquisition module is used to analyze the correlation between the differences in each region and the usage frequency based on the signal distribution reference value, and to determine the signal attenuation trend of each region at different usage stages. The region list acquisition module is used to determine the regions where the response signal deviates from the preset response threshold range as regions to be adjusted based on the signal attenuation trend, and to obtain a list of regions to be adjusted. The parameter calibration module is used to sort the list of areas to be adjusted and perform correlation matching with the acquired user behavior difference data to obtain preliminary calibration parameters; The calibration parameter correction module is used to collect external environmental data and correct the preliminary calibration parameters when the external environmental data exceeds the preset fluctuation range to obtain optimized calibration parameters. The calibration scheme output module is used to send the optimized calibration parameter set to the control units of each area, and adjust the optimized calibration parameters in combination with real-time user touch behavior to obtain the final calibration scheme.

[0116] It should be noted that the touch calibration system for a touch display screen provided in this embodiment of the invention is used to execute all the process steps of the touch calibration method for a touch display screen in the above embodiment. The working principles and beneficial effects of the two are one-to-one, so they will not be described again.

[0117] This invention also provides an electronic device. The electronic device includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps described in the embodiments of the touch calibration method for a touch display screen, for example... Figure 1 The step S101 shown. Alternatively, when the processor executes the computer program, it implements the functions of each module / unit in the above-described device embodiments, such as the parameter calibration module.

[0118] For example, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the electronic device.

[0119] The electronic device may be a desktop computer, laptop, handheld computer, or smart tablet, etc. The electronic device may include, but is not limited to, a processor and memory. Those skilled in the art will understand that the above components are merely examples of electronic devices and do not constitute a limitation on the electronic device. It may include more or fewer components than described above, or combine certain components, or different components. For example, the electronic device may also include input / output devices, network access devices, buses, etc.

[0120] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the electronic device, connecting all parts of the electronic device via various interfaces and lines.

[0121] The memory can be used to store the computer programs and / or modules. The processor implements various functions of the electronic device by running or executing the computer programs and / or modules stored in the memory and by calling data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the mobile phone (such as audio data, phonebook, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0122] Wherein, if the modules / units integrated in the electronic device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0123] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0124] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A touch calibration method of a touch display screen, characterized by, The computer is executed, comprising: Obtaining initial characteristic data of each region of the touch display screen and analyzing to obtain signal distribution reference value of each region; According to the signal distribution reference value, analyze the correlation between the difference of each region and the use frequency, determine the signal attenuation trend of each region in different use stages; According to the signal attenuation trend, the region whose response signal deviates from the preset response threshold range is determined as the region to be adjusted, and the region to be adjusted list is obtained; Sort the region to be adjusted list, and combine the obtained user behavior difference data to carry out correlation matching, and obtain preliminary calibration parameter; Collect external environment data, modify the preliminary calibration parameter which exceeds the preset fluctuation range of the external environment data, and obtain optimized calibration parameter; The optimized calibration parameter set is sent to the control unit of each region, and the optimized calibration parameter is adjusted combined with real-time user touch behavior, and the final calibration scheme is obtained.

2. The touch calibration method of claim 1, wherein, The initial characteristic data of each region of the touch display screen is obtained and analyzed, and the signal distribution reference value of each region is obtained, comprising: Divide the screen into multiple independent detection regions; Obtain the initial characteristic data of each region to obtain the preliminary signal distribution record; The initial characteristic data in the preliminary signal distribution record is analyzed to determine the response signal difference value of each region; The data whose response signal difference value exceeds the preset difference threshold is calibrated to obtain the signal distribution reference value of each region. 3.The touch calibration method of claim 1, wherein, According to the signal distribution reference value, analyze the correlation between the difference of each region and the use frequency, determine the signal attenuation trend of each region in different use stages, comprising: Obtain the use frequency, response characteristic data and stage change data of each region of the screen; According to the response characteristic data, analyze the change of the use frequency to obtain the signal change record of each region in different stages; Compare and analyze the signal change record and the signal distribution reference value to determine the correlation degree of the signal change of each region and the use frequency; According to the region whose correlation degree exceeds the preset correlation threshold and the stage change data, construct a signal monitoring file based on time sequence to obtain the signal attenuation trend of each region in different use stages.

4. The touch calibration method of claim 1, wherein, According to the signal attenuation trend, the region whose response signal deviates from the preset response threshold range is determined as the region to be adjusted, and the region to be adjusted list is obtained, comprising: According to the signal attenuation trend and the physical characteristics of each region, obtain the historical response signal of each region; Compare the historical response characteristic signal with the preset signal value abnormal threshold to obtain the region set whose signal deviates abnormally; Classify the region whose signal continuously exceeds the preset signal value abnormal threshold in the region set whose signal deviates abnormally to obtain the priority order of adjustment; According to the priority order of adjustment, integrate the signal data of the region to be adjusted into the preset database to obtain the region to be adjusted list.

5. The touch calibration method of claim 1, wherein, The region to be adjusted list is sorted, and the obtained user behavior difference data is associated and matched to obtain the preliminary calibration parameter, comprising: According to the region list to be adjusted, region priority data, touch precision data and response delay data are acquired; According to the region priority data, the touch precision data and the response delay data, each region is processed in a hierarchical manner to obtain a priority ranking of each region, and user behavior difference value data is acquired according to the priority ranking; The user behavior difference value data, the touch precision data and the delay data are matched to determine the adjustment direction of the region calibration parameter; According to the adjustment direction of the region calibration parameter and the region priority data, the calibration parameter information is integrated to obtain a preliminary calibration parameter.

6. The touch calibration method of claim 1, wherein, The preliminary calibration parameter is modified according to the external environment data that exceeds a preset fluctuation range to obtain an optimized calibration parameter, including: Acquiring the fluctuation of the environment data around the touch display screen; If the environment data fluctuation exceeds the preset fluctuation range, the preliminary calibration parameter and the environment data fluctuation are associated to obtain a secondary modification basis for the preliminary calibration parameter; According to the secondary modification basis, the preliminary calibration parameter is updated one by one to obtain an optimized calibration parameter.

7. The touch calibration method of claim 1, wherein, The optimized calibration parameter set is distributed to the region control unit, and the optimized calibration parameter is adjusted in combination with real-time user touch behavior to obtain a final calibration scheme, including: The optimized calibration parameter is distributed to the region control unit to obtain distribution state details; If the distribution state details show complete coverage, real-time user touch behavior data is acquired; The real-time user touch behavior data is compared with the historical archives established in advance one by one to obtain real-time user touch behavior data deviation; Abnormal fluctuation information is obtained from the real-time user touch behavior data deviation and converted into a parameter correction amount; The optimized calibration parameter is adjusted according to the parameter correction amount to obtain a final calibration scheme.

8. A touch calibration system for a touch display screen, characterized in that, Including: A data acquisition module is configured to acquire initial characteristic data of each region of a touch display screen and analyze the initial characteristic data to obtain signal distribution reference values of each region; An attenuation trend acquisition module is configured to analyze the correlation between the signal distribution reference values and the use frequency of each region according to the signal distribution reference values to determine signal attenuation trends of each region at different use stages; A region list acquisition module is configured to determine regions whose response signals deviate from a preset response threshold range as regions to be adjusted according to the signal attenuation trends to obtain a region list to be adjusted; A parameter calibration module is configured to sort the region list to be adjusted and associate and match the sorted region list to user behavior difference data to obtain a preliminary calibration parameter; A calibration parameter modification module is configured to acquire external environment data and modify the preliminary calibration parameter that exceeds a preset fluctuation range according to the external environment data to obtain an optimized calibration parameter; A calibration scheme output module is configured to distribute the optimized calibration parameter set to the region control unit and adjust the optimized calibration parameter in combination with real-time user touch behavior to obtain a final calibration scheme.

9. An electronic device, comprising: The computer readable storage medium comprises a stored computer program, wherein the computer readable storage medium controls a device in which the computer readable storage medium is located to execute the touch control calibration method of the touch display screen according to any one of claims 1 to 7 when the computer program runs.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium comprises a stored computer program, wherein the computer readable storage medium controls a device in which the computer readable storage medium is located to execute the touch control calibration method of the touch display screen according to any one of claims 1 to 7 when the computer program runs.

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