Performance detection method and system for visual interactive touch screen based on AI technology

Abstract

The invention is applicable to the technical field of touch screen performance detection, and provides a visual interactive touch screen performance detection method and system based on an AI (artificial intelligence) technology, and the method comprises the following steps: after multi-point touch detection is carried out on a detected display unit, if acquired capacitive sensor measurement data is abnormal, determining a current touch working mode, and obtaining an initial gain amplitude parameter prepared for the capacitive sensor and a historical processing record of a noise conditioning device for processing measurement data of the capacitive sensor. According to the invention, the first correction factor and the second correction factor are combined, the initial gain amplitude parameter is dynamically corrected, and adaptive optimization of the processing capability of the noise conditioning device is realized. The first correction factor is used for evaluating a noise baseline drift condition and reflecting a long-term noise change trend, and the second correction factor is used for analyzing a change condition of a calibration error and evaluating a real-time correction effect of the noise conditioning device.

Description

Technical Field

[0001] The present invention belongs to the technical field of touch screen performance detection, and in particular relates to a performance detection method and system for a visual interactive touch screen based on AI technology. Background Art

[0002] Visual interactive touchscreens are display devices that enable user input and interaction through touch. They are widely used in smart terminals, industrial control panels, medical equipment, and in-vehicle systems. These touchscreens are typically based on capacitive sensor technology, detecting changes in capacitance at the touch point to determine the user's input location and enabling complex interactive operations using multi-touch technology. Touchscreen performance directly impacts the user experience, with core testing criteria including touch sensitivity, response time, interference immunity, and signal stability. Capacitive sensor measurement data is a key indicator, directly determining the accuracy and stability of touch input. However, in real-world applications, touchscreens operate in complex and variable environments. Factors such as temperature fluctuations, electromagnetic interference, and device aging caused by long-term operation can all affect touch signals. Therefore, accurate performance testing of visual interactive touchscreens is crucial.

[0003] In existing technologies, touch screen performance testing typically relies on analyzing capacitive sensor measurement data and optimizing touch signal quality through gain compensation to reduce interference from environmental factors. Furthermore, to improve touch screen noise immunity, existing technologies commonly employ noise conditioning devices to process sensor signals, such as filtering, correction, and noise suppression, to ensure touch data stability. However, these traditional testing and optimization methods have limitations. First, the gain compensation setting is typically fixed, failing to adaptively adjust to noise variations in touch systems under different environments, resulting in limited touch accuracy. Second, the noise conditioning device's noise suppression capability may gradually decline over long-term operation, but existing technologies lack dynamic assessment of this change. Consequently, when the noise baseline drifts or calibration errors increase, compensation is still applied according to the existing strategy, potentially amplifying noise interference and affecting touch accuracy. Furthermore, existing technologies fail to effectively identify the relationship between abnormal capacitive sensor measurement data and noise variations. When the noise impact is significant, a fixed gain strategy is still used, ignoring the impact of noise amplification on measurement data. Summary of the Invention

[0004] The purpose of the present invention is to provide a performance detection method and system for a visual interactive touch screen based on AI technology, aiming to solve the problems raised in the background technology.

[0005] The present invention is implemented as follows: a performance detection method for a visual interactive touch screen based on AI technology, the method comprising:

[0006] After performing multi-touch detection on the display unit under test, if the obtained capacitive sensor measurement data is abnormal, determining the current touch operating mode, and obtaining initial gain amplitude parameters prepared for the capacitive sensor and historical processing records of the noise conditioning device used to process the capacitive sensor measurement data;

[0007] AI technology is used to analyze historical processing records and select specific time period logs where the processed display unit is of the same type as the tested display unit, the capacitive sensor measurement data has no anomalies, the time intervals are consistent, and there is a predetermined number of calibration events;

[0008] Determining, based on a preset reference model, a current test reference noise mean of the capacitive sensor measurement data processed by the noise conditioning device of the display unit under test in the current touch operating mode, determining a historical noise mean of the log for each specific time period, and generating a first correction factor based on a change trend of a deviation amplitude of each historical noise mean relative to the current test reference noise mean;

[0009] Parse the logs for a specific period of time, compare the noise mean values ​​within a predetermined time period before and after the calibration moment to obtain a calibration error value, and generate a second correction factor based on the changing trends of the calibration error values;

[0010] The initial gain amplitude parameter is adjusted by combining the first correction factor and the second correction factor.

[0011] As a further limitation of the technical solution of the embodiment of the present invention, the preset reference model refers to the baseline noise average obtained in each touch working mode when the noise conditioning device processes the capacitive sensor data measured by different types of display units under standard working conditions.

[0012] As a further limitation of the technical solution of the embodiment of the present invention, the steps of determining, based on a preset reference model, a current test reference noise mean of the capacitive sensor measurement data processed by the noise conditioning device of the tested display unit in the current touch operating mode, determining a historical noise mean of the log for each specific time period, and generating a first correction factor based on a change trend of a deviation amplitude of each historical noise mean relative to the current test reference noise mean include:

[0013] Retrieve a preset reference model to determine the specific type of the display unit under test, and use the specific type and the current touch operating mode as input to determine the corresponding current test reference noise mean;

[0014] Parse the logs for the selected specific period and calculate the historical noise mean corresponding to the logs for each specific period;

[0015] Quantify the deviation amplitude of each historical noise mean relative to the current test benchmark noise mean, plot these deviation amplitude values ​​in chronological order into a curve reflecting the noise baseline drift change trend, calculate the average slope of the curve, and use it as the first correction factor.

[0016] As a further limitation of the technical solution of the embodiment of the present invention, the steps of parsing the logs for a specific period of time, comparing the noise mean values ​​within a predetermined time period before and after the calibration moment to obtain a calibration error value, and generating a second correction factor based on a change trend of the calibration error values ​​include:

[0017] Analyze the logs for a specific period and calculate the mean noise value before and after calibration within the predetermined period.

[0018] The calibration error value is calculated based on the noise mean before calibration and the noise mean after calibration;

[0019] The corresponding calibration error values ​​in the logs of several specific time periods are plotted in chronological order to form a curve reflecting the changing trend of the calibration error. The average slope of the curve is calculated and used as the second correction factor.

[0020] As a further limitation of the technical solution of the embodiment of the present invention, the step of adjusting the initial gain amplitude parameter by combining the first correction factor and the second correction factor includes:

[0021] Calling a preset gain amplitude parameter adjustment formula, dynamically correcting the initial gain amplitude parameter by combining the first correction factor and the second correction factor to obtain the optimized gain amplitude parameter;

[0022] The optimized gain amplitude parameter is applied to the sensitivity adjustment unit of the capacitive sensor of the display unit under test.

[0023] As a further limitation of the technical solution of the embodiment of the present invention, the gain amplitude parameter adjustment formula is: ,in Refers to the optimized gain amplitude parameter, refers to the initial gain amplitude parameter, Refers to the first correction factor, that is, the average slope of the curve reflecting the trend of noise baseline drift. Refers to the adjustment coefficient corresponding to the first correction factor, Refers to the second correction factor, that is, the average slope of the curve reflecting the trend of calibration error change, Refers to the adjustment coefficient corresponding to the second correction factor;

[0024] In the gain amplitude parameter adjustment formula, ,in Refers to the total number of logs in a specific period. Refers to the The middle time point of a specific period log, Refers to the mean of the middle time point of all logs in a specific period. Indicates the The deviation between the historical noise mean value in the log of a specific period and the current test benchmark noise mean value, Refers to the mean of the deviation amplitude values ​​corresponding to all logs in a specific period;

[0025] ,in Refers to the The historical noise mean of the logs in a specific period, Refers to the current test benchmark noise mean;

[0026] ,in Refers to the The time point of the calibration event in the log of a specific period, Refers to the average time point of all calibration events in the logs of a specific period. Refers to the The calibration error value of the noise mean before calibration compared to the noise mean after calibration in the log of a specific period, Refers to the average value of the calibration error values ​​corresponding to all logs in a specific period;

[0027] ,in Refers to the The mean value of the noise before calibration in the log for a specific period, Refers to the The calibrated mean of the noise in the log for a specific period.

[0028] A performance detection system for a visual interactive touch screen based on AI technology, comprising: a data acquisition module, a history record screening module, a first correction factor determination module, a second correction factor determination module, and a gain amplitude parameter optimization module, wherein:

[0029] a data acquisition module for determining the current touch operating mode if the capacitive sensor measurement data obtained after performing multi-touch detection on the display unit under test is abnormal, and obtaining initial gain amplitude parameters prepared for the capacitive sensor and historical processing records of the noise conditioning device used to process the capacitive sensor measurement data;

[0030] A history record screening module is used to parse the historical processing records and screen out a predetermined number of specific period logs in which the processed display unit is consistent with the type of the tested display unit, the capacitive sensor measurement data has no anomalies, the time intervals are consistent, and there is a calibration event;

[0031] a first correction factor determination module, configured to determine, based on a preset reference model, a current test reference noise mean of the capacitive sensor measurement data processed by the noise conditioning device of the tested display unit in the current touch operating mode, determine a historical noise mean of the log for each specific time period, and generate a first correction factor based on a change trend of a deviation amplitude of each historical noise mean relative to the current test reference noise mean;

[0032] The preset reference model refers to the baseline noise mean value obtained in each touch operating mode when the noise conditioning device processes the capacitive sensor data measured by different types of display units under standard operating conditions;

[0033] A second correction factor determination module is configured to analyze logs for a specific period of time, compare the noise mean values ​​within a predetermined time period before and after the calibration moment to obtain a calibration error value, and generate a second correction factor based on the changing trends of the calibration error values;

[0034] The gain amplitude parameter optimization module is used to adjust the initial gain amplitude parameter by combining the first correction factor and the second correction factor.

[0035] As a further limitation of the technical solution of the embodiment of the present invention, the first correction factor determination module specifically includes:

[0036] A reference noise mean value determination unit is used to call a preset reference model to determine the specific type of the display unit under test, and use the specific type and the current touch operating mode as input to determine the corresponding current test reference noise mean value;

[0037] A historical noise mean calculation unit is used to parse the logs of a selected specific period and calculate the historical noise mean corresponding to the logs of each specific period;

[0038] The first correction factor calculation unit is used to quantify the deviation amplitude of each historical noise mean relative to the current test benchmark noise mean, plot these deviation amplitude values ​​in chronological order into a curve reflecting the noise baseline drift change trend, calculate the average slope of the curve, and use it as the first correction factor.

[0039] As a further limitation of the technical solution of the embodiment of the present invention, the second correction factor determination module specifically includes:

[0040] The noise mean calculation unit before and after calibration is used to analyze the logs of a specific period and calculate the noise mean before calibration and the noise mean after calibration within the predetermined time period;

[0041] a calibration error value determining unit, configured to calculate a calibration error value based on a noise mean value before calibration and a noise mean value after calibration;

[0042] The second correction factor calculation unit is used to plot the corresponding calibration error values ​​in the logs of several specific time periods in chronological order into a curve reflecting the calibration error change trend, calculate the average slope of the curve, and use it as the second correction factor.

[0043] As a further limitation of the technical solution of the embodiment of the present invention, the gain amplitude parameter optimization module specifically includes:

[0044] An initial gain amplitude parameter optimization unit is used to call a preset gain amplitude parameter adjustment formula, dynamically correct the initial gain amplitude parameter in combination with a first correction factor and a second correction factor, and obtain an optimized gain amplitude parameter;

[0045] an optimized gain amplitude parameter application unit, used for applying the optimized gain amplitude parameter to a sensitivity adjustment unit of the capacitive sensor of the display unit under test;

[0046] The gain amplitude parameter adjustment formula is: ,in Refers to the optimized gain amplitude parameter, refers to the initial gain amplitude parameter, Refers to the first correction factor, that is, the average slope of the curve reflecting the trend of noise baseline drift. Refers to the adjustment coefficient corresponding to the first correction factor, Refers to the second correction factor, that is, the average slope of the curve reflecting the trend of calibration error change, Refers to the adjustment coefficient corresponding to the second correction factor;

[0047] In the gain amplitude parameter adjustment formula, ,in Refers to the total number of logs in a specific period. Refers to the The middle time point of a specific period of time log, Refers to the mean of the middle time point of all logs in a specific period. Indicates the The deviation between the historical noise mean value in the log of a specific period and the current test benchmark noise mean value, Refers to the mean of the deviation amplitude values ​​corresponding to all logs in a specific period;

[0048] ,in Refers to the The historical noise mean of the logs in a specific period, Refers to the current test benchmark noise mean;

[0049] ,in Refers to the The time point of the calibration event in the log of a specific period, Refers to the average time point of all calibration events in the logs of a specific period. Refers to the The calibration error value of the noise mean before calibration compared to the noise mean after calibration in the log of a specific period, Refers to the average value of the calibration error values ​​corresponding to all logs in a specific period;

[0050] ,in Refers to the The mean value of the noise before calibration in the log for a specific period, Refers to the The calibrated mean of the noise in the log for a specific period.

[0051] Compared with the prior art, the present invention has the following beneficial effects:

[0052] The present invention dynamically modifies the initial gain amplitude parameter by combining a first correction factor and a second correction factor, achieving adaptive optimization of the noise conditioning device's processing capabilities. The first correction factor is used to assess noise baseline drift and reflect long-term noise trends, while the second correction factor is used to analyze changes in calibration error and assess the real-time correction effectiveness of the noise conditioning device.

[0053] By combining these two approaches, the present invention accurately identifies the correlation between abnormal capacitive sensor measurement data and the effects of noise. It also reduces the gain amplitude parameter when the noise influence is significant, avoiding the bias problem inherent in existing technologies that rely solely on gain amplitude compensation while ignoring the effects of noise amplification. Compared to existing technologies, the present invention effectively reduces measurement errors caused by noise interference, improves the stability and accuracy of touch signals, and makes gain adjustment more intelligent and adaptive. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 A flowchart of a method provided by an embodiment of the present invention;

[0055] Figure 2 A flowchart of determining a first correction factor in the method provided in an embodiment of the present invention;

[0056] Figure 3 A flow chart of determining a second correction factor in the method provided in an embodiment of the present invention;

[0057] Figure 4 A flow chart of optimizing the initial gain amplitude parameter in the method provided in an embodiment of the present invention;

[0058] Figure 5An application architecture diagram of the system provided by an embodiment of the present invention;

[0059] Figure 6 A structural block diagram of a first correction factor determination module in a system provided by an embodiment of the present invention;

[0060] Figure 7 A structural block diagram of a second correction factor determination module in a system provided by an embodiment of the present invention;

[0061] Figure 8 This is a structural block diagram of a gain amplitude parameter optimization module in a system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0062] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0063] Figure 1 A flow chart of a method provided by an embodiment of the present invention is shown.

[0064] Specifically, a performance detection method for a visual interactive touch screen based on AI technology includes the following steps:

[0065] In step S100, after performing multi-touch detection on the display unit under test, if the obtained capacitive sensor measurement data is abnormal, the current touch operating mode is determined, and the initial gain amplitude parameters prepared for the capacitive sensor and the historical processing records of the noise conditioning device used to process the capacitive sensor measurement data are obtained.

[0066] In an embodiment of the present invention, the display unit under test refers to a display device that undergoes multi-touch detection, specifically a visual interactive touchscreen. The display unit under test includes a capacitive touch sensor that senses touch operations and converts them into electrical signals for subsequent processing and analysis. Multi-touch detection refers to the simultaneous detection of multiple touch points on a display unit. It is commonly used in scenarios such as smart devices, industrial control panels, and medical touchscreens to test touch response accuracy, gesture recognition capabilities, and device stability.

[0067] Capacitive sensor measurement data refers to the electrical signal output by the sensor when it detects a touch event. This includes data such as capacitance, noise level, and response time. Deviating from the expected range, such as abnormal capacitance fluctuations, touch signal loss, or false touches, indicates a possible touch sensor anomaly. Specific anomaly detection can be based on historical data comparison, threshold detection, or AI-based anomaly recognition.

[0068] The current touch operating mode refers to the operating status of the touch sensor under different application scenarios or user operating habits. This includes touch sensitivity settings, signal sampling mode, input signal filtering, and more. Different touch modes may affect how touch data is collected. Therefore, when detecting an anomaly, it is important to clearly identify the current touch operating mode so that subsequent corrective measures are more targeted.

[0069] The initial gain amplitude parameter for this capacitive sensor refers to the signal gain adjustment parameter set for the sensor under default or preset system conditions. This parameter is used to optimize touch signal acquisition accuracy and noise suppression. This parameter can be obtained through experimental data, sensor calibration, or automatic system adjustment. In existing technologies, automatic gain control (AGC)-based methods are already available to dynamically adjust sensor gain.

[0070] A noise conditioning device used to process capacitive sensor measurement data refers to a hardware or software module that processes touch signals to reduce noise interference and improve signal accuracy. Its historical processing records include the conditioning processes and results performed by the device on capacitive sensor measurement data at different times and under different test conditions. This record can be obtained from device logs, noise filtering algorithm logs, or the sensor signal processing unit's data storage.

[0071] Historical processing records should at least include the following: First, each record needs to indicate the type of display unit being tested so that subsequent analysis can ensure that the devices being compared are of the same or similar types; second, the processing results of the noise conditioning device in different touch operating modes need to be included so that the noise characteristics in different modes can be compared; in addition, the specific time of the calibration event needs to be recorded, and the noise data before and after calibration needs to be stored so that the impact of calibration on noise can be analyzed; finally, the specific time of each measurement needs to be recorded so that subsequent trend analysis can be performed in chronological order.

[0072] Furthermore, the performance detection method of the visual interactive touch screen based on AI technology also includes the following steps:

[0073] Step S200 , analyzing historical processing records based on AI technology, screening out a predetermined number of specific period logs in which the processed display unit is consistent with the type of the tested display unit, the capacitive sensor measurement data has no abnormalities, the time intervals are consistent, and there is a calibration event.

[0074] In this embodiment of the present invention, the significance of selecting logs from a specific time period is to ensure the high quality and comparability of the historical data used to calculate the correction factor. By screening logs that meet specific conditions, interference from irrelevant factors and the impact of abnormal data can be reduced, thereby ensuring the accuracy of the correction factor calculation. This ensures that when adjusting the gain amplitude parameter, the data used as the basis truly reflects the changing trends of noise baseline drift and calibration error, making the ultimately optimized gain amplitude parameter more accurate and reliable.

[0075] Several factors need to be considered when screening logs, primarily to ensure data stability and representativeness. First, selecting logs that match the type of display unit being measured can avoid data mismatches due to differences in device characteristics. Second, the capacitive sensor measurement data must be free of anomalies to eliminate data deviations caused by device failure or environmental interference. Third, consistent time intervals must be ensured to ensure uniform data distribution and prevent trend analysis errors caused by uneven data density. Finally, each log must contain a calibration event to calculate the calibration error and generate a secondary correction factor, allowing the optimized gain amplitude parameter to adapt to performance changes in the device under different calibration conditions. Combining these screening criteria ensures a high degree of consistency in the data used for analysis, making the calculated correction factor more valuable for reference.

[0076] Furthermore, the performance detection method of the visual interactive touch screen based on AI technology also includes the following steps:

[0077] In step S300, a current test benchmark noise mean value of the capacitive sensor measurement data processed by the noise conditioning device of the tested display unit in the current touch working mode is determined based on a preset reference model, a historical noise mean value of the log for each specific time period is determined, and a first correction factor is generated based on a change trend of a deviation amplitude of each historical noise mean value relative to the current test benchmark noise mean value.

[0078] The preset reference model refers to a baseline noise average value obtained in each touch operating mode when the noise conditioning device processes capacitive sensor data measured by different types of display units under standard operating conditions.

[0079] Specifically, Figure 2 A flow chart for determining a first correction factor is shown.

[0080] The method of determining the current test reference noise mean of the capacitive sensor measurement data processed by the noise conditioning device of the tested display unit in the current touch working mode according to the preset reference model, determining the historical noise mean of the log in each specific time period, and generating the first correction factor according to the change trend of the deviation amplitude of each historical noise mean relative to the current test reference noise mean specifically includes the following steps:

[0081] Step S301: Retrieve a preset reference model to determine the specific type of the display unit under test, and use the specific type and the current touch operating mode as input to determine the corresponding current test reference noise mean;

[0082] Step S302: parse the selected specific period logs and calculate the historical noise mean corresponding to each specific period log;

[0083] Step S303: quantify the deviation amplitude of each historical noise mean relative to the current test reference noise mean, plot these deviation amplitude values ​​in chronological order into a curve reflecting the noise baseline drift change trend, calculate the average slope of the curve, and use it as the first correction factor.

[0084] In an embodiment of the present invention, the preset reference model is used to provide a baseline noise mean for capacitive sensor data measured by the noise conditioning device under different touch operating modes. This model is based on extensive experimental data covering different types of display units and their noise characteristics under standard operating conditions, and is optimized using existing noise analysis and filtering techniques. In existing technology, the noise characteristics of touch systems can be approximated through experimental measurement, statistical modeling, or machine learning methods. Therefore, this preset reference model is feasible and can be constructed based on existing technology.

[0085] The model construction process usually includes the following aspects:

[0086] First, standardized tests are conducted in a controlled environment for different types of display units, and noise data under different touch operating modes is recorded, including background noise, capacitive sensor response data, and processing results of the noise conditioning device. Second, based on the collected data, a mathematical model is established, such as one based on multivariate regression, probability distribution modeling, or neural network fitting, to analyze the variation pattern of the noise mean under different conditions. Finally, through data verification and model optimization, the preset reference model can accurately reflect the noise level under standard operating conditions, providing an accurate reference for subsequent noise deviation analysis.

[0087] The specific implementation process of step S303 is as follows: First, extract the historical noise mean from the log of a specific time period, and calculate the deviation amplitude of each historical noise mean relative to the current test benchmark noise mean. The deviation amplitude is calculated by subtracting the current test benchmark noise mean from the historical noise mean, and then dividing it by the current test benchmark noise mean, and taking the absolute value to measure the degree of deviation of the historical noise mean from the standard value.

[0088] Secondly, all calculated deviation amplitude values ​​are sorted according to the time points corresponding to the specific period logs, and a curve of the noise baseline drift change trend is plotted in chronological order. This curve can intuitively reflect the change pattern of noise deviation over time, so as to evaluate the long-term stability of the noise conditioning device on the capacitive sensor measurement data.

[0089] Finally, the linear regression method is used to analyze the curve and calculate the average slope of the trend change. The average slope reflects the degree of noise baseline drift. This slope is used as the first correction factor to adjust the initial gain amplitude parameter so that the adjusted gain amplitude parameter can compensate for the measurement error caused by noise baseline drift.

[0090] The significance of this method lies in the fact that noise baseline drift is a dynamic process that changes over time. Single-shot measurement data may be affected by random factors and cannot accurately describe the overall trend. Therefore, by analyzing historical noise deviation changes over a period of time, it is possible to more stably assess the performance fluctuations of the noise conditioning device, thereby improving the correction effect of capacitive sensor measurement data. Furthermore, this method can effectively reduce the impact of single-point abnormal data on the correction factor, making the calculated first correction factor more reliable and providing a more valuable reference for subsequent optimization of gain amplitude parameters.

[0091] Furthermore, the performance detection method of the visual interactive touch screen based on AI technology also includes the following steps:

[0092] Step S400 , analyzing the log of a specific period, comparing the noise mean values ​​in a predetermined time period before and after the calibration moment to obtain a calibration error value, and generating a second correction factor according to the change trend of the calibration error values.

[0093] Specifically, Figure 3 A flow chart for determining the second correction factor is shown.

[0094] The process of parsing the logs for a specific period of time, comparing the noise mean values ​​in a predetermined time period before and after the calibration moment to obtain a calibration error value, and generating a second correction factor based on the change trends of the calibration error values ​​specifically includes the following steps:

[0095] Step S401: parse the logs for a specific period of time and calculate the mean noise value before and after calibration within the predetermined time period;

[0096] Step S402, calculating a calibration error value based on the noise mean before calibration and the noise mean after calibration;

[0097] Step S403 : plotting the calibration error values ​​corresponding to the logs of several specific time periods into a curve reflecting the calibration error change trend in chronological order, calculating the average slope of the curve, and using it as the second correction factor.

[0098] In an embodiment of the present invention, the calibration error value is used to quantify the change in noise level before and after calibration. The calculation method is to subtract the mean noise value after calibration from the mean noise value before calibration, and then divide it by the mean noise value before calibration, and take the absolute value to measure the effect of calibration on noise suppression or correction.

[0099] The calibration error values ​​in multiple specific time period logs are sorted according to the corresponding calibration event time points, and a calibration error change trend curve is drawn in chronological order, so that the curve can intuitively reflect the calibration error change of the noise conditioning device over time.

[0100] The average slope of the trend curve is calculated using a linear regression method. This slope reflects the trend of the calibration error over time. This average slope is used as the second correction factor to adjust the initial gain amplitude parameter to compensate for the attenuation of the calibration effect due to long-term use of the equipment or environmental changes.

[0101] The significance of using this method to determine the second correction factor lies in the fact that the changing trend of the calibration error reflects the stability of the noise conditioning device in processing the capacitive sensor measurement data at different time points. A single calibration error value may be affected by factors such as the external environment and the device status, and cannot accurately describe the overall change pattern of the calibration error. Therefore, by analyzing the trend of the calibration error change over a period of time, it is possible to more stably assess whether the calibration effect of the device has gradually degraded or fluctuated during long-term operation, thereby providing a more reliable basis for parameter adjustment. In addition, this method can reduce the impact of random errors on calibration corrections, making the calculated second correction factor more stable and providing a more accurate reference for optimizing the gain amplitude parameter.

[0102] Furthermore, the performance detection method of the visual interactive touch screen based on AI technology also includes the following steps:

[0103] Step S500 : adjusting the initial gain amplitude parameter by combining the first correction factor and the second correction factor.

[0104] Specifically, Figure 4 A flow chart for optimizing the initial gain amplitude parameters is shown.

[0105] The adjustment of the initial gain amplitude parameter by combining the first correction factor and the second correction factor specifically includes the following steps:

[0106] Step S501: calling a preset gain amplitude parameter adjustment formula, dynamically correcting the initial gain amplitude parameter by combining a first correction factor and a second correction factor to obtain an optimized gain amplitude parameter;

[0107] Step S502 : applying the optimized gain amplitude parameter to the sensitivity adjustment unit of the capacitive sensor of the display unit under test.

[0108] The gain amplitude parameter adjustment formula is: ,in Refers to the optimized gain amplitude parameter, refers to the initial gain amplitude parameter, Refers to the first correction factor, that is, the average slope of the curve reflecting the trend of noise baseline drift. Refers to the adjustment coefficient corresponding to the first correction factor, Refers to the second correction factor, that is, the average slope of the curve reflecting the trend of calibration error change, Refers to the adjustment coefficient corresponding to the second correction factor.

[0109] In the gain amplitude parameter adjustment formula, ,in Refers to the total number of logs in a specific period. Refers to the The middle time point of a specific period of time log, Refers to the mean of the middle time point of all logs in a specific period. Indicates the The deviation between the historical noise mean value in the log of a specific period and the current test benchmark noise mean value, Refers to the mean of the deviation amplitude values ​​corresponding to all logs in a specific period.

[0110] ,in Refers to the The historical noise mean of the logs in a specific period, Refers to the current test benchmark noise mean;

[0111] ,in Refers to the The time point of the calibration event in the log of a specific period, Refers to the average time point of all calibration events in the logs of a specific period. Refers to the The calibration error value of the noise mean before calibration compared to the noise mean after calibration in the log of a specific period, Refers to the average value of the calibration error values ​​corresponding to all logs in a specific period;

[0112] ,in Refers to the The mean value of the noise before calibration in the log for a specific period, Refers to the The calibrated mean of the noise in the log for a specific period.

[0113] In the embodiment of the present invention, the significance of dynamically correcting the initial gain amplitude parameter by combining the first correction factor and the second correction factor is that the two correction factors respectively reflect the degree of noise baseline drift and the changing trend of the calibration error, thereby being able to comprehensively evaluate the processing effect of the noise conditioning device on the capacitive sensor measurement data and the impact of noise on the measurement data. Unlike the prior art, the core idea of ​​the present invention is that when the noise suppression effect of the noise conditioning device weakens, that is, the first correction factor indicates that the noise baseline has drifted significantly, and the second correction factor shows that the calibration error is increasing, it can be reasonably inferred that the abnormality of the capacitive sensor measurement data is related to the influence of noise. At this time, the adjustment of the gain amplitude parameter should not rely solely on the fixed gain strategy in the prior art, otherwise it may cause the noise amplification problem to be ignored, thereby causing deviations in the measurement data. The present invention performs adaptive adjustment by combining the processing power of the noise conditioning device, so that the gain amplitude parameter is appropriately reduced when the noise influence is large, avoiding excessive amplification of noise interference by the system, and improving the stability and accuracy of signal processing.

[0114] The first and second correction factors work together to form a mechanism that dynamically adjusts the gain amplitude parameter based on the processing power of the noise conditioning device, making the gain adjustment more intelligent and adaptive. For example, if the first correction factor is small, it indicates that the noise baseline is relatively stable, and if the second correction factor is small, it indicates that the calibration error of the noise conditioning device is low. This means that the current capacitive sensor measurement data is less affected by noise interference, and the gain amplitude parameter can be maintained at a high level to ensure sensitivity and signal response speed. However, if both the first and second correction factors are large, it means that the noise conditioning device's noise suppression capability has decreased and the current noise impact is more serious. The system should appropriately reduce the gain amplitude parameter to reduce the interference of noise in the signal, making the measurement data more stable and avoiding false touches or drift problems caused by signal amplification.

[0115] In addition to being applied to the sensitivity adjustment unit of the capacitive sensor of the display unit under test, the dynamic adjustment of the gain amplitude parameter can also be applied to other systems involving noise conditioning devices. For example, in the bioelectric signal detection of high-precision medical equipment, it can be used to optimize the gain adjustment of ECG signals or EEG signals to adapt to the physiological characteristics and environmental noise levels of different patients; in industrial automation systems, it can be used to optimize sensor signal processing to ensure that the equipment can still accurately detect the position of objects or pressure changes in high-noise environments; in avionics systems, it can be used to optimize the data of flight control sensors so that they can still maintain accurate measurement capabilities in different flight states and interference environments. The role of the noise conditioning device is to reduce the environmental noise interference in different application scenarios, so that the gain adjustment can play an effective role.

[0116] For example, in actual applications, suppose a certain smart touch screen device is running in a factory environment. Due to long-term exposure to a high electromagnetic interference environment, users find that the touch operation has become unstable, with occasional false touches or slow responses. System detection found that the first correction factor showed that the noise baseline drifted significantly, indicating that the suppression ability of the noise conditioning device decreased after long-term use, while the second correction factor showed that the calibration error was increasing, which means that even after multiple calibrations, the noise conditioning device still could not completely compensate for the noise interference. The existing technology may continue to increase the gain amplitude parameter to enhance the sensitivity of the sensor, but this will cause the noise to be further amplified, thereby affecting the accuracy of the measurement data. The solution of the present invention will be based on a comprehensive analysis of the two correction factors to identify the correlation between the anomaly and the decline in the processing ability of the noise conditioning device, and appropriately reduce the gain amplitude parameter to make the touch signal more stable and reduce the impact of noise on the measurement data. After the optimized gain amplitude parameter is applied to the sensitivity adjustment unit, the touch screen returns to stability, the touch false touch phenomenon is reduced, and the device operation is more reliable.

[0117] The present invention solves the problem of prior art relying solely on gain amplitude compensation while ignoring the impact deviation caused by noise amplification. Gain adjustment is no longer a fixed strategy, but can dynamically adapt to the processing capability of the noise conditioning device, ensuring the accuracy and reliability of the capacitive sensor measurement data, thereby improving the long-term stability and adaptability of the device.

[0118] Further, Figure 5 The application architecture diagram of the system provided by the embodiment of the present invention is shown.

[0119] Among them, in another preferred embodiment provided by the present invention, a performance detection system for a visual interactive touch screen based on AI technology includes:

[0120] The data acquisition module 100 is used to determine the current touch operating mode if the capacitive sensor measurement data obtained after multi-touch detection is performed on the display unit under test is abnormal, and to obtain the initial gain amplitude parameters prepared for the capacitive sensor and the historical processing records of the noise conditioning device used to process the capacitive sensor measurement data.

[0121] In an embodiment of the present invention, the display unit under test refers to a display device that undergoes multi-touch detection, specifically a visual interactive touchscreen. The display unit under test includes a capacitive touch sensor that senses touch operations and converts them into electrical signals for subsequent processing and analysis. Multi-touch detection refers to the simultaneous detection of multiple touch points on a display unit. It is commonly used in scenarios such as smart devices, industrial control panels, and medical touchscreens to test touch response accuracy, gesture recognition capabilities, and device stability.

[0122] Capacitive sensor measurement data refers to the electrical signal output by the sensor when it detects a touch event. This includes data such as capacitance, noise level, and response time. Deviating from the expected range, such as abnormal capacitance fluctuations, touch signal loss, or false touches, indicates a possible touch sensor anomaly. Specific anomaly detection can be based on historical data comparison, threshold detection, or AI-based anomaly recognition.

[0123] The current touch operating mode refers to the operating status of the touch sensor under different application scenarios or user operating habits. This includes touch sensitivity settings, signal sampling mode, input signal filtering, and more. Different touch modes may affect how touch data is collected. Therefore, when detecting an anomaly, it is important to clearly identify the current touch operating mode so that subsequent corrective measures are more targeted.

[0124] The initial gain amplitude parameter for this capacitive sensor refers to the signal gain adjustment parameter set for the sensor under default or preset system conditions. This parameter is used to optimize touch signal acquisition accuracy and noise suppression. This parameter can be obtained through experimental data, sensor calibration, or automatic system adjustment. In existing technologies, automatic gain control (AGC)-based methods are already available to dynamically adjust sensor gain.

[0125] A noise conditioning device used to process capacitive sensor measurement data refers to a hardware or software module that processes touch signals to reduce noise interference and improve signal accuracy. Its historical processing records include the conditioning processes and results performed by the device on capacitive sensor measurement data at different times and under different test conditions. This record can be obtained from device logs, noise filtering algorithm logs, or the sensor signal processing unit's data storage.

[0126] Historical processing records should at least include the following: First, each record needs to indicate the type of display unit being tested so that subsequent analysis can ensure that the devices being compared are of the same or similar types; second, the processing results of the noise conditioning device in different touch operating modes need to be included so that the noise characteristics in different modes can be compared; in addition, the specific time of the calibration event needs to be recorded, and the noise data before and after calibration needs to be stored so that the impact of calibration on noise can be analyzed; finally, the specific time of each measurement needs to be recorded so that subsequent trend analysis can be performed in chronological order.

[0127] Furthermore, the performance detection system of the visual interactive touch screen based on AI technology also includes:

[0128] The history record screening module 200 is used to parse the historical processing records and screen out a predetermined number of specific period logs in which the processed display unit is consistent with the type of the tested display unit, the capacitive sensor measurement data has no abnormalities, the time intervals are consistent, and there is a calibration event.

[0129] In this embodiment of the present invention, the significance of selecting logs from a specific time period is to ensure the high quality and comparability of the historical data used to calculate the correction factor. By screening logs that meet specific conditions, interference from irrelevant factors and the impact of abnormal data can be reduced, thereby ensuring the accuracy of the correction factor calculation. This ensures that when adjusting the gain amplitude parameter, the data used as the basis truly reflects the changing trends of noise baseline drift and calibration error, making the ultimately optimized gain amplitude parameter more accurate and reliable.

[0130] Several factors need to be considered when screening logs, primarily to ensure data stability and representativeness. First, selecting logs that match the type of display unit being measured can avoid data mismatches due to differences in device characteristics. Second, the capacitive sensor measurement data must be free of anomalies to eliminate data deviations caused by device failure or environmental interference. Third, consistent time intervals must be ensured to ensure uniform data distribution and prevent trend analysis errors caused by uneven data density. Finally, each log must contain a calibration event to calculate the calibration error and generate a secondary correction factor, allowing the optimized gain amplitude parameter to adapt to performance changes in the device under different calibration conditions. Combining these screening criteria ensures a high degree of consistency in the data used for analysis, making the calculated correction factor more valuable for reference.

[0131] Furthermore, the performance detection system of the visual interactive touch screen based on AI technology also includes:

[0132] The first correction factor determination module 300 is used to determine the current test benchmark noise mean of the capacitive sensor measurement data processed by the noise conditioning device of the tested display unit in the current touch working mode based on a preset reference model, determine the historical noise mean of the log for each specific time period, and generate a first correction factor based on the changing trend of the deviation amplitude of each historical noise mean relative to the current test benchmark noise mean.

[0133] The preset reference model refers to a baseline noise average value obtained in each touch operating mode when the noise conditioning device processes capacitive sensor data measured by different types of display units under standard operating conditions.

[0134] Specifically, Figure 6 FIG. 4 shows a structural block diagram of the first correction factor determination module 300 in the system provided by an embodiment of the present invention.

[0135] In a preferred embodiment of the present invention, the first correction factor determination module 300 specifically includes:

[0136] The reference noise mean value determination unit 301 is configured to retrieve a preset reference model, determine the specific type of the display unit under test, and use the specific type and the current touch operating mode as input to determine the corresponding current test reference noise mean value;

[0137] The historical noise mean value calculation unit 302 is used to analyze the logs of the selected specific period and calculate the historical noise mean value corresponding to the logs of each specific period;

[0138] The first correction factor calculation unit 303 is used to quantify the deviation amplitude of each historical noise mean relative to the current test reference noise mean, plot these deviation amplitude values ​​in chronological order into a curve reflecting the noise baseline drift change trend, calculate the average slope of the curve, and use it as the first correction factor.

[0139] In an embodiment of the present invention, the preset reference model is used to provide a baseline noise mean for capacitive sensor data measured by the noise conditioning device under different touch operating modes. This model is based on extensive experimental data covering different types of display units and their noise characteristics under standard operating conditions, and is optimized using existing noise analysis and filtering techniques. In existing technology, the noise characteristics of touch systems can be approximated through experimental measurement, statistical modeling, or machine learning methods. Therefore, this preset reference model is feasible and can be constructed based on existing technology.

[0140] The model construction process usually includes the following aspects:

[0141] First, standardized tests are conducted in a controlled environment for different types of display units, and noise data under different touch operating modes is recorded, including background noise, capacitive sensor response data, and processing results of the noise conditioning device. Second, based on the collected data, a mathematical model is established, such as one based on multivariate regression, probability distribution modeling, or neural network fitting, to analyze the variation pattern of the noise mean under different conditions. Finally, through data verification and model optimization, the preset reference model can accurately reflect the noise level under standard operating conditions, providing an accurate reference for subsequent noise deviation analysis.

[0142] The specific working process of the first correction factor calculation unit 303 is as follows: First, the historical noise mean is extracted from the log of a specific period, and the deviation amplitude of each historical noise mean relative to the current test benchmark noise mean is calculated. The deviation amplitude is calculated by subtracting the current test benchmark noise mean from the historical noise mean, and then dividing it by the current test benchmark noise mean, and taking the absolute value to measure the degree of deviation of the historical noise mean from the standard value.

[0143] Secondly, all calculated deviation amplitude values ​​are sorted according to the time points corresponding to the specific period logs, and a curve of the noise baseline drift change trend is plotted in chronological order. This curve can intuitively reflect the change pattern of noise deviation over time, so as to evaluate the long-term stability of the noise conditioning device on the capacitive sensor measurement data.

[0144] Finally, the linear regression method is used to analyze the curve and calculate the average slope of the trend change. The average slope reflects the degree of noise baseline drift. This slope is used as the first correction factor to adjust the initial gain amplitude parameter so that the adjusted gain amplitude parameter can compensate for the measurement error caused by noise baseline drift.

[0145] The significance of this method lies in the fact that noise baseline drift is a dynamic process that changes over time. Single-shot measurement data may be affected by random factors and cannot accurately describe the overall trend. Therefore, by analyzing historical noise deviation changes over a period of time, it is possible to more stably assess the performance fluctuations of the noise conditioning device, thereby improving the correction effect of capacitive sensor measurement data. Furthermore, this method can effectively reduce the impact of single-point abnormal data on the correction factor, making the calculated first correction factor more reliable and providing a more valuable reference for subsequent optimization of gain amplitude parameters.

[0146] Furthermore, the performance detection system of the visual interactive touch screen based on AI technology also includes:

[0147] The second correction factor determination module 400 is used to parse the log of a specific period, compare the noise mean values ​​in a predetermined time period before and after the calibration moment to obtain a calibration error value, and generate a second correction factor based on the change trend of the calibration error values.

[0148] Specifically, Figure 7 FIG. 4 is a structural block diagram of a second correction factor determination module 400 in a system provided by an embodiment of the present invention.

[0149] In a preferred embodiment of the present invention, the second correction factor determination module 400 specifically includes:

[0150] The pre-calibration noise mean calculation unit 401 is used to analyze the logs of a specific period and calculate the pre-calibration noise mean and the post-calibration noise mean within the predetermined time period;

[0151] A calibration error value determining unit 402 is configured to calculate a calibration error value based on a pre-calibration noise mean value and a post-calibration noise mean value;

[0152] The second correction factor calculation unit 403 is used to plot the calibration error values ​​corresponding to the logs in a number of specific time periods in chronological order into a curve reflecting the calibration error change trend, calculate the average slope of the curve, and use it as the second correction factor.

[0153] In an embodiment of the present invention, the calibration error value is used to quantify the change in noise level before and after calibration. The calculation method is to subtract the mean noise value after calibration from the mean noise value before calibration, and then divide it by the mean noise value before calibration, and take the absolute value to measure the effect of calibration on noise suppression or correction.

[0154] The calibration error values ​​in multiple specific time period logs are sorted according to the corresponding calibration event time points, and a calibration error change trend curve is drawn in chronological order, so that the curve can intuitively reflect the calibration error change of the noise conditioning device over time.

[0155] The average slope of the trend curve is calculated using a linear regression method. This slope reflects the trend of the calibration error over time. This average slope is used as the second correction factor to adjust the initial gain amplitude parameter to compensate for the attenuation of the calibration effect due to long-term use of the equipment or environmental changes.

[0156] The significance of using this method to determine the second correction factor lies in the fact that the changing trend of the calibration error reflects the stability of the noise conditioning device in processing the capacitive sensor measurement data at different time points. A single calibration error value may be affected by factors such as the external environment and the device status, and cannot accurately describe the overall change pattern of the calibration error. Therefore, by analyzing the trend of the calibration error change over a period of time, it is possible to more stably assess whether the calibration effect of the device has gradually degraded or fluctuated during long-term operation, thereby providing a more reliable basis for parameter adjustment. In addition, this method can reduce the impact of random errors on calibration corrections, making the calculated second correction factor more stable and providing a more accurate reference for optimizing the gain amplitude parameter.

[0157] Furthermore, the performance detection system of the visual interactive touch screen based on AI technology also includes:

[0158] The gain amplitude parameter optimization module 500 is used to adjust the initial gain amplitude parameter by combining the first correction factor and the second correction factor.

[0159] Specifically, Figure 8 FIG. 5 shows a structural block diagram of a gain amplitude parameter optimization module 500 in a system provided by an embodiment of the present invention.

[0160] In a preferred embodiment of the present invention, the gain amplitude parameter optimization module 500 specifically includes:

[0161] The initial gain amplitude parameter optimization unit 501 is used to call a preset gain amplitude parameter adjustment formula, dynamically correct the initial gain amplitude parameter in combination with the first correction factor and the second correction factor to obtain an optimized gain amplitude parameter;

[0162] The optimized gain amplitude parameter application unit 502 is configured to apply the optimized gain amplitude parameter to the sensitivity adjustment unit of the capacitive sensor of the display unit under test.

[0163] The gain amplitude parameter adjustment formula is: ,in Refers to the optimized gain amplitude parameter, refers to the initial gain amplitude parameter, Refers to the first correction factor, that is, the average slope of the curve reflecting the trend of noise baseline drift. Refers to the adjustment coefficient corresponding to the first correction factor, Refers to the second correction factor, that is, the average slope of the curve reflecting the trend of calibration error change, Refers to the adjustment coefficient corresponding to the second correction factor.

[0164] In the gain amplitude parameter adjustment formula, ,in Refers to the total number of logs in a specific period. Refers to the The middle time point of a specific period of time log, Refers to the mean of the middle time point of all logs in a specific period. Indicates the The deviation between the historical noise mean value in the log of a specific period and the current test benchmark noise mean value, Refers to the mean of the deviation amplitude values ​​corresponding to all logs in a specific period;

[0165] ,in Refers to the The historical noise mean of the logs in a specific period, Refers to the current test benchmark noise mean;

[0166] ,in Refers to the The time point of the calibration event in the log of a specific period, Refers to the average time point of all calibration events in the logs of a specific period. Refers to the The calibration error value of the noise mean before calibration compared to the noise mean after calibration in the log of a specific period, Refers to the average value of the calibration error values ​​corresponding to all logs in a specific period;

[0167] ,in Refers to the The mean value of the noise before calibration in the log for a specific period, Refers to the The calibrated mean of the noise in the log for a specific period.

[0168] It should be understood that although the steps in the flowcharts of the various embodiments of the present invention are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps may be executed in other orders.

Claims

1. A performance detection method for a visual interactive touch screen based on AI technology, characterized in that: The method comprises: After performing multi-touch detection on the display unit under test, if the obtained capacitive sensor measurement data is abnormal, determining the current touch operating mode, and obtaining initial gain amplitude parameters prepared for the capacitive sensor and historical processing records of the noise conditioning device used to process the capacitive sensor measurement data; Parse the historical processing records and select a predetermined number of specific time period logs in which the processed display unit is of the same type as the tested display unit, the capacitive sensor measurement data has no anomalies, the time intervals are consistent, and there is a calibration event; Determining, based on a preset reference model, a current test reference noise mean of the capacitive sensor measurement data processed by the noise conditioning device of the display unit under test in the current touch operating mode, determining a historical noise mean of the log for each specific time period, and generating a first correction factor based on a change trend of a deviation amplitude of each historical noise mean relative to the current test reference noise mean; Parse the logs for a specific period of time, compare the noise mean values ​​within a predetermined time period before and after the calibration moment to obtain a calibration error value, and generate a second correction factor based on the changing trends of the calibration error values; The initial gain amplitude parameter is adjusted by combining the first correction factor and the second correction factor.

2. The performance detection method of the visual interactive touch screen based on AI technology according to claim 1 is characterized in that: The preset reference model refers to a baseline noise average value obtained in each touch operating mode when the noise conditioning device processes capacitive sensor data measured by different types of display units under standard operating conditions.

3. The performance detection method of the visual interactive touch screen based on AI technology according to claim 2 is characterized in that: The steps of determining, based on a preset reference model, a current test reference noise mean of the capacitive sensor measurement data processed by the noise conditioning device of the tested display unit in the current touch operating mode, determining a historical noise mean of the log for each specific time period, and generating a first correction factor based on a change trend of a deviation amplitude of each historical noise mean relative to the current test reference noise mean include: Retrieve a preset reference model to determine the specific type of the display unit under test, and use the specific type and the current touch operating mode as input to determine the corresponding current test reference noise mean; Parse the logs for the selected specific period and calculate the historical noise mean corresponding to the logs for each specific period; Quantify the deviation amplitude of each historical noise mean relative to the current test benchmark noise mean, plot these deviation amplitude values ​​in chronological order into a curve reflecting the noise baseline drift change trend, calculate the average slope of the curve, and use it as the first correction factor.

4. The performance detection method of the visual interactive touch screen based on AI technology according to claim 3 is characterized in that: The steps of parsing the logs for a specific period, comparing the noise mean values ​​in a predetermined time period before and after the calibration moment to obtain a calibration error value, and generating a second correction factor based on the change trends of the calibration error values ​​include: Analyze the logs for a specific period and calculate the mean noise value before and after calibration within the predetermined period. The calibration error value is calculated based on the noise mean before calibration and the noise mean after calibration; The corresponding calibration error values ​​in the logs of several specific time periods are plotted in chronological order to form a curve reflecting the changing trend of the calibration error. The average slope of the curve is calculated and used as the second correction factor.

5. The performance detection method of the visual interactive touch screen based on AI technology according to claim 4 is characterized in that: The step of adjusting the initial gain amplitude parameter by combining the first correction factor and the second correction factor includes: Calling a preset gain amplitude parameter adjustment formula, dynamically correcting the initial gain amplitude parameter by combining the first correction factor and the second correction factor to obtain the optimized gain amplitude parameter; The optimized gain amplitude parameter is applied to the sensitivity adjustment unit of the capacitive sensor of the display unit under test.

6. The performance detection method of the visual interactive touch screen based on AI technology according to claim 5 is characterized in that: The gain amplitude parameter adjustment formula is: ,in Refers to the optimized gain amplitude parameter, refers to the initial gain amplitude parameter, Refers to the first correction factor, that is, the average slope of the curve reflecting the trend of noise baseline drift. Refers to the adjustment coefficient corresponding to the first correction factor, Refers to the second correction factor, that is, the average slope of the curve reflecting the trend of calibration error change, Refers to the adjustment coefficient corresponding to the second correction factor.

7. A performance detection system for a visual interactive touch screen based on AI technology, characterized in that: The system includes: a data acquisition module, a history record screening module, a first correction factor determination module, a second correction factor determination module, and a gain amplitude parameter optimization module, wherein: a data acquisition module for determining the current touch operating mode if the capacitive sensor measurement data obtained after performing multi-touch detection on the display unit under test is abnormal, and obtaining initial gain amplitude parameters prepared for the capacitive sensor and historical processing records of the noise conditioning device used to process the capacitive sensor measurement data; A history record screening module is used to parse the historical processing records and screen out a predetermined number of specific period logs in which the processed display unit is consistent with the type of the tested display unit, the capacitive sensor measurement data has no anomalies, the time intervals are consistent, and there is a calibration event; a first correction factor determination module, configured to determine, based on a preset reference model, a current test reference noise mean of the capacitive sensor measurement data processed by the noise conditioning device of the tested display unit in the current touch operating mode, determine a historical noise mean of the log for each specific time period, and generate a first correction factor based on a change trend of a deviation amplitude of each historical noise mean relative to the current test reference noise mean; The preset reference model refers to the baseline noise mean value obtained in each touch operating mode when the noise conditioning device processes the capacitive sensor data measured by different types of display units under standard operating conditions; A second correction factor determination module is configured to analyze logs for a specific period of time, compare the noise mean values ​​within a predetermined time period before and after the calibration moment to obtain a calibration error value, and generate a second correction factor based on the changing trends of the calibration error values; The gain amplitude parameter optimization module is used to adjust the initial gain amplitude parameter by combining the first correction factor and the second correction factor.

8. The performance detection system of the visual interactive touch screen based on AI technology according to claim 7 is characterized in that: The first correction factor determination module specifically includes: A reference noise mean value determination unit is used to call a preset reference model to determine the specific type of the display unit under test, and use the specific type and the current touch operating mode as input to determine the corresponding current test reference noise mean value; A historical noise mean calculation unit is used to parse the logs of a selected specific period and calculate the historical noise mean corresponding to the logs of each specific period; The first correction factor calculation unit is used to quantify the deviation amplitude of each historical noise mean relative to the current test benchmark noise mean, plot these deviation amplitude values ​​in chronological order into a curve reflecting the noise baseline drift change trend, calculate the average slope of the curve, and use it as the first correction factor.

9. The performance detection system of the visual interactive touch screen based on AI technology according to claim 8 is characterized in that: The second correction factor determination module specifically includes: The noise mean calculation unit before and after calibration is used to analyze the logs of a specific period and calculate the noise mean before calibration and the noise mean after calibration within the predetermined time period; a calibration error value determining unit, configured to calculate a calibration error value based on a noise mean value before calibration and a noise mean value after calibration; The second correction factor calculation unit is used to plot the corresponding calibration error values ​​in the logs of several specific time periods in chronological order into a curve reflecting the calibration error change trend, calculate the average slope of the curve, and use it as the second correction factor.

10. The performance detection system of the visual interactive touch screen based on AI technology according to claim 9 is characterized in that: The gain amplitude parameter optimization module specifically includes: An initial gain amplitude parameter optimization unit is used to call a preset gain amplitude parameter adjustment formula, dynamically correct the initial gain amplitude parameter in combination with a first correction factor and a second correction factor, and obtain an optimized gain amplitude parameter; an optimized gain amplitude parameter application unit, used for applying the optimized gain amplitude parameter to a sensitivity adjustment unit of the capacitive sensor of the display unit under test; The gain amplitude parameter adjustment formula is: ,in Refers to the optimized gain amplitude parameter, refers to the initial gain amplitude parameter, Refers to the first correction factor, that is, the average slope of the curve reflecting the trend of noise baseline drift. Refers to the adjustment coefficient corresponding to the first correction factor, Refers to the second correction factor, that is, the average slope of the curve reflecting the trend of calibration error change, Refers to the adjustment coefficient corresponding to the second correction factor.

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