Screen brightness uniformity detection method and system

Abstract

The invention discloses a screen brightness uniformity detection method and system, relates to the technical field of digital computing, and solves the problems of low detection efficiency, inaccurate fault positioning and insufficient environmental adaptability test through automation, precision and multi-scene adaptability improvement. A touch response dimension score Scx and an environmental adaptability dimension score Sty are extracted through an online analysis module in combination with a deep learning technology, and a performance evaluation index Pczs is calculated; the performance evaluation screening module classifies the performance of the touch display screen through the evaluation content of the performance evaluation index Pczs, generates optimization or maintenance suggestions, and improves the detection efficiency and the fault positioning precision; the environment simulation module dynamically tests touch performance parameters and calculates an environment adaptability evaluation factor Htyx through high-temperature, high-humidity, strong-light and low-pressure environment scenes, so that the reliability and the stability of the touch display screen in an extreme environment are ensured, and the brightness uniformity of the screen is detected and evaluated more accurately.

Description

Technical Field

[0001] The present invention relates to the field of digital computing technology, and in particular to a method and system for detecting brightness uniformity of a screen. Background Art

[0002] Touchscreen display technology originated in the 1960s and was initially limited to simple resistive designs for use in industrial and military fields. As an important medium for human-computer interaction, touchscreen displays are widely used in devices such as smartphones, tablets, and self-service terminals, and their performance and quality directly affect the user experience. After entering the 21st century, the rise of smart mobile devices has promoted the rapid development of touchscreen display technology, with touch accuracy, response speed, and durability becoming key indicators.

[0003] However, with the diversification of touchscreen application scenarios, quality inspection faces significant challenges, such as touch response delays, reduced sensitivity, and environmental adaptability issues. Traditional inspection methods rely heavily on manual testing, which is inefficient and highly subjective. In recent years, with the application of big data and intelligent analysis technologies, touchscreen performance quality inspection has gradually transitioned to automation and intelligence. Modern systems achieve comprehensive performance testing of touchscreens through data collection, algorithm analysis, and multi-dimensional performance evaluation. Analysis systems that combine fault diagnosis with optimization recommendations have become a key means of improving touchscreen product quality and user satisfaction, providing reliable technical support for related industries.

[0004] However, the existing touch screen display performance quality detection and analysis system still has the following technical shortcomings: 1. Low inspection efficiency: Traditional inspection methods rely on manual operation, which is inefficient and cannot meet the high-efficiency inspection requirements of large-scale production lines. For example, in batch inspection, manually checking the sensitivity or response time of touch screens one by one may lead to extended production cycles.

[0005] 2. Lack of accurate fault location: Existing detection systems are not accurate enough in locating abnormalities in touch screen performance, such as contact drift or regional failure. They can only indicate the existence of a fault but cannot accurately mark the fault area, increasing subsequent repair costs.

[0006] 3. Insufficient environmental adaptability testing: Many testing systems are unable to effectively simulate complex environmental conditions, such as high temperature, high humidity, and strong light. This makes it difficult to ensure the reliability of touch screen performance in extreme application scenarios. For example, in outdoor self-service terminals, touch screens may malfunction due to temperature fluctuations, making screen brightness uniformity testing inaccurate, and existing testing does not cover this scenario. Summary of the Invention

[0007] In view of the deficiencies in the prior art, the present invention provides a method and system for detecting brightness uniformity of a screen, which solves the technical shortcomings mentioned in the background art.

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions: a screen brightness uniformity detection system, including an image cache module, an image verification module, a performance preprocessing module, an environment simulation module, an online analysis module and a performance evaluation and screening module; The image cache module is used to store the received touch display performance test image information, including touch operation picture information and touch feedback video information, and mark the received image information according to the image information, including uploader information, device information and test environment information; The image verification module verifies the received touch display performance test image information, including touch display operation image parameters, image quantity, video duration, and touch response feature information, verifies the touch display performance characteristics in the image, and obtains the touch response characteristic verification factor Xcsd, and matches it with the preset touch response verification threshold T to generate a touch display performance verification solution; The performance preprocessing module preprocesses the touch display test image information after verification by the image verification module, including touch area integrity check, contact drift feature extraction and touch accuracy analysis, and normalizes the data to form a first performance data set and a second environment data set; The environmental simulation module simulates a variety of complex environmental conditions based on the second environmental data set, including high temperature, high humidity, strong light, and low pressure environments, and dynamically adjusts the performance parameters of the touch display screen in the environmental simulation scene to obtain the environmental adaptability evaluation factor Htyx of the touch display screen; The online analysis module uses deep learning technology to perform online analysis on the first performance data set and the environmental adaptability evaluation factor Htyx, and combines the touch response characteristic verification factor Xcsd to obtain the touch display performance evaluation index Pczs; The performance evaluation and screening module compares and evaluates the touch screen performance evaluation index Pczs through a preset touch screen performance evaluation threshold P, obtains a performance quality screening strategy based on the evaluation content of the touch screen performance evaluation index Pczs, and generates touch screen handling guidance suggestions based on the performance quality screening strategy to achieve automated execution.

[0009] Preferably, the image cache module parses and marks the received touch display performance test image information, records the identity information of the uploader, the model and unique identification information of the tested device, and the relevant parameters of the test environment, including temperature, humidity and light intensity, to improve the source traceability and integrity of the touch display performance test image information; at the same time, the stored touch display performance test image information is preliminarily classified and indexed.

[0010] Preferably, the image verification module includes a parameter calculation and evaluation unit and a solution generation unit; The parameter calculation and evaluation unit is used to parse and extract parameters of the received touch display performance test image information, including the resolution, number of images, video duration, and dynamic characteristic parameters of the touch display operation image, and obtain the touch start time Ts, touch feedback response time Tf, and touch operation duration Td by analyzing the time axis of the touch operation image sequence, and calculate the touch response characteristic calibration factor Xcsd using the following formula;

[0011] The touch response verification threshold T is preset and compared with the touch response characteristic verification factor Xcsd for evaluation. The specific evaluation contents are as follows: If the touch response characteristic verification factor Xcsd ≤ the touch response verification threshold T, it indicates that the touch response performance is qualified and the delay is within the acceptable range, and no further adjustment is required. If the touch response characteristic verification factor Xcsd is greater than the touch response verification threshold T, it indicates that the touch response performance is unqualified and the delay exceeds the preset standard. Further analysis is required.

[0012] Preferably, the solution generation unit is used to generate a touch display performance verification solution based on the evaluation content of the touch response characteristic verification factor Xcsd, and the specific content is as follows: When the touch response performance is unqualified, calculate and generate a verification strategy based on the deviation ratio P classification: The calculation formula of the deviation ratio P is as follows:

[0013] When the deviation ratio P is less than or equal to 10%, it indicates a slight deviation. In this case, it is recommended to check the touch screen driver or touch algorithm optimization. When 10% < deviation ratio P ≤ 30%, it indicates moderate deviation. In this case, it is recommended to test the sensor performance of the touch screen hardware module. When the deviation ratio P>30%, it indicates a serious deviation. In this case, it is recommended to replace the core hardware of the touch screen or redesign the touch module structure.

[0014] Preferably, the performance preprocessing module is used to check the integrity of the touch area, and identify the integrity status of the touch area by analyzing the boundary features and coverage range of the touch operation in the test image; secondly, extract the contact drift characteristics, and dynamically track and analyze the contact position information based on the time axis of the image sequence to obtain the contact drift trajectory and offset amplitude; then perform touch accuracy analysis, and evaluate the accuracy parameters of the touch operation by calculating the deviation value between the touch operation target point and the actual touch point; finally, normalize the preprocessed touch display test image information to generate standardized performance data, and at the same time, divide the feature data related to the touch operation performance into a first performance data set according to the data source, and divide the parameter data reflecting the test environment conditions into a second environment data set.

[0015] Preferably, the environmental simulation module is used to extract environmental data from the second environmental data set, including temperature, humidity, light intensity, and air pressure, and use an environmental simulation algorithm to generate four typical environmental scenarios of high temperature, high humidity, strong light, and low pressure. Subsequently, the touch display screen performance is dynamically tested in each simulation scenario to obtain environmental characteristic data, including touch simulation response time Msc, touch simulation accuracy Mjd, and touch simulation drift value Mpy. Then, based on product design and application requirements, target values ​​of the environmental characteristic data are preset. Finally, the environmental characteristic data and the target values ​​of the environmental characteristic data are associated to calculate and obtain the environmental adaptability evaluation factor Htyx of the touch display screen. The specific calculation formula is as follows:

[0016] Where, Indicates the preset touch simulation response time Msc target value, Indicates the preset touch simulation accuracy Mjd target value, Indicates the preset touch simulation drift value Mpy target value.

[0017] Preferably, the online analysis module is used to perform format conversion and normalization processing on the touch response-related data and the environmental adaptability evaluation factor Htyx in the first performance data set; secondly, calling a preset deep learning model to perform feature analysis and pattern recognition on the normalized touch response-related data and the environmental adaptability evaluation factor Htyx, and obtaining the touch response dimension score Scx and the environmental adaptability dimension score Sty through the multi-layer feature extraction network of the deep learning model; finally, the touch response characteristic verification factor Xcsd, the touch response dimension score Scx, and the environmental adaptability dimension score Sty are extracted, and the touch display performance evaluation index Pczs is calculated using the following formula:

[0018] Preferably, the performance evaluation and screening module includes an evaluation and comparison unit and a strategy generation unit; The evaluation and comparison unit is used to compare and analyze the preset touch screen performance evaluation threshold P with the touch screen performance evaluation index Pczs, and classify the touch screen performance quality; the specific contents are as follows: When the touch screen performance evaluation index Pczs ≥ the touch screen performance evaluation threshold, the touch screen performance is determined to meet the quality requirements and marked as "qualified"; When the touch screen performance evaluation index Pczs is less than the touch screen performance evaluation threshold, the touch screen performance is determined to be unqualified and marked as “unqualified”, and the corresponding performance defect information is recorded.

[0019] Preferably, the strategy generation unit formulates a performance quality screening strategy based on the classification result of the evaluation and comparison unit, and generates guidance suggestions for handling the touch display screen; specifically, the strategy generation unit comprises: For "qualified" touch screens, a pass mark is directly output; For "unqualified" touch screen displays, based on the performance defect information recorded by the evaluation comparison unit and combined with the built-in disposal rule library, optimization or repair guidance suggestions are generated, including recalibrating touch sensitivity, adjusting software algorithms or replacing hardware components.

[0020] A method for detecting brightness uniformity of a screen comprises the following steps: Step 1: storing the received touch screen performance test image information, including touch operation picture information and touch feedback video information, and marking the received image information, including uploader information, device information and test environment information; Step 2: Verify the received touch display performance test image information, including touch display operation image parameters, image quantity, video duration, and touch response feature information. Verify the touch display performance characteristics in the image, obtain the touch response characteristic verification factor Xcsd, and match it with the preset touch response verification threshold T to generate a touch display performance verification solution. Step 3: Preprocessing the touch screen test image information after verification by the image verification module, including touch area integrity check, contact drift feature extraction and touch accuracy analysis, and normalizing the data to form a first performance data set and a second environment data set; Step 4: Based on the second environmental data set, simulate multiple complex environmental conditions, including high temperature, high humidity, strong light, and low pressure environments, and dynamically adjust the performance parameters of the touch screen in the environmental simulation scenario to obtain the environmental adaptability evaluation factor Htyx of the touch screen; Step 5: Use deep learning technology to perform online analysis on the first performance data set and the environmental adaptability evaluation factor Htyx, and combine it with the touch response characteristic verification factor Xcsd to obtain the touch display performance evaluation index Pczs; Step 6: Compare and evaluate the touch screen performance evaluation index Pczs using the preset touch screen performance evaluation threshold P, obtain a performance quality screening strategy based on the evaluation content of the touch screen performance evaluation index Pczs, and generate touch screen handling guidance suggestions based on the performance quality screening strategy to achieve automated execution.

[0021] The present invention provides a method and system for detecting brightness uniformity of a screen. The method and system have the following beneficial effects: (1) The brightness uniformity detection method and system of a screen solves the problem of low detection efficiency. Through the coordinated work of the image cache module, the image verification module, the performance preprocessing module and the online analysis module, the full process automation of the touch display performance test is realized. Among them, the image cache module stores and marks the touch display performance test image information, including touch operation picture information and touch feedback video information, which reduces the time of manual recording. The image verification module automatically calculates and evaluates the touch response characteristic verification factor Xcsd in the touch display performance test image information through the parameter calculation and evaluation unit and the feature evaluation unit, and generates a touch display performance verification scheme after comparing it with the preset touch response verification threshold T, thereby improving the detection efficiency. The performance preprocessing module normalizes the verified information to generate a first performance data set and a second environmental data set, providing standardized input for subsequent deep learning analysis. The online analysis module uses deep learning technology to perform feature analysis on the normalized data, extracts the touch response dimension score Scx and the environmental adaptability dimension score Sty, and obtains the touch display performance evaluation index Pczs by calculation, thereby achieving efficient detection and more accurate detection and evaluation of the screen brightness uniformity.

[0022] (2) The brightness uniformity detection method and system of the screen solves the problem of lack of accurate fault location. Through the feature extraction and calculation of the performance preprocessing module and the online analysis module, the performance problem of the touch display screen can be accurately located. The performance preprocessing module extracts the touch start time Ts, the touch feedback response time Tf and the touch operation duration Td through the touch area integrity check, contact drift feature extraction and touch accuracy analysis, obtains the touch response feature verification factor Xcsd and normalizes it, and clarifies the source of the abnormal performance of the touch display screen. The online analysis module combines the first performance data set and the environmental adaptability evaluation factor Htyx to calculate the touch response dimension score Scx and the environmental adaptability dimension score Sty, and intuitively quantifies the performance problem of the touch display screen through the calculation formula of the touch display screen performance evaluation index Pczs, providing a high-precision performance quality classification basis for the performance evaluation screening module. The performance evaluation screening module further combines the touch display screen performance evaluation threshold P for comparison, clarifies the performance defect information and generates disposal guidance suggestions, including recalibrating the touch sensitivity, adjusting the software algorithm or replacing the hardware components, which significantly improves the accuracy of fault location.

[0023] (3) The brightness uniformity detection method and system of the screen solves the problem of insufficient environmental adaptability testing. The environmental simulation module constructs complex environmental scenes and evaluates the adaptability of the touch display performance. The environmental simulation module extracts temperature, humidity, light intensity and air pressure values ​​based on the second environmental data set, and generates high temperature, high humidity, strong light and low pressure environmental scenes respectively. In each scene, the touch simulation response time Msc, touch simulation accuracy Mjd and touch simulation drift value Mpy are dynamically tested, and the environmental adaptability evaluation factor Htyx of the touch display is calculated and obtained in combination with the target value of the preset environmental characteristic data, so as to accurately quantify the performance of the touch display in complex environments. The online analysis module incorporates the environmental adaptability evaluation factor Htyx of the touch display into the evaluation process, and generates a comprehensive performance evaluation result in combination with other performance parameters to ensure the reliability and stability of the touch display in extreme scenes, and provides a reliable guarantee for the application of touch display in diversified scenes. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 The figure is a schematic diagram of the framework structure of a screen brightness uniformity detection system of the present invention. DETAILED DESCRIPTION

[0025] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0026] Example 1: Please refer to Figure 1 , the present invention provides a screen brightness uniformity detection system, including an image cache module, an image verification module, a performance preprocessing module, an environment simulation module, an online analysis module and a performance evaluation and screening module; The image cache module is used to store the received touch display performance test image information, including touch operation picture information, touch feedback video information and screen brightness image information, and mark the received image information according to the information of the uploader, device information and test environment information; The image verification module verifies the received touch display performance test image information, including touch display operation image parameters, image quantity, video duration, touch response characteristic information, and screen brightness image parameters. It verifies the touch display performance characteristics in the image and obtains the touch response characteristic verification factor Xcsd. It matches it with the preset touch response verification threshold T and generates a touch display performance verification scheme. The performance preprocessing module preprocesses the touch screen test image information after verification by the image verification module, including touch area integrity check, contact drift feature extraction, touch accuracy analysis, and screen brightness uniformity analysis. After normalization, the data is formed into a first performance data set and a second environment data set. The environmental simulation module simulates a variety of complex environmental conditions based on the second environmental data set, including high temperature, high humidity, strong light, and low pressure environments. It dynamically adjusts the performance parameters of the touch screen in the environmental simulation scenario to obtain the environmental adaptability evaluation factor Htyx of the touch screen. The online analysis module uses deep learning technology to perform online analysis on the first performance data set and the environmental adaptability evaluation factor Htyx, and combines it with the touch response characteristic verification factor Xcsd to obtain the touch display performance evaluation index Pczs; The performance evaluation and screening module compares and evaluates the touch screen performance evaluation index Pczs through the preset touch screen performance evaluation threshold P, obtains the performance quality screening strategy based on the evaluation content of the touch screen performance evaluation index Pczs, and generates touch screen disposal guidance suggestions based on the performance quality screening strategy to achieve automatic execution.

[0027] In this embodiment, the image cache module ensures the traceability and integrity of the touch display performance test image information by storing touch operation image information and touch feedback video information, and marking the uploader information, device information, and test environment information. The image verification module verifies the touch display operation image parameters, image number, video duration, and touch response feature information to obtain the touch response characteristic verification factor Xcsd, and matches it with the preset touch response verification threshold T to generate a touch display performance verification scheme to improve detection accuracy. The performance preprocessing module checks the integrity of the touch area, extracts the contact drift characteristics, and analyzes the touch accuracy. It normalizes the verified data to generate a first performance data set and a second environment data set, providing standardized input for subsequent analysis. The environmental simulation module dynamically adjusts the touch simulation response time Msc, touch simulation accuracy Mjd and touch simulation drift value Mpy by simulating high temperature, high humidity, strong light and low pressure environments, and calculates the environmental adaptability evaluation factor Htyx of the touch display, thereby enhancing the system's ability to evaluate the performance of the touch display under complex environmental conditions; the online analysis module uses deep learning technology to extract features from the first performance data set and Htyx, and combines Xcsd to calculate the touch display performance evaluation index Pczs, comprehensively quantifying the performance of the touch display; the performance evaluation screening module generates performance quality screening strategies and touch display handling guidance suggestions based on a comparative analysis of Pczs and the preset touch display performance evaluation threshold P, thereby realizing the intelligent and automated fault location and optimization solutions.

[0028] Example 2: The image cache module parses and marks the received touch display performance test image information, records the identity information of the uploader, the model and unique identification information of the tested device, and the relevant parameters of the test environment, including temperature, humidity and light intensity, to improve the source traceability and integrity of the touch display performance test image information; at the same time, the stored touch display performance test image information is preliminarily classified and indexed.

[0029] The image verification module includes a parameter calculation and evaluation unit and a solution generation unit; The parameter calculation and evaluation unit is used to parse and extract parameters from the received touch display performance test image information, including the resolution, number of images, video duration, and dynamic characteristic parameters of the touch display operation image. By analyzing the time axis of the touch operation image sequence, the touch start time Ts, touch feedback response time Tf, and touch operation duration Td are obtained, and the touch response characteristic calibration factor Xcsd is calculated using the following formula;

[0030] The touch response verification threshold T is preset and compared with the touch response characteristic verification factor Xcsd for evaluation. The specific evaluation contents are as follows: If the touch response characteristic verification factor Xcsd ≤ the touch response verification threshold T, it indicates that the touch response performance is qualified and the delay is within the acceptable range, and no further adjustment is required. If the touch response characteristic verification factor Xcsd is greater than the touch response verification threshold T, it indicates that the touch response performance is unqualified and the delay exceeds the preset standard. Further analysis is required.

[0031] The solution generation unit is used to generate a touch display performance verification solution based on the evaluation content of the touch response characteristic verification factor Xcsd. The specific contents are as follows: When the touch response performance is unqualified, calculate and generate a verification strategy based on the deviation ratio P classification: The calculation formula of the deviation ratio P is as follows:

[0032] When the deviation ratio P is less than or equal to 10%, it indicates a slight deviation. In this case, it is recommended to check the touch screen driver or touch algorithm optimization. When 10% < deviation ratio P ≤ 30%, it indicates moderate deviation. In this case, it is recommended to test the sensor performance of the touch screen hardware module. When the deviation ratio P>30%, it indicates a serious deviation. In this case, it is recommended to replace the core hardware of the touch screen or redesign the touch module structure. Screen brightness uniformity analysis processes the screen brightness image and uses image segmentation technology to divide the screen area into multiple sub-areas. The average brightness value of each sub-area is calculated, and the standard deviation of the average brightness values ​​of all sub-areas is calculated to serve as the screen brightness uniformity evaluation index. The specific contents are as follows: For each sub-region , its average brightness value It can be obtained by averaging the brightness values ​​of all pixels in the sub-region; assuming that the sub-region Include pixels, the The brightness value of a pixel is ,but:

[0033] Calculate the average of the average brightness values ​​of all sub-regions : ; Calculate screen brightness uniformity evaluation index : Screen brightness uniformity evaluation index Defined as the standard deviation of the average brightness values ​​of all sub-areas. Standard deviation is an indicator to measure the degree of dispersion of a set of data. The smaller the standard deviation, the closer the average brightness values ​​of each sub-area are, and the more uniform the screen brightness is; conversely, the larger the standard deviation, the worse the screen brightness uniformity. According to the calculation formula of standard deviation, The calculation formula is: ; Among them, it is assumed that the screen area is divided into n sub-regions, respectively R 1, R 2,⋯, Rn , let the i-th sub-region Ri The average brightness value is Li, i =1,2,⋯, n , calculate the average of the average brightness values ​​of all sub-regions L .

[0034] Preset brightness difference threshold, if the screen brightness uniformity evaluation index If the brightness difference threshold is exceeded, it means that the screen brightness difference is unqualified. Use different grayscale compensation for each area, and increase the grayscale response of low brightness areas and reduce the grayscale response of high brightness areas according to the brightness amplitude of 2-4% step by step. Resample the brightness after each adjustment and repeat the above steps until the brightness uniformity evaluation index is met. Until the brightness difference threshold is less than or equal to it, and the maximum number of iterations is set to 5 to prevent dead loops; If the screen brightness uniformity evaluation index If the brightness difference threshold is not reached, it means that the screen brightness uniformity is qualified and continuous monitoring is required.

[0035] In this embodiment, the comprehensiveness, accuracy and automation of performance testing are achieved through the collaborative work of various modules; the image cache module parses and marks the touch display performance test image information, records the identity information of the uploader, the model and unique identification information of the tested device, and the relevant parameters of the test environment, including temperature, humidity and light intensity, to ensure the traceability and integrity of the source of the test data, and at the same time improves data management efficiency through preliminary classification and indexing; the parameter calculation and evaluation unit in the image verification module extracts the touch start time Ts, the touch feedback response time Tf and the touch operation duration Td. These lower-level parameters respectively reflect the start time of the touch operation, the system response time and the overall duration of the operation. , the touch response characteristic verification factor Xcsd is obtained through formula calculation to evaluate the performance of the touch display response delay; the feature evaluation unit compares and analyzes the touch response characteristic verification factor Xcsd with the preset touch response verification threshold T to generate a performance verification result, and clarify whether the touch display response performance meets expectations; the solution generation unit calculates the deviation ratio P and generates a verification strategy by classification to provide optimization or repair suggestions for touch displays with unqualified performance. The significance of the deviation ratio P is to quantify the degree of performance deviation, which helps to classify and formulate optimization strategies, such as touch driver optimization, hardware testing or module replacement. Ultimately, through effective collaboration between modules, the efficiency and intelligence level of touch display performance quality detection are improved.

[0036] Example 3: The performance preprocessing module is used to check the integrity of the touch area, and identify the integrity status of the touch area by analyzing the boundary features and coverage range of the touch operation in the test image; secondly, extract the contact drift characteristics, and dynamically track and analyze the offset of the touch position information based on the time axis of the image sequence to obtain the contact drift trajectory and offset amplitude; then perform touch accuracy analysis, and evaluate the accuracy parameters of the touch operation by calculating the deviation value between the touch operation target point and the actual touch point; finally, normalize the preprocessed touch display test image information to generate standardized performance data. At the same time, according to the data source, the feature data related to the touch operation performance is divided into a first performance data set, and the parameter data reflecting the test environment conditions is divided into a second environment data set.

[0037] The environmental simulation module is used to extract environmental data from the second environmental data set, including temperature, humidity, light intensity, and air pressure, and use the environmental simulation algorithm to generate four typical environmental scenarios: high temperature, high humidity, strong light, and low pressure. Subsequently, the touch screen performance is dynamically tested in each simulated scenario to obtain environmental characteristic data, including touch simulation response time Msc, touch simulation accuracy Mjd, and touch simulation drift value Mpy. Then, based on product design and application requirements, the target value of the environmental characteristic data is preset. Finally, the environmental characteristic data and the target value of the environmental characteristic data are correlated to calculate the environmental adaptability evaluation factor Htyx of the touch screen. The specific calculation formula is as follows:

[0038] Where, Indicates the preset touch simulation response time Msc target value, Indicates the preset touch simulation accuracy Mjd target value, Indicates the preset touch simulation drift value Mpy target value.

[0039] The online analysis module is used to perform format conversion and normalization processing on the touch response-related data and environmental adaptability evaluation factor Htyx in the first performance data set. Secondly, the preset deep learning model is called to perform feature analysis and pattern recognition on the normalized touch response-related data and environmental adaptability evaluation factor Htyx. The touch response dimension score Scx and the environmental adaptability dimension score Sty are obtained through the multi-layer feature extraction network of the deep learning model. Finally, the touch response characteristic verification factor Xcsd, the touch response dimension score Scx, and the environmental adaptability dimension score Sty are extracted, and the touch display performance evaluation index Pczs is calculated using the following formula:

[0040] The performance evaluation and screening module includes an evaluation and comparison unit and a strategy generation unit; The evaluation and comparison unit is used to compare and analyze the preset touch screen performance evaluation threshold P with the touch screen performance evaluation index Pczs, and classify the touch screen performance quality; the specific contents are as follows: When the touch screen performance evaluation index Pczs ≥ the touch screen performance evaluation threshold, the touch screen performance is determined to meet the quality requirements and marked as "qualified"; When the touch screen performance evaluation index Pczs is less than the touch screen performance evaluation threshold, the touch screen performance is determined to be unqualified and marked as “unqualified”, and the corresponding performance defect information is recorded.

[0041] The strategy generation unit formulates a performance quality screening strategy based on the classification results of the evaluation and comparison unit and generates guidance suggestions for handling the touch screen display. Specifically, it includes: For "qualified" touch screens, a pass mark is directly output; For "unqualified" touch screen displays, based on the performance defect information recorded by the evaluation comparison unit and combined with the built-in disposal rule library, optimization or repair guidance suggestions are generated, including recalibrating touch sensitivity, adjusting software algorithms or replacing hardware components.

[0042] In this embodiment, the performance preprocessing module extracts the boundary features and coverage of the touch operation through a touch area integrity check to determine the integrity status of the touch area. It analyzes the touch drift trajectory and offset amplitude through touch point drift feature extraction to evaluate touch point stability. It calculates the deviation between the touch operation target point and the actual touch point through touch accuracy analysis to quantify the touch accuracy performance. These data are normalized to generate a first performance dataset and a second environmental dataset, providing standardized input for subsequent analysis. The environmental simulation module extracts the temperature, humidity, light intensity, and air pressure values ​​from the second environmental dataset to generate high temperature, high humidity, strong light, and low pressure environmental scenarios, and obtains the touch simulation response time Msc, touch simulation accuracy Mjd, and touch simulation drift value Mpy. It calculates the touch display environmental adaptability evaluation factor Htyx based on preset target values ​​to evaluate the stability of the touch display in complex environments. The online analysis module normalizes the first performance dataset and the touch display environmental adaptability evaluation factor Htyx, calls a deep learning model to extract the touch response dimension score Scx and the environmental adaptability dimension score Sty, and combines them with the touch response characteristic verification factor Xcsd. By calculating the touch display performance evaluation index Pczs, the performance of the touch display is comprehensively quantified; the performance evaluation screening module compares and analyzes the touch display performance evaluation index Pczs with the touch display performance evaluation threshold P through the evaluation comparison unit to determine whether the performance is qualified and generate a performance quality screening strategy. The strategy generation unit generates touch display optimization or maintenance suggestions based on the screening strategy, including recalibrating touch sensitivity, adjusting software algorithms or replacing hardware components, thereby realizing intelligent and automated touch display performance optimization; through the above modules, comprehensive evaluation, accurate detection and efficient optimization of touch display performance are achieved.

[0043] Example 4: Please refer to Figure 1 A method for detecting brightness uniformity of a screen comprises the following steps: Step 1: storing the received touch display performance test image information, including touch operation picture information, touch feedback video information, and screen brightness image information, and marking the received image information, including uploader information, device information, and test environment information; Step 2: Verify the received touch screen performance test image information, including touch screen operation image parameters, image quantity, video duration, touch response feature information, and screen brightness image parameters. At the same time, perform preliminary verification of the screen brightness image to ensure that it can be used for subsequent brightness uniformity analysis. The brightness uniformity-related verification results are comprehensively matched with the preset touch response verification threshold T to generate a touch screen performance verification solution. Step 3: Preprocess the touch screen test image information after verification by the image verification module, including touch area integrity check, contact drift feature extraction, touch accuracy analysis, and screen brightness uniformity analysis. The data is normalized to form a first performance data set and a second environment data set. Touch area integrity check: Check whether the touch area in the touch operation image is complete and whether there are any missing or abnormal parts; Touch point drift feature extraction: Extract touch point drift-related features from touch operation images and feedback videos to evaluate touch accuracy; Touch accuracy analysis: Analyze touch accuracy based on extracted features to determine whether the touch operation is accurate; Screen brightness uniformity analysis: Process the screen brightness image and use image segmentation technology to divide the screen area into multiple sub-areas; Calculate the average brightness value of each sub-region; Calculate the standard deviation of the average brightness values ​​of all sub-areas and use it as an evaluation indicator of screen brightness uniformity ; Normalizing the processed data to form a first performance data set and a second environment data set; Step 4: Based on the second environmental data set, simulate multiple complex environmental conditions, including high temperature, high humidity, strong light, and low pressure environments, and dynamically adjust the performance parameters of the touch screen in the environmental simulation scenario to obtain the environmental adaptability evaluation factor Htyx of the touch screen; Step 5: Use deep learning technology to perform online analysis on the first performance data set and the environmental adaptability evaluation factor Htyx, and combine it with the touch response characteristic verification factor Xcsd to obtain the touch display performance evaluation index Pczs; Step 6. Compare and evaluate the touch screen performance evaluation index Pczs using the preset touch screen performance evaluation threshold P, obtain a performance quality screening strategy based on the evaluation content of the touch screen performance evaluation index Pczs, and generate touch screen handling guidance suggestions based on the performance quality screening strategy to achieve automated execution. Among them, the performance quality screening strategy and handling guidance suggestions will comprehensively consider various performance indicators such as brightness uniformity.

[0044] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A screen brightness uniformity detection system, characterized by: It includes image cache module, image verification module, performance preprocessing module, environment simulation module, online analysis module and performance evaluation and screening module; The image cache module is used to store the received touch display performance test image information, including touch operation picture information, touch feedback video information and screen brightness image information, and mark the received image information according to the information including uploader information, device information and test environment information; The image verification module verifies the received touch display performance test image information, including touch display operation image parameters, image quantity, video duration, touch response feature information, and screen brightness image parameters, verifies the touch display performance characteristics in the image, and obtains the touch response characteristic verification factor Xcsd, and matches it with the preset touch response verification threshold T to generate a touch display performance verification solution; The performance preprocessing module preprocesses the touch display test image information after verification by the image verification module, including touch area integrity check, contact drift feature extraction, touch accuracy analysis, and screen brightness uniformity analysis, and normalizes the data to form a first performance data set and a second environment data set; The environmental simulation module simulates a variety of complex environmental conditions based on the second environmental data set, including high temperature, high humidity, strong light, and low pressure environments, and dynamically adjusts the performance parameters of the touch display screen in the environmental simulation scene to obtain the environmental adaptability evaluation factor Htyx of the touch display screen; The online analysis module uses deep learning technology to perform online analysis on the first performance data set and the environmental adaptability evaluation factor Htyx, and combines the touch response characteristic verification factor Xcsd to obtain the touch display performance evaluation index Pczs; The performance evaluation and screening module compares and evaluates the touch screen performance evaluation index Pczs through a preset touch screen performance evaluation threshold P, obtains a performance quality screening strategy based on the evaluation content of the touch screen performance evaluation index Pczs, and generates touch screen handling guidance suggestions based on the performance quality screening strategy to achieve automated execution.

2. The screen brightness uniformity detection system according to claim 1, characterized in that: The image cache module parses and marks the received touch display performance test image information, records the identity information of the uploader, the model and unique identification information of the tested device, and the relevant parameters of the test environment, including temperature, humidity and light intensity, to improve the source traceability and integrity of the touch display performance test image information; at the same time, the stored touch display performance test image information is preliminarily classified and indexed.

3. The screen brightness uniformity detection system according to claim 1, characterized in that: The image verification module includes a parameter calculation and evaluation unit and a solution generation unit; The parameter calculation and evaluation unit is used to parse and extract parameters of the received touch display performance test image information, including the resolution, number of images, video duration, and dynamic characteristic parameters of the touch display operation image, and obtain the touch start time Ts, touch feedback response time Tf, and touch operation duration Td by analyzing the time axis of the touch operation image sequence, and calculate the touch response characteristic calibration factor Xcsd using the following formula; ; The touch response verification threshold T is preset and compared with the touch response characteristic verification factor Xcsd for evaluation. The specific evaluation contents are as follows: If the touch response characteristic verification factor Xcsd ≤ the touch response verification threshold T, it indicates that the touch response performance is qualified and the delay is within the acceptable range, and no further adjustment is required. If the touch response characteristic verification factor Xcsd is greater than the touch response verification threshold T, it indicates that the touch response performance is unqualified and the delay exceeds the preset standard. Further analysis is required.

4. The screen brightness uniformity detection system according to claim 1, characterized in that: The scheme generating unit is used to generate a touch display screen performance verification scheme based on the evaluation content of the touch response characteristic verification factor Xcsd, and the specific contents are as follows: When the touch response performance is unqualified, calculate and generate a verification strategy based on the deviation ratio P classification: The calculation formula of the deviation ratio P is as follows: ; When the deviation ratio P is less than or equal to 10%, it indicates a slight deviation. In this case, it is recommended to check the touch screen driver or touch algorithm optimization. When 10% < deviation ratio P ≤ 30%, it indicates moderate deviation. In this case, it is recommended to test the sensor performance of the touch screen hardware module. When the deviation ratio P>30%, it indicates a serious deviation. In this case, it is recommended to replace the core hardware of the touch screen or redesign the touch module structure. The screen brightness uniformity analysis processes the screen brightness image and uses image segmentation technology to divide the screen area into multiple sub-areas, calculates the average brightness value of each sub-area, and calculates the standard deviation of the average brightness values ​​of all sub-areas, which is used as the screen brightness uniformity evaluation index. , the specific contents are as follows: For each sub-region , its average brightness value It can be obtained by averaging the brightness values ​​of all pixels in the sub-region; assuming that the sub-region Include pixels, the The brightness value of a pixel is ,but: ， Calculate the average of the average brightness values ​​of all sub-regions : ； Calculate screen brightness uniformity evaluation index : Screen brightness uniformity evaluation index Defined as the standard deviation of the average brightness values ​​of all sub-areas. Standard deviation is an indicator to measure the degree of dispersion of a set of data. The smaller the standard deviation, the closer the average brightness values ​​of each sub-area are, and the more uniform the screen brightness is; conversely, the larger the standard deviation, the worse the screen brightness uniformity. According to the calculation formula of standard deviation, The calculation formula is: ； Among them, it is assumed that the screen area is divided into n sub-regions, respectively R 1, R 2,⋯, Rn , let the i-th sub-region Ri The average brightness value is Li, i =1,2,⋯, n , calculate the average of the average brightness values ​​of all sub-regions L ; Preset brightness difference threshold, if the screen brightness uniformity evaluation index If the brightness difference threshold is exceeded, it means that the screen brightness difference is unqualified. Use different grayscale compensation for each area, and increase the grayscale response of low brightness areas and reduce the grayscale response of high brightness areas according to the brightness amplitude of 2-4% step by step. Resample the brightness after each adjustment and repeat the above steps until the brightness uniformity evaluation index is met. Until the brightness difference threshold is less than or equal to it, and the maximum number of iterations is set to 5 to prevent dead loops; If the screen brightness uniformity evaluation index If the brightness difference threshold is not reached, it means that the screen brightness uniformity is qualified and continuous monitoring is required.

5. The screen brightness uniformity detection system according to claim 1, characterized in that: The performance preprocessing module is used to check the integrity of the touch area, identify the integrity status of the touch area by analyzing the boundary features and coverage range of the touch operation in the test image; secondly, extract the contact drift characteristics, dynamically track and analyze the offset of the touch position information based on the time axis of the image sequence, and obtain the contact drift trajectory and offset amplitude; then perform touch accuracy analysis, and evaluate the accuracy parameters of the touch operation by calculating the deviation value between the touch operation target point and the actual touch point; finally, normalize the preprocessed touch display test image information to generate standardized performance data. At the same time, according to the data source, the feature data related to the touch operation performance is divided into a first performance data set, and the parameter data reflecting the test environment conditions is divided into a second environment data set.

6. The screen brightness uniformity detection system according to claim 1, characterized in that: The environmental simulation module is used to extract environmental data from the second environmental data set, including temperature, humidity, light intensity, and air pressure, and use an environmental simulation algorithm to generate four typical environmental scenarios, namely, high temperature, high humidity, strong light, and low pressure. Subsequently, the performance of the touch display screen is dynamically tested in each simulation scenario to obtain environmental characteristic data, including touch simulation response time Msc, touch simulation accuracy Mjd, and touch simulation drift value Mpy. Then, target values ​​of the environmental characteristic data are preset based on product design and application requirements. Finally, the environmental characteristic data and the target values ​​of the environmental characteristic data are correlated to calculate the environmental adaptability evaluation factor Htyx of the touch display screen. The specific calculation formula is as follows: ; Where, Indicates the preset touch simulation response time Msc target value, Indicates the preset touch simulation accuracy Mjd target value, Indicates the preset touch simulation drift value Mpy target value.

7. The screen brightness uniformity detection system according to claim 1, characterized in that: The online analysis module is used to perform format conversion and normalization processing on the touch response-related data and the environmental adaptability evaluation factor Htyx in the first performance data set; secondly, calling a preset deep learning model to perform feature analysis and pattern recognition on the normalized touch response-related data and the environmental adaptability evaluation factor Htyx, and obtain the touch response dimension score Scx and the environmental adaptability dimension score Sty through the multi-layer feature extraction network of the deep learning model; finally, the touch response characteristic verification factor Xcsd, the touch response dimension score Scx, and the environmental adaptability dimension score Sty are extracted, and the touch display performance evaluation index Pczs is calculated using the following formula:

8. The screen brightness uniformity detection system according to claim 1, characterized in that: The performance evaluation and screening module includes an evaluation and comparison unit and a strategy generation unit; The evaluation and comparison unit is used to compare and analyze the preset touch screen performance evaluation threshold P with the touch screen performance evaluation index Pczs, and classify the touch screen performance quality; the specific contents are as follows: When the touch screen performance evaluation index Pczs ≥ the touch screen performance evaluation threshold, the touch screen performance is determined to meet the quality requirements and marked as "qualified"; When the touch screen performance evaluation index Pczs is less than the touch screen performance evaluation threshold, the touch screen performance is determined to be unqualified and marked as "unqualified", and the corresponding performance defect information is recorded.

9. The screen brightness uniformity detection system according to claim 1, characterized in that: The strategy generation unit formulates a performance quality screening strategy based on the classification result of the evaluation and comparison unit, and generates guidance suggestions for handling the touch display screen; Specifically include: For "qualified" touch screen displays, directly output a pass mark; For "unqualified" touch screen displays, based on the performance defect information recorded by the evaluation comparison unit and combined with the built-in disposal rule library, optimization or repair guidance suggestions are generated, including recalibrating touch sensitivity, adjusting software algorithms or replacing hardware components.

10. A method for detecting brightness uniformity of a screen, according to a system for detecting brightness uniformity of a screen according to any one of claims 1 to 9, characterized in that: The following steps are involved: Step 1: storing the received touch display performance test image information, including touch operation picture information, touch feedback video information, and screen brightness image information, and marking the received image information, including uploader information, device information, and test environment information; Step 2: Verify the received touch screen performance test image information, including touch screen operation image parameters, image quantity, video duration, touch response feature information, and screen brightness image parameters. At the same time, perform preliminary verification of the screen brightness image to ensure that it can be used for subsequent brightness uniformity analysis. The brightness uniformity-related verification results are comprehensively matched with the preset touch response verification threshold T to generate a touch screen performance verification solution. Step 3: Preprocessing the touch screen test image information after verification by the image verification module, including touch area integrity check, contact drift feature extraction, touch accuracy analysis, and screen brightness uniformity analysis, and normalizing the data to form a first performance data set and a second environment data set; Touch area integrity check: Check whether the touch area in the touch operation image is complete and whether there are any missing or abnormal parts; Touch point drift feature extraction: Extract touch point drift-related features from touch operation images and feedback videos to evaluate touch accuracy; Touch accuracy analysis: Analyze touch accuracy based on extracted features to determine whether the touch operation is accurate; Screen brightness uniformity analysis: Process the screen brightness image and use image segmentation technology to divide the screen area into multiple sub-areas; Calculate the average brightness value of each sub-region; Calculate the standard deviation of the average brightness values ​​of all sub-areas and use it as an evaluation indicator of screen brightness uniformity ; Normalizing the processed data to form a first performance data set and a second environment data set; Step 4: Based on the second environmental data set, simulate multiple complex environmental conditions, including high temperature, high humidity, strong light, and low pressure environments, and dynamically adjust the performance parameters of the touch screen in the environmental simulation scenario to obtain the environmental adaptability evaluation factor Htyx of the touch screen; Step 5: Use deep learning technology to perform online analysis on the first performance data set and the environmental adaptability evaluation factor Htyx, and combine it with the touch response characteristic verification factor Xcsd to obtain the touch display performance evaluation index Pczs; Step 6. Compare and evaluate the touch screen performance evaluation index Pczs using the preset touch screen performance evaluation threshold P, obtain a performance quality screening strategy based on the evaluation content of the touch screen performance evaluation index Pczs, and generate touch screen handling guidance suggestions based on the performance quality screening strategy to achieve automated execution. Among them, the performance quality screening strategy and handling guidance suggestions will comprehensively consider various performance indicators such as brightness uniformity.

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