Liquid crystal display function detection method, device, equipment and storage medium

Abstract

The present application relates to the technical field of display screen detection, and discloses a method, device, equipment and storage medium for detecting the functions of a liquid crystal display screen. The method includes: identifying parameters of the liquid crystal display screen to obtain display screen parameters and interface information; performing signal integrity tests on the interfaces in the interface information to obtain interface signal performance data; based on the interface signal performance data, performing dynamic pixel defect detection on the liquid crystal display screen to obtain dynamic pixel defect data; performing multi-dimensional analysis on the dynamic pixel defect data to obtain an image quality evaluation result; performing correlation analysis on the interface signal performance data and the dynamic pixel defect data to obtain the relationship between the interface and pixel response; and comprehensively evaluating the display screen parameters, the image quality evaluation result and the relationship between the interface and pixel response to obtain a display screen performance diagnosis report. The present application improves the accuracy of detecting the functions of a liquid crystal display screen.

Description

Technical Field

[0001] The present application relates to the technical field of display screen detection, and particularly to a method, device, equipment and storage medium for detecting the functions of a liquid crystal display screen. Background Art

[0002] With the rapid development of display technology, liquid crystal display screens are increasingly widely used in various electronic devices. However, with the continuous improvement of the display screen resolution, refresh rate and color depth, its function detection has become complex. Traditional static detection methods can no longer meet the quality control requirements of high-performance display screens, especially problems in dynamic display and high-speed signal transmission are difficult to be accurately identified and evaluated.

[0003] In addition, existing detection methods often regard the interface signal performance and pixel defects as independent test items, ignoring the potential correlation between the two. This leads to some display problems caused by signal quality being misjudged as panel defects, affecting the accuracy of problem diagnosis and the effectiveness of subsequent optimization measures. At the same time, traditional detection methods lack the dynamic performance evaluation under different interfaces and different display contents, and it is difficult to comprehensively reflect the performance of the display screen during actual use. Summary of the Invention

[0004] The present application provides a method, device, equipment and storage medium for detecting the functions of a liquid crystal display screen, which is used to improve the accuracy of detecting the functions of a liquid crystal display screen.

[0005] In a first aspect, the present application provides a method for detecting the functions of a liquid crystal display screen, and the method for detecting the functions of a liquid crystal display screen includes:

[0006] Identifying parameters of the liquid crystal display screen to obtain display screen parameters and interface information;

[0007] Performing a signal integrity test on the interface in the interface information to obtain interface signal performance data;

[0008] Based on the interface signal performance data, performing dynamic pixel defect detection on the liquid crystal display screen to obtain dynamic pixel defect data;

[0009] Performing multi-dimensional analysis on the dynamic pixel defect data to obtain an image quality evaluation result;

[0010] Performing correlation analysis on the interface signal performance data and the dynamic pixel defect data to obtain an interface-pixel response relationship;

[0011] Performing comprehensive evaluation on the display screen parameters, the image quality evaluation result and the interface-pixel response relationship to obtain a display screen performance diagnosis report.

[0012] Second aspect, the present application provides a liquid crystal display function detection device, and the liquid crystal display function detection device includes:

[0013] An identification module, configured to identify parameters of the liquid crystal display to obtain display screen parameters and interface information;

[0014] A test module, configured to perform signal integrity tests on the interfaces in the interface information to obtain interface signal performance data;

[0015] A detection module, configured to perform dynamic pixel defect detection on the liquid crystal display based on the interface signal performance data to obtain dynamic pixel defect data;

[0016] A multi-dimensional analysis module, configured to perform multi-dimensional analysis on the dynamic pixel defect data to obtain an image quality evaluation result;

[0017] A correlation analysis module, configured to perform correlation analysis on the interface signal performance data and the dynamic pixel defect data to obtain an interface-pixel response relationship;

[0018] A comprehensive evaluation module, configured to comprehensively evaluate the display screen parameters, the image quality evaluation result, and the interface-pixel response relationship to obtain a display screen performance diagnosis report.

[0019] The third aspect of the present application provides a liquid crystal display function detection device, including: a memory and at least one processor, where instructions are stored in the memory; the at least one processor calls the instructions in the memory to enable the liquid crystal display function detection device to execute the above-mentioned liquid crystal display function detection method.

[0020] The fourth aspect of the present application provides a computer-readable storage medium, where instructions are stored in the computer-readable storage medium, and when the instructions are run on a computer, the computer is enabled to execute the above-mentioned liquid crystal display function detection method.

[0021] In the technical solution provided by this application, precise detection of dynamic pixel defects is achieved: by combining interface signal performance data to generate dynamic test patterns and using high-speed acquisition and synchronous decoding technologies, dynamic defects that cannot be detected by traditional static tests can be captured, improving the accuracy and comprehensiveness of defect detection. An association analysis between interface signal performance and pixel response is established: by performing an association analysis on interface signal characteristics and pixel defect data, a probability model of the interface and pixel response is obtained, providing a more accurate basis for problem diagnosis and performance optimization. A multi-dimensional image quality assessment is provided: by analyzing dynamic pixel defect data in multiple dimensions such as time domain, space, color, and brightness, a more comprehensive image quality assessment result is obtained, which can better reflect the actual performance of the display screen. Intelligent diagnosis and optimization suggestions based on data are realized: by comprehensively evaluating display screen parameters, image quality assessment results, and the relationship between the interface and pixel response, key performance bottlenecks can be automatically identified, and targeted optimization suggestions can be generated, providing direct guidance for product improvement. The automation and standardization level of detection are improved: the entire detection process from parameter identification to performance diagnosis report generation is automated, reducing the influence of human factors and improving the consistency and comparability of detection results. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0023] Figure 1 It is a schematic diagram of an embodiment of the liquid crystal display screen function detection method in the embodiments of this application;

[0024] Figure 2 It is a schematic diagram of an embodiment of the liquid crystal display screen function detection device in the embodiments of this application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0025] The embodiments of the present application provide a method, device, equipment and storage medium for detecting the functions of a liquid crystal display screen. The terms "first", "second", "third", "fourth", etc. (if any) in the specification, claims and above-mentioned drawings of the present application are used to distinguish similar objects and do not necessarily describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments described herein can be implemented in an order other than those illustrated or described herein. In addition, the term "comprising" or "having" and any variation thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or equipment comprising a series of steps or units does not necessarily limit to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or equipment.

[0026] For ease of understanding, the specific process of the embodiments of the present application will be described below. Please refer to Figure 1 , an embodiment of the method for detecting the functions of a liquid crystal display screen in the embodiments of the present application includes:

[0027] Step S101: Identify the parameters of the liquid crystal display screen to obtain the display screen parameters and interface information;

[0028] It can be understood that the execution subject of the present application can be a device for detecting the functions of a liquid crystal display screen, or a terminal or a server. Specifically, it is not limited here. The embodiments of the present application will be described by taking the server as the execution subject as an example.

[0029] Specifically, read the EDID data of the liquid crystal display screen to obtain the original EDID information, which includes the basic attributes of the display screen and the supported function information. By parsing the original EDID information, basic parameters such as the size, maximum resolution and maximum refresh rate of the display screen are extracted. By extracting the interface descriptors in the original EDID information, the list of interface types supported by the display screen is identified. These descriptors record the types and quantities of physical interfaces that the display screen can support. Scan the physical interfaces of the display screen according to the interface type list to determine the actual available interface list, which helps the system understand the interface availability of the display screen in actual applications. Initialize and test each interface in the actual available interface list to obtain the specific information of the interface, including the working state and basic performance indicators of the interface. Identify and mark abnormal interfaces through test data to ensure the accuracy and reliability of subsequent tests. For the interfaces marked as valid, perform bandwidth tests to obtain the maximum bandwidth data of each interface. According to the maximum bandwidth data, perform performance grading on each interface to form an interface performance grade table. Integrate the parsed display screen size, maximum resolution, maximum refresh rate with the interface performance grade table to obtain the complete display screen parameters.

[0030] Step S102: Conduct signal integrity tests on the interfaces in the interface information to obtain interface signal performance data;

[0031] Specifically, generate a test signal sequence according to the interface information to obtain a signal sample set containing different combinations of resolutions and refresh rates, ensuring the diversity and coverage of the signal sample set, so as to comprehensively test the performance of the interface under various working conditions. Perform time encoding on the signal sample set to obtain an encoded signal sequence with unique identifiers, and input the encoded signal sequence into the liquid crystal display through the interface to obtain the output image of the display. Collect the output image of the display to obtain the actual display image sequence, and conduct time comparison analysis based on the encoded signal sequence and the actual display image sequence to obtain signal transmission delay data. Through time comparison analysis, measure the delay of the signal during transmission. At the same time, conduct spectral analysis on the actual display image sequence to obtain the signal bandwidth utilization rate curve, and calculate the signal integrity index based on the signal transmission delay data and the signal bandwidth utilization rate curve to obtain a preliminary signal performance score. Spectral analysis can reveal the energy distribution of the signal in different frequency bands, thereby evaluating the utilization efficiency of the signal bandwidth. The combination of signal transmission delay data and the signal bandwidth utilization rate curve makes the signal integrity index more comprehensive and accurate. Conduct electrical characteristic measurements on the interface to obtain signal jitter and crosstalk data, and perform weighted calculation based on the preliminary signal performance score and the signal jitter and crosstalk data to obtain the comprehensive signal quality index. Signal jitter and crosstalk are important influencing factors for signal integrity. Jitter reflects the instability of the signal in time, while crosstalk is the mutual interference between signals, which will have a significant impact on signal quality. By performing weighted calculation on the preliminary signal performance score and the signal jitter and crosstalk data, the overall quality of the signal is more comprehensively reflected.

[0032] Step S103: Based on the interface signal performance data, conduct dynamic pixel defect detection on the liquid crystal display to obtain dynamic pixel defect data;

[0033] Specifically, a dynamic test pattern sequence is generated based on the interface signal performance data to obtain a test image set containing various geometric shapes and color gradients, ensuring that the test image set can comprehensively cover different display scenarios, thereby detecting various potential pixel defects. The test image set is subjected to time encoding and spatial marking to obtain an encoded test sequence with position information, and the encoded test sequence is input into the liquid crystal display screen through the interface to obtain a dynamic display image. Time encoding and spatial marking enable each test image to carry unique time and space information. The dynamic display image is subjected to high-speed acquisition and synchronous decoding to obtain the actual display image data stream, and frame difference calculation is performed based on the actual display image data stream to obtain a pixel change mapping matrix. Threshold filtering is performed on the pixel change mapping matrix to obtain a preliminary pixel anomaly candidate set, and spatio-temporal clustering analysis is performed based on the preliminary pixel anomaly candidate set to obtain a potential defect area map. Threshold filtering can screen out pixels with changes exceeding a certain range and initially identify areas that may have defects, while spatio-temporal clustering analysis clusters these initially identified pixel anomalies according to spatial and temporal correlations to obtain a more accurate potential defect area. Morphological processing and edge detection are performed on the potential defect area map to obtain the target defect contour, and defect feature parameters are extracted based on the target defect contour to obtain the defect type recognition result. Morphological processing and edge detection can depict the contour of the defect. Statistical analysis and spatial distribution calculation are performed on the defect type recognition result to obtain dynamic pixel defect data. Statistical analysis can summarize the overall situation and occurrence frequency of the defects, while spatial distribution calculation reveals the specific distribution of the defects on the display screen.

[0034] Step S104: Perform multi-dimensional analysis on the dynamic pixel defect data to obtain an image quality evaluation result;

[0035] Specifically, perform time-domain analysis on the dynamic pixel defect data to obtain a defect occurrence frequency distribution diagram, and calculate the time consistency index based on the defect occurrence frequency distribution diagram to obtain the dynamic stability score. By analyzing the temporal variation of pixel defects, determine the frequency and pattern of defect occurrence, thereby evaluating the stability of the display screen during dynamic display. Perform spatial clustering analysis on the dynamic pixel defect data to obtain a defect density heat map, and calculate the spatial uniformity index based on the defect density heat map to obtain the regional consistency score. Through spatial clustering analysis, identify the distribution of defects in different regions of the display screen, thereby evaluating the overall uniformity and consistency of the display screen. Separate the color channels of the dynamic pixel defect data to obtain the defect characteristics of each RGB channel, and calculate the color balance index based on the defect characteristics of each RGB channel to obtain the color restoration score. Color channel separation can identify defects in different color channels and evaluate the accuracy and consistency of the display screen in color performance. Perform brightness distribution analysis on the dynamic pixel defect data to obtain a brightness deviation matrix, and calculate the dynamic contrast ratio index based on the brightness deviation matrix to obtain the contrast performance score. Brightness distribution analysis can reveal the performance of the display screen under different brightness conditions and evaluate its dynamic contrast ratio ability. Perform weighted fusion on the dynamic stability score, regional consistency score, color restoration score, and contrast performance score to obtain a comprehensive image quality index. Weighted fusion generates an index that comprehensively reflects the overall image quality of the display screen by considering the weights of each score. Perform quantitative grading based on the comprehensive image quality index and the preset rating criteria to obtain the image quality evaluation result.

[0036] Step S105: Perform correlation analysis on the interface signal performance data and the dynamic pixel defect data to obtain the relationship between the interface and pixel response;

[0037] Specifically, parameter extraction is performed on the interface signal performance data to obtain the signal feature vectors of each interface, and the dynamic pixel defect data is grouped according to the signal feature vectors to obtain the defect subsets corresponding to the interfaces. By extracting the signal feature vectors, the dynamic pixel defect data can be effectively classified according to the signal characteristics of different interfaces. Statistical analysis is performed on the defect subsets corresponding to the interfaces to obtain the defect-interface correlation matrix, and the Pearson correlation coefficient is calculated based on the defect-interface correlation matrix to obtain the preliminary correlation strength index. Statistical analysis can reveal the degree of association between different interfaces and defects, and the calculation of the Pearson correlation coefficient quantifies this association strength. Time series analysis is performed on the preliminary correlation strength index to obtain the dynamic correlation trend graph, and the abnormal fluctuation points are identified based on the dynamic correlation trend graph to obtain the list of potential problem interfaces. Time series analysis can identify the interfaces that may be abnormal by examining the changes in the correlation strength at different time points. In-depth signal integrity analysis is performed on the interfaces in the list of potential problem interfaces to obtain the signal anomaly characteristics, and pattern matching is performed on the corresponding dynamic pixel defects based on the signal anomaly characteristics to obtain the defect-signal mapping relationship. In-depth signal integrity analysis can reveal the specific problems existing in the signal transmission process, such as jitter, crosstalk, etc., and pattern matching associates these signal anomalies with specific pixel defects, clarifying the causes of the defects. Causal inference analysis is performed on the defect-signal mapping relationship to obtain the probability model of the interface affecting the pixel response, and interface optimization suggestions and pixel response prediction functions are generated based on the probability model to obtain the relationship between the interface and the pixel response. Causal inference analysis can quantify the influence degree of the interface signal on the pixel defects by establishing the causal relationship between the interface signal and the pixel response, and based on this model, optimization suggestions and prediction functions are proposed to provide guidance for improving the display screen performance.

[0038] Step S106: Comprehensively evaluate the display screen parameters, the image quality evaluation results, and the relationship between the interface and the pixel response to obtain a display screen performance diagnosis report.

[0039] Specifically, standardize the display screen parameters to obtain a normalized parameter set, and construct a display screen performance benchmark model based on the normalized parameter set to obtain theoretical performance indicators. Standardization processing can uniformly process parameters in different dimensions to make them comparable. Assign weights to the image quality evaluation results to obtain a weighted quality score, and perform a difference analysis based on the weighted quality score and the theoretical performance indicators to obtain a performance deviation matrix. The weight assignment is adjusted according to the importance of different quality indicators to ensure that the comprehensive score can better reflect the actual performance of the display screen. Through the difference analysis, clarify the gap between the actual performance and the theoretical performance of the display screen, so as to identify possible problems. Quantitatively encode the relationship between the interface and the pixel response to obtain an interface-pixel impact factor, and correct the performance deviation matrix according to the interface-pixel impact factor to obtain an adjusted performance evaluation result. Quantitative encoding simplifies the complex relationship between the interface and the pixel response into a quantifiable impact factor, enabling these relationships to be directly used for the adjustment of performance evaluation. By correcting the performance deviation matrix, the performance of the display screen under actual use conditions can be more accurately reflected. Perform multi-dimensional clustering analysis on the adjusted performance evaluation results to obtain a performance characteristic distribution map, and identify key performance bottlenecks based on the performance characteristic distribution map to obtain a list of optimization direction suggestions. Multi-dimensional clustering analysis can identify the distribution of different performance characteristics by classifying and clustering performance data, helping to find the key factors affecting performance. Based on the key factors, specific optimization direction suggestions are put forward. Evaluate the feasibility and prioritize the list of optimization direction suggestions to obtain an improvement strategy plan. Feasibility evaluation ensures that the proposed optimization suggestions are feasible in actual operation, while priority ranking is arranged according to the improvement effect and implementation difficulty, helping to formulate the optimal improvement strategy. Generate a display screen performance diagnosis report containing quantitative indicators, problem diagnosis, and optimization suggestions according to the improvement strategy plan. The diagnosis report lists the performance indicators, existing problems, and specific optimization suggestions of the display screen.

[0040] In the embodiments of the present application, accurate detection of dynamic pixel defects is achieved: by combining interface signal performance data to generate dynamic test patterns and using high-speed acquisition and synchronous decoding technologies, dynamic defects that cannot be detected by traditional static tests can be captured, improving the accuracy and comprehensiveness of defect detection. An association analysis between interface signal performance and pixel response is established: by performing an association analysis on interface signal characteristics and pixel defect data, a probability model of the interface and pixel response is obtained, providing a more accurate basis for problem diagnosis and performance optimization. Multi-dimensional image quality assessment is provided: by analyzing dynamic pixel defect data in multiple dimensions such as time domain, space, color, and brightness, more comprehensive image quality assessment results are obtained, which can better reflect the actual performance of the display screen. Intelligent diagnosis and optimization suggestions based on data are achieved: by comprehensively evaluating display screen parameters, image quality assessment results, and the relationship between the interface and pixel response, key performance bottlenecks can be automatically identified, and targeted optimization suggestions can be generated, providing direct guidance for product improvement. The automation and standardization levels of detection are improved: the entire detection process from parameter identification to the generation of performance diagnosis reports is automated, reducing the influence of human factors and improving the consistency and comparability of detection results.

[0041] In a specific embodiment, the process of executing step S101 may specifically include the following steps:

[0042] (1) Read the EDID data of the liquid crystal display screen to obtain the original EDID information, and parse the basic parameters of the display screen according to the original EDID information to obtain the display screen size, maximum resolution, and maximum refresh rate;

[0043] (2) Extract the interface descriptors in the original EDID information to obtain a list of supported interface types, and scan the physical interfaces of the display screen according to the list of interface types to obtain a list of actually available interfaces;

[0044] (3) Perform an initialization test on each interface in the list of actually available interfaces to obtain interface information, and mark abnormal interfaces according to the interface information to obtain a list of valid interfaces;

[0045] (4) Perform a bandwidth test on each interface in the list of valid interfaces to obtain the maximum bandwidth data of each interface, and classify the performance of the interfaces according to the maximum bandwidth data to obtain an interface performance grade table;

[0046] (5) Integrate the display screen size, maximum resolution, maximum refresh rate, and interface performance grade table to obtain display screen parameters.

[0047] Specifically, the EDID data of the liquid crystal display is an important data source for communication between the display and the host. By reading the EDID data, the original EDID information can be obtained. EDID (Extended Display Identification Data) is a data format transmitted by the display device through the I2C bus. It contains information such as the manufacturer information of the display device, product model, display parameters (such as size, maximum resolution, maximum refresh rate), and supported interface types. The process of reading the EDID data is generally completed by calling the system API or using a dedicated hardware device. The original EDID information read is a binary data, which needs to be parsed to obtain useful display parameters. For example, by parsing the EDID data, the size (Width and Height), maximum resolution (MaxResolution), and maximum refresh rate (MaxRefreshRate) of the display can be obtained. Assume that the relevant fields in the EDID data are:

[0048] ;

[0049] Among them, Horizontal Pixel Count is the total number of horizontal pixels of the display, Vertical PixelCount is the total number of vertical pixels, and Horizontal Resolution and Vertical Resolution are the resolutions in the horizontal and vertical directions respectively. For example, assume that in the EDID data read, the total number of horizontal pixels of the display is 1920, the total number of vertical pixels is 1080, and the horizontal and vertical resolutions are both 96 DPI (Dots Per Inch). Then the size of the display can be calculated as:

[0050] ;

[0051] Similarly, the maximum resolution and maximum refresh rate can be directly read from the relevant fields in the EDID data. Assume that the maximum resolution is , the maximum refresh rate is 60Hz. Extract the interface descriptors in the original EDID information to obtain a list of supported interface types. The EDID data contains the physical interface types supported by the display, such as HDMI, DisplayPort, DVI, etc. By parsing the interface descriptors, a list of interface types is obtained. According to this list, scan the physical interfaces of the display to determine the actual available interface list. Through the hardware detection function of the system, the interface types and quantities currently connected to the display can be identified. Perform initialization tests on each interface in the actual available interface list to obtain interface information. Initialization tests usually include checking the connection status, data transmission ability, and basic performance indicators of the interface. For example, verify the connection quality of the interface by sending and receiving test signals and record the test results. According to the test results, mark the abnormal interfaces and exclude those with poor connections or substandard performance to obtain a list of valid interfaces. Perform bandwidth tests on each interface in the valid interface list to obtain the maximum bandwidth data for each interface. Bandwidth tests are usually completed by transmitting test signals with a large amount of data, and the data transmission rate and stability are recorded during the test. Assume that the maximum bandwidth can be expressed by the following formula:

[0052] ;

[0053] where Data Rate is the data transmission rate and Signal Efficiency is the signal transmission efficiency. For example, assume that the data transmission rate of a certain HDMI interface is 10 Gbps and the signal transmission efficiency is 0.9, then the maximum bandwidth of this interface is: Bandwidth = 10 Gbps Gbps. According to the maximum bandwidth data of each interface, perform performance grading on the interfaces to obtain an interface performance grade table. Performance grading usually sets multiple levels according to the bandwidth data, such as high performance (bandwidth greater than 8 Gbps), medium performance (bandwidth between 4 - 8 Gbps), and low performance (bandwidth less than 4 Gbps), etc. Integrate the size, maximum resolution, maximum refresh rate of the display, and the interface performance grade table to obtain the display parameters. The integration process of the display parameters summarizes all the above information to form a comprehensive performance report. For example, for a certain display, its size is 20x11.25 inches, the maximum resolution is 1920x1080, the maximum refresh rate is 60Hz, and the interface performance grades are: HDMI (high performance), DisplayPort (medium performance), DVI (low performance).

[0054] In a specific embodiment, the process of executing step S102 may specifically include the following steps:

[0055] (1) Generate a test signal sequence based on the interface information to obtain a signal sample set containing different combinations of resolution and refresh rate;

[0056] (2) Perform time encoding on the signal sample set to obtain an encoded signal sequence with unique identifiers, and input the encoded signal sequence to the liquid crystal display through the interface to obtain the display output image;

[0057] (3) Collect the display output image to obtain the actual display image sequence, and perform time comparison analysis based on the encoded signal sequence and the actual display image sequence to obtain signal transmission delay data;

[0058] (4) Perform spectral analysis on the actual display image sequence to obtain a signal bandwidth utilization curve, and calculate the signal integrity index based on the signal transmission delay data and the signal bandwidth utilization curve to obtain a preliminary signal performance score;

[0059] (5) Measure the electrical characteristics of the interface to obtain signal jitter and crosstalk data, and perform weighted calculation based on the preliminary signal performance score and the signal jitter and crosstalk data to obtain a comprehensive signal quality index;

[0060] (6) Perform threshold judgment and classification on the comprehensive signal quality index to obtain the interface signal performance data.

[0061] Specifically, generate a test signal sequence based on the interface information to obtain a signal sample set containing different combinations of resolution and refresh rate. Consider various combinations of resolution and refresh rate supported by the display, such as 1920x1080@60Hz, 1280x720@60Hz, 3840x2160@30Hz, etc. These combinations can be determined by reading the EDID information of the display, which includes the highest resolution, maximum refresh rate, and other compatible modes supported by the display. The generated signal sample set should cover all possible working modes of the display to ensure the comprehensiveness of the test. Perform time encoding on the signal sample set to obtain an encoded signal sequence with unique identifiers. Time encoding can be achieved by adding timestamps or serial numbers to the signal sequence, and these identifiers can help distinguish different signal samples in subsequent data processing. For example, use a timestamp function to identify each signal sample, where Represents the number of signal samples. The encoded signal sequence is input into the liquid crystal display through an interface to obtain the image output by the display. The output image of the display is collected to obtain the actual display image sequence. A high-speed camera or other image acquisition device is used to capture the output image of the display, and these images are compared and analyzed with the original encoded signal sequence to obtain the signal transmission delay data. The time comparison analysis can be completed by calculating the difference between the timestamps of the actual display images and the input signal sequence. For example, assume the timestamp of the input signal is , and the timestamp of the actual display image is , then the signal transmission delay can be expressed as:

[0062] ;

[0063] After obtaining the signal transmission delay data, perform spectral analysis on the actual display image sequence to obtain the signal bandwidth utilization curve. Spectral analysis can reveal the energy distribution of the signal in different frequency bands, thereby evaluating the bandwidth utilization efficiency of the signal. Through Fourier transform, the signal in the time domain is converted to the frequency domain to obtain the spectrum of the signal. Assume the signal is represented in the time domain as , and its frequency domain representation is , then the Fourier transform can be expressed as:

[0064] ;

[0065] Among them, is the frequency domain signal, representing the components of the signal at different frequencies ; is the original time domain signal, representing the changes of the signal at different times ; represents the time position of the signal in the time domain; represents the frequency position of the signal in the frequency domain; , used to represent the complex frequency components in the Fourier transform; is the complex exponential function, used to convert the time domain signal to the frequency domain, where represents the angular frequency, and after multiplying by the time variable , an exponential operation is performed. By analyzing the spectrum, the bandwidth utilization rate of the signal is calculated, and combined with the signal transmission delay data, the signal integrity index is calculated. The signal integrity index can comprehensively reflect the quality of the signal during transmission, including factors such as transmission delay and bandwidth utilization rate. For example, the signal integrity index can be calculated through the following formula

[0066] ;

[0067] Among them, and is the weight coefficient, is the signal transmission delay, is the maximum tolerable delay, is the bandwidth utilization rate. By adjusting the weight coefficient, the impacts of transmission delay and bandwidth utilization rate on the signal integrity index are balanced. Electrical characteristics of the interface are measured to obtain signal jitter and crosstalk data. Signal jitter refers to the instability of the signal in time, while crosstalk refers to the mutual interference between signals. These factors will all affect the signal quality. Through electrical characteristics measurement, these data are obtained, and a weighted calculation is performed based on the preliminary signal performance score and the signal jitter and crosstalk data to obtain the comprehensive signal quality index. For example, the comprehensive signal quality index can be calculated by the following formula

[0068] ;

[0069] wherein, and are the weight coefficients, is the signal jitter, is the signal crosstalk. In this way, various influencing factors in the signal transmission process are comprehensively considered to obtain an index that comprehensively reflects the signal quality. Threshold judgment and grading are performed on the comprehensive signal quality index to obtain the interface signal performance data. Threshold judgment can be completed by setting different signal quality levels, such as high quality (SQI > 0.9), medium quality (0.7 < SQI ≤ 0.9), and low quality (SQI ≤ 0.7). Through grading, the signal performance of different interfaces is intuitively displayed.

[0070] In a specific embodiment, the process of executing step S103 may specifically include the following steps:

[0071] (1) Generate a dynamic test pattern sequence according to the interface signal performance data to obtain a test image set containing various geometric shapes and color gradients;

[0072] (2) Perform time encoding and spatial marking on the test image set to obtain an encoded test sequence with position information, and input the encoded test sequence into the liquid crystal display screen through the interface to obtain a dynamic display image;

[0073] (3) Perform high-speed acquisition and synchronous decoding on the dynamic display image to obtain the actual display image data stream, and perform inter-frame difference calculation according to the actual display image data stream to obtain the pixel change mapping matrix;

[0074] (4) Perform threshold filtering on the pixel change mapping matrix to obtain a preliminary pixel anomaly candidate set, and perform spatio-temporal clustering analysis according to the preliminary pixel anomaly candidate set to obtain a potential defect area map;

[0075] (5) Perform morphological processing and edge detection on the potential defect area map to obtain the target defect contour, and extract defect feature parameters based on the target defect contour to obtain the defect type recognition result;

[0076] (6) Perform statistical analysis and spatial distribution calculation on the defect type recognition result to obtain dynamic pixel defect data.

[0077] Specifically, generate a dynamic test pattern sequence according to the interface signal performance data to obtain a test image set containing various geometric shapes and color gradients. The interface signal performance data includes parameters such as resolution, refresh rate, and color depth. Different test patterns are generated through these parameters, such as geometric shapes of different sizes (such as circles, squares, triangles) and color gradients (such as gradients from red to blue, gradients from white to black), etc. These test patterns can cover all parts of the display screen to ensure comprehensive detection of the dynamic response performance of the display screen. Perform time encoding and spatial marking on the test image set to obtain an encoded test sequence with position information. Time encoding can be achieved by adding a timestamp to each test image, while spatial marking is to add a unique identifier to the pixel position in each image. At the same time, spatial marking is achieved by adding grid lines or other marks to the image. Input the encoded test sequence into the liquid crystal display screen through the interface to obtain a dynamic display image. Perform high-speed acquisition and synchronous decoding on the dynamic display image to obtain the actual display image data stream. Use a high-speed camera or other image acquisition devices to capture the output images of the display screen, and synchronously decode these images with the original encoded test sequence to ensure that the captured image data corresponds one-to-one with the input signal. The purpose of high-speed acquisition is to capture the output images of the display screen at different time points to analyze its dynamic performance. Perform inter-frame difference calculation according to the actual display image data stream to obtain a pixel change mapping matrix. Inter-frame difference calculation refers to comparing the pixel value changes in two consecutive frames of images and calculating the change amount of each pixel between these two frames. For example, assume that the pixel values of two consecutive frames of images are and , then the pixel change can be expressed as:

[0078] ;

[0079] By performing inter-frame difference calculation on all pixels, a pixel change mapping matrix is obtained, where each element represents the pixel change amount at position . Perform threshold filtering on the pixel change mapping matrix to obtain a preliminary pixel anomaly candidate set. Threshold filtering means setting a change amount threshold , and marking the pixels with a change amount greater than as abnormal pixels. For example, assume the threshold If it is 20, then for pixels, they are marked as the preliminary pixel anomaly candidate set, thus effectively filtering out normal pixel changes and only retaining pixels that may have defects. Based on the preliminary pixel anomaly candidate set, spatio-temporal clustering analysis is performed to obtain a potential defect area map. Spatio-temporal clustering analysis refers to clustering adjacent abnormal pixels in space and time to form potential defect areas. Common clustering algorithms such as K-means or DBSCAN are used to cluster the abnormal pixels to obtain a potential defect area map. Morphological processing and edge detection are performed on the potential defect area map to obtain the target defect contour. Morphological processing includes operations such as dilation, erosion, opening, and closing, which can eliminate noise and enhance the connectivity of the defect area. Edge detection can use common Canny edge detection algorithms to depict the edge contour of the defect. Defect feature parameters are extracted according to the target defect contour to obtain the defect type recognition result. Defect feature parameters include the area, perimeter, shape factor, color distribution, etc. of the defect, and the type of the defect can be recognized through these parameters. For example, by calculating the shape factor of the defect (the ratio of the area to the perimeter), it is judged whether the defect is a point-like, line-like or surface-like defect. Statistical analysis and spatial distribution calculation are performed on the defect type recognition result to obtain dynamic pixel defect data. Statistical analysis includes indicators such as the number of defects and the proportion of the defect area, and spatial distribution calculation is to analyze the position distribution of the defects on the display screen. For example, a two-dimensional histogram is used to represent the distribution density of the defects on the display screen to find the defect-dense area and the sparse area.

[0080] In a specific embodiment, the process of executing step S104 may specifically include the following steps:

[0081] (1) Perform time-domain analysis on the dynamic pixel defect data to obtain a defect occurrence frequency distribution map, and calculate a time consistency index based on the defect occurrence frequency distribution map to obtain a dynamic stability score;

[0082] (2) Perform spatial clustering analysis on the dynamic pixel defect data to obtain a defect density heat map, and calculate a spatial uniformity index based on the defect density heat map to obtain a regional consistency score;

[0083] (3) Separate the color channels of the dynamic pixel defect data to obtain defect characteristics of each RGB channel, and calculate a color balance index based on the defect characteristics of each RGB channel to obtain a color restoration score;

[0084] (4) Perform brightness distribution analysis on the dynamic pixel defect data to obtain a brightness deviation matrix, and calculate a dynamic contrast index based on the brightness deviation matrix to obtain a contrast performance score;

[0085] (5) Perform weighted fusion on the dynamic stability score, regional consistency score, color restoration score, and contrast performance score to obtain a comprehensive image quality index;

[0086] (6) Perform quantitative grading according to the comprehensive image quality index and the preset rating criteria to obtain the image quality assessment result.

[0087] Specifically, perform time-domain analysis on the dynamic pixel defect data to obtain a defect occurrence frequency distribution diagram, and calculate the time consistency index based on the defect occurrence frequency distribution diagram to obtain the dynamic stability score. The goal of time-domain analysis is to analyze the occurrence frequency of defects over time, count the number of defects at each time point, and draw a defect occurrence frequency distribution diagram. Based on the defect occurrence frequency distribution diagram, the time consistency index can be calculated to represent the stability of the number of defects over time. Assume that the time consistency index can be measured by the standard deviation of the number of defects. The formula is as follows:

[0088] ;

[0089] where represents the standard deviation of the number of defects. For example, assume that the standard deviation of the number of defects is calculated as 0.94, then the time consistency index is:

[0090] ;

[0091] Perform spatial clustering analysis on the dynamic pixel defect data to obtain a defect density heat map, and calculate the spatial uniformity index based on the defect density heat map to obtain the regional consistency score. The goal of spatial clustering analysis is to analyze the distribution of defects in space, divide the display screen into multiple regions, count the number of defects in each region, and generate a defect density heat map. Based on the defect density heat map, the spatial uniformity index can be calculated to represent the degree of uniformity of defects in space. Assume that the spatial uniformity index can be measured by the standard deviation of the number of regional defects. The formula is as follows:

[0092] ;

[0093] where represents the standard deviation of the number of regional defects. For example, assume that the standard deviation of the number of regional defects is calculated as 2.5, then the spatial uniformity index is:

[0094] ;

[0095] Separate the dynamic pixel defect data by color channels to obtain the defect characteristics of each RGB channel, and calculate the color balance index based on the defect characteristics of each RGB channel to obtain the color restoration score. The goal of color channel separation is to analyze the number and distribution of defects in each color channel. According to the defect characteristics of each RGB channel, calculate the color balance index, which represents the balance degree of the number of defects in each color channel. Assume that the color balance index can be calculated by the standard deviation of the number of defects in each RGB channel to measure, and the formula is as follows:

[0096] ;

[0097] where represents the standard deviation of the number of defects in each RGB channel. For example, assume that the standard deviation of the number of defects in each RGB channel is calculated as 2.08, then the color balance index is:

[0098] ;

[0099] Perform brightness distribution analysis on the dynamic pixel defect data to obtain a brightness deviation matrix, and calculate the dynamic contrast index based on the brightness deviation matrix to obtain the contrast performance score. The goal of brightness distribution analysis is to analyze the distribution of defects at different brightness levels. According to the brightness deviation matrix, calculate the dynamic contrast index, which represents the distribution of defects at different brightness levels. Assume that the dynamic contrast index can be measured by the standard deviation of the number of defects at the brightness level to measure, and the formula is as follows:

[0100] ;

[0101] where represents the standard deviation of the number of defects at the brightness level. For example, assume that the standard deviation of the number of defects at the brightness level is calculated as 4.36, then the dynamic contrast index is:

[0102] ;

[0103] Perform weighted fusion on the dynamic stability score, regional consistency score, color restoration score, and contrast performance score to obtain a comprehensive image quality index.

[0104] In a specific embodiment, the process of executing step S105 may specifically include the following steps:

[0105] (1) Extract parameters from the interface signal performance data to obtain the signal feature vectors of each interface, and group the dynamic pixel defect data according to the signal feature vectors to obtain the defect subsets corresponding to the interfaces;

[0106] (2) Statistically analyze the defect subset corresponding to the interface to obtain a defect-interface correlation matrix, and calculate the Pearson correlation coefficient based on the defect-interface correlation matrix to obtain a preliminary correlation strength index;

[0107] (3) Conduct a time series analysis on the preliminary correlation strength index to obtain a dynamic correlation trend graph, and identify abnormal fluctuation points based on the dynamic correlation trend graph to obtain a list of potential problem interfaces;

[0108] (4) Conduct an in-depth analysis of the signal integrity of the interfaces in the list of potential problem interfaces to obtain signal anomaly characteristics, and perform pattern matching on the corresponding dynamic pixel defects based on the signal anomaly characteristics to obtain a defect-signal mapping relationship;

[0109] (5) Conduct a causal inference analysis on the defect-signal mapping relationship to obtain a probability model of the interface affecting pixel response, and generate interface optimization suggestions and pixel response prediction functions based on the probability model to obtain the relationship between the interface and pixel response.

[0110] Specifically, extract parameters from the interface signal performance data to obtain the signal feature vectors of each interface. The signal feature vectors include parameters such as signal strength, signal bandwidth, jitter, and crosstalk. For example, assume there are three interfaces A, B, and C, and their signal feature vectors are respectively:

[0111] ;

[0112] Among them, the first element represents the signal strength, the second element represents the signal bandwidth (Gbps), the third element represents the jitter (ns), and the fourth element represents the crosstalk (V). Group the dynamic pixel defect data according to the signal feature vectors to obtain the defect subset corresponding to the interface. Assume that the dynamic pixel defect data includes information such as the location, time, and type of the defect. According to the signal feature vectors, the defect data is divided into three subsets corresponding to interfaces A, B, and C. Each subset contains all the dynamic pixel defect data that occurred under that interface. Statistically analyze the defect subset corresponding to the interface to obtain a defect-interface correlation matrix. The correlation matrix represents the occurrence frequency of different types of defects under each interface. For example, assume there are three defect types: bright spots, dark spots, and color deviation. Calculate the Pearson correlation coefficient based on the defect-interface correlation matrix to obtain a preliminary correlation strength index. The Pearson correlation coefficient is used to measure the linear correlation between two variables, and the formula is as follows:

[0113] ;

[0114] Among them, and respectively represent the values of the two variables, and Respectively represent the means of two variables. Perform time series analysis on the preliminary correlation strength index to obtain a dynamic correlation trend graph, identify abnormal fluctuation points based on the dynamic correlation trend graph, and obtain a list of potential problem interfaces. The goal of time series analysis is to analyze the change of the correlation strength over time and find abnormal fluctuation points. For example, if the correlation strength between the bright spot defects of interface A and the signal strength increases significantly within a certain period of time, it can be considered that there are potential problems with interface A. Conduct in-depth signal integrity analysis on the interfaces in the list of potential problem interfaces to obtain signal anomaly characteristics. Signal integrity analysis includes detailed indicators such as jitter, crosstalk, and amplitude attenuation. For example, the signal integrity analysis result of interface A shows that the jitter is 0.02 ns, the crosstalk is 0.01 V, and the amplitude attenuation is 0.05 V. Perform pattern matching on the corresponding dynamic pixel defects according to the signal anomaly characteristics to obtain the defect-signal mapping relationship. The goal of pattern matching is to find the corresponding relationship between signal anomalies and pixel defects. For example, if it is found that the bright spot defects of interface A are related to signal jitter, it can be considered that signal jitter is one of the main causes of bright spot defects. Conduct causal inference analysis on the defect-signal mapping relationship to obtain a probability model of the interface affecting pixel response. The goal of causal inference is to establish a causal relationship between signal anomalies and pixel defects and quantify this relationship. For example, assume the probability of the occurrence of bright spot defects Bright spot defects) and signal jitter The relationship between them is:

[0115] Bright spot defects ;

[0116] Among them, and Are regression coefficients and can be estimated through statistical data. For example, assume and , then when the signal jitter is 0.02 ns, the probability of the occurrence of bright spot defects is:

[0117] Bright spot defects ;

[0118] Generate interface optimization suggestions and pixel response prediction functions according to the probability model. For example, reducing signal jitter can reduce the occurrence probability of bright spot defects. The optimization suggestions include improving the electrical performance of the interface and using high-quality connecting wires, etc. The pixel response prediction function is used to predict the pixel response of the display screen under different signal conditions. For example, assume the signal jitter is reduced to 0.01 ns, then the probability of the occurrence of bright spot defects is: Bright spot defects .

[0119] In a specific embodiment, the process of executing step S106 may specifically include the following steps:

[0120] (1) Standardize the display parameters to obtain a normalized parameter set, and construct a display performance benchmark model based on the normalized parameter set to obtain theoretical performance indicators;

[0121] (2) Assign weights to the image quality evaluation results to obtain a weighted quality score, and perform a difference analysis based on the weighted quality score and the theoretical performance indicators to obtain a performance deviation matrix;

[0122] (3) Quantitatively encode the relationship between the interface and pixel response to obtain an interface-pixel impact factor, and correct the performance deviation matrix based on the interface-pixel impact factor to obtain an adjusted performance evaluation result;

[0123] (4) Perform multi-dimensional clustering analysis on the adjusted performance evaluation results to obtain a performance characteristic distribution map, and identify key performance bottlenecks based on the performance characteristic distribution map to obtain a list of optimization direction suggestions;

[0124] (5) Evaluate the feasibility and prioritize the list of optimization direction suggestions to obtain an improvement strategy plan;

[0125] (6) Generate a display performance diagnosis report containing quantitative indicators, problem diagnosis, and optimization suggestions according to the improvement strategy plan.

[0126] Specifically, standardize the display screen parameters to eliminate the dimensional differences between different parameters. The standardization process is carried out by subtracting the mean value of each parameter and dividing it by its standard deviation, so that all parameters follow the standard normal distribution. Construct a display screen performance benchmark model based on the normalized parameter set to obtain the theoretical performance indicators. The benchmark model can be established by statistically analyzing the normalized parameter sets of a large number of display screens, and the theoretical performance indicators are the average performance in the benchmark model. For example, assume that the theoretical performance indicators of display brightness and contrast in the benchmark model are 0.8 and 0.9 respectively. Assign weights to the image quality evaluation results to obtain the weighted quality score. The image quality evaluation results may include multiple indicators such as color accuracy, uniformity, and dynamic range. Conduct a difference analysis based on the weighted quality score and the theoretical performance indicators to obtain the performance deviation matrix. Quantitatively encode the relationship between the interface and pixel response to obtain the interface-pixel impact factor. The interface-pixel impact factor quantifies the degree of influence of the interface signal on pixel defects. For example, the impact factor of the interface signal strength on bright point defects is 0.02, and the impact factor of contrast on dark point defects is 0.03. Apply these impact factors to the performance deviation matrix to obtain the adjusted performance evaluation results. Conduct multi-dimensional clustering analysis on the adjusted performance evaluation results to obtain the performance characteristic distribution map. Multi-dimensional clustering analysis can use algorithms such as K-means or DBSCAN to cluster the display screen performance data into several groups, and each group represents a performance characteristic. For example, assume that the K-means clustering results show that there are three groups of performance characteristics, namely high performance, medium performance, and low performance. Identify the key performance bottlenecks based on the performance characteristic distribution map to obtain a list of optimization direction suggestions. For example, if it is found that the display screens in the low-performance group mainly have problems such as low contrast and many dark point defects, the optimization direction suggestions are to increase the contrast and reduce the dark point defects. Conduct a feasibility assessment and priority ranking on the list of optimization direction suggestions to obtain the improvement strategy plan. The feasibility assessment considers the technical feasibility and cost of the optimization measures, and the priority ranking is determined according to the optimization effect and implementation difficulty. For example, increasing the contrast has high technical feasibility and low cost, so it has a high priority; reducing the dark point defects has medium technical feasibility and high cost, so it has a medium priority. Generate a display screen performance diagnosis report containing quantitative indicators, problem diagnosis, and optimization suggestions according to the improvement strategy plan. For example, the report lists the normalized parameter values of brightness, contrast, resolution, and refresh rate, points out that the low contrast and many dark point defects are the main problems, and suggests increasing the contrast and improving the display screen manufacturing process to reduce the dark point defects.

[0127] The above describes the liquid crystal display screen function detection method in the embodiments of the present application. Next, the liquid crystal display screen function detection device in the embodiments of the present application will be described. Please refer to Figure 2 , an embodiment of the liquid crystal display screen function detection device in the embodiments of the present application includes:

[0128] An identification module 201, configured to identify parameters of a liquid crystal display screen to obtain display screen parameters and interface information;

[0129] A test module 202, configured to perform signal integrity tests on the interfaces in the interface information to obtain interface signal performance data;

[0130] An inspection module 203, configured to perform dynamic pixel defect detection on the liquid crystal display screen based on the interface signal performance data to obtain dynamic pixel defect data;

[0131] A multi-dimensional analysis module 204, configured to perform multi-dimensional analysis on the dynamic pixel defect data to obtain an image quality evaluation result;

[0132] A correlation analysis module 205, configured to perform correlation analysis on the interface signal performance data and the dynamic pixel defect data to obtain the relationship between the interface and pixel response;

[0133] A comprehensive evaluation module 206, configured to comprehensively evaluate the display screen parameters, the image quality evaluation result, and the relationship between the interface and pixel response to obtain a display screen performance diagnosis report.

[0134] Through the collaborative cooperation of the above-mentioned various components, the precise detection of dynamic pixel defects is achieved: by combining interface signal performance data to generate dynamic test patterns and using high-speed acquisition and synchronous decoding technologies, dynamic defects that cannot be discovered by traditional static tests can be captured, improving the accuracy and comprehensiveness of defect detection. A correlation analysis between interface signal performance and pixel response is established: by performing correlation analysis on interface signal characteristics and pixel defect data, a probability model of the interface and pixel response is obtained, providing a more accurate basis for problem diagnosis and performance optimization. A multi-dimensional image quality evaluation is provided: by performing analysis on the dynamic pixel defect data in multiple dimensions such as time domain, space, color, and brightness, a more comprehensive image quality evaluation result is obtained, which can better reflect the actual performance of the display screen. Intelligent diagnosis and optimization suggestions based on data are realized: by comprehensively evaluating the display screen parameters, the image quality evaluation result, and the relationship between the interface and pixel response, key performance bottlenecks can be automatically identified, and targeted optimization suggestions can be generated, providing direct guidance for product improvement. The automation and standardization level of detection are improved: the entire detection process from parameter identification to the generation of the performance diagnosis report is automated, reducing the influence of human factors and improving the consistency and comparability of detection results.

[0135] This application further provides a liquid crystal display screen function detection device, where the liquid crystal display screen function detection device includes a memory and a processor. Computer-readable instructions are stored in the memory. When the computer-readable instructions are executed by the processor, the processor executes the steps of the liquid crystal display screen function detection method in the above-mentioned various embodiments.

[0136] The present application also provides a computer-readable storage medium, which can be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions run on a computer, the computer is caused to execute the steps of the liquid crystal display screen function detection method.

[0137] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described system, system, and unit can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0138] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions to cause a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0139] As described above, the above embodiments are only used to illustrate the technical solutions of the present application and are not intended to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of various embodiments of the present application.

Claims

1. A method for detecting the function of a liquid crystal display screen, characterized in that: The method comprises: Perform parameter identification on the LCD screen to obtain screen parameters and interface information; Performing a signal integrity test on the interface in the interface information to obtain interface signal performance data; Based on the interface signal performance data, dynamic pixel defect detection is performed on the liquid crystal display screen to obtain dynamic pixel defect data; Performing multi-dimensional analysis on the dynamic pixel defect data to obtain an image quality assessment result; Performing correlation analysis on the interface signal performance data and the dynamic pixel defect data to obtain a relationship between an interface and a pixel response; The display screen parameters, the image quality evaluation results and the relationship between the interface and pixel response are comprehensively evaluated to obtain a display screen performance diagnosis report.

2. The method for detecting the function of a liquid crystal display screen according to claim 1, characterized in that: The step of performing parameter identification on the liquid crystal display screen to obtain display screen parameters and interface information includes: Reading the EDID data of the liquid crystal display screen to obtain original EDID information, and parsing basic parameters of the display screen according to the original EDID information to obtain the display screen size, maximum resolution and maximum refresh rate; Extracting the interface descriptor in the original EDID information to obtain a list of supported interface types, and scanning the physical interfaces of the display screen according to the list of interface types to obtain a list of actually available interfaces; Performing an initialization test on each interface in the actually available interface list to obtain interface information, and marking abnormal interfaces according to the interface information to obtain a valid interface list; Performing bandwidth testing on each interface in the valid interface list to obtain maximum bandwidth data of each interface, and performing performance grading on the interfaces according to the maximum bandwidth data to obtain an interface performance grade table; The display screen size, maximum resolution, maximum refresh rate and interface performance level table are integrated to obtain display screen parameters.

3. The method for detecting the function of a liquid crystal display screen according to claim 2, characterized in that: The performing of a signal integrity test on the interface in the interface information to obtain interface signal performance data includes: Generate a test signal sequence according to the interface information to obtain a signal sample set including different resolutions and refresh rate combinations; Time-encoding the signal sample set to obtain a coded signal sequence with a unique identifier, and inputting the coded signal sequence to a liquid crystal display screen through an interface to obtain an output image of the display screen; The output images of the display screen are collected to obtain an actual display image sequence, and a time comparison analysis is performed based on the coded signal sequence and the actual display image sequence to obtain signal transmission delay data; Performing spectrum analysis on the actual displayed image sequence to obtain a signal bandwidth utilization curve, and calculating a signal integrity index according to the signal transmission delay data and the signal bandwidth utilization curve to obtain a preliminary signal performance score; Measuring electrical characteristics of the interface to obtain signal jitter and crosstalk data, and performing weighted calculation based on the preliminary signal performance score and the signal jitter and crosstalk data to obtain a comprehensive signal quality index; Threshold determination and grading are performed on the comprehensive signal quality index to obtain interface signal performance data.

4. The method for detecting the function of a liquid crystal display screen according to claim 3, characterized in that: The step of performing dynamic pixel defect detection on the liquid crystal display screen based on the interface signal performance data to obtain dynamic pixel defect data includes: Generating a dynamic test pattern sequence according to the interface signal performance data to obtain a test image set including a variety of geometric shapes and color gradients; Performing time coding and space marking on the test image set to obtain a coding test sequence with position information, and inputting the coding test sequence to a liquid crystal display screen through an interface to obtain a dynamic display image; High-speed acquisition and synchronous decoding are performed on the dynamic display image to obtain an actual display image data stream, and inter-frame difference calculation is performed according to the actual display image data stream to obtain a pixel change mapping matrix; Performing threshold filtering on the pixel change mapping matrix to obtain a preliminary pixel anomaly candidate set, and performing spatiotemporal clustering analysis on the preliminary pixel anomaly candidate set to obtain a potential defect area map; Performing morphological processing and edge detection on the potential defect area map to obtain a target defect contour, and extracting defect feature parameters according to the target defect contour to obtain a defect type recognition result; Statistical analysis and spatial distribution calculation are performed on the defect type identification results to obtain dynamic pixel defect data.

5. The method for detecting the function of a liquid crystal display screen according to claim 1, characterized in that: The multi-dimensional analysis of the dynamic pixel defect data to obtain an image quality assessment result includes: Performing time domain analysis on the dynamic pixel defect data to obtain a defect occurrence frequency distribution diagram, and calculating a time consistency index based on the defect occurrence frequency distribution diagram to obtain a dynamic stability score; Performing spatial cluster analysis on the dynamic pixel defect data to obtain a defect density heat map, and calculating a spatial uniformity index based on the defect density heat map to obtain a regional consistency score; Performing color channel separation on the dynamic pixel defect data to obtain defect features of each RGB channel, and calculating a color balance index according to the defect features of each RGB channel to obtain a color restoration score; Performing brightness distribution analysis on the dynamic pixel defect data to obtain a brightness deviation matrix, and calculating a dynamic contrast index according to the brightness deviation matrix to obtain a contrast performance score; Performing weighted fusion on the dynamic stability score, the regional consistency score, the color reproduction score, and the contrast performance score to obtain a comprehensive image quality index; Quantitative grading is performed according to the comprehensive image quality index and a preset grading standard to obtain an image quality evaluation result.

6. The method for detecting the function of a liquid crystal display screen according to claim 1, characterized in that: The correlating analysis of the interface signal performance data and the dynamic pixel defect data to obtain the interface and pixel response relationship includes: Extracting parameters from the interface signal performance data to obtain a signal feature vector of each interface, and grouping the dynamic pixel defect data according to the signal feature vector to obtain a defect subset corresponding to the interface; Performing statistical analysis on the defect subset corresponding to the interface to obtain a defect-interface correlation matrix, and calculating the Pearson correlation coefficient based on the defect-interface correlation matrix to obtain a preliminary correlation strength index; Performing time series analysis on the preliminary correlation strength index to obtain a dynamic correlation trend graph, and identifying abnormal fluctuation points according to the dynamic correlation trend graph to obtain a list of potential problem interfaces; Performing an in-depth signal integrity analysis on the interfaces in the potential problem interface list to obtain signal abnormality features, and performing pattern matching on corresponding dynamic pixel defects based on the signal abnormality features to obtain a defect-signal mapping relationship; A causal inference analysis is performed on the defect-signal mapping relationship to obtain a probability model of the interface affecting the pixel response, and an interface optimization suggestion and a pixel response prediction function are generated based on the probability model to obtain the relationship between the interface and the pixel response.

7. The method for detecting the function of a liquid crystal display screen according to claim 1, characterized in that: The comprehensive evaluation of the display screen parameters, the image quality evaluation results and the relationship between the interface and the pixel response is performed to obtain a display screen performance diagnosis report, including: Standardizing the display screen parameters to obtain a normalized parameter set, and constructing a display screen performance benchmark model based on the normalized parameter set to obtain a theoretical performance index; Performing weight distribution on the image quality assessment results to obtain a weighted quality score, and performing difference analysis between the weighted quality score and the theoretical performance index to obtain a performance deviation matrix; Quantizing and encoding the relationship between the interface and the pixel response to obtain an interface-pixel impact factor, and correcting the performance deviation matrix according to the interface-pixel impact factor to obtain an adjusted performance evaluation result; Performing multi-dimensional cluster analysis on the adjusted performance evaluation results to obtain a performance characteristic distribution map, and identifying key performance bottlenecks based on the performance characteristic distribution map to obtain a list of optimization direction suggestions; Conduct feasibility assessment and priority sorting on the optimization direction suggestion list to obtain an improvement strategy plan; A display screen performance diagnosis report including quantitative indicators, problem diagnosis and optimization suggestions is generated according to the improvement strategy scheme.

8. A liquid crystal display function detection device, characterized in that: The device is used to perform the liquid crystal display function detection method according to any one of claims 1 to 7, and comprises: An identification module is used to identify the parameters of the LCD screen and obtain the screen parameters and interface information; A testing module, used to perform a signal integrity test on the interface in the interface information to obtain interface signal performance data; A detection module, used to perform dynamic pixel defect detection on the liquid crystal display screen based on the interface signal performance data to obtain dynamic pixel defect data; A multi-dimensional analysis module, used to perform multi-dimensional analysis on the dynamic pixel defect data to obtain an image quality assessment result; A correlation analysis module, used to perform correlation analysis on the interface signal performance data and the dynamic pixel defect data to obtain a relationship between the interface and the pixel response; The comprehensive evaluation module is used to comprehensively evaluate the display screen parameters, the image quality evaluation results and the relationship between the interface and pixel response to obtain a display screen performance diagnosis report.

9. A liquid crystal display function detection device, characterized in that: The liquid crystal display function detection device comprises: a memory and at least one processor, wherein instructions are stored in the memory; The at least one processor calls the instruction in the memory so that the liquid crystal display function detection device executes the liquid crystal display function detection method according to any one of claims 1 to 7.

10. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instructions are executed by the processor, the liquid crystal display function detection method according to any one of claims 1 to 7 is implemented.

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