Liquid crystal screen fault detection method and system

Abstract

The invention discloses a liquid crystal screen fault detection method and system, and relates to the technical field of liquid crystal screen fault detection, and the method comprises the following steps: employing a uniform grid division method to divide a whole liquid crystal screen display region into a plurality of sub-regions with the same area, enabling each sub-region to comprise a plurality of pixel points, a standard gray scale pattern is configured for each sub-region to ensure that all the sub-regions are subjected to unified driving stimulation; a control circuit is used for applying step-by-step changing driving voltage to the liquid crystal screen, so that each sub-region changes according to a preset gray-scale pattern under a unified time sequence, and liquid crystal molecules are driven to rotate and generate optical response. According to the method, accurate identification and dynamic amplification of nonlinear response coupling of the liquid crystal screen are realized through sub-region-level response modeling and a disturbance driving strategy, the detection capability of hidden defects is improved, the method is suitable for high-reliability display scenes such as vehicle-mounted display scenes and aviation display scenes, and the stability and the safety of a display system are guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of liquid crystal screen fault detection, and in particular to a liquid crystal screen fault detection method and system. Background Art

[0002] LCD screen fault detection refers to the process of identifying, diagnosing, and locating various faults that may occur during the use or production of LCD displays through software and hardware means, aiming to ensure display quality and operational stability. This detection typically covers the identification of bright spots, dark spots, color spots, screen flickering, dead zones, uneven brightness, color deviation, and response lag. It can be achieved through image processing algorithms, electrical signal analysis, visual inspection systems, or built-in self-test programs. LCD screen fault detection plays a vital role in production line quality control, terminal equipment maintenance, and after-sales service, helping to improve product yield, reduce return rates, and ensure user experience.

[0003] During the use of LCD screens, alignment layer disintegration testing aims to promptly identify abnormal liquid crystal molecular alignment caused by localized damage, aging, or electric field interference in the alignment layer, thereby preventing potential display defects such as localized grayscale drift, slow response, flicker, and color distortion. By early detecting alignment layer stability degradation, preventive measures can be taken before obvious visual failures occur on the screen, improving the display consistency and lifespan stability of the LCD screen. This is particularly applicable in critical applications such as medical, aviation, automotive, and industrial control, where display accuracy and reliability are extremely demanding, ensuring the reliability of terminal equipment and user experience.

[0004] The photoelectric response speed (grayscale switching speed) of liquid crystal pixels is highly dependent on the initial orientation state of the liquid crystal molecules and the effectiveness of the driving voltage. Disintegration of the alignment layer can lead to disordered initial alignment of liquid crystal molecules, decreased restorative force, or localized loss of control, resulting in slower response speed, delayed switching, or uneven changes. High-speed response time imaging, however, captures the response time changes of each pixel or region during the switching process by recording the dynamic process of the liquid crystal from its initial state to the target grayscale state frame by frame, thus enabling high-resolution identification of disintegrated regions.

[0005] The existing technology has the following deficiencies:

[0006] In the existing technology, although the alignment layer disintegration detection of LCD screens during use can be carried out through high-speed response time imaging methods, there are still technical limitations: when nonlinear response coupling occurs between multiple adjacent pixels, although the local area shows a consistent response delay trend as a whole, the response anomaly has time drift and hidden grayscale changes, which makes it difficult to effectively identify it during conventional detection. Especially in scenarios such as automotive or aviation that require extremely high image refresh stability, if such regional response delay is triggered by sudden changes in external lighting conditions, it may cause delayed or incomplete refresh of key screens such as navigation interfaces and instrument layers, which may easily cause misjudgment by drivers / flight personnel and pose a potential threat to system safety and reliability.

[0007] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0008] The purpose of the present invention is to provide a method and system for detecting LCD screen faults, which not only realizes the time series construction and spatial coupling modeling of pixel response behavior at the sub-region scale, but also significantly enhances the excitation ability and diagnostic accuracy of hidden and weakly responsive coupling regions by introducing a perturbation-driven mechanism and an adaptive perturbation-round control strategy. Compared with traditional methods that rely on mean response evaluation or static image detection, the present invention integrates multi-level grayscale perturbations and risk control logic in the dynamic response field, which can realize the region-by-region perception, graded diagnosis and visual calibration of nonlinear response coupling risks, thereby providing more stable, safe and accurate display quality assurance for LCD products in high-reliability application fields such as automotive, aviation, and medical, so as to solve the problems in the above-mentioned background technology.

[0009] In order to achieve the above object, the present invention provides the following technical solution: a method for detecting a fault in a liquid crystal display, comprising the following steps:

[0010] A uniform grid partitioning method is used to divide the entire LCD display area into several sub-regions of equal area (e.g., 20×20, 32×32 grids), each of which contains a number of pixels. A standard grayscale pattern is then assigned to each sub-region (e.g., a cyclic grayscale transition sequence of 0→128→255→128→0) to ensure that all sub-regions receive uniform driving stimuli during subsequent testing.

[0011] A control circuit applies a step-by-step driving voltage to the LCD screen, causing each sub-area to change in a preset grayscale pattern at a uniform timing, driving the liquid crystal molecules to rotate and produce an optical response.

[0012] When the LCD screen is transitioning from grayscale to grayscale, a high-speed camera (frame rate ≥ 1000fps) is used to capture all sub-areas frame by frame, collecting the brightness value (grayscale level) of each pixel in each frame. A record sequence of brightness changes over time is established for each pixel, and the response time required for the pixel to transition from one grayscale to another (e.g., the duration of the change from 10% to 90%) is accurately measured.

[0013] Convert the brightness change data of each pixel in different frames into a response time series, such as the time difference ΔT from T1 (10% grayscale) to T2 (90% grayscale), as the pixel response time. Synchronously extract the response time data of all pixels in each sub-region to form a response time distribution map under the same grayscale stimulus;

[0014] Feature engineering techniques are used to extract characteristic indicators reflecting the nonlinear response coupling characteristics between pixels from the response time distribution map. After in-depth analysis of the extracted characteristic indicators, the nonlinear response coupling risk between multiple adjacent pixels in the sub-region is quantified based on the analyzed characteristics.

[0015] The sub-region is divided into a nonlinear response coupling risk region and a normal region. For the nonlinear response coupling risk region, a very small disturbance (such as ±3 grayscales) is superimposed on the grayscale pattern to induce dynamic fluctuations in the response structure of the sub-region. The number of superpositions of grayscale disturbances is adaptively optimized according to the nonlinear response coupling risk value, so as to amplify the disturbance-excited response structure and make the coupling response in the original edge state show nonlinear behavior more obviously, thereby intelligently dealing with nonlinear response coupling problems of different degrees.

[0016] Preferably, a uniform grid division method is used to divide the entire LCD display area into several sub-areas of equal area. The specific steps are as follows:

[0017] First, obtain the overall resolution parameters of the LCD screen (e.g., 1920 × 1080 pixels) and select an appropriate number of grid divisions (e.g., dividing it into 30 × 20 sub-regions) based on the detection accuracy requirements.

[0018] Secondly, based on the set number of rows and columns, the pixel width and height of each sub-region are calculated in the horizontal and vertical directions respectively, dividing the entire screen into rectangular grids of equal area;

[0019] Finally, the boundary index of each sub-region (such as the coordinates of the upper left corner and the lower right corner) is established according to the pixel coordinates, which is used for the precise positioning and partitioning of pixels in each sub-region in subsequent image acquisition, completing the grid area division process of the LCD screen.

[0020] Preferably, a control circuit is used to apply a step-by-step driving voltage to the liquid crystal screen, so that each sub-area changes according to a preset grayscale pattern at a uniform timing, driving the liquid crystal molecules to rotate and generate an optical response. The specific steps are as follows:

[0021] First, a standard grayscale pattern sequence (e.g., 0→128→255→128→0) is preset and loaded into the LCD drive control system as a test signal.

[0022] Secondly, the control circuit applies a driving voltage corresponding to the grayscale pattern to each sub-area of the LCD screen synchronously, ensuring that all sub-areas complete grayscale switching step by step on the same time axis;

[0023] Finally, the liquid crystal molecules rotate in an orderly manner under the action of voltage, changing their transmittance to the backlight and producing a measurable optical response, thereby realizing dynamic response stimulation of the entire LCD screen under uniform grayscale drive, providing a basis for subsequent response time imaging and coupling behavior analysis.

[0024] Preferably, feature engineering technology is used to extract characteristic indicators reflecting the nonlinear response coupling characteristics between pixels from the response time distribution map, wherein the extracted characteristic indicators are the spatial gradient diffusion degree of the pixel response time in the sub-region. After an in-depth analysis of the spatial gradient diffusion degree of the pixel response time in the sub-region, a response gradient diffusion indicator is generated, and the nonlinear response coupling risk between multiple adjacent pixels in the sub-region is quantified based on the response gradient diffusion indicator.

[0025] Preferably, the specific steps of generating a response gradient diffusion index after performing a deep analysis on the spatial gradient diffusion degree of the pixel response time in the sub-region are as follows:

[0026] For each pixel in the sub-region, the response time gradient of the adjacent pixels is calculated. The calculation expression is:

[0027]

[0028] , where: T i is the response time of each pixel i, where i is the pixel index in the sub-region; is the response time gradient of pixel i, which represents the rate of change of response time between pixel i and its adjacent pixels; T i+1 and T i-1 are the response times of the adjacent pixels of pixel i respectively;

[0029] Based on the response time gradient of each pixel, the response gradient diffusion index of the entire sub-region is calculated to quantitatively measure the diffusion degree of the pixel response time difference within the sub-region. The response gradient diffusion index is calculated using the following formula:

[0030]

[0031] , where: R gdd is the response gradient diffusion index, N is the total number of pixels in the subregion; α is the sensitivity coefficient of the response time gradient, which is used to adjust the sensitivity of the response time gradient to the response coupling; neighbors(i) represents the neighborhood pixel set of pixel i, which usually refers to the upper, lower, left, and right pixels of the pixel, or the adjacent pixels selected according to some distance metric; j represents the neighborhood pixels of pixel i, that is, other pixels adjacent to pixel i; It represents the maximum value of the response time gradient in the neighborhood pixels of pixel i; β is the influence coefficient of the neighborhood maximum response gradient, which is used to control the influence of the maximum response of the gradient in the neighborhood on the response gradient diffusion index.

[0032] Preferably, a response gradient diffusion index generated after in-depth analysis of the spatial gradient diffusion degree of pixel response time in the sub-region is compared with a preset reference threshold of the response gradient diffusion index to divide the sub-region into a nonlinear response coupling risk region and a normal region. The specific division steps are as follows:

[0033] If the response gradient diffusion index is greater than the response gradient diffusion index reference threshold, it indicates that the risk of nonlinear response coupling between multiple adjacent pixels in the sub-region is high, and the sub-region is divided into a nonlinear response coupling risk region; if the response gradient diffusion index is less than or equal to the response gradient diffusion index reference threshold, it indicates that the risk of nonlinear response coupling between multiple adjacent pixels in the sub-region is low, and the sub-region is divided into a normal region.

[0034] Preferably, for the nonlinear response coupling risk area, a minimal disturbance is superimposed on the driving grayscale pattern to form a dither sequence, and the number of superpositions of the grayscale disturbance is adaptively optimized according to the nonlinear response coupling risk value. The specific steps are as follows:

[0035] For sub-regions identified as risk areas for nonlinear response coupling, a very small amplitude perturbation (e.g., ±3 grayscales) is superimposed on their standard grayscale driving pattern to induce dynamic fluctuations in the response structure of the sub-region;

[0036] During the disturbance driving process, the response time imaging of the sub-region is performed, the response gradient diffusion index after the disturbance is recalculated, and the dynamic amplification factor is constructed based on the reference threshold of the response gradient diffusion index. The calculation expression of the dynamic amplification factor is:

[0037]

[0038] , where: A is the response gradient diffusion indicator, R gdd' is the response gradient diffusion index after the disturbance, which quantifies the degree of change in the response structure in the sub-region after the disturbance is applied. The larger the value, the stronger the disturbance excitation effect and the more obvious the coupling risk; is the reference threshold of the response gradient diffusion index, which serves as a comparison baseline and is used to adjust the calculation intensity of the dynamic amplification factor and construct a unified comparison scale for the response coupling risk between different sub-regions; λ is the nonlinear amplification sensitivity factor, which is used to adjust the response intensity of the disturbance amplification effect to the change of the response structure;

[0039] According to the dynamic amplification factor Ψ A As well as the nonlinear response coupling risk of the sub-region initial response structure, the number of superpositions of adaptive perturbations is calculated to control the duration of perturbation injection. The calculation expression of the superposition number is:

[0040]

[0041] , where: η A is the superposition round of the disturbed grayscale sequence (rounded); R gdd is the initial response gradient diffusion index of the sub-region, which characterizes the initial nonlinear response coupling risk; β x is the disturbance superposition adjustment coefficient, which controls the growth rate of disturbance intensity with the adjustment of nonlinear response coupling risk; ln(1+Ψ A ln(1+R gdd ')) represents the amplified coupling factor of the response coupling risk to the perturbation persistence.

[0042] A liquid crystal screen fault detection system includes a sub-area division and drive configuration module, a drive voltage application and response excitation module, a high-speed image acquisition and response measurement module, a response time data extraction and distribution generation module, a feature extraction and coupling risk quantification module, and a nonlinear coupling risk amplification module;

[0043] The sub-area division and drive configuration module uses a uniform grid division method to divide the entire LCD display area into several sub-areas of equal area, so that each sub-area contains several pixels and configures a standard grayscale pattern for each sub-area to ensure that all sub-areas receive uniform drive stimulation;

[0044] The driving voltage application and response excitation module uses a control circuit to apply a step-by-step driving voltage to the LCD screen, causing each sub-area to change according to a preset grayscale pattern at a unified timing, driving the liquid crystal molecules to rotate and generate an optical response;

[0045] The high-speed image acquisition and response measurement module uses a high-speed camera to capture all sub-areas frame by frame when the LCD screen is transitioning from grayscale to grayscale. It collects the brightness value of each pixel in each frame, creates a record sequence of brightness changes over time for each pixel, and measures the response time required for the pixel to transition from one grayscale to another.

[0046] The response time data extraction and distribution generation module converts the brightness change data of each pixel in different frames into a response time series as the pixel response time. It then synchronously extracts the response time data of all pixels in each sub-region and forms a response time distribution map under the same grayscale stimulus.

[0047] The feature extraction and coupling risk quantification module uses feature engineering technology to extract characteristic indicators reflecting the nonlinear response coupling characteristics between pixels from the response time distribution map. After in-depth analysis of the extracted characteristic indicators, the nonlinear response coupling risk between multiple adjacent pixels in the sub-region is quantified based on the analyzed characteristics.

[0048] The nonlinear coupling risk amplification module divides the sub-region into a nonlinear response coupling risk region and a normal region. For the nonlinear response coupling risk region, a very small disturbance is superimposed on the grayscale pattern to induce dynamic fluctuations in the response structure of the sub-region. The number of superpositions of grayscale disturbances is adaptively optimized according to the nonlinear response coupling risk value, so that the coupling response in the original edge state exhibits nonlinear behavior.

[0049] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0050] This invention not only achieves time series construction and spatial coupling modeling of pixel response behavior at the sub-region scale, but also significantly enhances the excitation capability and diagnostic accuracy of hidden, weakly coupled response regions by introducing a perturbation-driven mechanism and an adaptive perturbation cycle control strategy. Compared to traditional methods that rely on mean response evaluation or static image detection, this invention integrates multi-level grayscale perturbations and risk control logic within the dynamic response field, enabling region-by-region perception, graded diagnosis, and visual calibration of nonlinear response coupling risks. This provides more stable, secure, and accurate display quality assurance for LCD products in high-reliability applications such as automotive, aviation, and medical applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction to the drawings required for use in the embodiments will be given below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0052] Figure 1 The present invention is a method flow chart of a liquid crystal screen fault detection method.

[0053] Figure 2 The figure is a module schematic diagram of a liquid crystal screen fault detection system of the present invention. DETAILED DESCRIPTION

[0054] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of this disclosure will be thorough and complete and will fully convey the concepts of the example embodiments to those skilled in the art.

[0055] The present invention provides Figure 1 A method for detecting a fault in an LCD screen is shown, comprising the following steps:

[0056] A uniform grid partitioning method is used to divide the entire LCD display area into several sub-regions of equal area (e.g., 20×20, 32×32 grids), each of which contains a number of pixels. A standard grayscale pattern is then assigned to each sub-region (e.g., a cyclic grayscale transition sequence of 0→128→255→128→0) to ensure that all sub-regions receive uniform driving stimuli during subsequent testing.

[0057] The entire LCD display area is divided into several sub-areas of equal area using a uniform grid division method. The specific steps are as follows:

[0058] First, obtain the overall resolution parameters of the LCD screen (e.g., 1920 × 1080 pixels) and select an appropriate number of grid divisions (e.g., dividing it into 30 × 20 sub-regions) based on the detection accuracy requirements.

[0059] Secondly, based on the set number of rows and columns, the pixel width and height of each sub-region are calculated in the horizontal and vertical directions respectively, dividing the entire screen into rectangular grids of equal area;

[0060] Finally, the boundary index of each sub-region (such as the coordinates of the upper left corner and the lower right corner) is established according to the pixel coordinates, which is used for the precise positioning and partitioning of pixels in each sub-region in subsequent image acquisition, completing the grid area division process of the LCD screen.

[0061] This step constructs a unified spatial basic unit for detection, facilitating subsequent targeted analysis of the response behavior of each small area. At the same time, the consistency of the grayscale pattern establishes a unified excitation condition for subsequent response time analysis, allowing differences between regions to be attributed to the internal response characteristics of the panel rather than driver differences.

[0062] A control circuit applies a step-by-step driving voltage to the LCD screen, causing each sub-area to change in a preset grayscale pattern at a uniform timing, driving the liquid crystal molecules to rotate and produce an optical response.

[0063] Grayscale switching, such as black → gray → white → gray → black, sets a specific time interval (e.g., each grayscale lasts 20ms) to ensure that the response process can be stably sampled. This process ensures that the response behavior of each area of the LCD screen occurs within a controllable and observable time window, providing a high-time-resolution measurement starting point for high-speed imaging equipment. It also avoids non-ideal transient response noise caused by grayscale mutations, ensuring subsequent data stability.

[0064] The control circuit applies a step-by-step driving voltage to the LCD screen, causing each sub-area to change according to a preset grayscale pattern at a uniform timing, driving the liquid crystal molecules to rotate and produce an optical response. The specific steps are as follows:

[0065] First, a standard grayscale pattern sequence (e.g., 0→128→255→128→0) is preset and loaded into the LCD drive control system as a test signal.

[0066] Secondly, the control circuit applies a driving voltage corresponding to the grayscale pattern to each sub-area of the LCD screen synchronously, ensuring that all sub-areas complete grayscale switching step by step on the same time axis;

[0067] Finally, the liquid crystal molecules rotate in an orderly manner under the action of voltage, changing their transmittance to the backlight and producing a measurable optical response, thereby realizing dynamic response stimulation of the entire LCD screen under uniform grayscale drive, providing a basis for subsequent response time imaging and coupling behavior analysis.

[0068] When using a control circuit to apply a step-by-step driving voltage to an LCD screen, causing each sub-area to change according to a preset grayscale pattern to drive the liquid crystal molecules to produce an optical response, several technical details require special attention to ensure the synchronization, stability, and measurability of the excitation process. First, the linear stability and level accuracy of the driving voltage must be ensured. This is because the rotation angle of liquid crystal molecules is extremely sensitive to voltage changes. If there is jitter or nonlinear error in the voltage control, it will directly lead to grayscale response deviation, affecting the accuracy of subsequent response time measurements. Second, the grayscale switching interval (i.e., frame duration) should be uniformly set to ensure that each grayscale is maintained for a sufficient time for the liquid crystal molecules to complete the response, avoiding the hysteresis caused by insufficient response. Furthermore, the driving signal should avoid voltage distortion such as overshoot and undershoot to prevent temporary electric field interference or the liquid crystal from entering an unstable state. Furthermore, when driving multiple screen areas simultaneously, strict clock synchronization of the control signals is required to avoid response phase misalignment due to drive delay differences. Finally, ambient light interference must be controlled. Testing should be conducted under standard lighting or darkroom conditions to improve the imaging contrast and analysis accuracy of the liquid crystal optical response changes. In general, this process requires a high degree of coordination among electrical excitation, liquid crystal physical response, and imaging acquisition. Instability in any one of them may interfere with the effectiveness of nonlinear response coupling analysis.

[0069] When the LCD screen is transitioning from grayscale to grayscale, a high-speed camera (frame rate ≥ 1000fps) is used to capture all sub-areas frame by frame, collecting the brightness value (grayscale level) of each pixel in each frame. A record sequence of brightness changes over time is established for each pixel, and the response time required for the pixel to transition from one grayscale to another (e.g., the duration of the change from 10% to 90%) is accurately measured.

[0070] To create a time-varying record of each pixel's brightness and accurately measure the response time required for a pixel to transition from one grayscale to another, a high-speed camera must be used to capture images at a high frame rate, ensuring that even the smallest brightness changes at each pixel are captured. During each grayscale transition, the LCD screen applies a different drive voltage to each pixel, causing the liquid crystal molecules to rotate and altering their transmittance. Because the LCD's response is not instantaneous but rather involves a delay, it's necessary to record the brightness changes of each pixel in the image frame by frame.

[0071] The specific steps are as follows:

[0072] Image acquisition and time synchronization: First, a high-speed camera (e.g., 1000 fps or higher) is used to capture each sub-area of the LCD screen, frame by frame. The camera must be synchronized with the LCD's grayscale drive signal to ensure that each frame is captured in sync with the LCD's grayscale changes. This can be achieved through a hardware trigger mechanism, allowing the camera to accurately capture the current image frame at each grayscale change. Each pixel is represented by a brightness value during this process, which can be considered the pixel's optical response.

[0073] Pixel brightness extraction and recording: The brightness value of each pixel is extracted from each image frame. Typically, brightness values are expressed as grayscale levels, ranging from 0 to 255. For each pixel, the sequence of its brightness changes over time is recorded to form a brightness-time curve for that pixel. This process ensures that the brightness data of each pixel is accurately measured at every point in time to capture subtle changes in brightness, especially at the beginning and end of grayscale transitions.

[0074] Grayscale transition measurement: Grayscale transition response time is typically measured using the 10%-90% change method. During the LCD screen's response, as the brightness transitions from 0 (or black) to the maximum grayscale value (e.g., 255), the response can be defined as starting at 10% brightness and ending at 90% brightness. The response time is the difference between these two points. By performing this process on all pixels within each subregion, the response time of each pixel can be determined, and statistical analysis can be used to determine the response characteristics of the region.

[0075] Accurately calculate response time: Calculate the response time based on the time-varying sequence of each pixel's brightness. The time required to transition from 10% to 90% grayscale can be calculated using numerical difference methods. For example, for a pixel, assuming its brightness-varying sequence is L(t), the response time T can be calculated using the following formula:

[0076] T=t 90 -t 10

[0077] , where: t 90 is the time point when the brightness reaches 90% of the maximum value, t 10 This is the time point when the brightness reaches 10% of its maximum value. Through this method, the response time required for each pixel to transition from one grayscale to another can be accurately measured, reflecting the response speed of the liquid crystal molecules in that sub-area.

[0078] This step constructs a pixel-level "grayscale-time response curve" that truly reflects the optical response capability of liquid crystal molecules under actual driving, with high accuracy and repeatability; at the same time, it provides a complete data basis for subsequent response anomaly point detection.

[0079] Convert the brightness change data of each pixel in different frames into a response time series, such as the time difference ΔT from T1 (10% grayscale) to T2 (90% grayscale), as the pixel response time. Synchronously extract the response time data of all pixels in each sub-region to form a response time distribution map under the same grayscale stimulus;

[0080] The response time distribution map establishes a basis for comparing lateral responses between pixels, supporting subsequent analysis of the spatial response structure. The spatial distribution of response time sets can reveal local response consistency or anomalous clustering trends, providing input for modeling nonlinear coupled behavior.

[0081] During the grayscale transition of an LCD screen, capturing the sequence of pixel brightness changes frame by frame using a high-speed camera is a key step in achieving accurate response time measurement. However, to ensure measurement accuracy, data reliability, and physical integrity, several key technical factors and environmental conditions must be considered.

[0082] First, the frame rate and exposure time settings of a high-speed camera are crucial. The frame rate must be high enough (typically no less than 1000 fps) to capture every critical transition point in the LCD's response. A frame rate that is too low may result in sparse sampling, missing critical data within the 10% to 90% brightness range and causing a misjudgment of the response time. Furthermore, the exposure time must be coordinated with the frame interval to avoid overexposure or underexposure that can lead to inaccurate pixel brightness values. Short exposure times combined with strong background lighting should be preferred to improve image clarity and grayscale response resolution.

[0083] Secondly, the accuracy of the pixel brightness extraction algorithm also requires special attention. Since the LCD screen has natural brightness noise, backlight fluctuations, edge halo and other interference phenomena during the grayscale change process, filtering or fitting techniques (such as polynomial fitting, moving average) must be used to smooth the original brightness sequence to ensure that the extracted brightness change curve can truly reflect the rotation process of the liquid crystal molecules. In addition, the grayscale response is usually measured using the "10%-90% rule", that is, the time difference between the brightness curve changing from the starting value to 90% of the target value. This requires accurate calibration of the pixel brightness values corresponding to the starting and target grayscales, and the establishment of a clear brightness-grayscale mapping relationship.

[0084] Furthermore, the optical coupling between the camera and the LCD screen must be stable. On the one hand, it's important to ensure that the camera's viewing angle remains aligned with the LCD screen during shooting to avoid polarization distortion caused by varying viewing angles. On the other hand, it's recommended to operate in a darkroom or light-controlled box to isolate ambient light interference and improve contrast and consistency of brightness changes.

[0085] Furthermore, the timing synchronization mechanism for response measurement is crucial. A hardware trigger or timestamp synchronization mechanism should be implemented between the drive control system (e.g., grayscale voltage loading) and the high-speed camera to ensure that the capture moment of each frame corresponds to the voltage change moment, thus avoiding timing errors caused by asynchrony between drive and acquisition.

[0086] Finally, attention must be paid to data consistency and processing efficiency when sampling large areas. Simultaneously collecting the brightness sequence of each pixel across multiple sub-areas of the entire LCD screen generates massive amounts of data, necessitating an efficient data caching and processing system, along with the use of parallel computing or GPU acceleration to achieve real-time or near-real-time response times.

[0087] Feature engineering techniques are used to extract characteristic indicators reflecting the nonlinear response coupling characteristics between pixels from the response time distribution map. After in-depth analysis of the extracted characteristic indicators, the nonlinear response coupling risk between multiple adjacent pixels in the sub-region is quantified based on the analyzed characteristics.

[0088] Feature engineering technology is used to extract characteristic indicators reflecting the nonlinear response coupling characteristics between pixels from the response time distribution map. The extracted characteristic indicators are the spatial gradient diffusion degree of the pixel response time in the sub-region. After in-depth analysis of the spatial gradient diffusion degree of the pixel response time in the sub-region, a response gradient diffusion index is generated. Based on the response gradient diffusion index, the nonlinear response coupling risk between multiple adjacent pixels in the sub-region is quantified.

[0089] A higher degree of spatial gradient diffusion of pixel response times within a subregion generally indicates a higher risk of nonlinear response coupling between adjacent pixels within that subregion. This is because, ideally, an LCD's response under the same grayscale drive should exhibit spatial continuity. That is, adjacent pixels should have highly similar response times due to identical driving conditions, liquid crystal structure, and alignment film state. However, when nonlinear response coupling occurs in a localized region, the affected pixel groups can exhibit abnormal response lags, instability, or mutual influence, leading to drastic variations in the response time differences between adjacent pixels, thus forming a "gradient diffusion" characteristic in the spatial dimension. This diffusion indicates that response behavior is no longer solely determined by the pixel itself, but is influenced by the nonlinear interference of the response states of surrounding pixels. This is particularly pronounced under coupling mechanisms such as localized alignment layer aging, residual charge, and abnormal drive voltage conduction. Therefore, the gradient diffusion degree can serve as a spatial characteristic indicator for identifying the strength of nonlinear coupling. A larger gradient diffusion indicates a more drastic response time deviation, a more complex coupling structure, and a higher coupling risk.

[0090] The specific steps for generating the response gradient diffusion index after performing a deep analysis of the spatial gradient diffusion degree of the pixel response time in the sub-region are as follows:

[0091] For each pixel in the sub-region, the response time gradient of the adjacent pixels is calculated. The calculation expression is:

[0092]

[0093] , where: T i is the response time of each pixel i, where i is the pixel index in the sub-region; is the response time gradient of pixel i, which represents the rate of change of response time between pixel i and its adjacent pixels; T i+1 and T i-1 are the response times of the adjacent pixels of pixel i respectively;

[0094] The purpose of this step is to identify areas with large response variations by calculating the response time differences between pixels, especially those areas with nonlinear coupling behavior, that is, multiple pixels respond asynchronously, lag behind, or fluctuate synchronously.

[0095] Based on the response time gradient of each pixel, the response gradient diffusion index of the entire sub-region is calculated to quantitatively measure the diffusion degree of the pixel response time difference within the sub-region. The response gradient diffusion index is calculated using the following formula:

[0096]

[0097] , where: R gdd is the response gradient diffusion index, N is the total number of pixels in the subregion; α is the sensitivity coefficient of the response time gradient, which is used to adjust the sensitivity of the response time gradient to the response coupling; neighbors(i) represents the neighborhood pixel set of pixel i, which usually refers to the upper, lower, left, and right pixels of the pixel, or the adjacent pixels selected according to some distance metric; j represents the neighborhood pixels of pixel i, that is, other pixels adjacent to pixel i; It represents the maximum value of the response time gradient in the neighborhood pixels of pixel i; β is the influence coefficient of the neighborhood maximum response gradient, which is used to control the influence of the maximum response of the gradient in the neighborhood on the response gradient diffusion index;

[0098] The purpose of this step is to quantify the amplification effect of nonlinear coupling on response time by weighting the maximum value of the gradient, reveal the potential nonlinear response coupling area through the size of the gradient diffusion, and further map the coupling risk of each sub-area.

[0099] The response gradient diffusion index (RGDI), generated by deeply analyzing the spatial gradient diffusion of pixel response times within a subregion, shows that a larger RGDI value indicates a higher risk of nonlinear response coupling between adjacent pixels within that subregion. Conversely, a smaller RGDI value indicates a lower risk of nonlinear response coupling between adjacent pixels within that subregion. This is because the RGDI essentially measures the intensity and diffusion of the spatial gradient variation of pixel response times. When the response behavior between adjacent pixels is linear and consistent, the spatial distribution of response times is typically smooth, continuous, and low in variance, resulting in a relatively low RGDI value. However, when nonlinear factors such as alignment layer damage, residual charge, or localized instability in the liquid crystal drive are present, the response times of adjacent pixels can vary dramatically, leading to a sudden change in the spatial gradient and a localized "mixed high and low response" diffusion trend, significantly increasing the RGDI value. Therefore, a larger RGDI value indicates a more chaotic response structure within the subregion, a stronger spatial coupling phenomenon, and more significant mutual interference or nonlinear cooperative behavior between liquid crystal pixels, thus increasing the coupling risk.

[0100] The sub-region is divided into a nonlinear response coupling risk region and a normal region. For the nonlinear response coupling risk region, a very small perturbation (such as ±3 grayscales) is superimposed on the driving grayscale pattern to induce dynamic fluctuations in the response structure of the sub-region. The number of superpositions of grayscale perturbations is adaptively optimized based on the nonlinear response coupling risk value to amplify the perturbation-induced response structure, causing the coupling response in the original edge state to exhibit more obvious nonlinear behavior, thereby intelligently dealing with different degrees of nonlinear response coupling problems.

[0101] The response gradient diffusion index generated by in-depth analysis of the spatial gradient diffusion degree of the pixel response time in the sub-region is compared with the pre-set reference threshold of the response gradient diffusion index to divide the sub-region into a nonlinear response coupling risk region and a normal region. The specific division steps are as follows:

[0102] If the response gradient diffusion index is greater than the response gradient diffusion index reference threshold, it indicates that the risk of nonlinear response coupling between multiple adjacent pixels in the sub-region is high, and the sub-region is divided into a nonlinear response coupling risk region; if the response gradient diffusion index is less than or equal to the response gradient diffusion index reference threshold, it indicates that the risk of nonlinear response coupling between multiple adjacent pixels in the sub-region is low, and the sub-region is divided into a normal region.

[0103] For areas with nonlinear response coupling risk, minimal perturbations are superimposed on the driving grayscale pattern to form a dither sequence. The number of grayscale perturbation superpositions is adaptively optimized based on the nonlinear response coupling risk value. The specific steps are as follows:

[0104] For sub-regions identified as risk areas for nonlinear response coupling, a very small amplitude perturbation (e.g., ±3 grayscales) is superimposed on their standard grayscale driving pattern to induce dynamic fluctuations in the response structure of the sub-region;

[0105] The purpose of this step is to construct an input signal with slight dynamic changes through the "nonlinear perturbation excitation" method, so that the pixel response in the critical unstable state will show more obvious dynamic response behavior in image acquisition.

[0106] During the disturbance driving process, the response time imaging of the sub-region is performed, the response gradient diffusion index after the disturbance is recalculated, and the dynamic amplification factor is constructed based on the reference threshold of the response gradient diffusion index. The calculation expression of the dynamic amplification factor is:

[0107]

[0108] , where: A is the response gradient diffusion indicator, R gdd ' is the response gradient diffusion index after the disturbance; is the reference threshold of the response gradient diffusion index, which serves as a comparison baseline and is used to adjust the calculation intensity of the dynamic amplification factor and construct a unified comparison scale for the response coupling risk between different sub-regions; λ is the nonlinear amplification sensitivity factor, which is used to adjust the response intensity of the disturbance amplification effect to the change of the response structure;

[0109] The dynamic amplification factor characterizes the nonlinear fluctuation capability of the response structure in the sub-region under disturbance driving. A >1, indicating that the disturbance effectively amplifies the original response difference; otherwise, it means that the disturbance does not trigger the explicitness of the response behavior.

[0110] The purpose of this step is to quantify the “effectiveness” of the disturbance-stimulated behavior and provide a decision-making basis for the adaptive optimization of the disturbance injection strategy.

[0111] According to the dynamic amplification factor Ψ A As well as the nonlinear response coupling risk of the sub-region initial response structure, the number of superpositions of adaptive perturbations is calculated to control the duration of perturbation injection. The calculation expression of the superposition number is:

[0112]

[0113] , where: η A is the superposition round of the disturbed grayscale sequence (rounded); R gdd is the initial response gradient diffusion index of the sub-region, which characterizes the initial nonlinear response coupling risk; β xis the disturbance superposition adjustment coefficient, which controls the growth rate of disturbance intensity with the adjustment of nonlinear response coupling risk; ln(1+Ψ A ln(1+R gdd ')) represents the amplified coupling factor of the response coupling risk to the perturbation persistence;

[0114] This design automatically increases the number of perturbation injections when the risk of response-structure coupling increases and the perturbation amplification effect becomes more pronounced, further amplifying abnormal response behavior in edge states. This step regulates the perturbation injection depth through self-feedback from the response behavior, forming a robust and sensitive closed-loop mechanism for nonlinear response amplification.

[0115] The purpose of this step is to achieve explicit amplification and fine identification of nonlinear response coupling phenomena within the sub-regions of the LCD screen through an active perturbation excitation mechanism, thereby enhancing the intelligent recognition capability of unstable states at the edges of the response structure. When the LCD unit is operating normally, it should exhibit high consistency and linear controllability in the response time of each pixel. However, in actual applications, due to factors such as alignment film aging, charge residue, local material defects or driving voltage fluctuations, nonlinear response coupling phenomena may occur between multiple pixels in some areas. Such coupling phenomena may be very weak in the early stage and belong to "metastable states" or "edge states". They are difficult to accurately detect under conventional grayscale drive and may even be mistaken for normal response fluctuations. Therefore, this step divides the sub-region into a "normal region" and a "nonlinear response coupling risk region" and introduces a slightly perturbation grayscale drive (such as ±3 grayscale) for the latter. The purpose is to induce stronger dynamic fluctuations in the pixel group that is originally close to the abnormal response threshold by constructing a critical response stimulation field. Specifically, this disturbance driving signal poses a continuous challenge to the stability of the liquid crystal molecular arrangement by constructing slight but periodic grayscale fluctuations, causing the coupling structure that was originally in the edge state to be "pushed" into the nonlinear response region, thereby showing characteristic changes such as tailing, fluctuation, increased delay or reverse response asymmetry in the response time curve. At the same time, this step introduces the response gradient diffusion index and its response gradient diffusion index reference threshold as a quantitative basis, and combines the amplification effect of the response structure after the disturbance, and further introduces an adaptive adjustment mechanism of the amplification factor and the number of superpositions, so that the number of disturbances is nonlinearly related to the response coupling risk, thereby realizing intelligent regulation of the disturbance depth. In this way, while avoiding misjudgment caused by excessive disturbance, it ensures that the response anomalies in potential problem areas are fully exposed, significantly improving the system's recognition resolution and robustness to weak coupling risks.

[0116] In summary, the core purpose of this step is to create a dynamic response excitation environment, induce potential coupling structures to amplify their abnormal behavior characteristics, and combine risk perception mechanisms to intelligently control the perturbation strategy. This effectively improves the system's sensitivity to nonlinear response coupling risks and its ability to grade and identify them, while ensuring detection accuracy. This mechanism not only offers the advantages of high detection sensitivity, strong adaptability, and sophisticated control strategies, but is also suitable for complex LCD display systems with high resolution, large size, and multiple layers. It represents an intelligent enhanced detection method with practical engineering value.

[0117] The LCD screen fault detection method proposed by the present invention can effectively overcome the problem of insufficient recognition ability of nonlinear response coupling phenomenon in the existing technology, and realize high-resolution and intelligent recognition of alignment layer collapse and regional response hysteresis phenomenon in the edge state of the LCD screen. This method not only realizes the time series construction and spatial coupling modeling of pixel response behavior at the sub-region scale, but also significantly enhances the excitation ability and diagnostic accuracy of hidden and weak response coupling areas by introducing a disturbance driving mechanism and an adaptive disturbance round control strategy. Compared with traditional methods that rely on mean response evaluation or static image detection, the present invention integrates multi-level grayscale disturbances and risk control logic in the dynamic response field, which can realize region-by-region perception, graded diagnosis and visual calibration of nonlinear response coupling risks, thereby providing more stable, safe and accurate display quality assurance for LCD display products in high-reliability application fields such as automotive, aviation, and medical.

[0118] A liquid crystal screen fault detection system includes a sub-area division and drive configuration module, a drive voltage application and response excitation module, a high-speed image acquisition and response measurement module, a response time data extraction and distribution generation module, a feature extraction and coupling risk quantification module, and a nonlinear coupling risk amplification module;

[0119] The sub-area division and drive configuration module uses a uniform grid division method to divide the entire LCD display area into several sub-areas of equal area, so that each sub-area contains several pixels and configures a standard grayscale pattern for each sub-area to ensure that all sub-areas receive uniform drive stimulation;

[0120] The driving voltage application and response excitation module uses a control circuit to apply a step-by-step driving voltage to the LCD screen, causing each sub-area to change according to a preset grayscale pattern at a unified timing, driving the liquid crystal molecules to rotate and generate an optical response;

[0121] The high-speed image acquisition and response measurement module uses a high-speed camera to capture all sub-areas frame by frame when the LCD screen is transitioning from grayscale to grayscale. It collects the brightness value of each pixel in each frame, creates a record sequence of brightness changes over time for each pixel, and measures the response time required for the pixel to transition from one grayscale to another.

[0122] The response time data extraction and distribution generation module converts the brightness change data of each pixel in different frames into a response time series as the pixel response time. It then synchronously extracts the response time data of all pixels in each sub-region and forms a response time distribution map under the same grayscale stimulus.

[0123] The feature extraction and coupling risk quantification module uses feature engineering technology to extract characteristic indicators reflecting the nonlinear response coupling characteristics between pixels from the response time distribution map. After in-depth analysis of the extracted characteristic indicators, the nonlinear response coupling risk between multiple adjacent pixels in the sub-region is quantified based on the analyzed characteristics.

[0124] The nonlinear coupling risk amplification module divides the sub-region into a nonlinear response coupling risk region and a normal region. For the nonlinear response coupling risk region, a very small disturbance is superimposed on the grayscale pattern to induce dynamic fluctuations in the response structure of the sub-region. The number of superpositions of grayscale disturbances is adaptively optimized according to the nonlinear response coupling risk value, so that the coupling response in the original edge state exhibits nonlinear behavior.

[0125] An embodiment of the present invention provides an LCD screen fault detection method, which is implemented by the above-mentioned LCD screen fault detection system. The specific method and process of an LCD screen fault detection system are detailed in the embodiment of the above-mentioned LCD screen fault detection method, which will not be repeated here.

[0126] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0127] The above description is merely illustrative of certain exemplary embodiments of the present invention. It goes without saying that those skilled in the art will be able to modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and description are illustrative in nature and should not be construed as limiting the scope of protection of the claims.

[0128] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0129] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0130] In the several embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection of some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0131] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0132] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0133] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A method for detecting a liquid crystal screen fault, characterized in that: The following steps are involved: The entire LCD display area is divided into several sub-areas of equal area using a uniform grid division method, so that each sub-area contains several pixels, and a standard grayscale pattern is configured for each sub-area to ensure that all sub-areas receive uniform driving stimuli. A control circuit applies a step-by-step driving voltage to the LCD screen, causing each sub-area to change in a preset grayscale pattern at a uniform timing, driving the liquid crystal molecules to rotate and produce an optical response. When the LCD screen is transitioning from grayscale to grayscale, a high-speed camera is used to capture all sub-areas frame by frame, collecting the brightness value of each pixel in each frame. A record sequence of brightness changes over time is established for each pixel, and the response time required for the pixel to transition from one grayscale to another is measured. The brightness change data of each pixel in different frames is converted into a response time series as the pixel response time. The response time data of all pixels in each sub-region are synchronously extracted to form a response time distribution map under the same grayscale stimulus. Feature engineering techniques are used to extract characteristic indicators reflecting the nonlinear response coupling characteristics between pixels from the response time distribution map. After in-depth analysis of the extracted characteristic indicators, the nonlinear response coupling risk between multiple adjacent pixels in the sub-region is quantified based on the analyzed characteristics. The sub-region is divided into a nonlinear response coupling risk region and a normal region. For the nonlinear response coupling risk region, a very small disturbance is superimposed on its driven grayscale pattern to induce dynamic fluctuations in the response structure of the sub-region. The number of superpositions of grayscale disturbances is adaptively optimized according to the nonlinear response coupling risk value, so that the coupling response in the original edge state exhibits nonlinear behavior.

2. A method for detecting LCD screen faults according to claim 1, characterized in that: The entire LCD display area is divided into several sub-areas of equal area using a uniform grid division method. The specific steps are as follows: Obtain the overall resolution parameters of the LCD screen and select the appropriate number of grid divisions based on the detection accuracy requirements; Based on the set number of rows and columns, the pixel width and height of each sub-area are calculated in the horizontal and vertical directions respectively, dividing the entire screen into rectangular grids of equal area; The boundary index of each sub-region is established according to the pixel coordinates, which is used for precise positioning and partitioning of pixels in each sub-region in subsequent image acquisition, completing the grid area division process of the LCD screen.

3. The LCD screen fault detection method according to claim 1, wherein: The control circuit applies a step-by-step driving voltage to the LCD screen, causing each sub-area to change according to a preset grayscale pattern at a uniform timing, driving the liquid crystal molecules to rotate and produce an optical response. The specific steps are as follows: Preset a standard grayscale pattern sequence and load the sequence into the liquid crystal drive control system as a test signal; The control circuit synchronously applies a driving voltage corresponding to the grayscale pattern to each sub-area of the LCD screen, ensuring that all sub-areas complete grayscale switching step by step on the same time axis; A measurable optical response is generated by liquid crystal molecules, stimulating the dynamic response of the entire LCD screen under uniform grayscale drive.

4. The method for detecting LCD screen faults according to claim 1, wherein: Feature engineering technology is used to extract characteristic indicators reflecting the nonlinear response coupling characteristics between pixels from the response time distribution map. The extracted characteristic indicators are the spatial gradient diffusion degree of the pixel response time in the sub-region. After in-depth analysis of the spatial gradient diffusion degree of the pixel response time in the sub-region, a response gradient diffusion index is generated. Based on the response gradient diffusion index, the nonlinear response coupling risk between multiple adjacent pixels in the sub-region is quantified.

5. A method for detecting LCD screen faults according to claim 4, characterized in that: The specific steps for generating the response gradient diffusion index after performing a deep analysis of the spatial gradient diffusion degree of the pixel response time in the sub-region are as follows: For each pixel in the sub-region, the response time gradient of the adjacent pixels is calculated. The calculation expression is: , where: T i is the response time of each pixel i, where i is the pixel index in the sub-region; is the response time gradient of pixel i, which represents the rate of change of response time between pixel i and its adjacent pixels; T i+1 and T i-1 are the response times of the adjacent pixels of pixel i respectively; Based on the response time gradient of each pixel, the response gradient diffusion index of the entire sub-region is calculated to quantitatively measure the diffusion degree of the pixel response time difference within the sub-region. The response gradient diffusion index is calculated using the following formula: , where: R gdd is the response gradient diffusion index, N is the total number of pixels in the sub-region; α is the sensitivity coefficient of the response time gradient, which is used to adjust the sensitivity of the response time gradient to the response coupling; neighbors(i) represents the neighborhood pixel set of pixel i; j represents the neighborhood pixel of pixel i, that is, other pixels adjacent to pixel i; It represents the maximum value of the response time gradient in the neighborhood pixels of pixel i; β is the influence coefficient of the neighborhood maximum response gradient, which is used to control the influence of the maximum response of the gradient in the neighborhood on the response gradient diffusion index.

6. A method for detecting LCD screen faults according to claim 4, characterized in that: The response gradient diffusion index generated by in-depth analysis of the spatial gradient diffusion degree of the pixel response time in the sub-region is compared with the pre-set reference threshold of the response gradient diffusion index to divide the sub-region into a nonlinear response coupling risk region and a normal region. The specific division steps are as follows: If the response gradient diffusion index is greater than the response gradient diffusion index reference threshold, it indicates that the nonlinear response coupling risk between multiple adjacent pixels in the sub-region is high, and the sub-region is then divided into a nonlinear response coupling risk region; If the response gradient diffusion index is less than or equal to the response gradient diffusion index reference threshold, it indicates that the risk of nonlinear response coupling between multiple adjacent pixels in the sub-region is low, and the sub-region is divided into a normal region.

7. A method for detecting LCD screen faults according to claim 6, characterized in that: For areas with nonlinear response coupling risk, minimal perturbations are superimposed on the driving grayscale pattern to form a dither sequence. The number of grayscale perturbation superpositions is adaptively optimized based on the nonlinear response coupling risk value. The specific steps are as follows: For sub-regions identified as risk areas for nonlinear response coupling, a very small amplitude perturbation is superimposed on their standard grayscale driving patterns to induce dynamic fluctuations in the response structure of the sub-region; During the disturbance driving process, the response time imaging of the sub-region is performed, the response gradient diffusion index after the disturbance is recalculated, and the dynamic amplification factor is constructed based on the reference threshold of the response gradient diffusion index. The calculation expression of the dynamic amplification factor is: , where: A R is the response gradient diffusion indicator. gdd ′ is the response gradient diffusion index after the disturbance, which quantifies the degree of change of the response structure in the sub-region after the disturbance is applied; is the reference threshold of the response gradient diffusion index, which serves as a comparison baseline and is used to adjust the calculation intensity of the dynamic amplification factor and construct a unified comparison scale for the response coupling risk between different sub-regions; λ is the nonlinear amplification sensitivity factor, which is used to adjust the response intensity of the disturbance amplification effect to the change of the response structure; According to the dynamic amplification factor Ψ A As well as the nonlinear response coupling risk of the sub-region initial response structure, the number of superpositions of adaptive perturbations is calculated to control the duration of perturbation injection. The calculation expression of the superposition number is: , where: η A is the superposition round of the disturbed grayscale sequence; R gdd is the initial response gradient diffusion index of the sub-region, which characterizes the initial nonlinear response coupling risk; β x is the disturbance superposition adjustment coefficient, which controls the growth rate of the disturbance intensity as the nonlinear response coupling risk is adjusted.

8. A liquid crystal screen fault detection system, used to implement the liquid crystal screen fault detection method according to any one of claims 1 to 7, characterized in that: It includes sub-area division and drive configuration module, drive voltage application and response excitation module, high-speed image acquisition and response measurement module, response time data extraction and distribution generation module, feature extraction and coupling risk quantification module, and nonlinear coupling risk amplification module; The sub-area division and drive configuration module uses a uniform grid division method to divide the entire LCD display area into several sub-areas of equal area, so that each sub-area contains several pixels and configures a standard grayscale pattern for each sub-area to ensure that all sub-areas receive uniform drive stimulation; The driving voltage application and response excitation module uses a control circuit to apply a step-by-step driving voltage to the LCD screen, causing each sub-area to change according to a preset grayscale pattern at a unified timing, driving the liquid crystal molecules to rotate and generate an optical response; The high-speed image acquisition and response measurement module uses a high-speed camera to capture all sub-areas frame by frame when the LCD screen is transitioning from grayscale to grayscale. It collects the brightness value of each pixel in each frame, creates a record sequence of brightness changes over time for each pixel, and measures the response time required for the pixel to transition from one grayscale to another. The response time data extraction and distribution generation module converts the brightness change data of each pixel in different frames into a response time series as the pixel response time. It then synchronously extracts the response time data of all pixels in each sub-region and forms a response time distribution map under the same grayscale stimulus. The feature extraction and coupling risk quantification module uses feature engineering technology to extract characteristic indicators reflecting the nonlinear response coupling characteristics between pixels from the response time distribution map. After in-depth analysis of the extracted characteristic indicators, the nonlinear response coupling risk between multiple adjacent pixels in the sub-region is quantified based on the analyzed characteristics. The nonlinear coupling risk amplification module divides the sub-region into a nonlinear response coupling risk region and a normal region. For the nonlinear response coupling risk region, a very small disturbance is superimposed on the grayscale pattern to induce dynamic fluctuations in the response structure of the sub-region. The number of superpositions of grayscale disturbances is adaptively optimized according to the nonlinear response coupling risk value, so that the coupling response in the original edge state exhibits nonlinear behavior.

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