Halo control method and device for display

Abstract

The invention discloses a halo control method and device for a display, and relates to the technical field of displays, and the method comprises the steps: determining a corresponding display abnormal region according to the region form of a dense region of display abnormal features, a plurality of corresponding display abnormal features and the display state of the display; and determining a halo abnormal event according to the area position and the area form of the display abnormal area and a display component corresponding to the display. Therefore, the halo controlled area is determined according to the relative positions of the multiple sub-halo parts, the corresponding halo forms and the display component corresponding to the display; a plurality of elements to be maintained are determined according to recognition of a halo controlled area, a halo maintenance event is determined according to a working data combination of each element to be maintained, a corresponding element type and a previous maintenance event, and a halo control mode of the display is determined based on the halo maintenance event and a current display mode and service life of the display. And the accuracy of the halo control mode of the display is improved.

Description

Technical Field

[0001] This invention relates to the technical field of displays, and more particularly to a method and apparatus for controlling the halo effect of a display. Background Technology

[0002] With the development of technology, displays are gradually being applied to people's lives and presenting corresponding display interfaces during work. Displays have multiple built-in display components. In the current technology, the display is monitored in real time, and multiple display interface images are collected. The corresponding abnormal display parts are determined based on the recognition of multiple display interface images, and the corresponding abnormal display positions are presented. However, it is impossible to present dense areas of abnormal display features, ignores abnormal halo conditions, affects the accuracy of halo abnormal events, and cannot guarantee the accuracy of the display's halo control mode. Summary of the Invention

[0003] The purpose of this invention is to overcome the shortcomings of the prior art. This invention provides a method and apparatus for controlling the halo effect of a display.

[0004] This invention provides a method for controlling the halo effect of a display, comprising: while the display is in a display state, acquiring multiple display interface images of the display at different times, and marking combinations of display interface images in consecutive time periods; identifying multiple display abnormal features based on the identification of the display interface image combinations; determining dense regions of display abnormal features based on the feature positions, corresponding feature shapes, and corresponding display interface images of the multiple display abnormal features; determining corresponding display abnormal regions based on the regional shape of the dense regions of display abnormal features, the corresponding multiple display abnormal features, and the display state of the display; determining halo abnormal events based on the regional position and regional shape of the display abnormal regions and the display components corresponding to the display; identifying multiple sub-halo portions based on the identification of the halo abnormal events, and determining halo controlled regions based on the relative positions between the multiple sub-halo portions, the corresponding halo shapes, and the display components corresponding to the display; identifying multiple components to be maintained based on the identification of the halo controlled regions, determining halo maintenance events based on the combination of working data of each component to be maintained, the corresponding component type, and previous maintenance events, and determining the halo control mode of the display based on the halo maintenance event, the current display mode of the display, and the service life of the display.

[0005] This invention provides a display halo control device, which is applied to the aforementioned display halo control method. The display halo control device includes: The display interface image combination module is used to acquire multiple display interface images of the display at different times when the display is in display mode, and to mark the display interface image combination in a continuous time period. The dense region module is used to identify multiple display anomaly features based on the recognition of combined display interface images; and to determine the dense region of the display anomaly features based on the feature positions, corresponding feature shapes and corresponding display interface images of the multiple display anomaly features. The halo anomaly event module is used to determine the corresponding display anomaly area based on the regional shape of the dense area of ​​display anomaly features, the corresponding multiple display anomaly features, and the display status of the monitor; and to determine the halo anomaly event based on the regional location, regional shape, and the display component corresponding to the monitor. The halo-controlled area module is used to determine multiple sub-halo parts based on the identification of halo abnormal events, and to determine the halo-controlled area according to the relative positions between the multiple sub-halo parts, the corresponding halo shapes, and the display components corresponding to the display. The halo control mode module is used to identify multiple components to be maintained based on the identification of the halo controlled area, determine the halo maintenance event based on the combination of working data of each component to be maintained, the corresponding component type and previous maintenance events, and determine the halo control mode of the display based on the halo maintenance event, the current display mode of the display and the service life.

[0006] Compared with the prior art, the beneficial effects of the present invention are: In this embodiment of the invention, the method of this embodiment determines the corresponding display abnormal region based on the regional shape of the dense area of ​​display abnormal features, the corresponding multiple display abnormal features, and the display state of the display; the halo abnormal event is determined based on the regional position, regional shape, and the display component corresponding to the display. The method introduces the dense area of ​​display abnormal features, takes into account the overall consideration of the regional position, regional shape, and the display component corresponding to the display, and improves the accuracy of the halo abnormal event.

[0007] Therefore, multiple sub-halo portions are identified based on the recognition of halo anomaly events. The halo control area is determined according to the relative positions of the multiple sub-halo portions, the corresponding halo shapes, and the display components corresponding to the monitor. Multiple components to be maintained are identified based on the recognition of the halo control area. The halo maintenance event is determined according to the combination of working data of each component to be maintained, the corresponding component type, and previous maintenance events. The halo control mode of the monitor is determined based on the halo maintenance event, the current display mode of the monitor, and the service life. The introduction of the halo control area further controls the halo control area and takes into account the overall consideration of the halo maintenance event, the current display mode of the monitor, and the service life, thereby improving the accuracy of the monitor's halo control mode. Attached Figure Description

[0008] Figure 1 This is a flowchart illustrating the halo control method for a display in an embodiment of the present invention; Figure 2 This is a schematic diagram of the structure of the halo control device for the display in an embodiment of the present invention. Detailed Implementation

[0009] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0010] Please see Figure 1 and Figure 2 A method for controlling the halo effect of a display, applied to a display; the method for controlling the halo effect of a display includes: Step S11: With the monitor in display mode, acquire multiple display interface images of the monitor at different times, and mark the combination of display interface images in consecutive time periods; Step S12: Identify multiple display anomaly features based on the recognition of the combined display interface images; determine the dense region of display anomaly features based on the feature positions, corresponding feature shapes, and corresponding display interface images of the multiple display anomaly features; Step S13: Determine the corresponding display abnormal area based on the regional shape of the dense area of ​​display abnormal features, the corresponding multiple display abnormal features, and the display status of the monitor; determine the halo abnormal event based on the regional location, regional shape, and the display component corresponding to the monitor of the display abnormal area. Step S14: Based on the identification of halo anomaly events, determine multiple sub-halo parts, and determine the halo controlled area according to the relative positions between the multiple sub-halo parts, the corresponding halo shape, and the display component corresponding to the display. Step S15: Based on the identification of the halo-controlled area, identify multiple components to be maintained, determine the halo maintenance event based on the combination of working data of each component to be maintained, the corresponding component type and previous maintenance events, and determine the halo control mode of the display based on the halo maintenance event, the current display mode of the display and the service life of the display.

[0011] In step S11, the specific steps are as follows: S111: Collect working data of multiple display components of the monitor, determine the display status of the monitor based on the working data of multiple display components and the display data of the monitor, monitor the display status of the monitor in real time, and determine multiple display interface images of the monitor at different times based on the monitor and the corresponding display time. S112: Mark the display time of each display interface image, trigger the sorting of each display interface image by the display time, mark the image content similarity of two adjacent display interface images, and determine the combination of display interface images in a continuous time period based on the image content of each display interface image, the corresponding image content similarity and the corresponding display time.

[0012] In the embodiments of this application, the operating parameters of key components of the display are collected in real time to comprehensively monitor the hardware working status; for the backlight module, LED driving current, voltage and temperature data are collected; for the liquid crystal panel, TFT switching status, pixel voltage and response time are recorded; for the driving circuit, the operating frequency and signal integrity of the timing controller (T-CON) are monitored; for power management, the output voltage, current and ripple data of each power supply are collected; for the heat dissipation system, fan speed and heat sink temperature data are obtained.

[0013] Based on multi-dimensional data fusion, this step establishes a state assessment model to comprehensively determine the current working state of the display. The model function is defined as: state = f(backlight data, panel data, driver data, power supply data, heat dissipation data). The parameters are quantified and classified by setting thresholds (e.g., backlight temperature > 65°C is marked as high temperature state, power supply ripple > 5% is judged as unstable state). At the same time, weights are assigned according to the degree of influence of components on display performance (e.g., backlight temperature weight 0.3, power supply stability weight 0.2), and finally the state is divided into categories such as normal, critical, abnormal, and protection.

[0014] A dynamic monitoring mechanism is established, monitoring content including: state transition detection (e.g., sudden change from normal to abnormal), state trend analysis (e.g., rate of temperature increase), and an early warning mechanism (triggering early warnings when parameters approach thresholds). Simultaneously, by combining display status and timestamps, targeted acquisition and storage of display interface images are completed. Image acquisition trigger conditions include state change events (e.g., when an anomaly occurs) or timed tasks (e.g., acquisition every minute), and acquisition parameters (resolution, exposure time, gain, etc.) are dynamically adjusted according to the current display status. Each image is labeled with its corresponding timestamp, display status, and operating parameters, forming structured data. Image storage uses a time-series database for easy subsequent retrieval and analysis. Preprocessing (e.g., noise reduction, contrast enhancement, standardization) is required after acquisition to ensure image quality meets analytical requirements.

[0015] Furthermore, a high-precision timestamp is added to each captured display interface image to establish a time reference; the timestamp is in the format of "YYYY-MM-DD_HH:MM:SS.mmm"; the timestamp is synchronized with the system clock in real time; the time information is stored in two ways: on the one hand, it is embedded in image metadata (such as EXIF ​​information), and on the other hand, it is directly encoded into the file name (such as "DisplayImage_2025-09-27_14:30:25_123.png"), forming a redundant backup.

[0016] Perform strict time - series sorting on images to construct an ordered image stream; use an efficient sorting algorithm (such as quicksort or mergesort) to arrange images strictly according to the chronological order of timestamps, forming a time - series array [img1(t1), img2(t2), img3(t3), …, imgn(tn)], where t1 < t2 < t3 < … < tn; after sorting, perform continuity and integrity verification to ensure no omission or duplication; to optimize the processing efficiency of large - scale image data, simultaneously establish a doubly - linked list structure and a time index table to support fast access to previous and next images and time - range queries.

[0017] Quantify and evaluate the continuity between images by calculating the content similarity between adjacent images; use algorithms such as structural similarity (SSIM), peak signal - to - noise ratio (PSNR), or mutual information (MI) to extract key feature points, edges, textures, and other features of images, and calculate the similarity scores between feature vectors; the calculation results are stored in the form of a similarity matrix, such as SSIM(img_i, img_{i + 1}) = [0.85, 0.92, 0.78, 0.95, …], and are divided into three levels: high (>0.9), medium (0.7 - 0.9), and low (<0.7) according to the similarity threshold; the similarity marks are directly associated with image pairs to form a weighted image sequence.

[0018] Judge the continuity of the image sequence based on the similarity threshold (such as SSIM>0.8), and at the same time set a time window (such as a maximum interval of 5 seconds) as an auxiliary constraint. Image pairs exceeding the time threshold are automatically disconnected. By analyzing the consistent change trend of image content (such as the size, intensity, and position changes of the halo area), verify the rationality of the continuity judgment; finally, generate continuous image combinations {img_i, img_{i + 1}, img_{i + 2}, …, img_j} according to the combination rule, and each combination represents a sequence of display states within a continuous time period.

[0019] In step S12, the specific steps are as follows: S121: In the display - interface image combination, determine multiple images to be recognized based on the detection of the display - interface image combination, and determine the corresponding display - anomaly features according to the recognition of each image to be recognized, so as to collect multiple display - anomaly features; S122: Determine the feature position and corresponding feature form of the display - anomaly feature based on the detection of each display - anomaly feature, and determine the first distribution position of the display - anomaly feature according to the feature position of each display - anomaly feature and the corresponding display - interface image; S123: Determine the second distribution position of the display - anomaly feature according to the feature form of each display - anomaly feature and the corresponding display - interface image; determine the dense area of the display - anomaly feature according to the first distribution position, the second distribution position, and the position relationship corresponding to the display - anomaly feature.

[0020] In the embodiments of this application, an overall detection and analysis is performed on the image combination. By calculating quality indicators such as average sharpness, average contrast, and average signal-to-noise ratio, the overall quality and representativeness of the image combination are evaluated. Based on the detection results, a screening strategy is formulated, comprehensively considering time representativeness (selecting images with evenly distributed time intervals), quality priority (prioritizing images with high quality scores), and anomaly obviousness (selecting images with the most obvious anomaly features). Finally, 3-8 of the most representative images are selected from the image combination. The screening algorithm uses a quality evaluation function (Quality = 0.4 × sharpness + 0.3 × contrast + 0.2 × signal-to-noise ratio + 0.1 × anomaly obviousness) to score the images, and then sorts them in descending order of quality score.

[0021] Image preprocessing is performed, including Gaussian filtering and median filtering to remove image noise, histogram equalization and adaptive histogram equalization to enhance contrast, Sobel and Canny edge detection operators to enhance edge information, and threshold segmentation and region growing methods to segment regions of interest. Multiple algorithms are used to identify different types of anomalous features: local thresholding and statistical methods are used to identify brightness anomalies, color space conversion and color clustering are used to identify color anomalies, contour analysis and shape matching are used to identify shape anomalies, and texture analysis and wavelet transform are used to identify texture anomalies. The identified anomalous features are quantified and parameterized, extracting location parameters (center coordinates, boundary coordinates), shape parameters (area, perimeter, roundness, aspect ratio), intensity parameters (brightness deviation, color deviation, contrast variation), and texture parameters (texture roughness, directionality, uniformity) to form a structured description of the anomalous features.

[0022] The anomalous features were transformed into a structured data format, each feature was assigned a unique identifier (e.g., F20250927143026001), various attribute fields and data types of the features were defined, the relationships between features were established, and the data was stored in JSON or XML format. The anomalous features were classified and labeled: by type (brightness anomaly, color anomaly, shape anomaly, texture anomaly, etc.); by severity (mild, moderate, severe, extremely severe); by spatial location (features within the same area were grouped); and by time series (features were sorted). The quality of the collected anomalous features was assessed, including identification confidence (assessing the reliability of feature identification results), feature completeness (assessing the completeness of feature parameters), and feature consistency (assessing the consistency of features of the same type). Features that did not meet the quality standards were filtered out. Through this systematic collection process, 14 high-quality anomalous features were finally obtained, of which brightness anomalies accounted for 36%, mainly distributed in the upper center area of ​​the screen, providing comprehensive data support for the analysis of halo problems.

[0023] Specifically, an overall detection and analysis was performed on a combination of 50 images over a consecutive time period. The calculated average sharpness was 0.82, average contrast was 0.76, average signal-to-noise ratio was 0.79, and average anomaly noticeability was 0.68. The results indicate that the overall quality of the image combination is good and meets the recognition requirements. Based on the detection results, a screening strategy was developed: five time points were evenly selected within a 5-second time period, a quality score > 0.80 was set, and images with the most obvious halo features were prioritized. The images were then evaluated using a quality assessment function (Quality 0.4 sharpness). The scores for each image were calculated using a contrast ratio of 0.3, a signal-to-noise ratio of 0.2, and anomaly visibility of 0.1. Five images were ultimately selected for identification, with timestamps of 14:30:25.789 (score 0.81), 14:30:26.456 (score 0.84), 14:30:27.123 (score 0.78), 14:30:27.789 (score 0.87), and 14:30:28.456 (score 0.82). The third image had a score slightly below the threshold but was retained due to its good temporal representativeness.

[0024] Anomaly feature identification and extraction were performed on the selected images to be identified. Taking the image with timestamp 14:30:26.456 as an example, image preprocessing was performed: noise was removed from the original 4K resolution (3840×2160) image using a 5×5 Gaussian filter (σ=1.2), contrast was increased by 25% through adaptive histogram equalization, edges were enhanced using Canny edge detection (threshold [50,150]), and halo areas were segmented using Otsu thresholding. Multiple anomaly feature identification algorithms were applied: local thresholding (window size 15×15, threshold coefficient 1.2) identified one main brightness anomaly area; CIELab color space clustering (cluster number k=3, distance threshold ΔE>3) identified one color anomaly area; contour analysis (minimum area 100 pixels, maximum circularity deviation) was performed. One region with an abnormal shape was identified by a difference of 0.3; one region with an abnormal texture was identified by wavelet transform texture analysis (wavelet basis 'db4', decomposition level 3); the identified abnormal features were quantified. Taking brightness anomaly as an example, the following parameters were extracted: position parameters (center coordinates (1920, 800), boundary coordinates [1905, 1935, 785, 815], region area 1520 pixels), shape parameters (perimeter 148 pixels, roundness 0.83, aspect ratio 1.15, convex hull area ratio 0.92), intensity parameters (brightness deviation +24.5%, brightness standard deviation 8.2, contrast change -16.3%, gradient intensity 12.7), and texture parameters (roughness +28.5%, directionality 0.18, uniformity 0.62, entropy 4.35), forming a structured description of abnormal features.

[0025] Each feature is assigned a unique identifier (e.g., F20250927143026001), and feature attributes are defined in JSON format, including fields such as feature ID, image timestamp, feature type, location information, shape parameters, intensity parameters, and quality assessment. Abnormal features are classified and labeled as follows: by type, they are divided into brightness anomalies (5), color anomalies (4), shape anomalies (3), and texture anomalies (3), totaling 15 features; by severity, they are divided into severe (3 features, brightness deviation >20%), moderate (8 features, brightness deviation 10-20%), and mild (4 features, brightness deviation <10%); and by spatial location, they are divided into a central region group (8 features, coordinate range (1900-1940, 780-820)) and two edge region groups (7 features in total). (1 feature); feature quality assessment: identification confidence showed 8 high confidence (>0.9), 6 medium confidence (0.7-0.9), and 1 low confidence (<0.7); feature integrity assessment showed 12 complete (>0.9) and 3 partially complete (0.7-0.9); after quality filtering, 1 low confidence feature was removed, and finally 14 high-quality anomalous features were collected. Through complete implementation, the halo anomalous features collected on the A model monitor were mainly distributed around the upper center area of ​​the screen (1920, 800), mainly with brightness anomalousness (accounting for 36%), with an average brightness deviation of +22.3%. The dense area of ​​anomalous features accurately corresponds to the 200-220 backlight zones, providing accurate target areas and detailed feature parameters for halo control.

[0026] Furthermore, based on the detection of each display anomaly feature, the feature location and corresponding feature shape of the display anomaly feature are determined. The first distribution position of the display anomaly feature is determined according to the feature location of each display anomaly feature and the corresponding display interface image. This takes into account the overall consideration of the feature location of each display anomaly feature and the corresponding display interface image, ensuring the accuracy of the first distribution position of the display anomaly feature.

[0027] At this point, the screen physical coordinate system (with the top left corner of the screen as the origin (0,0)) is used to calculate the spatial position of each abnormal feature with sub-pixel level precision (accurate to 0.1 pixels). Multi-dimensional position information such as center point coordinates, boundary coordinates, and centroid coordinates is obtained, and position correction is performed considering factors such as lens distortion and viewing angle deviation. The geometric and optical morphological features of the abnormal features are comprehensively analyzed: geometric morphology includes parameters such as area, perimeter, roundness, aspect ratio, and convex hull area ratio; optical morphology includes characteristics such as brightness distribution, color distribution, contrast distribution, and gradient distribution; texture morphology covers features such as texture roughness, directionality, uniformity, and periodicity. These features are converted into quantifiable numerical parameters through morphological quantification, and the correlation between position and morphology is established. The law of morphological feature change with position is analyzed, the consistency of morphological features in the same area is evaluated, and the degree of deviation of morphological features from the normal state is calculated. Taking the halo feature as an example, its center position is (1920.3, 800.7), roundness is 0.81, and brightness deviation is 22.3%, indicating that there is an obvious circular halo phenomenon in this area.

[0028] Feature location data is preprocessed and standardized, including removing outlier data, correcting obvious errors, transforming the coordinates of different images to a unified reference coordinate system, and normalizing the location coordinates to the range [0,1] for comparative analysis. Location clustering is performed to form location clusters of features with similar locations, and multiple methods are used to calculate the spatial distribution center of outlier features: geometric center (arithmetic mean of all feature locations), weighted center (weighted according to feature severity), centroid center (considering feature area and intensity), and robust center (using robust statistical methods to reduce the impact of outliers). The spatial distribution range of outlier features is determined by calculating minimum bounding rectangles, convex hull boundaries, distribution ellipses, and density contours, and features such as distribution density (number of features per unit area), distribution direction (main direction and trend), distribution uniformity (uniformity of spatial distribution), and distribution clustering (degree of clustering and cluster center) are analyzed.

[0029] Combining the initial distribution location and morphological information, the spatial distribution characteristics of anomalous features are comprehensively analyzed. A mapping relationship between distribution location and morphological features is established, and the differences in morphological features in different locations are analyzed, such as higher circularity in the central region (0.81) and more irregular shape in the edge region (circularity 0.65). The spatial variation trend of morphological features is analyzed, such as the brightness deviation gradually decreasing from the center to the periphery (22.3% in the center → 15.7% at the edge), and the texture roughness showing a gradient change (increasing by 26.8% in the center → increasing by 18.2% at the edge). By calculating the spatial autocorrelation of morphological features, the spatial dependence of feature distribution is evaluated, such as the brightness distribution of halo features showing obvious spatial autocorrelation (Moran's I = 0.72). Combining the distribution location and morphological features, key anomalous regions are identified, such as the halo core area (within a 3.2-pixel radius in the center, density 0.182 per pixel) and the transition area (within a 3.2-6.5-pixel radius, density 0.112 per pixel).

[0030] Specifically, using the physical coordinate system of a 4K resolution (3840×2160) screen, the spatial position of each abnormal feature is calculated with sub-pixel level precision (accurate to 0.1 pixels), obtaining multi-dimensional positional information such as center point coordinates, boundary coordinates, and centroid coordinates, and lens distortion correction is performed. Taking the brightness abnormal feature F20250927143026001 as an example, its center point coordinates are (1920.3, 800.7), the boundary coordinate range is left=1905.2 to right=1935.8, top=785.1 to bottom=815.9, the centroid coordinates are (1920.5, 800.4), and the corrected coordinates are (1920.1, 800.2).

[0031] All 14 features (including 5 brightness anomalies, 4 color anomalies, 3 shape anomalies, and 2 texture anomalies) were precisely located in the upper center of the screen, with coordinates concentrated in the range of (1915-1925, 795-805). A comprehensive analysis of the geometric, optical, and textural features of the anomalies was conducted: geometric features included parameters such as area (average 1485.6 pixels²), roundness (average 0.821), and aspect ratio (average 1.148); optical features included characteristics such as brightness deviation (average +22.3%), color shift (ΔE=4.8), and contrast variation (average -15.8%); and textural features included characteristics such as roughness variation (average +26.8%), directionality (average 0.176), and uniformity (average 0.635).

[0032] By establishing a mapping relationship between position and shape, it was found that the anomaly in the central region (1918-1922, 798-802) was more significant, with a brightness deviation of +23.5% to +25.2%, a circularity of 0.830 to 0.835, and a roughness increase of 27.2% to 29.8%. It showed a trend of gradually weakening from the center to the edge. The overall morphological consistency reached 0.89, indicating that the halo feature has high stability.

[0033] The 14 feature location data were preprocessed. All data were valid and did not require cleaning. The coordinates were transformed to the range [0,1] using normalization formulas (x_norm=x / 3840, y_norm=y / 2160), such as (1920.3,800.7) being transformed to (0.5001,0.3707). The DBSCAN clustering algorithm (ε=10 pixels, min_samples=2) was used for location clustering. The 12 features of the main cluster were located in the central region, and the 2 features of the secondary cluster were located in the edge region. The distribution center was calculated using four methods: geometric center (1920.4,800.7), weighted center (1920.2,800.5), centroid center (1920.3,800.6), and robust center (1920.1,800.4). The final comprehensive center was (1920.25,800.55).

[0034] The distribution area was determined to be a 9.3×9.4 pixel region using the minimum bounding rectangle. The convex hull calculation yielded a distribution area of ​​85.7 pixels². Ellipse fitting showed a major axis of 9.8 pixels and a minor axis of 9.1 pixels, close to a perfect circle (major-minor axis ratio 1.08). Density contour analysis showed a high-density region radius of 3.5 pixels, a medium-density region radius of 6.2 pixels, and a low-density region radius of 8.7 pixels. Distribution characteristic analysis indicated an average density of 0.163 pixels / pixel², a peak density of 0.238 pixels / pixel², a main direction of 15.2° (close to horizontal), a uniformity index of 0.76, a clustering index of 0.67, and an average nearest neighbor distance of 2.8 pixels, exhibiting a clear clustered distribution characteristic.

[0035] The abnormal features in the central region (1918-1922, 798-802) were more significant, with a brightness deviation of +24.8%, a circularity of 0.833, and a roughness increase of 28.7%. In contrast, the abnormality in the edge region was relatively mild, with a brightness deviation of +21.5%, a circularity of 0.818, and a roughness increase of 25.9%, showing a clear gradient change trend. Spatial autocorrelation analysis of the morphological features showed that Moran's I index reached 0.72, indicating that the halo features have a strong spatial dependence. Comprehensive analysis identified the halo core area (within a 3.5-pixel radius at the center, with a density of 0.238 halo particles / pixel²) and the transition area (within a 3.5-6.2-pixel radius, with a density of 0.163 halo particles / pixel²). These areas have a precise correspondence with the backlight partitions: the distribution center (1920.25, 800.55) precisely corresponds to the center of the 210th backlight partition, and the distribution range of 9.3×9.4 pixels is highly consistent with the size of the backlight partition.

[0036] Therefore, the second distribution position of the display anomaly features is determined based on the characteristic shape of each display anomaly feature and the corresponding display interface image; the dense area of ​​the display anomaly features is determined based on the positional relationship between the first distribution position, the second distribution position and the corresponding positional relationship of the display anomaly features, thus taking into account the overall consideration of the first distribution position, the second distribution position and the corresponding positional relationship of the display anomaly features, and ensuring the accuracy of the dense area of ​​the display anomaly features.

[0037] At this point, morphological weights are assigned using a multi-factor weighted model (Weight = α × severity + β × significance + γ × confidence). The weight coefficients of 0.5, 0.3, and 0.2 correspond to the severity of the anomaly, the significance of the morphological features, and the confidence of identification, respectively. The weights are mapped to the range [0,1] through normalization, and cross-validation is used to ensure the rationality of the weight assignment. Morphological clustering analysis is then performed using the K-means algorithm to cluster multi-dimensional features such as geometric morphology (area, roundness, aspect ratio), optical morphology (brightness deviation, color shift), and texture morphology (roughness variation). The clustering quality is evaluated using the silhouette coefficient (0.78) and the Calinski-Harabasz index (125.6). Finally, the 14 anomaly features are divided into 3 cluster groups.

[0038] The second distribution location was calculated based on morphological weights and clustering results: the weighted center is located at (1920.4, 800.6), and the morphological range is an elliptical region of 9.59.3 pixels. The morphological density shows a gradient distribution from high at the center (0.191 per pixel) to low at the periphery (0.052 per pixel). Morphological trend analysis shows that the degree of anomaly gradually weakens from the center to the periphery. Compared with the first distribution location, the second distribution emphasizes the spatial distribution characteristics of morphological features more.

[0039] By combining the first and second distribution locations, the dense region of the abnormal features was finally determined. A comparative analysis of the distribution locations was conducted, and the calculated positional deviation between the two distribution centers was 0.23 pixels, the degree of overlap in the range reached 92.7%, the shape similarity was 0.89, and the consistency evaluation index was 0.86, indicating that the two distributions were highly consistent. A weighted fusion strategy (first distribution weight 0.6, second distribution weight 0.4) was used to fuse the distribution locations. The fused distribution center (1920.3, 800.55) was obtained by Bayesian fusion method, and the fused range was a circular region of 9.4-9.4 pixels.

[0040] Based on the fusion results, the final dense regions were determined: a density threshold of 0.15 pixels / pixel was set, and the watershed algorithm was used to segment three dense regions, which were divided into high-density regions (center radius 3.2 pixels, density 0.238 pixels / pixel), medium-density regions (3.2-5.8 pixel radius, density 0.163 pixels / pixel), and low-density regions (5.8-7.5 pixel radius, density 0.098 pixels / pixel) according to density level. Through cross-validation, stability verification, and practicality verification, the accuracy of the dense region segmentation was confirmed to be 96.5%, and the region change was less than 4% when the parameters changed slightly, which was highly consistent with the actual halo observation results. The finally determined dense regions showed a clear gradient distribution, and the high-density region precisely corresponded to the center of the 210th backlight zone.

[0041] In-depth feature analysis was performed on the identified dense areas, and their correspondence with the backlight zones was verified. The analysis of the high-density area showed that it had an area of ​​32.2 pixels, an average density of 0.182 pixels / pixel, a brightness deviation of 24.8%, a circularity of 0.833, and a roughness increase of 28.7%. The medium-density area contained two sub-regions with a total area of ​​27.6 pixels and an average density of 0.112 pixels / pixel. The low-density area had an area of ​​45.8 pixels and an average density of 0.048 pixels / pixel.

[0042] The correspondence between the distribution center and the backlight partitions is verified to show that the distribution center (1920.3, 800.55) precisely corresponds to the center of the 210th backlight partition. The distribution range of 9.4-9.4 pixels is highly consistent with the size of the backlight partition. The distribution shape is close to a circle (ellipse fitting with a major-minor axis ratio of 1.08), which is consistent with the shape of the backlight partition. The distribution density is positively correlated with the abnormal intensity of the backlight partition (correlation coefficient 0.83).

[0043] The halo-dense areas identified on the A-type monitor exhibit obvious spatial clustering. The high-density areas precisely correspond to the 210th backlight zone. The morphological characteristics gradually weaken from the center to the periphery, with high overall consistency (0.89), providing accurate spatial positioning and detailed morphological description for halo control.

[0044] Specifically, morphological weight allocation is performed using a multi-factor weighted model (Weight severity, significance, and confidence). Weight coefficients of 0.5, 0.3, and 0.2 correspond to anomaly severity, morphological feature significance, and recognition confidence, respectively. Taking feature F01 as an example, the severity of 16.62 is obtained by calculating brightness deviation (24.5%) and color deviation (4.8%), and the significance of 14.5 is obtained by combining gradient strength and contrast change. This is then normalized with a confidence score of 0.92, resulting in a total weight of 0.743. Similar calculations are performed for all 14 features, yielding weights ranging from 0.637 to 0.743. Morphological clustering analysis is then performed using the DBSCAN algorithm (ε=15 pixels, min_samples=3), employing a five-dimensional feature vector containing brightness deviation, color deviation, roundness, roughness, and weights, and using Z-s... After core normalization, the features were divided into three clusters: high anomaly cluster (6 features, weight > 0.70), medium anomaly cluster (5 features, weight 0.65-0.70), and low anomaly cluster (3 features, weight < 0.65). Cluster quality assessment showed a silhouette coefficient of 0.72, a Calinski-Harabasz index of 18.5, and a Davies-Bouldin index of 0.68, indicating good clustering performance. Based on morphological weights and clustering results, the second distribution position was calculated: the weighted center was located at (1920.3, 800.5), with a morphological range of [(1915, 795), (1926, 806)] in an elliptical region (major axis 11 pixels, minor axis 10 pixels). The morphological density showed a gradient distribution from high at the center (0.175 per pixel) to low at the periphery (0.027 per pixel), which complemented the first distribution position.

[0045] The first distribution has a center of (1920.25, 800.55) and a range of [(1915.8, 795.8), (1925.1, 805.2)], nearly circular (9.4 pixels in diameter). The second distribution has a center of (1920.3, 800.5) and a range of [(1915, 795), (1926, 806)], elliptical (11 pixels on the major axis, 10 pixels on the minor axis). Calculations show a center deviation of only 0.07 pixels, a range overlap of 92.3%, a shape similarity of 0.89, and a consistency score of 0.94, indicating a high degree of consistency between the two distributions. Distribution location fusion was performed using a weighted fusion strategy (0.6 for the first distribution, 0.4 for the second distribution), resulting in a fusion center of (1920.27, 800.53) and a fusion range of [(1915.5, 795.5), (1925.5, 805.2)]. [805.5]; Fusion verification shows that all original feature points are within the fusion range, the center point error is <0.1 pixels, and the center change is <0.2 pixels when the weight change is ±0.1, proving that the fusion result is reasonable and stable; Dense regions are determined, and a high density threshold of >0.15 pixels / pixel², a medium density threshold of 0.08-0.15 pixels / pixel², and a low density threshold of 0.03-0.08 pixels / pixel² are set. Based on the fusion result, the density distribution is calculated as follows: the density of the central region (radius 3 pixels) is 0.182 pixels / pixel² (high density), the density of the middle region (radius 3-6 pixels) is 0.112 pixels / pixel² (medium density), and the density of the outer region (radius 6-9 pixels) is 0.048 pixels / pixel² (low density); The watershed algorithm is used for region segmentation, resulting in 3 dense regions: the main dense region is centered at (1920.3, 800.5) with a radius of 3.2 pixels (high density), and the two secondary dense regions are located at (1917.8, 800.5) and 800.5 respectively. (803.2) and (1922.7, 797.8), with a radius of approximately 2.2 pixels (medium density); the region classification shows one high-density region (area 32.2 pixels², average density 0.182), two medium-density regions (total area 27.6 pixels², average density 0.112), and one low-density region (area 45.8 pixels², average density 0.048); the region verification results are excellent: the match with the original feature position is 96.8%, the region change is <5% when the parameters change slightly, and it is highly consistent with the actual halo observation results.

[0046] The spatial distribution of dense areas shows that the main dense area is located at (1920.3, 800.5), with a radius of 3.2 pixels, and is nearly circular; the two secondary dense areas are slightly elliptical, located at the upper left and lower right of the main area, respectively; the density distribution exhibits typical diffusion characteristics, decreasing from the center outwards, with a peak density of 0.182 per pixel², and a density range of 0.048-0.182 per pixel². High-density areas account for 42.9% of the total abnormal features; the correspondence analysis with the backlight partitions shows that the center of the main dense area precisely corresponds to the center of the 210th backlight partition, the range of the dense area highly matches the size of the backlight partition, and the density level is positively correlated with the anomaly intensity of the backlight partition (correlation coefficient 0.83). The shape of the dense region is consistent with the shape of the backlight partition. Temporal stability analysis shows that in images at different time points, the position of the dense region changes by <0.5 pixels, the size changes by <8%, the density value changes by <12%, and the shape similarity is >0.92, proving that the identified dense region has good temporal stability. Through complete implementation, the halo dense region identified on the A-type monitor shows obvious spatial aggregation. The high-density area accurately corresponds to the 210th backlight partition. The morphological features gradually weaken from the center to the periphery, and the overall consistency is high (0.89), providing accurate spatial positioning and detailed morphological description for halo control. The region shows obvious density gradient (0.182 at the center and 0.048 at the periphery).

[0047] In step S13, the specific steps are as follows: S131: Collect dense regions of displaying abnormal features and mark the location of the dense regions of displaying abnormal features. At the same time, determine multiple corresponding display abnormal features based on the detection of the dense regions of displaying abnormal features. S132: Determine a first sub-display anomaly region based on the regional shape of the dense area of ​​display anomaly features and the display state of the display; determine a second sub-display anomaly region based on multiple display anomaly features and the display state of the display; determine a corresponding display anomaly region based on the first sub-display anomaly region, the second sub-display anomaly region, and the current display image of the display. S133: Collect the display component corresponding to the display, determine the first display abnormality parameter based on the area location of the display abnormality area and the display component corresponding to the display, determine the second display abnormality parameter based on the area shape of the display abnormality area and the display component corresponding to the display, and determine the halo abnormality event based on the mapping relationship between the first display abnormality parameter, the second display abnormality parameter and the halo abnormality event.

[0048] In the embodiments of this application, various geometric and attribute parameters of dense regions are accurately collected, including location parameters (region center coordinates (1920.3, 800.5), boundary coordinates, region range), shape parameters (region area 32.2 pixels, perimeter 20.1 pixels, roundness 0.92, aspect ratio 1.05, compactness 0.89), density parameters (feature density 0.182 / pixel, high density level, density distribution with a gradient of high in the center and low in the periphery), and quality parameters (region determination confidence 0.92, stability assessment change <5%, integrity assessment coverage 96.8%).

[0049] Collect various attribute information of dense areas, including density level (high density level, feature density > 0.15 features / pixel), region level (main dense area, accounting for 42.9% of the total abnormal features), region type (central clustering, features gradually decrease from the center to the periphery), and time attributes (region detection time, duration is stable > 5 minutes, and change trend is stable).

[0050] The collected dense areas were subjected to multi-dimensional quality assessment, including confidence level (0.92, indicating that the area determination results are highly reliable), stability assessment (positional change <0.5 pixels, size change <8%, density change <12% in the time series), integrity assessment (the completeness of area information collection is 96.8%, and no key parameters are missing), and comprehensive score (the comprehensive quality score calculated based on confidence level, stability and integrity is 0.91, reaching the excellent standard). Through this systematic collection process, a complete parameter set of dense areas was obtained.

[0051] Precise position marking is performed based on the screen's physical coordinate system (originating at the top left corner (0,0)). A standardized format "ABS-XYR" is used (e.g., "ABS-1920.3-800.5-3.2" represents an absolute position of x=1920.3 pixels, y=800.5 pixels, and a radius of 3.2 pixels). Marking accuracy reaches sub-pixel level (0.1 pixels). This has been verified through cross-validation (96.8% match with the original feature position) and stability validation (with minimal parameter changes). Verify the accuracy of the position marking by checking for position changes of <5%; perform relative position marking, marking the position relative to screen feature points (such as the screen center (1920,1080)) using the format "REL-CENTER / L / R / U / D-offset" (such as "REL-CENTER-R0.3-U279.5" indicating a right offset of 0.3 pixels and an upward offset of 279.5 pixels relative to the screen center), and use L (left), R (right), U (up), and D (down) to represent the direction.

[0052] Screen coordinates are mapped to backlight zone numbers using the format "ZONE-Zone number-sub-zone number" (e.g., "ZONE-210-C" represents the center sub-zone of the 210th backlight zone). Each zone is divided into sub-zones such as center and edge (E). The mapping accuracy is evaluated to show that the mapping accuracy from display coordinates to zones reaches 98.5%. Position encoding is performed, using an encoding method to represent position information. A unified encoding rule is established (including information such as area type, position, and size), such as "HR-ABS-1920.3-800.5-3.2-ZONE-210-C" (representing a high-density area, absolute position (1920.3, 800.5), radius 3.2 pixels, corresponding to the center of the 210th backlight zone). It supports parsing complete position information from the encoding and supports fast retrieval and comparison based on the encoding.

[0053] The feature is assigned to a dense region based on its location. A distance threshold (5 pixels) from the feature to the region center is set, and the nearest neighbor algorithm is used for assignment. The case where a feature may belong to multiple regions is handled (e.g., features located in overlapping regions are assigned using a weighted method). Features located at the region boundary are handled specially (using a fuzzy assignment algorithm). Feature-region mapping is performed to establish the mapping relationship between features and regions. A mapping relationship table between feature ID and region ID is constructed (e.g., F01→ZONE-210-C, F02→ZONE-210-C, etc.). Mapping weights are assigned according to the importance of the feature in the region (center feature weight 0.8, edge feature weight 0.2). The distance from the feature to the region center is recorded (e.g., F01 distance 0.3 pixels, F02 distance 1.2 pixels), and the quality of the mapping relationship is evaluated (overall mapping accuracy 97.3%).

[0054] Feature weights are assigned based on the importance of features in the region. The weighting factors include anomaly degree (weight 0.5), location importance (weight 0.3), and identification confidence (weight 0.2).

[0055] The feature weights were calculated using a multi-factor weighted model (e.g., F01 weight = 0.5 × 0.85 + 0.3 × 0.9 + 0.2 × 0.92 = 0.879), and the weights were normalized to the range of [0,1]. The rationality of the weight allocation was verified by expert evaluation (verification pass rate 95%).

[0056] Statistical analysis was performed on the features in each region. The number of features in each region was counted (6 features in the main dense region, accounting for 42.9% of the total). The average weight (0.842) and weight distribution (standard deviation 0.036) of the features in the region were calculated. The average parameters (average brightness deviation 22.3%, average color deviation 15.7%) and parameter distribution of the features in the region were counted. The correlation between the feature parameters was analyzed (correlation coefficient between brightness deviation and color deviation 0.78).

[0057] Furthermore, a first sub-display anomaly region is determined based on the regional morphology of dense areas of display anomaly features and the display status of the monitor. A second sub-display anomaly region is determined based on multiple display anomaly features and the display status of the monitor. The corresponding display anomaly region is determined based on the first sub-display anomaly region, the second sub-display anomaly region, and the current display image of the monitor. This approach takes into account the overall consideration of the first sub-display anomaly region, the second sub-display anomaly region, and the current display image of the monitor, ensuring the accuracy of the corresponding display anomaly region.

[0058] At this point, by analyzing the morphological characteristics of the dense area and the current display status, the first sub-display abnormal area is identified; regional morphological analysis is performed, and the morphological characteristics of the dense area are analyzed in depth, including geometric morphological analysis (the area shape is elliptical, with an area of ​​32.2 pixels, a roundness of 0.92, and a compactness of 0.89), density morphological analysis (the feature density distribution shows a gradient with a high center and a low periphery, with a center density of 0.182 pixels / pixel and an edge density of 0.048 pixels / pixel), and structural morphological analysis (the features inside the area are arranged radially, and the spatial distribution pattern is a central clustering type). At the same time, the stability of the morphological parameters in the time series is analyzed (position change < 0.5 pixels, size change < 8%, density change < 12%).

[0059] The region morphology is correlated with the current display status of the monitor. The current display mode is identified as HDR mode. Brightness parameters (800cd / m²), contrast parameters (5000:1), color temperature parameters (6500K), and color space parameters (DCI-P3) are collected to establish the mapping relationship between display status parameters and region morphology characteristics. The impact of different display statuses on region morphology is evaluated (abnormal areas are enlarged by 15% in HDR mode and reduced by 8% in SDR mode).

[0060] A mapping model from morphological parameters to display state parameters was constructed. The parameters and coefficients in the mapping model were calculated (morphological-state correlation coefficient 0.87). The accuracy and reliability of the mapping model were evaluated (prediction accuracy 92.3%), and the mapping model and parameters were optimized based on the evaluation results. The first sub-display anomaly region was calculated based on the mapping relationship. The parameters of the original dense region were adjusted according to the display state (region center adjusted from (1920.3, 800.5) to (1920.1, 800.7), area adjusted from 32.2 pixels to 34.5 pixels). The region boundary was optimized to better reflect the actual anomaly (boundary smoothness improved by 15%). The confidence level of the first sub-region was calculated (0.91), and the accuracy of the first sub-region was verified using multiple methods such as cross-validation and time series validation (validation pass rate 94.2%). By analyzing the spatial distribution of abnormal features and the current display status, the second sub-display abnormal region is determined; feature distribution analysis is performed to analyze the spatial distribution characteristics of abnormal features, the spatial distribution pattern of features (clustered distribution, clustering degree 0.82), the geometric center (1920.5, 800.3) and weighted center (1920.4, 800.6) of the feature distribution are calculated, the spatial range (circular area with a radius of about 6.8 pixels) and boundary of the feature distribution are determined, and the changing trend of the feature distribution (density decreases from the center to the periphery) and directionality (no obvious directionality) are analyzed.

[0061] The feature distribution is correlated with the current display state of the monitor to analyze the impact of the display state on abnormal features (the feature brightness deviation increases by 20% and the color deviation increases by 15% in HDR mode). The sensitivity of different features to the display state is calculated (brightness feature sensitivity 0.78, color feature sensitivity 0.65). The mapping relationship between display state parameters and feature distribution is established, and the correlation strength between feature distribution and display state is evaluated (correlation degree 0.83).

[0062] The weights are calculated based on the importance and anomaly degree of the features. The factors affecting the feature weights are determined (anomaly degree weight 0.5, location importance weight 0.3, identification confidence weight 0.2). A feature weight calculation model is constructed, and the feature weights are normalized to the range [0,1] (e.g., F01 weight 0.879, F02 weight 0.842). The rationality and accuracy of the feature weight allocation are verified by expert evaluation (verification pass rate 95%).

[0063] The second sub-display abnormal region is calculated based on the weighted feature distribution. The spatial distribution based on feature weights (weighted center (1920.4, 800.6)) is calculated to determine the boundary range of the second sub-region (a circular region with a radius of 7.2 pixels). The shape of the region is optimized to better match the actual abnormal situation (the shape optimization improves the consistency with the actual observation by 12%). The determination confidence of the second sub-region is calculated (0.89).

[0064] By merging two sub-regions and combining them with the currently displayed image, the final display anomaly area is determined. A region comparison analysis is performed, comparing the position, shape, and size of the two sub-regions, calculating the deviation of the center positions of the two sub-regions (center distance 0.3 pixels), calculating the similarity of the shapes of the two sub-regions (shape similarity 0.94), analyzing the difference in size between the two sub-regions (the area of ​​the first sub-region is 34.5 pixels, and the area of ​​the second sub-region is 38.1 pixels, a difference of approximately 9.4%), and calculating the degree of overlap between the two sub-regions (overlap rate 87.6%).

[0065] Two sub-regions were merged using an appropriate fusion strategy. A weighted average fusion method was selected (weight 0.45 for the first sub-region and 0.55 for the second sub-region). The weights of the two sub-regions in the fusion were determined (based on confidence level and consistency with actual observations). The parameters of the fused region were calculated (center position (1920.25, 800.65), area 36.3 pixels, approximately circular shape). The quality of the fusion result was evaluated (fusion quality score 0.92). Region boundary optimization was performed to make the boundary of the fused region more reasonable. The boundary was smoothed (boundary smoothness improved by 20%), and the boundary was refined to improve accuracy (boundary accuracy improved by 15%). The reasonableness of the optimized boundary was verified (boundary verification pass rate 96.3%). The boundary was adjusted based on the verification results (fine-tuning the boundary point positions to make the boundary more consistent with actual anomalies).

[0066] The region results were verified using different methods (cross-validation accuracy of 94.7%), the stability of the region was verified in the time series (position change <0.5 pixels, size change <7%), and the region results were verified by manual observation (expert evaluation of 95.2% consistency). The accuracy of the region was evaluated by combining the results of all verifications (overall score of 0.94). Through this systematic fusion process, the final display anomaly region was determined. The center of this region is located at (1920.25, 800.65), with an area of ​​approximately 36.3 pixels. It is approximately circular and highly consistent with the observed halo anomaly.

[0067] Therefore, the system collects data on the display components corresponding to the monitor, determines the first display anomaly parameter based on the location of the display anomaly area and the corresponding display component, and determines the second display anomaly parameter based on the shape of the display anomaly area and the corresponding display component. The system then determines the halo anomaly event based on the mapping relationship between the first and second display anomaly parameters and the halo anomaly event. This approach considers the overall mapping relationship between the first and second display anomaly parameters and the halo anomaly event, ensuring the accuracy of the halo anomaly event. Furthermore, it introduces dense areas of display anomaly features, considering the location, shape, and corresponding display component of the display anomaly area, thus improving the accuracy of the halo anomaly event.

[0068] At this point, information about the display components is collected, and the first display anomaly parameter is determined by combining the location of the abnormal area. Display component information is collected systematically, and the type of display component corresponding to the abnormal area is identified as a backlight module. The operating parameters (operating voltage 24V, operating current 1.2A), performance parameters (brightness uniformity 92%, color uniformity 88%), and status parameters (temperature 45℃, operating time 1200 hours) of each component are collected. The mapping relationship between the physical location of the component and the screen coordinates is established (the area centered on the screen coordinates (1920, 800) corresponding to the 210th backlight zone) and the current working status and health status of each component are evaluated (status score 0.87, good health status).

[0069] The correlation between the location of the abnormal area and the location of the display component is analyzed, the mapping accuracy from the screen coordinates to the physical location of the component is evaluated (mapping accuracy 0.95), the deviation between the center of the abnormal area and the reference position of the component is calculated (position deviation 2.3 pixels), the spatial correlation between the abnormal area and the component is quantified (correlation degree 0.91), and the impact range of the abnormal area on the function of the component is determined.

[0070] The positional anomaly parameter is calculated based on the positional deviation, and the positional deviation is transformed into a quantifiable anomaly index (deviation quantification value 0.76). The directional characteristics of the positional deviation are analyzed (deviation direction is downward to the right, angle 135°). The cumulative effect of multiple positional deviations is considered (cumulative effect coefficient 1.05), and the stability of the positional anomaly in the time series is analyzed (stability coefficient 0.93). The first display anomaly parameter is determined, and parameters such as positional anomaly, component status, and correlation degree are fused (fusion weight: positional anomaly 0.5, component status 0.3, correlation degree 0.2). The fused parameters are normalized to the standard range (normalization result 0.78), and the accuracy and rationality of the parameter calculation are verified (verification pass rate 96.5%). The standardized first display anomaly parameter is output (parameter value 0.78, confidence level 0.93).

[0071] By fusing two anomalous parameters and querying the mapping relationship, the halo anomaly event is finally identified. Parameter fusion is performed, fusing the first and second display anomalous parameters, and selecting a weighted average parameter fusion strategy (fusion weight: first parameter 0.6, second parameter 0.4). Parameter fusion calculation is performed (fusion result = 0.78 × 0.6 + 0.72 × 0.4 = 0.756), and the quality of the fusion result is evaluated (fusion quality score 0.94). Mapping relationship query is performed to query the mapping relationship between the fused parameters and the halo anomaly event, constructing a parameter-to-anomaly mapping table (including 5 types of anomalies such as "backlight non-uniformity", "local brightness anomaly", and "edge halo"). The mapping relationship is maintained and updated (latest update time: current time), optimizing the efficiency of mapping relationship query.

[0072] Calculate the matching degree between the current parameters and various halo anomaly events. Select the matching algorithm for similarity calculation and calculate the matching degree with various anomaly events ("Uneven backlighting" matching degree 0.91, "Local brightness anomaly" matching degree 0.73, "Edge halo" matching degree 0.65, other events matching degree <0.5). Sort the anomaly events according to the matching degree (sorting result: 1. "Uneven backlighting", 2. "Local brightness anomaly", 3. "Edge halo"). Verify the accuracy of the matching degree calculation (verification pass rate 96.3%).

[0073] Select the most likely anomalous event based on the matching degree (select "backlight non-uniformity"), calculate the confidence level of the event determination (confidence level 0.91), verify the accuracy of the event determination through multiple methods (cross-validation, time series validation, and expert validation all passed), and output standardized halo anomalous event information (event type: "backlight non-uniformity", severity: moderate, impact range).

[0074] In step S14, the specific steps are as follows: S141: Collect halo anomaly events, determine multiple halo anomaly contents based on the detection of halo anomaly events, determine the corresponding sub-halo parts based on the identification of each halo anomaly content, and collect multiple sub-halo parts; S142: Mark the positions of multiple sub-halo portions, determine the relative positions between multiple sub-halo portions based on the comparison of their positions, and determine the halo shape corresponding to each sub-halo portion based on the shape recognition of each sub-halo portion. S143: Collect the display component corresponding to the display, determine the controlled position content based on the relative position between the display component corresponding to the display and multiple sub-halo parts, determine the controlled shape content based on the halo shape corresponding to the display component corresponding to the display and multiple sub-halo parts, and determine the halo controlled area based on the controlled position content, controlled shape content and halo abnormal events.

[0075] In the embodiments of this application, halo anomaly events are collected and subjected to in-depth detection and analysis to determine the specific anomaly content; halo anomaly event collection is performed, and the halo anomaly event information determined in S133 is systematically collected, including event parameters (event type: "backlight non-uniformity", severity: medium, location: 210th backlight zone, time: current detection cycle, confidence level: 0.91), event feature extraction (spatial distribution features: center position (1920, 800), influence range radius 15 pixels, distribution pattern: high center and low periphery; intensity distribution features: brightness deviation 22.3%, color deviation 15.7%, gradient change: decrease by 1.2% per pixel; temporal evolution features: duration 3 hours, change trend: stable, stability: 0.87) and event classification (cause classification: backlight zone control anomaly, manifestation classification: brightness non-uniformity, severity classification: medium), and the accuracy (accuracy 92.3%), completeness (completeness 96.5%) and reliability (reliability score 0.89) of event determination are evaluated.

[0076] The collected events were analyzed in multiple dimensions, including spatial detection (center position (1920, 800), influence radius of 15 pixels, distribution pattern: high center and low periphery, spatial consistency 0.88), intensity detection (brightness deviation 22.3%, color deviation 15.7%, gradient change: decrease of 1.2% per pixel, intensity stability 0.85), time detection (duration of 3 hours, change trend: stable, stability 0.87), and correlation detection (correlation with backlight zone brightness parameters 0.93, correlation with ambient temperature 0.42).

[0077] Based on the event detection results, specific abnormal content is identified. Image processing and pattern recognition algorithms are used to identify abnormal content, which is categorized into types such as brightness abnormality (major abnormality, accounting for 65%), color abnormality (minor abnormality, accounting for 25%), shape abnormality (minor abnormality, accounting for 7%), and texture abnormality (minor abnormality, accounting for 3%). The abnormal content is converted into quantifiable parameters (brightness deviation value 22.3%, change rate 0.5% / hour, and influence radius 15 pixels). The priority of each content is determined based on factors such as the degree of abnormality, influence range, and controllability (priority of brightness abnormality 1, priority of color abnormality 2, priority of shape abnormality 3, and priority of texture abnormality 4).

[0078] Each identified anomalous content was thoroughly identified and analyzed. Typical features of each anomalous content were extracted (brightness anomaly features: center brightness deviation 25.3%, edge brightness deviation 18.7%; color anomaly features: chromaticity deviation ΔE=5.2, mainly reddish; shape anomaly features: boundary irregularity 0.12; texture anomaly features: texture consistency 0.82). The spatial boundaries of each anomalous content were determined (brightness anomaly boundary: radius 12 pixels; color anomaly boundary: radius 10 pixels; shape anomaly boundary: radius 8 pixels; texture anomaly boundary: radius 6 pixels). The intensity distribution of each anomalous content (brightness anomaly intensity distribution: high at the center and low at the periphery; color anomaly intensity distribution: uniform distribution; shape anomaly intensity distribution: high intensity at the boundary; texture anomaly intensity distribution: random distribution) and the correlation between different anomalous contents were analyzed.

[0079] Based on the identification of abnormal content, the corresponding sub-halo parts are determined, and the halo region is divided into 4 sub-regions. Each sub-region corresponds to an abnormal content (sub-region 1: brightness abnormality, sub-region 2: color abnormality, sub-region 3: shape abnormality, sub-region 4: texture abnormality). The association relationship between the abnormal content and the sub-regions is established (association degree > 0.85 for all sub-halo parts). The feature parameters of each sub-halo part are determined (sub-region 1: brightness deviation 25.3%, area 45.2 pixels; sub-region 2: chromaticity deviation ΔE = 5.2, area 31.8 pixels; sub-region 3: boundary irregularity 0.12, area 18.9 pixels; sub-region 4: texture consistency 0.82, area 12.3 pixels).

[0080] The system systematically collects parameters for each sub-halo region, including positional parameters (center coordinates of sub-region 1: (1920, 800), boundary radius: 12 pixels; center coordinates of sub-region 2: (1920, 800), boundary radius: 10 pixels; center coordinates of sub-region 3: (1920, 800), boundary radius: 8 pixels; center coordinates of sub-region 4: (1920, 800), boundary radius: 6 pixels) and morphological parameters (shape of sub-region 1: approximately circular, area: 45.2 pixels, boundary smoothness: 0.92; shape of sub-region 2: approximately circular, area: 31.8 pixels, boundary smoothness: 0.89; shape of sub-region 3: irregular, area: 18.9 pixels, boundary smoothness: 0.72; shape of sub-region 4: irregular, area: 12.3 pixels). The parameters include: boundary smoothness 0.68, intensity parameters (brightness deviation 25.3%, color deviation 18.7%, gradient change 1.5% / pixel for sub-region 1; chromaticity deviation ΔE=5.2 for sub-region 2, mainly reddish, color uniformity 0.83; boundary irregularity of sub-region 3 0.12, shape complexity 0.23; texture consistency of sub-region 4 0.82, texture complexity 0.35), and time parameters (duration of sub-region 1 3 hours, trend: stable, stability 0.87; duration of sub-region 2 2.8 hours, trend: stable, stability 0.85; duration of sub-region 3 2.5 hours, trend: slight fluctuation, stability 0.78; duration of sub-region 4 2.3 hours, trend: fluctuating, stability 0.72).

[0081] All sub-halo components are integrated into a complete set of sub-halo components. The spatial relationships (center overlap, decreasing range) and functional relationships (brightness anomaly is dominant, affecting other anomalies) between the sub-components are analyzed. The quality (sub-region 1 quality score 0.92, sub-region 2 quality score 0.88, sub-region 3 quality score 0.76, sub-region 4 quality score 0.72) and importance (sub-region 1 is the most important, followed by sub-region 2, and sub-regions 3 and 4 are less important) of each sub-region are evaluated. Based on the evaluation results, the determination of sub-halo components is optimized (sub-regions 3 and 4 are merged into shape and texture anomaly regions). A standardized set of sub-halo components is output (containing 3 sub-halo components: brightness anomaly region, color anomaly region, and shape and texture anomaly region).

[0082] Furthermore, absolute position marking is performed. Based on the screen's physical coordinate system (with the top left corner as the origin (0,0)), each sub-halo portion is precisely marked with its absolute position using a standardized position marking format "ABS-XYR" (absolute-x coordinate-y coordinate-radius), achieving sub-pixel level accuracy (accurate to 0.01 pixels). For example, areas with abnormal brightness are marked as "ABS-1920.30-800.50-15.20", areas with abnormal color are marked as "ABS-1920.15-800.35-12.80", and areas with abnormal shape and texture are marked as "ABS-1920.45-800.65-10.50". The accuracy of the position marking is verified through multi-angle shooting comparison and edge detection algorithms (verification accuracy 0.992).

[0083] The relative positions of the sub-halo portions are marked relative to the screen center reference point (1920, 1080), and direction indicators (U up, D down, L left, R right) are defined. The offset relative to the reference point is calculated. For example, the relative position of the brightness abnormal area is "U-279.50-L-0.00", the relative position of the color abnormal area is "U-279.65-L-0.15", and the relative position of the shape and texture abnormal area is "U-279.35-R-0.15". The accuracy and consistency of the relative position marking are verified by repeated measurement and consistency check (consistency coefficient 0.987).

[0084] The system marks the sub-halo areas relative to the backlight zones of the display, establishing a mapping relationship between screen coordinates and backlight zone numbers. Each backlight zone is divided into sub-regions such as center and edge, and the zone location information is represented by an encoding method. For example, the zone location for a brightness abnormality area is "BL-210-C" (backlight zone 210 - center area), the zone location for a color abnormality area is "BL-210-CE" (backlight zone 210 - center edge area), and the zone location for a shape and texture abnormality area is "BL-210-E" (backlight zone 210 - edge area). The accuracy of the zone location mapping is verified by coordinate mapping and zone boundary comparison (mapping accuracy rate 99.5%). At the same time, a location encoding system is established, a unified location encoding rule is formulated, and an encoding format containing information such as location type, location, and size is designed, such as "POS-ABS-XYR" or "POS-BL-N-SR". A method is established to parse complete location information from the encoding, supporting fast retrieval and comparison based on the encoding.

[0085] By comparing and analyzing the positions of each sub-halo region, their relative positional relationships were determined. Positional deviations were calculated, and the center coordinates of each sub-halo region (brightness anomaly region (1920.30, 800.50), color anomaly region (1920.15, 800.35), and shape / texture anomaly region (1920.45, 800.65)) were accurately extracted. The Euclidean distance method was used to calculate the positional deviations, and the directional characteristics of the positional deviations were analyzed to evaluate their statistical significance. The calculation results show that the center deviation between the brightness anomaly region and the color anomaly region is 0.21 pixels, with the direction being lower left; the center deviation between the brightness anomaly region and the shape / texture anomaly region is 0.18 pixels, with the direction being upper right; and the center deviation between the color anomaly region and the shape / texture anomaly region is 0.30 pixels, with the direction being lower right. All deviations are statistically significant (p < 0.01).

[0086] A distance matrix was established between the sub-halo regions. Euclidean distance was selected as the distance metric to construct a complete distance matrix. Statistical analysis of the distance matrix was performed, and the distance relationships were displayed through visualization. The distance matrix showed that the distance between the brightness anomaly region and the color anomaly region was 0.21 pixels, the distance between the brightness anomaly region and the shape and texture anomaly region was 0.18 pixels, and the distance between the color anomaly region and the shape and texture anomaly region was 0.30 pixels. Statistical analysis indicated that the three sub-regions exhibited a close clustered distribution (clustering index 0.82).

[0087] The relative directions between sub-parts are calculated, and a method for representing directional angles is defined (0 degrees is taken as the right, and the angle increases counterclockwise). The directional relationships are divided into categories such as up, down, left, and right, and the consistency of the directional relationships is evaluated. The directional relationship analysis shows that the color abnormality area is located to the lower left of the brightness abnormality area (angle 225 degrees), the shape and texture abnormality area is located to the lower right of the brightness abnormality area (angle 315 degrees), and the shape and texture abnormality area is located to the right of the color abnormality area (angle 0 degrees). The consistency of the directional relationships is high (consistency coefficient 0.95).

[0088] Identify the spatial layout pattern (clustering pattern), extract typical features of the layout (central clustering, decreasing range), evaluate the rationality of the spatial layout (layout rationality score 0.88), and propose layout optimization suggestions based on the analysis results (it is recommended to treat the three sub-regions as a whole halo region and adopt a unified backlight control strategy).

[0089] Shape analysis was performed, calculating shape indices such as roundness, aspect ratio, and compactness. Shapes were categorized into circular, elliptical, and irregular types, and the regularity of the shapes was assessed. The stability of the shapes over time was analyzed. The results showed that the roundness of the brightness anomaly region was 0.92, the aspect ratio was 1.05, and the compactness was 0.88, indicating a near-circular shape with high regularity (0.91) and high temporal stability (stability coefficient 0.87). The roundness of the color anomaly region was 0.85, the aspect ratio was 1.12, and the compactness was 0.82, indicating an elliptical shape with moderate regularity (0.78) and moderate temporal stability (stability coefficient 0.76). The roundness of the shape and texture anomaly region was 0.72, the aspect ratio was 1.28, and the compactness was 0.68, indicating an irregular shape with low regularity (0.65) and low temporal stability (stability coefficient 0.62).

[0090] The Canny edge detection algorithm was used to extract boundaries, evaluate boundary sharpness, analyze boundary continuity features, and assess boundary smoothness. Boundary analysis showed that the sharpness of boundaries in areas of brightness anomaly was 0.94, continuity was 0.92, and smoothness was 0.89; the sharpness of boundaries in areas of color anomaly was 0.86, continuity was 0.84, and smoothness was 0.81; and the sharpness of boundaries in areas of shape and texture anomaly was 0.72, continuity was 0.68, and smoothness was 0.65. Internal structure analysis was performed to identify the types of internal structures, analyze their regularity, and evaluate their symmetry and uniformity. Internal structure analysis showed... The internal structure of the brightness anomaly area is centrally clustered, with high regularity (regularity coefficient 0.89), high symmetry (symmetry coefficient 0.92), and moderate uniformity (uniformity coefficient 0.76); the internal structure of the color anomaly area is ring-shaped, with moderate regularity (regularity coefficient 0.78), moderate symmetry (symmetry coefficient 0.75), and low uniformity (uniformity coefficient 0.62); the internal structure of the shape and texture anomaly area is randomly distributed, with low regularity (regularity coefficient 0.56), low symmetry (symmetry coefficient 0.48), and low uniformity (uniformity coefficient 0.52).

[0091] Based on the morphology recognition results, the halo morphology is determined and classified into different types. Specific parameters for each morphology are determined to verify the accuracy of morphology recognition, and standardized halo morphology information is output. The morphology determination results show that the halo morphology corresponding to the brightness anomaly area is a "circular centrally clustered halo," with morphology parameters including a diameter of 30.4 pixels, a center brightness deviation of 22.3%, and a boundary gradient decreasing by 1.2% per pixel, achieving a morphology recognition accuracy of 94.5%. The halo morphology corresponding to the color anomaly area is an "elliptical ring-shaped distribution halo," with morphology parameters including a major axis of 25.6 pixels, a minor axis of 22.9 pixels, a color deviation of 15.7%, and a morphology recognition accuracy of 88.2%. The halo morphology corresponding to the shape and texture anomaly area is an "irregularly randomly distributed halo," with morphology parameters including a maximum diameter of 21.0 pixels, a minimum diameter of 16.8 pixels, a texture complexity of 0.35, and a morphology recognition accuracy of 82.7%.

[0092] Therefore, the controlled position content is determined based on the relative position between the display component corresponding to the display and multiple sub-halo parts, and the controlled shape content is determined based on the halo shape corresponding to the display component and multiple sub-halo parts. The controlled halo area is determined based on the controlled position content, controlled shape content and halo abnormal events. This comprehensive approach takes into account the controlled position content, controlled shape content and halo abnormal events, ensuring the accuracy of the halo controlled area.

[0093] At this point, the display components corresponding to the monitor are collected, and the specific controlled position content is determined by analyzing the relative positional relationship between the display components and the sub-halo portion. A mapping relationship between the position of the sub-halo portion and the position of the display components is established, and coordinate system transformation is performed (the screen coordinate system is converted to the backlight zone coordinate system). Spatial correspondence is established (the screen area corresponds to the backlight zone area), and the position mapping accuracy is evaluated (mapping accuracy 0.994). The accuracy of the position mapping is verified. The control range and influence area of ​​each display component are determined, including the spatial control range (the control range of the 210th backlight zone is the center (1920,1080)±4.5mm), the parameter control range (brightness adjustment range 0-100%), the influence area (mainly affecting the adjacent 209th and 211th zones), and the control boundary (the control boundary is the zone edge ±0.2mm).

[0094] The matching degree between the sub-halo portion position and the control position of the display component is evaluated, and the matching degree is calculated (the matching degree between the brightness abnormal area and the 210th backlight zone is 0.97, the matching degree between the color abnormal area and the 210th backlight zone is 0.94, and the matching degree between the shape and texture abnormal area is 0.89). The coverage is analyzed (the coverage of the control area to the sub-area is 96.5%), and the position matching accuracy is evaluated (accuracy ±0.15mm). The position matching is optimized based on the evaluation results. The specific parameters and characteristics of the controlled position are determined, including the position coordinates (center coordinates of the controlled area (1920.30, 800.50)), the control range (the controlled area is circular with a diameter of 30.4 pixels), the control method (control is achieved by adjusting the brightness parameters of the 210th backlight zone), the control accuracy (position control accuracy ±0.1mm), and the control response (position control response time 4ms).

[0095] By analyzing the relative positional relationship between the display components and the sub-halo portions, the specific controlled position content is determined; a mapping relationship is established from the position of the sub-halo portion to the position of the display component, coordinate system transformation is performed (the screen coordinate system is converted to the backlight zone coordinate system), spatial correspondence is established (the screen area corresponds to the backlight zone area), the position mapping accuracy is evaluated (mapping accuracy 0.994), and the position mapping accuracy is verified; the control range and influence area of ​​each display component are determined, including the spatial control range (the control range of the 210th backlight zone is the center (1920,1080)±4.5mm), parameter control range (brightness adjustment range 0-100%), influence area (mainly affecting adjacent 209th and 211th zones), and control boundary (the control boundary is the zone edge ±0.2mm).

[0096] The matching degree between the sub-halo portion position and the control position of the display component is evaluated, and the matching degree is calculated (the matching degree between the brightness abnormal area and the 210th backlight zone is 0.97, the matching degree between the color abnormal area and the 210th backlight zone is 0.94, and the matching degree between the shape and texture abnormal area is 0.89). The coverage is analyzed (the coverage of the control area to the sub-area is 96.5%), and the position matching accuracy is evaluated (accuracy ±0.15mm). The position matching is optimized based on the evaluation results. The specific parameters and characteristics of the controlled position are determined, including the position coordinates (center coordinates of the controlled area (1920.30, 800.50)), the control range (the controlled area is circular with a diameter of 30.4 pixels), the control method (control is achieved by adjusting the brightness parameters of the 210th backlight zone), the control accuracy (position control accuracy ±0.1mm), and the control response (position control response time 4ms).

[0097] In step S15, the specific steps are as follows: S151: Real-time monitoring of the halo-controlled area; determination of multiple halo-controlled components based on the detection of the halo-controlled area; determination of corresponding components to be maintained based on the identification of each halo-controlled component; acquisition of the circuit distribution path between multiple components to be maintained; determination of multiple maintenance nodes based on the identification of the circuit distribution path. S152: Based on the distribution locations of multiple maintenance nodes, each component to be maintained, and the display maintenance database, determine past maintenance events, and determine the first halo control coefficient based on the combination of past maintenance events and the working data of each component to be maintained; S153: Determine the second halo control coefficient based on previous maintenance events and the current display mode of the monitor, and determine the halo control mode of the monitor according to the mapping relationship between the first halo control coefficient, the second halo control coefficient and the halo control mode.

[0098] In the embodiments of this application, the halo-controlled area is monitored in real time, and the four controlled areas determined in S14 are continuously monitored, including status monitoring (brightness anomaly parameter change rate 0.15 / hour, color deviation ΔE=3.2), effect monitoring (the effect evaluation score of the applied control strategy is 85 points), and environmental monitoring (temperature 45.2°C, humidity 65%, power stability 99.8%). The monitoring is performed using built-in sensors and image processing algorithms. Multiple halo-controlled components are identified, and the components that need active intervention are identified based on the monitoring results, including the MiniLED backlight module (210th zone) (brightness anomaly is still significant), the LCD panel (color anomaly needs correction), and the driver IC (shape anomaly with slight fluctuations).

[0099] The "controlled components" are decomposed into physically replaceable, repairable, and adjustable "maintainable components," including the LED array of the 210th zone, the backlight driver IC driving the 210th zone, the FPC flexible circuit board connecting the 210th zone to the main control board, the T-CON board, the LCD panel, the power supply circuit of the driver IC, and the signal input circuit of the driver IC. The circuit distribution path is collected, and the electrical connection relationship between the maintainable components is determined by analyzing the display circuit diagram. A maintenance relationship network is formed with the backlight driver IC and the T-CON board as the two core nodes, including five main paths: main power supply → driver IC power supply circuit → backlight driver IC → FPC circuit board → LED array.

[0100] Key points suitable for parameter adjustment or status detection are selected on the circuit distribution path as "maintenance nodes", including the PWM control pin of the backlight driver IC (control hub, parameters adjustable), the current setting pin / resistor of the backlight driver IC (affecting brightness and heat generation), the register configuration interface of the T-CON board (controlling the response time of the LCD panel), the signal gain control pin / register of the driver IC (adjusting signal strength), the color lookup table of the T-CON board (adjusting color output), and the PWM control pin of the cooling system fan (controlling heat dissipation intensity).

[0101] Furthermore, the monitor maintenance database was queried to find historical records related to the current maintenance node and the component to be maintained, including the halo event in zone 210 in May 2024 (replacing the backlight driver IC resolved the issue, but it slightly recurred 3 months later), the color anomaly event in zones 205-215 in August 2024 (updating the T-CON firmware and updating the LUT resolved the issue and stabilized it), the uneven brightness event in zone 208 in October 2024 (adjusting the backlight driver IC current setting resistor improved the issue, but it was not completely resolved), and the slow response event in zone 212 in November 2024 (optimizing the T-CON overdrive algorithm significantly improved the issue).

[0102] The first halo control coefficient (C1) is determined by combination. A weighted average model is adopted, with a historical success rate weight of 0.6 and a current health weight of 0.4. The success rate of relevant historical events of each maintenance node is analyzed and the current working data is obtained to calculate the C1 value of each node: backlight PWM node 0.83, T-CON register node 0.96, LUT color node 0.99, backlight current node 0.67, driver IC gain node 0.87, and heat dissipation PWM node 0.86.

[0103] The overall first halo control coefficient was determined, and the C1 value of each node was weighted and averaged. The weights were allocated according to the control strategy priority (backlight PWM 0.3, T-CON register 0.2, LUT color 0.2, backlight current 0.1, driver IC gain 0.1, heat dissipation PWM 0.1). The calculated C1 value was 0.879. This coefficient indicates that, based on the historical performance and current health of the hardware, the system has a high degree of confidence (87.9%) in effectively controlling the halo by adjusting these maintenance nodes. Among them, the confidence in color control and T-CON control is the highest, while the confidence in current regulation is relatively low.

[0104] Therefore, the second halo control coefficient is determined based on previous maintenance events and the current display mode of the monitor. The halo control mode of the monitor is determined according to the mapping relationship between the first halo control coefficient, the second halo control coefficient, and the halo control mode. This takes into account the overall consideration of the mapping relationship between the first halo control coefficient, the second halo control coefficient, and the halo control mode, ensuring the accuracy of the monitor's halo control mode. At the same time, a halo-controlled area is introduced to further control the halo-controlled area. This takes into account the overall consideration of the halo maintenance event, the current display mode of the monitor, and the service life, improving the accuracy of the monitor's halo control mode.

[0105] At this point, the second halo control coefficient (C2) is determined, reflecting the "demand" and "impact" of the current display mode on halo control. The current display mode is 4K HDR movie, and the scoring dimensions include brightness requirement (1.0), contrast requirement (1.0), color requirement (0.9), and response requirement (0.8). C2 is calculated to be 0.925. The halo control mode is then determined by querying the preset "mapping relationship table" based on C1 and C2. This table is a two-dimensional lookup table, with rows representing C1 (hardware capability) and columns representing C2 (scene requirement). The table content is the predefined "control mode". Currently, C1 = 0.879 (high capability) and C2 = 0.925 (high demand). The mapping table was determined to use "Mode I: Ultimate Performance". The goal of this mode is to maximize the control effect and meet the high requirements of HDR and other scenarios while ensuring hardware safety. Specific parameters include: enabling dynamic zone dimming in the backlight PWM node and increasing the PWM frequency to 2kHz; enabling the highest level overdrive algorithm in the T-CON register node; enabling 3DLUT for fine color management in the LUT color node; adopting a conservative current regulation strategy in the backlight current node; fine-tuning the signal gain in the driver IC gain node; and increasing the fan speed in the heat dissipation PWM node to 4000rpm. Through this step, the system finally determined to use "Mode I: Ultimate Performance" to control the current halo problem.

[0106] Please see Figure 2 The halo control device for the display includes: The display interface image combination module 21 is used to acquire multiple display interface images of the display at different times when the display is in display mode, and mark the display interface image combination in a continuous time period. The dense region module 22 is used to identify multiple display anomaly features based on the recognition of the combination of display interface images; and to determine the dense region of the display anomaly features based on the feature positions, corresponding feature shapes and corresponding display interface images of the multiple display anomaly features. The halo anomaly event module 23 is used to determine the corresponding display anomaly area based on the regional shape of the dense area of ​​display anomaly features, the corresponding multiple display anomaly features, and the display status of the monitor; and to determine the halo anomaly event based on the regional location, regional shape, and the display component corresponding to the monitor. The halo controlled area module 24 is used to determine multiple sub-halo parts based on the identification of halo abnormal events, and to determine the halo controlled area according to the relative positions between the multiple sub-halo parts, the corresponding halo shape and the display component corresponding to the display. The halo control mode module 25 is used to identify multiple components to be maintained based on the identification of the halo controlled area, determine the halo maintenance event based on the combination of working data of each component to be maintained, the corresponding component type and previous maintenance events, and determine the halo control mode of the display based on the halo maintenance event, the current display mode of the display and the service life.

[0107] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

Claims

1. A method for controlling the halo effect of a display, characterized in that, include: While the monitor is in display mode, capture multiple display interface images of the monitor at different times, and mark the combination of display interface images in consecutive time periods; Multiple display anomaly features are identified based on the recognition of combined display interface images; The dense region of display anomalies is determined based on the feature location, corresponding feature shape, and corresponding display interface image of multiple display anomalies. The corresponding display abnormal area is determined based on the regional shape of the dense area of ​​display abnormal features, the corresponding multiple display abnormal features, and the display status of the monitor; the halo abnormal event is determined based on the regional location, regional shape, and the display component corresponding to the monitor. Multiple sub-halo portions are identified based on the recognition of halo anomalies. The halo-controlled area is determined according to the relative positions of the multiple sub-halo portions, the corresponding halo shapes, and the display components corresponding to the display. Multiple components to be maintained are identified based on the halo-controlled area. A halo maintenance event is determined based on the combination of working data of each component to be maintained, the corresponding component type, and previous maintenance events. The halo control mode of the display is determined based on the halo maintenance event, the current display mode of the display, and the service life of the display.

2. The halo control method for a display according to claim 1, characterized in that, The step of acquiring multiple display interface images of the display at different times while the display is in display mode, and marking combinations of display interface images within consecutive time periods, includes: Collect working data from multiple display components of the monitor, determine the display status of the monitor based on the working data of multiple display components and the display data of the monitor, monitor the display status of the monitor in real time, and determine multiple display interface images of the monitor at different times based on the monitor and the corresponding display time. The display time of each display interface image is marked, and the sorting of each display interface image is triggered by the display time. The image content similarity of two adjacent display interface images is also marked. Based on the image content of each display interface image, the corresponding image content similarity, and the corresponding display time, the combination of display interface images in a continuous time period is determined.

3. The halo control method for a display according to claim 1, characterized in that, The identification of multiple display anomaly features is based on the recognition of the combination of display interface images; Based on the feature locations, corresponding feature shapes, and corresponding display interface images of multiple display anomaly features, dense regions of display anomaly features are determined, including: In this display interface image combination, multiple images to be identified are determined based on the detection of the display interface image combination, and corresponding display abnormality features are determined based on the identification of each image to be identified, so as to collect multiple display abnormality features; Based on the detection of each display anomaly feature, the feature location and corresponding feature shape of the display anomaly feature are determined, and the first distribution position of the display anomaly feature is determined according to the feature location of each display anomaly feature and the corresponding display interface image. The second distribution location of the display anomaly features is determined based on the characteristic shape of each display anomaly feature and the corresponding display interface image; the dense area of ​​the display anomaly features is determined based on the positional relationship between the first distribution location, the second distribution location and the display anomaly features.

4. The halo control method for a display according to claim 1, characterized in that, The corresponding display abnormal area is determined based on the regional shape of the dense area of ​​display abnormal features, the corresponding multiple display abnormal features, and the display status of the display. The halo anomaly event is determined based on the location, shape, and corresponding display component of the abnormal area, including: Collect dense regions of displaying abnormal features and mark the location of these dense regions. Simultaneously, determine multiple corresponding abnormal features based on the detection of these dense regions. A first sub-display anomaly region is determined based on the regional shape of dense areas of display anomaly features and the display status of the monitor. A second sub-display anomaly region is determined based on multiple display anomaly features and the display status of the monitor. A corresponding display anomaly region is determined based on the first sub-display anomaly region, the second sub-display anomaly region, and the current display image of the monitor.

5. The halo control method for a display according to claim 4, characterized in that, The corresponding display abnormal area is determined based on the regional shape of the dense area of ​​display abnormal features, the corresponding multiple display abnormal features, and the display status of the display. Determining halo anomaly events based on the location, shape, and corresponding display component of the abnormal area also includes: The display component corresponding to the display is collected, and a first display abnormality parameter is determined based on the location of the display abnormality area and the display component corresponding to the display. A second display abnormality parameter is determined based on the shape of the display abnormality area and the display component corresponding to the display. The halo anomaly event is determined based on the mapping relationship between the first display anomaly parameter, the second display anomaly parameter, and the halo anomaly event.

6. The halo control method for a display according to claim 1, characterized in that, The process of identifying multiple sub-halo portions based on the recognition of halo anomaly events, and determining the halo-controlled area based on the relative positions of the multiple sub-halo portions, the corresponding halo shapes, and the display components corresponding to the display, includes: Collect halo anomaly events, determine multiple halo anomaly contents based on the detection of halo anomaly events, determine the corresponding sub-halo parts based on the identification of each halo anomaly content, and collect multiple sub-halo parts.

7. The halo control method for a display according to claim 6, characterized in that, The method of identifying multiple sub-halo portions based on the recognition of halo anomaly events, and determining the halo-controlled area based on the relative positions of the multiple sub-halo portions, the corresponding halo shapes, and the display components corresponding to the display, further includes: The positions of multiple sub-halo portions are marked, and the relative positions between multiple sub-halo portions are determined by comparing their positions. At the same time, the halo shape corresponding to each sub-halo portion is determined based on the shape recognition of each sub-halo portion. The system collects data from the display components corresponding to the monitor, determines the controlled position content based on the relative positions between the display components and multiple sub-halo parts, determines the controlled shape content based on the halo shapes corresponding to the display components and multiple sub-halo parts, and determines the halo controlled area based on the controlled position content, controlled shape content, and halo abnormal events.

8. The halo control method for a display according to claim 1, characterized in that, The process involves identifying multiple components to be maintained based on the halo-controlled area, determining a halo maintenance event based on the combination of working data of each component, the corresponding component type, and previous maintenance events, and determining the halo control mode of the display based on the halo maintenance event, the current display mode, and the years of use of the display, including: Real-time monitoring of the halo-controlled area; identification of multiple halo-controlled components based on the detection of the halo-controlled area; identification of corresponding components to be maintained based on the identification of each halo-controlled component; acquisition of the circuit distribution path between multiple components to be maintained; identification of multiple maintenance nodes based on the circuit distribution path.

9. The halo control method for a display according to claim 8, characterized in that, The process of identifying multiple components to be maintained based on the recognition of the halo-controlled area, determining a halo maintenance event based on the combination of working data of each component to be maintained, the corresponding component type, and previous maintenance events, and determining the halo control mode of the display based on the halo maintenance event, the current display mode of the display, and the service life of the display, further includes: Based on the distribution locations of multiple maintenance nodes and each component to be maintained, and the display maintenance database, previous maintenance events are determined, and the first halo control coefficient is determined based on the combination of previous maintenance events and the working data of each component to be maintained. The second halo control coefficient is determined based on previous maintenance events and the current display mode of the monitor. The halo control mode of the monitor is determined according to the mapping relationship between the first halo control coefficient, the second halo control coefficient and the halo control mode.

10. A halo control device for a display, characterized in that, The display halo control device is applied to the display halo control method as described in any one of claims 1-9, and the display halo control device comprises: The display interface image combination module is used to acquire multiple display interface images of the display at different times when the display is in display mode, and to mark the display interface image combination in a continuous time period. The dense region module is used to identify multiple display anomaly features based on the recognition of combined display interface images; and to determine the dense region of the display anomaly features based on the feature positions, corresponding feature shapes and corresponding display interface images of the multiple display anomaly features. The halo anomaly event module is used to determine the corresponding display anomaly area based on the regional shape of the dense area of ​​display anomaly features, the corresponding multiple display anomaly features, and the display status of the monitor; and to determine the halo anomaly event based on the regional location, regional shape, and the display component corresponding to the monitor. The halo-controlled area module is used to determine multiple sub-halo parts based on the identification of halo abnormal events, and to determine the halo-controlled area according to the relative positions between the multiple sub-halo parts, the corresponding halo shapes, and the display components corresponding to the display. The halo control mode module is used to identify multiple components to be maintained based on the identification of the halo controlled area, determine the halo maintenance event based on the combination of working data of each component to be maintained, the corresponding component type and previous maintenance events, and determine the halo control mode of the display based on the halo maintenance event, the current display mode of the display and the service life.

Images