Monitor Self-Monitoring Method, System, and Monitor

Abstract

The present invention relates to the field of display control technology, specifically a method and system for self-monitoring of a display and a display, including the following steps: collecting light intensity data and comparing it with a preset standard to classify ambient light as low light, strong light or reflected light, determining whether the difference between the real-time color temperature and the standard color temperature exceeds a threshold value, and if it exceeds, adjusting the color temperature and brightness; extracting the RGB channel ratio of pixels frame by frame and generating a color gamut distribution map; recording the time characteristics of repair instructions and statistically analyzing the color temperature adjustment trend to generate a backlight gradient compensation mode. In the present invention, by monitoring the light intensity around the display in real time and comparing it with the preset lighting conditions, the ambient light situation is automatically classified, enabling the display to adjust the color temperature and brightness according to different lighting environments. By analyzing the coverage areas of the red, green, and blue channels in the display frame frame by frame, the backlight drive of the display is adjusted in a timely manner, improving the adaptability and long-term stability of the display under changing lighting conditions and providing a more optimized visual experience for users.

Description

Technical Field

[0001] The present invention relates to the technical field of display control, and particularly to a method and system for self-monitoring of a display and a display device. Background Art

[0002] The technical field of display control encompasses various technologies for displays, televisions, and visual display devices. The core content of this technical field involves the control and optimization of the output of a display to ensure that the display device can clearly, stably, and accurately present images or information. The technical applications of display control are extensive, covering multiple aspects including screen display adjustment, color management, brightness and contrast adjustment, resolution adaptation, etc. With the continuous progress of display technology, the display control field also involves more complex adjustment methods, such as intelligent brightness adjustment and self-calibration of displays. The research and applications in this field are not limited to consumer electronics, but also extend to medical, industrial control, transportation, and industries that require high-precision displays.

[0003] Among them, the method for self-monitoring of a display refers to the way of real-time monitoring and self-detection of the state of the display to ensure the performance stability of the display during use. The technical matters covered by this method include the automatic monitoring of parameters such as the brightness, color, contrast, and resolution of the display. Specifically, the parameters are collected through internal sensors, and the system will detect the state of the display. If an abnormality is found, it will make timely adjustments or remind the user to handle it. This patented method realizes the continuous monitoring and automatic optimization adjustment of the display state through a built-in monitoring mechanism combined with specific control means to ensure the display quality and reliability of the display during operation.

[0004] Although the existing technologies are widely applied to various display devices, they still show deficiencies in automatic monitoring and responding to environmental changes. Especially in dealing with complex lighting environments such as low light, strong light, and reflected light, the existing technologies cannot accurately adjust the color temperature and brightness, resulting in a gap between the display effect and the actual viewing experience. This technical limitation leads to insufficient display accuracy in medical and industrial applications, affecting the accuracy of decision-making and operations. In addition, the lack of a function for dynamic gamut adjustment also makes the color reproduction less realistic, affecting the overall quality of the image. Due to technical limitations, users need to frequently manually adjust the display settings, increasing the operation complexity and inconvenience of use. Summary of the Invention

[0005] In view of the deficiencies of the existing technologies, the purpose of the present invention is to provide a method and system for self-monitoring of a display and a display device.

[0006] To achieve the above purpose, the technical solution adopted by the present invention is as follows:

[0007] The present invention provides a method for self-monitoring of a display, including the following steps:

[0008] S1: Collect the light intensity data around the display, compare the continuously collected light intensity data item by item with the low light, strong light, and reflected light conditions in the preset standard, and classify the real-time ambient light into low light, strong light, or reflected light categories according to the comparison differences, generating ambient light classification features;

[0009] S2: Based on the ambient light classification features, call the real-time color temperature value of the display and perform a difference operation with the preset standard color temperature value, determine whether the color temperature difference exceeds the corresponding item among the low light threshold, strong light threshold, and reflected light threshold. If it exceeds, synchronously adjust the color temperature and brightness to the preset standard range, generating a dynamic color calibration state;

[0010] S3: Utilize the dynamic color calibration state, extract the distribution ratios of the red, green, and blue channels for each pixel point of the real-time display frame of the display, statistically calculate the proportion of the coverage area of multiple channels in the display area, analyze the overlapping ratio of the coverage area ratio with the standard color gamut, and obtain a color gamut distribution map;

[0011] S4: According to the proportion of the coverage area of the red, green, and blue channels in the color gamut distribution map, calculate the deviation amounts of the overlapping ratios of multiple channels with the standard color gamut respectively. When the channel deviation amount exceeds the corresponding repair trigger threshold, adjust the hue and saturation of the display, generating a dynamic repair instruction.

[0012] As a further solution of the present invention, the ambient light classification features include illumination level, deviation range, and ambient matching degree. The dynamic color calibration state includes color temperature correction amplitude, brightness calibration amplitude, and synchronous adjustment flag. The color gamut distribution map includes color gamut coverage rate, channel saturation, and color gamut offset amount. The dynamic repair instruction includes hue compensation instruction, saturation correction instruction, and channel equalization instruction.

[0013] As a further solution of the present invention, the acquisition steps of the ambient light classification features are specifically as follows:

[0014] S101: Collect the light intensity data around the display, record the light intensity value at each moment, sort the continuously recorded light intensity values according to the sampling time interval to construct a light intensity data sequence, and call the light intensity values in the continuous time period in the sequence to compare with the threshold intervals in the standard classification values item by item, generating a light intensity interval offset difference;

[0015] S102: Based on the light intensity interval offset difference, judge the offset difference between each light intensity value and the threshold interval in the standard classification value, and record the real-time light intensity with the classification label corresponding to the offset difference, generating an ambient light classification frequency ratio;

[0016] S103: Invoke the ambient light classification frequency ratio, compare the differences in the three types of ambient light frequency ratios within each time window, mark the category of the ratio difference value as the dominant light type of the time period, summarize the dominant light type marks, identify the dominant light classification trend in the continuous environment, and generate ambient light classification features.

[0017] As a further solution of the present invention, the step of obtaining the dynamic color calibration state is specifically as follows:

[0018] S201: Based on the ambient light classification features, within the time period of the dominant light type, invoke the real-time color temperature value of the display and the preset standard color temperature value within the corresponding time period, calculate the color temperature difference value between the two within the time period respectively, and generate a time period color temperature difference value range;

[0019] The formula for calculating the color temperature difference value between the two within the time period is as follows:

[0020] ;

[0021] where ΔT is the dynamic color temperature difference value, AT is the real-time color temperature measurement value of the display within the time period, BT is the preset standard color temperature value within the time period corresponding to the dominant light type, t is the time stamp of the real-time time point, τ is the time decay coefficient, Δt j is the time interval between adjacent time points j, and m is the total number of time points;

[0022] S202: Invoke the time period color temperature difference value range, for the color temperature difference value corresponding to the real-time category in the ambient light classification features respectively, perform a difference judgment on the color temperature difference value with the low light color temperature threshold, strong light color temperature threshold or reflected light color temperature threshold set for the category, record the color temperature difference value points exceeding the threshold, and generate an abnormal color temperature deviation position index;

[0023] S203: According to the abnormal color temperature deviation position index, extract the real-time color temperature value and brightness value of the display within the corresponding time period, perform a difference comparison with the corresponding preset standard color temperature range and brightness range within the time period, and adjust the color temperature value and brightness value according to the preset adjustment parameters for the comparison difference items exceeding the limit to obtain a calibrated color temperature and brightness adjustment record;

[0024] S204: Invoke the calibrated color temperature and brightness adjustment record, fill it into the original color temperature and brightness data in chronological order, judge the calibration execution range by comparing the front and back difference trends, and mark the calibration state label sequence to generate a dynamic color calibration state.

[0025] As a further solution of the present invention, the step of obtaining the color gamut distribution map is specifically as follows:

[0026] S301: Based on the dynamic color calibration status, call the image data of each frame of the display, read the pixel point information frame by frame, extract the pixel values of the red, green, and blue channels from it, count the total amount of multi-channel pixel values in each frame, calculate the proportion in the total number of pixels in the entire frame, and generate the frame-level channel distribution ratio;

[0027] S302: Call the frame-level channel distribution ratio, combine it with the resolution data of each frame in the display area, count the number of pixel points covered by the multi-channel in the entire frame display area according to the channel markers, convert the proportion of the channel coverage area, and then summarize the area proportion of the channels along the time axis to generate a sequence of channel coverage area proportions;

[0028] S303: According to the sequence of channel coverage area proportions, compare the pixel distributions with the corresponding color gamut ranges of the red, green, and blue channels in the standard color gamut data in turn, calculate the ratio of pixel points overlapping with the standard color gamut in the real-time channel coverage area, and use the pixel point ratio as the overlapping ratio value of each frame to draw an image of the pixel point overlapping ratio and generate a color gamut distribution map.

[0029] As a further solution of the present invention, the formula for calculating the ratio of pixel points overlapping with the standard color gamut in the real-time channel coverage area is as follows:

[0030] ;

[0031] Where RW is the overlapping offset feature value, P fic is the pixel coverage value of the i-th pixel in the color channel c in the f-th frame, W fi is the channel weight coefficient of the i-th pixel in the f-th frame, QA c is the theoretical reference coverage area value of the color channel c in the standard color gamut, A f is the total pixel area value of the f-th frame image, A fc is the pixel area value covered by the color channel c in the f-th frame, and n is the number of pixels.

[0032] As a further solution of the present invention, the display self-monitoring method further includes the following steps:

[0033] S5: Record the trigger timestamp and duration of the dynamic repair instruction, extract the timing distribution characteristics of the color temperature adjustment, count the incremental direction of the number of color temperature adjustments in adjacent cycles and the change rate of the trigger interval of the repair instruction to obtain the display backlight gradient compensation mode;

[0034] The display gradient compensation mode includes a timestamp sequence, an adjustment frequency trend, and a backlight intensity gradient.

[0035] As a further solution of the present invention, the specific steps for obtaining the display gradient compensation mode are as follows:

[0036] S501: Based on the dynamic repair instruction, record the trigger timestamp and corresponding duration of each instruction, extract the distribution position of the dynamic repair instruction in the time series, construct a time series image of color temperature adjustment according to the time node distribution of each color temperature adjustment, and generate a time series feature interval for color temperature adjustment;

[0037] S502: Invoke the time series feature interval for color temperature adjustment, with adjacent time periods as sections, count the number of color temperature adjustments within each period, calculate the incremental direction of the number of adjustments between consecutive periods, measure the trigger interval between two consecutive dynamic repair instructions, judge the trend change rate based on the change amplitude of the trigger interval, and use the incremental direction and interval change rate jointly as the trigger condition to obtain the display backlight gradient compensation mode.

[0038] The present invention also provides a display self - monitoring system for executing the display self - monitoring method, including:

[0039] A light recognition module, configured to collect light intensity data around the display, invoke the standard thresholds for low light, strong light, and reflected light, judge the difference between the recorded value and the three types of thresholds, complete light classification according to the range to which the difference belongs, and generate ambient light classification features;

[0040] A color temperature calibration module, configured to invoke the real - time color temperature and brightness values of the display corresponding to the time period of the ambient light classification sequence, judge whether the color temperature deviation exceeds the color temperature adjustment threshold of the real - time type according to the standard color temperature and brightness interval, and if it exceeds the limit, adjust the color temperature and brightness simultaneously to generate a dynamic color calibration state;

[0041] A pixel distribution module, configured to invoke the frame number indicated by the dynamic color calibration state, read the red, green, and blue channel values in the image data frame by frame, count the number of pixels that meet the brightness threshold condition within the channel, invoke the resolution parameters of the corresponding frame, and calculate the pixel ratio of each of the three channels in the entire frame to obtain a color gamut distribution map;

[0042] A color repair module, configured to invoke the channel ratio value of each frame in the color gamut distribution map, judge whether it exceeds the corresponding repair trigger threshold, extract the hue value and saturation value of the trigger frame, judge whether it exceeds the standard control interval, and if there is an offset, perform adjustment to generate a dynamic repair instruction;

[0043] A backlight regulation module, configured to invoke the time point and duration of the dynamic repair instruction, count the change direction of the repair frequency within adjacent time periods, analyze the change trend of the trigger interval between instructions, judge whether the period triggers an unstable state of brightness response, and generate a display backlight gradient compensation mode.

[0044] The present invention also provides a display, which includes a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the display self-monitoring method is implemented.

[0045] The beneficial effects brought by the technical solution provided by the present invention at least include:

[0046] By real-time monitoring the light intensity around the display and comparing it with the preset lighting conditions, the ambient light situation is automatically classified, enabling the display to adjust the color temperature and brightness according to different lighting environments, ensuring the accuracy of image display and visual comfort. By analyzing the coverage areas of the red, green, and blue channels in each display frame frame by frame, the color gamut can be precisely adjusted, and dynamic repair is performed by calculating the deviation from the standard color gamut, enhancing the consistency of display quality and the authenticity of colors. Recording the specific timestamps and durations of dynamic repair, and timely adjusting the backlight drive of the display, improving the adaptability and long-term stability of the display under changing lighting conditions. This method of dynamically adjusting according to real-time data improves the response speed and accuracy of the display to environmental changes, providing a more optimized visual experience for users. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a schematic diagram of the working process of the present invention.

[0048] Figure 2 It is a system block diagram of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0049] In order to make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0050] In the description of the present invention, it should be understood that the orientation or positional relationships indicated by the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. are based on the orientation or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as limiting the present invention. In addition, in the description of the present invention, the meaning of "a plurality" is two or more unless otherwise specifically defined.

[0051] Please refer to Figure 1 , the embodiments of the present invention provide a display self-monitoring method, including the following steps:

[0052] S1: Collect the light intensity data around the display, compare the continuously collected light intensity data item by item with the low light, strong light, and reflected light conditions in the preset standard, and classify the real-time ambient light into low light, strong light, or reflected light categories according to the comparison differences, generating ambient light classification features;

[0053] S2: Based on the ambient light classification features, call the real-time color temperature value of the display and perform a difference operation with the preset standard color temperature value, determine whether the color temperature difference exceeds the corresponding item in the low light threshold, strong light threshold, and reflected light threshold. If it exceeds, synchronously adjust the color temperature and brightness to the preset standard range, generating a dynamic color calibration state;

[0054] S3: Using the dynamic color calibration state, for the pixel points of the real-time display frame of the display, extract the distribution ratios of the red, green, and blue channels frame by frame, count the proportion of the coverage area of multiple channels in the display area, analyze the overlapping ratio of the coverage area proportion with the standard color gamut, and obtain a color gamut distribution map;

[0055] S4: According to the proportion of the coverage area of the red, green, and blue channels in the color gamut distribution map, calculate the deviation amounts of the overlapping ratios of multiple channels with the standard color gamut respectively. When the channel deviation amount exceeds the corresponding repair trigger threshold, adjust the hue and saturation of the display, generating a dynamic repair instruction;

[0056] S5: Record the trigger timestamp and duration of the dynamic repair instruction, extract the timing distribution characteristics of the color temperature adjustment, count the incremental direction of the number of color temperature adjustments in adjacent periods and the change rate of the repair instruction trigger interval, and obtain the backlight gradient compensation mode of the display.

[0057] Among them, the ambient light classification features include illumination level, deviation interval, and ambient matching degree. The dynamic color calibration state includes color temperature correction amplitude, brightness calibration amplitude, and synchronous adjustment flag. The color gamut distribution map includes color gamut coverage rate, channel saturation, and color gamut offset amount. The dynamic repair instruction includes hue compensation instruction, saturation correction instruction, and channel equalization instruction. The gradient compensation mode includes timestamp sequence, adjustment frequency trend, and backlight intensity gradient.

[0058] The specific steps for obtaining the ambient light classification features are as follows:

[0059] S101: Collect the light intensity data around the display, record the light intensity value at each moment, sort the continuously recorded light intensity values according to the sampling time interval to construct a light intensity data sequence, call the light intensity values in consecutive time periods in the sequence and compare them item by item with the threshold intervals in the standard classification values, generating a light intensity interval offset difference;

[0060] When collecting the light intensity data around the display, it is necessary to install a light intensity sensor above the display or in the direction of the user's line of sight. The light intensity in the environment is recorded in real time through the sensor. The acquisition should be carried out at fixed time intervals. For example, the current light value is recorded every 1 second and continuously recorded for a whole day or a specific time period. Set from 8 am to 5 pm. During the acquisition process, the light data will be recorded as a continuous sequence in timestamp order. The data is arranged in time order to form a light sequence. It is necessary to compare each light intensity value in this data sequence with three preset standard light intervals one by one. The standard intervals can generally be set as a low light interval, a strong light interval, and a reflected light interval. Each light value will be compared with the boundary values of the three intervals according to its numerical size to confirm which interval it is closer to. Set to compare the difference between the value and the upper and lower limits of each interval to determine the interval type corresponding to the smallest difference. This comparison process is applicable to each light record. After traversing the records, a light intensity interval offset difference is generated.

[0061] S102: Based on the light intensity interval offset difference, for each light intensity value, judge its offset difference from the threshold interval in the standard classification value, and record the classification label corresponding to the offset difference as the real-time light intensity, generating an ambient light classification frequency ratio;

[0062] Obtain the real-time light intensity data stream in the target environment. In the office area, the light value is collected once per second through an ambient light sensor and continuously sampled during the whole-day working period. The collected data is preliminarily filtered and denoised to remove outliers caused by short-term occlusion, sensor jitter, etc. Interval judgment is carried out according to the set standard light classification model. The standard classification model can be preset as three categories according to specific scenario requirements. For example, the low light interval is defined as 0 to 300 lx, the medium light is 301 to 1000 lx, and the high light is 1001 to 10000 lx. Compare each sampled data with the interval it belongs to and analyze the degree of deviation between it and the central value of the interval. If the actual light value is at the edge of the medium light interval, the deviation degree is large. The system automatically records this data with a label classification of "low medium light" or "high medium light". Then, through real-time label identification, the light state at the current time point is mapped to a directional light deviation feature. All records are classified according to the label in time order in turn to construct a classification frequency data table. This data table is continuously updated and frequency statistics are carried out based on the set time window. For example, the label types and their occurrence frequencies of the recent 60 records are counted every 1 minute. For example, within one minute, the "low light" label appears 15 times, the "medium light" label appears 30 times, and the "high light" label appears 15 times. Then the frequency ratio of the three types of light in the current window is 15:30:15, which is recorded as a complete ambient light classification state. Continue to update the statistical data to generate an ambient light classification frequency ratio.

[0063] S103: Invoke the ambient light classification frequency ratio, compare the differences in the frequency ratios of the three types of ambient light within each time window, mark the category of the ratio difference value as the dominant light type for the time period, summarize the dominant light type marks, identify the dominant light classification trend in the continuous environment, and generate ambient light classification features;

[0064] It is necessary to analyze the dominant light type of each time window. The execution method of this analysis process is as follows: Compare the magnitude of the frequency ratios corresponding to the three classifications of low light, strong light, and reflected light within the current window, select the category with the highest frequency as the dominant type for this time period, and record it as a label. Suppose within a certain window, the strong light frequency is the highest, then the dominant label for this window is "strong light". This processing operation will be applied to the time window. By comparing the magnitude of the frequency values themselves, the dominant type can be determined. After that, record the dominant light types of the time windows in chronological order to form a sequence of dominant labels. Identify the continuity of the labels to determine whether there is a continuous dominant state of a certain type. If five consecutive windows are all labeled as "low light", it means that the overall time period is dominated by low light. The system can further summarize such continuous dominant states to identify the trend of changes in the lighting environment. The identification method is based on the continuity of the labels rather than the values themselves, and is especially suitable for judging problems such as whether the indoor lighting state is long-term dim or there is too much reflected interference in office areas, laboratories, or home usage scenarios, and generate ambient light classification features.

[0065] The specific steps for obtaining the dynamic color calibration state are as follows:

[0066] S201: Based on the ambient light classification features, within the time period to which the dominant light type belongs, invoke the real-time color temperature value of the monitor and the preset standard color temperature value within the corresponding time period, and calculate the color temperature difference value between the two within the time period to generate a color temperature difference value interval for the time period;

[0067] The formula for calculating the color temperature difference value between the two within the time period is as follows:

[0068] ;

[0069] Among them, ΔT is the dynamic color temperature difference value, AT is the real-time color temperature measurement value of the monitor within the time period, BT is the preset standard color temperature value for the time period corresponding to the dominant light type, t is the time stamp of the real-time time point, τ is the time decay coefficient, Δt j is the time interval between adjacent time points j, and m is the total number of time points;

[0070] Parameter meaning and formula calculation derivation process:

[0071] The real-time color temperature measurement value AT is collected through the built-in color temperature sensor of the monitor;

[0072] The preset standard color temperature value BT is obtained by querying the ambient light color temperature database corresponding to the dominant light type during the corresponding period;

[0073] The timestamp t is the difference (in seconds) between the current time point and the start time of the period obtained according to the clock;

[0074] The time decay coefficient τ is set to 7200 seconds (2 hours) verified in the study of ambient light color temperature stability;

[0075] The adjacent time point interval Δt j is determined by the data acquisition frequency (60 seconds);

[0076] The total number of time points m is obtained by dividing the total duration of the period (7200 seconds) by Δt j to get 120, and the sum of adjacent time point intervals is obtained by accumulating all Δt within the period (7200 seconds);

[0077] Taking a certain time point within the period as an example: AT is 5000K (measured in real time by the sensor), BT is 5500K (queried from data), t is 3600 seconds (1 hour after the start of the period), τ is 7200 seconds (based on experimental data), and Δt j is 60 seconds (the acquisition frequency is per minute), n is 120, and the formula operation process is as follows:

[0078] Calculate the absolute color temperature difference:

[0079] ∣5000 - 5500∣ = 500;

[0080] Calculate the time decay weight term:

[0081] ;

[0082] Calculate the time interval adjustment term:

[0083] 7200 / (120×60) = 7200 / 7200 = 1;

[0084] Substitute into the formula for calculation:

[0085] ΔT = 500×(0.816 + 1) = 500×1.816 = 908;

[0086] This result indicates that the dynamic color temperature difference value at the current time point is 908K, and the parameter values of all time points form the color temperature difference value interval of the time period, such as [500, 1200]K. The time decay coefficient τ is set according to the experimental data of the adaptation duration of the human eye to color temperature changes, and the time interval adjustment term realizes the optimization of parameter stability by balancing the discreteness of high-frequency acquired data.

[0087] S202: Invoke the color temperature difference value range for the time period. For the color temperature differences corresponding to the real-time categories in the ambient light classification features, judge the difference between the color temperature difference value and the low-light color temperature threshold, high-light color temperature threshold, or reflected light color temperature threshold set for the category, record the color temperature difference value points exceeding the threshold, and generate an abnormal color temperature deviation position index;

[0088] It is necessary to make corresponding judgments based on the deviation values in the difference range and the color temperature threshold associated with the current ambient light classification. The specific operation is as follows: Match each difference item with the ambient light category at its corresponding time point, call the corresponding color temperature deviation judgment threshold under this category, set the allowable deviation for low-light environments to ±200K, for high-light to ±300K, and for reflected light to ±400K. Compare whether each color temperature deviation value exceeds the allowable range in its corresponding environment. If the difference at a certain time point exceeds the corresponding threshold, mark it as an abnormal deviation point, and record its corresponding time index, actual color temperature value, and difference value. The exceeded items will be summarized uniformly, and the content will be arranged in ascending order of time. This processing method can be applied to various actual environments. In the display of a shopping mall large screen, for example, if the ambient light is high-light during a certain time period, and the real-time color temperature of the display is 5900K, the standard value is 6500K, the allowable deviation is ±300K, and the difference at this point is 600K, which exceeds the range and should be marked as an abnormal position. After completion, gather the abnormal points to form a complete deviation index structure for subsequent adjustment operations, and generate an abnormal color temperature deviation position index.

[0089] S203: According to the abnormal color temperature deviation position index, extract the real-time color temperature value and brightness value of the display within the corresponding time period, compare the differences with the corresponding preset standard color temperature range and brightness range within the time period, and adjust the color temperature value and brightness value according to the preset adjustment parameters for the items with exceeded comparison differences, to obtain a calibrated color temperature and brightness adjustment record;

[0090] For each time point marked as abnormal, it is necessary to further extract the corresponding real-time color temperature value and brightness value of the display, and at the same time call the preset standard color temperature range and brightness range corresponding to the ambient light classification to which this time point belongs. This process locates the abnormal moment through indexing. Set 10:15 in the morning, and obtain the real-time color temperature such as 6000 and brightness such as 250 at this moment. Obtain the color temperature setting under strong light environment such as 6500 and the brightness setting range such as 300 to 350 from the configuration parameters. Compare the current value with the standard range to judge whether the color temperature and brightness are simultaneously or separately too low or too high. For items with a difference exceeding the range, call the preset adjustment parameters for adjustment. For example, the color temperature adjustment step size is 50 each time, and the brightness adjustment step size is 20 each time. Determine the number of adjustment steps according to the difference magnitude. Set the difference to 500, then 10 steps of adjustment are required. Each adjustment operation will be recorded. This operation process can be automatically executed or intervened after manual review. For example, if the brightness at 10:15 is 250 and the standard is 320, then the brightness needs to be increased by 70, that is, 4 steps of adjustment are executed, and the result gradually approaches the standard value. The records are saved in chronological order for subsequent calibration tracking, and the calibrated color temperature and brightness adjustment records are obtained.

[0091] S204: Call the calibrated color temperature and brightness adjustment records, fill them into the original color temperature and brightness data in chronological order, compare the difference trends before and after to judge the calibration execution range, and mark the calibration status label sequence to generate the dynamic color calibration status;

[0092] Fill the adjusted record content back into the original data sequence in chronological order, merge it into the main color temperature and brightness record table as a data patch. The system compares the new and old data, calculates the overall difference trend in the same time period before and after the adjustment, and judges the coverage range of this calibration based on this. The coverage range refers to whether the current adjustment affects a single point, several consecutive points, or continues throughout the time period. According to the coverage degree and the data fluctuation change trend, set a calibration status label for each time point, set status identifiers such as "calibrated", "partially calibrated", or "uncalibrated". The status labels will be arranged along the time axis for subsequent system calls and analysis. This sequence can be used to monitor the stability of the color output status in real time, identify whether there are large-scale deviations occurring repeatedly within a certain period of time. For example, if the "calibrated" mark appears multiple times within an hour, it can be inferred that the light changes violently during this period, and the display parameter changes need to be monitored key points. The entire operation process is applicable in daily application scenarios such as monitoring centers and exhibition hall display systems. Through dynamic records, the system's automatic response ability to environmental adaptability can be improved, and the dynamic color calibration status can be generated.

[0093] The specific steps for obtaining the color gamut distribution map are as follows:

[0094] S301: Based on the dynamic color calibration status, call the image data of each frame of the display, read the pixel point information frame by frame, extract the pixel values of the red, green, and blue channels from it, count the total amount of multi-channel pixel values in each frame, calculate the proportion in the total number of pixels in the entire frame, and generate the frame-level channel distribution ratio.

[0095] It is necessary to read the image data of the currently playing screen of the display frame by frame. During the operation process, the system will automatically access each frame of the image in the graphics card cache or video memory, extract data for each pixel in the image. The image data is stored in the RGB three-channel format, that is, each pixel is composed of three channel values of red, green, and blue, with a range of 0 to 255. The system traverses the pixel points frame by frame, disassembles the RGB values of each pixel, and separately records the total number of pixel values corresponding to the three channels. It is set that in an image with a resolution of 1080p in one frame, there are 2,073,600 pixel points. During the traversal, it is statistically found that there are 400,000 points with a red channel pixel value higher than 200, 800,000 points in the green channel, and 500,000 points in the blue channel. After the statistics are completed, calculate the proportion of the total pixel values of the three channels in the total number of pixels in the entire frame, that is, the channel distribution ratio. This ratio will be marked in the feature data of this frame after the calculation of each frame is completed. The frame-level data after the above operations will be summarized by the system to form a sequence of frame-level channel distribution ratios over a period of time, which is applicable to dynamic picture change scenarios, such as monitoring and subsequent evaluation of color distribution in continuous pictures such as video display and advertisement playback, and generating the frame-level channel distribution ratio.

[0096] S302: Call the frame-level channel distribution ratio, combine it with the resolution data of each frame within the display area, count the number of pixel points covered by the multi-channel in the entire display area according to the channel markings, convert the proportion of the channel coverage area, and then summarize the area proportion of the channels along the time axis to generate a sequence of channel coverage area proportions.

[0097] It is necessary to further refine the number of pixel points marked by the three channels in combination with the resolution information of each frame in the display area. The specific operation is as follows: Read the resolution parameters of the current frame image, set it to 1920×1080, confirm that the total number of pixel points is 2,073,600, and then count the number of pixel points marked by each channel according to the RGB channel labels obtained in the previous stage. For example, if the red channel contains 540,000 pixel points, its ratio in the entire frame is 26.04%. Convert this ratio to the actual coverage area ratio. Considering that the pixel points are evenly distributed in the display area, the system calculates the display area of each channel proportionally. Set the monitor size to 24 inches, and the area is approximately 1272 square centimeters. Then the red channel accounts for approximately 330 square centimeters. The above steps will be executed once for each frame to ensure the generation of dynamically updated channel area ratio information. Arrange the channel area ratios of each frame in chronological order. This continuous change sequence can be used to monitor the area fluctuations of color channels in a certain video or dynamic picture, identify the time periods with heavy color distribution, such as the situation where the color tone in an advertisement segment is concentrated in red or blue, and form a channel coverage area ratio sequence.

[0098] S303: According to the channel coverage area ratio sequence, sequentially compare the pixel distributions with the corresponding color gamut ranges of the red, green, and blue channels in the standard color gamut data, calculate the ratio of the pixel points overlapping with the standard color gamut in the real-time channel coverage area, and use the pixel point ratio as the overlapping ratio value for each frame to draw an image of the pixel point overlapping ratio and generate a color gamut distribution map;

[0099] The formula for calculating the ratio of the pixel points overlapping with the standard color gamut in the real-time channel coverage area is as follows:

[0100] ;

[0101] Among them, RW is the overlapping offset feature value, P fic is the pixel coverage value of the i-th pixel in the color channel c in the f-th frame, W fi is the channel weight coefficient of the i-th pixel in the f-th frame, QA c is the theoretical reference coverage area value of the color channel c in the standard color gamut, A f is the total pixel area value of the f-th frame image, A fc is the pixel area value covered by the color channel c in the f-th frame, and n is the number of pixels;

[0102] Parameter meaning and formula calculation derivation process:

[0103] The frame number is the fifteenth frame, the frame image resolution is 1920 multiplied by 1080, and the total number of pixels in the display area is 2,073,600, that is, the parameter A f = 2073600;

[0104] The number of red-channel pixel points in this frame is obtained by extracting the color-channel values through point-by-point scanning. Among them, the brightness threshold of the red channel is set such that pixel points with a value exceeding 250 are included in the coverage statistics. According to the statistics of the color brightness distribution, the number of eligible pixels in this channel is 495,000, that is, n = 495000;

[0105] The weight coefficient W of each pixel in this channel fi is set according to the relative position of the pixel in the center of the image. The central area is set to 1, and the edge area gradually decays to 0.5. Considering the concentration of pixel distribution, the average weight value after sampling is 0.75;

[0106] The reference area value of the standard color gamut is 305,000, which is obtained from the calculation of the standard coverage pixel range of the red-channel color gamut based on the RGB standard, that is, QA c = 305000;

[0107] The actual coverage area value of the red channel in the current frame is obtained based on the number of pixel points and its equivalent statistical value of the occupied area. Calculated by taking each effective pixel point to represent one unit area, we get A fc = 495000;

[0108] Substitute the parameters into the formula item by item for derivation:

[0109] Calculate the weighted total pixel value:

[0110] ;

[0111] Calculate the denominator part as the total area value:

[0112] A f = 2073600;

[0113] Perform the operation of dividing the difference by the total area:

[0114] ;

[0115] Calculate the square root part:

[0116] ;

[0117] Then multiply the two parts to get the result:

[0118] RW = 0.03195×632.46 ≈ 20.21;

[0119] The result shows that there is an offset value of 20.21 between the red channel of the current frame and the standard color gamut within the effective color gamut range. This value is the channel overlap offset eigenvalue. The larger this value, the greater the difference between the actual area covered by the channels in the current image and the standard reference coverage, which is used to identify the standard overlap offset degree of the channels in the current frame. This eigenvalue will be used to draw the subsequent pixel point overlap ratio image and support the dynamic generation of the color gamut distribution map.

[0120] The specific steps for obtaining the dynamic repair instruction are as follows:

[0121] S401: Based on the area occupancy ratio data of the red, green, and blue channels in the color gamut distribution map, call the area occupancy ratio values of the channels in multiple frames of images, and calculate the difference with the reference area occupancy ratio of the same channel in the standard color gamut to obtain the channel overlap deviation record;

[0122] It is necessary to further process in depth the area occupancy ratio data of the red, green, and blue channels in multiple frames of images. The system will extract the area occupancy ratio of the three channels respectively in each frame of image. This data is derived from the analysis result of pixel distribution in the previous frame-level processing step and is recorded as a certain time series. The system calls the reference area occupancy ratio values of the red, green, and blue channels in the standard color gamut model. The standard values are set according to the international common color gamut standard and are preset in numerical form in color management. The standard red channel occupancy ratio is set to 30%, the green channel to 35%, and the blue channel to 35%. The system compares the area occupancy ratio value of each channel in the actual frame data with the standard value frame by frame, calculates the difference and records it. This recording process runs through the analysis cycle of each frame of image to ensure continuous monitoring of dynamic content. For example, when playing an animation segment, if the coverage area of the red channel in a certain frame is 20%, then the difference compared with the standard 30% is 10%. This difference will be written into the "channel overlap deviation record" for subsequent judgment on whether color repair intervention is needed. Each record item needs to indicate the frame number, channel type, actual value, standard value, and its deviation value to obtain the channel overlap deviation record.

[0123] S402: According to the channel overlap deviation record, respectively compare the deviation values of the red, green, and blue channels with the corresponding red channel repair trigger threshold, green channel repair trigger threshold, and blue channel repair trigger threshold, mark the channels whose deviation values exceed the corresponding thresholds, summarize and record the information of the trigger points, and generate the deviation trigger channel index group;

[0124] The deviation data of each item in the red, green, and blue channels will be compared with the preset channel repair trigger threshold in sequence. Each channel has an independent threshold. For example, the red channel is set to ±8%, the green channel ±10%, and the blue channel ±12%. The system reads the channel type and deviation value of each record item, and determines whether it exceeds the threshold of the corresponding channel. For example, if the area deviation of the green channel in a frame is 12%, exceeding its threshold of 10%, it is regarded as triggering the repair condition, and the channel is marked as the repair trigger state. The system records each trigger event, and the information content includes the frame number, channel type, deviation value, and deviation direction (greater than or less than the standard value). The trigger items will be classified into the "deviation trigger channel index group". This index group is used to track which frames and which channels have obvious abnormalities in color distribution and need to enter the repair process. In a video or dynamic image sequence, this operation can be executed in real time. The system can capture color deviation events and mark the repair positions with a second-level response, improving the overall color consistency and picture stability, especially suitable for occasions highly sensitive to color performance such as advertising production and exhibition displays, and generating a deviation trigger channel index group.

[0125] S403: Call the deviation trigger channel index group, extract the hue value and saturation value of the real-time frame according to the frame position and channel type recorded in the index, perform an offset calculation with the standard color control interval, perform an adjustment operation on the items with an offset value exceeding the limit, update the set of monitor color output parameters, and generate a dynamic repair instruction;

[0126] Extract the hue value and saturation value in the corresponding image frame according to the frame position and channel type listed in the index. The hue value represents the position of the current color on the color wheel, and the saturation represents the purity of the color. The system reads the hue and saturation parameters in the target channel pixel by pixel from the image data and forms the average color feature vector of the channel in this frame, and compares it with the preset standard color control interval in the color. The standard interval is generally set according to the application scenario. In the advertising scenario, it is required that the hue value is in the range of 100-130, and the saturation value is between 60% and 90%. During the comparison process, it is judged whether each item of hue and saturation exceeds this interval. If there are items with an offset exceeding the limit, the system starts an automatic repair operation. The repair method is to adjust the color output parameters. For example, by reducing the saturation or offsetting the hue value to make it return to the standard interval. The adjustment operation will be recorded in real time and update the set of monitor color output parameters in the system. The instruction includes the frame number, channel type, adjustment method, and target value. This color repair process based on the trigger mechanism can dynamically respond to color abnormalities and correct them immediately, especially suitable for complex scenarios with large changes in ambient light for indoor and outdoor screens, and generate a dynamic repair instruction.

[0127] The specific steps for obtaining the gradient compensation mode are as follows:

[0128] S501: Based on the dynamic repair instructions, record the trigger timestamp and the corresponding duration of each instruction, extract the distribution position of the dynamic repair instructions in the time series, construct a time series image of the color temperature adjustment according to the time node distribution of each color temperature adjustment, and generate a time series feature interval of the color temperature adjustment;

[0129] It is necessary to record the trigger timestamp and the continuously effective time of each instruction one by one to form the time trajectory of the color temperature adjustment behavior. When the system performs color temperature repair, it will automatically generate a unique identifier for each instruction and record the execution time. For example, it is recorded as "2025-04-14 14:23:15", and at the same time, a duration field is added. If the repair takes effect for 30 seconds, the system arranges the instructions in chronological order and draws their distribution on the time axis. If 100 instructions are generated in a day, the system will draw 100 time marker points in minutes and identify the density distribution of the repair behavior in the visualization image. By observing and measuring the distance between each time point, a time distribution feature image of the color temperature adjustment can be formed. The system further analyzes the dense interval and the sparse interval of the repair events, extracts the frequently repaired time periods, and constructs multiple time feature intervals. For example, 9:00 am is a frequently repaired segment, and 11:00 is a sparsely repaired segment. The feature intervals will be used to establish a time series image model of the color temperature adjustment, and the high-frequency and low-frequency repair sections are distinguished by different colors or shapes in the image, so that the system can form a complete behavior view of the time distribution of the color temperature adjustment, adapt to the management requirements of the picture color temperature stability in application scenarios such as offices, exhibition halls, and retail, and generate a time series feature interval of the color temperature adjustment.

[0130] S502: Call the time series feature interval of the color temperature adjustment, use adjacent time periods as segments, count the number of color temperature adjustments in each period, calculate the increment direction of the number of adjustments between consecutive periods, and measure the trigger interval between two consecutive dynamic repair instructions. Judge the trend change rate based on the change amplitude of the trigger interval, and use the increment direction and the interval change rate as the trigger conditions to obtain the display backlight gradient compensation mode;

[0131] The entire time period is divided into adjacent fixed cycles, with each cycle set to 10 minutes. The number of color temperature adjustment actions occurring within each cycle is counted, and the adjustment count for each cycle is recorded as a count item, forming a periodic adjustment frequency sequence. The system calculates the difference in the adjustment counts between two adjacent cycles to obtain the change direction of the color temperature adjustment frequency within each time period, and determines whether the current state is one of frequent increase, decrease, or stability. If there are 3 repairs in the first cycle and 7 in the second cycle, the change direction is "increase". At the same time, the system measures the time interval between any two dynamic repair instructions to determine whether the repair behavior is becoming denser. The measurement method is to extract the timestamps of two consecutive instructions, calculate the interval duration, and record it as an interval sequence. The system compares the change trend of the intervals to determine whether the current color temperature adjustment behavior shows an accelerating (interval shortening) or decelerating (interval lengthening) trend. The system jointly evaluates the increase or decrease direction of the adjustment frequency and the change trend of the trigger interval to form a composite trigger condition. If the number of repairs continuously increases and the interval significantly shortens within a certain time period, the system determines that the current screen requires backlight compensation and activates the display backlight gradient compensation mode accordingly. This mode is applicable to areas with drastic light changes, such as the period when the conference room curtains are opened or the period when the outdoor screen is shaded by sunlight, and has higher response requirements for color temperature stability, resulting in the display backlight gradient compensation mode.

[0132] Please refer to Figure 2 , a display self-monitoring system, including:

[0133] A light recognition module for collecting light intensity data around the display, invoking the standard thresholds for low light, strong light, and reflected light, judging the difference between the recorded value and the three types of thresholds, and completing light classification according to the range of the difference to generate ambient light classification features;

[0134] A color temperature calibration module for invoking the real-time color temperature and brightness values of the display corresponding to the ambient light classification sequence, judging whether the color temperature deviation exceeds the color temperature adjustment threshold of the real-time type according to the standard color temperature and brightness intervals, and if it exceeds the limit, adjusting the color temperature and brightness simultaneously to generate a dynamic color calibration state;

[0135] A pixel distribution module for invoking the frame numbers indicated by the dynamic color calibration state, reading the red, green, and blue channel values in the image data frame by frame, counting the number of pixels in the channel that meet the brightness threshold condition, invoking the resolution parameters of the corresponding frame, and calculating the pixel ratio of each of the three channels in the entire frame to obtain a color gamut distribution map;

[0136] A color repair module for invoking the channel ratio values of each frame in the color gamut distribution map, judging whether they exceed the corresponding repair trigger threshold, extracting the hue value and saturation value of the trigger frame, judging whether they exceed the standard control range, and if there is an offset, performing an adjustment to generate a dynamic repair instruction;

[0137] A backlight control module, which is used to call the time point and duration of the dynamic repair instruction, count the change direction of the repair frequency in adjacent time periods, analyze the change trend of the trigger interval between instructions, determine whether the cycle triggers an unstable state of brightness response, and generate a backlight gradient compensation mode for the display.

[0138] A display, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the above-mentioned display self-monitoring method is implemented.

[0139] The above are only the preferred embodiments of the present invention, and do not limit the present invention in other forms. Any person skilled in the art may use the technical content disclosed above to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, as long as it does not depart from the technical solution content of the present invention, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present invention still belong to the protection scope of the technical solution of the present invention.

Claims

1. A method for self-monitoring of a display, characterized in that, It includes the following steps: S1: Collect the light intensity data around the display, compare the continuously collected light intensity data item by item with the low light, strong light, and reflected light conditions in the preset standard, classify the real-time ambient light into low light, strong light, or reflected light categories according to the comparison differences, and generate ambient light classification features; S2: Based on the ambient light classification features, call the real-time color temperature value of the display and perform a difference operation with the preset standard color temperature value, determine whether the color temperature difference exceeds the corresponding item among the low light threshold, strong light threshold, and reflected light threshold. If it exceeds, synchronously adjust the color temperature and brightness to the preset standard range to generate a dynamic color calibration state; S3: Utilize the dynamic color calibration state to extract the distribution ratios of the red, green, and blue channels for each pixel point of the real-time display frame of the display, count the proportion of the coverage area of multiple channels in the display area, analyze the overlapping ratio of the coverage area proportion with the standard color gamut, and obtain a color gamut distribution map; The specific steps for obtaining the color gamut distribution map are as follows: S301: Based on the dynamic color calibration state, call the image data of each frame of the display, read the pixel point information frame by frame, extract the pixel values of the red, green, and blue channels therefrom, count the total amount of pixel values of multiple channels in each frame, and calculate the proportion in the total number of pixels in the entire frame to generate a frame-level channel distribution ratio; S302: Call the frame-level channel distribution ratio, combine the resolution data of each frame in the display area, count the number of pixel points covered by multiple channels in the entire frame display area according to the channel markers, convert the proportion of the channel coverage area, and then summarize the area proportion of the channels along the time axis to generate a sequence of the proportion of the channel coverage area; S303: According to the sequence of the proportion of the channel coverage area, sequentially compare the pixel distribution with the corresponding color gamut ranges of the red, green, and blue channels in the standard color gamut data, calculate the ratio of the pixel points overlapping with the standard color gamut in the real-time channel coverage area, and use the pixel point ratio as the overlapping ratio value for each frame to draw an image of the pixel point overlapping ratio and generate a color gamut distribution map; The formula for calculating the ratio of the pixel points overlapping with the standard color gamut in the real-time channel coverage area is as follows: ； Among them, RW is the overlapping offset feature value, P fic is the pixel coverage value of the i-th pixel in the f-th frame in color channel c, W fi is the channel weight coefficient of the i-th pixel in the f-th frame, QA c is the theoretical reference coverage area value of color channel c in the standard color gamut, A f is the total pixel area value of the f-th frame image, A fc is the pixel area value covered by color channel c in the f-th frame, and n is the number of pixels; S4: According to the proportion of the coverage area of the red, green, and blue channels in the color gamut distribution map, calculate the deviation amounts of the overlapping ratios of multiple channels with the standard color gamut respectively. When the channel deviation amount exceeds the corresponding repair trigger threshold, adjust the hue and saturation of the display to generate a dynamic repair instruction.

2. The self-monitoring method of a display according to claim 1, wherein The ambient light classification features include light level, deviation range, and ambient matching degree. The dynamic color calibration state includes color temperature correction amplitude, brightness calibration amplitude, and synchronous adjustment flag. The color gamut distribution map includes color gamut coverage rate, channel saturation, and color gamut offset amount. The dynamic repair instruction includes hue compensation instruction, saturation correction instruction, and channel equalization instruction.

3. The self-monitoring method of a display according to claim 1, wherein, The specific steps for obtaining the ambient light classification features are as follows: S101: Collect the light intensity data around the display, record the light intensity value at each moment, sort the continuously recorded light intensity values according to the sampling time interval, and construct a light intensity data sequence. Call the light intensity values in the continuous time period in the sequence and compare them item by item with the threshold interval in the standard classification value to generate a light intensity interval offset difference; S102: Based on the light intensity interval offset difference, judge the offset difference between each light intensity value and the threshold interval in the standard classification value, record the real-time light intensity with the classification label corresponding to the offset difference, and generate an ambient light classification frequency ratio; S103: Call the ambient light classification frequency ratio, compare the differences in the three types of ambient light frequency ratios within each time window, mark the category of the ratio difference value as the dominant light type of the time period, summarize the dominant light type marks, identify the dominant light classification trend in the continuous environment, and generate an ambient light classification feature.

4. The display self-monitoring method according to claim 3, characterized in that, The specific steps for obtaining the dynamic color calibration state are as follows: S201: Based on the ambient light classification feature, within the time period of the dominant light type, call the real-time color temperature value and the preset standard color temperature value of the display in the corresponding time period, and calculate the color temperature difference value between the two in the time period to generate a time period color temperature difference value interval; The formula for calculating the color temperature difference value between the two in the time period is as follows: ； Among them, ΔT is the dynamic color temperature difference value, AT is the real-time color temperature measurement value of the display within a time period, BT is the preset standard color temperature value corresponding to the dominant light type within the corresponding time period, t is the time stamp of the real-time time point, τ is the time decay coefficient, and Δt j is the time interval between adjacent time points j, and m is the total number of time points; S202: Call the time period color temperature difference value interval, for the color temperature difference value corresponding to the real-time category in the ambient light classification feature respectively, judge the difference between the color temperature difference value and the low light color temperature threshold, strong light color temperature threshold or reflected light color temperature threshold set by the category, record the color temperature difference value points exceeding the threshold, and generate an abnormal color temperature deviation position index; S203: According to the abnormal color temperature deviation position index, extract the real-time color temperature value and brightness value of the display in the corresponding time period, compare the differences with the corresponding preset standard color temperature interval and brightness interval in the time period, and perform adjustment operations on the color temperature value and brightness value according to the preset adjustment parameters for the comparison difference items to obtain a calibrated color temperature and brightness adjustment record; S204: Call the calibrated color temperature and brightness adjustment record, fill it into the original color temperature and brightness data in chronological order, judge the calibration execution range by comparing the difference trends before and after, and mark the calibration status label sequence to generate a dynamic color calibration state.

5. The display self-monitoring method according to claim 1, characterized in that, The display self-monitoring method further includes the following steps: S5: Record the trigger timestamp and duration of the dynamic repair instruction, extract the timing distribution characteristics of the color temperature adjustment, and count the increment direction of the color temperature adjustment times and the change rate of the repair instruction trigger interval in adjacent cycles to obtain the display backlight gradient compensation mode; The display backlight gradient compensation mode includes a timestamp sequence, an adjustment frequency trend, and a backlight intensity gradient.

6. The display self-monitoring method according to claim 5, wherein The specific steps for obtaining the display backlight gradient compensation mode are as follows: S501: Based on the dynamic repair instruction, record the trigger timestamp and the corresponding duration of each instruction, extract the distribution position of the dynamic repair instruction in the time sequence, and construct a time sequence image of the color temperature adjustment according to the time node distribution of each color temperature adjustment to generate a color temperature adjustment timing feature interval; S502: Invoke the color temperature adjustment timing feature interval. Use adjacent time periods as sections, count the number of color temperature adjustments within each period, calculate the incremental direction of the adjustment times between consecutive periods, and measure the trigger interval between two consecutive dynamic repair instructions. Determine the trend change rate based on the change amplitude of the trigger interval. Combine the incremental direction and the interval change rate as the trigger condition to obtain the display backlight gradient compensation mode.

7. A display self-monitoring system, characterized in that, For implementing the display self-monitoring method according to any one of claims 1 to 6, including: A light recognition module, configured to collect light intensity data around the display, invoke the standard thresholds for low light, strong light, and reflected light, judge the difference between the recorded value and the three types of thresholds, complete light classification according to the range of the difference, and generate ambient light classification features. A color temperature calibration module, configured to invoke the real-time color temperature and brightness values of the display corresponding to the time period of the ambient light classification features, judge whether the color temperature deviation exceeds the color temperature adjustment threshold of the real-time type according to the standard color temperature and brightness intervals. If it exceeds the limit, adjust the color temperature and brightness simultaneously to generate a dynamic color calibration state. A pixel distribution module, configured to invoke the frame number indicated by the dynamic color calibration state, read the red, green, and blue channel values in the image data frame by frame, count the number of pixels in the channel that meet the brightness threshold condition, invoke the resolution parameters of the corresponding frame, and calculate the pixel ratio of each of the three channels in the entire frame to obtain a color gamut distribution map. A color repair module, configured to invoke the channel ratio value of each frame in the color gamut distribution map, judge whether it exceeds the corresponding repair trigger threshold, extract the hue value and saturation value of the trigger frame, judge whether it exceeds the standard control interval, and if there is an offset, perform adjustment to generate a dynamic repair instruction. A backlight regulation module, configured to invoke the time point and duration of the dynamic repair instruction, count the change direction of the repair frequency in adjacent time periods, analyze the change trend of the trigger interval between instructions, judge whether the cycle triggers an unstable state of brightness response, and generate a display backlight gradient compensation mode.

8. A display, comprising a memory and a processor, characterized in that, The memory stores a computer program, and when the processor executes the computer program, it implements the display self-monitoring method according to any one of claims 1 to 6.

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