Display self-monitoring method and system and display

Abstract

The invention relates to the technical field of display control, in particular to a display self-monitoring method and system and a display, and the method comprises the following steps: collecting light intensity data, comparing and classifying ambient light as low light, strong light or reflected light with a preset standard, judging whether a real-time color temperature and a standard color temperature difference value exceed a threshold value, if yes, adjusting the color temperature and the brightness, and if not, adjusting the brightness; pixel RGB channel proportions are extracted frame by frame, a color gamut distribution diagram is generated, restoration instruction time features are recorded, the color temperature adjustment trend is counted, and a backlight gradient compensation mode is generated. In the invention, the light intensity around the display is monitored in real time, the light intensity is compared with the preset illumination condition, and the ambient light condition is automatically classified, so that the display can adjust the color temperature and the brightness according to different illumination environments, and the backlight drive of the display is adjusted in time by analyzing the coverage areas of the red, green and blue channels in the display frame frame by frame; the adaptive capacity and the long-term stability of the display under variable illumination conditions are improved, and more optimized visual experience is provided 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. 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 application 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 the function of dynamic gamut adjustment also makes the color reproduction less real, 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.

[0006] To achieve the above purpose, the technical solution adopted by the present invention is as follows: The present invention provides a method for self-monitoring of a display, including 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 status; S3: Utilize the dynamic color calibration status, extract the distribution ratios of the red, green, and blue channels for each pixel of the real-time display frame of the display 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 ratio with the standard color gamut, and obtain a color gamut distribution map; 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.

[0007] As a further solution of the present invention, the ambient light classification features include light intensity level, deviation range, and ambient matching degree. The dynamic color calibration status 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.

[0008] As a further solution of the present invention, 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 to 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 range 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 range in the standard classification value, record the real-time light intensity with the classification label corresponding to the offset difference to generate an ambient light classification frequency ratio; S103: Call the ambient light classification frequency ratio, compare the differences in the frequency ratios of the three types of ambient light in 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, and identify the dominant light classification trend in the continuous environment to generate ambient light classification features.

[0009] As a further solution of the present invention, the steps for obtaining the dynamic color calibration state are specifically as follows: S201: Based on the ambient light classification features, within the time period of the dominant light type, call 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; The formula for calculating the color temperature difference value between the two within the time period is as follows: ; where ΔT is the dynamic color temperature difference value, AT is the measured real-time color temperature value of the display within the time period, BT is the preset standard color temperature value within the corresponding time period of 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; S202: Call the time period color temperature difference value range, for the color temperature difference values corresponding to the real-time categories in the ambient light classification features respectively, make a difference judgment between the color temperature difference values and the low light color temperature threshold, strong light color temperature threshold or reflected light color temperature threshold set for the categories, 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 within the corresponding time period, compare the differences with the corresponding preset standard color temperature range and brightness range within 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.

[0010] As a further solution of the present invention, the steps for obtaining the color gamut distribution map are specifically 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 multi-channel pixel values in each frame, and calculate the proportion in the total number of pixels in the whole frame to generate a frame-level channel distribution ratio; S302: Call the frame-level channel distribution ratio, combine the resolution data of each frame within the display area, count the number of pixel points covered by the multi-channel in the whole frame display area according to the channel label, convert the proportion of the channel coverage area, and then summarize the area proportion of the channel along the time axis to generate a channel coverage area proportion sequence; S303: According to the sequence of the channel coverage area ratios, 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 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, draw an image of the pixel point overlapping ratio, and generate a color gamut distribution map.

[0011] As a further solution of the present invention, 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: ; 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.

[0012] As a further solution of the present invention, 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, 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, and obtain the display backlight gradient compensation mode; The display gradient compensation mode includes a timestamp sequence, an adjustment frequency trend, and a backlight intensity gradient.

[0013] As a further solution of the present invention, the obtaining steps of the display gradient compensation mode are specifically 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 series, and construct a time series 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: Call the color temperature adjustment timing feature interval, use adjacent time periods as sections, count the number of color temperature adjustments in each period, calculate the incremental direction of the number of adjustments in 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 incremental direction and the interval change rate jointly as the trigger condition to obtain the display backlight gradient compensation mode.

[0014] The present invention also provides a display self-monitoring system for executing the display self-monitoring method, including: A light recognition module, which is used to collect light intensity data around the display, call the standard thresholds of 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, which is used to call 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 at the same time to generate a dynamic color calibration state; A pixel distribution module, which is used to call 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 in the channel, call the resolution parameters of the corresponding frame, and calculate the pixel ratio of the three channels in the whole frame to obtain a color gamut distribution map; A color restoration module, which is used to call the channel ratio value of each frame in the color gamut distribution map, judge whether it exceeds the corresponding restoration 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 restoration instruction; A backlight control module, which is used to call the time point and duration of the dynamic restoration instruction, count the change direction of the restoration frequency in 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 backlight gradient compensation mode for the display.

[0015] 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.

[0016] The beneficial effects brought by the technical solution provided by the present invention at least include: By real-time monitoring the light intensity around the display and comparing it with the preset light conditions, the ambient light situation is automatically classified, so that the display can adjust the color temperature and brightness according to different light environments, ensuring the accuracy of image display and visual comfort. By analyzing the coverage area of the red, green and blue channels in the display frame frame by frame, the color gamut can be accurately adjusted, and dynamic restoration can be performed by calculating the deviation from the standard color gamut, enhancing the consistency of display quality and the authenticity of colors. Record the specific timestamps and durations of dynamic restoration, and adjust the backlight drive of the display in time to improve the adaptability and long-term stability of the display under changing light 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

[0017] Figure 1This is a schematic diagram of the workflow of the present invention.

[0018] Figure 2 This is a system block diagram of the present invention. Detailed implementation manners

[0019] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, 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.

[0020] 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 accompanying drawings. These are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as a limitation to 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.

[0021] Please refer to Figure 1 , the embodiment of the present invention provides a method for self-monitoring of a display, including 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, and 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, and 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 interval to generate a dynamic color calibration state; S3: Utilize the dynamic color calibration state, extract the distribution ratios of the red, green and blue channels for each pixel of the real-time display frame of the display, count the proportion of the coverage area of the multi-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; S4: According to the coverage area ratios of the red, green and blue channels in the color gamut distribution map, calculate the deviation amounts of the overlapping ratios of the multi-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; S5: Record the trigger timestamp and duration of the dynamic repair instruction, extract the timing distribution characteristics of color temperature adjustment, and statistically analyze the increment direction of the number of color temperature adjustments and the change rate of the trigger interval of the repair instruction within adjacent cycles to obtain the display backlight gradient compensation mode.

[0022] Among them, the ambient light classification features include light level, deviation range, and ambient matching degree; the dynamic color calibration status includes color temperature correction amplitude, brightness calibration amplitude, and synchronization adjustment flag; the color gamut distribution map includes color gamut coverage rate, channel saturation, and color gamut offset; the dynamic repair instructions include hue compensation instruction, saturation correction instruction, and channel equalization instruction; the gradient compensation mode includes timestamp sequence, adjustment frequency trend, and backlight intensity gradient.

[0023] 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 to construct a light intensity data sequence, and call the light intensity values in the continuous time period in the sequence to compare item by item with the threshold intervals in the standard classification values to generate the light intensity interval offset difference. 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 collection should be at a fixed time interval, such as recording the current light value every 1 second and continuously recording for a whole day or a specific time period, setting from 8 am to 5 pm. During the collection process, the light data will be recorded as a continuous sequence in timestamp order, and 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 item by item with the three preset standard light intervals in the system. The standard intervals can generally be set as the low light interval, the strong light interval, and the 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 the difference comparison 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, the light intensity interval offset difference is generated.

[0024] 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 in real-time light intensity to generate the ambient light classification frequency ratio. Obtain the real-time light intensity data stream in the target environment. In the office area, collect the light intensity value once per second through an ambient light sensor, and continuously sample during the entire working day. The collected data is subjected to preliminary filtering and denoising to remove outliers caused by short-term occlusion, sensor jitter, etc. Perform interval judgment based on the set standard light classification model. The standard classification model can be preset into 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 sampling data with the interval it belongs to, and analyze the deviation degree between it and the central value of the interval. If the actual light intensity 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, map the light state at the current time point to a directional light deviation feature. Sequentially classify all records according to the label in time order to construct a classification frequency data table. This data table is continuously updated and frequency statistics are performed based on the set time window. For example, every 1 minute, count the label types and their occurrence frequencies of the nearly 60 records. 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 light environment classification state. Continue to update the statistical data to generate the ambient light classification frequency ratio.

[0025] S103: Invoke the ambient light classification frequency ratio, compare the differences in the frequency ratios of the three types of ambient light in each time window, mark the category of the ratio difference value as the dominant light type in the time period, summarize the dominant light type marks, identify the dominant light classification trend in the continuous environment, and generate the ambient light classification feature; It is necessary to analyze the dominant light type in each time window. The execution method of this analysis process is: compare the frequency ratio sizes corresponding to the three classifications of low-light, strong light, and reflected light in the current window, and select the one with the highest frequency as the dominant type in this time period, and record it as a label setting in a certain window. If the frequency of strong light is the highest in a certain window, the dominant label of this window is "strong light". This processing operation will be applied to the time window. By comparing the magnitudes of the frequency values themselves, the dominant type can be judged. Then, record the dominant light types of the time windows in time order to form a sequence of dominant labels. Perform continuity identification on the labels to judge whether there is a continuous dominant state of a certain type. If five consecutive windows are all "low-light" labels, it means that the overall time period is dominated by low light. The system can further summarize such continuous dominant states to identify the change trend of the light environment. The identification method is based on label continuity rather than the numerical values themselves, and is especially suitable for judging whether the indoor lighting state is long-term dim or there is too much reflected interference in office areas, laboratories, or home use scenarios, and generate the ambient light classification feature.

[0026] The steps for obtaining the dynamic color calibration status are specifically as follows: S201: Based on the ambient light classification features, within the time period of the dominant light type, call the real-time color temperature value of the display 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 respectively to generate a color temperature difference value interval for the time period; The formula for calculating the color temperature difference value between the two within the time period is as follows: ; 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 corresponding to the dominant light type within the time period, 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; Meaning of parameters and derivation process of formula calculation: The real-time color temperature measurement value AT is collected by the built-in color temperature sensor of the display; The preset standard color temperature value BT is obtained by querying the ambient light color temperature database corresponding to the dominant light type; The time stamp t obtains the difference (in seconds) between the current time point and the start time of the time period according to the clock; The time decay coefficient τ is set to 7200 seconds (2 hours) verified in the study of ambient light color temperature stability; Adjacent time point interval Δt j is determined by the data acquisition frequency (60 seconds); The total number of time points m is obtained by dividing the total duration of the time period (7200 seconds) by Δt j to get 120, and the sum of adjacent time point intervals is accumulated by all Δt within the time period (7200 seconds); Taking a certain time point within the time period as an example: AT is 5000K (sensor real-time measurement), BT is 5500K (data query), t is 3600 seconds (1 hour after the start of the time period), τ is 7200 seconds (based on experimental data), Δt j is 60 seconds (acquisition frequency is per minute), and n is 120. The formula operation process is as follows: Calculate the absolute color temperature difference: ∣5000 - 5500∣ = 500; Calculate the time decay weight term: ; Calculate the time interval adjustment term: 7200 / (120×60) = 7200 / 7200 = 1; Substitute into the formula for calculation: ΔT = 500×(0.816 + 1) = 500×1.816 = 908; This result indicates that the dynamic color temperature difference value at the current time point is 908K. The parameter values at all time points form the color temperature difference value interval for the time period. For example, [500, 1200]K. The time decay coefficient τ is set based on the experimental data of the adaptation duration of the human eye to the change of color temperature. The time interval adjustment item optimizes the parameter stability by balancing the discreteness of the high-frequency collected data.

[0027] S202: Call the color temperature difference value interval for the time period. For the color temperature difference values corresponding to the real-time categories in the environmental light classification features respectively, make a difference judgment 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 for the category, record the points of the color temperature difference value exceeding the threshold, and generate an abnormal color temperature deviation position index; It is necessary to make corresponding judgments based on each deviation value in the difference interval and the color temperature threshold associated with the current environmental light classification. The specific operation is as follows: Match each difference item with the environmental light category at its corresponding time point, call the corresponding color temperature deviation judgment threshold under this category, set the allowable deviation for the low-light environment to be ±200K, for strong light to be ±300K, and for reflected light to be ±400K. Compare whether each color temperature deviation value exceeds the allowable range in its corresponding environment in turn. 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 large screen in a shopping mall, for example, if the environmental light is strong in 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, the abnormal points are gathered to form a complete deviation index structure for subsequent adjustment operations, and an abnormal color temperature deviation position index is generated.

[0028] 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, make a difference comparison with the corresponding preset standard color temperature interval and brightness interval within the time period, and adjust the color temperature value and brightness value according to the preset adjustment parameters for the items with the exceeded comparison difference to obtain the calibrated color temperature and brightness adjustment record; For each time point marked as an anomaly, 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 anomaly moment through indexing. Set 10:15 in the morning, and obtain the real-time color temperature at this moment, such as 6000, and the brightness, such as 250. Obtain the color temperature setting in strong light environment, such as 6500, and the brightness setting range, such as 300 to 350, from the configuration parameters. Compare the current values with the standard range to determine whether the color temperature and brightness are simultaneously or separately on the low side or high side. 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 step of the 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.

[0029] 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; Fill the adjusted record content back into the original data sequence in chronological order, merge it as a data patch into the main color temperature and brightness record table. The system compares the old and new 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 large-scale deviations occur 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 recording, the system's automatic response ability to environmental adaptability can be improved, and the dynamic color calibration status is generated.

[0030] The specific steps for obtaining the color gamut distribution map are as follows: 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; 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 in the video 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 sum of the 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 the 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.

[0031] 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 display area according to the channel label, convert the proportion of the channel coverage area, and then summarize the area proportion of the channel along the time axis to generate a sequence of channel coverage area proportions; It is necessary to further combine the resolution information of each frame of the display area to refine the number of pixels under the three channel labels. The specific operation is: read the resolution parameters of the current frame image, set 1920×1080, confirm that its total number of pixels is 2073600, and then count the number of pixels under each channel label according to the RGB channel labels obtained in the previous stage. For example, the red channel contains 540,000 pixels, which accounts for 26.04% of the entire frame. Convert this ratio to the actual coverage area ratio, considering that the pixels are evenly spaced in the display area. The display area of ​​each channel is calculated proportionally. The display size is set to 24 inches, with an area of ​​approximately 1272 square centimeters, and the red channel occupies approximately 330 square centimeters. The above steps are performed once for each frame to ensure the generation of dynamically updated channel area ratio information, and the channel area ratios of each frame are arranged in chronological order. This continuously changing sequence can be used to monitor the area fluctuations of the color channel in a certain video or dynamic picture, and identify time periods with heavy color distribution, such as when the color tone in an advertising clip is concentrated in red or blue, to form a channel coverage area ratio sequence.

[0032] S303: According to the channel coverage area ratio sequence, pixel distribution is compared with the color gamut ranges corresponding to the red, green and blue channels in the standard color gamut data in turn, the ratio of pixels overlapping with the standard color gamut in the real-time channel coverage area is calculated, and the pixel ratio is used as the overlap ratio value of each frame, and a pixel overlap ratio image is drawn to generate a color gamut distribution map; The formula for calculating the ratio of pixels in the real-time channel coverage area that overlaps with the standard color gamut is as follows: ; Where RW is the overlap offset eigenvalue, 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 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 fth frame, and n is the number of pixels; Parameter meaning and formula calculation derivation process: The frame number is the fifteenth frame, the frame image resolution is 1920 times 1080, and the total number of pixels in the display area is 2,073,600, that is, parameter A f = 2073600; 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 = 495,000; 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; The standard color gamut reference area value is 305,000, which is obtained from the calculation of the standard coverage pixel range of the red channel under the RGB standard, that is, QA c = 305,000; 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 = 495,000; Substitute the parameters of the formula item by item for derivation: Calculate the weighted total pixel value: ; Calculate the denominator part as the total area value: A f = 2,073,600; Perform the operation of dividing the difference by the total area: ; Calculate the square root part: ; Then multiply the two parts to get the result: RW = 0.03195 × 632.46 ≈ 20.21; This result indicates that there is an offset value of 20.21 between the red channel in the current frame within the effective color gamut range and the standard color gamut. This value is the channel overlap offset feature value. The larger this value, the greater the difference between the actual area covered by the channel in the current image and the standard reference coverage, which is used to identify the standard overlap offset degree of the channel in the current frame. This feature value will be used to draw the subsequent pixel point overlap ratio image and support the dynamic generation of the color gamut distribution map.

[0033] The specific steps for obtaining the dynamic repair instruction are as follows: S401: Based on the data of the area occupancy ratios of the red, green, and blue channels in the color gamut distribution map, call the area occupancy ratios of the channels in multiple frames of images, and calculate the difference from the reference area occupancy ratio of the same channel in the standard color gamut to obtain the channel overlap deviation record; It is necessary to further deeply process the data on the proportion of the coverage area of the red, green, and blue channels in multiple frames of images. The system will extract the proportion of the coverage area of the three channels separately in each frame of the image. This data is derived from the analysis results of the pixel distribution in the previous frame-level processing step and is recorded as a certain time series. The system calls the reference area proportion 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 proportion of the red channel is set to 30%, the green channel is 35%, and the blue channel is 35%. The system compares the area proportion 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 the image to ensure continuous monitoring of dynamic content. For example, when playing an animation clip, 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 restoration 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.

[0034] S402: According to the channel overlap deviation record, 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 respectively, mark the channels whose deviation values exceed the corresponding thresholds, summarize and record the information of the trigger points, and generate a deviation trigger channel index group; Each deviation data of 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 is ±10%, and the blue channel is ±12%. The system reads the channel type and deviation value of each record item to determine 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 this 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 performed 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 with high sensitivity to color performance such as advertising production and exhibition displays, and generate a deviation trigger channel index group.

[0035] S403: Invoke 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 adjustment operations on items with an offset value exceeding the limit, update the set of display color output parameters, and generate a dynamic repair instruction; 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 under 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 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, such as making it return to the standard interval by reducing the saturation or offsetting the hue value. The adjustment operation will record and update the set of display color output parameters in the system in real time. The instruction contains the frame number, channel type, adjustment method and target value. This color repair process based on the trigger mechanism can dynamically respond to color anomalies and correct them immediately, especially suitable for complex scenarios with large changes in ambient light for indoor and outdoor screens, and generate dynamic repair instructions.

[0036] The specific steps for obtaining the 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 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 color temperature adjustment time series feature interval; The trigger timestamps and durations of effectiveness of instructions need to be recorded item by item to form the time trajectory of the color temperature adjustment behavior. When the system performs color temperature restoration, it will automatically generate a unique identifier for each instruction and record the execution time, for example, recorded as "2025-04-14 14:23:15", and at the same time append a duration field. If the restoration takes effect for 30 seconds, the system arranges the instructions in chronological order and plots their distribution on the time axis. If 100 instructions are generated within a day, the system will plot 100 time marker points in minutes and identify the density distribution of the restoration behavior in the visualization image. By observing and measuring the distances between each time point, an image of the time distribution characteristics of color temperature adjustment can be formed. The system further analyzes the dense and sparse intervals of the restoration events, extracts the time periods with frequent restorations, and constructs multiple time characteristic intervals. For example, 9:00 am is a frequent restoration period, and 11:00 is a sparse restoration period. The characteristic intervals will be used to establish a time series image model of color temperature adjustment, and in the image, the high-frequency and low-frequency restoration sections are distinguished by different colors or shapes, so that the system can form a complete view of the time distribution of color temperature adjustment, adapt to the management requirements for the color temperature stability of the screen in application scenarios such as offices, exhibition halls, and retail, and generate the time series characteristic intervals of color temperature adjustment.

[0037] S502: Invoke the time series characteristic intervals of 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, and measure the trigger interval between two consecutive dynamic restoration instructions. Determine the trend change rate based on the change amplitude of the trigger interval, and use the incremental direction and the interval change rate jointly as the trigger condition to obtain the display backlight gradient compensation mode; The entire time period is divided into adjacent fixed cycles, with each cycle set to 10 minutes. The number of occurrences of color temperature adjustment actions is counted within each cycle, 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 actions tend to be intensive. 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 when the curtains in a meeting room are opened or when the sunlight occlusion of an outdoor screen changes, and places higher response requirements on color temperature stability, resulting in the display backlight gradient compensation mode.

[0038] Please refer to Figure 2 , a display self-monitoring system, including: 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 based on the range of the difference to generate ambient light classification features; A color temperature calibration module for invoking the real-time color temperature and brightness values of the display corresponding to the time period of the ambient light classification sequence, judging whether the color temperature deviation exceeds the color temperature adjustment threshold of the real-time type based on the standard color temperature and brightness intervals, and if it exceeds the limit, adjusting both the color temperature and brightness simultaneously to generate a dynamic color calibration state; A pixel distribution module for invoking the frame number 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 that meet the brightness threshold condition within the channel, invoking the resolution parameters of the corresponding frame, and calculating the pixel proportion of each of the three channels in the entire frame to obtain a color gamut distribution map; 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; The backlight control module 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 the unstable state of the brightness response, and generate the backlight gradient compensation mode of the display.

[0039] The display includes a memory and a processor. A computer program is stored in the memory. When the processor executes the computer program, the above-mentioned display self-monitoring method is implemented.

[0040] 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 disclosed technical content 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 content of the technical solution of the present invention, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. A display self-monitoring method, characterized in that: The following steps are involved: S1: Collect light intensity data around the display, compare the continuously collected light intensity data with low light, strong light and reflected light conditions in the preset standards one by one, classify the real-time ambient light into low light, strong light or reflected light categories according to the comparison difference, and generate ambient light classification features; S2: Based on the ambient light classification characteristics, the real-time color temperature value of the display is called to perform a difference operation with the preset standard color temperature value to determine whether the color temperature difference exceeds the corresponding items among the low light threshold, the strong light threshold and the reflected light threshold. If so, the color temperature and brightness are synchronously adjusted to the preset standard range to generate a dynamic color calibration state; S3: Utilizing the dynamic color calibration state, extracting the distribution ratio of the red, green and blue channels frame by frame for the pixel points of the real-time display frame of the display, counting the coverage area ratio of the multiple channels in the display area, analyzing the overlap ratio of the coverage area ratio and the standard color gamut, and obtaining a color gamut distribution map; S4: According to the coverage area ratios of the red, green and blue channels in the color gamut distribution diagram, the deviations of the overlapping ratios of the multi-channels and the standard color gamut are calculated respectively; when the channel deviation exceeds the corresponding repair trigger threshold, the hue and saturation of the display are adjusted to generate a dynamic repair instruction.

2. The display self-monitoring method according to claim 1, characterized in that: The ambient light classification features include light level, deviation range, and environmental matching degree; the dynamic color calibration status includes color temperature correction amplitude, brightness calibration amplitude, and synchronous adjustment mark; the color gamut distribution diagram includes color gamut coverage, channel saturation, and color gamut offset; the dynamic repair instructions include hue compensation instructions, saturation correction instructions, and channel equalization instructions.

3. The display self-monitoring method according to claim 1, characterized in that: The steps for obtaining the ambient light classification features are specifically as follows: S101: Collect 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 data sequence, call the light intensity values ​​of the continuous time period in the sequence and compare them with the threshold interval in the standard classification value item by item to generate the light intensity interval offset difference; S102: Based on the light intensity interval offset difference, for each light intensity value, determine its offset difference with the threshold interval in the standard classification value, record the classification label corresponding to the offset difference in real time, 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 in each time window, mark the categories of the ratio difference values ​​as the dominant lighting type of the time period, summarize the dominant lighting type marks, identify the dominant lighting classification trend in the continuous environment, and generate ambient light classification features.

4. The display self-monitoring method according to claim 3, characterized in that: The steps of acquiring the dynamic color calibration state are specifically as follows: S201: Based on the ambient light classification characteristics, within the time period to which the dominant lighting type belongs, calling the real-time color temperature value of the display within the corresponding time period and the preset standard color temperature value, respectively calculating the color temperature difference between the two within the time period, and generating the time period color temperature difference interval; The formulas for calculating the color temperature difference between the two in the time period are as follows: ； Among them, ΔT is the dynamic color temperature difference, 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 corresponding time period of the dominant lighting type, t is the timestamp of the real-time time point, τ is the time attenuation coefficient, Δt j is the time interval between adjacent time points j, and m is the total number of time points; S202: calling the color temperature difference interval of the time period, corresponding to the real-time category in the ambient light classification feature for the color temperature difference, performing difference judgment between the color temperature difference and the low light color temperature threshold, the strong light color temperature threshold or the reflected light color temperature threshold set by the category, recording the color temperature difference points exceeding the threshold, and generating an abnormal color temperature deviation position index; S203: extracting the real-time color temperature value and brightness value of the display within the corresponding time period according to the abnormal color temperature deviation position index, performing a difference comparison with the corresponding preset standard color temperature interval and brightness interval within the time period, and adjusting the color temperature value and brightness value according to the preset adjustment parameters for the items exceeding the comparison difference, to obtain a calibrated color temperature and brightness adjustment record; S204: calling the color temperature and brightness adjustment records after calibration, filling them into the original color temperature and brightness data in chronological order, comparing the difference trends before and after to determine the calibration execution range, marking the calibration status label sequence, and generating a dynamic color calibration status.

5. The display self-monitoring method according to claim 4, characterized in that: The steps of obtaining the color gamut distribution diagram are specifically as follows: S301: Based on the dynamic color calibration state, call the image data of each frame of the display, read the pixel information frame by frame, extract the pixel values ​​of the red, green and blue channels, count the total number of multi-channel pixel values ​​in each frame, and calculate the proportion of the total number of pixels in the whole frame to generate a frame-level channel distribution ratio; S302: calling the frame-level channel distribution ratio, combining the resolution data of each frame in the display area, counting the number of pixels covered by multiple channels in the entire frame display area according to the channel labels, and converting the channel coverage area ratio, and then summarizing the channel area ratio according to the time axis to generate a channel coverage area ratio sequence; S303: According to the channel coverage area ratio sequence, pixel distribution is compared with the color gamut range corresponding to the red, green and blue channels in the standard color gamut data in turn, the ratio of pixels overlapping with the standard color gamut in the real-time channel coverage area is calculated, and the pixel ratio is used as the overlapping ratio value of each frame, and a pixel overlap ratio image is drawn to generate a color gamut distribution diagram.

6. The display self-monitoring method according to claim 5, characterized in that: The formula for calculating the ratio of pixels overlapping the standard color gamut in the real-time channel coverage area is as follows: ； Where RW is the overlap offset eigenvalue, 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 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.

7. The display self-monitoring method according to claim 1, characterized in that: The display self-monitoring method also includes the following steps: S5: recording the triggering timestamp and duration of the dynamic repair instruction, extracting the timing distribution characteristics of the color temperature adjustment, and counting the incremental direction of the number of color temperature adjustments in adjacent cycles and the rate of change of the repair instruction triggering interval to obtain the display backlight gradient compensation mode; The display gradient compensation mode includes a time stamp sequence, an adjustment frequency trend, and a backlight intensity gradient.

8. The display self-monitoring method according to claim 7, characterized in that: The steps of acquiring the display gradient compensation mode are specifically as follows: S501: Based on the dynamic repair instructions, record the trigger timestamp and 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 color temperature adjustment time series feature interval; S502: Call the color temperature adjustment timing characteristic interval, with adjacent time periods as segments, count the number of color temperature adjustments in each cycle, calculate the incremental direction of the number of adjustments between consecutive cycles, and measure the trigger interval between two consecutive dynamic repair instructions. The trend change rate is determined based on the change amplitude of the trigger interval, and the incremental direction and interval change rate are combined as trigger conditions to obtain the display backlight gradient compensation mode.

9. A display self-monitoring system, characterized in that: Used to execute the display self-monitoring method according to any one of claims 1 to 8, comprising: The light recognition module is used to collect light intensity data around the display, call the standard thresholds of low light, strong light and reflected light, determine the difference between the recorded value and the three thresholds, complete the light classification according to the range of the difference, and generate the ambient light classification features; A color temperature calibration module is used to call the real-time color temperature and brightness values ​​of the display in the corresponding period of the ambient light classification sequence, and 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 range. If it exceeds the limit, the color temperature and brightness are adjusted at the same time to generate a dynamic color calibration state; A pixel distribution module is used to call 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, call the resolution parameter of the corresponding frame, calculate the pixel ratios of the three channels in the whole frame, and obtain a color gamut distribution map; A color restoration module is used to call the channel ratio value of each frame in the color gamut distribution map, determine whether it exceeds the corresponding restoration trigger threshold, extract the hue value and saturation value of the trigger frame, determine whether it exceeds the standard control range, and perform adjustment if there is an offset to generate a dynamic restoration instruction; The backlight control module is used to call the time point and duration of the dynamic repair instruction, count the direction of change of the repair frequency in adjacent time periods, analyze the changing trend of the trigger interval between instructions, determine whether the cycle triggers an unstable brightness response state, and generate a display backlight gradient compensation mode.

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

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