An adaptive color shift compensation method and device for liquid crystal display and storage medium

Abstract

The application relates to the technical field of display, and discloses a self-adaptive color deviation compensation method and device of a liquid crystal display screen and a storage medium, the method comprising the following steps: collecting real-time ambient light change data, and generating ambient light feature data set; analyzing the mapping relationship between screen color deviation and light parameters in the ambient light feature data set, determining the interference mode of ambient light on screen color; constructing a color rule database according to the interference mode, and generating a reference data matrix for real-time color deviation compensation; generating a self-adaptive color adjustment curve based on the reference data matrix, determining color compensation parameters under different ambient light conditions; and generating a color compensation scheme according to the color compensation parameters, and adjusting the color presentation of the display screen in real time. The application solves the problem of screen color deviation of the liquid crystal display screen under complex and changeable light environments, realizes real-time correction of the liquid crystal display screen, and ensures the accuracy of color display under different light environments.

Description

Technical Field

[0001] This application relates to the field of display technology, and in particular to an adaptive color deviation compensation method, device and storage medium for a liquid crystal display screen. Background Technology

[0002] In the field of display technology, liquid crystal displays (LCDs) occupy an important position in various display devices due to their mature technology, moderate cost, and excellent display effect. LCDs are widely used in numerous fields because of their high refresh rate, high resolution, and wide color gamut.

[0003] The accuracy and adaptability of LCD screen colors are key factors in improving user experience and device performance. With the widespread application of display devices in various scenarios, from bright outdoor environments to dimly lit indoor spaces, users have placed higher demands on the consistency of screen color performance in different environments. However, current LCD screens face a significant challenge in color adjustment: color adjustment methods for environmental changes often rely on fixed calibration modes.

[0004] However, this fixed calibration mode has obvious limitations and is difficult to dynamically adapt to complex and changing lighting conditions, which leads to deviations in screen color under dynamically changing lighting environments. Summary of the Invention

[0005] This application provides an adaptive color shift compensation method, device, and storage medium for a liquid crystal display (LCD). Related technologies, using calibration methods with fixed brightness adjustments, struggle to effectively address complex environmental light interference. This interference primarily manifests in the mixing of natural and artificial light sources, and the nonlinear dynamic changes in color shift patterns caused by instantaneous strong light. Large displays exhibit spatial heterogeneity across different areas, such as the influence of direct spotlights on the top and reflected light from the ground on the bottom. Furthermore, different LCD panels exhibit hardware-dependent color shift responses to the same lighting conditions. This application, through multi-sensor collaborative sensing, interference pattern modeling, and closed-loop control generating a dynamic compensation matrix, solves the problem of color shift in LCDs under complex lighting conditions, preventing normal display of content. It achieves real-time, accurate color correction, ensuring accurate color display under various lighting conditions.

[0006] This application provides an adaptive color shift compensation method for a liquid crystal display screen, the adaptive color shift compensation method for the liquid crystal display screen includes:

[0007] Collect real-time ambient light change data to generate an ambient light feature dataset;

[0008] The mapping relationship between screen color deviation and light parameters in the ambient light feature dataset is analyzed to determine the interference mode of ambient light on screen color.

[0009] A color pattern database is constructed based on the interference pattern, and a reference data matrix for real-time color shift compensation is generated.

[0010] Based on the reference data matrix, an adaptive color adjustment curve is generated to determine the color compensation parameters under different ambient light conditions.

[0011] A color compensation scheme is generated based on the color compensation parameters to adjust the color presentation of the display screen in real time.

[0012] Optionally, the step of analyzing the mapping relationship between screen color deviation and light parameters in the ambient light feature dataset to determine the interference mode of ambient light on screen color includes:

[0013] Obtain the difference between ambient light brightness and light intensity in the ambient light feature dataset;

[0014] If the difference value is not within the preset difference threshold range, the difference value is marked as an abnormal light range, and the color temperature distribution characteristics of the abnormal light range are extracted.

[0015] The interaction between the color temperature distribution characteristics and the incident angle of light is analyzed to extract the interference mode characteristics that cause color distortion of the display screen and to determine the interference mode.

[0016] Optionally, the step of constructing a color pattern database based on the interference pattern and generating a reference data matrix for real-time color shift compensation includes:

[0017] Based on the interference pattern, the ambient light feature dataset is classified to generate interference types containing color shift patterns, thus obtaining preliminary classification data.

[0018] The preliminary classification data is matched with the dynamic change parameters of ambient light to generate the color pattern database;

[0019] The color pattern database is matched with preset association rules to generate color deviation association quantification results;

[0020] Based on the color deviation correlation quantization result, the real-time ambient light data is compared with the screen color deviation to generate the reference data matrix.

[0021] Optionally, after the step of comparing the real-time ambient light data with the screen color deviation based on the color shift correlation quantization result to generate the reference data matrix, the method further includes:

[0022] Obtain the reference data content in the reference data matrix, and detect the deviation between the reference data content and the actual detected value;

[0023] If the deviation value is greater than the preset deviation value, the historical reference data content, the actual detection value, and the screen response data are fused together to adjust the reference data matrix.

[0024] Optionally, the step of generating an adaptive color adjustment curve based on the reference data matrix and determining color compensation parameters under different ambient light conditions includes:

[0025] By classifying and matching environmental variables and lighting conditions in the reference data matrix using preset data mapping rules, preliminary mapping data is generated.

[0026] The trend of changes in lighting conditions and color shifts in the preliminary mapping data is obtained to generate the adaptive color adjustment curve;

[0027] If a deviation is detected between the adaptive color adjustment curve and the expected adjustment curve, the adaptive color adjustment curve is corrected, and the range of compensation parameters corresponding to the correction is obtained, and a set of compensation parameters is generated.

[0028] The color compensation parameters are determined by adaptively matching the set of compensation parameters with the lighting conditions.

[0029] Optionally, after the step of adaptively matching the set of compensation parameters with the lighting conditions to determine the color compensation parameters, the method includes:

[0030] Dynamically monitor real-time light conditions and acquire at least one dataset of environmental variables related to light intensity;

[0031] If the light intensity in the monitored environmental variable dataset exceeds a preset light intensity threshold range, the environmental variable dataset is compared with the real-time light conditions to generate preliminary adjustment data corresponding to the current ambient light.

[0032] Obtain the color shift pattern from the historical database that matches the real-time lighting conditions, and correct the preliminary adjustment data based on the color shift pattern to update the compensation parameter set;

[0033] The updated set of compensation parameters is matched with the actual display requirements to determine the color compensation parameters that match the real-time lighting conditions.

[0034] Optionally, the step of generating a color compensation scheme based on the color compensation parameters and adjusting the color presentation of the display screen in real time includes:

[0035] The color compensation parameters are decomposed to obtain at least one adjustment dataset associated with the screen output;

[0036] The adjusted dataset is compared with the expected display requirements to generate the color compensation scheme;

[0037] An adjustment instruction is generated based on the color compensation scheme, and the color display content of the screen is controlled according to the adjustment instruction;

[0038] The color values ​​of the display screen are dynamically monitored, and the adjustment command is corrected according to the monitoring results to complete the real-time adjustment of the color presentation of the display screen.

[0039] Optionally, after the step of generating a color compensation scheme based on the color compensation parameters and adjusting the color presentation of the display screen in real time, the method includes:

[0040] Continuously monitor changes in ambient light, generate a light fluctuation dataset, and determine the fluctuation range of the light fluctuation dataset;

[0041] The display screen area corresponding to the fluctuation range exceeding the preset deviation range is obtained to obtain the screen deviation distribution.

[0042] Based on the screen deviation distribution, the display area is subjected to layer-by-layer color correction to complete the color deviation adjustment of the display.

[0043] In addition, to achieve the above objectives, embodiments of the present invention also provide a terminal device, including a memory, a processor, and an adaptive color shift compensation program for a liquid crystal display stored in the memory and executable on the processor. When the processor executes the adaptive color shift compensation program for the liquid crystal display, it implements the method described above.

[0044] In addition, to achieve the above objectives, embodiments of the present invention also provide a computer-readable storage medium storing an adaptive color shift compensation program for a liquid crystal display screen. When the adaptive color shift compensation program for the liquid crystal display screen is executed by a processor, it implements the method described above.

[0045] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0046] (1) This invention collects ambient light data in real time and constructs a feature dataset to analyze the mapping relationship between light parameters and screen color deviation, thereby identifying the interference pattern of ambient light on display color. It solves the problem that the nonlinear change of color deviation caused by the mixing of natural light and artificial light sources, and related technologies cannot effectively solve the problem of the influence of complex nonlinear light on the display screen through subjective calibration or fixed compensation, and realizes data-driven interference pattern recognition, providing a scientific basis for subsequent compensation.

[0047] (2) Based on the identified interference patterns, this invention establishes a color pattern database and generates a real-time compensation reference matrix, thereby inferring an adaptive adjustment curve and accurately calculating the color compensation parameters under different lighting conditions. Compared with static compensation schemes using preset modes, this dynamic compensation method can cover more complex lighting scenarios, such as day-night switching and mixed light sources, significantly improving the flexibility of color reproduction.

[0048] (3) After calculating the color compensation parameters, the present invention adjusts the color output of the display screen in real time according to the color compensation parameters to offset the problems of color deviation and contrast reduction of the liquid crystal display screen caused by ambient light, and ensures that the same content presents a consistent color performance under different lighting conditions. Attached Figure Description

[0049] Figure 1 This is a flowchart illustrating an embodiment of the adaptive color shift compensation method for a liquid crystal display screen according to this application.

[0050] Figure 2 This is a flowchart illustrating Embodiment 2 of the adaptive color shift compensation method for liquid crystal displays of this application;

[0051] Figure 3 This is a flowchart illustrating Embodiment 3 of the adaptive color shift compensation method for liquid crystal displays of this application;

[0052] Figure 4 This is a schematic diagram of the terminal structure of the hardware operating environment involved in one embodiment of this application. Detailed Implementation

[0053] To address display distortion issues such as color shift and decreased contrast in complex lighting environments involving a mixture of natural and artificial light sources, this solution collects ambient light data in real time and constructs a feature dataset. It analyzes the mapping relationship between light parameters and screen color deviation to identify interference patterns caused by ambient light. Based on these identified interference patterns, a color law database is established, and a real-time compensation reference matrix is ​​generated. This allows for the derivation of adaptive adjustment curves and the accurate calculation of color compensation parameters under different lighting conditions. Finally, the display's color output is adjusted in real time based on these color compensation parameters. This achieves stable color reproduction and display performance even in complex lighting environments.

[0054] To better understand the above technical solutions, exemplary embodiments of this application will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of this application are shown in the drawings, it should be understood that this application can be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of this application and to fully convey the scope of this application to those skilled in the art.

[0055] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0056] Example 1

[0057] In this embodiment, an adaptive color shift compensation method for a liquid crystal display screen is provided.

[0058] Reference Figure 1 The adaptive color shift compensation method for the liquid crystal display screen in this embodiment includes the following steps:

[0059] Step S100: Collect real-time ambient light change data and generate an ambient light feature dataset;

[0060] In this embodiment, by deploying a multi-point light sensor array, ambient light change data is collected in real time, covering light inputs of different angles and intensities, and an ambient light feature dataset containing brightness, color temperature and spectral distribution is generated to obtain preliminary results of ambient light change.

[0061] As an optional implementation, a deployed array of light sensors collects ambient light data in real time, covering light inputs of different angles and intensities, to obtain a raw dataset containing brightness, color temperature, and spectral distribution. This raw dataset is then preliminarily cleaned to remove outliers, resulting in a refined light feature set. Based on this light feature set, brightness and color temperature data are classified according to a preset feature threshold range. If the brightness value within a certain time period exceeds the preset feature threshold range, it is marked as an abnormal light interval. Simultaneously, the spectral distribution data within the abnormal light interval is acquired to determine the specific feature distribution of the abnormal light interval.

[0062] For example, in the process of real-time acquisition of ambient light data through a light sensor array, assume that the sensor array is deployed in various corners, covering multiple angles, and data is collected every 5 minutes, recording brightness, color temperature, and spectral distribution. Assume that the brightness data collected in a certain instance ranges from 200 to 800 lux, the color temperature data ranges from 3000 to 6500 Kelvin, and the spectral distribution is characterized by the intensity values ​​of the red, green, and blue channels. During initial data cleaning, if the brightness value of a certain sensor suddenly reaches 1200 lux, significantly deviating from the normal range, it can be considered an outlier and removed, resulting in a light feature set. When classifying the brightness and color temperature data, a preset brightness threshold of 300 to 700 lux and a color temperature threshold of 3500 to 6000 Kelvin can be used. If the brightness value reaches 850 lux within a certain time period, it is marked as an abnormal light range. For this abnormal light range, further extraction of spectral distribution data reveals an abnormally high proportion of red channel intensity, possibly due to interference from nearby red lights. Identifying this characteristic distribution helps pinpoint the specific source of anomalous light.

[0063] As another optional implementation, after determining the specific feature distribution of the abnormal light range, the feature distribution of the abnormal light range is compared and analyzed to obtain the data differences with those of the normal light range. The fluctuation trend of these data differences is then judged to obtain a quantitative fluctuation result of the ambient light change. Based on the quantitative fluctuation result, combined with light input data from different angles, the light features collected from multiple points are integrated and processed to determine the variation pattern of the ambient light in different time periods and spatial locations, thereby obtaining an ambient light feature dataset.

[0064] For example, when an abnormal light range is detected, retrospective analysis of historical illumination data reveals a clear periodic fluctuation in brightness values, while the color temperature parameter continuously deviates from the baseline value by approximately 500 Kelvin. Therefore, this fluctuation trend is determined to be significantly correlated with specific external interference sources, such as the headlights of passing vehicles outside the window. This fluctuation trend assessment provides a basis for subsequent quantification of ambient light changes. The quantification results show that the fluctuation amplitude is between ±150 lux, with a period of approximately 10 minutes. Weights are dynamically assigned based on the spatial location and degree of interference of each sensor. For example, sensor data near the window and significantly affected by external light are weighted at 0.6, while relatively stable indoor sensor data are weighted at 0.4. This differentiated weighting strategy preserves the characteristics of external light changes while ensuring the stability of basic illumination parameters. Through real-time fusion processing, not only are the significant brightness fluctuations in the window area during the morning period accurately captured, but the relatively stable illumination conditions deep inside the room are also found. The final generated ambient light feature dataset not only contains accurate quantification parameters but also fully records the spatial distribution patterns and temporal variation patterns of illumination features.

[0065] Step S200: Analyze the mapping relationship between screen color deviation and light parameters in the ambient light feature dataset to determine the interference mode of ambient light on screen color;

[0066] In this embodiment, a pre-defined light influence model is used to analyze the ambient light feature dataset, extract features from the mapping relationship between screen color deviation and light parameters, and determine the specific interference mode of ambient light changes on screen color. The light influence model is used to quantify the impact of ambient light on display color deviation. By analyzing the mapping relationship between ambient light parameters such as ambient light intensity, color temperature, and incident angle and screen color changes, prediction rules are established to provide a basis for real-time color compensation. The light influence model identifies key interference factors from the ambient light data, classifies typical interference scenarios, and outputs compensation parameters for use in real-time color correction. For example, it identifies the key interference factor "high color temperature light causing overexposure of the blue channel" and classifies it as "diffuse weak light."

[0067] As an optional implementation, the difference between ambient light brightness and light intensity in the ambient light feature dataset is obtained. If the difference value is not within a preset difference threshold range, the difference value is marked as an abnormal light interval, and the color temperature distribution feature of the abnormal light interval is extracted. The synergistic effect between the color temperature distribution feature and the incident angle of light is analyzed, and the interference mode feature that causes color distortion of the display screen is extracted to determine the interference mode.

[0068] For example, considering the differences in ambient light brightness and light intensity, a preset brightness threshold range of 200 to 700 lux is used. If the brightness value reaches 850 lux within a certain time period, this time period is marked as an abnormal light interval. Spectral data analysis is performed on this interval to obtain color temperature distribution characteristics. Assuming the color temperature data is concentrated in the low color temperature range of 3000 to 4000 Kelvin, combined with multi-angle light sensor recordings, it is confirmed that the light mainly illuminates the screen from the left-side floor-to-ceiling window at an incident angle of 45 to 60 degrees. Data comparison shows that under this abnormal lighting condition, a significant red-yellow bias appears in the left-side area of ​​the screen. Further analysis reveals that when the incident angle is less than 50 degrees, the degree of color bias increases exponentially with decreasing angle, and the red channel gain abnormally increases by 12%. These characteristic parameters [850 lux, 3500K, 50-degree incident angle, left-side red bias] are constructed as a "warm light oblique interference mode," and its spatial distribution characteristics are recorded as follows: the affected area is concentrated in the left 30% area of ​​the screen, and the color bias gradient decreases from left to right. This method, which generates interference pattern characteristics by analyzing color temperature distribution characteristics and light incident angle, can effectively solve the problem that related technologies cannot adjust color deviation when faced with significant differences in light angle and intensity in different areas of a large display screen.

[0069] As another optional implementation, after determining the interference mode, interference mode distribution data is acquired and integrated with the spatial location influence to determine the range of influence of ambient light changes on screen display adjustment. The interference mode distribution data is a multi-dimensional feature set, including the physical characteristics of ambient light itself, such as light intensity and color temperature, and also incorporating the spatial distortion characteristics generated after the light interacts with the display screen, such as the red-yellow bias appearing in the left 30% area and the abnormal 12% gain in the red channel. Spatial location influence refers to the spatial dependence exhibited when ambient light interacts with the display screen, including the variation of light source position, screen area, and interference intensity with screen position.

[0070] For example, interference pattern distribution data is acquired and combined with spatial location influences to establish a dynamic mapping relationship. For instance, when light at a specific incident angle is detected shining from the left, a three-dimensional compensation field is constructed. In this field, the adjustment amount of each pixel is determined by its spatial coordinates. The directly affected core area on the left adopts the maximum compensation intensity, such as reducing the red channel by 12%. As the position shifts to the right, the compensation intensity smoothly decreases according to the optical attenuation law until the unaffected area returns to zero. This combination is not a simple data superposition, but rather a cross-disciplinary algorithm combining light propagation models, display material properties, and human visual perception to transform abstract interference parameters into pixel-level operation instructions on the screen's physical coordinates. Ultimately, this achieves a display effect that eliminates local color shifts while maintaining overall image harmony.

[0071] For example, when the interference pattern distribution data is [850 lux, 3500K, 50-degree incident angle, left red shift, red channel +12%], the spatial location of the light source is affected by the light incident direction from 45 to 60 degrees to the left, and the physical range of the display affected by ambient light is 30% of the left side area. The interference intensity decreases with the screen position from left to right due to the color shift. The interference pattern distribution data is integrated with the spatial location to perform spatial location modeling. A geometric correspondence is established between the sensor coordinates, such as the position of sensor #3 on the left [x,y], and the screen partitions. The interference weight of each pixel is calculated, such as a weight of 1.0 for the directly illuminated area and a cosine-attenuated weight for the edge area. The color shift parameter "red channel +12%" in the interference pattern is converted into a compensation matrix according to the spatial weight distribution, such as a compensation of -12% for the red channel in the left 15% area, and a linear decrease to -5% in the 15%-30% area. Kalman filtering is then used to predict the movement trend of the interference area, such as the westward movement of the sun causing the affected area to expand to the right. Bilateral filtering is used to eliminate jagged edges at the compensation boundary, ensuring visual smoothness.

[0072] When integrating data on interference patterns and considering the influence of spatial location, the sensor data collected at different locations around the screen can be taken into account. For example, if a sensor near the left side detects higher warm light intensity while the right side shows normal data, fusion processing can determine that the impact of ambient light changes on screen display adjustment is mainly concentrated in the left area. This analysis helps to accurately locate the screen area requiring adjustment. By correcting screen color shift by region, efficiency can be effectively improved, ensuring the overall consistency of the display effect.

[0073] Optionally, to investigate the relationship between changes in light angle and screen color deviation, interference patterns can be analyzed by comparing light input data from different angles. For example, assuming light enters from a 45-degree angle on the left, the brightness of the left side of the screen increases by approximately 150 lux, and the color temperature decreases by approximately 500 Kelvin, resulting in a yellowish tint. Extracting the characteristics of the angle-related interference patterns provides a basis for subsequent correction. This refined analysis allows for targeted adjustments to screen parameters.

[0074] Step S300: Construct a color pattern database based on the interference pattern and generate a reference data matrix for real-time color shift compensation;

[0075] In this embodiment, by classifying and processing the interference patterns, a color pattern database is constructed, and the correlation between ambient light changes and screen color deviation is quantified and stored to obtain a reference data matrix that can be used for subsequent adjustments.

[0076] As an alternative implementation method, the ambient light feature dataset is classified based on the interference pattern to generate interference types containing color shift patterns, thus obtaining preliminary classification data.

[0077] For example, the impact of ambient light changes on screen deviation can be addressed through classification. First, different threshold ranges are set for the characteristics of the interference mode. Suppose that in an indoor office scenario, the brightness threshold range of ambient light is set to 100 to 600 lux, and the color temperature threshold range is 4000 to 6000 Kelvin. If a brightness of 650 lux and a color temperature of 3500 Kelvin are detected for a certain period of time, it is classified as an abnormal mode and marked as "warm light interference" in the preliminary classification data. This classification method can quickly filter out the light conditions that may affect the screen display.

[0078] Optionally, the classification of interference patterns can be further refined by incorporating the influence of light angle. For example, if light enters from a 30-degree angle on the left, the brightness of the left side of the screen increases significantly, while the right side changes less, then the classification data can be additionally labeled as "left-side light interference." This refined classification can provide a more accurate basis for subsequent local adjustments.

[0079] As another optional implementation, the preliminary classification data is matched with the dynamic change parameters of ambient light to generate a color pattern database; the color pattern database is then matched with preset association rules to generate color deviation association quantification results.

[0080] For example, when integrating the preliminary classification data, color data can be matched with dynamic ambient light change parameters. Suppose a periodic change in lighting is detected in a conference room during the morning. The collected light data is classified, identifying "intermittent warm light interference," characterized by color temperature fluctuations between 3000 and 3500 Kelvin, accompanied by a periodic 20% increase in red light intensity. These classification results are then matched with the dynamic ambient light change parameters, generating a record in the color pattern database: "When the color temperature is below 3500 Kelvin and the red light intensity increases by more than 15%, the red channel on the screen exhibits an average oversaturation of 12%." When the data recorded in the color pattern database matches the preset association rule of "low color temperature - red light oversaturation," the quantization calculation engine is activated. For example, if the current color temperature is detected to be 3200 Kelvin and the red light intensity is increased by 18%, relevant data in the color law database is obtained to get a preliminary compensation value. Then, it is matched with the association rule of "low color temperature - red light oversaturation" to correct the preliminary compensation value, obtain the accurate compensation value, and simultaneously optimize the compensation rule. Finally, a quantitative result containing the compensation intensity and execution parameters is generated.

[0081] As another optional implementation, based on the color deviation correlation quantization result, the real-time ambient light data is compared with the screen color deviation to generate the reference data matrix.

[0082] For example, after determining the correlation quantization results, the correspondence between ambient light changes and screen deviations is organized. Suppose that by comparison, it is found that when the color temperature is reduced to 3500 Kelvin, the blue channel value displayed on the screen is reduced by about 10%. Then, a storage matrix is ​​generated, and this correspondence is recorded in data form. This matrix-based storage method facilitates the quick retrieval of reference data content.

[0083] Optionally, time-dimensional data can be incorporated to supplement the generation and calibration of the reference data matrix. For example, assuming that light variations exhibit regular fluctuations at different times of the day, the reference data matrix can record reference data for specific time periods, such as the relationship between light parameters at 9 AM and screen deviation. This approach enhances the matrix's dynamic adaptability, ensuring suitable adjustment references are provided under different time conditions.

[0084] As another optional implementation, after generating the reference data matrix, the reference data content in the reference data matrix is ​​obtained, and the deviation value between the reference data content and the actual detection value is detected; if the deviation value is greater than a preset deviation value, the historical reference data content, the actual detection value and the screen response data are fused together, and the reference data matrix is ​​adjusted.

[0085] For example, if there is a discrepancy between the reference data and the adjustment criteria—such as the blue channel reduction value recorded in the reference data matrix being inconsistent with the actual detection value—calibration is performed. Assuming the blue channel decreased by 15% in the actual detection, while the matrix records a reduction of 10%, multiple sets of detection data are combined to adjust the matrix content and generate the final adjusted data matrix.

[0086] Optionally, when determining whether the adjusted reference data matrix meets the preset matching standard, a deviation tolerance range can be set, such as an allowable deviation value of plus or minus 5%. If the deviation between the adjusted value of the blue channel and the target value in the adjusted matrix data is within 3%, it is considered to meet the standard. This judgment method helps to ensure the applicability of the final adjusted data.

[0087] Step S400: Based on the reference data matrix, generate an adaptive color adjustment curve and determine the color compensation parameters under different ambient light conditions;

[0088] In this embodiment, a regression analysis algorithm can be applied to the reference data matrix to generate an adaptive color adjustment curve, thereby dynamically determining the optimal color compensation parameters according to different ambient light conditions.

[0089] As an optional implementation, environmental variables and lighting conditions in the reference data matrix are classified and matched using preset data mapping rules to generate preliminary mapping data.

[0090] For example, when constructing a reference data matrix and organizing the correspondence between environmental variables and lighting conditions, variables such as ambient light brightness and color temperature are classified and matched. Assuming that in a display application environment, the ambient light brightness ranges from 200 to 800 lux and the color temperature ranges from 3000 to 7000 Kelvin, these variables are associated with the screen color offset to generate preliminary mapping data.

[0091] Optionally, when compiling the correspondence between environmental variables and lighting conditions, the influence of the direction of light incidence can also be considered. For example, if the overall brightness is uniform when light shines directly from the front of the screen, but the color shift is more pronounced at the screen edges when light is incident from the side, this variable can be additionally recorded to form more comprehensive mapping data.

[0092] As another optional implementation, the trend of changes in lighting conditions and color shifts in the preliminary mapping data is obtained to generate the adaptive color adjustment curve.

[0093] For example, in processing the initial mapping data, the trend of changes in lighting conditions and color shift is analyzed. Assuming that the red channel value of the screen exhibits a non-linear upward trend as brightness gradually increases, an adjustment curve is plotted based on historical data, and the corresponding curve parameter combinations, such as slope and inflection point values, are extracted. If these parameters are found to be inconsistent with the corresponding preset threshold range, such as the slope value exceeding the expected range, further calibration is required to ensure the accuracy of the curve.

[0094] Optionally, a time-dimensional variable can be introduced when fitting the adjustment curve. Assuming that lighting conditions vary regularly throughout the day—for example, the color temperature of light is higher at 10 AM—different combinations of curve parameters can be generated based on the time of day. This approach makes the adjustment scheme more dynamically adaptable, ensuring appropriate color compensation is provided at various times of day.

[0095] As another optional implementation, if a deviation is detected between the adaptive color adjustment curve and the expected adjustment curve, the adaptive color adjustment curve is corrected, and the range of compensation parameters corresponding to the correction is obtained to generate a set of compensation parameters.

[0096] For example, when a deviation is detected in the color adjustment curve, the curve is corrected and optimized based on actual detection data. Suppose that under specific lighting conditions, the offset of the screen's blue channel is detected to be 3% to 5% higher than the curve's predicted value. Then, the curve parameters are adjusted to generate a compensation parameter range that matches the conditions, thus generating a set of compensation parameters. For instance, the final determined compensation parameter range might be that the blue channel value needs to be increased by 5% to 8% to adapt to changes in the current ambient light.

[0097] Optionally, user feedback data can be incorporated into the optimization process when adjusting compensation parameters. For example, if users report color cast on the screen under specific lighting conditions, this feedback can be analyzed first, and the range of compensation parameters can be adjusted to better reflect actual usage scenarios. This approach significantly improves the user experience and ensures the practicality of the adjustments.

[0098] As another alternative implementation, after generating the set of compensation parameters, the set of compensation parameters is adaptively matched with the lighting conditions to determine the color compensation parameters.

[0099] For example, the adaptability of the set of color compensation parameters to lighting conditions can be analyzed. Assuming that the performance of the compensation parameters differs under various environmental variables, such as natural light in the morning and artificial light at night, the optimal parameter combination—that is, the final color compensation parameters—can be determined by analyzing which set of parameters is more stable under different conditions. For instance, in low-light conditions at night, the compensation value of the blue channel might need to be adjusted to 6%, while during the day it might remain at 5%. This combination will be used as the basis for the final adjustment.

[0100] Step S500: Generate a color compensation scheme based on the color compensation parameters and adjust the color presentation of the display screen in real time.

[0101] In this embodiment, a specific color compensation scheme is generated based on the color compensation parameters, the adjustment instructions are transmitted to the display driver module, the screen color output value is updated in real time, and the final color presentation effect is determined.

[0102] As an alternative implementation, the color compensation parameters are first decomposed to obtain at least one adjustment dataset associated with the screen output.

[0103] For example, color compensation requirements can be decomposed, that is, the complex influence of ambient light can be broken down into multiple actionable data dimensions. By analyzing factors such as light intensity and color temperature distribution, an adjustment dataset directly related to screen output can be extracted. Assuming that the light intensity detected in an indoor environment is 600 lux and the color temperature is warm, a dataset containing a 5% increase in brightness and a fine-tuning of the color temperature to 5500 Kelvin can be generated.

[0104] As another optional implementation, the adjusted dataset is compared with the expected display requirements to generate the color compensation scheme.

[0105] For example, when comparing the adjusted dataset with the expected display requirements, a historical lighting conditions database can be used to find reference data that most closely matches the current environment. Assuming the current lighting conditions are similar to a high-brightness scene in the historical database, the comparison results might suggest reducing the screen contrast to meet the expected display requirements, thus forming a preliminary color compensation scheme. This approach helps ensure that the adjustment direction aligns with the actual scene.

[0106] As another optional implementation, after generating a color compensation scheme, an adjustment instruction is generated based on the color compensation scheme, and the color presentation content of the display screen is controlled based on the adjustment instruction.

[0107] For example, when converting a color compensation scheme into adjustment instructions, the key is to refine the abstract compensation scheme into specific operational parameters. Assuming the color compensation scheme requires a 10% reduction in brightness, a corresponding numerical instruction is generated. This instruction is then formatted based on light adaptation characteristics, such as the rate of change of ambient light, to ensure it is suitable for the display driver module. When transmitting the instruction set to the display driver module, real-time data transmission is crucial. If, during transmission, a sudden increase in light intensity from 600 lux to 850 lux is detected, exceeding the preset range, the system will correct the instruction set, for example, by temporarily increasing the brightness adjustment range, generating the final output control data. This dynamic correction mechanism ensures that the instructions always adapt to the current environment.

[0108] As another optional implementation, the color values ​​of the display screen are dynamically monitored, and the adjustment command is corrected according to the monitoring results to complete the real-time adjustment of the color presentation of the display screen.

[0109] For example, screen color values ​​can be dynamically monitored by collecting screen display data in real time through built-in sensors and adjusting them according to color presentation requirements. If the monitoring finds that the screen brightness is too high, the output value is immediately reduced to ensure that the final displayed content meets the visual comfort standard. This real-time adjustment capability can effectively improve the adaptability of the screen display.

[0110] Alternatively, user habits can be used as an auxiliary parameter when monitoring and adjusting the color values ​​of the display screen. For example, if the user prefers cool tones, this characteristic can be retained during adjustment to ensure that the final displayed content adapts to the ambient light, thereby meeting personalized needs.

[0111] It should be noted that the spatial distribution of the sensor array has a physical mapping relationship with the display screen's intervals, ensuring precise data acquisition positioning. Retrospective analysis of historical illumination data is used to optimize the initial parameters of the real-time model, improving compensation efficiency. The color law database is iteratively updated based on the coupling relationship between ambient light characteristics and panel properties, enabling the system to have adaptive evolution capabilities. All these components form a whole, jointly achieving dynamic color shift suppression effects that cannot be achieved by a single calibration method in related technologies.

[0112] In this embodiment, by collecting real-time ambient light data and analyzing its mapping relationship with screen color shift, specific lighting interference patterns are identified. Based on this, a color rule database is established and a compensation matrix is ​​generated, ultimately achieving adaptive real-time color correction based on changes in ambient light. This enables real-time adaptive and accurate color correction of the LCD screen under different ambient lighting conditions, ensuring that the displayed image maintains accurate color performance in various lighting environments.

[0113] Example 2

[0114] Based on Embodiment 1, another embodiment of this application is proposed, with reference to... Figure 2 After the step of adaptively matching the set of compensation parameters with the lighting conditions to determine the color compensation parameters, the following steps are included:

[0115] Step S410: Dynamically monitor real-time light conditions and obtain at least one dataset of environmental variables related to light intensity;

[0116] Step S420: If the light intensity in the monitored environmental variable dataset exceeds the preset light intensity threshold range, the environmental variable dataset is compared with the real-time light conditions to generate preliminary adjustment data corresponding to the current ambient light.

[0117] Step S430: Obtain the color shift pattern matching the real-time lighting conditions from the historical database, and correct the preliminary adjustment data according to the color shift pattern to update the compensation parameter set;

[0118] Step S440: Match the updated set of compensation parameters with the actual display requirements to determine the color compensation parameters that match the real-time lighting conditions.

[0119] In this embodiment, ambient light is continuously monitored. If the ambient light change under complex lighting conditions exceeds the preset light intensity threshold, a pre-established deep learning model is invoked to optimize the color adjustment compensation parameters in real time, thereby obtaining color compensation parameters for a specific scene.

[0120] As an optional implementation, the current light conditions are dynamically monitored based on changes in ambient light, and at least one environmental variable dataset related to light intensity is obtained. It is then determined whether the light intensity exceeds a preset threshold range. If it does, the environmental variable dataset is compared with the light conditions to determine an adjustment basis that is compatible with the current ambient light, and the preliminary adjustment data is generated.

[0121] For example, when dynamically monitoring current lighting conditions, the built-in light sensor can capture the intensity and changing trends of ambient light in real time. The core of this approach lies in its ability to convert light intensity into a quantifiable dataset of environmental variables, such as recording light intensity in lux. Suppose that in the environment where the display is used, the light intensity suddenly increases from 300 lux to 900 lux within a short period, exceeding a preset threshold range of 500 to 800 lux. Then, the light intensity is matched with historical lighting conditions to analyze color shift patterns under similar light intensities. Assuming the current light intensity is 900 lux, it is determined that the screen brightness needs to be reduced by a certain percentage to avoid overexposure, and this adjustment basis is recorded to generate preliminary adjustment data.

[0122] Optionally, when dynamically monitoring lighting conditions, the periodic characteristics of ambient light changes can also be considered. Assuming that light intensity fluctuates regularly throughout the day—for example, light is weaker in the morning and gradually increases in the afternoon—this time-dimensional variable can be additionally recorded to form a more comprehensive dataset. This refined processing helps to more accurately adapt to scene requirements during subsequent mapping and calibration, improving the targeted nature of adjustments.

[0123] As another optional implementation, after generating the initial adjustment data, it is necessary to correct the initial adjustment data, and combine the characteristics of light intensity and ambient light changes to update the curve parameters of color adjustment in real time to obtain an optimized set of compensation parameters.

[0124] For example, when correcting the parameter mapping results, the focus is on updating the color adjustment curve parameters in real time, taking into account the characteristics of light intensity and ambient light changes. The slope and offset of the curve are dynamically adjusted based on the rate of change in light intensity. For instance, when light intensity changes rapidly, priority is given to improving the adjustment response speed to ensure that screen colors do not cause discomfort due to delay. Assuming the light intensity increases from 300 lux to 900 lux within 5 minutes, the color adjustment curve parameters are updated to a steeper shape, generating an optimized set of compensation parameters.

[0125] As another alternative implementation, after updating the compensation parameter set, the compensation parameter set is matched with the actual display requirements to obtain the final adjustment basis and determine the parameter strategy for real-time lighting conditions, that is, to determine the final color compensation parameters.

[0126] For example, when matching the optimized set of compensation parameters with the actual scene adaptation requirements, the goal is to determine the parameter strategy for specific lighting conditions. Suppose that in a high-brightness environment, the screen needs to reduce overall brightness and fine-tune the color temperature to maintain visual comfort. The analysis focuses on which set of data in the optimized parameter set best meets the current needs. If the final determined strategy is a 15% reduction in brightness and a color temperature adjustment to 5000 Kelvin, this adjustment effectively alleviates visual fatigue under strong light while maintaining natural color reproduction.

[0127] Optionally, the distribution characteristics of ambient light can be incorporated as a reference during parameter calibration and comparison. Assuming the light primarily illuminates from the sides of the screen, resulting in uneven brightness in the edge areas, parameters can be adjusted to ensure color compensation in the edge areas remains consistent with the center area. This approach significantly improves the uniformity of screen display, providing users with a more comfortable visual experience.

[0128] In this embodiment, by monitoring changes in ambient light in real time, intelligently analyzing lighting conditions and dynamically calibrating display parameters, the screen color can adaptively adjust parameters under various lighting conditions to ensure the best visual effect.

[0129] Example 3

[0130] Based on Embodiment 1 and Embodiment 2, another embodiment of this application is proposed, with reference to... Figure 3 After the step of generating a color compensation scheme based on the color compensation parameters and adjusting the color presentation of the display screen in real time, the following steps are included:

[0131] Step S510: Continuously monitor changes in ambient light, generate a light fluctuation dataset, and determine the fluctuation range of the light fluctuation dataset;

[0132] Step S520: Obtain the display screen area corresponding to the fluctuation range exceeding the preset deviation range, and obtain the screen deviation distribution;

[0133] Step S530: Based on the screen deviation distribution, perform layer-by-layer color correction on the display screen area to complete the color deviation adjustment of the display screen.

[0134] In this embodiment, after adjusting the display screen's color presentation in real time, it is also necessary to continuously monitor the feedback data between changes in ambient light and screen color deviation, iterate and correct the color compensation scheme, obtain more accurate light perception accuracy and visual consistency adjustment results, and update the color data pattern database for the iteratively corrected color compensation scheme to ensure that targeted adjustment schemes can be quickly matched in subsequent changes in ambient light, resulting in long-term stable color performance.

[0135] As an optional implementation, the light intensity of the surrounding environment is continuously captured, and the frequency of environmental changes is recorded during the capture to obtain the corresponding light fluctuation dataset and determine the specific range of change of the light fluctuation dataset.

[0136] For example, by continuously capturing the ambient light intensity through built-in sensors, the core of this method lies in the precise recording of the frequency of light changes. Suppose that in an indoor scene, the light intensity fluctuates from 500 lux to 700 lux in a short period. Recording this frequency of change creates a light fluctuation dataset, which can then be analyzed to determine that the range of change is within 200 lux. This recording method facilitates subsequent adaptive adjustments to adapt to environmental changes.

[0137] As another alternative implementation, when analyzing the screen deviation distribution, the color values ​​of the current screen can be captured, and the deviation parts that do not conform to the preset threshold can be extracted from the captured data to obtain the screen color deviation distribution.

[0138] For example, assuming the preset brightness threshold is 100 nits, and the actual captured value is 110 nits, the deviation exceeding 10 nits is marked separately to form a deviation distribution. This extraction method provides a clear direction for subsequent correction, especially when the deviation exceeds the threshold range, it can quickly locate the problem area.

[0139] Optionally, when capturing deviations, historical data can be used for comparison. If the current deviation distribution is similar to that of a certain period in the past, the deviation portion can be quickly extracted by referring to historical adjustment schemes. This method can shorten the analysis time and improve adjustment efficiency.

[0140] As another optional implementation, after obtaining the screen deviation distribution, it is analyzed whether the screen deviation distribution is within a preset deviation range. If it exceeds the preset deviation range, the deviation portion is corrected layer by layer based on the attributes of color correction and correction accuracy, and the adjusted color value is obtained during the correction process. After the color value adjustment is completed, the adjusted color value is matched with the light fluctuation dataset according to the attributes of real-time adjustment and environmental adaptation. During the matching, optimization is performed for the needs of light perception to obtain the final screen output adjustment scheme.

[0141] For example, assuming the screen display is reddish, the red channel value is gradually adjusted through color mapping, decreasing from an initially excessively high value until it approaches the preset standard value of 5500 Kelvin color temperature. During the calibration process, the adjusted color value is continuously compared to ensure it meets the requirements, ensuring that each adjustment is close to the target. This step-by-step adjustment method effectively avoids abrupt color changes caused by a single adjustment. For real-time adjustment and environmental adaptation, the adjusted color value is matched with a light fluctuation dataset. Assuming the light fluctuation dataset shows that the current ambient light intensity is stable at 600 lux, and the adjusted color value is biased towards brightness, the matching is optimized according to the light perception requirements, appropriately reducing the brightness output value. This matching process ensures that the screen display remains consistent with changes in ambient light, improving visual comfort.

[0142] Optionally, the frequency characteristics of light changes can also be considered when matching. If the ambient light fluctuates frequently in a short period of time, a more conservative adjustment scheme will be preferred to avoid discomfort caused by frequent changes in screen output.

[0143] Optionally, data recording can also incorporate a time dimension. For example, if the light intensity gradually increases from 300 lux in the morning to 800 lux at noon within a day, forming a fluctuating dataset with time attributes, this multi-dimensional recording method can comprehensively reflect changes in ambient light.

[0144] As another optional implementation, after obtaining the final screen output adjustment scheme, the color adjustment records in the screen output adjustment scheme are extracted. During extraction, the color change points in the records are classified and labeled to obtain a classified color change dataset. The color change dataset is imported into a pre-established color rule database. During import, it is grouped and stored according to the characteristics of ambient light changes to determine the data storage structure after grouping.

[0145] For example, when analyzing color adjustment records in feedback data, one can start with the extraction logic. Key changes in the adjustment records are identified, such as significant fluctuations in brightness or color temperature, and categorized as high-priority or low-priority changes. This classification helps to quickly locate adjustment areas requiring focus. For instance, if in one record the color temperature abruptly changes from 5000 Kelvin to 6000 Kelvin, this is marked as a high-priority change, while a record where brightness is finely adjusted from 100 nits to 105 nits is marked as low-priority. This classification provides clear guidance for subsequent data processing. Then, the categorized color change datasets are grouped and stored according to the characteristics of ambient light changes. For example, data with ambient light intensity fluctuating between 300 and 500 lux are grouped together, while data above 600 lux are grouped together. This grouped storage structure makes the database more organized and facilitates quick matching of relevant records during subsequent retrievals.

[0146] Optionally, in the design of the grouped storage structure, if the data volume is large, a time dimension can be introduced for auxiliary grouping. Assuming that ambient light gradually increases from 200 lux in the morning to 700 lux at noon, data from different time periods can be stored separately and labeled with time characteristics. This multi-dimensional grouping method provides a more comprehensive reference for subsequent retrieval and verification, ensuring that the adjustment scheme conforms to the actual light change patterns.

[0147] As another optional implementation, if the grouped data storage structure meets the corresponding preset threshold requirements, adjustment records related to changes in the current ambient light are extracted from the color pattern database. During extraction, priority is prioritized according to dynamic monitoring needs to obtain a sorted set of matching schemes. Based on the sorted set of matching schemes, the adjustment schemes in the set are subjected to secondary verification, and parameters are adjusted to ensure the persistence of color performance during the verification process.

[0148] For example, when prioritizing, assuming the current ambient light intensity is 400 lux, historical adjustment records related to this intensity range are extracted first, and records with similar light change frequency to the current time period are ranked first, forming a set of sorted matching schemes. The sorted set of matching schemes is then validated a second time, with parameter fine-tuning to optimize the consistency of color performance. For instance, if the brightness output value of a scheme is too high, the output parameters need to be gradually adjusted, such as fine-tuning the brightness from 110 nits to 105 nits, while observing whether the adjustment affects the overall color temperature balance. This fine-tuning process ensures the adaptability of the scheme in different environments.

[0149] Optionally, user habits can be incorporated into the prioritization process. For example, if a user prefers lower brightness output in certain lighting conditions, historical data that matches this preference can be prioritized. This approach improves the personalization of the solution and enhances user comfort.

[0150] This solution continuously monitors ambient light fluctuations and analyzes abnormal areas, performing layered corrections for display deviations exceeding thresholds to achieve precise adaptive adjustment of screen colors. It enables intelligent real-time compensation of the display for changes in ambient light, ensuring accurate color reproduction under various lighting conditions.

[0151] Example 4

[0152] In this application embodiment, an adaptive color shift compensation device for a liquid crystal display screen is proposed.

[0153] Reference Figure 4 , Figure 4 This is a schematic diagram of the terminal structure of the hardware operating environment involved in one embodiment of this application.

[0154] like Figure 4 As shown, the control terminal may include: a processor 1001, such as a CPU, a network interface 1003, a memory 1004, and a communication bus 1002. The communication bus 1002 is used to enable communication between these components. The network interface 1003 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface). The memory 1004 may be high-speed RAM or stable non-volatile memory, such as disk storage. Alternatively, the memory 1004 may be a storage device independent of the aforementioned processor 1001.

[0155] Those skilled in the art will understand that Figure 4 The terminal structure shown does not constitute a limitation on the terminal and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0156] like Figure 4 As shown, the memory 1004, which serves as a computer storage medium, may include an operating system, a network communication module, and an adaptive color shift compensation program for the liquid crystal display screen.

[0157] exist Figure 4 In the hardware structure of the adaptive color shift compensation device for the liquid crystal display shown, the processor 1001 can call the adaptive color shift compensation program for the liquid crystal display stored in the memory 1004 and perform the following operations:

[0158] Collect real-time ambient light change data to generate an ambient light feature dataset;

[0159] The mapping relationship between screen color deviation and light parameters in the ambient light feature dataset is analyzed to determine the interference mode of ambient light on screen color.

[0160] A color pattern database is constructed based on the interference pattern, and a reference data matrix for real-time color shift compensation is generated.

[0161] Based on the reference data matrix, an adaptive color adjustment curve is generated to determine the color compensation parameters under different ambient light conditions.

[0162] A color compensation scheme is generated based on the color compensation parameters to adjust the color presentation of the display screen in real time.

[0163] Optionally, the processor 1001 may call the adaptive color shift compensation program for the liquid crystal display stored in the memory 1004, and further perform the following operations:

[0164] Obtain the difference between ambient light brightness and light intensity in the ambient light feature dataset;

[0165] If the difference value is not within the preset difference threshold range, the difference value is marked as an abnormal light range, and the color temperature distribution characteristics of the abnormal light range are extracted.

[0166] The interaction between the color temperature distribution characteristics and the incident angle of light is analyzed to extract the interference mode characteristics that cause color distortion of the display screen and to determine the interference mode.

[0167] Optionally, the processor 1001 may call the adaptive color shift compensation program for the liquid crystal display stored in the memory 1004, and further perform the following operations:

[0168] Based on the interference pattern, the ambient light feature dataset is classified to generate interference types containing color shift patterns, thus obtaining preliminary classification data.

[0169] The preliminary classification data is matched with the dynamic change parameters of ambient light to generate the color pattern database;

[0170] The color pattern database is matched with preset association rules to generate color deviation association quantification results;

[0171] Based on the color deviation correlation quantization result, the real-time ambient light data is compared with the screen color deviation to generate the reference data matrix.

[0172] Optionally, the processor 1001 may call the adaptive color shift compensation program for the liquid crystal display stored in the memory 1004, and further perform the following operations:

[0173] Obtain the reference data content in the reference data matrix, and detect the deviation between the reference data content and the actual detected value;

[0174] If the deviation value is greater than the preset deviation value, the historical reference data content, the actual detection value, and the screen response data are fused together to adjust the reference data matrix.

[0175] Optionally, the processor 1001 may call the adaptive color shift compensation program for the liquid crystal display stored in the memory 1004, and further perform the following operations:

[0176] By classifying and matching environmental variables and lighting conditions in the reference data matrix using preset data mapping rules, preliminary mapping data is generated.

[0177] The trend of changes in lighting conditions and color shifts in the preliminary mapping data is obtained to generate the adaptive color adjustment curve;

[0178] If a deviation is detected between the adaptive color adjustment curve and the expected adjustment curve, the adaptive color adjustment curve is corrected, and the range of compensation parameters corresponding to the correction is obtained, and a set of compensation parameters is generated.

[0179] The color compensation parameters are determined by adaptively matching the set of compensation parameters with the lighting conditions.

[0180] Optionally, the processor 1001 may call the adaptive color shift compensation program for the liquid crystal display stored in the memory 1004, and further perform the following operations:

[0181] Dynamically monitor real-time light conditions and acquire at least one dataset of environmental variables related to light intensity;

[0182] If the light intensity in the monitored environmental variable dataset exceeds a preset light intensity threshold range, the environmental variable dataset is compared with the real-time light conditions to generate preliminary adjustment data corresponding to the current ambient light.

[0183] Obtain the color shift pattern from the historical database that matches the real-time lighting conditions, and correct the preliminary adjustment data based on the color shift pattern to update the compensation parameter set;

[0184] The updated set of compensation parameters is matched with the actual display requirements to determine the color compensation parameters that match the real-time lighting conditions.

[0185] Optionally, the processor 1001 may call the adaptive color shift compensation program for the liquid crystal display stored in the memory 1004, and further perform the following operations:

[0186] The color compensation parameters are decomposed to obtain at least one adjustment dataset associated with the screen output;

[0187] The adjusted dataset is compared with the expected display requirements to generate the color compensation scheme;

[0188] An adjustment instruction is generated based on the color compensation scheme, and the color display content of the screen is controlled according to the adjustment instruction;

[0189] The color values ​​of the display screen are dynamically monitored, and the adjustment command is corrected according to the monitoring results to complete the real-time adjustment of the color presentation of the display screen.

[0190] Optionally, the processor 1001 may call the adaptive color shift compensation program for the liquid crystal display stored in the memory 1004, and further perform the following operations:

[0191] Continuously monitor changes in ambient light, generate a light fluctuation dataset, and determine the fluctuation range of the light fluctuation dataset;

[0192] The display screen area corresponding to the fluctuation range exceeding the preset deviation range is obtained to obtain the screen deviation distribution.

[0193] Based on the screen deviation distribution, the display area is subjected to layer-by-layer color correction to complete the color deviation adjustment of the display.

[0194] In addition, to achieve the above objectives, embodiments of the present invention also provide a device, including a memory, a processor, and an adaptive color shift compensation program for a liquid crystal display stored in the memory and executable on the processor. When the processor executes the adaptive color shift compensation program for the liquid crystal display, it implements the adaptive color shift compensation method for the liquid crystal display as described above.

[0195] In addition, to achieve the above objectives, embodiments of the present invention also provide a computer-readable storage medium storing an adaptive color shift compensation program for a liquid crystal display screen. When the adaptive color shift compensation program for the liquid crystal display screen is executed by a processor, the adaptive color shift compensation method for the liquid crystal display screen described above is implemented.

[0196] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0197] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0198] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0199] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0200] It should be noted that any reference signs placed between parentheses in the claims should not be construed as limiting the claims. The word "comprising" does not exclude the presence of components or steps not listed in the claims. The word "a" or "an" preceding a component does not exclude the presence of a plurality of such components. This application can be implemented by means of hardware comprising several different components and by means of a suitably programmed computer. In a unit claim enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, third, etc., does not indicate any order. These words can be interpreted as names.

[0201] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0202] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of the invention. Therefore, if these modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include these modifications and variations.

Claims

1. An adaptive color shift compensation method for a liquid crystal display screen, characterized in that, The steps of the adaptive color shift compensation method for the liquid crystal display screen include: Collect real-time ambient light change data to generate an ambient light feature dataset; The mapping relationship between screen color deviation and light parameters in the ambient light feature dataset is analyzed to determine the interference mode of ambient light on screen color. A color pattern database is constructed based on the interference pattern, and a reference data matrix for real-time color shift compensation is generated. Based on the reference data matrix, an adaptive color adjustment curve is generated to determine the color compensation parameters under different ambient light conditions. A color compensation scheme is generated based on the color compensation parameters to adjust the color presentation of the display screen in real time. The step of analyzing the mapping relationship between screen color deviation and light parameters in the ambient light feature dataset to determine the interference mode of ambient light on screen color includes: Obtain the difference between ambient light brightness and light intensity in the ambient light feature dataset; If the difference value is not within the preset difference threshold range, the difference value is marked as an abnormal light range, and the color temperature distribution characteristics of the abnormal light range are extracted. The interaction between the color temperature distribution characteristics and the incident angle of light is analyzed to extract the interference mode characteristics that cause color distortion of the display screen and to determine the interference mode.

2. The adaptive color shift compensation method for a liquid crystal display screen as described in claim 1, characterized in that, The step of constructing a color pattern database based on the interference pattern and generating a reference data matrix for real-time color shift compensation includes: Based on the interference pattern, the ambient light feature dataset is classified to generate interference types containing color shift patterns, thus obtaining preliminary classification data. The preliminary classification data is matched with the dynamic change parameters of ambient light to generate the color pattern database; The color pattern database is matched with preset association rules to generate color deviation association quantification results; Based on the color deviation correlation quantization result, the real-time ambient light data is compared with the screen color deviation to generate the reference data matrix.

3. The adaptive color shift compensation method for a liquid crystal display screen as described in claim 2, characterized in that, After the step of comparing the real-time ambient light data with the screen color deviation based on the color shift correlation quantization result to generate the reference data matrix, the following steps are included: Obtain the reference data content in the reference data matrix, and detect the deviation between the reference data content and the actual detected value; If the deviation value is greater than the preset deviation value, the historical reference data content, the actual detection value, and the screen response data are fused together to adjust the reference data matrix.

4. The adaptive color shift compensation method for a liquid crystal display screen as described in claim 1, characterized in that, The step of generating an adaptive color adjustment curve based on the reference data matrix and determining color compensation parameters under different ambient light conditions includes: By classifying and matching environmental variables and lighting conditions in the reference data matrix using preset data mapping rules, preliminary mapping data is generated. The trend of changes in lighting conditions and color shifts in the preliminary mapping data is obtained to generate the adaptive color adjustment curve; If a deviation is detected between the adaptive color adjustment curve and the expected adjustment curve, the adaptive color adjustment curve is corrected, and the range of compensation parameters corresponding to the correction is obtained, and a set of compensation parameters is generated. The color compensation parameters are determined by adaptively matching the set of compensation parameters with the lighting conditions.

5. The adaptive color shift compensation method for a liquid crystal display screen as described in claim 4, characterized in that, After the step of adaptively matching the set of compensation parameters with the lighting conditions to determine the color compensation parameters, the method includes: Dynamically monitor real-time light conditions and acquire at least one dataset of environmental variables related to light intensity; If the light intensity in the monitored environmental variable dataset exceeds a preset light intensity threshold range, the environmental variable dataset is compared with the real-time light conditions to generate preliminary adjustment data corresponding to the current ambient light. Obtain the color shift pattern from the historical database that matches the real-time lighting conditions, and correct the preliminary adjustment data based on the color shift pattern to update the compensation parameter set; The updated set of compensation parameters is matched with the actual display requirements to determine the color compensation parameters that match the real-time lighting conditions.

6. The adaptive color shift compensation method for a liquid crystal display screen as described in claim 1, characterized in that, The step of generating a color compensation scheme based on the color compensation parameters and adjusting the color presentation of the display screen in real time includes: The color compensation parameters are decomposed to obtain at least one adjustment dataset associated with the screen output; The adjusted dataset is compared with the expected display requirements to generate the color compensation scheme; An adjustment instruction is generated based on the color compensation scheme, and the color display content of the screen is controlled according to the adjustment instruction; The color values ​​of the display screen are dynamically monitored, and the adjustment command is corrected according to the monitoring results to complete the real-time adjustment of the color presentation of the display screen.

7. The adaptive color shift compensation method for a liquid crystal display screen as described in claim 1, characterized in that, After the step of generating a color compensation scheme based on the color compensation parameters and adjusting the color presentation of the display screen in real time, the following steps are included: Continuously monitor changes in ambient light, generate a light fluctuation dataset, and determine the fluctuation range of the light fluctuation dataset; The display screen area corresponding to the fluctuation range exceeding the preset deviation range is obtained to obtain the screen deviation distribution. Based on the screen deviation distribution, the display area is subjected to layer-by-layer color correction to complete the color deviation adjustment of the display.

8. A terminal device, characterized in that, The method includes a memory, a processor, and an adaptive color shift compensation program for a liquid crystal display stored in the memory and executable on the processor. When the processor executes the adaptive color shift compensation program for the liquid crystal display, it implements the method described in any one of claims 1-7.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores an adaptive color shift compensation program for the liquid crystal display screen. When the adaptive color shift compensation program for the liquid crystal display screen is executed by a processor, it implements the method described in any one of claims 1-7.

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