Display color adjusting method and system

Abstract

The invention discloses a display color adjustment method and system, and the method comprises the steps: collecting the spectral data of ambient light in real time through a multispectral sensor according to the environment where a display is located; according to the visual preference of the user, utilizing a visual response model to extract visual features of the user; according to the environment light field dynamic data set and the user visual preference data set, color parameters of a display are adjusted through a dynamic optimization algorithm, and optimized color parameters are generated; according to the optimized color parameters, color presentation of the display is adjusted in real time through a color management engine, and the color management engine ensures low delay and high precision of color adjustment through a streaming computing technology and a distributed architecture to generate a final display effect. According to the embodiment of the invention, the method can adapt to the change of ambient light in real time, considers the visual preference of a user, and achieves the efficient and precise color adjustment.

Description

Technical Field

[0001] The present invention belongs to the technical field of display technology, and in particular to a display color adjustment method and system. Background Art

[0002] With the popularization of electronic devices and the continuous development of display technology, displays play an increasingly important role in our daily life and work. Displays are not only widely used in consumer electronic products such as personal computers, TVs and mobile phones, but also widely used in professional fields such as graphic design, medical imaging, film and television post-production, etc. These scenes have high requirements for color performance, because accurate color reproduction directly affects the user's visual experience and work results. However, traditional display color calibration methods generally rely on static color standards and manual adjustments, which often cannot adapt to rapidly changing ambient light conditions and users' personalized needs. Changes in ambient light, such as the type, intensity, and color temperature of indoor light sources, can significantly affect the color effects presented by the display. For example, the color performance of the display may be distorted under strong sunlight, while the display may be too dim in a weak light environment. This situation requires users to manually adjust the color settings of the display regularly, which makes the operation cumbersome and does not necessarily achieve the best visual effect. Summary of the invention

[0003] The purpose of the present invention is to provide a display color calibration method and system to address the deficiencies in the prior art, which can adapt to changes in ambient light in real time while taking into account the user's visual preferences to achieve efficient and accurate color calibration.

[0004] An embodiment of the present application provides a display color calibration method, the method comprising: According to the environment in which the display is located, a multi-spectral sensor is used to collect spectral data of ambient light in real time, wherein the multi-spectral sensor captures the wavelength distribution and intensity change of ambient light through spectral analysis technology and adaptive sampling mechanism, generates ambient spectral data, and based on the ambient spectral data, a real-time light field model of ambient light is constructed using a dynamic light field modeling algorithm, wherein the light field model predicts the dynamic change trend of ambient light through spatiotemporal interpolation technology and light intensity distribution prediction mechanism, and generates a dynamic data set of ambient light field; According to the user's visual preferences, a visual response model is used to extract the user's visual features, wherein the visual response model uses eye tracking technology and a deep learning algorithm to analyze the user's sensitivity and preference for different color parameters, and generates a user visual feature vector. Based on the user's visual feature vector, a user visual preference model is constructed using a preference learning algorithm, and the preference learning algorithm dynamically updates the user's preferences through incremental learning to generate a user visual preference data set; According to the ambient light field dynamic data set and the user visual preference data set, the color parameters of the display are adjusted using a dynamic optimization algorithm, wherein the dynamic optimization algorithm adjusts the color saturation, contrast and color temperature in real time through a multi-objective optimization and adaptive feedback mechanism to generate optimized color parameters; According to the optimized color parameters, the color presentation of the display is adjusted in real time using the color management engine, wherein the color management engine ensures low latency and high precision of color adjustment through streaming computing technology and distributed architecture to generate the final display effect.

[0005] Optionally, according to the environment in which the display is located, a multispectral sensor is used to collect spectral data of ambient light in real time, wherein the multispectral sensor captures the wavelength distribution and intensity change of ambient light through spectral analysis technology and adaptive sampling mechanism to generate ambient spectral data, and based on the ambient spectral data, a real-time light field model of ambient light is constructed using a dynamic light field modeling algorithm, and the light field model predicts the dynamic change trend of ambient light through spatiotemporal interpolation technology and light intensity distribution prediction mechanism to generate a dynamic data set of ambient light field, including: According to the environment in which the display is located, a multi-spectral sensor is used to collect spectral data of ambient light in real time; wherein the sensor uses spectral analysis technology to capture the wavelength distribution and intensity changes of ambient light, and adjusts the sampling frequency and resolution through an adaptive sampling mechanism according to the dynamic changes of ambient light, so as to ensure that high-precision spectral data can still be captured when the light changes rapidly, and generate a real-time environmental spectral data set; Preprocessing the real-time environmental spectral data set, wherein the preprocessing removes high-frequency noise and outliers in the spectral data by wavelet transform technology and noise filtering algorithm, and smoothes the spectral data by digital signal processing technology to generate a denoised environmental spectral data set; Based on the denoised ambient spectral dataset, a real-time light field model of ambient light is constructed using a dynamic light field modeling algorithm. The light field modeling uses spatiotemporal interpolation technology to interpolate the ambient spectral data in spatial and temporal dimensions to generate continuous light field distribution data. The light intensity distribution prediction mechanism is used to combine historical spectral data and ambient light change trends to predict the dynamic changes of the light field in the future and generate a dynamic dataset of the ambient light field.

[0006] Optionally, the visual features of the user are extracted using a visual response model according to the user's visual preferences, wherein the visual response model analyzes the user's sensitivity and preference for different color parameters through eye tracking technology and deep learning algorithms to generate a user visual feature vector, and based on the user visual feature vector, a user visual preference model is constructed using a preference learning algorithm, and the preference learning algorithm dynamically updates the user preferences through incremental learning to generate a user visual preference data set, including: According to the user's visual preferences, the user's eye movement data when viewing the display is collected in real time. The user's gaze point distribution, gaze duration and scanning path are captured through high-precision infrared imaging technology and pupil positioning algorithm to generate an original eye movement data set; Preprocessing the original eye movement data set, wherein the noise and invalid data points in the eye movement data are removed by filtering algorithms and outlier detection techniques, and key visual features are extracted from the preprocessed eye movement data set by using feature extraction algorithms to generate a user visual feature data set, wherein the key visual features include gaze point density, scanning speed, and interest area distribution; Based on the user visual feature dataset, a visual response model is used to analyze the user's sensitivity and preference for different color parameters, wherein the visual response model uses a deep learning algorithm to construct a multi-layer neural network to model the relationship between the user's gaze behavior and the color parameters, and generates a user visual feature vector by training the multi-layer neural network to quantify the user's preference for color saturation, contrast and color temperature; Based on the user visual feature vector, a user visual preference model is constructed using a preference learning algorithm, wherein the preference learning algorithm dynamically updates the user preference model through incremental learning technology to ensure that it can adapt to changes in user preferences and generate a user visual preference data set.

[0007] Optionally, the color parameters of the display are adjusted using a dynamic optimization algorithm according to the ambient light field dynamic data set and the user visual preference data set, wherein the dynamic optimization algorithm adjusts the color saturation, contrast and color temperature in real time through a multi-objective optimization and adaptive feedback mechanism to generate optimized color parameters, including: Determining an optimization target of color parameters according to an ambient light field dynamic data set and a user visual preference data set, wherein the color parameters include color saturation, contrast, and color temperature; A color parameter optimization model is constructed using a multi-objective optimization algorithm, wherein the optimization model generates a multi-objective optimization problem according to the optimization target through the Pareto optimality theory, and limits the boundaries of the multi-objective optimization problem using constraint conditions to generate an initial optimization solution space; A dynamic optimization algorithm is used to solve the multi-objective optimization problem, wherein the dynamic optimization algorithm searches for the optimal color parameter combination in the initial optimization solution space through a genetic algorithm and / or a particle swarm optimization technique, and combines an adaptive feedback mechanism to adjust the search strategy of the dynamic optimization algorithm in real time to ensure that it can converge quickly and generate an optimized color parameter set when the ambient light field and user preferences change dynamically.

[0008] Optionally, the color presentation of the display is adjusted in real time using a color management engine according to the optimized color parameters, wherein the color management engine ensures low latency and high precision of color adjustment through streaming computing technology and distributed architecture to generate a final display effect, including: According to the optimized color parameters, the color parameters are loaded into the color management engine, so that the color management engine receives and analyzes the color parameters in real time, and converts the optimized color parameters into color instructions executable by the display by using a color mapping algorithm, wherein the color mapping algorithm ensures the matching of the color parameters with the hardware characteristics of the display through a color space conversion technology, and generates a color calibration instruction set; According to the color calibration instruction set, the color output of the display is adjusted in real time using the streaming computing technology, wherein the streaming computing technology distributes the color calibration instructions to multiple computing nodes through a distributed architecture, processes the color parameter adjustment in parallel, ensures low latency and high precision color calibration, and generates real-time calibration results; Based on the real-time adjustment result, the color output of the display is rendered using a color rendering engine, wherein the color rendering engine uses hardware acceleration technology to ensure the real-time and smoothness of color rendering to generate the final display effect.

[0009] Another embodiment of the present application provides a display color calibration system, the system comprising: An acquisition module is used to collect spectral data of ambient light in real time using a multispectral sensor according to the environment in which the display is located, wherein the multispectral sensor captures the wavelength distribution and intensity change of ambient light through spectral analysis technology and adaptive sampling mechanism to generate ambient spectral data, and based on the ambient spectral data, a real-time light field model of ambient light is constructed using a dynamic light field modeling algorithm, and the light field model predicts the dynamic change trend of ambient light through spatiotemporal interpolation technology and light intensity distribution prediction mechanism to generate a dynamic data set of ambient light field; An extraction module is used to extract the user's visual features according to the user's visual preferences using a visual response model, wherein the visual response model analyzes the user's sensitivity and preference for different color parameters through eye tracking technology and a deep learning algorithm to generate a user visual feature vector, and based on the user's visual feature vector, a user visual preference model is constructed using a preference learning algorithm, and the preference learning algorithm dynamically updates the user's preferences through incremental learning to generate a user visual preference data set; An adjustment module, used to adjust the color parameters of the display using a dynamic optimization algorithm according to the ambient light field dynamic data set and the user visual preference data set, wherein the dynamic optimization algorithm adjusts the color saturation, contrast and color temperature in real time through a multi-objective optimization and adaptive feedback mechanism to generate optimized color parameters; The adjustment module is used to use the color management engine to perform real-time adjustment of the color presentation of the display according to the optimized color parameters, wherein the color management engine ensures low latency and high precision of color adjustment through streaming computing technology and distributed architecture to generate the final display effect.

[0010] Yet another embodiment of the present application provides a storage medium, wherein the storage medium stores a computer program, wherein the computer program is configured to execute any of the above methods when running.

[0011] Yet another embodiment of the present application provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute any of the methods described above.

[0012] Compared with the prior art, the present invention provides a display color calibration method, which uses a multi-spectral sensor to collect spectral data of ambient light in real time according to the environment in which the display is located; uses a visual response model to extract the user's visual features according to the user's visual preference; uses a dynamic optimization algorithm to adjust the color parameters of the display according to the ambient light field dynamic data set and the user's visual preference data set to generate optimized color parameters; uses a color management engine to perform real-time calibration on the color presentation of the display according to the optimized color parameters, wherein the color management engine ensures low latency and high precision of color calibration through streaming computing technology and distributed architecture, generates a final display effect, and can adapt to changes in ambient light in real time, while taking into account the user's visual preferences, to achieve efficient and accurate color calibration. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 A hardware structure block diagram of a computer terminal for a display color calibration method provided by an embodiment of the present invention; Figure 2 A schematic diagram of a flow chart of a display color calibration method provided by an embodiment of the present invention; Figure 3 A schematic diagram of the structure of a display color calibration system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0014] The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, but should not be construed as limiting the present invention.

[0015] The embodiment of the present invention first provides a display color calibration method, which can be applied to electronic devices such as computer terminals, specifically ordinary computers, etc.

[0016] The following describes it in detail by taking running on a computer terminal as an example. Figure 1The hardware structure block diagram of a computer terminal of a display color calibration method provided by an embodiment of the present invention. Figure 1 As shown, the computer device includes a processor, a memory, and a network interface connected via a system bus, wherein the memory may include a non-volatile storage medium and an internal memory.

[0017] The non-volatile storage medium can store an operating system and a computer program. The computer program includes program instructions, and when the program instructions are executed, the processor can execute any display color calibration method.

[0018] The processor is used to provide computing and control capabilities and support the operation of the entire computer equipment.

[0019] The internal memory provides an environment for the operation of the computer program in the non-volatile storage medium. When the computer program is executed by the processor, the processor can execute any display color adjustment method.

[0020] The network interface is used for network communication, such as sending assigned tasks, etc. Those skilled in the art will appreciate that Figure 1 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0021] It should be understood that the processor may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0022] See also Figure 2 , an embodiment of the present invention provides a display color calibration method, which may include the following steps: S201, according to the environment in which the display is located, using a multi-spectral sensor to collect spectral data of ambient light in real time, wherein the multi-spectral sensor captures the wavelength distribution and intensity change of the ambient light through spectral analysis technology and adaptive sampling mechanism, generates ambient spectral data, and based on the ambient spectral data, uses a dynamic light field modeling algorithm to build a real-time light field model of the ambient light, wherein the light field model predicts the dynamic change trend of the ambient light through spatiotemporal interpolation technology and light intensity distribution prediction mechanism, and generates an ambient light field dynamic data set; The core of this step is to accurately obtain the lighting conditions around the display to ensure that the display can adjust its color output in real time according to changes in the environment. The application of multi-spectral sensors enables the system to capture light intensity information at different wavelengths, thereby generating more comprehensive and accurate environmental spectral data. This spectral data not only covers the intensity changes of light, but also reflects the color temperature and spectral composition of the light source, providing the necessary basic data support for subsequent light field modeling.

[0023] The significance of this process is to achieve intelligent adjustment of the display color to enhance the user's visual experience. By collecting detailed environmental spectral data in real time, the system can accurately identify the impact of light changes on the display effect. When the lighting conditions change, for example, switching from indoor lighting to sunlight, the display can instantly adjust the color parameters of its output to offset the interference of ambient light and ensure accurate color presentation. In addition, building a light field model based on environmental spectral data can predict the changing trend of lighting conditions, making the adjustment process of the display more predictable, thereby maintaining the best visual effect in a dynamic environment.

[0024] Specifically, a multi-spectral sensor can be used to collect spectral data of ambient light in real time according to the environment in which the display is located; wherein the sensor uses spectral analysis technology to capture the wavelength distribution and intensity changes of ambient light, and adjusts the sampling frequency and resolution through an adaptive sampling mechanism according to the dynamic changes of ambient light, so as to ensure that high-precision spectral data can still be captured when the light changes rapidly, and generate a real-time environmental spectral data set; The main purpose of this step is to ensure that spectral data is collected accurately and in real time under various ambient light conditions. The advanced technology of multispectral sensors enables the device to flexibly respond to various lighting conditions in a rapidly changing environment, and provides guarantee for data collection through adaptive mechanisms, avoiding missing or inaccurate data caused by changes in lighting.

[0025] The purpose of this process is to ensure that subsequent display calibration can be performed based on the latest and most accurate ambient light data. By acquiring real-time and high-precision spectral data, the display can dynamically adapt to changing light environments and provide users with a consistent and high-quality color experience. At the same time, this mechanism ensures that the display can maintain color accuracy and visual comfort under different lighting conditions, greatly improving user satisfaction and experience during use.

[0026] In the specific implementation, we first select a highly sensitive multi-spectral sensor, which has multi-channel spectral acquisition capabilities and can accurately obtain ambient light information within the wavelength range of 410nm to 700nm. The sensor will be installed in front of or to the side of the display to maximize the acquisition of real-time data on ambient light. The sensor will continue to operate and automatically adjust the sampling frequency when the light changes. For example, when the light changes drastically, the sensor can collect data 20 times per second, while when the light is relatively stable, the sampling frequency is reduced to 2 times per second. In this way, the sensor can flexibly respond to light changes in different environmental conditions. During the data collection process, the sensor will store the real-time spectral data in the internal cache and upload the data to the central processing unit regularly. At this time, the system will format the collected spectral data for subsequent analysis and processing. Ultimately, these spectral data will be converted into a structured real-time environmental spectral data set, including timestamps, wavelength distribution, light intensity and other information, to ensure that subsequent steps can obtain accurate and comprehensive ambient light data support.

[0027] Preprocessing the real-time environmental spectral data set, wherein the preprocessing removes high-frequency noise and outliers in the spectral data by wavelet transform technology and noise filtering algorithm, and smoothes the spectral data by digital signal processing technology to generate a denoised environmental spectral data set; This step is an important step to ensure that the collected ambient light data can accurately reflect the actual lighting conditions. By applying techniques such as wavelet transform, the system can effectively remove high-frequency noise caused by external interference, thereby improving data quality. After this processing, the generated data set will be cleaner and smoother, which is convenient for subsequent analysis and model building.

[0028] The key role of this preprocessing step is to improve the accuracy of subsequent analysis and dynamic light field modeling algorithms. The denoised data will reduce the impact of erroneous data on algorithm calculations, allowing the light field model to more accurately reflect the real changes in ambient light. At the same time, such a cleaning process can significantly improve the adaptability and stability of the algorithm under dynamic lighting conditions, providing reliable data support for overall color adjustment.

[0029] Specifically, the system first imports the real-time environmental spectral data set into the preprocessing module, which is equipped with a wavelet transform algorithm and a noise filtering algorithm that can effectively identify and remove noise from the spectral data. In the preliminary processing stage, the system uses wavelet transform to decompose the raw spectral data at multiple levels, extract the various frequency components, and thus identify the high-frequency noise in the signal. In this step, it is critical to use an appropriate wavelet basis function (for example, Daubechies wavelet) because it can adapt to the characteristics of spectral data. After completing the wavelet transform, the system will set a threshold to remove high-frequency noise to ensure that the actual spectral signal is retained.

[0030] Subsequently, the denoised spectral data needs to be further smoothed to eliminate small fluctuations and outliers. The system will apply digital signal processing techniques, such as moving average or Savitzky-Golay filter, to smooth the spectral data. After these processes, the final denoised environmental spectral data set will be more reliable and accurate, which provides a solid foundation for the subsequent steps to build a dynamic light field model.

[0031] Based on the denoised ambient spectral dataset, a real-time light field model of ambient light is constructed using a dynamic light field modeling algorithm. The light field modeling uses spatiotemporal interpolation technology to interpolate the ambient spectral data in spatial and temporal dimensions to generate continuous light field distribution data. The light intensity distribution prediction mechanism is used to combine historical spectral data and ambient light change trends to predict the dynamic changes of the light field in the future and generate a dynamic dataset of the ambient light field.

[0032] This step emphasizes converting ambient spectral data into a real-time light field model through mapping and modeling techniques, thereby providing the necessary foundation for dynamic adjustment of the display.

[0033] This step enables dynamic prediction of ambient light changes, providing forward-looking data support for the color calibration of the display. An effective light field model can not only reflect the current lighting conditions in real time, but also predict the changing lighting environment, so as to prepare the corresponding color adjustment plan in advance. This prediction capability greatly enhances the display's adaptive adjustment capability in complex environments and improves the user experience.

[0034] In the specific implementation, the denoised environmental spectral data must first be imported into the dynamic light field modeling module. This module will use the spatiotemporal interpolation algorithm to generate the continuity of the light field in space and time based on the relationship between the spectral data.

[0035] The spatiotemporal interpolation process will consider the spectral data at each time point and the data at its adjacent time points, and form a light field data grid through methods such as bilinear interpolation or Kriging interpolation to describe the distribution characteristics of the light field. This not only improves the accuracy of the model, but also ensures that the generated light field model can transition smoothly and avoid mutations caused by missing data. During this process, the system will also regularly update historical spectral data to incorporate the impact of the latest lighting environment, making the model more adaptable to dynamic changes.

[0036] Subsequently, combined with the light intensity distribution prediction mechanism, the system will analyze the trend of historical spectral data, such as the periodic changes in sunlight intensity, and predict the dynamic changes that may occur in the light field in the next few minutes or hours. By combining machine learning algorithms such as time series analysis or regression analysis, the system can identify the change pattern and generate a dynamic data set of the ambient light field. This series of data processing and analysis not only enhances the model's predictive ability, but also provides an accurate basis for the color adjustment of the display, helping to maintain the best display effect.

[0037] S202, extracting visual features of the user using a visual response model according to the user's visual preferences, wherein the visual response model analyzes the user's sensitivity and preference to different color parameters through eye tracking technology and deep learning algorithms, generates a user visual feature vector, and constructs a user visual preference model based on the user visual feature vector using a preference learning algorithm, wherein the preference learning algorithm dynamically updates the user preferences through incremental learning, and generates a user visual preference data set; The core of this step is to capture the user's real reaction through high-tech means to achieve personalized color optimization. Eye tracking technology can provide detailed data on the user's gaze point, focus area and visual fatigue level, while deep learning algorithms can extract the user's implicit preferences from complex data and form a sensitivity analysis of color parameters such as saturation, contrast and color temperature.

[0038] The purpose of this process is to eliminate the one-size-fits-all approach to color adjustment and allow each user to get the best visual experience. By analyzing the user's visual preferences in real time, the display can automatically adjust its color output to adapt to the unique color needs of different users. At the same time, the method can also dynamically update the user's visual preferences through incremental learning to achieve continuous optimization to cope with visual fatigue or color aesthetic changes that may occur after users use the display for a long time. This personalized adjustment can not only improve user comfort, but also improve work efficiency and entertainment experience.

[0039] Specifically, the eye movement data of the user when viewing the display can be collected in real time according to the user's visual preferences. The user's gaze point distribution, gaze duration and scanning path are captured through high-precision infrared imaging technology and pupil positioning algorithm to generate an original eye movement data set. At this stage, the system uses advanced infrared cameras to accurately capture the user's eye movement trajectory. By using the pupil positioning algorithm, the system can obtain the user's gaze point, gaze duration, and eye movement path to form a detailed raw eye movement data set. This data can provide deep insights into the user's attention distribution and help with subsequent analysis of the user's color preference.

[0040] The purpose of this process is to obtain the user's real visual feedback, thereby providing a basis for color adjustment. Eye movement data can not only reflect the user's focus when observing the interface, but also reveal their sensitivity and preference for specific colors or image content. By analyzing this data, the system can more accurately grasp the user's needs and then optimize the color settings of the display to make it more in line with the user's visual habits and aesthetic standards, which will significantly improve the user experience.

[0041] In the specific implementation, it is necessary to first deploy a high-precision infrared camera, which is usually installed above or on the side of the display to ensure that the user's eye movement information can be fully captured. This camera illuminates the user's eyes with an infrared light source so that subtle changes in eye movement can be accurately recorded by the sensor. During the data collection process, the system will monitor the user's eye movement trajectory in real time and analyze the captured video stream through an advanced pupil positioning algorithm. This algorithm can accurately detect the user's eye movement trajectory, the specific location of gaze, and the rotation angle of the eye. The system will also identify the different areas of gaze of the user and record the gaze duration of each area, thereby generating a detailed eye movement data set. Such a data set provides an important basis for subsequent analysis and contains rich information on the distribution of sight lines. Finally, the collected data will be formatted and stored in a database for subsequent analysis and feature extraction. The system will archive this data regularly to ensure that the user's changing trends can be tracked, especially the visual response under different lighting conditions or content types. Through these steps, the system fully ensures the accuracy and comprehensiveness of the user's eye movement data, laying a solid foundation for subsequent visual feature analysis and preference modeling.

[0042] Preprocessing the original eye movement data set, wherein the noise and invalid data points in the eye movement data are removed by filtering algorithms and outlier detection techniques, and key visual features are extracted from the preprocessed eye movement data set by using feature extraction algorithms to generate a user visual feature data set, wherein the key visual features include gaze point density, scanning speed, and interest area distribution; At this stage, the system passes the raw eye movement data to the preprocessing module, which applies filtering algorithms (such as high-pass filters) to remove noise caused by device jitter or external interference, and uses outlier detection technology to eliminate data points that significantly deviate from normal values ​​to ensure the accuracy of the final data.

[0043] The purpose of this process is to improve the validity and reliability of eye movement data. By removing noise and invalid data, the final data set obtained by the system will be clearer and can truly reflect the user's visual behavior. Those key visual features, such as gaze point density and scanning speed, will provide an accurate basis for subsequent visual preference analysis, thereby effectively supporting the goal of color adjustment.

[0044] At this stage, the system first needs to perform noise reduction on the collected raw eye movement data. Usually, a high-pass filter or a low-pass filter is used to remove noise caused by device jitter or unexpected interference. After filtering, the system will obtain a relatively clean eye movement data set. Next, the system applies outlier detection technology to identify and remove eye movement data points that are significantly out of the normal range. For example, when the user's eye movement speed exceeds a certain threshold (such as 300 degrees / second), this may indicate that the user is turning his head quickly or adjusting his visual focus, rather than normal eye movement behavior. By setting a reasonable threshold, the system can effectively exclude these abnormal data and ensure the accuracy of subsequent analysis. Finally, the pre-processed eye movement data will be input into the feature extraction algorithm, and the system will extract key visual features, such as the density distribution of gaze points (i.e. the number of times the user gazes in different areas), the scanning speed (i.e. the speed at which the user switches between different areas), and the distribution of interest areas (i.e. the areas that the user gazes most frequently). These feature information will be organized into a user visual feature dataset to facilitate the subsequent training of deep learning models and the analysis of user preferences.

[0045] Based on the user visual feature dataset, a visual response model is used to analyze the user's sensitivity and preference for different color parameters, wherein the visual response model uses a deep learning algorithm to construct a multi-layer neural network to model the relationship between the user's gaze behavior and the color parameters, and generates a user visual feature vector by training the multi-layer neural network to quantify the user's preference for color saturation, contrast and color temperature; During this process, the system will input the processed visual feature data into the designed visual response model, and train a multi-layer neural network based on the user's gaze behavior through deep learning technology. This network can learn the user's potential preferences from a large amount of sample data, thereby establishing a complex relationship between color parameters and user responses. The purpose of this process is to quantify the user's visual preferences, making the display adjustment more accurate and personalized. By analyzing the user's response to different color parameters, the system can not only identify the user's specific preferences, but also provide clear guidance for color adjustment, which helps to achieve the best visual effects and user experience.

[0046] In this process, the system first inputs the previously extracted user visual feature dataset into the designed deep learning model. This model uses a multi-layer neural network architecture that can process large amounts of complex data and improve its fit with user preferences through continuous training. Through the back-propagation algorithm, the system optimizes based on the user's gaze behavior (such as gaze duration, scanning speed, etc.) and the corresponding color parameters (such as saturation, contrast, color temperature, etc.). The model adjusts the weights in each training round to find the best parameter combination to maximize the user's preference for color. In order to ensure the effectiveness of the model, the system also needs to verify it regularly to improve the accuracy of the prediction. Finally, after sufficient training, the model will generate a user visual feature vector that can quantify the user's preference for different color parameters. For example, the vector may show that the user prefers highly saturated colors to 80%, while the preference for low-contrast colors is only 30%. Through these quantitative results, the system can more accurately understand and predict the user's visual needs.

[0047] Based on the user visual feature vector, a user visual preference model is constructed using a preference learning algorithm, wherein the preference learning algorithm dynamically updates the user preference model through incremental learning technology to ensure that it can adapt to changes in user preferences and generate a user visual preference data set.

[0048] In this step, the system uses the previously generated user visual feature vector as a basis and applies a preference learning algorithm to build a more comprehensive user visual preference model. Incremental learning technology enables the model to continuously receive new visual feature data and gradually improve its understanding of user preferences to ensure the long-term effectiveness and adaptability of the model. The role of this process is to provide the display with the ability to continuously optimize. As the user's visual preferences may change over time and usage habits, the dynamically updated user visual preference model ensures that the display can always adjust the color according to the current user needs. This flexibility can not only improve the user experience, but also reduce visual fatigue during long-term use, effectively protecting the user's vision health.

[0049] In this step, the system inputs the previously generated user visual feature vector into the preference learning algorithm, which allows the model to continuously adapt to changes in user preferences through incremental learning. Incremental learning allows the model to optimize and adjust as it receives new data without having to completely retrain, which saves time and computing resources. In specific implementation, the system will monitor the user's eye movement data when using the display at different times. Over time, new data will be continuously added to the existing dataset. By applying an online learning strategy, the model will analyze these new eye movement data and automatically update the visual preference model in combination with past preference data. For example, if the system detects that the user's preference for darker tones has increased over a period of time, the model will automatically adjust its parameters to reflect this change, ensuring that color calibration always meets the user's new needs. In addition, the system will also conduct regular model evaluation and performance monitoring to ensure its accuracy and effectiveness. During this evaluation process, the system can show optimized color settings to different users through A / B testing and collect their feedback. This real-time feedback mechanism will further enhance the adaptability of the model, enabling it to make quick adjustments based on user feedback, thereby generating a more comprehensive and accurate user visual preference data set.

[0050] S203, adjusting the color parameters of the display using a dynamic optimization algorithm according to the ambient light field dynamic data set and the user visual preference data set, wherein the dynamic optimization algorithm adjusts the color saturation, contrast and color temperature in real time through a multi-objective optimization and adaptive feedback mechanism to generate optimized color parameters; The core of the dynamic optimization algorithm lies in multi-objective optimization and adaptive feedback mechanism. Through real-time data analysis, the system can intelligently evaluate the current state of ambient light and the user's visual preferences, thereby optimizing key parameters such as color saturation, contrast, and color temperature. For example, if the ambient light changes significantly (such as from bright to dim), the system will automatically increase the color saturation and contrast to ensure that the image remains clear and the colors are still vivid in the new light environment. In this process, the dynamic optimization algorithm will take multiple goals into consideration to maintain the best visual effect under different conditions. The significance of this optimization step is that it can achieve a personalized visual experience. The user's visual perception is affected by ambient light and personal preferences. Continuous dynamic optimization can ensure that the displayed content is always in the best visual state. By adjusting the color parameters of the display, it can not only improve the user's viewing comfort, but also reduce visual fatigue. For example, watching the display content under unsuitable color settings for a long time may cause eye fatigue or discomfort, while through refined dynamic adjustment, consistent and comfortable visual effects can be provided under any ambient light conditions.

[0051] Specifically, the optimization target of the color parameters can be determined according to the ambient light field dynamic data set and the user visual preference data set, wherein the color parameters include color saturation, contrast and color temperature; In this step, the system first analyzes the real-time acquired ambient light field dynamic data set and the user visual preference data set. Through deep learning and data mining technology, the system can find out the user's color preferences under different lighting conditions. For example, in a bright environment, the user may prefer low saturation and high contrast color settings, while in a dim environment, the user may tend to enhance the color saturation to improve the visual effect. The system then sets the corresponding optimization goals based on this information. The significance of this process is to provide clear guidance for the subsequent optimization work. Once the specific optimization target is confirmed, the color adjustment of the entire system will become more targeted and ensure that the final color presentation meets the actual needs of the user. Through repeated testing under different lighting conditions, detailed user preference data can be obtained, and then the appropriate color parameter targets can be effectively defined.

[0052] In this step, the system first needs to integrate the dynamic data from the ambient light field with the user's visual preference dataset. The ambient light field dynamic dataset is the ambient light information captured in real time by a multispectral sensor, such as spectral distribution, intensity changes, etc., while the user visual preference dataset is the user's preference for color under different lighting conditions obtained by means such as eye tracking. By analyzing this data, the system can define the user's optimal color performance under specific ambient light conditions. For example, in bright natural light, users may prefer lower color saturation and higher contrast to enhance the clarity and visual impact of the picture; in dim environments, users may tend to choose higher color saturation to prevent the image from appearing too dark. During the implementation process, the system will use machine learning algorithms and historical data for training and analysis. By analyzing the user's behavior in different environments (such as gaze time and scanning path), the system can gradually build a model of user preferences. In this process, the system will also continuously adjust parameters based on real-time feedback to ensure that the optimization goals are highly consistent with user needs. For example, if the system monitors that the user frequently adjusts the brightness under a specific lighting condition, the system will infer the user's sensitivity to the brightness in that environment and make corresponding optimization settings. Ultimately, the core purpose of this step is to provide a clear target for subsequent color optimization. Once the system determines the optimization target for color parameters, the next step is to fine-tune the color parameters based on this target, which is crucial to improving the user's visual experience in various lighting environments. Setting optimization targets can not only improve the readability of displayed content, but also enhance user viewing comfort, thereby improving overall user satisfaction.

[0053] A color parameter optimization model is constructed using a multi-objective optimization algorithm, wherein the optimization model generates a multi-objective optimization problem according to the optimization target through the Pareto optimality theory, and limits the boundaries of the multi-objective optimization problem using constraint conditions to generate an initial optimization solution space; In this step, the system uses a multi-objective optimization algorithm to build a color parameter optimization model and adopts the Pareto optimality theory to deal with the trade-offs between multiple objectives. The application of the Pareto optimality theory enables the system to no longer pursue the optimality of a single color parameter, but to comprehensively consider the three aspects of color saturation, contrast and color temperature. For example, in some cases, increasing color saturation may affect contrast, and the optimization algorithm will ensure that a balance is maintained between different objectives while pursuing high satisfaction. The importance of this step is that it allows the system to find the best solution when faced with complex user needs. By establishing a multi-objective optimization model, the system can effectively handle the changes in user needs in different situations, thereby generating the optimal color parameter combination to ensure that users get the best visual experience when watching content.

[0054] In this step, the system will establish a multi-objective optimization model to meet different color parameter requirements at the same time. In the specific implementation, the system will apply the Pareto optimality theory, which allows the system to find a balance point when dealing with multiple goals. For example, if the user wants to obtain higher color saturation to improve the visual effect, but does not want too high contrast to cause visual fatigue, the system needs to find a balance between the two that can improve the visual experience without causing burden, which is achieved through multi-objective optimization. The first step in building this optimization model is to define the optimization goal and form a series of specific optimization parameters based on the user preferences and environmental data collected in the first step. The system uses historical data and user feedback to set the target ranges for color saturation, contrast, and color temperature. Next, the system will generate a multi-objective optimization problem and define the upper and lower limits of each color parameter by setting constraints. For example, color saturation may be limited to between 0-100, contrast to between 50-200, and color temperature to between 3000K-6500K to ensure that the generated parameter combination is optimal between physical feasibility and user preference. After the solution space of this multi-objective optimization problem is generated, the system will use computer optimization algorithms, such as genetic algorithms or particle swarm optimization, to solve it. Taking genetic algorithms as an example, the system will first randomly generate an initial population, each of which represents a possible combination of color parameters. Then, through operations such as gene crossover and mutation, it will evolve from generation to generation and gradually approach the optimal solution. In this process, the user's real-time feedback will be integrated into the optimization process to ensure that the final output color parameters meet the user's visual needs. This process not only provides a scientific basis for optimization, but also makes the entire adjustment process flexible and quickly adaptable.

[0055] A dynamic optimization algorithm is used to solve the multi-objective optimization problem, wherein the dynamic optimization algorithm searches for the optimal color parameter combination in the initial optimization solution space through a genetic algorithm and / or a particle swarm optimization technique, and combines an adaptive feedback mechanism to adjust the search strategy of the dynamic optimization algorithm in real time to ensure that it can converge quickly and generate an optimized color parameter set when the ambient light field and user preferences change dynamically.

[0056] In this final step, the system will use dynamic optimization algorithms, especially genetic algorithms and particle swarm optimization techniques, to solve the previously established multi-objective optimization model. Genetic algorithms simulate the process of natural selection, searching in the initial solution space through operations such as crossover and mutation to find the optimal color parameter combination. Particle swarm optimization technology simulates the behavior of bird flocks foraging to achieve efficient search for the optimal solution. The system will continuously monitor user reactions and environmental changes, and quickly feed back to the algorithm to correct its search strategy. The purpose of this step is to ensure the efficiency and real-time performance of the optimization process, especially when the user's preferences or ambient light change dramatically, the system can make timely adjustments to ensure the best visual effect. This flexible dynamic optimization algorithm can maintain the accuracy of the optimization direction in a rapidly changing environment, ensuring that users can always experience the most suitable display effect.

[0057] In this step, the system will apply dynamic optimization algorithms, mainly genetic algorithms and particle swarm optimization techniques, to solve the aforementioned multi-objective optimization problem. In order to achieve the best results, the system will first evaluate each color parameter combination in the initial solution space and compare its performance on all optimization goals. For example, the system may detect that a certain combination has high saturation but low contrast, resulting in poor overall visual effect. Therefore, through these evaluations, user satisfaction with different parameter combinations will become a key indicator for optimization. The use of genetic algorithms enables the system to quickly find high-quality color parameter combinations in the initial solution space by simulating the process of natural selection. Specifically, the system randomly generates a set of parameters, which are then evaluated based on the fitness function (i.e., user satisfaction), and selects parameters with better performance for crossover and mutation to generate new candidate parameters. This process is repeated until the preset convergence conditions are met, that is, when the parameters no longer change significantly or reach the user's expected satisfaction, the algorithm will stop calculating and output the final optimized parameters. In addition, the combination of adaptive feedback mechanism is the innovation of this step. The system will collect user feedback and ambient light change data in real time during operation, and incorporate it into the operation strategy of the optimization algorithm. For example, if the user still feels uncomfortable under the adjusted parameters, the system can adjust its search strategy in real time to quickly find a more suitable parameter combination. This adaptive adjustment capability ensures that the system can not only operate under ideal conditions, but also respond to user needs in actual applications, so that the final generated color parameter set is more in line with the user's visual experience requirements. This flexibility and responsiveness are of great significance to improving user satisfaction and optimizing the overall visual effect.

[0058] S204, using a color management engine to perform real-time adjustment on the color presentation of the display according to the optimized color parameters, wherein the color management engine ensures low latency and high precision of color adjustment through streaming computing technology and distributed architecture to generate a final display effect.

[0059] When the color management engine is used to adjust the color presentation of the display in real time based on the optimized color parameters, the system inputs the color parameters (such as color saturation, contrast, and color temperature) that have been optimized in the previous steps into the color management engine. The engine is responsible for parsing these parameters and converting them into color instructions that can be directly executed by the display. The color mapping algorithm here uses color space conversion technology to ensure that the optimized color parameters match the hardware characteristics of the display to achieve more accurate color presentation. For example, if the optimized color parameters show that the saturation needs to be improved, the color management engine will perform the necessary calculations and conversions internally to generate instructions suitable for the display, ensuring that the colors of the final picture are realistic and meet user expectations. The significance of this step is to ensure that users can enjoy the best visual experience under various ambient light conditions through efficient color calibration. Real-time calibration can adapt to the user's immediate feedback on color, thereby effectively reducing visual discomfort caused by light changes. Using streaming computing technology and distributed architecture, the system can respond quickly to user needs, help reduce delays and discomfort during the calibration process, and thus improve the overall user satisfaction. Such calibration not only improves the display effect, but also enhances the interactive experience between the user and the display, allowing users to feel the convenience of technology and personalized services during operation.

[0060] Specifically, the color parameters can be loaded into the color management engine according to the optimized color parameters, so that the color management engine receives and analyzes the color parameters in real time, and converts the optimized color parameters into color instructions executable by the display using a color mapping algorithm, wherein the color mapping algorithm ensures the matching of the color parameters with the hardware characteristics of the display through a color space conversion technology, and generates a color calibration instruction set; In this step, the system first needs to accurately load the optimized color parameters (such as color saturation, contrast, and color temperature) into the color management engine. To achieve this goal, the system converts these parameters into a common format so that the color management engine can quickly understand and process them. Specifically, the color management engine converts these input data into a color calibration instruction set, which clearly defines the specific values ​​and ranges of each color parameter. During this process, the color mapping algorithm uses color space conversion technology to ensure that the generated color instructions can well match the hardware characteristics of the display. For example, if the color display characteristics of the display deviate from the standard sRGB color space, the color management engine will make necessary adjustments to ensure the authenticity of the display effect. The significance of this process is that only when the color parameters are completely matched with the hardware characteristics of the display can the final image or video be guaranteed to achieve the expected effect. By analyzing the color parameters in real time, the system can respond to user needs and feedback in a timely manner, thereby ensuring that the content displayed each time provides the best visual experience. After this treatment, users will not experience color deviation and discomfort during viewing, which improves overall user satisfaction.

[0061] In this step, the system first needs to load the optimized color parameters (such as saturation, contrast, and color temperature) into the color management engine. The key to this process is to ensure that the color parameters can be input into the engine in an effective format so that it can be processed later. To achieve this goal, the system will design a data interface to ensure that the optimized color parameters can be quickly and accurately passed to the color management engine. The system usually uses JSON or XML format to transmit these parameters because these formats are well structured and readable. The loaded color parameters will be deeply analyzed through the color mapping algorithm. The algorithm takes into account the specific hardware characteristics of the display, such as color gamut, contrast, brightness and other factors. For example, if the optimized color parameter display requires a higher saturation, and the color gamut of the display itself is limited, the color management engine will adjust the saturation to a more suitable range according to the mapping algorithm to avoid picture distortion. At the same time, during the color mapping process, color space conversion technology can be used to achieve conversion between different color spaces (such as sRGB, Adobe RGB, etc.) to ensure that the final generated color instructions can accurately reflect the user's visual preferences. Finally, after being processed by the color mapping algorithm, the system generates a set of color calibration instruction sets that specify in detail how to adjust the color output of the display. For example, the instruction set may include "increase color saturation to 75%" or "adjust color temperature to 6500K", and each instruction is optimized and ensured to match the display hardware. This set of instruction sets will serve as the basis for subsequent steps and will be used by subsequent real-time adjustments and rendering processes to ensure that the user experience is always at its best.

[0062] According to the color calibration instruction set, the color output of the display is adjusted in real time using the streaming computing technology, wherein the streaming computing technology distributes the color calibration instructions to multiple computing nodes through a distributed architecture, processes the color parameter adjustment in parallel, ensures low latency and high precision color calibration, and generates real-time calibration results; In this step, the system will use streaming computing technology to process the color calibration instruction set and adjust the color output of the display in real time. Streaming computing technology allows the system to break down the calibration tasks into smaller units and improve the efficiency of the entire calibration process through parallel processing. Under the distributed computing architecture, the instruction set will be distributed to multiple computing nodes so that each node can independently adjust specific color parameters. This architecture ensures that when processing complex color adjustments, the system can still maintain low latency and high precision even under high load. The significance of this process is that through streaming computing, the system can not only respond quickly to new input color instructions, but also maintain high-quality output during the processing. When the color parameters are adjusted, the user hardly feels any delay, so the display can provide a smooth and consistent visual experience. For example, when watching dynamic video content, real-time color adjustment can ensure that each picture can display the best color effect according to the current lighting conditions and user preferences, greatly enhancing the user's immersion.

[0063] During this step, the system uses streaming computing technology to make real-time adjustments to the display's color output based on the previously generated color calibration instruction set. Streaming computing technology allows the system to split computing tasks into multiple independent subtasks, which are processed in parallel by different computing nodes to improve overall processing power. For example, if ten different pixel areas on the display need to be adjusted, streaming computing technology can assign the calibration instructions for each area to each independent computing node, allowing them to process almost simultaneously, which greatly reduces the time required for color adjustment. Each computing node will make corresponding color parameter adjustments based on the instructions it receives. For example, one node may be responsible for adjusting the contrast of a certain area, while another node is responsible for adjusting the color temperature. In this way, the system can quickly adapt to the user's input and present the effect of color adjustment in real time. During the implementation process, the system will ensure smooth information transmission between nodes so that data can be quickly shared and updated when needed, such as when a user adjusts the color preference, all relevant nodes can get timely feedback. Finally, after streaming computing, each node will summarize the processing results to generate an overall real-time calibration result. This result will be passed back to the color management engine for subsequent rendering. This process ensures that the color output of the display can be maintained in the best state at any time. Users can hardly feel any delay when watching content, and the smooth color performance greatly improves the viewing experience.

[0064] Based on the real-time adjustment result, the color output of the display is rendered using a color rendering engine, wherein the color rendering engine uses hardware acceleration technology to ensure the real-time and smoothness of color rendering to generate the final display effect.

[0065] In the last step, the system will use the color rendering engine to render the color output of the display based on the previous real-time adjustment results. The color rendering engine is a component specially designed to process and optimize color performance. Through hardware acceleration technology, it can process each pixel quickly and efficiently, thereby ensuring that the rendering process achieves the best results in terms of real-time and smoothness. For example, when the user adjusts the brightness or color preference of the content being watched, the rendering engine can immediately optimize the output based on the results of real-time adjustment without waiting for a long time of calculation and processing. The significance of this step is that through efficient color rendering, the display effect finally generated by the system can better meet the user's aesthetic needs and visual preferences. For example, when watching animated content with bright colors, the rendering engine can quickly adapt to changes in ambient light and adjust the color output in real time, so that the viewing effect always presents a vivid and eye-catching visual impact. Such a response can significantly enhance the user's immersion and comfort when watching visual content such as movies, games, etc.

[0066] In this step, the system will use the color rendering engine to render the real-time adjustment results to generate the final color display effect. The color rendering engine is a component designed specifically for processing and optimizing colors. Its core is to use hardware acceleration technology, such as GPU acceleration, to ensure high efficiency and low latency in color rendering. Through hardware acceleration, the rendering engine can process the color of each pixel in a short time to ensure the smoothness of real-time display content. Specifically, the system will configure the parameters of the rendering engine based on the real-time calibration results. The rendering engine will map the adjusted color data to the pixels of the display. In this process, the system will take into account the resolution and refresh rate of the display to ensure that the rendering effect can match the hardware. For example, when processing high-resolution 4K video, the rendering engine can quickly process large-scale pixels, while achieving accurate color rendering, avoiding frame rate reduction or screen freezes. Ultimately, the rendered color output will be what users expect, with vivid colors and distinct layers, which can truly reproduce the beauty of the content. This process not only improves the visual experience, but also enhances the user's immersion and satisfaction when watching different content (such as movies, games, etc.). For example, when watching an action movie, the color rendering engine can quickly adapt to the rapidly changing scenes, ensuring that each frame in the image can accurately display the color, so that the audience can better feel the compactness and tension of the plot. Through this series of processing, the system will provide users with a smooth and high-quality visual experience.

[0067] It can be seen that according to the environment in which the display is located, the multi-spectral sensor is used to collect the spectral data of the ambient light in real time; according to the user's visual preference, the visual response model is used to extract the user's visual features; according to the ambient light field dynamic data set and the user's visual preference data set, the dynamic optimization algorithm is used to adjust the color parameters of the display to generate optimized color parameters; according to the optimized color parameters, the color presentation of the display is adjusted in real time using the color management engine, wherein the color management engine uses streaming computing technology and distributed architecture to ensure low latency and high precision of color adjustment and generate the final display effect, so that it can adapt to changes in ambient light in real time, while taking into account the user's visual preferences, to achieve efficient and accurate color adjustment.

[0068] Another embodiment of the present invention provides a display color calibration system, see Figure 3 , the system may include: The acquisition module 301 is used to collect the spectrum data of the ambient light in real time using a multi-spectral sensor according to the environment in which the display is located, wherein the multi-spectral sensor captures the wavelength distribution and intensity change of the ambient light through spectral analysis technology and adaptive sampling mechanism, generates ambient spectrum data, and constructs a real-time light field model of the ambient light based on the ambient spectrum data using a dynamic light field modeling algorithm, and the light field model predicts the dynamic change trend of the ambient light through spatiotemporal interpolation technology and light intensity distribution prediction mechanism, and generates a dynamic data set of the ambient light field; An extraction module 302 is used to extract the user's visual features according to the user's visual preferences using a visual response model, wherein the visual response model analyzes the user's sensitivity and preference for different color parameters through eye tracking technology and a deep learning algorithm to generate a user visual feature vector, and based on the user's visual feature vector, a user visual preference model is constructed using a preference learning algorithm, and the preference learning algorithm dynamically updates the user's preferences through incremental learning to generate a user visual preference data set; An adjustment module 303 is used to adjust the color parameters of the display using a dynamic optimization algorithm according to the ambient light field dynamic data set and the user visual preference data set, wherein the dynamic optimization algorithm adjusts the color saturation, contrast and color temperature in real time through a multi-objective optimization and adaptive feedback mechanism to generate optimized color parameters; The adjustment module 304 is used to use the color management engine to perform real-time adjustment on the color presentation of the display according to the optimized color parameters, wherein the color management engine ensures low latency and high precision of color adjustment through streaming computing technology and distributed architecture to generate the final display effect.

[0069] It can be seen that according to the environment in which the display is located, the multi-spectral sensor is used to collect the spectral data of the ambient light in real time; according to the user's visual preference, the visual response model is used to extract the user's visual features; according to the ambient light field dynamic data set and the user's visual preference data set, the dynamic optimization algorithm is used to adjust the color parameters of the display to generate optimized color parameters; according to the optimized color parameters, the color presentation of the display is adjusted in real time using the color management engine, wherein the color management engine uses streaming computing technology and distributed architecture to ensure low latency and high precision of color adjustment and generate the final display effect, so that it can adapt to changes in ambient light in real time, while taking into account the user's visual preferences, to achieve efficient and accurate color adjustment.

[0070] An embodiment of the present invention further provides a storage medium, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above method embodiments when running.

[0071] Specifically, in this embodiment, the above storage medium may be configured to store a computer program for performing the following steps: S201, according to the environment in which the display is located, using a multi-spectral sensor to collect spectral data of ambient light in real time, wherein the multi-spectral sensor captures the wavelength distribution and intensity change of the ambient light through spectral analysis technology and adaptive sampling mechanism, generates ambient spectral data, and based on the ambient spectral data, uses a dynamic light field modeling algorithm to build a real-time light field model of the ambient light, wherein the light field model predicts the dynamic change trend of the ambient light through spatiotemporal interpolation technology and light intensity distribution prediction mechanism, and generates an ambient light field dynamic data set; S202, extracting visual features of the user using a visual response model according to the user's visual preferences, wherein the visual response model analyzes the user's sensitivity and preference to different color parameters through eye tracking technology and deep learning algorithms, generates a user visual feature vector, and constructs a user visual preference model based on the user visual feature vector using a preference learning algorithm, wherein the preference learning algorithm dynamically updates the user preferences through incremental learning, and generates a user visual preference data set; S203, adjusting the color parameters of the display using a dynamic optimization algorithm according to the ambient light field dynamic data set and the user visual preference data set, wherein the dynamic optimization algorithm adjusts the color saturation, contrast and color temperature in real time through a multi-objective optimization and adaptive feedback mechanism to generate optimized color parameters; S204, using a color management engine to perform real-time adjustment on the color presentation of the display according to the optimized color parameters, wherein the color management engine ensures low latency and high precision of color adjustment through streaming computing technology and distributed architecture to generate a final display effect.

[0072] It can be seen that according to the environment in which the display is located, the multi-spectral sensor is used to collect the spectral data of the ambient light in real time; according to the user's visual preference, the visual response model is used to extract the user's visual features; according to the ambient light field dynamic data set and the user's visual preference data set, the dynamic optimization algorithm is used to adjust the color parameters of the display to generate optimized color parameters; according to the optimized color parameters, the color presentation of the display is adjusted in real time using the color management engine, wherein the color management engine uses streaming computing technology and distributed architecture to ensure low latency and high precision of color adjustment and generate the final display effect, so that it can adapt to changes in ambient light in real time, while taking into account the user's visual preferences, to achieve efficient and accurate color adjustment.

[0073] An embodiment of the present invention further provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0074] Specifically, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0075] Specifically, in this embodiment, the processor may be configured to perform the following steps through a computer program: S201, according to the environment in which the display is located, using a multi-spectral sensor to collect spectral data of ambient light in real time, wherein the multi-spectral sensor captures the wavelength distribution and intensity change of the ambient light through spectral analysis technology and adaptive sampling mechanism, generates ambient spectral data, and based on the ambient spectral data, uses a dynamic light field modeling algorithm to build a real-time light field model of the ambient light, wherein the light field model predicts the dynamic change trend of the ambient light through spatiotemporal interpolation technology and light intensity distribution prediction mechanism, and generates an ambient light field dynamic data set; S202, extracting visual features of the user using a visual response model according to the user's visual preferences, wherein the visual response model analyzes the user's sensitivity and preference to different color parameters through eye tracking technology and deep learning algorithms, generates a user visual feature vector, and constructs a user visual preference model based on the user visual feature vector using a preference learning algorithm, wherein the preference learning algorithm dynamically updates the user preferences through incremental learning, and generates a user visual preference data set; S203, adjusting the color parameters of the display using a dynamic optimization algorithm according to the ambient light field dynamic data set and the user visual preference data set, wherein the dynamic optimization algorithm adjusts the color saturation, contrast and color temperature in real time through a multi-objective optimization and adaptive feedback mechanism to generate optimized color parameters; S204, using a color management engine to perform real-time adjustment on the color presentation of the display according to the optimized color parameters, wherein the color management engine ensures low latency and high precision of color adjustment through streaming computing technology and distributed architecture to generate a final display effect.

[0076] It can be seen that according to the environment in which the display is located, the multi-spectral sensor is used to collect the spectral data of the ambient light in real time; according to the user's visual preference, the visual response model is used to extract the user's visual features; according to the ambient light field dynamic data set and the user's visual preference data set, the dynamic optimization algorithm is used to adjust the color parameters of the display to generate optimized color parameters; according to the optimized color parameters, the color presentation of the display is adjusted in real time using the color management engine, wherein the color management engine uses streaming computing technology and distributed architecture to ensure low latency and high precision of color adjustment and generate the final display effect, so that it can adapt to changes in ambient light in real time, while taking into account the user's visual preferences, to achieve efficient and accurate color adjustment.

[0077] The above describes in detail the structure, features and effects of the present invention based on the embodiments shown in the drawings. The above is only a preferred embodiment of the present invention, but the present invention is not limited to the scope of implementation shown in the drawings. Any changes made according to the concept of the present invention, or modifications to equivalent embodiments with equivalent changes, which still do not exceed the spirit covered by the description and drawings, should be within the protection scope of the present invention.

Claims

1. A display color calibration method, characterized in that: The method comprises: According to the environment in which the display is located, a multi-spectral sensor is used to collect spectral data of ambient light in real time, wherein the multi-spectral sensor captures the wavelength distribution and intensity change of ambient light through spectral analysis technology and adaptive sampling mechanism, generates ambient spectral data, and based on the ambient spectral data, a real-time light field model of ambient light is constructed using a dynamic light field modeling algorithm, wherein the light field model predicts the dynamic change trend of ambient light through spatiotemporal interpolation technology and light intensity distribution prediction mechanism, and generates a dynamic data set of ambient light field; According to the user's visual preferences, a visual response model is used to extract the user's visual features, wherein the visual response model uses eye tracking technology and a deep learning algorithm to analyze the user's sensitivity and preference for different color parameters, and generates a user visual feature vector. Based on the user's visual feature vector, a user visual preference model is constructed using a preference learning algorithm, and the preference learning algorithm dynamically updates the user's preferences through incremental learning to generate a user visual preference data set; According to the ambient light field dynamic data set and the user visual preference data set, the color parameters of the display are adjusted using a dynamic optimization algorithm, wherein the dynamic optimization algorithm adjusts the color saturation, contrast and color temperature in real time through a multi-objective optimization and adaptive feedback mechanism to generate optimized color parameters; According to the optimized color parameters, the color presentation of the display is adjusted in real time using the color management engine, wherein the color management engine ensures low latency and high precision of color adjustment through streaming computing technology and distributed architecture to generate the final display effect.

2. The method according to claim 1, characterized in that According to the environment in which the display is located, the spectrum data of the ambient light is collected in real time by using a multi-spectral sensor, wherein the multi-spectral sensor captures the wavelength distribution and intensity change of the ambient light through spectral analysis technology and adaptive sampling mechanism, generates ambient spectrum data, and constructs a real-time light field model of the ambient light based on the ambient spectrum data by using a dynamic light field modeling algorithm, wherein the light field model predicts the dynamic change trend of the ambient light through spatiotemporal interpolation technology and light intensity distribution prediction mechanism, and generates a dynamic data set of the ambient light field, including: According to the environment in which the display is located, a multi-spectral sensor is used to collect spectral data of ambient light in real time; wherein the sensor uses spectral analysis technology to capture the wavelength distribution and intensity changes of ambient light, and adjusts the sampling frequency and resolution through an adaptive sampling mechanism according to the dynamic changes of ambient light, so as to ensure that high-precision spectral data can still be captured when the light changes rapidly, and generate a real-time environmental spectral data set; Preprocessing the real-time environmental spectral data set, wherein the preprocessing removes high-frequency noise and outliers in the spectral data by wavelet transform technology and noise filtering algorithm, and smoothes the spectral data by digital signal processing technology to generate a denoised environmental spectral data set; Based on the denoised ambient spectral dataset, a real-time light field model of ambient light is constructed using a dynamic light field modeling algorithm. The light field modeling uses spatiotemporal interpolation technology to interpolate the ambient spectral data in spatial and temporal dimensions to generate continuous light field distribution data. The light intensity distribution prediction mechanism is used to combine historical spectral data and ambient light change trends to predict the dynamic changes of the light field in the future and generate a dynamic dataset of the ambient light field.

3. The method according to claim 2, characterized in that The method extracts the visual features of the user by using a visual response model according to the user's visual preference, wherein the visual response model analyzes the user's sensitivity and preference to different color parameters through eye tracking technology and deep learning algorithm, generates a user visual feature vector, and constructs a user visual preference model based on the user visual feature vector by using a preference learning algorithm. The preference learning algorithm dynamically updates the user preference through incremental learning, and generates a user visual preference data set, including: According to the user's visual preferences, the user's eye movement data when viewing the display is collected in real time. The user's gaze point distribution, gaze duration and scanning path are captured through high-precision infrared imaging technology and pupil positioning algorithm to generate an original eye movement data set; Preprocessing the original eye movement data set, wherein the noise and invalid data points in the eye movement data are removed by filtering algorithms and outlier detection techniques, and key visual features are extracted from the preprocessed eye movement data set by using feature extraction algorithms to generate a user visual feature data set, wherein the key visual features include gaze point density, scanning speed, and interest area distribution; Based on the user visual feature dataset, a visual response model is used to analyze the user's sensitivity and preference for different color parameters, wherein the visual response model uses a deep learning algorithm to construct a multi-layer neural network to model the relationship between the user's gaze behavior and the color parameters, and generates a user visual feature vector by training the multi-layer neural network to quantify the user's preference for color saturation, contrast and color temperature; Based on the user visual feature vector, a user visual preference model is constructed using a preference learning algorithm, wherein the preference learning algorithm dynamically updates the user preference model through incremental learning technology to ensure that it can adapt to changes in user preferences and generate a user visual preference data set.

4. The method according to claim 3, characterized in that The method of adjusting the color parameters of the display using a dynamic optimization algorithm according to the ambient light field dynamic data set and the user visual preference data set, wherein the dynamic optimization algorithm adjusts the color saturation, contrast and color temperature in real time through a multi-objective optimization and adaptive feedback mechanism to generate optimized color parameters, includes: Determining an optimization target of color parameters according to an ambient light field dynamic data set and a user visual preference data set, wherein the color parameters include color saturation, contrast, and color temperature; A color parameter optimization model is constructed using a multi-objective optimization algorithm, wherein the optimization model generates a multi-objective optimization problem according to the optimization target through the Pareto optimality theory, and limits the boundaries of the multi-objective optimization problem using constraint conditions to generate an initial optimization solution space; A dynamic optimization algorithm is used to solve the multi-objective optimization problem, wherein the dynamic optimization algorithm searches for the optimal color parameter combination in the initial optimization solution space through a genetic algorithm and / or a particle swarm optimization technique, and combines an adaptive feedback mechanism to adjust the search strategy of the dynamic optimization algorithm in real time to ensure that it can converge quickly and generate an optimized color parameter set when the ambient light field and user preferences change dynamically.

5. The method according to claim 4, characterized in that According to the optimized color parameters, the color presentation of the display is adjusted in real time using the color management engine, wherein the color management engine ensures low latency and high precision of color adjustment through streaming computing technology and distributed architecture to generate the final display effect, including: According to the optimized color parameters, the color parameters are loaded into the color management engine, so that the color management engine receives and analyzes the color parameters in real time, and converts the optimized color parameters into color instructions executable by the display by using a color mapping algorithm, wherein the color mapping algorithm ensures the matching of the color parameters with the hardware characteristics of the display through a color space conversion technology, and generates a color calibration instruction set; According to the color calibration instruction set, the color output of the display is adjusted in real time using the streaming computing technology, wherein the streaming computing technology distributes the color calibration instructions to multiple computing nodes through a distributed architecture, processes the color parameter adjustment in parallel, ensures low latency and high precision color calibration, and generates real-time calibration results; Based on the real-time adjustment result, the color output of the display is rendered using a color rendering engine, wherein the color rendering engine uses hardware acceleration technology to ensure the real-time and smoothness of color rendering to generate the final display effect.

6. A display color calibration system, characterized in that: The system comprises: An acquisition module is used to collect spectral data of ambient light in real time using a multispectral sensor according to the environment in which the display is located, wherein the multispectral sensor captures the wavelength distribution and intensity change of ambient light through spectral analysis technology and adaptive sampling mechanism to generate ambient spectral data, and based on the ambient spectral data, a real-time light field model of ambient light is constructed using a dynamic light field modeling algorithm, and the light field model predicts the dynamic change trend of ambient light through spatiotemporal interpolation technology and light intensity distribution prediction mechanism to generate a dynamic data set of ambient light field; An extraction module is used to extract the user's visual features according to the user's visual preferences using a visual response model, wherein the visual response model analyzes the user's sensitivity and preference for different color parameters through eye tracking technology and a deep learning algorithm to generate a user visual feature vector, and based on the user's visual feature vector, a user visual preference model is constructed using a preference learning algorithm, and the preference learning algorithm dynamically updates the user's preferences through incremental learning to generate a user visual preference data set; An adjustment module, used to adjust the color parameters of the display using a dynamic optimization algorithm according to the ambient light field dynamic data set and the user visual preference data set, wherein the dynamic optimization algorithm adjusts the color saturation, contrast and color temperature in real time through a multi-objective optimization and adaptive feedback mechanism to generate optimized color parameters; The adjustment module is used to use the color management engine to perform real-time adjustment of the color presentation of the display according to the optimized color parameters, wherein the color management engine ensures low latency and high precision of color adjustment through streaming computing technology and distributed architecture to generate the final display effect.

7. The system according to claim 6, characterized in that The acquisition module is specifically used for: According to the environment in which the display is located, a multi-spectral sensor is used to collect spectral data of ambient light in real time; wherein the sensor uses spectral analysis technology to capture the wavelength distribution and intensity changes of ambient light, and adjusts the sampling frequency and resolution through an adaptive sampling mechanism according to the dynamic changes of ambient light, so as to ensure that high-precision spectral data can still be captured when the light changes rapidly, and generate a real-time environmental spectral data set; Preprocessing the real-time environmental spectral data set, wherein the preprocessing removes high-frequency noise and outliers in the spectral data by wavelet transform technology and noise filtering algorithm, and smoothes the spectral data by digital signal processing technology to generate a denoised environmental spectral data set; Based on the denoised ambient spectral dataset, a real-time light field model of ambient light is constructed using a dynamic light field modeling algorithm. The light field modeling uses spatiotemporal interpolation technology to interpolate the ambient spectral data in spatial and temporal dimensions to generate continuous light field distribution data. The light intensity distribution prediction mechanism is used to combine historical spectral data and ambient light change trends to predict the dynamic changes of the light field in the future and generate a dynamic dataset of the ambient light field.

8. The system according to claim 7, characterized in that The extraction module is specifically used for: According to the user's visual preferences, the user's eye movement data when viewing the display is collected in real time. The user's gaze point distribution, gaze duration and scanning path are captured through high-precision infrared imaging technology and pupil positioning algorithm to generate an original eye movement data set; Preprocessing the original eye movement data set, wherein the noise and invalid data points in the eye movement data are removed by filtering algorithms and outlier detection techniques, and key visual features are extracted from the preprocessed eye movement data set by using feature extraction algorithms to generate a user visual feature data set, wherein the key visual features include gaze point density, scanning speed, and interest area distribution; Based on the user visual feature dataset, a visual response model is used to analyze the user's sensitivity and preference for different color parameters, wherein the visual response model uses a deep learning algorithm to construct a multi-layer neural network to model the relationship between the user's gaze behavior and the color parameters, and generates a user visual feature vector by training the multi-layer neural network to quantify the user's preference for color saturation, contrast and color temperature; Based on the user visual feature vector, a user visual preference model is constructed using a preference learning algorithm, wherein the preference learning algorithm dynamically updates the user preference model through incremental learning technology to ensure that it can adapt to changes in user preferences and generate a user visual preference data set.

9. A storage medium, characterized in that: The storage medium stores a computer program, wherein the computer program is configured to execute the method according to any one of claims 1 to 5 when executed.

10. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to run the computer program to perform the method according to any one of claims 1 to 5.

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