A color calibration method and system for display screen

Abstract

The present invention discloses a color calibration method and system for a display screen, and relates to the field of display technology. A color calibration system for a display screen includes: a real-time ambient light capture module, an ambient color matching module, and a color calibration module. The present invention can ensure that the color output of the display screen matches the ambient lighting conditions by acquiring ambient light information in real time and applying it to color calibration, thereby improving the accuracy and naturalness of the displayed color; since the color calibration is continuously performed based on a preset time interval f, this means that the system can dynamically adapt to changes in ambient light and adjust the color settings of the display screen in real time to ensure that the best visual experience is provided at any time; by combining ambient light information with the current display information of the target display screen for color calibration, a more personalized and optimized visual experience can be provided to users according to different environmental and content requirements.

Description

Technical Field

[0001] The present invention relates to the field of display technology, and in particular to a color calibration method and system for a display screen. Background Art

[0002] In the era of big data, display screens are an important medium for information display, and their color accuracy is crucial to improving user experience. Traditional color calibration methods are usually based on professional equipment and software, and are operated by professionals. This is not only costly, but also inefficient, and it is difficult to meet users' large-scale and high-efficiency needs for color calibration. Under different ambient lighting conditions, the color temperature of ambient light will affect the visual perception of the display color, and the display color needs to be adjusted according to the external environment.

[0003] Therefore, we urgently need to provide a color calibration method and system for a display screen that is adaptively adjusted and meets user needs. Summary of the invention

[0004] The present invention aims to provide a color calibration method and system for a display screen, which meet the needs of users and improve visual satisfaction.

[0005] A color calibration method for a display screen, comprising:

[0006] Acquire ambient light information at a preset time interval f to obtain real-time ambient light information; input the real-time ambient light information into an ambient color matching model for matching to obtain a preset color target value; intercept display information of a target display screen to obtain a current target display screen image; input the current target display screen image and the preset color target value into a color calibration model for analysis to obtain a color calibration adjustment strategy; and perform color calibration on the target display screen using the color calibration adjustment strategy.

[0007] As a preferred technical solution of the present invention, the environmental color matching model includes a data preprocessing layer, an environmental light classification layer, a strategy matching layer and a result output layer;

[0008] The data preprocessing layer is used to preprocess the real-time ambient light information to obtain preprocessed real-time ambient light;

[0009] The ambient light classification layer is used to perform feature recognition based on the preprocessed real-time ambient light to obtain real-time ambient light classification;

[0010] The strategy matching layer is used to match the ambient light strategy matching library according to the real-time ambient light classification to obtain the preset color target value corresponding to the real-time ambient light classification; the ambient light strategy matching library contains M standard ambient light classifications G m Preset color target value C corresponding to standard ambient light classification m , m=1,2,…,M;

[0011] The resulting output layer is used to output a preset color target value.

[0012] As a preferred technical solution of the present invention, the specific steps of training the ambient light classification layer in the ambient color matching model include:

[0013] Collecting a number of pre-processed ambient light information with manually annotated ambient light classifications; combining a number of pre-processed ambient light information with manually annotated ambient light classifications to obtain an ambient light classification training set; and using the manually annotated ambient light classifications as target values ​​for the ambient light classification training set;

[0014] The SVM model is used as the initial model of the ambient light classification layer in the ambient color matching model, and the ambient light classification training set is input into the SVM model to train the ambient light classification layer with the target value as the target to obtain the initial ambient light classification layer; the initial ambient light classification layer is model evaluated to obtain the model evaluation result; if the model evaluation result is passed, the trained initial ambient light classification layer is used as the ambient light classification layer; otherwise, the ambient light classification training set is used to continue model training for the initial ambient light classification layer.

[0015] As a preferred technical solution of the present invention, it also includes building an ambient light strategy matching library, specifically the following steps:

[0016] L1. Collect Q users in different standard ambient light classification G m The following shows the color of different displays W mj The visual feedback result R mjq And comfort evaluation results D mjq , q=1,2,…,Q, j=1,2,…,J; J is the number of colors displayed by the display under the same standard ambient light classification; the visual feedback result R mjq And comfort evaluation results D mjq It is a numerical scoring format;

[0017] L2. For standard ambient light classification G m , construct K preset color target value individuals T mk , k=1, 2, ..., K; each preset color target value individual T mk Included in the standard ambient light classification G m The preset color target value Y to be optimized mk ; Set K preset color target value individuals T mk Combine to obtain the preset color target value iterative population; set the maximum number of iterations;

[0018] According to the preset color target value, iterate the preset color target value individuals T in the population mkPerform simulation calculation to obtain the preset color target value satisfaction result B mk ; Set the preset color target value satisfaction result B mk As the preset color target value individual T mk The fitness S mk ;

[0019] The steps of simulation calculation are:

[0020] Set the preset color target value individual T mk The preset color target value Y to be optimized mk With all displays showing colors W mj Match and obtain the preset color target value Y to be optimized m The closest display color W mj ; Weighted calculation of all display colors W mj The corresponding visual feedback result R mjq And comfort evaluation results D mjq , and obtain the preset color target value satisfaction result B mk ;

[0021] When the maximum number of iterations is reached, the preset color target value individual T corresponding to the maximum fitness is output mk , which is the optimal preset color target value individual; the corresponding preset color target value Y to be optimized in the optimal preset color target value individual mk That is the preset color target value C m ;

[0022] L3. Traverse and calculate all standard ambient light classifications G m , classify all standard ambient light into G m and the corresponding preset color target value C m Combine them to get the ambient light strategy matching library.

[0023] As a preferred technical solution of the present invention, it also includes establishing a special ambient light strategy library for users with special visual health conditions, specifically the following steps:

[0024] Collect the data of several users with special visual health conditions in different standard ambient light classifications G m Based on the visual feedback results and comfort evaluation results of the colors displayed on different display screens, repeat steps L2 and L3 to establish a special ambient light strategy library;

[0025] A special environment color matching model is established based on the environment color matching model and the special environment light strategy library; when users with special visual health conditions use the target display screen, the special environment color matching model and the color calibration model are used to perform color calibration on the target display screen.

[0026] As a preferred technical solution of the present invention, the color calibration model includes an image preprocessing layer, a strategy matching layer and a result output layer;

[0027] The image preprocessing layer is used to preprocess the current target display screen image to obtain a preprocessed target display screen image;

[0028] The feature extraction layer is used to extract features from the preprocessed target display screen image to obtain display color value information;

[0029] The strategy output layer is used to perform strategy analysis based on the displayed color value information and the preset color target value to obtain the color calibration adjustment strategy.

[0030] As a preferred technical solution of the present invention, the specific steps of training the strategy output layer in the color calibration model include:

[0031] The displayed color value information and the corresponding preset color target value are used as a set of training samples, the color calibration adjustment strategy corresponding to the training samples is manually labeled, several groups of different training samples with manually labeled color calibration adjustment strategies are collected, and the manually labeled color calibration adjustment strategies are used as target values; several groups of training samples are combined to obtain a color calibration training set;

[0032] The BP neural network model is used as the basic model of the strategy output layer in the color calibration model, and the color calibration training set is input into the BP neural network model to perform model training with the target value as the target to obtain the initial strategy output layer; the model evaluation is performed on the initial strategy output layer to obtain the strategy evaluation result; if the strategy evaluation result is within the error standard range, the trained initial strategy output layer is used as the strategy output layer in the color calibration model; otherwise, the color calibration training set is used to continue model training.

[0033] A color calibration system for a display screen, comprising:

[0034] A real-time ambient light capture module is used to obtain ambient light information at a preset time interval f to obtain real-time ambient light information;

[0035] The environmental color matching module is used to deploy the environmental color matching model, input the real-time ambient light information into the environmental color matching model for matching, and obtain the preset color target value;

[0036] The color calibration module is used to deploy a color calibration model, intercept the display information of the target display screen, and obtain the current target display screen image; input the current target display screen image and the preset color target value into the color calibration model for analysis to obtain a color calibration adjustment strategy; and use the color calibration adjustment strategy to perform color calibration on the target display screen.

[0037] The present invention has the following advantages:

[0038] 1. The present invention can ensure that the color output of the display screen matches the ambient light conditions by acquiring ambient light information in real time and applying it to color calibration, thereby improving the accuracy and naturalness of the displayed color; since the color calibration is continuously performed based on a preset time interval f, this means that the system can dynamically adapt to changes in ambient light and adjust the color settings of the display screen in real time to ensure that the best visual experience is provided at any time; by combining ambient light information with the current display information of the target display screen for color calibration, a more personalized and optimized visual experience can be provided to the user according to different environmental and content requirements; color calibration that adapts to ambient light conditions helps to reduce eye fatigue caused by color distortion or excessive brightness, especially when the display screen is used for a long time.

[0039] 2. The present invention establishes a special environment color matching model based on the environment color matching model and the special environment light strategy library; when a user with special visual health conditions uses the target display screen, the special environment color matching model and the color calibration model are used to calibrate the color of the target display screen; for users with special visual health conditions, such as color blindness or color weakness, the color of the display screen is adjusted by the special environment color matching model, which can significantly improve their visual comfort and reduce visual fatigue when using the display device for a long time; the use of the special environment light strategy library and the color matching model can provide users with special visual health conditions with a more personalized and optimized visual experience, thereby improving user satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 The present invention is a schematic structural diagram of a color calibration system for a display screen used in an embodiment of the present invention. DETAILED DESCRIPTION

[0041] In order to enable persons skilled in the art to better understand the technical solutions in the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0042] Embodiment 1, a color calibration method for a display screen, comprising:

[0043] Acquire ambient light information at a preset time interval f to obtain real-time ambient light information; input the real-time ambient light information into an ambient color matching model for matching to obtain a preset color target value; intercept display information of a target display screen to obtain a current target display screen image; input the current target display screen image and the preset color target value into a color calibration model for analysis to obtain a color calibration adjustment strategy; perform color calibration on the target display screen using the color calibration adjustment strategy;

[0044] The preset time interval f is set by professional technicians according to actual conditions; the ambient light information is obtained by using sensors, such as photoresistor sensors, photodiode sensors, color sensors, etc.;

[0045] The environmental color matching model includes a data preprocessing layer, an environmental light classification layer, a strategy matching layer, and a result output layer;

[0046] The data preprocessing layer is used to preprocess the real-time ambient light information to obtain preprocessed real-time ambient light;

[0047] The ambient light classification layer is used to perform feature recognition based on the preprocessed real-time ambient light to obtain real-time ambient light classification;

[0048] The strategy matching layer is used to match the ambient light strategy matching library according to the real-time ambient light classification to obtain the preset color target value corresponding to the real-time ambient light classification; the ambient light strategy matching library contains M standard ambient light classifications G m Preset color target value C corresponding to standard ambient light classification m , m=1, 2, ..., M; standard ambient light classification G m It is set by professional technicians according to the actual situation;

[0049] The result output layer is used to output the preset color target value;

[0050] By accurately matching ambient light conditions and preset color target values, the model can provide users with more natural and comfortable visual effects. This not only reduces visual fatigue, but also improves the realism and immersion of viewing content; by intelligently adjusting display settings to match ambient light conditions, the model can help reduce the energy consumption of display devices. For example, reducing backlight intensity in bright environments can not only provide a more comfortable viewing experience, but also extend the battery life of the device; whether outdoors or indoors, day or night, the ambient color matching model can provide appropriate color settings, which enables the display device to maintain the best display effect in a variety of application scenarios; the application of the ambient color matching model can not only significantly improve the color display quality and user experience of the display device, improve the energy efficiency of the device, but also support a wider range of application scenarios;

[0051] The specific steps for training the ambient light classification layer in the ambient color matching model include:

[0052] Collecting a number of pre-processed ambient light information with manually annotated ambient light classifications; combining a number of pre-processed ambient light information with manually annotated ambient light classifications to obtain an ambient light classification training set; and using the manually annotated ambient light classifications as target values ​​for the ambient light classification training set;

[0053] The SVM model is used as the initial model of the ambient light classification layer in the ambient color matching model, and the ambient light classification training set is input into the SVM model to train the ambient light classification layer with the target value as the target to obtain the initial ambient light classification layer; the model evaluation is performed on the initial ambient light classification layer to obtain the model evaluation result; if the model evaluation result is passed, the trained initial ambient light classification layer is used as the ambient light classification layer; otherwise, the ambient light classification training set is used to continue model training for the initial ambient light classification layer; by using the SVM model for ambient light classification, the model can more accurately identify ambient light features and match the most suitable color settings, which helps to reduce eye fatigue and improve reading or viewing comfort, thereby optimizing the user experience; the automation of the entire color calibration process reduces the need for manual intervention, making color calibration more efficient and convenient, and users do not need to manually adjust the display settings to enjoy the color performance that best suits the current environment;

[0054] By preprocessing and identifying the features of real-time ambient light information, and then matching the preset color target values ​​according to the classification of ambient light, this process can ensure that the color calibration reflects the actual situation of ambient light more accurately, thereby improving the color performance of the display device under different ambient light conditions; by continuously acquiring and analyzing real-time ambient light information, the model can dynamically adapt to changes in ambient light conditions and adjust the color settings of the display device in real time to ensure the best visual experience at all times;

[0055] Build an ambient light strategy matching library. Specific steps:

[0056] L1. Collect Q users in different standard ambient light classification G m The following shows the color of different displays W mj The visual feedback result R mjq And comfort evaluation results D mjq , q=1,2,…,Q, j=1,2,…,J; J is the number of colors displayed by the display under the same standard ambient light classification; the visual feedback result R mjq And comfort evaluation results D mjq It is a numerical rating form. The larger the number, the more positive the result. The magnitude of Q and J is large, which can ensure that the collected feedback results are more objective and more in line with the needs of the public. The visual feedback result is the degree of color display restoration, such as whether the color is beautiful and whether the brightness is appropriate. The comfort evaluation result is the fatigue of the glasses after a certain period of use. Different display screens display color W mj It comes from settings made by professional technicians or manual adjustments made by users;

[0057] L2. For standard ambient light classification G m , construct K preset color target value individuals Tmk , k=1, 2, ..., K; each preset color target value individual T mk Included in the standard ambient light classification G m The preset color target value Y to be optimized mk ; Set K preset color target value individuals T mk The preset color target value is obtained by combining the iterative population; the maximum number of iterations is set; the maximum number of iterations is set by professional technicians according to actual conditions; the preset color target value individual T mk The preset color target value Y to be optimized mk The preset color target value Y to be optimized after testing, adjustment and evaluation by professional technicians is set compared to the parameters adjusted manually by users. mk More professional. Professional technicians have a deep understanding of color theory and equipment characteristics. They can accurately evaluate and adjust color target values ​​to ensure that the output color is consistent with expectations. They can use professional tools and equipment to perform precise measurements, thereby ensuring that the accuracy of adjustments is far greater than that of ordinary users. When adjusting colors, professional technicians not only rely on tools, but also apply their professional knowledge, such as color psychology, visual perception theory, etc., which are difficult for ordinary users to possess.

[0058] According to the preset color target value, iterate the preset color target value individuals T in the population mk Perform simulation calculation to obtain the preset color target value satisfaction result B mk ; Set the preset color target value satisfaction result B mk As the preset color target value individual T mk The fitness S mk ;

[0059] The steps of simulation calculation are:

[0060] Set the preset color target value individual T mk The preset color target value Y to be optimized mk With all displays showing colors W mj Match and obtain the preset color target value Y to be optimized m The closest display color W mj ; Weighted calculation of all display colors W mj The corresponding visual feedback result R mjq And comfort evaluation results D mjq , and obtain the preset color target value satisfaction result B mk ; The weights in the weighted calculation are set by professional and technical personnel based on actual conditions;

[0061] Step L2 is to iterate the preset color target value individuals T in the preset color target value population based on the genetic algorithm. mk Screening, in the genetic algorithm, also includes the update operation of the iterative population of the preset color target value. Through the update operation of the iterative population, the preset color target value can be gradually optimized to more accurately match the display requirements under different ambient light conditions;

[0062] When the maximum number of iterations is reached, the preset color target value individual T corresponding to the maximum fitness is output mk , which is the optimal preset color target value individual; the corresponding preset color target value Y to be optimized in the optimal preset color target value individual mk That is the preset color target value C m ;

[0063] L3. Traverse and calculate all standard ambient light classifications G m , classify all standard ambient light into G m and the corresponding preset color target value C m Combine and obtain the ambient light strategy matching library;

[0064] By collecting a large number of users' visual feedback and comfort evaluation of display screen colors under different ambient light conditions, more accurate and diverse data support can be obtained. Using this data for color matching can ensure that the final color calibration solution is more in line with the user's actual visual experience and comfort needs; the preset color target values ​​optimized based on user feedback can provide the best display effect under different ambient light conditions, which can not only reduce eye fatigue, but also improve user satisfaction with color and overall viewing experience; by optimizing and selecting the optimal preset color target values ​​through genetic algorithms, the display device can provide a more ideal display effect under various ambient light conditions. This improvement in adaptability allows the display device to perform better in a wider range of usage scenarios; by analyzing users' visual feedback and comfort evaluation of different display colors, the construction process of the ambient light strategy matching library is actually a process of combining display technology with human visual science. This combination helps promote innovation in display technology so that it can better serve human visual health and comfort;

[0065] It also includes establishing a special ambient light strategy library for users with special visual health conditions. The specific steps are:

[0066] Collect the data of several users with special visual health conditions in different standard ambient light classifications G mRepeat steps L2 and L3 to build a special ambient light strategy library based on the visual feedback and comfort evaluation results of the colors displayed on different display screens under different standard ambient light classifications G. Users with special visual health conditions are users with color blindness or color weakness symptoms. By collecting visual feedback and comfort evaluation of users with special visual health conditions, more accurate color calibration can be provided for this specific group. Using steps L2 and L3, a number of users with special visual health conditions can be used to classify the color of the screen under different standard ambient light classifications G. m Based on the visual feedback results and comfort evaluation results of the colors displayed on different display screens, a special ambient light strategy iteration population is established, and a swarm optimization algorithm is used to iterate the special ambient light strategy iteration population to obtain a special ambient light strategy library for users with special visual health conditions;

[0067] A special environment color matching model is established based on the environment color matching model and the special environment light strategy library; when a user with special visual health conditions uses the target display screen, the target display screen is color calibrated using the special environment color matching model and the color calibration model; for users with special visual health conditions, such as color blindness or color weakness, the color of the display screen can be adjusted using the special environment color matching model, which can significantly improve their visual comfort and reduce visual fatigue when using the display device for a long time; the use of the special environment light strategy library and the color matching model can provide users with special visual health conditions with a more personalized and optimized visual experience, thereby improving user satisfaction;

[0068] The color calibration model includes an image preprocessing layer, a strategy matching layer, and a result output layer;

[0069] The image preprocessing layer is used to preprocess the current target display screen image to obtain a preprocessed target display screen image;

[0070] The feature extraction layer is used to extract features from the preprocessed target display screen image to obtain display color value information;

[0071] The strategy output layer is used to perform strategy analysis based on the displayed color value information and the preset color target value to obtain the color calibration adjustment strategy;

[0072] The image preprocessing layer ensures the quality of the images input to the model, thereby improving the accuracy of feature extraction. Accurate feature extraction is the basis for high-precision color calibration. Therefore, the accuracy of the entire color calibration process is significantly improved. The strategy output layer performs strategy analysis based on the actual color value information and the preset color target value. The generated color calibration adjustment strategy can provide users with a more natural and comfortable visual experience.

[0073] The specific steps for training the policy output layer in the color calibration model include:

[0074] The displayed color value information and the corresponding preset color target value are used as a set of training samples, the color calibration adjustment strategy corresponding to the training samples is manually labeled, several groups of different training samples with manually labeled color calibration adjustment strategies are collected, and the manually labeled color calibration adjustment strategies are used as target values; several groups of training samples are combined to obtain a color calibration training set;

[0075] The BP neural network model is used as the basic model of the strategy output layer in the color calibration model, and the color calibration training set is input into the BP neural network model to perform model training with the target value as the target to obtain the initial strategy output layer; the model evaluation is performed on the initial strategy output layer to obtain the strategy evaluation result; if the strategy evaluation result is within the error standard range, the trained initial strategy output layer is used as the strategy output layer in the color calibration model; otherwise, the color calibration training set is used to continue model training; the error standard range is set by professional and technical personnel according to actual conditions;

[0076] By using the BP neural network model to train the color calibration strategy, the model can learn the complex mapping relationship between the displayed color value information and the preset color target value, thereby achieving more accurate and efficient color calibration in practical applications. This method is more efficient than the traditional manual adjustment method and can save a lot of time and resources; by ensuring that the color of the display accurately reflects the preset color target value, the color calibration model can significantly improve the user's visual experience. This is especially important for color-sensitive application scenarios such as digital painting, image editing, gaming, and video playback; through the automated color calibration process, the need for manual intervention is reduced, and the automation and intelligence level of color calibration is improved.

[0077] Embodiment 2, a color calibration system for a display screen, see Figure 1 As shown, including:

[0078] A real-time ambient light capture module is used to obtain ambient light information at a preset time interval f to obtain real-time ambient light information;

[0079] The environmental color matching module is used to deploy the environmental color matching model, input the real-time ambient light information into the environmental color matching model for matching, and obtain the preset color target value;

[0080] The color calibration module is used to deploy a color calibration model, intercept the display information of the target display screen, and obtain the current target display screen image; input the current target display screen image and the preset color target value into the color calibration model for analysis to obtain a color calibration adjustment strategy; and use the color calibration adjustment strategy to perform color calibration on the target display screen.

[0081] It should be understood that those skilled in the art can make improvements or changes based on the above description, and all such improvements and changes should fall within the scope of protection of the appended claims of the present invention. Parts not described in detail in this specification belong to the prior art known to those skilled in the art.

Claims

1. A color calibration method for a display screen, characterized in that: include: Acquire ambient light information at a preset time interval f to obtain real-time ambient light information; Input the real-time ambient light information into the ambient color matching model for matching to obtain a preset color target value; intercept the display information of the target display screen to obtain a current target display screen image; input the current target display screen image and the preset color target value into the color calibration model for analysis to obtain a color calibration adjustment strategy; Performing color calibration on the target display screen using a color calibration adjustment strategy; The environmental color matching model includes a data preprocessing layer, an environmental light classification layer, a strategy matching layer, and a result output layer; The data preprocessing layer is used to preprocess the real-time ambient light information to obtain preprocessed real-time ambient light; The ambient light classification layer is used to perform feature recognition based on the preprocessed real-time ambient light to obtain real-time ambient light classification; The strategy matching layer is used to match the ambient light strategy matching library according to the real-time ambient light classification to obtain the preset color target value corresponding to the real-time ambient light classification; the ambient light strategy matching library contains M standard ambient light classifications G m Preset color target value C corresponding to standard ambient light classification m , m=1,2,…,M; The result output layer is used to output the preset color target value; The specific steps for training the ambient light classification layer in the ambient color matching model include: Collecting a number of pre-processed ambient light information with manually annotated ambient light classifications; combining a number of pre-processed ambient light information with manually annotated ambient light classifications to obtain an ambient light classification training set; and using the manually annotated ambient light classifications as target values ​​for the ambient light classification training set; The SVM model is used as the initial model of the ambient light classification layer in the ambient color matching model, and the ambient light classification training set is input into the SVM model to train the ambient light classification layer with the target value as the target, so as to obtain the initial ambient light classification layer; Perform model evaluation on the initial ambient light classification layer to obtain a model evaluation result; if the model evaluation result is passed, the trained initial ambient light classification layer is used as the ambient light classification layer; otherwise, continue to perform model training on the initial ambient light classification layer using the ambient light classification training set.

2. A color calibration method for a display screen according to claim 1, characterized in that: It also includes building an ambient light strategy matching library. The specific steps are: L1. Collect Q users in different standard ambient light classification G m The following shows the color of different displays W mj The visual feedback result R mjq And comfort evaluation results D mjq , q=1,2,…,Q, j=1,2,…,J; J is the number of colors displayed by the display under the same standard ambient light classification; the visual feedback result R mjq And comfort evaluation results D mjq It is a numerical scoring format; L2. For standard ambient light classification G m , construct K preset color target value individuals T mk , k = 1, 2, ..., K; each preset color target value individual T mk Included in the standard ambient light classification G m The preset color target value Y to be optimized mk ; Set K preset color target value individuals T mk Combining to obtain the preset color target value iterative population; Set the maximum number of iterations; According to the preset color target value, iterate the preset color target value individuals T in the population mk Perform simulation calculation to obtain the preset color target value satisfaction result B mk ; Set the preset color target value satisfaction result B mk As the preset color target value individual T mk The fitness S mk ; The steps of simulation calculation are: Set the preset color target value individual T mk The preset color target value Y to be optimized mk With all displays showing color W mj Match and obtain the preset color target value Y to be optimized m The closest display color W mj ; Weighted calculation of all display colors W mj The corresponding visual feedback result R mjq And comfort evaluation results D mjq , and obtain the preset color target value satisfaction result B mk ; When the maximum number of iterations is reached, the preset color target value individual T corresponding to the maximum fitness is output mk , which is the optimal preset color target value individual; The preset color target value Y to be optimized corresponding to the optimal preset color target value individual mk That is the preset color target value C m ; L3 .Traverse and calculate all standard ambient light classifications G m , classify all standard ambient light into G m and the corresponding preset color target value C m Combine them to get the ambient light strategy matching library.

3. A color calibration method for a display screen according to claim 2, characterized in that: It also includes establishing a special ambient light strategy library for users with special visual health conditions. The specific steps are: Collect the data of several users with special visual health conditions in different standard ambient light classifications G m Based on the visual feedback results and comfort evaluation results of the colors displayed on different display screens, repeat steps L2 and L3 to establish a special ambient light strategy library; Establish a special environment color matching model based on the environment color matching model and the special environment light strategy library; When a user with special visual health conditions uses a target display screen, a special environmental color matching model and a color calibration model are used to perform color calibration on the target display screen.

4. A color calibration method for a display screen according to claim 3, characterized in that: The color calibration model includes an image preprocessing layer, a strategy matching layer, and a result output layer; The image preprocessing layer is used to preprocess the current target display screen image to obtain a preprocessed target display screen image; The feature extraction layer is used to extract features from the preprocessed target display screen image to obtain display color value information; The strategy output layer is used to perform strategy analysis based on the displayed color value information and the preset color target value to obtain the color calibration adjustment strategy.

5. A color calibration method for a display screen according to claim 4, characterized in that: The specific steps for training the policy output layer in the color calibration model include: The displayed color value information and the corresponding preset color target value are used as a group of training samples, the color calibration adjustment strategy corresponding to the training samples is manually labeled, and several groups of different training samples with manually labeled color calibration adjustment strategies are collected, and the manually labeled color calibration adjustment strategies are used as target values; several groups of training samples are combined to obtain a color calibration training set; the BP neural network model is used as the basic model of the strategy output layer in the color calibration model, and the color calibration training set is input into the BP neural network model to perform model training with the target value as the target to obtain an initial strategy output layer; the initial strategy output layer is evaluated to obtain a strategy evaluation result; if the strategy evaluation result is within the error standard range, the trained initial strategy output layer is used as the strategy output layer in the color calibration model; otherwise, the color calibration training set is used to continue model training.

6. A color calibration system for a display screen, characterized in that: The system applies a color calibration method for a display screen as described in any one of claims 1 to 5, comprising: A real-time ambient light capture module is used to obtain ambient light information at a preset time interval f to obtain real-time ambient light information; The environmental color matching module is used to deploy the environmental color matching model, input the real-time ambient light information into the environmental color matching model for matching, and obtain the preset color target value; The color calibration module is used to deploy a color calibration model, intercept the display information of the target display screen, and obtain the current target display screen image; input the current target display screen image and the preset color target value into the color calibration model for analysis to obtain a color calibration adjustment strategy; and use the color calibration adjustment strategy to perform color calibration on the target display screen.

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