Method for uniformly adjusting brightness of multiple teaching display screens

Abstract

The invention relates to the technical field of display screen adjustment, and discloses a method for uniformly adjusting the brightness of multiple teaching display screens, and the method comprises the following steps: presetting and storing a corresponding relation table of users and display screens, to-be-displayed contents of the display screens, and a vision report of each user; obtaining a first brightness adjustment parameter of each display screen based on a corresponding relation table of the user and the display screen, the to-be-displayed content of the display screen and the vision report of each user; respectively adjusting the brightness of the display screen by using the first brightness adjustment parameter, and collecting the associated features of the display screen and the micro features of the user after the adjustment is finished; and forming a plurality of second brightness adjustment parameters based on the associated features of the display screens and the micro features of the user, and simultaneously adjusting the brightness of the plurality of display screens through the second brightness adjustment parameters and / or operation.

Description

Technical Field

[0001] The invention relates to the technical field of display screen adjustment, and in particular to a method for uniformly adjusting the brightness of multiple teaching display screens. Background Art

[0002] With the development of information technology, multimedia teaching has gradually become an important part of modern education. In multimedia teaching, the teaching display screen is the main teaching tool, and its brightness adjustment is of great significance to ensure the teaching quality and protect students' eyesight. The existing teaching display screen brightness adjustment methods mainly include manual adjustment and automatic adjustment.

[0003] For manual adjustment, users manually adjust the brightness of the display according to their own visual experience. This method is simple and intuitive, but it has obvious shortcomings. First, manual adjustment relies on the user's subjective feelings and it is difficult to achieve accurate brightness control. Second, different users have different requirements for brightness, and manual adjustment cannot meet personalized needs. In addition, long-term manual adjustment is not only time-consuming and laborious, but may also cause visual fatigue to users.

[0004] For automatic adjustment, the change of ambient light is detected by the sensor, and the brightness of the display screen is automatically adjusted. Although this adjustment method is smarter than manual adjustment, there are still some problems. First, most of the existing automatic adjustment methods only consider the change of ambient light, while ignoring the individual differences and specific needs of users. Secondly, a single ambient light detection cannot fully reflect the actual visual experience of users, especially in complex teaching environments, where the changes of ambient light may be very frequent and complex, and relying solely on ambient light adjustment is often not accurate enough. In summary, whether it is manual adjustment or automatic adjustment, the existing methods cannot fully consider the individual differences of users, such as vision status, age, gender and other factors, resulting in the brightness adjustment results cannot meet the specific needs of each user well. It also does not take into account the actual visual feedback of users and the use of the display screen. The adjustment accuracy is not high, which can easily lead to visual fatigue and discomfort. Summary of the invention

[0005] In view of the above-mentioned prior art, the present invention provides a method for uniformly adjusting the brightness of multiple teaching display screens, mainly to solve the technical problems existing in the above-mentioned background technology.

[0006] To achieve the above object, the technical solution of the embodiment of the present invention is implemented as follows:

[0007] A method for uniformly adjusting the brightness of multiple teaching display screens, the method comprising the following steps:

[0008] Preset and store a table of correspondence between users and display screens, content to be displayed on the display screen, and vision reports of each user;

[0009] Based on the correspondence table between users and display screens, the content to be displayed on the display screens, and the vision report of each user, obtaining a first brightness adjustment parameter for each of the display screens;

[0010] Applying the first brightness adjustment parameter to adjust the brightness of the display screen respectively, and collecting the associated features of the display screen and the micro features of the user after the adjustment is completed;

[0011] Based on the associated features of the display screens and the micro-features of the user, a plurality of second brightness adjustment parameters are formed, and the brightness of the plurality of display screens is adjusted simultaneously through the second brightness adjustment parameters and / or operations.

[0012] Optionally, a correspondence table between users and display screens and a vision report of each user are preset and stored, specifically including: pre-checking the user's vision health through an eye detection device to obtain the user's vision report; collecting the hardware information of the display screen to obtain the service life of the display screen, inputting a list of users who need to use the display screen, and allocating a corresponding display screen to each user based on the correspondence between vision and the service life of the display screen, and forming a correspondence table between users and display screens with the allocation results.

[0013] Optionally, the correspondence between vision and the service life of a display screen includes: the higher the user's vision score, the smaller the change in the brightness adjustment amplitude of the display screen, and the more conducive it is to extending the service life of the display screen; when the service life of the display screen is high, matching it with a user with a high vision score will extend the service life of the display screen.

[0014] Optionally, the content to be displayed on the display screen is preset and stored, specifically including: counting the number of all interfaces that need to be switched to run a computer program in the visible area of ​​the display screen, and counting the area ratio of each color value in each interface, and based on the area ratio of each color value in each interface, obtaining the area ratio of each color value in all interfaces as the content to be displayed on the display screen.

[0015] Optionally, the vision characteristics of each user are extracted from the vision report, and the vision characteristics of each user and the content to be displayed on the display screen are used as input of a first target deep learning model, and the output of the first target deep learning model is a first brightness adjustment parameter, and the first brightness adjustment parameter includes an adjustment parameter of a red component, an adjustment parameter of a green component, and an adjustment parameter of a blue component.

[0016] Optionally, collecting micro-features of the user specifically includes: when the user sits in front of the display screen, collecting the user's pupil contraction and expansion state and pupil reflection state through a micro-aperture camera group arranged on the display screen.

[0017] Optionally, the associated features of the display screen include the ambient brightness of the display screen and distance information between the user and the display screen.

[0018] Optionally, the associated features, the first brightness adjustment parameter, the pupil contraction and dilation state, and the pupil reflection state are used as inputs of a second target deep learning model, and the output of the second target deep learning model is the second brightness adjustment parameter.

[0019] Optionally, the first target deep learning model and the second target deep learning model are constructed based on any one of the following deep learning model architectures: convolutional neural network model architecture and self-attention mechanism model architecture.

[0020] Optionally, the brightness of multiple display screens can be adjusted simultaneously through operations, including:

[0021] The user adjusts the brightness of the display screen by inputting an adjustment instruction through an operation button, and obtains a brightness adjustment result;

[0022] According to the correspondence table between users and display screens, determine the user corresponding to the display screen whose brightness is adjusted, and determine the vision report corresponding to the user;

[0023] Compare the aforementioned vision report with other vision reports for similarity, select the most similar vision report, and determine the user and display screen corresponding to the most similar vision report;

[0024] The brightness adjustment result, the associated features of the user corresponding to the most similar vision report, the pupil contraction and dilation state, and the pupil reflection state are used as inputs of the second target deep learning model, which outputs the second brightness adjustment parameter of the display screen corresponding to the most similar vision report;

[0025] The brightness of the display screen is adjusted based on the aforementioned second brightness adjustment parameter.

[0026] The beneficial effects of the present invention are as follows: the present invention provides a method for uniformly adjusting the brightness of multiple teaching display screens, and realizes more accurate and personalized brightness adjustment by comprehensively considering the user's vision characteristics, the use content of the display screen and the environmental characteristics, and calculates the preliminary brightness adjustment parameters through the pre-trained first target deep learning model. These parameters include specific adjustment values ​​of the three color components of red, green and blue, the purpose of which is to make the brightness of the screen more in line with the user's vision requirements, and at the same time, it can also play a blue light protection effect according to the content to be displayed on the display screen, and can also effectively avoid visual discomfort caused by color distortion. After applying the first brightness adjustment parameter, the system will further collect feedback data in actual use, including related features such as ambient brightness, the distance between the user and the display screen, and the user's micro-features. These data can help the system evaluate the effect of the initial adjustment and provide a basis for further optimization. Finally, the actual use data collected is used to calculate the brightness adjustment parameters again through the second target deep learning model, and finally the brightness of the display screen that meets the personalized needs of different users is adjusted simultaneously. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 This is a flow chart of a method for uniformly adjusting the brightness of multiple teaching display screens in an embodiment of the present application. DETAILED DESCRIPTION

[0028] The technical solution of the present invention is further elaborated in detail below in conjunction with the drawings and specific embodiments of the specification. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by technicians in the technical field of the present invention. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention. In the following description, the expression "some embodiments" is related to a subset of all possible embodiments, but it should be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments, and can be combined with each other without conflict.

[0029] In the following description, a large number of specific details are provided to provide a more thorough understanding of the present invention. However, it is apparent to those skilled in the art that the present invention can be implemented without one or more of these details. In other examples, in order to avoid confusion with the present invention, some technical features well known in the art are not described.

[0030] It should be understood that the present invention can be implemented in different forms and should not be interpreted as being limited to the embodiments proposed herein. On the contrary, providing these embodiments will make the disclosure thorough and complete, and the scope of the present invention will be fully conveyed to those skilled in the art. And the purpose of the terms used herein is only to describe specific embodiments and is not intended to be a limitation of the present invention. When used herein, the singular forms of "one", "one" and "said / the" are also intended to include plural forms, unless the context clearly indicates another way. It should also be understood that the terms "compose" and / or "include" when used in this specification determine the presence of the features, integers, steps, operations, elements and / or parts, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, parts and / or groups. When used herein, the term "and / or" includes any and all combinations of the relevant listed items.

[0031] It should also be noted that when an element is referred to as being "fixed to" another element, it may be directly on the other element or there may be a central element. When an element is considered to be "connected to" another element, it may be directly connected to the other element or there may be a central element at the same time. The terms "vertical", "horizontal", "inside", "outside", "left", "right" and similar expressions used herein are for illustrative purposes only and are not intended to be the only implementation method.

[0032] In order to fully understand the present invention, a detailed structure will be proposed in the following description to illustrate the technical solution proposed by the present invention. The optional embodiments of the present invention are described in detail as follows, but in addition to these detailed descriptions, the present invention may also have other implementations.

[0033] Please refer to the attached Figure 1 The present application provides a method for uniformly adjusting the brightness of multiple teaching display screens, which is applied to uniformly adjust the brightness of multiple teaching display screens in a computer course classroom. The users of the teaching display screens include student users and teacher users. There are multiple teaching display screens, which are usually uniformly set in a classroom dedicated to computer courses. The method comprises the following steps:

[0034] S1. Preset and store a correspondence table between users and display screens, content to be displayed on the display screen, and vision reports of each user;

[0035] S2. Obtaining a first brightness adjustment parameter of each display screen based on a correspondence table between users and display screens, content to be displayed on the display screen, and a vision report of each user;

[0036] S3, applying the first brightness adjustment parameter to adjust the brightness of the display screen respectively, and collecting the associated features of the display screen and the micro features of the user after the adjustment is completed;

[0037] S4. Based on the associated features of the display screens and the micro-features of the user, a plurality of second brightness adjustment parameters are formed, and the brightness of the plurality of display screens is adjusted simultaneously through the second brightness adjustment parameters and / or operations.

[0038] Specifically, the implementation scheme of the present application needs to establish a data set including user information (such as vision report), display screen information (such as service life, content to be displayed) and the corresponding relationship between the two, and the user's vision characteristics, the content to be displayed on the display screen and other information are used as input, and the preliminary brightness adjustment parameters are calculated through the pre-trained first target deep learning model. These parameters include specific adjustment values ​​of the three color components of red, green and blue. The purpose is to make the brightness of the screen more in line with the user's vision needs. At the same time, it can also play a blue light protection effect according to the content to be displayed on the display screen, and can effectively avoid visual discomfort caused by color distortion. After applying the first brightness adjustment parameter, the system will further collect feedback data in actual use, including ambient brightness, user and display screen distance and other related features, and user's micro features (such as pupil reaction). These data can help the system evaluate the effect of the initial adjustment and provide a basis for further optimization. Finally, the actual use data collected is used to calculate the brightness adjustment parameters again through the second target deep learning model. In this process, the model will comprehensively consider factors such as ambient brightness, user and display screen distance, and user's vision characteristics to obtain the optimal solution. If the user makes manual adjustments, the system will also record these operations and use them as new input data to help the model learn the user's preferences.

[0039] In an optional implementation, presetting and storing a correspondence table between users and display screens and a vision report of each user specifically includes: pre-checking the vision health of the user by an eye detection device to obtain a vision report of the user, which reflects the vision condition of the user;

[0040] Collect the hardware information of the display screen, including but not limited to the model, usage time, etc., and know the service life of the display screen through the hardware information;

[0041] Then, a list of users who need to use the display screen is input, and a corresponding display screen is allocated to each user based on the correspondence between vision and the service life of the display screen, and the allocation results are formed into a correspondence table between users and display screens.

[0042] Furthermore, the corresponding relationship between vision and the service life of the display screen specifically includes: the higher the user's vision score, the smaller the change in the brightness adjustment amplitude of the display screen, and the more conducive it is to extending the service life of the display screen. When the service life of the display screen is high, matching it with a user with a high vision score will extend the service life of the display screen.

[0043] Specifically, users with good eyesight are usually able to see the screen content clearly at lower brightness. This means that they can turn down the screen brightness without affecting the reading or viewing experience. Lower brightness can reduce the working intensity of the screen backlight (such as LED), thereby reducing power consumption and heat generation, and reducing the aging rate of screen components. In addition, the smaller the change in brightness adjustment, the longer the screen life. Frequent adjustment of screen brightness will cause frequent switching of electronic components inside the screen, increasing the wear of these components. Users with good eyesight are more sensitive to brightness changes and are often able to adapt to smaller brightness changes. Therefore, they do not need to adjust the brightness frequently, thereby reducing the wear of the screen. Therefore, in order to extend the service life of the display, the display with a long service life will be allocated first.

[0044] In an optional embodiment, the content to be displayed on the display screen is preset and stored, specifically including: counting the number of all interfaces that need to be switched to run a computer program in the visible area of ​​the display screen, and counting the area ratio of each color value in each interface, and based on the area ratio of each color value in each interface, obtaining the area ratio of each color value in all interfaces as the content to be displayed on the display screen.

[0045] Specifically, the computer programs to be run usually include office software and video software, such as PPT, etc., and their PPT or word documents usually have multiple pages. When the teacher conducts each classroom teaching, the pages involved are relatively fixed content. Therefore, the application can be monitored and recorded through programming scripts or special tools, such as all different interfaces displayed by PPT during operation. It can also be achieved by taking screenshots and performing image recognition, and then it is necessary to count the area proportion of each color value in each interface. The purpose of this step is to understand the distribution of different colors in each interface. The color value is usually expressed as a combination of RGB (red, green, and blue) three-color channels, and the value range of each channel is usually 0 to 255. The area proportion refers to the proportion of the number of pixels occupied by a certain color in the interface to the total number of pixels. The screenshots of each interface can be analyzed at the pixel level through an image processing algorithm, the number of pixels of each color value is calculated, and then the area proportion is obtained by dividing it by the total number of pixels as the content to be displayed on the display screen for output.

[0046] In an optional embodiment, the vision characteristics of each user are extracted from the vision report, and the vision characteristics of each user and the content to be displayed on the display screen are used as inputs of the first target deep learning model, and the output of the first target deep learning model is a first brightness adjustment parameter, and the first brightness adjustment parameter includes an adjustment parameter of a red component, an adjustment parameter of a green component, and an adjustment parameter of a blue component. According to the vision characteristics of the user and the content to be displayed on the display screen, the blue light radiation intensity of each display is reduced by adjusting the red component, the green component, and the blue component, so that the blue light radiation intensity of the display screen can be made more in line with the specific situation of each user.

[0047] In an optional embodiment, collecting micro-features of the user specifically includes: when the user sits in front of the display screen, collecting the user's pupil contraction and expansion state and pupil reflection state through a micro-aperture camera group arranged on the display screen.

[0048] Specifically, when a user sits in front of a display screen, the user's pupil contraction and dilation state and pupil reflection state are collected through a micro-aperture camera group arranged on the display screen. The micro-aperture camera group is usually composed of multiple small cameras, which are installed on the frame or bottom of the display screen and can capture the user's facial and eye images from different angles. The aperture of each camera is very small to reduce the impact on the user's line of sight, and the size of the pupil will change according to the light intensity and the user's attention state. In strong light, the pupil will contract to reduce the light entering the eye; in weak light, the pupil will dilate to increase the amount of light entering. In other words, through the pupil state, it can be determined whether the brightness of the display screen still does not meet the user's physiological requirements after the brightness of the display screen is adjusted using the first brightness adjustment parameter;

[0049] Secondly, when the brightness of the display screen meets the physiological needs of the user in the short term, but within a certain period of time, such as 20 minutes, the user stares at the display screen for a long time, which will cause the eyes to be in a state of tension, resulting in eye fatigue. Eye fatigue will not only cause discomfort such as dryness and stinging, but will also further stimulate the lacrimal glands to secrete more tears. When the lacrimal glands secrete more tears, the degree of pupil reflection will increase. Therefore, by judging the pupil reflection state, it can assist in judging whether the brightness of the display screen meets the user's physiological requirements.

[0050] In an optional implementation, the associated features of the display screen include the ambient brightness of the display screen and distance information between the user and the display screen.

[0051] For example, the contrast between the ambient brightness and the display brightness has a great impact on the eyes. If the ambient brightness is low and the display brightness is high, the eyes will be strongly stimulated by the light, which may cause pupil contraction and increased tear secretion. Conversely, if the ambient brightness is high and the display brightness is low, the eyes need to constantly adjust to adapt to this change, which will also cause eye fatigue. Therefore, the ambient brightness of the display can be used as one of the associated features of the display;

[0052] Similarly, if the distance is too close, the focusing burden of the eyes will increase, causing pupil contraction and increased tear secretion. Therefore, when the distance is too close, the brightness should be appropriately lowered, and when the distance is too far, the brightness should be appropriately increased.

[0053] In an optional embodiment, the associated features, the first brightness adjustment parameter, the pupil contraction and dilation state, and the pupil reflection state are used as inputs of a second target deep learning model, and the output of the second target deep learning model is the second brightness adjustment parameter.

[0054] Furthermore, the first target deep learning model and the second target deep learning model are constructed based on any one of the following deep learning model architectures: convolutional neural network model architecture and self-attention mechanism model architecture.

[0055] In an optional implementation, the brightness of multiple display screens is adjusted simultaneously through operations, specifically including:

[0056] The user adjusts the brightness of the display screen by inputting an adjustment instruction through an operation button, and obtains a brightness adjustment result;

[0057] According to the correspondence table between users and display screens, determine the user corresponding to the display screen whose brightness is adjusted, and determine the vision report corresponding to the user;

[0058] Compare the aforementioned vision report with other vision reports for similarity, select the most similar vision report, and determine the user and display screen corresponding to the most similar vision report;

[0059] The brightness adjustment result, the associated features of the user corresponding to the most similar vision report, the pupil contraction and dilation state, and the pupil reflection state are used as inputs of the second target deep learning model, which outputs the second brightness adjustment parameter of the display screen corresponding to the most similar vision report;

[0060] The brightness of the display screen is adjusted based on the aforementioned second brightness adjustment parameter.

[0061] Exemplarily, the user can manually adjust the brightness of the display screen according to his or her visual comfort. These adjustment instructions will be recorded by the system for subsequent analysis and optimization. After the system receives the adjustment instructions input by the user, it will immediately adjust the brightness of the display screen and record the results of the brightness adjustment. These results include information such as the brightness value before and after the adjustment, the timestamp of the adjustment, etc., for subsequent analysis and use. The system determines the user corresponding to the display screen whose brightness is adjusted according to the corresponding relationship table between the user and the display screen. This relationship table usually contains the unique identifier of the user (such as the user ID) and the identifier of the display screen used by the user (such as the display screen ID). Through this corresponding relationship, the system can accurately identify which user has adjusted the brightness of which display screen. After obtaining the user's information, the user's vision report will be queried. The vision report usually contains the user's vision data, refractive power, astigmatism and other information. These data can help the system understand the user's visual characteristics, and then compare the current user's vision report with the vision reports of other users for similarity. Similarity comparison can be achieved by calculating the difference between various indicators in the vision report. For example, methods such as Euclidean distance or cosine similarity can be used to measure the similarity of two vision reports. The system will select the most similar vision report and determine the user and display screen corresponding to the vision report. Finally, the brightness adjustment result, the associated characteristics of the user corresponding to the most similar vision report (such as age, gender, usage habits, etc.), pupil contraction and expansion state, and pupil reflection state are used as inputs to the second target deep learning model. By learning from these input data, the deep learning model outputs the second brightness adjustment parameters of the display screen corresponding to the most similar vision report, and further adjusts the brightness of the display screen. These parameters are usually a specific brightness value or brightness range. The system will adjust the brightness of the display screen to this value or range to provide the best visual experience.

[0062] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. The protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for uniformly adjusting the brightness of multiple teaching display screens, characterized in that: The method comprises the following steps: Preset and store a table of correspondence between users and display screens, content to be displayed on the display screen, and vision reports of each user; Based on the correspondence table between users and display screens, the content to be displayed on the display screens, and the vision report of each user, obtaining a first brightness adjustment parameter for each of the display screens; Applying the first brightness adjustment parameter to adjust the brightness of the display screen respectively, and collecting the associated features of the display screen and the micro features of the user after the adjustment is completed; Based on the associated features of the display screens and the micro-features of the user, a plurality of second brightness adjustment parameters are formed, and the brightness of the plurality of display screens is adjusted simultaneously through the second brightness adjustment parameters and / or operations.

2. A method for uniformly adjusting the brightness of multiple teaching display screens according to claim 1, characterized in that: Preset and store a correspondence table between users and display screens and a vision report for each user, specifically including: pre-checking the user's vision health through an eye detection device to obtain the user's vision report; collecting the hardware information of the display screen to obtain the service life of the display screen, inputting a list of users who need to use the display screen, and allocating a corresponding display screen to each user based on the correspondence between vision and the service life of the display screen, and forming a correspondence table between users and display screens with the allocation results.

3. A method for uniformly adjusting the brightness of multiple teaching display screens according to claim 2, characterized in that: The corresponding relationship between vision and the service life of the display screen specifically includes: the higher the user's vision score, the smaller the change in the brightness adjustment amplitude of the display screen, and the more conducive it is to extending the service life of the display screen. When the service life of the display screen is high, matching it with a user with a high vision score will extend the service life of the display screen.

4. A method for uniformly adjusting the brightness of multiple teaching display screens according to claim 3, characterized in that: Presetting and storing the content to be displayed on the display screen specifically includes: counting the number of all interfaces that need to be switched when running a computer program in the visible area of ​​the display screen, and counting the area ratio of each color value in each interface, and based on the area ratio of each color value in each interface, obtaining the area ratio of each color value in all interfaces as the content to be displayed on the display screen.

5. A method for uniformly adjusting the brightness of multiple teaching display screens according to claim 4, characterized in that: The vision characteristics of each user are extracted from the vision report, and the vision characteristics of each user and the content to be displayed on the display screen are used as input of a first target deep learning model. The output of the first target deep learning model is a first brightness adjustment parameter, and the first brightness adjustment parameter includes an adjustment parameter of a red component, an adjustment parameter of a green component, and an adjustment parameter of a blue component.

6. A method for uniformly adjusting the brightness of multiple teaching display screens according to claim 5, characterized in that: Collecting the user's micro features specifically includes: when the user sits in front of the display screen, collecting the user's pupil contraction and expansion state and pupil reflection state through a micro-aperture camera group arranged on the display screen.

7. A method for uniformly adjusting the brightness of multiple teaching display screens according to claim 6, characterized in that: The associated features of the display screen include the ambient brightness of the display screen and the distance information between the user and the display screen.

8. A method for uniformly adjusting the brightness of multiple teaching display screens according to claim 7, characterized in that: The associated features, the first brightness adjustment parameter, the pupil contraction and dilation state, and the pupil reflection state are used as inputs of the second target deep learning model, and the output of the second target deep learning model is the second brightness adjustment parameter.

9. A method for uniformly adjusting the brightness of multiple teaching display screens according to claim 8, characterized in that: The first target deep learning model and the second target deep learning model are constructed based on any one of the following deep learning model architectures: convolutional neural network model architecture and self-attention mechanism model architecture.

10. A method for uniformly adjusting the brightness of multiple teaching display screens according to claim 9, characterized in that: Through operations, the brightness of multiple display screens can be adjusted simultaneously, including: The user adjusts the brightness of the display screen by inputting an adjustment instruction through an operation button, and obtains a brightness adjustment result; According to the correspondence table between users and display screens, determine the user corresponding to the display screen whose brightness is adjusted, and determine the vision report corresponding to the user; Compare the aforementioned vision report with other vision reports for similarity, select the most similar vision report, and determine the user and display screen corresponding to the most similar vision report; The brightness adjustment result, the associated features of the user corresponding to the most similar vision report, the pupil contraction and dilation state, and the pupil reflection state are used as inputs of the second target deep learning model, which outputs the second brightness adjustment parameter of the display screen corresponding to the most similar vision report; The brightness of the display screen is adjusted based on the aforementioned second brightness adjustment parameter.

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