A method for uniformly adjusting the brightness of multiple teaching display screens

Abstract

This invention relates to the field of display screen adjustment technology, and discloses a method for uniformly adjusting the brightness of multiple teaching display screens. The method includes the following steps: pre-setting and storing a 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 display screen based on the correspondence table, the content to be displayed on the display screens, and the vision report of each user; adjusting the brightness of each display screen using the first brightness adjustment parameter, and collecting the associated features of the display screens and the micro-features of the users after the adjustment is completed; forming multiple second brightness adjustment parameters based on the associated features of the display screens and the micro-features of the users, and simultaneously adjusting the brightness of multiple display screens through the second brightness adjustment parameters and / or operations.

Description

Technical Field

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

[0002] With the development of information technology, multimedia teaching has gradually become an important part of modern education. In multimedia teaching, the brightness adjustment of the teaching display screen is crucial for ensuring teaching quality and protecting students' eyesight, serving as a primary teaching tool. Existing methods for adjusting the brightness of teaching display screens mainly include manual and automatic adjustment.

[0003] Manual adjustment involves users adjusting the screen brightness based on their own visual perception. While simple and intuitive, this method has significant drawbacks. First, manual adjustment relies on the user's subjective experience, making precise brightness control difficult. Second, different users have different brightness needs, and manual adjustment cannot meet individual preferences. Furthermore, prolonged manual adjustment is not only time-consuming and laborious but can also lead to eye strain.

[0004] Automatic adjustment, which uses sensors to detect changes in ambient light and adjusts the screen brightness accordingly, is more intelligent than manual adjustment, but it still has some problems. First, most existing automatic adjustment methods only consider changes in ambient light, ignoring individual user differences and specific needs. Second, relying solely on ambient light detection cannot fully reflect the user's actual visual experience, especially in complex teaching environments where ambient light changes frequently and significantly. Simply relying on ambient light adjustment is often insufficiently precise. In summary, whether manual or automatic, existing methods fail to adequately consider individual user differences, such as vision, age, and gender. This results in brightness adjustments that do not adequately meet each user's specific needs, and they also fail to consider actual visual feedback and the content displayed on the screen, leading to low adjustment precision and potentially causing 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, which mainly solves the technical problems existing in the background art.

[0006] To achieve the above objectives, the technical solution of this 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] It presets and stores a table of correspondence between users and displays, the content to be displayed on the displays, and each user's vision report;

[0009] Based on the correspondence table between users and displays, the content to be displayed on the displays, and each user's vision report, the first brightness adjustment parameter of each display is obtained;

[0010] The brightness of the display screen is adjusted using the first brightness adjustment parameter, and the associated features of the display screen and the micro-features of the user are collected after the adjustment is completed.

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

[0012] Optionally, a user-display-to-user correspondence table and each user's vision report can be preset and stored. Specifically, this includes: pre-checking the user's vision health using an eye testing device to obtain the user's vision report; collecting the display screen's hardware information to obtain the display screen's lifespan; inputting a list of users who need to use the display screen; assigning a corresponding display screen to each user based on the correspondence between vision and display screen lifespan; and forming a user-display-to-user correspondence table from the allocation results.

[0013] Optionally, the relationship between vision and display lifespan includes: the higher the user's vision score, the smaller the brightness adjustment range of the display, which is more conducive to extending the display's lifespan. When the display's lifespan is high, matching it with a user with a high vision score will extend the display's lifespan.

[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 when running computer programs in the visible area of ​​the display screen, and counting the area ratio of each color value in each interface, and obtaining the area ratio of each color value in all interfaces based on the area ratio of each color value in each interface as the content to be displayed on the display screen.

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

[0016] Optionally, the user's micro-features are collected, specifically including: after the user sits in front of the display screen, the user's pupil constriction and dilation state and pupil reflection state are collected by a micro-hole camera group set on the display screen.

[0017] Optionally, 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.

[0018] Optionally, the associated features, the first brightness adjustment parameter, the pupil constriction and dilation state, and the pupil reflection state are used as inputs to the 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 of the following deep learning model architectures: convolutional neural network model architecture and self-attention mechanism model architecture.

[0020] Optionally, the brightness of multiple displays can be adjusted simultaneously via operation, specifically including:

[0021] Users can adjust the brightness of the display screen by inputting adjustment commands through operation buttons and obtain the brightness adjustment results;

[0022] Based on the user-display correspondence table, determine the user corresponding to the display whose brightness has been adjusted, and determine the vision report corresponding to that user;

[0023] The aforementioned vision reports are compared with other vision reports for similarity. The most similar vision report is selected, and the user and display screen corresponding to the most similar vision report are determined.

[0024] The brightness adjustment result, the association features of the user corresponding to the most similar vision report, the pupil constriction and dilation state, and the pupil reflection state are used as inputs to the second target deep learning model, which outputs the second brightness adjustment parameters 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 this invention are as follows: This invention provides a method for uniformly adjusting the brightness of multiple teaching displays. By comprehensively considering the user's visual characteristics, the content displayed on the screen, and environmental characteristics, it achieves more precise and personalized brightness adjustment. A pre-trained first-objective deep learning model calculates initial brightness adjustment parameters. These parameters include specific adjustment values ​​for the red, green, and blue color components, aiming to make the screen brightness more suitable for the user's visual needs. Simultaneously, it can also provide blue light protection based on the content to be displayed on the screen and effectively avoid visual discomfort caused by color distortion. After applying the first brightness adjustment parameters, the system further collects feedback data from actual use, including ambient brightness, the distance between the user and the screen, and other related features, as well as the user's micro-features. This data helps the system evaluate the effect of the initial adjustment and provides a basis for further optimization. Finally, using the collected actual usage data, a second-objective deep learning model is used to recalculate the brightness adjustment parameters, ultimately achieving simultaneous brightness adjustment of the displays to meet the personalized needs of different users. Attached Figure Description

[0027] Figure 1 This is a flowchart illustrating a method for uniformly adjusting the brightness of multiple teaching displays in an embodiment of this application. Detailed Implementation

[0028] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. In the following description, the expression "some embodiments" refers to a subset of all possible embodiments; however, 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, numerous specific details are set forth in order to provide a more thorough understanding of the invention. However, it will be apparent to those skilled in the art that the invention can be practiced without one or more of these details. In other instances, certain technical features well-known in the art have not been described in order to avoid obscuring the invention.

[0030] It should be understood that the present invention can be embodied in various forms and should not be construed as being limited to the embodiments set forth herein. Rather, providing these embodiments will make the disclosure thorough and complete, and will fully convey the scope of the invention to those skilled in the art. Furthermore, the terminology used herein is intended only to describe particular embodiments and is not intended to limit the invention. When used herein, the singular forms “a,” “an,” and “the” are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the terms “compose” and / or “comprising,” when used in this specification, identify the presence of the stated features, integers, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups. When used herein, the term “and / or” includes any and all combinations of the associated listed items.

[0031] It should also be noted that when an element is referred to as being "fixed to" another element, it can be directly attached to the other element or there may be an intervening element. When an element is referred to as being "connected to" another element, it can be directly connected to the other element or there may be an intervening element. The terms "vertical," "horizontal," "inner," "outer," "left," "right," and similar expressions used herein are for illustrative purposes only and do not represent the only possible implementation.

[0032] To fully understand this invention, a detailed structure will be presented in the following description to illustrate the technical solution proposed by this invention. Optional embodiments of the invention are described in detail below; however, in addition to these detailed descriptions, the invention may have other embodiments.

[0033] Please refer to the attached document. Figure 1 This application provides a method for uniformly adjusting the brightness of multiple teaching displays. This method is applied to uniformly adjust the brightness of multiple teaching displays in a computer classroom. The users of these displays include both students and teachers. There are multiple teaching displays, typically set up in a dedicated computer classroom. The method includes the following steps:

[0034] S1. Preset and store the correspondence table between users and displays, the content to be displayed on the displays, and the vision report of each user;

[0035] S2. Based on the correspondence table between users and displays, the content to be displayed on the displays, and the vision reports of each user, obtain the first brightness adjustment parameter for each of the displays;

[0036] S3. Adjust the brightness of the display screen using the first brightness adjustment parameters, and collect 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 screen and the micro-features of the user, multiple second brightness adjustment parameters are formed, and the brightness of multiple display screens is simultaneously adjusted through the second brightness adjustment parameters and / or operations.

[0038] Specifically, the implementation scheme of this application requires establishing a dataset that includes user information (such as vision reports), display screen information (such as lifespan and content to be displayed), and the correspondence between the two. The user's vision characteristics and the content to be displayed on the screen are used as inputs. A pre-trained first-objective deep learning model calculates initial brightness adjustment parameters. These parameters include specific adjustment values ​​for the red, green, and blue color components, aiming to make the screen brightness more suitable for the user's vision needs. Simultaneously, it can also provide blue light protection based on the content to be displayed on the screen and effectively avoid visual discomfort caused by color distortion. After applying the first brightness adjustment parameters, the system further collects feedback data from actual use, including ambient brightness, the distance between the user and the display screen, and other related features, as well as the user's micro-features (such as pupillary response). This data helps the system evaluate the effect of the initial adjustment and provides a basis for further optimization. Finally, using the collected actual usage data, the second-objective deep learning model recalculates the brightness adjustment parameters. In this process, the model comprehensively considers factors such as ambient brightness, the distance between the user and the display screen, and the user's vision characteristics to obtain the optimal solution. If the user makes manual adjustments, the system will also record these actions and use them as new input data to help the model learn the user's preferences.

[0039] In an optional implementation, a table of correspondence between users and displays and a vision report for each user are preset and stored. Specifically, this includes: pre-checking the user's vision health using an eye testing device to obtain the user's vision report, which reflects the user's vision status.

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

[0041] Then, input the list of users who need to use the display screen, and assign a corresponding display screen to each user based on the correlation between eyesight and the lifespan of the display screen, and form a table of correspondence between users and display screens.

[0042] Furthermore, the correlation between vision and display lifespan includes the following: the higher the user's vision score, the smaller the brightness adjustment range of the display, which is more conducive to extending the display's lifespan. When the display's lifespan is high, matching it with a user with a high vision score will extend the display's lifespan.

[0043] Specifically, users with good eyesight can typically see screen content clearly at lower brightness levels. This means they can lower the screen brightness without affecting their reading or viewing experience. Lower brightness reduces the workload of the screen backlight (such as LEDs), thereby reducing power consumption and heat generation, and slowing down the aging of screen components. In addition, the smaller the change in brightness adjustment, the longer the screen lifespan. Frequent adjustments to screen brightness cause frequent switching of internal electronic components, increasing wear and tear on these components. Users with good eyesight are more sensitive to changes in brightness and can often adapt to small changes, thus reducing the need for frequent brightness adjustments and minimizing screen wear. Therefore, to extend the lifespan of displays, those with longer lifespans are prioritized for allocation.

[0044] In an optional implementation, 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 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 obtaining the area ratio of each color value in all interfaces based on the area ratio of each color value in each interface as the content to be displayed on the display screen.

[0045] Specifically, the computer programs to be run typically include office software and video software, such as PowerPoint (PPT). PPT or Word documents usually have multiple pages. During each lesson, the pages covered by the teacher are relatively fixed. Therefore, it's possible to monitor and record the application, such as all the different interfaces displayed during a PPT presentation, using programming scripts or specialized tools. This can also be achieved by capturing screenshots and performing image recognition. Then, it's necessary to calculate the area percentage of each color value in each interface. The purpose of this step is to understand the distribution of different colors in each interface. Color values ​​are usually represented as a combination of RGB (red, green, blue) channels, with each channel typically ranging from 0 to 255. Area percentage refers to the proportion of pixels occupied by a particular color in the total number of pixels on the interface. Image processing algorithms can be used to perform pixel-level analysis on each screenshot, calculating the number of pixels for each color value, and then dividing by the total number of pixels to obtain the area percentage, which is then used as the content to be displayed on the screen.

[0046] In an optional implementation, visual characteristics of each user are extracted from the visual report. These characteristics, along with the content to be displayed on the screen, are used as input to a first target deep learning model. The output of this model is a first brightness adjustment parameter, which includes adjustment parameters for the red, green, and blue components. Based on the user's visual characteristics and the content to be displayed, the blue light radiation intensity of each display is reduced by adjusting the red, green, and blue components. This allows the blue light radiation intensity of the display to better suit the specific needs of each user.

[0047] In an optional implementation, the user's micro-features are collected, specifically including: after the user sits in front of the display screen, the user's pupil constriction and dilation state and pupil reflection state are collected by a micro-hole camera group set on the display screen.

[0048] Specifically, once a user sits in front of the display screen, a micro-aperture camera array mounted on the screen captures the user's pupil dilation and constriction, as well as pupil reflection. This micro-aperture camera array typically consists of multiple small cameras mounted on the bezel or bottom of the display screen, capable of capturing images of the user's face and eyes from different angles. Each camera has a very small aperture to minimize interference with the user's vision. The size of the pupil changes according to light intensity and the user's attention level. In bright light, the pupil constricts to reduce light entering the eye; in dim light, the pupil dilates to increase the amount of light entering. In other words, by observing the pupil state, it can be determined whether the display screen's brightness, after adjusting the brightness using the initial brightness adjustment parameters, still does not meet the user's physiological requirements.

[0049] Secondly, while the brightness of a display screen may meet a user's physiological needs in the short term, prolonged staring at the screen for extended periods, such as 20 minutes, can cause eye strain and fatigue. Eye fatigue not only leads to dryness and stinging, but also stimulates the tear glands to produce more tears. Increased tear production enhances pupillary reflectivity; therefore, assessing pupillary reflectivity can help determine whether the display screen's brightness meets the user's physiological requirements.

[0050] In an alternative 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 ambient brightness and display brightness has a significant impact on the eyes. If the ambient brightness is low and the display brightness is high, the eyes will be subjected to strong light stimulation, which may lead to pupil constriction and increased tear secretion. Conversely, if the ambient brightness is high and the display brightness is low, the eyes need to constantly adjust to this change, which can also cause eye fatigue. Therefore, the ambient brightness of the display can be considered as one of the associated characteristics of the display.

[0052] Similarly, being too close increases the focusing burden on the eyes, leading to pupil constriction and increased tear secretion. Therefore, when the distance is too close, the brightness should be appropriately reduced, while when the distance is too far, the brightness should be appropriately increased.

[0053] In an optional implementation, the associated features, the first brightness adjustment parameter, the pupil constriction and dilation state, and the pupil reflection state are used as inputs to the 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 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 displays is adjusted simultaneously through operation, specifically including:

[0056] Users can adjust the brightness of the display screen by inputting adjustment commands through operation buttons and obtain the brightness adjustment results;

[0057] Based on the user-display correspondence table, determine the user corresponding to the display whose brightness has been adjusted, and determine the vision report corresponding to that user;

[0058] The aforementioned vision reports are compared with other vision reports for similarity. The most similar vision report is selected, and the user and display screen corresponding to the most similar vision report are determined.

[0059] The brightness adjustment result, the association features of the user corresponding to the most similar vision report, the pupil constriction and dilation state, and the pupil reflection state are used as inputs to the second target deep learning model, which outputs the second brightness adjustment parameters 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] For example, users can manually adjust the screen brightness according to their visual comfort. These adjustment commands are recorded by the system for subsequent analysis and optimization. Upon receiving the user's input, the system immediately adjusts the screen brightness and records the result. This result includes the brightness values ​​before and after adjustment, the timestamp of the adjustment, and other information for later analysis. The system uses a user-screen correspondence table to determine the user associated with the screen whose brightness was adjusted. This table typically contains the user's unique identifier (e.g., user ID) and the identifier of the screen they are using (e.g., screen ID). Through this correspondence, the system can accurately identify which user adjusted the brightness of which screen. After obtaining the user's information, the system queries the user's vision report. The vision report typically contains the user's visual acuity data, refractive error, astigmatism, and other information. This data helps the system understand the user's visual characteristics. The system then compares the current user's vision report with the vision reports of other users. Similarity comparison can be achieved by calculating the differences in various indicators within the vision reports. For example, methods such as Euclidean distance or cosine similarity can be used to measure the similarity between two vision reports. The system selects the most similar vision report and identifies the user and display screen associated with that 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 constriction / dilation status, and pupil reflex status are used as input to the second objective deep learning model. By learning from these input data, the deep learning model outputs second brightness adjustment parameters for the display screen corresponding to the most similar vision report, further adjusting the screen's brightness. These parameters are typically a specific brightness value or range, and the system adjusts the display screen's brightness to this value or range to provide the best visual experience.

[0062] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. The scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for uniformly adjusting the brightness of multiple teaching display screens, characterized in that, The method includes the following steps: It presets and stores a table of correspondence between users and displays, the content to be displayed on the displays, and each user's vision report; Based on the correspondence table between users and displays, the content to be displayed on the displays, and each user's vision report, the first brightness adjustment parameter for each of the displays is obtained; The brightness of the display screen is adjusted by applying the first brightness adjustment parameter, and the associated features of the display screen and the micro features of the user are collected after the adjustment is completed. 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. The micro features of the user include the pupil contraction and dilation state and the pupil reflection state. Based on the associated features of the display screen and the micro-features of the user, multiple second brightness adjustment parameters are formed, and the brightness of multiple display screens is simultaneously adjusted through the second brightness adjustment parameters and operations. The system pre-sets and stores a mapping table between users and displays, as well as each user's vision report. Specifically, it includes: conducting a vision health check on users in advance using an eye testing device to obtain their vision reports; collecting hardware information of the displays to obtain their lifespan; inputting a list of users who need to use the displays; assigning a corresponding display to each user based on the mapping relationship between vision and display lifespan; and forming a mapping table between users and displays based on the assignment results. The relationship between vision and the lifespan of a display screen includes: the higher the user's vision score, the smaller the brightness adjustment range of the display screen, which is more conducive to extending the lifespan of the display screen. When the lifespan of the display screen is long, matching it with a user with a high vision score will extend the lifespan of the display screen. Simultaneous adjustment of the brightness of multiple displays through the second brightness adjustment parameter and operation specifically includes: Users can adjust the brightness of the display screen by inputting adjustment commands through operation buttons and obtain the brightness adjustment results; Based on the user-display correspondence table, determine the user corresponding to the display whose brightness has been adjusted, and determine the vision report corresponding to that user; The aforementioned vision reports are compared with other vision reports to determine the most similar vision report and the user and display screen corresponding to the most similar vision report. The brightness adjustment result, the association features of the user corresponding to the most similar vision report, the pupil constriction and dilation state, and the pupil reflection state are used as inputs to the second target deep learning model, which outputs the second brightness adjustment parameters 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.

2. The method for uniformly adjusting the brightness of multiple teaching display screens according to claim 1, characterized in that, 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 when running computer programs in the visible area of ​​the display screen, and counting the area ratio of each color value in each interface. Based on the area ratio of each color value in each interface, the area ratio of each color value in all interfaces is obtained as the content to be displayed on the display screen.

3. The method for uniformly adjusting the brightness of multiple teaching display screens according to claim 2, characterized in that, The vision features of each user are extracted from the vision report. The vision features of each user and the content to be displayed on the screen are used as the input of the first target deep learning model. The output of the first target deep learning model is the first brightness adjustment parameter, which includes the adjustment parameters of the red component, the green component and the blue component.

4. The method for uniformly adjusting the brightness of multiple teaching display screens according to claim 3, characterized in that, The collection of the user's micro-features specifically includes: after the user sits in front of the display screen, the user's pupil constriction and dilation state and pupil reflection state are collected by a micro-hole camera group set on the display screen.

5. The method for uniformly adjusting the brightness of multiple teaching display screens according to claim 4, characterized in that, The associated features, the first brightness adjustment parameter, the pupil constriction and dilation state, and the pupil reflection state are used as inputs to the second target deep learning model, and the output of the second target deep learning model is the second brightness adjustment parameter.

6. The method for uniformly adjusting the brightness of multiple teaching display screens according to claim 5, characterized in that, The first target deep learning model and the second target deep learning model are built based on any of the following deep learning model architectures: convolutional neural network model architecture and self-attention mechanism model architecture.

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