LED display backlight brightness control system and method based on visual comfort

Abstract

The invention provides an LED display backlight brightness control system and method based on visual comfort, and relates to the technical field of photoelectricity, the system comprises seven system composition subsystems and seven emerging technology subsystems, a user preference input subsystem supports multiple input modes, the individual needs of users can be better met, the user experience is improved, and the user experience is improved. The ambient light detection subsystem collects ambient light information by using a plurality of high-precision light sensors and a specific formula, realizes accurate detection of ambient light and provides an accurate basis for brightness adjustment, and the visual comfort evaluation subsystem calculates a comfort index according to a plurality of factors, so that the brightness adjustment better meets the visual comfort requirement of a human body, and the visual comfort of the human body is improved. The eyeball tracking subsystem has high tracking precision and is helpful for better performing targeted brightness adjustment according to the sight of the user, and the brain wave monitoring subsystem can provide more user state information for the system through specific frequency band monitoring analysis and assist in performing more reasonable brightness adjustment.

Description

Technical Field

[0001] The present invention belongs to the field of optoelectronic technology, and more specifically, particularly relates to a backlight brightness control system and method for an LED display based on visual comfort. Background Art

[0002] With the continuous development of display technology, LED displays have been widely used in people's lives and work. However, traditional LED displays often have some shortcomings in brightness control.

[0003] At present, the backlight brightness adjustment of most LED displays is relatively simple, usually only roughly adjusted based on simple ambient light detection, and it is difficult to fully consider multiple factors such as user visual comfort, personal preferences, and actual usage scenarios. This may lead to poor display effects, such as too high or too low brightness, affecting the user's visual experience and even causing damage to the user's eyes.

[0004] In addition, the existing technology still needs to improve the precision and accuracy of ambient light detection, and cannot accurately reflect the actual ambient light conditions. It also lacks consideration of user preferences and cannot meet the personalized needs of users. At the same time, the adaptability to display scenes is not flexible enough, and it is difficult to cope with complex and changeable actual usage situations.

[0005] In terms of heat dissipation, traditional systems may also have problems with low efficiency, affecting the performance and life of the display. In terms of intelligence and learning capabilities, existing technologies are also insufficient and cannot perform adaptive optimization and adjustments based on user habits and environmental changes.

[0006] Therefore, in order to solve the above-mentioned problems existing in the backlight brightness control of existing LED displays, it is particularly important to develop an LED display backlight brightness control system that can comprehensively consider multiple factors, achieve precise brightness adjustment, improve visual comfort, and have good heat dissipation performance and intelligent learning ability. Summary of the invention

[0007] In order to solve the above technical problems, the present invention provides a LED display backlight brightness control system and method based on visual comfort to solve the above problems.

[0008] LED display backlight brightness control system based on visual comfort, including seven system component subsystems and seven emerging technology subsystems;

[0009] Among them, the system component subsystems are composed of ambient light detection subsystem, visual comfort assessment subsystem, user preference input subsystem, data processing and analysis subsystem, brightness adjustment control subsystem, signal transmission subsystem, and feedback subsystem;

[0010] The emerging technology subsystem consists of an artificial intelligence learning subsystem, an eye tracking subsystem, a quantum dot color optimization subsystem, a wireless communication and cloud connection subsystem, an EEG monitoring subsystem, an adaptive scene recognition subsystem, and a nanomaterial heat dissipation management subsystem;

[0011] The ambient light detection subsystem, user preference input subsystem, eye tracking subsystem, brain wave monitoring subsystem and adaptive scene recognition subsystem respectively transmit the detected data or input information to the data processing and analysis subsystem through the signal transmission subsystem. The artificial intelligence learning subsystem is connected to the data processing and analysis subsystem, the quantum dot color optimization subsystem is connected to the brightness adjustment control subsystem, the wireless communication and cloud connection subsystem is connected to the data processing and analysis subsystem, the nanomaterial heat dissipation management subsystem is connected to the relevant parts of the LED display, and the feedback subsystem is connected to each subsystem to feed back information such as the system operation status and effect to other subsystems.

[0012] Preferably, the ambient light detection subsystem collects ambient light information through multiple high-precision light sensors, and its light intensity detection formula is L=k1 I1+k2I2+...+k n *I n , where L represents the total light intensity, I1, I2...I n are the detection values ​​of each sensor, k1, k2...k n is the corresponding weight coefficient.

[0013] Preferably, the visual comfort assessment subsystem calculates the comfort index based on factors such as ambient light and display content, and the algorithm formula is C=f(A, B, D...), where C represents the comfort index, A is the ambient light parameter, B is the display content characteristic parameter, and D etc. are other related parameters.

[0014] Preferably, the user preference input subsystem supports multiple input modes, including touch screen input, voice input and Bluetooth device input.

[0015] Preferably, the tracking accuracy error of the eye tracking subsystem does not exceed 0.5 degrees.

[0016] Preferably, the brain wave monitoring subsystem uses brain waves in the 1-10 Hz frequency band for monitoring and analysis.

[0017] Preferably, the adaptive scene recognition subsystem is capable of recognizing at least 10 common scene modes.

[0018] Preferably, the heat dissipation efficiency of the nanomaterial heat dissipation management subsystem is improved by no less than 30%.

[0019] Preferably, the data fed back in real time by the feedback subsystem includes a comparison of brightness values ​​before and after brightness adjustment and operating status information of each subsystem.

[0020] Another technical problem to be solved by the present invention is to provide a method for controlling the backlight brightness of an LED display based on visual comfort, comprising the following steps:

[0021] S1: Start the LED display backlight brightness control system based on visual comfort;

[0022] S2: The ambient light detection subsystem collects ambient light information in real time through multiple high-precision light sensors and calculates the total light intensity according to the light intensity detection formula;

[0023] S3: The visual comfort evaluation subsystem calculates the comfort index using an algorithm formula based on factors such as ambient light and display content;

[0024] S4: The user inputs personal preference information through a touch screen input, voice input, or Bluetooth device input of the user preference input subsystem;

[0025] S5: The eye tracking subsystem starts working and performs eye tracking with a tracking accuracy of no more than 0.5 degrees;

[0026] S6: The EEG monitoring subsystem uses 1-10 Hz frequency band EEG for monitoring and analysis;

[0027] S7: the adaptive scene recognition subsystem recognizes the current scene mode, and if it is one of at least 10 common scene modes, performs corresponding processing;

[0028] S8: The nanomaterial heat dissipation management subsystem ensures that the heat dissipation efficiency is increased by no less than 30%;

[0029] S9: The data processing and analysis subsystem receives data from the ambient light detection subsystem, the user preference input subsystem, the eye tracking subsystem, the brain wave monitoring subsystem, and the adaptive scene recognition subsystem, as well as user preference information, and performs comprehensive analysis and processing;

[0030] S10: The AI ​​learning subsystem learns and optimizes strategies based on the analysis results;

[0031] S11: The quantum dot color optimization subsystem works in conjunction with the brightness adjustment control subsystem to adjust brightness and color as needed;

[0032] S12: The feedback subsystem feeds back the brightness value comparison before and after brightness adjustment and the operation status information of each subsystem in real time;

[0033] S13: The system continues to run and repeats the above steps to make dynamic adjustments based on actual conditions.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] 1. Highly personalized: The user preference input subsystem supports multiple input methods, which can better meet the personalized needs of users and improve the user experience.

[0036] 2. Accurate environmental perception: The ambient light detection subsystem uses multiple high-precision light sensors and specific formulas to collect ambient light information, achieve accurate detection of ambient light, and provide an accurate basis for brightness adjustment.

[0037] 3. Scientifically evaluate comfort: The visual comfort evaluation subsystem calculates the comfort index based on a variety of factors to make brightness adjustment more in line with human visual comfort needs.

[0038] 4. High-precision eye tracking: The eye tracking subsystem has high tracking accuracy, which helps to better adjust the brightness according to the user's line of sight.

[0039] 5. Brainwave monitoring assistance: The brainwave monitoring subsystem can monitor and analyze specific frequency bands to provide the system with more user status information and assist in more reasonable brightness adjustment.

[0040] 6. Adaptive scene recognition: It can recognize a variety of common scene modes, automatically adapt to the needs of different scenes, and provide more suitable display effects.

[0041] 7. Efficient heat dissipation: The heat dissipation efficiency of the nanomaterial heat dissipation management subsystem is significantly improved, ensuring the stable operation and service life of the system.

[0042] 8. Real-time feedback and monitoring: The feedback subsystem provides real-time feedback of key data, making it convenient for the system and users to understand the operating status and effects in a timely manner, and facilitating adjustment and maintenance.

[0043] 9. Intelligent learning and optimization: The artificial intelligence learning subsystem can continuously learn and optimize the brightness adjustment strategy to improve the intelligence level and adaptability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a schematic diagram of the system structure of the present invention. DETAILED DESCRIPTION

[0045] The following embodiments of the present invention are described in further detail in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0046] See also Figure 1 ,The present invention provides a LED display backlight brightness control system based on visual comfort, including seven system component subsystems and seven emerging technology subsystems;

[0047] Among them, the system component subsystems are composed of ambient light detection subsystem, visual comfort assessment subsystem, user preference input subsystem, data processing and analysis subsystem, brightness adjustment control subsystem, signal transmission subsystem, and feedback subsystem;

[0048] The emerging technology subsystem consists of an artificial intelligence learning subsystem, an eye tracking subsystem, a quantum dot color optimization subsystem, a wireless communication and cloud connection subsystem, an EEG monitoring subsystem, an adaptive scene recognition subsystem, and a nanomaterial heat dissipation management subsystem;

[0049] The ambient light detection subsystem, user preference input subsystem, eye tracking subsystem, brain wave monitoring subsystem and adaptive scene recognition subsystem respectively transmit the detected data or input information to the data processing and analysis subsystem through the signal transmission subsystem. The artificial intelligence learning subsystem is connected to the data processing and analysis subsystem, the quantum dot color optimization subsystem is connected to the brightness adjustment control subsystem, the wireless communication and cloud connection subsystem is connected to the data processing and analysis subsystem, the nanomaterial heat dissipation management subsystem is connected to the relevant parts of the LED display, and the feedback subsystem is connected to each subsystem to feed back information such as the system operation status and effect to other subsystems.

[0050] The ambient light detection subsystem collects ambient light information through multiple high-precision light sensors. Its light intensity detection formula is L=k1I1+k2I2+...+kn*In, where L represents the total light intensity, I1, I2...In are the detection values ​​of each sensor, and k1, k2...kn are the corresponding weight coefficients. It can accurately quantify the intensity of the ambient light and obtain an accurate total light intensity value. It can more flexibly and accurately reflect the actual ambient light conditions based on the detection values ​​of different sensors and the corresponding weight coefficients, and provide an accurate ambient light data basis for the subsequent visual comfort evaluation subsystem, so as to more accurately calculate the comfort index, and help the data processing and analysis subsystem to reasonably adjust the backlight brightness of the LED display according to the ambient light intensity, so as to better adapt to different lighting environments and improve visual comfort. The whole system can be targeted and optimized according to the complex situation of the actual ambient light, thereby improving the adaptability and intelligence of the system.

[0051] The visual comfort evaluation subsystem calculates the comfort index based on factors such as ambient light and display content. The algorithm formula is C=f(A, B, D...), where C represents the comfort index, A is the ambient light parameter, B is the display content characteristic parameter, and D is other related parameters. It can comprehensively evaluate visual comfort based on multiple factors and derive a specific comfort index. It is presented in a formulaic way to facilitate accurate calculation and analysis, and provide a clear basis and standard for the system to perform operations such as brightness adjustment to ensure that the adjusted display effect meets the visual comfort requirements, help the system to achieve intelligent automatic adjustment, and make real-time adjustments according to different ambient light and display content, so that the operation and adjustment of the entire system are more scientific and reasonable, and the user experience is improved. The "f" represents a functional relationship, that is, a specific mapping relationship between input parameters (A, B, D, etc.) and the comfort index C. It contains specific calculation rules and algorithms for deriving the corresponding comfort index based on various input parameters.

[0052] The user preference input subsystem supports multiple input methods, including touch screen input, voice input and Bluetooth device input, providing a rich and diverse range of interaction methods to facilitate users to set preferences, enhancing the system's ease of use and flexibility, meeting the usage habits and needs of different users, allowing users to conveniently input preference information in their preferred way, allowing users to more directly participate in the system's settings and adjustments, improving users' sense of control and satisfaction with the system. Multiple input methods ensure that users can easily interact with the system in different scenarios, improving the overall adaptability of the system, and helping the system to better perform targeted brightness control and other operations based on the user's specific preferences, further improving visual comfort.

[0053] The tracking accuracy error of the eye tracking subsystem does not exceed 0.5 degrees. It can very accurately obtain the user's eye movement and gaze position, provide highly precise tracking data, and ensure that the system's related operations and responses based on eye tracking are highly accurate and reliable, so that the system can more accurately perform operations such as brightness adjustment according to the user's actual gaze point, thereby improving the pertinence and accuracy of visual comfort. It can provide strong data support for some advanced functions that rely on eye tracking (such as display optimization of specific areas, etc.), and achieve more refined control and services. High-precision eye tracking can enhance the user experience and make users feel that the system responds to their behavior more sensitively and accurately, which helps to enhance the performance and competitiveness of the entire system and reflect its technical advantages.

[0054] The brainwave monitoring subsystem uses brainwaves in the 1-10Hz frequency band for monitoring and analysis. It can obtain brainwave signals in a specific frequency band and achieve targeted monitoring of the user's brain activity. It may help to more accurately reflect the brainwave characteristics related to certain specific psychological or physiological states. The user's attention, fatigue level and other states can be inferred based on brainwave monitoring, providing more basis for the intelligent adjustment of the system. For example, when user fatigue is detected, parameters such as brightness can be automatically adjusted to improve visual comfort, which helps to gain a deeper understanding of the user's cognitive state and emotional response, so that the system can better adapt to and meet the user's potential needs. In some applications related to brain-computer interaction, brainwaves in a specific frequency band can be used to achieve more accurate interactive control, further improve the system's intelligence level and personalized service capabilities, and provide users with a display experience that is more in line with their brain state.

[0055] The adaptive scene recognition subsystem can recognize at least 10 common scene modes, which enables the system to adapt to different scenes more intelligently, provide brightness and display effects that are more in line with actual needs, and improve user experience. It can automatically optimize display parameters according to the characteristics of the scene, reduce the trouble of manual adjustment by users, and increase the convenience and practicality of the system. The 10 common modes may include: office scenes, movie scenes, game scenes, reading scenes, night mode, day mode, bright indoor scenes, dim indoor scenes, strong outdoor scenes, and cloudy outdoor scenes.

[0056] The heat dissipation efficiency of the nanomaterial heat dissipation management subsystem is increased by no less than 30%, ensuring that the LED display can operate stably for a long time, reducing performance degradation or failures caused by overheating, effectively controlling the temperature of the display, keeping its operating temperature within a more suitable range, extending the service life of the LED display, and reducing the risk of hardware damage. The improvement in heat dissipation efficiency allows the display to better cope with high-load working conditions, ensuring the reliability of the system, and is conducive to maintaining the stable performance of the entire brightness control system, without abnormalities due to overheating. It can create a good working environment for the efficient operation of other subsystems, ensure the coordinated operation of the entire system, improve user satisfaction and trust in the product, and enhance the market competitiveness of the product.

[0057] The data fed back in real time by the feedback subsystem includes the comparison of brightness values ​​before and after brightness adjustment and the operating status information of each subsystem, which allows users to intuitively understand the changing effects before and after brightness adjustment, and presents the operating status of each subsystem in real time, so as to find abnormalities in time. For users, they can judge whether the adjustment meets expectations based on the brightness value comparison, improve the user's sense of control and satisfaction with the system, help technicians or the system itself monitor the working conditions of each subsystem, find faults or abnormalities in time, and facilitate targeted maintenance and optimization. It can better evaluate the overall performance and operating stability of the system, provide a basis for subsequent improvements and upgrades, and enable the system to have better self-monitoring and self-adjustment capabilities to ensure that the system is always in good operating condition.

[0058] The user preference input subsystem supports multiple input methods, which can better meet the personalized needs of users and improve user experience. The ambient light detection subsystem uses multiple high-precision light sensors and specific formulas to collect ambient light information, realize accurate detection of ambient light, and provide accurate basis for brightness adjustment. The visual comfort evaluation subsystem calculates the comfort index based on multiple factors, so that the brightness adjustment is more in line with the visual comfort needs of the human body. The eye tracking subsystem has high tracking accuracy, which helps to better make targeted brightness adjustments according to the user's line of sight. The brain wave monitoring subsystem can provide the system with more user status information through monitoring and analysis in specific frequency bands, and assist in more reasonable brightness adjustment. It can identify a variety of common scene modes, automatically adapt to different scene requirements, and provide more suitable display effects. The heat dissipation efficiency of the nanomaterial heat dissipation management subsystem is significantly improved, which ensures the stable operation and service life of the system. The feedback subsystem feeds back key data in real time, which is convenient for the system and users to understand the operation status and effects in time, and is easy to adjust and maintain. The artificial intelligence learning subsystem can continuously learn and optimize the brightness adjustment strategy, improve the intelligence level and adaptability of the system, and integrate a variety of emerging technologies. The subsystems work together to make the entire system powerful and comprehensive.

[0059] The LED display backlight brightness control method based on visual comfort includes the following steps:

[0060] S1: Start the LED display backlight brightness control system based on visual comfort;

[0061] S2: The ambient light detection subsystem collects ambient light information in real time through multiple high-precision light sensors and calculates the total light intensity according to the light intensity detection formula;

[0062] S3: The visual comfort evaluation subsystem calculates the comfort index using an algorithm formula based on factors such as ambient light and display content;

[0063] S4: The user inputs personal preference information through a touch screen input, voice input, or Bluetooth device input of the user preference input subsystem;

[0064] S5: The eye tracking subsystem starts working and performs eye tracking with a tracking accuracy of no more than 0.5 degrees;

[0065] S6: The EEG monitoring subsystem uses 1-10 Hz frequency band EEG for monitoring and analysis;

[0066] S7: the adaptive scene recognition subsystem recognizes the current scene mode, and if it is one of at least 10 common scene modes, performs corresponding processing;

[0067] S8: The nanomaterial heat dissipation management subsystem ensures that the heat dissipation efficiency is increased by no less than 30%;

[0068] S9: The data processing and analysis subsystem receives data from the ambient light detection subsystem, the user preference input subsystem, the eye tracking subsystem, the brain wave monitoring subsystem, and the adaptive scene recognition subsystem, as well as user preference information, and performs comprehensive analysis and processing;

[0069] S10: The AI ​​learning subsystem learns and optimizes strategies based on the analysis results;

[0070] S11: The quantum dot color optimization subsystem works in conjunction with the brightness adjustment control subsystem to adjust brightness and color as needed;

[0071] S12: The feedback subsystem feeds back the brightness value comparison before and after brightness adjustment and the operation status information of each subsystem in real time;

[0072] S13: The system continues to run and repeats the above steps to make dynamic adjustments based on actual conditions.

[0073] The embodiments of the present invention are given for the purpose of illustration and description, and are not intended to be exhaustive or to limit the invention to the disclosed forms. Many modifications and variations will be apparent to those of ordinary skill in the art. The embodiments are selected and described in order to better illustrate the principles and practical applications of the present invention and to enable those of ordinary skill in the art to understand the present invention and thereby design various embodiments with various modifications suitable for specific uses.

Claims

1. An LED display backlight brightness control system based on visual comfort, characterized in that: It includes seven system component subsystems and seven emerging technology subsystems; Among them, the system component subsystems are composed of ambient light detection subsystem, visual comfort assessment subsystem, user preference input subsystem, data processing and analysis subsystem, brightness adjustment control subsystem, signal transmission subsystem, and feedback subsystem; The emerging technology subsystem consists of an artificial intelligence learning subsystem, an eye tracking subsystem, a quantum dot color optimization subsystem, a wireless communication and cloud connection subsystem, an EEG monitoring subsystem, an adaptive scene recognition subsystem, and a nanomaterial heat dissipation management subsystem; The ambient light detection subsystem, user preference input subsystem, eye tracking subsystem, brain wave monitoring subsystem and adaptive scene recognition subsystem respectively transmit the detected data or input information to the data processing and analysis subsystem through the signal transmission subsystem. The artificial intelligence learning subsystem is connected to the data processing and analysis subsystem, the quantum dot color optimization subsystem is connected to the brightness adjustment control subsystem, the wireless communication and cloud connection subsystem is connected to the data processing and analysis subsystem, the nanomaterial heat dissipation management subsystem is connected to the relevant parts of the LED display, and the feedback subsystem is connected to each subsystem to feed back information such as the system operation status and effect to other subsystems.

2. The LED display backlight brightness control system based on visual comfort as claimed in claim 1, characterized in that: The ambient light detection subsystem collects ambient light information through multiple high-precision light sensors. The light intensity detection formula is L = k1 I1 + k2I2 + ... + k n *I n , where L represents the total light intensity, I1, I2...I n are the detection values ​​of each sensor, k1, k2...k n is the corresponding weight coefficient.

3. The LED display backlight brightness control system based on visual comfort as claimed in claim 1, characterized in that: The visual comfort evaluation subsystem calculates the comfort index based on factors such as ambient light and display content. The algorithm formula is C=f(A,B,D...), where C represents the comfort index, A is the ambient light parameter, B is the display content characteristic parameter, and D is other related parameters.

4. The LED display backlight brightness control system based on visual comfort as claimed in claim 1, characterized in that: The user preference input subsystem supports multiple input methods, including touch screen input, voice input, and Bluetooth device input.

5. The LED display backlight brightness control system based on visual comfort as claimed in claim 1, characterized in that: The tracking accuracy error of the eye tracking subsystem does not exceed 0.5 degrees.

6. The LED display backlight brightness control system based on visual comfort as claimed in claim 1, characterized in that: The EEG monitoring subsystem uses 1-10Hz frequency band EEG for monitoring and analysis.

7. The LED display backlight brightness control system based on visual comfort as claimed in claim 1, characterized in that: The adaptive scene recognition subsystem is able to recognize at least 10 common scene modes.

8. The LED display backlight brightness control system based on visual comfort as claimed in claim 1, characterized in that: The heat dissipation efficiency of the nanomaterial heat dissipation management subsystem is improved by no less than 30%.

9. The LED display backlight brightness control system based on visual comfort as claimed in claim 1, characterized in that: The data fed back in real time by the feedback subsystem includes the comparison of brightness values ​​before and after brightness adjustment and the operating status information of each subsystem.

10. A method for controlling backlight brightness of an LED display based on visual comfort, characterized in that: The following steps are involved: S1: Start the LED display backlight brightness control system based on visual comfort; S2: The ambient light detection subsystem collects ambient light information in real time through multiple high-precision light sensors and calculates the total light intensity according to the light intensity detection formula; S3: The visual comfort evaluation subsystem calculates the comfort index using an algorithm formula based on factors such as ambient light and display content; S4: The user inputs personal preference information through a touch screen input, voice input, or Bluetooth device input of the user preference input subsystem; S5: The eye tracking subsystem starts working and performs eye tracking with a tracking accuracy of no more than 0.5 degrees; S6: The EEG monitoring subsystem uses 1-10 Hz frequency band EEG for monitoring and analysis; S7: the adaptive scene recognition subsystem recognizes the current scene mode, and if it is one of at least 10 common scene modes, performs corresponding processing; S8: The nanomaterial heat dissipation management subsystem ensures that the heat dissipation efficiency is increased by no less than 30%; S9: The data processing and analysis subsystem receives data from the ambient light detection subsystem, the user preference input subsystem, the eye tracking subsystem, the brain wave monitoring subsystem, and the adaptive scene recognition subsystem, as well as user preference information, and performs comprehensive analysis and processing; S10: The AI ​​learning subsystem learns and optimizes strategies based on the analysis results; S11: The quantum dot color optimization subsystem works in conjunction with the brightness adjustment control subsystem to adjust brightness and color as needed; S12: The feedback subsystem feeds back the brightness value comparison before and after brightness adjustment and the operation status information of each subsystem in real time; S13: The system continues to run and repeats the above steps to make dynamic adjustments based on actual conditions.

Images