CN119473202A - Liquid crystal display panel control method and system based on artificial intelligence - Google Patents
Liquid crystal display panel control method and system based on artificial intelligence Download PDFInfo
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Abstract
本发明提出了一种基于人工智能的液晶显示面板控制方法和系统，所述方法包括：使用高分辨率摄像头捕捉用户的面部图像，通过深度学习算法进行面部特征提取和识别，估计用户年龄；基于估计的用户年龄、环境光亮度、时间、用户所在地理位置和用户使用的应用类型调整液晶显示面板的蓝光；调整液晶显示面板的蓝光后，获取用户反馈，根据用户反馈训练深度学习模型，基于训练后的深度学习模型自适应调节液晶显示面板的蓝光以适合该用户需求；自适应调节后收集用户新的手动调节记录，更新数据集，使用更新后的数据集对深度学习模型进行再训练。通过此方法和对应的系统，能够依据使用时间及用户的个人偏好来动态调整蓝光输出。
The present invention proposes a liquid crystal display panel control method and system based on artificial intelligence, the method comprising: using a high-resolution camera to capture a user's facial image, extracting and identifying facial features through a deep learning algorithm, and estimating the user's age; adjusting the blue light of the liquid crystal display panel based on the estimated user age, ambient light brightness, time, user's geographical location, and the type of application used by the user; after adjusting the blue light of the liquid crystal display panel, obtaining user feedback, training a deep learning model based on the user feedback, and adaptively adjusting the blue light of the liquid crystal display panel based on the trained deep learning model to suit the user's needs; after adaptive adjustment, collecting the user's new manual adjustment record, updating the data set, and retraining the deep learning model using the updated data set. Through this method and the corresponding system, the blue light output can be dynamically adjusted according to the usage time and the user's personal preferences.
Description
Technical Field
The invention provides a liquid crystal display panel control method and system based on artificial intelligence, and relates to the field of display screen control.
Background
With the advent of the digital age, liquid crystal display panels (LCDs) have become an integral part of our daily lives, from smartphones, notebook computers to large flat panel displays, almost everywhere. However, blue light emitted during use of the screen is considered to be a major factor causing eyestrain and digital vision syndrome.
However, the conventional liquid crystal display has a limitation in blue light control, which cannot be intelligently adjusted according to a specific usage scenario and personal age of a user. For example, when a user uses the device at night in the home, the ideal blue light intensity should be softer than when used in the office during the day to reduce the potential impact on sleep quality. In addition, the conventional technology also ignores the user-personalized blue light comfort requirement, because the sensitivity and acceptance of blue light are different from user to user. Therefore, a more intelligent blue light adjusting system can dynamically adjust blue light output according to ambient light, service time and personal preference of a user, and user experience and visual health are greatly improved.
Disclosure of Invention
The invention provides a liquid crystal display panel control method and a system based on artificial intelligence, which are used for solving the problems mentioned above:
the invention provides a liquid crystal display panel control method based on artificial intelligence, which comprises the following steps:
Capturing facial images of a user by using a high-resolution camera, extracting and identifying facial features by a deep learning algorithm, and estimating the age of the user;
Adjusting blue light of the liquid crystal display panel based on the estimated age, ambient light level, time, geographical location of the user and application type used by the user;
After adjusting the blue light of the liquid crystal display panel, obtaining user feedback, training a deep learning model according to the user feedback, and adaptively adjusting the blue light of the liquid crystal display panel based on the trained deep learning model so as to meet the user requirement;
and after self-adaptive adjustment, collecting new manual adjustment records of a user, updating a data set, and retraining the deep learning model by using the updated data set.
Further, capturing a facial image of a user using a high resolution camera, extracting and identifying facial features by a deep learning algorithm, estimating the age of the user, including:
capturing a user face image by using a high-resolution camera, and preprocessing the captured user face image, wherein the preprocessing comprises gray level conversion, mean value normalization and denoising;
detecting a face region from the preprocessed face image by using a face detection algorithm;
Cutting out the detected Face region, scaling to a specified size, and extracting deep features from the cut and scaled Face region by utilizing a pre-trained deep convolutional neural network model VGG-Face;
And converting the extracted deep features into feature vectors, inputting the feature vectors into a pre-trained face recognition neural network model, and obtaining a user age estimation result.
Further, adjusting blue light of the liquid crystal display panel based on the estimated age, time, geographic location of the user, and application type used by the user, includes:
The liquid crystal display panel acquires the geographic position and time of a user through GPS equipment of connected host equipment;
The liquid crystal display panel monitors the name and the type of an application program of a current active window through a monitoring program in an operating system of a connected host;
Based on the estimated age, the ambient light brightness, the time, the geographical position of the user and the application type of the user, the blue light of the liquid crystal display panel is adjusted, the adjustment amplitude of the blue light of the liquid crystal display panel is calculated by a blue light adjustment model, and the blue light adjustment model is specifically:
;
Wherein, Representing the blue light modulation factor,Representing the basic blue light output value of the light source,Representing the application type factor(s),The geographical location factor is represented as such,Indicating the age of the user,The reference age is indicated as such,The age coefficient is represented by a coefficient of the age,The age constant is indicated as such,The age weight is indicated as being indicative of the age,The usage time weight is indicated as such,The maximum time of day is indicated,Representing a user usage device time;
Based on the calculated blue light adjustment factor, blue light is adjusted to an adjustment ratio with respect to the original blue light intensity.
Further, after adjusting the blue light of the liquid crystal display panel, obtaining user feedback, training a deep learning model according to the user feedback, and adaptively adjusting the blue light of the liquid crystal display panel based on the trained deep learning model to be suitable for the user demand, including:
according to the blue light adjusting model, the blue light output intensity of the liquid crystal display panel is adjusted, the blue light intensity is manually adjusted when and under any situation by a user is monitored and recorded, and the blue light intensity value before and after adjustment and the context information are recorded, wherein the context information comprises time, ambient light, application type and geographical position of the user;
cleaning the historical manual adjustment record, removing the invalid erroneous adjustment record, and extracting features from the manual adjustment record with the erroneous adjustment record removed, wherein the features comprise adjustment time, adjustment amplitude, ambient light intensity and current use application;
Constructing a deep learning model LSTM, wherein an input layer receives the extracted features, and an output layer predicts a blue light intensity regulating value wanted by a user;
performing preliminary training on the model by using historical manual adjustment data, and preliminarily learning the blue light adjustment preference of the user;
collecting context information in real time in the use process of a user, wherein the context information comprises time, ambient light, application type and geographic position of the user;
And inputting the data collected in real time into a trained deep learning model LSTM model, predicting the blue light intensity wanted by a user, and automatically adjusting the blue light intensity of the liquid crystal display panel according to the prediction result of the model.
Further, collecting new manual adjustment records of a user after self-adaptive adjustment, updating a data set, retraining the deep learning model by using the updated data set, and the method comprises the following steps:
When the user is still unsatisfied with the automatically adjusted blue light intensity and performs manual adjustment again, recording the new adjustment data, adding the new manual adjustment data into a training data set for incremental update of the model;
And retraining the deep learning model by using the updated data set.
The invention provides a liquid crystal display panel control system based on artificial intelligence, which comprises:
The estimated user age module is used for capturing facial images of the user by using the high-resolution camera, extracting and identifying facial features by a deep learning algorithm, and estimating the age of the user;
the preliminary adjustment module is used for adjusting blue light of the liquid crystal display panel based on the estimated age, ambient light brightness, time, geographical position of the user and application type used by the user;
the self-adaptive adjusting module is used for acquiring user feedback after adjusting the blue light of the liquid crystal display panel, training the deep learning model according to the user feedback, and self-adaptively adjusting the blue light of the liquid crystal display panel based on the trained deep learning model so as to meet the user requirement;
and the updating training module is used for collecting new manual adjustment records of the user after self-adaptive adjustment, updating the data set and retraining the deep learning model by using the updated data set.
Further, the estimating user age module includes:
The device comprises a user face image capturing module, a user face image capturing module and a user face image capturing module, wherein the user face image capturing module is used for capturing a user face image by using a high-resolution camera, and preprocessing the captured user face image, wherein the preprocessing comprises gray level conversion, mean value normalization and denoising;
the face detection module is used for detecting a face area from the preprocessed face image by using a face detection algorithm;
The feature extraction module is used for cutting out the detected Face region, scaling the Face region to a specified size and extracting deep features from the cut and scaled Face region by utilizing a pre-trained deep convolutional neural network model VGG-Face;
And the estimation module is used for converting the extracted deep features into feature vectors, inputting the feature vectors into a pre-trained face recognition neural network model and obtaining a user age estimation result.
Further, the preliminary adjustment module includes:
The geographic position and time acquisition module is used for acquiring the geographic position and time of a user through the GPS equipment of the connected host equipment by the liquid crystal display panel;
A monitoring application program module, configured to monitor, by using the monitor program in the operating system of the connected host, the name and type of the application program of the current active window;
the blue light adjustment proportion calculating module is used for adjusting blue light of the liquid crystal display panel based on estimated age, environment light brightness, time, geographical position of a user and application type of the user, the adjustment amplitude of the blue light of the liquid crystal display panel is calculated by a blue light adjustment model, and the blue light adjustment model is specifically:
;
Wherein, Representing the blue light modulation factor,Representing the basic blue light output value of the light source,Representing the application type factor(s),The geographical location factor is represented as such,Indicating the age of the user,The reference age is indicated as such,The age coefficient is represented by a coefficient of the age,The age constant is indicated as such,The age weight is indicated as being indicative of the age,The usage time weight is indicated as such,The maximum time of day is indicated,Representing a user usage device time;
and the adjusting module is used for adjusting the blue light to be an adjusting proportion relative to the original blue light intensity based on the calculated blue light adjusting factor.
Further, the adaptive adjustment module includes:
The manual adjustment recording module is used for adjusting the blue light output intensity of the liquid crystal display panel according to the blue light adjustment model, monitoring and recording when and under what circumstances a user manually adjusts the blue light intensity, and recording the blue light intensity values before and after adjustment and the context information, wherein the context information comprises time, ambient light, application type and geographic position of the user;
The cleaning module is used for cleaning the historical manual adjustment record, removing the invalid error adjustment record, and extracting features from the manual adjustment record with the error record removed, wherein the features comprise adjustment time, adjustment amplitude, ambient light intensity and application in current use;
the deep learning model building module is used for building a deep learning model LSTM, an input layer receives the extracted features, and an output layer predicts a blue light intensity regulating value wanted by a user;
The preliminary training module is used for performing preliminary training on the model by using historical manual adjustment data and preliminarily learning the blue light adjustment preference of the user;
The context information collecting module is used for collecting context information in real time in the use process of the user, wherein the context information comprises time, ambient light, application type and geographic position of the user;
And the prediction module is used for inputting the data collected in real time into a trained deep learning model LSTM model, predicting the blue light intensity wanted by a user, and automatically adjusting the blue light intensity of the liquid crystal display panel according to the prediction result of the model.
Further, the update training module includes:
The updating data set module is used for recording the new adjustment data when the user is still unsatisfied with the automatically adjusted blue light intensity and manually adjusts again, adding the new manual adjustment data into the training data set and used for incremental updating of the model;
and the retraining module is used for retraining the deep learning model by using the updated data set.
The liquid crystal display panel control method based on the artificial intelligence has the advantages that user experience is improved, screen blue light is adjusted based on user personalized requirements, visual comfort is improved, visual fatigue is reduced, potential health risks of blue light such as eye injury and biorhythm interference can be reduced through blue light management of different age levels, a system can continuously learn and adapt to personal requirements through a machine learning and intelligent feedback mechanism, optimal display effects can be provided under different conditions, unnecessary energy consumption is reduced through intelligent management of blue light, interference of excessive color temperature on biorhythm of a user is avoided, user satisfaction is improved along with improvement of visual experience along with continuous improvement and optimization of a model, longer-term use is encouraged, and the liquid crystal display panel control method based on the artificial intelligence is not only a blue light optimizing tool, but also a comprehensive personalized display solution, and provides healthy, safe and comfortable visual experience for the user through the combination of an AI technology.
Drawings
Fig. 1 is a schematic diagram of a liquid crystal display panel control method based on artificial intelligence according to the present invention.
Detailed Description
In order that the above-recited objects, features and advantages of the present application will be more clearly understood, a more particular description of the application will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings. It should be noted that, without conflict, the embodiments of the present application and features in the embodiments may be combined with each other.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, and the described embodiments are merely some, rather than all, embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Unless defined otherwise, 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 belongs. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.
In one embodiment of the invention, a liquid crystal display panel control method based on artificial intelligence comprises the following steps:
Capturing facial images of a user by using a high-resolution camera, extracting and identifying facial features by a deep learning algorithm, and estimating the age of the user;
Adjusting blue light of the liquid crystal display panel based on the estimated age, ambient light level, time, geographical location of the user and application type used by the user;
After adjusting the blue light of the liquid crystal display panel, obtaining user feedback, training a deep learning model according to the user feedback, and adaptively adjusting the blue light of the liquid crystal display panel based on the trained deep learning model so as to meet the user requirement;
and after self-adaptive adjustment, collecting new manual adjustment records of a user, updating a data set, and retraining the deep learning model by using the updated data set.
The technical scheme has the working principle and the effect that the high-resolution camera is used for capturing the facial image of the user, the deep learning algorithm is used for extracting facial features so as to estimate the age of the user, the age estimation is helpful for personalized adjustment of blue light output, because people in different age groups have different sensitivity and requirements on blue light, the ambient light sensor is used for acquiring the brightness of light around the liquid crystal display panel in real time, and other information such as time, geographic position and application type is collected. The method comprises the steps of integrating data to establish a comprehensive input vector for automatically adjusting blue light output, dynamically adjusting blue light intensity and color temperature of a liquid crystal display panel through a deep learning model, considering factors such as age, use environment and time period of a user, enabling a display effect to be more suitable for personal biorhythms and preferences, collecting user feedback, scoring visual comfort and manually adjusting histories, retraining the deep learning model by using the feedback to continuously optimize prediction accuracy and adjustment effects, recording and updating a data set by using new manual adjustment of the user to improve universality and individuation response capability of the model, and enabling the model to adapt to changes of user habits and preferences through a periodic retraining process. The method comprises the steps of improving user experience, adjusting screen blue light based on user personalized requirements, improving visual comfort, reducing visual fatigue, enabling blue light management of different age levels to reduce potential health risks of blue light, such as eye injury and biorhythm interference, enabling a system to continuously learn and adapt to personal requirements through a machine learning and intelligent feedback mechanism, enabling the system to provide optimal display effects in different environments, enabling unnecessary energy consumption to be reduced through intelligent management of blue light, simultaneously avoiding interference of excessive color temperature on the biorhythm of a user, enabling user satisfaction to be improved along with improvement of visual experience along with continuous improvement and optimization of a model, encouraging longer-term use, and enabling the liquid crystal display panel control method based on artificial intelligence to be not only a blue light optimizing tool, but also a comprehensive personalized display solution to provide healthy, safe and comfortable visual experience for the user through combination of AI technology.
One embodiment of the present invention captures a facial image of a user using a high resolution camera, performs facial feature extraction and recognition by a deep learning algorithm, estimates the age of the user, and includes:
capturing a user face image by using a high-resolution camera, and preprocessing the captured user face image, wherein the preprocessing comprises gray level conversion, mean value normalization and denoising;
detecting a face region from the preprocessed face image by using a face detection algorithm;
Cutting out the detected Face region, scaling to a specified size, and extracting deep features from the cut and scaled Face region by utilizing a pre-trained deep convolutional neural network model VGG-Face;
And converting the extracted deep features into feature vectors, inputting the feature vectors into a pre-trained face recognition neural network model, and obtaining a user age estimation result.
The working principle of the technical scheme has the advantages that the image preprocessing, the gray level conversion converts the color face image into the gray level image so as to reduce the calculation complexity, and meanwhile, the important characteristics of the image are reserved; the average normalization reduces image noise and enhances image quality by using a filter (such as Gaussian filtering) so as to improve the accuracy of face feature extraction, and identifies a face region from the preprocessed image by using a face detection algorithm (such as a Haar cascade classifier or a modern deep learning model MTCNN);
The method comprises the steps of ensuring reliable positioning, accurately detecting even if a human Face is partially shielded or under different illumination conditions, cutting a detected human Face region, scaling the detected human Face region to a specified size (224 x224 pixels for example) to adapt to the input requirement of a deep learning model, extracting deep features from the processed human Face region by utilizing a VGG-Face model, wherein the VGG-Face is a pre-trained deep convolutional neural network, optimizing a human Face recognition task, effectively extracting complex and identification features, converting the extracted deep features into feature vectors, inputting the feature vectors into the pre-trained human Face recognition neural network model for analysis, and obtaining the age estimation result of a user through a classification or regression mechanism. The method has the advantages of high accuracy, high efficiency in distinguishing different fine facial features by utilizing deep learning models such as VGG-Face and the like so as to improve the accuracy of age estimation, high efficiency in algorithm and model structure, suitability for real-time application in completing the whole age identification process in a short time, high robustness, capability of effectively detecting and identifying the facial features even under different shooting conditions (such as illumination change, partial shielding and the like), personalized display adjustment, and capability of better personalized adjustment of display equipment parameters for users based on estimated age information, such as blue light intensity and color temperature optimization.
One embodiment of the present invention adjusts blue light of a liquid crystal display panel based on estimated age, time, geographical location of a user, and application type used by the user, comprising:
The liquid crystal display panel acquires the geographic position and time of a user through GPS equipment of connected host equipment;
The liquid crystal display panel monitors the name and the type of an application program of a current active window through a monitoring program in an operating system of a connected host;
Based on the estimated age, the ambient light brightness, the time, the geographical position of the user and the application type of the user, the blue light of the liquid crystal display panel is adjusted, the adjustment amplitude of the blue light of the liquid crystal display panel is calculated by a blue light adjustment model, and the blue light adjustment model is specifically:
;
Wherein, Representing the blue light modulation factor,Representing the basic blue light output value of the light source,Representing the application type factor(s),The geographical location factor is represented as such,Indicating the age of the user,The reference age is indicated as such,The age coefficient is represented by a coefficient of the age,The age constant is indicated as such,The age weight is indicated as being indicative of the age,The usage time weight is indicated as such,The maximum time of day is indicated,Representing a user usage device time;
Based on the calculated blue light adjustment factor, blue light is adjusted to an adjustment ratio with respect to the original blue light intensity.
The reference age is set to 18 years, the geographical location factor is set to a different value depending on the current geographical location of the user,
At home, the lighting in the home is mild and the setting is carried outTo reduce eye irritation, offices typically have brighter artificial light sources, settingsBalance blue light output, outdoor, light condition is changeable, settingTo accommodate stronger natural light.
Acquiring application types used by users, if the application types belong to entertainment, low blue light is usually needed for comfort, and the application types are setIf the application type belongs to office reading or writing, moderate blue light is required to maintain high readability, andIf the application type is of a design or color sensitive job (e.g., image editing) a high blue light is required to ensure color accuracy, setting。
The blue adjustment factor calculated by a user in case 1:12 years old at 12 pm using the device is 58.32 and the blue adjustment factor calculated by a user in case 2:18 years old at 10 am using the device is 62.25.
The technical scheme has the working principle and the effect that the ambient light sensor detects the ambient light brightness around the liquid crystal display panel. This helps determine if the blue light needs to be increased or decreased to accommodate the change in ambient light, the GPS device determines the specific geographic location of the user, if at home, at night, and uses less blue light for entertainment applications than office, daytime, to avoid affecting user sleep, the host operating system monitors the program to monitor and identify the application program for the currently active window, this provides information on the type of application usage, estimates the user age from pre-obtained information (which may be registration information or through face recognition, etc.), uses the parameter settings in the formulas and the environment and user information (such as age, ambient light, time of use, etc.), Geographic position and application type) calculates a blue light adjustment factor age factor adjustment model and a sine model of a time factor, respectively considers the age difference of users and the requirements of different time periods in the day on blue light, applies type factors and geographic position factors to influence the actual output level of the blue light, adjusts the actually output blue light intensity according to the calculated blue light adjustment factor, and ensures that users obtain the most comfortable visual experience in the current use environment. The dynamic adjustment of the blue light output can adapt to different environment light changes and user activities, reduce eye fatigue and improve visual comfort, provide personalized blue light adjustment based on the age and application type of the user, help meet specific requirements of different users, protect eyesight and reduce potential damage to health by reducing excessively strong blue light output under dark conditions and moderate blue light contact under bright conditions, save energy and improve efficiency, reasonably adjust the blue light output, optimize user experience, save energy by reducing unnecessary light output, adjust in real time so that the system can quickly respond to the environment changes and the changes of user behaviors to provide continuously optimized visual experience, and perform intelligent blue light output in different occasions, Flexible adjustment to preserve user vision and provide an optimal visual experience. The method comprises the steps of taking a basic blue light value as a starting point of adjustment, determining the intensity of basic blue light output, taking the basic blue light value as a reference to carry out further adjustment, taking the age factor into consideration the different requirements of users with different ages on light sensitivity through a logic function and weight, ensuring that the blue light intensity is adapted to different age groups through setting a reference age and an age coefficient, avoiding discomfort or vision damage caused by excessively strong blue light to users with smaller ages or larger ages, and describing time variation in one day by using a sine function to adapt to natural circadian rhythms by using a time factor. The method can dynamically adjust blue light to match with the physiological cycle of a human body, the time weight controls the fluctuation amplitude of a time factor to ensure that the adjustment meets the natural demand cycle of the human body for the blue light, the application type factor and the field factor are used for adjusting the blue light intensity according to the current environment and the application type, the lower blue light is adjusted in the home environment to increase the comfort, the blue light is moderately improved to improve the attention in the office environment, the classification adjustment is beneficial to personalized user experience, the visual demand under different situations is adapted, the safety value control uses the minimum function min (1, the minimum function) to limit the upper limit of the blue light adjustment factor, the output is ensured not to be excessive, and the blue light is prevented from being excessively strong or weak due to the overlarge adjustment. The method has the advantages of improving safety, ensuring that the blue light output does not exceed the safety range by limiting conditions and using natural rhythms, protecting the vision health of users, providing balanced adjustment of each factor, avoiding single factor leading, having proper adjustment in different occasions, dynamically adapting, changing along with time, environment and usage scene, dynamically adjusting the blue light by a formula, providing comfortable usage experience for users even if the demands change at any time, being easy to update and adjust, flexible in design, capable of being quickly adjusted based on user feedback to improve the continuous improvement of the user experience, wide in practicability, applicable to various devices and usage scenes, suitable for different light conditions and user groups, optimizing the user personalized experience by comprehensively considering various factors, enhancing the safety of blue light adjustment, Rationality and adaptability.
According to one embodiment of the invention, after adjusting the blue light of the liquid crystal display panel, obtaining user feedback, training a deep learning model according to the user feedback, and adaptively adjusting the blue light of the liquid crystal display panel to meet the user requirement based on the trained deep learning model, wherein the method comprises the following steps:
according to the blue light adjusting model, the blue light output intensity of the liquid crystal display panel is adjusted, the blue light intensity is manually adjusted when and under any situation by a user is monitored and recorded, and the blue light intensity value before and after adjustment and the context information are recorded, wherein the context information comprises time, ambient light, application type and geographical position of the user;
cleaning the historical manual adjustment record, removing the invalid erroneous adjustment record, and extracting features from the manual adjustment record with the erroneous adjustment record removed, wherein the features comprise adjustment time, adjustment amplitude, ambient light intensity and current use application;
Constructing a deep learning model LSTM, wherein an input layer receives the extracted features, and an output layer predicts a blue light intensity regulating value wanted by a user;
performing preliminary training on the model by using historical manual adjustment data, and preliminarily learning the blue light adjustment preference of the user;
collecting context information in real time in the use process of a user, wherein the context information comprises time, ambient light, application type and geographic position of the user;
And inputting the data collected in real time into a trained deep learning model LSTM model, predicting the blue light intensity wanted by a user, and automatically adjusting the blue light intensity of the liquid crystal display panel according to the prediction result of the model.
The technical scheme has the working principle and the effects that when a user manually adjusts the blue light intensity of the liquid crystal display screen, the system automatically records relevant context information, including the time of adjustment, the ambient light intensity, the current application type used and the geographical position of the user;
For collected manual adjustment data, a purge is performed to remove invalid or abnormal adjustment records, e.g., to filter out erroneous inputs or abnormal data points, to extract core features of adjustment records, including adjustment time, adjustment amplitude, ambient light intensity, and type of application used. The characteristics are used for subsequent model training, a long-short-term memory (LSTM) neural network is built, the time sequence characteristics of blue light adjustment preference of a user are captured, the model is initially trained through inputting characteristic data so that the user can know adjustment habits and preferences of the user under different situations, new context information such as time, ambient light change, current application type and geographic position is collected in real time in the use process of the user, the real-time information is input into the trained LSTM model, the model outputs predicted ideal blue light intensity, and the blue light output of the liquid crystal display panel is automatically adjusted according to the prediction result of the model so as to meet the requirements of the user on expectations and comfort. Personalized user experience, personalized blue light intensity adjustment is realized, specific preference and visual comfort requirements of a user are met, and the user experience is improved; the LSTM model can identify and respond to the change of a user behavior mode, provide more accurate blue light adjustment suggestions, promote user health and comfort, automatically adjust blue light intensity to optimize visual comfort, reduce eye fatigue and health risks, save energy and intelligently manage, avoid unnecessary manual adjustment, optimize energy consumption and improve resource utilization efficiency by predicting user demands, continuously learn and self-optimize, enable the model to continuously learn the latest preferences and habits of a user along with the increase of service time by continuously updating and expanding training data, realize the self-adaptive optimization process, and play an important supporting role in vision protection and energy efficiency optimization while improving user experience through intelligent learning and dynamic response.
In one embodiment of the present invention, after adaptive adjustment, new manual adjustment records of a user are collected, a data set is updated, and the updated data set is used for retraining a deep learning model, including:
When the user is still unsatisfied with the automatically adjusted blue light intensity and performs manual adjustment again, recording the new adjustment data, adding the new manual adjustment data into a training data set for incremental update of the model;
And retraining the deep learning model by using the updated data set.
The technical scheme has the working principle and the effect that when a user is dissatisfied with the automatically adjusted blue light intensity and manually adjusts the blue light intensity, the system automatically records new manual adjustment data, including adjustment time, the blue light intensity before and after adjustment, the environment light intensity, the current application type, the geographical position of the user and other relevant context information, the newly recorded adjustment data is added into the existing training data set, so that the data set always reflects the latest adjustment behaviors and preferences of the user, the updated data set is used for carrying out incremental training on a deep learning model (such as an LSTM model), the purpose of incremental training is to gradually optimize the model under the condition that the model is not started from the beginning, so that the adjustment preferences of the user are better captured and prejudged, the incremental learning adapts to the new data by adjusting model parameters, the adaptability of the model to the latest user behavior mode can be improved while the existing learning results are maintained, the performance of the model is verified through cross verification and testing, and the prediction capability and accuracy of the model are improved after the new data is learned. The system can predict user preference more accurately through collecting and learning actual adjustment behaviors of users, so that satisfaction degree of the users on blue light automatic adjustment is improved, incremental training enables models to be continuously adapted to new user habits and environment changes, effectiveness of prediction capability is maintained, user intervention is reduced, the number of times of manual adjustment of users is reduced along with improvement of model performance, because the system can predict user requirements preliminarily, unnecessary manual operation is reduced, long-term optimization and individualization are achieved, the system can accumulate long-time user preference data, a unique user adjustment model is gradually formed, user experience is enabled to be more personalized and optimized, system efficiency is improved, automatic and intelligent adjustment processes are achieved, resource utilization is more efficient, operation is simplified, dispersion of user attention is reduced, flexibility and individualization of blue light adjustment of the system are enhanced through recording and learning continuous feedback of users, user experience is improved, and manual intervention requirements of users are reduced.
In one embodiment of the invention, an artificial intelligence based liquid crystal display panel control system comprises:
The estimated user age module is used for capturing facial images of the user by using the high-resolution camera, extracting and identifying facial features by a deep learning algorithm, and estimating the age of the user;
the preliminary adjustment module is used for adjusting blue light of the liquid crystal display panel based on the estimated age, ambient light brightness, time, geographical position of the user and application type used by the user;
the self-adaptive adjusting module is used for acquiring user feedback after adjusting the blue light of the liquid crystal display panel, training the deep learning model according to the user feedback, and self-adaptively adjusting the blue light of the liquid crystal display panel based on the trained deep learning model so as to meet the user requirement;
and the updating training module is used for collecting new manual adjustment records of the user after self-adaptive adjustment, updating the data set and retraining the deep learning model by using the updated data set.
The technical scheme has the working principle and the effect that the high-resolution camera is used for capturing the facial image of the user, the deep learning algorithm is used for extracting facial features so as to estimate the age of the user, the age estimation is helpful for personalized adjustment of blue light output, because people in different age groups have different sensitivity and requirements on blue light, the ambient light sensor is used for acquiring the brightness of light around the liquid crystal display panel in real time, and other information such as time, geographic position and application type is collected. The method comprises the steps of integrating data to establish a comprehensive input vector for automatically adjusting blue light output, dynamically adjusting blue light intensity and color temperature of a liquid crystal display panel through a deep learning model, considering factors such as age, use environment and time period of a user, enabling a display effect to be more suitable for personal biorhythms and preferences, collecting user feedback, scoring visual comfort and manually adjusting histories, retraining the deep learning model by using the feedback to continuously optimize prediction accuracy and adjustment effects, recording and updating a data set by using new manual adjustment of the user to improve universality and individuation response capability of the model, and enabling the model to adapt to changes of user habits and preferences through a periodic retraining process. The method comprises the steps of improving user experience, adjusting screen blue light based on user personalized requirements, improving visual comfort, reducing visual fatigue, enabling blue light management of different age levels to reduce potential health risks of blue light, such as eye injury and biorhythm interference, enabling a system to continuously learn and adapt to personal requirements through a machine learning and intelligent feedback mechanism, enabling the system to provide optimal display effects in different environments, enabling unnecessary energy consumption to be reduced through intelligent management of blue light, simultaneously avoiding interference of excessive color temperature on the biorhythm of a user, enabling user satisfaction to be improved along with improvement of visual experience along with continuous improvement and optimization of a model, encouraging longer-term use, and enabling the liquid crystal display panel control method based on artificial intelligence to be not only a blue light optimizing tool, but also a comprehensive personalized display solution to provide healthy, safe and comfortable visual experience for the user through combination of AI technology.
In one embodiment of the present invention, the estimating user age module includes:
The device comprises a user face image capturing module, a user face image capturing module and a user face image capturing module, wherein the user face image capturing module is used for capturing a user face image by using a high-resolution camera, and preprocessing the captured user face image, wherein the preprocessing comprises gray level conversion, mean value normalization and denoising;
the face detection module is used for detecting a face area from the preprocessed face image by using a face detection algorithm;
The feature extraction module is used for cutting out the detected Face region, scaling the Face region to a specified size and extracting deep features from the cut and scaled Face region by utilizing a pre-trained deep convolutional neural network model VGG-Face;
And the estimation module is used for converting the extracted deep features into feature vectors, inputting the feature vectors into a pre-trained face recognition neural network model and obtaining a user age estimation result.
The working principle of the technical scheme has the advantages that the image preprocessing, the gray level conversion converts the color face image into the gray level image so as to reduce the calculation complexity, and meanwhile, the important characteristics of the image are reserved; the average normalization reduces image noise and enhances image quality by using a filter (such as Gaussian filtering) so as to improve the accuracy of face feature extraction, and identifies a face region from the preprocessed image by using a face detection algorithm (such as a Haar cascade classifier or a modern deep learning model MTCNN);
The method comprises the steps of ensuring reliable positioning, accurately detecting even if a human Face is partially shielded or under different illumination conditions, cutting a detected human Face region, scaling the detected human Face region to a specified size (224 x224 pixels for example) to adapt to the input requirement of a deep learning model, extracting deep features from the processed human Face region by utilizing a VGG-Face model, wherein the VGG-Face is a pre-trained deep convolutional neural network, optimizing a human Face recognition task, effectively extracting complex and identification features, converting the extracted deep features into feature vectors, inputting the feature vectors into the pre-trained human Face recognition neural network model for analysis, and obtaining the age estimation result of a user through a classification or regression mechanism. The method has the advantages of high accuracy, high efficiency in distinguishing different fine facial features by utilizing deep learning models such as VGG-Face and the like so as to improve the accuracy of age estimation, high efficiency in algorithm and model structure, suitability for real-time application in completing the whole age identification process in a short time, high robustness, capability of effectively detecting and identifying the facial features even under different shooting conditions (such as illumination change, partial shielding and the like), personalized display adjustment, and capability of better personalized adjustment of display equipment parameters for users based on estimated age information, such as blue light intensity and color temperature optimization.
In one embodiment of the invention, the preliminary adjustment module comprises:
The geographic position and time acquisition module is used for acquiring the geographic position and time of a user through the GPS equipment of the connected host equipment by the liquid crystal display panel;
A monitoring application program module, configured to monitor, by using the monitor program in the operating system of the connected host, the name and type of the application program of the current active window;
the blue light adjustment proportion calculating module is used for adjusting blue light of the liquid crystal display panel based on estimated age, environment light brightness, time, geographical position of a user and application type of the user, the adjustment amplitude of the blue light of the liquid crystal display panel is calculated by a blue light adjustment model, and the blue light adjustment model is specifically:
;
Wherein, Representing the blue light modulation factor,Representing the basic blue light output value of the light source,Representing the application type factor(s),The geographical location factor is represented as such,Indicating the age of the user,The reference age is indicated as such,The age coefficient is represented by a coefficient of the age,The age constant is indicated as such,The age weight is indicated as being indicative of the age,The usage time weight is indicated as such,The maximum time of day is indicated,Representing a user usage device time;
and the adjusting module is used for adjusting the blue light to be an adjusting proportion relative to the original blue light intensity based on the calculated blue light adjusting factor.
The reference age is set to 18 years, the geographical location factor is set to a different value depending on the current geographical location of the user,
At home, the lighting in the home is mild and the setting is carried outTo reduce eye irritation, offices typically have brighter artificial light sources, settingsBalance blue light output, outdoor, light condition is changeable, settingTo accommodate stronger natural light.
Acquiring application types used by users, if the application types belong to entertainment, low blue light is usually needed for comfort, and the application types are setIf the application type belongs to office reading or writing, moderate blue light is required to maintain high readability, andIf the application type is of a design or color sensitive job (e.g., image editing) a high blue light is required to ensure color accuracy, setting。
The blue adjustment factor calculated by a user in case 1:12 years old at 12 pm using the device is 58.32 and the blue adjustment factor calculated by a user in case 2:18 years old at 10 am using the device is 62.25.
The technical scheme has the working principle and the effect that the ambient light sensor detects the ambient light brightness around the liquid crystal display panel. This helps determine if the blue light needs to be increased or decreased to accommodate the change in ambient light, the GPS device determines the specific geographic location of the user, if at home, at night, and uses less blue light for entertainment applications than office, daytime, to avoid affecting user sleep, the host operating system monitors the program to monitor and identify the application program for the currently active window, this provides information on the type of application usage, estimates the user age from pre-obtained information (which may be registration information or through face recognition, etc.), uses the parameter settings in the formulas and the environment and user information (such as age, ambient light, time of use, etc.), Geographic position and application type) calculates a blue light adjustment factor age factor adjustment model and a sine model of a time factor, respectively considers the age difference of users and the requirements of different time periods in the day on blue light, applies type factors and geographic position factors to influence the actual output level of the blue light, adjusts the actually output blue light intensity according to the calculated blue light adjustment factor, and ensures that users obtain the most comfortable visual experience in the current use environment. The dynamic adjustment of the blue light output can adapt to different environment light changes and user activities, reduce eye fatigue and improve visual comfort, provide personalized blue light adjustment based on the age and application type of the user, help meet specific requirements of different users, protect eyesight and reduce potential damage to health by reducing excessively strong blue light output under dark conditions and moderate blue light contact under bright conditions, save energy and improve efficiency, reasonably adjust the blue light output, optimize user experience, save energy by reducing unnecessary light output, adjust in real time so that the system can quickly respond to the environment changes and the changes of user behaviors to provide continuously optimized visual experience, and perform intelligent blue light output in different occasions, Flexible adjustment to preserve user vision and provide an optimal visual experience. The method comprises the steps of taking a basic blue light value as a starting point of adjustment, determining the intensity of basic blue light output, taking the basic blue light value as a reference to carry out further adjustment, taking the age factor into consideration the different requirements of users with different ages on light sensitivity through a logic function and weight, ensuring that the blue light intensity is adapted to different age groups through setting a reference age and an age coefficient, avoiding discomfort or vision damage caused by excessively strong blue light to users with smaller ages or larger ages, and describing time variation in one day by using a sine function to adapt to natural circadian rhythms by using a time factor. The method can dynamically adjust blue light to match with the physiological cycle of a human body, the time weight controls the fluctuation amplitude of a time factor to ensure that the adjustment meets the natural demand cycle of the human body for the blue light, the application type factor and the field factor are used for adjusting the blue light intensity according to the current environment and the application type, the lower blue light is adjusted in the home environment to increase the comfort, the blue light is moderately improved to improve the attention in the office environment, the classification adjustment is beneficial to personalized user experience, the visual demand under different situations is adapted, the safety value control uses the minimum function min (1, the minimum function) to limit the upper limit of the blue light adjustment factor, the output is ensured not to be excessive, and the blue light is prevented from being excessively strong or weak due to the overlarge adjustment. The method has the advantages of improving safety, ensuring that the blue light output does not exceed the safety range by limiting conditions and using natural rhythms, protecting the vision health of users, providing balanced adjustment of each factor, avoiding single factor leading, having proper adjustment in different occasions, dynamically adapting, changing along with time, environment and usage scene, dynamically adjusting the blue light by a formula, providing comfortable usage experience for users even if the demands change at any time, being easy to update and adjust, flexible in design, capable of being quickly adjusted based on user feedback to improve the continuous improvement of the user experience, wide in practicability, applicable to various devices and usage scenes, suitable for different light conditions and user groups, optimizing the user personalized experience by comprehensively considering various factors, enhancing the safety of blue light adjustment, Rationality and adaptability.
In one embodiment of the present invention, the adaptive adjustment module includes:
The manual adjustment recording module is used for adjusting the blue light output intensity of the liquid crystal display panel according to the blue light adjustment model, monitoring and recording when and under what circumstances a user manually adjusts the blue light intensity, and recording the blue light intensity values before and after adjustment and the context information, wherein the context information comprises time, ambient light, application type and geographic position of the user;
The cleaning module is used for cleaning the historical manual adjustment record, removing the invalid error adjustment record, and extracting features from the manual adjustment record with the error record removed, wherein the features comprise adjustment time, adjustment amplitude, ambient light intensity and application in current use;
the deep learning model building module is used for building a deep learning model LSTM, an input layer receives the extracted features, and an output layer predicts a blue light intensity regulating value wanted by a user;
The preliminary training module is used for performing preliminary training on the model by using historical manual adjustment data and preliminarily learning the blue light adjustment preference of the user;
The context information collecting module is used for collecting context information in real time in the use process of the user, wherein the context information comprises time, ambient light, application type and geographic position of the user;
And the prediction module is used for inputting the data collected in real time into a trained deep learning model LSTM model, predicting the blue light intensity wanted by a user, and automatically adjusting the blue light intensity of the liquid crystal display panel according to the prediction result of the model.
The technical scheme has the working principle and the effects that when a user manually adjusts the blue light intensity of the liquid crystal display screen, the system automatically records relevant context information, including the time of adjustment, the ambient light intensity, the current application type used and the geographical position of the user;
For collected manual adjustment data, a purge is performed to remove invalid or abnormal adjustment records, e.g., to filter out erroneous inputs or abnormal data points, to extract core features of adjustment records, including adjustment time, adjustment amplitude, ambient light intensity, and type of application used. The characteristics are used for subsequent model training, a long-short-term memory (LSTM) neural network is built, the time sequence characteristics of blue light adjustment preference of a user are captured, the model is initially trained through inputting characteristic data so that the user can know adjustment habits and preferences of the user under different situations, new context information such as time, ambient light change, current application type and geographic position is collected in real time in the use process of the user, the real-time information is input into the trained LSTM model, the model outputs predicted ideal blue light intensity, and the blue light output of the liquid crystal display panel is automatically adjusted according to the prediction result of the model so as to meet the requirements of the user on expectations and comfort. Personalized user experience, personalized blue light intensity adjustment is realized, specific preference and visual comfort requirements of a user are met, and the user experience is improved; the LSTM model can identify and respond to the change of a user behavior mode, provide more accurate blue light adjustment suggestions, promote user health and comfort, automatically adjust blue light intensity to optimize visual comfort, reduce eye fatigue and health risks, save energy and intelligently manage, avoid unnecessary manual adjustment, optimize energy consumption and improve resource utilization efficiency by predicting user demands, continuously learn and self-optimize, enable the model to continuously learn the latest preferences and habits of a user along with the increase of service time by continuously updating and expanding training data, realize the self-adaptive optimization process, and play an important supporting role in vision protection and energy efficiency optimization while improving user experience through intelligent learning and dynamic response.
In one embodiment of the present invention, the update training module includes:
The updating data set module is used for recording the new adjustment data when the user is still unsatisfied with the automatically adjusted blue light intensity and manually adjusts again, adding the new manual adjustment data into the training data set and used for incremental updating of the model;
and the retraining module is used for retraining the deep learning model by using the updated data set.
The technical scheme has the working principle and the effect that when a user is dissatisfied with the automatically adjusted blue light intensity and manually adjusts the blue light intensity, the system automatically records new manual adjustment data, including adjustment time, the blue light intensity before and after adjustment, the environment light intensity, the current application type, the geographical position of the user and other relevant context information, the newly recorded adjustment data is added into the existing training data set, so that the data set always reflects the latest adjustment behaviors and preferences of the user, the updated data set is used for carrying out incremental training on a deep learning model (such as an LSTM model), the purpose of incremental training is to gradually optimize the model under the condition that the model is not started from the beginning, so that the adjustment preferences of the user are better captured and prejudged, the incremental learning adapts to the new data by adjusting model parameters, the adaptability of the model to the latest user behavior mode can be improved while the existing learning results are maintained, the performance of the model is verified through cross verification and testing, and the prediction capability and accuracy of the model are improved after the new data is learned. The system can predict user preference more accurately through collecting and learning actual adjustment behaviors of users, so that satisfaction degree of the users on blue light automatic adjustment is improved, incremental training enables models to be continuously adapted to new user habits and environment changes, effectiveness of prediction capability is maintained, user intervention is reduced, the number of times of manual adjustment of users is reduced along with improvement of model performance, because the system can predict user requirements preliminarily, unnecessary manual operation is reduced, long-term optimization and individualization are achieved, the system can accumulate long-time user preference data, a unique user adjustment model is gradually formed, user experience is enabled to be more personalized and optimized, system efficiency is improved, automatic and intelligent adjustment processes are achieved, resource utilization is more efficient, operation is simplified, dispersion of user attention is reduced, flexibility and individualization of blue light adjustment of the system are enhanced through recording and learning continuous feedback of users, user experience is improved, and manual intervention requirements of users are reduced.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
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