Disclosure of Invention
The invention provides an intelligent screen refresh frequency adjusting system based on content analysis, which solves the problems, and comprises:
the multi-mode data acquisition module comprises an eye movement sensor for acquiring eye movement signals of a user, a sensor for acquiring heart rate variability, a sensor for acquiring skin electrical response and a temperature sensor for acquiring epidermis temperature;
Specifically, the system of the invention can be applied to not only fixed display equipment, but also portable equipment such as smart phones or tablets. By means of built-in or external sensors, the device can support real-time detection of eye movement signals, heart rate variability, galvanic skin response and epidermal temperature. Because GSR (GALVANIC SKIN Response ) needs to be in direct contact with skin, if the user does not hold the device, the system can continue to complete detection through external wearable devices (such as smart bracelets and finger rings) and transmit data to the display device through wireless connection, so that the method of the invention can be executed smoothly under any condition.
The data processing and fusion module is connected with the multi-mode data acquisition module and is used for processing and fusing the physiological data and generating a fatigue prediction result and concentration of the user;
the content perception module is connected with the data processing and fusion module and is used for analyzing the display content type and generating refresh rate adjustment parameters of each region according to the fatigue state of the user and the real-time screen content;
The regional adjustment module is connected to the content perception module and the data processing and fusion module and is used for adjusting the refresh rate of each region of the screen in real time based on a dynamic weight algorithm;
The control module is used for receiving the self-adaptive response curve generated by the data processing and fusing module and adjusting the refresh rate and brightness of the whole screen in real time.
Further, the eye movement sensor is a high frame rate camera;
The heart rate variability sensor adopts photoplethysmography (PPG) and electrode patches;
the galvanic skin response sensor is a high-sensitivity electrode;
The temperature sensor is a high-precision thermistor.
Further, the data processing and fusing module includes:
the multi-mode signal preprocessing unit is used for processing physiological signals by applying an adaptive filtering algorithm;
The feature extraction and analysis unit is used for extracting feature data related to the fatigue state of the user from the processed physiological signals;
The real-time state evaluation and weight distribution unit is used for dynamically adjusting the weight of the physiological signal;
and the fusion and decision unit is used for generating a fatigue prediction result and attention concentration of the user, wherein the fatigue prediction result is realized by fusing eye movement data, heart rate variability data and galvanic skin response data and combining a time sequence analysis model.
Further, the feature extraction and analysis unit extracts pupil dilation rate, HRV (HEART RATE Variability ) low frequency/high frequency ratio, skin conductivity variation amplitude and epidermis temperature fluctuation through Principal Component Analysis (PCA) and Independent Component Analysis (ICA) algorithms, respectively.
Further, the content perception module analyzes content types including text, video and game content displayed on a screen based on a Support Vector Machine (SVM) algorithm, and generates corresponding refresh rate adjustment parameters.
Furthermore, the regional adjustment module adjusts the refresh rate of each region of the screen in real time according to the focusing time of the user's sight and the physiological data through a preset dynamic weight algorithm.
Furthermore, the regional adjustment module comprises a multi-level regional division structure, divides the screen into a core region, a secondary core region and a peripheral region, and sets refresh rate weights for the regions respectively.
Further, the control module further comprises a task management unit, which is used for dynamically adjusting the refresh rates of different areas of the screen according to the task types and the priorities of the users.
Further, the task management unit monitors and analyzes the operation behavior and the application state of the user in real time through a Support Vector Machine (SVM) algorithm to identify the type of the task currently executed and assigns a corresponding refresh rate according to the priority.
The invention also provides a screen refreshing frequency intelligent adjusting method based on content analysis, which comprises the following steps:
step1, acquiring physiological data of a user, and generating a fatigue prediction result through a data processing and fusion module;
Step 2, analyzing the type of the display content of the screen and generating refresh rate adjustment parameters of each area;
Step 3, according to the sight focusing time and the fatigue state of the user, the refresh rate of each area is adjusted in real time through the regional adjustment module;
and 4, controlling the overall refresh rate and brightness of the screen.
The intelligent screen refreshing frequency adjusting system based on content analysis firstly acquires physiological data of a user, such as Heart Rate Variability (HRV), galvanic Skin Response (GSR), epidermis temperature and eye movement signals, in real time through a multi-mode data acquisition module. The data are preprocessed and then input to a data processing and fusing module. The data processing and fusion module processes the physiological data by using a high-precision fusion algorithm to generate a fatigue prediction result and concentration of attention of a user. These processing results provide accurate base data for subsequent screen refresh rate adjustments.
The user state data generated by the data processing and fusing module is transferred to the content perception module. The content perception module analyzes the displayed content types (such as characters, videos and games) according to the fatigue state of the user and the real-time screen content, and preliminarily sets the refresh rate adjustment parameters of each area. The content aware module then communicates these parameters to the regional adjustment module along with the real-time physiological state of the user. The regional adjustment module is based on a preset dynamic weight algorithm, and combines the sight focusing data of the user to adjust the refresh rate of each screen region in real time, so that the display effect of different regions is ensured to be matched with the requirements of the user.
The control module serves as a core coordination unit of the system and receives and analyzes the Adaptive Response Curve (ARC) generated by the data processing and fusion module. Through analyzing ARC, the control module adjusts the refresh rate and brightness of the whole screen in real time so as to adapt to the current state of a user and reduce visual fatigue. In the adjustment process, the control module also introduces a double-buffer technology and a DMA (direct memory access) technology to improve the screen refreshing efficiency and reduce the burden on the main control processor. Through the optimization technology, the system can improve the smoothness of the screen refreshing under the condition of not increasing the load of the processor.
The task management unit utilizes a Support Vector Machine (SVM) algorithm to identify the type of the currently running task by monitoring the operation behaviors (such as keyboard input, mouse clicking and window switching) of the user, and dynamically adjusts the refresh rate of each region according to the priority. For example, in a multitasking environment, the task management unit prioritizes the refresh rate of video conferences and important document editing areas while reducing the refresh rate of background tasks to save system resources. The task management unit is closely cooperated with the control module, and the refresh rate of the relevant area is adjusted in advance by intelligently pre-judging the operation trend of the user, so that the seamless transition of the display effect during task switching is ensured.
According to the intelligent screen refreshing frequency adjusting system and method based on content analysis, the screen refreshing rate and brightness are adjusted in real time, and the system can dynamically optimize the display effect of the screen according to the fatigue state and the current use scene of a user, so that visual fatigue is remarkably reduced, and the intelligent screen refreshing frequency adjusting system and method based on content analysis are particularly suitable for users using electronic equipment for a long time. The system adjusts the refresh rate of each area according to the screen content type and the attention distribution of the user, reduces the refresh rate of the non-core area, reduces unnecessary power consumption and realizes the energy-saving effect.
The multi-mode data acquisition module acquires physiological data (such as eye movement signals, heart rate variability, galvanic skin response and the like) of a user in real time, and generates fatigue prediction results and attention concentration of the user through data fusion, so that screen refresh rate adjustment is more intelligent and accurate.
The invention also dynamically adjusts the refresh rate of different areas according to the current task type and priority of the user through the task management unit, ensures the display effect of the key task area, reduces the refresh rate of the background task and optimizes the utilization of system resources. According to the invention, a double-buffering technology and a DMA technology are introduced, so that the burden on a main control processor is reduced, the smoothness of the screen in refreshing is improved, and the seamless transition of the display effect of a user in task switching is ensured.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
In the description of the present invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like indicate orientations or positional relationships based on the orientation or positional relationships shown in the drawings, merely to facilitate describing the present invention and simplify the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and therefore should not be construed as limiting the present invention. Furthermore, in the description of the present invention, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.
Referring to fig. 1 and fig. 2, in a possible implementation manner, the intelligent adjustment system for the screen refresh frequency based on content analysis provided in this embodiment implements intelligent dynamic adjustment of the screen refresh rate through organic combination among a multi-mode data acquisition module, a data processing and fusion module, a content sensing module, a regional adjustment module and a control module, so as to optimize the visual experience of a user and reduce visual fatigue.
The multi-mode data acquisition module in the embodiment of the invention acquires various physiological data such as eye movement signals, heart Rate Variability (HRV), galvanic Skin Response (GSR), skin temperature and the like of a user through the sensing device. The physiological data not only respectively provide important information related to the fatigue state of the user, but also realize the collaborative processing of the multi-source data through the combination of the physiological data and the data processing and fusion module.
Specifically, the system of the embodiment of the invention can be applied to fixed display equipment and portable equipment such as smart phones or tablets. By means of built-in or external sensors, the device can support real-time detection of eye movement signals, heart rate variability, galvanic skin response and epidermal temperature. Because Galvanic Skin Response (GSR) needs to be in direct contact with skin, if the user does not hold the device, the system can continue to complete detection through external wearable devices (such as smart bracelets and finger rings) and transmit data to the display device through wireless connection, so that the method of the invention can be successfully executed under any condition.
The sensing device includes:
(1) And the eye movement tracking sensor is used for combining laser scanning with the high-frame-rate camera, acquiring pupil movement data in real time through the combination of the laser scanner and the high-frame-rate camera, and acquiring eye movement signals of a user, including blink frequency, pupil dilation and sight line track, so as to monitor the vision concentration and attention change of the user.
(2) A heart rate variability sensor combining photoplethysmography (PPG) with an electrode patch, collecting multi-band heart rate signals through a miniature photoplethysmography sensor and an electrode patch, analyzing Heart Rate Variability (HRV), including low and high frequency HRV data, to assess the autonomic nervous system status of a user.
(3) Near infrared spectroscopy (NIRS) brain oxygen saturation monitoring sensor integrating miniature near infrared spectroscopy sensor, monitoring blood oxygen saturation of cerebral cortex by noninvasive mode, providing blood flow dynamics information of brain part, and identifying cognitive load and mental fatigue state of user.
(4) Galvanic Skin Response (GSR) sensors detect skin conductance changes using high sensitivity sensors and collect galvanic skin response data of a user in real time to reflect mood swings and stress levels as important parameters for assessing fatigue status.
(5) The high-precision thermistor temperature sensor adopts an embedded high-precision thermistor to collect tiny changes of the skin temperature in real time, and is combined with other physiological data to more accurately evaluate the overall fatigue state of a user.
The data processing and fusion module in this embodiment performs fusion processing on physiological data from the multi-modal data acquisition module based on a high-precision fusion algorithm. Through fusion processing, the system can dynamically adjust the weights of different physiological signals, generate a high-precision user fatigue prediction result and provide an accurate basis for subsequent screen refresh rate adjustment.
The content perception module is connected with the data processing and fusion module and obtains the fatigue prediction result of the user. Meanwhile, the content perception module recognizes different types of content (such as characters, videos, games and the like) through real-time analysis of screen display content, and generates regional refresh rate adjustment parameters in combination with the interaction frequency of users. The parameters not only consider the fatigue state of the user, but also reflect the complexity of the screen content and the attention area of the user.
The regional adjustment module is connected with the content perception module and the data processing and fusion module. The module dynamically adjusts the refresh rate of each area of the screen based on the parameters generated by the content perception module and the fatigue prediction result of the user. The regional classification and the preset dynamic weight algorithm adopted by the regional regulation module can provide a higher refreshing rate in the core region to ensure the best visual effect, and reduce the refreshing rate in the peripheral region to save resources. Through the mode, the system can finely adjust each area of the screen according to the real-time requirement of a user.
The control module serves as a core coordination part of the system, is responsible for coordinating data acquisition of the multi-mode data acquisition module, operation processing of the data processing and fusion module and content analysis of the content perception module, receives an adaptive response curve generated by the data processing and fusion module, and adjusts the refresh rate and brightness of the whole screen in real time. The refresh rate and brightness of the screen are adjusted in real time. Through the coordination function of the control modules, the functions of the modules are organically combined, so that the technical effect of the whole system is realized.
Referring to fig. 3, in one possible implementation manner, the present embodiment provides a data processing and fusion module based on a signal processing and fusion mechanism, which is configured to dynamically adjust weights from different physiological signals according to a real-time state of a user, so as to improve accuracy and sensitivity of fatigue prediction.
The data processing and fusing module is connected with the multi-mode data acquisition module to acquire various physiological signal data of the user, wherein the data include, but are not limited to, eye movement signals, heart Rate Variability (HRV), galvanic Skin Response (GSR), skin temperature and the like. The signal processing and fusion mechanism built in the module can dynamically adjust the importance of different physiological signals, so that the system can optimize the fatigue prediction precision according to the real-time state of a user.
The signal processing and fusion mechanism comprises:
(1) And the multi-mode signal preprocessing unit is used for processing the multi-mode physiological signals acquired in real time by applying an adaptive filtering algorithm (such as an LMS filter) and dynamically adjusting the filter coefficients so as to effectively remove environmental noise and equipment interference, thereby improving the signal-to-noise ratio of the signals. A Z-score normalization algorithm (Z-score) is applied to each type of physiological signal to convert the signal data into a form of zero mean and unit variance. Specifically, for each data point, a formula is usedConversion is performed, where x is the original signal value, μ is the mean of the signal, and σ is the standard deviation of the signal. The normalization process ensures that different signals have uniform processing scales, and facilitates subsequent fusion processing.
(2) The feature extraction and analysis unit processes the preprocessed physiological signals by first applying Principal Component Analysis (PCA) and Independent Component Analysis (ICA) algorithms, extracting blink frequency and pupil dilation rate from eye movement signals, extracting low frequency/high frequency ratio from Heart Rate Variability (HRV), extracting conductivity variation amplitude from Galvanic Skin Response (GSR), and extracting minute temperature fluctuations from epidermis temperature data. By these feature extraction algorithms, the data dimension is reduced and the most representative signal features are highlighted. And then, combining real-time and historical data, adopting a time sequence analysis method (such as ARIMA model or LSTM neural network) to perform time sequence analysis on the extracted signal characteristics, evaluating the change trend of the extracted signal characteristics, and predicting the future signal trend so as to understand the fatigue change of a user in different time periods and provide a basis for subsequent dynamic weight adjustment.
(3) And the real-time state evaluation and weight distribution unit is used for calculating the change rate and fluctuation range of each physiological signal characteristic in real time by combining the results of characteristic extraction and time sequence analysis and evaluating the current physiological state of the user by combining the historical data of the user. Based on the evaluation result, a Bayesian network or a fuzzy logic control algorithm is adopted to dynamically adjust the weight distribution of each physiological signal. For example, the system may increase the weight of the eye movement signal and heart rate variability when it detects that the user is looking at the screen for a long time, while the weight of the galvanic skin response and skin temperature increases when the user is in a relaxed state. The dynamic weight distribution mechanism enables the system to flexibly adapt to the real-time state of the user, and optimizes the precision and sensitivity of fatigue prediction.
(4) And the fusion and decision unit is used for carrying out weighted fusion on the characteristic values of the physiological signals according to the dynamically allocated weights to generate the comprehensive fatigue index. The fusion process can realize complex nonlinear fusion through weighted average, weighted accumulation or neural network, and ensure that the comprehensive index accurately reflects the overall fatigue state of the user. And then, the comprehensive fatigue index is subjected to multi-level decision analysis, such as using a decision tree or a Support Vector Machine (SVM) algorithm, and a final fatigue prediction result is generated. The prediction results can be directly used for the self-adaptive adjustment function of the system, such as dynamically adjusting the refresh rate and brightness of the screen, so as to optimize the user experience and reduce the accumulation of visual fatigue.
In summary, the embodiment can effectively improve the accuracy and sensitivity of fatigue prediction, ensure that the system can accurately respond to the real-time state of the user, and dynamically adjust the display parameters so as to optimize the visual experience of the user and reduce fatigue accumulation.
Embodiment III referring to FIGS. 4 and 5, in one possible implementation, the content aware module in this embodiment is configured to analyze different types of content displayed on the screen, including text, video, and games, and determine refresh rate adjustment parameters for each region. The content perception module is matched with the regional adjustment module, and based on a preset dynamic weight algorithm, the refreshing rate of each region of the screen is adjusted by combining the sight focusing time and the physiological data of the user.
In particular, the content perception module classifies screen content in real-time using classification algorithms, such as using Support Vector Machine (SVM) algorithms to distinguish between text, video, and game content. For text content, the content perception module determines a lower refresh rate parameter by analyzing the stationarity of the text content and utilizing a low-pass filter to reduce resource consumption, for video content, a frame rate detection algorithm is adopted to calculate the dynamic property of the video and generate a higher refresh rate parameter to ensure smooth playing, and for game content, particularly high-speed dynamic games, the content perception module determines the refresh rate parameter of the highest level by adopting an analysis method based on the frame rate and user operation response to ensure timely response of user operation and smoothness of visual experience.
The content perception module not only generates refresh rate parameters according to content types, but also adjusts the refresh rate of each region by using a preset dynamic weight algorithm in combination with the sight focusing time and physiological data of the user. Specifically, the regional adjustment module assigns refresh rate weights for each region in real time by a predetermined dynamic weight algorithm (e.g., a bayesian network or fuzzy logic control algorithm). The algorithm identifies the area with the longest stay time of the user on the screen by acquiring the line-of-sight focusing time data of the user, and sets the area as a high-weight area. According to a dynamic weight algorithm, the system automatically increases the refresh rate weight of the current area by combining the sight line data and the physiological state of the user so as to ensure that the display effect meets the user requirement. Meanwhile, the module also combines physiological data of the user, such as Heart Rate Variability (HRV) and Galvanic Skin Response (GSR), judges the fatigue state of the user, and correspondingly adjusts the refresh rate of each area. For example, when the user's line of sight is concentrated in a certain video area for a long period of time and the physiological data indicates that the user is in a low fatigue state, the system will assign a higher refresh rate to enhance the visual experience, whereas when the user's fatigue level is higher, the system may decrease the refresh rate to alleviate the visual burden.
Specifically, the preset dynamic weight algorithm refers to dynamically adjusting the refresh rate weight of each area according to the real-time state and content display requirement of a user in the running process of the system, and is specifically implemented in the following steps:
(1) And acquiring and preprocessing input data, namely acquiring physiological data of a user, including Heart Rate Variability (HRV), galvanic Skin Response (GSR) and epidermis temperature, through a multi-mode data acquisition module. The data are filtered and normalized and then used as input parameters for weight adjustment. The system acquires sight line data of the user, such as sight line residence time and sight line track, through an eye movement tracking technology, and identifies a core area, a secondary core area and a peripheral area where the user gazes.
(2) Analysis of content types and preliminary weight settings the content perception module analyzes the content types (e.g., text, video, games) displayed on the screen and generates preliminary refresh rate adjustment parameters based on the dynamics and complexity of the content. For example, static text content generates low weights, while dynamic video content generates high weights. And preliminarily distributing weight values to each region by the system according to the analysis result of the content perception module. For example, the refresh rate weight of the core region is set to 0.7, the sub-core region is set to 0.2, and the peripheral region is set to 0.1.
(3) And (3) real-time state evaluation and weight adjustment, wherein the system evaluates the fatigue state and the vision concentration degree of the user according to the real-time physiological data and the sight line data of the user. The data are processed by using a fuzzy logic control algorithm, and the attention distribution situation of the user in different areas is calculated. Based on the real-time evaluation result, the refresh rate weight of each region is dynamically adjusted through a Bayesian network or a fuzzy logic control algorithm. For example, when the user looks at a core area for a long time, the system will increase the weight value of that area to 0.8, and the weights of other areas will decrease accordingly.
(4) And (3) fusion and application, namely carrying out weighted fusion on the dynamically adjusted weight and the preliminarily generated refresh rate parameter to obtain a final refresh rate adjustment parameter. And the system adjusts the refresh rate of each region in real time according to the weighted fusion result, so as to ensure that the display effect is matched with the real-time requirement of the user.
Referring to fig. 4 and 5, the zoning adjustment module provided in this embodiment includes a configuration structure for dividing a screen into multiple layers, where the multiple layers include a core area, a sub-core area and a peripheral area. The core region refers to the main visual area where the user's vision is most often stopped, the secondary core region is located around the core region, and the peripheral region is the edge region where the user's vision is less focused. And the regional regulation module is used for independently regulating the refresh rate of each region according to the analysis result of the content perception module and the fatigue state of the user.
Specifically, the zoning adjustment module firstly analyzes the line-of-sight focusing data of the user based on a clustering algorithm (such as a K-means algorithm) to determine a core area, a secondary core area and a peripheral area which are most frequently watched on a screen. According to the regional divisions, the regional adjustment module receives the refresh rate adjustment parameters generated by the content perception module, and further dynamically adjusts the refresh rate of each region in combination with physiological feedback data of the user, such as analyzing the fatigue state of the user through a Heart Rate Variability (HRV) monitoring algorithm and Galvanic Skin Response (GSR).
In the adjustment process, the regional adjustment module adopts a multi-level weight distribution algorithm. Firstly, the system distributes higher refresh rate weight for the core area to ensure the visual experience of the main attention area of the user, the secondary core area distributes medium refresh rate weight according to the line-of-sight residence time and content dynamic property of the user, and the peripheral area is generally distributed with lower refresh rate weight to save resources due to lower visual importance of the peripheral area. In addition, the module is combined with a fuzzy logic control algorithm to dynamically adjust the refresh rate of each area according to the fatigue state of the user. For example, when the fatigue state of the user is detected to be aggravated, the system can reduce the refresh rate of the core area to relieve the eye burden, and conversely, when the user is concentrated, the system can improve the refresh rate of the core area to ensure the definition and fluency of the content.
Fifth embodiment referring to fig. 6, in one possible implementation, the control module includes a control unit for receiving an Adaptive Response Curve (ARC) generated by the data processing and fusion module, where the control unit is configured to adjust the refresh rate and brightness of the screen in real time. The control module also comprises a task management unit, wherein the task management unit is configured to adjust the refresh rates of different areas according to the task types and the priorities of users and preferentially guarantee the display effect of the key task areas in the multi-task scene.
Specifically, the control unit analyzes the fatigue state and visual demand information of the user provided by the ARC, optimizes the display parameters of the screen in real time by using a rule-based algorithm (such as a PID control algorithm), analyzes the interaction behavior and physiological state of the user by introducing a machine learning algorithm such as a Support Vector Machine (SVM), dynamically adjusts the refresh rate, adjusts the refresh rate and brightness of the screen in real time, ensures that the GRAM reading speed driven by the LCD is improved under the condition of not increasing the main control performance and the memory consumption, and further improves the smoothness of the screen during refreshing.
Specifically, the control unit strips the data reading task of the GRAM from the main control processor by introducing a DMA (direct memory access) technology, and directly transmits data through a DMA channel. The method avoids excessive participation of the main control processor in the data transmission process, reduces the load of the CPU and accelerates the data reading speed. In order to further optimize the smoothness of screen brushing, the control unit also integrates a double-buffering technology and a cache optimization strategy, so that data reading and display operation can be performed in parallel. The double-buffer technology ensures that when one buffer area refreshes display data, the other buffer area can synchronously read next frame data from the GRAM by arranging two independent display buffer areas, thereby avoiding the mutual interference of data transmission and display operation and ensuring that the phenomenon of blocking does not occur in the process of refreshing the screen.
In addition, the control unit dynamically adjusts the refresh rate through an intelligent algorithm to adapt to different content types and usage scenarios. For example, the system can reduce refresh rate and resource consumption when processing static content, and can automatically increase refresh rate and ensure smoothness of pictures in dynamic content such as video or game scene. All of these optimization measures aim to promote the refreshing effect of the screen while ensuring that the performance and memory usage of the host processor are not excessively consumed, thereby achieving efficient system resource management and excellent user visual experience.
In addition, the control module further comprises a task management unit, and the unit is configured to dynamically adjust refresh rates of different areas of the screen according to task types and priorities of users, so that display effects of key task areas in the multi-task scene are preferentially ensured. In one possible implementation manner, the specific implementation manner of the task management unit comprises the following parts:
First, the task management unit monitors and analyzes the user's operation behavior and application state in real time based on a Support Vector Machine (SVM) algorithm to identify the type of task currently being performed, such as office, entertainment, reading, etc. And extracting operation characteristics such as keyboard input, mouse clicking, window switching frequency and the like of a user, and accurately classifying task types to obtain a task identification result.
And secondly, according to the task identification result, corresponding priorities are allocated to the tasks. For example, video conferences or important document editing tasks are set to a high priority, ensuring that these critical task areas can get a higher refresh rate and brightness to ensure a smooth and clear display effect, and for background tasks or non-critical windows, a lower refresh rate is allocated, saving system resources.
In addition, the task management unit dynamically adjusts the refresh rate of each area according to the set priority and the user sight line data. By utilizing a prejudging mechanism, the task window which is possibly about to be activated by the user is identified in advance by analyzing the operation trend (such as hovering of a mouse and stay of a sight line) of the user, and the refresh rate of the task window is adjusted in advance, so that seamless transition of the display effect during task switching is ensured.
Finally, the task management unit adjusts the refresh rate allocation strategy in real time according to the use condition of the system resources. For example, when the system resource shortage is detected, the refresh rate of the low-priority task is reduced, and smooth display of the high-priority task is preferentially ensured. Meanwhile, the task management unit continuously optimizes the refresh rate adjustment strategy through user interaction feedback (such as mouse clicking and keyboard input frequency), so that the user is ensured to always obtain the best visual experience in the multitasking operation. Through the cooperative work of the modules, the task management unit realizes efficient resource allocation and intelligent display effect management.
In a sixth embodiment, referring to fig. 7, in a possible implementation manner, a method for intelligently adjusting a screen refresh frequency based on content analysis is provided, where the method is applied to the system. The method comprises the following steps:
Step 1, acquiring physiological data of a user, analyzing and processing the physiological data through a data processing and fusion module, and generating a fatigue prediction result and a sight focusing area of the user;
Step 2, analyzing the content type displayed on the current screen through a content analysis module, identifying the display content of each area of the screen, and generating refresh rate adjustment parameters of each area by combining the use scene of the user;
Step 3, according to the sight focusing time and the fatigue state of the user, the refresh rate of each area is adjusted in real time through the regional adjustment module;
and 4, combining the refresh rate adjustment parameters of the areas and the overall fatigue state of the user, dynamically controlling the overall refresh rate of the screen, and correspondingly adjusting the brightness of the screen.
The method first evaluates the real-time fatigue state of the user by acquiring physiological data of the user, such as heart rate, eye movement track, brain wave signals and the like. The data is comprehensively analyzed through the data processing and fusion module to generate a fatigue prediction result, and particularly, the method predicts the fatigue degree of a user through continuous monitoring of physiological data and comparison of historical data, and identifies key factors possibly causing visual fatigue.
Based on the method, the type of the screen display content is analyzed, and the content characteristics of each display area are distinguished. Through the content analysis module, the method can identify parameters such as image or video content, complexity of text content, dynamic change frequency, color contrast and the like of different areas. Based on these content features, refresh rate adjustment parameters for the respective regions are generated. For example, for areas that change more frequently dynamically, the method may suggest a higher refresh rate, while for static content areas, it may suggest a lower refresh rate to reduce power consumption.
In addition, the method adjusts the refresh rate of each area in real time according to the focusing time and the fatigue state of the user's sight through the regional adjusting module. When the user's sight is concentrated in a specific area for a long time, the method can preferentially ensure the refresh rate of the area to ensure the definition and fluency of the display effect, and simultaneously, the purposes of saving energy consumption and reducing unnecessary visual stimulus are achieved by reducing the refresh rate of other unfocused areas. In addition, the method can adjust the brightness and refresh rate of the whole screen in time according to the fatigue state of the user. When an increase in user fatigue is detected, the method may actively reduce screen brightness or reduce the overall refresh rate to reduce the visual burden and slow down the rate of fatigue exacerbation.
Through the intelligent refresh rate adjusting method, the visual experience of a user can be obviously optimized, and visual fatigue caused by long-time use of screen equipment is reduced. The method has the advantages that the method combines the feedback of the physiological data of the user and the analysis of the display content, thereby realizing a dynamic and accurate screen adjustment strategy, leading the user to keep a longer comfortable state in the use process, and being particularly suitable for the user who is engaged in activities such as reading, video watching or games for a long time.
In a specific application scenario, the intelligent screen refresh frequency adjusting system and the intelligent screen refresh frequency adjusting method are used for optimizing the visual experience of a user in a multi-task office environment and the system resource management efficiency. The scenario involves how the modules of the system work together to ensure optimal display and resource utilization during simultaneous processing of document editing, participation in video conferences, and browsing of web pages by the user.
1. The multi-mode data acquisition and fusion module is used for acquiring various physiological data of a user in real time through the multi-mode data acquisition module, wherein the physiological data comprise Heart Rate Variability (HRV), galvanic Skin Response (GSR) and epidermis temperature. The data are input to a data processing and fusion module after being filtered and standardized. At the same time, the system captures the user's gaze data via eye tracking technology, identifying the area of most interest to the user on the screen. The data processing and fusing module generates fatigue state and attention distribution information of the user based on the physiological data and the sight line data, and provides accurate basis for the refresh rate adjustment of each subsequent area.
2. Content perception and region refresh rate adjustment, wherein the content perception module analyzes the type of the content displayed on the screen in real time and classifies the content by using a Support Vector Machine (SVM) algorithm, such as characters, videos, web pages and the like. For different content, the system sets different refresh rate adjustment policies. For example, for a document editing window, the system identifies the document editing window as static content, sets a lower refresh rate for the region through a low-pass filter to reduce resource consumption, for a video conference window, identifies the document editing window as dynamic content, generates higher refresh rate parameters to ensure the fluency of video playing, and for a web browsing window, the system adjusts the refresh rate appropriately according to the page dynamics to ensure the timeliness of information presentation.
3. And the cooperation of regional adjustment and real-time weight distribution is that the regional adjustment module adjusts the refresh rate of each region in real time by utilizing a dynamic weight algorithm according to the sight focusing time and physiological data of the user. The task management unit sets the document editing and video conference window as high-priority task areas by identifying the priorities of the tasks, and assigns higher refresh rates to these areas. For example, when a user looks at a video conference window for a long time, the system automatically increases the refresh rate weight of the region, ensuring the sharpness and fluency of the video picture. At the same time, the web browsing area is identified as a low priority task, and the system reduces its refresh rate to optimize resource utilization.
4. And the control module dynamically adjusts the refreshing rate and brightness of the whole screen according to the self-Adaptive Response Curve (ARC) generated by the data processing and fusion module. When the user shows high concentration and low fatigue state during document editing, the system raises the refreshing rate and brightness of the area to ensure clear text presentation, and if the physiological data shows that the fatigue degree of the user is increased, the system lowers the whole brightness and reduces the refreshing rate of unnecessary area to lighten the visual burden. Through the self-adaptive adjustment mechanism, the system can effectively balance the visual demands and the resource consumption of users.
5. And the task management unit further predicts a task window which is likely to be switched by the user in advance by analyzing the mouse hovering position and the sight stay mode of the user. For example, when it is detected that the line of sight of the user is frequently switched between the document editing and the video conference window, the system promotes the refresh rate of the video conference window in advance, and ensures that the display effect is smooth and has no lag feeling when the task is switched. At the same time, the refresh rate of the web browsing window is properly reduced to realize reasonable allocation of resources.
Through the specific application scene, the system provided by the embodiment not only can optimize the display effect of each task in real time and meet the visual requirement of a user, but also can effectively manage system resources and ensure smooth display of key tasks, thereby remarkably improving the overall office experience of the user.
The present invention is not limited to the above embodiments, and any equivalent embodiments which can be changed or modified by the technical disclosure described above can be applied to other fields, but any simple modification, equivalent changes and modification made to the above embodiments according to the technical matter of the present invention will still fall within the scope of the technical disclosure.