A driving control method for reducing power consumption of liquid crystal display screen

Abstract

The present application relates to the technical field of liquid crystal display screens, and provides a driving control method for reducing the power consumption of liquid crystal display screens. The method includes: performing a self-check on the liquid crystal display screen to establish a mode setting result; configuring an optimization coefficient; performing real-time reading and identifying the screen to establish a dynamic and static identification and an aggregation identification; performing local optimization based on the dynamic and static identification and the optimization coefficient to establish a first driving result; activating a gaze sensor to perform eye tracking to establish a user focus area, performing a driving reconstruction based on the optimization coefficient to establish a second driving result; performing optimization on the screen that is not included in the first two driving results to establish a third driving result; and controlling the display screen according to the three driving results. The present application solves the technical problem that the existing liquid crystal display screen has high power consumption and is difficult to dynamically optimize according to the actual usage needs of the user, and realizes intelligent adjustment of the display screen power consumption according to the user's usage habits and display content, significantly reducing energy consumption while ensuring display effects.

Description

Technical Field

[0001] The present application relates to the technical field of liquid crystal display screens, and in particular to a driving control method for reducing the power consumption of liquid crystal display screens. Background Art

[0002] With the continuous development of liquid crystal display technology, its application in various electronic devices has become more and more extensive, especially in smart phones, tablet computers, smart TVs and other devices, where liquid crystal displays have become the main display medium. However, the power consumption of liquid crystal displays has always been one of the key issues of concern to users and device manufacturers. High power consumption will not only accelerate battery depletion and affect the endurance of the device, but may also cause the screen to heat up and affect the user experience. The existing power consumption optimization technology of liquid crystal displays mainly focuses on reducing power consumption by reducing brightness, reducing screen refresh rate or optimizing backlight control. Although these methods have slowed down the rate of power consumption increase to a certain extent, they have significant limitations. For example, excessive brightness reduction will affect the user's visual experience, while simply relying on reducing the refresh rate may lead to a significant decrease in display effect. In addition, most of the current power consumption control methods for liquid crystal displays are based on static parameter settings, and fail to perform intelligent optimization in combination with the user's actual usage behavior, viewing habits and dynamic changes in display content, and the power consumption optimization effect is limited. Therefore, how to provide a more intelligent power consumption control method for liquid crystal displays under the premise of ensuring the display effect, combined with factors such as user usage habits, real-time display content, and screen motion and stillness, has become an urgent problem to be solved in the current technical field. Summary of the invention

[0003] The present application provides a driving control method for reducing the power consumption of a liquid crystal display screen, aiming to solve the technical problem that the existing liquid crystal display screen has high power consumption and is difficult to dynamically optimize according to the actual usage needs of users.

[0004] In view of the above problems, the present application provides a driving control method for reducing the power consumption of a liquid crystal display screen.

[0005] The present application provides a driving control method for reducing the power consumption of a liquid crystal display screen, the method comprising: performing a display mode self-check on the liquid crystal display screen to establish a mode setting result, wherein the mode setting result is a display setting result of a user; configuring a power consumption optimization coefficient according to the mode setting result; performing real-time output reading of a display screen, and performing screen recognition on the real-time output reading result to establish a screen dynamic and static identification and a screen aggregation identification; performing local optimization of screen refresh under a static identification using the screen dynamic and static identification and the power consumption optimization coefficient to establish a first driving result; activating a gaze sensor to perform user eye tracking to establish a user focus area, and performing drive reconstruction of the user focus area, the screen dynamic and static identification, and the screen aggregation identification based on the power consumption optimization coefficient using a driving reconstruction network to establish a second driving result; obtaining a display screen that is not included in the first driving result and the second driving result, performing drive optimization based on the power consumption optimization coefficient, and establishing a third driving result; and performing driving control of the liquid crystal display screen according to the first driving result, the second driving result, and the third driving result.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0007] The above-mentioned driving control method for reducing the power consumption of a liquid crystal display screen, the method performs a self-check on the liquid crystal display screen, establishes the display setting result of the user, and this process can adjust the display mode according to the personalized needs of the user to improve the adaptability. Subsequently, a power consumption optimization coefficient is configured based on the setting result, and then the real-time output picture is read and identified to generate the dynamic and static identification and aggregation identification of the picture. This process helps to distinguish static and dynamic pictures, thereby reducing unnecessary refresh frequency under static pictures and improving energy efficiency. After that, the dynamic and static identification and the power consumption optimization coefficient are used to perform local refresh optimization for the picture with the static identification, generate the first driving result, and then activate the gaze sensor to track the user's eyeball, establish the user's focus area, and optimize the user's focus area, dynamic and static identification and aggregation identification through the driving reconstruction network to generate the second driving result. This step further reduces unnecessary energy consumption by concentrating resources in the area of ​​user focus. At the same time, the picture not included in the first and second driving results is obtained, and it is optimized using the power consumption optimization coefficient to generate the third driving result. This process ensures that the picture that is not directly processed can also achieve power consumption optimization. Ultimately, the three driving results are used to control the overall driving of the LCD screen, so that the LCD screen can intelligently identify user usage behavior and dynamically adjust the refresh area and frequency to effectively reduce power consumption while ensuring good display effects.

[0008] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0010] Figure 1 A schematic flow chart of a driving control method for reducing power consumption of a liquid crystal display screen in one embodiment;

[0011] Figure 2 The figure is a flowchart of a drive reconstruction of a drive control method for reducing power consumption of a liquid crystal display screen in one embodiment. DETAILED DESCRIPTION

[0012] The embodiment of the present application provides a driving control method for reducing the power consumption of a liquid crystal display screen, thereby solving the technical problem that the existing liquid crystal display screen has high power consumption and is difficult to dynamically optimize according to the actual usage requirements of users.

[0013] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0014] It should be noted that the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules that are not explicitly listed or inherent to these processes, methods, products or devices.

[0015] Examples, such as Figure 1 As shown, the present application provides a driving control method for reducing the power consumption of a liquid crystal display screen, the method comprising:

[0016] The liquid crystal display screen is self-checked for display mode to establish a mode setting result, wherein the mode setting result is a display setting result of the user.

[0017] In an embodiment of the present application, the system terminal starts the self-test program of the LCD screen and performs a comprehensive test on the current display mode, including display resolution, brightness, contrast, refresh rate and other parameters. In this process, display data is collected through built-in sensors to analyze the various states of the current operation of the screen. During the self-test process, combined with the user's historical usage data, for example, the screen brightness set by the user in different environments, the user's preferred color temperature, and commonly used display modes (such as reading mode, movie mode, etc.), these data will be stored in the user's display setting file. Subsequently, based on these test results and user historical data, a mode setting result is generated, which is a display setting result customized for the user, including specific display parameters, such as the adaptation parameters of the brightness in the reading mode, the refresh rate of dynamic and static images, etc. Through these parameters, it is intended to perform personalized configuration according to the user's habits, so as to ensure that the display effect of the screen meets the needs of the user, and at the same time lay the foundation for subsequent power consumption optimization.

[0018] The power consumption optimization factor is configured based on the mode setting result.

[0019] In one embodiment, the system terminal parses the obtained mode setting results and extracts parameters related to power consumption, including resolution, brightness, contrast, refresh rate, backlight intensity, etc. Subsequently, the historical maximum and historical minimum values ​​of each data type are extracted, and then the difference between the data in each data type and the corresponding historical minimum value is calculated by ratio with the difference between the corresponding historical maximum value and the corresponding historical minimum value to obtain a normalized mode setting result. Afterwards, the data in the normalized mode setting result is weighted according to the preset weight of each data type to configure a power consumption optimization coefficient. This coefficient is a trade-off adjustment factor for the energy consumption of the display screen, which is used to dynamically drive and optimize the power consumption of the screen to minimize energy consumption while ensuring the display effect.

[0020] Execute real-time output reading of the display screen, perform screen recognition on the real-time output reading result, and establish screen dynamic and static identification and screen aggregation identification.

[0021] In one embodiment, the system terminal reads the display screen in real time, continuously monitors the content displayed on the current screen, and analyzes and identifies the read screen data through pixel difference calculation, determines which parts are dynamic (such as video or animation) and which parts are static (such as text or static pictures), and establishes a dynamic and static identification of the screen. This identification can distinguish between dynamic areas and static areas. More processing resources may be required for dynamic areas, while unnecessary refresh frequencies can be reduced for static areas to save power consumption. In addition, the system terminal also establishes a screen aggregation identification, which is used to identify the concentration of displayed content, that is, whether there are multiple similar contents or repeated elements in the screen, to help optimize resource usage, reduce repeated processing of similar content, and further reduce power consumption. Specifically, the system terminal extracts feature values ​​from the segmentation results of the real-time output reading results, including color features, brightness features, and texture features, and calculates the corresponding mean based on these features. Among them, for color features, the RGB value of each pixel is extracted from all areas of the segmentation result, and the average values ​​of the red, green, and blue channels of each area are calculated respectively. For the brightness feature, the system terminal uses the brightness contribution formula Calculate the brightness value of each pixel in each area, where L represents the brightness value of the pixel, R represents the value of the red channel in the pixel, G represents the value of the green channel in the pixel, and B represents the value of the blue channel in the pixel. Then calculate the average of the L values ​​of all pixels in each area to obtain the average brightness of each area. For texture features, the system terminal uses an edge detection algorithm (Sobel operator, Laplacian operator, etc.) to detect the gradient intensity of each pixel in each area. Taking the Sobel operator as an example, the Sobel operator calculates the edges in the image through gradient detection in the horizontal and vertical directions. The convolution kernel of the Sobel operator is as follows: Horizontal direction: The system terminal combines the convolution kernel of the Sobel operator with the grayscale value of each pixel and its surrounding pixels to obtain the horizontal gradient of each pixel. and vertical gradient , and then use the gradient strength calculation formula The edge intensity G of each pixel is obtained. Subsequently, the G values ​​of all pixels in each area are averaged to obtain the intensity mean of each area. After obtaining the color mean, brightness mean and intensity mean, the system terminal calculates the similarity distance between every two areas through the Euclidean distance. After that, a judgment is made based on the preset similarity threshold, and all areas that are less than or equal to the preset similarity threshold are screened out, and then the areas are combined based on the regional similarity relationship to obtain multiple similar combination areas. For example, A is similar to B, and B is similar to C, so A is similar to B and C. Then, an aggregation identifier is established for each similar combination area, that is, the same identifier is marked for the areas belonging to the same similar combination area. In the subsequent processing process, the system terminal will merge similar areas into one according to these aggregation identifiers, thereby reducing redundant rendering of repeated content and achieving energy saving.

[0022] Furthermore, the present application provides the method of performing screen recognition on the real-time output reading result and establishing a screen motion and stillness identification, including:

[0023] The real-time output reading result is divided into sub-regions to establish the sub-region segmentation result; the pixel difference calculation is performed on the sub-region segmentation result to generate the pixel difference calculation result; the local threshold is used to trigger the recognition of the pixel difference calculation result to establish the image movement and stillness identification.

[0024] Preferably, the system terminal continuously monitors and reads each frame of the display screen, forms a real-time output reading result, and then performs sub-region segmentation according to a preset pixel grid method to establish multiple sub-region segmentation results. Each sub-region segmentation result will be used as an independent unit for subsequent analysis. Among them, the preset pixel grid method is determined according to the actual resolution, such as 16×16 or 32×32. Subsequently, the pixel difference calculation formula is used to calculate the pixel difference of each sub-region segmentation result to obtain the pixel difference calculation result of each sub-region segmentation result, which is used for subsequent motion and stillness discrimination. Afterwards, the system terminal uses the set local threshold Trigger recognition is performed on the pixel difference calculation results of each sub-region segmentation result, that is, it is determined whether each sub-region segmentation result belongs to a dynamic area or a static area. If the pixel difference calculation result is greater than , then the sub-region segmentation result is marked as a dynamic region, that is, the image dynamic and static flag of the sub-region segmentation result is set to 1. On the contrary, if the pixel difference calculation result is less than or equal to , then the sub-region segmentation result is marked as a static region, that is, the image dynamic and static flags of the sub-region segmentation result are set to 0. These image dynamic and static flags can be used for subsequent display optimization, such as maintaining a high refresh rate in the dynamic area and reducing the refresh rate in the static area, thereby saving power consumption;

[0025] Furthermore, the present application provides the method of performing pixel difference calculation on the sub-region segmentation result to generate a pixel difference calculation result, including:

[0026] The pixel difference of each sub-region segmentation result is calculated by the formula as follows: ;

[0027] Optionally, the system terminal calculates the pixel difference of each sub-region segmentation result by using a pixel difference calculation formula. This pixel difference calculation formula can measure the difference in pixel brightness of a sub-region between adjacent frames. The pixel difference calculation formula is as follows: ;in, Indicates The sub-region segmentation results are The pixel difference calculation result of the frame. Indicates the frame rate and represents the current frame in a video or image sequence. Indicates The pixel coordinates under the frame are The brightness value reflects the brightness of the pixel in this frame. Indicates The pixel coordinates under the frame are The brightness value of the Characterization The sub-region segmentation results are All pixel information under the frame includes all pixels and their brightness data of the sub-region. According to this formula, combined with the local threshold, it is possible to further determine whether the sub-region is static or dynamic, thus providing a basis for subsequent power consumption optimization.

[0028] The screen dynamic and static identification and the power consumption optimization coefficient are used to perform local optimization of screen refresh under static identification, and establish a first driving result.

[0029] In one embodiment, the system terminal selects all static areas from multiple sub-area segmentation results according to the obtained picture dynamic and static identification, that is, the sub-area segmentation results with the picture dynamic and static identification of 0. The pictures in these areas do not change significantly between adjacent frames, so power consumption optimization can be performed. Subsequently, the system terminal judges the power consumption optimization coefficient configured according to the mode setting result in combination with the power consumption optimization demarcation value. When the power consumption optimization coefficient is less than or equal to the power consumption optimization demarcation value, it means that a higher energy saving effect can be achieved in the current mode. Therefore, the refresh frequency of the static area will be greatly reduced, for example, the refresh rate will be reduced from 60Hz to 15Hz or lower to achieve the purpose of saving power. When the power consumption optimization coefficient is greater than the power consumption optimization demarcation value, it means that more attention is paid to the display effect. At this time, a relatively high refresh frequency will be maintained, for example, it will be reduced to the next refresh rate level to ensure that the display effect is still maintained to a certain extent. Similarly, local optimization will be performed on backlight brightness, data transmission and redrawing, etc. to reduce the energy consumption of static areas. For example, by reducing the backlight brightness of the area, the energy consumption of the static area can be reduced; by keeping the current frame content in the static area without repeated rendering, the data transmission and redrawing can be reduced to reduce the power consumption of image processing and transmission. After that, these optimization strategies are integrated to generate the first driving result. This first driving result is used to optimize the local refresh of the LCD screen, while maintaining the display effect of the static area and reducing unnecessary power consumption as much as possible.

[0030] Activate the gaze sensor to track the user's eyes, establish the user's focus area, use the drive reconstruction network to reconstruct the drive of the focus area based on the power consumption optimization coefficient for the user's focus area, the picture dynamic and static identification, and the picture aggregation identification, and establish the second drive result.

[0031] In one embodiment, the system terminal activates the eye tracking sensor, monitors the user's eye movement trajectory in real time through the built-in infrared or camera device, determines and establishes the area that the user is currently looking at, and uses this area as the user's attention area. Subsequently, the established user attention area is input into the drive reconstruction network for drive reconstruction of the attention area. Among them, the drive reconstruction network includes an input layer, a judgment layer, a processing layer, and an output layer. The judgment layer is used to judge whether there is a dynamic picture in the user's attention area, and the processing layer is used to perform coverage analysis and drive reconstruction. In the drive reconstruction network, the system terminal uses the judgment layer of the drive reconstruction network to make a coincidence judgment on the user's attention area and the picture dynamic and static identification to determine whether the user's attention area contains a dynamic picture. If it does, the processing layer combines the picture aggregation identification to perform coverage analysis, and aggregates similar parts in the dynamic identification area to reduce repeated calculations. Afterwards, according to the power consumption optimization coefficient and the dynamic aggregation result, the refresh frequency of the dynamic content in the user's attention area is adjusted. Even if there is repeated content in the dynamic area that the user is looking at, it only needs to be rendered once, thereby reducing unnecessary refreshes. Similarly, the backlight brightness of the area is adjusted according to the power consumption optimization coefficient. The dynamic area maintains a high brightness to ensure the display effect, but due to the aggregation of repeated content, excessive brightness adjustment can be avoided. Then, these optimization strategies are integrated in the output layer and output as the second drive result. This second drive result optimizes the user's attention area, which not only ensures the display effect of the user's attention area, but also reduces unnecessary display resource consumption through dynamic aggregation and power optimization.

[0032] Further, if Figure 2 As shown, the present application provides the method of using the drive reconstruction network to perform drive reconstruction of the user's attention area, the screen dynamic and static identification, and the screen aggregation identification based on the power consumption optimization coefficient of the attention area, and establish a second drive result, including:

[0033] The judgment layer of the drive reconstruction network is called to make a judgment on the overlap between the user's attention area and the dynamic and static identification of the screen. If the user's attention area contains a dynamic identification screen, a continue judgment instruction is generated; the continue judgment instruction is used to perform a coverage analysis on the dynamic identification screen based on the screen aggregation identification to establish a dynamic aggregation result; the dynamic aggregation result is subjected to a focus area drive reconstruction based on the power consumption optimization coefficient.

[0034] Preferably, the system terminal calls the judgment layer of the drive reconstruction network to make a coincidence judgment between the user's attention area and the picture's static and dynamic mark, that is, to compare the pixel coordinates of the user's gaze area with the coordinates of the dynamic area in the picture's static and dynamic mark. If there is a dynamic area in the user's gaze area, it is considered to be overlapped. At this time, the judgment layer will generate a continue judgment instruction and pass it to the processing layer for subsequent coverage analysis and drive reconstruction. After the processing layer receives the continue judgment instruction, the system terminal will call the picture aggregation mark, and divide the dynamic area according to the content recorded by the picture aggregation mark, that is, the dynamic areas belonging to the same picture aggregation mark are divided together to form multiple dynamic aggregation areas. Subsequently, the earliest dynamic area is extracted from each dynamic aggregation area and used as the main display area, and then these main display areas are summarized to establish a dynamic aggregation result. When other areas with the same content need to be rendered, they are covered by copying the content of the corresponding main display area. Afterwards, the dynamic aggregation results are reconstructed according to the power consumption optimization coefficient, that is, the display of the user's focus area is optimized and adjusted. The system terminal multiplies the power consumption optimization coefficient with the refresh frequency, backlight brightness and other parameters of the main display area in the dynamic aggregation results to obtain new refresh frequency, backlight brightness and other parameters. Similarly, the system terminal reduces the number of renderings of similar or repeated areas according to the dynamic aggregation results, and reduces the amount of data transmission by referencing the rendering results of the main display area, thereby reducing power consumption. After adjusting the refresh frequency, brightness, and data transmission, the system terminal generates a reconstructed driving result. This result reflects the optimized display strategy based on the power consumption optimization coefficient, thereby achieving a balance between power saving and display effect.

[0035] Acquire a display screen that is not included in the first driving result and the second driving result, perform driving optimization based on the power consumption optimization coefficient, and establish a third driving result; and perform liquid crystal display driving control according to the first driving result, the second driving result, and the third driving result.

[0036] In one embodiment, the system terminal removes the sub-region segmentation results with a picture motion and static identification of 0 and a picture motion and static identification of 1 belonging to the user's attention area from all sub-region segmentation results to generate display pictures. For these display pictures, the system terminal performs drive optimization based on the power consumption optimization coefficient, that is, multiplying the maximum refresh frequency, maximum backlight brightness and other parameters of these display pictures with the power consumption optimization coefficient to obtain new refresh frequency, backlight brightness and other parameters to reduce unnecessary screen updates and the brightness of non-critical parts, saving power consumption. In addition, the data transmission volume and rendering times of these display pictures are also reduced. For example, for content without significant changes, the data update volume between frames is reduced. For content with a low frequency of change, a cache mechanism can be used to avoid re-rendering of each frame. These optimization strategies are then sorted out to generate a third drive result. This drive result ensures that these remaining areas are reasonably optimized. After obtaining the first drive result (static area optimization), the second drive result (user attention area optimization) and the third drive result, the system terminal integrates these three results to form a final global display control scheme, and applies this scheme to the liquid crystal display screen to ensure that the display effect and power consumption requirements of different areas are balanced.

[0037] Furthermore, the present application provides optimization of the second driving result and the third driving result, including:

[0038] Obtain a historical gaze database of the attentive user, and use the historical gaze database to establish the user's gaze preference; perform screen subject recognition on the display screen corresponding to the third drive result, establish a screen subject recognition result, and establish a transfer association based on the focused screen subject of the user's focus area and the screen subject recognition result; perform a probability activation judgment of additional attention based on the transfer association and the gaze preference, generate a new coverage compensation for the second drive result based on the probability activation judgment result, and optimize the second drive result and the third drive result according to the new coverage compensation.

[0039] Optionally, the system terminal is connected to the historical gaze database of the user through a preset interface. This historical gaze database records the eye movement trajectory, gaze duration and gaze area of ​​the user in different display scenes, which is used to understand the user's visual preferences in different scenes. Then, the data in the historical gaze database is clustered and analyzed by clustering methods such as K-means to identify the area and content features that the user most often gazes at, and establish the user's gaze preference. Subsequently, the visual elements in the displayed screen are classified and labeled to determine the main content of the screen, such as a significant person, object, or text, and generate a screen subject recognition result. This recognition result records the main elements and their positions in each screen. After that, the screen subject in the user's attention area and the screen subject recognition result are combined to analyze the screen in the current display screen that may be associated with the main content currently being gazed by the user. In this process, the system terminal calculates the distance between other screen subjects and the main elements in the screen subject recognition result through the Euclidean distance, and judges it with the distance threshold. If the distance between other screen subjects and the main element is less than the distance threshold, it is determined to have a high potential for attention transfer. At this time, the system terminal establishes a transfer association between these areas and the screen subject recognition result. Then, based on the transfer association and the user's gaze preference, the system terminal makes an additional attention determination through a probabilistic activation model. Specifically, the system terminal calculates the probability that the user transfers from the current gaze area to the transfer-associated area. This probability is obtained by analyzing the visual transfer behavior pattern in the user's historical gaze preference, and combined with the characteristics of the current screen subject recognition result, it is determined whether the user is likely to transfer attention to these potential associated areas. In the process of probabilistic activation determination, a probabilistic prediction method based on a statistical model or a machine learning algorithm is used to analyze the user's attention transfer trend. If the activation probability of the transfer-associated area exceeds a preset threshold, the area is marked as an additional attention area, and a new coverage compensation for the area is generated. The new coverage compensation specifically includes the adjustment results of parameters such as the refresh frequency and backlight brightness of the area, and the adjustment method is the same as the aforementioned method for obtaining the second drive result, so as to ensure that when the user transfers attention to the area, the same high-quality display effect as the current gaze area can be obtained. For these areas, the system terminal preloads data in advance to ensure that there is no delay or freeze when the user shifts attention. According to the result of this new coverage compensation, the system terminal further optimizes the second drive result and the third drive result. The current gaze area corresponding to the second drive result may need to expand its coverage to include the additional focus area to ensure that the user does not perceive the difference in display quality when shifting attention. At the same time, the display images that need to be added to the second drive result are removed from the third drive result, so that these areas can quickly respond to the user's visual needs while optimizing power consumption, achieving a balance between display effect and energy consumption.

[0040] For the probability activation model, it can be constructed through the Logistic regression model, the Markov chain-based model, the KNN (K-nearest neighbor) algorithm, the neural network model, etc. Taking the Logistic regression model as an example, the system terminal extracts the user's sample gaze behavior data from the sample historical gaze database, and combines it with the sample screen body data to form a training data set for model training. Each piece of training data will correspond to a label, that is, the probability of whether the user will shift his attention to the target area. This probability is calculated by the frequency in the historical data, indicating the possibility of the user shifting from the current gaze area to the target area in history. Subsequently, the weights and bias items in the Logistic regression model are optimized through forward propagation, loss calculation, and back propagation, so that the model can most accurately predict the user's attention transfer probability. After that, the trained Logistic regression model is stored as a probability activation model.

[0041] Further, the present application provides a fourth driving result, including:

[0042] The input signal of the liquid crystal display screen is read, and the dynamic subject recognition is performed using the input signal to establish the time subject under the time series identification; the attention probability calculation based on the gaze preference is performed according to the attention picture subject and the time subject of the user's attention area, and the attention probability calculation result is established; the fourth driving result is established through the attention probability calculation result, and the fourth driving result is a pre-driving result; and the liquid crystal display screen drive control is performed according to the fourth driving result.

[0043] Optionally, the system terminal reads the input signal received by the LCD screen, which includes dynamic pictures such as video streams, image content, and animations. Based on the input signal, the dynamic subject recognition is performed on the picture content of each frame, that is, the image content of each frame is analyzed to identify different visual elements. Then, the elements that change with time in the picture are identified in the same way as mentioned above, and these elements are marked as dynamic subjects. After the dynamic subject is recognized, a time series identifier is generated for each identified dynamic subject through inter-frame analysis to establish a time subject. The time subject records the state information of each dynamic subject at different time points, such as position change, shape change, etc. Through the time subject, the change of an element over a period of time can be tracked. Subsequently, according to the focus picture subject of the user's focus area and the time subject obtained by the time series identifier, combined with the user's gaze preference, the probability that the user may continue to focus on these subjects is calculated through the focus probability model. This focus probability model can be constructed by a linear regression model, a neural network model, a random forest, a decision tree model, etc., and the construction method is similar to the above. Afterwards, according to the calculated attention probability, the attention probability calculation result is generated. This attention probability calculation result reflects the possibility of the user's attention to different dynamic subjects in the next time period, which will be used as the basis for subsequent drive control. Then, according to the attention probability calculation result of each dynamic subject, the system terminal determines whether to adjust the parameters such as the refresh frequency and backlight brightness of these areas. If the attention probability calculation result is higher than the attention probability threshold, the refresh frequency of these areas is increased, the backlight brightness and other parameters are increased to ensure that the areas that the user may pay attention to in the future have been optimized. On the contrary, it is possible to choose to reduce the parameters such as the refresh frequency and backlight brightness to save power consumption. Finally, the system terminal organizes these optimization strategies and generates a fourth drive result, that is, a drive result, which is used to predict the user's future attention changes and optimize in advance. According to this fourth drive result, the system terminal performs specific drive control on the LCD screen. At this time, according to the possible changes in the user's attention, the display parameters will be adjusted in advance to ensure the smoothness of the user experience and the optimization of power consumption.

[0044] Furthermore, the present application provides a method for adjusting the brightness of a static logo image, including:

[0045] Establish a static cumulative trigger threshold; determine whether the picture with the static mark meets the static cumulative trigger threshold; if it does, generate a brightness optimization instruction, and adjust the brightness of the picture with the static mark that meets the static cumulative trigger threshold based on the brightness optimization instruction.

[0046] Preferably, the system terminal establishes a static cumulative trigger threshold value based on user needs and mode setting results, combined with historical experience and expert advice, to determine whether certain elements or areas in the picture need to trigger the brightness adjustment operation after they remain in a static state for a certain period of time. The system terminal will monitor the picture area with a static mark, that is, the picture motion mark is 0 according to the preset monitoring frequency. Each area with a static mark has a static time counter for accumulating the static time of the area. When the accumulated static time reaches the set static cumulative trigger threshold, the brightness optimization instruction is generated to indicate that the backlight brightness of the area should be adjusted to reduce power consumption. Subsequently, the system terminal adjusts the backlight brightness of the static area that meets the conditions according to this brightness optimization instruction. The specific value of the brightness adjustment can be preset or dynamically calculated according to the current display scene. For example, the brightness is reduced to 50% or less of the original brightness, or the brightness is adjusted according to the importance of the area or the user's display preference. The brightness of important areas may remain unchanged, while unimportant areas will significantly reduce the brightness. After the brightness is adjusted, the system terminal will continue to monitor the area. If the area resumes dynamic changes (for example, user interaction causes the screen content to be updated), the system terminal will cancel the static identification and restore the original brightness or adjust it according to the new dynamic conditions to ensure that the user obtains the best display effect when needed.

[0047] Furthermore, the present application provides compensation for power consumption optimization coefficients, including

[0048] Connect a perception sensor, wherein the perception sensor is used to perceive the user's state; obtain a real-time perception data set of the perception sensor, use the real-time perception data set to analyze the fatigue state, and establish a fatigue level; generate fatigue optimization according to the fatigue level, compensate the power consumption optimization coefficient through fatigue optimization, and reconstruct the first drive result, the second drive result, and the third drive result with the compensated power consumption optimization coefficient.

[0049] Preferably, the system terminal is connected to the perception sensor through the Internet of Things technology. The perception sensor includes smart wearable devices (smart bracelets, smart watches, etc.), whole-house smart devices (cameras, smart lighting, thermostats, etc.), etc., which are used to monitor the user's status in real time, for example, whether there is a long period of immobility, sitting or lying. Subsequently, the user's real-time data is continuously collected through the perception sensor, and these data are collected into the system terminal to form a real-time perception data set. This real-time perception data set includes the user's heart rate, skin temperature, sitting time, static state, etc. After that, the real-time perception data set is input into the fatigue state model for fatigue state analysis to establish the user's current fatigue level. Among them, the fatigue state model can be constructed by decision trees, random forests, support vector machines, etc. Taking random forests as an example, the system terminal uses random forests to construct an initial fatigue state model structure, including input features, decision tree sets, voting mechanisms, etc. Then, the node splitting thresholds of each tree of the initial fatigue state model are initialized using random numbers, etc., and the training set is input into the initialized initial fatigue state model for forward propagation. Through the input layer, node splitting of each decision tree, leaf node voting and other steps, the user's fatigue level prediction result is calculated. Subsequently, the mean square error loss function is used to calculate the loss value between the prediction result and the sample data, and the tree structure in the forest is optimized by minimizing the error, and the splitting method of each tree is adjusted to minimize the error value. The random forest aggregates the prediction results of multiple trees through a voting mechanism to determine the final fatigue level output. Repeat the above process until the maximum number of iterations is reached. After the training is completed, the model performance is tested using the validation set to evaluate the accuracy of the model in the fatigue state classification task. If the accuracy meets the expected expectations, the current initial fatigue state model is output as the final fatigue state model. Otherwise, the hyperparameters such as the number of trees, depth, and minimum number of split samples are adjusted to further improve the fatigue level classification effect of the fatigue state model. Then, the system terminal uses the established fatigue level to generate fatigue optimization, that is, the degree of optimization of the power consumption optimization coefficient is obtained by using 1 and the product of the fatigue level and the corresponding fatigue influence factor for difference calculation. Then, this fatigue optimization is multiplied by the power consumption optimization coefficient to complete the compensation of the power consumption optimization coefficient. This compensated power consumption optimization coefficient will reduce the refresh frequency, backlight brightness and other parameters of the LCD screen to adapt to the user's fatigue state. Finally, the system terminal reconstructs the first driving result, the second driving result, and the third driving result according to the same steps as above by using the compensated power consumption optimization coefficient to achieve a balance between display quality and energy consumption.

[0050] In summary, the embodiments of the present application have at least the following technical effects:

[0051] The embodiment of the present application describes a drive control method for reducing the power consumption of a liquid crystal display screen, which mainly includes self-checking the display mode of the liquid crystal display screen, configuring the power consumption optimization coefficient according to the display mode set by the user, and establishing the dynamic and static identification of the screen and the aggregation identification of the screen by reading the display screen in real time and performing screen recognition. The static screen is locally refreshed and optimized using these identifications and power consumption optimization coefficients, and the screen of the user's attention area is driven and reconstructed in combination with the user's gaze sensor data. The method also involves further driving optimization of the area not included in the optimized screen, and adjusting the brightness of the static screen by accumulating the trigger threshold. In addition, based on the user's state perception, the fatigue state analysis is performed through the perception sensor, the fatigue level is generated, and the power consumption optimization coefficient is optimized to dynamically adjust the driving result of the display screen. These technical effects jointly solve the technical problems that the existing liquid crystal display screen has high power consumption and is difficult to be dynamically optimized according to the actual use needs of the user, and realizes the intelligent adjustment of the display screen power consumption according to the user's usage habits and display content, significantly reducing energy consumption while ensuring the display effect.

[0052] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. The processes depicted in the accompanying drawings do not necessarily require the specific order and continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0053] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0054] This specification and drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A driving control method for reducing power consumption of a liquid crystal display screen, characterized in that: The method comprises: Performing a display mode self-check on the liquid crystal display screen and establishing a mode setting result, wherein the mode setting result is a display setting result of the user; Configure the power consumption optimization coefficient based on the mode setting result; Execute real-time output reading of the display screen, and perform screen recognition on the real-time output reading result to establish screen dynamic and static identification and screen aggregation identification; Using the static and dynamic image identification and the power consumption optimization coefficient, the local optimization of the image refresh under the static identification is performed to establish the first driving result; Activate the gaze sensor to track the user's eyeballs, establish the user's focus area, and use the drive reconstruction network to reconstruct the drive of the focus area based on the power consumption optimization coefficient for the user's focus area, the image dynamic and static identifiers, and the image aggregation identifiers to establish a second drive result; Acquire a display screen that is not included in the first driving result and the second driving result, perform driving optimization based on the power consumption optimization coefficient, and establish a third driving result; Performing driving control of the liquid crystal display screen according to the first driving result, the second driving result, and the third driving result; The method of using the drive reconstruction network to perform drive reconstruction of the user's attention area, the screen dynamic and static identification, and the screen aggregation identification based on the power consumption optimization coefficient of the attention area to establish the second drive result also includes: The judgment layer of the driving reconstruction network is called to judge the overlap between the user's attention area and the dynamic and static marks of the screen. If the user's attention area contains a dynamic mark screen, a continue judgment instruction is generated; Using the continuation judgment instruction, the dynamic identification screen is subjected to coverage analysis based on the screen aggregation identification, and a dynamic aggregation result is established; Performing a focus area driven reconstruction based on a power consumption optimization coefficient on the dynamic aggregation result; Acquire a historical gaze database of a gazed user, and establish a gaze preference of the user using the historical gaze database; Performing screen subject recognition on the display screen corresponding to the third driving result, establishing a screen subject recognition result, and establishing a transfer association according to the screen subject of the user's focus area and the screen subject recognition result; A probability activation determination of additional attention is performed according to the transfer association and the gaze preference, a new coverage compensation of the second drive result is generated based on the probability activation determination result, and the second drive result and the third drive result are optimized according to the new coverage compensation.

2. A driving control method for reducing power consumption of a liquid crystal display screen as claimed in claim 1, characterized in that: The method further comprises: Reading an input signal from a liquid crystal display screen, using the input signal to perform dynamic subject recognition, and establishing a time subject under a time series identifier; Calculate the attention probability based on the gaze preference according to the attention picture subject and the time subject of the user's attention area, and establish the attention probability calculation result; Establishing a fourth driving result by focusing on the probability calculation result, wherein the fourth driving result is a pre-driving result; The liquid crystal display screen driving control is performed according to the fourth driving result.

3. A driving control method for reducing power consumption of a liquid crystal display screen as claimed in claim 1, characterized in that: The method further comprises: Establish static cumulative trigger threshold; Determine whether the screen with the static mark meets the static cumulative trigger threshold; If it can be satisfied, a brightness optimization instruction is generated, and based on the brightness optimization instruction, the brightness of the statically marked picture that meets the static cumulative trigger threshold is adjusted.

4. A driving control method for reducing power consumption of a liquid crystal display screen as claimed in claim 1, characterized in that: The method further comprises: A communication sensor is used to sense the state of a user. Acquire the real-time perception data set of the perception sensor, use the real-time perception data set to analyze the fatigue state, and establish the fatigue level; Fatigue optimization is generated according to the fatigue level, the power consumption optimization coefficient is compensated by the fatigue optimization, and the first driving result, the second driving result, and the third driving result are reconstructed with the compensated power consumption optimization coefficient.

5. A driving control method for reducing power consumption of a liquid crystal display screen as claimed in claim 1, characterized in that: The real-time output reading result is used for screen recognition to establish a screen dynamic and static identification, and further includes: The real-time output reading result is divided into sub-regions to establish the sub-region division result; Perform pixel difference calculation on the sub-region segmentation results to generate pixel difference calculation results; The local threshold is used to trigger the recognition of the pixel difference calculation results to establish the image movement and stillness identification.

6. A driving control method for reducing power consumption of a liquid crystal display screen as claimed in claim 5, characterized in that: The performing pixel difference calculation on the sub-region segmentation result to generate the pixel difference calculation result also includes: The pixel difference of each sub-region segmentation result is calculated by the formula as follows: in, Indicates Sub-region segmentation result pixel difference calculation result, Indicates the frame rate, Indicates The pixel coordinates under the frame are The brightness value, Indicates The pixel coordinates under the frame are The brightness value, Characterization The sub-region segmentation results are All pixel information under the frame.

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