A method and system for adjusting the viewing angle of a liquid crystal display screen.

Abstract

This invention relates to the field of image recognition and adjustment technology, specifically to a method and system for adjusting the viewing angle of a liquid crystal display screen. The method includes the following steps: acquiring the user's position point in front of the screen and its corresponding gaze direction information; mapping all the user's gaze direction points to a set of points in a three-dimensional coordinate system; and assigning importance weights to each point based on its distance from the screen to generate multi-region weighted equilibrium point distribution data. This invention, through the acquisition and three-dimensional mapping of real-time user position point and gaze direction data, combined with the weight calculation of gaze points and multi-region weighted processing, clarifies the spatial distribution characteristics of user attention points, improving the optimization capability of displayed content under dynamic viewing angles. It employs key focus point trajectory fitting and prediction technology to extract the dynamic change characteristics of densely populated user areas from multiple frames of images, and combines the trajectory analysis results with edge display adjustment signals to optimize the dynamic adaptation of the display signal.

Description

Technical Field

[0001] This invention relates to the field of image recognition and adjustment technology, and in particular to a method and system for adjusting the viewing angle of a liquid crystal display screen. Background Technology

[0002] Image recognition and adjustment technology primarily involves utilizing modern optical, electronic, and computer vision technologies to adjust the display, acquisition, processing, and optimization of images. This technology is widely used in the optimized design of display devices, enhancing the user's viewing experience through adjustments to brightness, contrast, color, and viewing angle.

[0003] Among them, the viewing angle adjustment method for LCD screens is a technology to improve the image clarity and color accuracy of LCD screens at different angles. Its main purpose is to maintain a high-quality display effect of LCD screens across a wide viewing angle range through hardware structure optimization and software algorithm adjustment. It is widely used in televisions, monitors, mobile devices, etc., to meet the visual requirements of multiple users watching at the same time or in special environments.

[0004] Current technologies primarily rely on hardware adjustments and static software algorithms to optimize viewing angles. They cannot dynamically perceive real-time changes in the user's gaze and struggle to adapt to the complex demands of rapidly switching viewing angles in multi-user scenarios. Static optimization methods for LCD screens often result in uneven brightness and color distortion at the edges of the displayed content, especially when viewed from multiple angles, where these problems are amplified by changes in the number of users and viewing angles. Furthermore, existing technologies lack the ability to dynamically analyze user behavior data, failing to accurately pinpoint and respond to user focus points in real time. This leads to lag in screen display effects at different viewing angles, significantly impacting the user experience. The limitations of static display optimization methods in dynamic scenarios make it difficult to meet the demands of dynamic display adaptation and multi-user interaction optimization for LCD screens. Summary of the Invention

[0005] To address the technical problems existing in the prior art, embodiments of the present invention provide a method and system for adjusting the viewing angle of a liquid crystal display screen. The technical solution is as follows:

[0006] A method for adjusting the viewing angle of a liquid crystal display screen includes the following steps:

[0007] S1: Collect the user's position point in front of the screen and its corresponding gaze direction information, map all the user's gaze direction points into a set of points in a three-dimensional coordinate system, and combine the importance weight of each point with the distance from the screen to generate multi-region weighted equilibrium point distribution data.

[0008] S2: Based on the user location and line-of-sight data in the multi-region weighted equilibrium point distribution data, key focal points are selected, and the user density data of the edge areas on both sides of the key focal points are compared with the central area to generate focal point distribution and edge density comparison data.

[0009] S3: Obtain information on key focal points in the focal point distribution and edge density comparison data, predict the coordinates and direction of change of the dense area in the future time period, and generate key focal point trajectory fitting prediction results;

[0010] S4: Compare the key focal point trajectory fitting prediction results with the focal point distribution and edge density comparison data, analyze the impact range of the predicted focal point trajectory changes on the edge region, and generate a dynamic edge correction signal by combining the user density distribution weight of the edge region.

[0011] S5: Analyze the adaptation range of the dynamic edge correction signal to the current screen angle, correct the tilt angle and rotation angle of the current screen, execute the adjustment action through the LCD screen driving module, and generate a screen angle control signal.

[0012] The present invention improves upon this invention by including the following: the multi-region weighted equilibrium point distribution data includes the three-dimensional coordinates of the user's position, the vector value of the viewing direction, and the density weight distribution of each sub-region; the focal point distribution and edge density comparison data includes the position coordinates of the key focal point, the number of users covered, the density values ​​of users in the left and right edge regions, and the weight comparison of the density distribution; the key focal point trajectory fitting prediction result includes the trajectory fitting path curve, the predicted future focal point position coordinates, and the change direction vector; the dynamic edge correction signal includes the display correction magnitude of the left and right edge regions, the clarity weight adjustment value of the central region, and the dynamic trajectory direction parameter; and the screen angle control signal includes the adjusted tilt angle value, rotation angle value, and the corresponding drive execution instruction set.

[0013] The present invention improves upon this by collecting the user's position point in front of the screen and its corresponding gaze direction information, mapping all the user's gaze direction points to a set of points in a three-dimensional coordinate system, and combining the importance weights of each point with the distance from the screen to the points, generating multi-region weighted balanced point distribution data. The specific steps are as follows:

[0014] S101: Based on a multi-point positioning sensor and camera array, it collects real-time location points and gaze direction data of the user in front of the screen, processes the data into three-dimensional coordinate points, maps them through a unified coordinate system, and generates a set of user gaze direction points.

[0015] S102: Based on the user's gaze direction point set, analyze the spatial distance of each point to the screen, and combine the distribution pattern of the points in three-dimensional coordinates to determine the influence intensity of the points on screen interaction. Perform weighted calculation on each point to generate user gaze point weight data.

[0016] S103: Based on the user gaze point weight data, the screen is divided into multiple sub-regions, and multi-region weighted equilibrium point distribution data is generated by referring to the gaze point density and weight distribution in each sub-region.

[0017] The present invention is improved by performing a weighted calculation on each point, using the following formula: Get the Weight of each viewpoint ;

[0018] in, For the first The three-dimensional coordinates of the points The three-dimensional coordinates of the center point of the screen. For the first The spatial distance from each point to the screen. For all points The total distance to the screen. This represents the total number of line-of-sight points collected.

[0019] The present invention improves upon this invention by filtering key focal points based on user location and line-of-sight data in the multi-region weighted equilibrium point distribution data, and comparing the user density data of the edge regions on both sides of the key focal points with the central region to generate focal point distribution and edge density comparison data. The specific steps are as follows:

[0020] S201: Based on the user position and gaze direction information in the multi-region weighted equilibrium point distribution data, detect and locate the gaze point position of each user on the screen, establish a gaze point distribution according to individual users, and generate user gaze point data.

[0021] S202: Based on the user gaze point data, analyze the spatial distribution of all user gaze points, refine the coverage area and number of users' gazes, use cluster analysis to extract the key positions of user focus, analyze the key focus points with reference to the key positions, and generate key focus point data;

[0022] S203: Based on the key focal point data, compare the user density in the core area and the two side edge areas of the focal point, and combine the difference analysis of the distribution data to generate focal point distribution and edge density comparison data.

[0023] The present invention improves upon this invention by obtaining information on key focal points from the focal point distribution and edge density comparison data, predicting the coordinates and direction of change of dense areas in future time periods, and generating key focal point trajectory fitting prediction results. The specific steps are as follows:

[0024] S301: Based on the key focal point location data and the distribution of covered users in the focal point distribution and edge density comparison data, generate multi-frame user dense area distribution data;

[0025] S302: Based on the multi-frame user dense area distribution data, extract the key focal point location data in each frame image, arrange the key focal point locations in chronological order, and generate key focal point time series data.

[0026] S303: Based on the time series data of the key focal points, analyze the location change trend and fit the motion trajectory. Combine the change trends of the key focal point location and user density to predict the location coordinates and change direction of the dense area in the future, and generate the key focal point trajectory fitting prediction result.

[0027] The present invention is improved by using the following formula to predict the location coordinates and direction of change of dense areas in the future: Key focus in future time trajectory position ;

[0028] in, This indicates that the key focus is on time. The velocity vector, It is the starting time point of the time series. These are the initial position coordinates of the key focal point. It represents the time increment during the integration process. Indicates from time arrive Integrating the velocity vector over the interval yields the displacement vector of the key focal point within that time period.

[0029] The present invention improves upon this by comparing the key focal point trajectory fitting prediction results with the focal point distribution and edge density comparison data, analyzing the impact range of the predicted focal point trajectory changes on the edge region, and combining the edge region user density distribution weights to generate a dynamic edge correction signal. The specific steps are as follows:

[0030] S401: Based on the trajectory fitting and prediction results of the key focal point and the comparison data of focal point distribution and edge density, analyze the impact range of trajectory changes on the edge region, extract the dynamic distribution data of the affected region, and generate edge region impact range data;

[0031] S402: Based on the edge region influence range data and combined with the edge region user density distribution weight, calculate the correction parameters for the display of the left and right edge display areas respectively, and generate edge correction parameters;

[0032] S403: Based on the edge correction parameters and the changing direction trend during the trajectory fitting process, dynamically adjust the output state of the edge display signal to generate a dynamic edge correction signal.

[0033] The present invention improves upon this invention by analyzing the adaptation range of the dynamic edge correction signal to the current screen angle, correcting the tilt angle and rotation angle of the current screen, and executing the adjustment action through the LCD screen driving module to generate the screen angle control signal. The specific steps are as follows:

[0034] S501: Based on the edge dynamic correction signal, analyze its adaptation range to the current screen angle adjustment, classify the screen adjustment direction, and generate screen angle adaptation analysis data.

[0035] S502: Based on the screen angle adaptation analysis data, and combined with the weight change parameters of the edge and center regions in the correction signal, analyze the adjustment amount of the display angle and generate display angle adjustment data;

[0036] S503: Based on the display angle adjustment data, integrate the parameters required for screen adjustment and generate a real-time control signal for angle changes.

[0037] A system for adjusting the viewing angle of a liquid crystal display screen, the system comprising:

[0038] The gaze acquisition module collects the user's position point in front of the screen and its corresponding gaze direction information, and maps it to a set of points in three-dimensional coordinates. It combines the importance weight of each point with the distance from the screen to generate multi-region weighted balanced point distribution data.

[0039] The focus point analysis module, based on the multi-region weighted equilibrium point distribution data, filters key focus points through line-of-sight direction and position data, compares the user density data of the edge areas on both sides of the key focus points with the central area, and generates focus point distribution and edge density comparison data.

[0040] Based on the information of key focal points in the focal point distribution and edge density comparison data, the trajectory prediction module predicts the coordinates and direction of change of the location of dense areas in the future time period, and generates a dynamic edge correction signal by combining the user density distribution weight of the edge area.

[0041] The dynamic adjustment module analyzes the adaptation range of the current screen angle based on the dynamic edge correction signal and generates a screen angle control signal according to the weight changes in the signal.

[0042] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0043] By collecting and 3D mapping user real-time location and gaze direction data, combined with weighted calculation of gaze points and multi-region weighted processing, the spatial distribution characteristics of user attention points are clarified, improving the optimization capability of displayed content under dynamic viewing angles. Key focus point trajectory fitting and prediction technology is employed to extract the dynamic change characteristics of densely populated user areas from multiple frames of images, and the trajectory analysis results are combined with edge display adjustment signals to optimize the dynamic adaptation of display signals. Through weighted analysis and mapping processing of user data and display signals, display angle adjustment data is further generated, providing real-time adjustment references for screen display optimization. This software-integrated processing logic achieves intelligent optimization across the entire chain from user behavior data to display parameters, avoiding the limitations of static adjustments, adapting to the needs of multiple users and multiple viewing angles in complex scenarios, and improving the adaptation range and display consistency of LCD screens. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0045] Figure 1 This is a flowchart of the method of the present invention;

[0046] Figure 2 This is a detailed flowchart of step S1 of the present invention;

[0047] Figure 3 This is a detailed flowchart of step S2 of the present invention;

[0048] Figure 4 This is a detailed flowchart of step S3 of the present invention;

[0049] Figure 5 This is a detailed flowchart of step S4 of the present invention;

[0050] Figure 6 This is a detailed flowchart of step S5 of the present invention;

[0051] Figure 7 This is a system module diagram of the present invention. Detailed Implementation

[0052] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0053] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as an "exemplary" in the present invention should not be interpreted as being preferred or advantageous over other embodiments or designs. Rather, the use of the word "exemplary" is intended to present concepts in a concrete manner. Furthermore, in the embodiments of the present invention, "and / or" can mean both or either of the two.

[0054] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0055] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0056] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0057] Please see Figure 1 This invention provides a method for adjusting the viewing angle of a liquid crystal display screen, comprising the following steps:

[0058] S1: Collect the user's position point in front of the screen and its corresponding gaze direction information, map all the user's gaze direction points into a set of points in a three-dimensional coordinate system, and combine the importance weight of each point with the distance from the screen to generate multi-region weighted equilibrium point distribution data.

[0059] S2: Based on the user location and line-of-sight data in the multi-region weighted equilibrium point distribution data, key focal points are filtered out. The user density data of the edge areas on both sides of the key focal points are compared with the central area to generate focal point distribution and edge density comparison data.

[0060] S3: Obtain information on key focal points in the focal point distribution and edge density comparison data, predict the coordinates and direction of change of dense areas in the future, and generate key focal point trajectory fitting prediction results;

[0061] S4: Compare the key focal point trajectory fitting prediction results with the focal point distribution and edge density comparison data, analyze the impact range of the predicted focal point trajectory changes on the edge region, and generate a dynamic edge correction signal by combining the user density distribution weight of the edge region.

[0062] S5: Analyze the adaptation range of the dynamic edge correction signal to the current screen angle, correct the tilt angle and rotation angle of the current screen, execute the adjustment action through the LCD screen driver module, and generate the screen angle control signal.

[0063] The multi-region weighted equilibrium point distribution data includes the three-dimensional coordinates of the user's position, the vector value of the viewing direction, and the density weight distribution of each sub-region. The focal point distribution and edge density comparison data includes the position coordinates of the key focal point, the number of users covered, the density values ​​of users in the left and right edge areas, and the weight comparison of the density distribution. The key focal point trajectory fitting prediction results include the trajectory fitting path curve, the predicted future focal point position coordinates, and the change direction vector. The dynamic edge correction signal includes the display correction magnitude of the left and right edge areas, the clarity weight adjustment value of the center area, and the dynamic trajectory direction parameters. The screen angle control signal includes the adjusted tilt angle value, rotation angle value, and the corresponding drive execution instruction set.

[0064] Please see Figure 2 The specific steps for collecting user position points in front of the screen and their corresponding gaze directions, mapping all user gaze direction points to a set of points in a three-dimensional coordinate system, and assigning importance weights to each point based on its distance from the screen to generate multi-region weighted equilibrium point distribution data are as follows:

[0065] S101: Based on a multi-point positioning sensor and camera array, it collects real-time location points and gaze direction data of the user in front of the screen, processes the data into three-dimensional coordinate points, maps them through a unified coordinate system, and generates a set of user gaze direction points.

[0066] First, multi-point positioning sensors are used to detect the user's spatial position data in front of the screen in real time. Using a multi-source data fusion method, the collected position information is matched with the image data acquired by the camera. The calibration parameters of the sensors and camera are set through a standardized calibration table, including focal length, field of view, and spatial offset. The position information collected by the sensors is corrected for timestamp differences using a linear interpolation method. Combined with the user's head movement trajectory, a geometrically constrained attitude calculation algorithm is used to convert the position data into three-dimensional coordinate points. At the same time, the user's gaze direction data is analyzed through camera image analysis. A facial key point detection algorithm is used to identify the user's pupil position and calculate the gaze direction vector. Based on a unified coordinate system, the position information and gaze direction are mapped into a set of three-dimensional coordinate points, ultimately generating a complete set of user gaze direction points.

[0067] S102: Based on the user's gaze direction point set, analyze the spatial distance of each point to the screen, and combine the distribution pattern of the points in three-dimensional coordinates to determine the influence intensity of the points on screen interaction. Perform weighted calculation on each point to generate user gaze point weight data.

[0068] A weighted calculation is performed for each point using the following formula: Get the Weight of each viewpoint ;

[0069] in, For the first The three-dimensional coordinates of each point are obtained through joint acquisition and calculation by a multi-point positioning sensor and camera array. The sensors provide position data, and the cameras provide image feature analysis results. The two are calibrated and fused to obtain the three-dimensional point data. The three-dimensional coordinates of the screen's center point are determined based on the screen's physical dimensions and installation location; for example, the geometric center is obtained after measuring the screen's width and height. For the first The spatial distance from each point to the screen. For all points The total distance to the screen. The total number of line-of-sight points collected is obtained directly through the sampling count of the sensor.

[0070] For example, if the screen width is 1000mm and the height is 500mm, its geometric center during installation can be defined as... Distance is calculated using the formula: For example, the coordinates of a certain point are collected. The distance is calculated as follows: .

[0071] Weight calculation for each point: For example, for point Assuming a total of 10 points and a total distance of 1000mm between all points, the weights are calculated as follows: By comparing the weight values ​​of different points in historical sampling, if the overall weight value of a certain area is relatively high, it indicates that the user's attention is more concentrated in that area. This result shows that the weight value distribution reflects the degree of user attention to different areas of the screen, and areas with higher weight values ​​are of reference value for subsequent multi-area weighted calculation and screen optimization division.

[0072] S103: Based on user gaze point weight data, the screen is divided into multiple sub-regions, and multi-region weighted equilibrium point distribution data is generated by referring to the gaze point density and weight distribution in each sub-region.

[0073] First, the pixel resolution is obtained based on the screen's physical parameters, such as calculating the actual physical resolution by measuring the screen width and height. Then, the division criteria are determined by combining the user's gaze point weight data. The screen region division adopts a quadtree-based partitioning method, dynamically adjusting the boundaries of sub-regions through recursive partitioning. After each partitioning, the density of user gaze points in each sub-region is counted. The number of points in each sub-region is calculated by the distribution of three-dimensional points, and the weight value distribution of each point is recorded. The weighted average method is used to calculate the weighted value distribution matrix of the sub-regions. Combined with the actual distribution characteristics of the points, the region division results are iteratively adjusted to make the density distribution of the divided sub-regions more uniform. Finally, multi-region weighted equilibrium point distribution data that can reflect the distribution characteristics of user gaze points is generated.

[0074] Please see Figure 3 The specific steps for selecting key focal points based on user location and line-of-sight data in multi-region weighted equilibrium point distribution data, and comparing user density data in the edge regions on both sides of the key focal points with the central region to generate focal point distribution and edge density comparison data are as follows:

[0075] S201: Based on the user position and gaze direction information in the multi-region weighted equilibrium point distribution data, detect and locate the gaze point position of each user on the screen, establish a gaze point distribution according to individual users, and generate user gaze point data;

[0076] First, a unified coordinate plane model of the screen is established. The coordinate plane is generated based on the actual physical size of the screen and the calibration data of the installation angle. Combined with the user's head posture and pupil position captured by the camera, the user's gaze direction vector is calculated using geometric optics methods. Then, the user's gaze point position is calculated using the formula of the intersection point of the gaze vector and the screen plane. During the detection process, linear interpolation is used to calibrate the data differences between cameras. Multi-source data collected by different cameras are matched by timestamps to ensure data synchronization. Finally, all users' gaze points are stored as independent user gaze trajectory data sets, generating individual user gaze point distribution records, and constructing overall user gaze point data for subsequent analysis.

[0077] S202: Based on user gaze point data, analyze the spatial distribution of all user gaze points, refine the coverage area and number of users' gazes, use cluster analysis to extract key user focus locations, analyze key focus points with reference to key locations, and generate key focus point data;

[0078] First, all gaze points on the screen are mapped to a two-dimensional coordinate plane. The gaze point coordinates are divided into regions using the K-Means clustering algorithm. The initial cluster center points are set based on the uniform grid distribution of the screen. The initial center positions are obtained by screen area segmentation. Then, the cluster center points are updated according to the distribution density of gaze points. This process is iterated until the classification results are stable. During the clustering process, the dwell time of gaze points is introduced as an additional parameter to improve the accuracy of the clustering results. Then, the dense locations of user gaze points in each cluster region are extracted using the clustering results. The number of users and the distribution characteristics of gaze points in each region are counted. The spatial coverage characteristics of the gaze data are analyzed by cross-comparison of multiple regions. Finally, the key focal point locations on the screen are determined and key focal point data is generated.

[0079] S203: Based on key focal point data, compare the user density in the core area and the two edge areas of the focal point, and combine the difference analysis of distribution data to generate focal point distribution and edge density comparison data;

[0080] The scope of the core area and the edge area is established by spatial division method. The core area is a circular area with a fixed radius centered on the key focal point. The two edge areas are generated with equal widths extending outward from the core area. User gaze point data within the defined area are sampled to obtain gaze point density and user number. The gaze point density of the core area and the edge area are counted separately. The distribution difference between the two areas is evaluated by density difference. Finally, a detailed matrix containing the density comparison of the core area and the edge area is generated to provide data support for subsequent screen content layout optimization.

[0081] Please see Figure 4 The specific steps for obtaining information on key focal points from the focal point distribution and edge density comparison data, predicting the coordinates and direction of change of dense areas in future time periods, and generating key focal point trajectory fitting prediction results are as follows:

[0082] S301: Based on the key focal point location data and the distribution of covered users in the focal point distribution and edge density comparison data, generate multi-frame user dense area distribution data;

[0083] First, the foreground region in the image is extracted using background modeling methods. Noise data is removed using dynamic thresholding. The pixel distribution data of the user's location is obtained by combining the mapping relationship between the image coordinate system and the actual screen coordinate system. The density of users in the foreground region is detected by a density analysis model. Furthermore, the user distribution region of each frame is correlated and matched using a time series analysis framework to record the location information of dense regions and track changes in user position across frames. Finally, the dynamic data of user dense regions in multiple frames are organized into a distribution matrix that can be analyzed, forming multi-frame user dense region distribution data.

[0084] S302: Based on multi-frame user dense area distribution data, extract the key focal point location data in each frame image, arrange the key focal point locations in chronological order, and generate key focal point time series data.

[0085] First, by utilizing the distribution information of dense user areas in each frame of multi-frame images, the coordinates of key focal points in each frame are obtained through the density center extraction method. Then, a weighted average method is used to calculate the weighted average of all pixels in the dense area as the key focal point position. Subsequently, the focal points of each frame are arranged in chronological order by combining the timestamp information, and the key focal points and their changing trends of each frame are recorded. All data is stored as a key focal point time series. The overall characteristics of this time series are summarized through dynamic data analysis technology to generate complete key focal point time series data, which is used to analyze the dynamic change patterns of dense user areas.

[0086] S303: Based on the time series data of key focal points, analyze the location change trend and fit the motion trajectory. Combine the change trend of key focal point location and user density to predict the location coordinates and change direction of dense areas in the future, and generate the key focal point trajectory fitting prediction results.

[0087] To predict the location coordinates and direction of change of densely populated areas in the future, the following formula is used: Key focus in the future trajectory position ;

[0088] in, This indicates that the key focus is on time. The velocity vector, through time and The change in the position of the key focal point is calculated using the following formula: , and They are time and The position coordinates of the key focal point, in pixels. This indicates a time interval in seconds, which is deduced from the video frame rate. For example, if the frame rate is 30 frames per second, then... , It is the starting time point of the time series, obtained through the timestamp of the first frame image. These are the initial coordinates of the key focal point, calculated using the weighted average center position of densely populated user areas in the first frame image. It represents a tiny time increment in the integration process, indicating a state where the change in time approaches infinitesimal. Indicates from time arrive Integrating the velocity vector over the interval yields the displacement vector of the key focal point within that time period.

[0089] For example, the initial focal point position is The frame rate is 30 frames per second, and the key focus points between two adjacent frames are... , Time interval Seconds, calculate the velocity vector as follows: pixels per second; integrate the velocity using the trajectory formula: , If we want to predict the focal point position in the next second, the calculation is as follows: ;

[0090] This result indicates that the key focus point is expected to be located at screen coordinates within the next second. By comparing and analyzing historical data, we can further optimize the dynamic content distribution across screen areas and enhance the user experience.

[0091] Please see Figure 5 By comparing the key focal point trajectory fitting prediction results with the focal point distribution and edge density comparison data, the influence range of the predicted focal point trajectory changes on the edge region is analyzed. Combined with the user density distribution weights in the edge region, the specific steps for generating a dynamic edge correction signal are as follows:

[0092] S401: Based on the key focal point trajectory fitting prediction results and the comparison data of focal point distribution and edge density, analyze the impact range of trajectory changes on the edge area, extract the dynamic distribution data of the affected area, and generate edge area impact range data;

[0093] First, the trajectory fitting results are mapped onto the two-dimensional coordinate plane of the screen. The range of influence of the trajectory is determined by comparing the spatial relationship between the trajectory points and the coordinates of the edge regions frame by frame. Then, the user distribution data of the edge regions is extracted by combining the edge density comparison data and overlaid with the trajectory-affected areas for analysis. The affected edge regions are divided into partitions using the region division method. A distribution matrix is ​​dynamically established by extracting the overlap ratio between the user distribution density of each partition and the trajectory intersection. The adjustment range of each region in the matrix is ​​recorded as the dynamic distribution data of the affected regions, generating the edge region influence range data.

[0094] S402: Based on the influence range data of the edge area, combined with the user density distribution weight of the edge area, calculate the correction parameters for the display of the left and right display areas of the edge respectively, and generate the edge correction parameters;

[0095] First, the edge region is divided into left and right parts. By statistically analyzing the user density distribution weights, the average user density in each region is extracted. The user distribution data is then mapped to the correction parameter matrix of the display area using weighted coefficients. Combined with the influence range data, the changing trend of user distribution density is mapped to the display correction ratio. Subsequently, the left and right region correction formulas are established based on the differences in the distribution weights of each region in the edge distribution matrix. The correction process is achieved by adjusting the brightness parameters, contrast parameters, and color correction data of the display area. The correction ratio results are stored in a parameter configuration table, and finally, edge correction parameters for dynamic display adjustment are generated.

[0096] S403: Based on edge correction parameters and combined with the changing direction trend during trajectory fitting, dynamically adjust the output state of the edge display signal to generate a dynamic edge correction signal;

[0097] First, the adjustment values ​​for each display sub-region in the edge correction parameters are obtained. The trajectory direction is matched with the correction change direction of the edge region through a real-time trend analysis model. The brightness distribution and contrast distribution parameters of the output signal are adjusted using a dynamic signal control mechanism. The partition mapping range of the display signal is adjusted according to the vector difference of the trajectory movement direction. The adjustment parameters are loaded into the display driver data in a frame-by-frame manner. Finally, the generated signal data is sent to the display control hardware to realize the dynamic optimization and adjustment of the edge display signal, and finally generate a dynamic edge correction signal.

[0098] Please see Figure 6 The specific steps for analyzing the adaptation range of the dynamic edge correction signal to the current screen angle, correcting the current screen tilt and rotation angles, and generating the screen angle control signal by executing adjustment actions through the LCD screen driver module are as follows:

[0099] S501: Based on the edge dynamic correction signal, analyze its adaptation range to the current screen angle adjustment, classify the screen adjustment direction, and generate screen angle adaptation analysis data.

[0100] First, time-series data and amplitude variation data of the edge dynamic correction signal are acquired. The signal partitions are mapped to the edge areas of the screen display. By calculating the amplitude weighting center position of each signal in the edge area, the concentrated area of ​​the signal is determined. Combined with the constraint range of the screen physical angle adjustment, the effect of each signal area is analyzed. Then, the signal effect data of different areas are classified and labeled. Based on the consistency between the angle change and the signal effect direction, the adaptation characteristics of the signal to the adjustment direction are classified and recorded, and the distribution data of adaptation in each direction is generated to form screen angle adaptation analysis data.

[0101] S502: Based on screen angle adaptation analysis data, combined with the weight change parameters of the edge and center regions in the correction signal, analyze the adjustment amount of the display angle and generate display angle adjustment data;

[0102] First, the signal strength weight change trend of each region is obtained, and the relative weight ratio of the signals in the edge and center regions is calculated. The target offset value for angle adjustment is derived by the change in the ratio. Combined with the direction distribution data in the adaptation analysis data, the adjustment amount data of the adaptation direction is extracted. Then, the adjustment values ​​of each direction are integrated through the direction weighted allocation model to obtain the overall angle adjustment data. The calculation results are recorded as the adjustment amount of the display angle change and used as the input parameter for subsequent dynamic adjustment of the screen angle.

[0103] S503: Based on display angle adjustment data, integrates the parameters required for screen adjustment and generates a real-time control signal for angle changes;

[0104] First, the adjustment value and direction data in the display angle adjustment data are extracted and converted into input parameters for the control signal. Combined with the speed limit and dynamic constraints required for screen adjustment, the dynamic trajectory of the output signal is constructed through the signal generation model. The adjustment direction and speed parameters are matched to the target state of the signal output, generating the amplitude and frequency distribution of the real-time signal. Finally, the control signal data containing the angle adjustment target is output.

[0105] Please see Figure 7 A system for adjusting the viewing angle of a liquid crystal display screen, the system comprising:

[0106] The gaze acquisition module collects the user's position point in front of the screen and its corresponding gaze direction information, and maps it to a set of points in three-dimensional coordinates. It combines the importance weight of each point with the distance from the screen to generate multi-region weighted balanced point distribution data.

[0107] The focus point analysis module is based on multi-region weighted equilibrium point distribution data. It filters key focus points by looking direction and location data, and compares the user density data of the edge areas on both sides of the key focus points with the central area to generate focus point distribution and edge density comparison data.

[0108] The trajectory prediction module predicts the coordinates and direction of change of dense areas in future time periods based on the information of key focal points in the focal point distribution and edge density comparison data, and generates dynamic edge correction signals by combining the user density distribution weights in the edge areas.

[0109] The dynamic adjustment module analyzes the adaptation range of the current screen angle based on the dynamic edge correction signal and generates a screen angle control signal according to the weight changes in the signal.

[0110] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0111] In this invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be a single item or multiple items.

[0112] It should be understood that, in various embodiments of the present invention, the order of the above-mentioned process numbers does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0113] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0114] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices, apparatuses, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0115] In the several embodiments provided by this invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0116] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0117] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0118] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0119] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for adjusting the viewing angle of a liquid crystal display screen, characterized in that, Includes the following steps: Collect the user's position point in front of the screen and its corresponding gaze direction information, map all the user's gaze direction points to a set of points in a three-dimensional coordinate system, and combine the importance weight of each point with the distance from the screen to generate multi-region weighted balanced point distribution data; Based on the user location and line-of-sight data in the multi-region weighted equilibrium point distribution data, key focal points are selected, and the user density data of the edge areas on both sides of the key focal points are compared with the central area to generate focal point distribution and edge density comparison data. Information on key focal points in the focal point distribution and edge density comparison data is obtained, the coordinates and direction of change of the dense area in the future time period are predicted, and the trajectory fitting prediction results of key focal points are generated. By comparing the key focal point trajectory fitting prediction results with the focal point distribution and edge density comparison data, the influence range of the predicted focal point trajectory changes on the edge region is analyzed, and a dynamic edge correction signal is generated by combining the user density distribution weight of the edge region. The dynamic edge correction signal is analyzed to determine its adaptation range to the current screen angle. The tilt and rotation angles of the current screen are corrected, and the adjustment action is executed through the LCD screen driving module to generate a screen angle control signal. The multi-region weighted equilibrium point distribution data includes the three-dimensional coordinates of the user's position, the vector value of the viewing direction, and the density weight distribution of each sub-region. The focal point distribution and edge density comparison data includes the position coordinates of the key focal point, the number of users covered, the density values ​​of users in the left and right edge regions, and the weight comparison of the density distribution. The key focal point trajectory fitting prediction result includes the trajectory fitting path curve, the predicted future focal point position coordinates, and the change direction vector. The dynamic edge correction signal includes the display correction magnitude of the left and right edge regions, the clarity weight adjustment value of the center region, and the dynamic trajectory direction parameters. The screen angle control signal includes the adjusted tilt angle value, rotation angle value, and the corresponding drive execution instruction set.

2. The method for adjusting the viewing angle of a liquid crystal display screen according to claim 1, characterized in that: The specific steps for collecting user position points in front of the screen and their corresponding gaze directions, mapping all user gaze direction points to a set of points in a three-dimensional coordinate system, and assigning importance weights to each point based on its distance from the screen to generate multi-region weighted equilibrium point distribution data are as follows: Based on multi-point positioning sensors and camera arrays, the real-time location points and gaze direction data of users in front of the screen are collected, the data is processed into the form of three-dimensional coordinate points, and mapped through a unified coordinate system to generate a set of user gaze direction points. Based on the user's gaze direction point set, the spatial distance from each point to the screen is analyzed, and the influence intensity of the points on screen interaction is determined by combining the distribution pattern of the points in three-dimensional coordinates. Weighted calculations are performed on each point to generate user gaze point weight data. Based on the user gaze point weight data, the screen is divided into multiple sub-regions. By referring to the gaze point density and weight distribution in each sub-region, multi-region weighted equilibrium point distribution data is generated.

3. The method for adjusting the viewing angle of a liquid crystal display screen according to claim 2, characterized in that: A weighted calculation is performed for each point using the following formula: Get the Weight of each viewpoint ; in, For the first The three-dimensional coordinates of the points The three-dimensional coordinates of the center point of the screen. For the first The spatial distance from each point to the screen. For all points The total distance to the screen. This represents the total number of line-of-sight points collected.

4. The method for adjusting the viewing angle of a liquid crystal display screen according to claim 1, characterized in that: Based on the user location and line-of-sight data in the multi-region weighted equilibrium point distribution data, key focal points are selected. The user density data of the edge regions on both sides of the key focal points are compared with the central region to generate focal point distribution and edge density comparison data. The specific steps are as follows: Based on the user position and gaze direction information in the multi-region weighted equilibrium point distribution data, the gaze point position of each user on the screen is detected and located, a gaze point distribution is established according to the individual user, and user gaze point data is generated. Based on the user gaze point data, the spatial distribution of all user gaze points is analyzed, the coverage area and number of users are refined, cluster analysis is used to extract the key positions of user focus, key focus points are analyzed with reference to the key positions, and key focus point data is generated. Based on the key focal point data, the user density in the core area and the two edge areas of the focal point is compared. Combined with the difference analysis of the distribution data, focal point distribution and edge density comparison data are generated.

5. The method for adjusting the viewing angle of a liquid crystal display screen according to claim 1, characterized in that: The specific steps for obtaining information on key focal points from the focal point distribution and edge density comparison data, predicting the coordinates and direction of change of dense areas in future time periods, and generating key focal point trajectory fitting prediction results are as follows: Based on the key focal point location data and the distribution of covered users in the focal point distribution and edge density comparison data, multi-frame user dense area distribution data is generated. Based on the multi-frame user dense area distribution data, the key focal point location data in each frame image is extracted, and the key focal point locations are arranged in chronological order to generate key focal point time series data. Based on the time series data of the key focal points, the location change trend is analyzed and the motion trajectory is fitted. Combining the change trends of the key focal point location and user density, the location coordinates and change direction of the dense area in the future are predicted, and the key focal point trajectory fitting prediction result is generated.

6. The method for adjusting the viewing angle of a liquid crystal display screen according to claim 5, characterized in that: To predict the location coordinates and direction of change of densely populated areas in the future, the following formula is used: Key focus in the future trajectory position ;in, This indicates that the key focus is on time. The velocity vector, It is the starting time point of the time series. These are the initial position coordinates of the key focal point. It represents the time increment during the integration process. Indicates from time arrive Integrating the velocity vector over the interval yields the displacement vector of the key focal point within that time period.

7. The method for adjusting the viewing angle of a liquid crystal display screen according to claim 1, characterized in that: By comparing the key focal point trajectory fitting prediction results with the focal point distribution and edge density comparison data, the influence range of the predicted focal point trajectory changes on the edge region is analyzed. Combined with the user density distribution weights in the edge region, the specific steps for generating a dynamic edge correction signal are as follows: Based on the trajectory fitting and prediction results of the key focal points and the comparison data of focal point distribution and edge density, the influence range of trajectory changes on the edge region is analyzed, the dynamic distribution data of the affected region is extracted, and the influence range data of the edge region is generated. Based on the data on the influence range of the edge region, and combined with the user density distribution weight of the edge region, the correction parameters for the display of the left and right display areas of the edge are calculated respectively, and the edge correction parameters are generated. Based on the edge correction parameters and the changing direction trend during the trajectory fitting process, the output state of the edge display signal is dynamically adjusted to generate a dynamic edge correction signal.

8. The method for adjusting the viewing angle of a liquid crystal display screen according to claim 1, characterized in that: The specific steps for analyzing the adaptation range of the dynamic edge correction signal to the current screen angle, correcting the current screen tilt and rotation angles, and generating the screen angle control signal by executing adjustment actions through the LCD screen driving module are as follows: Based on the edge dynamic correction signal, analyze its adaptation range to the current screen angle adjustment, classify the screen adjustment direction, and generate screen angle adaptation analysis data. Based on the screen angle adaptation analysis data, and combined with the weight change parameters of the edge and center regions in the correction signal, the adjustment amount of the display angle is analyzed, and display angle adjustment data is generated. Based on the display angle adjustment data, the parameters required for screen adjustment are integrated to generate a real-time control signal for angle changes.

9. A viewing angle adjustment system for a liquid crystal display screen, characterized in that, The system comprises: (1) the method for adjusting the viewing angle of a liquid crystal display screen according to any one of claims 1-8; (2) the system comprising: The gaze acquisition module collects the user's position point in front of the screen and its corresponding gaze direction information, and maps it to a set of points in three-dimensional coordinates. It combines the importance weight of each point with the distance from the screen to generate multi-region weighted balanced point distribution data. The focus point analysis module, based on the multi-region weighted equilibrium point distribution data, filters key focus points through line-of-sight direction and position data, compares the user density data of the edge areas on both sides of the key focus points with the central area, and generates focus point distribution and edge density comparison data. Based on the information of key focal points in the focal point distribution and edge density comparison data, the trajectory prediction module predicts the coordinates and direction of change of the location of dense areas in the future time period, and generates a dynamic edge correction signal by combining the user density distribution weight of the edge area. The dynamic adjustment module analyzes the adaptation range of the current screen angle based on the dynamic edge correction signal and generates a screen angle control signal according to the weight changes in the signal.

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