A standby picture processing method of a micro LED display screen

Abstract

The application provides a standby picture processing method of a Micro LED display screen, comprising: if the track overlap analysis result shows that a plurality of touch point track overlaps, separating the overlapping tracks through a clustering algorithm, calculating track overlap degree and separation confidence, and obtaining a touch track separation result; combining the pressure mutation detection result with the touch point coordinate change, calculating the touch offset through weighted average, obtaining the touch offset correction parameter, adjusting the touch coordinate mapping relationship according to the correction parameter; through the touch offset correction result, combining the user finger contact angle and contact area change, identifying the user touch gesture mode by adopting a gesture recognition technology, extracting the finger gesture feature parameter, and generating the user touch gesture data; according to the touch offset correction result and the pressure mutation detection result, combining the finger gesture feature parameter, modeling the user individual touch habit through gesture analysis, and obtaining the touch sensitivity adjustment parameter.

Description

Technical Field

[0001] This invention relates to the field of information technology, and in particular to a method for processing standby images on a Micro LED display screen. Background Technology

[0002] In the field of modern display technology, micro LED displays, due to their high brightness, high contrast, and low power consumption, have become an important development direction for intelligent interactive devices, especially in the interactive design of standby screens. The optimization of their touch guidance function directly affects the user experience and the level of device intelligence. However, current touch interaction methods for standby screens still have significant shortcomings. Many systems struggle to handle complex touch behaviors, easily exhibiting slow response times or misjudgments, leading to user operation disruptions and a degraded experience. Specifically, touch interaction in standby mode faces unique technical challenges. The primary issue lies in the complexity of user touch intentions. When users perform rapid multi-touch operations within a short period, touch trajectories often overlap, making it difficult for the system to accurately distinguish the user's true intention. This confusion of intentions can further trigger changes in user behavior, such as adjusting the operation rhythm through long presses, resulting in sudden changes in touch pressure. These sudden pressure changes can easily cause touch position shifts, leading to a series of mis-touch problems. These interconnected technical factors make touch parsing in standby screens extremely complex, requiring both rapid identification of user intentions and avoidance of operational confusion caused by misjudgments. Therefore, how to dynamically analyze touch trajectories, map pressure changes, and analyze user posture in the standby screen to generate adaptive guiding animations to improve the accuracy and smoothness of touch has become a key problem that urgently needs to be solved. Summary of the Invention

[0003] This invention provides a standby screen processing method for a Micro LED display, mainly including:

[0004] When a user's finger touches the surface of the Micro LED display, the screen switches from the standby screen to the active screen and displays the touch response interface. At the same time, it acquires multi-touch data, analyzes the coordinate position and timestamp information of each touch point, identifies the overlapping area of ​​different finger trajectories, and obtains the trajectory overlap analysis result.

[0005] If the trajectory overlap analysis results show that several touch point trajectories overlap, then the overlapping trajectories are separated by a clustering algorithm, and the trajectory overlap degree and separation confidence degree are calculated to obtain the touch trajectory separation result;

[0006] If the trajectory overlap analysis results show that there are multiple touch point trajectories overlapping, by analyzing the position density distribution of each touch point, adjacent touch points are identified and classified as the same finger operation, the degree of overlap and separation confidence between each trajectory are calculated, and the touch trajectory separation result is obtained.

[0007] By combining the pressure change detection results with the touch point coordinate changes, the touch offset is calculated by weighted averaging to obtain the touch offset correction parameters, and the touch coordinate mapping relationship is adjusted according to the correction parameters.

[0008] By combining the touch offset correction results with the changes in the user's finger contact angle and contact area, posture recognition technology is used to identify the user's touch posture pattern, extract finger posture feature parameters, and generate user touch posture data.

[0009] Based on the touch offset correction results and pressure change detection results, combined with finger posture feature parameters, the user's personalized touch habits are modeled through posture analysis to obtain touch sensitivity adjustment parameters;

[0010] Based on the current interactive scenario requirements, adaptive guidance animation data is generated. Combined with touch sensitivity adjustment parameters, interactive response data is generated by optimizing the animation playback rhythm and the priority order of user operation response. When the user completes the touch operation, the system maintains the current screen display state.

[0011] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0012] This invention discloses a standby screen processing method for Micro LED displays. Through multi-touch data analysis and trajectory overlap parsing, it achieves accurate recognition of complex touch operations. When a user's finger touches the display screen, this invention can accurately separate overlapping trajectories and, combined with pressure change detection and coordinate transformation, calculate the touch offset for correction. Simultaneously, this invention can also recognize the user's touch posture, extract finger posture feature parameters, and model personalized touch habits, thereby optimizing touch sensitivity. Finally, this invention generates adaptive guiding animations based on the needs of the interaction scenario and optimizes the interaction response based on touch parameters, providing a smooth and accurate touch experience. This intelligent touch technology significantly improves the recognition accuracy and operational sensitivity of multi-touch, providing strong support for complex touch interactions. Attached Figure Description

[0013] Figure 1 This is a flowchart of a standby screen processing method for a Micro LED display screen according to the present invention.

[0014] Figure 2 This is a schematic diagram of a standby screen processing method for a Micro LED display screen according to the present invention. Detailed Implementation

[0015] To further understand the content of this invention, a detailed description of the invention is provided in conjunction with the accompanying drawings and embodiments. The specific embodiments described herein are for illustrative purposes only and are not intended to limit the invention. It should also be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0016] like Figure 1-2 This embodiment of a standby screen processing method for a Micro LED display screen may specifically include:

[0017] Step S101: When the user's finger touches the surface of the Micro LED display screen, the system switches from the standby screen to the active screen and displays the touch response interface. At the same time, it acquires multi-touch data, analyzes the coordinate position and timestamp information of each touch point, identifies the overlapping area of ​​different finger trajectories, and obtains the trajectory overlap analysis result.

[0018] When a user's finger touches the surface of the MicroLED display, the system immediately records the pressure value and contact area of ​​the initial contact point upon detecting the touch signal. Simultaneously, it initiates an interface switching program, completing the rendering process from the standby screen to the active screen within a preset response time threshold. After the active screen is displayed, the corresponding touch response interface is loaded based on the pressure value and contact area. The sampling frequency is determined based on the initial contact pressure value and contact area to acquire the original capacitance value changes of all touch points on the display. The capacitance change is obtained by subtracting the capacitance values ​​of adjacent sampling points. Valid touch points are determined based on a preset capacitance threshold. For each valid touch point, its X-axis and Y-axis position values ​​in the screen coordinate system are extracted, and the corresponding system timestamp is recorded, forming a touch data sequence containing position and time information. Based on the continuity of the timestamps and the spatial proximity of the coordinate points in the touch data sequence, a clustering method based on Euclidean distance is used to group touch points belonging to the same finger. For each group of touch point sequences, the least squares method is used to fit the movement trajectory curve equation of the finger, and the set of boundary points of the region swept by each trajectory curve on the two-dimensional plane is calculated. For the set of boundary points of different finger trajectories, the intersecting regions are identified by determining whether the boundary points are inside other trajectory regions. If there is an intersection, all points located inside two or more trajectory regions are extracted as overlapping regions. The coordinates of the geometric center point and the area value of the overlapping region are calculated. The overlap ratio is obtained by dividing the area of ​​the overlapping region by the area of ​​each trajectory region. Combined with the overlap duration of the trajectory in the time dimension, the trajectory overlap analysis result containing the range of the overlapping region and the overlap ratio is obtained.

[0019] Specifically, in the multi-touch interaction process of MicroLED displays, the acquisition of pressure value and contact area is the foundation of the entire touch response system.

[0020] Specifically, when a finger touches the screen surface, a capacitance change occurs in the contact area. The intensity of this change is proportional to the pressure applied by the finger. The pressure value typically varies between 0 and 1024, with a light touch producing a value of around 200, while heavy pressure can reach over 800. The contact area is calculated by the number of pixels at the effective touch points; the typical contact area for an adult's fingertip is approximately 100 to 150 pixels. These two parameters together determine the sensitivity and accuracy of subsequent interface responses.

[0021] It should be noted that the capacitance change is calculated using the difference between adjacent sampling points because the environmental capacitance will have underlying noise.

[0022] For example, if the capacitance of a touch point is 520 kΩ at time t1 and 680 kΩ at time t2, the change in capacitance is 160 kΩ. When this change exceeds a preset threshold, such as 100 kΩ, it is considered a valid touch. This differential method can effectively eliminate environmental interference and improve the accuracy of touch detection.

[0023] In one possible implementation, the Euclidean distance-based clustering method assigns discrete touch points to different fingers, taking ergonomic characteristics into account. Within two adjacent sampling times, the movement distance of the same finger typically does not exceed 50 pixels; therefore, a distance threshold is set to this value. If two touch points are temporally adjacent and their spatial distance is less than the threshold, they are grouped into the same finger trajectory. This clustering method is particularly suitable for rapid swipe operations, accurately distinguishing the independent trajectories of multiple fingers. The process of fitting the trajectory curve using the least squares method is achieved by minimizing the sum of the squared vertical distances from all touch points to the fitted curve.

[0024] For example, if a finger draws an arc-shaped trajectory on the screen, containing 20 sampling points, the best-fit quadratic curve equation can be obtained by constructing an error function and taking its derivative. This fitting method can smooth out jitter in the original touch data, resulting in a smoother trajectory curve.

[0025] Preferably, the overlapping region identification uses a ray-mapping method to determine whether a point is inside a polygon. A ray is emitted from the point to be identified in any direction, and the number of intersections between the ray and the trajectory boundary is counted. An odd number of intersections indicates that the point is inside, and an even number indicates that it is outside. When there are mutually contained points in the boundary point sets of two trajectories, these points constitute an overlapping region. The calculation of the overlap ratio can quantify the degree of interaction between different finger trajectories, providing an important basis for subsequent gesture recognition.

[0026] Step S102: If the trajectory overlap analysis result shows that several touch point trajectories overlap, then the overlapping trajectories are separated by a clustering algorithm, the trajectory overlap degree and separation confidence degree are calculated, and the touch trajectory separation result is obtained.

[0027] If the trajectory overlap analysis results show that several touch point trajectories overlap, the timestamp sequence and coordinate sequence of each touch point in the overlapping area are obtained. The spatiotemporal distance value is obtained by calculating the Euclidean distance between two touch points and multiplying it by the normalization coefficient of the time difference. A touch point similarity matrix is ​​constructed based on the spatiotemporal distance values ​​between all touch point pairs, where each element represents the similarity between two touch points. The number of clusters K is determined based on the number of connected components in the touch point similarity matrix. The K-means clustering algorithm is used to group the touch point coordinates in the overlapping area. The input is the two-dimensional coordinate sequence of each touch point. The positions of K cluster centers are iteratively updated until the sum of the distances from each point to its respective cluster center no longer changes. The cluster label and coordinates of each cluster center are output. For the cluster label and cluster center coordinates, the average Euclidean distance from all touch points in each cluster to the cluster center is calculated as the cluster tightness. The minimum Euclidean distance between different cluster centers is calculated as the cluster margin. The separation confidence value is obtained by dividing the cluster margin by the cluster tightness. The separation confidence score is compared with a preset confidence threshold. If the separation confidence score is higher than the threshold, the touch point sequences with the same cluster label are recombined into independent trajectories. The proportion of the number of touch points contained in each independent trajectory to the total number of touch points in the original overlapping area is calculated as the trajectory overlap. The output is the touch trajectory separation result containing the coordinate sequence of each independent trajectory and the corresponding trajectory overlap.

[0028] Specifically, the calculation of spatiotemporal distance values ​​is a key foundation for separating overlapping trajectories.

[0029] Specifically, when two touch points are 20 pixels apart spatially and 0.1 seconds apart temporally, the differences in both dimensions need to be considered. Spatial distance is calculated directly using Euclidean distance, while the temporal difference needs to be normalized. The normalization coefficient is usually set to the reciprocal of the sampling period to make the temporal and spatial dimensions comparable.

[0030] For example, if the sampling period is 0.016 seconds, the normalization coefficient is 62.5, and the time difference of 0.1 seconds is normalized to 6.25. Multiplying this by the spatial distance of 20 yields a spatiotemporal distance value of 125. This calculation method can effectively distinguish between fast-moving and slow-moving trajectories.

[0031] It's important to note that the connected components in the touch point similarity matrix reflect the natural grouping of trajectories. A connected component is a set of interconnected elements in the matrix; when the similarity between two touch points exceeds a threshold, they are considered connected in the matrix. By traversing the matrix using a depth-first search, all connected components can be identified, with each component corresponding to an independent finger trajectory. This method avoids the subjectivity of manually setting the number of clusters, making the determination of the K value more objective and accurate.

[0032] In one possible implementation, the iterative process of the K-means clustering algorithm involves several key steps. Initially, K touch points are randomly selected as cluster centers. Then, the Euclidean distance from each touch point to each cluster center is calculated, and the touch point is assigned to the nearest cluster center. The center position of each cluster is recalculated, which is the average of the coordinates of all points within that cluster. This assignment and update process is repeated until the cluster center positions no longer change significantly. This iterative optimization process can find a locally optimal clustering scheme. The calculation of cluster density and cluster margin provides a quantitative evaluation standard for the separation results.

[0033] For example, a cluster contains 15 touch points, with distances from the cluster center of 8, 12, 10... pixels, and an average distance of 10 pixels. This is the cluster density. The cluster center is 50 pixels away from its nearest other cluster center; this is the cluster margin. The separation confidence score is calculated as 50 divided by 10, which equals 5, indicating that the separation between clusters is 5 times greater than the density within clusters, demonstrating good separation performance.

[0034] Preferably, the calculation of trajectory overlap can quantify the proportion of each trajectory in the overlapping area. If there are 100 touch points in the overlapping area, and after separation, the first trajectory contains 65 points and the second trajectory contains 35 points, then the overlap of the two trajectories is 0.65 and 0.35, respectively. This quantitative indicator not only reflects the relative importance of the trajectories but also provides a weight reference for subsequent gesture recognition and interaction response, enabling the system to more accurately understand the user's touch intent.

[0035] Step S103: If the trajectory overlap analysis result shows that there are multiple touch point trajectories overlapping, by analyzing the position density distribution of each touch point, adjacent touch points are identified and classified as the same finger operation, the degree of overlap and separation confidence between each trajectory are calculated, and the touch trajectory separation result is obtained.

[0036] If the trajectory overlap analysis results show that multiple touch point trajectories overlap, the two-dimensional coordinates of each touch point within the overlapping area are obtained. The display screen area is divided according to a preset grid size, and the number of touch points in each grid cell is counted. The position density value of each grid is obtained by dividing the number of touch points by the grid area, forming a position density distribution matrix. Based on the position density distribution matrix, the sum of the absolute values ​​of the density differences between each grid and its eight neighboring grids is calculated as the density gradient value of that grid. Grids with gradient values ​​below a preset threshold and density values ​​above a preset threshold are marked as density peak centers. Starting from each density peak center, neighboring grids with density values ​​above the threshold are gradually incorporated into the same region until a grid with a density value below the threshold is encountered, resulting in multiple independent high-density regions and their contained touch point sets. For each high-density region's touch point set, the Euclidean distance between any two touch points in the set is calculated. If the distance is less than a preset proximity threshold, a connection is established between the two points. All connection relationships are traversed using a depth-first search, and touch points that can reach each other through connection relationships are grouped into the same touch point group. Each touch point group corresponds to an independent trajectory. Based on the affiliation of each touch point group, the number of grids located at the intersection of two or more high-density areas is counted as the overlap value between trajectories. The standard deviation of the distance from all points in each touch point group to the center point of the group is calculated as the spatial dispersion. The separation confidence is obtained by dividing the minimum distance between different groups by the average spatial dispersion. The coordinate sequence of each touch point group and the corresponding overlap value and separation confidence value are output as the touch trajectory separation result.

[0037] Specifically, the construction of the location density distribution matrix is ​​the foundation for identifying overlapping trajectories.

[0038] Specifically, when the display screen is divided into a 10×10 pixel grid, each grid cell becomes a statistical unit. If a grid contains 5 touch points and the grid area is 100 square pixels, then the position density value of that grid is 0.05. This gridding method can convert discrete touch points into a continuous density field, facilitating subsequent area identification and analysis. The density value directly reflects the concentration of touch activity.

[0039] It should be noted that the density gradient value is calculated using the eight-neighbor difference method. For any grid, the density difference between it and its eight neighboring grids needs to be calculated, and then the absolute values ​​of these eight differences are added together.

[0040] For example, if the density of the central grid is 0.08, and the densities of the eight surrounding grids are 0.02, 0.03, 0.07, 0.06, 0.02, 0.01, 0.05, and 0.04 respectively, then the density gradient value is 0.34. A larger gradient value indicates that the location is a boundary of density change; a smaller gradient value indicates that the location is in a region of uniform density.

[0041] In one possible implementation, the region growth process starting from the center of the density peak is analogous to water wave diffusion. First, grids with gradient values ​​less than 0.1 and density values ​​greater than 0.05 are identified as seed points; these points are typically located in the central region of finger contact. Then, the neighboring grids of the seed points are checked; if the density values ​​of neighboring grids are still above a threshold of 0.03, they are included in the same region. This process continues until a grid with a density value below the threshold is encountered. In this way, the high-density touch area formed by each finger can be accurately identified. Depth-first search plays a crucial role in determining touch point connectivity. When the Euclidean distance between two touch points is less than 15 pixels, they are considered connected. Starting from any touch point, all reachable touch points are visited along the connectivity relationships; these points constitute a connected component.

[0042] For example, touch points A and B are 12 pixels apart, B and C are 14 pixels apart, but A and C are 28 pixels apart. Through depth-first search, A, B, and C are still grouped together because they form a connection chain through B.

[0043] Preferably, the spatial dispersion calculation reflects the degree of clustering of touch point groups. First, the geometric center of all touch points within a group is calculated, then the distance from each point to the center is calculated; the standard deviation of these distances is the spatial dispersion. A small dispersion indicates that the touch points are tightly clustered, typically corresponding to stable touch from a single finger; a large dispersion may indicate rapid finger swipes or complex trajectories formed by multiple intersecting fingers. Separation reliability is measured by the ratio of the minimum distance between groups to the average dispersion; a larger ratio indicates better separation of different trajectories and higher accuracy in touch recognition.

[0044] Step S104: Combine the pressure change detection result with the touch point coordinate change, calculate the touch offset by weighted average, obtain the touch offset correction parameter, and adjust the touch coordinate mapping relationship according to the correction parameter.

[0045] The system acquires the pressure value sequence at each sampling moment during the touch process, calculates the difference between pressure values ​​at adjacent moments, and marks any difference exceeding a preset abrupt change threshold as a pressure abrupt change point. The timestamp of the abrupt change point and the corresponding pressure change amplitude are recorded to form a pressure abrupt change detection result. Based on the timestamp in the pressure abrupt change detection result, the touch point coordinates at the abrupt change moment and the previous moment are extracted, and the difference between the two coordinates is calculated to obtain a coordinate offset vector. The pressure change amplitude is divided by a preset maximum pressure value to obtain a normalization coefficient as a weighting coefficient. The weighting coefficient is multiplied by the coordinate offset vector to obtain the weighted offset vector of the abrupt change point. For the weighted offset vectors of all pressure abrupt change points, the arithmetic mean of the horizontal component and the arithmetic mean of the vertical component are calculated respectively. The two averages are combined into a two-dimensional vector as the overall touch offset, which is the touch offset correction parameter. Based on the touch offset correction parameter, the horizontal component of the correction parameter is subtracted from the horizontal component of each original touch coordinate, and the vertical component of the correction parameter is subtracted from the vertical component to obtain the corrected touch coordinates. A linear transformation relationship from the original coordinates to the corrected coordinates is established as the adjusted touch coordinate mapping relationship.

[0046] Specifically, pressure change detection plays a crucial role in touch accuracy calibration.

[0047] Specifically, when a user's finger suddenly changes from a light touch to heavy pressure, the pressure value may jump dramatically from 200 units to 800 units. This 600-unit change far exceeds the gradual range of normal touch input. The pressure abrupt change threshold is typically set at three times the statistical average of the pressure difference between adjacent sampling points, approximately 150 units. When the system detects a pressure difference exceeding this threshold, it records that moment as the abrupt change point. This abrupt change is often accompanied by a sharp change in the finger's contact area, causing a shift in the touch center point.

[0048] It should be noted that the calculation of the coordinate offset vector needs to accurately capture the positional changes before and after the pressure change.

[0049] For example, at time t1, the pressure is 200, and the coordinates are (100, 150). At time t2, the pressure suddenly increases to 800, and the coordinates become (105, 148). The coordinate offset vector is (5, -2), indicating that the touch point is offset 5 pixels to the right and 2 pixels upward. This offset is mainly because as the finger pressure increases, the contact area expands, and the touch center calculated by the system changes accordingly.

[0050] In one possible implementation, the weighting coefficients are determined using a normalization method to reflect the relative degree of pressure change. Assuming the system's preset maximum pressure value is 1024 units, when the pressure change is 600 units, the normalization coefficient is 0.586. This coefficient, used as a weight, is multiplied by the coordinate offset vector (5, -2) to obtain the weighted offset vector (2.93, -1.17). The introduction of the weighting coefficients reduces the impact of small pressure change abrupt changes on the final correction parameters, while giving greater weight to abrupt pressure change abrupt changes. The touch offset correction parameters are calculated using statistical averaging to eliminate the influence of individual outliers.

[0051] For example, the system detects five pressure abrupt change points, with weighted offset vectors of (2.93, -1.17), (3.2, -1.5), (2.8, -1.0), (3.1, -1.3), and (2.9, -1.2), respectively. The average value of the horizontal component is 2.99, and the average value of the vertical component is -1.23. Combining these values ​​yields the correction parameter (2.99, -1.23). This two-dimensional vector represents the average offset trend throughout the entire touch process.

[0052] Preferably, the establishment of a linear transformation relationship makes the touch coordinate correction process simple and efficient. For any original touch coordinate (x, y), the corrected coordinates are (x - 2.99, y + 1.23). This simple subtraction operation can be completed in real time without imposing additional computational burden on the system. The corrected mapping relationship ensures that the system recognizes a consistent position when the user touches the screen under different pressures, significantly improving the accuracy of touch positioning, especially in applications requiring precise operation.

[0053] Step S105: Based on the touch offset correction results and the changes in the user's finger contact angle and contact area, the user's touch posture pattern is identified using posture recognition technology, finger posture feature parameters are extracted, and user touch posture data is generated.

[0054] The corrected touch coordinate sequence is obtained through touch offset correction results. All boundary points of the touch area are identified, and the minimum circumscribed ellipse containing all boundary points is calculated. The major and minor axes of the ellipse are extracted, and the angle between the major axis and the horizontal baseline of the display screen is calculated as the finger contact angle. Simultaneously, the number of effective pixels within the touch area at each sampling time is counted as the contact area, resulting in a contact angle sequence and a contact area sequence. Based on the contact angle sequence and the pressure values ​​of each point within the touch area, the weighted average coordinates of all touch point pressure values ​​are calculated as the pressure center position. The horizontal coordinate is equal to the sum of the products of the horizontal coordinates of each point and the pressure value, divided by the total pressure value. The vertical coordinate is calculated in the same way. The finger tilt angle is obtained by the angle between the line connecting the pressure center and the center of the ellipse and the vertical direction. For the finger tilt angle and the pressure center position, the tilt angle is classified according to a preset angle range. The Euclidean distance between the pressure center and the center of the ellipse is calculated. If this distance exceeds a preset threshold, it is determined to be a tilted touch posture; otherwise, it is determined to be a vertical touch posture, resulting in a touch posture category identifier. Based on the touch gesture category identifier and tilt angle value, the touch area is divided into multiple rectangular sub-regions according to a preset grid size. The average pressure value of all touch points in each sub-region is calculated, and a pressure distribution vector is formed according to the spatial arrangement order of the sub-regions. Combined with the tilt angle value, touch gesture category identifier, and pressure distribution vector, user touch gesture data containing finger gesture feature parameters is generated.

[0055] Specifically, calculating the minimum bounding ellipse is a key step in recognizing finger contact features.

[0056] Specifically, when a finger touches the screen at a certain angle, the touch area presents an irregular elliptical shape. By scanning all the boundary points of the touch area, the set of points farthest from the center can be found. These boundary points are fitted using the least squares method to determine an ellipse that contains all points and has the smallest area. The major axis of the ellipse typically extends along the longitudinal direction of the finger, and its length is approximately 2 to 3 times that of the minor axis. The angle between the major axis and the horizontal baseline directly reflects the direction of the finger's tilt, typically ranging from 0 to 90 degrees.

[0057] It should be noted that the calculation of the pressure center of gravity adopts the concept of the center of mass in physics. Each touch point can be regarded as a point mass, and its "mass" is the pressure value at that point.

[0058] For example, a touch area contains 100 touch points, with the coordinates of the i-th point being (xi, yi) and the pressure value being pi. The x-coordinate of the pressure center is calculated by summing the products of all xi and pi and dividing by the sum of all pi. When the finger presses vertically, the pressure center roughly coincides with the geometric center of the ellipse; however, when pressed at an angle, the pressure is concentrated at the tip of the finger, causing the pressure center to deviate significantly from the geometric center.

[0059] In one possible implementation, touch gestures are classified based on the degree of eccentricity in pressure distribution. The distance between the pressure center and the center of the ellipse reflects the non-uniformity of the pressure distribution. When this distance is less than 10% of the minor axis of the ellipse, it can be considered a uniform vertical press; more than 25% indicates a significant tilt. The tilt angle is typically divided into 15-degree increments, such as 0-15 degrees for a slight tilt, 15-30 degrees for a moderate tilt, and above 30 degrees for a severe tilt. This classification method can accommodate different users' operating habits. Constructing the pressure distribution vector requires meshing the touch area.

[0060] For example, the touch area is divided into a 4×4 grid, forming 16 sub-regions. The average pressure of all touch points within each sub-region is calculated; if there are no touch points in a sub-region, the pressure value is recorded as 0. The 16 pressure values ​​are arranged into a one-dimensional vector from left to right and top to bottom. During vertical touch, the pressure distribution is relatively uniform, and the differences between elements in the vector are small; during tilted touch, the pressure is concentrated in a specific direction, and the vector exhibits a significant gradient change.

[0061] Preferably, the user touch posture data includes three core elements: the tilt angle value provides a quantitative description of the finger direction, the touch posture category identifier enables fast pattern matching, and the pressure distribution vector preserves detailed pressure space features. This multi-dimensional posture description method enables the system to accurately distinguish different touch intentions, such as whether the user is making a precise click or a rapid swipe, thereby providing a more intelligent and personalized interactive experience.

[0062] Step S106: Based on the touch offset correction results and pressure change detection results, and combined with finger posture feature parameters, the user's personalized touch habits are modeled through posture analysis to obtain touch sensitivity adjustment parameters.

[0063] Based on the touch offset correction results, the mean and standard deviation of the user's historical touch offsets are calculated. The number of abrupt changes per unit time is extracted from the pressure abrupt change detection results as the abrupt change frequency, and the average pressure difference before and after the abrupt change is extracted as the abrupt change amplitude. The proportion of occurrences of each tilt angle interval in the finger posture feature parameters is statistically analyzed. The mean offset, standard deviation of offset, abrupt change frequency, abrupt change amplitude, and tilt angle proportion are combined to form a five-dimensional feature vector. K-means clustering is used to classify touch patterns based on the five-dimensional feature vector. The number of clusters K is set to 3, corresponding to three user types: light touch, standard touch, and heavy pressure. The current user's feature vector is input, and the cluster centers are iteratively updated until convergence. The Euclidean distance between the feature vector and the three cluster centers is calculated, and the user is classified into the touch habit category represented by the cluster center with the smallest distance. Based on the touch habit category index value, the built-in parameter mapping relationship is queried to obtain the corresponding baseline parameter set. For light touch, the baseline pressure is low, response time is short, and touch radius is small; for heavy touch, the opposite is true. The sum of the absolute values ​​of the differences between the current user's feature vector and the cluster center of its category for each dimension is calculated, and divided by the number of dimensions of the feature vector to obtain the average deviation. A normalized value is obtained by adding 1 to the average deviation to obtain the personalized adjustment coefficient. The baseline parameter set is then corrected using the personalized adjustment coefficient. The baseline pressure value is multiplied by the adjustment coefficient to obtain the pressure threshold suitable for the user; the baseline response time is divided by the adjustment coefficient to obtain a faster or slower response speed parameter; and the baseline touch radius is multiplied by the adjustment coefficient to obtain a larger or smaller touch area parameter. The output is a touch sensitivity adjustment parameter containing the pressure threshold, response speed parameter, and touch area parameter.

[0064] Specifically, the construction of the five-dimensional feature vector reflects the multi-dimensional characteristics of user touch behavior.

[0065] Specifically, the mean offset reflects the systematic bias of user touch. For example, if a user's historical average offset is 3.2 pixels, it indicates that their touch point is generally biased to the right. The standard deviation of the offset measures the stability of touch; users with a standard deviation of 1.5 have more stable touches than those with a standard deviation of 5.0. The mutation frequency is calculated by counting the number of times the pressure change exceeds a threshold per minute; frequent mutations indicate that the user tends to perform rapid clicks. The mutation amplitude is the average of the pressure difference across all mutation events, reflecting the intensity of the user's force application. The tilt angle percentage records the frequency of occurrence in three intervals: 0-15 degrees, 15-30 degrees, and above 30 degrees, forming a user posture preference profile.

[0066] It's important to note that the application of K-means clustering in touch behavior classification is based on the natural grouping of user behavior. The algorithm first randomly selects three initial cluster centers, representing light-touch, standard, and heavy-pressure users, respectively. In each iteration, the Euclidean distance from all user feature vectors to each cluster center is calculated, assigning the user to the nearest category. Then, the mean of all user feature vectors within each category is recalculated as the new cluster center. After multiple iterations, the cluster centers stabilize, forming a clear user type division. Light-touch users typically exhibit a combination of low-pressure, high-frequency, and small-biased features.

[0067] In one possible implementation, the parameter mapping relationship is designed to consider the operational characteristics of different user types. For light-touch users, the baseline pressure threshold is set to a low value, such as 100 units, because these users are accustomed to gentle touches; the response time is set to a short value, such as 30 milliseconds, to meet their need for fast operation; and the touch radius is set to a small value, such as 20 pixels, to suit their precise clicking characteristics. Conversely, for heavy-touch users, the baseline pressure threshold can reach 300 units, the response time is extended to 80 milliseconds to filter accidental touches, and the touch radius is expanded to 40 pixels to accommodate their larger contact area. The calculation of personalized adjustment coefficients achieves fine-grained adaptation from category to individual.

[0068] For example, a user is classified as standard type with a feature vector of [2.5, 1.8, 3.2, 180, 0.7], while the cluster centers for standard type are [2.0, 2.0, 3.0, 200, 0.6]. The absolute values ​​of the differences in each dimension are 0.5, 0.2, 0.2, 20, and 0.1, respectively. Normalization is required to make the data from different dimensions comparable. After normalization, summing the results and dividing by 5 yields an average deviation of 0.15, resulting in an adjustment factor of 1.15. This means that this user is slightly more prone to the heavy-pressure type compared to the standard type.

[0069] Preferably, the parameter correction process ensures personalized optimization of the touch experience. Multiplying the standard reference pressure of 200 by 1.15 yields a pressure threshold of 230, enabling the system to accurately identify the user's valid touch. The response time of 50 milliseconds divided by 1.15 yields 43 milliseconds, reducing accidental touches while maintaining responsiveness. The touch radius of 30 pixels multiplied by 1.15 yields 34.5 pixels, expanding the effective touch area. This dynamic adjustment mechanism allows each user to obtain touch parameters best suited to their operating habits, significantly improving touch accuracy and comfort.

[0070] Step S107: Generate adaptive guidance animation data based on the current interaction scenario requirements. Combined with touch sensitivity adjustment parameters, generate interactive response data by optimizing the animation playback rhythm and the priority order of user operation response. When the user completes the touch operation, the system maintains the current screen display state.

[0071] Based on the current interaction scenario, the system identifies the user's current operation stage, obtains the preset interface animation duration and animation type for that stage, determines the total animation playback duration based on the number of interface elements, and combines the animation type and total duration to generate guide animation data containing playback duration parameters. Based on the playback duration parameter in the guide animation data, and combined with the response speed value in the touch sensitivity adjustment parameters, the actual playback interval is obtained by dividing the playback duration by the response speed value. If the actual playback interval is less than the preset minimum interval, the minimum interval is used to determine the optimized animation playback rhythm. For the optimized animation playback rhythm, the time slice allocation ratio for user touch event processing is set higher than the time slice allocation ratio for animation rendering. The touch feedback intensity value is calculated based on the ratio of the pressure threshold in the touch sensitivity adjustment parameters to the actual touch pressure. The current brightness of the interface elements is obtained and multiplied by the touch feedback intensity value to obtain the target highlight brightness. The display attributes of the touched interface elements are updated by adjusting the target highlight brightness. The touch feedback intensity value is converted into a specific value within a preset vibration duration range. A semi-transparent circular confirmation icon is overlaid at the touch position. When the touch end signal is detected, the current display state of all interface elements is maintained, and interactive response data containing touch feedback intensity value, target highlight brightness and confirmation icon display parameters are output.

[0072] Specifically, recognizing the interactive scene is the foundation for generating appropriate guiding animations.

[0073] Specifically, when a user is in the application startup phase, the interface needs to display the brand logo and loading progress; the animation duration at this time is typically set to 2-3 seconds. During the function selection phase, users need to quickly browse options, so the animation duration is shortened to 0.5-1 second. The number of interface elements directly affects the animation complexity. For example, if the main interface contains 20 icons, the fade-in animation for each icon takes 50 milliseconds, for a total duration of 1 second. This dynamic adjustment based on the scenario avoids the monotony of fixed animations.

[0074] It's important to note that optimizing animation playback rhythm requires balancing smoothness and responsiveness. The responsiveness value reflects the user's operating habits; a fast-operating user might have a responsiveness value of 1.5, while a cautious user might only have 0.8. Personalized playback intervals can be obtained by dividing the playback duration by the responsiveness value.

[0075] For example, with an original playback duration of 1000 milliseconds and a response speed of 1.5, the actual interval would be 667 milliseconds. However, to prevent visual confusion caused by excessively fast animation, the system sets a minimum interval of 500 milliseconds, so 667 milliseconds is ultimately used as the playback interval.

[0076] In one possible implementation, a time-slice allocation mechanism ensures priority processing of touch responses. The system divides each rendering cycle into multiple time slices, with touch event processing occupying 70% of the time slices and animation rendering occupying 30%. This allocation ratio guarantees timely responses to user touch operations, even during complex animation playback. When a touch event is detected, the system immediately interrupts the current animation rendering, prioritizes touch logic processing, and resumes animation playback after processing is complete. The calculation of the touch feedback intensity value reflects the refined processing of pressure perception. When the user's actual touch pressure is 400 units, and their personalized pressure threshold is 200 units, the ratio is 2.0, indicating that the user has applied significant pressure. The system uses this ratio as the feedback intensity value to adjust various feedback effects. The current brightness of interface elements is typically between 0.6 and 0.8; multiplying this by the feedback intensity value of 2.0 results in a target highlight brightness range of 1.2 to 1.6, presenting a clear visual emphasis effect.

[0077] Preferably, the vibration feedback duration mapping adopts a piecewise linear approach. When the feedback intensity value is in the range of 0.5-1.0, the vibration duration is a slight feedback of 10-30 milliseconds; in the range of 1.0-2.0, it corresponds to a medium feedback of 30-80 milliseconds; and above 2.0, it provides a strong feedback of 80-150 milliseconds. The confirmation icon adopts a semi-transparent design with an opacity set to 0.7 and a diameter 1.5 times that of the touch area, ensuring that the user can clearly see the operation result. When the touch ends, the system maintains the screen state by locking the rendering buffer, avoiding unexpected interface changes and providing users with a stable and reliable interactive experience.

[0078] The above description is merely a specific implementation of this specification. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the scope of protection of this specification is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this specification, and these modifications or substitutions should all be covered within the scope of protection of this specification.

Claims

1.A method for processing a standby picture of a Micro LED display screen, characterized in that, The method comprises: When the user's finger contacts the surface of the Micro LED display screen, the standby picture is switched to the active picture, a touch response interface is displayed, multi-point touch data is acquired, the coordinate positions and timestamp information of each touch point are analyzed, the overlapping areas of different finger tracks are identified, and a track overlap analysis result is obtained; If the track overlap analysis result shows that the touch point tracks overlap, the overlapping tracks are separated by a clustering algorithm, the track overlap degree and separation confidence are calculated, and a touch track separation result is obtained; By analyzing the position density distribution of the touch points, adjacent touch points are identified and classified as the same finger operation, the overlapping degree and separation confidence between each track are calculated, and a touch track separation result is obtained; Combined with the pressure mutation detection result and the touch point coordinate change, the touch offset is calculated, the touch offset correction parameter is obtained, and the touch coordinate mapping relationship is adjusted; By combining the touch offset correction result, the pressure mutation detection result, and the finger posture feature parameter, the user's individual touch habits are modeled, and a touch sensitivity adjustment parameter is obtained; Based on the current interactive scene requirements, adaptive guide animation data is generated, the touch sensitivity adjustment parameter is combined, the animation playing rhythm and user operation response priority are optimized, interactive response data is generated, and the current picture display state is maintained. When the user's finger contacts the surface of the Micro LED display screen, the standby picture is switched to the active picture, a touch response interface is displayed, multi-point touch data is acquired, the coordinate positions and timestamp information of each touch point are analyzed, the overlapping areas of different finger tracks are identified, and a track overlap analysis result is obtained, comprising: 2.The method of claim 1, wherein, After detecting the touch signal, the pressure value and contact area of the initial contact point are recorded, the interface switching program is started, the rendering from the standby picture to the active picture is completed, and the corresponding touch response interface is loaded; according to the pressure value and contact area of the initial contact point, the sampling frequency is determined, the capacitance value change data of the touch point is acquired, the effective touch points are judged, the screen coordinate position and timestamp of each effective touch point are extracted, and a touch data sequence is formed; according to the timestamp continuity and coordinate space proximity of the touch data sequence, the touch points are classified by clustering method, the moving track curve of each group of touch points is fitted, and the track area boundary point set is calculated; by judging whether the boundary point set is in other track area, the overlapping area is identified, the geometric center coordinates and area value of the overlapping area are calculated, and a track overlap analysis result containing the overlapping area range is obtained. By analyzing the position density distribution of the touch points, adjacent touch points are identified and classified as the same finger operation, the overlapping degree and separation confidence between each track are calculated, and a touch track separation result is obtained, comprising: 3.The method of claim 1, wherein, ​ Obtain the two-dimensional coordinates of the touch points in the overlapping area, divide the display screen area by grid size, count the number of touch points in each grid, calculate the position density value, and form a position density distribution matrix; according to the position density distribution matrix, calculate the density difference value of each grid and its adjacent grid, mark the center of the density peak value, include the adjacent high-density grid in the same area, and obtain the touch point set of the high-density area; for the touch point set, calculate the Euclidean distance between touch points, establish a connection relationship, and classify the touch point groups through a search algorithm, each group corresponding to an independent trajectory; count the number of grids at the junction of the high-density area, calculate the spatial dispersion degree of the touch point group and the minimum distance between groups, obtain the overlapping degree and separation confidence, and output the touch trajectory separation result. 4.The method of claim 1, wherein, If the trajectory overlapping analysis result shows that the touch point trajectories overlap, separate the overlapping trajectories by clustering algorithm, calculate the trajectory overlapping degree and separation confidence, and obtain the touch trajectory separation result, including: Obtain the coordinate sequence of the touch points in the overlapping area, calculate the spatio-temporal distance value between touch points, and construct a similarity matrix; according to the similarity matrix, determine the number of clusters, group the touch points by clustering algorithm, and output the clustering label and center coordinates; calculate the average distance of the touch points corresponding to the clustering label to the clustering center and the minimum distance between the clustering centers, and obtain the separation confidence; according to the separation confidence, combine the touch point sequences with the same clustering label, calculate the trajectory overlapping degree, and output the touch trajectory separation result. 5.The method of claim 1, wherein, The combination of the pressure mutation detection result and the touch point coordinate change, the calculation of the touch offset, the obtaining of the touch offset correction parameter, and the adjustment of the touch coordinate mapping relationship, including: Obtain the pressure value sequence, calculate the pressure difference value of adjacent time, mark the pressure mutation point, record the timestamp and pressure change amplitude; extract the coordinates of the pressure mutation point, calculate the coordinate offset vector, and calculate the weighted offset vector combined with the pressure change amplitude; for the weighted offset vector, calculate the average value of the horizontal and vertical components, and obtain the touch offset; according to the touch offset, adjust the touch coordinates, and generate the corrected coordinate mapping relationship. 6.The method of claim 1, wherein, The touch offset correction result, the finger contact angle and the contact area change, the identification of the user touch gesture mode, the extraction of the finger gesture feature parameter, and the generation of the user touch gesture data, including: Through the touch offset correction result, obtain the corrected coordinate sequence, calculate the minimum circumscribed ellipse of the touch area boundary point, extract the length of the major axis and the minor axis and the included angle of the major axis, count the number of touch area pixels, and obtain the contact angle sequence; according to the contact angle sequence and the pressure value, calculate the pressure barycenter position, and determine the finger tilt angle; according to the finger tilt angle, classify the touch gesture, and generate the user touch gesture data containing the gesture feature parameter. 7.The method of claim 1, wherein, The combination of the touch offset correction result, the pressure mutation detection result and the finger gesture feature parameter, the modeling of the user's personalized touch habit, and the obtaining of the touch sensitivity adjustment parameter, including: According to the touch offset correction result, an offset statistical value is calculated, a mutation frequency and amplitude are extracted from the pressure mutation detection result, a tilt angle distribution of the finger posture feature parameter is counted, and a feature vector is generated; the feature vector is clustered to determine a touch habit category; according to the touch habit category, a parameter mapping relationship is queried, a reference parameter is corrected, and a touch sensitivity adjustment parameter is output. 8.The method of claim 1, wherein, The adaptive guide animation data is generated based on the current interactive scene requirement, the touch sensitivity adjustment parameter is combined, the animation playing rhythm and user operation response priority are optimized, and interactive response data is generated, including: An operation phase of an interactive scene is identified, guide animation data containing animation length and type is generated, an animation playing interval is determined according to the guide animation data and the touch sensitivity adjustment parameter, a touch event priority is set, a touch feedback intensity is calculated, an interface element brightness is updated, a confirmation icon is displayed, and interactive response data is output.

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