Method and system for simulating laser irradiation movement of electronic whiteboard large screen

Abstract

The invention relates to the technical field of electronic whiteboards, in particular to a method and a system for simulating laser irradiation movement of an electronic whiteboard large screen. The method comprises the following steps: monitoring a touch event, recording real-time data of a touch point, predicting the touch point by using a prediction algorithm, and generating a Bezier curve; adjusting the transparency, the width and the color of the Bezier curve in real time through a dynamic adjustment algorithm by utilizing the real-time data of the touch point, and drawing and simulating a laser track effect on a temporary layer canvas; gesture recognition is carried out through a machine learning algorithm, and different drawing modes are switched; and removing the outdated Bezier curve by using an optimization algorithm every preset time interval, and intelligently emptying the temporary layer canvas and the stored Bezier curve when detecting that the hand of the user is raised. According to the invention, high-precision simulation and intelligent management of the laser track in the large screen of the electronic whiteboard are realized, and the interactive experience and the operation efficiency of a user when the user uses the electronic whiteboard are greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of electronic whiteboards, and in particular to a method and system for simulating laser irradiation and movement of a large-screen electronic whiteboard. Background Art

[0002] As an indispensable device in modern education and conference scenarios, electronic whiteboards provide an intuitive and interactive way of display and communication. Traditional electronic whiteboards mainly rely on touch technology to achieve human-computer interaction by touching the screen surface. However, this method has certain limitations, especially when operating on a large screen, users may encounter problems such as insufficient touch accuracy and limited touch range.

[0003] Laser pointers are a common demonstration tool that is widely used in teaching, lectures, and meetings due to their precise pointing and ease of use. Introducing the advantages of laser pointers into electronic whiteboards can effectively improve the user's interactive experience. However, the prior art lacks a method and system that can simulate laser irradiation and track its movement in real time.

[0004] Electronic whiteboards currently on the market usually detect touch positions through technologies such as infrared, ultrasonic or electromagnetic induction, but these methods still have room for improvement in accuracy and response speed. The following problems still exist: In the prior art, laser trajectory simulation is usually based on simple line drawing, which cannot accurately reflect the real-time changes of touch points, resulting in the laser trajectory effect not being smooth and natural enough, and the user experience is poor; the trajectory drawn in the prior art is usually fixed, and the transparency, width and color of the trajectory cannot be dynamically adjusted according to actual needs, resulting in a single laser trajectory effect, which is difficult to meet the needs of different scenarios; the prior art mostly relies on manual operation of the user to switch the drawing mode, lacks intelligent gesture recognition function, resulting in cumbersome operations for users when switching the drawing mode, affecting the convenience and interactivity of use. Summary of the invention

[0005] To solve the above problems, the present invention provides a method and system for simulating laser irradiation movement on a large electronic whiteboard screen, which solves the problem of how to achieve touch detection with higher accuracy and response speed on the electronic whiteboard, and provides a smooth and natural laser trajectory that can dynamically adjust the transparency, width and color. At the same time, the problem of switching drawing modes is simplified through the intelligent gesture recognition function, and high-precision simulation and intelligent management of laser trajectories on the large electronic whiteboard screen are achieved, which greatly improves the user's interactive experience and operating efficiency when using the electronic whiteboard.

[0006] To achieve the above object, the technical solution adopted by the present invention is:

[0007] A method for simulating laser irradiation and movement of an electronic whiteboard large screen comprises the following steps:

[0008] S1: monitor touch events and record real-time data of touch points, use prediction algorithms to predict touch points, and generate Bezier curves;

[0009] S2: using the real-time data of the touch point, adjusting the transparency, width and color of the Bezier curve in real time through a dynamic adjustment algorithm, and drawing on a temporary layer canvas to simulate the laser track effect;

[0010] S3: Perform gesture recognition through machine learning algorithms and switch between different drawing modes;

[0011] S4: At preset time intervals, outdated Bezier curves are removed using an optimization algorithm, and the temporary layer canvas and stored Bezier curves are intelligently cleared when the user raises his hand.

[0012] Furthermore, the real-time data includes the coordinates, time, speed and pressure of the touch point.

[0013] Furthermore, the conditional judgment of the touch event is as follows:

[0014] If the touch event is a moving touch event, record the touch point data and go to the next step;

[0015] If the touch event is not a moving touch event, continue to monitor the touch event.

[0016] Furthermore, it also includes using multi-threading technology to separate touch event processing and Bezier curve drawing operations.

[0017] Furthermore, it also includes a trajectory cache for allowing the user to replay the movement process of the laser trajectory within a certain period of time.

[0018] Furthermore, the step S1 includes the following steps:

[0019] S11: monitor touch events, obtain the coordinates and timestamp of the initial touch point, and record the initial speed and pressure value of the touch point;

[0020] S12: using a Kalman filter algorithm to smooth the touch point data captured in real time;

[0021] S13: predicting the next position of the touch point based on a Gaussian process regression prediction model;

[0022] S14: using the predicted touch point position as a control point and generating a Bezier curve according to historical data of multiple touch points;

[0023] S15: The generated Bezier curve data is stored in a cache, and the historical data and prediction results of the touch points are synchronously updated to the cache.

[0024] Furthermore, the formula of the Bezier curve is as follows:

[0025] B(t)=(1-t) 3 P0+3(1-t) 2 tP1+3(1-t)t 2 P2+t 3 P3;

[0026] Wherein, B(t) represents the predicted position of the touch point at time t; t represents a parameter in the range of [0,1], which is used to represent the proportion of any point on the curve; P0 represents the initial position of the touch point; P1 represents the predicted point calculated by the velocity and pressure of the initial touch point; P2 represents the intermediate control point obtained by historical data and Gaussian process regression prediction model; P3 represents the coordinates of the current touch point.

[0027] Furthermore, the formula of the Gaussian process regression prediction model is as follows:

[0028]

[0029] in, represents the predicted touch point position at time t; k * represents the kernel function value vector between the current touch point) and the historical touch points; K represents the kernel function value matrix between the historical touch points; represents the measurement noise in the touch point data; I represents the identity matrix; y represents the coordinates of the historical touch point.

[0030] Furthermore, the step S2 includes the following steps:

[0031] S21: receiving real-time data of the touch points, and comprehensively processing the features of the touch points through a multi-dimensional data fusion algorithm;

[0032] S22: Use a dynamic adjustment algorithm based on a physical model to calculate the transparency, width, and color changes of the Bezier curve in real time according to the speed and pressure of the touch point;

[0033] S23: Draw the updated Bezier curve on the temporary layer canvas using GPU acceleration technology, and use the gradient effect to simulate the laser trajectory.

[0034] Furthermore, the step S22 includes the following steps:

[0035] S221: dynamically adjusting the transparency of the Bezier curve using a weighted average algorithm according to the speed of the touch point, so that the track is more transparent when moving quickly and clearer when moving slowly;

[0036] S222: According to the pressure of the touch point, the width of the Bezier curve is adjusted through a nonlinear mapping function. The greater the pressure, the thicker the line, and vice versa.

[0037] S223: Based on the historical data of the touch point, adjust the color of the Bezier curve using a color interpolation algorithm.

[0038] Furthermore, the step S3 includes the following steps:

[0039] S31: capturing and identifying the gesture trajectory of the touch point in real time, and generating a gesture feature vector;

[0040] S32: using a support vector machine algorithm to classify the gesture feature vector and identify the user's gesture type;

[0041] S33: switching to a corresponding drawing mode according to the recognized gesture type, including a laser pen mode, a brush mode, an eraser mode, and a selection mode;

[0042] S34: After each gesture switching, record the current drawing mode, and adjust the drawing parameters and effects according to the selected mode.

[0043] A system for simulating laser irradiation and movement of an electronic whiteboard large screen, comprising a touch event processing module, a dynamic adjustment module, a gesture recognition module and an optimization processing module which are sequentially connected in communication;

[0044] The touch event processing module is used to monitor and record real-time data of touch events, and generate a Bezier curve using a prediction algorithm;

[0045] The dynamic adjustment module is used to dynamically adjust the transparency, width and color of the Bezier curve according to the real-time data of the touch point, and draw it on the temporary layer canvas to simulate the laser track effect;

[0046] The gesture recognition module is used to perform gesture recognition through a machine learning algorithm and switch different drawing modes according to the recognition results;

[0047] The optimization processing module is used to regularly remove outdated Bezier curves, and intelligently clear the temporary layer canvas and stored Bezier curves when it is detected that the user raises his hand.

[0048] The beneficial effects of the present invention are:

[0049] The present invention uses a prediction algorithm to predict the touch points by monitoring touch events and recording the real-time data of the touch points, and generates a Bezier curve. This ensures that users can get a real-time and smooth operation experience when using the electronic whiteboard, reducing delays and freezes. Using the real-time data of the touch points, the transparency, width and color of the Bezier curve are adjusted in real time through a dynamic adjustment algorithm, and drawn on a temporary layer canvas to simulate the laser track effect. This not only improves the user's visual experience, but also makes the laser track more realistic and vivid, and enhances interactivity. Gesture recognition is performed through a machine learning algorithm, and different drawing modes can be automatically switched. This improves the intelligence of the system, and users can easily switch functions through different gestures, which improves the convenience and diversity of operation. At every preset time interval, the optimization algorithm is used to remove outdated Bezier curves, and the temporary layer canvas and stored Bezier curves are intelligently cleared when the user raises his hand. This not only effectively reduces the occupation of system resources, but also keeps the picture neat and clear, and improves the stability and sustainability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 The present invention is a flowchart of a method for simulating laser irradiation and movement of a large-screen electronic whiteboard.

[0051] Figure 2 It is a flowchart of step S1 provided in an embodiment of the present invention.

[0052] Figure 3 The present invention is a module schematic diagram of a system for simulating laser irradiation and movement of an electronic whiteboard large screen. DETAILED DESCRIPTION

[0053] See also Figure 1-3 As shown, the present invention relates to a method and system for simulating laser irradiation movement of a large electronic whiteboard screen.

[0054] Example 1

[0055] A method for simulating laser irradiation and movement of an electronic whiteboard large screen comprises the following steps:

[0056] S1: Monitor touch events and record real-time data of touch points, use prediction algorithms to predict touch points, and generate Bezier curves; the real-time data includes coordinates, time, speed, and pressure of touch points.

[0057] It should be noted that the system enters the touch event monitoring mode and continuously monitors the touch events on the touch screen in a cyclic or event-driven manner. The monitored touch events include three types: touch down, touch move, and touch up.

[0058] When a touch event is detected, first determine the event type:

[0059] Touch down event: records the coordinates, time, pressure, and speed of the initial touch point (the initial speed is 0).

[0060] Touch move event: Continue to record real-time data of the touch point, including the coordinates, time, pressure, and speed of the touch point.

[0061] Touch end event (touch up): ends the recording of the current touch track and enters the subsequent processing steps.

[0062] For touch movement events, the system records the data of the touch points in real time:

[0063] Coordinates: The touch sensor is used to obtain the two-dimensional coordinates of the touch point on the screen.

[0064] Timestamp: Records the timestamp of the current touch point for subsequent speed calculation and trajectory prediction.

[0065] Pressure: Some advanced touch screens can detect touch pressure and record the pressure value of the touch point.

[0066] Speed: Calculate the moving speed of the touch point based on the coordinates and time difference between the two touch points.

[0067] The touch point data is stored in a queue or array for subsequent processing.

[0068] The touch event condition is as follows:

[0069] If the touch event is a moving touch event, record the touch point data and go to the next step;

[0070] If the touch event is not a moving touch event, continue to monitor the touch event.

[0071] The step S1 comprises the following steps:

[0072] S11: monitor touch events, obtain the coordinates and timestamp of the initial touch point, and record the initial speed and pressure value of the touch point;

[0073] S12: using a Kalman filter algorithm to smooth the touch point data captured in real time;

[0074] Specifically, the Kalman filter is initialized and the initial state is set to the initial touch point data, including the initial position, speed and pressure. When a touch move event is detected, new touch point data (coordinates, timestamp, pressure and speed) is recorded. The newly captured touch point data is input into the Kalman filter for smoothing to obtain smoothed touch point data.

[0075] S13: predicting the next position of the touch point based on a Gaussian process regression prediction model;

[0076] S14: using the predicted touch point position as a control point and generating a Bezier curve according to historical data of multiple touch points;

[0077] S15: The generated Bezier curve data is stored in a cache, and the historical data and prediction results of the touch points are synchronously updated to the cache.

[0078] Furthermore, the formula of the Bezier curve is as follows:

[0079] B(t)=(1-t) 3 P0+3(1-t) 2 tP1+3(1-t)t 2 P2+t 3 P3;

[0080] Wherein, B(t) represents the predicted position of the touch point at time t; t represents a parameter in the range of [0,1], which is used to represent the proportion of any point on the curve; P0 represents the initial position of the touch point; P1 represents the predicted point calculated by the velocity and pressure of the initial touch point; P2 represents the intermediate control point obtained by historical data and Gaussian process regression prediction model; P3 represents the coordinates of the current touch point.

[0081] The formula of the Gaussian process regression prediction model is as follows:

[0082]

[0083] in, represents the predicted touch point position at time t; k * represents the kernel function value vector between the current touch point) and the historical touch points; K represents the kernel function value matrix between the historical touch points; represents the measurement noise in the touch point data; I represents the identity matrix; y represents the coordinates of the historical touch point.

[0084] S2: using the real-time data of the touch point, adjusting the transparency, width and color of the Bezier curve in real time through a dynamic adjustment algorithm, and drawing on a temporary layer canvas to simulate the laser track effect;

[0085] Wherein, the step S2 comprises the following steps:

[0086] S21: receiving real-time data of the touch points, and comprehensively processing the features of the touch points through a multi-dimensional data fusion algorithm;

[0087] Specifically, multiple features of the touch point (such as speed, pressure, and coordinates) are processed comprehensively, and a multi-dimensional data fusion algorithm (such as Kalman filtering, particle filtering, etc.) is used to improve the accuracy and reliability of the data.

[0088] Specific steps of multidimensional data fusion:

[0089] Data preprocessing: De-noise the touch point data to remove outliers and noise.

[0090] Feature extraction: Extract features from touch point data, including speed, pressure change rate, etc.

[0091] Fusion processing: Use fusion algorithm to comprehensively process multi-dimensional features to obtain more accurate touch point features.

[0092] S22: Use a dynamic adjustment algorithm based on a physical model to calculate the transparency, width, and color changes of the Bezier curve in real time according to the speed and pressure of the touch point;

[0093] S23: Draw the updated Bezier curve on the temporary layer canvas using GPU acceleration technology, and use the gradient effect to simulate the laser trajectory.

[0094] Specifically, the parallel computing capability of GPU is used to accelerate the drawing process of Bezier curves, improve drawing efficiency and real-time performance. GPU acceleration is achieved through graphics libraries such as OpenGL, WebGL or DirectX.

[0095] Draw the updated Bezier curve in real time on the temporary layer canvas to ensure that the drawing result is synchronized with the changes in touch data. The drawing steps are as follows:

[0096] Get the control points and parameters of a Bezier curve.

[0097] The GPU calculates each point of the Bezier curve and applies dynamically adjusted transparency, width, and color.

[0098] During the drawing process, a gradient effect is applied to simulate the laser trail.

[0099] Gradient effects can be achieved through color gradient, transparency gradient, etc. to enhance the visual effect.

[0100] The step S22 comprises the following steps:

[0101] S221: dynamically adjusting the transparency of the Bezier curve using a weighted average algorithm according to the speed of the touch point, so that the track is more transparent when moving quickly and clearer when moving slowly;

[0102] Specifically, the moving speed of the touch point is calculated in real time, and the transparency of the Bezier curve is adjusted according to the speed data. The speed calculation formula is as follows:

[0103]

[0104] Where v represents the moving speed of the touch point; x1, y1 and x2, y2 represent the coordinates of the two touch points before and after; t1 and t2 represent the timestamps of the two touch points before and after;

[0105] Use a weighted average algorithm to adjust transparency based on speed. When moving quickly, transparency is lower, and when moving slowly, transparency is higher. The transparency calculation formula is as follows:

[0106]

[0107] Among them, α represents transparency; k represents the adjustment coefficient; v represents the current speed; and v0 represents the reference speed.

[0108] S222: According to the pressure of the touch point, the width of the Bezier curve is adjusted through a nonlinear mapping function. The greater the pressure, the thicker the line, and vice versa.

[0109] Specifically, the pressure data of the touch point is acquired in real time, and the width of the Bezier curve is adjusted according to the pressure.

[0110] Use a nonlinear mapping function to adjust the line width based on the pressure data. The greater the pressure, the thicker the line; the smaller the pressure, the thinner the line. The width calculation formula is as follows:

[0111]

[0112] Where w represents the line width; w min and w max They represent the minimum and maximum values ​​of the line width respectively; p represents the current pressure; p min and p max Indicates the minimum and maximum values ​​of pressure;

[0113] S223: Based on the historical data of the touch point, adjust the color of the Bezier curve using a color interpolation algorithm.

[0114] Specifically, collect historical data of touch points and analyze the trend of color changes. Use a color interpolation algorithm to adjust the color of the Bezier curve based on historical data. The color interpolation formula is as follows:

[0115] C(t)=C0(1-t)+C1t

[0116] Where C(t) represents the current color; C0 and C1 represent the starting color and ending color of the interpolation; t represents the interpolation parameter, which ranges from [0,1].

[0117] In one embodiment, the operation details of drawing a Bezier curve are as follows: obtaining a temporary layer canvas; traversing the linked list in reverse order and drawing the remaining Bezier curves on the temporary canvas; adjusting the transparency and width of the curve according to its position in the linked list; and adjusting the color of the laser track according to changes in touch speed and pressure.

[0118] S3: Perform gesture recognition through machine learning algorithms and switch between different drawing modes;

[0119] Wherein, the step S3 comprises the following steps:

[0120] S31: capturing and identifying the gesture trajectory of the touch point in real time, and generating a gesture feature vector;

[0121] Specifically, the system uses the touch sensor module to capture the user's touch point trajectory on the electronic whiteboard in real time. The captured data includes information such as the coordinates, timestamp, pressure, and speed of the touch point. The captured touch point trajectory is preprocessed to remove noise and abnormal points. Gesture features are extracted, such as gesture path length, direction change, speed change, pressure change, etc. The extracted feature data is combined into a gesture feature vector for subsequent gesture recognition.

[0122] S32: using a support vector machine algorithm to classify the gesture feature vector and identify the user's gesture type;

[0123] Specifically, the generated gesture feature vector is input into the support vector machine (SVM) model for classification. The SVM model is pre-trained to recognize common gesture types, such as single-finger drag, two-finger zoom, rotation, etc. The SVM model classifies the input gesture feature vector and outputs the recognition result, i.e., the user's gesture type. Common gesture types include laser pointer mode gesture, brush mode gesture, eraser mode gesture, and selection mode gesture.

[0124] S33: switching to a corresponding drawing mode according to the recognized gesture type, including a laser pen mode, a brush mode, an eraser mode, and a selection mode;

[0125] Specifically, the user's gesture type is determined based on the output results of the SVM model. For example, if the recognition result is "single-finger drag", it is determined to be a laser pen mode gesture; if the recognition result is "two-finger zoom", it is determined to be a brush mode gesture. The system switches to the corresponding drawing mode based on the recognized gesture type. Functional description of each drawing mode:

[0126] Laser pointer mode is used to simulate the trajectory of a laser pointer and is suitable for demonstrations and instructions.

[0127] The brush mode is used to draw lines of different thicknesses and is suitable for writing and drawing.

[0128] Eraser mode is used to erase previously drawn tracks or contents.

[0129] Selection mode is used to select and move elements on the canvas.

[0130] S34: After each gesture switching, record the current drawing mode, and adjust the drawing parameters and effects according to the selected mode.

[0131] Specifically, after each gesture switch, the system records the current drawing mode for subsequent processing and state management. The recorded information includes the current mode, switch time, touch point data, etc. According to the current drawing mode, the system adjusts the drawing parameters and effects to ensure that the drawing behavior meets user expectations.

[0132] It should be noted that the drawing parameters of different modes are adjusted as follows:

[0133] Laser Pointer Mode: Set a higher transparency and color brightness to simulate a laser effect.

[0134] Brush Mode: Adjust line width based on pressure, and line smoothness based on speed.

[0135] Eraser mode: Set the transparency to 0 to simulate the erasing effect.

[0136] Selection Mode: Enables the Select and Move tools, allowing the user to select and manipulate elements on the canvas.

[0137] S4: At preset time intervals, outdated Bezier curves are removed using an optimization algorithm, and the temporary layer canvas and stored Bezier curves are intelligently cleared when the user raises his hand.

[0138] It should be noted that the system sets a preset time interval, such as 100 milliseconds. This time interval is used to regularly check and remove obsolete Bezier curves to ensure the real-time and smoothness of trajectory display. Obsolete Bezier curves are those that are no longer needed to be displayed at the current time point. Usually, the timestamps of these curves have exceeded a certain threshold (such as 2 seconds) compared to the current time.

[0139] Specifically, an optimization algorithm is used, such as a FIFO (first in, first out) queue or a timestamp-based cleanup algorithm: the Bezier curves are stored in the queue in the order of their generation time, and the earliest generated curve is removed each time it is checked. The stored list of Bezier curves is traversed, and the timestamp of each curve is checked. If the difference between the timestamp of a curve and the current time exceeds a set threshold, the curve is removed. At every preset time interval, the system automatically performs a cleanup operation: check the timestamps of the stored Bezier curves. Remove all Bezier curves that exceed the set time threshold. In this way, the system can maintain efficient resource utilization and prevent memory overflow or performance degradation.

[0140] The system detects the user's touch end event (touch up) by monitoring the touch sensor.

[0141] When the user's hand-raising action is detected, that is, the touch end event occurs, the system enters the clear mode.

[0142] After detecting a touch end event, the system performs the following actions:

[0143] Clear all drawn content on the temporary layer canvas to prepare for the next touch event.

[0144] Clears the queue or list used to store Bezier curves to ensure that no old trajectories remain.

[0145] To optimize the user experience, the system can use an intelligent clearing strategy: after detecting the touch end event, wait for a short period of time (for example, 500 milliseconds) before performing the clearing operation. This can prevent the loss of tracks caused by accidental touches. When multiple touch points are detected to be lifted at the same time, the clearing operation is performed. This strategy is suitable for multi-user or multi-touch environments. When clearing the temporary layer canvas, the user can be prompted through the interface (for example, through flashing or fading effects) to let the user know clearly that the track has been cleared. Prompts can enhance the user's understanding of system behavior and improve the user experience.

[0146] It also includes the use of multi-threading technology to separate touch event processing and Bezier curve drawing operations.

[0147] In one embodiment, multi-threading technology is used to separate the operations of different functional modules into different threads to improve the response speed and processing efficiency of the system.

[0148] Main thread: responsible for the overall coordination and management of the system, including thread creation and scheduling.

[0149] Touch event processing thread: dedicated to capturing and processing touch events to ensure real-time response to touch events.

[0150] Bezier curve drawing thread: responsible for drawing Bezier curves on the temporary layer canvas to ensure that the drawing process does not affect the capture and processing of touch events.

[0151] Touch event processing thread: When the system starts, a touch event processing thread is created and starts to monitor touch events on the touch screen. It continuously monitors touch events (including touch start, touch move, and touch end). When a touch event is detected, the event data (including the coordinates of the touch point, time, pressure, etc.) is recorded. The touch event processing thread will pass the recorded data to the Bezier curve drawing thread for subsequent processing.

[0152] Bezier curve drawing thread: When the system starts, a Bezier curve drawing thread is created and enters a waiting state, waiting for data passed by the touch event processing thread. After receiving the data passed by the touch event processing thread, a prediction algorithm is used to generate a Bezier curve. According to the real-time data of the touch point, the transparency, width and color of the Bezier curve are adjusted through a dynamic adjustment algorithm. The adjusted Bezier curve is drawn on the temporary layer canvas.

[0153] Use thread synchronization techniques (such as mutex locks or semaphores) to ensure that data transfer and processing between the touch event processing thread and the Bezier curve drawing thread are synchronized and conflict-free.

[0154] It also includes a trajectory cache that allows the user to replay the movement of the laser trajectory within a certain period of time.

[0155] In one embodiment, the trajectory cache module uses a queue or a double-ended queue (deque) data structure to store touch point data and generated Bezier curves. Each cache entry contains the coordinates, timestamp, pressure, speed, and generated Bezier curve of the touch point. The system sets a cache time window (for example, 5 seconds) and only stores the touch point data and Bezier curve within the time window. Data beyond the time window will be automatically removed to ensure that the amount of cached data is within a controllable range. After capturing a touch event, the touch event processing thread writes the touch point data into the trajectory cache. After generating the Bezier curve, the Bezier curve drawing thread also writes the generated curve data into the trajectory cache. When the user triggers the playback operation, the system reads the stored touch point data and Bezier curve from the trajectory cache and plays it back in chronological order. During playback, the system can redraw the Bezier curve on the temporary layer canvas to simulate the movement of the laser trajectory.

[0156] The user can trigger the track playback function through specific gestures or operations (such as double-click, long press, or sliding gestures). After the playback is triggered, the system pauses the processing of the current touch event and enters the track playback mode.

[0157] The system reads the touch point data and Bezier curves from the track cache in chronological order. Draws on the temporary layer canvas according to the cached timestamp to simulate the movement of the touch track. During playback, playback controls (such as pause, fast forward, slow play, etc.) can be provided to enhance the user experience. After playback, the system automatically returns to the normal touch event processing mode and continues to listen for and process new touch events.

[0158] Example 2

[0159] A system for simulating laser irradiation and movement of an electronic whiteboard large screen, comprising a touch event processing module, a dynamic adjustment module, a gesture recognition module and an optimization processing module which are sequentially connected in communication;

[0160] The touch event processing module is used to monitor and record real-time data of touch events, and generate a Bezier curve using a prediction algorithm;

[0161] The dynamic adjustment module is used to dynamically adjust the transparency, width and color of the Bezier curve according to the real-time data of the touch point, and draw it on the temporary layer canvas to simulate the laser track effect;

[0162] The gesture recognition module is used to perform gesture recognition through a machine learning algorithm and switch different drawing modes according to the recognition results;

[0163] The optimization processing module is used to regularly remove outdated Bezier curves, and intelligently clear the temporary layer canvas and stored Bezier curves when it is detected that the user raises his hand.

[0164] In this embodiment, a system for simulating laser irradiation and movement of a large electronic whiteboard screen is applied to a method for simulating laser irradiation and movement of a large electronic whiteboard screen described in Example 1, which will not be described in detail here.

[0165] In summary, the present invention uses the Kalman filter algorithm to smooth the touch point data, thereby improving the accuracy and reliability of the touch point data. Based on the Gaussian process regression prediction model, the touch point position is predicted, and the Bezier curve can be generated more accurately, thereby improving the smoothness and continuity of the touch trajectory. Through the multidimensional data fusion algorithm, the speed, pressure and other characteristics of the touch point are comprehensively processed, and the real-time dynamic adjustment of the transparency, width and color of the Bezier curve is realized, simulating the laser trajectory effect. By using GPU acceleration technology, the drawing efficiency of the Bezier curve is improved, ensuring the real-time and smoothness of the drawing process.

[0166] The present invention uses a support vector machine algorithm to classify gesture feature vectors, achieving accurate recognition of user gestures and rapid switching of corresponding drawing modes. According to the recognized gesture type, the system can intelligently switch between laser pen mode, brush mode, eraser mode and selection mode, providing multiple functions to adapt to different usage requirements. The optimization algorithm is used to regularly remove outdated Bezier curves to prevent memory overflow and ensure efficient use of system resources. When it is detected that the user raises his hand, the temporary layer canvas and stored Bezier curves are intelligently cleared to avoid the residue of old tracks and improve the user experience.

[0167] The present invention separates touch event processing and Bezier curve drawing operations through multi-threading technology, thereby improving the response speed and processing efficiency of the system. The main thread is responsible for the overall coordination and management of the system, the touch event processing thread is specifically used to capture and process touch events, and the Bezier curve drawing thread is responsible for drawing operations to ensure the collaborative work between the modules. The trajectory cache module allows users to replay the movement process of the laser trajectory within a certain period of time, provides a convenient backtracking function, and enhances the practicality and operability of the electronic whiteboard. Users can trigger the trajectory playback function through specific gestures or operations, and draw on the temporary layer canvas according to the cached timestamp, simulating the movement process of the touch trajectory, providing a rich interactive experience.

[0168] The above implementation modes are merely descriptions of the preferred implementation modes of the present invention, and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary engineering and technical personnel in the field shall fall within the protection scope determined by the claims of the present invention.

Claims

1. A method for simulating laser irradiation and movement of a large electronic whiteboard screen, characterized in that: The following steps are involved: S1: monitor touch events and record real-time data of touch points, use prediction algorithms to predict touch points, and generate Bezier curves; S2: using the real-time data of the touch point, adjusting the transparency, width and color of the Bezier curve in real time through a dynamic adjustment algorithm, and drawing on a temporary layer canvas to simulate the laser track effect; S3: Perform gesture recognition through machine learning algorithms and switch between different drawing modes; S4: At preset time intervals, outdated Bezier curves are removed using an optimization algorithm, and the temporary layer canvas and stored Bezier curves are intelligently cleared when the user raises his hand.

2. The method for simulating laser irradiation and movement of a large electronic whiteboard screen according to claim 1, characterized in that: The real-time data includes the coordinates, time, speed and pressure of the touch point.

3. The method for simulating laser irradiation and movement of a large electronic whiteboard screen according to claim 1, characterized in that: The conditional judgment of the touch event is as follows: If the touch event is a moving touch event, record the touch point data and go to the next step; If the touch event is not a moving touch event, continue to monitor the touch event.

4. The method for simulating laser irradiation and movement of a large electronic whiteboard screen according to claim 1, characterized in that: It also includes the use of multi-threading technology to separate touch event processing and Bezier curve drawing operations.

5. The method for simulating laser irradiation and movement of a large electronic whiteboard screen according to claim 1, characterized in that: It also includes a trajectory cache that allows the user to replay the movement of the laser trajectory within a certain period of time.

6. The method for simulating laser irradiation and movement of a large electronic whiteboard screen according to claim 1, characterized in that: The step S1 comprises the following steps: S11: monitor touch events, obtain the coordinates and timestamp of the initial touch point, and record the initial speed and pressure value of the touch point; S12: using a Kalman filter algorithm to smooth the touch point data captured in real time; S13: predicting the next position of the touch point based on a Gaussian process regression prediction model; S14: using the predicted touch point position as a control point and generating a Bezier curve according to historical data of multiple touch points; S15: The generated Bezier curve data is stored in a cache, and the historical data and prediction results of the touch points are synchronously updated to the cache.

7. The method for simulating laser irradiation and movement of a large electronic whiteboard screen according to claim 6, characterized in that: The formula of the Bezier curve is as follows: B(t)=(1-t) 3 P0+3(1-t) 2 tP1+3(1-t)t 2 P2+t 3 P3; Wherein, B(t) represents the predicted position of the touch point at time t; t represents a parameter in the range of [0,1], which is used to represent the proportion of any point on the curve; P0 represents the initial position of the touch point; P1 represents the predicted point calculated by the velocity and pressure of the initial touch point; P2 represents the intermediate control point obtained by historical data and Gaussian process regression prediction model; P3 represents the coordinates of the current touch point.

8. The method for simulating laser irradiation and movement of a large electronic whiteboard screen according to claim 6, characterized in that: The formula of the Gaussian process regression prediction model is as follows: in, represents the predicted touch point position at time t; k * represents the kernel function value vector between the current touch point) and the historical touch points; K represents the kernel function value matrix between the historical touch points; represents the measurement noise in the touch point data; I represents the identity matrix; y represents the coordinates of the historical touch point.

9. The method for simulating laser irradiation and movement of a large electronic whiteboard screen according to claim 1, characterized in that: The step S2 comprises the following steps: S21: receiving real-time data of the touch points, and comprehensively processing the features of the touch points through a multi-dimensional data fusion algorithm; S22: Use a dynamic adjustment algorithm based on a physical model to calculate the transparency, width, and color changes of the Bezier curve in real time according to the speed and pressure of the touch point; S23: Draw the updated Bezier curve on the temporary layer canvas using GPU acceleration technology, and use the gradient effect to simulate the laser trajectory.

10. The method for simulating laser irradiation and movement of a large electronic whiteboard screen according to claim 9, characterized in that: The step S22 comprises the following steps: S221: dynamically adjusting the transparency of the Bezier curve using a weighted average algorithm according to the speed of the touch point, so that the track is more transparent when moving quickly and clearer when moving slowly; S222: According to the pressure of the touch point, the width of the Bezier curve is adjusted through a nonlinear mapping function. The greater the pressure, the thicker the line, and vice versa. S223: Based on the historical data of the touch point, adjust the color of the Bezier curve using a color interpolation algorithm.

11. The method for simulating laser irradiation and movement of a large electronic whiteboard screen according to claim 1, characterized in that: The step S3 comprises the following steps: S31: capturing and identifying the gesture trajectory of the touch point in real time, and generating a gesture feature vector; S32: using a support vector machine algorithm to classify the gesture feature vector and identify the user's gesture type; S33: switching to a corresponding drawing mode according to the recognized gesture type, including a laser pen mode, a brush mode, an eraser mode, and a selection mode; S34: After each gesture switching, record the current drawing mode, and adjust the drawing parameters and effects according to the selected mode.

12. A system for simulating laser irradiation and movement of a large electronic whiteboard screen, characterized in that: The system is applied to a method for simulating laser irradiation movement of a large electronic whiteboard screen as described in any one of claims 1 to 11, comprising a touch event processing module, a dynamic adjustment module, a gesture recognition module and an optimization processing module which are sequentially communicatively connected; The touch event processing module is used to monitor and record real-time data of touch events, and generate a Bezier curve using a prediction algorithm; The dynamic adjustment module is used to dynamically adjust the transparency, width and color of the Bezier curve according to the real-time data of the touch point, and draw it on the temporary layer canvas to simulate the laser track effect; The gesture recognition module is used to perform gesture recognition through a machine learning algorithm and switch different drawing modes according to the recognition results; The optimization processing module is used to regularly remove outdated Bezier curves, and intelligently clear the temporary layer canvas and stored Bezier curves when it is detected that the user raises his hand.

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