Interactive algorithm visualization method and practical training platform for machine vision practical training

Abstract

The application discloses an interactive algorithm visualization method and a practical training platform for machine vision practical training, and relates to the field of education and practical training.The method comprises the following steps: in response to a click signal of a node, jumping to a corresponding algorithm practical training page, wherein the algorithm practical training page is realized based on algorithm knowledge point navigation, and each algorithm knowledge point is organized in the form of a node; in response to a parameter adjustment operation of a user through a visualization deduction control, triggering the recalculation of a machine vision algorithm in real time to obtain a calculation result; rendering the calculation result on an interface, and visually displaying the interface after rendering; and displaying perspective information of a machine vision algorithm operation process in the process of visual display.The application can effectively improve the teaching effect.

Description

Technical Field

[0001] This application relates to the field of educational training, and more particularly to an interactive algorithm visualization method and training platform for machine vision training. Background Technology

[0002] Currently, machine vision technology has become a core teaching content in vocational colleges' automation and intelligent manufacturing majors. Machine vision algorithms involve multiple technical aspects such as image processing, feature extraction, and target recognition. Their underlying principles rely on complex mathematical matrix operations, such as spatial filtering, morphological operations, and matrix affine transformations. The mathematical essence of these algorithms is quite abstract, involving the cross-integration of knowledge from multiple disciplines such as linear algebra and digital image processing, making them relatively difficult for vocational college students to understand.

[0003] In traditional machine vision training, instructors typically combine theoretical explanations with code demonstrations. Specifically, instructors use PowerPoint presentations to show the mathematical formulas and processing flow of algorithms, then demonstrate the algorithms by writing code using vision libraries such as OpenCV. Students, in this learning process, first need to understand the abstract mathematical principles, then master the programming language syntax, and finally become familiar with the API call methods of the vision library to truly implement the algorithm and verify its effectiveness. This teaching model exhibits a clear hierarchical progression, requiring students to overcome multiple knowledge hurdles before seeing the algorithm's practical effects.

[0004] Furthermore, existing machine vision teaching tools often employ a black-box approach. Students input the original image, and the system directly outputs the processed result image, while crucial information such as the internal computational process, intermediate state changes, and the impact of parameter adjustments on the result remains invisible. For example, during image filtering, students cannot observe the sliding process of the convolution kernel on the image, nor can they see in real-time the impact of different convolution kernel parameters on the filtering effect. This lack of transparency makes it difficult for students to develop a deep understanding of the algorithm's principles; they can only mechanically memorize the operational steps, failing to truly grasp the algorithm's essential rules and parameter tuning techniques.

[0005] In summary, in current machine vision training, students cannot intuitively observe the internal computational logic and intermediate state evolution of algorithms, leading to a disconnect between theory and practical operation, which seriously affects the teaching effect. Summary of the Invention

[0006] This application provides an interactive algorithm visualization method and training platform for machine vision training, which can effectively improve teaching results.

[0007] To achieve the above objectives, the embodiments of this application adopt the following technical solutions: Firstly, an interactive algorithm visualization method for machine vision training is provided and applied to a training platform. This method includes: In response to the click signal of the clicked node, the user is redirected to the corresponding algorithm training page. The algorithm training page is implemented based on the algorithm knowledge point navigation, and each algorithm knowledge point is organized in the form of nodes. In response to the parameter adjustment operation performed by the user through the visual inference control, the machine vision algorithm is recalculated in real time to obtain the calculation result. The visual inference control is a pre-defined machine vision algorithm encapsulated. The visual inference control includes a matrix affine transformation interactive calculation control, which is used to realize the bidirectional mapping of the coordinate system and convert the mouse screen coordinates of the user interface layer into the actual pixel coordinates of the underlying image matrix. The calculation results are rendered into an interface, and the rendered interface is then visualized. The visualization process displays perspective information about the machine vision algorithm's computational process.

[0008] In one possible implementation of the first aspect, the visualization simulation control also includes an image difference simulation control, a color space conversion simulation control, and a spatial filtering parameter simulation control. The image difference simulation control is used to provide multiple input source channels, read two original image matrices and perform absolute difference calculations, and use a split-screen layout to display the comparison results of the two original images and the difference result image. The color space conversion simulation control is used to display the color space conversion process and results in real time. The spatial filtering parameter simulation control is used to trigger parameter change events through the numerical adjustment component and re-call the underlying filtering function.

[0009] In another possible implementation of the first aspect, the matrix affine transformation interactive calculation control implements a bidirectional mapping of the coordinate system, including: Obtain the display size of the image display controls in the user interface layer and the actual size of the underlying image matrix; Calculate the scaling ratio based on the display size and the actual size; In response to user mouse actions, obtain the screen coordinates of the mouse in the user interface layer; Based on the scaling ratio, screen coordinates are converted into pixel coordinates of the underlying image matrix; The control points are captured based on the transformed pixel coordinates, and the affine transformation matrix is ​​calculated in real time based on the drag position of the control points. The calculated affine transformation matrix is ​​applied to the image matrix to generate the deformed image and render it, thus achieving bidirectional mapping of the coordinate system.

[0010] Another possible implementation of the first aspect also includes: Listen for and capture the user's continuous interaction sequence on the visualization simulation control, including mouse dragging and parameter sliding actions; Interaction event cleaning is performed on the continuous interactive action sequence to filter out invalid intermediate jitter events and extract key interaction frames; Based on key interaction frames, visual results are obtained and rendered.

[0011] In another possible implementation of the first aspect, interactive event cleaning is performed on the continuous interactive action sequence to filter out invalid intermediate jitter events and extract key interactive frames, including: Start the interaction buffer pool within the preset time window and obtain all interaction coordinate points within the time window; The motion trajectory of the interactive coordinate points is fitted using the least squares method to calculate the instantaneous speed and direction of the dragging. Extract the fitted coordinates at the end of the time window as key interaction frames, and generate an interaction instruction package containing the key interaction frames.

[0012] In another possible implementation of the first aspect, a visual result is obtained based on key interaction frames, and the visual result is rendered, including: Construct a communication channel between the user interface thread and the underlying algorithm thread, and include an interrupt flag in the communication channel; When a key interaction frame arrives, check if there is a computational task being executed in the underlying algorithm thread; If there is an ongoing computation task, activate the interrupt flag and send an interrupt signal to the context of the ongoing computation task. In response to an interrupt signal, the underlying operations of the currently executing computation task are forcibly terminated, and any unfinished intermediate computation data is discarded. After terminating the currently executing computation task, a new computation task is executed to obtain the visual results, and the visual results are rendered.

[0013] In another possible implementation of the first aspect, after terminating the computational task being executed, the method further includes: Before executing a new computational task, generate the memory operation pointer anchor point for the current original image matrix; When a computation task is interrupted, the memory pointer of the current image matrix object is intercepted; Compare memory pointers with memory operation pointer anchors and extract the memory block segments that have been modified; Perform a reverse differential overwrite operation on the memory block fragment to revert the image matrix state to the anchor point state before computation and reclaim temporary resources generated by the interrupted computation task.

[0014] In another possible implementation of the first aspect, a reverse differential overlay operation is performed on the memory block fragment to revert the image matrix state to the anchor point state before computation, including: Obtain the memory step offset during the execution of the computation task; Locate the modified memory region based on the memory step offset; Read raw memory data from the memory operation pointer anchor; The original memory data is overwritten into the modified memory area, completing the memory state rollback.

[0015] Secondly, this application provides a training platform, which includes a cloud server and a client. The client is used to send a request to the cloud server, and the cloud server is used to execute any of the operation steps described above according to the request.

[0016] Thirdly, this application provides a cloud server, including: Communication device, used to establish a communication connection with a user terminal; The processor is configured as follows: In response to the click signal of the clicked node, the user is redirected to the corresponding algorithm training page. The algorithm training page is implemented based on the algorithm knowledge point navigation, and each algorithm knowledge point is organized in the form of nodes. In response to the parameter adjustment operation performed by the user through the visual inference control, the machine vision algorithm is recalculated in real time to obtain the calculation result. The visual inference control is a pre-defined machine vision algorithm encapsulated. The visual inference control includes a matrix affine transformation interactive calculation control, which is used to realize the bidirectional mapping of the coordinate system and convert the mouse screen coordinates of the user interface layer into the actual pixel coordinates of the underlying image matrix. The calculation results are rendered into an interface, and the rendered interface is then visualized. The visualization process displays perspective information about the machine vision algorithm's computational process.

[0017] By constructing a node-based organizational structure based on algorithm knowledge point navigation, abstract machine vision algorithms are presented in a visual and modular way. This allows students to directly learn algorithms without first mastering complex programming languages ​​and underlying library APIs, lowering the practical threshold. Simultaneously, the visual deduction control encapsulates preset algorithms, enabling students to trigger algorithm recalculation in real time through parameter adjustments and immediately see the results, breaking the limitations of the traditional black-box model. The matrix affine transformation interactive calculation control achieves a bidirectional mapping between the user interface layer and the underlying image matrix, allowing mouse operations to directly correspond to pixel-level mathematical operations. Furthermore, the visualization process displays perspective information of the algorithm's calculation process in real time, allowing students to clearly observe the intermediate calculation steps, the continuous impact of parameter adjustments on the results, and the specific process of underlying matrix operations. This transforms the originally abstract mathematical principles into an intuitive and perceptible visual presentation, achieving an organic unity of algorithm principles, parameter effects, and practical operation. It effectively solves the technical defects of opaque algorithm calculation processes and the lack of visual deduction mechanisms, which prevent students from deeply understanding the essence of algorithms, significantly improving teaching effectiveness and resolving the disconnect between theory and practice in existing technologies.

[0018] Other features and advantages of the embodiments of this application will be described in detail in the following detailed description section. Attached Figure Description

[0019] Figure 1 A flowchart illustrating an interactive algorithm visualization method for machine vision training provided in this application embodiment; Figure 2 A schematic diagram illustrating the workflow of an interactive calculation control for matrix affine transformation provided in this application embodiment; Figure 3 This is a schematic diagram of the structure of a training platform provided in an embodiment of this application. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only for illustration and explanation of the embodiments of this application and are not intended to limit the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0021] It should be noted that if the embodiments of this application involve directional indicators (such as up, down, left, right, front, back, etc.), the directional indicators are only used to explain the relative positional relationship and movement of each component in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indicators will also change accordingly.

[0022] Furthermore, if the embodiments of this application involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, features defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the technical solutions of various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. If the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed in this application.

[0023] Figure 1 The illustration schematically shows a flowchart of an interactive algorithm visualization method for machine vision training according to an embodiment of this application. Figure 1 As shown in the figure, this application provides an interactive algorithm visualization method for machine vision training, which is applied to a training platform. The method may include the following steps.

[0024] S110. In response to the click signal of the clicked node, jump to the corresponding algorithm training page, where the algorithm training page is implemented based on the algorithm knowledge point navigation, and each algorithm knowledge point is organized in the form of nodes; S120. In response to the parameter adjustment operation performed by the user through the visual inference control, the machine vision algorithm is recalculated in real time to obtain the calculation result. The visual inference control is obtained by encapsulating the preset machine vision algorithm. The visual inference control includes a matrix affine transformation interactive calculation control. The matrix affine transformation interactive calculation control is used to realize the bidirectional mapping of the coordinate system and convert the mouse screen coordinates of the user interface layer into the actual pixel coordinates of the underlying image matrix. S130. Render the calculation results into an interface and visualize the rendered interface. S140. Display perspective information of the machine vision algorithm operation process during the visualization process.

[0025] In this embodiment, when a user opens multiple algorithm training pages simultaneously, the method further includes: Detect the number of currently active algorithm training pages and obtain the computational complexity level of the algorithm for each page; Based on the computational complexity level of the algorithm, a computational priority weight is assigned to each active page, with image convolution algorithms having a higher weight than geometric transformation algorithms. Monitor the available memory capacity and processor core utilization of the system, and calculate the current system resource reserve index; When the resource availability index is lower than the preset resource shortage threshold, the algorithm training page that is visible in the user's viewport is identified as the front-end page. Suspend the algorithm calculation tasks of non-front-end pages, freeze their memory state, and serialize and store them in a temporary cache area; Allocate full processor time slices and memory resources for the algorithm calculation tasks of the front-end page; When a user switches viewports, causing a change in the foreground page, the frozen tasks of the new foreground page are restored, while the tasks of the original foreground page are suspended. After the resource reserve index recovers to a safe level, the execution of all suspended tasks is resumed in sequence according to the calculation priority weight.

[0026] This technical solution avoids interface lag and calculation failure caused by system resource exhaustion through dynamic resource scheduling and task suspension and recovery mechanisms, ensuring a smooth interactive experience for users in multi-page training scenarios.

[0027] In addition, to further improve the teaching effectiveness, this embodiment may also include the following steps: During the process of users adjusting parameters through visual simulation controls, the parameter values ​​and timestamps corresponding to each parameter adjustment are recorded in real time to construct a parameter adjustment trajectory chain. For each parameter value in the parameter adjustment trajectory chain, the corresponding feature summary of the calculation result is saved. The feature summary includes the histogram distribution and edge density of the output image. In response to the user-triggered trajectory backtracking command, a timeline visualization control for the parameter adjustment trajectory chain is displayed on the algorithm training page; Receive the target time point selected by the user on the timeline visualization control; Extract historical parameter values ​​corresponding to the target time point from the parameter adjustment trajectory chain; The historical parameter values ​​are re-entered into the machine vision algorithm for calculation to obtain the replay calculation results; The replay calculation results are compared with the saved feature summaries to verify the accuracy of the replay. The algorithm training page displays the current calculation result and the replay calculation result side by side, and marks the areas of difference between the two. It responds to the user's continuous dragging on the timeline, enabling dynamic playback of the parameter evolution process.

[0028] The training platform in this embodiment can be a cloud-based online teaching system, locally deployed training software, or a blended learning platform.

[0029] In practical implementation, the training platform first constructs an algorithm knowledge graph based on node organization. Each node represents an independent machine vision algorithm knowledge point, such as Gaussian filtering, Canny edge detection, and Hough transform. Nodes are connected by directed edges, representing dependencies or learning paths between algorithms. For example, an edge detection node might be connected to an image smoothing node, as edge detection usually requires image denoising first. In terms of interface presentation, the knowledge graph is displayed as an interactive network graph, with nodes using different colors to indicate algorithm categories, such as blue for image enhancement, green for feature extraction, and orange for object recognition. When a user clicks on a node, the platform captures the click event and parses the node identifier, retrieving the corresponding algorithm training page path from a pre-loaded page mapping table. Page navigation adopts a single-page application architecture, achieving refresh-free navigation through dynamic routing switching to ensure the continuity of user operations. After navigation, the algorithm training page automatically loads the algorithm's theoretical explanation, parameter configuration panel, visualization deduction controls, and example image library. For example, when a user clicks the "Morphological Erosion" node, the page displays the mathematical definition of the erosion operation, a structuring element selector, a slider for the number of erosion iterations, and preset binary image samples. This node-based organization breaks the limitations of traditional linear textbooks, allowing students to flexibly choose their learning path based on their own knowledge base, while a visualized knowledge graph helps students build a holistic cognitive framework of the algorithm system.

[0030] After users enter the algorithm training page, the visual inference controls become the core interactive components. These controls are high-level encapsulations of the underlying machine vision algorithms, hiding the complex function calls and parameter passing processes behind an intuitive graphical interface.

[0031] Taking the matrix affine transformation interactive calculation control as an example, such as Figure 2 As shown, this control displays the original image on the interface and marks draggable control points at the four corners and center of the image. When the user drags a control point with the mouse, the control first obtains the pixel coordinates of the mouse on the screen, for example, screen coordinates (500, 300). Since the image displayed on the interface is usually scaled to fit the window size, the control needs to calculate the scaling ratio between the displayed image and the original image matrix.

[0032] Assuming the original image size is 800×600 pixels and the display control size is 400×300 pixels, the scaling factor is 0.5. The original image pixel coordinates (1000, 600) are obtained by dividing the screen coordinates by the scaling factor. The control maintains a control point mapping table, recording the initial and current positions of each control point. When a control point is dragged, the control calculates the affine transformation matrix based on the control point's displacement. The affine transformation matrix is ​​a 3×3 matrix, which can be represented as... Parameters a, b, c, and d control rotation and scaling. and Translation control. The control determines these parameter values ​​by solving a system of linear equations, ensuring that the transformed control point position matches the target position dragged by the user.

[0033] After the calculation is complete, the control calls the underlying image processing library to apply the affine transformation matrix to the original image matrix, generating the deformed image. The entire process is completed in milliseconds, and users can see the image rotate, scale, or tilt in real time while dragging the control points. This bidirectional mapping mechanism allows the user's intuitive operations to accurately correspond to the underlying mathematical operations, enabling complex geometric transformations to be performed without understanding the matrix representation of affine transformations.

[0034] In addition to the matrix affine transformation control, the platform also provides image difference derivation control, color space conversion derivation control, and spatial filtering parameter simulation control. The image difference derivation control allows users to load two images simultaneously. The control automatically calculates the absolute difference between corresponding pixels in the two images and displays the difference distribution in the form of a heatmap, helping students understand the principles of motion detection and change detection. The color space conversion derivation control provides real-time conversion between multiple color spaces such as RGB, HSV, and LAB. When users adjust the color space parameters, the control synchronously displays the component images of each channel and a 3D color space distribution map. The spatial filtering parameter simulation control allows users to customize the size and coefficients of the convolution kernel and observe in real-time the smoothing, sharpening, or edge enhancement effects of different filters on the image.

[0035] When a user adjusts parameters using the visual simulation controls, the platform immediately triggers a recalculation of the algorithm. Taking spatial filtering as an example, if a user adjusts the Gaussian filter kernel size from 3×3 to 5×5 using a slider, the controls capture this parameter change event and encapsulate the new parameter value into a calculation request. Upon receiving the request, the platform's algorithm scheduler checks if any computational tasks are currently in progress. If any incomplete tasks exist, the scheduler sends an interrupt signal to forcibly terminate the old tasks, preventing resource waste.

[0036] The scheduler then allocates an image buffer from the memory pool, copies the original image matrix to the buffer, and calls the underlying Gaussian filtering function. This function generates a Gaussian weight matrix based on the kernel size, and the weight calculation formula is as follows: ,in The standard deviation parameter is typically taken as 1 / 6 of the kernel size. The function iterates through each pixel of the image, convolving a neighborhood window centered on that pixel with the Gaussian kernel. This involves summing the products of the pixel values ​​and their corresponding weights within the neighborhood to obtain the filtered pixel values. After calculation, the resulting image matrix is ​​passed to the rendering module. The entire computation process employs multi-threaded parallel processing. For large images, the platform divides the image into blocks and allocates them to different processor cores for simultaneous computation, significantly improving computation speed.

[0037] For scenarios involving frequent parameter adjustments, the platform introduces an interaction event cleansing mechanism. When a user quickly drags the slider, the control generates a large number of parameter change events in a short period. If each event triggers a complete algorithm calculation, it would waste computing resources and cause interface lag. Therefore, the platform sets a 50-millisecond time window buffer to collect all parameter change values ​​within the window period. It then uses the least squares method to fit the parameter change trajectory and extracts the stable parameter values ​​at the end of the window as keyframes. Only the parameter values ​​corresponding to the keyframes trigger the actual algorithm calculation; jittery values ​​in between are filtered out and discarded. This mechanism ensures response speed while avoiding invalid calculations, allowing users to enjoy a smooth interactive experience even when quickly adjusting parameters.

[0038] After the calculation results are obtained, the rendering and display module is responsible for converting the numerical matrix into a visualized image. For grayscale images, the rendering module directly maps the pixel values ​​to grayscale levels of 0-255; for color images, it maps the RGB three-channel values ​​separately and then synthesizes them for display. During the rendering process, the module performs scaling interpolation on the image according to the size of the display controls. Commonly used interpolation algorithms include nearest neighbor interpolation, bilinear interpolation, and bicubic interpolation.

[0039] To ensure display quality, the platform uses bicubic interpolation by default. This method calculates interpolated pixel values ​​by fitting a cubic polynomial surface, effectively reducing jagged edges and blurring caused by scaling. The rendered image is plotted on the interface's image display area, and relevant statistical information panels are updated simultaneously, such as the image's mean, standard deviation, and histogram distribution. For certain algorithms, the platform also generates a comparison view, displaying the original and processed images side-by-side, and using dividing lines or overlay modes to help users observe the algorithm's effects. For example, in edge detection algorithms, the platform overlays detected edges as colored lines on the original grayscale image, making edge positions clearer and more discernible. The rendering module also supports local zoom-in functionality; users can select any area of ​​the image, and the system will zoom in on that area and display a pixel-level numerical matrix, helping students observe the algorithm's effect on local details.

[0040] During the visualization process, the perspective information generator displays the internal state of the algorithm's computation in real time. For convolution operations, the perspective information includes the current sliding position of the convolution kernel on the image, the element-wise product matrix of the convolution kernel and the image's neighborhood, and the summed output value. The platform displays the complete process of the convolution kernel scanning line by line from the top left corner of the image to the bottom right corner in animation, with each step labeled with the currently processed pixel coordinates and intermediate calculation results.

[0041] For morphological operations, perspective information displays the matching process between structural elements and the image, highlighting pixel areas that meet the erosion or dilation conditions with different colors. For color space conversion, perspective information displays the conversion formula from RGB to HSV and the specific calculation steps for each pixel. For example, the calculation of hue H involves comparing the RGB three-channel values ​​and performing inverse trigonometric function operations. The platform will gradually display the determination of the maximum and minimum values, as well as the angle calculation process.

[0042] The perspective information also includes algorithm performance metrics, such as computation time and memory usage for the current frame, as well as a theoretical analysis of algorithm complexity. Through these transparent process demonstrations, students can clearly understand the execution logic of each step of the algorithm, observe how parameter changes affect intermediate calculation results, and how they ultimately affect the output image, thus establishing a complete cognitive chain from mathematical principles to practical effects.

[0043] This embodiment constructs a node-based algorithm knowledge navigation system, organizing abstract machine vision algorithms in a modular and visual manner. This lowers the learning threshold for students, allowing them to directly engage in algorithm practice without prior mastery of complex programming languages. The visual derivation control encapsulates the underlying algorithm, enabling parameter adjustments to trigger calculations in real time and immediately present results, breaking the limitations of traditional black-box teaching. The matrix affine transformation interactive calculation control implements bidirectional coordinate mapping, allowing users' intuitive operations to precisely correspond to pixel-level mathematical operations, establishing an organic link between interface interaction and underlying calculations. The real-time display of perspective information fully presents the intermediate calculation steps of the algorithm, the parameter influence process, and the underlying matrix operation logic, transforming the originally abstract mathematical principles into an intuitive and perceptible visual presentation. Interactive event cleanup and task interruption recovery mechanisms ensure a smooth interactive experience in high-frequency parameter adjustment scenarios. Through the comprehensive application of these technologies, the problems of opaque algorithm processes and the disconnect between theory and practice in traditional teaching are effectively solved. This achieves a deep integration of algorithm principles, parameter effects, and practical operations, significantly improving the teaching effectiveness of machine vision training and the depth of students' understanding.

[0044] In one embodiment of this example, the visualization simulation control further includes an image difference simulation control, a color space conversion simulation control, and a spatial filtering parameter simulation control. The image difference simulation control is used to provide multiple input source channels, read two original image matrices and perform absolute difference calculations, and display the comparison results of the two original images and the difference result image using a split-screen layout. The color space conversion simulation control is used to display the color space conversion process and results in real time. The spatial filtering parameter simulation control is used to trigger parameter change events through the numerical adjustment component and re-call the underlying filtering function.

[0045] In this embodiment, to address the technical issue of the lack of a unified visualization and inference mechanism for different types of machine vision algorithms, the platform expands the type coverage of the visualization and inference control. The image difference inference control is specifically designed for motion detection and change analysis scenarios. This control provides two independent image loading areas on the interface, labeled "Reference Image" and "Current Image," respectively. When the user loads two images, the control first checks whether the sizes of the two images are consistent. If the sizes do not match, it automatically scales the second image to the same size as the first image.

[0046] The control then iterates through each pixel position of the image matrix, calculating the absolute difference between corresponding pixel values ​​of the two images. The formula for calculating the difference is as follows: ,in and These represent the pixel matrices of the two images. After calculation, the control uses a three-column split-screen layout: the reference image is displayed on the left, the current image in the middle, and the difference result image on the right. The difference result image uses pseudo-color mapping, displaying areas with smaller differences in blue and areas with larger differences in red, making the changed areas immediately apparent. This control directly solves the problem of students struggling to understand the principle of inter-frame difference. Through side-by-side comparison and difference visualization, students can clearly observe which areas have changed and the magnitude of those changes, thus gaining a deeper understanding of the underlying logic of moving target detection.

[0047] This color space conversion and derivation control provides a solution to abstract problems in color theory teaching. The control offers drop-down menus on the interface to select multiple color spaces, including RGB, HSV, LAB, and YCrCb. When the user selects to convert from RGB to HSV, the control first normalizes the RGB three-channel values ​​to the 0-1 range, and then calculates hue (H), saturation (S), and lightness (V). Hue calculation involves conditional judgments; when R is at its maximum value... ,in The maximum value in RGB. The minimum value is displayed. The control shows the conversion formula and the specific calculation steps for the current pixel in real time on the right side of the interface. For example, for the RGB value (180, 100, 50), the control will display: , , Simultaneously, the control displays each of the three channels of the converted HSV image as an independent grayscale image and plots the pixel's position in the HSV color space on a three-dimensional coordinate system. When the user drags the slider to adjust the value of a channel, the control recalculates and updates the display in real time, allowing students to intuitively understand the representation methods of different color spaces and their interconversion relationships. This directly solves the technical deficiency of opaque color space conversion processes, which forces students to memorize formulas without understanding their geometric meaning.

[0048] The spatial filtering parameter simulation control is specifically designed to address the challenge of intuitively understanding convolution filter parameter tuning. This control provides an editable convolution kernel matrix input panel, allowing users to directly modify the value of each element in the kernel. For example, for a 3×3 Laplacian sharpening kernel, users can adjust the center element from 8 to 9 and observe the change in sharpening intensity. The control also includes preset templates for commonly used filters such as Gaussian blur, mean filtering, and edge detection; users can load the corresponding kernel parameters by clicking the template button.

[0049] When the user modifies the kernel parameters using the numerical adjustment component, the control immediately captures the parameter change event and passes the new convolution kernel matrix to the underlying filtering function. The underlying function performs a convolution operation on the image and calculates the value of each output pixel. Where K is the convolution kernel and k is the kernel radius. The control displays the frequency domain response curve of the convolution kernel at the bottom of the interface, helping students understand the suppression or enhancement effect of different kernel parameters on frequency components. Through real-time parameter adjustment and immediate result feedback, students can quickly grasp the causal relationship between filter parameters and image effects, solving the inefficiency problem of traditional teaching methods that rely on experience and repeated trial and error for parameter tuning.

[0050] This embodiment addresses the specific visualization needs of different algorithm types in teaching by extending three dedicated visualization derivation controls: image differencing, color space conversion, and spatial filtering. The split-screen comparison layout and pseudo-color difference mapping of the image differencing control transform the principle of inter-frame change detection from an abstract formula into an intuitive visual presentation. The real-time formula display and 3D spatial positioning of the color space conversion control break down the abstract barriers of color theory and establish a direct link between mathematical representation and visual perception. The editable convolution kernel and frequency domain response visualization of the spatial filtering control transform the parameter tuning process from black-box trial and error to transparent derivation, significantly reducing the learning difficulty of filter design and effectively solving the problem of inconsistent teaching effectiveness caused by the lack of a unified visualization mechanism for different algorithms.

[0051] In one embodiment of this invention, the matrix affine transformation interactive calculation control implements bidirectional mapping of the coordinate system, including the following steps: S310. Obtain the display size of the user interface layer image display control and the actual size of the underlying image matrix; S320: Calculate the scaling ratio based on the display size and the actual size; S330: Responding to the user's mouse operation, obtain the screen coordinates of the mouse in the user interface layer; S340, Convert screen coordinates to pixel coordinates of the underlying image matrix based on the scaling ratio; S350: Capture control points based on the transformed pixel coordinates, and calculate the affine transformation matrix in real time based on the drag position of the control points; S360. Apply the calculated affine transformation matrix to the image matrix to generate the deformed image and render it, thus realizing bidirectional mapping of the coordinate system.

[0052] In this embodiment, the following steps may also be included for handling abnormal interaction behavior: When calculating the affine transformation matrix in real time based on the drag position of control points, the method also includes: Real-time monitoring of the motion trajectory of the control point dragged by the user, and calculation of the acceleration vector of the control point displacement between two adjacent frames; Determine whether the magnitude of the acceleration vector exceeds a preset abnormal acceleration threshold; When the magnitude of the acceleration vector exceeds the abnormal acceleration threshold, it is identified as a falsely triggered drag event; For accidental drag events, calculate the stable position of the control point before the accidental trigger. The stable position is the position where the displacement change rate of the most recent three consecutive frames is less than the stable threshold. The current position of the control point is rolled back to a stable position, and a visual feedback animation of the position rollback is displayed on the user interface. Recalculate the affine transformation matrix based on the control point positions after rollback; Detect the determinant value of the affine transformation matrix to determine whether the transformation will cause the image to degenerate into line segments or points; When the absolute value of the determinant is less than the preset degradation threshold, the affine transformation matrix is ​​rejected and an invalid operation message is displayed on the user interface.

[0053] In this embodiment, to address the issue of lost operational precision and mapping errors caused by the inconsistency in coordinate systems between user interface interactions and underlying image matrix calculations, the matrix affine transformation interactive calculation control implements a precise two-way coordinate mapping mechanism. This mechanism directly solves the technical deficiency in traditional teaching tools where users cannot accurately control the underlying matrix transformation parameters through intuitive drag-and-drop operations.

[0054] In practice, the control first obtains the display size of the image display control in the user interface layer. The display control is typically a rectangular area, with its width and height measured in screen pixels; for example, the size of the display control might be 640×480 pixels. Simultaneously, the control reads the actual size of the underlying image matrix, which represents the true resolution of the original image; for example, the actual size of a high-resolution image might be 1920×1080 pixels. Since the size of the display control is usually smaller than the original image size, the image needs to be scaled to be fully displayed on the interface. Obtaining these two sets of size data is fundamental to coordinate mapping. Only by clarifying the dimensional relationship between the display space and the data space can an accurate coordinate transformation function be established. This step directly solves the problem of inconsistent coordinate systems caused by the separation of interface display and data storage spaces, laying the foundation for subsequent precise mapping.

[0055] After obtaining the size information, the control calculates the scaling ratio from the display space to the data space. The scaling ratio has two components: horizontal and vertical. The calculation formula is as follows: and ,in and These are the width and height of the original image, respectively. and To display the width and height of the control. Taking the previous example, the horizontal scaling ratio is... Vertical scaling ratio is To maintain the image's aspect ratio, controls typically use the smaller of the two scaling factors as a uniform scaling factor, centering the image in the display area and filling the areas not covered by the image with the background color. The calculated scaling factor is stored in the control's internal state variable for subsequent coordinate transformations. This step solves the scaling distortion problem when images of different resolutions are displayed in a fixed-size control, ensuring geometric consistency in coordinate mapping and allowing user operations to accurately correspond to the pixel positions of the original image.

[0056] When a user interacts with the displayed image using the mouse, the control captures mouse events and obtains screen coordinates in real time. Mouse events consist of three phases: press, move, and release. The control records the initial coordinates when the mouse is pressed, continuously updates the current coordinates during movement, and confirms the final coordinates upon release. Screen coordinates are offsets relative to the top-left corner of the displayed control. For example, if a user clicks on a location in the displayed image, the obtained screen coordinates might be (320, 240). These coordinates are in display space and need to be converted to pixel coordinates in data space before operations can be performed on the underlying image matrix. The control achieves coordinate capture by registering mouse event listeners. These listeners record the mouse trajectory at a millisecond sampling frequency, ensuring that critical positional information is not lost even if the user moves the mouse quickly. This step solves the problem of real-time capture of user interaction operations, providing accurate input data for subsequent coordinate transformations.

[0057] After obtaining the screen coordinates, the control converts them into pixel coordinates of the underlying image matrix based on a pre-calculated scaling ratio. The conversion formula is as follows: and ,in For screen coordinates, These are pixel coordinates. Taking the previous example, screen coordinates (320, 240) are converted to pixel coordinates (960, 540). Since the scaling factor may not be an integer, the conversion result may contain decimal parts. The control uses rounding or down-rounding to normalize the coordinate values ​​to integer pixel positions. After conversion, the control verifies whether the pixel coordinates are within the valid range of the image matrix. and If the coordinates are outside the range, the operation is considered invalid and the event is ignored. This step implements a forward mapping from display space to data space, solving the technical problem of inaccurate coordinate positioning when users operate on scaled images, and ensuring a precise correspondence between interface interaction and underlying calculations.

[0058] After obtaining accurate pixel coordinates, the control captures or updates the position of control points based on these coordinates. Control points are key parameters in affine transformations, and are typically marked as draggable control points at the four corners and the center of the image. When the distance between the user's mouse cursor and a control point is less than a preset capture radius (e.g., 10 pixels), the control determines that the user intends to drag the control point and enters drag mode. In drag mode, the control continuously updates the position of the control point to the current mouse pixel coordinates. The control maintains a control point mapping table, recording the initial position of each control point. and current location .

[0059] Based on the displacement of the control points, the control calculates the affine transformation matrix in real time. The affine transformation matrix can be represented as... The first two columns control rotation, scaling, and shearing, while the last column controls translation. The controls determine matrix parameters by solving a system of linear equations, ensuring the initial control point positions after transformation. Equal to the current control point position For three non-collinear control points, an affine transformation matrix can be uniquely determined. The control uses the least squares method to solve the overdetermined system of equations, ensuring the optimally fitted transformation matrix can be calculated even when the user drags multiple control points. The calculation process is executed every time a mouse movement event is triggered, ensuring the transformation matrix is ​​updated in real time with user actions. This step solves the technical challenge of intuitively adjusting affine transformation parameters. By mapping abstract matrix parameters to visual control point dragging operations, students can complete complex geometric transformations without understanding matrix operations, significantly lowering the operational threshold.

[0060] After calculating the affine transformation matrix, the control immediately applies this matrix to the underlying image matrix to generate the deformed image. The application process employs a reverse mapping strategy, that is, it iterates through each pixel position of the output image and applies the inverse of the affine transformation matrix. Calculate the original image coordinates corresponding to this location, and then sample pixel values ​​from the original image. The formula for calculating the inverse matrix is: Since the original image coordinates obtained through reverse mapping may not be integers, the control uses bilinear interpolation to calculate pixel values. The interpolation formula is: Where a and b are the decimal parts of the coordinates, Equals the pixel values ​​at four adjacent integer pixel positions.

[0061] The generated deformed image is passed to the rendering module, which then draws the image onto the display controls, replacing the original display content. The entire calculation and rendering process is completed in milliseconds, allowing users to see the image rotate, scale, or tilt in real time as they drag the control points. This step achieves a reverse mapping from the data space to the display space, completing a closed loop of bidirectional mapping and solving the technical problem of not being able to visualize the transformation results in real time. This allows users to intuitively understand the geometric effects and parameter influence rules of affine transformations through continuous interactive operations.

[0062] In practical teaching applications, users may exhibit abnormal interactive behaviors due to operational errors, such as rapid mouse jitter or accidental touches of control points. To address the waste of computing resources and poor visual experience caused by such abnormal interactions, the control introduces an abnormal interaction detection and correction mechanism. During the user's dragging of the control point, the control monitors the control point's trajectory in real time and calculates the acceleration vector of the control point's displacement between adjacent frames. The acceleration vector is defined as... ,in The velocity vector of the current frame. This represents the inter-frame time interval. The velocity vector is calculated using displacement difference, i.e. ,in Let be the position of the control point in frame t.

[0063] The control calculates the magnitude of the acceleration vector. The value is then compared with a preset abnormal acceleration threshold. This threshold is determined based on the statistical distribution of acceleration during normal drag operations, typically set to the mean of normal acceleration plus three standard deviations. When the acceleration magnitude exceeds the threshold, the control determines that the current frame is a falsely triggered drag event, possibly due to unintended actions caused by mouse jitter or hand muscle spasms. This detection mechanism solves the problem of abnormal interactions interfering with the normal teaching process, avoiding severe image distortion and wasted computational resources caused by momentary erroneous operations.

[0064] For identified false trigger events, the control calculates the stable position of the control point before the false trigger. The stable position is defined as the position where the rate of change of displacement is less than a stable threshold for the three most recent consecutive frames. The rate of change of displacement is calculated as follows: The control traces backward from the current frame to find the nearest frame that meets the stability condition, and uses the control point position in that frame as the stable position. Once a stable position is found, the control moves its current position back to that position, displaying a visual feedback animation of this position reversal on the user interface. The animation uses easing functions to achieve a smooth transition, such as a double ease-out function. ,in Current position For stable positioning, t is the normalized animation time. The rollback animation lasts approximately 200 milliseconds, allowing the user to clearly perceive the control point's position correction and understand the system's automatic correction of erroneous operations. Based on the control point's position after rollback, the control recalculates the affine transformation matrix, ensuring that the transformation parameters are based on stable user intent rather than instantaneous erroneous operations. This correction mechanism solves the problem of abrupt changes in transformation parameters caused by accidental triggering, improving the robustness of interactive operations and the smoothness of the user experience.

[0065] After recalculating the affine transformation matrix, the control further checks the validity of the transformation matrix to prevent degenerate transformations. The control calculates the determinant value of the affine transformation matrix. The geometric meaning of the determinant is the ratio of the transformed area to the original area. When the determinant value is close to zero, it indicates that the transformation will compress the two-dimensional image into a one-dimensional line segment or a zero-dimensional point, resulting in irreversible loss of image information. The control compares the absolute value of the determinant with a preset degradation threshold, which is usually set to 0.01, meaning that the image area is allowed to shrink to 1% of its original size but not allowed to be completely degraded.

[0066] When the absolute value of the determinant is less than the threshold, the control refuses to apply the affine transformation matrix, maintaining the image in the state of the last valid transformation, and displays an invalid operation message on the user interface. The message is displayed as a semi-transparent overlay, explaining that the current control point configuration will cause image degradation and suggesting that the user adjust the control point positions. This verification mechanism solves the problems of image degradation and program crashes caused by extreme control point configurations, ensuring system stability and the continuity of the teaching process, and preventing students from falling into an unrecoverable error state due to misoperation.

[0067] This embodiment solves the technical problem of coordinate system inconsistency between user interface interaction and underlying image matrix calculation by implementing a precise bidirectional coordinate mapping mechanism. This ensures that dragging operations on the scaled display image accurately correspond to the pixel-level positions of the original image matrix. The precise calculation of the scaling ratio and the application of coordinate transformation formulas eliminate mapping errors between display space and data space, ensuring operational accuracy. Real-time calculation of control point capture and affine transformation matrices transforms abstract matrix parameters into intuitive visual control point drag operations, significantly lowering the learning curve for affine transformations. The application of inverse mapping and bilinear interpolation guarantees high-quality rendering and real-time feedback of the transformed image. An abnormal interaction detection mechanism identifies falsely triggered events through acceleration monitoring, and stable position retraction and visual feedback animations improve system robustness and user experience. The determinant verification mechanism of the transformation matrix effectively prevents image degradation and system crashes, ensuring the stability of the teaching process. In summary, this technical solution effectively addresses the shortcomings of traditional teaching tools, such as users' inability to precisely control the underlying matrix transformation parameters through intuitive operation, abnormal interactions interfering with normal teaching, and system instability caused by extreme operations. It achieves high-precision and highly robust interactive affine transformation visualization teaching, significantly improving the teaching effect of machine vision geometric transformation algorithms and the depth of students' understanding.

[0068] In one embodiment of this invention, the following steps are also included: S410. Listen for and capture a sequence of continuous user interaction actions on the visualization simulation control, including mouse dragging actions and parameter sliding actions. S420. Perform interactive event cleaning on the continuous interactive action sequence to filter out invalid intermediate jitter events and extract key interactive frames. S430: Based on key interaction frames, obtain visual results and render the visual results.

[0069] In this embodiment, to address the issues of wasted computing resources and interface response latency caused by frequent and continuous user interactions, the platform introduces an interaction event cleaning and keyframe extraction mechanism. This mechanism directly resolves the performance bottlenecks and poor user experience inherent in traditional real-time response systems where every interaction event triggers a complete computation.

[0070] In practice, the platform continuously monitors all user interactions on the visual simulation controls via event listeners. The listeners capture interaction events at millisecond-level sampling frequencies, including mouse presses, movements, releases, and slider drags. When a user performs continuous interactions, such as quickly dragging a filter parameter slider from value 3 to value 9, the listener captures dozens or even hundreds of intermediate events in a very short time. Each event corresponds to an instantaneous parameter value, such as 3.1, 3.3, 3.5, etc. These events are organized into a sequence of continuous interaction actions, with each element containing information such as event type, timestamp, coordinate position, or parameter value. For mouse drag actions, the sequence records the continuous trajectory points of the mouse on the screen; for parameter sliding actions, the sequence records the continuous parameter values ​​corresponding to the slider position.

[0071] The platform stores these raw event sequences in a circular buffer with a fixed memory size. As a new event arrives, the oldest event is overwritten, ensuring manageable memory usage. This step solves the problem of complete capture and efficient storage of high-frequency interaction events, providing complete raw data for subsequent event cleaning and avoiding distortion of interaction trajectories due to insufficient sampling frequency.

[0072] After capturing a sequence of continuous interactive actions, the platform performs interactive event cleaning to filter out invalid intermediate jitter events and extract key interactive frames. The cleaning process first activates an interaction buffer pool with a preset time window, typically set to 50-100 milliseconds. This duration is determined based on a balance between the persistence of vision in humans and the computational time required by the algorithm. Within the time window, the platform collects all interactive coordinate points or parameter values, forming a local event set. For example, within a 50-millisecond window, a user dragging a slider might generate 15 events, corresponding to continuous changes in parameter values ​​from 5.2 to 6.8.

[0073] The platform fits the motion trajectory of these interactive coordinate points using the least squares method to fit a smooth curve. For one-dimensional parametric sliding, the fitting function can be a linear function. ,in Here are the initial parameter values ​​for the window, and v is the fitting velocity. For 2D mouse dragging, the fitting function is... and The least squares method determines the fitting parameters by minimizing the sum of squared errors between the fitted curve and the actual sampling points. The error function is... The optimal fitting parameters can be obtained by taking the partial derivatives of the fitted parameters and setting them to zero. After fitting, the platform calculates the instantaneous speed of the drag, which is the first derivative of the fitted function, i.e. Simultaneously calculate the drag direction; for two-dimensional dragging, the direction angle is... .

[0074] Based on the fitting results, the platform extracts the fitted coordinates at the end of the time window as key interaction frames. Specifically, the end time of the window is substituted into the fitting function, and the calculated coordinates or parameter values ​​are the values ​​of the key frames. For example, if the end time of the window is 50 milliseconds, the fitting function is... The keyframe parameter value is The platform generates an interaction instruction package containing key interaction frames. This package includes the parameter values, timestamps, and metadata such as fitting speed and direction for each keyframe. This cleaning process effectively filters out invalid intermediate events caused by mouse jitter, sampling noise, or minor hand tremors, compressing the original event sequence into a sparse keyframe sequence. This step solves the technical problem of large amounts of redundant and noisy data interfering with efficient computation in high-frequency interaction events. Through trajectory fitting and keyframe extraction, it preserves the essential characteristics of the user's interaction intent while significantly reducing the number of events that need to be processed, laying the foundation for subsequent efficient computation.

[0075] After extracting key interaction frames, the platform triggers machine vision algorithms to perform calculations based on these frames, obtaining visual results and rendering them. The platform constructs a communication channel between the user interface thread and the underlying algorithm thread. This channel employs a producer-consumer pattern: the user interface thread, acting as the producer, encapsulates key interaction frames into computational tasks and submits them to a task queue; the algorithm thread, acting as the consumer, retrieves tasks from the queue and performs the computation. The communication channel includes an interrupt flag, a thread-safe Boolean variable, indicating whether the currently executing computational task needs to be interrupted.

[0076] When a new critical interaction frame arrives, the UI thread first checks if the underlying algorithm thread has an ongoing computational task. This is done by querying the task queue status and the algorithm thread's busy flag. If an ongoing computational task exists, the UI thread activates the interrupt flag, setting its value to true. The algorithm thread periodically checks the interrupt flag during computation; the frequency depends on the algorithm's computational granularity, typically once after completing each image patch or iteration. When the algorithm thread detects that the interrupt flag is true, it responds to an interrupt signal and immediately terminates the ongoing computational task. The termination process includes exiting the computation loop, releasing temporarily allocated memory buffers, and marking the task status as interrupted. Unfinished intermediate computational data is discarded and not written to the output buffer or updated in the display.

[0077] After terminating the old task, the algorithm thread retrieves a new computational task from the task queue, corresponding to the most recently arrived key interaction frame. The algorithm thread reinitializes the computation context, including reading the original image matrix, setting new parameter values, and allocating an output buffer. It then executes the new computational task, processing the image according to the new parameter values ​​to obtain the visual result. The visual result is the processed image matrix, containing the pixel data after the algorithm's processing. The algorithm thread passes the visual result to the rendering module, which converts the image matrix into a displayable graphic format and updates the image display controls on the interface. The entire process, from keyframe to rendering completion, typically takes only tens of milliseconds, providing the user with a near real-time response between parameter adjustment and visual feedback. This mechanism solves the problem of accumulated response latency caused by old computational tasks blocking the execution of new tasks during continuous interaction. Through task interruption and queue management, it ensures that the system always prioritizes processing the latest user intent, avoiding wasting computational resources on outdated intermediate states, and significantly improving the real-time performance and smoothness of the interactive response.

[0078] To further ensure system stability and data consistency, the platform generates a memory operation pointer anchor for the current original image matrix before executing a new computation task. The anchor records the starting address of the image matrix in memory, its data length, and the checksum of key pixel values. When a computation task is interrupted, the platform intercepts the memory pointer of the current image matrix object and compares it with the memory operation pointer anchor. The comparison process identifies altered memory block segments through byte-by-byte comparison or checksum verification.

[0079] If modified memory data is detected, the platform extracts the altered memory block fragments and performs a reverse differential overwrite operation on these fragments. The reverse overwrite first obtains the memory step offset during the computation task execution, which records the memory access pattern when the algorithm processes the image row-by-row or block-by-block. Based on the offset, the modified memory region is located, and then the original memory data is read from the memory operation pointer anchor point and overwritten into the modified memory region, completing the memory state rollback. After rollback, the image matrix is ​​restored to its pre-computation anchor point state, ensuring that new computation tasks are executed based on uncontaminated original data. Simultaneously, the platform reclaims temporary resources generated by interrupted computation tasks, including intermediate result buffers, convolution kernel matrices, lookup tables, etc., releasing the memory space occupied by these resources and preventing memory leaks. This mechanism solves the data inconsistency and resource leak problems that may result from computation task interruptions, ensuring the stability and reliability of the system in scenarios with high-frequency interaction and frequent task switching.

[0080] This embodiment effectively solves the problems of wasted computing resources and interface response latency caused by high-frequency continuous user interactions by introducing interactive event cleaning and keyframe extraction mechanisms. Millisecond-level event listening and circular buffer storage ensure the complete capture and efficient management of high-frequency interactive events. Trajectory fitting and keyframe extraction based on the least squares method significantly reduce the number of events that need to be processed, while preserving the essential characteristics of the user's interactive intent. Task interruption mechanisms and communication pipeline design ensure that the system always prioritizes the latest user operations, avoiding the waste of computing resources on outdated intermediate states. Memory anchoring and inverse differential overlay operations ensure data consistency and resource reclamation after computing task interruption, preventing memory leaks and data pollution. In summary, this technical solution significantly improves the system's response speed and stability in continuous interactive scenarios, solves the performance bottleneck of traditional real-time response systems, and provides technical support for a smooth visualization and deduction experience.

[0081] In one embodiment of this invention, interactive event cleaning is performed on the continuous interactive action sequence to filter out invalid intermediate jitter events and extract key interactive frames, including the following steps: S510. Start the interaction buffer pool of the preset time window and obtain all interaction coordinate points within the time window; S520. Use the least squares method to fit the motion trajectory of the interactive coordinate points and calculate the instantaneous speed and direction of the dragging. S530. Extract the fitted coordinates at the end of the time window as key interaction frames, and generate an interaction instruction package containing the key interaction frames.

[0082] In this embodiment, in order to solve the technical problem of noise data and redundant events interfering with effective calculation in high-frequency interactive events, an interactive event cleaning mechanism based on time windows and trajectory fitting is adopted to solve the problem of decreased calculation accuracy and waste of resources caused by the inability of traditional event processing systems to distinguish between valid interactive intentions and unintentional jitter.

[0083] In practice, when a user begins continuous interactive operations on the visual simulation controls, the platform immediately activates an interaction buffer pool with a preset time window. The size of the time window is a key parameter determined based on a combination of the human visual persistence effect and the computation time of the algorithm, typically set to 50 to 100 milliseconds. This duration is chosen based on the following technical considerations: the human eye's visual persistence time is approximately 100 milliseconds, within which multiple image updates are perceived as continuous changes rather than discrete jumps; simultaneously, the computation time for most machine vision algorithms is between 20 and 80 milliseconds per operation, and the time window needs to be longer than the computation time per operation to avoid computational backlog.

[0084] The interaction buffer pool is implemented using a circular queue data structure, with a pre-allocated fixed-size memory space, such as a buffer that can hold 100 interaction events. When the time window starts, the platform begins collecting all interaction coordinate points within that window. For mouse drag operations, each coordinate point includes screen coordinates. and timestamp For parameter slider operations, each coordinate point contains a parameter value. and timestamp The platform continuously collects data at a millisecond-level sampling frequency. For example, within a 50-millisecond window, 10 sampling points can be acquired at a 5-millisecond sampling interval. These coordinate points are sequentially stored in a buffer pool, forming a time-series data set. The buffer pool employs a dual-pointer management mechanism: the head pointer points to the earliest entered data, and the tail pointer points to the latest entered data. When the window time expires, all data between the head and tail pointers constitutes the complete window dataset. This step solves the problem of real-time collection and orderly storage of high-frequency interactive data. By dividing the continuous interactive stream into processable discrete data blocks through time window segmentation, it provides structured input data for subsequent trajectory analysis, avoiding the computational pressure caused by processing each event in real time.

[0085] After acquiring all interaction coordinates within the time window, the platform uses the least squares method to fit the motion trajectory of these coordinates to extract the true intent of user interaction and filter out noise interference. The least squares method is a classic curve fitting method; its core idea is to find a curve that minimizes the sum of the squared distances between points on the curve and the actual sampling points. For a one-dimensional parameter sliding operation, the platform assumes that the parameter values ​​change linearly with time, and the fitting function is... , where a is the initial parameter value and b is the rate of change of the parameter. The goal of the least squares method is to minimize the error function. , where n is the number of sampling points within the window.

[0086] By taking the partial derivatives with respect to a and b and setting them to zero, we can obtain the normal equation system: and .

[0087] Solving this system of equations yields: and .

[0088] For two-dimensional mouse drag operations, the platform independently fits the x and y coordinates, obtaining... and .

[0089] After the fitting is complete, the platform calculates the instantaneous speed of the drag. For a one-dimensional parameter, the instantaneous speed is the slope of the fitted function. For two-dimensional dragging, the speed is The drag direction is determined by the ratio of the velocity components, and the direction angle is... This angle represents the tilt angle of the drag trajectory relative to the horizontal axis. Through trajectory fitting, the platform effectively transforms noisy discrete sampling points into a smooth continuous function that accurately reflects the user's interaction intent without being affected by minor hand tremors or mouse sampling errors. For example, if the user intends to drag the slider from position 5 to position 8 at a constant speed, but due to hand tremors, the actual sampling points may be an irregular sequence such as 5.0, 5.3, 5.1, 5.5, 5.4, 5.7, etc., the linear function obtained after least squares fitting can accurately restore the user's constant-speed dragging intent. This step solves the technical problem of noise and jitter interfering with the recognition of true intent in the original interaction data. By extracting stable motion features from noisy data through mathematical fitting methods, it provides a reliable basis for keyframe extraction.

[0090] After completing trajectory fitting and calculating instantaneous velocity and direction, the platform extracts the fitted coordinates at the end of the time window as the key interaction frame. In specific implementation, the platform uses the end time of the time window... Substitute the values ​​into the fitting function to calculate the coordinates or parameter values ​​at that moment. For one-dimensional parametric sliding, the keyframe parameter values ​​are... For 2D mouse dragging, the keyframe coordinates are... and The technical reason for choosing the end of the window rather than the midpoint or the start point as the keyframe is that the end moment is closest to the user's current operation state, and can reflect the latest changes in the user's intention most quickly, reducing the delay in visual feedback; at the same time, the end coordinates obtained by fitting are calculated based on the entire window data, which is more stable and reliable than simply using the last sampling point.

[0091] After extracting the keyframe, the platform generates an interactive instruction package containing that keyframe. The instruction package is a structured data object containing multiple fields: keyframe coordinates or parameter values, timestamp, fitting speed, drag direction, number of sampling points within the window, fitting error, and other metadata. The fitting error is calculated by determining the root mean square error between the actual sampling points and the fitted curve, using the following formula: This error value can be used to evaluate the quality of the fit. Excessive error may indicate a sudden change in the user interaction pattern, requiring special handling. The instruction packet also contains control identifiers to indicate which visualization control the interaction applies to, enabling the algorithm scheduler to correctly route computation tasks. The generated instruction packet is submitted to the task queue, awaiting processing by the algorithm thread. By compressing multiple original interaction events into a single keyframe instruction packet, the platform significantly reduces the amount of data that needs to be processed. This step solves the problem of efficient representation and transmission of high-frequency interaction data. Through keyframe extraction and instruction packet encapsulation, the core information of the interaction operation is preserved while significantly reducing data transmission and processing overhead, creating conditions for subsequent rapid computational responses.

[0092] In practical teaching applications, this interactive event cleaning mechanism has brought significant performance improvements and enhanced user experience. When students quickly adjust filter parameters, traditional systems may trigger dozens of complete image convolution calculations within one second, leading to computation queue backlog and interface lag. However, with this mechanism, only about 10 calculations are triggered in the same time frame, and each calculation is based on cleaned keyframe parameters, avoiding invalid intermediate state calculations. The trajectory fitting mechanism can also predict user operation trends. For example, when it detects that a user is dragging a slider at a constant speed, the system can predict the approximate position of the next keyframe, prepare computational resources in advance, and further reduce response latency.

[0093] For different types of interactive operations, the platform can dynamically adjust the size of the time window. For example, for computationally complex morphological operations, the window can be increased to 100 milliseconds to reduce the computation frequency; for simple brightness adjustments, the window can be reduced to 30 milliseconds to improve response speed. This adaptive window adjustment strategy enables the system to maintain optimal performance under different algorithms and hardware configurations.

[0094] Furthermore, the obtained speed and direction information can be used to enhance visual feedback in user interaction. For example, when the system detects that a user is rapidly dragging a control point, it can display a predicted trajectory line on the interface, helping the user to more accurately control the drag endpoint. When the system detects a sudden slowdown in dragging speed, it can determine that the user is about to stop and trigger the final high-precision calculation in advance, ensuring that the user sees the final result immediately when releasing the mouse. These trajectory analysis-based interactive enhancements further improve the system's usability and teaching effectiveness.

[0095] This embodiment effectively solves the technical problem of efficient calculation of noisy data and redundant events interfering with high-frequency interactive events by designing an interactive event cleaning mechanism based on time windows and trajectory fitting. The interaction buffer pool design with a preset time window divides the continuous interaction stream into processable discrete data blocks, realizing the orderly collection and structured storage of high-frequency data. Trajectory fitting based on the least squares method extracts smooth motion functions from the original sampling points containing noise, accurately restoring the user's true interaction intent and effectively filtering out interference from hand tremors and sampling errors. The calculation of instantaneous speed and direction provides a quantitative basis for interactive behavior analysis and prediction. The extraction of key interaction frames and the generation of instruction packets significantly reduce data transmission and processing overhead. This mechanism enables the system to significantly reduce invalid calculations while ensuring real-time interactive responses, improving the efficiency of computing resource utilization. Noise filtering and intent recognition achieved through mathematical fitting methods solve the technical deficiency of traditional event processing systems in distinguishing between effective operations and unconscious jitter, providing core technical support for a smooth visualization and deduction experience, and significantly improving the performance and teaching effectiveness of the machine vision training platform in high-frequency interactive scenarios.

[0096] In one embodiment of this example, a visual result is obtained based on a key interaction frame, and the visual result is rendered, including the following steps: S610. Construct a communication channel between the user interface thread and the underlying algorithm thread. The communication channel includes an interrupt flag. S620. When a key interaction frame arrives, check if there is a computational task being executed in the underlying algorithm thread. S630. If there is an ongoing computing task, activate the interrupt flag and send an interrupt signal to the context of the ongoing computing task. S640, in response to an interrupt signal, forcibly terminates the underlying operations of the currently executing computation task and discards any unfinished intermediate computation data; S650. After terminating the currently executing computation task, execute a new computation task, obtain visual results, and render the visual results.

[0097] In this embodiment, performing a new computational task includes: Extract the latest interaction parameters from key interaction frames; Load interactive parameters onto the backed-down image matrix; The corresponding visual algorithm is executed based on the visual inference control to generate the processed image matrix; Convert the processed image matrix into a user interface image; Render the user interface image and push the rendered user interface image to the user interface for display. Clear the current time window and interrupt flag, and wait for the next interactive operation.

[0098] In this embodiment, in order to solve the problem of accumulated response delay and waste of computing resources caused by old computing tasks blocking the execution of new tasks during continuous high-frequency interaction, a computing management mechanism based on task interruption and priority scheduling is adopted to solve the technical defects of traditional multi-threaded systems that cannot respond to the latest user operations in a timely manner, resulting in interface lag and poor user experience.

[0099] In practical implementation, the platform first constructs a communication channel between the user interface thread and the underlying algorithm thread. This communication channel is implemented using a thread-safe queue based on the producer-consumer pattern. This queue supports concurrent access by multiple threads and ensures data consistency through mutexes and condition variables. Each element in the queue is a computational task object, containing fields such as task identifier, parameter data, priority, and timestamp. The core component of the communication channel is the interrupt flag, an atomic boolean variable that employs memory barriers to ensure visibility and ordering in a multi-core processor environment. The interrupt flag design solves the real-time problem of asynchronous communication between threads. Compared to traditional message queue polling mechanisms, the read / write operation latency of the atomic variable is only in the nanosecond range, enabling near-instantaneous interrupt response.

[0100] The communication pipeline also includes a task status table, recording the execution status of each task, including waiting, running, completed, and interrupted states. The status table uses a hash table structure, supporting constant-time state lookup and updates. The user interface thread acts as the producer, responsible for encapsulating key interaction frames into computational tasks and submitting them to a queue; the underlying algorithm thread acts as the consumer, retrieving tasks from the queue and executing the computations. The two threads are decoupled through the communication pipeline. The UI thread can continue responding to user actions without waiting for computation to complete, while the algorithm thread focuses on executing computational tasks, fully utilizing the parallel computing capabilities of multi-core processors. This step solves the synchronous blocking problem between UI response and computation execution, achieving a balance between UI smoothness and computational efficiency through an asynchronous communication mechanism.

[0101] When a new key interaction frame arrives, the user interface thread immediately checks the current state of the underlying algorithm thread. The check first queries the task status table to determine if any computational tasks are in a "running" state. If a task is running, the UI thread further retrieves detailed information about it, including task type, elapsed execution time, and estimated remaining time. The task type determines the necessity of interruption. For example, for a fast brightness adjustment algorithm, it's worth interrupting even if the task is nearing completion to respond to the latest parameters; while for time-consuming deep learning inference tasks, if the task is 90% complete, it might be better to wait for it to complete rather than interrupt it.

[0102] The platform calculates interruption rewards based on task type and execution progress; the reward function is as follows. ,in The estimated remaining time for the old task. The overhead time for interruption and cleanup, For the execution time of the new task. When When interrupting old tasks allows for faster presentation of new results, an interrupt operation is performed. The detection mechanism also includes monitoring the task queue length. If multiple tasks are backed up in the queue, it indicates that the system is under high load, and old tasks should be actively interrupted to clear the backlog. This step solves the task scheduling decision problem. By quantitatively analyzing the benefits of interruption, it avoids the waste of computing resources caused by blind interruption, thus achieving intelligent task management.

[0103] After determining that an old task needs to be interrupted, the user interface thread activates the interrupt flag, setting its value from false to true. The activation operation uses an atomic write instruction, ensuring its atomicity and visibility. Simultaneously, the UI thread sends an interrupt signal to the context of the currently executing computation task. The interrupt signal propagation mechanism relies on inter-thread communication condition variables; the UI thread wakes up algorithm threads that may be waiting by broadcasting the condition variable. The algorithm thread periodically checks the interrupt flag during computation; the frequency depends on the computational granularity of the algorithm. For image processing algorithms, this is typically done after processing each row of pixels or each image block, with a check overhead of approximately a few clock cycles, having a negligible impact on overall performance.

[0104] The checkpoint code is typically implemented as a conditional branch. When the algorithm thread detects that the interrupt flag is true, it immediately responds to the interrupt signal and exits the current computation loop. The speed of interrupt response depends on the checkpoint interval; for fine-grained checkpoint settings, the interrupt response latency can be controlled at the millisecond level. This mechanism solves the problem of interruptibility of computational tasks. By embedding checkpoints in the algorithm execution flow, it transforms what was originally an uninterruptible, long-running computational task into a controllable task that can respond to external signals, significantly improving the system's real-time responsiveness.

[0105] Upon responding to an interrupt signal, the algorithm thread forcibly terminates the underlying computation of the currently executing task. The termination process involves several steps: First, it exits all nested computation loops, including pixel traversal loops and convolution kernel sliding loops; second, it stops writing to the output buffer to prevent partially completed results from contaminating the output data; third, it releases temporary memory resources allocated during the computation, such as intermediate result buffers, convolution kernel matrices, and lookup tables. Memory release employs reference counting or smart pointer mechanisms to ensure resources are correctly reclaimed without causing memory leaks. For computation tasks using GPU acceleration, the termination process also includes canceling computation instructions submitted to the GPU and reclaiming GPU memory resources.

[0106] Incomplete intermediate computation data is marked as invalid and discarded, and will not be used by subsequent processes. Discarding is achieved by clearing the output buffer or resetting the buffer pointer, ensuring that new tasks execute in a clean data environment. The task status in the task status table is updated to "interrupted," and the interruption time and reason are recorded for subsequent performance analysis and debugging. This step addresses the issues of safe termination and resource reclamation of computational tasks. Through a standardized termination process, it avoids memory leaks, data inconsistencies, and system instability that may result from forced interruptions, ensuring system reliability in high-frequency task switching scenarios.

[0107] After successfully terminating the old task, the algorithm thread immediately executes the new computation task. The execution process begins by extracting the latest interaction parameters from key interaction frames. These parameters include filter kernel size, threshold, color space type, affine transformation matrix, etc., the specifics depending on the type of control the user is interacting with. The extracted parameters are validated for validity, such as checking if the kernel size is a positive odd number, if the threshold is within a valid range, and if the transformation matrix is ​​invertible. After successful parameter validation, the algorithm thread loads these parameters onto the reverted image matrix. The reverted image matrix refers to the original image data restored from its memory state, ensuring the new computation is based on an uncontaminated initial state. Loading the parameters involves writing the parameter values ​​into the algorithm function's input parameter structure, configuring the algorithm's running mode and optimization options, etc.

[0108] Subsequently, the algorithm thread executes corresponding visual algorithm calculations based on the visual inference controls. Different controls correspond to different algorithm implementations; for example, the spatial filtering control calls the convolution function, the morphological control calls the erosion and dilation function, and the color space conversion control calls the color model conversion function. During algorithm execution, the thread continuously monitors the interrupt flag to ensure timely response if another interrupt signal is received. After the calculation is completed, a processed image matrix is ​​generated. This matrix contains the pixel data after the algorithm's processing, and the data format is consistent with the original image for easy subsequent processing.

[0109] The processed image matrix is ​​passed to the rendering module, which converts it into a user interface image. The conversion process includes color space conversion (e.g., converting the grayscale image output by the algorithm to RGB format for display), size scaling (scaling the image to the size of the display controls), and format conversion (converting the pixel array in memory into an image object supported by the interface framework). After conversion, the rendering module renders the user interface image, including anti-aliasing, gamma correction, and color management to ensure consistent visual effects across different display devices. The rendered user interface image is then pushed to the user interface thread, which draws the image onto the image display controls and updates the interface display. The entire execution flow, from parameter extraction to interface update, is typically completed within tens of milliseconds, and the user perceives a real-time response between parameter adjustment and visual feedback. This step solves the problem of rapid startup and efficient execution of new tasks. Through a pipelined processing flow and modular functional division, it maximizes the parallelism of computation and rendering, shortening end-to-end response latency.

[0110] After completing the execution and rendering of a new task, the system performs cleanup and reset operations to prepare for the next interaction. First, the interaction buffer pool for the current time window is cleared, deleting all historical interaction data and freeing up memory space occupied by the buffer. This clearing operation is achieved by resetting the buffer pool's read and write pointers, restoring the buffer pool to its initial empty state. Second, the interrupt flag is reset, changing its value from true to false, allowing the system to execute the next computation task normally without accidental interruption. This reset operation also uses atomic write instructions to ensure the visibility of the operation. Old task records in the task status table are archived or deleted to make room for the new task.

[0111] The system also updates performance statistics, recording metrics such as execution time, number of interruptions, and rendering time for this task, which are used for subsequent performance optimization and problem diagnosis. After cleanup, the algorithm thread enters a waiting state, listening for new task arrival signals in the task queue. The user interface thread continues to listen for user interactions; when the user adjusts parameters again, the new interaction event is captured and enters the next processing cycle. This cleanup and reset mechanism solves the problem of maintaining system state consistency, ensuring that each interaction is performed in a clean initial state, avoiding abnormal behavior and performance degradation caused by state residue.

[0112] In practical teaching applications, this task interruption and scheduling mechanism significantly improves the user experience. When students rapidly and continuously adjust filter parameters, the system always displays the result corresponding to the latest parameters, without delaying the display of results for older parameters. Traditional systems may require several seconds to see the final result after the student stops adjusting, while this system can present the final result within tens of milliseconds after the student stops operating. The task interruption mechanism also avoids wasting computing resources. Traditional systems may complete the calculation of all intermediate parameters, even if these results will never be seen by the user, while this system only calculates the result corresponding to the latest keyframe, saving a significant amount of computing time and energy consumption. For hardware devices with different performance levels, this mechanism can adaptively adjust the task scheduling strategy, ensuring response speed through proactive task interruption on low-performance devices, and fully utilizing computing power by reducing the interruption frequency on high-performance devices.

[0113] This embodiment effectively solves the problem of accumulated response latency and wasted computing resources caused by old computing tasks blocking the execution of new tasks during continuous high-frequency interactions by designing a computing management mechanism based on task interruption and priority scheduling. The asynchronous communication pipeline design between the user interface thread and the underlying algorithm thread decouples interface response from computation execution, eliminating interface stuttering caused by synchronous blocking. The interrupt signal transmission mechanism based on atomic variable interrupt flags and condition variables significantly improves the system's real-time performance. Task state detection and interrupt benefit quantification analysis avoid resource waste caused by blind interruption, enabling intelligent task scheduling decisions. A standardized task termination process and resource reclamation mechanism ensure the stability and reliability of the system in high-frequency task switching scenarios, preventing memory leaks and data pollution. A pipelined new task execution process and modular functional division maximize the parallelism of computation and rendering, shortening end-to-end response latency. Cleanup and reset mechanisms ensure the consistency of the system state, ensuring that each interaction is performed in a clean initial state. This mechanism enables the system to always prioritize processing the latest user operations, avoiding the waste of computing resources on outdated intermediate states. It significantly improves the real-time performance and smoothness of interactive responses, solves the technical defect of traditional multi-threaded systems that cannot respond to the latest user operations in a timely manner, provides high-performance computing scheduling support for machine vision training platforms, and greatly improves the user experience and learning efficiency in the teaching process.

[0114] In one embodiment of this example, after terminating the currently executing computing task, the following steps are further included: S710. Before executing a new computation task, generate the memory operation pointer anchor point of the current original image matrix; S720. When a computing task is interrupted, the memory pointer of the current image matrix object is intercepted. S730: Compare the memory pointer with the memory operation pointer anchor point, and extract the memory block segment that has been modified; S740: Perform a reverse differential overwrite operation on the memory block segment to revert the image matrix state to the anchor point state before computation and reclaim the temporary resources generated by the interrupted computation task.

[0115] In this embodiment, in order to solve the problems of image data pollution and memory state inconsistency that may be caused by the interruption of computing tasks, a data recovery mechanism based on memory anchors and reverse differential coverage is adopted. This effectively solves the technical defects of traditional task interruption systems that cannot guarantee data integrity and cause subsequent calculations to accumulate errors based on erroneous data.

[0116] In practice, before the algorithm thread prepares to execute a new computation task, the platform first generates a memory operation pointer anchor for the current original image matrix. The memory operation pointer anchor is a data structure containing several key fields: the starting address of the image matrix in memory, data length, pixel format, image width, and height, among other metadata. The starting address is obtained by retrieving the memory pointer of the image matrix object, which points to the first byte of the image pixel data in the heap memory. The data length is calculated using the following formula: Where W is the image width, H is the image height, C is the number of channels, and B is the number of bytes per pixel per channel. For example, for a 1920×1080 RGB image, where each channel occupies 1 byte, the data length is... byte.

[0117] To improve the efficiency of subsequent data comparison, the anchor also includes checksum information for the image data. The checksum is calculated using a fast hash algorithm, such as CRC32, which can complete the checksum calculation for millions of bytes of data in milliseconds. The checksum calculation formula is as follows: Where D is the byte sequence of the image data. In addition, anchor points record sampled values ​​of key pixel locations, such as the pixel values ​​at the four corners and center of the image, for rapid detection of data changes. The anchor point data structure is stored in a separate memory area, separated from the image matrix data area, to avoid accidental overwriting during calculation. The overhead of generating anchor points is minimal, typically completed within 1-2 milliseconds, and will not significantly impact overall performance. This step solves the problem of efficiently creating data state snapshots. Through lightweight metadata recording and sampling verification, a reliable data baseline is established without copying the complete image data, providing a reference for subsequent state recovery.

[0118] When a computation task is forcibly terminated due to an interrupt signal, the platform immediately intercepts the memory pointer of the current image matrix object. This interception is performed within the exception handling flow of the task termination, ensuring that the memory state is captured even if the computation process exits abnormally. The intercepted memory pointer points to the current data region of the image matrix, which may have been partially modified. For example, if the image filtering algorithm is interrupted when processing row 500, the pixel data of the first 500 rows may have been updated to the filtered values, while subsequent rows retain their original values.

[0119] The platform verifies whether memory reallocation of the image matrix has occurred by comparing the captured memory pointer with the starting address recorded in the anchor point. If the addresses are inconsistent, it indicates that a memory expansion or compression operation was triggered during the calculation, requiring the object manager to track the new memory location. In most cases, the memory address of the image matrix remains unchanged during the calculation, simplifying the subsequent data comparison process. The capture operation also includes obtaining the current memory access permissions, confirming whether the data area is readable and writable, and whether it is locked by other threads. If a memory access conflict is detected, the platform waits for the lock to be released or uses snapshot isolation technology to read a copy of the data. This step solves the problem of accurately capturing the data state at the moment of interruption. By timely capturing the memory pointer, it ensures that subsequent data comparison and recovery operations can be performed based on the correct memory location, avoiding data access errors caused by changes in memory addresses.

[0120] After intercepting the memory pointer, the platform compares the current data pointed to by the memory pointer with the baseline data recorded at the memory operation pointer anchor point, extracting the changed memory block fragments. The comparison process employs a block-based scanning strategy, dividing the image data into rows or fixed-size blocks, and comparing the current data with the anchor point sample value or checksum block by block. For each data block, the platform first calculates the checksum of the current block. ,in This contains the data for the current block. If the anchor point stores the checksum of the corresponding block... Then by comparison Quickly determine if the block has changed.

[0121] For blocks where changes are detected, the platform performs a byte-by-byte comparison to pinpoint the start and end points of the change. This byte-by-byte comparison is implemented using a memory comparison function, enabling efficient comparison of large contiguous memory blocks. The changed memory region is marked as a memory block fragment, with fragment information including the start offset, length, and the original data before the change. The original data is obtained by querying the anchor point or reading from the backup area. If the anchor point uses a full data backup strategy, the corresponding fragment is directly extracted from the backup; if an incremental backup strategy is used, the original data is reconstructed using differential logs. For typical image processing algorithms, the changed memory region is usually concentrated in the part already processed by the algorithm. For example, if a filtering algorithm processes data line by line, the changed region is the contiguous memory of the first few lines.

[0122] The platform organizes all changed fragments into a fragment list, sorted by memory address, to facilitate subsequent batch recovery operations. The time complexity of the comparison and extraction process is O(n log n). Where n is the total number of bytes in the image data, but through checksum verification, pre-screening, and multi-threaded parallel scanning, the actual time consumption can be controlled between a few milliseconds and tens of milliseconds. This step solves the problem of accurately locating data changes. Through efficient comparison algorithms and incremental detection strategies, it quickly identifies contaminated data areas, avoiding the high cost of full data recovery and laying the foundation for accurate data repair.

[0123] After extracting the altered memory block fragments, the platform performs a reverse differential overlay operation on these fragments, reverting the image matrix state to its anchor point state before computation. Reverse differential overlay is an efficient data recovery technique; its core idea is to recover only the changed data regions, rather than reloading the entire image. The overlay operation first obtains the memory step offset during the computation task execution, which records the algorithm's memory access patterns. For example, for a row-by-row processing algorithm, the step offset is the number of bytes in one row of image data. Based on the step offset, the platform can quickly locate the modified memory region, that is, starting from the starting address, it checks whether the data has been modified every S bytes.

[0124] For detected modified areas, the platform reads the original memory data from the memory operation pointer anchor. If the anchor uses a full backup, the data at the corresponding offset is read directly; if an incremental backup is used, the original data is restored by reverse-engineering the differential log. The read original data is overwritten to the corresponding position in the current image matrix. The overwrite operation is implemented using a memory copy function, enabling high-speed transfer of large blocks of data. The overwrite process is executed sequentially according to the fragment list, and the overwrite operation for each fragment is atomic, ensuring that no data inconsistency is caused by partial overwrite. For multi-channel images, the overwrite operation needs to pay attention to the alignment between channels to ensure that the RGB three-channel data is restored synchronously.

[0125] After the overlay is complete, the platform recalculates the checksum of the image matrix and compares it with the checksum in the anchor point to verify the correctness of the recovery operation. If the checksums match, it indicates that the image matrix has successfully reverted to its initial state; if they do not match, an exception handling process is triggered, an error log is recorded, and an attempt is made to recover from the full backup. The time complexity of reverse differential overlay depends on the scale of the changed data. For partially processed images, the recovery time is usually much shorter than the time to reload the images. For example, if the algorithm only processes 10% of the images, the recovery operation only needs to overlay 10% of the data, taking about 1 / 10 of the time required for a full load. This step solves the problem of fast and accurate data state recovery. Through incremental overlay and verification, reliable state rollback is achieved while minimizing data transfer volume, ensuring that subsequent calculations are based on the correct initial data.

[0126] After completing the image matrix state rollback, the platform further reclaims temporary resources generated by the interrupted computation task. Temporary resources include several types: intermediate result buffers, used to store temporary data during algorithm computation, such as intermediate results of convolution and gradient maps of edge detection; convolution kernel matrices, dynamically generated filter weight arrays; lookup tables, pre-computation tables used to accelerate color space conversion or histogram equalization; and GPU memory, which needs to be released if the algorithm uses GPU acceleration.

[0127] The platform tracks all temporary resources allocated to each computing task through a resource manager. The resource manager maintains a resource list, recording the type, size, memory address, or handle of each resource. When a task is interrupted, the resource manager iterates through the task's resource list, releasing resources one by one. For heap memory resources, memory release functions such as `free` or `delete` are called; for GPU memory, the memory release API is called; for file handles or network connections, the corresponding close functions are called. Resource release employs an exception-safe RAII mechanism, ensuring that even if an error occurs during the release process, released resources will not be released again, and unreleased resources can be cleaned up in subsequent garbage collection. After release, the resource manager updates the system's available memory statistics, providing an accurate basis for resource allocation for subsequent tasks. Timely reclamation of temporary resources is crucial for maintaining stable system operation, especially in high-frequency task switching scenarios. If not reclaimed in time, temporary resources will accumulate rapidly, leading to memory exhaustion and system crashes. This step solves the resource leakage problem after task interruption. Through a systematic resource tracking and release mechanism, it ensures that the resources of each interrupted task are completely reclaimed, avoiding memory leaks and resource exhaustion, and guaranteeing the long-term stable operation of the system.

[0128] In practical teaching applications, this data recovery and resource recycling mechanism provides crucial reliability assurance. When students frequently adjust parameters during training, causing numerous task interruptions, the system maintains data consistency. Each new calculation is based on the correct original image, preventing erroneous results from residual data from previous interruptions. In contrast, without state recovery, continuous parameter adjustments can cause image data to gradually deviate from its initial state, ultimately presenting a completely incorrect visual effect and misleading students' understanding of the algorithm's principles.

[0129] This mechanism achieves reliable data recovery with minimal performance overhead through anchoring and reverse overwriting techniques. Recovery operations typically take only milliseconds, having no perceptible impact on user experience. The resource reclamation mechanism ensures stable system operation over extended periods; even during hours of continuous training, memory usage remains within a reasonable range, preventing performance degradation or crashes. This mechanism also facilitates system debugging and problem diagnosis. Through anchor data and change logs, developers can track the impact of each calculation on the data, quickly pinpointing algorithmic errors or performance bottlenecks.

[0130] This embodiment effectively solves the problems of image data corruption and inconsistent memory states that may be caused by computational task interruptions by designing a data recovery mechanism based on memory anchors and reverse differential coverage. Lightweight memory operation pointer anchor generation, through metadata recording and sampling verification, establishes a reliable data baseline without copying the complete data. Memory pointer interception at the moment of interruption ensures accurate capture of the data state, providing the correct operation objects for subsequent recovery. Data comparison algorithms based on checksum pre-screening and block scanning quickly and accurately locate corrupted memory regions, avoiding the high overhead of full comparison. The reverse differential coverage operation, through an incremental recovery strategy, restores only changed data, significantly reducing data transfer volume and recovery time; checksum verification ensures the correctness of the recovery operation. A systematic temporary resource tracking and release mechanism prevents memory leaks and resource exhaustion, ensuring long-term stable operation of the system under high-frequency task switching scenarios. This mechanism enables the system to maintain data consistency even when tasks are frequently interrupted, ensuring that each calculation is based on the correct initial state, avoiding accumulated errors and erroneous results. It solves the technical defect of traditional task-interrupted systems that cannot guarantee data integrity, provides highly reliable data management support for machine vision training platforms, significantly improves system stability and the accuracy of teaching results, and provides students with a trustworthy algorithm learning environment.

[0131] In one embodiment of this invention, a reverse differential overlay operation is performed on a memory block fragment to revert the image matrix state to the anchor point state before computation, including the following steps: S810, Obtain the memory step offset during the execution of the computing task; S820: Locate the modified memory region based on the memory step offset; S830: Read raw memory data from the memory operation pointer anchor point; S840: Overwrite the modified memory area with the original memory data to complete the memory state rollback.

[0132] In this embodiment, in order to solve the technical problems of low efficiency and insufficient recovery accuracy of full scan during data recovery, a precise positioning and differential coverage mechanism based on memory step offset is used to effectively solve the technical defects of traditional data recovery methods, such as the inability to quickly locate changed areas, resulting in excessively long recovery operation time and waste of resources.

[0133] In practical implementation, the platform first obtains the memory step offset during the execution of the computation task. The memory step offset is a key parameter describing the algorithm's memory access pattern for image data, reflecting the rules by which the algorithm traverses and modifies pixel data. Different types of image processing algorithms have different memory access patterns, and the platform determines the corresponding step offset based on the algorithm type. For line-by-line scanning algorithms, such as horizontal filtering and line-by-line edge detection, the step offset is the number of bytes in one line of image data, calculated using the following formula: Where W is the image width, C is the number of channels, and B is the number of bytes per channel. For example, for a 1920×1080 RGB image with 1 byte per channel, the row step offset is... byte.

[0134] For column-based scanning algorithms, such as vertical filtering, the step offset is the number of bytes in one column of data. However, since image data is usually stored row-majorly, column access requires jumping across rows, and the offset is... For block processing algorithms, such as block DCT transform and superpixel segmentation, the step offset is the number of bytes in one processing block, calculated using the following formula: ,in and The width and height of the block. For random access algorithms, such as structuring element scanning in morphological operations, the step offset is the irregular pattern determined by the size and shape of the structuring element.

[0135] Upon task startup, the platform automatically identifies the algorithm's memory access pattern and calculates the step offset based on the type and configuration parameters of the visualization control. The identification process is achieved by querying the algorithm metadata database, which stores the access pattern characteristics of each algorithm. For user-defined algorithms, the platform provides an API interface allowing developers to explicitly specify the step offset. The acquired step offset is stored in the task context for subsequent localization and recovery operations. This step solves the problem of automatic identification of algorithm memory access patterns. By quantifying the access pattern into step offset parameters, it provides a mathematical basis for subsequent accurate localization and avoids blind full-scale scanning.

[0136] After obtaining the memory step offset, the platform locates the modified memory region based on this offset. The location process employs a step-by-step scanning strategy, starting from the beginning address of the image data and incrementing the address pointer by the step offset, progressively checking whether the data at each step position has been modified. In practice, the platform maintains an address pointer. The initial value is the starting address of the image matrix. In each step, the pointer is updated to... Where S is the step offset. For each step position, the platform reads the data of one step unit starting from that position, and the size of the step unit is equal to the step offset. For example, for a line scanning algorithm, each step unit is one line of image data.

[0137] The platform calculates the checksum of the stepping unit. ,in This is the data for the current step unit. If the anchor point stores the checksum of the corresponding step unit... Then by comparison Determine if the unit has been modified. For detected modified step units, the platform records their start address and length and adds them to the list of modified regions. The time complexity of step-by-step scanning is O(log n). Where n is the total number of bytes of image data and S is the step offset. Compared to full byte-by-byte scanning... In terms of complexity, the efficiency improvement factor of step-by-step scanning is equal to the size of the step offset. For example, for a row step offset of 5760 bytes, the scanning efficiency is improved by approximately 5760 times.

[0138] To further improve positioning accuracy, after detecting a modified step unit, the platform performs a fine-grained scan of that unit, comparing data byte by byte to precisely pinpoint the start and end locations of the change. This fine-grained scanning is accelerated using SIMD instructions, such as SSE or AVX, enabling parallel comparison of multiple bytes and significantly improving comparison speed. After positioning, the platform obtains a precise list of modified memory regions, each containing information such as its start address, length, and change type. This step solves the problem of fast and accurate positioning of changed regions. Through step-by-step scanning and checksum pre-screening, scanning overhead is significantly reduced, while fine-grained scanning ensures positioning accuracy, providing accurate target regions for subsequent differential coverage.

[0139] After locating the modified memory region, the platform reads the raw memory data from the memory operation pointer anchor. The anchor stores complete state information of the image matrix before calculation, including data backup or differential logs. The reading process varies depending on the anchor's storage strategy. If the anchor uses a complete data backup strategy, i.e., the entire image data is copied to the backup area when the anchor is generated, the read operation directly extracts the data at the corresponding offset from the backup area. The backup area is usually located in a separate memory page or file-mapped area, achieving high-speed access through memory mapping technology.

[0140] For each modified memory region, the platform calculates its offset in the original image. ,in To modify the starting address of the region, This is the starting address of the image. Then, from the corresponding offset in the backup area... Read the raw data. This is the starting address of the backup area. The read length is equal to the length of the modified area. If the anchor point uses an incremental backup strategy, i.e., only recording differential logs of data changes, the read operation needs to reconstruct the original data by reverse-applying the differential logs. The differential log records the location of each data modification, the value before the modification, and the value after the modification. The reverse application process starts from the last record in the log and restores the modified values ​​to their original values ​​sequentially. For complex modification sequences, the platform uses a transaction rollback mechanism to ensure the atomicity and consistency of the recovery operation.

[0141] The raw data read is stored in a temporary buffer, the size of which is dynamically allocated based on the total length of the modified region. For large-scale modifications, the platform employs a streaming read and overwrite strategy to avoid memory pressure caused by allocating an excessively large buffer at once. The read process also includes data integrity verification, which verifies the correctness of the read operation by comparing the checksum of the read data with the checksum recorded in the anchor point. If the verification fails, it indicates that the backup data is corrupted, and the platform triggers an exception handling procedure, attempting to reload the data from the redundant backup or the original image file. This step solves the problem of reliable acquisition of raw data. Through flexible backup strategies and integrity verification, it ensures the correctness and availability of the read data, providing a reliable data source for subsequent overwrite operations.

[0142] After reading the raw memory data, the platform overwrites this data into the modified memory region, completing the memory state rollback. The overwrite operation employs efficient memory copy technology to ensure data transfer speed and accuracy. For each modified memory region, the platform calls an optimized memory copy function, such as a platform-specific optimized version. Modern processors typically utilize SIMD instructions and cache prefetching technology to achieve transfer speeds close to the memory bandwidth limit.

[0143] The overwrite operation is executed sequentially according to the list of modified regions. Overwriting each region is atomic; either it completely succeeds or it doesn't overwrite at all, avoiding data inconsistencies caused by partial overwrites. For multi-channel images, the overwrite operation must ensure alignment between channels to guarantee the synchronous recovery of RGB three-channel data. Before overwriting, the platform checks the access permissions of the target memory region to confirm that the region is writable and not locked by other threads. If an access conflict is detected, the platform uses a spin wait or condition variable notification mechanism to wait for the lock to be released before executing the overwrite.

[0144] After the overlay is complete, the platform updates the metadata of the image matrix object, such as modifying the timestamp and version number, and marks the data as having been rolled back to the anchor point state. To verify the correctness of the overlay operation, the platform recalculates the global checksum of the image matrix. ,in The overlaid image data, and compared with the original checksum recorded in the anchor point. Compare. If If the values ​​are equal, it indicates a successful rollback operation, and the image matrix has been accurately restored to its state before computation. If not, it indicates an error in the overlay process. The platform records the error log and triggers a full recovery process, reloading the data from the original image file. The time complexity of the overlay operation depends on the scale of the modified data. For partially modified images, the overlay time is much shorter than the full load time. For example, if the algorithm only modifies 20% of the image, the overlay operation only needs to transfer 20% of the data, taking approximately 1 / 5 of the time required for a full load. This step solves the problem of fast and accurate data state recovery. Through efficient memory copying and integrity verification, reliable state rollback is achieved while minimizing data transfer volume, ensuring that subsequent calculations are based on the correct initial data.

[0145] In practical teaching applications, this inverse differential coverage mechanism significantly improves the system's data recovery efficiency and reliability. For typical image processing algorithms, such as Gaussian filtering, which is interrupted halfway through processing, traditional full recovery methods require reloading the entire image, which can take tens of milliseconds. This mechanism, through step-by-step localization and differential coverage, only needs to recover 50% of the modified data, reducing recovery time to tens of milliseconds, resulting in a performance improvement of approximately 50%. For high-frequency parameter adjustment scenarios, such as students adjusting parameters 10 times per second, each triggering task interruption and data recovery, this mechanism can save hundreds of milliseconds of recovery time, significantly improving interactive response speed. The step-by-step scanning strategy also reduces CPU cache miss rate because accessing memory by step offset has good spatial locality, improving cache utilization efficiency. The checksum and verification mechanism ensures the reliability of the recovery operation; no abnormal calculation results due to recovery errors occurred during testing, guaranteeing the accuracy of teaching results.

[0146] This embodiment effectively solves the technical problems of low efficiency and insufficient recovery accuracy of full scans during data recovery by designing a precise positioning and differential overlay mechanism based on memory step offsets. Automatic identification and calculation of memory step offsets quantifies the algorithm's memory access patterns into mathematical parameters, providing a theoretical basis for precise positioning. The step-scanning strategy improves scanning efficiency by thousands of times through verification and pre-screening, significantly reducing the time overhead of locating changed areas. Fine-grained scanning and SIMD acceleration technology ensure byte-level accuracy in positioning. Flexible backup strategies and integrity checks ensure reliable acquisition of original data. Efficient memory copying and atomic overlay operations achieve fast and accurate state rollback; the overlay time is proportional to the scale of modified data, significantly outperforming full recovery methods. A global verification and validation mechanism ensures the correctness and reliability of the recovery operation. This solution overcomes the technical shortcomings of low efficiency in traditional recovery methods, provides high-performance data management support for high-frequency task switching scenarios, and significantly improves the system's response speed and reliability.

[0147] This application also provides a training platform, which is referred to... Figure 3 The training platform includes a cloud server and a client. The client is used to send requests to the cloud server, and the cloud server is used to execute the operation steps of any of the above methods according to the requests.

[0148] This application also provides a cloud server, including: Communication device, used to establish a communication connection with a user terminal; The processor is configured as follows: In response to the click signal of the clicked node, the user is redirected to the corresponding algorithm training page. The algorithm training page is implemented based on the algorithm knowledge point navigation, and each algorithm knowledge point is organized in the form of nodes. In response to the parameter adjustment operation performed by the user through the visual inference control, the machine vision algorithm is recalculated in real time to obtain the calculation result. The visual inference control is a pre-defined machine vision algorithm encapsulated. The visual inference control includes a matrix affine transformation interactive calculation control, which is used to realize the bidirectional mapping of the coordinate system and convert the mouse screen coordinates of the user interface layer into the actual pixel coordinates of the underlying image matrix. The calculation results are rendered into an interface, and the rendered interface is then visualized. The visualization process displays perspective information about the machine vision algorithm's computational process.

[0149] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0150] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0151] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0152] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0153] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0154] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0155] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0156] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0157] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. An interactive algorithm visualization method for machine vision training, characterized in that, Applied to a training platform, this method includes: In response to the click signal of the clicked node, the user is redirected to the corresponding algorithm training page. The algorithm training page is implemented based on the algorithm knowledge point navigation, and each algorithm knowledge point is organized in the form of nodes. In response to the parameter adjustment operation performed by the user through the visual inference control, the machine vision algorithm is recalculated in real time to obtain the calculation result. The visual inference control is a pre-defined machine vision algorithm encapsulated. The visual inference control includes a matrix affine transformation interactive calculation control, which is used to realize the bidirectional mapping of the coordinate system and convert the mouse screen coordinates of the user interface layer into the actual pixel coordinates of the underlying image matrix. The calculation results are rendered into an interface, and the rendered interface is then visualized. The visualization process displays perspective information about the machine vision algorithm's computational process.

2. The method according to claim 1, characterized in that, The visualization simulation control also includes image difference simulation control, color space conversion simulation control, and spatial filtering parameter simulation control. The image difference simulation control is used to provide multiple input source channels, read two original image matrices and perform absolute difference calculation, and use a split-screen layout to display the comparison results of the two original images and the difference result image. The color space conversion simulation control is used to display the color space conversion process and results in real time. The spatial filtering parameter simulation control is used to trigger parameter change events and re-invoke the underlying filtering function through the numerical adjustment component.

3. The method according to claim 1, characterized in that, The matrix affine transformation interactive calculation control realizes bidirectional mapping of the coordinate system, including: Obtain the display size of the image display controls in the user interface layer and the actual size of the underlying image matrix; Calculate the scaling ratio based on the display size and the actual size; In response to user mouse actions, obtain the screen coordinates of the mouse in the user interface layer; Based on the scaling ratio, screen coordinates are converted into pixel coordinates of the underlying image matrix; The control points are captured based on the transformed pixel coordinates, and the affine transformation matrix is ​​calculated in real time based on the drag position of the control points. The calculated affine transformation matrix is ​​applied to the image matrix to generate the deformed image and render it, thus achieving bidirectional mapping of the coordinate system.

4. The method according to claim 1, characterized in that, Also includes: Listen for and capture the user's continuous interaction sequence on the visualization simulation control, including mouse dragging and parameter sliding actions; Interaction event cleaning is performed on the continuous interactive action sequence to filter out invalid intermediate jitter events and extract key interaction frames; Based on key interaction frames, visual results are obtained and rendered.

5. The method according to claim 4, characterized in that, Interaction event cleaning is performed on the continuous sequence of interactive actions to filter out invalid intermediate jitter events and extract key interaction frames, including: Start the interaction buffer pool within the preset time window and obtain all interaction coordinate points within the time window; The motion trajectory of the interactive coordinate points is fitted using the least squares method to calculate the instantaneous speed and direction of the dragging. Extract the fitted coordinates at the end of the time window as key interaction frames, and generate an interaction instruction package containing the key interaction frames.

6. The method according to claim 4, characterized in that, Based on key interaction frames, visual results are obtained and rendered, including: Construct a communication channel between the user interface thread and the underlying algorithm thread, and include an interrupt flag in the communication channel; When a key interaction frame arrives, check if there is a computational task being executed in the underlying algorithm thread; If there is an ongoing computation task, activate the interrupt flag and send an interrupt signal to the context of the ongoing computation task. In response to an interrupt signal, the underlying operations of the currently executing computation task are forcibly terminated, and any unfinished intermediate computation data is discarded. After terminating the currently executing computation task, a new computation task is executed to obtain the visual results, and the visual results are rendered.

7. The method according to claim 6, characterized in that, After terminating the currently executing computation task, the method also includes: Before executing a new computational task, generate the memory operation pointer anchor point for the current original image matrix; When a computation task is interrupted, the memory pointer of the current image matrix object is intercepted; Compare memory pointers with memory operation pointer anchors and extract the memory block segments that have been modified; Perform a reverse differential overwrite operation on the memory block fragment to revert the image matrix state to the anchor point state before computation and reclaim temporary resources generated by the interrupted computation task.

8. The method according to claim 7, characterized in that, Perform a reverse differential overlay operation on the memory block fragment to revert the image matrix state to the anchor point state before computation, including: Obtain the memory step offset during the execution of the computation task; Locate the modified memory region based on the memory step offset; Read raw memory data from the memory operation pointer anchor; The original memory data is overwritten into the modified memory area, completing the memory state rollback.

9. A training platform, characterized in that, The training platform includes a cloud server and a client. The client is used to send a request to the cloud server, and the cloud server is used to execute the operation steps of any one of the methods described in claims 1-8 according to the request.

10. A cloud server, characterized in that, include: Communication device, used to establish a communication connection with a user terminal; The processor is configured as follows: In response to the click signal of the clicked node, the user is redirected to the corresponding algorithm training page. The algorithm training page is implemented based on the algorithm knowledge point navigation, and each algorithm knowledge point is organized in the form of nodes. In response to the parameter adjustment operation performed by the user through the visual inference control, the machine vision algorithm is recalculated in real time to obtain the calculation result. The visual inference control is a pre-defined machine vision algorithm encapsulated. The visual inference control includes a matrix affine transformation interactive calculation control, which is used to realize the bidirectional mapping of the coordinate system and convert the mouse screen coordinates of the user interface layer into the actual pixel coordinates of the underlying image matrix. The calculation results are rendered into an interface, and the rendered interface is then visualized. The visualization process displays perspective information about the machine vision algorithm's computational process.

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