Intelligent graph recognition method, device and equipment based on collaborative drawing and storage medium

Abstract

The invention provides an intelligent graph recognition method and device based on collaborative drawing, equipment and a storage medium. The method is applied to the technical field of computers, and comprises the following steps: acquiring a canvas for collaborative drawing, performing pattern recognition processing on a pattern in the canvas for collaborative drawing, determining a boundary of the pattern, and performing segmentation processing on the pattern according to the boundary of the pattern to obtain at least one pattern and a pattern block corresponding to the pattern; the canvas for collaborative drawing is a drawing result obtained by collaborative drawing of a plurality of users; performing intelligent pattern recognition processing on the pattern block, and determining a visual component corresponding to a pattern in the pattern block; pixel point pickup processing is carried out on the graphic block, and each pixel point of the graphic block is traversed to obtain a pixel color value of the graphic block; and the data board is generated according to the visual component corresponding to the graph in the graph block and the pixel color value of the graph block, so that the identification accuracy is improved, and the development efficiency is improved.

Description

Technical Field

[0001] This application belongs to the field of computer technology, and in particular relates to an intelligent map recognition method, apparatus, device and storage medium based on collaborative drawing. Background Technology

[0002] In the digital age, enterprises are increasingly demanding data visualization dashboards. This growth can be attributed to the explosive growth in data volume, the need for faster decision-making, the need for cross-departmental collaboration, and the need to improve user experience.

[0003] Currently, data dashboards have become an indispensable tool for enterprises. They not only help companies better understand and utilize data, but also promote cross-departmental collaboration, improve user experience, accelerate decision-making, and foster a data-driven culture. With continuous technological advancements and the expansion of application scenarios, data dashboards will play an even more important role in the future.

[0004] However, the development process of existing data dashboards resembles a single-threaded pipeline workflow. Each step requires the completion of the previous one before it can begin, and this strong coupling significantly limits work efficiency. More importantly, the existing model does not support multi-person collaborative work, resulting in long development cycles and low efficiency. Furthermore, the method of generating data dashboards through image recognition mainly relies on OCR recognition, which is easily affected by environmental factors such as lighting and angle, reducing the accuracy of recognition. Summary of the Invention

[0005] This application provides an intelligent map recognition method, apparatus, device, and storage medium based on collaborative drawing, to address the shortcomings of low recognition accuracy and low development efficiency in the prior art.

[0006] Firstly, this application provides an intelligent map recognition method based on collaborative drawing, the method comprising:

[0007] Obtain a collaborative drawing canvas, perform graphic recognition processing on the graphics in the collaborative drawing canvas, determine the boundaries of the graphics, and segment the graphics according to the boundaries of the graphics to obtain at least one graphic and the graphic block corresponding to the graphic. The collaborative drawing canvas is the drawing result obtained after collaborative drawing by multiple users.

[0008] Intelligent graphic recognition processing is performed on the graphic block to determine the visual component corresponding to the graphic within the graphic block;

[0009] Perform pixel picking processing on the graphic block, traverse each pixel of the graphic block, and obtain the pixel color value of the graphic block;

[0010] A data dashboard is generated based on the visualization components corresponding to the graphics within the graphic block and the pixel color values ​​of the graphic block.

[0011] Optionally, image recognition processing is performed on the images in the collaborative drawing canvas, including:

[0012] The graphics in the collaborative drawing canvas are detected and identified to obtain the type and position information of at least one graphic in the collaborative drawing canvas.

[0013] Calculate the gradient magnitude of each pixel in the graphic based on the type and location information of the graphic;

[0014] Determine whether the gradient magnitude is higher than the threshold.

[0015] If so, determine that the pixel is an edge pixel;

[0016] If not, the pixel is determined to be a non-edge pixel.

[0017] Optionally, before performing feature extraction processing on the graphic block, the method further includes:

[0018] Acquire multiple historical collaborative drawing data and their corresponding categories and labels. The historical collaborative drawing data includes the layout, boundaries, and color characteristics of various graphics and charts drawn by historical users. The corresponding categories and labels of the historical collaborative drawing data include the categories and labels of standard visualization components corresponding to various hand-drawn graphics and charts in the historical collaborative drawing data.

[0019] Based on the historical collaborative drawing data and the corresponding categories and labels, the image recognition model is trained to obtain the trained image recognition model.

[0020] Optionally, the step of performing intelligent graphic recognition processing on the graphic block to determine the visualization component corresponding to the graphic within the graphic block includes:

[0021] The trained image recognition model performs feature extraction on the image block to obtain the feature values ​​of the image within the image block;

[0022] Based on the feature values ​​of the graphic, feature matching processing is performed to obtain the category and label corresponding to the graphic;

[0023] Based on the category and label, determine the visualization component corresponding to the graphic.

[0024] Optionally, the step of performing pixel picking processing on the graphic block, traversing each pixel of the graphic block to obtain the pixel color value of the graphic block, includes:

[0025] Obtain the width and height of at least one graphic block in the canvas, and determine the extent of the graphic block;

[0026] Perform pixel traversal processing on each pixel within the graphic block range to obtain the color value of each pixel, and determine the color feature of each pixel based on the color value of the pixel.

[0027] The color features of the pixels are analyzed and processed to determine the color distribution and features within the graphic block, thereby obtaining the pixel color values ​​of the graphic block.

[0028] Optionally, generating a data dashboard based on the visualization components corresponding to the graphics within the graphic block and the pixel color values ​​of the graphic block includes:

[0029] Based on the visualization component corresponding to the graphic within the graphic block, the graphic within the graphic block is mapped to the corresponding visualization component;

[0030] The pixel color values ​​of the graphic block are mapped to the graphics of the corresponding visualization component to obtain the data dashboard of the graphic block;

[0031] Traverse each graphic block to obtain a data dashboard mapping at least one graphic block;

[0032] Based on the position information of the at least one graphic block, the data dashboard of the at least one graphic block is mapped to the corresponding position of the entire canvas to obtain the data dashboard corresponding to the canvas for collaborative drawing.

[0033] Optionally, the method further includes:

[0034] If there is no visualization component corresponding to the graphic within the graphic block, then the graphic within the graphic block is collected and calibrated to determine the category and label of the graphic within the graphic block;

[0035] The image recognition model is trained by iteratively training and adjusting the model parameters using the images within the image block and the categories and labels of the images within the image block.

[0036] Secondly, this application provides an intelligent map recognition device based on collaborative drawing, the device comprising:

[0037] The acquisition module is used to acquire the canvas for collaborative drawing;

[0038] The processing module is used to perform graphic recognition processing on the graphics in the collaborative drawing canvas, determine the boundaries of the graphics, and segment the graphics according to the boundaries of the graphics to obtain at least one graphic and the graphic block corresponding to the graphic. The collaborative drawing canvas is the drawing result obtained after collaborative drawing by multiple users.

[0039] The processing module is also used to perform intelligent graphic recognition processing on the graphic block to determine the visualization component corresponding to the graphic in the graphic block;

[0040] The processing module is also used to perform pixel picking processing on the graphic block, traversing each pixel of the graphic block to obtain the pixel color value of the graphic block;

[0041] The processing module is also used to generate a data dashboard based on the visualization components corresponding to the graphics within the graphic block and the pixel color values ​​of the graphic block.

[0042] Optionally, the intelligent map recognition device based on collaborative drawing further includes: a calculation module, a judgment module, and a determination module;

[0043] The processing module is also used to detect and identify the graphics in the collaborative drawing canvas to obtain the type and position information of at least one graphic in the collaborative drawing canvas.

[0044] The calculation module is used to calculate the gradient magnitude of each pixel of the graphic based on the type and position information of the graphic.

[0045] The judgment module is used to determine whether the gradient magnitude is higher than the threshold.

[0046] The determining module is used to determine whether the pixel is an edge pixel when the gradient magnitude is higher than a threshold.

[0047] The determining module is used to determine that the pixel is a non-edge pixel when the gradient magnitude is not higher than a threshold.

[0048] Optionally, the acquisition module is further configured to acquire multiple historical collaborative drawing data and their corresponding categories and labels. The historical collaborative drawing data includes: the layout, boundaries, and color features of various graphics and charts drawn by historical users. The corresponding categories and labels of the historical collaborative drawing data include: the categories and labels of standard visualization components corresponding to various hand-drawn graphics and charts in the historical collaborative drawing data.

[0049] The processing module is further configured to train the image recognition model based on the historical collaborative drawing data and the corresponding categories and labels, so as to obtain a trained image recognition model.

[0050] Optionally, the processing module is further configured to use the trained image recognition model to perform feature extraction processing on the image block to obtain the feature values ​​of the image within the image block;

[0051] The processing module is further configured to perform feature matching processing based on the feature values ​​of the graphic to obtain the category and label corresponding to the graphic;

[0052] The determination module is further configured to determine the visualization component corresponding to the graphic based on the category and label.

[0053] Optionally, the determining module is further configured to obtain the width and height of at least one graphic block in the canvas, and determine the range of the graphic block;

[0054] The processing module is also used to perform pixel traversal processing on each pixel within the range of the graphic block, obtain the color value of each pixel, and determine the color feature of each pixel based on the color value of the pixel.

[0055] The processing module is further configured to analyze and process the color features of the pixels, determine the color distribution and features within the graphic block, and obtain the pixel color value of the graphic block.

[0056] Optionally, the processing module is further configured to map the graphics in the graphics block to the corresponding visualization components based on the visualization components corresponding to the graphics in the graphics block;

[0057] The processing module is also used to map the pixel color values ​​of the graphic block to the graphics of the corresponding visualization component to obtain the data dashboard of the graphic block;

[0058] The processing module is also used to traverse each graphic block to obtain a data dashboard mapping at least one graphic block.

[0059] The processing module is further configured to map the data dashboard of the at least one graphic block to the corresponding position of the entire canvas based on the position information of the at least one graphic block, so as to obtain the data dashboard corresponding to the canvas for collaborative drawing.

[0060] Optionally, the processing module is further configured to, if there is no visualization component corresponding to the graphic in the graphic block, collect the graphic in the graphic block and perform calibration processing on the graphic in the graphic block to determine the category and label of the graphic in the graphic block;

[0061] The processing module is further configured to train the image recognition model by iteratively training and adjusting the model parameters using the images within the image block and the categories and labels of the images within the image block.

[0062] Thirdly, this application provides an intelligent map recognition device based on collaborative drawing, comprising:

[0063] Memory;

[0064] processor;

[0065] The memory stores computer-executed instructions;

[0066] The processor executes computer execution instructions stored in the memory to implement the intelligent map recognition method based on collaborative drawing as described in the first aspect and various possible implementations of the first aspect above.

[0067] Fourthly, this application provides a computer storage medium having a computer program stored thereon, the computer program being executed by a processor to implement the intelligent map recognition method based on collaborative drawing as described in the first aspect and various possible implementations of the first aspect above.

[0068] This application provides an intelligent image recognition method, apparatus, device, and storage medium based on collaborative drawing. The method acquires a collaborative drawing canvas, performs image recognition processing on the graphics within the canvas to determine the boundaries of the graphics, and segments the graphics according to these boundaries to obtain at least one graphic and its corresponding graphic block. The collaborative drawing canvas is the result of collaborative drawing by multiple users. Intelligent image recognition processing is performed on the graphic block to determine the visual component corresponding to the graphics within the block. Pixel picking processing is performed on the graphic block, traversing each pixel to obtain the pixel color value of the graphic block. A data dashboard is generated based on the visual component corresponding to the graphics within the block and the pixel color value of the graphic block, improving recognition accuracy and development efficiency. Attached Figure Description

[0069] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0070] Figure 1 The system architecture diagram of the intelligent map recognition system based on collaborative drawing provided in this application;

[0071] Figure 2 A flowchart illustrating the intelligent map recognition method based on collaborative drawing provided in this application. Figure 1 ;

[0072] Figure 3 A flowchart illustrating the intelligent map recognition method based on collaborative drawing provided in this application. Figure 2 ;

[0073] Figure 4A schematic diagram of the structure of the intelligent map recognition device based on collaborative drawing provided in this application;

[0074] Figure 5 A schematic diagram of the structure of the intelligent map recognition device based on collaborative drawing provided in this application.

[0075] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0076] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0077] The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein.

[0078] In this application, the terms "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0079] In the digital age, enterprises are increasingly demanding data visualization dashboards, and the explosive growth in data volume makes traditional data processing and display methods difficult to meet their needs.

[0080] Data dashboards allow businesses to visually display and analyze massive amounts of data, enabling them to identify problems and opportunities more quickly. The need for faster decision-making demands that businesses acquire and process data in real time to maintain a competitive edge in a fiercely competitive market. Data dashboards provide real-time data updates and dynamic displays, helping decision-makers make informed decisions quickly.

[0081] The need for cross-departmental collaboration has also driven the application of data dashboards. Different departments can use shared, visual data dashboards to view and analyze relevant data in real time, promoting transparency and efficient communication, thereby improving overall operational efficiency. Furthermore, the demand for improved user experience is another important driving factor for the rapid development of data dashboards. Modern enterprises increasingly value user experience; through visual data dashboards, users can understand data more intuitively and interactively, enhancing their sense of participation and satisfaction.

[0082] In summary, visual data dashboards have significant advantages in improving data processing efficiency, accelerating decision-making, promoting cross-departmental collaboration, and enhancing user experience, thus becoming an indispensable tool in the process of enterprise digital transformation.

[0083] However, existing methods for creating visual data dashboards have the following drawbacks:

[0084] 1) Low efficiency: In the traditional development process of large-screen visualizations, the entire process is similar to a single-threaded pipeline workflow. Each step can only begin after the previous step is completed, and this strong coupling greatly limits work efficiency. More importantly, the existing model does not support multi-person collaborative work, which results in long development cycles and low efficiency.

[0085] 2) Low accuracy: The method of generating data dashboards by recognizing images mainly relies on OCR recognition, but OCR recognition is easily affected by factors such as environment, lighting, and angle, which reduces the accuracy of recognition.

[0086] To address the aforementioned problems, this application proposes an intelligent map recognition method based on collaborative drawing. Figure 1 This is a system architecture diagram of the intelligent map recognition system based on collaborative drawing provided in this application. This embodiment aims to explain the functions and roles of the drawing module, collaboration module, intelligent recognition module, and personalized recommendation module in intelligent map recognition based on collaborative drawing. Figure 1 As shown, the intelligent map recognition system based on collaborative drawing provided in this embodiment includes:

[0087] Drawing Module: Users can perform various basic drawing operations using online drawing tools, such as drawing lines, circles, and selecting pen colors. All these operations are displayed on the dashboard in real time, allowing users to see their drawing progress immediately.

[0088] The drawing module provides a canvas for users to draw on by creating a drawing board. It listens for and processes user mouse events through event listeners, monitoring the drawing process in real time and capturing the results of the user's mouse movements on the canvas. It offers various brushes, erasers, and other functionalities, allowing users to draw on the canvas, select different types of brushes, adjust brush color and width, and erase drawings using the eraser. In short, the drawing module serves as a foundational module, providing basic configurations for multiple users to draw on.

[0089] Collaboration Module: Enables multi-user collaborative work. When one user performs a drawing operation, this module synchronizes the operation with all other users in real time, allowing them to see the latest, up-to-date view.

[0090] The collaboration module authenticates multi-user collaborative drawing by creating a project QR code, allowing only project-related personnel to access the drawing process by scanning the QR code. It also creates a unique identification code for each user, serving as their sole identifier within the system. This ensures the identification and management of each user's status and data, establishing a persistent connection with the system via a bidirectional, full-duplex communication channel provided by WebSocket. The connection status is established and the user is added to a shared WebSocket connection pool, enabling real-time sharing of multi-user drawings and facilitating collaborative drawing. Other project participants can see the latest, real-time visuals. A heartbeat detection mechanism is also implemented to monitor the heartbeat data of each participating user's client, understanding their online status and facilitating data synchronization for offline users.

[0091] Intelligent recognition module: It uses the latest machine learning technology and canvas pixel dot matrix method to accurately identify every detail on the canvas, including boundaries, layout, color, etc., and generate a visual data dashboard based on the recognition results.

[0092] The intelligent recognition module identifies the boundaries of graphics in the canvas by calculating the gradients of pixels in the x and y directions using filters to calculate the gradients in the horizontal and vertical directions. The gradient magnitude represents the rate of color change of a pixel, and the direction represents the direction of color change. The module calculates the gradient magnitude and direction for each pixel, applies a threshold to the gradient magnitude, and considers pixels below the threshold as non-edge pixels and pixels above the threshold as edge pixels. The intelligent recognition module also identifies the layout of graphics in the canvas by using machine learning techniques to perform contour detection and segmentation, and combines this with the identification of target objects in the image to analyze the position and relationships of the graphics relative to the entire canvas. Finally, feature extraction is performed on the graphics, including feature matching, graphic recognition, matching with corresponding visualization components, and generating a visual data dashboard.

[0093] Personalized recommendation module: This module can intelligently optimize and enhance the initial version of the visual data dashboard. By adjusting the dashboard's UI style, it can adjust the display effect of the data dashboard, creating a visual data dashboard that better suits the user's aesthetic preferences.

[0094] The intelligent image recognition method based on collaborative drawing provided in this application obtains a collaborative drawing canvas, performs image recognition processing on the graphics in the canvas to determine the boundaries of the graphics, and segments the graphics according to the boundaries to obtain at least one graphic and its corresponding graphic block. The collaborative drawing canvas is the drawing result obtained through collaborative drawing by multiple users. Intelligent image recognition processing is performed on the graphic block to determine the visualization component corresponding to the graphics within the graphic block. Pixel picking processing is performed on the graphic block, traversing each pixel to obtain the pixel color value of the graphic block. A data dashboard is generated based on the visualization component corresponding to the graphics within the graphic block and the pixel color value of the graphic block, improving recognition accuracy and development efficiency.

[0095] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0096] Figure 2 A flowchart illustrating the intelligent map recognition method based on collaborative drawing provided in the application embodiments. Figure 1 .like Figure 2 As shown, the intelligent map recognition method based on collaborative drawing provided in this embodiment includes:

[0097] S201. Obtain the canvas for collaborative drawing.

[0098] This involves acquiring a collaborative drawing canvas. This canvas represents not only the work of a single user, but also the final drawing result obtained through real-time interaction, communication, and collaboration among multiple users. The collaborative drawing canvas integrates the wisdom and creativity of multiple individuals, resulting in a richer and more diverse drawing outcome.

[0099] During collaborative drawing, the system records each user's drawing operations, including pen strokes, color selections, and graphic additions, and synchronizes these operations to the interfaces of all participating users. This allows each user to see the modifications and additions made by other users in real time, ensuring consistency and coordination throughout the collaborative drawing process. Once all users have completed their drawing tasks, the system integrates all users' drawing operations to generate a complete collaborative drawing canvas.

[0100] S202. Perform graphic recognition processing on the graphics in the collaborative drawing canvas, determine the boundaries of the graphics, and segment the graphics according to the boundaries of the graphics to obtain at least one graphic and the graphic block corresponding to the graphic.

[0101] This process involves using advanced image recognition algorithms to scan and analyze the graphics on the collaborative drawing canvas, identifying the outline and boundaries of each graphic to determine its boundaries and then segmenting it. The goal of segmentation is to divide the entire graphic into several independent parts, called graphic blocks, according to its boundaries. These graphic blocks can be simple geometric shapes, such as rectangles and circles, or complex irregular shapes. The accuracy and effectiveness of segmentation directly affect subsequent processing and applications; therefore, efficient segmentation algorithms are required to obtain accurately segmented graphic blocks.

[0102] The final result is at least one graphic and its corresponding graphic blocks. These graphic blocks can be used for further analysis, editing, and processing. For example, in a collaborative drawing environment, different users can independently operate and modify different graphic blocks, thereby improving collaboration efficiency and flexibility. Furthermore, these graphic blocks can also be used in advanced applications such as graphic recognition, pattern matching, and data mining, providing users with more functionality and possibilities.

[0103] S203. Perform intelligent graphic recognition processing on the graphic block to determine the visualization component corresponding to the graphic within the graphic block.

[0104] The intelligent graphic recognition process for graphic blocks aims to automatically identify and analyze various graphic elements within them using advanced algorithms and machine learning techniques. This process includes not only the recognition of basic shapes such as circles, squares, and triangles, but also the analysis of complex graphics and patterns.

[0105] Through intelligent graphic recognition processing, the system can accurately identify each graphic element within a graphic block and match these elements with predefined visualization components. For example, a graphic block may contain a pie chart, bar chart, or line chart; the intelligent graphic recognition system can quickly identify the type of these graphics and match the corresponding visualization components.

[0106] Intelligent image recognition and processing not only improves data processing efficiency but also significantly reduces the need for manual intervention, making the data visualization process more automated and intelligent. In this way, users can more quickly extract valuable information from complex data and present it in an intuitive format, thereby supporting more effective decision-making and analysis.

[0107] S204. Perform pixel picking processing on the graphic block, traverse each pixel of the graphic block, and obtain the pixel color value of the graphic block.

[0108] The process of pixel picking for graphic blocks involves first loading or generating the target graphic block, which is graphic data of image co-drawing; then, determining the width and height of the graphic block to accurately traverse each pixel; and finally, traversing each pixel of the graphic block to obtain the pixel color value of the graphic block.

[0109] During the traversal, a two-dimensional array is created to store the color value of each pixel. For each pixel, the pixel data is accessed through its two-dimensional array to obtain the pixel's color value. Color values ​​are typically represented in RGB (red, green, blue) or RGBA (red, green, blue, transparency) format. This yields the pixel color values ​​of the graphic block.

[0110] Pixel-by-pixel processing allows for a comprehensive understanding and manipulation of every pixel in a graphic block, laying the foundation for subsequent image processing and analysis.

[0111] S205. Generate a data dashboard based on the visualization components corresponding to the graphics within the graphic block and the pixel color values ​​of the graphic block.

[0112] The process of generating a data dashboard based on the corresponding visualization components within a graphic block and the pixel color values ​​of the graphic block involves parsing the graphics within the block and identifying the visualization component corresponding to each graphic; then, the pixel color values ​​of the graphic block are analyzed. These color values ​​not only determine the appearance of the graphics but can also carry data information. For example, different colors can represent different data categories or numerical ranges. By parsing these color values, the system can extract useful data and map it to the corresponding visualization components.

[0113] During the data dashboard generation process, an intelligent image recognition system based on collaborative drawing integrates the parsed graphics and data to generate a comprehensive, visualized data dashboard. This dashboard not only displays the data intuitively but also provides deeper data insights through interactive features such as hover tips and click events.

[0114] This embodiment proposes an intelligent image recognition method based on collaborative drawing. This method acquires a collaborative drawing canvas, performs image recognition processing on the graphics within the canvas to determine the boundaries of the graphics, and then segments the graphics according to these boundaries to obtain at least one graphic and its corresponding graphic block. The collaborative drawing canvas is the result of collaborative drawing by multiple users. Intelligent image recognition processing is then performed on the graphic blocks to determine the visual components corresponding to the graphics within the blocks. Pixel picking processing is then performed on the graphic blocks, traversing each pixel to obtain the pixel color value of the graphic block. Finally, a data dashboard is generated based on the visual components corresponding to the graphics within the graphic blocks and the pixel color values ​​of the graphic blocks, improving recognition accuracy and development efficiency.

[0115] Figure 3 This is a flowchart illustrating the intelligent map recognition method based on collaborative drawing provided in this application. Figure 2 This embodiment is... Figure 2 Based on the embodiments, the intelligent map recognition method based on collaborative drawing is described in detail. For example... Figure 3 As shown, the intelligent map recognition method based on collaborative drawing provided in this embodiment includes:

[0116] S301. Obtain the canvas for collaborative drawing.

[0117] Step S301 is the same as step S201 above, and will not be repeated here.

[0118] S302. Detect and recognize the graphics in the collaborative drawing canvas to obtain the type and position information of at least one graphic in the collaborative drawing canvas.

[0119] Specifically, the system detects the graphics in the collaborative drawing canvas to determine if any drawn graphics exist in the canvas, thereby obtaining at least one graphic in the collaborative drawing canvas. When at least one drawn graphic is detected, the system identifies the type of the at least one graphic and its position relative to the entire canvas.

[0120] Specifically, the canvas for collaborative drawing is preprocessed, for example: adjusting the brightness and contrast of the canvas to ensure image quality; using an edge detection algorithm to extract edges in the canvas, and using a contour detection algorithm to extract contours in the canvas. Based on the number of detected contours, it is determined whether a drawn graphic exists in the canvas; after detecting at least one graphic, graphic recognition is performed. Feature extraction is performed on the contours, and a pre-trained neural network model is used to classify the extracted features to identify the type of graphic, such as: circle, rectangle, triangle, etc.; the bounding box of each graphic is calculated to obtain the position information of the at least one graphic, and it is converted into a normalized coordinate representation relative to the entire canvas; the type and position information of each graphic in the at least one graphic are correlated to obtain the type and position information of at least one graphic in the collaborative drawing canvas.

[0121] S303. Calculate the gradient magnitude of each pixel in the graphic based on the graphic type and location information.

[0122] Based on the identified graphic type and location information, the gradient magnitude of each pixel in the graphic is further calculated. The gradient magnitude reflects the intensity of the image brightness change, the gradient magnitude represents the rate of color change of a pixel, and the direction represents the direction of color change. The gradient magnitude of each pixel can be obtained by calculating the gradient of each pixel in the x and y directions. The calculation of gradient magnitude helps to further analyze the detailed features and edge information of the graphic, providing an important basis for subsequent graphic processing and analysis.

[0123] Specifically, the Sobel filter is used to calculate the gradient value of each pixel within the graphic region, and the gradient magnitude of each pixel is calculated by combining the gradients in the x and y directions.

[0124] S304. Determine whether the gradient magnitude is higher than the threshold. If yes, proceed to step S305; otherwise, proceed to step S306.

[0125] The process involves determining whether the gradient magnitude of each pixel exceeds a threshold. If so, step S305 is executed; otherwise, step S306 is executed. The threshold can be fixed or dynamically calculated based on the image content. The threshold setting depends on the specific application requirements and image characteristics.

[0126] S305. Determine the pixel as an edge pixel.

[0127] S306. Determine that the pixel is a non-edge pixel.

[0128] S307. Determine the boundary of the graphic, and divide the graphic according to the boundary of the graphic to obtain at least one graphic and the graphic block corresponding to the graphic.

[0129] Step S307 is the same as step S202 above, and will not be repeated here.

[0130] S308. Obtain multiple historical collaborative drawing data and their corresponding categories and labels. The historical collaborative drawing data includes the layout, boundaries, colors, and other features of various graphics and charts drawn by historical users. The corresponding categories and labels of the historical collaborative drawing data include the categories and labels of the standard visualization components corresponding to various hand-drawn graphics and charts in the historical collaborative drawing data.

[0131] This involves collecting a large amount of historical collaborative drawing data. This data can come from various sources, such as user drawing records within collaborative drawing tools. The data includes, but is not limited to, the following: various hand-drawn graphics, such as lines, rectangles, circles, arrows, etc., and charts such as bar charts, line charts, pie charts, etc.; the position, size, and hierarchical structure of graphics and charts on the canvas; the bounding box information of graphics and charts to determine their spatial extent; and the color attributes of graphics and charts, including fill color, border color, and transparency.

[0132] Define the categories of standard visualization components corresponding to graphs and charts, such as bar charts, pie charts, flowcharts, etc.; assign corresponding category labels to each historical collaborative plotting dataset, which usually requires manual annotation or the use of semi-automated tools. Organize the collected data into a structured format and extract key features from the graphical data, such as shape, size, color, and position.

[0133] S309. Based on historical collaborative drawing data and corresponding categories and labels, train the image recognition model to obtain the trained image recognition model.

[0134] The historical collaborative drawing data includes various graphics drawn by users in different contexts. Each graphic is labeled with a corresponding category and tag, such as geometric shapes, handwritten characters, or specific symbols. The collected data undergoes preprocessing. Preprocessing steps may include graphic normalization, denoising, edge detection, and data augmentation. Normalization scales the graphics to a uniform size, while denoising and edge detection help extract key features. After preprocessing, this processed data is used to train the graphic recognition model. During training, the model continuously adjusts its internal parameters to minimize the error between the predicted result and the actual label. Once the model's loss function converges, the iteration stops, resulting in the trained graphic recognition model.

[0135] S310. The trained graphic recognition model performs feature extraction processing on the graphic block to obtain the feature values ​​of the graphic within the graphic block.

[0136] The image recognition model receives input image blocks and converts them into a format suitable for model processing. Then, the model optimizes the quality of the image blocks through a series of preprocessing steps, such as normalization and denoising, to improve the accuracy of feature extraction.

[0137] In the feature extraction stage, the model utilizes its internal structures, including convolutional layers, pooling layers, and fully connected layers, to extract feature information from graphic blocks layer by layer. Convolutional layers extract local features, such as edges and corners, through convolutional operations; pooling layers reduce the size of the feature map through downsampling operations while retaining important feature information. Through multiple layers of convolution and pooling operations, the model gradually extracts higher-level and more abstract features.

[0138] The model integrates the extracted feature information through fully connected layers to generate feature values ​​for the graphics within the graphic blocks. These feature values ​​can be used for subsequent classification, detection, or recognition tasks, helping the system accurately identify and understand the content within the graphic blocks.

[0139] S311. Perform feature matching processing based on the feature values ​​of the graphic to obtain the category and label corresponding to the graphic.

[0140] In this process, feature matching is performed based on the feature values ​​of the graphic. These feature values ​​may include information such as shape, color, texture, and edges. After the feature values ​​are extracted, they are matched with a pre-established feature database. Through feature matching, the category and label to which the graphic belongs can be identified.

[0141] S312. Based on the category and label, determine the visualization components corresponding to the graphic.

[0142] Identifying the corresponding visualization components based on categories and labels is a crucial step in data visualization and user interface design. This process involves transforming abstract data and information into intuitive, easy-to-understand graphical representations so that users can quickly access and analyze information.

[0143] The system classifies input data using machine learning algorithms or predefined rules and assigns category labels; based on the data's category and labels, it determines the most suitable visualization component. Common visualization components include: bar charts, line charts, pie charts, scatter plots, heatmaps, etc. Each component has its specific uses and advantages.

[0144] S313. Obtain the width and height of at least one graphic block in the canvas, and determine the range of the graphic block.

[0145] This process involves creating a bounding box for each detected graphic, with the top-left and bottom-right corners defining the graphic's position and extent. The width and height of at least one graphic block on the canvas are then obtained; specifically, the width and height are calculated from the bounding box coordinates. Based on the calculated width and height, the specific extent of the graphic block on the canvas is determined, which aids in subsequent layout analysis, graphic editing, and interaction design.

[0146] S314. Perform pixel traversal processing on each pixel within the graphic block range, obtain the color value of each pixel, and determine the color feature of each pixel based on the color value of the pixel.

[0147] This process involves iterating through each pixel within the graphic block, looping through the width and height of the graphic block, and then iterating through each pixel within the graphic block's range. For each pixel, its color value is obtained. In digital images, colors are typically represented using color models such as RGB (red, green, blue) or CMYK (cyan, magenta, yellow, black). Based on the pixel's color value, the color characteristics of each pixel can be determined. These color characteristics may include brightness, saturation, and hue.

[0148] S315. Analyze and process the color features of the pixels to determine the color distribution and features within the graphic block, and obtain the pixel color value of the graphic block.

[0149] The process involves converting pixel color values ​​from the original color space (such as RGB) to a more suitable color space for analysis (such as HSV or LAB); generating a color histogram for the graphic block and counting the frequency of each color in the graphic block; extracting color features, such as average color, dominant color, chromaticity distribution, and brightness, which can reflect the overall color characteristics of the graphic block; determining the color distribution and characteristics within the graphic block, and obtaining the pixel color values ​​of the graphic block.

[0150] S316. Map the graphics within the graphic block to the corresponding visualization components based on the graphics within the graphic block.

[0151] Based on the geometric characteristics, color, size, and position of the graphic elements, the most suitable visualization component is selected. For example, a rectangular graphic may be mapped to a bar chart, a circular graphic to a pie chart or a circular progress bar, and a line graphic to a line chart or flowchart. Abstract hand-drawn graphic elements are transformed into intuitive visualization components. By mapping the graphics within a graphic block to the corresponding visualization component, data can be displayed intuitively, helping users to better understand and analyze information.

[0152] S317. Map the pixel color values ​​of the graphic block to the graphics of the corresponding visualization component to obtain the data dashboard of the graphic block.

[0153] This process involves mapping the pixel color values ​​of graphic blocks to the corresponding visualization components. For example, color values ​​can be converted into color gradients in a heatmap to represent the heat or density of different areas; or color values ​​can be mapped to data points in bar charts, line charts, or other graphs to represent the changing trends of different data dimensions, thus creating a data dashboard for the graphic blocks. By mapping the pixel color values ​​of graphic blocks to the corresponding visualization components, a data dashboard can be generated. This type of data dashboard can not only intuitively display the data information in the graphic blocks, but also...

[0154] S318. Traverse each graphic block to obtain a data dashboard mapping at least one graphic block.

[0155] This process involves traversing each graphic block, extracting data from each block to obtain its pixel color values ​​or other feature information, processing and analyzing the extracted data, and mapping the processed data onto a visualization component to obtain a data dashboard of at least one graphic block.

[0156] S319. Based on the position information of at least one graphic block, map the data dashboard of at least one graphic block to the corresponding position of the entire canvas to obtain the data dashboard corresponding to the canvas for collaborative drawing.

[0157] The process involves parsing the positional information of each graphic block based on the positional information of at least one graphic block. This includes obtaining the graphic block's coordinates and dimensions on the canvas to determine its specific location and extent within the entire canvas. Each graphic block's data dashboard is then mapped to its corresponding canvas location. Processed data, such as color values ​​and numerical indicators, are combined with the graphic block's positional information to ensure the data dashboard accurately covers the graphic block's area. The generated collaborative drawing canvas integrates all the graphic block's data dashboards into a unified data display. Each graphic block's data dashboard corresponds to its actual location on the canvas, providing an intuitive global view.

[0158] Optionally, if there is no visualization component corresponding to the graphic within the graphic block, the graphic within the graphic block is collected and calibrated to determine the category and label of the graphic within the graphic block; the graphic recognition model is trained by iteratively training and adjusting the model parameters using the graphic within the graphic block and the category and label of the graphic within the graphic block.

[0159] If no corresponding visualization component is found within a graphic block, all graphics within the block are first collected and calibrated. The purpose of calibration is to determine the category and label of each graphic to provide an accurate data foundation for subsequent model training.

[0160] During the calibration process, manual intervention or the use of pre-trained models for initial classification and labeling may be necessary. After calibration, the graphics within the graphic blocks, along with their corresponding categories and labels, are input into the graphic recognition model as training data. The model is trained iteratively by adjusting its parameters. In each iteration, the model continuously optimizes its parameters based on the input data to improve the accuracy of recognizing different categories of graphics. After multiple iterations and parameter adjustments, the graphic recognition model will be able to accurately identify and classify various graphics within the graphic blocks, providing reliable support for the subsequent generation and application of visualization components.

[0161] This embodiment proposes an intelligent image recognition method based on collaborative drawing. This method detects and recognizes graphics in a collaborative canvas to obtain the type and location information of at least one graphic and calculates the gradient magnitude of each pixel. Edge pixels of the graphic are determined by judging whether the gradient magnitude is higher than a threshold. The graphic is segmented according to the edge pixels to obtain at least one graphic block. A trained graphic recognition model is used to extract features from the graphic blocks to obtain feature values ​​of the graphics within the blocks. Feature matching is then performed based on the feature values ​​to obtain the corresponding category and label of the graphic, thereby determining the corresponding visualization component. The range of the graphic block is obtained, and each pixel within the range is traversed to determine the color feature of each pixel. The pixel color features are analyzed to obtain the pixel color value of the graphic block. The visualization component corresponding to the graphic within the block and the pixel color value are mapped to the corresponding visualization component to obtain the data dashboard for the graphic block. Each graphic block is traversed, and each graphic block in at least one graphic block is mapped to the corresponding position on the entire canvas to obtain the data dashboard corresponding to the collaborative drawing canvas, improving recognition accuracy and development efficiency.

[0162] Figure 4 The schematic diagram of the intelligent map recognition device based on collaborative drawing provided in this application is as follows: Figure 4 As shown, the intelligent map recognition device 400 based on collaborative drawing provided in this embodiment includes:

[0163] Module 401 is used to acquire the canvas for collaborative drawing;

[0164] The processing module 402 is used to perform graphic recognition processing on the graphics in the collaborative drawing canvas, determine the boundaries of the graphics, and segment the graphics according to the boundaries of the graphics to obtain at least one graphic and the graphic block corresponding to the graphic. The collaborative drawing canvas is the drawing result obtained after collaborative drawing by multiple users.

[0165] The processing module 402 is further configured to perform intelligent graphic recognition processing on the graphic block to determine the visualization component corresponding to the graphic in the graphic block;

[0166] The processing module 402 is also used to perform pixel picking processing on the graphic block, traversing each pixel of the graphic block to obtain the pixel color value of the graphic block.

[0167] The processing module 402 is also used to generate a data dashboard based on the visualization components corresponding to the graphics within the graphic block and the pixel color values ​​of the graphic block.

[0168] Optionally, the intelligent map recognition device based on collaborative drawing further includes: a calculation module 403, a judgment module 404, and a determination module 405;

[0169] The processing module 402 is further configured to detect and identify the graphics in the collaborative drawing canvas to obtain the type and position information of at least one graphic in the collaborative drawing canvas.

[0170] The calculation module 403 is used to calculate the gradient magnitude of each pixel of the graphic based on the type and position information of the graphic.

[0171] The judgment module 404 is used to determine whether the gradient magnitude is higher than the threshold.

[0172] The determining module 405 is used to determine that the pixel is an edge pixel when the gradient magnitude is higher than the threshold.

[0173] The determining module 405 is used to determine that the pixel is a non-edge pixel when the gradient magnitude is not higher than the threshold.

[0174] Optionally, the acquisition module 401 is further configured to acquire multiple historical collaborative drawing data and corresponding categories and labels. The historical collaborative drawing data includes: the layout, boundaries, and color features of various graphics and charts drawn by historical users. The corresponding categories and labels of the historical collaborative drawing data include: the categories and labels of standard visualization components corresponding to various hand-drawn graphics and charts in the historical collaborative drawing data.

[0175] The processing module 402 is further configured to train the image recognition model based on the historical collaborative drawing data and the corresponding categories and labels, so as to obtain the trained image recognition model.

[0176] Optionally, the processing module 402 is further configured to use the trained image recognition model to perform feature extraction processing on the image block to obtain the feature values ​​of the image within the image block;

[0177] The processing module 402 is further configured to perform feature matching processing based on the feature values ​​of the graphic to obtain the category and label corresponding to the graphic;

[0178] The determination module 405 is further configured to determine the visualization component corresponding to the graphic based on the category and label.

[0179] Optionally, the determination module 405 is further configured to obtain the width and height of the at least one graphic block in the canvas and determine the range of the graphic block;

[0180] The processing module 402 is further configured to perform pixel traversal processing on each pixel within the range of the graphic block, obtain the color value of each pixel, and determine the color feature of each pixel based on the color value of the pixel.

[0181] The processing module 402 is further configured to analyze and process the color features of the pixel points, determine the color distribution and features within the graphic block, and obtain the pixel color value of the graphic block.

[0182] Optionally, the processing module 402 is further configured to map the graphics in the graphics block to the corresponding visualization components according to the visualization components corresponding to the graphics in the graphics block;

[0183] The processing module 402 is further configured to map the pixel color values ​​of the graphic block to the graphics of the corresponding visualization component to obtain the data dashboard of the graphic block;

[0184] The processing module 402 is also used to traverse each graphic block to obtain a data dashboard mapping at least one graphic block;

[0185] The processing module 402 is further configured to map the data dashboard of the at least one graphic block to the corresponding position of the entire canvas according to the position information of the at least one graphic block, so as to obtain the data dashboard corresponding to the canvas for collaborative drawing.

[0186] Optionally, the processing module 402 is further configured to, if there is no visualization component corresponding to the graphic in the graphic block, collect the graphic in the graphic block and perform calibration processing on the graphic in the graphic block to determine the category and label of the graphic in the graphic block;

[0187] The processing module 402 is further configured to train the image recognition model by iteratively training and adjusting the model parameters using the images within the image block and the categories and labels of the images within the image block.

[0188] Figure 5 This is a schematic diagram of the structure of the intelligent map recognition device based on collaborative drawing provided in this application. Figure 5 As shown, this application provides an intelligent map recognition device based on collaborative drawing. The intelligent map recognition device 500 based on collaborative drawing includes: a receiver 501, a transmitter 502, a processor 503, and a memory 504.

[0189] Receiver 501 is used to receive instructions and data;

[0190] Transmitter 502 is used to send commands and data;

[0191] Memory 504 is used to store instructions executed by the computer;

[0192] Processor 503 is used to execute computer execution instructions stored in memory 504 to implement the various steps of the intelligent map recognition method based on collaborative drawing in the above embodiments. For details, please refer to the relevant descriptions in the foregoing embodiments of the intelligent map recognition method based on collaborative drawing.

[0193] Optionally, the memory 504 can be either standalone or integrated with the processor 503.

[0194] When the memory 504 is set up independently, the electronic device also includes a bus for connecting the memory 504 and the processor 503.

[0195] This application also provides a computer storage medium storing computer execution instructions. When the processor executes the computer execution instructions, it implements the intelligent map recognition method based on collaborative drawing as described above by the intelligent map recognition device based on collaborative drawing.

[0196] It will be understood by those skilled in the art that all or some of the steps, systems, or apparatuses disclosed above, and their functional modules / units, can be implemented as software, firmware, hardware, or suitable combinations thereof. In hardware implementations, the division between functional modules / units mentioned in the above description does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed collaboratively by several physical components. Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit (ASIC). Such software may be distributed on a computer-readable medium, which may include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and can be accessed by a computer. Furthermore, it is well known to those skilled in the art that communication media typically contain computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0197] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the following claims.

[0198] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. An intelligent map recognition method based on collaborative drawing, characterized in that, The method includes: Obtain a collaborative drawing canvas, perform graphic recognition processing on the graphics in the collaborative drawing canvas, determine the boundaries of the graphics, and segment the graphics according to the boundaries of the graphics to obtain at least one graphic and the graphic block corresponding to the graphic. The collaborative drawing canvas is the drawing result obtained after collaborative drawing by multiple users. Intelligent graphic recognition processing is performed on the graphic block to determine the visual component corresponding to the graphic within the graphic block; Perform pixel picking processing on the graphic block, traverse each pixel of the graphic block, and obtain the pixel color value of the graphic block; A data dashboard is generated based on the visualization components corresponding to the graphics within the graphic block and the pixel color values ​​of the graphic block.

2. The method according to claim 1, characterized in that, Performing graphic recognition processing on the graphics in the collaborative drawing canvas includes: The graphics in the collaborative drawing canvas are detected and identified to obtain the type and position information of at least one graphic in the collaborative drawing canvas. Calculate the gradient magnitude of each pixel in the graphic based on the type and location information of the graphic; Determine whether the gradient magnitude is higher than the threshold; If so, determine that the pixel is an edge pixel; If not, the pixel is determined to be a non-edge pixel.

3. The method according to claim 1, characterized in that, Before performing feature extraction processing on the graphic block, the method further includes: Acquire multiple historical collaborative drawing data and their corresponding categories and labels. The historical collaborative drawing data includes the layout, boundaries, and color characteristics of various graphics and charts drawn by historical users. The corresponding categories and labels of the historical collaborative drawing data include the categories and labels of standard visualization components corresponding to various hand-drawn graphics and charts in the historical collaborative drawing data. Based on the historical collaborative drawing data and the corresponding categories and labels, the image recognition model is trained to obtain the trained image recognition model.

4. The method according to claim 3, characterized in that, The intelligent graphic recognition processing of the graphic block to determine the visualization component corresponding to the graphic within the graphic block includes: The trained image recognition model performs feature extraction on the image block to obtain the feature values ​​of the image within the image block; Based on the feature values ​​of the graphic, feature matching processing is performed to obtain the category and label corresponding to the graphic; Based on the category and label, determine the visualization component corresponding to the graphic.

5. The method according to claim 1, characterized in that, The step of performing pixel picking processing on the graphic block, traversing each pixel of the graphic block to obtain the pixel color value of the graphic block, includes: Obtain the width and height of at least one graphic block in the canvas, and determine the extent of the graphic block; Perform pixel traversal processing on each pixel within the graphic block range to obtain the color value of each pixel, and determine the color feature of each pixel based on the color value of the pixel. The color features of the pixels are analyzed and processed to determine the color distribution and features within the graphic block, thereby obtaining the pixel color values ​​of the graphic block.

6. The method according to claim 2, characterized in that, The step of generating a data dashboard based on the visualization components corresponding to the graphics within the graphic block and the pixel color values ​​of the graphic block includes: Based on the visualization component corresponding to the graphic within the graphic block, the graphic within the graphic block is mapped to the corresponding visualization component; The pixel color values ​​of the graphic block are mapped to the graphics of the corresponding visualization component to obtain the data dashboard of the graphic block; Traverse each graphic block to obtain a data dashboard mapping at least one graphic block; Based on the position information of the at least one graphic block, the data dashboard of the at least one graphic block is mapped to the corresponding position of the entire canvas to obtain the data dashboard corresponding to the canvas for collaborative drawing.

7. The method according to claim 4, characterized in that, The method further includes: If there is no visualization component corresponding to the graphic within the graphic block, then the graphic within the graphic block is collected and calibrated to determine the category and label of the graphic within the graphic block; The image recognition model is trained by iteratively training and adjusting the model parameters using the images within the image block and the categories and labels of the images within the image block.

8. An intelligent map recognition device based on collaborative drawing, characterized in that, The device includes: The acquisition module is used to acquire the canvas for collaborative drawing; The processing module is used to perform graphic recognition processing on the graphics in the collaborative drawing canvas, determine the boundaries of the graphics, and segment the graphics according to the boundaries of the graphics to obtain at least one graphic and the graphic block corresponding to the graphic. The collaborative drawing canvas is the drawing result obtained after collaborative drawing by multiple users. The processing module is also used to perform intelligent graphic recognition processing on the graphic block to determine the visualization component corresponding to the graphic in the graphic block; The processing module is also used to perform pixel picking processing on the graphic block, traversing each pixel of the graphic block to obtain the pixel color value of the graphic block; The processing module is also used to generate a data dashboard based on the visualization components corresponding to the graphics within the graphic block and the pixel color values ​​of the graphic block.

9. An intelligent map recognition device based on collaborative drawing, characterized in that, include: Memory; processor; The memory stores computer-executed instructions; The processor executes the computer execution instructions stored in the memory to implement the intelligent map recognition method based on collaborative drawing as described in any one of claims 1-7.

10. A computer storage medium, characterized in that, The computer storage medium stores computer execution instructions, which, when executed by a processor, are used to implement the intelligent map recognition method based on collaborative drawing as described in any one of claims 1-7.

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