A method and system for automatically generating graphic visualization

Abstract

The present invention discloses a method and system for automatically generating symbol visualizations, comprising: a receiving module for receiving user-uploaded data requiring symbol visualization; a metaphorical image search and processing module for searching for a series of metaphorical images for the received data based on metaphorical image standards, deconstructing and processing the metaphorical images to obtain visual elements, and forming a visual element list; a binding and drawing module for binding the received data to visual elements in the visual element list, assigning coding attributes to the bound visual elements based on data types, and drawing the visual elements based on the coding attributes to obtain a metaphor-based symbol visualization; and an editing module for providing an editing interface for the user, receiving the user's editing data, and drawing and generating a new metaphor-based symbol visualization based on the edited data. This system and method can automatically generate metaphor-based symbol visualizations.

Description

Technical Field

[0001] The present invention belongs to the technical field of multidimensional data visualization design, and in particular relates to an automatic generation method and system for graphic visualization. Background Art

[0002] Icons are widely used in the visualization of multidimensional data. However, excessive data dimensions can lead to overly complex icon designs, which in turn can cause problems with visual understanding. Researchers have attempted to use visual metaphors to aid understanding, using icons with representative appearances related to the data. Visual metaphors have been widely used in many fields, such as sports, urban applications, and blockchain. Related research has shown that appropriate metaphors can help people understand icons more quickly and accurately.

[0003] However, designing and using metaphors in icons is not an easy task. Researchers must balance various factors, such as the expressiveness of visual expressions and the effectiveness of data mapping. Currently, some visualization creation tools can help create icon-based visualizations. However, in the actual creation process, most tools find it difficult to strike a balance between automation and customization. Some tools only consider basic geometric shapes or limited shapes and do not support the creation of icons through metaphors. Some creation tools support customization more. They allow users to create graphic elements from scratch through sketching or interaction. However, creating graphic elements from scratch is difficult, and the quality of the final icon is highly dependent on the user's design experience and expertise. Some tools reduce customization costs and simplify the creation process by introducing online resources. However, excessive image design can easily make users lost, thus affecting the final creation.

[0004] Based on the above background, researchers hope to use online resources to automatically generate metaphor-based icon visualizations to help ordinary users who are not designers and visualization experts encode multidimensional data.

[0005] However, researchers need to overcome two difficulties: First, how to design metaphors into graphical visualizations. Currently, a large number of visualizations use metaphors to facilitate data presentation. Existing research has demonstrated that using metaphors can facilitate comprehension and is an effective design strategy. However, there is currently a lack of systematic research on this type of design to guide the creation of metaphor-based graphical visualizations.

[0006] The second challenge is automating the design process. This involves a series of design decisions, such as choosing an appropriate metaphor and binding data to the various elements of the metaphor. This requires comprehensive consideration of the entire process, but automated approaches to designing and generating metaphor-based visualizations have not received much attention. Summary of the Invention

[0007] In view of the above, the purpose of the present invention is to provide a method and system for automatically generating graphical visualizations, by searching for suitable metaphorical images as materials on the Internet and mapping the data to different visual elements of the graphics based on the matching degree.

[0008] To achieve the above-mentioned object of the invention, an embodiment provides a system for automatically generating icon visualization, comprising:

[0009] The receiving module is used to receive the data uploaded by the user that needs to be visualized;

[0010] a metaphorical image search and processing module, configured to search for a series of metaphorical images for received data according to metaphorical image standards, and deconstruct and process the metaphorical images to obtain visual elements, thereby forming a visual element list;

[0011] a binding and drawing module, configured to bind the received data to the visual elements in the visual element list, assign coding attributes to the bound visual elements according to the data types, and draw the visual elements according to the coding attributes to obtain a metaphor-based icon visualization;

[0012] The editing module is used to provide an editing interface for users, receive users' editing data, and draw and generate new metaphor-based icon visualization based on the editing data.

[0013] Preferably, the received data to be visualized by icons is represented in a table format, wherein the table name represents the subject, each column in the table represents a specific data dimension, and the column name is understood as the subject of the data dimension.

[0014] Preferably, the metaphor image standard is determined by analyzing existing metaphor-based icon visualizations, including:

[0015] First, we analyze existing metaphor-based icon visualizations to derive design rules. These rules include: considering metaphor types and icon placement types from the perspective of overall design. Metaphor types include semantic correlation, which indicates that the metaphor is related to the data theme, and structural correlation, which indicates that the metaphor is related to the data structure. Icon placement types include data-driven, which indicates that the position of the icon is related to a specific numerical value, and structural-driven, which indicates that the data has structural characteristics. From the perspective of individual icons, icon layout types are considered, which include radial and non-radial. From the perspective of visual elements in icons, visual element classification is considered, which includes shape and chart.

[0016] Then, according to the design rules, the metaphor image standard is determined as follows: a vector image that is relevant to the data semantics and has a simple structure.

[0017] Preferably, the metaphorical image is deconstructed and processed to obtain visual elements, including:

[0018] A single metaphor is divided into multiple visual elements, and the visual elements are filtered to remove the visual elements whose image area is smaller than the set threshold. The layout type of the metaphor image is determined to be radial or non-radial based on the remaining visual elements, and the remaining visual elements are formed into a visual element list and recorded.

[0019] The layout type of visual elements in the icon.

[0020] Preferably, the step of binding the received data to the visual elements in the visual element list includes:

[0021] Taking visual elements as nodes, a Monte Carlo search tree is used to search for visual elements to determine the set of visual elements corresponding to the path with the highest reward value as the optimal data mapping space. The received data is then bound to the visual elements according to the optimal data mapping space.

[0022] When using the Monte Carlo search tree to search for visual elements, the node reward value is updated based on three aspects: the importance of data attributes, the semantic relevance between data attributes and visual elements, and the overlap of visual elements. After the search is completed, the average reward value of all nodes in a single path is used as the reward value of the path.

[0023] Preferably, the editing data includes visual elements and / or coding attributes, and personalized design of the new icon visualization is achieved by modifying the visual elements and / or the coding attributes of the visual elements, wherein the coding attributes include color, numerical value, transparency, quantity, and rotation angle.

[0024] To achieve the above-mentioned object of the invention, an embodiment provides a method for automatically generating icon visualization, which uses the above-mentioned system and includes the following steps:

[0025] Utilize the receiving module to receive the data uploaded by the user that needs to be visualized;

[0026] Using the metaphor image search and processing module to search for a series of metaphor images for the received data according to the metaphor image standard, and deconstructing and processing the metaphor images to obtain visual elements, thereby forming a visual element list;

[0027] Using a binding and drawing module to bind the received data to the visual elements in the visual element list, assigning coding attributes to the bound visual elements according to the data type, and drawing the visual elements according to the coding attributes to obtain a metaphor-based icon visualization;

[0028] The editing interface provided by the editing module is used to receive the user's editing data, and a new metaphor-based icon visualization is generated based on the editing data.

[0029] Compared with the prior art, the present invention has at least the following beneficial effects:

[0030] (1) Automating icon design while using semantically related metaphor-based design. Metaphors are widely used in visualization to connect data semantics with themes. This invention aims to automatically generate icons and ensure that their visual elements are semantically related to the metaphorical graphics.

[0031] (2) Balancing design expressiveness and perceptual effectiveness. Increasing the number of data dimensions can complicate metaphor-based icon design and create comprehension issues. Some visual elements in metaphorical graphics are suitable for encoding data attributes, while others are more suitable for decorative purposes. This invention achieves a balance between encoding data dimensions and visual elements while maintaining perceptual constraints.

[0032] (3) Providing design inspiration. Considering metaphor selection and visual mapping, the design space for metaphor-based icon visualization can be very large, and it is impossible for designers to try all alternatives. Designers tend to start with the designs they are accustomed to. In contrast, machines will list all possibilities and may unexpectedly generate new designs, which in turn provide designers with new design inspiration. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0034] Figure 1 1 is a schematic diagram of the structure of the automatic generation system of the icon visualization provided by the embodiment;

[0035] Figure 2 This is a schematic diagram of an interface of a system for automatically generating visual icons provided by an embodiment;

[0036] Figure 3 is a flowchart of using a Monte Carlo search tree for exploration provided by an embodiment;

[0037] Figure 4 It is a flowchart of the method for automatically generating icon visualization provided by the embodiment. DETAILED DESCRIPTION

[0038] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and do not limit the scope of protection of the present invention.

[0039] Figure 11 is a schematic diagram of the structure of the automatic generation system of icon visualization provided by the embodiment. Figure 2 Schematic diagram of the interface of the automatic generation system of the icon visualization provided by the embodiment. Figure 1 and Figure 2 As shown, the embodiment provides an automatic generation system for icon visualization, including a receiving module, a metaphor image search and processing module, a binding and drawing module, and an editing module.

[0040] The receiving module is used to receive user-uploaded data to be visualized using graphical symbols. In one embodiment, the data to be visualized using graphical symbols is presented in a table format, where the table name represents the data subject, and each column in the table represents a specific data dimension. The column name can be understood as the subject of the data dimension. For example, for hamburger type data, the data subject is hamburger, and the information in each data dimension includes the type of hamburger, the hamburger's ingredients (such as bacon, chicken drumstick, etc.), the price of the hamburger, and the size of the hamburger.

[0041] The metaphor image search and processing module is used to search for a series of metaphor images for received data according to metaphor image standards, and deconstruct and process the metaphor images to obtain visual elements to form a visual element list.

[0042] In this embodiment, metaphorical image standards are determined by analyzing existing metaphor-based icon visualizations. This includes: first, analyzing existing metaphor-based icon visualizations to obtain design patterns, which provide guidance for automated design. Then, based on the analyzed design patterns, metaphorical image standards are determined as: vector images that are semantically relevant to the data and have a simple structure.

[0043] Specifically, the analysis of existing metaphor-based icon visualizations includes: Phase 1: Focusing on the overall design, understanding how designers design metaphors into visualizations, and how to place all icons in visualizations. In terms of metaphor types, it is divided into two categories: semantic relevance and structural relevance. The former indicates that the metaphor is related to the data theme, and designers usually use metaphors in the design of specific individual icons. The latter indicates that the metaphor is related to the data structure. In this case, the metaphor is mainly reflected in the visualization layout. Some designs meet both semantic relevance and structural relevance. In terms of icon placement, it is divided into two categories: data-driven and structure-driven. Data-driven means that the position of the icon is related to a specific numerical value. Such icons are usually placed in a Cartesian coordinate system, and some numerical values ​​can be directly used as x-values ​​or y-values. Structural-driven indicates that the data has structural characteristics. Designers tend to use metaphors in structure-driven icons, such as using maps and clocks;

[0044] Phase 2 focuses on the design of specific icons, particularly their layout. Icon layouts fall into two categories: radial and non-radial. Radial icons are placed in a polar coordinate system. Each element shares the coordinate system's origin and is related to a circle. Icons in non-radial layouts can be placed in a Cartesian coordinate system.

[0045] In the third stage, we focus on the visual elements in the icons. We divide the visual elements in the icons into two categories: shapes and charts. Shape-level elements refer to different shapes, including basic geometric figures (e.g., circles, polygons) and complex shapes (e.g., leaves). Chart-level elements are variant charts in icons that can convey more information. Designers mainly use pie charts, donut charts, star charts, heat maps, and box plots. The information conveyed by an icon is limited, and the number of elements encoded by metaphorical icons ranges from 2 to 6 elements.

[0046] The above-mentioned metaphorical image standard is embedded in the system. When receiving data that requires graphic visualization, the metaphorical image search and processing module searches for a series of metaphorical images for the received data according to the metaphorical image standard, and deconstructs and processes the metaphorical images to obtain visual elements, forming a visual element list.

[0047] In an embodiment, a metaphorical image is deconstructed and processed to obtain visual elements, including: first, dividing a single metaphor into multiple visual elements, converting each path in the SVG file into a separate SVG file, each file being a visual element; then, screening the visual elements to remove non-essential visual elements whose image area is smaller than a set threshold, where the threshold can be 0.5% of the area of ​​the metaphorical image; next, determining whether the layout type of the metaphorical image is radial or non-radial based on the remaining necessary visual elements; finally, forming a visual element list from the remaining visual elements, and recording the layout type of the visual elements in the icon.

[0048] The binding and drawing module is used to bind the received data to the visual elements in the visual element list, assign coding attributes to the bound visual elements according to the data type, and draw the visual elements according to the coding attributes to obtain metaphor-based icon visualization.

[0049] In the embodiment, the input data has fixed dimensions, and each dimension has corresponding data attributes, including numerical and categorical types. The number of elements in the element list is fixed, but not all data needs to be displayed in the final graphical visualization. Therefore, the data mapping space is explored by constructing a Monte Carlo search tree, each node of the tree is a specific visual element in the visual element list, and the height of the tree corresponds to a specific data dimension. This method needs to sort all data dimensions according to their importance, and the evaluation indicator is the relevance to the data theme. Specifically, the data dimensions are ranked by calculating the cosine distance between the data dimension description and the data theme.

[0050] In the embodiment, a Monte Carlo search tree is used to search for visual elements to determine the visual element set corresponding to the path with the highest reward value as the optimal data mapping space, and the received data is bound to the visual elements according to the optimal data mapping space. First, an empty root node is used to initialize the tree. Next, specifically, Figure 3 As shown, the search process is divided into the following four stages:

[0051] Phase 1: Selection. The goal of the selection phase is to find the most suitable element node. This phase begins at the root node and selects a child node with the highest confidence limit each time, considering a balance between nodes with low visit counts and nodes with high visit counts. The selection phase ends when the most urgent expandable node is reached. If a node has not been visited (i.e., not expanded), it is considered an expandable leaf node.

[0052] Phase 2, expansion. The tree is expanded by adding a child node. Specifically, this is achieved by adding a random visual element and initializing the visit count and reward to zero. Note that if this data dimension is not displayed in the visualization, it is treated as adding an empty node.

[0053] Phase 3, Simulation. The simulation process begins with a new node and rapidly expands to a randomly selected node until it reaches a point where it can no longer be expanded. To achieve faster simulations, this phase avoids the pursuit of high rewards, but rather the goal of simulating more times within a limited timeframe to achieve high rewards.

[0054] Phase 4: Backtracking. Using the simulation results, backtracking is used to update the selected nodes. New nodes are added to the tree after their reward values ​​are updated and their visit time is calculated. If other visited nodes have higher rewards, the reward values ​​are updated and the visit time is increased.

[0055] After the end of phase 4, a cycle ends and a new cycle begins again in phase 1. The search process will return to the selection phase or terminate when the time limit is exceeded or the search tree is exhausted.

[0056] In an embodiment, when a Monte Carlo search tree is used to search for visual elements, the reward value of the node is updated based on three aspects: the importance of the data attribute, the semantic relevance of the data attribute and the visual element, and the overlap of the visual element. After the search is completed, the average of the reward values ​​of all nodes in a single path is used as the reward value of the path, and the path with the highest reward value is determined as the optimal mapping space.

[0057] During the search process, a reward system is calculated based on three criteria to evaluate the quality of the final visualization. An importance score is used to estimate the importance of a data attribute. The importance scores are normalized when sorting data dimensions to ensure model feasibility. Specifically, the importance score calculation process involves obtaining the data topic from the table title, obtaining the description of each data dimension from the column name of each dimension, and calculating and normalizing the semantic relevance between two texts.

[0058] The semantic relevance between data attributes and visual elements is used to evaluate the semantic relevance between a visual element and a data attribute. The specific calculation is as follows: based on the description of each data dimension of the data and the metaphorical image, a heat map is obtained based on the metaphorical image. Each point on the heat map corresponds to the relevance of each point in the metaphorical image with the theme; for a given visual element, the mean relevance of all the pixels where it is located is calculated, which is the semantic relevance of the visual element with the data attribute.

[0059] The overlap score is used to evaluate the degree of overlap between all visual elements of the final icon visualization. The specific calculation is: calculate the overlapping area between visual elements. If the overlapping area exceeds 30% of the entire icon visualization bounding box, the overlapping area is too large and the overlap degree is 0, otherwise it is 1.

[0060] For a single path, the product of the importance and semantic relevance of each node is calculated. The average value of all nodes accumulated and multiplied by the overlap calculated for the path is the final reward value of the path.

[0061] After binding the data attributes to the visual elements through the optimal mapping space, encoding attributes are assigned to the bound visual elements based on the data type, and the visual elements are drawn based on the encoding attributes to obtain a metaphor-based icon visualization. In this embodiment, the encoding attributes include color, numerical value, transparency, quantity, and rotation angle. When the data attribute is a numerical value, the assigned encoding attribute is numerical value; when the data attribute is an image, the assigned encoding attribute is color, etc.

[0062] The editing module previews the metaphor-based icon visualization and provides an editing interface for users. It receives user edit data and generates a new metaphor-based icon visualization based on the edit data. The edit data includes bound visual elements and / or encoding attributes. By modifying the visual elements and / or their encoding attributes, the new icon visualization can be personalized.

[0063] The automatic generation system for visualizing icons provided in the embodiment mainly considers the following three points during design:

[0064] Semantic relevance. Appropriate metaphor-based visualizations can facilitate data interpretation. Therefore, data semantics are crucial for selecting appropriate metaphor designs, and the plausibility of mappings between specific data dimensions and individual visual elements needs to be considered.

[0065] Support automatic and efficient generation. Online image resources can provide design inspiration, however, it is difficult for designers to select a suitable metaphorical image from a large number of online resources. At the same time, manual data mapping is time-consuming and labor-intensive because users need to calculate different attributes (i.e., size and angle) for encoding. The simplified way is to automate the process, including image selection and data mapping. At the same time, the automated process needs to be efficient and fast, so multiple filtering conditions need to be set in different steps so that the final visualization can strike a balance between quality and efficiency.

[0066] Human-machine collaborative workflow. Despite the convenience provided by automated systems, the resulting visualizations may not meet user expectations. Therefore, users should be involved in the creation process. The system provides some preliminary results for users to select and modify. After that, users can modify specific data dimensions, and the system will update and provide alternative solutions based on user preferences.

[0067] Based on the same inventive concept, Figure 4 As shown, the embodiment further provides a method for automatically generating icon visualization, which applies the above system and includes the following steps:

[0068] Step 1: using a receiving module to receive the data uploaded by the user that needs to be visualized;

[0069] Step 2: using the metaphor image search and processing module to search for a series of metaphor images for the received data according to the metaphor image standard, and deconstructing and processing the metaphor images to obtain visual elements, thereby forming a visual element list;

[0070] Step 3: Using a binding and drawing module to bind the received data to the visual elements in the visual element list, assigning coding attributes to the bound visual elements according to the data type, and drawing the visual elements according to the coding attributes to obtain a metaphor-based icon visualization;

[0071] Step 4: Utilize the editing interface provided by the editing module to receive the user's editing data, and generate a new metaphor-based icon visualization based on the editing data.

[0072] like Figure 2 As shown in FIG, the specific process of designing using the above-mentioned automatic generation system of icon visualization is as follows:

[0073] First, upload the data table as input in the data menu bar. Click the Generate button to proceed to the next step (trigger the metaphor module). The system will automatically generate the initial several symbol visualizations based on the data and display them in the preview view and candidate list view (trigger the binding and drawing module). The symbol visualization in the preview view is the option with the highest score after the system calculation. The edit view provides a small panel for each data dimension. Each small panel corresponds to the detailed information of the data dimension, including the title of the data column, the data type, the mapped elements, and the corresponding encoding channel. Different data dimensions are arranged according to the importance score. Users can modify the mapping relationship of the data through the drop-down menu bar. After clicking the Update button, the system will recalculate and create a new symbol visualization based on the user's input. Finally, the user can click the Export button to export the designed visualization file for further creation.

[0074] The system and method provided by the above embodiments automatically generate metaphor-based graphical visualizations based on the data's characteristics and semantic information, given tabular data, to help users understand the data more intuitively. This method considers the importance and semantic relevance of the data to select metaphorical graphics and map the visual elements in the graphics to the data. Furthermore, the system supports interactive editing of the graphical visualization mapping relationships to meet diverse design needs.

[0075] The specific implementation methods described above provide a detailed description of the technical solutions and beneficial effects of the present invention. It should be understood that the above is only the most preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, supplements and equivalent substitutions made within the scope of the principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A system for automatically generating metaphor-based graphical visualizations, characterized in that: include: The receiving module is used to receive the data uploaded by the user that needs to be visualized; a metaphorical image search and processing module, configured to search for a series of metaphorical images for received data according to metaphorical image standards, and deconstruct and process the metaphorical images to obtain visual elements, thereby forming a visual element list; a binding and drawing module, configured to bind the received data to the visual elements in the visual element list, assign coding attributes to the bound visual elements according to the data types, and draw the visual elements according to the coding attributes to obtain a metaphor-based icon visualization; The editing module is used to provide an editing interface for users, receive users' editing data, and generate new metaphor-based icon visualizations based on the editing data; Bind the received data to the visual elements in the visual element list, including: Taking visual elements as nodes, a Monte Carlo search tree is used to search for visual elements to determine the set of visual elements corresponding to the path with the highest reward value as the optimal data mapping space. The received data is then bound to the visual elements according to the optimal data mapping space. When using the Monte Carlo search tree to search for visual elements, the node reward value is updated based on three aspects: the importance of the data attribute, the semantic relevance of the visual element of the data attribute, and the overlap of the visual elements. After the search is completed, the average reward value of all nodes in a single path is used as the reward value of the path.

2. The system for automatically generating metaphor-based graphical visualization according to claim 1, characterized in that: The received data to be visualized is represented in a table format, wherein the table name represents the data subject, each column in the table represents a specific data dimension, and the column name is understood as the subject of the data dimension.

3. The system for automatically generating metaphor-based graphical visualization according to claim 1, characterized in that: The metaphor image criteria are determined by analyzing existing metaphor-based icon visualizations, including: First, we analyze existing metaphor-based icon visualizations to derive design rules. These rules include: considering metaphor types and icon placement types from the perspective of overall design. Metaphor types include semantic correlation, which indicates that the metaphor is related to the data theme, and structural correlation, which indicates that the metaphor is related to the data structure. Icon placement types include data-driven, which indicates that the position of the icon is related to a specific numerical value, and structural-driven, which indicates that the data has structural characteristics. From the perspective of individual icons, icon layout types are considered, which include radial and non-radial. From the perspective of visual elements in icons, visual element classification is considered, which includes shape and chart. Then, according to the design rules, the metaphor image standard is determined as follows: a vector image that is relevant to the data semantics and has a simple structure.

4. The system for automatically generating metaphor-based graphical visualization according to claim 1, characterized in that: Deconstruct and process metaphorical images to obtain visual elements, including: A single metaphor is divided into multiple visual elements. After screening the visual elements to remove the visual elements whose image area is smaller than a set threshold, the layout type of the metaphor image is determined to be radial or non-radial based on the remaining visual elements. The remaining visual elements are then combined into a visual element list, and the layout type of the visual elements in the icon is recorded.

5. The system for automatically generating metaphor-based graphical visualization according to claim 1, characterized in that: The calculation of the importance of data attributes includes: obtaining the subject of the data from the table title, obtaining the description of each data dimension from the column name of each data dimension, calculating the semantic relevance of two texts and performing normalization processing; The calculation of the semantic relevance between data attributes and visual elements involves obtaining the description of the data dimension and a metaphorical image based on the column name of each dimension of the data. A heat map is then generated based on the metaphorical image. Each point on the heat map corresponds to the relevance of each point in the metaphorical image to the theme. For a given visual element, the mean relevance of all pixels within the element is calculated, which is the semantic relevance between the visual element and the data attribute. The calculation of the overlap degree of the visual elements includes: calculating the overlapping area between the visual elements, if the overlapping area exceeds 30% of the visual bounding box of the entire icon, the overlapping area is too large and the overlap degree is 0, otherwise it is 1; For a single path, the product of the importance and semantic relevance of each node is calculated. The average value of all nodes accumulated and multiplied by the overlap calculated for the path is the final reward value of the path.

6. The system for automatically generating metaphor-based graphical visualization according to claim 1, characterized in that: The editing data includes visual elements and / or coding attributes, and the personalized design of the new icon visualization is achieved by modifying the visual elements and / or the coding attributes of the visual elements, wherein the coding attributes include color, numerical value, transparency, quantity, and rotation angle.

7. A method for automatically generating metaphor-based graphical visualization, characterized in that: The method applies the system according to any one of claims 1 to 6, and the method comprises the following steps: Utilize the receiving module to receive the data uploaded by the user that needs to be visualized; Using the metaphor image search and processing module to search for a series of metaphor images for the received data according to the metaphor image standard, and deconstructing and processing the metaphor images to obtain visual elements, thereby forming a visual element list; Using a binding and drawing module to bind the received data to the visual elements in the visual element list, assigning coding attributes to the bound visual elements according to the data type, and drawing the visual elements according to the coding attributes to obtain a metaphor-based icon visualization; The editing interface provided by the editing module is used to receive the user's editing data, and a new metaphor-based icon visualization is generated based on the editing data.

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