Method, device, equipment, storage medium and product for generating data dashboard

Abstract

The embodiment of the disclosure provides a data dashboard generation method, device, equipment, storage medium and product, through responding to receiving a dashboard generation instruction, performing semantic recognition on a natural language in the instruction to obtain a dashboard parameter; based on the dashboard parameter, generating a layer generation instruction, the layer generation instruction containing a layer parameter of a to-be-generated layer; based on the layer generation instruction, generating a layer generation code corresponding to the to-be-generated layer; based on the layer generation code, rendering and generating a dashboard vector diagram, which realizes automatic and efficient generation of a dashboard vector diagram based on a natural language in a dashboard generation instruction, and further helps to improve the efficiency and flexibility of data visualization.

Description

Technical Field

[0001] This disclosure relates to the field of computer technology, and in particular to a method, apparatus, device, storage medium, and product for generating a data dashboard. Background Technology

[0002] A data dashboard is a visualization tool that centrally displays key data metrics and can be used for data monitoring, analysis, and decision support. However, the generation process of a data dashboard often relies heavily on manual intervention, especially when dealing with complex and irregularly shaped graphics. Developers often need to manually design the content and style of each layer and use complex programming to combine and display the layers. This not only significantly increases the development difficulty but also limits the flexibility and scalability of the dashboard.

[0003] How to automate the generation of dashboards has become a pressing technical challenge. Summary of the Invention

[0004] To address the aforementioned technical problems, this disclosure is proposed. Embodiments of this disclosure provide a method, apparatus, device, storage medium, and product for generating a data dashboard.

[0005] According to one aspect of the present disclosure, a method for generating a data dashboard is provided, comprising: In response to receiving a dashboard generation command, semantic recognition is performed on the natural language in the command to obtain dashboard parameters, which include at least one of dashboard background parameters, dashboard data type parameters, and dashboard description parameters. Based on the dashboard parameters, determine the layer parameters of the layer to be generated, and generate a layer generation instruction based on the layer parameters of the layer to be generated. Based on the layer generation instructions, generate the layer generation code corresponding to the layer to be generated; Based on the layer generation code, a customized irregular-shaped dashboard that matches the dashboard generation instruction is rendered.

[0006] In some optional implementations, based on the layer generation code, a customized irregular-shaped dashboard matching the dashboard generation instruction is rendered and generated, including: Based on the layer generation code, a layer vector image is rendered and generated. Based on the display priority of the layer vector graphics, the layer vector graphics are overlaid to obtain a customized irregular-shaped dashboard that matches the dashboard generation instruction.

[0007] In some optional implementations, after rendering a customized irregular-shaped dashboard that matches the dashboard generation instruction based on the layer generation code, the process includes: In response to receiving an adjustment operation triggered for the irregularly shaped dashboard, the layer to be adjusted and the adjustment parameters corresponding to the adjustment operation are obtained; Based on the correlation relationship of the vector graphics of each layer in the irregular instrument panel, obtain the associated layer of the layer to be adjusted; Based on the adjustment parameters, the layer to be adjusted and its associated layers are adjusted to obtain the adjusted irregular-shaped dashboard.

[0008] In some optional implementations, based on the dashboard parameters, layer parameters of the layer to be generated are determined, and based on the layer parameters of the layer to be generated, a layer generation instruction is generated, including: The instrument panel parameters are parsed to obtain key semantic information; Based on the key semantic information, the dashboard parameters are grouped to obtain layer generation parameters for at least one layer; For any one of the at least one layers, generate a layer generation instruction that includes the layer generation parameters of that any one layer.

[0009] In some optional implementations, based on the layer generation instructions, layer generation code corresponding to the layer to be generated is generated, including: Based on the layer generation instruction, the layer information of the layer to be generated is determined, and the layer information includes at least one of the following: constituent elements, element attributes, and dependencies between constituent elements; Using a code generation model, the layer generation code is generated based on the layer information of the layer to be generated.

[0010] In some optional implementations, based on the dashboard parameters, layer parameters of the layer to be generated are determined, and based on the layer parameters of the layer to be generated, a layer generation instruction is generated, including: Based on the pre-set layer classification, the dashboard parameters are grouped to obtain layer generation parameters for at least one layer; For any one of the at least one layers, generate a layer generation instruction that includes the layer generation parameters of that any one layer.

[0011] According to another aspect of the present disclosure, an apparatus for generating a data dashboard is provided, comprising: A semantic recognition module is used to perform semantic recognition on the natural language in the received dashboard generation instruction in response to receiving a dashboard generation instruction, and obtain dashboard parameters. The dashboard parameters include at least one of dashboard background parameters, dashboard data type parameters, and dashboard description parameters. The instruction generation module is used to determine the layer parameters of the layer to be generated based on the dashboard parameters, and to generate a layer generation instruction based on the layer parameters of the layer to be generated. The code generation module is used to generate layer generation code corresponding to the layer to be generated based on the layer generation instruction; The image rendering module is used to generate a customized irregular-shaped dashboard that matches the dashboard generation instruction based on the code generated by the layer.

[0012] In some alternative implementations, the image rendering module includes: The rendering submodule is used to generate code based on the layer and render the resulting vector image. The overlay submodule is used to overlay the layer vector graphics based on the display priority of the layer vector graphics to obtain a customized irregular-shaped dashboard that matches the dashboard generation instruction.

[0013] In some alternative embodiments, the apparatus further includes: The information acquisition module is used to respond to receiving an adjustment operation triggered for the irregularly shaped dashboard, and to acquire the layer to be adjusted and the adjustment parameters corresponding to the adjustment operation; The layer acquisition module is used to acquire the associated layer of the layer to be adjusted based on the relationship between the vector graphics of each layer in the irregular instrument panel. The adjustment module is used to adjust the layer to be adjusted and its associated layers based on the adjustment parameters to obtain the adjusted irregular-shaped dashboard.

[0014] In some optional implementations, the instruction generation module includes: The parsing submodule is used to parse the instrument panel parameters to obtain key semantic information; The first grouping submodule is used to group the dashboard parameters based on the key semantic information to obtain layer generation parameters for at least one layer. The first generation submodule is used to generate a layer generation instruction containing the layer generation parameters of any one of the at least one layers.

[0015] In some alternative implementations, the code generation module includes: The determination submodule is used to determine the layer information of the layer to be generated based on the layer generation instruction. The layer information includes at least one of the following: constituent elements, element attributes, and dependencies between constituent elements. The second generation submodule is used to generate the layer generation code based on the layer information of the layer to be generated, using a code generation model.

[0016] In some optional implementations, the instruction generation module includes: The second grouping submodule is used to group the dashboard parameters based on a pre-set layer classification to obtain layer generation parameters for at least one layer. The third generation submodule is used to generate a layer generation instruction containing the layer generation parameters of any one of the at least one layers.

[0017] According to another aspect of the present disclosure, a computer-readable storage medium is provided, the storage medium storing computer program instructions that, when executed, implement the above-described method for generating a data dashboard.

[0018] According to another aspect of the present disclosure, an electronic device is provided, the electronic device comprising: Memory, used to store computer program products; A processor is configured to execute a computer program product stored in the memory, and when the computer program product is executed, to implement the above-described method for generating a data dashboard.

[0019] According to another aspect of the present disclosure, a computer program product is provided, including computer program instructions that, when executed by a processor, implement the above-described method for generating a data dashboard.

[0020] Based on the embodiments of this disclosure, for scenarios requiring the generation of data dashboards, such as data monitoring, analysis, and decision support, after receiving the dashboard generation instruction, the system first performs semantic recognition on the natural language in the instruction to obtain dashboard parameters. Then, based on the dashboard parameters, it determines the layer parameters of the layer to be generated and generates a layer generation instruction based on these parameters. Next, based on the layer generation instruction, it generates the corresponding layer generation code for the layer to be generated. Finally, based on the layer generation code, it renders and generates a customized irregular-shaped dashboard that matches the dashboard generation instruction. Therefore, the technical solution of this disclosure can automatically generate dashboard vector graphics based on the natural language in the dashboard generation instruction, which helps to efficiently and flexibly generate complex irregular-shaped dashboards using natural language, thereby improving the efficiency and flexibility of data visualization. Furthermore, automatically generating dashboard vector graphics using natural language simplifies the development process of complex irregular-shaped dashboards, significantly reducing development time and costs.

[0021] The technical solutions of this disclosure will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0022] The above and other objects, features, and advantages of this disclosure will become more apparent from the more detailed description of the embodiments thereof in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of this disclosure and form part of the specification. They are used together with the embodiments of this disclosure to explain the disclosure and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same components or steps.

[0023] This disclosure will become clearer with reference to the accompanying drawings and the following detailed description, wherein: Figure 1 A flowchart illustrating one embodiment of the method for generating a data dashboard according to this disclosure; Figure 2 This is a flowchart of step 104 of the method for generating a data dashboard according to this disclosure; Figure 3 A flowchart illustrating another embodiment of the method for generating a data dashboard according to this disclosure; Figure 4 The flowchart of step 102 of the method for generating the data dashboard of this disclosure Figure 1 ; Figure 5 The flowchart of step 102 of the method for generating the data dashboard of this disclosure Figure 2 ; Figure 6 This is a schematic diagram of the structure of one embodiment of the data dashboard generation apparatus of this disclosure; Figure 7This is a schematic diagram of another embodiment of the data dashboard generation apparatus of this disclosure; Figure 8 This is a structural diagram of an electronic device provided as an illustrative embodiment of the present disclosure. Detailed Implementation

[0024] In the field of data visualization, dashboards, as a core tool, are widely used in scenarios such as data monitoring, analysis, and decision support. However, the generation process of data dashboards has many limitations: on the one hand, the generation of data dashboards usually relies on a large amount of manual intervention, especially when dealing with complex and irregular graphics, where the generation process becomes extremely cumbersome and time-consuming; on the other hand, users need to manually design the content and style of each layer and implement the combination and display of layers through complex programming, which not only significantly increases the difficulty of generating data dashboards but also limits their flexibility and scalability. Therefore, the dashboard generation solutions of related technologies cannot efficiently meet the diverse display needs of irregularly shaped dashboards, resulting in a significant limitation on the effectiveness and efficiency of data visualization.

[0025] To address the technical problems in related technologies, embodiments of this disclosure provide a method, apparatus, device, storage medium, and product for generating data dashboards. Through this technical solution, user input of natural language automatically generates dashboard vector graphics, simplifying the dashboard development process and reducing time costs.

[0026] The data dashboard generation method of this disclosure can be implemented by an intelligent agent. The intelligent agent refers to a proxy that can automatically generate a dashboard vector image based on the natural language in the dashboard generation instructions. This intelligent agent can perform semantic recognition of the natural language and automatically generate dashboard layers based on the recognized dashboard parameters. By overlaying these layers, complex and irregularly shaped dashboards can be generated. The intelligent agent designed in this disclosure can implement the above-mentioned data dashboard generation method, automatically learn from various dashboard diagrams, determine the layers of the dashboard to be generated based on the dashboard parameters obtained from natural language recognition, and then generate the dashboard vector image.

[0027] The technical solutions of the embodiments of this disclosure will now be described in detail with reference to the accompanying drawings.

[0028] Exemplary methods Figure 1 This is a flowchart illustrating one embodiment of the data dashboard generation method disclosed herein. This data dashboard generation method can be applied to electronic devices (such as computer systems, servers, etc.). Figure 1 As shown, the method for generating this data dashboard includes the following steps: In step 101, in response to receiving the dashboard generation instruction, semantic recognition is performed on the natural language in the instruction to obtain dashboard parameters.

[0029] In this embodiment of the disclosure, users can input natural language via voice or text to generate dashboard generation commands.

[0030] For example, a user can input via voice, "Please help me generate a dashboard that is semi-circular, 20 pixels wide, with rounded corners at the ends, to display business progress," and the device can automatically generate dashboard generation instructions based on this voice input.

[0031] In this embodiment of the disclosure, natural language recognition models, such as ERNIE (Enhanced Representation through Knowledge Integration) and GPT (Generative Pre-trained Transformer), can be used to perform semantic recognition on natural language and obtain dashboard parameters.

[0032] In this embodiment, dashboard parameters are parameters used to describe dashboard attributes, and may include at least one of dashboard background parameters, dashboard data type parameters, and dashboard description parameters. Dashboard background parameters may include shape parameters, color parameters, and texture parameters, and a background graphic of the data dashboard can be generated based on these parameters. Dashboard data type parameters indicate the type of data values ​​used to present the real-time status of data on the dashboard, such as completion percentages, progress bars, progress graphs, etc., and a dynamic progress layer of the data dashboard can be generated based on these parameters. Dashboard description parameters indicate the textual description information of the dashboard, such as data labels, data units, and explanatory text.

[0033] Continuing with the example above, after semantic recognition of "Please help me generate a semi-circular dashboard with a width of 20 and rounded corners at the ends to display business progress", we can obtain the dashboard's shape parameters "semi-circular, width 20, and rounded corners at the ends" and data type parameter "progress".

[0034] In this embodiment of the disclosure, streaming input of natural language can be supported to generate a series of dashboard generation instructions. The natural language recognition model can then perform progressive semantic understanding of the natural language in the series of dashboard generation instructions to obtain a series of dashboard parameters.

[0035] For example, when a user inputs via voice, "Please help me generate a dashboard that is semi-circular, 20 pixels wide, with rounded corners at the ends, to display business progress via a progress bar," the device can automatically generate a dashboard generation instruction based on the voice input. Then, it performs semantic recognition processing on the natural language in the dashboard generation instruction to obtain the dashboard's shape parameters "semi-circular, 20 pixels wide, with rounded corners at the ends" and the data type parameter "progress." The user can then continue by inputting, "Set the dashboard color to a gradient from yellow to red." The device can then automatically generate a dashboard generation instruction based on this voice input and perform semantic recognition processing on the natural language in the dashboard generation instruction to obtain the dashboard's color style parameter "a gradient from yellow to red."

[0036] In step 102, the layer parameters of the layer to be generated are determined based on the dashboard parameters, and a layer generation instruction is generated based on the layer parameters of the layer to be generated.

[0037] In this embodiment of the disclosure, in order to realize a complex irregularly shaped dashboard, multiple layers for dashboard generation and the parameters required to generate each layer can be pre-set in the intelligent agent, such as a basic background layer, a dynamic progress layer, an information description layer and a data display layer, with each layer having distinct layers and different functions. The dashboard is divided into several layers: a basic background layer, a dynamic progress layer, and a data display layer. The basic background layer is used to generate background graphics for the data dashboard. It can be generated based on parameters such as shape, color, and texture. For example, the background graphic could be a semi-circle with a diameter of 20 and rounded corners. The dynamic progress layer, located above the basic background layer, displays data progress information. It can be generated based on parameters of animated progress bars or progress rings, dynamically reflecting data trends. For example, the dynamic progress layer can be a graphical element with animated progress bars or progress rings, intuitively presenting the real-time status of the data and enhancing the interactivity and visual appeal of the dashboard. The information description layer displays relevant textual descriptions of the dashboard, such as data labels, units, and explanatory text. Located above the dynamic progress layer, it can intelligently generate clear and easy-to-read information descriptions based on user-input text and layout requirements, providing users with accurate data explanations and descriptions. The data display layer displays specific data values. It can be located at the top of all layers, intuitively presenting the dashboard data. Based on the data type and display format input by the user, it can generate data display elements in the form of numbers and charts. The data display layer can bind business data through data binding technology to update data content in real time, helping users quickly obtain key information.

[0038] The above-mentioned method of dividing the dashboard into layers is just an example. Depending on the complexity of the dashboard to be generated, fewer or more layers can be used to generate the data dashboard.

[0039] In this embodiment of the disclosure, a corresponding layer generation instruction can be generated for each layer. In this embodiment, the dashboard parameters identified based on the dashboard generation instruction can be matched with the pre-set parameters required for each layer to determine the layer parameters used to generate each layer to be generated in the dashboard parameters. Then, the layer generation instruction is determined based on the layer parameters of each layer to be generated.

[0040] The layer generation instruction is used to instruct the guide code generation model to generate layer code. The layer generation instruction can include the layer parameters to be generated, the required code language, and the layer functionality. The layer generation instruction can have a fixed template; by replacing the parameter parts in the template with specific parameters, the layer generation instruction can be obtained.

[0041] For example, the layer generation command is shown below: "Layer generation instructions: I need to generate a layer that displays a semicircle with a radius of 50, rounded corners, and a yellow color. This layer needs to be adapted for different devices. Please help me implement it using SVG or JS code."

[0042] In step 103, based on the layer generation instructions, the layer generation code corresponding to the layer to be generated is generated.

[0043] In this embodiment of the disclosure, a large code model can be used to automatically generate layer generation code for the layer to be generated based on the generation requirements in the layer generation instruction and the code language to be used. This large code model can be a pre-trained large model, such as a Generative Pre-Trained Transformer (GPT), which can automatically generate layer generation code for the layer to be generated based on the generation requirements in the layer generation instruction and the code language to be used.

[0044] In some optional implementations, a large number of training samples can be collected in advance. Each training sample may include layer parameters and layer codes for various layers. The initial model is trained using these large number of training samples. That is, by inputting a large number of training samples into the initial model, the initial model learns from these training samples. This ensures that after the model is trained, it can automatically generate the layer generation code for the layers to be generated according to the generation requirements in the layer generation instructions and the code language to be used.

[0045] In this embodiment of the disclosure, after each layer generation code corresponding to a layer to be generated is completed, code logic verification, unit testing, etc. can be performed on the layer generation code, and corresponding comment information can be added to the layer generation code to ensure the functionality and readability of the layer generation code.

[0046] In step 104, based on the layer generation code, a customized irregular-shaped dashboard that matches the dashboard generation instructions is rendered and generated.

[0047] In this embodiment of the disclosure, a graphics rendering engine can be used to render and display the layer generation code to obtain the layer vector image of each layer. Then, the layer vector images of each layer are superimposed to obtain the irregularly shaped dashboard.

[0048] Among them, the irregular instrument panel is an instrument panel that breaks away from the inherent form of traditional round or conventional rectangular instrument panels and adopts a non-standard, irregular or customized shape design.

[0049] In this embodiment of the disclosure, by generating a dashboard vector graphic, the good scalability and compatibility of the vector graphic can be utilized to ensure that the dashboard can present a high-quality visual effect on display devices of different sizes and resolutions.

[0050] Through steps 101 to 104 above, for scenarios requiring data dashboard generation, such as data monitoring, analysis, and decision support, after receiving the dashboard generation instruction, the system first performs semantic recognition on the natural language in the instruction to obtain dashboard parameters. Then, based on the dashboard parameters, it determines the layer parameters of the layer to be generated and generates a layer generation instruction based on these parameters. Next, based on the layer generation instruction, it generates the corresponding layer generation code for the layer to be generated. Finally, based on the layer generation code, it renders and generates a customized irregular-shaped dashboard that matches the dashboard generation instruction. Therefore, this disclosed technical solution can automatically generate dashboard vector graphics based on the natural language in the dashboard generation instruction, facilitating the efficient and flexible generation of complex irregular-shaped dashboards through natural language, thereby improving the efficiency and flexibility of data visualization. Furthermore, automatically generating dashboard vector graphics through natural language simplifies the development process of complex irregular-shaped dashboards, significantly reducing development time and costs.

[0051] Figure 2 This is a flowchart of step 104 of the method for generating a data dashboard according to this disclosure. Figure 2 As shown above, in the above Figure 1 Based on the illustrated embodiment, step 104 includes the following steps 141 to 142. The steps are explained below: In step 141, a layer vector image is generated based on the layer generation code.

[0052] In this embodiment of the disclosure, a graphics rendering engine can be used to render and display the layer generation code to obtain a layer vector image for each layer.

[0053] In step 142, based on the display priority of the layer vector graphics, the layer vector graphics are overlaid to obtain a customized irregular-shaped dashboard that matches the dashboard generation instruction.

[0054] In some implementations, the display priority of layer vector graphics can be a preset priority. For example, the display priority of the basic background layer is preset to be lower than that of the dynamic progress layer, the display priority of the dynamic progress layer is lower than that of the information description layer, and the display priority of the information description layer is lower than that of the data display layer.

[0055] In other implementations, a display priority can be set for each layer vector graphic according to user expectations. Alternatively, if the display effect does not meet user expectations after the layers are stacked and combined in an orderly manner according to the preset priority, the user can manually adjust the display order of the layers through interface operation or adjust the display order of the layers through voice command.

[0056] For example, a user can input the voice command "Move layer 1 above layer 2" to adjust the display priority between layer 1 and layer 2.

[0057] Based on the embodiments of this disclosure, multiple generated layers are combined in an orderly manner according to a preset layer display priority or a layer display priority adjusted by the user as desired. By supporting real-time response and recombination of layers based on user operations, the final display effect is ensured to meet the user's expectations, generating complex irregular-shaped dashboards that can meet different display scenarios and needs.

[0058] In some alternative implementations, after the above... Figure 1 After obtaining a customized irregular-shaped dashboard that matches the dashboard generation command in the illustrated embodiment, the device can detect and optimize the generated dashboard vector image to improve the visual effect of the dashboard. Figure 3 This is a flowchart illustrating another embodiment of the method for generating a data dashboard according to this disclosure. Figure 3 As shown, the method for generating this data dashboard includes the following steps 301 to 303. The steps are explained below: In step 301, in response to receiving an adjustment operation triggered for an irregularly shaped dashboard, the layer to be adjusted and the adjustment parameters corresponding to the adjustment operation are obtained.

[0059] In this embodiment of the disclosure, after generating the irregularly shaped dashboard, it can be displayed in the user interface. Based on the display effect of the irregularly shaped dashboard and personal expectations, the user can adjust the position, size, transparency and other attributes of a certain layer, thereby triggering an adjustment operation for the irregularly shaped dashboard. The device can then determine the layer to be adjusted and the adjustment parameters based on the adjustment operation.

[0060] For example, when a user drags layer 1, an adjustment operation for layer 1 can be triggered, with the adjustment parameter being the position parameter; or, when a user adjusts the font size of layer 2, an adjustment operation for layer 2 can be triggered, with the adjustment parameter being the font size.

[0061] In step 302, based on the relationship between the vector graphics of each layer in the irregular dashboard, the associated layer of the layer to be adjusted is obtained.

[0062] In the irregularly shaped dashboard, the relationships between the vector graphics of each layer are used to indicate the spatial, logical, or functional connections between layers. These relationships can include stacking relationships (top-bottom relationships), alignment relationships, blending modes, clipping masks, and more. Stacking relationships indicate the hierarchical relationship between layers, where the upper layer covers the lower layer; this stacking relationship is determined by the display priority of each layer. Alignment relationships indicate the alignment of multiple layers based on pixel edges. Blending modes indicate the relationship between upper and lower layers that produce specific visual effects through algorithmic calculations, such as overlay, darkening, or enhancing color saturation. Clipping masks indicate the relationship where the content of one layer acts as a mask for another layer, displaying only the portion of the lower layer that overlaps with the mask layer.

[0063] In this embodiment of the disclosure, during the generation of the irregularly shaped dashboard, the layering relationship between different layers can be automatically generated, the alignment relationship between different layers can be established by triggering the alignment operation, and the association relationship between clipping masks of different layers can be established by creating clipping masks.

[0064] In some implementations, after receiving an adjustment operation triggered by a user for a certain layer, the associated layer of the layer to be adjusted can be obtained based on the association relationship between the layer and the layer to be adjusted.

[0065] In step 303, based on the adjustment parameters, the layer to be adjusted and its associated layers are adjusted to obtain the adjusted irregular-shaped dashboard.

[0066] In this embodiment of the disclosure, the adjustment operation to be performed on the associated layer can be determined based on the association relationship between the layer to be adjusted and the layer. For example, after adjusting the position of layer 1, the position of the associated layer that is aligned with layer 1 can be adjusted; or, after adjusting the color saturation of layer 2, the color brightness of the associated layer that is in blending mode with layer 2 can be adjusted, etc.

[0067] Based on the embodiments of this disclosure, after the dashboard is generated, a manual fine-tuning tool can be provided to the user, allowing the user to trigger adjustment operations on the dashboard according to personal preferences, and to adjust the position, size, transparency and other attributes of related layers according to the adjustment operations triggered by the user, thereby further improving the visual effect and user experience of the dashboard.

[0068] In some alternative implementations, after generating the dashboard vector image, the system can also automatically detect potential problems in the overall display effect of the dashboard vector image, such as layer overlap and inaccurate data alignment, and automatically optimize and adjust them.

[0069] In this implementation, an intelligent algorithm can be pre-set in the intelligent agent to detect potential problems in the dashboard vector graphic. This intelligent algorithm may include a collision detection algorithm to detect and correct collisions between different layers that cause partial information to be covered; it may also include photometric blending algorithms, tone mapping algorithms, etc., to detect color balance and tone mapping problems in the dashboard; and it may include other algorithms that can automatically detect potential problems in the dashboard vector graphic. This disclosure does not specifically limit the type of intelligent algorithm.

[0070] Figure 4 The flowchart of step 102 of the method for generating the data dashboard of this disclosure Figure 1 .like Figure 4 As shown above, in the above Figure 1 Based on the illustrated embodiment, step 102 includes the following steps 121 to 123. The steps are explained below: In step 121, the dashboard parameters are parsed to obtain key semantic information.

[0071] The key semantic information can include information used to classify dashboard parameters. For example, if the dashboard generation instruction is "Please generate two layers: one is a background layer that is a semi-circle with a width of 20 and rounded corners; the other is a layer that displays dynamic data changes as a progress bar," the key semantic information obtained from the natural language in this instruction is the two layers and the dashboard parameters corresponding to each layer.

[0072] In step 122, based on key semantic information, the dashboard parameters are grouped to obtain layer generation parameters for at least one layer.

[0073] In this embodiment of the disclosure, the dashboard parameters are grouped to obtain the layer generation parameters corresponding to each layer.

[0074] In step 123, for any one of the at least one layers, a layer generation instruction containing the layer generation parameters of that layer is generated.

[0075] The layer generation command can have a pre-set template. By replacing the parameter part in the template with the specific layer generation parameters, the layer generation command can be obtained.

[0076] Based on the embodiments of this disclosure, by further parsing the dashboard parameters obtained from semantic recognition, the dashboard parameters can be grouped to obtain the layer generation parameters for each layer, and then the layer generation instructions for each layer can be automatically generated. This can reduce manual intervention, shorten data preprocessing time, and significantly improve data processing efficiency and visualization accuracy.

[0077] In some alternative implementations, dashboard parameters can be automatically grouped based on multiple pre-defined layers used for dashboard generation and the parameters required to generate each layer. Figure 5 The flowchart of step 102 of the method for generating the data dashboard of this disclosure Figure 2 .like Figure 5 As shown above, in the above Figure 1 Based on the illustrated embodiment, step 102 includes the following steps 124-125. The steps are explained below: In step 124, the dashboard parameters are grouped based on the pre-set layer classification to obtain layer generation parameters for at least one layer.

[0078] In some alternative implementations, the parameter type matching each layer can be obtained from the dashboard parameters as key semantic information, based on the multiple layers pre-set in the agent for dashboard generation and the parameters required to generate each layer.

[0079] For example, the dashboard generation instruction is "Please help me generate a dashboard that is semi-circular, 20 pixels wide, with rounded corners at the ends, and used to display business progress through a progress ring". After the device obtains the dashboard parameters "semi-circular, 20 pixels wide, rounded corners at the ends, and progress ring" based on the voice input, it can further parse the dashboard parameters to determine that they include shape parameters and data type parameters. The key semantic information is then "shape parameters and data type parameters".

[0080] In step 125, for any one of the at least one layers, a layer generation instruction containing the layer generation parameters of that layer is generated.

[0081] For example, after obtaining the dashboard parameters "semi-circle, width is 20, endpoints are arc corners, progress ring", by further parsing the dashboard parameters, the key semantic information can be determined as "shape parameters and data type parameters". The shape parameters can be obtained from the dashboard parameters as layer generation parameters for generating the basic background layer, and the data type parameter "progress bar" can be obtained from the dashboard parameters as layer generation parameters for generating the dynamic display layer.

[0082] Based on the embodiments of this disclosure, by automatically grouping the dashboard parameters to obtain the layer generation parameters for each layer, and then automatically generating the layer generation instructions for each layer, manual intervention can be reduced, data preprocessing time can be shortened, and data processing efficiency and visualization accuracy can be significantly improved.

[0083] In some alternative implementations, after generating a layer generation instruction containing layer generation parameters for any layer, the layer information of the layer to be generated can be determined based on the layer generation instruction. The layer information includes at least one of the following: constituent elements, element attributes, and dependencies between constituent elements. Then, using a code generation model, layer generation code is generated based on the layer information of the layer to be generated.

[0084] The constituent elements of a layer are used to indicate the elements contained in a layer, such as line elements (including straight lines, curves, polylines, etc.), geometric shapes (such as circles, semicircles, polygons, etc.), and fill patterns (such as materials or textures used to represent specific areas); element attributes are used to indicate the element value of each element, such as the element value of a geometric shape being a semicircle; the dependencies between constituent elements are used to indicate the relationships between the elements.

[0085] In this implementation, after determining the layer information of the layer to be generated, the layer generation code can be generated through the layer's constituent elements, element attributes, and the dependencies between the constituent elements.

[0086] Corresponding to the embodiments of the aforementioned data dashboard generation method, this disclosure also provides embodiments of the data dashboard generation apparatus.

[0087] Exemplary device Figure 6 This is a schematic diagram illustrating one embodiment of the data dashboard generation apparatus of this disclosure. The apparatus is applied to electronic devices (such as computer systems, servers, etc.). Figure 6 As shown, the data dashboard generation device may include: The semantic recognition module 61 is used to perform semantic recognition on the natural language in the command in response to receiving the dashboard generation command, and obtain dashboard parameters. The dashboard parameters include at least one of the following: dashboard background parameters, dashboard data type parameters, and dashboard description parameters. The instruction generation module 62 is used to determine the layer parameters of the layer to be generated based on the dashboard parameters, and generate a layer generation instruction based on the layer parameters of the layer to be generated. The layer generation instruction contains the layer parameters of the layer to be generated. The code generation module 63 is used to generate layer generation code corresponding to the layer to be generated based on the layer generation instruction. Image rendering module 64 is used to generate customized irregular-shaped dashboards that match the dashboard generation instructions based on layer generation code.

[0088] Figure 7 This is a schematic diagram of another embodiment of the data dashboard generation apparatus of this disclosure. Figure 7 As shown, in Figure 6 Based on the illustrated embodiment, in some alternative implementations, the image rendering module 64 may include: The rendering submodule 641 is used to generate layer vector graphics based on layer-based code. The overlay submodule 642 is used to overlay layer vector graphics based on the display priority of layer vector graphics to obtain a customized irregular-shaped dashboard that matches the dashboard generation command.

[0089] In some alternative implementations, the data dashboard generation apparatus may further include: The information acquisition module 65 is used to respond to receiving an adjustment operation triggered for an irregularly shaped dashboard, and to acquire the layer to be adjusted and the adjustment parameters corresponding to the adjustment operation. The layer acquisition module 66 is used to acquire the associated layers of the layer to be adjusted based on the relationship between the vector graphics of each layer in the dashboard vector graphic. Adjustment module 67 is used to adjust the layer to be adjusted and its associated layers based on adjustment parameters to obtain the adjusted dashboard vector image.

[0090] In some alternative implementations, the instruction generation module 62 may include: The parsing submodule 621 is used to parse the dashboard parameters to obtain key semantic information; The first grouping submodule 622 is used to group the dashboard parameters based on key semantic information to obtain layer generation parameters for at least one layer. The first generation submodule 623 is used to generate a layer generation instruction containing the layer generation parameters of any one of the at least one layers.

[0091] In some alternative implementations, the code generation module 63 may include: The determination submodule 631 is used to determine the layer information of the layer to be generated based on the layer generation instruction. The layer information includes at least one of the following: constituent elements, element attributes, and dependencies between constituent elements. The second generation submodule 632 is used to generate a model using code, and generate layer generation code based on the layer information of the layer to be generated.

[0092] In some alternative implementations, the instruction generation module 62 may include: The second grouping submodule 624 is used to group the dashboard parameters based on the pre-set layer classification to obtain the layer generation parameters of at least one layer. The third generation submodule 625 is used to generate a layer generation instruction containing the layer generation parameters of any one of the at least one layers.

[0093] The modules and units in this disclosed device can be further divided into finer-grained units according to actual needs, and the specific configuration can be set according to actual needs.

[0094] The apparatus of this disclosure embodiment can be used to implement the methods of the above embodiments of this disclosure. The two correspond to each other in specific implementation, and the specific implementation of related parts can be referred to each other, which will not be repeated here.

[0095] Exemplary electronic devices, computer program products, and computer-readable storage media This disclosure also provides an electronic device, including: a memory for storing a computer program; and a processor for executing the computer program stored in the memory, wherein when the computer program is executed, it implements the data dashboard generation method of any of the above embodiments of this disclosure.

[0096] Below, for reference Figure 8 This describes an electronic device according to embodiments of the present disclosure, wherein apparatus for implementing methods according to embodiments of the present disclosure may be integrated. Figure 8 This is a structural diagram of an electronic device provided in an illustrative embodiment of the present disclosure, such as... Figure 8 As shown, the electronic device includes one or more processors 81, one or more memory 82s of computer-readable storage media, and a computer program stored in the memory and executable on the processor. When the program in the memory 82 is executed, the method for generating the data dashboard described above can be implemented.

[0097] Specifically, in practical applications, the electronic device may also include components such as an input device 83 and an output device 84, which are interconnected via a bus system and / or other forms of connection mechanisms (not shown). Those skilled in the art will understand that... Figure 8 The structure of the electronic device shown does not constitute a limitation on the electronic device and may include more or fewer components than shown, or certain components, or different component arrangements. Wherein: The processor 81 may be a central processing unit (CPU) or other processing unit with error handling and / or instruction execution capabilities. It performs various functions and processes data by running or executing software programs and / or modules stored in memory 82 and calling data stored in memory 82, thereby providing overall monitoring of the electronic device.

[0098] The memory 82 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory. Non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 81 may execute the program instructions to implement the data dashboard generation method of the various embodiments of this disclosure described above and / or other desired functions. Various contents such as input signals, signal components, and noise components may also be stored in the computer-readable storage medium.

[0099] The input device 83 can be used to receive input digital or character information, and to generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control.

[0100] The output device 84 can output various information to the outside, including determined distance information, direction information, etc. The output device 84 may include, for example, a display, a speaker, a printer, and a communication network and its connected remote output devices, etc.

[0101] Electronic devices may also include a power supply for powering various components, which can be logically connected to the processor 81 through a power management system, thereby enabling functions such as charging, discharging, and power consumption management through the power management system. The power supply may also include one or more DC or AC power sources, recharging systems, power fault detection circuits, power converters or inverters, power status indicators, and any other components.

[0102] Of course, for the sake of simplicity, Figure 8Only some of the components of the electronic device relevant to this disclosure are shown, omitting components such as buses, input / output interfaces, etc. In addition, the electronic device may include any other suitable components depending on the specific application.

[0103] In addition to the methods and apparatus described above, embodiments of this disclosure may also be computer program products, including computer program instructions that, when executed by a processor, cause the processor to perform the steps in the methods for generating a data dashboard according to various embodiments of this disclosure as described in the "Exemplary Methods" section of this specification.

[0104] Computer program products can be written in any combination of one or more programming languages ​​to perform the operations of embodiments of this disclosure. The programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on a user's computing device, partially on a user's computing device, as a standalone software package, partially on a user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0105] Furthermore, embodiments of this disclosure may also be computer-readable storage media storing computer program instructions thereon, which, when executed by a processor, cause the processor to perform the steps in the methods for generating a data dashboard according to various embodiments of this disclosure as described in the "Exemplary Methods" section above.

[0106] Computer-readable storage media may take the form of any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may, for example, include, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0107] The basic principles of this disclosure have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in this disclosure are merely examples and not limitations, and should not be considered as essential features of each embodiment of this disclosure. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the scope of this disclosure to the necessity of employing the aforementioned specific details for implementation.

[0108] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For system embodiments, since they largely correspond to method embodiments, the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0109] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media that can store program code, such as ROM, RAM, magnetic disk, or optical disk.

[0110] The methods and apparatus of this disclosure may be implemented in many ways. For example, they may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above-described order of steps for the method is for illustrative purposes only, and the steps of the method of this disclosure are not limited to the order specifically described above, unless otherwise specifically stated. Furthermore, in some embodiments, this disclosure may also be implemented as a program recorded on a recording medium, the program including machine-readable instructions for implementing the method according to this disclosure. Thus, this disclosure also covers recording media storing programs for performing the method according to this disclosure.

[0111] The description in this disclosure is provided for illustrative and descriptive purposes only and is not intended to be exhaustive or to limit the disclosure to its forms. Many modifications and variations will be apparent to those skilled in the art. The embodiments were chosen and described in order to better illustrate the principles and practical application of this disclosure and to enable those skilled in the art to understand this disclosure and to design various embodiments with various modifications suitable for a particular purpose.

Claims

1. A method for generating a data dashboard, characterized in that, The method includes: In response to receiving a dashboard generation command, semantic recognition is performed on the natural language in the command to obtain dashboard parameters, which include at least one of dashboard background parameters, dashboard data type parameters, and dashboard description parameters. Based on the dashboard parameters, determine the layer parameters of the layer to be generated, and generate a layer generation instruction based on the layer parameters of the layer to be generated. Based on the layer generation instructions, generate the layer generation code corresponding to the layer to be generated; Based on the layer generation code, a customized irregular-shaped dashboard that matches the dashboard generation instruction is rendered.

2. The method according to claim 1, characterized in that, The step of generating a customized irregular-shaped dashboard based on the layer-generated code and rendering it to match the dashboard generation instructions includes: Based on the layer generation code, a layer vector image is rendered and generated. Based on the display priority of the layer vector graphics, the layer vector graphics are overlaid to obtain a customized irregular-shaped dashboard that matches the dashboard generation instruction.

3. The method according to any one of claims 1-2, characterized in that, After generating a customized irregular-shaped dashboard that matches the dashboard generation instruction based on the layer generation code, the process includes: In response to receiving an adjustment operation triggered for the irregularly shaped dashboard, the layer to be adjusted and the adjustment parameters corresponding to the adjustment operation are obtained; Based on the correlation relationship of the vector graphics of each layer in the irregular instrument panel, obtain the associated layer of the layer to be adjusted; Based on the adjustment parameters, the layer to be adjusted and its associated layers are adjusted to obtain the adjusted irregular-shaped dashboard.

4. The method according to any one of claims 1-3, characterized in that, The step of determining the layer parameters of the layer to be generated based on the dashboard parameters, and generating a layer generation instruction based on the layer parameters of the layer to be generated, includes: The instrument panel parameters are parsed to obtain key semantic information; Based on the key semantic information, the dashboard parameters are grouped to obtain layer generation parameters for at least one layer; For any one of the at least one layers, generate a layer generation instruction that includes the layer generation parameters of that any one layer.

5. The method according to claim 4, characterized in that, The step of generating layer generation code corresponding to the layer to be generated based on the layer generation instruction includes: Based on the layer generation instruction, the layer information of the layer to be generated is determined, and the layer information includes at least one of the following: constituent elements, element attributes, and dependencies between constituent elements; Using a code generation model, the layer generation code is generated based on the layer information of the layer to be generated.

6. The method according to any one of claims 1-5, characterized in that, The step of determining the layer parameters of the layer to be generated based on the dashboard parameters, and generating a layer generation instruction based on the layer parameters of the layer to be generated, includes: Based on the pre-set layer classification, the dashboard parameters are grouped to obtain layer generation parameters for at least one layer; For any one of the at least one layers, generate a layer generation instruction that includes the layer generation parameters of that any one layer.

7. A data dashboard generation device, characterized in that, The device includes: A semantic recognition module is used to perform semantic recognition on the natural language in the received dashboard generation instruction in response to receiving a dashboard generation instruction, and obtain dashboard parameters. The dashboard parameters include at least one of dashboard background parameters, dashboard data type parameters, and dashboard description parameters. The instruction generation module is used to determine the layer parameters of the layer to be generated based on the dashboard parameters, and to generate a layer generation instruction based on the layer parameters of the layer to be generated. The code generation module is used to generate layer generation code corresponding to the layer to be generated based on the layer generation instruction; The image rendering module is used to generate a customized irregular-shaped dashboard that matches the dashboard generation instruction based on the code generated by the layer.

8. A computer-readable storage medium, characterized in that, The storage medium stores computer program instructions, which, when executed, implement the method described in any one of claims 1-6.

9. An electronic device, characterized in that, The electronic device includes: Memory, used to store computer program products; A processor is configured to execute a computer program product stored in the memory, wherein, when the computer program product is executed, it implements the method described in any one of claims 1-6.

10. A computer program product comprising computer program instructions, characterized in that, When the computer program instructions are executed by the processor, they implement the method described in any one of claims 1-6.

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