Dynamic rendering method and device based on large model instruction

Abstract

The invention provides a method and a device for dynamically rendering based on a large model instruction. The method comprises the following steps: identifying a data demand and a chart type in an input text through a large model; wherein the chart type comprises a histogram, a bar chart or a table; finding a chart data structure and a rendering instruction matched with the chart type in a template library corresponding to the chart type through the large model; acquiring to-be-displayed data from a database according to the data demand; and rendering the to-be-displayed data according to the chart data structure and the rendering instruction. According to the method, the proper chart type can be automatically selected and rendered through model fine tuning and model rendering instruction returning.

Description

Technical Field

[0001] The present invention relates to the technical fields of artificial intelligence and data visualization, and particularly to a method and device for dynamic rendering based on large model instructions. Background Art

[0002] With the progress of artificial intelligence technology, AI voice chat systems have become the main interaction methods for intelligent customer service and voice assistants. In practical applications, users often need to view complex data displays such as charts and tables. However, most existing AI chat systems are limited to text and voice interactions and lack flexible chart and table display functions. Summary of the Invention

[0003] The present invention provides a method and device for dynamic rendering based on large model instructions to solve the defect that most existing AI chat systems are limited to text and voice interactions and to achieve flexible chart and table display functions.

[0004] The present invention provides a method for dynamic rendering based on large model instructions, including the following steps: Identifying data requirements and chart types in the input text through a large model; wherein, the chart types include: bar chart, column chart or table; Finding a chart data structure and rendering instructions matching the chart type in the template library corresponding to the chart type through a large model; Obtaining data to be displayed from a database according to the data requirements; Rendering the data to be displayed according to the chart data structure and rendering instructions.

[0005] According to the method for dynamic rendering based on large model instructions provided by the present invention, rendering the data to be displayed according to the chart data structure and rendering instructions includes the following steps: Performing data processing on the data to be displayed through a large model to obtain standard data to be displayed; wherein, the data processing includes: data cleaning and formatting processing; Rendering the standard data to be displayed according to the chart data structure and rendering instructions.

[0006] According to the method for dynamic rendering based on large model instructions provided by the present invention, rendering the data to be displayed according to the chart data structure and rendering instructions includes the following steps: Generating a display chart according to the chart data structure, rendering instructions and the data to be displayed; Adjusting the color scheme of the display chart according to a pre-set color scheme guide and chart type to obtain a color-schemed display chart; Render the color-matching display chart according to the rendering instruction. According to a method for dynamic rendering based on large model instructions provided by the present invention, after rendering the data to be displayed according to the chart data structure and the rendering instruction, the following steps are further included: Identify the zoom operation and zoom parameters in the user request through the large model, and match the zoom instruction corresponding to the zoom operation; Determine the data to be zoomed in the data to be displayed according to the zoom parameters; Display the data to be zoomed through a pop-up window according to the zoom instruction.

[0007] According to a method for dynamic rendering based on large model instructions provided by the present invention, after rendering the data to be displayed according to the chart data structure and the rendering instruction, the following steps are further included: Identify the download operation and specified parameters in the user request through the large model; Match the download instruction corresponding to the download operation through the large model; Determine the data to be downloaded in the data to be displayed according to the specified parameters; Download the data to be downloaded according to the download instruction.

[0008] The present invention also provides a device for dynamic rendering based on large model instructions, including the following modules: A requirement recognition module, configured to identify the data requirements and chart types in the input text through the large model; wherein, the chart types include: bar chart, column chart or table; A matching module, configured to find the chart data structure and rendering instruction matching the chart type in the template library corresponding to the chart type through the large model; A data-to-be-displayed acquisition module, configured to acquire the data to be displayed from the database according to the data requirements; A rendering module, configured to render the data to be displayed according to the chart data structure and the rendering instruction.

[0009] According to a device for dynamic rendering based on large model instructions provided by the present invention, the rendering module includes: A data processing sub-module, configured to perform data processing on the data to be displayed through the large model to obtain standard data to be displayed; wherein, the data processing includes: data cleaning and formatting processing; A rendering sub-module, configured to render the standard data to be displayed according to the chart data structure and the rendering instruction. The present invention also provides a device for dynamic rendering based on large model instructions, the rendering module includes: A display chart generation sub-module, configured to generate a display chart according to the chart data structure, the rendering instruction and the data to be displayed; A color-matching display chart generation sub-module, configured to adjust the color of the display chart according to the preset color-matching guide and the chart type to obtain a color-matching display chart; The color matching rendering sub-module is used to render the color matching display chart according to the rendering instruction.

[0010] The present invention also provides a device for dynamically rendering based on large model instructions, which further includes a magnification module: It is used to identify the magnification operation and magnification parameters in the user request through the large model, and match the magnification instruction corresponding to the magnification operation; determine the data to be magnified in the data to be displayed according to the magnification parameters; display the data to be magnified through a pop-up window according to the magnification instruction.

[0011] The present invention also provides a device for dynamically rendering based on large model instructions, which further includes a download module: It is used to identify the download operation and specified parameters in the user request through the large model; match the download instruction corresponding to the download operation through the large model; determine the data to be downloaded in the data to be displayed according to the specified parameters; download the data to be downloaded according to the download instruction.

[0012] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, it implements the method for dynamically rendering based on large model instructions as described in any one of the above.

[0013] The present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the method for dynamically rendering based on large model instructions as described in any one of the above.

[0014] The present invention also provides a computer program product, including a computer program. When the computer program is executed by a processor, it implements the method for dynamically rendering based on large model instructions as described in any one of the above.

[0015] The method and device for dynamically rendering based on large model instructions provided by the present invention identify the data requirements and chart types in the input text through the large model; wherein, the chart types include: bar chart, column chart or table; find the chart data structure and rendering instruction matching the chart type in the template library corresponding to the chart type through the large model; obtain the data to be displayed from the database according to the data requirements; Render the data to be displayed according to the chart data structure and rendering instruction. Compared with the existing AI chat systems, most of them are limited to text and voice interactions and lack flexible chart and table display functions. By fine-tuning the model and the model returning the rendering instruction, it is possible to automatically select and render the appropriate chart type. Description of the Drawings

[0016] To more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0017] Figure 1 It is one of the schematic flowcharts of the method for dynamic rendering based on large model instructions provided by the present invention.

[0018] Figure 2 It is the schematic structural diagram of the device for dynamic rendering based on large model instructions provided by the present invention.

[0019] Figure 3 It is the schematic structural diagram of the electronic device provided by the present invention. Specific embodiments

[0020] To make the objectives, technical solutions, and advantages of the present invention clearer, the following will clearly and completely describe the technical solutions in the present invention in conjunction with the drawings in the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0021] The present invention has significant advantages in the data visualization display of AI voice chat conversation boxes and plays a key role in various application scenarios. The present invention will pre-tune and improve the large model so that the large model can be applied to real-time data display in AI voice assistants, intelligent customer service, and big data analysis. After receiving the rendering instructions of the large model, the website front-end can intelligently screen and dynamically generate suitable charts and tables. The website front-end can also automatically adjust the column width according to the model optimization suggestions to ensure the integrity and visibility of the displayed content. In addition, the present invention can also implement operations such as chart magnification and download according to the large model instructions. It greatly improves the user interaction experience and widely covers fields such as big data analysis and real-time data monitoring.

[0022] The following will be combined with Figures 1-3 Describe the present invention.

[0023] Figure 1 It is one of the schematic flowcharts of a method for batch task processing based on a large model provided by the present invention. As Figure 1 shown, the method includes the following: Step 101: Identify the data requirements and chart types in the input text through the large model; among them, the chart types include: bar chart, column chart, pie chart, or table.

[0024] In step 101 above, the large model includes an input parsing module. The large model parses the input text through the input parsing module. In the embodiments of the present invention, the input parsing module of the large model is pre-strengthened to enable it to deeply identify the data requirements and chart types in the input text. The data requirements refer to the names of the data to be displayed. For example, "National Freight Index". The chart type can be selected according to different data display requirements. For example, when the large model receives the input text "Query the national freight index in the past week and display it in a bar chart", the input parsing module of the large model can quickly extract the data requirement of "National Freight Index" and the chart type of "bar chart".

[0025] Step 102: Use the large model to find the chart data structure and rendering instructions that match the chart type in the template library corresponding to the chart type.

[0026] In step 102 above, the template library corresponding to the chart type stores the corresponding chart data structures and rendering instructions for different chart types. In the knowledge reserve and instruction generation part of the model in the embodiments of the present invention, a rich template library corresponding to the chart type is constructed. When the large model identifies a specific chart type, it can quickly match and retrieve the appropriate template. For example, when the chart type is a bar chart, use the large model to find the data structure of the bar chart and the show_chart instruction for rendering the bar chart in the template library corresponding to the chart type. In the embodiments of the present invention, the data part in the rendering instruction is organized according to the echarts specification, so that it can be directly used for rendering after being received by the front end.

[0027] Step 103: Obtain the data to be displayed from the database according to the data requirements.

[0028] In step 103 above, generate an SQL query statement according to the data requirements, execute the SQL query statement, and obtain the data to be displayed from the database. The data to be displayed includes data requirements for different time periods. For example, the data to be displayed is the national freight index for different time periods. After the model receives the data to be displayed, it fine-tunes it. The fine-tuning includes clearing invalid data. And process the data to be displayed according to a custom data format to be rendered as a graph or table corresponding to the chart type. The data to be displayed can exist in the form of an array, an object, or a database query result.

[0029] Step 104: Render the data to be displayed according to the chart data structure and rendering instructions.

[0030] In the above step 104, the website front-end can call a chart library to render the data to be displayed according to the chart data structure and rendering instructions. Popular chart libraries include Chart.js, D3.js, ECharts, Highcharts, Plotly, etc. These libraries provide rich functions and tools that can simplify the process of creating and rendering charts.

[0031] The website front-end calls a chart library to render the data to be displayed according to the chart data structure and rendering instructions, including: configuring options using the API provided by the selected chart library to create a chart; using the rendering function or method provided by the chart library to render the configured chart into a specified container.

[0032] Among them, the above API configuration options specifically include: Data source: Specify the data to be rendered.

[0033] Chart type: Select a chart type suitable for your data.

[0034] Dimensions and layout: Set the width, height, and layout of the chart.

[0035] Color scheme: Select or define the colors of the chart.

[0036] Labels and titles: Add axis labels, legends, titles, subtitles, etc.

[0037] Interactivity: Enable or disable the interactive features of the chart, such as tooltips, zooming, click events, etc.

[0038] Using the rendering function or method provided by the chart library to render the configured chart into a specified container usually involves attaching the chart to an HTML element (such as or <canvas>), and trigger the rendering process.

[0039] Optionally, step 104 above includes steps A1 to A2: Step A1: Process the data to be displayed through a large model to obtain standard data to be displayed; wherein, the data processing includes: data cleaning and formatting processing.

[0040] Step A2: Render the standard data to be displayed according to the chart data structure and rendering instructions.

[0041] In the above steps A1 to A2, data cleaning includes deleting duplicate data, correcting errors, and filling in missing values. Data cleaning is a necessary step before formatting processing.

[0042] Formatting processing refers to converting the data to be displayed into a specific format for easy display. When performing formatting processing, attention should be paid to selecting the appropriate format and maintaining consistency.

[0043] Selecting the appropriate format means selecting the appropriate format for formatting according to the type, use, and storage requirements of the data to be displayed. For example, for numerical data, a numerical format can be selected; for date and time data, a date or time format can be selected.

[0044] Maintaining consistency means ensuring that the same type of data uses the same format during the formatting process to maintain data consistency.

[0045] Optionally, step 104 above includes steps B1 to B3: Step B1: Generate a display chart according to the chart data structure, rendering instructions, and data to be displayed.

[0046] Step B2: Adjust the color scheme of the display chart according to the pre-set color guide and chart type to obtain a color-schemed display chart; Step B3: Render the color-schemed display chart according to the rendering instructions.

[0047] In the above steps B1 to B3, the color guide refers to the guiding principles that help users select appropriate color combinations when creating charts to improve the readability and aesthetics of the charts.

[0048] The color guide includes the color theme and color gradient of the chart, which are adjusted by the website front-end according to the chart type and the characteristics of the data to be displayed.

[0049] Optionally, the method of dynamic rendering based on large model instructions further includes step 105: Step 105: Identify the zoom-in operation and zoom-in parameters in the user request through the large model, and match the zoom-in instruction corresponding to the zoom-in operation; determine the data to be zoomed in from the data to be displayed according to the zoom-in parameters; display the data to be zoomed in through a pop-up window according to the zoom-in instruction.

[0050] In the above Step 105, the user request can be: the user's voice instruction or the user's interface interaction operation. Exemplarily, the user's voice instruction contains "zoom in on the data for January 2024", and the user's interface interaction operation is to hold the data for January 2024 with two fingers at the same time and then slide in opposite directions.

[0051] After the large model identifies the word "zoom in" or the zoom-in action in the user request, it determines the zoom-in operation.

[0052] The zoom-in parameter refers to the data that needs to be zoomed in in the user request. Exemplarily, when the user's voice instruction contains "zoom in on the data for January 2024", or when the user's interface interaction operation is to hold the data for January 2024 with two fingers at the same time and then slide in opposite directions, the zoom-in parameter is the data for January 2024.

[0053] Match the data to be zoomed in that is consistent with the zoom-in parameter from the data to be displayed according to the zoom-in parameter.

[0054] The user can zoom in on the chart through voice instructions or interface interaction operations to view more detailed data. The enlarged chart displays more detailed information, enhancing the user's understanding of the data.

[0055] Optionally, based on the method of dynamic rendering of large model instructions, it further includes Step 106: Step 106: Identify the download operation and specified parameters in the user request through the large model; match the download instruction corresponding to the download operation through the large model; determine the data to be downloaded from the data to be displayed according to the specified parameters; download the data to be downloaded according to the download instruction.

[0056] In the above Step 106, the user request can be that the user's voice instruction contains the word "download" and the data for a specific time period, or the user clicks the download button for a specific time period. The specified parameter refers to the data for a specific time period in the user request.

[0057] In the embodiments of the present invention, key methods need to be predefined: deep_parse (parse text), find_adapt_template (match templates according to key information), and client_command (generate instructions and data required for rendering). The action fields of the instructions are also predefined: show_chart (chart), preview_graph (zoom in on the chart of the current conversation), and down_graph (download instruction).

[0058] When the action is "show_chart", the website front-end can process in multiple threads: 1. Intelligently parse the data to determine the chart type; 2. Synchronously preprocess the data and process it according to the configuration rules customized by the website front-end. If it is a table, the column width can be calculated by combining the interface, interaction, and data importance to prevent truncation, and caching can be used to accelerate subsequent responses.

[0059] When the action is "preview_graph", the website front-end extracts the chart data from the cache and sequentially displays the basic data and detailed data in a pop-up window. The speed is optimized by preloading to ensure the pop-up window zoom operation.

[0060] When the action is "down_graph", match the component id according to the returned id and perform the download operation on the matched component data.

[0061] The embodiments of the present invention support real-time data updates and can update the display content of charts or tables in real time according to data source changes and user interactions. It also supports real-time interaction operations and dynamically updates the content of charts or tables according to user input and data changes. The embodiments of the present invention can complete and clean the data according to the rules provided by the model, dynamically adjust the table column width, and ensure the integrity and clarity of data display.

[0062] The embodiments of the present invention can provide an effective chart zooming and data download interaction experience when the data volume is large or the content is complex. The embodiments of the present invention can achieve dynamic adjustment, data cleaning, and automated processing capabilities.

[0063] The embodiments of the present invention provide a method for dynamic rendering based on large model instructions. The large model is used to identify the data requirements and chart types in the input text; among them, the chart types include: bar chart, column chart, or table; the large model finds the chart data structure and rendering instructions that match the chart type in the corresponding template library of the chart type; the data to be displayed is obtained from the database according to the data requirements. Render the data to be displayed according to the chart data structure and rendering instructions. Compared with most existing AI chat systems that are mostly limited to text and voice interactions and lack flexible chart and table display functions, by fine-tuning the model and having the model return rendering instructions, it is possible to automatically select and render the appropriate chart type.

[0064] The device for dynamic rendering based on large model instructions provided by the present invention will be described below. The device for dynamic rendering based on large model instructions described below can be correspondingly referred to the method for dynamic rendering based on large model instructions described above.

[0065] The present invention also provides a device for dynamic rendering based on large model instructions, including the following modules: A requirement recognition module 201, configured to recognize data requirements and chart types in the input text through a large model; wherein, the chart types include: bar chart, column chart or table; A matching module 202, configured to find the chart data structure and rendering instructions matching the chart type in the template library corresponding to the chart type through a large model; A data to be displayed acquisition module 203, configured to acquire the data to be displayed from the database according to the data requirements; A rendering module 204, configured to render the data to be displayed according to the chart data structure and rendering instructions.

[0066] According to the device for dynamic rendering based on large model instructions provided by the present invention, the rendering module 204 includes: A data processing sub-module, configured to perform data processing on the data to be displayed through a large model to obtain standard data to be displayed; wherein, the data processing includes: data cleaning and formatting processing; A rendering sub-module, configured to render the standard data to be displayed according to the chart data structure and rendering instructions.

[0067] The present invention also provides a device for dynamic rendering based on large model instructions, the rendering module 204 includes: A display chart generation sub-module, configured to generate a display chart according to the chart data structure, rendering instructions and the data to be displayed; A color-matching display chart generation sub-module, configured to adjust the color matching of the display chart according to the preset color matching guide and chart type to obtain a color-matching display chart; A color-matching rendering sub-module, configured to render the color-matching display chart according to the rendering instructions.

[0068] The present invention also provides a device for dynamic rendering based on large model instructions, further including a magnification module: It is used to identify the zoom operation and zoom parameters in the user request through a large model, and match the zoom instruction corresponding to the zoom operation; determine the data to be zoomed in the data to be displayed according to the zoom parameters; and display the data to be zoomed through a pop-up window according to the zoom instruction.

[0069] The present invention also provides a device for dynamic rendering based on large model instructions, which further includes a download module: It is used to identify the download operation and specified parameters in the user request through a large model; match the download instruction corresponding to the download operation through the large model; determine the data to be downloaded in the data to be displayed according to the specified parameters; and download the data to be downloaded according to the download instruction.

[0070] An embodiment of the present invention provides a device for dynamic rendering based on large model instructions, which identifies the data requirements and chart types in the input text through a large model; wherein, the chart types include: bar chart, column chart or table; finds the chart data structure and rendering instruction matching the chart type in the template library corresponding to the chart type through the large model; and obtains the data to be displayed from the database according to the data requirements; Render the data to be displayed according to the chart data structure and rendering instruction. Compared with the existing AI chat systems, most of them are limited to text and voice interactions and lack flexible chart and table display functions. By model fine-tuning and the model returning rendering instructions, it is possible to automatically select and render the appropriate chart type.

[0071] Figure 3 An example of the physical structure diagram of an electronic device is shown as Figure 3 shown. The electronic device may include: a processor 810, a communication interface 820, a memory 830, and a communication bus 840. Among them, the processor 810, the communication interface 820, and the memory 830 complete mutual communication through the communication bus 840. The processor 810 can call the logical instructions in the memory 830 to execute the method for dynamic rendering based on large model instructions.

[0072] In addition, when the logical instructions in the above-mentioned memory 830 are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0073] An embodiment of the present invention provides an electronic device, in which the processor 810 can call the logical instructions in the memory 830 to execute a method for dynamically rendering based on large model instructions. The large model is used to identify the data requirements and chart types in the input text; wherein, the chart types include: bar charts, column charts, or tables; the large model finds a chart data structure and rendering instructions that match the chart type in the template library corresponding to the chart type; the data to be displayed is obtained from the database according to the data requirements; The data to be displayed is rendered according to the chart data structure and rendering instructions. Compared with existing AI chat systems, most of which are limited to text and voice interactions and lack flexible chart and table display functions, by fine-tuning the model and having the model return rendering instructions, it is possible to automatically select and render appropriate chart types.

[0074] On the other hand, the present invention also provides a computer program product. The computer program product includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the method for dynamically rendering based on large model instructions provided by the above-mentioned various methods.

[0075] An embodiment of the present invention provides a computer program product. When the computer program is executed by a processor, it is implemented to execute the method of dynamic rendering based on large model instructions provided by the above-mentioned various methods. The data requirements and chart types in the input text are identified through a large model; wherein, the chart types include: bar chart, column chart or table; the chart data structure and rendering instructions matching the chart type are found through the large model in the template library corresponding to the chart type; the data to be displayed is obtained from the database according to the data requirements; the data to be displayed is rendered according to the chart data structure and rendering instructions. Compared with the existing AI chat systems which are mostly limited to text and voice interactions and lack flexible chart and table display functions, through model fine-tuning and the model returning rendering instructions, it is possible to automatically select and render the appropriate chart type.

[0076] On the other hand, the present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it is implemented to execute the method of dynamic rendering based on large model instructions provided by the above-mentioned various methods.

[0077] An embodiment of the present invention provides a non-transitory computer-readable storage medium. When the computer program stored therein is executed by a processor, it is implemented to execute the method of dynamic rendering based on large model instructions provided by the above-mentioned various methods. The data requirements and chart types in the input text are identified through a large model; wherein, the chart types include: bar chart, column chart or table; the chart data structure and rendering instructions matching the chart type are found through the large model in the template library corresponding to the chart type; the data to be displayed is obtained from the database according to the data requirements; the data to be displayed is rendered according to the chart data structure and rendering instructions. Compared with the existing AI chat systems which are mostly limited to text and voice interactions and lack flexible chart and table display functions, through model fine-tuning and the model returning rendering instructions, it is possible to automatically select and render the appropriate chart type.

[0078] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative labor.

[0079] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the essence of the above technical solution, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0080] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.< / canvas>

Claims

1. A method for dynamic rendering based on large model instructions, characterized in that: include: Identifying data requirements and chart types in the input text through a large model; wherein the chart types include: a column chart, a bar chart or a table; Finding a chart data structure and rendering instructions matching the chart type in a template library corresponding to the chart type through the large model; Acquire the data to be displayed from the database according to the data requirements; The data to be displayed is rendered according to the chart data structure and the rendering instruction.

2. The method for dynamic rendering based on large model instructions according to claim 1, characterized in that: The rendering of the data to be displayed according to the chart data structure and the rendering instruction includes: Processing the data to be displayed by the large model to obtain standard data to be displayed; wherein the data processing includes: data cleaning and formatting; The standard data to be displayed is rendered according to the chart data structure and the rendering instruction.

3. The method for dynamic rendering based on large model instructions according to claim 1, characterized in that: The rendering of the data to be displayed according to the chart data structure and the rendering instruction includes: Generate a display chart according to the chart data structure, the rendering instruction and the data to be displayed; Adjusting the color matching of the display chart according to the preset color matching guide and the chart type to obtain a color matching display chart; The color matching display chart is rendered according to the rendering instruction.

4. The method for dynamic rendering based on large model instructions according to claim 1, characterized in that: After rendering the data to be displayed according to the chart data structure and the rendering instruction, the method further includes: Identify the zoom-in operation and zoom-in parameters in the user request through the large model, and match the zoom-in instruction corresponding to the zoom-in operation; Determining the data to be enlarged in the data to be displayed according to the enlargement parameter; The data to be enlarged is displayed through a pop-up window according to the enlargement instruction.

5. The method for dynamic rendering based on large model instructions according to claim 1, characterized in that: After rendering the data to be displayed according to the chart data structure and the rendering instruction, the method further includes: Identify download operations and specified parameters in user requests through a large model; Matching the download instruction corresponding to the download operation through the large model; Determining the data to be downloaded in the data to be displayed according to the specified parameters; The data to be downloaded is downloaded according to the download instruction.

6. A device for dynamic rendering based on large model instructions, characterized in that: include: A requirement identification module, used to identify data requirements and chart types in the input text through a large model; wherein the chart types include: a bar chart, a bar graph or a table; A matching module, used to find a chart data structure and rendering instructions matching the chart type in a template library corresponding to the chart type through a large model; A module for acquiring data to be displayed, used for acquiring data to be displayed from a database according to the data requirements; A rendering module is used to render the data to be displayed according to the chart data structure and the rendering instruction.

7. The device for dynamic rendering based on large model instructions according to claim 6, characterized in that: The rendering module includes: The data processing submodule is used to process the data to be displayed through the large model to obtain standard data to be displayed; wherein the data processing includes: data cleaning and formatting processing; A rendering submodule is used to render the standard data to be displayed according to the chart data structure and the rendering instruction.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the method for dynamic rendering based on large model instructions as described in any one of claims 1 to 5 is implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for dynamic rendering based on large model instructions as described in any one of claims 1 to 5 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for dynamic rendering based on large model instructions as described in any one of claims 1 to 5 is implemented.

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