Data visualization method and device, medium and equipment

Abstract

The invention provides a data visualization method and device, a medium and equipment, and the method comprises the steps: in response to a data visualization request which is input by a user and carries original data, firstly determining the original data in the request, and secondly, for each visualization type, obtaining preset prompt information corresponding to the visualization type, the method comprises the following steps of: firstly, establishing a large model of the original data, determining a preset data representation type suitable for the visualization type, then inputting the original data and prompt information into the trained large model to obtain a data view of the original data matched with the data representation type suitable for the visualization type in the original data, and finally, displaying the obtained data views. According to the data representation type, it is determined that each visualization type is matched with each piece of original data, then data visualization of each visualization type is carried out, and each data view is obtained.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a data visualization method, device, medium and equipment. Background Art

[0002] In recent years, with the development of computer technology, the speed of data generation has become faster and faster, and the amount of data generated has exploded. In order to facilitate users to quickly understand the patterns, trends and anomalies of data from massive data, data visualization technology that can transform abstract and complex data into intuitive graphics, charts or maps has become a key technology.

[0003] In the existing data visualization technology, users are required to first determine how to visualize the data to be visualized based on their own experience, and then determine the data visualization method to obtain various data views.

[0004] It is not difficult to see that the determination of the data visualization method determines the quality of the data view. However, the determination of the data visualization method also includes the user's subjective understanding of the data. For ordinary users, due to insufficient understanding of the data visualization method, they often cannot accurately determine the data visualization method of each data, so the efficiency of obtaining the data view is low, and ordinary users cannot quickly understand the data.

[0005] To this end, this specification provides a data visualization method. Summary of the invention

[0006] The present application provides a data visualization method to partially solve the above-mentioned problems existing in the prior art.

[0007] This application provides a data visualization method, including:

[0008] In response to a data visualization request containing various original data input by a user, determining the various original data in the request;

[0009] For each visualization type, obtaining preset prompt information corresponding to the visualization type, and determining a preset data representation type applicable to the visualization type;

[0010] Inputting the raw data and the prompt information into the trained large model to obtain a data view of the raw data that matches the data representation type applicable to the visualization type in the raw data;

[0011] Display the obtained data views.

[0012] Optionally, the method further comprises:

[0013] When it is determined that any original data corresponds to multiple data representation types, the data representation type of the original data is determined according to a preset data representation type priority.

[0014] Optionally, the raw data and the prompt information are input into a trained large model to obtain a data view of the raw data that matches a data representation type applicable to the visualization type, specifically including:

[0015] Inputting the original data and the prompt information into the trained big model, determining the data features of the original data through the matching module of the big model, and filtering out the original data matching the visualization type based on the data features corresponding to the visualization type;

[0016] The filtered raw data and the prompt information are input into the drawing module of the large model, and a data view of each raw data is generated based on the data visualization method of the visualization type.

[0017] Optionally, the filtered raw data and the prompt information are input into the drawing module of the large model, and a data view is generated based on a data visualization method of the visualization type, specifically including:

[0018] When the prompt information includes drawing code information, for each visualization type, at least one raw data corresponding to the visualization type is determined from the raw data as target data, and the target data and the prompt information are input into the trained large model, and a drawing code is generated based on a data visualization code generation method for the visualization type;

[0019] According to the original data of the visualization type matched in the original data and the drawing code of the visualization type, each data view of the visualization type of the original data is obtained.

[0020] Optionally, the obtained data views are displayed, including:

[0021] According to a preset combination method, the obtained data views are combined to obtain a composite view, and the obtained composite view is displayed.

[0022] Optionally, the method further comprises:

[0023] Determine whether there is raw data that does not match any of the preset data representation types;

[0024] If yes, the original data that does not match any of the preset data representation types is treated as pending data, and a request for related information of the pending data is sent to the user;

[0025] In response to the user inputting the relevant information of the pending data, a data representation type matching the relevant information is determined from the preset data representation types as the data representation type of the pending data.

[0026] Optionally, determining a data representation type matching the relevant information from the preset data representation types specifically includes:

[0027] When the relevant information does not match any of the preset data representation types, acquiring original data of the determined data representation type, and determining original data matching the relevant information from the acquired original data;

[0028] From the original data matched with the relevant information, according to the original data change causing the change of the pending data, determine the original data affecting the pending data;

[0029] The data representation type of each original data affecting the pending data is determined, and the data representation type of the pending data is determined according to the recorded data representation type of each original data and the preset data representation type priority.

[0030] The present application also provides a data visualization device, comprising:

[0031] A determination module, in response to a data visualization request with raw data input by a user, determines each raw data in the request;

[0032] An acquisition module, for each visualization type, acquires preset prompt information corresponding to the visualization type, and determines a preset data representation type applicable to the visualization type;

[0033] A matching module, inputting the original data and the prompt information into the trained large model, and obtaining a data view of the original data that matches the data representation type applicable to the visualization type in the original data;

[0034] The output module displays the obtained data views to the user.

[0035] This specification provides a computer storage medium, wherein the storage medium stores a computer program, and when the computer program is executed by a processor, a data visualization method is implemented.

[0036] This specification provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein a data visualization method is implemented when the processor executes the program.

[0037] In a data visualization method provided in the present specification, by responding to a data visualization request carrying various original data input by a user, first, the various original data in the request are determined, and then, for each visualization type, the preset prompt information corresponding to the visualization type is obtained, and the preset data representation type applicable to the visualization type is determined, and then, the various original data and the prompt information are input into a trained large model to obtain a data view of the original data that matches the data representation type applicable to the visualization type, and finally, the obtained data views are displayed.

[0038] It can be seen from the above method that based on the data representation type of the original data and the data representation type preset for each visualization type, each visualization type is determined to match each original data, and then data visualization of each visualization type is performed to obtain each data view. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The drawings described herein are used to provide a further understanding of this specification and constitute a part of this specification. The illustrative embodiments and descriptions of this specification are used to explain this specification and do not constitute an improper limitation on this specification. In the drawings:

[0040] Figure 1 A flowchart of a data visualization method provided in this specification;

[0041] Figure 2 A schematic diagram of a large model flow chart of a data visualization method provided in this specification;

[0042] Figure 3 A schematic diagram of a data view combination of a data visualization method provided in this specification;

[0043] Figure 4 A schematic diagram of a data visualization method provided in this specification;

[0044] Figure 5 A method corresponding to the Figure 1 Schematic diagram of electronic equipment. DETAILED DESCRIPTION

[0045] In order to make the purpose, technical solutions and advantages of this specification more clear, the technical solutions of this specification will be clearly and completely described below in combination with the specific embodiments of this specification and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments in this specification, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0046] The technical solutions provided by the embodiments of this specification are described in detail below in conjunction with the accompanying drawings.

[0047] Figure 1 A flowchart of a data visualization method provided in this specification specifically includes the following steps:

[0048] S101: In response to a data visualization request containing raw data input by a user, determining the raw data in the request.

[0049] The current data visualization technology requires users to first determine how to visualize the data to be visualized based on their own experience, and then determine the data visualization method to obtain the data views. However, for ordinary users, there is a lack of understanding of the data visualization method, and the data visualization method determined for each data is inaccurate, which leads to low efficiency in obtaining the data view. It can be seen that for ordinary users, data visualization methods cannot achieve a quick understanding of data.

[0050] To this end, this specification provides a data visualization method to obtain various data views based on the user's original data. The data visualization process can be performed by a device with computing capabilities such as a personal computer, a mobile terminal, and a server. This specification does not limit the specific device used to perform the data visualization process. For a unified description, this specification takes the server executing the data visualization method as an example for description.

[0051] In one or more embodiments of the present specification, the server does not generate each data view out of thin air, but determines the visualization type of the original data based on the data visualization request carrying each original data input by the user and the data representation type of each original data, thereby obtaining each data view.

[0052] Specifically, the server determines data that needs to be visualized from the received data visualization request as original data.

[0053] It should be noted here that the original data does not refer to a single data, but refers to multiple data required to generate a data view. This manual does not impose specific restrictions on how to determine the original data, and it can be selected as needed.

[0054] S103: For each visualization type, obtain preset prompt information corresponding to the visualization type, and determine a data representation type applicable to the preset visualization type.

[0055] In order to determine the data visualization method of the original data, the server determines the preset data representation type of each visualization type and the prompt information corresponding to each visualization type based on the existing visualization types, and then in the subsequent steps, the visualization type of the original data is determined through the large model in the form of prompt information according to the data representation type.

[0056] Specifically, the server determines each preset visualization type and prompt information corresponding to each visualization type, and determines each data representation type applicable to each visualization type.

[0057] In one or more embodiments of the present specification, the data representation types include at least integer type, currency type, time type and percentage type, and the preset corresponding visualization types include at least bar charts, histograms, line charts and pie charts.

[0058] In one or more embodiments of the present specification, the prompt information is information used to prompt the large model to determine the representation type of the original data based on the original data, for example: "Analyze the following data to determine its representation type", "Identify the following data to determine its data identification type" or "Analyze the following data to determine the representation type of its components".

[0059] S105: Inputting the original data and the prompt information into the trained large model to obtain a data view of the original data that matches the data representation type applicable to the visualization type.

[0060] Determining the visualization type of the original data based on the data representation type requires a lot of calculations. Therefore, the server inputs the original data and the preset prompts corresponding to each visualization type into the trained big model, and the trained big model is used for comparison to determine the original data corresponding to each visualization type. The original data is visualized according to the matching visualization types to obtain various data views.

[0061] Specifically, for each visualization data type, the server inputs the prompt information corresponding to the visualization type and each original data into the trained big model, determines the data representation type of each original data through the input big model, and determines the original data matching the visualization type based on the data representation type of the original data and the data representation type of the visualization type, and then performs data visualization of the visualization type on the original data matching the visualization type, thereby obtaining each data view of the visualization type.

[0062] It is necessary to further explain here that when it is determined that any raw data corresponds to multiple data representation types, the data representation type of the raw data is determined according to the data representation type priority preset in advance. For example, the data type of the raw data includes an integer type, and it changes over time. The visualization type corresponding to the integer type is a bar graph, and the visualization type corresponding to the time-varying type is a line graph. Since the preset priority of the line graph is greater than the bar graph, the server draws the raw data as a line graph.

[0063] S107: Displaying each obtained data view.

[0064] The server presents each data view of each visualization type to the user.

[0065] based on Figure 1 The data visualization method shown can achieve: by responding to a data visualization request with each raw data input by a user, first determining each raw data in the request, secondly, for each visualization type, obtaining the preset prompt information corresponding to the visualization type, and determining the preset data representation type applicable to the visualization type, and thirdly, inputting each raw data and the prompt information into the trained large model to obtain the data view of the raw data that matches the data representation type applicable to the visualization type, and finally, displaying each obtained data view. Based on the data representation type of the raw data and the data representation type preset for each visualization type, it is determined that each visualization type matches each raw data, and then data visualization of each visualization type is performed to obtain each data view.

[0066] This specification also provides a large model training method to generate a trained large model for Figure 1 The data visualization method shown is used, specifically including: for step S105, first determining the original data that matches the data representation type applicable to the visualization type, and then drawing a data view of the original data that matches the data representation type applicable to the visualization type. A matching module and a drawing module are preset in the large model to perform the content of this step.

[0067] First, according to each historical data view, each training data in each historical data view and the data representation type of each training data are determined, and the training data and the data representation type of the training data are used as matching training samples. Secondly, the training data in the matching training sample is input into the preset large model to be trained, and the data representation type of each training data matched by the matching module is obtained. If the data representation type of each training data matched is different from the data representation type of each training data matched in the matching training sample, the matching module parameters used to determine the data representation type are adjusted until the data representation type of each training data matched is the same as the data representation type of each training data matched in the matching training sample, and the matching module training is stopped. Thirdly, the training data and each historical data view from which the training data comes are used as drawing training samples. Finally, the training data in the drawing training sample is input into the large model to be trained with the matching module that has completed the training, and each data view output by the drawing module is obtained. If the data views obtained are different from the data views in the drawing training sample, the drawing module parameters used to generate the data view are adjusted until the data views obtained are the same as the data views in the drawing training sample, and the large model training is stopped.

[0068] In order to make the trained large model more accurately determine the data representation type, in step S105, this specification provides a large model process diagram, such as Figure 2 As shown, the big model includes a matching module and a drawing module. The server inputs each raw data and prompt information into the trained big model. Through the matching module of the big model, the data features of each raw data are first determined, and based on the data features corresponding to the visualization type, the raw data matching the visualization type is screened out. Secondly, the screened raw data and prompt information are input into the drawing module of the big model, and the data view of each raw data is generated based on the data visualization method of the visualization type.

[0069] Among them, the original data that matches the visualization type is determined by the server according to the visualization type, the data features of the visualization type are determined, and the data features determined according to the original data are referred to. From the data features determined according to the original data, the data features that match the data features determined according to the visualization type are determined as the data features of the original data that match the visualization type. Finally, from each original data, according to the data features of the original data that match the visualization type, the original data having the data features of the original data that match the visualization type is determined as the original data that matches the visualization type.

[0070] It should be noted here that the data feature is a feature determined according to the attributes of the original data. For example, if the value of any data in the original data changes with different time periods, it is determined that the original data has a time-related feature.

[0071] It is also necessary to explain here that for the data visualization method based on this visualization type, a data view of the original data is generated. This manual provides a method of visualization display, which is as follows: first, obtain the map json data, and then obtain the scattered coordinate data, organize the json file of the scattered coordinates by city, and the json name of the scattered coordinate file is the same as the city name. Thirdly, establish a Web project through Ec l ipse, and create a new jsp page for page display. Thirdly, based on the Echarts api, integrate the power supply unit code, power supply area, and collected data, encapsulate the js method, and finally, introduce the MAP.js method on the page to complete the map creation and display.

[0072] To further customize the visualization type, in step S105, when the prompt information includes drawing code information, the generation of data view is changed to generation of drawing code. For each visualization type, at least one raw data corresponding to the visualization type is first determined from each raw data as the target data. Secondly, the target data and the prompt information are input into the trained large model, and the drawing code is generated based on the data visualization code generation method of the visualization type. Thirdly, according to the raw data of the visualization type matched in each raw data and the drawing code of the visualization type, each data view of the visualization type of each raw data is obtained.

[0073] It is necessary to explain here that the drawing code information is information that prompts the large model to generate drawing code, for example, "generate detailed drawing code" or "generate drawing code first", or "generate in a form that can be drawn by ECharts", etc. The specific drawing code information is not limited in this manual and can be selected as needed. To facilitate understanding of drawing code information, this specification provides a method for implementing a data visualization page with zero code, as an example of drawing code information content: first, the server provides a configuration interface for the data visualization page, and the configuration interface provides a component list, a canvas, and a component property configuration panel, so that users can move the component on the canvas by dragging and dropping the component, and configure the properties of the component by filling in a form in the component property configuration panel. Secondly, the component list is provided by a component library, and the component library is encapsulated based on the ant-design-vue UI component library and the echarts chart library. Finally, after the user designs the page and triggers the one-click project generation function, the configured content is saved as json format data, and the vue code of the project is generated by FreeMarker and placed in the specified directory of the server; after the one-click publishing function is triggered, the project is automatically generated on the server, the project dependencies are downloaded, the project is packaged, and the project is deployed, and then the online project address is returned for the user to access the project online, thereby obtaining a page in the production environment.

[0074] To facilitate users to quickly obtain information from various data views, in step S107, the server may combine the obtained data views according to a preset combination method to obtain a composite view, and display the obtained composite view.

[0075] In one or more embodiments of this specification, taking the example of a large model using enterprise-level charts (EnterpriseCharts, ECharts) to draw various data views and combine them, the large model can set multiple coordinate systems in a chart through the multi-coordinate system combination function of Echarts, allowing different types of charts (such as line charts, bar charts, scatter plots, etc.) to use their own independent coordinate systems. And the layout and coordination of multiple coordinate systems are achieved by configuring the grid and adjusting related parameters. It is also possible to create one or more sub-charts within a chart through the nested chart function of Echarts. Of course, it is also possible to directly combine them in the same coordinate system through the side-by-side display function of Echarts, so that each chart has its own coordinate system and data. Through the above-mentioned various combination mechanisms, different charts are combined into a composite view.

[0076] In one or more embodiments of the present specification, the present specification also provides a composite view schematic diagram, such as Figure 3As shown, the user selects a bar chart to clearly display the data type, and pre-sets a bar chart that counts the number of data views of each visualization type as the main body. The server first determines the bar chart of the number of data views of each visualization type of the original data, and randomly displays the data views of other visualization types. Secondly, the data views of each visualization type of the original data, including line charts and pie charts, are displayed in a bar chart to the user. When the user clicks the rightmost rectangle in the bar chart, multiple pie charts of each original data are expanded; when the user clicks the bar chart except the rightmost rectangle on the right, multiple line charts of each original data are expanded.

[0077] In order to determine whether all raw data have applicable visualization types, the server first determines whether there is raw data that does not match any of the preset data representation types. If so, the raw data that does not match any of the preset data representation types is treated as pending data, and a request for related information of the pending data is sent to the user; if not, the applicable visualization type of each raw data is determined. Next, the relevant information of each pending data input in response to the user is determined, and the data representation type that matches the relevant information is determined from the preset data representation types, and then the data representation type of the pending data in each raw data is re-determined.

[0078] What needs to be additionally explained here is that the relevant information can be explained by the following example: the wind speed is increasing at 12 o'clock on the same day, and the temperature will decrease at 12 o'clock on the same day, but the big model cannot determine whether these two types of data are related. Therefore, by sending a request for relevant information, the user inputs the information that an increase in wind speed will cause a decrease in temperature as the relevant information, and then the big model uses the data type of the temperature change data as the data type of the wind speed change data.

[0079] Furthermore, when relevant information does not match all preset data representation types, in order to determine the visualization type applicable to the pending data, the server first obtains each original data of the determined data representation type, and determines each original data matching the relevant information from the obtained original data. Secondly, from the original data matching the relevant information, according to the original data change that causes the change of the pending data, the original data affecting the pending data is determined. Thirdly, the data representation type of each original data affecting the pending data is determined, and the data representation type of the pending data is determined according to the recorded data representation type of each original data and the preset data representation type priority.

[0080] It should be additionally explained here that data change can also be understood as data variation. For example, in the above-mentioned example where wind change affects temperature change, the local pressure difference in the original data affects the original data of wind change, and thus the pressure difference can be determined as the original data affecting the wind change. In one or more embodiments of this specification, the determination of the influence relationship can rely on the association obtained by training a large model, or it can be an association preset by the user. Therefore, this specification does not limit it and can be selected as needed.

[0081] This manual also provides Figure 1 The flow chart of the data visualization method corresponds to the device, such as Figure 4 As shown:

[0082] Determination module 201, in response to a data visualization request with raw data input by a user, determines each raw data in the request;

[0083] The acquisition module 203 acquires, for each visualization type, preset prompt information corresponding to the visualization type, and determines a preset data representation type applicable to the visualization type;

[0084] A matching module 205 inputs the original data and the prompt information into the trained large model to obtain a data view of the original data that matches the data representation type applicable to the visualization type in the original data;

[0085] The output module 207 displays the obtained data views to the user.

[0086] Optionally, the matching module 205 is configured to determine the data representation type of the original data according to a preset data representation type priority when it is determined that any original data corresponds to multiple data representation types.

[0087] Optionally, the matching module 205 is used to input each original data and prompt information into the trained large model, determine the data features of each original data through the matching module of the large model, and filter out the original data matching the visualization type based on the data features corresponding to the visualization type. Input the filtered original data and prompt information into the drawing module of the large model, and generate the data view of each original data based on the data visualization method of the visualization type.

[0088] Optionally, the matching module 205 is used for, when the prompt information includes drawing code information, determining, for each visualization type, from the raw data, at least one raw data corresponding to the visualization type as target data, and inputting the target data and the prompt information into the trained large model, and generating drawing codes based on the data visualization code generation method of the visualization type. According to the raw data of the visualization type matched in the raw data and the drawing code of the visualization type, each data view of the visualization type of each raw data is obtained.

[0089] Optionally, the output module 207 is used to combine the obtained data views according to a preset combination method to obtain a composite view, and display the obtained composite view.

[0090] Optionally, the above device further includes a feedback module 209, which is used to determine whether there is raw data that does not match any of the preset data representation types. If so, the raw data that does not match any of the preset data representation types is used as pending data, and a request for relevant information of the pending data is sent to the user. In response to the user inputting relevant information of the pending data, a data representation type that matches the relevant information is determined from the preset data representation types as the data representation type of the pending data.

[0091] Optionally, the device further includes a feedback module 209, which is used to obtain each original data of the determined data representation type when the relevant information does not match all preset data representation types, and determine each original data matching the relevant information from the obtained original data. From each original data matching the relevant information, according to the original data change that causes the change of the pending data, determine each original data affecting the pending data. Determine the data representation type of each original data affecting the pending data, and determine the data representation type of the pending data according to the recorded data representation type of each original data and the preset data representation type priority.

[0092] The present specification also provides a computer-readable storage medium, which stores a computer program, and the computer program can be used to execute the above method.

[0093] This manual also provides Figure 5 The one shown corresponds to Figure 1 A schematic diagram of the structure of an electronic device of a data visualization method. Figure 5 At the hardware level, the electronic device includes a processor, an internal bus, a network interface, a memory, and a non-volatile memory, and may also include other hardware required for the business. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it to achieve the above Figure 1Of course, in addition to the software implementation, this specification does not exclude other implementations, such as logic devices or a combination of software and hardware, etc., that is, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0094] In the 1990s, it was very clear whether the improvement of a technology was a hardware improvement (for example, improvements to the circuit structure of diodes, transistors, switches, etc.) or a software improvement (improvement of the method flow). However, with the development of technology, many improvements of the method flow today can be regarded as direct improvements to the hardware circuit structure. Designers almost always obtain the corresponding hardware circuit structure by programming the improved method flow into the hardware circuit. Therefore, it cannot be said that the improvement of a method flow cannot be implemented with a hardware entity module. For example, a programmable logic device (PLD) (such as a field programmable gate array (FPGA)) is such an integrated circuit whose logical function is determined by the user's programming of the device. Designers can "integrate" a digital system on a PLD by programming it themselves, without having to ask chip manufacturers to design and produce dedicated integrated circuit chips. Moreover, nowadays, instead of manually making integrated circuit chips, this kind of programming is mostly implemented by "logic compiler" software, which is similar to the software compiler used when developing and writing programs, and the original code before compilation must also be written in a specific programming language, which is called hardware description language (HDL). There is not only one HDL, but many kinds, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, RHDL (Ruby Hardware Description Language), etc. The most commonly used ones are VHDL (Very-High-Speed ​​Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should also know that it is only necessary to program the method flow slightly in the above-mentioned hardware description languages ​​and program it into the integrated circuit, and then it is easy to obtain the hardware circuit that implements the logic method flow.

[0095] The controller can be implemented in any appropriate manner, for example, the controller can take the form of a microprocessor or processor and a computer-readable medium storing a computer-readable program code (such as software or firmware) that can be executed by the (micro)processor, a logic gate, a switch, an application-specific integrated circuit (ASIC), a programmable logic controller, and an embedded microcontroller. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320. The memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art also know that in addition to implementing the controller in a purely computer-readable program code manner, the controller can be implemented in the form of a logic gate, a switch, an application-specific integrated circuit, a programmable logic controller, and an embedded microcontroller by logically programming the method steps. Therefore, this controller can be considered as a hardware component, and the devices included therein for implementing various functions can also be regarded as structures within the hardware component. Or even, the devices for implementing various functions can be regarded as both software modules for implementing the method and structures within the hardware component.

[0096] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a sensor phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0097] For the convenience of description, the above device is described in various units according to their functions. Of course, when implementing this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0098] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems, or computer program products. Therefore, this specification may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0099] This specification is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of this specification. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0100] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0101] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0102] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0103] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0104] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0105] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0106] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems or computer program products. Therefore, this specification may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0107] This specification may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. This specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0108] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0109] The above description is only an embodiment of the present specification and is not intended to limit the present specification. For those skilled in the art, the present specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present specification shall be included in the scope of the claims of the present specification.

Claims

1. A data visualization method, characterized in that: include: In response to a data visualization request containing various original data input by a user, determining the various original data in the request; For each visualization type, obtaining preset prompt information corresponding to the visualization type, and determining a preset data representation type applicable to the visualization type; Inputting the raw data and the prompt information into the trained large model to obtain a data view of the raw data that matches the data representation type applicable to the visualization type in the raw data; Display the obtained data views.

2. The method according to claim 1, characterized in that The method further comprises: When it is determined that any original data corresponds to multiple data representation types, the data representation type of the original data is determined according to a preset data representation type priority.

3. The method according to claim 1, characterized in that Inputting the original data and the prompt information into the trained large model to obtain a data view of the original data that matches the data representation type applicable to the visualization type in the original data, specifically including: Inputting the original data and the prompt information into the trained big model, determining the data features of the original data through the matching module of the big model, and filtering out the original data matching the visualization type based on the data features corresponding to the visualization type; The filtered raw data and the prompt information are input into the drawing module of the large model, and a data view of each raw data is generated based on the data visualization method of the visualization type.

4. The method according to claim 3, characterized in that The filtered raw data and the prompt information are input into the drawing module of the large model, and a data view is generated based on the data visualization method of the visualization type, specifically including: When the prompt information includes drawing code information, for each visualization type, at least one raw data corresponding to the visualization type is determined from the raw data as target data, and the target data and the prompt information are input into the trained large model, and a drawing code is generated based on a data visualization code generation method for the visualization type; According to the original data of the visualization type matched in the original data and the drawing code of the visualization type, each data view of the visualization type of the original data is obtained.

5. The method according to claim 1, characterized in that The obtained data views are displayed, including: According to a preset combination method, the obtained data views are combined to obtain a composite view, and the obtained composite view is displayed.

6. The method according to claim 1, characterized in that The method further comprises: Determine whether there is raw data that does not match any of the preset data representation types; If yes, the original data that does not match any of the preset data representation types is treated as pending data, and a request for related information of the pending data is sent to the user; In response to the user inputting the relevant information of the pending data, a data representation type matching the relevant information is determined from the preset data representation types as the data representation type of the pending data.

7. The method according to claim 6, characterized in that Determining a data representation type that matches the relevant information from the preset data representation types specifically includes: When the relevant information does not match any of the preset data representation types, acquiring original data of the determined data representation type, and determining original data matching the relevant information from the acquired original data; From the original data matched with the relevant information, according to the original data change causing the change of the pending data, determine the original data affecting the pending data; The data representation type of each original data affecting the pending data is determined, and the data representation type of the pending data is determined according to the recorded data representation type of each original data and the preset data representation type priority.

8. A data visualization device, characterized in that: include: A determination module, in response to a data visualization request with raw data input by a user, determines each raw data in the request; An acquisition module, for each visualization type, acquires preset prompt information corresponding to the visualization type, and determines a preset data representation type applicable to the visualization type; A matching module, inputting the original data and the prompt information into the trained large model, and obtaining a data view of the original data that matches the data representation type applicable to the visualization type in the original data; The output module displays the obtained data views to the user.

9. A computer storage medium, characterized in that The storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the method according to any one of claims 1 to 7 is implemented.

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