CN112445950A - Data visualization processing method and device and electronic equipment - Google Patents
Data visualization processing method and device and electronic equipment Download PDFInfo
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Abstract
The embodiment of the invention provides a data visualization processing method, a data visualization processing device and electronic equipment, wherein the method comprises the following steps: acquiring user data, and performing metadata analysis on the user data to generate metadata characteristic information; generating visual component characteristic information according to the metadata characteristic information; and configuring the visual components according to the visual component characteristic information, and recommending the visual components to the user. According to the embodiment of the invention, a complex analysis process of a user on the aspect of data visualization performance is omitted, so that a non-professional user is helped to better use a visualization component, and meanwhile, some repeated labor aiming at the aspect of data visualization analysis can be reduced, so that the business efficiency is further improved.
Description
Technical Field
The application relates to a data visualization processing method and device and electronic equipment, and belongs to the technical field of computers.
Background
In the data age, data analysis is increasingly important. The data report is the core output of the data analysis and is used for presenting conclusions and insights obtained by the data analysis through systematic expression, wherein the data visualization is the core element in the data report, namely, information hidden in the data is transmitted in a more intuitive form such as a graph, a table and the like. In the current data visualization tool, a user needs to understand the relationship between data and visualization like a professional data analyst, so that better data visualization can be realized. The difficulty of the processing can increase rapidly along with the complexity of the data, and an excellent visualization scheme is difficult to analyze by common users. In addition, even for professional data analysts, a large amount of data visualization work can also be repeated, and the work efficiency also has a space capable of being improved.
Disclosure of Invention
The embodiment of the invention provides a data visualization processing method and device and electronic equipment, and aims to improve convenience of a user for visualization presentation of data.
In order to achieve the above object, an embodiment of the present invention provides a data visualization processing method, which includes:
acquiring user data, and performing metadata analysis on the user data to generate metadata characteristic information;
generating visual component characteristic information according to the metadata characteristic information;
and configuring the visual components according to the visual component characteristic information, and recommending the visual components to the user.
An embodiment of the present invention further provides a data visualization processing apparatus, including:
the metadata characteristic information generation module is used for acquiring user data, analyzing the metadata of the user data and generating metadata characteristic information;
the visual component characteristic information generating module is used for generating visual component characteristic information according to the metadata characteristic information;
and the visual recommendation module is used for configuring visual components according to the visual component characteristic information and recommending the visual components to the user.
The embodiment of the invention also provides a data visualization processing method, which comprises the following steps:
receiving a data visualization request of a user, wherein the data visualization request comprises a visualization analysis requirement aiming at specified data content;
acquiring data to be analyzed according to the designated data content;
configuring a visual component according to the data characteristics of the data to be analyzed;
and generating a visual data view based on the data to be analyzed and the configured visual components, and recommending.
The embodiment of the invention also provides a data visualization processing method, which comprises the following steps:
acquiring visual clue information and user data provided by a user;
screening out matched visual components from the visual component library according to the visual clue information;
and configuring the visualization component according to the matched visualization component and the data characteristics of the user data, and recommending the visualization component to the user.
An embodiment of the present invention further provides an electronic device, including:
a memory for storing a program;
and the processor is used for operating the program stored in the memory so as to execute the data visualization processing method.
According to the embodiment of the invention, the metadata is analyzed for the user data, the metadata characteristic information is extracted, and the visual component characteristic information is further formed to configure and recommend the visual component for the user, the whole process can be executed by the data service platform, so that the complex analysis process of the user on the aspect of data visual representation is omitted, a non-professional user is helped to use the visual component better, and meanwhile, the repeated labor on the aspect of data visual analysis can be reduced, and the business efficiency is further improved.
The foregoing description is only an overview of the technical solutions of the present invention, and the present invention can be implemented according to the content of the description in order to make the technical means of the present invention more clearly understood, and the following detailed description of the present invention is provided in order to make the above and other objects, features, and advantages of the present invention more clearly understandable.
Drawings
FIG. 1 is a diagram illustrating an application scenario according to an embodiment of the present invention;
FIG. 1A is one example of a visualization of an embodiment of the present invention;
FIG. 1B is a second example of a visualization according to an embodiment of the present invention;
FIG. 2 is a second exemplary embodiment of an application scenario;
FIG. 3 is a schematic flow chart of a data visualization processing method according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a data visualization processing apparatus according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
The technical solution of the present invention is further illustrated by some specific examples.
The analysis of the data is generally presented in the form of a data analysis report, and the visualization of the data plays an important role in the data analysis report. The good data visualization presentation mode can better present data characteristics and viewpoints presented by data analysis. With the huge amount of data information and the increasing complexity of data sets, it is difficult for a common user to find a suitable data visualization form.
With the development of internet and cloud services, there are some data service platforms to provide various data services for users. The user performs visualization processing on user data to be displayed based on a visualization component provided by the data service platform to form visualization display states such as a data diagram or a table. In the prior art, although a data service platform provides rich visual components for users, users need to select and configure visual components according to the characteristics of user data, and this way brings great difficulty and inconvenience to users.
The embodiment of the invention provides a data visualization processing technology, which can fully utilize the existing samples of mass data analysis reports in a data service platform and extract the rule of visualization presentation based on user data, thereby being applied to automatically recommending visualization components for users. Among the existing data analysis reports in the data service platform, there are a large number of data analysis reports produced by professional data analysts, which contain many expert experiences, some reports are modified and updated many times, and these reports also contain some feedback of users. In addition, for the data analysis report of the large amount, a data analysis report with a better data visualization mode can be screened out as a sample in a manual mode or a user feedback mode, and a data visualization rule is extracted from the sample.
For the way of extracting the data visualization rules, a manual or computer analysis way can be adopted to extract the corresponding relation between the user data and the visualization components in the data analysis report, so as to form the configuration strategy of the visualization components, and thus some visualization components can be automatically matched based on the user data provided by the user. On the other hand, a machine learning mode can also be adopted, training samples are made based on samples of existing data analysis reports, and the mapping relation between the metadata characteristic information of the user data and the visual component characteristic information is learned, so that an intelligent visual recommendation system is formed. The visualization component in the embodiment of the present invention refers to unit modules for displaying visualization of data, and the unit modules may perform data configuration (for example, size, color, and the like), and display the data in a visualization manner after being filled with the data. The visualization component may include: for example, basic visualization units such as histograms, pie charts, graphs, tables, map data distributions, meter views, etc., or combinations of these visualization units.
Different user data may be suitable for different visual display modes, for example, sales data of various shops in a city may be more suitable for being displayed more intuitively in a map data distribution mode, and sales data of all the year around may be more suitable for being displayed in a graph mode, so that the change of all the year around data can be seen. For some complex user data, data characteristics may need to be displayed from multiple aspects, and therefore, combinations of multiple visualization components may be involved, for example, sales data of all shops in a city are also displayed, besides the display in the form of map data distribution, the sales proportions of all product categories can be displayed in the form of pie charts, and the like, so that a visualization display effect more meeting the requirements of customers can be recommended to users.
The visual recommendation system of the embodiment of the invention can recommend the configuration scheme of the visual component based on the characteristics of the user data to the user finally, and then the user can write the complete user data into the configured visual component, thereby forming the complete visual display scheme. In addition, after the visualization recommendation system generates the configuration scheme of the visualization component, the visualization recommendation system may also present the visualization example to the user based on part of the user data, and of course, if the user provides complete user data, the visualization recommendation system may also directly generate the visualization result to present to the user, that is, present to the user the final visualization view formed after the user data is filled into the visualization component. The visualization recommendation system may recommend the configuration schemes of the plurality of visualization components to the user for selection by the user, such as shown in fig. 1, and may ultimately present a visualization example of the configuration schemes of the plurality of visualization components to the user. As shown in fig. 1A and fig. 1B, which are examples of visualization component configuration schemes recommended to a user according to an embodiment of the present invention. The example shown in fig. 1A is a single visualization component example, which is a pie chart as shown in the figure, used to display the data scale of various sections below the pie chart (the names of the various sections are schematically represented by xxxxxx), such as may be suitable for type analysis that presents market share of different stores. FIG. 1B illustrates a visual example component in a combination that is more suitable for presenting data variations, using a combination of dashboard and graph forms. Different visualization schemes are provided for users, so that the users can flexibly select according to the requirements of the users.
Fig. 1B shows a configuration scheme of recommended visualization components to a user in a visualization example manner, where multiple visualization components of each part may be recommended to the user at the same time, and the display is switched by a switch button on an interaction interface. For example, the upper left part of fig. 1B shows a visualization component in a graph form as an example, in practice, the part may recommend two visualization forms, namely, a graph and a histogram, to the user at the same time, and the user may change the graph in the upper left part into a display example of the histogram by switching the key, and in addition, the user may delete the part of the visualization component or add a new visualization component. Similarly, the other parts of the visualization components can also provide multiple choices for the user, so that the user can observe the collocation scheme of various visualization components through self switching or deleting operation, thereby determining the final visualization scheme.
In order to obtain a better data visualization effect, the characteristics of user data need to be obtained, so that a reasonable visualization component and a specific component expression form can be determined. And the metadata analysis of the data can well extract the key characteristic information of the user data.
Some basic feature information behind the data described by the metadata, such as the number of rows and columns of the data, some statistical indicators of the data, and so on. These metadata feature information are the primary information that determines what visualization components to employ.
An application scenario of the embodiment of the present invention is explained with reference to fig. 1 and 2. In the embodiment of the invention, a machine learning model can be adopted on a data service platform to perform related recommendation of visual components. Based on the application scenario, a machine learning model of a sequence-to-sequence (seq2seq) model may be adopted, wherein an input sequence of the model is a metadata feature code generated by feature coding metadata of user data, and an output of the model is a visualization component feature code representing matching with the user data.
Specific feature encoding rules can be formulated based on data and information in the metadata and visualization component libraries, and the feature encoding rules can be updated as the content in the metadata and visualization component libraries continues to be enriched. For example, each element constituting a visual component in the visual component library is given a unique vector representation, and items of different metadata in the metadata library are also given a unique vector representation, thereby forming a feature encoding dictionary of the metadata and the visual component.
As shown in fig. 1, after user data is obtained, metadata extraction is performed on the user data, then metadata feature coding is performed with reference to a feature coding dictionary of the metadata, and the metadata feature coding is input into a sequence model, so that a visual component feature code is obtained, and based on a feature coding dictionary of a visual component, visual component decoding is performed, so that a corresponding visual component is obtained and recommended to a user. That is to say, in the sequence-to-sequence model, the mapping relationship from the metadata feature code to the visualization component feature code is obtained through a large number of training examples.
The training data for the sequence-to-sequence model may be from a large number of data reports accumulated on the aforementioned data service platform, which include a large number of combinations of user data and visualization components, which may be used as training data for the sequence-to-sequence model. As shown in fig. 2, the training data including the combination of the user data and the visualization component is subjected to visualization component parsing and metadata parsing, respectively, to form a visualization component feature code and a metadata feature code, respectively. The training is then performed in a training model input to the sequence-to-sequence model, thereby generating a trained sequence-to-sequence model, which is used on the data service platform as shown in fig. 1. The visual component analysis may include analyzing the expression type, content filling, and design rule angle of some visual components, so as to form a visual component feature code. Metadata parsing may include feature parsing for user data in terms of data dimensions and data aspects, such as single-dimensional parsing, multi-dimensional parsing, data distribution variance, data population, and the like. Further, parsing based on some expert cognitive aspects may also be included.
In addition, sometimes the mapping relationship between the visualization component and the user data is based on the difference of the business fields, that is, the visualization manners that may be adopted for the user data of different business fields are different, so that different sequence-to-sequence models may be adopted for the user data of different business fields, where the difference of the models refers to the difference of the final models caused by the difference of the training data. For some new business fields, there is probably no training data or less training data, and for this case, the model parameters of the model for other similar business fields may be migrated to the model for the new business field in a migration learning manner to perform data processing.
In addition, data burial points can be arranged to collect feedback data of the user, training data is formed based on the feedback data, and the model is further trained and updated. These user feedback data may include: and in the process of using the visual component recommendation service, the user serving as the data report generator feeds back the consumption data of the recommended visual components and the user serving as the consumer of the data reports to the consumption of the data reports.
In the embodiment of the present invention, the target user to which the visual recommendation scheme or the data service platform is directed may be a merchant operating based on a network platform, for example, an operator of an online store, an operator of an entity store managed based on a network platform, and the like, which may not be able to use a visual tool by the user himself.
For different users, besides visualization component recommendation based on the characteristics of user data, the use history of the user visualization component can be analyzed, the use preference of the user can be extracted, and the recommendation of the visualization component can be performed according to the characteristics of the user data and the use preference of the user.
In addition, although the visual component library of the data service platform stores abundant visual components, users can continuously innovate the visual components according to own data analysis requirements, and in some scenes, although the configuration scheme of the visual components is recommended to the data service platform, the users still design new visual components or modify the recommended visual components to adapt to specific requirements of the users. In such a case, the data service platform acquires feedback in this aspect through interaction with the user, and incorporates the newly designed or modified visualization component of the user into the visualization component library, so as to enrich the type and attribute of the visualization component for subsequent recommendation processing of the visualization component.
Example one
Fig. 3 is a schematic flow chart of a data visualization processing method according to an embodiment of the present invention, which may be executed on the foregoing data service platform, where the data service platform is capable of providing an online data visualization processing service for a user, and may further provide a data report generation service with functions of online editing, and the like. The method can comprise the following steps:
s101: and acquiring user data, and performing metadata analysis on the user data to generate metadata characteristic information. When a user uses a data processing service provided by the data service platform, user data, which may be formatted data such as a data table, is uploaded to the data service platform. After the data service platform obtains the user data, metadata analysis can be started, and particularly data dimension characteristic analysis and data distribution characteristic analysis can be carried out on the user data.
The data dimension feature analysis may include, for example, a combination of multiple dimensions formed based on the contents of each column in the data table, in the process of data visualization, a common representation is a two-dimensional data representation or a three-dimensional data representation, and for the data tables of multiple columns, a plurality of three-dimensional combinations or two-dimensional combinations may be formed, and a determination may be made as to which combination or combinations of combinations better represent the characteristics of the data. The analysis of the data distribution characteristics may include calculation of the total number of data, calculation of variance of data distribution, and the like, and these distribution characteristics are also important factors for determining which visualization form is used.
S102: and generating visual component characteristic information according to the metadata characteristic information. The processing of this step may be performed based on a preset policy, advantageously based on a piece of expert experience or some policy rules summarized from some data reports. The step may specifically be to determine, according to the metadata characteristic information, each visualization characteristic element of the visualization component, where the visualization characteristic element includes one item or a combination of any multiple items of a visualization display form, a visualization color matching, a visualization display dimension, and a visualization display size.
Preferably, the metadata feature information is input into the machine learning model for processing by using the machine learning model, so as to generate the visualized component feature information, and the machine learning model is trained to establish a mapping relationship between the metadata feature information and the visualized component feature information. In particular, the machine learning model may employ the sequence-to-sequence model described above. Here, the metadata feature information may be a metadata feature code for machine learning model processing, and a coding rule thereof may be formed based on the aforementioned metadata library. The visualization component feature information can be a visualization component feature code output after the machine learning model is processed, and the coding rule of the visualization component feature code can be formed based on the visualization component library, and the visualization component feature information can embody the visualization feature elements.
S103: and configuring the visual components according to the visual component characteristic information, and recommending the visual components to the user. After the visualization component feature information is determined, the visualization feature element is determined, and based on the visualization feature element, the visualization component or the combination of the visualization components is obtained from the visualization component library. Then, these visual components or combinations of visual components can be recommended to the user, the number of recommendations can be multiple, and certain ranking processing can be performed for the user to select. In the case of processing by using a machine learning model, the configuration of the visual components can be performed based on the visual component library according to the visual component feature codes output by the model.
In addition, corresponding to the machine learning model, model parameters of the machine learning model in a specified business field are obtained through a transfer learning process and are applied to the current machine learning model, wherein the specified business field can be a business field similar to the business field corresponding to the current machine learning model. In addition, in the embodiment of the invention, the use feedback information of the user on the recommended visual component can be acquired through the data embedding point; and updating the machine learning model based on the usage feedback information to provide a more suitable push process for the visualization component.
According to the data visualization processing method, the metadata is analyzed on the user data, the metadata characteristic information is extracted, and further the visualization component characteristic information is formed to configure and recommend the visualization component for the user, the whole process can be executed by the data service platform, the complex analysis process of the user on the data visualization representation aspect is omitted, so that the non-professional user is helped to use the visualization component better, meanwhile, the repeated labor aiming at the data visualization analysis aspect can be reduced, and the business efficiency is further improved.
In addition, the mapping relation between the user data and the visual components can be learned by adopting a machine learning model mode based on the advantage of a large amount of professional data reports in the data service platform, so that better visual components can be automatically and intelligently recommended for a user, and the user can be better assisted to use the visual components.
Example two
Fig. 4 is a schematic structural diagram of a data visualization processing apparatus according to an embodiment of the present invention, which may be disposed on the data service platform to provide a pushing process of a visualization component. The apparatus may include:
and the metadata characteristic information generating module 11 is configured to acquire user data, perform metadata analysis on the user data, and generate metadata characteristic information. When using the data processing service provided by the data service platform, the user uploads user data to the data service platform, where the user data may be formatted data, such as a data table. After the data service platform obtains the user data, metadata analysis can be started, and particularly data dimension characteristic analysis and data distribution characteristic analysis can be performed on the user data.
The visualized component characteristic information generating module 12 generates visualized component characteristic information according to the metadata characteristic information. The part of the processing may specifically be to determine, according to the metadata feature information, each visualization feature element of the visualization component, where the visualization feature element includes one item or a combination of any multiple items of visualization display form, visualization color matching, visualization display dimension, and visualization display size.
Preferably, the metadata feature information is input into the machine learning model for processing by using the machine learning model, so as to generate the visualized component feature information, and the machine learning model is trained to establish a mapping relationship between the metadata feature information and the visualized component feature information.
And the visual recommendation module 13 is used for configuring the visual components according to the visual component characteristic information and recommending the visual components to the user. The part of the processing may be specifically to obtain a visual component or a combination of visual components in a visual component library according to the visual feature elements.
The detailed description of the above processing procedure, the detailed description of the technical principle, and the detailed analysis of the technical effect are described in the foregoing embodiments, and are not repeated herein.
EXAMPLE III
In some cases, the user may already be substantially aware of the morphology of the visual components that he wishes or needs, but the user cannot make reasonable selections and configurations himself because he does not have the expertise in this and the visual component library. In such a case, besides providing the relevant features of the user data to be analyzed to the data service platform, the user may also provide some visual cue information, such as a picture of a visual effect, and the data service platform integrates the features of the user data and the cue information provided by the user to recommend visual components for the user.
Therefore, the present embodiment provides a data visualization processing method, which includes:
s201: and acquiring visual clue information and user data provided by a user. The visual clue information can be picture information or a text description for the component, and the like.
S202: and screening matched visual components from the visual component library according to the visual clue information. In this step, the selection range of the visual components is narrowed, corresponding to the clue information provided by the user.
S203: and configuring the visual components according to the matched visual components and the data characteristics of the user data, and recommending the visual components to the user. In this step, within the range of the visualized components screened in step S202, a visualization scheme using the user data is configured for the user further based on the characteristics of the user data.
The data visualization processing method provided by the embodiment recommends the visualization component for the user based on the clues provided by the user and the characteristics of the user data, so that the personalized requirements of the user are better met.
Example four
Most of the above embodiments are technical solutions for recommending visual components to a user based on data provided by the user. For some users, the users do not have the analyzed data, but look at the visual analysis of some public data, that is, the data source which wants to perform the visual analysis is not at the users. For example, for some general consumers or some data analysts, it is desirable to see data conditions on the network, such as consumption trends of a group of people of the same age, airline ticket statistics for international and domestic flights, and the like. Aiming at the requirements, the data service platform can also acquire related public data on the premise of reasonable legality, configure a visual component based on the public data, form a visual data view and present the visual data view to a user.
Therefore, the present embodiment provides a data visualization processing method, which includes:
s301: receiving a data visualization request of a user, wherein the data visualization request comprises a visualization analysis requirement aiming at specified data content. The data visualization request of the user needs to provide data content desired to be analyzed, and may include the location of the data source and the like, if necessary, and analysis requirements, such as whether the change rate or the occupation ratio is desired to be embodied.
S302: and acquiring data to be analyzed according to the specified data content. Specifically, the data to be analyzed may be obtained from a published data source.
S303: and configuring a visualization component according to the data characteristics of the data to be analyzed. The specific processing procedure of this step may adopt the related art described in the foregoing embodiments.
S304: and generating a visual data view based on the data to be analyzed and the configured visual components, and recommending the visual data view to the user.
The data visualization processing method provided by the embodiment provides data visualization analysis service for users such as general consumers or data analysts, so that the users can more easily perform visualization analysis on data from an open data source.
EXAMPLE III
The foregoing embodiment describes a flow process and a device structure according to an embodiment of the present invention, and the functions of the method and the device can be implemented by an electronic device, as shown in fig. 5, which is a schematic structural diagram of the electronic device according to the embodiment of the present invention, and specifically includes: a memory 110 and a processor 120.
And a memory 110 for storing a program.
In addition to the programs described above, the memory 110 may also be configured to store other various data to support operations on the electronic device. Examples of such data include instructions for any application or method operating on the electronic device, contact data, phonebook data, messages, pictures, videos, and so forth.
The memory 110 may be implemented by any type or combination of volatile or non-volatile memory devices, such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disks.
The processor 120, coupled to the memory 110, is used for executing the program in the memory 110 to perform the operation steps of the data visualization processing method described in the foregoing embodiments.
Furthermore, the processor 120 may also include various modules described in the foregoing embodiments to perform data visualization processing, and the memory 110 may be used, for example, to store data required by the modules to perform operations and/or data output.
The detailed description of the above processing procedure, the detailed description of the technical principle, and the detailed analysis of the technical effect are described in the foregoing embodiments, and are not repeated herein.
Further, as shown, the electronic device may further include: communication components 130, power components 140, audio components 150, display 160, and other components. Only some of the components are schematically shown in the figures and it is not meant that the electronic device comprises only the components shown in the figures.
The communication component 130 is configured to facilitate communication between the electronic device and other devices in a wired or wireless manner. The electronic device may access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component 130 receives a broadcast signal or broadcast associated information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 130 further includes a Near Field Communication (NFC) module to facilitate short range communication. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, Ultra Wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.
The power supply component 140 provides power to the various components of the electronic device. The power components 140 may include a power management system, one or more power sources, and other components associated with generating, managing, and distributing power for an electronic device.
The audio component 150 is configured to output and/or input audio signals. For example, the audio assembly 150 includes a Microphone (MIC) configured to receive external audio signals when the electronic device is in an operational mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signal may further be stored in the memory 110 or transmitted via the communication component 130. In some embodiments, audio assembly 150 also includes a speaker for outputting audio signals.
The display 160 includes a screen, which may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive an input signal from a user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation.
Those of ordinary skill in the art will understand that: all or a portion of the steps for implementing the above-described method embodiments may be performed by hardware associated with program instructions. The program may be stored in a computer-readable storage medium. When executed, the program performs steps comprising the method embodiments described above; and the aforementioned storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic or optical disks.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; although the present invention has been described in detail with reference to the foregoing embodiments, it should be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (13)
1. A data visualization processing method comprises the following steps:
acquiring user data, and performing metadata analysis on the user data to generate metadata characteristic information;
generating visual component characteristic information according to the metadata characteristic information;
and configuring the visual components according to the visual component characteristic information, and recommending the visual components to the user.
2. The method of claim 1, wherein performing metadata parsing on the user data, generating metadata characteristic information comprises:
and performing data dimension characteristic analysis and data distribution characteristic analysis on the user data to generate metadata characteristic information.
3. The method of claim 1, wherein said determining visualization component characteristic information from said metadata characteristic information comprises:
and determining each visual characteristic element of the visual assembly according to the metadata characteristic information, wherein the visual characteristic element comprises one item or any combination of multiple items of visual display form, visual color matching, visual display dimension and visual display size.
4. The method of claim 3, wherein said configuring a visualization component according to said visualization component characteristic information comprises:
and acquiring a visual component or a combination of visual components in the visual component library according to the visual characteristic elements.
5. The method of claim 1, wherein determining visualization component characteristic information from the metadata characteristic information comprises:
inputting the metadata feature information into a machine learning model for processing, and generating visual component feature information, wherein the machine learning model is trained to establish a mapping relation between the metadata feature information and the visual component feature information.
6. The method of claim 5, further comprising:
and obtaining model parameters of the machine learning model in the designated business field through transfer learning processing, and applying the model parameters to the current machine learning model.
7. The method of claim 5, further comprising:
acquiring user feedback data through a data buried point;
and updating the machine learning model according to the user feedback data.
8. A data visualization processing apparatus, comprising:
the metadata characteristic information generation module is used for acquiring user data, analyzing the metadata of the user data and generating metadata characteristic information;
the visual component characteristic information generating module is used for generating visual component characteristic information according to the metadata characteristic information;
and the visual recommendation module is used for configuring visual components according to the visual component characteristic information and recommending the visual components to the user.
9. The apparatus of claim 8, wherein the metadata parsing the user data, generating metadata characteristic information comprises:
and performing data dimension characteristic analysis and data distribution characteristic analysis on the user data to generate metadata characteristic information.
10. The apparatus of claim 8, wherein the generating visualization component characteristic information from the metadata characteristic information comprises:
and determining each visual characteristic element of the visual assembly according to the metadata characteristic information, wherein the visual characteristic element comprises one item or any combination of multiple items of visual display form, visual color matching, visual display dimension and visual display size.
11. A data visualization processing method comprises the following steps:
receiving a data visualization request of a user, wherein the data visualization request comprises a visualization analysis requirement aiming at specified data content;
acquiring data to be analyzed according to the designated data content;
configuring a visual component according to the data characteristics of the data to be analyzed;
and generating a visual data view based on the data to be analyzed and the configured visual components, and recommending.
12. A data visualization processing method comprises the following steps:
acquiring visual clue information and user data provided by a user;
screening out matched visual components from the visual component library according to the visual clue information;
and configuring the visualization component according to the matched visualization component and the data characteristics of the user data, and recommending the visualization component to the user.
13. An electronic device, comprising:
a memory for storing a program;
a processor for executing the program stored in the memory to execute the data visualization processing method of any one of claims 1 to 7, claim 11, and claim 12.
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