CN118247380A

CN118247380A - Data visualization method and device, storage medium and electronic equipment

Info

Publication number: CN118247380A
Application number: CN202410446805.2A
Authority: CN
Inventors: 周桂麟; 徐治钦
Original assignee: Anhui Sanqi Jiyu Network Technology Co ltd
Current assignee: Anhui Sanqi Jiyu Network Technology Co ltd
Priority date: 2024-04-12
Filing date: 2024-04-12
Publication date: 2024-06-25

Abstract

The specification discloses a data visualization method, a device, a storage medium and an electronic device, wherein the method comprises the following steps: in response to an input operation by a user, a descriptive text entered by the user is determined. And then the descriptive text is identified, and the identification result of the descriptive text is determined. And then, generating a visualization strategy by adopting a pre-trained large model according to the recognition result and the description text. And obtaining the data to be visualized, and visualizing the data to be visualized according to a visualization strategy to obtain a chart corresponding to the data to be visualized. The method comprises the steps of generating a visualization strategy of data to be visualized through a large model, and generating a chart corresponding to the data to be visualized based on the visualization strategy, so that manual coding of a user is not needed, the workload of the user is reduced, the automation degree of data visualization is improved, and the generation speed of the chart of the data to be visualized is increased.

Description

Data visualization method and device, storage medium and electronic equipment

Technical Field

The present disclosure relates to the field of computer technologies, and in particular, to a method, an apparatus, a storage medium, and an electronic device for data visualization.

Background

With the continuous development of technology, data visualization technology is continuously developed, and the data visualization technology has become a very important data analysis means.

Currently, when data visualization is performed, a user is generally required to perform the data visualization by adopting a data visualization technology so as to generate a chart corresponding to the data. The method requires the user to know the data visualization knowledge, grasp the data visualization technology and has stronger development capability, so that the development threshold is high and the development time is long. Therefore, how to visualize data is an important issue.

Based on this, the present description provides a method of data visualization.

Disclosure of Invention

The present disclosure provides a method, an apparatus, a storage medium, and an electronic device for visualizing data, so as to partially solve the foregoing problems in the prior art.

The technical scheme adopted in the specification is as follows:

The present specification provides a method of data visualization, comprising:

Determining a description text input by a user in response to an input operation of the user;

Identifying the description text, and determining an identification result of the description text;

Generating a visualization strategy by adopting a pre-trained large model according to the recognition result and the description text;

and obtaining the data to be visualized, and visualizing the data to be visualized according to the visualization strategy to obtain a chart corresponding to the data to be visualized.

Optionally, according to the recognition result and the description text, a pre-trained large model is adopted to generate a visualization strategy, which specifically comprises the following steps:

Obtaining data to be visualized;

extracting features of the data to be visualized, and determining data features corresponding to the data to be visualized;

And inputting the data characteristics, the identification result and the descriptive text into a pre-trained large model, and determining a visualization strategy of the data to be visualized.

Optionally, inputting the data features, the recognition result and the descriptive text into a pre-trained large model, and determining a visualization strategy of the data to be visualized specifically includes:

Inputting the data characteristics and the descriptive text into a pre-trained large model, and determining a first strategy of the data to be visualized;

Determining a target parameter in the identification result, determining a type corresponding to the target parameter, and taking the type as a type to be adjusted;

adjusting parameters corresponding to the type to be adjusted in the first strategy to target parameters;

and taking the adjusted first strategy as a visualization strategy of the data to be visualized.

checking the identification result by adopting a preset rule, and determining the checking result;

determining parameters to be deleted in the identification result according to the detection result;

Deleting the parameters to be deleted in the identification result;

And generating a visualization strategy by adopting a pre-trained large model according to the deleted recognition result and the description text.

Optionally, according to the visualization policy, visualizing the data to be visualized to obtain a chart corresponding to the data to be visualized, which specifically includes:

acquiring a preset grammar rule;

Inputting the grammar rule, the visualization strategy and the data to be visualized into the large model to generate a visualization code;

Testing the visual code;

and when the visual code test passes, generating a chart corresponding to the data to be visualized according to the visual code.

Identifying the data to be visualized, and determining target data contained in the data to be visualized;

determining a desensitization strategy corresponding to the target data;

Processing the target data in the data to be visualized according to the determined desensitization strategy;

Verifying the processed data to be visualized;

and when the processed data to be visualized passes verification, visualizing the processed data to be visualized according to the visualization strategy to obtain a chart corresponding to the data to be visualized.

Optionally, the method further comprises:

displaying the chart to the user;

according to a specified period, acquiring interaction data between the user and the chart;

according to the interaction data, the visualization strategy is adjusted;

according to the adjusted visualization strategy, visualizing the data to be visualized to obtain an optimized chart;

And displaying the optimized chart to the user.

The present specification provides an apparatus for data visualization, comprising:

the determining module is used for responding to the input operation of a user and determining the description text input by the user;

the recognition module is used for recognizing the description text and determining a recognition result of the description text;

The strategy generation module is used for generating a visual strategy by adopting a pre-trained large model according to the identification result and the description text;

The data visualization module is used for acquiring the data to be visualized, visualizing the data to be visualized according to the visualization strategy, and obtaining a chart corresponding to the data to be visualized.

The present specification provides a computer readable storage medium storing a computer program which when executed by a processor implements the method of data visualization described above.

The present specification provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing a method of visualizing the data as described above when executing the program.

The above-mentioned at least one technical scheme that this specification adopted can reach following beneficial effect:

The data visualization method provided by the specification is used for responding to input operation of a user to determine description text input by the user. And then the descriptive text is identified, and the identification result of the descriptive text is determined. And then, generating a visualization strategy by adopting a pre-trained large model according to the recognition result and the description text. And obtaining the data to be visualized, and visualizing the data to be visualized according to a visualization strategy to obtain a chart corresponding to the data to be visualized.

As can be seen from the method, the application can firstly respond to the input operation of the user to determine the description text input by the user when the data is visualized. And then the descriptive text is identified, and the identification result of the descriptive text is determined. And then, generating a visualization strategy by adopting a pre-trained large model according to the recognition result and the description text. And obtaining the data to be visualized, and visualizing the data to be visualized according to a visualization strategy to obtain a chart corresponding to the data to be visualized. The method comprises the steps of generating a visualization strategy of data to be visualized through a large model, and generating a chart corresponding to the data to be visualized based on the visualization strategy, so that manual coding of a user is not needed, the workload of the user is reduced, the automation degree of data visualization is improved, and the generation speed of the chart of the data to be visualized is increased.

Drawings

The accompanying drawings, which are included to provide a further understanding of the specification, illustrate and explain the exemplary embodiments of the present specification and their description, are not intended to limit the specification unduly. In the drawings:

FIG. 1 is a flow chart of a method of visualizing data provided in the present specification;

FIG. 2 is a schematic diagram of a device structure for visualizing data provided in the present specification;

fig. 3 is a schematic structural diagram of an electronic device corresponding to fig. 1 provided in the present specification.

Detailed Description

For the purposes of making the objects, technical solutions and advantages of the present specification more apparent, the technical solutions of the present specification will be clearly and completely described below with reference to specific embodiments of the present specification and corresponding drawings. It will be apparent that the described embodiments are only some, but not all, of the embodiments of the present specification. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are intended to be within the scope of the present disclosure.

The following describes in detail the technical solutions provided by the embodiments of the present specification with reference to the accompanying drawings.

Fig. 1 is a flow chart of a method for visualizing data provided in the present specification, including the following steps:

S100: and determining the description text input by the user in response to the input operation of the user.

In this specification, the apparatus for visualizing data may determine the descriptive text entered by the user in response to an input operation by the user. The device for data visualization may be a server or an electronic device such as a desktop computer or a notebook computer. For convenience of description, a method of visualizing data provided in the present specification will be described below with only a server as an execution subject.

The description text is text input by a user, and is text describing a chart which is obtained by visualizing data. The descriptive text may include text describing the type of chart, the color of the chart, the name of the chart, and the like. For example, the descriptive text entered by the user may be "i need a product sales trend chart, which is a line chart showing each product sales trend every month, and the line chart is blue in color. "wherein the chart type is a line chart, the chart color is blue, and the chart name is a product sales trend chart.

S102: and identifying the description text, and determining the identification result of the description text.

In this specification, the server may recognize the descriptive text and determine the recognition result of the descriptive text. Specifically, the server may use an identification algorithm to identify the descriptive text, and determine an identification result of the descriptive text. Of course, the server may also use a pre-trained recognition model to recognize the descriptive text and determine the recognition result of the descriptive text. The specific manner of determining the recognition result of the descriptive text is not particularly limited in this specification.

The identification algorithm may be preset by the server. The recognition model may be a model pre-trained by the server, and the recognition model may be a large model supporting text recognition, code generation and policy generation, and the model parameters of the large model may be in the range of billions to trillions, and the model parameters of the large model in the present specification may be in the range of billions. Of course, the recognition model may be any existing text recognition model, and the present specification is not limited specifically.

The identification result is determined according to the description text and comprises a plurality of parameters, and each parameter corresponds to one type. The identification result comprises parameters of the types such as chart types, chart colors, data ranges, chart names and the like, the identification result specifically comprises several types of parameters which can be preset by the server, namely, the number of the types of the parameters contained in the identification result can be preset by the server, the server sets up how many types of parameters, and the identification result comprises how many types of parameters. Since each parameter corresponds to one type, the number of parameters included in the recognition result may be preset by the server.

In this specification, taking the example of the parameters in which the recognition result includes three types of chart types, chart colors, and chart names, the representation form of the recognition result may be "chart types: x, chart color: y, chart name: z. "where x is the type of chart, y is the color of the chart, and z is the name of the chart. Parameters of this type of chart type may be bar charts, line charts, pie charts, bar charts, area charts, scatter charts, stock price charts, radar charts, bubble charts, surface charts, combination charts, and the like. The parameters of this type of chart color may be any color of blue, red, green, etc. When the server identifies which type of parameter from the descriptive text, which type of parameter in the identification result is the parameter identified by the server. Continuing to use the above example, the server identifies three types of parameters including a chart type, a chart color and a chart name in the descriptive text, wherein the parameters with the chart type are line diagrams, the parameters with the chart type are blue, and the parameters with the chart type are product sales figures. The recognition result is "chart type: line graph, chart color: blue, chart name: and (5) a product sales trend graph. ".

In this specification, when there is text describing a certain type of parameter in the description text, the server may identify the type of parameter from the description text, and the type of parameter in the identification result is the type of parameter identified by the server from the description text. However, when the description text does not contain text describing a certain type of parameter, the server cannot identify the type of parameter from the description text, and the type of parameter is null in the identification result.

S104: and generating a visualization strategy by adopting a pre-trained large model according to the recognition result and the description text.

In the specification, the server can generate a visualization strategy by adopting a pre-trained large model according to the recognition result and the descriptive text. Specifically, the server may input the recognition result and the descriptive text into a pre-trained large model, and determine a visualization strategy for the large model output. The visualization strategy comprises parameters required for generating the chart, the visualization strategy comprises a plurality of parameters, each parameter is used for generating the chart, namely the server can generate the chart according to the parameters in the visualization strategy. The visualization strategy comprises parameters of the types such as chart type, chart color, data range, chart name and the like, namely the parameters included in the visualization strategy are the same as the types of parameters included in the identification result, but the parameters included in the visualization strategy are not null values, and the parameters included in the identification result may have null values. The visualization strategy is used to generate a chart. The visualization strategy in the present specification includes at least two types of parameters, namely chart type and chart color. The large model may be a server pre-trained model that may support text recognition, code generation, and policy generation models, with model parameters of the large model being in the order of billions.

In this specification, in order to better understand the trend of data change so as to better generate a chart later, the server may determine a visualization policy based on the data to be visualized in addition to the recognition result and the descriptive text. Therefore, when the visual strategy is generated by adopting a pre-trained large model according to the identification result and the description text, the server can acquire the data to be visualized. Inputting the data to be visualized, the recognition result and the descriptive text into a pre-trained large model, and determining the visualization strategy of the data to be visualized.

The data to be visualized may be data uploaded by the user in advance, or may be data collected by the server in advance in a process of executing the service from the user, which is not specifically limited in this specification. Therefore, when the data to be visualized is obtained, the server can respond to the uploading operation of the user, determine the data uploaded by the user and serve as the data to be visualized. Specifically, taking sales data of each product in the past year as an example, a user may upload sales data of each product in the past year to a server, and the server may determine the sales data uploaded by the user in response to the upload operation of the user and serve as the sales data to be visualized. The sales data includes product type, product identification, product name, sales time, sales amount, and the like. The data to be visualized may be a CSV file, i.e., comma separated value (Comma-SEPARATED VALUES) file.

In the present specification, since the description text is a text input by the user, the description text may only include a text describing a part of parameters, that is, parameters with null values exist in the recognition result, so that the server may determine the first policy according to the description text and the data to be visualized, and then generate the visualization policy according to the recognition result and the first policy, so as to ensure that the generated visualization policy not only meets the requirements of the user, but also is more attractive and practical. Based on the above, when inputting the data to be visualized, the recognition result and the descriptive text into the pre-trained large model to determine the visualization strategy of the data to be visualized, the server may input the data to be visualized and the descriptive text into the pre-trained large model to determine the first strategy of the data to be visualized. And determining a visualization strategy of the data to be visualized according to the first strategy and the identification result. The first strategy is obtained based on the data to be visualized and the description text, and comprises parameters required for generating the chart, wherein the first strategy comprises parameters of types such as chart type, chart color, data range, chart name and the like, namely the parameters included in the first strategy are the same as the types of parameters included in the identification result, except that the parameters included in the first strategy are not null values, and the parameters included in the identification result may have null values.

When determining the visualization strategy of the data to be visualized according to the first strategy and the identification result, the server may aggregate the first strategy and the identification result by adopting an aggregation algorithm, and determine the visualization strategy of the data to be visualized, where the aggregation algorithm is preset by the server, and the aggregation algorithm is used for aggregating the first strategy and the identification result. In addition, the server can display the first strategy and the identification result to an operator, and the operator can determine a visualization strategy according to the first strategy and the identification result and upload the visualization strategy to the server. The server may determine a visualization policy for the data to be visualized in response to an upload policy operation by an operator. Of course, the server may also input the first policy and the recognition result into the large model, and determine a visualization policy of the data to be visualized output by the large model.

S106: and obtaining the data to be visualized, and visualizing the visualized data according to the visualization strategy to obtain a chart corresponding to the data to be visualized.

In the specification, the server may acquire the data to be visualized, and perform visualization on the visualized data according to the visualization policy, so as to obtain a chart corresponding to the data to be visualized. The data to be visualized may be data uploaded by the user in advance, or may be data collected by the server in advance in a process of executing the service from the user, which is not specifically limited in this specification. When the chart corresponding to the data to be visualized is obtained by visualizing the data to be visualized according to the visualization strategy, the server can adopt a data visualization technology to visualize the data to be visualized according to the visualization strategy, and the chart corresponding to the data to be visualized is obtained. The Data visualization technique may be a graph generation algorithm or a machine learning model, and of course, the Data visualization technique may be any existing Data visualization technique, such as D3 (Data-Driven Documents), which is not specifically limited in this specification.

In the present specification, when the data to be visualized is visualized according to the visualization policy, and the chart corresponding to the data to be visualized is obtained, the server may obtain a preset grammar rule. And inputting grammar rules, a visualization strategy and data to be visualized into the large model to generate a visualization code. And then, generating a chart corresponding to the data to be visualized according to the visualized code. The grammar rule is preset for the server, is a grammar rule of D3, and is used for generating the visual code. The large model may be a server pre-trained model that may support text recognition, code generation, and policy generation models, with model parameters of the large model being in the billions of levels. When the chart corresponding to the data to be visualized is generated according to the visualized code, the server can directly run the visualized code to generate the chart corresponding to the data to be visualized.

In addition, to ensure accuracy of the graph generated based on the visualization code, the server may test the visualization code. Specifically, the server may obtain a preset grammar rule. And inputting grammar rules, a visualization strategy and data to be visualized into a large model to generate a visualization code. The visual code is tested. And when the visual code test passes, generating a chart corresponding to the data to be visualized according to the visual code. When the visual code is tested, the server can display the visual code to an operator, so that the operator can check the visual code and return a check result. The server may receive the inspection result and, when the inspection result is pass, the server may determine that the visualization code verification passes. When the check result is that the visual code fails, the server may determine that the visual code fails verification.

Of course, the server may also use a testing tool to test the visual code to obtain a testing result. Then, when the test result is passing, the visual code verification is determined to pass. And when the test result is that the visual code fails to pass, determining that the visual code fails to pass the verification.

In the method, when the data is visualized, the server can firstly respond to the input operation of the user to determine the description text input by the user. And then the descriptive text is identified, and the identification result of the descriptive text is determined. And then, generating a visualization strategy by adopting a pre-trained large model according to the recognition result and the description text. And obtaining the data to be visualized, and visualizing the data to be visualized according to a visualization strategy to obtain a chart corresponding to the data to be visualized. The method comprises the steps of generating a visualization strategy of data to be visualized through a large model, and generating a chart corresponding to the data to be visualized based on the visualization strategy, so that manual coding of a user is not needed, the workload of the user is reduced, the automation degree of data visualization is improved, and the generation speed of the chart of the data to be visualized is increased. Meanwhile, a visualization strategy of the data to be visualized is generated through the large model, so that the generated visualization strategy is more in line with the requirements of users, and the visual effect and user experience of the chart are improved.

In the specification, the server may perform feature extraction on the data to be visualized first to obtain data features corresponding to the data to be visualized, and then generate a visualization policy based on the data features, the recognition result and the description text. The data features are used to characterize the trend of the data to be visualized, which may be increasing or decreasing, and may also be the trend of the differences between the data to be visualized. Taking sales data of each product in the past year as an example, the data characteristics corresponding to the data to be visualized are used for representing the change trend of the sales data, and the change trend can be the increase or decrease of the sales quantity and the increase or decrease of the sales amount because the sales data can comprise the sales quantity and the sales amount. Based on this, in step S104, the server may also acquire the data to be visualized, perform feature extraction on the data to be visualized, and determine the data features corresponding to the data to be visualized. Then, the data characteristics, the recognition results and the descriptive text are input into a pre-trained large model, and a visualization strategy of the data to be visualized is determined.

The server may perform feature extraction on the data to be visualized by using any existing feature extraction algorithm or feature extraction model to determine data features corresponding to the data to be visualized, and specifically, what mode is used for feature extraction, which is not specifically limited in this specification. Taking feature extraction model for feature extraction as an example, the server may input the data to be visualized into the feature extraction model trained in advance, and determine the data features corresponding to the data to be visualized. The data feature is used to characterize the trend of the data to be visualized. The feature extraction model may be a model pre-trained by the server, or may be any existing model, which is not specifically limited in this specification.

When the data characteristics, the recognition result and the descriptive text are input into the pre-trained large model to determine the visualization strategy of the data to be visualized, the server can input the data characteristics and the descriptive text into the pre-trained large model to determine the first strategy of the data to be visualized. Then, according to the identification result and the first policy, a visualization policy is generated, and the specific process is similar to the process in step S104, and will not be described herein.

In addition, the server may also input the data features and descriptive text into a pre-trained large model, determining a first policy for the data to be visualized. Then, the server can determine the target parameter in the identification result, determine the type corresponding to the target parameter and serve as the type to be adjusted. And then adjusting the parameters corresponding to the type to be adjusted in the first strategy into target parameters. And taking the adjusted first strategy as a visualization strategy of the data to be visualized. The target parameter is a parameter which is not null in the identification result.

In this specification, when there is no text describing a certain type of parameter in the description text, the type of parameter in the recognition result is a null value. In order to avoid the influence of the null value in the identification result, the speed of generating the visualization strategy is increased, and the server can delete the parameter with the null value in the identification result. Based on this, in step S104, when the pre-trained large model is used to generate the visualization strategy according to the recognition result and the description text, the server may delete the specified parameters in the recognition result, and use the pre-trained large model to generate the visualization strategy according to the deleted recognition result and description text. The specified parameter is null, that is, the parameter with the null parameter in the identification result is the specified parameter. The process of generating the visualization strategy according to the deleted recognition result and description text by adopting the pre-trained large model is similar to the process of generating the visualization strategy according to the recognition result and description text by adopting the pre-trained large model, and is not repeated here.

In addition, the server can also adopt preset rules to check the identification result and determine the check result. And determining parameters to be deleted in the identification result according to the checking result, and deleting the parameters to be deleted in the identification result. And then, generating a visualization strategy by adopting a pre-trained large model according to the deleted recognition result and the description text. The preset rule is a preset rule, and the preset rule can be used for checking whether parameters in the identification result are empty or not. The inspection result may be a type of parameter that identifies a null value in the result. The parameters to be deleted are parameters with null values in the identification result.

In the present specification, some parameters may be in the identification result in a format that does not conform to the preset format, that is, parameters may be in the identification result in a format error, for example, the preset format of the parameter of the date type is "a-b", and the parameter of the date type in the identification result is 3.1, where the format of the parameter does not conform to the preset format. Based on the method, the server can adjust parameters of which the format does not accord with the preset format in the identification result, so that a visualization strategy can be generated based on the identification result later, and a chart can be generated based on the visualization strategy better. Based on this, in step S104, according to the recognition result and the description text, when the pre-trained large model is adopted to generate the visualization strategy, the server may process the recognition result by adopting a preset format rule. And generating a visualization strategy by adopting a pre-trained large model according to the processed recognition result and the description text.

The preset format rule is a format rule preset by the server, and the preset format rule can be uploaded to the server by a user, so that the specification is not particularly limited. The preset format rule is used for adjusting the format of the parameters of which the format does not accord with the rule in the identification result. The preset format rule is a setting of the format of each type of parameter in the identification result, and comprises a target format of each type of parameter. When the format of a certain parameter in the identification result does not accord with the setting of the format of the parameter by the preset format rule, the server can adjust the format of the parameter according to the preset format rule.

Therefore, when the preset format rule is adopted to process the identification result, the server can judge whether the format of the parameter accords with the target format of the parameter in the preset format rule according to each parameter in the identification result, and if so, the server determines not to process the parameter. If not, the format of the parameter is adjusted to the target format. The process of generating the visualization strategy according to the processed recognition result and description text by adopting the pre-trained large model is similar to the process of generating the visualization strategy according to the recognition result and description text by adopting the pre-trained large model, and is not repeated here.

In the specification, in order to ensure that the privacy data in the data to be visualized is not revealed, the server can perform desensitization processing on the data to be visualized in the process of visualizing the data to be visualized, so that the safety of the data to be visualized is ensured, and the privacy of the data to be visualized is protected. Based on this, in step S106, the data to be visualized is visualized according to the visualization policy, and when the chart corresponding to the data to be visualized is obtained, the server may identify the data to be visualized, and determine the target data contained in the data to be visualized. And determining a desensitization strategy corresponding to the target data. And processing target data in the to-be-visualized data according to the determined desensitization strategy. And then, according to a visualization strategy, visualizing the processed data to be visualized to obtain a chart corresponding to the data to be visualized. The target data is sensitive data, the target data can be product cost price, product income and the like, and specific data is sensitive data, namely the target data can be preset by a server, namely the target data is preset by the server. When target data exists in the data to be visualized, the server can conduct mask processing on the target data in the data to be visualized so as to ensure sensitive data not to be revealed, and therefore data privacy is ensured.

The desensitization policy is a preset policy, the desensitization policy may include a substitution method, a masking method, a random substitution method, a generalization method, and the like, each target data has a corresponding desensitization policy, the server may directly determine the desensitization policy corresponding to the target data, and as to which target data corresponds to which desensitization policy, the server may preset. The substitution method is to directly substitute specific characters for the target data or part of the data in the target data, where the specific characters may be asterisks (i.e., ". For example, only the last two digits of the product return are retained, with the middle being replaced with an asterisk. The masking method is to mask the data according to a certain rule, for example, the first six bits and the last four bits of the identification card number are reserved, and the middle is replaced by an X. The random substitution method is to randomly map the target data of the numerical class, keep the format of the original target data, but change the value of the target data into a random value. The generalization is to convert precise target data into category or range data, such as age to age range, geographic location from a specific address to province or city.

When processing the target data in the data to be visualized according to the determined desensitization strategy, taking the desensitization strategy as a mask method as an example, the server can replace the target data in the data to be visualized with the specified data so as to mask the target data in the data to be visualized. Wherein, the designated data is preset for the server.

In the present specification, in order to ensure that the desensitized data does not include sensitive data, after the target data in the to-be-visualized data is processed, the server may verify the processed to-be-visualized data, and when the processed to-be-visualized data passes the verification, the processed to-be-visualized data is visualized according to a visualization policy, so as to obtain a chart corresponding to the to-be-visualized data. The process before processing the target data in the to-be-visualized data and the process of processing the target data in the to-be-visualized data are the above processes, and are not described herein. When verifying the processed data to be visualized, the server can continuously identify the processed data to be visualized to judge whether the processed data to be visualized includes target data or not, if yes, determining that the verification of the processed data to be visualized is not passed. If not, determining that the processed data to be visualized passes verification.

In the present disclosure, after a chart of data to be visualized is generated, the chart may be displayed to a user, and then the chart may be optimized according to interaction data between the user and the chart, so that the chart better meets the requirements of the user. Therefore, the server can display the chart to the user and acquire the interaction data between the user and the chart. And then optimizing the chart according to the interaction data, and displaying the optimized chart to a user. Wherein the interaction data is obtained based on user operation of the chart. After the chart is displayed to the user, the user can click, zoom in and zoom out on the chart, and the server can respond to the operation of the user to determine interaction data generated when the user operates the chart. Specifically, taking the operation as a click operation as an example, when the interactive data between the user and the chart is obtained, the server may determine the interactive data generated when the user performs the click operation on the chart in response to the click operation of the user, where the interactive data includes a click position, a click frequency and a click time. The clicking position is the position of the user clicking in the chart, the clicking times are the times of the user clicking the chart, and the clicking time is the time of the user clicking the chart.

When the chart is optimized according to the interactive data, the server can adjust the visualization strategy according to the interactive data, and visualize the data to be visualized according to the adjusted visualization strategy to obtain the chart after optimizing the data to be visualized. When the visualization strategy is adjusted according to the interaction data, the server can display the interaction data and the visualization strategy to an operator, the operator adjusts the visualization strategy according to the interaction data, and the adjusted visualization strategy is uploaded to the server, so that a subsequent server can respond to the uploading operation of the operator, determine the adjusted visualization strategy, and visualize the data to be visualized according to the adjusted visualization strategy, and obtain an optimized chart of the data to be visualized. In addition, when the visualization strategy is adjusted according to the interaction data, the server can input the interaction data and the visualization strategy into the large model, and the adjusted visualization strategy output by the large model is determined.

In the present specification, the interaction data may be collected by a server according to a specified period, and based on the collected interaction data, the chart is optimized, so that the chart is updated periodically, so as to ensure that the chart can meet the requirement of a user. Based on the above, the server may also display the chart to the user, and acquire interaction data between the user and the chart according to the specified period. And adjusting the visualization strategy according to the interaction data. And then, according to the adjusted visualization strategy, visualizing the data to be visualized to obtain an optimized chart. And displaying the optimized chart to a user. Wherein, the specified period is preset for the server, and the specified period can be one day.

In the present specification, in order to generate a chart of data to be visualized more quickly, speed of data visualization is increased, a server may perform data preprocessing on the data to be visualized, and then generate a chart based on the preprocessed data to be visualized. Based on this, in step S106, the data to be visualized is visualized according to the visualization policy, and when the chart corresponding to the data to be visualized is obtained, the server may perform preprocessing on the data to be visualized, and then perform visualization on the preprocessed data to be visualized according to the visualization policy, so as to obtain the chart. The data preprocessing includes data normalization, deletion value deletion and the like, and the specification is not particularly limited.

In the present specification, the above large model may be pre-trained by a server, and when the large model is pre-trained, the server may obtain a training sample and a label of the training sample, then input the training sample into the large model, determine an output result, and train the large model with a minimum difference between the output result and the label as a target, so that the trained large model may support text recognition, code generation, and policy generation.

The training sample can be descriptive text input by a user historically, and the label of the training sample can be an identification result of an operator for labeling the descriptive text. The training sample can also be descriptive text input by a user in history, data uploaded by the user or collected in advance by a server and an identification result of the descriptive text label by an operator, and the label of the training sample is a visual strategy of the operator label. The training sample can also be descriptive text input by a user historically and data uploaded by the user or collected in advance by a server, and then the label of the training sample is a visual strategy marked by an operator. Of course, the training samples may also be preset grammar rules, visualization strategies and data uploaded by a user or collected by a server in advance, the labels of the training samples are visual codes, the visualization strategies in the training samples may be strategies marked by operators or strategies output by a large model, and the specification is not limited specifically. The training sample may be a first strategy and an identification result, and the marking of the training sample is a visual strategy marked by an operator, the first strategy in the training sample may be a strategy output by a large model, and the identification result in the training sample may be an identification result marked by an operator or an identification result output by a large model, which is not specifically limited in this specification. In order to enable the large model to support text recognition, code generation and policy generation, the server may train the large model using the training samples and labels corresponding to the training samples.

In this specification, after obtaining a graph or an optimized graph of data to be visualized, the server may show the graph or the optimized graph on the target platform. The target platform is a data analysis platform on which a user can view a chart or an optimized chart.

The foregoing is a method of one or more implementations of the present disclosure, and based on the same concept, the present disclosure further provides a corresponding apparatus for visualizing data, as shown in fig. 2.

Fig. 2 is a schematic diagram of an apparatus for visualizing data provided in the present specification, including:

a determining module 200, configured to determine a description text input by a user in response to an input operation of the user;

the recognition module 202 is configured to recognize the description text and determine a recognition result of the description text;

the policy generation module 204 is configured to generate a visualization policy by using a pre-trained large model according to the recognition result and the description text;

The data visualization module 206 is configured to obtain data to be visualized, and visualize the data to be visualized according to the visualization policy, so as to obtain a chart corresponding to the data to be visualized.

Optionally, the policy generation module 204 is specifically configured to obtain data to be visualized; extracting features of the data to be visualized, and determining data features corresponding to the data to be visualized; and inputting the data characteristics, the identification result and the descriptive text into a pre-trained large model, and determining a visualization strategy of the data to be visualized.

Optionally, the policy generation module 204 is specifically configured to input the data feature and the descriptive text into a pre-trained large model, and determine a first policy of the data to be visualized; determining a target parameter in the identification result, determining a type corresponding to the target parameter, and taking the type as a type to be adjusted; adjusting parameters corresponding to the type to be adjusted in the first strategy to target parameters; and taking the adjusted first strategy as a visualization strategy of the data to be visualized.

Optionally, the policy generation module 204 is specifically configured to check the identification result by using a preset rule, and determine an inspection result; determining parameters to be deleted in the identification result according to the detection result; deleting the parameters to be deleted in the identification result; and generating a visualization strategy by adopting a pre-trained large model according to the deleted recognition result and the description text.

Optionally, the data visualization module 206 is specifically configured to obtain a preset grammar rule; inputting the grammar rule, the visualization strategy and the data to be visualized into the large model to generate a visualization code; testing the visual code; and when the visual code test passes, generating a chart corresponding to the data to be visualized according to the visual code.

Optionally, the data visualization module 206 is specifically configured to identify the data to be visualized, and determine target data included in the data to be visualized; determining a desensitization strategy corresponding to the target data; processing the target data in the data to be visualized according to the determined desensitization strategy; verifying the processed data to be visualized; and when the processed data to be visualized passes verification, visualizing the processed data to be visualized according to the visualization strategy to obtain a chart corresponding to the data to be visualized.

Optionally, the apparatus further comprises:

A chart optimization module 208, configured to display the chart to the user; according to a specified period, acquiring interaction data between the user and the chart; according to the interaction data, the visualization strategy is adjusted; according to the adjusted visualization strategy, visualizing the data to be visualized to obtain an optimized chart; and displaying the optimized chart to the user.

The present specification also provides a computer readable storage medium storing a computer program operable to perform a method of data visualization as provided in fig. 1 above.

The present specification also provides a schematic structural diagram of an electronic device corresponding to fig. 1 shown in fig. 3. At the hardware level, as shown in fig. 3, the electronic device includes a processor, an internal bus, a network interface, a memory, and a nonvolatile storage, and may of course include hardware required by other services. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs to implement the method of data visualization described above with respect to fig. 1.

Of course, other implementations, such as logic devices or combinations of hardware and software, are not excluded from the present description, that is, the execution subject of the following processing flows is not limited to each logic unit, but may be hardware or logic devices.

In the 90s of the 20 th century, improvements to one technology could clearly be distinguished as improvements in hardware (e.g., improvements to circuit structures such as diodes, transistors, switches, etc.) or software (improvements to the process flow). However, with the development of technology, many improvements of the current method flows can be regarded as direct improvements of hardware circuit structures. Designers almost always obtain corresponding hardware circuit structures by programming improved method flows into hardware circuits. Therefore, an improvement of a method flow cannot be said to be realized by a hardware entity module. For example, a programmable logic device (Programmable Logic Device, PLD) (e.g., field programmable gate array (Field Programmable GATE ARRAY, FPGA)) is an integrated circuit whose logic functions are determined by user programming of the device. A designer programs to "integrate" a digital system onto a PLD without requiring the chip manufacturer to design and fabricate application-specific integrated circuit chips. Moreover, nowadays, instead of manually manufacturing integrated circuit chips, such programming is mostly implemented with "logic compiler (logic compiler)" software, which is similar to the software compiler used in program development and writing, and the original code before being compiled is also written in a specific programming language, which is called hardware description language (Hardware Description Language, HDL), but HDL is not just one, but a plurality of 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), and VHDL (Very-High-SPEED INTEGRATED Circuit Hardware Description Language) and Verilog are currently most commonly used. It will also be apparent to those skilled in the art that a hardware circuit implementing the logic method flow can be readily obtained by merely slightly programming the method flow into an integrated circuit using several of the hardware description languages described above.

The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer readable medium storing computer readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, application SPECIFIC INTEGRATED Circuits (ASICs), programmable logic controllers, and embedded microcontrollers, examples of controllers include, but are not limited to, the following microcontrollers: ARC625D, atmel AT91SAM, microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic of the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller in a pure computer readable program code, it is well possible to implement the same functionality by logically programming the method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers, etc. Such a controller may thus be regarded as a kind of hardware component, and means for performing various functions included therein may also be regarded as structures within the hardware component. Or even means for achieving the various functions may be regarded as either software modules implementing the methods or structures within hardware components.

The system, apparatus, module or unit set forth in the above embodiments may be implemented in particular by a computer chip or entity, or by a product having a certain function. One typical implementation is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera 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.

For convenience of description, the above devices are described as being functionally divided into various units, respectively. Of course, the functions of each element may be implemented in one or more software and/or hardware elements when implemented in the present specification.

It will be appreciated by those skilled in the art that embodiments of the present description may be provided as a method, system, or computer program product. Accordingly, the present specification may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present description can take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.

The present description is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the specification. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

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

The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media.

Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer 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 disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.

It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element.

It will be appreciated by those skilled in the art that embodiments of the present description may be provided as a method, system, or computer program product. Accordingly, the present specification may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present description can take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.

The description may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.

In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for system embodiments, since they are substantially similar to method embodiments, the description is relatively simple, as relevant to see a section of the description of method embodiments.

The foregoing is merely exemplary of the present disclosure and is not intended to limit the disclosure. Various modifications and alterations to this specification will become apparent to those skilled in the art. Any modifications, equivalent substitutions, improvements, or the like, which are within the spirit and principles of the present description, are intended to be included within the scope of the claims of the present description.

Claims

1. A method of data visualization, comprising:

2. The method of claim 1, wherein generating a visualization strategy using a pre-trained large model based on the recognition result and the descriptive text, comprises:

Obtaining data to be visualized;

3. The method according to claim 2, wherein inputting the data features, the recognition results and the descriptive text into a pre-trained large model determines a visualization strategy for the data to be visualized, comprising in particular:

4. The method of claim 1, wherein generating a visualization strategy using a pre-trained large model based on the recognition result and the descriptive text, comprises:

Deleting the parameters to be deleted in the identification result;

5. The method of claim 1, wherein the visualizing the data to be visualized according to the visualizing strategy, to obtain a chart corresponding to the data to be visualized, specifically comprises:

acquiring a preset grammar rule;

Testing the visual code;

6. The method of claim 1, wherein the visualizing the data to be visualized according to the visualizing strategy, to obtain a chart corresponding to the data to be visualized, specifically comprises:

determining a desensitization strategy corresponding to the target data;

Verifying the processed data to be visualized;

7. The method of claim 1, wherein the method further comprises:

displaying the chart to the user;

according to the interaction data, the visualization strategy is adjusted;

And displaying the optimized chart to the user.

8. An apparatus for visualizing data, comprising:

9. A computer-readable storage medium, characterized in that the storage medium stores a computer program which, when executed by a processor, implements the method of any of the preceding claims 1-7.

10. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method of any of the preceding claims 1-7 when executing the program.