Data processing method and device, medium and equipment

Abstract

The invention relates to a data processing method and device, a medium and equipment. The method comprises the following steps: acquiring a natural language text; analyzing a to-be-analyzed entity of the natural language text to obtain an SQL statement for data query; wherein the to-be-analyzed entity at least comprises a target display form; performing data retrieval on a database according to the SQL statement; and visualizing a data retrieval result according to the target display form to generate a display result. According to the technical scheme, the data processing efficiency can be improved.

Description

Technical Field

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

[0002] In the fields of automobiles and artificial intelligence, there is a lot of data that needs to be analyzed. However, the people who have the need for data analysis and the people who can provide data are often from different departments. For example, for sales data and cost data, it is the business side personnel who put forward the data analysis visualization requirements to the database management personnel. The above results in extremely high communication and work costs for data analysis, and low data processing efficiency. Summary of the invention

[0003] In order to solve the above technical problems, the present disclosure provides a data processing method, device, medium and equipment to improve data processing efficiency.

[0004] The present disclosure provides a data processing method, comprising:

[0005] Get natural language text;

[0006] Parsing the entity to be analyzed in the natural language text to obtain an SQL statement for data query; wherein the entity to be analyzed at least includes a target display form;

[0007] Retrieve data from the database according to the SQL statement;

[0008] The data retrieval results are visualized according to the target display format to generate display results.

[0009] In some embodiments, the parsing of the entity to be analyzed in the natural language text to obtain an SQL statement for data query includes:

[0010] Parsing the entity to be analyzed in the natural language text through a preset text parsing model to obtain analysis dimensions, key indicators and the target display form;

[0011] According to the preset field mapping relationship, the analysis dimension, the key indicator and the target display form are mapped into SQL statements.

[0012] In some embodiments, the retrieving data from a database according to the SQL statement includes:

[0013] Searching a first data table preset in a database according to the fields of the analysis dimension in the SQL statement to obtain a dimension search result;

[0014] Based on the first data table and the fields of the key indicators in the SQL statement, a search is performed in the database to obtain an indicator search result.

[0015] In some embodiments, searching the database based on the first data table and the fields of the key indicators in the SQL statement to obtain indicator search results includes:

[0016] Determine in the first data table whether there are fields for all the key indicators in the SQL statement;

[0017] If yes, searching the first data table according to the field of the key indicator to obtain the indicator search result;

[0018] If not, obtaining a second data table associated with the first data table and the field of the target key indicator from the database; wherein the field of the target key indicator is a field of the key indicator that does not exist in the first data table;

[0019] The first data table and the second data table are searched according to the fields of the key indicators to obtain the indicator search results.

[0020] In some embodiments, visualizing the data retrieval results according to the target display format to generate display results includes:

[0021] Determine the target display chart corresponding to the target display form according to the preset display relationship; wherein the display relationship is used to represent the correspondence between different data display forms and different display charts;

[0022] The dimension search results and the indicator search results are visualized according to the target display chart to generate a display result.

[0023] In some embodiments, obtaining the natural language text includes:

[0024] Acquire the voice audio input by the user, and convert the voice audio into text to obtain natural language text;

[0025] or,

[0026] The original text input by the user is obtained, and text error correction is performed on the original text to obtain the natural language text.

[0027] The present disclosure provides a data processing device, comprising:

[0028] A text acquisition module, used to acquire natural language text;

[0029] An entity parsing module, used to parse the entity to be analyzed in the natural language text to obtain an SQL statement for data query; wherein the entity to be analyzed at least includes a target display form;

[0030] A data retrieval module, used to retrieve data from a database according to the SQL statement;

[0031] The visualization module is used to visualize the data retrieval results according to the target display form and generate display results.

[0032] In some embodiments, the entity resolution module is further used to:

[0033] Parsing the entity to be analyzed in the natural language text through a preset text parsing model to obtain analysis dimensions, key indicators and the target display form;

[0034] According to the preset field mapping relationship, the analysis dimension, the key indicator and the target display form are mapped into SQL statements.

[0035] The present disclosure also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a program or instruction, wherein the program or instruction enables a computer to execute the steps of any of the above methods.

[0036] The present disclosure also provides an electronic device, comprising:

[0037] one or more processors;

[0038] A memory for storing one or more programs or instructions;

[0039] The processor is used to execute the steps of any of the above methods by calling the program or instruction stored in the memory.

[0040] Compared with the prior art, the technical solution provided by the embodiments of the present disclosure has the following advantages:

[0041] The technical solution provided by the disclosed embodiment can skip the communication process between different staff members by parsing the natural language text expressing the user's data demand into SQL statements, directly convert the demand into SQL statements that can be processed by the machine, and then perform data retrieval based on the SQL statements to effectively improve the data processing efficiency. Therefore, this solution can reduce work costs and improve data processing efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0043] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0044] Figure 1 A flowchart of a data processing method provided by an embodiment of the present disclosure;

[0045] Figure 2 A schematic diagram of a display result provided by an embodiment of the present disclosure;

[0046] Figure 3 A schematic diagram of a data processing flow provided by an embodiment of the present disclosure;

[0047] Figure 4 A structural block diagram of a data processing device provided in an embodiment of the present disclosure;

[0048] Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0049] In order to more clearly understand the above-mentioned objectives, features and advantages of the present disclosure, the scheme of the present disclosure will be further described below. It should be noted that the embodiments of the present disclosure and the features in the embodiments can be combined with each other without conflict.

[0050] In the following description, many specific details are set forth to facilitate a full understanding of the present disclosure, but the present disclosure may also be implemented in other ways different from those described herein; it is obvious that the embodiments in the specification are only part of the embodiments of the present disclosure, rather than all of the embodiments.

[0051] Considering that the existing data analysis methods have high communication and work costs and low analysis efficiency, the embodiments of the present disclosure provide a data processing method, device, medium and equipment. The technology can be applied to scenarios such as automobiles and artificial intelligence that require data processing and data visualization. For ease of understanding, the embodiments of the present disclosure are described below.

[0052] Figure 1 The flowchart of a data processing method provided by the embodiment of the present disclosure is shown in FIG. The method can be executed by a data processing device, and the data processing device can be implemented in software and / or hardware. Figure 1 As shown, the method comprises the following steps:

[0053] Step S102, obtaining natural language text.

[0054] Among them, natural language text is text used to express the user's demand for data processing, for example: the percentage of automobile sales in Beijing in 2023.

[0055] In one possible implementation, the natural language text may be obtained through speech conversion. For example, speech audio input by a user is obtained, and the speech audio is converted into text to obtain the natural language text.

[0056] When the user inputs the data processing requirement in the form of voice, the user's voice audio is obtained; the voice audio is converted into natural language text through a preset voice conversion model, wherein the above-mentioned voice conversion model such as ASR (Automatic Speech Recognition, automatic speech recognition to text), Speech to Text under OpenAI, or voice conversion models based on convolutional neural networks, deep convolutional recurrent neural networks, etc. are not limited here.

[0057] Alternatively, in another possible implementation, the natural language text may be directly text. For example, the original text input by the user is obtained, and text error correction is performed on the original text to obtain the natural language text.

[0058] When the user inputs the data processing requirement in the form of text input, the original text input by the user is obtained. Since the original text input by the user in this scenario may contain typos, this embodiment can perform error correction processing on the original text based on the error correction dictionary. Specifically, models such as BiLSTM (Bi-directional Long Short-Term Memory) and RNN (Recurrent neural network) can be used to perform text correction on the original text to obtain natural language text.

[0059] Step S104, parsing the entity to be analyzed in the natural language text to obtain a SQL (Structured Query Language) statement for data query; wherein the entity to be analyzed at least includes a target display form.

[0060] After obtaining the natural language text that expresses the user's needs, it needs to be parsed to obtain the entities to be analyzed that can be effectively used for data retrieval. The entities to be analyzed include at least the target presentation form, and the entities to be analyzed may also include: analysis dimensions and / or key indicators.

[0061] The process of parsing the entities to be analyzed in the natural language text in this embodiment may include: firstly, parsing the entities to be analyzed in the natural language text through a preset text parsing model to obtain analysis dimensions, key indicators and target display forms.

[0062] Exemplarily, the text parsing model can be based on the GPT big model of the same framework as ChatGPT (Chat Generative Pre-trained Transformer), such as davinci, curie, babbage, etc. In practical applications, the above GPT big model can be fine-tuned so that the fine-tuned GPT big model can add a new model suitable for SQL text parsing, in order to achieve the effect of effectively parsing the text in which the user expresses SQL requirements.

[0063] The above text parsing model is used to parse the entities to be analyzed in the natural language text to obtain the analysis dimensions, key indicators and target display forms. Referring to Table 1, and taking the natural language text "the percentage of automobile sales in Beijing in 2023" as an example, the entities to be analyzed may include the analysis dimensions: sales volume, key indicators: city (Beijing), time (2023), vehicle model (all vehicle models), and target display form: percentage.

[0064] Then, according to the preset field mapping relationship, the analysis dimensions, key indicators and target display forms are mapped to SQL statements. The field mapping relationship in this embodiment is used to convert natural language into machine language that can be directly recognized by the machine; for example, according to the preset field mapping relationship, Beijing is mapped to WHERE region = 'Beijing', and 2023 is mapped to year = 2023. Referring to the above mapping method, the analysis dimensions, key indicators and target display forms are mapped to SQL statements. The SQL statements can be specifically shown in Table 1, where FROM car_sales indicates that the data comes from a data table named "car_sales", which can contain the following columns: car model (car_model), region (region), year (year), sales volume (sales), etc.

[0065] Interpreting the above SQL statements, the SELECT statement means selecting the "car_model" column and the "sales" column, SUM(sales)OVER(PARTITION BY region,year) means grouping and summing the sales by region and year, and sales / SUM(sales)OVER(PARTITION BY region,year) means calculating the percentage of sales of each car model in the total sales of Beijing in 2023; the WHERE statement limits the query region to "Beijing" and the year to "2023".

[0066] Table 1: Analysis table

[0067]

[0068] Step S106, searching the database for data according to the SQL statement.

[0069] In this embodiment, the first data table preset in the database is searched according to the field of the analysis dimension in the SQL statement to obtain the dimension search result. Specifically, the analysis dimension of sales volume can be searched first, that is, the first data table in the database is searched according to the SQL statement, and the first data table is the car_sales data table defined in the SQL statement, and the dimension search result about sales volume is obtained.

[0070] Next, a search is performed in the database based on the first data table and the fields of the key indicators in the SQL statement to obtain indicator search results.

[0071] Considering that in actual applications, the first data table about sales volume generally does not only record sales volume, but also records data of one or more key indicators associated with sales volume. In this case, the embodiment of searching the database based on the fields of the key indicators in the first data table and the SQL statement can refer to the following content.

[0072] It is determined whether there are fields for all key indicators in the SQL statement in the first data table; if so, the first data table is searched according to the fields for the key indicators to obtain the indicator search results. If the fields for all key indicators exist in the first data table, the first data table can satisfy the data search for the key indicators, and the first data table is searched according to the fields for each key indicator to obtain the indicator search results for each key indicator.

[0073] If not, a second data table associated with the first data table and the target key indicator fields is obtained from the database; wherein the target key indicator fields are the key indicator fields that do not exist in the first data table. In the case where only some of the key indicator fields exist in the first data table, a supplementary search is required from other data tables in the database; wherein the key indicator fields missing from the first data table can be determined as the target key indicator fields. An association relationship can be established in advance between the data tables in the database, and the data tables can also establish an association relationship with the key indicators and the analysis dimensions. Based on the above association relationship, a second data table associated with the first data table and the target key indicator fields is obtained from the database, and the second data table can be at least one.

[0074] The first data table and the second data table are searched according to the fields of the key indicators to obtain the indicator search results. In a specific example, for the fields of some key indicators existing in the first data table, the first data table can be searched using the fields; for the fields of the target key indicators not existing in the first data table, the second data table can be searched using the fields of the target key indicators. The search results of the above two parts are taken together as the indicator search results.

[0075] Step S108, visualizing the data retrieval results according to the target display format to generate display results.

[0076] This embodiment includes: determining a target display chart corresponding to a target display form according to a preset display relationship; wherein the display relationship is used to represent the correspondence between different data display forms and different display charts, as shown in the following Table 2.

[0077] Table 2: Display relationship

[0078]

[0079] Referring to Table 2, the data display form is a display form described in natural language or machine language, such as trend, comparison, percentage, etc., and the corresponding display chart is a visualized chart, such as a line chart, a bar chart, a pie chart, etc. Considering the data display in some special scenarios, the display relationship provided in this embodiment can also include remark information, such as limiting the heat map to be in two-dimensional space.

[0080] In one embodiment, if a presentation chart corresponding to the target presentation form cannot be found according to the presentation relationship, a preset default presentation chart is determined as the target presentation chart of the target presentation form.

[0081] After determining the target display chart corresponding to the target display form according to the above display relationship, the dimension retrieval results and the indicator retrieval results are visualized according to the target display chart to generate the display results.

[0082] According to the above embodiment, after parsing, data retrieval and visualization of the natural language text "Percentage of automobile sales in Beijing in 2023", the following can be generated: Figure 2 The display result shown in the figure uses a pie chart to show the sales share of Model 1, Model 2 and Model 3 in Beijing. Figure 2 This is only an example, not real data. This embodiment visualizes the dimension search results and the indicator search results according to the target display chart, which can better implement AIGC (Artificial Intelligence Generated Content). C is a visualization chart. This visualization method is highly efficient and the display results are intuitive and accurate.

[0083] According to the above embodiment, referring to Figure 3 , the data processing method may include the following process:

[0084] The voice audio input by the user is obtained, and the voice audio is converted into text to obtain natural language text; or, the original text input by the user is obtained, and text error correction is performed on the original text to obtain natural language text.

[0085] The entities to be analyzed in the natural language text are parsed through the preset text parsing model to obtain the analysis dimensions, key indicators and target presentation forms.

[0086] According to the preset field mapping relationship, the analysis dimensions, key indicators and target display forms are mapped into SQL statements.

[0087] Retrieve data from the database based on SQL statements.

[0088] Visualize the data retrieval results according to the target display format and generate display results.

[0089] In summary, the data processing method provided by the embodiment of the present disclosure first parses the entity to be analyzed of the acquired natural language text to obtain an SQL statement for data query; wherein the entity to be analyzed includes at least a target display form; then, data is retrieved from the database according to the SQL statement, and the data retrieval result is visualized according to the target display form to generate a display result. By parsing the natural language text that expresses the user's data needs into SQL statements, this technical solution can skip the communication process between different staff members, directly convert the needs into SQL statements that can be processed by the machine, and then perform data retrieval according to the SQL statements, which can effectively improve data processing efficiency. Therefore, this solution can reduce work costs and improve data processing efficiency.

[0090] Corresponding to the data processing method provided by the embodiment of the present disclosure, the embodiment of the present disclosure also provides a data processing device. Figure 4 A structural block diagram of a data processing device provided in an embodiment of the present disclosure, such as Figure 4 As shown, the data processing device includes:

[0091] A text acquisition module 202, used to acquire natural language text;

[0092] The entity parsing module 204 is used to parse the entity to be analyzed in the natural language text to obtain an SQL statement for data query; wherein the entity to be analyzed at least includes a target display form;

[0093] A data retrieval module 206, used to perform data retrieval on the database according to the SQL statement;

[0094] The visualization module 208 is used to visualize the data retrieval result according to the target display format and generate a display result.

[0095] In some embodiments, the entity resolution module 204 is further configured to:

[0096] Parsing the entity to be analyzed in the natural language text through a preset text parsing model to obtain analysis dimensions, key indicators and the target display form;

[0097] According to the preset field mapping relationship, the analysis dimension, the key indicator and the target display form are mapped into SQL statements.

[0098] In some embodiments, the data retrieval module 206 is further configured to:

[0099] Searching a first data table preset in a database according to the fields of the analysis dimension in the SQL statement to obtain a dimension search result;

[0100] Based on the first data table and the fields of the key indicators in the SQL statement, a search is performed in the database to obtain an indicator search result.

[0101] In some embodiments, the data retrieval module 206 is further configured to:

[0102] Determine in the first data table whether there are fields for all the key indicators in the SQL statement;

[0103] If yes, searching the first data table according to the field of the key indicator to obtain the indicator search result;

[0104] If not, obtaining a second data table associated with the first data table and the field of the target key indicator from the database; wherein the field of the target key indicator is a field of the key indicator that does not exist in the first data table;

[0105] The first data table and the second data table are searched according to the fields of the key indicators to obtain the indicator search results.

[0106] In some embodiments, the visualization module 208 is further configured to:

[0107] Determine the target display chart corresponding to the target display form according to the preset display relationship; wherein the display relationship is used to represent the correspondence between different data display forms and different display charts;

[0108] The dimension search results and the indicator search results are visualized according to the target display chart to generate a display result.

[0109] In some embodiments, the text acquisition module 202 is further used to:

[0110] Acquire the voice audio input by the user, and convert the voice audio into text to obtain natural language text;

[0111] or,

[0112] The original text input by the user is obtained, and text error correction is performed on the original text to obtain the natural language text.

[0113] The data processing device disclosed in the above embodiments can execute the data processing method disclosed in the above embodiments, and has the same or corresponding beneficial effects. To avoid repetition, it will not be described again here.

[0114] The embodiment of the present disclosure also provides a computer-readable storage medium, which stores a program or instruction, and the program or instruction enables a computer to execute the steps of any of the above methods.

[0115] Get natural language text;

[0116] Parsing the entity to be analyzed in the natural language text to obtain an SQL statement for data query; wherein the entity to be analyzed at least includes a target display form;

[0117] Retrieve data from the database according to the SQL statement;

[0118] The data retrieval results are visualized according to the target display format to generate display results.

[0119] Optionally, when executed by a computer processor, the computer executable instructions can also be used to execute the technical solution of any of the above-mentioned data processing methods provided in the embodiments of the present disclosure to achieve corresponding beneficial effects.

[0120] Through the above description of the implementation methods, the technicians in the relevant field can clearly understand that the embodiments of the present disclosure can be implemented with the help of software and necessary general hardware, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the embodiments of the present disclosure is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods described in each embodiment of the present disclosure.

[0121] The embodiment of the present disclosure also provides an electronic device, including: one or more processors; a memory for storing one or more programs or instructions; the processor calls the programs or instructions stored in the memory to execute the steps of any of the above methods to achieve corresponding beneficial effects.

[0122] Figure 5 Schematic diagram of the hardware structure of the electronic device provided by the embodiment of the present disclosure. Figure 5 As shown, the electronic device includes one or more processors 301 and a memory 302 .

[0123] The processor 301 may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions.

[0124] The memory 302 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, a random access memory (RAM) and / or a cache memory (cache), etc. The non-volatile memory may include, for example, a read-only memory (ROM), a hard disk, a flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 301 may run the program instructions to implement the data processing method of the embodiment of the present disclosure described above, and / or other desired functions. Various contents such as input signals, signal components, noise components, etc. may also be stored in the computer-readable storage medium.

[0125] In one example, the electronic device may further include: an input device 303 and an output device 304, and these components are interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0126] In addition, the input device 303 may also include, for example, a keyboard, a mouse, and the like.

[0127] The output device 304 can output various information to the outside, including the determined distance information, direction information, etc. The output device 304 can include, for example, a display, a speaker, a printer, a communication network and a remote output device connected thereto, and the like.

[0128] Of course, to simplify, Figure 5 Only some of the components related to the present disclosure in the electronic device are shown, and components such as a bus, an input / output interface, etc. are omitted. In addition, according to specific application situations, the electronic device may further include any other appropriate components.

[0129] It should be noted that, in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0130] The above description is only a specific embodiment of the present disclosure, so that those skilled in the art can understand or implement the present disclosure. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present disclosure. Therefore, the present disclosure will not be limited to the embodiments described herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A data processing method, characterized in that: include: Get natural language text; Parsing the entity to be analyzed in the natural language text to obtain an SQL statement for data query; wherein the entity to be analyzed at least includes a target display form; Retrieve data from the database according to the SQL statement; The data retrieval results are visualized according to the target display format to generate display results.

2. The method according to claim 1, characterized in that The parsing of the entity to be analyzed in the natural language text to obtain an SQL statement for data query includes: Parsing the entity to be analyzed in the natural language text through a preset text parsing model to obtain analysis dimensions, key indicators and the target display form; According to the preset field mapping relationship, the analysis dimension, the key indicator and the target display form are mapped into SQL statements.

3. The method according to claim 2, characterized in that The step of retrieving data from a database according to the SQL statement comprises: Searching a first data table preset in a database according to the fields of the analysis dimension in the SQL statement to obtain a dimension search result; Based on the first data table and the fields of the key indicators in the SQL statement, a search is performed in the database to obtain an indicator search result.

4. The method according to claim 3, characterized in that: The searching of the database based on the fields of the key indicators in the first data table and the SQL statement to obtain indicator search results includes: Determine in the first data table whether there are fields for all the key indicators in the SQL statement; If yes, searching the first data table according to the field of the key indicator to obtain the indicator search result; If not, obtaining a second data table associated with the first data table and the field of the target key indicator from the database; wherein the field of the target key indicator is a field of the key indicator that does not exist in the first data table; The first data table and the second data table are searched according to the fields of the key indicators to obtain the indicator search results.

5. The method according to claim 3, characterized in that: The step of visualizing the data retrieval results according to the target display form to generate display results includes: Determine the target display chart corresponding to the target display form according to the preset display relationship; wherein the display relationship is used to represent the correspondence between different data display forms and different display charts; The dimension search results and the indicator search results are visualized according to the target display chart to generate a display result.

6. The method according to claim 1, characterized in that The obtaining of natural language text includes: Acquire the voice audio input by the user, and convert the voice audio into text to obtain natural language text; or, The original text input by the user is obtained, and text error correction is performed on the original text to obtain the natural language text.

7. A data processing device, characterized in that: include: A text acquisition module, used to acquire natural language text; An entity parsing module, used to parse the entity to be analyzed in the natural language text to obtain an SQL statement for data query; wherein the entity to be analyzed at least includes a target display form; A data retrieval module, used to retrieve data from a database according to the SQL statement; The visualization module is used to visualize the data retrieval results according to the target display form and generate display results.

8. The device according to claim 7, characterized in that The entity resolution module is also used to: Parsing the entity to be analyzed in the natural language text through a preset text parsing model to obtain analysis dimensions, key indicators and the target display form; According to the preset field mapping relationship, the analysis dimension, the key indicator and the target display form are mapped into SQL statements.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a program or instruction, and the program or instruction enables a computer to execute the steps of the method according to any one of claims 1 to 6.

10. An electronic device, characterized in that: include: one or more processors; A memory for storing one or more programs or instructions; The processor is used to execute the steps of the method according to any one of claims 1 to 6 by calling the program or instruction stored in the memory.

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