CN116595029A - Automatic SQL sentence generation method and related equipment - Google Patents

Abstract

The application discloses a method for automatically generating SQL statements, including: setting corresponding rules for SQL statements, and the corresponding rules for SQL statements include: corresponding rules between natural language keywords and SQL keywords; obtaining test cases, the test A use case is a query requirement described in natural language; the test case is parsed based on a text parser to obtain the target natural language keywords included in the test case; based on the corresponding rules of the SQL statement, the corresponding A target SQL keyword; generating a target SQL statement based on the target SQL keyword. Based on this method, it can be seen that the beneficial effects of this application include: enhancing data query efficiency, using this method can automatically generate SQL statements according to query requirements described in natural language, reduce the work difficulty of non-professionals, and improve the efficiency of database operations.

Description

A method for automatically generating SQL statements and related equipment

technical field

The application belongs to the field of data processing, and in particular relates to a method for automatically generating SQL statements and related equipment.

Background technique

In today's big data testing process, when performing end-to-end big data testing, it is necessary to wait for all the test data to be processed. This process may be relatively long, which will lead to long testing time and redundant test execution. In addition, some timing processing tools (such as batch workflow task schedulers such as Azkaban) may not output the result data as expected due to the incomplete processing of the pre-processed data, which further reduces the test efficiency.

Considering the current layered governance of data warehouses, it is a better solution to refine data testing to each level. Most big data components open SQL interfaces at their respective levels for users to use. Therefore, during the testing process, we can use the SQL query method to ensure the quality of the data to ensure the accuracy and consistency of the test data at each level.

However, due to the different requirements and functions of the SQL used for querying at each level, the number of test cases using SQL for data testing will increase exponentially. As a result, the writing of SQL statements becomes complicated and trivial, consuming a lot of manpower and material resources.

Contents of the invention

The purpose of the present invention is to provide a method for automatically generating SQL statements, aiming to solve the problem that the existing SQL statement writing work is complicated and trivial, and consumes a large amount of manpower and material resources. The method for automatically generating SQL statements provided by the application includes:

The first aspect of the embodiment of the present application provides a method for automatically generating SQL statements, including:

The SQL statement correspondence rule is set, and the SQL statement correspondence correspondence rule comprises: the correspondence rule between the natural language keyword and the SQL keyword;

Obtain a test case, the test case is a query requirement described in natural language;

Analyzing the test case based on a text parser to obtain target natural language keywords included in the test case;

Determine the target SQL keyword corresponding to the target natural language keyword based on the SQL statement correspondence rule;

A target SQL statement is generated based on the target SQL keywords.

Based on the method for automatically generating SQL statements provided in the first aspect of the embodiment of the present application, optionally, the SQL statement correspondence rules further include: natural language keyword order adjustment rules.

Based on the method for automatically generating SQL statements provided in the first aspect of the embodiment of the present application, optionally, generating a target SQL statement based on the target SQL keyword includes:

A target SQL statement is generated based on the natural language key word order adjustment rule and the target SQL key word.

Based on the method for automatically generating SQL statements provided in the first aspect of the embodiment of the present application, optionally, the method further includes:

The target SQL statement is used to query the big data test database, and the big data test database has a hierarchical structure.

Based on the method for automatically generating SQL statements provided in the first aspect of the embodiment of the present application, optionally, the SQL statement corresponding rules further include:

keyword level setting rules, the keyword level setting rules are set based on the hierarchical structure of the big data test database;

The method also includes:

The target SQL statement is adjusted based on the keyword level setting rule.

Based on the method for automatically generating SQL statements provided in the first aspect of the embodiment of the present application, optionally, the method further includes:

Execute the target SQL statement to obtain query results and execution results.

The second aspect of the embodiment of the present application provides a method for automatically generating SQL statements, including:

The setting unit is used to set the corresponding rules of the SQL statement, and the corresponding corresponding rules of the SQL statement include: the corresponding rules between the natural language keywords and the SQL keywords;

An acquisition unit, configured to acquire a test case, where the test case is a query requirement described in natural language;

A parsing unit, configured to parse the test case based on a text parser to obtain target natural language keywords included in the test case;

A determining unit, configured to determine a target SQL keyword corresponding to the target natural language keyword based on the SQL statement correspondence rule;

A generating unit, configured to generate a target SQL statement based on the target SQL keyword.

Based on the device for automatically generating SQL statements provided in the second aspect of the embodiment of the present application, optionally, the SQL statement correspondence rules further include: natural language keyword order adjustment rules.

Based on the SQL statement automatic generation device provided in the second aspect of the embodiment of the present application, optionally, the generation unit is specifically used for:

A target SQL statement is generated based on the natural language key word order adjustment rule and the target SQL key word.

Based on the SQL statement automatic generation device provided in the second aspect of the embodiment of the present application, optionally, the target SQL statement is used to query a big data test database, and the big data test database has a hierarchical structure.

Based on the SQL statement automatic generation device provided in the second aspect of the embodiment of the present application, optionally, the SQL statement corresponding rules further include:

keyword level setting rules, the keyword level setting rules are set based on the hierarchical structure of the big data test database;

The device further includes: an adjustment unit, configured to adjust the target SQL statement based on the keyword level setting rule.

Based on the device for automatically generating SQL statements provided in the second aspect of the embodiment of the present application, optionally, the device further includes: an execution unit configured to execute the target SQL statement to obtain query results and execution results.

The third aspect of the embodiment of the present application provides a device for automatically generating SQL statements, including:

Central processing unit, memory, input and output interfaces, wired or wireless network interface and power supply;

The memory is a temporary storage memory or a persistent storage memory;

The central processing unit is configured to communicate with the memory, and execute instructions in the memory on the device for automatically generating SQL statements to perform the method described in any one of the first aspects of the embodiments of the present application.

The fourth aspect of the embodiments of the present application provides a computer-readable storage medium, including instructions, and when the instructions are run on a computer, the computer executes the method described in any one of the first aspects of the embodiments of the present application.

The fifth aspect of the embodiments of the present application provides a computer program product including instructions, which, when run on a computer, cause the computer to execute the method described in any one of the first aspects of the embodiments of the present application.

As can be seen from the above technical solutions, the embodiments of the present application have the following advantages: the present application provides a method for automatically generating SQL statements, including: setting the corresponding rules for SQL statements, and the corresponding corresponding rules for the SQL statements include: natural language keywords and Correspondence rule between SQL keywords; Obtain test case, described test case is the query requirement described in natural language; Analyze described test case based on text parser and obtain the target natural language keyword that described test case includes; Based on The SQL statement correspondence rule determines a target SQL keyword corresponding to the target natural language keyword; and generates a target SQL statement based on the target SQL keyword. Known based on the method, the beneficial effects of the present application include: enhanced data query efficiency: SQL statements are an important tool for performing database operations, and manual writing of SQL statements requires familiarity with SQL syntax and database structure, which is easy for non-professionals Mistakes and time consuming. Using this method, SQL statements can be automatically generated according to the query requirements described in natural language, reducing the work difficulty of non-professionals and improving the efficiency of database operations.

Description of drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings that need to be used in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only It is an embodiment of the present application, and those skilled in the art can also obtain other drawings according to the provided drawings without creative work. It can be understood that the drawings provided in this section are only used to better understand the present solution, and do not constitute a limitation to the present application.

Fig. 1 is a schematic flow sheet of the embodiment of the automatic generation method of SQL statement provided by the application;

Fig. 2 is another schematic flow sheet of the embodiment of the automatic generation method for SQL statements provided by the application;

Fig. 3 is a structural representation of the automatic generation equipment embodiment of SQL sentence that the application provides;

FIG. 4 is another schematic structural diagram of an embodiment of the device for automatically generating SQL statements provided by the present application.

Detailed ways

In order to enable those skilled in the art to better understand the solutions of the present application, the technical solutions in the embodiments of the present application are clearly and completely described below. Obviously, the described embodiments are only part of the embodiments of the present application, not all of them. the embodiment. Based on the embodiments in this application, all other embodiments obtained by persons of ordinary skill in the art without creative efforts shall fall within the scope of protection of this application. Meanwhile, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness of description.

The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present application and the above drawings are used to distinguish similar objects, and not necessarily Used to describe a specific sequence or sequence. It is to be understood that the terms so used are interchangeable under appropriate circumstances such that the embodiments described herein can be practiced in sequences other than those illustrated or described herein. Furthermore, the terms "comprising" and "having", as well as any variations thereof, are intended to cover a non-exclusive inclusion, for example, a process, method, product or apparatus comprising a series of steps or elements need not be limited to those steps explicitly listed or units, but may include other steps or units not explicitly listed or inherent to the process, method, product or apparatus.

In today's big data testing process, when performing end-to-end big data testing, it is necessary to wait for all the test data to be processed. This process may be relatively long, which will lead to long testing time and redundant test execution. In addition, some timing processing tools (such as azkaban) may not output the result data as expected because the pre-data processing is not completed in time, which further reduces the test efficiency.

Considering the current layered governance of data warehouses, it is a better solution to refine data testing to each level. Most big data components open SQL interfaces at their respective levels for users to use. Therefore, during the testing process, we can use the SQL query method to ensure the quality of the data to ensure the accuracy and consistency of the test data at each level.

However, due to the different requirements and functions of the SQL used for querying at each level, the number of test cases using SQL for data testing will increase exponentially. As a result, the writing of SQL statements becomes complicated and trivial, consuming a lot of manpower and material resources. In order to solve the above problems, the application provides a new method for automatically generating SQL statements, referring to Fig. 1, an embodiment of the method for automatically generating SQL statements provided by the application includes:

101. Set the SQL statement corresponding rules.

Specifically, in order to convert a natural language query into an SQL query, it is first necessary to establish a corresponding rule between natural language keywords and SQL keywords. It can be set by analyzing the corresponding relationship between common natural language queries and SQL queries. For example, "query" corresponds to the "select" keyword of SQL, "condition" corresponds to the "where" keyword of SQL, and so on. In addition, it is also necessary to classify and store configuration information such as connection information, fields, and blood relationship between tables involved in data query, so as to facilitate subsequent conversion processing. For general configuration information, it can be set in advance in the rule base; for business configuration information, it can be obtained by dynamically reading database metadata information.

It should be noted that different query syntaxes in SQL statements will have different SQL keywords and operators. Therefore, when establishing the corresponding rules for SQL statements, it is necessary to fully consider the grammatical structure of the SQL language and the needs of specific business queries, and set corresponding rules according to the actual situation to achieve the goal of successful conversion. At the same time, in actual application scenarios, it is also necessary to regularly maintain the accuracy and integrity of the rules corresponding to SQL statements to prevent rule matching errors or translation errors caused by changes in data structures. The specific configuration method can be determined according to the actual situation, which is not limited here.

102. Obtain a test case.

Specifically, a test case is obtained. The test case refers to a query requirement described by a user in natural language, which can be manually written or automatically generated. Test cases For example, to test whether the water cup can be used, filling water to see if the water cup will leak is one of the use cases. When writing test cases, it is necessary to take into account the relevant elements of the query in combination with the actual business scenario. Mind maps, text editors, etc. can be used to record for subsequent conversion processing. Later, according to the actual project, the file format will be converted through python. For test cases, it is necessary to ensure that the description of test cases is accurate and clear, and can fully describe the user's query requirements, and the tables, fields, and query conditions involved must be specified in detail. In order to ensure the accuracy and usability of subsequent automatically generated SQL statements.

103. Parse the test case based on a text parser to obtain target natural language keywords included in the test case.

Analyze the test case based on the text parser to obtain the included target natural language keywords: in step 102, we have obtained the test case, and the next task is to convert the test case into a computer language so that the computer can process and realize the user query request. To this end, you can use some open source tools and technologies, such as natural language processing technology, regular expressions, syntax analysis, etc., to analyze and identify the natural language text in the test case by writing a program, and extract the target natural language keywords . Among them, the target natural language keywords include the main information in the sense of user query, such as the name of the data table to be queried, the fields to be queried, query conditions, etc. The specific implementation method can be determined according to the actual situation, and will not be done here limited.

It should be noted that in practical applications, some test cases with complex or ambiguous natural language descriptions may be encountered. At this time, it is necessary to expand the rules of the parser, or increase the context, etc. to accurately extract the test cases. The target natural language keywords are converted into SQL statements. The specific parsing process depends on the actual situation of the parser, and is not limited here.

104. Determine a target SQL keyword corresponding to the target natural language keyword based on the SQL statement correspondence rule.

The target SQL keywords corresponding to the target natural language keywords are determined based on the SQL statement corresponding rules, and the target natural language keywords are converted into corresponding SQL keywords according to the SQL statement corresponding rules set in step 101 . This process needs to be matched and transformed according to the data structure and metadata information in the rule base.

For example, for the test case "Query the income of Table A in May", the target natural language keywords obtained after parsing are: "query", "Table A", "May", and "income". According to the rule base set in step 101, the SQL keyword corresponding to the "query" keyword can be converted into "select"; "A table" can be converted into "from A"; "May" can be converted into "wheremonth=' May'"; Convert "income" to "sum(income)", etc.

It should be noted that in the process of concept conversion, it is also necessary to consider that the table name and field name entered by the user may be misspelled, the capitalization is not standardized, and the use of aliases is confusing. You can set the corresponding rules in the rule base. Fault tolerance mechanisms and rules to increase the accuracy and stability of the system.

105. Generate a target SQL statement based on the target SQL keyword.

Specifically, a target SQL statement is generated based on the target SQL keyword, and a plurality of target SQL keywords obtained in step 104 are combined to generate a target SQL statement. When the generated target SQL statement is executed, it communicates with the user through natural language The written test case query requirements are consistent, such as the use case description input by the user: query the income of Table A in May. Based on the above steps 104 to 105, the result obtained is: select sum(income) from A where month='May'. Users can use the target SQL statement to query and obtain corresponding query results.

As can be seen from the above technical solutions, the embodiments of the present application have the following advantages: the present application provides a method for automatically generating SQL statements, including: setting the corresponding rules for SQL statements, and the corresponding corresponding rules for the SQL statements include: natural language keywords and Correspondence rule between SQL keywords; Obtain test case, described test case is the query requirement described in natural language; Analyze described test case based on text parser and obtain the target natural language keyword that described test case includes; Based on The SQL statement correspondence rule determines a target SQL keyword corresponding to the target natural language keyword; and generates a target SQL statement based on the target SQL keyword. Known based on this method, the beneficial effect that the present application has comprises:

1. Enhance data query efficiency: SQL statements are an important tool for performing database operations, and manually writing SQL statements requires familiarity with SQL syntax and database structure, which is prone to errors and time-consuming for non-professionals. Using this method, SQL statements can be automatically generated according to the query requirements described in natural language, reducing the work difficulty of non-professionals and improving the efficiency of database operations.

2. Reduce the error rate: Manually writing SQL statements is prone to inaccurate query results due to negligence or grammatical errors. Using this method can avoid the occurrence of this situation. By presetting the corresponding rules of SQL statements and analyzing the test cases based on the text parser, the target SQL keywords corresponding to the target natural language keywords are determined to generate accurate SQL statements.

3. Improve query accuracy: The traditional query method may require the user to write code or make multiple attempts to find the SQL statement that meets the query requirements, and the query efficiency is low. Using this method, accurate SQL statements can be automatically generated according to query requirements described in natural language, thereby improving query accuracy and efficiency.

4. Simplify the operation process: Using this method can omit the steps for users to write SQL statements, and convert the query requirements described in natural language into SQL statements, which simplifies the operation process and enables users to complete database queries more quickly.

5. Lower learning threshold: SQL language requires a certain degree of learning and understanding, and using this method allows users not to be proficient in SQL language, and only needs to use natural language to describe query requirements to generate SQL statements, which lowers the learning threshold.

In order to facilitate the use of this method in the implementation process, the optional application also provides a more detailed embodiment of an optional implementation, please refer to Fig. 2, a SQL statement automatic generation method provided by the application Examples include:

201. Set the rules corresponding to the SQL statement.

Set the rules corresponding to SQL statements. Specifically, step 201 is similar to the aforementioned step 101 during implementation, including the corresponding rules between natural language keywords and SQL keywords: for example, "select" corresponds to "SELECT", "from xx table" corresponds to "FROM", "condition " corresponds to "WHERE" and so on. In addition, the corresponding rules for SQL statements should also include the order adjustment rules for natural language keywords. In the query requirements described in different natural languages, the order of natural language keywords may be different. However, the order of keywords in SQL statements is fixed. It is necessary to adjust the order of natural language keywords to the correct order of SQL statements according to the rules, such as "from xx table + selection + condition" and so on.

In addition, the corresponding rules for SQL statements should also include the keyword level setting rules. When querying the big data test database, the data is usually organized according to a certain hierarchical structure, such as file directory structure, multi-level index, etc. At this time, it is necessary to adjust the keywords in the SQL statement to the keywords under the correct level according to the level setting rules, so as to ensure that the target data can be queried.

202. Obtain a test case.

This step is similar to step 102 in the above-mentioned embodiment corresponding to FIG. 1 , and details are not described here. It should be noted that the test case is used to query the big data test database, and the big data test database has a hierarchical structure.

203. Parse the test case based on a text parser to obtain target natural language keywords included in the test case.

Specifically, target natural language keywords included in the test case are parsed by a text parser. A text parser is a natural language processing tool that converts the text into a machine-readable format for subsequent processing by performing word segmentation and grammatical analysis on the input text. It specifically includes the following content: word segmentation: segment the input natural language text into independent words, and remove irrelevant components such as punctuation marks for subsequent processing and analysis. Part-of-speech tagging: For each word after word segmentation, determine the part of speech it belongs to in the sentence, such as noun, verb, adjective, etc. Grammatical analysis: According to word segmentation and part-of-speech tagging results, analyze the grammatical structure of sentences, including the construction of grammatical trees, the dependencies between words, and so on. Processing test cases through a text parser can convert natural language text into a machine-processable format, providing the basis for subsequent SQL statement generation.

204. Determine a target SQL keyword corresponding to the target natural language keyword based on the SQL statement correspondence rule.

Specifically, step 204 is similar to step 104 in the aforementioned embodiment corresponding to FIG. 1 , for details, reference may be made to the aforementioned content, and details are not repeated here.

205. Generate a target SQL statement based on the natural language keyword order adjustment rule and the target SQL keyword.

Specifically, a target SQL statement is generated based on the natural language keyword order adjustment rule and the target SQL keyword. Among them, the natural language keyword word order adjustment rules ensure the word order adjustment rules in the process of converting from natural language to SQL language. In the query requirements described in different natural languages, the order of natural language keywords may be different. However, the key words in the SQL statement The order of words is fixed. At this time, it is necessary to adjust the order of natural language keywords to the correct order of SQL statements according to the rules, such as "from xx table + selection + condition" and so on. Generate target SQL statements based on the word order adjustment rules and target SQL keywords obtained from test cases.

206. Adjust the target SQL statement based on the keyword level setting rule.

Specifically, the target SQL statement is adjusted based on the keyword level setting rule. When actual users write test cases, they may not pay extra attention to the hierarchy of keywords, but when querying big data test databases, the data is usually organized according to a certain hierarchical structure, such as file directory structure, multi-level index etc. At this time, it is necessary to adjust the keywords in the SQL statement to keywords under the correct level according to the level setting rules, so as to ensure that the correct interface is called and the target data is queried.

For example, the test case is to query the data in table A, but table A belongs to the statistical level in terms of hierarchy, so when generating the SQL statement corresponding to the test case, it is necessary to identify the query path corresponding to the statistical level when querying table A. The SQL keywords are added to the generated SQL statement, so that the target SQL statement can query the data at the correct level in the big data database.

207. Execute the target SQL statement to obtain a query result and an execution result.

Specifically, after the target SQL statement is obtained, the target SQL statement is executed to obtain a query result and an execution result. The query result is specific data, that is, the result obtained by executing the SQL statement. The execution result is the execution result of whether the target SQL statement is successfully executed, that is, the execution result of the use case. At the same time, if the execution result is an execution error, the cause of the error can also be fed back to the user etc., so that the user can obtain the execution status and execution results of the SQL statement, so that the user can adjust the SQL statement or test case in time. The details may be determined according to actual conditions, and are not limited here.

Known based on this method, the beneficial effect that the present application has comprises:

1. Enhance data query efficiency: SQL statements are an important tool for performing database operations, and manually writing SQL statements requires familiarity with SQL syntax and database structure, which is prone to errors and time-consuming for non-professionals. Using this method, SQL statements can be automatically generated according to the query requirements described in natural language, reducing the work difficulty of non-professionals and improving the efficiency of database operations.

2. Reduce the error rate: Manually writing SQL statements is prone to inaccurate query results due to negligence or grammatical errors. Using this method can avoid the occurrence of this situation. By presetting the corresponding rules of SQL statements and analyzing the test cases based on the text parser, the target SQL keywords corresponding to the target natural language keywords are determined to generate accurate SQL statements.

3. Improve query accuracy: The traditional query method may require the user to write code or make multiple attempts to find the SQL statement that meets the query requirements, and the query efficiency is low. Using this method, accurate SQL statements can be automatically generated according to query requirements described in natural language, thereby improving query accuracy and efficiency.

4. Simplify the operation process: Using this method can omit the steps for users to write SQL statements, and convert the query requirements described in natural language into SQL statements, which simplifies the operation process and enables users to complete database queries more quickly.

5. Lower learning threshold: SQL language requires a certain degree of learning and understanding, and using this method allows users not to be proficient in SQL language, and only needs to use natural language to describe query requirements to generate SQL statements, which lowers the learning threshold.

The above content has described the SQL statement automatic generation method provided by the application. In order to support the implementation of the above embodiments, the application also provides a SQL statement automatic generation device. Please refer to Fig. 3, the SQL statement provided by the application is automatically One embodiment of a generating device includes:

The setting unit 301 is used to set the corresponding rules of the SQL statement, and the corresponding corresponding rules of the SQL statement include: the corresponding rules between natural language keywords and SQL keywords;

An acquisition unit 302, configured to acquire a test case, which is a query requirement described in natural language;

A parsing unit 303, configured to parse the test case based on a text parser to obtain the target natural language keywords included in the test case;

A determining unit 304, configured to determine a target SQL keyword corresponding to the target natural language keyword based on the SQL statement correspondence rule;

A generating unit 305, configured to generate a target SQL statement based on the target SQL keywords.

Optionally, the SQL statement correspondence rule further includes: a natural language keyword sequence adjustment rule.

Optionally, the generating unit is specifically used for:

A target SQL statement is generated based on the natural language key word order adjustment rule and the target SQL key word.

Optionally, the target SQL statement is used to query a big data test database, and the big data test database has a hierarchical structure.

Optionally, the SQL statement corresponding rule also includes:

keyword level setting rules, the keyword level setting rules are set based on the hierarchical structure of the big data test database;

The device further includes: an adjustment unit, configured to adjust the target SQL statement based on the keyword level setting rule.

Optionally, the device further includes: an execution unit, configured to execute the target SQL statement to obtain a query result and an execution result.

In this embodiment, the processes executed by each unit in the device for automatically generating SQL statements are similar to the process described in the embodiment corresponding to FIG. 1 or FIG. 2 , and will not be repeated here.

FIG. 4 shows a schematic block diagram of an example electronic device 400 that may be used to implement embodiments of the present application. Electronic device is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other suitable computers. Electronic devices may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are by way of example only, and are not intended to limit implementations of the applications described and/or claimed herein.

As shown in FIG. 4, the device 400 includes a computing unit 401 that can execute according to a computer program stored in a read-only memory (ROM) 402 or loaded from a storage unit 408 into a random-access memory (RAM) 403. Various appropriate actions and treatments. In the RAM 403, various programs and data necessary for the operation of the device 400 can also be stored. The computing unit 401 , ROM 402 and RAM 403 are connected to each other through a bus 404 . An input/output (I/O) interface 405 is also connected to bus 404 .

Multiple components in the device 400 are connected to the I/O interface 405, including: an input unit 406, such as a keyboard, a mouse, etc.; an output unit 407, such as various types of displays, speakers, etc.; a storage unit 408, such as a magnetic disk, an optical disk, etc. ; and a communication unit 409, such as a network card, a modem, a wireless communication transceiver, and the like. The communication unit 409 allows the device 400 to exchange information/data with other devices over a computer network such as the Internet and/or various telecommunication networks.

The computing unit 401 may be various general-purpose and/or special-purpose processing components having processing and computing capabilities. Some examples of computing units 401 include, but are not limited to, central processing units (CPUs), graphics processing units (GPUs), various dedicated artificial intelligence (AI) computing chips, various computing units that run machine learning model algorithms, digital signal processing processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 401 executes various methods and processes described above, for example, a method for automatically generating SQL statements. For example, in some embodiments, may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as storage unit 408 . In some embodiments, part or all of the computer program may be loaded and/or installed on the device 400 via the ROM 402 and/or the communication unit 409 . When the computer program is loaded into RAM 403 and executed by computing unit 401, one or more steps of the method for automatically generating SQL statements described above can be executed. Alternatively, in other embodiments, the computing unit 401 may be configured in any other appropriate way (for example, by means of firmware) to execute the method for automatically generating SQL statements.

Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips Implemented in a system of systems (SOC), load programmable logic device (CPLD), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include being implemented in one or more computer programs executable and/or interpreted on a programmable system including at least one programmable processor, the programmable processor Can be special-purpose or general-purpose programmable processor, can receive data and instruction from storage system, at least one input device, and at least one output device, and transmit data and instruction to this storage system, this at least one input device, and this at least one output device an output device.

Program codes for implementing the methods of the present application may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special purpose computer, or other programmable data processing devices, so that the program codes, when executed by the processor or controller, make the functions/functions specified in the flow diagrams and/or block diagrams Action is implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of the present application, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media would include one or more wire-based electrical connections, portable computer discs, hard drives, random access memory (RAM), read only memory (ROM), erasable programmable read only memory (EPROM or flash memory), optical fiber, compact disk read only memory (CD-ROM), optical storage, magnetic storage, or any suitable combination of the foregoing.

To provide for interaction with the user, the systems and techniques described herein can be implemented on a computer having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user. ); and a keyboard and pointing device (eg, a mouse or a trackball) through which a user can provide input to the computer. Other kinds of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and can be in any form (including Acoustic input, speech input or, tactile input) to receive input from the user.

To implement the systems and techniques described herein in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., as a a user computer having a graphical user interface or web browser through which a user can interact with embodiments of the systems and techniques described herein), or including such backend components, middleware components, Or any combination of front-end components in a computing system. The components of the system can be interconnected by any form or medium of digital data communication, eg, a communication network. Examples of communication networks include: local area networks (LANs), wide area networks (WANs), the Internet, and blockchain networks.

A computer system may include clients and servers. Clients and servers are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, or a server of a distributed system, or a server combined with a blockchain.

The embodiment of the present application also provides a computer storage medium, the computer storage medium is used to store the computer software instructions used for the above SQL statement automatic generation method, which includes the program designed for executing the SQL statement automatic generation method.

The method for automatically generating SQL statements may be the same as the method for automatically generating SQL statements described in FIG. 1 .

The embodiment of the present application also provides a computer program product, the computer program product includes computer software instructions, and the computer software instructions can be loaded by a processor to implement the method for automatically generating SQL statements in any one of the above-mentioned Fig. 1 and Fig. 2 process.

In the several embodiments provided in this application, it should be understood that the disclosed system, device and method can be implemented in other ways. For example, the device embodiments described above are only illustrative. For example, the equivalent transformation of circuits and the division of units are only a logical function division. In actual implementation, there may be other division methods, such as multiple units or Components may be combined or integrated into another system, or some features may be omitted, or not implemented. In another point, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces, and the indirect coupling or communication connection of devices or units may be in electrical, mechanical or other forms.

The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple units. Part or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, each unit may exist separately physically, or two or more units may be integrated into one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

The above description is only a preferred embodiment of the present invention, and is not intended to limit the present invention. Any modification, equivalent replacement or improvement made within the spirit and principles of the present invention shall be included in the protection of the present invention. within range.

Claims (10)

1. A SQL statement automatic generation method is characterized in that, comprising: The SQL statement correspondence rule is set, and the SQL statement correspondence correspondence rule comprises: the correspondence rule between the natural language keyword and the SQL keyword; Obtain a test case, the test case is a query requirement described in natural language; Analyzing the test case based on a text parser to obtain target natural language keywords included in the test case; Determine the target SQL keyword corresponding to the target natural language keyword based on the SQL statement correspondence rule; A target SQL statement is generated based on the target SQL keywords. 2. The method for automatically generating SQL statements according to claim 1, wherein the SQL statement corresponding rules further comprise: natural language keyword order adjustment rules. 3. the SQL statement automatic generation method according to claim 2, is characterized in that, described target SQL statement is generated based on described target SQL keyword, comprises: A target SQL statement is generated based on the natural language key word order adjustment rule and the target SQL key word. 4. The method for automatically generating SQL statements according to claim 1, wherein the target SQL statement is used to query a large data test database, and the large data test database is a hierarchical structure. 5. the SQL statement automatic generation method according to claim 4, is characterized in that, described SQL statement correspondence rule also comprises: keyword level setting rules, the keyword level setting rules are set based on the hierarchical structure of the big data test database; The method also includes: The target SQL statement is adjusted based on the keyword level setting rule. 6. the SQL statement automatic generation method according to claim 1, is characterized in that, described method also comprises: Execute the target SQL statement to obtain query results and execution results. 7. A SQL statement automatic generation method is characterized in that, comprising: The setting unit is used to set the corresponding rules of the SQL statement, and the corresponding corresponding rules of the SQL statement include: the corresponding rules between the natural language keywords and the SQL keywords; An acquisition unit, configured to acquire a test case, where the test case is a query requirement described in natural language; A parsing unit, configured to parse the test case based on a text parser to obtain target natural language keywords included in the test case; A determining unit, configured to determine a target SQL keyword corresponding to the target natural language keyword based on the SQL statement correspondence rule; A generating unit, configured to generate a target SQL statement based on the target SQL keyword. 8. A SQL statement automatic generation device is characterized in that, comprising: Central processing unit, memory, input and output interfaces, wired or wireless network interface and power supply; The memory is a temporary storage memory or a persistent storage memory; The central processing unit is configured to communicate with the memory, and the SQL statement automatic generation device executes instructions in the memory to perform the method described in any one of claims 1 to 6. 9. A computer-readable storage medium, characterized by comprising instructions, and when the instructions are run on a computer, the computer is made to execute the method according to any one of claims 1 to 6. 10. A computer program product comprising instructions, characterized in that when it is run on a computer, it causes the computer to execute the method according to any one of claims 1 to 6.