CN114741459A - Data query method and device based on artificial intelligence, electronic equipment and medium - Google Patents
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Abstract
The invention relates to the technical field of artificial intelligence, and provides a data query method, a device, electronic equipment and a medium based on artificial intelligence, wherein the method comprises the following steps: acquiring a target code base and a table relation query condition; scanning database sentences jointly checked in a target code base to obtain database joint check sentences; analyzing the database joint check sentences to obtain a main table, a sub table and a corresponding relation between the main table and the sub table; creating a first entity relation graph according to the corresponding relation between the main table and the sub table; checking the first entity relation graph to obtain a second entity relation graph; and performing table relation query in the second entity relation graph according to the table relation query conditions to obtain a query result. According to the method and the device, the first entity relation graph is created according to the corresponding relation between the main table and the sub table, the first entity relation graph is verified, the association relation between the data tables is visually displayed, and the data table relation query efficiency and accuracy are improved.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a data query method and device based on artificial intelligence, electronic equipment and a medium.
Background
At present, table relations in a database are identified through foreign key relations, a user wants to know a bottom data storage structure of an item, and a data table relation graph can be reversely obtained directly according to the data table structure design.
However, when the control is converted into the code transaction, the data table relationship is difficult to find from the data table structure of the database, so that the efficiency and the accuracy of querying the data table relationship are low.
Disclosure of Invention
In view of the above, it is necessary to provide a data query method, an apparatus, an electronic device, and a medium based on artificial intelligence, wherein a first entity relationship diagram is created according to a correspondence between a main table and a sub table, and the first entity relationship diagram is verified, so that an association relationship between data tables is visually displayed, and the efficiency and accuracy of querying a data table relationship are improved.
A first aspect of the present invention provides a data query method based on artificial intelligence, the method comprising:
analyzing the received data table relation query request to obtain a target code base and a table relation query condition;
scanning the database sentences jointly searched in the target code base to obtain database joint search sentences;
analyzing the database joint check statement to obtain a main table, a sub table and a corresponding relation between the main table and the sub table;
creating a first entity relation graph according to the corresponding relation between the main table and the sub table;
checking the first entity relation graph to obtain a second entity relation graph;
and performing table relation query in the second entity relation graph according to the table relation query condition to obtain a query result.
Optionally, the scanning the database statement jointly searched in the target code library to obtain the database joint search statement includes:
driving a preset tool to initialize connection resources by using a connection pool of the target code library;
extracting database statements in the target code library into an XML configuration file based on the initialized connection resources;
and configuring the database statement in an XML mode, and mapping the attribute and the field of the entity of the configured database statement and the data table to obtain the database joint check statement.
Optionally, the analyzing the database joint-check statement to obtain a main table and a sub-table, and the corresponding relationship between the main table and the sub-table includes:
analyzing the database joint-check sentences, and screening out the data tables with the association relation;
identifying a connection mode of a data table with an incidence relation;
when the connection mode of the data tables with the association relation is left connection, determining that a left table in the data tables with the association relation is a main table, determining that a right table in the data tables with the association relation is a sub table, and determining that a corresponding relation between the main table and the sub table is a preset first corresponding relation;
and when the connection mode of the data tables with the association relation is right connection, determining that the right table in the data tables with the association relation is a main table, the left table is a sub table, and the corresponding relation between the main table and the sub table is a preset second corresponding relation.
Optionally, the method further comprises:
and when the connection mode of the data tables with the association relation is internal connection, determining that the corresponding relation between two tables in the data tables with the association relation is a preset third corresponding relation.
Optionally, the creating a first entity relationship diagram according to the correspondence between the main table and the sub table includes:
extracting field information with preset rules from the corresponding relation as an entity;
extracting the characteristics corresponding to the entities from the corresponding relations as attributes;
extracting the mapping relation and the mapping cardinality between the entities from the corresponding relation;
and generating a first entity relationship diagram by using a preset entity relationship diagram generation tool according to the entity, the attribute, the mapping relationship and the mapping cardinality.
Optionally, the verifying the first entity relationship diagram to obtain a second entity relationship diagram includes:
randomly extracting a code to be checked from the target code library;
scanning the code to be checked to obtain a joint check statement of the database to be checked;
analyzing the joint check sentences of the database to be checked to obtain a main table to be checked and a sub table to be checked, and the corresponding relation between the main table to be checked and the sub table to be checked;
judging whether the main table to be verified and the sub table to be verified are consistent with the main table and the sub table corresponding to the first entity relation graph;
when the main table to be verified and the sub table to be verified are consistent with the main table and the sub table corresponding to the first entity relational graph, determining the first entity relational graph as a second entity relational graph;
and when the main table to be verified and the sub table to be verified are not consistent with the main table and the sub table corresponding to the first entity relation graph, verifying the first entity relation graph according to a preset verification rule to obtain a verification result, and updating the first entity relation graph based on the verification result to obtain a second entity relation graph.
Optionally, after the verifying the first entity relationship diagram to obtain a second entity relationship diagram, the method further includes:
and mapping the second entity relationship graph into a relationship table according to a preset mapping rule.
A second aspect of the present invention provides an artificial intelligence based data query apparatus, the apparatus comprising:
the analysis and acquisition module is used for analyzing the received data table relation query request to acquire a target code base and a table relation query condition;
the scanning module is used for scanning the database sentences jointly searched in the target code base to obtain database joint search sentences;
the analysis module is used for analyzing the database joint check sentences to obtain a main table, a sub table and a corresponding relation between the main table and the sub table;
the creating module is used for creating a first entity relationship graph according to the corresponding relationship between the main table and the sub table;
the checking module is used for checking the first entity relationship diagram to obtain a second entity relationship diagram;
and the query module is used for performing table relation query in the second entity relation graph according to the table relation query condition to obtain a query result.
A third aspect of the invention provides an electronic device comprising a processor and a memory, the processor being configured to implement the artificial intelligence based data query method when executing a computer program stored in the memory.
A fourth aspect of the present invention provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the artificial intelligence based data query method.
In summary, according to the data query method, the data query device, the electronic device and the medium based on artificial intelligence, the database joint query statement is obtained by scanning the database statement of the joint query in the target code library, and the database joint query statement is analyzed to obtain the main table, the sub-table and the corresponding relationship between the main table and the sub-table, so that the data relationship does not need to be reversely pushed from a page by a foreign key or a developer, and the acquisition efficiency of the data table relationship is improved. And creating a first entity relationship diagram according to the corresponding relationship between the main table and the sub table, wherein developers can visually see the association relationship between the data tables in the target code library, and meanwhile, the first entity relationship diagram is verified to obtain a second entity relationship diagram, so that the accuracy of the association relationship between the data tables in the second entity relationship diagram is ensured, and the query efficiency and the accuracy of the data table relationship are improved.
Drawings
Fig. 1 is a flowchart of a data query method based on artificial intelligence according to an embodiment of the present invention.
Fig. 2 is a structural diagram of an artificial intelligence based data query apparatus according to a second embodiment of the present invention.
Fig. 3 is a schematic structural diagram of an electronic device according to a third embodiment of the present invention.
Detailed Description
In order that the above objects, features and advantages of the present invention can be more clearly understood, a detailed description of the present invention will be given below with reference to the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present invention and features of the embodiments may be combined with each other without conflict.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.
Example one
Fig. 1 is a flowchart of a data query method based on artificial intelligence according to an embodiment of the present invention.
In this embodiment, the artificial intelligence based data query method may be applied to an electronic device, and for an electronic device that needs to perform artificial intelligence based data query, the artificial intelligence based data query function provided by the method of the present invention may be directly integrated on the electronic device, or may be run in the electronic device in the form of a Software Development Kit (SDK).
The embodiment of the invention can acquire and process related data based on an artificial intelligence technology. Among them, Artificial Intelligence (AI) is a theory, method, technique and application system that simulates, extends and expands human Intelligence using a digital computer or a machine controlled by a digital computer, senses the environment, acquires knowledge and uses the knowledge to obtain the best result.
The artificial intelligence infrastructure generally includes technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning, deep learning and the like.
As shown in fig. 1, the artificial intelligence based data query method specifically includes the following steps, and the order of the steps in the flowchart may be changed and some may be omitted according to different requirements.
S11, analyzing the received data table relation query request to obtain the target code base and the table relation query condition.
In the embodiment, in the field of digital medical technology, for a medical research and development project, in order to quickly acquire project content, a client sends a data relationship query request to a server, the server acquires the project content according to the association between data tables in a target code library of a target project corresponding to the data relationship query request by analyzing the association between the data tables, that is, obtains the logical structure relationship of the project content, and can assist in later maintenance, increase, decrease and reconstruction of the project content according to the logical relationship of the project content, so as to shorten the project development period.
In this embodiment, the table relationship query condition may include: the time range of the query, the content of the query, the item information of the query, etc.
In an optional embodiment, the parsing the received data table relationship query request to obtain the target code base and the table relationship query condition includes:
analyzing the data table relation query request to obtain message information of the data table relation query request;
acquiring the name of a target code library and a table relation query condition from the message information;
determining an interface of the target code library according to the name of the target code library;
and calling an interface of the target code library to acquire the target code library.
In the embodiment, the name of each target code library corresponds to one target code library interface, the interface of the target code library is determined by analyzing the data table relation query request, the interface of the target code library is called to obtain the target code library, the obtaining efficiency of the target code library is improved, meanwhile, the problem that the target code library is obtained wrongly due to the fact that one interface corresponds to a plurality of target code libraries is avoided, and the accuracy of the obtained target code library is improved.
And S12, scanning the database sentences jointly searched in the target code library to obtain the database joint search sentences.
In this embodiment, the database joint query statement refers to a database statement of an association relationship existing in the target code library, and the database joint query statement may be obtained by analyzing the database statement.
In an optional embodiment, the scanning the database statement jointly searched in the target code library to obtain the database joint search statement includes:
driving a preset tool to initialize connection resources by using a connection pool of the target code library;
extracting database statements in the target code base into an XML configuration file based on the initialized connection resources;
and configuring the database statement in an XML mode, and mapping the attribute and the field of the entity of the configured database statement and the data table to obtain the database joint check statement.
In this embodiment, the preset tool may be Mybatis, where the Mybatis is a java-based persistent layer framework, and the jdbc is encapsulated inside the framework, and the developer only needs to focus on the database statement itself.
In this embodiment, the preset configuration mode may be an XML or annotation mode, the Mybatis configures various database statements to be executed in an XML or annotation mode, and performs attribute and field mapping on an entity and a table of the configured database statement to generate a database joint check statement, where the database joint check statement may include one or more database joint check statements.
In other optional embodiments, after obtaining the database co-check statement, the method further comprises:
and recording the association relation between the database joint check statement and the corresponding data table.
In the embodiment, the database joint-check sentences are obtained by adopting Mybatis, and since the jdbc is encapsulated by the Mybatis, the access details of the bottom layer of the jdbcapi are shielded, the database can be durably operated without making an intersection with the jdbcapi, and the efficiency of obtaining the database joint-check sentences is improved.
And S13, analyzing the database joint check statement to obtain a main table and a sub table and the corresponding relation between the main table and the sub table.
In this embodiment, the corresponding relationship includes all the associated field information of the data table and the association relationship between the fields.
In this embodiment, by analyzing the database joint-check statement, the association relationship between the data tables in the target code library can be obtained, and when the association relationship exists between the data tables and other data tables, the main table and the sub table in the data table having the association relationship and the corresponding relationship between the main table and the sub table are determined; when no association exists between the data table and other data tables, the data table without the association is used as a basic data table and is not processed, and meanwhile, the basic data table does not relate to project data, and example data, table structure comments and the like are mainly stored.
In an optional embodiment, the analyzing the database joint-check statement to obtain the main table and the sub-table, and the corresponding relationship between the main table and the sub-table includes:
analyzing the database joint-check sentences, and screening out the data tables with the association relation;
identifying a connection mode of a data table with an incidence relation;
when the connection mode of the data tables with the association relation is left connection, determining that a left table in the data tables with the association relation is a main table, a right table in the data tables with the association relation is a sub table, and the corresponding relation between the main table and the sub table is a preset first corresponding relation;
and when the connection mode of the data tables with the association relation is right connection, determining that the right table in the data tables with the association relation is a main table, the left table is a sub-table, and the corresponding relation between the main table and the sub-table is a preset second corresponding relation.
In other optional embodiments, the method further comprises:
and when the connection mode of the data tables with the association relation is internal connection, determining that the corresponding relation between two tables in the data tables with the association relation is a preset third corresponding relation.
In this embodiment, the preset first corresponding relationship is 1: n, the preset second corresponding relation is N: 1, the preset third corresponding relation is 1: 1, wherein N is a natural number, 1: n represents that the mapping relation between the data tables is 1 to more, N: 1 denotes that the mapping relationship between data tables is a plurality of pairs 1, 1: 1 indicates that the mapping relationship between the data tables is 1 to 1.
In this embodiment, the 1-to-many (1: N) is, for example: for the entities in the data table A, at least N entities in the data table B are related; and each entity in data table B has a relationship with at most one entity in data table a.
In the present embodiment, the plurality of pairs 1 (N: 1) are, for example: the entities in the data table B are related to at least N entities in the data table A; and each entity in data table a has a relationship with at most one entity in data table B.
In this embodiment, the 1 to 1 (1: 1) includes, for example: for data table a and data table B, each entity in data table a has a relationship with at most one entity in data table B; conversely, each entity in the data table B has a relationship with at most one entity in the data table a.
In this embodiment, the Entity (Entity) is a collection of objects that exist objectively and can be distinguished from each other, and an individual of each type of data object is referred to as an Entity.
In the embodiment, the main table, the sub-tables and the corresponding relation between the main table and the sub-tables are obtained by analyzing the database joint check statement, the data relation does not need to be reversely pushed from a page through foreign keys or developers, and the acquisition efficiency of the data table relation is improved.
S14, according to the corresponding relation between the main table and the sub table, a first entity relation graph is created.
In this embodiment, the first Entity Relationship Diagram is an ER Diagram (Entity Relationship Diagram) and is composed of entities, attributes, mapping relationships, and links. Wherein the entity is things which are distinguished from each other, and the entity can be a specific ward, a patient, a doctor or a department and is represented by a rectangular frame; one entity can be represented by a plurality of attributes which are represented by ellipses, for example, the attributes corresponding to a ward are a ward number and a bed number; the attributes corresponding to the patient are medical record number, name and gender; wherein the mapping reflects associations within or between entities, represented by diamonds, for example, associations between patients and physicians through diagnosis.
In an optional embodiment, the creating a first entity relationship diagram according to the correspondence between the main table and the sub table includes:
extracting field information with preset rules from the corresponding relation as an entity;
extracting the characteristics corresponding to the entities from the corresponding relations as attributes;
extracting the mapping relation and the mapping cardinality between the entities from the corresponding relation;
and generating a first entity relation graph by using a preset entity relation graph generation tool according to the entity, the attribute, the mapping relation and the mapping base number.
In this embodiment, the preset drawing tool may be DbSchema, PowerDesigner, ERStudio, or the like.
In this embodiment, after the first entity relationship diagram is generated, if attribute conflict, naming conflict, structural conflict, and relationship between redundant data and redundant entities occur in each entity relationship diagram, the first entity relationship diagram needs to be modified or reconstructed, and in addition, the relationship between the redundant data and redundant entities can be eliminated by using a normalization theory.
In this embodiment, according to the corresponding relationship between the main table and the sub-table and the field information, a first entity relationship diagram is generated by using a preset drawing tool, so that developers can visually see the association relationship between the data tables in the target code library, and the efficiency of querying the relationship between the data tables is improved.
S15, the first entity relation graph is checked to obtain a second entity relation graph.
In this embodiment, in order to further ensure the accuracy of the corresponding relationship between the data tables in the first entity relationship diagram, the first entity relationship diagram is checked.
In an optional embodiment, the checking the first entity relationship diagram to obtain the second entity relationship diagram includes:
randomly extracting a code to be checked from the target code library;
scanning the code to be checked to obtain a joint check statement of the database to be checked;
analyzing the joint check sentences of the database to be checked to obtain a main table to be checked and a sub table to be checked, and the corresponding relation between the main table to be checked and the sub table to be checked;
judging whether the main table to be verified and the sub table to be verified are consistent with the main table and the sub table corresponding to the first entity relation graph;
when the main table to be verified and the sub table to be verified are consistent with the main table and the sub table corresponding to the first entity relational graph, determining the first entity relational graph as a second entity relational graph;
and when the main table to be verified and the sub table to be verified are not consistent with the main table and the sub table corresponding to the first entity relation graph, verifying the first entity relation graph according to a preset verification rule to obtain a verification result, and updating the first entity relation graph based on the verification result to obtain a second entity relation graph.
In the embodiment, the codes to be checked are randomly extracted from the target code base, and the first entity relationship diagram is checked, so that the accuracy of the association relationship among the tables in the second entity relationship diagram is ensured, and the data table relationship query efficiency and accuracy are improved.
Further, after obtaining the second entity relationship diagram, the method further includes: and mapping the second entity relation graph into a relation table according to a preset mapping rule. The relationship table contains rows (not repeatable), columns (attributes), primary keys, foreign keys; the foreign key represents the correlation between the two relational tables. The relational table can display the incidence relation among the data tables more visually and in detail, developers can visually see the incidence relation among the data tables in the target code library, and further the efficiency and the accuracy of querying the relation of the data tables are improved.
And S16, performing table relation query in the second entity relation graph according to the table relation query condition to obtain a query result.
In this embodiment, based on the table relationship query condition, the relationship between the data tables is directly extracted from the second entity relationship diagram, and the data relationship does not need to be reversely deduced from a page by a foreign key or a developer, so that the data table relationship query efficiency is improved.
In summary, in the data query method based on artificial intelligence according to this embodiment, the database joint-check statements are obtained by scanning the database statements joint-checked in the target code library, and the database joint-check statements are analyzed to obtain the main table, the sub-tables, and the corresponding relationship between the main table and the sub-tables, so that it is not necessary to push back the data relationship from a page by using a foreign key or a developer, and the efficiency of obtaining the data table relationship is improved. And creating a first entity relationship diagram according to the corresponding relationship between the main table and the sub-table, so that a developer can visually see the association relationship between the data tables in the target code library, and meanwhile, the first entity relationship diagram is verified to obtain a second entity relationship diagram, thereby ensuring the accuracy of the association relationship between the tables in the second entity relationship diagram and further improving the query efficiency and accuracy of the data table relationship.
Example two
Fig. 2 is a structural diagram of an artificial intelligence based data query apparatus according to a second embodiment of the present invention.
In some embodiments, the artificial intelligence based data query device 20 may include a plurality of functional modules comprised of program code segments. The program code of the various program segments of the artificial intelligence based data query apparatus 20 may be stored in a memory of the electronic device and executed by the at least one processor to perform the functions of the artificial intelligence based data query (described in detail with reference to fig. 1).
In this embodiment, the artificial intelligence based data query device 20 may be divided into a plurality of functional modules according to the functions performed by the device. The functional module may include: a parsing and acquisition module 201, a scanning module 202, an analysis module 203, a creation module 204, a verification module 205, a mapping module 206, and a query module 207. The module referred to herein is a series of computer readable instruction segments stored in a memory that can be executed by at least one processor and that can perform a fixed function. In the present embodiment, the functions of the modules will be described in detail in the following embodiments.
The parsing and obtaining module 201 is configured to parse the received data table relationship query request to obtain a target code base and a table relationship query condition.
In the embodiment, in the field of digital medical technology, for a medical research and development project, in order to quickly acquire project content, a client sends a data relationship query request to a server, the server acquires the project content according to the association between data tables in a target code library of a target project corresponding to the data relationship query request by analyzing the association between the data tables, that is, obtains the logical structure relationship of the project content, and can assist in later maintenance, increase, decrease and reconstruction of the project content according to the logical relationship of the project content, so as to shorten the project development period.
In this embodiment, the table relationship query condition may include: the time range of the query, the content of the query, the item information of the query, etc.
In an optional embodiment, the parsing and obtaining module 201 parses the received data table relationship query request, and obtaining the target code base and the table relationship query condition includes:
analyzing the data table relation query request to obtain message information of the data table relation query request;
acquiring the name of a target code base and a table relation query condition from the message information;
determining an interface of the target code library according to the name of the target code library;
and calling an interface of the target code library to acquire the target code library.
In the embodiment, the name of each target code library corresponds to one target code library interface, the interface of the target code library is determined by analyzing a data table relation query request, the interface of the target code library is called to obtain the target code library, the obtaining efficiency of the target code library is improved, meanwhile, the problem that the target code library is obtained wrongly due to the fact that one interface corresponds to a plurality of target code libraries is avoided, and the accuracy of the obtained target code library is improved.
And the scanning module 202 is configured to scan the database statements co-checked in the target code base to obtain the database co-check statements.
In this embodiment, the database joint query statement refers to a database statement of an association relationship existing in the target code library, and the database joint query statement may be obtained by analyzing the database statement.
In an optional embodiment, the scanning module 202 scans the database statements co-searched in the target code library, and obtaining the database co-searched statements includes:
driving a preset tool to initialize connection resources by using a connection pool of the target code library;
extracting database statements in the target code base into an XML configuration file based on the initialized connection resources;
and configuring the database statement in an XML mode, and mapping the attribute and the field of the entity of the configured database statement and the data table to obtain the database joint check statement.
In this embodiment, the preset tool may be Mybatis, where the Mybatis is a java-based persistent layer framework, and the jdbc is encapsulated inside the framework, and the developer only needs to focus on the database statement itself.
In this embodiment, the preset configuration mode may be an XML or annotation mode, the Mybatis configures various database statements to be executed in an XML or annotation mode, and maps attributes and fields of an entity and a table of the configured database statement to generate a database joint check statement, where the database joint check statement may include one or more statements.
In other optional embodiments, after the database joint query statement is obtained, the association relationship between the database joint query statement and the corresponding data table is recorded.
In the embodiment, the database joint-check sentences are obtained by adopting Mybatis, and since the jdbc is encapsulated by the Mybatis, the access details of the bottom layer of the jdbcapi are shielded, the database can be durably operated without making a cross with the jdbcapi, and the efficiency of obtaining the database joint-check sentences is improved.
And the analysis module 203 is configured to analyze the database joint check statements to obtain a main table and a sub table, and a corresponding relationship between the main table and the sub table.
In this embodiment, the corresponding relationship includes all the associated field information of the data table and the association relationship between the fields.
In this embodiment, by analyzing the database joint-check statement, the association relationship between the data tables in the target code library can be obtained, and when the association relationship exists between the data tables and other data tables, the main table and the sub table in the data table having the association relationship and the corresponding relationship between the main table and the sub table are determined; when no association exists between the data table and other data tables, the data table without the association is used as a basic data table and is not processed, and meanwhile, the basic data table does not relate to project data, and example data, table structure comments and the like are mainly stored.
In an optional embodiment, the analyzing module 203 analyzes the database joint query statement to obtain a main table and a sub table, and the correspondence between the main table and the sub table includes:
analyzing the database joint-check sentences, and screening out the data tables with the association relation;
identifying a connection mode of a data table with an incidence relation;
when the connection mode of the data tables with the association relation is left connection, determining that a left table in the data tables with the association relation is a main table, a right table in the data tables with the association relation is a sub table, and the corresponding relation between the main table and the sub table is a preset first corresponding relation;
and when the connection mode of the data tables with the association relation is right connection, determining that the right table in the data tables with the association relation is a main table, the left table is a sub-table, and the corresponding relation between the main table and the sub-table is a preset second corresponding relation.
In other optional embodiments, when the connection manner of the data tables having the association relationship is internal connection, it is determined that the correspondence between two tables in the data tables having the association relationship is a preset third correspondence.
In this embodiment, the preset first corresponding relationship is 1: n, the preset second corresponding relation is N: 1, the preset third corresponding relation is 1: 1, wherein N is a natural number, 1: n represents that the mapping relation between the data tables is 1 to more, N: 1 denotes that the mapping relationship between data tables is a plurality of pairs 1, 1: 1 indicates that the mapping relationship between the data tables is 1 to 1.
In this embodiment, the 1-to-many (1: N) is, for example: for the entities in the data table A, at least N entities in the data table B are related; and each entity in data table B has a relationship with at most one entity in data table a.
In the present embodiment, the plurality of pairs 1 (N: 1) are, for example: the entities in the data table B are related to at least N entities in the data table A; and each entity in data table a has a relationship with at most one entity in data table B.
In this embodiment, the 1 to 1 (1: 1) includes, for example: for data table A and data table B, each entity in the data table A has a relationship with at most one entity in the data table B; conversely, each entity in the data table B has a relationship with at most one entity in the data table a.
In this embodiment, the Entity (Entity) is a collection of objects that exist objectively and can be distinguished from each other, and an individual of each type of data object is referred to as an Entity.
In the embodiment, the main table, the sub-tables and the corresponding relation between the main table and the sub-tables are obtained by analyzing the database joint check statement, the data relation does not need to be reversely pushed from a page through foreign keys or developers, and the acquisition efficiency of the data table relation is improved.
A creating module 204, configured to create a first entity relationship diagram according to the corresponding relationship between the main table and the sub table.
In this embodiment, the first Entity Relationship Diagram is an ER Diagram (Entity Relationship Diagram) and is composed of entities, attributes, mapping relationships, and links. Wherein the entity is things which are distinguished from each other, and the entity can be a specific ward, a patient, a doctor or a department and is represented by a rectangular frame; one entity can be represented by a plurality of attributes which are represented by ellipses, for example, the attributes corresponding to a ward are a ward number and a bed number; the attributes corresponding to the patient are medical record number, name and gender; wherein the mapping reflects associations within or between entities, represented by diamonds, for example, associations between patients and physicians through diagnosis.
In an optional embodiment, the creating module 204 creates the first entity relationship diagram according to the correspondence between the main table and the sub table, including:
extracting field information with preset rules from the corresponding relation as an entity;
extracting the characteristics corresponding to the entities from the corresponding relations as attributes;
extracting the mapping relation and the mapping cardinality between the entities from the corresponding relation;
and generating a first entity relation graph by using a preset entity relation graph generation tool according to the entity, the attribute, the mapping relation and the mapping base number.
In this embodiment, the preset drawing tool may be DbSchema, PowerDesigner, ERStudio, or the like.
In this embodiment, after the first entity relationship diagram is generated, if attribute conflict, naming conflict, structure conflict, and relation between redundant data and redundant entities occurs in each entity relationship diagram, the first entity relationship diagram needs to be modified or reconstructed, and in addition, the relation between the redundant data and redundant entities can be eliminated by using a normalization theory.
In this embodiment, according to the corresponding relationship between the main table and the sub-table and the field information, a first entity relationship diagram is generated by using a preset drawing tool, so that developers can visually see the association relationship between the data tables in the target code library, and the efficiency of querying the relationship between the data tables is improved.
The checking module 205 is configured to check the first entity relationship diagram to obtain a second entity relationship diagram.
In this embodiment, in order to further ensure the accuracy of the corresponding relationship between the data tables in the first entity relationship diagram, the first entity relationship diagram is verified.
In an optional embodiment, the checking module 205 checks the first entity relationship diagram to obtain a second entity relationship diagram, including:
randomly extracting a code to be checked from the target code library;
scanning the code to be checked to obtain a joint check statement of the database to be checked;
analyzing the joint check sentences of the database to be checked to obtain a main table to be checked and a sub table to be checked and the corresponding relation between the main table to be checked and the sub table to be checked;
judging whether the main table to be verified and the sub table to be verified are consistent with the main table and the sub table corresponding to the first entity relation graph;
when the main table to be checked and the sub table to be checked are consistent with the main table and the sub table corresponding to the first entity relationship diagram, determining the first entity relationship diagram as a second entity relationship diagram;
and when the main table to be verified and the sub table to be verified are not consistent with the main table and the sub table corresponding to the first entity relation graph, verifying the first entity relation graph according to a preset verification rule to obtain a verification result, and updating the first entity relation graph based on the verification result to obtain a second entity relation graph.
In the embodiment, codes to be checked are randomly extracted from the target code base, and the first entity relationship diagram is checked, so that the accuracy of the association relationship among the tables in the second entity relationship diagram is ensured, and the data table relationship query efficiency and accuracy are improved.
Further, after obtaining the second entity relationship diagram, the mapping module 206 is configured to map the second entity relationship diagram into the relationship table according to a preset mapping rule. The relationship table contains rows (not repeatable), columns (attributes), primary keys, foreign keys; the foreign key represents the correlation between two relation tables. The relational table can display the incidence relation among the data tables more visually and in detail, developers can visually see the incidence relation among the data tables in the target code library, and further the efficiency and the accuracy of querying the relation of the data tables are improved.
And the query module 207 is configured to perform table relationship query in the second entity relationship diagram according to the table relationship query condition to obtain a query result.
In this embodiment, based on the table relationship query condition, the relationship between the data tables is directly extracted from the second entity relationship diagram, and it is not necessary to perform reverse reasoning on the data relationship from a page by a foreign key or a developer, so that the data table relationship query efficiency is improved.
In summary, the data query device based on artificial intelligence according to this embodiment obtains the database joint-check statements by scanning the database statements joint-check in the target code library, and analyzes the database joint-check statements to obtain the main table, the sub-tables, and the corresponding relationship between the main table and the sub-tables, and does not need to push back the data relationship from a page by a foreign key or a developer, thereby improving the efficiency of obtaining the data table relationship. And creating a first entity relationship diagram according to the corresponding relationship between the main table and the sub table, wherein developers can visually see the association relationship between the data tables in the target code library, and meanwhile, the first entity relationship diagram is verified to obtain a second entity relationship diagram, so that the accuracy of the association relationship between the data tables in the second entity relationship diagram is ensured, and the query efficiency and the accuracy of the data table relationship are improved.
EXAMPLE III
Fig. 3 is a schematic structural diagram of an electronic device according to a third embodiment of the present invention. In the preferred embodiment of the present invention, the electronic device 3 comprises a memory 31, at least one processor 32, at least one communication bus 33 and a transceiver 34.
It will be appreciated by those skilled in the art that the configuration of the electronic device shown in fig. 3 does not constitute a limitation of the embodiment of the present invention, and may be a bus-type configuration or a star-type configuration, and the electronic device 3 may include more or less hardware or software than those shown in the figures, or different component arrangements.
In some embodiments, the electronic device 3 is an electronic device capable of automatically performing numerical calculation and/or information processing according to instructions set or stored in advance, and the hardware thereof includes but is not limited to a microprocessor, an application specific integrated circuit, a programmable gate array, a digital processor, an embedded device, and the like. The electronic device 3 may also include a client device, which includes, but is not limited to, any electronic product that can interact with a client through a keyboard, a mouse, a remote controller, a touch pad, or a voice control device, for example, a personal computer, a tablet computer, a smart phone, a digital camera, and the like.
It should be noted that the electronic device 3 is only an example, and other existing or future electronic products, such as may be adapted to the present invention, should also be included in the scope of the present invention, and is included by reference.
In some embodiments, the memory 31 is used for storing program codes and various data, such as the artificial intelligence based data query device 20 installed in the electronic device 3, and realizes high-speed and automatic access to programs or data during the operation of the electronic device 3. The Memory 31 includes a Read-Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Programmable Read-Only Memory (EPROM), a One-time Programmable Read-Only Memory (OTPROM), an electronically Erasable rewritable Read-Only Memory (Electrically-Erasable Programmable Read-Only Memory (EEPROM)), an optical Read-Only disk (CD-ROM) or other optical disk Memory, a magnetic disk Memory, a tape Memory, or any other medium readable by a computer capable of carrying or storing data.
In some embodiments, the at least one processor 32 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same or different functions, including one or more Central Processing Units (CPUs), microprocessors, digital Processing chips, graphics processors, and combinations of various control chips. The at least one processor 32 is a Control Unit (Control Unit) of the electronic device 3, connects various components of the whole electronic device 3 by using various interfaces and lines, and executes various functions of the electronic device 3 and processes data by running or executing programs or modules stored in the memory 31 and calling data stored in the memory 31.
In some embodiments, the at least one communication bus 33 is arranged to enable connection communication between the memory 31 and the at least one processor 32 or the like.
Although not shown, the electronic device 3 may further include a power supply (such as a battery) for supplying power to each component, and optionally, the power supply may be logically connected to the at least one processor 32 through a power management device, so as to implement functions of managing charging, discharging, and power consumption through the power management device. The power supply may also include any component of one or more dc or ac power sources, recharging devices, power failure detection circuitry, power converters or inverters, power status indicators, and the like. The electronic device 3 may further include various sensors, a bluetooth module, a Wi-Fi module, and the like, which are not described herein again.
It is to be understood that the described embodiments are for purposes of illustration only and that the scope of the appended claims is not limited to such structures.
The integrated unit implemented in the form of a software functional module may be stored in a computer-readable storage medium. The software functional module is stored in a storage medium and includes several instructions to enable a computer device (which may be a personal computer, an electronic device, or a network device) or a processor (processor) to execute parts of the methods according to the embodiments of the present invention.
In a further embodiment, in conjunction with fig. 2, the at least one processor 32 may execute operating means of the electronic device 3 and installed various types of applications (such as the artificial intelligence based data query device 20), program code, and the like, such as the various modules described above.
The memory 31 has program code stored therein, and the at least one processor 32 can call the program code stored in the memory 31 to perform related functions. For example, the modules illustrated in fig. 2 are program code stored in the memory 31 and executed by the at least one processor 32, so as to implement the functions of the modules for the purpose of artificial intelligence-based data query.
Illustratively, the program code may be partitioned into one or more modules/units that are stored in the memory 31 and executed by the processor 32 to accomplish the present application. The one or more modules/units may be a series of computer readable instruction segments capable of performing certain functions, which are used for describing the execution process of the program code in the electronic device 3. For example, the program code may be partitioned into a parsing and acquisition module 201, a scanning module 202, an analysis module 203, a creation module 204, a verification module 205, a mapping module 206, and a query module 207.
In one embodiment of the present invention, the memory 31 stores a plurality of computer-readable instructions that are executed by the at least one processor 32 to implement the functionality of artificial intelligence based data queries.
Specifically, the at least one processor 32 may refer to the description of the relevant steps in the embodiment corresponding to fig. 1, and details are not repeated here.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one position, or may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, functional modules in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional module.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned. Furthermore, it is obvious that the word "comprising" does not exclude other elements or that the singular does not exclude the plural. A plurality of units or means recited in the present invention may also be implemented by one unit or means through software or hardware. The terms first, second, etc. are used to denote names, but not any particular order.
Finally, it should be noted that the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (10)
1. A data query method based on artificial intelligence is characterized by comprising the following steps:
analyzing the received data table relation query request to obtain a target code library and a table relation query condition;
scanning the database sentences jointly searched in the target code base to obtain database joint search sentences;
analyzing the database joint check statement to obtain a main table, a sub table and a corresponding relation between the main table and the sub table;
creating a first entity relation graph according to the corresponding relation between the main table and the sub table;
checking the first entity relation graph to obtain a second entity relation graph;
and performing table relation query in the second entity relation graph according to the table relation query condition to obtain a query result.
2. The artificial intelligence based data query method of claim 1, wherein the scanning the database statements co-searched in the target code base to obtain the database co-searched statements comprises:
driving a preset tool to initialize connection resources by using a connection pool of the target code library;
extracting database statements in the target code base into an XML configuration file based on the initialized connection resources;
and configuring the database statement in an XML mode, and mapping the attribute and the field of the entity of the configured database statement and the data table to obtain the database joint check statement.
3. The artificial intelligence based data query method of claim 1, wherein analyzing the database joint query statement to obtain a main table and a sub table, and the correspondence between the main table and the sub table comprises:
analyzing the database joint-check sentences, and screening out the data tables with the association relation;
identifying a connection mode of a data table with an incidence relation;
when the connection mode of the data tables with the association relation is left connection, determining that a left table in the data tables with the association relation is a main table, a right table in the data tables with the association relation is a sub table, and the corresponding relation between the main table and the sub table is a preset first corresponding relation;
and when the connection mode of the data tables with the association relation is right connection, determining that the right table in the data tables with the association relation is a main table, the left table is a sub-table, and the corresponding relation between the main table and the sub-table is a preset second corresponding relation.
4. The artificial intelligence based data query method of claim 3, wherein the method further comprises:
and when the connection mode of the data tables with the association relation is internal connection, determining that the corresponding relation between two tables in the data tables with the association relation is a preset third corresponding relation.
5. The artificial intelligence based data query method of claim 1, wherein said creating a first entity relationship graph from correspondences between the main table and sub-tables comprises:
extracting field information with preset rules from the corresponding relation as an entity;
extracting the characteristics corresponding to the entities from the corresponding relations as attributes;
extracting the mapping relation and the mapping cardinality between the entities from the corresponding relation;
and generating a first entity relation graph by using a preset entity relation graph generation tool according to the entity, the attribute, the mapping relation and the mapping base number.
6. The artificial intelligence based data query method of claim 5, wherein said verifying said first entity relationship graph to obtain a second entity relationship graph comprises:
randomly extracting a code to be checked from the target code library;
scanning the code to be checked to obtain a joint check statement of the database to be checked;
analyzing the joint check sentences of the database to be checked to obtain a main table to be checked and a sub table to be checked and the corresponding relation between the main table to be checked and the sub table to be checked;
judging whether the main table to be verified and the sub table to be verified are consistent with the main table and the sub table corresponding to the first entity relation graph;
when the main table to be checked and the sub table to be checked are consistent with the main table and the sub table corresponding to the first entity relationship diagram, determining the first entity relationship diagram as a second entity relationship diagram;
and when the main table to be verified and the sub table to be verified are not consistent with the main table and the sub table corresponding to the first entity relation graph, verifying the first entity relation graph according to a preset verification rule to obtain a verification result, and updating the first entity relation graph based on the verification result to obtain a second entity relation graph.
7. The artificial intelligence based data query method of claim 6, wherein after said checking the first entity relationship graph to obtain a second entity relationship graph, the method further comprises:
and mapping the second entity relationship graph into a relationship table according to a preset mapping rule.
8. An artificial intelligence based data query apparatus, the apparatus comprising:
the analysis and acquisition module is used for analyzing the received data table relation query request to acquire a target code base and a table relation query condition;
the scanning module is used for scanning the database sentences jointly searched in the target code base to obtain database joint search sentences;
the analysis module is used for analyzing the database joint check sentences to obtain a main table, a sub table and a corresponding relation between the main table and the sub table;
the creating module is used for creating a first entity relationship graph according to the corresponding relationship between the main table and the sub table;
the verification module is used for verifying the first entity relationship diagram to obtain a second entity relationship diagram;
and the query module is used for performing table relation query in the second entity relation graph according to the table relation query condition to obtain a query result.
9. An electronic device, characterized in that the electronic device comprises a processor and a memory, the processor being configured to implement the artificial intelligence based data query method according to any one of claims 1 to 7 when executing a computer program stored in the memory.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the artificial intelligence based data query method according to any one of claims 1 to 7.
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