Data query method and device based on table association

Abstract

The present application provides a data query method and device based on table association, which relates to the field of data processing technology and solves the technical problem of low data query efficiency in multiple data tables. The method includes: extracting a first data table with query possibility from multiple data tables stored in a specified database through a neural network model based on the query semantics of the query content, and determining the query column in the first data table and the screening conditions for the first data table according to the query semantics; according to the table name of the first data table, in the data table knowledge graph, all related paths formed by the direction of each first data table pointing to the corresponding main table relationship are retrieved by pointing to the direction of the corresponding main table relationship; from all related paths, determine the target related path containing the least number of path nodes, and the target path direction corresponding to the target first data table in the target related path.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a data query method and device based on table association. Background Art

[0002] At present, for a data warehouse composed of multiple data tables, the query technology and products need to be integrated, that is, the product can perform specific data queries in these data tables according to the query request input by the user. In the process of performing data queries according to the query request input by the user, the product can clarify the query content of the data, but cannot find the queried data. In this case, it is necessary to obtain the data table to be queried, but the product cannot clarify which data table among the above multiple data tables to be queried, making the data query efficiency in multiple data tables low. Summary of the invention

[0003] The object of the present invention is to provide a data query method and device based on table association to solve the technical problem of low efficiency of data query in multiple data tables.

[0004] In a first aspect, an embodiment of the present application provides a data query method based on table association, the method comprising:

[0005] Acquire query content input by a user and identify query semantics of the query content;

[0006] Based on the query semantics, a first data table with query possibility is extracted from multiple data tables stored in a designated database through a neural network model, and a query column in the first data table and a screening condition for the first data table are determined according to the query semantics; wherein the query column includes fields; the designated database corresponds to a data table knowledge graph, and the data table knowledge graph is based on the multiple data tables stored in the designated database, through recommended query conditions, commonly used query conditions, and preset data table association contents, to determine the table association relationship between the multiple data tables, and a data table knowledge graph is formed through graph construction according to the table association relationship; the recommended query condition is a query condition determined by a query method in a report product; the commonly used query condition is a query condition determined by a query method collected and analyzed by a program in an analysis product; each of the data tables in the data table knowledge graph corresponds to a direction of its own data table pointing to a corresponding main table relationship;

[0007] According to the table name of the first data table, all relevant paths formed by the direction pointing to the corresponding main table relationship of each first data table are retrieved in the data table knowledge graph through the direction pointing to the corresponding main table relationship; the relevant path includes path nodes respectively formed by at least two of the first data tables, and the path direction corresponding to the direction of each first data table pointing to the corresponding main table relationship in the relevant path;

[0008] Determine the target related path containing the least number of the path nodes from all the related paths, and the target path direction corresponding to the target first data table in the target related path, and determine the associated fields between the multiple target first data tables according to the target fields contained in the target query column in the target first data table and the target screening condition for the target first data table;

[0009] According to the target direction of the target first data table corresponding to the target path direction pointing to the corresponding main table relationship, the target first data table that is pointed to as the corresponding main table relationship the most times from the multiple target first data tables is determined as the main table to be queried, the target first data tables in the multiple target first data tables that are directly and indirectly connected to the main table to be queried are used as the sub-tables to be queried corresponding to the main table to be queried, and the table association field relationship between the main table to be queried and at least one of the sub-tables to be queried is determined according to the association field;

[0010] According to the query semantics, query results corresponding to the query content are queried through the table association field relationship, the main table to be queried and the sub-table to be queried, with the main table to be queried as the center.

[0011] In a possible implementation, the step of obtaining the query content input by the user includes:

[0012] Acquire initial query content initially input by a user, as well as the user's position data and the user's work environment data, and identify initial query semantics of the initial query content;

[0013] Detecting the task requirements of the user according to the initial query semantics, the position data, and the work environment data;

[0014] Determine ambiguous initial query content and erroneous initial query content in the initial query content according to the task requirement, and exclude the ambiguous initial query content and the erroneous initial query content from the initial query content to obtain a target query content;

[0015] The target query content is determined as the formal query content formally input by the user.

[0016] In a possible implementation, after the step of determining the target query content as the formal query content formally input by the user, the method further includes:

[0017] At least one target data table is determined from the multiple data tables stored in the designated database according to the formal query content, as well as a query path corresponding to the formal query content in the at least one target data table; wherein the query path includes a query sequence relationship between the at least one target data table, a summary corresponding to each of the target data tables, and a query direction corresponding to each of the target data tables.

[0018] In a possible implementation, the at least one target data table includes a first text target data table, a second text target data table, and a third text target data table; and the step of determining, according to the formal query content, from the plurality of data tables stored in the designated database, a query path corresponding to the formal query content in the at least one target data table comprises:

[0019] Determine the formal query content from the plurality of data tables stored in the designated database according to the formal query content, obtain a first text query result through the first text target data table, obtain a second text query result through the second text target data table according to the first text query result, and obtain a third text query result through the third text target data table according to the second text query result;

[0020] Based on the first text query result, the second text query result, the third text query result, and the query order relationship between the first text target data table, the second text target data table and the third text target data table, the text query path corresponding to the formal query content in the multiple text target data tables is determined.

[0021] In a possible implementation, the at least one target data table includes a first digital target data table, a second digital target data table, and a third digital target data table; and the step of determining, according to the formal query content, from the plurality of data tables stored in the designated database, a query path corresponding to the formal query content in the at least one target data table comprises:

[0022] Determine the formal query content from the plurality of data tables stored in the designated database according to the formal query content, calculate the formal query content by using the first parameter value and the first parameter relationship in the first digital target data table, and obtain a first numerical query result, and calculate the second parameter value and the second parameter relationship in the second digital target data table according to the first numerical query result, and obtain a third numerical query result by using the third parameter value and the third parameter relationship in the third digital target data table according to the second numerical query result;

[0023] Based on the first numerical query result, the second numerical query result, the third numerical query result, and the query order sequence between the first digital target data table, the second digital target data table and the third digital target data table, the text query path corresponding to the formal query content in the multiple digital target data tables is determined.

[0024] In a possible implementation, the number of the query paths is multiple; after the step of determining at least one target data table from the multiple data tables stored in the designated database according to the formal query content, and the query path corresponding to the formal query content leading to the at least one target data table, the method further includes:

[0025] In response to a selection operation on a target query path among the multiple query paths, determining the target query path as an optimal query path among the multiple query paths, and searching for a query result corresponding to the formal query content through the optimal query path; and / or,

[0026] An optimal query path is selected from the multiple query paths through a neural network model, and a query result corresponding to the formal query content is queried through the optimal query path, wherein the neural network model is trained through query path samples with optimal path labels.

[0027] In a possible implementation, after the step of searching for the query result corresponding to the formal query content through the optimal query path, the method further includes:

[0028] Taking the query result corresponding to the formal query content queried through the optimal query path as the first query result, and respectively querying the second query result corresponding to the formal query content through at least one other query path except the optimal query path;

[0029] Comparing at least one of the second query results corresponding to the at least one other query path query with the first query result to obtain a comparison result;

[0030] The accuracy of the first query result is verified according to the comparison result to obtain an accuracy verification result for the first query result.

[0031] In a second aspect, a data query device based on table association is provided, comprising:

[0032] An acquisition module, used to acquire the query content input by the user and identify the query semantics of the query content;

[0033] An extraction module is used to extract a first data table with query possibility from multiple data tables stored in a designated database through a neural network model based on the query semantics, and determine the query column in the first data table and the screening conditions for the first data table according to the query semantics; wherein the query column includes fields; the designated database corresponds to a data table knowledge graph, and the data table knowledge graph is based on the multiple data tables stored in the designated database through recommended query conditions, common query conditions, and preset data table association contents to determine the table association relationship between the multiple data tables and build a data table knowledge graph based on the table association relationship; the recommended query condition is a query condition determined by a query method in a report product; the common query condition is a query condition determined by a query method collected and analyzed by a program in an analysis product; each of the data tables in the data table knowledge graph corresponds to a direction of its own data table pointing to a corresponding main table relationship;

[0034] A retrieval module, configured to retrieve, in the data table knowledge graph according to the table name of the first data table, all relevant paths formed by the direction pointing to the corresponding main table relationship of each first data table; the relevant path includes path nodes respectively formed by at least two of the first data tables, and the path direction corresponding to the direction of each first data table pointing to the corresponding main table relationship in the relevant path;

[0035] A first determination module is used to determine the target related path containing the least number of path nodes from all the related paths, and the target path direction corresponding to the target first data table in the target related path, and determine the associated fields between the multiple target first data tables according to the target fields contained in the target query column in the target first data table and the target screening condition for the target first data table;

[0036] A second determination module is used to determine a main table to be queried from a plurality of the target first data tables according to a target direction of a main table relationship corresponding to the target first data table corresponding to the target path direction, use target first data tables other than the main table to be queried in the plurality of the target first data tables as sub-tables to be queried corresponding to the main table to be queried, and determine a table association field relationship between the main table to be queried and at least one of the sub-tables to be queried according to the association field;

[0037] A query module is used to query the query result corresponding to the query content according to the query semantics through the table association field relationship, the main table to be queried and the sub-table to be queried and with the main table to be queried as the center.

[0038] In a third aspect, an embodiment of the present application provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program that can be executed on the processor, and when the processor executes the computer program, the method described in the first aspect is implemented.

[0039] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to execute the method described in the first aspect above.

[0040] The embodiments of the present application bring the following beneficial effects:

[0041] The embodiment of the present application provides a data query method and device based on table association, which can obtain the query content input by the user and identify the query semantics of the query content; based on the query semantics, a first data table with query possibility is extracted from multiple data tables stored in a specified database through a neural network model, and a query column in the first data table and a screening condition for the first data table are determined according to the query semantics; wherein the query column includes fields; the specified database corresponds to a data table knowledge graph, and the data table knowledge graph is based on the multiple data tables stored in the specified database through recommended query conditions, commonly used query conditions, and predicted Assume that the data table association content determines the table association relationship between the multiple data tables and forms a data table knowledge graph through the graph construction according to the table association relationship; the recommended query condition is the query condition determined by the query method in the report product; the commonly used query condition is the query condition determined by the query method of program collection and analysis in the analysis product; each of the data tables in the data table knowledge graph corresponds to the direction of its own data table pointing to the corresponding main table relationship; according to the table name of the first data table, all the directions of each first data table pointing to the corresponding main table relationship are retrieved in the data table knowledge graph through the direction pointing to the corresponding main table relationship. a related path; the related path includes path nodes respectively formed by at least two of the first data tables, and the path direction corresponding to the direction of each first data table pointing to the corresponding main table relationship in the related path; determining a target related path including the least number of the path nodes from all the related paths, and a target path direction corresponding to the target first data table in the target related path, and determining the associated fields between the multiple target first data tables according to the target fields included in the target query column in the target first data table and the target screening condition for the target first data table; ... A data table points to a target direction of a corresponding main table relationship, the target first data table that is pointed to as the corresponding main table relationship the most times from multiple target first data tables is determined as the main table to be queried, the target first data tables in the multiple target first data tables that are directly and indirectly connected to the main table to be queried are used as sub-tables to be queried corresponding to the main table to be queried, and the table association field relationship between the main table to be queried and at least one of the sub-tables to be queried is determined according to the association fields; according to the query semantics, the query result corresponding to the query content is queried through the table association field relationship, the main table to be queried and the sub-tables to be queried with the main table to be queried as the center.In this solution, the table association relationship between multiple data tables is determined by specifying multiple data tables stored in the database through the recommended query conditions determined by the query method in the report product, the common query conditions determined by the query method using program collection and analysis in the analysis product, and the preset data table association content, and the data table knowledge graph is constructed accordingly. According to the table name of the first data table with query possibility, all relevant paths formed by each first data table pointing to the corresponding main table relationship in the data table knowledge graph can be retrieved by pointing to the direction of the corresponding main table relationship, so that the determination of the data table to be queried is more accurate. Moreover, by determining the target related path containing the least number of path nodes from all the related paths, the number of data tables to be queried can be minimized, the query speed can be faster, and the data query efficiency can be improved. Furthermore, the associated fields between multiple target first data tables are determined through the target fields contained in the target query column in the target first data table and the target screening conditions for the target first data table, and then the query results corresponding to the query content of the secondary table to be queried and centered on the main table to be queried are used to further improve the data query efficiency, solving the technical problem of low data query efficiency in multiple data tables.

[0042] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, preferred embodiments are specifically cited below and described in detail with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the specific implementation methods of the present application or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0044] Figure 1 A flowchart of a data query method based on table association provided in an embodiment of the present application;

[0045] Figure 2 An example of a related path in a data table knowledge graph in a data query method based on table association provided in an embodiment of the present application;

[0046] Figure 3 A schematic diagram of the structure of a data query device based on table association provided in an embodiment of the present application;

[0047] Figure 4 A schematic structural diagram of an electronic device provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution of the present application will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application.

[0049] The terms "including" and "having" and any variations thereof mentioned in the embodiments of the present application are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device including a series of steps or units is not limited to the listed steps or units, but may optionally include other steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or devices.

[0050] At present, the query efficiency of data query in multiple data tables is low. Based on this, the embodiment of the present application provides a data query method and device based on table association, which can solve the technical problem of low data query efficiency in multiple data tables.

[0051] The embodiments of the present invention are further described below in conjunction with the accompanying drawings.

[0052] Figure 1 A flowchart of a data query method based on table association provided in an embodiment of the present application. Figure 1 As shown, the method includes:

[0053] Step S110, obtaining the query content input by the user and identifying the query semantics of the query content.

[0054] In an optional implementation, the process of obtaining the query content input by the user in this step may specifically include the following steps: first, obtaining the initial query content initially input by the user, the user's position data, and the user's work environment data, and identifying the initial query semantics of the initial query content; then, detecting the user's task requirements based on the initial query semantics, position data, and work environment data; determining ambiguous initial query content and erroneous initial query content in the initial query content based on the task requirements, and excluding the ambiguous initial query content and erroneous initial query content from the initial query content to obtain the target query content; and determining the target query content as the formal query content formally input by the user.

[0055] For example, based on the semantics of the user's speech, the user's position, and the user's work environment, AI can infer the specific problem the user wants to solve or infer what the user wants to do that causes the query to be made, thereby eliminating the ambiguous aspects of the question and the wrong questions asked. For example, based on the user's position and the user's current work environment, it is inferred that the user's task is to inquire about the production this month. However, the user actually asked the AI ​​system about the material purchase situation this month, indicating that the question asked by the user is an incorrect question. In this way, the accuracy of the query content can be improved, avoiding the reduction of the accuracy of the query content due to problems with the user's input operation.

[0056] Step S120, based on the query semantics, extract a first data table with query possibility from multiple data tables stored in the specified database through a neural network model, and determine the query columns in the first data table and the screening conditions for the first data table according to the query semantics.

[0057] As a possible implementation method, semantic recognition combined with data table range retrieval and large model can be used to extract data tables, query columns and screening conditions with query possibilities. For example, the input parameter is a human sentence, and the output parameter is a first data table with query possibilities, a query column in the first data table and a screening condition for the first data table. The query column contains fields.

[0058] In actual applications, the designated database corresponds to a data table knowledge graph. The data table knowledge graph is based on the multiple data tables stored in the designated database, and determines the table association relationship between multiple data tables through recommended query conditions, commonly used query conditions, and preset data table association contents, and builds a data table knowledge graph based on the table association relationship through a graph; the recommended query conditions are query conditions determined by the query method in report products; the commonly used query conditions are query conditions determined by the query method of program collection and analysis in analysis products; each data table in the data table knowledge graph corresponds to its own data table pointing to the direction of the corresponding main table relationship.

[0059] For example, the association rule is to use the data table knowledge graph corresponding to the database, which is formed by building the relationship between tables. The relationship between tables can be created by the relationship between the data table association content and the recommended queries used in report products and the common queries collected and analyzed by the program used by analysis products. In the data table knowledge graph, the direction of the sub-table pointing to the main table in the relationship between the sub-table and the main table can be clearly defined.

[0060] Step S130, according to the table name of the first data table, in the data table knowledge graph, all relevant paths formed by the direction of each first data table pointing to the corresponding main table relationship are retrieved by pointing to the direction of the corresponding main table relationship.

[0061] For example, Figure 2 As shown, the relevant path includes path nodes respectively formed by at least two first data tables, such as A table node, B table node, C table node, D table node, E table node, G table node, K table node, X table node, D table node, etc., and the direction of each first data table pointing to the corresponding main table relationship in the relevant path corresponds to the path direction, such as Figure 2 The direction of the arrow.

[0062] As a possible implementation, all relevant paths formed by the direction from each first data table to the corresponding main table relationship are retrieved in the data table knowledge graph according to the table name of the first data table, which can be implemented using a graph database (such as Neo4j). The following is a detailed step and example showing how to query these paths through Neo4j:

[0063] First, install and configure Neo4j. Make sure that the Neo4j database has been installed and configured, and can be operated through the Cypher query language. Then build a data table knowledge graph. Assume that the data table knowledge graph has been built, which contains nodes (representing data tables) and edges (representing relationships). For example, nodes: (:Table {name: 'Table1'}), (:Table {name: 'Table2'}); edges: (Table1)-[:RELATIONSHIP]->(Table2). Then query the path. Assume that you want to query the path formed by the direction of all main table relationships based on the table name of the first data table (such as Table1). You can use the Cypher query language to complete this task. Finally, use Python to interact with Neo4j. If you want to execute this query through a Python script, you can use the neo4j library. Through the above steps, you can retrieve all the relevant paths formed by the direction of each first data table pointing to the corresponding main table relationship in the data table knowledge graph based on the table name of the first data table. This process uses the Neo4j graph database and the Cypher query language, combined with Python scripts to achieve automated queries.

[0064] Step S140, determining the target related path containing the least number of path nodes from all related paths, and the target path direction corresponding to the target first data table in the target related path, and determining the associated fields between multiple target first data tables based on the target fields contained in the target query column in the target first data table, and the target screening conditions for the target first data table.

[0065] As for table association rules, among the multiple data tables in the above data table knowledge graph, the table with the most one-way pointing is identified as the main table, and all tables directly and indirectly connected to the main table are associated together.

[0066] Exemplarily, all relevant paths and path directions are retrieved in the data table knowledge graph using the table name of the target first data table obtained above, and all tables to be queried are generated by finding the shortest relevant path among all relevant paths.

[0067] For example, Figure 2 Among all the paths from the B table node to the E table node, the path formed by the B table node, the D table node, the A table node and the E table node is the shortest path formed by four table nodes, and the path formed by the E table node through the G table node, the X table node, the A table node, the G table node, the K table node and then to the B table node is the longest path formed by seven table nodes.

[0068] Then, in the shortest path formed by the B table node, the D table node, the A table node, and the E table node, find the main table and the relationship between the tables according to the path direction indicated by the arrow, and clarify the associated fields between the two tables according to the path information, such as Figure 2 The associated field of "name.id" in.

[0069] Step S150, according to the target direction of the target first data table corresponding to the target path direction pointing to the corresponding main table relationship, the target first data table that is pointed to as the corresponding main table relationship the most times among multiple target first data tables is determined as the main table to be queried, the target first data tables in the multiple target first data tables that are directly and indirectly connected to the main table to be queried are used as the sub-tables to be queried corresponding to the main table to be queried, and the table association field relationship between the main table to be queried and at least one sub-table to be queried is determined according to the association fields.

[0070] For example, Figure 2 As shown, in the shortest path formed by the B table node, the D table node, the A table node and the E table node, the table node with the most path directions pointed by arrows is the D table node. It can be seen that the D table (the target first data table) that is pointed to the most times as the corresponding main table relationship is determined as the main table to be queried.

[0071] As for the specific process of querying the path and determining the main table to be queried, illustratively, the first data table that is pointed to as the main table relationship with the most times is queried, and it is determined as the main table to be queried. Then, all the sub-tables directly and indirectly connected to the main table are found, and the table association field relationship between them is determined. For example, the number of times each first data table is pointed to as the main table relationship is first queried, and then all the sub-tables directly and indirectly connected to the main table to be queried and their paths are queried, and the table association field relationship between the main table and the sub-table is queried.

[0072] Through the above steps, the target first data table corresponding to the target path direction points to the target direction of the corresponding main table relationship, the main table to be queried can be determined from multiple target first data tables, and the secondary tables directly and indirectly connected to it can be found, and the table association field relationship between them can be determined. This process uses the Neo4j graph database and Cypher query language, combined with Python scripts to achieve automated query.

[0073] Step S160, querying the query results corresponding to the query content based on the query semantics through the table association field relationship, the main table to be queried and the sub-table to be queried and with the main table to be queried as the center.

[0074] In order to query the main table to be queried and the secondary table to be queried according to the query semantics through the table association field relationship, and query with the main table to be queried as the center, a complex Cypher query needs to be constructed. This query will start from the main table to be queried, connect to all related secondary tables through the association field relationship, and return the final query result. Assume that the main table to be queried (for example, Table1) and all secondary tables directly and indirectly connected to it (for example, Table2, Table3, etc.) have been determined, and the association field relationship between them is known (for example, Table1 is associated with Table2 through field1, Table2 is associated with Table3 through field2, etc.). First determine the main table to be queried, assuming it is Table1; then determine the secondary table and its associated field relationship, for example, Table1 is associated with Table2 through field1, and Table2 is associated with Table3 through field2; then construct a Cypher query, and use this information to construct a query to obtain the final query result.

[0075] In the embodiment of the present application, the table association relationship between multiple data tables is determined by specifying multiple data tables stored in the database through the recommended query conditions determined by the query method in the report product, the common query conditions determined by the query method using the program collection and analysis in the analysis product, and the preset data table association content, and the data table knowledge graph formed accordingly. According to the table name of the first data table with query possibility, all relevant paths formed by each first data table pointing to the corresponding main table relationship in the data table knowledge graph can be retrieved by pointing to the direction of the corresponding main table relationship, so that the determination of the data table to be queried is more accurate. Moreover, by determining the target related path containing the least number of path nodes from all the related paths, the number of data tables to be queried can be minimized, the query speed can be faster, and the data query efficiency can be improved. Furthermore, the associated fields between multiple target first data tables are determined through the target fields contained in the target query column in the target first data table and the target screening conditions for the target first data table, and then the query results corresponding to the query content of the query content of the sub-table to be queried and centered on the main table to be queried are used to further improve the data query efficiency, thereby solving the technical problem of low data query efficiency in multiple data tables.

[0076] The above steps are described in detail below.

[0077] In some embodiments, after the above process of determining the target query content as the formal query content formally input by the user, the method may further include the following steps:

[0078] At least one target data table is determined from multiple data tables stored in a designated database according to the formal query content, as well as a query path corresponding to the formal query content in the at least one target data table; wherein the query path includes a query sequence relationship between at least one target data table, a summary corresponding to each target data table, and a query direction corresponding to each target data table.

[0079] Exemplarily, the AI ​​system queries the database according to the AI ​​inference results described in step S110, obtains a query route map leading to each data table (such as querying table D first, then querying table A to obtain result a, then querying table B according to result a to obtain result b...), the summary corresponding to the data table, and the direction corresponding to the data table, and then determines the route. Then, the user inputs the parameters, and the AI ​​gives the results according to the chart (through the query table, various calculations of data related to the input parameters are performed, such as the user inputs parameter a, and the parameter a is converted through the D chart to obtain data b, and then the parameter b is converted through the A chart to obtain data c..., etc., such as when calculating the production profit of this month, it is necessary to divide each table into each stage and use the data tables corresponding to the nodes of different stages to query and calculate, and obtain the final query result of the production profit of this month), and finally give the result using all the above routes.

[0080] In the embodiment of the present application, by including the query order relationship between at least one target data table and the query path pointed to by the query corresponding to each target data table, the query order in multiple data tables can be accurately determined, making the query process more efficient.

[0081] As an example, the at least one target data table includes a first text target data table, a second text target data table, and a third text target data table; the process of determining the query path corresponding to the formal query content in the plurality of data tables stored in the designated database according to the formal query content in the at least one target data table may specifically include the following steps:

[0082] Determine the formal query content from a plurality of data tables stored in a designated database according to the formal query content, obtain a first text query result through a first text target data table, obtain a second text query result through a second text target data table according to the first text query result, and obtain a third text query result through a third text target data table according to the second text query result;

[0083] Based on the first text query result, the second text query result, the third text query result, and the query order relationship between the first text target data table, the second text target data table and the third text target data table, the text query path corresponding to the formal query content in the multiple text target data tables is determined.

[0084] In an embodiment of the present application, by including the query order relationship between text target data tables and the query path pointed to by each text target data table, the query order in multiple text target data tables can be accurately determined, making the text query process more efficient.

[0085] As another example, the at least one target data table includes a first digital target data table, a second digital target data table, and a third digital target data table; the process of determining the query path corresponding to the formal query content in the plurality of data tables stored in the designated database according to the formal query content in the at least one target data table may specifically include the following steps:

[0086] Determine the formal query content from a plurality of data tables stored in a designated database according to the formal query content, calculate through a first parameter value and a first parameter relationship in a first digital target data table, and obtain a first numerical query result, and calculate through a second parameter value and a second parameter relationship in a second digital target data table according to the first numerical query result, and obtain a second numerical query result, and calculate through a third parameter value and a third parameter relationship in a third digital target data table according to the second numerical query result, and obtain a third numerical query result;

[0087] Based on the first numerical query result, the second numerical query result, the third numerical query result, and the query order relationship between the first digital target data table, the second digital target data table and the third digital target data table, the text query path corresponding to the formal query content in the multiple digital target data tables is determined.

[0088] In an embodiment of the present application, by including the query order relationship between digital target data tables and the query path pointed to by the query corresponding to each digital target data table, the calculation order in multiple digital target data tables can be accurately determined. For example, only after the total revenue value is calculated through Table A which records the revenue status of each project, and the total cost value is calculated through Table C which records the cost status of each project, can the total profit value be calculated through Table B based on the total revenue value and the total cost value, thereby making the digital query process more efficient.

[0089] In some embodiments, there are multiple query paths; after the above process of determining at least one target data table from multiple data tables stored in a designated database according to the formal query content, and the query path corresponding to the formal query content leading to the at least one target data table, the method may further include the following steps:

[0090] In response to a selection operation on a target query path among the multiple query paths, determining the target query path as an optimal query path among the multiple query paths, and searching for a query result corresponding to the formal query content through the optimal query path; and / or,

[0091] The optimal query path is selected from multiple query paths through a neural network model, and the query result corresponding to the formal query content is queried through the optimal query path, wherein the neural network model is trained through query path samples with optimal path labels.

[0092] In an embodiment of the present application, a neural network model or a user can select the most efficient and lowest cost optimal query path from multiple routes (query route maps leading to each data table, routes leading to each data table node for querying and obtaining question query results), so that the query path used in the query process is the most preferred one, thereby further improving query efficiency.

[0093] In some embodiments, after the above process of searching for the query result corresponding to the formal query content through the optimal query path, the method may further include the following steps:

[0094] The query result corresponding to the formal query content is queried through the optimal query path as the first query result, and the second query result corresponding to the formal query content is queried through at least one other query path except the optimal query path;

[0095] Compare at least one second query result corresponding to at least one other query path query with the first query result to obtain a comparison result; verify the accuracy of the first query result according to the comparison result to obtain an accuracy verification result for the first query result.

[0096] As an optional implementation, the same question and result can be verified through different data table query paths. For example, a certain data can be queried through the path from table D to table A and then to table B, or through the path from table C to table F and then to table E. Then, calculations and verifications can be performed through different paths to further ensure the data accuracy of the query results.

[0097] Figure 3 A schematic diagram of the structure of a data query device based on table association is provided. Figure 3 As shown, the data query device 300 based on table association includes:

[0098] The acquisition module 301 is used to acquire the query content input by the user and identify the query semantics of the query content;

[0099] The extraction module 302 is used to extract a first data table with query possibility from multiple data tables stored in a designated database through a neural network model based on the query semantics, and determine the query column in the first data table and the screening conditions for the first data table according to the query semantics; wherein the query column includes fields; the designated database corresponds to a data table knowledge graph, and the data table knowledge graph is based on the multiple data tables stored in the designated database through recommended query conditions, common query conditions, and preset data table association contents to determine the table association relationship between the multiple data tables and build a data table knowledge graph according to the table association relationship through a graph; the recommended query condition is a query condition determined by a query method in a report product; the common query condition is a query condition determined by a query method collected and analyzed by a program in an analysis product; each of the data tables in the data table knowledge graph corresponds to a direction of its own data table pointing to a corresponding master table relationship;

[0100] A retrieval module 303 is used to retrieve all relevant paths formed by the direction of each first data table pointing to the corresponding main table relationship in the data table knowledge graph according to the table name of the first data table, through the direction pointing to the corresponding main table relationship; the relevant path includes path nodes formed by at least two of the first data tables respectively, and the path direction corresponding to the direction of each first data table pointing to the corresponding main table relationship in the relevant path;

[0101] A first determination module 304 is used to determine the target related path containing the least number of path nodes from all the related paths, and the target path direction corresponding to the target first data table in the target related path, and determine the associated fields between the multiple target first data tables according to the target fields contained in the target query column in the target first data table and the target screening condition for the target first data table;

[0102] The second determination module 305 is used to determine the main table to be queried from the multiple target first data tables according to the target direction of the target first data table corresponding to the target path direction pointing to the corresponding main table relationship, use the target first data tables other than the main table to be queried in the multiple target first data tables as the sub-tables to be queried corresponding to the main table to be queried, and determine the table association field relationship between the main table to be queried and at least one of the sub-tables to be queried according to the association field;

[0103] The query module 306 is used to query the query result corresponding to the query content through the table association field relationship, the main table to be queried and the sub-table to be queried according to the query semantics and with the main table to be queried as the center.

[0104] In some embodiments, the acquisition module is specifically used to:

[0105] Acquire initial query content initially input by a user, as well as the user's position data and the user's work environment data, and identify initial query semantics of the initial query content;

[0106] Detecting the task requirements of the user according to the initial query semantics, the position data, and the work environment data;

[0107] Determine ambiguous initial query content and erroneous initial query content in the initial query content according to the task requirement, and exclude the ambiguous initial query content and the erroneous initial query content from the initial query content to obtain a target query content;

[0108] The target query content is determined as the formal query content formally input by the user.

[0109] In some embodiments, the apparatus further comprises:

[0110] The third determination module is used to determine at least one target data table from the multiple data tables stored in the designated database according to the formal query content after the target query content is determined as the formal query content formally input by the user, and a query path corresponding to the formal query content in the at least one target data table; wherein the query path includes the query sequence relationship between the at least one target data table, the summary corresponding to each of the target data tables, and the query direction corresponding to each of the target data tables.

[0111] In some embodiments, the at least one target data table includes a first text target data table, a second text target data table, and a third text target data table; the third determination module is specifically used to:

[0112] Determine the formal query content from the plurality of data tables stored in the designated database according to the formal query content, obtain a first text query result through the first text target data table, obtain a second text query result through the second text target data table according to the first text query result, and obtain a third text query result through the third text target data table according to the second text query result;

[0113] Based on the first text query result, the second text query result, the third text query result, and the query order relationship between the first text target data table, the second text target data table and the third text target data table, the text query path corresponding to the formal query content in the multiple text target data tables is determined.

[0114] In some embodiments, the at least one target data table includes a first digital target data table, a second digital target data table, and a third digital target data table; the third determination module is specifically used to:

[0115] Determine the formal query content from the plurality of data tables stored in the designated database according to the formal query content, calculate the formal query content by using the first parameter value and the first parameter relationship in the first digital target data table, and obtain a first numerical query result, and calculate the second parameter value and the second parameter relationship in the second digital target data table according to the first numerical query result, and obtain a third numerical query result by using the third parameter value and the third parameter relationship in the third digital target data table according to the second numerical query result;

[0116] Based on the first numerical query result, the second numerical query result, the third numerical query result, and the query order sequence between the first digital target data table, the second digital target data table and the third digital target data table, the text query path corresponding to the formal query content in the multiple digital target data tables is determined.

[0117] In some embodiments, the number of the query paths is multiple; the device further includes:

[0118] A query module, used to determine at least one target data table from the multiple data tables stored in the designated database according to the formal query content, and after the formal query content leads to the corresponding query path in the at least one target data table, in response to a selection operation for a target query path in the multiple query paths, determine the target query path as an optimal query path among the multiple query paths, and query the query result corresponding to the formal query content through the optimal query path; and / or, select the optimal query path from the multiple query paths through a neural network model, and query the query result corresponding to the formal query content through the optimal query path, wherein the neural network model is trained by query path samples with optimal path labels.

[0119] In some embodiments, the apparatus further comprises:

[0120] A verification module is used to, after querying the query result corresponding to the formal query content through the optimal query path, use the query result corresponding to the formal query content through the optimal query path as the first query result, and query the second query result corresponding to the formal query content through at least one other query path except the optimal query path; compare at least one second query result corresponding to the at least one other query path query with the first query result to obtain a comparison result; and verify the accuracy of the first query result according to the comparison result to obtain an accuracy verification result for the first query result.

[0121] The data query device based on table association provided in the embodiment of the present application has the same technical features as the data query method based on table association provided in the above embodiment, so it can also solve the same technical problems and achieve the same technical effects.

[0122] An electronic device provided in an embodiment of the present application is Figure 4 As shown, the electronic device 400 includes a processor 402 and a memory 401, wherein the memory stores a computer program that can be run on the processor, and the processor implements the steps of the method provided in the above embodiment when executing the computer program.

[0123] See also Figure 4 The electronic device further includes: a bus 403 and a communication interface 404, a processor 402, a communication interface 404 and a memory 401 are connected via the bus 403; the processor 402 is used to execute an executable module stored in the memory 401, such as a computer program.

[0124] The memory 401 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk storage. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 404 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used.

[0125] The bus 403 may be an ISA bus, a PCI bus, or an EISA bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0126] Among them, the memory 401 is used to store programs, and the processor 402 executes the program after receiving the execution instruction. The method executed by the device defined by the process disclosed in any embodiment of the present application can be applied to the processor 402 or implemented by the processor 402.

[0127] The processor 402 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the processor 402. The above processor 402 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the embodiments of the present application can be directly embodied as a hardware decoding processor to execute, or the hardware and software modules in the decoding processor are combined to execute. The software module may be located in a storage medium mature in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 401, and the processor 402 reads the information in the memory 401 and completes the steps of the above method in combination with its hardware.

[0128] Corresponding to the above-mentioned data query method based on table association, an embodiment of the present application also provides a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to execute the steps of the above-mentioned data query method based on table association.

[0129] The data query device based on table association provided in the embodiment of the present application can be specific hardware on the device or software or firmware installed on the device. The device provided in the embodiment of the present application, its implementation principle and the technical effect produced are the same as those in the aforementioned method embodiment. For the sake of brief description, the parts not mentioned in the device embodiment can refer to the corresponding contents in the aforementioned method embodiment. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, devices and units described above can all refer to the corresponding processes in the aforementioned method embodiment, and will not be repeated here.

[0130] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0131] For another example, the flowchart and block diagram in the accompanying drawings show the possible architecture, function and operation of the device, method and computer program product according to multiple embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of the code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or the flowchart, and the combination of the boxes in the block diagram and / or the flowchart can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0132] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0133] In addition, each functional unit in the embodiments provided in the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0134] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the data query method based on table association described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store program codes.

[0135] It should be noted that similar numbers and letters represent similar items in the following figures. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. In addition, the terms "first", "second", "third", etc. are only used to distinguish the description and are not to be understood as indicating or implying relative importance.

[0136] Finally, it should be noted that the above-described embodiments are only specific implementation methods of the present application, which are used to illustrate the technical solution of the present application, rather than to limit it. The protection scope of the present application is not limited thereto. Although the present application is described in detail with reference to the aforementioned embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solution recorded in the aforementioned embodiments within the technical scope disclosed in the present application, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solution deviate from the scope of the technical solution of the embodiment of the present application. They should all be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. A data query method based on table association, characterized in that: The method comprises: Acquire query content input by a user and identify query semantics of the query content; Based on the query semantics, a first data table with query possibility is extracted from multiple data tables stored in a designated database through a neural network model, and a query column in the first data table and a screening condition for the first data table are determined according to the query semantics; wherein the query column includes fields; the designated database corresponds to a data table knowledge graph, and the data table knowledge graph is based on the multiple data tables stored in the designated database, through recommended query conditions, commonly used query conditions, and preset data table association contents, to determine the table association relationship between the multiple data tables, and a data table knowledge graph is formed through graph construction according to the table association relationship; the recommended query condition is a query condition determined by a query method in a report product; the commonly used query condition is a query condition determined by a query method collected and analyzed by a program in an analysis product; each of the data tables in the data table knowledge graph corresponds to a direction of its own data table pointing to a corresponding main table relationship; According to the table name of the first data table, all relevant paths formed by the direction pointing to the corresponding main table relationship of each first data table are retrieved in the data table knowledge graph through the direction pointing to the corresponding main table relationship; the relevant path includes path nodes respectively formed by at least two of the first data tables, and the path direction corresponding to the direction of each first data table pointing to the corresponding main table relationship in the relevant path; Determine the target related path containing the least number of the path nodes from all the related paths, and the target path direction corresponding to the target first data table in the target related path, and determine the associated fields between the multiple target first data tables according to the target fields contained in the target query column in the target first data table and the target screening condition for the target first data table; According to the target direction of the target first data table corresponding to the target path direction pointing to the corresponding main table relationship, the target first data table that is pointed to as the corresponding main table relationship the most times from the multiple target first data tables is determined as the main table to be queried, the target first data tables in the multiple target first data tables that are directly and indirectly connected to the main table to be queried are used as the sub-tables to be queried corresponding to the main table to be queried, and the table association field relationship between the main table to be queried and at least one of the sub-tables to be queried is determined according to the association field; According to the query semantics, query results corresponding to the query content are queried through the table association field relationship, the main table to be queried and the sub-table to be queried, with the main table to be queried as the center.

2. The method according to claim 1, characterized in that The step of obtaining the query content input by the user includes: Acquire initial query content initially input by a user, as well as the user's position data and the user's work environment data, and identify initial query semantics of the initial query content; Detecting the task requirements of the user according to the initial query semantics, the position data, and the work environment data; Determine ambiguous initial query content and erroneous initial query content in the initial query content according to the task requirement, and exclude the ambiguous initial query content and the erroneous initial query content from the initial query content to obtain a target query content; The target query content is determined as the formal query content formally input by the user.

3. The method according to claim 2, characterized in that After the step of determining the target query content as the formal query content formally input by the user, the method further includes: At least one target data table is determined from the multiple data tables stored in the designated database according to the formal query content, as well as a query path corresponding to the formal query content in the at least one target data table; wherein the query path includes a query sequence relationship between the at least one target data table, a summary corresponding to each of the target data tables, and a query direction corresponding to each of the target data tables.

4. The method according to claim 3, characterized in that The at least one target data table includes a first text target data table, a second text target data table, and a third text target data table; the step of determining, according to the formal query content, a query path corresponding to the formal query content in the plurality of data tables stored in the designated database leading to the at least one target data table comprises: Determine the formal query content from the plurality of data tables stored in the designated database according to the formal query content, obtain a first text query result through the first text target data table, obtain a second text query result through the second text target data table according to the first text query result, and obtain a third text query result through the third text target data table according to the second text query result; Based on the first text query result, the second text query result, the third text query result, and the query order relationship between the first text target data table, the second text target data table and the third text target data table, the text query path corresponding to the formal query content in the multiple text target data tables is determined.

5. The method according to claim 3, characterized in that: The at least one target data table includes a first digital target data table, a second digital target data table, and a third digital target data table; the step of determining, according to the formal query content, a query path corresponding to the formal query content in the plurality of data tables stored in the designated database leading to the at least one target data table comprises: Determine the formal query content from the plurality of data tables stored in the designated database according to the formal query content, calculate the formal query content by using the first parameter value and the first parameter relationship in the first digital target data table, and obtain a first numerical query result, and calculate the second parameter value and the second parameter relationship in the second digital target data table according to the first numerical query result, and obtain a third numerical query result by using the third parameter value and the third parameter relationship in the third digital target data table according to the second numerical query result; Based on the first numerical query result, the second numerical query result, the third numerical query result, and the query order sequence between the first digital target data table, the second digital target data table and the third digital target data table, the text query path corresponding to the formal query content in the multiple digital target data tables is determined.

6. The method according to claim 3, characterized in that: The number of the query paths is multiple; after the step of determining at least one target data table from the multiple data tables stored in the designated database according to the formal query content, and the query path corresponding to the formal query content leading to the at least one target data table, the method further includes: In response to a selection operation on a target query path among the multiple query paths, determining the target query path as an optimal query path among the multiple query paths, and searching for a query result corresponding to the formal query content through the optimal query path; and / or, An optimal query path is selected from the multiple query paths through a neural network model, and a query result corresponding to the formal query content is queried through the optimal query path, wherein the neural network model is trained by query path samples with optimal path labels.

7. The method according to claim 6, characterized in that After the step of searching for the query result corresponding to the formal query content through the optimal query path, the method further includes: Taking the query result corresponding to the formal query content queried through the optimal query path as the first query result, and respectively querying the second query result corresponding to the formal query content through at least one other query path except the optimal query path; Comparing at least one of the second query results corresponding to the at least one other query path query with the first query result to obtain a comparison result; The accuracy of the first query result is verified according to the comparison result to obtain an accuracy verification result for the first query result.

8. A data query device based on table association, characterized in that: include: An acquisition module, used to acquire the query content input by the user and identify the query semantics of the query content; An extraction module is used to extract a first data table with query possibility from multiple data tables stored in a designated database through a neural network model based on the query semantics, and determine the query column in the first data table and the screening conditions for the first data table according to the query semantics; wherein the query column includes fields; the designated database corresponds to a data table knowledge graph, and the data table knowledge graph is based on the multiple data tables stored in the designated database through recommended query conditions, common query conditions, and preset data table association contents to determine the table association relationship between the multiple data tables and build a data table knowledge graph based on the table association relationship; the recommended query condition is a query condition determined by a query method in a report product; the common query condition is a query condition determined by a query method collected and analyzed by a program in an analysis product; each of the data tables in the data table knowledge graph corresponds to a direction of its own data table pointing to a corresponding main table relationship; A retrieval module, configured to retrieve, in the data table knowledge graph according to the table name of the first data table, all relevant paths formed by the direction pointing to the corresponding main table relationship of each first data table; the relevant path includes path nodes respectively formed by at least two of the first data tables, and the path direction corresponding to the direction of each first data table pointing to the corresponding main table relationship in the relevant path; A first determination module is used to determine the target related path containing the least number of path nodes from all the related paths, and the target path direction corresponding to the target first data table in the target related path, and determine the associated fields between the multiple target first data tables according to the target fields contained in the target query column in the target first data table and the target screening condition for the target first data table; A second determination module is used to determine a main table to be queried from a plurality of the target first data tables according to a target direction of a main table relationship corresponding to the target first data table corresponding to the target path direction, use target first data tables other than the main table to be queried in the plurality of the target first data tables as sub-tables to be queried corresponding to the main table to be queried, and determine a table association field relationship between the main table to be queried and at least one of the sub-tables to be queried according to the association field; A query module is used to query the query result corresponding to the query content according to the query semantics through the table association field relationship, the main table to be queried and the sub-table to be queried and with the main table to be queried as the center.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to execute the method according to any one of claims 1 to 7.

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