SQL query visualization configuration method and system for realizing on-demand data screening

Abstract

The present invention discloses a SQL query visualization configuration method and system for realizing on-demand data screening, and relates to the field of SQL query technology. In order to meet the demand for user screening according to user preferences, the scheme includes: building a visualization configuration tool; using JSON as the front-end and back-end data exchange format, designing keys representing different query conditions in JSON, the value corresponding to each key is a JSON object, and each JSON object contains a judgment condition array and a logical relationship array; building a front-end page, and configuring the input parameters in the tool; designing a syntax tree algorithm in the back-end that traverses the array in sequence to generate a syntax tree; designing an algorithm that flattens the syntax tree into SQL statements in the back-end, and generating SQL statements that meet the requirements by performing in-order traversal on the syntax tree. The present invention implements the configuration of screening rules through a visual interface, and generates SQL statements that meet the screening rules.

Description

Technical Field

[0001] The present invention relates to the field of SQL query technology, in particular to a SQL query visualization configuration method and system for realizing on-demand data screening. Background Art

[0002] As business develops, many business systems accumulate a large amount of business data. At the system management level, how to use these business data to define personnel, organizations, etc. in a more generalized way, such as using labels, notes, and grouping, are also functions that managers believe the system must have.

[0003] In order to meet the needs of user filtering according to user preferences, a SQL query visualization configuration method and system for on-demand data filtering are designed to enable users to configure filtering rules through a visual interface and generate SQL statements that meet the user's filtering rules for easy user operation. Summary of the invention

[0004] In view of the needs and deficiencies of current technology development, the present invention provides a SQL query visualization configuration method and system for realizing on-demand data screening.

[0005] In a first aspect, the present invention provides a method for configuring SQL query visualization for on-demand data screening. The technical solution adopted to solve the above technical problem is as follows:

[0006] A method for visually configuring SQL queries to filter data on demand is provided. The implementation process of the method is as follows:

[0007] S1. Build a visual configuration tool for SQL query, which receives query conditions, field types, judgment conditions and logical relationships as input parameters, and dynamically splices SQL statements according to the input parameters;

[0008] S2. Use JSON as the front-end and back-end data exchange format. Design keys representing different query conditions in JSON. The value corresponding to each key is a JSON object. Each JSON object contains an array A for storing specific judgment conditions and an array B for defining the logical relationship between different judgment conditions. The order of the judgment conditions and logical relationships contained in the JSON object is arranged according to the configuration order of the user in the visual configuration tool.

[0009] S3. Use the ElementUI component library to build the front-end page, and configure the input parameters in the visual configuration tool through a combination of drop-down boxes, input boxes, and buttons;

[0010] S4. Design a syntax tree algorithm at the back end, which traverses array A and array B in sequence to generate a syntax tree; design an algorithm at the back end to flatten the syntax tree into SQL statements, which performs an in-order traversal of the syntax tree, generates a complete SQL statement that meets the user's query requirements based on the node type in the syntax tree, and stores the SQL statement in the database for persistence.

[0011] Optionally, the visualization configuration tool receives query conditions, field types, judgment conditions, and logical relationships as input parameters, where:

[0012] Query conditions include where query conditions and having query conditions;

[0013] Field types include numeric, string, and time types;

[0014] The judgment conditions include greater than, less than, equal to, not equal to, greater than or equal to, and less than or equal to.

[0015] Logical relations include and and or.

[0016] Optionally, use the ElementUI component library to build the front-end page, and configure the input parameters in the visual configuration tool through a combination of drop-down boxes, input boxes, and buttons, including:

[0017] Select the field name through the drop-down box. These field names are dynamically obtained by querying the database table structure through the backend;

[0018] Select the predefined judgment condition through the drop-down box. The judgment conditions include greater than, less than, equal to, not equal to, greater than or equal to, and less than or equal to.

[0019] Enter the specific value of the judgment condition through the input box;

[0020] By selecting the button, you can set the logical relationship between the current query condition and the next query condition.

[0021] Optionally, the syntax tree algorithm involved traverses array A and array B in order to generate a syntax tree. The specific implementation process is as follows:

[0022] (1) Initialize an empty root node;

[0023] (2) Traverse the query condition array, create a new leaf node for each query condition, and add it to the child node list of the current node;

[0024] (3) Traverse the logical relationship array and determine the type (and or) of each logical relationship.

[0025] (3a) If the logical relationship is and, set the current node to the newly created leaf node.

[0026] (3b) If the logical relationship is or, the current node is used as the parent node of the new leaf node, and the newly created leaf node is added to the list of sibling nodes of the current node;

[0027] (4) Return the root node as the generated syntax tree.

[0028] Further optionally, the algorithm involved in flattening the syntax tree into SQL statements performs an in-order traversal on the syntax tree, and generates a complete SQL statement that meets the user's query requirements according to the node type in the syntax tree. The specific implementation process is as follows:

[0029] (1) Initialize an empty string as the result;

[0030] (2) Perform in-order traversal of the syntax tree, and for each node:

[0031] (2.1) If the node is a leaf node, concatenate the query field, judgment condition, and judgment condition value into an SQL statement fragment and add it to the result string;

[0032] (2.2) If the node is a logical node:

[0033] (2.2a) If the current logical node is the same as the parent logical node, add brackets between the child nodes.

[0034] (2.2b) Otherwise, connect the child nodes directly;

[0035] (3) Return the result string as the generated SQL statement.

[0036] In a second aspect, the present invention provides a SQL query visualization configuration system for implementing on-demand data screening. The technical solution adopted to solve the above technical problems is as follows:

[0037] A SQL query visualization configuration system for realizing on-demand data screening, comprising:

[0038] A tool building module is used to build a visual configuration tool for SQL query, wherein the visual configuration tool receives query conditions, field types, judgment conditions and logical relationships as input parameters, and dynamically splices SQL statements according to the input parameters;

[0039] The format design module is used to use JSON as the front-end and back-end data exchange format, and to design keys representing different query conditions in JSON. The value corresponding to each key is a JSON object. Each JSON object contains an array A for storing specific judgment conditions and an array B for defining the logical relationship between different judgment conditions. The judgment conditions and logical relationships contained in the JSON object are arranged in the order configured by the user in the visual configuration tool.

[0040] The page construction module is used to build the front-end page using the ElementUI component library, and configure the input parameters in the visual configuration tool through a combination of drop-down boxes, input boxes, and buttons;

[0041] The algorithm design module is used to design a syntax tree algorithm on the back end. The syntax tree algorithm traverses array A and array B in sequence to generate a syntax tree. It is also used to design an algorithm on the back end to flatten the syntax tree into SQL statements. The algorithm performs an in-order traversal on the syntax tree, generates a complete SQL statement that meets the user's query requirements based on the node type in the syntax tree, and stores the SQL statement in the database for persistence.

[0042] Optionally, the visualization configuration tool receives query conditions, field types, judgment conditions, and logical relationships as input parameters, where:

[0043] Query conditions include where query conditions and having query conditions;

[0044] Field types include numeric, string, and time types;

[0045] The judgment conditions include greater than, less than, equal to, not equal to, greater than or equal to, and less than or equal to.

[0046] Logical relations include and and or.

[0047] Optionally, the page construction module involved uses the ElementUI component library to build the front-end page, and implements the configuration of input parameters in the visual configuration tool through a combination of drop-down boxes, input boxes and buttons, including:

[0048] Select the field name through the drop-down box. These field names are dynamically obtained by querying the database table structure through the backend;

[0049] Select the predefined judgment condition through the drop-down box. The judgment conditions include greater than, less than, equal to, not equal to, greater than or equal to, and less than or equal to.

[0050] Enter the specific value of the judgment condition through the input box;

[0051] By selecting the button, you can set the logical relationship between the current query condition and the next query condition.

[0052] Optionally, the syntax tree algorithm involved traverses array A and array B in order to generate a syntax tree. The specific implementation process is as follows:

[0053] (1) Initialize an empty root node;

[0054] (2) Traverse the query condition array, create a new leaf node for each query condition, and add it to the child node list of the current node;

[0055] (3) Traverse the logical relationship array and determine the type (and or) of each logical relationship.

[0056] (3a) If the logical relationship is and, set the current node to the newly created leaf node.

[0057] (3b) If the logical relationship is or, the current node is used as the parent node of the new leaf node, and the newly created leaf node is added to the list of sibling nodes of the current node;

[0058] (4) Return the root node as the generated syntax tree.

[0059] Further optionally, the algorithm involved in flattening the syntax tree into SQL statements performs an in-order traversal on the syntax tree, and generates a complete SQL statement that meets the user's query requirements according to the node type in the syntax tree. The specific implementation process is as follows:

[0060] (1) Initialize an empty string as the result;

[0061] (2) Perform in-order traversal of the syntax tree, and for each node:

[0062] (2.1) If the node is a leaf node, concatenate the query field, judgment condition, and judgment condition value into an SQL statement fragment and add it to the result string;

[0063] (2.2) If the node is a logical node:

[0064] (2.2a) If the current logical node is the same as the parent logical node, add brackets between the child nodes.

[0065] (2.2b) Otherwise, connect the child nodes directly;

[0066] (3) Return the result string as the generated SQL statement.

[0067] The method and system for configuring SQL query visualization for realizing on-demand data screening of the present invention have the following beneficial effects compared with the prior art:

[0068] 1. The present invention enables users to configure screening rules through a visual interface and generates SQL statements that meet the user's screening rules, which is convenient for user operation;

[0069] 2. The present invention generates a JSON data structure according to the layout configuration of the front-end page and passes it to the back-end. The back-end first converts the structure into a syntax tree according to recursion, and then converts the data into an SQL statement that can meet the user's query conditions, and stores it persistently in JSON format for other modules in the system to execute regularly to implement classification screening and labeling of users in the system, and meet the need to execute specified queries according to user needs for user screening, and the visual configuration function of the screening conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Attached Figure 1 is a flow chart of a method according to Embodiment 1 of the present invention;

[0071] Attached Figure 2 This is a module connection block diagram of the second embodiment of the present invention. DETAILED DESCRIPTION

[0072] In order to make the technical solution, the technical problem solved and the technical effect of the present invention more clearly understood, the technical solution of the present invention is clearly and completely described below in conjunction with specific embodiments.

[0073] Embodiment 1: Combining with Figure 1 This embodiment proposes a SQL query visualization configuration method for implementing on-demand data screening. The implementation process of the method is as follows:

[0074] S1. Build a visual configuration tool for SQL query, which receives query conditions, field types, judgment conditions and logical relationships as input parameters, and dynamically splices SQL statements according to the input parameters.

[0075] The visualization configuration tool involved receives query conditions, field types, judgment conditions and logical relationships as input parameters, where:

[0076] Query conditions include where query conditions and having query conditions;

[0077] Field types include numeric, string, and time types;

[0078] The judgment conditions include greater than, less than, equal to, not equal to, greater than or equal to, and less than or equal to.

[0079] Logical relations include and and or.

[0080] S2. Use JSON as the front-end and back-end data exchange format. Design keys representing different query conditions in JSON. The value corresponding to each key is a JSON object. Each JSON object contains an array A for storing specific judgment conditions and an array B for defining the logical relationship between different judgment conditions. The order of the judgment conditions and logical relationships contained in the JSON object is arranged according to the configuration order of the user in the visual configuration tool.

[0081] S3. Use the ElementUI component library to build the front-end page, and configure the input parameters in the visual configuration tool through a combination of drop-down boxes, input boxes, and buttons, including:

[0082] Select the field name through the drop-down box. These field names are dynamically obtained by querying the database table structure through the backend;

[0083] Select the predefined judgment condition through the drop-down box. The judgment conditions include greater than, less than, equal to, not equal to, greater than or equal to, and less than or equal to.

[0084] Enter the specific value of the judgment condition through the input box;

[0085] By selecting the button, you can set the logical relationship between the current query condition and the next query condition.

[0086] It should be added that: for complex fields that require the use of SQL statistical functions, a switch button is provided to allow users to manually enter the function expression.

[0087] S4. Design a syntax tree algorithm at the back end. The syntax tree algorithm traverses array A and array B in order to generate a syntax tree. The specific implementation process is as follows:

[0088] (1) Initialize an empty root node;

[0089] (2) Traverse the query condition array, create a new leaf node for each query condition, and add it to the child node list of the current node;

[0090] (3) Traverse the logical relationship array and determine the type (and or) of each logical relationship.

[0091] (3a) If the logical relationship is and, set the current node to the newly created leaf node.

[0092] (3b) If the logical relationship is or, the current node is used as the parent node of the new leaf node, and the newly created leaf node is added to the list of sibling nodes of the current node;

[0093] (4) Return the root node as the generated syntax tree.

[0094] An algorithm is designed on the backend to flatten the syntax tree into SQL statements. The algorithm performs in-order traversal on the syntax tree, generates complete SQL statements that meet user query requirements based on the node types in the syntax tree, and stores the SQL statements in the database for persistence. The specific implementation process is as follows:

[0095] (1) Initialize an empty string as the result;

[0096] (2) Perform in-order traversal of the syntax tree, and for each node:

[0097] (2.1) If the node is a leaf node, concatenate the query field, judgment condition, and judgment condition value into an SQL statement fragment and add it to the result string;

[0098] (2.2) If the node is a logical node:

[0099] (2.2a) If the current logical node is the same as the parent logical node, add brackets between the child nodes.

[0100] (2.2b) Otherwise, connect the child nodes directly;

[0101] (3) Return the result string as the generated SQL statement.

[0102] (4) Store the SQL statement in the database for persistence.

[0103] Take a case where a case handling and approval system wants to implement the function of designing and screening business capability labels for case handling personnel by integrating the method described in this embodiment. The specific implementation method is as follows:

[0104] 1. The document approval system organizes the data resources and data sources owned by the system, and conducts standardized management design, sorting out the indicators that system administrators pay attention to when managing system users, the sources of indicator data, and query methods, and describes these indicators in a natural language that users can accept more rather than a programming language.

[0105] 2. The document approval system integrates the front-end and back-end functional implementations of the method described in this embodiment, and configures the front-end functional implementation (front-end page and visual configuration tool) as an administrator menu.

[0106] 3. The administrator opens the function menu of the method described in this embodiment, and determines the indicator data for filtering the business capabilities of users in the case approval system according to his own needs, such as defining a person who handles more than 500 cases and takes less than 300 minutes to handle cases as an expert in case handling, etc. After the determination is completed, the front-end visualization function of this method is used, and the configuration rule is: "The number of cases handled is greater than 500 and the handling time is less than 300 minutes, which is equal to an expert in case handling." After the configuration is completed, click Save, and the front-end configuration content will be converted into the corresponding fields of the database, and then organized into the JSON format designed for this method, and passed to the back-end function implementation of this method.

[0107] 4. When the backend receives the parameters, it will first recursively organize the parameter array into a syntax tree based on the AND or OR logical relationship between the conditions, and then perform an in-order traversal of the syntax tree and generate a complete SQL statement based on the parameters in each node.

[0108] 5. After the statement is generated, the backend will persist the content based on the database provided by the business system. The business system can execute the statement regularly according to its actual needs (for example, if it is executed once a month, it can provide the administrator with the monthly data of the expert in handling cases).

[0109] Embodiment 2: Combining with Figure 2 This embodiment proposes a SQL query visualization configuration system for implementing on-demand data screening, which includes:

[0110] A tool building module is used to build a visual configuration tool for SQL query, wherein the visual configuration tool receives query conditions, field types, judgment conditions and logical relationships as input parameters, and dynamically splices SQL statements according to the input parameters;

[0111] The format design module is used to use JSON as the front-end and back-end data exchange format, and to design keys representing different query conditions in JSON. The value corresponding to each key is a JSON object. Each JSON object contains an array A for storing specific judgment conditions and an array B for defining the logical relationship between different judgment conditions. The judgment conditions and logical relationships contained in the JSON object are arranged in the order configured by the user in the visual configuration tool.

[0112] The page construction module is used to build the front-end page using the ElementUI component library, and configure the input parameters in the visual configuration tool through a combination of drop-down boxes, input boxes, and buttons;

[0113] The algorithm design module is used to design a syntax tree algorithm on the back end. The syntax tree algorithm traverses array A and array B in sequence to generate a syntax tree. It is also used to design an algorithm on the back end to flatten the syntax tree into SQL statements. The algorithm performs an in-order traversal on the syntax tree, generates a complete SQL statement that meets the user's query requirements based on the node type in the syntax tree, and stores the SQL statement in the database for persistence.

[0114] The visualization configuration tool involved in this embodiment receives query conditions, field types, judgment conditions and logical relationships as input parameters, where:

[0115] Query conditions include where query conditions and having query conditions;

[0116] Field types include numeric, string, and time types;

[0117] The judgment conditions include greater than, less than, equal to, not equal to, greater than or equal to, and less than or equal to.

[0118] Logical relations include and and or.

[0119] The page construction module involved in this embodiment uses the ElementUI component library to build the front-end page, and implements the configuration of input parameters in the visual configuration tool by combining a drop-down box, an input box, and a button, specifically including:

[0120] Select the field name through the drop-down box. These field names are dynamically obtained by querying the database table structure through the backend;

[0121] Select the predefined judgment condition through the drop-down box. The judgment conditions include greater than, less than, equal to, not equal to, greater than or equal to, and less than or equal to.

[0122] Enter the specific value of the judgment condition through the input box;

[0123] By selecting the button, you can set the logical relationship between the current query condition and the next query condition.

[0124] The syntax tree algorithm involved in this embodiment traverses array A and array B in order to generate a syntax tree. The specific implementation process is as follows:

[0125] (1) Initialize an empty root node;

[0126] (2) Traverse the query condition array, create a new leaf node for each query condition, and add it to the child node list of the current node;

[0127] (3) Traverse the logical relationship array and determine the type (and or) of each logical relationship.

[0128] (3a) If the logical relationship is and, set the current node to the newly created leaf node.

[0129] (3b) If the logical relationship is or, the current node is used as the parent node of the new leaf node, and the newly created leaf node is added to the list of sibling nodes of the current node;

[0130] (4) Return the root node as the generated syntax tree.

[0131] The algorithm for flattening the syntax tree into SQL statements in this embodiment performs an in-order traversal on the syntax tree, and generates a complete SQL statement that meets the user's query requirements according to the node types in the syntax tree. The specific implementation process is as follows:

[0132] (1) Initialize an empty string as the result;

[0133] (2) Perform in-order traversal of the syntax tree, and for each node:

[0134] (2.1) If the node is a leaf node, concatenate the query field, judgment condition, and judgment condition value into an SQL statement fragment and add it to the result string;

[0135] (2.2) If the node is a logical node:

[0136] (2.2a) If the current logical node is the same as the parent logical node, add brackets between the child nodes.

[0137] (2.2b) Otherwise, connect the child nodes directly;

[0138] (3) Return the result string as the generated SQL statement.

[0139] In summary, the SQL query visualization configuration method and system for implementing on-demand data filtering of the present invention enables users to configure filtering rules through a visual interface, and generates SQL statements that meet the user's filtering rules, which is convenient for user operation.

[0140] The above specific examples are used to explain the principles and implementation methods of the present invention in detail. These examples are only used to help understand the core technical content of the present invention. Based on the above specific embodiments of the present invention, any improvements and modifications made by technicians in this technical field without departing from the principles of the present invention should fall within the scope of patent protection of the present invention.

Claims

1. A method for visually configuring SQL queries to implement on-demand data screening, characterized in that: The implementation process of the method is as follows: S1. Build a visual configuration tool for SQL query, which receives query conditions, field types, judgment conditions and logical relationships as input parameters, and dynamically splices SQL statements according to the input parameters; S2. Use JSON as the front-end and back-end data exchange format. Design keys representing different query conditions in JSON. The value corresponding to each key is a JSON object. Each JSON object contains an array A for storing specific judgment conditions and an array B for defining the logical relationship between different judgment conditions. The order of the judgment conditions and logical relationships contained in the JSON object is arranged according to the configuration order of the user in the visual configuration tool. S3. Use the ElementUI component library to build the front-end page, and configure the input parameters in the visual configuration tool through a combination of drop-down boxes, input boxes, and buttons; S4. Design a syntax tree algorithm at the backend. The syntax tree algorithm traverses array A and array B in order to generate a syntax tree. The specific implementation process is as follows: (1) Initialize an empty root node; (2) Traverse the query condition array, for each query condition, create a new leaf node and add it to the child node list of the current node; (3) Traverse the logical relationship array, for each logical relationship, determine its type, (3a) If the logical relationship is and, set the current node to the newly created leaf node, (3b) If the logical relationship is or, set the current node as the parent node of the new leaf node, and add the newly created leaf node to the peer node list of the current node; (4) Return the root node as the generated syntax tree; An algorithm is designed on the backend to flatten the syntax tree into SQL statements. The algorithm performs an in-order traversal of the syntax tree, generates a complete SQL statement that meets the user's query requirements based on the node type in the syntax tree, and stores the SQL statement in the database for persistence. The specific implementation process is as follows: (1) Initialize an empty string as the result; (2) Perform an in-order traversal of the syntax tree, and for each node: (2.1) If the node is a leaf node, concatenate the query field, judgment condition, and judgment condition value into an SQL statement fragment and add it to the result string; (2.2) If the node is a logical node: (2.2a) If the current logical node is the same as the parent logical node, add brackets between the child nodes, (2.2b) Otherwise, directly connect the child nodes; (3) Return the result string as the generated SQL statement.

2. The SQL query visualization configuration method for realizing on-demand data screening according to claim 1 is characterized in that: The visualization configuration tool receives query conditions, field types, judgment conditions and logical relationships as input parameters, wherein: Query conditions include where query conditions and having query conditions; Field types include numeric, string, and time types; The judgment conditions include greater than, less than, equal to, not equal to, greater than or equal to, and less than or equal to. Logical relations include and and or.

3. The SQL query visualization configuration method for realizing on-demand data screening according to claim 1 is characterized in that: Use the ElementUI component library to build the front-end page, and configure the input parameters in the visual configuration tool through a combination of drop-down boxes, input boxes, and buttons, including: Select the field name through the drop-down box. These field names are dynamically obtained by querying the database table structure through the backend; Select the predefined judgment condition through the drop-down box. The judgment conditions include greater than, less than, equal to, not equal to, greater than or equal to, and less than or equal to. Enter the specific value of the judgment condition through the input box; By selecting the button, you can set the logical relationship between the current query condition and the next query condition.

4. A SQL query visualization configuration system for realizing on-demand data screening, characterized in that: It includes: A tool building module is used to build a visual configuration tool for SQL query, wherein the visual configuration tool receives query conditions, field types, judgment conditions and logical relationships as input parameters, and dynamically splices SQL statements according to the input parameters; The format design module is used to use JSON as the front-end and back-end data exchange format, and to design keys representing different query conditions in JSON. The value corresponding to each key is a JSON object. Each JSON object contains an array A for storing specific judgment conditions and an array B for defining the logical relationship between different judgment conditions. The judgment conditions and logical relationships contained in the JSON object are arranged in the order configured by the user in the visual configuration tool. The page construction module is used to build the front-end page using the ElementUI component library, and configure the input parameters in the visual configuration tool through a combination of drop-down boxes, input boxes, and buttons; The algorithm design module is used to design a syntax tree algorithm at the back end. The syntax tree algorithm traverses array A and array B in order to generate a syntax tree. The specific implementation process is as follows: (1) Initialize an empty root node; (2) Traverse the query condition array, for each query condition, create a new leaf node and add it to the child node list of the current node; (3) Traverse the logical relationship array, for each logical relationship, determine its type, (3a) If the logical relationship is and, set the current node to the newly created leaf node; (3b) If the logical relationship is or, set the current node as the parent node of the new leaf node and add the newly created leaf node to the parent node. Add to the list of peer nodes of the current node; (4) Return the root node as the generated syntax tree; It is also used to design an algorithm on the back end to flatten the syntax tree into SQL statements. The algorithm performs an in-order traversal of the syntax tree, generates a complete SQL statement that meets the user's query requirements based on the node type in the syntax tree, and stores the SQL statement in the database for persistence. The specific implementation process is as follows: (1) Initialize an empty string as the result; (2) Perform an in-order traversal of the syntax tree, and for each node: (2.1) If the node is a leaf node, concatenate the query field, judgment condition, and judgment condition value into an SQL statement fragment and add it to the result string; (2.2) If the node is a logical node: (2.2a) If the current logical node is the same as the parent logical node, add brackets between the child nodes, (2.2b) Otherwise, directly connect the child nodes; (3) Return the result string as the generated SQL statement.

5. The SQL query visualization configuration system for realizing on-demand data screening according to claim 4 is characterized in that: The visualization configuration tool receives query conditions, field types, judgment conditions and logical relationships as input parameters, wherein: Query conditions include where query conditions and having query conditions; Field types include numeric, string, and time types; The judgment conditions include greater than, less than, equal to, not equal to, greater than or equal to, and less than or equal to. Logical relations include and and or.

6. The SQL query visualization configuration system for realizing on-demand data screening according to claim 4, characterized in that: The page construction module uses the ElementUI component library to build the front-end page, and implements the configuration of input parameters in the visual configuration tool through a combination of drop-down boxes, input boxes and buttons, including: Select the field name through the drop-down box. These field names are dynamically obtained by querying the database table structure through the backend; Select the predefined judgment condition through the drop-down box. The judgment conditions include greater than, less than, equal to, not equal to, greater than or equal to, and less than or equal to. Enter the specific value of the judgment condition through the input box; By selecting the button, you can set the logical relationship between the current query condition and the next query condition.

Images