Multi-mode visual and intelligent rule configuration query system

Abstract

The invention provides a multi-mode visual and intelligent rule configuration query system. The multi-mode visual and intelligent rule configuration query system comprises a monitoring module, a management module and a query module, the monitoring module is used for monitoring user online conditions, system operation states and cache information in real time; the query module is used for determining target metadata according to a query instruction input by a user, a user online condition, a system running state and cache information so as to form a target condition input control; and the management module is used for determining and displaying a query result according to the target condition input control. According to the method, query is used as configurable metadata, and the mode that query logic is hard-coded in codes in a traditional system is changed. Any new query requirement can be online only by interface configuration without modifying a back-end code and redeploying a system, so that quick configuration and hot release, multi-scene multiplexing and servitization output of the query requirement are realized, and government affair data query efficiency and flexibility are remarkably improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of government affair data management, and in particular to a multi-mode visualized and intelligent rule configured query system. BACKGROUND

[0002] With the deep penetration and wide application of information technology in the field of government affairs, government affair data is gradually integrated at the provincial level, and the data scale is growing explosively. Meanwhile, the complex and changeable policy environment makes the demand for data extraction and statistical analysis in government affairs more frequent and diverse. Whether it is the regular business data aggregation or the temporary and short-term special data analysis demand, it shows a rapid growth trend, which puts forward higher requirements on the efficiency, flexibility and response speed of data processing.

[0003] The traditional data extraction and statistical analysis technology has significant defects under the background of government affair data integration at the provincial level and frequent and diverse data demand. The operation process needs to be approved level by level, which is tedious and inefficient, and it is difficult to quickly respond to temporary or short-term data demand. The query logic is hard-coded in the backend code, and new or adjusted requirements need to modify the code and redeploy, which responds slowly to business changes. A large number of operation and maintenance personnel are needed to handle diversified query requests from various cities, which consumes a lot of human resources and has high operation and maintenance costs. There is a lack of unified adaptation mechanism for heterogeneous data sources, the data extraction caliber is not unified, the management complexity is high, and the reuse and flexible expansion of query configuration cannot be realized, which makes it difficult to meet the efficient, flexible and low-cost data processing demand in the field of government affairs. SUMMARY

[0004] The embodiment of the present application provides a multi-mode visualized and intelligent rule configured query system, which is used to solve the problem of tedious operation process existing in the traditional data query and statistical method in the existing field of government affairs.

[0005] The first aspect of the embodiment of the present application provides a multi-mode visualized and intelligent rule configured query system, which comprises: a monitoring module, a management module and a query module; The monitoring module is used to monitor the user online situation, the system running state and the cache information in real time. The query module is used to determine the target metadata according to the query instruction input by the user, the user online situation, the system running state and the cache information, so as to form a target condition input control. The management module is used to determine and display the query result according to the target condition input control.

[0006] In a possible implementation manner, the query module is used to: Based on the query command, user online status, system running status, and cache information, determine the business requirements, query configuration mode, and target data source type; Configure target metadata based on the business requirements, the query configuration mode, and the target data source type; Based on the target metadata, determine the target condition input control.

[0007] In one possible implementation, the query module is used for: Parse the query command to determine business requirements; Determine user permissions and query configuration modes based on user online status; the query configuration modes include user data mode and developer mode. Determine the target data source type based on business needs, user permissions, system operating status, and cache information.

[0008] In one possible implementation, the query module is used for: Based on the query configuration mode, select the matching target metadata template from the preset metadata template library; Based on the target data source type, obtain the database dialect features corresponding to the data source type, and convert the general query logic in the target metadata template into a dialect query statement that adapts to the target data source type, generating adapted query logic metadata; Based on business requirements, semantic parsing is performed on the adapted query logic metadata to extract parameter metadata and result set structure metadata contained in the dialect query statement. Among them, parameter metadata is used to describe the input parameters required for the query, and result set structure metadata is used to describe the field information returned by the query. The adapted query logic metadata, parameter metadata, and result set structure metadata are combined and encapsulated to form the target metadata.

[0009] In one possible implementation, the query module is specifically used for: In developer mode, based on the preset metadata template corresponding to developer mode, the target metadata template containing SQL logic configuration fields and query basic information entry items is selected from the metadata template library; Parse the mapping relationship between custom SQL statements, query condition parameters, constraint rules, and result fields in the query command; Fill the SQL logic configuration field of the target metadata template with the custom SQL statement, and enter the basic information such as query ID, query name, and business number into the corresponding template items to form the initial query logic metadata; Based on the target data source type, obtain the corresponding database dialect features, perform dialect adaptation verification and conversion on the custom SQL statements in the initial query logic metadata, and generate query logic metadata adapted to the target data source. Based on business requirements, semantic parsing is performed on the adapted query logic metadata, the parsed query condition parameter information is associated, parameter metadata is extracted, and the result set structure metadata is determined according to the result field mapping relationship. The adapted query logic metadata, extracted parameter metadata, and result set structure metadata are combined and encapsulated, while embedding developer mode identifiers and basic query component reuse tags to form target metadata.

[0010] In one possible implementation, the query module is specifically used for: In user mode, the basic query components selected by the user in the query command are parsed, and the basic query components are expanded and combined to obtain custom query components. Among them, table query scenario and report query scenario are set in user mode. The basic query components in the table query scenario include the definition of related conditions, the selection of query fields, the sorting of result fields, and simple script editing. The basic query components in the report query scenario include the preset report template and the corresponding customized query conditions. Based on the preset metadata template corresponding to the user mode, the query logic, association conditions, and field configuration information in the custom query component are filled into the corresponding items of the template to form the initial query logic metadata. Based on the target data source type, obtain the corresponding database dialect features, perform dialect adaptation conversion on the initial query logic metadata, and generate query logic metadata adapted to the target data source. Based on business requirements, semantic parsing is performed on the adapted query logic metadata, the parameter information contained in the combined configuration is automatically identified, the parameter metadata is extracted, and the result set structure metadata is determined according to the result field information set by the user. The adapted query logic metadata, extracted parameter metadata, and result set structure metadata are combined and encapsulated, and user pattern identifiers and visual configuration tags are embedded to form target metadata.

[0011] In one possible implementation, the query module is specifically used for: Based on the metadata parsing engine, the parameter metadata in the target metadata is parsed to extract parameter names, parameter data types, optional value ranges, parameter constraint rules, and dictionary association mapping information; The system automatically matches the corresponding UI input control type based on the parameter data type and populates the control option data based on the range of selectable values ​​and dictionary association mapping information. Configure validation logic for input controls based on parameter constraint rules to complete real-time validity checks; The parsed parameter metadata and the generated conditional input control are two-way bound using a conditional dynamic binding mechanism, so that the parameter values ​​input by the control can be directly mapped to the parameter configuration of the target metadata, thus obtaining the target conditional input control.

[0012] In one possible implementation, the query module is specifically used for: Traverse the parameter configuration nodes in the target metadata and extract the parameter names and parameter data types from the nodes; Identify the data source reference identifier defined in the parameter configuration node, retrieve the system dictionary management module based on the data source reference identifier, obtain the corresponding standardized dictionary data and dictionary association mapping information, match the dictionary association mapping information with the obtained standardized dictionary data to parse out the range of optional values; Read the constraint configuration items in the parameter configuration node, convert the constraint configuration items into a structured set of constraint logic, and extract the parameter constraint rules.

[0013] In one possible implementation, the query module is specifically used for: Establish a mapping relationship between parameter metadata and conditional input controls, and map the attribute information of parameter metadata to the configuration items of conditional input controls to achieve the initialization and alignment of control attributes; The interaction events of the conditional input control are captured by the listening mechanism. When the user enters data, the operation data is converted into target data that conforms to the parameter format according to the mapping association, and after verification, it is synchronously updated to the parameter metadata. Listen for changes in parameter metadata. When a dynamic adjustment to the parameter metadata is detected, automatically trigger the property update of the conditional input control based on the changed content. During the binding process, access control logic is integrated to verify the access permissions of parameter metadata based on the current user's permission level.

[0014] In one possible implementation, the query module is also used for: Encapsulate the query logic, parameter configuration, and result set structure information in the target metadata to generate a calling document and access address that conforms to the general interface specification, so that external systems can call the query capability through the API interface.

[0015] Compared to traditional technologies, this invention provides a multi-mode visualization and intelligent rule configuration query system, including a monitoring module, a management module, and a query module. The monitoring module monitors user online status, system operating status, and cached information in real time. The query module determines target metadata based on user-input query commands, user online status, system operating status, and cached information to form a target condition input control. The management module determines and displays the query results based on the target condition input control. This invention changes the traditional method of hard-coding query logic in code by treating "query" itself as configurable metadata. Any new query requirement can be deployed without modifying backend code or redeploying the system; it only needs to be configured in the interface. This enables rapid configuration and hot deployment of query requirements, multi-scenario reuse, and service-oriented output, significantly improving the efficiency and flexibility of government data queries. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the structure of the query system with multi-mode visualization and intelligent rule configuration provided in the embodiments of the present invention. Detailed Implementation

[0017] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0018] Figure 1 This is a schematic diagram of the structure of the query system with multi-mode visualization and intelligent rule configuration provided in an embodiment of the present invention. For example... Figure 1 As shown, the system includes: Monitoring module 11, management module 12, and query module 13; Monitoring module 11 is used to monitor user online status, system operating status, and cache information in real time; The query module 12 is used to determine the target metadata based on the user's query command, user online status, system running status and cache information, so as to form the target condition input control; The management module 13 is used to input controls based on target conditions, determine and display query results.

[0019] In this embodiment of the invention, to address the current problem of long-term or temporary data query and statistics in the government sector, the Neusoft Government Intelligent Query and Statistics System was developed. This system adopts a platform-based data management model and a visual design concept, providing two configuration modes: development mode and user mode. It is suitable for different types of personnel to configure and can easily, quickly, and conveniently configure queries, statistics, and customized reports to meet various business needs of users, significantly improve work efficiency, and achieve "any query, any statistics".

[0020] The system includes system management, system monitoring, general query management, and report management. System management mainly includes user management, organization management, role management, menu management, position management, dictionary management, parameter management, notifications and announcements, and log management; system monitoring mainly includes online users, service monitoring, and cache monitoring; general query management includes data source definition, general query definition (development), and general query definition (user).

[0021] User management specifically includes: User information maintenance: Supports the creation, modification, and deletion of user accounts, including basic information such as username, password, and contact information.

[0022] Access control: Assign appropriate permissions to users based on their roles and responsibilities to control their access to and operation scope on the system.

[0023] Organization management specifically refers to the information of various organizations within the management system, including organization name, hierarchical relationship, contact information, etc.

[0024] Role management specifically involves creating and editing different system roles, each corresponding to a different set of permissions. These defined roles are then assigned to specific users or user groups, simplifying the permission management process.

[0025] Menu management specifically involves maintaining the system's menu structure, including menu names, URLs, display order, etc., setting menu access permissions for different roles or users, and controlling the functional modules that users can access.

[0026] Job management specifically involves defining job information within the system, including job titles and job scope. It also involves associating jobs with corresponding roles to map jobs to permissions.

[0027] Dictionary management specifically involves maintaining various dictionary data within the system, such as gender, education level, and professional title. It also provides dictionary value selection options for relevant fields in the system, facilitating data entry and retrieval for users.

[0028] Parameter management specifically involves setting and managing global parameters for the system, allowing administrators to dynamically adjust parameter values ​​as needed to adapt to changes in the system environment.

[0029] The notifications and announcements specifically involve publishing system notifications and announcements to remind users of important matters. Users can view published announcements, and administrators can edit and delete them.

[0030] Log management specifically involves recording all user actions within the system, including login, querying, and modification, and providing log query functionality to help administrators analyze user behavior and system operating status.

[0031] System monitoring specifically includes: It allows real-time monitoring of user online status, including login name, department name, host IP address, browser, operating system, and login time. It also allows for forced logout of users.

[0032] Responsible for real-time monitoring and early warning of the system's operational status to ensure system stability and availability. Real-time monitoring of system performance metrics such as CPU, memory, and disk usage to ensure efficient utilization of system resources.

[0033] Responsible for monitoring system cache information, including Redis version, runtime, memory usage, CPU usage, etc.

[0034] The general query definition specifically includes: 1. Data Source Definition. The data source definition is fundamental to the system and manages the data sources the platform needs to connect to, supporting Oracle and MySQL. After maintaining the database connection configuration, you can test whether the database can be accessed normally.

[0035] 2. General Query Definition (Development Mode). This feature is primarily for developers. It allows them to configure basic query information using pre-prepared SQL statements, including query ID, query name, business number, data source, and query SQL. After configuring the basic information, query conditions and results need to be configured.

[0036] 3. General Query Definition (User Mode). Users can define their own queries and statistics in a visual mode, with two options: table query and report query. Table query includes general query definition, related condition definition, setting query fields, setting result fields, setting other information, and script editing. After configuration, users can directly preview the configured query page and query the interfaces they want. Report query is mainly for customized queries. Users can design reports in advance according to their needs using reporting tools, and then configure the report's query conditions using this function.

[0037] This invention provides a dual-mode visual configuration interface, offering both development and user modes, to meet the different needs of technical personnel and general users respectively. In development mode, technical personnel can use SQL statements to define complex queries; in user mode, general users can perform query and statistical configurations by dragging and dropping interface elements.

[0038] This invention allows users to freely define query conditions and result display fields as needed, and configure them through a graphical interface. The system supports dynamic adjustment of query conditions and real-time preview of query results, improving user experience and operational efficiency.

[0039] This invention's system organically combines traditional access control mechanisms with custom query functionality, allowing users to configure corresponding permissions when defining query statements. Through a combination of roles and responsibilities, the system provides fine-grained control over query permissions, ensuring that different users access data according to their permissions. Embedding permission information within custom query statements enables automatic data filtering based on user permissions during query execution. Furthermore, the system allows administrators to dynamically adjust user query permissions, with real-time effects, flexibly responding to different business scenarios and ensuring both data access security and flexibility.

[0040] This invention combines workflow technology with custom query functionality to define and implement query processes of different methods and procedures, such as simple queries, user-defined queries, SQL-defined queries, and data permission queries.

[0041] In some embodiments, the query module is configured to: determine business requirements, query configuration mode, and target data source type based on query instructions, user online status, system operating status, and cache information; configure target metadata based on business requirements, query configuration mode, and target data source type; and determine target condition input controls based on target metadata.

[0042] In some embodiments, the query module is configured to: parse query instructions to determine business requirements; determine user permissions and query configuration modes based on user online status; wherein the query configuration modes include user data mode and developer mode; and determine the target data source type based on business requirements, user permissions, system operating status, and cache information.

[0043] In this embodiment of the invention, the query module first performs deep semantic analysis on the user-input query command, extracting core business information such as the data query scope, statistical dimensions, result display format, and customization requirements. This clarifies the data content and business objectives the user needs to obtain, while simultaneously verifying the completeness and legality of the command, laying the foundation for subsequent configuration processes. Based on this, the query module correlates with user online status obtained from the monitoring module, extracting key information such as user role, affiliated organization, and login identity. Combined with the permission configuration rules in the system management module, it accurately determines the user's data access permissions, operation permissions, and configuration permission levels. Furthermore, it automatically matches the corresponding query configuration mode based on the user's role type. If the user is a technical personnel with SQL writing permissions, the mode is determined to be developer mode; if the user is a regular business user without technical development capabilities, the mode is determined to be user mode, achieving precise adaptation of the configuration mode to the user's identity and permissions.

[0044] After clarifying business requirements, user permissions, and query configuration modes, the query module further integrates system operating status feedback from the monitoring module (including performance indicators such as CPU load, memory usage, disk I / O, and service response efficiency) and cache information (covering data source connection status, historical query configuration cache, and hot data cache status). Combined with the determined complexity of business requirements (such as whether multi-table joins or complex calculation logic are involved) and the scope of user permissions, the module comprehensively evaluates and filters available data sources. The system prioritizes data source types that are stable, have available connections, and are compatible with business requirements (such as Oracle and MySQL). If system resources are currently strained, cached data source connections or historical query configurations are reused first, based on cache information, to reduce system overhead. Simultaneously, it ensures that the selected target data source type meets user permission requirements and can fully support the implementation of business requirements. Finally, through collaborative analysis of multi-dimensional information, the optimal target data source type is determined.

[0045] In some embodiments, the query module is configured to: select a matching target metadata template from a preset metadata template library according to a query configuration mode; obtain the database dialect features corresponding to the target data source type according to the target data source type, and convert the general query logic in the target metadata template into a dialect query statement adapted to the target data source type, generating adapted query logic metadata; perform semantic parsing on the adapted query logic metadata according to business requirements, and extract the parameter metadata and result set structure metadata contained in the dialect query statement; wherein, the parameter metadata is used to describe the input parameters required for the query, and the result set structure metadata is used to describe the field information returned by the query; and combine and encapsulate the adapted query logic metadata, parameter metadata, and result set structure metadata to form target metadata.

[0046] In this embodiment of the invention, the query module first precisely selects matching target metadata templates from the system's preset metadata template library based on the determined query configuration mode. The metadata template library designs differentiated templates for development mode and user mode respectively: the templates for development mode include technically oriented configuration nodes such as SQL logic configuration fields and basic query information entry items, adapting to the entry and management of complex SQL statements; the templates for user mode simplify technical configuration items, highlighting query logic fields and component combination configuration items related to visual operations, conforming to the no-code operation habits of ordinary users. Through precise matching of modes and templates, a standardized foundation is laid for subsequent metadata configuration.

[0047] After determining the target data source type, the query module automatically retrieves the database adaptation information stored in the system's data source definition module to obtain the dialect characteristics corresponding to that data source type (e.g., Oracle, MySQL), including core differences such as syntax rules, function usage, and field naming conventions. Based on these dialect characteristics, the system performs targeted conversion and adaptation on the general query logic in the target metadata template, adjusting the query logic with a general syntax format to a specific statement that conforms to the dialect requirements of the target data source. Simultaneously, it verifies syntax compatibility and execution feasibility, generating adapted query logic metadata, thus achieving technical decoupling between the query logic and the heterogeneous data source.

[0048] Subsequently, the query module, based on the defined business requirements, performs deep semantic analysis on the adapted query logic metadata. By traversing the statement structure, field relationships, and operation rules in the query logic, it accurately identifies the input parameter information contained in the dialect query statement, extracts and forms parameter metadata, covering key attributes such as parameter name, data type, constraint rules, and optional value range, clearly defining the input conditions required for the query. At the same time, according to the requirements for result display in the business requirements, it parses the returned field structure of the query statement, clarifies the result set structure metadata, including field names, data types, display order, and associated dictionary information, ensuring the consistency of the result set with the business requirements.

[0049] Finally, the query module systematically combines and encapsulates the adapted query logic metadata, extracted parameter metadata, and result set structure metadata. During encapsulation, not only is the core information of the three types of metadata integrated, but also query configuration mode identifiers, data source type markers, and reuse permission attributes are embedded, forming a complete and logically consistent target metadata. This target metadata retains the query execution capability adapted to the data source and contains key information supporting interface interaction, conditional input, and result display, providing unified data support for subsequent dynamic generation of conditional input controls, query execution, and multi-scenario reuse.

[0050] In some embodiments, the query module is specifically used for: in developer mode, selecting a target metadata template containing SQL logic configuration fields and basic query information input items from the metadata template library according to the preset metadata template corresponding to developer mode; parsing the mapping relationship between the custom SQL statement, query condition parameters, constraint rules, and result fields in the query instruction; filling the custom SQL statement into the SQL logic configuration field of the target metadata template, and entering basic information such as query ID, query name, and business number into the corresponding template items to form initial query logic metadata; obtaining the corresponding database dialect features according to the target data source type, performing dialect adaptation verification and conversion on the custom SQL statement in the initial query logic metadata, and generating query logic metadata adapted to the target data source; performing semantic parsing on the adapted query logic metadata based on business requirements, associating the parsed query condition parameter information, extracting parameter metadata, and determining the result set structure metadata according to the result field mapping relationship; combining and encapsulating the adapted query logic metadata, the extracted parameter metadata, and the result set structure metadata, while embedding the developer mode identifier and the basic query component reuse tag to form target metadata.

[0051] In this embodiment of the invention, when the query configuration mode is developer mode, the query module first focuses on the target of precise matching of the metadata template. The system's built-in metadata template library, tailored to the characteristics of developer mode, pre-sets exclusive templates containing SQL logic configuration fields and basic query information input items. Based on the developer mode identifier, the query module filters out the fully compatible target metadata template from the template library. This template not only reserves an input area for custom SQL statements but also includes basic information input items such as query ID, query name, business number, and data source association, providing structural support for subsequent standardized configuration.

[0052] Subsequently, the query module performs in-depth analysis of the query commands, accurately extracting the core configuration information. On one hand, it extracts the custom SQL statements input by technical personnel, which carry complex query business logic, including advanced query requirements such as multi-table joins, customized filtering conditions, and aggregation calculations. On the other hand, it analyzes the query condition parameters, parameter constraint rules (such as required fields, numerical ranges, data type restrictions, etc.), and mapping relationships of result fields (such as field aliases, data format conversion rules, etc.) contained in the query commands, structuring and organizing this scattered configuration information to prepare for subsequent metadata population.

[0053] After completing information parsing, the query module initiates the metadata template population process. The parsed custom SQL statements are completely filled into the SQL logic configuration fields of the target metadata template, ensuring the accurate entry of the core query logic. Simultaneously, basic information such as query ID, query name, and business number are entered into the specified items of the template one by one, ensuring that all information in the template is complete and standardized. This ultimately forms the initial query logic metadata, containing complete query logic and basic configuration. This metadata initially presents the core framework of the query, but it has not yet been adapted to the technical characteristics of the specific data source.

[0054] To ensure compatibility between the query logic and the target data source, the query module, based on the determined target data source type (such as Oracle, MySQL, etc.), calls the database adaptation information stored in the system data source definition module to obtain the dialect characteristics corresponding to that data source, including key technical details such as syntax rules, function usage, field naming conventions, and keyword differences. Based on these dialect characteristics, the custom SQL statements in the initial query logic metadata undergo targeted adaptation, verification, and conversion. Syntactic structures incompatible with the target data source dialect are corrected, and dedicated functions and format identifiers are added to ensure that the SQL statements can execute correctly in the target data source, generating query logic metadata adapted to the target data source.

[0055] The adapted query logic metadata still needs to undergo deep semantic parsing based on business requirements to extract key metadata information. Guided by business needs, the query module traverses the SQL statement structure within the adapted query logic metadata, associates it with previously parsed query condition parameters, accurately identifies the input parameters contained in the statement, and extracts parameter metadata covering core attributes such as parameter name, data type, constraint rules, and default values. Simultaneously, based on the parsed result field mapping relationships, it clarifies the field composition of the query results and determines the result set structure metadata, including field names, data types, display order, and associated dictionary information, ensuring that the metadata can fully support subsequent query execution and result display.

[0056] After extracting all metadata, the query module initiates a combination and encapsulation process, systematically integrating the adapted query logic metadata, extracted parameter metadata, and result set structure metadata. During encapsulation, not only is the logical coherence and structural integrity of the three types of metadata ensured, but a developer mode identifier is also specifically embedded for subsequent system identification of the source mode of the query configuration. Simultaneously, a basic query component reuse marker is added, clearly indicating that the target metadata has the permission to be referenced, combined, and extended by user modes, making it a reusable standardized query unit in the system cache.

[0057] Ultimately, through the aforementioned series of standardized processes, the query module generates target metadata that is structurally complete and functionally comprehensive. This target metadata includes query execution logic adapted to specific data sources, as well as key information supporting interface interaction, conditional input, and result display. It also combines the technical characteristics of developer mode with the reusability of basic query components, satisfying the customization needs of technical personnel for complex queries while providing high-quality, reusable resources for ordinary user mode, thus achieving synergistic interaction between the two modes.

[0058] In some embodiments, the query module is specifically used for: in user mode, parsing the basic query components selected by the user in the query instruction, and expanding and combining the basic query components to obtain custom query components; wherein, in user mode, table query scenarios and report query scenarios are set; the basic query components in the table query scenario include the definition of association conditions, the selection of query fields, the sorting of result fields, and simple script editing; the basic query components in the report query scenario include a preset report template and corresponding customized query conditions; based on the preset metadata template corresponding to user mode, filling the query logic, association conditions, and field configuration information in the custom query components into the corresponding items of the template to form initial query logic metadata; obtaining the corresponding database dialect features according to the target data source type, performing dialect adaptation conversion on the initial query logic metadata to generate query logic metadata adapted to the target data source; performing semantic parsing on the adapted query logic metadata based on business requirements, automatically identifying the parameter information contained in the combined configuration, extracting parameter metadata, and determining the result set structure metadata according to the result field information set by the user; combining and encapsulating the adapted query logic metadata, the extracted parameter metadata, and the result set structure metadata, embedding user mode identifiers and visual configuration tags to form target metadata.

[0059] In this embodiment of the invention, when the query configuration mode is user mode, the query module first performs targeted parsing of the user's input query command, and extracts the basic query components selected by the user. These components are standardized query units pre-configured in development mode and stored in the system cache, possessing stable query logic and reusability. After clarifying the user's core needs, the query module supports users in flexibly expanding and combining the selected basic query components based on business requirements, breaking the functional limitations of a single component, forming custom query components that fit actual business scenarios, allowing ordinary users to achieve personalized query configuration without writing code.

[0060] The user mode is specifically divided into two main scenarios: table query and report query. Each scenario corresponds to a different set of basic query components to accurately match different business needs. The table query scenario focuses on routine data query and statistics, providing basic components such as defining association conditions, selecting query fields, sorting result fields, and simple script editing. Users can set multi-table association rules, filter required query fields, and adjust the display order of results through visual operations. They can also supplement personalized filtering logic through simple scripts. The report query scenario is designed for customized data display needs, providing preset report templates and corresponding customized query condition components. Users can directly select the system's pre-built standardized report templates and then configure query conditions to limit the data range to quickly generate customized reports that meet the format requirements.

[0061] After generating a custom query component, the query module calls the preset metadata template corresponding to the user mode. This template hides the complex underlying technical configurations, highlighting business-oriented configuration items and adapting to the operating habits of ordinary users. The system automatically fills in the integrated query logic, set association conditions, selected fields, and sorting rules from the custom query component according to the template's preset structure specifications, ensuring standardized storage and association of all configuration information. This ultimately forms the initial query logic metadata, laying the foundation for subsequent data source adaptation.

[0062] To ensure compatibility between query logic and the target data source, the query module automatically retrieves database adaptation information stored in the system's data source definition module based on the determined target data source type (e.g., Oracle, MySQL). This information reveals the unique dialect characteristics of the target data source, including core technical details such as syntax rules, function formats, and field naming conventions. Based on these dialect characteristics, the system automatically performs dialect adaptation conversion on the initial query logic metadata, shielding the underlying data source's technical differences. Users do not need to concern themselves with details such as database drivers and dialect syntax to generate query logic metadata that can execute correctly in the target data source, thus decoupling business configuration from technical implementation.

[0063] The query module is business-demand oriented, performing deep semantic analysis on the adapted query logic metadata. By traversing the query structure, field relationships, and configuration rules after component combination, it automatically identifies all parameter information contained therein, including user-defined query condition parameters and filtering thresholds, accurately extracting parameter metadata covering key attributes such as parameter name, data type, optional value range, and default configuration. Simultaneously, based on the result field information set by the user in the custom query component, it clarifies the field composition, data type, display format, and related dictionary mapping relationships of the query results, determining the complete result set structure metadata and ensuring a high degree of alignment between the metadata and business requirements.

[0064] After completing the adaptation of query logic metadata and the extraction of core metadata, the query module initiates a systematic combination and encapsulation process. The adapted query logic metadata, extracted parameter metadata, and result set structure metadata are organically integrated to ensure logical correlation and data consistency among the three types of metadata. During the encapsulation process, a user mode identifier is specifically embedded to facilitate the system's subsequent identification of the source mode of the query configuration; simultaneously, a visual configuration marker is added to clearly indicate that the metadata was generated through user mode visual operations, providing a basis for identification for subsequent reuse in multiple scenarios and permission verification.

[0065] Ultimately, the standardized process described above generates a complete and functional target metadata set. This target metadata not only includes query execution logic adapted to the target data source but also integrates key information supporting interface interaction, conditional input, and result display, fully adapting to no-code operation scenarios for ordinary users. Its embedded pattern identifiers and configuration tags ensure accurate metadata management by the system and support the core requirement of "configuration once, reuse in multiple scenarios," allowing user-configured queries to be directly applied to multiple scenarios such as query interface display, API calls, and form data rendering, significantly improving the data query and statistical efficiency for ordinary users.

[0066] In some embodiments, the query module is specifically used to: parse the parameter metadata in the target metadata according to the metadata parsing engine, extracting parameter names, parameter data types, optional value ranges, parameter constraint rules, and dictionary association mapping information; automatically match the corresponding interface input control type according to the parameter data type, and fill the control option data based on the optional value range and dictionary association mapping information; configure validation logic for the input control in combination with parameter constraint rules to complete real-time legality validation; and perform two-way binding between the parsed parameter metadata and the generated conditional input control using a conditional dynamic binding mechanism, so that the parameter values ​​input by the control can be directly mapped to the parameter configuration of the target metadata to obtain the target conditional input control.

[0067] In this embodiment of the invention, the query module is equipped with a professional metadata parsing engine to perform in-depth and precise parsing of parameter metadata in the target metadata. This engine comprehensively extracts core parameter attribute information by traversing parameter configuration nodes in the metadata structure. This includes parameter names that identify the parameter, parameter data types that define the data storage format (such as character, numeric, date, and boolean types), the range of selectable values ​​that limits the parameter input range, parameter constraint rules that standardize parameter input requirements (such as required fields, data length, numerical range, and date validity period), and dictionary association mapping information of standardized data from the associated system. This ensures that the key details of the parameter metadata are completely extracted, providing comprehensive data support for subsequent control generation.

[0068] After parsing the parameter metadata, the query module automatically matches the corresponding control type based on preset adaptation rules between the parameter data type and the interface input control type. If the parameter data type is character and has a limited range of selectable values, it automatically adapts to a dropdown control; if it is numeric, it is further subdivided into integer and floating-point types, matching integer input boxes and floating-point input boxes respectively; if it is date type, it adapts to a date picker that supports single date selection or date range selection; if it is Boolean, it matches a checkbox or radio button control. Simultaneously, combining the parsed range of selectable values ​​and dictionary association mapping information, the module retrieves the corresponding standardized data from the system dictionary management module and automatically populates it into the control options, ensuring the accuracy and standardization of the control option data, eliminating the need for users to manually enter options.

[0069] To ensure the validity of user input parameters, the query module transforms the parsed parameter constraint rules into validation logic executable by the input controls. For required field constraints, a starred indicator is added to the control, and a validation rule of "cannot submit without input" is configured. For data length constraints, an input character count function and over-limit interception logic are configured; when the input characters exceed the limit, a prompt is displayed in real time, and further input is prevented. For constraints such as numerical ranges and date validity periods, range boundary validation logic is configured; when the input value exceeds the set range, an error message is automatically triggered. Through this series of validation logic configurations, real-time guidance and validity verification of user input behavior are achieved, ensuring the accuracy of query parameters from the source.

[0070] The query module employs a dynamic condition binding mechanism to establish a bidirectional mapping between the parsed parameter metadata and the generated condition input controls. Through a declarative binding protocol, attributes such as parameter names, data types, and constraint rules in the parameter metadata are mapped one-to-one with the input fields, data formats, validation logic, and status feedback functions of the condition input controls, forming a stable association. When a user enters, modifies, or selects parameter values ​​through the condition input controls, the binding mechanism captures the operation events in real time, automatically converts and validates the input data according to the format requirements of the parameter metadata, and synchronously updates the parameter configuration items corresponding to the target metadata after successful validation, achieving real-time mapping from input parameters to metadata. Conversely, if the parameter metadata in the target metadata undergoes dynamic adjustments (such as updates to the selectable value range or changes to constraint rules), the binding mechanism listens for change events in real time and automatically triggers attribute updates to the condition input controls, ensuring that the controls and metadata remain synchronized and adapted.

[0071] Through the aforementioned series of standardized and automated processes, the query module ultimately generates a target condition input control that is highly adapted to business needs and supports dynamic interaction. This control not only features precise parameter input and real-time validity checks, but also achieves close linkage with target metadata through two-way dynamic binding. Users can configure and modify query conditions through visual operations without manually writing any code. This lowers the operational threshold for ordinary users while ensuring the efficiency and accuracy of query configuration, laying a solid foundation for the generation and execution of subsequent query commands.

[0072] In some embodiments, the query module is specifically used for: traversing parameter configuration nodes in the target metadata, extracting parameter names and parameter data types from the nodes; identifying data source reference identifiers defined in the parameter configuration nodes, retrieving the system dictionary management module based on the data source reference identifiers, obtaining the corresponding standardized dictionary data and dictionary association mapping information, matching the dictionary association mapping information with the obtained standardized dictionary data to parse out the range of optional values; reading constraint configuration items in the parameter configuration nodes, converting the constraint configuration items into a structured set of constraint logic to extract parameter constraint rules.

[0073] In this embodiment of the invention, when the query module initiates the parameter metadata parsing process, it first performs a comprehensive traversal of the parameter configuration nodes in the target metadata. This traversal covers all parameter-related configuration branches in the metadata structure, ensuring that no key parameter information is missed. During the traversal, the system accurately identifies the core attributes of each node, focusing on extracting the parameter name used to uniquely identify the parameter, as well as the parameter data type that defines the parameter data storage format and processing rules. This includes basic types such as character, numeric, date, and boolean types, while also supporting the recognition of complex data structures, providing basic attribute support for subsequent control matching, data validation, and other processes.

[0074] After extracting the basic attributes of the parameters, the query module further identifies the preset data source reference identifier in each parameter configuration node. This identifier is a key index for the standardized dictionary data of the associated system. Based on this reference identifier, the system automatically retrieves the corresponding standardized dictionary dataset from the system's dictionary management module. This dictionary data is uniformly maintained by the system and covers commonly used categories of data in government affairs (such as organization type, business status, approval level, etc.). Simultaneously, it obtains dictionary association mapping information to clarify the correspondence between parameters and dictionary data. By accurately matching the dictionary association mapping information with the standardized dictionary data, the system automatically parses out the range of possible parameter values, ensuring that parameter input options conform to business specifications and data standards.

[0075] The query module continues to read the constraint configuration items from the parameter configuration node. These items are stored in a structured or semi-structured format and contain various restrictions on parameter input. The system uses a pre-defined rule parsing engine to convert the scattered constraint configuration items into a unified set of structured constraint logic. For example, it converts "required" markers into non-empty validation logic, "length limit" into character counting constraints, "numerical range" into range boundary validation rules, and "date validity period" into time range filtering logic. During this process, the system performs syntax validation and logical integration on the constraint configuration items to ensure the completeness and executability of the constraint rules, ultimately forming a clear and standardized set of parameter constraint rules.

[0076] The entire parsing process centers on the target metadata, employing three key steps—node traversal, identifier retrieval, and rule transformation—to comprehensively extract parameter names, data types, optional value ranges, and parameter constraint rules. During parsing, the system deeply integrates with standardized data from the system's dictionary management module, ensuring consistency between parameter attributes and business specifications. Simultaneously, structured processing transforms non-standardized configuration information into system-recognizable and executable logical data, providing accurate and complete data support for subsequent conditional input control generation, dynamic binding, and validity verification, thus guaranteeing the standardization of query configuration and the accuracy of query execution.

[0077] In some embodiments, the query module is specifically used for: establishing a mapping association between parameter metadata and conditional input controls, mapping the attribute information of the parameter metadata to the configuration items of the conditional input controls, and realizing the initialization and alignment of control attributes; capturing the interaction events of the conditional input controls through a listening mechanism, and converting the operation data into target data conforming to the parameter format according to the mapping association when the user inputs data, and updating it synchronously to the parameter metadata after verification; listening for parameter metadata change events, and automatically triggering the attribute update of the conditional input controls according to the changed content when dynamic adjustment of parameter metadata is detected; and integrating permission control logic during the binding process to perform access permission verification on the parameter metadata according to the current user's permission level.

[0078] In this embodiment of the invention, the query module first establishes a precise mapping relationship between parameter metadata and conditional input controls. Based on preset mapping rules, it maps the core attribute information in the parameter metadata to the corresponding configuration items of the conditional input controls one by one. Specifically, it maps the parameter name to the control's label display text, the parameter data type to the control's input format constraints, the optional value range and dictionary association mapping information to the control's option data source, and the parameter constraint rules to the control's basic validation configuration. Through this series of mapping operations, the initialization of the conditional input control's attributes is completed, ensuring that the control and parameter metadata are fully aligned at the attribute level, laying the foundation for subsequent bidirectional interaction.

[0079] The query module uses a built-in listening mechanism to capture various interactive events of the conditional input controls in real time, including parameter entry, option selection, and data modification. When a user inputs data through the controls, the system immediately converts the raw data generated by the user's operation according to the format requirements specified by the parameter metadata, based on the established mapping associations. For example, it converts text input to numeric types and converts selection labels to corresponding encoded values. Subsequently, it calls the preset validation logic to perform legality checks on the converted target data, including data type matching, whether it conforms to the range of selectable values, and whether it meets the constraint rules. After the validation passes, the system automatically updates the target data to the parameter configuration items corresponding to the parameter metadata, realizing real-time feedback and storage of user input data to metadata.

[0080] Meanwhile, the query module continuously monitors parameter metadata change events. When dynamic adjustments to the parameter metadata are detected (such as updates to the range of selectable values, modifications to constraint rules, or changes to dictionary-related data), the attribute update process of the conditional input controls is immediately triggered. The corresponding configuration of the controls is automatically adjusted according to the changes, such as synchronously refreshing the control option list, updating data validation logic, and modifying label display text, ensuring that the conditional input controls and parameter metadata remain consistent at all times. Furthermore, throughout the entire two-way binding process, the system integrates fine-grained access control logic. Based on the current user's role, organization, and permission level, access permissions are verified for the parameter metadata. Only the input controls corresponding to parameters that the user has the authority to operate are displayed; for sensitive parameters that the user does not have access to, the relevant controls are hidden or made uneditable.

[0081] In some embodiments, the query module is further configured to: encapsulate the query logic, parameter configuration, and result set structure information in the target metadata, and generate a call document and access address that conform to the general interface specification, so that external systems can call the query capability through the API interface.

[0082] In this embodiment of the invention, after completing the target metadata configuration, the query module initiates an automatic conversion process from query configuration to external interface. The core of this process is the standardized encapsulation of key information in the target metadata. The system automatically extracts the adapted query logic, structured parameter configuration (including parameter names, data types, constraint rules, etc.), and clear result set structure information (including field names, data types, display formats, etc.) from the metadata. This information is then integrated and processed according to common interface specifications to ensure the universality and compatibility of the interface output format. Simultaneously, a corresponding interface call document is generated, detailing the interface request method, parameter descriptions, return result examples, error code explanations, and other key information, providing clear calling guidance for external system developers.

[0083] Based on this, the query module automatically generates a unique interface access address, which corresponds one-to-one with the encapsulated query configuration, shielding the differences in underlying data sources, query logic implementation details, and system internal architecture. External systems do not need to concern themselves with the technical implementation process of the query; they only need to pass the corresponding parameters according to the calling documentation through this standardized API interface to directly invoke the system's query capabilities and obtain standardized query results. This mechanism realizes the transformation from "data query" to "data service," directly converting user-configured query requirements into programmable and integrable software assets. It completely solves the problem of repetitive work in traditional development, where separate interfaces are developed for each query requirement. It fully embodies the deep application of the "low-code" concept at the service layer, greatly improving the reusability of the system's query capabilities and the efficiency of external services.

[0084] The multi-mode visualization and intelligent rule configuration query system provided by this invention consists of a monitoring module, a management module, and a query module, focusing on the efficient query needs in the field of government data management. The monitoring module monitors user online status, system operating status, and cached information in real time. The query module, based on user query commands and monitoring data, accurately determines business requirements, query configuration modes, and target data source types, then configures target metadata and forms target condition input controls. The management module then uses these controls to determine and display the query results. The system breaks away from the limitations of traditional hard-coded query logic, treating "query" as configurable metadata. New query requirements do not require modification of backend code or redeployment; they can be quickly deployed simply through interface configuration, enabling hot deployment of query requirements, multi-scenario reuse, and service-oriented output, significantly improving the efficiency and flexibility of government data queries.

[0085] The system is designed with differentiated query configuration modes to meet the needs of different users. Developer mode allows technical personnel to configure complex queries using custom SQL statements, generating target metadata through processes such as metadata template matching, dialect adaptation verification, and semantic parsing. User mode provides visual operation for ordinary users, covering table and report query scenarios. Users can complete configuration by combining and extending basic query components, and the system automatically handles metadata adaptation and generation. During the formation of target condition input controls, the system extracts core parameter information through a metadata parsing engine, automatically matches the interface input controls, and configures validation logic. A dynamic condition binding mechanism is used to achieve two-way linkage between parameter metadata and controls. Simultaneously, access control logic is integrated to verify parameter metadata access permissions based on user permission levels, ensuring data access security.

[0086] The system boasts powerful API service output capabilities, standardizing and encapsulating query logic, parameter configuration, and result set structure information from target metadata to generate call documents and unique access addresses that conform to common interface specifications, facilitating rapid integration and use by external systems. To comprehensively ensure the security and compliance of interface calls, the interface design incorporates multiple security mechanisms: interface input parameters are required to include identity fields such as the caller's unique identifier, request timestamp, and signature information. The caller's unique identifier is a dedicated credential assigned by the system to registered callers. The system verifies whether the caller has completed legal registration, whether the registration status is valid, and whether the authorization scope matches the current query requirements by searching the registration information database. Callers without registration or with expired registration will be directly blocked. Simultaneously, it supports associating the caller's list of legitimate access IPs with the caller's unique identifier. When the interface receives a request, it automatically extracts the source IP address and accurately matches it with the registered IP list, allowing only requests initiated by registered IPs to access the system, effectively preventing the risk of unauthorized IP access. In addition, the system is configured with request signature verification to prevent parameter tampering; differentiated call frequency thresholds are set based on the caller's unique identifier to avoid malicious high-frequency calls exhausting system resources; asymmetric encryption is used for sensitive parameters to ensure data transmission security; and interface call logs containing full information such as caller identity, IP verification results, request parameters, and response status are recorded, with a log retention period of no less than 90 days, supporting security event tracing and auditing. These multiple security mechanisms comprehensively prevent unauthorized access, parameter tampering, and data leakage risks, ensuring the legality, security, and traceability of interface calls, fully meeting the stringent requirements of the government sector for data query efficiency, flexibility, and security.

[0087] In the above embodiments, the descriptions of each embodiment have their own emphasis. Parts not detailed or described in a particular embodiment can be referred to in the relevant descriptions of other embodiments. Unless otherwise specified or in conflict with logic, the terminology and / or descriptions between different embodiments are consistent and can be referenced interchangeably. Technical features in different embodiments can be combined to form new embodiments based on their inherent logical relationships.

[0088] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A query system with multi-modal visualization and intelligent rule configuration, characterized in that, include: Monitoring module, management module, and query module; The monitoring module is used to monitor user online status, system operating status, and cache information in real time. The query module is used to determine target metadata based on the user's input query command, user online status, system running status, and cache information, so as to form a target condition input control; The management module is used to input controls based on the target conditions, determine and display the query results.

2. The query system with multi-mode visualization and intelligent rule configuration according to claim 1, characterized in that, The query module is used for: Based on the query command, user online status, system running status, and cache information, determine the business requirements, query configuration mode, and target data source type; Configure target metadata based on the business requirements, the query configuration mode, and the target data source type; Based on the target metadata, determine the target condition input control.

3. The query system with multi-mode visualization and intelligent rule configuration according to claim 2, characterized in that, The query module is used for: Parse the query command to determine the business requirements; Based on the user's online status, determine the user's permissions and query configuration mode; wherein, the query configuration mode includes user mode and developer mode; The target data source type is determined based on the business requirements, user permissions, system operating status, and cache information.

4. The query system with multi-mode visualization and intelligent rule configuration according to claim 3, characterized in that, The query module is used for: Based on the query configuration mode, select a matching target metadata template from the preset metadata template library; Based on the target data source type, obtain the database dialect features corresponding to the data source type, and convert the general query logic in the target metadata template into a dialect query statement that adapts to the target data source type to generate adapted query logic metadata; Based on the business requirements, semantic parsing is performed on the adapted query logic metadata to extract the parameter metadata and result set structure metadata contained in the dialect query statement; wherein, the parameter metadata is used to describe the input parameters required for the query, and the result set structure metadata is used to describe the field information returned by the query; The adapted query logic metadata, the parameter metadata, and the result set structure metadata are combined and encapsulated to form the target metadata.

5. The query system with multi-mode visualization and intelligent rule configuration according to claim 4, characterized in that, The query module is specifically used for: In developer mode, based on the preset metadata template corresponding to developer mode, the target metadata template containing SQL logic configuration fields and query basic information entry items is selected from the metadata template library; Parse the mapping relationship between custom SQL statements, query condition parameters, constraint rules, and result fields in the query command; Fill the SQL logic configuration field of the target metadata template with the custom SQL statement, and enter the basic information such as query ID, query name, and business number into the corresponding template items to form the initial query logic metadata; Based on the target data source type, obtain the corresponding database dialect features, perform dialect adaptation verification and conversion on the custom SQL statements in the initial query logic metadata, and generate query logic metadata adapted to the target data source. Based on business requirements, semantic parsing is performed on the adapted query logic metadata, the parsed query condition parameter information is associated, parameter metadata is extracted, and the result set structure metadata is determined according to the result field mapping relationship. The adapted query logic metadata, extracted parameter metadata, and result set structure metadata are combined and encapsulated, while embedding developer mode identifiers and basic query component reuse tags to form target metadata.

6. The query system with multi-mode visualization and intelligent rule configuration according to claim 4, characterized in that, The query module is specifically used for: In user mode, the basic query components selected by the user in the query command are parsed, and the basic query components are expanded and combined to obtain custom query components. Among them, table query scenario and report query scenario are set in user mode. The basic query components in the table query scenario include the definition of association conditions, the selection of query fields, the sorting of result fields, and the simple script editing. The basic query components in the report query scenario include the preset report template and the corresponding customized query conditions. Based on the preset metadata template corresponding to the user mode, the query logic, association conditions, and field configuration information in the custom query component are filled into the corresponding items of the template to form the initial query logic metadata. Based on the target data source type, obtain the corresponding database dialect features, perform dialect adaptation conversion on the initial query logic metadata, and generate query logic metadata adapted to the target data source. Based on business requirements, semantic parsing is performed on the adapted query logic metadata, the parameter information contained in the combined configuration is automatically identified, the parameter metadata is extracted, and the result set structure metadata is determined according to the result field information set by the user. The adapted query logic metadata, extracted parameter metadata, and result set structure metadata are combined and encapsulated, and user pattern identifiers and visual configuration tags are embedded to form target metadata.

7. The query system with multi-mode visualization and intelligent rule configuration according to claim 6, characterized in that, The query module is specifically used for: Based on the metadata parsing engine, the parameter metadata in the target metadata is parsed to extract parameter names, parameter data types, optional value ranges, parameter constraint rules, and dictionary association mapping information; The corresponding interface input control type is automatically matched according to the parameter data type, and the control option data is filled based on the optional value range and the dictionary association mapping information; Configure validation logic for the input control based on the aforementioned parameter constraint rules to complete real-time validity verification; The parsed parameter metadata and the generated conditional input control are bidirectionally bound using a conditional dynamic binding mechanism, so that the parameter values ​​input by the control can be directly mapped to the parameter configuration of the target metadata, thus obtaining the target conditional input control.

8. The query system with multi-mode visualization and intelligent rule configuration according to claim 7, characterized in that, The query module is specifically used for: Traverse the parameter configuration nodes in the target metadata and extract the parameter names and parameter data types from the nodes; Identify the data source reference identifier defined in the parameter configuration node, retrieve the system dictionary management module according to the data source reference identifier, obtain the corresponding standardized dictionary data and dictionary association mapping information, match the dictionary association mapping information with the obtained standardized dictionary data, and parse out the range of optional values; Read the constraint configuration items in the parameter configuration node, convert the constraint configuration items into a structured set of constraint logic, and extract the parameter constraint rules.

9. The query system with multi-mode visualization and intelligent rule configuration according to claim 7, characterized in that, The query module is specifically used for: Establish a mapping association between parameter metadata and conditional input controls, and map the attribute information of the parameter metadata to the configuration items of the conditional input controls to achieve the initialization and alignment of control attributes; The interaction events of the conditional input control are captured by a listening mechanism. When the user inputs data, the operation data is converted into target data that conforms to the parameter format according to the mapping association. After verification, the data is synchronously updated to the parameter metadata. Listen for changes in the parameter metadata. When a dynamic adjustment to the parameter metadata is detected, automatically trigger an attribute update for the conditional input control based on the changes. The binding process integrates access control logic to verify the access permissions of the parameter metadata based on the current user's permission level.

10. The query system with multi-mode visualization and intelligent rule configuration according to claim 9, characterized in that, The query module is also used for: The query logic, parameter configuration, and result set structure information in the target metadata are encapsulated to generate a calling document and access address that conform to the general interface specification, so that external systems can call the query capability through the API interface.

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