Quality inspection method and device supporting real-time geographic information data

Abstract

The invention discloses a quality inspection method and device supporting real-time geographic information data, and relates to the technical field of data processing, after data updating, centralized inspection does not need to be carried out through manpower or scripts, and the quality inspection can be carried out through a'geographic information system application platform-Java component-database storage layer 'linkage architecture. The quality inspection rule is triggered when the geographic information data is updated in real time, real-time quality inspection is carried out on the geographic information data stored in the data table of the geographic information system application platform, constraints of the quality inspection rule are dynamically generated according to data types, application scenes and the like, and the quality inspection rule can be dynamically adjusted according to different data changes. The problem that a traditional quality inspection mode is difficult to cover all application scenes is avoided, and therefore the requirement of a real-time service scene for real-time data verification is met. In addition, the quality inspection rules can be integrally exported as a template file, the reuse template file is imported in a new project through one key, the rules are automatically configured for the service data table, and the configuration efficiency is improved.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and more specifically, to a method and apparatus for quality inspection of real-time geographic information data. Background Technology

[0002] In Geographic Information System (GIS) industry applications, the quality of GIS data directly affects the accuracy of spatial analysis and decision-making. To improve GIS data quality, quality control of GIS data is necessary.

[0003] Traditional GIS data quality inspection methods mostly adopt offline batch processing mode, and perform centralized inspection by manual or script after the data is updated. Traditional GIS data quality inspection methods have problems such as high latency, slow response, and complex rule configuration, which cannot meet the needs of real-time business scenarios for instant data verification. Summary of the Invention

[0004] In view of this, this application discloses a quality inspection method and apparatus that supports real-time geographic information data, which aims to avoid the problem that traditional quality inspection methods are difficult to cover all application scenarios, thereby meeting the needs of real-time business scenarios for instant data verification.

[0005] To achieve the above objectives, the disclosed technical solution is as follows:

[0006] The first aspect of this application discloses a quality inspection method for real-time geographic information data, the method comprising:

[0007] When changes are detected in the geographic information data stored in the data table of the geographic information system application platform, a data change message is generated;

[0008] Identify the relational datasets involved in the data change messages and obtain the quality inspection rules corresponding to the relational datasets; the quality inspection rules are stored in a dynamic rule base; the quality inspection rules at least include the correspondence between rule tables, data tables, and association tables; the dynamic rule base at least includes relational datasets and attribute constraints;

[0009] The linkage architecture triggers the quality inspection rules to perform real-time verification of the data change message, and determines whether the data change message violates the quality inspection rules based on the verification results; wherein, the linkage architecture consists of at least a geographic information system application platform, Java components, and a database storage layer.

[0010] Optionally, when a change is detected in the geographic information data stored in the data table of the geographic information system application platform, generating a data change message includes:

[0011] The geographic information data stored in the data tables of the data storage layer of the geographic information system application platform is monitored through listeners in Java components.

[0012] When the listener detects that source table data in geographic information data has been edited, it generates a data change message.

[0013] Optional, also includes:

[0014] If the listener of a Java component detects that a relational dataset exists in the source table, it updates the target table and the related table in real time through data change messages in the source table.

[0015] Optional, also includes:

[0016] The relational dataset and attribute constraints are fused together, and the dynamic rule base is updated based on the fusion result; wherein, the attribute constraints consist of at least a value range, a subtype, and a condition value.

[0017] Optionally, the step of triggering the quality inspection rules through the linkage architecture to perform real-time verification of the data change message, and determining whether the data change message violates the quality inspection rules based on the verification result, includes:

[0018] During the process of real-time quality inspection of the data change message triggered by the linkage architecture, the preset building type is compared with the changed building type to obtain the first verification result, and the preset land type is compared with the changed land type to obtain the second verification result.

[0019] Based on the first verification result and the second verification result, determine whether the editing operation corresponding to the data change message violates the quality inspection rules.

[0020] Optional, also includes:

[0021] If the data change message violates the quality inspection rules, a constraint operation will be performed.

[0022] If the data change message violates the quality inspection rules, the constraint operation is executed, including:

[0023] If the first verification result indicates that the preset building type is inconsistent with the changed building type, or the second verification result indicates that the preset land type is inconsistent with the changed land type, it is determined that the data change message violates the quality inspection rules;

[0024] If the data change message violates the quality control rules, at least one of the following actions shall be taken: triggering a warning, performing a correction operation, or preventing data updates.

[0025] Optional, also includes:

[0026] If the first verification result indicates that the preset building type is consistent with the changed building type, and the second verification result indicates that the preset land type is consistent with the changed land type, then it is determined that the data change message does not violate the quality inspection rules;

[0027] Data change messages that do not violate the quality inspection rules are stored in the database.

[0028] Optional, also includes:

[0029] Once the industry standard data has been configured with rules, the API call will export all rules associated with the industry standard data as a template file.

[0030] When applying similar industry standard data, the template file is reused to parse out the template content and create a rule record corresponding to the template content in the database;

[0031] The rule records are bound to the target data table to achieve one-click configuration.

[0032] A second aspect of this application discloses a quality inspection device supporting real-time geographic information data, the device comprising:

[0033] The monitoring and generation unit is used to generate a data change message when changes are detected in the geographic information data stored in the data table of the geographic information system application platform.

[0034] The identification and acquisition unit is used to identify the relational dataset in which the data change message participates, and to acquire the quality inspection rules corresponding to the relational dataset; the quality inspection rules are stored in a dynamic rule base; the quality inspection rules include at least the correspondence between rule tables, data tables, and association tables; the dynamic rule base includes at least the relational dataset and attribute constraints;

[0035] The real-time verification unit is used to trigger the quality inspection rules through the linkage architecture to perform real-time verification on the data change message, and determine whether the data change message violates the quality inspection rules based on the verification result; wherein, the linkage architecture consists of at least a geographic information system application platform, Java components and a database storage layer.

[0036] Optionally, the monitoring generation unit includes:

[0037] The monitoring module is used to monitor the geographic information data stored in the data tables of the data storage layer of the geographic information system application platform through listeners in the Java component;

[0038] The first generation module is used to generate a data change message when the listener detects that the source table data in the geographic information data has been edited.

[0039] As can be seen from the technical solution, this application discloses a method and apparatus for quality inspection of real-time geographic information data. After data updates, centralized checks via manual methods or scripts are unnecessary. Instead, a linked architecture of the geographic information system application platform, Java components, and database storage layer is used to trigger quality inspection rules in real time when geographic information data is updated. This involves using rule tables from a dynamic rule base, relational information of structured stored geographic information data, attribute constraints, and other quality inspection rules to perform real-time quality inspection on the geographic information data stored in the data tables of the geographic information system application platform. If geographic information data changes, a data change message is generated, and constraints for the quality inspection rules can be dynamically generated based on data type and application scenario. Furthermore, the quality inspection rules can be dynamically adjusted according to different data changes, avoiding the problem that traditional quality inspection methods cannot cover all application scenarios, thus meeting the needs of real-time business scenarios for immediate data verification. In addition, the quality inspection rules are stored in the database and can be exported as a template file. This template file can be imported and reused in new projects with a single click, automatically configuring rules for business data tables and improving configuration efficiency. Attached Figure Description

[0040] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0041] Figure 1 This is a flowchart illustrating a quality inspection method for real-time geographic information data disclosed in an embodiment of this application.

[0042] Figure 2 This is a schematic diagram of the architecture of the GIS application platform disclosed in the embodiments of this application;

[0043] Figure 3 This is an example diagram illustrating the storage of condition value constraint information disclosed in the embodiments of this application;

[0044] Figure 4 This is an entity relationship diagram of the land use information table and rule table disclosed in the embodiments of this application;

[0045] Figure 5 This is an entity relationship diagram of the building-land association table and rule table disclosed in the embodiments of this application;

[0046] Figure 6 This is a schematic diagram of the structure of a quality inspection device that supports real-time geographic information data, as disclosed in an embodiment of this application.

[0047] Figure 7 This is a schematic diagram of the structure of the electronic device disclosed in the embodiments of this application. Detailed Implementation

[0048] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0049] In this application, the terms "comprising," "including," or any other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0050] As the background technology shows, after data updates, there is no need for centralized checks through manual methods or scripts. Instead, a real-time monitoring mechanism is used. This involves real-time monitoring of the geographic information data stored in the data tables of the geographic information system application platform. When geographic information data changes, a data change message is generated. The data change message is then subjected to real-time quality checks using the quality inspection rules of the relational dataset in which it participates. Since the quality inspection rules of the rule table in the dynamic rule base can be abstracted into structured data such as value ranges, condition values, and relational data, rule constraints can be dynamically generated based on data types and application scenarios. Furthermore, the quality inspection rules can be dynamically and automatically adjusted according to different data changes. This avoids the problem that traditional quality inspection methods cannot cover all application scenarios, thereby meeting the real-time business scenario's need for immediate data verification.

[0051] To address the aforementioned issues, this application discloses a method and apparatus for quality inspection of real-time geographic information data. After data updates, centralized manual or script-based checks are unnecessary. Instead, a linked architecture of the geographic information system application platform, Java components, and database storage layer triggers quality inspection rules in real-time upon data updates. This involves using rule tables from a dynamic rule base, relational information of structured geographic information data, attribute constraints, and other quality inspection rules to perform real-time quality inspection on the geographic information data stored in the data tables of the geographic information system application platform. If geographic information data changes, a data change message is generated, and constraints for the quality inspection rules can be dynamically generated based on data type and application scenario. Furthermore, the quality inspection rules can be dynamically adjusted according to different data changes, avoiding the problem of traditional quality inspection methods failing to cover all application scenarios. This meets the real-time data verification requirements of business scenarios. In addition, the quality inspection rules are stored in the database and can be exported as a template file. This template file can be imported and reused in new projects with a single click, automatically configuring rules for business data tables and improving configuration efficiency. The specific implementation is described in detail in the following embodiments.

[0052] It should be noted that the quality inspection method and apparatus for real-time geographic information data provided in this application relates to technical fields such as data processing. The above is only an example and does not limit the application field of the quality inspection method and apparatus for real-time geographic information data provided in this application.

[0053] refer to Figure 1 The diagram shown is a flowchart illustrating a quality inspection method for real-time geographic information data disclosed in this application. This method mainly includes the following steps:

[0054] S101: When changes are detected in the geographic information data stored in the data table of the geographic information system application platform, a data change message is generated.

[0055] In S101, a linkage editing mechanism for relational datasets is used. This involves monitoring the geographic information data stored in the data tables of the data storage layer of the GIS application platform through listeners in Java components. When the listener detects that the source table data in the geographic information data is being edited, the target table and related tables are updated in real time, generating a data change message.

[0056] A relational dataset is a dataset used to store the relationships between objects in two datasets. Relational datasets can permanently store the relationships between objects in one dataset and objects in another dataset, providing an advanced management mechanism that is compatible with both spatial and non-spatial data.

[0057] The architecture of the specific GIS application platform is as follows: Figure 2 As shown.

[0058] Figure 2 In China, a GIS application platform includes at least a storage layer, an application layer, and a component layer (such as a Java component layer).

[0059] The application layer provides interactive operations such as interface display and editing. When data is updated, the GIS application platform transmits the updated geographic information data to the Java component layer via API. These geographic information data change messages mainly include: adding records, deleting records, and modifying field values.

[0060] The component layer, a middleware layer developed in Java, provides interfaces for basic data reading and writing. Operations such as creating, deleting, and modifying relational datasets and attribute constraints are handled by the component layer's interfaces. Additionally, the component layer provides listeners for triggering actions. Through these listeners, the component layer detects data changes and immediately triggers a message mechanism, which then initiates validation.

[0061] The storage layer serves as the data storage medium, storing actual data, data rule information, etc. The storage layer is designed using data tables, rule tables, and relational tables. The following example illustrates the main storage design aspects of this technical solution:

[0062] Data Table: Used to store actual GIS data. Taking land use information as an example, the data table includes fields such as Land ID (SmID), Land Type ID (LandtypeID), Land Use Type (LandtypeName), Land Subtype Name (LandSubtypeName), and Land Area (Area), as shown in Table 1.

[0063] Table 1

[0064] Serial Number SmID LandtypeID LandtypeName LandSubtypeID LandSubtypeName Area 1 1 5 Construction land 501 Residential land 5000 2 2 5 Construction land 501 Residential land 2000 3 3 5 Construction land 501 Residential land 2000 4 4 5 Construction land 502 Industrial land 20000 5 5 5 Construction land 501 Industrial land 10000

[0065] S102: Identify the relational datasets involved in the data change messages and obtain the corresponding quality inspection rules for the relational datasets; the quality inspection rules are stored in the dynamic rule base; the quality inspection rules include at least the correspondence between rule tables, data tables, and association tables; the dynamic rule base includes at least the relational datasets and attribute constraints.

[0066] Dynamic rule base: By abstracting business rules into structured data such as value ranges, condition values, and relational data, rule constraints are dynamically generated based on data type and application scenario. This allows for automatic adjustment of quality inspection rules according to different data changes, avoiding the problem of traditional methods where constraint rules cannot cover all application scenarios. Abstracting business rules into structured data such as value range constraints, condition value restrictions, subtypes, and relational data allows for flexible modification and expansion of quality inspection rules.

[0067] In this context, the value range describes the range of values ​​a field can take, representing the set of all valid values ​​a field can have in a database table. Conditional values ​​are an attribute management function suitable for vector datasets and attribute table datasets. They make the value in one field dependent on the value in another field, thus constraining attribute editing. Fields participating in conditional values ​​must have a value range. Conditional values ​​further constrain the valid values ​​that can be entered into a field based on its value range. This is mainly achieved by setting the available field values ​​for one or more other fields based on the selected value in one field. In this way, the valid values ​​that can be entered into a set of fields mutually restrict each other, thereby ensuring data integrity and correctness.

[0068] Rule table: Used to store quality inspection rules, including but not limited to value range constraints, subclass relationships (associations between subtypes), and conditional value rules. Detailed constraints in the rule table include, but are not limited to, storage in JSON format. A subtype refers to dividing objects into different subsets within the same dataset based on common attributes. This method enables the categorized management of all objects in the dataset. For example, in a road dataset, different subtypes can be defined based on road type (e.g., highways, national roads, provincial roads).

[0069] Taking setting conditional value constraints for the land use type and land subtype fields as an example, when the land use type is construction land, the land subtype can only be residential land or industrial land. The conditional value constraint information can be stored in JSON format as follows: Figure 3 As shown. The entity relationship between the rule table and the land use information table is as follows. Figure 4 As shown.

[0070] Field constraints, including those related to value range, subtype, and condition values, are also implemented through a message mechanism. When the application layer edits a field value, the Java component detects that the record has field constraints. For example, if a field has a condition value constraint, the condition value sets the dependency relationship between fields. When editing the attribute table, a value from one field is selected to define and restrict the list of valid values ​​for another field. The following example illustrates how condition values ​​can be used to implement these mutual constraints between fields:

[0071] First: Set value ranges for the three fields of primary land category, secondary land category, and tertiary land category. For example, primary land category can be cultivated land, orchard, forest land, residential land, etc.; secondary land category can be dry land, irrigated land, shrubland, rural residential land, urban residential land, etc.; tertiary land category includes: sloping cultivated land, paddy field, artificial shrubland, street block, etc.

[0072] Second: Create conditions to manage these fields, adding primary land categories, secondary land categories, and tertiary land categories to the same condition.

[0073] Third: Set condition values ​​to implement mutual constraints between field values. For example, select the value for the primary land use category, such as forest land. For secondary land use categories, different primary land use categories will correspond to different secondary land use categories. If the primary land use category is forest land, the available secondary land use categories are: arbor forest land, bamboo forest land, mangrove forest land, forest swamp, shrubland land, thicket swamp, and other forest land. For example, select shrubland land. For tertiary land use categories, different secondary land use categories correspond to different tertiary land use categories. For example, if the primary land use category is shrubland land, the available tertiary land use categories are: natural shrubland land and artificial shrubland land. After setting the condition values, when editing the attribute table, an option list will be provided based on your previous selections. Selecting different primary land use categories will provide corresponding valid secondary land use category values, and then, based on the selected secondary land use category, selectable tertiary land use categories will be provided. The selection at each stage may result in different condition values, for example: forest land -> shrubland land -> natural shrubland land.

[0074] Figure 4 In this table, the land use information table includes land use type, land subtype, area, etc. The rules table includes rule type, table ID, constraint information, etc. Multiple rules can be configured for a single land use information table, linked through primary and foreign keys.

[0075] By leveraging the listener and message triggering mechanism of Java components, the system can dynamically verify data during real-time updates, instantly detect data errors or inconsistencies, and immediately correct or return a prompt.

[0076] If the listener of a Java component detects that a relational dataset exists in the source table, it updates the target table and the related table in real time through data change messages in the source table.

[0077] Association table: This is an attribute table of a relationship (such as a relational dataset). Association tables are used to store information about relationships between data tables. They are associated with the source table (the source table that builds the relational dataset) through foreign keys. When the source table is edited, the association table will be updated accordingly. Association tables can also be configured with rules for linkage constraints.

[0078] For example, to verify the consistency between building type and land type, this technical solution can create a dataset of land use and building relationships on a GIS platform, generating a "Building and Land Use Association Table." Constraints can then be added to this table to set the correspondence between building types and land subtypes. An example of the entity relationship between the specific building-land association table and rule table is shown below. Figure 5 As shown.

[0079] Figure 5In the table, the building information table includes building type, building name, number of floors, etc.; the building-land use association table includes building ID, building type, land use ID, land subtype, etc.; the rule table includes rule type, data table ID, constraint information; and the land use information table includes land use type, land subtype, area, etc.

[0080] The quality inspection rules include, but are not limited to, rule tables in a dynamic rule base, relational information of structured stored geographic information data, and attribute constraints.

[0081] The relational dataset and attribute constraints are merged, and the dynamic rule base is updated based on the fusion result; wherein, the attribute constraints consist of at least a value range, a subtype, and a condition value.

[0082] For example, when GIS data is manipulated and changes occur on the platform, the system automatically generates a "data change message," which is received and processed by a Java component, triggering the execution of corresponding quality inspection rules. This series of message transmissions and processing is automatic, ensuring that every data modification is detected and processed by the system, thus achieving real-time quality inspection. Real-time quality inspection is based on Java component listening and message triggering to achieve automatic verification and immediate feedback when GIS data is updated.

[0083] S103: Real-time verification of data change messages is performed by triggering quality inspection rules through a linkage architecture, and the verification results are used to determine whether the data change messages violate the quality inspection rules; the linkage architecture consists of at least a geographic information system application platform, Java components, and a database storage layer.

[0084] When the value of a GIS data field changes, the field value will be validated in real time according to preset constraints such as value range, subtype, and condition values. If it does not meet expectations, the system will trigger a warning or automatically correct it. Through the linkage editing mechanism of relational datasets, the listener detects changes in the source table, updates the target table and related tables in real time, and triggers cross-table data consistency checks according to the rules of the related tables. The system obtains the check results and determines whether the data change message violates the quality inspection rules. If it does not meet expectations, the system will trigger a warning or prevent data updates.

[0085] The verification results include the first verification result comparing the preset building type with the changed building type, and the second verification result comparing the preset land type with the changed land type.

[0086] The specific process of performing real-time verification (i.e., consistency verification) on data change messages and determining whether the data change messages violate quality inspection rules based on the verification results is shown in A1-A2.

[0087] A1: In the process of real-time quality inspection of data change messages triggered by the linkage architecture, the preset building type is compared with the changed building type to obtain the first verification result, and the preset land type is compared with the changed land type to obtain the second verification result.

[0088] The preset building type is a pre-set building type used to compare with the changed building type in the data change message to determine whether it meets the quality inspection rules.

[0089] The preset land type is a pre-set land type used to compare with the changed land type in the data change message to determine whether it meets the quality inspection rules.

[0090] A2: Based on the first and second verification results, determine whether the editing operation corresponding to the data change message violates the quality inspection rules.

[0091] If a data change message violates quality control rules, a constraint operation will be performed. The specific process is shown in B1-B2.

[0092] B1: If the first verification result indicates that the preset building type is inconsistent with the changed building type, or the second verification result indicates that the preset land type is inconsistent with the changed land type, the data change message is determined to violate the quality inspection rules.

[0093] To better understand the data consistency verification process, an example is provided below:

[0094] Taking the "Building-Land Relationship Table" as an example, the following describes how cross-table consistency verification can be achieved using the linked editing of relational datasets:

[0095] (1) Triggering phase: The source table data (such as the building information table) is edited, and the building type is changed from the original value "residential building" to "industrial building".

[0096] (2) Message generation stage: The edit in (1) will be captured by the listener in the Java component and generate an internal message, namely the data change message. The data change message contains information such as the modified data table, the modified field, the modified value, and the record ID involved.

[0097] (3) Relationship linkage stage: When the Java component processes the data change message, it will recognize that the dataset has participated in the relationship dataset, and therefore will further check the rules of the relationship dataset.

[0098] (4) Cross-table rule verification stage: The Java component reads the rules of this relational dataset, verifies the edited data, finds that the record "residential building" in the source table is cross-table related to the record in the land use type table, and the land use type is "residential land". Now this record has been changed to "industrial land". The Java component will immediately determine that the editing operation violates the rule.

[0099] B2: If a data change message violates quality control rules, execute at least one of the following: trigger a warning, perform a correction, or prevent data updates.

[0100] If the first verification result indicates that the preset building type is consistent with the changed building type, and the second verification result indicates that the preset land type is consistent with the changed land type, it is determined that the data change message does not violate the quality inspection rules, and the data change message that does not violate the quality inspection rules is stored in the database.

[0101] The Java component returns the execution result to the application layer. If it is determined that the data change message does not violate the quality inspection rules and passes the quality inspection, the system saves the updated data to the database. If it fails the quality inspection, the system will return log information (error or warning message). Some operations may trigger correction operations, such as modifying the data to the default value or preventing the data update.

[0102] (5) Result feedback stage: The verification result is returned to the application layer through the message mechanism. The forms of expression include, but are not limited to: preventing the current saving, giving a clear warning, and recording the quality inspection log.

[0103] Data change messages that do not violate quality inspection rules are stored in the database for subsequent rule template reuse. The specific rule template reuse process is shown in C1-C3.

[0104] C1: When the industry standard data has completed the rule configuration, call the interface to export all the rules associated with the industry standard data as a template file.

[0105] After configuring rules for a specific industry standard data set (such as "land use status data" or "building planning data"), the GIS application platform can call an interface to export all rules associated with the selected data into a template file, forming a rule template. The template file records information such as the data table name, the rule ID associated with the data table, the rule type, and detailed rules.

[0106] C2: When applying similar industry standard data, reuse the template file to parse out the template content and create the corresponding rule record in the database.

[0107] Real-time quality inspection during data updates on the GIS platform allows for the export of existing rules from the database as template files, enabling quick import and reuse of these template files in similar industry-standard data. While rules are stored in the database, the entire set of rules for a specific industry standard dataset can be exported as a template file, and rule items from the database can be exported as local rule template files. These template files can then be imported with a single click for rule reuse. For example, in new projects, rules can be imported and reused with a single click, automatically configuring rules for business data tables and improving configuration efficiency.

[0108] C3: Binds rule records to the target data table to achieve one-click configuration.

[0109] When a GIS system applies similar industry-standard data, users can directly import saved rule templates into the platform. The system parses the template content and automatically creates corresponding rule records in the database, binding them to the target data table for one-click configuration, thereby improving configuration efficiency.

[0110] This solution ensures that the data in the system meets the predetermined business constraints with each update through the following interaction process:

[0111] (1) Data Update and Quality Inspection: The GIS application layer provides interactive operations such as interface display and editing. When data is updated, the GIS application platform transmits the data update information (mainly including: adding records, deleting records, and modifying field values) to the Java component layer via API. The Java component layer detects data changes through listeners and immediately triggers a message mechanism to trigger verification. When the value of a GIS data field changes, the field value will be verified in real time according to preset constraint rules such as value range, subtype, and condition value. If it does not meet expectations, the system will trigger a warning or automatically correct it. Through the linkage editing mechanism of relational datasets, the listener detects changes in the source table, updates the target table and related tables in real time, and triggers cross-table data consistency verification according to the rules of the related tables. If it does not meet expectations, the system will trigger a warning or prevent data updates.

[0112] (2) Verification result feedback: The Java component returns the execution result to the application layer. If the updated data passes the quality inspection, the system will save the updated data to the database. If it fails the quality inspection, the system will return log information (error or warning). Some operations may trigger correction operations, such as modifying the data to the default value or preventing the data from updating.

[0113] This application utilizes a collaborative architecture of GIS application platform, Java components, and database storage layer to trigger rule verification in real time during data updates; it integrates structured business rule objects such as relational datasets and constraint rules (value domain, subtype, condition value) to generate a dynamic rule library, which can adjust constraint rules in real time according to data conditions; and it supports exporting and importing rule templates to achieve efficient reuse and rapid deployment of industry standard rules.

[0114] The constraint rules can be adjusted in real time based on the data. For example, structured rule objects such as relational datasets and attribute constraints (value range, subtype, condition value) can be edited on the platform at any time. This solution provides a visual interactive interface for editing these rule objects. For instance, in a land use dataset, the value range of the Type field is [1, 2, 3]. If the value range needs to be changed to [1, 2] during use, the value range object can be modified directly through the platform in real time. After modification, this value range rule will take effect immediately, and all data referencing this value range object will be applied in real time. Furthermore, the GIS application platform supports access to multiple databases, such as SQLite, PostgreSQL, and Oracle. Therefore, the methods in this solution can run on different databases.

[0115] In this embodiment, after data updates, centralized checks via manual methods or scripts are unnecessary. Instead, a linked architecture of the Geographic Information System (GIS) application platform, Java components, and database storage layer triggers quality control rules in real-time when geographic information data is updated. This involves using rule tables from a dynamic rule base, relational information of structured geographic information data, and attribute constraints to perform real-time quality checks on the geographic information data stored in the GIS application platform's data tables. If geographic information data changes, a data change message is generated, and constraints for the quality control rules are dynamically generated based on data type and application scenario. Furthermore, the quality control rules can be dynamically adjusted according to different data changes, avoiding the problem of traditional quality control methods failing to cover all application scenarios. This meets the real-time data verification needs of business scenarios. In addition, the quality control rules are stored in the database and can be exported as template files. These template files can be imported and reused in new projects with a single click, automatically configuring rules for business data tables and improving configuration efficiency.

[0116] Based on the above embodiments Figure 1 This application discloses a quality inspection method for real-time geographic information data, and also provides a corresponding quality inspection device for real-time geographic information data. Figure 6 The quality inspection device shown, which supports real-time geographic information data, includes:

[0117] The monitoring generation unit 601 is used to generate a data change message when changes are detected in the geographic information data stored in the data table of the geographic information system application platform.

[0118] The identification and acquisition unit 602 is used to identify the relational datasets in which the data change message participates, and to acquire the quality inspection rules corresponding to the relational datasets; the quality inspection rules are stored in the dynamic rule base; the quality inspection rules include at least the correspondence between the rule table, the data table, and the association table; the dynamic rule base includes at least the relational datasets and attribute constraints;

[0119] The real-time verification unit 603 is used to trigger quality inspection rules through the linkage architecture to perform real-time verification of data change messages, and to determine whether the data change messages violate the quality inspection rules based on the verification results; wherein, the linkage architecture consists of at least a geographic information system application platform, Java components and a database storage layer.

[0120] Furthermore, the monitoring generation unit 601 includes:

[0121] The monitoring module is used to monitor the geographic information data stored in the data tables of the data storage layer of the geographic information system application platform through listeners in the Java component;

[0122] The first generation module is used to generate a data change message when the listener detects that the source table data in the geographic information data has been edited.

[0123] Furthermore, it also includes:

[0124] The update unit is used to update the target table and related tables in real time by means of data change messages in the source table if the listener of the Java component detects that a relational dataset exists in the source table.

[0125] Furthermore, it also includes:

[0126] The fusion unit is used to merge relational datasets and attribute constraints, and update the dynamic rule base based on the fusion result; wherein, the attribute constraint consists of at least a value range, a subtype, and a condition value.

[0127] Furthermore, the real-time verification unit 603 includes:

[0128] The comparison module is used to compare the preset building type with the changed building type to obtain the first verification result, and to compare the preset land type with the changed land type to obtain the second verification result during the real-time quality inspection of data change messages triggered by the linkage architecture and quality inspection rules.

[0129] The judgment module is used to determine whether the editing operation corresponding to the data change message violates the quality inspection rules based on the first and second verification results.

[0130] Furthermore, quality inspection devices that support real-time geographic information data also include:

[0131] The execution unit is used to perform constraint operations if the data change message violates the quality inspection rules;

[0132] Execution unit, including:

[0133] The first determination module is used to determine that the data change message violates the quality inspection rules if the first verification result indicates that the preset building type is inconsistent with the changed building type, or the second verification result indicates that the preset land type is inconsistent with the changed land type.

[0134] The execution module is used to perform at least one of the following actions when a data change message violates quality control rules: triggering a warning, performing a correction operation, or preventing data updates.

[0135] Furthermore, quality inspection devices that support real-time geographic information data also include:

[0136] The second determination module is used to determine that the data change message does not violate the quality inspection rules if the first verification result indicates that the preset building type is consistent with the changed building type and the second verification result indicates that the preset land type is consistent with the changed land type.

[0137] The storage module is used to store data change messages that do not violate quality inspection rules into the database.

[0138] Furthermore, it also includes:

[0139] The calling module is used to call the interface to export all rules associated with the industry standard data as a template file when the rule configuration of the industry standard data is completed;

[0140] The reuse parsing module is used to reuse template files when applying similar industry standard data, to parse out the template content, and create rule records corresponding to the template content in the database;

[0141] The binding module is used to bind rule records to the target data table to achieve one-click configuration.

[0142] In this embodiment, after data updates, centralized checks via manual methods or scripts are unnecessary. Instead, a linked architecture of the Geographic Information System (GIS) application platform, Java components, and database storage layer triggers quality control rules in real-time when geographic information data is updated. This involves using rule tables from a dynamic rule base, relational information of structured geographic information data, and attribute constraints to perform real-time quality checks on the geographic information data stored in the GIS application platform's data tables. If geographic information data changes, a data change message is generated, and constraints for the quality control rules are dynamically generated based on data type and application scenario. Furthermore, the quality control rules can be dynamically adjusted according to different data changes, avoiding the problem of traditional quality control methods failing to cover all application scenarios. This meets the real-time data verification needs of business scenarios. In addition, the quality control rules are stored in the database and can be exported as template files. These template files can be imported and reused in new projects with a single click, automatically configuring rules for business data tables and improving configuration efficiency.

[0143] This application embodiment also provides a storage medium, the storage medium including stored instructions, wherein, when the instructions are executed, the device where the storage medium is located is controlled to perform the quality inspection method for supporting real-time geographic information data as described above.

[0144] This application also provides an electronic device, the structural schematic diagram of which is shown below. Figure 7 As shown, it specifically includes a memory 701 and one or more instructions 702, wherein one or more instructions 702 are stored in the memory 71 and configured to be executed by one or more processors 703 to perform the above-mentioned quality inspection method supporting real-time geographic information data.

[0145] For the foregoing method embodiments, in order to simplify the description, they are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, because according to this application, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0146] It should be noted that the various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For apparatus embodiments, since they are basically similar to method embodiments, the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0147] The steps in the methods of the various embodiments of this application can be adjusted, combined, or deleted according to actual needs.

[0148] Finally, it should be noted that in this paper, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations.

[0149] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0150] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A quality inspection method supporting real-time geographic information data, characterized in that, The method includes: When changes are detected in the geographic information data stored in the data table of the geographic information system application platform, a data change message is generated; Identify the relational datasets involved in the data change messages and obtain the quality inspection rules corresponding to the relational datasets; the quality inspection rules are stored in a dynamic rule base; the quality inspection rules at least include the correspondence between rule tables, data tables, and association tables; the dynamic rule base at least includes relational datasets and attribute constraints; The linkage architecture triggers the quality inspection rules to perform real-time verification of the data change message, and determines whether the data change message violates the quality inspection rules based on the verification results; wherein, the linkage architecture consists of at least a geographic information system application platform, Java components, and a database storage layer.

2. The method according to claim 1, characterized in that, When a change is detected in the geographic information data stored in the data table of the geographic information system application platform, a data change message is generated, including: The geographic information data stored in the data tables of the data storage layer of the geographic information system application platform is monitored through listeners in Java components. When the listener detects that source table data in geographic information data has been edited, it generates a data change message.

3. The method according to claim 1, characterized in that, Also includes: If the listener of a Java component detects that a relational dataset exists in the source table, it updates the target table and the related table in real time through data change messages in the source table.

4. The method according to claim 1, characterized in that, Also includes: The relational dataset and attribute constraints are fused together, and the dynamic rule base is updated based on the fusion result; wherein, the attribute constraints consist of at least a value range, a subtype, and a condition value.

5. The method according to claim 1, characterized in that, The step of triggering the quality inspection rules through a linkage architecture to perform real-time verification of the data change message, and determining whether the data change message violates the quality inspection rules based on the verification result, includes: During the process of real-time quality inspection of the data change message triggered by the linkage architecture, the preset building type is compared with the changed building type to obtain the first verification result, and the preset land type is compared with the changed land type to obtain the second verification result. Based on the first verification result and the second verification result, determine whether the editing operation corresponding to the data change message violates the quality inspection rules.

6. The method according to claim 5, characterized in that, Also includes: If the data change message violates the quality inspection rules, a constraint operation will be performed. If the data change message violates the quality inspection rules, the constraint operation is executed, including: If the first verification result indicates that the preset building type is inconsistent with the changed building type, or the second verification result indicates that the preset land type is inconsistent with the changed land type, it is determined that the data change message violates the quality inspection rules; If the data change message violates the quality control rules, at least one of the following actions shall be taken: triggering a warning, performing a correction operation, or preventing data updates.

7. The method according to claim 5, characterized in that, Also includes: If the first verification result indicates that the preset building type is consistent with the changed building type, and the second verification result indicates that the preset land type is consistent with the changed land type, then it is determined that the data change message does not violate the quality inspection rules; Data change messages that do not violate the quality inspection rules are stored in the database.

8. The method according to claim 1, characterized in that, Also includes: Once the industry standard data has been configured with rules, the API call will export all rules associated with the industry standard data as a template file. When applying similar industry standard data, the template file is reused to parse out the template content and create a rule record corresponding to the template content in the database; The rule records are bound to the target data table to achieve one-click configuration.

9. A quality inspection device supporting real-time geographic information data, characterized in that, The device includes: The monitoring and generation unit is used to generate a data change message when changes are detected in the geographic information data stored in the data table of the geographic information system application platform. The identification and acquisition unit is used to identify the relational dataset in which the data change message participates, and to acquire the quality inspection rules corresponding to the relational dataset; the quality inspection rules are stored in a dynamic rule base; the quality inspection rules include at least the correspondence between rule tables, data tables, and association tables; the dynamic rule base includes at least the relational dataset and attribute constraints; The real-time verification unit is used to trigger the quality inspection rules through the linkage architecture to perform real-time verification on the data change message, and determine whether the data change message violates the quality inspection rules based on the verification result; wherein, the linkage architecture consists of at least a geographic information system application platform, Java components and a database storage layer.

10. The apparatus according to claim 9, characterized in that, The monitoring generation unit includes: The monitoring module is used to monitor the geographic information data stored in the data tables of the data storage layer of the geographic information system application platform through listeners in the Java component; The first generation module is used to generate a data change message when the listener detects that the source table data in the geographic information data has been edited.

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