Multi-dimensional error coding and rapid positioning system for GIS quality inspection

Abstract

The invention relates to the technical field of geographic information system (GIS) data quality control, and discloses a GIS quality inspection-oriented multi-dimensional error coding and rapid positioning system, which comprises a multi-dimensional error coding system, a functional module and an error processing feedback-driven MDEC quality inspection rule dynamic optimization module, the multi-dimensional error coding system is of a segmented combined code structure and is marked as an MDEC code, and the MDEC code sequentially comprises a data field used for identifying a data category or a product specification where errors are located. By constructing a multi-dimensional error coding system, a unified and structured coding standard is provided for GIS data errors, information deviation of staff in different posts in error description, transmission and tracking is effectively eliminated, standardized management of error information is achieved, and the information can also be used as a core index, space coordinates, attribute information and repair knowledge of associated error elements, so that the error coding efficiency is improved. A unified data basis is provided for subsequent error positioning, root analysis and repair operation, and the collaborative efficiency and normalization of GIS quality inspection work are remarkably improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of geographic information system (GIS) data quality control, in particular to a multi-dimensional error coding and rapid positioning system for GIS quality inspection. BACKGROUND

[0002] With the in-depth application of concepts such as smart city and digital twin in the fields of land surveying and mapping, urban planning, pipeline management, etc., GIS data has become a key infrastructure supporting various spatial decision-making and upper-layer applications, and its data quality directly determines the accuracy and reliability of the application results. Therefore, GIS data quality inspection has become a core link to ensure data usability.

[0003] In the prior art, GIS data quality inspection mainly relies on the basic inspection functions integrated by traditional GIS tools such as ArcGIS and QGIS, and performs error detection through preset simple rules. After detecting errors, quality inspection personnel need to manually record fuzzy descriptions such as topological errors and attribute missing, and then manually pan and zoom to the suspected area to locate errors in the map interface. The repair scheme is mostly dependent on personal experience or scattered document records.

[0004] However, in the existing GIS quality inspection process, the error description lacks a unified and standard expression form, and different production, quality inspection and development personnel have different understandings and records of the same error, which leads to deviations in the transmission, tracking and subsequent processing of error information, not only reducing the collaboration efficiency, but also making it difficult to trace the error handling process and unable to form a standardized quality inspection process, which seriously affects the overall standardization and efficiency of GIS data quality inspection work. SUMMARY

[0005] In view of the deficiencies of the prior art, the present application provides a multi-dimensional error coding and rapid positioning system for GIS quality inspection, which solves the problems of non-uniform GIS quality inspection error coding, low positioning efficiency and difficult knowledge sedimentation.

[0006] To achieve the above purpose, the present application realizes the following technical scheme: a multi-dimensional error coding and rapid positioning system for GIS quality inspection, comprising a multi-dimensional error coding system, a functional module and an MDEC quality inspection rule dynamic optimization module driven by error handling feedback; The multi-dimensional error coding system is a segmented combination code structure, denoted as MDEC code. The MDEC code comprises, in sequence, a data field for identifying the data category or product specification where the error is located, an error category for identifying the basic type of the error, an error subclass or unique ID for uniquely identifying a specific error rule, a severity level for identifying the severity of the error, and additional information for carrying error auxiliary positioning information. The functional modules include an automated quality inspection and coding generation module, an error knowledge base management module, and an interactive visualization and rapid location module. The automated quality inspection and coding generation module integrates or encapsulates GIS quality inspection algorithms, performs GIS data inspection according to a predefined quality inspection rule base, and automatically captures the data domain, error category, error subclass or unique ID corresponding to the error when an error is found. It combines the pre-configured severity level and additional information generated from the data context to form a complete MDEC code, and stores the MDEC code together with the geometric coordinates and attribute information of the error element into the error database. The error knowledge base management module stores and manages MDEC codes and their corresponding associated information. The information associated with each MDEC code includes an error description, an error example, a repair suggestion, a possible root cause, and association rules. The interactive visualization and rapid positioning module receives user operations and generates error repair result records; it integrates map controls to support the filtering, sorting, statistics, and spatial positioning of error records and error elements. The error handling feedback-driven MDEC quality inspection rule dynamic optimization module receives error repair result records pushed by the interactive visualization and rapid location module. Using the MDEC code as the core index, it synchronously updates the associated information of the error knowledge base management module and triggers the quality inspection rule optimization of the automated quality inspection and coding generation module, forming a closed loop of error handling feedback optimization.

[0007] Preferably, the interactive visualization and quick location module is configured with an error repair feedback entry. After the user completes the error repair operation, he / she submits the repair result through the feedback entry, and the module automatically encapsulates it to form an error repair result record. The interactive visualization and quick location module provides an error list view, a map view, and an intelligent diagnostic panel; The error list view displays errors in tabular form and supports arbitrary segmentation, sorting, and statistics based on multi-dimensional error coding systems; After the user selects an error record, the system reads the geometric coordinates of the corresponding error element, drives the map control to pan and zoom to the target area, highlights and flashes the error element, and places it in the center of the view; The intelligent diagnostic panel retrieves and displays the repair suggestions and possible root causes associated with the corresponding MDEC code from the error knowledge base management module.

[0008] Preferably, the values ​​of the data field include G, D, P, and T, where G represents basic geographic information data, D represents geological data, P represents pipeline data, and T represents traffic data.

[0009] Preferably, the values ​​of the error categories include T, G, A, D, R, and C, where T represents topological error, G represents geometric error, A represents attribute error, D represents domain error, R represents logical consistency error, and C represents integrity error.

[0010] Preferably, the error subclass or unique ID is a four-digit code.

[0011] Preferably, the values ​​of the error subclass or unique ID include 5001, 3002, and 1001, where 5001 represents self-overlapping polygon features, 3002 represents overhanging pipeline connection points, and 1001 represents an empty required attribute field.

[0012] Preferably, the severity level includes values ​​of A, B, C, and D, where A represents a fatal error, B represents a serious error, C represents a minor error, and D represents a warning.

[0013] Preferably, the additional information is a variable-length structure, specifically a variable-length string or JSON key-value pair, used to carry auxiliary positioning information for error occurrence. The auxiliary positioning information includes map sheet number, batch number, and responsible person number. The geometric coordinates of the erroneous elements are stored internally in a unified projection, which is either the project's preset projection or the WGS84 coordinate system, and the original projection information of the data is recorded.

[0014] Preferably, the automated quality inspection and coding generation module uses an adapter or conversion layer to standardize the outputs of various heterogeneous GIS quality inspection algorithms into error data records within the system. The heterogeneous GIS quality inspection algorithms include ArcGIS algorithm, QGIS algorithm, FME algorithm and self-developed GIS quality inspection algorithm; The error data records within the system include at least the MDEC code, the geometric coordinates of the error element, and attribute information.

[0015] Preferably, the error repair result record includes at least the error repair status and the actual root cause of the error confirmed by the user; The error handling feedback-driven MDEC quality inspection rule dynamic optimization module is configured to perform the following operations: Based on the MDEC code, the failure rate of the error repair is statistically analyzed. Based on the statistics and analysis results of the repair failure rate, update the repair suggestion priority and possible root cause information of the corresponding MDEC code in the error knowledge base management module; When the failure to repair a specific MDEC code meets the predefined optimization trigger conditions, the optimization process for the quality inspection rules associated with that MDEC code is initiated. The optimization process includes an administrator review and confirmation mechanism. Record the change history of the quality inspection rules. The change history shall include at least the operator who made the optimization, the optimization time, the configuration of the quality inspection rules before and after the optimization, and the review comments.

[0016] This invention provides a multi-dimensional error coding and rapid location system for GIS quality inspection. It has the following beneficial effects: 1. This invention constructs a multi-dimensional error coding system to provide a unified and structured coding standard for GIS data errors, effectively eliminating information discrepancies in error description, transmission, and tracking among personnel in different positions, achieving standardized management of error information, and can also serve as a core index to associate the spatial coordinates, attribute information, and repair knowledge of error elements, providing a unified data foundation for subsequent error location, root cause analysis, and repair operations, and significantly improving the collaborative efficiency and standardization of GIS quality inspection work.

[0017] 2. Based on the actual repair results of users, this invention can automatically adjust the priority of repair suggestions and the probability distribution of error root causes in the error knowledge base. It can also trigger adaptive optimization of quality inspection rules, avoid the recurrence of similar errors, reduce the cost of manual intervention, and continuously improve the system's adaptability to GIS data quality inspection requirements.

[0018] 3. This invention deeply integrates the error list with the map view through an interactive visualization and rapid positioning module, supporting one-click location of error elements and highlighting them, eliminating the need for manual map zooming to find targets. The intelligent diagnostic panel can retrieve the corresponding repair suggestions and possible root causes of errors in real time, helping users quickly master the handling solutions, reducing the learning cost for new employees, ensuring the consistency of quality inspection levels among different personnel, and improving the overall error handling efficiency. Attached Figure Description

[0019] Figure 1 This is a system block diagram of the present invention. Detailed Implementation

[0020] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] Example: Please see the appendix Figure 1 This invention provides a multi-dimensional error coding and rapid location system for GIS quality inspection, specifically including: I. System Overall Architecture The system uses the Multidimensional Error Coding System (MDEC) as its core index, connecting automated quality inspection and code generation modules, error knowledge base management modules, and interactive visualization and rapid location modules. It is supplemented by an error handling feedback-driven MDEC quality inspection rule dynamic optimization module. The system runs on CentOS 7 operating system, with the front end using the Vue 3 framework for the interactive interface and the back end using the Spring Boot framework for service provision. Data storage uses MySQL 8.0 (error database) and PostgreSQL 14 (error knowledge base). All modules interact with each other through RESTful APIs to ensure the real-time performance and consistency of the data flow.

[0022] II. Specific Implementation of the Multidimensional Error Coding System (MDEC) The MDEC coding system is scalable and automatically updated. When a new data field is added, the system reads the data type tag from the new data metadata, automatically generates a new data field enumeration value (e.g., adding 'S' as a data field identifier when ecological monitoring data is detected), and synchronously updates the error category mapping table. When a new error type is added, the administrator uploads the new error rule configuration file through the system backend. The system automatically parses and adds an error subclass or unique ID, while associating it with a pre-configured severity level, completing the dynamic update of the coding system. The five dimensions of MDEC codes are all automatically extracted or pre-configured by the system without manual intervention. The specific generation logic is as follows: Data domain determination: The system identifies the major data categories by reading the metadata tags of GIS data. When the data type field in the metadata of the input data is labeled as topographic data or cadastral data, the data domain is automatically determined to be G; when labeled as pipeline ledger data, it is determined to be P; when labeled as road vector data, it is determined to be T; and when labeled as geological borehole data, it is determined to be D.

[0023] Error Category Determination: The algorithm integration unit in the automated quality inspection and coding generation module matches the output results of heterogeneous GIS quality inspection algorithms with a predefined error category mapping table. When the ArcGIS topology check algorithm outputs a description of overlapping polygon features, it is matched as a topology error; when the QGIS geometry check algorithm outputs a description of feature nodes exceeding limits, it is matched as a geometry error; when the FME attribute check algorithm outputs a description of empty required fields, it is matched as an attribute error.

[0024] Error Subclass / Unique ID Matching: The system predefines an XML-formatted quality inspection rule base, which stores the correspondence between error categories and subclass IDs. When the error category is a topological error, the rule base predefines 5001 as corresponding to self-overlapping polygon features and 5002 as corresponding to intersecting line features. When the error category is an attribute error, 1001 as corresponding to an empty required attribute field and 1002 as corresponding to an incorrect attribute value format. When a specific error is detected, the system retrieves the unique subclass ID from the rule base based on the error category.

[0025] Severity level configuration: The rule base stores the association between error subclass IDs and severity levels. It is pre-configured according to the degree of impact of errors on data application. Self-overlapping polygon features will cause data entry failure and are pre-configured as fatal errors; format errors of non-mandatory attribute fields do not affect core functions and are pre-configured as warnings; overhanging of pipeline connection points affects the accuracy of pipeline network analysis and are pre-configured as severe errors.

[0026] Additional information extraction: Additional information is automatically extracted from the context information of GIS data, using a variable-length string format. The map sheet number is read from the map sheet number field of the data file's name suffix or metadata; the batch number is extracted from the batch label of the data production; and the responsible person number is obtained from the system login account of the data production personnel.

[0027] Taking the self-overlap error of polygon features in basic geographic information data as an example, the MDEC code generation process is: data field G, error category T, subclass ID5001, severity level A, additional information 108, and the final combination is GT-5001-A-108.

[0028] III. Specific Implementation of Functional Modules (a) Automated quality inspection and code generation module This module includes an algorithm integration unit, a rule parsing unit, an encoding generation unit, and a data storage unit. Its specific functions are implemented as follows: Algorithm Integration Unit: This unit integrates heterogeneous GIS quality inspection algorithms through adapters or conversion layers. The adapters are designed with parsing logic tailored to the output formats of different algorithms. For example, ArcGIS algorithms output as feature class files, and the adapter parses the error codes and feature ID fields in these files; QGIS algorithms output as GeoJSON files, and the adapter extracts the properties.errorType field from the features array; FME algorithms output as custom XML files, and the adapter parses the description attribute under the error node; and self-developed algorithms output as structured arrays, and the adapter directly reads the error description field from the array. The conversion layer maps the information parsed by the adapters into a unified internal intermediate data format, which includes error type, feature identifier, and attribute information.

[0029] The adapter layer adopts a plug-in architecture. When adding a new GIS quality inspection tool (such as SuperMap or MapGIS), the administrator uploads the corresponding adapter plug-in (including the JAR package containing the parsing logic) to the system plug-in directory. The system automatically loads the plug-in and registers the parsing logic without restarting the service. The plug-in must comply with the system's preset adapter interface specifications, which include three core methods: parsing the native format, extracting error information, and mapping the internal format.

[0030] Rule parsing unit: Reads the predefined XML format quality inspection rule base, parses it to generate a mapping table of error categories, subclass IDs, and severity levels. The XML structure of the rule base contains rule nodes, and each node has errorCategory nodes, errorSubtype nodes, and severityLevel nodes. The rule parsing unit reads the content of these nodes through DOM parsing technology, generates a hash table and stores it in memory for the encoding generation unit to call.

[0031] The XML structure of the rule base also adds `errorDefinition` and `adaptedAlgorithms` nodes. The `errorDefinition` node stores the standard definitions of error types. For example, self-overlapping polygons are defined as an overlapping area ≥ 0.01㎡ within a single polygon or an overlapping area ≥ 0.1㎡ between multiple polygons; drooping pipeline connection points are defined as a distance > 0.5m from the pipeline endpoint to the nearest connected object. The `adaptedAlgorithms` node stores the adapted GIS quality inspection algorithms. For example, the content of the `adaptedAlgorithms` node for self-overlapping polygons is ArcGIS topology algorithm or a self-developed topology algorithm; the content of the `adaptedAlgorithms` node with empty required attribute fields is FME attribute algorithm or QGIS field validation algorithm. The rule parsing unit reads the content of these nodes and generates an associated hash table of error types, standard definitions, and adapted algorithms for the encoding generation unit to call.

[0032] The rule base supports dynamic updates. Administrators can upload new XML format rule files through the rule management interface in the system backend. The system automatically verifies the integrity of the file structure (checks whether it contains all necessary nodes). After the verification is passed, the old rule file is overwritten and the rule parsing unit is triggered to regenerate the mapping table. The update process does not interrupt the current quality inspection task, and the new rules only take effect on the quality inspection tasks submitted after the update.

[0033] The encoding generation unit extracts the error type from the intermediate data of the transformation layer, matches it with the mapping table to obtain the error category and sub-category ID; reads the severity level of the corresponding sub-category ID from the mapping table; extracts additional information from the data context; combines the five pieces of information in the order of data field, error category, sub-category ID, severity level, and additional information to generate a complete MDEC code; at the same time, it calculates the geometric coordinates of the error feature. The center point coordinates are calculated using the feature geometric center formula, where x equals the average of the x coordinates of all nodes and y equals the average of the y coordinates of all nodes; the bounding box is calculated using the feature's minimum bounding rectangle formula, where minX equals the minimum x coordinate of all nodes, minY equals the minimum y coordinate of all nodes, maxX equals the maximum x coordinate of all nodes, and maxY equals the maximum y coordinate of all nodes.

[0034] Data storage unit: MDEC code, geometric coordinates, and attribute information are stored in the error database. The geometric coordinates include center point x, center point y, and bounding box minX, minY, maxX, and maxY. The attribute information includes fields such as feature name, code, and production time. The error_info table structure of the error database is designed as follows: mdec_code is of type VARCHAR(50) and is the primary key, geometry_center_x is of type DOUBLE, geometry_center_y is of type DOUBLE, geometry_minX is of type DOUBLE, geometry_minY is of type DOUBLE, geometry_maxX is of type DOUBLE, geometry_maxY is of type DOUBLE, attribute_name is of type VARCHAR(100), attribute_value is of type VARCHAR(200), and create_time is of type DATETIME.

[0035] (II) Error Knowledge Base Management Module This module includes a knowledge base storage unit and an association maintenance unit, realizing the association management of MDEC codes and full error information: Knowledge base storage unit: A PostgreSQL database is used, and an error_knowledge table is created. The table structure includes mdec_code, error_description, error_example_path, repair_suggestion, possible_root_cause, and related_rule. mdec_code is of type VARCHAR(50) and is the primary key; error_description is of type TEXT and stores detailed text descriptions of errors, such as self-overlapping polygon features, which refers to overlapping areas within a single polygon feature, or spatial overlap between multiple polygon features, which can lead to deviations in spatial analysis results; error_example_path is of type VARCHAR(50). The HAR(200) type stores the server path of example error images, such as dataerror_examplesG-T-5001-A.png, where the image is a map screenshot of the erroneous feature; repair_suggestion is of type TEXT[], storing multiple repair suggestions in array form, such as using ArcGIS's repair geometry tools to eliminate overlapping areas, or manually editing polygon feature boundaries to delete overlapping parts; possible_root_cause is of type JSONB, storing the probability distribution of possible root causes, such as data acquisition stage 0.7, data transformation stage 0.2, and data entry stage 0.1; related_rule is of type TEXT, storing the path of the quality inspection rule script that triggered the error, such as datarulesT-5001.xml.

[0036] Association Maintenance Unit: When the automated quality inspection and coding generation module generates a new MDEC code, the association maintenance unit automatically queries the error_knowledge table. If the MDEC code does not exist, it retrieves the basic information of the corresponding error category from the predefined knowledge template, including the error description and initial repair suggestions, and generates a new record. If the MDEC code already exists, it updates the association information of the MDEC code, such as adding repair suggestions. Administrators can manually add example error images and edit repair suggestions through the knowledge base management interface in the system backend. The interface supports drag-and-drop image uploads and rich text editing of repair suggestions. The edited data is synchronized to the error_knowledge table in real time.

[0037] (III) Interactive Visualization and Quick Location Module This module includes a user interaction unit, a map rendering unit, and a diagnostic display unit, enabling visualized viewing and quick location of errors: The user interaction unit configures an error repair feedback entry point. A repair feedback button is set after each record in the error list view. After the user completes the error repair operation, clicking this button pops up a feedback form. The form includes two required fields: repair status and actual error root cause. The repair status dropdown selects success or failure, and the actual error root cause dropdown selects the data collection stage, data processing stage, data conversion stage, or data entry stage. After the user submits, the unit automatically encapsulates the error repair result record, which includes mdec_code, repair_status, actual_root_cause, and repair_time. repair_time is the current time. This record is pushed to the MDEC quality inspection rule dynamic optimization module driven by error handling feedback via a POST request. Simultaneously, the user interaction unit provides an error list view implemented using the VueTable component. The table columns include mdec_code, error_description, severity_level, and map_sheet. map_sheet contains the map sheet number from the additional information. Sorting by any column is supported; clicking the column header triggers sorting. Filtering by MDEC code segment is also supported; for example, entering "G" filters errors with data field "G," and selecting "A" filters errors with severity level "A."

[0038] Map rendering unit: Integrates OpenLayers7 map control, loads basic map services, such as Tianditu vector base map. When the user selects an error record in the error list, the unit reads the center point coordinates and bounding box of the error feature from the error database, calls the setView method of the map control, sets the map center to the center point coordinates, and sets the zoom level to 12. At the same time, it creates a red polygon vector layer, draws a rectangle with the bounding box as the range, and sets the layer style to a border width of 2px, a fill opacity of 0.3, and a blinking frequency of 1 time per second to achieve the highlighted blinking display of the error feature and ensure that the feature is placed in the center of the map view.

[0039] Diagnostic Display Unit: A smart diagnostic panel is set up on the right side of the map view. When the user selects an error record, the unit calls the getKnowledgeByMdec interface of the error knowledge base management module via AJAX request, with the request parameter being mdec_code. It retrieves the repair_suggestion and possible_root_cause corresponding to the MDEC code. The panel displays repair suggestions according to their priority, which is sorted by the number of successful applications read from the knowledge base. The root cause analysis is displayed according to the probability distribution of possible root causes, such as 70% for data collection, 20% for data transformation, and 10% for data entry.

[0040] IV. Error Handling Feedback-Driven Dynamic Optimization Module for MDEC Quality Inspection Rules This module includes a feedback receiving unit, a failure rate analysis unit, a knowledge base update unit, a rule optimization unit, and a log recording unit, enabling system self-optimization based on repair feedback: Feedback Receiving Unit: Provides a feedbackreceive RESTful API interface to receive error repair result records pushed by the interactive visualization and quick location module. The interface receives data in JSON format. After receiving the data, the unit stores the records in the feedback_record table, which is a MySQL database table. The table structure includes feedback_id, mdec_code, repair_status, actual_root_cause, and repair_time. feedback_id is of type INT and is an auto-incrementing primary key; mdec_code is of type VARCHAR(50); repair_status is of type VARCHAR(10); actual_root_cause is of type VARCHAR(50); and repair_time is of type DATETIME.

[0041] Failure Rate Analysis Unit: Centered on MDEC codes, this unit calculates the repair failure rate daily. The failure rate equals the number of failures divided by the total number of repairs. The unit executes a statistical task every morning at midnight, querying records from the past 24 hours in the feedback_record table and calculating the failure rate by grouping by mdec_code. Simultaneously, it groups by data field to calculate the failure rate of each MDEC code under the same error category. For example, it calculates the failure rate of all MDEC codes for topology errors under the G field. It filters MDEC codes corresponding to A and B level errors by severity level, generates statistical reports of MDEC codes and failure rates, and stores them in the failure_analysis table.

[0042] Knowledge base update unit: Every 3 days, the feedback_record and error_knowledge tables are queried, and the knowledge base is updated according to the following logic: The number of successful applications of each repair suggestion under the same MDEC code is counted; the corresponding repair suggestions are matched according to the successful repair records reported by users; the sorting of the repair_suggestion array is adjusted according to the percentage of successful applications, with the repair suggestion with the highest percentage placed first; the actual root causes of errors confirmed by users are summarized, the percentage of each production link in the root causes of errors for this MDEC code is calculated, and the JSONB data of the possible_root_cause field is updated. For example, if the data collection link accounts for 80% of the actual root causes of an MDEC code, then possible_root_cause is updated to 0.8 for data collection link, 0.15 for data conversion link, and 0.05 for data entry link.

[0043] Rule Optimization Unit: The predefined optimization trigger condition is that the failure rate of a certain MDEC code's repair exceeds 50% for 30 consecutive days. The unit queries the `failure_analysis` table daily to determine if any MDEC codes meet the condition. If so, it automatically marks the quality inspection rules associated with that MDEC code, obtains the rule path from the `related_rule` field of the `error_knowledge` table, and extracts the current configuration parameters of the rules. For example, the threshold for the self-overlapping rule of polygon features is an overlap area greater than 0.1 square meters. The marked rules, current parameters, failure rate trend chart, and data domain distribution table are pushed to the administrator's review interface. The failure rate trend chart is the failure rate change curve over the past 30 days, and the data domain distribution table shows the number of errors for that MDEC code in each data domain. The administrator views the information through the interface and fills in review comments, including pass or fail. If pass, rule parameters can be modified, such as adjusting the threshold to an overlap area greater than 0.05 square meters. After clicking the "Confirm Optimization" button, the system triggers a secondary confirmation pop-up. After confirmation, the rule is updated, and the optimized rule script is written to the original rule path. If fail, the reason for failure must be filled in, the system records the reason, and the optimization process is terminated.

[0044] Log recording unit: Configure change logging or audit logging mechanism, create the rule_change_log table, which is a MySQL database table. The table structure includes log_id, operation_user, operation_time, mdec_code, rule_before, rule_after, audit_opinion, audit_user, and audit_time. log_id is of type INT and is an auto-incrementing primary key; operation_user is of type VARCHAR(50) and records the person performing the optimization operation; operation_time is of type DATETIME and is in YYYY format. -MM-DDHH:MM:SS, records the optimization time; mdec_code is of type VARCHAR(50), records the associated MDEC code; rule_before is of type TEXT, records the rule script content before optimization; rule_after is of type TEXT, records the rule script content after optimization; audit_opinion is of type VARCHAR(200), records the administrator's review comments; audit_user is of type VARCHAR(50), records the reviewer; audit_time is of type DATETIME, records the review time. When the rule optimization is completed, the unit automatically writes the above information into the table to realize the full traceability of the optimization operation.

[0045] V. System Workflow Error detection and code generation: Users upload GIS data through the system. The automated quality inspection and code generation module reads the data and calls the integrated heterogeneous algorithm to perform quality inspection. The algorithm outputs error information, which is parsed by the adapter and normalized by the transformation layer. The rule parsing unit matches the rule base to obtain the MDEC code's dimensions. The code generation unit combines these into a complete MDEC code. The data storage unit stores the MDEC code, geometric coordinates, and attribute information into the error database, and simultaneously synchronizes the MDEC code to the error knowledge base management module, associating it with basic error information.

[0046] Error location and repair feedback: Users log in to the system and view the error list in the interactive visualization and quick location module. They can filter or sort to find the target error. When an error record is selected, the map rendering unit automatically locates and highlights the error elements, and the diagnostic display unit displays repair suggestions and possible root causes. Users complete the repair according to the suggestions and submit the repair status and actual root cause through the feedback entry. The module pushes the feedback record to the dynamic optimization module.

[0047] System optimization and rule updates: The feedback receiving unit of the dynamic optimization module stores feedback records, and the failure rate analysis unit counts the failure rate; the knowledge base update unit adjusts the knowledge base information based on the feedback; the rule optimization unit determines whether the optimization conditions are met, and if so, pushes the rules to the administrator for review. After the review is approved, the rules are updated, and the log recording unit records the optimization process; the optimized rules are used for the next quality inspection, forming a closed loop.

[0048] Taking a dangling error (error category T, subcategory ID 3002) at a connection point in pipeline data (data domain P) as an example, the system workflow is as follows: Detection and Coding: The FME quality inspection algorithm outputs a dangling error. The adapter parses the XML and the rule base matches the MDEC segment as PT-3002-B. Combined with the batch number BATCH-2024A, the complete MDEC code PT-3002-B-BATCH-2024A is generated and stored in the database.

[0049] Location and Feedback: When a user finds the error in the list, the overhang point is highlighted on the map after a one-click location update. After fixing the issue based on knowledge base suggestions, the user submits the feedback entry: repair_status:'success' and actual_root_cause:'data collection phase'.

[0050] Optimization: The dynamic optimization module receives feedback and updates the priority and root cause probability of the 3002 repair suggestions in the knowledge base. If the failure rate of this error remains high over a long period, a rule optimization process will be triggered, and the administrator may adjust the overhang tolerance threshold from 0.5 meters to 0.3 meters.

[0051] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A multi-dimensional error coding and rapid location system for GIS quality inspection, characterized in that, This includes a multi-dimensional error coding system, functional modules, and an MDEC quality inspection rule dynamic optimization module driven by error handling feedback; The multi-dimensional error coding system is a segmented combined code structure, denoted as MDEC code. The MDEC code includes, in sequence, a data field for identifying the data category or product specification where the error is located, an error category for identifying the basic type of error, an error subclass or unique ID for uniquely identifying the specific error rule, a severity level for identifying the severity of the error, and additional information for carrying error auxiliary location information. The functional modules include an automated quality inspection and coding generation module, an error knowledge base management module, and an interactive visualization and rapid location module. The automated quality inspection and coding generation module integrates or encapsulates GIS quality inspection algorithms, performs GIS data inspection according to a predefined quality inspection rule base, and automatically captures the data domain, error category, error subclass or unique ID corresponding to the error when an error is found. It combines the pre-configured severity level and additional information generated from the data context to form a complete MDEC code, and stores the MDEC code together with the geometric coordinates and attribute information of the error element into the error database. The error knowledge base management module stores and manages MDEC codes and their corresponding associated information. The information associated with each MDEC code includes an error description, an error example, a repair suggestion, a possible root cause, and association rules. The interactive visualization and rapid positioning module receives user operations and generates error repair result records; it integrates map controls to support the filtering, sorting, statistics, and spatial positioning of error records and error elements. The error handling feedback-driven MDEC quality inspection rule dynamic optimization module receives error repair result records pushed by the interactive visualization and rapid location module. Using the MDEC code as the core index, it synchronously updates the associated information of the error knowledge base management module and triggers the quality inspection rule optimization of the automated quality inspection and coding generation module, forming a closed loop of error handling feedback optimization.

2. The multi-dimensional error coding and rapid location system for GIS quality inspection according to claim 1, characterized in that, The interactive visualization and quick location module is configured with an error repair feedback entry. After the user completes the error repair operation, the user submits the repair result through the feedback entry, and the module automatically encapsulates it to form an error repair result record. The interactive visualization and quick location module provides an error list view, a map view, and an intelligent diagnostic panel; The error list view displays errors in tabular form and supports arbitrary segmentation, sorting, and statistics based on multi-dimensional error coding systems; After the user selects an error record, the system reads the geometric coordinates of the corresponding error element, drives the map control to pan and zoom to the target area, highlights and flashes the error element, and places it in the center of the view; The intelligent diagnostic panel retrieves and displays the repair suggestions and possible root causes associated with the corresponding MDEC code from the error knowledge base management module.

3. The multi-dimensional error coding and rapid location system for GIS quality inspection according to claim 1, characterized in that, The data field can take the values ​​G, D, P, and T, where G represents basic geographic information data, D represents geological data, P represents pipeline data, and T represents traffic data.

4. The multi-dimensional error coding and rapid location system for GIS quality inspection according to claim 1, characterized in that, The error categories can take the values ​​T, G, A, D, R, and C, where T represents topological error, G represents geometric error, A represents attribute error, D represents domain error, R represents logical consistency error, and C represents integrity error.

5. The multi-dimensional error coding and rapid location system for GIS quality inspection according to claim 1, characterized in that, The error subclass or unique ID is a four-digit code.

6. The multi-dimensional error coding and rapid location system for GIS quality inspection according to claim 1, characterized in that, The values ​​of the error subclass or unique ID include 5001, 3002, and 1001. 5001 represents self-overlapping polygon features, 3002 represents overhanging pipeline connection points, and 1001 represents an empty required attribute field.

7. The multi-dimensional error coding and rapid location system for GIS quality inspection according to claim 1, characterized in that, The severity level can be A, B, C, or D, where A represents a fatal error, B represents a serious error, C represents a minor error, and D represents a warning.

8. The multi-dimensional error coding and rapid location system for GIS quality inspection according to claim 1, characterized in that, The additional information is a variable-length structure, specifically a variable-length string or a JSON key-value pair, used to carry auxiliary location information for error occurrence. The auxiliary location information includes map sheet number, batch number, and responsible person number. The geometric coordinates of the erroneous elements are stored internally in a unified projection, which is either the project's preset projection or the WGS84 coordinate system, and the original projection information of the data is recorded.

9. The multi-dimensional error coding and rapid location system for GIS quality inspection according to claim 1, characterized in that, The automated quality inspection and coding generation module uses an adapter or conversion layer to standardize the outputs of various heterogeneous GIS quality inspection algorithms into error data records within the system. The heterogeneous GIS quality inspection algorithms include ArcGIS algorithm, QGIS algorithm, FME algorithm and self-developed GIS quality inspection algorithm; The error data records within the system include at least the MDEC code, the geometric coordinates of the error element, and attribute information.

10. The multi-dimensional error coding and rapid location system for GIS quality inspection according to claim 1, characterized in that, The error repair result record shall include at least the error repair status and the actual root cause of the error confirmed by the user; The error handling feedback-driven MDEC quality inspection rule dynamic optimization module is configured to perform the following operations: Based on the MDEC code, the failure rate of the error repair is statistically analyzed. Based on the statistics and analysis results of the repair failure rate, update the repair suggestion priority and possible root cause information of the corresponding MDEC code in the error knowledge base management module; When the failure to repair a specific MDEC code meets the predefined optimization trigger conditions, the optimization process for the quality inspection rules associated with that MDEC code is initiated. The optimization process includes an administrator review and confirmation mechanism. Record the change history of the quality inspection rules. The change history shall include at least the operator who made the optimization, the optimization time, the configuration of the quality inspection rules before and after the optimization, and the review comments.

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