A system and method for offline comprehensive analysis, mapping, and evidence collection of natural resource big data

Abstract

This application discloses a system and method for offline comprehensive analysis, mapping, and evidence collection of natural resource big data, belonging to the field of natural resource informatization and geographic information processing technology. The system includes the following components: a spatial data fusion module for unified management and efficient processing of heterogeneous spatial data; a spatial index optimization module for rapid spatial data retrieval and high-performance query optimization; an intelligent mapping and arrangement module for low-code rapid configuration and automated business process arrangement for different natural resource survey scenarios; an offline analysis module for buffer analysis, spatial overlay, and terrain analysis in offline environments; a real-time visualization module for multi-level map display, trajectory recording, vector element editing, and multimedia evidence collection; and a data quality management module for automated quality checks of geometry, topology, and attributes, as well as statistical analysis, version control, and export management of mapping results.

Description

Technical Field

[0001] This application relates to the field of natural resource informatization and geographic information processing technology, and more specifically, to a system and method for offline comprehensive analysis, mapping and evidence collection of natural resource big data, used for efficient data management, spatial analysis, automated scheduling of survey tasks and field evidence collection and recording of forest land, minerals, water areas, land and other natural resources. Background Technology

[0002] With the increasing complexity of natural resource management tasks, geospatial data and natural resource information are characterized by their massive volume, diverse sources, heterogeneous formats, and dynamic updates. Modern natural resource surveys not only cover various resource types such as forest land, minerals, water bodies, and land, but also involve cross-domain data from environmental protection, urban and rural construction, and emergency management. Traditional manual surveying and stand-alone digital processing methods have significant limitations in data acquisition efficiency, format uniformity, spatial analysis capabilities, and results sharing and traceability, making it difficult to meet the current needs of refined management.

[0003] Existing natural resource information systems are mostly geared towards online environments, offering insufficient support for survey areas in remote locations or with limited network access. This results in difficulties in achieving real-time and complete data collection, spatial analysis, and field evidence recording. Furthermore, existing systems lack unified support for intelligent task allocation, dynamic form generation, data quality monitoring, and anomaly warnings, leading to low levels of automation in the survey process and compromising task execution efficiency and data reliability.

[0004] Furthermore, with the increasing demand for offline analysis, survey work not only needs to support multi-dimensional spatial computations such as buffer analysis, spatial overlay, terrain analysis, spatial clustering, and trajectory analysis, but also needs to achieve statistical summarization, version management, and multimedia evidence association of survey results. However, existing technologies still have significant shortcomings in unified management of heterogeneous spatial data, efficient index retrieval, offline comprehensive analysis, and integrated processing of field evidence, making it difficult to form a traceable and verifiable survey results system.

[0005] In summary, how to achieve efficient management, offline comprehensive analysis, intelligent mapping, and integrated processing of heterogeneous spatial data in the context of natural resource big data has become an urgent technical problem to be solved. Summary of the Invention

[0006] In order to overcome a series of defects in the existing technology, the purpose of this application is to provide a system for offline comprehensive analysis, mapping and evidence collection of natural resource big data, which includes the following components.

[0007] The spatial data fusion module is used to achieve unified management and efficient processing of heterogeneous spatial data.

[0008] The spatial index optimization module is used to achieve fast retrieval of spatial data and high-performance query optimization.

[0009] The intelligent mapping and orchestration module is used to enable low-code, rapid configuration and automated orchestration of business processes for different natural resource survey scenarios.

[0010] The offline analysis module is used to perform buffer analysis, spatial overlay, and terrain analysis in offline environments.

[0011] The real-time visualization module is used to realize multi-level map display, trajectory recording, vector feature editing, and multimedia evidence collection.

[0012] The data quality management module is used to automate the quality checks of geometry, topology, and attributes, as well as the statistical analysis, version control, and export management of mapping results.

[0013] Furthermore, the spatial data fusion module adopts a multi-level storage architecture based on the DuckDB-Spatial database, including: a memory-level spatial object caching layer for storing frequently accessed spatial geometric objects and index structures; a local disk-level columnar storage layer for partitioning and storing spatial data according to geometric type, attribute dimension, and acquisition time, and constructing a storage structure based on the native columnar storage format of DuckDB; and a data synchronization layer that, under network access conditions, identifies the differences between local data and server data through an incremental synchronization algorithm and only transmits the spatial elements and their attribute records that have changed.

[0014] Furthermore, the intelligent mapping and orchestration module includes a form template management unit, a form dynamic rendering unit, a data real-time verification unit, a task allocation rule management unit, a real-time progress tracking unit, and an intelligent early warning notification unit. Specifically: the form template management unit pre-sets corresponding JSON Schema templates based on standards and technical procedures for different natural resource types, defining survey field types, data verification rules, field dependencies, and interface display logic; the form dynamic rendering unit uses native JavaScript DOM manipulation and CSS style control to render the JSON... The schema configuration is converted into an interactive survey form interface, supporting text input, numerical selection, date and time pickup, file attachment upload, and GPS coordinate acquisition. The real-time data verification unit performs field mandatory checks, data format validation, numerical range constraints, and cross-field logical consistency verification on the mobile device. The task allocation rule management unit formulates automatic task allocation decision rules based on the survey personnel's professional qualifications, geographical distribution, current workload, and historical work quality factors, achieving scientific and intelligent task assignment. The real-time progress tracking unit monitors the survey task execution status in real time through GPS positioning and data synchronization on the mobile device, including task start time, completion percentage, data collection quality, and abnormal situation recording information. The intelligent early warning notification unit automatically generates early warning information and pushes it to relevant personnel when it detects task execution delays, substandard data quality, or abnormal device disconnection.

[0015] Furthermore, the offline analysis module includes a buffer analysis unit, a spatial overlay unit, a terrain analysis unit, a spatial clustering analysis unit, a trajectory data analysis unit, a spatial correlation analysis unit, and a statistical report generation unit. Specifically: the buffer analysis unit implements fixed-distance, variable-distance, and multi-ring buffering based on the GEOS geometry library, and optimizes boundary smoothing; the spatial overlay unit performs spatial topology operations, including point-in-plane judgment, line-plane intersection, and plane-plane overlay; the terrain analysis unit calculates slope, aspect, watershed, and visible area based on a digital elevation model; the spatial clustering analysis unit identifies the distribution patterns and outliers of spatial elements based on distance clustering; the trajectory data analysis component performs trajectory segmentation, stop point identification, and movement pattern extraction on the collected GPS movement trajectories; the spatial correlation analysis unit identifies spatial correlation characteristics between different resource types through spatial autocorrelation analysis; and the statistical report generation unit automatically calculates the area, distribution density, and change trends of various resources based on the spatial analysis results, and generates standardized statistical analysis reports.

[0016] The purpose of this application is also to provide a method for offline comprehensive analysis, mapping and evidence collection of natural resource big data, including the following steps.

[0017] Under good network conditions, acquire basic natural resource data and survey task configuration information of the area to be investigated, complete the basic information configuration of the project, and perform unified format conversion and storage management of heterogeneous spatial data through the spatial data fusion module. Store the spatial data composed of points, lines and surfaces in the server database as the basis for field collection and evidence collection.

[0018] Based on the geometric characteristics of the natural resource basic data, a differentiated hierarchical index structure is constructed to achieve rapid retrieval and optimization of spatial data.

[0019] Based on the survey task configuration information, a dynamic survey form is generated, and the survey operation process is automatically arranged according to the preset business process template.

[0020] In a good network environment, the unified spatial data and the survey workflow are downloaded and synchronized to the DuckDB-Spatial database of the mobile survey device.

[0021] Whenever investigators arrive at an investigation point, the current point is marked as the target investigation area, and the spatial location of the investigators is collected and stored in the DuckDB-Spatial database.

[0022] Based on the resource type and survey requirements of the target survey area, the offline analysis module is used to perform real-time geographic information processing and spatial relationship analysis to generate corresponding analysis results.

[0023] The real-time visualization module is used to edit vector elements and collect relevant multimedia evidence data. The editing results and evidence data are stored together and the field evidence record is constructed by combining the spatial location of the operators.

[0024] During the data acquisition process, the data quality management module is used to identify data quality problems and provide correction suggestions.

[0025] If a data quality issue is detected, the investigators are prompted to correct the data and re-perform the quality check until it meets the quality standards.

[0026] If the data quality check passes, the data collection for the current survey point is completed, and the process moves to the next survey point according to the survey path. At the same time, the completed survey results are statistically analyzed and version controlled.

[0027] When the network environment is good, the field evidence records stored in the DuckDB-Spatial database will be sent back to the management platform server.

[0028] Furthermore, the construction of the differentiated hierarchical index structure includes the following steps.

[0029] Perform distribution statistics on spatial data to analyze its density distribution, geometric complexity, and characteristics of query hotspot areas.

[0030] For point elements, a dynamic quadtree segmentation method is adopted, which automatically adjusts the segmentation depth and node capacity according to the distribution density of points, so as to achieve efficient point query and proximity retrieval.

[0031] For line features, a hierarchical index structure based on line segment direction and length is established to support fast line segment intersection detection and retrieval of adjacent line features.

[0032] For polygon features, a hybrid structure combining multi-level grid indexes and R-trees is constructed to optimize the performance of polygon feature inclusion queries and overlay analysis.

[0033] During the index building process, memory usage and building time are monitored in real time, and index parameters are dynamically adjusted based on resource consumption and performance to achieve a balance between query efficiency and resource utilization.

[0034] After the index is built, the performance of the index structure is evaluated, and corresponding optimization suggestions are generated to ensure that the established hierarchical index can significantly improve the execution efficiency in subsequent spatial queries.

[0035] Furthermore, the generation of the dynamic survey form includes the following steps.

[0036] Based on the resource type, accuracy requirements, and business specifications of the survey task, the system automatically matches applicable form templates, enabling metadata-driven template selection.

[0037] The selected form template is parsed to read field types, validation rules, display styles, and interaction logic information, providing a basis for control generation and field configuration.

[0038] Generate various form controls based on the template definition, including text input, numeric selection, date and time, geographic coordinates, image upload, and drop-down selection.

[0039] Based on the field dependencies and display conditions defined in the template, the display and hiding rules of each field are dynamically configured to ensure that the generated form can intelligently adjust the field layout and visibility according to task requirements.

[0040] Configure validation rules and linkage logic for each field, including data type checks, mandatory field constraints, and range limits, so that the generated form has real-time validation and error message capabilities when used.

[0041] It automatically performs structural integrity checks and logical consistency verifications, and optimizes field order, dependencies, and control configurations based on the detection results to ensure that the generated forms meet business specifications and usage requirements.

[0042] Furthermore, the synchronization of the spatial data and the survey operation process includes the following steps.

[0043] By comparing the data version identifiers on the server and mobile devices, the newly added, modified, and deleted data items that need to be synchronized are identified, thereby determining the target dataset for synchronization.

[0044] The data to be synchronized is compressed, including geometric simplification, attribute field optimization, and file compression, to reduce the amount of data transmitted and reduce network bandwidth usage.

[0045] Large files are split into multiple smaller data blocks and transmitted step by step to reduce the impact of a single transmission failure on the overall synchronization.

[0046] During the synchronization process, the transmission progress and network connection status are monitored in real time, and the transmission strategy is dynamically adjusted according to the network quality to ensure data transmission efficiency and stability.

[0047] After data transmission is complete, an integrity check is performed on the mobile device to verify the received data and update the local database to ensure the accuracy and consistency of the synchronized data.

[0048] Record historical information for each synchronization operation and enable data version management and synchronization status tracking for a single device.

[0049] Furthermore, vector feature editing operations include the creation, modification, deletion, and copying of point, line, and polygon features. Editing tools include a vertex editor, geometric transformation tools, an attribute editor, and a style settings panel.

[0050] Multimedia evidence data collection integrates multiple collection methods, including high-resolution cameras, high-definition video recording, stereo recording, and handwritten signatures. It supports customizable collection parameters and quality settings. The collected multimedia data is automatically embedded with geographic coordinates, timestamps, and device information, establishing a correlation with spatial elements.

[0051] Compared with the prior art, this application has the following beneficial effects: This application realizes efficient management and synchronization of heterogeneous spatial data through a multi-level storage architecture based on DuckDB-Spatial, and combines differentiated hierarchical indexes, dynamic metadata-driven survey forms, offline spatial analysis and real-time visualization to achieve full-process low-code automated mapping, field evidence collection and intelligent control of data quality. Attached Figure Description

[0052] Figure 1 This is a schematic diagram of a system for offline comprehensive analysis, mapping, and evidence collection of natural resource big data, as disclosed in an embodiment of this application.

[0053] Figure 2This is a flowchart illustrating a method for offline comprehensive analysis, mapping, and evidence collection of natural resource big data, as disclosed in an embodiment of this application. Detailed Implementation

[0054] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of the embodiments of this invention will be described in more detail below with reference to the accompanying drawings. In the drawings, the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The described embodiments are some embodiments of this invention, but not all embodiments.

[0055] 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.

[0056] The embodiments and directional terms described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0057] like Figure 1 As shown, a system for offline comprehensive analysis, mapping, and evidence collection of natural resource big data includes the following components.

[0058] The spatial data fusion module is used to achieve unified management and efficient processing of heterogeneous spatial data.

[0059] The spatial index optimization module is used to achieve fast retrieval of spatial data and high-performance query optimization.

[0060] The intelligent mapping and orchestration module is used to enable low-code, rapid configuration and automated orchestration of business processes for different natural resource survey scenarios.

[0061] The offline analysis module is used to perform buffer analysis, spatial overlay, and terrain analysis in offline environments.

[0062] The real-time visualization module is used to realize multi-level map display, trajectory recording, vector feature editing, and multimedia evidence collection.

[0063] The data quality management module is used to automate the quality checks of geometry, topology, and attributes, as well as the statistical analysis, version control, and export management of mapping results.

[0064] This offline comprehensive analysis and mapping system for natural resource big data significantly enhances the comprehensive processing and analysis capabilities of natural resource big data in an offline environment through the collaborative work of multiple modules. It achieves an integrated technical effect encompassing data fusion, index optimization, intelligent mapping, offline analysis, visualization, and quality management. Specifically, the spatial data fusion module can uniformly convert and efficiently manage multi-source, heterogeneous spatial data, solving problems such as inconsistent data standards, difficult interaction, and storage redundancy in traditional natural resource surveys, thereby ensuring the integrity and consistency of data processing. The spatial index optimization module, by constructing a high-performance index structure, greatly improves the retrieval speed and query efficiency of spatial data, enabling rapid positioning and real-time retrieval of large-scale geographic information, effectively supporting complex analysis tasks. The intelligent mapping and orchestration module, leveraging low-code and automated orchestration technologies, achieves flexible adaptation to different natural resource survey scenarios, lowering the technical threshold for system configuration and business process setup, and improving work efficiency and scalability. Offline analysis… The module possesses multi-dimensional functions such as buffer analysis, spatial overlay, and terrain analysis, enabling users to complete critical data processing and geographic analysis tasks even in environments without or with weak network connectivity, thus improving the reliability and continuity of field surveys and emergency reconnaissance. The real-time visualization module integrates multi-level map display, trajectory recording, vector element editing, and multimedia evidence collection, achieving intuitive display and dynamic updates of data results and strengthening the linkage between data analysis, mapping, and evidence collection. The data quality management module, through automated geometric, topological, and attribute checks, as well as result statistics, version control, and export management, ensures the accuracy, integrity, and traceability of mapping data, significantly reducing human error rates and improving data delivery quality. In summary, this system constructs an efficient, scalable, and reliable offline comprehensive analysis and mapping evidence collection technology platform for natural resource big data. It not only improves the speed and accuracy of data processing and spatial analysis but also optimizes multi-scenario adaptability and full-process quality control, providing technical assurance and support for natural resource surveys, supervision, and evidence collection.

[0065] The spatial data fusion module adopts a multi-level storage architecture based on the DuckDB-Spatial database, specifically including: a memory-level spatial object caching layer for storing frequently accessed spatial geometric objects and index structures; a local disk-level columnar storage layer for partitioning spatial data according to geometric type, attribute dimension, and acquisition time, and constructing a storage structure based on DuckDB's native columnar storage format; and a data synchronization layer that, under network access conditions, identifies differences between local data and server-side data through an incremental synchronization algorithm, transmitting only the changed spatial elements and attribute records.

[0066] As can be seen from the above, this spatial data fusion module, through a multi-level storage architecture based on the DuckDB-Spatial database, achieves efficient access to memory cache, structured storage on local disks, and efficient data synchronization under network access conditions. This significantly improves the reading speed and processing performance of spatial data, reduces redundant storage and transmission overhead, and ensures data consistency and real-time performance in both offline and online environments.

[0067] The spatial index optimization module constructs specialized index structures for the three basic geometric types: points, lines, and polygons. For point features, a space-filling curve index based on Hilbert curves is used to map two-dimensional spatial coordinates into a one-dimensional sequence, thereby enabling fast range queries and nearest neighbor searches for point features. For line features, a minimum bounding rectangle index based on R-trees is constructed, while maintaining the start and end coordinates and length information of line segments to calculate line segment intersections, inclusions, and adjacencies. For polygon features, a hierarchical index structure combining quadtrees and R-trees is used, embedding R-tree indexes in the leaf nodes of the quadtrees to optimize the performance of multi-scale spatial queries. In addition, the spatial index optimization module integrates a query pattern recognition unit, an index efficiency evaluation unit, and an index strategy optimization unit. The query pattern recognition unit analyzes spatial query behavior to identify high-frequency query regions, common query types, and query time distribution. The index efficiency evaluation unit periodically evaluates the query performance and storage overhead of different index structures, calculating index selectivity, query response time, and disk I / O counts. Based on the query pattern recognition results and index efficiency evaluation results, the index strategy optimization unit automatically selects the optimal index parameter configuration, including the quadtree splitting threshold, R-tree node capacity, and the order of the space fill curve, thereby achieving efficient management and fast retrieval of spatial data.

[0068] As can be seen from the above, this spatial index optimization module achieves efficient management and rapid retrieval of various types of spatial data by constructing differentiated index structures for point, line, and surface elements and combining query pattern recognition, index efficiency evaluation, and strategy optimization. It significantly improves the response speed of range queries, proximity searches, and multi-scale spatial analysis, reduces storage and computing overhead, and can dynamically adjust index parameters according to actual query characteristics, thereby achieving higher retrieval performance and stability in large-scale natural resource data processing.

[0069] The intelligent mapping and arrangement module includes a form template management unit, a form dynamic rendering unit, a real-time data verification unit, a task allocation rule management unit, a real-time progress tracking unit, and an intelligent early warning notification unit. Specifically: the form template management unit pre-sets corresponding JSON Schema templates based on standards and technical procedures for different natural resource types, defining survey field types, data verification rules, field dependencies, and interface display logic to ensure the standardization and consistency of data collection; the form dynamic rendering unit uses native JavaScript DOM manipulation and CSS style control to render the JSON... The schema configuration is converted into an interactive survey form interface, supporting text input, numerical selection, date and time pickup, file attachment upload, and GPS coordinate acquisition, achieving operability and flexibility of mobile forms. The real-time data verification unit performs field mandatory checks, data format validation, numerical range constraints, and cross-field logical consistency verification locally on the mobile device to ensure the accuracy and completeness of the collected data. The task allocation rule management unit formulates automatic task allocation decision rules based on the survey personnel's professional qualifications, geographical distribution, current workload, and historical work quality factors, achieving scientific and intelligent task assignment. The real-time progress tracking unit monitors the survey task execution status in real time through GPS positioning and data synchronization on the mobile device, including task start time, completion percentage, data collection quality, and abnormal situation recording information. The intelligent early warning notification unit automatically generates early warning information and pushes it to relevant personnel when it detects task execution delays, substandard data quality, or abnormal device disconnection, thereby achieving full monitoring and timely intervention of the survey process.

[0070] As can be seen from the above, this intelligent mapping and arrangement module, through the synergistic effects of form template management, dynamic rendering, real-time data verification, intelligent task allocation, progress tracking, and early warning notification, achieves standardization, dynamism, and automation of the data collection process. This effectively improves the standardization, completeness, and consistency of data entry and reduces the rate of human error. At the same time, it enhances operational efficiency and resource utilization through scientific task assignment and real-time progress monitoring, and issues timely warnings in abnormal situations, realizing visualized management and risk control of the entire survey process, significantly enhancing the reliability and precision of natural resource survey work.

[0071] The offline analysis module includes a buffer analysis unit, a spatial overlay unit, a terrain analysis unit, a spatial clustering analysis unit, a trajectory data analysis unit, a spatial correlation analysis unit, and a statistical report generation unit. Specifically: the buffer analysis unit implements fixed-distance, variable-distance, and multi-ring buffering based on the GEOS geometry library and optimizes boundary smoothing; the spatial overlay unit performs spatial topology operations, including point-in-plane judgment, line-plane intersection, and plane-plane overlay; the terrain analysis unit calculates slope, aspect, watershed, and visible area based on a digital elevation model and supports contour line generation and 3D visualization; the spatial clustering analysis unit identifies the distribution patterns and outliers of spatial elements based on distance clustering; the trajectory data analysis component performs trajectory segmentation, stop point identification, and movement pattern extraction on the collected GPS movement trajectories; the spatial correlation analysis unit identifies spatial correlation characteristics between different resource types through spatial autocorrelation analysis; and the statistical report generation unit automatically calculates the area, distribution density, and change trends of various resources based on the spatial analysis results and generates standardized statistical analysis reports.

[0072] As can be seen from the above, this offline analysis module integrates multiple functions such as buffer analysis, spatial overlay, terrain analysis, cluster analysis, trajectory analysis, spatial correlation analysis, and statistical report generation. It enables multi-dimensional processing and comprehensive analysis of natural resource spatial data under network-free conditions. It effectively supports fixed and variable distance buffering, complex topology operations, and digital elevation model calculations, improving the accuracy and depth of spatial pattern recognition, trajectory feature extraction, and resource correlation judgment. Furthermore, it enhances the efficiency of results processing and presentation through automated report output, thereby significantly strengthening the ability to process natural resource data and support decisions in offline environments.

[0073] The real-time visualization module includes a map rendering unit, a vector feature editing unit, and a multimedia evidence management unit. Specifically: the map rendering unit uses a WebGL graphics interface to achieve smooth display of multi-level tile maps, supports the mixed display of online raster base maps, offline vector data, and 3D terrain models, and optimizes mobile device rendering performance through viewport clipping and multi-level detail control; the vector feature editing unit enables the creation, deletion, movement, scaling, and attribute management of point, line, and polygon features, and provides operation history, undo / redo, and geometric snap functions; the multimedia evidence management unit uses the mobile device's camera, audio recording, and file system interface to collect various types of evidence on-site, and optimizes storage space usage through file compression and format conversion technologies.

[0074] As can be seen from the above, this real-time visualization module, through the synergy of map rendering, vector feature editing, and multimedia evidence management, achieves intuitive display and efficient interaction of multi-source spatial data and multimedia information, significantly improving map loading and rendering performance on mobile devices and supporting smooth display of 3D terrain and multi-level data. It also provides comprehensive capabilities for editing and managing the attributes of point, line, and polygon features, enhancing the flexibility and accuracy of data processing. Furthermore, it optimizes storage and transmission efficiency through on-site collection, compression, and format conversion of multimedia evidence, thereby comprehensively improving the visualization, real-time performance, and reliability of the natural resource survey and evidence collection process.

[0075] The statistical analysis and version control processing of the mapping results includes the following steps.

[0076] Spatial distribution statistics: By calculating the survey coverage, element density and spatial clustering, a comprehensive assessment of the distribution characteristics of the survey results in geospatial space is conducted.

[0077] Time progress statistics: Statistics on work progress, including work progress monitoring, efficiency comparison analysis and expected completion time prediction, to provide quantitative basis for project progress management.

[0078] Quality indicator statistics: Calculate the accuracy and completeness scores of the data, and conduct consistency checks to comprehensively evaluate the quality of the mapping results.

[0079] Version control management: It adopts a snapshot-based incremental storage mechanism to record the detailed content and context information of each data change, enabling comparison of differences between versions, tracking of change paths, and rollback and recovery of specified versions.

[0080] Visualized version management: The data evolution process is displayed through version trees and change timelines, which makes it easier for project managers to monitor changes in results, coordinate multi-person collaboration, and handle possible version conflict issues.

[0081] As can be seen from the above, the statistical analysis and version control processing of the survey results, through multi-dimensional quantitative evaluation of spatial distribution, time progress, and quality indicators, has achieved a comprehensive grasp of the survey results' coverage, density characteristics, operational efficiency, and data integrity. At the same time, the use of a snapshot-based incremental storage mechanism to record data change details supports comparison of differences between versions, trajectory tracing, and rollback recovery, ensuring data traceability and reliability. Furthermore, the visualization of version trees and change timelines makes the evolution process of the results clear at a glance, facilitating multi-person collaboration, version conflict handling, and project process management, thereby significantly improving the management accuracy and work efficiency of the survey results.

[0082] like Figure 2 As shown, a method for offline comprehensive analysis, mapping, and evidence collection of natural resource big data includes the following steps.

[0083] Step 1: Under a good network environment, acquire basic natural resource data and survey task configuration information of the area to be investigated, complete the basic information configuration of the project, and perform unified format conversion and storage management of heterogeneous spatial data through the spatial data fusion module. Store the spatial data composed of points, lines and surfaces in the server database as the basis for field collection and evidence collection.

[0084] The process of unifying the format of heterogeneous spatial data includes the following steps.

[0085] By analyzing file header information, extension characteristics, and content structure, the system automatically determines the data format type and distinguishes between vector data and raster data.

[0086] For data identified as vector type, the vertex coordinates, attribute information and style definitions of geometric features are extracted. At the same time, coordinate system identification and transformation calculations are performed to ensure that data from different coordinate reference systems can achieve spatial consistency and correct geometric position expression under a unified format.

[0087] For raster data, pixel data, georeferenced information, and metadata description are read, and resampling and registration are performed to eliminate deviations caused by differences in resolution, projection, or original reference, so as to achieve accurate overlay and consistent expression of raster data in a unified format.

[0088] Throughout the entire transformation process, the source, processing steps, and change history of each data element are tracked and archived, thereby achieving data traceability and the ability to trace back the processing steps.

[0089] After the data conversion is completed, the results are verified for integrity and consistency to ensure that the accuracy and consistency of the converted data in terms of geometric location, attribute information, and spatial reference meet the expected requirements.

[0090] The final output conforms to a unified format, and a conversion log and quality report are generated, providing a detailed description of the key parameters, processing methods, error messages, and correction records throughout the conversion process.

[0091] As can be seen from the above, step 1, by acquiring basic natural resource data and survey task configuration information, configuring basic project information, and unifying the format conversion and storage management of heterogeneous spatial data under a good network environment, achieves efficient integration and standardized processing of multi-source data under a unified technical framework, thus providing reliable data foundation support for field collection and evidence collection. During data processing, it can automatically identify different file types and data structures, distinguish between vector and raster data, and selectively extract geometric elements, attribute information, and geographic reference parameters. Coordinate system identification and transformation ensure that data under different coordinate reference systems maintain consistency and accurate geometric position expression within the same spatial framework. For raster data, by reading pixel, metadata, and projection information, resampling and registration are performed to eliminate differences in resolution, projection, and original benchmarks among multi-source data, achieving seamless overlay and consistent expression of multi-scale, multi-source raster data. Throughout the entire conversion process, the data source, processing steps, and change trajectory are recorded and archived, providing comprehensive traceability and backtracking, and providing a basis for subsequent quality checks and results audits. After the data conversion is completed, integrity and consistency checks are performed to ensure that geometric location, attribute information, and spatial references all meet the expected accuracy and consistency standards, and output data results in a unified format. At the same time, a conversion log and quality report containing key parameters, processing methods, error messages, and correction records are generated. Thus, while ensuring data reliability, standardization, and traceability, the efficiency of natural resource survey preparation and the accuracy and consistency of field evidence data are significantly improved.

[0092] Step 2: Based on the geometric type characteristics of the natural resource basic data, construct a differentiated hierarchical index structure to achieve rapid retrieval and optimization of spatial data.

[0093] The construction of the differentiated hierarchical index structure includes the following steps.

[0094] Perform distribution statistics on spatial data to analyze its density distribution, geometric complexity, and characteristics of query hotspot areas.

[0095] For point elements, a dynamic quadtree segmentation method is adopted, which automatically adjusts the segmentation depth and node capacity according to the distribution density of points, thereby achieving efficient point query and proximity retrieval.

[0096] For line features, a hierarchical index structure based on line segment direction and length is established to support fast line segment intersection detection and retrieval of adjacent line features.

[0097] For polygon features, a hybrid structure combining multi-level grid indexes and R-trees is constructed to optimize the performance of polygon feature inclusion queries and overlay analysis.

[0098] During the index building process, memory usage and building time are monitored in real time, and index parameters are dynamically adjusted based on resource consumption and performance to achieve a balance between query efficiency and resource utilization.

[0099] After the index is built, the performance of the index structure is evaluated, and corresponding optimization suggestions are generated to ensure that the established hierarchical index can significantly improve the execution efficiency in subsequent spatial queries.

[0100] As can be seen from the above, step 2, by constructing a differentiated hierarchical index structure based on the geometric type characteristics of natural resource basic data, achieves efficient organization and rapid retrieval optimization of multi-type spatial data, significantly improving the execution performance and overall operating efficiency of spatial queries. In the specific process, the spatial data is first statistically analyzed to comprehensively assess its density distribution, geometric complexity, and characteristics of query hotspots, providing a quantitative basis for subsequent indexing strategies. For point features, a dynamic quadtree segmentation method is used to automatically adjust the segmentation depth and node capacity based on point distribution density, achieving efficient point location, rapid range retrieval, and proximity search. For line features, a hierarchical index structure based on line segment direction and length is constructed, significantly improving the speed and accuracy of line segment intersection detection and adjacent line feature retrieval. For polygon features, a hybrid structure combining multi-level grid indexes and R-trees is adopted, effectively optimizing performance for queries, overlay analysis, and complex spatial calculations. During index construction, memory consumption and construction time are monitored in real time, and index parameters are dynamically adjusted based on resource utilization and performance, thereby reducing computational and storage overhead while ensuring high performance, achieving a balance between query efficiency and hardware resources. After indexing is completed, performance evaluation and optimization suggestions are provided to offer technical feedback and adjustment guidelines for subsequent spatial retrieval and analysis, ensuring that the constructed hierarchical index has stable, efficient, and scalable technical effects in complex natural resource data environments.

[0101] Step 3: Based on the survey task configuration information, generate a dynamic survey form and automatically arrange the survey operation process according to the preset business process template.

[0102] The generation of the dynamic survey form includes the following steps.

[0103] Based on the resource type, accuracy requirements, and business specifications of the survey task, the system automatically matches applicable form templates, enabling metadata-driven template selection.

[0104] The selected form template is parsed to read field types, validation rules, display styles, and interaction logic information, providing a basis for control generation and field configuration.

[0105] Generate various form controls based on the template definition, including text input, numeric selection, date and time, geographic coordinates, image upload, and drop-down selection.

[0106] Based on the field dependencies and display conditions defined in the template, the display and hiding rules of each field are dynamically configured to ensure that the generated form can intelligently adjust the field layout and visibility according to task requirements.

[0107] Configure validation rules and linkage logic for each field, including data type checks, mandatory field constraints, and range limits, so that the generated form has real-time validation and error message capabilities when used.

[0108] It automatically performs structural integrity checks and logical consistency verifications, and optimizes field order, dependencies, and control configurations based on the detection results to ensure that the generated forms meet business specifications and usage requirements.

[0109] As can be seen from the above, step 3 generates dynamic survey forms based on survey task configuration information and automatically arranges survey operation processes, thereby achieving a high degree of standardization, intelligence and process-orientation in natural resource survey work. Specifically, it can automatically match applicable form templates based on the type of survey resources, accuracy requirements, and business specifications, achieving metadata-driven template selection to ensure that form content is highly aligned with task requirements. During template parsing, it reads field types, validation rules, display styles, and interaction logic information, providing accurate basis for form control generation and field configuration, ensuring that various controls such as text input, numerical selection, date and time, geographic coordinates, image uploads, and dropdown selections can meet different data collection needs. Simultaneously, based on the field dependencies and display conditions defined in the template, it dynamically adjusts field display and hiding rules, enabling the form to intelligently adapt to specific scenarios during task execution, improving data entry flexibility and operational efficiency. It also configures validation rules and linkage logic for each field, including data type checks, mandatory constraints, and range limitations, enabling real-time data verification and error prompts, thereby significantly reducing data entry errors and inconsistencies. After generating the form, it automatically performs structural integrity checks and logical consistency verification, and optimizes field order, dependencies, and control configurations based on the detection results, ensuring that the generated form conforms to business specifications and meets actual usage requirements. Overall, this step significantly improved the standardization of survey operations, the accuracy of data collection, and the efficiency of task execution. It achieved full-process control from template-driven to intelligent workflow, ensuring the high quality, high efficiency, and traceability of natural resource surveys. At the same time, it provided reliable and standardized basic data support for subsequent data analysis, mapping, and evidence collection.

[0110] Step 4: Under a good network environment, download and synchronize the unified storage spatial data and the survey workflow to the DuckDB-Spatial database of the mobile survey device.

[0111] The synchronization of spatial data and survey operation processes includes the following steps.

[0112] By comparing the data version identifiers on the server and mobile devices, the newly added, modified, and deleted data items that need to be synchronized are identified, thereby determining the target dataset for synchronization.

[0113] The data to be synchronized is compressed, including geometric simplification, attribute field optimization, and file compression, to reduce the amount of data transmitted and reduce network bandwidth usage.

[0114] Large files are split into multiple smaller data blocks and transmitted step by step to reduce the impact of a single transmission failure on the overall synchronization.

[0115] During the synchronization process, the transmission progress and network connection status are monitored in real time, and the transmission strategy is dynamically adjusted according to the network quality to ensure data transmission efficiency and stability.

[0116] After data transmission is complete, an integrity check is performed on the mobile device to verify the received data and update the local database to ensure the accuracy and consistency of the synchronized data.

[0117] Record historical information for each synchronization operation and enable data version management and synchronization status tracking for a single device.

[0118] As can be seen from the above, step 4 efficiently synchronizes the unified stored spatial data and survey workflow to the DuckDB-Spatial database of the mobile survey device, thereby achieving the integrity, real-time performance, and reliability of mobile data and providing a stable data foundation for field surveys. During synchronization, the system automatically compares the data version identifiers between the server and mobile devices to accurately identify newly added, modified, and deleted data items, thereby constructing the target dataset for synchronization and ensuring that the content obtained by the mobile device is consistent with that of the server. For the data to be transmitted, geometric simplification, attribute field optimization, and file compression are performed to effectively reduce data volume and network bandwidth consumption. At the same time, large files are split into multiple data blocks for gradual transmission, reducing the impact of a single transmission failure on the overall synchronization and improving the transmission success rate and reliability. During synchronization, the system monitors the transmission progress and network status in real time, dynamically adjusts the transmission strategy based on network quality, optimizes data transmission efficiency, and ensures process stability. After transmission is completed, the mobile device performs integrity verification, verifies the received data, and updates the local database to ensure the consistency and accuracy of geometric, attribute, and spatial reference information. In addition, detailed historical information is recorded for each synchronization operation, enabling single-device data version management and synchronization status tracking, thereby ensuring data traceability, reliability, and continuity in a multi-device collaborative and version iteration environment. Overall, this step significantly improves the data availability, synchronization efficiency, and management accuracy of mobile natural resource surveys, providing efficient, reliable, and controllable data support for field tasks and effectively supporting data collection, mapping, and evidence collection in offline environments.

[0119] Step 5: Whenever the investigators arrive at a survey point, they mark the current point as the target survey area, and at the same time collect the spatial location of the workers and store it in the DuckDB-Spatial database.

[0120] As can be seen from the above, step 5, by marking the target area in real time at the survey points and collecting the spatial location of the workers, achieves precise positioning and trajectory recording of the mobile survey operation, ensuring that the spatial information of each survey point and the activity data of the workers are stored synchronously in the local database, thereby improving the traceability of the survey process, the integrity of the data and the accuracy of subsequent analysis, and providing a reliable spatial information foundation for natural resource surveys and evidence collection.

[0121] Step 6: Based on the resource type and survey requirements of the target survey area, use the offline analysis module to perform real-time geographic information processing and spatial relationship analysis to generate corresponding analysis results.

[0122] The real-time geographic information processing and spatial relationship analysis includes the following steps.

[0123] It acquires various types of geographic information data in real time, including spatial data and attribute information of land, water and forest resources, and performs cleaning, format standardization and preliminary correction.

[0124] Based on different survey subjects, the system automatically identifies the resource type to which the data belongs and calls the corresponding set of analysis tools. Specifically, for land resources, it performs plot boundary extraction, area calculation, and land use type identification; for water resources, it performs watershed analysis, water system network construction, and catchment area delineation; and for forest resources, it performs vegetation coverage calculation, forest stand boundary delineation, and biomass estimation.

[0125] The processed geographic information data is subjected to basic spatial operator operations, including topological relationship determination, distance measurement, azimuth calculation and visibility analysis, to achieve quantitative expression of multidimensional spatial relationships.

[0126] As can be seen from the above, step 6, based on the resource types and survey requirements of the target survey area, utilizes the offline analysis module to perform real-time processing and spatial relationship analysis of geographic information data, achieving efficient, accurate, and multi-dimensional information acquisition for natural resource surveys. Specifically, it can collect various spatial data and their attribute information, such as land, water bodies, and forests, in real time, and clean, standardize, and preliminarily correct the data to ensure consistency and reliability in subsequent analyses. In terms of resource type identification, it can automatically determine the resource category to which different data belong and call the corresponding analysis toolset for targeted processing: land resource analysis includes plot boundary extraction, area calculation, and land use type identification to ensure accurate measurement of land cover and utilization status; water resource analysis, through watershed division, water system network construction, and catchment area calculation, realizes the distribution, flow direction, and catchment of water bodies. The analysis of forest resources includes the quantification of features, such as vegetation cover calculation, stand boundary delineation, and biomass estimation, providing accurate basic data for ecological status assessment and forestry management. In the spatial relationship analysis stage, the processed data undergoes topological relationship determination, distance measurement, azimuth calculation, and visibility analysis to achieve a quantitative expression of multidimensional spatial relationships among various elements, providing a scientific basis for resource distribution patterns, spatial interactions, and potential risk assessment. The entire process supports real-time computation and dynamic data updates, enabling the analysis results to reflect the on-site situation of the field survey in real time, significantly improving data processing efficiency and analytical accuracy. Through this step, natural resource surveys can not only obtain spatial distribution and attribute information of various resources but also quantify the spatial relationships between elements, providing reliable, comprehensive, and traceable technical support for subsequent mapping, decision analysis, and evidence collection.

[0127] Step 7: Perform vector element editing operations through the real-time visualization module, collect relevant multimedia evidence data, associate and store the editing results and evidence data, and construct field evidence records by combining the spatial location of the operators.

[0128] The vector feature editing operations include the creation, modification, deletion, and copying of point, line, and polygon features. The editing tools include a vertex editor, geometric transformation tools, attribute editor, and style settings panel. The multimedia evidence data acquisition integrates multiple acquisition methods, including high-resolution cameras, high-definition video recording, stereo recording, and handwritten signatures. It supports customized acquisition parameters and quality settings. The acquired multimedia data is automatically embedded with geographic coordinates, timestamps, and device information, establishing a relationship with spatial features.

[0129] As can be seen from the above, step 7 enables comprehensive editing of vector elements and high-precision collection of multimedia evidence through the real-time visualization module, thereby constructing a complete and traceable field evidence record, significantly improving the data integrity and on-site operational accuracy of natural resource surveys. During operation, the system supports the creation, modification, deletion, and copying of various spatial elements, including points, lines, and polygons. Combined with a vertex editor, geometric transformation tools, attribute editor, and style settings panel, it enables precise control over geometric shapes, attribute information, and visual styles, ensuring that the editing results meet actual investigation needs. Simultaneously, multimedia evidence collection integrates multiple methods such as high-resolution cameras, high-definition video recording, stereo audio recording, and handwritten signatures, and supports customized collection parameters and quality settings. This ensures that images, videos, audio, and handwritten records acquired on-site meet the evidentiary requirements of different types of resources. Collected multimedia data is automatically appended with geographic coordinates, timestamps, and device information, establishing a precise association with the edited vector elements, achieving unified management of spatial data, work records, and evidentiary information. Furthermore, by integrating the spatial location of personnel with the editing results and multimedia evidence, a complete field evidence record can be generated, ensuring that every operation and data collection step is traceable and verifiable, contributing to improved accuracy, reliability, and legality of investigation results. Overall, this step achieves a close integration of spatial element editing and multimedia evidence collection, making field data management more standardized, refined, and intelligent.

[0130] Step 8: During the data acquisition process, the data quality management module is used to identify data quality problems and provide correction suggestions.

[0131] The quality problem identification of the data quality management module includes the following steps.

[0132] Real-time checks are performed during data entry and editing, including data format verification, value range checks, and mandatory field validation, to ensure that the data meets basic specifications in the initial stage.

[0133] After data collection is completed, a comprehensive check is performed on the entire dataset, including geometric validity verification, topological consistency check, and attribute correlation verification, in order to discover potential problems across data records.

[0134] Conduct in-depth data inspections, including cross-table join checks, business rule compliance verification, and data logic consistency analysis, to identify potential business logic and relationship issues.

[0135] Based on the severity of the inspection results, problems are classified into three levels: error, warning, and alert.

[0136] As can be seen from the above, step 8 implements multi-level and multi-dimensional quality control throughout the data acquisition process through the data quality management module, achieving high reliability and availability of natural resource data. During the data entry and editing stages, real-time format verification, value range checks, and mandatory field validation are performed to ensure that initial data conforms to basic specifications, reducing human input errors and standardization deviations. After data acquisition, a comprehensive check is conducted on the entire dataset, including geometric validity verification, topological consistency checks, and attribute correlation verification, which can identify potential problems across data records, such as spatial misalignment, attribute conflicts, and missing data. Furthermore, in-depth data analysis is performed through cross-table correlation checks, business rule compliance verification, and data logic consistency analysis, which can identify potential anomalies in complex business logic or resource relationships, ensuring data coordination and consistency across multiple tables, attributes, and resource types. For the various problems discovered, hierarchical management is implemented according to severity, classifying problems into three levels: errors, warnings, and alerts, and providing targeted correction suggestions for each type of problem, enabling operators to correct data defects promptly and accurately, improving data integrity and accuracy. Overall, this step effectively ensures quality control throughout the entire process of data entry and editing, from initial collection to in-depth analysis, significantly improving the scientific nature and reliability of natural resource data management.

[0137] Step 9: If a data quality problem is detected, the investigators are prompted to correct the data and re-perform the quality check until it meets the quality standards.

[0138] The process for correcting the data quality issues includes the following steps.

[0139] Based on the type and severity of data quality issues, it automatically identifies geometric or attribute errors and generates personalized correction suggestions and operational instructions.

[0140] For identified geometric errors, automatic repair tools are invoked to perform processing, including self-intersection elimination, geometric simplification, and topology reconstruction, to ensure the correctness of the spatial data structure.

[0141] For identified attribute errors, data cleaning tools are used to process them, including format standardization, outlier correction, and missing value filling operations, to ensure the integrity and consistency of attribute data.

[0142] During the correction process, the results of the modifications are verified in real time to ensure that the repair operation does not introduce new data quality issues.

[0143] All corrective actions are fully recorded, including the time of correction, the operator, the content of the correction, and the basis for the correction, thus establishing a data change audit trail for easy traceability and management.

[0144] Once the correction is complete, the relevant quality check process will be automatically triggered to verify whether the correction results meet the data quality standards.

[0145] As can be seen from the above, step 9, by guiding investigators to make targeted corrections and repeatedly performing quality checks when data quality issues are detected, achieves end-to-end quality assurance for natural resource data from initial collection to final compliance. During the correction process, geometric or attribute errors can be automatically identified based on the type and severity of the problem, generating personalized correction suggestions and operational guidance, enabling investigators to address various anomalies in a targeted manner. For geometric errors, automatic repair tools are invoked to perform self-intersection elimination, geometric simplification, and topological reconstruction, ensuring the accuracy and integrity of the spatial data structure. For attribute errors, data cleaning tools are used for format standardization, outlier correction, and missing value imputation, ensuring the integrity, consistency, and analyzability of attribute information. During the correction process, the modification results are verified in real time to prevent the introduction of new data quality issues and ensure reliable and effective repair. Simultaneously, all correction operations are fully recorded, including operation time, personnel involved, correction content, and basis, forming a traceable data change audit trail, providing a basis for subsequent management, verification, and quality control. After correction is completed, subsequent quality check processes are automatically triggered to verify the repair results and ensure that the data meets preset quality standards. Overall, this step achieves closed-loop management of data quality through automatic identification, targeted correction, real-time verification, and traceable recording. It not only improves the accuracy, completeness, and consistency of the data, but also ensures the reliability and scientific nature of the field survey results.

[0146] Step 10: If the data quality check passes, the data collection for the current survey point is completed, and the process moves to the next survey point according to the survey path. At the same time, the completed survey results are statistically analyzed and version controlled.

[0147] As can be seen from the above, step 10 completes the data collection of the current survey point after the data quality check is passed, realizing the continuous execution of the survey task and ensuring data reliability. At the same time, it guides the survey personnel to move to the next point according to the preset survey path, ensuring the systematicness and completeness of the survey coverage. Statistical analysis is performed on the completed data results to quantify spatial distribution, work progress and quality indicators, and the data change and evolution process is recorded through version control management, realizing the traceability and standardized management of the survey results, thereby improving the efficiency, accuracy and data management level of the entire field survey.

[0148] Step 11: When the network environment is good, send the field evidence record data stored in the DuckDB-Spatial database back to the management platform server.

[0149] The process of transmitting field evidence record data back includes the following steps.

[0150] Periodically check the current network connection status, signal strength, and available bandwidth parameters to determine whether the preset threshold has been reached to initiate data backhaul.

[0151] When network conditions meet the requirements, the appropriate transmission strategy is automatically selected based on the size of the data file to be transmitted and the real-time network status. For large files, a fragmented transmission method is used to improve the transmission success rate.

[0152] During data transmission, the transmission progress and connection status are continuously monitored. If a network interruption is detected, the transmission is automatically paused, and the unfinished transmission task is resumed from the interruption point after the network is restored, ensuring the integrity and continuity of the data.

[0153] After the transmission is completed, the receiving server returns an acknowledgment message to the mobile device and updates the local data status flag to indicate that the transmission has been completed.

[0154] As can be seen from the above, step 11 efficiently transmits field evidence records from the DuckDB-Spatial database on the mobile device to the management platform server when network conditions are good, achieving centralized management and integrity assurance of field data. It can periodically monitor network connection status, signal strength, and available bandwidth, and initiate transmission when preset thresholds are reached, ensuring data transmission occurs in a reliable network environment. It automatically selects appropriate transmission strategies based on different data file sizes and real-time network conditions, using fragmented transmission for large files to improve success rates. During transmission, it continuously monitors progress and connection status; if a network interruption occurs, it automatically pauses transmission and resumes unfinished tasks from the interrupted point after network recovery, ensuring the integrity and continuity of data transmission. After transmission is complete, the receiving server returns confirmation information to the mobile device and synchronously updates the local data status identifier, indicating that the transmission is complete. Overall, this step effectively ensures the secure, efficient transmission and traceability of field evidence data, providing a reliable data foundation for centralized storage, analysis, and auditing on the management platform, while improving the efficiency and reliability of collaborative data management between the mobile device and the server.

[0155] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A natural resource big data offline comprehensive analysis and evidence system, characterized in that, Includes the following components: The spatial data fusion module is used to achieve unified management and efficient processing of heterogeneous spatial data; The spatial index optimization module is used to achieve fast retrieval of spatial data and high-performance query optimization; The intelligent mapping and orchestration module is used to enable low-code, rapid configuration and automated orchestration of business processes for different natural resource survey scenarios. The offline analysis module is used to perform buffer analysis, spatial overlay, and terrain analysis in offline environments. The real-time visualization module is used to realize multi-level map display, trajectory recording, vector feature editing, and multimedia evidence collection; The data quality management module is used to automate the quality checks of geometry, topology, and attributes, as well as the statistical analysis, version control, and export management of mapping results. The spatial data fusion module adopts a multi-level storage architecture based on the DuckDB-Spatial database, including: a memory-level spatial object caching layer for storing frequently accessed spatial geometric objects and index structures; a local disk-level columnar storage layer for partitioning spatial data according to geometric type, attribute dimension, and acquisition time, and constructing a storage structure based on DuckDB's native columnar storage format; and a data synchronization layer that, under network access conditions, identifies differences between local data and server-side data through an incremental synchronization algorithm and only transmits spatial elements and their attribute records that have changed.

2. The natural resource big data offline comprehensive analysis and evidence system of claim 1, wherein, The intelligent mapping and arrangement module includes a form template management unit, a form dynamic rendering unit, a real-time data verification unit, a task allocation rule management unit, a real-time progress tracking unit, and an intelligent early warning notification unit. Specifically: the form template management unit pre-sets corresponding JSON Schema templates based on standards and technical procedures for different natural resource types, defining survey field types, data verification rules, field dependencies, and interface display logic; the form dynamic rendering unit uses native JavaScript DOM manipulation and CSS style control to render the JSON... The schema configuration is converted into an interactive survey form interface, supporting text input, numerical selection, date and time pickup, file attachment upload, and GPS coordinate acquisition. The real-time data verification unit performs field mandatory checks, data format validation, numerical range constraints, and cross-field logical consistency verification on the mobile device. The task allocation rule management unit formulates automatic task allocation decision rules based on the survey personnel's professional qualifications, geographical distribution, current workload, and historical work quality factors, achieving scientific and intelligent task assignment. The real-time progress tracking unit monitors the survey task execution status in real time through GPS positioning and data synchronization on the mobile device, including task start time, completion percentage, data collection quality, and abnormal situation recording information. The intelligent early warning notification unit automatically generates early warning information and pushes it to relevant personnel when it detects task execution delays, substandard data quality, or abnormal device disconnection.

3. The system according to claim 2, wherein, The offline analysis module includes a buffer analysis unit, a spatial overlay unit, a terrain analysis unit, a spatial clustering analysis unit, a trajectory data analysis unit, a spatial correlation analysis unit, and a statistical report generation unit. Specifically: the buffer analysis unit implements fixed-distance, variable-distance, and multi-ring buffering based on the GEOS geometry library and optimizes boundary smoothing; the spatial overlay unit performs spatial topology operations, including point-in-plane determination, line-plane intersection, and plane-plane overlay; the terrain analysis unit calculates slope, aspect, watershed, and visible area based on a digital elevation model; the spatial clustering analysis unit identifies the distribution patterns and outliers of spatial elements based on distance clustering; the trajectory data analysis unit performs trajectory segmentation, stop point identification, and movement pattern extraction on the collected GPS movement trajectories; the spatial correlation analysis unit identifies spatial correlation characteristics between different resource types through spatial autocorrelation analysis; and the statistical report generation unit automatically calculates the area, distribution density, and trend of various resources based on the spatial analysis results and generates standardized statistical analysis reports.

4. A method for offline comprehensive analysis and evidence mapping of natural resource big data, applied to the system for offline comprehensive analysis and evidence mapping of natural resource big data according to claim 3, characterized in that, Includes the following steps: Under good network conditions, acquire basic natural resource data and survey task configuration information of the area to be investigated, complete the basic information configuration of the project, and perform unified format conversion and storage management of heterogeneous spatial data through the spatial data fusion module. Store the spatial data composed of points, lines and surfaces in the server database as the basis for field collection and evidence collection. Based on the geometric type characteristics of the aforementioned natural resource basic data, a differentiated hierarchical index structure is constructed to achieve rapid retrieval and optimization of spatial data; Based on the survey task configuration information, a dynamic survey form is generated, and the survey operation process is automatically arranged according to the preset business process template. Under a good network environment, download and synchronize the uniformly stored spatial data and the aforementioned survey workflow to the DuckDB-Spatial database of the mobile survey device; Whenever investigators arrive at an investigation point, the current point is marked as the target investigation area, and the spatial location of the investigators is collected and stored in the DuckDB-Spatial database. Based on the resource type and survey requirements of the target survey area, the offline analysis module is used to perform real-time geographic information processing and spatial relationship analysis to generate corresponding analysis results. The real-time visualization module is used to edit vector elements and collect relevant multimedia evidence data. The editing results and evidence data are stored together and the field evidence record is constructed by combining the spatial location of the operators. During the data acquisition process, the data quality management module is used to identify data quality problems and provide correction suggestions; If a data quality issue is detected, the investigators will be prompted to correct the data and re-perform the quality check until it meets the quality standards. If the data quality check passes, the data collection for the current survey point is completed, and the survey proceeds to the next survey point according to the survey path. At the same time, the completed survey results are statistically analyzed and version controlled. When the network environment is good, the field evidence records stored in the DuckDB-Spatial database will be sent back to the management platform server.

5. The method of claim 4, wherein, The construction of the differentiated hierarchical index structure includes the following steps: Perform distribution statistics on spatial data to analyze its density distribution, geometric complexity, and characteristics of query hotspot areas; For point elements, a dynamic quadtree segmentation method is adopted, which automatically adjusts the segmentation depth and node capacity according to the distribution density of points to achieve efficient point query and proximity retrieval. For line features, a hierarchical index structure based on line segment direction and length is established to support fast line segment intersection detection and retrieval of adjacent line features. For polygon features, a hybrid structure combining multi-level grid index and R-tree is constructed to optimize the performance of polygon feature inclusion query and overlay analysis. During the index building process, memory usage and building time are monitored in real time, and index parameters are dynamically adjusted based on resource consumption and performance to achieve a balance between query efficiency and resource utilization. After the index is built, the performance of the index structure is evaluated, and corresponding optimization suggestions are generated to ensure that the established hierarchical index can significantly improve the execution efficiency in subsequent spatial queries.

6. The method of claim 4, wherein, The generation of the dynamic survey form includes the following steps: Based on the resource type, accuracy requirements, and business specifications of the survey task, the system automatically matches the appropriate form template, enabling metadata-driven template selection. The selected form template is parsed to read field types, validation rules, display styles, and interaction logic information, providing a basis for control generation and field configuration; Generate various form controls based on the template definition, including text input, numeric selection, date and time, geographic coordinates, image upload, and drop-down selection; Based on the field dependencies and display conditions defined in the template, the display and hiding rules of each field are dynamically configured to ensure that the generated form can intelligently adjust the field layout and visibility according to task requirements; Configure validation rules and linkage logic for each field, including data type checks, mandatory field constraints and range limits, so that the generated form has real-time validation and error prompt capabilities when used; It automatically performs structural integrity checks and logical consistency verifications, and optimizes field order, dependencies, and control configurations based on the detection results to ensure that the generated forms meet business specifications and usage requirements.

7. The method of claim 4, wherein, The synchronization of spatial data and survey workflow includes the following steps: By comparing the data version identifiers on the server and mobile devices, we can identify the new, modified, and deleted data items that need to be synchronized, thereby determining the target dataset for synchronization. The data to be synchronized is compressed, including geometric simplification, attribute field optimization, and file compression, to reduce the amount of data transmitted and reduce network bandwidth usage. Large files are split into multiple smaller data blocks and transmitted step by step to reduce the impact of a single transmission failure on the overall synchronization. During the synchronization process, the transmission progress and network connection status are monitored in real time, and the transmission strategy is dynamically adjusted according to the network quality to ensure data transmission efficiency and stability. After the data transmission is completed, an integrity check is performed on the mobile device to verify the received data and update the local database to ensure the accuracy and consistency of the synchronized data. Record historical information for each synchronization operation and enable data version management and synchronization status tracking for a single device.

8. The method of claim 4, wherein, Vector feature editing operations include creating, modifying, deleting, and copying point, line, and polygon features. Editing tools include the vertex editor, geometric transformation tools, attribute editor, and style settings panel. Multimedia evidence data collection integrates multiple collection methods, including high-resolution cameras, high-definition video recording, stereo recording, and handwritten signatures. It supports customizable collection parameters and quality settings. The collected multimedia data is automatically embedded with geographic coordinates, timestamps, and device information, establishing a correlation with spatial elements.

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