Geological data interaction method and system based on Ovi interaction map

Abstract

The invention relates to the technical field of Otwei interactive maps, and discloses a geological data interaction method and system based on an Otwei interactive map. The method comprises the following steps: carrying out feature recognition and structural analysis on original geological data to obtain a standardized data packet, and establishing a geographic space reference conversion index table; performing feature extraction and classification on geological elements in the standardized data packet to obtain structured geological data; importing the structured geological data into an Ovoucher interactive map, and carrying out local registration through a mesh generation technology to obtain a visual geological element map layer; geological element drawing and attribute input are carried out, and an edited geological data set is obtained; and carrying out structure recombination and reverse coordinate conversion to obtain a standard format data file adaptive to the target geological information system. According to the method, accurate conversion of multi-source geological data between different coordinate systems and measuring scales is realized, and the problem of spatial dislocation during integration of different-source geological data in a traditional method is effectively solved.

Description

Technical Field

[0001] The present invention relates to the technical field of Orvis interactive maps, and particularly to a geological data interaction method and system based on Orvis interactive maps. Background Art

[0002] With the progress of technology, different from target geological software / platforms such as the Digital Geological Survey System (DGSS) Geological Cloud and 3DMine, interactive map popular software with multi-functions such as satellite (2D / 3D) images, traffic maps, design, and mobile terminal positioning has been widely used in many industries such as geology, surveying and mapping, water conservancy, environment, forestry, agriculture, electric power, construction, transportation, and communication. The main ones include mainstream map software such as Orvis interactive maps, Hydrographic Micro Map, 91 Satellite Map Assistant, Earth Viewer, and Bigemap. However, there are obvious deficiencies in the interaction methods between geological target data and these popular interactive map platforms in the prior art. The data interaction methods in processes such as importing geological maps / Mapgis vector data into Orvis and exporting Orvis elements / satellite images to Mapgis are single, the process is cumbersome and complex, and it requires multiple-step operations and various intermediate conversion tools.

[0003] Currently, there is a gap in the data flow between on-site survey data collection by geological workers and the target geological system. Field workers usually need to first record the original data and then return to the office to use the target software for data processing and analysis. This working mode leads to problems such as delayed data processing, information loss, and recording deviation. Although Orvis interactive maps have portability and intuitive map expression capabilities, their data interaction capabilities with the target geological information system are limited. There are technical obstacles especially in aspects such as coordinate system conversion, geological target symbol expression, and attribute data association, resulting in insufficient data conversion accuracy and obvious spatial position deviation. Summary of the Invention

[0004] The present invention provides a geological data interaction method and system based on Orvis interactive maps. The present invention realizes the precise conversion of multi-source geological data between different coordinate systems and scales, and effectively solves the spatial misalignment problem in the integration of geological data from different sources in traditional methods.

[0005] In the first aspect, the present invention provides a geological data interaction method based on Orvis interactive maps. The geological data interaction method based on Orvis interactive maps includes: Performing feature recognition and structural analysis on the original geological data to obtain a standardized data packet and establishing a geospatial reference conversion index table; Performing feature extraction and classification on the geological elements in the standardized data packet according to the geospatial reference conversion index table to obtain structured geological data; Import the structured geological data into the Ovi Interactive Map, and perform local registration through grid meshing technology to obtain a visualized geological feature layer; Add a target geological editing tool to the visualized geological feature layer to draw geological features and enter attributes, obtaining a completed geological data set; Perform structural reorganization and reverse coordinate transformation on the completed geological data set to obtain a standard format data file adapted to the target geological information system.

[0006] In a second aspect, the present invention provides a geological data interaction system based on the Ovi Interactive Map. The geological data interaction system based on the Ovi Interactive Map includes: A feature recognition module for performing feature recognition and structural analysis on the original geological data to obtain a standardized data packet and establish a geospatial reference conversion index table; A classification module for performing feature extraction and classification on the geological features in the standardized data packet according to the geospatial reference conversion index table to obtain structured geological data; A local registration module for importing the structured geological data into the Ovi Interactive Map and performing local registration through grid meshing technology to obtain a visualized geological feature layer; An editing module for adding a target geological editing tool to the visualized geological feature layer to draw geological features and enter attributes, obtaining a completed geological data set; A conversion module for performing structural reorganization and reverse coordinate transformation on the completed geological data set to obtain a standard format data file adapted to the target geological information system.

[0007] In the technical solution provided by the present invention, a two-way seamless interaction between target geological data and the Ovital Map Platform is achieved, solving the problems of single data interaction method and cumbersome process in the prior art, simplifying the operation process, reducing the technical threshold for users, and reducing the operation steps. By introducing the concept of Geospatial Reference Consistency (GRC) and an intelligent coordinate matching algorithm, an accurate coordinate system conversion mechanism is established, realizing the accurate conversion of multi-source geological data between different coordinate systems and scales, effectively solving the problem of spatial misalignment during the integration of geological data from different sources in traditional methods. Using a feature extraction engine and a target geological element recognition algorithm, geological elements are accurately extracted and classified, and complex geological element relationships are retained to ensure the deep integration of geological target attributes with Ovital objects, so that the converted data still retains the target geological meaning on the Ovital platform. Based on the grid meshing technology, local accurate registration is achieved, breaking through the accuracy limitation of traditional global transformation and effectively eliminating image deformation and stitching gaps. The geological target editing function is seamlessly integrated into the Ovital platform, changing the traditional workflow of editing in the target GIS system and then exporting, supporting real-time field data collection and editing, and significantly improving the efficiency of geological field operations. A multi-person collaborative editing and version management mechanism is established to adapt to the geological survey work mode of team collaboration, realizing seamless data transfer and integration from the Ovital platform to the target GIS system. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0009] Figure 1 It is a schematic diagram of an embodiment of the geological data interaction method based on the Ovital Map in the embodiments of the present invention; Figure 2 It is a schematic diagram of an embodiment of the geological data interaction system based on the Ovital Map in the embodiments of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0010] An embodiment of the present invention provides a geological data interaction method and system based on Ovital Interactive Map. Terms such as "first", "second", "third", "fourth", etc. (if any) in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and do not necessarily describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "comprising" or "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or devices.

[0011] For ease of understanding, the specific process of the embodiment of the present invention will be described below. Please refer to Figure 1 , an embodiment of the geological data interaction method based on Ovital Interactive Map in the embodiment of the present invention includes: Step S101: Perform feature recognition and structural analysis on the original geological data to obtain a standardized data packet and establish a geospatial reference conversion index table; It can be understood that the execution entity of the present invention can be a geological data interaction system based on Ovital Interactive Map, or a terminal or a server. Specifically, it is not limited here. The embodiment of the present invention takes the server as the execution entity as an example for illustration.

[0012] Specifically, perform file header information parsing on the original geological data. By reading the header structure of the data, parse key parameters such as file type identifier, storage format, coordinate projection information, etc., so as to judge the basic attributes of the data. Classify and process the original geological data based on the data type identifier, and divide the data into two major categories: geological image data and MapGIS vector data, and import them into the corresponding processing modules respectively. Geological image data includes remote sensing images, scanned geological maps, etc. Its characteristics are large data volume and complex content, while MapGIS vector data contains geometric elements such as points, lines, and faces, and is accompanied by rich attribute information. Perform image enhancement processing on the geological image data to improve the readability and data accuracy of the images. Image enhancement mainly involves operations such as contrast adjustment, noise removal, and edge sharpening, making geological features clearer and reducing blurring and distortion that occur during image acquisition. At the same time, the system automatically detects and extracts control points in the images. The control points include map coordinate markings, grid intersection points, known geological boundary intersection points, etc. Use edge detection and image matching algorithms to accurately locate the control points, and record their pixel coordinates and corresponding spatial coordinate information. At the same time, perform structure parsing and feature extraction on the imported MapGIS vector data. The structure parsing of MapGIS vector data mainly includes reading its topological relationship, attribute table fields, coordinate information, etc., and converting the complex data structure into a standardized format. In the feature extraction stage, identify the point, line, and face features in the data, and extract their geometric coordinates, spatial relationships, and associated attributes. For example, point features are used to represent sampling points, ore points, etc., line features can represent faults, stratigraphic boundaries, and face features are used to represent different stratigraphic units or rock mass distribution areas. Establish feature classification rules during the parsing process to ensure that the data structures of different geological features are unified, and provide a standardized data basis for subsequent spatial analysis and visualization. Perform spatial integrity checks on the enhanced image data and the set of point, line, and face features to ensure the correctness and coherence of the data. The content of the spatial integrity check includes marking data missing areas and topological abnormal points, analyzing the geological image data, detecting problems such as image stitching errors, data truncation, or missing coordinate information, and evaluating the geographic positioning accuracy of the image by comparing the positional relationships of known control points. For vector data, check whether the topological structure of the point, line, and face features is complete, such as whether there are hanging lines, unclosed polygons, duplicate points, etc. By analyzing the integrity of the attribute table, judge whether there are cases of missing fields or abnormal data formats, and record all abnormal information in the data quality assessment report for subsequent correction and optimization. Based on the data quality assessment results, combined with the control point position information, the set of point, line, and face features and their attribute information, construct a standardized data packet containing spatial reference information and feature structure data.The data packet contains all processed geological data, along with detailed spatial information, including coordinate system definition, projection parameters, datum, etc. At the same time, the attribute structure of geological elements is recorded to enable seamless docking during subsequent data interaction and system integration. Based on the standardized data packet and spatial reference information, a geospatial reference conversion index table is established to record the conversion parameters between the original data coordinate system and the target coordinate system (WGS84), including translation amounts, rotation angles, scale factors, and projection transformation matrices, etc. At the same time, for data with different scales and coordinate systems, hierarchical conversion rules are established to ensure spatial consistency of data at different scales.

[0013] Extract spatial reference information from the standardized data packet. This information includes coordinate system parameters, projection methods, and datum information, which together constitute the spatial positioning basis of the data. By analyzing these parameters, determine the original coordinate datum of the input data and compare it with the reference frame of the target coordinate system WGS84. During this process, consider the differences in coordinate definitions from different data sources to ensure that the extracted parameters can accurately describe the spatial position of the data. Perform coordinate system analysis on the extracted spatial positioning data and apply four-parameter or six-parameter transformation models for different types of data respectively. For MapGIS vector data, utilize its own coordinate definition to directly calculate the translation, rotation, and scaling relationships with the WGS84 system and adopt a four-parameter model for transformation. For geological image data after image enhancement processing, due to more complex deformations and error accumulations, a six-parameter model is used for more accurate coordinate transformation. This model not only considers translation, rotation, and scaling but also introduces tilt compensation additionally to correct the errors caused by image deformation. Through these mathematical transformations, calculate the initial coordinate transformation parameters and store them for the construction of subsequent mapping relationships. Construct a global coordinate mapping table based on the initial coordinate transformation parameters to record the transformation relationship between the original coordinate system and the WGS84 coordinate system of the AnyView Interactive Map, ensuring that all geological data are fused on a unified spatial datum. The establishment of the global coordinate mapping table is based on multiple key variables, including the original projection method, datum, transformation matrix, and offset parameters of the data. Through matrix operations and coordinate point fitting methods, globally adjust the coordinates of the entire dataset so that it can be seamlessly docked to the AnyView Interactive Map platform and maintain high spatial consistency at different scales. Perform multi-scale hierarchical processing on the basic mapping model. According to the accuracy requirements of geological data and the visualization levels of the AnyView Interactive Map, calculate the sampling point density at each scale level. The calculation of the sampling point density depends on the spatial distribution characteristics of the data. By analyzing the distribution density of geological elements, resolution requirements, and error accumulation, the system adaptively adjusts the interval of sampling points to ensure sufficient positioning accuracy in a large-scale range and avoid unnecessary computational overhead in a small-scale range. At the same time, calculate the transformation accuracy to determine the adaptability of coordinate transformation parameters at each scale level and generate a hierarchical transformation parameter set. This parameter set includes transformation matrices, error compensation parameters, and local adjustment factors under different scale conditions. Use known control points to verify the forward and reverse accuracy of the transformation parameter set. Select multiple control points with known coordinates, perform the transformation from the original data coordinates to WGS84 coordinates respectively, and then map the transformed coordinates back to the original coordinate system again. By calculating the error between the forward transformation and the reverse transformation, evaluate the coordinate transformation accuracy of each region and draw a spatial registration accuracy distribution map accordingly. If the transformation error exceeds the preset threshold in some regions, automatically mark these regions and recommend increasing the local control point density to improve the transformation accuracy.Based on the spatial registration accuracy distribution map, the entire data area is divided into grids, and local precise transformation parameters are assigned to each grid cell, thereby constructing a geospatial reference transformation index table with a two-level index structure. The grid division adopts an adaptive method, that is, smaller grid cells are set in areas with larger errors to ensure higher accuracy, while larger grids are used in areas with smaller errors to reduce computational overhead. Each grid cell records the transformation parameters of a specific area, including the local transformation matrix, control point compensation value, and corresponding spatial index information, so as to achieve adaptive adjustment for different accuracy requirements.

[0014] Step S102: Extract and classify the geological elements in the standardized data packet according to the geospatial reference transformation index table to obtain structured geological data; Specifically, the mapping relationship between the original coordinate system and the AutoNavi interactive map coordinate system is extracted from the geospatial reference conversion index table. This mapping relationship includes core information such as coordinate transformation parameters, scale factors, rotation angles, and projection methods, which are used to ensure the accurate conversion of data between different coordinate systems and maintain its geospatial consistency. Based on the mapping relationship, feature analysis and processing are performed on the geological data in the standardized data packet. The geometric features and spatial distribution features of geological elements are identified through a feature extraction engine to obtain a preliminary feature set of geological elements. The feature extraction engine adopts a variety of pattern recognition algorithms and computer vision technologies, and uses differential processing for different types of data, so that the extracted features have high accuracy and reliability. After the preliminary feature extraction is completed, different types of geological elements are classified. For the linear elements included in the preliminary feature set, such as faults and stratigraphic boundaries, the system uses an improved combination algorithm of Canny edge detection and Hough transformation. This method can effectively detect the edge structure in geological data and achieve accurate fitting of straight lines or curves through Hough transformation to ensure the coherence and integrity of linear elements. For planar elements, such as stratigraphic units and rock masses, region growing and color clustering algorithms are used. Different geological units are identified by analyzing features such as the color and texture of pixels, and they are automatically classified according to their spatial adjacency relationships to maintain their geological integrity and correctness. At the same time, for point elements, such as sampling points and attitude points, template matching technology is applied. Point elements in the data are identified by matching the morphological features of known geological elements, and their spatial positions are optimized using the adjacent point analysis method to accurately reflect the distribution of geological observation data. The classified element set is input into the geological element classification model. This classification model is based on a rich geological feature description library, covering multiple categories such as strata, structures, lithologies, and minerals. By comparing the extracted geological features with the existing standardized data, clear geological attribute tags are assigned to each geological element, giving it professional geological significance. Semantic enhancement processing is performed on the element data with geological attribute tags. The stratigraphic relationship and cutting relationship between geological elements are identified through spatial association analysis. For example, whether a certain stratum is cut by a fault, whether a certain rock mass is located below another rock mass, or whether multiple ore points are distributed along a certain structural line. Through topological analysis, calculation of spatial adjacency relationships, and matching of geological rules, the hierarchical structure between geological elements is automatically deduced, and a structured geological element relationship network is formed. According to the data structure specification of the AutoNavi interactive map, the point elements in the geological element relationship network are converted into AutoNavi label objects, the line elements are converted into AutoNavi track objects, and the surface elements are converted into AutoNavi graphic objects. During the conversion process, the display styles of the elements are automatically adjusted, and their visualization performance is optimized to meet the rendering requirements of the AutoNavi platform.Meanwhile, during the conversion process, instead of simply mapping the geometric data to the OV format, the spatial relationships and attribute information of the original geological data are retained, enabling the converted data to still possess complete geological significance and ensuring that its presentation on the OV Interactive Map is not limited to the graphical level but can carry professional geological data analysis functions. The original geological attribute information is attached to the preliminarily adapted OV format data, and this attribute information is stored as metadata in the OV object, enabling each geological element to be associated with complete geological descriptions, measurement data, and related explanations. Meanwhile, the structured geological data finally adapted to the OV Interactive Map is generated, and the data quality during the conversion process is evaluated to ensure the integrity and accuracy of the data.

[0015] Step S103: Import the structured geological data into the OV Interactive Map and perform local registration through grid meshing technology to obtain a visual geological element layer; Specifically, perform API specification conversion on structured geological data to make it meet the data import requirements of the Ovital Map. According to the data interface standard of the Ovital platform, encapsulate the geometric information, attribute information, and style definition of geological elements, and format and store them in the KML / KMZ format to ensure that the geological data can be correctly parsed and displayed on the Ovital platform. The KML / KMZ file contains the coordinate information of geological elements, as well as the attributes, annotation information, and visualization styles of geological elements such as lines, surfaces, and points. Input the standard KML / KMZ file into the Ovital Map engine interface and execute data loading by calling the import function. Based on the definition of geological elements in the KML / KMZ file, generate the corresponding geological data layer on the Ovital Map. Perform real-time coordinate conversion on the preliminarily imported geological data layer and locate it according to the mapping relationship in the geospatial reference conversion index table. The geospatial reference conversion index table stores the conversion parameters between the original coordinate system and the Ovital Map WGS84 coordinate system, including information such as translation, rotation, scaling, and projection transformation matrices. During the real-time coordinate conversion process, the system performs coordinate alignment calculations on the geological data based on these parameters to ensure that all data is accurately matched with the base map of the Ovital Map and eliminate the errors between different coordinate systems, obtaining spatially located geological data. Perform spatial grid meshing processing on the located geological data, divide the data area into regular grid cells, and apply local transformation parameters to each grid cell. The purpose of grid meshing is to improve the local accuracy of the data, so that geological elements can maintain high accuracy in different spatial ranges, and correct the existing geometric errors through local transformation parameters. For example, in some areas, due to the problem of the original coordinate conversion accuracy, local deformation or dislocation occurs. Through the method of grid-based zoning correction, a conversion matrix is set for each small area respectively, and the boundary area is smoothed by combining the interpolation algorithm, thereby eliminating deformation and splicing gaps and ensuring the spatial consistency of geological data. After the spatial position adjustment of the data is completed, implement a transparency optimization algorithm on the geological image part in the corrected data to improve the visualization effect of the map. Since geological data contains a large amount of image information, directly overlaying it on the base map of the Ovital Map will affect the overall readability. An intelligent transparency adjustment method is adopted to adaptively adjust the semi-transparency according to the characteristics of the image content, so that the geological information can be clearly visible and can be best integrated with the base map satellite image. The core of transparency optimization is to dynamically adjust the transparency according to the color distribution, boundary characteristics, and background information of geological elements, so that important geological information is prominently displayed, while the transparency of the background part is appropriately increased to avoid blocking the topographic information of the base map. While performing transparency optimization, classify and organize the corrected data according to the categories of geological elements, and classify different types of geological elements such as strata, structures, and ore points into independent layers, improving the visualization management ability of the map, so that users can selectively display or hide certain specific types of geological information according to their needs.In addition, a two-way data association mechanism is established to ensure the interaction function of geological elements on the Ovi Map. Bind the geological objects on the Ovi Map to the original geological database, so that when the user clicks on a certain geological element, detailed attribute information can be queried, including geological classification, mineral distribution, fault characteristics, etc. At the same time, allow the user to locate the specific position of the element on the Ovi Map in the geological database. Generate a visual geological element layer that supports layer-level display control.

[0016] Step S104: Add a target geological editing tool to the visual geological element layer, draw geological elements and enter attributes to obtain a completed geological data set; Specifically, the native interaction interface of the Ovi Interactive Map is functionally extended to meet the professional requirements of geological data editing. A complete set of target data editing tools is added to the basic interface of the Ovi Interactive Map. This toolset covers core geological data editing functions such as formation addition, fault drawing, attitude measurement, and sample point recording. Through the introduction of these functions, geological workers can directly draw, adjust, and manage geological data in the environment of the Ovi Interactive Map without relying on external GIS software, thus improving the convenience and real-time nature of data editing. While constructing the geological target editing interface, intelligent drawing assistance functions are provided to optimize the user's drawing experience and ensure the spatial consistency of newly drawn elements with existing data. The core of the intelligent drawing assistance function is to automatically detect the extension trend of existing boundaries in the vicinity, enabling newly drawn elements such as fault lines and formation boundaries to be automatically aligned with the existing data. Using spatial topology analysis methods, the direction of the lines drawn by the user is analyzed in real time, and intelligent snapping and smoothing functions are provided to ensure that newly drawn geological elements do not have problems such as breaks, discontinuities, or distortions, thus guaranteeing the coherence and accuracy of the entire geological element network. During the drawing process, based on known geological laws, the rationality of the drawn formation boundaries is judged, and optimization suggestions are provided when necessary to reduce errors in manual drawing. In addition to the drawing function, a standard geological symbol library is integrated to ensure that the visual expression of geological data conforms to industry standards. The standard geological symbol library contains a rich variety of geological markings, such as filling styles for different lithologies, structural symbols, ore point symbols, etc. These symbols provide a clear way to label geological elements and also ensure that the edited geological data is consistent with standardized geological legends. In the geological target editing interface, users directly select appropriate symbols from the symbol library and apply them to geological elements such as drawn formations, faults, and ore points, thereby enhancing the professionalism and readability of the map. At the same time, corresponding attribute forms are dynamically generated according to the target attribute requirements of different types of geological elements, enabling users to input geological description data that conforms to the specifications for different geological elements. For formation elements, an input interface containing formation name, lithology description, sedimentary environment, age information, etc. is provided; for fault elements, input options for attitude, displacement amount, activity, etc. are provided; for fold elements, input boxes for fold axis, plunge angle, related geological structures, etc. are provided; for rock mass elements, the system allows users to input key information such as lithology category, structural characteristics, and mineralization conditions. All these dynamically generated attribute forms are designed based on the standard data structure of the geological industry to ensure that the data entered by users can meet professional requirements and can be seamlessly connected to other geological information systems during subsequent data processing and export. To ensure the accuracy of the geological description data entered by users, legality checks and consistency validations are performed on all input data, with a focus on detecting the rationality of the formation age sequence and the compliance with structural laws.For example, the system automatically verifies whether the stratum order entered by the user conforms to the superposition law of stratigraphy, that is, younger strata should not appear below older strata. At the same time, it detects whether the fault attitude conforms to the basic principles of structural geology, such as whether the fracture system in the same area has a reasonable dip and strike distribution. If the system detects unreasonable data, it will prompt the user to make adjustments and provide modification suggestions that conform to geological logic to prevent incorrect data from entering the final geological database. When all geological attribute data pass the verification, the audited geological attribute data is associated and integrated with the geometric information of geological elements to ensure that each geological element not only has an accurate spatial position and shape but also comes with a complete geological attribute description. To enhance the traceability of the data editing process, an editing operation log recording mechanism is established to record all data modification histories, including operation information such as adding elements, editing attributes, and adjusting geometric shapes, enabling users to trace back the editing process at any time, understand the evolution track of the data, and perform version comparison and rollback operations when necessary. The edited geological data set is obtained.

[0017] Step S105: Restructure the edited geological data set and perform reverse coordinate transformation to obtain a standard format data file adapted to the target geological information system.

[0018] Specifically, perform data structure reorganization on the edited geological data set to ensure that the data exported from the OvitalMap platform restores its original hierarchical relationship and topological relationship of geological elements. In this process, convert the tags, trajectories, and graphic objects in the OvitalMap platform back to the target geological data structure, so that the geometric relationships of point elements, line elements, and surface elements are restored, and the connection relationships between geological elements are reconstructed through topological analysis methods. For point elements, restore their attribute associations in the target system, such as the geological attributes of ore points and sampling points; for line elements, ensure that elements such as faults and stratigraphic boundaries can maintain coherence in the topological structure; for surface elements, ensure the closure of stratigraphic units through topological repair techniques, so that they can be correctly parsed in the target GIS system and used for spatial analysis. After completing the structure reorganization, perform enhanced conversion of attribute data to ensure that the geological description information entered in the OvitalMap platform is standardized and adapted to the target geological database structure. Since the OvitalMap is mainly used for visual display and its data structure is relatively free, while the target geological information system follows strict database specifications, standardize the conversion of geological description information. Analyze the geological attribute data stored in the OvitalMap platform and map it to the standard attribute table structure according to the specifications of the geological database. For example, the stratigraphic attributes include name, lithology, sedimentary age, etc., and the fault attributes include attitude, displacement, tectonic type, etc. At the same time, construct a relationship table to maintain the logical association between different geological elements. For example, a certain stratigraphic unit interacts with multiple faults, and a certain ore point belongs to a specific rock mass unit. And perform code table conversion on the attribute data so that all text description information can be mapped to the standard geological data dictionary to ensure the readability and consistency of the data in the target system. After the data structure reorganization and attribute conversion are completed, perform inverse coordinate conversion on the standardized geological data according to the geospatial reference conversion index table to achieve accurate conversion from the WGS84 coordinate system to the coordinate system of the target geological information system. Since the OvitalMap uses the globally common WGS84 coordinate, and the target geological information system uses a local coordinate system or a specific projection coordinate system, apply the conversion parameters stored in the geospatial reference conversion index table, including translation, rotation, scaling, and projection transformation matrices, to ensure the accuracy of coordinate conversion. During the conversion process, adopt a multi-scale accuracy control mechanism, calculate the coordinate conversion error for data in different geographical ranges respectively, and optimize the conversion accuracy through an error compensation model, so that the converted data can be seamlessly docked with the target GIS platform. After completing the coordinate conversion, apply an intelligent coordinate matching algorithm to the geologically data with corrected coordinates to ensure the spatial consistency of the data in the target system. The intelligent coordinate matching algorithm automatically identifies control points and establishes a conversion relationship, so that the original geological data can be accurately aligned with the existing data in the target system.The system extracts known control points from the target system, matches and analyzes them with the converted data, calculates the deviation between the two through the least squares registration algorithm, and dynamically adjusts the coordinate transformation parameters to eliminate the position error caused by the coordinate system difference. This matching process improves the spatial accuracy of the data and enables the newly imported data to be correctly superimposed on the existing geological information, thus avoiding data misalignment or overlap problems. After precise registration is completed, the geological spatial data is converted into a standard format file of the target geological information system, and format adaptation is performed for different GIS platforms. For example, for the MapGIS system, standard MAP, DAT, WAT, and TAB file structures are generated, where the MAP file stores geospatial features, the DAT file contains attribute data, the WAT file records topological relationships, and the TAB file is used to manage the mapping relationships between different data files. During the data export process, the system performs format optimization to reduce file redundancy, improve data loading efficiency, and ensure that the target system can correctly parse the exported geological data. After exporting the standard format file, data integrity verification is performed to ensure that no elements are lost, no attribute information is missing, or no coordinate transformation anomalies occur during the conversion process. The first step in integrity verification is to compare the number, type, and attribute information of the original data with the converted data to ensure that all geological elements are correctly converted. Secondly, topological consistency is checked, such as whether the intersections of faults and strata match and whether the formation units still maintain a closed structure. Statistical analysis of the coordinate transformation errors is performed to ensure that the transformation errors of all points are within the preset range, and an automatic correction option is provided for areas where the errors exceed the threshold. A data update tracking mechanism is established to record the conversion process of the data from the Ovi platform to the target geological information system, including key steps such as data structure adjustment, attribute matching, and coordinate transformation, enabling users to trace back the data modification history at any time and perform incremental updates when needed without re-exporting the entire dataset. Generate a standard format data file adapted to the target geological information system.

[0019] In the embodiments of the present invention, a two-way seamless interaction between target geological data and the Avemap platform is realized, solving the problems of single data interaction method and cumbersome process in the prior art, simplifying the operation process, reducing the technical threshold for users, and reducing the number of operation steps. By introducing the concept of Geospatial Reference Consistency (GRC) and an intelligent coordinate matching algorithm, an accurate coordinate system conversion mechanism is established, realizing the accurate conversion of multi-source geological data between different coordinate systems and scales, effectively solving the spatial misalignment problem during the integration of geological data from different sources in traditional methods. Using a feature extraction engine and a target geological element recognition algorithm, geological elements are accurately extracted and classified, and complex geological element relationships are retained to ensure the deep integration of geological target attributes with Ave objects, so that the converted data still retains the target geological meaning on the Ave platform. Based on the grid subdivision technology, local accurate registration is realized, breaking through the accuracy limitation of traditional global transformation and effectively eliminating image deformation and stitching gaps. The geological target editing function is seamlessly integrated into the Ave platform, changing the traditional workflow of editing in a target GIS system and then exporting, supporting real-time field data collection and editing, and significantly improving the efficiency of geological field operations. A multi-person collaborative editing and version management mechanism is established, adapting to the geological survey work mode of team collaboration, and realizing seamless data transfer and integration from the Ave platform to the target GIS system.

[0020] In a specific embodiment, the process of executing step S101 may specifically include the following steps: Perform file header information parsing on the original geological data to obtain a data type identifier and format parameters; Based on the data type identifier, classify and process the original geological data, and import the geological image data and MapGIS vector data into the corresponding processing modules respectively; Perform image enhancement processing and control point detection on the imported geological image data to obtain enhanced image data and control point position information; Perform structure parsing and element extraction on the imported MapGIS vector data to obtain point, line, and surface element sets and their attribute information; Perform spatial integrity checks on the enhanced image data and the point, line, and surface element sets, mark data missing areas and topological abnormal points, and obtain a data quality assessment result; According to the data quality assessment result, control point position information, point, line, and surface element sets and their attribute information, construct a standardized data packet containing spatial reference information and element structure data; Based on the standardized data packet and spatial reference information, establish a geospatial reference conversion index table.

[0021] Specifically, file header information parsing is performed on the original geological data. The original data includes various formats, such as geological image data (JPG, TIFF, etc.) and MapGIS vector data. The system extracts the data type identifier and format parameters by parsing the file header information. For example, the geological image in TIFF format contains coordinate reference information, while the vector data file of MapGIS stores topological relationships and projection coordinate system information. By analyzing this file header information, the data type is automatically determined, and key parameters related to the coordinate system, storage structure, data resolution, etc. are extracted. The original geological data is classified according to the data type identifier and imported into different processing modules respectively. Among them, the geological image data is sent to the image processing module, while the MapGIS vector data enters the structure parsing module. For the geological image data, image enhancement processing is performed to improve the data quality and recognizability. Since the original geological images have problems such as noise, insufficient contrast, or blurred edges, a multi-level image enhancement strategy is adopted, including histogram equalization to optimize the contrast, high-pass filtering to remove noise, and edge sharpening algorithms to highlight the geological boundary features. In order to achieve accurate positioning in subsequent geospatial reference conversion, control point detection is performed after image enhancement. Control points are geological annotation points with known coordinates, such as scale marks on geological maps, known ore points, or tectonic intersection points. Using template matching and feature point detection algorithms, these control points are automatically identified in the image, and their pixel coordinates and corresponding geographical coordinates are recorded, thus establishing preliminary spatial reference information. At the same time, structure parsing and feature extraction are performed on the MapGIS vector data imported into the structure parsing module. MapGIS data contains elements such as points, lines, and polygons, and each element carries different geological information. For example, point elements represent ore points, sampling points, or observation points, line elements correspond to faults, fold axes, or stratigraphic boundaries, and polygon elements are used to represent the distribution range of stratigraphic units or rock masses. By parsing the topological structure of the data file, the geometric information and attribute information of these elements are extracted, and their spatial relationship model is established. During the parsing process, a topological consistency check algorithm is used to ensure that all line elements are in a connected state, all polygon elements are closed areas, and duplicate points, hanging lines, or overlapping polygons are identified for correction in subsequent spatial integrity checks. After image enhancement and feature extraction are completed, spatial integrity checks are performed on the data to ensure the correctness and usability of the data. For geological image data, detect whether there are image stitching errors, missing coordinates, or deformed areas, and evaluate its geographical accuracy based on the control point information. For vector data, check the spatial relationships of point, line, and polygon elements, such as whether there are two fault lines intersecting but not forming nodes, or whether there are topological errors in some polygon-shaped geological units. Analyze whether there are missing areas in the data. For example, some stratigraphic units are not defined in a certain area, but there are complete records in the surrounding areas, indicating that there are blank areas or storage errors in the data.To quantify data quality, a geological data integrity metric formula is defined: ; where Q represents the data integrity score, represents the total number of elements that should exist in the dataset, represents the number of missing elements, and represents the number of elements with topological errors. The closer the integrity score Q is to 1, the higher the data quality; if Q is below a certain threshold, the system will mark the dataset as incomplete and generate a data quality assessment report, prompting the user to supplement data or correct topological errors. Using the data quality assessment results in combination with control point information, point-line-surface element sets and their attribute information, a standardized data packet is constructed to ensure that all geological data adopts a unified spatial reference system and has a complete element structure. The control point information of the image data is converted into spatial reference markers in the vector data, enabling the image data and vector data to be superimposed and analyzed in the same coordinate system. At the same time, based on the topological structure of points, lines, and surfaces, their hierarchical relationships in the standardized data format are established. For example, the attribute information of a certain ore point is bound to the corresponding stratigraphic unit, and its spatial relationship with the surrounding faults is defined. The standardized data packet also contains geographic projection information to ensure accurate conversion between different coordinate systems and provides a complete element attribute table, making subsequent data query and visualization more convenient. Based on the standardized data packet and spatial reference information, a geospatial reference conversion index table is finally established. The role of this index table is to provide a precise coordinate conversion scheme for geological data and ensure its spatial alignment on the Ovital Map. The index table records the conversion parameters between the coordinate system of the original data and the target system (WGS84), including translation vectors, rotation matrices, and scaling factors. The calculation of the conversion parameters uses the least squares fitting method to ensure the spatial accuracy of the control points after conversion. The index table supports multi-scale conversion to meet the data matching requirements at different zoom levels. For example, at a large scale, global conversion parameters are used to ensure the spatial consistency of the overall data, while at a small scale, local adjustment parameters are introduced to correct small-scale coordinate deviations caused by different data sources.

[0022] In a specific embodiment, the process of establishing a geospatial reference conversion index table based on the standardized data packet and spatial reference information may specifically include the following steps: Extract spatial reference information from the standardized data packet to obtain spatial positioning data containing coordinate system parameters, projection methods, and datum information; Perform coordinate system parsing on the spatial positioning data, and apply a four-parameter or six-parameter conversion model to MapGIS vector data and enhanced image data to obtain initial coordinate conversion parameters; Construct a global coordinate mapping table according to the initial coordinate conversion parameters, record the transformation relationship between the original coordinate system and the WGS84 coordinate system of the OvitalMap, and obtain the basic mapping model; Perform multi-scale hierarchical processing on the basic mapping model, calculate the sampling point density and transformation accuracy according to different scale levels, and obtain the hierarchical conversion parameter set; Use the known control points to perform forward and backward accuracy verification on the hierarchical conversion parameter set, calculate the conversion error distribution of each region, and obtain the spatial registration accuracy distribution map; Based on the spatial registration accuracy distribution map, divide the data area into grid cells, assign local precise conversion parameters to each grid cell, and obtain the geospatial reference conversion index table containing the secondary index structure.

[0023] Specifically, spatial reference information is extracted from the standardized data packets to obtain coordinate system parameters, projection methods, and datum information. The standardized data packets contain different types of data, including MapGIS vector data, enhanced geological image data, and corresponding geospatial information. By parsing the metadata fields of these data, the definition of the coordinate system is automatically read, and the projection method and datum parameters are extracted. For example, in MapGIS data, its projection file is directly parsed to obtain the coordinate datum, while in geological image data, the geographic range and control point coordinates of the image are extracted by detecting the embedded georegistration information, thus constructing spatial positioning data. The coordinate system of the spatial positioning data is analyzed, and appropriate coordinate transformation models are applied to different types of data to ensure that all data can be matched under a unified spatial datum. Since geological data is stored in different coordinate systems, such as MapGIS vector data using a local projection coordinate system, and geological image data using a geographic coordinate system or other projection methods, coordinate transformation is performed to unify all data into the WGS84 coordinate system. For vector data, a four-parameter transformation model is adopted, which includes a translation vector and rotation parameters, while for image data, a six-parameter transformation model is used to consider scaling and tilt compensation. This transformation model ensures the matching accuracy between different coordinate systems and takes into account tilt and scaling errors in the image data conversion, enabling the geological images to be precisely aligned with the vector data. After calculating the initial coordinate transformation parameters, a global coordinate mapping table is constructed to record the transformation relationship between the original coordinate system and the WGS84 coordinate system of the OvitalMap. This mapping table stores the coordinate transformation parameters of different data sources and provides a basis for subsequent spatial alignment and data matching. During the construction of the mapping table, the transformation matrix of each dataset is recorded, including the transformation error information of the control points, thus ensuring the spatial consistency of all geological elements. The mapping table supports multi-scale transformation to meet the requirements of different resolutions and map zoom levels, enabling geological data to maintain the correct spatial position at different observation scales. To improve the adaptability of data transformation, multi-scale hierarchical processing is performed on the basic mapping model, and the sampling point density and transformation accuracy are calculated according to different scale levels. At large scales, a lower sampling point density is adopted to reduce the computational cost, while at small scales, the number of sampling points is increased to ensure higher transformation accuracy. The sampling point density is dynamically adjusted through an adaptive algorithm, and error compensation techniques are combined during the transformation process, enabling the transformation parameters to be accurately adapted to each scale. After completing the multi-scale parameter calculation, forward and backward accuracy verification is performed on the hierarchical transformation parameter set using known control points, and the transformation error distribution of each region is calculated. The key to forward and backward accuracy verification lies in transforming through the known control point coordinates and then transforming back to the original coordinate system, and calculating the error before and after the transformation. The error distribution is calculated for all control points, and a spatial registration accuracy distribution map is generated to visually display the transformation accuracy of each region.Regions with large errors will be automatically marked by the system for local optimization and adjustment during subsequent processing. Based on the spatial registration accuracy distribution map, the entire data region is divided into grids, and local precise transformation parameters are assigned to each grid cell to obtain a geospatial reference transformation index table containing a secondary index structure. The grid division adopts a dynamic segmentation strategy, that is, smaller grid cells are applied in regions with larger transformation errors to improve the transformation accuracy, while larger grid cells are used in regions with smaller errors to reduce the computational amount. Each grid cell records the local transformation matrix and control point compensation parameters, so that in practical applications, the system can quickly search for and apply the optimal coordinate transformation parameters according to the map zoom level of the user's location area.

[0024] In the construction process of the geospatial reference transformation index table in this embodiment, collaborative data transformation based on GRC and intelligent coordinate matching algorithms is also applied, including: extracting GRC features from the spatial reference information in the standardized data packet, identifying the key feature points and core reference lines for coordinate system transformation, and obtaining the GRC feature point set; inputting the GRC feature point set into the sub-region intelligent matching model, dividing the matching regions according to the terrain features and geological unit boundaries to obtain spatial partition matching units; performing adaptive weight assignment on each spatial partition matching unit, and obtaining the point priority sequence within the region according to the geological element type, density distribution, and position importance; selecting the optimal control point combination based on the point priority sequence, applying different matching strategies for different geological structure units to obtain a multi-scale collaborative control point network; using the multi-scale collaborative control point network to construct a local non-linear transformation model, calculating the spatial deformation parameters through the tensor spline interpolation algorithm to obtain a set of local precise transformation matrices; performing boundary condition smoothing processing on the set of local precise transformation matrices to ensure the continuity and smooth transition between adjacent regions, and obtaining a seamlessly spliced regional transformation model; fusing and integrating the regional transformation model with the basic mapping model, calculating the global and local transformation parameters through multi-level weighted combination to obtain a comprehensive transformation matrix; performing iterative verification and optimization on the comprehensive transformation matrix, adjusting the control point weights and distributions through minimizing the back-projection error to obtain the final set of collaborative transformation parameters; organizing the set of collaborative transformation parameters according to the spatial grid index structure, assigning precise transformation parameters to each grid cell, and establishing a parameter query acceleration mechanism to obtain the geospatial reference transformation index table.

[0025] In a specific embodiment, the process of executing step S102 may specifically include the following steps: Extract the mapping relationship between the original coordinate system and the AutoNavi Interactive Map coordinate system in the geospatial reference transformation index table; Based on the mapping relationship, perform feature analysis and processing on the geological data in the standardized data packet, and identify the geometric features and spatial distribution features of the geological elements through the feature extraction engine to obtain the preliminary feature set of the geological elements; Apply the improved combination algorithm of Canny edge detection and Hough transform to the linear elements in the preliminary feature set, apply the region growing and color clustering algorithms to the planar elements, and apply the template matching technology to the point elements to obtain the classified geological element set; Input the classified geological element set into the geological element classification model, which contains the feature description libraries of strata, structures, lithologies, and minerals, to obtain the element data with geological attribute tags; Perform semantic enhancement processing on the element data with geological attribute tags, identify the stratigraphic and cutting relationships between elements through spatial association analysis, and obtain the structured geological element relationship network; According to the data structure specification of the AnyView Interactive Map, convert the point elements in the geological element relationship network into AnyView label objects, the line elements into AnyView track objects, and the surface elements into AnyView graphic objects to obtain the preliminarily adapted AnyView format data; Attach the original geological attribute information as metadata to the preliminarily adapted AnyView format data and generate the structured geological data adapted to the AnyView Interactive Map.

[0026] Specifically, extract the mapping relationship between the original coordinate system and the AutoNavi interactive map coordinate system from the geospatial reference conversion index table. Since different data sources adopt different coordinate systems, such as local projection coordinate systems, UTM coordinate systems, or geographic coordinate systems, the conversion parameters in the index table are parsed, including coordinate translation amounts, rotation angles, scaling factors, and projection transformation matrices, and a spatial conversion model is constructed based on these parameters to ensure consistent spatial matching of all data on the AutoNavi interactive map. Perform feature analysis processing on the geological data in the standardized data packet based on the coordinate mapping relationship, and identify the geometric features and spatial distribution features of geological elements through a feature extraction engine to generate a preliminary feature set of geological elements. The feature extraction engine adopts different analysis strategies according to the data type. For example, for raster data, morphological analysis methods are used to extract the main geological boundaries, while for vector data, the spatial relationships of points, lines, and surfaces are identified through topological structure analysis. Use Gaussian filtering to remove noise and calculate the spatial density of geological elements to determine whether a certain area has significant geological features. After preliminary feature extraction, apply corresponding feature recognition algorithms to different types of geological elements. For linear elements, such as faults and stratigraphic boundaries, an improved combination algorithm of Canny edge detection and Hough transform is used to ensure the accuracy of edge recognition and the coherence of linear structures. Canny edge detection is used to extract high-gradient regions in the image and remove redundant noise through non-maximum suppression; Hough transform is used to detect linear features and improve the recognition ability for curved faults through parameter optimization. For planar elements, such as stratigraphic units and rock mass distributions, region growing and color clustering algorithms are used. By analyzing the color and texture features of adjacent pixels, region division is achieved, and the integrity of geological units is ensured through spatial connectivity analysis. For point elements, such as ore points and sampling points, template matching technology is used. By calculating the feature similarity of different sample points, the target points are accurately located, and abnormal points are removed through spatial density analysis methods to ensure the stability and reliability of the data. After completing the classification of geological elements, input the classified element set into the geological element classification model. This classification model contains feature description libraries for multiple categories such as strata, structures, lithologies, and minerals, and uses pattern matching and deep learning technologies for automatic classification. Learn the morphological features and attribute relationships of different geological elements through the training set. For example, some stratigraphic units have specific color distributions and spatial texture features, while some ore points are concentrated near specific geological structures. Therefore, classification rules are established based on these features, and corresponding geological attribute tags are automatically assigned to each identified element, so that all data can be consistent with the standard geological classification system. After obtaining the element data with geological attribute tags, perform semantic enhancement processing, and identify the stratigraphic relationship and cutting relationship between geological elements through spatial association analysis to construct a complete geological element relationship network.The core of semantic enhancement processing is to analyze the interaction of different geological elements based on topological rules, such as whether a rock layer is cut by a fault, or whether certain mineral points are concentrated in a specific structural unit. The system adopts the stratigraphic relationship modeling method to calculate the relative time relationship between stratigraphic units, automatically identify the upper and lower superposition relationship of the stratigraphic units, and verify its rationality through geological rules. At the same time, for the structural cutting relationship, the impact of geological faults on the surrounding strata is calculated through spatial analysis, and the contact relationship between different strata is identified, so as to construct a geological relationship network, so that the data not only has geometric information, but also can express geological process and structural evolution information. After establishing a complete geological element relationship network, the geological elements are converted into an object format compatible with the Ovi interactive map according to the data structure specification of the Ovi interactive map. Among them, the point elements are converted into the Ovi label objects, the line elements are converted into the Ovi trajectory objects, and the surface elements are converted into the Ovi graphic objects. During the conversion process, the attribute information of the geological elements is retained, and the geographic coordinates are attached to each object so that it can be correctly displayed on the Ovi interactive map. At the same time, the layer management mechanism is adopted to organize different types of geological data into independent layers, so that users can flexibly control the display mode of the data on the map interface. After the initial adaptation of the OV format data, the original geological attribute information is added and stored as metadata in the OV object to ensure the integrity of the geological data. For example, each mineral point object contains not only its spatial coordinate information, but also attributes such as mineral type, grade, and mineralization environment, while each fault object contains key information such as its occurrence, dislocation direction, and activity. The data modification history during all conversion processes is recorded so that users can trace back to the original data when needed and ensure data traceability.

[0027] In a specific embodiment, the process of executing step S103 may specifically include the following steps: The structured geological data is converted to API specifications, and the geometric information, attribute information and style definition of geological elements are encapsulated according to the requirements of the Aowei interactive map to obtain a standard KML / KMZ format file; Input the standard KML / KMZ format file into the Aowei interactive map engine interface, and load the data by calling the import function to obtain the preliminary imported geological data layer; Based on the initially imported geological data layer, real-time coordinate conversion is performed, and the mapping relationship in the geographic spatial reference conversion index table is used for positioning to obtain the spatially positioned geological data; Perform spatial gridding processing on the spatially located geological data, divide the data area into regular grid cells, apply local transformation parameters to each grid cell, and obtain corrected data that eliminates deformation and splicing gaps; Implement a transparency optimization algorithm for the geological image part in the calibration data, intelligently adjust the semi-transparency according to the image content characteristics to obtain the best overlay effect for fusing with the base map satellite image. At the same time, classify and organize the calibration data according to the geological element types, assign the formation, structure, and ore point elements to independent layers, and establish a two-way data association mechanism to obtain a visual geological element layer that supports layer-level display control.

[0028] Specifically, perform API specification conversion on the structured geological data to make it conform to the standardized storage format of the Ovital Map. In this process, parse the geometric information, attribute information, and style definitions of the geological elements and encapsulate them in the KML / KMZ format. KML is an XML-based geographic data storage format, and KMZ is its compressed version, which can store complex geological data more efficiently. During the conversion process, encode the geometric information of point elements, line elements, and polygon elements, and add detailed information of the geological data through attribute fields. For example, for ore points, record the ore type, grade, occurrence status, etc.; for faults, record the attitude, displacement direction, activity, etc.; for formation units, append sedimentary environment, lithology, and age information. To ensure the visualization effect of the geological data on the Ovital Map, set style definitions according to different element types. For example, fill different formation units with different colors, or add different line types to the fault lines to distinguish normal faults, reverse faults, and strike-slip faults. After completing the KML / KMZ format encapsulation, input the generated file into the Ovital Map engine interface and execute data loading by calling the import function. In this process, the Ovital Map parses the KML / KMZ file, extracts the geological elements therein, and generates corresponding geological data layers on the map interface according to the coordinate information. Since the coordinate system of the original data is different from the WGS84 coordinate system used by the Ovital Map, position deviation or deformation is likely to occur after data loading. Therefore, perform real-time coordinate conversion to ensure the accurate positioning of the geological data on the Ovital Map. The real-time coordinate conversion uses the mapping relationship in the geospatial reference conversion index table for data positioning. Extract the transformation parameters between the original coordinate system and the WGS84 coordinate system from the index table, including the translation vector, rotation matrix, and scaling factor. Convert the coordinates of each geological element to ensure its spatial position on the Ovital Map is accurately matched. The coordinate conversion process uses the following transformation formula: ; where, represents the coordinates of the original data, is the target coordinate after conversion, respectively represent the scaling factors of the X-axis and Y-axis, are the non-diagonal elements of the rotation matrix, and Represents the translation amount. After performing this transformation on the coordinates of all geological elements, ensure their accurate spatial positions on the Ovi map. After completing the real-time coordinate transformation, perform spatial grid meshing on the spatially located geological data to improve the local accuracy of the data and eliminate the stitching gaps or local deformations caused by coordinate transformation. The grid meshing divides the entire data area into regular grid cells, and the geological data within each grid cell adopts independent local transformation parameters to ensure high-precision matching in the local area. Calculate an appropriate grid division granularity according to the distribution density of the geological data and the conversion error. Use smaller grid cells in areas with larger errors to improve the conversion accuracy, while use larger grid cells in areas with smaller errors to reduce the computational amount. Calculate the local transformation parameters separately within each grid cell and apply these parameters to fine-tune the geological data to eliminate stitching errors and geometric deformations. On the basis of optimizing the data accuracy, implement a transparency optimization algorithm for the geological image part in the corrected data to enhance the fusion effect of the geological data and the base map satellite image. Since the geological image will cover the topographic information of the base map, thus affecting the readability of the map, an intelligent transparency adjustment method is adopted to dynamically adjust the transparency according to the characteristics of the image content. By analyzing the color distribution and boundary features of the image, calculate the importance weights of each pixel point and adjust the transparency according to these weights. For example, for the formation boundary area, reduce the transparency to highlight the boundary details, while for the uniformly filled area, increase the transparency to ensure that the base map information is still visible. Through this method, intelligently adjust the transparency of the geological image to achieve the best fusion with the satellite image on the Ovi interactive map. At the same time, classify and organize the corrected data according to the types of geological elements, and classify different types of elements into independent layers so that users can flexibly control the display mode of the data. For example, classify the formation units into the formation layer, the structural information into the structure layer, and the ore point data into the mineral resources layer. To improve the interactivity and traceability of the data, establish a two-way data association mechanism so that each geological element on the Ovi map can be linked back to its original data source. Store the index information of the original data in the geological objects on the Ovi map, and record the unique identifier of the Ovi map object in the geological database to ensure that users can perform data query and correlation analysis between the two systems. For example, when the user clicks on a certain fault on the Ovi map, automatically jump to the corresponding record in the geological database and display the detailed attribute information of the fault; conversely, when selecting a certain geological element in the geological database, highlight its corresponding geographical location on the Ovi map, thus realizing two-way interaction of the data. Generate a visual geological element layer that supports layer-level display control.

[0029] In a specific embodiment, the process of performing step S104 may specifically include the following steps: Functionally expand the native interaction interface of the Ovital Map, add target data editing tools including formation addition, fault drawing, attitude measurement, and sample point recording to obtain a geological target editing interface; Provide an intelligent drawing assistance function based on the geological target editing interface, perform intelligent adsorption and smoothing processing by detecting the extension trend of existing boundaries around, and obtain new drawn geological elements that maintain spatial consistency with the existing data; Integrate the standard geological symbol library into the geological target editing interface to obtain a geological editing system that supports target annotation; Generate dynamic attribute forms according to the target attribute requirements of different types of geological elements, provide corresponding target attribute input interfaces for formations, faults, folds, and rock masses, and obtain standardized geological description data; Perform legality checks and consistency verification on the standardized geological description data input by the user, detect the rationality of the formation age sequence and the compliance of the tectonic laws, and obtain verified geological attribute data; Associate and integrate the verified geological attribute data with the geometric information of geological elements, and at the same time establish an editing operation log to record the data modification history, and obtain a completed geological data set.

[0030] Specifically, the native interaction interface of the AutoNavi Interactive Map is functionally extended to support the visual drawing and editing of geological data. By adding a geological data editing toolbar to the basic interface of the AutoNavi Map and adding functions specifically for formation addition, fault drawing, attitude measurement, and sample point recording, geological workers can directly input and adjust professional geological data on the map. These tools support manual drawing of geological elements and are linked with existing geological data to ensure that users can perform geological editing work efficiently and accurately. On the basis of providing drawing tools, the drawing accuracy and data consistency are enhanced, and the user's drawing experience is optimized through intelligent drawing assistance functions. The system automatically detects the extension direction of the formation boundary, fault, or tectonic line being drawn by the user and, in combination with the topological relationship of the existing geological elements in the vicinity, intelligently predicts the reasonable trend of the geological boundary. For example, when the user draws a new formation boundary, the system analyzes its boundary relationship with adjacent formations and automatically adjusts the drawing path to smoothly connect to the existing formation boundary, avoiding unnecessary breaks or overlaps. The intelligent snap function can identify the point, line, and surface elements drawn by the user and automatically align them to the existing geological data structure, such as making the new fault line close to the known fault or making the new sample point automatically align with the existing ore point distribution range. The system provides a drawing smoothing function to geometrically optimize the lines drawn by the user to reduce human errors and make the spatial expression of geological elements more accurate. To enhance the professional expression ability of geological data, a standard geological symbol library is integrated into the geological target editing interface, allowing users to directly use graphic symbols that conform to geological industry standards for geological annotation. The symbol library covers common formation filling patterns, fault symbols, lithology identifiers, and ore point markers, etc. Users can select appropriate symbols for application according to different geological objects. For example, when drawing a fault, users can select different types of fault symbols, such as normal faults, reverse faults, or strike-slip faults, to clearly distinguish different geological structural features on the map. When annotating ore points, users can select symbols for specific ore types and customize their colors and sizes to visually express the distribution of mineral resources on the map. After the drawing is completed, to ensure that the geological data has complete attribute information, corresponding attribute input forms are dynamically generated according to different types of geological elements, enabling users to enter standardized geological description data. For example, when the user draws a new formation unit, the system automatically pops up a formation attribute input interface, asking the user to fill in information such as the formation name, sedimentary age, lithology, and thickness; when the user adds a fault element, the system provides input options related to the fault, such as attitude, displacement direction, and activity. For fold elements, attribute fields such as axial azimuth, plunge angle, and tectonic type are provided, while for rock mass units, users are allowed to input key geological information such as lithological classification, magma intrusion mode, and mineralization characteristics. The dynamic form mechanism of the system enables different types of geological data to have structured attribute descriptions that conform to industry standards, thus ensuring the integrity and consistency of the data.After the user inputs geological attribute data, perform legality checks and consistency validations on the input data to ensure that all data conforms to the basic laws of geology. Detect the rationality of the stratigraphic age sequence. For example, if the stratigraphic order input by the user conflicts with the known geological ages, the system will issue a warning and suggest that the user adjust the stratigraphic order or check the geological era. At the same time, check the compliance with tectonic laws. For example, for the fault attitude, calculate the spatial relationship between this fault and adjacent faults and ensure that its dip and dip angle conform to the tectonic characteristics of the region. If it is found that the data input by the user does not conform to the existing geological laws, such as inputting an unreasonable high-angle thrust fault in a stable sedimentary basin, the system prompts the user to re-check the input and provides modification suggestions that conform to the tectonic laws. When all geological attribute data pass the validation, associate the audited data with the geometric information of geological elements and store it in a unified geological database to ensure data integrity and traceability. To enhance data management capabilities, establish an edit operation log to record all data modification histories of the user, including operation information such as adding elements, editing attributes, and adjusting geometric forms. The log mechanism allows the user to trace back the data modification process in the future and supports version comparison and rollback operations.

[0031] In a specific embodiment, the process of performing step S105 may specifically include the following steps: Perform data structure reorganization processing on the edited geological data set, convert the tags, tracks, and graphics in the OVITO platform back to the target geological data structure, and obtain restructured data that restores the hierarchical relationship and topological relationship of geological elements; Perform enhanced conversion of attribute data based on the restructured data, normalize the geological description information input in the OVITO platform into the target geological database structure, and obtain normalized geological data including standard attribute tables, relationship tables, and code tables; Perform reverse coordinate conversion on the normalized geological data according to the geospatial reference conversion index table, convert the data in the WGS84 coordinate system back to the target system coordinate system, and obtain geologically corrected data; Apply an intelligent coordinate matching algorithm to the geologically corrected data, automatically identify control points and establish conversion relationships, and obtain accurately registered geological spatial data; Convert the accurately registered geological spatial data into the file format of the target geological information system, generate standard MAP, DAT, WAT, and TAB file structures for the MapGIS system, and obtain a preliminarily exported standard format file; Perform data integrity verification on the preliminarily exported standard format file, check for problems such as missing elements, incomplete attribute information, and abnormal coordinate transformation, and establish a data update tracking mechanism to obtain a standard format data file adapted to the target geological information system.

[0032] Specifically, the data structure is reorganized to restore the hierarchical and topological relationships of geological elements. The geological data in the OV platform is stored in the form of objects such as tags, trajectories, and graphics. These objects can clearly express geological information during visualization, but in the target geological information system, they need to be converted back to the standard geological data structure. The system parses the data files in the OV platform, identifies different types of geological elements, and maps them to standard geographical element types such as points, lines, and surfaces. For example, the trajectory data in OV corresponds to linear geological structures such as fault lines and fold axes, while the tag data is used to represent discrete geological elements such as ore points and sampling points, and the surface data is used to define regional geological units such as strata and rock masses. Through topological analysis, the connection relationships between geological elements are reconstructed. For example, it is ensured that correct nodes are formed at the intersections of fault lines, and the formation boundary lines can form closed polygons to maintain the consistency and integrity of geological data in subsequent spatial analysis. After the data structure reorganization is completed, an enhanced conversion of attribute data is performed to standardize the geological description information in the OV platform to adapt to the standard format of the target geological database. Since the data in the OV platform is stored in formats such as KML / KMZ, the attribute information is unstructured text or simple tag fields, so these information are mapped to the standard attribute table of the target database. For example, the strata data needs to include fields such as name, lithology, sedimentary environment, and age, while the fault data needs to include attributes such as attitude, displacement, and activity. The system automatically parses the data content of the OV platform and constructs a structured attribute table according to the field definitions of the target geological database, and ensures that all attribute data can be accurately matched. For example, if the fault attribute in OV only includes the item of "direction", while the target database requires three fields including strike, dip, and dip angle, it is automatically supplemented according to the existing data, and the user is prompted to complete the information when necessary. At the same time, a relationship table is established to maintain the logical association between different elements. For example, the data of a certain ore point is associated with the mining area where it is located, and the mutual relationship between lithology and strata is established. The code table conversion is also a key step in the enhanced conversion of attribute data. According to the standard coding specifications of the geological industry, the text information in the OV platform is converted into standardized codes. For example, "sandstone" is converted into the standard lithology code to ensure the compatibility of data between different systems. After the normalization of the data structure and attribute data is completed, an inverse coordinate transformation is performed on the normalized geological data according to the geographical space reference transformation index table to ensure that the data can match the coordinate system of the target system. The OV interactive map uses the WGS84 coordinate system, while the target geological information system uses a local coordinate system or a specific projection coordinate system, so the coordinates of all geological elements are transformed. The parameters in the transformation index table are read, including translation amount, rotation angle, scaling ratio, etc., and these parameters are applied to perform coordinate transformation on all data.During the conversion process, a high-precision coordinate conversion algorithm is adopted to minimize the conversion error. After conversion, key control points are verified to ensure that the converted coordinates are consistent with the geographic datum of the target coordinate system. If the error of some points exceeds the allowable range during the conversion process, the conversion parameters are automatically adjusted, and a manual correction option is provided if necessary to ensure the accuracy of the data. After the coordinate conversion is completed, an intelligent coordinate matching algorithm is applied to optimize the spatial accuracy of geological data in the target system. By automatically identifying control points and establishing the spatial conversion relationship between control points, all geological elements can be accurately matched with the existing data in the target system. For example, if there are already some formation boundaries in the target system and the converted data has a slight offset near these boundaries, the system will automatically detect these deviations and apply a local adjustment algorithm to align the new formation boundaries with the existing data. For point elements, such as ore points or sampling points, calculate the position deviation before and after conversion and adjust the converted coordinates to maintain their original spatial consistency in the target system. Optimize for data at different scales. For example, use global conversion parameters for large-scale data and local optimization parameters for small-scale data to ensure that the data can maintain high precision at different map zoom levels. When the coordinate and attribute information of all geological data have been optimized, convert them into the standard file format of the target geological information system. For example, in the MapGIS system, geological data consists of multiple files such as MAP, DAT, WAT, and TAB. The MAP file stores spatial geometric information, the DAT file stores attribute data, the WAT file is used to record topological relationships, and the TAB file is used to manage the index between different data files. During the data export process, the system will split the processed geological data into corresponding files according to the storage requirements of the target system and ensure that all data structures can be correctly mapped to the MapGIS format. After generating the preliminary exported standard format file, perform data integrity verification to ensure that no element loss, incomplete attribute information, or abnormal coordinate transformation occurs during all data conversion processes. By comparing the data records before and after conversion, check whether all geological elements are complete. For example, calculate the total length of faults, the total area of formation units, etc., and compare with the original data to ensure that no information is missing in the converted data. Also check the integrity of attribute fields. For example, ensure that each formation unit has complete lithology, age and other attribute information, and analyze the coordinate conversion error to ensure that the conversion error of all points is within the allowable range. If the system finds that some data is missing, attribute fields are missing, or the coordinate error exceeds the threshold, these problems will be automatically marked and correction suggestions will be provided. To ensure the traceability of data and the convenience of subsequent maintenance, establish a data update tracking mechanism to record the conversion process of data from the Ovi platform to the target geological information system.Store all historical versions of data conversion and record the specific content of each modification, enabling users to trace back the data modification history at any time and restore to a specific version when needed.

[0033] The above described the geological data interaction method based on the Ovital interactive map in the embodiments of the present invention. Next, the geological data interaction system based on the Ovital interactive map in the embodiments of the present invention will be described. Please refer to Figure 2 , an embodiment of the geological data interaction system based on the Ovital interactive map in the embodiments of the present invention includes: A feature recognition module 201, configured to perform feature recognition and structural analysis on the original geological data, obtain a standardized data packet, and establish a geospatial reference conversion index table; A classification module 202, configured to perform feature extraction and classification on the geological elements in the standardized data packet according to the geospatial reference conversion index table to obtain structured geological data; A local registration module 203, configured to import the structured geological data into the Ovital interactive map and perform local registration through a grid dissection technique to obtain a visualized geological element layer; An editing module 204, configured to add a target geological editing tool to the visualized geological element layer, perform geological element drawing and attribute entry to obtain a completed geological data set; A conversion module 205, configured to perform structural reorganization and inverse coordinate conversion on the completed geological data set to obtain a standard format data file adapted to the target geological information system.

[0034] Through the collaborative cooperation of the above-mentioned various components, two-way seamless interaction between the target geological data and the AnyView Interactive Map Platform is achieved, solving the problems of single data interaction method and cumbersome process in the prior art, simplifying the operation process, reducing the technical threshold for users, and reducing the operation steps. By introducing the concept of Geospatial Reference Consistency (GRC) and the intelligent coordinate matching algorithm, an accurate coordinate system conversion mechanism is established, realizing the accurate conversion of multi-source geological data between different coordinate systems and scales, effectively solving the spatial misalignment problem during the integration of geological data from different sources in the traditional method. Using the feature extraction engine and the target geological element recognition algorithm, geological elements are accurately extracted and classified, and the complex relationships of geological elements are retained to ensure the deep integration of the geological target attributes and the AnyView objects, so that the converted data still retains the target geological meaning on the AnyView platform. Based on the grid meshing technology, local accurate registration is achieved, breaking through the accuracy limitation of the traditional global transformation and effectively eliminating image deformation and stitching gaps. The geological target editing function is seamlessly integrated into the AnyView platform, changing the traditional workflow of editing in the target GIS system and then exporting, supporting real-time field data collection and editing, and significantly improving the efficiency of geological field operations. A multi-person collaborative editing and version management mechanism is established, adapting to the geological survey work mode of team collaboration, and realizing seamless data transfer and integration from the AnyView platform to the target GIS system.

[0035] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described systems, systems, and units can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0036] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a geological data interaction device based on the AnyView Interactive Map (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0037] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements 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 geological data interaction method based on Ovi interactive map, characterized in that: include: Perform feature recognition and structural analysis on the original geological data to obtain standardized data packages and establish a geospatial reference conversion index table; Extracting and classifying features of geological elements in the standardized data package according to the geospatial reference conversion index table to obtain structured geological data; Importing the structured geological data into the Aowei interactive map, and performing local registration through grid generation technology to obtain a visualized geological element layer; Adding a target geological editing tool to the visualized geological element layer, performing geological element drawing and attribute entry, and obtaining an edited geological data set; The edited geological data set is restructured and reversely converted to obtain a data file in a standard format that is suitable for a target geological information system.

2. The geological data interaction method based on the Aowei interactive map according to claim 1 is characterized in that: The feature recognition and structural analysis of the original geological data are performed to obtain a standardized data package and establish a geographic space reference conversion index table, including: Parse the file header information of the original geological data to obtain the data type identifier and format parameters; Classify the original geological data based on the data type identifier, and import the geological image data and MapGIS vector data into corresponding processing modules respectively; Perform image enhancement processing and control point detection on the geological image data imported into the processing module to obtain enhanced image data and control point location information; Perform structural analysis and feature extraction on the MapGIS vector data imported into the processing module to obtain point, line, and surface feature sets and their attribute information; Performing a spatial integrity check on the enhanced image data and the point, line, and surface element sets, marking data missing areas and topological anomalies, and obtaining a data quality assessment result; Constructing a standardized data package including spatial reference information and feature structure data according to the data quality assessment result, the control point location information, the point, line, and surface feature sets and their attribute information; A geographic spatial reference conversion index table is established based on the standardized data package and the spatial reference information.

3. The geological data interaction method based on the Aowei interactive map according to claim 2 is characterized in that: The step of establishing a geographic spatial reference conversion index table based on the standardized data package and the spatial reference information includes: Extracting spatial reference information from the standardized data package to obtain spatial positioning data including coordinate system parameters, projection mode and reference surface information; Performing coordinate system analysis on the spatial positioning data, applying a four-parameter or six-parameter transformation model to the MapGIS vector data and the enhanced image data to obtain initial coordinate transformation parameters; Constructing a global coordinate mapping table according to the initial coordinate conversion parameters, recording the transformation relationship between the original coordinate system and the WGS84 coordinate system of the Ovi interactive map, and obtaining a basic mapping model; Performing multi-scale hierarchical processing on the basic mapping model, calculating sampling point density and transformation accuracy according to different scale levels, and obtaining a hierarchical transformation parameter set; Using known control points to perform forward and reverse accuracy verification on the hierarchical transformation parameter set, calculate the distribution of transformation errors in each region, and obtain a spatial registration accuracy distribution map; The data area is divided into grid cells based on the spatial registration accuracy distribution map, and local precise conversion parameters are allocated to each grid cell to obtain a geographic spatial reference conversion index table containing a secondary index structure.

4. The geological data interaction method based on the Aowei interactive map according to claim 1 is characterized in that: The feature extraction and classification of the geological elements in the standardized data package according to the geographic space reference conversion index table to obtain structured geological data includes: Extract the mapping relationship between the original coordinate system and the Aowei interactive map coordinate system in the geographic space reference conversion index table; Based on the mapping relationship, feature analysis is performed on the geological data in the standardized data package, and the geometric features and spatial distribution features of the geological elements are identified by a feature extraction engine to obtain a preliminary feature set of the geological elements; Applying an improved Canny edge detection and Hough transform combination algorithm to linear elements in the preliminary feature set, applying region growing and color clustering algorithms to area elements, and applying template matching technology to point elements, to obtain a classified geological element set; Inputting the classified geological element set into a geological element classification model, wherein the geological element classification model includes a characteristic description library of strata, structures, lithology, and minerals, to obtain element data with geological attribute tags; Performing semantic enhancement processing on the element data with geological attribute tags, identifying the layer relationship and cutting relationship between elements through spatial association analysis, and obtaining a structured geological element relationship network; According to the data structure specification of the Ovi interactive map, the point elements in the geological element relationship network are converted into Ovi label objects, the line elements are converted into Ovi trajectory objects, and the surface elements are converted into Ovi graphic objects to obtain preliminary adapted Ovi format data; The original geological attribute information is added to the initially adapted Ovi format data as metadata, and structured geological data adapted to the Ovi interactive map is generated.

5. The geological data interaction method based on the Aowei interactive map according to claim 1 is characterized in that: The structured geological data is imported into the Aowei interactive map, and local registration is performed through the grid generation technology to obtain a visualized geological element layer, including: The structured geological data is converted to API standards, and the geometric information, attribute information and style definition of geological elements are encapsulated according to the requirements of the Aowei interactive map to obtain a standard KML / KMZ format file; The standard KML / KMZ format file is input into the Aowei interactive map engine interface, and the data loading is performed by calling the import function to obtain the preliminary imported geological data layer; Based on the geological data layer initially imported, real-time coordinate conversion is performed, and positioning is performed using the mapping relationship in the geographic space reference conversion index table to obtain spatially positioned geological data; Performing spatial grid subdivision processing on the spatially located geological data, dividing the data area into regular grid cells, applying local transformation parameters to each grid cell, and obtaining corrected data that eliminates deformation and splicing gaps; A transparency optimization algorithm is implemented for the geological image part in the correction data, and the semi-transparency is intelligently adjusted according to the image content characteristics to obtain an overlay effect that is optimally integrated with the base map satellite image. At the same time, the correction data is classified and organized according to the type of geological elements, and the strata, structures, and mineral point elements are classified into independent layers. A two-way data association mechanism is established to obtain a visualized geological element layer that supports layer-level display control.

6. The geological data interaction method based on the Aowei interactive map according to claim 1 is characterized in that: The step of adding a target geological editing tool to the visualized geological element layer, drawing geological elements and entering attributes to obtain an edited geological data set includes: The original interactive interface of the Aowei interactive map is expanded to include target data editing tools such as stratum addition, fault drawing, occurrence measurement, and sample point recording, thus obtaining a geological target editing interface; Based on the geological target editing interface, an intelligent drawing auxiliary function is provided, and by detecting the extension trend of the existing boundary lines around, intelligent adsorption and smoothing processing are performed to obtain new geological elements that maintain spatial consistency with the existing data; Integrating a standard geological symbol library into the geological target editing interface to obtain a geological editing system that supports target annotation; Generate dynamic attribute forms according to the target attribute requirements of different types of geological elements, provide corresponding target attribute input interfaces for strata, faults, folds, and rock masses, and obtain standardized geological description data; Performing a legality check and consistency verification on the standardized geological description data input by the user, detecting the rationality of the stratigraphic age sequence and the conformity with the structural law, and obtaining verified geological attribute data; The verified geological attribute data is associated and integrated with the geometric information of geological elements, and an editing operation log is established to record the data modification history, so as to obtain an edited geological data set.

7. The geological data interaction method based on the Aowei interactive map according to claim 1 is characterized in that: The edited geological data set is restructured and reversely converted to obtain a standard format data file adapted to the target geological information system, including: Performing data structure reorganization processing on the edited geological data set, converting the labels, tracks, and graphics in the Aowei platform back to the target geological data structure, and obtaining reorganized data that restores the hierarchical relationship and topological relationship of geological elements; Based on the reorganized data, attribute data enhancement conversion is performed to normalize the geological description information entered in the Aowei platform into a target geological database structure, and normalized geological data including a standard attribute table, a relationship table and a code table are obtained; Performing reverse coordinate transformation on the normalized geological data according to the geospatial reference transformation index table, transforming the data in the WGS84 coordinate system back to the target system coordinate system, and obtaining the geological data after coordinate correction; Applying an intelligent coordinate matching algorithm to the coordinate-corrected geological data, automatically identifying control points and establishing conversion relationships, to obtain accurately aligned geological spatial data; Convert the precisely registered geological spatial data into the file format of the target geological information system, generate standard MAP, DAT, WAT, TAB file structures for the MapGIS system, and obtain a preliminary exported standard format file; Data integrity verification is performed on the initially exported standard format file to check for missing elements, incomplete attribute information and abnormal coordinate transformation, and a data update tracking mechanism is established to obtain a standard format data file adapted to the target geological information system.

8. A geological data interactive system based on Aowei interactive map, characterized in that: Used to implement the geological data interaction method based on the Aowei interactive map as described in any one of claims 1 to 7, the geological data interaction system based on the Aowei interactive map comprises: The feature recognition module is used to perform feature recognition and structural analysis on the original geological data, obtain standardized data packages and establish a geospatial reference conversion index table; A classification module, used for extracting and classifying features of geological elements in the standardized data package according to the geospatial reference conversion index table to obtain structured geological data; A local registration module is used to import the structured geological data into the Aowei interactive map, and perform local registration through grid generation technology to obtain a visual geological element layer; An editing module is used to add a target geological editing tool to the visual geological element layer, draw geological elements and enter attributes to obtain an edited geological data set; The conversion module is used to restructure and reversely convert the edited geological data set to obtain a standard format data file that is suitable for the target geological information system.

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