A cross-platform geological vector attribute mapping method based on geometric data transformation

Abstract

This invention relates to the field of data mapping technology, specifically a cross-platform geological vector attribute mapping method based on geometric data transformation. The method includes separating the geometric sequence, original symbol index, and attribute items of the source platform data; generating a transformed coordinate sequence using the Bursa seven-parameter matrix and an elastic grid deformation algorithm; normalizing the attributes and binding them with unique symbol identifiers to the coordinate sequence to generate geometric feature objects; extracting multi-level primitive logic protocols through semantic translation operators and constructing a multi-dimensional index mapping matrix using manifold learning; matching the entity handles of the target platform, calculating the expression difference compensation factor, and performing adaptive alignment; and performing integration and encapsulation according to the target platform standard. This invention effectively solves the problems of symbol loss, topological distortion, and visual distortion in cross-platform migration through low-level logic decoupling and semantic projection, eliminating the need for manual secondary redrawing.

Description

Technical Field

[0001] This invention relates to the field of data mapping technology, specifically to a cross-platform geological vector attribute mapping method based on geometric data transformation. Background Technology

[0002] Massive amounts of vector geographic information data have been accumulated in fields such as geological surveys, spatial planning, and mineral resource management. Due to the influence of different historical periods and technical systems, these data are often scattered and stored in various heterogeneous geographic information system platforms. In the process of realizing the construction of a "single map" of the entire spatial data, cross-platform vector data migration and integration has become a core requirement of the industry.

[0003] However, existing cross-platform geological vector data conversion technologies still face the following serious challenges in practical applications: Geological vector data not only contains basic geometric coordinates (points, lines, and surfaces), but also carries rich professional geological semantics; geological maps contain a large number of custom symbolic expressions, such as fault lines with specific shapes, filled patterns representing different strata lithologies, and complex annotation combinations. Current conversion methods mostly adopt physical migration based on intermediate formats. These formats can only record pure geometric attributes and cannot transfer symbol library indexes and rendering rules across platforms. This results in the loss of all professional geological visual features after data migration, requiring operators to perform tedious manual redrawing on the target platform, which greatly reduces the efficiency of data integration.

[0004] Different GIS platforms exhibit significant differences in their underlying geocomputing engine designs, particularly in their logic for determining geometric topology tolerance. For instance, a polygon identified as closed in MapGIS may be identified as a self-intersecting polygon or contain tiny dangling lines in ArcGIS due to fluctuations in topology tolerance. If only a pure geometric coordinate transformation is performed without correction, these potential topological defects will directly trigger anomalies in the target platform's rendering engine, potentially even causing the software system to crash. At the same time, geometric distortions (such as the generation of fragmented polygons) can also severely impair the accuracy of geological spatial analysis.

[0005] To address this, a cross-platform geological vector attribute mapping method based on geometric data transformation is proposed. Summary of the Invention

[0006] The purpose of this invention is to provide a cross-platform geological vector attribute mapping method based on geometric data transformation to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] A cross-platform geological vector attribute mapping method based on geometric data transformation includes:

[0009] Obtain the geological vector dataset from the source platform, separate the geometric coordinate sequence, the original symbol index, and the original attribute items, and obtain the multi-level primitive logical description protocol corresponding to the original symbol index;

[0010] The transformed coordinate sequence in the heterogeneous coordinate system is calculated based on the geometric coordinate sequence using the Bursa seven-parameter matrix; the original attribute items are normalized to generate attribute metadata; a unique symbol is assigned to the transformed coordinate sequence and logically bound to the attribute metadata to generate a geometric feature object;

[0011] The multi-level primitive logical description protocol is converted into a markup language sequence. Logical path features and operator weight vectors of the markup language sequence are extracted. A multi-dimensional index mapping matrix is ​​constructed by feature space projection. The target platform entity handle is matched in the multi-dimensional index mapping matrix based on the unique symbol identifier. The expression difference compensation factor between the source platform and the target platform is calculated and updated in the geometric element object.

[0012] According to the serialization standard of the target platform, the updated geometric feature objects are integrated and encapsulated with the style definition items generated based on the markup language sequence to produce the native map project engineering document of the target platform.

[0013] Preferably, the process of separating the geometric coordinate sequence, the original symbol index, and the original attribute items is as follows:

[0014] Retrieve the parsing template corresponding to the source platform file format and load the source platform geological vector dataset into a binary byte stream; use the parsing template to locate the offset in the byte stream and extract the geometric topology block, symbol description block and extended attribute table respectively;

[0015] The geometric coordinate sequence is reconstructed from the geometric topology block using a variable-length parsing algorithm; the original symbol index associated with the rendering engine is extracted from the symbol description block; the original attribute items are extracted from the extended attribute table using a field mapping mechanism; during the disassembly process, a temporary memory handle table is generated synchronously to record the original mapping relationship between the geometric coordinate sequence, the original symbol index, and the original attribute items in physical storage.

[0016] Preferably, the process of obtaining the multi-level primitive logic description protocol involves parsing symbol description data based on the original symbol index, wherein the symbol description data is obtained from the symbol description area of ​​the source platform's exported interface; semantically decomposing the symbol description data using a semantic translation operator to extract atomic operator sequences; performing correlation analysis on the atomic operator sequences to identify the coordinate reference relationships and dynamic constraints between operators, and establishing a topological logic graph model describing the internal structure of the symbols; and converting the topological logic graph model into a serialized logic instruction stream containing attribute state machine descriptions and operator execution chains according to the parsing granularity of the target platform, thereby generating the multi-level primitive logic description protocol.

[0017] Preferably, the semantic translation operator includes:

[0018] Feature fingerprint matching unit: Retrieves preset instruction feature codes in the symbol description data using a sliding window matching algorithm, identifies instruction boundaries, and determines the logical type of drawing actions; Parameter field decoder: Extracts original parameter values ​​related to geometry, color space, and fill style from the variable-length bitstream of the symbol description data, and performs normalization processing on the parameter dimensions; Semantic operator reducer: Performs semantic fusion between the normalized original parameter values ​​and the logical type, eliminates source platform-specific rendering pipeline dependencies, and decouples the original instruction actions in the symbol description data into cross-platform universal logical expressions, generating atomic operator sequences.

[0019] Preferably, the specific process for calculating the transformed coordinate sequence in the heterogeneous coordinate system is as follows: retrieval of common control points in the spatial reference systems of the source platform and the target platform; solving the Bursa seven-parameter matrix using the least squares method; constructing the geometric coordinate sequence into a spatial position vector matrix; performing coordinate transformation operations on the spatial position vector matrix using the Bursa seven-parameter matrix to generate preliminary estimated transformed coordinates; calculating the unit transformation residual vector of the preliminary estimated transformed coordinates in the target coordinate system; determining the residual weight allocation coefficient; and simultaneously using an elastic mesh deformation algorithm to perform nonlinear fine-tuning of the preliminary estimated transformed coordinates to generate the transformed coordinate sequence.

[0020] Preferably, the specific process of assigning a unique symbol identifier to the transformed coordinate sequence is as follows: performing spatial topological feature dimensionality reduction on the transformed coordinate sequence to extract geometric feature fingerprints containing geometric central moments, boundary closure curvature, and direction vectors; performing a hash operation on the attribute metadata to generate a semantic feature check code; concatenating the geometric feature fingerprint and the semantic feature check code to generate a unique symbol identifier with both spatial and semantic attributes; and using the unique symbol identifier as a global logical primary key to atomically bind the transformed coordinate sequence, attribute metadata, and reference handles of the corresponding multi-level primitive logical description protocol.

[0021] Preferably, the specific process of constructing the multidimensional index mapping matrix includes performing a depth-first traversal on the markup language sequence, extracting the recursive nesting depth and tag topology between primitive levels, and generating a logical path feature vector; statistically analyzing the occurrence frequency and logical entropy value of each atomic operator in the markup language sequence, calculating the gain based on the contribution of the operator to the visual features, and generating an operator weight vector; performing a tensor product operation on the logical path feature vector and the operator weight vector, projecting the generated composite feature tensor onto a low-dimensional linear metric space using a manifold learning algorithm, and calculating the feature centroid coordinates of each symbol protocol in the metric space; using the feature centroid coordinates as index keys, searching and matching the target platform standard entity library, establishing a bidirectional associative topology array between the source platform logical protocol and the target platform entity handle, and generating a multidimensional index mapping matrix.

[0022] Preferably, the expression difference compensation factor is a floating-point feature vector containing color offset, transparency gain parameter, and geometric visual scaling ratio, generated by identifying parameter conflict domains including color space, transparency algorithm, and display resolution, based on the benchmarking results of the multi-level primitive logical description protocol and the rendering capacity of the target platform; the expression difference compensation factor is used to perform semantic-level adaptive alignment of the rendering parameters of the geometric element object during the encapsulation process of the target platform.

[0023] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0024] 1. This application achieves deep logical reconstruction of geological symbols from underlying binary instructions to atomic operators by constructing a multi-level primitive logical description protocol and semantic translation operator; it uses manifold learning algorithm to extract the logical features of primitive protocol and performs semantic projection in high-dimensional feature space, ensuring accurate semantic alignment of complex geological elements between heterogeneous platforms. This mechanism reconstructs the expression logic of geological mapping from the bottom layer, solves the stubborn problem of loss of symbolic information in the traditional physical migration process, realizes the automated reorganization of professional geological mapping features, and eliminates the dependence on manual secondary redrawing.

[0025] 2. This application establishes a complete spatial data topology protection mechanism by introducing unit transformation residual vector monitoring and elastic grid deformation correction in the heterogeneous coordinate transformation process. While performing the Bursa seven-parameter matrix transformation, nonlinear fine-tuning is used to dynamically correct coordinate distortion caused by the step size difference of geospatial computing engines on different platforms, effectively eliminating the common topological cracks, hanging lines and self-intersection of patches when loading data across platforms. This process not only ensures the geometric legitimacy of spatial data, but also enhances the logical robustness of geological elements in the complex analysis environment of the target platform, avoiding system operation abnormalities caused by geometric defects.

[0026] 3. This application achieves adaptive energy level alignment between the rendering pipelines of the source and target platforms by establishing an expression difference compensation vector covering color offset, transparency gain, and visual scaling parameters. Through quantified compensation for differences in the underlying graphics engine algorithms of different platforms, visual expression distortion caused by inconsistent pixel density and color space standards is eliminated. This method ensures that key elements such as geological boundaries and stratigraphic filling patterns can present consistent visual saliency and information transmission effects under different hardware and software environments, providing a reliable guarantee of expression consistency for cross-system and cross-industry geological data integration and full-domain display. Attached Figure Description

[0027] Figure 1 This is a schematic diagram of a cross-platform geological vector attribute mapping method based on geometric data transformation;

[0028] Figure 2 This is a flowchart illustrating the overall process of cross-platform geological vector attribute mapping according to the present invention.

[0029] Figure 3 This is a schematic diagram of the multi-level primitive logic description protocol acquisition process of the present invention. Detailed Implementation

[0030] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0031] Example 1:

[0032] Please see Figure 1 This invention provides a cross-platform geological vector attribute mapping method based on geometric data transformation, the technical solution of which is as follows:

[0033] A cross-platform geological vector attribute mapping method based on geometric data transformation includes:

[0034] Obtain the geological vector dataset from the source platform, separate the geometric coordinate sequence, the original symbol index, and the original attribute items, and obtain the multi-level primitive logical description protocol corresponding to the original symbol index;

[0035] The transformed coordinate sequence in the heterogeneous coordinate system is calculated based on the geometric coordinate sequence using the Bursa seven-parameter matrix; the original attribute items are normalized to generate attribute metadata; a unique symbol is assigned to the transformed coordinate sequence and logically bound to the attribute metadata to generate a geometric feature object;

[0036] The multi-level primitive logical description protocol is converted into a markup language sequence. Logical path features and operator weight vectors of the markup language sequence are extracted. A multi-dimensional index mapping matrix is ​​constructed by feature space projection. The target platform entity handle is matched in the multi-dimensional index mapping matrix based on the unique symbol identifier. The expression difference compensation factor between the source platform and the target platform is calculated and updated in the geometric element object.

[0037] See Figure 2 According to the serialization standard of the target platform, the updated geometric feature objects are integrated and encapsulated with the style definition items generated based on the markup language sequence to produce the native map project engineering document of the target platform.

[0038] Obtain the geological vector dataset from the source platform, and separate the geometric coordinate sequence, the original symbol index, and the original attribute items. The process of separating the geometric coordinate sequence, the original symbol index, and the original attribute items is as follows:

[0039] Retrieve the parsing template corresponding to the source platform file format and load the source platform geological vector dataset into a binary byte stream; use the parsing template to locate the offset in the byte stream and extract the geometric topology block, symbol description block and extended attribute table respectively;

[0040] The geometric coordinate sequence is reconstructed from the geometric topology block using a variable-length parsing algorithm; the original symbol index associated with the rendering engine is extracted from the symbol description block; the original attribute items are extracted from the extended attribute table using a field mapping mechanism; during the disassembly process, a temporary memory handle table is generated synchronously to record the original mapping relationship between the geometric coordinate sequence, the original symbol index, and the original attribute items in physical storage.

[0041] Specifically, a parsing template matching the source platform file format is invoked. The parsing template predefines the physical organization structure of the file, and the geological vector dataset is fully loaded into the computer memory buffer in the form of a binary byte stream using the file reading component.

[0042] After acquiring the binary byte stream, the address mapping rules recorded in the parsing template are used to locate the offset in the byte stream; first, the file header information at the beginning of the byte stream is read to obtain the address pointers pointing to different data areas; according to the offset values ​​represented by these pointers, the corresponding positions are accurately jumped to in the byte stream, thereby identifying and extracting the geometric topology block, symbol description block and extended attribute table respectively.

[0043] For the extracted geometric topology blocks, a variable-length parsing algorithm is used to reconstruct the geometric coordinate sequence. Since the number of inflection points contained in geological elements (such as faults and rock strata boundaries) varies, the variable-length parsing algorithm first reads the point count field of each element in the geometric topology block, determines the length of bytes to be read subsequently based on the count value, and reads the floating-point bytes representing coordinate values ​​in sequence. These bytes are then converted into specific values ​​according to a preset bit width (64-bit double precision in this embodiment) to recover the complete geometric coordinate point sequence, realizing the reconstruction from binary data to spatial geometry.

[0044] During the processing of symbol description blocks, the original symbol indexes associated with the rendering engine are extracted. These indexes are usually unique numbers used to identify geological legends, line types, or fill styles. By parsing the identification fields in the symbol description blocks, the style number corresponding to each geometric feature is obtained. These numbers directly point to the symbol library resources in the source platform's rendering engine and record the visual characteristics that the feature should present in the source platform.

[0045] For the extended attribute table, the original attribute items are extracted through a field mapping mechanism. First, the definition area of ​​the attribute table is parsed to obtain the field arrangement order, name, and data format definition. According to the correspondence between rows and columns, the attribute values ​​of each geological element (such as strike, dip, dip angle, and stratigraphic code) are read in a loop from the attribute data area of ​​the byte stream. The field mapping mechanism ensures that the program can identify the business meaning represented by each segment of the byte stream and transform the messy binary data into structured original attribute information.

[0046] During the data disassembly and extraction process, a temporary memory handle table is generated synchronously in memory. This temporary memory handle table serves as an intermediate mapping record, specifically used to record the original mapping relationship between geometric coordinate sequences, original symbol indexes, and original attribute items in physical storage. In specific implementation, a unique memory handle (i.e., a memory address identifier) ​​is assigned to each geological element, and the corresponding coordinate data pointer, symbol index value, and attribute table record address are associated and stored under this handle.

[0047] By parsing templates and processing binary byte streams, a deep deconstruction of multi-source heterogeneous geological data formats was achieved. Using offset positioning mechanisms and variable-length parsing algorithms, the completeness and accuracy of extraction and expression of complex geological elements in spatial coordinates, symbol indexes, and extended attributes were ensured. In particular, by constructing a temporary memory handle table, a stable mapping record was established between the physical storage layer and the logical application layer, eliminating the risk of misalignment of coordinates and attributes that may occur during cross-platform data decomposition from the underlying logic, and ensuring the logical consistency and data structure stability of geological vector elements before and after heterogeneous system conversion.

[0048] Obtain the multi-level primitive logical description protocol corresponding to the original symbol index;

[0049] See Figure 3 The process of obtaining the multi-level primitive logic description protocol involves parsing symbol description data based on the original symbol index, where the symbol description data is obtained from the symbol description area of ​​the source platform's exported interface; semantically decomposing the symbol description data using semantic translation operators to extract atomic operator sequences; performing correlation analysis on the atomic operator sequences to identify the coordinate reference relationships and dynamic constraints between operators, and establishing a topological logic graph model describing the internal structure of the symbols; and converting the topological logic graph model into a serialized logic instruction stream containing attribute state machine descriptions and operator execution chains according to the parsing granularity of the target platform, thereby generating the multi-level primitive logic description protocol.

[0050] Specifically, in the process of obtaining the multi-level primitive logical description protocol, the corresponding symbol description data is first obtained and parsed based on the original symbol index. The symbol description data can be obtained by calling the standard export interface provided by the source platform, or extracted from the symbol description data area in the legally held data file. These symbol description data record the original instructions required by the source platform to generate complex geological symbols (such as strike lines with different dip angles and lithological blocks with specific filling densities).

[0051] After intercepting the drawing symbol description data, a semantic translation operator is used to perform semantic decomposition; the semantic translation operator includes:

[0052] The semantic translation operator includes:

[0053] Feature fingerprint matching unit: Retrieves preset instruction feature codes in the symbol description data using a sliding window matching algorithm, identifies instruction boundaries, and determines the logical type of drawing actions; Parameter field decoder: Extracts original parameter values ​​related to geometry, color space, and fill style from the variable-length bitstream of the symbol description data, and performs normalization processing on the parameter dimensions; Semantic operator reducer: Performs semantic fusion between the normalized original parameter values ​​and the logical type, eliminates source platform-specific rendering pipeline dependencies, and decouples the original instruction actions in the symbol description data into cross-platform universal logical expressions, generating atomic operator sequences.

[0054] Specifically, the feature fingerprint matching unit first initializes a sliding window in memory with the same length as the preset feature code. The sliding window shifts bit-by-bit across the received symbol description data. After each shift, the matching unit performs a logical XOR comparison between the binary sequence within the window and the known instruction fingerprints in the feature code library. When the comparison result is zero, the current window position is determined as the start boundary of the instruction, and the termination boundary of the instruction is determined based on the instruction length attribute associated with the feature fingerprint. This allows for precise truncation and extraction of independent instruction logic units from the bit stream without fixed delimiters.

[0055] The parameter field decoder retrieves the field layout rules for the identified instruction logic type from a preset instruction architecture description table. Based on the bit offsets and bit widths defined in the layout rules, the decoder uses shift operations and bitmasking operations to directly extract the original values ​​related to geometry, color, and fill from the variable-length bitstream of the symbol description data. After extraction, the original values ​​are mapped to the standard coordinate system and color space of the target platform, and the parameter dimensions are normalized using a floating-point scaling algorithm, outputting a standardized parameter set.

[0056] The semantic operator reducer encapsulates the normalized parameter set and logical type to construct intermediate operators. The reducer recursively scans the intermediate operators by retrieving the source platform's private dependency feature table, identifying and removing private data items such as specific rendering pipeline identifiers, proprietary memory addresses, or hardware acceleration instructions. After stripping, the reducer calls a cross-platform semantic mapping dictionary to map the purified drawing actions into general logical expression operators. By reconstructing the execution order and parameter combinations of these general operators, it ultimately generates an atomic operator sequence, achieving complete decoupling between the geological symbol description logic and the underlying rendering environment.

[0057] A deep correlation analysis is performed on the atomic operator sequence, focusing on identifying the coordinate reference relationships between each operator. By constructing a coordinate state tracking stack, the atomic operator sequence is sequentially traversed and its coordinate mode flags are identified to distinguish between absolute positioning and incremental offset modes. For incremental operators, vector superposition operations based on the previous landing point are performed to establish linear following reference relationships. For complex symbols with nested levels, the local logical origin is established by identifying the starting operator of the sub-primitive, and the differential offset between each internal operator and the origin is calculated to lock the parent-child topological dependencies within the symbol. At the same time, by analyzing the range of action of transformation operators (such as rotation and scaling) on ​​geometric operators, dynamic geometric conditions such as vertical constraints and equidistant arrangement are extracted. The reference object numbers, offset parameters, and constraint rules are uniformly recorded in the node attributes of the topological logic graph model.

[0058] The nodes of the topological logic graph model are used to represent atomic operators or state transition events. Spatial location dependencies and logical constraints are used as edges to transform the static instruction flow into a structured expression with logical hierarchy. According to the parsing granularity of the target platform (i.e., the upper limit of the target platform's support for path drawing or filling methods), the model is transformed into a serialized logical instruction flow containing attribute state machine descriptions and operator execution chains. The attribute state machine is used to record and switch drawing environment parameters (such as the current pen color and transparency state) in real time, while the operator execution chain determines the logical triggering order of drawing actions and generates a multi-level primitive logic description protocol.

[0059] By implementing the above-mentioned technical solution based on multi-level primitive logical description protocol and semantic translation operator, this invention achieves deep logical deconstruction and precise semantic alignment of heterogeneous geological symbol description data; feature fingerprint matching and variable-length bitstream decoding technology ensure the accuracy of extracting primitive instructions from the variable-length bitstream of symbol description data, and can effectively identify instruction boundaries without fixed delimiters; the semantic reduction mechanism completely removes the hardware rendering dependency of the source platform through logical decoupling, and transforms complex drawing actions into a cross-platform universal atomic operator sequence; this not only ensures a high degree of visual consistency in the cross-platform conversion of complex geological elements, but also maintains the spatial reference and logical constraint relationship within the symbol from the topological level.

[0060] The transformed coordinate sequence in the heterogeneous coordinate system is calculated based on the geometric coordinate sequence using the Bursa seven-parameter matrix; the original attribute items are normalized to generate attribute metadata;

[0061] The specific process for calculating the transformed coordinate sequence in the heterogeneous coordinate system involves: retrieving the common control points of the spatial reference systems of the source and target platforms; solving the Bursa seven-parameter matrix using the least squares method; constructing the geometric coordinate sequence into a spatial position vector matrix; performing coordinate transformation operations on the spatial position vector matrix using the Bursa seven-parameter matrix to generate the initial estimated transformed coordinates; calculating the unit transformation residual vector of the initial estimated transformed coordinates in the target coordinate system; determining the residual weight allocation coefficients; and simultaneously using an elastic mesh deformation algorithm to perform nonlinear fine-tuning on the initial estimated transformed coordinates to generate the transformed coordinate sequence.

[0062] Specifically, the control point retrieval process involves searching the built-in geospatial database for common control point pairs in the spatial reference systems of the source and target platforms. These control points are typically terrain feature points or mapping reference points with known coordinates. A set of spatial transformation equations is then established using these common control points. By calling the least squares iterative program, the goal is to minimize the sum of squared projection residuals of all control point pairs, thereby solving for a Bursa seven-parameter matrix containing three translation offsets, three rotation angles, and a scale factor.

[0063] After obtaining the Bursa seven-parameter matrix, the geometric coordinate sequence is structurally reorganized to construct a spatial position vector matrix. The coordinate sequence is read sequentially through a temporary memory handle table, and the horizontal, vertical, and height three-dimensional coordinate values ​​are extracted according to the bit width step size and encapsulated into a 3-row, 1-column basic vector. According to the total number of points to be transformed, a 3-row, N-column matrix storage space is pre-allocated in memory. Each basic vector is inserted into the corresponding column position in the original index order, and a memory alignment check is performed synchronously, thereby reorganizing the scattered geometric point set into a spatial position vector matrix.

[0064] The spatial position vector matrix is ​​subjected to an affine transformation using the solved seven-parameter matrix; rotation matrix multiplication is performed on each coordinate vector, and the vectors are scaled according to the scale factor. Finally, the translation offset vectors are accumulated to generate the initial estimated transformed coordinates.

[0065] Because geological data transformation often involves nonlinear deformation, after generating the initial estimated transformation coordinates, the unit transformation residual vector of the initial estimated transformation coordinates in the target coordinate system is further calculated. In practice, the coordinates of the initially estimated control points are compared again with the actual control point coordinates in the target platform, and the deviation values ​​between the two in the three coordinate axes are calculated. Based on these deviation values, the residual weight allocation coefficient corresponding to each geometric point to be transformed is determined through distance-weighted inverse proportion.

[0066] The initial estimated transformation coordinates are finely adjusted nonlinearly using an elastic grid deformation algorithm. The elastic grid deformation algorithm treats the entire geological vector layer as a virtual grid with elastic characteristics and uses the previously determined residual vector and weight allocation coefficient as internal driving forces. By simulating the stress deformation of the elastic thin plate or grid, local smooth displacement adjustment is performed on each initial estimated coordinate point to absorb and compensate for nonlinear distortion caused by surveying errors or differences in projection models, thereby generating a transformation coordinate sequence.

[0067] By combining the Bursa seven-parameter matrix with the least squares method, a rigorous mathematical transformation relationship between the source and target platforms was established. Furthermore, the spatial location vector matrix was used to achieve the standardized reorganization of large-scale discrete geometric points into standardized computational objects. Addressing the projection differences and mapping errors that easily occur during cross-platform migration of geological data, a residual weight allocation mechanism and an elastic grid deformation algorithm were introduced to effectively absorb and compensate for local distortions that linear transformations cannot cover.

[0068] The original attribute items are normalized to generate attribute metadata. Specifically, the original attribute items are cleaned by standardizing numerical precision, standardizing date formats, and removing redundant characters to eliminate data expression differences between source platforms. Semantic mapping relationships are established based on the geological ontology library to align non-standard field names to industry standard key values, resolving semantic conflicts. For key attributes such as geological occurrence elements, dimensional conversion and standardization calibration of value ranges are further performed, and qualitative descriptions are converted into standard level codes. The processed attribute data, its field characteristics, and coding rules are encapsulated together as attribute metadata.

[0069] A unique symbol is assigned to the transformed coordinate sequence and logically bound to the attribute metadata to generate a geometric feature object;

[0070] The specific process of assigning a unique symbol identifier to the transformed coordinate sequence is as follows: dimensionality reduction of the transformed coordinate sequence using spatial topological features; extraction of geometric feature fingerprints including geometric central moments, boundary curvature, and direction vectors; hashing of the attribute metadata to generate a semantic feature check code; concatenation of the geometric feature fingerprint and the semantic feature check code to generate a unique symbol identifier with both spatial and semantic attributes; and atomic binding of the transformed coordinate sequence, attribute metadata, and reference handles of the corresponding multi-level primitive logical description protocol using the unique symbol identifier as the global logical key.

[0071] Specifically, the spatial distribution characteristics of each point in the transformed coordinate sequence are extracted, and the geometric central moments of the point set are calculated: First, the geometric center is determined by calculating the arithmetic mean of all coordinate points in the sequence. Then, the second and higher-order moments of each point relative to the center are calculated to describe the overall shape distribution characteristics of the feature. Simultaneously, the boundary curvature is calculated. For linear or planar features, the rate of change of the angle between adjacent polyline segments is analyzed to extract numerical features reflecting the smoothness or complexity of the boundary. Furthermore, the direction vector is determined by calculating the displacement components between the starting and ending points of the geometric feature. These extracted values ​​are normalized and combined to form the geometric feature fingerprint of the geometric feature.

[0072] A hash operation is performed on the attribute metadata. In specific implementation, the field names, data types, and specific attribute values ​​in the attribute metadata are arranged in a preset logical order and converted into a continuous binary bit stream. A hash algorithm with high discreteness is used to calculate the bit stream to generate a set of fixed-length numerical sequences, which serve as the semantic feature check code of the element.

[0073] After obtaining the geometric fingerprint and semantic checksum, the two are concatenated. The binary value of the geometric fingerprint is used as a prefix, and the semantic checksum is used as a suffix to form a composite feature vector. A secure hash operation module with a 256-bit output width is initialized, which receives the composite feature vector formed by concatenating the geometric fingerprint and semantic checksum as logical input. At the beginning of the operation, the composite feature vector is padded to ensure that its length meets the preset grouping operation standard. Using a multi-round iterative compression function, a combination of bit operations, including cyclic shift, XOR logic superposition, and nonlinear permutation, is performed on the input bit stream to map the variable-length feature information into a fixed-length initial binary hash digest.

[0074] Using the unique symbol identifier as the global logical primary key, atomic binding is performed. In the memory management unit, the system creates a data record block with atomic operation characteristics, storing the unique symbol identifier, the corresponding transformed coordinate sequence, the normalized attribute metadata, and the reference handle of the previously obtained multi-level primitive logical description protocol in the same logical unit. This binding method ensures that in the subsequent encapsulation, transmission, or rendering process, the spatial geometry information, attribute information, and style protocol always maintain a strong and consistent association, and any call to any part can be instantly traced back to other related items through the global primary key.

[0075] By implementing a unique symbolic identifier allocation scheme based on dual hashing of spatial topological features and semantic features, this invention achieves extremely high data association stability in cross-platform data mapping. Utilizing a cascaded algorithm of geometric fingerprint and semantic checksum, a unique digital identity label is constructed for each geological element, fundamentally solving the problem of spatial element and attribute information disconnection or mismatch that easily occurs during the conversion of large-scale geological vector data.

[0076] The multi-level primitive logic description protocol is converted into a markup language sequence, the logical path features and operator weight vectors of the markup language sequence are extracted, and a multi-dimensional index mapping matrix is ​​constructed by feature space projection.

[0077] The specific process of constructing the multidimensional index mapping matrix includes performing a depth-first traversal on the markup language sequence, extracting the recursive nesting depth and label topology between primitive levels, and generating a logical path feature vector; statistically analyzing the occurrence frequency and logical entropy value of each atomic operator in the markup language sequence, calculating the gain based on the contribution of the operator to the visual features, and generating an operator weight vector; performing a tensor product operation on the logical path feature vector and the operator weight vector, projecting the generated composite feature tensor onto a low-dimensional linear metric space using a manifold learning algorithm, and calculating the feature centroid coordinates of each symbol protocol in the metric space; using the feature centroid coordinates as the index key, searching and matching the target platform standard entity library, establishing a bidirectional associative topology array between the source platform logical protocol and the target platform entity handle, and generating a multidimensional index mapping matrix.

[0078] Specifically, the multi-level primitive logical description protocol is parsed and reconstructed into a hierarchical markup language sequence according to preset syntax rules. A depth-first traversal is then performed on this sequence: using an explicitly maintained logical stack structure, the nesting levels of primitives are probed level by level, starting from the root label of the sequence. During the traversal, the recursion depth of the current node is recorded in real time, and the parent-child and sibling topological connections between labels are identified. A logical path feature vector is generated by serializing and encoding the depth value of each traversal path with the topological connection identifier.

[0079] The entire markup language sequence is scanned, and the frequency of each type of atomic operator in the sequence is counted using a counter. Simultaneously, the logical distribution uniformity of each operator in the instruction stream is evaluated using the information entropy calculation formula, yielding its logical entropy value. Based on this, a pre-defined contribution evaluation model is invoked, and gain is calculated according to the degree of contribution of each operator to the final visual contour of the symbol: key operators that directly determine the geometric shape are assigned higher contribution weights, while auxiliary operators that only handle environmental parameter switching are assigned lower weights. The frequency, entropy value, and gain score are weighted and accumulated to generate an operator weight vector reflecting the center of gravity of the symbol's expression.

[0080] The generated logical path feature vector and operator weight vector are subjected to tensor product operation, that is, by performing a multiplication operation on each element of the logical path vector and all elements of the weight vector in sequence, a high-dimensional composite feature tensor is constructed. In order to eliminate the redundancy of high-dimensional data, a manifold learning algorithm is called to perform nonlinear dimensionality reduction on this tensor: the manifold learning algorithm calculates the local neighborhood structure of feature points in high-dimensional space and finds a low-dimensional linear metric space that can preserve the local structure, and projects the complex composite feature tensor into the low-dimensional space; during the projection process, iterative optimization is used to ensure that the projected data points can retain the logical feature differences of the original symbols to the greatest extent.

[0081] In this embodiment, the manifold learning algorithm employs a unified manifold approximation and projection algorithm. First, the K-nearest neighbor algorithm is used to search the local neighborhood of each feature data point in the original high-dimensional tensor space. To balance the coherence of the logical paths of geological symbols with computational resource overhead, the neighborhood size k is set to 15 to 30; smaller neighborhood values ​​help the system capture subtle logical differences such as fault lines with different dip angles, while larger neighborhood values ​​focus on converging major features of point-like, line-like, and area-like symbols.

[0082] After establishing local neighborhood relationships, the high-dimensional composite feature tensor is projected onto a low-dimensional linear metric space. Considering the clustering and matching retrieval characteristics of geological symbols, the dimension of the target low-dimensional metric space is preferably set to 3 to 5 dimensions, and the minimum distance parameter between data points in the projected space is set to 0.1 to control the compactness of the feature clusters. Subsequently, the system uses cross-entropy as the loss function and iteratively optimizes the low-dimensional feature embedding using stochastic gradient descent. During optimization, the initial learning rate is set to 1.0 and decreases linearly with iteration, the maximum number of iterations is set to 200 to 500, and the automatic convergence termination condition is set to a loss function change rate of less than 0.001 for 10 consecutive iterations, ensuring that the projection results eliminate data redundancy to the greatest extent possible.

[0083] After establishing the low-dimensional linear metric space, the feature centroid coordinates of each symbol protocol in the space are calculated. Specifically, all feature components projected onto each symbol are extracted, and their arithmetic mean along each coordinate axis is calculated to obtain a unique coordinate point representing the logical feature center of the symbol. This unique coordinate point is then encapsulated into a standardized feature vector. Using this feature centroid coordinate as an index key, feature matching retrieval is performed in the target platform's standard entity library. The specific matching process is as follows: the cosine similarity or Euclidean distance between the feature vector and the feature vectors of each entity in the target library is calculated as a similarity metric. Based on this similarity metric, all entities in the target library are sorted from highest to lowest similarity score, and the top K entities are extracted to form a Top-K candidate queue. A matching threshold T is preset, and the highest similarity score in the Top-K candidate queue is compared with the threshold T for confidence. If the highest score is greater than or equal to the threshold T, the automated matching is considered successful, and the top-ranked candidate entity handle is directly selected as the best matching object. If the highest score is less than the threshold T, it indicates that the target platform library lacks symbol representations that are highly compatible with the source platform, and a rule fallback mechanism is triggered. The rule fallback mechanism first calls a pre-set geological expert rule library for downgraded matching. If the rule library also cannot match completely, the Top-K candidate queue and the source symbol graphic are thrown into an exception handling queue for manual review and manual matching by technical personnel. In this embodiment, the threshold T is set to 0.85. Experimental verification shows that this achieves the best balance between ensuring automated conversion efficiency by compatibility with minor underlying rendering differences between heterogeneous platforms and strictly adhering to the professional semantic bottom line of geological symbols to prevent mismatches of symbols that are similar in form but different in logic.

[0084] A multidimensional array space is allocated in memory and defined as a topological logical array with source protocol logical dimension, spatial feature dimension, and target entity dimension. The logical path features and operator weight vectors of the source platform symbols are stored in the logical dimension, the calculated feature centroid coordinates are stored as the index layer in the feature dimension, and the target platform entity handle and corresponding rendering instruction interface finally determined by the automatic matching or rule backoff mechanism are stored in the entity dimension. A multidimensional index mapping matrix is ​​constructed by establishing bidirectional index pointers between the dimensions.

[0085] By constructing a three-dimensional topological logic array with source protocol dimension, spatial feature dimension and target entity dimension in memory, the multidimensional reorganization of symbolic logic and platform entities is realized; a spatial segmentation index structure is constructed using feature centroid coordinates to transform abstract feature matching into fast physical addressing based on metric space, and a multidimensional index mapping matrix between source logic and target handle is established in conjunction with bidirectional associative pointers.

[0086] Based on the unique symbol identifier, the target platform entity handle is matched in the multidimensional index mapping matrix, the expression difference compensation factor between the source platform and the target platform is calculated, and the result is updated in the geometric feature object.

[0087] The expression difference compensation factor is a floating-point feature vector containing color offset, transparency gain parameter, and geometric visual scaling ratio, generated by identifying parameter conflict domains including color space, transparency algorithm, and display resolution, based on the benchmarking results between the multi-level primitive logical description protocol and the target platform rendering level. The expression difference compensation factor is used to perform semantic-level adaptive alignment of the rendering parameters of the geometric element object during the target platform encapsulation process. The expression difference compensation factor includes color parameter correction, transparency parameter correction, and symbol size correction. Its calculation process includes: reading the color parameters, transparency parameters, and size parameters from the source platform primitive protocol, and reading the corresponding parameter definitions from the target platform rendering configuration; generating corresponding correction parameters based on the differences in parameter value ranges and encoding methods between the two platforms; and writing the correction parameters into the rendering extension field of the geometric element object.

[0088] Specifically, the pre-stored target platform rendering energy level configuration file is retrieved. This file defines the color model standard, transparency blending mode logic, and display device reference resolution supported by the target platform. The drawing instruction parameters in the multi-level primitive logic description protocol are compared with the target platform's energy level configuration field by field. By extracting the numerical differences in color space definition, transparency algorithm weight, and pixel density definition, parameter mapping sets for color space conflict domain, transparency algorithm conflict domain, and display resolution conflict domain are divided and established in memory.

[0089] In the processing of color space conflict domain, the color component values ​​in the source protocol are extracted and mapped to the three-dimensional coordinate system corresponding to the color space model of the target platform. The system calculates the algebraic difference between the source component coordinates and the target space standard coordinates on the color wheel angle and the lightness axis. Through color conversion matrix operation, the algebraic difference is converted into a color offset containing the adjustment components of each red, green and blue channel. This offset is stored in a temporary buffer as the first set of floating-point feature parameters of the compensation factor.

[0090] In handling the conflict domain of the transparency algorithm, the layer overlay parameters defined in the source protocol are extracted and substituted into the alpha blending equation of the target platform's underlying rendering engine. The output brightness fitting curves of the source platform and the target platform under the same input values ​​are compared, and the deviation ratio of the two in the transparency gradient is calculated. By performing a normalization mapping on this deviation ratio, transparency gain parameters are generated and used as the second set of floating-point feature parameters of the compensation factor.

[0091] In the processing of display resolution conflict domains, the number of pixels per inch of the reference screen defined by the source platform is extracted and divided by the physical display resolution value of the target platform to obtain the pixel density ratio; further, the pixel conversion logic of the target platform for geometric measurement units such as line width and dot diameter is retrieved, and the pixel density ratio is converted into the corresponding linear scaling factor to determine the geometric visual scaling ratio, which serves as the third set of floating-point feature parameters of the compensation factor.

[0092] The color offset, transparency gain parameter, and geometric visual scaling ratio are concatenated and encapsulated according to a preset positional order to construct a multi-dimensional floating-point feature vector; a unique symbol identifier is used as the primary key index to locate the corresponding geometric element object in the memory data object pool; by calling the object's attribute writing interface, the floating-point feature vector is directly filled into the rendering extended attribute field of the geometric element object, thus completing the physical integration of the expression difference compensation factor and the geometric element object.

[0093] By utilizing multi-dimensional floating-point compensation logic based on color offset, transparency gain, and geometric scaling, it can accurately eliminate technical pain points such as color distortion, layer logic occlusion, and symbol visual distortion caused by differences in rendering energy levels between platforms. While maintaining the rigor of the original geometric topology, it endows geological element objects with cross-platform semantic expression resilience, solving the common problem of multiple representations of a single map in the field of geological mapping from the underlying logic.

[0094] According to the serialization standard of the target platform, the updated geometric feature objects are integrated and encapsulated with the style definition items generated based on the markup language sequence to produce the native map project engineering document of the target platform;

[0095] Specifically, the process loads the target platform's native project template, writes the coordinate reference system and global metadata into the file header, and initializes the physical offset index table. It serializes the markup language into style resource blocks conforming to the target protocol and assigns a unique resource reference number to each style item, storing it in the symbol library storage area. It iterates through geometric feature objects, writing the transformation coordinate sequence, attribute metadata, and expression difference compensation factors in a stream according to the target byte order, synchronously recording the starting offset and data length of each feature to reconstruct the physical addressing table. It uses unique symbols to establish logical pointer associations between geometric features and style blocks, updates the offset address item in the file header, and performs data flow consistency checks, ultimately producing the target platform's native map project engineering document.

[0096] This invention achieves deep compatibility and high-fidelity migration of geological vector data across heterogeneous platforms by constructing a complete technical chain from data decomposition and spatial transformation to style semantic alignment. Utilizing a multidimensional index mapping matrix and an expression difference compensation factor, it fundamentally solves common technical challenges in cross-platform transformation, such as spatial displacement deviation, attribute information disconnect, and symbol visual distortion. This method not only ensures the topological stability and semantic consistency of geological elements in complex transformation environments but also significantly improves the efficiency of cross-platform geological mapping through a standardized integration and encapsulation process, providing core technical support for the standardized sharing and engineering application of geological information results.

[0097] Example 2:

[0098] This embodiment applies a cross-platform geological vector attribute mapping method based on geometric data conversion to the engineering document format of a large coal mine geological map to migrate the geological map of a large coal mine area from a proprietary geological data format (Type A) to a native map project on a Type B platform.

[0099] In a digital handover project for a coal mine with a capacity of tens of millions of tons, technicians first retrieved a parsing template for a proprietary geological data format (Type A). They then loaded a geological dataset containing 15,000 fault lines and stratigraphic boundaries into a binary byte stream. By locating the byte stream offset, they used a variable-length parsing algorithm to reconstruct the geometric coordinate sequence. During this process, a temporary memory handle (e.g., HND_GEOL_001) was synchronously generated for each geological element (e.g., "F1 fault") to lock the mapping relationship between physical storage and logical attributes, preventing data misalignment.

[0100] For symbol indexes unique to geological maps (e.g., index number 1025 represents "thrust fault"), their symbol description data (obtained through data files exported from the source platform interface) is acquired and parsed. Using the sliding window matching algorithm in the semantic translation operator, the symbol is identified as being composed of equally spaced serrated triangles and a main line. Through the parameter field decoder, the color space (e.g., RGB: 255, 0, 0) and geometric parameters are normalized to remove rendering dependencies unique to the source data and decouple it into a cross-format universal atomic operator sequence.

[0101] Six sets of common control points in the mining area were retrieved under the source and target format spatial reference frames, and the Bursa seven-parameter matrix was solved using the least squares method. In this implementation scenario, the translation parameter was set to 112.54m, and the rotation parameter and scale factor were calculated and applied. To address the differences in topology tolerance determination between different engines, an elastic mesh deformation algorithm was introduced to perform nonlinear fine-tuning on the initially estimated coordinates after transformation.

[0102] Spatial topological feature dimensionality reduction is performed on the transformed coordinate sequence to extract geometric feature fingerprints such as geometric central moments and boundary closure curvature. Simultaneously, hash operations are performed on attribute metadata such as stratigraphic codes, strike, and dip to generate semantic feature check codes. These two are concatenated, and a non-collision hash algorithm is used to generate a unique symbol identifier UID_2026_COAL_001. This identifier serves as the global primary key, atomically binding coordinates, attributes, and rendering protocols to ensure they remain consistent in subsequent encapsulation. The logical protocol of the symbol is converted into a markup language sequence, and nesting depth and structural features are extracted using depth-first traversal to generate path feature vectors. A manifold learning algorithm is used to project complex feature tensors onto a low-dimensional linear metric space, and the feature centroid coordinates are calculated. Using these coordinates as keys, a search is conducted in the target format's standard entity library to automatically associate with the native entity handles of the target data structure, establishing a bidirectional associative topological array between the source and target.

[0103] Considering the differences in color space and pixel density between the underlying rendering engines of different systems, a difference compensation factor is calculated. In this scenario, a floating-point feature vector containing color offset (+3% brightness), transparency gain parameter (1.05), and geometric visual scaling ratio (1.2 times) is generated. These factors are updated in the geometric feature object to automatically correct the problem of smaller signs or color distortion caused by different resolutions when the target format is packaged.

[0104] Following the serialization standard of the Class B target platform, the updated geometric feature objects and style definition items are integrated and encapsulated. In specific implementation, the native project template of the Class B platform is loaded, the coordinate reference system and global metadata are written to the file header, the coordinate sequence, attribute metadata and compensation factors are streamed, and the physical addressing table is reconstructed. The resulting native map project engineering document of the target platform can be opened directly in the target system environment without the need for manual secondary symbolization and redrawing.

[0105] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A cross-platform geological vector attribute mapping method based on geometric data transformation, characterized in that, include: The process involves acquiring a geological vector dataset from a source platform, separating the geometric coordinate sequence, original symbol index, and original attribute items, and obtaining a multi-level primitive logical description protocol corresponding to the original symbol index. The acquisition process of the multi-level primitive logical description protocol involves parsing the symbol description data based on the original symbol index, where the symbol description data is obtained from the symbol description area of ​​the source platform's exported interface. Semantic translation operators are used to semantically decompose the symbol description data and extract atomic operator sequences. Correlation analysis is performed on the atomic operator sequences to identify the coordinate reference relationships and dynamic constraints between operators, establishing a topological logic graph model describing the internal structure of the symbols. According to the parsing granularity of the target platform, the topological logic graph model is transformed into a serialized logical instruction stream containing attribute state machine descriptions and operator execution chains, generating the multi-level primitive logical description protocol. The semantic translation operator includes: Feature fingerprint matching unit: Retrieves preset instruction feature codes in the symbol description data using a sliding window matching algorithm, identifies instruction boundaries, and determines the logical type of drawing actions; Parameter field decoder: Extracts original parameter values ​​related to geometry, color space, and fill style from the variable-length bitstream of the symbol description data, and performs normalization processing on the parameter dimensions; Semantic operator reducer: Performs semantic fusion between the normalized original parameter values ​​and the logical type, eliminates source platform-specific rendering pipeline dependencies, and decouples the original instruction actions in the symbol description data into cross-platform universal logical expressions, generating atomic operator sequences; The transformed coordinate sequence in the heterogeneous coordinate system is calculated based on the geometric coordinate sequence using the Bursa seven-parameter matrix; the original attribute items are normalized to generate attribute metadata; a unique symbol is assigned to the transformed coordinate sequence and logically bound to the attribute metadata to generate a geometric feature object; The multi-level primitive logical description protocol is converted into a markup language sequence. Logical path features and operator weight vectors of the markup language sequence are extracted. A multi-dimensional index mapping matrix is ​​constructed by feature space projection. The target platform entity handle is matched in the multi-dimensional index mapping matrix based on the unique symbol identifier. The expression difference compensation factor between the source platform and the target platform is calculated and updated in the geometric element object. According to the serialization standard of the target platform, the updated geometric feature objects are integrated and encapsulated with the style definition items generated based on the markup language sequence to produce the native map project engineering document of the target platform.

2. The cross-platform geological vector attribute mapping method based on geometric data transformation according to claim 1, characterized in that, The process of separating the geometric coordinate sequence, the original symbol index, and the original attribute items is as follows: Retrieve the parsing template corresponding to the source platform file format and load the source platform geological vector dataset into a binary byte stream; use the parsing template to locate the offset in the byte stream and extract the geometric topology block, symbol description block and extended attribute table respectively; The geometric coordinate sequence is reconstructed from the geometric topology block using a variable-length parsing algorithm; the original symbol index associated with the rendering engine is extracted from the symbol description block; the original attribute items are extracted from the extended attribute table using a field mapping mechanism; during the disassembly process, a temporary memory handle table is generated synchronously to record the original mapping relationship between the geometric coordinate sequence, the original symbol index, and the original attribute items in physical storage.

3. The cross-platform geological vector attribute mapping method based on geometric data transformation according to claim 1, characterized in that, The specific process for calculating the transformed coordinate sequence in the heterogeneous coordinate system involves: retrieving the common control points of the spatial reference systems of the source and target platforms; solving the Bursa seven-parameter matrix using the least squares method; constructing the geometric coordinate sequence into a spatial position vector matrix; performing coordinate transformation operations on the spatial position vector matrix using the Bursa seven-parameter matrix to generate the initial estimated transformed coordinates; calculating the unit transformation residual vector of the initial estimated transformed coordinates in the target coordinate system; determining the residual weight allocation coefficients; and simultaneously using an elastic mesh deformation algorithm to perform nonlinear fine-tuning on the initial estimated transformed coordinates to generate the transformed coordinate sequence.

4. The cross-platform geological vector attribute mapping method based on geometric data transformation according to claim 1, characterized in that, The specific process of assigning a unique symbol identifier to the transformed coordinate sequence is to perform spatial topological feature dimensionality reduction on the transformed coordinate sequence and extract geometric feature fingerprints containing geometric central moments, boundary closure curvature, and direction vectors; Perform a hash operation on the attribute metadata to generate a semantic feature check code; The geometric feature fingerprint and the semantic feature check code are concatenated to generate a unique symbol identifier with both spatial and semantic attributes; Using the unique symbol identifier as the global logical primary key, the reference handles of the transformation coordinate sequence, attribute metadata, and the corresponding multi-level primitive logical description protocol are atomically bound.

5. The cross-platform geological vector attribute mapping method based on geometric data transformation according to claim 1, characterized in that, The specific process of constructing the multidimensional index mapping matrix includes performing a depth-first traversal on the markup language sequence, extracting the recursive nesting depth and label topology between primitive levels, and generating a logical path feature vector; statistically analyzing the occurrence frequency and logical entropy value of each atomic operator in the markup language sequence, calculating the gain based on the contribution of the operator to the visual features, and generating an operator weight vector; performing a tensor product operation on the logical path feature vector and the operator weight vector, projecting the generated composite feature tensor onto a low-dimensional linear metric space using a manifold learning algorithm, and calculating the feature centroid coordinates of each symbol protocol in the metric space; using the feature centroid coordinates as the index key, searching and matching the target platform standard entity library, establishing a bidirectional associative topology array between the source platform logical protocol and the target platform entity handle, and generating a multidimensional index mapping matrix.

6. The cross-platform geological vector attribute mapping method based on geometric data transformation according to claim 1, characterized in that, The expression difference compensation factor is a floating-point feature vector containing color offset, transparency gain parameter, and geometric visual scaling ratio, generated by identifying parameter conflict domains including color space, transparency algorithm, and display resolution, based on the benchmarking results of the multi-level primitive logical description protocol and the rendering capacity of the target platform. The expression difference compensation factor is used to perform semantic-level adaptive alignment of the rendering parameters of the geometric element object during the encapsulation process of the target platform.

Images