Graph symbol library unified management method and device based on geological data

Abstract

The invention provides a geological data-based graph symbol library unified management method and device, and belongs to the technical field of geological data graph symbol management. The method provided by the invention comprises the steps of generating a standard symbol library for managing basic symbol data of a standard geological map; extracting a sub-graph information contour and text information from the to-be-stored geological data according to the storage data standard format, and generating sub-graph information; calculating the visual similarity between the sub-graph information and the same type of sub-graphs in the standard symbol library; calculating the semantic matching degree of information of the standard symbol data and the geological data to be stored under each symbol of the same type; the visual similarity and the semantic matching degree are synthesized, and based on the comprehensive matching degree, warehousing data sub-graph matching information is determined, and a to-be-added sub-graph is positioned; and updating the in-storage data sub-graph reference based on the in-storage data sub-graph matching information. The geological data-based graph symbol library unified management method and device provided by the invention are used for realizing fusion and management of multi-source geological symbol libraries.

Description

Technical Field

[0001] This application relates to the field of geological data map symbol management technology, and in particular to a unified management method and apparatus for a map symbol library based on geological data. Background Technology

[0002] Geological data, as core foundational data in fields such as geological surveys, resource exploration, and environmental assessments, directly impacts the quality and efficiency of geological work through its standardized representation and efficient sharing. With the large-scale development of geological surveys, data such as medium- and large-scale regional geological maps and thematic geological maps have been continuously accumulated. Significant differences exist in the symbol libraries used by different periods and survey units, forming a... Figure 1 The fragmentation of databases is a growing problem. In scenarios such as geological data integration and mapping, spatial database construction, and geological map publication, the lack of unified symbol libraries leads to inconsistencies in data expression, contradictions in map integration, and difficulties in controlling the quality of outputs.

[0003] Currently, geological symbol library technologies primarily focus on the construction and integration of standard symbol libraries. By integrating symbol library resources from various industries and sources, and standardizing elements such as symbols, colors, and fonts, a universal standard geological symbol library is ultimately formed, providing a unified symbol reference benchmark for geological data production. This approach first establishes a standardized symbol library system and then requires subsequent data production to adhere to this standard for symbol application. Essentially, it is a standard-driven symbol library management model, which has played a certain role in the standardized production of data for new geological projects.

[0004] However, this type of technology relies heavily on a pre-defined industry standard symbol library, lacks dynamic adaptation capabilities, and cannot effectively address the numerous non-standard symbol differences that exist in historical geological data. Figure 1 For integrating existing data in the current database, achieving precise matching and unification of symbols is difficult. Furthermore, the logic of prioritizing standards over data is disconnected from the current state of geological data. Full retrospective modification of historical data to adapt to the standard symbol library is extremely costly and impractical in real-world applications. In addition, existing technologies manage the symbol library as an independent entity, neglecting the semantic relationship between the library and specific geological data. A symbol library detached from data lacks practical application value and cannot meet the data-driven requirement of achieving unified display based on current data during data production. This results in poor symbol library unification and fails to address the core pain points of geological data integration and mapping. Summary of the Invention

[0005] In view of this, this application provides a method and apparatus for unified management of geological symbol libraries based on geological data, so as to realize the fusion and management of multi-source geological symbol libraries.

[0006] Specifically, this application is implemented through the following technical solution:

[0007] The first aspect of this application provides a unified management method for a map symbol library based on geological data, the method comprising:

[0008] Based on the standard geological map basic symbol library, a standard symbol library for managing the standard geological map basic symbol data is generated;

[0009] Based on the standard format of the data to be stored in the standard geological map basic symbol library, extract the outline and text information of the sub-map information from the layer information of the geological data to be stored, and combine them to generate the sub-map information.

[0010] The visual similarity between the subgraph information and the subgraphs of the same type in the standard symbol library is calculated based on structural similarity.

[0011] Calculate the semantic matching degree between the standard symbol data in the standard symbol library and the geological data to be added to the library under each symbol of the same type;

[0012] The overall matching score is obtained by combining the visual similarity and the semantic matching score.

[0013] Based on the comprehensive matching degree, determine the subgraph matching information of the data to be entered into the database and locate the subgraph to be added in the standard symbol library;

[0014] Subgraph information is added based on the subgraph to be added, and the reference of the subgraph in the database is updated based on the subgraph matching information of the inbound data.

[0015] A second aspect of this application provides a unified management device for a map symbol library based on geological data. The device includes a construction module, a calculation module, and an update module; wherein...

[0016] The construction module is used to generate a standard symbol library for managing the basic symbol data of the standard geological map based on the standard geological map basic symbol library;

[0017] The calculation module is used to extract sub-map information outlines and text information from the layer information of the geological data to be entered into the database according to the standard format of the data to be entered into the database in the standard geological map basic symbol library, and combine them to generate sub-map information.

[0018] The calculation module is also used to calculate the visual similarity between the subgraph information and the subgraphs of the same type in the standard symbol library based on structural similarity;

[0019] The calculation module is also used to calculate the semantic matching degree between the standard symbol data in the standard symbol library and the geological data to be added to the library under each symbol of the same type;

[0020] The calculation module is also used to combine the visual similarity and the semantic matching degree to obtain a comprehensive matching degree;

[0021] The update module is used to determine the subgraph matching information of the data entering the database and locate the subgraph to be added in the standard symbol library based on the comprehensive matching degree;

[0022] The update module is also used to add subgraph information based on the subgraph to be added, and to update the reference of the subgraph in the database based on the subgraph matching information of the inbound data.

[0023] The unified management method and apparatus for map symbol libraries based on geological data provided in this application, upon receiving geological data to be added to the library, achieves dual matching of semantic and structural information through simultaneous comparison of legend data and sub-map data. This accurately locates the related sub-maps that need updating, synchronizes and updates information at the sub-map level, and automatically updates the symbol library based on the updated sub-maps. This achieves precise matching of synchronized objects while avoiding the complex operation of directly updating the symbol library, effectively improving the management efficiency of the map symbol library. Specifically, using a standard geological map basic symbol library as a benchmark and a standard symbol library as the management method, the standardization of symbols is ensured. By extracting the outline and text information of the sub-maps in the data to be added to the library, the completeness of data feature capture is ensured. Subsequently, a comprehensive matching degree is formed by combining visual similarity calculated based on structural similarity with the semantic matching degree of the information under the symbols. This avoids the problem of confusion between similar symbols in pure visual matching and solves the problem of mismatch caused by differences in semantic expression. Based on the comprehensive matching degree, the sub-maps to be updated are located and updated, and an updated symbol library is generated. This not only achieves dynamic supplementation of missing sub-maps and lossless expansion of the symbol library, but also effectively solves the problem of multiple sources of geological data... Figure 1 The fragmentation problem of the database, while ensuring the accuracy and consistency of data content expression, has improved the efficiency of geological data integration, spatial database construction and geological map publication. Attached Figure Description

[0024] Figure 1 A flowchart of an embodiment of the unified management method for map symbol libraries based on geological data provided in this application;

[0025] Figure 2 A schematic diagram of the second embodiment of the unified management device for the map symbol library based on geological data provided in this application. Detailed Implementation

[0026] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application.

[0027] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used herein are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any and all possible combinations of one or more of the associated listed items.

[0028] It should be understood that although the terms first, second, third, etc., may be used in this application to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, without departing from the scope of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to determination."

[0029] The following specific embodiments are given to illustrate the technical solution of this application in detail.

[0030] Figure 1 This is a flowchart of an embodiment of the unified management method for map symbol libraries based on geological data provided in this application. Please refer to... Figure 1 The method provided in this embodiment may include:

[0031] S101. Based on the standard geological map basic symbol library, generate a standard symbol library to manage the standard geological map basic symbol data.

[0032] Specifically, the core basis and data source for constructing the basic symbol library are determined; data source information is integrated to build a standard geological map base; based on the content of the basic symbol library, a corresponding standard symbol library is generated; and the management attributes of the standard symbol library are improved to ensure traceability and maintenance.

[0033] Furthermore, using the symbol library of the Digital Geological Survey System (DGSS) as the data source, and adhering to the definition and specifications of legends in the national standard GB / T 958-2015 "Regional Geological Map Legends", the sub-maps in the DGSS symbol library are classified and organized into three types: points, lines, and areas. Semantic information of the sub-maps is supplemented based on the national standard legend definitions, such as sub-map name, the type of geological element represented, and usage scenarios. A standard geological map basic symbol library framework is constructed, including basic sub-map information, graphic parameters, and semantic descriptions, forming a semantically complete and clearly defined basic symbol library structure. Based on the sub-map information in the basic symbol library, corresponding standard symbol libraries are generated. Each sub-map corresponds to a unique identifier in the symbol library, associated with its graphic file, color reference value, and other information. Furthermore, version management and update logs are added to the standard symbol library to record information such as the symbol library's construction time, updaters, and modifications, facilitating subsequent maintenance and traceability of the symbol library and ensuring that the standard symbol library can effectively manage the sub-map resources in the standard geological map basic symbol library.

[0034] Furthermore, the basic symbol library is generated based on the symbol library of existing geological map data. Its stored content includes original sub-map information (points, lines, filled areas), font library, and color library information. The standard symbol library integrates symbols defined in the 958 national standard and existing map sheets that are not in the national standard. This integration includes unique symbol identification, semantic standardization, merging of duplicate symbols, and removal of non-standard symbols, forming a new symbol library with standardized semantics, complete elements, and strong compatibility.

[0035] S102. Extract sub-map information outlines and text information from the layer information of the geological data to be entered into the database according to the standard format of the data to be entered into the database in the standard geological map basic symbol library, and combine them to generate sub-map information.

[0036] Specifically, the process involves constructing or reading project engineering files for geological data to be included in the database; loading layer data from the engineering files and associating them with the original symbol library; extracting submap information and contour-related parameters from the layer data; extracting submap text information from the layer data; and integrating the extracted contour and text information to generate submap information. Geological data to be included in the database refers to various types of geological data that are not yet included in the standard geological map basic symbol library for unified management and require symbol library matching, information verification, and format standardization before being integrated into the standardized geological data system.

[0037] Furthermore, a MAPX project engineering file is constructed, adding the geological data layers to be imported that require symbol library unification to the project. If an existing project engineering file already exists, it is directly read. After loading the layer data into the engineering file, the original symbol libraries corresponding to each layer are associated to ensure that the layer data can present its original display effect. The project file is then saved to ensure data stability. The feature layers contained in the project file are read, and all features under the simple feature class are queried. Graphical parameters are obtained by traversing the features: for point type submaps, SymID submap number and OutClr point color group are extracted; for line type submaps, LinStyID line type, LibID auxiliary line type number, and OutClr line color group are extracted; for zone type submaps, PatID fill pattern number, FillClr fill color, EndClr termination color, and PatClr pattern color are extracted. Simultaneously, based on the associated symbol library and the read PicType submap type and SymID submap number, the specified symbols are obtained, the submap is converted into an SVG vector graphic of a specified size, and then the SVG is deserialized and converted into a raster image and stored as a lossless compressed image file, thereby obtaining the visualization data of the submap information outline; through preprocessing of the layer-related images, legend outline recognition, text region positioning, and OCR text extraction, the text information of the submap, such as the submap name and geological attribute description, is obtained; using the submap number as the key, the extracted submap information outline-related parameters, such as the submap number, graphic file path, and color group information, are associated and stored with the text information, such as the submap name and geological attributes, in JSON format, and combined to generate complete submap information.

[0038] Furthermore, the specific steps for extracting the sub-image information contour include:

[0039] (1) Extract candidate contours from the layer information that conform to the features of the geological map legend;

[0040] Specifically, before extracting candidate contours, the layer information needs to be preprocessed to eliminate noise interference and enhance contour recognition. The image file associated with the geological data layer to be entered into the database is read, converted to OpenCV-compatible BGR format, loaded using the PIL library and converted to an RGB format NumPy array, and then format conversion is completed using cv2.cvtColor(image_rgb,cv2.COLOR_RGB2BGR) to ensure the image can be processed by subsequent OpenCV algorithms. The RGB format color image is converted to a grayscale image, and the CLAHE algorithm is used to enhance the contrast of the grayscale image. The enhanced grayscale image is output, highlighting the difference in brightness between the legend and the background, making the contours easier to identify.

[0041] Furthermore, based on the rectangular border feature commonly found in geological map legends, the contours in the layer information are filtered. First, border recognition is performed using grayscale values ​​to determine the contour boundary positions. Based on the determined border and contour boundary positions, contours are extracted. Candidate contours are determined from the extracted contours using a custom rectangular contour detection function. Preferably, the candidate contours are rectangular in shape.

[0042] (2) Calculate multiple geometric features of each candidate contour, and select the target contour from the candidate contours as the sub-image information contour by combining the geometric features.

[0043] Specifically, geometric features include at least contour area, aspect ratio, compactness, convexity, and rectangularity. Contours with excessively small or large areas are excluded by calculating their actual area. The minimum bounding rectangle of the contour is obtained, along with its top-left corner coordinates, width, and height. The aspect ratio of the contour is calculated based on these coordinates, and candidate contours with aspect ratios exceeding a preset threshold are deleted. The closed perimeter of the candidate contours is calculated, and the corresponding compactness is then calculated. The convex hull of the candidate contours is obtained, and its area is calculated. Rectangularity is calculated based on the width and height. Geological map legend contours have a very high degree of overlap with their bounding rectangles, typically resulting in a rectangularity greater than 0.9. Contours with a rectangularity lower than 0.8, or significant differences between the contour and its bounding rectangle, may be image noise and are therefore excluded.

[0044] Furthermore, based on the calculated geometric features, combined with the actual morphological characteristics of the geological map legend and the project data test results, a set screening threshold is determined, and candidate contours are screened in multiple rounds to obtain sub-map information contours. Contours with area and aspect ratio exceeding the preset range are excluded, and obviously non-legendary noisy contours are quickly removed. For the screened candidate contours, compactness, convexity, and rectangularity are verified, and only contours that meet all three indicators are retained to complete the extraction of sub-map information contours.

[0045] By first extracting candidate contours that conform to the characteristics of geological map legends, and then filtering target contours based on geometric features, accurate positioning and efficient filtering of sub-map information contours in geological data to be entered into the database can be achieved. On the one hand, the preliminary extraction of candidate contours through image preprocessing and multi-algorithm combination can comprehensively cover geological map legends of different colors and resolutions, effectively avoiding contour omissions caused by legends that are too light in color or have incomplete borders, and ensuring the integrity of candidate contours. On the other hand, by calculating basic geometric features and performing multi-indicator joint filtering, image noise and non-legendary contours can be accurately eliminated. At the same time, the validity of the legend can be further confirmed by combining position verification, ultimately ensuring the accuracy of sub-map information contour extraction. This provides an accurate regional positioning basis for subsequent sub-map visual similarity calculation and semantic information extraction, avoiding symbol matching errors caused by contour deviations.

[0046] Furthermore, the specific steps for extracting text information include:

[0047] (1) Determine the target region based on the outline of the sub-graph information;

[0048] Specifically, the geometric parameters corresponding to the extracted sub-map information contour are read to obtain the coordinate data of the smallest bounding rectangle of the contour, including the coordinates of the top left vertex, the width and height of the rectangle, thus clarifying the spatial location range of the sub-map information contour in the image. Combining the conventional layout characteristics of geological map legends, the legend graphics and explanatory text are mostly arranged with the graphics on the left and the text on the right, or the graphics on top and the text on the bottom, and the spacing between them is usually between 5 and 20 pixels, thus determining the search benchmark for the text association area. Taking any boundary of the sub-map information contour as the starting line, it extends to the right by N pixels. The extension range can be adjusted according to the length of the legend text. At the same time, the top and bottom of the contour are used as the upper and lower boundaries to form the initial text search target area, ensuring that the target area can completely cover the explanatory text corresponding to the legend, avoiding omissions due to text position offset.

[0049] Furthermore, considering that actual geological maps may have dense legend arrangements and text wrapping, the initial target area needs to be optimized. If the adjacent contours on the right side of the initial target area are too close, with a spacing of less than 5 pixels, the right-side expansion range should be reduced accordingly to avoid interference from cross-legend text. If a significant horizontal pixel blank band is detected in the initial target area, and the pixel value within the blank band is continuously above 20 and is the background color, the blank band is used as a text wrapping separator to divide it into multiple sub-target areas. Each sub-target area corresponds to a different line of text, ensuring that each target area contains only continuous text content, thus reducing interference for subsequent pixel density detection and text recognition.

[0050] (2) Pixel density detection is performed on the target region to determine candidate text regions;

[0051] Specifically, the image fragments corresponding to the target area are converted from their original format to grayscale images to eliminate color interference; binarization processing is performed, and a threshold is set. Pixels above the threshold are judged as white background, and pixels below the threshold are judged as black text. By processing to highlight the difference between text pixels and background pixels, the text area appears as a continuous black pixel block, and the background appears as white, providing a clear image basis for pixel density calculation.

[0052] Next, pixel density detection is performed on the remaining candidate contours after screening to verify their association with the text region. The detection area is the right side of the contour and the area above and below it within 50 pixels. The pixel density of this area is calculated. The pixel density of the text region is higher than that of the background. If a continuous text region is detected, it means that the contour is a complete geological map legend with explanatory text and can be identified as the target contour. If no text region is detected, it is further confirmed by combining the semantic information of the layer to avoid mis-screening of valid legends. The final selected target contours are classified according to the sub-map type, that is, the legends corresponding to points, lines and areas. The coordinate range, the size of the bounding rectangle and the position of the associated text region of each contour are recorded as the basis for subsequent extraction of sub-map graphic parameters and semantic information.

[0053] Furthermore, the pixel density within the target area is calculated using a sliding window combined with density statistics. A sliding window of a preset pixel size is created, its size adapting to the character size of typical geological map legend text, such as font sizes 5 to 12. The window slides within the preprocessed target area in 5-pixel increments to ensure it covers all pixels. Density statistics are then performed: for each sliding window, the number of black pixels (text pixels) within the window is counted, and the window density value is calculated based on the ratio of black pixels to the total number of pixels in the window (100 pixels). Candidate regions are then identified by setting a pixel density threshold. If pixels within a window are above the threshold and thus considered text pixels, the area containing that window is considered a text region. Multiple consecutive sliding windows with density values ​​above the threshold are merged to form a connected, densely populated region. The bounding rectangle of this region is then obtained, representing the candidate text region. Simultaneously, isolated windows with density values ​​below the threshold are removed; these are considered background noise or redundant pixels, ensuring that candidate text regions contain only valid text content and reducing unnecessary calculations in subsequent text recognition.

[0054] (3) Use the OCR recognition algorithm to extract the text information of the candidate text region.

[0055] Specifically, OCR recognition algorithms are a technique used to convert text information in images into editable and storable text formats. They simulate the human visual process of recognizing text, using computer algorithms to automatically detect, segment, and recognize printed or handwritten text. The process involves normalizing the size of all candidate text regions; then removing noise by using median filtering to eliminate isolated noise points, such as salt-and-pepper noise, within the candidate text regions; and finally, using morphological opening operations to repair broken edges of text characters, such as missing strokes, resulting in clearer text outlines.

[0056] Furthermore, a concurrent service is built, using multi-threading technology to construct a concurrent OCR recognition service. Images of multiple candidate text regions are batched and distributed to different threads, simultaneously calling the OCR recognition interface. Recognition parameters are set to support both Chinese and English, as geological texts often contain both Chinese and English terminology. The recognition mode is set to single-line text, adapting to the legend text layout, and the output format is set to plain text to improve batch text extraction efficiency. Recognition results are verified and corrected using a geological terminology database containing common geological terms such as porphyritic, basaltic, and stratigraphic codes. Redundant characters unrelated to geological semantics, such as garbled characters and special symbols, are removed. For texts with significant recognition errors, such as misidentifying geological symbols as ordinary letters, fuzzy terminology matching is used for correction. The edit distance between the recognized text and keywords in the terminology database is calculated; when the distance is less than a preset value, the recognized text is replaced with the correct term. Finally, the text information is associated and stored. The corrected text information, such as sub-map name and geological attributes, is bound to the corresponding sub-map information outline and stored in the source symbol information file in JSON format with the sub-map number as the unique identifier. This provides accurate textual basis for subsequent semantic matching with standard legend data.

[0057] By identifying the target area based on the spatial location of the sub-map information outline and the layout features of the geological map legend, the text search range can be accurately locked, avoiding invalid searches of other irrelevant areas of the image and ensuring targeted text extraction. Pixel density detection is used to filter candidate text areas, effectively distinguishing text pixels from background noise and eliminating isolated interfering pixels, ensuring that candidate areas contain only valid text content and reducing interference for subsequent recognition. Combined with an OCR recognition algorithm adapted to the characteristics of geological text, it can efficiently convert image-based legend text into structured text, and reduce errors in recognizing mixed Chinese and English text and geological symbols through geological terminology adaptation and post-processing correction. Ultimately, high-quality extraction of sub-map text information is achieved, providing accurate and usable textual basis for subsequent semantic matching between sub-maps and standard symbol libraries, thus contributing to improved accuracy of semantic dimension matching in the unified process of geological data symbol libraries.

[0058] S103. Calculate the visual similarity between the subgraph information and the subgraphs of the same type in the standard symbol library based on structural similarity.

[0059] Specifically, the image formats and specifications of the sub-images to be matched and the standard sub-images are standardized; the core parameters of the structural similarity calculation algorithm are determined; visual similarity is calculated according to the sub-image type; and the original visual similarity results are non-linearly adjusted.

[0060] Furthermore, visual similarity can be calculated using the SSIM algorithm, in which image similarity data consists of three parts: brightness, contrast, and structure.

[0061] set up , These are the image data to be compared. For image The pixel value, For image The pixel value;

[0062] Brightness is measured by average grayscale value, which are respectively , This is obtained by averaging the values ​​of all pixels:

[0063] ;

[0064] ;

[0065] in, The average grayscale value of the sub-image information;

[0066] The average gray value of the sub-image in the standard symbol library;

[0067] Total number of pixels;

[0068] For the subgraph information Each pixel value;

[0069] For the subgraph in the standard symbol library Each pixel value.

[0070] Image brightness contrast function:

[0071] ;

[0072] in, The average grayscale value of the sub-image information;

[0073] The average gray value of the sub-image in the standard symbol library;

[0074] It is a constant.

[0075] Contrast ratio is measured by the standard deviation of gray levels. , Unbiased estimate of standard deviation:

[0076] ;

[0077] ;

[0078] in, The grayscale standard deviation of the sub-image information;

[0079] The grayscale standard deviation of the sub-image in the standard symbol library;

[0080] The average grayscale value of the sub-image information;

[0081] The average gray value of the sub-image in the standard symbol library;

[0082] Total number of pixels;

[0083] For the subgraph information Each pixel value;

[0084] For the subgraph in the standard symbol library Each pixel value.

[0085] Image contrast function:

[0086] ;

[0087] in, The grayscale standard deviation of the sub-image information;

[0088] The grayscale standard deviation of the sub-image in the standard symbol library;

[0089] It is a constant.

[0090] The structural comparison is based on the normalized values. and The comparison can be measured by the correlation coefficient:

[0091] ;

[0092] ;

[0093] in, This represents the covariance between subgraph information and subgraphs in the standard symbol library. , , Constant avoidance When the value is close to 0, it is unstable and can be adjusted according to the characteristics of the data. , , Commonly used =0.01, =0.03, Pixel dynamic value range ;

[0094] Finally, the formula for calculating SSIM is:

[0095] ;

[0096] If let If the value is 1, then the commonly used SSIM calculation formula is obtained:

[0097] ;

[0098] in, The grayscale standard deviation of the sub-image information;

[0099] The grayscale standard deviation of the sub-image in the standard symbol library;

[0100] The average grayscale value of the sub-image information;

[0101] The average gray value of the sub-image in the standard symbol library;

[0102] It is a constant;

[0103] It is a constant.

[0104] Furthermore, the raster images in the geological data submaps to be added to the database are uniformly adjusted to the same size as the raster images of the same type of submaps in the standard symbol library to ensure consistency in pixel count, resolution, and other specifications, avoiding the impact of image specification differences on similarity calculation results. Secondly, the core parameters of the SSIM (Structural Similarity) algorithm are determined; these parameters are used to avoid instability during brightness and contrast calculations. Similarity is calculated according to submap type. For point-type submaps, the submap to be matched is compared with all point-type standard submaps in the standard symbol library one by one using the SSIM algorithm to calculate similarity; the same applies to line-type and area-type submaps, obtaining linResults (line symbol similarity data), pntResults (point symbol similarity data), and regResults (area symbol similarity data), respectively.

[0105] Furthermore, the specific implementation steps include:

[0106] (1) Convert the sub-graph in the sub-graph information into a raster sub-graph;

[0107] Specifically, from the sub-map information of the geological data to be entered into the database, the original graphic data corresponding to the sub-map is extracted. The original graphic data may be in vector format and needs to be converted into a unified raster image format first. Based on the sub-map type and sub-map identifier recorded in the sub-map information, a dedicated method provided by the symbol conversion tool is called to standardize the sub-map according to a preset fixed size, such as uniformly converting it into a 100×100 pixel image. This ensures that different sub-maps have the same size benchmark when compared later, avoiding the impact of size differences on the similarity calculation results. During the conversion process, the original line style, fill pattern, color information and other core visual features of the sub-map are preserved without loss. Finally, a raster sub-map that can be used for pixel-level comparison is generated, laying the foundation for subsequent visual feature comparison with the standard sub-map.

[0108] (2) Determine the type of the subgraph information, filter target subgraphs of the same type from the standard symbol library, and convert the target subgraphs into target raster subgraphs;

[0109] Specifically, analyze the type attributes of the sub-map information to be processed, and determine which category it belongs to: point, line, or area. For example, the type can be determined by the graphic parameters recorded in the sub-map information, such as point color group, line type, and fill color. Sub-maps containing point color groups are point type sub-maps, those containing line types are line type sub-maps, and those containing fill colors are area type sub-maps. Based on the determined sub-map type, filter in the standard symbol library and extract only the standard sub-maps that are consistent with the type of the sub-map to be processed as target sub-maps to eliminate interference from sub-maps of different types and ensure that subsequent comparisons are made between sub-maps of the same type, which conforms to the logic of geological symbol classification and matching. Using the same conversion method and size standard as in step (1), the selected target sub-maps are also converted into raster image format to generate target raster sub-maps, ensuring that the raster images of the sub-map to be processed and the target sub-maps are completely consistent in format and size, and meet the conditions for subsequent pixel-level calculations.

[0110] (3) Calculate the covariance and standard deviation between the raster sub-image and the target raster sub-image;

[0111] Specifically, the raster sub-image and the target raster sub-image are preprocessed by converting both into grayscale images to eliminate the interference of color information on pixel value calculation, so that each pixel is represented only by a grayscale value (0-255). All pixels in the two grayscale images are traversed to obtain the grayscale value of each corresponding pixel. Assume that the set of pixel grayscale values ​​of the raster sub-image is x, the set of pixel grayscale values ​​of the target raster sub-image is y, and the total number of pixels is N.

[0112] Furthermore, the average values ​​of the pixel grayscale values ​​of the two images are calculated separately. The average value of the raster sub-image is obtained by summing the pixel values ​​in the set of all pixel grayscale values ​​and dividing by the total number of pixels. The average value of the target raster sub-image is obtained by summing the pixel values ​​in the set of all pixel grayscale values ​​and dividing by the total number of pixels. According to the standard deviation formula, the standard deviation of the raster sub-image and the standard deviation of the target raster sub-image are calculated separately.

[0113] Furthermore, based on the covariance formula, the covariance between the pixel grayscale values ​​of the raster sub-image and the target raster sub-image is calculated to measure the correlation between the pixel change trends of the two and reflect the degree of structural similarity of the images.

[0114] (4) Calculate the structural comparison value based on the ratio of the covariance and the standard deviation, and use it as the visual similarity.

[0115] Specifically, constant parameters are introduced to optimize the calculation process. The value of the constant is determined based on the dynamic range of pixel values. The constant is introduced to avoid unstable calculation results when the covariance or standard deviation is too small, thus ensuring the validity of the structural comparison values.

[0116] Furthermore, the sum of the covariance and the constant is used as the numerator, and the sum of the product of the standard deviation of the raster sub-image and the standard deviation of the target raster sub-image and the constant is used as the denominator to calculate the structural comparison value. The structural comparison value can comprehensively reflect the similarity between the raster sub-image and the target raster sub-image in terms of pixel distribution trends and structural features. Therefore, it is used as the visual similarity between sub-images, providing key visual dimension data support for subsequent comprehensive matching degree calculation, and helping to accurately determine the visual matching degree between the sub-image to be added to the database and the standard sub-image.

[0117] Sub-images are uniformly converted into raster sub-images, and combined with fixed-size standardization, eliminating the interference of original format and size differences of sub-images on subsequent comparisons, and laying a unified benchmark for pixel-level calculations. Target sub-images of the same type in the standard library are selected according to sub-image type and simultaneously converted to raster format, ensuring that visual comparisons are only performed between sub-images of the same type (points, lines, areas), conforming to the logic of geological symbol classification and matching, and avoiding misjudgments caused by cross-type comparisons. Through grayscale preprocessing and pixel-level covariance and standard deviation calculations, the contrast characteristics of each of the two sub-images can be quantified, and the correlation of pixel change trends can be captured, comprehensively extracting key features for visual matching. The introduction of constant optimization in the calculation of structural comparison values ​​effectively avoids the instability of results caused by extreme values. The final structural comparison values ​​can accurately reflect the visual similarity between sub-images, providing a reliable visual dimension basis for subsequent comprehensive matching degree calculations.

[0118] S104. Calculate the semantic matching degree between the standard legend data in the standard symbol library and the geological data to be added to the library under each symbol.

[0119] Specifically, the standard semantic information of the standard sub-maps in the standard symbol library is extracted; the semantic text information of the geological data sub-maps to be included in the database is extracted; the two types of semantic information are preprocessed for standardization; and the semantic matching degree is calculated according to preset rules and the results are recorded.

[0120] Furthermore, standard legend data corresponding to each standard sub-map is retrieved from the standard symbol library, and the standard semantic information specific to that sub-map is extracted, including the sub-map's name, type definition, and core attribute keywords. A standard semantic database is constructed to provide a benchmark for semantic matching. Sub-map information stored in JSON format from the geological data to be entered into the database is retrieved, and the semantic text information corresponding to each sub-map to be matched is extracted using the sub-map number as the association identifier. This includes the associated title, type description, and attribute description of the sub-map to be matched. The standard semantic information and the semantic text information of the sub-maps to be matched are standardized, removing punctuation marks, redundant modifiers, and meaningless placeholder characters. Terminology is also standardized, such as unifying "porphyritic structure" as "porphyritic structure," ensuring consistency in the expression of the two types of semantic information and reducing matching errors. The semantic matching degree is calculated according to two-level semantic matching rules: if the semantic text of the sub-map to be matched matches the standard legend... If the semantic information of the data is completely consistent, such as if the name of the sub-map to be matched is "porphyritic granite sub-map" and the name of the standard sub-map is also "porphyritic granite sub-map", then the semantic matching degree is assigned a value of 0.4. If the semantic text of the sub-map to be matched and the semantic information of the standard legend data have a mutual inclusion relationship, such as if the name of the sub-map to be matched is "porphyritic rock sub-map" and the name of the standard sub-map is "porphyritic granite sub-map", and the former contains the core keywords of the latter, or vice versa, then the semantic matching degree is assigned a value of 0.2. If neither of the above two matching relationships is satisfied, then the semantic matching degree is assigned a value of 0, and the semantic matching results of each sub-map to be matched and the corresponding standard sub-map are recorded to form a semantic matching degree comparison table.

[0121] Furthermore, the specific implementation steps include:

[0122] (1) Extract the structured information of the standard symbol data under each symbol;

[0123] Specifically, locate the storage path and format of the standard symbol data; filter the target source symbols by subgraph type; and extract fields from the JSON information.

[0124] Furthermore, a standard symbol dataset of the same type as the data to be imported is retrieved from the standard symbol library. The dataset is stored in JSON format, categorized by submap type. Source symbols of the corresponding category are filtered according to the submap type of the data to be imported to ensure accurate extraction range. For each selected source symbol, its JSON structure is parsed to extract fields containing at least a name and a type. The name is the geological element identifier of the standard submap, and the type is the submap geometric classification.

[0125] (2) Extract the semantic text information of the geological data to be stored;

[0126] Specifically, the process involves loading engineering files and associated symbol libraries of geological data to be imported; extracting semantically relevant text by sub-map number; and integrating multi-dimensional semantic information.

[0127] Furthermore, the MAPX project engineering file for the geological data to be imported is constructed or read, layer data is loaded and associated with the original symbol library to ensure that the data fully presents its original attributes. Using the sub-map number as a unique association identifier, the point, line, and area elements in the layers are traversed to extract semantic text information related to the sub-map, including sub-map association titles, geological attribute descriptions, type annotations, and other multi-dimensional content. The extracted scattered text information is then integrated to form a semantic text set corresponding to each sub-map.

[0128] (3) Calculate the semantic relationship between each structured information and the corresponding part of the semantic text information, and calculate the semantic matching degree based on the semantic relationship.

[0129] Specifically, a clear two-level semantic matching rule is established to adapt to the semantic representation characteristics of geological data. The first level is exact matching, meaning the semantic text of the geological data to be included in the database is completely identical to the name in the JSON information of the standard source symbol. The second level is inclusion matching, meaning the names of the two data have a mutual inclusion relationship (e.g., the standard name is porphyritic granite, and the semantic text name to be included in the database is porphyritic rock, or vice versa). The semantic text information of each sub-map to be included in the database is compared with the JSON information of the corresponding standard source symbol. If the exact matching rule is met, the semantic matching degree is assigned a value of 0.4; if the inclusion matching rule is met, the semantic matching degree is assigned a value of 0.2; if neither of the above two rules is met, the semantic matching degree is assigned a value of 0. Using the sub-map number as an index, a semantic matching degree lookup table is generated. The table records the JSON information of the standard source symbol (name, type), the semantic text information of the sub-map to be included in the database, the matching rule type, and the corresponding semantic matching degree, providing accurate semantic dimension data support for subsequent comprehensive matching degree calculation.

[0130] By extracting the names and types from the standard source symbol JSON information and combining this with standardized processing to unify the expression, an authoritative and consistent benchmark is provided for semantic matching. Multi-dimensional semantic text is integrated around the sub-map number of the data to be imported, and invalid content is cleaned up to ensure that the semantic information focuses on core geological attributes and avoids redundant interference. The third step quantifies semantic relationships through two-level matching rules, accurately distinguishing between three semantic states: completely consistent, partially related, and unrelated, and assigning matching scores accordingly. This approach not only aligns with the characteristics of geological terminology but also effectively avoids misjudgments caused by differences in semantic expression. This achieves efficient semantic integration between the standard and the data to be imported, providing a reliable semantic basis for subsequent comprehensive matching score calculations, helping to improve the overall accuracy of geological symbol matching, and laying the foundation for the semantic unification and integration of multi-source geological data.

[0131] S105. Combine the visual similarity and semantic matching to obtain the comprehensive matching degree.

[0132] Specifically, the weighting ratio of visual similarity and semantic matching is determined; the comprehensive matching degree of each sub-image is calculated according to the weighting formula; the comprehensive matching degree results are numerically normalized; and a comprehensive matching degree comparison table is established and associated with relevant information.

[0133] Furthermore, the weight allocation is determined based on the core attribute characteristics of geological symbols and the needs of practical application scenarios. The visual form of geological symbols is the core identifier that distinguishes different symbols, directly determining the uniqueness of the symbol's recognition on the map. It has a higher priority in affecting matching accuracy, hence the higher weight of visual similarity. Semantic information, on the other hand, is a supplementary explanation of visual symbols, used to correct mismatches of visually similar but semantically different symbols. It belongs to the auxiliary matching dimension, therefore the weight of semantic matching degree is less than that of visual similarity. Weighted calculations are performed using pre-set ratios, and the calculated comprehensive matching degree is numerically normalized to ensure that the result is within the range of 0-1. If the calculation result is below 0, such as a negative value due to calculation errors caused by abnormal data, it is corrected to 0 according to the boundary value. If the calculation result is above 1.0, such as in extreme cases where visual similarity is adjusted to 1.0 and semantic matching degree is 0.4, 1.0 × 0.6 + 0.4 = 1.0 is calculated, requiring no correction. If it exceeds 1.0, it is corrected to 1.0 to ensure the rationality and comparability of the comprehensive matching degree result. A comprehensive matching degree comparison table is established, which records the sub-image number to be matched, the corresponding standard sub-image number, the visual similarity (after adjustment), the semantic matching degree, and the final comprehensive matching degree. For example, the sub-image number to be matched is 208, the corresponding standard sub-image number is 127, the adjusted visual similarity is 1.0, the semantic matching degree is 0.4, and the comprehensive matching degree is 1.0, which provides clear and accurate data support for the subsequent location of the sub-image to be updated.

[0134] Furthermore, the specific implementation steps include:

[0135] (1) Determine the first weight of visual similarity and the second weight of semantic matching degree based on the attribute characteristics of geological symbols;

[0136] Specifically, by analyzing the core attribute characteristics of geological symbols, the differences in the roles of visual morphology and semantic information in symbol recognition are clarified. As a visual carrier of geological elements, the visual morphology of geological symbols is the primary identifier distinguishing different symbols, directly determining the uniqueness of a symbol on the map. For example, the difference between a porphyritic granite dot symbol and a basalt dot symbol lies in their visual outline. Relying solely on semantic descriptions can easily lead to confusion due to differences in expression; therefore, visual similarity has a higher priority in influencing matching accuracy. Semantic information, on the other hand, supplements the visual morphology and is mainly used to correct special cases where visual similarities exist but semantic differences are not. For instance, two line symbols may both appear as solid lines, but their semantic meanings are stratigraphic contact lines and fault lines, respectively. This is an auxiliary matching dimension, and its impact on the matching result is lower than that of visual similarity.

[0137] Optionally, typical sub-map samples from 1:250,000 regional geological maps were selected, covering 200 sets of sub-maps in three categories: points, lines, and areas, to test the matching accuracy under different weight combinations. When the visual similarity weight is higher than the semantic matching weight, it can effectively reduce mismatches caused by non-standard semantic descriptions in historical data; if the semantic weight is too high, it will ignore effective matching cases that are visually highly similar but have subtle differences in semantic description. After multiple rounds of parameter optimization and demonstration by geological experts, the first weight of visual similarity was finally determined to be 0.6, and the second weight of semantic matching was determined to be 0.4. This ensures the dominant role of the core visual features and avoids the problem of confusion between similar symbols in pure visual matching through the auxiliary constraints of semantic information, which fully meets the business requirements of a unified geological data symbol library.

[0138] (2) The visual similarity and semantic matching degree are weighted and summed based on the first weight and the second weight to obtain the comprehensive matching degree.

[0139] Specifically, the weighted sum can be calculated using the following formula:

[0140] ;

[0141] in, For overall matching degree;

[0142] It is the first weight;

[0143] For semantic matching degree;

[0144] Visual similarity;

[0145] It is the second weight.

[0146] S106. Based on the comprehensive matching degree, determine the matching information of the subgraphs in the database and locate the subgraphs to be added in the standard symbol library.

[0147] Specifically, the process involves setting a classification threshold for overall matching degree; determining the matching status of subgraphs to be added based on the classification threshold; organizing the structured subgraph matching information of the data to be added; filtering out subgraphs with no matching items and locating subgraphs to be added; and recording the core information of the subgraphs to be added to provide a basis for the addition operation.

[0148] Furthermore, a comprehensive matching degree threshold range is set to achieve subgraph classification, with a comprehensive matching degree ≥ 95%. Subgraphs are categorized into several types based on their matching criteria. A perfect match (no additional confirmation required) is defined as a subgraph matching with a comprehensive matching degree of 80% to 95% (requiring manual verification of accuracy) and a non-match (requiring further retrieval or being considered missing). Based on the comprehensive matching degree comparison table, subgraphs corresponding to matches to be confirmed and non-matches are selected, forming a multi-dimensional candidate set of subgraphs to be updated. This set includes subgraph number, subgraph type, original image, standard subgraph matching candidates, comprehensive matching degree, and semantic association information. Processing strategies are developed for different subgraph categories. For subgraphs to be confirmed, a manual interactive confirmation interface is provided, displaying the original subgraph information, standard library matching candidates, and matching criteria (visual similarity details, semantic matching keywords). Each subgraph is manually checked; if correct, it is marked as a valid association and designated as a subgraph to be updated. If an error is found, the subgraph search function retrieves subgraphs of the same type from the standard symbol library, a new target subgraph is selected and associated, the comprehensive matching degree is updated, and it is marked as a manually associated subgraph to be added to the list of subgraphs to be updated. For unmatched subgraphs, the search function is first used to expand the search scope in the standard symbol library based on the semantic keywords and graphic features of the subgraph. If a suitable subgraph is found, the association is manually established and marked as a supplementary association. If no corresponding subgraph is found after the search, it is determined to be a missing subgraph and marked as to be added.

[0149] Furthermore, for missing submaps marked as to be added, their metadata information is first collected, covering key information such as submap name, data source, project, adding unit, and adding personnel. This information is then entered according to the format specifications of submap information in the standard symbol library and submitted to the standard symbol library extension queue, completing the dynamic creation of the missing submap in the standard symbol library. The newly created submap is automatically assigned a unique submap number and associated with corresponding semantic information, such as geological element type and usage scenario, forming a new standard submap in the standard symbol library. Simultaneously, a unique matching association is established between this new standard submap and the original missing submap, directly incorporating it into the set of submaps to be updated. The original missing submap is no longer retained as an unmatched state; instead, through forced matching with the newly created standard submap, it is ensured that each submap to be added ultimately corresponds to a standard submap in the standard symbol library—either an existing successfully matched submap or a newly created submap. This achieves matching association for all submaps to be added, laying a complete data foundation for subsequent unified symbol library replacement based on the submaps to be updated. All subgraphs are compiled to form a final list of subgraphs to be updated. The list clearly defines the original identifier, target subgraph identifier, association method, and update priority of each subgraph to be updated, providing a clear basis for subsequent subgraph updates.

[0150] Furthermore, the specific implementation steps include:

[0151] (1) Classify the subgraphs based on the comprehensive matching degree, wherein the categories of the subgraphs include at least subgraphs to be confirmed as matched and unmatched subgraphs;

[0152] Specifically, based on the actual needs of geological data symbol matching, a classification threshold for comprehensive matching degree is set. Referring to historical test data of symbol matching for 1:250,000 regional geological maps, sub-maps with a comprehensive matching degree ≥ 95% are classified as precisely matched sub-maps. These sub-maps have a high degree of visual and semantic fit and can be directly associated with standard sub-maps without additional processing. Sub-maps with a comprehensive matching degree of 80% ≤ comprehensive matching degree < 95% are classified as sub-maps to be confirmed as matched. These sub-maps may have visual similarities but subtle differences in semantic expression, or semantic consistency but slight differences in visual details, requiring further manual verification. Sub-maps with a comprehensive matching degree < 80% are classified as unmatched sub-maps. These sub-maps have a low degree of matching with existing standard sub-maps and require further retrieval or classification as missing.

[0153] The system iterates through the comprehensive matching score data of all subgraphs to be added to the database, classifies them according to the aforementioned thresholds, and automatically compares the comprehensive matching score of each subgraph with the classification threshold to generate a classification list. The list clearly indicates the subgraph's ID, corresponding standard subgraph ID, comprehensive matching score, and category. Simultaneously, the classification results are synchronized to a visual interactive interface, using different colors to label each type of subgraph (e.g., green for precisely matched subgraphs, yellow for subgraphs awaiting confirmation of a match, and red for unmatched subgraphs). This facilitates subsequent manual operations and data management, ensuring a clear and traceable classification process and laying the foundation for subsequent targeted processing.

[0154] (2) For the subgraphs to be confirmed, the matching relationship is manually verified. Subgraphs to be confirmed that have the correct verification result are marked as valid associations, and subgraphs to be confirmed that have the incorrect verification result are re-matched.

[0155] Specifically, sub-maps to be matched are grouped and displayed according to their type. Each group presents the raster image and semantic text information of the sub-map to be added to the database, along with the raster image and standard semantic information of its corresponding standard sub-map. The overall matching degree of the two is also labeled, including specific numerical values ​​for visual similarity and semantic matching, providing a complete basis for manual verification. Verifiers examine each group of sub-maps one by one to determine whether the two correspond to the same geological element. If the visual features and semantic connotations of the sub-map to be added to the database are consistent with those of the standard sub-map, the verification result is considered correct; if there are significant differences, the verification result is considered incorrect.

[0156] Furthermore, for sub-maps with correct verification results, they are marked as valid associations, and the association status is updated in the matching association table. At the same time, the name of the verifier and the verification time are recorded to form a traceable verification log. For sub-maps with incorrect verification results, the re-matching process is initiated, returning to the sub-map type filtering stage. The search scope of standard sub-maps of the same type is expanded again (e.g., if only standard sub-maps under a certain geological classification were originally searched, the search scope is expanded to all standard sub-maps of the same geometric shape during re-matching). The comprehensive matching degree between the sub-map to be added to the database and the newly filtered standard sub-maps is recalculated to generate a new matching candidate list for manual secondary verification until a correctly matching standard sub-map is found or it is confirmed that there is no matching sub-map.

[0157] (3) For the unmatched subgraph, expand the matching range and continue searching. When the search result is none, determine that the unmatched subgraph is a missing subgraph;

[0158] Specifically, the search criteria for standard sub-images have been adjusted, breaking the original search restrictions. Only conditions with the same geometric type are retained, while the semantic matching restrictions have been relaxed. Previously, the requirement was that the names be completely identical or that the core keywords be included. Now, the search only requires semantic relevance (e.g., if the semantics of the sub-image to be added to the database is volcanic clastic rock area, both volcanic rock areas and clastic rock areas in the standard sub-images are included in the search scope). The search dimensions for standard sub-images have been increased. In addition to the conventional visual similarity and semantic matching, application scenario tags for sub-images are added as a search basis. If the application scenario of the sub-image to be added to the database is similar to that of a certain standard sub-image... Figure 1 Even if the overall match is slightly lower, it will still be included in the candidate list.

[0159] Furthermore, after re-searching the standard symbol library according to the new search strategy, a search results report is generated. If the report contains at least one candidate standard sub-map, the comprehensive matching degree between the sub-map to be added to the library and the candidate sub-map is recalculated and processed through a manual verification process. If the report shows no candidate standard sub-map, further confirmation is made in conjunction with the evaluation of geological experts. Experts in the field of geology are invited to analyze the visual features and semantic information of the unmatched sub-map to determine whether it is a new geological symbol not covered by the existing standards. If it is confirmed that there is no corresponding standard sub-map, the unmatched sub-map is officially determined to be a missing sub-map, and its missing status is marked in the system. At the same time, the basis for the missing determination and the expert opinions are recorded.

[0160] (4) Collect the metadata information of the missing subgraph, enter it in the standard format and submit it to the standard symbol library to complete the addition of the missing subgraph, and associate the added missing subgraph with the standard symbol library.

[0161] Specifically, a standardized metadata information collection form is designed. The form content covers the core attributes of the missing submap, including basic information (submap name, submap type, geometric shape description), visual parameters (raster image file, color parameters (such as point color group, line color group, area fill color), size specifications), semantic information (corresponding geological element name, geological attribute description, application scenario, data source), and management information (collection personnel, collection time, review personnel). The metadata of the missing submap is collected one by one according to the form requirements.

[0162] Furthermore, following the format requirements of the standard symbol library, the collected metadata information is entered into the database. New records are added to the sub-map data table of the standard symbol library, and the information in the metadata form is filled in. Simultaneously, the raster image files of the missing sub-maps are stored in the image resource directory of the standard symbol library according to the naming rule of sub-map type + sub-map number, and the image file paths are associated in the data table. A unique standard sub-map number is assigned to each new sub-map, completing the addition of the missing sub-map to the standard symbol library. In the association data table of the standard symbol library, a new association record is added. The association fields include the newly assigned standard sub-map number, the symbol library category (e.g., structural geological symbols, lithostratigraphic symbols), and symbol call interface parameters (for subsequent geological data production software). At the same time, the version information of the standard symbol library is updated (e.g., version number upgrade, record update time, and update content) to ensure that the added missing sub-maps can be effectively managed by the standard symbol library. Subsequent sub-maps to be added to the library can be directly matched with this newly added sub-map, achieving synchronous updates and collaborative applications between standard symbol libraries.

[0163] By classifying subgraphs into categories such as pending confirmation and unmatched based on comprehensive matching scores, we can quickly identify subgraphs that require priority processing, avoiding indiscriminate processing that leads to wasted efficiency and providing clear direction for subsequent operations. For subgraphs requiring confirmation, manual verification and correction of erroneous matches not only leverages human expertise to compensate for the algorithm's shortcomings in recognizing complex semantics and detailed visual differences, but also ensures accurate subgraph association through re-matching. Expanding the search scope for unmatched subgraphs before determining their absence reduces misjudgments caused by search limitations and ensures the rigor of missing subgraph determination. Standardized collection of missing subgraph metadata and its integration into the database and symbol library not only enables dynamic expansion of the standard symbol library, filling gaps in existing standard symbols, but also ensures that newly added subgraphs can be integrated into the symbol library management system, providing a standard basis for subsequent matching of similar subgraphs. This improves the accuracy of subgraph matching, promotes the continuous improvement of the standard geological data system, and strongly supports the unified and efficient application of the geological data symbol library.

[0164] S107. Add subgraph information based on the subgraph to be added, and update the reference of the subgraph to be added based on the subgraph matching information of the data to be added.

[0165] Specifically, the process involves: constructing new and old graphical parameter objects for the subgraphs to be updated; using temporary coding as a transition method to batch modify subgraph association information; integrating the updated subgraphs to be updated with standard symbol library resources; generating an updated symbol library and improving its management attributes; collecting complete metadata information for the subgraphs to be added; entering the subgraphs to be added into the standard symbol library in a standard format; assigning unique identifiers to the new subgraphs and associating them with semantic information; locating the subgraph references to be updated based on the subgraph matching information in the database; and batch updating the subgraph reference relationships in the data to be entered into the database.

[0166] Furthermore, based on the sub-map information of the geological data to be added to the database and the standard information corresponding to the sub-maps to be updated in the standard symbol library, graphic parameter objects for the old and new parameters are constructed according to the sub-map type: For point-type sub-maps to be updated, a PntInfo point graphic parameter object is constructed (containing the old SymID sub-map number, old OutClr point color group, and new SymID sub-map number, new OutClr point color group); for line-type sub-maps to be updated, a LinInfo line graphic parameter object is constructed (containing the old LinStyID line model, old LibID auxiliary line type number, old OutClr line color group, and new LinStyID line model, new LibID auxiliary line type number, and new OutClr line color group); for area-type sub-maps to be updated, a RegInfo area graphic parameter object is constructed (containing the old PatID fill pattern number, old FillClr fill color, old EndClr termination color, old PatClr pattern color, and new PatID fill pattern number, new FillClr fill color, new EndClr termination color, and new PatClr pattern color). To avoid cyclic mapping errors (such as cyclic conversions of 1→2, 2→1) during subgraph number conversion, a temporary encoding transition method is adopted. First, it is confirmed that the added temporary encodings (such as T1, T2) do not exist in the symbol library. Then, the original subgraph number of the subgraph to be updated is converted to the temporary encoding (such as original subgraph number 1→T1, original subgraph number 2→T2). Finally, the temporary encoding is converted to the target subgraph number (such as T1→2, T2→3), achieving accurate conversion from the original subgraph number to the target subgraph number. Based on automatically associated color numbers and manually confirmed subgraph number relationship data, according to graphic information conditions such as subgraph type and original subgraph number, the graphic information of objects meeting the conditions is modified in batches, completing the batch update of the original data referencing subgraph numbers and color information. The updated subgraph information to be updated is integrated with the standard symbol library resources. Dynamically added missing subgraphs, i.e., subgraphs already assigned unique subgraph numbers, associated semantic information, and graphic parameters, are included in the standard symbol library. Simultaneously, the association information of the original subgraphs to be updated in the standard symbol library is updated to ensure consistency between the standard symbol library and the updated subgraph information. An updated symbol library is generated, which contains all updated submap resources, including the original standard submaps and dynamically added missing submaps. The management attributes of the updated symbol library are improved, such as recording the update version number, update time, update content, update personnel, etc. At the same time, relying on the management conversion API provided by Desktop, the updated symbol library can be exported and imported as a single legend and format converted, which facilitates the use and management of the symbol library in subsequent geological data production and application.

[0167] Furthermore, for the identified submaps to be added, complete metadata information is collected, including submap name, data source, project affiliation, graphic parameters, semantic description, and usage scenario, ensuring comprehensive and accurate information. Following the standard symbol library's entry format requirements, the collected metadata information is standardized and entered, assigning a unique standard number to each new submap, associating it with corresponding semantic information and graphic files, and completing the addition operation in the standard symbol library. Based on the submap matching information of the data to be entered, the standard submap identifier corresponding to each submap in the geological data to be entered is found, including existing and newly added standard submaps. Using a batch data processing tool, the original submap references in the data to be entered, such as original submap numbers and local file paths, are uniformly updated to the corresponding standard submap numbers and storage paths in the standard symbol library, ensuring that the submap references in the data to be entered are fully aligned with the standard symbol library, achieving precise data-standard compatibility.

[0168] Furthermore, the standard symbol library stores point graphic parameter objects, line graphic parameter objects, and area graphic parameters of the subgraph; the implementation steps of generating an updated symbol library based on the subgraph to be updated and the standard symbol library include:

[0169] (1) Construct the Association object;

[0170] Specifically, the core attributes and data sources of the Association object must be clearly defined. The association object needs to fully record the relationship between the subgraph to be updated and the subgraph in the standard symbol library to ensure traceability of subsequent replacements and updates. The attributes of the association object include: original symbol library name, subgraph type, original subgraph number, original subgraph image path, association method, standard subgraph number, standard subgraph name, comprehensive matching degree, manual confirmation status, and temporary code.

[0171] Data is extracted from the preliminary processing results and the associated objects are populated: the original symbol library name, original sub-figure number, and sub-figure type are obtained from the layer information of the data to be imported; the original sub-figure image path is associated with the raster sub-figure file stored in the previous step; the standard sub-figure number and standard sub-figure name are extracted from the target sub-figure information matched in the standard symbol library; the association method and manual confirmation status are determined according to the sub-figure processing flow; the comprehensive matching degree directly references the previous calculation results; the temporary code will be assigned in subsequent steps. After all attribute data is format-validated, such as whether the sub-figure number format conforms to the standard library rules and whether the type matches the parameter object, a structured Association associated object set is generated, which is grouped and stored according to the sub-figure type, providing a foundation for subsequent encoding conversion and parameter construction.

[0172] (2) The original sub-figure number is converted into a temporary code using a temporary coding transition method, and the temporary code is mapped to the sub-figure number of the standard symbol library;

[0173] Specifically, temporary coding rules are established to ensure the uniqueness and identifiability of the codes: the temporary code format is a type identifier plus a random 6-digit number, where the type identifier corresponds to the subgraph type: D for point types, X for line types, and Q for area types. For example, the temporary code for a point type subgraph can be D123456. The system automatically detects all existing codes in the standard symbol library and the subgraph to be updated, excludes existing code combinations, and generates a pool of unused temporary codes to avoid coding conflicts.

[0174] Furthermore, the conversion from original submap numbers to temporary codes is performed in batches. The Association object set is traversed, and a unique code from the temporary code pool is assigned to each object. The temporary code field is updated in the object's properties, and a conversion mapping table is recorded, including the original submap number, submap type, temporary code, and conversion time. During the conversion process, the original submap number is simultaneously replaced with the temporary code in the layer properties of the geological data to be imported, ensuring code consistency between the data layer and the associated objects. This avoids circular mapping issues that may occur when directly replacing standard submap numbers later.

[0175] Furthermore, based on the standard sub-map number attribute in the Association object, a mapping relationship between temporary codes and standard sub-map numbers is established, generating a mapping lookup table. This table includes the temporary code, standard sub-map number, sub-map type, and association object ID, ensuring that each temporary code uniquely corresponds to one standard sub-map number. Then, mapping replacement is performed on the geological data to be imported. Using a batch data processing tool, all temporary codes in the layers are replaced with their corresponding standard sub-map numbers according to the mapping lookup table. Simultaneously, the temporary code field status of the Association object is updated to "mapped," and the mapping time and operator are recorded. After the replacement is completed, accuracy is verified through sampling checks to ensure no codes are missing or mismatched, laying a correct coding foundation for subsequent parameter modifications.

[0176] (3) Construct new and old parameters for point graphic parameter objects, line graphic parameter objects, and area graphic parameter objects;

[0177] Specifically, old parameters are extracted from the layer information and Association objects of the geological data to be imported into the database. For point-type submaps, the extracted old parameters include the original SymID submap number, OutClr point color group, point size, and point style. For line-type submaps, the extracted old parameters include the original LinStyID line type, LibID auxiliary line type number, OutClr line color group, line width, and line endpoint style. For zone-type submaps, the extracted old parameters include the original PatID fill pattern number, FillClr fill color, EndClr boundary color, PatClr pattern color, and fill transparency. All old parameters are stored according to submap type, forming an old parameter set to ensure parameter integrity and traceability.

[0178] Furthermore, based on the standard sub-figure number in the Association object, the matching parameter record is queried in the point / line / area graphic parameter data table of the standard symbol library. The new parameters for point types must be consistent with the PntInfo object of the standard sub-figure in the standard symbol library; the new parameters for line types must be consistent with the LinInfo object; and the new parameters for area types must be consistent with the RegInfo object. If it is a newly added missing sub-figure, its new parameters are the standardized parameters entered when it was added to the library, ensuring that the new parameters fully comply with the specifications of the standard symbol library.

[0179] Furthermore, a comparison table of old and new parameters is constructed, grouped by sub-figure type and standard sub-figure number. The table includes sub-figure type, standard sub-figure number, parameter type, old parameter value, new parameter value, and explanation of parameter differences. For example, the old color group RGB (255,0,0) is updated to the standard RGB (200,0,0). The comparison table must ensure that each parameter type has corresponding old and new values, with no omissions. For sub-figures with significant parameter differences, such as line width changing from 2 pixels to 3 pixels, key verification markers are added to facilitate verification after subsequent batch modifications, ensuring a clear mapping between old and new parameters and providing a clear parameter basis for batch modifications.

[0180] (4) Modify the graphic information of the geological data that meets the graphic information conditions in batches according to the graphic information conditions, and complete the replacement update.

[0181] Specifically, the graphic information conditions are set based on the submap type and standard submap number. For example, submap type = point and standard submap number = D001, submap type = line and standard submap number = X005. Auxiliary conditions can also be added, such as layer name = geological point layer and data acquisition time = 2024, to further narrow the scope and ensure that only the graphic information of the target submap to be updated is modified. The condition expressions must conform to the syntax rules of the geological data processing tool. The system's pre-validation function checks the validity of the conditions, such as for syntax errors and whether they can match the features, to avoid batch modification failures due to incorrect conditions.

[0182] The system utilizes a professional geological data processing interface to batch-read the elements to be modified based on the graphic information conditions. Then, it updates the graphic parameters of the elements according to a new / old parameter comparison table: for point elements, it batch-replaces old OutClr color groups, point sizes, and other parameters with new parameters; for line elements, it batch-updates LinStyID line type, line width, and other parameters; for area elements, it batch-modifies PatID fill pattern number, FillClr fill color, and other parameters. A transaction management mechanism is enabled during the modification process. If an error occurs in a batch of modifications, all modifications in that batch are automatically rolled back to prevent data corruption. An error log is also recorded for subsequent troubleshooting and repair.

[0183] After batch modifications are completed, the symbol library is updated to include both existing and newly added sub-maps from the standard symbol library. All modification records are also recorded, and the symbol library version number is updated. The update effect is ensured through dual verification: first, visual verification, by loading the updated geological data into the GIS software and comparing the display effects of the sub-maps before and after the modifications, such as whether the colors and styles match the standard sub-maps. Figure 1 The first step is data verification. Modified elements are randomly selected to verify whether their graphic parameters are completely consistent with the new parameters in the old-new parameter comparison table. Simultaneously, the spatial location and related attributes of the elements are checked to ensure that batch modifications only update graphic information without changing other key data. Finally, the generation and confirmation of the updated symbol library are completed, achieving seamless integration between the sub-graph to be updated and the standard symbol library.

[0184] By constructing associated objects, the relationships and core attributes of sub-maps are fully recorded, providing a traceable data foundation for subsequent operations and avoiding confusion in associated information. Temporary coding transitions effectively avoid potential circular mapping conflicts when directly mapping original sub-map numbers to standard sub-map numbers, ensuring the accuracy and security of coding conversion. A comparison of old and new graphic parameters is constructed according to point, line, and area categories, clearly defining the direction of parameter updates and providing precise parameter basis for batch modifications, reducing the risk of parameter mismatch. Based on graphic information conditions, element graphic information is modified in batches, and a transaction management mechanism ensures data security during the modification process. Subsequent verification ensures that modifications only target specific elements and conform to standards. The resulting updated symbol library integrates standard symbol library resources and newly added missing sub-maps, achieving standardization and unification of graphic information from geological data to be added to the library. This improves the efficiency of symbol library updates and ensures the consistency of geological data symbol expression, laying a standardized data foundation for subsequent applications such as geological data map integration and spatial database construction.

[0185] In addition to the methods described above, the method provided in this embodiment also includes:

[0186] (1) Extract color parameters from the layer information of the geological data to be stored;

[0187] Specifically, the storage location and type of color parameters in the geological data layer information to be entered into the database are determined. Different sub-map types correspond to different color parameters. The color parameters of point type sub-maps are OutClr point color groups, stored in the point graphic attribute field of the layer element, recording the fill color, border color, and other color identifiers of the point symbol. The color parameters of line type sub-maps are OutClr line color groups, associated with the line graphic attribute field, containing information such as the main color and endpoint color of the line. The color parameters of area type sub-maps cover FillClr fill color, EndClr boundary color, and PatClr pattern color, stored in the area graphic attribute related fields, corresponding to the internal fill color, boundary line color, and fill pattern color of the area symbol, respectively.

[0188] The layer data parsing tool iterates through all feature layers of the geological data to be imported, extracting color parameters according to sub-map type. For each layer, it first filters out different types of features: points, lines, and areas. Then, it retrieves the color parameter values ​​of the corresponding fields through the attribute reading interface. For example, it reads the OutClr field value C001 for point features. These identifiers, which exist in the form of letter + number combinations, are the color codes. The extracted color parameters are organized according to the structure of layer name-sub-map type-feature ID-color parameter type-color code to form a color parameter extraction list. This ensures that the color information of each feature is accurately matched, avoiding omissions or confusion, and laying the foundation for subsequent RGB value queries.

[0189] (2) Based on the color number of the color parameter, look up the corresponding RGB value;

[0190] Specifically, the color codes of the geological data to be imported typically correspond to preset colors in their original symbol library. The mapping rules need to be read from the color configuration file of the original symbol library. For example, C001 in the original symbol library corresponds to the RGB value (255,0,0) (red). If the original symbol library does not have a specific configuration file, the pixel colors of the raster images of the sub-images to be imported are extracted using image analysis tools. Representative pixels in the sub-image raster images are selected (such as the center pixel of a dot symbol, the middle pixel of a line symbol, and the internal filling pixels of a region symbol). The RGB value of this point is obtained using a pixel color reading function and used as the RGB value of the corresponding color code, ensuring accurate numerical representation of the color even without a configuration file.

[0191] Each color number in the color parameter extraction list is iterated through, and the corresponding RGB value is matched for each color number according to the above mapping relationship or image analysis results. The original geological data document is manually checked to confirm that each color number corresponds to a unique and accurate RGB value, forming a color number-RGB value lookup table, which provides a numerical basis for subsequent matching with the standard color dataset.

[0192] (3) Call the standard color dataset in the standard geological map basic symbol library, and match the target RGB value from the standard color dataset based on the RGB value.

[0193] Specifically, the standard color dataset in the standard geological map basic symbol library is a structured color set constructed according to the "GB / T 958-2015 Regional Geological Map Legend" and geological industry standards. It is stored in the standard color data table of the basic symbol library. Each record contains fields such as standard color ID, color purpose, standard RGB value, and color deviation tolerance. All records in the standard color dataset are read through a database API, categorized and organized by color purpose, forming standard color subsets divided by submap type and geological element purpose, facilitating targeted matching.

[0194] Furthermore, using the RGB values ​​in the RGB value lookup table to be confirmed as a reference, the Euclidean distance between the reference RGB value and each standard RGB value is calculated in the standard color subset of the corresponding type. The smaller the Euclidean distance, the higher the color similarity. If the Euclidean distance is less than or equal to the color deviation tolerance of the standard color, it is determined to be a successful match, and the standard RGB value is the target RGB value.

[0195] First, the color parameters of different sub-map types (points, lines, and areas) are accurately located from the layer information. Then, the abstract color codes are converted into quantifiable RGB values. Finally, the target RGB values ​​are matched based on the standard color dataset. Euclidean distance calculation is used to ensure the accuracy of the matching. Combined with manual evaluation to cover special color scenarios, this method not only eliminates color differences between data from different sources and ensures that the color expression of the data to be entered into the database meets the requirements of the geological industry specifications and the basic symbol library of standard geological maps, but also provides a unified color benchmark for the visual consistency of the geological data symbol library, the accuracy of geological map splicing and integration, and the batch update of graphic parameters. This strongly supports the overall goal of unified management of geological data symbol libraries from multiple sources.

[0196] This embodiment provides a unified management method for geological data-based map symbol libraries. By constructing a standard geological map basic symbol library and a standard symbol library conforming to national and industry standards, it provides a standardized benchmark for symbol unification. Simultaneously, relying on a precise sub-map information extraction process, it fully captures the outline and textual features of the data to be added to the library, avoiding resource waste caused by synchronizing the entire library. The dual-dimensional fusion calculation of visual similarity and semantic matching, combined with scientific weight allocation, ensures accurate matching of visual features through structural similarity algorithms and avoids confusion of similar symbols through semantic association correction, improving the overall matching accuracy. A hierarchical processing mechanism for sub-maps with different matching degrees, combined with manual verification and dynamic supplementation of missing sub-maps, achieves 100% matching and association of sub-maps, enabling lossless replacement of the geological data symbol library. Standardized matching of color parameters further ensures the consistency of data visual expression, while temporary encoding transition and batch parameter update technology effectively avoid conversion conflicts, ensuring a safe and efficient replacement process. The overall process adapts to the needs of multiple scenarios such as geological data map integration, spatial database construction, and geological map publication, strengthening the quality control of geological results and promoting data sharing and integration, providing strong support for the standardized and efficient conduct of geological work.

[0197] Corresponding to the aforementioned embodiment of a unified management method for a map symbol library based on geological data, this application also provides an embodiment of a unified management device for a map symbol library based on geological data.

[0198] Figure 2 This is a schematic diagram of the second embodiment of the unified management device for the map symbol library based on geological data provided in this application. Please refer to... Figure 2The apparatus provided in this embodiment includes a construction module 210, a calculation module 220, and an update module 230; wherein,

[0199] The construction module 210 is used to generate a standard symbol library for managing the basic symbol data of the standard geological map based on the standard geological map basic symbol library;

[0200] The calculation module 220 is used to extract sub-map information outlines and text information from the layer information of the geological data to be entered into the database according to the standard format of the data to be entered into the database in the standard geological map basic symbol library, and combine them to generate sub-map information.

[0201] The calculation module 220 is also used to calculate the visual similarity between the subgraph information and the subgraphs of the same type in the standard symbol library based on structural similarity;

[0202] The calculation module 220 is also used to calculate the semantic matching degree between the standard symbol data in the standard symbol library and the geological data to be added to the library under each symbol of the same type;

[0203] The calculation module 220 is also used to combine the visual similarity and the semantic matching degree to obtain a comprehensive matching degree;

[0204] The update module 230 is used to determine the subgraph matching information of the data entering the database and to locate the subgraph to be added in the standard symbol library based on the comprehensive matching degree.

[0205] The update module 230 is also used to add subgraph information based on the subgraph to be added, and to update the reference of the subgraph in the database based on the subgraph matching information of the inbound data.

[0206] The apparatus of this embodiment can be used to perform... Figure 1 The steps of the method embodiment shown are similar in principle and process, and will not be repeated here.

[0207] The specific implementation process of the functions and roles of each unit in the above device can be found in the implementation process of the corresponding steps in the above method, and will not be repeated here.

[0208] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this application according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0209] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for unified management of a symbol library based on geological data, characterized in that, The method comprises: generating a standard symbol library for managing standard geological map basic symbol data based on a standard geological map basic symbol library; extracting subgraph information contours and text information from layer information of to-be-warehoused geological data according to a standard format of warehoused data in the standard geological map basic symbol library, and combining to generate subgraph information; calculating visual similarity between the subgraph information and the same type of subgraph in the standard symbol library based on structural similarity; calculating semantic matching degrees of information of standard symbol data in the standard symbol library and the to-be-warehoused geological data under each same type of symbol; comprehensively matching degrees are obtained by comprehensively matching the visual similarity and the semantic matching degrees; determining to-be-added subgraph in the standard symbol library based on the comprehensive matching degrees; based on the to-be-added subgraph, adding subgraph information, and updating subgraph reference of the warehoused data based on the to-be-added subgraph.

2. The method of claim 1, wherein, The method comprises: extracting candidate contours conforming to legend features of the geological map from the layer information; calculating a plurality of geometric features of each of the candidate contours, and screening a target contour from the candidate contours as the subgraph information contour by comprehensively matching the geometric features.

3. The method of claim 1, wherein, The method comprises: determining a target region based on the subgraph information contour; performing pixel density detection on the target region to determine a candidate text region; calling an OCR recognition algorithm to extract text information of the candidate text region.

4. The method of claim 1, wherein, The method comprises: converting a subgraph in the subgraph information into a raster subgraph; determining a type of the subgraph information, screening a target subgraph of the same type from the standard symbol library, and converting the target subgraph into a target raster subgraph; calculating a covariance and a standard deviation between the raster subgraph and the target raster subgraph; calculating a structural contrast value based on a ratio of the covariance and the standard deviation as the visual similarity.

5. The method of claim 1, wherein, The method comprises: extracting structured information of the standard symbol data under each symbol; extracting semantic text information of the to-be-warehoused geological data; calculating semantic relationships of corresponding parts in each structured information and semantic text information, and calculating a semantic matching degree based on the semantic relationships.

6. The method of claim 1, wherein, The method comprises: determining a first weight of the visual similarity and a second weight of the semantic matching degree according to attribute features of the geological symbol; performing weighted summation on the visual similarity and the semantic matching degree based on the first weight and the second weight to obtain the comprehensive matching degree.

7. The method of claim 1, wherein, The method comprises: Classify the subgraphs based on the comprehensive matching degree, the categories of the subgraphs at least including to-be-confirmed matching subgraphs and non-matching subgraphs; For the to-be-confirmed matching subgraphs, manually check the matching relationship, mark the to-be-confirmed matching subgraphs with correct checking results as valid correlations, and re-match the to-be-confirmed matching subgraphs with incorrect checking results; For the non-matching subgraphs, continue searching by expanding the matching range, and when the searching result is null, judge that the non-matching subgraph is a missing subgraph; Collect the metadata information of the missing subgraph, enter the missing subgraph in a standard format, and submit the missing subgraph to the standard symbol library to complete the addition of the missing subgraph and associate the added missing subgraph to the standard symbol library.

8. The method of claim 1, wherein, The standard symbol library stores point graphic parameter objects, line graphic parameter objects and area graphic parameters of subgraphs; and the subgraph information is added based on the to-be-added subgraph, and the in-storage data subgraph reference is updated based on the in-storage data subgraph matching information; It comprises: building an Association association object; using a temporary coding transition mode to convert an original subgraph number into a temporary code, and mapping the temporary code to a subgraph number in the standard symbol library; building new and old parameters of point graphic parameter objects, line graphic parameter objects and area graphic parameter objects; According to the graphic information condition, the method further comprises:

9. The method of claim 1, wherein, extracting color parameters from layer information of the to-be-stored geological data; According to the color number of the color parameters, the corresponding RGB value is queried; The standard color data set in the standard geological map basic symbol library is called, and the target RGB value is matched from the standard color data set based on the RGB value. The device comprises a construction module, a calculation module and an update module; wherein, 10. A geological data-based pictogram library unified management apparatus, characterized by comprising: The construction module is used to generate a standard symbol library for managing standard geological map basic symbol data based on a standard geological map basic symbol library; The calculation module is used to extract subgraph information contours and text information from layer information of to-be-stored geological data according to a standard format of in-storage data in the standard geological map basic symbol library, and combine to generate subgraph information; The calculation module is further used to calculate visual similarity between the subgraph information and the same type of subgraph in the standard symbol library based on structural similarity; The calculation module is further used to calculate semantic matching degrees of standard symbol data in the standard symbol library and information under each same type of symbol in the to-be-stored geological data; The calculation module is further used to obtain a comprehensive matching degree by comprehensively considering the visual similarity and the semantic matching degree; The update module is used to determine in-storage data subgraph matching information and locate to-be-added subgraphs in the standard symbol library based on the comprehensive matching degree; The update module is further used to add subgraph information based on the to-be-added subgraph, and update in-storage data subgraph reference based on the in-storage data subgraph matching information. ​

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