Geographic entity construction method and device based on DLG data analysis

Abstract

The invention discloses a geographic entity construction method and device based on DLG data analysis, and relates to the technical field of data analysis. Analyzing the DLG data in the graphic file format into space vector data, generating a vector data set, completing element type conversion, and synchronously extracting text attributes associated with elements; traversing the vector data set, and performing original structured classification on the element objects based on a preset rule; selecting a data extraction method to extract geographic entity elements according to the difference between the original structured classification and the geometrical morphology to be generated in the mapping table; and according to casscodes carried by the elements converted into the geographic coordinate system, performing contrast mapping with classification codes of basic geographic entity elements, and respectively outputting extraction results to corresponding element data sets according to geometric types. The problems that an existing conversion method is poor in universality, and most conversion tools cannot independently complete batch processing of graphs and are not closed are solved.

Description

Technical Field

[0001] This application relates to the field of data parsing technology, and in particular to a method and apparatus for constructing geographic entities based on DLG data parsing. Background Technology

[0002] Basic geographic entity elements, as the core achievement of basic surveying and mapping and real-scene 3D construction, are the key foundation for building a unified spatial positioning framework and supporting information applications in multiple fields. They are widely used in natural resource management, smart cities, digital economy, ecological protection and other fields.

[0003] DLG data originates from as-built CAD data that integrates multiple measurements. However, this type of data has problems such as non-closed graphics, non-standardization, and missing surface data, which require regularization and transformation of the expression form.

[0004] However, existing conversion methods have poor versatility, and most conversion tools cannot independently complete batch processing of non-closed graphics, resulting in difficulties in data conversion and low usage and automation rates. Summary of the Invention

[0005] In this embodiment of the application, a geographic entity construction method based on DLG data parsing is provided, which solves the problem that existing conversion methods have poor universality and that most conversion tools cannot independently complete batch processing of non-closed graphics.

[0006] In a first aspect, embodiments of this application provide a method for constructing geographic entities based on DLG data parsing. This method includes: parsing DLG data in graphic file format into spatial vector data, generating a vector dataset, performing feature type conversion based on geometric features, and simultaneously extracting text attributes associated with the features; traversing the vector dataset and performing original structured classification of feature objects based on preset rules; the preset rules include: classifying polygon data generated from closed line features into polygon data class, line data generated from non-closed line features into line data class, point data generated from point features into point data class, and text information on the DLG data into annotation class, with the stored geometric type being point type; and based on the original... To address the gap between the structured classification and the geometric shapes to be generated in the mapping table, a data extraction method is selected to extract geographic entity features. This data extraction method includes direct surface mapping, auxiliary surface mapping, fused surface mapping, direct derivation, point derivation, and line derivation. The extracted geographic entity features are then converted from the projected coordinate system to the geographic coordinate system. After the coordinate system conversion, the CASSCode carried by the converted features is compared and mapped with the classification code of the basic geographic entity features to obtain the extraction results. The extraction results are then output to the feature datasets corresponding to point geographic entity features, line geographic entity features, and polygon geographic entity features, respectively, according to their geometric types.

[0007] In one possible implementation, parsing DLG data in graphic file format into spatial vector data to generate a vector dataset and completing feature type conversion based on geometric features includes: taking a DWG file or DXF file as input, extracting the geometric and attribute information of the DLG data, and generating a vector dataset including spatial location, layer classification, casscode, and annotation content; during the parsing process, distinguishing polygon / line feature types based on line closure, generating polygon / line data respectively, and directly mapping independent point symbols or zero-length line features to point data.

[0008] In one possible implementation, the direct surface construction method, the auxiliary surface construction method, or the fused surface construction method also includes a basic processing flow. This basic processing flow includes: decomposing both line and surface features into connected vector line segments to generate a line segment set; expanding the endpoints of the decomposed line segments towards both ends according to a tolerance setting, with the expansion length equal to the tolerance value; obtaining the line set of the expanded line segments and the point set of the expanded line segment endpoints after expansion; rasterizing the expanded line segment line set and the expanded line segment endpoint point set to generate a rasterized image; vectorizing the rasterized image; where closed surfaces are converted into surface vectors; and smoothing the surface vectors to complete the basic processing flow.

[0009] In one possible implementation, for DLG data containing only master codes, where the master codes are polygon features but there are unclosed line features, a direct polygonization method is used to generate closed polygon features; a basic processing flow is executed to implement the direct polygonization method; and after completing the basic processing flow, polygon geographic entity features generated by the direct polygonization method are obtained.

[0010] In one possible implementation, when the main coded polygon features in the DLG data are not closed and the missing boundaries are composed of auxiliary coded line features, an auxiliary polygon construction method is used to generate closed polygon features; a basic processing flow and filtering steps are executed to implement the auxiliary polygon construction method; the polygon geographic entity features generated by executing the basic processing flow are stored in the polygon geographic entity feature dataset; the filtering steps include: determining whether the polygon geographic entity features generated by executing the basic processing flow contain annotations; if no annotations are contained, the polygon geographic entity feature is deleted from the polygon geographic entity feature dataset.

[0011] In one possible implementation, when the main coded polygon feature in the DLG data is not closed, and the missing boundary is composed of auxiliary coded line features, and it is necessary to merge adjacent polygon features coded with fusion codes, a fusion polygon construction method is adopted, using auxiliary coded line features to help the main coded polygon feature close; a basic processing flow, a polygon fusion step, and a filtering step are executed to implement the fusion polygon construction method; the polygon geographic entity features generated by executing the basic processing flow are stored in the polygon geographic entity feature dataset; the filtering step includes: determining whether the polygon geographic entity features generated by executing the basic processing flow contain annotations; if no annotations are contained, the polygon geographic entity feature is deleted from the polygon geographic entity feature dataset; the polygon fusion step includes: selecting polygon geographic entity features whose casscode only contains the main code or the fusion code for fusion.

[0012] In one possible implementation, when the geometry of the DLG data is accurate, there are no topological errors or deformations, and the attribute information is complete, a direct export method is used. Direct export methods include: exporting point data as point geographic entity features; exporting line data as line geographic entity features; and exporting polygon data as polygon geographic entity features.

[0013] In one possible implementation, when a geographic entity is presented as a polygon or line feature during drawing but the production requirement is for a point feature, the point export method is used to convert the polygon or line features in the DLG data into point features. The point export method includes: directly exporting point data as point geographic entity features; using the centroid of the line as a point geographic entity feature for line data, while retaining the attributes of the line data; and using the centroid of the polygon as a point geographic entity feature for polygon data, while retaining the attributes of the polygon data.

[0014] In one possible implementation, when a geographic entity is incorrectly parsed as a polygon feature due to its closure characteristics during the drawing process, and the polygon features and boundaries that are suitable to be represented by line features are incorrectly parsed as polygon features, the line export method is used to convert the polygon features in the DLG data into line features. The line export method includes: directly exporting line data as line geographic entity features; and splitting polygon data into polylines, and using the exported polylines as line geographic entity features, while retaining the attributes of the polygon data.

[0015] Secondly, embodiments of this application provide a geographic entity construction device based on DLG data parsing. The device includes: a parsing module, used to parse DLG data in graphic file format into spatial vector data, generate a vector dataset, complete feature type conversion based on geometric features, and simultaneously extract text attributes associated with the features; a classification module, used to traverse the vector dataset and perform original structured classification of feature objects based on preset rules; the preset rules include: classifying polygon data generated from closed line features into polygon data class, line data generated from non-closed line features into line data class, point data generated from point features into point data class, and text information on the DLG data into annotation class, with the stored geometric type being point type; and an extraction module, used to extract based on the original structure... The difference between the structured classification and the geometric shape to be generated in the mapping table is identified, and a data extraction method is selected to extract geographic entity features. The data extraction methods include direct surface construction, auxiliary surface construction, fused surface construction, direct export, point export, and line export. A coordinate transformation module is used to convert the extracted geographic entity features from the projected coordinate system to the geographic coordinate system. A mapping module is used to map the CASS codes carried by the features after the coordinate system transformation to the classification codes of the basic geographic entity features based on the CASS codes carried by the features after the transformation to the geographic coordinate system, thereby obtaining the extraction results. An output module is used to output the extraction results to the feature datasets corresponding to point geographic entity features, line geographic entity features, and polygon geographic entity features according to their geometric types.

[0016] The one or more technical solutions provided in this application embodiment have at least the following technical effects: This application embodiment provides a geographic entity construction method based on DLG data parsing. It parses DLG data in graphic file format into spatial vector data, generates a vector dataset, completes feature type conversion based on geometric features, and simultaneously extracts the text attributes associated with the features. It traverses the vector dataset and performs original structured classification of feature objects based on preset rules. Based on the difference between the original structured classification and the geometric shape to be generated in the mapping table, a data extraction method is selected to extract geographic entity features, making the graphics closed, improving the utilization rate of DLG data and the reliability of the final data. The extracted geographic entity features are converted from the projected coordinate system to the geographic coordinate system, eliminating the problem of inconsistency in coordinates between multi-source data. After the coordinate system conversion is completed, the CASSCode carried by the features after conversion to the geographic coordinate system is compared and mapped with the classification code of the basic geographic entity features to obtain the extraction results. The extraction results are output to the feature datasets corresponding to point geographic entity features, line geographic entity features, and polygon geographic entity features according to their geometric types. This solves the problem of poor universality of existing conversion methods and the inability of most conversion tools to independently complete batch processing of non-closed graphics. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments of this application or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart illustrating a method for constructing geographic entities based on DLG data parsing, provided in an embodiment of this application; Figure 2 A schematic diagram of a geographic entity construction device based on DLG data parsing provided in an embodiment of this application; Figure 3 This is a schematic diagram of a geographic entity builder server based on DLG data parsing, provided as an embodiment of this application. Detailed Implementation

[0019] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0020] The following description of some technologies involved in the embodiments of this application is provided to aid understanding and should be considered merely exemplary. Therefore, those skilled in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this application. Similarly, for clarity and brevity, some descriptions of well-known functions and structures are omitted in the following description.

[0021] This application provides a method for constructing geographic entities based on DLG data parsing, such as... Figure 1 As shown, the method includes steps S101 to S106. Wherein, Figure 1 This is merely one execution order shown in the embodiments of this application and does not represent the only execution order for a geographic entity construction method based on DLG data parsing. Where the final result can be achieved, Figure 1 The steps shown can be performed in parallel or in reverse order.

[0022] S101: Parse DLG data in graphic file format into spatial vector data, generate vector dataset, complete feature type conversion based on geometric features, and simultaneously extract text attributes associated with features.

[0023] The DLG data in graphic file format is parsed into spatial vector data to generate a vector dataset. The feature type is then converted based on geometric features, including the following:

[0024] Using DWG or DXF files as input, the geometric and attribute information of the DLG data is extracted to generate a vector dataset that includes spatial location, layer classification, casscode, and annotation content.

[0025] Specifically, the graphic file format in this application can be DWG (Binary File Format) or DXF (Graphics Exchange Format). DLG data is digital line drawing data, and DLG data originates from the as-built CAD data that integrates multiple measurements.

[0026] Geometric information includes the spatial location of features, i.e., the specific coordinate values ​​of features in the coordinate system, used to determine the precise location of features in geographic space. Layer classification information categorizes and manages different types of geographic entity features, facilitating data organization and analysis. Regarding attribute information, casscodes (the feature codes in Southern CASS software) serve as classification identifiers for geographic entity features, accurately reflecting the type and characteristics of the features. Annotations provide detailed textual descriptions of geographic entity features, enhancing data readability and usability. By extracting this information, a vector dataset including spatial location, layer classification, casscode, and annotations is generated.

[0027] During the parsing process, the type of surface / line feature is distinguished based on the closure of the line, and surface / line data is generated respectively. Independent point symbols or zero-length line features are directly mapped to point data.

[0028] Specifically, if a line is closed, it is parsed as a polygon feature, generating polygon data. Polygon data is typically used in geographic information systems to represent areas with closed boundaries, such as buildings and lakes. If a line is not closed, it is parsed as a line feature, generating line data. Line data is commonly used to represent linear features of geographic elements such as boundaries, roads, and rivers. Independent point symbols or zero-length line features are directly mapped to point data. Point data is used in geographic information systems to represent discrete geographic entities, such as streetlights and manhole covers.

[0029] Furthermore, text attributes, as important supplementary information to geographic features, can provide more detailed and specific descriptions. For example, annotations can include information such as the feature's name, specifications, and purpose. Storing these text attributes as independent fields facilitates subsequent data querying, analysis, and visualization.

[0030] S102: Traverse the vector dataset and perform raw structured classification of feature objects based on preset rules. Preset rules include: classifying polygon data generated from closed line features into the polygon data class, line data generated from non-closed line features into the line data class, point data generated from point features into the point data class, and text information on DLG data into the annotation class. The stored geometry type is point type.

[0031] S103: Based on the difference between the original structured classification and the geometric shape to be generated in the mapping table, select a data extraction method to extract geographic entity features. The data extraction methods include direct surface mapping, auxiliary surface mapping, fused surface mapping, direct derivation, point derivation, and line derivation.

[0032] Data extraction methods include direct surface construction, auxiliary surface construction, fused surface construction, direct export, point export, and line export.

[0033] Direct surface construction methods, auxiliary surface construction methods, or fused surface construction methods also include basic processing procedures.

[0034] The basic processing flow includes: breaking down line features and polygon features into vector line segments that are connected end to end, and generating a set of line segments.

[0035] For example, in DLG data, there may be a graphic that should form a closed surface (such as a pond) composed of multiple polylines, but it is not actually drawn as a closed line when plotting. In this case, these polylines are decomposed into vector line segments.

[0036] Based on the tolerance setting, the endpoints of the disassembled line segments are expanded to both ends, with the expansion length being the tolerance size.

[0037] After expansion, we obtain the line set of the expanded line segment and the point set of the endpoints of the expanded line segment.

[0038] The rasterized image is generated by rasterizing the line set of the expanded line segment and the point set of the endpoints of the expanded line segment.

[0039] Specifically, the rasterization process converts continuous vector data into discrete raster data.

[0040] The rasterized image is then vectorized. Closed surfaces are converted into surface vectors.

[0041] Specifically, vectorization converts discrete raster data back into continuous vector data, restoring the geometric features of geographic elements.

[0042] The surface vector is smoothed to complete the basic processing flow. For DLG data containing only the master code, where the master code is a polygon feature but there are unclosed line features, the direct polygon construction method is used to generate closed polygon features.

[0043] Perform basic processing to implement the direct surface method.

[0044] Complete the basic processing flow to obtain polygon geographic entity features generated by the direct polygonization method.

[0045] Specifically, since the resulting surface vector boundaries often exhibit jagged edges and irregular feature shapes, a B-spline curve smoothing algorithm can be used to smooth the surface vectors. After smoothing, the entire basic processing flow is completed, ultimately yielding surface geographic entity features generated by the direct surface construction method, making the shape of geographic features more consistent with actual needs and standardized specifications.

[0046] When the main coded surface features in the DLG data are not closed and the missing boundaries are composed of auxiliary coded line features, the auxiliary surface construction method is used to generate closed surface features.

[0047] For example, when depicting a building that is adjacent to a wall, due to factors such as drawing operations, the building may not be drawn as a closed surface. Specifically, the building's graphic is not closed, and the line segment on the side adjacent to the wall actually belongs to the wall itself, not to the building as a geographical entity.

[0048] Perform basic processing flows and filtering steps to implement auxiliary surface construction methods.

[0049] The polygonal geographic entity features generated by the basic processing flow are stored in the polygonal geographic entity feature dataset.

[0050] The filtering step includes: determining whether the polygonal geographic entity features generated by the basic processing flow contain annotations. If no annotations are found, the polygonal geographic entity feature is deleted from the polygonal geographic entity feature dataset.

[0051] Specifically, in real-world geographic information scenarios, some polygon geographic entity features may be mistakenly identified as successfully polygonized during the auxiliary polygonization process, even though these features do not actually belong to the target geographic entity (such as buildings). The system determines whether the polygon geographic entity features generated by the basic processing flow contain annotations. If no annotations are found, it indicates that the feature does not belong to the object requiring polygonization. In this case, the polygon geographic entity feature is removed from the polygon geographic entity feature dataset to ensure data quality and accuracy.

[0052] When the main coded surface features in DLG data are not closed, and the missing boundaries are formed by auxiliary coded line features, and it is necessary to merge adjacent surface features coded as fusion codes, the fusion surface construction method is adopted, using auxiliary coded line features to help the main coded surface features close.

[0053] For example, when depicting a building against a wall, due to drawing operations and other factors, the building may not be drawn as a closed surface. Specifically, the building's graphic is not closed, and the line segment on the wall side actually belongs to the wall itself, not the building as a geographical entity. Furthermore, it's necessary to merge balconies and other ancillary facilities with the building into a single surface. The building's casscode can be selected as the primary code, and the wall's casscode as the secondary code. Elements with the primary code are the core elements, and elements with the secondary code are auxiliary, completing the surface construction and generating a building against the wall that meets the requirements. Then, adjacent surface elements with fusion codes (such as balconies) are merged with the constructed surface, ultimately generating a closed surface element of the building against the wall that meets the requirements. The adjacent elements to be merged are used as the fusion code; for example, balconies and other ancillary facilities are coded as fusion elements.

[0054] Perform basic processing steps, surface blending steps, and filtering steps to achieve the blended surface method.

[0055] The polygonal geographic entity features generated by the basic processing flow are stored in the polygonal geographic entity feature dataset.

[0056] The filtering step includes: determining whether the polygonal geographic entity features generated by the basic processing flow contain annotations. If no annotations are found, the polygonal geographic entity feature is deleted from the polygonal geographic entity feature dataset.

[0057] The surface fusion step includes: filtering out surfaces in the surface geographic entity features that only have a primary code or a fusion code in their casscode and then fusion them.

[0058] When the geometry of the DLG data is accurate, there are no topological errors or deformations, and the attribute information is complete, the direct export method is used.

[0059] Direct export methods include: exporting point data as point geographic entity features; exporting line data as line geographic entity features; and exporting polygon data as polygon geographic entity features.

[0060] Specifically, if the original data is point data, since it already meets the basic requirements of point geographic entity features, no additional processing is needed, and it can be directly exported as point geographic entity features. During the export process, it is essential to ensure that the coordinate information and attribute information of the point data are accurately preserved in the exported file. When the original data is line data, similarly, no complex processing is required, and it is directly exported as line geographic entity features. It is necessary to check whether the topological relationship of the line data is correct, such as whether the line segments are continuous and whether there are any dangling nodes, to ensure the quality of the exported line geographic entity features. If the original data is polygon data, after confirming that its geometry is accurate, there are no topological errors or deformation issues, and the attribute information is complete, it is directly exported as polygon geographic entity features. During export, it is crucial to ensure that the boundary information of the polygon features is accurate and can correctly reflect the extent of the geographic entity.

[0061] When a geographic entity is presented as a polygon or line feature during drawing but the production requirement is for a point feature, the point export method is used to convert the polygon or line feature in the DLG data into a point feature.

[0062] For example, a substation is drawn as a polygon feature, and a garbage dump is drawn as a symbol (composed of lines), but in production requirements, they must be represented as points. By using the point export method, polygon or line features in DLG data can be converted into point features.

[0063] Methods for exporting points include: directly exporting point data as point geographic entity features; exporting line data by using the centroid of the line as the point geographic entity feature, while retaining the attributes of the line data; and exporting polygon data by using the centroid of the polygon as the point geographic entity feature, while retaining the attributes of the polygon data.

[0064] When, during the drawing process, due to the closure characteristics, the orientation and boundary of geographic entities that are suitable to be represented by line features are incorrectly parsed as polygon features, the line export method is used to convert the polygon features in the DLG data into line features.

[0065] For example, roads surrounding gardens, parks, and playgrounds, which are drawn as lines, are parsed as polygons due to their closure, but the production process requires them to be represented as line features. The line-derived method is designed to address this situation by converting polygon features in DLG data into line features to meet production requirements.

[0066] Methods for exporting lines include: directly exporting line data as line geographic entity features; and splitting polygon data into polylines, exporting the polylines as line geographic entity features, and retaining the attributes of the polygon data.

[0067] S104: Convert the extracted geographic entity features from the projected coordinate system to the geographic coordinate system.

[0068] Specifically, in real-world scenarios, the integration of multiple data sources is often involved. Data from different sources, especially geographic entity features, frequently uses projected coordinate systems, and these systems often differ. This inconsistency in coordinate systems can cause numerous problems for subsequent data fusion and analysis. To effectively avoid these issues arising from different coordinate systems in large volumes of data and to ensure accurate spatial matching and unified analysis of results from multiple data sources, the coordinate systems of the extracted geographic entity features are converted to the CGCS2000 geographic coordinate system.

[0069] S105: After completing the coordinate system transformation, the CASS code carried by the elements after conversion to the geographic coordinate system is compared and mapped with the classification code of the basic geographic entity elements to obtain the extraction results.

[0070] Specifically, after completing the coordinate system transformation, a classification and coding mapping process needs to be carried out. This involves using the casscode carried in the transformed data as an index, combined with a pre-built mapping table to achieve the coding mapping. This mapping table is compiled by integrating the casscode classification dictionary built into the CASS11.0 software with the basic geographic information entity element classification standards. The extracted result is a standardized set of geographic entity data that conforms to the basic geographic entity element standards and can be directly used for GIS (Geographic Information System) analysis or database construction, obtained through classification and coding mapping.

[0071] S106: Output the extraction results to the feature datasets corresponding to point geographic entity features, line geographic entity features, and polygon geographic entity features according to their geometric types.

[0072] Specifically, the original data source can be identified through the project name field within each dataset, enabling the classification, storage, and management of multi-source data.

[0073] This application also provides a geographic entity construction device 200 based on DLG data parsing, such as... Figure 2 As shown, the device includes: a parsing module 201, a classification module 202, an extraction module 203, a coordinate transformation module 204, a mapping module 205, and an output module 206.

[0074] The parsing module 201 is used to parse DLG data in graphic file format into spatial vector data, generate a vector dataset, complete the feature type conversion based on geometric features, and simultaneously extract the text attributes associated with the features.

[0075] The classification module 202 is used to traverse the vector dataset and perform raw structured classification of feature objects based on preset rules. The preset rules include: classifying polygon data generated from closed line features into the polygon data class, line data generated from non-closed line features into the line data class, point data generated from point features into the point data class, and text information on DLG data into the annotation class. The stored geometric type is point type.

[0076] The extraction module 203 is used to select a data extraction method to extract geographic entity features based on the difference between the original structured classification and the geometric shape to be generated in the mapping table. The data extraction methods include direct surface mapping, auxiliary surface mapping, fused surface mapping, direct export, point export, and line export.

[0077] The coordinate transformation module 204 is used to convert the extracted geographic entity features from the projected coordinate system to the geographic coordinate system.

[0078] The mapping module 205 is used to complete the coordinate system transformation and then compare and map the CASS code carried by the feature after the transformation to the geographic coordinate system with the classification code of the basic geographic entity feature to obtain the extraction result.

[0079] The output module 206 is used to output the extraction results to the feature datasets corresponding to point geographic entity features, line geographic entity features, and polygon geographic entity features according to their geometric types.

[0080] Some modules in the apparatus described in this application can be described in the general context of computer-executable instructions that are executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, classes, etc., that perform a specific task or implement a specific abstract data type. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0081] The apparatus or module described in the above embodiments can be implemented by a computer chip or physical entity, or by a product with a certain function. For ease of description, the above apparatus is described by dividing it into various modules according to their functions. When implementing the embodiments of this application, the functions of each module can be implemented in one or more software and / or hardware. Of course, a module that implements a certain function can also be implemented by combining multiple sub-modules or sub-units.

[0082] The methods, apparatus, or modules described in this application can be implemented in a computer-readable program code manner. The controller can be implemented in any suitable manner, for example, as a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro)processor, logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers, and embedded microcontrollers. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicon Labs C8051F320. A memory controller can also be implemented as part of the control logic of a memory. Those skilled in the art will also recognize that, in addition to implementing the controller in purely computer-readable program code manner, the same functionality can be achieved by logically programming the method steps to make the controller take the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers. Therefore, such a controller can be considered a hardware component, and the means included within it for implementing various functions can also be considered as structures within the hardware component. Alternatively, the device used to implement various functions can be viewed as either a software module that implements the method or a structure within a hardware component.

[0083] like Figure 3 As shown in the figure, this application embodiment also provides a geographic entity construction server based on DLG data parsing, including a memory 301 and a processor 302; the memory 301 is used to store computer-executable instructions; the processor 302 is used to execute computer-executable instructions to implement the geographic entity construction method based on DLG data parsing described above in this application embodiment.

[0084] This application also provides a computer-readable storage medium storing executable instructions, which, when executed by a computer, enable the implementation of the geographic entity construction method based on DLG data parsing described above in this application embodiment.

[0085] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary hardware. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product, or it can be embodied in the process of data migration. The computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, mobile terminal, server, or network device, etc.) to execute the methods described in the embodiments of this application.

[0086] The various embodiments described in this specification are presented in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on its differences from other embodiments. All or part of this application can be used in numerous general-purpose or special-purpose computer system environments or configurations.

[0087] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit this application. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of this application.

Claims

1. A method for constructing geographic entities based on DLG data parsing, characterized in that, include: DLG data in graphic file format is parsed into spatial vector data to generate a vector dataset. Feature type conversion is completed based on geometric features, and text attributes associated with the features are extracted simultaneously. Traverse the vector dataset and perform raw structured classification of feature objects based on preset rules; The preset rules include: classifying polygon data generated by closed line features into polygon data class, classifying line data generated by non-closed line features into line data class, classifying point data generated by point features into point data class, classifying text information on DLG data into annotation class, and storing the geometry type as point type; Based on the gap between the original structured classification and the geometric shape to be generated in the mapping table, a data extraction method is selected to extract geographic entity features; the data extraction methods include direct surface mapping method, auxiliary surface mapping method, fused surface mapping method, direct derivation method, point derivation method, and line derivation method; Convert the extracted geographic entity features from the projected coordinate system to the geographic coordinate system; After completing the coordinate system transformation, the CASS code carried by the feature after conversion to the geographic coordinate system is compared and mapped with the classification code of the basic geographic entity feature to obtain the extraction result. The extracted results are output to the feature datasets corresponding to point geographic entities, line geographic entities, and polygon geographic entities, respectively, according to their geometric types.

2. The geographic entity construction method based on DLG data parsing according to claim 1, characterized in that, The process of parsing DLG data in graphic file format into spatial vector data, generating a vector dataset, and performing feature type conversion based on geometric features includes: Using DWG or DXF files as input, extract the geometric and attribute information of the DLG data to generate a vector dataset that includes spatial location, layer classification, casscode, and annotation content. During the parsing process, the type of surface / line feature is distinguished based on the closure of the line, and surface / line data is generated respectively. Independent point symbols or zero-length line features are directly mapped to point data.

3. The geographic entity construction method based on DLG data parsing according to claim 1, characterized in that, Direct surface construction methods, auxiliary surface construction methods, or fused surface construction methods also include basic processing procedures; The basic processing flow includes: breaking down line features and polygon features into vector line segments that are connected end to end, and generating a set of line segments; Based on the tolerance setting, the endpoints of the disassembled line segments are expanded to both ends, with the expansion length being the tolerance size; After expansion, we obtain the line set of the expanded line segment and the point set of the endpoints of the expanded line segment; The rasterized image is generated by rasterizing the line set of the expanded line segment and the point set of the endpoints of the expanded line segment. The rasterized image is vectorized; closed surfaces are converted into surface vectors. The surface vector is smoothed to complete the basic processing flow.

4. The geographic entity construction method based on DLG data parsing according to claim 3, characterized in that, For DLG data that contains only the master code, and the master code is a polygon feature but there are unclosed line features, the direct polygon construction method is used to generate closed polygon features. Execute the basic processing flow to implement the direct surface construction method; Complete the basic processing flow to obtain polygon geographic entity features generated by the direct polygonization method.

5. The geographic entity construction method based on DLG data parsing according to claim 3, characterized in that, When the main coded surface features in the DLG data are not closed and the missing boundaries are composed of auxiliary coded line features, the auxiliary surface construction method is used to generate closed surface features. Perform basic processing flows and filtering steps to implement auxiliary surface construction methods; The polygonal geographic entity features generated by executing the basic processing flow are stored in the polygonal geographic entity feature dataset. The filtering steps include: determining whether the polygon geographic entity features generated by the basic processing flow contain annotations; If no annotation is included, the polygon geographic entity feature will be removed from the polygon geographic entity feature dataset.

6. The geographic entity construction method based on DLG data parsing according to claim 3, characterized in that, When the main coded surface features in DLG data are not closed, and the missing boundaries are formed by auxiliary coded line features, and it is necessary to merge adjacent surface features coded as fused, the fused surface construction method is adopted, and auxiliary coded line features are used to help the main coded surface features close. The basic processing flow, surface blending step, and filtering step are executed to achieve the surface blending method; The polygonal geographic entity features generated by executing the basic processing flow are stored in the polygonal geographic entity feature dataset. The filtering steps include: determining whether the polygon geographic entity features generated by the basic processing flow contain annotations; if no annotations are contained, deleting the polygon geographic entity feature from the polygon geographic entity feature dataset; The surface fusion step includes: filtering out surfaces in the surface geographic entity features that only have a primary code or a fusion code in their casscode and then fusion them.

7. The method for constructing geographic entities based on DLG data parsing according to claim 1, characterized in that, When the geometry in the DLG data is accurate, there are no topological errors or deformations, and the attribute information is complete, the direct export method is used. Direct export methods include: exporting point data as point geographic entity features; exporting line data as line geographic entity features; and exporting polygon data as polygon geographic entity features.

8. The method for constructing geographic entities based on DLG data parsing according to claim 1, characterized in that, When a geographic entity is presented as a polygon or line feature during drawing but the production requirement is for a point feature, the point export method is used to convert the polygon or line feature in the DLG data into a point feature. The methods for exporting points include: directly exporting point data as point geographic entity features; using the centroid of the line as the point geographic entity feature for line data, while retaining the attributes of the line data; and using the centroid of the polygon as the point geographic entity feature for polygon data, while retaining the attributes of the polygon data.

9. The method for constructing geographic entities based on DLG data parsing according to claim 1, characterized in that, When a geographic entity is incorrectly parsed as a polygon feature during the drawing process due to its closure characteristics, and the direction and boundary of the geographic entity are not represented by line features, the line export method is used to convert the polygon features in the DLG data into line features. Methods for exporting lines include: directly exporting line data as line geographic entity features; Polygonal data is broken down into polylines, and the exported polylines are used as line geographic entity features, while retaining the attributes of the polygonal data.

10. A geographic entity construction device based on DLG data parsing, characterized in that, include: The parsing module is used to parse DLG data in graphic file format into spatial vector data, generate vector datasets, complete feature type conversion based on geometric features, and simultaneously extract text attributes associated with features. The classification module is used to traverse the vector dataset and perform raw structured classification of feature objects based on preset rules; The preset rules include: classifying polygon data generated by closed line features into polygon data class, classifying line data generated by non-closed line features into line data class, classifying point data generated by point features into point data class, classifying text information on DLG data into annotation class, and storing the geometry type as point type; The extraction module is used to select a data extraction method to extract geographic entity features based on the difference between the original structured classification and the geometric shape to be generated in the mapping table; the data extraction methods include direct surface construction method, auxiliary surface construction method, fused surface construction method, direct export method, export point method, and export line method. The coordinate transformation module is used to convert extracted geographic entity features from the projected coordinate system to the geographic coordinate system; The mapping module is used to perform a mapping between the CASSCode carried by the elements after the coordinate system transformation and the classification code of the basic geographic entity elements to obtain the extraction results. The output module is used to output the extraction results to the feature datasets corresponding to point geographic entity features, line geographic entity features, and polygon geographic entity features, respectively, according to their geometric types.

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