A method for mining area data coordinate conversion and conversion parameter solidification

Abstract

The present invention discloses a method for coordinate conversion and conversion parameter solidification of mining area data, which relates to the field of digital geographic information technology. Based on the common points of mining area coordinates, the accuracy of conversion parameters is analyzed from aspects such as the number of common points and the spatial distribution of common points. A set of coordinate reference conversion parameters with high accuracy and strong applicability is customized for each mining area, and the parameters are stored in a PostgreSQL database adopted by the system. The coordinate conversion algorithm adopts point-by-point conversion, and the conversion accuracy depends only on the accuracy of the conversion parameters. Except for the change in the coordinate position of the composition node of the graphic entity, the layer, color, line type and line width and other attributes of the entity are not changed, and there is no additional accuracy loss. The accuracy of mining area data coordinate conversion is improved, and the integrity of mining area data attributes before and after the data coordinate conversion is guaranteed, which provides assistance for the effective management, sharing and fusion analysis of mining area data.

Description

Technical Field

[0001] The invention belongs to the field of digital geographic information technology, and in particular relates to a method for converting mining area data coordinates and solidifying conversion parameters. Background Art

[0002] With the development of surveying and mapping science and technology, AutoCAD has gradually become one of the important geomorphic mapping, cadastral mapping, and engineering surveying software in the surveying and mapping industry due to its strong operability, powerful editing functions, wide application, and easy learning and mastering. It has produced a large number of surveying and mapping results for the surveying and mapping departments. Mineral resources, as the material basis of national economic and social development, are increasingly valued for their sustainable development, rational development, and effective utilization. With the exploitation of mineral resources, the mining department has used AutoCAD to produce a large amount of mining area topographic data, mining engineering drawings, mining area cross-section data, and tunnel three-dimensional maps. However, due to historical reasons, these data are based on different collection methods, different spatial benchmarks, and different time nodes. As a result, there are large differences in these data spatial coordinate benchmarks, data types, and timeliness. It is difficult to share and integrate data for analysis, which can easily lead to "information islands". Decision makers cannot coordinate the overall situation and make timely decisions, which is very serious. It has a great impact on the scientificity, rationality and effectiveness of decision-making and planning, and brings great challenges to the effective management and utilization of data. Therefore, in order to make full use of mining results in different coordinate systems and realize the sharing and coexistence of resources, it is necessary to solve the problem of mutual conversion of mining results data between different coordinate systems. At present, there are many coordinate conversion software based on AutoCAD, but most of their conversion algorithms adopt the overall method, that is, according to the calculated conversion parameters, the graphic entity is translated, rotated and scaled as a whole based on the base point position. This conversion method is applicable when the graphic coverage area is small, but when the graphic coverage area is large, this method not only has poor accuracy, but may also cause topological relationship confusion between graphic entities, and the graphics after block or frame conversion need to be re-joined, which generates a lot of new workload, and the conversion efficiency decreases instead of increases. Therefore, in order to solve the above problems, the present invention proposes a method for mining data coordinate conversion and conversion parameter solidification. Summary of the Invention

[0003] In view of the shortcomings of the existing technology, the purpose of the present invention is to provide a method for coordinate conversion and conversion parameter solidification of mining area data, so as to solve the technical problems of topological relationship disorder, inability to connect edges of graphics and low conversion efficiency existing in the current coordinate conversion algorithm; at the same time, the mining area conversion parameters are solidified to improve the efficiency of unified coordinate benchmark of mining area data, and provide assistance for effective management, sharing and fusion analysis of mining area data.

[0004] The purpose of the present invention can be achieved by the following technical solution: a method for converting mining area data coordinates and solidifying conversion parameters, the method comprising the following steps:

[0005] Step 1: Develop a mining area data coordinate conversion system on the Visual Studio 2010 platform and test the system's algorithm accuracy;

[0006] Step 2: Summarize the coordinate references to which the CAD data of the mining area is applied, and record the names of the coordinate references and their ellipsoid parameters in the database of the mining area data coordinate conversion system;

[0007] Step 3: Evenly select control points covering the entire mining area, and measure the coordinate values of the control points under various coordinate references in the mining area;

[0008] Step 4: Analyze the influence of control points on the accuracy of transformation parameters from three aspects: the accuracy of control point coordinates, the spatial distribution of control points, and the number of control points. Finally, select the control points with the highest influence on the accuracy of transformation parameters as the common points for calculating transformation parameters.

[0009] Step 5: Use the mining area data coordinate conversion system to customize a set of high-precision and highly applicable coordinate reference conversion parameters for each mining area, and store the coordinate reference conversion parameters in the system database;

[0010] Step 6: Use the mining area data coordinate conversion system and the coordinate reference conversion parameters solidified in the database to perform coordinate conversion on the mining area data to achieve the unification of the mining area data coordinate reference.

[0011] Furthermore, the mining area data coordinate conversion system is written in C development language based on AutoCAD.NET API and PostgreSQL database.

[0012] Furthermore, the process of coordinate conversion of the mining area data includes the following steps:

[0013] Step S1: traverse all layer tables in the DWG drawing file, traverse all layer table records, unlock all frozen or locked layers in the layer table records, and make all layers editable;

[0014] Step S2: Obtain the block table and block table record of the DWG graphic file, traverse all entity index ObjectIds in the block table record, and obtain the entity type through forced type conversion;

[0015] Step S3: Select different entity node acquisition methods based on different entity types, read the coordinate values of all nodes of each entity, then perform coordinate conversion on the coordinates of each point one by one, and reassign the converted coordinate values to the position attribute of the point. For some special entities, their size attributes also need to be modified;

[0016] Step S4: After all entities have completed the coordinate transformation, the frozen and locked states of the layers in the layer table records are restored.

[0017] Furthermore, the mining area data coordinate conversion system only changes the coordinate values of the entity nodes when performing coordinate conversion.

[0018] Furthermore, the mining area data coordinate conversion system will automatically add a file with the same name with the .cr suffix to the imported data and the data exported after coordinate conversion. The coordinate reference information of the mining area data is recorded in the file with the same name using special coding. When the file with the same name is called next time or opened in CAD, the coordinate reference information of the exported data can be automatically identified.

[0019] Furthermore, the solidified coordinate reference conversion parameters are analyzed from three aspects: common point accuracy, common point quantity, and common point spatial distribution. The steps are as follows:

[0020] Calculate the residuals of all known common points, eliminate points with excessive residuals, and record the residuals of all remaining common points. In subsequent steps, give priority to common points with small residuals.

[0021] According to the following situations, the common points are evenly distributed in the whole mining area, partially distributed in the north of the mining area, partially distributed in the south of the mining area, partially distributed in the east of the mining area, partially distributed in the west of the mining area, partially distributed in the center of the mining area, horizontal linear distribution of the common points, vertical linear distribution of the common points, and oblique linear distribution of the common points, 5 groups of samples are set for each situation. The number of common points selected for all samples is the same. The final position error of all samples is calculated to determine the spatial distribution of the common points. The results show that the distribution of the common points is evenly distributed in the whole mining area.

[0022] According to the distribution method of common points evenly distributed in the entire mining area, 10 groups of samples are set, and the number of common points in each group of samples increases. The final point position error of all samples is calculated to determine the number of common points to be selected.

[0023] Beneficial effects of the present invention:

[0024] During use, the present invention analyzes the accuracy of conversion parameters from aspects such as the number of common points and the spatial distribution of common points based on the common points of the mining area coordinates, customizes a set of coordinate reference conversion parameters with high accuracy and strong applicability for each mining area, and stores them in the PostgreSQL database adopted by the system. The coordinate conversion algorithm adopts point-by-point conversion, and the conversion accuracy depends only on the accuracy of the conversion parameters. Except for the change in the coordinate position of the graphic entity composition node, the entity's layer, color, line type and line width and other attributes are not changed, and there is no additional loss of accuracy. This not only improves the accuracy of mining area data coordinate conversion, but also ensures the integrity of mining area data attributes before and after data coordinate conversion, providing assistance for the effective management, sharing and fusion analysis of mining area data. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0026] Figure 1 is a flow chart of the method of the present invention;

[0027] Figure 2 This is a flow chart of the coordinate conversion algorithm of the present invention. DETAILED DESCRIPTION

[0028] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0029] like Figure 1 As shown, a method for converting mining area data coordinates and solidifying conversion parameters includes the following steps:

[0030] Step 1: Develop a mining area data coordinate conversion system on the Visual Studio 2010 platform and test the system's algorithm accuracy;

[0031] Step 2: Summarize the coordinate references to which all mining area CAD data are applied, and record the names of these coordinate references and their ellipsoid parameters in the system database;

[0032] Step 3: Evenly select a large number of control points covering the entire mining area, and measure the coordinate values of these control points under various coordinate references in the mining area;

[0033] Step 4: Analyze the influence of control points on the accuracy of transformation parameters from three aspects: the accuracy of control point coordinates, the spatial distribution of control points, and the number of control points. Finally, select the most appropriate control points as the common points for calculating the transformation parameters.

[0034] Step 5: Use the mining area data coordinate conversion system to customize a set of high-precision and highly applicable coordinate reference conversion parameters for each mining area, and store them in the system's database;

[0035] Step 6: Use the developed mining area data coordinate conversion system and the conversion parameters solidified in the database to perform coordinate conversion on the mining area data to achieve the unification of the mining area data coordinate reference.

[0036] It is necessary to further explain that, in the specific implementation process, a mining area data coordinate conversion system was developed;

[0037] A mine data coordinate conversion system was developed using the C# development language on the Visual Studio 2010 platform, based on the AutoCAD.NET API and the PostgreSQL database. PostgreSQL serves as the data layer, storing coordinate reference names, ellipsoid parameters, mine index information, coordinate conversion parameters, and more. The coordinate conversion algorithm steps are as follows:

[0038] Step S1: Based on the CAD secondary development component Teigha, the database storage organization structure of DWG graphic files is studied to understand all entity types of AutoCAD and summarize the entity attribute acquisition, editing and modification methods of each entity class;

[0039] Step S2: traverse all layer tables in the DWG drawing file, and traverse all layer table records, unlock all frozen or locked layers in the layer table records, so that all layers are in an editable state;

[0040] Step S3: Obtain the layer table and layer table records of the DWG drawing file, traverse all entity index ObjectIds in the layer table records, and obtain the entity type through forced type conversion;

[0041] Step S4: Select different entity node acquisition methods based on different entity types, read the position attributes of all nodes of each entity, then perform coordinate conversion on the coordinates of each point one by one, and reassign the converted coordinate values to the position attributes of the point. For some special entities, their size attributes also need to be modified, such as arcs and ellipses;

[0042] Step S5: After all entities have completed the coordinate transformation, the frozen and locked states of the layers in the layer table records are restored.

[0043] Stores coordinate reference information.

[0044] Summarize the coordinate references to which all mine CAD data are applied, and record the names of these coordinate references and their ellipsoid parameters in the system's database.

[0045] Measure the coordinates of the control points.

[0046] A large number of control points covering the entire mining area are uniformly selected in the mining area, and the coordinate values of these control points under various coordinate references are measured as common points for obtaining conversion parameters. The coordinate values of some common points in the embodiment are as follows:

[0047]

[0048] Select the common point for evaluating the transformation parameters.

[0049] First, select high-precision common points. Calculate the residuals of all known common points, remove points with excessive residuals, and record the residuals of all remaining common points. In subsequent steps, give priority to public points with relatively small residuals. The residuals of some common points in the embodiment are shown below:

[0050]

[0051]

[0052] Secondly, to study the impact of common point distribution on the accuracy of conversion parameters, the same number of common points were selected and three common point layout schemes were used for sample selection. The specific layout schemes are as follows:

[0053] The specific layout plan is as follows:

[0054] The common points are distributed in local areas: the common points are locally distributed in the north of the mining area, the common points are locally distributed in the south of the mining area, the common points are locally distributed in the east of the mining area, the common points are locally distributed in the west of the mining area, and the common points are locally distributed in the center of the mining area;

[0055] The common points are evenly distributed throughout the mining area;

[0056] The common points are distributed in a linear manner: horizontal linear distribution of common points, vertical linear distribution of common points and oblique linear distribution of common points.

[0057] Five experimental samples were selected for each layout method. By using different layout schemes, the sample accuracy was compared and analyzed in combination with the coordinate residuals and mean square error. The results are shown below:

[0058]

[0059]

[0060] Comparative analysis shows that the common points evenly distributed throughout the mining area have the smallest point position error, and the accuracy of its conversion parameters is significantly better than other common point layout schemes. Therefore, when laying out common points, they should be distributed evenly throughout the mining area as much as possible to avoid localized and linear distribution.

[0061] Finally, according to the distribution of common points evenly distributed throughout the mining area, 10 groups of samples are set, and the number of common points in each group of samples increases. The final point error of all samples is calculated to determine the most appropriate number of common points. If it is as follows:

[0062]

[0063] As shown in the table above, the number of common points significantly impacts the accuracy of conversion parameters. When the number of common points is less than 8, the mean error in the point position fluctuates significantly. When the number of common points exceeds 8, the mean error in the point position gradually stabilizes. Generally speaking, the greater the number of common points, the smaller the error and the higher the accuracy of the conversion parameters. However, when the number of common points exceeds 8, while the accuracy of the conversion parameters still improves, the improvement is minimal, and the benefits of increasing the number of common points are negligible. Furthermore, when the number of common points is too high, the accuracy of the common point coordinates themselves cannot be guaranteed, and using them in calculations may even reduce the accuracy of the conversion parameters. Therefore, a minimum of 8 to 10 common points is sufficient, ensuring high conversion parameter accuracy while minimizing measurement costs.

[0064] Conversion parameters are solidified and stored in the database.

[0065] Based on the conclusions in the previous step, 8 to 10 control points are evenly selected within the mining area. A set of high-precision and highly applicable coordinate reference conversion parameters is customized for the mining area in the mining area data coordinate conversion system and stored in the system database.

[0066] Coordinate conversion of mining area data.

[0067] The mining area data in DWG format is imported into the mining area data coordinate conversion system, and the solidified conversion parameters are called from the database to perform coordinate conversion on the mining area data, thus completing the unification of the mining area data coordinate reference.

[0068] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0069] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention, and such changes and modifications are intended to fall within the scope of the present invention.

Claims

1. A method for converting mining area data coordinates and solidifying conversion parameters, characterized in that: The method comprises the following steps: Step 1: Develop a mining area data coordinate conversion system on the Visual Studio 2010 platform and test the system's algorithm accuracy; Step 2: Summarize the coordinate references to which the CAD data of the mining area is applied, and record the names of the coordinate references and their ellipsoid parameters in the database of the mining area data coordinate conversion system; Step 3: Evenly select control points covering the entire mining area, and measure the coordinate values of the control points under various coordinate references in the mining area; Step 4: Analyze the influence of control points on the accuracy of transformation parameters from three aspects: the accuracy of control point coordinates, the spatial distribution of control points, and the number of control points. Finally, select the control points with the highest influence on the accuracy of transformation parameters as the common points for calculating transformation parameters. Step 5: Use the mining area data coordinate conversion system to customize a set of high-precision and highly applicable coordinate reference conversion parameters for each mining area, and store the coordinate reference conversion parameters in the system database; The process of coordinate conversion of the mining area data includes the following steps: Step S1: traverse all layer tables in the DWG drawing file, traverse all layer table records, unlock all frozen or locked layers in the layer table records, and make all layers editable; Step S2: Obtain the block table and block table record of the DWG graphic file, traverse all entity index ObjectIds in the block table record, and obtain the entity type through forced type conversion; Step S3: Select different entity node acquisition methods based on different entity types, read the coordinate values of all nodes of each entity, then perform coordinate conversion on the coordinates of each point one by one, and reassign the converted coordinate values to the position attribute of the point. For some special entities, their size attributes also need to be modified; Step S4: After all entities have completed the coordinate transformation, the frozen and locked states of the layers in the layer table records are restored; The mining area data coordinate conversion system automatically adds a file with the same name and a .cr suffix to the imported data and the data exported after coordinate conversion. The coordinate reference information of the mining area data is recorded in the file with a special code. When the file with the same name is called or opened in CAD next time, the coordinate reference information of the exported data can be automatically identified. Step 6: Use the mining area data coordinate conversion system and the coordinate reference conversion parameters solidified in the database to perform coordinate conversion on the mining area data to achieve the unification of the mining area data coordinate reference; The solidified coordinate reference conversion parameters analyze the influence of common point selection on conversion parameter accuracy from three aspects: common point accuracy, common point quantity and common point spatial distribution.

2. A method for converting mining area data coordinates and solidifying conversion parameters according to claim 1, characterized in that: The mining area data coordinate conversion system is written in C development language based on AutoCAD.NET API and PostgreSQL database.

3. A method for converting mining area data coordinates and solidifying conversion parameters according to claim 2, characterized in that: The mining area data coordinate conversion system only changes the coordinate values of the entity nodes when performing coordinate conversion.

4. The method for converting mining area data coordinates and solidifying conversion parameters according to claim 1, characterized in that: The solidified coordinate reference conversion parameters analyze the impact of common point selection on conversion parameter accuracy from three aspects: common point accuracy, common point quantity, and common point spatial distribution. The steps are as follows: Calculate the residuals of all known common points, eliminate points with excessive residuals, and record the residuals of all remaining common points. In subsequent steps, give priority to common points with relatively small residuals. According to the common points uniformly distributed in the entire mining area, localized in the north of the mining area, localized in the south of the mining area, localized in the east of the mining area, localized in the west of the mining area, localized in the center of the mining area, horizontal linear distribution of common points, vertical linear distribution of common points, and oblique linear distribution of common points, 5 groups of samples were set for each case. The number of common points selected for all samples was the same. The final position error of all samples was calculated to determine the spatial distribution of the common points. The results showed that the distribution of the common points was uniform in the entire mining area. According to the distribution method of common points evenly distributed in the entire mining area, 10 groups of samples are set, and the number of common points in each group of samples increases. The final point position error of all samples is calculated to determine the number of common points to be selected.

Images