A method and system for correcting the offset of geographic entity position

Abstract

The present application discloses a method and system for correcting the offset of the location of a geographic entity, and relates to the field of data information processing. The method includes creating a longitude and latitude grid network of a target area; establishing a coordinate mapping network and a positive and negative coordinate transformation relationship between spherical coordinates and plane coordinates of an online map; obtaining the spatial data of the target entity from the longitude and latitude grid network of the target area; based on the coordinate mapping network and the positive and negative coordinate transformation relationship, converting the spatial data of the target entity into the surface data of the target entity, and converting the surface data of the target entity into the point data of the target entity through an entity matching algorithm; geometrically correcting the point data of the target entity to obtain the geometrically corrected spatial data corresponding to the target entity. This solution eliminates the position deviation caused by coordinate offset in the map through coordinate transformation and geometric correction, improves the accuracy and reliability of geographic entity positioning, and provides accurate and reliable data support for building planning and decision-making.

Description

Technical Field

[0001] The present invention relates to the field of data information processing, and in particular to a method and system for correcting the offset of a geographic entity position. Background Art

[0002] High-precision maps are of great significance in the development planning of the housing and construction industry, especially in the case of large amounts of structured data, intuitively displaying data information through geographic positioning has become an indispensable technology. However, existing map products generally have the problem of geographic coordinate offset.

[0003] Currently, existing map products need to use a specific offset algorithm to process the real coordinates of the map. Since this offset algorithm is nonlinear and the offset direction is not fixed, there is a certain degree of offset error in the map coordinates. These offset errors will seriously affect the accuracy of the results when used in high-precision positioning and data analysis.

[0004] Therefore, how to effectively solve the problem of geographic coordinate offset, reduce the error caused by nonlinear offset, and meet the needs of the housing and construction field for high-precision positioning and data analysis has become an urgent problem to be solved in the current technical field. Summary of the invention

[0005] The purpose of the present invention is to provide a method and system for correcting the offset of geographic entity positions, so as to solve the problem of geographic entity position error caused by coordinate offset in existing maps and improve the accuracy and reliability of geographic entity positioning.

[0006] To achieve the above object, the present invention discloses the following technical solution:

[0007] A first aspect of the present invention provides a method for correcting the offset of a geographic entity position, the method being applied to an online map, comprising:

[0008] Create a longitude and latitude grid network for the target area;

[0009] Establish the coordinate mapping network and the forward and inverse coordinate transformation relationship between spherical coordinates and online map plane coordinates;

[0010] Acquire spatial data of a target entity from a latitude and longitude grid network of the target area;

[0011] Based on the coordinate mapping network and the forward and inverse coordinate transformation relationship, the spatial data of the target entity is converted into the surface data of the target entity, and the surface data of the target entity is converted into the point data of the target entity through an entity matching algorithm;

[0012] The point data of the target entity is geometrically corrected to obtain the geometrically corrected spatial data corresponding to the target entity.

[0013] Optionally, in the above-mentioned offset correction method, the spherical coordinates are WGS84 coordinates, which are used to represent the geographical location of the target entity; the spatial data of the target entity is vector GIS data, including geometric data of points, lines and surfaces, which are used to represent the spatial shape and attribute characteristics of the target entity.

[0014] Optionally, in the above-mentioned offset correction method, the step of creating a longitude and latitude grid network of the target area includes:

[0015] Set the target longitude difference and target latitude difference to determine the accuracy of grid division;

[0016] Based on the target longitude difference and the target latitude difference, a longitude and latitude grid network of the target area is created, and each grid node of the longitude and latitude grid network corresponds to a specific longitude and latitude position.

[0017] Optionally, the above-mentioned offset correction method, converting the spatial data of the target entity into the surface data of the target entity based on the coordinate mapping network and the forward and inverse coordinate transformation, includes:

[0018] Acquiring the plane coordinates of the target entity based on the spatial data of the target entity;

[0019] Based on the forward and inverse coordinate transformation relationship, the plane coordinates of the target entity are converted into the WGS84 spherical coordinates of the target entity;

[0020] Based on the coordinate mapping network, the WGS84 spherical coordinates of the target entity are converted into the online map plane coordinates of the target entity to generate the surface data of the target entity.

[0021] Optionally, in the above-mentioned offset correction method, the surface data of the target entity is stored in 3D Tiles format or GeoJSON format, and the surface data is represented by a closed polygon composed of multiple arc segments, which is used to describe the spatial boundary and geometric shape of the target entity; the point data of the target entity is a set of coordinate values, which is used to represent the spatial position of the target entity.

[0022] Optionally, the above-mentioned offset correction method, wherein the surface data of the target entity is converted into point data of the target entity by an entity matching algorithm, further comprises:

[0023] Calculate the geometric center of the surface data of the target entity through multi-scale point, line and surface matching, and extract the spatial coordinate value corresponding to the geometric center;

[0024] Based on the geometric features of the target entity, the ICP algorithm is used to extract feature points from the surface data of the target entity to generate a set of key feature points;

[0025] The geometric center of the surface data or the extracted key feature points are used as the point data of the target entity to generate a point data set corresponding to the target entity.

[0026] Optionally, in the above-mentioned offset correction method, the step of geometrically correcting the point data of the target entity to obtain geometrically corrected spatial data corresponding to the target entity includes:

[0027] Overlay two or more sets of surface data of the same scale, merge the overlapping areas, and generate a new data set;

[0028] Based on preset selection parameters, data screening is performed on the overlapping part of the superimposed data set, wherein the selection parameters include height information and spatial coordinate information of the white model entity corresponding to the target entity;

[0029] The filtered data is processed by a geometric correction algorithm to obtain geometrically corrected spatial data corresponding to the target entity.

[0030] Optionally, in the above-mentioned offset correction method, the filtered data is processed by a geometric correction algorithm to obtain geometrically corrected spatial data corresponding to the target entity, including:

[0031] The screened data were geometrically corrected using the inverse ratio transformation model to eliminate the spatial bias of the data;

[0032] Based on the geometric features and geographic location of the target entity, the corrected data is optimized using an ICP algorithm to generate geometrically corrected spatial data;

[0033] A spatial data model is generated according to the geometrically corrected spatial data.

[0034] Optionally, the above offset correction method further includes:

[0035] Performing pattern processing on the geometrically corrected spatial data corresponding to the target entity to generate entity contour data corresponding to the target entity;

[0036] Based on the entity contour data, a three-dimensional model corresponding to the target entity is generated using a three-dimensional modeling method.

[0037] Another aspect of the present invention provides a system for correcting the offset of a geographic entity position, which is applied to an online map, and the system comprises:

[0038] A grid creation module is used to create a longitude and latitude grid network of the target area;

[0039] Coordinate mapping module, used to establish the coordinate mapping network and the positive and negative coordinate transformation relationship between spherical coordinates and online map plane coordinates;

[0040] A data acquisition module, used to acquire spatial data of a target entity from a latitude and longitude grid network of the target area;

[0041] A data conversion module, for converting the spatial data of the target entity into the surface data of the target entity based on the coordinate mapping network and the forward and inverse coordinate transformation relationship, and converting the surface data of the target entity into the point data of the target entity through an entity matching algorithm;

[0042] The data correction module is used to perform geometric correction on the point data of the target entity to obtain the geometrically corrected spatial data corresponding to the target entity.

[0043] The effects provided in the content of the invention are only the effects of the embodiments, not all the effects of the invention. One of the above technical solutions has the following advantages or beneficial effects:

[0044] The geographic entity position offset correction method and system of the present application, by establishing a longitude and latitude grid network, a coordinate mapping network and a forward and inverse coordinate transformation relationship, and combining a geometric correction algorithm, effectively and accurately converts the spatial data of the geographic entity, eliminating coordinate offset and projection errors. By creating an accurate grid network and establishing a mapping relationship between spherical coordinates and plane coordinates, it is ensured that the spatial data of the geographic entity can be accurately converted between different coordinate systems, avoiding the common error problems in traditional map products. Through the application of the geometric correction algorithm, the accuracy of the data is further optimized, and the errors caused by coordinate conversion and projection are eliminated, making the positioning of geographic entities on the map more accurate and reliable. Compared with the prior art, this solution solves the problem of geographic entity position error caused by coordinate offset in map products, and provides reliable and accurate data support for the subsequent planning and decision-making of building entities. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0046] Figure 1 A schematic diagram of a method for correcting an offset of a geographic entity position according to an embodiment of the present application;

[0047] Figure 2 for Figure 1 A flowchart of a specific implementation method of step S140;

[0048] Figure 3A schematic diagram of the structure of a system for correcting the offset of a geographic entity position according to an embodiment of the present application;

[0049] Figure 4 A schematic diagram of the structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0051] It should be noted that references to "one embodiment", "embodiment", "example embodiment", etc. in this specification refer to the embodiment being described which may include specific features, structures or characteristics, but not every embodiment must include these specific features, structures or characteristics. In addition, such expressions do not refer to the same embodiment. Furthermore, when describing specific features, structures or characteristics in conjunction with an embodiment, whether or not there is an explicit description, it is indicated that incorporating such features, structures or characteristics into other embodiments is within the knowledge of those skilled in the art.

[0052] In practical applications, drawings from different sources usually use different coordinate systems and projection methods, even including hand-drawn drawings, whose coordinate systems are significantly different from the standard geographic coordinate system. In particular, engineering drawings in DWG format may use different coordinate systems (such as local coordinate systems or custom coordinate systems), and the projection parameters of these coordinate systems are inconsistent with standard geographic coordinate systems (such as WGS-84 or Geodesy 2000 coordinate systems). Therefore, directly mapping these drawings to the standard geographic coordinate system based on the spatial data of the target entity will cause coordinate errors and affect the final spatial data accuracy. In addition, hand-drawn drawings often lack standardized coordinate systems and even do not have enough reference points, which makes direct spatial data mapping more complicated and cannot guarantee high-precision data output. In order to solve these problems, effective calibration and conversion methods are needed.

[0053] Normally, when processing drawings, technicians may try to convert the coordinate data on the drawings into standard spatial data through direct mapping. However, due to the existence of different coordinate systems and projection methods, direct mapping will lead to errors, especially when facing unknown coordinate systems and hand-drawn drawings. At this time, the traditional direct mapping method may not be able to accurately handle the spatial data differences in the drawings, especially when the drawings use non-standard coordinate systems, the accuracy of coordinate conversion will be affected. Therefore, relying solely on direct mapping cannot ensure the accuracy of coordinate conversion.

[0054] Therefore, due to the nonlinear characteristics of the coordinate offset algorithm, existing map products generally have the problem of geographic entity position deviation, especially in high-precision positioning and data analysis, the offset error will seriously affect the accuracy of the results, thereby interfering with project planning and decision-making. In order to solve this problem, the embodiment of the present application provides a method and system for correcting the offset of the geographic entity position, which eliminates the deviation of the geographic entity position through coordinate transformation and geometric correction, and improves the accuracy and reliability of geographic entity positioning.

[0055] refer to Figure 1 , Figure 1 A schematic flow chart of a method for correcting the offset of a geographic entity position according to an embodiment of the present application is shown. The method package is applied to an online map, and includes:

[0056] S110, creating a longitude and latitude grid network of the target area;

[0057] Since the longitude and latitude lines are interwoven to form a longitude and latitude grid, the geographical locations of various locations and regions within the target area can be accurately calibrated through the intersection of these longitude and latitude lines.

[0058] S120, establishing a coordinate mapping network and a forward and reverse coordinate transformation relationship between spherical coordinates and online map plane coordinates;

[0059] The coordinate mapping network can realize the conversion from spherical coordinates to plane coordinates, and the positive and negative coordinate transformation relationships support bidirectional conversion, ensuring accurate interchange between geographic coordinates and map coordinates for subsequent data processing and geographic entity positioning.

[0060] S130, acquiring spatial data of the target entity from the longitude and latitude grid network of the target area;

[0061] This step uses the created longitude and latitude grid network to determine the specific location in the target area and obtain the corresponding spatial data. The spatial data contains the geographic location, shape and attribute characteristics of the target entity, such as point, line or surface data.

[0062] S140, based on the coordinate mapping network and the forward and inverse coordinate transformation relationship, converting the spatial data of the target entity into the surface data of the target entity, and converting the surface data of the target entity into the point data of the target entity through an entity matching algorithm;

[0063] First, the spatial data of the target entity (such as longitude and latitude information) is converted into surface data (geometric surface of the target entity, such as the outline of buildings and roads, etc.) through the coordinate mapping network and the positive and negative coordinate transformation relationship. Then, the surface data is processed using the entity matching algorithm and converted into point data for subsequent analysis and operation.

[0064] S150, geometrically correcting the point data of the target entity to obtain geometrically corrected spatial data corresponding to the target entity;

[0065] By using a geometric correction algorithm to correct the point data obtained from the above conversion, the spatial position deviation caused by coordinate offset or projection error can be eliminated, and more accurate geometrically corrected spatial data can be obtained.

[0066] The method of this embodiment, by establishing a longitude and latitude grid network, a coordinate mapping network and a forward and reverse coordinate transformation relationship, and combining a geometric correction algorithm, effectively converts the spatial data of geographic entities accurately, eliminates coordinate offset and projection errors, solves the problem of geographic entity position error caused by coordinate offset in traditional map products, and realizes high-precision positioning and spatial data correction of target entities. Specifically, by establishing a coordinate mapping network, the mapping relationship between different coordinate systems in the drawing and the standard geographic coordinate system can be effectively established. The coordinate mapping network can achieve accurate coordinate conversion by learning existing known point data, such as known positions in the standard coordinate system, and avoid errors caused by the direct mapping method. Positive and reverse coordinate transformation can solve the offset problem in the coordinate system conversion process. The positive coordinate transformation converts the coordinates in the drawing from the offset coordinate system to the standard spatial coordinate system, while the reverse coordinate transformation can be reversely converted in actual applications to perform interactive operations between different coordinate systems. The combination of these two conversion steps can ensure that the spatial data finally obtained has high accuracy and reliability. These steps play a vital role in processing drawings of different coordinate systems, especially when precise calibration is required, they provide effective guarantees for the standardized conversion of drawing data.

[0067] In addition, geometric errors in drawings, such as deformation caused by hand-drawn drawings or different projection methods, often cannot be eliminated by simple coordinate mapping, so additional geometric correction steps are required. Drawings may contain geometric deformations caused by drawing errors, scale distortion of hand-drawn drawings, or projection distortion. These problems will cause errors in the position, size, or scale of objects on the drawings, affecting subsequent data analysis and processing. Geometric correction corrects the deformation in the drawings by referencing known points such as known geographic coordinates in a standard coordinate system. By comparing with standard maps or high-precision geographic data, the errors on the drawings can be accurately corrected to ensure that the output spatial data meets the requirements in terms of geographic accuracy. Geometric correction not only eliminates potential errors in coordinate mapping, but also further ensures that the geometric form of the spatial data conforms to the actual situation and avoids position deviations caused by drawing deformation.

[0068] Preferably, in the above embodiment, the spherical coordinates are WGS84 coordinates, which are used to represent the geographic location of the target entity. The spatial data of the target entity is vector GIS data, including geometric data of points, lines and surfaces, which are used to represent the spatial shape and attribute characteristics of the target entity.

[0069] In some embodiments, the longitude and latitude grid network of the target area created in the above-mentioned step S110 specifically includes: first, setting the target longitude difference and the target latitude difference to determine the accuracy of the grid division. The two parameters of the target longitude difference and the target latitude difference determine the accuracy of the grid. The longitude difference refers to the distance between two adjacent longitudes, and the latitude difference refers to the distance between two adjacent latitudes. By accurately controlling these differences, the accuracy of the grid division can be ensured, thereby ensuring the accuracy and positioning accuracy of subsequent data. Next, based on the target longitude difference and the target latitude difference, a longitude and latitude grid network of the target area is created. Each grid node of the longitude and latitude grid network corresponds to a specific longitude and latitude position, and these nodes provide a basic framework for the spatial positioning of the entire area. The grid network can help to quickly calibrate the geographical location of each location in the target area, ensuring the accuracy of subsequent data processing and coordinate conversion.

[0070] refer to Figure 2 In some embodiments, the above step S140 converts the spatial data of the target entity into the surface data of the target entity based on the coordinate mapping network and the forward and reverse coordinate transformation, specifically including:

[0071] S141, acquiring the plane coordinates of the target entity based on the spatial data of the target entity;

[0072] The spatial data of the target entity is obtained through satellites, surveying equipment or GIS geographic information systems. These data are usually expressed in high-precision three-dimensional spatial coordinates. In order to apply these data to the map, they need to be converted into values ​​in a plane coordinate system. In the plane coordinate system, the position of the target entity is simplified to two-dimensional coordinates, which makes it easier to project and display the map.

[0073] S142, based on the forward and reverse coordinate transformation relationship, converting the plane coordinates of the target entity into the WGS84 spherical coordinates of the target entity;

[0074] The plane coordinates of the target entity are usually expressed based on a local coordinate system (such as the UTM coordinate system). These coordinate systems are usually two-dimensional projection systems for a specific area and are used to express the location of geographic entities on a plane. In order to ensure the compatibility and uniformity of geographic data, these coordinates need to be further converted into WGS84 spherical coordinates. By applying the forward and reverse coordinate transformation relationship, the plane coordinates can be accurately converted into spherical coordinates, thereby ensuring that the data can be docked and unified across different coordinate systems.

[0075] S143, based on the coordinate mapping network, converting the WGS84 spherical coordinates of the target entity into the online map plane coordinates of the target entity, and generating the surface data of the target entity;

[0076] In order to accurately display the target entity on the map, it is necessary to convert the WGS84 spherical coordinates back to the plane coordinates of the online map. At this time, through the constructed coordinate mapping network, the mapping relationship between the spherical coordinates and the plane coordinates on the online map can be established. Online maps usually use a specific projection system (such as Web Mercator), and the WGS84 spherical coordinates need to be accurately converted to ensure that the geographic location can be correctly mapped to the two-dimensional map plane. The surface data represents the geometric shape of the target entity on the map, usually a polygon composed of multiple coordinate points, which determine the boundary and spatial position of the entity on the map. Preferably, the surface data of the target entity can be stored in 3D Tiles format or GeoJSON format to display the shape, area and position of the target entity in the online map, which is convenient for further geographic analysis, such as measuring area, shape recognition, spatial relationship analysis, etc.

[0077] Through the above process, spatial data can be accurately converted from the local coordinate system to the global coordinate system WGS84, and finally converted to the plane coordinates used by online maps. This process not only ensures the accurate conversion of geographic entities between different coordinate systems, but also eliminates data inconsistency problems caused by coordinate offset, projection error, etc. The final surface data can be correctly displayed in the map application, providing a data basis for subsequent spatial analysis, decision support, etc.

[0078] The point data of the target entity can be a set of coordinate values, which are used to represent the spatial position of the target entity. Figure 2 In the above step S140, the surface data of the target entity is converted into point data of the target entity by using an entity matching algorithm, further comprising:

[0079] S144, calculating the geometric center (center of mass) of the surface data of the target entity through a multi-scale point, line, and surface matching method, and extracting the spatial coordinate value corresponding to the geometric center;

[0080] The geometric center is the spatial center position of the surface data, which can be obtained by weighted average of the coordinates of all vertices in the surface data. By extracting the geometric center of the target entity in space, the spatial expression of the target entity can be simplified and a reference point can be provided for the generation of point data.

[0081] S145, based on the geometric features of the target entity, using the ICP algorithm to extract feature points from the surface data of the target entity to generate a set of key feature points;

[0082] The ICP (Iterative Closest Point) algorithm is an algorithm used for point cloud alignment and feature point matching. It can efficiently identify key feature points by iteratively calculating the input surface data. The extracted feature point set reflects the shape, boundary, and unique structure of the surface data, providing a basis for surface data simplification and point data generation.

[0083] S146, using the geometric center of the surface data or the extracted key feature points as point data of the target entity, and generating a point data set corresponding to the target entity;

[0084] The point data of the point data set can be a single geometric center point or a collection of multiple key feature points, which can be adjusted according to application requirements. For example, in a high-precision map, more feature points can be selected to enhance the representation capability, while in a simplified map, only the geometric center point can be used.

[0085] Through the above process, the complex surface data of the target entity is accurately converted into point data, which simplifies the representation of the target entity and adapts to the application needs of geographic information at different levels. At the same time, the extraction of geometric center and key feature points ensures the spatial accuracy of the data while retaining the geometric features. This method is particularly suitable for high-precision map display and complex geographic analysis scenarios. It reduces the computational complexity while improving the expressiveness and practicality of the data.

[0086] In some embodiments, the step S150 described above performs geometric correction on the point data of the target entity to obtain geometrically corrected spatial data corresponding to the target entity, including:

[0087] First, two or more sets of surface data of the same scale are superimposed to generate a new data set. In this step, the surface data used for superposition usually have the same scale to ensure that their geometric characteristics remain consistent in space. The superposition process merges the overlapping areas of the surface data to generate a new data set containing overlapping and non-overlapping parts.

[0088] Then, based on the preset selection parameters, the overlapping parts in the superimposed data set are screened, and the selection parameters include the height information and spatial coordinate information of the white model entity corresponding to the target entity. In this step, the selection parameters include the height information and spatial coordinate information of the white model entity corresponding to the target entity, so as to judge the priority of the data. For example, the height information reflects the physical characteristics of the entity (such as the building height or the terrain undulation), and the spatial coordinate information is used to judge the geographic location accuracy of the data. Through these parameters, the geometric data that meets the actual characteristics of the target entity can be retained first, and the part with large errors can be eliminated.

[0089] Next, the filtered data is processed by a geometric correction algorithm to obtain geometrically corrected spatial data corresponding to the target entity. After the screening is completed, the data is further processed using a geometric correction algorithm. The geometric correction algorithm comprehensively considers factors such as the boundary deviation of the superimposed area, the spatial coordinate error, and the topological structure to adjust and optimize the filtered data. In some embodiments, the filtered data is processed by a geometric correction algorithm to obtain geometrically corrected spatial data corresponding to the target entity, further comprising:

[0090] First, the inverse transformation model is used to geometrically correct the filtered data to eliminate the spatial deviation of the data. The inverse transformation model is a geometric correction method based on the coordinate ratio relationship, which can effectively correct the spatial deviation introduced by projection transformation or other coordinate transformation processes to ensure the accuracy and consistency of data ratio. Then, based on the geometric characteristics and geographic location of the target entity, the ICP algorithm is used to further optimize the corrected data. The ICP algorithm continuously adjusts the position, rotation and scaling of the data to maximize the geometric consistency between matching points, thereby enhancing the geometric accuracy of the target entity. Finally, a spatial data model is generated based on the geometrically corrected spatial data. This model is based on the corrected point, line and surface data, combined with the geometric and attribute information of the geographic entity, to form an accurate spatial data representation that can be used in a variety of geographic information applications.

[0091] Specifically, the geometric correction algorithm is as follows: The current mapping of dwg drawings is mainly used in two-dimensional scenes. Therefore, the current geographic calibration can be simplified to the coordinate system conversion problem of the two-dimensional plane. The points are fixed in the two-dimensional plane, and the transformation matrices corresponding to the two coordinate systems are calculated to realize the conversion of all points and complete the mapping. The transformation matrix acts on the coordinates of the original point, including (translation, rotation, scaling) to obtain new coordinates. In the embodiment of this application, only three conversion methods are considered, and the case of miscutting is not considered.

[0092] First, the explanation of translation: Assume that point ( x,y,z ) is translated in the three coordinate axes by [ Tx, Ty, Tz ] , then the translation relationship is as follows:

[0093] ;

[0094] In a two-dimensional scene, the translation matrix Mt can be obtained as:

[0095] .

[0096] Secondly, regarding the explanation of rotation: Assume that the point ( x,y,z ) rotate around the three coordinate axes θ Angle, then the transformation relationship after rotation around the X axis is:

[0097] ;

[0098] The transformation relationship for rotation around the y-axis is:

[0099] ;

[0100] The transformation relationship for rotation around the z-axis is:

[0101] ;

[0102] The rotation of the two-dimensional plane can refer to the z-axis rotation to get the rotation matrix Mr:

[0103] .

[0104] For scaling, you need to determine the scaling factor t , assuming the same scaling factor in the x-axis and y-axis directions, the scaling matrix Ms is:

[0105] .

[0106] The specific calculation process is as follows: Assume that the coordinate system to be transformed is set as A, and the two selected points are set as a1, a2 , set the target coordinate system to B (usually the 4490 coordinate system), and set the corresponding two selected points to b1, b2 .

[0107] In this case, two vectors can be obtained, namely and , the transformation to be calculated is the transformation between these two vectors. The transformation process can be divided into the following four steps:

[0108] (1) Pan to the origin.

[0109] In this step, it is equivalent to a1 The point is translated to the origin, assuming a1 The coordinates of , we can get the translation matrix T1 of this step as:

[0110] ;

[0111] (2) Rotate to Same angle.

[0112] In this step, you need to first calculate the angle between the two vectors , the corresponding formula is , that is, assuming for( x1,y1) , for( m,n ), we can get the inner product , , , and thus the value obtained. When calculating the rotation matrix, since the sin value needs to be calculated, it is necessary to determine the positive and negative values ​​of the angle. Clockwise is negative and counterclockwise is positive. It is necessary to use vector cross multiplication to determine the positive and negative values ​​of the angle. The formula is , if the result is <0, it is clockwise and the angle needs to be negated. Then we can get the rotation matrix R1 as follows:

[0113] .

[0114] (3) Zoom to Same length.

[0115] In this step, you need to calculate the lengths of the two vectors separately, and then calculate the scaling factor, refer to formula (2) , , and then calculate the scaling factor t1 as , the scaling matrix S1 can be obtained as follows:

[0116] .

[0117] (4) Pan to Location.

[0118] In this step, it is equivalent to translating the origin to b1 point, assuming b1 The coordinates of p,q ), then the translation matrix T2 of this step can be obtained as:

[0119] .

[0120] After four steps, we can map any point in the original coordinate system to the target coordinate system. Assuming that we need to convert point C, we can apply the T1, R1, S1 and T2 matrices in the above steps in turn to get the corresponding point in the target coordinate system. It should be noted that the coordinate units of the two coordinate systems must be consistent. For example, the default units are both meters.

[0121] In some embodiments, the above-mentioned method for correcting the offset of the geographic entity position further comprises the following steps:

[0122] First, the geometrically corrected spatial data corresponding to the target entity is processed to generate entity contour data corresponding to the target entity. By analyzing the geometric features in the spatial data, the spot processing performs region segmentation and contour recognition on the target entity, eliminates interference from irrelevant areas, and ensures the accuracy of the contour data. Then, based on the entity contour data, a three-dimensional modeling method is used to generate a three-dimensional model corresponding to the target entity. The three-dimensional modeling method can specifically use stretching modeling, surface fitting, or refined modeling based on point cloud data, combining contour data with height information, material properties, etc., to accurately reproduce the three-dimensional form of the target entity.

[0123] In other embodiments, the forward transformation can also be used to perform geometric correction on the vector GIS data, and publish related geoprocessing GP services. The GP service supports calling through a deep learning interface (such as a Python-based method) to perform overlay calculations on data files of two or more groups of polygonal elements in the same area and the same scale. The system generates a new data set for the overlay area, and screens the overlapping parts based on preset selection parameters (such as geometric features, position deviations, height information, etc.), and extracts the geometrically corrected spatial data of the target entity, that is, the point data of the target entity. In addition, the corrected data can be converted into a format through the CesiumLab tool to adapt to the Cesium API and generate three-dimensional model data. These three-dimensional models can be loaded and parsed on the virtual earth platform to provide data support for the accurate display and spatial analysis of housing entity information.

[0124] In addition, in some embodiments, the conversion from the plane coordinate system to the latitude and longitude coordinate system can be implemented by the following code:

[0125] var x_PI = 3.14159265358979324 3000.0 / 180.0;

[0126] var PI = 3.1415926535897932384626;

[0127] var a = 6378245.0;

[0128] varee = 0.00669342162296594323;

[0129] var MCBAND = [12890594.86, 8362377.87, 5591021, 3481989.83,1678043.12, 0];

[0130] var MC2LL = [

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[0133] <h2 style=";text-align:left;direction:ltr"> [-3.030883460898826e-8, 0.00000898305509983578, 0.30071316287616,59.74293618442277, 7.357984074871, -25.38371002664745, 13.45380521110908, -3.29883767235584, 0.32710905363475, 6856817.37],<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr">

[0134] <h2 style=";text-align:left;direction:ltr"> [-1.981981304930552e-8, 0.000008983055099779535, 0.03278182852591,40.31678527705744, 0.65659298677277, -4.44255534477492, 0.85341911805263,0.12923347998204, -0.04625736007561, 4482777.06],<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr">

[0135] [3.09191371068437e-9, 0.000008983055096812155, 0.00006995724062, 23.10934304144901, -0.00023663490511, -0.6321817810242, -0.00663494467273,0.03430082397953, -0.00466043876332, 2555164.4]

[0136] The MCBAND array defines the different levels of division, and the MC2LL array defines the corresponding conversion parameters. According to the input plane coordinates, these parameters can be used to calculate and obtain the corresponding longitude and latitude coordinates. The specific implementation logic of this code also includes other functions, which need to be determined according to the specific application scenario.

[0137] x_PI: represents the correction factor for longitude, used to convert longitude values ​​from degrees to radians.

[0138] PI: stands for pi, which is the number of circles per circumference of a circle.

[0139] a: represents the length of the semi-major axis of the Earth's ellipsoid.

[0140] ee: represents the square of the eccentricity of the ellipsoid.

[0141] MCBAND: An array of six numbers used to divide the different levels of the plane coordinate system.

[0142] MC2LL: A two-dimensional array used to perform conversion calculations from a plane coordinate system to longitude and latitude.

[0143] In summary, the offset correction method of the geographic entity position of the present embodiment ensures that the spatial data of the geographic entity is accurate and consistent through multi-level coordinate conversion and geometric correction. First, by establishing a longitude and latitude grid network and a coordinate mapping network, the spatial data of the target entity is converted from the local coordinate system to the standard WGS84 spherical coordinates, eliminating coordinate offset and projection errors. Then, the data is geometrically corrected through the forward and reverse coordinate transformation model to optimize the consistency of the data, and the position, rotation and scaling of the target entity are further optimized using the ICP algorithm to improve the geometric accuracy of the data. Next, the point data of the target entity is generated by using a multi-scale point, line, and surface matching algorithm and geometric center extraction, simplifying the surface data representation. Through data screening and processing of the geometric correction algorithm, the geometrically corrected spatial data is generated, which can be further applied to three-dimensional modeling and geographic information analysis. It can be seen that this method provides reliable and accurate data support for high-precision map display, spatial analysis and decision-making, and solves the problem of geographic entity position error caused by coordinate offset in existing maps.

[0144] It should be noted that, for the method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the embodiments of the present application are not limited by the described order of actions, because according to the embodiments of the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by the embodiments of the present application.

[0145] refer to Figure 3 , Figure 3 A schematic diagram of the structure of a system for correcting the offset of a geographic entity position according to an embodiment of the present application is shown. The offset correction system is applied to online maps and includes:

[0146] A grid creation module 310, for creating a longitude and latitude grid network of a target area;

[0147] A coordinate mapping module 320, for establishing a coordinate mapping network and a positive and negative coordinate transformation relationship between spherical coordinates and online map plane coordinates;

[0148] A data acquisition module 330 is used to acquire spatial data of a target entity from a latitude and longitude grid network of the target area;

[0149] A data conversion module 340, for converting the spatial data of the target entity into the surface data of the target entity based on the coordinate mapping network and the forward and reverse coordinate transformation relationship, and converting the surface data of the target entity into the point data of the target entity through an entity matching algorithm;

[0150] The data correction module 350 is used to perform geometric correction on the point data of the target entity to obtain geometrically corrected spatial data corresponding to the target entity.

[0151] Regarding the processes not described in detail in the system for correcting the offset of the geographic entity position in this embodiment, reference may be made to the relevant parts in the above embodiment of the method for correcting the offset of the geographic entity position, which will not be described in detail here.

[0152] refer to Figure 4 The present application also provides an electronic device for executing the offset correction method of the geographic entity position in the above embodiment. Figure 4As shown, the electronic device is an example of a hardware device that can be applied to various aspects of the present invention. The electronic device is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are only examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0153] like Figure 4 As shown, the electronic device includes a computing unit, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) or a computer program loaded from a storage unit into a random access memory (RAM). In the RAM, various programs and data required for the operation of the device can also be stored. The computing unit, ROM, and RAM are connected to each other through a bus. The input / output (I / O) interface is also connected to the bus.

[0154] Multiple components in the electronic device are connected to the I / O interface, including: an input unit, an output unit, a storage unit, and a communication unit. The input unit can be any type of device that can input information to the electronic device, and the input unit can receive input digital or character information, and generate key signal input related to user settings and / or function control of the electronic device. The output unit can be any type of device that can present information, and can include but is not limited to a display, a speaker, a video / audio output terminal, a vibrator, and / or a printer. The storage unit can include but is not limited to a disk, an optical disk. The communication unit allows the electronic device to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks, and can include but is not limited to a modem, a network card, an infrared communication device, a wireless communication transceiver, and / or a chipset, such as a Bluetooth™ device, a WiFi device, a WiMax device, a cellular communication device, and / or the like.

[0155] The computing unit may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of computing units include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit performs the various methods and processes described above.

[0156] Regarding the processes not described in detail in the electronic device in this embodiment, reference may be made to the relevant parts of the above-mentioned embodiment of the method for correcting the offset of the geographic entity position, which will not be described in detail here.

[0157] The above is a detailed introduction to the offset correction method and system for the location of a geographic entity provided in the embodiment of the present application. The various embodiments in the specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same and similar parts between the various embodiments can be referenced to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description. It should be pointed out that for ordinary technicians in this field, without departing from the principles of the present application, several improvements and modifications can be made to the present application, and these improvements and modifications also fall within the scope of protection of the claims of the present application.

Claims

1. A method for correcting the offset of a geographic entity position, characterized in that: Applied to online maps, the method includes: Create a longitude and latitude grid network for the target area; Establish the coordinate mapping network and the forward and inverse coordinate transformation relationship between spherical coordinates and online map plane coordinates; Acquire spatial data of a target entity from a latitude and longitude grid network of the target area; Based on the coordinate mapping network and the forward and reverse coordinate transformation relationship, the spatial data of the target entity is converted into the surface data of the target entity, and the surface data of the target entity is converted into the point data of the target entity through an entity matching algorithm; the surface data is represented by a closed polygon composed of multiple arc segments, which is used to describe the spatial boundary and geometric shape of the target entity; the point data of the target entity is a set of coordinate values, which is used to represent the spatial position of the target entity; The step of converting the surface data of the target entity into the point data of the target entity by the entity matching algorithm includes: calculating the geometric center of the surface data of the target entity by multi-scale point, line and surface matching, and extracting the spatial coordinate value corresponding to the geometric center; extracting feature points from the surface data of the target entity by the ICP algorithm based on the geometric features of the target entity, and generating a set of key feature points; using the geometric center of the surface data or the extracted key feature points as the point data of the target entity, and generating a set of point data corresponding to the target entity; The point data of the target entity are geometrically corrected to obtain the geometrically corrected spatial data corresponding to the target entity, including: superimposing two or more groups of surface data of the same scale, merging the superimposed areas, and generating a new data set; based on preset selection parameters, filtering the overlapping parts in the superimposed data set, the selection parameters including the height information and spatial coordinate information of the white model entity corresponding to the target entity; processing the filtered data by a geometric correction algorithm to obtain the geometrically corrected spatial data corresponding to the target entity.

2. The offset correction method according to claim 1, characterized in that: The spherical coordinates are WGS84 coordinates, which are used to represent the geographical location of the target entity; the spatial data of the target entity are vector GIS data, including geometric data of points, lines and surfaces, which are used to represent the spatial shape and attribute characteristics of the target entity.

3. The offset correction method according to claim 2, characterized in that: The step of creating a latitude and longitude grid network of the target area includes: Set the target longitude difference and target latitude difference to determine the accuracy of grid division; Based on the target longitude difference and the target latitude difference, a longitude and latitude grid network of the target area is created, and each grid node of the longitude and latitude grid network corresponds to a specific longitude and latitude position.

4. The offset correction method according to claim 3, characterized in that: The step of converting the spatial data of the target entity into the surface data of the target entity based on the coordinate mapping network and the forward and inverse coordinate transformation includes: Acquiring the plane coordinates of the target entity based on the spatial data of the target entity; Based on the forward and inverse coordinate transformation relationship, the plane coordinates of the target entity are converted into the WGS84 spherical coordinates of the target entity; Based on the coordinate mapping network, the WGS84 spherical coordinates of the target entity are converted into the online map plane coordinates of the target entity to generate the surface data of the target entity.

5. The offset correction method according to any one of claims 1 to 4, characterized in that: The surface data of the target entity is stored in 3D Tiles format or GeoJSON format.

6. The offset correction method according to claim 1, characterized in that: The step of processing the filtered data by a geometric correction algorithm to obtain geometrically corrected spatial data corresponding to the target entity includes: The screened data were geometrically corrected using the inverse ratio transformation model to eliminate the spatial bias of the data; Based on the geometric features and geographic location of the target entity, the corrected data is optimized using an ICP algorithm to generate geometrically corrected spatial data; A spatial data model is generated according to the geometrically corrected spatial data.

7. The offset correction method according to claim 1, characterized in that: The method further comprises: Performing pattern processing on the geometrically corrected spatial data corresponding to the target entity to generate entity contour data corresponding to the target entity; Based on the entity contour data, a three-dimensional model corresponding to the target entity is generated using a three-dimensional modeling method.

8. A system for correcting the offset of a geographic entity position, characterized in that: Applied in online maps, the system includes: A grid creation module is used to create a longitude and latitude grid network of the target area; Coordinate mapping module, used to establish the coordinate mapping network and the positive and negative coordinate transformation relationship between spherical coordinates and online map plane coordinates; A data acquisition module, used to acquire spatial data of a target entity from a latitude and longitude grid network of the target area; A data conversion module, for converting the spatial data of the target entity into the surface data of the target entity based on the coordinate mapping network and the forward and reverse coordinate transformation relationship, and converting the surface data of the target entity into the point data of the target entity through an entity matching algorithm; the surface data is represented by a closed polygon composed of multiple arc segments, which is used to describe the spatial boundary and geometric shape of the target entity; the point data of the target entity is a set of coordinate values, which is used to represent the spatial position of the target entity; The step of converting the surface data of the target entity into the point data of the target entity by the entity matching algorithm includes: calculating the geometric center of the surface data of the target entity by multi-scale point, line and surface matching, and extracting the spatial coordinate value corresponding to the geometric center; extracting feature points from the surface data of the target entity by the ICP algorithm based on the geometric features of the target entity, and generating a set of key feature points; using the geometric center of the surface data or the extracted key feature points as the point data of the target entity, and generating a set of point data corresponding to the target entity; The data correction module is used to perform geometric correction on the point data of the target entity to obtain the geometrically corrected spatial data corresponding to the target entity, including: superimposing two or more groups of surface data of the same scale, merging the superimposed areas, and generating a new data set; based on preset selection parameters, filtering the overlapping parts in the superimposed data set, the selection parameters including the height information and spatial coordinate information of the white model entity corresponding to the target entity; processing the filtered data through a geometric correction algorithm to obtain the geometrically corrected spatial data corresponding to the target entity.

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