Three-dimensional map making system capable of automatically converting data

Abstract

The invention relates to the technical field of cartographic systems, in particular to a three-dimensional map making system capable of automatically converting data, comprising a data acquisition module for acquiring different types of geographic information data from a plurality of data sources; the automatic data conversion module is used for performing format conversion on the acquired geographic information data and specifically comprises a self-adaptive analysis sub-module for converting the data into a uniform intermediate format; the multi-level precision control and error compensation sub-module is used for carrying out precision analysis and multi-level error compensation on spatial point coordinates in the data conversion process; and the intelligent modeling and optimizing module is used for modeling a three-dimensional map model based on the compensated and converted data, wherein the modeling process comprises automatic topological optimization, texture mapping enhancement and geometric structure refinement. According to the method, the problems of low processing efficiency, inconsistent data precision and the like caused by incompatibility of multi-source data and manual intervention in a traditional method are solved, and the automation degree and the processing efficiency of three-dimensional map making are greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of mapping systems, and in particular to a three-dimensional map making system capable of automatic data conversion. Background Art

[0002] With the rapid development of geographic information system (GIS) technology, 3D map production is increasingly used in urban planning, navigation, geological exploration, virtual reality and other fields. Traditional 3D map production usually relies on multi-source geographic information data (such as lidar point cloud, satellite imagery, building information model (BIM), global positioning system (GPS) data, etc.) for processing and fusion. Due to different sources, these data have significant differences in format, resolution, coordinate system and accuracy, resulting in the need for complex manual data conversion and processing in the 3D map production process, which not only increases the workload, but also easily leads to problems such as inconsistent data accuracy, incompatible formats, and low processing efficiency.

[0003] At present, the common 3D map production tools on the market are often unable to automatically handle the conversion and precision control of heterogeneous data from multiple sources. In particular, the existing technology is particularly insufficient in application scenarios with high processing accuracy requirements. For example, in high-precision terrain modeling or architectural simulation, the resolution and coordinate system conversion of the data require manual intervention, and error accumulation and data redundancy problems are difficult to avoid, which in turn affects the accuracy and expressiveness of the final 3D map. In addition, the unstable topological structure of the 3D model, insufficient geometric details, inaccurate texture mapping and other problems also limit the realism and application scope of the 3D map. Summary of the invention

[0004] The invention provides a three-dimensional map making system capable of automatic data conversion.

[0005] A 3D map production system with automatic data conversion, including:

[0006] Data acquisition module: acquires different types of geographic information data from multiple data sources, including lidar point cloud data, satellite image data, building information model (BIM) data, global positioning system (GPS) data, and geographic information system (GIS) data;

[0007] The automatic data conversion module converts the format of the acquired geographic information data, including:

[0008] The adaptive parsing submodule converts the data into a unified intermediate format according to the format, resolution, and coordinate system of the input data;

[0009] Multi-level precision control and error compensation submodule: During the data conversion process, the spatial point coordinates (x, y, z) are subjected to precision analysis and multi-level error compensation. The multi-level error compensation is based on the precision weight of the data source, spatial distribution data, and historical error model, and the compensated data is output for subsequent 3D map modeling.

[0010] Intelligent modeling and optimization module: 3D map modeling is performed based on the compensated and converted data, wherein the modeling process includes automatic topology optimization, texture mapping enhancement and geometric structure refinement.

[0011] Optionally, the data acquisition module specifically includes:

[0012] A data source access unit, connected to and receiving geographic information data from multiple heterogeneous data sources, wherein the data source access unit interacts with each data source using an interface protocol, wherein the protocol includes a lidar sensor interface protocol, a remote sensing satellite data transmission protocol, a building information model interface standard, a global positioning system data stream protocol, and a geographic information system (GIS) data exchange protocol;

[0013] The data synchronization unit ensures the synchronization of geographic information data received from different data sources in time and space. The data synchronization unit automatically adjusts the data timestamp of each data source in combination with timestamp calibration.

[0014] Optionally, the adaptive parsing submodule specifically includes:

[0015] A data format recognition unit, which automatically recognizes the format type of the data according to the metadata and content characteristics of the input geographic information data, wherein the format types include laser radar point cloud data in LAS format, satellite image data in GeoTIFF format, building information model data in IFC format, global positioning system data in NMEA format, and geographic information system data in Shapefile format;

[0016] Resolution and coordinate system analysis unit analyzes the resolution and coordinate system of the input data, and calculates the resolution adjustment parameters and coordinate system conversion matrix required for data conversion based on the spatial resolution information and coordinate system parameters (including WGS84, NAD83, UTM, etc.) of the input data;

[0017] The intermediate format conversion unit is used to convert the identified geographic information data into a unified intermediate format, where the intermediate format adopts the GeoJSON format.

[0018] Optionally, the multi-level precision control and error compensation submodule specifically includes:

[0019] Precision weight calculation unit: Calculates the precision weight of each data source in the data fusion process according to the characteristics and reliability of different data sources to ensure that high-precision data contributes more to the final result and improves the overall accuracy of data fusion;

[0020] Spatial error analysis unit: performs error analysis on the spatial distribution data of each data source, identifies and quantifies the error distribution at different spatial locations, and provides a basis for error compensation;

[0021] Historical error model construction and adjustment unit: Use historical error analysis results to build and update historical error models, and combine current error analysis results to compensate for data errors, thereby improving the long-term stability and accuracy of data;

[0022] Data accuracy optimization and output unit: Utilize the updated historical error model to optimize the accuracy and correct the errors of spatial point coordinates (x, y, z), and output high-quality data with minimized errors for subsequent 3D map modeling.

[0023] Optionally, the precision weight calculation unit includes calculating the precision weight w of the i-th data source i : Among them, k is the adjustment coefficient, which is used to balance the impact of the average error and standard deviation on the weight. It takes a positive value, is determined based on historical experience, and is adjusted according to the specific application scenario. i is the average error of the data source, indicating the overall accuracy level of the data source, σ i is the standard deviation of the data source error, reflecting the degree of dispersion of the data error, c i is the credibility coefficient, which is the credibility evaluated based on the acquisition method of the data source, device performance and environmental conditions, and its value range is [0,1];

[0024] Normalize so that the sum of all weights equals 1:

[0025] Optionally, the input data of the spatial error analysis unit includes:

[0026] Spatial data sets of each data source: For the i-th data source, it includes a series of spatial points P i ={(x ij ,y ij ,z ij )};

[0027] Corresponding measurement value: measurement value V of each spatial point ij ;

[0028] Precision weight W i : From the precision weight calculation unit;

[0029] The spatial error analysis unit specifically includes:

[0030] Error calculation: For each spatial point, the error between its observed value and the reference value is calculated, and a high-precision reference data set P is collected in advance by measuring equipment. ref and the corresponding measured value V ref , then the error e ij Calculated as: e ij =V ij -V ref (x ij ,y ij ,z ij ), if there is a lack of unified high-precision reference data, the relative error can be calculated by comparing the data sources with each other;

[0031] Weighted average error field construction: Construct the weighted average error field E(x,u,z) within the overall space: Among them, e i (x, y, z) is the error of the ith data source at position (x, y, z), which is obtained from the discrete point error e by spatial interpolation. ij Inferred;

[0032] Error statistical analysis: Perform statistical analysis on the error field E(x,y,z) and calculate global and local error indicators.

[0033] Optionally, the input parameters of the historical error model construction and adjustment unit include:

[0034] Historical error data set: contains error field data for multiple time periods in the past Where t=1,2,…,T represents different time points;

[0035] Current error field: Current error field E from the spatial error analysis unit current (x,y,z);

[0036] The historical error model is expressed as E model (x,y,z), constructed based on temporal and spatial statistical analysis of historical error data: The model reflects the average distribution of errors in different time periods and captures the error characteristics;

[0037] Error model adjustment: Taking into account the variability of the environment and data sources, the error model is dynamically updated using the exponentially weighted moving average method, expressed as E updated (x,y,z).

[0038] Optionally, the input parameters of the data precision optimization and output unit include the spatial point coordinates (x, y, z) and E updated(x,y,z);

[0039] Error compensation calculation: For each spatial point coordinate (x, y, z), correction is performed according to the corresponding error value:

[0040]

[0041] Among them, (x′, y′, z′) is the corrected spatial coordinate, δ x ,δ y ,δ z are the error correction amounts in the three coordinate axis directions respectively.

[0042] Optionally, the data accuracy optimization and output unit also includes data quality assessment. After error compensation is completed, the corrected data is subjected to quality assessment to calculate the overall accuracy index, including correction of the global average error and correction of the global error standard deviation.

[0043] Optionally, the intelligent modeling and optimization module specifically includes:

[0044] Data point cloud generation unit: extract the coordinate point P from the compensated data i ,y i , z i ), and generate the corresponding three-dimensional point cloud data, and construct the basic geometric structure of the three-dimensional space by preliminary triangulation of the coordinate points;

[0045] Automatic topology optimization unit: Based on the basic geometric structure generated by the point cloud, the Delaunay triangulation algorithm is used to generate a triangular mesh model to ensure that the internal angles of all triangles are maximized to improve the uniformity and stability of the mesh;

[0046] Texture mapping enhancement unit: Based on the generated 3D mesh model, the texture coordinates (u, v) are mapped to each triangle face f. The mapping process uses the following formula:

[0047] T(u,v)=Image(x,y), where (u,v) is the texture coordinate on the 3D mesh model, and (x,y) is the pixel coordinate of the corresponding image texture;

[0048] Geometry refinement unit: It performs geometry refinement on the established triangular mesh model and uses the geometry refinement technology based on the Catmull-Clark subdivision algorithm to refine the original rough mesh.

[0049] 3D map model generation unit: Integrates the topology optimized mesh, enhanced texture mapping and geometric refinement results to generate the final 3D map model.

[0050] Beneficial effects of the present invention:

[0051] The present invention realizes the automatic conversion of multi-source heterogeneous geographic information data through an adaptive data parsing submodule, and can automatically convert the data into a unified intermediate format (GeoJSON) according to the format, resolution, coordinate system and accuracy requirements of the input data. At the same time, through the embedded multi-level accuracy control and error compensation submodule, real-time accuracy analysis and error compensation are performed during the data conversion process to ensure that the data reaches high accuracy and consistency before entering the subsequent processing link. This effectively solves the problems of low processing efficiency and inconsistent data accuracy caused by incompatible multi-source data and manual intervention in traditional methods, and greatly improves the automation level and processing efficiency of three-dimensional map production.

[0052] The present invention introduces automatic topology optimization and geometric structure refinement technology in the process of three-dimensional map model building, and uses the Delaunay triangulation algorithm and the Catmull-Clark subdivision algorithm to realize the topology optimization and geometric refinement of the three-dimensional model. By automatically optimizing the grid structure, redundant nodes and small triangles are eliminated to ensure the stability and uniformity of the three-dimensional model structure; the detail expression of the model is improved through geometric refinement, so that the three-dimensional map performs better in high-precision scene applications. Compared with traditional three-dimensional modeling technology, the problem of unstable model topology structure and insufficient geometric details is solved, and a more refined and smooth three-dimensional map model can be generated, which is suitable for high-precision applications such as architectural planning and terrain analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0054] Figure 1 A schematic diagram of system function modules according to an embodiment of the present invention;

[0055] Figure 2 Schematic diagram of a multi-level precision control and error compensation submodule according to an embodiment of the present invention. DETAILED DESCRIPTION

[0056] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0057] It should be noted that the references to "one embodiment", "an embodiment", "an exemplary embodiment", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0058] In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0059] like Figure 1-Figure 2 As shown, the three-dimensional map making system capable of automatic data conversion comprises:

[0060] Data acquisition module: acquires different types of geographic information data from multiple data sources, including lidar point cloud data, satellite image data, building information model (BIM) data, global positioning system (GPS) data, and geographic information system (GIS) data;

[0061] The automatic data conversion module converts the format of the acquired geographic information data, including:

[0062] The adaptive parsing submodule converts the data into a unified intermediate format according to the format, resolution, and coordinate system of the input data;

[0063] Multi-level precision control and error compensation submodule: During the data conversion process, the spatial point coordinates (x, y, z) are subjected to precision analysis and multi-level error compensation. The multi-level error compensation is based on the precision weight of the data source, spatial distribution data, and historical error model, and the compensated data is output for subsequent 3D map modeling.

[0064] Intelligent modeling and optimization module: 3D map modeling is performed based on compensated and converted data. The modeling process includes automatic topology optimization, texture mapping enhancement and geometric structure refinement.

[0065] The data acquisition module specifically includes:

[0066] A data source access unit connects and receives geographic information data from multiple heterogeneous data sources. The data source access unit uses interface protocols to interact with each data source. The protocols include lidar sensor interface protocol, remote sensing satellite data transmission protocol, building information model interface standard, global positioning system data stream protocol, and geographic information system (GIS) data exchange protocol.

[0067] The data synchronization unit ensures the synchronization of geographic information data received from different data sources in time and space. The data synchronization unit automatically adjusts the data timestamps of each data source in combination with timestamp calibration to ensure the uniformity and accuracy of subsequent data processing. Assume that the geographic information data obtained from different data sources have different timestamps, denoted by T 1 ,T 2 ,…,T n In order to ensure the time consistency of data, a unified reference timestamp T is used. ref , perform calibration, and the timestamp after calibration is T i ' is calculated as: T i ′=T i +ΔT i , where ΔT i From timestamp T i To the reference timestamp T ref The calibration deviation is calculated based on the acquisition delay and clock difference of the data source.

[0068] The adaptive parsing submodule specifically includes:

[0069] A data format recognition unit automatically recognizes the format type of the data according to the metadata and content characteristics of the input geographic information data. The format types include laser radar point cloud data in LAS format, satellite image data in GeoTIFF format, building information model data in IFC format, global positioning system data in NMEA format, and geographic information system data in Shapefile format;

[0070] Resolution and coordinate system analysis unit analyzes the resolution and coordinate system of the input data, and calculates the resolution adjustment parameters and coordinate system conversion matrix required for data conversion based on the spatial resolution information and coordinate system parameters (including WGS84, NAD83, UTM, etc.) of the input data;

[0071] The resolution is adjusted as follows: Assume that the original resolution of the input geographic information data is R orig , the target resolution required by the system is R target , in order to adjust the resolution, the following formula is used for calculation: Where S is the scaling factor, calculated as follows: The resolution adjustment here is based on the resolution R of the original data. orig and the target resolution R target By calculating the scaling factor S, the system can adjust the resolution of the input data to adapt to the target resolution requirement, ensuring that data can be seamlessly integrated at different resolutions.

[0072] The coordinate system transformation is as follows: Assume that the original coordinate system of the input data is (x orig ,y orig ,z orig ), the target coordinate system of the system is (x target ,y target ,z target ), the coordinate system transformation is calculated using the following formula:

[0073] Among them, R is the rotation matrix and T is the translation vector. The specific form is as follows:

[0074]

[0075] The coordinate system conversion formula here uses the rotation matrix R and translation vector T to transform the coordinates of the input data to ensure that the spatial coordinate systems of different data sources can be unified into the system target coordinate system. The rotation matrix R is responsible for processing the rotation transformation of the coordinate system, while the translation vector T processes the offset of the origin of different coordinate systems.

[0076] The intermediate format conversion unit is used to convert the identified geographic information data into a unified intermediate format. The intermediate format adopts the GeoJSON format, which has high compatibility, good spatial data expression capabilities and lightweight characteristics, and is suitable for the integration and processing of different types of geographic information data.

[0077] Point cloud data (LAS format): Each point (x i ,y i ,z i ) to a GeoJSON "Point" object;

[0078] Image data (GeoTIFF format): Each pixel of the image data (u i ,v i ) corresponds to the spatial coordinate (x i ,y i ) is converted to a GeoJSON "Polygon" object, which is used to represent the area in the image;

[0079] Building Information Model (IFC format): Building components in IFC format (b i) is converted to a GeoJSON "MultiPolygon" object, which is used to represent the geometric shape of a three-dimensional building;

[0080] Global Positioning System (NMEA format): Each coordinate point of GPS data (lat i ,lon i ) to a GeoJSON "Point" object;

[0081] Geographic Information System (Shapefile format): Each geographic object in a Shapefile (geo i ) is converted to the corresponding GeoJSON object "LineString";

[0082] GeoJSON was chosen as the intermediate format because of its lightweight, openness, and support for various geographic spatial data structures. GeoJSON can flexibly represent spatial elements such as points, lines, and surfaces, allowing multiple heterogeneous data sources to be expressed in a unified manner, facilitating subsequent data processing and fusion operations.

[0083] The multi-level precision control and error compensation submodules specifically include:

[0084] Precision weight calculation unit: Calculates the precision weight of each data source in the data fusion process according to the characteristics and reliability of different data sources to ensure that high-precision data contributes more to the final result and improves the overall accuracy of data fusion;

[0085] Spatial error analysis unit: performs error analysis on the spatial distribution data of each data source, identifies and quantifies the error distribution at different spatial locations, and provides a basis for error compensation;

[0086] Historical error model construction and adjustment unit: Use historical error analysis results to build and update historical error models, and combine current error analysis results to compensate for data errors, thereby improving the long-term stability and accuracy of data;

[0087] Data accuracy optimization and output unit: Utilize the updated historical error model to optimize the accuracy and correct the errors of spatial point coordinates (x, y, z), and output high-quality data with minimized errors for subsequent 3D map modeling.

[0088] The precision weight calculation unit includes calculating the precision weight w of the i-th data source i : Among them, k is the adjustment coefficient, which is used to balance the impact of the average error and standard deviation on the weight. It takes a positive value, is determined based on historical experience, and is adjusted according to the specific application scenario. i is the average error of the data source, indicating the overall accuracy level of the data source, σ iis the standard deviation of the data source error, reflecting the degree of dispersion of the data error, c i is the credibility coefficient, which is the credibility evaluated based on the acquisition method of the data source, device performance and environmental conditions, and its value range is [0,1];

[0089] Normalize so that the sum of all weights equals 1: Average error μ i and standard deviation σ i Together they reflect the accuracy and stability of the data source. The smaller the error, the higher the accuracy and the greater the weight.

[0090] Through the precision weight calculation unit, the system can objectively evaluate the accuracy level of each data source. In the subsequent data fusion and error compensation process, it will rely more on high-precision data sources to reduce overall errors and improve data quality.

[0091] The evaluation of credibility CI is based on the data source acquisition method, equipment performance and environmental conditions, and can be quantified and evaluated through the following steps:

[0092] Evaluation of the acquisition method of the data source: Satellite remote sensing data is usually affected by atmospheric interference, imaging angle and satellite orbit. Different weights can be set for these factors to evaluate the credibility of the acquisition method. For example, data with greater atmospheric interference has lower credibility. The closer the imaging angle is to vertical, the higher the credibility. LiDAR data is affected by terrain, vegetation density and equipment installation height. In areas with large terrain fluctuations or high vegetation density, data acquisition may be hindered and the credibility is relatively reduced.

[0093] Equipment performance evaluation: The higher the point cloud density of the lidar device, the finer the spatial resolution obtained, the higher the measurement accuracy of the device and the higher the credibility. For different devices, quantitative scores are given according to their technical indicators (such as resolution, accuracy, and sampling frequency).

[0094] Environmental condition assessment: When acquiring satellite remote sensing data, it is greatly affected by weather conditions. Cloud thickness, humidity, etc. will affect the data quality. Therefore, the better the meteorological conditions, the higher the data credibility. Ground data collection may be affected by building obstructions and traffic conditions during collection. In scenes with many obstructions or complex collection environments, the credibility is lower.

[0095] Based on the above factors, the credibility ci can be evaluated by weighted scoring.

[0096] The input data of the spatial error analysis unit includes:

[0097] Spatial data sets of each data source: For the i-th data source, it includes a series of spatial points P i ={(x ij ,yij ,z ij )};

[0098] Corresponding measurement value: measurement value V of each spatial point ij ;

[0099] Precision weight W i : From the precision weight calculation unit;

[0100] The spatial error analysis unit specifically includes:

[0101] Error calculation: For each spatial point, the error between its observed value and the reference value is calculated, and a high-precision reference data set P is collected in advance by measuring equipment. ref and the corresponding measured value V ref , then the error e ij Calculated as: e ij =V ij -V ref (x ij ,y ij ,z ij ), if there is a lack of unified high-precision reference data, the relative error can be calculated by comparing the data sources with each other;

[0102] Weighted average error field construction: Construct the weighted average error field E(x,y,z) within the overall space: Among them, e i (x, y, z) is the error of the ith data source at position (x, y, z), which is obtained from the discrete point error e by spatial interpolation. ij Inferred;

[0103] Error statistical analysis: Perform statistical analysis on the error field E(x,y,z) and calculate global and local error indicators:

[0104] Global average error:

[0105] Global error standard deviation: Where V is the volume of the space region.

[0106] The spatial error analysis unit accurately quantifies the error distribution of spatial data, providing detailed and accurate basic information for the subsequent error compensation process, ensuring the pertinence and effectiveness of error compensation.

[0107] Spatial interpolation is a method of extrapolating data at discrete points to the entire spatial range. Specifically, the error e at the position (x, y, z) is extrapolated by inverse distance weighted interpolation. i (x, y, z), let the error e at position (x, y, z) be i(x, y, z) is composed of multiple surrounding known error points e ij The interpolation formula is calculated as follows: Among them, e ij is the error value of the jth known discrete point; d ij The target point (x, y, z) and the known point (x ij ,y ij , z ij ):

[0108]

[0109] p is a parameter used to control the effect of distance on the interpolation result. p = 2 means that the greater the distance, the smaller the effect on the interpolation result.

[0110] N is the number of known points used for interpolation.

[0111] The input parameters of the historical error model construction and adjustment unit include:

[0112] Historical error data set: contains error field data for multiple time periods in the past Where t=1,2,…,T represents different time points;

[0113] Current error field: Current error field E from the spatial error analysis unit current (x,y,z);

[0114] The historical error model is expressed as E model (x,y,z), constructed based on temporal and spatial statistical analysis of historical error data: The model reflects the average distribution of errors in different time periods and captures the error characteristics;

[0115] Error model adjustment: Taking into account the variability of the environment and data sources, the error model is dynamically updated using the exponentially weighted moving average method, expressed as E updated (x,y,z), expressed as:

[0116] E updated (x,y,z)=α·E current (x,y,z)+(1-α)·E model (x, y, z), where α is the smoothing coefficient, ranging from [0, 1], which controls the influence of current error and historical error. A larger α value makes the model more sensitive to changes in the current error and is suitable for situations where the environment changes rapidly. A smaller α value emphasizes historical errors and is suitable for situations where the environment is relatively stable.

[0117] The historical error model construction and adjustment unit provides a dynamic and stable error compensation reference by combining historical and current error information, enabling the system to adapt to different environmental changes and data source characteristics and continuously output high-precision data.

[0118] The input parameters of the data precision optimization and output unit include the spatial point coordinates (x, y, z) and E updataed (x,y,z);

[0119] Error compensation calculation: For each spatial point coordinate (x, y, z), correction is performed according to the corresponding error value:

[0120]

[0121] Among them, (x′, y′, z′) is the corrected spatial coordinate, δ x ,δ y ,δ z They are the error correction amounts in the three coordinate axis directions, and are calculated as follows:

[0122] δ x (x,y,z)=E updated (x,y,z)·cosθ x ;

[0123] δ y (x,y,z)=E updated (x,y,z)·cosθ y ;

[0124] δ z (x,y,z)=E updated (x,y,z)·cosθ z ;

[0125] Among them, θ x ,θ y ,θ z They are the distribution angles of the errors in the directions of each coordinate axis, which are determined through error analysis. By performing detailed error correction on each spatial point, the accuracy of the data is improved at the microscopic level. The correction amount in the direction of each coordinate axis is calculated separately, which can compensate for the spatial error more accurately.

[0126] The data accuracy optimization and output unit also includes data quality assessment. After error compensation, the corrected data is evaluated for quality and the overall accuracy index is calculated, including the correction of the global average error and the correction of the global error standard deviation:

[0127] Global average error after correction:

[0128]

[0129] The global error standard deviation after correction:

[0130] Where N is the total number of data points, (x ref,i ,y ref,i , z ref,i ) is the reference coordinate;

[0131] Through quality assessment, the effect of error compensation is verified to ensure that the data meets the expected accuracy requirements. If the evaluation results do not meet the standards, the parameters of the previous steps can be iteratively adjusted for further optimization.

[0132] The intelligent modeling and optimization module specifically includes:

[0133] Data point cloud generation unit: extract the coordinate point P from the compensated data i ,y i , z i ), and generate the corresponding three-dimensional point cloud data, and construct the basic geometric structure of the three-dimensional space by preliminary triangulation of the coordinate points;

[0134] Automatic topology optimization unit: Based on the basic geometric structure generated by the point cloud, the Delaunay triangulation algorithm is used to generate a triangular mesh model to ensure that the internal angles of all triangles are maximized to improve the uniformity and stability of the mesh. The optimization process is expressed as follows: Maximizemin i θ i , where θ i It represents the minimum inner angle of the i-th triangle in the mesh. By continuously adjusting the positions of mesh vertices, the topological structure of the model is optimized, small and distorted triangles are eliminated, and the robustness of the model is improved.

[0135] Texture mapping enhancement unit: Based on the generated 3D mesh model, the texture coordinates (u, v) are mapped to each triangle face f. The mapping process uses the following formula:

[0136] T(u,v)=Image(x,y), where (u,v) is the texture coordinate on the 3D mesh model, and (x,y) is the pixel coordinate of the corresponding image texture. The accuracy and fidelity of the texture are ensured by accurately mapping high-resolution images (such as satellite images or aerial photography images) onto the surface of the 3D model.

[0137] Geometric structure refinement unit: The established triangular mesh model is geometrically refined. The original rough mesh is refined using the geometric refinement technology based on the Catmull-Clark subdivision algorithm. The refinement formula is:

[0138] Among them, P faceis the center point of each patch, P edge is the midpoint of each side, P original is the original vertex.

[0139] By refining the geometric structure and increasing mesh vertices, more geometric details are generated to improve the surface smoothness and accuracy of the model.

[0140] 3D map model generation unit: Integrate the topology optimized mesh, enhanced texture mapping and geometric refinement results to generate the final 3D map model. The generated 3D map model has a high-precision geometric structure and clear texture mapping, and can be applied to a variety of scenarios, such as urban planning, virtual simulation and navigation systems.

[0141] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0142] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A three-dimensional map making system capable of automatic data conversion, characterized in that: include: Data acquisition module: acquires different types of geographic information data from multiple data sources, including lidar point cloud data, satellite image data, building information model data, global positioning system data, and geographic information system data; The automatic data conversion module converts the format of the acquired geographic information data, including: The adaptive parsing submodule converts the data into a unified intermediate format according to the format, resolution, and coordinate system of the input data; Multi-level precision control and error compensation submodule: During the data conversion process, the spatial point coordinates (x, y, z) are subjected to precision analysis and multi-level error compensation. The multi-level error compensation is based on the precision weight of the data source, spatial distribution data, and historical error model, and the compensated data is output for subsequent 3D map modeling. Intelligent modeling and optimization module: 3D map modeling is performed based on the compensated and converted data, wherein the modeling process includes automatic topology optimization, texture mapping enhancement and geometric structure refinement.

2. The three-dimensional map making system capable of automatic data conversion according to claim 1, characterized in that: The data acquisition module specifically includes: A data source access unit, connected to and receiving geographic information data from multiple heterogeneous data sources, wherein the data source access unit interacts with each data source using an interface protocol, wherein the protocol includes a lidar sensor interface protocol, a remote sensing satellite data transmission protocol, a building information model interface standard, a global positioning system data stream protocol, and a geographic information system (GIS) data exchange protocol; A data synchronization unit, wherein the data synchronization unit automatically adjusts the data timestamps of each data source in combination with the timestamp calibration.

3. The three-dimensional map making system capable of automatic data conversion according to claim 1, characterized in that: The adaptive parsing submodule specifically includes: A data format recognition unit, which automatically recognizes the format type of the data according to the metadata and content characteristics of the input geographic information data, wherein the format types include laser radar point cloud data in LAS format, satellite image data in GeoTIFF format, building information model data in IFC format, global positioning system data in NMEA format, and geographic information system data in Shapefile format; A resolution and coordinate system analysis unit analyzes the resolution and coordinate system of the input data, and calculates the resolution adjustment parameters and coordinate system conversion matrix required for data conversion according to the spatial resolution information and coordinate system parameters of the input data; The intermediate format conversion unit is used to convert the identified geographic information data into a unified intermediate format, where the intermediate format adopts the GeoJSON format.

4. The three-dimensional map making system capable of automatic data conversion according to claim 1, characterized in that: The multi-level precision control and error compensation submodule specifically includes: Precision weight calculation unit: Calculates the precision weight of each data source in the data fusion process according to the characteristics and reliability of different data sources to ensure that high-precision data contributes more to the final result; Spatial error analysis unit: performs error analysis on the spatial distribution data of each data source, identifies and quantifies the error distribution at different spatial locations, and provides a basis for error compensation; Historical error model construction and adjustment unit: Use historical error analysis results to build and update the historical error model, and combine the current error analysis results to compensate for the data errors; Data accuracy optimization and output unit: Utilize the updated historical error model to optimize the accuracy and correct the errors of spatial point coordinates (x, y, z), and output high-quality data with minimized errors for subsequent 3D map modeling.

5. The three-dimensional map making system capable of automatic data conversion according to claim 4, characterized in that: The precision weight calculation unit includes calculating the precision weight w of the i-th data source i : Among them, k is the adjustment coefficient, which is used to balance the impact of the average error and standard deviation on the weight, and takes a positive value, μ i is the average error of the data source, indicating the overall accuracy level of the data source, σ i is the standard deviation of the data source error, reflecting the degree of dispersion of the data error, c i is the credibility coefficient, and its value range is [0,1]; Normalize so that the sum of all weights equals 1:

6. The three-dimensional map making system capable of automatic data conversion according to claim 5, characterized in that: The input data of the spatial error analysis unit includes: Spatial data sets of each data source: For the i-th data source, it includes a series of spatial points P i ={(x ij ,y ij ,z ij )}; Corresponding measurement value: measurement value V of each spatial point ij ; Precision weight W i : From the precision weight calculation unit; The spatial error analysis unit specifically includes: Error calculation: For each spatial point, the error between its observed value and the reference value is calculated, and a high-precision reference data set P is collected in advance by measuring equipment. ref and the corresponding measured value V ref , then the error e ij Calculated as: e ij =V ij -V ref (x ij ,y ij ,z ij ); Weighted average error field construction: Construct the weighted average error field E(x,y,z) within the overall space: Among them, e i (x, y, z) is the error of the ith data source at position (x, y, z), which is obtained from the discrete point error e by spatial interpolation. ij Inferred; Error statistical analysis: Perform statistical analysis on the error field E(x,y,z) and calculate global and local error indicators.

7. The three-dimensional map making system capable of automatic data conversion according to claim 6, characterized in that: The input parameters of the historical error model construction and adjustment unit include: Historical error data set: contains error field data for multiple time periods in the past Where t=1,2,…,T represents different time points; Current error field: Current error field E from the spatial error analysis unit current (x,y,z); The historical error model is expressed as E model (x,y,z), constructed based on temporal and spatial statistical analysis of historical error data: The model reflects the average distribution of errors in different time periods and captures the error characteristics; Error model adjustment: Taking into account the variability of the environment and data sources, the error model is dynamically updated using the exponentially weighted moving average method, expressed as E updated (x,y,z).

8. The three-dimensional map making system capable of automatic data conversion according to claim 7, characterized in that: The input parameters of the data precision optimization and output unit include the spatial point coordinates (x, y, z) and E updated (x,y,z); Error compensation calculation: For each spatial point coordinate (x, y, z), correction is performed according to the corresponding error value: Among them, (x′, y′, z′) is the corrected spatial coordinate, δ x ,δ y ,δ z are the error correction amounts in the three coordinate axis directions respectively.

9. The three-dimensional map making system capable of automatic data conversion according to claim 8, characterized in that: The data accuracy optimization and output unit also includes data quality assessment. After error compensation is completed, the corrected data is evaluated for quality and the overall accuracy index is calculated, including correction of the global average error and correction of the global error standard deviation.

10. The three-dimensional map making system capable of automatic data conversion according to claim 1, characterized in that: The intelligent modeling and optimization module specifically includes: Data point cloud generation unit: extract the coordinate point P from the compensated data i ,y i , z i ), and generate the corresponding three-dimensional point cloud data, and construct the basic geometric structure of the three-dimensional space by preliminary triangulation of the coordinate points; Automatic topology optimization unit: Based on the basic geometric structure generated by the point cloud, the Delaunay triangulation algorithm is used to generate a triangular mesh model to ensure that the internal angles of all triangles are maximized to improve the uniformity and stability of the mesh; Texture mapping enhancement unit: Based on the generated 3D mesh model, the texture coordinates (u, v) are mapped to each triangle face f. The mapping process uses the following formula: T(u,v)=Image(x,y), where (u,v) is the texture coordinate on the 3D mesh model, and (x,y) is the pixel coordinate of the corresponding image texture; Geometry refinement unit: It performs geometry refinement on the established triangular mesh model and uses the geometry refinement technology based on the Catmull-Clark subdivision algorithm to refine the original rough mesh. 3D map model generation unit: Integrates the topology optimized mesh, enhanced texture mapping and geometric refinement results to generate the final 3D map model.

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