Three-dimensional modeling method and system based on surveying and mapping multi-source data

Abstract

The invention relates to the field of data modeling, in particular to a three-dimensional modeling method and system based on surveying and mapping multi-source data. The method comprises the following steps: analyzing acquired surveying and mapping multi-source data, dividing a target area, and generating a three-dimensional disturbance layering graph; analyzing the three-dimensional disturbance layering atlas, and extracting disturbance level attributes and modeling complexity data; analyzing the disturbance level attribute and the modeling complexity data to generate a modeling subarea attribute set; constructing a modeling boundary relationship and a modeling priority based on the modeling subarea attribute set, analyzing the modeling boundary relationship in combination with the modeling priority, and generating a modeling control structure; analyzing the target area based on the modeling control structure to generate candidate point cloud data; processing the candidate point cloud data to generate a modeling candidate data set; and generating a three-dimensional modeling fusion result according to the modeling control structure and the modeling candidate data set. According to the invention, the continuity and regional adaptability of three-dimensional modeling can be improved.

Description

Technical Field

[0001] This invention relates to the field of data modeling, and specifically to a three-dimensional modeling method and system based on multi-source surveying data. Background Technology

[0002] In fields such as 3D modeling, geographic information systems, digital twins, and infrastructure planning, high-precision 3D modeling based on multi-source surveying data (such as lidar point clouds, remote sensing imagery, oblique photogrammetry, and DEMs) has become a key supporting technology. This type of modeling method typically relies on the spatial analysis of multi-source heterogeneous data, integrating multi-dimensional spatial features and terrain structure information to achieve 3D reconstruction of the target area. Especially in scenarios such as urban renewal, disaster monitoring, transportation engineering, and ecological assessment, accurately and efficiently reconstructing the 3D morphology of the land surface is a prerequisite for subsequent analysis, simulation, and decision-making.

[0003] Chinese patent CN116778105A discloses a method for modeling based on multi-precision 3D surveying data fusion, including: acquiring surveying data at surveying points, summarizing it to establish a first topographic surveying dataset, establishing a surveying condition set and generating a surveying condition coefficient Cxs, filtering surveying images using a first image quality coefficient when the surveying condition coefficient Cxs is not higher than a condition threshold, and filtering or recombining the first candidate images to determine the target image; establishing a second topographic surveying dataset, and performing 3D processing on the planar real-scene map, fusing the two generated 3D topographic models to generate a candidate model, and if there is a fusion error, filtering out the error area; and collecting topographic data at the surveying point to correct the candidate model.

[0004] In existing technologies, there is a lack of dynamic identification and hierarchical control mechanisms for the degree of disturbance within the modeling region, resulting in mixed modeling of highly disturbed and stable regions, which affects the overall model accuracy and stability. The modeling task lacks an execution rhythm control mechanism driven by the level of disturbance and the complexity of modeling, which is prone to problems such as modeling overfitting, boundary discontinuity, or uneven distribution of modeling computational resources. After modeling multiple sub-regions, it is difficult to achieve continuous fusion between modeling results, often causing problems such as regional boundary discontinuity and structural jumps, which are problems that we need to solve. Summary of the Invention

[0005] The purpose of this invention is to address the problems existing in the background technology by proposing a three-dimensional modeling method and system based on multi-source surveying data.

[0006] The technical solution of this invention: a three-dimensional modeling method based on multi-source surveying data, comprising the following steps: S1. Analyze the acquired multi-source survey data, divide the target area, and generate a three-dimensional perturbation layer map; S2. Analyze the 3D disturbance layer map to extract disturbance level attributes and modeling complexity data; analyze the disturbance level attributes and modeling complexity data to generate a modeling sub-region attribute set; construct modeling boundary relationships and modeling priorities based on the modeling sub-region attribute set; analyze the modeling boundary relationships in conjunction with the modeling priorities to generate a modeling control structure. S3. Analyze the target region based on the modeling control structure to generate candidate point cloud data; process the candidate point cloud data to generate a modeling candidate dataset; S4. Based on the modeling control structure and modeling candidate dataset, perform 3D modeling and region fusion to generate 3D modeling fusion results.

[0007] Preferably, the process of analyzing the acquired multi-source mapping data, dividing the target area, and generating a three-dimensional perturbation layer map includes: Data format parsing is performed on multi-source surveying data, and three-dimensional coordinate benchmark alignment is performed on data from different sources based on a unified spatial reference coordinate system, so that all multi-source surveying data are expressed as point information in the same three-dimensional space; spatial resolution is unified on various types of data after three-dimensional coordinate benchmark alignment, the target area is divided into three-dimensional space according to the preset grid size, a three-dimensional spatial grid structure is generated, and the point information of multi-source data is mapped to the corresponding three-dimensional spatial grid cells respectively; For each mapped 3D spatial grid cell, the 3D point cloud continuity index, direction vector stability index, and local height change gradient are calculated. Based on the 3D point cloud continuity index, direction vector stability index, and local height change gradient, the mirror perturbation intensity of all 3D spatial grid cells in the target area is estimated, and a perturbation intensity field model is constructed. According to the perturbation intensity field model, grid cells with comprehensive perturbation intensity values ​​in different segments are divided into stable modeling sub-regions, light perturbation modeling sub-regions, medium perturbation modeling sub-regions, and strong perturbation modeling sub-regions, and corresponding 3D perturbation layer maps are generated.

[0008] Preferably, the process of analyzing the 3D perturbation layer map to extract perturbation level attributes and modeling complexity data, and analyzing the perturbation level attributes and modeling complexity data to generate a modeling sub-region attribute set includes: The three-dimensional perturbation layer map is analyzed to obtain the three-dimensional spatial grid number sets of stable modeling sub-regions, lightly perturbed modeling sub-regions, moderately perturbed modeling sub-regions, and strongly perturbed modeling sub-regions, and the perturbation level attribute corresponding to each grid cell is extracted; the number of grids, grid density, point cloud sparsity, point position direction change rate, and local height change gradient in each modeling sub-region are calculated to obtain modeling complexity data. Based on the modeling complexity data and the disturbance level attributes of each modeling sub-region, modeling response control parameters are set for each modeling sub-region. The modeling response control parameters include: modeling triggering method, modeling start time conditions, modeling allowable space range, modeling pause conditions, and modeling delay window length. A corresponding modeling execution monitoring table is created for all modeling sub-regions, and the modeling response control parameters and the modeling execution monitoring table are combined to generate the modeling sub-region attribute set.

[0009] Preferably, the process of constructing modeling boundary relationships and modeling priorities based on the modeling sub-region attribute set, analyzing the modeling boundary relationships in conjunction with the modeling priorities, and generating the modeling control structure is as follows: The attribute set of the modeling sub-region is read. Based on the three-dimensional spatial range, disturbance level label and modeling task complexity data of each modeling sub-region recorded in the attribute set, the three-dimensional spatial mesh unit within the boundary of the modeling sub-region is subjected to region boundary extraction processing. Based on the region boundary extraction results, the three-dimensional spatial adjacency relationship between stable modeling sub-region, lightly disturbed modeling sub-region, mediumly disturbed modeling sub-region and strongly disturbed modeling sub-region is identified, and the modeling boundary relationship is constructed. For 3D spatial mesh elements located in the boundary region of the modeling sub-region, the modeling priority is calculated based on the perturbation level label, point cloud continuity index, direction vector change, local height change gradient, and modeling task stacking depth in the modeling sub-region attribute set. The modeling task queues corresponding to each modeling sub-region are sorted according to the modeling priority to generate a modeling execution order associated with the disturbance level; based on the sorted modeling task queues and modeling boundary relationships, a modeling control structure is generated.

[0010] Preferably, the process of analyzing the target region based on the modeling control structure to generate candidate point cloud data includes: Read the spatial range of the light disturbance modeling sub-region and the medium disturbance modeling sub-region from the modeling control structure, obtain the 3D point cloud data of the current period within the corresponding spatial range, and call the point cloud data of the previous modeling period from the historical modeling data cache as the reference point cloud data. The point cloud data input in the current cycle is used to identify the spatially overlapping area with the reference point cloud data. The local surface normal vector, local curvature vector and point cloud density change rate of the corresponding points in the spatially overlapping area are calculated. By comparing the change amplitude of the main direction vector of the same grid cell in two consecutive modeling cycles, if it exceeds the threshold, the corresponding grid cell is marked as "directionally unstable grid". For regions not marked as directionally unstable grids, calculate the local boundary segment displacement, local corner offset, and surface slope change. If these values ​​are not within the tolerance range, mark the corresponding point set as a "structurally inconsistent point set". Assign low modeling weight labels to the data points marked as directionally unstable grids or structurally inconsistent point sets to generate candidate point cloud data.

[0011] Preferably, the process of processing candidate point cloud data to generate a candidate dataset for modeling includes: Multidimensional feature extraction is performed on the candidate point cloud data. Feature parameters, including the local curvature change amplitude, direction vector continuity score, neighborhood height fluctuation factor, and historical disturbance record amount, are calculated for each data point. Based on the extracted feature parameters, a feature description vector for each data point is constructed. The feature description vector is then input into the disturbance absorption evaluation rule set to determine the geometric confidence level of each data point. Candidate point cloud data are classified according to geometric confidence level. By analyzing the proportion of stable points in the corresponding grid neighborhood of each data point, a candidate dataset for modeling is generated.

[0012] Preferably, the process of performing 3D modeling and region fusion based on the modeling control structure and the modeling candidate dataset to generate 3D modeling fusion results includes: Prioritize 3D modeling of stable modeling sub-regions to construct a 3D mesh structure for the stable region. For lightly disturbed modeling sub-regions, after the modeling delay window set by the modeling rhythm control structure ends, combine the point data with high and medium geometric confidence levels in the modeling candidate dataset to perform a local weighted fitting algorithm under directional constraints, and adopt a boundary adsorption strategy to ensure its connection continuity with the stable modeling sub-regions. For moderately disturbed modeling sub-regions, perform continuous boundary fusion processing based on normal vector consistency and curvature compatibility. Perform 3D modeling on the three types of modeling sub-regions respectively, and fuse the modeling results of each sub-region to generate a consistent 3D modeling fusion result for the entire region.

[0013] This invention also discloses a 3D modeling system based on multi-source surveying and mapping data, including a management center, which is communicatively connected to a data layering module, a data control module, a modeling candidate module, and a modeling fusion module. The data layering module is used to analyze the acquired multi-source surveying data, divide the target area, and generate a three-dimensional perturbation layered map. The data control module is used to analyze the 3D disturbance layer map, extract disturbance level attributes and modeling complexity data; analyze the disturbance level attributes and modeling complexity data to generate a modeling sub-region attribute set; construct modeling boundary relationships and modeling priorities based on the modeling sub-region attribute set; analyze the modeling boundary relationships in combination with the modeling priorities to generate a modeling control structure. The modeling candidate module is used to analyze the target region based on the modeling control structure and generate candidate point cloud data; it also processes the candidate point cloud data to generate a modeling candidate dataset. The modeling fusion module is used to perform 3D modeling and region fusion based on the modeling control structure and modeling candidate dataset, and generate 3D modeling fusion results.

[0014] Compared with the prior art, the above-mentioned technical solution of the present invention has the following beneficial technical effects: by constructing a three-dimensional perturbation layer map and setting modeling response control parameters, it is possible to realize differentiated modeling response control for regions with different perturbation levels, improve the stability of the regional modeling process and the rationality of resource allocation, and help to improve the problems of uncontrollable modeling rhythm and single processing strategy in existing three-dimensional modeling; by constructing a modeling priority and candidate point cloud data credibility classification mechanism, progressive three-dimensional modeling and boundary fusion are carried out, so that the three-dimensional modeling process has higher continuity and adaptability under multi-region collaboration, optimizes the modeling boundary stitching quality, and improves the consistency and accuracy of the overall modeling results. Attached Figure Description

[0015] Figure 1 This is a flowchart of one embodiment of the present invention. Detailed Implementation

[0016] Example 1, as Figure 1 As shown, the present invention proposes a 3D modeling method based on multi-source surveying data, which includes the following steps: S1. Analyze the acquired multi-source survey data, divide the target area, and generate a three-dimensional perturbation layer map; S2. Analyze the 3D disturbance layer map to extract disturbance level attributes and modeling complexity data; analyze the disturbance level attributes and modeling complexity data to generate a modeling sub-region attribute set; construct modeling boundary relationships and modeling priorities based on the modeling sub-region attribute set; analyze the modeling boundary relationships in conjunction with the modeling priorities to generate a modeling control structure. S3. Analyze the target region based on the modeling control structure to generate candidate point cloud data; process the candidate point cloud data to generate a modeling candidate dataset; S4. Based on the modeling control structure and modeling candidate dataset, perform 3D modeling and region fusion to generate 3D modeling fusion results.

[0017] It should be further explained that, in the specific implementation process, the process of analyzing the acquired multi-source surveying data, dividing the target area, and generating a three-dimensional perturbation layer map is as follows: The multi-source mapping data includes lidar point cloud data, oblique photogrammetry data, remote sensing image data, and digital elevation model data. Data format parsing is performed on lidar point cloud data, oblique photogrammetry data, remote sensing image data, and digital elevation model data. Based on a unified spatial reference coordinate system, three-dimensional coordinate benchmark alignment processing is performed on data from different sources, so that multi-source surveying data can be expressed as point information or patch information in the same three-dimensional space. After aligning various data with the three-dimensional coordinate reference, the spatial resolution is unified. The target area is divided into three-dimensional spaces according to the preset grid size, generating a three-dimensional spatial grid structure with a fixed spatial unit scale. The point information or patch information of the multi-source data is mapped to the corresponding three-dimensional spatial grid unit. For each mapped 3D spatial grid cell, the 3D point cloud continuity index, the orientation vector stability index, and the local height change gradient are calculated. The 3D point cloud continuity index is used to characterize the density and geometric coherence of the point cloud distribution in 3D space. The orientation vector stability index is used to characterize the orientation offset of the main orientation vector of the point set in the grid between its neighbors. The local height change gradient is used to characterize the magnitude of the height difference change of the point set in the grid along the vertical direction. Based on the continuity index of three-dimensional point cloud, the stability index of direction vector, and the gradient of local height change, the mirror perturbation intensity of all three-dimensional spatial grid cells in the target area is estimated. The three indices are weighted to obtain the comprehensive perturbation intensity value of each grid cell. The perturbation intensity field model is constructed according to the magnitude of the comprehensive perturbation intensity value and its changing trend in three-dimensional space. Based on the disturbance intensity field model, the grid cells with comprehensive disturbance intensity values ​​in different segments are divided into stable modeling sub-regions, light disturbance modeling sub-regions, medium disturbance modeling sub-regions, and strong disturbance modeling sub-regions, and corresponding three-dimensional disturbance layer maps are generated.

[0018] It should be further explained that, in the specific implementation process, the following steps are taken: First, the three-dimensional disturbance layer map is analyzed to extract disturbance level attributes and modeling complexity data. Then, the disturbance level attributes and modeling complexity data are analyzed to generate a modeling sub-region attribute set. Based on the modeling sub-region attribute set, modeling boundary relationships and modeling priorities are constructed. Finally, the modeling boundary relationships are analyzed in conjunction with the modeling priorities to generate the modeling control structure. The three-dimensional perturbation layer map is analyzed to obtain the three-dimensional spatial grid number sets of the stable modeling sub-region, the light perturbation modeling sub-region, the medium perturbation modeling sub-region, and the strong perturbation modeling sub-region, and the perturbation level attribute corresponding to each grid cell is extracted. Specifically, the process of extracting the disturbance level attribute corresponding to each grid cell includes: for each three-dimensional spatial grid cell in the three-dimensional disturbance layer map, reading the three-dimensional point cloud continuity index, direction vector stability index, and local height change gradient of the three-dimensional spatial grid cell respectively, comparing the three-dimensional point cloud continuity index, direction vector stability index, and local height change gradient with the preset disturbance level division intervals in sequence; when the three-dimensional point cloud continuity index, direction vector stability index, and local height change gradient are all in the same disturbance level interval, the corresponding grid cell is marked as the corresponding disturbance level; when the three-dimensional point cloud continuity index, direction vector stability index, and local height change gradient are in different intervals, according to the preset index weights, the level corresponding to the index with the highest weight is selected as the final disturbance level; and the disturbance level of each grid cell and its grid number are recorded as the disturbance level attribute. Statistical analysis is performed on the three-dimensional spatial mesh units belonging to each modeling sub-region to calculate the number of meshes, mesh density, point cloud sparsity, point orientation change rate, and local height change gradient in each modeling sub-region, thereby obtaining modeling complexity data. Specifically, the process of obtaining the modeling complexity data includes: based on the set of grid numbers of the modeling sub-region, counting the total number of grid cells in each modeling sub-region; calculating the number of grid cells per unit volume to obtain the grid density; counting the number of points in the point cloud of each grid cell, and calculating the average distance and distance difference rate between adjacent points; calculating the point orientation change rate based on the change amplitude of the principal direction vector of the point cloud within the grid cell; calculating the local height change gradient based on the change in the height difference of the point cloud in the grid cell; and normalizing the above statistical results to generate modeling complexity data representing the modeling computational load, fitting difficulty, and local geometric change complexity. Based on the modeling complexity data and the disturbance level attributes of each modeling sub-region, modeling response control parameters are set for each modeling sub-region. The modeling response control parameters include: modeling triggering method, modeling start time conditions, modeling allowable space range, modeling pause conditions, and modeling delay window length. Among them, the stable modeling sub-region is set to real-time triggering, the lightly disturbed modeling sub-region is set to a first modeling delay window, the mediumly disturbed modeling sub-region is set to limit the modeling space range, and the strongly disturbed modeling sub-region is set to modeling freeze conditions. Create a corresponding modeling execution monitoring table for all modeling sub-regions. The modeling execution monitoring table is used to record the modeling trigger satisfaction status, modeling delay countdown, modeling range restriction status, and freezing condition satisfaction status of the modeling sub-regions. The modeling response control parameters are combined with the modeling execution monitoring table to generate a modeling sub-region attribute set. The modeling sub-region attribute set is used to provide a basis for subsequent modeling region sorting and modeling execution order generation.

[0019] The attribute set of the modeling sub-region is read, and based on the three-dimensional spatial range, disturbance level label and modeling task complexity data of each modeling sub-region recorded in the attribute set, the region boundary extraction process is performed on the three-dimensional spatial mesh unit within the boundary of the modeling sub-region. Based on the region boundary extraction results, the three-dimensional spatial adjacency relationships between stable modeling sub-regions, lightly disturbed modeling sub-regions, moderately disturbed modeling sub-regions, and strongly disturbed modeling sub-regions are identified, and modeling boundary relationships are constructed. These modeling boundary relationships are used to represent the spatial contact surfaces and geometric connection features between different modeling sub-regions. For 3D spatial mesh elements located in the boundary region of the modeling sub-region, the modeling priority is calculated based on the perturbation level label, point cloud continuity index, direction vector change, local height change gradient, and modeling task stacking depth in the modeling sub-region attribute set. Specifically, the modeling priority calculation process is as follows: calculate the change range of the directional vector angle between the grid cell and its adjacent grid cells, which is recorded as the directional perturbation score; calculate the point cloud density change rate of the grid cell within its sub-region, which is recorded as the density perturbation score; combine the local height change gradient and the perturbation level to calculate the local terrain complexity score; integrate the directional perturbation score, the density perturbation score, and the terrain complexity score according to a preset weighting rule to generate a modeling priority score, and divide the modeling priority into three levels: high, medium, and low, to obtain the modeling priority. The modeling task queues corresponding to each modeling sub-region are sorted according to the modeling priority, and a modeling execution order associated with the perturbation level is generated. This prioritizes the modeling tasks of stable modeling sub-regions, followed by lightly perturbed modeling sub-regions and then mediumly perturbed modeling sub-regions, while strongly perturbed modeling sub-regions remain frozen until their perturbation level is reassessed. Based on the sorted modeling task queue and modeling boundary relationships, a modeling control structure is generated. The modeling control structure is used to control the execution order of 3D modeling tasks, the coordination relationship between modeling regions, and the geometric connection method of modeling boundaries.

[0020] It should be further explained that, in the specific implementation process, the target area is analyzed based on the modeling control structure to generate candidate point cloud data; the process of processing the candidate point cloud data to generate the modeling candidate dataset is as follows: Read the spatial range of the light disturbance modeling sub-region and the medium disturbance modeling sub-region from the modeling control structure, obtain the 3D point cloud data of the current period within the corresponding spatial range, and call the point cloud data of the previous modeling period from the historical modeling data cache as the reference point cloud data. The point cloud data input in the current cycle is used to identify the spatially overlapping area with the reference point cloud data. The local surface normal vector, local curvature vector and point cloud density change rate of the corresponding points in the spatially overlapping area are calculated. By comparing the change amplitude of the main direction vector of the same grid cell in two consecutive modeling cycles, it is determined whether the change amplitude exceeds the set disturbance level threshold. If it exceeds the threshold, the corresponding grid cell is marked as "directionally unstable grid". For regions not marked as directionally unstable grids, calculate the local boundary segment displacement, local corner offset, and surface slope change, and determine whether they are within the set structural consistency tolerance. If they are not within the tolerance, mark the corresponding point set as "structurally inconsistent point set". Data points marked as directionally unstable grids or structurally inconsistent point sets are assigned low modeling weight labels, while point data that meet the conditions of trend alignment, directional stability, and structural consistency are retained to generate candidate point cloud data.

[0021] Multidimensional feature extraction processing is performed on the candidate point cloud data, and feature parameters including the local curvature change amplitude, direction vector continuity score, neighborhood height fluctuation factor and historical disturbance record amount are calculated for each data point. Based on the extracted feature parameters, a feature description vector is constructed for each data point. The feature description vector is used to represent the overall behavior of the data point in local geometry, orientation stability and disturbance history. The feature description vector is input into the perturbation absorption evaluation rule set, which includes curvature stability criteria, direction consistency criteria, height fluctuation constraint criteria, and historical perturbation cumulative weight update rules to determine the geometric confidence level of each data point. Specifically, the process for determining the geometric reliability level of each data point is as follows: The curvature values ​​of the data point and its neighboring points are compared; if the deviation is lower than a preset threshold, it is marked as "curvature stable"; based on the direction consistency criterion, the average angle between the direction vector of the data point and the direction vectors of several adjacent points is calculated; if the angle is less than the angle tolerance threshold, it is marked as "direction consistent"; based on the height fluctuation constraint criterion, the height dispersion of the data point within its local neighborhood is calculated; if the neighborhood height fluctuation factor is less than a set threshold, it is marked as "height stable"; the disturbance level records of the areas in which the data point participated during the historical modeling process are extracted, and the disturbance frequency weighting value is calculated; based on the disturbance frequency weighting value, the data points are divided into three levels: "high reliability," "medium reliability," and "low reliability," and a corresponding geometric reliability level is assigned. Candidate point cloud data are classified according to geometric confidence level. Data points with high geometric confidence level are marked as "primary modeling points", data points with medium geometric confidence level are marked as "auxiliary modeling points", and data points with low geometric confidence level are marked as "low weight points" and their participation in the modeling and fitting process is restricted. By analyzing the proportion of stable points in the corresponding grid neighborhood of each data point, it is determined whether to adjust its modeling weight label to avoid the loss of structural information due to local occasional disturbances. The point cloud data after multi-dimensional feature evaluation, credibility classification and neighborhood consistency compensation are integrated and output to generate a modeling candidate dataset.

[0022] It should be further explained that, in the specific implementation process, the process of performing 3D modeling and region fusion based on the modeling control structure and the modeling candidate dataset to generate the 3D modeling fusion result is as follows: Prioritize 3D modeling of stable modeling sub-regions, call main modeling points and auxiliary modeling points, and use a topology fitting algorithm based on boundary constraints to construct the 3D mesh structure of stable regions; For the lightly disturbed modeling sub-region, after the modeling delay window set by the modeling rhythm control structure ends, a local weighted fitting algorithm under directional constraints is performed by combining the point data with high and medium geometric confidence levels in the modeling candidate dataset, and a boundary adsorption strategy is adopted to ensure its connection continuity with the stable modeling sub-region. For the perturbation modeling sub-region, the modeling space is limited to the "directionally stable region" marked in the modeling control structure. Only the data marked as the main modeling points in the modeling candidate dataset are selected. The fitted boundary points and the neighboring sub-regions are subjected to continuous boundary fusion processing based on normal vector consistency and curvature compatibility. After modeling is completed in the three types of modeling sub-regions, based on the three-dimensional boundary position between the sub-regions, the boundary point set and the main modeling points of the surrounding area are called to perform continuity correction based on minimizing the surface fitting residual, so as to ensure the consistency of cross-region modeling results in terms of height, normal vector and curvature index; the modeling results of each sub-region are merged to generate a consistent three-dimensional modeling fusion result for the whole region.

[0023] Example 2: The 3D modeling system based on multi-source surveying data proposed in this invention is applied to the 3D modeling method based on multi-source surveying data described in Example 1. Specifically, it includes a management center, which is communicatively connected to a data layering module, a data control module, a modeling candidate module, and a modeling fusion module. The data layering module is used to analyze the acquired multi-source surveying data, divide the target area, and generate a three-dimensional perturbation layered map. The data control module is used to analyze the 3D disturbance layer map, extract disturbance level attributes and modeling complexity data; analyze the disturbance level attributes and modeling complexity data to generate a modeling sub-region attribute set; construct modeling boundary relationships and modeling priorities based on the modeling sub-region attribute set; analyze the modeling boundary relationships in combination with the modeling priorities to generate a modeling control structure. The modeling candidate module is used to analyze the target region based on the modeling control structure and generate candidate point cloud data; it also processes the candidate point cloud data to generate a modeling candidate dataset. The modeling fusion module is used to perform 3D modeling and region fusion based on the modeling control structure and modeling candidate dataset, and generate 3D modeling fusion results.

[0024] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited thereto. Various changes can be made within the scope of knowledge possessed by those skilled in the art without departing from the spirit of the present invention.

Claims

1. A three-dimensional modeling method based on multi-source surveying data, characterized in that, Includes the following steps: S1. Analyze the acquired multi-source survey data, divide the target area, and generate a three-dimensional perturbation layer map; S2. Analyze the three-dimensional disturbance layer map and extract the disturbance level attributes and modeling complexity data; The disturbance level attributes and modeling complexity data are analyzed to generate a modeling sub-region attribute set; based on the modeling sub-region attribute set, modeling boundary relationships and modeling priorities are constructed; the modeling boundary relationships are analyzed in combination with the modeling priorities to generate a modeling control structure. S3. Analyze the target region based on the modeling control structure to generate candidate point cloud data; The candidate point cloud data is processed to generate a candidate dataset for modeling; S4. Based on the modeling control structure and modeling candidate dataset, perform 3D modeling and region fusion to generate 3D modeling fusion results.

2. The three-dimensional modeling method based on multi-source surveying data according to claim 1, characterized in that, The process of analyzing the acquired multi-source mapping data, dividing the target area, and generating a three-dimensional perturbation layer map includes: Data format parsing is performed on multi-source surveying data, and three-dimensional coordinate benchmark alignment is performed on data from different sources based on a unified spatial reference coordinate system, so that all multi-source surveying data are expressed as point information in the same three-dimensional space; spatial resolution is unified on various types of data after three-dimensional coordinate benchmark alignment, the target area is divided into three-dimensional space according to the preset grid size, a three-dimensional spatial grid structure is generated, and the point information of multi-source data is mapped to the corresponding three-dimensional spatial grid cells respectively; For each mapped 3D spatial grid cell, the 3D point cloud continuity index, direction vector stability index, and local height change gradient are calculated. Based on the 3D point cloud continuity index, direction vector stability index, and local height change gradient, the mirror perturbation intensity of all 3D spatial grid cells in the target area is estimated, and a perturbation intensity field model is constructed. According to the perturbation intensity field model, grid cells with comprehensive perturbation intensity values ​​in different segments are divided into stable modeling sub-regions, light perturbation modeling sub-regions, medium perturbation modeling sub-regions, and strong perturbation modeling sub-regions, and corresponding 3D perturbation layer maps are generated.

3. The three-dimensional modeling method based on multi-source surveying data according to claim 2, characterized in that, Analyze the three-dimensional perturbation layer map to extract perturbation level attributes and modeling complexity data; The process of analyzing disturbance level attributes and modeling complexity data to generate a modeling sub-region attribute set includes: The three-dimensional perturbation layer map is analyzed to obtain the three-dimensional spatial grid number sets of stable modeling sub-regions, lightly perturbed modeling sub-regions, moderately perturbed modeling sub-regions, and strongly perturbed modeling sub-regions, and the perturbation level attribute corresponding to each grid cell is extracted; the number of grids, grid density, point cloud sparsity, point position direction change rate, and local height change gradient in each modeling sub-region are calculated to obtain modeling complexity data. Based on the modeling complexity data and the disturbance level attributes of each modeling sub-region, modeling response control parameters are set for each modeling sub-region. The modeling response control parameters include: modeling triggering method, modeling start time conditions, modeling allowable space range, modeling pause conditions, and modeling delay window length. A corresponding modeling execution monitoring table is created for all modeling sub-regions, and the modeling response control parameters and the modeling execution monitoring table are combined to generate the modeling sub-region attribute set.

4. The three-dimensional modeling method based on multi-source surveying data according to claim 3, characterized in that, The process of constructing modeling boundary relationships and modeling priorities based on the attribute set of the modeling sub-region, analyzing the modeling boundary relationships in conjunction with the modeling priorities, and generating the modeling control structure is as follows: The attribute set of the modeling sub-region is read. Based on the three-dimensional spatial range, disturbance level label and modeling task complexity data of each modeling sub-region recorded in the attribute set, the three-dimensional spatial mesh unit within the boundary of the modeling sub-region is subjected to region boundary extraction processing. Based on the region boundary extraction results, the three-dimensional spatial adjacency relationship between stable modeling sub-region, lightly disturbed modeling sub-region, mediumly disturbed modeling sub-region and strongly disturbed modeling sub-region is identified, and the modeling boundary relationship is constructed. For 3D spatial mesh elements located in the boundary region of the modeling sub-region, the modeling priority is calculated based on the perturbation level label, point cloud continuity index, direction vector change, local height change gradient, and modeling task stacking depth in the modeling sub-region attribute set. The modeling task queues corresponding to each modeling sub-region are sorted according to the modeling priority to generate a modeling execution order associated with the disturbance level; Based on the sorted modeling task queue and modeling boundary relationships, a modeling control structure is generated.

5. A three-dimensional modeling method based on multi-source surveying data according to claim 4, characterized in that, The process of analyzing the target region based on the modeling control structure and generating candidate point cloud data includes: Read the spatial range of the light disturbance modeling sub-region and the medium disturbance modeling sub-region from the modeling control structure, obtain the 3D point cloud data of the current period within the corresponding spatial range, and call the point cloud data of the previous modeling period from the historical modeling data cache as the reference point cloud data. The point cloud data input in the current cycle is used to identify the spatially overlapping area with the reference point cloud data. The local surface normal vector, local curvature vector and point cloud density change rate of the corresponding points in the spatially overlapping area are calculated. By comparing the change amplitude of the main direction vector of the same grid cell in two consecutive modeling cycles, if it exceeds the threshold, the corresponding grid cell is marked as "directionally unstable grid". For regions not marked as directionally unstable grids, calculate the local boundary segment displacement, local corner offset, and surface slope change. If these values ​​are not within the tolerance range, mark the corresponding point set as a "structurally inconsistent point set". Assign low modeling weight labels to the data points marked as directionally unstable grids or structurally inconsistent point sets to generate candidate point cloud data.

6. A three-dimensional modeling method based on multi-source surveying data according to claim 5, characterized in that, The process of processing candidate point cloud data to generate a candidate dataset for modeling includes: Multidimensional feature extraction is performed on the candidate point cloud data. Feature parameters, including the local curvature change amplitude, direction vector continuity score, neighborhood height fluctuation factor, and historical disturbance record amount, are calculated for each data point. Based on the extracted feature parameters, a feature description vector for each data point is constructed. The feature description vector is then input into the disturbance absorption evaluation rule set to determine the geometric confidence level of each data point. Candidate point cloud data are classified according to geometric confidence level. By analyzing the proportion of stable points in the corresponding grid neighborhood of each data point, a candidate dataset for modeling is generated.

7. A three-dimensional modeling method based on multi-source surveying data according to claim 6, characterized in that, The process of generating 3D modeling fusion results by performing 3D modeling and region fusion based on the modeling control structure and modeling candidate dataset includes: Prioritize 3D modeling of stable modeling sub-regions to construct a 3D mesh structure for the stable region. For lightly disturbed modeling sub-regions, after the modeling delay window set by the modeling rhythm control structure ends, combine the point data with high and medium geometric confidence levels in the modeling candidate dataset to perform a local weighted fitting algorithm under directional constraints, and adopt a boundary adsorption strategy to ensure its connection continuity with the stable modeling sub-regions. For moderately disturbed modeling sub-regions, perform continuous boundary fusion processing based on normal vector consistency and curvature compatibility. Perform 3D modeling on the three types of modeling sub-regions respectively, and fuse the modeling results of each sub-region to generate a consistent 3D modeling fusion result for the entire region.

8. A 3D modeling system based on multi-source surveying data, specifically applied to the 3D modeling method based on multi-source surveying data as described in any one of claims 1 to 7, comprising a management center, characterized in that, The management center communication connection includes a data layering module, a data control module, a modeling candidate module, and a modeling fusion module. The data layering module is used to analyze the acquired multi-source surveying data, divide the target area, and generate a three-dimensional perturbation layered map. The data control module is used to analyze the three-dimensional disturbance layer map, extract disturbance level attributes and modeling complexity data; The disturbance level attributes and modeling complexity data are analyzed to generate a modeling sub-region attribute set; based on the modeling sub-region attribute set, modeling boundary relationships and modeling priorities are constructed; the modeling boundary relationships are analyzed in combination with the modeling priorities to generate a modeling control structure. The candidate modeling module is used to analyze the target region based on the modeling control structure and generate candidate point cloud data; The candidate point cloud data is processed to generate a candidate dataset for modeling; The modeling fusion module is used to perform 3D modeling and region fusion based on the modeling control structure and modeling candidate dataset, and generate 3D modeling fusion results.

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