A high-precision 3D geological modeling system for mining drones

Abstract

The present invention relates to the technical fields of geological exploration and three-dimensional modeling, and specifically to a high-precision three-dimensional geological modeling system for mining drones. The system comprises a multispectral imaging acquisition module, a high-precision point cloud-image registration module, a geological feature fusion and extraction module, a three-dimensional geological model construction module, and a geological attribute visualization output module. The system comprises the following components: the multispectral imaging acquisition module, which acquires and registers image data and raw point cloud data; the high-precision point cloud-image registration module, which performs consistency matching and outputs a registered point cloud-image dataset; the geological feature fusion and extraction module, which identifies geological boundaries and outputs geological feature vector data; and the three-dimensional geological model construction module, which generates a three-dimensional grid model with attribute topology. Through precise three-dimensional modeling and lithologic visualization, the system efficiently combines geological features with lithologic information, providing reliable data support for rock mass stability analysis.

Description

Technical Field

[0001] The present invention relates to the technical field of geological exploration and three-dimensional modeling, and in particular to a high-precision three-dimensional geological modeling system for mining unmanned aerial vehicles. Background Art

[0002] With the continuous development of mining and resource exploration, the application of 3D geological modeling technology in mining areas has become more and more extensive. Traditional geological modeling methods rely on 2D images, manual drawing and limited measurement data. These methods are usually unable to fully reflect the complex geological structure and spatial distribution of rock masses in mining areas. In order to more accurately reflect the geological characteristics of mining areas, 3D geological modeling technology has gradually developed, using a variety of advanced data acquisition and processing technologies, such as laser radar (LiDAR), multispectral imaging and point cloud data.

[0003] The main challenge with existing 3D geological modeling technologies lies in efficiently and accurately combining different types of geological data. Specifically, how to effectively map lithologic information into 3D models, ensuring precise alignment of lithologic distribution with geological features. While some methods can perform 3D reconstruction of point cloud data, they lack effective visual representation for mapping and displaying complex lithologic information, resulting in low accuracy in rock stability analysis. Therefore, improving the visualization of geological models through high-precision 3D modeling, combined with geological features and lithologic properties, to support rock stability analysis remains a pressing technical challenge. Summary of the Invention

[0004] Based on the above objectives, the present invention provides a high-precision three-dimensional geological modeling system for mining drones.

[0005] A high-precision 3D geological modeling system for mining drones, comprising a multispectral imaging acquisition module, a point cloud-image high-precision registration module, a geological feature fusion extraction module, a 3D geological model construction module, and a geological attribute visualization output module; wherein:

[0006] Multispectral imaging acquisition module: used to acquire mining area image data through a multispectral camera equipped with near-infrared and visible light bands, simultaneously acquire point cloud data through lidar, and output registered image data and original point cloud data;

[0007] Point cloud-image high-precision registration module: used to receive registration image data and original point cloud data, extract image SIFT feature points and perform consistency matching with point cloud FPFH feature descriptors, and output the registered point cloud-image dataset;

[0008] Geological feature fusion extraction module: used to receive the registered point cloud-image dataset, identify geological boundaries based on the grayscale gradient changes of rock mass fractures and the fusion of point cloud curvature feature values, and output geological feature vector data;

[0009] 3D geological model construction module: used to receive geological feature vector data, perform Delaunay triangulation based on geological boundaries as constraints, and generate a 3D mesh model with attribute topology;

[0010] Geological attribute visualization output module: used to receive a 3D mesh model with attribute topology, map lithology codes to mesh vertex color values, and output a 3D geological model that supports rock mass stability analysis.

[0011] Optionally, the multispectral imaging acquisition module includes a near-infrared band acquisition unit, a visible light band acquisition unit, an image synchronization control unit, a lidar point cloud acquisition unit, and a data fusion and output unit; wherein:

[0012] Near-infrared band acquisition unit: used to collect near-infrared image data of the mining area through the onboard near-infrared band multispectral camera;

[0013] Visible light band acquisition unit: used to collect visible light image data of the mining area through the onboard visible light band multispectral camera;

[0014] Image synchronization control unit: used to synchronously control the operation of the near-infrared band acquisition unit and the visible light band acquisition unit, ensure the temporal consistency of the image data collected by the two, and output the registered image data;

[0015] LiDAR point cloud acquisition unit: This unit integrates a LiDAR device that emits a laser beam and measures the time difference of the reflected signal to calculate the distance each laser pulse is reflected back, thereby acquiring point cloud data of the mining area.

[0016] Data fusion and output unit: used to receive image data collected by multispectral cameras in the near-infrared band and visible light band and point cloud data collected by lidar, perform data synchronization processing and fusion, and output registered image data and original point cloud data.

[0017] Optionally, the data fusion and output unit includes:

[0018] Data synchronization control subunit: used to synchronize image data and point cloud data to ensure the consistency of data in time;

[0019] Registration data processing subunit: used to receive time-synchronized image data and point cloud data, optimize the registration accuracy based on the least squares method by matching the feature points in the image data with the geometric feature points in the point cloud data, and output the registered image data and original point cloud data;

[0020] Data fusion subunit: used to perform spatial fusion processing on the registered image data and the original point cloud data, combining the color information of the image data and the geometric position information of the point cloud data, fusing the two information in the same spatial coordinate system through the weighted average method, and outputting the fused point cloud data and image data;

[0021] Data output subunit: used to convert the format of the registered image data and original point cloud data after data synchronization processing and fusion and output them to the subsequent processing module.

[0022] Optionally, the point cloud-image high-precision registration module includes a feature extraction unit, a feature matching unit, a registration calculation unit, and an output unit; wherein:

[0023] Feature extraction unit: used to extract SIFT feature points from the received registered image data and extract FPFH feature descriptors from the received original point cloud data, generating image feature point sets and point cloud feature descriptor subsets respectively;

[0024] Feature matching unit: performs consistency matching based on the correspondence between SIFT feature points and FPFH feature descriptors, and selects the best matching pair by calculating the similarity between feature points and using the minimum matching error criterion;

[0025] Registration calculation unit: used to calculate the transformation matrix based on the best matching pair of image feature points and point cloud feature descriptors, perform spatial transformation processing, and optimize the alignment accuracy of image data and point cloud data;

[0026] Output unit: used to combine the registered image data with the point cloud data and output the registered point cloud-image dataset.

[0027] Optionally, the registration calculation unit includes:

[0028] Transformation matrix calculation subunit: used to calculate the rotation matrix between image data and point cloud data through least squares method or singular value decomposition based on the best matching pair of image feature points and point cloud feature descriptors and translation vectors , and get the transformation matrix ;

[0029] Spatial transformation processing subunit: used to transform the matrix Perform spatial transformation on the image data, and transform each point in the image data Convert to the coordinate system of point cloud data, optimize the alignment accuracy between image data and point cloud data, and output the image data after spatial transformation;

[0030] Registration accuracy optimization subunit: It is used to optimize the alignment accuracy based on the error evaluation of the transformed image data and point cloud data using the iterative closest point algorithm to ensure the best registration of the image data and point cloud data.

[0031] Optionally, the geological feature fusion extraction module includes a fracture grayscale gradient calculation unit, a point cloud curvature calculation unit, and a feature fusion unit; wherein:

[0032] Fracture grayscale gradient calculation unit: used to extract the grayscale information of the image data from the received registered point cloud-image data set and calculate the grayscale gradient change vector of the rock mass fracture area in the image data;

[0033] Point cloud curvature calculation unit: calculates the curvature feature value of each point based on the point cloud data in the registered point cloud-image dataset;

[0034] Feature fusion unit: used to spatially fuse the fracture grayscale gradient changes and point cloud curvature features, combine the feature information of the two through a weighted fusion method, identify geological boundaries, and output geological feature vector data.

[0035] Optionally, the feature fusion unit includes:

[0036] Feature weighted calculation subunit: used to perform weighted calculation on crack gray gradient and point cloud curvature features by setting the corresponding weight coefficient and , calculate the weighted sum of the two to obtain fusion feature information ;

[0037] Boundary recognition calculation subunit is used to calculate the boundary based on the fused feature information , by setting the threshold Identify geological boundaries and regard areas with characteristic information greater than a threshold as geological boundary areas;

[0038] The output subunit is used to output geological feature vector data based on the identified geological boundary information.

[0039] Optionally, the three-dimensional geological model construction module includes a geological boundary constraint processing unit, a Delaunay triangulation unit, and an attribute topology generation unit; wherein:

[0040] Geological boundary constraint processing unit: used to receive geological feature vector data, extract geological boundary information, and use the boundary information as a constraint condition;

[0041] Delaunay triangulation unit: Based on geological boundary constraints, the Delaunay triangulation algorithm is executed to triangulate the point set within the constraint area, thereby generating a triangular mesh that meets the topological requirements;

[0042] Attribute topology generation unit: used to associate the generated triangular mesh with the attribute information of the geological features to generate a three-dimensional mesh model with attribute topology, in which each triangular mesh node has a one-to-one correspondence with its corresponding attribute information.

[0043] Optionally, the Delaunay triangulation unit includes:

[0044] Point set preparation subunit: used to extract valid point sets within the constraint area from geological feature vector data according to preset conditions;

[0045] Delaunay triangulation algorithm subunit: Executes the Delaunay triangulation algorithm based on the point set within the constraint area, performs triangle division according to the Delaunay criterion, and ensures that the circumscribed circle of each generated triangular mesh does not contain other points;

[0046] Boundary constraint processing subunit: used to use geological boundaries as hard constraints when performing Delaunay triangulation to ensure that the generated triangular mesh does not cross the geological boundary area.

[0047] Optionally, the geological attribute visualization output module includes a lithology code extraction unit, a color value mapping unit, a three-dimensional model updating unit, and a stability analysis support unit; wherein:

[0048] Lithology code extraction unit: used to extract lithology information from the received geological feature vector data and convert the lithology information into a unified lithology code. Different lithology types have unique code values.

[0049] Color value mapping unit: used to map different lithology codes to corresponding grid vertex color values ​​based on the extracted lithology codes and the preset color value mapping rule table;

[0050] 3D model updating unit: used to update the mapped color value information to each vertex of the 3D mesh model;

[0051] Stability analysis support unit: used to output a three-dimensional geological model that supports rock mass stability analysis based on the updated three-dimensional geological model, combined with lithology distribution and geological attribute information.

[0052] Beneficial effects of the present invention:

[0053] The present invention combines multispectral imaging and lidar technology, uses the Delaunay triangulation algorithm to accurately construct a three-dimensional geological model, and combines it with lithology coding for visual display, achieving high-precision geological boundary identification and lithology distribution mapping; this method can accurately map lithology information into a three-dimensional grid model and intuitively display the distribution of different lithologies through color value mapping, thereby effectively improving the accuracy and operability of geological modeling in mining areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0055] Figure 1 A schematic diagram of a three-dimensional geological modeling system according to an embodiment of the present invention;

[0056] Figure 2 Schematic diagram of a three-dimensional geological model construction module according to an embodiment of the present invention. DETAILED DESCRIPTION

[0057] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments. It is also noted that, to provide a more detailed description, the following embodiments are best and preferred embodiments, and those skilled in the art may employ alternative methods for implementing certain known technologies. Furthermore, the accompanying drawings are intended only to provide a more detailed description of the embodiments and are not intended to limit the present invention.

[0058] It should be noted that references in the specification to "one embodiment," "an embodiment," "exemplary embodiments," "some embodiments," etc. indicate that the described embodiments may include specific features, structures, or characteristics, but not necessarily every embodiment will include such specific features, structures, or characteristics. Furthermore, when specific features, structures, or characteristics are described in conjunction with an embodiment, it is within the knowledge of persons skilled in the relevant art to implement such features, structures, or characteristics in conjunction with other embodiments (whether or not explicitly described).

[0059] In general, terms can be understood, at least in part, from their 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.

[0060] like Figure 1-Figure 2 As shown, a high-precision 3D geological modeling system for mining drones includes a multispectral imaging acquisition module, a point cloud-image high-precision registration module, a geological feature fusion extraction module, a 3D geological model construction module, and a geological attribute visualization output module; wherein:

[0061] Multispectral imaging acquisition module: used to acquire mining area image data through a multispectral camera equipped with near-infrared and visible light bands, simultaneously acquire point cloud data through lidar, and output registered image data and original point cloud data;

[0062] The multispectral imaging acquisition module includes a near-infrared band acquisition unit, a visible light band acquisition unit, an image synchronization control unit, a lidar point cloud acquisition unit, and a data fusion and output unit; wherein:

[0063] Near-infrared band acquisition unit: used to collect near-infrared image data of the mining area through the onboard near-infrared band multispectral camera;

[0064] Visible light band acquisition unit: used to collect visible light image data of the mining area through the onboard visible light band multispectral camera;

[0065] Image synchronization control unit: used to synchronously control the operation of the near-infrared band acquisition unit and the visible light band acquisition unit, ensure the temporal consistency of the image data collected by the two, and output the registered image data;

[0066] LiDAR point cloud acquisition unit: This unit integrates a LiDAR device. The LiDAR emits a laser beam and measures the time difference of the reflected signal to calculate the distance of each reflected laser pulse, thereby acquiring point cloud data of the mining area. The distance calculation formula is: ,in, is the distance between the laser pulse and the target; is the speed of light, approximately ; is the time delay from the laser pulse being emitted to the time it is reflected back to the receiver;

[0067] The calculation principle is as follows:

[0068] Laser emission: The lidar system emits laser pulses toward the target;

[0069] Time measurement: The laser pulse hits the target and reflects back, and the receiver records the time delay from the pulse transmission to the reception ;

[0070] Distance calculation: According to the speed of light formula Calculate the total distance the pulse travels; since the laser pulse undergoes a round trip during propagation, the final distance value needs to be divided by 2 to obtain the actual distance to the target; this calculation is used to calculate the precise position of each point cloud collected by the lidar in order to generate high-precision three-dimensional point cloud data of the mining area.

[0071] Data fusion and output unit: used to receive image data collected by multispectral cameras in the near-infrared band and visible light band and point cloud data collected by lidar, perform data synchronization processing and fusion, and output registered image data and original point cloud data; through the collaborative work of multispectral cameras and lidar, it can accurately and synchronously obtain image data and point cloud data of the mining area and ensure high-precision data registration. This multi-dimensional data collection method provides high-quality input for subsequent geological feature extraction and three-dimensional modeling, effectively improving the accuracy and reliability of geological modeling in the mining area.

[0072] The data fusion and output unit includes:

[0073] Data synchronization control subunit: used to synchronize image data and point cloud data to ensure the consistency of data in time;

[0074] Registration data processing subunit: It is used to receive the time-synchronized image data and point cloud data, optimize the registration accuracy based on the least squares method by matching the feature points in the image data with the geometric feature points in the point cloud data, and output the registered image data and original point cloud data. The objective function of the least squares method optimization is defined as: ,in, The point cloud data Point coordinates; The corresponding image data feature point coordinates; is the rotation matrix, which is used to describe the rotation transformation between the point cloud and the image; is the translation vector, which is used to describe the translation transformation between the point cloud and the image; is the number of feature points for registration;

[0075] Data fusion subunit: It is used to perform spatial fusion processing on the registered image data and the original point cloud data. It combines the color information of the image data with the geometric position information of the point cloud data, fuses the two information into the same spatial coordinate system through the weighted average method, and outputs the fused point cloud data and image data to the subsequent modules for further analysis.

[0076] Data output subunit: used to convert the format of the registered image data and original point cloud data after data synchronization processing and fusion and output them to the subsequent processing module to ensure the compatibility of the data in format and accuracy; the above steps can efficiently and accurately align the image data and point cloud data in time and space through data synchronization processing and fusion, thereby optimizing the data fusion accuracy; this high-precision data synchronization and fusion processing technology ensures the efficiency and accuracy of subsequent geological feature extraction and three-dimensional modeling, and improves the quality of geological modeling in the mining area.

[0077] Point cloud-image high-precision registration module: used to receive registration image data and original point cloud data, extract image SIFT feature points and perform consistency matching with point cloud FPFH feature descriptors, and output the registered point cloud-image dataset;

[0078] The point cloud-image high-precision registration module includes a feature extraction unit, a feature matching unit, a registration calculation unit, and an output unit; wherein:

[0079] Feature extraction unit: used to extract SIFT (Scale Invariant Feature Transform) feature points from the received registered image data, and extract FPFH (Fast Point Feature History) feature descriptors from the received raw point cloud data, generating image feature point sets and point cloud feature descriptor subsets respectively;

[0080] Feature matching unit: performs consistency matching based on the correspondence between SIFT feature points and FPFH feature descriptors, and selects the best matching pair by calculating the similarity between feature points and using the minimum matching error criterion;

[0081] The steps to select the best matching pair are as follows:

[0082] Feature point similarity calculation: SIFT feature points and FPFH feature descriptors are extracted from image data and point cloud data respectively; for each pair of feature points, Euclidean distance is used as the measurement method to calculate their similarity, the formula is: ,in, The image data SIFT feature point descriptors; The point cloud data FPFH feature descriptors; is a feature point and Similarity measure between ;

[0083] Matching error calculation: After calculating the similarity between all feature points, the minimum matching error criterion is used to evaluate the matching quality of each pair of feature points. The minimum matching error criterion is to select the matching pair with the smallest matching error by calculating the similarity difference between the image feature points and the point cloud feature descriptors; the formula is: ,in, is the matching error, which represents the sum of similarities of all matching pairs; is a feature point and Similarity measure between ; is the number of feature points;

[0084] Best matching pair selection: By comparing all possible feature point matching pairs, the best matching pair with the smallest matching error is selected to ensure high-quality alignment of image data and point cloud data, avoiding the introduction of false matching and registration errors.

[0085] Registration calculation unit: used to calculate the transformation matrix based on the best matching pair of image feature points and point cloud feature descriptors, perform spatial transformation processing, and optimize the alignment accuracy of image data and point cloud data;

[0086] Output unit: used to combine the registered image data with the point cloud data and output the registered point cloud-image dataset for use by subsequent modules; the above unit can accurately register the image data with the point cloud data with high precision by matching SIFT feature points with FPFH feature descriptors and combining the RANSAC algorithm to optimize the registration accuracy; this process ensures efficient alignment of image and point cloud data, providing a high-quality data foundation for subsequent geological feature extraction and 3D modeling.

[0087] The registration calculation unit includes:

[0088] Transformation matrix calculation subunit: used to calculate the rotation matrix between image data and point cloud data through least squares method or singular value decomposition based on the best matching pair of image feature points and point cloud feature descriptors and translation vectors , and get the transformation matrix , the transformation matrix is ​​used to align the image data to the coordinate system of the point cloud data;

[0089] Spatial transformation processing subunit: used to transform the matrix Perform spatial transformation on the image data, and transform each point in the image data Convert to the coordinate system of the point cloud data, optimize the alignment accuracy of the image data and the point cloud data, and output the image data after spatial transformation; the spatial transformation processing formula is: ,in, The image data feature point coordinates; is the first feature point coordinates; is the rotation matrix, is the translation vector;

[0090] Registration accuracy optimization subunit: It is used to optimize the alignment accuracy based on the error evaluation between the transformed image data and the point cloud data using the iterative closest point algorithm to ensure the best registration of the image data and the point cloud data. The iterative closest point algorithm optimizes by minimizing the error between the image data and the point cloud data. Its expression is: ,in, The point cloud data Point coordinates; The error function of the iterative closest point algorithm is minimized to optimize the registration accuracy. The above subunits can effectively align the image data to the coordinate system of the point cloud data through the calculation of the transformation matrix and spatial transformation processing. Combined with the optimization steps of the ICP algorithm, the alignment accuracy between the image data and the point cloud data can be further improved, providing more reliable data support for high-precision geological modeling.

[0091] Geological feature fusion extraction module: used to receive the registered point cloud-image dataset, identify geological boundaries based on the grayscale gradient changes of rock mass fractures and the fusion of point cloud curvature feature values, and output geological feature vector data;

[0092] The geological feature fusion extraction module includes a fracture gray gradient calculation unit, a point cloud curvature calculation unit, and a feature fusion unit; wherein:

[0093] Fracture grayscale gradient calculation unit: used to extract the grayscale information of the image data from the received registered point cloud-image data set and calculate the grayscale gradient change vector of the rock mass fracture area in the image data; the formula is:

[0094] ,in, For image data at point The gradient vector at ; For the image at point Gray value of and For the image The gray value at and The partial derivative in the direction represents the rate of change of the grayscale value;

[0095] Point cloud curvature calculation unit: Based on the point cloud data in the registered point cloud-image dataset, the curvature characteristic value of each point is calculated; the formula is: ,in, The midpoint of the point cloud data The curvature eigenvalue at ; is the second-order derivative of the point cloud data, indicating the change in surface curvature; The midpoint of the point cloud data The gradient value at , represents the change of the surface normal;

[0096] Feature fusion unit: used to spatially fuse the grayscale gradient changes of fractures and the curvature features of the point cloud. The feature information of the two is combined through a weighted fusion method to identify geological boundaries and output geological feature vector data. By combining the grayscale gradient changes of rock fractures with the curvature features of the point cloud, the above unit can accurately identify geological boundaries, especially key geological features such as fractures and faults. This method optimizes the recognition accuracy of geological boundaries, provides high-quality geological feature vector data, and greatly improves the accuracy and reliability of geological modeling in mining areas.

[0097] The feature fusion unit includes:

[0098] Feature weighted calculation subunit: used to perform weighted calculation on crack gray gradient and point cloud curvature features by setting the corresponding weight coefficient and , calculate the weighted sum of the two to obtain fusion feature information , whose expression is: ,in, is the fused feature information, indicating that The weighted eigenvalue at ;

[0099] Boundary recognition calculation subunit is used to calculate the boundary based on the fused feature information , by setting the threshold Identify geological boundaries and regard areas with feature information greater than the threshold as geological boundary areas; the judgment expression is: ;

[0100] in, For the point If it is a geological boundary, it is a geological boundary; if it is 0, it is not a geological boundary; is the threshold set to distinguish geological boundaries from non-geological boundary areas;

[0101] The output subunit is used to output geological feature vector data based on the identified geological boundary information for further analysis by subsequent modules. The above subunit can combine the advantages of the fracture grayscale gradient and point cloud curvature features by weighted fusion to accurately identify geological boundaries. The weighted fusion method improves the sensitivity of boundary recognition and accurately distinguishes geological boundary areas through threshold judgment, providing a reliable data basis for subsequent three-dimensional modeling and significantly improving the precision and accuracy of mining area geological analysis.

[0102] 3D geological model construction module: used to receive geological feature vector data, perform Delaunay triangulation based on geological boundaries as constraints, and generate a 3D mesh model with attribute topology;

[0103] The 3D geological model construction module includes a geological boundary constraint processing unit, a Delaunay triangulation unit, and an attribute topology generation unit; among which:

[0104] Geological boundary constraint processing unit: used to receive geological feature vector data, extract geological boundary information, and use the boundary information as a constraint to ensure that the generated triangular mesh is consistent with the geological boundary during Delaunay triangulation, avoiding the mesh crossing the boundary area;

[0105] Delaunay triangulation unit: Based on geological boundary constraints, the Delaunay triangulation algorithm is executed to triangulate the point set within the constraint area, thereby generating a triangular mesh that meets the topological requirements;

[0106] Attribute topology generation unit: used to associate the generated triangular mesh with the attribute information of geological features (such as lithology, curvature, etc.) to generate a three-dimensional mesh model with attribute topology, in which each triangular mesh node has a one-to-one correspondence with its corresponding attribute information, and provide topological structure support for subsequent analysis; the above unit can generate an accurate three-dimensional mesh model that conforms to the geological boundary through Delaunay triangulation with geological boundaries as constraints. Combined with geological attribute information, it generates a three-dimensional mesh model with attribute topology, providing high-quality structural support for subsequent geological analysis and mining area modeling.

[0107] Delaunay triangulation units include:

[0108] Point set preparation subunit: used to extract valid point sets within the constraint area from geological feature vector data according to preset conditions;

[0109] Condition 1: select all points located inside the geological boundary, and the minimum distance between points is not less than the preset threshold;

[0110] Condition 2: remove points outside the boundary to ensure that the point set contains only valid points within the boundary;

[0111] Delaunay triangulation algorithm subunit: Executes the Delaunay triangulation algorithm based on the point set within the constraint area, performs triangle division according to the Delaunay criterion, and ensures that the circumscribed circle of each generated triangular mesh does not contain other points;

[0112] Boundary constraint processing subunit: This subunit is used to use geological boundaries as hard constraints when performing Delaunay triangulation to ensure that the generated triangular mesh does not cross the geological boundary area. If triangles that cross the boundary appear, they are corrected by adjusting the triangle nodes or splitting the boundary area to ensure that the mesh meets the geological boundary constraints. The above subunit uses clear point set standards and Delaunay triangulation based on geological boundary constraints to ensure that the generated triangular mesh meets the topological requirements and does not cross the geological boundary, providing accurate structural data for subsequent 3D modeling and geological analysis.

[0113] Geological attribute visualization output module: used to receive a 3D mesh model with attribute topology, map lithology codes to mesh vertex color values, and output a 3D geological model that supports rock mass stability analysis;

[0114] The geological attribute visualization output module includes a lithology code extraction unit, a color value mapping unit, a 3D model update unit, and a stability analysis support unit; among them:

[0115] Lithology code extraction unit: used to extract lithology information from the received geological feature vector data and convert the lithology information into a unified lithology code. Different lithology types have unique code values, which facilitates subsequent mapping and analysis.

[0116] Color value mapping unit: used to map different lithology codes to corresponding grid vertex color values ​​based on the extracted lithology codes and the preset color value mapping rule table, ensuring that each lithology has a unique color representation in the 3D geological model;

[0117] Table 1 Color value mapping rules table

[0118]

[0119] In Table 1 above, lithology type represents different geological units or rock types in the mining area, each with different physical and chemical properties; mapping color is used to map each lithology type to a specific color; color value (RGB) represents the RGB value of each mapped color, which is used for color coding in 3D visualization. Through such a mapping table, a clear relationship can be established between lithology and its corresponding color, allowing the 3D geological model to intuitively present the distribution of different lithologies during visualization. Using different colors to represent different types of rock can provide higher accuracy and more intuitive results in the geological analysis of the mining area, helping engineers and geologists better conduct rock stability analysis, resource exploration, and mine development decisions.

[0120] 3D model update unit: used to update the mapped color value information to each vertex of the 3D mesh model, update the visualization effect of the mesh model, and ensure the correct presentation of lithology information in the model;

[0121] Stability analysis support unit: used to output a three-dimensional geological model that supports rock stability analysis based on the updated three-dimensional geological model, combined with lithologic distribution and geological attribute information, and provide visualization data for rock stability assessment; the above unit can intuitively present the lithologic distribution of the mining area by mapping lithologic codes to grid vertex color values ​​and updating the three-dimensional geological model. This method effectively combines geological attributes with the visualization of the three-dimensional model, providing operational data support for rock stability analysis and improving the accuracy and efficiency of geological analysis in the mining area.

[0122] The present invention encompasses any alternatives, modifications, equivalents, and solutions that fall within the spirit and scope of the present invention. To provide a thorough understanding of the present invention, specific details are described in detail below in connection with the preferred embodiments of the present invention, but those skilled in the art will be able to fully understand the present invention without these detailed descriptions. Furthermore, to avoid unnecessary confusion regarding the essence of the present invention, well-known methods, processes, procedures, components, and circuits have not been described in detail.

[0123] 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 principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A high-precision three-dimensional geological modeling system for mining drones, characterized by: It includes a multispectral imaging acquisition module, a point cloud-image high-precision registration module, a geological feature fusion extraction module, a 3D geological model construction module, and a geological attribute visualization output module; among which: Multispectral imaging acquisition module: used to acquire mining area image data through a multispectral camera equipped with near-infrared and visible light bands, simultaneously acquire point cloud data through lidar, and output registered image data and original point cloud data; Point cloud-image high-precision registration module: used to receive registration image data and original point cloud data, extract image SIFT feature points and perform consistency matching with point cloud FPFH feature descriptors, and output the registered point cloud-image dataset; Geological feature fusion extraction module: used to receive the registered point cloud-image dataset, identify geological boundaries based on the grayscale gradient changes of rock fractures and the fusion of point cloud curvature feature values, and output geological feature vector data; 3D geological model construction module: used to receive geological feature vector data, perform Delaunay triangulation based on geological boundaries as constraints, and generate a 3D mesh model with attribute topology; Geological attribute visualization output module: used to receive a 3D mesh model with attribute topology, map lithology codes to mesh vertex color values, and output a 3D geological model that supports rock mass stability analysis.

2. A high-precision three-dimensional geological modeling system for mining drones according to claim 1, characterized in that: The multispectral imaging acquisition module includes a near-infrared band acquisition unit, a visible light band acquisition unit, an image synchronization control unit, a lidar point cloud acquisition unit, and a data fusion and output unit; wherein: Near-infrared band acquisition unit: used to collect near-infrared image data of the mining area through the onboard near-infrared band multispectral camera; Visible light band acquisition unit: used to collect visible light image data of the mining area through the onboard visible light band multispectral camera; Image synchronization control unit: used to synchronously control the operation of the near-infrared band acquisition unit and the visible light band acquisition unit, ensure the temporal consistency of the image data collected by the two, and output the registered image data; LiDAR point cloud acquisition unit: This unit integrates a LiDAR device that emits a laser beam and measures the time difference of the reflected signal to calculate the distance each laser pulse is reflected back, thereby acquiring point cloud data of the mining area. Data fusion and output unit: used to receive image data collected by multispectral cameras in the near-infrared band and visible light band and point cloud data collected by lidar, perform data synchronization processing and fusion, and output registered image data and original point cloud data.

3. The high-precision three-dimensional geological modeling system for mining drones according to claim 2 is characterized in that: The data fusion and output unit includes: Data synchronization control subunit: used to synchronize image data and point cloud data to ensure the consistency of data in time; Registration data processing subunit: used to receive time-synchronized image data and point cloud data, optimize the registration accuracy based on the least squares method by matching the feature points in the image data with the geometric feature points in the point cloud data, and output the registered image data and original point cloud data; Data fusion subunit: used to perform spatial fusion processing on the registered image data and the original point cloud data, combining the color information of the image data and the geometric position information of the point cloud data, fusing the two information in the same spatial coordinate system through the weighted average method, and outputting the fused point cloud data and image data; Data output subunit: used to convert the format of the registered image data and original point cloud data after data synchronization processing and fusion and output them to the subsequent processing module.

4. The high-precision three-dimensional geological modeling system for mining drones according to claim 1 is characterized in that: The point cloud-image high-precision registration module includes a feature extraction unit, a feature matching unit, a registration calculation unit, and an output unit; wherein: Feature extraction unit: used to extract SIFT feature points from the received registered image data and extract FPFH feature descriptors from the received original point cloud data, generating image feature point sets and point cloud feature descriptor subsets respectively; Feature matching unit: performs consistency matching based on the correspondence between SIFT feature points and FPFH feature descriptors, and selects the best matching pair by calculating the similarity between feature points and using the minimum matching error criterion; Registration calculation unit: used to calculate the transformation matrix based on the best matching pair of image feature points and point cloud feature descriptors, perform spatial transformation processing, and optimize the alignment accuracy of image data and point cloud data; Output unit: used to combine the registered image data with the point cloud data and output the registered point cloud-image dataset.

5. The high-precision three-dimensional geological modeling system for mining drones according to claim 4 is characterized in that: The registration calculation unit includes: Transformation matrix calculation subunit: used to calculate the rotation matrix between image data and point cloud data through least squares method or singular value decomposition based on the best matching pair of image feature points and point cloud feature descriptors and translation vectors , and get the transformation matrix ; Spatial transformation processing subunit: used to transform the matrix Perform spatial transformation on the image data, and transform each point in the image data Convert to the coordinate system of point cloud data, optimize the alignment accuracy between image data and point cloud data, and output the image data after spatial transformation; Registration accuracy optimization subunit: It is used to optimize the alignment accuracy based on the error evaluation of the transformed image data and point cloud data using the iterative closest point algorithm to ensure the best registration of the image data and point cloud data.

6. The high-precision three-dimensional geological modeling system for mining drones according to claim 1 is characterized in that: The geological feature fusion extraction module includes a fracture gray gradient calculation unit, a point cloud curvature calculation unit and a feature fusion unit; wherein: Fracture grayscale gradient calculation unit: used to extract the grayscale information of the image data from the received registered point cloud-image data set and calculate the grayscale gradient change vector of the rock mass fracture area in the image data; Point cloud curvature calculation unit: calculates the curvature feature value of each point based on the point cloud data in the registered point cloud-image dataset; Feature fusion unit: used to spatially fuse the fracture grayscale gradient changes and point cloud curvature features, combine the feature information of the two through a weighted fusion method, identify geological boundaries, and output geological feature vector data.

7. The high-precision three-dimensional geological modeling system for mining drones according to claim 6, characterized in that: The feature fusion unit includes: Feature weighted calculation subunit: used to perform weighted calculation on crack gray gradient and point cloud curvature features by setting the corresponding weight coefficient and , calculate the weighted sum of the two to obtain fusion feature information ; Boundary recognition calculation subunit is used to calculate the boundary based on the fused feature information , by setting the threshold Identify geological boundaries and regard areas with characteristic information greater than a threshold as geological boundary areas; The output subunit is used to output geological feature vector data based on the identified geological boundary information.

8. The high-precision three-dimensional geological modeling system for mining drones according to claim 1 is characterized in that: The three-dimensional geological model construction module includes a geological boundary constraint processing unit, a Delaunay triangulation unit, and an attribute topology generation unit; wherein: Geological boundary constraint processing unit: used to receive geological feature vector data, extract geological boundary information, and use the boundary information as a constraint condition; Delaunay triangulation unit: Based on geological boundary constraints, the Delaunay triangulation algorithm is executed to triangulate the point set within the constraint area, thereby generating a triangular mesh that meets the topological requirements; Attribute topology generation unit: used to associate the generated triangular mesh with the attribute information of the geological features to generate a three-dimensional mesh model with attribute topology, in which each triangular mesh node has a one-to-one correspondence with its corresponding attribute information.

9. The high-precision three-dimensional geological modeling system for mining drones according to claim 8, characterized in that: The Delaunay triangulation unit includes: Point set preparation subunit: used to extract valid point sets within the constraint area from geological feature vector data according to preset conditions; Delaunay triangulation algorithm subunit: Executes the Delaunay triangulation algorithm based on the point set within the constraint area, performs triangle division according to the Delaunay criterion, and ensures that the circumscribed circle of each generated triangular mesh does not contain other points; Boundary constraint processing subunit: used to use geological boundaries as hard constraints when performing Delaunay triangulation to ensure that the generated triangular mesh does not cross the geological boundary area.

10. The high-precision three-dimensional geological modeling system for mining drones according to claim 1, characterized in that: The geological attribute visualization output module includes a lithology code extraction unit, a color value mapping unit, a three-dimensional model update unit, and a stability analysis support unit; wherein: Lithology code extraction unit: used to extract lithology information from the received geological feature vector data and convert the lithology information into a unified lithology code. Different lithology types have unique code values. Color value mapping unit: used to map different lithology codes to corresponding grid vertex color values ​​based on the extracted lithology codes and the preset color value mapping rule table; 3D model updating unit: used to update the mapped color value information to each vertex of the 3D mesh model; Stability analysis support unit: used to output a three-dimensional geological model that supports rock mass stability analysis based on the updated three-dimensional geological model, combined with lithology distribution and geological attribute information.

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