Surface mine photography model analysis system
Patent Information
- Application Number
- CN202511004391.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-07-21
- Publication Date
- 2025-10-31
Smart Images
Figure CN120876756A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to model analysis, specifically to a photographic model analysis system for open-pit mines. Background Technology
[0002] In the mining industry, mine monitoring and management are crucial for ensuring production safety, improving mining efficiency, and protecting the environment. Traditional mine monitoring and management methods mainly rely on manual inspections and two-dimensional maps, which are not only inefficient but also fail to comprehensively and accurately reflect the actual conditions of the mine. With the advancement of technology, especially the development of drone technology and photogrammetry, open-pit mine photogrammetry analysis systems have emerged, providing new solutions for mine monitoring and management.
[0003] Existing open-pit mine photogrammetry analysis systems have the following main drawbacks: 1) Bottlenecks in massive data processing Some systems cannot achieve parallel computing during aerial triangulation, resulting in low efficiency when processing massive amounts of oblique photogrammetry data, which becomes the main bottleneck affecting data processing speed. 2) The data processing and storage process is not closed-loop. When existing applications view data, they still view local data. For data scanned by drones, they still rely on storing it on a single PC and forwarding it via USB drive, which makes data processing, storage, management, and viewing inconvenient. 3) Insufficient data processing efficiency and automation In mining scenarios, slope estimation and measurement require manual setup or manual parameter adjustment, which is complex and cannot effectively detect all slope problems. 4) Display lag Some systems lack real-time 3D visualization capabilities, making it impossible to verify data quality in the field immediately, requiring post-processing to identify problems. Summary of the Invention
[0004] (a) Technical problems to be solved In view of the above-mentioned shortcomings of the existing technology, the present invention provides an open-pit mine photographic model analysis system, which can effectively overcome the shortcomings of the existing technology in that it is difficult to conduct comprehensive, accurate, efficient analysis and three-dimensional visualization of mine oblique photographic models.
[0005] (II) Technical Solution To achieve the above objectives, the present invention provides the following technical solution: An open-pit mine photogrammetry model analysis system includes the following components: The slope boundary line recognition module automatically identifies the top and bottom slope lines of the loaded 3D mine model; The slope analysis unit calculates the slope by analyzing the elevation gradient of neighboring points to quickly and preliminarily assess terrain changes; it calculates the slope by fitting the optimal plane of neighboring points and then calculating the normal vector to adapt to complex 3D mine models; it projects point cloud data onto a 2D grid, calculates the slope by calculating the ratio between the elevation difference of adjacent grid cells and the grid resolution after statistically analyzing the lowest elevation value of all grid cells, and filters out non-ground points based on a threshold to handle the automated processing of massive point cloud data; The slope rendering module renders the 3D model of the mine using different color steps based on the calculated slope index. The monitoring and early warning module integrates calculated slope indicators, displacement sensor data, and video surveillance data to build a risk early warning model, enabling abnormal alarms and emergency responses.
[0006] Preferably, the slope analysis unit includes a gradient-based slope calculation module, a normal vector-based slope calculation module, and a grid elevation value-based slope calculation module; The gradient-based slope calculation module calculates the slope by analyzing the elevation gradient of neighboring points, so as to quickly and preliminarily assess the terrain changes. The slope calculation module based on normal vectors calculates the slope by fitting the best plane of the neighborhood points and then calculating the normal vectors to adapt to complex 3D mine models. The slope calculation module based on grid elevation values projects point cloud data onto a two-dimensional grid. After calculating the lowest elevation value of all grid cells, it calculates the slope by the ratio between the elevation difference of adjacent grid cells and the grid resolution. It also filters out non-ground points based on a threshold to handle the automated processing of massive point cloud data.
[0007] Preferably, the gradient-based slope calculation module calculates the slope by analyzing the elevation gradient of neighboring points to quickly and preliminarily assess terrain changes, including: The slope S is calculated by analyzing the elevation gradient of neighboring points: ; Where S is the slope, representing the degree of inclination of the terrain at a certain point, expressed as a percentage; (x, y) are the coordinates of the point on the horizontal plane; and z is the elevation of the terrain at that point. Let X be the rate of change of elevation at a point on the terrain along the X direction, i.e., the elevation gradient along the X direction. This is the rate of change of elevation of a point in the terrain along the Y direction, i.e., the elevation gradient along the Y direction.
[0008] Preferably, the slope calculation module based on normal vectors calculates the slope by fitting the optimal plane of neighboring points and then calculating the normal vector, in order to adapt to complex 3D mine models, including: S11. Calculate the centroid coordinates of neighboring points. : ; ; ; Among them, (x i ,y i ,z i Let be the coordinates of the i-th neighboring point, and n be the number of neighboring points; S12. Construct the covariance matrix M: ; The covariance matrix M is used to describe the positional changes of neighborhood points in various directions. S13. Calculate the eigenvalues and eigenvectors of the covariance matrix M, and take the eigenvector corresponding to the smallest eigenvalue. If C < 0, then the eigenvector is taken as the normal vector of the best-fit plane for the neighborhood points. The normal vector of the best-fitting plane for neighborhood points is used to ensure that the normal vector points upwards. Where A, B, and C are the components of the normal vector in the X, Y, and Z directions, respectively, and the component C in the Z direction is used to determine whether the normal vector is pointing upwards or downwards. S14. The normal vector of the best-fit plane based on the neighborhood points. Calculate slope : ; in, Slope is the degree of inclination of the terrain at a point, expressed in degrees.
[0009] Preferably, the slope calculation module based on grid elevation values projects point cloud data onto a two-dimensional grid, calculates the slope by calculating the ratio between the elevation difference of adjacent grid cells and the grid resolution after statistically analyzing the lowest elevation value of all grid cells, and filters out non-ground points based on a threshold to handle the automated processing of massive point cloud data, including: S21, Aggregate the point cloud All point cloud data are projected onto a two-dimensional grid; Where, x j y j Let z be the x-coordinate and y-coordinate of the j-th measurement point, respectively. j Let R be the elevation value of the j-th measurement point. 3Represents a three-dimensional real space, where N is the number of measurement points; S22. Calculate the minimum elevation value of all grid cells, and then calculate the slope by the ratio of the elevation difference between adjacent grid cells to the grid resolution. ; Where S(x) m ,y m ,x n ,y n ) is the m-th grid cell (x m ,y m ) and the nth grid cell (x n ,y n The slope between ) and Z min (x m ,y m Z min (x n ,y n ) are respectively the m-th grid cell (x m ,y m ), the nth grid cell (x n ,y n The lowest elevation value, Grid resolution represents the size of each grid cell in the horizontal direction; S23. Filter out non-ground points based on threshold: ; Among them, P ground For the set of ground points, Let x represent the k-th measurement point in the point cloud set P, and Neighbors represent the points connected to the m-th grid cell (x). m ,y m Adjacent grid cell sets, This indicates the filtering condition, when the m-th grid cell (x m ,y m ) and the nth grid cell (x n ,y n The slope S(x) between ) m ,y m ,x n ,y n Less than the slope threshold S thres When, the nth grid cell (x) is considered to be n ,y n The k-th measurement point (x) included k ,y k ,z k () represents the ground point.
[0010] Preferably, it also includes an information annotation and manual measurement module, which supports information annotation at fixed locations on the 3D model of the mine and provides manual measurement tools to facilitate users to directly obtain key parameters.
[0011] Preferably, it also includes a data storage and management module, which can store and manage more than 1,000 mine-related data and support the efficient loading and display of more than 10GB of mine 3D models, meeting the management needs of large-scale mine data. At the same time, combined with a NAS file storage system, it can realize the long-term preservation and fast retrieval of mine oblique photogrammetry models.
[0012] Preferably, it also includes a system integration and expansion module, which adopts a Web architecture to support online collaboration between regulatory authorities and mining enterprises, including task allocation, data reporting and early warning information processing functions. At the same time, it accesses the video monitoring data of mining enterprises through API interfaces to realize unified data analysis and early warning view generation.
[0013] (III) Beneficial Effects Compared with the prior art, the open-pit mine photographic model analysis system provided by the present invention has the following beneficial effects: 1) High efficiency and automation Multiple slope analysis algorithms (including gradient-based slope calculation, normal vector-based slope calculation, and grid elevation value-based slope calculation) significantly improve data processing efficiency, and are especially suitable for processing massive point cloud data generated by UAV scanning. Automated identification and early warning functions (such as automatic identification of slope boundary lines and automatic early warning of risk warning models) reduce manual operations and improve the timeliness of safety management; 2) High precision and reliability The slope is calculated by analyzing the elevation gradient of neighboring points, by fitting the best plane of neighboring points and then calculating the normal vector, and by calculating the slope by calculating the ratio between the elevation difference of adjacent grid cells and the grid resolution after statistically analyzing the lowest elevation values of all grid cells. These methods ensure the accuracy of slope calculation and meet the strict requirements of mine safety assessment. Establish a mechanism for regularly saving and preventing tampering of data to ensure the reliability and traceability of analysis results; 3) Safety and Risk Control Real-time monitoring of key indicators such as slope stability and transport road slope, combined with risk warning models to identify potential safety hazards (such as exceeding design slope, spoil heap risks, etc.), significantly reducing the probability of safety accidents. 4) User-friendliness and usability The 3D model interactive functions (such as information annotation and manual measurement) and the rendering of the 3D model of the mine using different step colors based on the calculated slope index, intuitively display the analysis results and realize the 3D visualization of the analysis results; 5) Scalability and compatibility It can store and manage more than 1,000 mine-related data, support the efficient loading and display of 3D mine models of more than 10GB, meet the management needs of large-scale mine data, and, combined with a NAS file storage system, realize the long-term preservation and fast retrieval of mine oblique photogrammetry models, which can be expanded to manage larger-scale data. Attached Figure Description
[0014] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.
[0015] Figure 1 This is a schematic diagram of the system of the present invention. Detailed Implementation
[0016] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.
[0017] An open-pit mine photographic model analysis system, such as Figure 1 As shown, it includes the following parts: The slope boundary line recognition module automatically identifies the top and bottom slope lines of the loaded 3D mine model; The slope analysis unit calculates the slope by analyzing the elevation gradient of neighboring points to quickly and preliminarily assess terrain changes; it calculates the slope by fitting the optimal plane of neighboring points and then calculating the normal vector to adapt to complex 3D mine models; it projects point cloud data onto a 2D grid, calculates the slope by calculating the ratio between the elevation difference of adjacent grid cells and the grid resolution after statistically analyzing the lowest elevation value of all grid cells, and filters out non-ground points based on a threshold to handle the automated processing of massive point cloud data; The slope rendering module renders the 3D model of the mine using different color steps based on the calculated slope index. The monitoring and early warning module integrates calculated slope indicators, displacement sensor data, and video surveillance data to build a risk early warning model, enabling abnormal alarms and emergency responses.
[0018] The slope analysis unit includes a gradient-based slope calculation module, a normal vector-based slope calculation module, and a grid elevation value-based slope calculation module. The gradient-based slope calculation module calculates the slope by analyzing the elevation gradient of neighboring points, so as to quickly and preliminarily assess the terrain changes. The slope calculation module based on normal vectors calculates the slope by fitting the best plane of the neighborhood points and then calculating the normal vectors to adapt to complex 3D mine models. The slope calculation module based on grid elevation values projects point cloud data onto a two-dimensional grid. After calculating the lowest elevation value of all grid cells, it calculates the slope by the ratio between the elevation difference of adjacent grid cells and the grid resolution. It also filters out non-ground points based on a threshold to handle the automated processing of massive point cloud data.
[0019] 1) The gradient-based slope calculation module calculates the slope by analyzing the elevation gradient of neighboring points to quickly and preliminarily assess terrain changes, including: The slope S is calculated by analyzing the elevation gradient of neighboring points: ; Where S is the slope, representing the degree of inclination of the terrain at a certain point, expressed as a percentage; (x, y) are the coordinates of the point on the horizontal plane; and z is the elevation of the terrain at that point. Let X be the rate of change of elevation at a point on the terrain along the X direction, i.e., the elevation gradient along the X direction. This is the rate of change of elevation of a point in the terrain along the Y direction, i.e., the elevation gradient along the Y direction.
[0020] 2) The slope calculation module based on normal vectors calculates the slope by fitting the optimal plane of neighboring points and then calculating the normal vector, in order to adapt to complex 3D mine models, including: S11. Calculate the centroid coordinates of neighboring points. : ; ; ; Among them, (x i ,y i ,z i Let be the coordinates of the i-th neighboring point, and n be the number of neighboring points; S12. Construct the covariance matrix M: ; The covariance matrix M is used to describe the positional changes of neighborhood points in various directions. S13. Calculate the eigenvalues and eigenvectors of the covariance matrix M, and take the eigenvector corresponding to the smallest eigenvalue. If C < 0, then the eigenvector is taken as the normal vector of the best-fit plane for the neighborhood points. The normal vector of the best-fitting plane for neighborhood points is used to ensure that the normal vector points upwards. Where A, B, and C are the components of the normal vector in the X, Y, and Z directions, respectively, and the component C in the Z direction is used to determine whether the normal vector is pointing upwards or downwards. S14. The normal vector of the best-fit plane based on the neighborhood points. Calculate slope : ; in, Slope is the degree of inclination of the terrain at a point, expressed in degrees.
[0021] 3) The slope calculation module based on grid elevation values projects point cloud data onto a two-dimensional grid. After calculating the lowest elevation value of all grid cells, it calculates the slope by the ratio of the elevation difference between adjacent grid cells to the grid resolution. It also filters out non-ground points based on a threshold to handle automated processing of massive point cloud data, including: S21, Aggregate the point cloud All point cloud data are projected onto a two-dimensional grid; Where, x j y j Let z be the x-coordinate and y-coordinate of the j-th measurement point, respectively. j Let R be the elevation value of the j-th measurement point. 3 Represents a three-dimensional real space, where N is the number of measurement points; S22. Calculate the minimum elevation value of all grid cells, and then calculate the slope by dividing the elevation difference between adjacent grid cells by the grid resolution. ; Where S(x) m ,y m ,x n ,y n ) is the m-th grid cell (x m ,y m ) and the nth grid cell (x n ,y n The slope between ) and Z min (x m ,y m Z min(x n ,y n ) are respectively the m-th grid cell (x m ,y m ), the nth grid cell (x n ,y n The lowest elevation value, Grid resolution represents the size of each grid cell in the horizontal direction; S23. Filter out non-ground points based on threshold: ; Among them, P ground For the set of ground points, Let x represent the k-th measurement point in the point cloud set P, and Neighbors represent the points connected to the m-th grid cell (x). m ,y m Adjacent grid cell sets, This indicates the filtering condition, when the m-th grid cell (x m ,y m ) and the nth grid cell (x n ,y n The slope S(x) between ) m ,y m ,x n ,y n Less than the slope threshold S thres When, the nth grid cell (x) is considered to be n ,y n The k-th measurement point (x) included k ,y k ,z k () represents the ground point.
[0022] In the technical solution of this application, such as Figure 1 As shown, it also includes an information annotation and manual measurement module. The information annotation and manual measurement module supports information annotation at fixed positions on the 3D model of the mine and provides manual measurement tools to facilitate users to directly obtain key parameters (such as distance, angle, etc.).
[0023] In the technical solution of this application, such as Figure 1 As shown, it also includes a data storage and management module, which can store and manage more than 1,000 mine-related data and support the efficient loading and display of more than 10GB of mine 3D models, meeting the management needs of large-scale mine data. At the same time, combined with a NAS file storage system, it can realize the long-term preservation and fast retrieval of mine oblique photogrammetry models.
[0024] In the technical solution of this application, such as Figure 1As shown, it also includes a system integration and extension module. The system integration and extension module adopts a web architecture to support online collaboration between regulatory authorities and mining enterprises, including task allocation, data reporting and early warning information processing functions. At the same time, it accesses the video monitoring data of mining enterprises through API interfaces to realize unified data analysis and early warning view generation.
[0025] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims
1. A photographic model analysis system for open-pit mines, characterized in that: Includes the following parts: The slope boundary line recognition module automatically identifies the top and bottom slope lines of the loaded 3D mine model; The slope analysis unit calculates the slope by analyzing the elevation gradient of neighboring points to quickly and preliminarily assess terrain changes; it calculates the slope by fitting the optimal plane of neighboring points and then calculating the normal vector to adapt to complex 3D mine models; it projects point cloud data onto a 2D grid, calculates the slope by calculating the ratio between the elevation difference of adjacent grid cells and the grid resolution after statistically analyzing the lowest elevation value of all grid cells, and filters out non-ground points based on a threshold to handle the automated processing of massive point cloud data; The slope rendering module renders the 3D model of the mine using different color steps based on the calculated slope index. The monitoring and early warning module integrates calculated slope indicators, displacement sensor data, and video surveillance data to build a risk early warning model, enabling abnormal alarms and emergency responses.
2. The open-pit mine photographic model analysis system according to claim 1, characterized in that: The slope analysis unit includes a gradient-based slope calculation module, a normal vector-based slope calculation module, and a grid elevation value-based slope calculation module. The gradient-based slope calculation module calculates the slope by analyzing the elevation gradient of neighboring points, so as to quickly and preliminarily assess the terrain changes. The slope calculation module based on normal vectors calculates the slope by fitting the best plane of the neighborhood points and then calculating the normal vectors to adapt to complex 3D mine models. The slope calculation module based on grid elevation values projects point cloud data onto a two-dimensional grid. After calculating the lowest elevation value of all grid cells, it calculates the slope by the ratio between the elevation difference of adjacent grid cells and the grid resolution. It also filters out non-ground points based on a threshold to handle the automated processing of massive point cloud data.
3. The open-pit mine photographic model analysis system according to claim 2, characterized in that: The gradient-based slope calculation module calculates the slope by analyzing the elevation gradient of neighboring points to quickly and preliminarily assess terrain changes, including: The slope S is calculated by analyzing the elevation gradient of neighboring points: ； Where S is the slope, representing the degree of inclination of the terrain at a certain point, expressed as a percentage; (x, y) are the coordinates of the point on the horizontal plane; and z is the elevation of the terrain at that point. Let X be the rate of change of elevation at a point on the terrain along the X direction, i.e., the elevation gradient along the X direction. This is the rate of change of elevation of a point in the terrain along the Y direction, i.e., the elevation gradient along the Y direction.
4. The open-pit mine photographic model analysis system according to claim 2, characterized in that: The slope calculation module based on normal vectors calculates the slope by fitting the optimal plane of neighboring points and then calculating the normal vector, in order to adapt to complex 3D mine models, including: S11. Calculate the centroid coordinates of neighboring points. : ； ； ； Among them, (x i ,y i ,z i Let be the coordinates of the i-th neighboring point, and n be the number of neighboring points; S12. Construct the covariance matrix M: ； The covariance matrix M is used to describe the positional changes of neighborhood points in various directions. S13. Calculate the eigenvalues and eigenvectors of the covariance matrix M, and take the eigenvector corresponding to the smallest eigenvalue. If C < 0, then the eigenvector is taken as the normal vector of the best-fit plane for the neighborhood points. The normal vector of the best-fitting plane for neighborhood points is used to ensure that the normal vector points upwards. Where A, B, and C are the components of the normal vector in the X, Y, and Z directions, respectively, and the component C in the Z direction is used to determine whether the normal vector is pointing upwards or downwards. S14. The normal vector of the best-fit plane based on the neighborhood points. Calculate slope : ； in, Slope is the degree of inclination of the terrain at a point, expressed in degrees.
5. The open-pit mine photographic model analysis system according to claim 2, characterized in that: The slope calculation module based on grid elevation values projects point cloud data onto a two-dimensional grid, calculates the slope by calculating the ratio between the elevation difference of adjacent grid cells and the grid resolution after statistically analyzing the lowest elevation value of all grid cells, and filters out non-ground points based on a threshold to handle the automated processing of massive point cloud data, including: S21, Aggregate the point cloud All point cloud data are projected onto a two-dimensional grid; Where, x j y j Let z be the x-coordinate and y-coordinate of the j-th measurement point, respectively. j Let R be the elevation value of the j-th measurement point. 3 Represents a three-dimensional real space, where N is the number of measurement points; S22. Calculate the minimum elevation value of all grid cells, and then calculate the slope by the ratio of the elevation difference between adjacent grid cells to the grid resolution. ； Where S(x) m ,y m ,x n ,y n ) is the m-th grid cell (x m ,y m ) and the nth grid cell (x n ,y n The slope between ) and Z min (x m ,y m Z min (x n ,y n ) are respectively the m-th grid cell (x m ,y m ), the nth grid cell (x n ,y n The lowest elevation value, Grid resolution represents the size of each grid cell in the horizontal direction; S23. Filter out non-ground points based on threshold: ； Among them, P ground For the set of ground points, Let x represent the k-th measurement point in the point cloud set P, and Neighbors represent the points connected to the m-th grid cell (x). m ,y m Adjacent grid cell sets, This indicates the filtering condition, when the m-th grid cell (x m ,y m ) and the nth grid cell (x n ,y n The slope S(x) between ) m ,y m ,x n ,y n Less than the slope threshold S thres When, the nth grid cell (x) is considered to be n ,y n The k-th measurement point (x) included k ,y k ,z k () represents the ground point.
6. The open-pit mine photographic model analysis system according to claim 1, characterized in that: It also includes an information annotation and manual measurement module, which supports information annotation at fixed locations on the 3D model of the mine and provides manual measurement tools to facilitate users to directly obtain key parameters.
7. The open-pit mine photographic model analysis system according to claim 6, characterized in that: It also includes a data storage and management module, which can store and manage more than 1,000 mine-related data and support the efficient loading and display of more than 10GB of mine 3D models, meeting the management needs of large-scale mine data. At the same time, combined with a NAS file storage system, it can realize the long-term preservation and fast retrieval of mine oblique photogrammetry models.
8. The open-pit mine photographic model analysis system according to claim 7, characterized in that: It also includes a system integration and extension module, which adopts a web architecture to support online collaboration between regulatory authorities and mining enterprises. This includes functions such as task allocation, data reporting, and early warning information processing. At the same time, it accesses video surveillance data from mining enterprises through API interfaces to achieve unified data analysis and early warning view generation.