An open-pit mine annual resource consumption investigation method, system, device and medium based on unmanned aerial vehicle oblique photography

Abstract

The application discloses an open-pit mine annual resource consumption survey method, system, device and medium based on unmanned aerial vehicle oblique photography, relates to the field of mineral resources, and comprises the following steps: acquiring image data of at least two years of an open-pit mine by using an unmanned aerial vehicle carrying an oblique photography system; laying out ground control points and acquiring a reference data set; generating a three-dimensional real scene model of a corresponding year by means of aerial triangulation operation based on the image data and the reference data set; constructing a three-dimensional vector model of a mine body, and connecting the three-dimensional vector model of the mine body with the three-dimensional real scene model to obtain a connected three-dimensional model of the mine body; calculating a soil and stone quota based on connected three-dimensional models of the mine body of adjacent years; and calculating an annual resource consumption of the open-pit mine based on the soil and stone quota. The application can realize rapid and accurate statistics of the annual resource consumption of the open-pit mine.

Description

Technical Field

[0001] This application relates to the field of mineral resources, and in particular to a method, system, equipment and medium for surveying the annual resource utilization of open-pit mines based on UAV oblique photography. Background Technology

[0002] As a crucial material foundation for national economic development, accurate surveys and statistics on annual resource utilization are essential prerequisites for mineral resource planning, management, rational development and utilization, and ecological environmental protection. Open-pit mines, as one of the main forms of mineral resource extraction, are characterized by their wide operating range, frequent dynamic changes in earthwork and rock excavation, and continuous reshaping of topography, making real-time and accurate surveys of annual resource utilization a technical challenge within the industry.

[0003] Traditional methods for surveying annual operational activity in open-pit mines primarily rely on manual measurement, total station observation, and GPS point positioning. Manual measurement is inefficient, labor-intensive, and poses safety risks in complex terrain, making it difficult to achieve large-scale, high-frequency dynamic monitoring. While total stations and GPS point positioning can obtain high-precision data in localized areas, their limitations in measurement range and operational efficiency prevent them from comprehensively reflecting the overall three-dimensional spatial changes of the open-pit mine, resulting in delayed and limited survey results. Furthermore, traditional methods are mostly based on two-dimensional planar data, lacking a precise depiction of the three-dimensional spatial characteristics of the mine's topography, making it difficult to accurately obtain the actual volume changes of earth and rock, thus affecting the accuracy of annual operational activity statistics and failing to provide timely and reliable technical support for mineral resource management decisions. Summary of the Invention

[0004] The purpose of this application is to provide a method, system, equipment, and medium for investigating the annual resource utilization of open-pit mines based on UAV oblique photography. This method can achieve rapid and accurate statistics on the annual resource utilization of open-pit mines, providing a new technical approach for investigating the annual consumption of mineral resources. It has important practical significance for improving the level of mineral resource management and promoting the rational development and utilization of mineral resources.

[0005] To achieve the above objectives, this application provides the following solution: Firstly, this application provides a method for investigating the annual resource utilization of open-pit mines based on UAV oblique photography, including: The image data of the open-pit mine for at least two years was acquired using a drone equipped with an oblique photography system; the image data included orthophoto data and oblique photo data. Ground control points are established, and a reference dataset is acquired; the ground control points include calibration points and ranging control points, and the reference dataset includes a correction dataset and a deviation correction dataset; Based on the image data and the benchmark dataset, a three-dimensional real-scene model corresponding to the year is generated through aerial triangulation. A three-dimensional vector model of the ore body is constructed, and the three-dimensional vector model of the ore body is docked with the three-dimensional real scene model to obtain the docked three-dimensional model of the ore body; Earthwork quotas are calculated based on the three-dimensional model of the ore body connecting adjacent years. The annual resource utilization of the open-pit mine is calculated based on the earthwork quota; the annual resource utilization of the open-pit mine includes the amount of ore and metal utilized.

[0006] Secondly, this application provides a system for surveying the annual resource utilization of open-pit mines based on UAV oblique photography, including: The image data acquisition module is used to acquire image data of an open-pit mine for at least two years using a drone equipped with an oblique photography system; the image data includes orthophoto image data and oblique image data. The benchmark dataset acquisition module is used to deploy ground control points and acquire benchmark datasets; the ground control points include calibration points and ranging control points, and the benchmark datasets include correction datasets and deviation correction datasets. A 3D reality model building module is used to generate a 3D reality model for the corresponding year based on the image data and the benchmark dataset through aerial triangulation. The ore body 3D model construction module is used to construct a 3D vector model of the ore body and connect the 3D vector model of the ore body with the 3D reality model to obtain the docked ore body 3D model; The earthwork quota calculation module is used to calculate the earthwork quota based on the three-dimensional model of the docking ore body in adjacent years. The open-pit mine annual resource utilization calculation module is used to calculate the annual resource utilization of the open-pit mine based on the earthwork quota; the annual resource utilization of the open-pit mine includes the amount of ore utilized and the amount of metal utilized.

[0007] Thirdly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described method for investigating the annual resource utilization of open-pit mines based on UAV oblique photography.

[0008] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method for investigating the annual resource utilization of open-pit mines based on UAV oblique photography.

[0009] According to the specific embodiments provided in this application, this application has the following technical effects: (1) Significantly improve survey efficiency and coverage: Using drones equipped with multi-view oblique photography systems, three-dimensional data collection of open-pit mines with large-scale and complex terrain can be completed in a short time, overcoming the shortcomings of low efficiency and limited coverage of traditional manual measurement, total station or single-point GPS operations, and realizing high-frequency, dynamic monitoring of the entire mining area. (2) Achieve high-precision three-dimensional spatial modeling: By setting up ground control points to form a benchmark dataset, and combining it with aerial triangulation calculations, the absolute positional accuracy and geometric scale accuracy of the three-dimensional real scene model can be effectively controlled, ensuring that the plane and elevation errors of the model are controlled at the centimeter level, thus meeting the core requirement of spatial coordinate accuracy for resource quantity calculation; (3) Realistic depiction of the three-dimensional dynamic changes of mine terrain: The three-dimensional real scene model generated based on image data fully preserves the landform details such as mining face, slope, and platform. Compared with the traditional two-dimensional planar method, it can realistically restore the evolution process of the three-dimensional spatial form of open-pit mine from year to year. (4) Achieve accurate earthwork calculation under orebody constraints: By connecting the three-dimensional vector model of the orebody with the three-dimensional real scene model, a three-dimensional model of the orebody is constructed to ensure that the earthwork quota calculation is strictly limited to the actual distribution range of the orebody, eliminate the interference of the surrounding rock, and achieve accurate extraction of the earthwork quota. (5) Improve the accuracy and reliability of the annual resource utilization estimate: The annual resource utilization of open-pit mines calculated based on the high-precision earthwork quota has both spatial accuracy and geological rationality. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 A flowchart illustrating a method for investigating the annual resource utilization of open-pit mines based on UAV oblique photography, provided as an embodiment of this application; Figure 2 This is a flowchart illustrating the image data acquisition process. Figure 3 A schematic diagram of flight route planning; Figure 4 A schematic diagram showing the layout of calibration points and distance control points; Figure 5 This is a schematic diagram showing spatial relationships of intersection and containment. Detailed Implementation

[0012] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0013] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0014] In one exemplary embodiment, such as Figure 1 As shown, a method for investigating the annual resource utilization of open-pit mines based on UAV oblique photography is provided. This method is executed by computer equipment, specifically by a terminal or server alone, or by both a terminal and a server. In this embodiment, the method is described using a server as an example, and includes the following steps S1 to S6. Wherein: S1: Use a drone equipped with an oblique photography system to acquire image data of the open-pit mine for at least two years; the image data includes orthophoto data and oblique photo data.

[0015] S2: Deploy ground control points and acquire a reference dataset; the ground control points include calibration points and distance control points, and the reference dataset includes a correction dataset and a deviation correction dataset.

[0016] S3: Based on the image data and the benchmark dataset, generate a three-dimensional real-world model for the corresponding year through aerial triangulation.

[0017] S4: Construct a three-dimensional vector model of the ore body, and then connect the three-dimensional vector model of the ore body with the three-dimensional real scene model to obtain the connected three-dimensional model of the ore body.

[0018] S5: Calculate the earthwork quota based on the three-dimensional model of the docking ore body in adjacent years.

[0019] S6: Calculate the annual resource utilization amount of the open-pit mine based on the earthwork quota; the annual resource utilization amount of the open-pit mine includes the utilization amount of ore and the utilization amount of metal.

[0020] Implementing steps S1 to S6 above solves the problem of the difficulty in real-time investigation of the annual utilization of open-pit mines, provides a quick new method for the annual consumption of mineral resources, and is of great significance for the investigation and evaluation of the utilization of mineral resources.

[0021] In a specific embodiment, step S1 specifically includes: determining the oblique photography acquisition range based on the estimated boundary range of mineral resources; planning a flight route based on the oblique photography acquisition range and flight altitude; and using a UAV equipped with an oblique photography system to fly along the flight route to acquire image data of the open-pit mine for at least two years.

[0022] Image data acquisition mainly includes range determination, flight route planning, and flight data acquisition. The overall process is as follows: Figure 2 As shown.

[0023] ① Determining the scope of data collection: This mainly involves including the estimated boundary range of resources in open-pit mines and creating a vector file for this range.

[0024] ② Plan flight routes: Based on the required oblique photography acquisition range and flight altitude, plan flight routes at certain intervals to ensure that the forward overlap rate of the two routes is designed to be no less than 80%, the lateral overlap rate is designed to be no less than 60%, and the edge area retains 30% overlap to ensure continuity; the forward coverage extends beyond the boundary line of the survey area by at least 3 baselines; the difference between the actual flight altitude and the designed flight altitude should not exceed 50 meters, the flight altitude difference between adjacent images on the same route should not exceed 30 meters, and the difference between the maximum flight altitude and the minimum flight altitude should not exceed 50 meters; the terrain height difference should not exceed 1 / 4 of the flight altitude, provided that the ground resolution of the images and the correct connection of adjacent image pairs are guaranteed.

[0025] Figure 3 This is a schematic diagram of the flight path planning, and the flight altitude is calculated based on the ground resolution (GSD). : In the formula, This is the camera's actual focal length (mm). Pixel size (mm).

[0026] Image width The calculation formula is: In the formula, From the camera's perspective.

[0027] With a 60% lateral overlap rate, the flight path distance... It can be set to: With an 80% heading overlap, the flight speed... It can be set to: In the formula, The lateral width is (m). Frames per second (frames / s).

[0028] Figure 3 In the figure, d represents the actual width of the camera sensor (mm).

[0029] ③ Acquiring image data by UAV flight: The operation is carried out using GNSS network RTK mode. The UAV adopts a synchronous oblique photography gimbal with an oblique angle between 15 and 45 degrees. It includes five cameras: a downward-looking camera, a forward-looking camera, a rear-looking camera, a left-looking camera, and a right-looking camera. The downward-looking camera performs vertical photography, while the other cameras perform oblique photography. The frame rate is no less than 3 frames per second. Vertical and oblique photography are carried out according to the planned flight path. The UAV equipped with the oblique photography system is used to shoot from multiple angles, directions, and perspectives to acquire a large amount of high-definition, high-precision orthophoto and oblique image data. The image data includes camera parameters, timestamps, POS positions, etc.

[0030] In one specific embodiment, step S2 specifically includes: Calibration point and distance control point layout: High-precision RTK is configured on the ground, and multiple control terrain calibration points are set according to the oblique photography acquisition range. RTK is used for measurement to ensure horizontal and vertical accuracy. Handheld distance measuring instruments are used to measure multiple control points at certain intervals to assist in image correction. Figure 4 This is a schematic diagram showing the layout of calibration points and distance control points.

[0031] First, select prominent buildings within the oblique photography acquisition range, or manually drive stakes or draw crosses with paint. Distribute calibration points evenly throughout the survey area. For these points, use GNSS network RTK to measure the horizontal and vertical positions of the reference points. The RTK satellite cutoff elevation angle is 15 degrees, the number of observed satellites is greater than 5, the PDOP value is less than 6, the horizontal convergence threshold before RTK observation is no greater than 2 cm, the vertical convergence threshold is no greater than 3 cm, the number of observations is greater than 2, the number of observation epochs is no less than 10, and the difference in horizontal coordinate components between each observation is no greater than 2 cm, and the difference in elevation is no greater than 3 cm. Take the average as the final result to form the "calibration dataset".

[0032] Secondly, two distance measurement control points, horizontal and vertical, are selected, and distance is measured using a distance measuring instrument to obtain the horizontal and vertical distances. Based on the distance measured by the distance measurement control points and the distance measured by the distance measuring instrument, a "correction dataset" is formed.

[0033] Finally, the “correction dataset” and the “skew correction dataset” were combined as the baseline dataset for oblique photogrammetry.

[0034] In a specific embodiment, step S3 specifically includes: preprocessing the image data; registering the preprocessed image data based on the correction dataset; performing aerial triangulation (hereinafter referred to as aerial triangulation) based on the registration result and the correction dataset to generate a sparse point cloud; optimizing the sparse point cloud; and generating a three-dimensional real-scene model corresponding to the year based on the optimized point cloud data using triangulation reconstruction or implicit surface reconstruction methods.

[0035] Step S3 mainly includes five steps: image preprocessing, image registration, aerial triangulation, 3D modeling, and fine-tuning.

[0036] ① Preprocessing: The acquired image data is preprocessed, including border removal, distortion correction, and image segmentation, to improve the efficiency and accuracy of subsequent processing.

[0037] Radial distortion: The distortion is the same at points with equal radiative distances along the radial line centered on the principal point. The correction function model is as follows: in, and These are the radial distortion corrections in the x and y directions. , and And so on, ( , ) represents the coordinates of the image point, ( , () represents the coordinates of the principal point of the image. , , is the radial distortion coefficient.

[0038] Tangential distortion: The direction is perpendicular to the radial direction, and the correction function model is: in, and This represents the correction amount in the x and y directions of tangential distortion. , denoted as the tangential distortion coefficient.

[0039] CCD array deformation: The correction function model is: in, This is the CCD array deformation correction amount. , These are the coefficients of the correction function model.

[0040] The summation correction model for the three distortions is as follows: .

[0041] in, , This represents the total distortion correction amount.

[0042] The three distortion biases are superimposed to obtain the final image coordinate correction result, which directly provides accurate image point coordinates (x, y) for subsequent collinearity equations.

[0043] ② Image registration: Register images from different perspectives to ensure they are accurately aligned in order to achieve higher modeling accuracy. Import the control points of the "calibration dataset" into GCP coordinates. Each point is marked with no less than 3 images, and the residual after adjustment is no more than 1 pixel.

[0044] ③ Aerial triangulation: Extract corresponding points from the image, i.e., image spikes, including the center of the cross, the left and right points of the line, the interior corner points of the right angle, etc. The density of the sparse point cloud is not less than 50 points / square meter. Next, input and verify the camera parameters and set the coordinate system. Then, calculate the aerial triangulation position of the sparse point cloud based on inertial navigation data and POS data. Finally, correct the horizontal and vertical distances based on the ranging control points of the "correction dataset" and perform aerial triangulation calculation.

[0045] Assume geodetic coordinates: X, Y, Z; camera coordinates: XS, YS, ZS; rotation angle: Image point coordinates: x, y; Image principal point coordinates: x0, y0; Principal distance: f.

[0046] Sub-camera parameter calibration: Four tilting cameras W, A, X, D, and a downward-looking camera S. Through spatial back intersection, the exterior orientation elements of individual sub-cameras can be determined, and then the relative angles and position parameters of angular and linear elements can be calculated. Assume the exterior orientation elements of camera A are... The exterior orientation elements of the W camera are The rotation matrix of camera A relative to camera W can be calculated. : in, , Given a rotation matrix composed of corresponding angular elements, the relative line elements between cameras in the object space coordinate system are: In a spatial coordinate system, the relative position of camera A with respect to camera W is: The parameter relationships for other sub-cameras are the same as above.

[0047] Calculation of exterior orientation elements of oblique images: The calculation of exterior orientation elements of the downward-looking camera can be carried out by calculating the relative angle elements and image element parameters between the POS and the downward-looking camera through the air-to-ground calibration field. By calculating the relative relationship between the sub-cameras, the relative relationship between the POS and each sub-camera is obtained. Then, the combined navigation results are decomposed to each sub-camera to calculate the exterior orientation elements of the image.

[0048] In oblique photography, the object point, the center of the photograph, and the image point lie on a straight line, satisfying the collinearity equation: Among them, (XS) A YS A ZS A () represents the x, y, z coordinates of the photographed object point. , , (x, y, z) represents the coordinates of the center point of the photograph. , ,…, Image exterior azimuth elements The direction cosine of the symbol.

[0049] .

[0050] The transformation yields the central projection image equations for ground level and image level, which are perspective transformation formulas and represent the transformation relationship between corresponding points on the image plane and the ground plane.

[0051] The principal point of image A is the origin of the image plane coordinate system, with image plane coordinates of (x, y) and object space coordinates of (X, Y, Z). Therefore, the collinearity equation is: The coordinates of the airborne RTK antenna center A and the camera center S in the geodetic coordinate system are (XA, YA, ZA) and (XS, YS, ZS), respectively. The coordinates of A in the image space coordinate system are (u, v, w). The image attitude angle can be used to determine the coordinates. ω and κ form an orthogonal transformation matrix R: The camera attitude angle measured by the IMU is , , We can conclude that: in, , for , ,…, first derivative The principle of adjustment is that in a single photograph, the image point, the center of the photograph, and the ground point form a beam of light. The core and foundation of adjustment is the collinearity equation, which is also the core of the entire photogrammetry. Each beam of light composed of photographs is translated and rotated in the air so that the light rays of common points between models intersect and are incorporated into a unified coordinate system. Through adjustment calculations, the position and orientation of the photograph and the ground coordinates of all points, i.e., the sparse point cloud, are obtained.

[0052] ④ Point Cloud Processing and Optimization: The obtained sparse point cloud may contain noise, errors, and incompleteness, requiring point cloud processing and optimization. This includes operations such as noise point removal, filtering, point cloud registration (aligning point cloud data acquired from different perspectives to a unified coordinate system), and point cloud interpolation to improve the quality and completeness of the point cloud data.

[0053] Sampling points The points after filtering It is the normal vector. , The standard deviation σ C σ S The Gaussian kernel function.

[0054] .

[0055] ⑤ 3D Modeling and Refinement: Based on the optimized point cloud data, a 3D reality model is constructed using surface reconstruction algorithms. This step can be carried out using two methods: one is to use triangular mesh reconstruction algorithms, such as triangulation, voxel-based methods, or deep learning-based methods; the other is a reconstruction method based on implicit surfaces, which defines an implicit function to represent the surface of the ground features and then solves for this implicit function based on the point cloud data to obtain the 3D reality model. The generated 3D reality model is then refined according to specific needs, including texture mapping, surface smoothing, and texturing, to improve the model's quality and visual effects. Texture mapping maps the texture information in the oblique image onto the surface of the 3D reality model. By mapping image pixels to points in the 3D reality model, the color and texture information of the image are assigned to the corresponding positions on the surface of the 3D reality model, thus adding realistic textures to the 3D reality model and giving it a more lifelike appearance.

[0056] The equations for the collinearity of spatial recursion and spatial forward intersection are: in, - The rotation matrix parameters can be determined according to... Calculation of ω and κ: .

[0057] equations for collinearity of exterior elements: The relative positional relationship between adjacent images is calculated using vertices with the same name.

[0058] Single-image spatial resection: Based on a single image, using the image coordinates and ground coordinates of several known control points within the corresponding ground area, the exterior orientation elements XS, YS, ZS of the image at the aerial photography timestamp are calculated according to the collinearity equation. , ω, κ.

[0059] Forward intersection of stereo image pairs: The coordinates of corresponding ground points are determined by the internal and external orientation elements and image point coordinates of the stereo image pairs. Among them, (XS1, YS1, ZS1) and (X1, Y1, Z1) are the geodetic coordinates and camera coordinates of the first image in the stereo image pair, and (XS2, YS2, ZS2) and (X2, Y2, Z2) are the geodetic coordinates and camera coordinates of the selected second image in the stereo image pair.

[0060] Where (XA, YA, ZA) are the coordinates of the center A of the airborne RTK antenna in the geodetic coordinate system. , .

[0061] Solution formula: in, Let x, y be the coordinates of the image. For camera focal length, for , ,…, The first derivative, for , ,…, The second derivative, Let be the first derivatives of u, v, and w. , , Let f be the first derivative of x, y, and f, and q be the solution factor.

[0062] Absolute orientation: By measuring the coordinates of the corresponding image points of the ground control points, the external orientation elements are calculated, and the model is incorporated into the geodetic coordinate system. The origins of the image space coordinates and the object space coordinates are made consistent with the scale. Spatial similarity transformation is performed, resulting in image space coordinates (X,Y,Z) and object space coordinates (XTP,YTP,ZTP).

[0063] Where λ is the scaling factor. , ,…, For corner elements The direction cosines are composed of functions of ω and κ, and ΔX, ΔY, and ΔZ are the translations of the origin.

[0064] In one specific embodiment, step S4 specifically includes: determining the pinch-out point of the ore body based on borehole data; Based on the pinch-out point of the ore body, an interpolation algorithm is used to generate a three-dimensional vector model of the ore body; the three-dimensional vector model of the ore body is converted and registered with the three-dimensional real scene model to obtain a registered three-dimensional vector model of the ore body; the spatial relationship between the registered ore body model and the three-dimensional real scene model is determined; and the docking ore body three-dimensional model is determined based on the spatial relationship.

[0065] Aerial triangulation modeling yields three-dimensional spatial coordinates in a coordinate system. Most mines use cross-sectional descriptions of ore bodies, requiring the creation of a three-dimensional vector model of the ore body. Both the 3D reality model and the 3D vector model of the ore body visually represent surface features. After docking, various relationships arise, including intersection, inclusion, and irrelevance. These relationships need to be interpreted before remodeling.

[0066] ① Ore body modeling: First, determine the pinch-out point of the ore body, and then use interpolation, finite inference and infinite inference methods.

[0067] Interpolation is mainly used to find the vanishing point. The specific formula is as follows: In the formula, X is the distance from the borehole where the ore was found to the boundary of the ore body, i.e., the pinch-out point. Grade at the boundary of the ore body , Let L represent the ore grade of industrially mined borehole A and non-industrially mined borehole B, and L be the distance between the two boreholes. If the ore body thickness in the non-mineralized borehole is less than the minimum minable thickness but the grade meets the requirements, then: In the formula, , The thickness of the ore body is represented by borehole A, which shows the mineralized ore-bearing borehole, and borehole B, which shows the non-industrial mineralized ore-bearing ore body. n is the minimum mineable thickness.

[0068] ② Three-dimensional vector modeling of the ore body: The modeling is carried out using interpolation methods such as the natural critical point method, the inverse power distance method, and the Kriging method.

[0069] The natural critical point method uses the dual graph of a triangular network to perform arbitrary point interpolation, as shown in the following formula: In the formula, is the information value of a known point; D is the distance between the point to be interpolated and its naturally nearest known points; The critical influence distance is n, where n is the number of relevant known points. The value of the interpolation point is obtained separately for the k-th known point.

[0070] Inverse power law of distance: This method assumes that the closer the known point and the interpolation point are, the greater the correlation. The main calculation formula is: In the formula, denoted as , where is the distance from the interpolation point to the known point, and μ is the exponent.

[0071] Kriging: Based on covariance, it minimizes the interpolation points using linear regression. In the formula, Given the point value, These are weights, which can be solved using a combination of functions: In the formula, It is the value of the distance model variable graph between points i and j.

[0072] ③ The three-dimensional vector model of the ore body is registered with the three-dimensional real scene model.

[0073] First, the ore body and the image need to be transformed into the same coordinate system, that is, (X1,Y1,Z1) to (X2,Y2,Z2). The origins O1 and O2 of the two are different, and the coordinate axes are not parallel. During the transformation, in addition to the translation parameters (ΔX,ΔY,ΔZ), the rotation parameters (α,β,γ) corresponding to the Euler angles (εX,εY,εZ) are also needed. In addition, the scale change parameter κ needs to be set, for a total of 7 transformation parameters.

[0074] In the formula, .

[0075] The converted coordinates are consistent with the coordinates of the 3D reality model.

[0076] ④ Processing the docking between the registered ore body model and the 3D reality model: such as Figure 5 As shown, the docking of the two models mainly includes intersection, inclusion, and unrelated. The monitoring of open-pit mine activity is either intersection or unrelated. If the ore body is not fully mined, it is intersection; if the ore body is fully mined, it is unrelated.

[0077] Intersection determination: Calculate the intersection line L using the triangular mesh T1 of the 3D real-world model and the triangular mesh T2 of the registered ore body model; determine whether the intersection line L and the intersection points A, B, C, D of the triangular meshes T1 and T2 form the intervals [A,B] and [C,D]. If they overlap, they intersect; otherwise, they do not intersect.

[0078] For an unrelated ore body A, consisting of n segments, the volume of the trapezoidal frustum is... ,density ,grade The ore quantity dA and metal quantity mA directly incorporate the total resource quantity of the ore body into the utilization quantity.

[0079] .

[0080] For intersecting ore bodies, it is necessary to cut out the intersecting ore body portion and then perform calculations.

[0081] The intersection line between the registered ore body model and the ground of the 3D reality model is considered the intersecting part. The area enclosed by this intersection line is taken as the top surface of the ore body, and modeling is performed based on this. The intersection line and the triangular mesh form a closed polygon, and triangular meshes are constructed for the vertices of the polygon. The vertices of the formed polygon are [V1,V2,V3,…,Vn]. Three points are selected in sequence, and it is determined whether they are collinear. If they are collinear, the middle point is deleted, and the above process is repeated until the end. These triangular meshes are used to construct surfaces through TIN, which serve as the top surface of the docking 3D ore body model.

[0082] In a specific embodiment, step S5 specifically includes: constructing earthwork based on the three-dimensional model of the docking ore body in adjacent years and the side of the ore body in the open-pit mine; dividing the earthwork into multiple segments and calculating the area of ​​each segment and the area of ​​each segment; calculating the volume of the trapezoidal frustum formed by every two segments based on the area; and summing the volumes of the trapezoidal frustum to obtain the earthwork quota.

[0083] Open-pit mining involves not only mining the ore body but also the surrounding rock. Therefore, the earthwork quota refers to the earthwork of the ore body. Thus, the calculation mainly focuses on the earthwork quota for the mined portion of the ore body. Assuming the top surface of the ore body's 3D model in the first year is S1, and the top surface in the ore body's 3D model in the second year is S2, with the ore body's side surface considered as the side surface, these three elements together form a volume. This volume represents the earthwork quota.

[0084] The earthwork formed by S1, S2, and the side of the ore body is divided into n segments along a certain direction. The areas of the two cross-sections D1 and D2 of each segment are calculated using a DTM network. The area is then divided into m small triangular mesh areas. The cross-sectional area of ​​D1 is... The area of ​​D2 is ,So: For the area of ​​the small triangular mesh, , , Given the side lengths of the three sides of a triangular mesh, the volume of the trapezoidal frustum formed by every two cross-sections. Then sum them up: V represents the earthwork quota. This represents the distance between every two cross sections.

[0085] In one specific embodiment, step S6 specifically includes: ①Estimation of the amount of resources to be used.

[0086] The basic parameters for estimating the amount of resources to be utilized include the area, average thickness, dip angle, grade, and average weight of the ore body within the utilization range. Sometimes, ore moisture content and mineralization coefficient are also included. The data in the most recent exploration report should be used as the standard. Based on factors such as the geological body occurrence, faults, and exploration projects, the location of the profile lines is selected. The vertical profile method is used to extract the boundary lines of each profile line, which are then projected onto the reserve estimation map. Connecting the boundary points of each profile line delineates the range of resources to be utilized.

[0087] Depending on the scope of resource utilization, geometric methods (arithmetic mean method, geological block method, mining block method, cross-section method, contour line method, linear reserve method, trigonometric method, nearest area method, polygonal method), statistical analysis methods (distance-weighted method, kriging method), and SD method are used to calculate the amount of land covered. The geological block method and cross-section method are commonly used.

[0088] Quantity of ore used The basic formula for calculation: ; Basic formula for calculating the amount of metal used, M: In the formula, For the first The volume of the trapezoidal frustum. For the first The density of the trapezoidal frustum, For the first The taste of a trapezoidal frustum. This represents the number of trapezoidal frustums.

[0089] ② Verification of resource utilization: Verify the estimated ore volume V to be utilized. K If the difference between the two is less than the given limit ε, the calculation is considered valid and is taken as the amount of mobilization. The amount of mobilization calculated each year is the annual amount of mobilization. Otherwise, the calculation starts from the beginning.

[0090] ; .

[0091] The process after step S6 also includes: final drawing.

[0092] ① Vector and raster overlay.

[0093] The input range is a vector file containing three bands (R (red, 669.43 nm), G (green, 538.96 nm), and B (blue, 479.25 nm)) acquired from oblique photography. A color image is synthesized in RGB order, with vectors represented by points, lines, and surfaces with the same projection. The raster and vector images are then overlaid using coordinate layering.

[0094] vector x and y are the corresponding coordinates, z is the feature value, and the grid is... , and These are the corresponding coordinates.

[0095] make, Thus achieving a grid and vector The superposition of.

[0096] By using corresponding coordinates to overlay raster and vector data, the output is a GIS vector and raster overlay image, thus creating an image that is suitable for human vision.

[0097] ② Final image.

[0098] Input oblique image data and extent vector file, output a superimposed image (GIS) suitable for human visual observation, which can be output as a final image in JPG or TIF format by the software.

[0099] Based on the same inventive concept, this application also provides a system for implementing the above-mentioned method for investigating the annual resource utilization of open-pit mines based on UAV oblique photography. The solution provided by this system is similar to the implementation described in the above method. Therefore, the specific limitations of one or more embodiments of the system for investigating the annual resource utilization of open-pit mines based on UAV oblique photography provided below can be found in the limitations of the method for investigating the annual resource utilization of open-pit mines based on UAV oblique photography described above, and will not be repeated here.

[0100] In one exemplary embodiment, a system for surveying the annual resource utilization of open-pit mines based on UAV oblique photography is provided, including the following modules.

[0101] The image data acquisition module is used to acquire image data of an open-pit mine for at least two years using a drone equipped with an oblique photography system; the image data includes orthophoto image data and oblique image data. The benchmark dataset acquisition module is used to deploy ground control points and acquire benchmark datasets; the ground control points include calibration points and ranging control points, and the benchmark datasets include correction datasets and deviation correction datasets. A 3D reality model building module is used to generate a 3D reality model for the corresponding year based on the image data and the benchmark dataset through aerial triangulation. The ore body 3D model construction module is used to construct a 3D vector model of the ore body and connect the 3D vector model of the ore body with the 3D reality model to obtain the docked ore body 3D model; The earthwork quota calculation module is used to calculate the earthwork quota based on the three-dimensional model of the docking ore body in adjacent years. The open-pit mine annual resource utilization calculation module is used to calculate the annual resource utilization of the open-pit mine based on the earthwork quota; the annual resource utilization of the open-pit mine includes the amount of ore utilized and the amount of metal utilized.

[0102] In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments. The computer device may be a server or a terminal. The computer device includes a processor, a memory, an input / output interface (I / O), and a communication interface. The processor, memory, and I / O are connected via a system bus, and the communication interface is connected to the system bus via the I / O interface. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device stores data to be processed. The I / O interface of the computer device is used for exchanging information between the processor and external devices. The communication interface of the computer device is used for communicating with an external terminal via a network connection. When the computer program is executed by the processor, it implements the steps in the above-described method embodiments.

[0103] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0104] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0105] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0106] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0107] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0108] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for investigating the annual resource utilization of open-pit mines based on UAV oblique photography, characterized in that, include: The image data of the open-pit mine for at least two years was acquired using a drone equipped with an oblique photography system; the image data included orthophoto data and oblique photo data. Ground control points are established, and a reference dataset is acquired; the ground control points include calibration points and ranging control points, and the reference dataset includes a correction dataset and a deviation correction dataset; Based on the image data and the benchmark dataset, a three-dimensional real-scene model corresponding to the year is generated through aerial triangulation. A three-dimensional vector model of the ore body is constructed, and the three-dimensional vector model of the ore body is docked with the three-dimensional real scene model to obtain the docked three-dimensional model of the ore body; Earthwork quotas are calculated based on the three-dimensional model of the ore body connecting adjacent years. The annual resource utilization of the open-pit mine is calculated based on the earthwork quota; the annual resource utilization of the open-pit mine includes the amount of ore and metal utilized.

2. The method for investigating the annual resource utilization of open-pit mines based on UAV oblique photography according to claim 1, which utilizes a UAV equipped with an oblique photography system to acquire image data of the open-pit mine for at least two years, specifically includes: The oblique photography acquisition range is determined based on the boundary range estimated from the mineral resource quantity. Based on the oblique photography acquisition range and flight altitude, a flight route is planned; Using a drone equipped with an oblique photography system, the open-pit mine was captured by flying along the flight path for at least two years of image data.

3. The method for investigating the annual resource utilization of open-pit mines based on UAV oblique photography according to claim 1, wherein a three-dimensional real-scene model for the corresponding year is generated through aerial triangulation based on the image data and the benchmark dataset, specifically including: The image data is preprocessed; The preprocessed image data is registered based on the aforementioned calibration dataset; Based on the registration results and the correction dataset, aerial triangulation is performed to generate a sparse point cloud. The sparse point cloud is then optimized. Based on the optimized point cloud data, a 3D reality model corresponding to the year is generated using triangular mesh reconstruction or implicit surface reconstruction methods.

4. The method for investigating the annual resource utilization of open-pit mines based on UAV oblique photography according to claim 1, characterized in that, Constructing a three-dimensional vector model of the ore body, specifically including: Determine the pinch-out point of the ore body based on borehole data; Based on the pinch-out point of the ore body, an interpolation algorithm is used to generate a three-dimensional vector model of the ore body.

5. The method for investigating the annual resource utilization of open-pit mines based on UAV oblique photography according to claim 1, characterized in that, The 3D vector model of the ore body is docked with the 3D reality model to obtain the docked 3D ore body model, specifically including: The 3D vector model of the ore body is converted and registered with the 3D real scene model to obtain the registered 3D vector model of the ore body. Determine the spatial relationship between the registered ore body model and the three-dimensional reality model; Based on the spatial relationship, a three-dimensional model of the docking ore body was determined.

6. The method for investigating the annual resource utilization of open-pit mines based on UAV oblique photography according to claim 1, characterized in that, The earthwork quota is calculated based on the three-dimensional model of the ore body connecting adjacent years, specifically including: Earthwork was constructed based on the three-dimensional model of the ore body in adjacent years and the side of the ore body in the open-pit mine. The earthwork is divided into multiple sections, and the area of ​​each section is calculated. Based on the area, calculate the volume of the trapezoidal frustum formed by each pair of cross-sections; The earthwork quota is obtained by summing the volumes of the trapezoidal frustum.

7. The method for investigating the annual resource utilization of open-pit mines based on UAV oblique photography according to claim 1, characterized in that, The formula for calculating the annual resource utilization of the open-pit mine is as follows: in, In order to utilize the amount of ore, In order to utilize the amount of metal, For the first The volume of the trapezoidal frustum. For the first The density of the trapezoidal frustum, For the first The taste of a trapezoidal frustum. This represents the number of trapezoidal frustums.

8. A system for surveying annual resource utilization in open-pit mines based on UAV oblique photography, characterized in that, include: The image data acquisition module is used to acquire image data of an open-pit mine for at least two years using a drone equipped with an oblique photography system; the image data includes orthophoto image data and oblique image data. The benchmark dataset acquisition module is used to deploy ground control points and acquire benchmark datasets; the ground control points include calibration points and ranging control points, and the benchmark datasets include correction datasets and deviation correction datasets. A 3D reality model building module is used to generate a 3D reality model for the corresponding year based on the image data and the benchmark dataset through aerial triangulation. The ore body 3D model construction module is used to construct a 3D vector model of the ore body and connect the 3D vector model of the ore body with the 3D reality model to obtain the docked ore body 3D model; The earthwork quota calculation module is used to calculate the earthwork quota based on the three-dimensional model of the docking ore body in adjacent years. The open-pit mine annual resource utilization calculation module is used to calculate the annual resource utilization of the open-pit mine based on the earthwork quota; the annual resource utilization of the open-pit mine includes the amount of ore utilized and the amount of metal utilized.

9. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the method for investigating the annual resource utilization of open-pit mines based on UAV oblique photography as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the method for investigating the annual resource utilization of open-pit mines based on UAV oblique photography as described in any one of claims 1-7.

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