Method and system for stratified measurement of capacity of a heap of a waste dump

Abstract

This invention provides a method and system for layered measurement of the capacity of spoil heaps. The method includes: using a scanning array composed of L three-dimensional laser stations to synchronously scan layer by layer at preset elevation intervals to acquire multiple sets of topographic point cloud data; performing elevation point matching on each layer of data to locate K elevation coordinate points corresponding to surface feature points; establishing a unified reference coordinate system through digital elevation model fusion technology; classifying and processing feature points and constructing a spoil heap contour mesh model, which is then superimposed with a base three-dimensional model to generate a composite model; performing terrain consistency calibration on internal points of the spoil heap to acquire calibrated elevation data; calculating the three-dimensional coordinates of internal points using a spatial triangulation algorithm, and integrating them to construct a layered spoil heap three-dimensional model; finally, calculating the total spoil heap capacity based on the volume difference of the multi-level models. This invention can achieve high-precision automated measurement of spoil heap capacity, improving the accuracy of volume calculation and operational efficiency.

Description

Technical Field

[0001] This invention relates to the field of engineering measurement technology, and more specifically, to a method and system for stratified measurement of the capacity of a spoil heap. Background Technology

[0002] In engineering construction and mining, measuring the storage capacity of spoil heaps is crucial. Spoil heaps are used to store waste generated during construction, and accurate measurement of their capacity is essential for controlling project progress, rational resource utilization, and assessing environmental impact. Traditional measurement methods rely primarily on manual measurement and simple geometric calculations, such as estimating volume by measuring the length, width, and height of the spoil heap. While simple, this method has limitations, including low accuracy, low efficiency, and difficulty adapting to complex terrain. With technological advancements, 3D laser scanning technology has been gradually introduced into the field of volume measurement. 3D laser scanners can quickly acquire 3D point cloud data of an object's surface, allowing for the construction of a 3D model and subsequent volume calculation. However, existing 3D laser scanning-based volume measurement methods are mostly designed for single objects or regular shapes. They fall short for measuring complex terrain like spoil heaps, especially in multi-station scanning and layered measurement. Existing 3D laser scanning methods typically only provide point cloud data from a single scan, lacking effective layered scanning and data fusion technologies for scenarios requiring layered measurement, such as spoil heaps. Furthermore, existing methods often struggle to achieve accurate elevation point matching and terrain consistency calibration when processing multi-station scanning data, which affects the accuracy and reliability of the measurement results.

[0003] In the process of implementing the embodiments of the present invention, the inventors discovered that the prior art has at least the following problems or defects: First, the existing measurement methods cannot meet the high-precision measurement requirements of complex terrain at spoil disposal sites, especially in terms of multi-station layered scanning and data fusion, where there are technical bottlenecks; second, the existing technology is not accurate enough in terms of elevation point matching and terrain consistency calibration, resulting in insufficient reliability of the measurement results; third, the existing methods lack effective means to perform uncertainty analysis on the measurement results, and cannot provide users with reliable confidence intervals, affecting the practical application value of the measurement results. Summary of the Invention

[0004] This invention provides a method and system for stratified measurement of the capacity of a spoil heap.

[0005] In a first aspect of the present invention, a method for stratified measurement of the capacity of a spoil heap is provided, comprising: Using a scanning array consisting of L three-dimensional laser stations, a synchronous layered elevation scanning operation is carried out on the surface of the spoil disposal site at a preset elevation interval, thereby obtaining multiple sets of terrain point cloud data. Each set of terrain point cloud data consists of a scan dataset from L three-dimensional laser stations. For each level of L 3D laser scanning datasets, perform the following operations sequentially to calculate the corresponding level's stack volume: Elevation point matching is performed on L three-dimensional laser scanning datasets to accurately locate the K three-dimensional laser scanning datasets corresponding to each surface feature point, and to identify the K elevation coordinate points in these K three-dimensional laser scanning datasets that match the surface feature points. By using digital elevation model fusion technology, the scanning datasets of L three-dimensional laser stations are deeply fused with the pre-constructed three-dimensional model of the spoil disposal site base. This establishes a precise correlation between the spatial coordinate system of the L three-dimensional laser station scanning datasets and the unified reference coordinate system of the spoil disposal site base three-dimensional model. Based on this unified reference coordinate system, the three-dimensional reference coordinates of the positions of the L three-dimensional laser stations are determined. The surface feature points are classified and processed to identify the storage boundary points and internal points of the storage; a storage body outline mesh model is constructed based on the storage boundary points. The outline mesh model of the stockpile is spatially superimposed with the three-dimensional model of the spoil heap base to generate a stockpile-base composite model. The stockpile-base composite model performs terrain consistency calibration on the K elevation coordinate points and K three-dimensional laser station scan datasets corresponding to each point inside the stockpile to obtain the calibrated J elevation coordinate points and J three-dimensional laser station scan datasets. Based on the J elevation coordinates of each internal point of the stack and the J three-dimensional laser station scanning dataset, the three-dimensional spatial coordinates of each internal point of the stack are calculated by the spatial triangulation algorithm; the three-dimensional spatial coordinates of all internal points of the stack are integrated with the stack body contour mesh model to construct a layered stack body three-dimensional model. Based on the volume difference of the three-dimensional model of the multi-level layered storage body, the total storage capacity of the spoil disposal site is calculated.

[0006] Furthermore, the step of calculating the total storage capacity of the spoil heap based on the volume difference of the multi-level layered three-dimensional model of the spoil heap is as follows: A volume rasterization process is applied to the 3D model of each layered stack to produce a set of spatial voxels for that layer of stack. By combining the vertical projection overlap rate of the voxel sets of adjacent stacked volumes and the elevation interval between levels, the volume of each stacked volume at each level is calculated using the voxel difference method. The total storage capacity of the spoil heap is obtained by performing cumulative integration on the volume of all levels of the spoil heap.

[0007] Furthermore, the elevation interval between the layers is determined based on the scanning layer thickness parameters of the three-dimensional laser station, and satisfies the following relationship:

[0008] in, Indicates the elevation interval value. This indicates the maximum storage height of the spoil disposal site.

[0009] Furthermore, the specific process for terrain consistency calibration is as follows: For each point within the heap, perform the following operations: Select a reference elevation point from the K elevation coordinate points, and integrate the remaining K-1 three-dimensional laser station scanning datasets (excluding the three-dimensional laser station scanning datasets corresponding to the reference elevation point) into an elevation calibration queue. The system iterates through each 3D laser station scan dataset in the elevation calibration queue. For each traversed 3D laser station scan dataset, it verifies whether the benchmark elevation point has a spatially matching point in the 3D laser station scan dataset using spatial projection constraints. If it does, the 3D laser station scan dataset is retained, and its corresponding elevation coordinates are updated synchronously. Otherwise, the 3D laser station scan dataset is removed from the set of 3D laser station scan datasets corresponding to the internal points in the stack, and the corresponding elevation coordinates are also removed.

[0010] Furthermore, the specific method for verifying spatial matching points using spatial projection constraints is as follows: Based on the three-dimensional reference coordinates of the reference elevation point and the laser scanning elevation angle parameters of the three-dimensional laser station, a spatial projection ray corresponding to the reference elevation point is constructed. Calculate the coordinates of the intersection point between the spatial projection ray and the surface of the stack-substrate composite model; If the elevation deviation between the coordinates of the intersection point on the surface and the corresponding point coordinates in the scanning data of another three-dimensional laser station is less than the preset allowable measurement error threshold, then it is determined that a spatial matching point exists.

[0011] Furthermore, the operation of updating the elevation coordinate points is as follows: In the 3D laser station scanning dataset where spatial matching points exist, a 3D calibration cube is constructed with the surface intersection coordinates as the geometric center. The point cloud data within the 3D calibration cube is resampled using the inverse distance weighted interpolation method to generate calibrated elevation coordinate points.

[0012] Furthermore, the specific steps for calculating the three-dimensional spatial coordinates using the spatial triangulation algorithm are as follows: Input the three-dimensional reference coordinates of J elevation coordinate points into the Delaunay triangulation generator to construct a local triangulation surface related to the internal points of the heap. Extract the set of terrain triangles that are associated with points inside the stack from the local triangular mesh surface; Calculate the geometric centroid coordinates of the vertices of each triangular facet, and determine these geometric centroid coordinates as the three-dimensional spatial coordinates of the points inside the stack.

[0013] Furthermore, the formula for calculating the geometric centroid coordinates is as follows:

[0014] in, This represents the three-dimensional spatial coordinates of a point inside the heap, where n represents the number of triangular faces associated with that point. This represents the coordinates of the vertex of the i-th triangle.

[0015] Furthermore, the elevation point matching steps are as follows: Perform point cloud density normalization processing on the scanning datasets of L three-dimensional laser stations to generate a point cloud set with uniform resolution. The iterative nearest-point algorithm is used to perform registration processing on a point cloud with uniform resolution. Extract the set of points that meet the curvature consistency condition and use them as surface feature points; By using kd-tree spatial indexing technology, a precise correspondence between surface feature points and multiple 3D laser scanning datasets is established.

[0016] In a second aspect of the invention, a system for measuring the stratified capacity of a spoil heap is provided, comprising: Three-dimensional laser scanning array: It consists of L three-dimensional laser stations equipped with multi-echo detection modules, which synchronously collect layered terrain point cloud data according to preset elevation intervals; Point cloud preprocessing module: used to perform noise reduction filtering and coordinate unification processing on the collected layered terrain point cloud data in sequence, and output a standardized 3D laser station scan dataset; The stacking model calculation engine has the following main functions: constructing a three-dimensional model of the stacked waste dump based on the stratified measurement method of the waste dump capacity described in the first aspect; conducting uncertainty analysis on the volume calculation results based on the Monte Carlo method; and outputting a waste dump capacity report containing confidence intervals.

[0017] The embodiments of the present invention have at least the following beneficial effects: 1. Multi-station synchronous layered scanning technology combined with elevation point matching algorithm can eliminate the blind zone problem of traditional single-station scanning. Through the collaborative operation of L three-dimensional laser stations, complete terrain point cloud data can be obtained. Combined with the feature point correspondence established by kd tree spatial indexing technology, the elevation coordinate matching accuracy can be improved to the millimeter level, thereby ensuring that the accuracy of the volume calculation of the stockpile meets the requirements of engineering measurement standards.

[0018] 2. The digital elevation model fusion technology, combined with the terrain consistency calibration process, can solve the industry problem of inconsistent coordinate systems of multi-source data. By constructing a unified reference coordinate system and adopting a spatial projection constraint verification mechanism, it can achieve seamless fusion of data from L scanning stations with the base model. Its calibration cube resampling method can control the model splicing error within a small range, providing an accurate spatial reference for the stack-base composite model.

[0019] 3. The intelligent spatial triangulation algorithm combined with the layered volume difference calculation method can significantly improve measurement efficiency. The local surface model constructed by the Delaunay triangulation generator, combined with the volume calculation process of the voxel difference method, can realize fully automated data processing, which can shorten the operation time compared with traditional measurement methods. Attached Figure Description

[0020] The above and other objects, features, and advantages of exemplary embodiments of the present invention will become readily apparent from the following detailed description taken in conjunction with the accompanying drawings. Several embodiments of the invention are illustrated in the drawings by way of example and not limitation, wherein: Figure 1 This is a flowchart illustrating a method for stratified measurement of the capacity of a spoil heap according to an embodiment of the present invention. Figure 2 This is a schematic diagram of a stratified measurement system for the capacity of a spoil heap provided in an embodiment of the present invention; Figure 3 A schematic diagram of the structure of an electronic device according to an embodiment of the present invention is shown. Detailed Implementation

[0021] The principles and spirit of the invention will now be described with reference to several exemplary embodiments. It should be understood that these embodiments are provided merely to enable those skilled in the art to better understand and implement the invention, and are not intended to limit the scope of the invention in any way. Rather, these embodiments are provided to make the invention more thorough and complete, and to fully convey the scope of the invention to those skilled in the art.

[0022] Those skilled in the art will recognize that embodiments of the present invention can be implemented as a system, apparatus, device, method, or computer program product. Therefore, the present invention can be specifically implemented in the following forms: entirely hardware, entirely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software.

[0023] It should be noted that the number of any elements in the accompanying drawings is for illustrative purposes only and not as a limitation, and any naming is for distinction only and has no limiting meaning.

[0024] The following is for reference. Figure 1 , Figure 1 This is a schematic flowchart illustrating a method for stratified measurement of the capacity of a spoil heap according to an embodiment of the present invention. Figure 1 As shown, a method for stratified measurement of the capacity of a spoil heap includes: S1. Using a scanning array consisting of L three-dimensional laser stations, synchronous layered elevation scanning is carried out on the surface of the spoil disposal site at preset elevation intervals to obtain multiple sets of terrain point cloud data. Each set of terrain point cloud data consists of scanning datasets from L three-dimensional laser stations. S2. For the scan dataset of L 3D laser stations at each level, perform the following operations sequentially to calculate the volume of the corresponding level's stack: S3. Perform elevation point matching on the L three-dimensional laser scanning datasets to accurately locate the K three-dimensional laser scanning datasets corresponding to each surface feature point, and identify the K elevation coordinate points in these K three-dimensional laser scanning datasets that match the surface feature points. S4. Using digital elevation model fusion technology, the scanning datasets of L three-dimensional laser stations are deeply fused with the pre-constructed three-dimensional model of the spoil disposal site base. This establishes a precise correlation between the spatial coordinate system of the scanning datasets of L three-dimensional laser stations and the unified reference coordinate system of the spoil disposal site base. Based on this unified reference coordinate system, the three-dimensional reference coordinates of the positions of the L three-dimensional laser stations are determined. S5. Classify and process the surface feature points, and filter out the storage boundary points and internal points of the storage; construct the storage body outline mesh model based on the storage boundary points. S6. The outline mesh model of the stockpile body and the three-dimensional model of the spoil disposal site base are spatially superimposed to generate a stockpile-base composite model. The stockpile-base composite model performs terrain consistency calibration on the K elevation coordinate points and K three-dimensional laser station scan datasets corresponding to each point inside the stockpile to obtain the calibrated J elevation coordinate points and J three-dimensional laser station scan datasets. S7. Based on the J elevation coordinate points and J three-dimensional laser station scanning datasets corresponding to each internal point of the stack, calculate the three-dimensional spatial coordinates of each internal point of the stack using the spatial triangulation algorithm; integrate the three-dimensional spatial coordinates of all internal points of the stack with the stack body contour mesh model to construct a layered stack body three-dimensional model. S8. Based on the volume difference of the three-dimensional model of the multi-level layered storage body, the total storage capacity of the waste disposal site is calculated.

[0025] It should be noted that a scanning array consisting of L three-dimensional laser stations is used to simultaneously perform layered elevation scanning of the spoil heap surface at preset elevation intervals, thereby acquiring multiple sets of terrain point cloud data. Each set of terrain point cloud data consists of scan datasets from L three-dimensional laser stations. Here, a three-dimensional laser station is a high-precision measuring device that measures the three-dimensional coordinates of a target object by emitting laser beams and receiving reflected signals. The scanning array refers to arranging multiple three-dimensional laser stations in a specific layout to form a unified measurement system for comprehensive scanning of the spoil heap. The preset elevation interval refers to the height difference between each scan in the vertical direction, used to scan the spoil heap layer by layer for more accurate measurement of the terrain data of each layer. The terrain point cloud data is a set of three-dimensional coordinates containing a large number of points obtained from the scan, with each point representing a location on the spoil heap surface. This data forms the basis for subsequent volume calculations.

[0026] Specifically, the L three-dimensional laser scanning stations can be rationally arranged according to the actual size and shape of the spoil heap, such as being evenly distributed around the spoil heap and at key locations to ensure comprehensiveness and accuracy of the scan. The preset elevation interval can be determined based on the maximum stockpiling height of the spoil heap and the required measurement accuracy. For example, if the maximum stockpiling height of the spoil heap is 30 meters and high measurement accuracy is required, the elevation interval can be set between 0.2 meters and 0.5 meters. Each three-dimensional laser scanning station's scan dataset contains the three-dimensional coordinate information of all points acquired by that station in a single scan. These datasets will serve as the basis for subsequent processing. The higher-level designations, such as "first three-dimensional laser scanning station," "second three-dimensional laser scanning station," etc., refer to stations at different locations within the scanning array. Each station has its unique coordinates and scanning range, collectively forming a complete scanning array.

[0027] Preferably, to ensure the accuracy and integrity of the scanned data, the 3D laser stations need to be precisely calibrated before scanning, including adjusting parameters such as laser emission angle and receiver sensitivity to guarantee the measurement accuracy of each station. During the scanning process, a certain overlap area can also be set to avoid data loss due to gaps between stations. The acquired terrain point cloud data can be preliminarily processed using data fusion technology to eliminate coordinate deviations caused by different station locations, providing a more accurate data foundation for subsequent elevation point matching and model construction. For example, during data fusion, a feature point matching method can be used to align data obtained from different stations to the same coordinate system, thereby improving data consistency and usability.

[0028] In some embodiments, the step of calculating the total storage capacity of the spoil heap based on the volume difference of the three-dimensional model of the multi-level layered storage body is as follows: A volume rasterization process is applied to the 3D model of each layered stack to produce a set of spatial voxels for that layer of stack. By combining the vertical projection overlap rate of the voxel sets of adjacent stacked volumes and the elevation interval between levels, the volume of each stacked volume at each level is calculated using the voxel difference method. The total storage capacity of the spoil heap is obtained by performing cumulative integration on the volume of all levels of the spoil heap.

[0029] It should be noted that a volumetric rasterization process is performed on the 3D model of each layer of the stack, producing a set of voxels for that layer's stack space. This volumetric rasterization process converts a 3D model into a discrete representation composed of small cubes and voxels, similar to pixelation in 2D images. This method simplifies complex 3D models into a computable set of voxels, facilitating subsequent volume calculations. The vertical projection overlap rate of adjacent stack space voxel sets refers to the degree of overlap between the projections of voxels in the vertical direction, used to assess the spatial relationship between adjacent layers. The elevation interval between layers refers to the height difference between adjacent layers, a key parameter for volume difference calculation. The voxel difference method is a method that calculates volume changes by comparing the differences between adjacent voxel sets. This method can accurately calculate the volume contribution of each layer, and then accumulate them to obtain the total stack capacity.

[0030] Specifically, the implementation of the voxel difference method requires clarifying several key parameters. First, the voxel size in volume rasterization should be selected based on the required accuracy and computational efficiency. For example, if high-precision calculation is required, a smaller voxel size, such as 0.1 m × 0.1 m × 0.1 m, can be chosen. The vertical projection overlap rate can be calculated by comparing the projection ranges of two adjacent voxel layers in the vertical direction. For example, if the projections of two voxel layers overlap by 50%, the overlap rate is 50%. The elevation interval between layers can be determined based on the actual measured elevation data, usually consistent with the scanning layer thickness parameters of the 3D laser station. For example, if the scanning layer thickness is 0.5 m, the elevation interval is also 0.5 m. These parameters collectively determine the accuracy and efficiency of volume calculation.

[0031] Preferably, volume rasterization can be achieved through the following steps: First, determine the size and shape of the voxels, typically cubes or cuboids; then, map each point of the 3D model of the layered stockpile to the corresponding voxel, forming a voxel set. When calculating the volume difference between adjacent layers, the volume change can be determined by comparing the overlapping and non-overlapping portions of the two voxel sets. For example, if a voxel in one layer is not present in the next layer, its volume should be included in the volume difference. Furthermore, to improve calculation accuracy, a weighting factor can be introduced into the voxel difference method to adjust the volume difference based on the voxel's position and shape. Finally, by summing the volume differences across all layers, the total stockpile capacity of the spoil heap can be obtained.

[0032] In some embodiments, the elevation interval between layers is determined based on the scanning layer thickness parameters of the three-dimensional laser station, and satisfies the following relationship:

[0033] in, Indicates the elevation interval value. This indicates the maximum storage height of the spoil disposal site.

[0034] It should be noted that the elevation interval between layers is determined based on the scanning layer thickness parameters of the 3D laser survey station, and must meet a specific condition: the elevation interval should not exceed one-tenth of the maximum stockpile height of the spoil heap. Here, the elevation interval between layers refers to the vertical distance between two adjacent scanning layers when performing layered scanning of the spoil heap. This parameter is crucial for ensuring the accuracy and efficiency of the measurement. The scanning layer thickness parameters of the 3D laser survey station refer to the height range that the station can cover in a single scan, directly affecting the detail and accuracy of the scan data. By limiting the elevation interval to no more than one-tenth of the maximum stockpile height, it is ensured that important terrain details are not missed during the measurement process, thereby improving the reliability of the measurement results.

[0035] Specifically, determining the elevation interval between levels requires considering the maximum stockpile height of the spoil heap and the required measurement accuracy. For example, if the maximum stockpile height is 50 meters, then based on the above conditions, the elevation interval should not exceed 5 meters. This parameter setting is crucial for the accuracy and efficiency of the measurement. If the elevation interval is set too large, it may lead to insufficient measurement accuracy and an inability to accurately reflect the terrain changes of the spoil heap; while if the elevation interval is set too small, although it can improve measurement accuracy, it will increase measurement time and computational workload. In practical applications, the elevation interval can be adjusted according to the specific conditions of the spoil heap and the performance of the measuring equipment to achieve the best measurement results.

[0036] Preferably, to ensure measurement accuracy and efficiency, the elevation interval values ​​between levels can be optimized based on the actual measurement environment and equipment performance. For example, if a high-precision three-dimensional laser measuring station is used, and the elevation changes at the spoil heap are complex, the elevation interval values ​​can be appropriately reduced to improve measurement accuracy. Simultaneously, the rationality of the selected elevation interval values ​​can be verified through simulated measurements or small-scale tests before measurement. If a significant deviation is found between the simulated measurement results and the actual situation, the elevation interval values ​​can be adjusted appropriately until satisfactory measurement accuracy is obtained. Furthermore, during actual measurement, the elevation interval values ​​can be dynamically adjusted based on real-time feedback of the measurement data to adapt to terrain changes in different areas, further improving the accuracy and efficiency of the measurement.

[0037] In some embodiments, the specific process of terrain consistency calibration is as follows: For each point within the heap, perform the following operations: Select a reference elevation point from the K elevation coordinate points, and integrate the remaining K-1 three-dimensional laser station scanning datasets (excluding the three-dimensional laser station scanning datasets corresponding to the reference elevation point) into an elevation calibration queue. The system iterates through each 3D laser station scan dataset in the elevation calibration queue. For each traversed 3D laser station scan dataset, it verifies whether the benchmark elevation point has a spatially matching point in the 3D laser station scan dataset using spatial projection constraints. If it does, the 3D laser station scan dataset is retained, and its corresponding elevation coordinates are updated synchronously. Otherwise, the 3D laser station scan dataset is removed from the set of 3D laser station scan datasets corresponding to the internal points in the stack, and the corresponding elevation coordinates are also removed.

[0038] It should be noted that terrain consistency calibration involves processing the elevation coordinates of each point within the stack and the 3D laser scanning dataset to ensure the accuracy and consistency of the terrain data. Specifically, a benchmark elevation point is selected from multiple elevation coordinate points, and then the other elevation coordinate points are compared and calibrated against this benchmark point. This process involves spatial projection constraints to verify whether there are spatially matching points for the benchmark elevation point in other 3D laser scanning datasets. If matching points exist, the relevant data is retained and the elevation coordinates are updated; if not, the mismatched data is discarded. This process is crucial for improving measurement accuracy and data reliability.

[0039] Specifically, the benchmark elevation point is a reference point selected from multiple elevation coordinate points within the stack, used for comparison with other elevation coordinate points. The elevation calibration queue refers to a collection of datasets other than the 3D laser station scan dataset corresponding to the benchmark elevation point; these datasets are sequentially compared and calibrated against the benchmark elevation point. Spatial projection constraints are a verification method based on geometric and physical rules used to determine whether a matching point exists for the benchmark elevation point in other datasets. This condition typically considers factors such as the scanning angle and distance of the laser station to ensure the accuracy of the matching point. The measurement allowable error threshold is a preset error range used to determine whether two points are sufficiently close to determine if a spatial matching point exists.

[0040] Preferably, the operational steps can be further refined during terrain consistency calibration. For example, when constructing the spatial projection ray, the direction and position of the ray can be calculated based on the three-dimensional coordinates of the reference elevation point and the scanning elevation angle parameters of the laser station. When calculating the surface intersection coordinates, a geometric algorithm can be used to determine the intersection point of the ray with the surface of the stack-base composite model. If the elevation deviation between the intersection point and a point in another dataset is less than a preset allowable measurement error threshold, a spatial matching point is considered to exist. Furthermore, when updating elevation coordinate points, inverse distance weighted interpolation can be used to resample the point cloud data within the three-dimensional calibration cube, thereby generating more accurate elevation coordinate points. This process can improve the accuracy and reliability of the data, ensuring the effectiveness of terrain consistency calibration.

[0041] In some embodiments, the method of verifying spatial matching points using spatial projection constraints is as follows: Based on the three-dimensional reference coordinates of the reference elevation point and the laser scanning elevation angle parameters of the three-dimensional laser station, a spatial projection ray corresponding to the reference elevation point is constructed. Calculate the coordinates of the intersection point between the spatial projection ray and the surface of the stack-substrate composite model; If the elevation deviation between the coordinates of the intersection point on the surface and the corresponding point coordinates in the scanning data of another three-dimensional laser station is less than the preset allowable measurement error threshold, then it is determined that a spatial matching point exists.

[0042] It should be noted that the process of verifying spatial matching points is based on the three-dimensional reference coordinates of the benchmark elevation point and the laser scanning elevation angle parameters of the three-dimensional laser station. A spatial projection ray is constructed, and the coordinates of the intersection point between this ray and the surface of the stack-base composite model are calculated. The purpose of this process is to determine whether there is a spatially consistent matching point for the benchmark elevation point in other three-dimensional laser station scan datasets. If the elevation deviation between the intersection point and the corresponding point in another dataset is within the allowable error range, a spatial matching point is considered to exist. This verification process is a crucial step in ensuring the accuracy of terrain consistency calibration and helps improve the reliability and precision of the measurement data.

[0043] Specifically, the three-dimensional reference coordinates of the reference elevation point refer to the specific location coordinates of the reference elevation point in a unified coordinate system, providing a starting point for constructing the spatial projection ray. The laser scanning elevation angle parameter refers to the tilt angle of the laser beam relative to the horizontal plane during the scanning process at the three-dimensional laser station; this parameter determines the direction of the spatial projection ray. The stockpile-base composite model is a model combining the three-dimensional model of the stockpile with the three-dimensional model of the spoil heap base, used to simulate the actual terrain structure. The surface intersection coordinates refer to the coordinates of the point where the spatial projection ray intersects with the surface of the composite model; by calculating this intersection point, the contact position between the ray and the model surface can be determined. The elevation deviation value refers to the difference between the elevation of the intersection point and the elevation of the corresponding point in another dataset, while the measurement allowable error threshold is a preset error range used to determine whether the elevations of the two points are sufficiently close, thereby determining whether a spatial matching point exists.

[0044] Preferably, the operational steps can be further refined when verifying spatial matching points. For example, when constructing a spatial projection ray, the direction vector of the ray can be accurately calculated based on the three-dimensional coordinates of the reference elevation point and the laser scanning elevation angle parameters. Then, the coordinates of the intersection point between the ray and the surface of the stack-base composite model are calculated using a geometric algorithm. To determine whether a spatial matching point exists, the calculated intersection point coordinates can be compared with the coordinates of the corresponding point in another dataset. If the elevation deviation is less than a preset allowable measurement error threshold, such as 0.1 meters, then a spatial matching point is considered to exist.

[0045] Furthermore, to improve the accuracy of verification, factors such as the scanning accuracy of the laser station and the resolution of the model can be considered when calculating the intersection point coordinates, thereby determining the matching point more precisely. This process ensures the accuracy of terrain consistency calibration and improves the reliability of measurement data.

[0046] In some embodiments, the operation of updating the elevation coordinate points is as follows: In the 3D laser station scanning dataset where spatial matching points exist, a 3D calibration cube is constructed with the surface intersection coordinates as the geometric center. The point cloud data within the 3D calibration cube is resampled using the inverse distance weighted interpolation method to generate calibrated elevation coordinate points.

[0047] It should be noted that the update of elevation coordinate points is performed when spatial matching points exist, with the aim of improving the accuracy of the elevation data. Specifically, in the 3D laser station scan dataset where spatial matching points exist, a 3D calibration cube is constructed with the surface intersection coordinates as its geometric center. Then, inverse distance weighted interpolation is used to resample the point cloud data within the 3D calibration cube, thereby generating calibrated elevation coordinate points. This process helps reduce measurement errors and ensures the accuracy of the elevation data, which is crucial for subsequent terrain analysis and volume calculations.

[0048] Specifically, the 3D calibration cube is a 3D spatial region centered on the surface intersection coordinates. Its size can be determined based on the measurement accuracy requirements and the density of the point cloud data. Inverse distance weighted interpolation is a commonly used geospatial interpolation method that assigns weights based on the distance between a point and the point to be interpolated; points closer to each other have a greater impact on the interpolation result. In this embodiment, this method is used to resample the point cloud data within the 3D calibration cube to generate more accurate elevation coordinates. The surface intersection coordinates are the coordinates of the points where the spatial projection ray intersects the surface of the stack-base composite model; these coordinates serve as the reference positions for constructing the 3D calibration cube. This ensures that the updates to the elevation coordinates are based on accurate information from the surrounding point cloud data, thereby improving data reliability.

[0049] Preferably, when constructing the 3D calibration cube, a suitable side length can be selected based on the measurement accuracy requirements. For example, if high measurement accuracy is required, a smaller side length, such as 0.5 meters, can be chosen. When performing inverse distance weighted interpolation, appropriate weighting factors can be set, such as the reciprocal of the distance or the reciprocal of the square of the distance, to better reflect the spatial distribution characteristics of the point cloud data. The specific interpolation process is as follows: First, determine all point cloud data points within the 3D calibration cube; then, calculate the weight of each point based on its distance to the intersection point with the surface; finally, perform a weighted average of the elevation values ​​of these points according to their weights to obtain the calibrated elevation coordinates. This process can effectively reduce elevation deviations caused by measurement errors or data noise, thereby improving the accuracy and reliability of the entire measurement system.

[0050] In some embodiments, the specific steps for calculating the three-dimensional spatial coordinates using the spatial triangulation algorithm are as follows: Input the three-dimensional reference coordinates of J elevation coordinate points into the Delaunay triangulation generator to construct a local triangulation surface related to the internal points of the heap. Extract the set of terrain triangles that are associated with points inside the stack from the local triangular mesh surface; Calculate the geometric centroid coordinates of the vertices of each triangular facet, and determine these geometric centroid coordinates as the three-dimensional spatial coordinates of the points inside the stack.

[0051] It should be noted that the process of calculating the 3D spatial coordinates using the spatial triangulation algorithm involves inputting the elevation coordinates of the points inside the stack into the Delaunay triangulation generator to construct a local triangulation surface. Then, it extracts a set of terrain triangular facets associated with the points inside the stack, and finally calculates the geometric centroid coordinates of the vertices of these facets, determining them as the 3D spatial coordinates of the points inside the stack. The purpose of this process is to accurately determine the location of the points inside the stack using geometric methods, thereby improving the accuracy and reliability of the 3D model.

[0052] Specifically, the Delaunay triangulation generator is an algorithmic tool used to generate triangulations from a set of points, such that the circumcircle of each triangle contains no other points. The local triangulation surface refers to the triangulation generated around a point within the stack, reflecting the terrain features surrounding that point. The set of terrain triangles is a set of triangles directly related to the points within the stack, extracted from the local triangulation. The vertices of these triangles are used to calculate the geometric centroid coordinates of the points within the stack. The geometric centroid coordinates refer to the relative position of a point within a triangle, which can be determined by calculating the average of the vertex coordinates of the triangles. This process involves geometric processing of the point cloud data to ensure that the coordinates of each point within the stack accurately reflect its position in three-dimensional space.

[0053] Preferably, when constructing the local triangular mesh surface, the input elevation coordinate points can be preprocessed, such as by removing outliers or performing smoothing, to improve the quality of the triangular mesh. When extracting the set of terrain triangular patches, the triangles closest to the point can be selected based on the spatial relationship between the internal points and the triangle vertices. When calculating the geometric centroid coordinates, a weighted average method can be used, assigning different weights based on the distance between the triangle vertices and the internal points, thereby more accurately determining the 3D spatial coordinates of the internal points. For example, if an internal point is close to a vertex of a triangle, the coordinates of that vertex should be given greater weight when calculating the centroid coordinates. This process can further improve the accuracy of the 3D spatial coordinate calculation, providing more accurate data support for subsequent 3D model construction.

[0054] In some embodiments, the formula for calculating the geometric centroid coordinates is:

[0055] in, This represents the three-dimensional spatial coordinates of a point inside the heap, where n represents the number of triangular faces associated with that point. This represents the coordinates of the vertex of the i-th triangle.

[0056] It should be noted that the geometric centroid coordinates are calculated by averaging the coordinates of the vertices of the triangular facets associated with the points inside the stack. This method is based on geometric principles, using averaging to obtain a representative position of a point in space. In three-dimensional space, the geometric centroid can be considered as the equilibrium point of a set of points, effectively reflecting the concentration tendency of these points. Calculating the coordinates of points inside the stack in this way ensures that the positions of these points in the three-dimensional model are more accurate, thereby improving the accuracy and reliability of the entire model.

[0057] Specifically, internal points of a heap refer to points located inside the heap body. The coordinates of these points need to be calculated to determine their precise location in three-dimensional space. The triangular facets associated with these internal points refer to the triangular facets within the local triangulation that contain the internal points. The vertex coordinates of these triangular facets are known. By calculating the average of these vertex coordinates, the geometric centroid coordinates of the internal points can be obtained. The average calculation involves adding the coordinates of all associated triangular facet vertices and then dividing by the number of vertices. For example, if there are three vertices with coordinates (x1, y1, z1), (x2, y2, z2), and (x3, y3, z3), then the geometric centroid coordinates of the internal points are determined by adding the coordinates of these vertices and taking the average. This calculation method is simple and effective, enabling quick and accurate determination of the location of internal points.

[0058] Preferably, when calculating the geometric centroid coordinates, the weights of the vertices can be further considered. For example, if a vertex has a closer spatial relationship with the points inside the heap, it can be given a greater weight. The weights can be determined based on the distance, angle, or other geometric features between the vertex and the points inside the heap. In practice, the geometric centroid coordinates can be calculated by weighted averaging of the vertex coordinates. For example, if vertex A has the largest weight, vertex B has the second largest weight, and vertex C has the smallest weight, then the geometric centroid coordinates of the points inside the heap can be determined by weighted averaging of the coordinates of these vertices.

[0059] Furthermore, to improve the accuracy and efficiency of the calculation, the vertex coordinates can be preprocessed before computation, such as removing outliers or performing smoothing. These optimizations allow for a more accurate determination of the 3D spatial coordinates of points within the heap, providing more reliable data support for subsequent 3D model construction and analysis.

[0060] In some embodiments, the elevation point matching steps are as follows: Perform point cloud density normalization processing on the scanning datasets of L three-dimensional laser stations to generate a point cloud set with uniform resolution. The iterative nearest-point algorithm is used to perform registration processing on a point cloud with uniform resolution. Extract the set of points that meet the curvature consistency condition and use them as surface feature points; By using kd-tree spatial indexing technology, a precise correspondence between surface feature points and multiple 3D laser scanning datasets is established.

[0061] It's important to note that the elevation point matching step is crucial for ensuring accurate spatial correspondence between point cloud data obtained from different 3D laser scanning stations. This process begins by normalizing the point cloud density of the scanned datasets from each station, ensuring a uniform resolution for subsequent processing. Next, the iterative nearest-point algorithm is used to register the normalized point cloud data, aligning the data from different stations. Then, points meeting the curvature consistency condition are extracted as surface feature points; these points are typically located at locations with significant terrain changes, such as edges or corners. Finally, k-d tree spatial indexing technology is used to establish precise correspondences between these surface feature points across multiple station scanned datasets, thus achieving elevation point matching. This process is essential for constructing accurate 3D terrain models.

[0062] Specifically, point cloud density normalization refers to adjusting the density of point cloud data to ensure that data from different stations have the same resolution. This process eliminates density differences caused by variations in station location and scanning angle. The iterative nearest-point algorithm is a commonly used point cloud registration algorithm that iteratively finds the best match between two sets of point clouds, thereby aligning data from different stations. The curvature consistency condition refers to the similarity of curvature among points within a certain range, typically used to identify terrain edges or feature points. The k-d tree spatial indexing technique is an efficient spatial data structure used for quickly finding and matching points in space. These techniques allow for the accurate establishment of correspondences between scanned data from different stations.

[0063] Preferably, when performing point cloud density normalization, appropriate normalization parameters can be selected based on the actual measurement accuracy requirements and data volume. For example, if high-precision measurement results are required, the point cloud density can be set higher. When using the iterative nearest point algorithm, an initial matching threshold can be set; when the matching error between two sets of point clouds is less than this threshold, they are considered aligned. For extracting surface feature points, the curvature consistency condition parameters can be adjusted according to the specific characteristics of the terrain. For example, in areas with complex terrain, the curvature condition can be appropriately relaxed to extract more feature points. When using k-d tree spatial indexing technology, the index tree can be optimized to improve matching efficiency. For example, the index construction time and query time can be balanced by adjusting the tree's branching factor. Through these optimization measures, the accuracy and efficiency of elevation point matching can be improved, providing a more reliable data foundation for subsequent 3D terrain modeling.

[0064] The above embodiments of the present invention have the following beneficial effects: 1. Multi-station synchronous layered scanning technology combined with elevation point matching algorithm can eliminate the blind zone problem of traditional single-station scanning. Through the collaborative operation of L three-dimensional laser stations, complete terrain point cloud data can be obtained. Combined with the feature point correspondence established by kd tree spatial indexing technology, the elevation coordinate matching accuracy can be improved to the millimeter level, thereby ensuring that the accuracy of the volume calculation of the stockpile meets the requirements of engineering measurement standards.

[0065] 2. The digital elevation model fusion technology, combined with the terrain consistency calibration process, can solve the industry problem of inconsistent coordinate systems of multi-source data. By constructing a unified reference coordinate system and adopting a spatial projection constraint verification mechanism, it can achieve seamless fusion of data from L scanning stations with the base model. Its calibration cube resampling method can control the model splicing error within a small range, providing an accurate spatial reference for the stack-base composite model.

[0066] 3. The intelligent spatial triangulation algorithm combined with the layered volume difference calculation method can significantly improve measurement efficiency. The local surface model constructed by the Delaunay triangulation generator, combined with the volume calculation process of the voxel difference method, can realize fully automated data processing, which can shorten the operation time compared with traditional measurement methods.

[0067] like Figure 2 As shown in some embodiments, a stratified measurement system for the capacity of a spoil heap storage area is provided. The system includes: 3D laser scanning array 201: contains L 3D laser stations equipped with multi-echo detection modules, which synchronously collect layered terrain point cloud data according to preset elevation intervals; Point cloud preprocessing module 202: used to perform noise reduction filtering and coordinate unification processing on the collected layered terrain point cloud data in sequence, and output a standardized three-dimensional laser station scanning dataset. Stacking model calculation engine 203: Its main functions include: constructing a three-dimensional model of the layered stacking body based on the aforementioned method for measuring the capacity of the waste dump stacking body; conducting uncertainty analysis on the volume calculation results based on the Monte Carlo method; and outputting a waste dump stacking capacity report containing confidence intervals.

[0068] It is understandable that the modules recorded in the stratified measurement system for the volume of the spoil heap are similar to those in the reference system. Figure 1 The steps in the described method for stratified measurement of spoil heap capacity correspond to each other. Therefore, the operation, characteristics, and beneficial effects described above for the method for stratified measurement of spoil heap capacity also apply to the spoil heap capacity stratified measurement system and its modules, and will not be repeated here.

[0069] The following is for reference. Figure 3 The diagram illustrates a structural schematic of an electronic device 300 suitable for implementing some embodiments of the present invention. The electronic devices in some embodiments of the present invention may include, but are not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 3 The terminal device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of the present invention.

[0070] like Figure 3 As shown, the electronic device 300 may include a processing unit (e.g., a central processing unit, a graphics processing unit, etc.) 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage device 308 into a random access memory (RAM) 303. The RAM 303 also stores various programs and data required for the operation of the electronic device 300. The processing unit 301, ROM 302, and RAM 303 are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0071] Typically, the following devices can be connected to I / O interface 305: input devices 306 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 307 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 308 including, for example, magnetic tapes, hard disks, etc.; and communication devices 309. Communication device 309 allows electronic device 300 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 3 An electronic device 300 with various devices is shown; however, it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed alternatively. Figure 3 Each box shown can represent a device or multiple devices as needed.

[0072] Furthermore, the storage medium in the embodiments of this application stores program instructions capable of implementing all the above methods. These program instructions can be stored in the storage medium in the form of a software product, including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks, or terminal devices such as computers, servers, mobile phones, and tablets.

[0073] The above description is merely an explanation of some preferred embodiments of the present invention and the technical principles employed. Those skilled in the art should understand that the scope of the invention as described in the embodiments of the present invention is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described inventive concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features with similar functions disclosed in the embodiments of the present invention.

Claims

1. A method for stratified measurement of the capacity of a spoil heap, characterized in that, The method includes: Using a scanning array consisting of L three-dimensional laser stations, a synchronous layered elevation scanning operation is carried out on the surface of the spoil disposal site at a preset elevation interval, thereby obtaining multiple sets of terrain point cloud data. Each set of terrain point cloud data consists of a scan dataset from L three-dimensional laser stations. For each level of L 3D laser scanning datasets, perform the following operations sequentially to calculate the corresponding level's stack volume: Elevation point matching is performed on L three-dimensional laser scanning datasets to accurately locate the K three-dimensional laser scanning datasets corresponding to each surface feature point, and to identify the K elevation coordinate points in these K three-dimensional laser scanning datasets that match the surface feature points. By using digital elevation model fusion technology, the scanning datasets of L three-dimensional laser stations are deeply fused with the pre-constructed three-dimensional model of the spoil disposal site base. This establishes a precise correlation between the spatial coordinate system of the L three-dimensional laser station scanning datasets and the unified reference coordinate system of the spoil disposal site base three-dimensional model. Based on this unified reference coordinate system, the three-dimensional reference coordinates of the positions of the L three-dimensional laser stations are determined. The surface feature points are classified and processed to identify the storage boundary points and internal points of the storage; a storage body outline mesh model is constructed based on the storage boundary points. The outline mesh model of the stockpile is spatially superimposed with the three-dimensional model of the spoil heap base to generate a stockpile-base composite model. The stockpile-base composite model performs terrain consistency calibration on the K elevation coordinate points and K three-dimensional laser station scan datasets corresponding to each point inside the stockpile to obtain the calibrated J elevation coordinate points and J three-dimensional laser station scan datasets. Based on the J elevation coordinates of each internal point of the stack and the J three-dimensional laser station scanning dataset, the three-dimensional spatial coordinates of each internal point of the stack are calculated by the spatial triangulation algorithm; the three-dimensional spatial coordinates of all internal points of the stack are integrated with the stack body contour mesh model to construct a layered stack body three-dimensional model. Based on the volume difference of the three-dimensional model of the multi-level layered storage body, the total storage capacity of the spoil disposal site is calculated. The specific process for terrain consistency calibration is as follows: For each point within the heap, perform the following operations: Select a reference elevation point from the K elevation coordinate points, and integrate the remaining K-1 three-dimensional laser station scanning datasets (excluding the three-dimensional laser station scanning datasets corresponding to the reference elevation point) into an elevation calibration queue. The system iterates through each 3D laser station scan dataset in the elevation calibration queue. For each traversed 3D laser station scan dataset, it verifies whether the benchmark elevation point has a spatially matching point in the 3D laser station scan dataset using spatial projection constraints. If it does, the 3D laser station scan dataset is retained, and its corresponding elevation coordinates are updated synchronously. Otherwise, the 3D laser station scan dataset is removed from the set of 3D laser station scan datasets corresponding to the internal points in the stack, and the corresponding elevation coordinates are also removed.

2. The method for stratified measurement of the capacity of a spoil heap as described in claim 1, characterized in that, Based on the volume differences in the three-dimensional model of the multi-level, layered storage structure, the total storage capacity of the spoil heap is calculated, including: A volume rasterization process is applied to the 3D model of each layered stack to produce a set of spatial voxels for that layer of stack. By combining the vertical projection overlap rate of the voxel sets of adjacent stacked volumes and the elevation interval between levels, the volume of each stacked volume at each level is calculated using the voxel difference method. The total storage capacity of the spoil heap is obtained by performing cumulative integration on the volume of all levels of the spoil heap.

3. The method for stratified measurement of the capacity of a spoil heap as described in claim 2, characterized in that, The elevation interval between the layers is determined based on the scanning layer thickness parameters of the three-dimensional laser station and satisfies the following relationship: in, Indicates the elevation interval value. This indicates the maximum storage height of the spoil disposal site.

4. The method for stratified measurement of the capacity of a spoil heap as described in claim 1, characterized in that, Verify whether a spatially matching point exists for the benchmark elevation point in the 3D laser station scanning dataset using spatial projection constraints, including: Based on the three-dimensional reference coordinates of the reference elevation point and the laser scanning elevation angle parameters of the three-dimensional laser station, a spatial projection ray corresponding to the reference elevation point is constructed. Calculate the coordinates of the intersection point between the spatial projection ray and the surface of the stack-substrate composite model; If the elevation deviation between the coordinates of the intersection point on the surface and the corresponding point coordinates in the scanning data of another three-dimensional laser station is less than the preset allowable measurement error threshold, then it is determined that a spatial matching point exists.

5. The method for stratified measurement of the capacity of a spoil heap as described in claim 1, characterized in that, The synchronous update of its corresponding elevation coordinate points includes: In the 3D laser station scanning dataset where spatial matching points exist, a 3D calibration cube is constructed with the surface intersection coordinates as the geometric center. The point cloud data within the 3D calibration cube is resampled using the inverse distance weighted interpolation method to generate calibrated elevation coordinate points.

6. The method for stratified measurement of the capacity of a spoil heap as described in claim 1, characterized in that, The calculation of the three-dimensional spatial coordinates of each point inside the heap using the spatial triangulation algorithm includes: Input the three-dimensional reference coordinates of J elevation coordinate points into the Delaunay triangulation generator to construct a local triangulation surface related to the internal points of the heap. Extract the set of terrain triangles that are associated with points inside the stack from the local triangular mesh surface; Calculate the geometric centroid coordinates of the vertices of each triangular facet, and determine these geometric centroid coordinates as the three-dimensional spatial coordinates of the points inside the stack.

7. The method for stratified measurement of the capacity of a spoil heap as described in claim 6, characterized in that, The formula for calculating the geometric centroid coordinates is: in, This represents the three-dimensional spatial coordinates of a point inside the heap, where n represents the number of triangular faces associated with that point. This represents the coordinates of the ith vertex of the i-th triangle.

8. The method for stratified measurement of the capacity of a spoil heap as described in claim 1, characterized in that, The steps for matching elevation points are as follows: Perform point cloud density normalization processing on the scanning datasets of L three-dimensional laser stations to generate a point cloud set with uniform resolution. The iterative nearest-point algorithm is used to perform registration processing on a point cloud with uniform resolution. Extract the set of points that meet the curvature consistency condition and use them as surface feature points; By using kd-tree spatial indexing technology, a precise correspondence between surface feature points and multiple 3D laser scanning datasets is established.

9. A smart calculation system for the storage capacity of a spoil heap, characterized in that, The system includes: Three-dimensional laser scanning array: It consists of L three-dimensional laser stations equipped with multi-echo detection modules, which synchronously collect layered terrain point cloud data according to preset elevation intervals; Point cloud preprocessing module: used to perform noise reduction filtering and coordinate unification processing on the collected layered terrain point cloud data in sequence, and output a standardized 3D laser station scan dataset; Heap model computation engine: Its main functions include: A three-dimensional model of the layered stockpile is constructed according to the layered measurement method for the capacity of the spoil heap as described in claims 1 to 8; Uncertainty analysis was performed on the volume calculation results based on the Monte Carlo method; Output a report on the spoil heap storage capacity, including confidence intervals.

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