Layered measurement method and system for capacity of stockpiling body in waste slag field

Abstract

The invention provides a waste slag field stockpiling body capacity layered measurement method and system, and the method comprises the steps: carrying out the synchronous layered scanning according to a preset elevation interval through a scanning array composed of L three-dimensional laser observation stations, and obtaining a plurality of groups of topographic point cloud data; performing elevation point matching on each layer of data, and positioning K elevation coordinate points corresponding to the earth surface feature points; establishing a unified reference coordinate system through a digital elevation model fusion technology; classifying and processing the feature points, constructing a stockpiling body contour mesh model, and superposing the stockpiling body contour mesh model with the substrate three-dimensional model to generate a composite model; performing terrain consistency calibration on the stockpiling internal points to obtain calibrated elevation data; internal point three-dimensional coordinates are calculated through a space triangulation algorithm, and a layered stockpiling body three-dimensional model is constructed in an integrated mode; and finally, calculating the total stockpiling capacity based on the volume difference value of the multi-level model. According to the invention, high-precision automatic measurement of the volume of the stockpiling body in the waste slag field can be realized, and the accuracy and the operation efficiency of volume calculation are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of engineering measurement, and more particularly, to a method and system for measuring the capacity of a waste dump storage volume in layers. Background Art

[0002] Measuring the storage capacity of waste dumps is a crucial task in fields such as construction and mining. Waste dumps are used to store waste generated during construction. Accurately measuring their capacity is crucial for project progress control, resource utilization, and environmental impact assessment. Traditional measurement methods rely primarily on manual measurement and simple geometric calculations, such as estimating the volume by measuring the length, width, and height of a waste dump. While simple, this method has numerous limitations, including low accuracy, low efficiency, and difficulty adapting to complex terrain. With technological advancements, 3D laser scanning technology has gradually been introduced into the field of volume measurement. 3D laser scanners can rapidly acquire 3D point cloud data from an object's surface, from which a 3D model of the object can be constructed and its volume calculated. However, existing volume measurement methods based on 3D laser scanning are mostly designed for measuring the volume of single objects or regular shapes. They remain insufficient for measuring complex terrain, such as waste dumps, particularly in terms of multi-station scanning and layered measurement. Existing 3D laser scanning measurement methods typically only provide point cloud data from a single scan. For scenarios like waste dumps, which require layered measurement, effective layered scanning and data fusion technologies are lacking. In addition, existing methods often find it difficult 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 found that there are at least the following problems or defects in the existing technology: First, the existing measurement methods cannot meet the high-precision measurement requirements of the complex terrain of the waste dump, especially there are technical bottlenecks in multi-station layered scanning and data fusion; Second, the existing technology is not accurate enough in 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] The present invention provides a method and system for measuring the storage volume of a waste dump in layers.

[0005] In a first aspect of the present invention, a method for measuring the volume of a waste dump in layers is provided, comprising: Using a scanning array consisting of L 3D laser stations, the surface of the waste dump is synchronously layered and scanned at preset elevation intervals to obtain multiple sets of terrain point cloud data. Each set of terrain point cloud data is composed of L 3D laser station scanning data sets. For each level of the L 3D laser station scan datasets, perform the following operations in sequence to calculate the volume of the corresponding level: Perform elevation point matching on L 3D laser station scan datasets, accurately locate the K 3D laser station scan datasets corresponding to each surface feature point, and identify the K elevation coordinate points in these K 3D laser station scan datasets that match the surface feature points; Using digital elevation model fusion technology, the L 3D laser station scanning datasets were deeply fused with a pre-built 3D model of the spoil dump base. This established a precise relationship between the spatial coordinate systems of the L 3D laser station scanning datasets and the unified reference coordinate system of the 3D spoil dump base model. The 3D reference coordinates of the L 3D laser station locations were then determined based on this unified reference coordinate system. Surface feature points are classified and processed to screen out stockpile boundary points and stockpile internal points; a stockpile contour grid model is constructed based on the stockpile boundary points; The stockpile contour grid model and the 3D model of the spoil dump base are spatially superimposed to generate a stockpile-base composite model. This stockpile-base composite model is then used to calibrate the terrain consistency of K elevation coordinate points and K 3D laser station scanning datasets corresponding to each internal point of the stockpile, obtaining J calibrated elevation coordinate points and J 3D laser station scanning datasets. Based on the J elevation coordinate points corresponding to each internal point of the stockpile and J 3D laser station scanning data sets, the 3D spatial coordinates of each internal point of the stockpile are calculated using a spatial triangulation algorithm. The 3D spatial coordinates of all internal points of the stockpile are integrated with the stockpile contour grid model to construct a layered 3D stockpile model. The total storage capacity of the waste dump is calculated based on the volume difference of the multi-level layered storage three-dimensional model.

[0006] Furthermore, the step of calculating the total storage capacity of the waste dump based on the volume difference of the multi-level layered storage body three-dimensional model is as follows: Implementing a volume rasterization process on each layered stockpile 3D model to generate a set of spatial voxels of the stockpile at that level; Combining the vertical projection overlap rate of the voxel sets of the adjacent layers of the stockpile space and the elevation interval between layers, the voxel difference method is used to calculate the volume of the stockpile at each layer. The cumulative integral operation is performed on the stockpile volumes of all levels to ultimately obtain the total stockpile capacity of the waste dump.

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

[0008] in, Indicates the elevation interval value, Indicates the maximum storage height of the waste dump.

[0009] Furthermore, the specific process of the terrain consistency calibration is as follows: For each stockpile interior point, do the following: A benchmark elevation point is selected from the K elevation coordinate points, and the remaining K-1 3D laser station scanning datasets other than the 3D laser station scanning dataset corresponding to the benchmark elevation point are integrated into an elevation calibration queue; Traverse each 3D laser station scan dataset in the elevation calibration queue, and for each traversed 3D laser station scan dataset, use the spatial projection constraint condition to verify whether the benchmark elevation point has a spatial matching point in the 3D laser station scan dataset; if so, retain the 3D laser station scan dataset and synchronously update its corresponding elevation coordinate point; otherwise, remove the 3D laser station scan dataset from the 3D laser station scan dataset set corresponding to the internal point in the storage, and remove the corresponding elevation coordinate point at the same time.

[0010] Furthermore, the spatial projection constraint condition is used to verify the spatial matching points, specifically in the following manner: Based on the three-dimensional coordinates of the benchmark elevation point and the laser scanning elevation angle parameters of the three-dimensional laser survey station, a spatial projection ray corresponding to the benchmark elevation point is constructed; Calculate the coordinates of the intersection of the spatial projection ray and the surface of the deposit-base composite model; If the elevation deviation between the surface intersection coordinates and the corresponding point coordinates in another 3D laser station scanning data set is less than a preset measurement allowable error threshold, it is determined that a spatial matching point exists.

[0011] Furthermore, the operation of updating the elevation coordinate point is: In the 3D laser station scanning data set where the spatial matching point exists, a 3D calibration cube is constructed with the surface intersection coordinates as the geometric center; The inverse distance weighted interpolation method is used to resample the point cloud data in the 3D calibration cube to generate calibrated elevation coordinate points.

[0012] Furthermore, the three-dimensional space coordinates are calculated by using a spatial triangulation algorithm, and the specific steps 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 stockpile; Extracting a set of terrain triangles associated with the internal points of the stockpile from the local triangulated surface; The geometric center of gravity coordinates of each triangle vertex are calculated and determined as the three-dimensional space coordinates of the internal points of the stack.

[0013] Furthermore, the calculation formula of the geometric center of gravity coordinates is:

[0014] in, Represents the three-dimensional space coordinates of the internal point of the stockpile, n represents the number of triangles associated with the internal point of the stockpile, Represents the coordinates of the i-th triangle vertex.

[0015] Furthermore, the steps of matching elevation points are as follows: Perform point cloud density normalization on L 3D laser station scanning data sets to generate a uniform resolution point cloud set. The iterative closest point algorithm is used to perform registration processing on the uniform resolution point cloud set; Extract the point set that meets the curvature consistency condition and use it as the surface feature points; Through the kd-tree spatial indexing technology, an accurate correspondence between surface feature points in multiple 3D laser station scanning data sets is established.

[0016] In a second aspect of the present invention, a system for measuring the volume of a waste dump in layers is provided, comprising: 3D laser scanning array: It includes L 3D laser measuring 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 denoising and filtering on the collected layered terrain point cloud data and coordinate unification, and output a standardized 3D laser station scanning data set; Stockpile model calculation engine: Its main functions include: constructing a layered three-dimensional stockpile model based on the layered measurement method for the waste dump stockpile capacity recorded in the first aspect; conducting uncertainty analysis on the volume calculation results based on the Monte Carlo method; and outputting a waste dump stockpile capacity report containing confidence intervals.

[0017] The above embodiments of the present invention have at least the following beneficial effects: 1. Multi-station synchronous layered scanning technology combined with the elevation point matching algorithm can eliminate the blind spot problem of traditional single-station scanning. Through the collaborative operation of L 3D laser measurement 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 stockpile volume calculation meets the requirements of engineering measurement standards.

[0018] 2. Digital elevation model fusion technology combined with the terrain consistency calibration process can solve the industry problem of inconsistent coordinate systems for multi-source data. By building a unified reference coordinate system and adopting a spatial projection constraint verification mechanism, seamless fusion of data from L scanning stations and the base model can be achieved. Its calibration cube resampling method can control the model splicing error within a small range, providing an accurate spatial reference for the stockpile-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 and the volume calculation process of the voxel difference method can achieve fully automated data processing, which can shorten the operation time compared with traditional measurement methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily apparent by reading the following detailed description with reference to the accompanying drawings, in which several embodiments of the present invention are shown by way of example and not limitation, in which: Figure 1 A schematic flow chart of a method for measuring the volume of a waste dump in layers according to an embodiment of the present invention; Figure 2 A schematic diagram of the structure of a system for measuring the volume of a waste dump in layers according to an embodiment of the present invention; Figure 3 The figure schematically shows the structure of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

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

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

[0023] It should be noted that any number of elements in the drawings is for illustration only and not for limitation, and any naming is only for distinction and does not have any limiting meaning.

[0024] Reference below Figure 1 , Figure 1 This is a flow chart of a method for measuring the capacity of a waste dump in layers according to an embodiment of the present invention. Figure 1 As shown, a method for measuring the volume of a waste dump in layers includes: S1. Using a scanning array consisting of L three-dimensional laser measurement stations, perform synchronous layered elevation scanning of the surface of the waste dump at preset elevation intervals, thereby obtaining multiple sets of terrain point cloud data, each set of terrain point cloud data consisting of L three-dimensional laser measurement station scanning data sets; S2. For each level of the L 3D laser station scanning data sets, perform the following operations in sequence to calculate the volume of the corresponding level: S3. Perform elevation point matching on the L 3D laser station scan datasets, accurately locate the K 3D laser station scan datasets corresponding to each surface feature point, and identify the K elevation coordinate points in these K 3D laser station scan datasets that match the surface feature points. S4. Using digital elevation model fusion technology, deeply fuse the L 3D laser station scanning datasets with the pre-built 3D model of the spoil dump basement. Establish a precise relationship between the spatial coordinate systems of the L 3D laser station scanning datasets and the unified reference coordinate system of the 3D model of the spoil dump basement. Determine the 3D reference coordinates of the L 3D laser station locations based on this unified reference coordinate system. S5. Classify the surface feature points to select the stockpile boundary points and stockpile interior points; and construct a stockpile body contour grid model based on the stockpile boundary points; S6. Spatially superimposing the stockpile contour grid model with the three-dimensional model of the spoil dump base to generate a stockpile-base composite model for use; using the stockpile-base composite model, terrain consistency calibration is performed on K elevation coordinate points and K three-dimensional laser station scanning data sets corresponding to each internal point of the stockpile, thereby obtaining J calibrated elevation coordinate points and J three-dimensional laser station scanning data sets; S7. Calculate the three-dimensional coordinates of each stockpile interior point using a spatial triangulation algorithm based on the J elevation coordinate points corresponding to each stockpile interior point and the J three-dimensional laser station scanning data sets; integrate the three-dimensional coordinates of all stockpile interior points with the stockpile body contour grid model to construct a layered stockpile body three-dimensional model; S8. Calculate the total storage capacity of the waste dump based on the volume difference of the multi-level layered storage three-dimensional model.

[0025] It should be noted that a scanning array consisting of L three-dimensional laser measuring stations is used to perform synchronous layered elevation scanning of the waste dump surface at preset elevation intervals, thereby obtaining multiple sets of topographic point cloud data. Each set of topographic point cloud data is composed of L three-dimensional laser measuring station scanning data sets. Here, a three-dimensional laser measuring station is a high-precision measuring device that measures the three-dimensional coordinates of a target object by emitting a laser beam and receiving the reflected signal. A scanning array refers to the arrangement of multiple three-dimensional laser measuring stations in a certain layout to form an integrated measurement system to achieve a comprehensive scan of the waste dump. The preset elevation interval refers to the height difference between each scan in the vertical direction, which is used to scan the waste dump in layers to more accurately measure the topographic data of each layer. Topographic point cloud data is a set of three-dimensional coordinates containing a large number of points obtained by scanning. Each point represents a location on the surface of the waste dump. This data forms the basis for subsequent volume calculations.

[0026] Specifically, L three-dimensional laser survey stations can be reasonably arranged according to the actual size and shape of the waste dump, for example, evenly distributed around the waste dump and at key locations to ensure the comprehensiveness and accuracy of the scan. The preset elevation interval can be determined based on the maximum stockpile height of the waste dump and the required measurement accuracy. For example, if the maximum stockpile height of the waste dump is 30 meters and higher measurement accuracy is required, the elevation interval can be set to between 0.2 meters and 0.5 meters. Each three-dimensional laser survey station scanning data set contains the three-dimensional coordinate information of all points obtained by the survey station in a single scan. These data sets will serve as the basic data for subsequent processing. Among them, the first three-dimensional laser survey station, the second three-dimensional laser survey station and other superordinate expressions refer to survey stations at different positions in the scanning array, respectively. Each survey station has its own unique coordinates and scanning range, which together constitute a complete scanning array.

[0027] Preferably, in order to ensure the accuracy and completeness of the scanned data, the 3D laser measuring station needs to be precisely calibrated before scanning, including the adjustment of parameters such as the laser emission angle and receiving sensitivity, to ensure the measurement accuracy of each measuring station. During the scanning process, a certain overlapping area can also be set to avoid data loss due to gaps between measuring stations. The acquired terrain point cloud data can be preliminarily processed using data fusion technology to eliminate coordinate deviations caused by different measuring station locations, providing a more accurate data foundation for subsequent elevation point matching and model construction. For example, when performing data fusion, a method based on feature point matching can be used to align the data scanned by different measuring stations into the same coordinate system, thereby improving data consistency and availability.

[0028] In some embodiments, the step of calculating the total storage capacity of the waste dump based on the volume difference of the multi-level layered storage body three-dimensional model is specifically as follows: Implementing a volume rasterization process on each layered stockpile 3D model to generate a set of spatial voxels of the stockpile at that level; Combining the vertical projection overlap rate of the voxel sets of the adjacent layers of the stockpile space and the elevation interval between layers, the voxel difference method is used to calculate the volume of the stockpile at each layer. The cumulative integral operation is performed on the stockpile volumes of all levels to ultimately obtain the total stockpile capacity of the waste dump.

[0029] It should be noted that a volume rasterization process is performed on each layered 3D model to produce a set of voxels in the volume space at that level. Volume rasterization, as used here, converts a 3D model into a discrete representation consisting of small cubes or voxels, similar to pixelation in 2D images. This process simplifies complex 3D models into a computable set of voxels, facilitating subsequent volume calculations. The vertical projection overlap rate of voxel sets in adjacent layers of the volume space refers to the degree of vertical overlap between the projections of voxels in two adjacent layers and is used to assess the spatial relationship between adjacent layers. The inter-layer elevation interval, which refers to the height difference between two adjacent layers, is a key parameter for volume difference calculations. The voxel difference method calculates volume changes by comparing the differences between adjacent voxel sets. This method accurately calculates the volume contribution of each layer and then accumulates them to obtain the total volumetric capacity.

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

[0031] Preferably, the volume rasterization process can be achieved by the following steps: first, determine the size and shape of the voxel, which is usually a cube or a cuboid; then, map each point of the layered stockpile three-dimensional model to the corresponding voxel to form a voxel set. When calculating the volume difference between adjacent levels, the volume change can be determined by comparing the overlapping and non-overlapping parts of the two layers of voxel sets. For example, if a voxel in a voxel set of one layer does not exist in the next layer, the volume of the voxel should be included in the volume difference. In addition, in order to improve the calculation accuracy, a weight factor can be introduced into the voxel difference method to adjust the volume difference according to the position and shape of the voxel. Finally, by accumulating the volume differences of all levels, the total storage capacity of the waste dump can be obtained.

[0032] In some embodiments, the inter-layer elevation interval value is determined based on a scanning layer thickness parameter of a three-dimensional laser survey station and satisfies the following relationship:

[0033] in, Indicates the elevation interval value, Indicates the maximum storage height of the waste dump.

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

[0035] Specifically, the determination of the elevation interval between layers needs to consider the maximum stockpile height of the waste dump and the measurement accuracy requirements. For example, if the maximum stockpile height of the waste dump is 50 meters, then based on the above conditions, the elevation interval value should not exceed 5 meters. The setting of this parameter is crucial to the accuracy and efficiency of the measurement. If the elevation interval value is set too large, it may lead to insufficient measurement accuracy and fail to accurately reflect the topographic changes of the waste dump; if the elevation interval value is set too small, although it can improve measurement accuracy, it will increase measurement time and calculation complexity. In actual application, the elevation interval value can be adjusted according to the specific conditions of the waste dump and the performance of the measuring equipment to achieve the best measurement effect.

[0036] Preferably, in order to ensure the accuracy and efficiency of the measurement, when determining the elevation interval value between layers, it can be optimized in combination with the actual measurement environment and equipment performance. For example, if a high-precision three-dimensional laser measuring station is used and the height changes of the waste dump are more complex, the elevation interval value can be appropriately reduced to improve the measurement accuracy. At the same time, the rationality of the selected elevation interval value can be verified by simulation measurement or small-scale test before measurement. If it is found in the simulation measurement that the measurement result deviates significantly from the actual situation, the elevation interval value can be appropriately adjusted until satisfactory measurement accuracy is obtained. In addition, during the actual measurement process, the elevation interval value can be dynamically adjusted based on the real-time feedback of the measurement data to adapt to the terrain changes in different areas, further improving the accuracy and efficiency of the measurement.

[0037] In some embodiments, the specific process of the terrain consistency calibration is: For each stockpile interior point, do the following: A benchmark elevation point is selected from the K elevation coordinate points, and the remaining K-1 3D laser station scanning datasets other than the 3D laser station scanning dataset corresponding to the benchmark elevation point are integrated into an elevation calibration queue; Traverse each 3D laser station scan dataset in the elevation calibration queue, and for each traversed 3D laser station scan dataset, use the spatial projection constraint condition to verify whether the benchmark elevation point has a spatial matching point in the 3D laser station scan dataset; if so, retain the 3D laser station scan dataset and synchronously update its corresponding elevation coordinate point; otherwise, remove the 3D laser station scan dataset from the 3D laser station scan dataset set corresponding to the internal point in the storage, and remove the corresponding elevation coordinate point at the same time.

[0038] It should be noted that the terrain consistency calibration is performed by processing the elevation coordinate points of each internal point of the stockpile and the three-dimensional laser station scanning data set to ensure the accuracy and consistency of the terrain data. Specifically, a reference elevation point is selected from multiple elevation coordinate points, and then the other elevation coordinate points are compared and calibrated with the reference point. This process involves spatial projection constraints for verifying whether there is a spatial matching point of the reference elevation point in other three-dimensional laser station scanning data sets. If there is a matching point, the relevant data is retained and the elevation coordinate point is updated; if not, the unmatched data is excluded. This process is crucial for improving measurement accuracy and data reliability.

[0039] Specifically, the reference elevation point is a reference point selected from multiple elevation coordinate points of the internal points of the stockpile, which is used for comparison with other elevation coordinate points. The elevation calibration queue refers to a set of data sets other than the three-dimensional laser station scanning data set corresponding to the reference elevation point, which will be compared and calibrated with the reference elevation point in turn. The spatial projection constraint condition is a verification method based on geometric and physical rules to determine whether there is a matching point of the reference elevation point in other data sets. This condition usually 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 for determining whether two points are close enough to determine whether there is a spatial matching point.

[0040] Preferably, when performing terrain consistency calibration, the operation steps can be further refined. For example, when constructing the spatial projection ray, the direction and position of the ray can be calculated according to 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 of the ray and the surface of the stockpile-base composite model. If the elevation deviation of the intersection point from the point in another data set is less than the preset measurement allowable error threshold, it is considered that there is a spatial matching point. In addition, when updating the elevation coordinate point, an inverse distance weighted interpolation method can be used to resample the point cloud data within the three-dimensional calibration cube to generate more accurate elevation coordinate points. This process can improve the accuracy and reliability of the data and ensure the effectiveness of the terrain consistency calibration.

[0041] In some embodiments, the spatial matching point is verified by applying the spatial projection constraint condition in the following specific manner: 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; The surface intersection coordinates of the spatial projection ray and the stockpile-base composite model are calculated; If the elevation deviation between the surface intersection coordinates and the corresponding point coordinates in another 3D laser station scanning data set is less than a preset measurement allowable error threshold, it is determined that a spatial matching point exists.

[0042] It should be noted that the process of verifying spatial matching points involves constructing a spatial projection ray based on the 3D reference coordinates of the benchmark elevation point and the laser scanning elevation parameters of the 3D laser survey station, and calculating the coordinates of the intersection of this ray with the surface of the stockpile-basement composite model. The purpose of this process is to determine whether there are spatially consistent matching points for the benchmark elevation point in other 3D laser survey station scan datasets. If the elevation deviation between the intersection point and the corresponding point in the other dataset is within the allowable error range, a spatial matching point is considered to exist. This verification process is a key step in ensuring the accuracy of terrain consistency calibration and helps improve the reliability and accuracy of measurement data.

[0043] Specifically, the three-dimensional reference coordinates of the reference elevation point refer to the specific position coordinates of the reference elevation point in the unified coordinate system, which provides the starting point for constructing the spatial projection ray. The laser scanning elevation parameter refers to the inclination angle of the laser beam relative to the horizontal plane during the scanning process of the three-dimensional laser measuring station. This parameter determines the direction of the spatial projection ray. The stockpile-base composite model is a model that combines the three-dimensional model of the stockpile with the three-dimensional model of the waste dump base, and is 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, the contact position of the ray with 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, and the measurement allowable error threshold is a preset error range used to determine whether the elevations of two points are close enough, thereby determining whether there is a spatial matching point.

[0044] Preferably, when verifying spatial matching points, the operating steps can be further refined. 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 parameters. Then, the coordinates of the intersection of the ray and the surface of the stockpile-base composite model are calculated using a geometric algorithm. When determining whether a spatial matching point exists, the calculated intersection coordinates can be compared with the coordinates of the corresponding point in another data set. If the elevation deviation value is less than a preset measurement allowable error threshold, such as 0.1 meter, it is considered that a spatial matching point exists.

[0045] Furthermore, to improve verification accuracy, factors such as the laser station's scanning accuracy and the model's resolution can be taken into account when calculating intersection coordinates, allowing for more precise determination of matching points. This process ensures accurate terrain consistency calibration and improves the reliability of the measurement data.

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

[0047] It should be noted that the operation of updating elevation coordinate points is performed in the presence of spatial matching points in order to improve the accuracy of the elevation data. Specifically, a 3D calibration cube is constructed with the surface intersection coordinates as the geometric center within the 3D laser station scan dataset where the spatial matching points exist. The point cloud data within the 3D calibration cube is then resampled using inverse distance weighted interpolation to generate calibrated elevation coordinate points. This process helps reduce measurement errors and ensures the accuracy of elevation data, which is crucial for subsequent terrain analysis and volume calculations.

[0048] Specifically, the three-dimensional calibration cube is a three-dimensional spatial area centered on the surface intersection coordinates, and its size can be determined based on the measurement accuracy requirements and the density of the point cloud data. The inverse distance weighted interpolation method is a commonly used geospatial interpolation method that assigns weights based on the distance between the point and the point to be interpolated. The closer the point, the greater the impact on the interpolation result. In this embodiment, the method is used to resample the point cloud data within the three-dimensional calibration cube to generate more accurate elevation coordinate points. The surface intersection coordinates are the coordinates of the point where the spatial projection ray intersects the surface of the stockpile-base composite model, which is the reference position for constructing the three-dimensional calibration cube. In this way, it can be ensured that the update of the elevation coordinate points is based on the accurate information of the surrounding point cloud data, thereby improving the reliability of the data.

[0049] Preferably, when constructing a three-dimensional calibration cube, an appropriate side length can be selected according to the measurement accuracy requirements. For example, if the measurement accuracy requirements are high, a smaller side length, such as 0.5 meters, can be selected. When performing inverse distance weighted interpolation, an appropriate weight factor 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: first, determine all the point cloud data points in the three-dimensional calibration cube; then, calculate the weight of each point based on the distance between the point and the surface intersection; finally, perform a weighted average of the elevation values ​​of these points based on the weights to obtain the calibrated elevation coordinate points. This process can effectively reduce the elevation deviation caused by measurement errors or data noise, thereby improving the accuracy and reliability of the entire measurement system.

[0050] In some embodiments, the three-dimensional space coordinates are calculated by using a spatial triangulation algorithm, and the specific steps 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 stockpile; Extracting a set of terrain triangles associated with the internal points of the stockpile from the local triangulated surface; The geometric center of gravity coordinates of each triangle vertex are calculated and determined as the three-dimensional space coordinates of the internal points of the stack.

[0051] It's important to note that the process of calculating 3D coordinates using a spatial triangulation algorithm involves inputting the elevation coordinates of internal points into a Delaunay triangulation generator, constructing a local triangulated surface, extracting a set of terrain triangles associated with the internal points, and finally calculating the geometric barycentric coordinates of these triangle vertices as the 3D coordinates of the internal points. The goal of this process is to precisely determine the positions of internal points through 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 a triangulated mesh based on a set of points, so that the circumcircle of each triangle does not contain other points. A local triangulated surface refers to a triangulated mesh generated around a point inside the stockpile, which reflects the terrain characteristics around the point. The terrain triangle patch set is a set of triangles directly related to the stockpile internal points extracted from the local triangulated mesh. The vertices of these triangles are used to calculate the geometric barycentric coordinates of the stockpile internal points. The geometric barycentric coordinates refer to the relative position of a point in a triangle, which can be determined by calculating the average coordinates of the triangle vertices. This process involves geometric processing of the point cloud data to ensure that the coordinates of each stockpile internal point accurately reflect its position in three-dimensional space.

[0053] Preferably, when constructing a local triangulated surface, the input elevation coordinate points can be pre-processed first, such as removing outliers or performing smoothing, to improve the quality of the triangulated network. When extracting a set of terrain triangles, the triangles closest to the point can be selected based on the spatial relationship between the internal point of the stockpile and the triangle vertex. When calculating the geometric centroid coordinates, a weighted average method can be used to assign different weights according to the distance between the triangle vertex and the internal point of the stockpile, so as to more accurately determine the three-dimensional space coordinates of the internal point of the stockpile. For example, if a stockpile internal point is close to the vertex of a triangle, the coordinates of the vertex should be given a greater weight when calculating the centroid coordinates. This process can further improve the calculation accuracy of the three-dimensional space coordinates and provide more accurate data support for the subsequent three-dimensional model construction.

[0054] In some embodiments, the calculation formula of the geometric center of gravity coordinates is:

[0055] in, Represents the three-dimensional space coordinates of the internal point of the stockpile, n represents the number of triangles associated with the internal point of the stockpile, Represents the coordinates of the i-th triangle vertex.

[0056] It should be noted that the geometric centroid coordinates are calculated by averaging the coordinates of the triangle vertices associated with the internal points of the stockpile. This method, based on geometric principles, uses averaging to obtain a representative position of a point in space. In three-dimensional space, the geometric centroid can be regarded as the equilibrium point of a collection of points, which effectively reflects the concentration trend of these points. Calculating the coordinates of internal points of the stockpile in this way ensures that the positions of these points in the 3D model are more accurate, thereby improving the accuracy and reliability of the entire model.

[0057] Specifically, a stockpile interior point is a point located within the stockpile volume. The coordinates of these points must be calculated to determine their exact location in three-dimensional space. The triangles associated with these stockpile interior points are the triangles in the local triangulation mesh that contain the stockpile interior point. The vertex coordinates of these triangles are known, and the geometric centroid coordinates of the stockpile interior point can be obtained by calculating the average of these vertex coordinates. This average calculation involves adding the coordinates of all associated triangle vertices and 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), the geometric centroid coordinates of the stockpile interior point 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 stockpile interior point's location.

[0058] Preferably, when calculating the geometric center of gravity coordinates, the weights of the vertices can be further considered. For example, if a vertex has a closer spatial relationship with an internal point of the stockpile, the vertex can be given a greater weight. The setting of the weight can be determined based on the distance, angle or other geometric features between the vertex and the internal point of the stockpile. In actual operation, the geometric center of gravity coordinates can be calculated by taking a weighted average of the vertex coordinates. For example, if the weight of vertex A is the largest, the weight of vertex B is the second largest, and the weight of vertex C is the smallest, then the geometric center of gravity coordinates of the internal point of the stockpile can be determined by taking a weighted average of the coordinates of these vertices according to their weights.

[0059] Furthermore, to improve calculation accuracy and efficiency, vertex coordinates can be preprocessed before calculation, such as removing outliers or performing smoothing. These optimization measures can more accurately determine the 3D coordinates of points within the stockpile, providing more reliable data support for subsequent 3D model construction and analysis.

[0060] In some embodiments, the steps of matching elevation points are as follows: Perform point cloud density normalization on L 3D laser station scanning data sets to generate a uniform resolution point cloud set. The iterative closest point algorithm is used to perform registration processing on the uniform resolution point cloud set; Extract the point set that meets the curvature consistency condition and use it as the surface feature points; Through the kd-tree spatial indexing technology, an accurate correspondence between surface feature points in multiple 3D laser station scanning data sets is established.

[0061] It should be noted that the elevation point matching step ensures accurate spatial correspondence between the point cloud data obtained from different 3D laser scanning stations. This process first normalizes the point cloud density of the scanned datasets from each station to achieve uniform resolution across all stations, facilitating subsequent processing. Next, the normalized point cloud data are registered using an iterative closest point algorithm to align the data from different stations. Points that meet the curvature consistency criteria are then extracted as surface feature points. These points are typically located at locations with significant terrain changes, such as edges or corners. Finally, a k-d tree spatial indexing technique is used to establish a precise correspondence between these surface feature points across the scanned datasets from multiple stations, enabling elevation point matching. This process is crucial for constructing an accurate 3D terrain model.

[0062] Specifically, point cloud density normalization processing refers to adjusting the density of point cloud data so that the data scanned by different stations have the same resolution. This processing can eliminate the difference in point cloud density caused by different station positions and scanning angles. The iterative closest point algorithm is a commonly used point cloud registration algorithm. It iteratively finds the best match between two sets of point clouds to align data from different stations. The curvature consistency condition means that the curvature of the points is similar within a certain range, and is usually used to identify the edges or feature points of the terrain. The k-d tree spatial indexing technology is an efficient spatial data structure used to quickly find and match points in space. Through these technologies, the correspondence between the scanning data of different stations can be accurately established.

[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 closest point algorithm, an initial matching threshold can be set. When the matching error between two sets of point clouds is less than the threshold, they are considered to be aligned. For extracting surface feature points, the parameters of the curvature consistency condition can be adjusted according to the specific characteristics of the terrain. For example, in areas with more 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 branching factor of the tree. Through these optimization measures, the accuracy and efficiency of elevation point matching can be improved, providing a more reliable data foundation for subsequent three-dimensional terrain modeling.

[0064] The above embodiments of the present invention have the following beneficial effects: 1. Multi-station synchronous layered scanning technology combined with the elevation point matching algorithm can eliminate the blind spot problem of traditional single-station scanning. Through the collaborative operation of L 3D laser measurement 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 stockpile volume calculation meets the requirements of engineering measurement standards.

[0065] 2. Digital elevation model fusion technology combined with the terrain consistency calibration process can solve the industry problem of inconsistent coordinate systems for multi-source data. By building a unified reference coordinate system and adopting a spatial projection constraint verification mechanism, seamless fusion of data from L scanning stations and the base model can be achieved. Its calibration cube resampling method can control the model splicing error within a small range, providing an accurate spatial reference for the stockpile-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 and the volume calculation process of the voxel difference method can achieve fully automated data processing, which can shorten the operation time compared with traditional measurement methods.

[0067] like Figure 2 As shown, some embodiments provide a system for measuring the volume of a waste dump in layers, the system comprising: 3D laser scanning array 201: comprising L 3D laser measuring stations equipped with multi-echo detection modules, synchronously collecting layered terrain point cloud data according to preset elevation intervals; Point cloud pre-processing module 202: used to perform denoising and filtering and coordinate unification processing on the collected layered terrain point cloud data, and output a standardized 3D laser station scanning data set; Stockpile model calculation engine 203: Its main functions include: constructing a layered stockpile three-dimensional model based on the above-mentioned method for measuring the capacity of the waste dump stockpile; conducting uncertainty analysis on the volume calculation results based on the Monte Carlo method; and outputting a waste dump stockpile capacity report including a confidence interval.

[0068] It is understandable that the modules recorded in the waste dump storage volume layer measurement system are consistent with the reference Figure 1 The steps in the method for measuring the volume of a waste dump stockpile by layers correspond to each other. Therefore, the operations, features, and beneficial effects described above for the method for measuring the volume of a waste dump stockpile by layers are also applicable to the system for measuring the volume of a waste dump stockpile by layers and the modules contained therein, and will not be repeated here.

[0069] Reference below Figure 3 , which shows a schematic structural diagram 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, laptop computers, 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 only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

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

[0071] Typically, the following devices may be connected to the I / O interface 305: an input device 306 including, for example, a touch screen, a touchpad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 308 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 309. The communication device 309 may allow the electronic device 300 to communicate with other devices wirelessly or by wire to exchange data. Figure 3 The electronic device 300 is shown with various devices, but 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 instead. Figure 3 Each block shown in the figure may represent one device, or may represent multiple devices as needed.

[0072] Furthermore, the storage medium of the embodiment of the present application stores program instructions that can implement all the above methods, wherein the program instructions can be stored in the above storage medium in the form of a software product, including a number of instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) or a processor to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or a terminal device such as a computer, server, mobile phone, or tablet.

[0073] The above descriptions merely illustrate some preferred embodiments of the present invention and the underlying technical principles. Those skilled in the art should understand that the scope of the invention encompassed by the embodiments of the present invention is not limited to technical solutions formed by specific combinations of the aforementioned technical features. It also encompasses other technical solutions formed by any combination of the aforementioned technical features or their equivalents, without departing from the aforementioned inventive concept. For example, a technical solution formed by replacing the aforementioned features with (but not limited to) technical features with similar functions disclosed in the embodiments of the present invention.

Claims

1. A method for measuring the volume of a waste dump in layers, characterized in that: The method comprises: Using a scanning array consisting of L 3D laser stations, the surface of the waste dump is synchronously layered and scanned at preset elevation intervals to obtain multiple sets of terrain point cloud data. Each set of terrain point cloud data is composed of L 3D laser station scanning data sets. For each level of the L 3D laser station scan datasets, perform the following operations in sequence to calculate the volume of the corresponding level: Perform elevation point matching on L 3D laser station scan datasets, accurately locate the K 3D laser station scan datasets corresponding to each surface feature point, and identify the K elevation coordinate points in these K 3D laser station scan datasets that match the surface feature points; Using digital elevation model fusion technology, the L 3D laser station scanning datasets were deeply fused with a pre-built 3D model of the spoil dump base. This established a precise relationship between the spatial coordinate systems of the L 3D laser station scanning datasets and the unified reference coordinate system of the 3D spoil dump base model. The 3D reference coordinates of the L 3D laser station locations were then determined based on this unified reference coordinate system. Surface feature points are classified and processed to screen out stockpile boundary points and stockpile internal points; a stockpile contour grid model is constructed based on the stockpile boundary points; The stockpile contour grid model and the 3D model of the spoil dump base are spatially superimposed to generate a stockpile-base composite model. This stockpile-base composite model is then used to calibrate the terrain consistency of K elevation coordinate points and K 3D laser station scanning datasets corresponding to each internal point of the stockpile, obtaining J calibrated elevation coordinate points and J 3D laser station scanning datasets. Based on the J elevation coordinate points corresponding to each internal point of the stockpile and J 3D laser station scanning data sets, the 3D spatial coordinates of each internal point of the stockpile are calculated using a spatial triangulation algorithm. The 3D spatial coordinates of all internal points of the stockpile are integrated with the stockpile contour grid model to construct a layered 3D stockpile model. The total storage capacity of the waste dump is calculated based on the volume difference of the multi-level layered storage three-dimensional model.

2. The method for measuring the volume of a waste dump according to claim 1, characterized in that: The total storage capacity of the waste dump is calculated based on the volume difference of the multi-level layered storage 3D model, including: Implementing a volume rasterization process on each layered stockpile 3D model to generate a set of spatial voxels of the stockpile at that level; Combining the vertical projection overlap rate of the voxel sets of the adjacent layers of the stockpile space and the elevation interval between layers, the voxel difference method is used to calculate the volume of the stockpile at each layer. The cumulative integral operation is performed on the stockpile volumes of all levels to ultimately obtain the total stockpile capacity of the waste dump.

3. The method for measuring the volume of a waste dump according to claim 2, characterized in that: The elevation interval between the layers is determined based on the scanning layer thickness parameter of the 3D laser survey station and satisfies the following relationship: in, Indicates the elevation interval value, Indicates the maximum storage height of the waste dump.

4. The method for measuring the volume of a waste dump according to claim 1, wherein: The specific process of the terrain consistency calibration is as follows: For each stockpile interior point, do the following: A benchmark elevation point is selected from the K elevation coordinate points, and the remaining K-1 3D laser station scanning datasets other than the 3D laser station scanning dataset corresponding to the benchmark elevation point are integrated into an elevation calibration queue; Traverse each 3D laser station scan dataset in the elevation calibration queue, and for each traversed 3D laser station scan dataset, use the spatial projection constraint condition to verify whether the benchmark elevation point has a spatial matching point in the 3D laser station scan dataset; if so, retain the 3D laser station scan dataset and synchronously update its corresponding elevation coordinate point; otherwise, remove the 3D laser station scan dataset from the 3D laser station scan dataset set corresponding to the internal point in the storage, and remove the corresponding elevation coordinate point at the same time.

5. The method for measuring the volume of a waste dump according to claim 4, characterized in that: Use spatial projection constraints to verify whether the benchmark elevation point has a spatial matching point in the 3D laser station scanning dataset, including: Based on the three-dimensional coordinates of the benchmark elevation point and the laser scanning elevation angle parameters of the three-dimensional laser survey station, a spatial projection ray corresponding to the benchmark elevation point is constructed; Calculate the coordinates of the intersection of the spatial projection ray and the surface of the deposit-base composite model; If the elevation deviation between the surface intersection coordinates and the corresponding point coordinates in another 3D laser station scanning data set is less than a preset measurement allowable error threshold, it is determined that a spatial matching point exists.

6. The method for measuring the volume of a waste dump according to claim 4, characterized in that: The synchronous updating of the corresponding elevation coordinate points includes: In the 3D laser station scanning data set where the spatial matching point exists, a 3D calibration cube is constructed with the surface intersection coordinates as the geometric center; The inverse distance weighted interpolation method is used to resample the point cloud data in the 3D calibration cube to generate calibrated elevation coordinate points.

7. The method for measuring the capacity of a waste dump according to claim 1, characterized in that: The three-dimensional spatial coordinates of each internal point of the stockpile are calculated by a spatial triangulation algorithm, including: 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 stockpile; Extracting a set of terrain triangles associated with the internal points of the stockpile from the local triangulated surface; The geometric center of gravity coordinates of each triangle vertex are calculated and determined as the three-dimensional space coordinates of the internal points of the stack.

8. The method for measuring the volume of a waste dump according to claim 7, characterized in that: The calculation formula of the geometric center of gravity coordinates is: in, Represents the three-dimensional space coordinates of the internal point of the stockpile, n represents the number of triangles associated with the internal point of the stockpile, Represents the coordinates of the i-th triangle vertex.

9. The method for measuring the volume of a waste dump according to claim 1, characterized in that: The steps of elevation point matching are as follows: Perform point cloud density normalization on L 3D laser station scanning data sets to generate a uniform resolution point cloud set. The iterative closest point algorithm is used to perform registration processing on the uniform resolution point cloud set; Extract the point set that meets the curvature consistency condition and use it as the surface feature points; Through the kd-tree spatial indexing technology, an accurate correspondence between surface feature points in multiple 3D laser station scanning data sets is established.

10. An intelligent calculation system for the storage capacity of a waste dump, characterized in that: The system comprises: 3D laser scanning array: It includes L 3D laser measuring 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 denoising and filtering on the collected layered terrain point cloud data and coordinate unification, and output a standardized 3D laser station scanning data set; Stockpile model calculation engine: Its main functions include: Constructing a three-dimensional model of a layered dump according to the layered measurement method of the dump volume in claims 1 to 9; Uncertainty analysis of volume calculation results was conducted based on Monte Carlo method; Outputs a report on the dump storage capacity including confidence intervals.

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