Method, device and equipment for determining surface volume of material stack, medium and product

Abstract

The invention provides a method, device and equipment for determining the volume of a material pile surface, a medium and a product. The method is applied to the field of mining electric shovel excavation volume prediction. The method comprises the following steps: determining the type of a material stack surface based on obtained initial point cloud data of the material stack surface; performing grid division on the initial point cloud data corresponding to the material heap surface type to obtain a grid corresponding to the material heap surface type; projecting a grid corresponding to the material stack surface type to a first plane to obtain a projected grid; the first plane represents a plane formed by a first dimension and a second dimension, and the first dimension is perpendicular to the second dimension; and determining the volume of the material stack surface based on the grid corresponding to the type of the material stack surface and the projected grid.

Description

Technical Field

[0001] The embodiments of the present application relate to, but are not limited to, the field of predicting the excavation volume of mining electric shovels, and relate to, but are not limited to, a method, device, equipment, medium and product for determining the volume of a material pile surface. Background Art

[0002] In the related art, mainly the calculation of the excavation volume of the material in the bucket after the mining electric shovel finishes excavation is carried out, and it is impossible to effectively and accurately predict the volume of the material in the bucket during the excavation process for different material pile surface morphologies (concave pile surface, convex pile surface, stepped pile surface, etc.). Summary of the Invention

[0003] The embodiments of the present application provide a method, device, equipment, medium and product for determining the volume of a material pile surface.

[0004] The technical solution of the embodiments of the present application is implemented as follows: In a first aspect, the embodiments of the present application provide a method for determining the volume of a material pile surface, the method including: determining the type of the material pile surface based on the acquired initial point cloud data of the material pile surface; performing grid division on the initial point cloud data corresponding to the type of the material pile surface to obtain a grid corresponding to the type of the material pile surface; projecting the grid corresponding to the type of the material pile surface onto a first plane to obtain a projected grid; the first plane represents a plane formed by a first dimension and a second dimension, and the first dimension is perpendicular to the second dimension; determining the volume of the material pile surface based on the grid corresponding to the type of the material pile surface and the projected grid.

[0005] In a second aspect, the embodiments of the present application provide a device for determining the volume of a material pile surface, the device including: A first determination module, configured to determine the type of the material pile surface based on the acquired initial point cloud data of the material pile surface; A division module, configured to perform grid division on the initial point cloud data corresponding to the type of the material pile surface to obtain a grid corresponding to the type of the material pile surface; A projection module, configured to project the grid corresponding to the type of the material pile surface onto a first plane to obtain a projected grid; the first plane represents a plane formed by a first dimension and a second dimension, and the first dimension is perpendicular to the second dimension; A second determination module, configured to determine the volume of the material pile surface based on the grid corresponding to the type of the material pile surface and the projected grid.

[0006] In a third aspect, the embodiments of the present application provide an electronic device, including a memory and a processor, the memory storing a computer program that can run on the processor, and the processor implementing some or all of the steps in the above method when executing the program.

[0007] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, some or all of the steps in the above method are implemented.

[0008] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program or instruction, and when the computer program or instruction is executed by a processor, some or all of the steps in the above method are implemented.

[0009] In the embodiments of the present application, on the one hand, the initial point cloud data of the material pile surface obtained can accurately reflect the three-dimensional shape of the material pile surface; on the other hand, for different types of material pile surfaces, by performing mesh division on the initial point cloud data corresponding to the material pile surface type, mesh division of different types of material pile surfaces can be achieved, which helps to effectively and accurately predict the volume of the material in the bucket during the excavation process. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings, where: Figure 1 is a schematic flowchart of the implementation of a method for determining the volume of a material pile surface provided by an embodiment of the present application; Figure 2 is a schematic diagram of a concave material pile surface provided by an embodiment of the present application; Figure 3 is a schematic diagram of a convex material pile surface provided by an embodiment of the present application; Figure 4 is a schematic diagram of a stepped material pile surface provided by an embodiment of the present application; Figure 5 is a schematic flowchart of the implementation of a dynamic excavation volume calculation method based on the finite element method provided by an embodiment of the present application; Figure 6 is a schematic diagram of the point cloud data after horizontal calibration provided by an embodiment of the present application; Figure 7 is a schematic diagram of the material pile surface mesh provided by an embodiment of the present application; Figure 8 is a schematic diagram of the excavated material provided by an embodiment of the present application; Figure 9 is a schematic diagram of a single finite element mesh provided by an embodiment of the present application; Figure 10 Schematic diagram of the composition structure of a device for determining the volume of a material pile surface provided by an embodiment of the present application; Figure 11 Schematic diagram of the hardware entity of an electronic device provided by an embodiment of the present application. Detailed implementation manners

[0011] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Apparently, the described embodiments are some, but not all, of the embodiments of the present application. The following embodiments are used to illustrate the present application, but are not used to limit the scope of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without making creative efforts shall fall within the protection scope of the present application.

[0012] In the following description, reference is made to "some embodiments", which describe subsets of all possible embodiments. However, it can be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments, and can be combined with each other without conflict.

[0013] It should be noted that the terms "first / second / third" involved in the embodiments of the present application are only used to distinguish similar objects, and do not represent a specific order for the objects. It can be understood that "first / second / third" can be interchanged with a specific order or sequence when allowed, so that the embodiments of the present application described here can be implemented in an order other than that illustrated or described here.

[0014] Those skilled in the art of the present technology can understand that, unless otherwise defined, all terms (including technical terms and scientific terms) used here have the same meaning as the general understanding of those of ordinary skill in the art in the field to which the embodiments of the present application belong. It should also be understood that terms such as those defined in a general dictionary should be understood to have a meaning consistent with the meaning in the context of the prior art, and will not be interpreted with an idealized or overly formal meaning unless specifically defined as here.

[0015] An embodiment of the present application provides a method for determining the volume of a material pile surface. As Figure 1 shown, the method for determining the volume of a material pile surface may include the following steps S101 to step S104, where: Step S101, based on the acquired initial point cloud data of the material pile surface, determine the type of the material pile surface; Here, the material pile surface can be formed by piling up various materials (such as ores, coal, sand, etc.). The material pile surface can be regular or irregular. The initial point cloud data can be a set of unprocessed three-dimensional coordinate points of the material pile surface, which contains the three-dimensional shape information of the material pile surface. The material pile surface type can be classified into different types according to the shape of the material pile surface. For example, concave pile surface, convex pile surface, stepped pile surface, etc.

[0016] It should be noted that a concave pile surface refers to a material pile surface that presents a concave shape, that is, the highest point of the pile surface is not at the center or edge of the pile surface, but at a certain local position, while other positions are relatively lower. A convex pile surface refers to a material pile surface that presents a convex shape, that is, the highest point of the pile surface is located at the center or a relatively wide position of the pile surface, while other positions gradually decrease. A stepped pile surface refers to a material pile surface that presents a stepped change, that is, the pile surface presents multiple platforms or stepped structures at different heights.

[0017] In some embodiments, three-dimensional scanning technology is used to scan the material pile surface, so as to obtain the initial point cloud data of the material pile surface, and the initial point cloud data contains the three-dimensional shape information of the material pile surface. Then, based on the initial point cloud data, a specific algorithm is used to determine the type of the material pile surface.

[0018] Step S102, perform mesh division on the initial point cloud data corresponding to the material pile surface type to obtain the mesh corresponding to the material pile surface type; Here, mesh division can be to divide the continuous object surface into a series of small, interconnected mesh units. These mesh units can be in the shapes of triangles, quadrilaterals, etc.

[0019] In some embodiments, after determining the type of the material pile surface (such as concave pile surface, convex pile surface, stepped pile surface, etc.), for each type of material pile surface, perform mesh division on its corresponding initial point cloud data to obtain the mesh corresponding to the material pile surface type. The mesh corresponding to the material pile surface type can be in the shapes of triangles, quadrilaterals or other shapes, specifically depending on the mesh division algorithm used and the characteristics of the material.

[0020] Step S103, project the mesh corresponding to the material pile surface type onto the first plane to obtain the projected mesh; the first plane represents the plane formed by the first dimension and the second dimension, and the first dimension is perpendicular to the second dimension; Here, the first plane is a two-dimensional plane formed by two mutually perpendicular dimensions (the first dimension and the second dimension). These two dimensions can be any two independent parameters used to describe the position of the material pile surface, such as the X-axis and the Y-axis.

[0021] In some embodiments, the grid corresponding to the material heap surface type is projected onto a first plane to obtain a projected grid. Since the first plane is a two-dimensional plane, the projected grid is a two-dimensional grid, but it retains the shape and characteristics of the material heap surface from a specific perspective (XOY).

[0022] Step S104, determine the volume of the material heap surface based on the grid corresponding to the material heap surface type and the projected grid.

[0023] In some embodiments, a specific algorithm or method is used to calculate the volume of the material heap surface according to the grid corresponding to the material heap surface type and the projected grid. The calculation method can be integration, summation, or other mathematical methods to accurately estimate the volume of the material heap surface.

[0024] In the embodiments of the present application, on the one hand, the initial point cloud data of the material heap surface obtained can accurately reflect the three-dimensional shape of the material heap surface; on the other hand, for different types of material heap surfaces, by performing grid division on the initial point cloud data corresponding to the material heap surface type, grid division of different types of material heap surfaces can be achieved, which helps to effectively and accurately predict the volume of the material in the bucket during the excavation process.

[0025] In some embodiments, the implementation of step S104, "determine the volume of the material heap surface based on the grid corresponding to the material heap surface type and the projected grid", may include the following steps S111 to S114, where: Step S111, project each vertex in the grid corresponding to the material heap surface type onto the electric shovel excavation trajectory curve to obtain the third-dimensional coordinate value of each projected vertex; Here, the electric shovel excavation trajectory curve can be the movement trajectory of the electric shovel when excavating the material heap. The third-dimensional coordinate value can be the coordinate value in the height (such as the Z-axis).

[0026] In some embodiments, each vertex in the grid corresponding to the material heap surface type is projected onto the electric shovel excavation trajectory curve. After projection, each vertex will obtain a coordinate value in the third dimension (such as the Z-axis).

[0027] Step S112, determine the area of the projected grid based on the first-dimensional coordinate value and the second-dimensional coordinate value of each vertex in the projected grid; In some embodiments, the projected grid consists of multiple vertices, each having coordinate values in a first dimension (such as the X-axis) and a second dimension (such as the Y-axis). Next, the coordinate values of these vertices in the first and second dimensions are used to calculate the area of the projected grid. This typically involves geometric algorithms, such as the formula for calculating the area of a polygon. For a polygon grid formed by connecting straight line segments, the area of the polygon grid is obtained by dividing the polygon into multiple triangles, calculating the area of each triangle separately, and then summing them up.

[0028] Step S113: Based on the third-dimensional coordinate values of the vertices after projection, the third-dimensional coordinate values of the vertices in the grid corresponding to the material pile surface type, and the area of the projected grid, determine the prism volume of the grid corresponding to the material pile surface type; In some embodiments, for the area between each small region (the polygon surface formed by adjacent vertices) in the material pile surface grid and the corresponding projected grid, it can be regarded as a prism. The base of the prism is a small polygon in the projected grid, and the height of the prism is the third-dimensional coordinate value obtained after projecting the vertices of the polygon onto the curve of the electric shovel excavation trajectory. By multiplying the area of each small polygon by the corresponding height, the volume of each small prism can be obtained.

[0029] Step S114: Add up the prism volumes of the grids corresponding to the material pile surface type to obtain the volume of the material pile surface.

[0030] In some embodiments, the volumes of all small prisms are added up to obtain the total volume of the entire material pile surface.

[0031] In the embodiments of the present application, on the one hand, by projecting the vertices in the grid corresponding to the material pile surface type onto the curve of the electric shovel excavation trajectory, the change in height of the material pile surface can be captured more accurately, thereby obtaining accurate third-dimensional coordinate values, which helps to improve the accuracy of volume calculation; on the other hand, by dividing the material pile surface into multiple prisms, the complex volume calculation problem is simplified into a more intuitive geometric problem, which can flexibly adapt to various geometric shapes and make the volume calculation more accurate.

[0032] In some embodiments, the implementation of step S113 “Based on the third-dimensional coordinate values of the vertices after projection, the third-dimensional coordinate values of the vertices in the grid corresponding to the material pile surface type, and the area of the projected grid, determine the prism volume of the grid corresponding to the material pile surface type” may include the following steps S121 to S124, where: Step S121: Based on the first-dimensional coordinate values and the second-dimensional coordinate values of the vertices in the projected grid, determine the area of the projected grid; In some embodiments, the first - dimension coordinate values (usually X - values) and the second - dimension coordinate values (Y - values) of each vertex in the projected grid are used to calculate the area of the projected grid on the two - dimensional plane through a geometric algorithm, and the area of the projected grid will be used for subsequent volume calculations.

[0033] Step S122: Based on the third - dimension coordinate values of the projected vertices, determine the average value of the third - dimension coordinates of the projected vertices. Here, the third - dimension coordinate values of the projected vertices can be the average values of the coordinate values of the projected vertices in the third dimension, and can be used to approximately represent the average height of the projected vertices in the third dimension.

[0034] In some embodiments, in addition to having coordinate values in the first and second dimensions, each projected vertex also has a coordinate value in the third dimension (Z - axis). Calculate the average value of the third - dimension coordinate values of the projected vertices, and this average value will be used as the average height when calculating the volume of each prism in subsequent calculations.

[0035] It should be noted that if the grid corresponding to the material pile surface type is a triangular grid, then the projected grid formed after projecting the grid corresponding to the material pile surface type onto the first plane is also triangular, and the shape formed by the projected vertices obtained after projecting each vertex in the grid corresponding to the material pile surface type onto the electric shovel excavation trajectory curve is also triangular. Next, taking the grid corresponding to the material pile surface type as △abc, and the triangle formed by the projected vertices obtained after projecting each vertex in the grid corresponding to the material pile surface type onto the electric shovel excavation trajectory curve as △a 1 b 1 c 1 , and the projected grid is △a 0 b 0 c 0 as an example for illustration.

[0036] In some embodiments, if the electric shovel excavation trajectory curve is T(X, Y), the coordinates of vertex a in △abc are (X 1 , Y 1 , Z 1 ), the coordinates of vertex b are (X 2 , Y 2 , Z 2 ), and the coordinates of vertex c are (X 3 , Y 3 , Z 3 ), then after projecting vertex a, vertex b, and vertex c onto T(X, Y) respectively, the third - dimension coordinate values of vertex a 1 , vertex b 1 , and vertex c 1 are respectively , , 。

[0037] In some embodiments, the average value of the third - dimension coordinates of each vertex after projection is determined by the following formula (1): (1); Wherein, represents the average value of the third - dimension coordinates of each vertex after projection, represents the third - dimension coordinate value of vertex a after projecting vertex a onto T(X, Y), 1 of vertex a, represents the third - dimension coordinate value of vertex b after projecting vertex b onto T(X, Y), 1 of vertex b, represents the third - dimension coordinate value of vertex c after projecting vertex c onto T(X, Y), 1 of vertex c.

[0038] Step S123: Based on the third - dimension coordinate values of each vertex in the grid corresponding to the material pile surface type, determine the average value of the third - dimension coordinates of each vertex in the grid corresponding to the material pile surface type; Here, the average value of the third - dimension coordinates of each vertex in the grid corresponding to the material pile surface type can be the average value of the coordinate values of each vertex in the third dimension in the grid corresponding to the material pile surface type, and can be used to approximately represent the average height of each vertex in the third dimension in the grid corresponding to the material pile surface type.

[0039] In some embodiments, the average value of the third - dimension coordinates of each vertex in the grid corresponding to the material pile surface type is determined by the following formula (2): (2); Wherein, represents the average value of the third - dimension coordinates of each vertex in the grid corresponding to the material pile surface type, represents the third - dimension coordinate value of vertex a, represents the third - dimension coordinate value of vertex b, represents the third - dimension coordinate value of vertex c.

[0040] Step S124: Based on the area of the projected grid, the average value of the third - dimension coordinates of each vertex after projection, and the average value of the third - dimension coordinates of each vertex in the grid corresponding to the material pile surface type, determine the prism volume of the grid corresponding to the material pile surface type.

[0041] In some embodiments, the projected grid is divided into multiple small regions (each small region can be regarded as the bottom surface of a prism). Then, for each small region (the bottom surface of the prism), the prism volume of the grid corresponding to the material pile surface type is calculated using its area, the average value of the third-dimensional coordinates of each vertex after projection, and the average value of the third-dimensional coordinates of each vertex in the grid corresponding to the material pile surface type.

[0042] In some embodiments, the prism volume of the grid corresponding to the material pile surface type is determined by the following formula (3): (3); where represents the prism volume of the grid corresponding to the material pile surface type, represents the volume of a triangular prism of, represents the volume of a triangular prism of, represents △a 0 b 0 c 0 area.

[0043] In the embodiments of the present application, according to the area of the projected grid and the coordinate values of the vertices in the third dimension, the prism volume of the grid corresponding to the material pile surface type can be calculated more accurately, which helps to more accurately evaluate the volume of the material pile.

[0044] In some embodiments, the implementation of step S121, "Based on the first-dimensional coordinate values and the second-dimensional coordinate values of the vertices in the projected grid, determine the area of the projected grid", may include the following steps S131 to S133, where: Step S131, based on the first-dimensional coordinate values and the second-dimensional coordinate values of the vertices in the projected grid, determine the lengths of the three sides of the projected grid; In some embodiments, taking the projected grid as △a 0 b 0 c 0 as an example, the coordinates of vertex a 0 b 0 c 0 in △a 0 are (X 1 , Y 1 , 0), the coordinates of vertex b 0 are (X 2 , Y 2 , 0), and the coordinates of vertex c 0 are (X 3 , Y 3 , 0).

[0045] Determine the lengths of the three sides of the projected grid through the following formulas (4) to (6): (4); (5); (6); Wherein, represents the length of side a 0 b 0 side, represents the length of side a 0 c 0 side, represents the length of side b 0 c 0 side.

[0046] Step S132, determine the semi-perimeter of the projected grid based on the lengths of the three sides of the projected grid; In some embodiments, determine the semi-perimeter of the projected grid through the following formula (7): (7); Wherein, represents the semi-perimeter of the projected grid.

[0047] Step S133, determine the area of the projected grid based on the lengths of the three sides and the semi-perimeter.

[0048] In some embodiments, determine the area of the projected grid through the following formula (8): (8); Wherein, represents the area of the projected grid.

[0049] In the embodiments of the present application, by calculating the lengths of the three sides and the semi-perimeter of the grid and then applying geometric formulas to obtain the area, the area of the projected grid is made more accurate.

[0050] In some embodiments, the implementation of step S102 "perform grid division on the initial point cloud data corresponding to the material pile surface type to obtain the grid corresponding to the material pile surface type" may include the following steps S141 and S142, wherein: Step S141, preprocess the initial point cloud data corresponding to the material pile surface type to obtain preprocessed point cloud data; Here, preprocessing is a series of operations performed on the initial point cloud data, aiming to improve the data quality and prepare for the subsequent grid division step. Preprocessing may include operations such as horizontal calibration, filtering, downsampling, and projection transformation.

[0051] In some embodiments, the initial point cloud data corresponding to the material pile surface type is preprocessed so that the initial point cloud data is converted into a form with higher quality and easier to mesh.

[0052] Step S142, perform meshing on the preprocessed point cloud data to obtain the mesh corresponding to the material pile surface type.

[0053] In some embodiments, perform meshing on the preprocessed point cloud data so that the preprocessed point cloud data is converted into a polygon mesh, thereby obtaining the mesh corresponding to the material pile surface type.

[0054] In the embodiments of the present application, by preprocessing the initial point cloud data corresponding to the material pile surface type, noise points and abnormal points can be removed, thereby improving the overall quality of the point cloud data, helping to improve the accuracy and reliability of meshing, and enabling the mesh obtained by meshing to better reflect the true shape of the material pile surface.

[0055] In some embodiments, the preprocessing includes horizontal calibration, filtering, downsampling, and projection transformation; the implementation of step S141 "perform preprocessing on the initial point cloud data corresponding to the material pile surface type to obtain the preprocessed point cloud data" may include the following steps S151 to S154, where: Step S151, perform horizontal calibration on the initial point cloud data corresponding to the material pile surface type to obtain the horizontally calibrated point cloud data; Here, horizontal calibration is the process of adjusting the points in the point cloud data to a unified horizontal reference plane.

[0056] In some embodiments, performing horizontal calibration on the initial point cloud data so that the point cloud data is adjusted to a unified horizontal reference plane can reduce the deviation of the point cloud data in the horizontal direction caused by factors such as the position of the scanning device and the inclination of the material pile surface, making the point cloud data more consistent in the horizontal direction.

[0057] Step S152, perform filtering on the horizontally calibrated point cloud data to obtain the filtered point cloud data; Here, filtering is the process of removing noise point clouds and abnormal point clouds from the point cloud data.

[0058] In some embodiments, performing filtering on the horizontally calibrated point cloud data can remove noise points and abnormal points, making the filtered point cloud data smoother.

[0059] Step S153, perform downsampling on the filtered point cloud data to obtain the downsampled point cloud data; Here, downsampling is the process of reducing the number of point clouds in the point cloud data while trying to maintain the overall characteristics and shape of the data.

[0060] In some embodiments, the filtered point cloud data is downsampled to reduce the number of points in the downsampled point cloud data while attempting to maintain the overall characteristics and shape of the data as much as possible.

[0061] Step S154, perform a projection transformation on the downsampled point cloud data to obtain the preprocessed point cloud data.

[0062] Here, the projection transformation is a process of mapping three-dimensional point cloud data onto a two-dimensional plane.

[0063] In some embodiments, performing a projection transformation on the downsampled point cloud data generally involves mapping the three-dimensional point cloud data onto a two-dimensional plane to obtain the preprocessed point cloud data.

[0064] In the embodiments of the present application, the horizontal calibration operation can eliminate the deviation of the point cloud data in the horizontal direction, making the data more accurate and consistent. The filtering operation can effectively remove noise points and outliers, improving the smoothness and clarity of the point cloud data. The downsampling operation can reduce the number of points in the point cloud, reducing the complexity of the data and the storage space requirements. The projection transformation operation can map the three-dimensional point cloud data onto a two-dimensional plane, making the preprocessed point cloud data convenient for subsequent processing.

[0065] In some embodiments, the implementation of step S151, "perform horizontal calibration on the initial point cloud data corresponding to the material heap surface type to obtain the horizontally calibrated point cloud data", may include the following steps S161 to S163, where: Step S161, obtain the roll angle and pitch angle of the initial point cloud data corresponding to the material heap surface type with respect to the horizontal plane; Here, the roll angle represents the left-right inclination degree of the point cloud data with respect to the horizontal plane. The pitch angle represents the front-back inclination degree of the point cloud data with respect to the horizontal plane.

[0066] In some embodiments, the roll angle and pitch angle of the initial point cloud data corresponding to the material heap surface type with respect to the horizontal plane are obtained by using an inclination sensor installed on the electric shovel.

[0067] Step S162, determine the transformation matrix of the initial point cloud data based on the roll angle and the pitch angle; Here, the transformation matrix can be a matrix used to transform point cloud data from one coordinate system to another coordinate system.

[0068] In some embodiments, the transformation matrix of the initial point cloud data is determined by the following formula (9): (9); Where, The transformation matrix representing the initial point cloud data, represents the roll angle, represents the pitch angle.

[0069] Step S163, using the transformation matrix to perform horizontal calibration on the initial point cloud data to obtain the horizontally calibrated point cloud data.

[0070] In some embodiments, the horizontally calibrated point cloud data is determined by the following formula (10): (10); where, , represents the horizontally calibrated point cloud data, , , represent the initial point cloud data.

[0071] In the embodiments of the present application, using the transformation matrix to perform horizontal calibration on the initial point cloud data can eliminate the roll angle and pitch angle between the initial point cloud data and the horizontal plane, so that the point cloud data obtained at different scanning angles is unified to a horizontal reference plane.

[0072] In some embodiments, the filtering includes direct filtering, radius filtering, and statistical filtering; the implementation of step S152 "filter the horizontally calibrated point cloud data to obtain the filtered point cloud data" may include the following steps S171 to S174, where: Step S171, perform a first direct filtering on the horizontally calibrated point cloud data to obtain the point cloud data after the first direct filtering; Here, direct filtering can be a filtering method based on the coordinate values of the point cloud data in a certain dimension (such as the X, Y, Z axes). By setting the dimension threshold range, the point cloud within the dimension threshold range can be retained, while the point cloud outside the dimension threshold is removed.

[0073] In some embodiments, performing a first direct filtering on the horizontally calibrated point cloud data to obtain the point cloud data after the first direct filtering can remove the noise or background points that deviate significantly from the main data range in a certain dimension.

[0074] Step S172, perform a second direct filtering on the point cloud data after the first direct filtering to obtain the point cloud data after the second direct filtering; In some embodiments, performing a second direct filtering on the point cloud data after the first direct filtering to obtain the point cloud data after the second direct filtering can remove the points that were not removed in the first direct filtering but still deviate in another dimension.

[0075] Step S173: Perform radius filtering on the point cloud data after the second pass-through filtering to obtain the point cloud data after radius filtering. Here, radius filtering can be a filtering method that screens based on the neighborhood distance of the point cloud. For each point cloud, calculate the distance from it to all point clouds within its neighborhood. If there are not enough point clouds within the neighborhood of a certain point cloud (i.e., less than the set threshold), then this point cloud will be considered a noise point and be removed.

[0076] In some embodiments, performing radius filtering on the point cloud data after the second pass-through filtering to obtain the point cloud data after radius filtering can remove isolated noise points or sparse regions, making the point cloud data denser and more regular.

[0077] Step S174: Perform statistical filtering on the point cloud data after the radius filtering to obtain the point cloud data after statistical filtering.

[0078] Here, statistical filtering is a filtering method that screens based on the statistical characteristics of the point cloud data. For each point cloud, calculate the average distance from it to all point clouds within its neighborhood, and judge whether this point is a noise point based on this average distance and its standard deviation. If the difference between the average distance of a point cloud and the average distance of the point clouds within its neighborhood is greater than the set multiple of the standard deviation, then this point cloud will be considered a noise point and be removed.

[0079] In some embodiments, performing statistical filtering on the point cloud data after the radius filtering to obtain the point cloud data after statistical filtering can remove those noise points with a large distance difference from the points within the neighborhood, making the point cloud data smoother and more accurate.

[0080] In the embodiments of the present application, through pass-through filtering, point clouds that deviate significantly in a specific dimension can be removed; through radius filtering and statistical filtering, isolated points and point clouds with a large difference from neighboring points can be further removed, which helps to improve the overall quality of the filtered point cloud data.

[0081] In some embodiments, the implementation of step S171, "Perform the first pass-through filtering on the point cloud data after the horizontal calibration to obtain the point cloud data after the first pass-through filtering", may include the following steps S181 and S182, where: Step S181: Determine the three-dimensional coordinate values of the point cloud data after the horizontal calibration. Here, each point in the point cloud data after the horizontal calibration has three-dimensional coordinate values, that is, the coordinate values in the three directions of the X-axis, Y-axis, and Z-axis.

[0082] In some embodiments, perform a preliminary analysis on the point cloud data after the horizontal calibration to obtain the three-dimensional coordinate values of each point cloud. The three-dimensional coordinate values describe the specific position of the point cloud in the three-dimensional space.

[0083] Step S182, filter out the horizontally calibrated point cloud data corresponding to the three-dimensional coordinate values less than the first preset threshold to obtain the first pass-filtered point cloud data.

[0084] Here, the first preset threshold can be a preset threshold for determining whether the point cloud in the point cloud data needs to be filtered out.

[0085] In some embodiments, first set the first preset threshold, which can be set for one or more of the X, Y, and Z axes. Then, for each point cloud in the horizontally calibrated point cloud data, compare its coordinate value on the specified axis with the first preset threshold. If the coordinate value of a certain point cloud on the specified axis is less than the first preset threshold, then this point cloud is considered to need to be filtered out, and the remaining point clouds constitute the first pass-filtered point cloud data.

[0086] In the embodiments of the present application, the first pass filtering filters out the point clouds with three-dimensional coordinate values less than the threshold by setting the threshold, thereby reducing the quantity of the point cloud data.

[0087] In some embodiments, the implementation of step S172 "perform a second pass filtering on the first pass-filtered point cloud data to obtain the second pass-filtered point cloud data" may include the following steps S191 and S192, where: Step S191, determine the first-dimensional coordinate value, the second-dimensional coordinate value, and the third-dimensional coordinate value of the first pass-filtered point cloud data; In some embodiments, further analyze the first pass-filtered point cloud data to obtain the coordinate values of each point cloud in three dimensions (the first dimension, the second dimension, and the third dimension).

[0088] Step S192, filter out the first pass-filtered point cloud data corresponding to the first-dimensional coordinate value greater than the preset first-dimensional threshold, the second-dimensional coordinate value greater than the preset second-dimensional threshold, and the third-dimensional coordinate value greater than the preset third-dimensional threshold to obtain the second pass-filtered point cloud data.

[0089] Here, the first-dimensional threshold represents twice the bucket width, the second-dimensional threshold represents the sum of the stick extension length and the bucket length, and the third-dimensional threshold represents the sum of the product of the stick extension length and the sine value of the angle between the boom and the horizontal line and the height from the saddle rotation center to the ground.

[0090] In some embodiments, for each point cloud, the coordinate values in the first dimension, second dimension, and third dimension are compared with corresponding thresholds. If the coordinate value of a certain point cloud in any dimension is greater than the corresponding threshold, then this point cloud is considered to be filtered out, and the remaining point clouds constitute the point cloud data after the second pass-through filtering.

[0091] In the embodiments of the present application, on the basis of the first pass-through filtering, the second pass-through filtering further filters out the points that do not meet the size or range requirements by setting more specific dimensional thresholds, which helps to reduce the quantity of the point cloud data.

[0092] In some embodiments, the implementation of step S173 "performing radius filtering on the point cloud data after the second pass-through filtering to obtain the point cloud data after radius filtering" may include the following step S1001 and step S1002, where: Step S1001: Taking each point cloud in the point cloud data after the second pass-through filtering as a central point cloud, determining the Euclidean distance between the central point cloud and other point clouds within a preset neighborhood distance from the central point cloud; In some embodiments, each point cloud in the point cloud data after the second pass-through filtering is regarded as a central point cloud. For each central point cloud, it is necessary to determine the Euclidean distance between it and other point clouds within the surrounding preset neighborhood.

[0093] In some embodiments, the Euclidean distance between the central point cloud and other point clouds within a preset neighborhood distance from the central point cloud is determined by the following formula (11): (11); Wherein, represents the Euclidean distance between the central point cloud and other point clouds within a preset neighborhood distance from the central point cloud, represents the central point cloud, represents other point clouds within a preset neighborhood distance from the central point cloud, , , represent the three-dimensional coordinate values of the central point cloud, , , represent the three-dimensional coordinate values of other point clouds within a preset neighborhood distance from the central point cloud.

[0094] Step S1002: Filtering out the central point clouds for which the Euclidean distance is greater than the second preset threshold to obtain the point cloud data after radius filtering.

[0095] Here, the second preset threshold can be a preset threshold for determining whether the Euclidean distance between the central point cloud and its surrounding point clouds is too large, so as to decide whether the central point cloud needs to be filtered out.

[0096] In some embodiments, for each central point cloud, if the Euclidean distance between it and other point clouds within a preset neighborhood is greater than a second preset threshold, then the central point cloud is considered isolated or does not meet the density requirement, and thus needs to be filtered out. The remaining points constitute the point cloud data after radius filtering.

[0097] In the embodiments of the present application, radius filtering effectively removes isolated points or noise points by calculating the Euclidean distance between the central point cloud and other point clouds within its preset neighborhood, and filtering out the central point clouds with a distance greater than the second preset threshold.

[0098] In some embodiments, the implementation of step S174, "performing statistical filtering on the point cloud data after radius filtering to obtain the point cloud data after statistical filtering", may include the following steps S1101 to S1105, where: Step S1101: Taking each point cloud in the point cloud data after radius filtering as the central point cloud, determining the Euclidean distance between the central point cloud and other point clouds within a preset neighborhood of the central point cloud; In some embodiments, taking each point in the point cloud data after radius filtering as the central point cloud, calculating the Euclidean distance between the central point cloud and other point clouds within its preset neighborhood.

[0099] Step S1102: Based on the Euclidean distance and the number of point clouds within the preset neighborhood, determining the average Euclidean distance of the point cloud data after radius filtering; In some embodiments, for each central point cloud, based on the Euclidean distance between it and the point clouds within the preset neighborhood, calculating the average of these distances. This average reflects the average spatial distance between the central point cloud and the point clouds within its neighborhood.

[0100] In some embodiments, the average Euclidean distance of the point cloud data after radius filtering is determined by the following formula (12): (12); Where: represents the average Euclidean distance of the point cloud data after radius filtering, represents the Euclidean distance between the central point cloud and other point clouds within a preset neighborhood of the central point cloud, represents the number of point clouds within the preset neighborhood.

[0101] Step S1103: Based on the average Euclidean distance of the point cloud data after radius filtering and the number of the point cloud data after radius filtering, determining the global neighborhood average of the point cloud data after radius filtering; In some embodiments, based on the mean Euclidean distance of all center point clouds and the number of point cloud data after radius filtering, the global neighborhood mean of the entire point cloud data is calculated. This global mean reflects the average spatial distribution characteristics of the entire point cloud data.

[0102] In some embodiments, the global neighborhood mean of the point cloud data after radius filtering is determined by the following formula (13): (13); Where, represents the global neighborhood mean of the point cloud data after radius filtering, represents the total number of point clouds.

[0103] Step S1104, based on the mean Euclidean distance of the point cloud data after radius filtering and the number of the point cloud data after radius filtering, determine the standard deviation of the point cloud data after radius filtering; In some embodiments, based on the mean Euclidean distance and the number of the point cloud data after radius filtering, the standard deviation of the point cloud data is calculated. The standard deviation is an important indicator for measuring the degree of data dispersion and reflects the fluctuation range of the mean Euclidean distance.

[0104] In some embodiments, the global neighborhood mean of the point cloud data after radius filtering is determined by the following formula (14): (14); Where, represents the global neighborhood mean of the point cloud data after radius filtering, represents the neighborhood variance, assuming that the mean Euclidean distance of the k-neighborhood of the point cloud follows a normal distribution, that is .

[0105] Step S1105, filter out the point cloud data after radius filtering for which the Euclidean norm of the difference between the mean Euclidean distance and the global neighborhood mean is greater than the product of the standard deviation and the third preset threshold, to obtain the point cloud data after statistical filtering.

[0106] Here, the third preset threshold can be a preset threshold for determining which center point clouds are abnormal.

[0107] In some embodiments, calculate the Euclidean norm of the difference between the mean Euclidean distance of each center point cloud and the global neighborhood mean, and then compare the Euclidean norm with the product value of the standard deviation and the third preset threshold. If the norm value is greater than the product value, it is considered that the center point cloud is abnormal and needs to be filtered out, and the remaining points constitute the point cloud data after statistical filtering.

[0108] In some embodiments, the filtered radius-filtered point cloud data is determined by the following formula (15): (15); wherein, represents the Euclidean norm of the difference between the mean Euclidean distance and the global neighborhood mean, represents the third preset threshold value.

[0109] In the embodiments of the present application, by comparing the Euclidean norm of the difference between the mean Euclidean distance of each central point cloud and the point cloud within its preset neighborhood and the global neighborhood mean with the product value of the standard deviation and the third preset threshold value, it is possible to identify and filter out the point cloud with a large difference from the overall data distribution, which helps to enhance the consistency of the point cloud data.

[0110] In some embodiments, the implementation of step S153 "downsample the filtered point cloud data to obtain the downsampled point cloud data" may include the following steps S1201 to S1204, wherein: Step S1201, determine the maximum coordinate value of the first dimension, the minimum coordinate value of the first dimension, the maximum coordinate value of the second dimension, the minimum coordinate value of the second dimension, the maximum coordinate value of the third dimension, and the minimum coordinate value of the third dimension of the filtered point cloud data; In some embodiments, the maximum coordinate value and the minimum coordinate value of the filtered point cloud data in the first dimension (X-axis), the second dimension (Y-axis), and the third dimension (Z-axis) are respectively found.

[0111] Step S1202, subtract the minimum coordinate value of the first dimension from the maximum coordinate value of the first dimension, subtract the minimum coordinate value of the second dimension from the maximum coordinate value of the second dimension, and subtract the minimum coordinate value of the third dimension from the maximum coordinate value of the third dimension, respectively, to obtain the first dimension range, the second dimension range, and the third dimension range of the filtered point cloud data; In some embodiments, the first dimension range, the second dimension range, and the third dimension range of the filtered point cloud data are determined by the following formula (16): (16); wherein, represents the first dimension range of the filtered point cloud data, represents the second dimension range of the filtered point cloud data, represents the third dimension range of the filtered point cloud data, represents the maximum coordinate value of the first dimension, represents the minimum coordinate value of the first dimension, represents the maximum coordinate value of the second dimension, Represents the minimum coordinate value of the second dimension, Represents the maximum coordinate value of the third dimension, Represents the minimum coordinate value of the third dimension.

[0112] Step S1203, based on the first dimension range, the second dimension range, the third dimension range, and a preset three-dimensional voxel grid side length, determine the size of the three-dimensional voxel grid of the filtered point cloud data; In some embodiments, the size of the three-dimensional voxel grid of the filtered point cloud data is determined by the following formula (17): (17); Wherein, 、 、 Represents the size of the three-dimensional voxel grid of the filtered point cloud data, Represents the preset three-dimensional voxel grid side length.

[0113] Step S1204, based on the size of the three-dimensional voxel grid, determine the downsampled point cloud data.

[0114] In the embodiments of the present application, according to the maximum coordinate value and the minimum coordinate value of the filtered point cloud data in each dimension, determine the range of the filtered point cloud data in each dimension, and according to the preset three-dimensional voxel grid side length, further determine the size of the three-dimensional voxel grid, which can quickly determine the overall distribution of the point cloud data.

[0115] In some embodiments, the implementation of step S154 "perform a projection transformation on the downsampled point cloud data to obtain the preprocessed point cloud data" may include the following steps S1301 to step S1303, where: Step S1301, determine the angle between the tangent plane normal vector of the downsampled point cloud data and the third dimension; Here, the tangent plane normal vector is the normal direction of the plane tangent to the downsampled point cloud data.

[0116] In some embodiments, it is necessary to determine the tangent plane normal vector of each point in the downsampled point cloud data. Then, calculate the angle between the tangent plane normal vector and the third dimension (Z-axis). This angle reflects the degree of inclination of the normal vector in the third dimension.

[0117] Step S1302, based on the angle between the tangent plane normal vector and the third dimension, determine the rotation matrix of the downsampled point cloud data; In some embodiments, the rotation matrix of the downsampled point cloud data is determined by the following formula (18):

[0118]

[0119] (18); Among them, 、 、 represent the rotation matrix of the downsampled point cloud data, 、 、 represent the angle between the tangent plane normal vector of the downsampled point cloud data and the third dimension.

[0120] Step S1303, perform a projection transformation on the downsampled point cloud data by using the rotation matrix to obtain the preprocessed point cloud data.

[0121] In some embodiments, perform a projection transformation on the downsampled point cloud data by using the calculated rotation matrix, so that the downsampled three-dimensional point cloud data is mapped to a two-dimensional plane to obtain the preprocessed point cloud data.

[0122] In the embodiments of the present application, through the projection transformation, complex three-dimensional point cloud data can be converted into a simpler form, thereby simplifying the subsequent processing flow. For example, in a three-dimensional reconstruction task, projecting the point cloud data onto a two-dimensional plane can simplify the reconstruction process.

[0123] Electric shovels for mines are widely used in the ore excavation operations of open-pit mines and play a key role in open-pit mining. Currently, mainly through an operator driving the electric shovel to complete four actions of "loading - slewing - unloading - returning", and cooperating with mining trucks to realize the transportation of ore materials. The randomness of manual operation of electric shovels will lead to a series of problems. The overall structure of the electric shovel is very large and the operation is complex. Different geological conditions result in complex and variable morphologies of the ore stockpile surfaces (concave stockpile surfaces, convex stockpile surfaces, stepped stockpile surfaces, etc.). It requires operators to have certain skills and experience. Different operators have different proficiency levels, resulting in overloading or underloading during the excavation of materials, causing low excavation efficiency and high failure rate of the electric shovel.

[0124] In addition, currently, mainly the calculation of the excavation volume of the materials in the bucket after excavation is completed, and it is impossible to obtain the excavation volume of the materials in the bucket at any time. Therefore, for different material stockpile surface morphologies, realizing the effective and accurate prediction of the volume of the materials in the bucket during the excavation process has very important engineering practical significance for both the electric shovel for mining operations and the mining truck for transporting materials.

[0125] Based on this, the embodiments of the present application provide a dynamic excavation volume calculation method based on the finite element method. First, the initial point cloud data of the material heap surface is obtained by scanning the material heap surface with a 3D lidar, and then the initial point cloud data is processed (including point cloud horizontal calibration, two-pass through filtering, radius filtering, statistical filtering, and point cloud downsampling processing). Then, the processed point cloud is subjected to a projection transformation (converting the point cloud data from a three-dimensional space to a two-dimensional plane), and finally, a triangular mesh is generated using MATLAB tools, and the dynamic excavation volume of the material in the bucket is calculated based on the triangular mesh. The embodiments of the present application comprehensively consider different types of material heap surfaces (concave, convex, stepped), and realize the real-time calculation of the excavated material volume in the bucket during the excavation process, providing technical support for the intelligent development of electric shovels in mines.

[0126] The embodiments of the present application provide a concave material heap surface, as Figure 2 shown.

[0127] The embodiments of the present application provide a convex material heap surface, as Figure 3 shown.

[0128] The embodiments of the present application provide a stepped material heap surface, as Figure 4 shown.

[0129] The embodiments of the present application provide a dynamic excavation volume calculation method based on the finite element method, as Figure 5 shown, including the following steps S501 to step S510, where: Step S501, obtain the initial point cloud data of the material heap surface; In some embodiments, a 3D lidar is installed on the boom of the electric shovel to scan the material heap surface to obtain the initial point cloud data of the material heap surface.

[0130] Step S502, perform horizontal calibration on the initial point cloud data to obtain the horizontally calibrated point cloud data; In some embodiments, since the ground in the mine is uneven, it may cause a certain side tilt angle δ and pitch angle λ error of the initial point cloud data relative to the horizontal plane, and horizontal calibration is required. The horizontally calibrated point cloud data is as Figure 6 shown.

[0131] In some embodiments, the transformation matrix of the initial point cloud data is determined by the following formula (9): (9); In some embodiments, the horizontally calibrated point cloud data is determined by the following formula (10): (10); where, , represents the horizontally calibrated point cloud data, , , represents the initial point cloud data.

[0132] Step S503: Perform two-pass filtering on the horizontally calibrated point cloud data to obtain the point cloud data after two-pass filtering; In some embodiments, the first pass filtering is used to filter out the ground noise points outside the excavation range. It is removed by setting the height threshold of the material. If the z value of the point cloud data is less than the minimum height, it is recognized as a ground point and discarded; if the z value is greater than the minimum height and less than the maximum height, it is recognized as a material surface point and retained. Similarly, in the width and length directions, by correspondingly setting the thresholds in the x and y directions, the outlier points can be quickly filtered out to achieve the purpose of the first-step rough processing.

[0133] In some embodiments, in order to further improve the efficiency of processing the point cloud, the second pass filtering is used to retain the point cloud within the excavable range of the bucket. As shown in the right formula shown, in the x-axis direction, with the center point of the bucket teeth as the symmetry point, retain the material point cloud within twice the width of the bucket; in the y-axis direction, retain the point cloud within the maximum length when the boom is fully extended; in the z-axis direction, retain the point cloud within the maximum height that the boom can lift. Among them, D is the width of the bucket, is the maximum length when the boom is fully extended, is the length of the bucket, H is the height from the saddle rotation center to the ground, and β is the angle between the boom and the horizontal line.

[0134] Step S504: Perform radius filtering on the point cloud data after two-pass filtering to obtain the point cloud data after radius filtering; In some embodiments, the Euclidean distance between the center point cloud and other point clouds within the preset neighborhood of the center point cloud is determined by the following formula (11): (11); In some embodiments, the Euclidean distance between the center point cloud and other point clouds within the preset neighborhood of the center point cloud is compared with the preset threshold and the center points greater than are filtered out.

[0135] Step S505: Perform statistical filtering on the point cloud data after radius filtering to obtain the point cloud data after statistical filtering; In some embodiments, based on the Euclidean distance between the center point cloud and other point clouds within the preset neighborhood of the center point cloud and the number of point clouds within the preset neighborhood, the average Euclidean distance of the point cloud data after radius filtering is determined.

[0136] In some embodiments, the Euclidean distance mean of the point cloud data after radius filtering is determined by the following formula (12): (12); In some embodiments, based on the Euclidean distance mean of the point cloud data after radius filtering and the number of the point cloud data after radius filtering, the global neighborhood mean of the point cloud data after radius filtering is determined.

[0137] In some embodiments, the global neighborhood mean of the point cloud data after radius filtering is determined by the following formula (13): (13); In some embodiments, based on the Euclidean distance mean of the point cloud data after radius filtering and the number of the point cloud data after radius filtering, the standard deviation of the point cloud data after radius filtering is determined.

[0138] In some embodiments, the global neighborhood mean of the point cloud data after radius filtering is determined by the following formula (14): (14); A filtering threshold factor α is reasonably set. When is satisfied, the statistical filtering algorithm will automatically identify those material point clouds that do not meet the expectations as noise points and filter them out.

[0139] Step S506: Downsample the point cloud data after statistical filtering to obtain the downsampled point cloud data; In some embodiments, the number of point clouds after filtering is still large. To reduce the point cloud density and improve the calculation efficiency, the uniform voxel algorithm is used for downsampling processing. By constructing a three-dimensional voxel grid, calculating the centroid of the points in each grid, and replacing other points in the grid with one point, the reduction of the data volume is achieved. A relatively dense number of point clouds will result in an unreasonable triangular mesh generated later.

[0140] In some embodiments, the first-dimensional range, the second-dimensional range, and the third-dimensional range of the point cloud data after filtering are determined by the following formula (16): (16); In some embodiments, the size of the three-dimensional voxel grid of the point cloud data after filtering is determined by the following formula (17): (17); Step S507: Perform a projection transformation on the downsampled point cloud data to obtain the point cloud data after the projection transformation; In some implementations, according to the actual scene and requirements of the material pile surface, the horizontal plane that passes through the best plane fit of the edge of the material pile is used as the reference plane. The downsampled point cloud data in the three-dimensional space is calculated by the rotation matrix to obtain its coordinates in the two-dimensional reference plane.

[0141] In some embodiments, the rotation matrix of the downsampled point cloud data is determined by the following formula (18):

[0142]

[0143] (18); in, , , Represents the rotation matrix of the downsampled point cloud data, , , Represents the angle between the tangent plane normal vector of the downsampled point cloud data and the third dimension.

[0144] Step S508, meshing the point cloud data after the projection transformation to obtain a triangular mesh; In some implementations, the function in the MATLAB point cloud toolbox is used to generate a complete, non-overlapping triangular mesh structure from the projected two-dimensional point cloud data. Then, by mapping the two-dimensional topological relationship to the three-dimensional space, and performing union operations and deduplication processing, a material pile surface mesh is finally constructed, such as Figure 7 shown.

[0145] In some embodiments, based on the distance formula between two points Calculate the lengths of the three sides of the generated triangular mesh. If the lengths of the three sides are similar, it means that the generated triangular mesh meets the requirements. On the contrary, if the lengths of the three sides are very different, it means that the generated triangular mesh does not meet the requirements. At this time, it is necessary to re-process the point cloud data scanned by the 3D laser radar through straight-through filtering, radius filtering and statistical filtering, and point cloud downsampling, and then divide the triangular mesh again.

[0146] Step S509, determining whether the triangle mesh meets the requirements; Here, if yes, that is, the triangular mesh meets the requirements, the process proceeds to step S510; if no, that is, the triangular mesh does not meet the requirements, the process proceeds to step S503.

[0147] In some embodiments, when the length ratio of the longest side to the shortest side of the three sides of the triangular mesh is less than 2, the generated triangular mesh is considered to be good. When the length ratio of the longest side to the shortest side is greater than 3, the generated triangular mesh is considered to be poor, and the lengths of the three sides differ greatly.

[0148] Step S510: Determine the volume of the material heap surface to be excavated based on the triangular mesh.

[0149] In some embodiments, based on the established triangular mesh of the material heap surface, the idea of the finite element method is adopted to calculate the excavation volume of the material in the bucket. First, project the coordinates of the triangular mesh onto a plane and calculate the area of each projected triangle. Then, for each projected triangle, multiply its area by the average value of the z - coordinate values of the projected points of the triangle to obtain the volume of the triangular prism corresponding to the triangle. Finally, sum up the finite - element excavation volumes within the excavation range to obtain the volume of the material heap surface to be excavated at any time.

[0150] The embodiments of the present application provide a method for excavating materials. For example, Figure 8 as shown, F(x, y) represents the curve of the material heap surface, T(x, y) represents the excavation trajectory curve, the dashed line represents the excavation trajectory, the gray part S is the projection of the excavated material part on the XOY horizontal plane, and the material between the first plane 81 and the second plane 82 is the excavated material, and ∆s i is the area of a single triangular mesh of the material heap surface projected onto the XOY horizontal plane (the first mesh 83 represents the mesh corresponding to the material surface, the second mesh 84 represents the mesh corresponding to the material surface after excavation, and the third mesh 85 represents the mesh corresponding to the projection of the first mesh 83 onto the XOY horizontal plane). The overall idea is to divide it into small triangular prisms, calculate the volume of the triangular prism formed by the triangle in the first mesh 83 and the triangle in the second mesh 84, and sum up to obtain the volume of the excavated material heap surface.

[0151] The embodiments of the present application provide a single finite - element excavation of the material heap surface. For example, Figure 9 as shown. Taking the area calculation of △a 0 b 0 c 0 as an example, the coordinates of vertex a 0 in △a 0 b 0 c 0 are (X 1 , Y 1 , 0), the coordinates of vertex b 0 are (X 2 , Y 2 , 0), and the coordinates of vertex c 0 are (X 3 , Y 3 , 0).

[0152] Determine the lengths of the three sides of △a 0 b 0 c 0 through the following formulas (4) to (6): (4); (5); (6); Wherein, represents the length of side, represents the length of side, represents the length of side.

[0153] In some embodiments, △a is determined by the following formula (7) 0 b 0 c 0 the semi-perimeter of: (7); Wherein, represents the semi-perimeter of △a 0 b 0 c 0 of.

[0154] In some embodiments, △a is determined by the following formula (8) 0 b 0 c 0 the area of: (8); Wherein, represents the area of △a 0 b 0 c 0 of.

[0155] In some embodiments, the coordinates of vertex a in △abc are (X 1 , Y 1 , Z 1 ), the coordinates of vertex b are (X 2 , Y 2 , Z 2 ), and the coordinates of vertex c are (X 3 , Y 3 , Z 3 ). The average value of the third-dimensional coordinates of points a, b, and c is determined by the following formula (2): (2); The volume of the triangular prism abc-a 0 b 0 c 0 is

[0156] If the electric shovel excavation trajectory curve is T(X, Y), then the z coordinate values of the projections of points a, b, and c on T(X, Y) are respectively , , .

[0157] In some embodiments, the average value of the third-dimensional coordinates of points a 1 , b 1 , c 1 is determined by the following formula (1): (1); The volume of the triangular prism a 1 b 1 c 1 - a 0 b 0 c 0 is

[0158] In some embodiments, the volume of the material excavated by a single finite element is determined by the following formula (3): (3); Since is constantly changing, and when the bucket leaves the material pile surface after excavation, the value of the volume excavated by a single finite element is negative. Therefore the following requirements should be followed:

[0159] Finally, by adding up the volumes of all the finite elements of the excavated material, the volume of the material in the bucket at any excavation moment can be obtained.

[0160] Compared with the prior art, the embodiments of the present application have the following advantages: 1. Use a 3D lidar to scan the pile surface to obtain point cloud data, and process the point cloud data through methods such as horizontal calibration, filtering, and downsampling to filter out interference and noise, ensuring that the point cloud data can accurately and reliably reflect the information of the material pile surface, and realizing a comprehensive perception of the excavation operation environment.

[0161] 2. Based on the idea of the finite element method, grid division of different types of pile surfaces can be realized, and the volume of the material excavated by each finite element can be obtained through simple calculations. It has a wide applicability to different pile surface types and can calculate the dynamic excavation volume at any excavation moment.

[0162] 3. The dynamic excavation volume calculation method based on the finite element method is fast in calculation speed and high in efficiency while meeting the calculation accuracy of the excavation volume.

[0163] The embodiments of the present application provide a device for determining the volume of a material pile surface. As Figure 10 shown, the device 1000 for determining the volume of a material pile surface includes: The first determination module 1010 is configured to determine the type of the material pile surface based on the acquired initial point cloud data of the material pile surface; The partitioning module 1020 is configured to partition the initial point cloud data corresponding to the type of the material pile surface into grids to obtain the grids corresponding to the type of the material pile surface; The projection module 1030 is configured to project the grids corresponding to the type of the material pile surface onto a first plane to obtain the projected grids; the first plane represents a plane formed by a first dimension and a second dimension, and the first dimension is perpendicular to the second dimension; The second determination module 1040 is configured to determine the volume of the material pile surface based on the grids corresponding to the type of the material pile surface and the projected grids.

[0164] In some embodiments, the second determination module 1040 includes: a projection sub-module configured to project each vertex in the grids corresponding to the type of the material pile surface onto the electric shovel excavation trajectory curve to obtain the third dimension coordinate values of each projected vertex; a first determination sub-module configured to determine the area of the projected grids based on the first dimension coordinate values and the second dimension coordinate values of each vertex in the projected grids; a second determination sub-module configured to determine the prism volume of the grids corresponding to the type of the material pile surface based on the third dimension coordinate values of each projected vertex, the third dimension coordinate values of each vertex in the grids corresponding to the type of the material pile surface, and the area of the projected grids; and an adding module configured to add up the prism volumes of the grids corresponding to the type of the material pile surface to obtain the volume of the material pile surface.

[0165] In some embodiments, the second determination sub-module includes: a first determination unit configured to determine the area of the projected grids based on the first dimension coordinate values and the second dimension coordinate values of each vertex in the projected grids; a second determination unit configured to determine the average value of the third dimension coordinates of each projected vertex based on the third dimension coordinate values of each projected vertex; a third determination unit configured to determine the average value of the third dimension coordinates of each vertex in the grids corresponding to the type of the material pile surface based on the third dimension coordinate values of each vertex in the grids corresponding to the type of the material pile surface; and a fourth determination unit configured to determine the prism volume of the grids corresponding to the type of the material pile surface based on the area of the projected grids, the average value of the third dimension coordinates of each projected vertex, and the average value of the third dimension coordinates of each vertex in the grids corresponding to the type of the material pile surface.

[0166] In some embodiments, the first determination unit includes: a first determination subunit, configured to determine the lengths of the three sides of the projected grid based on the first-dimensional coordinate values and the second-dimensional coordinate values of the vertices in the projected grid; a second determination subunit, configured to determine the semi-perimeter of the projected grid based on the lengths of the three sides of the projected grid; and a third determination subunit, configured to determine the area of the projected grid based on the lengths of the three sides and the semi-perimeter.

[0167] In some embodiments, the partitioning module 1020 includes: a preprocessing sub-module, configured to preprocess the initial point cloud data corresponding to the material pile surface type to obtain preprocessed point cloud data; and a partitioning sub-module, configured to perform grid partitioning on the preprocessed point cloud data to obtain the grid corresponding to the material pile surface type.

[0168] In some embodiments, the preprocessing includes horizontal calibration, filtering, downsampling, and projection transformation; the preprocessing sub-module includes: a calibration unit, configured to perform horizontal calibration on the initial point cloud data corresponding to the material pile surface type to obtain horizontally calibrated point cloud data; a filtering unit, configured to filter the horizontally calibrated point cloud data to obtain filtered point cloud data; a downsampling unit, configured to perform downsampling on the filtered point cloud data to obtain the downsampled point cloud data; and a projection transformation unit, configured to perform projection transformation on the downsampled point cloud data to obtain the preprocessed point cloud data.

[0169] In some embodiments, the calibration unit includes: an acquisition subunit, configured to acquire the roll angle and the pitch angle of the initial point cloud data corresponding to the material pile surface type with respect to the horizontal plane; a fourth determination subunit, configured to determine the transformation matrix of the initial point cloud data based on the roll angle and the pitch angle; and a calibration subunit, configured to perform horizontal calibration on the initial point cloud data by using the transformation matrix to obtain the horizontally calibrated point cloud data.

[0170] In some embodiments, the filtering includes direct filtering, radius filtering, and statistical filtering; the filtering unit includes: a first filtering subunit, configured to perform a first direct filtering on the horizontally calibrated point cloud data to obtain first direct-filtered point cloud data; a second filtering subunit, configured to perform a second direct filtering on the first direct-filtered point cloud data to obtain second direct-filtered point cloud data; a third filtering subunit, configured to perform radius filtering on the second direct-filtered point cloud data to obtain radius-filtered point cloud data; and a fourth filtering subunit, configured to perform statistical filtering on the radius-filtered point cloud data to obtain statistically filtered point cloud data.

[0171] In some embodiments, the first filtering subunit is used to determine the three-dimensional coordinate value of the horizontally calibrated point cloud data; filter out the horizontally calibrated point cloud data corresponding to the three-dimensional coordinate value being less than a first preset threshold, to obtain the first straight-through filtered point cloud data.

[0172] In some embodiments, a first filtering subunit is used to determine the first dimension coordinate value, the second dimension coordinate value and the third dimension coordinate value of the point cloud data after the first straight-pass filtering; the point cloud data after the first straight-pass filtering corresponding to the first dimension coordinate value greater than the preset first dimension threshold, the second dimension coordinate value greater than the preset second dimension threshold and the third dimension coordinate value greater than the preset third dimension threshold are filtered out to obtain the point cloud data after the second straight-pass filtering; wherein the first dimension threshold represents twice the bucket width, the second dimension threshold represents the sum of the stick extension length and the bucket length, and the third dimension threshold represents the sum of the stick extension length and the sine value of the angle between the boom and the horizontal line multiplied by the height from the saddle rotation center to the ground.

[0173] In some embodiments, a third filtering subunit is used to take each point cloud in the point cloud data after the second through-filtering as the center point cloud, determine the Euclidean distance between the center point cloud and other point clouds within a preset neighborhood of the center point cloud; filter out the center point cloud corresponding to the Euclidean distance greater than the second preset threshold, and obtain the point cloud data after the radius filtering.

[0174] In some embodiments, a fourth filtering subunit is used to determine the Euclidean distance between the center point cloud and other point clouds within a preset neighborhood of the center point cloud, taking each point cloud in the radius-filtered point cloud data as the center point cloud; determining the mean Euclidean distance of the radius-filtered point cloud data based on the Euclidean distance and the number of point clouds in the preset neighborhood; determining the global neighborhood mean of the radius-filtered point cloud data based on the mean Euclidean distance of the radius-filtered point cloud data and the number of radius-filtered point cloud data; determining the standard deviation of the radius-filtered point cloud data based on the mean Euclidean distance of the radius-filtered point cloud data and the number of radius-filtered point cloud data; filtering out the radius-filtered point cloud data whose Euclidean norm of the difference between the mean Euclidean distance and the global neighborhood mean is greater than the product of the standard deviation and a third preset threshold value, to obtain the statistically filtered point cloud data.

[0175] In some embodiments, the downsampling unit includes: a fifth determination subunit configured to determine the maximum coordinate value of the first dimension, the minimum coordinate value of the first dimension, the maximum coordinate value of the second dimension, the minimum coordinate value of the second dimension, the maximum coordinate value of the third dimension, and the minimum coordinate value of the third dimension of the filtered point cloud data; a difference calculation subunit configured to calculate the difference between the maximum coordinate value and the minimum coordinate value of the first dimension, the difference between the maximum coordinate value and the minimum coordinate value of the second dimension, and the difference between the maximum coordinate value and the minimum coordinate value of the third dimension, respectively, to correspondingly obtain the range of the first dimension, the range of the second dimension, and the range of the third dimension of the filtered point cloud data; a sixth determination subunit configured to determine the size of the three-dimensional voxel grid of the filtered point cloud data based on the range of the first dimension, the range of the second dimension, the range of the third dimension, and a preset side length of the three-dimensional voxel grid; and a seventh determination subunit configured to determine the downsampled point cloud data based on the size of the three-dimensional voxel grid.

[0176] In some embodiments, the projection transformation unit includes: an eighth determination subunit configured to determine the angle between the tangent plane normal vector of the downsampled point cloud data and the third dimension; a ninth determination subunit configured to determine the rotation matrix of the downsampled point cloud data based on the angle between the tangent plane normal vector and the third dimension; and a projection transformation subunit configured to perform projection transformation on the downsampled point cloud data by using the rotation matrix to obtain the preprocessed point cloud data.

[0177] It should be noted here that: the description of the above device embodiments is similar to the description of the above method embodiments and has similar beneficial effects to those of the method embodiments. For the technical details not disclosed in the device embodiments of the present application, please refer to the description of the method embodiments of the present application for understanding.

[0178] It should be noted that in the embodiments of the present application, if the above method is implemented in the form of software function modules and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the embodiments of the present application, in essence, or the part that contributes to the related technology can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing an electronic device to execute all or part of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROMs), magnetic disks, or optical discs that can store program codes. In this way, the embodiments of the present application are not limited to any specific combination of hardware and software.

[0179] An embodiment of the present application further provides an electronic device, including a memory and a processor. The memory stores a computer program that can run on the processor, and when the processor executes the program, some or all of the steps in the above method are implemented.

[0180] An embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, some or all of the steps in the above method are implemented. The computer-readable storage medium can be transient or non-transient.

[0181] An embodiment of the present application further provides a computer program, including computer-readable code. When the computer-readable code runs in a computing device, the processor in the computing device executes to implement some or all of the steps in the above method.

[0182] An embodiment of the present application further provides a computer program product. The computer program product includes a non-transient computer-readable storage medium storing a computer program. When the computer program is read and executed by a computer, some or all of the steps in the above method are implemented. The computer program product can be specifically implemented by means of hardware, software, or a combination thereof. In some embodiments, the computer program product is specifically embodied as a computer storage medium. In other embodiments, the computer program product is specifically embodied as a software product, such as a Software Development Kit (SDK), etc.

[0183] It should be noted here that the descriptions of the above storage medium and device embodiments are similar to those of the above method embodiments and have beneficial effects similar to those of the method embodiments. For technical details not disclosed in the storage medium and device embodiments of the present application, please refer to the descriptions of the method embodiments of the present application for understanding.

[0184] An embodiment of the present application provides a hardware entity of an electronic device, such as Figure 11 As shown, the hardware entity of the electronic device 1100 includes: The processor 1101 generally controls the overall operation of the electronic device 1100. The communication interface 1102 can enable the electronic device to communicate with other terminals or servers through a network. The memory 1103 is configured to store instructions and applications executable by the processor 1101, and can also cache data to be processed or already processed by the processor 1101 and each module in the electronic device 1100 (for example, image data, audio data, voice communication data, and video communication data), and can be implemented by flash memory (FLASH) or random access memory (Random Access Memory, RAM). Data transmission can be carried out between the processor 1101, the communication interface 1102, and the memory 1103 through the bus 1104.

Claims

1. A method for determining the volume of a material pile surface, characterized in that: The method comprises: Determining the type of the material pile surface based on the acquired initial point cloud data of the material pile surface; Meshing the initial point cloud data corresponding to the material pile surface type to obtain a mesh corresponding to the material pile surface type; Projecting the grid corresponding to the material pile surface type onto a first plane to obtain a projected grid; the first plane represents a plane formed by a first dimension and a second dimension, and the first dimension and the second dimension are perpendicular to each other; The volume of the material pile surface is determined based on the grid corresponding to the material pile surface type and the projected grid.

2. The method according to claim 1, characterized in that Determining the volume of the material pile surface based on the grid corresponding to the material pile surface type and the projected grid, including: Projecting each vertex in the grid corresponding to the material pile surface type onto the excavation trajectory curve of the electric shovel to obtain the third-dimensional coordinate value of each vertex after projection; Determine the area of ​​the projected mesh based on the first dimension coordinate value and the second dimension coordinate value of each vertex in the projected mesh; Determine the prism volume of the mesh corresponding to the material pile surface type based on the third dimensional coordinate values ​​of each vertex after the projection, the third dimensional coordinate values ​​of each vertex in the mesh corresponding to the material pile surface type, and the area of ​​the mesh after the projection; The prism volumes of the grids corresponding to the material pile surface type are added together to obtain the volume of the material pile surface.

3. The method according to claim 2, characterized in that Determining the prism volume of the mesh corresponding to the material pile surface type based on the third dimensional coordinate values ​​of each vertex after the projection, the third dimensional coordinate values ​​of each vertex in the mesh corresponding to the material pile surface type, and the area of ​​the mesh after the projection, including: Determine the area of ​​the projected mesh based on the first dimension coordinate value and the second dimension coordinate value of each vertex in the projected mesh; Based on the third-dimensional coordinate values ​​of the vertices after the projection, determining the mean value of the third-dimensional coordinates of the vertices after the projection; Determine the mean value of the third dimensional coordinates of each vertex in the mesh corresponding to the material pile surface type based on the third dimensional coordinate values ​​of each vertex in the mesh corresponding to the material pile surface type; The prism volume of the mesh corresponding to the material pile surface type is determined based on the area of ​​the projected mesh, the average third-dimensional coordinates of each vertex after the projection, and the average third-dimensional coordinates of each vertex in the mesh corresponding to the material pile surface type.

4. The method according to claim 3, characterized in that Determining the area of ​​the projected mesh based on the first dimension coordinate value and the second dimension coordinate value of each vertex in the projected mesh includes: Determine the lengths of three sides of the projected mesh based on the first dimension coordinate value and the second dimension coordinate value of each vertex in the projected mesh; Determine the semi-perimeter of the projected grid based on the lengths of the three sides of the projected grid; The area of ​​the projected grid is determined based on the lengths of the three sides and the semi-perimeter.

5. The method according to any one of claims 1 to 4, characterized in that: Meshing the initial point cloud data corresponding to the material pile surface type to obtain a mesh corresponding to the material pile surface type includes: Preprocessing the initial point cloud data corresponding to the material pile surface type to obtain preprocessed point cloud data; The preprocessed point cloud data is meshed to obtain a mesh corresponding to the material pile surface type.

6. The method according to claim 5, characterized in that The preprocessing includes level calibration, filtering, down sampling and projection transformation; Preprocessing the initial point cloud data corresponding to the material pile surface type to obtain preprocessed point cloud data includes: Performing horizontal calibration on the initial point cloud data corresponding to the material pile surface type to obtain point cloud data after horizontal calibration; Filtering the point cloud data after the horizontal calibration to obtain filtered point cloud data; Down-sampling the filtered point cloud data to obtain the down-sampled point cloud data; Projection transformation is performed on the downsampled point cloud data to obtain the preprocessed point cloud data.

7. The method according to claim 6, characterized in that Performing horizontal calibration on the initial point cloud data corresponding to the material pile surface type to obtain point cloud data after horizontal calibration, including: Obtaining the roll angle and pitch angle of the horizontal plane between the initial point cloud data corresponding to the material pile surface type; Based on the roll angle and the pitch angle, determining a transformation matrix of the initial point cloud data; The initial point cloud data is horizontally calibrated using the conversion matrix to obtain the point cloud data after horizontal calibration.

8. The method according to claim 6, characterized in that The filtering includes through filtering, radius filtering and statistical filtering; Filtering the point cloud data after the horizontal calibration to obtain filtered point cloud data includes: Performing a first straight-pass filtering on the horizontally calibrated point cloud data to obtain first straight-pass filtered point cloud data; Performing a second straight-through filtering on the point cloud data after the first straight-through filtering to obtain the point cloud data after the second straight-through filtering; Performing radius filtering on the point cloud data after the second through-filtering to obtain radius-filtered point cloud data; Perform statistical filtering on the radius-filtered point cloud data to obtain statistically filtered point cloud data.

9. The method according to claim 8, characterized in that Performing a first straight-pass filtering on the horizontally calibrated point cloud data to obtain the first straight-pass filtered point cloud data, including: Determining the three-dimensional coordinate values ​​of the point cloud data after the horizontal calibration; The point cloud data after horizontal calibration corresponding to the three-dimensional coordinate value being less than the first preset threshold is filtered out to obtain the point cloud data after the first through-filtering.

10. The method according to claim 8, characterized in that Performing a second straight-through filtering on the point cloud data after the first straight-through filtering to obtain the point cloud data after the second straight-through filtering includes: Determine a first dimension coordinate value, a second dimension coordinate value, and a third dimension coordinate value of the point cloud data after the first through-filtering; Filter out the first straight-through filtered point cloud data corresponding to the coordinate values ​​of the first dimension being greater than a preset first dimension threshold, the coordinate values ​​of the second dimension being greater than a preset second dimension threshold, and the coordinate values ​​of the third dimension being greater than a preset third dimension threshold, to obtain the second straight-through filtered point cloud data; Among them, the first dimension threshold represents twice the bucket width, the second dimension threshold represents the sum of the arm extension length and the bucket length, and the third dimension threshold represents the product of the arm extension length and the sine value of the angle between the boom and the horizontal line and the height from the saddle rotation center to the ground.

11. The method according to claim 8, characterized in that Performing radius filtering on the point cloud data after the second through-filtering to obtain point cloud data after the radius filtering includes: Taking each point cloud in the point cloud data after the second straight-through filtering as a central point cloud, determining the Euclidean distance between the central point cloud and other point clouds within a preset neighborhood of the central point cloud; The central point cloud corresponding to the Euclidean distance greater than the second preset threshold is filtered out to obtain the point cloud data after the radius filtering.

12. The method according to claim 8, characterized in that Performing statistical filtering on the radius-filtered point cloud data to obtain statistically filtered point cloud data, including: Taking each point cloud in the point cloud data after the radius filtering as the central point cloud, determining the Euclidean distance between the central point cloud and other point clouds within a preset neighborhood of the central point cloud; Determining a mean Euclidean distance of the radius-filtered point cloud data based on the Euclidean distance and the number of point clouds in the preset neighborhood; Determining a global neighborhood mean of the radius-filtered point cloud data based on the Euclidean distance mean of the radius-filtered point cloud data and the amount of the radius-filtered point cloud data; Determining a standard deviation of the radius-filtered point cloud data based on a mean value of the Euclidean distance of the radius-filtered point cloud data and the amount of the radius-filtered point cloud data; The point cloud data after radius filtering whose Euclidean norm of the difference between the mean of the Euclidean distance and the global neighborhood mean is greater than the product of the standard deviation and the third preset threshold is filtered out to obtain the point cloud data after statistical filtering.

13. The method according to claim 6, characterized in that Downsampling the filtered point cloud data to obtain the downsampled point cloud data includes: Determine the maximum coordinate value of the first dimension, the minimum coordinate value of the first dimension, the maximum coordinate value of the second dimension, the minimum coordinate value of the second dimension, the maximum coordinate value of the third dimension, and the minimum coordinate value of the third dimension of the filtered point cloud data; Subtract the maximum coordinate value of the first dimension from the minimum coordinate value of the first dimension, the maximum coordinate value of the second dimension from the minimum coordinate value of the second dimension, and the maximum coordinate value of the third dimension from the minimum coordinate value of the third dimension, respectively, to obtain the first dimension range, the second dimension range, and the third dimension range of the filtered point cloud data; Determining a size of a three-dimensional voxel grid of the filtered point cloud data based on the first dimensional range, the second dimensional range, the third dimensional range, and a preset three-dimensional voxel grid side length; The downsampled point cloud data is determined based on a size of the three-dimensional voxel grid.

14. The method according to claim 6, characterized in that Performing a projection transformation on the downsampled point cloud data to obtain the preprocessed point cloud data includes: Determine the angle between the tangent plane normal vector of the downsampled point cloud data and the third dimension; Determine a rotation matrix of the downsampled point cloud data based on the angle between the tangent plane normal vector and the third dimension; The down-sampled point cloud data is projected using the rotation matrix to obtain the pre-processed point cloud data.

15. A device for determining the volume of a pile of materials, characterized in that: The device comprises: A first determination module, configured to determine the type of a material pile surface based on the acquired initial point cloud data of the material pile surface; A division module, used for performing grid division on the initial point cloud data corresponding to the material pile surface type to obtain a grid corresponding to the material pile surface type; A projection module, used for projecting the grid corresponding to the material pile surface type onto a first plane to obtain a projected grid; the first plane represents a plane formed by a first dimension and a second dimension, and the first dimension and the second dimension are perpendicular to each other; The second determination module is used to determine the volume of the material pile surface based on the grid corresponding to the material pile surface type and the projected grid.

16. An electronic device comprising a processor and a memory, wherein the memory stores a computer program that can be run on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 14 are implemented.

17. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a processor, the steps in the method according to any one of claims 1 to 14 are implemented.

18. A computer program product comprising a computer program or instructions, characterized in that When the computer program or instruction is executed by a processor, the steps in the method according to any one of claims 1 to 14 are implemented.

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