A method and apparatus for measuring the volume of material in an irregular bin

Abstract

The application provides a method for measuring the volume of irregular silo materials, comprising: obtaining point cloud data of a silo to be measured; preprocessing the point cloud data to obtain material data; determining whether the silo to be measured is an irregular silo, and if so, obtaining the volume of the material in the silo to be measured based on the material data according to a first volume calculation algorithm, and if not, obtaining the volume of the material in the silo to be measured based on the material data according to a second volume calculation algorithm. The application solves the problem that the existing equipment for measuring the volume of materials cannot meet the complex full-silo condition, has low accuracy in real-time measurement of the volume of materials and has high cost.

Description

Technical Field

[0001] This invention relates to the field of measurement technology, and in particular to a method and apparatus for measuring the volume of materials in an irregularly shaped silo. Background Technology

[0002] In applications such as ores, grain silos, and coal mines, calculating storage capacity is a crucial task, impacting material disbursement statistics and material production capacity assessment. However, due to limitations imposed by the storage environment, warehouse shape, irregular mineral stacking patterns, and high frequency of material handling, accurately measuring the volume of stored materials is challenging, time-consuming, and prone to significant errors.

[0003] Traditional measurement methods typically employ measuring tools like tape measures to calculate volume based on geometry. This is labor-intensive, resource-intensive, cumbersome, inaccurate, and lacks traceability. Weighing methods are ill-suited to accurately reflect changes in volume due to variations in material moisture content and density. They are also subject to measurement errors due to the limitations of weighbridge range accuracy. Furthermore, in typical silo clusters, only one weighbridge is installed per factory area to monitor the overall weight, failing to reflect volume changes in individual silos. Level gauges, installed on top, only measure material height, ignoring silo and level shapes, resulting in significant errors closely related to silo shape, especially for irregularly shaped silos. High-frequency radar (80-90Hz) measures the surface shape of the material level. However, limitations in beam angle and silo size necessitate multiple radars in large-scale applications, hindering widespread adoption due to cost constraints. Portable laser volume measuring instruments require multiple manual inspections, are time-consuming, inaccurate, and cannot provide real-time feedback on silo volume changes. Fixed laser volume measuring instruments cannot move with the silo, creating blind spots. For material piles with complex geometries and significant obstruction, measurements can result in substantial errors. For silos with wall partitions, multiple devices need to be installed, increasing operating costs.

[0004] In summary, existing equipment for measuring material stockpiles cannot meet the complex conditions of full silos, and real-time measurement of material volume is inaccurate and costly. Summary of the Invention

[0005] In order to overcome the shortcomings of the prior art, the purpose of this invention is to provide a method and apparatus for measuring the volume of materials in irregular silos. This invention solves the problems that the existing equipment for measuring stockpiled materials cannot meet the complex conditions of full silos, and that the accuracy of real-time material volume measurement is low and the cost is high.

[0006] To achieve the above objectives, the present invention provides the following solution:

[0007] A method for measuring the volume of material in an irregularly shaped silo includes:

[0008] Obtain point cloud data of the silo to be tested;

[0009] The point cloud data is preprocessed to obtain material data;

[0010] Determine whether the silo to be tested is an irregular silo. If so, obtain the material volume of the silo to be tested based on the material data and according to the first volume algorithm. If not, obtain the material volume of the silo to be tested based on the material data and according to the second volume algorithm.

[0011] Preferably, the preprocessing of the point cloud data to obtain material data includes:

[0012] The point cloud data is sampled to obtain sampled data;

[0013] The sampled data is filtered to obtain filtered data;

[0014] The filtered data is segmented and processed to obtain material data.

[0015] Preferably, the point cloud data is sampled to obtain sampled data, including:

[0016] Create a 3D voxel mesh on the point cloud data;

[0017] Determine the voxel center point within each voxel in the 3D voxel mesh;

[0018] The sampling data is obtained by replacing the points within each voxel with the voxel center point.

[0019] Preferably, the sampled data is filtered to obtain filtered data, including:

[0020] Based on the sampled data, a coordinate system is established with the ground directly below the docking point of the silo to be tested as the origin.

[0021] Three pass-through filters are used to filter the data in the X-axis, Y-axis and Z-axis ranges respectively to obtain filtered data.

[0022] Preferably, the step of using three through filters to filter the data within the X-axis, Y-axis, and Z-axis ranges respectively to obtain filtered data includes:

[0023] The first pass filter is used to determine whether the value of the X-axis of the sampled data is within the range of the X-axis of the established coordinate system. If not, the data point is deleted; if so, the data point is retained.

[0024] The second pass-through filter is used to determine whether the value of the Y-axis of the sampled data is within the range of the Y-axis of the established coordinate system. If not, the data point is deleted; if so, the data point is retained.

[0025] The third pass-through filter is used to determine whether the value of the Z-axis of the sampled data is within the range of the Z-axis of the established coordinate system. If not, the data point is deleted; if so, the data point is retained.

[0026] Based on all the retained data points, the filtered data is obtained.

[0027] Preferably, the step of segmenting the filtered data to obtain material data includes:

[0028] The filtered data is cut using the RANSAC method to fit the planar formula for each wall.

[0029] Based on the planar formula for each wall, construct the planar distance formula;

[0030] Based on the filtered data, the distance between each point and each plane is obtained according to the planar distance formula;

[0031] Determine whether the distance between each point and each plane is within the threshold range; if so, delete the data point.

[0032] Based on the remaining data points, the material data is obtained.

[0033] Preferably, the expression for the planar distance formula is:

[0034]

[0035] Where d is the distance from the point to the plane.

[0036] Preferably, based on the material data, the material volume of the silo to be tested is obtained according to the first volume algorithm, including:

[0037] Based on the maximum and minimum values ​​of the X-axis and the maximum and minimum values ​​of the Y-axis of the silo detection range, grids are established on the X-axis and Y-axis, and the initial Z-axis average value of each grid is recorded. The silo detection range is larger than the range where the material data is located.

[0038] The material data is stored in a grid, and the average value of the Z-axis of the material data points in each grid is calculated.

[0039] Calculate the volume of each grid cell based on the average Z-axis value and the initial average Z-axis value;

[0040] The material volume of the silo to be tested is obtained based on the volume of each grid.

[0041] Preferably, based on the material data, the material volume of the silo to be tested is obtained according to the second volume algorithm, including:

[0042] Determine the maximum and minimum values ​​of the material data on the X-axis and the maximum and minimum values ​​on the Y-axis;

[0043] Based on the maximum and minimum values ​​of the X-axis and the maximum and minimum values ​​of the Y-axis of the material data, a grid is established on the X-axis and Y-axis of the material data;

[0044] The material data is stored in a grid, and the average Z-axis value of the material data in the grid is calculated.

[0045] Calculate the volume of each grid cell based on the Z-axis average value;

[0046] The material volume of the silo to be tested is obtained based on the volume of each grid.

[0047] An apparatus for measuring the volume of material in an irregularly shaped silo, the apparatus comprising:

[0048] The computing processor and the 3D LiDAR and rail-mounted crane, both connected to the computing processor;

[0049] The 3D LiDAR is used to acquire point cloud data of the silo to be tested. The guide rail trolley is used to drive the 3D LiDAR to the location of the silo to be tested. The computing processor is used to guide the guide rail trolley and obtain the material volume of the silo to be tested based on the point cloud data.

[0050] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0051] This invention provides a method for measuring the volume of materials in an irregularly shaped silo, comprising: acquiring point cloud data of the silo to be measured; preprocessing the point cloud data to obtain material data; determining whether the silo to be measured is an irregularly shaped silo; if so, obtaining the material volume of the silo to be measured based on the material data and according to a first volume algorithm; if not, obtaining the material volume of the silo to be measured based on the material data and according to a second volume algorithm. This invention provides real-time and accurate measurement of material volume; meets the needs of large-scale applications; generates a three-dimensional point cloud map of the material for intuitive display and convenient later traceability; is not limited by warehouse partitions, curved walls, uneven ground, etc.; allows for remote operation, convenient maintenance, high availability, and low cost. Attached Figure Description

[0052] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0053] Figure 1A flowchart illustrating a method for measuring the volume of material in an irregular silo, as provided in an embodiment of the present invention;

[0054] Figure 2 This is a detailed schematic diagram illustrating a method for measuring the volume of materials in an irregularly shaped silo, as provided in an embodiment of the present invention.

[0055] Figure 3 This is a schematic diagram of the sampling point cloud provided in an embodiment of the present invention;

[0056] Figure 4 A flowchart of the filtering algorithm provided in an embodiment of the present invention;

[0057] Figure 5 The filtered point cloud image provided in this embodiment of the invention;

[0058] Figure 6 This is a schematic diagram of the point cloud after fitting the plane parameters of the empty warehouse according to an embodiment of the present invention;

[0059] Figure 7 The flowchart provided for this embodiment of the invention is a segmentation algorithm flowchart;

[0060] Figure 8 The point cloud image provided for this embodiment of the invention is a segmented point cloud image;

[0061] Figure 9 A flowchart of the volume measurement algorithm provided in an embodiment of the present invention;

[0062] Figure 10 This is a point cloud map of uneven ground in the silo provided in an embodiment of the present invention;

[0063] Figure 11 This is an irregular point cloud diagram of the silo wall provided in an embodiment of the present invention;

[0064] Figure 12 This is a schematic diagram of device installation provided in an embodiment of the present invention, wherein, Figure 12 (a) is a schematic diagram of the installation at the first angle. Figure 12 (b) is a schematic diagram of the second angle installation. Figure 12 (c) is a schematic diagram of the installation at the third angle. Detailed Implementation

[0065] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0066] The purpose of this invention is to provide a method and apparatus for measuring the volume of materials in irregular silos. This invention solves the problems of existing equipment for measuring stockpiled materials being unable to meet the complex conditions of full silos, having low accuracy in real-time material volume measurement, and being costly.

[0067] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0068] like Figure 1 As shown, the present invention provides a method for measuring the volume of material in an irregularly shaped silo, comprising:

[0069] Step 100: Obtain point cloud data of the silo to be tested;

[0070] Step 200: Preprocess the point cloud data to obtain material data;

[0071] Step 300: Determine whether the silo to be tested is an irregular silo. If so, obtain the material volume of the silo to be tested based on the material data and according to the first volume algorithm. If not, obtain the material volume of the silo to be tested based on the material data and according to the second volume algorithm.

[0072] Furthermore, such as Figure 2 As shown, the preprocessing of the point cloud data to obtain material data includes:

[0073] The point cloud data is sampled to obtain sampled data;

[0074] The sampled data is filtered to obtain filtered data;

[0075] The filtered data is segmented and processed to obtain material data.

[0076] Furthermore, the point cloud data is sampled to obtain sampled data, including:

[0077] Create a 3D voxel mesh on the point cloud data;

[0078] Determine the voxel center point within each voxel in the 3D voxel mesh;

[0079] The sampling data is obtained by replacing the points within each voxel with the voxel center point.

[0080] Specifically, the point cloud sampling algorithm, based on the characteristics of laser ranging, shows that the point cloud is denser closer to the laser device and sparser farther away. A uniform sampling algorithm is used to achieve a consistent point cloud density. The sampling algorithm involves creating a 5*5*5cm 3D voxel grid on the point cloud data. The size of the voxel grid is adjusted according to the site environment. Then, within each voxel, the point closest to the voxel center replaces all other points within that voxel. This sampling algorithm improves the efficiency of subsequent algorithms without affecting the calculation results. The sampled point cloud is shown below. Figure 3 As shown.

[0081] Furthermore, the sampled data is filtered to obtain filtered data, including:

[0082] Based on the sampled data, a coordinate system is established with the ground directly below the docking point of the silo to be tested as the origin.

[0083] Three pass-through filters are used to filter the data in the X-axis, Y-axis and Z-axis ranges respectively to obtain filtered data.

[0084] Furthermore, the data within the X-axis, Y-axis, and Z-axis ranges are filtered using three through-pass filters respectively to obtain filtered data, including:

[0085] The first pass filter is used to determine whether the value of the X-axis of the sampled data is within the range of the X-axis of the established coordinate system. If not, the data point is deleted; if so, the data point is retained.

[0086] The second pass-through filter is used to determine whether the value of the Y-axis of the sampled data is within the range of the Y-axis of the established coordinate system. If not, the data point is deleted; if so, the data point is retained.

[0087] The third pass-through filter is used to determine whether the value of the Z-axis of the sampled data is within the range of the Z-axis of the established coordinate system. If not, the data point is deleted; if so, the data point is retained.

[0088] Based on all the retained data points, the filtered data is obtained.

[0089] Specifically, the flowchart of the point cloud filtering method is as follows: Figure 4 As shown, a coordinate system is established, with the ground directly below each silo's docking point as the origin. Following a right-handed coordinate system, the Z-axis points above the ground, and the X and Y axes intersect perpendicularly at the ground plane. Typically, for rectangular silos, the Y-axis is parallel to the length of the silo, and the X-axis is parallel to its width. Three pass-through filters are used to filter data along the X, Y, and Z axes. The filtered point cloud data contains only data points from the silo walls, ground, and material. The filtered point cloud is shown below. Figure 5 As shown.

[0090] Furthermore, the step of segmenting the filtered data to obtain material data includes:

[0091] The filtered data is cut using the RANSAC method to fit the planar formula for each wall.

[0092] Based on the planar formula for each wall, construct the planar distance formula;

[0093] Based on the filtered data, the distance between each point and each plane is obtained according to the planar distance formula;

[0094] Determine whether the distance between each point and each plane is within the threshold range; if so, delete the data point.

[0095] Based on the remaining data points, the material data is obtained.

[0096] Specifically, the method for segmenting the environment, including filtering out walls, firstly involves using the RANSAC method to cut and fit a planar formula for each silo wall, A·x+B·y+C·z+D=0. The parameters of each plane are recorded. Typically, a silo is a three-walled warehouse, resulting in four planar formulas: the three walls plus the ground. The point cloud after fitting the planes of an empty silo is shown below. Figure 6 As shown, the point cloud fitted to a plane is labeled with a specific color. Based on the distance formula from a point to a plane, the point cloud data is traversed, and data points whose distance d from the plane is less than 100mm are filtered out. The algorithm flowchart is shown below. Figure 7 As shown, the filtered point cloud data only contains data points of the materials, such as... Figure 8 As shown.

[0097] Figure 7 In the diagram, Plane 1, Plane 2, Plane 3, and Plane 4 represent three walls and the ground, respectively. The specific number of planes is determined by the actual situation.

[0098] Specifically, the expression for the planar distance formula is:

[0099]

[0100] Where d is the distance between the point and the plane, the parameters ABCD are the parameters of the plane distance formula, and xyz represent the coordinates of the point on the xyz axis of the coordinate system, respectively.

[0101] Furthermore, based on the material data, the material volume of the silo to be tested is obtained according to the first volume algorithm, including:

[0102] Based on the maximum and minimum values ​​of the X-axis and the maximum and minimum values ​​of the Y-axis of the silo detection range, a grid is established on the X-axis and Y-axis, and the initial Z-axis average value of each grid is recorded. The silo detection range is pre-measured and is larger than the range where the material data is located.

[0103] The material data is stored in a grid, and the average value of the Z-axis of the material data points in each grid is calculated.

[0104] Calculate the volume of each grid cell based on the average Z-axis value and the initial average Z-axis value;

[0105] The material volume of the silo to be tested is obtained based on the volume of each grid.

[0106] Furthermore, based on the material data, the material volume of the silo to be tested is obtained according to the second volume algorithm, including:

[0107] Determine the maximum and minimum values ​​of the material data on the X-axis and the maximum and minimum values ​​on the Y-axis;

[0108] Based on the maximum and minimum values ​​of the X-axis and the maximum and minimum values ​​of the Y-axis of the material data, a grid is established on the X-axis and Y-axis of the material data;

[0109] The material data is stored in a grid, and the average Z-axis value of the material data in the grid is calculated.

[0110] Calculate the volume of each grid cell based on the Z-axis average value;

[0111] The material volume of the silo to be tested is obtained based on the volume of each grid.

[0112] The first volumetric algorithm determines the maximum and minimum values ​​of the X and Y axes of the detection range based on the warehouse's location. The difference between this and the second volumetric algorithm is that the maximum and minimum values ​​of the X and Y axes for the warehouse's location are known beforehand and measured manually. The X and Y ranges are larger than the material's range, and the grid positions established with fixed X and Y axes will not change. The second volumetric algorithm requires dynamic calculation based on the material's data, so the grid positions change with the material's point cloud, resulting in greater accuracy.

[0113] Specifically, materials are typically stacked and distributed in warehouses. Traditional grid-based processing methods only require accumulating grid volumes to calculate the final volume. However, traditional measurement methods have limitations in adaptability when encountering special situations, requiring optimization of the algorithm's logic. For example, if the warehouse is divided by walls, the point cloud of the wall portion needs to be removed, and only the point cloud outside the walls needs to be calculated. If the ground is uneven, the height of each grid cell needs to be distinguished, and each grid cell needs to be calculated separately. If the wall is curved, the processing method is similar to that for uneven ground, distinguishing the height of the curved wall surface using separate grid cells.

[0114] The flowchart of the method for measuring material volume is as follows: Figure 9As shown, the material is divided into regular and irregular silos. First, the input parameters are checked to determine if the silo being measured is a regular silo. If it is, the material data points are iterated through to find the maximum value (Xmax) and minimum value (Xmin) on the X-axis, and the maximum and minimum values ​​(Ymax and Ymin) on the Y-axis. The range of material data points on the X-axis is Xd = Xmax - Xmin, and the range on the Y-axis is Yd = Ymax - Ymin. A 200*200mm grid is created on the X and Y axes, with (Xd / 200) rows on the X-axis and (Yd / 200) columns on the Y-axis. The material data points are iterated through, and the data for each point is stored in the corresponding grid array. The average value (Zavgn) of the Z-axis for each grid data point is calculated. For some grids with obstructed views, there may be no data points; in this case, the average value of the surrounding grids is taken as the value of that grid. The volume of each grid is Vn = 200*200*Zagvn. The final volume of the material is obtained by summing the volumes of all grids. If it is an irregularly shaped silo, such as one with an uneven floor... Figure 10 As shown, the wall is slanted and has an arc shape. Figure 11 In cases like these, a 200*200mm grid will be created based on the maximum and minimum values ​​of the X-axis and Y-axis within the detection range of that warehouse location. The grid positions are fixed during measurement and will not change with the material's location. When the silo is empty, the initial average value of the Z-axis, Zintn, for each grid data point is calculated and saved. When the silo contains material, the average value of the Z-axis, Zavgn, for each grid data point is calculated, and the volume of each grid, Vn = 200*200*(Zagvn - Zintn). The sum of the volumes of all grids gives the final material volume.

[0115] like Figure 12 As shown, this embodiment also discloses an apparatus for measuring the volume of material in an irregularly shaped silo, the apparatus comprising:

[0116] The computing processor and the 3D LiDAR and rail-mounted crane, both connected to the computing processor;

[0117] The 3D LiDAR is used to acquire point cloud data of the silo to be tested. The guide rail trolley is used to drive the 3D LiDAR to the location of the silo to be tested. The computing processor is used to guide the guide rail trolley and obtain the material volume of the silo to be tested based on the point cloud data.

[0118] The scanning light curtain is a two-dimensional plane perpendicular to the side of the hopper. The motor drives this two-dimensional plane to rotate, which can convert two-dimensional coordinate points into three-dimensional points.

[0119] Specifically, the 3D LiDAR uses a two-dimensional linear LiDAR with a gimbal rotation to generate a three-dimensional point cloud. The processing processor is connected to the overhead crane, guiding and positioning it to the designated hopper. It then connects to the LiDAR, acquires radar data, runs processing algorithms, and outputs the material volume for each hopper.

[0120] The unit for point cloud coordinate data is mm, and the LiDAR wavelength is 905nm. The scanning angle of the 2D linear LiDAR must be greater than 180°, and the scanning angular resolution must be less than 0.1°. The turntable rotation angle must be greater than ±80°, the angular resolution less than 0.1°, and the angular velocity greater than 30 rpm. The guide rail accuracy must be within 1mm, and the repeatability accuracy must be within 1mm, ensuring that it stops at the same position on the top of the hopper each time. The appropriate tracks should be laid out according to the specific steps of the hopper.

[0121] The optimal location for the overhead crane to stop and measure is at the top center of each silo, within a 40-meter rectangular area. The measurement range is related to the radar's ranging capability. If there are walls obstructing the view, the measurement range should be reduced, measuring only the material inside the wall, while measuring the material outside at a different location.

[0122] When the processing unit receives the start detection command, it controls the overhead crane to stop at the designated silo location and controls the turntable and LiDAR to begin scanning, acquiring point cloud data within the measurement range. After scanning is complete, the algorithm runs, sampling, filtering, segmenting the environment, and performing volume measurement to calculate the final volume of the material inside the silo.

[0123] The beneficial effects of this invention are as follows:

[0124] This invention addresses the challenge of rapid and effective volume measurement in complex situations, such as irregular warehouse shapes (e.g., warehouses divided by walls, uneven floors and walls), by employing an overhead crane to drive a 3D radar to collect the three-dimensional point cloud coordinates of materials on a guide rail. The data is then sent to a data processor for processing and calculation. The volume of each silo is calculated by accumulating the volume using a grid-based method. The optimized measurement algorithm demonstrates excellent adaptability to material volume measurement in irregular silos and other special conditions, enabling rapid and accurate volume calculation.

[0125] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0126] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for measuring the volume of material in an irregularly shaped silo, characterized in that, include: Obtain point cloud data of the silo to be tested; The point cloud data is preprocessed to obtain material data; Determine whether the silo to be tested is an irregular silo. If so, obtain the material volume of the silo to be tested based on the material data and according to the first volume algorithm. If not, obtain the material volume of the silo to be tested based on the material data and according to the second volume algorithm. The preprocessing of the point cloud data to obtain material data includes: The point cloud data is sampled to obtain sampled data; The sampled data is filtered to obtain filtered data; The filtered data is segmented and processed to obtain material data; The point cloud data is sampled to obtain sampled data, including: Create a 3D voxel mesh on the point cloud data; Determine the voxel center point within each voxel in the 3D voxel mesh; The sampling data is obtained by replacing the points within each voxel with the voxel center point. The sampled data is filtered to obtain filtered data, including: Based on the sampled data, a coordinate system is established with the ground directly below the docking point of the silo to be tested as the origin. Three pass-through filters are used to filter the data in the X-axis, Y-axis and Z-axis ranges respectively to obtain filtered data; The process involves using three through filters to filter the data within the X, Y, and Z axis ranges, respectively, to obtain filtered data, including: The first pass filter is used to determine whether the value of the X-axis of the sampled data is within the range of the X-axis of the established coordinate system. If not, the data point is deleted; if so, the data point is retained. The second pass-through filter is used to determine whether the value of the Y-axis of the sampled data is within the range of the Y-axis of the established coordinate system. If not, the data point is deleted; if so, the data point is retained. The third pass-through filter is used to determine whether the value of the Z-axis of the sampled data is within the range of the Z-axis of the established coordinate system. If not, the data point is deleted; if so, the data point is retained. Based on all the retained data points, the filtered data is obtained; The step of segmenting the filtered data to obtain material data includes: The filtered data is cut using the RANSAC method to fit the planar formula for each wall. Based on the planar formula for each wall, construct the planar distance formula; Based on the filtered data, the distance between each point and each plane is obtained according to the planar distance formula; Determine whether the distance between each point and each plane is within the threshold range; if so, delete the data point. Based on the remaining data points, the material data is obtained.

2. The method for measuring the volume of material in an irregular silo according to claim 1, characterized in that, The expression for the planar distance formula is: ； Where d is the distance from the point to the plane.

3. The method for measuring the volume of material in an irregular silo according to claim 1, characterized in that, Based on the material data, the material volume of the silo to be tested is obtained according to the first volume algorithm, including: Based on the maximum and minimum values ​​of the X-axis and the maximum and minimum values ​​of the Y-axis of the silo detection range, grids are established on the X-axis and Y-axis, and the initial Z-axis average value of each grid is recorded. The silo detection range is larger than the range where the material data is located. The material data is stored in a grid, and the average value of the Z-axis of the material data points in each grid is calculated. Calculate the volume of each grid cell based on the average Z-axis value and the initial average Z-axis value; The material volume of the silo to be tested is obtained based on the volume of each grid.

4. The method for measuring the volume of material in an irregular silo according to claim 1, characterized in that, Based on the material data, the material volume of the silo to be tested is obtained according to the second volume algorithm, including: Determine the maximum and minimum values ​​of the material data on the X-axis and the maximum and minimum values ​​on the Y-axis; Based on the maximum and minimum values ​​of the X-axis and the maximum and minimum values ​​of the Y-axis of the material data, a grid is established on the X-axis and Y-axis of the material data; The material data is stored in a grid, and the average Z-axis value of the material data in the grid is calculated. Calculate the volume of each grid cell based on the Z-axis average value; The material volume of the silo to be tested is obtained based on the volume of each grid.

5. An apparatus for measuring the volume of material in an irregularly shaped silo, applied to the method according to any one of claims 1-4, characterized in that, The device includes: The computing processor and the 3D LiDAR and rail-mounted crane, both connected to the computing processor; The 3D LiDAR is used to acquire point cloud data of the silo to be tested. The guide rail trolley is used to drive the 3D LiDAR to the location of the silo to be tested. The computing processor is used to guide the guide rail trolley and obtain the material volume of the silo to be tested based on the point cloud data.

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