A method and a scanning device for identifying the distribution of garbage in a garbage pit based on laser scanning

Abstract

The present application relates to garbage distribution identification technical field, specifically to a kind of garbage pool garbage distribution identification method and scanning device based on laser scanning;Method includes: according to the geometric characteristic value of stockpile generation grabbing position recommended coordinate and stacking position recommended coordinate, and output to grab bucket dispatching control system;The surface slope change of stockpile and the point cloud depth of garbage pool bottom drainage ditch area are analyzed, when the slope exceeds preset collapse threshold or drainage ditch depth is lower than preset grab ditch threshold, generate collapse early warning signal or grab ditch early warning signal;The total inventory volume of entire garbage pool is calculated using grid integration method, and inventory data is automatically refreshed after each scanning update;Through the cooperation of multiple laser scanners fixedly installed with pan-tilt rotation acquisition point cloud, grabbing stacking coordinate and volume data are generated after splicing, clustering segmentation and feature extraction, to solve the problems of low efficiency, large error and high safety hazards in manual management.

Description

Technical Field

[0001] This invention relates to the field of waste distribution identification technology, and in particular to a method and scanning device for identifying waste distribution in waste pits based on laser scanning. Background Technology

[0002] Waste-to-energy plants use waste pits to store and ferment municipal solid waste. Their inventory management and grab location determination have long relied on manual operation. This involves manually recording the information of waste entering the plant and the weight of waste being fed into the furnace, estimating the inventory by the difference, and simultaneously judging the grab location by visual observation. In the existing technology, a few companies have tried to use two-dimensional cameras or laser rangefinders to assist in monitoring, but these can only provide local height data and cannot obtain the three-dimensional shape of the entire pit. There are also solutions that use mobile scanning equipment, but these require frequent movement and repeated calibration, making it difficult to meet the requirements of continuous operation.

[0003] However, in the aforementioned existing technologies, the methods of manual recording and visual judgment suffer from serious data lag and location misjudgment. Specifically, the moisture loss caused by waste fermentation leads to large deviations in weight estimation, and the limited field of view and large height differences on site result in frequent errors in judging the grabbing position. Furthermore, it is impossible to obtain the three-dimensional distribution and inventory volume changes of the waste pool in real time. Therefore, there is an urgent need for a waste pool modeling and imaging method and device that can be fixedly installed, automatically scanned, 3D modeled, and output grabbing and stacking coordinates to solve the problems of low efficiency, large errors, and high safety risks of manual management. Summary of the Invention

[0004] The purpose of this invention is to provide a method and scanning device for identifying the distribution of waste in a waste pit based on laser scanning. This invention aims to solve the problems of serious data lag and location misjudgment caused by manual recording and visual judgment in the prior art. Specifically, the waste fermentation leads to moisture loss, resulting in large deviations in weight estimation. Limited field of view and large height differences on site lead to frequent errors in judging the grab position. Furthermore, it is impossible to obtain the three-dimensional distribution and inventory volume changes of the waste pit in real time.

[0005] To achieve the above objectives, the present invention employs a laser scanning-based method for identifying the distribution of waste in a waste pit, comprising the following steps: Multiple high-precision 3D laser scanners, fixedly installed above the waste pool, are used in conjunction with a rotating gimbal to collect raw 3D point cloud data of each area of ​​the waste pool. The point cloud data collected by each scanner is preprocessed, including outlier filtering and downsampling, and the point cloud data from multiple scanners are stitched together into a complete 3D point cloud model of the waste pool through an iterative nearest point registration algorithm. Clustering and segmentation are performed on the stitched 3D point cloud model to extract independent point cloud clusters of waste piles, and geometric feature values ​​of each pile are calculated, including the coordinates of the highest point, the coordinates of the lowest point, the pile volume, and the surface slope. Based on the geometric feature values ​​of the material pile, recommended coordinates for grab position and stacking position are generated and output to the grab bucket scheduling and control system. Analyze the surface slope changes of the material pile and the point cloud depth of the drainage ditch area at the bottom of the waste pit. When the slope exceeds the preset collapse threshold or the drainage ditch depth is lower than the preset ditch grabbing threshold, generate a collapse warning signal or a ditch grabbing warning signal. Based on a complete 3D point cloud model, the total inventory volume of the entire waste pool is calculated using the grid integration method, and the inventory data is automatically refreshed after each scan update.

[0006] In the step of collecting original 3D point cloud data of each area of ​​the waste pool by using multiple high-precision 3D laser scanners fixedly installed above the waste pool and rotating a pan-tilt unit: Each scanner is equipped with a Class II explosion-proof rating and an IP protection rating of no less than IP65. It uses a high-precision lightweight pan-tilt head to rotate the scanner in both horizontal and vertical directions, and the scanning angle covers the entire area of ​​the waste pool. Both the scanner and the pan-tilt unit are fixedly mounted on the steel structure beam at the top of the waste pit to avoid vibration interference during the scanning process.

[0007] Among the steps, the preprocessing of point cloud data collected by each scanner, including outlier filtering and downsampling, and the stitching of point cloud data from multiple scanners into a complete 3D point cloud model of the waste pool using an iterative nearest-neighbor registration algorithm are as follows: First, the point cloud data of each scanner is timestamped and synchronized, and initial coarse registration is performed using the pre-calibrated relative pose matrix between scanners. Then, the iterative nearest point algorithm is used for fine registration, and the convergence condition for the iteration is set to the root mean square error being less than 1 mm.

[0008] Among the steps, the preprocessing of point cloud data collected by each scanner, including outlier filtering and downsampling, and the stitching of point cloud data from multiple scanners into a complete 3D point cloud model of the waste pool using an iterative nearest-neighbor registration algorithm are as follows: Outlier filtering uses a statistical filtering method to calculate the average neighborhood distance of each point and deletes points whose distance exceeds three times the global mean as outliers. Downsampling employs a voxel grid filter with a voxel side length of 20 mm to reduce point cloud density while preserving overall geometric features.

[0009] In the steps of clustering and segmenting the stitched 3D point cloud model, extracting independent point cloud clusters of waste piles, and calculating the geometric feature values ​​of each pile, including the coordinates of the highest point, the coordinates of the lowest point, the pile volume, and the surface slope: A clustering algorithm based on Euclidean distance is used to divide spatially separated material piles into different point cloud clusters by setting a threshold for the cluster neighborhood radius and a minimum number of cluster points. Each point cloud cluster represents an independent waste pile.

[0010] In the steps of clustering and segmenting the stitched 3D point cloud model, extracting independent point cloud clusters of waste piles, and calculating the geometric feature values ​​of each pile, including the coordinates of the highest point, the coordinates of the lowest point, the pile volume, and the surface slope: The coordinates of the highest point are taken from the point with the largest Z-axis coordinate in the point cloud cluster, and the coordinates of the lowest point are taken from the point with the smallest Z-axis coordinate. The accumulation volume is obtained by summing the tetrahedral volumes after triangular meshing the point cloud cluster. The surface slope is obtained by fitting a local plane of the material pile and calculating the average angle between the normal vector and the horizontal plane.

[0011] In the step of generating recommended coordinates for the grab position and the stacking position based on the geometric feature values ​​of the material pile, and outputting them to the grab bucket scheduling and control system: The recommended coordinates for grabbing position are the center of gravity of the highest point of the material pile, to ensure that the grab can grab the most material. The recommended location for stacking materials is an area within the current waste pit that is not covered by material piles and has a lower ground elevation, in order to balance the distribution of materials within the waste pit.

[0012] In the step of analyzing the surface slope changes of the material pile and the point cloud depth of the drainage ditch area at the bottom of the waste pit, when the slope exceeds a preset collapse threshold or the drainage ditch depth is lower than a preset ditch-catching threshold, the following steps are taken: The preset collapse threshold is a surface slope greater than 35 degrees. When the slope of the same stockpile exceeds this threshold in three consecutive scanning cycles, a collapse warning is generated. The preset threshold for ditch detection is that the difference between the point cloud depth of the drainage ditch area and the initial depth is greater than 200 mm. When the depth difference exceeds the threshold, a ditch detection warning is generated.

[0013] Among them, in the step of calculating the total inventory volume of the entire waste pool based on a complete 3D point cloud model using the grid integration method, and automatically refreshing the inventory data after each scan update: The ground area of ​​the waste pit is divided into uniform grid units, each with a length and width of 100 mm; The material thickness is obtained by subtracting the ground reference height from the average height of the point cloud within each grid cell. Multiply the thickness by the grid area and then sum all the grid areas to get the total volume.

[0014] This invention also provides a laser scanning-based waste distribution scanning device for waste pits, comprising multiple fixedly installed 3D laser scanners, a high-precision lightweight pan-tilt unit, a data acquisition and processing unit, an early warning output interface, and a scheduling and control interface, wherein: The 3D laser scanner and the high-precision lightweight gimbal are fixedly installed on the steel structure beam at the top of the garbage pit. The 3D laser scanner is used to collect raw 3D point cloud data, and the high-precision lightweight gimbal is used to drive the scanner to rotate to cover the entire area. The data acquisition and processing unit is used to receive point cloud data from each scanner and perform preprocessing, stitching, clustering and segmentation, feature value extraction and volume calculation. The early warning output interface is used to output an early warning signal to the scheduling and control system when a risk of material collapse is detected or when it is necessary to clear the ditch. The scheduling and control interface is used to provide the grab bucket control system with recommended coordinates for the grab position and the stacking position.

[0015] This invention discloses a method and scanning device for identifying waste distribution in a waste pit based on laser scanning. Multiple high-precision 3D laser scanners, fixedly mounted above the waste pit and rotated with a pan-tilt unit, collect raw 3D point cloud data of various areas within the waste pit. The point cloud data collected by each scanner is preprocessed, including outlier filtering and downsampling. The point cloud data from multiple scanners are then stitched together into a complete 3D point cloud model of the waste pit using an iterative nearest-neighbor registration algorithm. The stitched 3D point cloud model is then clustered and segmented to extract independent point cloud clusters of waste piles. Geometric feature values ​​for each pile are calculated, including the coordinates of the highest and lowest points, the pile volume, and the surface slope. Based on these geometric feature values, a grab position is generated. Recommended coordinates and stacking location coordinates are output to the grab bucket scheduling and control system; the surface slope changes of the material pile and the point cloud depth of the drainage ditch area at the bottom of the waste pit are analyzed. When the slope exceeds the preset collapse threshold or the drainage ditch depth is lower than the preset grab ditch threshold, a collapse warning signal or a grab ditch warning signal is generated; based on the complete 3D point cloud model, the total inventory volume of the entire waste pit is calculated using the grid integration method, and the inventory data is automatically refreshed after each scan update; multiple fixedly installed laser scanners are used in conjunction with a rotating gimbal to collect point clouds. After stitching, clustering, segmentation, and feature extraction, grab stacking coordinates and volume data are generated, and warnings are issued based on slope and depth thresholds, solving the problems of low efficiency, large errors, and high safety hazards of manual management; By using multiple fixed, high-precision 3D laser scanners in conjunction with a rotating gimbal, a comprehensive, low-vibration scan of the entire waste pit area was achieved, significantly improving the accuracy and stability of the point cloud data. Outlier filtering, downsampling preprocessing, and iterative nearest-point registration algorithms effectively reduced noise interference and enabled high-precision stitching of point clouds from multiple scanners, resolving the blind spot problem. Clustering and segmentation extracted independent material piles, and geometric features such as the highest and lowest points, volume, and slope were calculated, providing accurate coordinates for grabbing and stacking, avoiding misjudgments from manual visual inspection. Analysis of surface slope changes and drainage ditch depth automatically generated collapse and ditch-grabbing warnings, enhancing operational safety. Real-time calculation of inventory volume using grid integration and automatic updates after scanning enabled the digitalization and automation of inventory management. This effectively reduced manual labor intensity, improved work efficiency and measurement accuracy, and eliminated the risk of misjudgments caused by visual fatigue. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art 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.

[0017] Figure 1 This is a flowchart of the steps of the laser scanning-based waste distribution identification method in the waste pool of the present invention.

[0018] Figure 2 This is a flowchart of steps S100 of the present invention.

[0019] Figure 3 This is a flowchart of steps S200 of the present invention.

[0020] Figure 4 This is a flowchart of steps S300 of the present invention.

[0021] Figure 5 This is a flowchart of steps S400 of the present invention.

[0022] Figure 6 This is a flowchart of steps S500 of the present invention.

[0023] Figure 7 This is a flowchart of steps S600 of the present invention.

[0024] Figure 8 This is a schematic diagram of the structural principle of the waste distribution scanning device for waste pits based on laser scanning according to the present invention.

[0025] Figure 9This is a diagram of the three-dimensional scanning processing framework of the present invention.

[0026] Figure 10 This is a schematic diagram of the electronic device of the present invention.

[0027] 701-3D laser scanner, 702-high-precision lightweight gimbal, 703-data acquisition and processing unit, 704-early warning output interface, 705-dispatch control interface. Detailed Implementation

[0028] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application.

[0029] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0030] It should be understood that although the terms first, second, third, etc., may be used in this application to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, without departing from the scope of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to determination."

[0031] Please see Figures 1-4 This invention provides a method for identifying the distribution of waste in a waste pool based on laser scanning, comprising the following steps: S100: Multiple high-precision 3D laser scanners fixedly installed above the waste pool, along with a rotating gimbal, collect the original 3D point cloud data of each area of ​​the waste pool. In this embodiment, feature 0+ effect. Specifically, it includes: S101: Each scanner has a Class II explosion-proof rating and an IP protection rating of no less than IP65. It uses a high-precision lightweight pan-tilt head to rotate the scanner in the horizontal and vertical directions, and the scanning angle covers the entire area of ​​the waste pool. S102: Both the scanner and the pan-tilt unit are fixedly installed on the steel structure beam at the top of the waste pit to avoid vibration interference during the scanning process.

[0032] Two Class II explosion-proof, IP65-rated 3D laser scanners were selected and fixedly installed on the steel structural beams on the east and west sides of the top of the waste pit. Each scanner was equipped with a high-precision, lightweight gimbal, which drove the scanner to rotate uniformly within a horizontal range of 0 to 180 degrees and a pitch range of 0 to 90 degrees. The scanning frequency was set to 100,000 points per second, with a scanning time of 30 seconds per area, ensuring full coverage of the waste pit. Rubber shock-absorbing pads were installed between the beams and the waste pit walls to prevent vibrations caused by the grab bucket operation from being transmitted to the scanners, thus obtaining high-precision, low-noise raw 3D point cloud data.

[0033] S200: Preprocesses the point cloud data collected by each scanner, including outlier filtering and downsampling, and stitches the point cloud data from multiple scanners into a complete 3D point cloud model of the waste pool through an iterative nearest point registration algorithm. In this embodiment, feature 0+ effect. Specifically, it includes: S201: First, the point cloud data of each scanner is timestamped and synchronized, and initial coarse registration is performed using the pre-calibrated relative pose matrix between scanners. S202: Then, the iterative nearest point algorithm is used for fine registration, and the iterative convergence condition is set to the root mean square error being less than 1 mm. S203: Outlier filtering uses a statistical filtering method to calculate the average neighborhood distance of each point and deletes points whose distance exceeds three times the standard deviation of the global mean as outliers. S204: Downsampling uses a voxel grid filter with a voxel side length of 20 mm to reduce point cloud density while preserving overall geometric features.

[0034] In this process, point cloud data collected by each scanner is transmitted to a data processing server via industrial Ethernet. The server first timestamps the data from the two scanners based on a GPS clock. Then, initial coarse registration is performed using the relative pose matrix between the two scanners, pre-calculated using a checkerboard calibration method. Next, fine registration is performed using an iterative nearest-neighbor algorithm, with the iterative convergence condition set to a root mean square error of less than 1 mm. After fine registration, outlier filtering is applied to the stitched point cloud: the average distance to the 50 neighboring points around each point is calculated, and points whose distance exceeds three standard deviations of the global mean are deleted. Finally, downsampling is performed: a voxel grid filter is used, with the voxel side length set to 20 mm, preserving overall geometric features such as the edges of the material pile while reducing the point cloud density to an acceptable range to accelerate subsequent processing.

[0035] S300: Cluster and segment the stitched 3D point cloud model, extract independent point cloud clusters of waste piles, and calculate the geometric feature values ​​of each pile, including the coordinates of the highest point, the coordinates of the lowest point, the pile volume, and the surface slope. In this embodiment, feature 0+ effect. Specifically, it includes: S301: Employs a clustering algorithm based on Euclidean distance, sets a threshold for the cluster neighborhood radius and a minimum number of cluster points, and divides spatially separated material piles into different point cloud clusters; each point cloud cluster represents an independent waste pile. S302: The coordinates of the highest point are taken from the point with the largest Z-axis coordinate in the point cloud cluster, and the coordinates of the lowest point are taken from the point with the smallest Z-axis coordinate; S303: The accumulation volume is obtained by summing the tetrahedral volumes after triangular meshing the point cloud cluster; S304: The surface slope is obtained by fitting the local plane of the material pile and calculating the average angle between the normal vector and the horizontal plane.

[0036] In this study, a clustering algorithm based on Euclidean distance was used to segment the preprocessed point cloud. A cluster neighborhood radius threshold of 0.5 meters and a minimum cluster size of 100 points were set. Point clouds spatially separated by a distance greater than 0.5 meters were segmented into different clusters, each representing an independent waste pile. For each pile's point cloud cluster, the coordinates of its highest point (maximum Z-axis point) and lowest point (minimum Z-axis point) were calculated. The pile volume was calculated by performing Delaunay triangulation on the point cloud cluster, obtaining a series of tetrahedrons, and summing the volumes of all tetrahedrons to obtain the approximate volume of the pile. The surface slope was determined by fitting the local plane of the pile using the least squares method and calculating the average angle between the plane's normal vector and the horizontal plane's normal vector. In this embodiment, the coordinates of the highest point of a typical pile are (12.5, 8.2, 3.8), the coordinates of the lowest point are (12.5, 8.2, 0.5), the volume is 24.6 cubic meters, and the surface slope is 28 degrees.

[0037] S400: Generates recommended coordinates for grab position and stacking position based on the geometric feature values ​​of the material pile, and outputs them to the grab bucket scheduling and control system; In this embodiment, feature 0+ effect. Specifically, it includes: S401: Recommended coordinates for grab position: Grab the center of gravity of the highest point of the material pile to ensure that the grab can grab the most material. S402: Recommended coordinates for stacking location are areas in the current waste pit that are not covered by material piles and have a lower ground elevation, in order to balance the distribution of materials in the waste pit.

[0038] For each material pile, the recommended grab position coordinates are taken from the centroid coordinates of the highest point area of ​​the pile. Specifically, the average coordinates of all points within a 0.8-meter radius around the highest point are calculated to obtain the recommended grab point. For example, if the centroid coordinates of a material pile are (12.5, 8.2, 3.6), the grab bucket can grab the most material by descending at these coordinates. The recommended placement coordinates are taken from the area in the current waste pool that is not covered by any material pile and whose ground elevation is 0.3 meters lower than the overall average ground elevation. The system scans the ground point cloud of the blank area, calculates the ground elevation of each candidate point, and selects the point with the lowest elevation as the recommended placement point. The generated coordinates are sent to the grab bucket scheduling control system in structured message format via TCP / IP protocol. The message format includes the material pile ID, recommended coordinates, and recommendation type (grab or place).

[0039] S500: Analyzes the surface slope changes of the material pile and the point cloud depth of the drainage ditch area at the bottom of the waste pit. When the slope exceeds the preset collapse threshold or the drainage ditch depth is lower than the preset ditch grabbing threshold, a collapse warning signal or a ditch grabbing warning signal is generated. In this embodiment, feature 0+ effect. Specifically, it includes: S501: The preset collapse threshold is a surface slope greater than 35 degrees. When the slope of the same stockpile exceeds this threshold in three consecutive scanning cycles, a collapse warning is generated. S502: The preset ditch grabbing threshold is when the difference between the point cloud depth of the drainage ditch area and the initial depth is greater than 200 mm. When the depth difference exceeds the threshold, a ditch grabbing warning is generated.

[0040] The system performs a complete scan and analysis process every 15 minutes. For each material pile, historical surface slope data is recorded. The preset collapse threshold is a surface slope greater than 35 degrees. When the slope of the same material pile exceeds 35 degrees for three consecutive scan cycles (i.e., 45 minutes), the material pile is deemed to have a collapse risk, and a collapse warning signal is immediately generated, including the material pile ID, current location coordinates, and slope value. For the drainage ditch area, the average depth of the point cloud at the bottom of the drainage ditch is recorded as the initial depth benchmark during the first scan. Subsequent scans calculate the difference between the point cloud depth in the drainage ditch area and the initial depth. When the difference is greater than 200 mm (i.e., the depth decreases by 200 mm), it indicates that the drainage ditch is buried by garbage and needs to be cleared. The system generates a clearing warning signal, including the depth difference and the location range of the drainage ditch. In addition, "ditch grabbing" is a common operational term in the waste-to-energy incineration industry. It specifically refers to the operation of using a garbage crane grab bucket to clean up the accumulated garbage, silt, and leachate sediment in the drainage ditch at the bottom of the garbage pit, restoring the drainage duct's smooth flow and leachate discharge function. The drainage ditch at the bottom of the garbage pit is used to collect the leachate produced by garbage fermentation and guide it to the leachate collection tank. When garbage accumulates and buries the drainage ditch or debris blocks the drainage ditch, the leachate cannot be discharged normally, affecting the garbage fermentation effect and increasing the safety hazards of the garbage pit. Therefore, ditch grabbing operations need to be carried out regularly.

[0041] S600: Based on a complete 3D point cloud model, it uses the grid integration method to calculate the total inventory volume of the entire waste pool and automatically refreshes the inventory data after each scan update.

[0042] In this embodiment, feature 0+ effect. Specifically, it includes: S601: Divide the ground area of ​​the waste pit into uniform grid units, each grid unit being 100 mm in length and width; S602: Calculate the material thickness by subtracting the ground reference height from the average height of the point cloud within each grid cell; S603: Multiply the thickness by the grid area and sum all the grids to get the total volume.

[0043] The system divides the area within the boundary of the waste disposal site into uniform grid cells, each 100 mm in length and width. For each grid cell, the height values ​​of all point clouds within that cell are retrieved, and their average value is calculated. If a grid cell has no point cloud, the average height of the surrounding eight grid cells is used for interpolation to fill the cell. The material thickness of the grid cell is equal to the average height minus the pre-calibrated ground reference height at that grid cell (obtained during the initial emptying scan). The volume of the grid cell is equal to the thickness multiplied by the grid area (0.01 square meters). The total inventory volume of the entire waste disposal site is obtained by summing the volumes of all grid cells. After each scan, the system automatically updates the inventory volume value and stores historical records. For example, if a scan calculates a total volume of 1250.6 cubic meters, the interface displays this value in real time and plots a curve showing its change for management personnel's reference. In addition, regarding the volume of the inventory, the volume of the inventory is calculated using a complete 3D model of the waste pool; after establishing a 3D model of the waste pool and refreshing the waste pool model, the volume of the corresponding waste pile is automatically calculated. This represents the relative height of the (x, y) coordinates of the l-th cell to the ground. This represents the absolute height value of the (x, y) coordinates of the l-th cell. The (x, y) coordinates of the ll-th cell represent the absolute height of the waste pit floor. This represents the volume of the l-th cell.

[0044] Corresponding to the aforementioned embodiments of the laser scanning-based waste distribution identification method for waste pits, this application also provides embodiments of a laser scanning-based waste distribution scanning device for waste pits.

[0045] Figure 8 This is a block diagram illustrating a laser scanning-based waste distribution scanning device for a waste disposal site, according to an exemplary embodiment. (Refer to...) Figure 8 The device may include: multiple fixedly installed 3D laser scanners 701, a high-precision lightweight pan-tilt unit 702, a data acquisition and processing unit 703, an early warning output interface 704, and a scheduling and control interface 705, wherein: The 3D laser scanner 701 and the high-precision lightweight gimbal 702 are fixedly installed on the steel structure beam at the top of the garbage pit. The 3D laser scanner 701 is used to collect raw 3D point cloud data, and the high-precision lightweight gimbal 702 is used to drive the scanner to rotate to cover the entire area. The data acquisition and processing unit 703 is used to receive point cloud data from each scanner and perform preprocessing, stitching, clustering and segmentation, feature value extraction and volume calculation. The early warning output interface 704 is used to output an early warning signal to the scheduling and control system when a risk of material collapse is detected or when it is necessary to clear the ditch. The scheduling control interface 705 is used to provide the grab bucket control system with recommended coordinates for the grab position and the stacking position.

[0046] In this embodiment, the 3D laser scanner 701 and the high-precision lightweight gimbal 702 are fixedly installed on the steel structure beam at the top of the waste pit. The 3D laser scanner 701 is used to collect raw 3D point cloud data, and the high-precision lightweight gimbal 702 is used to drive the scanner to rotate to cover the entire area. The data acquisition and processing unit 703 is used to receive the point cloud data from each scanner and perform preprocessing, stitching, clustering and segmentation, feature value extraction, and volume calculation. The early warning output interface 704 is used to output an early warning signal to the scheduling control system when a risk of material collapse is detected or when grabbing is required. The scheduling control interface 705 is used to provide the grabbing position and stacking position recommended coordinates to the grab bucket control system.

[0047] Regarding the system in the above embodiments, the specific ways in which each module performs operations have been described in detail in the embodiments related to the method, and will not be elaborated here.

[0048] For the system embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this application according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0049] Accordingly, this application also provides an electronic device, comprising: one or more processors; a memory for storing one or more programs; and when the one or more programs are executed by the one or more processors, causing the one or more processors to implement the laser scanning-based waste distribution identification method for waste pits as described above. Figure 10 The diagram shown is a hardware structure diagram of any device with data processing capabilities, including a laser scanning-based waste distribution scanning device for waste pits, as provided in an embodiment of the present invention. (Except for...) Figure 10 In addition to the processor, memory, and network interface shown, any data processing device in the embodiment may also include other hardware depending on the actual function of the data processing device, which will not be described in detail here.

[0050] Accordingly, this application also provides a computer-readable storage medium storing computer instructions, which, when executed by a processor, implement the laser scanning-based waste distribution identification method for waste pits as described above. The computer-readable storage medium can be an internal storage unit of any data-processing device as described in any of the foregoing embodiments, such as a hard disk or memory. The computer-readable storage medium can also be an external storage device, such as a plug-in hard disk, smart media card (SMC), SD card, flash card, etc., equipped on the device. Furthermore, the computer-readable storage medium can include both internal storage units of any data-processing device and external storage devices. The computer-readable storage medium is used to store the computer program and other programs and data required by the data-processing device, and can also be used to temporarily store data that has been output or will be output.

[0051] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the disclosure herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein.

[0052] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope.

Claims

1. A laser scanning modeling and imaging method for landfill pits, characterized in that, Includes the following steps: Multiple high-precision 3D laser scanners, fixedly installed above the waste pool, are used in conjunction with a rotating gimbal to collect raw 3D point cloud data of each area of ​​the waste pool. The point cloud data collected by each scanner is preprocessed, including outlier filtering and downsampling, and the point cloud data from multiple scanners are stitched together into a complete 3D point cloud model of the waste pool through an iterative nearest point registration algorithm. Clustering and segmentation are performed on the stitched 3D point cloud model to extract independent point cloud clusters of waste piles, and geometric feature values ​​of each pile are calculated, including the coordinates of the highest point, the coordinates of the lowest point, the pile volume, and the surface slope. Based on the geometric feature values ​​of the material pile, recommended coordinates for grab position and stacking position are generated and output to the grab bucket scheduling and control system. Analyze the surface slope changes of the material pile and the point cloud depth of the drainage ditch area at the bottom of the waste pit. When the slope exceeds the preset collapse threshold or the drainage ditch depth is lower than the preset ditch grabbing threshold, generate a collapse warning signal or a ditch grabbing warning signal. Based on a complete 3D point cloud model, the total inventory volume of the entire waste pool is calculated using the grid integration method, and the inventory data is automatically refreshed after each scan update.

2. The laser scanning modeling and imaging method for landfills as described in claim 1, characterized in that, In the step of collecting raw 3D point cloud data of each area of ​​the waste pool by using multiple high-precision 3D laser scanners fixedly installed above the waste pool and rotating a pan-tilt unit: Each scanner is equipped with a Class II explosion-proof rating and an IP protection rating of no less than IP65. It uses a high-precision lightweight pan-tilt head to rotate the scanner in both horizontal and vertical directions, and the scanning angle covers the entire area of ​​the waste pool. Both the scanner and the pan-tilt unit are fixedly mounted on the steel structure beam at the top of the waste pit to avoid vibration interference during the scanning process.

3. The laser scanning modeling and imaging method for garbage pits as described in claim 1, characterized in that, In the steps of preprocessing the point cloud data collected by each scanner, including outlier filtering and downsampling, and stitching the point cloud data from multiple scanners into a complete 3D point cloud model of the waste pool using an iterative nearest-point registration algorithm: First, the point cloud data of each scanner is timestamped and synchronized, and initial coarse registration is performed using the pre-calibrated relative pose matrix between scanners. Then, the iterative nearest point algorithm is used for fine registration, and the convergence condition for the iteration is set to the root mean square error being less than 1 mm.

4. The laser scanning modeling and imaging method for landfills as described in claim 1, characterized in that, In the steps of preprocessing the point cloud data collected by each scanner, including outlier filtering and downsampling, and stitching the point cloud data from multiple scanners into a complete 3D point cloud model of the waste pool using an iterative nearest-point registration algorithm: Outlier filtering uses a statistical filtering method to calculate the average neighborhood distance of each point and deletes points whose distance exceeds three times the global mean as outliers. Downsampling employs a voxel grid filter with a voxel side length of 20 mm to reduce point cloud density while preserving overall geometric features.

5. The laser scanning modeling and imaging method for garbage pits as described in claim 1, characterized in that, In the steps of clustering and segmenting the stitched 3D point cloud model, extracting independent point cloud clusters of waste piles, and calculating the geometric feature values ​​of each pile, including the coordinates of the highest point, the coordinates of the lowest point, the pile volume, and the surface slope: A clustering algorithm based on Euclidean distance is used to divide spatially separated material piles into different point cloud clusters by setting a threshold for the cluster neighborhood radius and a minimum number of cluster points. Each point cloud cluster represents an independent waste pile.

6. The laser scanning modeling and imaging method for landfills as described in claim 1, characterized in that, In the steps of clustering and segmenting the stitched 3D point cloud model, extracting independent point cloud clusters of waste piles, and calculating the geometric feature values ​​of each pile, including the coordinates of the highest point, the coordinates of the lowest point, the pile volume, and the surface slope: The coordinates of the highest point are taken from the point with the largest Z-axis coordinate in the point cloud cluster, and the coordinates of the lowest point are taken from the point with the smallest Z-axis coordinate. The accumulation volume is obtained by summing the tetrahedral volumes after triangular meshing the point cloud cluster. The surface slope is obtained by fitting a local plane of the material pile and calculating the average angle between the normal vector and the horizontal plane.

7. The laser scanning modeling and imaging method for landfills as described in claim 1, characterized in that, In the steps of generating recommended coordinates for the grab position and the stacking position based on the geometric feature values ​​of the material pile, and outputting them to the grab bucket scheduling and control system: The recommended coordinates for grabbing position are the center of gravity of the highest point of the material pile, to ensure that the grab can grab the most material. The recommended location for stacking materials is an area within the current waste pit that is not covered by material piles and has a lower ground elevation, in order to balance the distribution of materials within the waste pit.

8. The laser scanning modeling and imaging method for landfills as described in claim 1, characterized in that, In the step of generating a collapse warning signal or a drainage ditch warning signal when analyzing the surface slope changes of the material pile and the point cloud depth of the drainage ditch area at the bottom of the waste pit: The preset collapse threshold is a surface slope greater than 35 degrees. When the slope of the same stockpile exceeds this threshold in three consecutive scanning cycles, a collapse warning is generated. The preset threshold for ditch detection is that the difference between the point cloud depth of the drainage ditch area and the initial depth is greater than 200 mm. When the depth difference exceeds the threshold, a ditch detection warning is generated.

9. The laser scanning modeling and imaging method for garbage pits as described in claim 1, characterized in that, In the step of calculating the total inventory volume of the entire waste pool based on a complete 3D point cloud model using the mesh integration method, and automatically refreshing the inventory data after each scan update: The ground area of ​​the waste pit is divided into uniform grid units, each with a length and width of 100 mm; The material thickness is obtained by subtracting the ground reference height from the average height of the point cloud within each grid cell. Multiply the thickness by the grid area and then sum all the grid areas to get the total volume.

10. A laser scanning garbage pit modeling and imaging device, employing the laser scanning garbage pit modeling and imaging method as described in claim 1, characterized in that, It includes multiple fixed-mount 3D laser scanners, a high-precision lightweight pan-tilt unit, a data acquisition and processing unit, an early warning output interface, and a scheduling and control interface, among which: The 3D laser scanner and the high-precision lightweight gimbal are fixedly installed on the steel structure beam at the top of the garbage pit. The 3D laser scanner is used to collect raw 3D point cloud data, and the high-precision lightweight gimbal is used to drive the scanner to rotate to cover the entire area. The data acquisition and processing unit is used to receive point cloud data from each scanner and perform preprocessing, stitching, clustering and segmentation, feature value extraction and volume calculation. The early warning output interface is used to output an early warning signal to the scheduling and control system when a risk of material collapse is detected or when it is necessary to clear the ditch. The scheduling and control interface is used to provide the grab bucket control system with recommended coordinates for the grab position and the stacking position.

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