A method and system for map area updating for a loading area of an open pit mine

Abstract

The application discloses a kind of map area updating method and system for open-pit mine loading area, method includes: obtaining initial global map data of loading area, and dividing region boundary line and region attribute;Obtain local map data of operation end;According to the local map data of operation end obtained, calculate the flatness of operation end;According to the local map data of operation end obtained and the region boundary line and region attribute, different probability update strategy is implemented to different regions using improved ray casting method, to obtain the local map data of operation end after updating;According to the local map data of operation end after updating and the flatness of operation end, construct global map and carry out earthwork volume change calculation;According to the constraint condition of global map, the flatness of operation end and region boundary, divide the region boundary line and region attribute after updating.The application can accurately and carefully divide the region boundary line and region attribute after updating, and operation is simple, and calculation is small.

Description

Technical Field

[0001] This invention belongs to the field of engineering machinery technology, specifically relating to a method and system for updating map areas in open-pit mine loading areas. Background Technology

[0002] With the development of intelligent technologies, the traditional mining industry will gradually move towards unmanned operation. On the one hand, the mining industry is a high-risk industry, and the application of unmanned systems urgently needs to be implemented. On the other hand, mechanical operations in mining scenarios are simple and repetitive, and there are relatively few uncontrollable dynamic factors on mine roads, making them suitable for the application of autonomous driving.

[0003] In mining environments, autonomous mining trucks need to perform tasks autonomously in various work areas such as driving, loading, and unloading zones. This places high demands on the interaction between the autonomous mining trucks and their environment. To address these challenges, the primary task is to achieve localization of the unmanned mining trucks and the construction of scene maps of their surroundings. GNSS technology can solve the localization problem by measuring the absolute position of a location, while environmental perception sensors such as LiDAR can be used to construct scene maps.

[0004] Roads in mining environments are unstructured roads characterized by numerous curves, steep gradients, and significant surface undulations. The boundaries between these roads and their surroundings are often blurred; for example, mining trucks may encounter cliffs and rock faces, necessitating the planning of a safe driving range. By performing regional detection on unstructured road environments, the changing states of different areas can be tracked based on their varying frequencies of change. For scenarios with low-frequency changes in drivable road areas, tracking environmental changes can be reduced to conserve computational resources. However, given the highly dynamic nature of materials in loading areas, the calculation of earthwork volume and the ability to dynamically update maps are particularly crucial. Unmanned mining trucks, while achieving autonomous driving based on maps, should also dynamically update point cloud maps in real time and automatically calculate earthwork volume based on the updated results. However, current technologies cannot simultaneously meet all of these requirements.

[0005] Patent CN114708218A discloses a method for calculating road surface smoothness. This method acquires road surface data using a panoramic camera, selects a relatively smooth road surface as the target base, extracts data from the target base and the surface to be tested, converts the image data into three-dimensional coordinate data, and compares the three-dimensional data of the road surface to be tested with a reference target to accurately determine the smoothness of the road surface and the location coordinates of defects. However, this method requires good prior road surface data, which is difficult to find directly without fitting on unstructured roads in mines. Furthermore, the method involves converting the smoothness data recorded by the camera into three-dimensional coordinates, increasing the computational complexity of the algorithm, and the smoothness data acquired by the camera is easily affected by lighting conditions.

[0006] Patent CN114001678A discloses a road surface smoothness detection method based on vehicle-mounted LiDAR. The method involves acquiring point cloud data from LiDAR scanning the road surface, filtering the point cloud data to obtain a first point cloud set, fitting the first point cloud set to obtain a fitted straight line, downsampling the point cloud data to obtain a second point cloud set, obtaining multiple unilateral distance extreme points based on the second point cloud set and the fitted straight line, determining a road surface reference straight line based on these multiple unilateral distance extreme points (equivalent to a virtual three-meter ruler), and finally determining the road surface smoothness based on the road surface reference straight line and the point cloud data. However, on the one hand, the process of fitting the road surface reference line is relatively complex, requiring two fittings based on the least squares method and the unilateral distance extreme points, which may result in significant errors; on the other hand, using a straight line instead of a plane as the reference may omit some road surface information, leading to incomplete road surface data covered in the road surface smoothness calculation.

[0007] Patent CN114037800A provides an octree map construction method. It pre-integrates acceleration and angular velocity information to obtain the current pose information of a LiDAR sensor, and uses this pose information to correct distortion in the point cloud data. It then detects feature points in the point cloud data using the curvature method, and uses these feature points to perform scene association calculations to obtain the pose transformation information of the LiDAR sensor and the spatial position information of the current point cloud. Based on the pose transformation information and position information, an octree map is created and updated. However, updating the octree map requires calculating all free grid cells in a 3D grid through ray projection, which significantly increases the computational load when the laser point is far from the sensor. In reality, the area with the highest frequency of change is the working area near the excavator. For distant environments with low frequency of change, performing ray projection every time would be a huge waste of computational resources. Therefore, the problem of how to rationally allocate computational resources according to the rate of change of the environment needs to be solved.

[0008] Patent CN114612525A uses an octree map ray traversal method on keyframes to filter static map points and update the sparse point cloud map, ensuring positioning accuracy. This enables real-time construction and updating of the static octree map for the scene. However, the ray casting method used for constructing local maps on the vehicle side can cause plane vanishing during map updates. This is because, to ensure a large field of view, LiDAR is typically placed vertically on vehicles, which can lead to excessively large angles between the LiDAR's rays and the road surface during data acquisition, resulting in accidental deletion. Therefore, how to improve this problem needs to be addressed.

[0009] Patent CN113963050A relates to a method for calculating earthwork volume based on point clouds. The method involves acquiring terrain point cloud data and a design plane, dividing the point cloud data into several elevation regions, calculating the average elevation of each elevation region by averaging the point cloud elevation coordinates, dividing the design plane into several polygons, determining the elevation region where each polygon's vertices are located, and obtaining the average elevation of that region as the average elevation of the polygon's vertices. The calculated elevation of each polygon is then obtained using the average elevation of its vertices. The earthwork volume is calculated from the area of ​​the polygons and the calculated elevation, and the sign of the earthwork volume indicates whether it is excavation or fill. However, the earthwork volume calculation method employed is complex, involving multiple segmentations and elevation measurements, which may introduce significant measurement errors. Traditional earthwork measurement mainly uses surveying instruments such as total stations or GPS-RTK to acquire ground point data for measurement. This is not only labor-intensive but also time-consuming and labor-intensive. In particular, the data measurement process will be more difficult in areas with complex terrain. Therefore, it is very important to choose an efficient automatic data acquisition and processing method. Summary of the Invention

[0010] The purpose of this invention is to overcome the shortcomings of the prior art and provide a map area updating method and system for open-pit mine loading areas, which can accurately and meticulously divide the updated area boundary lines and area attributes, and is simple to operate and has a small computational load.

[0011] This invention provides the following technical solution:

[0012] Firstly, a method for updating map areas in open-pit mine loading zones is provided, including:

[0013] Obtain the initial global map data for the loading area, and delineate the area boundary lines and area attributes;

[0014] Obtain local map data from the client;

[0015] The flatness of the work site is calculated based on the acquired local map data of the work site;

[0016] Based on the acquired local map data of the work terminal, as well as the regional boundary lines and regional attributes, an improved ray casting method is used to implement an update strategy with different probabilities for different regions, thereby obtaining updated local map data of the work terminal.

[0017] Based on the updated local map data of the work site and the flatness of the work site, a global map is constructed and the earthwork volume change is calculated.

[0018] Based on the constraints of the global map, the flatness of the work site, and the regional boundaries, the updated regional boundary lines and regional attributes are defined.

[0019] Furthermore, the operating end includes one or more of the following: unmanned mining trucks, excavating machinery, and auxiliary operating vehicles.

[0020] Furthermore, the local map data of the operating terminal includes point cloud data that has been synchronized with the time, data on the working status of the acquisition unit, and attribute data of the current road.

[0021] Furthermore, the method for calculating the flatness of the working end includes:

[0022] The point cloud data in the local map data of the operation terminal is denoised and segmented in sequence to obtain the reference horizontal plane;

[0023] The point cloud data within the safe range of the work terminal is divided into horizontal and vertical grid blocks, and each block is marked and numbered in the order of horizontal first and then vertical.

[0024] Extract the center point of each region and calculate the vector and distance from the center point to the reference horizontal plane;

[0025] The region division conditions are set according to the vector and distance changes from the center point to the reference horizontal plane. The grids in each region are connected in a four-neighbor manner to form a region block.

[0026] The flatness is determined and graded according to the regional division conditions.

[0027] Furthermore, the region division conditions include:

[0028] The distances from the center point to the reference plane are L1, L2, L3, L4, and L5, where L1 represents a negative flatness, L2 represents a negative flatness, L3 represents a positive flatness, L4 represents a positive flatness, and L5 represents a positive flatness.

[0029] The minimum bounding polygon area of ​​each region block and its proportion of the overall region are output, along with the 3D coordinates of the boundary line.

[0030] Output the minimum bounding polygon area of ​​the combined region of L2 and L3 and its proportion of the whole region, and output the three-dimensional coordinates of the boundary line.

[0031] Furthermore, methods for determining and classifying flatness based on regional division criteria include:

[0032] L1 accounts for no more than 5% of the total area, and its flatness is negative, indicating an unusable pit.

[0033] L2 accounts for 30% to 40%, and its flatness is negative small, which means that it is generally usable pit.

[0034] The proportion of L3 is between 30% and 40%, and its flatness is positively low, indicating that it is a generally usable protrusion.

[0035] L4 accounts for 1% to 5% of the total area, and its flatness is in the middle, representing obstacles and ruts.

[0036] L5 accounts for 30% to 40%, and its flatness is positive, representing a retaining wall;

[0037] The combined L2 and L3 areas account for 30% to 40% of the area, and their flatness is rated as positive, representing areas prone to rockfall.

[0038] Furthermore, the method for obtaining the updated local map data of the work terminal includes:

[0039] The acquired local map data from the work site is preprocessed, including gridding the point cloud data, denoising the point cloud data, and time synchronization, to obtain preprocessed point cloud data.

[0040] Divide the preprocessed point cloud data into regions;

[0041] The preprocessed point cloud data of different regions is traversed according to the improved ray casting method to extract the empty and occupied grid cells in each region.

[0042] Different probabilities are set for different regions to enable mapping and updating of different regions.

[0043] Furthermore, the area boundary includes at least the excavation area boundary, the retaining wall boundary, the mountain boundary, the rockfall area boundary, and the drivable area boundary.

[0044] Furthermore, the constraints on the boundary of the excavation area include at least the following: the maximum height of the area with a flatness of L5 detected by the unmanned mining truck does not exceed 0.5m; the minimum length of the continuous area with a flatness of L5 detected by the unmanned mining truck is 5m; and the proportion of the L2 and L3 combined area detected by the excavating machinery is more than 50%.

[0045] The constraints on the retaining wall boundary include at least the following: the maximum change in ground curvature detected by the unmanned mining truck does not exceed a set range; the maximum height of the area with a flatness of L5 detected by the unmanned mining truck does not exceed 1.5m; and the minimum length of the continuous area with a flatness of L5 detected by the unmanned mining truck is 10m.

[0046] The constraints on the mountain boundary include at least the following: the maximum change in ground curvature detected by the unmanned mining truck does not exceed a set range; the maximum height of the area with a flatness of L5 detected by the unmanned mining truck exceeds 1.5m; and the minimum length of the continuous area with a flatness of L5 detected by the unmanned mining truck is 10m.

[0047] The constraints on the boundary of the rockfall area include at least the following: the proportion of the L2 and L3 combined area detected by the unmanned mining truck is between 30% and 40%; the variation range of the rockfall area boundary does not exceed the set value; the minimum obstacle size does not exceed the set value; and the minimum pit depth does not exceed the set value.

[0048] The constraints on the boundary of the drivable area include at least the following: the maximum change in ground curvature detected by the unmanned mining truck does not exceed a set range; conditions L1, L2, L3, L4, and L5 are met simultaneously; the maximum road width is 1.5 to 2 times the vehicle width; the minimum obstacle size does not exceed a set value; and the minimum pit depth does not exceed a set value.

[0049] Secondly, a map area update system for open-pit mine loading areas is provided, including:

[0050] The initial data acquisition and processing unit is used to acquire the initial global map data of the loading area and to delineate the area boundary lines and area attributes;

[0051] The work-side data acquisition unit is used to acquire local map data from the work-side.

[0052] The flatness calculation unit at the work end is used to calculate the flatness of the work end based on the acquired local map data of the work end.

[0053] The local map update unit at the work end is used to apply an improved ray casting method to different regions with different probabilities to update the local map data at the work end based on the acquired local map data at the work end, as well as the region boundary line and region attributes, so as to obtain the updated local map data at the work end.

[0054] The global map management unit in the map management terminal is used to construct a global map based on the updated local map data of the work terminal and the flatness of the work terminal.

[0055] The map management terminal area division unit is used to divide the updated area boundary lines and area attributes based on the constraints of the global map, the flatness of the operation terminal, and the area boundary.

[0056] Compared with the prior art, the beneficial effects of the present invention are:

[0057] (1) The present invention calculates the flatness of the work end based on the acquired local map data of the work end, and then uses it to construct a global map and divide the updated regional boundary lines and regional attributes, providing parameter basis for further detailed division of the region;

[0058] (2) Based on the acquired local map data of the work terminal and the regional boundary lines and regional attributes divided based on the initial global map data, the present invention adopts an improved ray casting method to implement different probability update strategies for different regions, thereby obtaining updated local map data of the work terminal, which helps to accurately divide the region and has a small amount of computation.

[0059] (3) Based on the updated local map data of the working end and the flatness of the working end, the present invention constructs a global map and calculates the change in earthwork volume. Furthermore, based on the global map, the flatness of the working end and the constraints of the regional boundary, the updated regional boundary line and regional attributes are divided in detail. The divided regional boundaries include at least the excavation area boundary, retaining wall boundary, mountain boundary, rockfall area boundary and drivable area boundary. Attached Figure Description

[0060] Figure 1 This is a flowchart of a map area updating method for an open-pit mine loading area in an embodiment of the present invention;

[0061] Figure 2 This is a flowchart of the flatness calculation process at the working end in an embodiment of the present invention;

[0062] Figure 3 This is a flowchart of the local map data update process at the working end in an embodiment of the present invention;

[0063] Figure 4 This is a schematic diagram of the cloud region division of the work endpoint in an embodiment of the present invention;

[0064] Figure 5 This is a schematic diagram of the loading area division in an embodiment of the present invention;

[0065] Figure 6 This is a block diagram of a map area update system for open-pit mine loading areas in an embodiment of the present invention. Detailed Implementation

[0066] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and should not be used to limit the scope of protection of the present invention.

[0067] Example 1

[0068] like Figure 1As shown in the figure, this embodiment provides a method for updating map areas in open-pit mine loading areas, the steps of which are as follows:

[0069] Step 1: Obtain the initial global map data of the loading area and divide the area boundary lines and area attributes.

[0070] The work unit first collects the initial global map data of the loading area. The work unit includes unmanned mining trucks, excavating machinery, and other auxiliary work vehicles. Each vehicle combines its own multiple sensors and outputs the initial global point cloud map data of the loading area after fusion.

[0071] Step 2: Obtain local map data from the client.

[0072] The local map data on the work end includes point cloud data that has been synchronized with the completion time, data on the working status of the acquisition unit, and attribute data such as the current road slope, marker points, boundary curvature, and obstacle coordinates.

[0073] Step 3: Calculate the flatness of the work area based on the acquired local map data. For example... Figure 2 As shown, the specific method is as follows:

[0074] Step 3.1: Denoise and segment the point cloud data in the local map data of the working end in sequence to obtain the reference horizontal plane.

[0075] Step 3.2: Divide the point cloud data within the safe range of the working end into horizontal and vertical grid blocks, and label and number each area in the order of horizontal first and then vertical.

[0076] Step 3.3: Extract the center point of each region and calculate the vector and distance from the center point to the reference horizontal plane.

[0077] Step 3.4: Set the region division conditions based on the vector and distance changes from the center point to the reference horizontal plane. The grids in each region are connected in a four-neighbor manner to form a region block.

[0078] The conditions for dividing the region include:

[0079] (1) The distances from the center point to the reference plane are L1, L2, L3, L4, and L5, where L1 represents a negative flatness, L2 represents a negative flatness, L3 represents a positive flatness, L4 represents a positive flatness, and L5 represents a positive flatness.

[0080] (2) Output the area of ​​the minimum bounding polygon of each region block and its proportion of the whole region, and output the three-dimensional coordinates of the boundary line;

[0081] (3) The area of ​​the minimum circumscribed polygon of the L2 and L3 combined region and its proportion to the whole region, and output the three-dimensional coordinates of the boundary line.

[0082] Step 3.5: Determine and classify the flatness according to the area division conditions. The specific method is as follows:

[0083] (1) L1 accounts for no more than 5% of the total area, and its flatness is negative, which means it is an unusable pit.

[0084] (2) When L2 accounts for 30% to 40%, its flatness is negative small, which means that it is generally usable pit;

[0085] (3) The proportion of L3 is between 30% and 40%, and its flatness is positive, which means that the protrusion is generally usable.

[0086] (4) L4 accounts for 1% to 5%, and its flatness is in the middle, representing obstacles and ruts;

[0087] (5) L5 accounts for 30% to 40%, and its flatness is positive, representing a retaining wall;

[0088] (6) The area where L2 and L3 are combined accounts for 30% to 40%, and its flatness is positive, representing the area where rocks have fallen.

[0089] Step 4: Based on the acquired local map data of the work terminal, as well as the regional boundary lines and regional attributes, an improved ray casting method is used to implement an update strategy with different probabilities for different regions, resulting in updated local map data of the work terminal. For example... Figure 3 As shown, the specific method is as follows:

[0090] Step 4.1: Preprocess the acquired local map data of the work end, including gridding the point cloud data, denoising and time synchronization of the point cloud map data initially scanned by the unmanned mining trucks, excavating machinery and auxiliary work vehicles at the work end, to obtain the preprocessed point cloud data.

[0091] Step 4.2: Divide the preprocessed point cloud data into regions.

[0092] like Figure 4 As shown, boundary A represents the mountain boundary, boundary B represents the boundary of the combined L2 and L3 region, and boundary C represents the boundary of L5. Only the area between boundary B and boundary C is the drivable area. Based on the boundary attributes, the point clouds of different grids at the working end can be divided into different regions.

[0093] Step 4.3: Using the improved ray casting method, traverse the preprocessed point cloud data of different regions to extract the empty and occupied grid cells in each region.

[0094] Step 4.4: Set different probabilities for different areas and update the local map data on the client side.

[0095] Based on the raster extracted by the improved ray casting method, confidence is updated using a static binary Bayesian method according to the idle and occupied states. This probabilistic update achieves the effects of removing moving objects and reducing noise. High-probability update values ​​are selected in drivable and excavated areas, while low-probability update values ​​are selected in non-drivable areas, enabling mapping and updating of different regions.

[0096] Step 5: Based on the updated local map data and flatness of the work site, construct a global map and calculate the change in earthwork volume.

[0097] Step 6: Based on the global map, the flatness of the workpiece, and the constraints of the region boundaries, define the updated region boundary lines and region attributes. The final result is as follows. Figure 5 As shown.

[0098] The area boundary includes at least the excavation area boundary, the retaining wall boundary, the mountain boundary, the rockfall area boundary, and the drivable area boundary.

[0099] The constraints on the boundary of the excavation area include at least the following:

[0100] (1) The area with a flatness of L5 detected by the unmanned mining truck has a maximum height of no more than 0.5m;

[0101] (2) The area with a flatness of L5 detected by the unmanned mining truck has a minimum continuous length of 5m;

[0102] (3) The excavating machinery detected that the proportion of the L2 and L3 combined area was more than 50%.

[0103] The constraints on the boundary of the retaining wall include at least the following:

[0104] (1) The maximum change in ground curvature detected by the unmanned mining truck does not exceed the set range;

[0105] (2) The maximum height of the area with a flatness of L5 detected by the unmanned mining truck does not exceed 1.5m; (3) The minimum length of the continuous area with a flatness of L5 detected by the unmanned mining truck is 10m. The constraints of the mountain boundary include at least:

[0106] (1) The maximum change in ground curvature detected by the unmanned mining truck does not exceed the set range;

[0107] (2) The area with a flatness of L5 detected by the unmanned mining truck has a maximum height of more than 1.5m;

[0108] (3) The area with a flatness of L5 detected by the unmanned mining truck has a minimum continuous length of 10m. The constraint conditions of the boundary of the rockfall area include at least:

[0109] (1) The proportion of L2 and L3 combined areas detected by the unmanned mining truck is between 30% and 40%;

[0110] (2) The range of change of the boundary of the rockfall area (expansion or contraction) shall not exceed the set value;

[0111] (3) The minimum obstacle size does not exceed the set value;

[0112] (4) The minimum pit depth shall not exceed the set value (including ruts and pits).

[0113] The constraints on the boundary of the permissible area include at least the following:

[0114] (1) The maximum change in ground curvature detected by the unmanned mining truck does not exceed the set range;

[0115] (2) Simultaneously satisfy conditions L1, L2, L3, L4, and L5;

[0116] (3) The maximum width of the road surface is 1.5 to 2 times the width of the vehicle;

[0117] (4) The minimum obstacle size does not exceed the set value;

[0118] (5) The minimum pit depth shall not exceed the set value (including ruts and pits).

[0119] Example 2

[0120] like Figure 6 As shown, this embodiment provides a map area update system for loading areas in open-pit mines, including: an initial data acquisition and processing unit, a work-end data acquisition unit, a work-end flatness calculation unit, a work-end local map update unit, a map management terminal global map management unit, and a map management terminal area division unit;

[0121] The initial data acquisition and processing unit is used to acquire the initial global map data of the loading area and to delineate the area boundary lines and area attributes.

[0122] In some other embodiments, the global map management unit and the region division unit of the map management terminal can serve as initial data acquisition and processing units. Specifically, the global map management unit of the map management terminal is used to acquire the initial global map data of the loading area, and the region division unit of the map management terminal is used to divide the region boundary lines and region attributes.

[0123] The data acquisition unit at the work site is used to collect local map data. The local map data at the work site includes point cloud data synchronized with the completion time, data on the working status of the acquisition unit, and attribute data such as the current road slope, marker points, boundary curvature, and obstacle coordinates.

[0124] The work-end data acquisition unit is used to acquire local map data from the work-end of the data acquisition unit.

[0125] The flatness calculation unit at the work site is used to calculate the flatness of the work site based on the acquired local map data of the work site, so as to achieve fine area division.

[0126] The local map update unit at the work end is used to apply an improved ray casting method to different regions with different probabilities to obtain updated local map data at the work end, based on the acquired local map data at the work end, the region boundary lines, and the region attributes.

[0127] The global map management unit in the map management terminal is used to construct a global map based on the updated local map data of each region and the flatness of the work terminal.

[0128] The map management terminal area division unit is used to divide the updated area boundary lines and area attributes based on the constraints of the global map, the flatness of the operation terminal, and the area boundary.

[0129] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0130] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0131] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1The function specified in one or more boxes.

[0132] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0133] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for updating map areas in open-pit mine loading zones, characterized in that, include: Obtain the initial global map data for the loading area, and delineate the area boundary lines and area attributes; Obtain local map data from the client; The flatness of the work site is calculated based on the acquired local map data of the work site; Based on the acquired local map data of the work terminal, as well as the regional boundary lines and regional attributes, an improved ray casting method is used to implement an update strategy with different probabilities for different regions, thereby obtaining updated local map data of the work terminal. Based on the updated local map data of the work site and the flatness of the work site, a global map is constructed and the earthwork volume change is calculated. Based on the constraints of the global map, the flatness of the work terminal, and the regional boundaries, the updated regional boundary lines and regional attributes are defined. The area boundary includes at least the excavation area boundary, the retaining wall boundary, the mountain boundary, the rockfall area boundary, and the drivable area boundary; The constraints on the boundary of the excavation area include at least the following: the maximum height of the area with a flatness of L5 detected by the unmanned mining truck is no more than 0.5m; the minimum length of the continuous area with a flatness of L5 detected by the unmanned mining truck is 5m; and the proportion of the area with a combination of L2 and L3 detected by the excavating machinery is more than 50%. The constraints on the retaining wall boundary include at least the following: the maximum change in ground curvature detected by the unmanned mining truck does not exceed a set range; the maximum height of the area with a flatness of L5 detected by the unmanned mining truck does not exceed 1.5m; and the minimum length of the continuous area with a flatness of L5 detected by the unmanned mining truck is 10m. The constraints on the mountain boundary include at least the following: the maximum change in ground curvature detected by the unmanned mining truck does not exceed a set range; the maximum height of the area with a flatness of L5 detected by the unmanned mining truck exceeds 1.5m; and the minimum length of the continuous area with a flatness of L5 detected by the unmanned mining truck is 10m. The constraints on the boundary of the rockfall area include at least the following: the proportion of the L2 and L3 combined area detected by the unmanned mining truck is between 30% and 40%; the variation range of the rockfall area boundary does not exceed the set value; the minimum obstacle size does not exceed the set value; and the minimum pit depth does not exceed the set value. The constraints on the boundary of the drivable area include at least the following: the maximum change in ground curvature detected by the unmanned mining truck does not exceed a set range; conditions L1, L2, L3, L4, and L5 are met simultaneously; the maximum road width is 1.5 to 2 times the vehicle width; the minimum obstacle size does not exceed a set value; and the minimum pit depth does not exceed a set value. Among them, L1 represents a negative medium flatness, L2 represents a negative small flatness, L3 represents a positive small flatness, L4 represents a positive medium flatness, and L5 represents a positive large flatness.

2. The map area updating method for open-pit mine loading areas according to claim 1, characterized in that, The operating end includes one or more of the following: unmanned mining trucks, excavating machinery, and auxiliary operation vehicles.

3. The map area updating method for loading areas in open-pit mines according to claim 1, characterized in that, The local map data of the operating terminal includes point cloud data that has been synchronized with the time, data on the working status of the acquisition unit, and attribute data of the current road.

4. The map area updating method for open-pit mine loading areas according to claim 1, characterized in that, The method for calculating the flatness of the working end includes: The point cloud data in the local map data of the operation terminal is denoised and segmented in sequence to obtain the reference horizontal plane; The point cloud data within the safe range of the work terminal is divided into horizontal and vertical grid blocks, and each block is marked and numbered in the order of horizontal first and then vertical. Extract the center point of each region and calculate the vector and distance from the center point to the reference horizontal plane; The region division conditions are set according to the vector and distance changes from the center point to the reference horizontal plane. The grids in each region are connected in a four-neighbor manner to form a region block. The flatness is determined and graded according to the regional division conditions.

5. The map area updating method for open-pit mine loading areas according to claim 4, characterized in that, The conditions for dividing the region include: The distances from the center point to the reference plane are L1, L2, L3, L4, and L5. The minimum bounding polygon area of ​​each region block and its proportion of the overall region are output, along with the 3D coordinates of the boundary line. Output the minimum bounding polygon area of ​​the combined region of L2 and L3 and its proportion of the whole region, and output the three-dimensional coordinates of the boundary line.

6. The map area updating method for loading areas in open-pit mines according to claim 5, characterized in that, Methods for determining and classifying flatness based on regional division criteria include: L1 accounts for no more than 5% of the total area, and its flatness is negative, which means that the pit is unusable. L2 accounts for 30% to 40%, and its flatness is negative small, which means that it is a generally usable pit. L3 accounts for 30% to 40%, and its flatness is positive and small, which means that the protrusion is generally usable. L4 accounts for 1% to 5% of the total area, and its flatness is in the middle, representing obstacles and tire tracks. L5 accounts for 30% to 40%, and its flatness is positive, representing a retaining wall; The combined L2 and L3 areas account for 30% to 40% of the area, and their flatness is rated as positive, representing areas prone to rockfall.

7. The map area updating method for open-pit mine loading areas according to claim 1, characterized in that, The method for obtaining the updated local map data of the client includes: The acquired local map data from the work site is preprocessed, including gridding the point cloud data, denoising the point cloud data, and time synchronization, to obtain preprocessed point cloud data. Divide the preprocessed point cloud data into regions; The preprocessed point cloud data of different regions is traversed according to the improved ray casting method to extract the empty and occupied grid cells in each region. Different probabilities are set for different regions to enable mapping and updating of different regions.

8. A map area updating system for loading areas in open-pit mines, characterized in that, The system for implementing the method according to any one of claims 1 to 7 comprises: The initial data acquisition and processing unit is used to acquire the initial global map data of the loading area and to delineate the area boundary lines and area attributes; The work-side data acquisition unit is used to acquire local map data from the work-side. The flatness calculation unit at the work end is used to calculate the flatness of the work end based on the acquired local map data of the work end. The local map update unit at the work end is used to apply an improved ray casting method to different regions with different probabilities to update the local map data at the work end based on the acquired local map data at the work end, as well as the region boundary line and region attributes, so as to obtain the updated local map data at the work end. The global map management unit in the map management terminal is used to construct a global map based on the updated local map data of the work terminal and the flatness of the work terminal. The map management terminal area division unit is used to divide the updated area boundary lines and area attributes based on the constraints of the global map, the flatness of the operation terminal, and the area boundary.

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