Method for determining dumping priority of dumping site and related device thereof

Abstract

The invention relates to a method for determining the dumping priority of a dumping site and a related device thereof. The method comprises the following steps: acquiring multi-source sensing data of target operation equipment; wherein the multi-source sensing data at least comprises point cloud data, position data and image data; generating a target operation map according to the multi-source sensing data; wherein the target operation map comprises a plurality of operation sub-regions; calculating the operation priority of each operation sub-region based on a preset priority algorithm; and based on the operation priority, controlling the target operation equipment to discharge soil from the operation sub-region. According to the scheme, the waste dump can be dynamically partitioned, the transportation cost of a transportation machine is reduced, and the operation efficiency and safety of the waste dump are improved.

Description

Technical Field

[0001] This application relates to the field of mining technology, and in particular to a method for determining the priority of spoil disposal at a spoil heap and related apparatus. Background Technology

[0002] In earthwork engineering, the spoil heap is the core area for receiving stripped soil and waste rock, and its management efficiency directly affects the project progress and cost.

[0003] In related technologies, spoil heap zoning is usually determined once based on initial topographic surveys, failing to consider dynamic changes such as topographic uplift and regional saturation during spoil disposal. This results in low spoil heap capacity utilization and unreasonable spoil heap zoning. Secondly, the spoil disposal sequence mainly relies on manual experience and judgment, lacking a comprehensive assessment of multiple factors such as transport distance, spoil disposal capacity, and slope. This leads to low transport efficiency, as transport machinery cannot prioritize spoil disposal according to the priority of each spoil heap zone, significantly reducing the operational efficiency of the spoil heap. Summary of the Invention

[0004] To address or partially address the problems existing in related technologies, this application provides a method and related apparatus for determining the priority of spoil disposal at a spoil heap, which can dynamically divide the spoil heap, reduce the transportation costs of transport machinery, and improve the operational efficiency and safety of the spoil heap.

[0005] The first aspect of this application provides a method for determining the priority of spoil disposal at a spoil heap, comprising: acquiring multi-source sensing data of a target operating device; wherein the multi-source sensing data includes at least point cloud data, location data, and image data; generating a target operating map based on the multi-source sensing data; wherein the target operating map includes multiple operating sub-regions; calculating the operating priority of each operating sub-region based on a preset priority algorithm; and controlling the target operating device to perform spoil disposal in the operating sub-region based on the operating priority.

[0006] In conjunction with the first aspect, in one possible implementation of the first aspect, acquiring the multi-source sensing data of the target working equipment includes: acquiring point cloud data, location data, and image data collected in real time by at least one sensor on the target working equipment; and obtaining the earthwork volume data and slope data of the working sub-area where the target working equipment is located based on the point cloud data.

[0007] In conjunction with the first aspect, in one possible implementation of the first aspect, generating a target operation map based on the multi-source sensing data includes: performing spatiotemporal alignment processing on the multi-source sensing data to obtain processed multi-source sensing data; acquiring verification data collected in real time by the target verification device; wherein the collection time of the verification data is the same as the collection time of the multi-source sensing data; comparing the verification data with the processed multi-source sensing data, and if the processed multi-source sensing data meets preset conditions, generating the target operation map based on the processed multi-source sensing data.

[0008] In conjunction with the first aspect, in one possible implementation of the first aspect, calculating the operation priority of each operation sub-region based on a preset priority algorithm includes: calculating a capacity factor for the operation sub-region based on the earthwork volume data and preset capacity data of the operation sub-region, wherein the capacity factor is positively correlated with the earthwork volume data; calculating the average transport distance of the target operation equipment based on the location data, and calculating an efficiency factor for the operation sub-region based on the average transport distance, wherein the efficiency factor is negatively correlated with the average transport distance; calculating a safety factor for the operation sub-region based on the slope data, wherein the safety factor is negatively correlated with the slope data; and calculating the operation priority of the operation sub-region based on the capacity factor, the efficiency factor, and the safety factor; wherein the operation priority is positively correlated with the capacity factor, the efficiency factor, and the safety factor.

[0009] In conjunction with the first aspect, in one possible implementation of the first aspect, the preset priority algorithm is as follows: P i =αC i +βE i +γS i Wherein, P i The priority of the job sub-region is defined by α, which is the first parameter, and C is the job priority. i E is the capacity factor, β is the second parameter, and E i S is the efficiency factor, γ is the third parameter, and S i α is the safety factor; where α+β+γ=1.

[0010] In conjunction with the first aspect, one possible implementation of the first aspect further includes: expanding the soil discharge boundary of the work sub-region when the work priority of the work sub-region is greater than a first threshold; and narrowing the soil discharge boundary of the work sub-region when the work priority of the work sub-region is less than a second threshold.

[0011] In conjunction with the first aspect, one possible implementation of the first aspect further includes: determining the soil removal rotation time for each work sub-region based on the work priority of the work sub-region; and controlling the target work equipment to remove soil from the work sub-region based on the work priority and the soil removal rotation time.

[0012] A second aspect of this application provides an apparatus for determining the priority of spoil disposal at a spoil heap, comprising: an acquisition module for acquiring multi-source sensing data of a target operating device; wherein the multi-source sensing data includes at least point cloud data, location data, and image data; a generation module for generating a target operating map based on the multi-source sensing data; wherein the target operating map includes multiple operating sub-regions; a calculation module for calculating the operating priority of each operating sub-region based on a preset priority algorithm; and a control module for controlling the target operating device to perform spoil disposal in the operating sub-regions based on the operating priorities.

[0013] A third aspect of this application provides an electronic device, comprising: Processor; and A memory that stores executable code, which, when executed by the processor, causes the processor to perform the method described above.

[0014] A fourth aspect of this application provides a computer-readable storage medium having executable code stored thereon, which, when executed by a processor of an electronic device, causes the processor to perform the method described above.

[0015] The fifth aspect of this application provides a computer program product, including a computer program / instructions that, when executed by a processor, implement the method described above.

[0016] The technical solution provided in this application may include the following beneficial effects: This application discloses a method and related apparatus for determining the priority of spoil disposal at a spoil heap. The method includes: acquiring multi-source sensing data of the target operating equipment; wherein the multi-source sensing data includes at least point cloud data, location data, and image data; generating a target operating map based on the multi-source sensing data; wherein the target operating map includes multiple operating sub-regions; calculating the operating priority of each operating sub-region based on a preset priority algorithm; and controlling the target operating equipment to dump spoil in the operating sub-regions based on the operating priorities. This method enables dynamic zoning of the spoil heap, reduces transportation costs for transport machinery, and improves the operating efficiency and safety of the spoil heap.

[0017] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0018] The above and other objects, features and advantages of this application will become more apparent from the following description of exemplary embodiments of this application in conjunction with the accompanying drawings, wherein the same reference numerals generally represent the same components.

[0019] Figure 1 This is a flowchart illustrating the method for determining the priority of spoil disposal sites according to an embodiment of this application; Figure 2 This is a schematic diagram of the structure of the device for determining the priority of spoil disposal in a spoil heap, as shown in an embodiment of this application; Figure 3 This is a schematic diagram of the structure of an electronic device shown in an embodiment of this application. Detailed Implementation

[0020] Embodiments of this application will now be described in more detail with reference to the accompanying drawings. While embodiments of this application are shown in the drawings, it should be understood that this application may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to make this application more thorough and complete, and to fully convey the scope of this application to those skilled in the art.

[0021] 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” and “the” as 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.

[0022] It should be understood that although the terms "first," "second," "third," etc., may be used in this application to describe various information, this 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. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0023] In large-scale earthwork projects such as open-pit mining, highway and railway infrastructure construction, spoil heaps are the core areas for receiving and stockpiling stripped soil and waste rock. The efficiency and safety of their operation and management directly affect the progress, cost and environmental impact of the entire project.

[0024] In related technologies, traditional spoil heaps typically involve a one-time, fixed zoning based on initial topographic surveys before operations begin, such as simply dividing the area into zones A, B, and C according to location. This static zoning scheme completely ignores the dramatic dynamic changes in topography during spoil heap operations, such as rapid uplift of local spoil surfaces and gradual saturation of areas. This directly leads to a severe misallocation of spoil heap space resources, often resulting in some areas being over-accumulated and creating safety hazards, while other areas remain idle for extended periods. Consequently, the overall capacity utilization rate of the spoil heap is usually only maintained at a low level of 60% to 70%. Secondly, the sequence and scheduling of spoil heap operations largely rely on the personal experience of on-site management personnel, such as using simple and intuitive rules like "nearest before farthest." This scheduling method lacks comprehensive quantitative evaluation and dynamic optimization of multi-dimensional factors such as transportation distance, real-time equipment capacity, spoil surface slope stability, and multi-equipment coordination efficiency. As a result, transportation equipment (such as mining trucks) frequently makes long-distance detours or engages in ineffective transportation, and transportation machines cannot prioritize spoil heaps according to the priority of each zone, significantly reducing the operational efficiency of the spoil heap.

[0025] To address the aforementioned issues, this application provides a method and related apparatus for determining the priority of spoil disposal at a spoil heap, which enables dynamic zoning of the spoil heap, reduces transportation costs for transport machinery, and improves the operational efficiency and safety of the spoil heap.

[0026] The technical solutions of the embodiments of this application are described in detail below with reference to the accompanying drawings.

[0027] Figure 1 This is a flowchart illustrating the method for determining the priority of spoil disposal sites as shown in the embodiments of this application.

[0028] See Figure 1 A method for determining the priority of spoil disposal at a spoil heap, comprising: S110: Acquire multi-source sensing data of the target operating equipment; wherein, the multi-source sensing data includes at least point cloud data, location data and image data.

[0029] Specifically, target operation equipment refers to engineering machinery that performs core transportation and dumping tasks at the spoil heap, such as large mining trucks, articulated dump trucks, or unmanned transport vehicles. Target operation equipment is the main execution unit in the spoil heap operation process, and it can acquire multi-source sensing data of its current location and surrounding environment. Multi-source sensing data refers to heterogeneous datasets collected in real time from various types of sensors installed on the target operation equipment. This data can include point cloud data, location data, image data, and other collectable data, such as temperature data, adjusted according to actual needs. Point cloud data can be generated by LiDAR sensors, accurately describing the geometry and spatial structure of the surrounding environment in the form of massive three-dimensional coordinate points (x, y, z). Location data can be provided by high-precision global navigation satellite systems (such as RTK-GPS supporting real-time dynamic differential technology) to obtain the precise geospatial location (longitude, latitude, elevation) of the equipment in the global coordinate system. Image data can be acquired by cameras, providing rich two-dimensional visual information that can be used for environmental texture recognition, boundary detection, or obstacle recognition.

[0030] For example, the target operating equipment could be an unmanned mining truck with a payload capacity of 200 tons. The truck is equipped with a 16-line LiDAR at the upper rear, used to perform a rearward scan of the newly formed dump pile after each dumping operation. The truck body is also equipped with a GPS device and multiple cameras to obtain location and image data. When the truck completes a dumping operation and leaves the dumping point, the onboard control system automatically triggers these sensors to perform a coordinated data acquisition. The LiDAR generates a 3D point cloud covering an area of ​​approximately 20 meters behind the truck, the GPS records the precise coordinates of the dumping operation, and the cameras capture images of the site, which are then uploaded in real time to an edge computing node or central server deployed at the dumping site command center.

[0031] In one possible implementation, point cloud data, location data, and image data collected in real time by at least one sensor on the target work equipment are acquired; based on the point cloud data, the earthwork volume data and slope data of the work sub-area where the target work equipment is located are obtained.

[0032] Specifically, sensors can include lidar, GPS, and cameras to collect point cloud, location, and image information of the work area in real time. The volume of earthwork dumped refers to the precise volume of earth and rock dumped in a single earthwork dumping operation. For example, the volume change of the point cloud coverage area can be calculated using a three-dimensional mesh division method. The slope data refers to the tilt angle of the terrain surface near the earthwork dumping point. It can be obtained by fitting the point cloud to a plane and then calculating the tilt angle of the normal vector. For example, the slope angle can be obtained by fitting the plane equation using the least squares method.

[0033] For example, the lidar installed on the target operation equipment scans the spoil disposal area at a frequency of 10 frames per second, generating a high-density point cloud. After receiving the point cloud, the edge computing node performs differential processing between the current point cloud and the historical baseline point cloud to calculate the increased spoil volume. For example, if a scan detects that the point cloud volume of area A has increased by 50 cubic meters, it is marked as spoil volume data. At the same time, after the point cloud data is fitted to a plane, the average slope of the area is calculated to be 15 degrees, which is used as slope data. This allows for real-time acquisition of soil changes and terrain features in the spoil disposal area, improving the capacity utilization rate of the spoil disposal site and operational safety.

[0034] S120: Generate a target operation map based on multi-source sensing data; the target operation map includes multiple operation sub-regions.

[0035] Specifically, the target operation map can be a spatial distribution map that divides the spoil heap into multiple dynamically adjustable operation sub-regions. It can be dynamically updated through multi-source sensing data acquired by the target operation equipment, including multi-dimensional information such as topography, regional division, and equipment status. The target operation map can include multiple operation sub-regions, which are multiple independent management units divided on the target operation map. The initial division of operation sub-regions can be based on terrain features, and can also be dynamically adjusted according to subsequent priority calculation results.

[0036] For example, after receiving multi-source sensing data uploaded from multiple mining trucks, the edge computing node first performs data fusion processing. Using the RTK-GPS location data contained in each data packet, it converts and registers all the local point cloud data collected by different mining trucks at different times and locations into a unified global coordinate system. Subsequently, the data is fused to generate a high-precision 3D map with a resolution of 0.1 meters. The target operation map can be divided into several 50m × 50m operation sub-areas, and the capacity, slope, transportation distance and other parameters of each sub-area are updated in real time.

[0037] In one possible implementation, generating a target operation map based on multi-source sensing data includes: performing spatiotemporal alignment processing on the multi-source sensing data to obtain processed multi-source sensing data; acquiring verification data collected in real time by the target verification device; wherein the acquisition time of the verification data is the same as the acquisition time of the multi-source sensing data; comparing the verification data with the processed multi-source sensing data, and if the processed multi-source sensing data meets preset conditions, generating a target operation map based on the processed multi-source sensing data.

[0038] Specifically, through spatiotemporal alignment processing, multi-source sensing data collected at different times or spaces can be timestamped and their coordinates unified to eliminate time discrepancies in data collection from different sensors. The target verification device can be a drone inspecting the target work area. During the inspection, the drone collects verification data at the same time. This verification data can include various data from different sub-areas, such as soil dumping volume data and slope data. Preset conditions can include point cloud registration error less than 5cm and feature point matching rate higher than 95%.

[0039] For example, point cloud data, location data, and image data from multi-source sensing data are first transmitted to edge computing nodes. A timestamp matching algorithm aligns the acquisition times of different sensors to the same reference time. Then, a coordinate transformation algorithm unifies the coordinate systems of the LiDAR point cloud and GPS positioning to the global coordinate system of the spoil heap. The processed multi-source sensing data is then compared with verification data collected by a drone within the same time window. For instance, the difference between the spoil volume data uploaded by the mining truck and the volume data scanned by the drone is calculated. If the difference is within 5%, the point cloud data is considered valid. When preset conditions are met, the processed multi-source sensing data is fused to generate a high-precision 3D map, including terrain and slope information. This high-precision fusion and verification of multi-source heterogeneous data ensures the accuracy and reliability of the generated map. Furthermore, the verification data allows for timely detection and correction of data anomalies, improving data accuracy.

[0040] S130: Calculate the job priority for each job sub-region based on a preset priority algorithm.

[0041] Specifically, the priority of each sub-region can be calculated using a priority algorithm. The priority is a numerical score calculated by the algorithm for each sub-region. This score indicates the degree to which the sub-region is recommended for the next soil disposal operation. The higher the score, the higher the priority of the sub-region in terms of overall conditions.

[0042] For example, every 5 minutes or after receiving a certain number (e.g., 10) updates of the soil disposal data, the priority of all work sub-areas (areas A to E) can be recalculated, and a comprehensive priority score ranging from 0 to 1 can be calculated for each sub-area. The priority can reflect the soil disposal situation of each work sub-area. In one possible implementation, the operation priority of each operation sub-region is calculated based on a preset priority algorithm, including: calculating the capacity factor of the operation sub-region based on the earthwork volume data and the preset capacity data of the operation sub-region, wherein the capacity factor is positively correlated with the earthwork volume data; calculating the average transportation distance of the target operation equipment based on the location data, and calculating the efficiency factor of the operation sub-region based on the average transportation distance, wherein the efficiency factor is negatively correlated with the average transportation distance; calculating the safety factor of the operation sub-region based on the slope data, wherein the safety factor is negatively correlated with the slope data; and calculating the operation priority of the operation sub-region based on the capacity factor, efficiency factor, and safety factor; wherein the operation priority is positively correlated with the capacity factor, efficiency factor, and safety factor.

[0043] Specifically, the capacity factor is determined by the ratio of the earthwork volume data to the preset capacity data; the larger the earthwork volume data, the higher the capacity factor. The efficiency factor is calculated based on the average transport distance of the target operating equipment; the shorter the average transport distance, the higher the efficiency factor. The safety factor is determined based on the slope data; the greater the slope, the lower the safety factor. The priority of each operating sub-area can be calculated based on the capacity factor, efficiency factor, and safety factor.

[0044] Specifically, the process begins by acquiring the earthwork volume data and preset capacity data for the sub-area where the target equipment is located, and then calculating the capacity factor. The earthwork volume data is obtained through point cloud volume analysis, and the preset capacity data is determined based on the initial terrain of the sub-area. Subsequently, the average transportation distance is calculated based on the GPS trajectory data of the mining trucks, and an efficiency factor is calculated. Simultaneously, the slope of the sub-area is fitted using LiDAR point cloud data, and a safety factor is calculated based on the slope data. Finally, the capacity factor, efficiency factor, and safety factor are substituted into a preset linear weighted formula to generate a work priority value. A higher value indicates that the sub-area has a higher priority for earthwork disposal.

[0045] In one possible implementation, the preset priority algorithm is as follows: P i =αC i +βE i +γS i Among them, P i The priority of a task is defined as the task sub-region, where α is the first parameter and C is the second parameter. i E is the capacity factor, β is the second parameter, and E i S is the efficiency factor, γ is the third parameter, and S i α is the safety factor; where α+β+γ=1.

[0046] Specifically, the first, second, and third parameters can be preset. For example, when the spoil heap is near a slope, the third parameter γ can be increased to 0.5 to reduce the priority of areas with a slope of more than 25 degrees; when the project progress is lagging behind, the second parameter β can be increased to 0.4 to improve work efficiency.

[0047] Specifically, the volume of excavated soil can be obtained through LiDAR scanning and compared with the pre-set maximum capacity of a sub-region to obtain a capacity factor. For example, if the maximum capacity of a sub-region is 1 million cubic meters and 800,000 cubic meters have been excavated so far, the capacity factor is 0.8. The trajectory of the mining trucks from the loading point to the excavation point can be recorded using a GPS positioning system, and the average distance of multiple transports can be calculated. For example, if the average transport distance of a sub-region is 2 kilometers, the efficiency factor can be set to 0.5. The slope of the excavation surface can be obtained by fitting LiDAR point cloud data; the steeper the slope, the lower the safety factor. For example, if the average slope of a sub-region is 20 degrees, the safety factor can be set to 0.7. Finally, based on the capacity factor, efficiency factor, and safety factor, the operation priority of the sub-area is calculated. For example, if the first parameter is set to 0.4, the second parameter to 0.3, and the third parameter to 0.3, then the operation priority of the sub-area is 0.68. By introducing quantitative indicators of capacity, efficiency, and safety, and combining them with adjustable weight parameters, the actual needs of the soil dumping operation can be more comprehensively reflected, improving both soil dumping efficiency and operational safety.

[0048] In one possible implementation, the method further includes: expanding the soil discharge boundary of the work sub-region when the work priority of the work sub-region is greater than a first threshold; and narrowing the soil discharge boundary of the work sub-region when the work priority of the work sub-region is less than a second threshold.

[0049] Specifically, the first threshold and the second threshold can be preset. The first threshold can be greater than the second threshold. When the priority calculation result of a certain sub-area exceeds the first threshold, it indicates that the area can carry out more soil removal. At this time, its soil removal boundary is automatically expanded to increase the workable area. Conversely, when the priority is lower than the second threshold, it indicates that the area is approaching saturation or inefficiency. At this time, its soil removal boundary can be reduced.

[0050] For example, a first threshold can be set to 0.8 and a second threshold to 0.6. After calculation, the priority P of area B is... B =0.85. Since 0.85 > 0.8, area B can be marked as a "priority area," and its allowable boundary for dumping operations will be automatically extended outward by 10 meters on the target operation map. For example, if the original boundary coordinates are (x1, y1) - (x2, y2), the expanded boundary coordinates become (x1 - 10, y1) - (x2 + 10, y2), providing more operating space for dumping vehicles and avoiding congestion. Area A's priority P...A =0.5. Since 0.5 < 0.6, the system marks it as a "semi-saturated zone" and shrinks its boundary inward by 5 meters. For example, the original boundary coordinates are (x3,y3)-(x4,y4), and the new boundary coordinates after shrinking become (x3-5,y3)-(x4-5,y4). This can prevent the mine from continuing to dump soil at the edge of the near-saturated area, avoid the formation of excessively steep slopes, and reduce safety risks. By dynamically adjusting the boundary through priority, the efficiency of dumping can be improved.

[0051] S140: Based on the task priority, control the target task equipment to remove soil from the task sub-area.

[0052] Specifically, based on the task priority, the operating areas of equipment such as mining trucks and bulldozers can be automatically scheduled, and the target operating equipment can be controlled to dump soil in the operating sub-area. Sub-areas with higher priority will be assigned to tasks first, and equipment scheduling and path planning will also be optimized accordingly.

[0053] For example, after calculation using a preset priority algorithm, it is determined that area B has the highest priority score (e.g., area B's priority is 0.85), while area A has a lower priority score (e.g., area A's priority is 0.5). At this point, a new job instruction can be sent to a mining truck that has just finished loading and is in standby mode. This instruction explicitly instructs the mining truck to proceed to a specific, optimized coordinate point within area B for soil removal, for example, the coordinate point is (x...). B ,y B ,z B Meanwhile, the instruction also includes an optimal driving route generated based on a real-time operation map, which avoids other operating equipment and high-risk areas (such as areas with a slope of more than 30°). After receiving the instruction, the mining truck's autonomous driving system will automatically drive to the designated location to complete the soil dumping operation.

[0054] In one possible implementation, the method further includes: determining the soil removal rotation time for each work sub-region based on its work priority; and controlling the target work equipment to remove soil from the work sub-region based on the work priority and the soil removal rotation time.

[0055] Specifically, the soil dumping rotation time refers to the work time interval allocated to each work sub-area during the soil dumping operation. For example, high-priority sub-areas are allocated shorter rotation intervals, while low-priority sub-areas have longer rotation intervals. When the priority of a work sub-area is increased, its rotation time can be shortened and the number of mine card allocations can be increased.

[0056] For example, work sub-regions can be sorted according to real-time calculated task priorities, with higher-priority sub-regions assigned shorter turnaround times. Secondly, considering the current operating status and transport capacity of the equipment, the maximum dumping frequency that each sub-region can handle is calculated. When controlling equipment dumping, turnaround time and priority data can be synchronously sent to the mining truck's onboard terminal. The equipment automatically adjusts its travel route and work sequence according to a preset path planning algorithm. For instance, when the priority of work sub-region A increases from 0.7 to 0.9, its turnaround time is shortened from 30 minutes to 15 minutes. After completing its current task, the mining truck immediately turns to work in that area. Through dynamic matching of priority and turnaround time, it ensures timely dumping in high-priority areas while preventing excessive concentration of work equipment in low-priority areas.

[0057] This application discloses a method for determining the priority of spoil disposal at a spoil heap. The method includes: acquiring multi-source sensing data of the target operating equipment; wherein the multi-source sensing data includes at least point cloud data, location data, and image data; generating a target operating map based on the multi-source sensing data; wherein the target operating map includes multiple operating sub-regions; calculating the operating priority of each operating sub-region based on a preset priority algorithm; and controlling the target operating equipment to dump spoil in the operating sub-regions based on the operating priorities. This method enables dynamic zoning of the spoil heap, reduces transportation costs for transport machinery, and improves the operating efficiency and safety of the spoil heap.

[0058] Corresponding to the aforementioned application function implementation method embodiments, this application also provides a device for determining the priority of spoil disposal at a spoil heap, an electronic device, and corresponding embodiments.

[0059] Figure 2 This is a schematic diagram of the structure of the device for determining the priority of spoil disposal in a spoil heap, as shown in an embodiment of this application.

[0060] See Figure 2 A device 200 for determining the priority of spoil disposal at a spoil heap, comprising: The acquisition module 210 is used to acquire multi-source sensing data of the target operating equipment; wherein, the multi-source sensing data includes at least point cloud data, location data and image data.

[0061] The generation module 220 is used to generate a target operation map based on multi-source sensing data; wherein the target operation map includes multiple operation sub-regions.

[0062] The calculation module 230 is used to calculate the job priority of each job sub-region based on a preset priority algorithm.

[0063] The control module 240 is used to control the target work equipment to discharge soil in the work sub-area based on the work priority.

[0064] In one possible implementation, the acquisition module 210 is further configured to acquire point cloud data, location data, and image data collected in real time by at least one sensor on the target working equipment; and based on the point cloud data, obtain the earthwork volume data and slope data of the working sub-area where the target working equipment is located.

[0065] In one possible implementation, the generation module 220 is further configured to perform spatiotemporal alignment processing on the multi-source sensing data to obtain processed multi-source sensing data; acquire verification data collected in real time by the target verification device; wherein the acquisition time of the verification data is the same as the acquisition time of the multi-source sensing data; compare the verification data with the processed multi-source sensing data, and if the processed multi-source sensing data meets the preset conditions, generate a target operation map based on the processed multi-source sensing data.

[0066] In one possible implementation, the calculation module 230 is further configured to calculate the capacity factor of the work sub-region based on the earthwork volume data and the preset capacity data of the work sub-region, wherein the capacity factor is positively correlated with the earthwork volume data; calculate the average transport distance of the target work equipment based on the location data, and calculate the efficiency factor of the work sub-region based on the average transport distance, wherein the efficiency factor is negatively correlated with the average transport distance; calculate the safety factor of the work sub-region based on the slope data, wherein the safety factor is negatively correlated with the slope data; and calculate the work priority of the work sub-region based on the capacity factor, efficiency factor, and safety factor, wherein the work priority is positively correlated with the capacity factor, efficiency factor, and safety factor.

[0067] In one possible implementation, the calculation module 230 is further configured to use a preset priority algorithm as follows: P i =αC i +βE i +γS i Among them, P i The priority of a task is defined as the task sub-region, where α is the first parameter and C is the second parameter. i E is the capacity factor, β is the second parameter, and E i S is the efficiency factor, γ is the third parameter, and S i α is the safety factor; where α+β+γ=1.

[0068] In one possible implementation, the control module 240 is further configured to expand the soil discharge boundary of the work sub-region when the work priority of the work sub-region is greater than a first threshold, and to shrink the soil discharge boundary of the work sub-region when the work priority of the work sub-region is less than a second threshold.

[0069] In one possible implementation, the control module 240 is further configured to determine the soil removal rotation time of each work sub-area according to the work priority of the work sub-area; and control the target work equipment to remove soil from the work sub-area based on the work priority and the soil removal rotation time.

[0070] This application discloses a device for determining the priority of spoil disposal at a spoil heap. The device includes: an acquisition module for acquiring multi-source sensing data of a target operating device, wherein the multi-source sensing data includes at least point cloud data, location data, and image data; a generation module for generating a target operating map based on the multi-source sensing data, wherein the target operating map includes multiple operating sub-regions; a calculation module for calculating the operating priority of each operating sub-region based on a preset priority algorithm; and a control module for controlling the target operating device to discharge spoil into the operating sub-regions based on the operating priority. This device enables dynamic zoning of the spoil heap, reduces transportation costs for transport machinery, and improves the operating efficiency and safety of the spoil heap.

[0071] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated further here.

[0072] This application also provides an electronic device. Figure 3 This is a schematic diagram of the hardware structure of an embodiment of the electronic device of this application. The electronic device includes a memory 320 and at least one processor 310. The memory 320 is electrically connected to the at least one processor 310. The memory 320 stores instructions. The at least one processor 310 calls the instructions in the memory 320 to cause the electronic device to execute the method for determining the priority of spoil disposal according to any of the foregoing embodiments of this application.

[0073] Specifically, the processor 310 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0074] Memory 320 may include a mass storage device for data or instructions. For example, and not limitingly, memory 320 may include a hard disk drive (HDD), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 320 may include removable or non-removable (or fixed) media. Where appropriate, memory 320 may be internal or external to the integrated gateway disaster recovery device. In a particular embodiment, memory 320 is non-volatile solid-state memory. In a particular embodiment, memory 320 includes read-only memory (ROM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically rewritable ROM (EAROM), or flash memory, or a combination of two or more of these.

[0075] In one example, the control device may also include a communication interface 330 and a bus 340. The processor 310, memory 320, and communication interface 330 are connected via the bus 340 and communicate with each other.

[0076] The communication interface 330 is mainly used to realize communication between various modules, devices, units and / or equipment in the embodiments of this application.

[0077] Bus 340 includes hardware, software, or both, that couples components of an online data traffic metering device together. For example, and not limitingly, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, bus 340 may include one or more buses. Although specific buses are described and illustrated in embodiments of this application, any suitable bus or interconnect is contemplated herein.

[0078] Furthermore, in conjunction with the method for determining the priority of spoil heaps in the above embodiments, this application embodiment can provide a computer-readable storage medium for implementation. This computer-readable storage medium stores executable code, which, when executed by a processor, implements any of the methods for determining the priority of spoil heaps in the above embodiments.

[0079] This application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of this application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application.

[0080] The functional blocks shown in the above block diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. Programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried on a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0081] It should also be noted that the exemplary embodiments mentioned in this application describe methods or systems based on a series of steps or apparatus. However, this application is not limited to the order of the above steps; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0082] The above are merely specific embodiments of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the protection scope of this application.

Claims

1. A method for determining the priority of spoil disposal at a spoil heap, characterized in that, include: Acquire multi-source sensing data of the target operating equipment; wherein, the multi-source sensing data includes at least point cloud data, location data, and image data; Based on the multi-source sensing data, a target operation map is generated; wherein the target operation map includes multiple operation sub-regions; Based on a preset priority algorithm, the job priority of each job sub-region is calculated; Based on the work priority, the target work equipment is controlled to discharge soil in the work sub-area.

2. The method according to claim 1, characterized in that, The acquisition of multi-source sensing data of the target operating equipment includes: Acquire point cloud data, location data, and image data collected in real time by at least one sensor on the target operating equipment; Based on the point cloud data, the earthwork volume and slope data of the sub-area where the target operating equipment is located are obtained.

3. The method according to claim 1, characterized in that, The step of generating a target operation map based on the multi-source sensing data includes: The multi-source sensing data is spatiotemporally aligned to obtain the processed multi-source sensing data. Acquire verification data collected in real time by the target verification device; wherein the acquisition time of the verification data is the same as the acquisition time of the multi-source sensing data; The verification data is compared with the processed multi-source sensing data. If the processed multi-source sensing data meets the preset conditions, the target operation map is generated based on the processed multi-source sensing data.

4. The method according to claim 2, characterized in that, The calculation of the job priority for each job sub-region based on the preset priority algorithm includes: Based on the earthwork volume data and the preset capacity data of the work sub-area, the capacity factor of the work sub-area is calculated, and the capacity factor is positively correlated with the earthwork volume data. Based on the location data, the average transport distance of the target operating equipment is calculated, and based on the average transport distance, the efficiency factor of the operating sub-area is calculated, wherein the efficiency factor is negatively correlated with the average transport distance. Based on the slope data, a safety factor for the work sub-area is calculated, and the safety factor is negatively correlated with the slope data. The task priority of the task sub-region is calculated based on the capacity factor, the efficiency factor, and the safety factor; wherein the task priority is positively correlated with the capacity factor, the efficiency factor, and the safety factor.

5. The method according to claim 4, characterized in that, The preset priority algorithm is as follows: P i =αC i +βE i +γS i Wherein, P i The priority of the job sub-region is defined by α, which is the first parameter, and C is the job priority. i E is the capacity factor, β is the second parameter, and E i S is the efficiency factor, γ is the third parameter, and S i α is the safety factor; where α+β+γ=1.

6. The method according to claim 1, characterized in that, Also includes: If the operation priority of the operation sub-region is greater than the first threshold, then the soil removal boundary of the operation sub-region is expanded. If the operation priority of the operation sub-region is less than the second threshold, then the soil removal boundary of the operation sub-region is reduced.

7. The method according to claim 1, characterized in that, Also includes: The soil removal rotation time for each work sub-region is determined based on the work priority of the aforementioned work sub-regions; Based on the operation priority and the soil removal rotation time, the target operation equipment is controlled to remove soil from the operation sub-area.

8. A device for determining the priority of spoil disposal at a spoil heap, characterized in that, include: The acquisition module is used to acquire multi-source sensing data of the target operating equipment; wherein, the multi-source sensing data includes at least point cloud data, location data and image data; A generation module is used to generate a target operation map based on the multi-source sensing data; wherein the target operation map includes multiple operation sub-regions; The calculation module is used to calculate the job priority of each of the job sub-regions based on a preset priority algorithm; The control module is used to control the target work equipment to discharge soil in the work sub-area based on the work priority.

9. An electronic device, characterized in that, include: processor; as well as A memory having executable code stored thereon, which, when executed by the processor, causes the processor to perform the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, It stores executable code that, when executed by a processor of an electronic device, causes the processor to perform the method as described in any one of claims 1-7.

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