Bucket-wheel reclaimer operation control method and system, terminal and medium

Abstract

The invention relates to the field of bucket-wheel reclaiming, and particularly provides a bucket-wheel reclaimer operation control method and system, a terminal and a medium, and the method comprises the steps: scanning a material accumulation area through a laser radar to construct a real-time material three-dimensional model; acquiring equipment position and attitude information in real time through a sensor, wherein the equipment position and attitude information comprises a walking position of the whole machine, a pitching angle and a rotation angle of a cantilever and a rotation speed of a bucket wheel; according to the current position and attitude information of the equipment, searching an optimal material taking path in the current material three-dimensional model by using an A * algorithm; the optimization target of the A * algorithm is to select an area with the material accumulation thickness greater than a preset threshold value as a material taking point, and search is carried out by taking the highest path efficiency and the lowest energy consumption as a comprehensive cost function; and according to the optimal material taking path, executing parameters of all executing mechanisms are generated in combination with the current position and posture of the equipment, and the executing parameters are constructed into control instructions to be sent to the corresponding executing mechanisms. According to the invention, efficient, stable and energy-saving automatic operation of the bucket-wheel reclaimer is realized.

Description

Technical Field

[0001] This application relates to the field of bucket wheel reclaimers, specifically to a bucket wheel reclaimer operation control method, system, terminal, and medium. Background Technology

[0002] Bucket wheel reclaimers are highly efficient continuous material handling equipment widely used in industrial bulk material yards such as ports. Traditional bucket wheel reclaimers primarily rely on manual operator control, resulting in high labor intensity, high labor costs, and susceptibility to human error leading to inconsistent operational quality. Currently, some bucket wheel reclaimers have achieved preliminary automation control, such as setting fixed material handling paths through programming. However, these automation solutions are mostly based on preset programs and cannot effectively perceive and adapt to the actual three-dimensional shape of the material pile. When material accumulation is irregular or site conditions change, the system struggles to dynamically adjust its handling strategy, easily leading to risks such as bucket wheel idling, drastic fluctuations in material flow, or collisions with the material pile, failing to achieve truly efficient and continuous fully automated operation. Furthermore, the control between the various actuators (such as traveling, rotating, pitching, and bucket wheel drive) in these solutions is often independent or simply linked, lacking globally optimal collaborative planning. This results in inconsistent equipment movements, operating paths that are not optimal in terms of both energy consumption and efficiency, and a large number of ineffective actions and energy waste, leading to poor equipment operating economy. Summary of the Invention

[0003] To address the aforementioned problems, this invention provides a method, system, terminal, and medium for controlling the operation of a bucket wheel reclaimer, enabling efficient, stable, and energy-saving automatic operation of the bucket wheel reclaimer.

[0004] In a first aspect, the technical solution of the present invention provides a method for controlling the operation of a bucket wheel reclaimer, comprising the following steps: The material accumulation area is scanned by lidar to obtain the three-dimensional shape information of the material in real time, and a real-time three-dimensional model of the material is constructed based on the three-dimensional shape information. The equipment's position and attitude information is acquired in real time through sensors, including the overall machine's walking position, the cantilever's pitch and slewing angles, and the bucket wheel's rotation speed. Based on the current position and attitude information of the equipment, the A* algorithm is used to search for the optimal material picking path in the current three-dimensional material model. The optimization objective of the A* algorithm is to select the area where the material accumulation thickness is greater than a preset threshold as the picking point, and to search with the highest path efficiency and the lowest energy consumption as the comprehensive cost function. Based on the optimal material handling path, and combined with the current position and attitude of the equipment, the execution parameters of each actuator are generated, and the execution parameters are constructed into control commands and sent to the corresponding actuators.

[0005] In an optional implementation, based on the current position and attitude information of the equipment, the A* algorithm is used to search for the optimal material handling path in the current 3D model of the material, specifically including: Based on the current position and attitude information of the equipment, the real-time three-dimensional coordinates of the material collection point of the bucket wheel reclaimer are calculated, and the coordinates are defined as the starting node of the A* algorithm path search. Based on the current 3D model of the material, identify the area where the material accumulation thickness is greater than a preset threshold, and use this area as the target material extraction area. A comprehensive cost function is constructed based on the highest path efficiency and lowest energy consumption; wherein, the evaluation of path efficiency and energy consumption is calculated based on the changes in the device motion state from the starting node to the target node; Using the starting node as the path origin and the target material picking area as the search target, run the A* algorithm to output the optimal material picking path as the movement path with the minimum overall cost from the starting point to the target material picking area.

[0006] In one optional implementation, a comprehensive cost function is constructed based on maximizing path efficiency and minimizing energy consumption, specifically including: The real-time material 3D module is discretized into a 3D raster map, where each raster cell is a node; Define actual cost Used to calculate from the starting node to the current node. The cumulative cost is expressed as,

[0007] in, parent node Move to the current node Euclidean distance, To be based on each actuator of the device from the parent node Move to the current node The energy consumption change calculated based on the estimated range of motion , These are the path length weighting coefficient and the energy consumption weighting coefficient, respectively. Define nodes heuristic function , is represented as ,

[0008] in, For the set of target points in the target material picking area, For the current node Nodes in the target point set Euclidean distance; Constructing a comprehensive cost function .

[0009] In an optional implementation, the method further includes calculating from the parent node Move to the current node The steps for predicting energy consumption changes in the range of motion specifically include: For the parent node Move to the current node Based on the spatial coordinate difference between the two nodes, the estimated motion amplitude required by each actuator of the bucket wheel reclaimer to complete this movement is calculated for the path segment. The estimated motion amplitude is input into the pre-built energy consumption model of the corresponding actuator to obtain the estimated energy consumption of the corresponding actuator. The total energy consumption change is obtained by linearly superimposing the estimated energy consumption of each actuator.

[0010] In one optional implementation, the material accumulation area is scanned by lidar to obtain the three-dimensional shape information of the material in real time, and a real-time three-dimensional model of the material is constructed based on the three-dimensional shape information, specifically including: Real-time acquisition of raw point cloud data characterizing the surface morphology of an object using lidar; Preprocess the raw point cloud data, including noise reduction filtering and coordinate system unification; Based on the preprocessed point cloud data, a random sampling consensus algorithm is used to segment the point cloud, separating the point cloud representing the ground or base plane from the point cloud representing the material. Based on the point cloud of the material surface obtained after segmentation, a real-time three-dimensional model of the material is constructed using a surface reconstruction algorithm.

[0011] In one optional implementation, based on the optimal material handling path and the current position and attitude of the equipment, execution parameters for each actuator are generated. These execution parameters are then used to construct control commands, which are sent to the corresponding actuators. Specifically, this includes: The optimal material handling path is calculated and converted into a continuous time series setpoint. Each setpoint contains the target values ​​of each actuator of the equipment at that moment, including the target travel position of the whole machine, the target rotation angle of the cantilever, the target pitch angle of the cantilever, and the target rotation speed of the bucket wheel. Based on the current position and attitude of the equipment, the control algorithm calculates the execution parameters required to drive each actuator to reach the next set point. The calculated execution parameters are constructed into control instructions that can be recognized by the lower-level driver or actuator according to a predetermined communication protocol; Control commands are sent to the corresponding lower-level drivers or actuators in real time.

[0012] In an optional implementation, after the execution parameters are constructed into control commands and sent to the corresponding actuators, the following steps are also included: During the material handling process, the system receives real-time data streams from the target sensors, including the instantaneous weight of the material grabbed by the bucket wheel, the actual rotation angle and pitch angle of the cantilever, and the actual speed, position, and operating current of each actuator motor. The received real-time data is compared with the preset safe operation threshold to determine whether there is an abnormal state; If an abnormal state exists, the corresponding adjustment strategy will be matched to generate the corresponding adjustment instruction.

[0013] Secondly, the technical solution of the present invention provides a bucket wheel reclaimer operation control system, comprising: The material 3D modeling module is used to scan the material accumulation area with LiDAR, obtain the 3D shape information of the material in real time, and construct a real-time 3D model of the material based on the 3D shape information. The operation information acquisition module is used to acquire the equipment's position and attitude information in real time through sensors, including the overall machine's walking position, the cantilever's pitch and slewing angles, and the bucket wheel's rotation speed; The optimal material picking path generation module is used to search for the optimal material picking path in the current material 3D model based on the current position and attitude information of the equipment using the A* algorithm. The optimization objective of the A* algorithm is to select the area where the material accumulation thickness is greater than a preset threshold as the picking point, and to search using the highest path efficiency and lowest energy consumption as the comprehensive cost function. The execution control module is used to generate execution parameters for each actuator based on the optimal material handling path and the current position and attitude of the equipment, and then construct control commands from the execution parameters and send them to the corresponding actuators.

[0014] Thirdly, the technical solution of the present invention provides a terminal, comprising: The memory is used to store the operating control program of the bucket wheel reclaimer; A processor is used to implement the steps of the bucket wheel reclaimer operation control method as described above when executing the bucket wheel reclaimer operation control program.

[0015] Fourthly, the present invention provides a computer-readable storage medium storing a bucket wheel reclaimer operation control program, wherein the bucket wheel reclaimer operation control program, when executed by a processor, implements the steps of the bucket wheel reclaimer operation control method as described in any of the above claims.

[0016] As can be seen from the above technical solutions, this application has the following advantages: 1. This application constructs a three-dimensional model of the material through real-time scanning with LiDAR, and performs path planning based on this model, enabling the bucket wheel reclaimer to actively adapt to irregular material piles and changing site conditions. This achieves a leap from fixed program to intelligent perception, realizes automated operation, and enhances the intelligence and adaptability of the material reclaiming operation. 2. This application employs intelligent search algorithms such as A* to plan the optimal material handling path based on the material accumulation thickness, ensuring that the bucket wheel always operates within the high-efficiency material handling area, reducing idle travel and ineffective movements, and improving material handling efficiency. Simultaneously, through the coordinated control of various actuators, the uniformity and continuity of the material handling process are guaranteed, preventing localized excessively high or low stockpiles and improving overall operational quality. 3. This application uses both path length and motion energy consumption as optimization objectives for path planning, directly seeking the optimal balance between energy consumption and efficiency at the algorithm level. By selecting the path with the lowest energy consumption, the energy consumption of the equipment during movements such as walking, turning, and pitching is reduced, thus improving the economic efficiency of operation. 4. This application further uses sensors to monitor the material handling process in real time and makes dynamic adjustments based on feedback, which can respond to complex working conditions in a timely manner, effectively prevent abnormal situations such as equipment overload and collision, extend the service life of the equipment, and ensure operational safety. Attached Figure Description

[0017] To more clearly illustrate the technical solution of this application, the accompanying drawings used in the description will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a schematic flowchart of a bucket wheel reclaimer operation control method provided in an embodiment of the present invention.

[0019] Figure 2 This is a schematic block diagram of the operating control system of a bucket wheel reclaimer provided in an embodiment of the present invention.

[0020] Figure 3 This is a schematic diagram of the structure of a terminal provided in an embodiment of the present invention. Detailed Implementation

[0021] To make the purpose, features, and advantages of this application more apparent and understandable, specific embodiments and accompanying drawings will be used to clearly and completely describe the technical solution protected by this application. Obviously, the embodiments described below are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0022] Unless otherwise defined, all technical and scientific terms used in this application have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used in this application and in the specification of this invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.

[0023] Figure 1 This is a schematic flowchart illustrating a bucket wheel reclaimer operation control method according to an embodiment of the present invention. Figure 1 The executing entity can be a bucket wheel reclaimer operation control system. The bucket wheel reclaimer operation control method provided in this embodiment of the invention is executed by computer equipment; correspondingly, the bucket wheel reclaimer operation control system runs within the computer equipment. Depending on different requirements, the order of steps in this flowchart can be changed, and some steps can be omitted.

[0024] like Figure 1 As shown, the method includes the following steps.

[0025] S1, the material accumulation area is scanned by lidar to obtain the three-dimensional shape information of the material in real time, and a real-time three-dimensional model of the material is constructed based on the three-dimensional shape information.

[0026] S2 acquires real-time equipment position and attitude information through sensors, including the overall machine's walking position, the cantilever's pitch and slewing angles, and the bucket wheel's rotation speed.

[0027] S3. Based on the current position and attitude information of the equipment, the A* algorithm is used to search for the optimal material picking path in the current three-dimensional material model. The optimization objective of the A* algorithm is to select the area where the material accumulation thickness is greater than the preset threshold as the picking point, and to search with the highest path efficiency and the lowest energy consumption as the comprehensive cost function.

[0028] S4. Based on the optimal material handling path, and combined with the current position and attitude of the equipment, the execution parameters of each actuator are generated, and the execution parameters are constructed into control commands and sent to the corresponding actuators.

[0029] Furthermore, as a refinement and extension of the specific implementation of the above embodiments, in order to fully illustrate the specific implementation process in this embodiment, another bucket wheel reclaimer operation control method is provided, which includes the following steps.

[0030] S101, Construct a real-time 3D model of the material.

[0031] The material accumulation area is scanned by LiDAR to obtain the three-dimensional shape information of the material in real time, and a real-time three-dimensional model of the material is constructed based on the three-dimensional shape information. The specific steps include the following steps.

[0032] S101.1 acquires raw point cloud data characterizing the surface morphology of an object in real time using lidar.

[0033] The lidar, mounted on the cantilever or frame of the bucket wheel reclaimer, is controlled to periodically or continuously scan the material accumulation area in front. The lidar emits a laser beam and receives the returned point cloud data, which contains a large amount of three-dimensional coordinate information (X, Y, Z) and reflection intensity information of points, thereby obtaining raw point cloud data characterizing the surface morphology of the material in real time.

[0034] S101.2 preprocesses the raw point cloud data, including noise reduction filtering and coordinate system unification.

[0035] Noise reduction filtering: Statistical filtering or radius filtering algorithms are used to remove outlier noise caused by dust, rain, snow or measurement errors.

[0036] Coordinate System 1: The point cloud data is transformed from the local coordinate system of the lidar to the global world coordinate system with the rotation center of the bucket wheel reclaimer as the origin through a pre-calibrated transformation matrix, thereby realizing the fusion of point cloud data and equipment position information.

[0037] S101.3 Based on the preprocessed point cloud data, a random sampling consensus algorithm is used to segment the point cloud, separating the point cloud representing the ground or base plane from the point cloud representing the material.

[0038] S101.4, based on the point cloud of the material surface obtained after segmentation, constructs a real-time three-dimensional model of the material through a surface reconstruction algorithm.

[0039] Surface reconstruction algorithms can be either triangulation algorithms or voxel mesh algorithms. Triangulation algorithms use Delaunay triangulation or Poisson surface reconstruction algorithms to connect discrete point clouds into a continuous triangular mesh model. Voxel mesh algorithms discretize the point cloud space into voxel grids and assign an occupancy probability to each voxel based on the point cloud density, generating a voxelized 3D model.

[0040] The reconstructed 3D model can then undergo post-processing, including mesh smoothing and simplification, to eliminate jagged edges and reduce computational load. Further, based on the reflection intensity of the point cloud or preset material density information, attribute information is assigned to different regions of the model. This attribute information includes material type and bulk density, providing data for subsequent material reclaiming path planning. During the operation of the bucket wheel reclaimer, the above steps are executed cyclically, continuously updating the 3D material model at a set frequency. This dynamically reflects the real-time changes in the material pile shape after the reclaiming operation, ensuring the accuracy of the path planning.

[0041] S102, Obtain equipment operation information.

[0042] The equipment's position and attitude information is acquired in real time through sensors, including the overall machine's walking position, the cantilever's pitch and slewing angles, and the bucket wheel's rotation speed.

[0043] The overall travel position of the machine is the absolute or relative position coordinate of the material handling machine on the track, measured by an encoder or GPS (for open-air trackless mobile equipment).

[0044] The rotational speed of the bucket wheel is the rotational speed of the bucket wheel itself. It is a direct parameter for controlling the material handling efficiency and flow rate, and is measured in real time by an incremental rotary encoder or speed sensor installed on the bucket wheel drive motor or bucket wheel shaft.

[0045] The cantilever pitch angle is measured in real time by an absolute rotary encoder or tilt sensor mounted on the hinge axis between the cantilever and the portal frame. The rotary encoder directly measures the rotation angle of the pitch hinge axis; the tilt sensor directly measures the tilt angle of the cantilever relative to the horizontal reference plane.

[0046] The cantilever rotation angle is measured in real time by an absolute multi-turn rotary encoder mounted on the slewing bearing of the gantry frame or the slewing drive. This encoder directly measures the horizontal rotation angle of the gantry frame relative to the bottom traveling mechanism.

[0047] S103, generate the optimal material handling path.

[0048] Based on the current position and attitude information of the equipment, the A* algorithm is used to search for the optimal material picking path in the current three-dimensional material model. The optimization objective of the A* algorithm is to select the area where the material accumulation thickness is greater than a preset threshold as the picking point, and to search using the highest path efficiency and lowest energy consumption as the comprehensive cost function. The specific steps include the following steps.

[0049] S103.1 Calculate the real-time three-dimensional coordinates of the bucket wheel reclaimer's material collection point based on the current position and attitude information of the equipment, and define these coordinates as the starting node for the A* algorithm path search.

[0050] The starting node of the search path in the A* algorithm is dynamically defined by the current position and attitude information of the equipment. Specifically, the overall walking position determines the plane reference coordinates (X, Y) of the equipment in the global coordinate system of the yard, the rotation angle of the boom determines the horizontal direction of the boom relative to the central axis of the whole machine, the pitch angle of the boom determines the tilt angle of the bucket wheel relative to the horizontal plane, and the rotation speed of the bucket wheel is not directly used to define the position, but is used to evaluate the current motion state of the equipment.

[0051] By integrating the above parameters, the real-time three-dimensional coordinates (X, Y, Z) of the bucket wheel tip (i.e., the material handling point) in the global coordinate system are calculated. These coordinates are the starting node for the A* algorithm path search. Each path planning begins from the absolute real-time state of the equipment, ensuring that the planned path aligns with the current state of the equipment and avoiding theoretically feasible paths that conflict with the current state.

[0052] S103.2 Based on the current three-dimensional model of the material, identify the area where the material accumulation thickness is greater than the preset threshold, and take the area as the target material taking area.

[0053] It should be noted that when searching for the optimal path, the real-time material 3D module is first discretized into a 3D grid map, where each grid cell is a node, and each node contains its spatial coordinate information.

[0054] The optimization objective of this method is to prioritize regions with large material accumulation thickness. Therefore, the model considers all material accumulation thicknesses... Greater than the preset threshold The grid nodes constitute the target point set . S103.3, construct a comprehensive cost function based on the highest path efficiency and lowest energy consumption; wherein, the evaluation of path efficiency and energy consumption is calculated based on the change of device motion state from the starting node to the target node.

[0055] The total cost estimation function of the A* algorithm From the starting node to the current node The actual cost and from the current node Heuristic cost estimation to the target node It consists of two parts, represented as:

[0056] in, For nodes Total cost estimate, From the starting node to the node The actual accumulated cost, which comprehensively considers the energy consumption of path length, bucket wheel speed variation, and cantilever attitude adjustment, is... For nodes A heuristic cost function to the target node is used to estimate the minimum cost required for subsequent steps.

[0057] Define actual cost , is represented as ,

[0058] in, parent node Move to the current node Euclidean distance, To be based on each actuator of the device from the parent node Move to the current node The energy consumption change calculated based on the estimated range of motion , These are the path length weighting coefficient and the energy consumption weighting coefficient, respectively, used to adjust the proportion of path efficiency and energy consumption in the optimization objective. Indicates the starting index or starting point for summation, referring to the starting node of the path.

[0059] Define nodes heuristic function , is represented as ,

[0060] in, For the set of target points in the target material picking area, For the current node Nodes in the target point set Euclidean distance.

[0061] In some alternative implementations, the calculation from the parent node is performed through the following steps. Move to the current node Estimate the energy consumption changes of the amplitude of movement.

[0062] Step 1), for the parent node Move to the current node Based on the spatial coordinate difference between the two nodes, the estimated motion amplitude required by each actuator of the bucket wheel reclaimer to complete this movement is calculated for the path segment.

[0063] The range of motion includes the estimated travel distance of the entire machine's traveling mechanism. The estimated change in rotation angle of the cantilever slewing mechanism The estimated pitch angle change of the cantilever pitch mechanism The estimated change in rotation angle of the bucket wheel drive mechanism . Step 2) Input the estimated motion amplitude into the pre-built energy consumption model of the corresponding actuator to obtain the estimated energy consumption of the corresponding actuator.

[0064] Energy consumption model of walking mechanism ,in This is the energy consumption coefficient per unit distance traveled.

[0065] Energy consumption model of rotary mechanism: ,in The energy consumption coefficient is the energy consumption per unit angle of rotation.

[0066] Pitch mechanism energy consumption model: ,in This is the energy consumption coefficient per unit pitch angle.

[0067] Bucket wheel mechanism energy consumption model: ,in This is the energy consumption coefficient per unit angle of bucket wheel rotation.

[0068] Step 3) The estimated energy consumption of each actuator is linearly superimposed to obtain the total energy consumption change.

[0069] Will , , , Adding them together gives the change in total energy consumption. .

[0070] S103.4, taking the starting node as the path starting point and the target material picking area as the search target, run the A* algorithm and output the movement path with the minimum comprehensive cost from the starting point to the target material picking area as the optimal material picking path.

[0071] Open an open list and a closed column. Add the starting node to the open list and repeat the following process until the target node is found or the open list is empty: a) Select the total cost estimate from the open list smallest node As the current node; b) If the current node Belongs to the set of target points If so, the search is completed by tracing back the path. c) Move the current node Move from the open list to the closed list; d) Traverse the current node All neighboring nodes : If node If the path is impassable (e.g., due to collision with an obstacle) or is already on the closed list, skip it; Calculate the nodes from the starting node through the nodes Reaching the node Temporary actual cost ; If node If a node is not in the open list, or its temporary actual cost is less than its original actual cost, then update the node. The value of Calculate its total cost and set its parent node to Finally, add or update it to the open list.

[0072] After the algorithm terminates, a complete motion path from the current position to the optimal material picking point is generated by backtracking from the target node back to the starting node. This path is the optimal material picking path.

[0073] During the node expansion process, unreachable nodes are eliminated based on the mechanical operation constraints of the equipment: When expanding the search nodes, the A* algorithm must determine whether the adjacent nodes are "feasible", that is, whether the equipment's motion mechanism can reach the point without collision, which depends on the current posture of the equipment and its mechanical constraints.

[0074] Mechanical constraint verification: Based on the current cantilever pitch and slewing angles, the achievable working range of the cantilever in the next movement can be calculated. When generating adjacent nodes, the algorithm excludes points that require the cantilever to exceed its maximum pitch angle, maximum slewing angle, or maximum working radius to reach, ensuring that the planned path is within the mechanical capabilities of the equipment.

[0075] Collision avoidance verification: The algorithm incorporates the equipment's own outline as a dynamic obstacle into the collision detection consideration. When planning the path, it ensures that not only can the bucket wheel reach the target point, but other parts of the equipment (such as the cantilever and frame) will not collide with the material pile or other fixed facilities.

[0076] S104, execute control according to the optimal material handling path.

[0077] Based on the optimal material handling path, the execution parameters of each actuator are generated by combining the current position and attitude of the equipment. The execution parameters are then used to construct control commands and sent to the corresponding actuators. The specific steps include the following steps.

[0078] S104.1, calculate the optimal material picking path and convert it into a continuous time series setpoint. Each setpoint contains the target values ​​of each actuator of the equipment at that moment, including the target travel position of the whole machine, the target rotation angle of the cantilever, the target pitch angle of the cantilever, and the target rotation speed of the bucket wheel.

[0079] S104.2, combining the current position and attitude of the equipment, the control algorithm calculates the execution parameters required to drive each actuator to reach the next set point.

[0080] The control algorithm can be a PID controller, and the actuators include a traveling mechanism, a slewing mechanism, a pitching mechanism, a bucket wheel drive mechanism, and a conveying mechanism.

[0081] The traveling mechanism is used to drive the whole machine forward or backward along the track. The execution parameters include the speed command and displacement command of the traveling motor, and control the whole machine to accurately position itself to the target traveling position specified in the path planning.

[0082] The slewing mechanism is used to drive the cantilever to rotate horizontally around the central axis of the machine. The execution parameters include the torque command and speed command of the slewing motor, which controls the cantilever to rotate smoothly and accurately to the target slewing angle.

[0083] The pitch mechanism is used to drive the boom to perform pitching motions, either upwards or downwards. The execution parameters include the hydraulic pressure command and displacement command of the pitch hydraulic cylinder, controlling the boom to precisely adjust to the target pitch angle in order to determine the material taking depth of the bucket wheel.

[0084] The bucket wheel drive mechanism is used to drive the bucket wheel to rotate and perform the action of digging materials. The execution parameters include the torque command and speed command of the bucket wheel drive motor, which controls the bucket wheel to rotate at the target speed to ensure that the material handling flow rate matches the system requirements.

[0085] The conveying mechanism is used to transport the material picked up by the bucket wheel to the rear system. The execution parameters include the speed command of the cantilever belt conveyor motor, so that its running speed is matched with the material picked up by the bucket wheel in real time to prevent material accumulation or interruption of flow.

[0086] S104.3, The calculated execution parameters are constructed into control instructions that can be recognized by the lower-level driver or actuator according to a predetermined communication protocol.

[0087] S104.4 sends control commands to the corresponding lower-level drivers or actuators in real time.

[0088] In some alternative implementations, after the execution parameters are constructed into control commands and sent to the corresponding actuators, the following steps are also included.

[0089] Step 1) During the material handling operation, receive the real-time data stream from the target sensor, including the instantaneous weight of the material grabbed by the bucket wheel, the actual rotation angle and pitch angle of the cantilever, and the actual speed, position, and operating current of each actuator motor.

[0090] The weight sensor installed on the bucket wheel drive mechanism detects the instantaneous weight of the material grabbed by the bucket wheel; the angle sensor installed on the cantilever rotation and pitch hinge points detects the actual rotation angle and actual pitch angle of the cantilever; the encoder and current sensor installed on each motor detect the actual speed, position and operating current of the actuator.

[0091] Step 2) Compare the received real-time data with the preset safe operation threshold to determine whether there is an abnormal state.

[0092] The instantaneous weight of the material grabbed by the bucket wheel is compared with the preset maximum allowable load threshold of the bucket wheel. The actual operating current of each motor is compared with its rated current. If the actual value is greater than the preset value, the current state is determined to be overload or abnormal weight.

[0093] Step 3): If an abnormal state exists, generate the corresponding adjustment instruction by matching the appropriate adjustment strategy.

[0094] Once an abnormal state is determined, the control system's adjustment strategy module immediately generates corresponding adjustment instructions. The adjustment strategies include: generating instructions to reduce the target speed of the bucket wheel drive motor to reduce the amount of material taken out per unit time; generating instructions to control the pitch mechanism to lift the cantilever to reduce the depth of the bucket wheel cutting into the material, thereby reducing the load; and simultaneously generating the above-mentioned speed reduction and lifting instructions to perform a combined adjustment to quickly eliminate overload.

[0095] In some optional implementations, the operating parameters of various parts of the equipment are monitored in real time, and a preset fault diagnosis algorithm is used to determine whether the equipment is faulty. For example, when the bucket wheel motor current is detected to be too high and the duration exceeds a set threshold, the system determines that the bucket wheel may be overloaded, immediately issues a shutdown command, sends an alarm to the operator through the alarm device, and displays fault information and handling suggestions on the display screen.

[0096] The above text provides a detailed description of an embodiment of a bucket wheel reclaimer operation control method. Based on the bucket wheel reclaimer operation control method described in the above embodiment, this invention also provides a bucket wheel reclaimer operation control system corresponding to the method.

[0097] Figure 2 This is a schematic block diagram of the operating control system of a bucket wheel reclaimer provided in an embodiment of the present invention. In this embodiment, the bucket wheel reclaimer operating control system 200 can be divided into multiple functional modules according to the functions it performs, such as... Figure 2 As shown. The module referred to in this invention is a series of computer program segments that can be executed by at least one processor and perform a fixed function, and which are stored in memory.

[0098] The material 3D modeling module 210 is used to scan the material accumulation area with LiDAR, obtain the 3D shape information of the material in real time, and construct a real-time 3D model of the material based on the 3D shape information.

[0099] The operation information acquisition module 220 is used to acquire the equipment position and attitude information in real time through sensors, including the overall walking position of the machine, the pitch and rotation angles of the cantilever, and the rotation speed of the bucket wheel.

[0100] The optimal material picking path generation module 230 is used to search for the optimal material picking path in the current material 3D model based on the current position and attitude information of the equipment using the A* algorithm. The optimization objective of the A* algorithm is to select the area where the material accumulation thickness is greater than a preset threshold as the picking point, and to search using the highest path efficiency and lowest energy consumption as the comprehensive cost function.

[0101] The execution control module 240 is used to generate execution parameters for each actuator based on the optimal material handling path and the current position and attitude of the equipment, and to construct control commands from the execution parameters and send them to the corresponding actuators.

[0102] The bucket wheel reclaimer operation control system of this embodiment is used to implement the aforementioned bucket wheel reclaimer operation control method. Therefore, the specific implementation of this system can be found in the embodiment section of the bucket wheel reclaimer operation control method above. Thus, the specific implementation can be referred to the description of the corresponding embodiments, and will not be elaborated here.

[0103] Furthermore, since the bucket wheel reclaimer operation control system of this embodiment is used to implement the aforementioned bucket wheel reclaimer operation control method, its function corresponds to the function of the above method, and will not be repeated here.

[0104] Figure 3 This is a schematic diagram of a terminal 300 provided in an embodiment of the present invention, including: a processor 310, a memory 320, and a communication unit 330. The processor 310 is used to implement the flow steps of the above-described bucket wheel reclaimer operation control method embodiment when implementing the bucket wheel reclaimer operation control program stored in the memory 320.

[0105] The terminal 300 includes a processor 310, a memory 320, and a communication unit 330. These components communicate via one or more buses. Those skilled in the art will understand that the server structure shown in the figure does not constitute a limitation of the present invention. It can be a bus topology or a star topology, and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0106] The memory 320 can be used to store the execution instructions of the processor 310. The memory 320 can be implemented by any type of volatile or non-volatile memory terminal or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. When the execution instructions in the memory 320 are executed by the processor 310, the terminal 300 is able to perform some or all of the steps in the above method embodiments.

[0107] The processor 310 serves as the control center of the storage terminal, connecting various parts of the electronic terminal via various interfaces and lines. It executes software programs and / or modules stored in the memory 320, and calls data stored in the memory to perform various functions of the electronic terminal and / or process data. The processor can be composed of integrated circuits (ICs), such as a single packaged IC or multiple packaged ICs with the same or different functions connected together. For example, the processor 310 may consist only of a central processing unit (CPU). In this embodiment of the invention, the CPU may have a single processing core or include multiple processing cores.

[0108] The communication unit 330 is used to establish a communication channel, enabling the storage terminal to communicate with other terminals. It can receive user data sent by other terminals or send user data to other terminals.

[0109] This invention also provides a computer storage medium, which may be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc. The computer storage medium stores a bucket wheel reclaimer operation control program. When the bucket wheel reclaimer operation control program is executed by a processor, it implements the process steps of the above-described bucket wheel reclaimer operation control method embodiment.

[0110] Those skilled in the art will clearly understand that the techniques in the embodiments of the present invention can be implemented using software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solutions in the embodiments of the present invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium such as a USB flash drive, mobile hard drive, read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk, or other media capable of storing program code. It includes several instructions to cause a computer terminal (which may be a personal computer, server, or a second terminal, network terminal, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention.

[0111] In the embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0112] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0113] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0114] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for controlling the operation of a bucket wheel reclaimer, characterized in that, Includes the following steps: The material accumulation area is scanned by lidar to obtain the three-dimensional shape information of the material in real time, and a real-time three-dimensional model of the material is constructed based on the three-dimensional shape information. The equipment's position and attitude information is acquired in real time through sensors, including the overall machine's walking position, the cantilever's pitch and slewing angles, and the bucket wheel's rotation speed. Based on the current position and attitude information of the equipment, the A* algorithm is used to search for the optimal material picking path in the current three-dimensional material model. The optimization objective of the A* algorithm is to select the area where the material accumulation thickness is greater than a preset threshold as the picking point, and to search with the highest path efficiency and the lowest energy consumption as the comprehensive cost function. Based on the optimal material handling path, and combined with the current position and attitude of the equipment, the execution parameters of each actuator are generated, and the execution parameters are constructed into control commands and sent to the corresponding actuators.

2. The method according to claim 1, characterized in that, Based on the current position and attitude information of the equipment, the A* algorithm is used to search for the optimal material handling path in the current 3D model of the material, specifically including: Based on the current position and attitude information of the equipment, the real-time three-dimensional coordinates of the material collection point of the bucket wheel reclaimer are calculated, and the coordinates are defined as the starting node of the A* algorithm path search. Based on the current 3D model of the material, identify the area where the material accumulation thickness is greater than a preset threshold, and use this area as the target material extraction area. A comprehensive cost function is constructed based on the highest path efficiency and lowest energy consumption; wherein, the evaluation of path efficiency and energy consumption is calculated based on the changes in the device motion state from the starting node to the target node; Using the starting node as the path origin and the target material picking area as the search target, run the A* algorithm to output the optimal material picking path as the movement path with the minimum overall cost from the starting point to the target material picking area.

3. The method according to claim 2, characterized in that, The comprehensive cost function is constructed based on the highest path efficiency and lowest energy consumption, specifically including: The real-time material 3D module is discretized into a 3D raster map, where each raster cell is a node; Define actual cost Used to calculate from the starting node to the current node. The cumulative cost is expressed as, in, parent node Move to the current node Euclidean distance, To be based on each actuator of the device from the parent node Move to the current node The energy consumption change calculated based on the estimated range of motion , These are the path length weighting coefficient and the energy consumption weighting coefficient, respectively. Define nodes heuristic function , is represented as , in, For the set of target points in the target material picking area, For the current node Nodes in the target point set Euclidean distance; Constructing a comprehensive cost function .

4. The method according to claim 1, characterized in that, The method also includes calculating from the parent node Move to the current node The steps for predicting energy consumption changes in the range of motion specifically include: For the parent node Move to the current node Based on the spatial coordinate difference between the two nodes, the estimated motion amplitude required by each actuator of the bucket wheel reclaimer to complete this movement is calculated for the path segment. The estimated motion amplitude is input into the pre-built energy consumption model of the corresponding actuator to obtain the estimated energy consumption of the corresponding actuator. The total energy consumption change is obtained by linearly superimposing the estimated energy consumption of each actuator.

5. The method according to claim 1, characterized in that, The material accumulation area is scanned by lidar to obtain the three-dimensional shape information of the material in real time. A real-time three-dimensional model of the material is constructed based on the three-dimensional shape information, specifically including: Real-time acquisition of raw point cloud data characterizing the surface morphology of an object using lidar; Preprocess the raw point cloud data, including noise reduction filtering and coordinate system unification; Based on the preprocessed point cloud data, a random sampling consensus algorithm is used to segment the point cloud, separating the point cloud representing the ground or base plane from the point cloud representing the material. Based on the point cloud of the material surface obtained after segmentation, a real-time three-dimensional model of the material is constructed using a surface reconstruction algorithm.

6. The method according to claim 1, characterized in that, Based on the optimal material handling path, and combined with the current position and attitude of the equipment, execution parameters for each actuator are generated. These execution parameters are then used to construct control commands, which are sent to the corresponding actuators. Specifically, this includes: The optimal material handling path is calculated and converted into a continuous time series setpoint. Each setpoint contains the target values ​​of each actuator of the equipment at that moment, including the target travel position of the whole machine, the target rotation angle of the cantilever, the target pitch angle of the cantilever, and the target rotation speed of the bucket wheel. Based on the current position and attitude of the equipment, the control algorithm calculates the execution parameters required to drive each actuator to reach the next set point. The calculated execution parameters are constructed into control instructions that can be recognized by the lower-level driver or actuator according to a predetermined communication protocol; Control commands are sent to the corresponding lower-level drivers or actuators in real time.

7. The method according to claim 1, characterized in that, After constructing the execution parameters into control commands and sending them to the corresponding actuators, the following steps are also included: During the material handling process, the system receives real-time data streams from the target sensors, including the instantaneous weight of the material grabbed by the bucket wheel, the actual rotation angle and pitch angle of the cantilever, and the actual speed, position, and operating current of each actuator motor. The received real-time data is compared with the preset safe operation threshold to determine whether there is an abnormal state; If an abnormal state exists, the corresponding adjustment strategy will be matched to generate the corresponding adjustment instruction.

8. A bucket wheel reclaimer operation control system, characterized in that, include: The material 3D modeling module is used to scan the material accumulation area with LiDAR, obtain the 3D shape information of the material in real time, and construct a real-time 3D model of the material based on the 3D shape information. The operation information acquisition module is used to acquire the equipment's position and attitude information in real time through sensors, including the overall machine's walking position, the cantilever's pitch and slewing angles, and the bucket wheel's rotation speed; The optimal material picking path generation module is used to search for the optimal material picking path in the current material 3D model based on the current position and attitude information of the equipment using the A* algorithm. The optimization objective of the A* algorithm is to select the area where the material accumulation thickness is greater than a preset threshold as the picking point, and to search using the highest path efficiency and lowest energy consumption as the comprehensive cost function. The execution control module is used to generate execution parameters for each actuator based on the optimal material handling path and the current position and attitude of the equipment, and then construct control commands from the execution parameters and send them to the corresponding actuators.

9. A terminal, characterized in that, include: The memory is used to store the operating control program of the bucket wheel reclaimer; A processor is configured to implement the steps of the bucket wheel reclaimer operation control method as described in any one of claims 1 to 7 when executing the bucket wheel reclaimer operation control program.

10. A computer-readable storage medium, characterized in that, The readable storage medium stores a bucket wheel reclaimer operation control program, which, when executed by a processor, implements the steps of the bucket wheel reclaimer operation control method as described in any one of claims 1 to 7.

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