Automatic control method and system for unmanned bucket wheel excavator

Abstract

The present invention provides an unmanned bucket wheel machine automatic control method and system, relating to the technical field of unmanned bucket wheel machines. The method comprises the following steps: obtaining a grasping task requirement, a three-dimensional operation area model, the real-time position and posture data of the unmanned bucket wheel machine, the distribution status information of the material pile, and the operating status data of the transportation equipment; performing path planning based on the grasping task requirement, the three-dimensional operation area model, and the real-time position and posture data to generate a preliminary grasping path; performing transportation collaborative planning based on the operating status data and the preliminary grasping path to obtain a collaborative grasping solution; predicting a collapse area based on the distribution status information to obtain a prediction result; and adjusting the collaborative grasping solution based on the prediction result, dynamically avoiding collapse areas with a risk level above a threshold, and obtaining a grasping operation solution. Through dynamic path planning and a real-time updated three-dimensional operation area model, the present invention can quickly generate an optimal grasping path and adjust the operation sequence in real time.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned bucket wheel machines, and in particular to an automatic control method and system for an unmanned bucket wheel machine. Background Art

[0002] With the rapid development of industrial automation and intelligent technologies, unmanned operations have become a key research direction in the control of large-scale industrial equipment. Bucket-wheel excavators, the core equipment for material handling and transportation in bulk material storage yards, have traditionally relied on manual operation or semi-automated technologies. However, these approaches face numerous challenges in practical application. First, manual operation is inefficient, requiring operators to monitor the complex operating environment for extended periods and manually adjust the grasping path. This is labor-intensive and inefficient, and grasping accuracy is severely limited by manual experience. Second, the environmental complexity of material storage yards places high demands on grasping path planning and equipment stability. In particular, dynamic deformation of the material pile during the grasping process can lead to path failure or equipment damage, making traditional technologies unable to respond in real time. While existing automated control technologies incorporate path planning and sensor feedback mechanisms, they typically rely on fixed path planning and static scenario modeling, operating only under minimal environmental changes. In the event of dynamic collapse of the material pile, complex path interference, or other unpredictable factors, these systems struggle to quickly adjust grasping strategies and path planning, compromising operational efficiency and system safety. Furthermore, they lack effective support for the coordinated operation of transport equipment.

[0003] Based on the above shortcomings of the prior art, there is an urgent need for an unmanned bucket wheel excavator automatic control method and system. Summary of the Invention

[0004] The purpose of the present invention is to provide an automatic control method and system for an unmanned bucket wheel excavator to improve the above-mentioned problems. In order to achieve the above-mentioned purpose, the technical solution adopted by the present invention is as follows:

[0005] In a first aspect, the present application provides an automatic control method for an unmanned bucket wheel excavator, comprising:

[0006] Obtaining grasping task requirements, 3D operation area models, real-time position and posture data of unmanned bucket wheel excavators, distribution status information of material piles, and operating status data of transportation equipment;

[0007] Performing grasping path planning processing according to the grasping task requirements, the three-dimensional operating area model, and the real-time position and posture data, and generating a preliminary grasping path by analyzing the operating area of the bucket wheel excavator, the target position of the grasping task, and the dynamic posture of the bucket wheel excavator;

[0008] Performing transport collaborative planning based on the operating status data and the preliminary grabbing path, dynamically adjusting the bucket wheel excavator's operating sequence and path planning by analyzing the real-time position, operating speed, and load status of the transport equipment, and obtaining a collaborative grabbing solution;

[0009] The collapse area is predicted based on the distribution state information, and the prediction result is obtained by analyzing the morphology and structure information of the material pile and simulating the impact of different grasping schemes on the morphology change of the material pile;

[0010] The collaborative grasping scheme is adjusted according to the prediction results, and the grasping path is optimized to dynamically avoid the collapsed area with a risk level higher than a threshold, and a specific grasping action strategy is generated to obtain a grasping operation plan.

[0011] In a second aspect, the present application also provides an unmanned bucket wheel machine automatic control system, comprising:

[0012] The acquisition module is used to obtain the grasping task requirements, the three-dimensional operation area model, the real-time position and posture data of the unmanned bucket wheel excavator, the distribution status information of the material pile, and the operating status data of the transportation equipment;

[0013] a planning module for performing grasping path planning based on the grasping task requirements, the three-dimensional operating area model, and the real-time position and posture data, and generating a preliminary grasping path by analyzing the operating area of the bucket wheel excavator, the target position of the grasping task, and the dynamic posture of the bucket wheel excavator;

[0014] An analysis module is configured to perform transport collaborative planning based on the operating status data and the preliminary grabbing path, dynamically adjust the bucket wheel excavator's operating sequence and path planning by analyzing the real-time position, operating speed, and load status of the transport equipment, and obtain a collaborative grabbing solution;

[0015] A prediction module is used to predict the collapse area based on the distribution state information, analyze the morphology and structure information of the material pile, and simulate the impact of different grabbing schemes on the morphology change of the material pile to obtain a prediction result;

[0016] The optimization module is used to adjust the collaborative grasping plan according to the prediction results, dynamically avoid the collapsed area with a risk level higher than the threshold by optimizing the grasping path, and generate a specific grasping action strategy to obtain a grasping operation plan.

[0017] The beneficial effects of the present invention are:

[0018] Through dynamic path planning and real-time updated three-dimensional working area models, the present invention enables the bucket wheel excavator to quickly generate the optimal grasping path and adjust the working sequence in real time, significantly improving the grasping efficiency compared with traditional fixed path planning; the discrete element method is used to dynamically simulate the changes in the material pile morphology, predict the collapse areas that may occur during the grasping process, and optimize the grasping path in combination with the dynamic avoidance algorithm, effectively avoiding high-risk areas and ensuring operational safety; by analyzing the real-time operating status of the transportation equipment, the grasping rhythm and operating path of the bucket wheel excavator are dynamically adjusted to ensure seamless connection between grasping operations and material transportation, thereby improving overall operational efficiency and reducing waiting time. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying any creative work.

[0020] Figure 1 This is a flow chart of an automatic control method for an unmanned bucket wheel excavator according to an embodiment of the present invention;

[0021] Figure 2 Schematic diagram of the structure of an unmanned bucket wheel excavator automatic control system according to an embodiment of the present invention;

[0022] Figure 3 This is a schematic structural diagram of an automatic control device for an unmanned bucket wheel excavator described in an embodiment of the present invention.

[0023] Markings in the figure: 800, an unmanned bucket wheel machine automatic control device; 801, processor; 802, memory; 803, multimedia component; 804, I / O interface; 805, communication component; 901, acquisition module; 902, planning module; 903, analysis module; 904, prediction module; 905, optimization module. DETAILED DESCRIPTION

[0024] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0025] It should be noted that similar reference numerals and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings. At the same time, in the description of the present invention, the terms "first", "second", etc. are used only to distinguish the description and should not be understood as indicating or implying relative importance.

[0026] Example 1:

[0027] This embodiment provides an automatic control method for an unmanned bucket wheel excavator.

[0028] See also Figure 1 , the figure shows that the method includes steps S100 to S500.

[0029] Step S100: Acquire the grasping task requirements, the three-dimensional operation area model, the real-time position and posture data of the unmanned bucket wheel excavator, the distribution status information of the material pile, and the operating status data of the transportation equipment;

[0030] In practice, grasping task requirements are issued by the higher-level scheduling system, including target material type, grasp quantity, quality requirements, and operation time constraints, ensuring that the UWBW operation meets global production scheduling requirements. The construction of a 3D operation area model relies on point cloud data captured by lidar or high-precision cameras. Algorithms are used to perform modeling and extract spatial features, generating a dynamic 3D spatial map of the yard environment, providing comprehensive awareness of the yard's structure, terrain characteristics, and obstacle distribution. Simultaneously, GPS and IMU sensors collect and fuse the UWBW's real-time position and attitude data to obtain its current 3D coordinates and attitude information, laying the foundation for dynamic path planning. The distribution status of the material pile is scanned in real time by radar or vision sensors, generating a 3D material pile model. Density estimation algorithms are then used to further analyze the distribution characteristics of the material pile, such as pile height, volume, and boundary features. Furthermore, operational status data of transport equipment, including its current position, speed, and load status, is acquired via wireless communication networks to support coordinated transport planning.

[0031] Step S200: performing grabbing path planning based on the grabbing task requirements, the three-dimensional operating area model, and the real-time position and posture data, and generating a preliminary grabbing path by analyzing the bucket wheel excavator's operating area, the target position of the grabbing task, and the bucket wheel excavator's dynamic posture;

[0032] It is understandable that the grasping task requirements in this step define the specific target areas in the material pile that need to be grasped (including material type, grasp quantity, and priority order, etc.), and these requirements become constraints for path planning. Then, based on the 3D work area model, through spatial feature extraction and terrain analysis of point cloud data, the range of feasible paths within the work area is determined. At the same time, combined with the bucket wheel excavator's real-time position and attitude data (such as 3D coordinates and pitch and yaw angles), the bucket wheel excavator's current state is calibrated through multi-sensor fusion technology, providing an accurate reference for the selection of the path starting point.

[0033] Step S300: Performing transport collaborative planning based on the operating status data and the preliminary grabbing path. By analyzing the real-time position, operating speed, and load status of the transport equipment, the bucket wheel excavator's operating sequence and path planning are dynamically adjusted to obtain a collaborative grabbing solution.

[0034] It should be noted that this step achieves a high degree of coordination between the bucket wheel excavator and the transportation equipment, and significantly improves the overall efficiency of the system by optimizing the operation sequence and path planning; secondly, by dynamically analyzing the load status and operation trajectory of the transportation equipment, task conflicts and waiting time between grasping and transportation are avoided, and resource utilization is improved; finally, the collaborative planning scheme can adapt to the status changes of the transportation equipment in real time, ensuring the stability and efficiency of the unmanned bucket wheel excavator in complex operating environments.

[0035] Step S400: predicting the collapsed area based on the distribution state information, analyzing the morphology and structure information of the material pile, and simulating the effects of different grabbing schemes on the morphology of the material pile to obtain a prediction result;

[0036] It can be understood that this step conducts an in-depth analysis of the morphological and structural information of the material pile and uses simulation technology to evaluate the dynamic impact of different grasping schemes on the morphological changes of the material pile, thereby identifying possible collapse areas in advance and providing a reliable basis for subsequent path optimization and action adjustment.

[0037] Step S500: Adjust the collaborative grasping plan according to the prediction results, optimize the grasping path, dynamically avoid the collapsed area with a risk level higher than the threshold, and generate a specific grasping action strategy to obtain a grasping operation plan.

[0038] It's important to note that this step is the decision-making component of the unmanned bucket wheel machine's automatic control method. Its purpose is to dynamically adjust the collaborative grabbing plan based on the collapse area prediction results, optimize the grabbing path, avoid high-risk collapse areas, and generate a specific grabbing action strategy, ultimately forming a safe and efficient grabbing operation plan. This step, through path adjustment and action optimization, ensures the unmanned bucket wheel machine's adaptability and safety in complex yard environments.

[0039] Furthermore, step S200 includes steps S210 to S240.

[0040] Step S210: Perform positioning processing based on the grasping task requirements and the 3D operation area model. By performing cluster analysis on the point cloud data of the 3D operation area model, the material pile locations and boundaries that meet the grasping requirements are screened out, and the positioning data of the target grasping area, the positioning data spatial position and boundary data are obtained;

[0041] Specifically, this step first combines information such as the target material type, grasping priority, and work area restrictions in the grasping task requirements, and converts them into constraints for point cloud data screening, such as the material's height range, density distribution characteristics, and specific spatial range. Subsequently, the point cloud data in the three-dimensional work area model is preprocessed and analyzed using a DBSCAN clustering algorithm. DBSCAN is a density-based clustering method that can effectively identify high-density areas in point clouds while separating boundary points and noise points. The clustering results reflect the distribution characteristics of the material pile, and the high-density clustered areas screened out are potential grasping targets.

[0042] After completing the initial clustering, the spatial distribution characteristics of the material pile are further analyzed in conjunction with the grasping task requirements. For example, by calculating the center point coordinates, height gradient, volume, and boundary shape of the clustered areas, the clustered areas are screened and prioritized, eliminating areas that do not meet the grasping requirements. Ultimately, the location and boundary data of the material pile that meet the grasping task are extracted. This data is output as the positioning data of the target grasping area, including the center coordinates of the material pile, the spatial boundary outline, and the surface height distribution, providing accurate initial input for subsequent path planning.

[0043] Step S220: Optimize the path starting point based on the spatial position and real-time position and attitude data. A cost function is constructed with path distance, attitude adjustment energy consumption, and obstacle avoidance constraints as weights. The path starting point is optimized and calculated using a gradient descent algorithm to obtain optimized path starting point parameters. The path starting point parameters include path starting point coordinates and attitude parameters.

[0044] Specifically, the geometric relationship between the bucket wheel crane and the target area and the required attitude adjustment are first analyzed based on the target area's location and the bucket wheel crane's current three-dimensional coordinates, pitch angle, and yaw angle. Path start point optimization constructs a cost function based on path distance, attitude adjustment energy consumption, and obstacle avoidance constraints. This function is used to balance path length and energy consumption while ensuring the safety of the path start point. A gradient descent algorithm is used to iteratively optimize the cost function, gradually adjusting the starting point's spatial position and attitude parameters to minimize energy consumption from the start point to the target area while satisfying obstacle avoidance requirements. The resulting path start point parameters include the three-dimensional coordinates of the path and optimized attitude parameters. These parameters reduce the transition time and energy consumption from the bucket wheel crane's current state to the operation starting point, while also providing a high-quality starting point for subsequent path planning. The technical benefits are reflected in the efficiency and safety of the path start point, optimizing path length and attitude adjustment processes while reducing energy consumption and collision risks during equipment operation, laying the foundation for smooth path planning and operation.

[0045] Step S230: Planning a local grasping path based on the boundary data and path starting point parameters. Path nodes are generated by gridding the boundary of the target grasping area. Path generation is combined with the A* algorithm and gradient field constraints to generate a path, resulting in a sequence of local grasping path nodes from the path starting point to the target grasping area.

[0046] As can be understood, this step first utilizes the boundary data of the target grasping area, combined with a three-dimensional operating area model, to divide the target area into a regular grid node structure. This gridding generates discrete nodes for path search, ensuring computational efficiency and area coverage for path planning. Subsequently, the path starting point parameters are used as the planning starting point, connecting the path starting point to the grid nodes of the target area, and employing the A* algorithm for path search. The A* algorithm is a classic heuristic path planning method that combines the cumulative path cost with a heuristic estimation function to rapidly calculate the optimal path from the starting point to the boundary of the target area while avoiding redundant computations. To further enhance the feasibility of the path, gradient field constraints are introduced into the planning process to perform constrained optimization of the path. Gradient field constraints optimize the path node connections by analyzing the mechanical motion characteristics of the bucket wheel machine (such as turning radius and tilt angle limits), ensuring a physically feasible and smooth path. The resulting local grasping path is a sequence of nodes that define the trajectory from the path starting point to the target grasping area, ensuring that the path is consistent with the dynamic operating capabilities of the bucket wheel machine while avoiding potential obstacles or unstable areas.

[0047] Step S240: Perform path smoothing processing based on the local grasping path node sequence, generate a continuous path function through interpolation, and optimize the control point position in combination with the dynamic constraint of attitude adjustment to obtain a preliminary grasping path. The preliminary grasping path includes the continuous path function and the bucket wheel machine attitude adjustment instruction.

[0048] It should be noted that path smoothing first uses an interpolation algorithm to make the path nodes continuous, generating a highly smooth continuous path function. This interpolation algorithm not only smoothes the transitions between discrete nodes on the path but also adjusts the position and curvature of control points to avoid sharp corners or unnecessary bends in the path. Furthermore, to ensure the feasibility of the smoothed path in actual mechanical operation, this step further optimizes the positions of the path control points by incorporating the bucket wheel machine's dynamic attitude adjustment constraints (such as the physical limitations of pitch, yaw, and bucket wheel rotation angles). The optimization process modifies the interpolation curve using dynamic constraints to ensure that the generated path meets smoothness requirements while also conforming to the bucket wheel machine's kinematic characteristics and physical operating range. The resulting preliminary grasping path consists of a continuous path function and corresponding bucket wheel machine attitude adjustment instructions. These instructions provide the bucket wheel machine with specific operating parameters (such as steering angle, lift height, and grasping depth), ensuring efficient and smooth operation along the grasping path.

[0049] Furthermore, step S300 includes steps S310 to S340.

[0050] Step S310: Performing a transport equipment status assessment based on the operating status data, analyzing the real-time location, operating speed, and current load status of the transport equipment, calculating the idle capacity and dynamic accessibility of the transport equipment, and obtaining a transport equipment coordination index;

[0051] In this embodiment, the status assessment of the transport equipment first starts with the real-time position and running speed data, and estimates the moving path of the transport equipment in the short future time and the time window for reaching a specific location through a trajectory prediction algorithm (such as a prediction model based on Kalman filtering). Combined with the grabbing position and path planning results of the bucket wheel excavator, the spatial distance and time matching between the transport equipment and the bucket wheel excavator are evaluated. At the same time, combined with the current load status of the transport equipment (such as the remaining carrying capacity or the volume of the material bin), the idle capacity of the transport equipment is calculated to determine whether it can undertake the amount of material for the current grabbing task. For the dynamic accessibility of the transport equipment, a comprehensive assessment is made of whether the transport equipment can successfully reach the target area within the specified time by combining the speed, acceleration and environmental constraints of the equipment. Finally, by combining the above analysis results, a transport equipment coordination index is generated. This index is a quantitative result, including the arrival time prediction, spatial adaptability and load matching of the transport equipment, and serves as an important input basis for transport coordination planning.

[0052] Step S320: Based on the coordination index and the preliminary grasping path, the operation sequence is adjusted. By prioritizing the idle state of the transport equipment and the order constraints of the grasping path, the operation sequence of the grasping tasks is dynamically adjusted in combination with the optimal scheduling algorithm of the transport equipment to obtain the optimized grasping sequence.

[0053] Specifically, this step first prioritizes the current status of transporters using their interoperability metrics (including free capacity, dynamic reachability, and arrival time window). For example, a transporter with sufficient free capacity and a high-matching arrival time at the target area within the bucket wheel excavator's grabbing window is prioritized. Simultaneously, based on the sequence constraints of the preliminary grabbing paths, the material pile location and estimated grabbing time corresponding to each grabbing path are analyzed and matched to the dynamic state of the transporter. This determines the optimal relationship between different transporters and grabbing tasks. To achieve optimal scheduling, this step combines a transporter scheduling algorithm, preferably a heuristic rule-based task allocation algorithm or a dynamic programming algorithm, to adjust the order of grabbing tasks. Within the scheduling algorithm, grasping tasks are dynamically reordered, balancing the load capacity of the transporters, the bucket wheel excavator's path planning, and transport time constraints, with the overall system efficiency as the goal. For example, priority is given to transporters that are closest to full capacity but still capable of handling the current grabbing task, or the order of grabbing tasks is adjusted based on the transporter's shortest travel path, ensuring seamless transport and grabbing.

[0054] Step S330: Perform path synchronization adjustment processing based on the optimized grabbing sequence. By combining the position of the transport equipment with the dynamic time window matching of the bucket wheel excavator grabbing path, a path adjustment algorithm based on time window optimization is used to generate a synchronous grabbing path that matches the operating status of the transport equipment, thereby obtaining a preliminary collaborative path solution.

[0055] Specifically, the system first analyzes the current position of the transporter, the estimated time window for reaching the target grabbing area, and the transporter's dynamic operating status (such as speed and acceleration) based on the optimized grabbing sequence. Combined with the bucket wheel excavator's current grabbing path, a dynamic time window matching algorithm is used to evaluate the timing of coordination between the transporter and the bucket wheel excavator in the grabbing area. The core of time window matching is to predict whether the arrival time of the transporter coincides with the completion time of the bucket wheel excavator's grabbing task, thus avoiding situations where the transporter is waiting or material is accumulated due to delays in the transporter after the bucket wheel excavator has completed its operation.

[0056] During path synchronization adjustment, a path adjustment algorithm based on time window optimization is used to optimize the bucket wheel excavator's grabbing path. This algorithm dynamically adjusts the node sequence in the preliminary grabbing path to precisely align the timing of grabbing tasks with the arrival time of the transport equipment. For example, if the transport equipment is expected to arrive late, the execution order of the bucket wheel excavator's grabbing path is adjusted to advance lower-priority grabbing tasks to fill the idle time. If the transport equipment arrives early, the path is adjusted to quickly complete the grabbing tasks in the target area. In addition, the path adjustment process takes into account the bucket wheel excavator's motion characteristics (such as path smoothness and mechanical constraints) to ensure that the adjusted path is feasible in actual operation.

[0057] The final generated synchronous grasping path is a path plan that is dynamically adjusted according to the operating status of the transportation equipment. It not only meets the arrival time requirements of the transportation equipment, but also ensures the smooth connection of the grasping tasks.

[0058] Step S340: Perform transport collaborative optimization processing based on the preliminary collaborative path plan, dynamically adjust the operating speed and loading rhythm of the bucket wheel excavator's grabbing path, and iteratively optimize the collaborative plan in combination with the load state constraint to obtain a collaborative grabbing plan.

[0059] In this embodiment, the real-time operating status of the transport equipment is first analyzed based on a preliminary collaborative path plan, including information such as the transport equipment's current location, estimated arrival time, current load level, and remaining carrying capacity. Then, the bucket wheel excavator's grabbing path execution rhythm is dynamically adjusted based on the transport equipment's load state constraints (such as the transport equipment's maximum load capacity and loading speed). Specifically, when the transport equipment is close to full load, the bucket wheel excavator's operating speed is controlled to reduce the material grabbing rhythm to avoid excessive material accumulation and overloading of the transport equipment. When the transport equipment has a large idle capacity, the bucket wheel excavator's grabbing rhythm and loading rate are increased to fully utilize the transport equipment's carrying capacity and improve operational efficiency.

[0060] To achieve collaborative optimization, this step employs an iterative optimization algorithm. Each iteration updates the collaborative solution based on the bucket wheel excavator's speed adjustment and the transporter's load status. The optimization goal is to minimize the waiting time between the bucket wheel excavator and the transporter while maximizing the transporter's load utilization and the bucket wheel excavator's efficiency. After each iteration, the operating speed and loading cadence are updated based on the transporter's latest operating status until the collaborative solution converges to the optimal solution.

[0061] Furthermore, step S400 includes steps S410 to S440.

[0062] Step S410: Extracting morphological features of the material pile based on the distribution state information. By using normal vector calculation and surface fitting algorithms on the three-dimensional point cloud data of the material pile, surface gradients, local curvatures, and boundary morphological features are extracted, and key areas are marked to obtain three-dimensional morphological feature parameters of the material pile.

[0063] First, a high-precision three-dimensional model of the material pile is constructed through the preprocessing of the three-dimensional point cloud data. Subsequently, the normal vector calculation technology is used to calculate the normal vector distribution of the surface based on the local neighborhood of each point in the point cloud, which is used to characterize the inclination direction and gradient of various parts of the material pile surface. The surface gradient is one of the important characteristics of the material pile morphology and can be used to identify steep areas or potential slip risk points. Next, the local curvature of the material pile surface is calculated through a surface fitting algorithm (such as polynomial fitting or quadratic surface fitting based on the least squares method). The local curvature reflects the degree of convexity and concavity of the material pile surface, and can effectively identify high convex areas (such as the top of the pile) and low concave areas (such as the bottom accumulation point) in the pile body. These areas are often the key points of changes in the morphology of the material pile.

[0064] Furthermore, to further identify the boundary morphological characteristics of the material pile, this step combines the spatial distribution information of the point cloud data with boundary detection algorithms (such as the Alpha shape algorithm) to extract the boundary contours of the material pile. Key areas within the pile where morphological changes may occur, such as steep slopes and weak boundaries, are marked. These marked key areas are not only used for subsequent collapse risk prediction but also provide important reference for dynamic simulation and grasping path planning.

[0065] The final extracted 3D morphological parameters of the material pile include surface gradient, local curvature, boundary shape, and key area marking information. These parameters fully describe the geometric and structural characteristics of the material pile.

[0066] Step S420: Perform structural stability analysis based on the three-dimensional morphological characteristic parameters. By constructing a stability discrimination model based on morphological characteristics and combining gravity field analysis with contact surface distribution calculation, the overall stability of the material pile is evaluated by partitioning to obtain a stability distribution map of the material pile.

[0067] First, based on the three-dimensional morphological characteristic parameters extracted in step S410, a stability discrimination model based on the morphological characteristics of the material pile is constructed. This model is mainly used to identify weak areas and potentially unstable areas in the pile, and by combining morphological characteristics with mechanical properties, the overall and local stability of the pile is evaluated. Subsequently, the stress state inside the material pile is calculated through gravity field analysis. Based on the three-dimensional model of the material pile and the material density parameters, the pile is divided into finite volume units, and the gravity vector of each unit and its distribution on the contact surface are calculated. Gravity field analysis can reflect the stress and force concentration areas of each part of the pile. Combined with the morphological characteristic discrimination model, the stability assessment can be further refined. Next, the contact conditions between the material pile and the ground and the contact characteristics between the particles are analyzed through contact surface distribution calculation. The contact surface distribution calculation adopts the normal force and friction force model, combined with the Coulomb friction condition to evaluate the anti-slip ability between the particles and the support force distribution in the contact area between the pile and the ground.

[0068] Ultimately, the results of the morphological feature discrimination model, gravity field analysis, and contact surface distribution calculations were combined to calibrate the overall stability of the material pile using a zoning assessment method. A stability distribution map visually displays the stability levels of different regions of the material pile. High-stability areas are used for path planning and task prioritization, while low-stability areas serve as the focus for collapse prediction and dynamic avoidance.

[0069] Step S430: Performing morphological change simulation based on the stability distribution diagram of the material pile. By using the discrete element method to dynamically simulate different grasping schemes, the influence of the grasping path on the material pile structure is simulated, the morphological changes caused by the grasping process are predicted, and the dynamic correspondence between the grasping path and the morphological changes of the material pile is obtained.

[0070] It is understood that this step first determines the morphological characteristics and stability levels of key areas based on the material pile's stability distribution map, identifying high-risk areas that may be affected by the grasping (such as low-stability areas or boundary areas with weak support). Combined with the bucket wheel excavator's grasping path and motion parameters, the material pile model is discretized into a simulation system composed of particle units. Each particle unit is assigned physical properties and an initial position, and dynamic calculations are performed using the discrete element method. In dynamic simulation, the discrete element method iteratively calculates the morphological changes of the material pile by simulating the contact forces between particles (including normal force, tangential force, friction, and adhesion) and the interaction between particles and the bucket wheel excavator's grasping tool. During the simulation, as the grasping path is executed, the particle motion state (such as displacement and rotation) and the pile morphology (such as slip, sedimentation, or local collapse) are updated in real time. For example, when the grasping path passes through a low-stability area, the simulation may predict the expansion of the slip boundary or a significant change in the pile's center of gravity. These dynamic changes directly affect the overall stability of the material pile. By comparing and analyzing simulation results from different grasping schemes, a dynamic relationship between grasping paths and changes in the material pile's shape is generated. This relationship is represented as a data graph or 3D model, describing the degree and range of changes in the pile's shape that each grasping path may cause.

[0071] Step S440: Perform collapse risk prediction processing based on the dynamic correspondence, calculate the risk probability distribution of each area during the grasping action, mark the high-risk collapse area on the grasping path, and obtain the collapse area prediction result.

[0072] Specifically, first, based on the dynamic correspondence between the grasping path generated in step S430 and the morphological changes of the material pile, the direct impact of the grasping action on the material pile is analyzed. On this basis, the grasping path is divided into several key points or areas, and the risk probability is calculated for each area. The risk probability distribution is comprehensively evaluated by combining the morphological change simulation results, the stability distribution diagram of the material pile, and the stress state of the particles. Specifically, weak areas (such as low stability areas or areas with insufficient support) are given higher basic risk weights because their structures are easily destroyed; and the risk probability of path points where large particle displacement or slip extension occurs in the dynamic simulation is further increased.

[0073] The risk prediction model incorporates a method combining probabilistic calculations with mechanical analysis. Furthermore, based on Coulomb friction theory and particle contact mechanics, the probability of slippage or collapse in each region during a grasping action is calculated. Furthermore, based on time series data of dynamic correspondences, the system predicts the gradual accumulation and evolution of risks during the grasping process, identifying critical nodes that could trigger chain reactions. High-risk areas are categorized and labeled based on their predicted probabilities and system-defined risk thresholds. These labeled results are visualized to form a collapse risk map for the grasping path.

[0074] Furthermore, step S420 includes steps S421 to S424.

[0075] Step S421: Perform contact surface extraction based on the three-dimensional morphological feature parameters. By analyzing the material pile point cloud data and normal vector distribution, the material pile surface is triangulated using an Alpha shape algorithm to extract contact areas with contact force. The normal angle distribution and normal area weight of each contact area are calculated to obtain the contact surface distribution characteristics of the material pile.

[0076] As can be understood, the material pile surface is first geometrically reconstructed based on the material pile's point cloud data and normal vector information. After preprocessing the point cloud data, a normal vector field for the material pile surface is generated. The normal vector distribution reflects the local directional characteristics and inclination of the material pile surface, providing an important basis for subsequent contact surface identification. Next, the Alpha shape algorithm is used to reconstruct the surface and extract the contact surface from the material pile's point cloud data. The Alpha shape algorithm is a geometric reconstruction method suitable for processing three-dimensional point cloud data. By adjusting the value of the parameter α, it can accurately capture the boundary shape and detailed features of the material pile. By converting the point cloud into a triangulated mesh, the Alpha shape algorithm can effectively identify the geometric relationship between the material pile surface and the contact area, and extract key areas where contact forces act (such as the contact area between the material pile and the ground, and the local contact surface between particles). After extracting the contact surface, the mechanical properties of the contact surface are further quantified by calculating the normal angle distribution and normal area weight of the contact area. The normal angle distribution reflects the inclination of the contact surface and the direction of the force applied. Areas with larger inclination angles tend to have a higher risk of slip. The normal area weight represents the support capacity of the contact surface. Larger and more evenly distributed areas generally have higher stability. These contact surface distribution characteristics provide highly accurate input parameters for subsequent mechanical analysis.

[0077] Step S422: Calculate the gravity distribution based on the contact distribution characteristics. This involves dividing the three-dimensional shape of the material pile into finite volume units, using the finite element method to calculate the center of gravity coordinates and gravity distribution of each unit. Combined with the normal characteristics of the contact surface, the support force and sliding force of each unit are mechanically decomposed to obtain the gravity field distribution inside the material pile.

[0078] It should be noted that this step can simplify the calculation process and improve the calculation accuracy by decomposing complex geometric shapes into simple units. The finite element method is used to calculate the center of gravity coordinates and gravity distribution of each unit, which can handle complex boundary conditions and material properties, and determine the stress and displacement state of each unit by solving the equilibrium equation. Combined with the normal characteristics of the contact surface, the support force and sliding force of each unit are mechanically decomposed. This process takes into account the mechanical behavior of the material pile when it contacts the ground or other objects. The normal characteristics reflect the inclination degree and force direction of the contact surface, which is crucial for evaluating the stability of the material pile. Through this decomposition, the gravity field distribution inside the material pile can be calculated more accurately, thereby providing reliable data support for subsequent stability analysis.

[0079] Step S423: Perform stability analysis based on the gravity field distribution. Calculate the anti-slip force of each area on the contact surface based on a preset Coulomb friction model. Calculate the stability factor by combining the gravity field distribution and the support force of the local contact point. Classify the stability of each contact surface area based on the critical value to obtain the local distribution of the stability factor.

[0080] It should be noted that the Coulomb friction model is a classic model for describing the frictional properties of granular materials. It takes into account the internal friction angle and cohesion of the material and can effectively predict the slip behavior between particles. By calculating the anti-slip force on the contact surface, the ability of the material pile to resist slip in different regions can be determined. Next, the stability factor is calculated by combining the gravity field distribution and the support force at the local contact point. The stability factor is an important indicator for measuring the stability of the material pile. It comprehensively considers the relationship between the sliding force under gravity and the anti-slip force on the contact surface. By calculating the stability factor for each contact surface region, the stability of the material pile in different regions can be quantitatively assessed. Finally, the stability of each contact surface region is classified according to the critical value, and the local distribution of the stability factor is obtained. The critical value is an important reference value for judging the stability of the material pile. When the stability factor is below the critical value, the material pile may be unstable and at risk of collapse. By classifying the stability of the contact surface region, weak links and potential risk areas in the material pile can be identified, providing important information for subsequent collapse risk prediction and path planning.

[0081] Step S424: Perform zoning processing based on the local distribution of stability factors. By constructing a global assessment model based on stability factors, weighted interpolation of the locally distributed stability factors is performed and combined with the transition continuity between regions. High-risk areas are marked and risk level classification is performed to obtain an overall stability distribution map of the material pile.

[0082] Specifically, this step first performs zoning processing based on the local distribution of the stability factor, dividing the material pile into different areas, and each area is classified according to the value of its stability factor. Zoning processing helps to focus on areas with poor stability and higher risks. Next, a global assessment model based on the stability factor is constructed. This model expands the local stability factor data to the entire material pile through the weighted interpolation method. Weighted interpolation takes into account the relative importance of stability factors in different areas, ensuring the accuracy and reliability of the assessment results. At the same time, combined with the transition continuity between regions, it ensures a smooth transition in space for the stability assessment, avoids sudden changes in the assessment results caused by zoning, and makes the overall stability distribution map more realistically reflect the actual situation of the material pile. Finally, the high-risk areas are marked and risk level classified. This step clearly identifies the areas with the lowest stability and highest risk in the material pile in a visual way, and provides a clear risk warning and decision-making basis by grading them according to the degree of risk.

[0083] Furthermore, step S430 includes steps S431 to S434.

[0084] Step S431: Constructing a grasping motion mechanics model based on the stability distribution diagram of the material pile. By combining the stability distribution diagram of the material pile with the geometric model of the grasping tool, a particle contact mechanics algorithm based on the discrete element method is used to decompose the material pile into a number of discrete particle units. Combined with the calculation of contact points, contact forces, and friction forces between the particles, a grasping motion mechanics model of the material pile is constructed to obtain initial state parameters describing the internal stress of the material pile and the movement of the particles.

[0085] It is understandable that the stability distribution map of the material pile provides stability information of each area of the material pile, providing important basic data for the construction of the model. These stability data are combined with the geometric model of the gripping tool to ensure that the model can accurately reflect the interaction between the gripping tool and the material pile. The discrete element method is a numerical simulation technology used to simulate the mechanical behavior of granular materials. By decomposing the material pile into several discrete particle units, the interaction between particles can be captured in detail, including contact points, contact forces, and friction forces. The discrete element method can accurately calculate the forces and motion states between particles, thereby constructing a comprehensive mechanical model of the gripping action. This model can describe the stress distribution inside the material pile and the initial state parameters of the particle motion, providing the necessary initial conditions for subsequent dynamic simulations. The specific calculation formula is as follows:

[0086] The total force formula is:

[0087] F i =∑ j∈C(i) (F n,ij +F t,ij +F d,ij )+F g,i +F e,i +F c,i ;

[0088] The formula for normal contact force is:

[0089] F n,ij =k n δ n,ij n ij -η n v n,ij ;

[0090] The formula for tangential contact force is:

[0091] F t,ij =min(k t δ t,ij ,μ|F n,ij |)t ij -η t v t,ij ;

[0092] The collision force formula is:

[0093]

[0094] Rotational dynamics formula:

[0095]

[0096] Where i represents the number of the particle; j represents the number of the particle that contacts particle i; C(i) represents the set of particles in the contact area of particle i; F irepresents the total force on particle i; F n,ij represents the normal contact force between particles i and j; k n represents the normal elastic coefficient between particles; δ n,ij represents the normal overlap between particles i and j; n ij represents the normal unit vector from particle i to particle j; η n represents the normal damping coefficient; v n,ij represents the normal relative velocity between particles i and j; F t,ij represents the tangential contact force between particles i and j; k t represents the tangential elastic coefficient between particles; δ t,ij represents the relative sliding displacement between particles i and j; μ represents the sliding friction resistance between particles; t ij represents the tangential unit vector between particles i and j; η t represents the tangential damping coefficient; v t,ij represents the tangential relative velocity between particles i and j; F c,i represents the collision force of particle i; ξ represents the collision energy dissipation coefficient; U represents the potential energy function; r i represents the position vector of the center of mass of particle i; T i represents the total torque of particle i; r ij represents the displacement vector from the center of mass of particle i to the contact point; η r represents the rotational damping coefficient; ω i represents the angular velocity of particle i; F g,i represents the gravity on particle i; F e,i Represents the external force exerted by the gripping tool; F d,ij represents the viscous damping force between particles i and j.

[0097] Step S432: Dynamic simulation of particle motion is performed based on the initial state parameters. By analyzing the particle force at each point along the grasping path, the force equilibrium state and motion trajectory of the particles under the action of the grasping tool are gradually and iteratively calculated. The contact relationship and stacking morphology between the particles are dynamically updated to obtain the dynamic morphological changes in the distribution of particles in the material pile due to the grasping path.

[0098] It should be noted that this step performs a detailed force analysis of the particles at each path point of the grasping path. Specifically, it involves calculating the various forces that the particles are subjected to under the action of the grasping tool, such as gravity, friction, contact force, etc., and how these forces affect the motion state of the particles. Through step-by-step iterative calculations, the simulation model can accurately simulate the force balance state and motion trajectory of the particles during the grasping process. As the grasping tool moves, the contact relationship between the particles will change, such as the formation of new contact points and the breaking of old contact points. The simulation model will update these contact relationships in real time to ensure that the interaction between the particles always accurately reflects the actual situation. At the same time, the stacking morphology of the particles will also change accordingly, such as the rearrangement, slippage or local collapse of the particles. By simulating the entire grasping path, the dynamic morphological changes of the particle distribution in the material pile can be obtained, that is, the changes in the position, stacking morphology and interaction relationship of the particles at different time points.

[0099] Step S433: Extract the affected area of the morphological change based on the dynamic morphological change. By performing spatial cluster analysis on the areas of change in particle displacement and accumulation morphology in the simulation results, the high displacement areas, slip boundary areas, and local collapse areas within the action range of the grasping path are identified to obtain the affected area of the morphological change.

[0100] It can be understood that this step first analyzes the displacement and stacking morphology changes of the particles in the simulation results. By monitoring the position changes and the evolution of the stacking morphology of the particles, it is possible to determine which areas have undergone significant displacement or morphological changes during the grasping process. Then, spatial clustering analysis technology is used to classify these change areas. Spatial clustering analysis can identify areas with similar characteristics in space, thereby extracting areas with large particle displacement, boundary areas where slip occurs, and areas where local collapse occurs. High displacement areas refer to areas where particles move a large distance during the grasping process. These areas may become the core areas of material pile morphology changes; slip boundary areas are boundaries where relative sliding occurs between particles. The stability of these areas has an important impact on the stability of the entire material pile; local collapse areas refer to areas where local collapse occurs under the action of grasping. These areas are weak links in the morphological changes of the material pile.

[0101] Step S434: Establish a dynamic correspondence based on the impact area of the morphological change. By matching and analyzing the dynamic characteristic parameters of the impact area with the time series of the grasping path, a correlation modeling method based on time series interpolation and regional influence intensity weighting is used to generate a grasping dynamic correspondence diagram to obtain the dynamic correspondence between the grasping path and the morphological change of the material pile.

[0102] As can be understood, this step extracts dynamic characteristic parameters, such as particle displacement velocity, slip rate, and collapse degree, from the affected regions of the morphological change. These parameters can quantitatively describe how the region changes during the grasping process. These dynamic characteristic parameters are then matched and analyzed with the time series of the grasping path. The grasping path time series records the position and motion of the grasping tool at different time points. By mapping changes in the affected regions with the time points of the grasping path, it is possible to determine which regions experienced significant changes at which stages of the grasping process. A time series interpolation method is used to interpolate the dynamic characteristic parameters of the affected regions, filling in data gaps in the time series and ensuring a more continuous and complete change process. Furthermore, the regional impact strength is weighted. Considering the varying degrees of influence of different regions on the overall stability of the material pile, this weighting can highlight the overall impact of changes in key regions. Using an association modeling approach, the above analysis results are integrated to generate a dynamic grasping correspondence diagram. This diagram intuitively demonstrates the dynamic connection between the grasping path and the morphological changes of the material pile, clarifying the response and changing trends of each region of the material pile at each stage of the grasping process. The dynamic correspondence between the grasping path and the changes in the material pile shape obtained provides an important decision-making basis for optimizing the grasping path, avoiding risk areas, and improving work efficiency.

[0103] Furthermore, step S500 includes steps S510 to S540.

[0104] Step S510: Dynamically identify high-risk areas based on the prediction results and the collaborative grasping scheme. Spatial overlap calculation is performed on the marked high-risk areas of collapse. A KD tree is used to locate the intersection nodes of the grasping path and the collapsed areas. Areas to be avoided are screened based on the risk level threshold to obtain a set of conflicting nodes between the grasping path and the high-risk areas.

[0105] It should be noted that the prediction results provide an assessment of the material pile's stability under different grasping schemes, while the collaborative grasping scheme defines the grasping path and operation sequence. Combining these two methods can determine which areas within a specific grasping path present a higher risk of collapse. Next, a spatial overlap calculation is performed, spatially comparing the marked high-risk collapse areas with the grasping path to identify any overlap. This process requires precise spatial positioning and computing power to ensure accurate identification results. A KD tree algorithm is used to locate the intersection nodes between the grasping path and the collapse area. The KD tree is an efficient spatial search data structure that can quickly find the nearest neighbor in space. By constructing a KD tree, the specific nodes where the grasping path intersects the collapse area can be quickly located. These nodes are potential risk points. Finally, identified intersection nodes are screened based on a preset risk level threshold. The risk level threshold is determined based on factors such as the material pile's stability, the potential for collapse, and the impact on operational safety. Only intersection nodes with a risk level above the threshold are included in the avoidance zone. Through this screening process, a set of conflicting nodes between the grasping path and the high-risk area is obtained, which provides a clear goal for subsequent path adjustment and avoidance strategy formulation.

[0106] Step S520: Path avoidance processing is performed based on the conflict node set. An alternative path from the grabbing start point to the target area is regenerated in the three-dimensional working area model. Constraints are defined as avoiding conflict nodes, prioritizing the shortest path, and limiting the bucket wheel machine's posture adjustment, to obtain an optimized grabbing path.

[0107] Specifically, within the 3D work area model, the first consideration is how to generate a new alternative path from the grasping starting point to the target area. This path must meet several key constraints: First, it must avoid all identified conflicting nodes to ensure that the bucket wheel machine does not enter high-risk areas during the grasping process, thereby preventing potential collapse accidents. Second, it must adhere to the principle of shortest path priority to improve operational efficiency and reduce energy consumption. While ensuring safety and efficiency, the bucket wheel machine's posture adjustment limitations must also be considered to ensure that the path is executable within the machine's physical and operational capabilities. This requires the rational planning of the machine's posture parameters, such as steering angle and lifting height, to ensure smooth and accurate grasping movements. By comprehensively considering these constraints, a path planning algorithm is used to search and generate an optimal alternative path in 3D space. This path not only avoids risks but also takes into account operational efficiency and the bucket wheel machine's operational characteristics.

[0108] Step S530: Optimize the grasping action strategy based on the optimized grasping path. By sampling each path point and combining the real-time position and posture data of the bucket wheel excavator, a gradient descent-based action parameter optimization algorithm is used to calculate the grasping depth, angle adjustment, and force control of the grasping action, thereby obtaining the action strategy corresponding to each position on the grasping path.

[0109] In this embodiment, the optimized grasping path is first uniformly sampled, dividing the path into several key points, each of which represents a location that the bucket wheel machine needs to pass through during the grasping process. Then, the real-time position and posture data of the bucket wheel machine at each sampling point, including the bucket wheel machine's three-dimensional coordinates, pitch angle, yaw angle, etc., are combined to provide an accurate reference for optimizing the motion parameters. Next, a gradient descent-based motion parameter optimization algorithm is used. By calculating the gradient of the objective function (such as grasping efficiency, energy consumption, etc.), the motion parameters are adjusted along the direction of gradient descent, gradually approaching the optimal solution. In this step, the objective function is to maximize grasping efficiency or minimize energy consumption. Using the gradient descent algorithm, motion parameters such as grasping depth, angle adjustment, and force control are optimized. Optimizing the grasping depth ensures that the bucket wheel machine can accurately grasp materials at different locations; optimizing the angle adjustment helps the bucket wheel machine maintain a stable grasp in complex terrain; and optimizing the force control can avoid excessive damage to the material pile or a loose grasp. Ultimately, an optimal motion strategy is calculated for each location along the grasping path, including the appropriate grasping depth, angle adjustment, and force applied at that location. These strategies guide the bucket wheel excavator in precisely adjusting its movements during the actual grasping process, ensuring efficient and safe grasping operations.

[0110] Step S540: Generate an operation plan based on the grasping action strategy and the optimized grasping path. By integrating the dynamic avoidance path with the grasping action strategy, the path is continuously optimized using cubic B-spline curve interpolation. At the same time, the rhythm of the grasping action is synchronously adjusted in combination with the operating status of the transportation equipment to obtain an optimized grasping operation plan.

[0111] As you can understand, this step first integrates the dynamic avoidance path with the grasping action strategy to ensure smooth grasping while avoiding risks. The dynamic avoidance path provides a safe path selection, while the grasping action strategy defines specific grasping parameters for each path point. The combination of the two ensures both safe and efficient grasping operations. Next, the path is continuously optimized using cubic B-spline interpolation. Cubic B-spline curves are a smooth curve interpolation method that generates a smooth and continuous path, reducing the potential sharp turns or sudden changes during the bucket wheel crane's operation, thereby improving the stability and smoothness of the grasping action. Simultaneously, the grasping action rhythm is synchronized and adjusted based on the operating status of the transport equipment. Taking into account the real-time position, speed, and load of the transport equipment, the grasping action rhythm is rationally arranged to achieve a seamless connection between grasping and transporting, reducing waiting time and improving overall operation efficiency. Through these comprehensive adjustments, an optimized grasping operation plan is ultimately obtained. This plan not only takes into account safety and efficiency, but also takes into account the coordinated operation with the transport equipment, providing a strong guarantee for the efficient and safe grasping operation of the unmanned bucket wheel crane. This step achieves a perfect combination of grasping path, motion strategy, and transport coordination by generating a fully optimized grasping operation plan, significantly improving the overall performance of the operation.

[0112] Example 2:

[0113] like Figure 2 As shown, this embodiment provides an unmanned bucket wheel machine automatic control system, the system includes:

[0114] Acquisition module 901, used to obtain the grasping task requirements, the three-dimensional operation area model, the real-time position and posture data of the unmanned bucket wheel excavator, the distribution status information of the material pile, and the operating status data of the transportation equipment;

[0115] Planning module 902 is used to plan the grasping path based on the grasping task requirements, the three-dimensional working area model, and the real-time position and posture data. It generates a preliminary grasping path by analyzing the working area of the bucket wheel excavator, the target position of the grasping task, and the dynamic posture of the bucket wheel excavator;

[0116] Analysis module 903 is used to perform transport collaborative planning based on the operating status data and the preliminary grabbing path. By analyzing the real-time position, operating speed, and load status of the transport equipment, the bucket wheel excavator's operating sequence and path planning are dynamically adjusted to obtain a collaborative grabbing solution.

[0117] Prediction module 904 is used to predict the collapse area based on the distribution state information. It analyzes the shape and structure information of the material pile and simulates the impact of different grabbing schemes on the shape change of the material pile to obtain the prediction result.

[0118] The optimization module 905 is used to adjust the collaborative grasping plan according to the prediction results, optimize the grasping path, dynamically avoid the collapsed area with a risk level higher than the threshold, and generate a specific grasping action strategy to obtain a grasping operation plan.

[0119] In a specific embodiment of the present invention, the planning module 902 includes:

[0120] The first planning unit is used to perform positioning processing based on the grasping task requirements and the 3D operation area model. By performing cluster analysis on the point cloud data of the 3D operation area model, the location and boundary of the material pile that meets the grasping requirements are screened out, and the positioning data of the target grasping area, the spatial position of the positioning data, and the boundary data are obtained;

[0121] The second planning unit is used to optimize the path starting point based on the spatial position and real-time position and attitude data. By constructing a cost function with path distance, attitude adjustment energy consumption and obstacle avoidance constraints as weights, and using the gradient descent algorithm to optimize and calculate the path starting point, the optimized path starting point parameters are obtained. The path starting point parameters include the path starting point coordinates and attitude parameters;

[0122] The third planning unit is used to plan the local grasping path based on the boundary data and the path starting point parameters. It generates path nodes by gridding on the boundary of the target grasping area and combines the A* algorithm with the gradient field constraint to generate the path, thus obtaining the local grasping path node sequence from the path starting point to the target grasping area.

[0123] The fourth planning unit is used to perform path smoothing processing based on the local grasping path node sequence, generate a continuous path function through interpolation, and optimize the control point position in combination with the dynamic constraints of the attitude adjustment to obtain a preliminary grasping path. The preliminary grasping path includes a continuous path function and bucket wheel machine attitude adjustment instructions.

[0124] In a specific embodiment of the present invention, the analysis module 903 includes:

[0125] The first analysis unit is configured to perform a transport equipment status evaluation process based on the operating status data, calculate the idle capacity and dynamic accessibility of the transport equipment by analyzing the real-time position, operating speed, and current load status of the transport equipment, and obtain a transport equipment coordination index;

[0126] The second analysis unit is used to adjust the operation sequence based on the coordination index and the preliminary grasping path. By prioritizing the idle state of the transport equipment and the order constraints of the grasping path, combined with the optimal scheduling algorithm of the transport equipment, the operation sequence of the grasping tasks is dynamically adjusted to obtain the optimized grasping sequence;

[0127] The third analysis unit is used to perform path synchronization adjustment processing based on the optimized grabbing sequence. By combining the position of the transport equipment with the dynamic time window matching of the bucket wheel excavator grabbing path, a path adjustment algorithm based on time window optimization is used to generate a synchronous grabbing path that matches the operating status of the transport equipment, thereby obtaining a preliminary collaborative path solution.

[0128] The fourth analysis unit is used to perform transport collaborative optimization processing based on the preliminary collaborative path plan. By dynamically adjusting the operating speed and loading rhythm of the bucket wheel excavator's grabbing path, the collaborative plan is iteratively optimized in combination with the load state constraints to obtain a collaborative grabbing plan.

[0129] Example 3:

[0130] Corresponding to the above method embodiment, this embodiment also provides an unmanned bucket wheel machine automatic control device. The unmanned bucket wheel machine automatic control device described below and the unmanned bucket wheel machine automatic control method described above can be referenced to each other.

[0131] Figure 3 FIG. 8 is a block diagram of an unmanned bucket wheel machine automatic control device 800 according to an exemplary embodiment. Figure 3 As shown, the unmanned bucket wheel machine automatic control device 800 may include: a processor 801, a memory 802. The unmanned bucket wheel machine automatic control device 800 may also include one or more of a multimedia component 803, an I / O interface 804, and a communication component 805.

[0132] The processor 801 is used to control the overall operation of the unmanned bucket wheel machine automatic control device 800 to complete all or part of the steps in the unmanned bucket wheel machine automatic control method described above. The memory 802 is used to store various types of data to support the operation of the unmanned bucket wheel machine automatic control device 800. This data may include, for example, instructions for any application or method operating on the unmanned bucket wheel machine automatic control device 800, as well as application-related data such as contact information, sent and received messages, images, audio, video, etc. The memory 802 can be implemented by any type of volatile or non-volatile storage device 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 memory, flash memory, magnetic disk or optical disk. The multimedia component 803 may include a screen and an audio component. The screen may be, for example, a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal may be further stored in the memory 802 or transmitted via the communication component 805. The audio component also includes at least one speaker for outputting audio signals. The I / O interface 804 provides an interface between the processor 801 and other interface modules, which may include a keyboard, a mouse, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 805 is used for wired or wireless communication between the unmanned bucket wheel machine automatic control device 800 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, or 4G, or a combination of one or more thereof, may include a Wi-Fi module, a Bluetooth module, or an NFC module.

[0133] In an exemplary embodiment, an unmanned bucket wheel machine automatic control device 800 can be implemented by one or more application specific integrated circuits (ASIC), digital signal processors (DSP), digital signal processing devices (DSPD), programmable logic devices (PLD), field programmable gate arrays (FPGA), controllers, microcontrollers, microprocessors or other electronic components to execute the above-mentioned unmanned bucket wheel machine automatic control method.

[0134] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided. When executed by a processor, the program instructions implement the steps of the aforementioned unmanned bucket wheel machine automatic control method. For example, the computer-readable storage medium may be the aforementioned memory 802 including the program instructions. The program instructions may be executed by the processor 801 of the unmanned bucket wheel machine automatic control device 800 to implement the aforementioned unmanned bucket wheel machine automatic control method.

[0135] The above description is only a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed by the present invention, which should be covered by the scope of protection of the present invention.

Claims

1. An automatic control method for an unmanned bucket wheel excavator, characterized in that: include: Obtaining grasping task requirements, 3D operation area models, real-time position and posture data of unmanned bucket wheel excavators, distribution status information of material piles, and operating status data of transportation equipment; Performing grasping path planning processing according to the grasping task requirements, the three-dimensional operating area model, and the real-time position and posture data, and generating a preliminary grasping path by analyzing the operating area of the bucket wheel excavator, the target position of the grasping task, and the dynamic posture of the bucket wheel excavator; Performing transport collaborative planning based on the operating status data and the preliminary grabbing path, dynamically adjusting the bucket wheel excavator's operating sequence and path planning by analyzing the real-time position, operating speed, and load status of the transport equipment, and obtaining a collaborative grabbing solution; The collapse area is predicted based on the distribution state information, and the prediction result is obtained by analyzing the morphology and structure information of the material pile and simulating the impact of different grasping schemes on the morphology change of the material pile; The collaborative grasping scheme is adjusted according to the prediction results, the grasping path is optimized, the collapsed area with a risk level higher than a threshold is dynamically avoided, and a specific grasping action strategy is generated to obtain a grasping operation plan; The collapse area is predicted based on the distribution state information. By analyzing the morphology and structure information of the material pile and simulating the effects of different grabbing schemes on the morphology of the material pile, the prediction results are obtained, including: Performing morphological feature extraction processing on the material pile based on the distribution state information, extracting surface gradients, local curvatures, and boundary morphological features from the three-dimensional point cloud data of the material pile using normal vector calculation and surface fitting algorithms, and performing feature marking on key areas to obtain three-dimensional morphological feature parameters of the material pile; Performing a structural stability analysis based on the three-dimensional morphological characteristic parameters, constructing a stability discrimination model based on the morphological characteristics, combining gravity field analysis with contact surface distribution calculation, and conducting an overall stability zoning assessment of the material pile to obtain a stability distribution map of the material pile; A morphological change simulation is performed based on the stability distribution diagram of the material pile. Different grasping schemes are dynamically simulated using the discrete element method to simulate the impact of the grasping path on the material pile structure, predict the morphological changes caused by the grasping process, and obtain a dynamic correspondence between the grasping path and the morphological changes of the material pile; The collapse risk prediction process is performed according to the dynamic correspondence, and the risk probability distribution of each area during the grasping action is calculated, and the high-risk collapse areas on the grasping path are marked to obtain the collapse area prediction result.

2. The automatic control method of an unmanned bucket wheel excavator according to claim 1, characterized in that: Performing a grasping path planning process based on the grasping task requirements, the three-dimensional operating area model, and the real-time position and posture data, and generating a preliminary grasping path by analyzing the bucket wheel machine's operating area, the target position of the grasping task, and the bucket wheel machine's dynamic posture, including: Perform positioning processing based on the grasping task requirements and the three-dimensional operation area model, and screen out the material pile positions and boundaries that meet the grasping requirements by clustering the point cloud data of the three-dimensional operation area model to obtain the positioning data of the target grasping area, including the spatial position and boundary data of the positioning data; performing path starting point optimization processing based on the spatial position and the real-time position and attitude data, constructing a cost function with path distance, attitude adjustment energy consumption, and obstacle avoidance constraints as weights, and using a gradient descent algorithm to optimize and calculate the path starting point, thereby obtaining optimized path starting point parameters, the path starting point parameters including path starting point coordinates and attitude parameters; Planning a local grasping path based on the boundary data and the path starting point parameters, generating path nodes by gridding on the boundary of the target grasping area, and combining the A* algorithm with gradient field constraints to generate a path, thereby obtaining a local grasping path node sequence from the path starting point to the target grasping area; Path smoothing is performed according to the local grasping path node sequence, a continuous path function is generated by interpolation, and the control point positions are optimized in combination with the dynamic constraints of the posture adjustment to obtain a preliminary grasping path. The preliminary grasping path includes the continuous path function and the bucket wheel machine posture adjustment instruction.

3. The automatic control method of an unmanned bucket wheel excavator according to claim 1, characterized in that: The collaborative transport planning is performed based on the operating status data and the preliminary grabbing path. By analyzing the real-time position, operating speed, and load status of the transport equipment, the operation sequence and path planning of the bucket wheel excavator are dynamically adjusted to obtain a collaborative grabbing solution, including: Performing a transport equipment status assessment based on the operating status data, calculating the idle capacity and dynamic accessibility of the transport equipment by analyzing the real-time position, operating speed, and current load status of the transport equipment, and obtaining a transport equipment coordination index; According to the coordination index and the preliminary grasping path, the operation sequence is adjusted. By prioritizing the matching of the idle state of the transport equipment and the order constraints of the grasping path, the operation sequence of the grasping tasks is dynamically adjusted in combination with the optimal scheduling algorithm of the transport equipment to obtain the optimized grasping sequence; Performing path synchronization adjustment processing based on the optimized grabbing sequence, combining the position of the transport equipment with the dynamic time window matching of the bucket wheel excavator grabbing path, and adopting a path adjustment algorithm based on time window optimization to generate a synchronous grabbing path that matches the operating status of the transport equipment, thereby obtaining a preliminary collaborative path solution; The transport collaborative optimization process is performed according to the preliminary collaborative path plan. The operating speed and loading rhythm of the bucket wheel excavator grabbing path are dynamically adjusted. The collaborative plan is iteratively optimized in combination with the load state constraints to obtain a collaborative grabbing plan.

4. The automatic control method of an unmanned bucket wheel excavator according to claim 1, characterized in that: Structural stability analysis is performed based on the three-dimensional morphological characteristic parameters. By constructing a stability discrimination model based on morphological characteristics and combining gravity field analysis with contact surface distribution calculation, an overall stability zoning assessment of the material pile is performed, and a stability distribution map of the material pile is obtained, including: Performing contact surface extraction processing based on the three-dimensional morphological characteristic parameters, analyzing the material pile point cloud data and normal vector distribution, and triangulating the material pile surface using an Alpha shape algorithm to extract contact areas with contact forces, and calculating the normal angle distribution and normal area weight of each contact area to obtain the contact surface distribution characteristics of the material pile; The gravity distribution is calculated based on the contact surface distribution characteristics. The three-dimensional shape of the material pile is divided into finite volume units. The finite element method is used to calculate the center of gravity coordinates and gravity distribution of each unit. The support force and sliding force of each unit are mechanically decomposed in combination with the normal characteristics of the contact surface to obtain the gravity field distribution inside the material pile. Performing a stability analysis based on the gravity field distribution, calculating the anti-slip force of each area on the contact surface based on a preset Coulomb friction model, calculating the stability factor by combining the gravity field distribution and the support force of the local contact point, and classifying the stability of each contact surface area according to the critical value to obtain a local distribution of the stability factor; According to the local distribution of the stability factor, zoning is performed. By constructing a global assessment model based on the stability factor, the locally distributed stability factor is weighted interpolated and combined with the transition continuity between regions, the high-risk areas are marked and the risk level is classified to obtain the overall stability distribution map of the material pile.

5. The automatic control method of an unmanned bucket wheel excavator according to claim 1, characterized in that: Based on the stability distribution diagram of the material pile, a morphological change simulation is performed. By using the discrete element method to dynamically simulate different grasping schemes, the influence of the grasping path on the material pile structure is simulated, the morphological changes caused by the grasping process are predicted, and the dynamic correspondence between the grasping path and the morphological changes of the material pile is obtained, including: A mechanical model of the grasping action is constructed based on the stability distribution diagram of the material pile. By combining the stability distribution diagram of the material pile with the geometric model of the grasping tool, a particle contact mechanics algorithm based on the discrete element method is used to decompose the material pile into a number of discrete particle units. Combined with the calculation of contact points, contact forces, and friction forces between the particles, a mechanical model of the grasping action of the material pile is constructed to obtain initial state parameters describing the internal stress of the material pile and the movement of the particles; Performing dynamic simulation of particle motion based on the initial state parameters, analyzing the particle force at each point along the grasping path, iteratively calculating the force equilibrium state and motion trajectory of the particles under the action of the grasping tool, dynamically updating the contact relationship and stacking morphology between the particles, and obtaining the dynamic morphological changes in the distribution of particles in the material pile due to the grasping path; Extracting the affected area of the morphological change according to the dynamic morphological change, performing spatial cluster analysis on the areas of change in particle displacement and accumulation morphology in the simulation results, identifying high displacement areas, slip boundary areas, and local collapse areas within the action range of the grasping path, and obtaining the affected area of the morphological change; A dynamic correspondence is established based on the impact area of the morphological change. By matching and analyzing the dynamic characteristic parameters of the impact area with the time series of the grasping path, an association modeling method based on time series interpolation and regional influence intensity weighting is used to generate a grasping dynamic correspondence diagram, and the dynamic correspondence between the grasping path and the morphological change of the material pile is obtained.

6. The automatic control method of an unmanned bucket wheel excavator according to claim 1, characterized in that: The collaborative grasping scheme is adjusted based on the prediction results. By optimizing the grasping path, collapse areas with risk levels higher than a threshold are dynamically avoided, and a specific grasping action strategy is generated to obtain an optimized grasping operation plan, including: Dynamically identify high-risk areas based on the prediction results and the collaborative grasping scheme. By performing spatial overlap calculation on the marked high-risk areas of collapse, a KD tree is used to locate the intersection nodes of the grasping path and the collapsed area. Areas to be avoided are screened based on the risk level threshold to obtain a set of conflicting nodes between the grasping path and the high-risk area. Performing path avoidance processing based on the conflict node set, regenerating an alternative path from the grasping start point to the target area in the three-dimensional working area model, and defining constraints such as avoiding conflict nodes, shortest path priority, and bucket wheel machine posture adjustment restrictions to obtain an optimized grasping path; The grasping action strategy is optimized according to the optimized grasping path. By sampling each path point and combining the real-time position and posture data of the bucket wheel excavator, a gradient descent-based action parameter optimization algorithm is used to calculate the grasping depth, angle adjustment, and force control of the grasping action, and the action strategy corresponding to each position on the grasping path is obtained; An operation plan is generated based on the grasping action strategy and the optimized grasping path. By integrating the dynamic avoidance path with the grasping action strategy, the path is continuously optimized using cubic B-spline curve interpolation. At the same time, the rhythm of the grasping action is synchronously adjusted in combination with the operating status of the transportation equipment to obtain an optimized grasping operation plan.

7. An unmanned bucket wheel machine automatic control system, characterized in that: include: The acquisition module is used to obtain the grasping task requirements, the three-dimensional operation area model, the real-time position and posture data of the unmanned bucket wheel excavator, the distribution status information of the material pile, and the operating status data of the transportation equipment; a planning module for performing grasping path planning based on the grasping task requirements, the three-dimensional operating area model, and the real-time position and posture data, and generating a preliminary grasping path by analyzing the operating area of the bucket wheel excavator, the target position of the grasping task, and the dynamic posture of the bucket wheel excavator; An analysis module is configured to perform transport collaborative planning based on the operating status data and the preliminary grabbing path, dynamically adjust the bucket wheel excavator's operating sequence and path planning by analyzing the real-time position, operating speed, and load status of the transport equipment, and obtain a collaborative grabbing solution; A prediction module is used to predict the collapse area based on the distribution state information, analyze the morphology and structure information of the material pile, and simulate the impact of different grabbing schemes on the morphology change of the material pile to obtain a prediction result; An optimization module is used to adjust the collaborative grasping plan according to the prediction results, dynamically avoid collapse areas with a risk level higher than a threshold by optimizing the grasping path, and generate a specific grasping action strategy to obtain a grasping operation plan; The collapse area is predicted based on the distribution state information. By analyzing the morphology and structure information of the material pile and simulating the effects of different grabbing schemes on the morphology of the material pile, the prediction results are obtained, including: Performing morphological feature extraction processing on the material pile based on the distribution state information, extracting surface gradients, local curvatures, and boundary morphological features from the three-dimensional point cloud data of the material pile using normal vector calculation and surface fitting algorithms, and performing feature marking on key areas to obtain three-dimensional morphological feature parameters of the material pile; Performing a structural stability analysis based on the three-dimensional morphological characteristic parameters, constructing a stability discrimination model based on the morphological characteristics, combining gravity field analysis with contact surface distribution calculation, and conducting an overall stability zoning assessment of the material pile to obtain a stability distribution map of the material pile; A morphological change simulation is performed based on the stability distribution diagram of the material pile. Different grasping schemes are dynamically simulated using the discrete element method to simulate the impact of the grasping path on the material pile structure, predict the morphological changes caused by the grasping process, and obtain a dynamic correspondence between the grasping path and the morphological changes of the material pile; The collapse risk prediction process is performed according to the dynamic correspondence, and the risk probability distribution of each area during the grasping action is calculated, and the high-risk collapse areas on the grasping path are marked to obtain the collapse area prediction result.

8. The automatic control system of an unmanned bucket wheel excavator according to claim 7, characterized in that: The planning module includes: A first planning unit is configured to perform positioning processing based on the grasping task requirements and the three-dimensional operation area model, screen out the material pile positions and boundaries that meet the grasping requirements by performing cluster analysis on the point cloud data of the three-dimensional operation area model, and obtain positioning data of the target grasping area, including spatial position and boundary data of the positioning data; a second planning unit, configured to perform path starting point optimization processing based on the spatial position and the real-time position and attitude data, by constructing a cost function with path distance, attitude adjustment energy consumption, and obstacle avoidance constraints as weights, and optimizing and calculating the path starting point using a gradient descent algorithm, to obtain optimized path starting point parameters, the path starting point parameters including path starting point coordinates and attitude parameters; a third planning unit, configured to plan a local grasping path based on the boundary data and the path starting point parameters, by generating path nodes by gridding on the boundary of the target grasping area and combining the A* algorithm with gradient field constraints to generate a path, thereby obtaining a local grasping path node sequence from the path starting point to the target grasping area; The fourth planning unit is used to perform path smoothing processing based on the local grasping path node sequence, generate a continuous path function through interpolation, and optimize the control point position in combination with the dynamic constraints of the posture adjustment to obtain a preliminary grasping path. The preliminary grasping path includes a continuous path function and a bucket wheel machine posture adjustment instruction.

9. The automatic control system of an unmanned bucket wheel excavator according to claim 7, characterized in that: The analysis module includes: a first analysis unit configured to perform a transport equipment status evaluation process based on the operating status data, calculate the idle capacity and dynamic accessibility of the transport equipment by analyzing the real-time position, operating speed, and current load status of the transport equipment, and obtain a transport equipment coordination index; The second analysis unit is configured to adjust the operation sequence based on the coordination index and the preliminary grasping path, and dynamically adjust the operation sequence of the grasping tasks by preferentially matching the idle state of the transport equipment and the sequence constraints of the grasping path in combination with the optimal scheduling algorithm of the transport equipment to obtain an optimized grasping sequence; A third analysis unit is configured to perform path synchronization adjustment processing based on the optimized grabbing sequence, by combining the position of the transport equipment with the dynamic time window matching of the bucket wheel excavator grabbing path, and using a path adjustment algorithm based on time window optimization to generate a synchronized grabbing path that matches the operating state of the transport equipment, thereby obtaining a preliminary collaborative path solution; The fourth analysis unit is used to perform transportation collaborative optimization processing based on the preliminary collaborative path plan, dynamically adjust the operating speed and loading rhythm of the bucket wheel excavator's grasping path, and iteratively optimize the collaborative plan in combination with the load state constraints to obtain a collaborative grasping plan.

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