Intelligent control method and system for operation of a magnetic ship in rotation

Abstract

The application provides a kind of spin empty magnetic ship operation intelligent control method and system, method contains the three-dimensional model of surrounding virtual environment is built, with the map data imported is fused, generates real-time updated environment model;Based on the real-time updated environment model, an optimal path from the current location to the target location is planned, avoiding obstacles;The planned path is converted into specific control instructions to guide the running direction and speed of spin empty magnetic;Through the feedback of real-time GPS positioning and environmental scanning data, the optimal path is constantly adjusted;During the operation of spin empty magnetic, the changes of the surrounding environment are continuously monitored;When potential collision risk is detected, adjust the running direction to avoid obstacles. The system includes an environment model update module, a path optimization adjustment module, and a running obstacle avoidance monitoring module. The application improves the technical effect of the system, and also achieves the important significance of improving environmental perception, enhancing safety, achieving precise control and ensuring stable operation.

Description

Technical Field

[0001] This invention relates to the field of control and regulation technology for non-electrical variables, and in particular to an intelligent control method and system for the operation of a rotating magnetic ship. Background Technology

[0002] Podded Propulsion Vessels are vessels that employ a podded propulsion system. This system integrates an electric motor and a propeller into a single, rotatable pod, which can be mounted directly on the exterior of the hull. This allows the propeller and motor to rotate 360 ​​degrees, enabling omnidirectional propulsion and precise heading control. Heading control methods include: azimuth propulsion, where the pod's 360-degree rotation allows for precise heading control; power distribution, where adjusting the propulsion force and direction of different pods enables complex heading control and maneuvering; and automatic control systems, which combine GPS, laser scanning, and environmental sensing technologies to adjust the propulsion force and direction of the pods based on real-time data, achieving intelligent heading control and obstacle avoidance. However, these control methods suffer from limitations such as poor environmental sensing accuracy, weak obstacle avoidance capabilities, and an inability to automatically adjust control strategies according to different environments and mission requirements, resulting in poor adaptability.

[0003] Existing technology 1, application number: CN202410758928.X, discloses a ship heading control method based on adaptive angular rate calculation, including the following steps: real-time acquisition of ship heading, rudder angle, and speed signals; online identification of the ship's motion equations to obtain the turning index and rudder response index at a specific speed; dimensionless transformation of the ship's motion equations to obtain the dimensionless turning index and dimensionless rudder response index; calculation of the turning index and rudder response index at real-time speed, and construction of a heading state observation model to calculate the adaptive angular rate; calculation of the commanded rudder angle, and heading control based on the calculated commanded rudder angle. Although this method is beneficial for reducing steering frequency, reducing mechanical wear, and lowering cabin noise, it lacks environmental awareness, resulting in poor obstacle avoidance capabilities during ship navigation.

[0004] Prior art two, application number CN202411159851.0, discloses a course-keeping control method for large ships based on composite function nonlinear feedback. By obtaining the difference between the preset course and the actual course, the expressions for the first and second layers of the composite function are obtained, leading to the course difference after the second layer of the composite function is applied. Finally, the rudder angle output by the controller and the actual course are obtained, achieving course-keeping control for large ships. While composite function nonlinear feedback technology is more robust than simple function nonlinear feedback technology for course-keeping control of ultra-large ships, considering the characteristics of the steering gear and wind and wave interference, and also shortens the adjustment time, reduces the maximum yaw angle, the average yaw angle, the average rudder angle, and the rate of change of the average rudder angle, and reduces energy consumption during course-keeping, resulting in better overall performance and achieving the goal of strong robustness and low energy consumption, its control process is relatively complex, placing higher demands on the intelligence level of the ship's controller and increasing the ship's navigation costs to some extent.

[0005] Existing technology three, application number CN202411121733.0, discloses an adaptive neural network unmanned vessel heading control method with input quantization and output constraints. The method includes: acquiring information about the surrounding environment and sea conditions of other vessels, establishing a mathematical model for the heading control of the unmanned vessel; quantizing the control inputs in the control system using a composite quantizer, and describing the input quantization process using a linear analytical model; designing a barrier Lyapunov function based on output constraint theory to obtain the heading controller of the unmanned vessel; and proving the stability of the designed adaptive neural network unmanned vessel heading control system with input quantization and output constraints without prior information on quantization parameters, and ensuring that all signals in the closed-loop control system are consistent and ultimately bounded. While this method can improve the heading performance of the unmanned vessel, it does not achieve adaptive heading adjustment, resulting in a low level of intelligence in heading adjustment and compromising navigation safety.

[0006] Current technologies 1, 2, and 3 suffer from poor environmental perception accuracy and weak obstacle avoidance capabilities; they also lack the ability to automatically adjust control strategies according to different environments and mission requirements, resulting in poor adaptability. Therefore, this invention provides an intelligent control method and system for the operation of rotating magnetic ships. Summary of the Invention

[0007] To address the aforementioned technical problems, this invention provides an intelligent control method for the operation of a rotating magnetic ship, comprising the following steps:

[0008] Construct a 3D model of the surrounding virtual environment and generate an environment model that is updated in real time;

[0009] Based on a real-time updated environment model, an optimal path from the current location to the target location is planned.

[0010] When a potential collision risk is detected, the obstacle avoidance procedure is initiated, and the running direction is adjusted.

[0011] Optionally, the process of translating the planned optimal path into specific control commands includes the following steps:

[0012] Based on a real-time updated environment model, the shortest path from the current location to the target location is calculated; the optimized path is decomposed into a series of small path segments, each corresponding to a control command.

[0013] Based on the length and curvature of the path segment, the speed of the rotary magnetic field on each path segment is planned; the path segment and speed plan are converted into specific control commands and sent to the actuator of the rotary magnetic field.

[0014] During the operation of the rotating magnetic field, the execution of the path and environmental changes are continuously monitored through real-time GPS positioning and environmental scanning data feedback; if path deviation or environmental changes are detected, the path planning and control commands are regenerated.

[0015] Optionally, the process of calculating the shortest path from the current location to the target location includes the following steps:

[0016] Based on a real-time updated environment model, a graph structure is constructed, with the current position as the starting point and the target position as the ending point.

[0017] Create a priority queue to store nodes to be processed, and initialize the distance of all nodes to infinity, and the distance of the starting point to 0; take the node with the smallest distance from the priority queue, traverse all its adjacent nodes, and calculate the distance from the starting point through the current node to the adjacent node.

[0018] The algorithm stops when the node taken from the priority queue is the target location node; by backtracking the parent node of each node, the algorithm backtracks from the target location node to the starting point to generate an initial path; with the starting point of the initial path as the root node, the algorithm randomly samples near the key points of the path and expands from the nearest neighbor node to the random point to generate new path nodes.

[0019] New path nodes undergo collision detection with obstacles in the environment; when a node in the random tree is rapidly explored and approaches the target location, a new optimized path, i.e. the shortest path, is generated by backtracking.

[0020] Optionally, each node represents a location, and edges represent connections between nodes, with corresponding weights assigned.

[0021] Optionally, the process of calculating the distance from the starting point through the current node to the adjacent node includes the following steps:

[0022] Determine the positions of the current node and its neighboring nodes; obtain the known shortest distance from the current node to the starting point;

[0023] Calculate the direct distance from the current node to its neighboring nodes; add the distance from the starting point to the current node to the distance from the current node to its neighboring nodes to obtain the total distance from the starting point through the current node to the neighboring nodes;

[0024] Compare the calculated total distance with the current distance of adjacent nodes.

[0025] Optionally, if the total distance is less than the current distance of the adjacent nodes, update the distance of the adjacent nodes to the total distance and add the adjacent nodes to the priority queue; repeat the above steps until the node taken out of the priority queue is the target location node, and the calculation stops.

[0026] Optionally, the process of generating new path nodes includes the following steps:

[0027] At critical turning points in the path, in narrow passages, expand the sampling range to capture potential paths; in straight sections of the path, narrow the sampling range.

[0028] Utilize historical path data to predict the optimal sampling point; select sampling points by learning from historical path planning data.

[0029] By combining real-time environmental perception technology, environmental information is acquired in real time and the path planning strategy is dynamically adjusted; in a dynamic environment, path nodes are updated in real time in response to changes in obstacles; the path planning task is assigned to multiple computing nodes to accelerate the path generation process through parallel computing.

[0030] Optionally, the process of translating path segments and speed plans into specific control commands includes the following steps:

[0031] The path segment is decomposed into a series of key nodes and intermediate nodes, and the coordinate information and connection relationship of each node are extracted; the speed requirement of each path segment is calculated based on the length and curvature of the path segment.

[0032] Based on the curvature and connectivity of the path segments, calculate the steering angle of each node; based on the velocity planning information, calculate the acceleration change of each path segment; based on the length of the path segment and the velocity planning, generate the velocity control command for each path segment.

[0033] Optionally, the actuator of the rotary magnet performs steering operation according to the optimized steering angle command; the actuator of the rotary magnet performs acceleration change according to the optimized acceleration command; and the actuator of the rotary magnet performs speed change according to the optimized speed control command.

[0034] The present invention provides an intelligent control system for the operation of a rotating magnetic ship, comprising:

[0035] The environment model update module is responsible for constructing a 3D model of the surrounding virtual environment and generating an environment model that is updated in real time.

[0036] The path optimization and adjustment module is responsible for planning an optimal path from the current location to the target location based on a real-time updated environment model.

[0037] The obstacle avoidance monitoring module is responsible for initiating the obstacle avoidance procedure when a potential collision risk is detected.

[0038] This invention's data import and environmental modeling features the ability to import pre-stored map data, including terrain and obstacle distribution, and combine it with real-time sensor data to achieve multi-source data fusion, improving the accuracy and comprehensiveness of the environmental model. It constructs a 3D model of the surrounding virtual environment, enabling the maglev train to understand its surroundings more intuitively and comprehensively, providing foundational data for path planning and obstacle avoidance. The generated environmental model is updated in real-time, dynamically reflecting environmental changes and ensuring the maglev train always makes decisions based on the latest environmental information during operation. Path planning and control command generation, based on the real-time updated environmental model, plan an optimal path from the current location to the target location, avoiding obstacles and ensuring efficiency and safety. The planned path is then converted into specific control commands to guide the maglev train's direction and speed, achieving seamless integration between path planning and actual operation. Through feedback from real-time GPS positioning and environmental scanning data, the optimal path is continuously adjusted to ensure dynamic adaptability and real-time performance. The environmental perception and obstacle avoidance strategy continuously monitors changes in the surrounding environment during the operation of the rotating magnetic field, ensuring comprehensive environmental perception and timely detection of potential risks. When a potential collision risk is detected, the obstacle avoidance program is activated to adjust the operating direction and avoid obstacles, ensuring the safe operation of the rotating magnetic field.

[0039] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings.

[0040] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0041] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0042] Figure 1 This is a flowchart of the intelligent control method for the operation of a rotary magnetic ship in Embodiment 1 of the present invention;

[0043] Figure 2 This is a process diagram of generating a real-time updated environment model in Embodiment 2 of the present invention;

[0044] Figure 3 This is a flowchart illustrating the process of converting a planned path into specific control commands in Embodiment 3 of the present invention.

[0045] Figure 4 This is a diagram illustrating the process of calculating the shortest path from the current position to the target position in Embodiment 4 of the present invention.

[0046] Figure 5 This is a diagram illustrating the process of calculating the distance from the starting point through the current node to the adjacent node in Embodiment 5 of the present invention.

[0047] Figure 6 This is a process diagram of generating new path nodes in Embodiment 6 of the present invention;

[0048] Figure 7 This is a flowchart illustrating the process of selecting sampling points in Embodiment 7 of the present invention;

[0049] Figure 8 This is a flowchart illustrating the process of converting path segment and speed planning into specific control commands in Embodiment 8 of the present invention.

[0050] Figure 9 This is a diagram illustrating the process of calculating the steering angle of each node, i.e., calculating the acceleration change of each path segment, in Embodiment 9 of the present invention.

[0051] Figure 10 This is a flowchart illustrating the process of initiating the obstacle avoidance procedure in Embodiment 10 of the present invention;

[0052] Figure 11 This is a block diagram of the intelligent control system for the operation of a rotary magnetic ship in Embodiment 11 of the present invention. Detailed Implementation

[0053] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0054] The terminology used in the embodiments of this application is for the purpose of describing particular embodiments only and is not intended to limit the embodiments of this application. The singular forms “a,” “the,” and “the” used in the embodiments of this application are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0055] In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application. In the description of this application, it should be understood that the terms "first," "second," "third," etc., are used only to distinguish similar objects and are not necessarily used to describe a specific order or sequence, nor should they be construed as indicating or implying relative importance. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0056] Example 1: As Figure 1 As shown, this embodiment of the invention provides an intelligent control method for the operation of a rotating magnetic ship, comprising the following steps:

[0057] S100: Import pre-stored map data containing information such as terrain and obstacle distribution; initialize the operating parameters of the rotating magnetic field, set the initial position and target position; construct a 3D model of the surrounding virtual environment, merge it with the imported map data, and generate a real-time updated environment model;

[0058] S200: Based on a real-time updated environmental model, it performs path planning, planning an optimal path from the current position to the target position, avoiding obstacles; it converts the planned path into specific control commands to guide the direction and speed of the rotating magnetic field; and it continuously adjusts the optimal path through feedback from real-time GPS positioning and environmental scanning data.

[0059] S300: During the operation of the rotating magnetic field, it continuously monitors changes in the surrounding environment; when a potential collision risk is detected, it initiates an obstacle avoidance program, adjusts its operating direction, and avoids obstacles.

[0060] The working principle and beneficial effects of the above technical solution are as follows: This embodiment first imports pre-stored map data containing information such as terrain and obstacle distribution; initializes the operating parameters of the rotating magnetic field, sets the initial position and target position; constructs a three-dimensional model of the surrounding virtual environment, merges it with the imported map data, and generates a real-time updated environment model; secondly, based on the real-time updated environment model, performs path planning, plans an optimal path from the current position to the target position, and avoids obstacles; converts the planned path into specific control commands to guide the running direction and speed of the rotating magnetic field; continuously adjusts the optimal path through feedback from real-time GPS positioning and environmental scanning data; finally, during the operation of the rotating magnetic field, continuously monitors changes in the surrounding environment; when a potential collision risk is detected, an obstacle avoidance program is initiated, the running direction is adjusted, and obstacles are avoided. Step S100 of the above scheme, data import and environmental modeling, imports pre-stored map data such as terrain and obstacle distribution, and combines it with real-time sensor data to achieve multi-source data fusion, improving the accuracy and comprehensiveness of the environmental model; it constructs a 3D model of the surrounding virtual environment, enabling the vortex magneto system to understand the surrounding environment more intuitively and comprehensively, providing basic data for path planning and obstacle avoidance; the generated environmental model is updated in real time, dynamically reflecting environmental changes, ensuring that the vortex magneto system always makes decisions based on the latest environmental information during operation. Significance achieved: Through multi-source data fusion and a real-time updated environmental model, the vortex magneto system can perceive the surrounding environment more accurately, providing reliable data support for intelligent decision-making; the real-time updated environmental model can cope with environmental changes, improving the robustness and adaptability of the system, ensuring the stable operation of the vortex magneto system in complex environments. Step S200: Path Planning and Control Command Generation. Based on a real-time updated environmental model, path planning is performed to generate an optimal path from the current location to the target location, avoiding obstacles and ensuring efficiency and safety. The planned path is then converted into specific control commands to guide the maglev's direction and speed, achieving seamless integration between path planning and actual operation. Through feedback from real-time GPS positioning and environmental scanning data, the optimal path is continuously adjusted to ensure dynamic adaptability and real-time performance. Significance: Intelligent path planning optimizes path selection, reduces unnecessary detours and delays, and improves the maglev's operating efficiency. By avoiding obstacles and dynamically adjusting the path, the safety of the maglev during operation is ensured, reducing collision risks. Converting path planning into specific control commands ensures the maglev operates precisely according to the planned path, improving system control accuracy and stability. Step S300: Environmental Perception and Obstacle Avoidance Strategy. During the maglev's operation, continuous monitoring of changes in the surrounding environment ensures comprehensive environmental perception and timely detection of potential risks. When a potential collision risk is detected, an obstacle avoidance program is initiated, adjusting the operating direction to avoid obstacles and ensuring the safe operation of the maglev.Significance achieved: Through continuous monitoring and obstacle avoidance algorithms, the system can avoid obstacles in a timely manner during operation, improving system safety; the application of obstacle avoidance algorithms enables the system to cope with sudden environmental changes, enhancing the system's adaptability and flexibility; timely obstacle avoidance ensures the stable operation of the system and reduces system failures and downtime caused by collisions.

[0061] In summary, the intelligent control method for rotating magnetic ship operation in this embodiment not only improves the technical performance of the system, but also achieves important results such as enhancing environmental perception capabilities, strengthening safety, realizing precise control, and ensuring stable operation.

[0062] Example 2: Figure 2 As shown, based on Example 1, the process of generating a real-time updated environment model provided by this embodiment of the invention includes the following steps:

[0063] S1011: The map data is processed in layers according to different levels of detail to generate terrain and obstacle models of different precision; spatial interpolation is used to fill the gaps and details in the map data to generate a continuous and smooth three-dimensional spatial model; the generated three-dimensional model is simplified and redundant information is removed through optimization algorithms.

[0064] The expression for filling gaps and details in map data using spatial interpolation is as follows:

[0065]

[0066] The Kriging equations are used to solve for the weighting coefficients λ. i Its form is as follows:

[0067]

[0068] In the formula, Z(x0) represents the interpolation result at location x0 in the map data, Z(x i ) represents the known location x in the map data. i The measured value at λ i The weighting coefficients are determined by the Kriging equations; C(x) i ,x j ) is the position x i and x j The covariance function between x1, x2, ..., x n x is a known location, x0 is an unknown location in the map data, and interpolation is performed at the location in the map data; x i and x j It is an index of a known location in the map data;

[0069] Obtain the vertex coordinates v of the 3D model i and v j; Calculate the quadratic error matrix Q for each vertex; for each pair of adjacent vertices v i and v j Calculate the merger cost E(v) i ,v j Using equation E(v) i ,v j )=(v i +v j ) T Q(v i +v j Calculate the merging cost; select the vertex pair with the minimum merging cost for merging; update the model, remove the merged vertices, and update the quadratic error matrix Q of the relevant vertices; repeat the above steps until the predetermined simplification goal is achieved (such as reducing the number of vertices to a certain extent).

[0070] S1012: Collect dynamic data of the current environment, including real-time location and obstacle changes, through real-time GPS positioning and environmental scanning equipment; match and align the collected dynamic data with preprocessed static map data;

[0071] Kalman filtering is used to fuse dynamic and static map data, thereby improving the accuracy and reliability of positioning. For example, in urban environments, GPS signals may be interfered with by tall buildings, trees and other obstructions, leading to increased positioning errors. By fusing with static map data, the system can better understand the current environment, thus providing more accurate positioning services.

[0072]

[0073] Pk|k=(IK k H k )P k|k-1

[0074] In the formula, F represents state prediction. k B represents the state transition matrix. k Represents the control input matrix, u k P represents the control input vector. k|k-1 Let Q represent the prediction error covariance matrix. k K represents the process noise covariance matrix. k H represents the Kalman gain. k Let R represent the observation matrix. k Let z represent the observation noise covariance matrix. k Represents the observation vector. This represents the state update, where Pk|k represents the update error covariance matrix. The first step in using Kalman filtering is to perform state prediction based on the state estimate from the previous time step. and state transition matrix F k Predict the state at the current moment. In addition to predicting the state, it is also necessary to predict the error covariance matrix P. k|k-1 Calculate the Kalman gain K k This determines the extent to which new observation data affects state updates; using observation data z k The Kalman gain is used to correct the predicted state, resulting in the final state estimate. In essence, it is the process of matching and aligning dynamic data with static map data;

[0075] S1013: Using a fusion algorithm, dynamic data and static map data are fused to generate a real-time updated environment model; the fusion algorithm includes steps such as data weight allocation, data smoothing, and conflict detection and resolution;

[0076] The Bayesian fusion equation is used to calculate the weight of each data source based on prior probability and likelihood. The prior probability assumes that the static map data is highly accurate and is given a high prior probability. The marginal probability is the overall probability of new evidence and does not consider the influence of the assumption. The posterior probability is the degree of confidence in updating the hypothesis after considering the new evidence.

[0077] Suppose there are multiple hypotheses H1, H2, ..., H n And multiple pieces of evidence E1, E2, ..., E m The following extended Bayes formula can be used:

[0078]

[0079] Where: P(H i |E1,E2,…,E m () indicates that given multiple pieces of evidence E1, E2, ..., E m At that time, assume H i The posterior probability; P(E1,E2,…,E m |H i )·P(H i ) indicates that under the assumption H i When established, multiple pieces of evidence E1, E2, ..., E m The joint probability, P(H) i ) indicates hypothesis H i The prior probability, P(E1,E2,…,E) m () represents multiple pieces of evidence E1, E2, ..., E m The joint marginal probability;

[0080] Data smoothing: The Kalman smoothing equation is used to smooth dynamic data and reduce the impact of noise.

[0081] predict:

[0082]

[0083] χ k+1|k =f(χ) k|k ,u k )

[0084]

[0085] renew:

[0086]

[0087] γ k+1 =h(χ k+1|k )

[0088]

[0089] smooth:

[0090]

[0091] In the formula: σ is the unscented transformation function used to generate the σ point, W i (m) and W i (c) χ is the weight of point σ, and n is the dimension of the state vector; k|k This represents the estimated state at time step k. It represents a transformation or update of the state estimate, and is a state vector. The function of the covariance matrix Pk|k, χ k+1|k f(χ) represents the state estimate based on time step k at time step k+1. k|k ,u k ) represents the state transition function, describing how the system state transitions from one time step to the next. k It is a control input; P represents the prior estimate of the state at time step k+1. k+1|k Let Q represent the prior covariance matrix of the state estimate at time step k+1. k The covariance matrix representing process noise; χ k+1|k γ represents the prior estimate of the state at time step k+1. k+1 This represents the prediction of the measured value at time step k+1. P represents the estimated value of the measurement at time step k+1. zz,k+1 P represents the covariance matrix of the predicted measurements at time step k+1. xz,k+1K represents the cross-covariance matrix between the state estimate and the predicted measurement at time step k+1. k+1 Kalman gain represents the weighting of predicted and measured values. P represents the posterior estimate of the state at time step k+1. k+1|k+1 R represents the posterior covariance matrix of the state estimate at time step k+1. k The covariance matrix representing the measurement noise; C represents the smoothed state estimate based on all available data at time step k. k F represents the smoothing gain, used to feed future information into the current estimate. k Represents the state transition matrix;

[0092] The least squares optimization equation is used to resolve the conflict between dynamic data and static map data, ensuring the accuracy and consistency of the model.

[0093]

[0094] In the formula: y i Let f(x) represent the i-th measurement value. i ) represents the model prediction value, and m represents the number of measurements.

[0095] The working principle and beneficial effects of the above technical solution are as follows: First, the map data is processed in layers according to different levels of detail to generate terrain and obstacle models of varying precision. Spatial interpolation is used to fill gaps and details in the map data, generating a continuous and smooth three-dimensional spatial model. An optimization algorithm simplifies the generated three-dimensional model, removing redundant information. Second, dynamic data of the current environment, including real-time location and obstacle changes, is collected using real-time GPS positioning and environmental scanning equipment. The collected dynamic data is matched and aligned with the preprocessed static map data. Finally, a fusion algorithm is used to fuse the dynamic data with the static map data, generating a real-time updated environmental model. The fusion algorithm includes steps such as data weight allocation, data smoothing, and conflict detection and resolution. Step S1011 of the above solution involves layered processing and model optimization. Through layered processing, terrain and obstacle models of varying precision can be generated, ensuring the model remains efficient and accurate in different application scenarios. Spatial interpolation fills gaps in the map data, ensuring the continuity and smoothness of the three-dimensional spatial model. The optimization algorithm removes redundant information, making the model more concise and efficient. Significance: Step S1012 ensures the quality and efficiency of the basic model, providing a solid foundation for real-time updates; it enables the environmental model to not only perform well under static conditions but also remain stable and reliable under dynamic changes. Step S1012 Dynamic Data Acquisition and Matching: Real-time GPS positioning and environmental scanning equipment collect dynamic data, providing real-time location and obstacle change information of the current environment; matching and aligning this dynamic data with static map data ensures the model's real-time performance and accuracy. Significance: This enables the environmental model to reflect environmental changes in real time, whether it's moving obstacles or environmental changes, they can be captured and updated promptly; this is especially important for applications requiring real-time environmental perception, such as autonomous driving and drone navigation. Step S1013 Data Fusion and Real-time Update: The fusion algorithm seamlessly combines dynamic data with static map data to generate a real-time updated environmental model; data weight allocation ensures that the importance of different data sources is reasonably reflected, data smoothing reduces data noise, and conflict detection and resolution mechanisms ensure data accuracy and consistency. Significance: This endows the model with the ability to update in real time, enabling the environmental model to continuously adapt to environmental changes, not only improving the model's practicality and reliability but also providing strong support for various applications requiring real-time environmental perception.

[0096] In summary, this embodiment constructs an efficient, accurate, and real-time updated environment model, providing solid technical support for various complex application scenarios.

[0097] Example 3: As Figure 3 As shown, based on Embodiment 1, the process of converting a planned path into specific control commands provided by this embodiment of the invention includes the following steps:

[0098] S201: Based on a real-time updated environment model, calculate the shortest path from the current location to the target location; decompose the optimized path into a series of small path segments, each path segment corresponding to a control command;

[0099] S202: Based on the length and curvature of the path segment, plan the speed of the rotating magnetic field on each path segment; convert the path segment and speed plan into specific control commands, including steering angle, acceleration and speed, and send them to the actuator of the rotating magnetic field;

[0100] S203: During the operation of the rotating magnetic field, the execution of the path and environmental changes are continuously monitored through real-time GPS positioning and environmental scanning data feedback; if path deviation or environmental changes are detected, the path planning and control commands are regenerated.

[0101] The working principle and beneficial effects of the above technical solution are as follows: This embodiment first calculates the shortest path from the current position to the target position based on a real-time updated environmental model; the optimized path is decomposed into a series of small path segments, each corresponding to a control command; secondly, the speed of the rotating magnetic field on each path segment is planned according to the length and curvature of the path segment; the path segment and speed planning are converted into specific control commands, including steering angle, acceleration, and speed, and sent to the actuator of the rotating magnetic field; finally, during the operation of the rotating magnetic field, the execution status of the path and environmental changes are continuously monitored through feedback from real-time GPS positioning and environmental scanning data; if path deviation or environmental changes are detected, the path planning and control command generation are re-performed. Step S201 of the above solution, path decomposition and control command generation, decomposes the optimized path into a series of small path segments, each corresponding to a control command; the decomposition method makes the path planning more refined and can better cope with subtle changes in complex environments; the control command corresponding to each path segment includes specific steering angle, acceleration, and speed information, ensuring that the rotating magnetic field can accurately execute the path planning. Significance: By decomposing the path into smaller segments, the rotary magnetic actuator can execute each segment more precisely, reducing the possibility of path deviation. Path decomposition and control command generation enable the system to flexibly respond to environmental changes and adjust its operating strategy in a timely manner. Step S202: Speed ​​Planning and Command Encoding. Based on the length and curvature of the path segments, the speed of the rotary magnetic actuator on each segment is planned. Speed ​​planning considers factors such as acceleration limitations and energy consumption optimization to ensure the operating efficiency and safety of the rotary magnetic actuator on different path segments. The path segments and speed planning are converted into specific control commands, including steering angle, acceleration, and speed, and sent to the actuator of the rotary magnetic actuator. Command encoding ensures that the control commands are accurately transmitted to the actuator. Significance: By rationally planning the speed, the rotary magnetic actuator can maximize operating efficiency and reduce operating time and energy consumption while ensuring safety. Command encoding ensures the accurate transmission of control commands, reduces the risk of misoperation and failure, and improves the overall reliability of the system. Step S203 involves real-time feedback and dynamic adjustment. Through real-time GPS positioning and environmental scanning data feedback, the system continuously monitors the path execution and environmental changes. Real-time feedback allows the system to promptly acquire current status and environmental information. If path deviation or environmental changes are detected, the system will re-plan the path and generate control commands, dynamically adjusting to ensure the rotating magnetic field always operates along the optimal path. Significance: Real-time feedback and dynamic adjustment enable the rotating magnetic field to adapt to environmental changes and adjust its operating strategy in a timely manner, ensuring the real-time nature and effectiveness of path planning. Through dynamic adjustment, the system can react quickly to emergencies, reducing accident risks and improving system robustness and safety.

[0102] In summary, this embodiment plays an irreplaceable technical role in transforming the planned path into specific control commands, significantly impacting system performance and reliability. Path decomposition and control command generation improve the accuracy of path execution and system flexibility; speed planning and command encoding optimize operational efficiency and system reliability; real-time feedback and dynamic adjustment enhance the system's adaptability and robustness. Together, these factors ensure the efficient and safe operation of the rotary magnetic system in complex environments.

[0103] Example 4: Figure 4 As shown, based on Example 3, the process of calculating the shortest path from the current position to the target position provided by this embodiment of the invention includes the following steps:

[0104] S2011: Based on a real-time updated environment model, including information such as obstacles and terrain, a graph structure is constructed; each node represents a location, edges represent connections between nodes, and corresponding weights are assigned; the current location is set as the starting point, and the target location is set as the ending point;

[0105] S2012: Create a priority queue to store nodes to be processed, and initialize the distance of all nodes to infinity, and the distance of the starting point to 0; take the node with the smallest distance (initially the starting point) from the priority queue, traverse all its adjacent nodes, and calculate the distance from the starting point through the current node to the adjacent node; if the calculated distance is less than the current distance of the adjacent node, update the distance of the adjacent node and add the adjacent node to the priority queue.

[0106] S2013: The algorithm stops when the node taken out from the priority queue is the target location node; by backtracking the parent node of each node, the algorithm backtracks from the target location node to the starting point to generate an initial path; with the starting point of the initial path as the root node, the algorithm randomly samples near the key points of the path and expands from the nearest neighbor node to the random point to generate new path nodes.

[0107] S2014: Collision detection is performed between the new path node and obstacles in the environment; if there is no collision, the new node is added to the fast exploration random tree; when a node in the fast exploration random tree approaches the target position, a new optimized path, i.e. the shortest path, is generated by backtracking.

[0108] The working principle and beneficial effects of the above technical solution are as follows: This embodiment first constructs a graph structure based on a real-time updated environment model, including information such as obstacles and terrain; each node represents a location, edges represent connections between nodes, and are assigned corresponding weights; the current location is set as the starting point, and the target location is set as the ending point; secondly, a priority queue is created to store nodes to be processed, and the distance of all nodes is initialized to infinity, while the distance of the starting point is 0; the node with the smallest distance (initially the starting point) is taken from the priority queue, and all its adjacent nodes are traversed to calculate the distance from the starting point through the current node to the adjacent node. If the calculated distance is less than the current distance of the adjacent node, the distance of the adjacent node is updated, and the adjacent node is added to the priority queue. Then, when the node taken from the priority queue is the target location node, the algorithm stops. By backtracking the parent node of each node, the algorithm backtracks from the target location node to the starting point to generate an initial path. Taking the starting point of the initial path as the root node, random sampling is performed near the key points of the path, expanding from the nearest neighbor node to the random point to generate new path nodes. Finally, the new path nodes are subjected to collision detection with obstacles in the environment. If there is no collision, the new node is added to the fast exploration random tree. When a node in the fast exploration random tree approaches the target location, a new optimized path, i.e., the shortest path, is generated by backtracking. Step S2011 of the above scheme provides a basic framework for path planning, ensuring that path search can be performed systematically in complex environments. Through graph structure, environmental information is processed systematically, providing a clear logical basis for path search and optimization. Step S2012 uses a priority queue to ensure that each node processed is the one with the smallest current distance, thus efficiently expanding the shortest path tree and avoiding unnecessary node traversal. By continuously updating node distances, it ensures that the path search always proceeds in the direction of the shortest path, improving the efficiency and accuracy of path search. Step S2013 generates an initial path and performs random sampling. The algorithm stops when the node taken from the priority queue is the target location node. By backtracking the parent node of each node, from the target location node back to the starting point, an initial path is generated. Using the starting point of the initial path as the root node, random sampling is performed near the key points of the path, expanding from the nearest neighbor node towards the random point to generate new path nodes. Significance: An initial path from the starting point to the target location is generated, providing a foundation for path optimization; random sampling increases the diversity and flexibility of the path, avoiding the uniformity and rigidity of path planning, and providing more possibilities for subsequent path optimization. Step S2014 ensures the safety and feasibility of the path through collision detection, avoiding potential safety hazards in path planning; by rapidly exploring the expansion and backtracking of the random tree, the shortest and safest path is generated, improving the efficiency and accuracy of path planning.

[0109] In summary, this embodiment achieves shortest path planning from the current location to the target location, which not only improves the efficiency and accuracy of path planning, but also ensures the safety and feasibility of the path. Through systematic graph structure construction, efficient priority queue path search, diversified random sampling, and secure collision detection, the optimal path is finally generated, meeting various requirements of path planning.

[0110] Example 5: Figure 5 As shown in Example 4, the process of calculating the distance from the starting point through the current node to the adjacent node provided in this embodiment of the invention includes the following steps:

[0111] S20121: Determine the positions of the current node and its neighboring nodes; obtain the known shortest distance from the current node to the starting point;

[0112] S20122: Calculate the direct distance from the current node to its neighboring nodes; add the distance from the starting point to the current node to the distance from the current node to its neighboring nodes to obtain the total distance from the starting point through the current node to the neighboring nodes;

[0113] S20123: Compare the calculated total distance with the current distance of the adjacent nodes; if the total distance is less than the current distance of the adjacent nodes, update the distance of the adjacent nodes to the total distance and add the adjacent nodes to the priority queue; repeat the above steps until the node taken out of the priority queue is the target location node, and the calculation stops.

[0114] The working principle and beneficial effects of the above technical solution are as follows: This embodiment first clarifies the positions of the current node and adjacent nodes; obtains the known shortest distance from the current node to the starting point; secondly, calculates the direct distance from the current node to adjacent nodes; adds the distance from the starting point to the current node and the distance from the current node to adjacent nodes to obtain the total distance from the starting point through the current node to adjacent nodes; finally, compares the calculated total distance with the current distance of adjacent nodes; if the total distance is less than the current distance of adjacent nodes, updates the distance of adjacent nodes to the total distance and adds the adjacent nodes to the priority queue; repeats the above steps until the node taken from the priority queue is the target location node, and the calculation stops. Step S20121 of the above solution clarifies the positions of the current node and adjacent nodes; obtains the known shortest distance from the current node to the starting point. By clarifying the node positions and the known shortest distance, basic data is provided for distance calculation. Significance: Ensures that the starting point and current node information in the calculation process are accurate, providing reliable data support for the steps. Step S20122 calculates the direct distance using the Euclidean distance formula and combines it with the known shortest distance to accurately calculate the total distance. Significance: This ensures the accuracy of the calculated total distance, providing a reliable basis for distance comparison and path updates. Step S20123 ensures the path information is always optimal by comparing and updating distances; the introduction of a priority queue enables efficient node processing. Significance: This ensures the final generated path is the shortest, improving the efficiency and accuracy of path planning; the use of a priority queue optimizes the algorithm's execution efficiency, making the path planning process more efficient.

[0115] In summary, this embodiment can accurately calculate the distance from the starting point through the current node to the adjacent node, and update the path information as needed to ensure that the final generated path is the shortest; together, it achieves efficient and accurate path planning.

[0116] Example 6: As Figure 6 As shown, based on Example 4, the process of generating new path nodes provided in this embodiment of the invention includes the following steps:

[0117] S20131: At critical turning points in the path, in narrow passages, expand the sampling range to capture potential paths; in straight sections of the path, narrow the sampling range.

[0118] S20132: Predict the optimal sampling point using historical path data; select sampling points by learning from historical path planning data;

[0119] S20133: Combining real-time environmental perception technology, it acquires environmental information in real time and dynamically adjusts the path planning strategy; in a dynamic environment, it responds to changes in obstacles and updates path nodes in real time; it distributes path planning tasks to multiple computing nodes and accelerates the path generation process through parallel computing.

[0120] The working principle and beneficial effects of the above technical solution are as follows: Firstly, in narrow passages at key turning points of the path, the sampling range is expanded to capture potential paths; in straight sections of the path, the sampling range is narrowed. Secondly, historical path data is used to predict the optimal sampling point; sampling points are selected by learning historical path planning data; finally, real-time environmental perception technology is combined to acquire environmental information in real time and dynamically adjust the path planning strategy; in dynamic environments, path nodes are updated in real time in response to changes in obstacles; the path planning task is assigned to multiple computing nodes, accelerating the path generation process through parallel computing. Step S20131 of the above solution involves adaptive sampling range adjustment. At key turning points of the path (such as narrow passages), expanding the sampling range can capture more potential paths, avoiding omissions and errors in path planning; in straight sections of the path, narrowing the sampling range reduces unnecessary calculations and improves the efficiency of path planning. The significance achieved is that by adaptively adjusting the sampling range, path planning can better adapt to the needs of different environments, whether complex or simple paths; while ensuring the accuracy of path planning, it optimizes the utilization of computing resources by reducing unnecessary calculations, thereby improving the overall system efficiency. Step S20132 Intelligent sampling point selection based on historical data: Utilizing historical path data, machine learning algorithms predict the optimal sampling points, enabling more accurate selection and improving the quality of path generation. Intelligent sampling point selection also reduces the number of sampling points, improving path planning efficiency. Significance: By learning from historical data, path planning can more intelligently select sampling points, reducing human intervention and improving automation. Intelligent sampling point selection also results in more optimized paths, meeting the needs of different application scenarios. Step S20133 Dynamic environment perception and distributed path planning: Combining real-time environment perception technology, this step acquires environmental information in real time and dynamically adjusts path planning strategies, ensuring the real-time performance and safety of the path. In dynamic environments, it can quickly respond to changes in obstacles and update path nodes in real time, ensuring path robustness. Distributing path planning tasks to multiple computing nodes through parallel computing accelerates the path generation process and improves path planning efficiency. Significance achieved: Through dynamic environment perception, path planning can better adapt to changes in the dynamic environment, improving its adaptability and flexibility; through distributed computing, it improves the efficiency of path planning and meets the needs of path planning in large-scale complex environments; through real-time updating of path nodes and parallel computing, it enhances the overall performance of the system and ensures the efficiency and security of path planning.

[0121] In summary, the process of generating new path nodes in this embodiment not only improves the accuracy, efficiency, and real-time performance of path planning, but also enhances its intelligence, adaptability, and robustness. This enables path planning to better meet the needs of different application scenarios, improving the overall system performance and user experience.

[0122] Example 7: Figure 7 As shown, based on Example 6, the process of selecting sampling points provided in this embodiment of the invention includes the following steps:

[0123] S201321: Collect a large amount of path data from past path planning tasks, including path nodes, environmental information, and obstacle locations; label the collected data, marking key path nodes and sampling points;

[0124] S201322: Extract key features from the marked critical path nodes and sampling points, such as path length, curvature, obstacle distance, and environmental complexity, and screen out the features that have the greatest impact on path planning; perform statistical analysis on the extracted features, calculate the mean, variance, and correlation of each feature, and find the relationships between the features.

[0125] S201323: Common path patterns and sampling point distribution patterns in historical data are identified through pattern recognition; based on the results of statistical analysis and pattern recognition, the optimal sampling points are selected to generate new path nodes.

[0126] The working principle and beneficial effects of the above technical solution are as follows: This embodiment first collects a large amount of path data from past path planning tasks, including path nodes, environmental information, and obstacle locations; the collected data is labeled to mark key path nodes and sampling points; secondly, key features, such as path length, curvature, obstacle distance, and environmental complexity, are extracted from the marked key path nodes and sampling points, and the features with the greatest impact on path planning are selected; statistical analysis is performed on the extracted features to calculate the mean, variance, and correlation of each feature, and to find the relationships between the features; finally, common path patterns and sampling point distribution patterns in historical data are identified through pattern recognition; based on the results of statistical analysis and pattern recognition, the optimal sampling points are selected to generate new path nodes. Step S201321, data collection and labeling, in the above solution, allows the system to obtain rich historical path information by collecting a large amount of path data, providing a solid foundation for analysis; labeling the data, especially marking key path nodes and sampling points, helps the system more accurately identify and understand important nodes in the path. Significance: This step provides necessary data support for feature extraction and pattern recognition, ensuring the comprehensiveness and accuracy of the analysis. Through annotation, the system can better identify key nodes in the path, which is crucial for optimizing path planning. Step S201322 Feature Extraction and Statistical Analysis: By extracting key features such as path length, curvature, and obstacle distance, the system can filter out the features that have the greatest impact on path planning, thereby improving the efficiency of path planning. Calculating the mean, variance, and correlation of each feature helps the system understand the relationships between features and further optimize the path planning strategy. Significance: Through feature filtering and statistical analysis, path features can be understood more accurately, thereby optimizing path planning and reducing unnecessary computation and resource consumption. Statistical analysis helps the system understand the relationships between features, which is very important for generating more reasonable path planning strategies. Step S201323 Pattern Recognition and Sampling Point Selection: Through pattern recognition, common path patterns and sampling point distribution patterns in historical data can be discovered, thereby better predicting future path requirements. Based on the results of statistical analysis and pattern recognition, the optimal sampling points are selected, which can generate more reasonable and efficient path nodes. Significance: Pattern recognition helps the system predict future path requirements, thereby enabling advance path planning and improving the predictability and accuracy of path planning; selecting the optimal sampling points can generate more efficient path nodes, reducing the time and resource consumption of path planning and improving overall efficiency.

[0127] In summary, the sampling point selection process in this embodiment, through data collection, feature extraction, statistical analysis, and pattern recognition, achieves optimized and efficient path planning, which is of great significance for improving the accuracy and efficiency of path planning.

[0128] Example 8: As Figure 8As shown, based on Embodiment 3, the process of converting path segment and speed planning into specific control commands provided by this embodiment of the invention includes the following steps:

[0129] S2021: Decompose the path segment into a series of key nodes and intermediate nodes, extract the coordinate information and connection relationship of each node; calculate the speed requirement of each path segment based on the length and curvature of the path segment.

[0130] S2022: Calculate the steering angle of each node based on the curvature and connection relationship of the path segment; calculate the acceleration change of each path segment based on the speed planning information; generate the speed control command for each path segment based on the length of the path segment and the speed planning.

[0131] S2023: According to the optimized steering angle command, the actuator of the rotary magnet performs steering operation; according to the optimized acceleration command, the actuator of the rotary magnet performs acceleration change; according to the optimized speed control command, the actuator of the rotary magnet performs speed change.

[0132] The working principle and beneficial effects of the above technical solution are as follows: This embodiment first decomposes the path segment into a series of key nodes and intermediate nodes, and extracts the coordinate information and connection relationship of each node; calculates the speed requirement of each path segment based on the length and curvature of the path segment; secondly, calculates the turning angle of each node based on the curvature and connection relationship of the path segment; calculates the acceleration change of each path segment based on the speed planning information; generates speed control commands for each path segment based on the length and speed planning of the path segment; finally, the actuator of the rotary magnetic field performs the turning operation based on the optimized turning angle command; the actuator of the rotary magnetic field performs the acceleration change based on the optimized acceleration command; and the actuator of the rotary magnetic field performs the speed change based on the optimized speed control command. The above scheme includes step S2021, path segment decomposition and speed requirement calculation. By decomposing the path segment into a series of key nodes and intermediate nodes, the system can more precisely control the trajectory of the vortex magnet. The coordinate information and connection relationships of each node provide the basic data for calculation. Based on the length and curvature of the path segment, the speed requirement for each path segment is calculated to ensure that the vortex magnet maintains a suitable speed on different path segments, avoiding instability or inefficiency caused by excessively fast or slow speeds. The significance is that through refined path decomposition, the vortex magnet can more accurately follow the planned path, reducing errors; reasonable speed planning ensures that the vortex magnet travels at the optimal speed on different path segments, improving overall motion efficiency. Step S2022 involves calculating the steering angle and acceleration. Based on the curvature and connectivity of the path segments, the steering angle of each node is calculated to ensure a smooth transition during turns, avoiding loss of control or wear caused by sharp turns. Based on speed planning information, the acceleration variation of each path segment is calculated to ensure a smooth transition during acceleration and deceleration, avoiding shocks or energy waste caused by excessive acceleration changes. Based on the length of the path segments and speed planning, speed control commands are generated for each path segment to ensure the maglev travels at the planned speed throughout the entire path. The significance of this step is that precise steering angle and acceleration calculations enable the maglev to maintain stability during turns and speed changes, reducing vibration and wear. Reasonable acceleration variations and speed control reduce unnecessary energy consumption and improve the maglev's endurance. Step S2023: Actuator operation. According to the optimized steering angle command, the actuator of the rotating magnetic field performs steering operation to ensure that the rotating magnetic field can accurately complete the turning action; according to the optimized acceleration command, the actuator of the rotating magnetic field performs acceleration change to ensure that the rotating magnetic field can smoothly transition during acceleration and deceleration; according to the optimized speed control command, the actuator of the rotating magnetic field performs speed change to ensure that the rotating magnetic field can travel at the planned speed throughout the entire path.Significance achieved: Through the precise operation of the actuator, the rotary magnetic actuator can complete the task according to the planned path and speed requirements, improving the accuracy and reliability of control; smooth motion and precise control can enhance the user's operating experience and reduce operational errors or discomfort caused by inaccurate control.

[0133] In summary, this embodiment achieves precise, smooth, and efficient motion control, thereby improving the overall task completion quality and user experience.

[0134] Example 9: As Figure 9 As shown, based on Example 8, the process of calculating the turning angle of each node, i.e., calculating the acceleration change of each path segment, provided in this embodiment of the invention includes the following steps:

[0135] S20221: Obtain the coordinate information and connection relationship of key nodes and intermediate nodes of the path segment, calculate the distance and angle difference between adjacent nodes, and determine the curvature of the path segment; fit the path segment to an arc, calculate the central angle of the arc, and calculate the angle between the vectors using the coordinate vectors of adjacent nodes; calculate the tangent angle of the curve through the control points of the Bézier curve.

[0136] S20222: Determine the initial speed and target speed for each path segment based on the length and speed requirements of the path segment;

[0137] S20223: Calculate acceleration using the formula for uniformly accelerated motion and optimize acceleration changes; adjust acceleration in real time using model predictive control.

[0138] The working principle and beneficial effects of the above technical solution are as follows: This embodiment first obtains the coordinate information and connection relationship of the key nodes and intermediate nodes of the path segment, calculates the distance and angle difference between adjacent nodes, and determines the curvature of the path segment; fits the path segment to an arc, calculates the central angle of the arc, and calculates the angle between the vectors using the coordinate vectors of adjacent nodes; calculates the tangent angle of the curve using the control points of the Bézier curve; secondly, determines the initial velocity and target velocity of each path segment according to the length and speed requirements of the path segment; finally, calculates the acceleration using the uniform acceleration motion formula and optimizes the acceleration change; and adjusts the acceleration in real time using model predictive control. Step S20221 of the above solution, geometric analysis and fitting of the path segment, by obtaining the coordinate information of the key nodes and intermediate nodes of the path segment and calculating the distance and angle difference between adjacent nodes, can accurately determine the curvature of the path segment; fitting the path segment to an arc, calculating the central angle of the arc, and calculating the tangent angle of the curve using the control points of the Bézier curve—these operations provide detailed geometric information about the path segment. Significance: This step provides fundamental data for speed and acceleration calculations, ensuring the accuracy of path planning and enabling robots or vehicles to smoothly travel along predetermined paths, reducing unnecessary vibrations and energy consumption. Step S20222 Speed ​​Planning: Based on the length and speed requirements of each path segment, the initial and target speeds are determined. This helps achieve smooth speed transitions and avoids sudden acceleration or deceleration. Significance: Rational speed planning improves driving comfort and safety, while optimizing energy consumption and extending equipment lifespan. Step S20223 Acceleration Calculation and Optimization: Acceleration is calculated using the uniform acceleration motion formula and adjusted in real time through Model Predictive Control (MPC) to ensure smooth acceleration changes that meet path requirements. Significance: This ensures the dynamic performance of the vehicle or robot during travel, enabling it to respond quickly to path changes while maintaining operational stability and safety. Optimizing acceleration changes can further reduce energy consumption and improve the overall efficiency of the system.

[0139] In summary, this embodiment constitutes a complete path planning and control process, which not only improves the accuracy of path following, but also optimizes the dynamic performance and energy efficiency during driving, and has important application value for fields such as autonomous driving and robot navigation.

[0140] Example 10: As Figure 10 As shown, based on Embodiment 1, the process of activating the obstacle avoidance procedure provided in this embodiment of the invention includes the following steps:

[0141] S301: Based on a real-time perceived environment model, calculate the relative distance and velocity between the vortex magnet and obstacles to assess potential collision risks; classify the risks into low, medium and high levels according to their severity.

[0142] S302: When a high risk is detected, initiate path replanning. Based on the current environmental model and the operating status of the vortex magnet, replan a path to avoid obstacles. Select an obstacle avoidance strategy, such as emergency braking, lateral avoidance, or detour, according to the risk level and environmental complexity.

[0143] S303: Transforms the selected obstacle avoidance strategy into specific control commands, and adjusts the running direction and speed of the rotating magnetic field in real time.

[0144] The working principle and beneficial effects of the above technical solution are as follows: Firstly, based on a real-time perceived environmental model, the relative distance and speed between the rotating magnetic field and obstacles are calculated to assess potential collision risks. Based on the severity of the collision risk, the risks are classified into low, medium, and high levels. Secondly, when a high risk is detected, path replanning is initiated. Based on the current environmental model and the operating status of the rotating magnetic field, a new path is planned to avoid the obstacle. Based on the risk level and environmental complexity, obstacle avoidance strategies are selected, such as emergency braking, lateral avoidance, and detour. Finally, the selected obstacle avoidance strategies are converted into specific control commands to adjust the rotating magnetic field's operating direction and speed in real time. Step S301 of the above solution, collision risk assessment, accurately calculates the relative distance and speed between the rotating magnetic field and obstacles through a real-time perceived environmental model, ensuring the accuracy of the risk assessment. Classifying the risks into low, medium, and high levels based on their severity provides a clear basis for selecting obstacle avoidance strategies. Significance: Through accurate risk assessment, potential collision risks can be identified in advance, providing a time window for taking effective obstacle avoidance measures and significantly improving the operational safety of the rotary magnetic field system. The risk level classification allows the system to adopt corresponding obstacle avoidance strategies based on different risk levels, optimizing the decision-making process and improving obstacle avoidance efficiency. Step S302: Path replanning and strategy selection. Based on the current environmental model and the rotary magnetic field system's operating status, the path is replanned in real time to ensure that the planned path avoids obstacles. Appropriate obstacle avoidance strategies, such as emergency braking, lateral avoidance, and detour, are selected according to the risk level and environmental complexity to ensure the flexibility and efficiency of obstacle avoidance. Significance: Through dynamic path planning and multi-strategy selection, the rotary magnetic field system can flexibly cope with various complex environments, ensuring that the optimal obstacle avoidance path can be found under different conditions. Reasonable strategy selection can reduce unnecessary obstacle avoidance actions, improve the operational efficiency of the rotary magnetic field system, and ensure that it can quickly and smoothly avoid obstacles. Step S303 generates control commands, translating the selected obstacle avoidance strategy into specific control commands. This involves real-time adjustment of the rotating magnetic field's direction and speed to ensure smooth and rapid obstacle avoidance. A closed-loop feedback mechanism monitors the obstacle avoidance effect in real time, further fine-tuning the control commands based on actual operating conditions to ensure the accuracy and stability of the obstacle avoidance process. Significance: Through real-time control adjustments, the rotating magnetic field can precisely execute the obstacle avoidance strategy, ensuring smooth and safe operation in complex environments. The introduction of the feedback mechanism allows the system to monitor the obstacle avoidance effect in real time, adjust control commands promptly, and improve the stability and reliability of the obstacle avoidance process.

[0145] In summary, the obstacle avoidance procedure of the vortex magnetic field in this embodiment can not only assess collision risks in real time and accurately, but also flexibly select and execute obstacle avoidance strategies, ensuring its efficient and safe operation in complex environments. The obstacle avoidance procedure not only improves the operational safety of the vortex magnetic field, but also enhances its reliability and adaptability in practical applications, possessing significant technical importance and application value.

[0146] Example 11: As Figure 11 As shown, based on Embodiments 1-10, the intelligent control system for vortex-magnetic ship operation provided in this embodiment of the invention includes:

[0147] The environment model update module is responsible for importing pre-stored map data containing information such as terrain and obstacle distribution; initializing the operating parameters of the rotating magnetic field, setting the initial position and target position; constructing a 3D model of the surrounding virtual environment, and merging it with the imported map data to generate a real-time updated environment model.

[0148] The path optimization and adjustment module is responsible for planning a path based on a real-time updated environmental model, planning an optimal path from the current position to the target position, avoiding obstacles; converting the planned path into specific control commands to guide the direction and speed of the rotating magnetic field; and continuously adjusting the optimal path based on feedback from real-time GPS positioning and environmental scanning data.

[0149] The obstacle avoidance monitoring module is responsible for continuously monitoring changes in the surrounding environment during the operation of the rotating magnetic field; when a potential collision risk is detected, the obstacle avoidance program is activated to adjust the running direction and avoid obstacles.

[0150] The working principle and beneficial effects of the above technical solution are as follows: The environment model update module of this embodiment imports pre-stored map data containing information such as terrain and obstacle distribution; initializes the operating parameters of the rotating magnetic field, sets the initial position and target position; constructs a three-dimensional model of the surrounding virtual environment, merges it with the imported map data, and generates a real-time updated environment model; the path optimization and adjustment module performs path planning based on the real-time updated environment model, plans an optimal path from the current position to the target position, and avoids obstacles; the planned path is converted into specific control commands to guide the running direction and speed of the rotating magnetic field; the optimal path is continuously adjusted through feedback from real-time GPS positioning and environmental scanning data; the obstacle avoidance monitoring module continuously monitors changes in the surrounding environment during the operation of the rotating magnetic field; when a potential collision risk is detected, the obstacle avoidance program is activated, the running direction is adjusted, and obstacles are avoided. The environmental model update module of the above scheme imports pre-stored map data such as terrain and obstacle distribution, and combines it with real-time sensor data to achieve multi-source data fusion, improving the accuracy and comprehensiveness of the environmental model. It constructs a 3D model of the surrounding virtual environment, enabling the vortex magneto system to understand its surroundings more intuitively and comprehensively, providing foundational data for subsequent path planning and obstacle avoidance. The generated environmental model is updated in real time, dynamically reflecting environmental changes and ensuring that the vortex magneto system always makes decisions based on the latest environmental information during operation. The significance is that through multi-source data fusion and a real-time updated environmental model, the vortex magneto system can perceive its surroundings more accurately, providing reliable data support for intelligent decision-making; the real-time updated environmental model can cope with environmental changes, improving the system's robustness and adaptability, and ensuring the stable operation of the vortex magneto system in complex environments. The path optimization and adjustment module, based on a real-time updated environmental model, plans the optimal path from the current location to the target location, avoiding obstacles and ensuring efficiency and safety. It translates the planned path into specific control commands, guiding the maglev's direction and speed, achieving seamless integration between path planning and actual operation. Through feedback from real-time GPS positioning and environmental scanning data, it continuously adjusts the optimal path, ensuring dynamic adaptability and real-time performance. The significance is that intelligent path planning optimizes path selection, reducing unnecessary detours and delays, and improving the maglev's operational efficiency; by avoiding obstacles and dynamically adjusting the path, it ensures the maglev's safety during operation, reducing collision risks; and by translating path planning into specific control commands, it ensures the maglev operates precisely according to the planned path, improving system control accuracy and stability. The obstacle avoidance monitoring module continuously monitors changes in the surrounding environment during maglev operation, ensuring comprehensive environmental awareness and timely detection of potential risks. When a potential collision risk is detected, it initiates an obstacle avoidance program, adjusting the operating direction to avoid obstacles and ensuring the safe operation of the maglev.Significance achieved: Through continuous monitoring and obstacle avoidance algorithms, the system can avoid obstacles in a timely manner during operation, improving system safety; the application of obstacle avoidance algorithms enables the system to cope with sudden environmental changes, enhancing the system's adaptability and flexibility; timely obstacle avoidance ensures the stable operation of the system and reduces system failures and downtime caused by collisions.

[0151] In summary, the intelligent control system for vortex-magnetic ship operation in this embodiment not only improves the technical performance of the system, but also achieves important results such as enhancing environmental perception capabilities, strengthening safety, realizing precise control, and ensuring stable operation.

[0152] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of equivalents of this invention, this invention is also intended to include these modifications and variations.

Claims

1. A method for intelligent control of vortex-guided ship operation, characterized in that, Includes the following steps: Construct a 3D model of the surrounding virtual environment and generate an environment model that is updated in real time; Based on a real-time updated environment model, an optimal path from the current location to the target location is planned. When a potential collision risk is detected, the obstacle avoidance procedure is initiated, and the running direction is adjusted; The process of generating a real-time updated environment model includes the following steps: Map data is processed in layers according to different levels of detail to generate terrain and obstacle models of varying precision; spatial interpolation is used to fill in gaps and details in the map data to generate a continuous and smooth 3D spatial model; and the generated 3D model is simplified and redundant information is removed through optimization algorithms. The system collects dynamic data of the current environment, including real-time location and obstacle changes, using real-time GPS positioning and environmental scanning equipment; the collected dynamic data is then matched and aligned with preprocessed static map data. By using a fusion algorithm, dynamic data and static map data are merged to generate a real-time updated environment model. The fusion algorithm includes data weight allocation, data smoothing, and conflict detection and resolution steps.

2. The intelligent control method for the operation of a rotary magnetic ship as described in claim 1, characterized in that, The process of translating the planned optimal path into specific control commands includes the following steps: Based on a real-time updated environment model, the shortest path from the current location to the target location is calculated; the optimized path is decomposed into a series of small path segments, each corresponding to a control command. Based on the length and curvature of the path segment, the speed of the rotary magnetic field on each path segment is planned; the path segment and speed plan are converted into specific control commands and sent to the actuator of the rotary magnetic field. During the operation of the rotating magnetic field, the execution of the path and environmental changes are continuously monitored through real-time GPS positioning and environmental scanning data feedback; if path deviation or environmental changes are detected, the path planning and control commands are regenerated.

3. The intelligent control method for the operation of a rotating magnetic ship as described in claim 2, characterized in that, The process of calculating the shortest path from the current location to the target location includes the following steps: Based on a real-time updated environment model, a graph structure is constructed, with the current position as the starting point and the target position as the ending point. Create a priority queue to store nodes to be processed, and initialize the distance of all nodes to infinity, and the distance of the starting point to 0; take the node with the smallest distance from the priority queue, traverse all its adjacent nodes, and calculate the distance from the starting point through the current node to the adjacent node. The algorithm stops when the node retrieved from the priority queue is the target location node; the initial path is generated by backtracking the parent node of each node from the target location node back to the starting point. Using the starting point of the initial path as the root node, random sampling is performed near the key points of the path, and new path nodes are generated by expanding from the nearest neighbor node to the random point. New path nodes undergo collision detection with obstacles in the environment; when a node in the random tree is rapidly explored and approaches the target location, a new optimized path, i.e. the shortest path, is generated by backtracking.

4. The intelligent control method for the operation of a rotating magnetic ship as described in claim 3, characterized in that, Each node represents a location, and edges represent connections between nodes, each with a corresponding weight.

5. The intelligent control method for the operation of a rotating magnetic ship as described in claim 3, characterized in that, The process of calculating the distance from the starting point through the current node to adjacent nodes includes the following steps: Determine the positions of the current node and its neighboring nodes; obtain the known shortest distance from the current node to the starting point; Calculate the direct distance from the current node to its neighboring nodes; add the distance from the starting point to the current node to the distance from the current node to its neighboring nodes to obtain the total distance from the starting point through the current node to the neighboring nodes; Compare the calculated total distance with the current distance of adjacent nodes.

6. The intelligent control method for the operation of a rotating magnetic ship as described in claim 5, characterized in that, If the total distance is less than the current distance of the adjacent node, update the distance of the adjacent node to the total distance and add the adjacent node to the priority queue; repeat the above steps until the node taken out of the priority queue is the target location node, then the calculation stops.

7. The intelligent control method for the operation of a rotating magnetic ship as described in claim 3, characterized in that, The process of generating a new path node includes the following steps: At critical turning points in the path, in narrow passages, expand the sampling range to capture potential paths; in straight sections of the path, narrow the sampling range. Utilize historical path data to predict the optimal sampling point; select sampling points by learning from historical path planning data. By combining real-time environmental perception technology, environmental information can be acquired in real time, and path planning strategies can be dynamically adjusted. In dynamic environments, the path nodes are updated in real time in response to changes in obstacles; the path planning task is assigned to multiple computing nodes to accelerate the path generation process through parallel computing.

8. The intelligent control method for the operation of a rotating magnetic ship as described in claim 3, characterized in that, The process of translating path segments and speed plans into specific control commands includes the following steps: The path segment is decomposed into a series of key nodes and intermediate nodes, and the coordinate information and connection relationship of each node are extracted; the speed requirement of each path segment is calculated based on the length and curvature of the path segment. Calculate the turning angle of each node based on the curvature and connectivity of the path segments; Based on the velocity planning information, calculate the acceleration change for each path segment; based on the length of the path segment and the velocity planning, generate the velocity control command for each path segment.

9. The intelligent control method for the operation of a rotating magnetic ship as described in claim 8, characterized in that, Based on the optimized steering angle command, the actuator of the rotary magnet performs steering operations; based on the optimized acceleration command, the actuator of the rotary magnet performs acceleration changes; based on the optimized speed control command, the actuator of the rotary magnet performs speed changes.

10. A spiral magnetic ship operation intelligent control system, used to implement the spiral magnetic ship operation intelligent control method according to any one of claims 1-9, characterized in that, Include: The environment model update module is responsible for constructing a 3D model of the surrounding virtual environment and generating an environment model that is updated in real time. The path optimization and adjustment module is responsible for planning an optimal path from the current location to the target location based on a real-time updated environment model. The obstacle avoidance monitoring module is responsible for initiating the obstacle avoidance procedure when a potential collision risk is detected.

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