Flexible magnetic levitation transportation method, device, equipment and medium based on edge computing

Abstract

This invention relates to a flexible magnetic levitation conveying method, apparatus, equipment, and medium based on edge computing. The method includes acquiring preliminary motion planning data from a main controller to a distributed driver via a standard EtherCAT bus; performing kinematic decomposition on the data by the edge computing unit of the distributed driver to generate detailed trajectory planning data for each axis, and generating a position closed-loop control signal by combining motor current and position encoder data; establishing a synchronous link between the drivers using VoE technology to calculate compensation values ​​for phase and speed differences between adjacent axes; inputting the compensation values ​​into the edge computing unit to correct the trajectory data and update the control signal; and finally driving the magnetic levitation conveying system according to the updated signal to achieve high-precision, high-reliability, and high-flexibility multi-axis flexible synchronous motion, meeting the stringent requirements of modern industry for material conveying.

Description

Technical Field

[0001] This invention relates to the field of industrial automation and intelligent control technology, and in particular to a flexible magnetic levitation transport method, device, equipment and medium based on edge computing. Background Technology

[0002] In modern industrial production, extremely high demands are placed on the precision, flexibility, and efficiency of material conveying systems. Traditional conveying systems, such as those based on belts and chains, suffer from severe wear, high maintenance costs, limited positioning accuracy, and difficulty in achieving complex motion trajectories, and are gradually failing to meet the production needs of high-end industries such as precision manufacturing and electronic semiconductors.

[0003] With the deepening development of industrial automation, magnetic levitation conveyor technology has emerged. However, early magnetic levitation conveyor systems typically relied on a central controller for centralized control, where all motion planning and control commands were calculated and issued uniformly. This architecture revealed significant shortcomings when facing complex multi-axis motions and scenarios with extremely high real-time requirements. The central controller not only easily became a system performance bottleneck, causing data processing delays, but also paralyzed the entire system if it failed. Furthermore, the centralized control mode struggled to make flexible and precise real-time adjustments based on the actual operating status of each axis, failing to achieve truly flexible synchronous multi-axis motion. Therefore, how to fully utilize distributed computing resources to improve the control accuracy, flexibility, and reliability of magnetic levitation conveyor systems has become a critical issue that urgently needs to be addressed. Summary of the Invention

[0004] The main objective of this invention is to provide a flexible magnetic levitation conveying method, device, equipment, and medium based on edge computing, so as to achieve high-precision, high-reliability, and high-flexibility multi-axis flexible synchronous motion of the magnetic levitation conveying system, thereby meeting the stringent requirements of modern industrial production for material conveying.

[0005] To achieve the above objectives, the present invention provides a flexible magnetic levitation transportation method based on edge computing, comprising the following steps:

[0006] The preliminary motion planning data sent by the main controller to the distributed driver is obtained through the standard EtherCAT bus in the magnetic levitation system. The main controller only completes the preliminary motion planning, and the preliminary motion planning data includes global parameters of the transport path.

[0007] The edge computing unit of the distributed driver deployed in the magnetic levitation system independently performs kinematic decomposition on the preliminary motion planning data to generate detailed trajectory planning data for each motion axis. Combined with the motor current and position encoder data collected in real time by the distributed driver, a position closed-loop control signal is generated.

[0008] A synchronous link is established between each distributed driver using VoE technology to exchange the actual position and velocity data of adjacent motion axes in real time. Based on the actual position and velocity data, the phase difference compensation value and velocity difference compensation value between adjacent axes are calculated. The calculation process does not depend on the participation of the main controller.

[0009] The phase difference compensation value and velocity difference compensation value are input to the edge computing unit of the corresponding driver to dynamically correct the detailed trajectory planning data, and the position closed-loop control signal is updated based on the corrected detailed trajectory planning data.

[0010] The magnetic levitation conveyor system is driven by the updated position closed-loop control signal to perform multi-axis flexible synchronous motion.

[0011] Further, the step of performing kinematic decomposition on the preliminary motion planning data includes:

[0012] The global parameters are decomposed based on the inverse kinematics algorithm to obtain the displacement and velocity commands for each motion axis;

[0013] The displacement and velocity commands are smoothed using a B-spline curve trajectory planning algorithm to generate detailed trajectory planning data that satisfies C2 continuity.

[0014] Furthermore, the step of generating the position closed-loop control signal includes:

[0015] Based on the model predictive control algorithm, combined with real-time acquired motor current data and position encoder data, the position tracking error of each motion axis is calculated;

[0016] A position closed-loop control signal is generated based on the position tracking error, with a control period of ≤100μs.

[0017] Furthermore, the step of establishing synchronization links between distributed drivers using VoE technology includes:

[0018] Local clock information is obtained based on the virtual timestamp counters configured in each distributed driver, wherein the resolution of the counters is ≤10ns;

[0019] A synchronization link is established between distributed drives through a precision time protocol to synchronize the virtual timestamps of adjacent drives with a synchronization error of ≤100ns.

[0020] When network latency fluctuations of ≥50μs are detected, switch to local clock compensation mode with a compensation accuracy of ≤200ns.

[0021] Further, the step of calculating the phase difference compensation value and velocity difference compensation value between adjacent axes includes:

[0022] The real-time phase difference is calculated using interpolation based on the actual position data of adjacent motion axes.

[0023] The velocity difference is obtained by differential calculation based on the velocity data of adjacent motion axes.

[0024] When the phase difference or velocity difference exceeds a preset threshold, a compensation calculation mechanism is triggered, which generates compensation values ​​for the phase difference and velocity difference through a proportional-integral control algorithm. The proportional coefficient of the proportional-integral control algorithm is 0.5, and the integral time is 0.1s.

[0025] Furthermore, the step of dynamically correcting the detailed trajectory planning data includes:

[0026] The phase difference compensation value and velocity difference compensation value are iteratively adjusted using the gradient descent algorithm. The gradient descent algorithm is a parameter optimization method that gradually approximates the optimal solution by calculating the gradient direction of the error function. The learning rate is set to 0.01.

[0027] If the absolute values ​​of both the phase difference compensation value and the velocity difference compensation value are less than the preset tolerance threshold, the corrected trajectory data will be injected into the closed-loop control signal.

[0028] Furthermore, prior to the step of driving the magnetic levitation transport system based on the updated position closed-loop control signal, the method further includes:

[0029] The communication data frames are parsed using the EtherCAT slave controller, and the data integrity is verified using the CRC32 algorithm.

[0030] In the edge computing unit, trajectory tracking error data and positioning repeatability accuracy data are extracted based on the updated detailed trajectory planning data, and an optimized motion planning scheme is generated by combining the stability constraints of the magnetic levitation system.

[0031] The position closed-loop control signal is adjusted and updated based on the optimized motion planning scheme.

[0032] Furthermore, the edge computing unit also includes:

[0033] Vibration suppression algorithms for flexible magnetic levitation systems are integrated into each edge computing unit. Control parameters are dynamically adjusted based on real-time collected motor current fluctuation data. The motor current fluctuation data is acquired by a current sensor with a sampling period of ≤50us to suppress low-frequency oscillations during flexible transport.

[0034] The present invention also provides a flexible magnetic levitation transport device based on edge computing, comprising:

[0035] The data acquisition module is used to acquire preliminary motion planning data sent by the main controller to the distributed driver through the standard EtherCAT bus in the magnetic levitation system. The main controller only completes the preliminary motion planning, and the preliminary motion planning data includes global parameters of the transport path.

[0036] The trajectory generation module is used to independently perform kinematic decomposition on the preliminary motion planning data through the edge computing unit of the distributed driver deployed in the magnetic levitation system, generate detailed trajectory planning data for each motion axis, and generate a position closed-loop control signal by combining the motor current and position encoder data collected in real time by the distributed driver.

[0037] The synchronization compensation module is used to establish a synchronization link between each distributed driver through VoE technology, exchange the actual position and speed data of adjacent motion axes in real time, and calculate the phase difference compensation value and speed difference compensation value between adjacent axes based on the actual position and speed data. The calculation process does not depend on the participation of the main controller.

[0038] The data compensation module is used to input the phase difference compensation value and velocity difference compensation value to the edge computing unit of the corresponding driver, dynamically correct the detailed trajectory planning data, and update the position closed-loop control signal based on the corrected detailed trajectory planning data.

[0039] The system drive module is used to drive the magnetic levitation conveyor system to perform multi-axis flexible synchronous motion based on the updated position closed-loop control signal.

[0040] The present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described edge computing-based flexible magnetic levitation transport method.

[0041] The flexible magnetic levitation conveying method, device, equipment, and medium based on edge computing provided by this invention have the following beneficial effects: This invention overcomes the bottleneck of centralized processing by the main controller by independently computing the edge computing units of the distributed driver, quickly completing kinematic decomposition and flexibly responding to changing conveying needs; by using real-time acquired data to generate position closed-loop control signals with a control cycle as short as ≤100μs, the positioning accuracy is greatly improved, meeting the high-precision requirements of precision manufacturing; and by autonomously establishing a synchronous link through VoE technology, the drivers exchange adjacent axis data in real time, calculate compensation values ​​to ensure multi-axis synchronization, and the system can operate stably even if the main controller fails. Attached Figure Description

[0042] Figure 1 This is a flowchart illustrating a flexible magnetic levitation transport method based on edge computing in one embodiment of the present invention.

[0043] Figure 2 This is a structural block diagram of a flexible magnetic levitation transport device based on edge computing in one embodiment of the present invention;

[0044] Figure 3 This is a schematic block diagram of the structure of a computer device according to an embodiment of the present invention.

[0045] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0047] Reference Figure 1 This is a flowchart illustrating a flexible magnetic levitation transport method based on edge computing proposed in this invention, which includes the following steps:

[0048] S1, the preliminary motion planning data sent by the main controller to the distributed driver is obtained through the standard EtherCAT bus in the magnetic levitation system. The main controller only completes the preliminary motion planning, and the preliminary motion planning data includes the global parameters of the transport path.

[0049] S2, through the edge computing unit of the distributed driver deployed in the magnetic levitation system, independently perform kinematic decomposition on the preliminary motion planning data to generate detailed trajectory planning data for each motion axis, and combine it with the motor current and position encoder data collected in real time by the distributed driver to generate a position closed-loop control signal;

[0050] S3, establishes a synchronous link between each distributed driver through VoE technology, exchanges the actual position and speed data of adjacent motion axes in real time, and calculates the phase difference compensation value and speed difference compensation value between adjacent axes based on the actual position and speed data. The calculation process does not depend on the participation of the main controller.

[0051] S4, input the phase difference compensation value and velocity difference compensation value to the edge computing unit of the corresponding driver, dynamically correct the detailed trajectory planning data, and update the position closed-loop control signal based on the corrected detailed trajectory planning data;

[0052] S5 drives the magnetic levitation conveyor system based on the updated position closed-loop control signal to perform multi-axis flexible synchronous motion.

[0053] As described in step S1 above, the main controller completes the necessary global planning and is responsible for generating preliminary motion planning data, including global parameters of the transport path (such as start point, end point, path shape, speed range, etc.), avoiding tasks with high computing power requirements (such as real-time trajectory optimization and closed-loop control), and reducing dependence on high-performance main control hardware. The generated preliminary motion planning data only contains global parameters, reducing bus communication and providing a foundation for subsequent edge computing. Afterwards, it is distributed to the distributed driver via the standard EtherCAT bus. The main controller in step S1 does not need to participate in real-time control at high frequency, but only needs to intervene at key nodes, significantly reducing the communication frequency and effectively alleviating the load pressure on the main station, while ensuring that the system's real-time performance is not affected. In addition, efficient and reliable data transmission is achieved through the EtherCAT bus, compatible with industrial standard equipment.

[0054] As described in step S2 above, the edge computing units (MCUs) of each distributed driver perform kinematic decomposition, trajectory planning, and closed-loop control operations based on the received preliminary planning data. An inverse kinematics algorithm is used to decompose global parameters (such as paths in Cartesian coordinates) into displacement and velocity commands for each motion axis (such as the X / Y / Z axes). A smooth, detailed trajectory (C2 continuity) is generated using a B-spline curve algorithm (control point spacing ≤ 10 mm, curvature change rate ≤ 5% / mm). The B-spline trajectory ensures smooth motion and avoids mechanical shock. Combining real-time acquired motor current (reflecting load status) and position encoder data (feedback of actual position), a model predictive control (MPC) algorithm (prediction time domain 20 ms, control period ≤ 100 μs) is used to generate a position closed-loop control signal. The real-time acquired motor current data is used to dynamically adjust control parameters to adapt to different load scenarios (such as no-load and full-load), and the MPC algorithm is used to dynamically compensate for disturbances. Step S2 completes detailed planning and closed-loop control independently through each driver, without the need for the main controller, thus improving system scalability; microsecond-level closed-loop control is achieved through local computing (MCU), eliminating the impact of master-slave communication delay in the traditional architecture.

[0055] As described in step S3 above, a virtual timestamp counter (resolution ≤ 10ns) is configured for each driver, and the clocks of adjacent drivers are synchronized via Precision Time Protocol (PTP), with a synchronization error ≤ 100ns. When network latency fluctuations ≥ 50μs, the system switches to local clock compensation mode (compensation accuracy ≤ 200ns). The local clock compensation mechanism can cope with network fluctuations, ensure system stability, and guarantee link robustness. The actual position and speed data of adjacent motion axes are exchanged in real time. The phase difference is calculated based on interpolation (such as linear interpolation), and the speed difference is calculated using differentiation. When the deviation exceeds the threshold (phase difference ≤ 0.1°, speed difference ≤ 0.1mm / s, meeting precision conveying requirements), the PI control algorithm (Kp = 0.5, Ti = 0.1s) is triggered to generate compensation values. In step S3, VoE technology achieves synchronization through direct communication between drivers, with the main controller only handling data forwarding, avoiding the occupation of main station bandwidth and computing resources. By adjusting the phase difference and speed difference in real time, the accumulated error in multi-axis cooperative motion is eliminated.

[0056] As described in step S4 above, the gradient descent algorithm (learning rate 0.01) is used to iteratively adjust the phase difference and velocity difference compensation values, gradually approaching the optimal solution. This gradient descent algorithm enables parameter self-tuning, reducing manual debugging costs. When the absolute value of the compensation is less than the tolerance threshold (phase difference ≤ 0.1°, velocity difference ≤ 0.1 mm / s), the corrected trajectory data is injected into the closed-loop control signal. Motor current fluctuation data is collected in real time using a current sensor (sampling period ≤ 50 μs), and control parameters are dynamically adjusted to suppress low-frequency oscillations in the flexible conveyor. In step S4, the trajectory is continuously optimized based on real-time data, improving motion accuracy and stability. For the flexible characteristics of the magnetic levitation system (such as low-frequency vibration), a special algorithm enhances the control effect.

[0057] As described in step S5 above, data frames are parsed by the EtherCAT slave controller, and CRC32 checksum ensures communication integrity. Tracking error (≤15μm) and positioning repeatability (≤5μm) are extracted from the updated trajectory data, and combined with magnetic levitation stability constraints (e.g., magnetic stiffness ≥5kN / mm), an optimized scheme is generated. Based on the optimized control signal, the magnetic levitation system is driven to achieve multi-axis flexible synchronous motion.

[0058] In one embodiment, a flexible magnetic levitation transport system based on edge computing is applied in semiconductor wafer transport. This embodiment is designed for semiconductor wafer transport scenarios, requiring the magnetic levitation transport system to achieve high-precision positioning (error ≤ 5μm), multi-axis synchronization (phase difference ≤ 0.1°), and low communication frequency (≤ 20Hz). The system uses standard EtherCAT bus communication. The main controller is only responsible for issuing global parameters, while the core control logic is independently completed by the edge computing unit of the distributed driver. The magnetic levitation platform uses a 6-DOF motion mechanism, equipped with a magnetic field sensor (resolution 0.1mT) and a brushless motor (rated current 10A). The edge computing unit is equipped with a high-performance computing module (200TOPS computing power) and a specific type of FPGA module. The main controller uses a highly reliable embedded controller, transmitting initial planning data at a frequency of 20Hz via the EtherCAT bus. The distributed driver integrates an EtherCAT slave module with a built-in virtual timestamp counter (resolution 5ns), supporting multi-axis synchronous operation under VoE technology.

[0059] In trajectory planning, the B-spline curve algorithm generates a C2 continuous trajectory with a control point spacing of 8mm and a curvature change rate ≤4.2% / mm, ensuring smooth motion. The Model Predictive Control (MPC) algorithm generates a closed-loop signal with a 20ms prediction time domain and a 100μs control cycle, combining real-time acquired motor current (sampling period 50µs) and position encoder data (resolution 0.1μm) to dynamically compensate for disturbances. In actual testing, the trajectory tracking error is ≤12μm, a 60% improvement over traditional centralized control. VoE technology establishes a distributed synchronization link through Precision Time Protocol (PTP), with a synchronization error ≤80ns. When network latency fluctuations ≥50μs, the system automatically switches to local clock compensation mode (compensation accuracy 150ns). Adjacent motion axes exchange position and velocity data in real time, using linear interpolation to calculate the phase difference and differential operations to calculate the velocity difference. When the phase difference is ≥0.08° or the velocity difference is ≥0.08mm / s, the proportional-integral (PI) control algorithm is triggered to generate compensation values ​​(Kp=0.5, Ti=0.1s), and the final synchronization error is ≤0.08°, which is 84% ​​higher than the traditional solution.

[0060] In the dynamic correction phase, the gradient descent algorithm iteratively optimizes the compensation value with the mean squared error (MSE) as the objective function (learning rate 0.01), converging to the tolerance threshold (phase difference ≤ 0.05°, velocity difference ≤ 0.05 mm / s) after 10 iterations. The vibration suppression algorithm predicts low-frequency oscillation trends through an LSTM neural network, dynamically adjusts MPC parameters, and uses a current sensor to collect fluctuation data at a 50µs cycle, achieving a suppression rate of 70%. Before system operation, the EtherCAT slave controller parses data frames and performs CRC32 verification to ensure communication integrity. The optimization module extracts trajectory tracking errors (delay standard deviation ≤ 0.1ms, positioning repeatability ≤ 5μm), combines them with magnetic stiffness constraints (≥ 5.2kN / mm), and uses a genetic algorithm to optimize path parameters, reducing energy consumption by 23%. Actual tests show that under a 4kg load and 200Hz electromagnetic interference, the system's positioning accuracy is ≤ 5μm, response time is ≤ 1.8ms, and communication frequency is 20Hz (traditional solutions require 8kHz).

[0061] Reference Figure 2 Here is a structural block diagram of a flexible magnetic levitation transport device based on edge computing in one embodiment of the present invention, comprising:

[0062] The data acquisition module is used to acquire preliminary motion planning data sent by the main controller to the distributed driver through the standard EtherCAT bus in the magnetic levitation system. The main controller only completes the preliminary motion planning, and the preliminary motion planning data includes global parameters of the transport path.

[0063] The trajectory generation module is used to independently perform kinematic decomposition on the preliminary motion planning data through the edge computing unit of the distributed driver deployed in the magnetic levitation system, generate detailed trajectory planning data for each motion axis, and generate a position closed-loop control signal by combining the motor current and position encoder data collected in real time by the distributed driver.

[0064] The synchronization compensation module is used to establish a synchronization link between each distributed driver through VoE technology, exchange the actual position and speed data of adjacent motion axes in real time, and calculate the phase difference compensation value and speed difference compensation value between adjacent axes based on the actual position and speed data. The calculation process does not depend on the participation of the main controller.

[0065] The data compensation module is used to input the phase difference compensation value and velocity difference compensation value to the edge computing unit of the corresponding driver, dynamically correct the detailed trajectory planning data, and update the position closed-loop control signal based on the corrected detailed trajectory planning data.

[0066] The system drive module is used to drive the magnetic levitation conveyor system to perform multi-axis flexible synchronous motion based on the updated position closed-loop control signal.

[0067] For the specific implementation of each module in the above device example, please refer to the above method embodiments, which will not be repeated here.

[0068] Reference Figure 3 This invention also provides a computer device, which can be a server, and its internal structure can be as follows: Figure 3 As shown, the computer device includes a processor, memory, display screen, input device, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores the data corresponding to this embodiment. The network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements the above-described method.

[0069] Those skilled in the art will understand that Figure 3 The structures shown are merely block diagrams of some structures related to the present invention and do not constitute a limitation on the computer devices on which the present invention is applied.

[0070] In summary, this invention acquires preliminary motion planning data from the main controller to the distributed actuators via the standard EtherCAT bus in the magnetic levitation system. The edge computing units of the distributed actuators deployed in the magnetic levitation system independently perform kinematic decomposition on the preliminary motion planning data, generating detailed trajectory planning data for each motion axis. This data, combined with real-time motor current and position encoder data collected by the distributed actuators, generates a position closed-loop control signal. A synchronous link is established between the distributed actuators using VoE technology, exchanging real-time actual position and velocity data of adjacent motion axes. Based on this actual position and velocity data, phase difference compensation values ​​and velocity difference compensation values ​​between adjacent axes are calculated. These compensation values ​​are input to the edge computing units of the corresponding actuators to dynamically correct the detailed trajectory planning data, and the position closed-loop control signal is updated based on the corrected detailed trajectory planning data. The updated position closed-loop control signal drives the magnetic levitation conveying system to perform multi-axis flexible synchronous motion, achieving high-precision, high-reliability, and high-flexibility multi-axis flexible synchronous motion to meet the stringent requirements of modern industrial production for material conveying.

[0071] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the present invention and embodiments can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual-rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM, etc.

[0072] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, apparatus, article, or method. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, apparatus, article, or method that includes that element.

[0073] The above description is merely a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A flexible magnetic levitation transport method based on edge computing, characterized in that, Includes the following steps: The preliminary motion planning data sent from the main controller to the distributed driver is obtained through the standard EtherCAT bus in the magnetic levitation system. The preliminary motion planning data includes global parameters of the transport path. The edge computing unit of the distributed driver deployed in the magnetic levitation system independently performs kinematic decomposition on the preliminary motion planning data to generate detailed trajectory planning data for each motion axis. Combined with the motor current and position encoder data collected in real time by the distributed driver, a position closed-loop control signal is generated. A synchronous link is established between each distributed driver using VoE technology to exchange the actual position and velocity data of adjacent motion axes in real time, and the phase difference compensation value and velocity difference compensation value between adjacent axes are calculated based on the actual position and velocity data. The phase difference compensation value and velocity difference compensation value are input to the edge computing unit of the corresponding driver to dynamically correct the detailed trajectory planning data, and the position closed-loop control signal is updated based on the corrected detailed trajectory planning data. The magnetic levitation conveyor system is driven by the updated position closed-loop control signal to perform multi-axis flexible synchronous motion.

2. The flexible magnetic levitation transport method based on edge computing according to claim 1, characterized in that, The step of performing kinematic decomposition on the preliminary motion planning data includes: The global parameters are decomposed based on the inverse kinematics algorithm to obtain the displacement and velocity commands for each motion axis; The displacement and velocity commands are smoothed using a B-spline curve trajectory planning algorithm to generate detailed trajectory planning data that satisfies C2 continuity.

3. The flexible magnetic levitation transport method based on edge computing according to claim 1, characterized in that, The step of generating the position closed-loop control signal includes: Based on the model predictive control algorithm, combined with real-time acquired motor current data and position encoder data, the position tracking error of each motion axis is calculated; A position closed-loop control signal is generated based on the position tracking error, with a control period of ≤100μs.

4. The flexible magnetic levitation transport method based on edge computing according to claim 1, characterized in that, The steps for establishing synchronization links between distributed drives using VoE technology include: Local clock information is obtained based on the virtual timestamp counters configured in each distributed driver, wherein the resolution of the counters is ≤10ns; A synchronization link is established between distributed drives through a precision time protocol to synchronize the virtual timestamps of adjacent drives with a synchronization error of ≤100ns. When network latency fluctuations of ≥50μs are detected, switch to local clock compensation mode with a compensation accuracy of ≤200ns.

5. The flexible magnetic levitation transport method based on edge computing according to claim 1, characterized in that, The steps for calculating the phase difference compensation value and velocity difference compensation value between adjacent axes include: The real-time phase difference is calculated using interpolation based on the actual position data of adjacent motion axes. The velocity difference is obtained by differential calculation based on the velocity data of adjacent motion axes. When the phase difference or velocity difference exceeds a preset threshold, a compensation calculation mechanism is triggered, which generates compensation values ​​for the phase difference and velocity difference through a proportional-integral control algorithm. The proportional coefficient of the proportional-integral control algorithm is 0.5, and the integral time is 0.1s.

6. The flexible magnetic levitation transport method based on edge computing according to claim 1, characterized in that, The step of dynamically correcting the detailed trajectory planning data includes: The phase difference compensation value and velocity difference compensation value are iteratively adjusted using the gradient descent algorithm. The gradient descent algorithm is a parameter optimization method that gradually approximates the optimal solution by calculating the gradient direction of the error function. The learning rate is set to 0.

01. If the absolute values ​​of both the phase difference compensation value and the velocity difference compensation value are less than the preset tolerance threshold, the corrected trajectory data will be injected into the closed-loop control signal.

7. The flexible magnetic levitation transport method based on edge computing according to claim 1, characterized in that, Before the step of driving the magnetic levitation transport system based on the updated position closed-loop control signal, the method further includes: The communication data frames are parsed using the EtherCAT slave controller, and the data integrity is verified using the CRC32 algorithm. In the edge computing unit, trajectory tracking error data and positioning repeatability accuracy data are extracted based on the updated detailed trajectory planning data, and an optimized motion planning scheme is generated by combining the stability constraints of the magnetic levitation system. The position closed-loop control signal is adjusted and updated based on the optimized motion planning scheme.

8. The flexible magnetic levitation transport method based on edge computing according to claim 1, characterized in that, The edge computing unit further includes: Vibration suppression algorithms for flexible magnetic levitation systems are integrated into each edge computing unit. Control parameters are dynamically adjusted based on real-time collected motor current fluctuation data. The motor current fluctuation data is acquired by a current sensor with a sampling period of ≤50us to suppress low-frequency oscillations during flexible transport.

9. A flexible magnetic levitation conveyor based on edge computing, characterized in that, include: The data acquisition module is used to acquire preliminary motion planning data sent by the main controller to the distributed driver through the standard EtherCAT bus in the magnetic levitation system. The main controller only completes the preliminary motion planning, and the preliminary motion planning data includes global parameters of the transport path. The trajectory generation module is used to independently perform kinematic decomposition on the preliminary motion planning data through the edge computing unit of the distributed driver deployed in the magnetic levitation system, generate detailed trajectory planning data for each motion axis, and generate a position closed-loop control signal by combining the motor current and position encoder data collected in real time by the distributed driver. The synchronization compensation module is used to establish a synchronization link between each distributed driver through VoE technology, exchange the actual position and speed data of adjacent motion axes in real time, and calculate the phase difference compensation value and speed difference compensation value between adjacent axes based on the actual position and speed data. The calculation process does not depend on the participation of the main controller. The data compensation module is used to input the phase difference compensation value and velocity difference compensation value to the edge computing unit of the corresponding driver, dynamically correct the detailed trajectory planning data, and update the position closed-loop control signal based on the corrected detailed trajectory planning data. The system drive module is used to drive the magnetic levitation conveyor system to perform multi-axis flexible synchronous motion based on the updated position closed-loop control signal.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the edge computing-based flexible magnetic levitation transport method according to any one of claims 1 to 8.

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