Flexible magnetic suspension conveying method, device and equipment based on edge calculation and medium

Abstract

The invention relates to a flexible magnetic suspension conveying method and device based on edge calculation, equipment and a medium. The method comprises the steps that preliminary motion planning data issued by a main controller to a distributed driver are acquired through a standard EtherCAT bus; a distributed driver edge calculation unit executes kinematics decomposition on the data to generate detailed trajectory planning data of each axis, and a position closed-loop control signal is generated in combination with motor current and position encoder data; establishing a synchronous link between drivers by using a VoE technology, and calculating a phase difference and a speed difference compensation value of adjacent shafts; the compensation value is input into an edge calculation unit to correct trajectory data, and a control signal is updated; finally, the magnetic suspension conveying system is driven according to the updated signal, high-precision, high-reliability and high-flexibility multi-axis flexible synchronous movement is achieved, and the strict requirement of modern industry for material conveying is met.

Description

Technical Field

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

[0002] In modern industrial production, extremely high requirements are placed on the accuracy, flexibility and efficiency of material conveying systems. Traditional conveying systems, such as conveying methods based on mechanical structures such as belts and chains, have problems such as severe wear, high maintenance costs, limited positioning accuracy, and difficulty in achieving complex motion trajectories. They have gradually been unable to meet the production needs of high-end industries such as precision manufacturing and electronic semiconductors.

[0003] With the in-depth development of industrial automation, magnetic levitation transportation technology has emerged. However, early magnetic levitation transportation systems usually rely on a central main controller for centralized control, and all motion planning and control instructions are uniformly calculated and issued by the main controller. This architecture exposes obvious defects when facing multi-axis complex motion and extremely high real-time requirements. The main controller is not only likely to become a system performance bottleneck, resulting in data processing delays, but once the main controller fails, the entire system will be paralyzed. At the same time, the centralized control mode is difficult to make flexible and accurate real-time adjustments according to the actual operating status of each axis, and it is impossible to achieve truly multi-axis flexible synchronous motion. How to make full use of distributed computing resources to improve the control accuracy, flexibility and reliability of the magnetic levitation transportation system has become a key issue that needs to be solved urgently. Summary of the invention

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

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

[0006] Acquire preliminary motion planning data sent by the main controller to the distributed drive through the standard EtherCAT bus in the magnetic suspension system, wherein the main controller only completes preliminary motion planning, and the preliminary motion planning data includes global parameters of the conveying path;

[0007] The edge computing unit of the distributed drive arranged in the magnetic suspension system independently performs kinematic decomposition on the preliminary motion planning data to generate detailed trajectory planning data of each motion axis, and generates a position closed-loop control signal in combination with the motor current and position encoder data collected in real time by the distributed drive;

[0008] By using VoE technology, a synchronous link is established between each distributed drive, and the actual position and speed data of adjacent motion axes are exchanged in real time. Based on the actual position and speed data, the phase difference compensation value and speed difference compensation value between adjacent axes are calculated, and the calculation process does not rely on the participation of the main controller.

[0009] Inputting the phase difference compensation value and the speed difference compensation value into the edge computing unit of the corresponding driver, dynamically correcting the detailed trajectory planning data, and updating the position closed-loop control signal based on the corrected detailed trajectory planning data;

[0010] The magnetic suspension conveying system is driven based on the updated position closed-loop control signal to perform multi-axis flexible synchronous motion.

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

[0012] Decomposing the global parameters based on the inverse kinematics algorithm to obtain the displacement command and speed command of each motion axis;

[0013] The displacement instruction and the speed instruction are smoothed by a B-spline curve trajectory planning algorithm to generate detailed trajectory planning data that meets C2 continuity.

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

[0015] Based on the model predictive control algorithm, the position tracking error of each motion axis is calculated by combining the motor current data and position encoder data collected in real time;

[0016] A position closed-loop control signal is generated according to the position tracking error, and the control period is ≤100 μs.

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

[0018] Obtain local clock information according to a virtual timestamp counter configured in each distributed driver, wherein the resolution of the counter is ≤10ns;

[0019] Establish a synchronization link between distributed drives through the precision time protocol, synchronize the virtual timestamps of adjacent drives, and the synchronization error is ≤100ns;

[0020] When the network delay fluctuation is detected to be ≥50μs, it switches to the local clock compensation mode with a compensation accuracy of ≤200ns.

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

[0022] According to the actual position data of adjacent motion axes, the real-time phase difference is calculated by interpolation method;

[0023] Based on the speed data of adjacent motion axes, the speed difference is obtained through differential operation;

[0024] When the phase difference or speed difference exceeds the preset threshold, the compensation calculation mechanism is triggered to generate compensation values ​​of the phase difference and speed difference through a proportional-integral control algorithm, wherein the proportional coefficient of the proportional-integral control algorithm is 0.5 and the integration time is 0.1s.

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

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

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

[0028] Furthermore, before the step of driving the magnetic suspension conveying system based on the updated position closed-loop control signal, the method further includes:

[0029] The EtherCAT slave controller parses the communication data frame and uses the CRC32 algorithm to verify data integrity;

[0030] In the edge computing unit, the trajectory tracking error data and positioning repeatability data are extracted based on the updated detailed trajectory planning data, and the optimized motion planning scheme is generated in combination with the stability constraints of the magnetic levitation system;

[0031] The updated position closed-loop control signal is adjusted according to the optimized motion planning scheme.

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

[0033] A vibration suppression algorithm for the flexible magnetic levitation system is integrated in each edge computing unit, and the control parameters are dynamically adjusted based on the 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 transportation.

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

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

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

[0037] A synchronous compensation module is used to establish a synchronous link between distributed drives through VoE technology, exchange actual position and speed data of adjacent motion axes in real time, and calculate phase difference compensation values ​​and speed difference compensation values ​​between adjacent axes based on the actual position and speed data, wherein the calculation process does not rely on the participation of the main controller;

[0038] A data compensation module, used for inputting the phase difference compensation value and the speed difference compensation value into the edge computing unit of the corresponding driver, dynamically correcting the detailed trajectory planning data, and updating 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 suspension conveying system based on the updated position closed-loop control signal to perform multi-axis flexible synchronous motion.

[0040] The present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the above-mentioned flexible magnetic levitation transportation method based on edge computing are implemented.

[0041] The flexible magnetic levitation transportation method, device, equipment and medium based on edge computing provided by the present invention have the following beneficial effects: the present invention overcomes the bottleneck of centralized processing of the main controller through independent calculation of the distributed drive edge computing unit, quickly completes the kinematic decomposition, and can flexibly respond to changing transportation needs; by utilizing real-time acquisition data to generate a position closed-loop control signal, the control cycle is as short as ≤100μs, which greatly improves the positioning accuracy and meets the high-precision requirements such as precision manufacturing; it also independently establishes a synchronization link through VoE technology, and the adjacent axis data are exchanged in real time between the drivers, and the compensation value is calculated to ensure multi-axis synchronization. Even if the main controller fails, the system can still operate stably. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a schematic flow chart of a flexible magnetic suspension transportation method based on edge computing in one embodiment of the present invention;

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

[0044] Figure 3 It 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 purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0047] Reference Figure 1 , which is a flow chart of a flexible magnetic levitation transportation method based on edge computing proposed by the present invention, comprising the following steps:

[0048] S1, obtaining preliminary motion planning data sent by the main controller to the distributed drive through the standard EtherCAT bus in the magnetic suspension system, wherein the main controller only completes the preliminary motion planning, and the preliminary motion planning data includes the global parameters of the conveying path;

[0049] S2, independently performing kinematic decomposition on the preliminary motion planning data through the edge computing unit of the distributed drive arranged in the magnetic levitation system, generating detailed trajectory planning data of each motion axis, and generating a position closed-loop control signal in combination with the motor current and position encoder data collected in real time by the distributed drive;

[0050] S3, establishing a synchronous link between each distributed drive through VoE technology, exchanging actual position and speed data of adjacent motion axes in real time, and calculating the phase difference compensation value and speed difference compensation value between adjacent axes based on the actual position and speed data, wherein the calculation process does not rely on the participation of the main controller;

[0051] S4, inputting the phase difference compensation value and the speed difference compensation value into the edge computing unit of the corresponding driver, dynamically correcting the detailed trajectory planning data, and updating the position closed-loop control signal based on the corrected detailed trajectory planning data;

[0052] S5, driving the magnetic suspension conveying 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 the global parameters of the conveying path (such as starting point, end point, path shape, speed range, etc.), avoiding tasks with high computing power requirements (such as real-time trajectory optimization, 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 volume, and providing a basis for subsequent edge computing. Afterwards, it is sent to the distributed drive through the standard EtherCAT bus. The main controller of step S1 does not need to participate in real-time control at a high frequency, but only needs to intervene at key nodes, which greatly reduces the communication frequency and effectively reduces the load pressure of the main station, while ensuring that the real-time performance of the system is not affected; in addition, efficient and reliable data transmission is achieved through the EtherCAT bus, which is compatible with industrial standard equipment.

[0054] As described in step S2 above, the edge computing unit (MCU) of each distributed drive performs kinematic decomposition, trajectory planning and closed-loop control operations based on the received preliminary planning data. The inverse kinematics algorithm is used to decompose the global parameters (such as the path in the Cartesian coordinate system) into displacement instructions and speed instructions for each motion axis (such as the X / Y / Z axis). A smooth detail trajectory (C2 continuity) is generated by the B-spline curve algorithm (control point spacing ≤10mm, curvature change rate ≤5% / mm). The B-spline trajectory can ensure smooth motion and avoid mechanical shock. Combined with the real-time collected motor current (reflecting the load state) and position encoder data (feedback actual position), the model predictive control (MPC) algorithm (prediction time domain 20ms, control cycle ≤100μs) is used to generate a position closed-loop control signal, wherein the real-time collected motor current data is used to dynamically adjust the control parameters to adapt to different load scenarios (such as no load, full load), and the MPC algorithm is used to dynamically compensate for disturbances. In step S2, each driver independently completes detailed planning and closed-loop control without the involvement of the main controller, thereby improving the scalability of the system; microsecond-level closed-loop control is achieved through local computing (MCU), eliminating the impact of master-slave station communication delays in traditional architectures.

[0055] As described in step S3 above, a virtual timestamp counter (resolution ≤ 10ns) is configured for each drive, and the clocks of adjacent drives are synchronized through the precision time protocol (PTP), with a synchronization error of ≤ 100ns. When the network delay fluctuation is ≥ 50μs, switch to the local clock compensation mode (compensation accuracy ≤ 200ns). The local clock compensation mechanism can cope with network fluctuations, ensure system stability, and ensure link robustness. The actual position and speed data of adjacent motion axes are exchanged in real time, the phase difference is calculated based on the interpolation method (such as linear interpolation), and the speed difference is calculated by differential operation. When the deviation exceeds the threshold (phase difference ≤ 0.1°, speed difference ≤ 0.1mm / s, meeting the precision transportation requirements), the PI control algorithm (Kp = 0.5, Ti = 0.1s) is triggered to generate the compensation value. In step S3, VoE technology achieves synchronization through direct communication between drives, and the main controller only undertakes data forwarding to avoid occupying the bandwidth and computing power resources of the master station. By adjusting the phase difference and speed difference in real time, the accumulated error in multi-axis collaborative motion is eliminated.

[0056] As described in step S4 above, a gradient descent algorithm (learning rate 0.01) is used to iteratively adjust the phase difference and speed difference compensation values ​​to gradually approach the optimal solution. The gradient descent algorithm can achieve parameter self-tuning and reduce the cost of manual debugging. When the absolute value of the compensation value is less than the tolerance threshold (phase difference ≤ 0.1°, speed difference ≤ 0.1mm / s), the corrected trajectory data is injected into the closed-loop control signal. The motor current fluctuation data is collected in real time through the current sensor (sampling period ≤ 50us), and the control parameters are dynamically adjusted to suppress low-frequency oscillations in flexible transportation. In step S4, the trajectory is continuously optimized based on real-time data to improve motion accuracy and stability; in view of the flexible characteristics of the magnetic levitation system (such as low-frequency vibration), the control effect is enhanced through special algorithms.

[0057] As described in step S5 above, the data frame is parsed by the EtherCAT slave controller, and the CRC32 check ensures the integrity of the communication. The tracking error (≤15μm) and positioning repeatability (≤5μm) are extracted from the updated trajectory data, and the optimization scheme is generated in combination with the magnetic suspension stability constraint (such as magnetic stiffness ≥5kN / mm). The magnetic suspension system is driven based on the optimized control signal to achieve multi-axis flexible synchronous motion.

[0058] In one embodiment, a flexible magnetic levitation conveying system based on edge computing is applied in semiconductor wafer transmission. This embodiment is designed for semiconductor wafer transmission scenarios, requiring the magnetic levitation conveying 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, and the core control logic is independently completed by the edge computing unit of the distributed drive. The magnetic levitation platform adopts a 6-degree-of-freedom 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 (computing power 200TOPS) and a specific model of FPGA module. The main controller uses an embedded controller with high reliability, and transmits preliminary planning data at a frequency of 20Hz through the EtherCAT bus. The distributed drive integrates an EtherCAT slave module with a built-in virtual timestamp counter (resolution 5ns), which supports 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 of ≤4.2% / mm to ensure smooth motion. The model predictive control (MPC) algorithm generates a closed-loop signal with a 20ms prediction time domain and a 100μs control period, and dynamically compensates for disturbances by combining the real-time collected motor current (sampling period 50us) and position encoder data (resolution 0.1μm). In actual tests, the trajectory tracking error is ≤12μm, which is 60% higher than traditional centralized control. VoE technology establishes a distributed synchronization link through the Precision Time Protocol (PTP), and the synchronization error is ≤80ns. When the network delay fluctuates ≥50μs, the system automatically switches to the local clock compensation mode (compensation accuracy 150ns). Adjacent motion axes exchange position and velocity data in real time, and linear interpolation is used to calculate the phase difference, and differential operations are used to calculate the velocity difference. When the phase difference is ≥0.08° or the speed difference is ≥0.08mm / s, the proportional-integral (PI) control algorithm is triggered to generate a compensation value (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 link, the gradient descent algorithm uses the mean square error (MSE) as the objective function to iteratively optimize the compensation value (learning rate 0.01), and converges to the tolerance threshold (phase difference ≤ 0.05°, speed difference ≤ 0.05mm / s) after 10 iterations. The vibration suppression algorithm predicts the trend of low-frequency oscillation through the LSTM neural network, dynamically adjusts the MPC parameters, and the current sensor collects fluctuation data with a period of 50us, with a suppression rate of 70%. Before the system is driven, the EtherCAT slave controller parses the data frame and performs CRC32 check to ensure communication integrity. The optimization module extracts the trajectory tracking error (delay standard deviation ≤ 0.1ms, positioning repeatability ≤ 5μm), combines the magnetic stiffness constraint (≥ 5.2kN / mm), and uses the genetic algorithm to optimize the path parameters, reducing energy consumption by 23%. Actual tests show that under a 4kg load and 200Hz electromagnetic interference, the system positioning accuracy is ≤ 5μm, the response time is ≤ 1.8ms, and the communication frequency is 20Hz (the traditional solution requires 8kHz).

[0061] Reference Figure 2 , is a structural block diagram of a flexible magnetic suspension conveying device based on edge computing in one embodiment of the present invention, including:

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

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

[0064] A synchronous compensation module is used to establish a synchronous link between distributed drives through VoE technology, exchange actual position and speed data of adjacent motion axes in real time, and calculate phase difference compensation values ​​and speed difference compensation values ​​between adjacent axes based on the actual position and speed data, wherein the calculation process does not rely on the participation of the main controller;

[0065] A data compensation module, used for inputting the phase difference compensation value and the speed difference compensation value into the edge computing unit of the corresponding driver, dynamically correcting the detailed trajectory planning data, and updating 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 suspension conveying system based on the updated position closed-loop control signal to perform multi-axis flexible synchronous motion.

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

[0068] Reference Figure 3 In an embodiment of the present invention, a computer device is also provided. The computer device may be a server, and its internal structure may be as follows: Figure 3 As shown. The computer device includes a processor, a memory, a display screen, an input device, a network interface and a database connected through a system bus. Among them, the processor designed by the computer is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the corresponding data in this embodiment. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the above method is implemented.

[0069] Those skilled in the art will understand that Figure 3 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied.

[0070] In summary, the present invention obtains the preliminary motion planning data sent by the main controller to the distributed drive through the standard EtherCAT bus in the magnetic levitation system; the edge computing unit of the distributed drive arranged in the magnetic levitation system independently performs kinematic decomposition on the preliminary motion planning data to generate detailed trajectory planning data of each motion axis, and generates a position closed-loop control signal in combination with the motor current and position encoder data collected in real time by the distributed drive; a synchronous link is established between the distributed drives through the VoE technology, the actual position and speed data of adjacent motion axes are exchanged in real time, and the phase difference compensation value and the speed difference compensation value between the adjacent axes are calculated based on the actual position and speed data; the phase difference compensation value and the speed difference compensation value are input into the edge computing unit of the corresponding drive, the detailed trajectory planning data is dynamically corrected, and the position closed-loop control signal is updated based on the corrected detailed trajectory planning data; the magnetic levitation conveying system is driven based on the updated position closed-loop control signal to perform multi-axis flexible synchronous motion, so as to realize the high-precision, high-reliability and high-flexibility multi-axis flexible synchronous motion of the magnetic levitation conveying system, and meet the strict requirements of modern industrial production for material transportation.

[0071] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media provided by the present invention and used in the 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. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double-speed data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM.

[0072] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, device, article or method. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the presence of other identical elements in the process, device, article or method including the element.

[0073] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A flexible magnetic levitation transportation method based on edge computing, characterized in that: The following steps are involved: Obtaining preliminary motion planning data sent by the main controller to the distributed drive through the standard EtherCAT bus in the magnetic suspension system, wherein the preliminary motion planning data includes global parameters of the conveying path; The edge computing unit of the distributed drive arranged in the magnetic suspension system independently performs kinematic decomposition on the preliminary motion planning data to generate detailed trajectory planning data of each motion axis, and generates a position closed-loop control signal in combination with the motor current and position encoder data collected in real time by the distributed drive; A synchronous link is established between each distributed drive through VoE technology to exchange the actual position and speed data of adjacent motion axes in real time, and the phase difference compensation value and speed difference compensation value between adjacent axes are calculated based on the actual position and speed data; Inputting the phase difference compensation value and the speed difference compensation value into the edge computing unit of the corresponding driver, dynamically correcting the detailed trajectory planning data, and updating the position closed-loop control signal based on the corrected detailed trajectory planning data; The magnetic suspension conveying system is driven based on the updated position closed-loop control signal to perform multi-axis flexible synchronous motion.

2. The flexible magnetic levitation transportation method based on edge computing according to claim 1 is characterized in that: The step of performing kinematic decomposition on the preliminary motion planning data comprises: Decomposing the global parameters based on the inverse kinematics algorithm to obtain the displacement command and speed command of each motion axis; The displacement instruction and the speed instruction are smoothed by a B-spline curve trajectory planning algorithm to generate detailed trajectory planning data that meets C2 continuity.

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

4. The flexible magnetic levitation transportation method based on edge computing according to claim 1 is characterized in that: The step of establishing a synchronous link between distributed drivers by using VoE technology includes: Obtain local clock information according to a virtual timestamp counter configured in each distributed driver, wherein the resolution of the counter is ≤10ns; Establish a synchronization link between distributed drives through the precision time protocol, synchronize the virtual timestamps of adjacent drives, and the synchronization error is ≤100ns; When the network delay fluctuation is detected to be ≥50μs, it switches to the local clock compensation mode with a compensation accuracy of ≤200ns.

5. The flexible magnetic levitation transportation method based on edge computing according to claim 1 is characterized in that: The step of calculating the phase difference compensation value and the speed difference compensation value between adjacent axes includes: According to the actual position data of adjacent motion axes, the real-time phase difference is calculated by interpolation method; Based on the speed data of adjacent motion axes, the speed difference is obtained through differential operation; When the phase difference or speed difference exceeds the preset threshold, the compensation calculation mechanism is triggered to generate compensation values ​​of the phase difference and speed difference through a proportional-integral control algorithm, wherein the proportional coefficient of the proportional-integral control algorithm is 0.5 and the integration time is 0.1s.

6. The flexible magnetic levitation transportation method based on edge computing according to claim 1 is characterized in that: The step of dynamically correcting the detailed trajectory planning data comprises: The phase difference compensation value and the speed difference compensation value are iteratively adjusted using a gradient descent algorithm, wherein the gradient descent algorithm is a parameter optimization method that gradually approaches the optimal solution by calculating the gradient direction of the error function, and the learning rate is set to 0.01; If the absolute values ​​of the phase difference compensation value and the speed difference compensation value are both smaller than the preset tolerance threshold, the corrected trajectory data is injected into the closed-loop control signal.

7. The flexible magnetic levitation transportation method based on edge computing according to claim 1 is characterized in that: Before the step of driving the magnetic suspension conveying system based on the updated position closed-loop control signal, the method further includes: The EtherCAT slave controller parses the communication data frame and uses the CRC32 algorithm to verify data integrity; In the edge computing unit, the trajectory tracking error data and positioning repeatability data are extracted based on the updated detailed trajectory planning data, and the optimized motion planning scheme is generated in combination with the stability constraints of the magnetic levitation system; The updated position closed-loop control signal is adjusted according to the optimized motion planning scheme.

8. The flexible magnetic levitation transportation method based on edge computing according to claim 1 is characterized in that: The edge computing unit further includes: A vibration suppression algorithm for the flexible magnetic levitation system is integrated in each edge computing unit, and the control parameters are dynamically adjusted based on the 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 transportation.

9. A flexible magnetic levitation conveying device based on edge computing, characterized in that: include: A data acquisition module is used to acquire preliminary motion planning data sent by the main controller to the distributed drive through the standard EtherCAT bus in the magnetic suspension system. The main controller only completes the preliminary motion planning, and the preliminary motion planning data includes the global parameters of the conveying path; A trajectory generation module is used to independently perform kinematic decomposition on the preliminary motion planning data through an edge computing unit of a distributed drive arranged in the magnetic levitation system, generate detailed trajectory planning data for each motion axis, and generate a position closed-loop control signal in combination with the motor current and position encoder data collected in real time by the distributed drive; A synchronous compensation module is used to establish a synchronous link between distributed drives through VoE technology, exchange actual position and speed data of adjacent motion axes in real time, and calculate phase difference compensation values ​​and speed difference compensation values ​​between adjacent axes based on the actual position and speed data, wherein the calculation process does not rely on the participation of the main controller; A data compensation module, used for inputting the phase difference compensation value and the speed difference compensation value into the edge computing unit of the corresponding driver, dynamically correcting the detailed trajectory planning data, and updating the position closed-loop control signal based on the corrected detailed trajectory planning data; The system drive module is used to drive the magnetic suspension conveying system based on the updated position closed-loop control signal to perform multi-axis flexible synchronous motion.

10. A computer device comprising a memory and a processor, wherein a computer program is stored in the memory, wherein: When the processor executes the computer program, the steps of the flexible magnetic levitation transportation method based on edge computing described in any one of claims 1 to 8 are implemented.

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