A Method and System for Wireless Power Transfer of Magnetic Levitation Mooring Vehicles Based on Contactless Power Supply

Abstract

This invention provides a method and system for wireless power transmission of a magnetically levitated mover based on contactless power supply, relating to the field of contactless power supply technology. The method includes: reconstructing the motion state vector of the magnetically levitated mover and outputting a pose priority weight vector; setting a data acquisition scheduling strategy; receiving a six-degree-of-freedom native state set; reconstructing and outputting the real-time pose of the mover after receiving transient response data of the load impedance; fitting the instantaneous optimal frequency of the resonant cavity and the phase compensation amount of the current waveform; loading it into an adaptive power mapping model to predict the trend of power demand changes; dynamically tuning the output parameters of the high-frequency inverter and performing coupling efficiency offset compensation. This invention solves the technical problem that existing wireless power transmission methods rely on static frequency and current waveform settings, making it difficult to adapt in real time to the rapidly changing motion state and load demands of the magnetically levitated mover, thus affecting the performance of the entire magnetic levitation system.

Description

Technical Field

[0001] This invention relates to the field of contactless power supply technology, and specifically to a wireless power transmission method and system for magnetically levitated motors based on contactless power supply. Background Technology

[0002] In a magnetic levitation system, the moving part is suspended by a magnetic field and runs on a track, while energy is supplied wirelessly. Traditional magnetic levitation systems mainly rely on the magnetic field coupling between the stator and the moving part to transfer energy. However, the high-speed motion of the moving part and the complex track environment greatly affect the stability of the magnetic field. Most existing wireless energy transfer methods rely on static frequency and current waveform settings, which are difficult to adapt to the rapidly changing motion state and load requirements of the moving part in real time. Specifically, the acceleration, deceleration, and turning of the moving part on the track cause fluctuations in energy transfer efficiency. Especially when the load changes significantly, the energy transfer efficiency may drop sharply. This leads to unstable energy transfer efficiency in high-speed or high-dynamic environments, and may even result in unstable power supply, thus affecting the performance of the entire magnetic levitation system. Summary of the Invention

[0003] This application provides a wireless power transmission method and system for magnetic levitation movers based on contactless power supply, aiming to solve the technical problem that existing wireless power transmission methods rely on static frequency and current waveform settings, making it difficult to adapt to the rapidly changing motion state and load requirements of the magnetic levitation movers in real time, thus affecting the performance of the entire magnetic levitation system.

[0004] The first aspect disclosed in this application provides a method for wireless power transmission of a magnetically levitated mover based on contactless power supply. The method includes: a main control unit receiving multi-dimensional spatiotemporal synchronous sampling data transmitted from a mover state sensing array pre-deployed on the stator track; reconstructing the motion state vector of the magnetically levitated mover; and outputting a pose priority weight vector; setting a data acquisition scheduling strategy for the mover state sensing array based on the pose priority weight vector; the main control unit receiving a six-degree-of-freedom native state set transmitted from the mover state sensing array, using the data acquisition scheduling strategy as a shielding constraint; receiving load impedance transient response data transmitted from the current and voltage sampling circuit, and reconstructing and outputting the real-time pose of the mover based on the six-degree-of-freedom native state set; fitting the instantaneous optimal frequency of the resonant cavity and the phase compensation amount of the current waveform based on a preset motion trajectory and the real-time pose of the mover; loading the load impedance transient response data into an adaptive power mapping model to predict power demand change trends; and dynamically tuning the output parameters of a high-frequency inverter based on the instantaneous optimal frequency of the resonant cavity and the phase compensation amount of the current waveform, and performing coupling efficiency offset compensation based on the predicted power demand change trends.

[0005] The second aspect of this application discloses a wireless energy transmission system for a magnetically levitated ... The system comprises: a native state set; a mover real-time pose output module, used by the main control unit to reconstruct and output the mover real-time pose by combining the load impedance transient response data returned by the current and voltage sampling circuit with the six-degree-of-freedom native state set; a fitting module, used to fit the instantaneous optimal frequency of the resonant cavity and the phase compensation amount of the current waveform based on a preset motion trajectory and the mover real-time pose; a trend prediction module, used to load the load impedance transient response data into an adaptive power mapping model to predict the trend of power demand changes; and a coupling efficiency offset compensation module, used by the main control unit to dynamically tune the high-frequency inverter output parameters according to the instantaneous optimal frequency of the resonant cavity and the phase compensation amount of the current waveform, and to perform coupling efficiency offset compensation according to the predicted trend of power demand changes.

[0006] One or more technical solutions provided in this application have at least the following beneficial effects: The main control unit receives multi-dimensional spatiotemporal synchronous data transmitted back from the mover state sensing array pre-deployed on the stator track, reconstructs the motion state vector, and outputs a pose priority weight vector, which provides the foundation for subsequent motion state monitoring and energy transmission regulation. Based on the pose priority weight vector, data acquisition can be prioritized according to different stages of the mover's motion on the track, thereby improving overall sensing efficiency. By using a data acquisition scheduling strategy as a shielding constraint, system resource allocation is optimized, redundant data transmission is avoided, data transmission efficiency is improved, and computational resources are saved. The main control unit combines the load impedance transient response data transmitted back from the current and voltage sampling circuit with the six-degree-of-freedom native state set to reconstruct the real-time pose of the mover, enabling the system to adapt to load fluctuations and further improving the stability and accuracy of energy transmission. By fitting the instantaneous optimal frequency of the resonant cavity and the phase compensation amount of the current waveform... The system can dynamically adjust the operating frequency and current waveform of the resonant cavity based on the real-time pose and preset motion trajectory of the mover. This ensures that the energy transmission system remains in a resonant state, thereby maximizing energy coupling efficiency. By loading the transient response data of the load impedance into the adaptive power mapping model, future power demand trends are predicted. This allows the main control unit to adjust the energy transmission strategy in advance, reducing energy fluctuations caused by load changes and improving system stability and reliability. The main control unit dynamically tunes the output parameters of the high-frequency inverter based on the instantaneous optimal frequency and current waveform phase compensation of the resonant cavity, ensuring a high degree of matching between the system output frequency and waveform and the operating state of the resonant cavity. Simultaneously, coupling efficiency offset compensation is performed based on the predicted power demand trends. This dynamic tuning mechanism enables rapid response to instantaneous changes, ensuring efficient wireless energy transmission for the magnetically levitated mover under various motion states. The above description is merely an overview of the technical solution of this application. To better understand the technical means of this application and to implement it according to the contents of the specification, and to make the above and other objects, features, and advantages of this application more apparent, specific embodiments of this application are described below. Attached Figure Description

[0007] Figure 1 This is a schematic diagram of the wireless power transmission method for a magnetically levitated motor based on contactless power supply, provided in an embodiment of this application.

[0008] Figure 2 This is a schematic diagram of a wireless energy transmission system for a magnetically levitated motor based on contactless power supply, provided in an embodiment of this application. Reference numerals: 10 for motion state vector reconstruction module, 20 for scheduling strategy setting module, 30 for native state set receiving module, 40 for real-time pose output module of the motor, 50 for fitting module, 60 for trend prediction module, and 70 for coupling efficiency offset compensation module. Detailed Implementation

[0009] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0010] Example 1, as Figure 1 As shown, this application provides a method for wireless power transmission of a magnetically levitated motor based on contactless power supply, the method comprising: The main control unit receives multi-dimensional spatiotemporal synchronous sampling data from the mover state sensing array pre-deployed on the stator track, performs motion state vector reconstruction of the magnetically levitated mover, and outputs a pose priority weight vector.

[0011] The mover state sensing array is a set of sensors pre-deployed on the stator track, capable of collecting multi-dimensional data related to the mover's motion state in real time. The mover is the levitated body, and the multi-dimensional data includes information such as acceleration, velocity, displacement, and angle. This sampled data is spatiotemporally synchronized, meaning that the data points in time and space are consistent, reflecting the actual motion of the mover on the magnetic levitation track. The main control unit uses this multi-dimensional data and mathematical methods, such as Kalman filtering, particle filtering, and interpolation, to reconstruct the motion state vector. This reconstruction process involves integrating and fusing sensor data to calculate the real-time state of the mover in three-dimensional space.

[0012] After reconstructing the motion state vector, the pose priority weight vector is output. The purpose of this vector is to determine which positions and attitudes are more important based on the motion state of the mover, or which need to be given priority during data acquisition and energy transfer, so as to optimize subsequent control and scheduling.

[0013] The data acquisition scheduling strategy of the moving part state sensing array is set according to the pose priority weight vector.

[0014] The main control unit performs multi-dimensional situational analysis based on the output pose priority weight vector, constructing a spatial weight distribution heatmap. This means that by analyzing the energy transmission and dynamic monitoring needs of each location point (different regions of the mover's trajectory), the main control unit determines which regions require higher-frequency data acquisition. The spatial weight distribution heatmap visually displays the data acquisition needs at different locations. For example, some regions are displayed as high priority, indicating that the motion state of these regions changes rapidly and more data needs to be collected in real time; other regions are displayed as low priority, indicating that the motion in these regions is relatively stable, and the data acquisition frequency can be appropriately reduced. Based on the heatmap and the orbital physical topology, the main control unit optimizes the operation of the mover state sensing array by dynamically adjusting the data acquisition frequency. Some nodes, especially in high-priority regions, will execute higher sampling frequencies, while other regions will execute lower frequencies.

[0015] The main control unit receives the six-degree-of-freedom native state set transmitted back by the moving part state sensing array, using the data acquisition and scheduling strategy as a shielding constraint.

[0016] By implementing hardware-level shielding for inactive areas (such as unloaded straight sections), the main control unit avoids receiving sensing array data from these areas. This is because in unloaded or static areas, the state changes of the mover are minimal, eliminating the need for frequent data acquisition and thus saving computational resources and energy. According to a defined data acquisition scheduling strategy, the main control unit obtains a six-degree-of-freedom (6DOF) raw state set from the mover state sensing array. This state set is the raw sensing data, unprocessed or uncompensated. The six degrees of freedom refer to: the mover's displacement in three-dimensional space, including its position along the x, y, and z axes; and the mover's attitude in three-dimensional space, including its angles around the x, y, and z axes. These data determine the relative position and alignment between the mover and the power source in the wireless power transfer system.

[0017] After receiving the load impedance transient response data returned by the current and voltage sampling circuit, the main control unit reconstructs the real-time pose of the mover by combining it with the six-degree-of-freedom native state set.

[0018] Load impedance transient response refers to the response of a load, i.e., a magnetically levitated mover, to instantaneous changes in current or voltage. Due to electromagnetic coupling, changes in the mover's state lead to changes in load impedance. Therefore, by collecting load impedance transient response data, the main control unit can indirectly infer the mover's physical state. By combining the load impedance transient response data with the six-degree-of-freedom native state set, the mover's real-time pose, including position and attitude, can be estimated. For example, changes in the mover's displacement, acceleration, and coupling with the power supply system can all be reflected in this data. Kalman filtering and other methods can be used to fuse these two sets of data to improve the accuracy of pose estimation.

[0019] Based on the preset motion trajectory and the real-time pose of the mover, the instantaneous optimal frequency of the resonant cavity and the phase compensation amount of the current waveform are fitted.

[0020] In the control of a magnetically levitated mover, there is a preset motion trajectory. This trajectory defines the desired motion path of the mover on the stator track, such as its velocity and acceleration within a certain time period. The real-time pose of the mover is used to adjust the frequency and current waveform of the power supply system, because the electromagnetic coupling performance of the magnetically levitated mover is closely related to its motion state. Based on the preset motion trajectory and the real-time pose of the mover, the instantaneous optimal frequency of the resonant cavity in the magnetic levitation system is fitted. Optimizing the resonant cavity frequency is key to improving the efficiency of wireless power transmission. This frequency is related to factors such as the relative position and velocity of the mover at a specific moment. In addition to frequency adjustment, the phase of the current waveform also needs to be compensated. The phase compensation amount is calculated to ensure that the phase of the current waveform remains synchronized with the motion state of the mover, thus optimizing the efficiency of power transmission and avoiding energy loss. The fitting process will be detailed in subsequent steps and will not be elaborated here.

[0021] The transient response data of the load impedance is loaded into the adaptive power mapping model to predict the trend of power demand changes.

[0022] The adaptive power mapping model is a computational model based on the physical characteristics of the system, such as current, voltage, load impedance, and frequency. It can predict future power demand based on real-time data and optimize power scheduling strategies. The adaptive aspect means that the model can be dynamically adjusted according to real-time feedback and automatically updated as the system state and load change.

[0023] By loading the transient response data of load impedance into the adaptive power mapping model, the power demand trend is predicted, including the prediction of power fluctuations, load changes, frequency adjustments and other situations that the system will experience in the future. The model can identify load changes or other factors that affect power demand in advance, and thus adjust the power output in advance. For example, the acceleration or deceleration of the motor causes changes in current demand, or the impedance characteristics of the system load change, resulting in different power demand.

[0024] The main control unit dynamically tunes the output parameters of the high-frequency inverter based on the instantaneous optimal frequency of the resonant cavity and the phase compensation amount of the current waveform, and performs coupling efficiency offset compensation based on the predicted trend of power demand changes.

[0025] High-frequency inverters are used to convert electrical energy from a power source into high-frequency alternating current suitable for wireless energy transmission, and then transmit it to a resonant cavity to drive a magnetically levitated actuator. Their output parameters, such as frequency and waveform, directly affect the efficiency and stability of energy transmission.

[0026] During dynamic tuning, based on the fitted instantaneous optimal frequency of the resonant cavity and the phase compensation amount of the current waveform, the main control unit dynamically adjusts the output frequency and current waveform of the high-frequency inverter so that the output signal of the high-frequency inverter is always consistent with the resonant cavity requirements of the magnetic levitation system. This adjustment process is continuous and is constantly adjusted according to the motion state of the mover and load changes.

[0027] Because energy transfer between the power source and load in a magnetic levitation system is nonlinear and affected by various factors such as position changes, frequency deviations, and phase mismatches, the system's coupling efficiency can change constantly. To address these changes, the main control unit needs to perform coupling efficiency offset compensation. This is a compensatory adjustment process that aims to adjust the output of the high-frequency inverter based on the predicted power demand trend, further optimizing the system's coupling efficiency. By compensating for the coupling efficiency offset, efficient energy transfer can be maintained continuously without affecting the system's stability or performance due to load changes or other dynamic factors.

[0028] Furthermore, based on the preset motion trajectory and the real-time pose of the mover, the instantaneous optimal frequency of the resonant cavity and the phase compensation amount of the current waveform are fitted, including: The real-time pose of the mover is decomposed to obtain linear velocity vectors and angular velocity vectors; the preset motion trajectory is segmented and identified to obtain the trajectory radius of curvature and a reference motion acceleration sequence; the spatial offset of the receiving coil is calculated based on the angular velocity vectors and the trajectory radius of curvature; the real-time acceleration increment is extracted from the reference motion acceleration sequence based on the real-time spatial position of the magnetically levitated mover; the phase compensation of the current waveform is calculated based on the real-time acceleration increment and the spatial offset of the receiving coil; after generating the fundamental frequency of the resonant cavity proportionally based on the linear velocity vectors, the spatial offset of the receiving coil is superimposed to perform negative frequency correction, and the instantaneous optimal frequency of the resonant cavity is output.

[0029] The linear velocity vector describes the linear velocity of a mover in space, that is, the rate at which the mover's position changes with time. By performing differential calculations on the mover's pose, the linear velocity vector of the mover can be obtained, that is, the components in the x-axis, y-axis, and z-axis directions. Numerical differentials, such as finite difference, can be used to estimate the position change at each moment, thereby obtaining the linear velocity vector.

[0030] Angular velocity vectors describe the rotational speed of a mover in space, that is, the rate at which the mover's attitude changes with time. Angular velocity vectors are obtained by differential calculation of the mover's attitude data, representing the rotational speed around the x, y, and z axes. Calculating angular velocity involves using tools such as quaternions, Euler angles, or direction cosine matrices to represent attitude changes, and extrapolating the angular velocity based on the attitude differences between moments.

[0031] The preset motion trajectory defines the desired motion path of the magnetically levitated mover on the stator track. In practical applications, this trajectory is specified through design and can be a straight line, a circular arc, or a more complex curve. The preset motion trajectory consists of different segments, such as straight line segments, circular arc segments, or curve segments. The entire preset motion trajectory is segmented and identified to handle different types of motion segments separately. This can be done by calculating the geometric properties of the trajectory, such as tangent and curvature. The radius of curvature of the trajectory is a quantity describing the degree of curvature of the trajectory curve, defined as the radius of the tangent to the curve at a certain point. For a circular arc segment, the radius of curvature is a constant; for a general curve segment, the radius of curvature varies with position. The acceleration sequence refers to the acceleration of the mover at each moment on the trajectory. The acceleration can be obtained by differential calculation of the velocity changes of the trajectory. The reference motion acceleration sequence is calculated based on the trajectory design and desired motion characteristics, representing the ideal acceleration of the mover at each moment.

[0032] The spatial offset of the receiving coil refers to the deviation of the receiving coil from its expected position in space. Since the mover may rotate along its trajectory, the position of the receiving coil will shift. The offset is determined by the angular velocity of the mover and the radius of curvature of the trajectory. The calculation method includes: based on the real-time angular velocity vector of the mover and the radius of curvature of the trajectory, the spatial offset of the receiving coil at the current moment is calculated. A larger angular velocity and a smaller radius of curvature will result in a larger offset.

[0033] The real-time acceleration increment refers to the difference between the actual acceleration of the mover at the current moment and the reference acceleration sequence. Due to external interference or actual deviation of the motion trajectory, the real-time acceleration may differ from the ideal acceleration. This increment represents the change in the acceleration of the mover at the current moment, that is, the acceleration error that deviates from the preset trajectory. Extracting this increment is for subsequent adjustment of the phase and frequency of the current waveform, thereby optimizing wireless power transmission.

[0034] Current waveform phase compensation refers to the adjustment of the current waveform phase required to optimize wireless power transmission. The current waveform phase must be synchronized with the relative position and motion state of the receiving coil to ensure maximum energy coupling efficiency. The required phase adjustment is calculated by combining the real-time acceleration increment and the spatial offset of the receiving coil. The acceleration increment affects the acceleration state of the mover, and the offset affects the coupling angle of the electromagnetic field. Therefore, the combination of these two factors determines an optimized current waveform phase, thereby compensating for energy transmission errors caused by changes in mover acceleration and the positional offset of the receiving coil.

[0035] The linear velocity vector is the linear velocity of the mover in three-dimensional space. The velocity of the mover directly affects the resonant frequency of the wireless power transmission system because there is a close relationship between the frequency and the relative velocity of the mover. The fundamental frequency of the resonant cavity is an important parameter in wireless power transmission. It determines the oscillation frequency of the electromagnetic field and affects the energy coupling efficiency. The fundamental frequency of the resonant cavity is generated proportionally based on the linear velocity vector of the mover. This means that the fundamental frequency is proportional to the linear velocity of the mover. The faster the mover's velocity, the more the resonant frequency needs to be adjusted accordingly.

[0036] The spatial offset of the receiving coil indicates the degree of deviation of the receiving coil from its ideal position. To maintain optimal electromagnetic coupling, the frequency needs to be corrected; the larger the offset, the greater the frequency correction. Negative frequency correction refers to adjusting the fundamental frequency based on the spatial offset of the receiving coil, typically by lowering the frequency. This is because when the receiving coil is offset, the efficiency of electromagnetic coupling decreases, and lowering the frequency is necessary to compensate for this offset and ensure efficient energy transfer. This frequency correction optimizes energy transfer efficiency by applying the offset to the fundamental frequency, thus maintaining maximum electromagnetic coupling even with displacement and velocity changes in the mover.

[0037] Through comprehensive calculations, the output instantaneous optimal frequency of the resonant cavity is the final frequency after considering factors such as the real-time velocity, acceleration, and position offset of the mover. This instantaneous optimal frequency of the resonant cavity is the ideal frequency in the wireless energy transmission process, which can ensure that the system can perform energy coupling with the highest efficiency under the current dynamic conditions.

[0038] Furthermore, the main control unit receives multi-dimensional spatiotemporal synchronous sampling data from the mover state sensing array pre-deployed on the stator track, performs magnetic levitation mover motion state vector reconstruction, and outputs a pose priority weight vector, including: After performing spatiotemporal alignment compensation on the multi-dimensional spatiotemporal synchronous sampling data based on the spatiotemporal labels, inertial compensation is performed based on the regional dynamic attribute distribution of the stator track to obtain a six-degree-of-freedom initial state set; six-degree-of-freedom vector fusion is performed on the six-degree-of-freedom initial state set to output the reconstructed motion state vector; the out-of-tolerance dimension of the motion state vector is detected based on a preset risk threshold to generate the pose priority weight vector.

[0039] During wireless power transfer, the mover travels at high speed on the track, and multi-dimensional sensor data is collected in a spatiotemporally synchronized manner. Each sensor's data is tagged with spatiotemporal labels, including time and spatial coordinates, indicating the specific time and location of data acquisition. Spatiotemporal alignment compensation refers to aligning data from different sensors into a unified spatiotemporal framework to ensure data consistency and synchronization, thereby ensuring the synchronization of different data sources. Spatiotemporal alignment uses timestamp synchronization and spatial coordinate mapping to precisely match multi-dimensional data from different sensors in time and space.

[0040] The regional dynamic properties of the stator track include the track's geometry, friction, and magnetic field strength. Different track regions have different effects on the motion of the mover. For example, some regions have strong magnetic field coupling, while others are less affected by friction or other factors. By compensating for the different effects of the track regions on the mover's motion when estimating the mover's state, the accuracy of the state estimation can be improved.

[0041] Inertial compensation corrects errors caused by inertia during the motion of a mover. The inertia of the mover causes deviations in sensor data; inertial compensation eliminates or reduces these errors. Inertial compensation is achieved by combining the mover's acceleration sensor data with modeled kinematic relationships, ensuring accurate motion state estimation. The resulting six-degree-of-freedom initial state set provides preliminary estimates of the mover's six degrees of freedom, laying the foundation for subsequent motion state reconstruction.

[0042] Six-degree-of-freedom (6DOF) vector fusion integrates and fuses 6DOF state data from different sensors and computational sources to obtain a more accurate and reliable motion state vector. The 6DOF state set includes the position (x, y, z) and attitude (pitch, roll, yaw) of the mover in three-dimensional space. Through vector fusion, these data are weighted and averaged or filtered using techniques such as Kalman filtering or particle filtering to integrate data from different sources, thereby eliminating errors and noise from single-sensor data. The reconstructed motion state vector represents a precise estimate of the mover's motion state at a given moment, containing information such as the mover's precise position, velocity, acceleration, and attitude.

[0043] The preset risk threshold is a limit set based on the system design and the moving part's operating environment. It is used to determine whether the moving part's current motion state deviates from the expected state. The risk threshold includes the maximum permissible error in aspects such as position, velocity, acceleration, and attitude. An out-of-tolerance dimension refers to the moving part's motion state in a certain degree of freedom, such as position, velocity, or attitude, exceeding the preset permissible error range. When the deviation of a certain dimension of the moving part exceeds the threshold, it indicates a large error in that degree of freedom, which may lead to a decrease in energy transfer efficiency or even system failure.

[0044] The pose priority weight vector is generated based on the out-of-tolerance dimension of the motion state vector, representing the priority of each degree of freedom at the current moment. For degrees of freedom with larger deviations, higher weights are assigned, and their energy transfer parameters, such as current waveform and frequency, are adjusted first. A high-weighted degree of freedom means that it has a greater impact on the system's energy transfer efficiency, and therefore requires more frequent and prioritized adjustments. By prioritizing the adjustment of these degree of freedom parameters, deviations can be quickly corrected, and wireless energy transfer can be optimized.

[0045] Furthermore, the data acquisition scheduling strategy for the moving part state sensing array is set according to the pose priority weight vector, including: Multidimensional situational analysis is performed on the pose priority weight vector to construct a spatial weight distribution heatmap; based on the orbital physical topology of the stator track, the spatial weight distribution heatmap is projected onto the mover state sensing array to perform heterogeneous sampling frequency dynamic mapping and output a heterogeneous sampling time slot allocation matrix as the data acquisition scheduling strategy.

[0046] Multidimensional situational awareness refers to a comprehensive analysis of motion states from different dimensions. In practical applications, situational awareness involves mathematical and physical models to describe the electromagnetic coupling relationship between the mover and stator under different states, and how to adjust energy transfer strategies based on these states. Specifically, a heatmap is generated using a scalar-space transformation algorithm. This algorithm transforms one-dimensional scalar data into two-dimensional or three-dimensional data related to spatial distribution. In this process, scalar weights (pose priority weights) are converted into a spatial heatmap, representing the weight distribution in different regions. This transformation uses interpolation or other spatial mapping algorithms to map the one-dimensional weight data to the spatial region of the stator track, making the weight value of each spatial point intuitively representable. High-weight regions correspond to higher energy transfer requirements for the mover in these regions, thus requiring more attention and sampling resources.

[0047] The stator track's physical topology describes its geometry, structure, and characteristics, including curvature, length, and twist, all of which affect the mover's trajectory and the distribution of the stator's electromagnetic field. Mapping the generated spatial weight distribution heatmap onto the mover state sensing array allows for adjustments to the sampling frequency and resource allocation based on the weight of each sensor's region. Heterogeneous sampling frequency dynamic mapping refers to dynamically allocating different sampling frequencies to different regions based on the spatial weight heatmap. In high-weight regions, i.e., regions with a greater impact on energy transmission, more sampling resources are needed, and the sampling frequency should be higher; while in low-weight regions, the sampling frequency can be appropriately reduced. The heterogeneous sampling time slot allocation matrix represents how sensor sampling time slots are allocated in different time and spatial regions. Elements in the matrix represent the sampling frequency or time slot allocation at a specific time and spatial location. Specifically, it defines which sensors require higher sampling frequencies in different regions and at different times, ensuring the system can respond to mover state changes in real time and effectively.

[0048] Furthermore, it also includes: The current and voltage sampling circuit is pre-deployed on the magnetically levitated mover, wherein the current and voltage sampling circuit is connected to the master control unit through a single master-multiple slave wireless communication architecture; an adaptive impedance matching network is pre-deployed on the magnetically levitated mover for dynamic tuning of the resonant frequency; and the mover state sensing array is connected to the master control unit through an EtherCAT bus.

[0049] The current and voltage sampling circuit is used in the magnetically levitated mover to monitor current and voltage in real time, providing data on the mover's load and energy transfer efficiency. The single-master, multi-slave wireless communication architecture refers to a system with one master control unit and multiple slave movers (i.e., multiple mover devices). The master control unit acts as the sole master station, broadcasting control commands to all movers wirelessly. The multi-slave architecture means that each mover communicates with the master control unit as a slave station. The master control unit sends commands to all movers, and the movers transmit data back according to these commands using time-division multiplexing principles. This mechanism ensures that multiple movers can efficiently exchange data on the same wireless channel, avoiding communication conflicts and signal interference.

[0050] Impedance matching networks are used to optimize energy coupling efficiency in wireless power transfer systems. In such systems, the impedance of the resonant cavity must match the load impedance to maximize energy transfer efficiency. Adaptive impedance matching networks automatically adjust the impedance to ensure that the load impedance matches the system's resonant frequency. Frequency tuning involves adjusting the resonant frequency in the circuit to adapt to changes in the state of the actuator and load requirements. The actuator's trajectory, acceleration, and different loads all cause variations in load impedance, thus requiring real-time adjustment of the resonant frequency to maintain optimal coupling efficiency. Adaptive impedance matching networks monitor current and voltage data as well as changes in load impedance, adjust impedance matching, and dynamically regulate the resonant frequency to ensure maximum efficiency in wireless power transfer.

[0051] EtherCAT is a real-time Ethernet protocol widely used in automated control systems, particularly suitable for scenarios requiring high-speed data transmission and real-time feedback. Through the EtherCAT bus, the moving part state sensing array can quickly and reliably transmit the sensed multi-dimensional data to the main control unit. The main control unit then adjusts parameters such as the frequency and power of energy transmission in real time based on this data, ensuring efficient wireless energy transmission.

[0052] Furthermore, after receiving the load impedance transient response data returned by the current and voltage sampling circuit, the main control unit reconstructs the real-time pose of the mover by combining it with the six-degree-of-freedom native state set, including: The motioner physical dynamic compensation is performed on the six-degree-of-freedom native state set to obtain the spatial pose compensation vector. The motioner physical dynamic compensation includes acceleration lag compensation and centrifugal force deviation compensation. After aligning the spatiotemporal labels of the load impedance transient response data and the spatial pose compensation vector based on the EtherCAT global clock, extended Kalman filtering is performed to fuse them and output the real-time pose of the motioner.

[0053] In a magnetically levitated motion system, due to factors such as the high-speed motion of the motioner, interference from external forces, and limitations in sensor accuracy, the original six-degree-of-freedom state set contains certain errors or deviations. To obtain an accurate motion state, dynamic compensation is required. Through acceleration lag compensation and centrifugal force deviation compensation, a spatial pose compensation vector is obtained, which represents the correction value of the motion state of the motioner.

[0054] Acceleration hysteresis refers to the delay in the motion accelerometer's response to changes in motion acceleration. Due to the time difference between sensor response and processing, the actual acceleration change may lag behind the data acquisition. Acceleration hysteresis compensation adjusts the data error caused by the hysteresis of the accelerometer, ensuring the accuracy of acceleration data and thus affecting subsequent pose estimation. Centrifugal force deviation refers to the error caused by centrifugal force on the sensor (especially the accelerometer) when the motion accelerometer rotates or moves at high speed on the track. The magnitude of the centrifugal force is related to the motion trajectory, velocity, and mass distribution of the motion accelerometer. Therefore, under high-speed motion, the sensor will be subject to considerable deviation. Centrifugal force deviation compensation accurately calculates the influence of centrifugal force by physically modeling the motion of the motion accelerometer and corrects it, thereby eliminating the influence of this deviation on the motion state.

[0055] A global clock refers to a precise, synchronized clock shared by all devices connected via an EtherCAT network. Through the global clock, high-precision timing synchronization can be achieved between devices, ensuring accurate data transmission and processing. Spatiotemporal tag alignment synchronizes the load impedance transient response data and the spatial pose compensation vector to ensure they have the same temporal and spatial reference. Since different sensors may have different sampling times and delays, spatiotemporal alignment is necessary for subsequent fusion and filtering operations. Extended Kalman filtering is a filtering algorithm for nonlinear state estimation. It linearizes the system state and continuously updates and optimizes the state estimate by combining the predictive model and sensor data. In this step, extended Kalman filtering is used to fuse the spatial pose compensation vector and the load impedance transient response data, combining their real-time data to eliminate noise and errors, thereby improving the accuracy of the mover's real-time pose estimation.

[0056] Furthermore, the main control unit dynamically tunes the output parameters of the high-frequency inverter based on the instantaneous optimal frequency of the resonant cavity and the phase compensation amount of the current waveform, including: Based on the instantaneous optimal frequency of the resonant cavity and the phase compensation amount of the current waveform, the target switching frequency deviation value and the PWM waveform turn-on timing adjustment amount are calculated respectively; the switching frequency of the high-frequency inverter is adjusted using the target switching frequency deviation value; the output waveform phase of the high-frequency inverter is adjusted using the PWM waveform turn-on timing adjustment amount, wherein the target switching frequency deviation value and the PWM waveform turn-on timing adjustment amount are performed in parallel with dual threads.

[0057] The switching frequency deviation is the difference between the current inverter operating frequency and the instantaneous optimal frequency of the resonant cavity. This deviation reflects the gap between the current frequency and the optimal frequency. Based on the optimal frequency of the resonant cavity and the phase compensation of the current waveform, the target switching frequency deviation is calculated. This deviation is used to adjust the switching frequency of the high-frequency inverter to ensure that the system operates at the optimal frequency.

[0058] PWM (Pulse Width Modulation) waveforms control the inverter's output waveform by adjusting the on-time and off-time. Adjusting the on-time sequence is crucial for accurately controlling the output voltage, frequency, and phase. The on-time adjustment of the PWM waveform is calculated by using the phase compensation of the current waveform. This adjustment ensures that the output PWM waveform is aligned with the optimal operating state of the resonant cavity, avoiding a decrease in energy transfer efficiency due to phase deviation.

[0059] The switching frequency of a high-frequency inverter is adjusted using a target switching frequency deviation value. The switching frequency of the high-frequency inverter determines the efficiency and accuracy of power conversion. Adjusting the switching frequency to match the instantaneous optimal frequency of the resonant cavity can improve the system's coupling efficiency and ensure the stability and efficiency of wireless power transmission. The switching frequency of the high-frequency inverter is controlled by PWM; by adjusting the target switching frequency deviation value, the inverter's switching frequency can be precisely controlled, ensuring alignment with the system's optimal frequency.

[0060] Based on the PWM waveform turn-on timing adjustment, the output waveform phase of the high-frequency inverter is adjusted to ensure phase matching of the current and voltage waveforms, maximizing energy transfer efficiency. Dual-thread parallel tuning means that the inverter adjusts simultaneously according to two adjustments: on the one hand, the switching frequency is adjusted, and on the other hand, the turn-on timing of the PWM waveform is adjusted. These two adjustments are executed in parallel, which can significantly improve the system response speed and ensure that parameters can be adjusted in real time under dynamically changing load and motion conditions.

[0061] Furthermore, it also includes: Historical motion characteristics of the mover are obtained interactively, and distributed high-dynamic region and distributed steady-state region are fitted and output in the stator orbital spatial domain according to the motion characteristics of the mover. Based on the kinematic characteristics, Hall sensors are deployed in a non-uniform topology in the distributed high-dynamic region to obtain a high-density sensing sub-array. Based on a preset spatial sampling interval, Hall sensors are deployed uniformly in the distributed steady-state region to obtain a low-density sensing sub-array. The high-density sensing sub-array and the low-density sensing sub-array are aligned by spatiotemporal coordinate registration to obtain the mover state sensing array.

[0062] Historical motion characteristics of the stator are obtained through data storage and analysis of historical motion trajectories. The stator track spatial domain refers to the operating region of the magnetically levitated stator along the stator track. The motion characteristics of the stator vary at different positions on the track. For example, in the high-speed region, the speed and acceleration of the stator change significantly, i.e., the high-dynamic region; while in the region with smaller and more stable speed changes, the state of the stator changes less, i.e., the steady-state region. Based on historical motion characteristics, the stator track spatial domain is analyzed to fit distributed high-dynamic regions and distributed steady-state regions.

[0063] Kinematic characteristics such as acceleration, velocity, and trajectory curvature reflect the motion state of a mover. In high-dynamic regions, the velocity and acceleration of the mover change drastically, thus requiring more precise monitoring of these areas. Hall sensors are used to measure changes in the magnetic field, making them suitable for monitoring the dynamic state of magnetic levitation systems. Based on the kinematic characteristics of high-dynamic regions, more Hall sensors are deployed in these areas to provide more refined motion monitoring. Non-uniform topology deployment means deploying dense sensor arrays in high-dynamic regions and reducing sensor density in regions with less dynamic change. This deployment method can effectively reduce costs while improving sensing accuracy in high-dynamic regions.

[0064] The steady-state region is typically an area where the motion of the mover is relatively stable, with minimal changes in velocity and acceleration. In this region, the motion state of the mover does not change significantly, and the response requirements of the sensors are relatively low. Therefore, in the steady-state region, Hall effect sensors are uniformly deployed at preset spatial sampling intervals. This deployment method reduces the number of sensors and deployment costs while still being sufficient to monitor motion changes in the steady-state region. Although the sensing density is low, it still provides sufficient monitoring accuracy to meet the energy transmission requirements of the steady-state region.

[0065] Spatiotemporal registration refers to synchronizing and aligning data collected from high-density and low-density sensing arrays in time and space. Because the high-density and low-density sensing subarrays are deployed in different locations, their collected data may have temporal and spatial deviations, thus requiring precise alignment and synchronization. After spatiotemporal registration, the data from the high-density and low-density arrays are fused together to form a complete motion sensor array. This array can provide full-process state monitoring of the motion sensor on its orbit, covering both high-dynamic and steady-state regions, ensuring that the system can acquire motion information of the motion sensor in real time.

[0066] Furthermore, based on kinematic characteristics, a non-uniform topological deployment of Hall sensors is performed in the distributed high-dynamic region to obtain a high-density sensing subarray, including: Kinematic feature key points are located in the distributed high dynamic region to obtain distributed centrifugal force abrupt change points and distributed acceleration inflection points; using the distributed centrifugal force abrupt change points and distributed acceleration inflection points as deployment target points, Hall sensors are deployed in a non-uniform topology in the distributed high dynamic region to obtain the high-density sensing subarray.

[0067] The motion characteristics of a mover vary in different orbital regions. In the high-dynamic region, the mover's velocity and acceleration change drastically. Centrifugal force is a force related to the mover's rotational speed and trajectory. Its magnitude varies with the mover's velocity. In the high-dynamic region, especially during acceleration or deceleration, the changes in centrifugal force are very drastic. These points of change are called centrifugal force abrupt change points, corresponding to the moments or locations of rapid changes in the mover's state, especially when the mover is moving along curves or changing its velocity. Acceleration inflection points refer to the moments or locations where significant changes in acceleration occur, appearing during the mover's acceleration or deceleration phases. Acceleration inflection points reflect the instantaneous changes in external forces acting on the mover on the orbit, especially when the mover turns or changes its motion, resulting in drastic changes in acceleration.

[0068] The centrifugal force abrupt change points and acceleration inflection points are targeted as sensor deployment points. These points reflect the most dramatic changes in the motion of the mover on the track, making them the most sensitive locations to changes in the mover's motion state. The non-uniform topology deployment of Hall sensors means that the sensors are not distributed uniformly at these key points, but rather arranged according to the density of changes in motion state. More sensors are deployed at centrifugal force abrupt change points and acceleration inflection points to ensure real-time monitoring of these highly dynamic regions. Sensor deployment involves not only increasing the number of sensors but also densely deploying them according to the dynamic needs of these specific points to provide higher sensing accuracy. By densely deploying Hall sensors in these key areas, a high-density sensing subarray is obtained, which can accurately capture the motion state of the mover in highly dynamic regions and provide precise real-time data feedback.

[0069] Example 2, based on the same inventive concept as the contactless power supply-based magnetic levitation motor wireless power transmission method in the foregoing examples, such as... Figure 2 As shown in the figure, this application provides a wireless power transmission system for a magnetically levitated motor based on contactless power supply. The system includes: The motion state vector reconstruction module 10 is used by the main control unit to receive multi-dimensional spatiotemporal synchronous sampling data from the mover state sensing array pre-deployed on the stator track, perform motion state vector reconstruction of the magnetically levitated mover, and output a pose priority weight vector; the scheduling strategy setting module 20 is used to set the data acquisition scheduling strategy of the mover state sensing array according to the pose priority weight vector; the native state set receiving module 30 is used by the main control unit to receive the six-degree-of-freedom native state set returned by the mover state sensing array with the data acquisition scheduling strategy as a shielding constraint; and the mover real-time pose output module 40 is used by the main control unit to receive the current and voltage sampling circuit feedback. After receiving the transient response data of the load impedance, the real-time pose of the output mover is reconstructed by combining the six-degree-of-freedom native state set; the fitting module 50 is used to fit the instantaneous optimal frequency of the resonant cavity and the phase compensation amount of the current waveform based on the preset motion trajectory and the real-time pose of the mover; the trend prediction module 60 is used to load the transient response data of the load impedance into the adaptive power mapping model to predict the trend of power demand change; the coupling efficiency offset compensation module 70 is used by the main control unit to dynamically tune the output parameters of the high-frequency inverter according to the instantaneous optimal frequency of the resonant cavity and the phase compensation amount of the current waveform, and to perform coupling efficiency offset compensation according to the predicted trend of power demand change.

[0070] Furthermore, the fitting module 50 is used to perform the following operation steps: The real-time pose of the mover is decomposed to obtain linear velocity vectors and angular velocity vectors; the preset motion trajectory is segmented and identified to obtain the trajectory radius of curvature and a reference motion acceleration sequence; the spatial offset of the receiving coil is calculated based on the angular velocity vectors and the trajectory radius of curvature; the real-time acceleration increment is extracted from the reference motion acceleration sequence based on the real-time spatial position of the magnetically levitated mover; the phase compensation of the current waveform is calculated based on the real-time acceleration increment and the spatial offset of the receiving coil; after generating the fundamental frequency of the resonant cavity proportionally based on the linear velocity vectors, the spatial offset of the receiving coil is superimposed to perform negative frequency correction, and the instantaneous optimal frequency of the resonant cavity is output.

[0071] Furthermore, the motion state vector reconstruction module 10 is used to perform the following operation steps: After performing spatiotemporal alignment compensation on the multi-dimensional spatiotemporal synchronous sampling data based on the spatiotemporal labels, inertial compensation is performed based on the regional dynamic attribute distribution of the stator track to obtain a six-degree-of-freedom initial state set; six-degree-of-freedom vector fusion is performed on the six-degree-of-freedom initial state set to output the reconstructed motion state vector; the out-of-tolerance dimension of the motion state vector is detected based on a preset risk threshold to generate the pose priority weight vector.

[0072] Furthermore, the scheduling strategy setting module 20 is used to perform the following operation steps: Multidimensional situational analysis is performed on the pose priority weight vector to construct a spatial weight distribution heatmap; based on the orbital physical topology of the stator track, the spatial weight distribution heatmap is projected onto the mover state sensing array to perform heterogeneous sampling frequency dynamic mapping and output a heterogeneous sampling time slot allocation matrix as the data acquisition scheduling strategy.

[0073] Furthermore, the motion state vector reconstruction module 10 is used to perform the following operation steps: The current and voltage sampling circuit is pre-deployed on the magnetically levitated mover, wherein the current and voltage sampling circuit is connected to the master control unit through a single master-multiple slave wireless communication architecture; an adaptive impedance matching network is pre-deployed on the magnetically levitated mover for dynamic tuning of the resonant frequency; and the mover state sensing array is connected to the master control unit through an EtherCAT bus.

[0074] Furthermore, the real-time pose output module 40 of the moving part is used to perform the following operation steps: The motioner physical dynamic compensation is performed on the six-degree-of-freedom native state set to obtain the spatial pose compensation vector. The motioner physical dynamic compensation includes acceleration lag compensation and centrifugal force deviation compensation. After aligning the spatiotemporal labels of the load impedance transient response data and the spatial pose compensation vector based on the EtherCAT global clock, extended Kalman filtering is performed to fuse them and output the real-time pose of the motioner.

[0075] Furthermore, the coupling efficiency offset compensation module 70 is used to perform the following operation steps: Based on the instantaneous optimal frequency of the resonant cavity and the phase compensation amount of the current waveform, the target switching frequency deviation value and the PWM waveform turn-on timing adjustment amount are calculated respectively; the switching frequency of the high-frequency inverter is adjusted using the target switching frequency deviation value; the output waveform phase of the high-frequency inverter is adjusted using the PWM waveform turn-on timing adjustment amount, wherein the target switching frequency deviation value and the PWM waveform turn-on timing adjustment amount are performed in parallel with dual threads.

[0076] Furthermore, the motion state vector reconstruction module 10 is used to perform the following operation steps: Historical motion characteristics of the mover are obtained interactively, and distributed high-dynamic region and distributed steady-state region are fitted and output in the stator orbital spatial domain according to the motion characteristics of the mover. Based on the kinematic characteristics, Hall sensors are deployed in a non-uniform topology in the distributed high-dynamic region to obtain a high-density sensing sub-array. Based on a preset spatial sampling interval, Hall sensors are deployed uniformly in the distributed steady-state region to obtain a low-density sensing sub-array. The high-density sensing sub-array and the low-density sensing sub-array are aligned by spatiotemporal coordinate registration to obtain the mover state sensing array.

[0077] Furthermore, the motion state vector reconstruction module 10 is used to perform the following operation steps: Kinematic feature key points are located in the distributed high dynamic region to obtain distributed centrifugal force abrupt change points and distributed acceleration inflection points; using the distributed centrifugal force abrupt change points and distributed acceleration inflection points as deployment target points, Hall sensors are deployed in a non-uniform topology in the distributed high dynamic region to obtain the high-density sensing subarray.

[0078] Through the foregoing detailed description of the wireless power transmission method for a magnetically levitated motor based on contactless power supply, those skilled in the art can clearly understand the wireless power transmission system for a magnetically levitated motor based on contactless power supply in this embodiment. Since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant details can be found in the method section. The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention still fall within the scope of the present invention.

Claims

1. A method for wireless energy transfer for a magnetic levitation vehicle based on non-contact power supply, characterized in that, The method includes: The main control unit receives multi-dimensional spatiotemporal synchronous sampling data from the mover state sensing array pre-deployed on the stator track, performs motion state vector reconstruction of the magnetically levitated mover, and outputs a pose priority weight vector. The data acquisition scheduling strategy of the moving part state sensing array is set according to the pose priority weight vector; The main control unit receives the six-degree-of-freedom native state set transmitted back by the moving part state sensing array, using the data acquisition and scheduling strategy as a shielding constraint. After receiving the load impedance transient response data returned by the current and voltage sampling circuit, the main control unit reconstructs and outputs the real-time pose of the mover by combining the six-degree-of-freedom native state set. Based on the preset motion trajectory and the real-time pose of the mover, the instantaneous optimal frequency of the resonant cavity and the phase compensation amount of the current waveform are fitted. The load impedance transient response data is loaded into the adaptive power mapping model to predict the power demand change trend. The main control unit dynamically tunes the output parameters of the high-frequency inverter based on the instantaneous optimal frequency of the resonant cavity and the phase compensation amount of the current waveform, and performs coupling efficiency offset compensation based on the predicted trend of power demand changes.

2. The non-contact power-based magnetic levitation vehicle wireless power transfer method of claim 1, wherein, Based on the preset motion trajectory and the real-time pose of the mover, the instantaneous optimal frequency of the resonant cavity and the phase compensation amount of the current waveform are fitted, including: The real-time pose of the mover is decomposed to obtain the linear velocity vector and the angular velocity vector; The preset motion trajectory is segmented and identified to obtain the trajectory curvature radius and reference motion acceleration sequence; The spatial offset of the receiving coil is calculated based on the angular velocity vector and the radius of curvature of the trajectory. Real-time acceleration increments are extracted from the reference motion acceleration sequence based on the real-time spatial position of the magnetically levitated mover. The phase compensation amount of the current waveform is calculated based on the real-time acceleration increment and the spatial offset of the receiving coil. After generating the fundamental frequency of the resonant cavity proportionally based on the linear velocity vector, the spatial offset of the receiving coil is superimposed to perform negative frequency correction, and the instantaneous optimal frequency of the resonant cavity is output.

3. The wireless power transmission method for a magnetically levitated motor based on contactless power supply as described in claim 1, characterized in that, The main control unit receives multi-dimensional spatiotemporal synchronous sampling data from the mover state sensing array pre-deployed on the stator track, performs motion state vector reconstruction of the magnetically levitated mover, and outputs a pose priority weight vector, including: After performing spatiotemporal alignment compensation on the multi-dimensional spatiotemporal synchronous sampling data based on the spatiotemporal tags, inertial compensation is performed based on the regional dynamic attribute distribution of the stator orbit to obtain a six-degree-of-freedom initial state set. The six-degree-of-freedom initial state set is fused with six-degree-of-freedom vectors to output the reconstructed motion state vectors; Based on a preset risk threshold, the out-of-tolerance dimension of the motion state vector is detected, and the pose priority weight vector is generated.

4. The wireless power transmission method for a magnetically levitated motor based on contactless power supply as described in claim 1, characterized in that, The data acquisition scheduling strategy for the moving part state sensing array is set according to the pose priority weight vector, including: Perform multi-dimensional situational analysis on the pose priority weight vector to construct a spatial weight distribution heatmap; Based on the physical topology of the stator track, the spatial weight distribution heatmap is projected onto the mover state sensing array to perform heterogeneous sampling frequency dynamic mapping and output a heterogeneous sampling time slot allocation matrix as the data acquisition scheduling strategy.

5. The wireless power transmission method for a magnetically levitated motor based on contactless power supply as described in claim 1, characterized in that, Also includes: The current and voltage sampling circuit is pre-deployed in the magnetically levitated actuator, wherein the current and voltage sampling circuit is connected to the master control unit through a single master-multiple slave wireless communication architecture; The resonant frequency is dynamically tuned by pre-deploying an adaptive impedance matching network on the magnetically levitated mover. The moving part state sensing array is connected to the main control unit via an EtherCAT bus.

6. The wireless power transmission method for a magnetically levitated motor based on contactless power supply as described in claim 5, characterized in that, After receiving the load impedance transient response data returned by the current and voltage sampling circuit, the main control unit reconstructs the real-time pose of the mover by combining it with the six-degree-of-freedom native state set, including: The physical dynamic compensation of the six-degree-of-freedom original state set is performed to obtain the spatial pose compensation vector, wherein the physical dynamic compensation of the motioner includes acceleration lag compensation and centrifugal force deviation compensation; After aligning the temporal and spatial labels of the load impedance transient response data and the spatial pose compensation vector based on the EtherCAT global clock, extended Kalman filtering is performed to fuse them and output the real-time pose of the mover.

7. The wireless power transmission method for a magnetically levitated motor based on contactless power supply as described in claim 5, characterized in that, The main control unit dynamically tunes the output parameters of the high-frequency inverter based on the instantaneous optimal frequency of the resonant cavity and the phase compensation amount of the current waveform, including: Based on the instantaneous optimal frequency of the resonant cavity and the phase compensation amount of the current waveform, the target switching frequency deviation value and the PWM waveform turn-on timing adjustment amount are calculated respectively. The switching frequency of the high-frequency inverter is adjusted using the target switching frequency deviation value; The output waveform phase of the high-frequency inverter is adjusted using the WM waveform turn-on timing adjustment, wherein the target switching frequency deviation and the PWM waveform turn-on timing adjustment are performed in parallel with dual threads.

8. The wireless power transmission method for a magnetically levitated motor based on contactless power supply as described in claim 1, characterized in that, Also includes: Historical motion characteristics of the mover are obtained interactively, and distributed high dynamic region and distributed steady-state region are fitted and output in the stator orbital space domain based on the motion characteristics of the mover. Based on kinematic characteristics, a non-uniform topological deployment of Hall sensors is carried out in the distributed high-dynamic region to obtain a high-density sensing subarray. Hall sensors are uniformly deployed in the distributed steady-state region based on a preset spatial sampling interval to obtain a low-density sensing subarray. The motion state sensing array is obtained by aligning the high-density sensing subarray and the low-density sensing subarray through spatiotemporal coordinate registration.

9. The wireless power transmission method for a magnetically levitated motor based on contactless power supply as described in claim 8, characterized in that, Based on kinematic characteristics, a non-uniform topological deployment of Hall sensors is performed in the distributed high-dynamic region to obtain a high-density sensing subarray, including: Kinematic feature key points are located in the distributed high dynamic region to obtain distributed centrifugal force abrupt change points and distributed acceleration inflection points. Using the distributed centrifugal force mutation point and the distributed acceleration inflection point as deployment targets, a non-uniform topology deployment of Hall sensors is carried out in the distributed high dynamic region to obtain the high-density sensing subarray.

10. A wireless power transmission system for a magnetically levitated motor based on contactless power supply, characterized in that, For implementing the contactless power supply-based magnetic levitation levitated wireless power transmission method according to any one of claims 1-9, the system comprises: The motion state vector reconstruction module is used by the main control unit to receive multi-dimensional spatiotemporal synchronous sampling data transmitted back by the motion state sensing array pre-deployed on the stator track, perform motion state vector reconstruction of the magnetically levitated motion state vector, and output the pose priority weight vector. The scheduling strategy setting module is used to set the data acquisition scheduling strategy of the moving part state sensing array according to the pose priority weight vector. The native state set receiving module is used by the main control unit to receive the six-degree-of-freedom native state set transmitted back by the moving sub-state sensing array, with the data acquisition scheduling strategy as a masking constraint. The real-time pose output module for the mover is used by the main control unit to reconstruct and output the real-time pose of the mover after receiving the load impedance transient response data returned by the current and voltage sampling circuit and combining it with the six-degree-of-freedom native state set. The fitting module is used to fit the instantaneous optimal frequency of the resonant cavity and the phase compensation amount of the current waveform based on the preset motion trajectory and the real-time pose of the mover. The trend prediction module is used to load the transient response data of the load impedance into the adaptive power mapping model to predict the trend of power demand change; the coupling efficiency offset compensation module is used by the main control unit to dynamically tune the output parameters of the high-frequency inverter according to the instantaneous optimal frequency of the resonant cavity and the phase compensation amount of the current waveform, and to perform coupling efficiency offset compensation according to the predicted trend of power demand change.

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