Method for acquiring rotor position data of magnetic drive conveying system and related equipment

Abstract

The embodiment of the invention provides a rotor position data acquisition method of a magnetic drive conveying system and related equipment, and the method comprises the steps: obtaining position collection data obtained through the simultaneous collection of a plurality of magnetic receiving devices for a rotor when the rotor operates at one side of a stator, and obtaining preset reference supplementary data and phase nonlinear supplementary data; performing feature extraction calculation based on each piece of position acquisition data to obtain a corresponding data feature value, determining a target receiving device, and obtaining initial position data of the rotor based on set position data of the target receiving device; target collection data and adjacent collection data are selected from the multiple pieces of position collection data; performing coordinate rotation iterative calculation based on the target acquisition data and the adjacent acquisition data to obtain a phase increment; and obtaining the target position data of the rotor based on the reference supplementary data, the phase nonlinear supplementary data, the phase increment and the initial position data, thereby effectively improving the accuracy and stability of the finally obtained rotor position data.

Description

Methods and related equipment for acquiring the position data of the moving part of a magnetic drive conveyor system Technical Field

[0001] This application relates to the field of control technology, and in particular to a method and related equipment for acquiring the position data of the mover in a magnetic drive conveyor system. Background Technology

[0002] In the operation control of magnetic drive conveyor systems, high-precision and high-response speed detection of the real-time position of the mover on the stator track is fundamental to achieving closed-loop motion control and multi-motor coordinated scheduling. Current magnetic drive conveyor systems typically employ non-contact detection methods. Among these, schemes based on the principle of electromagnetic induction (such as arranging an array of magnetic receivers on the stator to sense the magnetic field of the mover's magnetic transmitter) have become a mainstream position feedback method due to their simple structure, low cost, and strong adaptability to harsh industrial environments such as oil and dust. These systems typically collect analog signals sensed by the receivers and use specific signal processing algorithms to determine the absolute position of the mover relative to the stator, thus providing crucial feedback data for the control system.

[0003] However, practical systems are limited by hardware manufacturing and assembly processes. On the one hand, there are macroscopic reference errors caused by deviations in the spacing of the sensor array and welding tolerances. On the other hand, there are microscopic phase errors caused by the inductive nonlinearity of the Hall element itself and the high-order harmonic distortion of the magnetic field distribution. This makes it difficult to simultaneously ensure measurement continuity over a large stroke range and micron-level local measurement accuracy when using a position detection method based on a magnetic receiving array. As a result, position data jumps or accuracy fluctuations are likely to occur when the mover crosses different sensor sensing areas at high speed, leading to low accuracy and stability of the final mover position data. Summary of the Invention

[0004] This application provides a method and related equipment for acquiring the position data of the mover in a magnetic drive conveyor system, which can improve the accuracy and stability of acquiring the mover position data.

[0005] To achieve the above objectives, a first aspect of this application proposes a method for acquiring the position data of a moving part in a magnetic drive conveyor system. The magnetic drive conveyor system includes a stator and a moving part. The stator is equipped with multiple magnetic receiving devices, and the moving part is equipped with a magnetic transmitting device. The method includes: when the moving part is running on one side of the stator, acquiring position data simultaneously collected by the multiple magnetic receiving devices for the moving part, and acquiring preset reference supplementary data and phase nonlinear supplementary data; performing feature extraction calculations based on each of the position data to obtain corresponding data feature values, and based on the multiple data feature values, extracting data from the multiple magnetic receiving devices... A target receiving device is selected from the magnetic receiving devices, and the initial position data of the mover is obtained based on the setting position data of the target receiving device. Target acquisition data corresponding to the target receiving device and adjacent acquisition data corresponding to adjacent receiving devices are selected from multiple position acquisition data, wherein the adjacent receiving devices are the magnetic receiving devices set adjacent to the target receiving device. Coordinate rotation iterative calculation is performed based on the target acquisition data and the adjacent acquisition data to obtain the phase increment. The target position data of the mover is obtained based on the reference supplementary data, the phase nonlinear supplementary data, the phase increment, and the initial position data.

[0006] In some embodiments, the step of performing feature extraction calculation based on the data collected at each location to obtain corresponding data feature values ​​includes: extracting sine and cosine components corresponding to each location data based on the arrangement position and phase difference of the multiple magnetic receiving devices; for each location data, substituting the corresponding sine and cosine components into the trigonometric inverse tangent function to calculate a feature phase angle, and calculating the vector amplitude based on the sine and cosine components to obtain a feature amplitude; and obtaining the data feature value corresponding to the location data based on the feature amplitude and the feature phase angle.

[0007] In some embodiments, obtaining the data feature value corresponding to the location acquisition data based on the feature amplitude and the feature phase angle includes: matching the feature amplitude with a preset amplitude range to obtain an amplitude matching result; when the amplitude matching result indicates that the feature amplitude is not within the preset amplitude range, obtaining the data feature value corresponding to the location acquisition data based on invalid representation information; when the amplitude matching result indicates that the feature amplitude is within the preset amplitude range, obtaining the data feature value corresponding to the location acquisition data based on valid representation information and the feature phase angle.

[0008] In some embodiments, selecting a target receiving device from a plurality of magnetic receiving devices based on a plurality of data feature values ​​includes: selecting at least one magnetic receiving device from the plurality of magnetic receiving devices whose data feature values ​​contain the effective characterization information to obtain a candidate magnetic receiving device; and selecting the candidate magnetic receiving device with the smallest difference between its feature phase angle and a preset angle value from the plurality of candidate magnetic receiving devices as the target receiving device.

[0009] In some embodiments, the step of performing coordinate rotation iteration calculation based on the target acquisition data and the adjacent acquisition data to obtain the phase increment includes: in each coordinate rotation iteration, determining the corresponding rotation direction based on the sign of the sine input component; obtaining an iteration coefficient term based on the exponential processing of a preset value and the number of iterations; obtaining a sine update term based on the cumulative product of the sine input component, the rotation direction, and the iteration coefficient term; obtaining a cosine update term based on the cumulative product of the cosine input component, the rotation direction, and the iteration coefficient term; obtaining an updated sine input component based on the difference between the sine input component and the cosine update term; and obtaining an updated sine input component based on the difference between the cosine input component and the adjacent acquisition data. The accumulated value of the sine update term is used to obtain the updated cosine input component; the arctangent is processed based on the iteration coefficient term, and then multiplied by the rotation direction to obtain the angle update term; the updated rotation angle component is obtained based on the accumulated value of the rotation angle component and the angle update term; the updated sine input component is used as the new sine input component, the updated cosine input component is used as the new cosine input component, and the updated rotation angle component is used as the new rotation angle component, and the coordinate rotation iteration is performed again until the number of iterations reaches the preset number of iterations; wherein, the initial value of the sine input component is the target acquired data, and the initial value of the cosine input component is the adjacent acquired data.

[0010] In some embodiments, obtaining the target position data of the mover based on the reference supplementary data, the phase nonlinear supplementary data, the phase increment, and the initial position data includes: correcting the initial position data based on the reference supplementary data to obtain corrected reference position data; correcting the phase increment based on the phase nonlinear supplementary data to obtain corrected nonlinear position data; and accumulating the corrected reference position data and the corrected nonlinear position data to obtain the target position data.

[0011] In some embodiments, acquiring position acquisition data simultaneously collected by multiple magnetic receiving devices for the mover includes: performing multi-channel synchronous scanning of multiple magnetic receiving devices using an analog-to-digital converter to obtain multiple initial position acquisition data, and acquiring temperature data of the magnetic drive conveyor system; performing bias removal processing on each initial position acquisition data to obtain filtered position acquisition data; performing normalization mapping on each filtered position acquisition data based on a standard numerical range to obtain normalized position acquisition data; and performing correction processing on each normalized position acquisition data based on the temperature coefficient corresponding to the temperature data to obtain the position acquisition data.

[0012] To achieve the above objectives, a second aspect of this application provides a device for acquiring position data of a moving part in a magnetic drive conveyor system. The magnetic drive conveyor system includes a stator and a moving part. The stator is equipped with multiple magnetic receiving devices, and the moving part is equipped with a magnetic transmitting device. The device includes: a data acquisition module, used to acquire position data simultaneously collected by the multiple magnetic receiving devices for the moving part when the moving part is running on one side of the stator, and to acquire preset reference supplementary data and phase nonlinearity supplementary data; and an initial data determination module, used to perform feature extraction calculations based on each position acquisition data to obtain corresponding data feature values, and to determine the initial data based on the multiple data feature values ​​from the multiple magnetic receiving devices. A target receiving device is selected, and the initial position data of the mover is obtained based on the setting position data of the target receiving device; a data acquisition determination module is used to select the target acquisition data corresponding to the target receiving device and the adjacent acquisition data corresponding to the adjacent receiving device from multiple position acquisition data, wherein the adjacent receiving device is the magnetic receiving device set adjacent to the target receiving device; a phase increment determination module is used to perform coordinate rotation iteration calculation based on the target acquisition data and the adjacent acquisition data to obtain the phase increment; a target data determination module is used to obtain the target position data of the mover based on the reference supplementary data, the phase nonlinear supplementary data, the phase increment and the initial position data.

[0013] To achieve the above objectives, a third aspect of this application provides a magnetic drive motor conveying system, which includes a conveying line body formed by sequentially splicing multiple stators along the conveying line direction, a mover magnetically coupled to the stators, and a servo control component. The servo control component includes a processor and a memory; wherein the memory stores a computer program adapted to be loaded by the processor and executed as described in the first aspect, the mover position data acquisition method of the magnetic drive conveying system.

[0014] To achieve the above objectives, a fourth aspect of the present application provides a storage medium, which is a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the method for acquiring the mover position data of the magnetic drive conveyor system described in the first aspect.

[0015] This application proposes a method and related equipment for acquiring the position data of a moving part in a magnetic drive conveyor system. The magnetic drive conveyor system includes a stator and a moving part. The stator is equipped with multiple magnetic receiving devices, and the moving part is equipped with a magnetic transmitting device. The method includes: First, when the moving part is running on one side of the stator, acquiring position acquisition data simultaneously collected by multiple magnetic receiving devices for the moving part, and acquiring preset reference supplementary data and phase nonlinear supplementary data; then, performing feature extraction calculation based on each position acquisition data to obtain corresponding data feature values, and selecting a target receiving device from multiple magnetic receiving devices based on multiple data feature values, and obtaining the initial position data of the moving part based on the set position data of the target receiving device; then, selecting the target acquisition data corresponding to the target receiving device and the adjacent acquisition data corresponding to the adjacent receiving device from multiple position acquisition data, wherein the adjacent receiving device is a magnetic receiving device set adjacent to the target receiving device; next, performing coordinate rotation iterative calculation based on the target acquisition data and the adjacent acquisition data to obtain the phase increment; finally, obtaining the target position data of the moving part based on the reference supplementary data, phase nonlinear supplementary data, phase increment, and initial position data. This application embodiment achieves dual decoupling compensation for macroscopic geometric errors and microscopic phase errors by introducing preset reference supplementary data and phase nonlinear supplementary data, combined with the phase increment obtained by coordinate rotation iteration calculation. Specifically, the method first uses the reference supplementary data to correct the initial position data obtained based on the target receiving device setting position, effectively eliminating macroscopic reference errors caused by hardware manufacturing processes such as sensor array spacing deviation and welding tolerance; simultaneously, it uses the phase nonlinear supplementary data to refine the high-precision phase increment calculated based on coordinate rotation iteration, effectively suppressing microscopic phase errors caused by Hall element induction nonlinearity and high-order harmonic distortion of magnetic field distribution; finally, by synthesizing the corrected macroscopic position reference and microscopic phase increment to obtain the target position data, it not only significantly improves the absolute accuracy of mover position detection, but also ensures the measurement continuity when the mover crosses different sensor sensing areas at high speed, avoiding position data jumps, thereby effectively improving the accuracy and stability of the finally acquired mover position data and meeting the high-precision feedback requirements of closed-loop motion control.

[0016] Other features and advantages of this application will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the application. The objectives and other advantages of this application may be realized and obtained by means of the structures particularly pointed out in the description, claims and drawings. Attached Figure Description

[0017] Figure 1 is a schematic diagram of a magnetic drive conveying system provided in an embodiment of this application.

[0018] Figure 2 is a schematic diagram of the working principle and signal flow of a linear incremental feedback device provided in another embodiment of this application.

[0019] Figure 3 is a schematic diagram of the hardware circuit structure of a magnetic receiving device provided in another embodiment of this application.

[0020] Figure 4 is a flowchart of a method for acquiring the position data of a moving part of a magnetic drive conveyor system according to another embodiment of this application.

[0021] Figure 5 is a flowchart of step 401 in Figure 4.

[0022] Figure 6 is a flowchart of step 402 in Figure 4.

[0023] Figure 7 is a flowchart of step 603 in Figure 6.

[0024] Figure 8 is another flowchart of step 402 in Figure 4.

[0025] Figure 9 is a flowchart of step 404 in Figure 4.

[0026] Figure 10 is a flowchart of step 405 in Figure 4.

[0027] Figure 11 is a schematic diagram of the main information flow structure of a linear incremental feedback device provided in another embodiment of this application.

[0028] Figure 12 is a flowchart of the program processing of a linear incremental feedback device provided in another embodiment of this application.

[0029] Figure 13 is a schematic diagram of the structure of the mover position data acquisition device of the magnetic drive conveying system provided in another embodiment of this application.

[0030] Figure 14 is a structural schematic diagram of a magnetic drive motor conveying system provided in another embodiment of this application. Detailed Implementation

[0031] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0032] It should be noted that although functional modules are divided in the device schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart.

[0033] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0034] In the operation control of magnetic drive conveyor systems, high-precision and high-response speed detection of the real-time position of the mover on the stator track is fundamental to achieving closed-loop motion control and multi-motor coordinated scheduling. Current magnetic drive conveyor systems typically employ non-contact detection methods. Among these, schemes based on the principle of electromagnetic induction (such as arranging an array of magnetic receivers on the stator to sense the magnetic field of the mover's magnetic transmitter) have become a mainstream position feedback method due to their simple structure, low cost, and strong adaptability to harsh industrial environments such as oil and dust. These systems typically collect analog signals sensed by the receivers and use specific signal processing algorithms to determine the absolute position of the mover relative to the stator, thus providing crucial feedback data for the control system.

[0035] However, practical systems are limited by hardware manufacturing and assembly processes. On the one hand, there are macroscopic reference errors caused by deviations in the spacing of the sensor array and welding tolerances. On the other hand, there are microscopic phase errors caused by the inductive nonlinearity of the Hall element itself and the high-order harmonic distortion of the magnetic field distribution. This makes it difficult to simultaneously ensure measurement continuity over a large stroke range and micron-level local measurement accuracy when using a position detection method based on a magnetic receiving array. As a result, position data jumps or accuracy fluctuations are likely to occur when the mover crosses different sensor sensing areas at high speed, leading to low accuracy and stability of the final mover position data.

[0036] To improve the accuracy and stability of mover position data acquisition, this application embodiment introduces preset reference supplementary data and phase nonlinear supplementary data, and combines the phase increment obtained by coordinate rotation iteration calculation to achieve dual decoupling compensation for macroscopic geometric errors and microscopic phase errors. Specifically, the method first uses the reference supplementary data to correct the initial position data obtained based on the target receiving device setting position, effectively eliminating macroscopic reference errors caused by hardware manufacturing processes such as sensor array spacing deviation and welding tolerance; simultaneously, it uses the phase nonlinear supplementary data to refine the high-precision phase increment calculated based on coordinate rotation iteration, effectively suppressing microscopic phase errors caused by Hall element induction nonlinearity and high-order harmonic distortion of magnetic field distribution; finally, by synthesizing the corrected macroscopic position reference and microscopic phase increment to obtain the target position data, it not only significantly improves the absolute accuracy of mover position detection, but also ensures the measurement continuity when the mover crosses different sensor sensing areas at high speed, avoiding position data jumps, thereby effectively improving the accuracy and stability of the finally acquired mover position data and meeting the high-precision feedback requirements of closed-loop motion control.

[0037] To better illustrate the motion control method for the synchronous transition track provided in this application embodiment, this embodiment first describes a magnetic drive conveyor system applying the motion control method. Referring to Figure 1, which is a structural schematic diagram of a magnetic drive conveyor system provided in this application embodiment, the magnetic drive conveyor system mainly consists of a stator and a motion unit. The stator (combined with the magnetic drive conveyor track) is linearly arrayed with multiple magnetic receiving devices along the motion direction of the motion unit to construct a continuous position detection area. The motion unit is equipped with a magnetic transmitting device. When the motion unit runs on the stator along the direction of the arrow shown in the figure, the magnetic field generated by the magnetic transmitting device can be sensed by the magnetic receiving device below it, thereby providing raw acquisition data for subsequent position data acquisition and decoupling compensation calculation.

[0038] Referring to Figure 2, a schematic diagram illustrating the working principle and signal flow of a linear incremental feedback device provided in this application embodiment is shown. As shown in Figure 2, the magnetic drive conveyor system includes a moving element (magnetic emitting device) undergoing linear motion and a stationary stator (feedback ruler / magnetic receiving device). The moving element is equipped with a magnetic emitting unit (i.e., a magnetic grating) containing an NSN magnetic pole arrangement. When the moving element moves relative to the stator in the direction of the arrow, the linear magnetic sensor array on the stator senses the magnetic characteristics emitted by the magnetic grating and generates a continuously changing analog signal. This analog signal is transmitted to an analog-to-digital converter chip for digital processing and then transmitted to the core processing chip. The core processing chip executes a coarse-fine combined positioning algorithm based on the received data and uses pre-stored reference supplementary data and phase nonlinear supplementary data for dual correction. The final calculated absolute position information of the moving element is output through a standard interface, thereby achieving non-contact, high-precision, and low-cost continuous positioning of the moving element during linear motion.

[0039] Referring to Figure 3, it is a schematic diagram of the hardware circuit structure of a magnetic receiving device provided in an embodiment of this application. As shown in Figure 3, the device is integrated on a PCB substrate and mainly consists of multiple linear magnetic sensors (S1~Sn+2) arranged in a linear array, multiple analog-to-digital converter chips (ADC1~ADCn), a voltage reference chip (VREF), a core computing chip (CPU), and a signal interface (Connector). Among them, the linear magnetic sensors (S1~Sn+2) are responsible for sensing external magnetic field signals and converting them into analog voltage signals; the analog-to-digital converter chips (ADC1~ADCn) are connected to multiple sensors and are used for multi-channel synchronous acquisition and digital conversion of analog signals; the voltage reference chip (VREF) provides a stable reference voltage for the system to eliminate power supply fluctuation errors; the core computing chip (CPU, such as FPGA or MCU) receives digitized position acquisition data, performs feature extraction, coordinate rotation iteration, and decoupling compensation calculations, and finally outputs high-precision target position data through the signal interface (such as a 485 interface).

[0040] Based on the aforementioned magnetic drive conveyor system, the method for acquiring the mover position data of the magnetic drive conveyor system in this application embodiment will be described in detail below. Referring to Figure 4, an optional flowchart of the method for acquiring the mover position data of the magnetic drive conveyor system provided in this application embodiment is shown. The method in Figure 4 may include, but is not limited to, steps 401 to 405. It is also understood that this embodiment does not specifically limit the order of steps 401 to 405 in Figure 4, and the order of steps can be adjusted or certain steps can be reduced or added according to actual needs. The method for acquiring the mover position data of the magnetic drive conveyor system provided in this application embodiment can be applied to a control and processing system (such as a smart terminal, server, computer, etc.) connected to the magnetic drive conveyor track.

[0041] Step 401: When the mover is running on one side of the stator, acquire position data collected simultaneously by multiple magnetic receiving devices for the mover, and acquire preset reference supplementary data and phase nonlinear supplementary data.

[0042] Step 401 will be described in detail below.

[0043] In some embodiments, when the control processing system responds to the acquisition of position data of the mover, it first performs synchronous data acquisition and preparation. As the mover travels along the transport path on one side of the stator track, multiple magnetic receiving devices (e.g., linear Hall sensors) arranged in a linear array on the stator simultaneously sense the magnetic field generated by the magnetic transmitting device on the mover. To ensure data time consistency, the system uses an analog-to-digital converter to perform multi-channel synchronous scanning or parallel sampling of these magnetic receiving devices, thereby acquiring a set of real-time analog voltage signals, i.e., "position acquisition data".

[0044] The following section will further describe how to acquire position data simultaneously collected by multiple magnetic receiving devices for the mover.

[0045] Referring to Figure 5, position data collected simultaneously by multiple magnetic receiving devices for the mover is obtained, including the following steps 501 to 504.

[0046] Step 501: Use an analog-to-digital converter to perform multi-channel synchronous scanning on multiple magnetic receiving devices to obtain multiple initial position acquisition data and acquire temperature data of the magnetic drive conveyor system.

[0047] Step 502: Perform offset removal processing on the data collected at each initial position to obtain filtered data collected at the position.

[0048] Step 503: Based on the standard numerical range, normalize the data collected at each filter location to obtain normalized location data.

[0049] Step 504: Based on the temperature coefficient corresponding to the temperature data, perform correction processing on each normalized location acquisition data to obtain the location acquisition data.

[0050] Steps 501 to 504 are described in detail below.

[0051] In some embodiments, the system utilizes an analog-to-digital converter (ADC) to perform multi-channel synchronous scanning of multiple magnetic receiving devices arranged in an array on the stator. This means that the ADC simultaneously latches the analog signals of all receiving devices and converts them into digital quantities within a very short time window, thereby avoiding the sampling time difference between channels caused by the high-speed movement of the mover and ensuring the acquisition of multiple initial position data (i.e., , The analog voltage signal output by the k-th sensor exhibits strict consistency over time. Simultaneously, the system also acquires real-time temperature data from the magnetic drive conveyor system via an integrated temperature sensor. This provides the necessary environmental parameters for subsequently eliminating thermal drift errors caused by changes in ambient temperature.

[0052] Next, the processor performs preliminary signal conditioning on the acquired raw signals. For each initial position data point x0, the system performs bias removal processing. This involves subtracting the sensor's static output value under zero magnetic field conditions from the raw data x0. (That is, the zero-point bias value of the k-th sensor pre-measured point, i.e., the static output value when there is no magnetic field), to eliminate the inherent DC component of the device, so as to obtain the filtered position acquisition data, i.e. This process transforms the original unipolar or biased signal into an AC component with zero reference, resulting in purer filtered position acquisition data and providing an accurate zero-point reference for subsequent amplitude calculations.

[0053] To eliminate the impact of hardware inconsistencies, the system standardizes the signal amplitude. The processor normalizes the data acquired at each filtering location based on a preset standard numerical range (e.g., the amplitude range of a unit circle or a specific voltage interval). This process typically involves calculating the ratio of the data's amplitude to the ideal maximum amplitude and multiplying it by a corresponding gain coefficient to obtain the normalized location acquisition data. , This represents the normalized gain coefficient corresponding to the k-th sensor. Normalization mapping aims to eliminate the sensitivity inhomogeneity caused by differences in manufacturing processes among different magnetic receiving devices, ensuring that the signals from all channels maintain a consistent numerical order of magnitude, thereby obtaining normalized position acquisition data.

[0054] Next, the system incorporates environmental parameters for final data calibration. The processing system queries or calculates the corresponding temperature coefficient based on the temperature data. (e.g., based on temperature data) Comparison with calibration reference temperature data The temperature coefficient corresponding to the difference between them. Since the magnetic sensitivity of the magnetic receiving device (usually a Hall element) drifts with temperature changes, the system uses this temperature coefficient to correct each normalized position acquisition data point, compensating for gain or linearity errors caused by temperature fluctuations, to obtain the position acquisition data, i.e. After this series of rigorous signal preprocessing steps, the final output is highly stable and linear position acquisition data, which can be used for subsequent feature extraction and position calculation.

[0055] Through steps 501 to 504 above, a highly reliable data front-end is constructed. Dynamic sampling errors are effectively avoided through multi-channel synchronous scanning. The static deviation and sensitivity inconsistency of individual sensors are eliminated by bias removal and normalization mapping. Real-time compensation is performed in conjunction with temperature data to overcome environmental thermal drift, which significantly improves the signal-to-noise ratio and consistency of the original signal. This enables the final generated position acquisition data to reflect the physical characteristics of the mover magnetic field in a true and pure manner, providing a solid data foundation for subsequent high-precision position calculation and decoupling compensation.

[0056] Simultaneously, the system retrieves pre-stored "preset reference supplementary data" and "phase nonlinearity supplementary data" from its internal non-volatile memory. The "preset reference supplementary data" typically corresponds to low-precision high-level compensation values ​​obtained during the factory calibration phase, used to characterize sensor physical installation errors or macroscopic sector deviations; while the "phase nonlinearity supplementary data" corresponds to high-precision low-level compensation values ​​used to characterize internal sensor inductive nonlinearity or higher-order harmonic distortion of the magnetic field. These two sets of data are decoupled and stored through a pre-calibration process (such as using a laser interferometer for comparison), laying the data foundation for subsequent high-precision position calculations.

[0057] Understandably, "preset reference supplementary data" and "phase nonlinearity supplementary data" are typically acquired during the system's factory calibration or initialization phase by entering "external compensation mode." Specifically, a high-precision external positioning device (such as a laser interferometer) drives the mover to move synchronously within the stator's stroke, recording a series of precise theoretical position values ​​and the raw measurement values ​​output by the magnetic receiving device. By calculating the deviation between the theoretical and measured values, the total error is decoupled and separated into two parts: one part is low-precision high-order data corresponding to sensor physical installation errors or macroscopic sector deviations, i.e., "preset reference supplementary data"; the other part is high-precision low-order data corresponding to sensor inductive nonlinearity or magnetic field distortion, i.e., "phase nonlinearity supplementary data." Finally, these two sets of data are mapped into lookup tables (LUTs) and burned into the system's fixed memory for real-time retrieval during operation.

[0058] Step 402: Perform feature extraction calculations based on the data collected at each location to obtain the corresponding data feature values. Based on multiple data feature values, select a target receiving device from multiple magnetic receiving devices, and then obtain the initial position data of the mover based on the set position data of the target receiving device.

[0059] Step 402 will be described in detail below.

[0060] Next, the processing system collects data for each of the acquired locations. Deep signal analysis and feature extraction are performed. This process typically involves bias adjustment, normalization, and trigonometric function transformation calculations (such as arctangent operation and vector amplitude calculation) of the original signal to generate "data feature values" (i.e., R values) that characterize the current magnetic field state. These data feature values ​​contain not only the phase angle information of the magnetic field but also amplitude intensity information used to verify signal validity. The system iterates through and compares the data feature values ​​output by multiple magnetic receiving devices, selecting the device in the linear operating region with the optimal signal-to-noise ratio (e.g., the characteristic phase angle closest to the zero-degree linear center) as the "target receiving device." Subsequently, based on the physical hardware number or ID of the target receiving device in the stator array, the system retrieves its preset physical installation coordinates (i.e., "set position data") and uses them as the current macroscopic anchor point of the mover, i.e., the "initial position data." This step enables rapid locking from full-field scanning to local sectors.

[0061] Referring to Figure 6, feature extraction calculations are performed based on the data collected at each location to obtain the corresponding data feature values, including the following steps 601 to 603.

[0062] Step 601: Based on the arrangement and phase difference of multiple magnetic receiving devices, data is collected from each corresponding position and extracted to obtain the sine and cosine components corresponding to the data collected at each position.

[0063] Step 602: For each location, collect data, substitute the corresponding sine and cosine components into the trigonometric inverse tangent function to calculate the characteristic phase angle, and calculate the vector amplitude based on the sine and cosine components to obtain the characteristic amplitude.

[0064] Step 603: Based on the characteristic amplitude and characteristic phase angle, obtain the data feature values ​​corresponding to the location acquisition data.

[0065] Steps 601 to 603 are described in detail below.

[0066] In some embodiments, the processing system begins by constructing an orthogonal signal basis for mathematical solutions. The system acquires data from each corresponding location based on the arrangement and phase difference of multiple magnetic receiving devices (e.g., the electrical angle difference corresponding to the physical spacing of the sensors on the stator). Extraction is performed. Since a single sensor typically only outputs a single analog signal, the system selects the current sensor signal and its associated signal with a specific phase relationship in space (e.g., a 90-degree electrical angle difference) based on the array's physical topology, or based on the sensor's internal dual-output mechanism, thereby resolving the sine and cosine components corresponding to the data collected at each location (e.g., extracting the data from the current location (e.g., the k-th sensor)). As a sine component, i.e. And collect position data from sensors with a 90-degree phase difference (such as the k'th sensor). As a cosine component, i.e. This step transforms a one-dimensional voltage amplitude signal into orthogonal vector components in a two-dimensional plane coordinate system, providing the necessary mathematical input for subsequent vector operations.

[0067] Then, the system uses mathematical tools to solve for the spatial characteristics of the above orthogonal components. For data collected at each location, the processor substitutes the corresponding sine and cosine components into the trigonometric inverse tangent function for calculation (usually the four-quadrant inverse tangent function Atan2, i.e., ...). Its return value is Thus, the characteristic phase angle is obtained, i.e. This angle directly reflects the electrical angular position of the mover magnetic pole relative to the sensor. Simultaneously, the system calculates the vector amplitude based on the sine and cosine components (usually the square root of the sum of their squares) to obtain the characteristic amplitude, i.e. The characteristic amplitude here is a key indicator for measuring the strength of the magnetic field sensed by the current sensor, used to characterize whether the signal is in the linear operating region or whether magnetic saturation exists.

[0068] The system then integrates the calculated multi-dimensional information into a comprehensive state descriptor. The processor combines and encapsulates the characteristic amplitude and characteristic phase angle to obtain the data feature values ​​corresponding to the location acquisition data, i.e. , This refers to the initial data feature value structure corresponding to the k-th sensor. This data feature value is not merely a numerical value, but a data packet containing "position information" (represented by the phase angle) and "confidence state" (represented by the amplitude). It serves as the basis for subsequent logical judgments by the system, enabling it to quickly identify which sensors are in their optimal sensing areas, thus providing quantitative decision support for the selection of the "target receiving device," as described below.

[0069] Referring to Figure 7, the data feature values ​​corresponding to the location acquisition data are obtained based on the feature amplitude and feature phase angle, including the following steps 701 to 703.

[0070] Step 701: Match the feature amplitude with the preset amplitude range to obtain the amplitude matching result.

[0071] Step 702: When the amplitude matching result characterization feature amplitude is not within the preset amplitude range, obtain the data feature value corresponding to the location acquisition data based on the invalid characterization information.

[0072] Step 703: When the amplitude matching result characterization feature amplitude is within the preset amplitude range, the data feature value corresponding to the location acquisition data is obtained based on the effective characterization information and feature phase angle.

[0073] Steps 701 to 703 are described in detail below.

[0074] After obtaining the characteristic amplitude and characteristic phase angle, the processing system initiates the logic for evaluating the sensor signal quality. The system performs a numerical comparison and matching between the previously calculated characteristic amplitude and a preset amplitude range stored internally. This preset amplitude range typically corresponds to the linear operating range of a magnetic receiving device (such as a Hall sensor), with its upper limit defining the magnetic saturation threshold and its lower limit defining the minimum effective sensing intensity. Through this comparison process, the system obtains the amplitude matching result. This result is a logical criterion used to indicate whether the magnetic field strength sensed by the current sensor is within a reliable range, as shown in the following formula.

[0075]

[0076] in, These are the lower and upper limits of the preset amplitude range, namely the magnetic saturation threshold and the minimum induction threshold.

[0077] When the amplitude matching result indicates that the characteristic amplitude is outside the preset amplitude range (for example, an excessively large amplitude indicates that the sensor is in a magnetic saturation state, leading to a sharp increase in nonlinear error; or an excessively small amplitude indicates that the mover is too far from the sensor, resulting in a low signal-to-noise ratio), the system determines that the data is unusable. In this case, the system generates an identifier representing the unusable state, that is, based on the invalid characterization information, assigning or associating it with the data characteristic value corresponding to the location acquisition data. This operation is equivalent to "shielding" the sensor at the logical level, preventing inferior data from contaminating subsequent calculation processes.

[0078] When the amplitude matching result indicates that the characteristic amplitude is within the preset amplitude range, it means that the sensor is in the optimal sensing area, and the output signal has high linearity and reliability. At this time, the system generates an identifier representing the availability status, which is based on the effective characterization information and combined with the characteristic phase angle carrying position information to obtain the data feature value corresponding to the position acquisition data. This composite feature value contains both the "data availability" authentication and the angular information of "where the mover is," providing complete input for subsequent target selection.

[0079] Through steps 701 to 703 above, an adaptive data filtering mechanism based on signal amplitude is constructed. Using the characteristic amplitude as a "gating switch", it can automatically identify and eliminate invalid signals caused by mechanical installation errors (excessive air gap) or strong magnetic interference (magnetic saturation). This design ensures that when the system selects the target receiving device and calculates the position in the subsequent process, it only uses high-quality data that is in the linear operating range and has a high signal-to-noise ratio. This effectively avoids overall positioning deviation or misjudgment caused by abnormal operating conditions of individual sensors, and significantly enhances the robustness and fault tolerance of the position detection of the magnetic drive conveyor system.

[0080] Through the feature extraction calculations in steps 601 to 603 above, the original time-domain analog signal is effectively transformed into spatial vector features with clear physical meaning. By separating and calculating the feature phase angle and feature amplitude, the system can not only obtain the rough position information of the mover, but more importantly, it can use the feature amplitude as an indicator of signal quality to effectively identify and eliminate invalid signals caused by magnetic saturation or excessive air gap. This ensures that the subsequent position calculation relies only on effective data with high signal-to-noise ratio and high linearity, greatly improving the anti-interference capability and positioning robustness of the magnetic drive conveyor system in complex electromagnetic environments.

[0081] Referring to Figure 8, selecting a target receiving device from multiple magnetic receiving devices based on multiple data feature values ​​includes the following steps 801 to 802.

[0082] Step 801: Select at least one magnetic receiving device from multiple magnetic receiving devices whose data feature values ​​contain valid characterization information to obtain a candidate magnetic receiving device.

[0083] Step 802: Select the candidate magnetic receiving device with the smallest difference between the characteristic phase angle and the preset angle value from multiple candidate magnetic receiving devices, and use it as the target receiving device.

[0084] Steps 801 to 802 are described in detail below.

[0085] In some embodiments, the processing system further performs preliminary screening logic based on signal state to eliminate unreliable signal sources from the entire array. The system iterates through and examines the data characteristic values ​​output by all magnetic receiving devices to identify whether they contain identifiers indicating that the signal is within the normal linear operating range. Specifically, the system selects at least one magnetic receiving device from a pool of magnetic receiving devices whose data characteristic values ​​contain valid characterizing information. This means that the system automatically ignores devices marked as invalid due to magnetic saturation, malfunction, or excessive mover distance, retaining only those devices with moderate induction intensity and reliable data, thus obtaining a candidate set of magnetic receiving devices, as shown in the following formula.

[0086]

[0087] A function that represents the extraction of status flags from feature value data packets; It represents effective representation information (indicating that the signal is in the linear operating region and is not saturated). This forms the index set of the selected candidate magnetic receiving devices. This process constructs a high-confidence candidate pool, eliminating noise and outlier data interference for subsequent optimization decisions.

[0088] Furthermore, the system executes an optimal selection strategy based on linearity within the candidate pool to pinpoint the sensor with the highest measurement accuracy. The processor compares and analyzes the characteristic phase angle of each candidate device in the set, searching for the device closest to the ideal operating point. Specifically, it selects the candidate magnetic receiving device with the smallest difference between its characteristic phase angle and a preset angle value from multiple candidate magnetic receiving devices. This preset angle value is typically set to the phase point where the sensor has the best linearity (e.g., 0 degrees corresponding to the zero-crossing point of a sine wave or a specific linear center). Through this comparison, the system can accurately pinpoint the device currently located at the center of the mover's magnetic pole or the optimal sensing position, identifying it as the target receiving device. This device serves as the physical anchor point for subsequently acquiring position data (coarse positioning) and the main reference source for extracting orthogonal signals (fine positioning), as shown in the following formula.

[0089]

[0090] in, Let be the characteristic phase angle of the k-th candidate device. The preset angle value is usually 0 degrees because the Hall sensor has the highest linearity and the smallest measurement error near the zero-crossing point (phase 0) of the sine wave. The index ID of the finally selected target receiving device.

[0091] Through steps 801 to 802 above, threshold filtering is first performed using effective characterization information to ensure the availability of candidate sources; then, linear optimization is performed using phase angle proximity to ensure the accuracy of the final target source. This mechanism fully utilizes the physical characteristic of Hall sensors having the highest linearity at a specific phase, ensuring that the system automatically switches and uses the best-performing sensor in the current array as the measurement benchmark at any time, thereby minimizing measurement errors caused by sensors operating in nonlinear edge regions and improving the accuracy and stability of position detection in the magnetic drive conveyor system.

[0092] Step 403: Select the target data corresponding to the target receiving device and the adjacent data corresponding to the adjacent receiving device from the data collected from multiple locations. The adjacent receiving device is a magnetic receiving device set up adjacent to the target receiving device.

[0093] Step 403 will be described in detail below.

[0094] Next, to perform high-precision phase subdivision, the system needs to construct a set of orthogonal signal pairs. After locking onto the target receiver, the system extracts the target receiver from the position data pool of the entire array. The output "target acquisition data" is used to select a magnetic receiving device that is spatially adjacent to the target (usually differing by about 1 / 4 of the magnetic pole moment or a specific phase difference) as a neighboring receiving device based on the physical arrangement, and extract its output "neighboring acquisition data". The purpose of selecting neighboring devices is to utilize the phase difference between the two signals in space (e.g., approximately 90 degrees) as the sine and cosine components required for subsequent mathematical operations, thereby constructing a complete vector rotation space and providing the necessary orthogonal signal sources for subsequent fine interpolation operations.

[0095] Step 404: Perform coordinate rotation iterative calculation based on the target acquisition data and adjacent acquisition data to obtain the phase increment.

[0096] Step 404 will be described in detail below.

[0097] In some embodiments, the processing system further utilizes digital signal processing algorithms to perform high-precision phase calculation on the extracted orthogonal signal pairs. Specifically, the system employs the Coordinate Rotating Digital Computer (CORDIC) algorithm to map the "target acquisition data" and "adjacent acquisition data" into vector components in a two-dimensional plane coordinate system. Through a series of preset shift and addition / subtraction iteration operations, the vector is driven to rotate until one of the components approaches zero. The accumulated rotation angle during this process is the "phase increment." This phase increment represents the microscopic fine position (i.e., subdivided phase s) of the mover within the current magnetic pole sector, with a resolution far exceeding the accuracy achievable by directly reading the sensor voltage. This achieves micrometer-level positioning within the sensing range of a single sensor, as described below.

[0098] Referring to Figure 9, the phase increment is obtained by performing coordinate rotation iteration calculation based on the target acquisition data and adjacent acquisition data, including the following steps 901 to 905.

[0099] Step 901: In each coordinate rotation iteration, determine the corresponding rotation direction based on the sign of the sinusoidal input component.

[0100] Step 902: Based on the exponential processing of the preset value and the number of iterations, obtain the iteration coefficient term. Based on the cumulative multiplication of the sine input component, the rotation direction, and the iteration coefficient term, obtain the sine update term. Based on the cumulative multiplication of the cosine input component, the rotation direction, and the iteration coefficient term, obtain the cosine update term.

[0101] Step 903: Based on the difference between the sine input component and the cosine update term, obtain the updated sine input component, and based on the sum of the cosine input component and the sine update term, obtain the updated cosine input component.

[0102] Step 904: Perform arctangent processing based on the iteration coefficient term, then multiply by the rotation direction to obtain the angle update term, and obtain the updated rotation angle component based on the cumulative value of the rotation angle component and the angle update term.

[0103] Step 905: Use the updated sine input component as the new sine input component, the updated cosine input component as the new cosine input component, and the updated rotation angle component as the new rotation angle component, and perform coordinate rotation iteration again until the number of iterations reaches the preset number of iterations; wherein, the initial value of the sine input component is the target acquisition data, and the initial value of the cosine input component is the adjacent acquisition data.

[0104] Steps 901 to 905 are described in detail below.

[0105] In some embodiments, the processing system first initiates the logical judgment phase of the Coordinate Rotation Digital Computation (CORDIC) algorithm. During each tiny coordinate rotation iteration, the system first needs to determine the current quadrant of the vector or its direction of deviation from the target axis (usually the X-axis) to decide on the subsequent rotation strategy. Specifically, in each coordinate rotation iteration (e.g., in the i-th iteration), the system determines the corresponding rotation direction based on the sign of the sinusoidal input component (i.e., whether the Y-axis component is greater than 0 or less than 0). If the sinusoidal component is positive, it indicates that the vector is biased upwards and needs to rotate clockwise (the direction is usually marked as -1); otherwise, it rotates counterclockwise (the direction is marked as +1). This judgment provides directional guidance for subsequently "driving" the vector to zero phase, as shown in the following formula.

[0106]

[0107] The direction of rotation This is the current sinusoidal input component.

[0108] Then, the system performs preprocessing of the mathematical quantities required for rotation, using efficient hardware shift operations to replace complex multiplication operations. Based on a preset value (usually base 2) and the exponential processing of the number of iterations, the system obtains the iteration coefficient term, i.e. Subsequently, the system calculates the projection increment of this rotation on each coordinate axis: based on the cumulative product of the sinusoidal input components, the rotation direction, and the iteration coefficients, the sinusoidal update term is obtained, i.e. Simultaneously, based on the cumulative product of the cosine input components, rotation direction, and iteration coefficients, the cosine update term is obtained, i.e. These update terms represent the relative changes in the x and y coordinates of a vector as it rotates within the two-dimensional plane at the current iteration step.

[0109] Afterwards, the system performs a physical update operation on the vector coordinates, completing a "pseudo-rotation." At this point, based on the geometric principles of vector rotation, the system updates the coordinate values ​​using a cross-addition / subtraction method: based on the difference between the sine input component and the cosine update term, the updated sine input component (i.e., the new Y-axis coordinate) is obtained. Based on the accumulated value of the cosine input component and the sine update term, the updated cosine input component (i.e., the new X-axis coordinate) is obtained. Through this calculation, the endpoint of the vector undergoes a position shift in the planar coordinate system, bringing it closer to the target axis (i.e., the Y component tends to zero), thus gradually approaching the true phase angle.

[0110] Furthermore, while rotating the vector, the system simultaneously accumulates and records the total number of angles rotated. The system performs arctangent processing based on the iteration coefficients (usually by consulting a pre-defined arctangent constant table) to determine the inherent angle value corresponding to the current iteration step, then multiplies it by the rotation direction to obtain the angle update term (i.e., whether the rotation increased or decreased the angle). Finally, the system is based on the rotation angle component (i.e., the currently accumulated total angle). The sum of the sum of the sum of the sum of the sum of the sum of the sum of the sums ... This variable acts as a phase accumulator, storing the final, highly accurate phase value at the end of the iteration.

[0111] The system then approximates the theoretical accuracy through an iterative loop mechanism. The system passes the state variables calculated in the preceding steps to the next iteration: updating the sine input component as the new sine input component, updating the cosine input component as the new cosine input component, updating the rotation angle component as the new rotation angle component, and performing coordinate rotation iterations again. This process is repeated until the preset number of iterations (e.g., 8 to 16) is reached, and the phase increment is obtained based on the final rotation angle component. It is particularly important to note that, in order to initiate this iterative process, the initial value of the sine input component is the target acquired data, and the initial value of the cosine input component is the adjacent acquired data, thereby transforming the physically orthogonal signals acquired by the sensor into the initial vector state of the algorithm.

[0112] Through steps 901 to 905 above, complex trigonometric function or square root operations are avoided in embedded chips with limited computing resources. By using simple shifting, addition, subtraction and table lookup operations, the composite vector composed of target acquisition data and adjacent acquisition data is gradually "rotated" to zero phase, thereby accurately resolving the angle information contained in the vector. This iterative convergence method can obtain extremely high phase resolution (i.e. phase increment), effectively converting the analog signal of the sensor array into micron-level micro position readings, providing a key high-precision input for subsequent precise positioning by combining nonlinear compensation data.

[0113] Step 405: Based on the reference supplementary data, phase nonlinear supplementary data, phase increment and initial position data, obtain the target position data of the mover.

[0114] Step 405 will be described in detail below.

[0115] Finally, the system performs final position synthesis and dual error compensation. The processor first uses "preset reference supplementary data" to correct the coarse "initial position data," eliminating macroscopic geometric errors caused by manufacturing and assembly processes. Simultaneously, it uses "phase nonlinearity supplementary data" to perform microscopic corrections on the calculated "phase increment," offsetting local accuracy fluctuations caused by Hall effect nonlinearity or magnetic field distortion. Finally, the corrected macroscopic position and the corrected microscopic increment are numerically superimposed to obtain the high-precision absolute coordinates of the mover throughout its entire stroke, i.e., the "target position data." This layered and decoupled compensation strategy ensures that the final output position data maintains continuity over a long stroke while possessing extremely high local linearity and accuracy, as described below.

[0116] Referring to Figure 10, the target position data of the mover is obtained based on the reference supplementary data, the phase nonlinear supplementary data, the phase increment and the initial position data, including the following steps 1001 to 1003.

[0117] Step 1001: Correct the initial position data based on the supplementary reference data to obtain the corrected reference position data.

[0118] Step 1002: Correct the phase increment based on the phase nonlinearity supplementary data to obtain the corrected nonlinear position data.

[0119] Step 1003: Accumulate the corrected reference position data and the corrected nonlinear position data to obtain the target position data.

[0120] Steps 1001 to 1003 are described in detail below.

[0121] In some embodiments, the processing system performs macroscopic error calibration. The system corrects the coarse initial position data based on previously acquired preset reference supplementary data. This "reference supplementary data" typically corresponds to the high-order portion of fixed-data storage, specifically used to characterize physical installation errors of the magnetic receiver on the stator, array spacing deviations, or sector start-point offsets. By applying this compensation value to the initial position data generated based on the physical coordinates of the target receiver, the system effectively eliminates systematic errors in the macroscopic geometric dimension, thereby obtaining calibrated sector anchor point coordinates, i.e., corrected reference position data, as shown in the following formula.

[0122]

[0123] in, The index ID of the selected target receiving device (sensor). This refers to the location data of the target sensor (i.e., the physical installation coordinates, corresponding to the initial location data). Supplement data to the preset benchmark found by indexing the sensor ID (used to compensate for macroscopic deviations such as installation spacing errors). The corrected reference position data obtained from the calculation.

[0124] Furthermore, the system performs microscopic linearity calibration in parallel or serially. The system corrects the phase increment calculated through coordinate rotation iterations based on phase nonlinearity supplementary data. This "phase nonlinearity supplementary data" corresponds to the low-order portion stored in fixed memory, specifically used to compensate for the nonlinear response of the Hall effect within the sensor and the high-order harmonic distortion of the magnetic field distribution of the magnetic grating. The system uses the original phase increment as an index to find the corresponding microscopic error value and compensates for it, thereby straightening out potentially fluctuating angle values ​​into highly linear displacement values. This yields corrected nonlinear position data that accurately reflects the minute displacement of the mover within the current sector, as shown in the following formula.

[0125]

[0126] in, The phase increment of the CORDIC output (usually in radians), i.e., the above , It is the magnetic pole moment of the magnetic grating (i.e., the physical length corresponding to a complete phase period). To convert the phase increment into a nominal value of physical distance, This is supplementary data for phase nonlinearity found by indexing phase increment (used to compensate for Hall effect nonlinearity or harmonic distortion). The corrected nonlinear position data obtained from the calculation.

[0127] Finally, the system completes the final data synthesis and outputs the absolute coordinates of the entire journey. The processor performs numerical calculations, accumulating and correcting the reference position data and the nonlinear position data. Mathematically, this step superimposes the high-precision displacement within the micro-sector onto the high-accuracy macro-sector starting reference to obtain the final output target position data. As shown in the formula below.

[0128]

[0129] Through this synthesis, the system integrates calibration results from two different dimensions to obtain the target position data of the mover. This data is output through a standard interface, representing the precise linear absolute position of the mover relative to the stator.

[0130] Through steps 1001 to 1003 above, by correcting the macroscopic reference error and the microscopic nonlinear error respectively, the contradiction between the traditional single compensation method and the difficulty in balancing the continuity of large stroke and the high precision of local areas is resolved. Correcting the reference position data ensures a smooth transition when the mover crosses different sensors, eliminating step jumps; while correcting the nonlinear position data significantly improves the linearity within the sensor's sensing range. The combination of the two enables the magnetic drive conveyor system using a low-cost Hall sensor array to output micron-level industrial-grade positioning data, greatly improving the system's cost-effectiveness and control performance.

[0131] Referring to Figure 11, it is a schematic diagram of the main information flow structure of a linear incremental feedback device provided in an embodiment of this application. Figure 11 clearly shows the entire data link from physical signal sensing to final data output. The system mainly includes three input data streams: the first stream is the raw magnetic data A sensed by the linear sensor array (S1~Sn+2) during the movement of the mover magnetic grating, which is converted into digital data B by the analog-to-digital converter (ADC) chip and then enters the CPU; the second stream is the reference voltage data C provided by the reference chip, which directly enters the CPU for signal normalization and error correction; the third stream is the external compensation positioning data (i.e., true value data) acquired during the calibration stage. These three data streams converge in the algorithm processing unit inside the CPU (i.e., in Figure 11). At the node, the processing system executes core algorithms including feature extraction, coordinate rotation iteration, and decoupling compensation, ultimately calculating the precise location information P and sending it outward through the communication interface.

[0132] Referring to Figure 12, a flowchart of the program processing of a linear incremental feedback device provided in an embodiment of this application is shown. As shown in Figure 12, the process is mainly divided into two branches: "external compensation mode" and "position calculation mode". After the program is powered on, it first determines whether to enter the compensation mode. If it does, the system will receive "external position data" transmitted by an external high-precision positioning device through the interface, and compare it with the original sensor data to generate and update "fixed storage data" (i.e., a lookup table containing reference supplementary data and phase nonlinear supplementary data). If the position calculation mode is entered, the process follows a "coarse-to-fine" strategy: first, the sine and cosine values ​​of all sensors are calculated to generate characteristic values ​​R (R value determination), based on which the coarse position of the magnetic grating is estimated and the specific target sensor is locked; then, the signal is conditioned using the reference voltage value (VREF) and the CORDIC algorithm is executed for fine phase calculation; finally, the calculated phase increment is compared and synthesized with the calibration value in the "fixed storage data" (incremental value confirmation), thereby outputting a high-precision incremental value after double correction.

[0133] Through steps 401 to 405 above, by employing a combined strategy of "feature extraction to lock coarse position" and "coordinate rotation iterative calculation of fine phase," the hardware resources of the magnetic receiving device array are fully utilized. Furthermore, by introducing a dual decoupling compensation mechanism that includes reference supplementary data and phase nonlinearity supplementary data, macroscopic installation errors (such as spacing deviations) and microscopic signal distortions (such as harmonic nonlinearities) of the sensor array are independently corrected. This mechanism effectively solves the problems of position jumps and insufficient accuracy caused by poor sensor consistency or uneven magnetic field distribution in existing technologies. It enables low-cost Hall sensor arrays to achieve measurement accuracy comparable to expensive optical grating rulers, significantly improving the stability and reliability of the magnetic drive conveyor system in closed-loop control.

[0134] In one example, this embodiment is applied to a high-precision magnetic drive conveyor system. The system includes a long-stroke stator track, within which several linear Hall sensors (e.g., spaced 5mm apart) are closely arranged along a straight line. to The mover has a matching permanent magnet grating at its bottom. Before the system is put into operation, a calibration process is performed: a high-precision laser interferometer is used to drive the mover through its full stroke, and the original measurement error of the sensor array is recorded. Through decoupling calculation, the error is separated into two sets of data stored in the controller's non-volatile memory: one set is "preset reference supplementary data" corresponding to macroscopic geometric installation errors (stored in the high bit of the lookup table), and the other set is "phase nonlinearity supplementary data" corresponding to the microscopic nonlinearity and harmonic distortion of the Hall element (stored in the low bit of the lookup table).

[0135] When the mover is running on the track in real time, the controller's analog-to-digital converter (ADC) performs multi-channel synchronous scanning of all Hall sensors to acquire the raw analog signals, and then sequentially performs debiasing, normalization, and temperature drift compensation to obtain standard "position acquisition data." Subsequently, the processor extracts features from each data stream, calculating its respective "data feature value" (R value) using trigonometric inverse tangent operations. The processor first checks the amplitude component in the R value, eliminating invalid sensors that are magnetically saturated (amplitude too large) or have excessively large air gaps (amplitude too small); then, from the remaining valid sensors, it selects the sensor with the smallest phase angle difference at the zero-degree linear center (e.g., [missing information]). (This is) used as a "target receiving device". The processor reads... The physical installation coordinates, combined with their characteristic phase angles, are used to obtain the "initial position data" of the mover.

[0136] To achieve micrometer-level displacement accuracy, the processor locks onto the target sensor. and its physically adjacent sensors The processor extracts the orthogonal signal pairs from their outputs as input. Using the CORDIC (Coordinate Rotating Digital Computer) algorithm, the processor rotates the vector synthesized from these two signals to zero phase through iterative shift and addition / subtraction operations, thereby calculating a high-precision "phase increment." This process avoids complex square root and floating-point operations, ensuring real-time performance under high-speed motion.

[0137] Finally, the dual decoupling compensation stage begins. The processor uses the current sector ID as an index to retrieve the corresponding "reference supplementary data" from the lookup table to macroscopically correct the "initial position data," eliminating installation errors. Simultaneously, using the calculated phase increment as an index, it retrieves "phase nonlinearity supplementary data" to microscopically correct the "phase increment," eliminating waveform distortion. The system accumulates the corrected macroscopic position and the corrected microscopic increment, ultimately outputting the mover's "target position data." This data not only achieves seamless continuity throughout the entire stroke but also possesses extremely high linearity locally, meeting the stringent requirements of scenarios such as semiconductor wafer transport or precision assembly.

[0138] This application embodiment also provides a mover position data acquisition device for a magnetic drive conveyor system, which can implement the above-mentioned mover position data acquisition method for the magnetic drive conveyor system. Referring to FIG13, the device 1300 includes: a data acquisition module 1310, used to acquire position acquisition data simultaneously collected by multiple magnetic receiving devices for the mover when the mover is running on one side of the stator, and to acquire preset reference supplementary data and phase nonlinearity supplementary data; and an initial data determination module 1320, used to perform feature extraction calculation based on each position acquisition data to obtain the corresponding data feature value, and to select a target receiving device from multiple magnetic receiving devices based on multiple data feature values. Then, based on the setting position data of the target receiving device, the initial position data of the mover is obtained; the data acquisition determination module 1330 is used to select the target acquisition data corresponding to the target receiving device and the adjacent acquisition data corresponding to the adjacent receiving device from multiple position acquisition data, wherein the adjacent receiving device is a magnetic receiving device set adjacent to the target receiving device; the phase increment determination module 1340 is used to perform coordinate rotation iteration calculation based on the target acquisition data and the adjacent acquisition data to obtain the phase increment; the target data determination module 1350 is used to obtain the target position data of the mover based on the reference supplementary data, the phase nonlinear supplementary data, the phase increment and the initial position data.

[0139] In some embodiments, the initial data determination module 1320 is further configured to: extract data from each corresponding location based on the arrangement position and phase difference of the multiple magnetic receiving devices to obtain the sine component and cosine component corresponding to the data collected at each location; for each data collected at a location, substitute the corresponding sine component and cosine component into the trigonometric inverse tangent function for calculation to obtain the characteristic phase angle, and calculate the vector amplitude based on the sine component and cosine component to obtain the characteristic amplitude; and obtain the data feature value corresponding to the data collected at the location based on the characteristic amplitude and the characteristic phase angle.

[0140] In some embodiments, the initial data determination module 1320 is further configured to: match the feature amplitude with a preset amplitude range to obtain an amplitude matching result; when the amplitude matching result indicates that the feature amplitude is not within the preset amplitude range, obtain the data feature value corresponding to the location acquisition data based on invalid representation information; when the amplitude matching result indicates that the feature amplitude is within the preset amplitude range, obtain the data feature value corresponding to the location acquisition data based on valid representation information and feature phase angle.

[0141] In some embodiments, the initial data determination module 1320 is further configured to: select at least one magnetic receiving device from a plurality of magnetic receiving devices whose data feature values ​​contain valid characterization information to obtain a candidate magnetic receiving device; and select the candidate magnetic receiving device with the smallest difference between the feature phase angle and the preset angle value from a plurality of candidate magnetic receiving devices as the target receiving device.

[0142] In some embodiments, the phase increment determination module 1340 is further configured to: determine the corresponding rotation direction based on the sign of the sine input component in each coordinate rotation iteration; obtain an iteration coefficient term based on the exponential processing of a preset value and the number of iterations; obtain a sine update term based on the cumulative product of the sine input component, the rotation direction, and the iteration coefficient term; obtain a cosine update term based on the cumulative product of the cosine input component, the rotation direction, and the iteration coefficient term; obtain an updated sine input component based on the difference between the sine input component and the cosine update term; and obtain an updated sine input component based on the cumulative sum of the cosine input component and the sine update term. The process involves obtaining the updated cosine input component; performing arctangent processing based on the iteration coefficient term, then multiplying by the rotation direction to obtain the angle update term; and obtaining the updated rotation angle component based on the cumulative value of the rotation angle component and the angle update term; using the updated sine input component as the new sine input component, the updated cosine input component as the new cosine input component, and the updated rotation angle component as the new rotation angle component, and performing coordinate rotation iterations again until the number of iterations reaches the preset number of iterations; wherein, the initial value of the sine input component is the target acquisition data, and the initial value of the cosine input component is the adjacent acquisition data.

[0143] In some embodiments, the target data determination module 1350 is further configured to: correct the initial position data based on the reference supplementary data to obtain corrected reference position data; correct the phase increment based on the phase nonlinear supplementary data to obtain corrected nonlinear position data; and accumulate the corrected reference position data and the corrected nonlinear position data to obtain target position data.

[0144] In some embodiments, the data acquisition module 1310 is further configured to: perform multi-channel synchronous scanning of multiple magnetic receiving devices using an analog-to-digital conversion unit to obtain multiple initial position acquisition data, and acquire temperature data of the magnetic drive conveyor system; perform bias removal processing on each initial position acquisition data to obtain filtered position acquisition data; perform normalization mapping on each filtered position acquisition data based on a standard numerical range to obtain normalized position acquisition data; and perform correction processing on each normalized position acquisition data based on the temperature coefficient corresponding to the temperature data to obtain position acquisition data.

[0145] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, the specific implementation of the mover position data acquisition device of the magnetic drive conveyor system is basically the same as the specific implementation of the mover position data acquisition method of the magnetic drive conveyor system, and will not be repeated here.

[0146] Please refer to Figure 14, which is a schematic diagram of a magnetic drive motor conveying system provided in an exemplary embodiment of this application. The magnetic drive motor conveying system 1400 includes a conveying line body formed by sequentially splicing multiple stators 1410 along the conveying line direction, a mover 1420 magnetically coupled to the stators 1410, and a servo control component 1430. The servo control component 1430 includes a processor 1431 and a memory 1432.

[0147] The processor 1431 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application. The memory 1432 can be implemented using ROM (Read-Only Memory), static storage device, dynamic storage device, or RAM (Random Access Memory). The memory 1432 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 1432, and the processor 1431 calls and executes the mover position data acquisition method of the magnetic drive conveyor system in the embodiments of this application.

[0148] This application embodiment also provides a storage medium, which is a computer-readable storage medium, storing a computer program. When the computer program is executed by a processor, it implements the above-described method for acquiring the position data of the moving part of the magnetic drive conveyor system.

[0149] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0150] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0151] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0152] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0153] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0154] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0155] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0156] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. The coupling or direct coupling or communication connection between the shown or discussed units may be through some interfaces, or indirect coupling or communication connection between the apparatus or units, and may be electrical, mechanical, or other forms.

[0157] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0158] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0159] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0160] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A method for acquiring the position data of the mover in a magnetic drive conveyor system, characterized in that, The magnetic drive conveying system includes a stator and a mover. The stator is equipped with multiple magnetic receiving devices, and the mover is equipped with a magnetic transmitting device. The method includes: when the mover is running on one side of the stator, acquiring position acquisition data simultaneously collected by the multiple magnetic receiving devices for the mover, and acquiring preset reference supplementary data and phase nonlinear supplementary data; performing feature extraction calculation based on each position acquisition data to obtain corresponding data feature values, and selecting a target receiving device from the multiple magnetic receiving devices based on the multiple data feature values, and obtaining the initial position data of the mover based on the set position data of the target receiving device; selecting target acquisition data corresponding to the target receiving device and adjacent acquisition data corresponding to adjacent receiving devices from the multiple position acquisition data, wherein the adjacent receiving device is a magnetic receiving device arranged adjacent to the target receiving device; performing coordinate rotation iterative calculation based on the target acquisition data and the adjacent acquisition data to obtain the phase increment; and obtaining the target position data of the mover based on the reference supplementary data, the phase nonlinear supplementary data, the phase increment, and the initial position data.

2. The method for acquiring the position data of the moving part of the magnetic drive conveyor system according to claim 1, characterized in that, The step of performing feature extraction calculations based on the data collected at each of the locations to obtain corresponding data feature values ​​includes: extracting sine and cosine components corresponding to each of the data collected at each of the locations based on the arrangement positions and phase differences of the multiple magnetic receiving devices; for each of the data collected at the locations, substituting the corresponding sine and cosine components into the trigonometric inverse tangent function for calculation to obtain a feature phase angle, and calculating the vector amplitude based on the sine and cosine components to obtain a feature amplitude; and obtaining the data feature value corresponding to the data collected at each location based on the feature amplitude and the feature phase angle.

3. The method for acquiring the position data of the moving part of the magnetic drive conveyor system according to claim 2, characterized in that, The step of obtaining the data feature value corresponding to the location acquisition data based on the feature amplitude and the feature phase angle includes: matching the feature amplitude with a preset amplitude range to obtain an amplitude matching result; when the amplitude matching result indicates that the feature amplitude is not within the preset amplitude range, obtaining the data feature value corresponding to the location acquisition data based on invalid representation information; when the amplitude matching result indicates that the feature amplitude is within the preset amplitude range, obtaining the data feature value corresponding to the location acquisition data based on valid representation information and the feature phase angle.

4. The method for acquiring the position data of the moving part of the magnetic drive conveyor system according to claim 1, characterized in that, The step of selecting a target receiving device from a plurality of magnetic receiving devices based on a plurality of data feature values ​​includes: selecting at least one magnetic receiving device from the plurality of magnetic receiving devices whose data feature values ​​contain valid characterization information to obtain a candidate magnetic receiving device; and selecting the candidate magnetic receiving device with the smallest difference between its feature phase angle and a preset angle value from the plurality of candidate magnetic receiving devices as the target receiving device.

5. The method for acquiring the position data of the moving part of the magnetic drive conveyor system according to claim 1, characterized in that, The step of performing coordinate rotation iterative calculation based on the target acquisition data and the adjacent acquisition data to obtain the phase increment includes: in each coordinate rotation iteration, determining the corresponding rotation direction based on the sign of the sine input component; obtaining an iteration coefficient term based on the exponential processing of a preset value and the number of iterations; obtaining a sine update term based on the cumulative product of the sine input component, the rotation direction, and the iteration coefficient term; obtaining a cosine update term based on the cumulative product of the cosine input component, the rotation direction, and the iteration coefficient term; obtaining an updated sine input component based on the difference between the sine input component and the cosine update term; and obtaining an updated sine input component based on the difference between the cosine input component and the sine update term. The accumulated value of the terms is used to obtain the updated cosine input component; the arctangent is processed based on the iteration coefficient term, and then multiplied by the rotation direction to obtain the angle update term, and the updated rotation angle component is obtained based on the accumulated value of the rotation angle component and the angle update term; the updated sine input component is used as the new sine input component, the updated cosine input component is used as the new cosine input component, and the updated rotation angle component is used as the new rotation angle component, and the coordinate rotation iteration is performed again until the number of iterations reaches the preset number of iterations; wherein, the initial value of the sine input component is the target acquired data, and the initial value of the cosine input component is the adjacent acquired data.

6. The method for acquiring the position data of the moving part of the magnetic drive conveyor system according to claim 1, characterized in that, The step of obtaining the target position data of the mover based on the reference supplementary data, the phase nonlinear supplementary data, the phase increment, and the initial position data includes: correcting the initial position data based on the reference supplementary data to obtain corrected reference position data; correcting the phase increment based on the phase nonlinear supplementary data to obtain corrected nonlinear position data; and accumulating the corrected reference position data and the corrected nonlinear position data to obtain the target position data.

7. The method for acquiring the position data of the mover in a magnetic drive conveyor system according to claim 1, characterized in that, The step of acquiring position acquisition data simultaneously collected by multiple magnetic receiving devices for the mover includes: using an analog-to-digital converter to perform multi-channel synchronous scanning of multiple magnetic receiving devices to obtain multiple initial position acquisition data, and acquiring temperature data of the magnetic drive conveyor system; performing bias removal processing on each initial position acquisition data to obtain filtered position acquisition data; performing normalization mapping on each filtered position acquisition data based on a standard numerical range to obtain normalized position acquisition data; and performing correction processing on each normalized position acquisition data based on the temperature coefficient corresponding to the temperature data to obtain the position acquisition data.

8. A device for acquiring the position data of the mover in a magnetic drive conveyor system, characterized in that, The magnetic drive conveying system includes a stator and a mover. The stator is equipped with multiple magnetic receiving devices, and the mover is equipped with a magnetic transmitting device. Each device includes: a data acquisition module, used to acquire position data simultaneously collected by the multiple magnetic receiving devices for the mover when the mover is running on one side of the stator, and to acquire preset reference supplementary data and phase nonlinearity supplementary data; and an initial data determination module, used to perform feature extraction calculations based on each position data acquisition to obtain corresponding data feature values, and to select a target receiving device from the multiple magnetic receiving devices based on the multiple data feature values, and then based on the target receiving device... The initial position data of the mover is obtained by setting position data; the data acquisition determination module is used to select target acquisition data corresponding to the target receiving device and adjacent acquisition data corresponding to adjacent receiving devices from multiple position acquisition data, wherein the adjacent receiving device is the magnetic receiving device arranged adjacent to the target receiving device; the phase increment determination module is used to perform coordinate rotation iterative calculation based on the target acquisition data and the adjacent acquisition data to obtain the phase increment; the target data determination module is used to obtain the target position data of the mover based on the reference supplementary data, the phase nonlinear supplementary data, the phase increment and the initial position data.

9. An electronic device, characterized in that, The system includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method for acquiring the position data of the moving part of the magnetic drive conveyor system according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method for acquiring the position data of the moving part of the magnetic drive conveyor system according to any one of claims 1 to 7.

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