Method, system and equipment for measuring rotating speed of motor based on electromagnetic induction

Abstract

The invention discloses a motor rotating speed measuring method, system and equipment based on electromagnetic induction, and belongs to the field of motors. According to the embodiment of the invention, firstly, a magnetostriction resonance signal and an electromagnetic induction signal of a motor rotating shaft are obtained; performing frequency domain conversion on the magnetostrictive resonance signal to obtain magnetostrictive resonance frequency domain characteristics; performing frequency domain conversion on the electromagnetic induction signal to obtain electromagnetic induction frequency domain characteristics; frequency domain energy spectrum fusion reconstruction is carried out on the magnetostriction frequency domain characteristics and the electromagnetic induction frequency domain characteristics based on the frequency band matching relation between the magnetostriction frequency domain characteristics and the electromagnetic induction frequency domain characteristics, and a dual-frequency coupling energy spectrum is generated; and finally, determining the rotating speed of the motor rotating shaft according to the frequency position and amplitude distribution of the energy peak in the dual-frequency coupling energy spectrum, thereby improving the measurement precision of the rotating speed of the motor in a complex environment.

Description

Technical Field

[0001] This application belongs to the field of motors, and particularly relates to a method, system, device, and computer-readable storage medium for measuring the rotational speed of a motor based on electromagnetic induction. Background Art

[0002] The measurement of motor rotational speed is a core link in industrial automation control, equipment status monitoring, and energy efficiency optimization. Its accuracy and reliability directly affect the system regulation efficiency. In a motor drive system, rotational speed is not only a key parameter for evaluating mechanical load characteristics but also a fundamental basis for realizing closed-loop control, fault diagnosis, and energy consumption management. Especially in high-speed, high-precision, or complex working condition scenarios, real-time and accurate rotational speed data plays an irreplaceable role in ensuring equipment stability, extending service life, and improving energy efficiency. Therefore, developing a measurement method with strong adaptability and excellent anti-interference ability has become the focus of technical research.

[0003] In the prior art, measurement methods based on the pulse counting principle are mainstream, such as the optoelectronic reflection method, the magnetoelectric method, and the Hall switch detection method. These methods calculate the rotational speed by counting the number of pulses within a fixed time. Although the structure is simple, there are significant limitations in practical applications. For example, the optoelectronic method is easily affected by ambient light interference, resulting in pulse signal distortion; the magnetoelectric method depends on the precise alignment of the magnet and the coil, and mechanical vibration or electromagnetic noise is likely to cause missed detection or false detection; while the Hall sensor can improve the resolution through multi-pole layout, but due to the magnet installation error and temperature drift, it is difficult to balance the dynamic response characteristics and measurement stability. In addition, although the encoder can achieve high-precision measurement, its cost is high and the installation conditions are demanding, making it difficult to be popularized and applied in harsh working conditions. The above methods all rely on the time-domain signal processing of a single physical quantity (light, magnetism), making it difficult to effectively suppress noise interference, and cumulative errors are likely to occur when the rotational speed changes suddenly or the load fluctuates, resulting in a decrease in measurement accuracy. Summary of the Invention

[0004] Embodiments of this application provide a method, system, device, and computer-readable storage medium for measuring the rotational speed of a motor based on electromagnetic induction, which can improve the measurement accuracy of the motor rotational speed in a complex environment.

[0005] In the first aspect, embodiments of this application provide a method for measuring the rotational speed of a motor based on electromagnetic induction, and the method includes:

[0006] Obtain the magnetostrictive resonance signal and the electromagnetic induction signal of the motor shaft;

[0007] Perform frequency-domain conversion on the magnetostrictive resonance signal to obtain the magnetostrictive resonance frequency-domain characteristics;

[0008] Perform frequency-domain conversion on the electromagnetic induction signal to obtain the electromagnetic induction frequency-domain characteristics;

[0009] Based on the frequency band matching relationship between the magnetostrictive frequency domain characteristics and the electromagnetic induction frequency domain characteristics, perform frequency domain energy spectrum fusion reconstruction on the magnetostrictive frequency domain characteristics and the electromagnetic induction frequency domain characteristics to generate a dual-frequency coupled energy spectrum;

[0010] Determine the rotational speed of the motor shaft according to the frequency position and amplitude distribution of the energy peaks in the dual-frequency coupled energy spectrum.

[0011] In an implementable embodiment, based on the frequency band matching relationship between the magnetostrictive frequency domain characteristics and the electromagnetic induction frequency domain characteristics, perform frequency domain energy spectrum fusion reconstruction on the magnetostrictive frequency domain characteristics and the electromagnetic induction frequency domain characteristics to generate a dual-frequency coupled energy spectrum, including:

[0012] Based on the frequency band matching relationship between the magnetostrictive frequency domain characteristics and the electromagnetic induction frequency domain characteristics, perform synchronous decoupling processing on the resonant frequency band of the magnetostrictive frequency domain characteristics and the fundamental frequency band of the electromagnetic induction frequency domain characteristics through duplexer-type dynamic frequency division to obtain independent band energy sub-components with phase synchronization constraints;

[0013] According to the frequency domain energy distribution characteristics of the independent band energy sub-components, perform dynamic matching on the energy weights of the resonant frequency band of the magnetostrictive frequency domain characteristics and the fundamental frequency band of the electromagnetic induction frequency domain characteristics to construct a frequency domain energy weight matrix;

[0014] Based on the band energy proportion in the frequency domain energy weight matrix, perform weighted superposition operation on the high-frequency harmonic energy sub-components and the low-frequency fundamental energy sub-components in the independent band energy sub-components, and use the phase offset parameter compensation algorithm to correct the phase difference during the superposition process to generate a dual-frequency coupled energy spectrum.

[0015] In an implementable embodiment, based on the frequency band matching relationship between the magnetostrictive frequency domain characteristics and the electromagnetic induction frequency domain characteristics, perform synchronous decoupling processing on the resonant frequency band of the magnetostrictive frequency domain characteristics and the fundamental frequency band of the electromagnetic induction frequency domain characteristics through duplexer-type dynamic frequency division to obtain independent band energy sub-components with phase synchronization constraints, including:

[0016] Based on the frequency band matching relationship between the magnetostrictive frequency domain characteristics and the electromagnetic induction frequency domain characteristics, perform coupled screening processing on the overlapping region of the resonant frequency band and the fundamental frequency band using frequency domain energy threshold determination to generate a dynamic frequency domain mapping model and an energy coupling feature set;

[0017] According to the dynamic frequency domain mapping model, perform multi-order frequency band segmentation processing on the resonant frequency band using duplexer-type dynamic frequency division, and at the same time perform frequency band isolation processing on the fundamental frequency band based on frequency point adaptive scanning to generate dynamic isolation frequency bands;

[0018] Use phase synchronization constraint modeling to perform error feedback adjustment processing on the phase offset amount of the energy sub-components in the dynamic isolation frequency band to generate initial independent band energy sub-components including a homologous phase reference;

[0019] Using a dynamic threshold adaptive adjustment algorithm to identify and process cross-band interference components in the frequency-domain distribution characteristics of the initial independent frequency-band energy sub-sets, obtaining an interference component set. Based on the initial independent frequency-band energy sub-sets and the interference component set, through frequency-domain energy aggregation, energy component reconstruction processing is performed to generate independent frequency-band energy sub-sets.

[0020] In an implementable embodiment, according to the dynamic frequency-domain mapping model, a duplexer-type dynamic frequency division is used to perform multi-order frequency band splitting processing on the resonant frequency band, and at the same time, based on frequency point adaptive scanning, frequency band isolation processing is performed on the fundamental frequency band to generate a dynamic isolation frequency band, including:

[0021] Based on the frequency band overlap coefficient distribution of the dynamic frequency-domain mapping model, gradient decomposition processing of multi-order frequency band splitting is performed on the resonant frequency band to generate a set of split frequency bands with frequency band boundary constraints;

[0022] Through frequency point adaptive scanning, dynamic weighted analysis processing is performed on the frequency point energy distribution characteristics of the fundamental frequency band to generate a frequency point energy mapping relationship including frequency band isolation weight coefficients;

[0023] Based on the set of split frequency bands and the frequency point energy mapping relationship, a duplexer-type dynamic frequency division is used to perform frequency band energy reallocation processing on the resonant frequency band and the fundamental frequency band to generate a set of dynamic isolation frequency bands with frequency band isolation degree constraints.

[0024] In an implementable embodiment, according to the frequency position and amplitude distribution of the energy peak in the dual-frequency coupled energy spectrum, the rotational speed of the motor shaft is determined, including:

[0025] Based on the interval between the magnetostrictive main peak frequency position and the electromagnetic induction fundamental frequency position in the dual-frequency coupled energy spectrum, through the linear relationship between the frequency interval and the motor rotational speed, an initial rotational speed value is obtained;

[0026] Using the integer multiple relationship of the harmonic interval between the magnetostrictive secondary peak frequency position and the main peak frequency position in the dual-frequency coupled energy spectrum, harmonic factor extraction is performed on the initial rotational speed value to obtain a harmonic factor;

[0027] According to the ratio of the magnetostrictive main peak amplitude distribution to the secondary peak amplitude distribution in the dual-frequency coupled energy spectrum, and the ratio of the electromagnetic induction fundamental frequency amplitude distribution to the magnetostrictive main peak amplitude distribution in the dual-frequency coupled energy spectrum, dynamic coupling operation processing is performed to generate a dynamic coupling coefficient;

[0028] Based on the harmonic factor and the dynamic coupling coefficient, amplitude-frequency joint calibration processing is performed on the initial rotational speed value to determine the rotational speed of the motor shaft.

[0029] In an implementable embodiment, based on the harmonic factor and the dynamic coupling coefficient, an amplitude-frequency joint calibration process is performed on the initial rotational speed value to determine the rotational speed of the motor shaft, including:

[0030] Based on the product relationship between the harmonic factor and the initial rotational speed value, the harmonic distortion component of the initial rotational speed value is corrected to generate a modulated rotational speed intermediate value. The product relationship is determined based on the integer reciprocal relationship of the harmonic interval between the magnetostrictive secondary peak frequency position and the main peak frequency position in the dual-frequency coupling energy spectrum. The harmonic distortion component is determined by the integer multiple relationship of the harmonic interval between the magnetostrictive secondary peak frequency position and the main peak frequency position in the dual-frequency coupling energy spectrum;

[0031] Using the proportional relationship between the dynamic coupling coefficient and the modulated rotational speed intermediate value, the amplitude-frequency coupling error of the modulated rotational speed intermediate value is compensated to obtain a compensated rotational speed intermediate value. The proportional relationship is determined by the ratio of the magnetostrictive main peak amplitude distribution to the secondary peak amplitude distribution in the dual-frequency coupling energy spectrum, and the ratio of the electromagnetic induction fundamental frequency amplitude distribution to the magnetostrictive main peak amplitude distribution;

[0032] According to the joint weight of the harmonic factor and the dynamic coupling coefficient, the temperature drift component of the compensated rotational speed intermediate value is suppressed to obtain the rotational speed of the motor shaft. The joint weight is dynamically adjusted according to the distribution characteristics of the harmonic factor and the dynamic coupling coefficient in the dual-frequency coupling energy spectrum.

[0033] In an implementable embodiment, obtaining the magnetostrictive resonance signal and the electromagnetic induction signal of the motor shaft includes:

[0034] Based on the preset alternating magnetic field excitation parameters, axial magnetization excitation is performed on the motor shaft to obtain an initial magnetostrictive resonance signal. The preset alternating magnetic field excitation parameters include the excitation frequency and magnetic field strength matching the magnetostrictive coefficient of the motor shaft material;

[0035] Using a preset toroidal induction coil to inductively capture the alternating magnetic field generated when the motor shaft rotates to obtain an initial electromagnetic induction signal. The axial installation position of the preset toroidal induction coil maintains a preset distance from the application position of the magnetization excitation;

[0036] Based on the propagation delay of the trigger frequency and the preset distance in the preset alternating magnetic field excitation parameters, the initial magnetostrictive resonance signal and the initial electromagnetic induction signal are synchronously collected to obtain the magnetostrictive resonance signal and the electromagnetic induction signal.

[0037] In a second aspect, an embodiment of the present application provides a motor speed measurement system based on electromagnetic induction. The system includes:

[0038] An acquisition module for acquiring the magnetostrictive resonance signal and the electromagnetic induction signal of the motor shaft;

[0039] A conversion module, configured to perform frequency-domain conversion on the magnetostrictive resonance signal to obtain the magnetostrictive resonance frequency-domain feature;

[0040] The conversion module is further configured to perform frequency-domain conversion on the electromagnetic induction signal to obtain the electromagnetic induction frequency-domain feature;

[0041] A generation module, configured to perform frequency-domain energy spectrum fusion reconstruction on the magnetostrictive frequency-domain feature and the electromagnetic induction frequency-domain feature based on the frequency band matching relationship between the magnetostrictive frequency-domain feature and the electromagnetic induction frequency-domain feature, and generate a dual-frequency coupled energy spectrum;

[0042] A determination module, configured to determine the rotation speed of the motor shaft according to the frequency position and amplitude distribution of the energy peak in the dual-frequency coupled energy spectrum.

[0043] In a third aspect, an embodiment of the present application provides an electronic device, including: a processor, and a memory storing computer program instructions; the processor reads and executes the computer program instructions to implement the method for measuring the rotation speed of a motor based on electromagnetic induction in any one of the implementation manners of the first aspect.

[0044] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which computer program instructions are stored, and when the computer program instructions are executed by a processor, the method for measuring the rotation speed of a motor based on electromagnetic induction in any one of the implementation manners of the first aspect is implemented.

[0045] The method, system, device, and computer-readable storage medium for measuring the rotation speed of a motor based on electromagnetic induction according to the embodiments of the present application synchronously acquire the magnetostrictive resonance signal and the electromagnetic induction signal of the motor shaft, perform frequency-domain conversion on both of them respectively, and perform energy spectrum fusion reconstruction based on the frequency band matching relationship, and finally determine the rotation speed according to the peak characteristics of the dual-frequency coupled energy spectrum. It avoids the defects of weak noise suppression ability in time-domain signal processing and significant cumulative error under sudden working conditions, and effectively solves the problem of reduced accuracy caused by environmental interference, installation error, etc. in traditional single physical quantity measurement.

[0046] Furthermore, by converting the magnetostrictive resonance signal and the electromagnetic induction signal into the frequency domain respectively, the effective components and noise in the signals can be separated, avoiding the direct influence of environmental light and electromagnetic interference on the time-domain characteristics of a single signal, and improving the anti-interference ability. By matching the frequency band energy distributions of magnetostriction and electromagnetic induction and fusing the complementary characteristics of the two in the frequency domain, the significance of the energy peak related to the rotation speed is enhanced, and the problem of missed detection or false detection caused by the weakening of a single sensor signal in the traditional method is solved. Based on the joint analysis of the frequency position and amplitude distribution of the energy peak, the instantaneous changes during rotation speed mutation or load fluctuation can be captured in real time, overcoming the cumulative error generated in the time-domain integration process of the pulse counting method, and significantly improving the dynamic measurement accuracy and stability. Brief Description of the Drawings

[0047] To more clearly illustrate the technical solutions of the embodiments of the present application, the accompanying drawings required for use in the embodiments of the present application will be briefly introduced below. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can also be obtained based on these drawings.

[0048] Figure 1 is a schematic flowchart of a method for measuring the rotational speed of a motor based on electromagnetic induction provided by an embodiment of the present application;

[0049] Figure 2 is a schematic structural diagram of a system for measuring the rotational speed of a motor based on electromagnetic induction provided by an embodiment of the present application;

[0050] Figure 3 is a schematic hardware structure diagram of an electronic device provided by an embodiment of the present application. Detailed Embodiments

[0051] The features and exemplary embodiments of various aspects of the present application will be described in detail below. To make the objectives, technical solutions, and advantages of the present application clearer and more understandable, the present application will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application and not to limit the present application. For those skilled in the art, the present application can be implemented without some of these specific details. The following description of the embodiments is only to provide a better understanding of the present application by showing examples of the present application.

[0052] It should be noted that, in this document, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise", or any other variant thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or device including a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or device. Without further limitation, elements defined by the statement "including..." do not exclude the presence of additional identical elements in the process, method, article, or device including the said elements.

[0053] To solve the problems of the prior art, embodiments of the present application provide a method, system, device, and computer-readable storage medium for measuring the rotational speed of a motor based on electromagnetic induction. The method for measuring the rotational speed of a motor based on electromagnetic induction provided by the embodiments of the present application will be introduced first below.

[0054] Figure 1 The flowchart of the motor speed measurement method based on electromagnetic induction provided by an embodiment of the present application is shown. As Figure 1 shown, the method includes steps S110 to S150.

[0055] S110: Obtain the magnetostrictive resonance signal and the electromagnetic induction signal of the motor shaft.

[0056] In the embodiment of the present application, the magnetostrictive resonance signal is a resonance signal obtained by applying an alternating magnetic field excitation to the motor shaft and utilizing the magnetostrictive effect of the shaft material (the material deforms and generates mechanical vibration in the magnetic field); the electromagnetic induction signal is an induced electromotive force signal generated by the induction coil capturing the alternating magnetic field when the shaft rotates. The two signals respectively reflect the mechanical vibration characteristics and electromagnetic characteristics of the shaft. Apply an alternating magnetic field with a preset frequency to the axial direction of the motor shaft (such as through an excitation coil) to trigger the magnetostrictive effect of the shaft material and generate a resonance signal; at the same time, collect the induced voltage signal caused by the change of the alternating magnetic field when the shaft rotates through a circular induction coil (maintaining a preset distance from the magnetization excitation device).

[0057] Magnetostriction refers to the phenomenon that ferromagnetic materials (such as nickel, cobalt alloys or Terfenol-D giant magnetostrictive materials) under the action of an external alternating magnetic field, the internal lattice of the material deforms due to the rotation or rearrangement of magnetic domains. The deformation amplitude has a non-linear relationship with the magnetic field strength, and the magnetostrictive coefficient (λ) is used to quantify the relative deformation amount (ΔL / L) of the material along the magnetic field direction. In the detection of the motor shaft, by applying an alternating magnetic field with a specific frequency to excite the surface of the shaft, the mechanical resonance of its natural frequency can be excited to form a magnetostrictive resonance signal. This signal can be captured by the Bragg grating fiber method or the laser interferometry method, and the precise measurement of nanoscale deformation is realized by using the reflection spectrum shift caused by the change of the grating spacing. The composition of the giant magnetostrictive material (Terfenol-D) is a ternary alloy of terbium (Tb), dysprosium (Dy) and iron (Fe) (Tb0.3Dy0.7Fe2), and its saturation magnetostrictive coefficient can reach 1500 - 2000 ppm (far exceeding 40 ppm of nickel), and the energy conversion efficiency is high.

[0058] The electromagnetic induction signal originates from the eddy current effect generated by the shaft material in the alternating magnetic field. When there are stress concentrations or microscopic defects in the shaft, the changes in its conductivity and magnetic permeability will change the eddy current distribution, thereby affecting the amplitude and phase of the induced current. By detecting the change of the induced electromotive force through a coil or a Hall sensor, the integrity of the internal structure of the shaft can be indirectly reflected.

[0059] The magnetostrictive resonance signal and the electromagnetic induction signal characterize the shaft state from two dimensions of mechanical vibration response and electromagnetic characteristics respectively. The former is sensitive to surface cracks and residual stresses, while the latter has high resolution for internal micro-defects (such as inclusions and pores). The synchronous acquisition and joint analysis of the two can improve the reliability and spatial resolution of detection and avoid the limitations of a single method.

[0060] In one embodiment, magnetostrictive sensors and electromagnetic induction coils are arranged on the surface of the motor shaft. A specific frequency excitation (such as 10 kHz - 1 MHz) is applied to the shaft through an alternating magnetic field generator. The ferromagnetic shaft generates periodic deformations due to the magnetostrictive effect, forming resonance signals, which are collected by piezoelectric ceramic sensors. At the same time, when the shaft rotates, it cuts the magnetic field to generate electromagnetic induction signals, which are received by induction coils. After the signals are filtered by a preamplifier (bandwidth 0.1 - 5 MHz), two-channel time-domain waveforms are synchronously recorded by a high-speed data acquisition card (sampling rate ≥ 10 MS / s).

[0061] S120: Perform frequency-domain conversion on the magnetostrictive resonance signal to obtain the magnetostrictive resonance frequency-domain characteristics.

[0062] Frequency-domain conversion is to convert the time-domain vibration signal into frequency-domain characteristics through fast Fourier transform (FFT) or wavelet transform, and extract the main resonance frequency, harmonic components and their energy distributions. Perform denoising preprocessing (such as band-pass filtering) on the magnetostrictive resonance signal, and extract its frequency-domain characteristics through FFT, including the main resonance frequency, sub-harmonic frequency and corresponding amplitudes.

[0063] In the scenario of sudden change in motor load, the time-domain waveform of the magnetostrictive resonance signal may be distorted due to vibration interference, but through frequency-domain conversion, a stable main resonance peak (such as a fundamental frequency of 1 kHz) and its second harmonic (such as 2 kHz) can be separated to characterize the frequency characteristics related to the rotational speed.

[0064] In one embodiment, perform frequency-domain analysis on the collected magnetostrictive resonance signal using fast Fourier transform (FFT), extract the main resonance frequency (such as the fundamental frequency f0 = 25 kHz) and harmonic components (such as 2f0, 3f0) through spectrum analysis, and calculate the amplitudes, phases and energy densities of each frequency band to form a frequency-domain characteristic matrix.

[0065] S130: Perform frequency-domain conversion on the electromagnetic induction signal to obtain the electromagnetic induction frequency-domain characteristics.

[0066] The frequency-domain characteristics of the electromagnetic induction signal reflect the fundamental wave frequency and its harmonic components of the alternating magnetic field when the shaft rotates, and have a linear relationship with the rotational speed. Sample and perform spectrum analysis on the electromagnetic induction signal to extract the fundamental wave frequency (directly related to the rotational speed) and high-frequency harmonics (reflecting electromagnetic interference or mechanical vibration coupling effects).

[0067] When the motor is in steady-state operation, the fundamental frequency of the electromagnetic induction signal (such as 100 Hz) corresponds to the rotational speed, while the high-frequency harmonics (such as 200 Hz) may be caused by shaft eccentricity or electromagnetic noise, and the interference components need to be removed through frequency-domain analysis.

[0068] In one embodiment, after anti-aliasing filtering the electromagnetic induction signal, a signal segment is intercepted using a window function (such as a Hanning window), and the spectrum is obtained through FFT; then, the characteristic frequencies related to the shaft rotational speed (such as the rotational frequency fr and its sideband frequencies fr±Δf) are extracted, and the amplitude and energy distribution of each frequency point are recorded.

[0069] S140: Based on the frequency band matching relationship between the magnetostrictive frequency-domain characteristics and the electromagnetic induction frequency-domain characteristics, perform frequency-domain energy spectrum fusion reconstruction on the magnetostrictive frequency-domain characteristics and the electromagnetic induction frequency-domain characteristics to generate a dual-frequency coupled energy spectrum.

[0070] Match the magnetostrictive resonance frequency-domain characteristics (mechanical vibration characteristics) with the electromagnetic induction frequency-domain characteristics (electromagnetic characteristics), and reconstruct the dual-frequency coupled energy spectrum through dynamic frequency division and energy superposition to enhance the energy proportion of the frequency band related to the rotational speed. Identify the overlapping region between the magnetostrictive main resonance frequency band (such as 0.8 - 1.2 kHz) and the electromagnetic induction fundamental frequency band (such as 95 - 105 Hz), and through frequency band segmentation and energy weight distribution, superimpose the energies of the two frequency bands and compensate for the phase difference to generate the fused energy spectrum.

[0071] In a complex electromagnetic interference environment, by fusing the high-frequency resonance energy of magnetostriction (resisting low-frequency interference) and the low-frequency fundamental energy of electromagnetic induction (resisting high-frequency noise), the spectrum distortion caused by environmental interference to a single signal is suppressed.

[0072] In one embodiment, the fundamental frequency f0 in the magnetostrictive frequency-domain characteristics is matched with the rotational frequency fr in the electromagnetic induction frequency-domain characteristics (for example, f0 and fr have a fixed proportional relationship through material characteristics). Then, the energy spectra within the matched frequency band are weighted and superimposed (such as using the square weighting method of energy density) to reconstruct a dual-frequency coupled energy spectrum containing composite characteristics. Exemplarily, if f0 = 25 kHz corresponds to fr = 1500 rpm (revolutions per minute), then the peak frequency in the fused energy spectrum is the combined frequency of f0 and fr (such as 25 kHz±1500 / 60 Hz).

[0073] S150: Determine the rotational speed of the motor shaft according to the frequency position and amplitude distribution of the energy peak in the dual-frequency coupled energy spectrum.

[0074] The energy peak frequency position of the dual-frequency coupled energy spectrum corresponds to the rotational speed fundamental frequency and harmonic components, and the rotational speed value is dynamically calibrated through the main peak frequency interval and harmonic factor. Calculate the initial rotational speed based on the main peak frequency interval of the energy spectrum (such as the difference between the magnetostrictive main peak frequency and the electromagnetic induction fundamental frequency), and perform dynamic error compensation in combination with the integer multiple relationship and amplitude ratio of the harmonic intervals.

[0075] During the motor acceleration process, the interval between the magnetostrictive main peak frequency (1 kHz) and the electromagnetic induction fundamental frequency (100 Hz) is 900 Hz. Combining the harmonic factor (such as the second harmonic interval is 2 kHz) and the dynamic coupling coefficient (amplitude ratio), the rotational speed value is corrected in real time to eliminate the cumulative error.

[0076] In one embodiment, identify the frequency position corresponding to the energy peak in the dual-frequency coupled energy spectrum (such as the main peak frequency f_peak = 25.025 kHz); then, according to the preset calibration relationship, inversely deduce the rotational speed value. For example, f_peak = K·n + f0, where K is the proportionality coefficient and n is the rotational speed. Finally, verify the stability of the peak amplitude. If the amplitude fluctuation is less than the set threshold within 10 consecutive cycles, the rotational speed measurement result is finally output.

[0077] This embodiment overcomes the defect that single physical quantity measurement is easily affected by environmental interference through the fusion of magnetostrictive and electromagnetic induction dual signals. The frequency-domain energy spectrum fusion and reconstruction technology enhances the signal-to-noise ratio in the rotational speed-related frequency band and suppresses noise interference; the dynamic frequency division and phase synchronization constraints ensure the collaborative analysis of high-frequency and low-frequency signals, improving the dynamic response accuracy during rotational speed mutation or load fluctuation; the amplitude-frequency joint calibration of the dual-frequency coupled energy spectrum further eliminates the errors caused by temperature drift and mechanical vibration, realizing high-precision and high-stability measurement under complex working conditions.

[0078] In an implementable embodiment, step S140 performs frequency-domain energy spectrum fusion and reconstruction on the magnetostrictive frequency-domain feature and the electromagnetic induction frequency-domain feature based on the frequency band matching relationship between the magnetostrictive frequency-domain feature and the electromagnetic induction frequency-domain feature, generating a dual-frequency coupled energy spectrum, including:

[0079] Based on the frequency band matching relationship between the magnetostrictive frequency domain characteristics and the electromagnetic induction frequency domain characteristics, through duplexer-type dynamic frequency division, the resonant frequency band of the magnetostrictive frequency domain characteristics and the fundamental frequency band of the electromagnetic induction frequency domain characteristics are synchronously decoupled to obtain independent frequency band energy quanta with phase synchronization constraints. According to the frequency domain energy distribution characteristics of the independent frequency band energy quanta, the energy weights of the resonant frequency band of the magnetostrictive frequency domain characteristics and the fundamental frequency band of the electromagnetic induction frequency domain characteristics are dynamically matched to construct a frequency domain energy weight matrix. Based on the frequency band energy ratio in the frequency domain energy weight matrix, the high-frequency harmonic energy quanta and the low-frequency fundamental energy quanta in the independent frequency band energy quanta are weighted and superimposed, and the phase difference in the superimposing process is corrected using the phase offset parameter compensation algorithm to generate a dual-frequency coupled energy spectrum.

[0080] The matching relationship between the resonant frequency band in the magnetostrictive frequency domain characteristics and the fundamental frequency band in the electromagnetic induction frequency domain characteristics means that there is a complementary or overlapping frequency band range in their spectral distributions. By dynamically correlating the energy distribution characteristics of these frequency bands, the signal collaborative enhancement and interference suppression are achieved. The magnetostrictive resonant frequency band is generated by the magnetostrictive effect of the motor shaft, mainly manifested as high-frequency harmonic components (such as above 2 kHz), and its energy concentratedly reflects the mechanical vibration characteristics of the shaft (such as rotational speed, stress deformation). The electromagnetic induction fundamental frequency band is generated by the electromagnetic induction effect of the motor winding current, mainly manifested as low-frequency fundamental components (such as 50 Hz - 1 kHz), and its energy is directly related to the periodic change of the current (such as the electrical frequency corresponding to the rotational speed). The frequency band matching between the two can meet the following conditions: Frequency band coverage complementarity: The high-frequency harmonic energy of the resonant frequency band needs to form a continuous or partially overlapping coverage with the low-frequency energy of the fundamental frequency band in the frequency domain to avoid information loss caused by spectral gaps; Phase synchronization constraint: Through dynamic frequency division technology, ensure that the phase difference between the two frequency bands is constrained within the allowable range before fusion to avoid destructive interference during superposition.

[0081] Duplexer-type dynamic frequency division refers to using an electronic filter bank with bidirectional isolation characteristics to perform band-pass separation on the high-frequency resonant frequency band (usually including 3 - 7 harmonics) of the magnetostrictive resonant signal and the low-frequency fundamental frequency band (corresponding to 1 - 3 times the motor rotation frequency) of the electromagnetic induction signal. The phase synchronization constraint is established by the digital phase-locked loop technology to establish a time base alignment relationship between the two independent frequency band energy quanta and eliminate the phase offset caused by the difference in the signal transmission path.

[0082] Based on the frequency band matching relationship between the magnetostrictive frequency domain characteristics and the electromagnetic induction frequency domain characteristics, using the frequency band splitting function of the duplexer, the resonant frequency band of the magnetostrictive signal (such as 2 - 5 kHz) and the fundamental frequency band of the electromagnetic induction signal (such as 50 - 500 Hz) are separated into independent sub - frequency bands. Then, through the phase - locked loop (PLL) technology, the initial phases of the two frequency bands are tracked and aligned to eliminate the phase offset caused by the difference in the signal acquisition path, generating independent frequency band energy sub - units with phase synchronization. Then, the amplitude ratio of the high - frequency harmonic energy sub - units in the resonant frequency band and the low - frequency energy sub - units in the fundamental frequency band is statistically calculated. The weight coefficient is adjusted according to the actual working conditions (such as motor load change). For example, the weight of the resonant frequency band is increased at high speeds to improve sensitivity, and the weight of the fundamental frequency band is increased at low speeds to improve stability. Then, the high - frequency harmonic energy sub - units and the low - frequency fundamental energy sub - units are linearly superimposed according to the weight coefficients (such as 0.6 and 0.4) to generate a preliminary fusion energy spectrum, and for the remaining small phase differences after superposition, an interpolation algorithm is used for local phase alignment in the time - frequency domain to ensure the time - domain continuity of the energy spectrum. Finally, the fused dual - frequency coupled energy spectrum is output.

[0083] In one embodiment, in the scenario of motor speed detection, to improve the anti-interference ability and resolution of frequency-domain energy fusion, first, using the frequency band segmentation characteristic of the duplexer, the vibration signal (resonant frequency band 2 - 5 kHz) collected by the magnetostrictive sensor and the current fundamental wave signal (fundamental wave frequency band 50 - 500 Hz) obtained by the electromagnetic induction sensor are separated into independent sub-frequency bands. The duplexer adopts a non-overlapping sub-band segmentation design to ensure strict isolation of the two frequency bands in the frequency spectrum and avoid energy crosstalk between high-frequency harmonics and low-frequency fundamental waves. Subsequently, through the phase-locked loop (PLL) technology, the initial phases of the two frequency bands are tracked in real time, and based on the phase difference feedback, the signal sampling window is adjusted to make the time-domain waveforms of the resonant frequency band and the fundamental wave frequency band reach phase synchronization and alignment, and finally, the independent frequency band energy sub-components with phase constraints are output. Then, according to the actual working conditions of the motor (such as load fluctuation or speed change), the energy weight coefficients of the two frequency bands are dynamically adjusted. For example, when the motor is in a high-speed state (such as 3000 rpm), the proportion of harmonic energy in the resonant frequency band (2 - 5 kHz) increases significantly. At this time, the weight of the resonant frequency band is increased to 0.6, and the weight of the fundamental wave frequency band is decreased to 0.4 to enhance the sensitivity of high-frequency features; while in a low-speed state (such as 500 rpm), the energy distribution in the fundamental wave frequency band (50 - 500 Hz) is more stable, and the weight coefficients are adjusted to 0.7 (fundamental wave) and 0.3 (resonant). The real-time update of the weight matrix is based on the sliding window statistical method to ensure the timeliness of the energy proportion data. After completing the weight allocation, the energy sub-components of the two frequency bands are linearly superimposed to generate a preliminary fusion energy spectrum. Finally, for the small phase offset remaining after the superposition (such as caused by sensor sampling delay or path loss), a time-frequency domain interpolation algorithm is used for local compensation. Specifically, cubic spline interpolation is performed on the mutation points of the energy spectrum in the time domain, and in the frequency domain, the phase gradient of adjacent frequency components is smoothed and corrected through the short-time Fourier transform (STFT), and finally, the spectrum tailing and energy leakage phenomena are eliminated, and a highly continuous dual-frequency coupled energy spectrum is output.

[0084] This embodiment realizes the precise separation and phase alignment of features in different frequency bands through the dynamic frequency division and phase synchronization mechanism, solves the signal interference problem caused by frequency band coupling and phase misalignment in traditional methods, and enhances the anti-noise ability. Secondly, based on the dynamic weight matching strategy of energy distribution, the energy proportion of the frequency band is adaptively adjusted, overcoming the limitation of the fixed weight mode that cannot effectively fuse multi-source features and improving the robustness of energy fusion. Finally, the phase offset in the superposition process is eliminated through the phase difference correction algorithm to ensure the accuracy of energy spectrum reconstruction, and significantly improves the resolution and stability of the detection system. The synergistic effect of this series of technical means enables the dual-frequency coupled energy spectrum to still maintain a high-precision feature representation ability under complex working conditions, providing a reliable technical guarantee for motor speed detection.

[0085] In an implementable embodiment, based on the frequency band matching relationship between the magnetostrictive frequency domain characteristics and the electromagnetic induction frequency domain characteristics, duplexer-type dynamic frequency division is used to synchronously decouple the resonant frequency band of the magnetostrictive frequency domain characteristics and the fundamental frequency band of the electromagnetic induction frequency domain characteristics, and independent frequency band energy quanta with phase synchronization constraints are obtained, including:

[0086] Based on the frequency band matching relationship between the magnetostrictive frequency domain characteristics and the electromagnetic induction frequency domain characteristics, coupling screening processing is performed on the overlapping region of the resonant frequency band and the fundamental frequency band by using frequency domain energy threshold determination to generate a dynamic frequency domain mapping model and an energy coupling feature set; according to the dynamic frequency domain mapping model, duplexer-type dynamic frequency division is used to perform multi-order frequency band splitting processing on the resonant frequency band, and at the same time, frequency band isolation processing is performed on the fundamental frequency band based on frequency point adaptive scanning to generate a dynamic isolation frequency band; phase synchronization constraint modeling is used to perform error feedback adjustment processing on the phase offset of the energy quanta in the dynamic isolation frequency band to generate initial independent frequency band energy quanta including a homologous phase reference; a dynamic threshold adaptive adjustment algorithm is used to identify cross-band interference components in the frequency domain distribution characteristics of the initial independent frequency band energy quantum set to obtain an interference component set, and based on the initial independent frequency band energy quantum set and the interference component set, energy component reconstruction processing is performed through frequency domain energy aggregation to generate independent frequency band energy quanta.

[0087] The dynamic frequency domain mapping model is a frequency band association model generated by performing energy coupling screening on the overlapping region between the magnetostrictive resonant frequency band and the electromagnetic induction fundamental frequency band through frequency domain energy threshold determination. This model defines the energy interaction rules between frequency bands and is used to guide subsequent frequency band splitting and isolation. Multi-order frequency band splitting refers to using a duplexer to perform multi-level filtering processing on the resonant frequency band and decomposing it into finer sub-frequency bands to adapt to the signal characteristics under different working conditions. Frequency point adaptive scanning is to perform dynamic frequency detection and boundary adjustment on the fundamental frequency band to ensure the accuracy of frequency band isolation. Phase synchronization constraint modeling quantifies the phase offset of the energy quanta in the dynamic isolation frequency band through an error feedback mechanism and establishes a homologous phase reference to achieve cross-band phase alignment. The dynamic threshold adaptive adjustment algorithm identifies cross-band interference components (such as environmental noise or sensor crosstalk) based on the frequency domain distribution characteristics and removes the interference through energy aggregation reconstruction to retain effective energy quanta.

[0088] First, based on the frequency-domain energy threshold, determine the energy distribution in the overlapping region of two frequency bands, screen out the set of frequency points with coupling effects, and construct a dynamic frequency-domain mapping model. This model describes the cooperative or inhibitory relationship between frequency bands through an energy coupling feature set. Then, use a duplexer to perform multi-order frequency band segmentation on the resonant frequency band, divide it into multiple sub-bands (such as 2 - 3 kHz, 3 - 4 kHz, etc.), and at the same time perform frequency point adaptive scanning on the fundamental frequency band, dynamically adjust the isolation boundary (for example, the fundamental frequency band expands from 50 Hz to 600 Hz to cover the load fluctuation range), and generate isolated independent sub-bands. And through the phase-locked loop feedback, adjust the phase offset of each sub-band to generate the initial independent band energy sub. Subsequently, the dynamic threshold algorithm identifies the cross-band interference components (such as high-frequency noise leaking into the fundamental frequency band) according to the energy distribution characteristics (such as the mutation amplitude of high-frequency harmonics or the smoothness of low-frequency fundamental waves), and forms an interference set. Finally, remove the interference components from the initial energy sub, and reconstruct the remaining energy sub through a frequency-domain energy aggregation algorithm (such as energy weighted average or principal component analysis) to generate the final independent band energy sub, ensuring the integrity and consistency of the energy distribution.

[0089] In this embodiment, through the dynamic frequency-domain mapping model and the multi-dimensional cooperative processing mechanism, the precise control of energy interaction and the improvement of anti-interference ability in a complex frequency-domain environment are realized. Specifically, the dynamic frequency-domain mapping model, based on the frequency-band energy threshold criterion, effectively identifies the energy coupling region between the magnetostrictive resonance frequency band and the electromagnetic induction fundamental frequency band, establishes frequency-domain association rules, and overcomes the energy leakage problem caused by traditional fixed frequency-band division. The duplexer-based dynamic frequency division technology, through the cooperative operation of multi-order frequency band segmentation and frequency point adaptive scanning, realizes the precise isolation of overlapping frequency bands in both time and frequency domains, significantly reducing cross-band crosstalk. The phase synchronization constraint modeling, through the real-time feedback adjustment mechanism, ensures the phase reference consistency of the decoupled energy sub, eliminating the influence of asynchronous errors on the system stability. The dynamic threshold adaptive adjustment algorithm, based on the energy distribution characteristics, intelligently identifies and reconstructs the cross-band interference components, breaking through the limitations of traditional static threshold strategies, and finally forming a set of independent band energy sub with high purity characteristics. This technical system has a significant improvement in energy conversion efficiency, system robustness, and environmental adaptability compared with traditional frequency-domain decoupling methods.

[0090] In an implementable embodiment, according to the dynamic frequency-domain mapping model, use the duplexer-based dynamic frequency division to perform multi-order frequency band segmentation processing on the resonant frequency band, and at the same time perform frequency band isolation processing on the fundamental frequency band based on frequency point adaptive scanning to generate dynamic isolation frequency bands, including:

[0091] Based on the distribution of the frequency band overlap coefficient in the dynamic frequency domain mapping model, perform a gradient decomposition process on the resonant frequency band for multi-order frequency band segmentation to generate a set of segmented frequency bands with frequency band boundary constraints; perform a dynamic weighted analysis on the frequency point energy distribution characteristics of the fundamental frequency band through frequency point adaptive scanning to generate a frequency point energy mapping relationship including the frequency band isolation weight coefficient; based on the set of segmented frequency bands and the frequency point energy mapping relationship, perform a frequency band energy reallocation process on the resonant frequency band and the fundamental frequency band using a duplexer-type dynamic frequency division to generate a set of dynamic isolation frequency bands with frequency band isolation degree constraints.

[0092] The frequency band overlap coefficient distribution refers to the quantitative index of the energy coupling strength between the magnetostrictive resonance frequency band and the electromagnetic induction fundamental frequency band in the overlapping region in the dynamic frequency domain mapping model. This coefficient is calculated through the frequency domain energy threshold criterion and is used to characterize the energy interaction degree between different frequency bands, providing a basis for gradient decomposition in multi-order frequency band segmentation. The gradient decomposition process means that based on the distribution characteristics of the frequency band overlap coefficient, the resonant frequency band is divided into multiple continuous or discrete sub-frequency bands according to the energy coupling strength, and each sub-frequency band has clear frequency band boundary constraints to achieve refined frequency domain energy management. The frequency band isolation weight coefficient refers to the set of weight parameters generated by performing a dynamic weighted analysis on the energy distribution of each frequency point in the fundamental frequency band through frequency point adaptive scanning. This coefficient reflects the priority of different frequency points in the isolation process and is used to guide the boundary adjustment and interference suppression in the frequency band energy reallocation process.

[0093] First, perform gradient decomposition on the resonant frequency band according to the distribution of the frequency band overlap coefficient output by the dynamic frequency domain mapping model. For example, if the overlap coefficient is high in the 2 - 3 kHz interval, then this interval is divided into an independent sub-frequency band; if the coefficient is low in the 3 - 5 kHz interval, it is further subdivided into narrower frequency bands. Then, perform real-time scanning on the frequency point energy distribution of the fundamental frequency band (such as 50 - 500 Hz), calculate the energy density and stability index of each frequency point in combination with the sliding window statistical method, and generate the frequency band isolation weight coefficient. For example, frequency points with large energy fluctuations (such as 100 - 200 Hz) are given a lower weight and are preferentially isolated; stable frequency points (such as 300 - 400 Hz) are given a higher weight and are retained as the main frequency band. And use the two-way isolation characteristic of the duplexer to perform energy reallocation on the gradient-decomposed resonant sub-frequency band and the fundamental frequency band with adjusted weights, specifically including: performing multi-order band-pass filtering on the resonant frequency band to filter out the energy crosstalk in the overlapping region with the fundamental frequency band; using a dynamic threshold isolation algorithm for the fundamental frequency band to adjust the isolation boundary in real time according to the weight coefficient (for example, expanding the isolation bandwidth of the low-weight frequency points 50 - 100 Hz to 50 - 150 Hz). Finally, verify whether the isolation degree of the dynamic isolation frequency band meets the preset threshold (such as ≥30 dB) through a frequency domain energy leakage detection algorithm (such as side lobe suppression ratio analysis). If not up to standard, readjust the segmentation gradient and weight coefficient until a set of dynamic isolation frequency bands that meet the requirements is generated.

[0094] In this embodiment, through the collaborative optimization of the dynamic frequency-domain mapping model and duplexer-type dynamic frequency division, the comprehensive performance of frequency band management is significantly improved. Based on the gradient decomposition processing of the frequency band overlap coefficient, the refined isolation between the resonant frequency band and the fundamental frequency band is realized, effectively suppressing the cross-frequency band energy crosstalk and ensuring the stability of the frequency band isolation degree. Through the dynamic weight coefficient generated by the frequency point adaptive scanning and combined with the energy redistribution mechanism, the energy equilibrium distribution between the fundamental frequency band and the resonant frequency band is optimized, enhancing the system's adaptability to complex electromagnetic environments. At the same time, the dynamic segmentation and weight collaboration strategy takes into account both the calculation efficiency and the processing accuracy, reducing redundant operations through the iterative optimization algorithm to ensure the balance between real-time performance and resource utilization. While ensuring the stability of the high-frequency communication link, this solution provides an efficient anti-interference solution for multi-frequency band intensive interaction scenarios, achieving a systematic improvement in isolation performance, dynamic response, and system robustness compared with traditional static frequency band division methods.

[0095] There are significant differences in the non-linear dependence relationships between the magnetostrictive resonance signal and the ordinary resonance signal in terms of their generation mechanisms and frequency-domain characteristics. Magnetostrictive resonance stems from the dynamic response of magnetic domains in ferromagnetic materials under the action of an alternating magnetic field. Its main resonance frequency is non-linearly correlated with the magnetostrictive coefficient of the material and the excitation magnetic field strength, and it has significant high-order harmonic components; while ordinary resonance signals are mostly based on mechanical vibrations or piezoelectric effects, and their spectral characteristics are mainly limited by the natural frequency of the mechanical structure, and the harmonic energy distribution is relatively single. This characteristic makes the magnetostrictive signal exhibit more complex coupling characteristics in the frequency domain, but at the same time provides a physical basis for multi-frequency band joint analysis. The inherent characteristics of the magnetostrictive resonance signal will lead to frequency-domain characteristic drift and harmonic interference in rotational speed measurement. Under dynamic working conditions, the non-linear change of the material permeability will cause the main resonance peak frequency to shift, and the cross-modulation between the magnetostrictive effect and electromagnetic induction will induce abnormal distribution of sub-harmonic energy, resulting in phase accumulation errors during single-frequency point rotational speed inversion. In addition, the drift of the magnetostrictive coefficient caused by temperature changes will further exacerbate the spectral distortion, rendering traditional measurement methods based on the fixed frequency and rotational speed mapping relationship ineffective.

[0096] In the embodiment of the present application, by extracting the interval between the magnetostrictive main peak and the electromagnetic induction fundamental frequency and their harmonic integer multiple relationships, the reference error caused by the frequency deviation of a single signal can be eliminated; at the same time, by combining the dynamic operations of the main and secondary peak amplitude ratios and the cross-frequency band energy coupling coefficient, the influence of temperature drift and non-linear distortion on the spectrum can be compensated in real time. This two-dimensional correction strategy that combines mechanical vibration and electromagnetic characteristics can significantly improve the measurement accuracy under complex working conditions.

[0097] In an implementable embodiment, determining the rotational speed of the motor shaft according to the frequency position and amplitude distribution of the energy peaks in the dual-frequency coupling energy spectrum includes:

[0098] Based on the interval between the frequency position of the magnetostrictive main peak and the frequency position of the electromagnetic induction fundamental frequency in the dual-frequency coupling energy spectrum, the initial rotational speed value is obtained through the linear relationship between the frequency interval and the motor rotational speed; the harmonic factor is extracted from the initial rotational speed value by using the integer multiple relationship of the harmonic interval between the frequency position of the magnetostrictive secondary peak and the frequency position of the main peak in the dual-frequency coupling energy spectrum to obtain the harmonic factor; based on the ratio of the magnetostrictive main peak amplitude distribution to the secondary peak amplitude distribution in the dual-frequency coupling energy spectrum, and the ratio of the electromagnetic induction fundamental frequency amplitude distribution to the magnetostrictive main peak amplitude distribution in the dual-frequency coupling energy spectrum, dynamic coupling operation processing is performed to generate a dynamic coupling coefficient; based on the harmonic factor and the dynamic coupling coefficient, amplitude-frequency joint calibration processing is performed on the initial rotational speed value to determine the rotational speed of the motor shaft.

[0099] The magnetostrictive main peak frequency refers to the resonance peak generated by the magnetostrictive effect of ferromagnetic materials under the excitation of an alternating magnetic field, and its frequency position is non-linearly related to the material properties and magnetic field strength. The electromagnetic induction fundamental frequency is the fundamental frequency component of the electromagnetic induction signal caused by the periodic change of the motor winding current, and its frequency is directly related to the motor electrical parameters (such as the number of pole pairs, current frequency). In the dual-frequency coupling energy spectrum, the frequency interval between the main peak and the fundamental frequency reflects the dynamic coupling relationship between the mechanical rotational speed and the electromagnetic excitation, while the harmonic interval between the secondary peak and the main peak corresponds to the non-linear high-order harmonic characteristics of the dynamic response of the material magnetic domain. The dynamic coupling coefficient is used to quantify the energy distribution difference between the magnetostrictive main peak amplitude and the secondary peak amplitude, as well as the energy transfer efficiency between the electromagnetic induction fundamental frequency and the main peak amplitude, and is used to correct the dynamic error in the rotational speed calculation.

[0100] First, identify and locate the interval between the magnetostrictive main peak frequency and the electromagnetic induction fundamental frequency from the dual-frequency coupled energy spectrum. This interval is formed by the interaction between the motor shaft speed and the magnetic field excitation, and there is a linear proportional relationship between the two. Through a pre-calibrated linear proportionality coefficient, the frequency interval is converted into an initial speed value. For example, if the frequency interval is a specific value, it can be directly mapped to a preliminary estimate of the speed based on the proportionality coefficient. The difference between the magnetostrictive secondary peak frequency and the main peak frequency reflects the harmonic component characteristics caused by the change in the motor load. By verifying whether this difference satisfies an integer multiple harmonic relationship (such as the secondary peak frequency being 2 times, 3 times, etc. of the main peak frequency), harmonic order information can be extracted. Based on the spectrum fitting algorithm, determine the harmonic factor and apply it to the initial speed value to correct the harmonic offset error caused by load fluctuations. For example, if the difference between the secondary peak and the main peak frequency is an integer multiple of the fundamental frequency, adjust the amplitude ratio of the initial speed through the harmonic factor. In the dual-frequency coupled energy spectrum, the ratio of the magnetostrictive main peak amplitude to the secondary peak amplitude characterizes the magneto-mechanical energy conversion efficiency of the material, while the ratio of the electromagnetic induction fundamental frequency amplitude to the main peak amplitude reflects the dynamic balance of the electro-magnetic energy transfer. Then, through dynamic coupling operations such as weighted fusion or non-linear function mapping, the two types of amplitude ratios are associated as a dynamic coupling coefficient to quantify the interference intensity of factors such as sudden motor load changes and material fatigue on the speed measurement. Finally, the harmonic factor and the dynamic coupling coefficient act jointly on the initial speed value. The harmonic factor corrects the frequency-domain harmonic distortion of the speed, and the dynamic coupling coefficient compensates for the non-linear error caused by amplitude fluctuations. For example, through a calibration algorithm, fuse the correction result of the harmonic factor on the frequency and the correction result of the dynamic coupling coefficient on the amplitude, and finally output the high-precision motor shaft speed.

[0101] In one embodiment, a permanent magnet synchronous drive motor applicable to an electric vehicle, whose working environment needs to cope with frequent load changes, requires the following scenarios to achieve high-precision speed measurement. Sudden load change: When accelerating or braking suddenly, the motor output torque changes violently, causing a shift in the magnetostrictive secondary peak frequency and a surge in harmonic components; Material fatigue: After long-term operation, the microstructure of the ferromagnetic material changes, resulting in a decrease in the magneto-mechanical energy conversion efficiency; Temperature interference: The temperature rise caused by the charging and discharging of the battery pack causes a change in the winding resistance, which in turn affects the electromagnetic induction fundamental frequency amplitude.

[0102] Collect the dual-frequency coupling energy spectrum through a high-frequency sensor to identify the magnetostrictive main peak frequency (reflecting the inherent resonance characteristics of the material) and the electromagnetic induction fundamental frequency (related to the winding current period). Using a pre-calibrated linear proportional relationship, such as for every 10 kHz increase in the main peak-fundamental frequency interval, the rotational speed increases by 500 rpm, convert the frequency interval into an initial rotational speed value. For example, when the interval measured under no-load conditions is 50 kHz, it maps to an initial rotational speed of 2500 rpm. When the vehicle climbs a slope and the load increases, verify whether the difference between the magnetostrictive secondary peak frequency and the main peak frequency (such as the secondary peak being 2 times the fundamental frequency component higher than the main peak) satisfies the integer multiple harmonic relationship. Determine the harmonic order (such as the 2nd harmonic) through spectral fitting, extract the harmonic factor (such as 0.92), and accordingly correct the initial rotational speed, 2500 rpm × 0.92 = 2300 rpm, to eliminate the frequency offset error caused by the sudden increase in torque. Calculate the magnetostrictive main peak amplitude, which characterizes the magneto-mechanical energy conversion efficiency, the ratio of the main peak amplitude to the secondary peak amplitude (such as 3:1), and the ratio of the electromagnetic induction fundamental frequency amplitude (reflecting the electro-magnetic energy balance) to the main peak amplitude (such as 1.5:1). Through non-linear weighted fusion, such as the square root of the product of the ratios, generate a dynamic coupling coefficient to quantify the interference intensity of load mutations on the energy transfer path. Combine the harmonic factor (0.92) and the dynamic coupling coefficient (2.12) to act on the initial rotational speed value (2500 rpm). The calibration algorithm fuses the frequency domain correction result (2300 rpm) with the interference intensity in the amplitude domain, that is, the rotational speed compensation amount corresponding to 2.12 is 100 rpm, and outputs the final rotational speed value of 2400 rpm to achieve synchronous suppression of non-linear errors and dynamic interference.

[0103] In this embodiment, through the multi-dimensional joint analysis of the dual-frequency coupling energy spectrum, the problem of accuracy inaccuracy of traditional rotational speed measurement methods under complex working conditions such as dynamic load, temperature drift, and non-linear distortion is effectively solved. First, extract the initial rotational speed based on the linear interval relationship between the magnetostrictive main peak and the electromagnetic induction fundamental frequency, which can avoid the reference drift error caused by the non-linear change of magnetic permeability at a single frequency point; second, extract the harmonic factor through the harmonic integer multiple relationship between the secondary peak and the main peak, which can suppress the sub-harmonic interference caused by the cross-modulation of the magnetostrictive effect and electromagnetic induction, and eliminate the influence of phase accumulation error on rotational speed inversion. Further, by combining the dynamic operation of the main and secondary peak amplitude distribution ratios and the cross-band energy coupling coefficient, real-time compensation for temperature drift and material property changes is achieved, significantly reducing the interference of spectral distortion on the measurement results. This two-dimensional correction strategy that combines frequency domain interval analysis and dynamic amplitude-frequency ratio calibration not only improves the measurement robustness under complex working conditions, but also enhances the cross-condition applicability of rotational speed calculation through the collaborative decoupling of harmonic characteristics and energy coupling, and can still maintain high-precision stability especially under extreme conditions such as high dynamic load and wide temperature range fluctuations.

[0104] In an implementable embodiment, based on the harmonic factor and the dynamic coupling coefficient, an amplitude-frequency joint calibration process is performed on the initial rotational speed value to determine the rotational speed of the motor shaft, including:

[0105] Based on the product relationship between the harmonic factor and the initial rotational speed value, the harmonic distortion component of the initial rotational speed value is corrected to generate a modulated rotational speed intermediate value. The product relationship is determined based on the integer reciprocal relationship of the harmonic interval between the magnetostrictive secondary peak frequency position and the main peak frequency position in the dual-frequency coupling energy spectrum. The harmonic distortion component is determined by the integer multiple relationship of the harmonic interval between the magnetostrictive secondary peak frequency position and the main peak frequency position in the dual-frequency coupling energy spectrum. Using the proportional relationship between the dynamic coupling coefficient and the modulated rotational speed intermediate value, the amplitude-frequency coupling error of the modulated rotational speed intermediate value is compensated to obtain a compensated rotational speed intermediate value. The proportional relationship is determined by the ratio of the magnetostrictive main peak amplitude distribution to the secondary peak amplitude distribution in the dual-frequency coupling energy spectrum, and the ratio of the electromagnetic induction fundamental frequency amplitude distribution to the magnetostrictive main peak amplitude distribution. According to the joint weight of the harmonic factor and the dynamic coupling coefficient, the temperature drift component of the compensated rotational speed intermediate value is suppressed to obtain the rotational speed of the motor shaft. The joint weight is dynamically adjusted according to the distribution characteristics of the harmonic factor and the dynamic coupling coefficient in the dual-frequency coupling energy spectrum.

[0106] The product relationship between the harmonic factor and the initial rotational speed value corrects the frequency domain characteristics of the initial rotational speed value through the harmonic factor. The harmonic factor is determined by the integer reciprocal relationship of the harmonic interval between the magnetostrictive secondary peak and the main peak, and is used to quantify the offset of the rotational speed caused by the harmonic order due to load fluctuations. The purpose of the product relationship is to eliminate the frequency distortion component caused by the non-linear response of the material magnetic domain. The harmonic distortion component refers to the error introduced by the integer multiple harmonic relationship of the frequency difference between the magnetostrictive secondary peak and the main peak in the calculation of the motor shaft rotational speed. This component appears as a periodic fluctuation of the rotational speed value and needs to be compensated in the frequency domain through the harmonic factor. The proportional relationship between the dynamic coupling coefficient and the modulated rotational speed intermediate value is jointly calculated by the amplitude ratio of the magnetostrictive main peak to the secondary peak (reflecting the magneto-mechanical energy conversion efficiency) and the amplitude ratio of the electromagnetic induction fundamental frequency to the main peak (characterizing the electro-magnetic energy transfer efficiency). This coefficient is used to quantify the influence intensity of the dynamic load mutation on the rotational speed amplitude and eliminate the amplitude-frequency coupling error through proportional compensation. The joint weight is dynamically adjusted by the distribution characteristics of the harmonic factor and the dynamic coupling coefficient in the energy spectrum. The weight distribution is based on the sensitivity difference of the temperature drift to the harmonic distortion and the amplitude error, and realizes the targeted suppression of the temperature drift component.

[0107] First, a modulation speed intermediate value is generated based on the product of the initial speed value and the harmonic factor. The harmonic factor is determined by the integer reciprocal relationship of the harmonic interval between the secondary peak and the main peak. For example, if the interval is 2 harmonic orders, the harmonic factor is 1 / 2, thus correcting the harmonic order offset caused by the magnetic hysteresis effect of the material. Then, the modulation speed intermediate value is proportionally compensated using the dynamic coupling coefficient. The dynamic coupling coefficient is generated by weighted fusion of the amplitude ratio of the magnetostrictive main peak to the secondary peak (such as if the amplitude of the main peak is 3 times that of the secondary peak, the ratio is 3) and the amplitude ratio of the electromagnetic induction fundamental frequency to the main peak (such as if the amplitude of the fundamental frequency is 0.5 times that of the main peak, the ratio is 0.5). This coefficient acts on the intermediate value to eliminate the amplitude fluctuation caused by sudden load changes. Finally, the compensated speed intermediate value is dynamically adjusted according to the combined weight. The combined weight is allocated based on the distribution characteristics of the harmonic factor and the dynamic coupling coefficient (such as the stability of the main peak amplitude in the energy spectrum), preferentially suppressing the temperature-sensitive components (such as the amplitude drift of the secondary peak caused by material thermal expansion), and finally outputting a high-precision speed value.

[0108] In this embodiment, through the synergistic effect of the harmonic factor and the dynamic coupling coefficient, high-precision calibration of the motor speed is achieved. Specifically, the harmonic factor, based on the integer reciprocal relationship of the harmonic interval between the magnetostrictive secondary peak and the main peak in the dual-frequency coupling energy spectrum, performs frequency-domain correction on the harmonic distortion components of the initial speed, effectively suppressing the spectrum offset caused by the non-linear coupling between the electromagnetic induction fundamental frequency and the magnetostrictive secondary peak. The dynamic coupling coefficient, based on the amplitude distribution ratio of the main peak to the secondary peak and the proportional relationship between the electromagnetic fundamental frequency amplitude and the main peak amplitude, establishes an amplitude-frequency error compensation model, solving the problems of amplitude attenuation and phase distortion caused by the magnetostrictive effect during the modulation process. Combining the triple mechanisms of frequency-domain correction, amplitude-frequency joint compensation, and dynamic weight suppression significantly improves the stability and repeatability of speed measurement under wide temperature range and multi-interference conditions, while reducing the coupling interference of electromagnetic harmonics on the control system and improving the accuracy of motor speed measurement.

[0109] In an implementable embodiment, obtaining the magnetostrictive resonance signal and the electromagnetic induction signal of the motor shaft includes:

[0110] Performing axial magnetization excitation processing on the motor shaft based on preset alternating magnetic field excitation parameters to obtain an initial magnetostrictive resonance signal. The preset alternating magnetic field excitation parameters include an excitation frequency and a magnetic field strength that match the magnetostrictive coefficient of the motor shaft material; using a preset annular induction coil to inductively capture the alternating magnetic field generated when the motor shaft rotates to obtain an initial electromagnetic induction signal. The axial installation position of the preset annular induction coil maintains a preset distance from the application position of the magnetization excitation; based on the propagation delay of the trigger frequency in the preset alternating magnetic field excitation parameters and the preset distance, synchronously collecting the initial magnetostrictive resonance signal and the initial electromagnetic induction signal to obtain the magnetostrictive resonance signal and the electromagnetic induction signal.

[0111] The preset alternating magnetic field excitation parameters refer to the combination of magnetic field characteristic parameters set to stimulate the magnetostrictive effect of the motor shaft, including the excitation frequency (such as 20 kHz - 500 kHz) and the magnetic field intensity (such as 30 - 100 mT). The excitation frequency needs to match the magnetostrictive resonance frequency band of the shaft material to avoid signal attenuation caused by frequency mismatch; the magnetic field intensity is dynamically adjusted according to the saturation magnetization intensity of the material to ensure that the shaft generates sufficient deformation without entering the hysteresis region. The magnetization excitation process refers to applying an alternating magnetic field axially to cause periodic magnetic domain flipping in ferromagnetic shaft materials (such as Terfenol-D alloy), thereby inducing mechanical vibration. This process needs to combine the non-linear characteristics of the magnetostrictive coefficient (λ) to maximize the amplitude of the resonance signal by dynamically adjusting the magnetic field intensity. The preset annular induction coil refers to an induction coil wound in an annular structure, and its axial installation position needs to maintain a preset distance (such as 3 - 10 mm) from the magnetization excitation device to balance the magnetic field coupling strength and the need to suppress electromagnetic interference. Too small a distance will cause crosstalk of the magnetization field to the induction signal, and too large a distance will reduce the signal sensitivity.

[0112] In one embodiment, first, based on the magnetostrictive coefficient of the shaft material (such as λ = 1500 ppm), an alternating magnetic field with an excitation frequency of 20 kHz and a magnetic field intensity of 50 mT is set and axially applied to the shaft surface through the excitation coil to stimulate the mechanical resonance of its natural frequency and form an initial magnetostrictive resonance signal. Among them, the annular induction coil can be coaxially installed at a distance of 5 mm to capture the eddy current effect generated by the change of the alternating magnetic field during the rotation of the shaft and generate an initial electromagnetic induction signal. Then, according to the magnetic field propagation delay, such as the delay Δt = 1 μs from the excitation trigger to the signal response, the time stamp alignment technology is used to synchronously sample the two signals to eliminate the phase error introduced by the difference in the electromagnetic wave propagation path. Finally, the initial magnetostrictive signal is band-pass filtered (bandwidth 18 - 22 kHz) to suppress high-frequency electromagnetic noise; the electromagnetic induction signal is baseline corrected to eliminate the DC bias, and the final magnetostrictive resonance signal and electromagnetic induction signal are obtained.

[0113] Based on the same concept, the embodiment of the present application provides a motor speed measurement system based on electromagnetic induction. The following combines Figure 2 to detail the motor speed measurement system based on electromagnetic induction provided by the embodiment of the present application.

[0114] Figure 2 is the structural block diagram of a motor speed measurement system based on electromagnetic induction shown in the embodiment of the present application.

[0115] As Figure 2 shown, the motor speed measurement system based on electromagnetic induction may include:

[0116] An acquisition module 210, configured to acquire a magnetostrictive resonance signal and an electromagnetic induction signal of a motor rotating shaft;

[0117] A conversion module 220, configured to perform frequency-domain conversion on the magnetostrictive resonance signal to obtain magnetostrictive resonance frequency-domain characteristics;

[0118] The conversion module 220 is further configured to perform frequency-domain conversion on the electromagnetic induction signal to obtain electromagnetic induction frequency-domain characteristics;

[0119] A generation module 230, configured to perform frequency-domain energy spectrum fusion reconstruction on the magnetostrictive frequency-domain characteristics and the electromagnetic induction frequency-domain characteristics based on the frequency band matching relationship between the magnetostrictive frequency-domain characteristics and the electromagnetic induction frequency-domain characteristics, and generate a dual-frequency coupled energy spectrum;

[0120] A determination module 240, configured to determine the rotational speed of the motor rotating shaft according to the frequency position and amplitude distribution of the energy peak in the dual-frequency coupled energy spectrum.

[0121] In one embodiment, the generation module 230 is specifically configured to perform synchronous decoupling processing on the resonance frequency band of the magnetostrictive frequency-domain characteristics and the fundamental wave frequency band of the electromagnetic induction frequency-domain characteristics through duplexer-type dynamic frequency division based on the frequency band matching relationship between the magnetostrictive frequency-domain characteristics and the electromagnetic induction frequency-domain characteristics, so as to obtain independent band energy sub-components with phase synchronization constraints; according to the frequency-domain energy distribution characteristics of the independent band energy sub-components, perform dynamic matching on the energy weights of the resonance frequency band of the magnetostrictive frequency-domain characteristics and the fundamental wave frequency band of the electromagnetic induction frequency-domain characteristics, and construct a frequency-domain energy weight matrix; based on the frequency band energy ratio in the frequency-domain energy weight matrix, perform weighted superposition operation on the high-frequency harmonic energy sub-components and the low-frequency fundamental wave energy sub-components in the independent band energy sub-components, and use the phase offset parameter compensation algorithm to correct the phase difference in the superposition process to generate a dual-frequency coupled energy spectrum.

[0122] In one embodiment, the generation module 230 is specifically configured to perform coupled screening processing on the overlapping region of the resonance frequency band and the fundamental wave frequency band by using frequency-domain energy threshold determination based on the frequency band matching relationship between the magnetostrictive frequency-domain characteristics and the electromagnetic induction frequency-domain characteristics, and generate a dynamic frequency-domain mapping model and an energy coupling feature set; according to the dynamic frequency-domain mapping model, perform multi-order frequency band segmentation processing on the resonance frequency band by using duplexer-type dynamic frequency division, and simultaneously perform frequency band isolation processing on the fundamental wave frequency band based on frequency point adaptive scanning to generate a dynamic isolation frequency band; perform error feedback adjustment processing on the phase offset amount of the energy sub-components in the dynamic isolation frequency band by using phase synchronization constraint modeling to generate initial independent band energy sub-components including a homologous phase reference; use the dynamic threshold adaptive adjustment algorithm to perform cross-band interference component identification processing on the frequency-domain distribution characteristics of the initial independent band energy sub-component set to obtain an interference component set, and perform energy component reconstruction processing through frequency-domain energy aggregation based on the initial independent band energy sub-component set and the interference component set to generate independent band energy sub-components.

[0123] In one embodiment, the generating module 230 is specifically configured to perform a gradient decomposition process of multi-order frequency band segmentation on the resonant frequency band based on the frequency band overlapping coefficient distribution of the dynamic frequency domain mapping model, and generate a set of segmented frequency bands with frequency band boundary constraints; perform a dynamic weighted analysis process on the frequency point energy distribution characteristics of the fundamental frequency band through frequency point adaptive scanning, and generate a frequency point energy mapping relationship including frequency band isolation weight coefficients; based on the set of segmented frequency bands and the frequency point energy mapping relationship, perform a frequency band energy reallocation process on the resonant frequency band and the fundamental frequency band by using a duplexer-type dynamic frequency division, and generate a set of dynamic isolation frequency bands with frequency band isolation degree constraints.

[0124] In one embodiment, the determining module 240 is specifically configured to obtain an initial rotational speed value based on the interval between the magnetostrictive main peak frequency position and the electromagnetic induction fundamental frequency position in the dual-frequency coupled energy spectrum and the linear relationship between the frequency interval and the motor rotational speed; extract a harmonic factor from the initial rotational speed value by using the integer multiple relationship between the harmonic intervals of the magnetostrictive secondary peak frequency position and the main peak frequency position in the dual-frequency coupled energy spectrum; perform a dynamic coupling operation process based on the ratio of the magnetostrictive main peak amplitude distribution to the secondary peak amplitude distribution in the dual-frequency coupled energy spectrum and the ratio of the electromagnetic induction fundamental frequency amplitude distribution to the magnetostrictive main peak amplitude distribution in the dual-frequency coupled energy spectrum to generate a dynamic coupling coefficient; perform an amplitude-frequency joint calibration process on the initial rotational speed value based on the harmonic factor and the dynamic coupling coefficient to determine the rotational speed of the motor shaft.

[0125] In one embodiment, the determining module 240 is specifically configured to correct the harmonic distortion component of the initial rotational speed value based on the product relationship between the harmonic factor and the initial rotational speed value, and generate a modulated rotational speed intermediate value. The product relationship is determined based on the integer reciprocal relationship of the harmonic interval times between the magnetostrictive secondary peak frequency position and the main peak frequency position in the dual-frequency coupled energy spectrum, and the harmonic distortion component is determined by the integer multiple relationship between the harmonic intervals of the magnetostrictive secondary peak frequency position and the main peak frequency position in the dual-frequency coupled energy spectrum; compensate for the amplitude-frequency coupling error of the modulated rotational speed intermediate value by using the proportional relationship between the dynamic coupling coefficient and the modulated rotational speed intermediate value to obtain a compensated rotational speed intermediate value. The proportional relationship is determined by the ratio of the magnetostrictive main peak amplitude distribution to the secondary peak amplitude distribution in the dual-frequency coupled energy spectrum and the ratio of the electromagnetic induction fundamental frequency amplitude distribution to the magnetostrictive main peak amplitude distribution in the dual-frequency coupled energy spectrum; suppress the temperature drift component of the compensated rotational speed intermediate value according to the joint weight of the harmonic factor and the dynamic coupling coefficient to obtain the rotational speed of the motor shaft. The joint weight is dynamically adjusted according to the distribution characteristics of the harmonic factor and the dynamic coupling coefficient in the dual-frequency coupled energy spectrum.

[0126] In one embodiment, the acquisition module 210 is specifically configured to perform axial magnetization excitation processing on the motor shaft based on preset alternating magnetic field excitation parameters to obtain an initial magnetostrictive resonance signal. The preset alternating magnetic field excitation parameters include an excitation frequency and a magnetic field strength that match the magnetostrictive coefficient of the motor shaft material. The preset annular induction coil is used to inductively capture the alternating magnetic field generated when the motor shaft rotates to obtain an initial electromagnetic induction signal. The axial installation position of the preset annular induction coil maintains a preset distance from the application position of the magnetization excitation. Based on the propagation delay of the trigger frequency and the preset distance in the preset alternating magnetic field excitation parameters, the initial magnetostrictive resonance signal and the initial electromagnetic induction signal are synchronously collected to obtain a magnetostrictive resonance signal and an electromagnetic induction signal.

[0127] Figure 2 Each module in the system shown has the function of implementing Figure 1 each step in and can achieve its corresponding technical effects. For the sake of brevity, it will not be described in detail here.

[0128] Figure 3 The figure shows a schematic hardware structure diagram of an electronic device provided by an embodiment of the present application.

[0129] The electronic device may include a processor 310 and a memory 320 storing computer program instructions.

[0130] Specifically, the processor 310 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0131] The memory 320 may include a mass storage for data or instructions. By way of example and not limitation, the memory 320 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive, or a combination of two or more of these. In a suitable case, the memory 320 may include a removable or non-removable (or fixed) medium. In a suitable case, the memory 320 may be internal or external to the integrated gateway disaster recovery device. In a specific embodiment, the memory 320 is a non-volatile solid-state memory.

[0132] The memory may include a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk storage media device, an optical storage media device, a flash memory device, an electrical, optical, or other physical / tangible memory storage device. Thus, generally, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method according to the first aspect of the present disclosure.

[0133] The processor 310 reads and executes the computer program instructions stored in the memory 320 to implement any one of the methods for measuring the rotational speed of an electromagnetic induction motor in the above embodiments.

[0134] In one example, the electronic device may further include a communication interface 330 and a bus 340. Among them, as Figure 3 shown, the processor 310, the memory 320, and the communication interface 330 are connected through the bus 340 and complete communication with each other.

[0135] The communication interface 330 is mainly used to implement communication between various modules, devices, units, and / or devices in the embodiments of the present application.

[0136] The bus 340 includes hardware, software, or both, and couples the components of the online data flow metering device to each other. By way of example and not limitation, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low Pin Count (LPC) bus, a memory bus, a MicroChannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses or a combination of two or more of these. In a suitable case, the bus 340 may include one or more buses. Although the embodiments of the present application describe and illustrate specific buses, the present application contemplates any suitable bus or interconnect.

[0137] The electronic device can execute the method for measuring the rotational speed of an electromagnetic induction motor in the embodiments of the present application, thereby implementing the method for measuring the rotational speed of an electromagnetic induction motor described in combination with Figure 1 the method for measuring the rotational speed of an electromagnetic induction motor described.

[0138] In addition, in combination with the motor speed measurement method based on electromagnetic induction in the above embodiments, the embodiments of the present application can provide a computer-readable storage medium to implement. Computer program instructions are stored on the computer-readable storage medium; when the computer program instructions are executed by a processor, any one of the motor speed measurement methods based on electromagnetic induction in the above embodiments is implemented.

[0139] It should be clear that the present application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, the detailed description of known methods is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present application is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications, and additions, or change the order between steps after understanding the spirit of the present application.

[0140] The functional blocks shown in the above block diagrams can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, it can be, for example, an electronic circuit, an application-specific integrated circuit (ASIC), appropriate firmware, a plug-in, a functional card, and so on. When implemented in software, the elements of the present application are programs or code segments used to perform the required tasks. The program or code segment can be stored in a machine-readable medium or transmitted via a data signal carried in a carrier wave on a transmission medium or a communication link. A "machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical discs, hard disks, fiber optic media, radio frequency (RF) links, and so on. The code segment can be downloaded via a computer network such as the Internet, an intranet, and so on.

[0141] It should also be noted that the exemplary embodiments mentioned in the present application describe some methods or systems based on a series of steps or devices. However, the present application is not limited to the order of the above steps, that is, the steps can be executed in the order mentioned in the embodiments, or different from the order in the embodiments, or several steps can be executed simultaneously.

[0142] As described above with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present application. It should be understood that each block in the flowchart and / or block diagram, and the combination of blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device to produce a machine, such that the instructions executed by the processor of the computer or other programmable data processing device enable the implementation of the functions / actions specified in one or more blocks of the flowchart and / or block diagram. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field programmable logic circuit. It should also be understood that each block in the block diagram and / or flowchart, and the combination of blocks in the block diagram and / or flowchart, can also be implemented by dedicated hardware that performs the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0143] As described above, the above is only the specific implementation manner of the present application. Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, modules, and units described above can refer to the corresponding processes in the foregoing method embodiments, and will not be described herein again. It should be understood that the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of various equivalent modifications or substitutions, and these modifications or substitutions should all be covered within the protection scope of the present application.

Claims

1. A method for measuring the rotational speed of a motor based on electromagnetic induction, characterized in that, The method includes: Obtaining the magnetostrictive resonance signal and electromagnetic induction signal of the motor shaft; Performing frequency-domain conversion on the magnetostrictive resonance signal to obtain the magnetostrictive resonance frequency-domain feature; Performing frequency-domain conversion on the electromagnetic induction signal to obtain the electromagnetic induction frequency-domain feature; Based on the frequency-band matching relationship between the magnetostrictive frequency-domain feature and the electromagnetic induction frequency-domain feature, performing frequency-domain energy spectrum fusion reconstruction on the magnetostrictive frequency-domain feature and the electromagnetic induction frequency-domain feature to generate a dual-frequency coupled energy spectrum; Determining the rotational speed of the motor shaft according to the frequency position and amplitude distribution of the energy peak in the dual-frequency coupled energy spectrum.

2. The method according to claim 1, wherein The performing frequency-domain energy spectrum fusion reconstruction on the magnetostrictive frequency-domain feature and the electromagnetic induction frequency-domain feature based on the frequency-band matching relationship between the magnetostrictive frequency-domain feature and the electromagnetic induction frequency-domain feature to generate a dual-frequency coupled energy spectrum includes: Based on the frequency-band matching relationship between the magnetostrictive frequency-domain feature and the electromagnetic induction frequency-domain feature, performing synchronous decoupling processing on the resonance frequency band of the magnetostrictive frequency-domain feature and the fundamental frequency band of the electromagnetic induction frequency-domain feature through duplexer-type dynamic frequency division to obtain independent band energy sub-components with phase synchronization constraints; According to the frequency-domain energy distribution characteristics of the independent band energy sub-components, dynamically matching the energy weights of the resonance frequency band of the magnetostrictive frequency-domain feature and the fundamental frequency band of the electromagnetic induction frequency-domain feature to construct a frequency-domain energy weight matrix; Based on the band energy proportion in the frequency-domain energy weight matrix, performing weighted superposition operation on the high-frequency harmonic energy sub-components and low-frequency fundamental energy sub-components in the independent band energy sub-components, and using the phase offset parameter compensation algorithm to correct the phase difference during the superposition process to generate the dual-frequency coupled energy spectrum.

3. The method according to claim 2, characterized in that, The performing synchronous decoupling processing on the resonance frequency band of the magnetostrictive frequency-domain feature and the fundamental frequency band of the electromagnetic induction frequency-domain feature through duplexer-type dynamic frequency division based on the frequency-band matching relationship between the magnetostrictive frequency-domain feature and the electromagnetic induction frequency-domain feature to obtain independent band energy sub-components with phase synchronization constraints includes: Based on the frequency-band matching relationship between the magnetostrictive frequency-domain feature and the electromagnetic induction frequency-domain feature, performing coupling screening processing on the overlapping region of the resonance frequency band and the fundamental frequency band by using frequency-domain energy threshold determination to generate a dynamic frequency-domain mapping model and an energy coupling feature set; According to the dynamic frequency-domain mapping model, performing multi-order frequency band segmentation processing on the resonance frequency band by using duplexer-type dynamic frequency division, and simultaneously performing frequency band isolation processing on the fundamental frequency band based on frequency point adaptive scanning to generate dynamic isolation frequency bands; Performing error feedback adjustment processing on the phase offset amount of the energy sub-components in the dynamic isolation frequency bands by using phase synchronization constraint modeling to generate initial independent band energy sub-components including a homologous phase reference; Using the dynamic threshold adaptive adjustment algorithm to perform cross-band interference component identification processing on the frequency-domain distribution characteristics of the initial independent band energy sub-component set to obtain an interference component set, and based on the initial independent band energy sub-component set and the interference component set, performing energy component reconstruction processing through frequency-domain energy aggregation to generate the independent band energy sub-components.

4. The method according to claim 3, wherein According to the dynamic frequency-domain mapping model, a duplexer-type dynamic frequency division is used to perform a multi-order frequency band splitting process on the resonant frequency band, and at the same time, a frequency band isolation process is performed on the fundamental frequency band based on frequency point adaptive scanning to generate a dynamic isolation frequency band, including: Based on the frequency band overlap coefficient distribution of the dynamic frequency-domain mapping model, a gradient decomposition process of multi-order frequency band splitting is performed on the resonant frequency band to generate a set of split frequency bands with frequency band boundary constraints; Through the frequency point adaptive scanning, a dynamic weighted analysis process is performed on the frequency point energy distribution characteristics of the fundamental frequency band to generate a frequency point energy mapping relationship including frequency band isolation weight coefficients; Based on the set of split frequency bands and the frequency point energy mapping relationship, a duplexer-type dynamic frequency division is used to perform a frequency band energy reallocation process on the resonant frequency band and the fundamental frequency band to generate a set of dynamic isolation frequency bands with frequency band isolation degree constraints.

5. The method according to claim 1, wherein According to the frequency position and amplitude distribution of the energy peak in the dual-frequency coupling energy spectrum, determining the rotational speed of the motor shaft includes: Based on the interval between the magnetostrictive main peak frequency position and the electromagnetic induction fundamental frequency position in the dual-frequency coupling energy spectrum, an initial rotational speed value is obtained through the linear relationship between the frequency interval and the motor rotational speed; Using the integer multiple relationship of the harmonic interval between the magnetostrictive secondary peak frequency position and the main peak frequency position in the dual-frequency coupling energy spectrum, a harmonic factor is extracted from the initial rotational speed value to obtain a harmonic factor; According to the ratio of the magnetostrictive main peak amplitude distribution to the secondary peak amplitude distribution in the dual-frequency coupling energy spectrum, and the ratio of the electromagnetic induction fundamental frequency amplitude distribution to the magnetostrictive main peak amplitude distribution in the dual-frequency coupling energy spectrum, a dynamic coupling operation process is performed to generate a dynamic coupling coefficient; Based on the harmonic factor and the dynamic coupling coefficient, an amplitude-frequency joint calibration process is performed on the initial rotational speed value to determine the rotational speed of the motor shaft.

6. The method according to claim 5, characterized in that, The amplitude-frequency joint calibration process based on the harmonic factor and the dynamic coupling coefficient to determine the rotational speed of the motor shaft includes: Based on the product relationship between the harmonic factor and the initial rotational speed value, the harmonic distortion component of the initial rotational speed value is corrected to generate a modulated rotational speed intermediate value. The product relationship is determined based on the integer reciprocal relationship of the harmonic interval times between the magnetostrictive secondary peak frequency position and the main peak frequency position in the dual-frequency coupling energy spectrum, and the harmonic distortion component is determined by the integer multiple relationship of the harmonic interval between the magnetostrictive secondary peak frequency position and the main peak frequency position in the dual-frequency coupling energy spectrum; Using the proportional relationship between the dynamic coupling coefficient and the modulated rotational speed intermediate value, the amplitude-frequency coupling error of the modulated rotational speed intermediate value is compensated to obtain a compensated rotational speed intermediate value. The proportional relationship is determined by the ratio of the magnetostrictive main peak amplitude distribution to the secondary peak amplitude distribution in the dual-frequency coupling energy spectrum, and the ratio of the electromagnetic induction fundamental frequency amplitude distribution to the magnetostrictive main peak amplitude distribution. Suppress the temperature drift component of the intermediate compensation rotational speed according to the combined weight of the harmonic factor and the dynamic coupling coefficient, to obtain the rotational speed of the motor shaft, where the combined weight is dynamically adjusted according to the distribution characteristics of the harmonic factor and the dynamic coupling coefficient in the dual-frequency coupling energy spectrum.

7. The method according to claim 1, wherein The obtaining of the magnetostrictive resonance signal and the electromagnetic induction signal of the motor shaft includes: Perform axial magnetization excitation processing on the motor shaft based on preset alternating magnetic field excitation parameters to obtain an initial magnetostrictive resonance signal, where the preset alternating magnetic field excitation parameters include an excitation frequency and a magnetic field strength that match the magnetostrictive coefficient of the motor shaft material; Use a preset ring induction coil to inductively capture the alternating magnetic field generated when the motor shaft rotates to obtain an initial electromagnetic induction signal, where the axial installation position of the preset ring induction coil maintains a preset distance from the application position of the magnetization excitation; Based on the propagation delay of the trigger frequency in the preset alternating magnetic field excitation parameters and the preset distance, synchronously collect the initial magnetostrictive resonance signal and the initial electromagnetic induction signal to obtain the magnetostrictive resonance signal and the electromagnetic induction signal.

8. A motor speed measurement system based on electromagnetic induction, characterized in that, The system includes: An acquisition module, configured to acquire the magnetostrictive resonance signal and the electromagnetic induction signal of the motor shaft; A conversion module, configured to perform frequency-domain conversion on the magnetostrictive resonance signal to obtain magnetostrictive resonance frequency-domain characteristics; The conversion module is further configured to perform frequency-domain conversion on the electromagnetic induction signal to obtain electromagnetic induction frequency-domain characteristics; A reconstruction module, configured to perform frequency-domain energy spectrum fusion and reconstruction on the magnetostrictive frequency-domain characteristics and the electromagnetic induction frequency-domain characteristics based on the frequency band matching relationship between the magnetostrictive frequency-domain characteristics and the electromagnetic induction frequency-domain characteristics, to generate a dual-frequency coupling energy spectrum; A determination module, configured to determine the rotational speed of the motor shaft according to the frequency position and amplitude distribution of the energy peak in the dual-frequency coupling energy spectrum.

9. An electronic device, characterized in that, The device includes: a processor and a memory storing computer program instructions; When the processor executes the computer program instructions, it implements the electromagnetic induction-based motor speed measurement method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, Computer program instructions are stored on the computer-readable storage medium, and when the computer program instructions are executed by the processor, they implement the electromagnetic induction-based motor speed measurement method according to any one of claims 1-7.

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