A motor rotational speed measurement method, system and device based on electromagnetic induction

Abstract

The application discloses a motor rotating speed measurement method, system and device based on electromagnetic induction, and belongs to the motor field. The motor rotating shaft magnetostrictive resonance signal and the electromagnetic induction signal are acquired first in the embodiment of the application; the magnetostrictive resonance signal is converted in the frequency domain to obtain the magnetostrictive resonance frequency domain feature; the electromagnetic induction signal is converted in the frequency domain to obtain the electromagnetic induction frequency domain feature; then, based on the frequency band matching relationship of the magnetostrictive frequency domain feature and the electromagnetic induction frequency domain feature, the magnetostrictive frequency domain feature and the electromagnetic induction frequency domain feature are fused and reconstructed in the frequency domain to generate a double-frequency coupling energy spectrum; finally, according to the frequency position and amplitude distribution of the energy peak value in the double-frequency coupling energy spectrum, the rotating speed of the motor rotating shaft is determined, and the measurement precision of the motor rotating speed in a complex environment can be improved.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of electric machines, and particularly relates to an electric machine rotating speed measurement method, system, device and computer readable storage medium based on electromagnetic induction. BACKGROUND

[0002] As a core link in industrial automation control, equipment state monitoring and energy efficiency optimization, the precision and reliability of electric machine rotating speed measurement directly affect the system control efficiency. In an electric machine driving system, the rotating speed is not only a key parameter for evaluating mechanical load characteristics, but also a basic 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 rotating speed data plays an irreplaceable role in ensuring equipment stability, prolonging service life and improving energy efficiency. Therefore, developing a measurement method with strong adaptability and good anti-interference ability has become a key point of technical research.

[0003] In the prior art, measurement methods based on pulse counting principle occupy the mainstream, such as photoelectric reflection method, magneto-electric method and Hall switch detection method. Such methods convert the rotating speed by counting the number of pulses in a fixed time, although the structure is simple, but there are significant limitations in actual application. For example, the photoelectric method is easily disturbed by environmental light, resulting in distortion of the pulse signal; the magneto-electric method relies on the accurate alignment of the magnet and the coil, and mechanical vibration or electromagnetic noise easily causes missed detection or false detection; while the Hall sensor can improve the resolution through multi-magnetic pole layout, but is limited by the installation error of the magnet and the temperature drift, and the dynamic response characteristics and measurement stability are difficult to balance. In addition, although the encoder can realize high-precision measurement, its cost is high and the installation conditions are harsh, and it is difficult to popularize and apply in harsh working conditions. The above methods all rely on time domain signal processing of a single physical quantity (light, magnetism), it is difficult to effectively suppress noise interference, and when the rotating speed suddenly changes or the load fluctuates, cumulative error is easily generated, resulting in a decrease in measurement accuracy. SUMMARY

[0004] The embodiments of the present application provide an electric machine rotating speed measurement method, system, device and computer readable storage medium based on electromagnetic induction, which can improve the rotating speed measurement precision of the electric machine in a complex environment.

[0005] In a first aspect, the embodiments of the present application provide an electric machine rotating speed measurement method based on electromagnetic induction, which comprises:

[0006] Obtaining a magnetostrictive resonance signal and an electromagnetic induction signal of a rotating shaft of an electric machine;

[0007] Performing frequency domain conversion on the magnetostrictive resonance signal to obtain a magnetostrictive resonance frequency domain feature;

[0008] Performing frequency domain conversion on the electromagnetic induction signal to obtain an electromagnetic induction frequency domain feature;

[0009] The magnetostrictive frequency domain feature and the electromagnetic induction frequency domain feature are fused and reconstructed 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 coupling energy spectrum.

[0010] The rotating speed of the motor rotating shaft is determined according to the frequency position and amplitude distribution of the energy peak in the dual-frequency coupling energy spectrum.

[0011] In an implementable embodiment, the magnetostrictive frequency domain feature and the electromagnetic induction frequency domain feature are fused and reconstructed 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 coupling energy spectrum, including:

[0012] The resonant frequency band of the magnetostrictive frequency domain feature and the fundamental frequency band of the electromagnetic induction frequency domain feature are synchronously decoupled based on the frequency band matching relationship between the magnetostrictive frequency domain feature and the electromagnetic induction frequency domain feature to obtain an independent frequency band energy quantum with phase synchronization constraint.

[0013] The energy weight of the resonant frequency band of the magnetostrictive frequency domain feature and the fundamental frequency band of the electromagnetic induction frequency domain feature is dynamically matched according to the frequency energy distribution characteristics of the independent frequency band energy quantum to construct a frequency energy weight matrix.

[0014] The high-frequency harmonic energy quantum and the low-frequency fundamental energy quantum in the independent frequency band energy quantum are weighted and superimposed based on the frequency band energy proportion in the frequency energy weight matrix, and the phase difference in the superimposition process is corrected using a phase offset parameter compensation algorithm to generate a dual-frequency coupling energy spectrum.

[0015] In an implementable embodiment, the resonant frequency band of the magnetostrictive frequency domain feature and the fundamental frequency band of the electromagnetic induction frequency domain feature are synchronously decoupled based on the frequency band matching relationship between the magnetostrictive frequency domain feature and the electromagnetic induction frequency domain feature to obtain an independent frequency band energy quantum with phase synchronization constraint, including:

[0016] The overlapping area of the resonant frequency band and the fundamental frequency band is coupled and screened based on the frequency band matching relationship between the magnetostrictive frequency domain feature and the electromagnetic induction frequency domain feature to generate a dynamic frequency domain mapping model and an energy coupling feature set.

[0017] The resonant frequency band is subjected to multi-order frequency band division processing based on the dynamic frequency domain mapping model, and the fundamental frequency band is subjected to frequency band isolation processing based on frequency point adaptive scanning to generate a dynamic isolated frequency band.

[0018] The phase offset amount of the energy quantum in the dynamic isolated frequency band is subjected to error feedback adjustment processing using phase synchronization constraint modeling to generate an initial independent frequency band energy quantum including a homophase reference.

[0019] The dynamic threshold self-adaptive adjustment algorithm is used for cross-frequency interference component identification processing on the frequency domain distribution characteristics of the initial independent frequency band energy sub-set, to obtain an interference component set; and based on the initial independent frequency band energy sub-set and the interference component set, energy component reconstruction processing is performed through frequency energy aggregation to generate an independent frequency band energy sub-set.

[0020] In an implementable embodiment, according to a dynamic frequency domain mapping model, a duplex dynamic frequency division is used to perform multi-order frequency band division 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 isolated frequency band, including:

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

[0022] Through dynamic weighting analysis processing on the frequency point energy distribution characteristics of the fundamental frequency band by frequency point adaptive scanning, a frequency point energy mapping relationship including a frequency band isolation weight coefficient is generated;

[0023] Based on the divided frequency band set and the frequency point energy mapping relationship, frequency band energy redistribution processing is performed on the resonant frequency band and the fundamental frequency band by using a duplex dynamic frequency division to generate a dynamic isolated frequency band set with a frequency band isolation degree constraint.

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

[0025] Based on the interval between the magnetostriction 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] The initial rotational speed value is subjected to harmonic factor extraction using the integer multiple relationship of the harmonic interval between the magnetostriction secondary peak frequency position and the main peak frequency position in the dual-frequency coupled energy spectrum to obtain a harmonic factor;

[0027] According to the ratio of the magnetostriction 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 magnetostriction 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 rotating shaft.

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

[0030] The harmonic distortion component of the initial rotating speed value is corrected based on the product relationship of the harmonic factor and the initial rotating speed value, to generate a modulated rotating speed intermediate value, the product relationship being determined based on the integer reciprocal relationship of the harmonic interval number between the magnetostriction secondary peak frequency position and the main peak frequency position in the double-frequency coupling energy spectrum, and the harmonic distortion component being determined based on the integer multiple relationship of the harmonic interval between the magnetostriction secondary peak frequency position and the main peak frequency position in the double-frequency coupling energy spectrum;

[0031] The amplitude-frequency coupling error of the modulated rotating speed intermediate value is compensated using the proportional relationship of the dynamic coupling coefficient and the modulated rotating speed intermediate value, to obtain a compensated rotating speed intermediate value, the proportional relationship being determined by the ratio of the magnetostriction main peak amplitude distribution to the secondary peak amplitude distribution in the double-frequency coupling energy spectrum, and the ratio of the electromagnetic induction base frequency amplitude distribution to the magnetostriction main peak amplitude distribution;

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

[0033] In an implementable embodiment, the magnetostriction resonance signal and the electromagnetic induction signal of the motor rotating shaft are obtained, comprising:

[0034] The motor rotating shaft is subjected to axial magnetization excitation processing based on preset alternating magnetic field excitation parameters, to obtain an initial magnetostriction resonance signal, the preset alternating magnetic field excitation parameters including an excitation frequency and a magnetic field strength matched with the magnetostriction coefficient of the motor rotating shaft material;

[0035] The alternating magnetic field generated by the rotation of the motor rotating shaft is inductively captured using a preset annular induction coil, to obtain an initial electromagnetic induction signal, the axial installation position of the preset annular induction coil and the application position of the magnetization excitation maintaining a preset interval;

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

[0037] In a second aspect, the embodiments of the present application provide an electromagnetic induction-based motor rotating speed measurement system, which comprises:

[0038] The acquisition module is configured to acquire the magnetostriction resonance signal and the electromagnetic induction signal of the motor rotating shaft;

[0039] The conversion module is configured to perform frequency domain conversion on the magnetostriction resonance signal to obtain magnetostriction resonance frequency domain characteristics.

[0040] The conversion module is further configured to perform frequency domain conversion on the electromagnetic induction signal to obtain electromagnetic induction frequency domain characteristics.

[0041] The generation module is configured to perform frequency domain energy spectrum fusion reconstruction on the magnetostriction frequency domain characteristics and the electromagnetic induction frequency domain characteristics based on a frequency band matching relationship between the magnetostriction frequency domain characteristics and the electromagnetic induction frequency domain characteristics, to generate double-frequency coupling energy spectrum.

[0042] The determination module is configured to determine the rotation speed of the motor rotating shaft according to a frequency position and an amplitude distribution of an energy peak value in the double-frequency coupling energy spectrum.

[0043] In a third aspect, an electronic device is provided, which includes a processor and a memory storing computer program instructions; the processor reads and executes the computer program instructions to implement the motor rotation speed measurement method based on electromagnetic induction in any of the embodiments of the first aspect.

[0044] In a fourth aspect, a computer readable storage medium is provided, which stores computer program instructions; the computer program instructions are executed by a processor to implement the motor rotation speed measurement method based on electromagnetic induction in any of the embodiments of the first aspect.

[0045] The motor rotation speed measurement method, system, device and computer readable storage medium based on electromagnetic induction provided in the embodiments of the present application can avoid the defects of weak noise suppression ability in time domain signal processing and significant cumulative error under sudden change conditions, and effectively solve the problem of precision decline caused by environmental interference, installation error and other problems in traditional single physical quantity measurement.

[0046] Further, the magnetostriction resonance signal and the electromagnetic induction signal are respectively converted to the frequency domain, which can separate the effective components and noise in the signals, avoid the direct influence of environmental light and electromagnetic interference on the time domain characteristics of a single signal, and improve the anti-interference ability. By matching the frequency band energy distribution of magnetostriction and electromagnetic induction, the complementary characteristics of the two in the frequency domain are fused, the significance of the rotation speed related energy peak value is enhanced, and the missing detection or false detection problem 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 value, the instantaneous change when the rotation speed suddenly changes or the load fluctuates can be captured in real time, the cumulative error generated in the time domain integration process of the pulse counting method is overcome, and the dynamic measurement precision and stability are significantly improved. BRIEF DESCRIPTION OF DRAWINGS

[0047] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required to be used in the embodiments of the present application will be briefly introduced, and other drawings can be obtained by those of ordinary skill in the art without creative labor on the premise of not paying the creative labor.

[0048] Figure 1 is a flow diagram of a motor speed measurement method based on electromagnetic induction provided by an embodiment of the present application;

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

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

[0051] The features and exemplary embodiments of various aspects of the present application will be described in detail below, in order to make the purposes, technical solutions and advantages of the present application more clear and apparent, the present application will be further described in detail below in combination with the drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application, but not to limit the present application. The present application can be implemented without some of these specific details by those skilled in the art. 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 paper, relationship 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 such actual relationship or order between the entities or operations. Moreover, the terms "include", "contain" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or device. Without more limitations, the elements defined by the statement "include" do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0053] In order to solve the problems of the prior art, the embodiments of the present application provide a motor speed measurement method, system, device and computer readable storage medium based on electromagnetic induction. First, the motor speed measurement method based on electromagnetic induction provided by the embodiments of the present application will be introduced.

[0054] Figure 1 A flowchart of a motor speed measurement method based on electromagnetic induction is shown. As shown in the figure, the method includes steps S110-S150. Figure 1

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

[0056] In the embodiment of the application, the magnetostriction resonance signal is obtained by applying an alternating magnetic field to the motor rotating shaft to excite the magnetostriction effect of the rotating shaft material (the material deforms in the magnetic field and generates mechanical vibration); the electromagnetic induction signal is the induced electromotive force signal generated by the alternating magnetic field when the rotating shaft rotates, which is captured by the induction coil. The two signals respectively reflect the mechanical vibration characteristics and electromagnetic characteristics of the rotating shaft. An alternating magnetic field of a predetermined frequency is applied to the motor rotating shaft in the axial direction (such as through the excitation coil), triggering the magnetostriction effect of the rotating shaft material, generating a resonance signal; at the same time, the induction voltage signal caused by the change of the alternating magnetic field when the rotating shaft rotates is collected through the annular induction coil (maintaining a predetermined distance from the magnetization excitation device).

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

[0058] The electromagnetic induction signal is derived from the eddy current effect of the rotating shaft material in the alternating magnetic field. When the rotating shaft has stress concentration or micro defects, the changes in its conductivity and permeability will change the eddy current distribution, and then affect the amplitude and phase of the induced current. By detecting the change of the induced electromotive force through the coil or the Hall sensor, the integrity of the internal structure of the rotating shaft can be indirectly reflected.

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

[0060] In one embodiment, magnetostrictive sensors and electromagnetic induction coils are arranged on the surface of the motor rotating shaft. By applying excitation of a specific frequency (such as 10 kHz-1 MHz) to the rotating shaft through an alternating magnetic field generator, the ferromagnetic rotating shaft produces periodic deformation due to the magnetostrictive effect, forming a resonance signal, which is collected by a piezoelectric ceramic sensor. At the same time, the rotating shaft cuts the magnetic field to produce an electromagnetic induction signal, which is received by the induction coil. After filtering (bandwidth 0.1-5 MHz) by a preamplifier, the two time-domain waveforms are recorded synchronously by a high-speed data acquisition card (sampling rate ≥10 MS / s).

[0061] S120: Frequency domain conversion is performed on the magnetostrictive resonance signal to obtain magnetostrictive resonance frequency domain features.

[0062] Frequency domain conversion is the conversion of time domain vibration signal to frequency domain features through fast Fourier transform (FFT) or wavelet transform, extracting the main frequency, harmonic components and their energy distribution. The magnetostrictive resonance signal is preprocessed (such as band-pass filtering) to extract its frequency domain features, including the main resonance frequency, the secondary harmonic frequency and the corresponding amplitude.

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

[0064] In one embodiment, the collected magnetostrictive resonance signal is subjected to fast Fourier transform (FFT) for frequency domain analysis, and the main resonance frequency (such as the fundamental frequency f0=25 kHz) and harmonic components (such as 2f0, 3f0) are extracted through frequency spectrum analysis, and the amplitude, phase and energy density of each frequency band are calculated to form a frequency domain feature matrix.

[0065] S130: Frequency domain conversion is performed on the electromagnetic induction signal to obtain electromagnetic induction frequency domain features.

[0066] The frequency domain features of the electromagnetic induction signal reflect the fundamental frequency and its harmonic components of the alternating magnetic field when the rotating shaft rotates, which is linearly related to the rotating speed. The fundamental frequency (directly related to the rotating speed) and high-frequency harmonics (reflecting electromagnetic interference or mechanical vibration coupling effect) are extracted through sampling and frequency spectrum analysis of the electromagnetic induction signal.

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

[0068] In one embodiment, after anti-aliasing filtering is performed on 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 speed (such as the rotation frequency f_r and its sideband frequencies f_r±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, the frequency domain energy spectrum of the magnetostrictive frequency domain characteristics and the electromagnetic induction frequency domain characteristics are fused and reconstructed to generate a dual-frequency coupled energy spectrum.

[0070] The frequency domain characteristics of magnetostrictive resonance (mechanical vibration characteristics) and electromagnetic induction (electromagnetic characteristics) are frequency-matched, and a dual-frequency coupled energy spectrum is reconstructed through dynamic frequency division and energy superposition to enhance the energy contribution of the speed-related frequency band. The overlapping region of the main magnetostrictive resonance frequency band (such as 0.8-1.2kHz) and the fundamental electromagnetic induction frequency band (such as 95-105Hz) is identified. Through frequency band division and energy weighting, the energy of the two frequency bands is superimposed and the phase difference is compensated to generate a fused energy spectrum.

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

[0072] In one embodiment, the fundamental frequency f0 in the magnetostrictive frequency domain characteristics and the rotation frequency f_r in the electromagnetic induction frequency domain characteristics are frequency-band matched (for example, f0 and f_r have a fixed proportional relationship due to material properties). The energy spectra within the matching frequency bands are then weighted and superimposed (e.g., using an energy density square weighting method) to reconstruct a dual-frequency coupled energy spectrum containing composite characteristics. For example, if f0 = 25 kHz corresponds to f_r = 1500 rpm (revolutions per minute), the peak frequency in the fused energy spectrum is the composite frequency of f0 and f_r (e.g., 25 kHz ± 1500 / 60 Hz).

[0073] S150: Determine the rotation speed of the motor shaft according to the frequency position and amplitude distribution of the energy peak in the dual-frequency coupling 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. The rotational speed value is dynamically calibrated through the main peak frequency interval and the harmonic factor. The initial rotational speed is calculated based on the main peak frequency interval of the energy spectrum (e.g., the difference between the magnetostrictive main peak frequency and the electromagnetic induction fundamental frequency), and the dynamic error is compensated by combining the integer multiple relationship of the harmonic interval and the amplitude ratio.

[0075] During the acceleration of the motor, the interval between the magnetostrictive main peak frequency (1 kHz) and the electromagnetic induction fundamental frequency (100 Hz) is 900 Hz. By combining the harmonic factor (e.g., 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 cumulative errors.

[0076] In one embodiment, the frequency position corresponding to the energy peak (e.g., the main peak frequency f_peak = 25.025 kHz) is identified in the dual-frequency coupled energy spectrum. Then, according to the preset calibration relationship, the rotational speed value is back calculated, for example, f_peak = K·n + f0, where K is the proportional coefficient and n is the rotational speed. Finally, the stability of the peak amplitude is verified, such as the amplitude fluctuation within 10 consecutive cycles being less than a set threshold, and the final rotational speed measurement result is output.

[0077] This embodiment overcomes the defect that single physical quantity measurement is easily disturbed by the environment by fusing magnetostrictive and electromagnetic induction dual signals. The frequency energy spectrum fusion reconstruction technology enhances the signal-to-noise ratio of the rotational speed related frequency band and suppresses noise interference. Dynamic frequency division and phase synchronization constraint ensure the cooperative analysis of high-frequency and low-frequency signals, and improve the dynamic response accuracy when the rotational speed suddenly changes or the load fluctuates. The amplitude-frequency joint calibration of the dual-frequency coupled energy spectrum further eliminates the errors caused by temperature drift and mechanical vibration, and realizes high-precision and high-stability measurement under complex working conditions.

[0078] In one implementable embodiment, step S140 performs frequency 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, including:

[0079] Based on the frequency band matching relationship of magnetostrictive frequency domain features and electromagnetic induction frequency domain features, the resonant frequency band of magnetostrictive frequency domain features and the fundamental frequency band of electromagnetic induction frequency domain features are synchronously decoupled by a diplexer type dynamic frequency splitting, and independent frequency band energy sub-quantums with phase synchronization constraint are obtained. According to the frequency energy distribution characteristics of the independent frequency band energy sub-quantums, the energy weights of the resonant frequency band of magnetostrictive frequency domain features and the fundamental frequency band of electromagnetic induction frequency domain features are dynamically matched, and a frequency energy weight matrix is constructed. Based on the frequency band energy proportion in the frequency energy weight matrix, the high-frequency harmonic energy sub-quantum and the low-frequency fundamental energy sub-quantum in the independent frequency band energy sub-quantum are subjected to weighted superposition operation, and the phase difference in the superposition process is corrected by using a 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 features and the fundamental frequency band in the electromagnetic induction frequency domain features refers to the existence of complementary or overlapping frequency band ranges in the frequency spectrum distribution. By dynamically correlating the energy distribution characteristics of these frequency bands, the signal can be synergistically enhanced and interference can be suppressed. The magnetostrictive resonant frequency band is generated by the magnetostrictive effect of the motor shaft, mainly represented by high-frequency harmonic components (such as 2 kHz or above), and its energy concentration reflects the mechanical vibration characteristics (such as rotational speed, stress deformation) of the shaft. The electromagnetic induction fundamental frequency band is generated by the electromagnetic induction effect of the motor winding current, mainly represented by low-frequency fundamental components (such as 50 Hz to 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 of the two can satisfy 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 splitting technology, the phase difference between the two frequency bands is constrained within an allowable range before fusion, to avoid destructive interference when superimposed.

[0081] The diplexer type dynamic frequency splitting refers to using an electronic filter group with bidirectional isolation characteristics to separate the high-frequency resonant frequency band (usually containing 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 a digital phase-locked loop technology between the two independent frequency band energy sub-quantums, to eliminate the phase offset caused by the difference in signal transmission paths.

[0082] Based on the frequency band matching relationship of magnetostrictive frequency domain characteristics and electromagnetic induction frequency domain characteristics, the resonant frequency band (such as 2-5 kHz) of the magnetostrictive signal and the fundamental frequency band (such as 50-500 Hz) of the electromagnetic induction signal are separated into independent sub-frequency bands by using the frequency band division function of the duplexer. Then the initial phase of the two frequency bands is tracked and aligned by using the phase-locked loop (PLL) technology, the phase offset caused by the difference in signal collection path is eliminated, and the phase-synchronized independent frequency band energy quantum is generated. Then the amplitude ratio of the high-frequency harmonic energy quantum of the resonant frequency band and the low-frequency energy quantum of the fundamental frequency band is counted. According to the actual working condition (such as the change of motor load), the weight coefficient is adjusted. For example, the resonant frequency band weight is increased at high speed to improve the sensitivity, and the fundamental frequency band weight is increased at low speed to improve the stability. Then the high-frequency harmonic energy quantum and the low-frequency fundamental energy quantum are linearly superimposed according to the weight coefficient (such as 0.6 and 0.4) to generate the preliminary fusion energy spectrum, and for the small phase difference remaining after superposition, the 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 motor speed detection scene, in order to improve the anti-interference ability and resolution of frequency domain energy fusion, first, the frequency band division characteristics of the duplexer are used to separate the vibration signals (resonant frequency band 2-5 kHz) collected by the magnetostrictive sensor and the current fundamental wave signals (fundamental wave frequency band 50-500 Hz) obtained by the electromagnetic induction sensor into independent sub-bands. The duplexer adopts a non-overlapping sub-band division design to ensure that the two frequency bands are strictly isolated in the frequency spectrum, avoiding energy crosstalk between high-frequency harmonics and low-frequency fundamental waves. Subsequently, the initial phase of the two frequency bands is tracked in real time through the phase-locked loop (PLL) technology, and the signal sampling window is adjusted based on the phase difference feedback to make the time domain waveforms of the resonant frequency band and the fundamental wave frequency band phase synchronized and aligned, and finally the independent frequency band energy sub is output. Phase-constrained. Then, according to the actual working condition 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 harmonic energy proportion of the resonant frequency band (2-5 kHz) increases significantly, at this time the resonant frequency band weight is increased to 0.6 and the fundamental wave frequency band weight is reduced to 0.4, in order to enhance the sensitivity of high frequency characteristics; while in the low speed state (such as 500 rpm), the fundamental wave frequency band (50-500 Hz) energy distribution is more stable, and the weight coefficient is 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 two frequency band energy sub is linearly superimposed to generate a preliminary fusion energy spectrum. Finally, for the small phase offset (such as caused by sensor sampling delay or path loss) remaining after superposition, a time-frequency domain interpolation algorithm is used for local compensation. Specifically, in the time domain, the mutation points of the energy spectrum are cubic spline interpolated, and in the frequency domain, the phase gradient of adjacent frequency components is smoothed and corrected through short-time Fourier transform (STFT), finally eliminating the spectrum tailing and energy leakage phenomenon, and outputting a high continuity double frequency coupled energy spectrum.

[0084] This embodiment realizes accurate separation and phase alignment of different frequency band features through 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 frequency band energy proportion is adaptively adjusted, which overcomes the limitation that multiple source features cannot be effectively fused in the fixed weight mode, and improves the robustness of energy fusion. Finally, through the phase difference correction algorithm, the phase offset in the superposition process is eliminated, ensuring the accuracy of energy spectrum reconstruction, and significantly improving the resolution and stability of the detection system. The synergistic effect of a series of technical means makes the double frequency coupled energy spectrum still maintain high precision feature representation ability under complex working conditions, providing reliable technical support for motor speed detection.

[0085] In an implementable embodiment, based on the frequency band matching relationship between the magnetostrictive frequency domain feature and the electromagnetic induction frequency domain feature, the resonant frequency band of the magnetostrictive frequency domain feature and the fundamental frequency band of the electromagnetic induction frequency domain feature are synchronously decoupled by a duplexer type dynamic frequency division, and an independent frequency band energy quantum with phase synchronization constraint is obtained, including:

[0086] Based on the frequency band matching relationship between the magnetostrictive frequency domain feature and the electromagnetic induction frequency domain feature, the overlapping area of the resonant frequency band and the fundamental frequency band is coupled and screened by frequency energy threshold judgment, a dynamic frequency domain mapping model and an energy coupling feature set are generated; according to the dynamic frequency domain mapping model, the resonant frequency band is subjected to multi-order frequency band division processing by a duplexer type dynamic frequency division, and at the same time, the fundamental frequency band is subjected to frequency band isolation processing based on frequency point adaptive scanning, a dynamic isolated frequency band is generated; the phase offset of the energy quantum in the dynamic isolated frequency band is subjected to error feedback adjustment processing by phase synchronization constraint modeling, and an initial independent frequency band energy quantum including a homophase reference is generated; the frequency domain distribution features of the initial independent frequency band energy quantum set are subjected to cross-frequency interference component identification processing by a dynamic threshold adaptive adjustment algorithm, and a interference component set is obtained, based on the initial independent frequency band energy quantum set and the interference component set, energy component reconstruction processing is performed by frequency energy aggregation, and an independent frequency band energy quantum is generated.

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

[0088] Firstly, the energy distribution of the overlapping region of two frequency bands is determined based on a frequency energy threshold, a set of frequency points with coupling effect is screened out, and a dynamic frequency domain mapping model is constructed. The model describes the synergistic or inhibitory relationship between frequency bands through an energy coupling feature set. Then, the multi-order frequency band division is performed on the resonant frequency band by using the duplexer, and the resonant frequency band is divided into multiple sub-frequency bands (for example, 2-3 kHz, 3-4 kHz, etc.). At the same time, the fundamental frequency band is adaptively scanned, and the isolation boundary is dynamically adjusted (for example, the fundamental frequency band is expanded from 50 Hz to 600 Hz to cover the load fluctuation range), to generate isolated independent sub-frequency bands. The phase offset of each sub-frequency band is adjusted by the phase-locked loop feedback to generate an initial independent frequency band energy sub. Subsequently, the dynamic threshold algorithm identifies the cross-frequency band interference components (such as high-frequency noise leakage to the fundamental frequency band) according to the energy distribution characteristics (such as the sudden amplitude of high-frequency harmonics or the smoothness of low-frequency fundamental frequency). Finally, the interference components are removed from the initial energy sub, and the remaining energy sub is reconstructed by the frequency energy aggregation algorithm (such as energy weighted average or principal component analysis) to generate the final independent frequency band energy sub, ensuring the integrity and consistency of the energy distribution.

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

[0090] In an implementable embodiment, according to the dynamic frequency domain mapping model, the duplexer type dynamic frequency division is used to perform multi-order frequency band division on the resonant frequency band, and the frequency band isolation is performed on the fundamental frequency band based on the frequency point adaptive scanning, to generate a dynamic isolated frequency band, including:

[0091] Based on the frequency band overlap coefficient distribution of the dynamic frequency domain mapping model, a gradient decomposition processing of multi-order frequency band segmentation is performed on the resonant frequency band to generate a segmented frequency band set with frequency band boundary constraints; a dynamic weighting analysis processing of the frequency point energy distribution characteristics of the fundamental frequency band is performed through frequency point adaptive scanning to generate a frequency point energy mapping relationship including frequency band isolation weight coefficients; based on the segmented frequency band set and the frequency point energy mapping relationship, a frequency band energy redistribution processing is performed on the resonant frequency band and the fundamental frequency band by using a duplexer type dynamic frequency splitting to generate a dynamic isolated frequency band set with frequency band isolation degree constraints.

[0092] The frequency band overlap coefficient distribution refers to a quantitative index of energy coupling intensity of the magnetostrictive resonant frequency band and the electromagnetic induction fundamental frequency band in the overlapping area in the dynamic frequency domain mapping model. The coefficient is calculated by a frequency energy threshold criterion and is used to represent the energy interaction degree between different frequency bands and provide a gradient decomposition basis for multi-order frequency band segmentation. The gradient decomposition processing refers to dividing the resonant frequency band into multiple continuous or discrete sub-frequency bands according to the energy coupling intensity based on the distribution characteristics of the frequency band overlap coefficient, each sub-frequency band having a clear frequency band boundary constraint to achieve fine frequency energy management. The frequency band isolation weight coefficient refers to a weight parameter set generated by dynamically weighting and analyzing the energy distribution of each frequency point in the fundamental frequency band through frequency point adaptive scanning. The coefficient reflects the priority of different frequency points in the isolation processing and is used to guide the boundary adjustment and interference suppression in the frequency band energy redistribution process.

[0093] First, the resonant frequency band is gradiently decomposed according to the frequency band overlap coefficient distribution output by the dynamic frequency domain mapping model. For example, if the overlap coefficient is high in the 2-3 kHz interval, the interval is divided into independent sub-frequency bands; if the coefficient is low in the 3-5 kHz interval, the interval is further subdivided into narrower frequency bands. Then, the fundamental frequency band (such as 50-500 Hz) is scanned in real time for frequency point energy distribution, and the energy density and stability index of each frequency point are calculated by using a sliding window statistical method to generate a frequency band isolation weight coefficient. For example, a frequency point with large energy fluctuation (such as 100-200 Hz) is given a low weight and is preferentially isolated; a stable frequency point (such as 300-400 Hz) is given a high weight and is reserved as a main frequency band. The energy of the gradiently decomposed resonant sub-frequency band and the weight-adjusted fundamental frequency band is redistributed by using the bidirectional isolation characteristics of a duplexer, specifically including: performing multi-order bandpass filtering on the resonant frequency band to filter out energy crosstalk in the overlapping area with the fundamental frequency band; and using a dynamic threshold isolation algorithm on 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 point 50-100 Hz to 50-150 Hz). Finally, whether the isolation degree of the dynamic isolated frequency band meets a preset threshold (such as ≥ 30 dB) is verified by a frequency energy leakage detection algorithm (such as a sidelobe suppression ratio analysis), and if not, the segmentation gradient and weight coefficient are adjusted again until a dynamic isolated frequency band set meeting the requirements is generated.

[0094] The embodiment significantly improves the comprehensive performance of frequency band management through the cooperative optimization of the dynamic frequency domain mapping model and the duplexer type dynamic frequency division. The gradient decomposition processing based on the frequency band overlap coefficient realizes the fine isolation of the resonant frequency band and the fundamental frequency band, effectively suppresses the cross-frequency band energy crosstalk, and guarantees the stability of the frequency band isolation degree. Through the dynamic weight coefficient generated by the frequency point adaptive scanning, combined with the energy redistribution mechanism, the energy equalization distribution of the fundamental frequency band and the resonant frequency band is optimized, and the adaptability of the system to the complex electromagnetic environment is enhanced. At the same time, the dynamic segmentation and weight cooperative strategy takes into account the calculation efficiency and processing accuracy, reduces redundant operation through an iterative optimization algorithm, and ensures the balance between real-time performance and resource utilization. The scheme guarantees the stability of the high-frequency communication link while providing an efficient anti-interference solution for multi-frequency band intensive interaction scenarios. Compared with the traditional static frequency band division method, the system has achieved systematic improvement in isolation performance, dynamic response and system robustness.

[0095] The magnetostrictive resonance signal and the ordinary resonance signal have significant nonlinear dependence on their generation mechanism and frequency domain characteristics. Magnetostrictive resonance originates from the dynamic response of the magnetic domain of ferromagnetic materials under the action of alternating magnetic field, and its main resonance frequency is nonlinearly related to the material magnetostrictive coefficient and the intensity of the excitation magnetic field, and has significant harmonic components. While ordinary resonance signals are mostly based on mechanical vibration or piezoelectric effect, and their frequency spectrum characteristics are mainly limited by the inherent frequency of the mechanical structure, and the harmonic energy distribution is relatively simple. This characteristic makes the magnetostrictive signal present 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 magnetostrictive resonance signal will cause frequency domain feature drift and harmonic interference in the speed measurement. Under dynamic working conditions, the nonlinear change of material permeability will cause the main resonance peak frequency to shift, and the cross modulation of magnetostrictive effect and electromagnetic induction will induce abnormal distribution of harmonic energy, resulting in phase accumulation error in single frequency point speed inversion. In addition, the drift of magnetostrictive coefficient caused by temperature change will further aggravate the spectrum distortion, making the traditional measurement method based on fixed frequency and speed mapping relationship invalid.

[0096] The embodiment of the present application can eliminate the reference error caused by single signal frequency deviation by extracting the interval between the magnetostrictive main peak and the electromagnetic induction fundamental frequency and its harmonic integer multiple relationship. At the same time, combined with the dynamic operation of the main peak amplitude ratio and the cross-frequency band energy coupling coefficient, the influence of temperature drift and nonlinear distortion on the frequency spectrum can be compensated in real time. This dual-dimension correction strategy that integrates mechanical vibration and electromagnetic characteristics can significantly improve the measurement accuracy under complex working conditions.

[0097] In an implementable embodiment, the speed of the motor rotating shaft is determined according to the frequency position and amplitude distribution of the energy peak in the dual-frequency coupled energy spectrum, comprising:

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

[0099] The magnetostriction main peak frequency refers to the resonance peak generated by the magnetostriction effect of the ferromagnetic material under the excitation of the alternating magnetic field, and its frequency position is nonlinearly related to the material characteristics and the magnetic field strength. The electromagnetic induction base frequency is the base 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 and the current frequency). In the dual-frequency coupled energy spectrum, the frequency interval between the main peak and the base frequency reflects the dynamic coupling relationship between the mechanical speed and the electromagnetic excitation, and the harmonic interval between the secondary peak and the main peak corresponds to the nonlinear high-order harmonic characteristics of the material magnetic domain dynamic response. The dynamic coupling coefficient is used to quantify the energy distribution difference between the magnetostriction main peak amplitude and the secondary peak amplitude, and the energy transfer efficiency between the electromagnetic induction base frequency and the main peak amplitude, and is used to correct the dynamic error in the speed calculation.

[0100] Firstly, the interval between the magnetostriction main peak frequency and the electromagnetic induction base frequency is identified and located from the dual-frequency coupled energy spectrum. The interval is formed by the interaction of the motor shaft speed and the magnetic field excitation, and there is a linear proportional relationship between them. Through the linear proportional coefficient calibrated in advance, the frequency interval is converted into the initial speed value. For example, if the frequency interval is a certain value, it can be directly mapped to the preliminary estimated value of the speed based on the proportional coefficient. The difference between the magnetostriction secondary peak frequency and the main peak frequency reflects the characteristics of the harmonic components caused by the load change of the motor. By verifying whether the difference satisfies the integer multiple harmonic relationship (such as the secondary peak frequency being 2 times, 3 times, etc. of the main peak frequency), the harmonic order information can be extracted. Based on the spectrum fitting algorithm, the harmonic factor is determined, and it is applied to the initial speed value to correct the harmonic offset error caused by load fluctuation. For example, if the difference between the secondary peak and the main peak frequency is an integer multiple of the base frequency, the amplitude proportional coefficient of the initial speed is adjusted through the harmonic factor. In the dual-frequency coupled energy spectrum, the ratio of the magnetostriction main peak amplitude to the secondary peak amplitude represents the material magnetic-mechanical energy conversion efficiency, and the ratio of the electromagnetic induction base frequency amplitude to the main peak amplitude reflects the dynamic balance of the electric-magnetic energy transfer. Then through dynamic coupling operation such as weighted fusion or nonlinear function mapping, the two types of amplitude ratios are associated into a dynamic coupling coefficient, which is used to quantify the interference strength of factors such as motor load mutation and material fatigue on the speed measurement. Finally, the harmonic factor and the dynamic coupling coefficient are combined to act on the initial speed value. The harmonic factor corrects the frequency domain harmonic distortion of the speed, and the dynamic coupling coefficient compensates the nonlinear error caused by amplitude fluctuation. For example, through the calibration algorithm, the correction results of the harmonic factor on the frequency and the correction results of the dynamic coupling coefficient on the amplitude are fused, and finally the high-precision motor shaft speed is output.

[0101] In one embodiment, the permanent magnet synchronous drive motor applicable to electric vehicles needs to cope with frequent load changes in its working environment, and the following scenarios are required to achieve high-precision speed measurement. Load mutation: when accelerating or braking, the motor output torque changes dramatically, causing the magnetostriction secondary peak frequency to shift and the harmonic component to surge; material fatigue: after long-term operation, the microstructure of the ferromagnetic material changes, resulting in a decrease in the magnetic-mechanical energy conversion efficiency; temperature interference: the temperature rise caused by battery charging and discharging leads to a change in winding resistance, which in turn affects the electromagnetic induction base frequency amplitude.

[0102] The double-frequency coupled energy spectrum is collected by a high-frequency sensor to identify the magnetostrictive main peak frequency (reflecting the material's inherent resonance characteristics) and the electromagnetic induction base frequency (related to the winding current period). Using a pre-calibrated linear proportional relationship, such as a 10 kHz increase in the main peak-base frequency interval corresponding to a 500 rpm increase in rotational speed, the frequency interval is converted to an initial rotational speed value. For example, in the no-load state, the interval is measured to be 50 kHz, which maps to an initial rotational speed of 2500 rpm. When the vehicle climbs a slope, causing an increase in load, the difference between the magnetostrictive secondary peak frequency and the main peak frequency (such as the secondary peak being 2 times the base frequency component higher than the main peak) verifies whether it satisfies the integer harmonic relationship. By spectral fitting, the harmonic order (such as the 2nd harmonic) is determined, the harmonic factor (such as 0.92) is extracted, and the initial rotational speed is corrected accordingly, 2500 rpm x 0.92 = 2300 rpm, eliminating the frequency offset error caused by a sudden increase in torque. The magnetostrictive main peak amplitude, which represents the magnetic-machine energy conversion efficiency, is calculated, and the ratio of the secondary peak amplitude (such as 3:1) and the ratio of the electromagnetic induction base frequency amplitude (reflecting the electric-magnetic energy balance) to the main peak amplitude (such as 1.5:1) are calculated. Through nonlinear weighted fusion, such as the square root of the ratio product, a dynamic coupling coefficient is generated to quantify the interference intensity of load sudden changes on the energy transfer path. The harmonic factor (0.92) and the dynamic coupling coefficient (2.12) are combined to apply to the initial rotational speed value (2500 rpm). The calibration algorithm fuses the frequency domain correction result (2300 rpm) with the amplitude domain interference intensity, i.e., the dynamic coupling coefficient 2.12 corresponding to a rotational speed compensation of 100 rpm, to output the final rotational speed value of 2400 rpm, achieving simultaneous suppression of nonlinear errors and dynamic interference.

[0103] This embodiment effectively solves the precision misalignment problem of traditional rotational speed measurement methods under complex working conditions such as dynamic load, temperature drift, and nonlinear distortion through multi-dimensional joint analysis of double-frequency coupled energy spectrum. First, based on the linear interval relationship between the magnetostrictive main peak and the electromagnetic induction base frequency, the initial rotational speed is extracted, which can avoid the reference drift error caused by the nonlinear change of magnetic permeability; second, the harmonic factor is extracted through the integer multiple relationship of the secondary peak and the main peak, which can suppress the secondary harmonic interference caused by the cross modulation of magnetostrictive effect and electromagnetic induction, and eliminate the influence of phase accumulation error on rotational speed inversion. Further combined with the dynamic operation of the main-secondary peak amplitude distribution ratio and the cross-band energy coupling coefficient, real-time compensation of temperature drift and material property changes is realized, significantly reducing the interference of spectral distortion on the measurement result. This dual-dimensional correction strategy that fuses frequency 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 harmonic characteristics and energy coupling decoupling, especially under extreme conditions such as high dynamic load and wide temperature range fluctuations, while maintaining high precision stability.

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

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

[0106] The product relationship of the harmonic factor and the initial rotating speed value corrects the frequency domain characteristics of the initial rotating speed value by the harmonic factor. The harmonic factor is determined by the integer reciprocal relationship of the harmonic interval number between the magnetostriction secondary peak and the main peak, and is used to quantify the offset of the harmonic order caused by the load fluctuation. The purpose of the product relationship is to eliminate the frequency distortion component caused by the nonlinear response of the material magnetic domain. The harmonic distortion component refers to the error introduced in the calculation of the motor rotating shaft rotating speed due to the integer multiple harmonic relationship of the frequency difference between the magnetostriction secondary peak and the main peak. This component shows periodic fluctuations in the rotating speed value, which needs to be compensated in the frequency domain by the harmonic factor. The proportional relationship of the dynamic coupling coefficient and the modulated rotating speed intermediate value is calculated by the amplitude ratio of the magnetostriction main peak and the secondary peak (reflecting the magnetic-mechanical energy conversion efficiency) and the amplitude ratio of the electromagnetic induction base frequency and the main peak (characterizing the electric-magnetic energy transfer efficiency), and this coefficient is used to quantify the influence strength of the dynamic load mutation on the rotating speed amplitude, and to eliminate the amplitude-frequency coupling error by 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 to achieve targeted inhibition of the temperature drift component.

[0107] Firstly, a modulation speed intermediate value is generated based on the product of an initial speed value and a harmonic factor. The harmonic factor is determined by the integer reciprocal relationship of the harmonic interval number between the secondary peak and the primary peak, for example, if the interval number is the second harmonic, the harmonic factor is 1 / 2, which corrects the harmonic order offset caused by the material hysteresis effect. Then, the dynamic coupling coefficient is used to proportionally compensate the modulation speed intermediate value. The dynamic coupling coefficient is generated by the amplitude ratio of the magnetostriction primary peak and the secondary peak (for example, if the primary peak amplitude is 3 times that of the secondary peak, the ratio is 3) and the amplitude ratio of the electromagnetic induction base frequency and the primary peak (for example, if the base frequency amplitude is 0.5 times that of the primary peak, the ratio is 0.5). The coefficient acts on the intermediate value to eliminate the amplitude fluctuation caused by the load mutation. Finally, the compensated speed intermediate value is dynamically adjusted according to the joint weight. The joint weight is distributed according to the distribution characteristics of the harmonic factor and the dynamic coupling coefficient (such as the stability of the primary peak amplitude in the energy spectrum), which preferentially suppresses the temperature sensitive component (such as the secondary peak amplitude drift caused by material thermal expansion), and finally outputs a high precision speed value.

[0108] In this embodiment, the harmonic factor and the dynamic coupling coefficient work together to achieve high-precision calibration of the motor speed. Specifically, the harmonic factor is based on the integer reciprocal relationship of the harmonic interval between the magnetostriction secondary peak and the primary peak in the double-frequency coupled energy spectrum, which corrects the harmonic distortion component of the initial speed in the frequency domain, effectively suppressing the frequency spectrum offset caused by the nonlinear coupling between the electromagnetic induction base frequency and the magnetostriction secondary peak. The dynamic coupling coefficient establishes an amplitude-frequency error compensation model according to the amplitude distribution ratio of the primary peak and the secondary peak, and the proportional relationship between the electromagnetic base frequency amplitude and the primary peak amplitude, solving the problem of amplitude attenuation and phase distortion caused by the magnetostriction effect in the modulation process. Combined with the triple mechanisms of frequency domain correction, amplitude-frequency joint compensation and dynamic weight suppression, the stability and repeatability of the speed measurement under wide temperature range and multiple interference conditions are significantly improved, while the coupling interference of electromagnetic harmonics on the control system is reduced, and the accuracy of motor speed measurement is improved.

[0109] In an implementable embodiment, the magnetostriction resonance signal and the electromagnetic induction signal of the motor rotating shaft are obtained, including:

[0110] The motor rotating shaft is subjected to axial magnetization excitation processing based on preset alternating magnetic field excitation parameters, to obtain an initial magnetostriction resonance signal, the preset alternating magnetic field excitation parameters including an excitation frequency and a magnetic field strength matched with the magnetostriction coefficient of the material of the motor rotating shaft; an initial electromagnetic induction signal is obtained by using a preset annular induction coil to induce and capture the alternating magnetic field generated when the motor rotating shaft rotates, the axial installation position of the preset annular induction coil and the application position of the magnetization excitation maintaining a preset interval; the initial magnetostriction resonance signal and the initial electromagnetic induction signal are synchronously collected based on the propagation delay of the trigger frequency in the preset alternating magnetic field excitation parameters and the preset interval, to obtain the magnetostriction resonance signal and the electromagnetic induction signal.

[0111] The preset alternating magnetic field excitation parameter refers to a combination of magnetic field characteristic parameters set for exciting the magnetostrictive effect of the motor shaft, including excitation frequency (such as 20 kHz-500 kHz) and magnetic field strength (such as 30-100 mT). The excitation frequency needs to be matched with the magnetostrictive resonance frequency band of the shaft material to avoid signal attenuation due to frequency mismatch; the magnetic field strength is dynamically adjusted according to the material saturation magnetization to ensure that the shaft generates sufficient deformation without entering the magnetic hysteresis region. The magnetization excitation process refers to applying an alternating magnetic field axially to cause periodic magnetic domain flipping of the ferromagnetic shaft material (such as Terfenol-D alloy), thereby inducing mechanical vibration. This process needs to combine the nonlinear characteristics of the magnetostriction coefficient (λ) to maximize the resonance signal amplitude by dynamically adjusting the magnetic field strength. The preset annular induction coil refers to an induction coil wound in an annular structure, which needs to be installed axially at a preset distance (such as 3-10 mm) from the magnetization excitation device to balance the magnetic field coupling strength and electromagnetic interference suppression requirements. If the distance is too small, the magnetization field will cause crosstalk to the induction signal, and if the distance is too large, the signal sensitivity will be reduced.

[0112] In one embodiment, first, based on the magnetostriction coefficient (such as λ = 1500 ppm) of the shaft material, an alternating magnetic field with an excitation frequency of 20 kHz and a magnetic field strength of 50 mT is set, which is applied axially to the surface of the shaft by the excitation coil to excite the mechanical resonance of its natural frequency, forming an initial magnetostrictive resonance signal, wherein the annular induction coil can be coaxially installed at a 5 mm interval to capture the eddy current effect generated by the change of the alternating magnetic field during the rotation of the shaft, generating an initial electromagnetic induction signal. Then, according to the magnetic field propagation delay, such as the delay Δt = 1 μs from excitation triggering to signal response, a time stamp alignment technique is used to synchronize sampling of the two signals to eliminate phase errors introduced by differences in electromagnetic wave propagation paths. 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 DC bias, obtaining the final magnetostrictive resonance signal and electromagnetic induction signal.

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

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

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

[0116] The acquisition module 210 is configured to acquire a magnetostrictive resonance signal and an electromagnetic induction signal of a motor rotating shaft.

[0117] The conversion module 220 is configured to perform frequency domain conversion on the magnetostrictive resonance signal to obtain a magnetostrictive resonance frequency domain feature.

[0118] The conversion module 220 is further configured to perform frequency domain conversion on the electromagnetic induction signal to obtain an electromagnetic induction frequency domain feature.

[0119] The generation module 230 is configured to perform frequency domain energy spectrum fusion reconstruction on the magnetostrictive resonance frequency domain feature and the electromagnetic induction frequency domain feature based on a frequency band matching relationship between the magnetostrictive resonance frequency domain feature and the electromagnetic induction frequency domain feature, to generate a dual-frequency coupling energy spectrum.

[0120] The determination module 240 is configured to determine a rotating speed of the motor rotating shaft according to a frequency position and an amplitude distribution of an energy peak in the dual-frequency coupling energy spectrum.

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

[0122] In one embodiment, the generation module 230 is specifically configured to perform coupling screening processing on an overlapping area of the resonance frequency band and the fundamental wave frequency band by using a frequency domain energy threshold judgment based on a frequency band matching relationship between the magnetostrictive resonance frequency domain feature and the electromagnetic induction frequency domain feature, to generate a dynamic frequency domain mapping model and an energy coupling feature set; perform multi-order frequency band division processing on the resonance frequency band by using a duplexer type dynamic frequency splitting based on the dynamic frequency domain mapping model, and perform frequency band isolation processing on the fundamental wave frequency band based on frequency point adaptive scanning, to generate a dynamic isolated frequency band; perform error feedback adjustment processing on phase offsets of energy subscripts in the dynamic isolated frequency band by using phase synchronization constraint modeling, to generate initial independent frequency band energy subscripts including a same-phase reference; perform cross-frequency interference component identification processing on frequency domain distribution characteristics of the initial independent frequency band energy subscript set by using a dynamic threshold adaptive adjustment algorithm, to obtain an interference component set; and perform energy component reconstruction processing by frequency domain energy aggregation based on the initial independent frequency band energy subscript set and the interference component set, to generate the independent frequency band energy subscripts.

[0123] In one embodiment, the generating module 230 is specifically configured to perform gradientization decomposition processing of multi-order frequency band segmentation on the resonant frequency band based on a frequency band overlap coefficient distribution of a dynamic frequency domain mapping model, to generate a segmented frequency band set with a frequency band boundary constraint; to perform dynamic weighting analysis processing on a frequency point energy distribution feature of the fundamental frequency band through frequency point adaptive scanning, to generate a frequency point energy mapping relationship including a frequency band isolation weight coefficient; and to perform frequency band energy redistribution processing on the resonant frequency band and the fundamental frequency band based on the segmented frequency band set and the frequency point energy mapping relationship, using a duplexer type dynamic frequency splitting, to generate a dynamic isolated frequency band set with a frequency band isolation degree constraint.

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

[0125] In one embodiment, the determining module 240 is specifically configured to correct a harmonic distortion component of the initial rotation speed value based on a product relationship of the harmonic factor and the initial rotation speed value, to generate a modulation rotation speed intermediate value, the product relationship being determined based on an integer reciprocal relationship of a harmonic interval number between the magnetostriction secondary peak frequency position and the main peak frequency position in the dual-frequency coupling energy spectrum, and the harmonic distortion component being determined based on an integer multiple relationship of the harmonic interval between the magnetostriction secondary peak frequency position and the main peak frequency position; to compensate an amplitude-frequency coupling error of the modulation rotation speed intermediate value based on a proportional relationship of the dynamic coupling coefficient and the modulation rotation speed intermediate value, to obtain a compensated rotation speed intermediate value, the proportional relationship being determined based on the ratio of the magnetostriction main peak amplitude distribution to the secondary peak amplitude distribution, and the ratio of the electromagnetic induction fundamental frequency amplitude distribution to the magnetostriction main peak amplitude distribution in the dual-frequency coupling energy spectrum; and to obtain the rotation speed of the motor rotation shaft by performing suppression processing on a temperature drift component of the compensated rotation speed intermediate value based on a joint weight of the harmonic factor and the dynamic coupling coefficient, the joint weight being dynamically adjusted based on distribution characteristics of the harmonic factor and the dynamic coupling coefficient in the dual-frequency coupling energy spectrum.

[0126] In one embodiment, the acquisition module 210 is specifically configured to perform axial magnetization excitation processing on the motor rotating shaft based on preset alternating magnetic field excitation parameters, to obtain an initial magnetostrictive resonance signal, the preset alternating magnetic field excitation parameters including an excitation frequency and a magnetic field strength matched with a magnetostrictive coefficient of the material of the motor rotating shaft; an initial electromagnetic induction signal is obtained by using a preset annular induction coil to inductively capture an alternating magnetic field generated when the motor rotating shaft rotates, the axial installation position of the preset annular induction coil being kept at a preset interval from the application position of the magnetization excitation; the initial magnetostrictive resonance signal and the initial electromagnetic induction signal are synchronously collected based on a trigger frequency in the preset alternating magnetic field excitation parameters and a propagation delay of the preset interval, 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 each step in the method and can achieve the corresponding technical effects. For brevity, no further description is given here. Figure 1 Each step in the method has the function of implementing each module in the system and can achieve the corresponding technical effects. For brevity, no further description is given here.

[0128] Figure 3 A hardware structure schematic diagram of an electronic device provided by one embodiment of the present application is shown.

[0129] The electronic device can include a processor 310 and a memory 320 having computer program instructions stored therein.

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

[0131] The memory 320 can include a mass storage for data or instructions. By way of example and not limitation, the memory 320 can 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. Where appropriate, the memory 320 can include removable or non-removable (or fixed) media. Where appropriate, the memory 320 can be internal or external to the integrated gateway disaster recovery device. In a particular embodiment, the memory 320 is a non-volatile solid-state memory.

[0132] The memory can include read-only memory (ROM), random access memory (RAM), a magnetic disk storage medium device, an optical storage medium 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., a memory device) encoded with software that, when executed (e.g., by one or more processors), 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 implements the electromagnetic induction-based motor speed measurement method in any of the above-described embodiments by reading and executing computer program instructions stored in the memory 320.

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

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

[0136] The bus 340 includes hardware, software, or both, that couples components of the online data traffic billing device to each other in such a manner that information can be passed therebetween. By way of example, and not limitation, the bus can 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 (IB) interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel 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 another suitable bus or a combination of two or more of these. Where appropriate, the bus 340 can include one or more buses. Although the present application describes and illustrates a particular bus, the present application contemplates any suitable bus or interconnect.

[0137] The electronic device can perform the electromagnetic induction-based motor speed measurement method in the embodiments of the present application, thereby realizing the electromagnetic induction-based motor speed measurement method described in combination Figure 1 with the above-described embodiments.

[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 for implementation. The computer readable storage medium has computer program instructions stored thereon; the computer program instructions are executed by a processor to implement any one of the motor speed measurement methods based on electromagnetic induction in the above embodiments.

[0139] It should be noted that the present application is not limited to the specific configurations and processes described above and shown in the drawings. For the sake of brevity, detailed descriptions of well-known methods are omitted. In the above embodiments, several specific steps are described and shown as examples. However, the method processes of the present application are 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 of the steps, after understanding the spirit of the present application.

[0140] The functional blocks shown in the structural block diagrams described above 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, etc. When implemented in software, the elements of the present application are program or code segments used to perform the required tasks. The program or code segments can be stored in a machine readable medium or transmitted through a data signal carried in a carrier wave over a transmission medium or communication link. The "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 disks, hard disks, optical fiber media, radio frequency (RF) links, etc. The code segments can be downloaded via a computer network such as the Internet, an intranet, etc.

[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 performed in the order mentioned in the embodiments, or in an order different from the embodiments, or several steps can be performed simultaneously.

[0142] The computer program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other processing device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other processing device to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0143] The above only describes specific implementation of the present application. For the convenience and brevity of description, the specific working processes of the above-described system, module and unit can refer to the corresponding processes in the foregoing method embodiments, which will not be described herein. It should be understood that the protection scope of the present application is not limited in this way. Any person skilled in the art can easily think of various equivalent modifications or replacements within the technical range disclosed in the present application, and these modifications or replacements should be covered by the protection scope of the present application.

Claims

1. A motor speed measurement method based on electromagnetic induction, characterized in that: The method comprises: Obtaining magnetostrictive resonance signals and electromagnetic induction signals of the motor shaft; Performing frequency domain conversion on the magnetostrictive resonance signal to obtain a magnetostrictive resonance frequency domain feature; Performing frequency domain conversion on the electromagnetic induction signal to obtain an 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, the magnetostrictive frequency domain feature and the electromagnetic induction frequency domain feature are subjected to frequency domain energy spectrum fusion and reconstruction 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 coupling energy spectrum; The determining the rotation speed of the motor shaft according to the frequency position and amplitude distribution of the energy peak in the dual-frequency coupling energy spectrum includes: Based on the interval between the main peak frequency position of magnetostriction and the fundamental frequency position of electromagnetic induction in the dual-frequency coupling energy spectrum, an initial speed value is obtained through a linear relationship between the frequency interval and the motor speed; Extracting harmonic factors from the initial rotational speed value 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 coupling energy spectrum to obtain harmonic factors; Performing dynamic coupling calculation processing to generate a dynamic coupling coefficient based on a ratio of a magnetostrictive main peak amplitude distribution to a secondary peak amplitude distribution in the dual-frequency coupling energy spectrum, and a ratio of an electromagnetic induction fundamental frequency amplitude distribution to a magnetostrictive main peak amplitude distribution in the dual-frequency coupling energy spectrum; Based on the harmonic factor and the dynamic coupling coefficient, performing amplitude-frequency joint calibration processing on the initial speed value to determine the speed of the motor shaft; The performing amplitude-frequency joint calibration processing on the initial speed value based on the harmonic factor and the dynamic coupling coefficient to determine the speed of the motor shaft includes: Based on a product relationship between the harmonic factor and the initial speed value, a harmonic distortion component of the initial speed value is corrected to generate an intermediate modulation speed value, wherein the product relationship is determined based on an integer reciprocal relationship of harmonic intervals 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 an integer multiple relationship of harmonic intervals between the magnetostrictive secondary peak frequency position and the main peak frequency position in the dual-frequency coupling energy spectrum; Compensating for an amplitude-frequency coupling error of the modulation speed intermediate value by utilizing a proportional relationship between the dynamic coupling coefficient and the modulation speed intermediate value to obtain a compensated speed intermediate value, wherein the proportional relationship is determined by a ratio of a magnetostrictive main peak amplitude distribution to a secondary peak amplitude distribution, and a ratio of an electromagnetic induction fundamental frequency amplitude distribution to a magnetostrictive main peak amplitude distribution in the dual-frequency coupling energy spectrum; Suppressing the temperature drift component of the intermediate value of the compensation speed according to the joint weight of the harmonic factor and the dynamic coupling coefficient to obtain the speed of the motor shaft, wherein 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; The method of 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 characteristics and the electromagnetic induction frequency domain characteristics, 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 by duplexer-type dynamic frequency division 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, dynamically matching 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 is performed to construct a frequency domain energy weight matrix; Based on the frequency band energy proportions in the frequency domain energy weight matrix, a weighted superposition operation is performed on the high-frequency harmonic energy quanta and the low-frequency fundamental energy quanta in the independent frequency band energy quanta, and a phase offset parameter compensation algorithm is used to correct the phase difference in the superposition process to generate the dual-frequency coupled energy spectrum; The method of performing synchronous decoupling processing on the resonant frequency band of the magnetostrictive frequency domain feature and the fundamental frequency band of 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 through duplexer-type dynamic frequency division to obtain independent frequency band energy quanta with phase synchronization constraints includes: Based on the frequency band matching relationship between the magnetostrictive frequency domain characteristics and the electromagnetic induction frequency domain characteristics, a frequency domain energy threshold is used to determine the overlapping area of ​​the resonant frequency band and the fundamental frequency band, thereby generating a dynamic frequency domain mapping model and an energy coupling feature set; According to the dynamic frequency domain mapping model, a duplexer-type dynamic frequency division is used to perform multi-order frequency band segmentation processing on the resonant frequency band, and at the same time, a frequency band isolation processing is performed on the fundamental frequency band based on frequency adaptive scanning to generate a dynamic isolation frequency band; Performing error feedback adjustment processing on the phase offset of the energy quantum in the dynamic isolation frequency band by using phase synchronization constraint modeling to generate initial independent frequency band energy quantum including a homologous phase reference; A dynamic threshold adaptive adjustment algorithm is used to perform cross-band interference component identification processing on the frequency domain distribution characteristics of the initial independent frequency band energy subset to obtain an interference component set. Based on the initial independent frequency band energy subset and the interference component set, energy component reconstruction processing is performed through frequency domain energy aggregation to generate the independent frequency band energy quanta.

2. The method according to claim 1, characterized in that The method of performing multi-order frequency band segmentation processing on the resonant frequency band using a duplexer-type dynamic frequency division according to the dynamic frequency domain mapping model, and performing frequency band isolation processing on the fundamental frequency band based on frequency adaptive scanning to generate a dynamic isolation frequency band includes: Based on the frequency band overlap coefficient distribution of the dynamic frequency domain mapping model, performing a gradient decomposition process of multi-order frequency band segmentation on the resonant frequency band to generate a set of segmented frequency bands with frequency band boundary constraints; Performing dynamic weighted analysis on the frequency energy distribution characteristics of the fundamental frequency band through the frequency adaptive scanning to generate a frequency energy mapping relationship including a frequency band isolation weight coefficient; Based on the mapping relationship between the divided frequency band set and the frequency point energy, a duplexer-type dynamic frequency division is used to perform frequency band energy redistribution processing on the resonant frequency band and the fundamental frequency band to generate a dynamic isolation frequency band set with frequency band isolation constraints.

3. The method according to claim 1, characterized in that The obtaining of the magnetostrictive resonance signal and the electromagnetic induction signal of the motor shaft includes: performing axial magnetization excitation processing on the motor shaft based on preset alternating magnetic field excitation parameters to obtain an initial magnetostrictive resonance signal, wherein the preset alternating magnetic field excitation parameters include an excitation frequency and a magnetic field intensity that match the magnetostriction coefficient of the motor shaft material; An alternating magnetic field generated by the rotation of the motor shaft is sensed and captured using a preset annular induction coil to obtain an initial electromagnetic induction signal, wherein the axial installation position of the preset annular induction coil maintains a preset distance from the application position of the magnetizing excitation; Based on the trigger frequency in the preset alternating magnetic field excitation parameters and the propagation delay of the preset distance, 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.

4. A motor speed measurement system based on electromagnetic induction, used to perform a motor speed measurement method based on electromagnetic induction according to any one of claims 1 to 3, characterized in that: The system comprises: An acquisition module, used to acquire a magnetostrictive resonance signal and an electromagnetic induction signal of a motor shaft; a conversion module, configured to perform frequency domain conversion on the magnetostrictive resonance signal to obtain a magnetostrictive resonance frequency domain feature; The conversion module is further configured to perform frequency domain conversion on the electromagnetic induction signal to obtain electromagnetic induction frequency domain features; a reconstruction 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 a frequency band matching relationship between the magnetostrictive frequency domain feature and the electromagnetic induction frequency domain feature, so as to generate a dual-frequency coupled energy spectrum; The determination module is used 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 coupling energy spectrum.

5. 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, the motor speed measurement method based on electromagnetic induction according to any one of claims 1 to 3 is implemented.

6. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer program instructions, and when the computer program instructions are executed by a processor, the motor speed measurement method based on electromagnetic induction according to any one of claims 1 to 3 is implemented.

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