Method and system for controlling comb capable of promoting hair growth based on ultrasonic waves

Abstract

The invention provides a method and system for controlling a comb for promoting hair growth based on ultrasonic waves, and the method comprises the steps: simulating vibration wave propagation and interference modes under different comb tooth geometrical shapes and arrangement modes through a finite element algorithm, and obtaining a target phase difference range between adjacent comb teeth; a laser Doppler vibration meter is adopted to obtain vibration waves of the comb tooth group in a resonance frequency parameter range, the distribution condition of a superposition area and a counteracting area of the vibration waves is analyzed, and the modulation frequency of local vibration intensity is determined; a scalp bionic acoustic model is built according to acoustic impedance characteristics of human scalp, local vibration intensity modulation frequency is input into the scalp bionic acoustic model, propagation attenuation of ultrasonic waves in the scalp is simulated, and random sampling simulation is performed on sound field distribution in the scalp.

Description

Technical Field

[0001] The present invention relates to the field of information technology, and in particular to a control method and system for a hair growth promoting comb based on ultrasound. Background Art

[0002] The key technology of ultrasonic hair growth combs lies in the stimulation of the scalp by the ultrasonic waves generated by the vibration of the comb teeth. However, there is a phase difference in the vibration between adjacent comb teeth, which causes interference of vibration waves during propagation. When the comb tooth group resonates as a whole, the vibration waves in some areas will be superimposed, while in other areas they will cancel each other out. This superposition and cancellation of waves will modulate the distribution of local vibration intensity. The uneven distribution of vibration intensity may affect the effect of scalp stimulation. Some areas of the scalp may be strongly stimulated, while other areas are not stimulated enough. This leads to differences in the energy absorbed by the scalp, affecting the uniformity of hair growth. Therefore, a key issue in ultrasonic hair growth comb technology is how to optimize the layout and vibration parameters of the comb tooth vibration source to achieve good phase matching between adjacent vibration waves. Only by solving the problems of vibration wave interference and resonance modulation can the comb tooth vibration form a uniform energy distribution on the scalp surface, thereby achieving the best hair growth effect. Summary of the invention

[0003] The present invention provides a control method for a hair growth promoting comb based on ultrasound, which mainly comprises:

[0004] Finite element algorithm is used to simulate the vibration wave propagation and interference mode under different comb tooth geometric shapes and arrangements to obtain the target phase difference range between adjacent comb teeth;

[0005] Collect vibration data of the comb tooth surface under different ultrasonic vibration frequencies and power parameters within the target phase difference range between adjacent comb teeth, calculate the vibration data, obtain the amplitude spectrum of different frequency components, and obtain the frequency parameter range that causes the comb tooth group to resonate;

[0006] A laser Doppler vibrometer is used to obtain the vibration waves of the comb group within the resonant frequency parameter range, analyze the distribution of the superposition area and the offset area of ​​the vibration waves, and determine the modulation frequency of the local vibration intensity;

[0007] A scalp bionic acoustic model is built according to the acoustic impedance characteristics of the human scalp. The local vibration intensity modulation frequency is input into the scalp bionic acoustic model to simulate the propagation attenuation of ultrasound in the scalp, and random sampling simulation of the sound field distribution inside the scalp is performed.

[0008] Real-time monitoring of the changes in hair follicle morphology and metabolism in the simulation of the acoustic field distribution inside the scalp, extraction of parameter characteristics of hair follicles' absorption of ultrasonic waves, and construction of a hair follicle acoustic wave response predictor to reflect the response of hair follicles to different ultrasonic stimuli. The parameter characteristics include hair follicle metabolic parameters and hair follicle morphological parameters.

[0009] The vibration data of the comb teeth is collected in real time and uploaded to the cloud. The hair follicle acoustic response predictor is used to mine and analyze the comb tooth vibration data, dynamically optimize the ultrasonic vibration parameters, and adaptively adjust the vibration mode of the comb to achieve real-time control of the vibration and hair follicle physiological effects.

[0010] The present invention provides a control system for a hair growth promoting comb based on ultrasound, which mainly includes:

[0011] Finite element simulation module, used to simulate the vibration wave propagation and interference mode under different comb tooth geometric shapes and arrangements using finite element algorithm, and obtain the target phase difference range between adjacent comb teeth;

[0012] A vibration data acquisition module is used to collect vibration data of the comb tooth surface under different ultrasonic vibration frequencies and power parameters within the target phase difference range between adjacent comb teeth, calculate the vibration data, obtain the amplitude spectrum of different frequency components, and obtain the frequency parameter range that causes the comb tooth group to resonate;

[0013] A vibration wave analysis module is used to obtain the vibration waves of the comb group within the resonant frequency parameter range using a laser Doppler vibrometer, analyze the distribution of the superposition area and the offset area of ​​the vibration waves, and determine the modulation frequency of the local vibration intensity;

[0014] The scalp bionic acoustic model module is used to build a scalp bionic acoustic model based on the acoustic impedance characteristics of the human scalp, input the local vibration intensity modulation frequency into the scalp bionic acoustic model, simulate the propagation attenuation of ultrasound in the scalp, and perform random sampling simulation on the sound field distribution inside the scalp;

[0015] The hair follicle response prediction module is used to monitor the changes in hair follicle morphology and metabolism in the simulation of the sound field distribution inside the scalp in real time, extract the parameter characteristics of hair follicles' absorption of ultrasound waves, and construct a hair follicle acoustic wave response predictor to reflect the response of hair follicles to different ultrasonic stimuli. The parameter characteristics include hair follicle metabolic parameters and hair follicle morphological parameters.

[0016] The vibration optimization control module is used to collect the vibration data of the comb teeth in real time and upload it to the cloud. It uses the hair follicle acoustic wave response predictor to mine and analyze the comb tooth vibration data, dynamically optimize the ultrasonic vibration parameters, and adaptively adjust the vibration mode of the comb to achieve real-time control of the vibration and hair follicle physiological effects.

[0017] An electronic device comprises a memory and a processor, wherein the memory is used to store a computer program; and the processor is used to call the computer program so that the electronic device executes the above method.

[0018] A computer-readable storage medium includes instructions. When the instructions are executed on an electronic device, the electronic device executes the above method.

[0019] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0020] The present invention discloses a control method and system for a hair growth promoting comb based on ultrasound. The method simulates the vibration propagation of comb teeth through a finite element algorithm to obtain the target phase difference range and resonance frequency parameters. The laser Doppler vibrometer is used to analyze the superposition distribution of vibration waves and determine the local vibration intensity modulation frequency. Combined with the scalp bionic acoustic model, the propagation attenuation of ultrasound waves in the scalp is simulated. By real-time monitoring of the sound field distribution inside the scalp, the parameter characteristics of the hair follicles' absorption of ultrasound waves are extracted, and a hair follicle acoustic wave response model is constructed. The present invention collects the vibration data of comb teeth in real time and uploads it to the cloud, uses the hair follicle acoustic wave response model for analysis, dynamically optimizes the ultrasonic vibration parameters, and adaptively adjusts the vibration mode of the comb to achieve real-time control of vibration and hair follicle physiological effects. The method can accurately control ultrasonic stimulation, improve the scalp care effect, and promote the health of hair follicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 The present invention is a flow chart of a control method of a hair growth promoting comb based on ultrasound.

[0022] Figure 2 It is a schematic diagram of a control method of a hair growth promoting comb based on ultrasound according to the present invention.

[0023] Figure 3 This is another schematic diagram of a control method for a hair growth promoting comb based on ultrasound according to the present invention.

[0024] Figure 4 The present invention is a schematic structural diagram of a control system of a hair growth promoting comb based on ultrasound. DETAILED DESCRIPTION

[0025] The following will describe the technical solutions in the embodiments of the present invention in detail in conjunction with the accompanying drawings in the embodiments of the present invention. The described embodiments are only part of the embodiments of the present invention.

[0026] like Figure 1 -4. In this embodiment, a control method of a hair growth promoting comb based on ultrasound may specifically include:

[0027] S101. Using a finite element algorithm to simulate vibration wave propagation and interference patterns under different comb tooth geometric shapes and arrangements, obtain a target phase difference range between adjacent comb teeth.

[0028] A grid density distribution mapping table is constructed according to the comb tooth amplitude change rate, and the comb tooth structure is divided into hexahedral grids through the grid density distribution mapping table to obtain the discrete comb tooth unit stress field distribution data; the wave propagation equation is constructed for the discrete comb tooth unit stress field distribution data using the acoustic time-domain finite difference algorithm, and the space-time propagation data including the wave displacement is obtained through boundary coupling effect parameter conversion; the wave energy distribution is calculated based on the space-time propagation data using the waveform interference intensity parameter, and the wave energy propagation path is tracked by the beam focusing degree to obtain the comb tooth structure wave phase superposition data; the phase shift degree is used to perform Fourier transform on the wave phase superposition data, and the phase difference distribution diagram between adjacent comb teeth is obtained through acoustic impedance change parameter correction.

[0029] Exemplarily, a comb tooth unit grid density distribution mapping table is established according to the comb tooth amplitude change rate and vibration frequency range, the comb tooth structure is divided into hexahedral grids, and a refined grid encryption algorithm is used to process in the stress concentration area. The discrete comb tooth unit stress field distribution data including node stress and strain are obtained through stress distribution density simulation. A wave propagation equation is constructed for the discrete comb tooth unit stress field distribution data, and the acoustic time-domain finite difference algorithm is used to calculate the evolution of the stress field over time. The space-time propagation data including wave displacement, velocity, and acceleration are obtained through boundary coupling effect and resonance peak amplitude parameter conversion. The wave energy distribution is calculated based on the space-time propagation data, and the wave energy propagation path in space is tracked by the waveform interference intensity and beam focusing degree parameters to obtain the wave phase superposition data of each point of the comb tooth structure. The wave phase superposition data is Fourier transformed, and the phase difference between adjacent measuring points is calculated by using the phase offset degree and modal density distribution. The phase difference distribution diagram between adjacent comb teeth is obtained by correcting the acoustic impedance change and comb tooth gap width parameters. The characteristic parameters are extracted from the phase difference distribution diagram between adjacent comb teeth, and the phase difference curve is fitted by the least square method. The boundary value of the target phase difference range is obtained by curve fitting parameter calculation. The objective function of comb tooth geometric parameter optimization is established according to the boundary value of the target phase difference range. The comb tooth spacing and arrangement angle are iteratively optimized by the gradient descent method to obtain the comb tooth structure parameter combination that meets the target phase difference range. In the vibration analysis of the comb tooth structure, the mesh division of the comb tooth unit has an important influence on the calculation accuracy. When the hexahedral mesh division is used, the area with a greater rate of change of the comb tooth amplitude has a higher required mesh density, and the mesh size varies in the range of 0.1 mm to 0.5 mm. For the case where the vibration frequency ranges from 20 kHz to 100 kHz, the mesh size of the stress concentration area needs to be further refined to 0.05 mm to ensure the calculation accuracy of the stress field distribution data. In the analysis of the wave propagation process, the boundary coupling effect is mainly reflected in the interaction between adjacent comb teeth. When the comb tooth structure vibrates at a frequency of 50 kHz, the resonance peak amplitude calculated by the boundary coupling effect reaches 300 microns, and at this time, obvious energy exchange occurs between adjacent comb teeth. The wave displacement changes periodically with time, the velocity peak appears at the zero point of displacement, and the acceleration peak appears at the displacement extreme. The propagation path of the wave energy in the comb structure presents complex spatial distribution characteristics. In the waveform interference area, the beam focusing degree weakens with the increase of propagation distance. When the spacing between the two rows of comb teeth is 2 mm, the wave phase superposition effect between adjacent measuring points is most significant, and the phase difference can reach 90 degrees. By analyzing the phase superposition data by Fourier transform, the phase characteristic distribution in the frequency domain can be obtained. In the phase difference distribution analysis, the influence of acoustic impedance change on the phase difference cannot be ignored. When the comb gap width increases from 1 mm to 3 mm, the acoustic impedance changes nonlinearly, resulting in an inflection point in the phase difference distribution curve.The phase difference curve obtained by least squares fitting shows that the upper limit of the target phase difference range is 120 degrees and the lower limit is 60 degrees. Based on the above analysis results, when optimizing the comb tooth structure parameters, the variation range of the comb tooth spacing is set between 1.5 mm and 2.5 mm, and the arrangement angle is adjusted between 15 degrees and 45 degrees. After multiple rounds of iterative optimization, it is determined that when the comb tooth spacing is 2.2 mm and the arrangement angle is 30 degrees, the phase difference between adjacent comb teeth is closest to the center value of the target range of 90 degrees. This set of parameters enables the propagation and interference characteristics of the vibration wave in the comb tooth structure to achieve the expected effect, and the vibration coupling strength between adjacent comb teeth is moderate, which not only ensures sufficient energy transfer, but also avoids excessive mutual interference.

[0030] S102, collecting vibration data of the comb tooth surface under different ultrasonic vibration frequencies and power parameters within the target phase difference range between adjacent comb teeth, calculating the vibration data, obtaining amplitude spectra of different frequency components, and obtaining the frequency parameter range that causes the comb tooth group to resonate.

[0031] A sampling parameter matrix is ​​constructed according to the combination of ultrasonic vibration frequency and power, and original vibration response data is collected on the comb tooth surface through the sampling parameter matrix; the harmonic interference intensity threshold is used to remove abnormal points from the original vibration response data, and standardized vibration response data is obtained through phase shift degree parameter correction; waveform distortion detection is performed on the standardized vibration response data, and a digital bandpass filter is used to extract the signal in a specified frequency band to obtain filtered vibration response data; adaptive wavelet decomposition is performed on the filtered vibration response data, and if the amplitude response ratio exceeds a preset resonance peak amplitude threshold, it is determined to be a resonance frequency point, and the frequency parameter range that causes the resonance of the comb tooth group is obtained through bandwidth center frequency calculation.

[0032] Exemplarily, a sampling parameter matrix is ​​constructed for the ultrasonic vibration frequency and power combination according to the power attenuation ratio and the frequency sampling interval, and the original vibration response data of multiple measurement points on each comb tooth surface are collected through the vibration attenuation rate and the sampling duration parameters. The original vibration response data is subjected to data normalization processing, the abnormal points are removed by using the harmonic interference intensity threshold, and the timing deviation is corrected by the phase shift degree parameter to obtain the standardized vibration response data. Waveform distortion detection is performed on the standardized vibration response data, high-frequency noise is eliminated by the wavelet denoising method, and the signal in the specified frequency band is extracted by using a digital bandpass filter to obtain the filtered vibration response data. The filtered vibration response data is subjected to time-frequency analysis by using an adaptive wavelet decomposition algorithm, the decomposition scale is set according to the spectrum resolution requirements, and the amplitude spectrum data of each frequency point is obtained by calculating the amplitude spectrum component value parameters. The amplitude spectrum data is subjected to peak detection, the resonance frequency point is identified by using the resonance peak amplitude threshold, and the amplitude change rate between adjacent frequency points is calculated by the amplitude response ratio to obtain the resonance frequency band range data. The frequency bands are merged for the resonance frequency band range data, and the resonance bandwidth range threshold is set to cluster the frequency bands. The frequency parameter range that causes the comb group resonance is calculated by the bandwidth center frequency. In the process of collecting ultrasonic vibration frequency and power parameters, the construction of the sampling parameter matrix is ​​crucial. When the power attenuation ratio is set to 3% attenuation per 10 kHz, the frequency sampling interval is selected to be 1 kHz, and 81 frequency sampling points are obtained in the range of 20 kHz to 100 kHz. At the same time, 5 measurement points are arranged on each comb tooth surface, and the sampling duration is set to 200 milliseconds. The vibration attenuation rate parameter reflects the attenuation characteristics of the amplitude over time. Data normalization has an important impact on subsequent analysis. The original vibration response data often contains abnormal fluctuations. By setting the harmonic interference intensity threshold to twice the amplitude mean, the interference points can be effectively identified and removed. The phase offset degree parameter is used to correct the phase error caused by the asynchronous sampling timing. When the phase offset exceeds 30 degrees, it is corrected to make the standardized data more reliable. In waveform distortion detection, the nonlinear distortion characteristics of the signal are mainly concerned. When using the wavelet denoising method, the db4 wavelet basis function is selected for 4-layer decomposition, and the threshold is set to 1.5 times the standard deviation. The passband range of the digital bandpass filter is 15 kHz to 120 kHz, ensuring that the filtered vibration response data retains the main frequency components. Adaptive wavelet decomposition is used in the time-frequency analysis stage, and the spectrum resolution is set to 0.5 Hz. For the calculation of the amplitude spectrum component value, normalization is used to make the maximum amplitude 1, and the amplitudes of other frequency points are scaled proportionally. In the resonance feature identification, when the amplitude of a certain frequency point exceeds 0.7 and the amplitude ratio with the adjacent frequency point is greater than 3, it is judged as a resonance frequency point. The determination of the resonance frequency band involves comprehensive consideration of multiple parameters. The amplitude response ratio characterizes the sensitivity of the amplitude to the frequency change. When the amplitude change rate between adjacent frequency points exceeds 50%, it is considered to have entered a new frequency band.The resonance bandwidth range threshold is set to 5 kHz, that is, the resonance frequency points with an interval less than this value are classified into the same frequency band. By calculating the center frequency of each frequency band, the frequency parameter range that causes the resonance of the comb group is finally obtained to be 45 kHz to 75 kHz. In practical applications, the selection of these parameters needs to be appropriately adjusted according to the specific situation. For example, when working in the high frequency band, it may be necessary to increase the sampling rate and frequency resolution, while reducing the amplitude response ratio threshold to capture more subtle resonance features. In an environment with strong background noise, it may be necessary to increase the harmonic interference intensity threshold and the wavelet denoising threshold to obtain clearer signal characteristics.

[0033] S103, using a laser Doppler vibrometer to obtain vibration waves of the comb tooth group within the resonant frequency parameter range, analyzing the distribution of superposition areas and cancellation areas of the vibration waves, and determining the modulation frequency of the local vibration intensity.

[0034] The Doppler frequency shift is used to scan the resonant frequency range of the comb group, and the scanning obtains the original vibration displacement data of the measuring point through the vibration wave amplitude; spatial alignment is performed according to the original vibration displacement data, and the spatial alignment calibrates the position correspondence between adjacent measuring points through waveform overlap parameters to obtain a vibration displacement distribution diagram; waveform feature analysis is performed on the vibration displacement distribution diagram, and the waveform feature analysis sets the waveform superposition threshold through the phase difference rate and the interference intensity ratio to obtain vibration wave interference distribution data; wavelet packet transform decomposition is performed on the vibration wave interference distribution data, and the wavelet packet transform decomposition calculates the spatial distribution characteristics of the superposition area and the offset area through the spatial distribution rate parameter to obtain the regional boundary contour data; spectrum analysis is performed on the vibration intensity modulation curve, and the modulation frequency value of the local vibration intensity is obtained through frequency domain feature extraction.

[0035] Exemplarily, the Doppler frequency shift and vibration wave amplitude are used to scan and sample the resonant frequency range of the comb group, and the energy attenuation of different positions is calculated by the attenuation rate ratio to obtain the original vibration displacement data of multiple measurement points. The original vibration displacement data is spatially aligned, and the position correspondence between adjacent measurement points is calibrated by the waveform overlap parameter, and a continuous vibration displacement distribution map is generated by the data interpolation algorithm. The waveform characteristics of the vibration displacement distribution map are analyzed according to the phase difference rate and the interference intensity ratio, and the waveform superposition threshold is set to determine the superposition and cancellation areas to obtain the marked vibration wave interference distribution data. The wavelet packet transform algorithm is used to perform multi-scale decomposition on the vibration wave interference distribution data, and the spatial distribution characteristics of the superposition area and the cancellation area are calculated by the spatial distribution rate parameter to obtain the regional boundary contour data. The regional boundary contour data is used to construct the intensity distribution surface, and the vibration intensity ratio of the superposition area and the cancellation area is calculated according to the resonance amplitude difference parameter to obtain the local area vibration intensity data. The local area vibration intensity data is subjected to time series analysis, and the vibration envelope characteristics are extracted by the modulation depth value parameter to obtain the vibration intensity modulation curve. The vibration intensity modulation curve is subjected to spectrum analysis, and the main frequency components are identified by frequency offset and waveform symmetry parameters. The modulation frequency value of the local vibration intensity is obtained by extracting frequency domain features. In the laser Doppler vibration measurement process, the Doppler frequency shift directly reflects the vibration velocity information. When the comb group vibrates at a frequency of 50 kHz, the measured Doppler frequency shift varies from 20 Hz to 200 Hz, and the vibration wave amplitude ranges from 10 microns to 100 microns. By calculating the attenuation rate ratio, it is found that the vibration energy decays by about 5% for every 1 mm of propagation during the propagation process. This attenuation characteristic is crucial for determining the spatial distribution of the measurement points. In the spatial registration stage, the waveform overlap parameter reflects the data continuity between adjacent measurement points. When the measurement point spacing is set to 0.5 mm, the waveform overlap of adjacent points reaches more than 85%. The cubic spline interpolation algorithm can generate a continuous vibration displacement distribution map with a resolution of 0.1 mm. This high-precision spatial distribution data lays the foundation for subsequent waveform analysis. In waveform feature analysis, the phase difference rate and interference intensity ratio are key indicators for judging waveform superposition and cancellation. When the phase difference rate is less than 30 degrees and the interference intensity ratio is greater than 1.5, it is determined to be a waveform superposition area; when the phase difference rate is close to 180 degrees and the interference intensity ratio is less than 0.5, it is determined to be a waveform cancellation area. Statistics show that in the 2 mm × 2 mm observation area, the superposition area and the cancellation area show an obvious periodic distribution. The wavelet packet transform adopts a 5-layer decomposition structure, and the spatial distribution rate reflects the spatial arrangement characteristics of the superposition area and the cancellation area. In the process of boundary contour extraction, a normalized threshold of 0.7 is used to determine the region boundary, and it is found that the area ratio of the superposition area to the cancellation area is about 3:2. The resonance amplitude difference parameter shows that the vibration intensity of the superposition area is 2.8 times higher than that of the cancellation area on average.The timing analysis stage focuses on the dynamic change characteristics of vibration intensity. The modulation depth value reflects the fluctuation of the vibration envelope, and the value varies between 0.35 and 0.65, indicating that there is a significant intensity modulation phenomenon. After the envelope curve is Fourier transformed, multiple characteristic frequency components appear in the spectrum, among which the most significant modulation frequency is located near 15 kHz, and the frequency offset remains within the range of ±0.5 kHz. The waveform symmetry parameter further verifies the stability of the modulation feature, and its value fluctuates between 0.85 and 0.95. This highly symmetrical modulation feature shows that the vibration energy is regularly redistributed in space. The periodic energy redistribution process is the essential manifestation of local vibration intensity modulation, which provides an important basis for understanding the vibration mode of the comb tooth group.

[0036] S104. A scalp bionic acoustic model is constructed according to the acoustic impedance characteristics of the human scalp, and the local vibration intensity modulation frequency is input into the scalp bionic acoustic model to simulate the propagation attenuation of ultrasound waves in the scalp, and a random sampling simulation is performed on the sound field distribution inside the scalp.

[0037] The acoustic parameter data of the scalp epidermis, dermis and subcutaneous tissue layer are obtained by ultrasonic reflection method, and the acoustic parameter data include tissue density ratio and acoustic refractive index; a scalp bionic acoustic model is established according to the acoustic parameter data through sound wave scattering ratio and tissue thickness value parameters, and the scalp bionic acoustic model adopts an adaptive grid division algorithm; the propagation process of sound waves in the scalp tissue is calculated by using frequency response parameters for the scalp bionic acoustic model to obtain an initial sound field distribution map; the energy attenuation of sound waves in each layer of tissue is calculated by using propagation velocity difference and vibration attenuation parameters for the initial sound field distribution map to obtain a corrected sound field intensity distribution map; random sampling points are generated according to the corrected sound field intensity distribution map by using Monte Carlo method, and the sound field characteristic parameters of each sampling point are calculated by using sound field intensity ratio parameters to obtain a random sampling data set.

[0038] Exemplarily, the scalp epidermis, dermis, and subcutaneous tissue layers are measured in layers according to the tissue density ratio and acoustic refractive index, and the acoustic parameter data of each layer of tissue is obtained by ultrasonic reflection method. The acoustic parameter data of each layer of tissue are spatially reconstructed, the acoustic characteristics of the tissue interface are calculated by the acoustic wave scattering ratio and tissue thickness value parameters, and the scalp bionic acoustic model is established by an adaptive meshing algorithm. Acoustic boundary conditions are constructed for the scalp bionic acoustic model, and the tissue interface propagation characteristics are set according to the acoustic impedance value and the acoustic wave attenuation rate parameters to obtain the model boundary parameter data. The local vibration intensity modulation frequency is converted into an acoustic excitation signal by using the frequency response parameter, and the propagation process of the sound wave in the scalp tissue is calculated by the time domain finite difference method to obtain the initial sound field distribution map. The initial sound field distribution map is meshed, and the energy attenuation of the sound wave in each layer of tissue is calculated by the propagation velocity difference and the vibration attenuation parameter to obtain the corrected sound field intensity distribution map. The sound field intensity distribution map is divided into layers according to the waveform distortion rate and the beam penetration, and the grid sampling density threshold is set to obtain the sampling area marking data. The Monte Carlo method was used to generate random sampling points for the sampling area marking data, and the acoustic field characteristic parameters of each sampling point were calculated by the acoustic field intensity ratio parameter to obtain a random sampling data set. The acoustic properties of scalp tissue are closely related to its anatomical structure, showing multi-layer structural characteristics from the epidermis to the subcutaneous tissue layer. Measurements show that the tissue density ratio of the epidermis is about 1.1 and the acoustic refractive index is 1.6, while these two parameters of the dermis are 1.2 and 1.8, respectively, and those of the subcutaneous tissue layer are 0.9 and 1.3. The acoustic wave scattering ratio data measured by the ultrasonic reflection method show that the scattering ratio of the epidermis-dermis interface is 0.15, and that of the dermis-subcutaneous tissue interface is 0.25. This difference reflects the change in acoustic impedance of tissue structures at different levels. When constructing a scalp bionic acoustic model, the tissue thickness value is a key parameter. The thickness of the epidermis is between 0.07 mm and 0.12 mm, the thickness of the dermis is between 1.5 mm and 2.0 mm, and the thickness of the subcutaneous tissue layer varies between 3.0 mm and 4.5 mm. When adaptive meshing is performed, the mesh size near the interface needs to be refined to 0.02 mm to accurately capture the propagation characteristics of sound waves at the tissue interface. The setting of acoustic boundary conditions is based on the measured acoustic impedance value and attenuation characteristics. The acoustic impedance value of the epidermis is 1.6 megareis and the acoustic wave attenuation rate is 1.2 dB / cm / MHz, while the two parameters of the dermis are 1.8 megareis and 1.5 dB / cm / MHz, and the subcutaneous tissue layer is 1.4 megareis and 0.9 dB / cm / MHz. When the 15 kHz local vibration intensity modulation frequency is converted into an acoustic excitation signal, the frequency response curve shows obvious nonlinear characteristics. In the finite difference time domain calculation, the time step is set to 0.02 microseconds and the spatial step is 0.05 mm. The propagation velocity difference between different layers reaches 120 m / s to 180 m / s. The calculated vibration attenuation shows that the sound wave energy is attenuated by about 25% each time it passes through a tissue interface during propagation.The grid refinement processing of the sound field intensity distribution adopts a quadtree structure, and the grid size in the area with large sound field gradient can be refined to 0.01 mm. The waveform distortion rate threshold is set to 0.3, and the beam penetration threshold is set to 0.5. Based on these two parameters, the sound field area is divided into three intensity levels: high, medium, and low. The grid density of the sampling area is set to 100 points per square millimeter in the high-intensity area, 50 points in the medium-intensity area, and 20 points in the low-intensity area. Monte Carlo random sampling generates random sampling points in each intensity area. The total number of sampling points is about 1,000, of which 40% is in the high-intensity area, 35% is in the medium-intensity area, and 25% is in the low-intensity area. The sound field intensity ratio parameter reflects the ratio of the sound field intensity at the sampling point to the average intensity of the layer. The value is distributed between 0.5 and 2.0. The stratified random sampling strategy ensures the full characterization of different sound field feature areas.

[0039] S105. Real-time monitoring of changes in hair follicle morphology and metabolism in the simulation of the sound field distribution inside the scalp, extracting parameter characteristics of hair follicles' absorption of ultrasonic waves, and constructing a hair follicle acoustic wave response predictor to reflect the response of hair follicles to different ultrasonic stimuli. The parameter characteristics include hair follicle metabolic parameters and hair follicle morphological parameters.

[0040] The simulation data of the sound field distribution inside the scalp is identified according to the hair follicle diameter value and the hair follicle angle value, and the hair follicle morphological feature vector is obtained through the morphological change rate; the hair follicle morphological feature vector is tracked by an image acquisition device, and the hair follicle dynamic morphological data is obtained through a cell vitality index; the acoustic characteristics of the hair follicle dynamic morphological data are analyzed, and the ultrasonic energy attenuation characteristic data is obtained through the sound wave absorption rate parameter; a recursive neural network model is trained for the ultrasonic energy attenuation characteristic data, and the hair follicle acoustic wave response prediction data is obtained through a sliding time window method.

[0041] Exemplarily, the scalp internal acoustic field distribution simulation data is used to identify the follicle region according to the follicle diameter value and the follicle angle value, the follicle morphology feature vector is constructed by the morphology change rate and tissue density difference parameters, and the follicle morphology parameters are calculated by a deep convolutional neural network to obtain the initial follicle morphology data. The blood flow rate ratio and the nutrient supply rate baseline values ​​are calibrated for the initial follicle morphology data, and the follicle morphology changes are tracked by a high-speed image acquisition device to obtain the follicle dynamic morphology data. According to the follicle dynamic morphology data, the cell activity index is collected in real time, the follicle growth state is calculated by the metabolic activity parameter, and the follicle metabolic sequence data is obtained by recording at fixed time intervals. The acoustic characteristics of the follicle metabolic sequence data are analyzed, and the ultrasonic energy attenuation characteristics are calculated by the acoustic absorption rate and the acoustic field attenuation parameters, and the acoustic absorption characteristic data is obtained by the energy conversion ratio parameter. The follicle acoustic response function is constructed according to the acoustic absorption characteristic data, and the follicle morphology and metabolic changes are modeled by the growth cycle value parameter to obtain the follicle ultrasonic response mapping relationship. A recursive neural network was trained for the mapping relationship of the ultrasonic response of hair follicles, and the sliding time window method was used to extract features from the real-time data stream to obtain a hair follicle acoustic response predictor. The ultrasonic stimulation response was calculated in real time using the hair follicle acoustic response predictor, and the characteristic spectrum of the hair follicle response was obtained through the dynamic change curves of morphological parameters and metabolic parameters. The morphological characteristics of hair follicles directly affect their response characteristics to ultrasound. The diameter of hair follicles usually varies between 0.06 mm and 0.12 mm, and the angle value of hair follicles is inclined from 15 degrees to 75 degrees on the skin surface. Through morphological change rate monitoring, it was found that the cross-sectional area of ​​hair follicles during the growth period changes by about 2% to 5% per day, and the tissue density difference reflects the density ratio of hair follicle tissue to surrounding dermal tissue, which usually fluctuates between 0.85 and 1.15. The blood supply of hair follicles is closely related to their metabolic activity. The blood flow rate ratio characterizes the ratio of the microcirculatory blood flow velocity of the hair follicle to the basal blood flow velocity, which can reach 2.5 to 3.0 during the growth period. The nutritional supply rate reflects the nutritional status of hair follicles by monitoring the local concentration changes of glucose and amino acids. The value between 80% and 95% indicates a normal metabolic level. The high-speed image acquisition device can record 1,000 frames of images per second to capture the slight changes in the morphology of hair follicles. The cell viability index is mainly evaluated by measuring ATP concentration and mitochondrial activity. Under normal conditions, the ATP concentration is maintained at 2.5 to 3.5 micromoles / gram of tissue. The metabolic activity parameter combines multiple biochemical indicators, including oxygen consumption rate and carbon dioxide production rate. These indicators are recorded every 5 minutes to form continuous metabolic sequence data. The propagation characteristics of sound waves in hair follicle tissue show obvious nonlinear characteristics. The sound wave absorption rate is frequency-dependent in the frequency range of 20 kHz to 100 kHz, and the average absorption rate is 1.2 dB / cm / MHz. The attenuation of the sound field increases with the increase of the propagation distance. When passing through the hair follicle tissue, the energy attenuation can reach 40% to 60%.The growth cycle of hair follicles usually includes three stages: growth phase, regression phase and rest phase, and the response characteristics of each stage to ultrasonic stimulation are different. The energy conversion of hair follicles in the growth phase is relatively high, reaching 0.7 to 0.8, while it drops to about 0.3 in the rest phase. Based on these timing characteristics, a recursive layer of 256 neurons and a time window of 64 milliseconds are used for feature extraction, and the sliding step size is set to 16 milliseconds. The acoustic response predictor dynamically evaluates the effect of ultrasonic stimulation by analyzing the changing trends of morphological parameters and metabolic parameters in real time. Morphological parameters include real-time changes in hair follicle diameter, angle and density, and metabolic parameters include dynamic response curves of indicators such as ATP concentration and oxygen consumption rate. These parameters are updated every 50 milliseconds to form a continuous response feature map.

[0042] S106. Collect the vibration data of the comb teeth in real time and upload it to the cloud. Use the hair follicle acoustic wave response predictor to mine and analyze the comb teeth vibration data, dynamically optimize the ultrasonic vibration parameters, adaptively adjust the vibration mode of the comb, and achieve real-time control of the vibration and hair follicle physiological effects.

[0043] The data collected by the comb tooth vibration sensor is compressed and encoded according to the vibration sampling rate, and uploaded to the cloud storage through the wireless sensor network protocol to obtain the original vibration data stream; the original vibration data stream is used to import the hair follicle acoustic wave response predictor, and the correlation strength between the vibration mode and the hair follicle state is calculated through the response matching parameter to obtain the vibration and response mapping relationship; the state and action space are constructed according to the vibration and response mapping relationship by using a deep reinforcement learning algorithm, and a vibration parameter optimization scheme is obtained through gradient strategy search; a real-time vibration control function is constructed according to the vibration parameter optimization scheme, the vibrator control amount is calculated through the amplitude change rate, and the vibration control instruction is obtained by real-time compensation of the feedback delay value.

[0044] Exemplarily, the data collected by the comb-tooth vibration sensor is compressed and encoded according to the vibration sampling rate and vibration power ratio, the data frames are packaged and grouped by the transmission delay value parameter, and the wireless sensor network protocol is used to upload to the cloud storage at fixed time intervals to obtain the original vibration data stream. The original vibration data stream is cleaned, the vibration feature sequence is extracted by the sliding window method, and the vibration mode characteristics are calculated by the time-frequency analysis method to obtain the pre-processed vibration data. The pre-processed vibration data is imported into the hair follicle acoustic wave response predictor, the response matching degree parameter is used to calculate the correlation strength between the vibration mode and the hair follicle state, and the response effect is evaluated by the physiological response ratio parameter to obtain the vibration and response mapping relationship. Data mining is performed on the vibration-response mapping relationship, the state and action space is constructed by the deep reinforcement learning algorithm, and the vibration parameter optimization scheme is obtained by gradient strategy search. A real-time vibration control function is constructed according to the vibration parameter optimization scheme, the vibrator control amount is calculated by the amplitude change rate and frequency modulation parameters, and the vibration control instruction is obtained by real-time compensation using the feedback delay value. The execution process of the vibration control instruction is monitored, the output deviation is calculated by the vibration stability parameter, and the control parameter is calibrated by the control accuracy value parameter to obtain the corrected control instruction. The vibration mode configuration is updated according to the modified control instruction, the control effect is evaluated by the control accuracy parameter, and the adaptive control strategy is obtained by dynamically adjusting the control parameters through the parameter optimization index. The collection of comb vibration data involves the coordinated control of multiple key parameters. When the vibration sampling rate is set to 1000 Hz, the vibration characteristics in the range of 20 Hz to 100 Hz can be accurately captured. The vibration power ratio reflects the ratio of output power to reference power, which is dynamically adjusted between 0.5 and 2.0. The data compression adopts a lossless compression algorithm, the compression ratio reaches 3:1, and the transmission delay value is controlled within 20 milliseconds. In the data cleaning stage, a sliding window with a length of 2 seconds is used, and the window overlap rate is 50%. For sudden abnormal vibrations, the amplitude threshold is set to 3 times the mean for filtering. The time-frequency analysis adopts the short-time Fourier transform method with a time resolution of 5 milliseconds and a frequency resolution of 0.5 Hz. The extracted features include the main frequency, spectrum energy distribution, etc. In the correlation analysis of vibration and hair follicle response, the response matching parameter quantifies the consistency of the vibration pattern with the expected response. When the matching degree exceeds 0.8, it indicates that the vibration mode is highly matched with the state of the hair follicle. The physiological response ratio reflects the ratio of the actual response intensity to the expected response intensity. When the value fluctuates between 0.7 and 1.3, the response is considered normal. The state space constructed by the deep reinforcement learning algorithm contains 16 vibration parameter dimensions, and the action space covers three adjustment dimensions: frequency, amplitude, and phase. The gradient strategy search method is adopted, 128 sampling points are updated each iteration, the learning rate is set to 0.01, and it converges to a stable solution after 500 iterations. In real-time vibration control, the amplitude change rate is limited to 20% per second, and the adjustment step of the frequency modulation is 0.5 Hz. The feedback delay value is usually 15 milliseconds, and the control delay is reduced to less than 5 milliseconds through feedforward compensation.The vibration stability parameter is calculated using the root mean square error, and the control target is set to less than 2%. The execution monitoring of the control instructions uses a sampling frequency of 100 Hz, and the parameter calibration is triggered when the vibration stability deviation exceeds 5%. The control accuracy value represents the deviation between the actual output and the target value, which is controlled within the range of ±3%. The control accuracy parameter takes into account the frequency deviation, phase deviation and amplitude deviation, and is calculated by weighted average. The adaptive control strategy dynamically adjusts the control parameters based on the parameter optimization index, and the optimization index is updated every 500 milliseconds. When the optimization degree is lower than 0.7, the adaptive adjustment of the control parameters is triggered, and the adjustment step size is automatically calculated according to the deviation size. Through this real-time feedback mechanism, the control system can continuously optimize the vibration mode and maintain a good match with the physiological response of the hair follicles.

[0045] The present invention provides a control system for a hair growth promoting comb based on ultrasound, which mainly includes:

[0046] Finite element simulation module, used to simulate the vibration wave propagation and interference mode under different comb tooth geometric shapes and arrangements using finite element algorithm, and obtain the target phase difference range between adjacent comb teeth;

[0047] A vibration data acquisition module is used to collect vibration data of the comb tooth surface under different ultrasonic vibration frequencies and power parameters within the target phase difference range between adjacent comb teeth, calculate the vibration data, obtain the amplitude spectrum of different frequency components, and obtain the frequency parameter range that causes the comb tooth group to resonate;

[0048] A vibration wave analysis module is used to obtain the vibration waves of the comb group within the resonant frequency parameter range using a laser Doppler vibrometer, analyze the distribution of the superposition area and the offset area of ​​the vibration waves, and determine the modulation frequency of the local vibration intensity;

[0049] The scalp bionic acoustic model module is used to build a scalp bionic acoustic model based on the acoustic impedance characteristics of the human scalp, input the local vibration intensity modulation frequency into the scalp bionic acoustic model, simulate the propagation attenuation of ultrasound in the scalp, and perform random sampling simulation on the sound field distribution inside the scalp;

[0050] The hair follicle response prediction module is used to monitor the changes in hair follicle morphology and metabolism in the simulation of the sound field distribution inside the scalp in real time, extract the parameter characteristics of hair follicles' absorption of ultrasound waves, and construct a hair follicle acoustic wave response predictor to reflect the response of hair follicles to different ultrasonic stimuli. The parameter characteristics include hair follicle metabolic parameters and hair follicle morphological parameters.

[0051] The vibration optimization control module is used to collect the vibration data of the comb teeth in real time and upload it to the cloud. It uses the hair follicle acoustic wave response predictor to mine and analyze the comb tooth vibration data, dynamically optimize the ultrasonic vibration parameters, and adaptively adjust the vibration mode of the comb to achieve real-time control of the vibration and hair follicle physiological effects.

[0052] The above describes the specific embodiments of the present invention. It should be understood that the present invention is not limited to the above specific embodiments, and those skilled in the art can make various changes or modifications within the scope of the claims, which does not affect the essence of the present invention. In the absence of conflict, the embodiments of the present application and the features in the embodiments can be combined with each other arbitrarily.

Claims

1. A control method for a hair growth promoting comb based on ultrasound, characterized in that: The method comprises: Finite element algorithm is used to simulate the vibration wave propagation and interference mode under different comb tooth geometric shapes and arrangements to obtain the target phase difference range between adjacent comb teeth; Collect vibration data of the comb tooth surface under different ultrasonic vibration frequencies and power parameters within the target phase difference range between adjacent comb teeth, calculate the vibration data, obtain the amplitude spectrum of different frequency components, and obtain the frequency parameter range that causes the comb tooth group to resonate; A laser Doppler vibrometer is used to obtain the vibration waves of the comb group within the resonant frequency parameter range, analyze the distribution of the superposition area and the offset area of ​​the vibration waves, and determine the modulation frequency of the local vibration intensity; A scalp bionic acoustic model is built according to the acoustic impedance characteristics of the human scalp. The local vibration intensity modulation frequency is input into the scalp bionic acoustic model to simulate the propagation attenuation of ultrasound in the scalp, and random sampling simulation of the sound field distribution inside the scalp is performed. Real-time monitoring of the changes in hair follicle morphology and metabolism in the simulation of the acoustic field distribution inside the scalp, extraction of parameter characteristics of hair follicles' absorption of ultrasonic waves, and construction of a hair follicle acoustic wave response predictor to reflect the response of hair follicles to different ultrasonic stimuli. The parameter characteristics include hair follicle metabolic parameters and hair follicle morphological parameters. The vibration data of the comb teeth is collected in real time and uploaded to the cloud. The hair follicle acoustic response predictor is used to mine and analyze the comb tooth vibration data, dynamically optimize the ultrasonic vibration parameters, and adaptively adjust the vibration mode of the comb to achieve real-time control of the vibration and hair follicle physiological effects.

2. The method according to claim 1, characterized in that The finite element algorithm is used to simulate the vibration wave propagation and interference mode under different comb tooth geometric shapes and arrangements to obtain the target phase difference range between adjacent comb teeth, including: A grid density distribution mapping table is constructed according to the comb tooth amplitude change rate, and the comb tooth structure is divided into hexahedral grids by the grid density distribution mapping table to obtain the discrete comb tooth unit stress field distribution data; The acoustic finite-difference time-domain algorithm is used to construct the wave propagation equation for the stress field distribution data of discrete comb units, and the space-time propagation data including wave displacement is obtained through boundary coupling effect parameter conversion. The wave energy distribution is calculated using the waveform interference intensity parameter according to the spatiotemporal propagation data, and the wave energy propagation path is tracked by the beam focusing degree to obtain the comb-tooth structure wave phase superposition data; The phase shift degree is used to perform Fourier transform on the wave phase superposition data, and a phase difference distribution diagram between adjacent comb teeth is obtained by correcting the acoustic impedance change parameters.

3. The method according to claim 1, characterized in that The collecting of vibration data of the comb tooth surface under different ultrasonic vibration frequencies and power parameters within the target phase difference range between adjacent comb teeth, calculating the vibration data, obtaining amplitude spectra of different frequency components, and obtaining the frequency parameter range causing the comb tooth group resonance includes: Constructing a sampling parameter matrix according to the combination of ultrasonic vibration frequency and power, and collecting original vibration response data on the comb tooth surface through the sampling parameter matrix; The harmonic interference intensity threshold is used to remove abnormal points from the original vibration response data, and the standardized vibration response data is obtained by correcting the phase shift degree parameter; Performing waveform distortion detection on the standardized vibration response data, and extracting signals within a specified frequency band using a digital bandpass filter to obtain filtered vibration response data; The filtered vibration response data is subjected to adaptive wavelet decomposition. If the amplitude response ratio exceeds a preset resonance peak amplitude threshold, it is determined to be a resonance frequency point, and the frequency parameter range causing the comb group resonance is obtained by calculating the bandwidth center frequency.

4. The method according to claim 1, characterized in that: The laser Doppler vibrometer is used to obtain the vibration waves of the comb group within the resonant frequency parameter range, analyze the distribution of the superposition area and the offset area of ​​the vibration waves, and determine the modulation frequency of the local vibration intensity, including: The Doppler frequency shift is used to scan the resonant frequency range of the comb group, and the scanning obtains the original vibration displacement data of the measurement point through the vibration wave amplitude; Performing spatial registration according to the original vibration displacement data, wherein the spatial registration calibrates the position correspondence between adjacent measurement points by using waveform overlap parameters to obtain a vibration displacement distribution map; Performing waveform feature analysis on the vibration displacement distribution diagram, wherein the waveform feature analysis sets a waveform superposition threshold by using a phase difference rate and an interference intensity ratio to obtain vibration wave interference distribution data; The vibration wave interference distribution data is decomposed by wavelet packet transform, wherein the wavelet packet transform decomposition calculates the spatial distribution characteristics of the superposition area and the offset area through a spatial distribution rate parameter to obtain the regional boundary contour data; The spectrum analysis of the vibration intensity modulation curve is performed, and the modulation frequency value of the local vibration intensity is obtained by extracting the frequency domain features.

5. The method according to claim 1, characterized in that The method of building a scalp bionic acoustic model according to the acoustic impedance characteristics of the human scalp, inputting the local vibration intensity modulation frequency into the scalp bionic acoustic model, simulating the propagation attenuation of ultrasound waves in the scalp, and performing random sampling simulation on the sound field distribution inside the scalp includes: Acquiring acoustic parameter data of the epidermis, dermis and subcutaneous tissue layers of the scalp using an ultrasonic reflection method, wherein the acoustic parameter data includes a tissue density ratio and an acoustic refractive index; Establishing a scalp bionic acoustic model according to the acoustic parameter data through sound wave scattering ratio and tissue thickness value parameters, wherein the scalp bionic acoustic model adopts an adaptive meshing algorithm; The frequency response parameters are used to calculate the propagation process of sound waves in the scalp tissue for the scalp bionic acoustic model to obtain an initial sound field distribution diagram; Calculating the energy attenuation of the sound wave in each layer of tissue by using the propagation velocity difference and the vibration attenuation parameter for the initial sound field distribution diagram to obtain a corrected sound field intensity distribution diagram; A Monte Carlo method is used to generate random sampling points according to the corrected sound field intensity distribution diagram, and a sound field characteristic parameter of each sampling point is calculated through a sound field intensity ratio parameter to obtain a random sampling data set.

6. The method according to claim 1, characterized in that The real-time monitoring of the changes in hair follicle morphology and metabolism in the simulation of the sound field distribution inside the scalp, extracting the parameter characteristics of the hair follicles' absorption of ultrasonic waves, constructing a hair follicle acoustic wave response predictor, and reflecting the response of the hair follicles to different ultrasonic stimuli, the parameter characteristics include hair follicle metabolic parameters and hair follicle morphological parameters, including: The simulation data of the sound field distribution inside the scalp is identified according to the hair follicle diameter value and the hair follicle angle value, and the hair follicle morphology feature vector is obtained through the morphology change rate; An image acquisition device is used to track the hair follicle morphological feature vector, and the hair follicle dynamic morphological data is obtained through a cell vitality index; Performing acoustic characteristic analysis on the hair follicle dynamic morphology data, and obtaining ultrasonic energy attenuation characteristic data through acoustic wave absorption rate parameters; A recursive neural network model is trained based on the ultrasonic energy attenuation characteristic data, and hair follicle acoustic wave response prediction data is obtained through a sliding time window method.

7. The method according to claim 1, characterized in that The real-time collection of the vibration data of the comb teeth is uploaded to the cloud, and the hair follicle acoustic wave response predictor is used to mine and analyze the vibration data of the comb teeth, dynamically optimize the ultrasonic vibration parameters, and adaptively adjust the vibration mode of the comb to achieve real-time control of the vibration and hair follicle physiological effects, including: The data collected by the comb-tooth vibration sensor is compressed and encoded according to the vibration sampling rate, and uploaded to the cloud storage through the wireless sensor network protocol to obtain the original vibration data stream; The original vibration data stream is introduced into the hair follicle acoustic wave response predictor, and the correlation strength between the vibration mode and the hair follicle state is calculated through the response matching parameter to obtain the vibration and response mapping relationship; A deep reinforcement learning algorithm is used to construct a state and action space for the vibration and response mapping relationship, and a vibration parameter optimization scheme is obtained through a gradient strategy search; A real-time vibration control function is constructed according to the vibration parameter optimization scheme, the vibrator control quantity is calculated by the amplitude change rate, and the vibration control instruction is obtained by using the feedback delay value for real-time compensation.

8. A control system for a hair growth promoting comb based on ultrasound, characterized in that: The system comprises: Finite element simulation module, used to simulate the vibration wave propagation and interference mode under different comb tooth geometric shapes and arrangements using finite element algorithm, and obtain the target phase difference range between adjacent comb teeth; A vibration data acquisition module is used to collect vibration data of the comb tooth surface under different ultrasonic vibration frequencies and power parameters within the target phase difference range between adjacent comb teeth, calculate the vibration data, obtain the amplitude spectrum of different frequency components, and obtain the frequency parameter range that causes the comb tooth group to resonate; A vibration wave analysis module is used to obtain the vibration waves of the comb group within the resonant frequency parameter range using a laser Doppler vibrometer, analyze the distribution of the superposition area and the offset area of ​​the vibration waves, and determine the modulation frequency of the local vibration intensity; The scalp bionic acoustic model module is used to build a scalp bionic acoustic model based on the acoustic impedance characteristics of the human scalp, input the local vibration intensity modulation frequency into the scalp bionic acoustic model, simulate the propagation attenuation of ultrasound in the scalp, and perform random sampling simulation on the sound field distribution inside the scalp; The hair follicle response prediction module is used to monitor the changes in hair follicle morphology and metabolism in the simulation of the sound field distribution inside the scalp in real time, extract the parameter characteristics of hair follicles' absorption of ultrasound waves, and construct a hair follicle acoustic wave response predictor to reflect the response of hair follicles to different ultrasonic stimuli. The parameter characteristics include hair follicle metabolic parameters and hair follicle morphological parameters. The vibration optimization control module is used to collect the vibration data of the comb teeth in real time and upload it to the cloud. It uses the hair follicle acoustic wave response predictor to mine and analyze the comb tooth vibration data, dynamically optimize the ultrasonic vibration parameters, and adaptively adjust the vibration mode of the comb to achieve real-time control of the vibration and hair follicle physiological effects.

9. An electronic device, characterized in that: The electronic device comprises a memory and a processor, wherein the memory is used to store a computer program; and the processor is used to call the computer program so that the electronic device executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium comprising instructions, characterized in that: When the instructions are executed on an electronic device, the electronic device executes the method according to any one of claims 1 to 7.

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