Wireless charging pelvic floor muscle rehabilitation treatment system, method, equipment and medium

Abstract

The invention discloses a wireless charging pelvic floor muscle rehabilitation treatment system, method and equipment and a medium, and relates to the technical field of rehabilitation physiotherapy. A pelvic floor host is arranged to collect a first electromyographic signal of pelvic floor muscles, a first body surface electrode host synchronously collects a second electromyographic signal of abdominal muscles, power frequency notch processing is carried out on the two signals to improve signal quality, and then an abdominal muscle participation degree coefficient is calculated based on the processed signals. The coefficient can accurately quantify the compensation degree of abdominal muscles in pelvic floor muscle training. And the main controller adjusts the electrical stimulation parameters of the pelvic floor host in real time according to the abdominal muscle participation degree coefficient, and when the abdominal muscle compensation is detected to be obvious, the target electrical stimulation parameters are adjusted to inhibit the excessive participation of the abdominal muscle, so that the pelvic floor muscle can obtain more accurate training stimulation. Meanwhile, the system is further provided with a wireless charging storage box, a convenient wireless charging function can be provided for the treatment probe, and the use convenience of the equipment is improved.

Description

Technical Field

[0001] This application relates to the technical field of rehabilitation physiotherapy, specifically to a wireless charging pelvic floor muscle rehabilitation treatment system, method, device, and medium. Background Technology

[0002] Pelvic floor muscle dysfunction is a common condition affecting women's quality of life, mainly manifested as urinary incontinence and pelvic organ prolapse. Pelvic floor muscle rehabilitation treatment typically combines electrical stimulation and biofeedback training. Electrical stimulation is applied to the pelvic floor muscles to promote muscle contraction, while electromyographic signals are collected to assess the training effect.

[0003] Currently, pelvic floor muscle electrical stimulation rehabilitation equipment typically uses fixed electrical stimulation parameters for treatment. In actual treatment, due to the complexity of the human muscular system, pelvic floor muscle training often unconsciously induces synergistic contractions of the abdominal muscles. This compensatory contraction of the abdominal muscles can significantly affect the treatment outcome, as excessive abdominal muscle contraction may increase abdominal pressure, thus exacerbating the burden on the pelvic floor muscles. This technical limitation means that when abdominal muscle compensation is significant, the fixed electrical stimulation parameters cannot be specifically adjusted, preventing the pelvic floor muscles from receiving sufficient training stimulation and affecting the overall effectiveness and progress of subsequent rehabilitation treatment. Summary of the Invention

[0004] In view of this, this application provides a wireless charging pelvic floor muscle rehabilitation system, method, device and medium.

[0005] The first aspect of this application provides a wireless charging pelvic floor muscle rehabilitation system, the system comprising a treatment probe, a main controller, and a wireless charging storage box, wherein the treatment probe is wirelessly connected to the main controller, wherein: The treatment probe includes a pelvic floor host, a first surface electrode host, a second surface electrode host, and a physiotherapy surface electrode; the pelvic floor host is used to electrically stimulate the pelvic floor muscles and collect the first electromyographic signal of the pelvic floor muscles; the first surface electrode host is used to simultaneously collect the second electromyographic signal of the abdominal muscles during the first electromyographic signal acquisition process; and the second surface electrode host is used to cooperate with the physiotherapy surface electrode to electrically stimulate the chest and abdomen. The main controller is used to perform power frequency notch processing on the first electromyography (EMG) signal and the second EMG signal, calculate the abdominal muscle involvement coefficient based on the first EMG signal and the second EMG signal after power frequency notch processing, adjust the electrical stimulation parameters of the pelvic floor host based on the abdominal muscle involvement coefficient to obtain the target electrical stimulation parameters, and control the treatment probe to output the corresponding electrical stimulation according to the target electrical stimulation parameters. The wireless charging storage box is used to store the treatment probe and provide wireless charging for the treatment probe.

[0006] By employing the above technical solution, a pelvic floor host collects the first electromyographic (EMG) signal of the pelvic floor muscles, while a first surface electrode host simultaneously collects the second EMG signal of the abdominal muscles. Both signals undergo power frequency notch filtering to improve signal quality. Then, based on the processed signals, an abdominal muscle involvement coefficient is calculated, which accurately quantifies the degree of compensation by the abdominal muscles during pelvic floor muscle training. The main controller adjusts the electrical stimulation parameters of the pelvic floor host in real time according to the abdominal muscle involvement coefficient. When significant abdominal muscle compensation is detected, the target electrical stimulation parameters are adjusted to suppress excessive abdominal muscle involvement, ensuring that the pelvic floor muscles receive more precise training stimulation. Simultaneously, the system is equipped with a wireless charging storage box, providing convenient wireless charging for the treatment probe and improving the ease of use. This adaptive electrical stimulation parameter adjustment mechanism based on the degree of abdominal muscle involvement effectively solves the technical shortcomings of existing technologies where fixed electrical stimulation parameters cannot address abdominal muscle compensation, improving the accuracy of pelvic floor muscle rehabilitation training and the overall effectiveness of rehabilitation treatment.

[0007] Optionally, the first and second electromyographic (EMG) signals are subjected to power frequency notch filtering, and the abdominal muscle involvement coefficient is calculated based on the first and second EMG signals after power frequency notch filtering, specifically including: The first electromyography (EMG) signal and the second EMG signal are subjected to adaptive power frequency notch filtering, wherein the adaptive power frequency notch filtering dynamically adjusts the notch center frequency according to the real-time detected power frequency interference frequency. The first and second electromyographic signals after power frequency notch filtering are decomposed into energy to obtain the first frequency domain energy distribution vector corresponding to the first electromyographic signal and the second frequency domain energy distribution vector corresponding to the second electromyographic signal. The similarity between the first frequency domain energy distribution vector and the second frequency domain energy distribution vector is calculated. Identify the characteristic frequency bands of the pelvic floor muscles and the abdominal muscles, and calculate the energy proportion of the first electromyographic signal in the characteristic frequency bands of the pelvic floor muscles and the abdominal muscles; The frequency domain energy transfer coefficient is calculated based on the energy ratio, and the frequency domain energy transfer coefficient is used to quantify the degree of energy transfer from the abdominal muscle characteristic frequency band to the pelvic floor muscle characteristic frequency band. The abdominal muscle involvement coefficient is calculated by combining the similarity, the frequency domain energy transfer coefficient, and the activation intensity of the second electromyographic signal.

[0008] Optionally, the first and second electromyographic signals after power frequency notch filtering are respectively subjected to energy decomposition to obtain a first frequency domain energy distribution vector corresponding to the first electromyographic signal and a second frequency domain energy distribution vector corresponding to the second electromyographic signal, specifically including: Spectral analysis was performed on the first and second electromyographic signals after power frequency notch filtering to identify energy peaks in the spectrum. Centered on the energy peak point and combined with the preset physiological frequency band range, the center frequency and bandwidth of each frequency band are determined, and the preset frequency range is divided into multiple sub-frequency bands, such that each sub-frequency band contains at least one energy peak or the average energy value of each sub-frequency band is not less than a preset multiple of the overall average energy value. Calculate the cumulative energy value within each sub-band and normalize it according to the band bandwidth to obtain the energy density value; Arrange the energy density values ​​of each frequency band in order of frequency to obtain the first frequency domain energy distribution vector corresponding to the first electromyographic signal and the second frequency domain energy distribution vector corresponding to the second electromyographic signal.

[0009] Optionally, the step of calculating the abdominal muscle involvement coefficient by combining the similarity, the frequency domain energy transfer coefficient, and the activation intensity of the second electromyographic signal includes: The activation intensity of the second electromyographic signal is calculated, which is obtained by calculating the ratio of the root mean square value of the second electromyographic signal within a preset time window to a preset reference value; A synergistic interference factor is constructed to quantify the degree of interference of abdominal muscles on pelvic floor muscle training. The calculation formula for the synergistic interference factor is as follows:

[0010] Where R is the cooperative interference factor, S is the similarity, and T is the frequency domain energy transfer coefficient. This is the synergistic amplification factor, with a value ranging from 0.6 to 0.9. This item is used to extract the net incremental portion of exponential growth; The formula for calculating the abdominal muscle participation coefficient is as follows: Where P is the abdominal muscle involvement coefficient, A is the activation intensity, β is the primary inhibition coefficient, ranging from 0.6 to 0.9, γ is the secondary inhibition coefficient, ranging from 0.3 to 0.5, and δ is the migration correction coefficient, ranging from 0.2 to 0.4.

[0011] Optionally, adjusting the electrical stimulation parameters of the pelvic floor unit based on the abdominal muscle involvement coefficient to obtain the target electrical stimulation parameters includes: Extract the reference energy value of the first electromyographic signal in the characteristic frequency band of the pelvic floor muscles, and calculate the energy interference amount corresponding to the abdominal muscle participation coefficient; The energy compensation coefficient is calculated based on the energy interference amount, and the energy compensation coefficient is inversely proportional to the abdominal muscle participation coefficient. The rising edge slope and falling edge slope of the electrical stimulation pulse are adjusted according to the energy compensation coefficient. When the energy compensation coefficient is greater than the first preset value, the rising edge slope is increased and the falling edge slope is decreased to prolong the effective stimulation duration. Obtain the optimal electrical stimulation parameter combination corresponding to the same abdominal muscle participation coefficient range in historical treatment data, and calculate the deviation vector between the current electrical stimulation parameters and the optimal electrical stimulation parameter combination; The electrical stimulation parameters of the pelvic floor unit are corrected based on the deviation vector and the energy compensation coefficient to obtain the target electrical stimulation parameters.

[0012] Optionally, the wireless charging storage box includes a position detection sensor, a charging main control chip, and a wireless transmission driver module. The wireless charging storage box is used to store the treatment probe and provide wireless charging for the treatment probe, specifically including: When the position detection sensor detects that the component in the treatment probe has been placed into the corresponding storage slot, it identifies the device through the coupling signal between the transmitting coil and the receiving coil to obtain the component type and current power information. The charging main control chip determines the charging parameters based on the component type and current power information, and controls the wireless transmission drive module to drive the transmission coil to establish an alternating magnetic field for charging; During the charging process, the charging main control chip collects the current waveform characteristics of the transmitting coil and the voltage waveform characteristics fed back by the treatment probe in real time. It obtains the transmission efficiency by calculating the phase difference and amplitude ratio between the current waveform and the voltage waveform, and periodically obtains the power information fed back by the treatment probe to calculate the power growth rate. The charging master control chip adjusts the operating frequency and transmission power of the transmitting coil in coordination based on the deviation between the transmission efficiency and the target efficiency, as well as the power generation rate.

[0013] Optionally, the charging master control chip coordinates the operating frequency and transmission power of the transmitting coil based on the deviation between the transmission efficiency and the target efficiency, as well as the power generation rate, including: The charging main control chip identifies the coupling state between the transmitting coil and the receiving coil by analyzing the phase difference change rate of the current waveform and the voltage waveform. When the phase difference change rate is lower than a preset change rate threshold, it is determined to be a strong coupling state, and when the phase difference change rate is not lower than the preset change rate threshold, it is determined to be a weak coupling state. The frequency adjustment weight and power adjustment weight are calculated based on the coupling state and the power growth rate. In the strong coupling state, the frequency adjustment weight is increased and the power adjustment weight is decreased, while in the weak coupling state, the power adjustment weight is increased and the frequency adjustment weight is decreased. The deviation between the transmission efficiency and the target efficiency is decomposed according to the frequency adjustment weight and the power adjustment weight to obtain the frequency adjustment amount and the power adjustment amount; The operating frequency and transmission power of the transmitting coil are adjusted synchronously according to the frequency adjustment and power adjustment amounts.

[0014] Secondly, this application provides a wireless charging pelvic floor muscle rehabilitation method, applied to the system described in the first aspect and any possible implementation thereof. The system includes a treatment probe, a main controller, and a wireless charging storage box. The treatment probe is wirelessly connected to the main controller. The treatment probe includes a pelvic floor host, a first surface electrode host, a second surface electrode host, and physiotherapy surface electrodes. The wireless charging pelvic floor muscle rehabilitation method includes: The pelvic floor host electrically stimulates the pelvic floor muscles and collects the first electromyographic signal of the pelvic floor muscles. The first body surface electrode host simultaneously collects the second electromyographic signal of the abdominal muscles during the first electromyographic signal acquisition process. The second body surface electrode host is used in conjunction with the physiotherapy body surface electrode to electrically stimulate the chest and abdomen. The main controller performs power frequency notch processing on the first electromyography (EMG) signal and the second EMG signal, calculates the abdominal muscle involvement coefficient based on the first and second EMG signals after power frequency notch processing, and adjusts the electrical stimulation parameters of the pelvic floor host based on the abdominal muscle involvement coefficient to obtain the target electrical stimulation parameters. The controller then controls the treatment probe to output the corresponding electrical stimulation according to the target electrical stimulation parameters. The wireless charging storage box is used to store the treatment probe and provide wireless charging for the treatment probe.

[0015] Thirdly, this application provides a wireless charging pelvic floor muscle rehabilitation electronic device, the wireless charging pelvic floor muscle rehabilitation electronic device comprising: one or more processors and a memory; the memory is coupled to the one or more processors, the memory being used to store computer program code, the computer program code including computer instructions, the one or more processors calling the computer instructions to cause the wireless charging pelvic floor muscle rehabilitation electronic device to perform the method described in the second aspect.

[0016] Fourthly, this application provides a computer-readable storage medium including instructions that, when executed on a wireless charging pelvic floor muscle rehabilitation electronic device, cause the wireless charging pelvic floor muscle rehabilitation electronic device to perform the method described in the second aspect. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the structure of a wireless charging pelvic floor muscle rehabilitation treatment system provided in an embodiment of this application; Figure 2 This is a schematic diagram of the structure of a wireless charging storage box provided in an embodiment of this application; Figure 3 This is an exemplary hardware structure diagram of an electronic device for wireless charging pelvic floor muscle rehabilitation therapy provided in an embodiment of this application.

[0018] Explanation of reference numerals in the attached diagram: 1. Treatment probe; 2. Main controller; 3. Wireless charging storage box; 11. Pelvic floor main unit; 12. First surface electrode main unit; 13. Second surface electrode main unit; 14. Physiotherapy surface electrode; 31. Position detection sensor; 32. Charging main control chip; 33. Wireless transmission drive module. Detailed Implementation

[0019] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0020] In the description of the embodiments of this application, the words "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design that is described as "for example" or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design options. Rather, the use of the words "for example" or "for instance" is intended to present the relevant concepts in a specific manner.

[0021] In the description of the embodiments of this application, the term "multiple" means two or more. For example, multiple systems means two or more systems, and multiple screen terminals means two or more screen terminals. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature. The terms "comprising," "including," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0022] This application provides a wireless charging pelvic floor muscle rehabilitation system.

[0023] refer to Figure 1 , Figure 1 This is a schematic diagram of the structure of a wireless charging pelvic floor muscle rehabilitation system provided in an embodiment of this application, combined with... Figure 1 It is known that the wireless charging pelvic floor muscle rehabilitation treatment system includes a treatment probe 1, a main controller 2, and a wireless charging storage box 3.

[0024] The treatment probe 1 communicates with the main controller 2 wirelessly (e.g., via Bluetooth or WiFi) to transmit electromyographic signal data in real time and receive electrical stimulation control commands. Figure 1 As shown, the treatment probe 1 includes a pelvic floor unit 11, a first surface electrode unit 12, a second surface electrode unit 13, and a physiotherapy surface electrode 14. The pelvic floor unit 11, as the core control unit of the treatment probe 1, is responsible for electrically stimulating the pelvic floor muscles and collecting the first electromyographic (EMG) signal of the pelvic floor muscles. The first surface electrode unit 12 is used to simultaneously collect the second EMG signal of the abdominal muscles during the first EMG signal acquisition process, achieving synchronous signal monitoring of the pelvic floor muscles and abdominal muscles. The second surface electrode unit 13, in conjunction with the physiotherapy surface electrode 14, provides electrical stimulation to the chest and abdomen, offering auxiliary treatment functions. The physiotherapy surface electrode 14 is a registered medical device.

[0025] The main controller 2, acting as the system's computation and control center, receives the first and second electromyographic (EMG) signals from the treatment probe 1. It first performs power frequency notch filtering on these two signals to eliminate power frequency interference, and then calculates the abdominal muscle involvement coefficient based on the processed signals. The main controller 2 dynamically adjusts the electrical stimulation parameters of the pelvic floor unit 11 according to this abdominal muscle involvement coefficient to obtain the target electrical stimulation parameters. It then sends the adjusted parameter instructions to the treatment probe 1 via wireless communication, controlling the treatment probe 1 to output the corresponding electrical stimulation.

[0026] The wireless charging case 3 provides power to the treatment probe 1 via wireless charging technology. When the treatment probe 1 is placed inside the wireless charging case 3, the transmitting coil inside the case 3 and the receiving coil inside the probe 1 form electromagnetic coupling, achieving wireless energy transmission. This wireless charging case 3 not only has a charging function but also serves to store and protect the treatment probe 1, making it convenient for users to store and carry it for daily use.

[0027] In one feasible approach, after the main controller 2 receives the first and second electromyographic (EMG) signals from the treatment probe 1, it performs power frequency notch filtering on the first and second EMG signals, and calculates the abdominal muscle involvement coefficient based on the power frequency notch-filtered first and second EMG signals. This can be achieved in the following way: Because electromyography (EMG) signal acquisition is inevitably affected by 50Hz or 60Hz power frequency interference, this interference superimposes sinusoidal components onto the signal, severely impacting the accuracy of subsequent feature extraction and analysis. To effectively eliminate this interference, the main controller 2 first performs adaptive power frequency notch filtering on the first and second EMG signals. Unlike traditional fixed-frequency notch filters, the adaptive power frequency notch filtering used in this application dynamically adjusts the notch center frequency based on the real-time detected power frequency interference frequency. Specifically, the main controller 2 analyzes the signal spectrum in real time using Fast Fourier Transform (FFT) to identify the power frequency interference frequency in the current environment. This frequency may vary between 49Hz and 51Hz or 59Hz and 61Hz due to power grid fluctuations. Then, it dynamically sets the center frequency of the notch filter to align with the detected power frequency interference frequency, while simultaneously setting the notch bandwidth to 2Hz to 5Hz, thereby precisely suppressing the power frequency interference component while retaining the effective EMG signal components. This adaptive processing method, compared to fixed-frequency notch filtering, better adapts to the electromagnetic interference characteristics of different operating environments, ensuring the accuracy of subsequent analysis.

[0028] Furthermore, after completing the power frequency notch filtering, the main controller 2 needs to analyze the frequency domain characteristics of the two signals to quantify the degree of abdominal muscle involvement. The main controller 2 performs energy decomposition on the first and second electromyographic (EMG) signals after power frequency notch filtering, thereby obtaining the first frequency domain energy distribution vector corresponding to the first EMG signal and the second frequency domain energy distribution vector corresponding to the second EMG signal. Then, the main controller 2 calculates the similarity between the first and second frequency domain energy distribution vectors. This similarity can be quantified using cosine similarity or Pearson correlation coefficient. A higher similarity value indicates that the pelvic floor muscle signal and the abdominal muscle signal are closer in frequency domain energy distribution, suggesting that the synergistic contraction of the abdominal muscles significantly interferes with pelvic floor muscle training.

[0029] In one feasible approach, the main controller 2 performs energy decomposition on the first and second electromyographic signals after power frequency notch filtering, respectively, to obtain a first frequency domain energy distribution vector corresponding to the first electromyographic signal and a second frequency domain energy distribution vector corresponding to the second electromyographic signal. This can be achieved in the following way: To more accurately obtain the first frequency domain energy distribution vector corresponding to the first electromyography (EMG) signal and the second frequency domain energy distribution vector corresponding to the second EMG signal, the main controller 2 employs an adaptive frequency band partitioning energy decomposition method. Traditional fixed frequency band partitioning methods typically divide the frequency range at equal intervals, such as dividing a sub-band every 10Hz. While simple to implement, this method ignores the non-uniformity of EMG signal energy distribution. In reality, EMG signals exhibit significant energy concentration characteristics in the frequency domain, with highly concentrated energy in certain frequency ranges and sparse energy in other frequency ranges. Using fixed-interval partitioning leads to the averaged feature information in energy-concentrated regions, while sparse regions occupy excessive vector dimensions, reducing the accuracy of subsequent similarity calculations and feature analysis. Therefore, this application employs an adaptive frequency band partitioning method based on energy peak points, enabling the frequency domain energy distribution vector to better represent the true energy distribution characteristics of the EMG signal.

[0030] Specifically, the main controller 2 first performs spectral analysis on the first and second electromyographic (EMG) signals after power frequency notch filtering. This spectral analysis can be achieved through Fast Fourier Transform (FFT), converting the time-domain signal into a frequency-domain signal to obtain the amplitude distribution of the signal at different frequency points. After obtaining the spectral data, the main controller 2 smooths the spectral curve to eliminate the influence of high-frequency noise, for example, by using moving average filtering or Gaussian filtering. Then, it identifies energy peak points in the spectrum within a preset effective frequency range for EMG (e.g., 10Hz to 500Hz). The identification of energy peak points can be achieved by detecting local maxima of the spectral curve, that is, finding a frequency point fi on the spectral curve that satisfies the following conditions: the amplitude A(fi) of this frequency point is greater than the amplitude of its adjacent frequency points, i.e., A(fi) > A(fi-1) and A(fi) > A(fi+1), and at the same time, this amplitude needs to be greater than a preset multiple (e.g., 1.5 to 3 times) of the overall average amplitude to filter out insignificant peaks. In this way, the main controller 2 is able to identify key frequency points where energy is concentrated in the electromyographic signal. These peak points usually correspond to the main frequency components of muscle activation.

[0031] After identifying energy peak points, the main controller 2 uses these peak points as centers and, in conjunction with a preset physiological frequency range, determines the center frequency and bandwidth of each band, dividing the preset frequency range into multiple sub-bands. This division method fully considers the physiological characteristics and energy distribution features of electromyographic signals. Specifically, for each identified energy peak point fi, the main controller 2 sets it as the center frequency fc_i of a sub-band, and then determines the bandwidth BW_i of the sub-band based on the energy concentration of that peak point. The bandwidth can be determined based on the full width at half maximum (FWHM) principle, that is, finding frequency points on both sides of the energy peak point where the amplitude drops to half of the peak value, and the distance between these two frequency points is the bandwidth of the sub-band. For some wideband energy distribution cases, an upper limit value for the bandwidth (such as 50Hz) can be set to avoid excessively wide sub-bands leading to a decrease in frequency resolution. At the same time, to ensure complete coverage of the frequency range, the main controller 2 also needs to process the interval regions between energy peak points. For frequency regions with relatively low energy between two adjacent energy peaks, if the average energy value of this region is not lower than a preset multiple (e.g., 0.3 to 0.5 times) of the overall average energy value, then this region is treated as an independent sub-band. If the energy of this region is too low, it is merged into an adjacent sub-band centered on the peak. Furthermore, the main controller 2 also needs to consider preset physiological frequency band constraints. For example, the pelvic floor muscle characteristic frequency band is mainly concentrated between 20Hz and 150Hz, and the abdominal muscle characteristic frequency band is mainly concentrated between 30Hz and 200Hz. When determining the sub-band division, it should be ensured that these key physiological frequency bands have sufficient frequency resolution. Through this adaptive division method, key frequency regions with concentrated energy are divided into sub-bands with narrower bandwidths to preserve detailed features, while sparse energy regions are divided into sub-bands with wider bandwidths or merged. This ensures that each sub-band contains at least one energy peak or that the average energy value within each sub-band is not lower than a preset multiple of the overall average energy value, ensuring that the frequency domain energy distribution vector can capture key features without increasing computational complexity due to excessive dimensionality.

[0032] After completing the sub-band division, the main controller 2 calculates the cumulative energy value within each sub-band. For the i-th sub-band [fi_start, fi_end], the main controller 2 sums the energy values ​​(i.e., the squares of the amplitudes) of all frequency points within that sub-band range to obtain the cumulative energy value Ei_sum for that sub-band. The energy value is higher in some sub-bands and lower in others, and this difference does not accurately reflect the energy distribution intensity per unit frequency. To eliminate the influence of bandwidth differences, the main controller 2 performs normalization based on the band bandwidth to obtain the energy density value. Specifically, the energy density value EDi of the i-th sub-band is Ei_sum / BWi, where BWi is the bandwidth of that sub-band. Through this normalization process, the energy density value reflects the average energy intensity per unit frequency, making the energy values ​​of sub-bands with different bandwidths comparable.

[0033] Finally, the main controller 2 arranges the energy density values ​​of each frequency band in frequency order to obtain the first frequency domain energy distribution vector E1=[ED] corresponding to the first electromyographic signal. 1，1 ED 1，2 , ..., ED 1，m The second frequency domain energy distribution vector E2 corresponding to the second electromyographic signal is E2=[ED 2，1 ED 2，2 , ..., ED 2，m ], where m is the total number of sub-bands.

[0034] After calculating the first and second frequency domain energy distribution vectors and performing similarity analysis, the main controller 2 needs to further identify the characteristic frequency bands of the pelvic floor muscles and abdominal muscles to more accurately quantify the degree of interference of the abdominal muscles on pelvic floor muscle training. Since the frequency domain characteristics of muscles vary among different individuals, at different training stages, and under different contraction intensities, this application employs a dynamic identification method that combines prior physiological knowledge with real-time signal characteristics to determine the characteristic frequency bands.

[0035] The main controller 2 first establishes an initial search interval based on a preset physiological frequency range. According to extensive clinical data, the main activation frequency band of the pelvic floor muscles is typically concentrated in the range of 20Hz to 150Hz, with energy peaks mostly occurring in the range of 40Hz to 80Hz; while the main activation frequency band of the abdominal muscles is typically concentrated in the range of 30Hz to 200Hz, with energy peaks mostly occurring in the range of 50Hz to 120Hz. Within these initial search intervals, the main controller 2 performs energy distribution pattern analysis on the first frequency domain energy distribution vector E1. The main controller 2 calculates the weighted sum of the energy density values ​​of each sub-band within the initial pelvic floor muscle search interval [20Hz, 150Hz]. The weights are set inversely proportional to the distance between the sub-band and a known typical pelvic floor muscle frequency (e.g., 60Hz), with closer bands having higher weights. By finding the continuous frequency band region with the highest weighted energy density, the main controller 2 determines the pelvic floor muscle characteristic frequency band [f_p1, f_p2] of the current first electromyographic signal. Similarly, the main controller 2 searches for the continuous frequency band region with the highest weighted energy density within the initial abdominal muscle search interval [30Hz, 200Hz] to determine the abdominal muscle characteristic frequency band [f_a1, f_a2] of the current first electromyography (EMG) signal. To improve recognition accuracy, the main controller 2 also analyzes the frequency domain energy distribution of the second EMG signal. Since the second EMG signal is directly acquired from the abdominal muscle, the frequency band where its energy peak is located can more accurately reflect the current activation characteristics of the abdominal muscle. The main controller 2 extracts the frequency band with the highest energy density in the second frequency domain energy distribution vector E2 as a reference for the abdominal muscle characteristic frequency band. If this reference frequency band deviates significantly from the abdominal muscle characteristic frequency band identified from the first EMG signal (e.g., the center frequency difference exceeds 20Hz), the abdominal muscle characteristic frequency band of the first EMG signal is corrected to align it with the abdominal muscle characteristic frequency band of the second EMG signal. The correction magnitude can be dynamically determined based on the signal-to-noise ratio and reliability of the two signals. By using this dynamic recognition method that combines dual-channel signal features, the main controller 2 can more accurately locate the actual activation frequency range of the pelvic floor muscles and abdominal muscles in the current training state.

[0036] After determining the characteristic frequency bands of the pelvic floor muscles and abdominal muscles, the main controller 2 calculates the energy proportion of the first electromyographic signal in these two characteristic frequency bands. Specifically, the main controller 2 traverses each sub-frequency band in the first frequency domain energy distribution vector E1, and accumulates the energy density values ​​of all sub-frequency bands whose center frequencies fall within the range of the pelvic floor muscle characteristic frequency band [f_p1, f_p2], to obtain the total energy of the pelvic floor muscle characteristic frequency band E_p = Σ(EDi × BWi), where EDi is the energy density value of the i-th sub-frequency band, and BWi is the bandwidth of the sub-frequency band. The bandwidth needs to be multiplied during accumulation to restore the energy value. Similarly, the main controller 2 accumulates the energy density values ​​of all sub-frequency bands whose center frequencies fall within the range of the abdominal muscle characteristic frequency band [f_a1, f_a2], to obtain the total energy of the abdominal muscle characteristic frequency band E_a. To calculate the energy percentage, the main controller 2 also needs to obtain the total energy E_total of the first electromyography (EMG) signal across the entire effective frequency range. This total energy is obtained by summing the energy values ​​of all sub-bands in the first frequency domain energy distribution vector. Subsequently, the main controller 2 calculates the energy percentage of the pelvic floor muscle characteristic frequency band Rp = E_p / E_total and the energy percentage of the abdominal muscle characteristic frequency band Ra = E_a / E_total. These two energy percentages reflect the relative intensity of pelvic floor muscle activation and abdominal muscle compensation in the first EMG signal. Ideally, during pelvic floor muscle training, Rp should be significantly higher than Ra, typically Rp should be greater than 0.6 while Ra should be less than 0.3. However, when the patient's training movements are incorrect, leading to excessive abdominal muscle involvement, Ra will abnormally increase or even exceed Rp, indicating poor training results and requiring timely intervention.

[0037] Based on the aforementioned energy proportions, the main controller 2 further calculates the frequency domain energy transfer coefficient (EMT), which quantifies the degree of energy migration from the abdominal muscle characteristic frequency band to the pelvic floor muscle characteristic frequency band. When abdominal muscle compensation is significant, not only does the energy proportion Ra of the abdominal muscle characteristic frequency band increase, but also, due to the synergy between the pelvic floor muscles and abdominal muscles in their physiological structure, strong abdominal muscle contractions affect the electrical activity of the pelvic floor muscles through mechanisms such as changes in intra-abdominal pressure. This results in the mixing of abdominal muscle frequency components into the pelvic floor muscle signal, manifested as a shift in the energy of the first electromyography (EMG) signal from the pure pelvic floor muscle characteristic frequency band to the abdominal muscle characteristic frequency band in the frequency domain. To quantify the degree of this shift, embodiments of this application provide the calculation of the frequency domain energy transfer coefficient (EMT), which comprehensively considers both the absolute value of the energy proportion of the abdominal muscle characteristic frequency band and its proportional relationship relative to the energy proportion of the pelvic floor muscle characteristic frequency band. The specific calculation formula is: EMT = (Ra / (Rp + ε)) × (1 + λ × ΔE), where ε is a very small positive number to prevent division by zero (value 0.001), λ is the energy change sensitivity coefficient (value 0.5 to 2.0), and ΔE is the change in the proportion of abdominal muscle characteristic frequency band energy relative to the baseline value. The baseline value Ra_baseline can be obtained by averaging multiple sets of signals collected from the patient in a relaxed state or in the correct training posture at the beginning of training. ΔE = Ra - Ra_baseline. When ΔE is positive, it indicates that the abdominal muscle involvement has increased relative to the baseline. By introducing the energy change ΔE, the frequency domain energy transfer coefficient can more sensitively capture the dynamic changes in abdominal muscle involvement during training. When the pelvic floor muscles are correctly activated and the abdominal muscles remain relaxed, Rp is large and Ra is small, and the EMT calculation result is close to 0; while when abdominal muscle compensation is obvious, Ra increases significantly and Rp decreases relatively, and the EMT calculation result increases rapidly. In one specific embodiment, for the case of Rp=0.65, Ra=0.25, Ra_baseline=0.15, and λ=1.0, the calculated EMT = (0.25 / 0.651) × (1 + 1.0 × 0.10) ≈ 0.422, which is at a moderate level, indicating that there is some abdominal muscle involvement but has not yet seriously affected the training effect. However, for the abnormal case of Rp=0.45 and Ra=0.48, the EMT = (0.48 / 0.451) × (1 + 1.0 × 0.33) ≈ 1.415, which is significantly higher, indicating that the abdominal muscle compensation is severe and the electrical stimulation parameters need to be adjusted immediately.

[0038] After identifying the characteristic frequency bands of the pelvic floor muscles and the abdominal muscles and calculating their energy proportions, the main controller 2 needs to calculate the abdominal muscle participation coefficient by combining the similarity, frequency domain energy transfer coefficient and the activation intensity of the second electromyographic signal.

[0039] In one feasible approach, the main controller 2 calculates the abdominal muscle involvement coefficient by combining similarity, frequency domain energy transfer coefficient, and activation intensity of the second electromyographic signal. This can be achieved in the following way: The main controller 2 first calculates the activation intensity A of the second electromyographic signal. This activation intensity is obtained by calculating the ratio of the root mean square (RMS) value of the second electromyographic signal within a preset time window to a preset baseline value. The main controller 2 sets the preset time window to 500ms to 1000ms, within which the RMS value of the second electromyographic signal is calculated. The preset baseline value A_ref represents the background activation level of the abdominal muscles in a relaxed state, which is obtained by collecting the abdominal muscle electromyographic signals of the patient in a resting state before the start of training. The activation intensity is calculated using the formula A = RMS / A_ref, which normalizes the signal amplitude differences between different patients. When the abdominal muscles are relaxed, A is close to 1, while when the abdominal muscles are contracted, A can reach 5 to 20, directly reflecting the actual degree of abdominal muscle involvement.

[0040] After obtaining the activation intensity, the main controller 2 constructs a cooperative interference factor R to quantify the degree of interference of abdominal muscles on pelvic floor muscle training. When both the similarity S and the frequency domain energy transfer coefficient T increase simultaneously, the abdominal muscle interference exhibits cooperative amplification characteristics.

[0041] The formula for calculating the cooperative interference factor is: Where α is the synergistic amplification factor, ranging from 0.6 to 0.9. Basic Term A product relationship between similarity and energy transfer was established, where T² reflects the squared property of energy proportion, and the exponential term... To capture the nonlinear synergistic amplification effect, the interference grows exponentially when both S and T are large. Extracting the net increment avoids excessively large values; this nonlinear design accurately reflects the actual interference mechanism of abdominal muscle compensation. In a specific embodiment, assuming the similarity S = 0.72, the frequency domain energy transfer coefficient T = 0.48, and the cooperative amplification coefficient α = 0.75 obtained through the aforementioned calculations, then R ≈ 1.16 is calculated. This value is greater than 1, indicating the existence of a significant cooperative interference effect.

[0042] Finally, the main controller 2 calculates the abdominal muscle involvement coefficient P, using the following formula: Where P is the abdominal muscle involvement coefficient, A is the activation intensity, β is the primary inhibition coefficient (ranging from 0.6 to 0.9), γ is the secondary inhibition coefficient (ranging from 0.3 to 0.5), and δ is the migration correction coefficient (ranging from 0.2 to 0.4). In this calculation formula, the molecule... The product of interference intensity and activation intensity is represented; a primary inhibition term βR and a secondary inhibition term γ(R·A)² are introduced into the denominator to prevent P from growing indefinitely under high interference conditions, thus conforming to physiological saturation characteristics; a correction factor is also included. Logarithmic correction is performed based on the energy transfer coefficient to describe diminishing marginal returns. In one specific embodiment, using the calculated R = 1.16 and A = 3.62, and setting β = 0.75, γ = 0.4, and δ = 0.3, the abdominal muscle involvement coefficient P = 0.526 is calculated. This value is between 0.3 and 0.6, indicating a moderate level of abdominal muscle involvement. Based on this, the main controller 2 determines that the electrical stimulation parameters need to be adjusted appropriately to reduce abdominal muscle compensation and optimize the pelvic floor muscle training effect.

[0043] In one feasible embodiment, the electrical stimulation parameters of the pelvic floor host are adjusted based on the abdominal muscle participation coefficient to obtain the target electrical stimulation parameters, which can be achieved in the following way: First, the main controller 2 extracts the baseline energy value of the first electromyography (EMG) signal in the characteristic frequency band of the pelvic floor muscles and calculates the energy interference corresponding to the abdominal muscle involvement coefficient. The main controller 2 calculates the energy value by bandpass filtering the first EMG signal in the characteristic frequency band of the pelvic floor muscles (20Hz to 120Hz). The baseline energy value E_base is obtained by integrating the square of the filtered signal, calculated as E_base = Σ[x1_filtered(n)]² / N, where x1_filtered(n) is the number of sampling points of the filtered first EMG signal, and N is the total number of sampling points. The energy interference ΔE represents the energy loss due to the effective stimulation of the pelvic floor muscles caused by abdominal muscle involvement, calculated as ΔE = E_base × P × k, where k is the energy loss conversion coefficient (ranging from 0.4 to 0.7), which is obtained based on clinical data statistics. In one specific embodiment, assuming that the main controller 2 calculates the baseline energy value E_base = 850μV²·s, the abdominal muscle involvement coefficient P = 0.526, and the energy loss conversion coefficient k = 0.55, then the energy interference ΔE = 850×0.526×0.55 = 246μV²·s, indicating that approximately 29% of the stimulation energy in the current training is not effectively applied to the pelvic floor muscles.

[0044] Then, the main controller 2 calculates the energy compensation coefficient C based on the energy interference amount. This coefficient is inversely proportional to the abdominal muscle involvement coefficient, and the calculation formula is C = 1 / (1 + λP), where λ is the compensation sensitivity coefficient (ranging from 1.2 to 1.8), which controls the rate of change of the compensation intensity with the abdominal muscle involvement. In the above embodiment, λ = 1.5 is set, then the energy compensation coefficient C = 1 / (1 + 1.5 × 0.526) = 1 / 1.789 = 0.559. A value less than 1 indicates that energy loss needs to be compensated by increasing the electrical stimulation parameters; the smaller the energy compensation coefficient, the greater the required compensation intensity.

[0045] The main controller 2 adjusts the rise and fall slopes of the electrical stimulation pulses based on the energy compensation coefficient. The rise slope of the electrical stimulation pulse determines the speed at which the stimulation intensity reaches its peak, while the fall slope determines the speed at which the stimulation intensity decreases; both together affect the effective stimulation duration. When the energy compensation coefficient C is greater than the first preset value (set to 0.7), it indicates low abdominal muscle involvement, and the main controller 2 maintains the current rise and fall slopes. When C is less than or equal to 0.7, it indicates high abdominal muscle involvement requiring compensation, and the main controller 2 increases the rise slope and decreases the fall slope to prolong the effective stimulation duration. The formula for adjusting the rising edge slope is SR_rise = SR_base × (1 + μ(0.7−C)), and the formula for adjusting the falling edge slope is SR_fall = SR_base × (1 − ν(0.7−C)), where SR_base is the base slope (typically 5mA / ms to 15mA / ms), μ is the rising edge adjustment coefficient (value from 0.3 to 0.6), and ν is the falling edge adjustment coefficient (value from 0.2 to 0.5). In the above embodiment, assuming the base slope SR_base = 10mA / ms, μ = 0.4, ν = 0.3, and since C = 0.559 < 0.7, the calculated rising slope SR_rise = 10×(1+0.4×0.141) = 10.564mA / ms and the falling slope SR_fall = 10×(1−0.3×0.141) = 9.577mA / ms are obtained. By accelerating the rising speed and slowing down the falling speed, the effective stimulation duration is extended from the original 50ms to approximately 58ms.

[0046] The main controller 2 acquires the optimal electrical stimulation parameter combination corresponding to the same abdominal muscle involvement coefficient range from historical treatment data. The main controller 2 divides the abdominal muscle involvement coefficient into several ranges (e.g., 0-0.3, 0.3-0.6, 0.6-1.0), each range corresponding to a set of optimal electrical stimulation parameters validated through extensive clinical trials, including pulse frequency, pulse width, and stimulation intensity. In the above embodiment, P = 0.526 falls within the 0.3-0.6 range. The main controller 2 retrieves the optimal parameter combination for this range from the historical database: pulse frequency f_opt = 38Hz, pulse width w_opt = 280μs, and stimulation intensity I_opt = 22mA. The current electrical stimulation parameters are: pulse frequency f_cur = 35Hz, pulse width w_cur = 250μs, and stimulation intensity I_cur = 20mA. The main controller 2 calculates the deviation vector D = [f_opt−f_cur, w_opt−w_cur, I_opt−I_cur] = [3Hz,30μs, 2mA], which indicates the direction and magnitude of the adjustment of the current parameters relative to the optimal parameters.

[0047] Finally, the main controller 2 corrects the electrical stimulation parameters of the pelvic floor host 11 based on the deviation vector and energy compensation coefficient to obtain the target electrical stimulation parameters. The correction formula is: target parameter = current parameter + D × (1−C) × η, where η is the correction intensity coefficient (value from 0.5 to 0.9), and the (1−C) term ensures that the correction amplitude is proportional to the abdominal muscle involvement. In the above embodiment, η = 0.7 is set, then the target pulse frequency f_target = 35 + 3×(1−0.559)×0.7 = 35 + 0.926 = 35.9Hz, the target pulse width w_target = 250 + 30×0.441×0.7 = 259.3μs, and the target stimulation intensity I_target = 20 + 2×0.441×0.7 = 20.6mA. The main controller 2 sends these target electrical stimulation parameters to the pelvic floor host 11. The pelvic floor host 11 adjusts the stimulation output of each electrode accordingly, thereby ensuring that the pelvic floor muscles receive sufficient and effective stimulation through parameter optimization when the abdominal muscles are highly involved, thus improving the training effect.

[0048] Please see Figure 2 This is a schematic diagram of the structure of a wireless charging storage box provided in an embodiment of this application. The wireless charging storage box 3 includes a position detection sensor 31, a charging main control chip 32, and a wireless transmission drive module 33.

[0049] The position detection sensor 31 is located inside the wireless charging storage box 3 and is used to detect whether the treatment probe 1 is placed in the preset charging position. The charging main control chip 32 receives the detection signal from the position detection sensor 31 and determines whether the treatment probe 1 is placed correctly. When the treatment probe 1 is detected to be correctly placed, the charging main control chip 32 generates a control signal and sends it to the wireless transmission drive module 33. The charging main control chip 32 can also receive voltage, current, and temperature information fed back by the treatment probe 1 through the wireless charging magnetic field, and adjust the charging control parameters in real time according to the feedback signal to achieve closed-loop charging control. The wireless transmission drive module 33 drives the transmitting coil to generate an alternating magnetic field according to the control signal of the charging main control chip 32. This magnetic field induces electromagnetic induction with the receiving coil inside the treatment probe 1, generating an induced current in the receiving coil to charge the treatment probe 1. The transmitting coil of the wireless transmission drive module 33 is designed as a planar spiral structure and is placed in the charging area at the bottom of the wireless charging storage box 3 to achieve efficient magnetic coupling energy transmission.

[0050] In one feasible embodiment, the wireless charging storage box 3, used to store the treatment probe 1 and provide wireless charging for the treatment probe 1, can be implemented in the following way: When the position detection sensor 31 detects that a component in the treatment probe 1 has been placed in the corresponding storage slot, the charging control chip 32 identifies the device and obtains the component type and current power information through the coupling signal between the transmitting coil of the wireless transmission drive module 33 and the receiving coil inside the treatment probe 1. Specifically, the components in the treatment probe 1 include the pelvic floor host 11, the first surface electrode host 12, the second surface electrode host 13, and the physiotherapy surface electrode 14. Each of these components has an independent power system and a receiving coil. The wireless charging storage box 3 is designed with multiple corresponding storage slots, each equipped with an independent transmitting coil for wireless charging of the corresponding component. For example, when the pelvic floor host 11 is placed in the first storage slot, the Hall sensor of the position detection sensor 31 senses a change in the magnetic field and outputs a detection signal to the charging control chip 32. The charging control chip 32 initiates the initial communication process, sending a low-power interrogation signal through the transmitting coil of the wireless transmission drive module 33. After receiving the interrogation signal, the receiving coil of the pelvic floor host 11 feeds back the device information to the charging control chip 32 through load modulation. In one specific embodiment, the device information fed back by the pelvic floor host 11 includes: component type identification code 0x1A (representing the pelvic floor host), battery capacity 2000mAh, current battery level 65%, and battery temperature 28℃. The charging main control chip 32 parses the received device information, identifies the currently placed component as the pelvic floor host 11, the current battery level as 65%, and the remaining charging requirement as 700mAh.

[0051] The charging control chip 32 determines the charging parameters based on the component type and current battery level, and controls the wireless transmission drive module 33 to drive the transmission coil to establish an alternating magnetic field for charging. The charging parameters are determined based on a preset charging strategy table, which corresponds to different combinations of charging parameters for different component types and battery levels. In the above embodiment, for the pelvic floor unit 11 with a current battery level of 65%, the charging control chip 32 determines the charging parameters as follows: charging frequency f_charge = 125kHz, initial transmission power P_init = 10W, and target transmission efficiency η_target = 90%. The charging control chip 32 sends the control signal to the wireless transmission drive module 33, which adjusts the internal oscillator frequency to 125kHz, sets the output power of the drive circuit to 10W, and drives the transmission coil to generate an alternating magnetic field. This magnetic field couples with the receiving coil inside the pelvic floor unit 11, inducing an AC voltage in the receiving coil, which is then rectified and filtered to charge the battery of the pelvic floor unit 11.

[0052] During charging, the charging main control chip 32 collects the current waveform characteristics of the transmitting coil and the voltage waveform characteristics fed back by the treatment probe 1 in real time. It obtains the transmission efficiency by calculating the phase difference and amplitude ratio between the current and voltage waveforms, and periodically acquires the power information fed back by the treatment probe 1 to calculate the power growth rate. The charging main control chip 32 has a built-in high-speed ADC with a sampling rate set to 1MHz, used to collect the current waveforms at both ends of the transmitting coil. In one specific embodiment, the charging main control chip 32 collects 8 current sampling points within one charging cycle (8μs), extracts the fundamental component through FFT, and obtains the effective current value I_rms = 0.72A and the current phase angle φ_I = 12°. Simultaneously, the pelvic floor host 11 feeds back the voltage waveform characteristics of the receiving coil through load modulation, and the charging main control chip 32 analyzes and obtains the effective voltage value V_rms = 8.5V and the voltage phase angle φ_V = 27°. The charging main control chip 32 calculates the phase difference Δφ = φ_V - φ_I = 15°. According to the formula η = (V_rms × I_rx) / (V_coil × I_rms) × 100%, the transmission efficiency is calculated. The receiving current I_rx is obtained through feedback and is 0.42A. The transmitting coil voltage V_coil = 48V. The calculated transmission efficiency η = (8.5 × 0.42) / (48 × 0.72) × 100% = 10.3%. After considering actual optimization, the measured transmission efficiency η = 86%. The charging main control chip 32 acquires the power information fed back by the pelvic floor host 11 every 30 seconds. 30 seconds after charging begins, the power level is 66%, and after 60 seconds, it is 67.5%. The calculated power growth rate is (67.5% - 65%) / 1 minute = 2.5% / minute.

[0053] In one feasible embodiment, the charging master control chip 32 coordinates the operating frequency and transmission power of the transmitting coil based on the deviation between the transmission efficiency and the target efficiency, as well as the power generation rate. This can be achieved in the following way: First, the charging main control chip 32 identifies the coupling state between the transmitting coil and the receiving coil by analyzing the phase difference change rate of the current waveform and the voltage waveform. The phase difference change rate reflects the stability of the coupling degree between the coils. When the phase difference change rate is lower than a preset change rate threshold, it is determined to be a strong coupling state, indicating that the transmitting coil and the receiving coil are well aligned and the distance is appropriate; when the phase difference change rate is not lower than the preset change rate threshold, it is determined to be a weak coupling state, indicating that there is a misalignment or excessive distance between the coils. In a specific embodiment, the charging main control chip 32 measures the phase difference as Δφ1 = 15°, Δφ2 = 16°, and Δφ3 = 15.5° in three consecutive sampling cycles (30 seconds per cycle), respectively, and calculates the phase difference change rate as dΔφ / dt = |Δφ3 - Δφ1| / (2×30s) = |15.5° - 15°| / 60s = 0.0083° / s. The preset change rate threshold is set to 0.02° / s. Since 0.0083° / s < 0.02° / s, the charging main control chip 32 determines that the current state is a strong coupling state, indicating that the bottom host 11 is in a stable position in the storage slot and the coil is well coupled.

[0054] The charging main control chip 32 calculates the frequency adjustment weight W_f and power adjustment weight W_p based on the coupling state and the power generation rate. In a strongly coupled state, since the coil coupling is already optimal, the frequency is fine-tuned to optimize resonance matching and improve transmission efficiency; therefore, the frequency adjustment weight is increased and the power adjustment weight is decreased. In a weakly coupled state, due to poor coil coupling leading to significant energy transmission loss, power is increased to compensate for the loss; therefore, the power adjustment weight is increased and the frequency adjustment weight is decreased. The weight calculation formulas are: in a strongly coupled state, W_f = 0.7 + 0.2×(G_target - G_current) / G_target, W_p = 1 - W_f; in a weakly coupled state, W_p = 0.7 + 0.2×(G_target - G_current) / G_target, W_f = 1 - W_p, where G_target is the target power generation rate and G_current is the current power generation rate. In the above strongly coupled embodiment, the target power growth rate G_target = 3% / minute, and the current power growth rate G_current = 2.5% / minute. The calculated frequency adjustment weight W_f = 0.7 + 0.2×(3 - 2.5) / 3 = 0.7 + 0.033 = 0.733, and the power adjustment weight W_p = 1 - 0.733 = 0.267. This weight allocation indicates that under the current strongly coupled state, approximately 73.3% of the adjustment effort is applied to frequency adjustment, and 26.7% is applied to power adjustment.

[0055] The charging main control chip 32 decomposes the deviation between the transmission efficiency and the target efficiency according to the frequency adjustment weight and the power adjustment weight, obtaining the frequency adjustment amount and the power adjustment amount. The efficiency deviation Δη = η_target - η = 90% - 86% = 4%, which needs to be eliminated through coordinated adjustment of frequency and power. The formula for calculating the frequency adjustment amount is Δf = K_f × Δη × W_f, and the formula for calculating the power adjustment amount is ΔP = K_p × Δη × W_p, where K_f is the frequency adjustment coefficient (ranging from 0.4kHz / % to 0.8kHz / %), and K_p is the power adjustment coefficient (ranging from 0.3W / % to 0.6W / %). In the above embodiment, K_f = 0.6kHz / %, K_p = 0.5W / %, the calculated frequency adjustment Δf = 0.6 × 4 × 0.733 = 1.76kHz and the power adjustment ΔP = 0.5 × 4 × 0.267 = 0.534W. This means that to eliminate the 4% efficiency deviation, the frequency needs to be increased by 1.76kHz and the power by 0.534W, with the frequency adjustment playing the main optimization role.

[0056] Finally, the charging main control chip 32 synchronously adjusts the operating frequency and transmission power of the transmitting coil according to the frequency and power adjustment amounts. In the above embodiment, the current operating frequency f_current = 125kHz, the current transmission power P_current = 10W, the adjusted operating frequency f_new = f_current + Δf = 125 + 1.76 = 126.76kHz, and the adjusted transmission power P_new = P_current + ΔP = 10 + 0.534 = 10.534W. The charging main control chip 32 converts the new frequency and power parameters into control signals and synchronously sends them to the wireless transmission driver module 33 via the internal bus. After receiving the control signals, the wireless transmission driver module 33 first adjusts the oscillator frequency to 126.76kHz, and then adjusts the output power of the driver circuit to 10.534W, ensuring that the frequency and power adjustments take effect at the same time and avoiding transient mismatch during the adjustment process. After adjustment, the charging master control chip 32 measures the transmission efficiency again in the next sampling cycle (30 seconds later), finding that the transmission efficiency has increased to 88.5% and the power rate increase has increased to 2.85% / minute. Since the target value has not yet been reached, the charging master control chip 32 repeats the above process, measuring the phase difference change rate again at 0.0075° / s, determining that it is still in a strongly coupled state. It calculates a new efficiency deviation of 1.5%, a new frequency adjustment weight of 0.718, a frequency adjustment amount of 0.647kHz, and a power adjustment amount of 0.211W, continuing to optimize the adjustment. Through this adaptive collaborative adjustment strategy based on coupling state, the charging master control chip 32 can dynamically allocate the frequency and power adjustment ratios according to the actual operating conditions. In a strongly coupled state, it focuses on frequency optimization to improve efficiency, while in a weakly coupled state, it focuses on power compensation to maintain the charging speed, achieving intelligent and precise control of the wireless charging process.

[0057] For example, assume that the first surface electrode host 12 is in a weak coupling state due to positional offset after being placed in the storage slot. At this time, the transmission efficiency η = 72%, the target efficiency η_target = 90%, the efficiency deviation Δη = 18%, and the current power growth rate G_current = 1.5% / minute. The charging main control chip 32 calculates the power adjustment weight W_p = 0.7 + 0.2×(3 -1.5) / 3 = 0.8, the frequency adjustment weight W_f = 0.2, the frequency adjustment amount Δf = 0.6×18×0.2 = 2.16kHz, and the power adjustment amount ΔP = 0.5×18×0.8 = 7.2W. The adjusted operating frequency f_new = 125 + 2.16 = 127.16kHz, and the adjusted transmission power P_new = 10 + 7.2 = 17.2W. In the weak coupling state, the coupling loss is compensated by significantly increasing the power (accounting for 80% of the adjustment), while the frequency is moderately adjusted (accounting for 20% of the adjustment) for auxiliary optimization, so as to ensure that the effective charging function can still be maintained even if the coil alignment is poor.

[0058] This application also provides a wireless charging pelvic floor muscle rehabilitation method. This method is applied to a wireless charging pelvic floor muscle rehabilitation system, which includes a treatment probe 1, a main controller 2, and a wireless charging storage box 3. The treatment probe 1 is wirelessly connected to the main controller 2. The treatment probe 1 includes a pelvic floor host 11, a first surface electrode host 12, a second surface electrode host 13, and physiotherapy surface electrodes 14. The method includes steps S101 to S103, as follows: S101: The pelvic floor host 11 electrically stimulates the pelvic floor muscles and collects the first electromyographic signal of the pelvic floor muscles. The first surface electrode host 12 simultaneously collects the second electromyographic signal of the abdominal muscles during the first electromyographic signal acquisition process. The second surface electrode host 13 is used in conjunction with the physiotherapy surface electrode 14 to electrically stimulate the chest and abdomen. S102: The main controller 2 performs power frequency notch processing on the first electromyography signal and the second electromyography signal, calculates the abdominal muscle participation coefficient based on the first electromyography signal and the second electromyography signal after power frequency notch processing, and adjusts the electrical stimulation parameters of the pelvic floor host based on the abdominal muscle participation coefficient to obtain the target electrical stimulation parameters. The treatment probe 1 is controlled to output the corresponding electrical stimulation according to the target electrical stimulation parameters. S103: Wireless charging storage box 3 is used to store the treatment probe 1 and provide wireless charging for the treatment probe 1.

[0059] The implementation process of the above method can be referred to the embodiments of the above system, and will not be repeated here.

[0060] The following describes an exemplary wireless charging electronic device for pelvic floor muscle rehabilitation therapy provided in an embodiment of this application. Figure 3 This is an exemplary hardware structure diagram of an electronic device for wireless charging pelvic floor muscle rehabilitation therapy provided in an embodiment of this application.

[0061] In some embodiments, the electronic device for wireless charging pelvic floor muscle rehabilitation therapy is a computer device or includes a computer device. The computer device includes a processor, memory, and a network interface connected via a system bus. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database of the computer device stores data. The network interface of the computer device is used to communicate with other external terminals or servers via a network connection. In some embodiments, the network interface can be a wired network interface; in some embodiments, the network interface can also be a wireless network interface. When the computer program is executed by the processor, it implements the methods in the embodiments of this application.

[0062] Those skilled in the art will understand that Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0063] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

[0064] As used in the above embodiments, depending on the context, the term "when..." can be interpreted as meaning "if...", "after...", "in response to determining...", or "in response to detecting...". Similarly, depending on the context, the phrase "when determining..." or "if (the stated condition or event) is interpreted as meaning "if determining...", "in response to determining...", "when (the stated condition or event) is detected", or "in response to detecting (the stated condition or event)".

[0065] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state drive), etc.

[0066] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A wireless charging pelvic floor muscle rehabilitation system, characterized in that, The system includes a treatment probe (1), a main controller (2), and a wireless charging storage box (3). The treatment probe (1) is connected to the main controller (2) via wireless communication. The treatment probe (1) includes a pelvic floor host (11), a first surface electrode host (12), a second surface electrode host (13), and a physiotherapy surface electrode (14); the pelvic floor host (11) is used to electrically stimulate the pelvic floor muscles and collect the first electromyographic signal of the pelvic floor muscles; the first surface electrode host (12) is used to simultaneously collect the second electromyographic signal of the abdominal muscles during the first electromyographic signal acquisition process; and the second surface electrode host (13) is used in conjunction with the physiotherapy surface electrode (14) to electrically stimulate the chest and abdomen. The main controller (2) is used to perform power frequency notch processing on the first electromyographic signal and the second electromyographic signal, calculate the abdominal muscle participation coefficient based on the first electromyographic signal and the second electromyographic signal after power frequency notch processing, and adjust the electrical stimulation parameters of the pelvic floor host (11) based on the abdominal muscle participation coefficient to obtain the target electrical stimulation parameters, and control the treatment probe (1) to output the corresponding electrical stimulation according to the target electrical stimulation parameters. The wireless charging storage box (3) is used to store the treatment probe (1) and provide wireless charging for the treatment probe (1).

2. The wireless charging pelvic floor muscle rehabilitation system according to claim 1, characterized in that, The first and second electromyographic (EMG) signals are subjected to power frequency notch filtering. Based on the processed EMG signals, the abdominal muscle involvement coefficient is calculated, specifically including: The first electromyography (EMG) signal and the second EMG signal are subjected to adaptive power frequency notch filtering, wherein the adaptive power frequency notch filtering dynamically adjusts the notch center frequency according to the real-time detected power frequency interference frequency. The first and second electromyographic signals after power frequency notch filtering are decomposed into energy to obtain the first frequency domain energy distribution vector corresponding to the first electromyographic signal and the second frequency domain energy distribution vector corresponding to the second electromyographic signal. The similarity between the first frequency domain energy distribution vector and the second frequency domain energy distribution vector is calculated. Identify the characteristic frequency bands of the pelvic floor muscles and the abdominal muscles, and calculate the energy proportion of the first electromyographic signal in the characteristic frequency bands of the pelvic floor muscles and the abdominal muscles; The frequency domain energy transfer coefficient is calculated based on the energy ratio, and the frequency domain energy transfer coefficient is used to quantify the degree of energy transfer from the abdominal muscle characteristic frequency band to the pelvic floor muscle characteristic frequency band. The abdominal muscle involvement coefficient is calculated by combining the similarity, the frequency domain energy transfer coefficient, and the activation intensity of the second electromyographic signal.

3. The wireless charging pelvic floor muscle rehabilitation system according to claim 2, characterized in that, The first and second electromyographic (EMG) signals after power frequency notch filtering are respectively subjected to energy decomposition to obtain the first frequency domain energy distribution vector corresponding to the first EMG signal and the second frequency domain energy distribution vector corresponding to the second EMG signal, specifically including: Spectral analysis was performed on the first and second electromyographic signals after power frequency notch filtering to identify energy peaks in the spectrum. Centered on the energy peak point and combined with the preset physiological frequency band range, the center frequency and bandwidth of each frequency band are determined, and the preset frequency range is divided into multiple sub-frequency bands, such that each sub-frequency band contains at least one energy peak or the average energy value of each sub-frequency band is not less than a preset multiple of the overall average energy value. Calculate the cumulative energy value within each sub-band and normalize it according to the band bandwidth to obtain the energy density value; Arrange the energy density values ​​of each frequency band in order of frequency to obtain the first frequency domain energy distribution vector corresponding to the first electromyographic signal and the second frequency domain energy distribution vector corresponding to the second electromyographic signal.

4. The wireless charging pelvic floor muscle rehabilitation system according to claim 2, characterized in that, The calculation of the abdominal muscle involvement coefficient, which combines the similarity, the frequency domain energy transfer coefficient, and the activation intensity of the second electromyographic signal, includes: The activation intensity of the second electromyographic signal is calculated, which is obtained by calculating the ratio of the root mean square value of the second electromyographic signal within a preset time window to a preset reference value; A synergistic interference factor is constructed to quantify the degree of interference of abdominal muscles on pelvic floor muscle training. The calculation formula for the synergistic interference factor is as follows: ； Where R is the cooperative interference factor, S is the similarity, and T is the frequency domain energy transfer coefficient. This is the synergistic amplification factor, with a value ranging from 0.6 to 0.

9. This item is used to extract the net incremental portion of exponential growth; The formula for calculating the abdominal muscle participation coefficient is as follows: ； Where P is the abdominal muscle involvement coefficient, A is the activation intensity, β is the primary inhibition coefficient, ranging from 0.6 to 0.9, γ is the secondary inhibition coefficient, ranging from 0.3 to 0.5, and δ is the migration correction coefficient, ranging from 0.2 to 0.

4.

5. The wireless charging pelvic floor muscle rehabilitation system according to claim 2, characterized in that, The process of adjusting the electrical stimulation parameters of the pelvic floor host (11) based on the abdominal muscle participation coefficient to obtain the target electrical stimulation parameters includes: Extract the reference energy value of the first electromyographic signal in the characteristic frequency band of the pelvic floor muscles, and calculate the energy interference amount corresponding to the abdominal muscle participation coefficient; The energy compensation coefficient is calculated based on the energy interference amount, and the energy compensation coefficient is inversely proportional to the abdominal muscle participation coefficient. The rising edge slope and falling edge slope of the electrical stimulation pulse are adjusted according to the energy compensation coefficient. When the energy compensation coefficient is greater than the first preset value, the rising edge slope is increased and the falling edge slope is decreased to prolong the effective stimulation duration. Obtain the optimal electrical stimulation parameter combination corresponding to the same abdominal muscle participation coefficient range in historical treatment data, and calculate the deviation vector between the current electrical stimulation parameters and the optimal electrical stimulation parameter combination; The electrical stimulation parameters of the pelvic floor host (11) are corrected according to the deviation vector and the energy compensation coefficient to obtain the target electrical stimulation parameters.

6. The wireless charging pelvic floor muscle rehabilitation system according to claim 1, characterized in that, The wireless charging storage box (3) includes a position detection sensor (31), a charging main control chip (32), and a wireless transmission drive module (33). The wireless charging storage box (3) is used to store the treatment probe (1) and provide wireless charging for the treatment probe (1), specifically including: When the position detection sensor (31) detects that the component in the treatment probe (1) is placed in the corresponding storage slot, the device is identified by the coupling signal between the transmitting coil and the receiving coil, and the component type and current power information are obtained. The charging main control chip (32) determines the charging parameters according to the component type and the current power information, and controls the wireless transmission drive module (33) to drive the transmission coil to establish an alternating magnetic field for charging; During the charging process, the charging main control chip (32) collects the current waveform characteristics of the transmitting coil and the voltage waveform characteristics fed back by the treatment probe (1) in real time. It obtains the transmission efficiency by calculating the phase difference and amplitude ratio between the current waveform and the voltage waveform, and periodically obtains the power information fed back by the treatment probe (1) to calculate the power growth rate. The charging master control chip (32) adjusts the operating frequency and transmission power of the transmitting coil in coordination according to the deviation between the transmission efficiency and the target efficiency and the power growth rate.

7. The wireless charging pelvic floor muscle rehabilitation system according to claim 6, characterized in that, The charging main control chip (32) adjusts the operating frequency and transmission power of the transmitting coil in coordination according to the deviation between the transmission efficiency and the target efficiency and the power growth rate, including: The charging main control chip (32) identifies the coupling state between the transmitting coil and the receiving coil by analyzing the phase difference change rate of the current waveform and the voltage waveform. When the phase difference change rate is lower than the preset change rate threshold, it is determined to be a strong coupling state, and when the phase difference change rate is not lower than the preset change rate threshold, it is determined to be a weak coupling state. The frequency adjustment weight and power adjustment weight are calculated based on the coupling state and the power growth rate. In the strong coupling state, the frequency adjustment weight is increased and the power adjustment weight is decreased, while in the weak coupling state, the power adjustment weight is increased and the frequency adjustment weight is decreased. The deviation between the transmission efficiency and the target efficiency is decomposed according to the frequency adjustment weight and the power adjustment weight to obtain the frequency adjustment amount and the power adjustment amount; The operating frequency and transmission power of the transmitting coil are adjusted synchronously according to the frequency adjustment and power adjustment amounts.

8. A wireless charging pelvic floor muscle rehabilitation treatment method, characterized in that, The wireless charging pelvic floor muscle rehabilitation treatment system as described in claim 1 includes a treatment probe (1), a main controller (2), and a wireless charging storage box (3). The treatment probe (1) is connected to the main controller (2) via wireless communication. The treatment probe (1) includes a pelvic floor host (11), a first surface electrode host (12), a second surface electrode host (13), and a physiotherapy surface electrode (14). The wireless charging pelvic floor muscle rehabilitation treatment method includes: The pelvic floor host (11) provides electrical stimulation to the pelvic floor muscles and collects the first electromyographic signal of the pelvic floor muscles. The first body surface electrode host (12) simultaneously collects the second electromyographic signal of the abdominal muscles during the first electromyographic signal collection process. The second body surface electrode host (13) is used in conjunction with the physiotherapy body surface electrode (14) to provide electrical stimulation to the chest and abdomen. The main controller (2) performs power frequency notch processing on the first electromyographic signal and the second electromyographic signal, calculates the abdominal muscle participation coefficient based on the first electromyographic signal and the second electromyographic signal after power frequency notch processing, and adjusts the electrical stimulation parameters of the pelvic floor host (11) based on the abdominal muscle participation coefficient to obtain the target electrical stimulation parameters, and controls the treatment probe (1) to output the corresponding electrical stimulation according to the target electrical stimulation parameters. The wireless charging storage box (3) is used to store the treatment probe (1) and provide wireless charging for the treatment probe (1).

9. An electronic device for wireless charging pelvic floor muscle rehabilitation therapy, characterized in that, The wireless charging pelvic floor muscle rehabilitation electronic device includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code including computer instructions, and the one or more processors call the computer instructions to cause the wireless charging pelvic floor muscle rehabilitation electronic device to perform the method as described in claim 8.

10. A computer-readable storage medium comprising instructions, characterized in that, When the instructions are executed on the wireless charging pelvic floor muscle rehabilitation electronic device, the wireless charging pelvic floor muscle rehabilitation electronic device performs the method as described in claim 8.

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