High-frequency high-field-intensity magnetic stimulator

Abstract

The invention discloses a high-frequency high-field-intensity magnetic stimulator, and relates to the technical field of magnetic therapy. The system comprises a power supply module, a control module, a driving module, a switching circuit and a magnetic stimulation actuator array, and bioelectricity signals, temperature data and motion parameters of a human body injury part are collected in real time through a multi-mode sensor unit on a contact surface of the magnetic stimulation actuator array; the control module generates an output label by using a pre-trained neural network model after extracting features, and further queries a control database to obtain matched magnetic stimulation control parameters to generate a control instruction, the driving module is matched with the switching circuit to realize magnetic stimulation, and the power supply module adaptively adjusts an output voltage according to an input current of the switching circuit; the problems that in the prior art, parameter adjustment depends on manual work, the treatment efficiency is low, and preventive repair is lacked are effectively solved, and the intelligent level and effectiveness of magnetic stimulation treatment are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the field of magnetic therapy technology, and more particularly to a high-frequency and high-field strength magnetic stimulator. Background Art

[0002] At present, high-frequency magnetic stimulation technology has been applied in the field of sports injury repair. It can produce biological effects through high-frequency magnetic fields and provide care for muscles, joints and other tissues damaged after exercise.

[0003] However, existing technologies still have significant defects: first, they lack multimodal perception capabilities for sports injuries and are unable to monitor key parameters such as deformation, skin temperature, and microcirculatory blood flow of damaged tissue in real time, resulting in inaccurate injury positioning and assessment; second, parameter adjustment relies on manual presets, making it difficult to dynamically optimize stimulation plans based on injury types (such as muscle strains, ligament injuries) and repair processes, resulting in low treatment efficiency; third, there is a lack of a linkage mechanism with sports data, making it difficult to achieve preventive repair.

[0004] Therefore, how to propose a high-frequency and high-field strength magnetic stimulator to realize intelligent treatment of magnetic stimulators and improve efficiency is an urgent problem that needs to be solved by technical personnel in this field. Summary of the Invention

[0005] In view of this, the present invention provides a high-frequency and high-field strength magnetic stimulator to improve the intelligence level and effectiveness of magnetic stimulation therapy.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] A high-frequency and high-field strength magnetic stimulator, comprising: a power supply module, a control module, a drive module, a switching circuit and a magnetic stimulation actuator array;

[0008] The contact surface of the magnetic stimulation actuator array is integrated with a multimodal sensor unit for real-time acquisition of sensor data, wherein the sensor data includes bioelectric signals, temperature data and motion parameters of the injured part of the human body;

[0009] The control module generates a control instruction according to the sensor data;

[0010] The driving module converts the control instruction into a high-voltage driving signal;

[0011] The switch circuit controls the magnetic stimulation actuator array to perform magnetic stimulation in response to the high-voltage driving voltage;

[0012] The power supply module adaptively adjusts the power supply output voltage according to the input current of the switching circuit to meet the power demand of the switching circuit; the input current of the switching circuit is determined according to the high-voltage driving voltage.

[0013] Preferably, the control module generates a control instruction according to the sensor data, including:

[0014] extracting features of the sensor data;

[0015] Input the features into the pre-trained neural network model to obtain the corresponding output labels;

[0016] querying a pre-stored control database according to the output tag to obtain magnetic stimulation control parameters matching the tag;

[0017] A control instruction is generated based on the control parameter.

[0018] Preferably, the neural network model is one or a combination of a convolutional neural network, a deep neural network or a recurrent neural network, which is used to classify and process the extracted bioelectric signal features, temperature data features and motion parameter features, and output corresponding damage type labels or repair stage labels;

[0019] The neural network model performs supervised learning training based on the mapping relationship between historical sensor data and magnetic stimulation parameters. The training data includes bioelectric signal waveforms, temperature change curves and motion parameter thresholds of different injury types.

[0020] Preferably, the driving module includes an FPGA and a GaN driver;

[0021] The FPGA generates a PWM modulation signal according to the control instruction, and the GaN driver converts the PWM modulation signal into a high-voltage driving voltage.

[0022] Preferably, the power supply module includes a rectifier and filter unit and an LLC resonant circuit. The rectifier and filter unit is used to convert AC mains power into DC bus voltage; the LLC resonant circuit converts the DC bus voltage into high-frequency AC, and outputs adjustable DC high voltage after transformer boosting and synchronous rectification.

[0023] Preferably, the LLC resonant circuit includes a half-bridge MOSFET, a resonant network, a high-frequency transformer, and a synchronous rectifier;

[0024] The working process of the LLC resonant circuit includes:

[0025] The half-bridge switch unit chops the DC input into a high-frequency square wave voltage;

[0026] The resonant network filters the high-frequency square wave voltage into a fundamental frequency quasi-sinusoidal current;

[0027] The fundamental frequency quasi-sinusoidal current flows through the primary side of the high-frequency transformer, and a quasi-sinusoidal voltage is induced on the secondary side;

[0028] The synchronous rectifier rectifies the quasi-sinusoidal voltage into a direct current output.

[0029] Preferably, the LLC resonant circuit further includes a feedback control unit;

[0030] The feedback control unit includes a current sampling unit, an error amplifier and a PWM controller, wherein:

[0031] The current sampling unit samples the input current of the switch circuit in real time and converts it into a sampling voltage;

[0032] The error amplifier compares the sampled voltage with a reference voltage to generate an error signal;

[0033] The PWM controller adjusts the duty cycle of the half-bridge MOSFET according to the error signal, so that the output voltage of the adjustable DC high voltage increases as the input current of the switching circuit increases.

[0034] Through the above technical solution, it can be seen that compared with the prior art, the present invention discloses a high-frequency, high-field strength magnetic stimulator, including a power supply module, a control module, a drive module, a switching circuit and a magnetic stimulation actuator array. The multimodal sensor unit on the contact surface of the magnetic stimulation actuator array collects bioelectric signals, temperature data and motion parameters of the injured part of the human body in real time. After extracting the features, the control module uses the pre-trained neural network model to generate an output label, and then queries the control database to obtain the matching magnetic stimulation control parameters to generate control instructions. The drive module cooperates with the switching circuit to realize magnetic stimulation, and the power supply module adaptively adjusts the output voltage according to the input current of the switching circuit. The present invention realizes the precision of injury location assessment and dynamic optimization of stimulation parameters through the combination of multimodal perception and neural network, and with the help of the adaptive adjustment of the power supply module and the motion data linkage mechanism, it effectively solves the problems of manual parameter adjustment, low treatment efficiency and lack of preventive repair in the prior art, and significantly improves the intelligence level and effectiveness of magnetic stimulation treatment. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0036] Figure 1 A schematic diagram of the overall structure of the high-frequency and high-field strength magnetic stimulator provided by the present invention;

[0037] Figure 2This is a schematic diagram of the LLC resonant circuit provided by the present invention. DETAILED DESCRIPTION

[0038] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0039] The embodiment of the present invention discloses a high-frequency and high-field strength magnetic stimulator, such as Figure 1-2 As shown, it includes: a power supply module, a control module, a drive module, a switching circuit and a magnetic stimulation actuator array;

[0040] The contact surface of the magnetic stimulation actuator array is integrated with a multimodal sensor unit for real-time acquisition of sensor data, including bioelectric signals, temperature data and motion parameters of the injured part of the human body.

[0041] The control module generates control instructions based on sensor data, including:

[0042] Extract features from sensor data;

[0043] Input the features into the pre-trained neural network model to obtain the corresponding output labels;

[0044] Querying a pre-stored control database according to the output label to obtain magnetic stimulation control parameters that match the label;

[0045] A control instruction is generated based on the control parameters.

[0046] Preferably, the neural network model is one or a combination of a convolutional neural network, a deep neural network or a recurrent neural network, which is used to classify and process the extracted bioelectric signal features, temperature data features and motion parameter features, and output corresponding damage type labels or repair stage labels;

[0047] The neural network model performs supervised learning training based on the mapping relationship between historical sensor data and magnetic stimulation parameters. The training data includes bioelectric signal waveforms, temperature change curves and motion parameter thresholds of different injury types.

[0048] The driver module converts the control instructions into high-voltage drive signals. The driver module includes FPGA and GaN driver;

[0049] The FPGA generates a PWM modulation signal according to the control instructions, and the GaN driver converts the PWM modulation signal into a high-voltage driving voltage.

[0050] The switching circuit responds to the high-voltage driving voltage and controls the magnetic stimulation actuator array to perform magnetic stimulation.

[0051] The power supply module adaptively adjusts the power supply output voltage according to the input current of the switching circuit to meet the power demand of the switching circuit; the input current of the switching circuit is determined according to the high-voltage drive voltage.

[0052] The power supply module includes a rectifier and filter unit and an LLC resonant circuit. The rectifier and filter unit is used to convert AC mains power into DC bus voltage; the LLC resonant circuit converts the DC bus voltage into high-frequency AC, which is then boosted by a transformer and synchronously rectified to output adjustable DC high voltage.

[0053] The LLC resonant circuit includes a half-bridge MOSFET, a resonant network, a high-frequency transformer, and a synchronous rectifier;

[0054] The working process of the LLC resonant circuit includes:

[0055] The half-bridge switching unit chops the DC input into a high-frequency square wave voltage;

[0056] The resonant network filters the high-frequency square wave voltage into a fundamental frequency quasi-sinusoidal current;

[0057] A fundamental frequency quasi-sinusoidal current flows through the primary side of the high-frequency transformer, and a quasi-sinusoidal voltage is induced on the secondary side;

[0058] The synchronous rectifier rectifies the quasi-sinusoidal voltage into a DC output.

[0059] Furthermore, the LLC resonant circuit further includes a feedback control unit;

[0060] The feedback control unit includes a current sampling unit, an error amplifier and a PWM controller, wherein:

[0061] The current sampling unit samples the input current of the switching circuit in real time and converts it into a sampling voltage;

[0062] The error amplifier compares the sampled voltage with the reference voltage to generate an error signal;

[0063] The PWM controller adjusts the duty cycle of the half-bridge MOSFET according to the error signal, so that the output voltage of the adjustable DC high voltage increases as the input current of the switching circuit increases.

[0064] Specifically, the contact surface of the magnetic stimulation actuator array is integrated with multimodal sensor units such as pressure sensors, temperature sensors and myoelectric sensors, which can collect myoelectric signals, skin temperature data and joint mobility and other motion parameters of the injured part of the human body in real time; the control module uses an ARM processor to sample the sensor data through the ADC circuit to extract characteristic parameters such as the frequency characteristics, temperature change rate and motion angle threshold of the bioelectric signal; the drive module consists of an FPGA chip and a GaN driver. The FPGA generates a frequency-adjustable PWM modulation signal according to the control instruction, and the GaN driver amplifies it into a high-voltage drive signal of more than 1000V; the switching circuit uses IGB The T power module controls the coils in the magnetic stimulation actuator array to generate a high-frequency magnetic field in response to the high-voltage drive signal. The power supply module converts the 220V AC power into a 310V DC bus voltage through the rectifier and filter unit, and then chops it into a 200kHz high-frequency square wave through the half-bridge MOSFET in the LLC resonant circuit. It is filtered into a fundamental frequency quasi-sinusoidal current through the resonant network, and then boosted by the high-frequency transformer. The synchronous rectifier outputs a 0-3000V adjustable DC high voltage. The power supply module monitors the input current of the switching circuit in real time through the current sampling unit. When the current increases, the PWM controller automatically adjusts the duty cycle of the half-bridge MOSFET to increase the output voltage synchronously to meet the power demand.

[0065] The specific process of the control module generating control instructions based on sensor data is as follows: de-noising the electromyographic signal through wavelet transform to extract time domain features such as root mean square value (RMS) and mean absolute value (MAV); extracting frequency domain features such as main frequency and frequency band energy through fast Fourier transform (FFT); and simultaneously obtaining the gradient change of temperature data and the real-time displacement of joint angle. In this embodiment, the above features are input into a pre-trained CNN-RNN hybrid neural network model (CNN uses ResNet structure to extract spatial features, and RNN uses LSTM unit to process temporal features). The model outputs an injury type label (such as muscle strain, ligament injury) or a repair stage label (acute phase, subacute phase, recovery phase). Based on the label, the control database (which stores magnetic stimulation parameter mapping relationships for more than 2000 sets of clinical cases) is queried to obtain corresponding parameters such as stimulation frequency (50-500Hz), pulse width (10-200μs), and magnetic field strength (1-3T). Finally, a control instruction containing PWM frequency, duty cycle and pulse sequence is generated and sent to the drive module.

[0066] In the drive module, the FPGA generates a 20-500kHz PWM signal according to the control instructions, with a duty cycle adjustment range of 0-90%. After amplification by the GaN driver, the output voltage amplitude is 1200V, which drives the IGBT switching circuit to control the on-off timing of the magnetic stimulation coil. When the LLC resonant circuit of the power supply module is working, the half-bridge MOSFET is alternately turned on at a frequency of 200kHz, chopping the DC bus voltage into a square wave. The resonant inductor and resonant capacitor in the resonant network form a series resonance, filtering the square wave into a quasi-sinusoidal current with a peak current of 20A, which is then passed through a high-frequency transformer with a transformation ratio of 1:30. After the voltage is boosted, a quasi-sinusoidal voltage is induced on the secondary side, which is then rectified into a DC high voltage by the synchronous rectifier MOSFET. The feedback control unit monitors the input current of the switching circuit in real time through a 0.1Ω sampling resistor. When the current rises from 5A to 10A, the error amplifier compares the sampled voltage (0.5-1V) with the reference voltage (1V) to generate an error signal. Based on this signal, the PWM controller adjusts the duty cycle of the half-bridge MOSFET from 40% to 60%, increasing the output voltage from 1500V to 3000V, which is used to drive the coil of the magnetic stimulation actuator array to generate a high-frequency magnetic field, thereby realizing adaptive adjustment of the supply voltage with the load current.

[0067] When the magnetic stimulation actuator array contacts the injured area, the multimodal sensor unit synchronously collects electromyographic signals (sampling rate 1000Hz), temperature (accuracy ±0.5℃), and joint angle (resolution 0.1°) data. If the control module detects a sudden drop in the electromyographic signal amplitude by 30% and a temperature increase by 1.5℃, it is determined to be an acute muscle strain. The neural network then outputs an "acute phase" label, and the control database matches the corresponding stimulation parameters: frequency 100Hz, pulse width 50μs, magnetic field strength 1.2T, and continuous stimulation for 20 minutes. If the sensor data shows that the electromyographic signal amplitude has recovered to 80% of the normal level and the temperature has dropped during treatment, the neural network updates and outputs a "subacute phase" label, and the control module automatically increases the stimulation frequency to 200Hz and the magnetic field strength to 1.8T to promote tissue repair. At the same time, the power supply module adaptively adjusts the output voltage from 2000V to 2800V based on the change in the switching circuit input current from 8A to 12A to ensure stable output of magnetic stimulation intensity.

[0068] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[0069] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A high-frequency and high-field strength magnetic stimulator, characterized in that: include: Power supply module, control module, drive module, switch circuit and magnetic stimulation actuator array; The contact surface of the magnetic stimulation actuator array is integrated with a multimodal sensor unit for real-time acquisition of sensor data, wherein the sensor data includes bioelectric signals, temperature data and motion parameters of the injured part of the human body; The control module generates a control instruction according to the sensor data; The driving module converts the control instruction into a high-voltage driving signal; The switch circuit controls the magnetic stimulation actuator array to perform magnetic stimulation in response to the high-voltage driving voltage; The power supply module adaptively adjusts the power supply output voltage according to the input current of the switching circuit to meet the power demand of the switching circuit; the input current of the switching circuit is determined according to the high-voltage driving voltage.

2. A high-frequency and high-field strength magnetic stimulator according to claim 1, characterized in that: The control module generates a control instruction according to the sensor data, including: extracting features of the sensor data; Input the features into the pre-trained neural network model to obtain the corresponding output labels; querying a pre-stored control database according to the output tag to obtain magnetic stimulation control parameters matching the tag; A control instruction is generated based on the control parameter.

3. A high frequency and high field strength magnetic stimulator according to claim 2, characterized in that: The neural network model is one or a combination of a convolutional neural network, a deep neural network or a recurrent neural network, and is used to classify and process the extracted bioelectric signal features, temperature data features and motion parameter features, and output corresponding damage type labels or repair stage labels; The neural network model performs supervised learning training based on the mapping relationship between historical sensor data and magnetic stimulation parameters. The training data includes bioelectric signal waveforms, temperature change curves and motion parameter thresholds of different injury types.

4. A high-frequency and high-field strength magnetic stimulator according to claim 1, characterized in that: The driving module includes an FPGA and a GaN driver; The FPGA generates a PWM modulation signal according to the control instruction, and the GaN driver converts the PWM modulation signal into a high-voltage driving voltage.

5. A high frequency and high field strength magnetic stimulator according to claim 1, characterized in that: The power supply module includes a rectifier and filter unit and an LLC resonant circuit. The rectifier and filter unit is used to convert AC mains power into a DC bus voltage; the LLC resonant circuit converts the DC bus voltage into high-frequency AC, and outputs an adjustable DC high voltage after transformer boosting and synchronous rectification.

6. A high-frequency and high-field strength magnetic stimulator according to claim 5, characterized in that: The LLC resonant circuit includes a half-bridge MOSFET, a resonant network, a high-frequency transformer, and a synchronous rectifier; The working process of the LLC resonant circuit includes: The half-bridge switch unit chops the DC input into a high-frequency square wave voltage; The resonant network filters the high-frequency square wave voltage into a fundamental frequency quasi-sinusoidal current; The fundamental frequency quasi-sinusoidal current flows through the primary side of the high-frequency transformer, and a quasi-sinusoidal voltage is induced on the secondary side; The synchronous rectifier rectifies the quasi-sinusoidal voltage into a direct current output.

7. A high-frequency and high-field strength magnetic stimulator according to claim 6, characterized in that: The LLC resonant circuit further includes a feedback control unit; The feedback control unit includes a current sampling unit, an error amplifier and a PWM controller, wherein: The current sampling unit samples the input current of the switch circuit in real time and converts it into a sampling voltage; The error amplifier compares the sampled voltage with a reference voltage to generate an error signal; The PWM controller adjusts the duty cycle of the half-bridge MOSFET according to the error signal, so that the output voltage of the adjustable DC high voltage increases as the input current of the switching circuit increases.

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