Dynamic magnetic field control method of non-invasive brain physiotherapy resonance pestle needle

Abstract

The invention provides a dynamic magnetic field control method of a non-invasive brain physiotherapy resonance pestle needle, and relates to the technical field of medical care information. A flexible piezoelectric sensing unit, a dynamic magnetic field generator and a multi-frequency algorithm control terminal are organically fused; by calculating a pressure-tissue stiffness correlation factor, a pressure-temperature coupling change factor and a verification area physiological mutation factor in real time, a self-adaptive dynamic magnetic field regulation and control model is constructed, a first regulation strategy of magnetic field frequency, intensity and needle pressure is automatically generated, and an indication signal is efficiently issued through wireless communication, so that the needle pressure is regulated and controlled. The resonance pestle needle can dynamically switch the frequency band and the intensity of the magnetic field according to the real-time physiological state of a patient in the physiotherapy process, and therefore the accuracy, the safety and the repeatability of physiotherapy are remarkably improved. According to the scheme, the defects in multi-mode closed-loop control capability and real-time adaptive adjustment level in the prior art are overcome, and a brand new technical path is provided for non-invasive brain physiotherapy.

Description

Technical Field

[0001] The present invention relates to the field of medical care information technology, and in particular to a dynamic magnetic field control method for a non-invasive brain physiotherapy resonance pestle needle. Background Art

[0002] In the existing technology, the announcement number is CN116313029B, and the name is a solution for dynamic optimization control of digital acupuncture, which realizes dynamic optimization control of the digital acupuncture process, completes real-time multi-body dynamic acupoint identification, and real-time detects and quantifies the overall physiological and psychological feedback, local acupoint feedback, acupuncture environmental factors and acupuncture process parameters of the subjects, further evaluates physiological safety, emotional and psychological stress levels and acupuncture effect levels in real time, conducts target dynamic planning and generates multi-dimensional acupuncture optimization strategies, and conducts comprehensive and intelligent auxiliary optimization and adjustment of the acupuncture process to help achieve a more efficient, safer and more personalized acupuncture experience, and assist in clinical treatment and medical care.

[0003] Although existing technologies have achieved initial success in the field of non-invasive neuromodulation, they are still unable to meet the needs of refined and individualized clinical practice: First, single-frequency or single-intensity magnetic stimulation lacks the ability to adapt to the patient's tissue stiffness and temperature coupling response in real time, which can easily lead to over- or under-stimulation, reducing efficacy and potentially creating safety risks. Second, traditional TMS and tDCS systems struggle to obtain immediate pressure and thermal feedback from the subscalp tissue, making it impossible to dynamically assess the actual effects of the resonant pestle's needles or electrodes at different acupoint depths and tissue hardnesses, thus limiting the stability and reproducibility of therapeutic effects. Third, multimodal sensors often operate independently of magnetic field generators, lacking a unified multi-frequency algorithm control terminal to integrate multi-source physiological data. This results in fragmented closed-loop control strategies and difficulty in real-time execution. Finally, current clinical devices often rely on wired connections for data transmission and control. Downstream actuators have high signal latency and bandwidth requirements, making it difficult to achieve true patient mobility and portability.

[0004] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0005] The purpose of the present invention is to provide a dynamic magnetic field control method for a non-invasive brain physiotherapy resonance pestle needle to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions: A method for controlling a dynamic magnetic field of a non-invasive brain physiotherapy resonance pestle needle comprises the following steps: Step S1: Marking a target acupoint area A and a verification acupoint area B on the scalp surface of the patient to be treated; wherein the verification acupoint area B is used to collect preset physiological response characteristics generated during the treatment of the target acupoint area A; Step S2: Real-time collection of pressure feedback indicators and thermal feedback indicators of the resonant needle pestle needle during the treatment process at the target acupoint area A; and collection of preset physiological response characteristics of the verification acupoint area B on the scalp surface of the patient to be treated; Step S3: analyzing the pressure feedback index and thermal feedback index of the target acupoint area A during the current monitoring period to obtain a pressure-tissue stiffness correlation factor and a pressure-temperature coupling change factor, respectively; Analyze the preset physiological response characteristics of the verification acupoint area B during the current monitoring period to obtain the physiological mutation factor of the verification area; Step S4: Receive and integrate the pressure-tissue stiffness correlation factor, the pressure-temperature coupling change factor, and the physiological mutation factor of the verification area, and construct an adaptive dynamic magnetic field control model through a deep learning or fuzzy logic module preset in the multi-frequency algorithm control terminal. The adaptive dynamic magnetic field control model outputs a first adjustment strategy for magnetic field frequency, magnetic field intensity, and theoretical needle pressure; Step S5: After receiving the output of the first adjustment strategy, a real-time adjustment indication signal is sent to the intelligent magnetic field control module in the resonant pestle needle through a wireless or wired communication link to drive the dynamic magnetic field generator to switch the frequency band, thereby generating an alternating magnetic field with different magnetic field strengths and magnetic field frequencies, and generating a needle theoretical pressure adjustment warning.

[0007] Compared with existing technologies, the present invention has the following advantages: by organically integrating a flexible piezoelectric sensing unit, a dynamic magnetic field generator, and a multi-frequency algorithm control terminal, and by real-time calculation of the pressure-tissue stiffness correlation factor, the pressure-temperature coupling variation factor, and the physiological mutation factor of the verification area, an adaptive dynamic magnetic field control model is constructed. This automatically generates a first adjustment strategy for magnetic field frequency, intensity, and needle pressure, and efficiently transmits instruction signals via wireless communication. This allows the resonant pestle needle to dynamically switch the magnetic field frequency and intensity according to the patient's real-time physiological state during physical therapy, significantly improving the accuracy, safety, and repeatability of physical therapy. This solution overcomes the shortcomings of existing technologies in multimodal closed-loop control capabilities and real-time adaptive adjustment levels, providing a new technical path for non-invasive brain physical therapy. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 Schematic diagram of the process of the present invention; Figure 2 A schematic diagram of the present invention performing a resonant pestle needle operation on the scalp surface of a patient to be treated; Figure 3This is a diagram showing the structure of the traditional pestle needle end of the present invention; Figure 4 This is a schematic diagram of the dynamic magnetic field resonance pestle structure of the multi-frequency intelligent algorithm of the present invention. DETAILED DESCRIPTION

[0009] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0010] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0011] Example 1: See also Figure 1 、 Figure 2 、 Figure 3 and Figure 4 , the present invention provides a technical solution: A method for dynamic magnetic field control of a resonant needle pestle for non-invasive brain physiotherapy is disclosed. The resonant needle pestle is provided with an intelligent magnetic field control module, which consists of a flexible piezoelectric sensor unit, a dynamic magnetic field generator, and a multi-frequency algorithm control terminal. Dynamic magnetic field control includes the following steps: Step S1: Marking a target acupoint area A and a verification acupoint area B on the scalp surface of the patient to be treated; wherein the verification acupoint area B is used to collect preset physiological response characteristics generated during the treatment of the target acupoint area A; Further explanation: The resonant pestle needle includes a needle head, a needle handle and a needle body; precise treatment is achieved through real-time pressure feedback and dynamic adjustment of the magnetic field; The determination of target acupoint area A and verification acupoint area B specifically includes: 1.1) Use a laser positioning device combined with a meridian atlas or MRI (Magnetic Resonance Imaging) to determine the coordinates of target acupoint area A on the scalp of the patient being treated. The specific steps are as follows: Determine the three-dimensional coordinates of the target acupuncture point area A using a laser positioning system or three-dimensional optical scanning technology; Use a harmless marker pen to mark the location of the target acupuncture point area A on the scalp surface, ensuring that the location accurately corresponds to the target acupuncture point known from the meridian or imaging.

[0012] The calibration position is checked twice to ensure the accuracy of the position, with an error of no more than 1mm.

[0013] 1.2) With target acupoint region A as the center, a radius of r1 is set as the selection range for verification acupoint region B to ensure that verification acupoint region B has preset physiological response characteristics. The preset physiological response characteristics can be compared with the physiological response characteristics of target acupoint region A and used to verify the subsequent treatment effect. In this embodiment, the selection of verification acupoint region B must meet the following criteria: Verify that acupoint area B is at least 5 cm away from the target acupoint area A, and the location must have a significant contrast effect in the patient's physiological response in order to detect the difference in physiological response with the target acupoint A.

[0014] Use the same positioning method as the target acupoint area A to ensure that the position of the verification acupoint area B is stable and the error range is controlled within 2 mm.

[0015] Step S2: Real-time collection of pressure feedback indicators and thermal feedback indicators of the resonant needle pestle needle during the treatment process at the target acupoint area A; and collection of preset physiological response characteristics of the verification acupoint area B on the scalp surface of the patient to be treated; Further explanation: 1.3) When the target acupoint area A is calibrated and treatment begins, the preset physiological response characteristics of acupoint area B are monitored and verified in real time; the specific steps are as follows: Non-invasive skin electrical impedance sensors, temperature sensors, and blood oxygen monitors are used to collect real-time data on physiological status changes in target acupoint area A and verification acupoint area B.

[0016] Apply 0.1N-0.5N of therapeutic pressure to the target acupoint area A, and record physiological data such as temperature, blood oxygen saturation, and skin electrical impedance changes in the target acupoint area A and the verification acupoint area B; The data of the target acupoint area A and the verification acupoint area B are collected synchronously; the system sets the synchronous collection timestamp and ensures the timeliness of the data of both. This process includes: The time interval for each data collection was set to 1 second to ensure that the collected physiological data could be compared and analyzed within the same time frame.

[0017] For each data collection, ensure that the data recording accuracy is 1 decimal place and that the data accuracy is not less than 0.1N pressure value and 0.1°C temperature change.

[0018] The preset physiological response characteristics specifically include: The actual measured distance between verification point area B and target point area A is at least 5 cm. This distance is chosen because it ensures that verification point area B has a clear contrast effect on the patient's physiological response compared to target point area A and does not overlap with interference signals that may be generated during treatment. Blood flow change: Use a non-invasive blood flow sensor to measure and verify the local blood flow in acupoint area B, reflecting the response of this area to pressure stimulation. The unit of blood flow change is mL / min, and the data range should be set within (0,1).

[0019] Skin impedance: The skin impedance sensor measures the impedance change in acupoint area B. The unit is Ω, which reflects the electrophysiological characteristics of the local tissue. The impedance change range should be limited to (0, 1).

[0020] Temperature change: Use a temperature sensor to monitor and verify the skin surface temperature change in acupoint area B. The unit is °C. The range of change should be 0°C to 45°C, and the data should be controlled in the interval (0,1).

[0021] Assume that the local blood flow is Q, in mL / min, and the value of Q is in the range [0,1]: Set the skin electrical impedance to R, in Ω, and the R value should be in the range [0,1]: Set the skin temperature to Tw in °C, and the T value should be in the range of 0°C to 45°C, scaled to the interval [0,1]: 1.4) During the treatment process, continuously monitor and record the preset physiological response characteristics of the verified acupoint area B. The steps include: While the target acupuncture point area A is being treated, the physiological data of the verification area B is recorded in real time. High-precision sensors are used to capture the changes in local blood flow Q, skin electrical impedance R, and skin temperature Tw in real time.

[0022] The time interval for data collection is 1 second, and the data collection accuracy is controlled within 1 decimal place.

[0023] 1.5) Analyze the collected data and use calculation formulas to determine the physiological change indicators of the validation acupoint area B. Calculate the standard deviation of local blood flow Q, skin electrical impedance R, and skin temperature Tw to check the data volatility and ensure that it is within the preset physiological response characteristic range.

[0024] Calculate the trend and correlation of the data to confirm whether acupoint area B shows significant physiological response differences during treatment: Standard deviation of changes in local blood flow Q ;in, is the mean of blood flow data, is the blood flow value collected for the i-th time, and N is the total number of data points.

[0025] Standard deviation of skin temperature Tw variation ;in, is the mean of the skin temperature data, is the temperature value collected for the i-th time.

[0026] Standard deviation of the change in skin electrical impedance R ;in, is the mean value of skin electrical impedance data, is the electrical impedance value collected for the i-th time.

[0027] Set separately , and The preset threshold , and ;when , and When any two values ​​exceed the corresponding preset thresholds, it is determined that the preset physiological response characteristics exist; Through the above steps, it is ensured that the verified acupoint area B can effectively provide the preset physiological response characteristics and can generate comparable data with the target acupoint area A during the treatment process, thereby supporting the optimization and adjustment of personalized treatment.

[0028] 1.6) Using a flexible piezoelectric sensing unit, real-time acquisition of pressure feedback P(t) from the resonant needle tip at the target acupuncture point region A is performed. The specific steps are as follows: The piezoelectric sensor mounted on the tip of the needle detects the instantaneous pressure applied to the skin and converts it into an electrical signal. This pressure signal is then converted into digital data via an amplification circuit and transmitted to a data acquisition system.

[0029] The pressure change is recorded at an interval of 1 second, and the pressure value is obtained in real time. In this embodiment, the safety range of the pressure value is set to be within 15N. The obtained pressure value range is limited to the interval (0, 1) through normalization, and is recorded as the pressure feedback index P(t).

[0030] At the end of each therapy cycle, calculate the maximum pressure value collected by the piezoelectric sensor With minimum pressure value The difference ensures pressure stability; 1.7) Use the integrated temperature sensor to collect the thermal feedback index T(t) of the target acupoint area A in real time. The specific steps are as follows: The skin surface temperature of the target acupoint area A is monitored in real time by a micro temperature sensor, and the data acquisition accuracy of the sensor is 0.1°C.

[0031] The value range of the temperature data T(t) is limited to the interval (0,1) by normalization. Its unit is °C and is controlled within the normal physiological range of 0°C to 45°C. The normalized temperature data is recorded as the thermal feedback index T(t); Record the maximum temperature during each treatment With minimum value , to analyze the temperature fluctuation range.

[0032] Step S3: analyzing the pressure feedback index and thermal feedback index of the target acupoint area A during the current monitoring period to obtain a pressure-tissue stiffness correlation factor and a pressure-temperature coupling change factor, respectively; Analyze the preset physiological response characteristics of the verification acupoint area B during the current monitoring period to obtain the physiological mutation factor of the verification area; Further explanation: 2.1) Based on the pressure feedback index P(t) and strain of the target acupoint area A at time t Data, calculate the pressure-tissue stiffness correlation factor G; Normalize the pressure-tissue stiffness correlation factor G to the interval (0,1); When the pressure-tissue stiffness correlation factor G is closer to 1, it means that the pressure feedback index P(t) is closer to the strain. The greater the degree of response between The specific calculation steps are as follows: A flexible strain gauge is installed at the contact point between the needle and the scalp to measure the normalized instantaneous strain of the tissue in real time. , ;Data acquisition frequency is 1Hz, accuracy is 0.01.

[0033] Calculation of tissue elastic modulus ;in, It is the instantaneous pressure corresponding to the pressure feedback index of the target acupoint area A, the unit is N, and P(t)∈(0,1).

[0034] Further mapping E(t) to (0,1), we get ;in, and They are the lower and upper limits of the tissue elastic modulus calibrated in advance.

[0035] In the current monitoring period Calculate the mean of the normalized elastic modulus ; The pressure-tissue stiffness correlation factor G is defined as ; T is the length of the current monitoring period; Since P(t) is are normalized to (0,1), so G∈(0,1); when P(t) or When increases, the product of the two increases, resulting in an increase in G; conversely, G decreases, reflecting the intensity of the response of tissue stiffness to pressure.

[0036] 2.2) Based on the pressure feedback index P(t) and thermal feedback index T(t) of the target acupoint area A at time t, calculate the pressure-temperature coupling change factor C through covariance and standard deviation; Normalize the pressure-temperature coupling variation factor C to the interval (0,1); When the pressure-temperature coupling variation factor C is closer to 1, it means that the correlation between the pressure feedback index P(t) and the thermal feedback index T(t) is greater; The specific calculation steps are as follows: Normalize the thermal feedback index T(t) at time t to , specifically ; In the current monitoring period In the N time points, the sequence is obtained ; represents the instantaneous pressure corresponding to the pressure feedback index collected for the i-th time; represents the normalized value of the thermal feedback index collected for the i-th time; Calculating covariance ; is the mean of the normalized thermal feedback index, calculated in the same way as the mean of the normalized elastic modulus Same, no further elaboration; During the current monitoring period The average value of the internal heat feedback index; Calculate the standard deviation: ; ; The calculation formula for the pressure-temperature coupling variation factor C is defined as follows: ; C∈(0,1); when P and When the consistency of the same-direction changes is high, Cov increases and C increases; if the two changes are unrelated or in opposite directions, C tends to 0.

[0037] 2.3) Calculate the physiological mutation factor M of the verification area based on the monitoring data of local blood flow Q, skin electrical impedance R, and skin temperature Tw in the verification acupoint area B; Normalize the physiological mutation factor M in the validation area to the interval (0,1); The closer the physiological mutation factor M in the verification area is to 1, the more unstable the relationship between the local blood flow Q, skin electrical impedance R and skin temperature Tw is.

[0038] The specific calculation steps are as follows: Normalize the local blood flow Q, skin electrical impedance R, and skin temperature Tw to (0,1): ; The upper and lower limits of the parameters are determined by clinical standards to ensure that the three normalized data are all ∈(0,1); In the current monitoring period Within N time points, sample and calculate the instantaneous deviation of the three at the i-th sampling time :

[0039] in is the mean of each; the physiological mutation factor of the validation area is calculated as ; Since each deviation is ∈(0,1), M is also normalized to the interval (0,1).

[0040] When any physiological signal deviates more from the mean, If all three are stable, M tends to 0.

[0041] Step S4: Receive and integrate the pressure-tissue stiffness correlation factor, the pressure-temperature coupling change factor, and the physiological mutation factor of the verification area, and construct an adaptive dynamic magnetic field control model through a deep learning or fuzzy logic module preset in the multi-frequency algorithm control terminal. The adaptive dynamic magnetic field control model outputs a first adjustment strategy for magnetic field frequency, magnetic field intensity, and theoretical needle pressure; Further explanation: the multi-frequency algorithm control terminal includes an adaptive frequency adjustment unit; the dynamic magnetic field generator includes a dynamic magnetic field generation module and a core control unit; The flexible piezoelectric sensing unit includes: Electromagnetic compatibility: Differential-mode inductors and common-mode inductors, as well as X capacitors and Y capacitors, are introduced to suppress differential-mode interference and common-mode interference, ensuring stable and reliable operation of the system. Slow-start protection: When the device is initially powered on, there is no energy in the energy storage capacitor. If there is no slow-start function, the DC bus power will be directly applied to the piezoelectric sensor unit through the inductor, resulting in a very large current at the power-on moment, which may cause overcurrent damage to the piezoelectric sensor unit. Dual LC filtering: Utilizes the synergistic effect of inductance and capacitance to efficiently filter out noise, select specific frequency signals, and balance high power adaptability and reliability; Selection of piezoelectric material: The pestle needle head is embedded with a PZT-5A piezoelectric film array (thickness 0.1mm, sensitivity 10mV / N) to collect acupoint pressure signals in real time; the normal range of pressure values ​​corresponding to the preset acupoint pressure signals is 0.1N-15N; Signal processing mechanism: The signal is processed by DSP (AVP32F335) and then input into the adaptive frequency adjustment unit.

[0042] Further explanation: The adaptive frequency adjustment unit includes: Based on the PWM signal output by the flexible piezoelectric sensor module, a complementary conduction switch drive signal is output to the dynamic magnetic field generator through the drive circuit. Among them, the output mode is divided into low-frequency mode, medium-frequency mode, high-frequency mode and safety protection mode according to different needle theoretical pressures; Low frequency mode, 1Hz-10Hz: activated when the theoretical needle pressure is less than 2N, used for soothing superficial tissues; Medium frequency mode, 10Hz-30Hz: activated when the theoretical pressure of the needle is 2N-5N, stimulating deep fascia and nerve endings; High-frequency mode, 30Hz-100Hz: activated when the theoretical needle pressure is greater than 5N, targeting chronic pain and inflammation inhibition; Safety protection mode: Activated when pressure > 15N or temperature of the dynamic magnetic field generator > 50°C. Abnormal pressure is detected, output is immediately cut off and vibration alarm is triggered.

[0043] Further explanation: The dynamic magnetic field generating module converts the electrical signal into a magnetic field signal through a micro magnetron array; The core control unit includes four independently driven electromagnetic coil MOSFETs, designated S1 and S3, and S2 and S4, respectively. S1 and S3, and S2 and S4, conduct synchronously, while S1 and S2, and S1 and S4 conduct complementary. The output is driven by an H-bridge chip (DRV8873) and a DAC module (AD5696R), resulting in a controllable magnetic field with an output current accuracy of ±1mA. The wire diameter is 0.1mm, and the inductance is 2.2mH. MOSFET in this embodiment stands for Metal-Oxide-Semiconductor-Field-Effect-Transistor. Dynamic magnetic field generator: supports multi-frequency magnetic field output, specifically 1Hz-100Hz, intensity 0.1mT-5mT, and magnetic field uniformity error <5%; Further explanation: 3.1) At startup, the multi-frequency algorithm control terminal, adaptive frequency adjustment unit and dynamic magnetic field generator are initialized for electromagnetic compatibility, soft start protection and dual LC filter configuration to ensure that the system is in a stable and controllable state during the subsequent multi-frequency algorithm operation; Connect differential mode inductors and common mode inductors in parallel on the signal line of the flexible piezoelectric sensor unit, and connect X capacitors in parallel at the input end. With Y capacitor , to suppress differential and common mode interference in the range of 100kHz–1MHz.

[0044] Connect a current limiting resistor in series at the main power input , and further connect the slow start control chip in parallel, waiting for the energy storage capacitor After the voltage rises to 90% of the rated value, the current limit is released to prevent overcurrent of the PZT-5A piezoelectric array.

[0045] An LC filter is placed before the DSP output: the first filter stage is L1 = 47µH, C1 = 22nF; the second filter stage is L2 = 22µH, C2 = 10nF, representing the specific values ​​of the inductor (L) and capacitor (C) in the two-stage LC filter. These components are combined to form two filter stages to achieve effective filtering of signals within a specific frequency range; the filter cutoff frequency is set to , to ensure the signal integrity of the bandwidth 0.1Hz–100Hz.

[0046] Read the sensitivity of the PZT-5A thin film array S = 10mV / N, perform zero point calibration, and ensure that the output baseline voltage V0 maps the pressure P0 = 0.1N.

[0047] After this initialization is completed, the system enters the multi-frequency algorithm data fusion phase to ensure that the hardware status meets the algorithm execution requirements; 3.2) Based on the obtained pressure-tissue stiffness correlation factor G, pressure-temperature coupling variation factor C, and verification area physiological mutation factor M, a normalized fusion factor for frequency, intensity, and pressure mapping is generated in the multi-frequency algorithm control terminal through preset weights. 、 、 ; Its calculation formula is characterized as follows: ; in , , is the weight coefficient of the corresponding parameter; and , , The sum of is 1, , , The values ​​of are all selected in the interval (0,1); This embodiment sets , , The optimal weight values ​​are 0.5, 0.3, and 0.2, respectively; and this weight is selected in this embodiment because tissue stiffness has the greatest impact on the brain tissue stimulation threshold, followed by temperature coupling, and the verification area mutation weight is the smallest.

[0048] set up ; and normalize the fusion factor 、 、 Converted into the magnetic field frequency actually output by the first adjustment strategy according to linear mapping , magnetic field strength and needle theoretical pressure Specific: The mapping ranges of preset magnetic field frequency, magnetic field strength, and needle theoretical pressure are as follows: The lower and upper limits of the magnetic field frequency are ; The lower and upper limits of magnetic field strength are ; The lower and upper limits of the needle theoretical pressure are ; The linear mapping formula of the first adjustment strategy is expressed as ; From this we get ; 3.3) According to the magnetic field frequency , magnetic field strength and needle theoretical pressure ,Configure PWM and DAC in the MCU to control the H-bridge driver of the dynamic magnetic field generator and the needle pressure adjustment unit, respectively, to achieve the first adjustment strategy; The PWM signal configuration is as follows: Set the PWM period to ; Duty cycle is ; MCU (STM32H743) is set via built-in timer: period , complementary output, dead time is configured as 500ns, driving S1 / S3 and S2 / S4 of DRV8873 respectively, ensuring synchronous and complementary conduction of the coils.

[0049] The DAC signal configuration is as follows: Based on the physiological mutation factor M in the validation region, the driving voltage is mapped to ; is the driving voltage; in 、 The minimum and maximum driving voltages required for 0.1N and 15N respectively correspond to 0.5V-3.3V in this embodiment.

[0050] MCU is configured through AD5696R , drive amplifier to set gain, output to piezoelectric driver to achieve the needle theoretical pressure .

[0051] The PWM signal is sent to the dynamic magnetic field generating module through the driving circuit; the DAC voltage is input to the needle driving unit after passing through the amplifier.

[0052] The system detects the output current in real time and feeds it back to the MCU through the ADC for loop correction.

[0053] 3.4) It should be noted that the "needle pressure regulating unit" refers to the unit integrated at the end of the resonant pestle needle, which is used to control the analog driving voltage signal sent by the terminal according to the multi-frequency algorithm. The execution and feedback device for real-time adjustment of the needle axial force is specifically composed of the following: Miniature piezoelectric linear actuator: Model: XYZ-PZT10; piezoelectric actuated stack unit, 0.1mm stroke, maximum output force 20N; function is to receive the amplified drive signal from the DAC (AD5696R), convert the voltage change into axial displacement, and thus accurately adjust the theoretical pressure acting on the needle. ; Force feedback sensor: Piezoresistive force sensor is used to detect the actual pressure output by the actuator in real time and send the signal to the amplification and filtering circuit; Driving and signal processing circuit: It is an amplifier, a low-noise op amp with a bandwidth of 1kHz, used to amplify the piezoresistive sensor signal The filter is a second-order low-pass filter to remove high-frequency interference DAC interface is AD5696R output , driving a high-voltage amplifier to drive a piezoelectric actuator MCU sampling: STM32H743 built-in ADC collects and amplifies data at a rate of 1kHz signal and calculate the pressure error The closed-loop control logic is as follows: Input: Target needle theoretical pressure ;actual: Measured by the sensor and normalized to ;error: ; Output: If , MCU adjusts according to PID algorithm ,until , complete precise pressure control The tail of the actuator is connected to the tail needle handle top cover through threads to maintain coaxial alignment; The force sensor surrounds the front end of the actuator and is rigidly fixed to the base of the needle body to ensure measurement accuracy; Through the above structure and closed-loop control, the needle pressure adjustment unit can dynamically adjust and maintain the required pressure within the range of 0.1N–15N with an accuracy of ±0.1N, and is directly implemented in the resonant pestle needle.

[0054] 3.5) After the first adjustment strategy is executed, the mode is automatically switched or protection is triggered based on the needle theoretical pressure and the temperature threshold of the magnetic field generator. Specifically including: like or dynamic magnetic field generator temperature ,but: Immediately set the PWM duty cycle D to 0, and the DAC output Place ; Trigger the MCU peripheral GPIO to output a vibration alarm.

[0055] The mode switching logic is as follows: according to Corresponding The value automatically switches, characterized by ; Among them, MS represents the mode; mode MS includes DP1-low frequency mode, DP2-medium frequency mode, and DP3-high frequency mode; in this embodiment, the rate of change of the duty cycle is limited to within 0.1 / s to avoid sudden changes in the magnetic field.

[0056] During operation, G, C, M and 、 、 , dynamically update the magnetic field frequency , magnetic field strength and needle theoretical pressure , realizing closed-loop adaptive control.

[0057] Step S5: After receiving the output of the first adjustment strategy, a real-time adjustment indication signal is sent to the intelligent magnetic field control module in the resonant pestle needle through a wireless or wired communication link to drive the dynamic magnetic field generator to switch the frequency band, thereby generating an alternating magnetic field with different magnetic field strengths and magnetic field frequencies, and generating a needle theoretical pressure adjustment warning.

[0058] Further explanation: 4.1) According to the normalized fusion factor of the output 、 、 , generating a real-time adjustment indication signal X for subsequent communication; the specific steps are: Map each normalized fusion factor to an 8-bit integer ;in, ; The mapping resolution is 1 / 255.

[0059] Defines the real-time adjustment indication signal of a 3-byte data packet ;Will Encapsulated as a GATT characteristic value.

[0060] 4.2) Generating real-time adjustment indication signal After that, the preset BLE-GATT service will be Write the intelligent magnetic field control module at the resonant pestle needle end.

[0061] BLE module initialization: The control terminal starts the BLE peripheral device mode and broadcasts the custom Service UUID.

[0062] GATT characteristic write: After the connection is established, a 3-byte data packet X is written to the Characteristic handle 0x0025 every 1 second using the "Write Without Response" method.

[0063] In the write operation completion interrupt callback, no response is required and the 1 s cycle continues to be maintained to ensure the real-time performance of the instruction.

[0064] 4.3) Receive and analyze the real-time adjustment indication signal at the resonant pestle needle end After that, restore according to the mapping formula 、 、 , and switch the magnetic field frequency band and magnetic field intensity of the dynamic magnetic field generator. The specific instructions are as follows: Parse the 3 bytes received ; is the normalized value of the corresponding parameter; Recover the following real physical quantities: Magnetic field frequency: ; Magnetic field strength: ; Theoretical needle pressure: ; The MCU drives the H-bridge (DRV8873) to generate complementary PWM to control the electromagnetic coil to switch to the required frequency band and intensity; the DAC (AD5696R) outputs a reference voltage to adjust the current to the corresponding .

[0065] 4.4) After the dynamic magnetic field and needle theoretical pressure output are completed, according to the restored needle theoretical pressure Generates theoretical pressure regulation warning signal with set threshold.

[0066] Defining alert factors ; express The normalized value of High voltage warning: If ,correspond ; Low voltage warning: If ,correspond ; Otherwise there is no warning.

[0067] When high voltage is applied, GPIO outputs PWM warning to drive the vibration motor; When the voltage is low, the GPIO outputs PWM warning and flashes the LED.

[0068] Further explanation: The resonant pestle needle in this embodiment is made of copper alloy, with the upper and lower ends of the needle body connected to the head needle handle and the tail needle handle respectively; the needle head is connected to the head needle handle through a joint; the tail needle handle is a hollow cylinder with a closed bottom and an open top, with a top cover on the top of the needle handle, the needle body is fixed on the top cover, and the top cover and the needle handle are connected by a thread; a charging port is set at the bottom of the tail needle handle; Further explanation: If Figure 3 As shown, the needles include a five-star pestle needle 1, a three-platform pestle needle 2, a diamond pestle needle 3 and a spherical platform pestle needle 4; Figure 4 As shown, the needles include the Qiyaochu needles; Figure 4 5, 6, 7, 8, 901, 902, 903 and 9 respectively represent the needle head, the head needle handle, the needle body, the tail needle handle, the flexible piezoelectric sensing unit, the adaptive frequency adjustment unit and the dynamic magnetic field generator; The body of the Qiyao Pestle needle consists of seven parallel cylindrical needle tips of equal height, arranged in a single row in the middle of the top cover of the Qiyao Pestle needle joint. The needle body of the five-star pestle needle has five cylindrical needle tips, which are distributed in a plum blossom shape at the center of the top cover end face of the five-star pestle needle joint. The distance from the top of the five needle tips to the top cover end face is equal. The needle body of the three-piece pestle needle is composed of three cylindrical needle tips that are parallel to each other and of equal height, and are arranged in a single row in the middle of the top cover end surface of the three-piece needle connector; The resonant pestle needle of this embodiment has a height of 70 mm and a diameter of 25 mm. The area of ​​contact between the needle and the human body is 6 square millimeters. The head handle is 28 mm high and 30 mm in diameter, while the tail handle is 20 mm high and 27 mm in diameter. It also has a 22V AC charging port. The working steps of this embodiment are as follows: First, a PZT-5A piezoelectric thin film array (0.1 mm thick) embedded in the resonant pestle needle tip acquires real-time pressure and thermal feedback indicators from target acupoint area A and verifies the pre-set physiological response characteristics of acupoint area B. These signals are processed by an AD8421 low-noise amplifier and a bandpass filter (0.1 Hz-100 Hz) before being fed into an STM32H743 microcontroller (MCU) for analysis. The MCU controls four independently driven electromagnetic coils (10 mm × 10 mm, 0.1 mm wire diameter, 2.2 mH inductance) in a micromagnetic control array to generate alternating magnetic fields of varying intensities (0.1 mT-5 mT) and frequencies (1 Hz-100 Hz). This magnetic field is precisely regulated to ±1 mA output current using a DRV8873 H-bridge chip and an AD5696R DAC module, ensuring magnetic field uniformity error of less than 5%.

[0069] During treatment, the system automatically switches to different magnetic field modes based on the first adjustment strategy: Low-frequency mode (1Hz-10Hz): Activated when pressure is less than 2N, for superficial tissue soothing; Medium-frequency mode (10Hz-30Hz): Activated when pressure is between 2-5N, for stimulating deep fascia and nerve endings; High-frequency mode (30Hz-100Hz): Activated when pressure exceeds 5N, specifically for chronic pain and inflammation suppression. If abnormally high needle theoretical pressure or magnetic field overheating is detected, the system immediately cuts off output and triggers a vibration alarm to ensure safe use.

[0070] Furthermore, thanks to its built-in flexible piezoelectric sensing unit, each resonant pestle needle can adjust magnetic field parameters in real time based on external pressure changes, enabling precise treatment. Ultimately, this resonant pestle needle not only provides traditional mechanical stimulation but also, through precise magnetic field control, promotes the transformation of the molecular chain structure at the intervention site from a disordered state to an ordered arrangement, enhancing the therapeutic effect. The entire process forms a closed-loop control system, achieving a seamless transition from pressure sensing to magnetic field regulation to optimal treatment effect.

[0071] The beneficial effects brought about by this embodiment are: 1. Non-invasive treatment: Resonant pestle needles can be applied to the body through surface or close-range contact, eliminating the need for surgery or injections, and avoiding the complications and infection risks associated with traditional acupuncture. Because no puncture is required, patients experience no pain and are more likely to accept this treatment, thus reducing the difficulty of clinical promotion. This non-invasive treatment method is not only safer and more comfortable, but also significantly improves patient compliance and satisfaction.

[0072] 2. Compared to existing Trikombin natural therapy devices and Microwave-Resonance-Therapy (MRT), MRT is a non-invasive therapy that uses microwave-frequency electromagnetic waves to adjust their frequency and intensity to match the natural vibration frequency of specific tissues or molecules in the human body, thereby generating a resonance effect and achieving therapeutic purposes; While these devices incorporate some concepts from Traditional Chinese Medicine (TCM), their integration is limited, and their high cost and complex operation, requiring specialized personnel, significantly limit their widespread adoption. The present invention, on the other hand, offers a simpler design and ease of operation. Its intelligent control module automatically adjusts magnetic field parameters, making it accessible to a wider range of medical institutions and individual users. This simplified design not only lowers the barrier to entry but also reduces operating costs.

[0073] 3. Application of Intelligent Magnetic Field Regulation Technology: Integrating the principles of magnetic fields in modern physics, this technology conducts energy waves through resonance within the body, thereby influencing the function of cells and tissues. This integration of intelligent control and magnetic field technology offers new insights and technical means for promoting the modernization of Traditional Chinese Medicine. This groundbreaking combination not only helps expand the role and advantages of traditional Chinese medicine treatment techniques in inheritance and innovation, but also provides new therapeutic possibilities.

[0074] 4. Multi-Frequency Vibration Principle: The dynamic magnetic field resonance pestle needle utilizes a multi-frequency intelligent algorithm to output alternating magnetic field signals at multiple frequencies, more comprehensively influencing the body's physiological functions and metabolic processes, enhancing therapeutic efficacy. Compared to single-frequency treatments, multi-frequency vibration can better adapt to individual differences and provide more precise treatment plans. This multi-frequency vibration principle not only enhances treatment effectiveness but also allows for flexible adjustments based on the patient's specific condition to achieve optimal therapeutic results.

[0075] 5. Personalized Treatment: The resonant needle pestle, powered by a multi-frequency intelligent algorithm, can adjust frequency and intensity in real time based on the patient's specific condition, enabling highly personalized treatment plans. By collecting pressure signals from acupoints in real time through a flexible piezoelectric sensing unit and dynamically adjusting magnetic field parameters based on a fuzzy PID control algorithm, the system precisely tailors treatment to the patient's condition and physiological characteristics. This personalized treatment not only improves treatment efficacy but also optimizes patient outcomes, providing a more scientific and precise basis for clinical practice.

[0076] Example 2: To verify the effectiveness of the present invention's approach in non-invasive brain therapy, six volunteers (designated A1–A6) were selected and had target acupoints (Area A) and verification acupoints (Area B) marked on their scalp surfaces. Each volunteer wore a resonant pestle needle and received the first adjustment strategy indication signal generated in step S4 via BLE wireless communication. The magnetic field frequency and intensity were then switched in real time during the treatment. The experimental process was as follows: The volunteer lies quietly on a biosafety chair with the head fixed on a positioning bracket to ensure stable contact between the scalp and the pestle.

[0077] The resonant pestle has a built-in flexible piezoelectric sensing unit (PZT-5A thin film array, sensitivity 10mV / N) and a dynamic magnetic field generator (1Hz–100Hz, 0.1mT–5mT). The BLE communication module has been paired and can read and write GATT characteristic handles within a 1s cycle.

[0078] Verification settings: Each volunteer was tested for 10 minutes, with an initial magnetic field frequency of 50 Hz, an intensity of 2.5 mT, and a reference pressure of 3 N. Functional near-infrared blood oxygen, skin temperature, and subjective VAS pain score tests were performed before and after physical therapy.

[0079] The pressure and temperature of area A and the skin electrical response of area B are collected every second, and the control terminal calculates the pressure-tissue stiffness factor G, the pressure-temperature coupling factor C, and the physiological mutation factor M of the verification area.

[0080] Multi-frequency algorithm control terminal will 、 、 Mapped as a 3-byte indication signal and written to the resonant pestle pin via the BLE "WriteWithoutResponse" method once per second.

[0081] After the analysis of the needle end, the magnetic field frequency is restored according to the mapping , magnetic field strength and needle theoretical pressure , and adjust PWM and DAC in real time to drive the magnetic field generator to complete frequency band and intensity switching.

[0082] Compared with the traditional TMS mode with a constant frequency of 50Hz and a constant intensity of 2.5mT, the experimental group dynamically adjusted according to this scheme and recorded the changes in local blood oxygen concentration of each volunteer during the treatment. , skin temperature changes , VAS pain relief degree ΔVAS.

[0083] Six volunteers were divided into control group and implementation group 、 , ΔVAS statistics are shown in Table 1 to verify the accuracy, safety and repeatability advantages of the present invention.

[0084] Table 1 Example data comparison:

[0085] The table description is as follows: The control group adopted the traditional constant frequency and constant intensity TMS mode; the implementation group adopted the dynamic wireless instruction real-time switching mode of the present invention.

[0086] : Increase in blood oxygen saturation before and after physical therapy; : the increase in local scalp temperature; ΔVAS: the decrease in subjective pain score.

[0087] The implementation group was significantly better than the control group in all three indicators, which verified the accuracy of the present invention ( ↑3 times), safety ( Beneficial effects were observed in terms of temperature controllability (↑3-fold gentler) and repeatability (ΔVAS↑3-fold pain relief).

[0088] It should be noted that: All calculation formulas in this application document use regression analysis including but not limited to machine learning algorithms to deeply analyze the relevant parameters collected and identify their natural trends and relationships. Use professional software, such as Python's Scikit-learn library or R language, to automatically generate mathematical models that match the data. Then, objectively evaluate the performance of the model through methods such as cross-validation, and combine continuous feedback and optimization to ensure that the created formula truly reflects the inherent laws of the data, thereby ensuring its effectiveness and accuracy. In all calculation formulas in this application, the parameters in each formula are dimensionlessly processed in a consistent range to ensure that different physical quantities are compared on the same scale; dimensionless technical means include but are not limited to Min-Max Normalization and Z-Score standardization; The technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random-access memory (RAM), flash memory (FLASH), hard disk or optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods of various embodiments of the present invention.

[0089] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0090] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for controlling the dynamic magnetic field of a resonant needle pestle for non-invasive brain physiotherapy, wherein an intelligent magnetic field control module is provided on the resonant needle pestle. The intelligent magnetic field control module is composed of a flexible piezoelectric sensor unit, a dynamic magnetic field generator, and a multi-frequency algorithm control terminal. The method is characterized in that: The specific steps include: Step S1: Marking a target acupoint area A and a verification acupoint area B on the scalp surface of the patient to be treated; wherein the verification acupoint area B is used to collect preset physiological response characteristics generated during the treatment of the target acupoint area A; Step S2: Real-time collection of pressure feedback indicators and thermal feedback indicators of the resonant needle pestle needle during the treatment process at the target acupoint area A; and collection of preset physiological response characteristics of the verification acupoint area B on the scalp surface of the patient to be treated; Step S3: analyzing the pressure feedback index and thermal feedback index of the target acupoint area A during the current monitoring period to obtain a pressure-tissue stiffness correlation factor and a pressure-temperature coupling change factor, respectively; Analyze the preset physiological response characteristics of the verification acupoint area B during the current monitoring period to obtain the physiological mutation factor of the verification area; Step S4: Receive and integrate the pressure-tissue stiffness correlation factor, the pressure-temperature coupling change factor, and the physiological mutation factor of the verification area, and construct an adaptive dynamic magnetic field control model through a deep learning or fuzzy logic module preset in the multi-frequency algorithm control terminal. The adaptive dynamic magnetic field control model outputs a first adjustment strategy for magnetic field frequency, magnetic field intensity, and theoretical needle pressure; Step S5: After receiving the output of the first adjustment strategy, a real-time adjustment indication signal is sent to the intelligent magnetic field control module in the resonant pestle needle through a wireless or wired communication link to drive the dynamic magnetic field generator to switch the frequency band, thereby generating an alternating magnetic field with different magnetic field strengths and magnetic field frequencies, and generating a needle theoretical pressure adjustment warning.

2. The method for controlling the dynamic magnetic field of a non-invasive resonant pestle needle for brain physiotherapy according to claim 1, characterized in that: The determination of target acupoint area A and verification acupoint area B specifically includes: On the scalp surface of the patient to be treated, the coordinates of the target acupuncture point area A are determined using a laser positioning device combined with magnetic resonance imaging (MRI) of the meridian map; With the target acupoint area A as the center, a range of radius r1 is set as the selection range of the verification acupoint area B to ensure that the verification acupoint area B has the preset physiological response characteristics; the preset physiological response characteristics can be compared with the physiological response characteristics of the target acupoint area A.

3. The method for controlling the dynamic magnetic field of a non-invasive resonance pestle needle for brain physiotherapy according to claim 2, characterized in that: When the target acupoint area A is calibrated and treatment begins, the preset physiological response characteristics of the verification acupoint area B are monitored in real time; the data of the target acupoint area A and the verification acupoint area B are collected synchronously; A flexible piezoelectric sensing unit is used to collect the pressure feedback index P(t) of the resonant pestle needle in the target acupoint area A in real time; an integrated temperature sensor is used to collect the thermal feedback index T(t) of the target acupoint area A in real time.

4. The method for controlling the dynamic magnetic field of a non-invasive resonance pestle needle for brain physiotherapy according to claim 3, characterized in that: Obtain the pressure feedback index P(t) and strain of the target acupoint area A at time t Data, calculate the pressure-tissue stiffness correlation factor G; Normalize the pressure-tissue stiffness correlation factor G to the interval (0,1); When the pressure-tissue stiffness correlation factor G is closer to 1, it means that the pressure feedback index P(t) is closer to the strain. The greater the degree of response between Based on the pressure feedback index P(t) and thermal feedback index T(t) of the target acupoint area A at time t, the pressure-temperature coupling change factor C is calculated through covariance and standard deviation; Normalize the pressure-temperature coupling variation factor C to the interval (0,1); When the pressure-temperature coupling variation factor C is closer to 1, it means that the correlation between the pressure feedback index P(t) and the thermal feedback index T(t) is greater; Based on the monitoring data of local blood flow Q, skin electrical impedance R and skin temperature Tw in the verification acupoint area B, the physiological mutation factor M of the verification area is calculated; Normalize the physiological mutation factor M in the validation area to the interval (0,1); The closer the physiological mutation factor M in the verification area is to 1, the more unstable the relationship between the local blood flow Q, skin electrical impedance R and skin temperature Tw is.

5. The method for controlling the dynamic magnetic field of a non-invasive resonant pestle needle for brain physiotherapy according to claim 4, characterized in that: The multi-frequency algorithm control terminal includes an adaptive frequency adjustment unit; the dynamic magnetic field generator includes a dynamic magnetic field generation module and a core control unit; The adaptive frequency adjustment unit includes: outputting a PWM signal from the flexible piezoelectric sensor module, and outputting a complementary conduction switch tube drive signal to the dynamic magnetic field generator through a drive circuit; wherein, according to different needle theoretical pressures, the output mode is divided into a low frequency mode, a medium frequency mode, a high frequency mode and a safety protection mode; The dynamic magnetic field generating module converts the electrical signal into a magnetic field signal through a micro magnetron array; The core control unit contains 4 sets of independently driven electromagnetic coil MOSFETs.

6. The method for controlling the dynamic magnetic field of a non-invasive resonant pestle needle for brain physiotherapy according to claim 5, characterized in that: Based on the obtained pressure-tissue stiffness correlation factor G, pressure-temperature coupling change factor C, and verification area physiological mutation factor M, a normalized fusion factor for frequency, intensity, and pressure mapping is generated in the multi-frequency algorithm control terminal through preset weights. 、 、 ; set up ; And normalize the fusion factor 、 、 Converted into the magnetic field frequency actually output by the first adjustment strategy according to linear mapping , magnetic field strength and needle theoretical pressure ; According to the magnetic field frequency , magnetic field strength and needle theoretical pressure , PWM and DAC are configured in the MCU to control the H-bridge drive of the dynamic magnetic field generator and the needle pressure adjustment unit respectively, to realize the first adjustment strategy.

7. The method for controlling the dynamic magnetic field of a non-invasive resonance pestle needle for brain physiotherapy according to claim 6, characterized in that: After the first adjustment strategy is executed, the mode is automatically switched or protection is triggered according to the needle theoretical pressure and the temperature threshold of the magnetic field generator.

8. The method for controlling the dynamic magnetic field of a non-invasive resonance pestle needle for brain physiotherapy according to claim 7, characterized in that: According to the normalized fusion factor of the output 、 、 , generating a real-time adjustment indication signal X for subsequent communication; Generating real-time adjustment indication signals After that, the preset BLE-GATT service will be Write the intelligent magnetic field control module at the resonant pestle needle end; Receive and analyze the real-time adjustment indication signal at the resonant pestle needle end After that, restore according to the mapping formula 、 、 , and switch the magnetic field frequency band and magnetic field intensity of the dynamic magnetic field generator.

9. The method for controlling the dynamic magnetic field of a non-invasive resonant pestle needle for brain physiotherapy according to claim 8, characterized in that: After the dynamic magnetic field and the needle theoretical pressure output are completed, according to the restored needle theoretical pressure Generates theoretical pressure regulation warning signal with set threshold.

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