Multifunctional magnetic stimulation treatment system and application method thereof

Abstract

The invention discloses a multifunctional magnetic stimulation treatment system and an application method thereof, relates to the technical field of medical equipment, and solves the defects of unstable curative effect and limited application range caused by individual difference in the existing system. The precision of the laser beam is automatically adjusted through a self-adaptive calibration algorithm, and it is ensured that a treatment area is accurately positioned; optimal stimulation parameters are calculated through a self-adaptive magnetic field depth optimization algorithm, and pertinence and effectiveness of treatment parameters are ensured; the treatment effect is predicted through a feedback mapping algorithm, and the treatment parameters are dynamically adjusted to ensure the safety and effectiveness of the treatment process; physiological data, symptom description and medical history information of a patient are comprehensively analyzed and recognized through the complex disease pattern recognition module, a detailed disease pattern recognition report is output, the application range of the system is expanded, and personalized disease prediction and risk assessment are provided; the applicability and the curative effect of an existing system are greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of medical equipment, and more specifically to a multifunctional magnetic stimulation treatment system and an application method thereof. Background Art

[0002] As a non-invasive physical therapy, magnetic stimulation therapy has shown great potential in recent years in neuromodulation, improving blood circulation, promoting metabolism, anti-inflammatory and analgesic, and tissue repair. As people's understanding of neurological diseases continues to deepen, magnetic stimulation technology has become an important part of the field of neuroscience and rehabilitation medicine. Since the late 1980s, magnetic stimulation technology has gradually moved from the initial experimental stage to clinical application. After decades of development, it has not only achieved remarkable results in the treatment of mental illnesses such as depression and anxiety, but also shown good treatment prospects in neurodegenerative diseases such as Parkinson's disease and multiple sclerosis.

[0003] The working principle of the magnetic stimulation therapy system is to induce electric current by generating a short and powerful magnetic field, thereby stimulating the cerebral cortex or peripheral nerve tissue. This process does not require surgery, does not damage the skin and other tissues, and is a relatively safe treatment method. For example, a transcranial magnetic stimulation (TMS) system usually consists of a pulse generator, a coil, a positioning device, and control software. Among them, the coil is responsible for generating the magnetic field, while the pulse generator controls the intensity and frequency of the magnetic field. The positioning device ensures that the coil can be accurately placed above the target area, and the control software is used to set treatment parameters such as magnetic field intensity, pulse frequency and duration. In recent years, with the advancement of computer technology and imaging technology, such as functional magnetic resonance imaging (fMRI) and positron emission tomography (PET), these technologies have been integrated into the magnetic stimulation therapy system, improving the accuracy and personalization of treatment.

[0004] Although magnetic stimulation therapy systems have achieved remarkable success in clinical practice, they still face some challenges. For example, when conducting magnetic stimulation therapy, even if the same magnetic field strength and frequency are used, factors such as body tissue density and water content of different patients will cause differences in the actual stimulation intensity, thus affecting the treatment effect. In addition, factors such as individual age, gender, and health status will also affect the consistency of treatment results; in addition, the existing magnetic stimulation therapy systems are still limited in their scope of application. Although they can effectively treat certain specific conditions, the treatment effects are not ideal for some more complex diseases, such as chronic pain and systemic inflammatory response syndrome. This is because the pathological mechanisms of these diseases are complex, often involving interactions between multiple tissues and organs, and existing magnetic stimulation technologies are difficult to accurately regulate these complex biological responses.

[0005] Therefore, the present invention discloses a multifunctional magnetic stimulation treatment system and an application method thereof to solve the shortcomings of the existing magnetic stimulation treatment system that individual differences lead to unstable efficacy and limited scope of application. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention discloses a multifunctional magnetic stimulation treatment system and an application method thereof, which improve the efficacy and application scope of the magnetic stimulation treatment system.

[0007] The present invention adopts the following technical solutions:

[0008] A multifunctional magnetic stimulation treatment system, the system comprising a laser positioning module, a personalized treatment module, a biofeedback monitoring module and a complex disease pattern recognition module;

[0009] The laser positioning module is used to locate the treatment area through the laser beam; the laser positioning module automatically adjusts the accuracy of the laser indication according to the individual differences of the patients through an adaptive calibration algorithm; the individual differences include but are not limited to body shape and skin tension parameters;

[0010] The personalized treatment module is used to output optimal stimulation parameters according to the patient's physiological data and the positioning position of the laser positioning module, and dynamically adjust the stimulation parameters based on the real-time monitoring and analysis of the biofeedback monitoring module; the personalized treatment module measures the patient's bioimpedance and tissue density through a non-invasive sensor based on the position information output by the laser positioning module, and calculates the optimal stimulation parameters through an adaptive magnetic field depth optimization algorithm; the stimulation parameters include magnetic field intensity, frequency and pulse waveform;

[0011] The biofeedback monitoring module is used to monitor the biological response during the treatment in real time through multi-channel EEG monitoring, electromyography recording and nerve conduction velocity detection based on the optimal stimulation parameters output by the personalized treatment module and the actual output of the magnetic stimulation therapeutic device, and to predict the treatment effect and adjust the treatment plan through a feedback mapping algorithm, and output it to the personalized treatment module;

[0012] The complex disease pattern recognition module is used to achieve personalized disease prediction and risk assessment based on the real-time collected data of the biological monitoring feedback module and the expert system, and output a disease pattern recognition report; the disease pattern recognition report includes the disease type, an overview of the pathological mechanism and recommended treatment directions; the expert system identifies and classifies the patient's physiological data, symptom description and medical history information through data mining methods and rule engines, constructs a disease pattern library, and outputs a disease prediction and risk assessment report.

[0013] As a further technical solution of the present invention, the adaptive calibration algorithm first captures the patient's body surface contour information through an infrared sensor, and the body surface contour information includes body shape, curves and skin smoothness; then, the adaptive calibration algorithm extracts body surface parameters through edge detection, smoothing and feature extraction methods, and the body surface parameters include but are not limited to curvature and slope to evaluate skin tension and predict soft tissue deformation; based on the extracted body surface geometric parameters and skin tension parameters, the adaptive calibration algorithm constructs a virtual model of the patient's body surface through an adaptive modeling mechanism; and based on the virtual model, the current position of the laser beam and the relative position data of the magnetic field coil, the deviation of the laser beam is calculated through a feedback adjustment method, and a calibration instruction is generated; the calibration instruction includes but is not limited to real-time adjustment of the emission angle, direction and focal length parameters of the laser beam; during the adaptive calibration process, the adaptive calibration algorithm also continuously corrects the calibration parameters through an iterative optimization strategy to improve the laser positioning accuracy.

[0014] As a further technical solution of the present invention, the working method of the personalized treatment module is:

[0015] S301, data reception and preprocessing; realizing data interaction with the laser positioning module and the biofeedback monitoring module through a serial communication protocol, and filtering and correcting the raw data in real time through data cleaning and denoising methods;

[0016] S302, physiological characteristics analysis: measuring the bioimpedance of the patient's treatment area by bioimpedance analysis, and measuring tissue density by ultrasound imaging;

[0017] S303, calculation of personalized treatment parameters; based on the patient's bioimpedance, tissue density and positioning position information of the laser positioning module, the optimal magnetic field strength, frequency and pulse waveform are calculated by an adaptive magnetic field deep optimization algorithm, and the position and direction of the magnetic field generator are adjusted according to the position information output by the laser positioning module by a reverse engineering method; the adaptive magnetic field deep optimization algorithm analyzes historical data and treatment effects by a deep learning method to predict the optimal parameter combination; the reverse engineering method optimizes the magnetic field distribution by geometric modeling and simulation based on the patient's anatomical structure information;

[0018] S304, dynamic parameter adjustment; dynamically adjusting the magnetic field intensity, frequency and pulse waveform parameters through a feedback control mechanism according to the real-time monitoring results of the biofeedback monitoring module;

[0019] S305, treatment parameter execution: sending the calculated optimal stimulation parameters to the magnetic field generator through digital signal processing to start the treatment process.

[0020] As a further technical solution of the present invention, the adaptive magnetic field depth optimization algorithm comprises the following steps:

[0021] Step 401, calculating the complex impedance of biological tissue at a given time point by a bioimpedance measurement formula; the bioimpedance measurement formula generates a current of known frequency and amplitude through a current source and measures the voltage generated thereby; the real part and the imaginary part of the voltage are converted into a complex form by a phase difference to obtain the bioimpedance; the formula expression of the bioimpedance measurement formula is:

[0022]

[0023] In formula (1), Z blo (t) is the bioimpedance value; V man Represents the measured voltage, including the real part V sman and the imaginary part V xman ; j is an imaginary unit; is the time-varying applied current;

[0024] Step 402: Estimate the tissue density p at each spatial position in the three-dimensional space (x, y, z) by combining the tissue density estimation formula with bioimpedance and optical properties. tis (x, y, z); the optical properties include absorption coefficient u (x，y，z) and the reduced scattering coefficient P IR ; The formula expression of the tissue density estimation formula is:

[0025]

[0026] In formula (2), p tis (x, y, z) represents the tissue density value; P IR represents the reduced scattering coefficient, w1 represents the empirical coefficient of water content deviation; w2 represents the empirical coefficient of power limitation, u (x，y，z) represents the absorption coefficient of the tissue at position (x, y, z); g (x，y，z) represents the anisotropy factor, which is used to describe the directionality of scattering;

[0027] Step 403, the initial magnetic field strength is calculated by an initial magnetic field strength calculation formula according to the maximum power, transmission efficiency, stimulation area, tissue conductivity and average tissue density of the device; the initial magnetic field strength calculation formula calculates the magnetic field strength proportional factor according to the power, transmission efficiency and area, and then adjusts the magnetic field strength proportional factor by comparing the average tissue density with the density threshold to reflect the difference in the demand for magnetic field strength of tissues with different densities; the formula expression of the initial magnetic field strength calculation formula is:

[0028]

[0029] In formula (3), B0 represents the initial magnetic field strength; Pmax Indicates the maximum power of the device in watts; m tran represents the transmission efficiency; e(t) represents the area of ​​the stimulation area, in square meters; f tisme Represents tissue conductivity in Siemens per meter; r tissue represents the average tissue density in kilograms per cubic meter; r threch Represents the density threshold, which is used to adjust the sensitivity of magnetic field strength to density; T cycle Indicates the initial setting period, in seconds;

[0030] Step 404: adjusting the magnetic field parameters according to the real-time biofeedback through the treatment effect optimization objective function to optimize the stimulation depth; the real-time biofeedback includes the muscle contraction intensity M and the nerve response speed x; the formula expression of the treatment effect optimization objective function is:

[0031]

[0032] In formula (4), M targct represents the expected muscle contraction strength; x nerver represents the expected neural response speed; M targct (t) represents the actual muscle contraction strength; x nerver (t) represents the actual neural response speed, which varies with time and is affected by stimulation parameters; D represents the duty cycle; f represents the initial frequency and waveform; B0 represents the initial magnetic field strength; T obs Indicates the length of observation time in seconds;

[0033] Step 405: updating the magnetic field strength parameter by a magnetic field strength gradient descent update rule according to the gradient information of the optimization target, so as to reduce the value of the treatment effect optimization objective function G; the magnetic field strength gradient descent update rule obtains the magnetic field strength update amount by the chain rule;

[0034] Step 406: Update the stimulation system based on the acquired magnetic field strength update amount, and continuously monitor the treatment effect, and repeat steps 401 to 406 until the preset treatment time is reached.

[0035] As a further technical solution of the present invention, the biofeedback monitoring module includes a multi-channel EEG monitoring submodule, an electromyography recording submodule, a nerve conduction velocity detection submodule and a feedback mapping algorithm submodule; the multi-channel EEG monitoring submodule includes a signal acquisition unit, a signal amplification and filtering unit and a signal digitization unit; the signal acquisition unit collects EEG signals from the surface of the patient's scalp through an electrode array; the signal amplification and filtering unit amplifies and filters the collected weak EEG signals through an analog front-end circuit; the analog front-end circuit amplifies and processes the weak electrical signals through a preamplifier, and removes redundant frequency components through a bandpass filter; the signal digitization unit converts the analog signal into a digital signal through an analog-to-digital converter; the electromyography recording submodule includes a muscle activity detection unit and a signal transmission unit; the muscle activity detection unit collects electrical signals generated by muscle activity from the surface of the patient's skin through surface electromyography electrodes; and processes the collected electromyography signals through a digital signal processor; the digital signal processor extracts the electromyography signals through Hilbert transform The signal instantaneous amplitude and phase information of the signal; the signal transmission unit sends the processed electromyographic signal to the central processing unit through a wireless transmission method; the nerve conduction velocity detection submodule includes a nerve stimulation unit, a nerve response detection unit, and a conduction velocity calculation unit; the nerve stimulation unit generates a stimulation pulse through a pulse generator; the pulse generator generates a pulse signal through a digital signal processor and a digital-to-analog converter; the nerve response detection unit captures the response signal of the stimulated nerve through a photomultiplier tube; the conduction velocity calculation unit determines the time delay between stimulation and response through a cross-correlation function and calculates the nerve conduction velocity; the feedback mapping algorithm submodule includes a data fusion unit, a pattern recognition unit, and a treatment effect prediction unit; the data fusion unit assigns different weights to sensors according to the reliability and importance of the signal source through a weighted average method; the pattern recognition unit performs pattern recognition on the fused biofeedback signal through a convolutional neural network; the treatment effect prediction unit observes the patient's response to different treatment parameters through a reinforcement learning algorithm and gradually optimizes the treatment plan.

[0036] As a further technical solution of the present invention, the feedback mapping algorithm includes a data layer, a feature extraction layer, a pattern recognition layer, a prediction optimization layer and a decision feedback layer. The operation process steps of the feedback mapping algorithm in the system are as follows:

[0037] Step 601, preprocessing the original signals collected by multi-channel EEG monitoring, electromyography recording and nerve conduction velocity detection through the data layer; the data layer removes interference signals through wavelet denoising and frequency domain filtering to enhance the signal-to-noise ratio of the signal, the wavelet denoising decomposes the signal into different scales through wavelet transform, and then removes high-frequency noise through threshold setting; the frequency domain filtering retains the signal components within a specific frequency range through a bandpass filter;

[0038] Step 602: extract feature parameters from the preprocessed signal through the feature extraction layer; the feature extraction layer extracts spatial features through a convolutional neural network and extracts time series features through a recurrent neural network; the feature extraction formula of the feature extraction layer is:

[0039]

[0040] In formula (5), R total is the total feature, which is used for the next step of pattern recognition; f i (X;θ i ) represents the i-th convolution kernel function, θ i represents the parameter corresponding to the i-th convolution kernel function, which is used to extract spatial features; X represents the input signal at the current moment, which is used to update the hidden state; g represents the activation function, which is used for nonlinear transformation; b h-1 represents the hidden state of the previous moment, which is used to capture time series dependencies; x t and a t are weight matrices representing hidden states and inputs, respectively, used to extract time series features; n is the number of feature vectors;

[0041] Step 603: Classify and cluster the extracted features through the pattern recognition layer to identify different types of biofeedback signal patterns; the pattern recognition layer classifies the features through a support vector machine, and clusters similar features through adaptive fuzzy C-means clustering; the support vector machine achieves optimal separation between different categories by constructing a maximum margin hyperplane, and the adaptive fuzzy C-means clustering achieves fuzzy division of samples by dynamically adjusting the membership function; the formula for classifying features through the support vector machine is:

[0042] V(X)=log2(1+Q θ (X+β) 2 +b (6)

[0043] In formula (6), V(X) represents the classification result; β is the weight vector used for classification; Q θ is a mapping function used to map features to a high-dimensional space; b represents a bias term used to adjust the position of the classification hyperplane; the formula for clustering similar features by the adaptive fuzzy C-means clustering is:

[0044]

[0045] In formula (7), E(X) is the clustering result, which is used for the next step of prediction and optimization; U ij Represents sample x i For the cluster center cj The membership degree of is used for clustering; c j represents the jth cluster center, which is used for clustering; σ represents the fuzzy parameter, which is used to control the width of the membership function;

[0046] Step 604: Based on the output result of the pattern recognition layer, the prediction optimization layer predicts the treatment effect, the prediction optimization layer predicts the treatment effect through the deep Q network, and optimizes the treatment parameters through the proximal strategy optimization PPO; the deep Q network guides the selection of treatment parameters through the state action value function, and the proximal strategy optimization PPO optimizes the treatment plan through the policy gradient update function to maximize the treatment effect; the formula expression of the state action value function is:

[0047] Q(s,a)=∑log2(r(s,a) 2 -τMAX s+1 Q(s+1,a+1,μ)) 2 (8)

[0048] In formula (8), Q(s, a) is the state-action value function; τ is the discount factor, which is used to balance the importance of immediate rewards and future rewards; r(s, a) is the immediate reward for taking action a in state s, which is used to evaluate the effect of the current state-action pair; s+1 is the next state, which is used to update the state-action value function; μ represents the target network parameter, which is used to stabilize the training process;

[0049] The formula expression of the policy gradient update function is:

[0050] π new = argmax π (D a～π min(Q1(s,a) 2 -Q2(s,a)) 2 ) (9)

[0051] In formula (9), π new is the updated strategy; Q1 represents the first value function, Q2 represents the second value function, which is used to minimize the deviation of value estimation; D a～π represents the expectation operator; π is the current strategy;

[0052] Step 605: Feedback the prediction results to the personalized treatment module through the decision feedback layer, and adjust the next treatment plan based on the current treatment effect, and output the final treatment parameter P final; The decision feedback layer formulates a treatment strategy through a fuzzy logic controller and searches for the optimal treatment parameters through a genetic algorithm; the fuzzy logic controller formulates a treatment strategy through fuzzy inference rules, and the genetic algorithm searches for the optimal combination of treatment parameters through an evolutionary strategy; the formula expression of the decision feedback layer is:

[0053]

[0054] In formula (10), P final Represents the final treatment parameters, which are used to update the treatment plan; P old is the old treatment parameter, which is used to calculate the parameter increment; Δp is the parameter increment, which is used to adjust the treatment parameter; A t represents the parameter population, which is used for the exploration space of the legacy algorithm; δ is the exploration rate, which is used to control the conservatism of the strategy update; y represents the fitness function, which is used to evaluate the quality of the parameters.

[0055] As a further technical solution of the present invention, the complex disease pattern recognition module first extracts information fragments from physiological data through association rule mining and cluster analysis methods, and the information fragments include but are not limited to abnormal changes in physiological indicators and periodic fluctuation parameters, and improves the efficiency and accuracy of data processing through decision trees; then, the complex disease pattern recognition module integrates the information obtained from data mining with the knowledge base in the medical field through an expert system; the expert system combines unstructured clinical information with structured medical knowledge through knowledge representation and reasoning mechanisms to preliminarily determine the patient's disease type; based on the patient's disease type, the complex disease pattern recognition module learns and extracts typical characteristics of the disease from case data through a convolutional neural network to form the disease pattern library.

[0056] As a further technical solution of the present invention, the system also includes a decision support module, which is used to display the positioning information of the laser positioning module, the stimulation parameter setting of the personalized treatment module, and the treatment effect evaluation of the biofeedback monitoring module through a human-computer interaction interface and generate treatment decision suggestions; the decision support module extracts features from the laser positioning information, personalized treatment parameters and biofeedback monitoring data through a neural network model and performs fusion analysis, stores the patient's personal information, medical history records and previous treatment data through a database management system, and generates treatment suggestions through an expert system.

[0057] As a further technical solution of the present invention, a method for applying the multifunctional magnetic stimulation treatment system comprises the following steps:

[0058] Step 1: Patient assessment and preparation: Collect but not limited to the patient's basic information, physiological data, symptom description and medical history, and enter them into the system;

[0059] Step 2: Laser positioning and treatment area determination: Start the laser positioning module, mark the treatment area on the patient's body surface with a laser beam, and use an adaptive calibration algorithm to automatically adjust the accuracy of the laser indication according to the individual difference data of the patient's body shape and skin tension;

[0060] Step 3: Setting and implementing personalized treatment parameters; measuring the patient's bioimpedance and tissue density through non-invasive sensors, and calculating the optimal stimulation parameters through an adaptive magnetic field depth optimization algorithm based on the bioimpedance and tissue density data and the position information of laser positioning; then, starting the magnetic stimulation therapy device according to the calculated optimal stimulation parameters to perform magnetic stimulation treatment on the target area; the stimulation parameters include magnetic field intensity, frequency and pulse waveform;

[0061] Step 4: Real-time monitoring and feedback adjustment: Real-time monitoring of biological responses during treatment is performed through multi-channel EEG monitoring, electromyography recording, and nerve conduction velocity detection; at the same time, the monitored biofeedback signals are deeply analyzed through feedback mapping algorithms to predict the treatment effect and evaluate whether the treatment plan needs to be adjusted. If adjustment is required, the stimulation parameters are dynamically adjusted through the personalized treatment module to optimize the treatment effect;

[0062] Step 5: Complex disease pattern recognition and report output: Based on the patient's individual characteristics, physiological data, symptom description and medical history information, the information is identified and classified through data mining methods and expert systems, and finally a disease pattern recognition report is output, which includes the disease type, pathological mechanism overview and recommended treatment direction;

[0063] Step 6: Post-treatment evaluation and follow-up plan; after the treatment, based on the evaluation results and disease pattern recognition report, a subsequent follow-up plan is formulated through expert experience, including regular review and treatment adjustment.

[0064] Positive beneficial effects

[0065] The invention discloses a multifunctional magnetic stimulation treatment system and an application method thereof. The invention automatically adjusts the accuracy of the laser beam according to individual differences such as the patient's body shape and skin tension through a laser positioning module combined with an adaptive calibration algorithm to ensure accurate positioning of the treatment area. This greatly improves the accuracy of the treatment and avoids the difference in efficacy caused by inaccurate positioning. The patient's bioimpedance and tissue density are measured by a personalized treatment module, and based on these data and the position information of the laser positioning, the optimal stimulation parameters are calculated by an adaptive magnetic field depth optimization algorithm. This personalized treatment plan fully considers the patient's physiological characteristics, ensures the pertinence and effectiveness of the treatment parameters, and thus improves the stability of the treatment effect. The feedback mapping algorithm is used to predict the treatment effect and dynamically adjust the treatment parameters to ensure the safety and effectiveness of the treatment process. This real-time feedback adjustment mechanism significantly improves the flexibility and adaptability of the treatment and further stabilizes the efficacy. The patient's physiological data, symptom description and medical history information are comprehensively analyzed and identified through a complex disease pattern recognition module, a disease pattern library is constructed, and a detailed disease pattern recognition report is output, which not only expands the scope of application of the system so that it can cope with a variety of complex diseases, but also provides personalized disease prediction and risk assessment, providing strong support for doctors to formulate treatment plans. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 This is a schematic diagram of the overall architecture of a multifunctional magnetic stimulation treatment system of the present invention;

[0067] Figure 2 It is a workflow framework diagram of the adaptive calibration algorithm of the present invention;

[0068] Figure 3 It is a schematic diagram of the principle framework of the feedback mapping algorithm of the present invention;

[0069] Figure 4 This is a working step diagram of the personalized treatment module of the present invention. DETAILED DESCRIPTION

[0070] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0071] In the above embodiment, if Figure 1-Figure 4 As shown: a multifunctional magnetic stimulation treatment system, the system includes a laser positioning module, a personalized treatment module, a biofeedback monitoring module and a complex disease pattern recognition module;

[0072] The laser positioning module is used to locate the treatment area through the laser beam; the laser positioning module automatically adjusts the accuracy of the laser indication according to the individual differences of the patients through an adaptive calibration algorithm; the individual differences include but are not limited to body shape and skin tension parameters;

[0073] The personalized treatment module is used to output optimal stimulation parameters according to the patient's physiological data and the positioning position of the laser positioning module, and dynamically adjust the stimulation parameters based on the real-time monitoring and analysis of the biofeedback monitoring module; the personalized treatment module measures the patient's bioimpedance and tissue density through a non-invasive sensor based on the position information output by the laser positioning module, and calculates the optimal stimulation parameters through an adaptive magnetic field depth optimization algorithm; the stimulation parameters include magnetic field intensity, frequency and pulse waveform;

[0074] The biofeedback monitoring module is used to monitor the biological response during the treatment in real time through multi-channel EEG monitoring, electromyography recording and nerve conduction velocity detection based on the optimal stimulation parameters output by the personalized treatment module and the actual output of the magnetic stimulation therapeutic device, and to predict the treatment effect and adjust the treatment plan through a feedback mapping algorithm, and output it to the personalized treatment module;

[0075] The complex disease pattern recognition module is used to achieve personalized disease prediction and risk assessment based on the real-time collected data of the biological monitoring feedback module and the expert system, and output a disease pattern recognition report; the disease pattern recognition report includes the disease type, an overview of the pathological mechanism and recommended treatment directions; the expert system identifies and classifies the patient's physiological data, symptom description and medical history information through data mining methods and rule engines, constructs a disease pattern library, and outputs a disease prediction and risk assessment report.

[0076] Furthermore, a method for applying the multifunctional magnetic stimulation treatment system comprises the following steps:

[0077] Step 1: Patient assessment and preparation: Collect but not limited to the patient's basic information, physiological data, symptom description and medical history, and enter them into the system;

[0078] Step 2: Laser positioning and treatment area determination: Start the laser positioning module, mark the treatment area on the patient's body surface with a laser beam, and use an adaptive calibration algorithm to automatically adjust the accuracy of the laser indication according to the individual difference data of the patient's body shape and skin tension;

[0079] Step 3: Setting and implementing personalized treatment parameters; measuring the patient's bioimpedance and tissue density through non-invasive sensors, and calculating the optimal stimulation parameters through an adaptive magnetic field depth optimization algorithm based on the bioimpedance and tissue density data and the position information of laser positioning; then, starting the magnetic stimulation therapy device according to the calculated optimal stimulation parameters to perform magnetic stimulation treatment on the target area; the stimulation parameters include magnetic field intensity, frequency and pulse waveform;

[0080] Step 4: Real-time monitoring and feedback adjustment: Real-time monitoring of biological responses during treatment is performed through multi-channel EEG monitoring, electromyography recording, and nerve conduction velocity detection; at the same time, the monitored biofeedback signals are deeply analyzed through feedback mapping algorithms to predict the treatment effect and evaluate whether the treatment plan needs to be adjusted. If adjustment is required, the stimulation parameters are dynamically adjusted through the personalized treatment module to optimize the treatment effect;

[0081] Step 5: Complex disease pattern recognition and report output: Based on the patient's individual characteristics, physiological data, symptom description and medical history information, the information is identified and classified through data mining methods and expert systems, and finally a disease pattern recognition report is output, which includes the disease type, pathological mechanism overview and recommended treatment direction;

[0082] Step 6: Post-treatment evaluation and follow-up plan; after the treatment, based on the evaluation results and disease pattern recognition report, a subsequent follow-up plan is formulated through expert experience, including regular review and treatment adjustment.

[0083] In a specific embodiment, the technical principle of the multifunctional magnetic stimulation treatment system is deeply rooted in the intersection of electromagnetism and neurophysiology. The core of the system is to use high-intensity pulsed magnetic fields to penetrate human tissue in a non-invasive way and directly act on target nerves or muscle tissues. These pulsed magnetic fields induce induced currents in the target area. According to Faraday's law of electromagnetic induction, the changing magnetic field will generate an electromotive force in the conductor (such as nerve fibers), thereby stimulating the potential change of the nerve cell membrane, that is, generating action potentials. This process simulates the mechanism of natural neural electrical activity, but can regulate neural excitability in vitro through precisely controlled parameters (such as magnetic field intensity, pulse frequency, waveform, etc.). Specifically, the high-energy capacitor group inside the system is discharged in a very short time, and a strong magnetic field that changes instantly is generated through a specific coil structure. The magnetic field quickly penetrates non-conductive media such as the scalp and skull, acts on deep nerve tissues such as the cerebral cortex or spinal cord, or directly acts on muscle tissue, inducing the desired physiological effects. By adjusting the direction, intensity and pulse sequence of the magnetic field, precise stimulation of neural networks in different depths and regions can be achieved, thereby achieving the purpose of treatment or rehabilitation.

[0084] The working mode of the multifunctional magnetic stimulation treatment system is as follows: before treatment, the exact position of the target nerve or muscle tissue is first located through advanced imaging technology (such as MRI, fMRI) to ensure the accuracy of the stimulation. Subsequently, according to the patient's specific condition and treatment needs, the therapist sets the corresponding stimulation parameters on the system interface, including magnetic field strength, pulse frequency, stimulation duration, interval time, and stimulation mode (such as single pulse, repeated pulse, burst pulse, etc.). Once the parameters are set, the system enters the automatic working mode. Under the precise control of the control circuit, the high-energy capacitor group discharges according to the preset parameter sequence and generates the required pulsed magnetic field through the coil. These magnetic field pulses act continuously or intermittently on the target area, inducing a series of electrophysiological reactions, including neuronal depolarization, enhancement or inhibition of synaptic transmission, muscle contraction, etc. During the treatment process, the system also has real-time monitoring and feedback functions, which can dynamically adjust the stimulation parameters according to the patient's physiological response to ensure the safety and effectiveness of the treatment.

[0085] When the multifunctional magnetic stimulation therapy system is in working condition, it shows extremely high flexibility and therapeutic potential. The system can not only target specific neurological or muscle diseases, such as Parkinson's disease, depression, hemiplegia after stroke, etc., but also improve the overall neurological function of patients by adjusting the excitation-inhibition balance of neural networks. In addition, due to its non-invasive and non-invasive characteristics, the system avoids the side effects and risks that may be caused by traditional surgery or drug therapy during the treatment process, and improves the patient's acceptance and comfort. In the specific implementation, through the precise positioning of the laser positioning module, the parameter optimization of the personalized treatment module, and the real-time monitoring of the biofeedback monitoring module, the system can provide customized treatment plans for each patient, significantly improving the treatment effect while reducing adverse reactions. In addition, the complex disease pattern recognition module enables doctors to diagnose diseases more accurately and predict the development trend of diseases through big data analysis and expert system assistance, thereby providing patients with more effective treatment strategies. Overall, the system not only improves the safety and effectiveness of treatment, but also provides powerful tool support for clinical practice.

[0086] Furthermore, the adaptive calibration algorithm first captures the patient's body surface contour information through an infrared sensor, and the body surface contour information includes body shape, curves and skin smoothness; then, the adaptive calibration algorithm extracts body surface parameters through edge detection, smoothing and feature extraction methods, and the body surface parameters include but are not limited to curvature and slope to evaluate skin tension and predict soft tissue deformation; based on the extracted body surface geometric parameters and skin tension parameters, the adaptive calibration algorithm constructs a virtual model of the patient's body surface through an adaptive modeling mechanism; and based on the virtual model, the current position of the laser beam and the relative position data of the magnetic field coil, the deviation of the laser beam is calculated through a feedback adjustment method, and a calibration instruction is generated; the calibration instruction includes but is not limited to real-time adjustment of the emission angle, direction and focal length parameters of the laser beam; during the adaptive calibration process, the adaptive calibration algorithm also continuously corrects the calibration parameters through an iterative optimization strategy to improve the laser positioning accuracy.

[0087] In a specific embodiment, the infrared sensor emits continuous or pulsed infrared light. When the light encounters the surface of an object, it is reflected. The sensor receives the reflected light and measures its round-trip time or phase difference, thereby calculating the distance information of the object surface. The system realizes high-resolution body surface contour scanning through a densely arranged infrared sensor array. Then, by preprocessing the image captured by the infrared sensor (such as denoising, contrast enhancement, etc.), edge detection algorithms (such as Canny edge detection), smoothing (such as Gaussian blur) and feature extraction methods (such as Hough transform) are used to extract key body surface parameters. The edge detection algorithm is used to identify the mutation points on the body surface contour, the smoothing process can remove noise interference, and the feature extraction further refines this information and extracts important parameters such as curvature and slope for subsequent modeling and analysis. Then, a virtual patient body surface model is constructed through an adaptive modeling mechanism, which not only contains the geometric information of the body surface, but also reflects the skin tension and deformation characteristics of soft tissue. That is, based on the extracted body surface parameters, the adaptive modeling mechanism uses numerical simulation methods (such as finite element analysis FEM) to construct a three-dimensional model of the patient's body surface. Finite element analysis can decompose complex geometric shapes into many small units, and the physical properties of each unit (such as elastic modulus, Poisson's ratio, etc.) can be set according to known body surface characteristics. By adjusting the properties of these units, a virtual model that can accurately reflect the patient's body surface characteristics can be constructed. Finally, control algorithms such as PID controllers are used to adjust the angle, direction, and focal length parameters of the laser beam in real time to ensure that the laser beam is always aimed at the target area. The iterative optimization strategy can use gradient descent or other optimization algorithms to gradually approach the optimal solution. Specifically, the adaptive calibration algorithm first captures the patient's body surface contour information, including body shape, curves, and skin smoothness, through an infrared sensor, and then analyzes the body surface contour information, and through data preprocessing steps, including edge detection, smoothing, and feature extraction, to extract key body surface parameters such as curvature and slope; these parameters are crucial for evaluating skin tension and predicting soft tissue deformation. Based on these parameters, the algorithm starts the adaptive modeling mechanism to build a virtual model that accurately reflects the patient's body surface characteristics, then synchronizes the current position of the laser beam and the relative position data of the magnetic field coil, calculates the deviation that needs to be adjusted for the laser beam, and generates calibration instructions including adjustment of the emission angle, direction, and focal length parameters. The algorithm uses a real-time feedback adjustment mechanism to accurately adjust the laser beam according to the generated calibration instructions, and continuously corrects the calibration parameters through iterative optimization strategies to improve the laser positioning accuracy. In the calibration result verification stage, if the results meet the predetermined requirements, the calibration data is recorded and a calibration report is generated to complete the entire adaptive calibration process; if the verification fails, it returns for re-identification and calibration to ensure that the laser beam positioning is ultimately accurately controlled to adapt to the body surface characteristics of different patients and maximize the treatment effect.

[0088] During treatment, the adaptive calibration algorithm is in a state of dynamic adjustment, constantly monitoring changes in the patient's body surface and adjusting the position and focus of the laser beam accordingly. This dynamic adjustment ensures that the laser beam can always accurately irradiate the intended treatment area even if the patient moves slightly or the skin changes due to treatment. In addition, the algorithm will periodically perform self-calibration to ensure the stability and reliability of the entire treatment process.

[0089] Compared with the traditional static calibration method, the adaptive calibration algorithm has higher flexibility: it can cope with slight changes in the patient's body surface and provide a more flexible calibration scheme. In addition, it is suitable for patients with different body shapes and skin conditions, which improves the universality of treatment. Secondly, the automated calibration process significantly shortens the preparation time before treatment. At the same time, it reduces human errors and reduces the requirements for the operator's skill level. Through precise positioning and personalized treatment plans, the effectiveness and safety of treatment are also improved.

[0090] Furthermore, the working method of the personalized treatment module is:

[0091] S301, data reception and preprocessing; realizing data interaction with the laser positioning module and the biofeedback monitoring module through a serial communication protocol, and filtering and correcting the raw data in real time through data cleaning and denoising methods;

[0092] S302, physiological characteristics analysis: measuring the bioimpedance of the patient's treatment area by bioimpedance analysis, and measuring tissue density by ultrasound imaging;

[0093] S303, calculation of personalized treatment parameters; based on the patient's bioimpedance, tissue density and positioning position information of the laser positioning module, the optimal magnetic field strength, frequency and pulse waveform are calculated by an adaptive magnetic field deep optimization algorithm, and the position and direction of the magnetic field generator are adjusted according to the position information output by the laser positioning module by a reverse engineering method; the adaptive magnetic field deep optimization algorithm analyzes historical data and treatment effects by a deep learning method to predict the optimal parameter combination; the reverse engineering method optimizes the magnetic field distribution by geometric modeling and simulation based on the patient's anatomical structure information;

[0094] S304, dynamic parameter adjustment; dynamically adjusting the magnetic field intensity, frequency and pulse waveform parameters through a feedback control mechanism according to the real-time monitoring results of the biofeedback monitoring module;

[0095] S305, treatment parameter execution: sending the calculated optimal stimulation parameters to the magnetic field generator through digital signal processing to start the treatment process.

[0096] Furthermore, the adaptive magnetic field depth optimization algorithm comprises the following steps:

[0097] Step 401, calculating the complex impedance of biological tissue at a given time point by a bioimpedance measurement formula; the bioimpedance measurement formula generates a current of known frequency and amplitude through a current source and measures the voltage generated thereby; the real part and the imaginary part of the voltage are converted into a complex form by a phase difference to obtain the bioimpedance; the formula expression of the bioimpedance measurement formula is:

[0098]

[0099] In formula (1), Z blo (t) is the bioimpedance value; V man Represents the measured voltage, including the real part V sman and the imaginary part V xman ; j is an imaginary unit; is the applied current that varies with time;

[0100] Step 402: Estimate the tissue density p at each spatial position in the three-dimensional space (x, y, z) by combining the tissue density estimation formula with bioimpedance and optical properties. tis (x, y, z); the optical properties include absorption coefficient u (x，y，z) and the reduced scattering coefficient P IR ; The formula expression of the tissue density estimation formula is:

[0101]

[0102] In formula (2), p tis (x, y, z) represents the tissue density value; P IR represents the reduced scattering coefficient, w1 represents the empirical coefficient of water content deviation; w2 represents the empirical coefficient of power limitation, u (x，y，z) represents the absorption coefficient of the tissue at position (x, y, z); g (x，y，z) represents the anisotropy factor, which is used to describe the directionality of scattering;

[0103] Step 403, the initial magnetic field strength is calculated by an initial magnetic field strength calculation formula according to the maximum power, transmission efficiency, stimulation area, tissue conductivity and average tissue density of the device; the initial magnetic field strength calculation formula calculates the magnetic field strength proportional factor according to the power, transmission efficiency and area, and then adjusts the magnetic field strength proportional factor by comparing the average tissue density with the density threshold to reflect the difference in the demand for magnetic field strength of tissues with different densities; the formula expression of the initial magnetic field strength calculation formula is:

[0104]

[0105] In formula (3), B0 represents the initial magnetic field strength; P maxIndicates the maximum power of the device in watts; m tran represents the transmission efficiency; e(t) represents the area of ​​the stimulation area, in square meters; f tisme Represents tissue conductivity in Siemens per meter; r tissue represents the average tissue density in kilograms per cubic meter; r threch Represents the density threshold, which is used to adjust the sensitivity of magnetic field strength to density; T cycle Indicates the initial setting period, in seconds;

[0106] Step 404: adjusting the magnetic field parameters according to the real-time biofeedback through the treatment effect optimization objective function to optimize the stimulation depth; the real-time biofeedback includes the muscle contraction intensity M and the nerve response speed x; the formula expression of the treatment effect optimization objective function is:

[0107]

[0108] In formula (4), M targct represents the expected muscle contraction strength; x nerver represents the expected neural response speed; M targct (t) represents the actual muscle contraction strength; x nerver (t) represents the actual neural response speed, which varies with time and is affected by stimulation parameters; D represents the duty cycle; f represents the initial frequency and waveform; B0 represents the initial magnetic field strength; T obs Indicates the length of observation time in seconds;

[0109] Step 405: updating the magnetic field strength parameter by a magnetic field strength gradient descent update rule according to the gradient information of the optimization target, so as to reduce the value of the treatment effect optimization objective function G; the magnetic field strength gradient descent update rule obtains the magnetic field strength update amount by the chain rule;

[0110] Step 406: Update the stimulation system based on the acquired magnetic field strength update amount, and continuously monitor the treatment effect, and repeat steps 401 to 406 until the preset treatment time is reached.

[0111] In a specific embodiment, the personalized treatment module first realizes efficient data interaction with the laser positioning module and the biofeedback monitoring module through a serial communication protocol (such as RS-232, USB or Ethernet, etc.) to ensure precise control and real-time response during the treatment process. Data cleaning and denoising techniques, such as wavelet transform and median filtering, are used in the data preprocessing stage to effectively eliminate noise interference, improve data quality, and provide a reliable basis for subsequent analysis. Bioimpedance analysis is based on the obstruction of tissue to the passage of electric current. It measures the electrical properties of the patient's treatment area in a non-invasive manner to reflect the composition, structure and functional status of the tissue. Ultrasonic imaging uses the propagation characteristics of ultrasound in human tissue to construct a three-dimensional image of tissue density, providing accurate anatomical structure information for the calculation of treatment parameters.

[0112] The calculation of personalized treatment parameters is the core of the module, which relies on an adaptive magnetic field deep optimization algorithm. The algorithm uses a deep learning model to learn and predict the optimal magnetic field parameters (intensity, frequency, pulse waveform) for specific patients and conditions by training a large amount of historical treatment data and corresponding efficacy feedback. This process not only takes into account the patient's physiological characteristics (such as bioimpedance, tissue density), but also combines the precise position information provided by the laser positioning module to ensure accurate treatment positioning. The reverse engineering method further optimizes the position and direction of the magnetic field generator through geometric modeling and simulation to maximize the distribution effect of the magnetic field in the treatment area and reduce side effects in non-treatment areas.

[0113] In the specific implementation, after the system is started, the data of the laser positioning module and the biofeedback monitoring module are first received through serial communication, and preprocessing is performed to ensure the data quality. Subsequently, based on the preprocessed data and the initial physiological characteristics of the patient, the preliminary treatment parameters are calculated through the adaptive magnetic field depth optimization algorithm. During the treatment process, the biofeedback monitoring module continuously monitors the patient's physiological responses, such as muscle activity, nerve conduction velocity, etc., and feeds back real-time data to the control system. The control system dynamically adjusts the magnetic field parameters through the feedback control mechanism according to the feedback results to ensure the optimization of the treatment effect. Finally, through digital signal processing technology, the adjusted optimal stimulation parameters are sent to the magnetic field generator to execute the treatment process.

[0114] In the adaptive magnetic field depth optimization algorithm, the bioimpedance value is calculated by measuring the relationship between voltage (including real and imaginary parts) and current (I, which changes with time). Imaginary units are used to convert the real and imaginary parts of the voltage into complex forms, thereby reflecting the impedance characteristics of biological tissues. This process utilizes the complex form of Ohm's law and can capture the electrical behavior of biological tissues at different frequencies, including resistance and capacitance effects. The measurement of bioimpedance is crucial to understanding the conductivity and dielectric properties of tissues and is the basis for subsequent parameter calculations. By accurately measuring bioimpedance, the algorithm can distinguish the conductive properties of different tissue types (such as muscle, fat, bone, etc.), providing an important basis for subsequent tissue density estimation and magnetic field strength calculation. Formula (2) combines bioimpedance, optical properties (such as absorption coefficient and reduced scattering coefficient) and empirical coefficients (such as water content deviation coefficient and power limit coefficient) to estimate tissue density. Here, the optical properties are obtained by other imaging techniques (such as near-infrared spectroscopy or optical coherence tomography), while the empirical coefficients are derived based on a large amount of experimental data and are used to adjust the accuracy of the estimation. Accurate estimation of tissue density is crucial to determine the penetration depth and distribution of the magnetic field in the treatment area. Tissues of different densities respond differently to magnetic fields, so this step is key to optimizing magnetic field parameters. Formula (3) takes into account the maximum power of the device, transmission efficiency, stimulation area, tissue conductivity, and average tissue density to calculate the initial magnetic field strength. By comparing the average tissue density with the density threshold, the algorithm can adjust the magnetic field strength proportional factor to meet the needs of tissues of different densities. This step ensures that the magnetic field strength matches the patient's physiological characteristics at the beginning of treatment, laying a good foundation for subsequent dynamic adjustments. Formula (4) defines the treatment effect optimization objective function, which aims to minimize the objective function value, that is, the difference between the expected muscle contraction intensity and nerve response speed and the actual measured value, by adjusting the magnetic field parameters (such as duty cycle, frequency, waveform, and magnetic field strength). The introduction of real-time biofeedback enables the algorithm to dynamically adjust the magnetic field parameters to maximize the treatment effect. This closed-loop control mechanism ensures the accuracy and effectiveness of the treatment process. Next, the chain rule is used to calculate the gradient of the treatment effect optimization objective function with respect to the magnetic field strength, and the magnetic field strength parameters are updated according to the gradient descent method. This step involves complex mathematical operations, including the calculation of partial derivatives and the formulation of iterative update rules. The gradient descent method can efficiently find the magnetic field strength value that minimizes the objective function, thereby optimizing the treatment effect. Through continuous iterative updates, the algorithm can gradually approach the optimal magnetic field parameter combination. Finally, step 406 ensures the continuity and dynamics of the treatment process, so that the algorithm can be adaptively adjusted according to the patient's real-time physiological changes, thereby maintaining the optimization of the treatment effect.

[0115] Among them, the model of the bioimpedance analyzer is BI-9400; the frequency range is 1kHz-1MHz; the impedance measurement range is 0.1Ω-100kΩ; the measurement accuracy is ±1%; the model of the ultrasonic imaging equipment is USI-5000; the probe type is linear array; the frequency range is 5MHz-10MHz; the image resolution is 0.1mm; the scanning depth is 1cm-10cm;

[0116] In the specific implementation, the system first obtains the patient's bioimpedance data through the bioimpedance measurement module, and these data are used in the tissue density estimation module to generate a tissue density map in three-dimensional space. Then, the initial magnetic field strength calculation module calculates the initial magnetic field strength and sets the stimulation parameters based on the physical parameters of the equipment and the estimated tissue density map. During the treatment process, the real-time biofeedback module continuously monitors the patient's muscle contraction strength and nerve response speed, and feeds these data back to the optimization control module. Based on the feedback data, the optimization control module uses optimization algorithms such as the gradient descent method to calculate the adjustment amount of the magnetic field parameters and update the stimulation system. This process is repeated until the preset treatment time is reached or the treatment effect meets the requirements. Through this closed-loop control method, the algorithm can adapt to the patient's physiological changes in real time to ensure the optimization of the treatment effect.

[0117] The working state of the adaptive magnetic field depth optimization algorithm can be divided into several key stages. In the initialization stage, the algorithm determines the initial magnetic field strength and stimulation parameters through bioimpedance measurement and tissue density estimation. At the beginning of treatment, the algorithm starts the stimulation system with these initial parameters and starts real-time biofeedback monitoring. During the treatment, the algorithm continuously adjusts the magnetic field parameters according to the real-time biofeedback data to optimize the treatment effect. This adjustment process may involve multiple iterative cycles, each of which includes steps such as data collection, parameter calculation, stimulation update, and effect evaluation. When the preset treatment time is reached or the treatment effect meets the requirements, the algorithm stops adjusting and ends the treatment process. Throughout the working process, the algorithm always maintains precise control and deep optimization of the treatment area to ensure the reliability and safety of the treatment effect.

[0118] Specifically, a practical design is made to compare the application effects of the traditional algorithm and the adaptive magnetic field depth optimization algorithm in the multifunctional magnetic stimulation therapy system; the experiment includes: Multifunctional magnetic stimulation therapy system: including the traditional algorithm control module and the adaptive magnetic field depth optimization algorithm control module. The traditional algorithm is set by fixed frequency and intensity; biological tissue simulation material: used to simulate human tissue and evaluate the magnetic field penetration depth and uniformity. Energy measurement equipment: used to measure the energy transmission efficiency and loss of the system under different algorithms. Temperature sensor: installed near the coil to monitor temperature changes after long-term operation. Patient comfort assessment tool: including a questionnaire or a comfort scoring system to collect patient feedback. The specific experimental methods are as follows:

[0119] 1. Use biological tissue simulation materials to build a test model, and measure the depth of magnetic field penetration into the simulation material under the control of traditional algorithms and adaptive algorithms. 2. Record and compare the penetration depth data under the two algorithms. 3. Set multiple measurement points in the target area and record the magnetic field strength under the control of traditional algorithms and adaptive algorithms respectively. 4. Calculate and compare the standard deviation of magnetic field strength under the two algorithms to evaluate the uniformity of the magnetic field. 5. Use energy measurement equipment to record the stimulation energy delivered by the system under different algorithms per unit time. 6. Calculate and compare the stimulation efficiency (stimulation energy / time) under the two algorithms. 7. Select a certain number of patients or volunteers to receive magnetic stimulation treatment under the control of traditional algorithms and adaptive algorithms respectively. 8. Use questionnaires or comfort scoring systems to collect patient feedback and evaluate patient comfort under the two algorithms. 9. Under the same conditions, run the multifunctional magnetic stimulation treatment system controlled by traditional algorithms and adaptive algorithms respectively. 10. Use energy measurement equipment to record system energy consumption and use temperature sensors to monitor coil temperature. 11. Record and compare system energy consumption and coil temperature data under the two algorithms. According to the above experimental method, the data of various test indicators are recorded as shown in Table 1:

[0120] Table 1 Experimental comparison of adaptive magnetic field depth optimization algorithm

[0121]

[0122] As can be seen from the data table 1, the optimization algorithm significantly improves the penetration depth of the magnetic field in biological tissues by precisely controlling the magnetic field distribution, which is particularly important for stimulation therapy of deep tissues. In addition, the optimization algorithm improves the uniformity of the magnetic field in the target area, reduces the fluctuation of the magnetic field intensity, and helps to improve the consistency of the treatment effect. Secondly, by optimizing the energy transmission path and method, the adaptive algorithm improves the efficiency of stimulation energy transfer per unit time and enhances the treatment effect. In addition, based on patient feedback, the optimization algorithm improves the patient's treatment experience and comfort score while improving the treatment effect. At the same time, the optimization algorithm reduces the overall energy consumption of the system and improves energy efficiency by finely controlling the magnetic field generation and energy transmission process. Finally, after long-term operation, the traditional algorithm may cause the coil temperature to be too high, affecting the service life of the equipment. The optimization algorithm effectively controls the coil temperature by optimizing the heat dissipation and energy management strategies.

[0123] Furthermore, the biofeedback monitoring module includes a multi-channel EEG monitoring submodule, an electromyography recording submodule, a nerve conduction velocity detection submodule and a feedback mapping algorithm submodule; the multi-channel EEG monitoring submodule includes a signal acquisition unit, a signal amplification and filtering unit and a signal digitization unit; the signal acquisition unit collects EEG signals from the surface of the patient's scalp through an electrode array; the signal amplification and filtering unit amplifies and filters the collected weak EEG signals through an analog front-end circuit; the analog front-end circuit amplifies and processes the weak electrical signals through a preamplifier, and removes redundant frequency components through a bandpass filter; the signal digitization unit converts the analog signals into digital signals through an analog-to-digital converter; the electromyography recording submodule includes a muscle activity detection unit and a signal transmission unit; the muscle activity detection unit collects electrical signals generated by muscle activity from the surface of the patient's skin through surface electromyography electrodes; and processes the collected electromyography signals through a digital signal processor; the digital signal processor extracts the instantaneous amplitude of the electromyography signal through Hilbert transform degree and phase information; the signal transmission unit sends the processed electromyographic signal to the central processing unit through a wireless transmission method; the nerve conduction velocity detection submodule includes a nerve stimulation unit, a nerve response detection unit, and a conduction velocity calculation unit; the nerve stimulation unit generates a stimulation pulse through a pulse generator; the pulse generator generates a pulse signal through a digital signal processor and a digital-to-analog converter; the nerve response detection unit captures the response signal of the stimulated nerve through a photomultiplier tube; the conduction velocity calculation unit determines the time delay between stimulation and response through a cross-correlation function and calculates the nerve conduction velocity; the feedback mapping algorithm submodule includes a data fusion unit, a pattern recognition unit, and a treatment effect prediction unit; the data fusion unit assigns different weights to sensors according to the reliability and importance of the signal source through a weighted average method; the pattern recognition unit performs pattern recognition on the fused biofeedback signal through a convolutional neural network; the treatment effect prediction unit observes the patient's response to different treatment parameters through a reinforcement learning algorithm and gradually optimizes the treatment plan.

[0124] Furthermore, the feedback mapping algorithm includes a data layer, a feature extraction layer, a pattern recognition layer, a prediction optimization layer and a decision feedback layer. The operation process steps of the feedback mapping algorithm in the system are as follows:

[0125] Step 601, preprocessing the original signals collected by multi-channel EEG monitoring, electromyography recording and nerve conduction velocity detection through the data layer; the data layer removes interference signals through wavelet denoising and frequency domain filtering to enhance the signal-to-noise ratio of the signal, the wavelet denoising decomposes the signal into different scales through wavelet transform, and then removes high-frequency noise through threshold setting; the frequency domain filtering retains the signal components within a specific frequency range through a bandpass filter;

[0126] Step 602: extract feature parameters from the preprocessed signal through the feature extraction layer; the feature extraction layer extracts spatial features through a convolutional neural network and extracts time series features through a recurrent neural network; the feature extraction formula of the feature extraction layer is:

[0127]

[0128] In formula (5), R total is the total feature, which is used for the next step of pattern recognition; f i (X;θ i ) represents the i-th convolution kernel function, θ i represents the parameter corresponding to the i-th convolution kernel function, which is used to extract spatial features; X represents the input signal at the current moment, which is used to update the hidden state; g represents the activation function, which is used for nonlinear transformation; b h-1 represents the hidden state of the previous moment, which is used to capture time series dependencies; x t and a t are weight matrices representing hidden states and inputs, respectively, used to extract time series features; n is the number of feature vectors;

[0129] Step 603: Classify and cluster the extracted features through the pattern recognition layer to identify different types of biofeedback signal patterns; the pattern recognition layer classifies the features through a support vector machine, and clusters similar features through adaptive fuzzy C-means clustering; the support vector machine achieves optimal separation between different categories by constructing a maximum margin hyperplane, and the adaptive fuzzy C-means clustering achieves fuzzy division of samples by dynamically adjusting the membership function; the formula for classifying features through the support vector machine is:

[0130] V(X)=log2(1+Q θ (X+β) 2 +b (6)

[0131] In formula (6), V(X) represents the classification result; β is the weight vector used for classification; Q θ is a mapping function used to map features to a high-dimensional space; b represents a bias term used to adjust the position of the classification hyperplane; the formula for clustering similar features by the adaptive fuzzy C-means clustering is:

[0132]

[0133] In formula (7), E(X) is the clustering result, which is used for the next step of prediction and optimization; U ij Represents sample x i For the cluster center c j The membership degree of is used for clustering; cj represents the jth cluster center, which is used for clustering; σ represents the fuzzy parameter, which is used to control the width of the membership function;

[0134] Step 604: Based on the output result of the pattern recognition layer, the prediction optimization layer predicts the treatment effect, the prediction optimization layer predicts the treatment effect through the deep Q network, and optimizes the treatment parameters through the proximal strategy optimization PPO; the deep Q network guides the selection of treatment parameters through the state action value function, and the proximal strategy optimization PPO optimizes the treatment plan through the policy gradient update function to maximize the treatment effect; the formula expression of the state action value function is:

[0135] Q(s,a)=∑log2(r(s,a) 2 -τMAX s+1 Q(s+1,a+1,μ)) 2 (8)

[0136] In formula (8), Q(s, a) is the state-action value function; τ is the discount factor, which is used to balance the importance of immediate rewards and future rewards; r(s, a) is the immediate reward for taking action a in state s, which is used to evaluate the effect of the current state-action pair; s+1 is the next state, which is used to update the state-action value function; μ represents the target network parameter, which is used to stabilize the training process;

[0137] The formula expression of the policy gradient update function is:

[0138] π new = argmax π (D a～π min(Q1(s,a) 2 -Q2(s,a)) 2 ) (9)

[0139] In formula (9), π new is the updated strategy; Q1 represents the first value function, Q2 represents the second value function, which is used to minimize the deviation of value estimation; D a～π represents the expectation operator; π is the current strategy;

[0140] Step 605: Feedback the prediction results to the personalized treatment module through the decision feedback layer, and adjust the next treatment plan based on the current treatment effect, and output the final treatment parameter P final ; The decision feedback layer formulates a treatment strategy through a fuzzy logic controller and searches for the optimal treatment parameters through a genetic algorithm; the fuzzy logic controller formulates a treatment strategy through fuzzy inference rules, and the genetic algorithm searches for the optimal combination of treatment parameters through an evolutionary strategy; the formula expression of the decision feedback layer is:

[0141]

[0142] In formula (10), P final Represents the final treatment parameters, which are used to update the treatment plan; P old is the old treatment parameter, which is used to calculate the parameter increment; Δp is the parameter increment, which is used to adjust the treatment parameter; A t represents the parameter population, which is used for the exploration space of the legacy algorithm; δ is the exploration rate, which is used to control the conservatism of the strategy update; y represents the fitness function, which is used to evaluate the quality of the parameters.

[0143] In a specific embodiment, the EEG uses a multi-channel EEG amplifier to collect EEG signals at multiple locations. Each channel is connected to a high-quality dry electrode that minimizes skin impedance and improves signal quality. To identify abnormal EEG wave patterns, thereby guiding treatment strategies. Electromyography (EMG) recording is performed using a multi-channel EMG amplifier, each channel is equipped with surface electrodes or inserted fine needle electrodes, which can accurately capture the electrical activity of muscles. EMG recording helps to evaluate the function of the neuromuscular system, identify nerve damage or muscle disease, and monitor muscle response during treatment. Nerve conduction velocity (NCS) measurement is to calculate the speed of nerve signal transmission by stimulating nerves and recording electrical signals at different points. A nerve conduction velocity tester is used, which is equipped with stimulating electrodes and recording electrodes. The conduction velocity is calculated by giving a brief current stimulus and then recording the time difference of the response at the other end of the nerve. NCS detection is very useful for evaluating problems such as peripheral neuropathy and radiculopathy, and can provide important objective indicators for treatment.

[0144] In the feedback mapping algorithm, the wavelet transform decomposes the signal into multiple frequency components, and the high-frequency noise is removed by setting the threshold to enhance the signal-to-noise ratio. The selection of the threshold is dynamically adjusted according to the signal noise level. Common methods include fixed thresholds and adaptive thresholds based on noise level estimation. The signal components within a specific frequency range, such as α waves (8-12Hz) and β waves (13-30Hz), are retained by a bandpass filter to exclude unnecessary low-frequency and high-frequency interference. The classification effect of the support vector machine depends on the selection of kernel functions (such as linear kernels, RBF kernels, etc.) and parameter optimization (such as C value, γ value). Adaptive fuzzy C-means clustering can more flexibly handle the fuzzy boundaries between samples by dynamically adjusting the membership function, and the clustering results are more reasonable. The support vector machine accurately classifies different biofeedback signal patterns, and the adaptive fuzzy C-means clustering clusters similar features, providing a clear category division for subsequent prediction optimization; the deep Q network stabilizes the training process through experience replay and target network technology, and effectively predicts the treatment effect. Proximal policy optimization avoids the performance degradation caused by large updates in traditional policy gradient methods by limiting the policy update step size. The combination of deep Q network and proximal strategy optimization not only improves the prediction accuracy of treatment effect, but also realizes the dynamic adjustment of treatment parameters through strategy optimization; the fuzzy logic controller formulates preliminary treatment strategies according to fuzzy rules, and the genetic algorithm searches for the optimal parameter combination based on these strategies. The exploration rate α controls the balance between the algorithm exploring new solutions and using the current optimal solution. The decision feedback layer can quickly respond to feedback on treatment effects and dynamically adjust the treatment plan.

[0145] When working, the feedback mapping algorithm remains highly dynamic and adaptive. It can monitor the changes in the patient's physiological signals in real time and quickly adjust the treatment parameters and treatment strategies according to these changes. Through continuous iteration and optimization, the system can gradually approach the optimal treatment effect, improve treatment efficiency and patient satisfaction.

[0146] In specific implementation, the multifunctional magnetic stimulation therapy system realizes the accurate analysis of the patient's physiological signals and the formulation of personalized treatment plans through the combined use of deep learning algorithms and intelligent optimization algorithms. Secondly, it can monitor the changes in the patient's physiological signals in real time and quickly adjust the treatment parameters and treatment strategies, thereby improving the treatment efficiency and response speed. The feedback mapping algorithm converts the monitored biological signals into actionable feedback information so that patients can learn to control their own physiological reactions. At the same time, the feedback mapping algorithm enables patients to learn how to adjust their brain activity or muscle tension through observed or heard feedback, thereby improving symptoms or accelerating the recovery process.

[0147] Specifically, the feedback mapping algorithm predicts the treatment effect and dynamically adjusts the treatment plan by real-time analysis of data from multi-channel EEG monitoring, electromyography recording, and nerve conduction velocity detection. The algorithm can accurately identify the patient's response to magnetic stimulation therapy and adjust the stimulation parameters in time to achieve the best treatment effect. This real-time monitoring and adjustment capability significantly improves the targetedness and effectiveness of the treatment; in specific implementations, the feedback mapping algorithm is able to process complex biological signals and has the ability to learn and predict. The algorithm can intelligently adjust the treatment plan based on the patient's real-time physiological data, thereby achieving personalized treatment. In addition, the feedback mapping algorithm can maintain the intrinsic structure of the data when processing high-dimensional data, which is particularly important for precision medicine; specifically,

[0148] Experiment 2 was designed to evaluate the effect of the feedback mapping algorithm in the multifunctional magnetic stimulation treatment system. The experimental method is as follows: First, select subjects who meet the inclusion criteria, such as patients with neurological diseases, musculoskeletal diseases or chronic pain. Before magnetic stimulation treatment, use multi-channel EEG monitors, electromyograms, nerve conduction velocity meters and other equipment to collect baseline data of the subjects. Then, according to the specific condition and baseline data of the subjects, the personalized treatment module is used to output the optimal stimulation parameters. Then, the magnetic stimulation therapeutic device is used to treat the subjects according to the optimal stimulation parameters, and the biofeedback monitoring module is turned on for real-time monitoring. The feedback mapping algorithm is used to predict the treatment effect, and the treatment plan is adjusted according to the real-time biological response. After the treatment, the multi-channel EEG, electromyography and nerve conduction velocity data of the subjects are collected again, and the subjective pain score of the patients is calculated. Finally, the data changes before and after the experiment are compared, the improvement percentage is calculated, and the positive and beneficial effects of the feedback mapping algorithm are evaluated. The experimental data record table is shown in Table 2:

[0149] Table 2 Feedback mapping algorithm experimental test table

[0150]

[0151] As shown in Table 2, the alpha wave amplitude increased significantly after the experiment, indicating that brain function was improved. The RMS value of the electromyogram increased after treatment, indicating that muscle strength was enhanced. At the same time, the nerve conduction velocity was accelerated, reflecting the improvement of nerve conduction efficiency. In addition, the pain score was significantly reduced, proving that the treatment effectively reduced the patient's pain. By comprehensively evaluating various indicators, it was found that the treatment efficiency was as high as 85%, indicating that the feedback mapping algorithm has a significant positive and beneficial effect in the multifunctional magnetic stimulation treatment system.

[0152] Furthermore, the complex disease pattern recognition module first extracts information fragments from physiological data through association rule mining and cluster analysis methods, and the information fragments include but are not limited to abnormal changes in physiological indicators and periodic fluctuation parameters, and improves the efficiency and accuracy of data processing through decision trees; then, the complex disease pattern recognition module integrates the information obtained from data mining with the knowledge base in the medical field through an expert system; the expert system combines unstructured clinical information with structured medical knowledge through knowledge representation and reasoning mechanisms to preliminarily determine the patient's disease type; based on the patient's disease type, the complex disease pattern recognition module learns and extracts typical characteristics of the disease from case data through a convolutional neural network to form the disease pattern library.

[0153] In a specific embodiment, the complex disease model first collects physiological data from patients, which may include multiple indicators such as electrocardiogram (ECG), electroencephalogram (EEG), blood pressure, and blood sugar level. Then, association rule mining and cluster analysis techniques are applied to extract meaningful information fragments from the data, such as abnormal changes in physiological indicators and periodic fluctuation parameters. Association rule mining uses classic methods such as Apriori algorithm or FP-growth algorithm. The system scans historical physiological data to find frequent item sets, and then generates association rules based on these frequent item sets, which can help identify those physiological indicator changes that usually appear together, such as increased heart rate and increased blood pressure may appear at the same time; then the decision tree algorithm is used to classify the extracted information fragments and identify potential disease patterns. Decision trees can quickly classify newly collected physiological data, improve the efficiency and accuracy of data processing, and quickly identify the type of disease that the patient may have; then, the expert system combines the above information with the knowledge base in the medical field to determine the type of disease through knowledge representation and reasoning mechanisms. Finally, CNN is used to learn and extract typical characteristics of diseases from a large amount of case data, and a disease pattern library is established for subsequent disease identification and treatment reference.

[0154] During operation, the module is in a state of continuous monitoring, receiving real-time physiological data from sensors and other data sources. The disease pattern library is continuously updated and adjusted based on the newly collected data to ensure the accuracy and timeliness of diagnosis. Therefore, the system can automatically adapt to new disease patterns and development trends, and maintain its performance through regular model training and verification. In specific implementation, through continuous data accumulation and model training, the system can continuously improve its recognition ability and adapt to new disease patterns.

[0155] Furthermore, the system also includes a decision support module, which is used to display the positioning information of the laser positioning module, the stimulation parameter setting of the personalized treatment module, and the treatment effect evaluation of the biofeedback monitoring module through a human-computer interaction interface and generate treatment decision suggestions; the decision support module extracts features from the laser positioning information, personalized treatment parameters and biofeedback monitoring data through a neural network model and performs fusion analysis, stores the patient's personal information, medical history records and previous treatment data through a database management system, and generates treatment suggestions through an expert system.

[0156] Furthermore, the system also includes a safety assurance module, which ensures the safety of the treatment process through multiple safety mechanisms and physiological signal monitoring methods; the multiple safety mechanisms include an overheating protection unit and an overload protection unit; the overheating protection unit monitors the temperature status of the treatment equipment in real time through a temperature sensor, and automatically starts a cooling program or cuts off the power supply through a rule engine to prevent the equipment from overheating; the overload protection unit monitors the current load during the treatment process in real time through current monitoring; once the current exceeds the safety range, the safety assurance module limits the current through a current limiting control method; the physiological signal monitoring method displays the physiological signals through a data visualization method based on the output data of the biofeedback monitoring module.

[0157] In a specific embodiment, the decision support module first extracts the features most relevant to the treatment effect by analyzing the laser positioning information, personalized treatment parameters and biofeedback monitoring data. The decision support module uses convolutional neural networks (CNNs) and recurrent neural networks (RNNs) in deep learning to process different types of input data. CNN is good at processing image data, such as the positioning images generated by the laser positioning module; while RNN is suitable for sequence data, such as the time series data collected by the biofeedback monitoring module. Through feature extraction, redundant information can be reduced and the focus can be placed on the factors that have the greatest impact on the treatment effect. Then, the features from different sources are integrated to obtain a more comprehensive treatment evaluation result. The decision support module uses an attention mechanism to weight the importance of each feature to ensure that all key information is taken into account in the decision-making process. In addition, advanced recurrent networks such as gated recurrent units (GRUs) or long short-term memory networks (LSTMs) can also be used to process the interdependencies between features. Through fusion analysis, the decision support module can more accurately evaluate the treatment effect and make more targeted treatment recommendations based on this.

[0158] The database management system uses a relational database management system (RDBMS) or a NoSQL database to store and manage patients' personal information, medical history records, and previous treatment data. This ensures data consistency and integrity, making it easy for doctors to access detailed patient information at any time, which helps to develop a more reasonable treatment plan. Data retrieval uses database indexing technology and query optimizers to quickly retrieve required data. By creating indexes, data retrieval is accelerated, and query optimization algorithms are used to select the best query execution path. With fast data retrieval, doctors can obtain the required patient information in a short time, improving work efficiency.

[0159] Expert systems simulate the decision-making process of human experts through a rule-based reasoning mechanism. Expert system development tools (such as CLIPS or Jess) are used to build a rule base, where each rule represents a medical principle or treatment guideline. Through the rule engine, the system can derive appropriate treatment recommendations based on current treatment data and the patient's medical history. At the same time, the expert system establishes a knowledge base containing medical knowledge and clinical experience to support the decision-making process of the expert system. The knowledge base is jointly built by doctors and researchers and contains treatment guidelines for a variety of diseases, clinical research results, and information from other medical literature. By integrating the knowledge and experience of experts, the professionalism and accuracy of treatment recommendations can be improved.

[0160] During the treatment process, the decision support module is always active, constantly receiving and processing data from other modules, updating the patient's treatment status in real time through feature extraction and fusion analysis, and generating treatment recommendations as needed. In addition, the decision support module can also dynamically adjust its recommendation strategy based on the patient's latest biofeedback monitoring data and changes in personalized treatment parameters to ensure that the treatment plan is always optimal. In specific implementation, compared with the traditional magnetic stimulation treatment system, this multifunctional magnetic stimulation treatment system integrates multiple data sources such as laser positioning information, personalized treatment parameters and biofeedback monitoring data to provide comprehensive treatment decision support. At the same time, the use of neural network models for feature extraction and fusion analysis can more accurately capture key information in the treatment process. In addition, through the knowledge base of the expert system, personalized treatment recommendations are generated for each patient to improve the treatment effect. Secondly, the treatment recommendations are displayed through a friendly human-computer interaction interface, which is convenient for doctors to understand and apply. At the same time, the data is stored in a secure database management system to ensure patient privacy and data security.

[0161] When the temperature exceeds the preset safety threshold, the corresponding protection measures are initiated through the rule engine. The rule engine automatically starts the cooling program or cuts off the power supply according to the preset rules based on the data transmitted by the temperature sensor. Through the rule engine, the system can automatically respond to temperature anomalies to avoid safety accidents caused by overheating of the equipment.

[0162] When the current exceeds the safe range, current limiting elements such as relays or solid-state relays (SSR) are used to automatically disconnect the circuit or reduce the current intensity when the current exceeds the preset threshold. Through current limiting control, the equipment can be prevented from being damaged due to overload and the safety of the treatment process can be guaranteed. Professional physiological signal acquisition equipment, such as heart rate monitors and blood pressure monitors, is used to collect the patient's physiological signals. By monitoring physiological signals in real time, physiological changes in patients during treatment can be discovered in a timely manner.

[0163] During the treatment process, the safety assurance module is always in a monitoring state, constantly monitoring the temperature, current load of the equipment, and the physiological signals of the patient. Once an abnormal situation is detected, corresponding protection measures will be taken immediately, such as starting the cooling program, cutting off the power supply, or starting the current limiting control, to ensure the safety of the entire treatment process. In specific implementation, the safety of the treatment process is ensured by real-time monitoring of the equipment status and the patient's physiological signals. When overheating or overload is detected, the protection mechanism is automatically activated to avoid safety accidents. At the same time, by visualizing physiological signals through data, doctors can understand the patient's status more intuitively and take necessary measures in time.

[0164] Although the specific embodiments of the present invention are described above, it should be understood by those skilled in the art that these specific embodiments are only illustrative, and those skilled in the art may omit, replace, and change the details of the above methods and systems in various ways without departing from the principles and essence of the present invention. For example, merging the above method steps so as to perform substantially the same functions in substantially the same manner to achieve substantially the same results is within the scope of the present invention. Therefore, the scope of the present invention is limited only by the appended claims.

Claims

1. A multifunctional magnetic stimulation treatment system, characterized in that: The system includes a laser positioning module, a personalized treatment module, a biofeedback monitoring module and a complex disease pattern recognition module; The laser positioning module is used to locate the treatment area through the laser beam; the laser positioning module automatically adjusts the accuracy of the laser indication according to the individual differences of the patients through an adaptive calibration algorithm; the individual differences include but are not limited to body shape and skin tension parameters; The personalized treatment module is used to output optimal stimulation parameters according to the patient's physiological data and the positioning position of the laser positioning module, and dynamically adjust the stimulation parameters based on the real-time monitoring and analysis of the biofeedback monitoring module; the personalized treatment module measures the patient's bioimpedance and tissue density through a non-invasive sensor based on the position information output by the laser positioning module, and calculates the optimal stimulation parameters through an adaptive magnetic field depth optimization algorithm; the stimulation parameters include magnetic field intensity, frequency and pulse waveform; The biofeedback monitoring module is used to monitor the biological response during the treatment in real time through multi-channel EEG monitoring, electromyography recording and nerve conduction velocity detection based on the optimal stimulation parameters output by the personalized treatment module and the actual output of the magnetic stimulation therapeutic device, and to predict the treatment effect and adjust the treatment plan through a feedback mapping algorithm, and output it to the personalized treatment module; The complex disease pattern recognition module is used to achieve personalized disease prediction and risk assessment based on the real-time collected data of the biological monitoring feedback module and the expert system, and output a disease pattern recognition report; the disease pattern recognition report includes the disease type, an overview of the pathological mechanism and recommended treatment directions; the expert system identifies and classifies the patient's physiological data, symptom description and medical history information through data mining methods and rule engines, constructs a disease pattern library, and outputs a disease prediction and risk assessment report.

2. A multifunctional magnetic stimulation treatment system according to claim 1, characterized in that: The adaptive calibration algorithm first captures the patient's body surface contour information through an infrared sensor, and the body surface contour information includes body shape, curves and skin smoothness; then, the adaptive calibration algorithm extracts body surface parameters through edge detection, smoothing and feature extraction methods, and the body surface parameters include but are not limited to curvature and slope to evaluate skin tension and predict soft tissue deformation; based on the extracted body surface geometric parameters and skin tension parameters, the adaptive calibration algorithm constructs a virtual model of the patient's body surface through an adaptive modeling mechanism; Based on the virtual model, the current position of the laser beam and the relative position data of the magnetic field coil, the deviation of the laser beam is calculated through a feedback adjustment method, and a calibration instruction is generated; the calibration instruction includes but is not limited to real-time adjustment of the emission angle, direction and focal length parameters of the laser beam; during the adaptive calibration process, the adaptive calibration algorithm also continuously corrects the calibration parameters through an iterative optimization strategy to improve the laser positioning accuracy.

3. A multifunctional magnetic stimulation treatment system according to claim 1, characterized in that: The working method of the personalized treatment module is: S301, data reception and preprocessing; Realize data interaction with the laser positioning module and the biofeedback monitoring module through a serial communication protocol, and filter and correct the raw data in real time through data cleaning and denoising methods; S302, physiological characteristics analysis; The patient's bioimpedance of the treatment area is measured by bioimpedance analysis, and tissue density is measured by ultrasound imaging; S303, calculation of personalized treatment parameters; Based on the patient's bioimpedance, tissue density and the positioning position information of the laser positioning module, the optimal magnetic field strength, frequency and pulse waveform are calculated by an adaptive magnetic field deep optimization algorithm, and the position and direction of the magnetic field generator are adjusted according to the position information output by the laser positioning module by a reverse engineering method; the adaptive magnetic field deep optimization algorithm analyzes historical data and treatment effects by a deep learning method to predict the optimal parameter combination; the reverse engineering method optimizes the magnetic field distribution by geometric modeling and simulation based on the patient's anatomical structure information; S304, dynamic parameter adjustment; According to the real-time monitoring results of the biofeedback monitoring module, the magnetic field intensity, frequency and pulse waveform parameters are dynamically adjusted through the feedback control mechanism; S305, treatment parameter execution; The calculated optimal stimulation parameters are sent to the magnetic field generator through digital signal processing to start the treatment process.

4. A multifunctional magnetic stimulation treatment system according to claim 1, characterized in that: The adaptive magnetic field depth optimization algorithm comprises the following steps: Step 401, calculating the complex impedance of biological tissue at a given time point by a bioimpedance measurement formula; the bioimpedance measurement formula generates a current of known frequency and amplitude through a current source and measures the voltage generated thereby; the real part and the imaginary part of the voltage are converted into a complex form by a phase difference to obtain the bioimpedance; the formula expression of the bioimpedance measurement formula is: In formula (1), Z blo (t) is the bioimpedance value; V max Represents the measured voltage, including the real part V sman and the imaginary part V xman ; j is an imaginary unit; is the applied current that varies with time; Step 402: Estimate the tissue density p at each spatial position in the three-dimensional space (x, y, z) by combining the tissue density estimation formula with bioimpedance and optical properties. tis (x, y, z); the optical properties include absorption coefficient u (x，y，z) and the reduced scattering coefficient P IR ; The formula expression of the tissue density estimation formula is: In formula (2), p tis (x, y, z) represents the tissue density value; P IR represents the reduced scattering coefficient, w1 represents the empirical coefficient of water content deviation; w2 represents the empirical coefficient of power limitation, u (x，y，z) represents the absorption coefficient of the tissue at position (x, y, z); g (x，y，z) represents the anisotropy factor, which is used to describe the directionality of scattering; Step 403, the initial magnetic field strength is calculated by an initial magnetic field strength calculation formula according to the maximum power, transmission efficiency, stimulation area, tissue conductivity and average tissue density of the device; the initial magnetic field strength calculation formula calculates the magnetic field strength proportional factor according to the power, transmission efficiency and area, and then adjusts the magnetic field strength proportional factor by comparing the average tissue density with the density threshold to reflect the difference in the demand for magnetic field strength of tissues with different densities; the formula expression of the initial magnetic field strength calculation formula is: In formula (3), B0 represents the initial magnetic field strength; P max Indicates the maximum power of the device in watts; m tran represents the transmission efficiency; e(t) represents the area of ​​the stimulation area, in square meters; f tisme Represents tissue conductivity in Siemens per meter; r tissue represents the average tissue density in kilograms per cubic meter; r threch Represents the density threshold, which is used to adjust the sensitivity of magnetic field strength to density; T cycle Indicates the initial setting period, in seconds; Step 404: adjusting the magnetic field parameters according to the real-time biofeedback through the treatment effect optimization objective function to optimize the stimulation depth; the real-time biofeedback includes the muscle contraction intensity M and the nerve response speed x; the formula expression of the treatment effect optimization objective function is: In formula (4), M targct represents the expected muscle contraction strength; x nerver represents the expected neural response speed; M targct (t) represents the actual muscle contraction strength; x nerver (t) represents the actual neural response speed, which varies with time and is affected by stimulation parameters; D represents the duty cycle; f represents the initial frequency and waveform; B0 represents the initial magnetic field strength; T obs Indicates the length of observation time in seconds; Step 405: updating the magnetic field strength parameter by a magnetic field strength gradient descent update rule according to the gradient information of the optimization target, so as to reduce the value of the treatment effect optimization objective function G; the magnetic field strength gradient descent update rule obtains the magnetic field strength update amount by the chain rule; Step 406: Update the stimulation system based on the acquired magnetic field strength update amount, and continuously monitor the treatment effect, and repeat steps 401 to 406 until the preset treatment time is reached.

5. A multifunctional magnetic stimulation treatment system according to claim 1, characterized in that: The biofeedback monitoring module includes a multi-channel EEG monitoring submodule, an electromyography recording submodule, a nerve conduction velocity detection submodule and a feedback mapping algorithm submodule; the multi-channel EEG monitoring submodule includes a signal acquisition unit, a signal amplification and filtering unit and a signal digitization unit; the signal acquisition unit collects EEG signals from the surface of the patient's scalp through an electrode array; the signal amplification and filtering unit amplifies and filters the collected weak EEG signals through an analog front-end circuit; the analog front-end circuit amplifies and processes the weak electrical signals through a preamplifier, and removes redundant frequency components through a bandpass filter; the signal digitization unit converts the analog signal into a digital signal through an analog-to-digital converter; the electromyography recording submodule includes a muscle activity detection unit and a signal transmission unit; the muscle activity detection unit collects electrical signals generated by muscle activity from the surface of the patient's skin through surface electromyography electrodes; And the collected electromyographic signals are processed by a digital signal processor; The digital signal processor extracts the instantaneous amplitude and phase information of the electromyographic signal through Hilbert transform; the signal transmission unit sends the processed electromyographic signal to the central processing unit through a wireless transmission method; the nerve conduction velocity detection submodule includes a nerve stimulation unit, a nerve response detection unit, and a conduction velocity calculation unit; the nerve stimulation unit generates a stimulation pulse through a pulse generator; the pulse generator generates a pulse signal through a digital signal processor and a digital-to-analog converter; the nerve response detection unit captures the response signal of the stimulated nerve through a photomultiplier tube; the conduction velocity calculation unit determines the time delay between stimulation and response through a cross-correlation function and calculates the nerve conduction velocity; the feedback mapping algorithm submodule includes a data fusion unit, a pattern recognition unit, and a treatment effect prediction unit; the data fusion unit assigns different weights to sensors according to the reliability and importance of the signal source through a weighted average method; the pattern recognition unit performs pattern recognition on the fused biofeedback signal through a convolutional neural network; the treatment effect prediction unit observes the patient's response to different treatment parameters through a reinforcement learning algorithm and gradually optimizes the treatment plan.

6. A multifunctional magnetic stimulation treatment system according to claim 1, characterized in that: The feedback mapping algorithm includes a data layer, a feature extraction layer, a pattern recognition layer, a prediction optimization layer and a decision feedback layer. The operation process steps of the feedback mapping algorithm in the system are as follows: Step 601, preprocessing the original signals collected by multi-channel EEG monitoring, electromyography recording and nerve conduction velocity detection through the data layer; the data layer removes interference signals through wavelet denoising and frequency domain filtering to enhance the signal-to-noise ratio of the signal, the wavelet denoising decomposes the signal into different scales through wavelet transform, and then removes high-frequency noise through threshold setting; the frequency domain filtering retains the signal components within a specific frequency range through a bandpass filter; Step 602: extract feature parameters from the preprocessed signal through the feature extraction layer; the feature extraction layer extracts spatial features through a convolutional neural network and extracts time series features through a recurrent neural network; the feature extraction formula of the feature extraction layer is: In formula (5), R total is the total feature, which is used for the next step of pattern recognition; f i (X;θ i ) represents the i-th convolution kernel function, θ i Represents the parameters corresponding to the i-th convolution kernel function, which is used to extract spatial features; X represents the input signal at the current moment, which is used to update the hidden state g represents the activation function, which is used for nonlinear transformation; b h-1 represents the hidden state of the previous moment, which is used to capture time series dependencies; x t and a t are weight matrices representing hidden states and inputs, respectively, used to extract time series features; n is the number of feature vectors; Step 603: Classify and cluster the extracted features through the pattern recognition layer to identify different types of biofeedback signal patterns. The pattern recognition layer classifies the features through a support vector machine and clusters similar features through adaptive fuzzy C-means clustering; the support vector machine achieves optimal separation between different categories by constructing a maximum margin hyperplane, and the adaptive fuzzy C-means clustering achieves fuzzy division of samples by dynamically adjusting the membership function; the formula for classifying features through the support vector machine is: y(X)=log2(1+Q θ (X+β) 2 +b (6) In formula (6), V(X) represents the classification result; β is the weight vector used for classification; Q θ is a mapping function used to map features to a high-dimensional space; b represents a bias term used to adjust the position of the classification hyperplane; the formula for clustering similar features by the adaptive fuzzy C-means clustering is: In formula (7), E(X) is the clustering result, which is used for the next step of prediction and optimization; U ij Represents sample x i For the cluster center c j The membership degree of is used for clustering; c j represents the jth cluster center, which is used for clustering; σ represents the fuzzy parameter, which is used to control the width of the membership function; Step 604: Based on the output result of the pattern recognition layer, the prediction optimization layer predicts the treatment effect, the prediction optimization layer predicts the treatment effect through the deep Q network, and optimizes the treatment parameters through the proximal strategy optimization PPO; the deep Q network guides the selection of treatment parameters through the state action value function, and the proximal strategy optimization PPO optimizes the treatment plan through the policy gradient update function to maximize the treatment effect; the formula expression of the state action value function is: Q(s,a)=∑log2(r(s,a) 2 -τMAX s+1 Q(s+1,a+1,μ)) 2 (8) In formula (8), Q(s, a) is the state-action value function; τ is the discount factor, which is used to balance the importance of immediate rewards and future rewards; r(s, a) is the immediate reward for taking action a in state s, which is used to evaluate the effect of the current state-action pair; s+1 is the next state, which is used to update the state-action value function; μ represents the target network parameter, which is used to stabilize the training process; The formula expression of the policy gradient update function is: π new =argmax π (D a～π min(Q1(s,a) 2 -Q2(s,a)) 2 ) (9) In formula (9), π new is the updated strategy; Q1 represents the first value function, Q2 represents the second value function, which is used to minimize the deviation of value estimation; D a～π represents the expectation operator; π is the current strategy; Step 605: Feedback the prediction results to the personalized treatment module through the decision feedback layer, and adjust the next treatment plan based on the current treatment effect, and output the final treatment parameter P final ; The decision feedback layer formulates a treatment strategy through a fuzzy logic controller and searches for the best treatment parameters through a genetic algorithm; the fuzzy logic controller formulates a treatment strategy through fuzzy inference rules, and the genetic algorithm searches for the best combination of treatment parameters through an evolutionary strategy; the formula expression of the decision feedback layer is: In formula (10), P final Represents the final treatment parameters, which are used to update the treatment plan; P old is the old treatment parameter, used to calculate the parameter increment; Δp is the parameter increment, which is used to adjust the treatment parameters; A t Represents the parameter population, used for the exploration space of the legacy algorithm; δ is the exploration rate, which is used to control the conservatism of the strategy update; y represents the fitness function, which is used to evaluate the quality of the parameters.

7. A multifunctional magnetic stimulation treatment system according to claim 1, characterized in that: The complex disease pattern recognition module first extracts information fragments from physiological data through association rule mining and cluster analysis methods, and the information fragments include but are not limited to abnormal changes in physiological indicators and periodic fluctuation parameters, and improves the efficiency and accuracy of data processing through decision trees; then, the complex disease pattern recognition module integrates the information obtained from data mining with the knowledge base in the medical field through an expert system; the expert system combines unstructured clinical information with structured medical knowledge through knowledge representation and reasoning mechanisms to preliminarily determine the patient's disease type; based on the patient's disease type, the complex disease pattern recognition module learns and extracts typical characteristics of the disease from case data through a convolutional neural network to form the disease pattern library.

8. A multifunctional magnetic stimulation treatment system according to claim 1, characterized in that: The system also includes a decision support module for displaying the positioning information of the laser positioning module, the stimulation parameter setting of the personalized treatment module, and the treatment effect evaluation of the biofeedback monitoring module through a human-computer interaction interface and generating treatment decision suggestions; the decision support module extracts features from the laser positioning information, personalized treatment parameters and biofeedback monitoring data through a neural network model and performs fusion analysis, stores the patient's personal information, medical history records and previous treatment data through a database management system, and generates treatment suggestions through an expert system.

9. A multifunctional magnetic stimulation treatment system according to claim 1, characterized in that: The system also includes a safety assurance module, which ensures the safety of the treatment process through multiple safety mechanisms and physiological signal monitoring methods; the multiple safety mechanisms include an overheat protection unit and an overload protection unit; the overheat protection unit monitors the temperature state of the treatment device in real time through a temperature sensor, and automatically starts a cooling program or cuts off the power supply through a rule engine to prevent the device from overheating; the overload protection unit monitors the current load during the treatment process in real time through current monitoring; Once the current exceeds the safety range, the safety assurance module limits the current through a current limiting control method; the physiological signal monitoring method displays the physiological signal through a data visualization method based on the output data of the biofeedback monitoring module.

10. An application method of a multifunctional magnetic stimulation treatment system, characterized in that: A multifunctional magnetic stimulation therapy system as claimed in any one of claims 1 to 9, comprising the following steps: Step 1: Patient assessment and preparation; Collect, including but not limited to, basic information of the patient, physiological data, symptom descriptions and medical history records, and enter them into the system; Step 2: Laser positioning and treatment area determination; Start the laser positioning module to mark the treatment area on the patient's body surface with a laser beam, and use an adaptive calibration algorithm to automatically adjust the accuracy of the laser indication according to the individual difference data of the patient's body shape and skin tension; Step 3: Setting and implementing personalized treatment parameters; The patient's bioimpedance and tissue density are measured by a non-invasive sensor, and based on the bioimpedance and tissue density data and the position information of the laser positioning, the optimal stimulation parameters are calculated by an adaptive magnetic field depth optimization algorithm; then, according to the calculated optimal stimulation parameters, the magnetic stimulation therapeutic device is started to perform magnetic stimulation treatment on the target area; the stimulation parameters include magnetic field intensity, frequency and pulse waveform; Step 4: Real-time monitoring and feedback adjustment; Through multi-channel EEG monitoring, electromyography recording and nerve conduction velocity detection, biological responses during treatment are monitored in real time; at the same time, the monitored biofeedback signals are deeply analyzed through feedback mapping algorithms to predict treatment effects and assess whether the treatment plan needs to be adjusted. If adjustment is required, the stimulation parameters are dynamically adjusted through the personalized treatment module to optimize the treatment effect; Step 5: Complex disease pattern recognition and report output; Based on the individual characteristics, physiological data, symptom descriptions and medical history information of the patient, the information is identified and classified through data mining methods and expert systems, and a disease pattern recognition report is finally output, which includes the disease type, pathological mechanism overview and recommended treatment direction; Step 6: Post-treatment evaluation and follow-up plan; After the treatment, a follow-up plan will be developed based on the evaluation results and disease pattern recognition report, and through expert experience, including regular review and treatment adjustment.

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