A double-target synchronous stimulation method and system based on a magnetic stimulator

Abstract

The application discloses a double-target synchronous stimulation method and system based on a magnetic stimulator, and belongs to the field of medical instruments. The method comprises the following steps: determining a first comprehensive behavior feedback value of a user; determining a first basic stimulation frequency of a first target point and a second basic stimulation frequency of a second target point; calculating a first adjusted stimulation frequency of the first target point and a second adjusted stimulation frequency of the second target point according to the first comprehensive behavior feedback value, the first basic stimulation frequency and the second basic stimulation frequency; and controlling the magnetic stimulator to perform double-target synchronous stimulation according to the first adjusted stimulation frequency and the second adjusted stimulation frequency. The scheme can ensure that the frequency and intensity of each treatment are suitable for the current health condition and treatment target of the patient. By automatically calculating the stimulation frequency, intensity and adjustment parameters, the interference of human operation is reduced, the consistency and standardization of each treatment are ensured, the repeatability of the treatment is improved, and the treatment effect is more stable.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of medical devices, and particularly relates to a double-target point synchronous stimulation method and system based on a magnetic stimulator. BACKGROUND

[0002] As a non-invasive physical treatment method, the magnetic stimulator uses the pulsed magnetic field generated by the magnetic field coil to directly act on the cerebral cortex, adjusts the nerve excitability to improve the neural electrical activity, and safely and effectively treats Parkinson's disease, stroke, depression and other diseases, providing new treatment options and rehabilitation hopes for patients.

[0003] The existing magnetic stimulator treatment process includes device preparation and inspection, patient preparation, and determination of treatment parameters including stimulation frequency, intensity, etc. according to the patient's condition and treatment target, and then precise positioning of the patient is performed using the magnetic stimulator and treatment is implemented.

[0004] However, the existing magnetic stimulator treatment process lacks sufficient consideration of the real-time state and treatment response of the patient, and the treatment parameters are often based on preset values, which may limit the personalization and maximization of treatment effectiveness, and the real-time feedback is not fully utilized to optimize the treatment experience. SUMMARY

[0005] The present application provides a double-target point synchronous stimulation method and system based on a magnetic stimulator, which solves the problem that the existing magnetic stimulator treatment process lacks sufficient consideration of the real-time state and treatment response of the patient, and the treatment parameters are often based on preset values, which may limit the personalization and maximization of treatment effectiveness, and the real-time feedback is not fully utilized to optimize the treatment experience.

[0006] According to a first aspect of the present application, a double-target point synchronous stimulation method based on a magnetic stimulator is provided, the method comprising:

[0007] Obtaining a first cognitive performance feedback value, a first emotional response score feedback value and a first physiological data feedback value of a user, and generating a first comprehensive behavior feedback value according to the first cognitive performance feedback value, the first emotional response score feedback value and the first physiological data feedback value;

[0008] Obtaining condition data and treatment target data of the user, determining a first basic stimulation frequency of a first target point according to the condition data and the treatment target data, and determining a second basic stimulation frequency of a second target point;

[0009] The first adjustment stimulation frequency of the first target point is calculated according to the first comprehensive behavior feedback value, the first basic stimulation frequency and a preset first target point stimulation intensity calculation formula, and the second adjustment stimulation frequency of the second target point is calculated according to the first comprehensive behavior feedback value, the second basic stimulation frequency and a preset second target point stimulation intensity calculation formula.

[0010] The magnetic stimulator is controlled to perform double-target point synchronous stimulation according to the first adjustment stimulation frequency and the second adjustment stimulation frequency.

[0011] According to a second aspect of the present application, a double-target point synchronous stimulation system based on a magnetic stimulator is provided, and the system comprises:

[0012] A comprehensive evaluation module is configured to acquire a first cognitive performance feedback value, a first emotional reaction score feedback value and a first physiological data feedback value of a user, and generate a first comprehensive behavior feedback value according to the first cognitive performance feedback value, the first emotional reaction score feedback value and the first physiological data feedback value.

[0013] A basic stimulation frequency determination module is configured to acquire disease data and treatment target data of the user, determine a first basic stimulation frequency of a first target point and a second basic stimulation frequency of a second target point according to the disease data and the treatment target data.

[0014] An adjustment stimulation frequency determination module is configured to calculate a first adjustment stimulation frequency of a first target point according to the first comprehensive behavior feedback value, the first basic stimulation frequency and a preset first target point stimulation intensity calculation formula, and calculate a second adjustment stimulation frequency of a second target point according to the first comprehensive behavior feedback value, the second basic stimulation frequency and a preset second target point stimulation intensity calculation formula.

[0015] A control module is configured to control the magnetic stimulator to perform double-target point synchronous stimulation according to the first adjustment stimulation frequency and the second adjustment stimulation frequency.

[0016] In the embodiments of the present application, the first cognitive performance feedback value, the first emotional response score feedback value and the first physiological data feedback value of the user are obtained, and a first comprehensive behavior feedback value is generated according to the first cognitive performance feedback value, the first emotional response score feedback value and the first physiological data feedback value; the disease data and the treatment target data of the user are obtained, and a first basic stimulation frequency of the first target point and a second basic stimulation frequency of the second target point are determined according to the disease data and the treatment target data; a first adjustment stimulation frequency of the first target point is calculated according to the first comprehensive behavior feedback value, the first basic stimulation frequency and a preset first target point stimulation intensity calculation formula, and a second adjustment stimulation frequency of the second target point is calculated according to the first comprehensive behavior feedback value, the second basic stimulation frequency and a preset second target point stimulation intensity calculation formula; the magnetic stimulator is controlled to perform double-target point synchronous stimulation according to the first adjustment stimulation frequency and the second adjustment stimulation frequency. Through the above-mentioned double-target point synchronous stimulation method based on the magnetic stimulator, the basic stimulation frequency of each target point is determined according to the disease data and the treatment target, and then the adjustment is performed in combination with the feedback value of the patient, so that the frequency and intensity of each treatment can be ensured to be most suitable for the current health status and the treatment target of the patient. Through automatic calculation of the stimulation frequency, the intensity and the adjustment parameter, the interference of human operation is reduced, the consistency and standardization of each treatment are ensured to be high, which is helpful to improve the repeatability of the treatment and ensure that the effect of the treatment is more stable.

[0017] It should be understood that the content described in the summary section is not intended to limit the key or important features of the embodiments of the present application, nor to limit the scope of the present application. Other features of the present application will become apparent through the following description. BRIEF DESCRIPTION OF DRAWINGS

[0018] The above and other features, advantages and aspects of the embodiments of the present application will become more apparent by describing in detail the following embodiments with reference to the attached drawings. The attached drawings are intended to better understand the present application and do not limit the present application. In the drawings, the same or similar reference numerals refer to the same or similar elements, and:

[0019] Figure 1 is a flowchart of the double-target point synchronous stimulation method based on the magnetic stimulator provided by the first embodiment of the present application;

[0020] Figure 2 is a flowchart of the double-target point synchronous stimulation method based on the magnetic stimulator provided by the second embodiment of the present application;

[0021] Figure 3 is a structural schematic diagram of the double-target point synchronous stimulation system based on the magnetic stimulator provided by the fourth embodiment of the present application;

[0022] Figure 4A block diagram of an example electronic device is shown in accordance with embodiments of the present disclosure. DETAILED DESCRIPTION

[0023] To make the objectives, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0024] In addition, the term "and / or" herein merely describes an association relationship of associated objects, and indicates that there can be three relationships, for example, A and / or B can represent the three cases of A alone, A and B together, and B alone. In addition, the character " / " herein generally represents an "or" relationship between the front and rear associated objects.

[0025] The magnetic stimulation instrument-based double-target point synchronous stimulation method provided by the embodiments of the present application will be described in detail below in combination with the drawings, specific embodiments and application scenarios.

[0026] Embodiment one

[0027] Figure 1 is a flowchart of the magnetic stimulation instrument-based double-target point synchronous stimulation method provided by the first embodiment of the present application. As shown in Figure 1 , specifically includes the following steps:

[0028] S101, obtaining a first cognitive performance feedback value, a first emotional response score feedback value and a first physiological data feedback value of a user, and generating a first comprehensive behavior feedback value according to the first cognitive performance feedback value, the first emotional response score feedback value and the first physiological data feedback value.

[0029] Firstly, the use scenario of the present solution can be to obtain cognitive, emotional and physiological feedback data of a user, generate a comprehensive behavior feedback value, determine a basic stimulation frequency of double-target points in combination with disease and treatment target data, and then adjust the stimulation frequency according to the comprehensive behavior feedback value and a preset formula, so as to finally realize the scenario of individualized synchronous stimulation of a magnetic stimulation instrument on double-target points.

[0030] Based on the above use scenario, it can be understood that the execution subject of the present application can be a magnetic stimulation instrument-based double-target point synchronous stimulation system integrating the functions of determining a document modality, determining document feature information, determining an alternative theme and determining a target theme, which is not limited here.

[0031] In this solution, the first cognitive performance feedback value can be an assessment of the user's cognitive ability. It can be obtained through various cognitive tests, which are usually used to assess the user's thinking, memory, attention, etc.

[0032] The first emotional response score feedback value can be used to assess the user's emotional state at the beginning of treatment. The emotional response can be quantified through standardized emotional assessment tools, which can help understand the user's emotional fluctuations or mental state.

[0033] The first physiological data feedback value can reflect the user's physiological state before treatment. The user's physiological data, such as heart rate, blood pressure, skin electrical response, electromyogram, etc., are usually monitored through sensor devices.

[0034] The first comprehensive behavior feedback value can be a comprehensive assessment of the user's overall behavior state, usually combining cognitive performance feedback value, emotional response score feedback value and physiological data feedback value to obtain a comprehensive score.

[0035] Cognitive performance feedback value usually reflects the user's performance in cognitive ability (such as memory, attention, information processing, etc.). The way to obtain these data is usually through specific cognitive tests, which can include Stroop test: used to measure reaction time, information processing speed and cognitive flexibility. The user needs to identify the color of the color name, which corresponds to the color of the displayed text. Digit span test: used to assess the capacity of short-term memory and working memory. By asking the user to remember a series of numbers in reverse, the user's memory is tested. Other cognitive tasks: such as digit symbol substitution task, attention concentration task, etc., are used to assess different dimensions of cognitive function. These tests can be conducted through professional cognitive assessment tools or on smart devices. The results obtained by the user during the test can be used as the first cognitive performance feedback value, usually represented as a numerical value, indicating the test score or reaction time.

[0036] Emotional response score feedback value can be obtained by assessing the user's emotional state (such as anxiety, depression, stress, etc.). It can be done through standardized emotional assessment questionnaires or self-rating scales, which can include PHQ-9: used to assess the user's depressive symptoms, the higher the score, the more severe the depressive symptoms. GAD-7: used to assess the user's anxiety symptoms. Self-emotional assessment: emotional diary or quick self-rating scale can be used to understand the user's emotional state on that day, the score can include emotional fluctuations (such as anxiety, depression, anger, etc.). These assessment tools obtain a quantitative emotional response score through user self-rating or clinical rating, which can be used as the first emotional response score feedback value.

[0037] The physiological data feedback value can reflect the user's physiological state, and physiological parameters can be monitored in real time through various sensors. Specifically, heart rate: measured in real time through wearable devices (such as smart watches, heart rate bands, etc.), reflecting cardiovascular health and emotional response. Galvanic skin response (GSR): reflects the user's physiological stress response, which can be measured by a galvanic skin response sensor. Blood pressure: measure the user's blood pressure condition through a sphygmomanometer. Body temperature, blood oxygen: can be monitored by temperature sensors and pulse oximeters, providing feedback on the body's health status. After collecting these physiological data, they are converted into numerical values by the physiological monitoring device as the first physiological data feedback value.

[0038] Then the first comprehensive behavior feedback value is formed in the form of weighted average:

[0039] CBF1 = w1 x CBF cognitive + w2 x CBF emotion + w3 x CBF physio ;

[0040] Wherein, CBF1 is the first comprehensive behavior feedback value; w1 is the preset cognitive performance weight; CBF cognitive is the first cognitive performance feedback value; w2 is the preset emotional response weight; CBF emotion is the first emotional response score feedback value; w3 is the preset physiological data weight; CBF physio is the first physiological data feedback value.

[0041] S102, obtain the user's illness data and treatment target data, determine the first basis stimulation frequency of the first target point according to the illness data and the treatment target data, and determine the second basis stimulation frequency of the second target point.

[0042] Illness data can be the patient's medical history, current disease clinical manifestations, signs, symptoms, and health status data related to treatment. These data can include: disease type (such as depression, chronic pain, anxiety, etc.), clinical assessment results (such as symptom scores, disease course, severity), patient signs (such as physiological parameters, body temperature, blood pressure, etc.), relevant examination results (such as imaging, laboratory examination results, etc.).

[0043] Treatment target data can describe specific goals set during treatment, usually based on patient illness data and expected efficacy. These target data can include: primary treatment goals (such as improving symptoms, reducing pain, improving cognitive function, etc.), secondary treatment goals (such as improving quality of life, improving sleep, etc.), specific treatment indicators (such as target pain score, cognitive ability score, emotional stability, etc.).

[0044] The first target point can refer to the first target area or site selected during the treatment process, which is usually directly related to the patient's condition. According to different treatment methods (such as magnetic stimulation, neural regulation, etc.), the target point can be: a specific brain area (such as the left prefrontal lobe, hippocampus, etc.), a specific area in the nervous system, a certain part of the body (such as a pain point, muscle group, etc.).

[0045] The first basic stimulation frequency can be the initial stimulation frequency determined according to the patient's condition, treatment target and characteristics of the first target point. It is usually the frequency used to start treatment in the treatment plan.

[0046] The second target point can be the second target area selected during the treatment process, which is usually complementary to the first target point or the area that needs to be regulated in multi-point treatment.

[0047] The second basic stimulation frequency can be the initial stimulation frequency setting for the second target point. It is a frequency that matches the treatment target and the needs of the target point, and provides a preliminary set frequency for subsequent treatment.

[0048] The condition data (for example, diagnosis results, disease types, previous treatment records, etc.) can be obtained from the patient's historical medical records. These data can be automatically read from the hospital's EMR system to obtain treatment target data. Then input the location of the two target points, the user's condition data and the treatment target data into the machine learning model (the location of the target point is selected by the doctor according to the condition and the treatment target), which can automatically calculate and adjust the basic stimulation frequency of the target point. The model will output the optimal basic stimulation frequency of each target point according to the individual differences of the patient, the condition data and the treatment target.

[0049] The model training steps are:

[0050] Define inputs and outputs: Input data: target location data, condition data, and treatment goal data. Target location data: the location of the target can be spatial coordinates (e.g., coordinates of a specific region in the brain) or categorical information derived from a doctor's diagnosis (e.g., target type: cerebral cortex, thalamus, etc.). If it is spatial coordinates, consider inputting the location as a vector into the model; if it is categorical information, perform category encoding. For spatial location, the input can be a vector [x, y, z], where [x, y, z] are the three-dimensional coordinates of the target. For target type, the input can be a categorical variable (e.g., "Target A," "Target B," etc.), which can be encoded using one-hot encoding. The output data is the baseline stimulation frequency for each target. Then read the historical condition data, historical treatment goal data, their corresponding historical target location, and the historical baseline stimulation frequency of the historical target location. Then convert the historical target location, historical condition data, and historical treatment goal data into a format suitable for the model, especially for categorical variables (such as target type) one-hot encoding, etc., and form a data set according to the above data. Then select the model and algorithm, the model can choose the regression algorithm in supervised learning, the following are several possible choices: 1. Linear regression: if the relationship between data is relatively simple, start with the most basic linear regression model. It is suitable for linear relationships between target location, condition data, treatment goal data, and stimulation frequency. 2. Random forest regression: Random forest is an ensemble learning method suitable for handling non-linear problems. It can handle more complex input data, automatically extract features from data, and is suitable for cases without explicit linear relationships. 3. Gradient Boosting Regression: Gradient Boosting is a decision tree-based regression method suitable for handling complex feature interactions and non-linear problems, especially in cases with large amounts of data and complex features. 4. Neural network: If the input data is very complex and contains non-linear relationships, a deep learning model (such as a multi-layer perceptron) may be a suitable choice. Neural networks can automatically extract features through multiple layers of computation and handle complex patterns. 5. Support Vector Machine Regression: Support Vector Machine Regression is suitable for handling data with non-linear relationships, and can map data to high-dimensional space through kernel functions to find the best regression line. 6. Reinforcement Learning: If the model is dynamically adjusting treatment plans in real-time and optimizing based on real-time feedback from patients, consider using reinforcement learning. Through the design of the reward function, reinforcement learning can gradually adjust and optimize the stimulation frequency to maximize the treatment effect of the treatment plan for the patient. Then divide the data set into training set, validation set and test set. The training set is used for model training, the validation set is used for adjusting hyperparameters, and the test set is used for evaluating the performance of the model. Then select the loss function, specifically, you can choose mean square error (MSE), for regression problems, mean square error is a commonly used loss function. The loss function can measure the difference between the predicted stimulation frequency and the actual treatment effect.Then use the training set data for model training, by minimizing the loss function (such as MSE) to optimize the model parameters (such as weights and biases). Use the validation set to evaluate the generalization ability of the model, adjust the hyperparameters (such as learning rate, regularization term, etc.). If there is overfitting, you can consider using cross-validation, regularization, etc. Use the test set to evaluate the final performance of the model, to ensure that the model also has good prediction ability on unknown data.

[0051] S103, according to the first comprehensive behavior feedback value, the first basis stimulation frequency and the preset first target point stimulation intensity calculation formula, the first adjustment stimulation frequency of the first target point is calculated, and the second adjustment stimulation frequency of the second target point is calculated according to the first comprehensive behavior feedback value, the second basis stimulation frequency and the preset second target point stimulation intensity calculation formula.

[0052] The preset first target point stimulation intensity calculation formula and the preset second target point stimulation intensity calculation formula ky are formulas for adjusting the intensity of magnetic stimulation of each target point according to the feedback data of the patient.

[0053] The first adjustment stimulation frequency and the second adjustment stimulation frequency can be new stimulation frequencies of each target point compared with the original basis frequency after feedback calculation. This frequency will reflect the treatment feedback of the patient and the response of the target point.

[0054] The first comprehensive behavior feedback value, the first basis stimulation frequency can be substituted into the preset first target point stimulation intensity calculation formula to calculate the first adjustment stimulation frequency of the first target point. The first comprehensive behavior feedback value, the second basis stimulation frequency is substituted into the preset second target point stimulation intensity calculation formula, and the preset second target point stimulation intensity calculation formula.

[0055] On the basis of the above technical solutions, optionally, the preset first target point stimulation intensity calculation formula is:

[0056] I1(t)=α·(B(t)) δ ·F1(t)+β·(1-B(t)) δ ·F2(t);

[0057] Wherein, I1(t) is the first adjustment stimulation frequency; alpha is the preset first target point stimulation weight; (B(t)) is the first comprehensive behavior feedback value; delta is the preset behavior feedback adjustment factor; F1(t) is the first basis stimulation frequency of the first target point;

[0058] The preset second target point stimulation intensity calculation formula is:

[0059] I2(t)=β·(1-B(t)) δ ·F2(t);

[0060] wherein I2(t) is the second adjusted stimulation frequency; β is the preset second target stimulation weight; and F2(t) is the second basal stimulation frequency of the second target.

[0061] In this scheme, δ is used to control how the stimulation intensity is adjusted during the treatment according to the changes in different conditions (such as cognitive, emotional or physiological feedback). This parameter determines how the basal stimulation intensity is preliminarily adjusted according to the individual characteristics of the patient or the treatment target. It is generally set by the clinician based on the patient's condition, historical treatment feedback and treatment plan. For example, for different diseases or target areas, the amplitude of the change in stimulation intensity may be different, so different δ values need to be set.

[0062] α represents the relative importance or priority of the first target during the treatment. It determines the relative influence of the stimulation intensity of the first target on other targets or treatment targets. A larger α means that the first target contributes more to the treatment effect, so a higher stimulation intensity or higher attention is needed.

[0063] β represents the relative importance or priority of the second target in the treatment. Through β, the stimulation intensity of the second target can be controlled to balance the effects of the two targets.

[0064] The clinician will set the initial weights based on a large amount of historical treatment data of patients or the results of clinical research. According to the patient's condition and treatment target and the specific needs of the patient, these data can help the doctor understand which target has a greater impact on the treatment effect of a specific patient group, so as to adjust α and β.

[0065] S104, controlling the magnetic stimulator to perform double-target synchronous stimulation according to the first adjusted stimulation frequency and the second adjusted stimulation frequency;

[0066] The magnetic stimulator can be a medical device that generates a high-intensity magnetic field through electromagnetic principles, commonly used for the treatment of nervous system-related diseases such as depression, anxiety, chronic pain, cognitive impairment, etc. The magnetic stimulator acts on the brain or other target areas through a non-invasive method, using the strength and frequency of the magnetic field to regulate neural activity. Common forms of magnetic stimulation include transcranial magnetic stimulation and repetitive transcranial magnetic stimulation.

[0067] The probe of the magnetic stimulator can be positioned at the exact location of the first target and the second target, and the stimulation parameters of the magnetic stimulator are set according to the first adjusted stimulation frequency and the second adjusted stimulation frequency, and then the magnetic stimulator is started to perform double-target synchronous stimulation. At this time, the magnetic stimulator will act on both targets at the same time, applying electromagnetic stimulation.

[0068] In the embodiments of the present application, the first cognitive performance feedback value, the first emotional response score feedback value and the first physiological data feedback value of the user are obtained, and a first comprehensive behavior feedback value is generated according to the first cognitive performance feedback value, the first emotional response score feedback value and the first physiological data feedback value; the disease data and the treatment target data of the user are obtained, and a first basic stimulation frequency of the first target point and a second basic stimulation frequency of the second target point are determined according to the disease data and the treatment target data; a first adjustment stimulation frequency of the first target point is calculated according to the first comprehensive behavior feedback value, the first basic stimulation frequency and a preset first target point stimulation intensity calculation formula, and a second adjustment stimulation frequency of the second target point is calculated according to the first comprehensive behavior feedback value, the second basic stimulation frequency and a preset second target point stimulation intensity calculation formula; the magnetic stimulator is controlled to perform double-target point synchronous stimulation according to the first adjustment stimulation frequency and the second adjustment stimulation frequency. Through the above-mentioned double-target point synchronous stimulation method based on the magnetic stimulator, the basic stimulation frequency of each target point is determined according to the disease data and the treatment target, and then the adjustment is performed in combination with the feedback value of the patient, so that the frequency and intensity of each treatment can be ensured to be most suitable for the current health status and treatment target of the patient. By automatically calculating the stimulation frequency, intensity and adjustment parameters, the interference of human operation is reduced, the consistency and standardization of each treatment are ensured to be high, which is helpful to improve the repeatability of the treatment and ensure that the effect of the treatment is more stable.

[0069] Embodiment two

[0070] Figure 2 is a flowchart of the double-target point synchronous stimulation method based on the magnetic stimulator provided in the embodiments of the present application, as Figure 2 shown, the specific method comprises the following steps:

[0071] S201, obtaining Stroop test data, digital span test data and WCST test data of the user, and determining a first cognitive performance feedback value according to the Stroop test data, the digital span test data and the WCST test data.

[0072] The Stroop test is a classic cognitive psychology test used to assess attention control and information processing speed. The test tests the conflict resolution ability of the subject by requiring the subject to match color words with stimuli that are inconsistent with the color itself. The Stroop test data can be performance data in the test, which usually includes the following contents: reaction time: the time required by the subject to answer correctly. Accuracy: the percentage of the subject to complete the test correctly. Number of errors: the number of questions answered incorrectly by the subject during the test. Then the Stroop test data is determined by the following formula:

[0073] Stroop test data = a1 x Reaction Time + a2 x Accuracy + a3 x Error Count

[0074] where a1 is a preset reaction time weight; Reaction Time is the reaction time; a2 is a preset accuracy weight; Accuracy is the accuracy; a3 is a preset error count weight; and Error Count is the error count.

[0075] The number span test is used to assess working memory and attention. In the test, the subject needs to hear a series of numbers and repeat them after listening. The difficulty of the test will gradually increase, and the tester needs to recall the sequence and restate the numbers in the correct order. The number span test data usually includes: direct number span: the length of the number sequence that the subject can correctly recall. Reverse number span: the length of the number sequence that the subject can correctly recall in reverse order. The number span test data can be determined by the following formula:

[0076] Number span test data = b1 x Direct Span + b2 x Reverse Span

[0077] where b1 is a preset direct number span weight; Direct Span is the direct number span weight; b2 is a preset reverse number span weight; and Reverse Span is the reverse number span.

[0078] WCST can be a cognitive psychology task used to assess cognitive flexibility, abstract reasoning ability and problem solving ability. In the test, the subject needs to classify the cards according to different rules, and adjust their classification strategy according to the feedback. WCST test data usually includes: number of categories: the number of correct classifications completed. Error number: the number of incorrect classifications. The number of times of first correct functional classification. The WCST test data can be calculated by the following formula:

[0079] WCST test data = c1 x Number + c2 x Error + c3 x First Correct

[0080] where c1 is a preset category number weight; Number is the category number; c2 is a preset error number weight; Error is the error number; c3 is a preset functional classification weight; and First Correct is the number of first correct functional classification.

[0081] The first cognitive performance feedback value can be a composite index calculated from the results of multiple cognitive tests (such as Stroop, Digit Span Test, WCST) to assess the cognitive performance of the subject at a specific time point. It reflects the subject's performance on these cognitive tasks, which is usually calculated based on the following factors: Stroop test data (reaction time, accuracy, etc.), digit span test data (correct digit sequence length, etc.), WCST test data (number of errors, classification completion, etc.).

[0082] Each test data (Stroop test data, digit span test data, and WCST test data) can be collected. Due to the different scales and units of each test, the data of each test needs to be standardized to ensure that they can be compared under the same standard. Common standardization methods include: Z-score standardization: subtract the mean from each test data and divide by the standard deviation. Percentile: convert test results into percentages to reflect relative performance. Then, the standardized data is weighted according to certain weights to obtain the first cognitive performance feedback value. The first cognitive performance feedback value can be determined by the following formula:

[0083] The first cognitive performance feedback value

[0084] = d1 x Stroop test data + d2 x digit span test data + d3

[0085] x WCST test data

[0086] Where d1 is the preset Stroop test data weight; d2 is the preset digit span test data weight; d3 is the preset WCST test data weight.

[0087] S202, obtain the user's emotional self-rating scale, and determine the user's first emotional response score feedback value according to the user's emotional self-rating scale.

[0088] Emotional self-rating scale is a tool for assessing an individual's current emotional state, usually consisting of a series of questions designed to understand the individual's emotional experience at a specific time point. They are usually filled out through self-reporting. Emotional self-rating scales can help quantify an individual's emotional state for assessment in psychology, medicine or other fields.

[0089] The first emotional response score feedback value can be a numerical value based on the results of the user's emotional self-rating scale. It is a quantitative expression of the user's emotional state, usually used to reflect the intensity, duration and type of emotion. This value can be calculated by weighted average or through a certain model based on multiple items in the emotional scale (such as pleasure, activation, anxiety, depression, etc.).

[0090] The user fills out a mood self-report scale, such as the Self-Assessment Manikin (SAM) or the DASS-21. These scales can be presented in the form of a questionnaire and ask the user to choose the appropriate answer based on their actual feelings. Depending on the design of the mood self-report scale, there will be different scoring criteria for each dimension (e.g. pleasantness, activation, depression, anxiety, etc.) (e.g. Likert scale, usually 1 to 5 or 1 to 7 levels). These data need to be converted into standardized numerical values (e.g. Z-score standardization or normalized to the range of 0-100). For example, in the DASS-21 scale, the score of anxiety is a 1-4 scale (e.g. 1 = none, 4 = very severe). These scores may need to be converted into numerical form or normalized. Based on the standardized scale scores, the following methods can be used to calculate the first emotional response score feedback value: If the scale contains multiple dimensions (e.g. pleasantness, activation, anxiety, depression, etc.), each dimension can be assigned a weight and a weighted average can be calculated. Standardize all scale results to a unified range (e.g. 0 to 100), then synthesize a feedback value according to the positive and negative values of the emotions (e.g. pleasantness is a positive emotion, anxiety is a negative emotion). The final output is the first emotional response score feedback value, which reflects the user's emotional state. In health management, psychological intervention or treatment, this feedback value will help to develop subsequent treatment or intervention strategies.

[0091] S203, obtaining the overall physiological data of the user, and determining the first physiological data feedback value of the user according to the overall physiological data of the user.

[0092] The overall physiological data can be a set of various data related to the physiological state of an individual, which is usually used to reflect the individual's health status, physical condition, or other physiological indicators. Specifically, the overall physiological data can include the following categories: heart rate: usually represents the number of heartbeats per minute, reflecting heart health and physical activity level. Blood pressure: reflects the pressure generated when the heart pumps blood, usually including systolic and diastolic blood pressure. Respiratory rate: the number of breaths per minute, usually related to the individual's lung function and metabolic state. Body temperature: reflects the individual's thermal balance state, usually used to monitor the presence of infection or other health problems. Blood oxygen saturation: usually measured by pulse oximeter, reflecting the concentration of oxygen in the blood, indicating lung and heart function. Weight, height, BMI: by calculating the ratio of weight to height, reflecting the individual's obesity level. These data can be collected in real time by various devices (such as sphygmomanometer, electrocardiograph, thermometer, pulse oximeter, smart bracelet, etc.).

[0093] The first physiological data feedback value can be a comprehensive feedback value derived from the overall physiological data of the user. This value is used to reflect the physiological state of the user, which can be calculated according to multiple physiological parameters. This feedback value is usually used to evaluate the health status of the individual or whether there is a potential physiological problem.

[0094] The physiological data of the user can be collected using different measurement tools or devices. For example, heart rate data is obtained through a smart bracelet, blood oxygen level is obtained using a pulse oximeter, body temperature is obtained using a thermometer, blood pressure is obtained using a sphygmomanometer, etc. Ensure that the time and conditions of data collection are relatively consistent to ensure the accuracy and comparability of the data. The data can be collected through regular monitoring (such as every hour or every day) or at a specific health check. Standardize or normalize the collected physiological data so that different indicators can be compared on the same scale. For example, the normal range of blood pressure is usually 90 / 60mmHg to 120 / 80mmHg, while the normal range of heart rate is 60-100bpm. Through standardization, these indicators can be converted to the same scale, such as the range of 0 to 100. For each physiological indicator, convert each physiological data to a standardized score between 0 and 100, then assign different weights to each physiological data item (such as heart rate, blood oxygen, body temperature, etc.), and then calculate their weighted average. After calculation, a value (for example, in the range of 0 to 100) is obtained, which can be used as the first physiological data feedback value.

[0095] S204, generating a first comprehensive behavior feedback value according to the first cognitive performance feedback value, an emotional response score feedback value, and the first physiological data feedback value.

[0096] The first comprehensive behavior feedback value can be formed in the form of a weighted average:

[0097] CBF1 = w1 × CBF cognitive + w2 × CBF emotion + w3 × CBF physio ;

[0098] CBF1 is the first comprehensive behavior feedback value; w1 is the preset cognitive performance weight; CBF is the first cognitive performance feedback value; w2 is the preset emotional response weight; CBF is the first emotional response score feedback value; w3 is the preset physiological data weight; CBF is the first physiological data feedback value. cognitive emotion physio

[0099] S205, obtaining disease data and treatment target data of the user, determining a first base stimulation frequency of a first target point and a second base stimulation frequency of a second target point according to the disease data and the treatment target data.​​​

[0100] S206, calculating a first adjustment stimulation frequency of the first target point according to the first comprehensive behavior feedback value, the first basic stimulation frequency, and a preset first target point stimulation intensity calculation formula, and calculating a second adjustment stimulation frequency of the second target point according to the first comprehensive behavior feedback value, the second basic stimulation frequency, and a preset second target point stimulation intensity calculation formula.

[0101] S207, controlling the magnetic stimulator to perform double-target point synchronous stimulation according to the first adjustment stimulation frequency and the second adjustment stimulation frequency.

[0102] In the embodiment, cognitive data such as Stroop test, digital span test, and WCST test, physiological and emotional data such as emotional self-rating scale and overall physiological data are integrated to generate the first comprehensive behavior feedback value, which can provide comprehensive and personalized health feedback for the user and greatly improve the accuracy, effect, and experience of health management and treatment.

[0103] On the basis of the above technical solution, after the double-target point synchronous stimulation of the magnetic stimulator is controlled according to the first adjustment stimulation frequency and the second adjustment stimulation frequency, the method further includes:

[0104] real-time updating of overall physiological state data of the user, real-time acquisition of overall pain data of the user, input of the preset behavior feedback adjustment factor, the overall physiological state data, and the overall pain data into a preset adjustment model, and determination of whether the preset behavior feedback adjustment factor needs to be updated;

[0105] If the preset behavior feedback adjustment factor needs to be updated, the preset behavior feedback adjustment factor is updated through the preset adjustment model;

[0106] real-time acquisition of brain region activity monitoring data of each target point of the user, physiological state data of each target point, and pain data of each target point, input of the preset first target point stimulation weight, the preset second target point stimulation weight, the brain region activity monitoring data of each target point, the physiological state data of each target point, and the pain data of each target point into a preset adjustment model, and determination of whether the preset first target point stimulation weight and the preset second target point stimulation weight need to be updated;

[0107] If the preset first target point stimulation weight and the preset second target point stimulation weight need to be updated, the preset first target point stimulation weight and the preset second target point stimulation weight are updated through the preset adjustment model;

[0108] Correspondingly, after the preset first target point stimulation weight and the preset second target point stimulation weight are updated through the preset adjustment model, the method further includes:

[0109] The preset first target point stimulation intensity calculation formula is updated according to the updated preset behavior feedback adjustment factor and the preset first target point stimulation weight, and the preset second target point stimulation intensity calculation formula is updated according to the updated preset behavior feedback adjustment factor and the preset second target point stimulation weight.

[0110] In this solution, the overall pain data can refer to the user's pain perception level within a certain time period, which is usually quantified by standardized pain scales or scoring systems. Common pain assessment tools include: Visual Analog Scale: Users select a value within the range of 0 to 10 to express the intensity of pain, with 0 representing no pain and 10 representing the most intense pain. Numerical Rating Scale: Users select a number within the range of 1 to 10 to represent the intensity of pain. Pain mask: used to assess the nature, intensity of pain and its impact on mood and function. These data can be collected from user self-report, clinical observation or sensors (such as pressure sensors, galvanic skin response, etc.) to monitor changes in pain in real time.

[0111] The preset adjustment model can be a calculation model or algorithm, usually implemented through machine learning or rule engine, used to dynamically adjust behavior feedback factors, stimulation weights and other parameters based on real-time physiological, brain activity, pain and other data. The purpose of the adjustment model is to ensure that the intervention measures (such as magnetic stimulation) can be adjusted according to the real-time state of the individual to achieve the best effect. The model can update the preset parameters in real time according to the user's physiological and psychological state. For example: Behavior feedback adjustment factor: adjust the stimulation frequency or intensity based on real-time cognitive performance, emotional feedback and physiological data. Stimulation weight: in the case of double target point stimulation, adjust the relative weight of each target point according to real-time feedback to ensure the accuracy of stimulation.

[0112] Brain activity monitoring data can be real-time data reflecting the activity of different brain regions collected through electroencephalogram, functional magnetic resonance imaging, near-infrared spectroscopy or other brain function monitoring devices. These data help analyze the brain's response patterns, especially when performing neuroregulation (such as magnetic stimulation). Electroencephalogram: measures the electrical activity of the brain through electrodes, commonly used to analyze the activity status of different brain regions such as alpha waves and beta waves.

[0113] Functional magnetic resonance imaging: detects changes in brain blood flow to indirectly reflect brain activity. Near-infrared spectroscopy: assesses brain activity by detecting changes in oxygenated hemoglobin concentration in the brain.

[0114] Physiological state data for each target can be physiological responses specific to the target area during treatment or stimulation. These data do not involve the whole body's physiological state, but focus on the specific area stimulated during treatment. Specifically, it can include: local blood flow (e.g. blood circulation in skin or muscle area), local skin temperature: especially when heat therapy, cold therapy or other local stimulation, the temperature change of the stimulation area can affect the treatment effect. Local skin electrical response: unlike the whole body's GSR, the local skin electrical response reflects the skin sweat gland activity of the stimulation area.

[0115] Pain data for each target can refer to the pain level feedback for each target during or after stimulation. These data are usually collected through self-reporting or sensory devices. Each target can correspond to different parts or targets, for example, stimulation of head targets and back targets can cause different pain responses. Pain data can include: pain intensity: record the user's subjective feeling of pain through VAS, NRS and other scales. Pain location: record the specific area where the pain occurs, help to identify which target stimulation causes pain. Pain duration: measure the duration of pain, reflect the discomfort degree of stimulation to the user.

[0116] The overall physiological state data can come from wearable devices or real-time monitoring systems. Common devices include heart rate monitors, blood pressure monitors, thermometers, galvanic skin response sensors, respiration sensors, etc. Real-time collected physiological data needs to be connected to the data processing platform through sensors. The platform needs to clean, filter, synchronize and standardize the collected data to ensure the accuracy and stability of the data. By setting the data transmission and processing frequency, real-time updating of physiological state data can be achieved. The device and data processing system need to have low delay response capability. Overall pain data is usually obtained through user's subjective feedback (such as pain rating scale or questionnaire) and possible auxiliary sensors (such as pain rating sensors, body temperature sensors, etc.). The user's pain state can be inferred by combining physiological sensor data through regular surveys or user-initiated feedback during treatment. According to the pain rating (for example, a rating system from 0 to 10) and physiological data (such as muscle tension, galvanic skin response, etc.), the pain state is quantified in real time. Then input the above data into the preset adjustment model, the model needs to determine whether to update the behavior feedback adjustment factor according to these inputs. This process can be achieved through a feedback control system. For example, when the overall physiological data and pain data deviate from the expected threshold, the model may require adjustment of the behavior feedback factor. The output of the model determines whether the behavior feedback adjustment factor needs to be updated. The model can determine whether to update based on certain rules, algorithms or thresholds. If it needs to be updated, it can be done through the calculation formula of the adjustment factor. For example, use weighted average, optimization algorithm or other control strategies to update the factor, so that future feedback and treatment adjustment are more accurate.

[0117] Then real-time data monitoring is performed for each target point, common methods include using electroencephalogram (EEG), functional magnetic resonance imaging (fMRI), local electromyography (EMG), galvanic skin response sensor, temperature sensor, etc. According to the characteristics of each target point, analyze its brain activity, local physiological state (such as blood flow, muscle response, temperature) and pain perception. The data of each target point will have separate monitoring and feedback. The data of each target point (including brain activity, local physiological state and pain data) is input into the model together with the preset stimulation weights of the first target point and the second target point. The model will calculate and adjust the weights of the target point stimulation according to these data. If the model judges that the physiological state or pain response of the target point exceeds a certain threshold, the stimulation weight may be updated. The model outputs the new stimulation weight to control the stimulation intensity of the target point. Finally, by updating the calculation results of the model, combining the real-time data of the user, the weights of each factor in the formula are adjusted to achieve the best treatment effect.

[0118] The training steps of the preset adjustment model are:

[0119] First, collect historical data: overall physiological state data of the user: such as heart rate, blood pressure, body temperature, respiratory rate, etc. Overall pain data of the user: including pain score, pain area, pain change, etc. Brain region activity monitoring data of each target point: such as electroencephalogram (EEG) data, brain function imaging data, etc. Physiological state data of each target point: for example, physiological response data for a specific target point, such as local blood flow change, temperature change, etc. Pain data of each target point: pain perception data related to each target point. Preset behavior feedback adjustment factor: historical feedback factor for model training, including previously adjusted factor values. Preset target point stimulation weight: stimulation weight in historical data, used to learn the relationship between stimulation intensity and feedback adjustment. Ensure that all collected historical data is consistent in format, remove noise and normalize. Extract important features from historical data to form a training set. Then train a multi-input model, the input features include historical overall physiological state data, pain data, brain region activity data, etc. The output of the model is the behavior feedback adjustment factor and the target point stimulation weight, that is, the model will predict the adjustment factor and stimulation weight needed under a certain physiological and pain state. By using the behavior feedback adjustment factor in the historical data as the target variable, the model learns how to optimize these factors according to the physiological, pain and brain region activity data. Then calculate the loss between the historical feedback behavior adjustment factor and the adjustment factor output by the model to optimize the prediction ability of the model. Use optimization algorithms such as gradient descent to iteratively update the model weights so that the prediction result approaches the expected feedback factor. The trained model can accept real-time updated user data (such as real-time physiological state, pain data, etc.) and adjust the behavior feedback factor and target point stimulation weight in real time according to these data.

[0120] In this scheme, by updating the physiological state, pain data and brain region activity monitoring data in real time, the system can dynamically adjust the behavior feedback factor and stimulation weight to achieve personalized and precise treatment. According to the changes of the user, continuous optimization is carried out to ensure the continuity and stability of the treatment. At the same time, the intelligent adjustment process reduces the manual intervention and improves the efficiency and convenience of the treatment.

[0121] On the basis of the above technical scheme, optionally, after updating the preset first target point stimulation weight to update the preset first target point stimulation intensity calculation formula, and updating the preset second target point stimulation weight to update the preset second target point stimulation intensity calculation formula, the method further comprises:

[0122] calculating a first adjusted stimulation frequency of the first target according to the first comprehensive behavior feedback value, the first basic stimulation frequency and the updated preset first target stimulation intensity calculation formula, and calculating a second adjusted stimulation frequency of the second target according to the first comprehensive behavior feedback value, the second basic stimulation frequency and the updated preset second target stimulation intensity calculation formula;

[0123] controlling the magnetic stimulator to perform double-target synchronous stimulation according to the first adjusted stimulation frequency and the second adjusted stimulation frequency;

[0124] Correspondingly, after controlling the magnetic stimulator to perform double-target synchronous stimulation according to the first adjusted stimulation frequency and the second adjusted stimulation frequency, the method further comprises:

[0125] If it is identified that there is an updated preset behavior feedback adjustment factor and / or an updated preset first target stimulation weight and a preset second target stimulation weight, updating the preset first target stimulation intensity calculation formula according to the updated preset behavior feedback adjustment factor and / or the updated preset first target stimulation weight, and updating the preset second target stimulation intensity calculation formula according to the updated preset behavior feedback adjustment factor and / or the updated preset second target stimulation weight;

[0126] calculating a first adjusted stimulation frequency of the first target according to the first comprehensive behavior feedback value, the first basic stimulation frequency and the updated preset first target stimulation intensity calculation formula, and calculating a second adjusted stimulation frequency of the second target according to the first comprehensive behavior feedback value, the second basic stimulation frequency and the updated preset second target stimulation intensity calculation formula;

[0127] controlling the magnetic stimulator to perform double-target synchronous stimulation according to the first adjusted stimulation frequency and the second adjusted stimulation frequency until the treatment process is completed.

[0128] In the present solution, the first adjustment stimulation frequency of the first target point can be calculated by combining the first comprehensive behavior feedback value, the first basic stimulation frequency, and the updated preset first target point stimulation intensity calculation formula. Specifically, the stimulation frequency of the first target point needs to be dynamically adjusted according to the user's comprehensive behavior feedback state, which involves information obtained from the user's current physiological, emotional, and cognitive data to accurately set the stimulation frequency to achieve optimal treatment effect. The calculation formula will combine the first basic stimulation frequency (which is the initial preset value) and the first target point stimulation intensity calculation formula, which will be dynamically adjusted according to the user's feedback value to determine the adjusted first target point stimulation frequency. Similarly, the second adjustment stimulation frequency of the second target point can also be calculated according to similar logic. By inputting the first comprehensive behavior feedback value, the second basic stimulation frequency, and the updated preset second target point stimulation intensity calculation formula, the adjustment frequency of the second target point can be obtained. Here, the stimulation frequency of the second target point also needs to consider the user's current physiological, emotional, and other states to ensure the accuracy and individualization of the treatment. After calculating the first adjustment stimulation frequency of the first target point and the second adjustment stimulation frequency of the second target point, these two frequencies will be used to control the magnetic stimulator to achieve synchronous stimulation of the two target points. This synchronous stimulation can ensure that the treatment process applies appropriate stimulation to both target points at the same time, thereby achieving more effective treatment. During the treatment process, if the updated preset behavior feedback adjustment factor or the changes in the updated preset first target point stimulation weight and second target point stimulation weight are identified, the system will adjust the stimulation intensity calculation formula according to these updated values. That is, when the preset behavior feedback adjustment factor or the target point stimulation weight changes, the updated formula will be used to recalculate the stimulation intensity of the first target point and the second target point. The updated factors and weights adjust the stimulation intensity calculation formula so that the stimulation frequency and intensity of each target point are more in line with the treatment needs and the user's real-time state until the treatment process is completed.

[0129] In the present solution, by continuously monitoring the user's physiological state, pain level, and behavior feedback, the treatment strategy can be adjusted in a timely manner to ensure that the stimulation frequency and intensity always match the user's current state, improving the effectiveness and comfort of the treatment.

[0130] On the basis of the above technical solution, optionally, the overall pain data of the user is acquired in real time, including:

[0131] The physiological stress index data, pain score data, and facial expression image of the user are acquired in real time, and the physiological stress index data, pain score data, and facial expression image are input into a preset pain analysis model to obtain overall pain data.

[0132] In this solution, physiological stress indicator data can be obtained by monitoring physiological responses to reflect the body's stress state, including but not limited to heart rate, blood pressure, respiratory rate, skin electrical response (EDA), blood oxygen saturation (SpO2), body temperature, etc. These indicators reflect the human body's response to pain, stress or other stressors. Physiological monitoring devices such as heart rate monitors, blood pressure monitors, respiratory monitors, skin electrical response sensors, etc. can be used. Real-time data is collected and transmitted to the system for analysis.

[0133] Pain score data can be the subjective report of pain perception by the patient, usually using a numerical rating scale (such as the Visual Analog Scale VAS, the Numerical Pain Rating Scale NRS) or a descriptive scale to record the patient's assessment of the intensity of pain. It can be obtained through a self-assessment questionnaire by the patient (e.g. NRS score 0-10, 0 representing no pain, 10 representing the most intense pain). It can also be obtained through interaction with the patient or through an automatic scoring system of medical devices.

[0134] Facial expression images can be images of the user's face captured by a camera, through image processing and facial recognition technology (such as facial expression recognition algorithms), analyzing the user's facial muscle activity to assess their emotions and pain response. Common expressions include frowning, pursed lips, etc., which are often associated with pain or discomfort. High-definition cameras or facial recognition devices can be used for real-time shooting. Facial expression recognition technology (e.g. OpenFace, Face++, etc.) is used to analyze the user's facial expressions.

[0135] The pre-set pain analysis model can be a multi-dimensional analysis model that combines machine learning, data analysis and other technologies to integrate the user's physiological data, pain score data and facial expression data to obtain a comprehensive pain status assessment. The purpose of this model is to predict the user's pain perception based on multiple input data sources and provide data support for subsequent treatment or intervention.

[0136] Physiological stress indicator data, pain score data and facial expression images can be obtained in real time from sensors or devices. Physiological stress indicator data is obtained by connecting to physiological monitoring devices such as heart rate bands, skin electrical sensors. Pain score data is obtained through a self-assessment questionnaire by the patient (numerical scale) or real-time input. Facial expression data is captured by a camera and analyzed in real time using facial expression recognition algorithms. The collected data is then cleaned, standardized and feature extracted to ensure that the data input into the model is standardized and free of noise interference. The physiological data, pain score and facial expression image are integrated into a comprehensive data set for subsequent analysis. The integrated data is input into the pre-set pain analysis model. The model will derive "overall pain data" from the input data, which may be a comprehensive pain score or pain level reflecting the user's current pain perception.

[0137] The training step of the pre-set pain analysis model is:

[0138] A large amount of historical data can be collected, which should include: Physiological stress indicator data: such as heart rate, blood pressure, skin galvanic response, respiratory rate, etc. Pain score data: such as patient's self-reported pain scores (such as visual analog scale, numerical rating scale, etc.). Facial expression image data: facial expression images obtained through the camera, which are converted into numerical features through facial expression recognition algorithms. Then remove missing values, outliers, duplicate data, etc. to ensure data quality. Label the pain score, pain level, etc. information to ensure that the data can be aligned with the target of model training. Different types of data may have different dimensions and distributions, and need to be normalized or standardized to ensure that the scales of all input data are consistent and suitable for model training. According to the needs of the model, extract effective features from the original data, feature extraction is crucial to the performance of the model, usually includes: Physiological data features: such as average heart rate, heart rate variability, skin galvanic response, etc. Pain score features: directly use the patient's pain score data. Facial expression image features: extract numerical features of facial expressions through facial expression recognition algorithms (such as OpenFace, deep convolutional neural network, etc.), such as facial muscle activity intensity, facial expression change, etc. Then create labels: generate corresponding label data through the collected pain score data. The label can be: the severity of pain (pain score) the overall pain perception score or numerical value (overall pain data). Then select the model architecture, multi-input model: the model needs to handle different types of data at the same time, so using a multi-input model (such as parallel input layer in neural network) will be an ideal choice. The main inputs include: Physiological stress indicator data (such as heart rate, skin galvanic response, etc.) Pain score data (provided by the user), facial expression image data (image features extracted through CNN). Model type: Deep neural network (DNN): suitable for handling high-dimensional data, especially for extracting complex features from physiological data and images. Convolutional neural network (CNN): used to process facial expression image data and extract features from images. Regression model: suitable for predicting continuous pain scores (such as pain intensity or pain perception numerical value). Then pass all input data (physiological stress indicator data, pain score data, and facial expression image data) into the neural network for forward propagation, calculate the predicted value of the model. Use appropriate loss functions, such as mean squared error (MSE), to measure the difference between the model's predicted pain score and the true label. Calculate the gradient of the loss function through the backpropagation algorithm and update the model's parameters (i.e. neural network weights) to minimize the prediction error. Use common optimization algorithms (such as Adam, SGD, etc.) to update the model weights to improve prediction accuracy. Then divide the training set and validation set, use the validation set to evaluate the generalization ability of the model. Common evaluation indicators include: prediction error: such as mean squared error (MSE) or mean absolute error (MAE), to measure the accuracy of pain score prediction.Training stability: Check if the loss value of the model is steadily decreasing, and if there are signs of overfitting or underfitting. Further evaluate the stability and robustness of the model through cross-validation (e.g. K-fold cross-validation). Then adjust the hyperparameters of the model (e.g. learning rate, number of layers, number of neurons per layer, etc.) to improve the performance of the model. Methods such as grid search, random search or Bayesian optimization can be used for hyperparameter tuning. An important value of the model output is the overall pain perception data predicted, i.e. the intensity of pain perception predicted from physiological stress indicators, pain scores and facial expression images.

[0139] In this solution, by real-time acquisition of physiological stress data, pain scores and facial expression images and combining with the pain analysis model, a more accurate and personalized pain management plan can be provided for the patient, improving the efficiency and effectiveness of treatment.

[0140] On the basis of the above technical solution, optionally, after controlling the magnetic stimulation instrument to perform double-target point synchronous stimulation according to the first adjusted stimulation frequency and the second adjusted stimulation frequency until the completion of the treatment process, the method further comprises:

[0141] If the preset evaluation time interval is reached, a second cognitive performance feedback value, a second emotional response score feedback value and a second physiological data feedback value of the user are obtained, and a second comprehensive behavior feedback value is generated according to the second cognitive performance feedback value, the second emotional response score feedback value and the second physiological data feedback value;

[0142] According to the first comprehensive behavior feedback value, the second comprehensive behavior feedback value and a preset score formula, a treatment effect score is calculated, and the treatment effect score is sent to the control center.

[0143] In this solution, the preset evaluation time interval refers to a specific time interval set during the treatment process. Whenever this time point is reached, the system will perform an evaluation and generate a new comprehensive behavior feedback value according to the feedback data, and calculate the treatment effect score.

[0144] The second cognitive performance feedback value can refer to the performance score of the user in the cognitive aspect within the evaluation time interval. It can be measured through a series of standardized cognitive tests, questionnaires, task performance (such as memory, attention, reaction time, etc.). This data reflects the changes or improvements in cognitive ability during treatment.

[0145] The second emotional response score feedback value can refer to the emotional response score of the user within the evaluation time interval. It is usually measured through standardized emotional scales (such as anxiety, depression, degree of happiness, etc.) or physiological emotional response monitoring (such as facial expressions, heart rate changes, etc.). It reflects the changes in the emotional state of the user.

[0146] The second physiological data feedback value can refer to the physiological data feedback (such as heart rate, skin electric response, body temperature, blood pressure, etc.) of the user within the evaluation time interval. This can reflect the changes in the physiological stress or health status of the user during the treatment process.

[0147] The second comprehensive behavior feedback value can be a comprehensive value calculated by combining the second cognitive performance feedback value, the second emotional response score feedback value, and the second physiological data feedback value through a weighted formula. This is an indicator that comprehensively reflects the overall performance (cognitive, emotional, physiological) and behavior state of the user within the evaluation time interval.

[0148] The preset scoring formula can be a pre-defined calculation method for calculating the treatment effect score based on different feedback values (such as the first comprehensive behavior feedback value, the second comprehensive behavior feedback value).

[0149] The treatment effect score can be a quantitative indicator for evaluating the performance and treatment effect of the user during the treatment process. It is a result calculated based on multiple feedback data, reflecting whether the treatment is effective and whether the user's indicators (cognitive, emotional, physiological) have improved.

[0150] The control center can be a management and monitoring system that receives the treatment effect score and makes corresponding adjustments or decisions based on the score. The control center can be a software platform or an automated system responsible for real-time tracking of the treatment process, evaluation of the treatment effect, adjustment of the treatment plan, and feedback to relevant personnel.

[0151] Every time the preset evaluation time interval is reached, the second cognitive performance feedback value, the second emotional response score feedback value, and the second physiological data feedback value of the user are re-collected, the second comprehensive behavior feedback value is calculated by weighting, and then the first comprehensive behavior feedback value and the second comprehensive behavior feedback value are substituted into the preset scoring formula to calculate the treatment effect score, which is sent to the control center through wireless communication technology.

[0152] In this scheme, by regularly evaluating the cognitive, emotional, and physiological state of the user, the treatment effect score is calculated in real time by combining the comprehensive behavior feedback value. This mechanism can provide accurate treatment effect data for the control center, thereby achieving the goal of dynamically adjusting the treatment strategy and ensuring the continuous optimization and personalized management of the treatment.

[0153] On the basis of the above technical scheme, optionally, the preset scoring formula is:

[0154]

[0155] wherein Score is the treatment effect score; D i (t) is the second comprehensive behavior feedback value; D i(0) is a first comprehensive behavior feedback value; k is a preset behavior adjustment constant; t is a preset evaluation time interval.

[0156] Embodiment three

[0157] Figure 3 is a structural schematic diagram of a dual-target synchronous stimulation system based on a magnetic stimulator provided by Embodiment three of the present application, as Figure 3 shown, specifically comprising the following:

[0158] The comprehensive evaluation module 301 is configured to acquire a first cognitive performance feedback value, a first emotional response score feedback value and a first physiological data feedback value of a user, and generate a first comprehensive behavior feedback value according to the first cognitive performance feedback value, the first emotional response score feedback value and the first physiological data feedback value.

[0159] The basic stimulation frequency determination module 302 is configured to acquire disease data and treatment target data of the user, determine a first basic stimulation frequency of a first target according to the disease data and the treatment target data, and determine a second basic stimulation frequency of a second target.

[0160] The adjustment stimulation frequency determination module 303 is configured to calculate a first adjustment stimulation frequency of the first target according to the first comprehensive behavior feedback value, the first basic stimulation frequency and a preset first target stimulation intensity calculation formula, and calculate a second adjustment stimulation frequency of the second target according to the first comprehensive behavior feedback value, the second basic stimulation frequency and a preset second target stimulation intensity calculation formula.

[0161] The control module 304 is configured to control the magnetic stimulator to perform dual-target synchronous stimulation according to the first adjustment stimulation frequency and the second adjustment stimulation frequency.

[0162] Embodiment four

[0163] Figure 4 A schematic block diagram of an electronic device 400 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptops, desktops, tablets, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown here, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the present disclosure described and / or claimed in this document.

[0164] The electronic device 400 includes a computing unit 401 that can perform various appropriate actions and processes in accordance with a computer program stored in the ROM 402 or a computer program loaded into the RAM 404 from the storage unit 408. In the RAM 404, various programs and data required for the operation of the electronic device 400 can also be stored. The computing unit 401, the ROM 402, and the RAM 404 are connected to each other through a bus 404. The I / O interface 405 is also connected to the bus 404.

[0165] A plurality of components in the electronic device 400 are connected to the I / O interface 405, including: an input unit 406, such as a keyboard, a mouse, and the like; an output unit 407, such as various types of displays, a speaker, and the like; a storage unit 408, such as a magnetic disk, an optical disk, and the like; and a communication unit 409, such as a network card, a modem, a wireless communication transceiver, and the like. The communication unit 409 allows the electronic device 400 to exchange information / data with other devices through a computer network, such as the Internet, and / or various telecommunication networks.

[0166] The computing unit 401 can be various general-purpose and / or special-purpose processing components having processing and computing capabilities. Some examples of the computing unit 401 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, and the like. The computing unit 401 performs various methods and processes described above, such as the one kind of dual-target synchronous stimulation method based on a magnetic stimulator. For example, in some embodiments, the one kind of dual-target synchronous stimulation method based on a magnetic stimulator can be implemented as a computer software program that is tangibly embodied in a machine-readable medium, such as the storage unit 408. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 400 via the ROM 402 and / or the communication unit 409. When the computer program is loaded into the RAM 404 and executed by the computing unit 401, one or more steps of the one kind of dual-target synchronous stimulation method based on a magnetic stimulator described above can be performed. Alternatively, in other embodiments, the computing unit 401 can be configured to perform the one kind of dual-target synchronous stimulation method based on a magnetic stimulator by any other appropriate means, such as by means of firmware.

[0167] Various implementations of the systems and techniques described above can be realized in digital electronic circuitry, integrated circuitry, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on a chip (SOC), a programmable logic device (PLD), a computer hardware, firmware, software, and / or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0168] Program code for carrying out methods of the present disclosure can be written in any combination of one or more programming languages. The program code can be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the program code, when executed by the processor or controller, produces the functions / operations specified in the flowcharts and / or the block diagrams. The program code can be executed entirely on a machine, partially on a machine, partially on a machine as a stand-alone software package, partially on a machine and partially on a remote machine or entirely on a remote machine or server.

[0169] In the context of the present disclosure, a machine-readable medium can be a tangible medium that contains or stores a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include but is not limited to an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium will include one or more lines of electrical connections, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0170] To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having a display device for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0171] The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0172] The computer system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server can arise by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0173] It should be understood that the various forms of flow shown above can be re-ordered, added to, or deleted from without departing from the scope of the present disclosure. For example, the steps recited in the present disclosure can be performed in parallel, in series, or in a different order, without departing from the desired results of the technology disclosed in the present disclosure, and are not limited herein.

[0174] The specific embodiments described above are not intended to limit the scope of the present disclosure. Those skilled in the art will understand that various modifications, combinations, sub-combinations, and alternatives can be made to the specific embodiments without departing from the spirit and principles of the present disclosure. Any further modifications, equivalent substitutions, improvements, and the like, either presently known or later developed, that do not depart from the spirit and principles of the present disclosure are to be encompassed within the scope of the present disclosure.

[0175] The above merely provides the preferred embodiments of the present application and the applied technical principles. The present application is not limited to the specific embodiments described herein, and various obvious changes, re-adjustments and substitutions made by those skilled in the art without departing from the scope of the present application. Therefore, although the present application is described in detail through the above embodiments, the present application is not limited to the above embodiments, and can include more other equivalent embodiments without departing from the concept of the present application, and the scope of the present application is determined by the scope of the claims.

[0176] It should be understood that various forms of flow shown above can be used to reorder, add or delete steps. For example, each step described in the present application can be executed in parallel, sequentially or in a different order, as long as the desired results of the technical solutions disclosed in the present application can be achieved, which is not limited herein.

Claims

1. A dual target synchronous stimulation system based on a magnetic stimulator, characterized in that, The system comprises: a comprehensive evaluation module configured to obtain a first cognitive performance feedback value, a first emotional response score feedback value, and a first physiological data feedback value of a user, and generate a first comprehensive behavior feedback value according to the first cognitive performance feedback value, the first emotional response score feedback value, and the first physiological data feedback value; a basic stimulation frequency determination module configured to obtain disease data and treatment target data of the user, determine a first basic stimulation frequency of a first target point and a second basic stimulation frequency of a second target point according to the disease data and the treatment target data; an adjusted stimulation frequency determination module configured to calculate a first adjusted stimulation frequency of the first target point according to the first comprehensive behavior feedback value, the first basic stimulation frequency, and a preset first target point stimulation intensity calculation formula, and calculate a second adjusted stimulation frequency of the second target point according to the first comprehensive behavior feedback value, the second basic stimulation frequency, and a preset second target point stimulation intensity calculation formula; wherein the preset first target point stimulation intensity calculation formula is: I1(t) = a • (B(t)) δ • F1(t) + β • (1 - B(t)) δ • F2(t); wherein I1(t) is the first adjusted stimulation frequency, α is a preset first target point stimulation weight, (B(t)) is the first comprehensive behavior feedback value, δ is a preset behavior feedback adjustment factor, and F1(t) is the first basic stimulation frequency of the first target point; the preset second target point stimulation intensity calculation formula is: I2(t) = β · (1 - B(t)) δ • F2(t); wherein I2(t) is the second adjusted stimulation frequency, β is a preset second target point stimulation weight, and F2(t) is the second basic stimulation frequency of the second target point; a control module configured to control a magnetic stimulator to perform double-target-point synchronous stimulation according to the first adjusted stimulation frequency and the second adjusted stimulation frequency.

2. An electronic device comprising: at least one processor; and a memory connected in communication with the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform a double-target-point synchronous stimulation method based on a magnetic stimulator, the double-target-point synchronous stimulation method based on the magnetic stimulator comprising: obtaining a first cognitive performance feedback value, a first emotional response score feedback value, and a first physiological data feedback value of a user, and generating a first comprehensive behavior feedback value according to the first cognitive performance feedback value, the first emotional response score feedback value, and the first physiological data feedback value; obtaining disease data and treatment target data of the user, and determining a first basic stimulation frequency of a first target point and a second basic stimulation frequency of a second target point according to the disease data and the treatment target data; calculating a first adjusted stimulation frequency of the first target point according to the first comprehensive behavior feedback value, the first basic stimulation frequency, and a preset first target point stimulation intensity calculation formula, and calculating a second adjusted stimulation frequency of the second target point according to the first comprehensive behavior feedback value, the second basic stimulation frequency, and a preset second target point stimulation intensity calculation formula; wherein the preset first target point stimulation intensity calculation formula is: I1(t) = a • (B(t)) δ • F1(t) + β • (1 - B(t)) δ • F2(t); Wherein, I1(t) is the first adjustment stimulation frequency; a is the preset first target stimulation weight; (B(t)) is the first comprehensive behavior feedback value; δ is the preset behavior feedback adjustment factor; F1(t) is the first basis stimulation frequency of the first target; The preset second target stimulation intensity calculation formula is: I2(t) = β · (1 - B(t)) δ • F2(t); Wherein, I2(t) is the second adjustment stimulation frequency; β is the preset second target stimulation weight; F2(t) is the second basis stimulation frequency of the second target; According to the first adjustment stimulation frequency and the second adjustment stimulation frequency, control the magnetic stimulator to carry out double target synchronous stimulation.

3. The electronic device of claim 2, wherein, Obtain the first cognitive performance feedback value, the first emotional response score feedback value and the first physiological data feedback value of the user, and generate the first comprehensive behavior feedback value according to the first cognitive performance feedback value, the first emotional response score feedback value and the first physiological data feedback value, including: Obtain the Stroop test data, the digital span test data and the WCST test data of the user, and determine the first cognitive performance feedback value according to the Stroop test data, the digital span test data and the WCST test data; Obtain the emotional self-rating scale of the user, and determine the first emotional response score feedback value of the user according to the emotional self-rating scale of the user; Obtain the overall physiological data of the user, and determine the first physiological data feedback value of the user according to the overall physiological data of the user; Generate the first comprehensive behavior feedback value according to the first cognitive performance feedback value, the emotional response score feedback value and the first physiological data feedback value.

4. The electronic device of claim 3, wherein, After controlling the magnetic stimulator to carry out double target synchronous stimulation according to the first adjustment stimulation frequency and the second adjustment stimulation frequency, the method further comprises: Real-time update the overall physiological state data of the user, and real-time obtain the overall pain data of the user, input the preset behavior feedback adjustment factor, the overall physiological state data and the overall pain data into the preset adjustment model, and determine whether the preset behavior feedback adjustment factor needs to be updated; If the preset behavior feedback adjustment factor needs to be updated, update the preset behavior feedback adjustment factor through the preset adjustment model; Real-time obtain the brain region activity monitoring data of each target of the user, the physiological state data of each target and the pain data of each target, input the preset first target stimulation weight, the preset second target stimulation weight, the brain region activity monitoring data of each target, the physiological state data of each target and the pain data of each target into the preset adjustment model, and determine whether the preset first target stimulation weight and the preset second target stimulation weight need to be updated; If the preset first target stimulation weight and the preset second target stimulation weight need to be updated, update the preset first target stimulation weight and the preset second target stimulation weight through the preset adjustment model; Correspondingly, after updating the preset first target stimulation weight and the preset second target stimulation weight through the preset adjustment model, the method further comprises: updating the preset first target stimulation intensity calculation formula according to the updated preset behavior feedback adjustment factor and the preset first target stimulation weight, and updating the preset second target stimulation intensity calculation formula according to the updated preset behavior feedback adjustment factor and the preset second target stimulation weight.

5. The electronic device of claim 4, wherein, After updating the preset first target stimulation intensity calculation formula according to the updated preset first target stimulation weight, and updating the preset second target stimulation intensity calculation formula according to the updated preset second target stimulation weight, the method further comprises: calculating a first adjusted stimulation frequency of the first target according to the first comprehensive behavior feedback value, the first basic stimulation frequency and the updated preset first target stimulation intensity calculation formula, and calculating a second adjusted stimulation frequency of the second target according to the first comprehensive behavior feedback value, the second basic stimulation frequency and the updated preset second target stimulation intensity calculation formula; controlling the magnetic stimulator to perform double-target synchronous stimulation according to the first adjusted stimulation frequency and the second adjusted stimulation frequency; correspondingly, after controlling the magnetic stimulator to perform double-target synchronous stimulation according to the first adjusted stimulation frequency and the second adjusted stimulation frequency, the method further comprises: if the updated preset behavior feedback adjustment factor and / or the updated preset first target stimulation weight and the preset second target stimulation weight are identified, updating the preset first target stimulation intensity calculation formula according to the updated preset behavior feedback adjustment factor and / or the updated preset first target stimulation weight, and updating the preset second target stimulation intensity calculation formula according to the updated preset behavior feedback adjustment factor and / or the updated preset second target stimulation weight; updating the first adjusted stimulation frequency of the first target according to the first comprehensive behavior feedback value, the first basic stimulation frequency and the updated preset first target stimulation intensity calculation formula, and updating the second adjusted stimulation frequency of the second target according to the first comprehensive behavior feedback value, the second basic stimulation frequency and the updated preset second target stimulation intensity calculation formula; controlling the magnetic stimulator to perform double-target synchronous stimulation according to the first adjusted stimulation frequency and the second adjusted stimulation frequency until the treatment process is completed.

6. The electronic device of claim 4, wherein, real-time acquisition of overall pain data of the user, including: real-time acquisition of physiological stress index data, pain score data and facial expression images of the user, inputting the physiological stress index data, pain score data and facial expression images into a preset pain analysis model to obtain overall pain data.

7. The electronic device of claim 5, wherein, After controlling the magnetic stimulator to perform double-target synchronous stimulation according to the first adjusted stimulation frequency and the second adjusted stimulation frequency until the treatment process is completed, the method further comprises: if a preset evaluation time interval is reached, acquiring a second cognitive performance feedback value, a second emotional response score feedback value and a second physiological data feedback value of the user, and generating a second comprehensive behavior feedback value according to the second cognitive performance feedback value, the second emotional response score feedback value and the second physiological data feedback value; According to the first comprehensive behavior feedback value, the second comprehensive behavior feedback value and a preset scoring formula, a treatment effect score is calculated, and the treatment effect score is sent to a control center.

8. The electronic device of claim 7, wherein, The preset scoring formula is: wherein Score is a treatment effect score; D i (t) is a second integrated behavior feedback value; D i (0) is a first integrated behavior feedback value; k is a preset behavior adjustment constant; and t is a preset evaluation time interval.

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