A gyromagnetic pulse action path optimization method and system

Abstract

The application relates to the medical field and discloses a gyromagnetic pulse action path optimization method and system, which comprises the following steps: S1, collecting pulse data of a patient by using a pulse appearance analysis device to generate original pulse data; carrying out denoising treatment on the original pulse data to obtain clear pulse data; S2, based on the four-diagnosis combined reference concept, fusing the clear pulse data and other diagnosis information of the patient to generate comprehensive health evaluation data; and collecting and analyzing simulated finger manipulation according to the comprehensive health evaluation data, simulating the influence of different finger manipulations on pulse appearance, and obtaining simulated finger manipulation analysis results; through multi-site synchronous pulse appearance collection, intelligent denoising and a deep learning filtering model, the application effectively improves the clarity and authenticity of pulse data and reduces the influence of environmental noise; pulse data, tongue appearance, facial expressions, voiceprint features and metabolomics detection information are cross-modally fused, the four-diagnosis combined reference concept of traditional Chinese medicine is combined, and comprehensive evaluation of the health state of the patient is realized.

Description

Technical Field

[0001] This invention relates to the field of medical technology, specifically to a method and system for optimizing the action path of a rotating magnetic pulse. Background Technology

[0002] Among traditional medical treatments, rotating magnetic pulse therapy, as an emerging physical therapy method, has shown certain efficacy in the treatment of various diseases. However, rotating magnetic pulse therapy still faces many challenges in practical applications.

[0003] Existing magnetic pulse therapy pathways often lack precision and personalization. They typically employ general treatment pathways without fully considering individual patient differences, such as body type, disease severity, and physical condition, leading to inconsistent treatment outcomes. Some patients may not achieve optimal treatment results.

[0004] The optimization of treatment pathways lacks scientific basis and systematic evaluation methods. Determining treatment pathways often relies heavily on physician experience, lacking objective and accurate data support and scientific analytical models. Furthermore, during treatment, it is difficult to monitor treatment effects in real time and dynamically adjust the treatment pathway, hindering timely optimization of the treatment plan based on the patient's physical response, thus limiting further improvements in the efficacy of rotary magnetic pulse therapy.

[0005] Furthermore, traditional medical diagnostic information is fragmented, and multi-dimensional diagnostic information such as pulse, tongue appearance, facial expression, and voiceprint is not effectively integrated, making it impossible to comprehensively and accurately assess a patient's health status. This also poses challenges to the precise optimization of the rotating magnetic pulse therapy pathway. Therefore, developing a scientific and effective method and system for optimizing the rotating magnetic pulse action pathway is of significant practical importance. Summary of the Invention

[0006] The purpose of this invention is to provide a method for optimizing the action path of rotating magnetic pulses, in order to solve the problem that traditional medical diagnostic information is relatively scattered, and multi-dimensional diagnostic information such as pulse, tongue, facial expression, and voiceprint cannot be effectively integrated, making it impossible to comprehensively and accurately assess the patient's health status.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a method for optimizing the action path of a gyromagnetic pulse, comprising:

[0008] S1. Collect pulse data from the patient using pulse analysis equipment to generate raw pulse data; perform noise reduction processing on the raw pulse data to obtain clear pulse data.

[0009] S2. Based on the concept of integrating the four diagnostic methods, the clear pulse data is integrated with other diagnostic information of the patient to generate comprehensive health assessment data; based on the comprehensive health assessment data, simulated finger technique collection and analysis are performed to simulate the influence of different finger techniques on the pulse and obtain simulated finger technique analysis results;

[0010] S3. Based on the results of the simulated fingering analysis, the initial action path of the vortex magnetic pulse is initially adjusted to generate preliminary optimized path data; the simulated action effect of the preliminary optimized path data is evaluated to obtain the simulated action effect evaluation data.

[0011] S4. Using simulated effect evaluation data and combined with individual patient health characteristic data, further optimize the preliminary optimized path data to generate deeply optimized path data; predict the effect of the deeply optimized path data to generate effect prediction data.

[0012] S5. Based on the effect prediction data, use the four diagnostic methods combined with the health self-test function to predict the patient's health status and generate health status prediction data; compare the health status prediction data with the preset health standards to generate comparative analysis data.

[0013] S6. Based on comparative analysis data, the deep optimization path data is adjusted to generate the final optimized path data; the final optimized path data is recorded and tracked to generate path record tracking data, providing a reference for subsequent rotating magnetic pulse therapy.

[0014] Furthermore, step S1 includes the following steps:

[0015] The pulse analysis equipment is used to collect pulse data from multiple sites of the patient and generate raw pulse data from multiple sites.

[0016] Time synchronization processing is performed on raw pulse data from multiple locations to obtain synchronized pulse data.

[0017] Noise type identification is performed on the synchronous pulse data to obtain noise type data;

[0018] Select an appropriate denoising algorithm based on the noise type of the data, and perform denoising processing on the synchronous pulse data to obtain clear pulse data.

[0019] Furthermore, step S2 includes the following steps:

[0020] Extract pulse feature parameters from clear pulse data to generate pulse feature parameter data;

[0021] The pulse characteristic parameter data is correlated and matched with other diagnostic information of the patient to generate correlation matching data;

[0022] Based on the concept of integrating the four diagnostic methods, comprehensive health assessment data is generated by analyzing the relevant and matched data.

[0023] Based on comprehensive health assessment data, the effects of different finger techniques on pulse were simulated, including changes in finger pressure, speed, and position parameters, resulting in simulated finger technique analysis results.

[0024] Furthermore, step S3 includes the following steps:

[0025] Based on the results of the simulated finger technique analysis, the adjustment direction of the initial action path of the vortex pulse is determined, and adjustment direction data is generated;

[0026] Based on the adjustment direction data, the initial action path is initially adjusted to generate preliminary optimized path data;

[0027] The simulation effect of the preliminary optimized path data was evaluated using simulation software, including the evaluation of pulse energy distribution and depth parameters, to obtain simulation effect evaluation data.

[0028] Furthermore, step S4 includes the following steps:

[0029] Extract individual health characteristic data of patients from comprehensive health assessment data, including physical type, severity of disease, etc.

[0030] Based on the patient's individual health characteristics data, the preliminary optimized path data is further optimized, taking into account the impact of individual differences on pulse action, to generate deeply optimized path data;

[0031] A deep learning model is used to predict the effect of deep optimization path data, generating effect prediction data.

[0032] Furthermore, step S5 includes the following steps:

[0033] Based on the effect prediction data, the relevant algorithms in the four diagnostic methods combined with the health self-test function are used to predict the health status of patients and generate health status prediction data.

[0034] By comparing health status prediction data with preset health standards, parameters of abnormal health status are identified, and comparative analysis data is generated.

[0035] Furthermore, step S6 includes the following steps:

[0036] Based on comparative analysis data, determine the adjustment range and method of the deep optimization path data, and generate adjustment parameter data;

[0037] Based on the adjusted parameter data, the deep optimization path data is finally adjusted to generate the final optimized path data.

[0038] The database is used to record and track the final optimized path data, including information such as adjustment time and adjustment parameters, and to generate path tracking data.

[0039] Furthermore, step S7 includes the following steps:

[0040] Based on the path recording and tracking data, a detailed analysis of the optimization process of the vortex pulse action path is conducted, including the reasons and effects of each adjustment, and an optimization process analysis report is generated.

[0041] The optimization process analysis report is stored in a knowledge base to provide a reference for subsequent magnetic pulse therapy, including the selection of treatment pathways for patients with similar conditions.

[0042] Furthermore, step S2 also includes the following steps:

[0043] Using a flexible electronic pulse patch, multidimensional pulse data are simultaneously collected at the cun, guan, and chi positions of the patient to generate a raw dynamic pulse dataset. Through a deep learning denoising model, combined with environmental electromagnetic interference parameters, intelligent filtering is performed to obtain high-fidelity spatiotemporal sequence data of pulse data.

[0044] High-fidelity pulse spatiotemporal sequence data is fused with patient tongue image spectral analysis results, facial micro-expression emotion data, and voiceprint features across modalities to generate a four diagnostic features fusion matrix.

[0045] By utilizing stress concentration region data from simulated effect heatmaps and combining it with individualized biomarker characteristics obtained from patient metabolomics testing, a privacy-preserving path re-optimization is performed using a federated learning framework to generate a deeply optimized path program with adaptive adjustment capabilities. After importing this program into a smart treatment device, the device's built-in sensor array continuously collects real-time biological signals during the treatment process, dynamically corrects the treatment parameters, and generates a health management report that includes short-term efficacy predictions and long-term health trend analysis.

[0046] A gyratory magnetic pulse action path optimization system is provided for implementing the aforementioned gyratory magnetic pulse action path optimization method. The system includes:

[0047] The data acquisition unit is used to collect pulse data from patients using pulse analysis equipment, generate raw pulse data, and perform noise reduction processing on the raw pulse data to obtain clear pulse data.

[0048] The data analysis unit is used to integrate clear pulse data with other diagnostic information of patients based on the concept of combining the four diagnostic methods to generate comprehensive health assessment data; based on the comprehensive health assessment data, simulated finger technique collection and analysis is performed to simulate the influence of different finger techniques on the pulse and obtain simulated finger technique analysis results;

[0049] The data optimization unit is used to make preliminary adjustments to the initial action path of the gyromagnetic pulse based on the results of the simulated fingering analysis, and generate preliminary optimized path data; the preliminary optimized path data is then used to evaluate the simulated action effect to obtain simulated action effect evaluation data.

[0050] The effect prediction unit is used to further optimize the preliminary optimization path data by using simulated effect evaluation data and combining it with individual patient health characteristic data to generate deep optimization path data; and to predict the effect of the deep optimization path data to generate effect prediction data.

[0051] The data comparison unit is used to predict the health status of patients based on the effect prediction data and the health self-test function of the four diagnostic methods, and generate health status prediction data; the health status prediction data is compared with the preset health standards to generate comparative analysis data.

[0052] The data recording and tracking unit is used to make final adjustments to the deep optimization path data based on comparative analysis data, and generate final optimized path data; it also records and tracks the final optimized path data to generate path recording and tracking data, providing a reference for subsequent rotating magnetic pulse therapy.

[0053] Compared with the prior art, the beneficial effects of the present invention are:

[0054] (1) By simultaneously collecting pulse data from multiple sites, using intelligent noise reduction and deep learning filtering models, the clarity and authenticity of pulse data are effectively improved, and the impact of environmental noise is reduced.

[0055] (2) The pulse data is fused with information such as tongue appearance, facial expression, voiceprint features and metabolomics detection in a cross-modal manner, combined with the TCM concept of "four diagnostic methods", to achieve a comprehensive assessment of the patient's health status;

[0056] (3) By simulating the effects of different finger techniques on the pulse, and combining energy distribution and depth of action assessment, the action path of the rotating magnetic pulse is gradually optimized to improve the targetedness and effectiveness of the treatment;

[0057] (4) Introduce deep learning models and federated learning frameworks to achieve differentiated path optimization and effect prediction under privacy protection based on patient physical type, disease severity and individualized biomarkers;

[0058] (5) During the treatment process, the sensor array of the intelligent treatment device is used to collect real-time biological signals, dynamically correct the action parameters, form a closed-loop regulation mechanism, and improve the adaptability and safety of the treatment process. Attached Figure Description

[0059] Figure 1 This is a flowchart of the method of the present invention;

[0060] Figure 2 This is a system block diagram of the present invention. Detailed Implementation

[0061] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0062] Please see Figure 1-2 This invention provides a technical solution: a method for optimizing the action path of a gyromagnetic pulse, comprising:

[0063] S1. Collect pulse data from the patient using pulse analysis equipment to generate raw pulse data; perform noise reduction processing on the raw pulse data to obtain clear pulse data.

[0064] Among them, rotating magnetic pulse is a physical therapy method that combines rotating magnetic fields with pulsed magnetic fields. It generates a rotating magnetic field with pulsed characteristics through specific equipment, which acts on human tissue, utilizing the biological effects of the magnetic field on the organism. Pulse analysis equipment is a medical device used to collect and analyze human pulse information. Raw pulse data is pulse information data directly collected by the pulse analysis equipment without any processing. Denoising processing, in the field of signal processing, refers to the process of extracting useful signals from noisy signals using various mathematical methods and algorithms. In pulse data processing, denoising processing aims to remove noise interference from the raw pulse data, such as high-frequency noise and power frequency interference, to improve the quality and accuracy of the pulse data and obtain clear pulse data.

[0065] S2. Based on the concept of integrating the four diagnostic methods, the clear pulse data is integrated with other diagnostic information of the patient to generate comprehensive health assessment data; based on the comprehensive health assessment data, simulated finger technique collection and analysis are performed to simulate the influence of different finger techniques on the pulse and obtain simulated finger technique analysis results;

[0066] Among them, the concept of integrating the four diagnostic methods (inspection, auscultation and olfaction, inquiry, and palpation) is one of the basic principles of TCM diagnostics. It refers to the comprehensive analysis and judgment of information obtained from the four diagnostic methods to fully and accurately understand the patient's condition and physical status. The comprehensive health assessment data is based on the concept of integrating the four diagnostic methods and is generated by merging and analyzing clear pulse data with other diagnostic information of the patient. The simulated finger technique acquisition and analysis uses computer simulation technology to simulate the influence of different finger techniques on the pulse during TCM pulse diagnosis.

[0067] S3. Based on the results of the simulated fingering analysis, the initial action path of the vortex magnetic pulse is initially adjusted to generate preliminary optimized path data; the simulated action effect of the preliminary optimized path data is evaluated to obtain the simulated action effect evaluation data.

[0068] Among them, the initial action path of the magnetic pulse is the pre-set initial route or method of the magnetic pulse acting on the human body before the optimization of the magnetic pulse action path begins; the preliminary optimized path data is the data generated after the initial action path of the magnetic pulse is preliminarily adjusted based on the results of simulated finger technique analysis; the simulated effect evaluation is the evaluation of the magnetic pulse effect corresponding to the preliminary optimized path data using computer simulation technology.

[0069] S4. Using simulated effect evaluation data and combined with individual patient health characteristic data, further optimize the preliminary optimized path data to generate deeply optimized path data; predict the effect of the deeply optimized path data to generate effect prediction data.

[0070] Among them, the patient's individual health characteristics data reflects the unique health status and characteristics of each patient, including the patient's age, gender, medical history, genetic factors, lifestyle habits, and physical characteristics; the deeply optimized path data is generated by further optimizing the preliminary optimized path data by combining the simulated effect evaluation data with the patient's individual health characteristics data; the effect prediction is based on the deeply optimized path data, using mathematical models and computer simulation technology to predict the possible effects of the magnetic pulse acting on the human body along this path.

[0071] S5. Based on the effect prediction data, use the four diagnostic methods combined with the health self-test function to predict the patient's health status and generate health status prediction data; compare the health status prediction data with the preset health standards to generate comparative analysis data.

[0072] Among them, the Four Diagnostic Methods Integrated Health Self-Assessment Function is a function that combines the concept of the Four Diagnostic Methods Integrated Health Self-Assessment Function with modern technology to enable patients to self-test and assess their health status; the health status prediction data is data generated after predicting the health status of patients using the Four Diagnostic Methods Integrated Health Self-Assessment Function based on the effect prediction data; the preset health standards are pre-set reference indicators and ranges used to measure the human body's health status; and the comparative analysis data is data generated after comparing and analyzing the health status prediction data with the preset health standards.

[0073] S6. Based on the comparative analysis data, the deep optimization path data is adjusted to generate the final optimized path data; the final optimized path data is recorded and tracked to generate path record tracking data, providing a reference for subsequent rotating magnetic pulse therapy.

[0074] The final optimized path data is generated by making final adjustments to the deeply optimized path data based on comparative analysis data; the path record tracking data is generated by recording and tracking the final optimized path data.

[0075] It should be noted that during operation, pulse data is collected and denoised to ensure data accuracy, laying the foundation for subsequent analysis. Based on the integration of multiple diagnostic information from the four diagnostic methods, a comprehensive health assessment is conducted, making the simulated finger technique analysis more realistic. The path is gradually adjusted based on the simulation results. Through simulation evaluation, in-depth optimization, and effect prediction, it can accurately match individual patient characteristics, improve treatment targeting, and use the four diagnostic methods to predict health status. After comparison with standards, the path is finally adjusted to ensure treatment effectiveness. The final path data is recorded and tracked to provide a reference for subsequent treatment, forming a complete closed loop, which helps to improve the scientific, effective, and personalized level of rotating magnetic pulse therapy.

[0076] In one embodiment, step S1 includes the following steps:

[0077] The pulse analysis equipment is used to collect pulse data from multiple sites of the patient and generate raw pulse data from multiple sites.

[0078] Time synchronization processing is performed on raw pulse data from multiple locations to obtain synchronized pulse data.

[0079] Noise type identification is performed on the synchronous pulse data to obtain noise type data;

[0080] Select an appropriate denoising algorithm based on the noise type of the data, and perform denoising processing on the synchronous pulse data to obtain clear pulse data.

[0081] This design, which collects pulse data from multiple sites simultaneously, comprehensively reflects the body's condition. After identifying the type of noise, an appropriate algorithm is selected for denoising, which can accurately remove interference and obtain clear and accurate pulse data. The advantages are that multi-site collection avoids information omission and provides a comprehensive understanding of the body's pulse; time synchronization processing ensures the consistency of data in the time dimension, which is convenient for analysis; targeted denoising improves data quality and provides a reliable foundation for subsequent pulse data-based analysis, diagnosis, and optimization of the rotating magnetic pulse path. This allows the entire treatment process to be based on accurate data, improving the scientific nature and effectiveness of the treatment.

[0082] In one embodiment, step S2 includes the following steps:

[0083] Extract pulse feature parameters from clear pulse data to generate pulse feature parameter data;

[0084] The pulse characteristic parameter data is correlated and matched with other diagnostic information of the patient to generate correlation matching data;

[0085] Pulse feature parameter extraction involves extracting various feature parameters from clear pulse data. Assuming that the extracted parameters are...

[0086] k feature parameters constitute the pulse feature parameter vector ;

[0087] Based on the concept of integrating the four diagnostic methods, comprehensive health assessment data is generated by analyzing the relevant and matched data.

[0088] pulse feature parameter vector The data was correlated and matched with other diagnostic information of the patient, and a weighted comprehensive scoring method was used to generate comprehensive health assessment data. :

[0089]

[0090] in and These are the weights of pulse characteristics and blood test indicators, respectively. and These are the sub-weights of each pulse characteristic parameter and blood test index, and they satisfy the following conditions: ;

[0091] Based on comprehensive health assessment data, the effects of different finger techniques on pulse were simulated, including changes in finger pressure, speed, and position parameters, resulting in simulated finger technique analysis results.

[0092] This design extracts pulse characteristic parameters and correlates them with other diagnostic information, enabling the integration of multi-dimensional data. Based on comprehensive analysis using the four diagnostic methods (palpation, palpation, inquiry, and palpation), it can comprehensively assess health, simulate the effects of different finger techniques, and provide a reference for adjusting the magnetic resonance pulse path. The fusion of multi-dimensional data breaks through the limitations of single diagnosis, providing a comprehensive and accurate assessment of health. Simulating changes in finger techniques visually presents their impact on the pulse, helping to find a more realistic approach to magnetic resonance pulse action. This allows for treatment path optimization that better aligns with the patient's actual health condition, enhancing the targeted and personalized nature of treatment.

[0093] In one embodiment, step S3 includes the following steps:

[0094] Based on the results of the simulated finger technique analysis, the adjustment direction of the initial action path of the vortex pulse is determined, and adjustment direction data is generated;

[0095] Based on the adjustment direction data, the initial action path is initially adjusted to generate preliminary optimized path data;

[0096] The simulation effect of the preliminary optimized path data was evaluated using simulation software, including the evaluation of pulse energy distribution and depth parameters, to obtain simulation effect evaluation data.

[0097] This design, based on the results of simulated finger movements to determine the adjustment direction, clarifies the optimization path. After the initial adjustment, the simulation evaluates the pulse energy distribution and depth of action, allowing for timely identification of problems, clarifying the adjustment direction, avoiding blind optimization, and improving efficiency. The simulation evaluation can predict the effect of the initial optimization path in advance, identifying problems such as uneven energy distribution and insufficient depth of action, allowing for timely adjustments to make the initial optimization path more reasonable, laying a good foundation for subsequent in-depth optimization, and improving the accuracy of the overall optimization process.

[0098] In one embodiment, step S4 includes the following steps:

[0099] Extract individual health characteristic data of patients from comprehensive health assessment data, including physical type, severity of disease, etc.

[0100] Based on the patient's individual health characteristics data, the preliminary optimized path data is further optimized, taking into account the impact of individual differences on pulse action, to generate deeply optimized path data;

[0101] A deep learning model is used to predict the effect of deep optimization path data, generating effect prediction data.

[0102] This design extracts individual patient health characteristic data and considers the impact of individual differences on the pulse effect, enabling personalized optimization. It utilizes deep learning models to predict the effect, improving prediction accuracy. By considering individual differences, such as different physical conditions and illnesses, the optimized path is more closely aligned with the patient's own characteristics, enhancing the treatment effect. The deep learning model, based on a large amount of data, can more accurately predict the effect, providing a reliable basis for deep optimization of the path, making the optimization path more scientific and forward-looking, and improving the effectiveness and safety of treatment.

[0103] In one embodiment, step S5 includes the following steps:

[0104] Based on the effect prediction data, the relevant algorithms in the four diagnostic methods combined with the health self-test function are used to predict the health status of patients and generate health status prediction data.

[0105] By comparing health status prediction data with preset health standards, parameters of abnormal health status are identified, and comparative analysis data is generated.

[0106] This design, by comparing abnormal parameters with preset standards, can accurately pinpoint problems. Health status prediction allows doctors and patients to know in advance the possible effects of treatment and prepare accordingly. Comparative analysis can quickly identify health abnormalities, providing a clear direction for subsequent adjustments to the treatment path, making treatment more targeted, and allowing for timely adjustments to the plan to improve patient health, thereby enhancing the timeliness and effectiveness of treatment.

[0107] In one embodiment, step S6 includes the following steps:

[0108] Based on comparative analysis data Determine the adjustment range and method of the deep optimization path data, and generate adjustment parameter data;

[0109] The proportional-integral-derivative (PID) control algorithm is used to determine the adjustment range and method of the deep optimization path data. Let the output of the PID controller be... ,but:

[0110]

[0111] in It is an error signal. , , These are the proportional, integral, and differential coefficients, respectively.

[0112] Based on the adjusted parameter data, the deep optimization path data is finally adjusted to generate the final optimized path data.

[0113] The database is used to record and track the final optimized path data, including information such as adjustment time and adjustment parameters, and to generate path tracking data.

[0114] This design utilizes database records for tracking, facilitating review and analysis, accurately determining adjustment parameters, avoiding over- or under-adjustment, and making the final optimized path more reasonable and effective. The database records provide detailed information on the treatment process, making it convenient for doctors to review and summarize their experience, and also providing a reference for the treatment of similar cases in the future. This helps to continuously improve the level of optimization of the rotating magnetic pulse therapy path and promotes the continuous progress of treatment technology.

[0115] In one embodiment, step S7 includes the following steps:

[0116] Based on the path recording and tracking data, a detailed analysis of the optimization process of the vortex pulse action path is conducted, including the reasons and effects of each adjustment, and an optimization process analysis report is generated.

[0117] The optimization process analysis report is stored in a knowledge base to provide a reference for subsequent magnetic pulse therapy, including the selection of treatment pathways for patients with similar conditions.

[0118] This design allows for the storage of information in a knowledge base to provide reference for subsequent treatments. It enables detailed analysis and optimization of the process, clarifying the reasons and effects of each adjustment, allowing doctors to clearly understand the evolution logic of the treatment path and accumulate experience. The knowledge base stores reports, providing ready-made treatment path selection criteria for patients with similar conditions, avoiding repeated exploration, improving treatment efficiency, promoting the inheritance and development of rotating magnetic pulse therapy technology, and enhancing the overall level of medical care.

[0119] In one embodiment, step S2 further includes the following steps:

[0120] Using a flexible electronic pulse patch, multidimensional pulse data are simultaneously collected at the cun, guan, and chi positions of the patient to generate a raw dynamic pulse dataset. Through a deep learning denoising model, combined with environmental electromagnetic interference parameters, intelligent filtering is performed to obtain high-fidelity spatiotemporal sequence data of pulse data.

[0121] High-fidelity pulse spatiotemporal sequence data is fused with patient tongue image spectral analysis results, facial micro-expression emotion data, and voiceprint features across modalities to generate a four diagnostic features fusion matrix.

[0122] Cross-modal fusion integrates high-fidelity pulse spatiotemporal sequence data. Results of spectral analysis of the patient's tongue image Facial micro-expression emotion data Voiceprint features Cross-modal fusion is performed using a multimodal fusion method to generate a four-diagnostic feature fusion matrix. :

[0123]

[0124] in, This represents the mapping relationship of the multimodal fusion function;

[0125] By utilizing stress concentration region data from simulated effect heatmaps and combining it with individualized biomarker characteristics obtained from patient metabolomics testing, a privacy-preserving path re-optimization is performed using a federated learning framework to generate a deeply optimized path program with adaptive adjustment capabilities. After importing this program into a smart treatment device, the device's built-in sensor array continuously collects real-time biological signals during the treatment process, dynamically corrects the treatment parameters, and generates a health management report that includes short-term efficacy predictions and long-term health trend analysis.

[0126] This design combines deep learning for noise reduction with environmental parameters to improve data quality, and cross-modal fusion of multiple data points for a comprehensive health assessment. The federated learning framework optimizes the path while protecting privacy; multi-dimensional dynamic data collection more accurately reflects the pulse; intelligent noise reduction improves data reliability; cross-modal fusion breaks down data boundaries for a comprehensive assessment; federated learning optimizes the path while protecting privacy, adapting to different patients; dynamically corrects parameters and generates health management reports, enabling personalized and continuous health management, improving treatment outcomes and patient health levels.

[0127] A gyratory magnetic pulse action path optimization system is provided for implementing the aforementioned gyratory magnetic pulse action path optimization method. The system includes:

[0128] The data acquisition unit is used to collect pulse data from patients using pulse analysis equipment, generate raw pulse data, and perform noise reduction processing on the raw pulse data to obtain clear pulse data.

[0129] The data analysis unit is used to integrate clear pulse data with other diagnostic information of patients based on the concept of combining the four diagnostic methods to generate comprehensive health assessment data; based on the comprehensive health assessment data, simulated finger technique collection and analysis is performed to simulate the influence of different finger techniques on the pulse and obtain simulated finger technique analysis results;

[0130] The data optimization unit is used to make preliminary adjustments to the initial action path of the gyromagnetic pulse based on the results of the simulated fingering analysis, and generate preliminary optimized path data; the preliminary optimized path data is then used to evaluate the simulated action effect to obtain simulated action effect evaluation data.

[0131] The effect prediction unit is used to further optimize the preliminary optimization path data by using simulated effect evaluation data and combining it with individual patient health characteristic data to generate deep optimization path data; and to predict the effect of the deep optimization path data to generate effect prediction data.

[0132] The data comparison unit is used to predict the health status of patients based on the effect prediction data and the health self-test function of the four diagnostic methods, and generate health status prediction data; the health status prediction data is compared with the preset health standards to generate comparative analysis data.

[0133] The data recording and tracking unit is used to make final adjustments to the deep optimization path data based on comparative analysis data, and generate final optimized path data; it also records and tracks the final optimized path data to generate path recording and tracking data, providing a reference for subsequent rotating magnetic pulse therapy.

[0134] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited thereto. Various changes can be made within the scope of knowledge possessed by those skilled in the art without departing from the spirit of the present invention.

Claims

1. A gyromagnetic pulse action path optimization method characterized by, Comprise: S1, collecting pulse data of a patient by using a pulse analysis device to generate raw pulse data; Denoising the raw pulse data to obtain clear pulse data; S2, based on the four diagnostic methods, the clear pulse data is fused with other diagnostic information of the patient to generate comprehensive health assessment data; According to the comprehensive health assessment data, simulate the influence of different finger methods on pulse, and obtain the simulation finger analysis result; S3, according to the simulation finger analysis result, the initial action path of the rotating magnetic pulse is preliminarily adjusted to generate preliminary optimization path data; The simulation effect evaluation data is obtained by evaluating the simulation effect of the preliminary optimization path data; S4, using the simulation effect evaluation data, combining with the individual health characteristic data of the patient, further optimizing the preliminary optimization path data to generate deep optimization path data; the effect prediction data is generated by predicting the effect of the deep optimization path data; S5, according to the effect prediction data, using the four diagnostic methods health self-test function to predict the health status of the patient, generating health status prediction data; comparing the health status prediction data with the preset health standard to generate comparison analysis data; S6, based on the comparison analysis data, the deep optimization path data is finally adjusted to generate the final optimization path data; The final optimization path data is recorded and tracked to generate path record tracking data, which provides reference for subsequent rotating magnetic pulse treatment.

2. A method of optimizing a gyromagnetic pulse action path according to claim 1, characterized in that, Step S1 includes the following steps: Collecting multi-site pulse data of a patient by using a pulse analysis device to generate multi-site raw pulse data; Synchronizing the multi-site raw pulse data to obtain synchronized pulse data; Identify the noise type of the synchronized pulse data to obtain noise type data; According to the noise type data, select appropriate denoising algorithm to denoise the synchronized pulse data to obtain clear pulse data.

3. A method of optimizing a gyromagnetic pulse action path according to claim 2, characterized in that Step S2 includes the following steps: Extracting pulse feature parameters from clear pulse data to generate pulse feature parameter data; Correlate and match the pulse feature parameter data with other diagnostic information of the patient to generate correlation matching data; Based on the four diagnostic methods, the correlation matching data is comprehensively analyzed to generate comprehensive health assessment data; According to the comprehensive health assessment data, simulate the influence of different finger methods on pulse, including the change of finger method strength, speed and position parameters, to obtain the simulation finger analysis result.

4. A gyromagnetic pulse action path optimization method according to claim 3, characterized by, Step S3 includes the following steps: According to the simulation finger analysis result, determine the adjustment direction of the initial action path of the rotating magnetic pulse to generate adjustment direction data; Based on the adjustment direction data, the initial action path is preliminarily adjusted to generate preliminary optimization path data; The simulation software is used to evaluate the simulation effect of the preliminary optimization path data, including the evaluation of pulse energy distribution and action depth parameters, to obtain the simulation effect evaluation data.

5. A gyromagnetic pulse action path optimization method according to claim 4, characterized by, Step S4 includes the following steps: Extracting individual health characteristic data of the patient from the comprehensive health assessment data, including constitution type, disease severity, etc. According to the patient individual health characteristic data, the preliminary optimization path data is further optimized, considering the influence of individual differences on pulse effect, to generate deep optimization path data; Using a deep learning model, the deep optimization path data is used to predict the effect, and the effect prediction data is generated.

6. A gyromagnetic pulse action path optimization method according to claim 5, characterized by, Step S5 includes the following steps: According to the effect prediction data, the health state of the patient is predicted by using the related algorithm in the four diagnosis combined health self-test function, and the health state prediction data is generated; Compare the health state prediction data with the preset health standard, identify the abnormal parameters of the health state, and generate comparison analysis data.

7. A gyromagnetic pulse action path optimization method according to claim 6, characterized by, Step S6 includes the following steps: Based on the comparison analysis data, the adjustment range and adjustment method of the deep optimization path data are determined, and the adjustment parameter data is generated; According to the adjustment parameter data, the deep optimization path data is finally adjusted, and the final optimization path data is generated; Using the database, the final optimization path data is recorded and tracked, including recording the adjustment time, adjustment parameters and other information, and generating path record tracking data.

8. A gyromagnetic pulse action path optimization method according to claim 7, characterized by, Step S7 includes the following steps: According to the path record tracking data, the optimization process of the spin magnetic pulse effect path is analyzed in detail, including the reason and effect of each adjustment, and the optimization process analysis report is generated; The optimization process analysis report is stored in the knowledge base, providing a reference for subsequent spin magnetic pulse treatment, including the treatment path selection of patients with similar conditions.

9. A method of optimizing a gyromagnetic pulse action path according to claim 3, characterized in that, Step S2 further includes the following steps: Using a flexible electronic pulse patch, multi-dimensional pulse data is synchronously collected at the patient's inch, Guan and Chi parts to generate a raw dynamic pulse data set; through a deep learning denoising model, intelligent filtering is performed in combination with environmental electromagnetic interference parameters to obtain high-fidelity pulse spatiotemporal sequence data; Cross-modal fusion is performed on the high-fidelity pulse spatiotemporal sequence data, tongue spectrum analysis results, facial micro-expression emotion data, and voice voiceprint characteristics of the patient to generate a four-diagnosis feature fusion matrix; Using stress concentration area data in the simulated effect thermal map, in combination with individualized biomarker characteristics obtained through patient metabolomics detection, path re-optimization is performed through a federated learning framework with privacy protection to generate a deep optimization path program with adaptive adjustment function; after the program is imported into the intelligent treatment device, real-time biological signals during the treatment process are continuously collected through the sensor array built-in the device, the action parameters are dynamically corrected, and a health management report containing short-term efficacy prediction and long-term health trend analysis is generated.

10. A gyromagnetic pulse action path optimization system for implementing the gyromagnetic pulse action path optimization method of any one of claims 1-9, characterized by, The system includes: A data acquisition unit is used to collect pulse data from patients using a pulse analysis device to generate raw pulse data; and to obtain clear pulse data by denoising the raw pulse data; A data analysis unit is used to generate comprehensive health assessment data by fusing clear pulse data with other diagnostic information of the patient based on the four diagnosis combined concept; and to obtain simulated finger manipulation analysis results by simulating the influence of different finger manipulations on pulse based on the comprehensive health assessment data; The data optimization unit is configured to preliminarily adjust the initial action path of the spin magnetic pulse according to the simulation fingering analysis result, to generate preliminary optimization path data; and to evaluate the simulation action effect of the preliminary optimization path data, to obtain simulation action effect evaluation data. The effect prediction unit is configured to further optimize the preliminary optimization path data by using the simulation action effect evaluation data and combining the individual health feature data of the patient, to generate deep optimization path data; and to predict the action effect of the deep optimization path data, to generate action effect prediction data. The data comparison unit is configured to predict the health state of the patient by using the four diagnostic health self-test function according to the action effect prediction data, to generate health state prediction data; and to compare the health state prediction data with a preset health standard, to generate comparison analysis data. The data record tracking unit is configured to finally adjust the deep optimization path data based on the comparison analysis data, to generate final optimization path data; and to record and track the final optimization path data, to generate path record tracking data, to provide a reference basis for subsequent spin magnetic pulse treatment.

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