Postpartum pelvic floor muscle function rehabilitation system based on magnetoelectric combined stimulation

Abstract

The invention discloses a postpartum pelvic floor muscle function rehabilitation system based on magnetoelectric combined stimulation, and particularly relates to the field of postpartum pelvic floor muscle rehabilitation assisted by electrotherapy in the biomedical engineering industry. Comprising a stimulation parameter setting module, a tissue response behavior modeling module, a soft tissue drift anomaly detection module, a self-adaptive stimulation parameter reconstruction module, a closed-loop feedback regulation and control module and a case data archiving and self-learning module. The stimulation parameter setting module generates a basic stimulation parameter set by analyzing user input data, and outputs the basic stimulation parameter set for subsequent modules to use. A tissue drifting state is identified in real time through a soft tissue drifting anomaly detection module, magnetoelectric stimulation parameters are dynamically optimized based on a drifting trend by combining an adaptive stimulation parameter reconstruction module, and a magnetoelectric combined stimulation closed-loop control mechanism capable of synchronously following target tissue migration is formed. The problem that stimulation focuses are mismatched under dynamic displacement of postpartum pelvic floor soft tissue is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of electrotherapy-assisted stimulation of postpartum pelvic floor muscle rehabilitation in the biomedical engineering industry, and more specifically, to a postpartum pelvic floor muscle functional rehabilitation system based on combined magnetic and electric stimulation. Background Art

[0002] In current postpartum pelvic floor muscle rehabilitation technologies based on combined magnetic and electrical stimulation, the devices generally rely on fixed body positions and static tissue models to preset stimulation parameters, ignoring the dynamic variability of postpartum pelvic floor tissues over short timescales. Especially in the acute postpartum period, the pelvic floor soft tissues (including the vaginal wall, uterine isthmus, and bladder fundus) often experience uncontrollable drifting displacement due to exudate accumulation, edema, tissue relaxation, and ligament damage. Anatomical studies have shown that this displacement can reach 2-4 cm, forming so-called "floating soft tissue islands." This spatial displacement of tissue disrupts the spatial focusing consistency of the magnetic and electric fields, causing the stimulation focus to deviate from the target tissue during electrotherapy intervention, resulting in a significant decrease in rehabilitation efficacy and even causing inadvertent stimulation of adjacent sensitive tissues (such as the rectum and urethra), leading to the risk of secondary injury such as pain and loss of muscle control. Existing technologies lack the ability to track and dynamically correct tissue displacement in real time, resulting in severe failure of magnetoelectric stimulation under dynamic anatomical conditions. This situation not only restricts the clinical reliability and widespread application of high-end postpartum rehabilitation equipment in the biomedical engineering industry, but also exposes the control blind spots of current technologies in complex anatomical dynamic environments. Therefore, the current fundamental technical bottleneck in this field lies in how to achieve adaptive tracking and dynamic synchronous control of the focus of combined magnetic and electric stimulation while the postpartum pelvic tissue continues to migrate. Summary of the Invention

[0003] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a postpartum pelvic floor muscle function rehabilitation system based on combined magnetoelectric stimulation, which identifies the tissue drift state in real time through a soft tissue drift abnormality detection module, and dynamically optimizes the magnetoelectric stimulation parameters based on the drift trend in combination with an adaptive stimulation parameter reconstruction module, forming a magnetoelectric combined stimulation closed-loop control mechanism that can synchronously follow the migration of the target tissue, so as to solve the problem of stimulation focus mismatch under dynamic displacement of the postpartum pelvic floor soft tissue.

[0004] To achieve the above objectives, the present invention provides the following technical solutions: a postpartum pelvic floor muscle function rehabilitation system based on combined magnetic and electric stimulation, comprising a stimulation parameter setting module, a tissue response behavior modeling module, a soft tissue drift abnormality detection module, an adaptive stimulation parameter reconstruction module, a closed-loop feedback control module, and a case data archiving and self-learning module; The stimulation parameter setting module generates a basic stimulation parameter set by analyzing the user input data and outputs the basic stimulation parameter set for use by subsequent modules; The tissue response behavior modeling module applies a basic stimulus parameter set to the target tissue and collects tissue response behavior data to establish a basic tissue response database for anomaly detection and parameter reconstruction; The soft tissue drift anomaly detection module performs a difference analysis between the current tissue response behavior data and the basic tissue response database, identifies and outputs drift trend parameters or normal signals for use by subsequent modules; The adaptive stimulation parameter reconstruction module generates and outputs a final stimulation parameter set by receiving the drift trend parameter and the basic stimulation parameter set to optimize the stimulation effect; The closed-loop feedback control module applies the final stimulation parameter set and monitors the tissue response behavior data in real time, determines and outputs a treatment completion signal or feeds back to the adaptive stimulation parameter reconstruction module to continue optimization; The case data archiving and self-learning module generates and outputs stimulation parameter optimization rule data by archiving the complete treatment process data to update the basic tissue response database for subsequent rehabilitation cycles.

[0005] In a preferred embodiment, the stimulation parameter setting module is configured to receive user input data, the user input data including tissue state parameters and treatment target parameters; parse the user input data into control parameters, the control parameters including electrical stimulation parameters and magnetic stimulation parameters; the electrical stimulation parameters including frequency, intensity, and pulse width; the magnetic stimulation parameters including magnetic flux density, frequency, and waveform; and combine the control parameters to form a basic stimulation parameter set; Determine whether the basic stimulation parameter set meets the following conditions: the stimulation frequency is greater than the set frequency threshold lower limit, and the stimulation intensity is lower than the set safety threshold upper limit; if both conditions are met, the data is output to the tissue response behavior modeling module; if not, the data is returned to the user input data analysis to adjust the control parameters; Output the basic stimulus parameter set to the tissue response behavior modeling module.

[0006] In a preferred embodiment, the tissue response behavior modeling module includes a magnetoelectric combined stimulation hardware unit and a biofeedback detection unit; the magnetoelectric combined stimulation hardware unit includes an electrical stimulation unit and a magnetic stimulation unit; The tissue response behavior modeling module is used to receive a basic stimulation parameter set, transmit the basic stimulation parameter set to the magnetoelectric combined stimulation hardware unit, and apply a stimulation signal; the magnetoelectric combined stimulation hardware unit applies an electrical signal through the electrical stimulation unit, and the magnetoelectric combined stimulation hardware unit applies a magnetic field signal through the magnetic stimulation unit; The biofeedback detection unit is used to collect tissue response behavior data to form a basic response behavior data set; establish a mapping relationship between the stimulation parameters and the basic response behavior data set to form a basic tissue response database; Determine whether all data points in the basic response behavior data set meet the following conditions: the tissue impedance change rate is lower than the preset fluctuation threshold, and the magnetic deformation is within the set offset range; if all conditions are met, enter the soft tissue drift anomaly detection module; if not, return to receiving the basic stimulation parameter set and re-apply stimulation.

[0007] In a preferred embodiment, the soft tissue drift anomaly detection module is used to receive the basic tissue response database, construct a progressive stimulation parameter sequence, and apply it to the target tissue; the current tissue response behavior data collected by the biofeedback detection unit is used to form current response data, and the soft tissue drift anomaly detection module compares the current response data with the basic tissue response database to form differential behavior feature data; Determine whether the differential behavior characteristic data meets the following conditions: the tissue impedance change is greater than the product of the basic response data average value and the preset drift coefficient, and the magnetostrictive deformation offset is greater than the product of the basic response data standard deviation and the set anomaly coefficient; if all conditions are met, drift is identified and a drift trend parameter is generated; if not, the parameter is output to the closed-loop feedback control module; Determine whether the drift trend parameters meet the following conditions: the drift speed is higher than the set response threshold lower limit, and the drift direction is consistent for three consecutive times. If all conditions are met, the drift trend parameters are output to the adaptive stimulation parameter reconstruction module. If not, return to "Construct progressive stimulation parameter sequence and apply it to target tissue" to continue progressive scanning.

[0008] In a preferred embodiment, the adaptive stimulation parameter reconstruction module is used to receive the drift trend parameter and the basic stimulation parameter set, construct a target stimulation area model, and combine the drift trend parameter with the tissue space data to form stimulation reconstruction requirement data; Construct a candidate stimulus parameter matrix; input the candidate stimulus parameter matrix into the basic tissue response database, and simulate and generate predicted behavioral feedback data; Determine whether the predicted behavior feedback data meets the following requirements: the upper limit of the tissue response value is lower than the set tissue safety limit, and the uniformity of the stimulation effect is higher than the lower limit of the set effectiveness threshold. If all of these conditions are met, output the final stimulation parameter set. If not, return to constructing the candidate stimulation parameter matrix and continue optimization.

[0009] In a preferred embodiment, the closed-loop feedback control module is configured to receive a final set of stimulation parameters; apply electrical and magnetic field signals according to the final set of stimulation parameters via a combined magnetic and electric stimulation hardware unit; and synchronously collect tissue response behavior data via a biofeedback detection unit to generate real-time feedback data; compare the real-time feedback data with the expected behavior feedback data corresponding to the final set of stimulation parameters to generate deviation determination data; Determine whether the deviation judgment data meets the following requirements: the deviation rate is lower than the set upper limit of the allowable error, and the tissue displacement change is lower than the preset displacement fluctuation range; if all are met, output the treatment completion signal to the case data archiving and self-learning module; if not, return to the adaptive stimulation parameter reconstruction module to re-optimize the stimulation parameters.

[0010] In a preferred embodiment, the case data archiving and self-learning module is used to archive all basic stimulation parameter sets, basic tissue response databases, abnormal behavior characteristic data, drift trend parameters, final stimulation parameter sets, and real-time feedback data during the treatment process as a case history data set; input the case history data set into the self-learning algorithm model, analyze the case history data set based on the self-learning algorithm model, and generate stimulation parameter optimization rule data; Determine whether the stimulation parameter optimization rule data meets the following requirements: the stimulation intensity after optimization is lower than the average intensity of the previous treatment, and the efficacy score after optimization is higher than the average efficacy score of the previous treatment; if both are met, update the basic tissue response database; if not, retain the original parameters; save the updated basic tissue response database to the postpartum pelvic floor muscle functional rehabilitation system based on combined magnetic and electric stimulation for storage.

[0011] In a preferred embodiment, the soft tissue drift anomaly detection module receives the basic tissue response database and the current tissue response behavior data, calculates the drift anomaly comprehensive index based on the difference between the tissue impedance response value and the magnetostrictive response value and their time change rate, and forms a drift trend parameter. Based on this, a soft tissue drift anomaly comprehensive index model is constructed: ; in is the drift anomaly comprehensive index, which is used as the output for determining whether soft tissue drift exists; is the tissue impedance response value in the current tissue response behavior data; is the tissue impedance response value in the basic tissue response database; is the magnetic deformation response value in the current tissue response behavior data; is the magneto-induced deformation response value in the basic tissue response database; The time interval between the current organizational response behavior data and the basic organizational response database; is the drift speed in the multi-time series difference behavior feature data; is the drift path curvature in the multi-time series difference behavior feature data; The soft tissue reference length set by the system is derived from the basic tissue space data; The first half of the above formula uses the difference in response values ​​between the current tissue response behavior data and the basic tissue response database, combined with the time difference, to reflect the mutation rate of abnormal drift. The second half of the above formula normalizes the product of drift speed and path curvature as a dynamic enhancement factor of the drift trend. The above formula comprehensively reflects the collaborative judgment of current differences and dynamic trends, and outputs a single drift anomaly comprehensive index.

[0012] In a preferred embodiment, the adaptive stimulation parameter reconstruction module receives the drift trend parameter and the basic stimulation parameter set to form a candidate stimulation parameter matrix. The final stimulation parameter set is solved based on the tissue response behavior prediction data and the expected energy input. Based on this, a stimulation parameter target solution search model is constructed: ; in is the final stimulation parameter set, which is output by the system and used in the closed-loop feedback control module; is the candidate stimulation parameter matrix, which is derived from the basic stimulation parameter set; is a single set of stimulation parameters in the candidate stimulation parameter matrix; is the tissue impedance response value predicted by the system under the candidate stimulation parameters; The target tissue impedance response value set by the system is derived from the basic tissue response database; is the magnetostrictive response value predicted by the system under the candidate stimulation parameters; The target magneto-deformation response value set by the system is derived from the basic tissue response database; is the expected energy input under the candidate stimulation parameters, and the expected energy input is calculated by the system; is the current soft tissue drift abnormality comprehensive index , drift anomaly comprehensive index from the soft tissue drift anomaly detection module; Drift reference threshold preset for the system; The system extracts historical tissue response behavior data corresponding to the current candidate stimulation parameter set from the basic tissue response database, establishes a multidimensional interpolation model and response behavior fitting function based on the candidate stimulation parameter set, inputs the parameter data in the current candidate stimulation parameter set, and through interpolation calculation and fitting function solution, solves the tissue impedance response value and magnetostrictive deformation response value under the current candidate stimulation parameter set as the system prediction result; exist The numerator of the first term in represents the sum of squares of the prediction errors of the candidate stimulation parameters on tissue impedance and magnetostriction, which is used to ensure the target response accuracy. The denominator of the first term represents the energy input used to penalize high-energy consumption solutions and improve the feasibility of the system in practical applications; exist The outer multiplier part of the second term in represents the dynamic adjustment of the weight of the stimulation parameter optimization process by combining the ratio of the soft tissue drift abnormality comprehensive index to the reference threshold; exist In the embodiment, the triple optimization logic of error minimization, energy consumption optimization and drift dynamic feedback is realized by combining the two items, reflecting the adaptive stimulation parameter reconstruction technology route of the present invention.

[0013] The technical effects and advantages of the present invention are as follows: 1. The soft tissue drift anomaly detection module, based on differential analysis of tissue response behavior data and a basic response database, identifies and generates drift trend parameters in real time, resolving the fundamental bottleneck of existing technologies that prevent them from adaptively tracking the focus of magnetic and electrical stimulation under dynamic displacement of postpartum pelvic tissue. 2. The adaptive stimulation parameter reconstruction module combines the drift trend parameters with the basic stimulation parameter set to construct a target stimulation area model and a candidate stimulation parameter matrix, dynamically optimizing the stimulation parameters and improving the accuracy and individual adaptability of combined magnetic and electric stimulation in the target tissue. 3. This system uses a closed-loop feedback control module to collect real-time tissue response behavior data after stimulation, judge and output whether the preset stimulation effect has been achieved, and form an automatically iterative adaptive closed-loop control, thereby improving stimulation accuracy and consistency of therapeutic effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 Schematic diagram of the system module of the present invention. DETAILED DESCRIPTION

[0015] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. Figure 1 According to an embodiment of the present invention, a postpartum pelvic floor muscle functional rehabilitation system based on combined magnetic and electric stimulation includes a stimulation parameter setting module, a tissue response behavior modeling module, a soft tissue drift abnormality detection module, an adaptive stimulation parameter reconstruction module, a closed-loop feedback control module, and a case data archiving and self-learning module. The stimulation parameter setting module generates a basic stimulation parameter set by analyzing the user input data and outputs the basic stimulation parameter set for use by subsequent modules; The tissue response behavior modeling module applies a basic stimulus parameter set to the target tissue and collects tissue response behavior data to establish a basic tissue response database for anomaly detection and parameter reconstruction; The soft tissue drift anomaly detection module performs a difference analysis between the current tissue response behavior data and the basic tissue response database, identifies and outputs drift trend parameters or normal signals for use by subsequent modules; The adaptive stimulation parameter reconstruction module generates and outputs a final stimulation parameter set by receiving the drift trend parameter and the basic stimulation parameter set to optimize the stimulation effect; The closed-loop feedback control module applies the final stimulation parameter set and monitors the tissue response behavior data in real time, determines and outputs a treatment completion signal or feeds back to the adaptive stimulation parameter reconstruction module to continue optimization; The case data archiving and self-learning module generates and outputs stimulation parameter optimization rule data by archiving the complete treatment process data to update the basic tissue response database for subsequent rehabilitation cycles.

[0016] The stimulation parameter setting module is used to receive user input data, including tissue state parameters and treatment target parameters; interpret the user input data into control parameters, including electrical stimulation parameters and magnetic stimulation parameters; electrical stimulation parameters include frequency, intensity, and pulse width; magnetic stimulation parameters include magnetic flux density, frequency, and waveform; and combine the control parameters to form a basic stimulation parameter set; Determine whether the basic stimulation parameter set meets the following conditions: the stimulation frequency is greater than the set frequency threshold lower limit, and the stimulation intensity is lower than the set safety threshold upper limit; if both conditions are met, the data is output to the tissue response behavior modeling module; if not, the data is returned to the user input data analysis to adjust the control parameters; Output the basic stimulus parameter set to the tissue response behavior modeling module.

[0017] The tissue response behavior modeling module includes a magnetoelectric combined stimulation hardware unit and a biofeedback detection unit; the magnetoelectric combined stimulation hardware unit includes an electrical stimulation unit and a magnetic stimulation unit; The tissue response behavior modeling module is used to receive a basic stimulation parameter set, transmit the basic stimulation parameter set to the magnetoelectric combined stimulation hardware unit, and apply a stimulation signal; the magnetoelectric combined stimulation hardware unit applies an electrical signal through the electrical stimulation unit, and the magnetoelectric combined stimulation hardware unit applies a magnetic field signal through the magnetic stimulation unit; The biofeedback detection unit is used to collect tissue response behavior data to form a basic response behavior data set; establish a mapping relationship between the stimulation parameters and the basic response behavior data set to form a basic tissue response database; Determine whether all data points in the basic response behavior data set meet the following conditions: the tissue impedance change rate is lower than the preset fluctuation threshold, and the magnetic deformation is within the set offset range; if all conditions are met, enter the soft tissue drift anomaly detection module; if not, return to receiving the basic stimulation parameter set and re-apply stimulation.

[0018] The soft tissue drift anomaly detection module is used to receive the basic tissue response database, construct a progressive stimulation parameter sequence, and apply it to the target tissue; the current tissue response behavior data collected by the biofeedback detection unit is used to form current response data, and the current response data is compared with the basic tissue response database through the soft tissue drift anomaly detection module to form differential behavior feature data; Determine whether the differential behavior characteristic data meets the following conditions: the tissue impedance change is greater than the product of the basic response data average value and the preset drift coefficient, and the magnetostrictive deformation offset is greater than the product of the basic response data standard deviation and the set anomaly coefficient; if all conditions are met, drift is identified and a drift trend parameter is generated; if not, the parameter is output to the closed-loop feedback control module; Determine whether the drift trend parameters meet the following conditions: the drift speed is higher than the set response threshold lower limit, and the drift direction is consistent for three consecutive times. If all conditions are met, the drift trend parameters are output to the adaptive stimulation parameter reconstruction module. If not, return to "Construct progressive stimulation parameter sequence and apply it to target tissue" to continue progressive scanning.

[0019] The adaptive stimulation parameter reconstruction module is used to receive the drift trend parameter and the basic stimulation parameter set, build a target stimulation area model, and combine the drift trend parameter with the tissue space data to form stimulation reconstruction demand data; Construct a candidate stimulus parameter matrix; input the candidate stimulus parameter matrix into the basic tissue response database, and simulate and generate predicted behavioral feedback data; Determine whether the predicted behavior feedback data meets the following requirements: the upper limit of the tissue response value is lower than the set tissue safety limit, and the uniformity of the stimulation effect is higher than the lower limit of the set effectiveness threshold. If all of these conditions are met, output the final stimulation parameter set. If not, return to constructing the candidate stimulation parameter matrix and continue optimization.

[0020] The closed-loop feedback control module is used to receive the final stimulation parameter set; the magnetic and electric combined stimulation hardware unit applies electrical and magnetic field signals according to the final stimulation parameter set, and the biofeedback detection unit synchronously collects tissue response behavior data to form real-time feedback data; the real-time feedback data is compared with the expected behavior feedback data corresponding to the final stimulation parameter set to solve the deviation judgment data; Determine whether the deviation judgment data meets the following requirements: the deviation rate is lower than the set upper limit of the allowable error, and the tissue displacement change is lower than the preset displacement fluctuation range; if all are met, output the treatment completion signal to the case data archiving and self-learning module; if not, return to the adaptive stimulation parameter reconstruction module to re-optimize the stimulation parameters.

[0021] The case data archiving and self-learning module is used to archive all basic stimulation parameter sets, basic tissue response databases, abnormal behavior characteristic data, drift trend parameters, final stimulation parameter sets, and real-time feedback data during the treatment process as case history data sets; input the case history data sets into the self-learning algorithm model, analyze the case history data sets based on the self-learning algorithm model, and generate stimulation parameter optimization rule data; Determine whether the stimulation parameter optimization rule data meets the following requirements: the stimulation intensity after optimization is lower than the average intensity of the previous treatment, and the efficacy score after optimization is higher than the average efficacy score of the previous treatment; if both are met, update the basic tissue response database; if not, retain the original parameters; save the updated basic tissue response database to the postpartum pelvic floor muscle functional rehabilitation system based on combined magnetic and electric stimulation for storage.

[0022] It should be noted that in the formula structure involved in this solution, dimensionless terms can serve as proportionality or structural adjustment factors. When combined with quantities with units, they only play a numerical scaling role and do not introduce new physical dimensions. Therefore, they will not change or confuse the overall unit system of expression. This combination of "dimensionless terms and units" can be understood as a composite structural expression commonly used in mathematical and physical modeling, conforming to the principle of dimensional consistency and having a clear physical interpretation basis. Secondly, in the formula structure of this scheme, if multiple variables with different physical units are involved, including but not limited to time, mass or energy variables, their joint appearance is to express the collaborative modeling relationship of multiple physical mechanisms. Each variable can be formed into a unified structure through function mapping, ratio combination or normalization adjustment. The units and meanings are clear, and the overall expression conforms to the principle of dimensional consistency and the common formula of engineering modeling. In this solution, any design constants, weights, adjustment factors, threshold parameters, and proportional coefficients are adjustable control parameters for different application environments. Their values ​​depend on the target device configuration, data input characteristics, and performance optimization goals. During the implementation phase, they are set within a reasonable range through model verification, performance constraints, or engineering calibration. Although these parameters do not have unique preset values, they have clear adjustment logic and calculation paths, and are part of the deterministic setting process in engineering implementation. The purpose of such setting is to ensure that the solution is both universally adaptable, reproducible, and operable, without affecting its technical clarity and feasibility. The soft tissue drift anomaly detection module receives the basic tissue response database and the current tissue response behavior data. The drift anomaly comprehensive index is calculated based on the difference between the tissue impedance response value and the magnetostrictive response value and their time change rate, and a drift trend parameter is formed. Based on this, a soft tissue drift anomaly comprehensive index model is constructed: ; in is the drift anomaly comprehensive index, which is used as the output for determining whether soft tissue drift exists; is the tissue impedance response value in the current tissue response behavior data; is the tissue impedance response value in the basic tissue response database; is the magnetic deformation response value in the current tissue response behavior data; is the magneto-induced deformation response value in the basic tissue response database; The time interval between the current organizational response behavior data and the basic organizational response database; is the drift speed in the multi-time series difference behavior feature data; is the drift path curvature in the multi-time series difference behavior feature data; The soft tissue reference length set by the system is derived from the basic tissue space data; The first half of the above formula uses the difference in response values ​​between the current tissue response behavior data and the basic tissue response database, combined with the time difference, to reflect the mutation rate of abnormal drift. The second half of the above formula normalizes the product of drift speed and path curvature as a dynamic enhancement factor of the drift trend. The above formula comprehensively reflects the collaborative judgment of current differences and dynamic trends, and outputs a single drift anomaly comprehensive index.

[0023] The adaptive stimulation parameter reconstruction module receives the drift trend parameters and the basic stimulation parameter set to form a candidate stimulation parameter matrix. Based on the tissue response behavior prediction data and the expected energy input, the final stimulation parameter set is solved and the stimulation parameter target solution search model is constructed based on this: ; in is the final stimulation parameter set, which is output by the system and used in the closed-loop feedback control module; is the candidate stimulation parameter matrix, which is derived from the basic stimulation parameter set; is a single set of stimulation parameters in the candidate stimulation parameter matrix; is the tissue impedance response value predicted by the system under the candidate stimulation parameters; The target tissue impedance response value set by the system is derived from the basic tissue response database; is the magnetostrictive response value predicted by the system under the candidate stimulation parameters; The target magneto-deformation response value set by the system is derived from the basic tissue response database; is the expected energy input under the candidate stimulation parameters, and the expected energy input is calculated by the system; is the current soft tissue drift abnormality comprehensive index , drift anomaly comprehensive index from the soft tissue drift anomaly detection module; Drift reference threshold preset for the system; The system extracts historical tissue response behavior data corresponding to the current candidate stimulation parameter set from the basic tissue response database, establishes a multidimensional interpolation model and response behavior fitting function based on the candidate stimulation parameter set, inputs the parameter data in the current candidate stimulation parameter set, and through interpolation calculation and fitting function solution, solves the tissue impedance response value and magnetostrictive deformation response value under the current candidate stimulation parameter set as the system prediction result; exist The numerator of the first term in represents the sum of squares of the prediction errors of the candidate stimulation parameters on tissue impedance and magnetostriction, which is used to ensure the target response accuracy. The denominator of the first term represents the energy input used to penalize high-energy consumption solutions and improve the feasibility of the system in practical applications; exist The outer multiplier part of the second term in represents the dynamic adjustment of the weight of the stimulation parameter optimization process by combining the ratio of the soft tissue drift abnormality comprehensive index to the reference threshold; exist In the embodiment, the triple optimization logic of error minimization, energy consumption optimization and drift dynamic feedback is realized by combining the two items, reflecting the adaptive stimulation parameter reconstruction technology route of the present invention.

[0024] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A postpartum pelvic floor muscle functional rehabilitation system based on combined magnetic and electric stimulation, comprising a stimulation parameter setting module, a tissue response behavior modeling module, a soft tissue drift anomaly detection module, an adaptive stimulation parameter reconstruction module, a closed-loop feedback control module, and a case data archiving and self-learning module, characterized by: The stimulation parameter setting module generates a basic stimulation parameter set by analyzing the user input data and outputs the basic stimulation parameter set for use by subsequent modules; The tissue response behavior modeling module applies a basic stimulus parameter set to the target tissue and collects tissue response behavior data to establish a basic tissue response database for anomaly detection and parameter reconstruction; The soft tissue drift anomaly detection module performs a difference analysis between the current tissue response behavior data and the basic tissue response database, identifies and outputs drift trend parameters or normal signals for use by subsequent modules; The adaptive stimulation parameter reconstruction module generates and outputs a final stimulation parameter set by receiving the drift trend parameter and the basic stimulation parameter set to optimize the stimulation effect; The closed-loop feedback control module applies the final stimulation parameter set and monitors the tissue response behavior data in real time, determines and outputs a treatment completion signal or feeds back to the adaptive stimulation parameter reconstruction module to continue optimization; The case data archiving and self-learning module generates and outputs stimulation parameter optimization rule data by archiving the complete treatment process data to update the basic tissue response database for subsequent rehabilitation cycles.

2. The postpartum pelvic floor muscle function rehabilitation system based on combined magnetic and electric stimulation according to claim 1, characterized in that: The stimulation parameter setting module is used to receive user input data, which includes tissue state parameters and treatment target parameters; interpret the user input data into control parameters, which include electrical stimulation parameters and magnetic stimulation parameters; electrical stimulation parameters include frequency, intensity, and pulse width; Magnetic stimulation parameters include magnetic flux density, frequency and waveform; the combined control parameters form a basic stimulation parameter set; Determine whether the basic stimulation parameter set meets the following conditions: the stimulation frequency is greater than the set frequency threshold lower limit, and the stimulation intensity is lower than the set safety threshold upper limit; if both conditions are met, the data is output to the tissue response behavior modeling module; if not, the data is returned to the user input data analysis to adjust the control parameters; Output the basic stimulus parameter set to the tissue response behavior modeling module.

3. The postpartum pelvic floor muscle function rehabilitation system based on combined magnetic and electric stimulation according to claim 2, characterized in that: The tissue response behavior modeling module includes a magnetoelectric combined stimulation hardware unit and a biofeedback detection unit; the magnetoelectric combined stimulation hardware unit includes an electrical stimulation unit and a magnetic stimulation unit; The tissue response behavior modeling module is used to receive a basic stimulation parameter set, transmit the basic stimulation parameter set to the magnetoelectric combined stimulation hardware unit, and apply a stimulation signal; the magnetoelectric combined stimulation hardware unit applies an electrical signal through the electrical stimulation unit, and the magnetoelectric combined stimulation hardware unit applies a magnetic field signal through the magnetic stimulation unit; The biofeedback detection unit is used to collect tissue response behavior data to form a basic response behavior data set; Establish a mapping relationship between the stimulus parameters and the basic response behavior data set to form a basic tissue response database; Determine whether all data points in the basic response behavior data set meet the following conditions: the tissue impedance change rate is lower than the preset fluctuation threshold, and the magnetic deformation is within the set offset range; if all conditions are met, enter the soft tissue drift anomaly detection module; if not, return to receiving the basic stimulation parameter set and re-apply stimulation.

4. The postpartum pelvic floor muscle function rehabilitation system based on combined magnetic and electric stimulation according to claim 3, characterized in that: The soft tissue drift anomaly detection module is used to receive the basic tissue response database, construct a progressive stimulation parameter sequence, and apply it to the target tissue; The current tissue response behavior data collected by the biofeedback detection unit is used to form current response data, and the current response data is compared with the basic tissue response database through the soft tissue drift abnormality detection module to form differential behavior feature data; Determine whether the differential behavior characteristic data meets the following conditions: the tissue impedance change is greater than the product of the basic response data average value and the preset drift coefficient, and the magnetostrictive deformation offset is greater than the product of the basic response data standard deviation and the set anomaly coefficient; if all conditions are met, drift is identified and a drift trend parameter is generated; if not, the parameter is output to the closed-loop feedback control module; Determine whether the drift trend parameters meet the following conditions: the drift velocity is higher than the set response threshold lower limit, and the drift direction is consistent three times in a row. If both conditions are met, the drift trend parameters are output to the adaptive stimulation parameter reconstruction module. If not, the module returns to the "Construct progressive stimulation parameter sequence and apply it to the target tissue" step to continue the progressive scan.

5. The postpartum pelvic floor muscle function rehabilitation system based on combined magnetic and electric stimulation according to claim 4, characterized in that: The adaptive stimulation parameter reconstruction module is used to receive the drift trend parameter and the basic stimulation parameter set, build a target stimulation area model, and combine the drift trend parameter with the tissue space data to form stimulation reconstruction demand data; Construct a candidate stimulus parameter matrix; input the candidate stimulus parameter matrix into the basic tissue response database, and simulate and generate predicted behavioral feedback data; Determine whether the predicted behavior feedback data meets the following requirements: the upper limit of the tissue response value is lower than the set tissue safety limit, and the uniformity of the stimulation effect is higher than the lower limit of the set effectiveness threshold. If all of these conditions are met, output the final stimulation parameter set. If not, return to constructing the candidate stimulation parameter matrix and continue optimization.

6. The postpartum pelvic floor muscle function rehabilitation system based on combined magnetic and electric stimulation according to claim 5, characterized in that: The closed-loop feedback control module is used to receive the final set of stimulation parameters; the magnetic and electric combined stimulation hardware unit applies electrical and magnetic field signals according to the final set of stimulation parameters, and the biofeedback detection unit synchronously collects tissue response behavior data to form real-time feedback data; Compare the real-time feedback data with the expected behavior feedback data corresponding to the final stimulation parameter set to obtain the deviation judgment data; Determine whether the deviation judgment data meets the following requirements: the deviation rate is lower than the set upper limit of the allowable error, and the tissue displacement change is lower than the preset displacement fluctuation range; if all are met, output the treatment completion signal to the case data archiving and self-learning module; if not, return to the adaptive stimulation parameter reconstruction module to re-optimize the stimulation parameters.

7. The postpartum pelvic floor muscle function rehabilitation system based on combined magnetic and electric stimulation according to claim 6, characterized in that: The case data archiving and self-learning module is used to archive all basic stimulation parameter sets, basic tissue response databases, abnormal behavior characteristic data, drift trend parameters, final stimulation parameter sets, and real-time feedback data during the treatment process as case history data sets; input the case history data sets into the self-learning algorithm model, analyze the case history data sets based on the self-learning algorithm model, and generate stimulation parameter optimization rule data; Determine whether the stimulation parameter optimization rule data meets the following requirements: the stimulation intensity after optimization is lower than the average intensity of the previous treatment, and the efficacy score after optimization is higher than the average efficacy score of the previous treatment; if both are met, update the basic tissue response database; if not, retain the original parameters; save the updated basic tissue response database to the postpartum pelvic floor muscle functional rehabilitation system based on combined magnetic and electric stimulation for storage.

8. The postpartum pelvic floor muscle function rehabilitation system based on combined magnetic and electric stimulation according to claim 7, characterized in that: The soft tissue drift anomaly detection module receives the basic tissue response database and the current tissue response behavior data. The drift anomaly comprehensive index is calculated based on the difference between the tissue impedance response value and the magnetostrictive response value and their time change rate, and a drift trend parameter is formed. Based on this, a soft tissue drift anomaly comprehensive index model is constructed: ； in is the drift anomaly comprehensive index, which is used as the output for determining whether soft tissue drift exists; is the tissue impedance response value in the current tissue response behavior data; is the tissue impedance response value in the basic tissue response database; is the magnetic deformation response value in the current tissue response behavior data; is the magneto-induced deformation response value in the basic tissue response database; The time interval between the current organizational response behavior data and the basic organizational response database; is the drift speed in the multi-time series difference behavior feature data; is the drift path curvature in the multi-time series difference behavior feature data; The soft tissue reference length set by the system.

9. The postpartum pelvic floor muscle function rehabilitation system based on combined magnetic and electric stimulation according to claim 8, characterized in that: The adaptive stimulation parameter reconstruction module receives the drift trend parameters and the basic stimulation parameter set to form a candidate stimulation parameter matrix. Based on the tissue response behavior prediction data and the expected energy input, the final stimulation parameter set is solved and the stimulation parameter target solution search model is constructed based on this: ； in is the final stimulus parameter set; is the candidate stimulus parameter matrix; is a single set of stimulation parameters in the candidate stimulation parameter matrix; is the tissue impedance response value predicted by the system under the candidate stimulation parameters; The target tissue impedance response value set for the system; is the magnetostrictive response value predicted by the system under the candidate stimulation parameters; The target magnetostrictive response value set for the system; is the expected energy input under the candidate stimulation parameters; is the current soft tissue drift abnormality comprehensive index ; The drift reference threshold preset for the system.

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