Infrared low-frequency magnetic pulse therapeutic instrument optimization system based on feedback regulation

Abstract

The invention belongs to the technical field of therapeutic instrument optimization, and particularly relates to an infrared low-frequency magnetic pulse therapeutic instrument optimization system based on feedback regulation, which comprises an infrared low-frequency module, a magnetic pulse module, a physiological feedback module, a self-adaptive regulation module and a multiplexing coordination module. According to the scheme, the PID adjusting method combining fuzzy logic and interpolation is provided, interpolation is introduced into the fuzzy PID parameter adjusting process, the smoother and more accurate control effect is achieved, parameters can be adjusted in a self-adaptive mode under unknown interference, and the anti-interference capacity and the steady-state performance of the system are remarkably improved; according to the simulation electric field optimization method, the treatment time is divided into time sequence sections where an infrared low-frequency module and a magnetic pulse module alternately act, a time division multiplexing coordination control strategy is adopted, the time division proportion and the protection interval of the time division multiplexing coordination control strategy are optimized, the treatment effect is ensured to be maximized, and overstimulation is avoided.

Description

Technical Field

[0001] The invention relates to the technical field of therapeutic apparatus optimization, and in particular to an infrared low-frequency magnetic pulse therapeutic apparatus optimization system based on feedback regulation. Background Art

[0002] The infrared low-frequency magnetic pulse therapeutic apparatus optimization system based on feedback regulation is a system that optimizes the infrared low-frequency magnetic pulse therapeutic apparatus by means of feedback regulation.

[0003] Among the existing similar solutions, for example, CN115252117B is an intense pulsed light therapy device and energy monitoring method that can monitor the luminous energy of a light source. This solution aims to solve the technical problem that traditional sensors are easily saturated due to excessive intense pulsed light energy, and the existing technology relies on empirical records or electrical parameter estimation and cannot reflect the actual light energy in real time. This solution first captures part of the light signal from the intense pulsed light source through a small aperture light sampling hole, and combines a filter and a multi-segment cavity to significantly attenuate the light signal, so that the therapy device is adapted to the measurement range of the photodetector, avoiding the technical effect of sensor saturation. In addition, by pre- First, the environmental noise value is collected, the background interference is dynamically eliminated, and the effective light signal is integrated and calculated to achieve the technical effect of accurate energy calibration. Finally, when the light source decays, the light source output is adjusted in real time according to the fitting function to achieve the technical effect of ensuring that the actual luminous energy is always consistent with the set value. However, there is physical aging of the device, such as electrode loss and spectral shift, which may exceed the compensation range of dynamic calibration, resulting in irreversible decline in energy output or wavelength shift, affecting the long-term use of the therapeutic device. In addition, there is a technical problem that the calibration process requires reliance on professional equipment or technicians, and improper operation by ordinary users may cause calibration failure.

[0004] For example, CN116602760B is an intelligent automatic intense pulsed light therapy device. This solution aims to solve the technical problems of incomplete skin condition monitoring, cumbersome manual adjustment, unstable treatment effect, insufficient skin temperature management and burn risk of existing equipment. This solution first achieves the technical effect of detailed analysis of skin condition by calculating the thickness of skin hair, hair follicle size, and the ratio of pits and protrusions. In addition, multi-level intense pulsed light therapy is adopted to automatically adjust the range and intensity of intense pulsed light according to skin condition data, so as to achieve the technical effect of improving treatment accuracy without manual operation. Finally, the therapy device has temperature monitoring, cooling and emergency treatment functions, so as to achieve the technical effect that the skin will not overheat during treatment and the safety of treatment is improved. However, there are technical problems that the use of image processing technology to monitor temperature through skin and hair status will have large errors, and the image acquisition quality is limited by factors such as lighting conditions, skin color, hair density, etc., which seriously affects the accuracy of temperature feature recognition. In addition, there is a technical problem that if the temperature feedback and adjustment of the temperature monitoring and cooling functions are delayed, it may still cause temporary skin overheating. Summary of the invention

[0005] In view of the above situation, in order to overcome the defects of the prior art, the present invention provides an infrared low-frequency magnetic pulse therapeutic device optimization system based on feedback regulation. In view of the physical aging of the device, such as electrode loss and spectral shift, it may exceed the compensation range of dynamic calibration, resulting in irreversible decline in energy output or wavelength shift, affecting the long-term use of the therapeutic device. In addition, there is a technical problem that the calibration process needs to rely on professional equipment or technicians, and improper operation by ordinary users may cause calibration failure. This solution proposes a PID adjustment method combining fuzzy logic and interpolation, introduces interpolation into the fuzzy PID parameter adjustment process, achieves smoother and more precise control effects, and can adaptively adjust parameters under unknown interference, which significantly improves the system's anti-interference ability and steady-state Performance, to achieve automatic calibration of energy output equipment; in view of the technical problems that there will be large errors in monitoring temperature through skin and hair status by using image processing technology, the quality of image acquisition is limited by factors such as lighting conditions, skin color, hair density, etc., which seriously affects the accuracy of temperature feature recognition, and there is a technical problem that if the temperature feedback and adjustment of temperature monitoring and cooling functions are delayed, it may still cause temporary skin overheating. This scheme proposes a simulated electric field optimization method, which divides the treatment time into a timing segment in which the infrared low-frequency module and the magnetic pulse module act alternately, adopts a time-sharing multiplexing coordinated control strategy, optimizes the time-sharing ratio and protection interval of the time-sharing multiplexing coordinated control strategy, ensures the maximum treatment effect and avoids excessive stimulation and burns.

[0006] The technical solution adopted by the present invention is as follows: The present invention provides an infrared low-frequency magnetic pulse therapeutic device optimization system based on feedback regulation, and the infrared low-frequency magnetic pulse therapeutic device optimization system based on feedback regulation includes an infrared low-frequency module, a magnetic pulse module, a physiological feedback module, an adaptive adjustment module and a multiplexing coordination module;

[0007] The infrared low-frequency module acts on the superficial tissue of the human body through thermal effect and electrical stimulation;

[0008] The magnetic pulse module generates a pulse signal through an electromagnetic field to act on the deep tissues of the human body;

[0009] The physiological feedback module is used to collect physiological feedback data and upload the collected physiological feedback data to the adaptive adjustment module. The physiological feedback module includes an infrared thermal imaging unit, an impedance spectrum analysis unit, a surface electromyography sensor, a magnetic flux sensor and an optical power meter;

[0010] The infrared thermal imaging unit monitors the changes in body surface temperature and local blood flow in the treatment area in real time;

[0011] The impedance spectrum analysis unit detects the degree of tissue edema through multi-frequency bioelectrical impedance technology;

[0012] The surface electromyography sensor collects electromyography signals in the treatment area and quantifies the degree of muscle tension;

[0013] The magnetic flux sensor detects the magnetic field strength of the magnetic pulse module in a closed loop;

[0014] The optical power meter monitors the output stability of the infrared low-frequency module;

[0015] The adaptive adjustment module monitors the physiological feedback data uploaded by the physiological feedback module in real time, adopts a PID controller, and adopts a PID adjustment method combining fuzzy logic and interpolation to obtain an actual control value, and dynamically adjusts the infrared low-frequency module and the magnetic pulse module according to the actual control value;

[0016] The multiplexing coordination module adopts a time-sharing multiplexing coordination control strategy to divide the treatment time into time segments in which the infrared low-frequency module and the magnetic pulse module act alternately, and optimizes the time-sharing ratio and protection interval of the time-sharing multiplexing coordination control strategy by simulating the electric field optimization method.

[0017] The adaptive adjustment module adopts a PID adjustment method combining fuzzy logic and interpolation. The PID adjustment method combining fuzzy logic and interpolation specifically includes the following steps:

[0018] Step A1: Construct the input-output relationship, specifically, define the input variables and output variables, and calculate the error and error change rate between the actual measurement of the input variables and the expected results. The specific steps are as follows:

[0019] Step A11: defining input variables, specifically, the input variables include the change in body surface temperature, the change in local blood flow, the degree of tissue edema, the degree of muscle tension, the magnetic field strength and the output stability;

[0020] Step A12: defining output variables, specifically, the output variables include infrared power and infrared frequency of the infrared low-frequency module, and magnetic pulse intensity, magnetic pulse frequency and magnetic pulse duration of the magnetic pulse module;

[0021] Step A2: Use a Gaussian membership function to cover the dynamic range of each unit in the physiological feedback module and divide the membership of the error, error change rate and output variable respectively. The Gaussian membership function formula is as follows; ;

[0022] In the formula, represents the center point, Indicates width, represents the membership of error and error change rate, Values ​​representing errors and error rate of change;

[0023] Step A3: Establishing a fuzzy rule base, wherein the fuzzy rule base includes fuzzy rules for dynamically adjusting output variables through errors and error change rates;

[0024] Step A4: adjusting the fuzzy rule parameters of the fuzzy rule base, the fuzzy rule base parameters include membership function parameters, rule weights, and consequent parameters;

[0025] Step A5: Fuzzy reasoning, using the Mamdani reasoning method, in the area covered by the fuzzy rule base, matching the input variables and output variables according to the fuzzy rules to obtain the fuzzy set of the output variables;

[0026] Step A6: Use interpolation technology to smooth the fuzzy set of output variables through the PID controller in the area not covered by the fuzzy rule base. The smoothing formula of the fuzzy set of output variables used is as follows: ;

[0027] In the formula, represents the fuzzy set of the smoothed output variable, Indicates the minimum limit of the output variable in the PID controller, Indicates the maximum limit of the output variable in the PID controller, represents a fuzzy set, represents the minimum fuzzy value of the output variable output by the fuzzy inference system, The maximum fuzzy value of the output variable representing the output of the fuzzy inference system;

[0028] Step A7: Defuzzification is used to convert the fuzzy set into a clear value. The formula used is as follows: ;

[0029] In the formula, represents the clear value after deblurring, represents the discrete sampling points of the fuzzy set, express The corresponding membership degree is Represents the number of discrete sampling points;

[0030] Step A8: Denormalization, specifically, mapping the defuzzified clarity value to the actual physical range through a denormalization operation to obtain the actual control value;

[0031] If the clarity value range is [0, 1], the denormalization formula used is as follows: ;

[0032] In the formula, represents the actual control value after denormalization, Indicates the minimum limit of the output variable in the PID controller, Indicates the maximum limit of the output variable in the PID controller, Represents the clarity value after deblurring;

[0033] If the clarity value range is [-1, 1], the denormalization formula used is as follows: ;

[0034] In the formula, represents the actual control value after denormalization, Indicates the minimum limit of the output variable in the PID controller, Indicates the maximum limit of the output variable in the PID controller, Indicates the sharp value after deblurring.

[0035] The multiplexing coordination module adopts a simulated electric field optimization method, and the simulated electric field optimization method specifically includes the following steps:

[0036] Step B1: taking the therapeutic effect of the therapeutic device as a performance indicator;

[0037] Step B2: randomly generate M groups of time-reuse coordinated control strategies, and calculate the performance index corresponding to each group of time-reuse coordinated control strategies;

[0038] Step B3: updating the group, specifically, introducing a mutation operator and a crossover operator, presetting a threshold, and performing crossover and mutation operations on the time-sharing multiplexing coordination control strategy whose performance index is lower than the threshold;

[0039] Step B4: Introduce the interaction mechanism of Coulomb force, record the group as a charged particle, initialize the velocity and position of the charged particle, calculate the particle charge, which is used to reflect the performance index of the charged particle, simulate the attraction and repulsion behavior between groups, and calculate the particle acceleration according to the particle charge. The formula used is as follows: ;

[0040] In the formula, represents the charged particle at time t The amount of charge, Represents the best performance indicator among all current groups, Indicates the worst performance indicator among all current groups. Represents charged particles performance indicators; ;

[0041] In the formula, represents the charged particle at time t From charged particles The Coulomb force, is the Coulomb constant, and Represents charged particles and charged particles The amount of charge, represents the distance between groups; ;

[0042] In the formula, Represents charged particles The acceleration of represents the charged particle at time t From charged particles Coulomb force;

[0043] Step B5: Update the velocity and position of the charged particles according to their acceleration, so that the coordinated control strategy of each group can approach the global optimum.

[0044] The beneficial effects achieved by the present invention using the above scheme are as follows:

[0045] (1) In view of the physical aging of the device, such as electrode loss and spectral shift, it may exceed the compensation range of dynamic calibration, resulting in irreversible decrease in energy output or wavelength shift, affecting the long-term use of the therapeutic device. In addition, there is a technical problem that the calibration process requires reliance on professional equipment or technicians, and improper operation by ordinary users may cause calibration failure. This scheme proposes a PID adjustment method that combines fuzzy logic and interpolation, introduces interpolation into the fuzzy PID parameter adjustment process, achieves smoother and more precise control effects, and can adaptively adjust parameters under unknown interference, significantly improving the system's anti-interference ability and steady-state performance, and realizing automatic calibration of energy output equipment;

[0046] (2) In view of the technical problems that the use of image processing technology to monitor temperature through skin and hair status will result in large errors, and the quality of image acquisition is limited by factors such as lighting conditions, skin color, and hair density, which seriously affects the accuracy of temperature feature recognition. In addition, there is a technical problem that if the temperature feedback and adjustment of the temperature monitoring and cooling functions are delayed, it may still cause temporary skin overheating. This scheme proposes a simulated electric field optimization method, which divides the treatment time into a time sequence in which the infrared low-frequency module and the magnetic pulse module act alternately, adopts a time-sharing coordinated control strategy, optimizes the time-sharing ratio and protection interval of the time-sharing coordinated control strategy, ensures that the treatment effect is maximized and excessive stimulation is avoided to prevent burns. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1A module connection diagram of an infrared low-frequency magnetic pulse therapeutic apparatus optimization system based on feedback regulation provided by the present invention;

[0048] Figure 2 It is a flow chart of the PID control method combining fuzzy logic and interpolation;

[0049] Figure 3 Schematic diagram of the flow chart of the simulation electric field optimization method.

[0050] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention. DETAILED DESCRIPTION

[0051] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all 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.

[0052] Example 1: See Figures 1 to 3 , this embodiment provides an infrared low-frequency magnetic pulse therapeutic device optimization system based on feedback regulation, the infrared low-frequency magnetic pulse therapeutic device optimization system based on feedback regulation includes an infrared low-frequency module, a magnetic pulse module, a physiological feedback module, an adaptive adjustment module and a multiplexing coordination module;

[0053] The infrared low-frequency module acts on the superficial tissue of the human body through thermal effect and electrical stimulation;

[0054] The magnetic pulse module generates a pulse signal through an electromagnetic field to act on the deep tissues of the human body;

[0055] The physiological feedback module is used to collect physiological feedback data and upload the collected physiological feedback data to the adaptive adjustment module. The physiological feedback module includes an infrared thermal imaging unit, an impedance spectrum analysis unit, a surface electromyography sensor, a magnetic flux sensor and an optical power meter;

[0056] The infrared thermal imaging unit monitors the changes in body surface temperature and local blood flow in the treatment area in real time;

[0057] The impedance spectrum analysis unit detects the degree of tissue edema through multi-frequency bioelectrical impedance technology;

[0058] The surface electromyography sensor collects electromyography signals in the treatment area and quantifies the degree of muscle tension;

[0059] The magnetic flux sensor detects the magnetic field strength of the magnetic pulse module in a closed loop;

[0060] The optical power meter monitors the output stability of the infrared low-frequency module;

[0061] The adaptive adjustment module monitors the physiological feedback data uploaded by the physiological feedback module in real time, adopts a PID controller, and adopts a PID adjustment method combining fuzzy logic and interpolation to obtain an actual control value, and dynamically adjusts the infrared low-frequency module and the magnetic pulse module according to the actual control value;

[0062] The multiplexing coordination module adopts a time-sharing multiplexing coordination control strategy to divide the treatment time into time segments in which the infrared low-frequency module and the magnetic pulse module act alternately, and optimizes the time-sharing ratio and protection interval of the time-sharing multiplexing coordination control strategy by simulating the electric field optimization method.

[0063] Example 2: See Figure 1 to Figure 2 This embodiment is based on the above embodiment. The adaptive adjustment module adopts a PID adjustment method combining fuzzy logic and interpolation. The PID adjustment method combining fuzzy logic and interpolation specifically includes the following steps:

[0064] Step A1: Construct the input-output relationship, specifically, define the input variables and output variables, and calculate the error and error change rate between the actual measurement of the input variables and the expected results. The specific steps are as follows:

[0065] Step A11: defining input variables, specifically, the input variables include the change in body surface temperature, the change in local blood flow, the degree of tissue edema, the degree of muscle tension, the magnetic field strength and the output stability;

[0066] Step A12: defining output variables, specifically, the output variables include infrared power and infrared frequency of the infrared low-frequency module, and magnetic pulse intensity, magnetic pulse frequency and magnetic pulse duration of the magnetic pulse module;

[0067] Step A2: Use a Gaussian membership function to cover the dynamic range of each unit in the physiological feedback module and divide the membership of the error, error change rate and output variable respectively. The Gaussian membership function formula is as follows; ;

[0068] In the formula, represents the center point, Indicates width, represents the membership of error and error change rate, Values ​​representing errors and error rate of change;

[0069] Step A3: Establishing a fuzzy rule base, wherein the fuzzy rule base includes fuzzy rules for dynamically adjusting output variables through errors and error change rates;

[0070] Step A4: adjusting the fuzzy rule parameters of the fuzzy rule base, the fuzzy rule base parameters include membership function parameters, rule weights, and consequent parameters;

[0071] Step A5: Fuzzy reasoning, using the Mamdani reasoning method, in the area covered by the fuzzy rule base, matching the input variables and output variables according to the fuzzy rules to obtain the fuzzy set of the output variables;

[0072] Step A6: Use interpolation technology to smooth the fuzzy set of output variables through the PID controller in the area not covered by the fuzzy rule base. The smoothing formula of the fuzzy set of output variables used is as follows: ;

[0073] In the formula, represents the fuzzy set of the smoothed output variable, Indicates the minimum limit of the output variable in the PID controller, Indicates the maximum limit of the output variable in the PID controller, represents a fuzzy set, represents the minimum fuzzy value of the output variable output by the fuzzy inference system, The maximum fuzzy value of the output variable representing the output of the fuzzy inference system;

[0074] Step A7: Defuzzification is used to convert the fuzzy set into a clear value. The formula used is as follows: ;

[0075] In the formula, represents the clear value after deblurring, represents the discrete sampling points of the fuzzy set, express The corresponding membership degree is Represents the number of discrete sampling points;

[0076] Step A8: Denormalization, specifically, mapping the defuzzified clarity value to the actual physical range through a denormalization operation to obtain the actual control value;

[0077] If the clarity value range is [0, 1], the denormalization formula used is as follows: ;

[0078] In the formula, represents the actual control value after denormalization, Indicates the minimum limit of the output variable in the PID controller, Indicates the maximum limit of the output variable in the PID controller, Represents the clarity value after deblurring;

[0079] If the clarity value range is [-1, 1], the denormalization formula used is as follows: ;

[0080] In the formula, represents the actual control value after denormalization, Indicates the minimum limit of the output variable in the PID controller, Indicates the maximum limit of the output variable in the PID controller, Indicates the sharp value after deblurring.

[0081] Example 3: See Figures 1 to 3 This embodiment is based on the above embodiment. The multiplexing coordination module adopts a simulated electric field optimization method. The simulated electric field optimization method specifically includes the following steps:

[0082] Step B1: taking the therapeutic effect of the therapeutic device as a performance indicator;

[0083] Step B2: randomly generate M groups of time-reuse coordinated control strategies, and calculate the performance index corresponding to each group of time-reuse coordinated control strategies;

[0084] Step B3: updating the group, specifically, introducing a mutation operator and a crossover operator, presetting a threshold, and performing crossover and mutation operations on the time-sharing multiplexing coordination control strategy whose performance index is lower than the threshold;

[0085] Step B4: Introduce the interaction mechanism of Coulomb force, record the group as a charged particle, initialize the velocity and position of the charged particle, calculate the particle charge, which is used to reflect the performance index of the charged particle, simulate the attraction and repulsion behavior between groups, and calculate the particle acceleration according to the particle charge. The formula used is as follows: ;

[0086] In the formula, represents the charged particle at time t The amount of charge, Represents the best performance indicator among all current groups, Indicates the worst performance indicator among all current groups. Represents charged particles performance indicators; ;

[0087] In the formula, represents the charged particle at time t From charged particles The Coulomb force, is the Coulomb constant, and Represents charged particles and charged particles The amount of charge, represents the distance between groups; ;

[0088] In the formula, Represents charged particles The acceleration of represents the charged particle at time t From charged particles Coulomb force;

[0089] Step B5: Update the velocity and position of the charged particles according to their acceleration, so that the coordinated control strategy of each group can approach the global optimum.

[0090] Example 4: See Figures 1 to 3 This embodiment is based on the above embodiment. In step A3, the temperature measured by the infrared thermal imaging unit is taken as the error, and the difference between the current temperature collected by the infrared thermal imaging unit and the last measured temperature is divided by the time interval as the error change rate.

[0091] Example 5: See Figures 1 to 3 This embodiment is based on the above embodiment. This embodiment differs from the above embodiment only in error and error change rate. The muscle tension measured by the surface electromyography sensor is taken as the error, and the difference between the current muscle tension collected by the surface electromyography sensor and the last measured muscle tension divided by the time interval is taken as the error change rate.

[0092] Example 6: See Figures 1 to 3 This embodiment is based on the above embodiment. This embodiment differs from the above embodiment only in error and error change rate. The degree of tissue edema measured by the impedance spectrum analysis unit is taken as the error, and the difference between the current degree of tissue edema collected by the impedance spectrum analysis unit and the last measured degree of tissue edema divided by the time interval is taken as the error change rate.

[0093] Example 7: See Figures 1 to 3 ,This embodiment is based on the above embodiment. In step A2, the error, error change rate and membership of the output variable are divided into 7 levels, namely: NL (large negative), NA (medium negative), NT (small negative), ZE (zero), PT (small positive), PA (medium positive), and PL (large positive).

[0094] Example 8: See Figures 1 to 3 This embodiment is based on the above embodiment. In step A3, the fuzzy rule is that if the temperature error is NL (large negative) and the rate of change is PL (large positive), the infrared power is increased and the magnetic field strength is reduced.

[0095] Example 9: See Figures 1 to 3 This embodiment is based on the above embodiment. In step A3, this embodiment differs from the above embodiment only in the fuzzy rule. The fuzzy rule is that if the electromyographic error is PA (medium positive) and the rate of change is NT (slightly negative), the duration of the magnetic pulse is extended.

[0096] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0097] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

[0098] The present invention and its embodiments are described above, and such description is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. In short, if ordinary technicians in the field are inspired by it, without departing from the purpose of the invention, they can design a structure and embodiment similar to the technical solution without creativity, which should belong to the protection scope of the present invention.

Claims

1. An infrared low-frequency magnetic pulse therapeutic device optimization system based on feedback regulation, characterized in that: It includes infrared low-frequency module, magnetic pulse module, physiological feedback module, adaptive adjustment module and multiplexing coordination module; The infrared low-frequency module acts on the superficial tissue of the human body through thermal effect and electrical stimulation; The magnetic pulse module generates a pulse signal through an electromagnetic field to act on the deep tissues of the human body; The physiological feedback module is used to collect physiological feedback data and upload the collected physiological feedback data to the adaptive adjustment module. The physiological feedback module includes an infrared thermal imaging unit, an impedance spectrum analysis unit, a surface electromyography sensor, a magnetic flux sensor and an optical power meter; The adaptive adjustment module monitors the physiological feedback data uploaded by the physiological feedback module in real time, adopts a PID controller, and adopts a PID adjustment method combining fuzzy logic and interpolation to obtain an actual control value, and dynamically adjusts the infrared low-frequency module and the magnetic pulse module according to the actual control value; The multiplexing coordination module adopts a time-sharing multiplexing coordination control strategy to divide the treatment time into time segments in which the infrared low-frequency module and the magnetic pulse module act alternately, and optimizes the time-sharing ratio and protection interval of the time-sharing multiplexing coordination control strategy by simulating an electric field optimization method; The PID adjustment method combining fuzzy logic and interpolation specifically includes the following steps: Step A1: Construct the input-output relationship, specifically, define the input variables and output variables, and calculate the error and error change rate between the actual measurement of the input variables and the expected results. The specific steps are as follows: Step A2: Use a Gaussian membership function to cover the dynamic range of each unit in the physiological feedback module and divide the membership of the error, error change rate and output variable respectively. The Gaussian membership function formula is as follows; ； In the formula, represents the center point, Indicates width, represents the membership of error and error change rate, Values ​​representing errors and error rate of change; Step A3: Establishing a fuzzy rule base, wherein the fuzzy rule base includes fuzzy rules for dynamically adjusting output variables through errors and error change rates; Step A4: adjusting the fuzzy rule parameters of the fuzzy rule base, the fuzzy rule base parameters include membership function parameters, rule weights, and consequent parameters; Step A5: Fuzzy reasoning, using the Mamdani reasoning method, in the area covered by the fuzzy rule base, matching the input variables and output variables according to the fuzzy rules to obtain the fuzzy set of the output variables; Step A6: Use interpolation technology to smooth the fuzzy set of output variables through the PID controller in the area not covered by the fuzzy rule base. The smoothing formula of the fuzzy set of output variables used is as follows: ； In the formula, represents the fuzzy set of the smoothed output variable, Indicates the minimum limit of the output variable in the PID controller, Indicates the maximum limit of the output variable in the PID controller, represents a fuzzy set, represents the minimum fuzzy value of the output variable output by the fuzzy inference system, The maximum fuzzy value of the output variable representing the output of the fuzzy inference system; Step A7: Defuzzification is used to convert the fuzzy set into a clear value. The formula used is as follows: ； In the formula, represents the clear value after deblurring, represents the discrete sampling points of the fuzzy set, express The corresponding membership degree is Represents the number of discrete sampling points; Step A8: Denormalization, specifically, the defuzzified clarity value is mapped to the actual physical range through a denormalization operation to obtain the actual control value.

2. According to the feedback-adjusted infrared low-frequency magnetic pulse therapeutic apparatus optimization system of claim 1, it is characterized in that: The multiplexing coordination module adopts a simulated electric field optimization method, and the simulated electric field optimization method specifically includes the following steps: Step B1: taking the therapeutic effect of the therapeutic device as a performance indicator; Step B2: randomly generate M groups of time-reuse coordinated control strategies, and calculate the performance index corresponding to each group of time-reuse coordinated control strategies; Step B3: updating the group, specifically, introducing a mutation operator and a crossover operator, presetting a threshold, and performing crossover and mutation operations on the time-sharing multiplexing coordination control strategy whose performance index is lower than the threshold; Step B4: Introduce the interaction mechanism of Coulomb force, record the group as a charged particle, initialize the velocity and position of the charged particle, calculate the particle charge, which is used to reflect the performance index of the charged particle, simulate the attraction and repulsion behavior between groups, and calculate the particle acceleration according to the particle charge. The formula used is as follows: ; In the formula, represents the charged particle at time t The amount of charge, Represents the best performance indicator among all current groups, Indicates the worst performance indicator among all current groups. Represents charged particles performance indicators; ； In the formula, represents the charged particle at time t From charged particles The Coulomb force, is the Coulomb constant, and Represents charged particles and charged particles The amount of charge, represents the distance between groups; ； In the formula, Represents charged particles The acceleration of represents the charged particle at time t From charged particles Coulomb force; Step B5: Update the velocity and position of the charged particles according to their acceleration, so that the coordinated control strategy of each group can approach the global optimum.

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