Closed-loop feedback regulation and control system based on magnetothermal effect of magnetic particles

Abstract

The invention provides a closed-loop feedback regulation and control system based on a magnetic particle magnetothermal effect, and the system comprises a magnetic generation module which is used for heating and exciting magnetic particles injected into a nerve regulation and control target region based on a generated magnetic field, and obtaining a magnetic particle response signal; the magnetic particle signal receiving module is used for receiving the magnetic particle response signal and calculating the ambient temperature around the magnetic particles; and the closed-loop control module is used for adaptively adjusting heating parameters based on the calculated ambient temperature around the magnetic particles and a preset target temperature so as to realize closed-loop nerve temperature regulation and control. According to the invention, precise closed-loop control of temperature can be realized, and the requirement of a nerve regulation and control system for high-precision temperature control is met.

Description

Technical Field

[0001] The present invention belongs to the technical field of neural network control, and in particular relates to a closed-loop feedback control system based on the magnetothermal effect of magnetic particles. Background Art

[0002] With the aging of the population, increasing life pressure and changes in the external environment, the incidence of functional brain diseases such as neurodegenerative diseases, drug-resistant epilepsy and mental illness continues to rise, becoming one of the major causes of disability and death in the world. These diseases not only bring heavy burdens to individuals and society, but also their treatment methods and mechanisms of action have become scientific problems that need to be solved urgently.

[0003] In this context, neuromodulation, as a cutting-edge field based on neuroscience and biomedical engineering technology, has developed rapidly. Neuromodulation technology uses implantable or non-implantable means to excite, inhibit or regulate the nervous system through physical (such as electricity, magnetism, light, ultrasound, etc.) or chemical methods. It is widely used in the treatment of neurological diseases, the improvement of patients' quality of life, and the study of the mechanisms of neurological diseases, thereby promoting the innovation of related treatment methods.

[0004] Magnetic particle neuromodulation technology is a minimally invasive neuromodulation method that uses magnetic particles (usually superparamagnetic iron oxide nanoparticles, SPIO) to inject magnetic particles into the target site and utilize the magnetothermal effect or magnetic force effect generated by the external magnetic field to regulate neural activity. Studies have shown that neural activity is significantly affected by the temperature of its environment. Through genetic engineering technology, neuronal cells express temperature-gated TRPV1 ion channels, and local thermal effects are used to induce Ca 2+ The magnetothermal effect can effectively regulate nerve activity by precisely controlling the local temperature, providing new methods and ideas for the treatment and pathogenesis of neurological diseases.

[0005] In a neuromodulation system, closed-loop control is a key technology. Especially in temperature-based neuromodulation, it is crucial for the safety and effectiveness of the system. Research shows that target neurons are highly sensitive to temperature changes. Generally, an increase in temperature enhances neural activity, while a deviation of temperature from the safe range may lead to regulatory failure and even irreversible damage to neural tissue. Due to the differences in temperature tolerance and response among different individuals and regulatory sites, real-time monitoring of the target temperature and dynamic adjustment of heating parameters become necessary conditions for achieving precise regulation. Closed-loop control dynamically adjusts external heating parameters through real-time temperature feedback to ensure that the target temperature is always maintained within a safe and effective range, thereby significantly improving the safety, stability, and effectiveness of neuromodulation.

[0006] To construct a closed-loop feedback regulation system based on the magnetothermal effect of magnetic particles, a magnetic-thermal neuromodulation system usually has two working modes: a heating mode and a measurement mode. In the measurement mode, traditional magnetic particle thermometry methods estimate the ambient temperature through the relaxation phenomenon of the magnetic moments of magnetic particles under the excitation of an alternating magnetic field. Specifically, at different temperatures, the magnetic moments of magnetic particles deflect and delay differently due to relaxation, which causes changes in the harmonic ratio of the magnetic particle response signal. Based on this, the temperature can be estimated by measuring the harmonic ratio.

[0007] However, the existing temperature measurement methods based on the harmonic ratio have obvious deficiencies in accuracy and precision. Especially in neuromodulation applications, when high-precision temperature measurement and precise regulation are required within a small temperature range, this method cannot meet the actual needs. This deficiency limits the application of the magnetothermal effect of magnetic particles in closed-loop neuromodulation systems. Summary of the Invention

[0008] To solve the problems existing in the prior art, the present invention provides a closed-loop feedback regulation system based on the magnetothermal effect of magnetic particles. By introducing a bias magnetic field on the basis of the original excitation magnetic field, the high-precision measurement of the temperature around the magnetic particles is realized by measuring the change relationship between the harmonic phase deflection of the magnetic particles caused by temperature and the value of the bias magnetic field. Based on this, the system can accurately and real-time monitor the temperature, and through a closed-loop algorithm, adaptively adjust the heating parameters to achieve precise closed-loop control of the temperature, meeting the requirements of the neuromodulation system for high-precision temperature control.

[0009] A closed-loop feedback regulation system based on the magnetothermal effect of magnetic particles includes:

[0010] A magnetic generation module for heating and exciting the magnetic particles injected into the neuromodulation target area based on the generated magnetic field to obtain a magnetic particle response signal;

[0011] A magnetic particle signal receiving module for receiving the magnetic particle response signal and calculating the ambient temperature around the magnetic particles;

[0012] The closed-loop control module is used to adaptively adjust the heating parameters based on the calculated ambient temperature of the magnetic particles and the preset target temperature, so as to achieve closed-loop neural temperature regulation.

[0013] Preferably, the magnetic generation module includes: a signal generator, a first power amplifier and a second power amplifier connected to the signal generator, an excitation coil connected to the first power amplifier, and a bias magnetic field coil connected to the second power amplifier; wherein,

[0014] The signal generator is used to generate a high-frequency sine signal for heating the magnetic particles, a low-frequency sine signal for detecting temperature, and a DC signal;

[0015] The first power amplifier is used to amplify the high-frequency sine signal and the low-frequency sine signal;

[0016] The excitation coil is used to generate a high-frequency sine current based on the amplified high-frequency sine signal, and generate a high-frequency sine alternating magnetic field based on the high-frequency sine current; generate a low-frequency sine current based on the amplified low-frequency sine signal, and generate a low-frequency sine alternating magnetic field based on the low-frequency sine current;

[0017] The second power amplifier is used to amplify the DC signal;

[0018] The bias magnetic field coil is used to generate a DC current based on the amplified DC signal, and generate a bias magnetic field based on the DC current.

[0019] Preferably, in the magnetic generation module,

[0020] The high-frequency sine alternating magnetic field is used to heat the magnetic particles and the surrounding nerve tissues through the magnetothermal effect;

[0021] The low-frequency sine alternating magnetic field is used to excite the deflection of the magnetic moment of the magnetic particles to obtain a magnetic particle response signal;

[0022] The bias magnetic field is used to be applied when the magnetic moment of the magnetic particles deflects.

[0023] Preferably, the magnetic particle signal receiving module includes: a compensation coil, a receiving coil and a data acquisition unit connected in sequence;

[0024] The receiving coil is used to detect the magnetic particle response signal;

[0025] The compensation coil is used to cancel the feedthrough signal in the system; wherein, the feedthrough signal is mixed with the magnetic particle response signal;

[0026] The data acquisition unit is used to acquire the magnetic particle response signal detected by the receiving coil.

[0027] Preferably, the excitation coil, the receiving coil, and the compensation coil are coaxial and concentric solenoid structures; the bias magnetic field coil is a Helmholtz coil;

[0028] Among them, the bias magnetic field coil is placed at the upper and lower ends of the excitation coil;

[0029] The receiving coil and the compensation coil are separated up and down and embedded in the excitation coil.

[0030] Preferably, the closed-loop control module includes:

[0031] An actuator, an arithmetic unit, and a user interface unit respectively connected to the actuator; the arithmetic unit is connected to the data acquisition unit; among them,

[0032] The arithmetic unit is used to calculate the ambient temperature around the magnetic particles based on the magnetic particle response signal, and perform operations on the calculated ambient temperature around the magnetic particles and the preset target temperature based on a mathematical model to obtain control parameters;

[0033] The actuator is connected to the signal generator and is used to adjust the heating parameters based on the control parameters;

[0034] The user interface unit is used to set the preset target temperature and perform parameter visualization.

[0035] Preferably, the mathematical model formula is as follows:

[0036]

[0037] Among them, A n (H DC ) is the change of the nth harmonic complex number with the bias magnetic field, T is the temperature, M'(H DC ) is the derivative of the magnetic moment response of the magnetic particles, that is, the magnetic particle response signal measured by the receiving coil, U n-1 is the second-kind Chebyshev polynomial, H DC is the bias magnetic field amplitude, H AC is the amplitude of the low-frequency sinusoidal alternating magnetic field.

[0038] Preferably, the closed-loop control module is built with a PID control model, and the expression of the PID control model is as follows:

[0039]

[0040] Among them, u(t) is the output of the PID control model, e(t) is the control deviation, K p is the proportional gain coefficient, K i is the integral gain coefficient, K d is the differential gain coefficient.

[0041] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0042] In view of the problems of inaccurate temperature control and limited regulation accuracy in the existing magnetothermal nerve regulation system, the present invention proposes an innovative closed-loop magnetothermal nerve regulation system. Compared with the traditional magnetothermal regulation system, the present invention has made a major breakthrough in the technical architecture. For the first time, an in-vivo temperature measurement mode of magnetic particles is introduced to achieve real-time and accurate monitoring of the temperature around the magnetic particles. To improve the temperature measurement accuracy in the in-vivo temperature measurement mode, the present invention innovatively combines the bias magnetic field and the excitation magnetic field, and realizes accurate temperature measurement by detecting the bias magnetic field value when the harmonic phase of the magnetic particle response signal deflects. The introduction of the bias magnetic field makes the magnetic moment response signal of the magnetic particles asymmetric, resulting in a sudden change in the harmonic phase of the magnetic particle response signal. When the bias magnetic field intensity reaches a specific threshold, the harmonic phase will undergo a sudden change, and this critical bias magnetic field value is determined by the relaxation characteristics of the magnetic particles. Since the relaxation characteristics of the magnetic particles are closely related to the surrounding temperature, by accurately measuring the critical bias magnetic field value corresponding to the phase mutation, the temperature change of the environment around the magnetic particles can be accurately reflected. Compared with the traditional method of measuring the temperature of magnetic particles using only the excitation magnetic field, this method significantly improves the accuracy and reliability of temperature measurement, providing key technical support for the accurate implementation of magnetothermal nerve regulation. The system feeds back the accurately measured temperature of the magnetic particles to the closed-loop control module in real time, and adaptively adjusts the heating parameters through the built-in algorithm to achieve high-precision closed-loop magnetothermal nerve regulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions of the present invention, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can also obtain other drawings based on these drawings without creative efforts.

[0044] Figure 1 It is a schematic structural diagram of a closed-loop feedback regulation system based on the magnetothermal effect of magnetic particles according to an embodiment of the present invention;

[0045] Figure 2 It is a structural sectional view of the excitation coil, receiving coil, compensation coil and bias magnetic field coil in the magnetic field generation module;

[0046] Figure 3 It is a working flow chart of a closed-loop feedback regulation system based on the magnetothermal effect of magnetic particles.

[0047] BRIEF DESCRIPTION OF THE DRAWINGS: 01 - excitation coil, 02 - bias magnetic field coil, 03 - receiving coil, 04 - compensation coil. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0048] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0049] To make the above objects, features, and advantages of the present invention more obvious and understandable, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0050] Embodiment 1

[0051] As Figure 1 shown, a closed-loop feedback regulation system based on the magnetothermal effect of magnetic particles includes: a magnetic generation module, a magnetic particle signal receiving module, and a closed-loop control module. In this embodiment, the magnetic particles are used to receive magnetic field energy to heat nerve tissue and generate response signals to detect the temperature around them. The magnetic particles have superparamagnetic characteristics. Under the action of an alternating magnetic field, their magnetization curve shows a non-linear response, generating high-order harmonic signals. When a bias magnetic field is applied, the asymmetry of the response signal is enhanced, resulting in changes in the real and imaginary parts of the harmonic signal. When the bias magnetic field strength reaches a specific value, the harmonic phase will deflect by 180 degrees. In this embodiment, the magnetic particles are superparamagnetic iron oxide nanoparticles SPIO.

[0052] The magnetic generation module is used to heat and excite the magnetic particles injected into the nerve regulation target area based on the generated magnetic field to obtain the magnetic particle response signal.

[0053] A further implementation is that the magnetic generation module includes: a signal generator, a first power amplifier and a second power amplifier connected to the signal generator, an excitation coil connected to the first power amplifier, and a bias magnetic field coil 02 connected to the second power amplifier; wherein,

[0054] The signal generator is used to generate a high-frequency sine signal required to heat the magnetic particles, a low-frequency sine signal required to detect the temperature, and a DC signal;

[0055] The first power amplifier is used to amplify the high-frequency sine signal and the low-frequency sine signal;

[0056] The excitation coil 01 is used to generate a high-frequency sine current based on the amplified high-frequency sine signal and generate a high-frequency sine alternating magnetic field based on the high-frequency sine current; generate a low-frequency sine current based on the amplified low-frequency sine signal and generate a low-frequency sine alternating magnetic field based on the low-frequency sine current;

[0057] The second power amplifier is used to amplify the DC signal;

[0058] The bias magnetic field coil 02 is configured to generate a direct current based on the amplified direct current signal, and generate a bias magnetic field based on the direct current.

[0059] A further embodiment lies in that, in the magnetic generation module,

[0060] The high-frequency sinusoidal alternating magnetic field is used to heat the magnetic particles and the surrounding nerve tissues through the magnetothermal effect;

[0061] The low-frequency sinusoidal alternating magnetic field is used to excite the deflection of the magnetic moment of the magnetic particles to obtain a magnetic particle response signal;

[0062] The bias magnetic field is used to be applied when the magnetic moment of the magnetic particles deflects.

[0063] In this embodiment, the frequency of the high-frequency sinusoidal alternating magnetic field is: 100 kHz - 40 MHz, and the frequency of the lower-frequency sinusoidal alternating magnetic field is: 1 - 25 kHz.

[0064] The magnetic particle signal receiving module is configured to receive the magnetic particle response signal and calculate the ambient temperature around the magnetic particles.

[0065] A further embodiment lies in that the magnetic particle signal receiving module includes: a compensation coil, a receiving coil, and a data acquisition unit connected in sequence; wherein,

[0066] The receiving coil 03 is configured to detect the magnetic particle response signal;

[0067] The compensation coil 04 is configured to cancel the feedthrough signal in the system; wherein, the feedthrough signal is mixed with the magnetic particle response signal; specifically, the feedthrough signal is the electrical signal induced and received by the receiving coil from the sinusoidal alternating magnetic field used to excite the magnetic particle signal in the outside world; the feedthrough signal is mixed with the response signal; it can be cancelled by the compensation coil inducing the same but opposite-sign signals.

[0068] The data acquisition unit is configured to acquire the magnetic particle response signal detected by the receiving coil 03.

[0069] A further embodiment lies in that the excitation coil 01, the receiving coil 03, and the compensation coil 04 are of a coaxial and concentric solenoid structure; the bias magnetic field coil 02 is a Helmholtz coil, and the nerve to be regulated is placed in the nerve regulation area. As Figure 2 shown.

[0070] Among them, the bias magnetic field coil 02 is placed at the upper and lower ends of the excitation coil 01;

[0071] The receiving coil 03 and the compensation coil 04 are vertically separated and embedded in the excitation coil 01. They have the same specifications and are vertically separated. The winding directions of the coils are opposite.

[0072] A closed-loop control module, which is used to adaptively adjust the heating parameters based on the calculated ambient temperature of the magnetic particles and a preset target temperature, so as to achieve closed-loop neural temperature regulation.

[0073] A further implementation manner is that the closed-loop control module includes:

[0074] An actuator, an arithmetic unit and a user interface unit which are respectively connected to the actuator; the arithmetic unit is connected to the data acquisition unit; wherein,

[0075] The arithmetic unit is used to calculate the ambient temperature of the magnetic particles based on the magnetic particle response signal, and perform operations on the calculated ambient temperature of the magnetic particles and the preset target temperature based on a mathematical model to obtain control parameters; wherein, the control parameters include the amplitude and frequency of the heating magnetic field (high-frequency sinusoidal alternating magnetic field), etc. (If only one parameter is adjusted to simplify the system, only the amplitude can be retained).

[0076] The actuator is connected to the signal generator and is used to adjust the heating parameters based on the control parameters, specifically adjust parameters such as the frequency and amplitude of the heating magnetic field;

[0077] The user interface unit is used to set the preset target temperature and perform parameter visualization, including other parameters and relevant information.

[0078] A further implementation manner is that the mathematical model formula is as follows:

[0079]

[0080] Wherein, A n (H DC ) is the change of the nth harmonic complex number with the bias magnetic field, T is the temperature, i represents the imaginary unit in mathematics, M'(H DC ) is the derivative of the magnetic moment response of the magnetic particles, that is, the magnetic particle response signal measured by the receiving coil 03, U n-1 is the second-kind Chebyshev polynomial, H DC is the amplitude of the bias magnetic field, H AC is the amplitude of the low-frequency sinusoidal alternating magnetic field.

[0081] A further implementation manner is that the closed-loop control module is built with a PID control model, and the expression of the PID control model is as follows:

[0082]

[0083] Wherein, u(t) is the output of the PID control model, which can be the magnetic field amplitude, frequency, power or other control signals; e(t) is the control deviation, that is, the difference between the set point and the actual output; K pis the proportional gain coefficient, representing the response intensity of the controller to the current deviation, and its output is proportional to the deviation; proportional control can quickly respond to the deviation and reduce the steady-state error of the system; K i is the integral gain coefficient, which is used to eliminate the steady-state error and ensure that the long-term deviation is completely eliminated by accumulating the time integral of the deviation; K d is the derivative gain coefficient, which is used to predict the future trend of the deviation, reduce the overshoot and oscillation of the system by responding to the deviation change rate, and improve the dynamic performance. The gain coefficients K p 、K i and K d need to be adjusted according to the dynamic characteristics and control objectives of the system to ensure that the controller can optimize the system stability and response speed.

[0084] Embodiment 2

[0085] As Figure 3 shown, this embodiment provides a method for applying the system described in Embodiment 1, and the specific implementation steps are as follows:

[0086] S1: Inject magnetic particles into the position of the neuromodulation target area;

[0087] S2: Set the target temperature in the closed-loop control module and start running the system;

[0088] S3: After the system runs, the system enters the heating mode, and the magnetic field generation module generates a high-frequency sinusoidal alternating magnetic field, and heats the magnetic particles and the surrounding tissues through the magnetothermal effect;

[0089] S4: After running for a certain time, the system switches to the temperature measurement mode, the magnetic field generation module generates a lower-frequency sinusoidal alternating magnetic field and a bias magnetic field. At the same time, the magnetic particle signal receiving module starts to collect the magnetic particle response signal; during the signal collection process, the bias magnetic field current scans near the harmonic phase signal deflection critical point, so as to obtain the bias magnetic field intensity at the critical point; and according to the bias magnetic field intensity and the mathematical model, the temperature of the environment around the magnetic particles is calculated;

[0090] S5: Input the measured temperature and the preset temperature into the closed-loop control module, and adaptively adjust the heating parameters through the built-in arithmetic unit and actuator of the module;

[0091] S6: Through the continuous switching between the heating mode and the temperature measurement mode, and the parameter adjustment of the closed-loop control module, the temperature reaches and remains at the preset temperature value.

[0092] Further, in step S4, the mathematical model for calculating the temperature includes:

[0093] Under the combined action of the bias magnetic field and the lower-frequency alternating magnetic field, the harmonic signal of the magnetic particles can be expressed by the following mathematical expression:

[0094]

[0095] Among them, A n (H DC ) is the variation of the nth harmonic complex with respect to the bias magnetic field, T is the temperature, M'(H DC ) is the derivative of the particle magnetic moment response, that is, the particle response signal measured by the detection coil, U n-1 is the second kind of Chebyshev polynomial, H DC is the amplitude of the bias magnetic field, H AC is the amplitude of the lower-frequency sinusoidal alternating magnetic field. The change in temperature T will cause a corresponding change in the amplitude H DC of the bias magnetic field when the harmonic phase deflects, thereby reflecting the change in the temperature of the environment around the magnetic particles.

[0096] Furthermore, in step S5, the built-in algorithm of the closed-loop control module includes the PID control algorithm:

[0097] The mathematical expression of the PID control algorithm is:

[0098]

[0099] Among them, u(t) is the output of the PID controller, which can be the magnetic field amplitude, frequency, power or other control signals; e(t) is the control deviation, that is, the difference between the set point and the actual output; K p is the proportional gain coefficient, which represents the response intensity of the controller to the current deviation, and its output is proportional to the deviation; proportional control can quickly respond to the deviation and reduce the steady-state error of the system; K i is the integral gain coefficient, which is used to eliminate the steady-state error and ensure that the long-term deviation is completely eliminated by accumulating the time integral of the deviation; K d is the derivative gain coefficient, which is used to predict the future trend of the deviation and reduce the overshoot and oscillation of the system by responding to the deviation change rate, thereby improving the dynamic performance. The gain coefficients K p , K i and K d need to be adjusted according to the dynamic characteristics and control objectives of the system to ensure that the controller can optimize the system stability and response speed.

[0100] The embodiments described above are only descriptions of the preferred embodiments of the present invention, and do not limit the scope of the present invention. Without departing from the design spirit of the present invention, various deformations and improvements made by those of ordinary skill in the art to the technical solutions of the present invention should fall within the protection scope determined by the claims of the present invention.

Claims

1. A closed-loop feedback control system based on the magnetocaloric effect of magnetic particles, characterized in that: include: A magnetic generation module, used to heat and stimulate the magnetic particles injected into the target area of ​​nerve regulation based on the generated magnetic field, and obtain a magnetic particle response signal; A magnetic particle signal receiving module, used to receive the magnetic particle response signal and calculate the ambient temperature of the magnetic particle; The closed-loop control module is used to adaptively adjust the heating parameters based on the calculated ambient temperature of the magnetic particles and the preset target temperature to achieve closed-loop neural temperature regulation.

2. The system according to claim 1, characterized in that The magnetic generating module comprises: a signal generator, a first power amplifier and a second power amplifier connected to the signal generator, an excitation coil connected to the first power amplifier, and a bias magnetic field coil connected to the second power amplifier; wherein, The signal generator is used to generate a high-frequency sinusoidal signal required for heating the magnetic particles, a low-frequency sinusoidal signal required for detecting the temperature, and a DC signal; The first power amplifier is used to amplify the high-frequency sinusoidal signal and the low-frequency sinusoidal signal; The excitation coil is used to generate a high-frequency sinusoidal current based on the amplified high-frequency sinusoidal signal, and generate a high-frequency sinusoidal alternating magnetic field based on the high-frequency sinusoidal current; generate a low-frequency sinusoidal current based on the amplified low-frequency sinusoidal signal, and generate a low-frequency sinusoidal alternating magnetic field based on the low-frequency sinusoidal current; The second power amplifier is used to amplify the DC signal; The bias magnetic field coil is used to generate a direct current based on the amplified direct current signal, and to generate a bias magnetic field based on the direct current.

3. The system according to claim 2, characterized in that In the magnetic generating module, The high-frequency sinusoidal alternating magnetic field is used to heat the magnetic particles and surrounding nerve tissues through magnetocaloric effect; The low-frequency sinusoidal alternating magnetic field is used to excite the magnetic moment deflection of the magnetic particles to obtain a magnetic particle response signal; The bias magnetic field is used to apply when the magnetic moment of the magnetic particles is deflected.

4. The system according to claim 2, characterized in that The magnetic particle signal receiving module comprises: a compensation coil, a receiving coil and a data acquisition unit connected in sequence; The receiving coil is used to detect the magnetic particle response signal; The compensation coil is used to offset the feedthrough signal in the system; wherein the feedthrough signal is mixed with the magnetic particle response signal; The data acquisition unit is used to acquire the magnetic particle response signal detected by the receiving coil.

5. The system according to claim 4, characterized in that The excitation coil, the receiving coil and the compensation coil are coaxial concentric solenoid structures; the bias magnetic field coil is a Helmholtz coil; Wherein, the bias magnetic field coil is placed at the upper and lower ends of the excitation coil; The receiving coil and the compensating coil are separated up and down and embedded in the exciting coil.

6. The system according to claim 4, characterized in that The closed-loop control module includes: an actuator, and a computing unit and a user interface unit respectively connected to the actuator; the computing unit is connected to the data acquisition unit; wherein, An operator, used to calculate the ambient temperature of the magnetic particles based on the magnetic particle response signal, and to calculate the ambient temperature of the magnetic particles and a preset target temperature based on a mathematical model to obtain a control parameter; an actuator, connected to the signal generator, for adjusting the heating parameter based on the control parameter; The user interface unit is used to set the preset target temperature and perform parameter visualization.

7. The system according to claim 6, characterized in that The mathematical model formula is as follows: Among them, A n (H DC ) is the variation of the nth harmonic complex number with the bias magnetic field, T is the temperature, M'(H DC ) is the derivative of the magnetic moment response of the magnetic particle, that is, the magnetic particle response signal measured by the receiving coil, U n-1 is a Chebyshev polynomial of the second kind, H DC is the bias magnetic field amplitude, H AC is the amplitude of the low-frequency sinusoidal alternating magnetic field.

8. The system according to claim 5, characterized in that The closed-loop control module has a built-in PID control model, and the expression of the PID control model is as follows: Among them, u(t) is the output of the PID control model, e(t) is the control deviation, K p is the proportional gain coefficient, K i is the integral gain coefficient, K d is the differential gain coefficient.

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