System and method for realizing adjustable rigidity of tissue scaffold based on magnetorheological fluid

Abstract

The invention provides a system and method for achieving adjustable rigidity of a tissue scaffold based on magnetorheological fluid, and relates to the technical field of intelligent biomedical engineering.The system comprises a scaffold module, a sensing feedback module, a control center module, a magnetic field module and a user interaction module.The magnetic response microchannel tissue scaffold is prepared through 3D printing and a photoetching machine; the method comprises the following steps: acquiring a mechanical state and an environment signal of a magnetic response micro-channel tissue scaffold, processing to obtain mechanical state and environment data, generating a PWM pulse signal through an LSTM model and a self-adaptive PID algorithm, generating a controllable magnetic field according to the PWM pulse signal, adjusting the rigidity of magnetorheological fluid according to the PWM pulse signal, adjusting parameters of the self-adaptive PID algorithm through a parameter display adjustment area, and meanwhile, adjusting the rigidity of the magnetorheological fluid according to the controllable magnetic field. And displaying the mechanical state data and the environmental data through a data display area. According to the system, through cooperative work of all the modules, precise regulation and control of the rigidity of the tissue scaffold are achieved, and the system has important application value in the field of biomedical engineering.

Description

Technical Field

[0001] The present invention mainly relates to the technical field of intelligent biomedical engineering, and particularly relates to a system and method for realizing adjustable stiffness of tissue scaffolds based on magnetorheological fluid. Background Art

[0002] In the field of intelligent biomedical engineering, tissue scaffolds play a crucial supporting role in cell growth and tissue repair.

[0003] Traditional tissue scaffolds have fixed stiffness and are difficult to adapt to complex biological environments and the changing mechanical requirements during the tissue repair process. At different stages of cell growth and tissue repair, the requirements for scaffold stiffness vary greatly. Scaffolds with fixed stiffness cannot provide a matching mechanical microenvironment, which limits cell proliferation, differentiation, and tissue regeneration effects. At the same time, existing technologies are difficult to obtain the mechanical state and environmental signals of tissue scaffolds in real time and accurately, and cannot provide a reliable basis for the dynamic adjustment of scaffold stiffness. In terms of technical means for controlling scaffold stiffness, there is a lack of efficient and intelligent regulation methods, and it is impossible to quickly and accurately adjust scaffold stiffness according to actual needs, affecting the treatment effect and application scope of tissue engineering. Therefore, how to obtain relevant data of tissue scaffolds in real time and accurately, and quickly and accurately adjust the stiffness of tissue scaffolds according to the relevant data has become an urgent problem. For this reason, a system and method for realizing adjustable stiffness of tissue scaffolds based on magnetorheological fluid are proposed. Summary of the Invention

[0004] The purpose of the present invention is to provide a system and method for realizing adjustable stiffness of tissue scaffolds based on magnetorheological fluid to solve the problems raised in the above background art.

[0005] To solve the above technical problems, the technical solutions adopted by the present invention are as follows:

[0006] A system for realizing adjustable stiffness of tissue scaffolds based on magnetorheological fluid includes a scaffold module, a sensing and feedback module, a control center module, a magnetic field module, and a user interaction module;

[0007] The scaffold module is used to construct a tissue scaffold. The magnetorheological fluid microcapsules and the scaffold material are printed along the periphery of the negative microchannel mold using 3D printing technology to obtain a microchannel scaffold. The microchannel scaffold is processed by model removal and PNIPAM thermosensitive layer modification to obtain a magnetoresponsive microchannel tissue scaffold;

[0008] The sensing and feedback module is used to obtain the mechanical state signal and environmental signal of the magnetoresponsive microchannel tissue scaffold, and through amplification, filtering, analog-to-digital conversion, calculation, or demodulation processing of the obtained mechanical state signal and environmental signal, obtain mechanical state data and environmental data, and upload the mechanical state signal, mechanical state data, and environmental data to the control center and the database;

[0009] The control center module is used to dynamically adjust the magnetic field intensity to achieve rigid control. It obtains prediction data through the LSTM model, normalizes the multi-modal data including prediction data, mechanical state data, and environmental data through a normalization formula, and obtains a PWM pulse signal through an adaptive PID algorithm for the normalized multi-modal data;

[0010] The magnetic field module is used to drive the magnetorheological fluid in the magnetoresponsive microchannel tissue scaffold to achieve stiffness adjustment. According to the PWM pulse signal, a controllable magnetic field is generated by controlling an array of micro-coils through a high-frequency drive circuit, and the stiffness of the magnetorheological fluid is adjusted accordingly;

[0011] The user interaction module is used to adjust the parameters of the adaptive PID algorithm and display the mechanical state data and environmental data.

[0012] Preferably, the process of obtaining the microchannel scaffold:

[0013] Carbonyl iron powder with a particle size of 5 to 10 μm and a saturation magnetization intensity of greater than or equal to 140 emu / g is dispersed in a silicone oil-based carrier liquid. Through ultrasonic dispersion with a power of 200 W and a time of 15 min combined with mechanical stirring at a rotation speed of 500 rpm and a time of 30 min, a magnetorheological fluid is obtained. A microcapsule shell is formed by poly(lactic-co-glycolic acid) (PLGA) to encapsulate the magnetorheological fluid to obtain magnetorheological fluid microcapsules. A negative microchannel mold made of SU-8 photoresist is prepared on a silicon wafer with a diameter of 100 mm through photolithography technology. The negative microchannel mold is fixed on a printing platform, and the material obtained by mixing magnetorheological fluid microcapsules and scaffold material in a ratio of 1:3 is printed along the periphery of the negative microchannel mold through 3D printing technology to obtain a microchannel scaffold.

[0014] Preferably, the process of obtaining the magnetoresponsive microchannel tissue scaffold:

[0015] The microchannel scaffold is subjected to model removal treatment. Utilizing the property that SU-8 photoresist is soluble in a mixed solvent of acetone and ethanol, ultrasonic cleaning is performed with a mixed solvent of acetone and ethanol with a ratio of 1:1 at a power of 100 W and a time of 20 min to remove the negative microchannel mold;

[0016] The microchannel scaffold after model removal treatment is subjected to PNIPAM thermosensitive layer modification treatment,

[0017] NIPAM monomer, MBA crosslinker and photoinitiator were dissolved in deionized water to obtain a PNIPAM precursor solution. The ratio of NIPAM monomer, MBA crosslinker, photoinitiator Irgacure 2959 and deionized water in the PNIPAM precursor solution was 200:5:1:794. The PNIPAM precursor solution was injected into the microchannels of the microchannel scaffold after model removal treatment. In-situ polymerization was initiated by irradiating with ultraviolet light at 365 nm and an intensity of 5 mW / cm 2 for 10 min to convert the monomer into a polymer in-situ, forming a temperature-sensitive layer on the inner wall of the microchannel. Thus, a magnetic-responsive microchannel tissue scaffold was obtained.

[0018] Preferably, the process of obtaining the mechanical state signals and environmental signals of the magnetic-responsive microchannel tissue scaffold, processing the obtained mechanical state signals and environmental signals through amplification, filtering, analog-to-digital conversion, calculation or demodulation to obtain mechanical state data and environmental data, and uploading the mechanical state signals, mechanical state data and environmental data to the control center and database:

[0019] Embedded sensors including a K-type thermocouple, a microstrip antenna, a piezoresistive sensor and a fiber Bragg grating sensor were embedded inside the magnetic-responsive microchannel tissue scaffold to obtain mechanical state signals and environmental signals. The mechanical state signals included analog pressure signals and fiber wavelength shift signals, and the environmental signals included temperature signals and reflection coefficients. The analog pressure signals and temperature signals were amplified by an operational amplifier, the amplified analog pressure signals and temperature signals were filtered by a low-pass filter, and the filtered analog pressure signals and temperature signals were subjected to analog-to-digital conversion by an analog-to-digital converter ADC with a resolution of 12 bits and a sampling rate of 100 Hz. Thus, pressure data P(t) and temperature data T(t) were obtained;

[0020] The reflection coefficient was calculated by the ultrasonic time domain reflection formula to obtain the scaffold degradation rate n(t);

[0021] The ultrasonic time domain reflection formula is:

[0022] n(t) = 30·|τ(t)| (frequency 2.45 GHz);

[0023] where n(t) is the scaffold degradation rate and τ(t) is the reflection coefficient;

[0024] The wavelength shift signal was demodulated by a demodulator to obtain strain data ε(t);

[0025] The mechanical state data including pressure data and strain data, mechanical state signals and environmental data including temperature data and scaffold degradation rate were uploaded to the control center and database via Bluetooth 5.0.

[0026] Preferably, the process of obtaining prediction data through the LSTM model and normalizing multi-modal data including prediction data, mechanical state data, and environmental data using a normalization formula:

[0027] Obtain the stiffness values within the past 10 seconds from the database according to the timestamp, map the stiffness values, mechanical state data, and environmental data within the past 10 seconds to the interval [-1, 1] using the normalization formula. Meanwhile, extract the time-frequency feature f of the mechanical state signal through wavelet transform dominant , construct a feature vector from the stiffness values, mechanical state data, environmental data, time features, and frequency features within the past 10 seconds, and input the feature vector into the LSTM model to obtain the future 10-second stiffness demand prediction sequence {E pred (t + 1), E pred (t + 2),..., E pred (t + 10)}, and the future 10-second stiffness demand prediction sequence is the prediction data;

[0028] Map the multi-modal data including prediction data, mechanical state data, and environmental data to the interval [-1, 1] using the normalization formula to obtain the normalized multi-modal data;

[0029] The normalization formula is:

[0030]

[0031] where x max and x min are the minimum and maximum values of the data, and x norm is the normalized data.

[0032] Preferably, the process of obtaining the PWM pulse signal from the normalized multi-modal data through the adaptive PID algorithm:

[0033] The outer loop and the inner loop are components of the adaptive PID algorithm. The outer loop is responsible for receiving the normalized multi-modal data, obtaining the mechanical term E mech (t) for the mechanical state data through the mechanical target formula, obtaining the prediction term E pred (t) through the prediction target formula, obtaining the stiffness value E target (t) for the mechanical term and the prediction term through the stiffness formula. The outer loop, based on the stent degradation rate and temperature data, obtains the preliminary corrected stiffness value E’ target (t) for the stiffness value through the degradation compensation formula, and obtains the target stiffness value E” target (t) for the preliminary corrected stiffness value through the temperature compensation formula, and stores it in the database. The outer loop queries the magnetic field-stiffness mapping table that maps the target stiffness value to the target magnetic field strength value based on the target stiffness value to obtain the target magnetic field strength value B target ;

[0034] The mechanical target formula is as follows:

[0035] E mech (t) = α·P(t) + β·ε(t) + γ·f dominant ;

[0036] where α is 0.7, β is 0.2, γ is 0.1, P(t) is pressure data, T(t) is temperature data, and f dominant is the time-frequency feature, and E mech (t) is the mechanical term;

[0037] The prediction target formula is as follows:

[0038]

[0039] where λ is the attenuation factor and its value is 0.9, E pred (t) is the prediction term, and E pred (t + k) is an element in the prediction sequence;

[0040] The stiffness formula is: E target (t) = ω mech ·E mech (t) + ω pred ·E pred (t);

[0041] where E pred (t) is the prediction term, E mech (t) is the mechanical term, E target (t) is the stiffness value, ω mech is the weight of the mechanical term, ω pred is the weight of the prediction term and ω mech + ω pred = 1;

[0042] The degradation compensation formula is as follows:

[0043] where E target (t) is the stiffness value, E’ target (t) is the preliminary corrected stiffness value, n(t) is the stent degradation rate, and n max is the degradation compensation coefficient;

[0044] The temperature compensation formula is as follows:

[0045] E” target (t) = E’ target (t)·[1 - α·(T(t) - T ref )];

[0046] where E”target (t) is the target stiffness value, E’ target (t) is the preliminary corrected stiffness value, α is the temperature compensation coefficient and its value is 0.05 / °C, T ref is 37°C, T(t) is the temperature data;

[0047] The inner loop is responsible for receiving the target magnetic field intensity value B from the outer loop target , meanwhile, the actual magnetic field intensity value B is obtained in real time through a Hall sensor actual , according to the target magnetic field intensity, the feedforward current I is obtained through a magnetic field-current mapping table that maps the target magnetic field intensity to the feedforward current ff , the target magnetic field intensity value is subtracted from the real-time magnetic field intensity value to obtain the deviation ΔB, and the correction current I is obtained for the deviation ΔB through the discrete PID formula pid , the correction current I pid is added to the feedforward current I ff to obtain the target current. The inner loop outputs a PWM pulse signal according to the target current. The inner loop continuously obtains the actual magnetic field intensity value B in real time actual , and accordingly continuously obtains the real-time correction current I pid , outputs the real-time PWM pulse signal until the deviation ΔB is 0, then stops outputting;

[0048] The discrete PID formula is:

[0049]

[0050] where, I pid (k) is the correction current in the k-th control period, K p is the proportionality coefficient, ΔB(t) is the deviation between the target magnetic field intensity and the actual magnetic field intensity in the k-th control period, K i is the integral coefficient, T S is the control period and its value is 1 ms, K d is the differential coefficient, is the rate of change of the deviation.

[0051] Preferably, the process of controlling the array of micro-coils to generate a controllable magnetic field according to the PWM pulse signal:

[0052] The control center module calculates the PWM pulse signal according to the adaptive PID algorithm. The high-frequency drive circuit is connected to the control center module through an interface to obtain the PWM pulse signal. The initial power of the PWM pulse signal is low. The power of the PWM pulse signal is amplified by a power amplifier. The amplified PWM pulse signal is filtered by a low-pass filter with a cut-off frequency of 20 kHz to obtain the amplified and filtered PWM pulse signal. The amplified and filtered PWM pulse signal is transmitted to the GaN power module of the high-frequency drive circuit. The GaN power module controls the magnitude of the coil current according to the duty cycle of the PWM pulse signal. When the PWM pulse signal is at a high level, the GaN power module is turned on and the current passes through the coil. When the PWM pulse signal is at a low level, the GaN power module is turned off and the coil current is interrupted. By adjusting the duty cycle of the PWM pulse signal, the magnitude of the coil current is controlled. According to Ampere's law, a current passing through a coil generates a magnetic field, and the magnetic field strength is proportional to the magnitude of the current. The coil current passes through the array of micro-coils to generate a controllable magnetic field.

[0053] Preferably, the process of adjusting the stiffness of the magnetorheological fluid:

[0054] A controllable magnetic field is applied to the magnetorheological fluid. The magnetic domains of the carbonyl iron powder in the magnetorheological fluid change their orientation under the action of the controllable magnetic field. The magnetic domains gradually align along the magnetic field direction, causing the carbonyl iron powder to be polarized. Magnetic domains are small regions of spontaneous magnetization inside the carbonyl iron powder. The polarized carbonyl iron powders attract each other in the controllable magnetic field and are arranged in a chain-like structure along the controllable magnetic field direction. The magnetic field strength is proportional to the firmness of the chain-like structure. By adjusting the magnetic field strength of the controllable magnetic field, the stiffness of the magnetorheological fluid is adjusted. The PWM pulse signal drives the array of micro-coils to generate a controllable magnetic field with different magnetic field strengths. By increasing or decreasing the magnetic field strength, the stiffness of the magnetorheological fluid is increased or decreased.

[0055] Preferably, the process of adjusting the parameters of the adaptive PID algorithm and displaying the mechanical state data and environmental data:

[0056] The user delivery module includes a parameter display and adjustment area and a data display area. The display and adjustment area is used to display the parameters of the adaptive PID algorithm and adjust the parameters of the adaptive PID algorithm. The data display area is used to display the mechanical state data and environmental data in real time;

[0057] The user adjusts the mechanical term coefficient ω in the stiffness formula mech and the prediction term weight coefficient ω pred in the parameter display and adjustment area, adjusts the degradation compensation coefficient n of the degradation compensation formula max , adjusts the temperature compensation coefficient α of the temperature compensation formula, and adjusts the proportional coefficient K p , integral coefficient K i and differential coefficient K d, for the adaptive PID algorithm parameters including the mechanical term weight coefficient ω mech , the prediction term weight coefficient ω pred , the degradation compensation coefficient n max , the temperature compensation coefficient α, the proportionality coefficient K p , the integral coefficient K i and the differential coefficient K d , verify the adjusted adaptive PID algorithm parameters through the parameter display adjustment area, which is used to ensure that the adaptive PID algorithm parameters are within a reasonable range. If the adaptive PID algorithm parameters exceed the reasonable range, the parameter display adjustment area pops up a prompt box and re-enters;

[0058] If the verification process passes, save the adjusted adaptive PID algorithm parameters to the database and update the adaptive PID algorithm parameters;

[0059] The data display area obtains the mechanical state data and environmental data from the database. For the real-time mechanical state data and environmental data, they are displayed in digital form in the data display area. For the historical mechanical state data and environmental data, a line chart is drawn using the Matplotlib chart library for display;

[0060] The reasonable range is: the proportionality coefficient K p is 0.5 to 2.0 A / T, the integral coefficient K i is 0.05 to 0.3 A / (T·s), the differential coefficient K d is 0.01 to 0.1 A·s / T, the mechanical term weight ω mech is 0.5 to 0.8, the prediction term weight ω pred is 0.2 to 0.5, and ω mech +ω pred = 1, the degradation compensation coefficient n max is 20% to 40%, and the temperature compensation coefficient α is 0.03 to 0.07.

[0061] Due to the adoption of the above technical solutions, the technical progress achieved by the present invention compared with the prior art is:

[0062] 1. The present invention realizes the dynamic and precise regulation of the stiffness of the tissue scaffold. By using magnetorheological fluid, through the LSTM model and the adaptive PID algorithm of the control central module, combined with the mechanical state and environmental data obtained in real time by the sensing feedback module, a PWM pulse signal is generated to adjust the magnetic field strength, and then the stiffness of the magnetorheological fluid is dynamically adjusted, providing a matching mechanical microenvironment for cell proliferation, differentiation and tissue regeneration, and significantly improving the tissue repair effect.

[0063] 2. The present invention realizes real-time and accurate data acquisition and feedback regulation. By embedding K-type thermocouples, microstrip antennas, piezoresistive sensors, and fiber optic sensors in the magnetoresponsive microchannel tissue scaffold, it can acquire various signals such as temperature, reflection coefficient, analog pressure, and fiber optic wavelength shift in real time. After amplification, filtering, analog-to-digital conversion, calculation, or demodulation processing, accurate mechanical state data and environmental data are obtained, which are used to adjust the stiffness of the scaffold in real time and stored in the database for subsequent analysis, realizing all-round real-time monitoring and accurate feedback regulation of the tissue scaffold state. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings.

[0065] The following are the drawings of the system structure module flowchart of the present invention.

[0066] Figure 1 This is the system structure module flowchart of the present invention.

[0067] DETAILED DESCRIPTION OF THE EMBODIMENTS

[0068] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0069] Embodiment 1, as Figure 1 described, a system for realizing adjustable stiffness of a tissue scaffold based on magnetorheological fluid includes a scaffold module, a sensing and feedback module, a control center module, a magnetic field module, and a user interaction module. Each module cooperates to realize adjustable stiffness of the tissue scaffold.

[0070] The scaffold module is used to construct a tissue scaffold. The magnetorheological fluid microcapsules and the scaffold material are printed along the periphery of the negative microchannel mold using 3D printing technology to obtain a microchannel scaffold. The microchannel scaffold is processed by model removal and PNIPAM temperature-sensitive layer modification to obtain a magnetoresponsive microchannel tissue scaffold;

[0071] The sensing feedback module is used to obtain the mechanical state signals and environmental signals of the magnetoresponsive microchannel tissue scaffold, and through amplification, filtering, digital-to-analog conversion, calculation or demodulation processing of the obtained mechanical state signals and environmental signals, obtain mechanical state data and environmental data, and upload the mechanical state signals, mechanical state data and environmental data to the control center and the database;

[0072] The control center module is used to dynamically adjust the magnetic field intensity to achieve rigid control, obtain prediction data through the LSTM model, normalize the multi-modal data including prediction data, mechanical state data and environmental data through the normalization formula, and obtain the PWM pulse signal through the adaptive PID algorithm for the normalized multi-modal data;

[0073] The magnetic field module is used to drive the magnetorheological fluid in the magnetoresponsive microchannel tissue scaffold to achieve stiffness adjustment. According to the PWM pulse signal, the array-type microcoils are controlled by a high-frequency drive circuit to generate a controllable magnetic field, and accordingly, the stiffness of the magnetorheological fluid is adjusted;

[0074] The user interaction module is used to adjust the parameters of the adaptive PID algorithm and display the mechanical state data and environmental data.

[0075] Furthermore, the working principle of the present invention is illustrated by the following embodiments:

[0076] Carbonyl iron powder with a particle size of 5 to 10 μm and a saturation magnetization intensity of greater than or equal to 140 emu / g is dispersed in a silicone oil-based carrier liquid, ultrasonically dispersed for 15 minutes with a power of 200 W, combined with mechanical stirring at 500 rpm for 30 minutes to form a uniform magnetorheological fluid. The magnetorheological fluid is encapsulated with poly(lactic-co-glycolic acid) PLGA to form magnetorheological fluid microcapsules. The mass ratio of the magnetorheological fluid to PLGA is 1:3. On a 100-mm diameter silicon wafer, an SU-8 photoresist negative microchannel mold is prepared by lithography technology. The magnetorheological fluid microcapsules and the scaffold material are mixed in a ratio of 1:3 and printed along the periphery of the mold to form a microchannel scaffold. The negative microchannel mold is removed by ultrasonic cleaning in a mixed solvent of acetone and ethanol for 20 minutes. A PNIPAM precursor solution is injected into the microchannels in the negative microchannel mold. The ratio of NIPAM monomer:MBA crosslinker:Irgacure2959:deionized water is 200:5:1:794, and irradiated with ultraviolet light at 365 nm for 10 minutes to form a temperature-sensitive layer on the inner wall of the channel, and finally a magnetoresponsive microchannel tissue scaffold is obtained.

[0077] Embedded sensors including K-type thermocouples, piezoresistive sensors, microstrip antennas, and fiber Bragg grating sensors are embedded inside the scaffold to obtain the mechanical state signals and environmental signals of the magnetoresponsive microchannel tissue scaffold. The temperature measurement range of the K-type thermocouple is 0 to 100 °C, the range of the piezoresistive sensor is 0 to 10 kPa, the frequency of the microstrip antenna is 2.45 GHz, and the wavelength resolution of the fiber Bragg grating sensor is 0.1 nm. The simulated pressure signals and temperature signals are amplified by an operational amplifier with a gain of 100 times, filtered by a low-pass filter with a cut-off frequency of 50 Hz, and the analog signals are converted into digital signals using a 12-bit ADC to obtain the pressure data P(t) and temperature data T(t). The scaffold degradation rate n(t) is calculated through the ultrasonic time-domain reflection formula n(t) = 30·|τ(t)|. The fiber wavelength shift signal is processed by a demodulator to output the strain data ε(t). The mechanical state data and environmental data are uploaded to the control center and database via Bluetooth 5.0.

[0078] Extract the stiffness value of the past 10 seconds from the database, combine it with the real-time data, and through the normalization formula map it to [-1, 1]. The normalized multi-modal data is input into the LSTM model to output the predicted sequence of stiffness requirements for the next 10 seconds {E pred (t + 1), E pred (t + 2),..., E pred (t + 10)}. The outer loop, based on the mechanical state data and the predicted data, calculates the stiffness value through the mechanical target formula E mech (t) = 0.7·P(t) + 0.2·ε(t) + 0.1·f dominant , the prediction target formula and the stiffness formula E target (t) = 0.6·E mech (t) + 0.4·E pred (t). Then, combined with the scaffold degradation rate and temperature data, it is corrected through the degradation compensation formula and the temperature compensation formula E” target (t) = E’ target (t)·[1 - 0.05·(T(t) - 37)]. The target magnetic field intensity value is obtained by querying the magnetic field-stiffness mapping table. The inner loop receives the target magnetic field intensity value, obtains the actual magnetic field intensity value through a Hall sensor, gets the feedforward current according to the magnetic field-current mapping table, calculates the deviation ΔB, and obtains the corrected current through the discrete PID formula The corrected current is added to the feedforward current to obtain the target current, and then a PWM pulse signal is output.

[0079] The high-frequency drive circuit receives a PWM pulse signal, amplifies it to 5V through a power amplifier, sets the cut-off frequency of the low-pass filter to 20kHz, and the GaN power module controls the coil current according to the duty cycle of the PWM pulse signal. The number of turns of the coil is 100, and the radius is 10mm. According to Ampere's law, the coil current generates a controllable magnetic field through the array-type micro-coil, and the carbonyl iron powder in the magnetorheological fluid polarizes to form a chain-like structure under the action of the controllable magnetic field. The magnetic field strength varies in the range of 0-1T, and the stiffness of the magnetorheological fluid can be adjusted between 10-100kPa.

[0080] The parameter display and adjustment area of the user interaction module shows the parameters of the adaptive PID algorithm. The user can adjust the weight coefficient of the mechanical term, the weight coefficient of the prediction term in the stiffness formula, the degradation compensation coefficient of the degradation compensation formula, the temperature compensation coefficient of the temperature compensation formula, and the proportional coefficient, integral coefficient, and differential coefficient of the discrete PID formula. The data display area obtains the mechanical state data and environmental data from the database. The real-time data is displayed in digital form, and the historical data is displayed as a line chart using the Matplotlib chart library. The time resolution of the abscissa is 1s, and the appropriate scale range is set for the ordinate according to different data types.

[0081] This embodiment verifies the effectiveness of the system in real-time monitoring, intelligent prediction, and precise regulation through specific parameters and data, providing a dynamically adaptable mechanical microenvironment for tissue engineering. The above is the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of modifications or substitutions, which should be covered by the protection scope of the present invention.

Claims

1. A system for realizing adjustable stiffness of tissue scaffolds based on magnetorheological fluid, characterized in that, Comprising: A stent module for constructing a tissue scaffold. Using 3D printing technology, magnetorheological fluid microcapsules and a scaffold material are printed along the periphery of a negative microchannel mold to obtain a microchannel scaffold. The microchannel scaffold is processed by model removal and PNIPAM thermosensitive layer modification to obtain a magnetoresponsive microchannel tissue scaffold; A sensing feedback module for acquiring the mechanical state signals and environmental signals of the magnetoresponsive microchannel tissue scaffold, processing the acquired mechanical state signals and environmental signals through amplification, filtering, analog-to-digital conversion, calculation, or demodulation to obtain mechanical state data and environmental data, and uploading the mechanical state signals, mechanical state data, and environmental data to a control center and a database; A control center module for dynamically adjusting the magnetic field intensity to achieve rigid control, obtaining prediction data through an LSTM model, normalizing the multimodal data including prediction data, mechanical state data, and environmental data through a normalization formula, and obtaining a PWM pulse signal through an adaptive PID algorithm for the normalized multimodal data; A magnetic field module for driving the magnetorheological fluid in the magnetoresponsive microchannel tissue scaffold to achieve stiffness adjustment. According to the PWM pulse signal, an array of micro coils is controlled by a high-frequency drive circuit to generate a controllable magnetic field, thereby adjusting the stiffness of the magnetorheological fluid; A user interaction module for adjusting the parameters of the adaptive PID algorithm and displaying the mechanical state data and environmental data.

2. The system for realizing adjustable stiffness of tissue scaffolds based on magnetorheological fluid according to claim 1, wherein The process of obtaining the microchannel scaffold: Carbonyl iron powder is dispersed in a silicone oil-based carrier liquid to obtain a magnetorheological fluid. The magnetorheological fluid is encapsulated by a microcapsule shell formed by poly(lactic-co-glycolic acid) to obtain magnetorheological fluid microcapsules. A negative microchannel mold made of SU-8 photoresist is prepared on a silicon wafer by photolithography technology. The negative microchannel mold is fixed on a printing platform, and the material obtained by mixing the magnetorheological fluid microcapsules and the scaffold material is printed along the periphery of the negative microchannel mold through 3D printing technology to obtain a microchannel scaffold.

3. According to claim 2, a system for realizing adjustable stiffness of tissue scaffolds based on magnetorheological fluid, characterized in that, The process of obtaining the magnetoresponsive microchannel tissue scaffold: The microchannel scaffold is subjected to model removal treatment. Utilizing the property that SU-8 photoresist is soluble in a mixed solvent of acetone and ethanol, the negative microchannel mold is removed by cleaning with the mixed solvent of acetone and ethanol; The microchannel scaffold after model removal treatment is subjected to PNIPAM thermosensitive layer modification treatment. NIPAM monomer, MBA crosslinking agent, and photoinitiator are dissolved in deionized water to obtain a PNIPAM precursor solution. The microchannels of the microchannel scaffold after model removal treatment are injected with the PNIPAM precursor solution, and in-situ polymerization is initiated by ultraviolet light irradiation to form a thermosensitive layer on the inner wall of the microchannels, thereby obtaining a magnetoresponsive microchannel tissue scaffold; The in-situ polymerization is a process of directly carrying out a polymerization reaction on a specific target material to convert monomers into polymers in-situ.

4. The system for realizing adjustable stiffness of tissue scaffolds based on magnetorheological fluid according to claim 3, wherein The process of acquiring the mechanical state signals and environmental signals of the magnetoresponsive microchannel tissue scaffold, processing the acquired mechanical state signals and environmental signals through amplification, filtering, analog-to-digital conversion, calculation, or demodulation to obtain mechanical state data and environmental data, and uploading the mechanical state signals, mechanical state data, and environmental data to a control center and a database: Embed embedded sensors including K-type thermocouples, microstrip antennas, piezoresistive sensors, and fiber Bragg grating sensors inside the magnetoresponsive microchannel tissue scaffold to obtain temperature signals, reflection coefficients, analog pressure signals, and fiber wavelength shift signals. Amplify the analog pressure signals and temperature signals through an operational amplifier, filter the amplified analog pressure signals and temperature signals through a low-pass filter, and perform analog-to-digital conversion processing on the filtered analog pressure signals and temperature signals through an analog-to-digital converter ADC to obtain pressure data and temperature data accordingly; Calculate and process the reflection coefficient through the ultrasonic time-domain reflection formula to obtain the scaffold degradation rate; Demodulate the wavelength shift signal through a demodulator to obtain strain data; Upload the mechanical state data including pressure data and strain data, the mechanical state signals, and the environmental data including temperature data and scaffold degradation rate to the control center and database through Bluetooth 5.0; The mechanical state signals are analog pressure signals and fiber wavelength shift signals; The environmental signals are temperature signals and reflection coefficients.

5. According to claim 4, a system for realizing adjustable stiffness of tissue scaffolds based on magnetorheological fluid, characterized in that, The process of obtaining prediction data through the LSTM model and normalizing the multi-modal data including prediction data, mechanical state data, and environmental data through the normalization formula: Obtain the stiffness values within the past 10 seconds from the database, map the stiffness values, mechanical state data, and environmental data within the past 10 seconds to the [-1, 1] interval through the normalization formula. At the same time, extract the time features and frequency features of the mechanical state signals through wavelet transform, construct a feature vector from the stiffness values, mechanical state data, environmental data, time features, and frequency features within the past 10 seconds, and input the feature vector into the LSTM model to obtain the predicted sequence of stiffness requirements for the next 10 seconds. The predicted sequence of stiffness requirements for the next 10 seconds is the prediction data; Map the multi-modal data including prediction data, mechanical state data, and environmental data to the [-1, 1] interval through the normalization formula to obtain the normalized multi-modal data; The wavelet transform is a mathematical tool for signal processing and analysis; The LSTM model is a special recurrent neural network RNN that processes long-term dependencies in time series data.

6. The system for realizing adjustable stiffness of a tissue scaffold based on magnetorheological fluid according to claim 5, wherein The process of obtaining the PWM pulse signal through the adaptive PID algorithm using the normalized multi-modal data: The outer loop receives the normalized multi-modal data, obtains the mechanical term from the mechanical state data through the mechanical target formula, obtains the prediction term through the prediction target formula, obtains the stiffness value from the mechanical term and the prediction term through the stiffness formula. The outer loop, based on the scaffold degradation rate and temperature data, obtains the preliminary corrected stiffness value from the stiffness value through the degradation compensation formula, obtains the target stiffness value from the preliminary corrected stiffness value through the temperature compensation formula, and stores it in the database. The outer loop queries the magnetic field-stiffness mapping table based on the target stiffness value to obtain the target magnetic field intensity value; The inner loop receives the target magnetic field intensity value from the outer loop. At the same time, it obtains the actual magnetic field intensity value in real time through a Hall sensor, obtains the feedforward current according to the target magnetic field intensity through a magnetic field-current mapping table, subtracts the real-time magnetic field intensity value from the target magnetic field intensity value to obtain the deviation ΔB, obtains the correction current for the deviation ΔB through a discrete PID formula, adds the correction current and the feedforward current to obtain the target current, and the inner loop outputs a PWM pulse signal according to the target current until the deviation ΔB is 0, then stops outputting; The outer loop and the outer loop are components of an adaptive PID algorithm; The magnetic field-stiffness mapping table is a look-up table used to map the target stiffness value to the target magnetic field intensity value during the outer loop control process; The magnetic field-current mapping table is a look-up table used to map the target magnetic field intensity to the feedforward current during the inner loop control process.

7. The system for realizing adjustable stiffness of tissue scaffolds based on magnetorheological fluid according to claim 6, characterized in that, The process of controlling the array of microcoils to generate a controllable magnetic field according to the PWM pulse signal: The high-frequency drive circuit receives the PWM pulse signal output from the control center module, amplifies the PWM pulse signal through a power amplifier. At the same time, it filters the amplified PWM pulse signal through a low-pass filter. The PWM pulse signal after amplification and filtering drives the GaN power module of the high-frequency drive circuit to control the magnitude of the coil current. According to Ampere's law, the coil current generates a controllable magnetic field through the array of microcoils; Ampere's law states that current passing through a coil generates a magnetic field, and the magnetic field intensity is proportional to the magnitude of the current.

8. The system for realizing adjustable stiffness of a tissue scaffold based on magnetorheological fluid according to claim 7, characterized in that, The process of adjusting the stiffness of the magnetorheological fluid: The carbonyl iron powder in the magnetorheological fluid is polarized along the direction of the controllable magnetic field through the controllable magnetic field and forms a chain-like structure. The magnetic field intensity is proportional to the firmness of the chain-like structure. The stiffness of the magnetorheological fluid is adjusted by adjusting the magnetic field intensity of the controllable magnetic field.

9. The system for realizing adjustable stiffness of tissue scaffolds based on magnetorheological fluid according to claim 8, wherein The process of adjusting the parameters of the adaptive PID algorithm and displaying mechanical state data and environmental data: The user delivery module includes a parameter display and adjustment area and a data display area. The display and adjustment area is used to display the parameters of the adaptive PID algorithm and adjust the parameters of the adaptive PID algorithm. The data display area is used to display the mechanical state data and environmental data in real time; The user adjusts the weight coefficients of the mechanical term and the prediction term in the stiffness formula, adjusts the degradation compensation coefficient of the degradation compensation formula, adjusts the temperature compensation coefficient of the temperature compensation formula, adjusts the proportional coefficient, integral coefficient, and differential coefficient of the discrete PID formula for the parameters of the adaptive PID algorithm including the weight coefficient of the mechanical term, the weight coefficient of the prediction term, the degradation compensation coefficient, the temperature compensation coefficient, the proportional coefficient, the integral coefficient, and the differential coefficient. The adjusted parameters of the adaptive PID algorithm are verified through the parameter display and adjustment area to ensure that the parameters of the adaptive PID algorithm are within a reasonable range. If the parameters of the adaptive PID algorithm exceed the reasonable range, a prompt box will pop up in the parameter display and adjustment area, and re-enter; If the verification process passes, the adjusted parameters of the adaptive PID algorithm will be saved to the database and the parameters of the adaptive PID algorithm will be updated; The data display area obtains mechanical state data and environmental data from the database. For real-time mechanical state data and environmental data, they are displayed in digital form in the data display area. For historical mechanical state data and environmental data, a line chart is plotted using the Matplotlib chart library for display; The reasonable range is as follows: the proportionality coefficient is from 0.5 to 2.0 A / T, the integral coefficient is from 0.05 to 0.3 A / (T·s), the differential coefficient is from 0.01 to 0.1 A·s / T, the weight of the mechanical term is from 0.5 to 0.8, the weight of the prediction term is from 0.2 to 0.5, and the sum of the weight of the mechanical term and the weight of the prediction term is 1. The degradation compensation coefficient is from 20% to 40%, and the temperature compensation coefficient is from 0.03 to 0.

07.

10. A method for realizing adjustable stiffness of a tissue scaffold based on magnetorheological fluid, the method is used to realize a system for realizing adjustable stiffness of a tissue scaffold based on magnetorheological fluid in claim 1, and the method includes the following steps: S1. Prepare magnetorheological fluid microcapsules and a negative microchannel mold, use 3D printing technology to print the magnetorheological fluid microcapsules and the scaffold material along the periphery of the negative microchannel mold to obtain a microchannel scaffold, and perform model removal and PNIPAM thermosensitive layer modification on the microchannel scaffold to obtain a magnetoresponsive microchannel tissue scaffold; S2. Embed sensors in the magnetoresponsive microchannel tissue scaffold to obtain the mechanical state signals and environmental signals of the magnetoresponsive microchannel tissue scaffold. The obtained mechanical state signals and environmental signals are processed through amplification, filtering, analog-to-digital conversion, calculation or demodulation to obtain mechanical state data and environmental data, and the mechanical state signals, mechanical state data and environmental data are uploaded to the control center and the database; S3. Obtain prediction data through the LSTM model from the stiffness values, mechanical state data, environmental data and mechanical state signals within the past 10 seconds obtained from the database. The multi-modal data including the prediction data, mechanical state data and environmental data are normalized through the normalization formula, and the normalized multi-modal data are used to obtain PWM pulse signals through the adaptive PID algorithm; S4. For the PWM pulse signals, through amplification and filtering processing, the amplified and filtered PWM pulse signals drive the GaN power module of the high-frequency drive circuit to control the magnitude of the coil current. According to Ampere's law, the coil current generates a controllable magnetic field through the array of microcoils, making the carbonyl iron powder in the magnetorheological fluid polarize to form a chain structure, thereby changing the stiffness of the magnetorheological fluid; S5. The user adjusts the parameters of the adaptive PID algorithm in the parameter display and adjustment area, saves and updates after verification, and prompts to re-enter if it exceeds the reasonable range. The data display area obtains data from the database, displays real-time data digitally, and plots a line chart of historical data using Matplotlib.

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