An oral soft tissue repair device and method based on a biological cell scaffold

Abstract

The present invention provides an oral soft tissue repair device and method based on a biological cell scaffold, relating to the field of biological oral technologies. The device includes a bionic adaptive biological cell scaffold, a multi-dimensional precision 3D printing system, and an intelligent implantation and regulation device. The bionic adaptive biological cell scaffold is made of a composite of collagen, hyaluronic acid, and a biodegradable poly(lactic-co-glycolic acid) copolymer. The bionic adaptive biological cell scaffold has a multi-layer spiral pore network, and the inner wall of the pore network is embedded with microfluidic channels. Chemical-sensitive microvalves are arranged in the microfluidic channels. By integrating the bionic adaptive biological cell scaffold, the multi-dimensional precision three-dimensional printing system, and the intelligent implantation and regulation device, and combining finite element analysis and the particle swarm optimization algorithm, the present invention can accurately simulate the chewing dynamics and saliva secretion characteristics of patients, effectively solving the problem of the lack of dynamic adaptability in the static design of traditional scaffolds.

Description

Technical Field

[0001] The present invention relates to the technical field of biological oral cavity, and particularly to an oral soft tissue repair device and method based on a biological cell scaffold. Background Art

[0002] With the continuous progress of modern medical technology, the demand for oral soft tissue repair in the fields of periodontal disease, trauma, congenital defects, etc. is increasing day by day. Oral soft tissue defects not only affect the chewing function and aesthetics of patients, but may also cause complications such as infection and inflammation, seriously affecting the quality of life. Therefore, how to achieve precise, efficient and safe oral soft tissue repair has become a key research direction in the field of oral medicine.

[0003] At present, oral soft tissue repair mainly relies on the following technical means:

[0004] Traditional surgical repair: Transplanting autologous or allogeneic tissues (such as gingival tissue) to the defect site through surgical operations to restore the function and morphology of the soft tissue.

[0005] Biomaterial scaffolds: Using biomaterials such as collagen and polylactic acid to make scaffolds and implanting them into the defect site to promote tissue regeneration.

[0006] Tissue engineering technology: Combining stem cells and growth factors and using in vitro cultured tissues or cell-scaffold composites for repair.

[0007] Three-dimensional printing technology: Manufacturing personalized scaffolds through three-dimensional printing technology to adapt to the anatomical structure of the patient's oral cavity.

[0008] Although the existing technologies and methods have achieved certain results in oral soft tissue repair, there are still the following deficiencies:

[0009] Problem 1: Traditional surgical repair relies on autologous or allogeneic tissue transplantation, which has problems such as limited donor tissue sources, large surgical trauma, and long postoperative recovery time, and it is difficult to meet the needs of complex defect repair;

[0010] Problem 2: Most of the existing biomaterial scaffolds are statically designed and lack adaptability to the oral dynamic environment (such as chewing force and saliva flow field). The pore structure and mechanical properties of the scaffolds cannot accurately match the individual differences of patients, resulting in unsatisfactory repair effects and the risk of mechanical mismatch or chemical erosion;

[0011] Problem 3: Although current tissue engineering technology has introduced stem cells and growth factors, it lacks real-time regulation ability. The release of growth factors, etc. is mostly passive and cannot be dynamically adjusted according to the postoperative repair process of patients, resulting in low repair efficiency or excessive inflammatory reactions;

[0012] Problem 4: Although existing 3D printing technologies can manufacture personalized scaffolds, the printing process lacks precise analysis of oral biomechanics and metabolic data. Scaffold design is mostly based on experience and cannot fully consider chewing dynamics and saliva secretion characteristics. In addition, postoperative monitoring methods are mostly off-line detections, lacking real-time and continuous bio-signal feedback, and unable to adjust treatment plans in a timely manner.

[0013] Therefore, an oral soft tissue repair device and method based on a biological cell scaffold are needed to solve the above problems. Summary of the Invention

[0014] Technical Problems to be Solved

[0015] In view of the deficiencies of the prior art, the present invention provides an oral soft tissue repair device and method based on a biological cell scaffold, which solves the problems in the above background technology.

[0016] Technical Solutions

[0017] To achieve the above objectives, the present invention is realized through the following technical solutions: An oral soft tissue repair device based on a biological cell scaffold, comprising a bionic adaptive biological cell scaffold, a multi-dimensional precision 3D printing system, and an intelligent implantation and regulation device. The bionic adaptive biological cell scaffold is made of a composite of collagen, hyaluronic acid, and biodegradable poly(lactic-co-glycolic acid). The bionic adaptive biological cell scaffold has a multi-layer spiral pore network, and a microfluidic channel is embedded in the inner wall of the pore network. A chemical-sensitive microvalve is arranged in the microfluidic channel, and the chemical-sensitive microvalve is composed of a composite material of polyacrylate and diatomaceous earth. An self-assembled micro-biochip is integrated inside the bionic adaptive biological cell scaffold, and the micro-biochip is composed of a porous silicon substrate and a temperature-sensitive polymer coating. The temperature-sensitive polymer coating is formed by a composite of poly(N-isopropylacrylamide) and an enzyme-responsive hydrogel. Growth factors, anti-inflammatory factors, and gene regulation molecules are encapsulated in the micro-biochip. The multi-dimensional precision 3D printing system is configured to generate a scaffold model based on the patient's oral biomechanics and tissue metabolism data. The structure of the multi-dimensional precision 3D printing system includes a multi-material print head, an adaptive air flow stabilization module, and an in-situ infrared spectroscopy detector. The multi-material print head is composed of a ceramic-based nozzle and a microfluidic dispenser. The adaptive air flow stabilization module is composed of an annular air flow generator and a temperature compensation unit. The in-situ infrared spectroscopy detector is composed of an infrared emitter and a reflective optical fiber probe.

[0018] Preferably, the intelligent implantation and regulation device includes:

[0019] A flexible biosensor network is composed of polymer conductive fibers and a self-healing polymer layer. The polymer conductive fibers are made of a composite material of polypyrrole and carbon nanotubes. The self-healing polymer layer is composed of a composite material of polyurethane and microcapsules, and repair enzymes are encapsulated in the microcapsules.

[0020] A bio-information collaborative regulation module is composed of a microfluidic chip, a storage bin for autologous fibroblasts, a storage bin for RNA interference molecules, and a storage bin for antibacterial polypeptides. The microfluidic chip is composed of a polydimethylsiloxane substrate and a multi-channel dispenser.

[0021] A self-learning computing core is composed of a fuzzy logic controller, a microcurrent generator, and a data interaction interface. The fuzzy logic controller includes a fuzzy rule memory and a membership degree calculation unit. The microcurrent generator is composed of a signal modulation circuit and a conductive fiber connector. The data interaction interface is composed of a Bluetooth transmission module and an encryption chip. The self-learning computing core stores the correspondence between biological signals and repair processes through the fuzzy rule memory, analyzes biological signal data through the membership degree calculation unit, and outputs a regulation signal to the micro-biochip through the microcurrent generator.

[0022] The specific preparation process of the oral soft tissue repair device based on a biological cell scaffold is as follows:

[0023] Prepare the scaffold substrate: Mix collagen, hyaluronic acid, and a degradable poly(lactic-co-glycolic acid) copolymer according to a mass ratio, and form a multi-layer spiral pore network matrix through electrospinning technology. The diameter of the matrix fibers gradually changes from the inner layer to the outer layer.

[0024] Integrate microfluidic channels and microvalves: Etch microfluidic channels in the matrix through laser micromachining technology, and deposit a composite material of polyacrylate and diatomite on the inner wall of the channels to form chemically sensitive microvalves.

[0025] Integrate the micro-biochip: In a sterile environment, place a porous silicon substrate inside the matrix through a microdroplet ejection technology. Coat a composite layer of poly(N-isopropylacrylamide) and an enzyme-responsive hydrogel on the surface of the substrate, and fill growth factors, anti-inflammatory factors, and gene regulation molecules in the pores of the substrate.

[0026] 3D printing and molding: Place the substrate in a multi-dimensional precision 3D printing system, and layer by layer print through a ceramic-based nozzle and a microfluidic dispenser of a multi-material print head. An annular air flow generator and a temperature compensation unit maintain the stability of the printing environment, and an infrared emitter and a reflective fiber optic probe detect the chemical composition in real time.

[0027] Sensor and regulation module assembly: After the stent is formed, polymer conductive fibers and self-healing polymer layers are embedded inside the stent through electrospinning technology. The microfluidic chip, autologous fibroblast storage bin, RNA interference molecule storage bin, and antibacterial polypeptide storage bin are connected to the stent through ultrasonic welding technology. The fuzzy logic controller, micro-current generator, and data interaction interface are assembled to the stent through conductive fiber connectors.

[0028] Preferably, the bionic adaptive biological cell stent is internally embedded with a bionic microcapsule array. The microcapsule array consists of a chitosan outer shell and a thermosensitive gel core. The thermosensitive gel core is composed of a composite material of polyethylene glycol and hyaluronic acid ester.

[0029] Preferably, the multi-dimensional precision 3D printing system includes a micro-vibration calibration device. The micro-vibration calibration device consists of a piezoelectric ceramic oscillator and a feedback sensor. The piezoelectric ceramic oscillator is made of lead zirconate titanate material. The feedback sensor consists of a strain gauge and a signal amplifier. The surface of the flexible biosensor network is covered with a bionic adhesion coating. The bionic adhesion coating is composed of a composite material of dopamine-modified hyaluronic acid and collagen fibers.

[0030] Preferably, the self-learning computing core includes a micro heat dissipation unit. The micro heat dissipation unit consists of a graphene heat sink and a micro fan. The graphene heat sink is composed of a composite material of single-layer graphene and a polymer substrate.

[0031] An oral soft tissue repair method based on a biological cell stent includes the following steps:

[0032] Sp1. Precise preoperative modeling:

[0033] Through high-resolution cone beam CT and tissue metabolic imaging, three-dimensional structures and dynamic metabolic data of the patient's oral soft tissue defects are obtained;

[0034] Using finite element analysis and particle swarm optimization algorithm, an adaptive stent model is generated based on the three-dimensional structures and dynamic metabolic data of the patient's oral soft tissue defects, specifically including:

[0035] Simulating the patient's chewing dynamics: Establishing a three-dimensional mechanical model of the patient's oral cavity through finite element analysis, inputting the patient's chewing frequency and bite force data, calculating the stress distribution in the gingiva and mucosa areas, and generating a stress distribution map;

[0036] Simulating the characteristics of saliva secretion: Through fluid mechanics simulation, simulating the saliva secretion rate and pH value changes, analyzing the chemical erosion effect of the saliva flow field on the stent surface, and generating a chemical distribution map;

[0037] Optimize the stent pore network: Based on the particle swarm optimization algorithm, combine the stress distribution map and the chemical distribution map to adjust the pore size distribution and connectivity of the stent pore network;

[0038] Use a multi-dimensional precision 3D printing system to print the stent through a multi-material print head and an adaptive air flow stabilization module;

[0039] Sp2, Intelligent implantation and activation:

[0040] Through minimally invasive robotic-assisted surgery, implant the stent into the oral soft tissue defect site of the patient under local anesthesia;

[0041] Use the intraoperative optical navigation system to adjust the fit of the stent and the tissue interface;

[0042] Through temperature-controlled pulses and enzyme-triggered signals, activate the micro-biochip to release growth factors, anti-inflammatory factors, and gene regulatory molecules;

[0043] Sp3, Postoperative dynamic regulation:

[0044] Use a flexible bio-sensor network to collect multi-dimensional bio-signals and transmit them to the self-learning computing core;

[0045] Through the bio-information collaborative regulation module, use a microfluidic chip to adjust the release of fibroblasts and RNA interference molecules;

[0046] Combine with a wearable oral monitoring device and transmit data to the doctor's end through a data interaction interface.

[0047] Preferably, in the preoperative precise modeling step, the finite element analysis further adjusts the mechanical rigidity of the stent pore network by iteratively calculating the strain distribution in the gingival area during the patient's chewing cycle.

[0048] Preferably, in the intelligent implantation and activation step, the minimally invasive robot includes a force-electricity dual feedback device, and the force-electricity dual feedback device is composed of a force sensor and an electric signal generator.

[0049] Preferably, in the postoperative dynamic regulation step, the bio-information collaborative regulation module releases RNA interference molecules in layers through the multi-channel distributor of the microfluidic chip.

[0050] Preferably, the method includes a data optimization step, and the fuzzy logic controller of the self-learning computing core analyzes multi-patient data to adjust the release strategy of the micro-biochip.

[0051] The wearable oral monitoring device is a dental brace - type device for postoperative dynamic monitoring, specifically designed to collect patients' bio - signal data and oral environment parameters in real - time and transmit the data to the doctor's end to support remote monitoring and treatment plan adjustment. The device can monitor the repair process of patients' oral soft tissues, helping doctors timely understand the repair status and optimize treatment strategies.

[0052] Device Structure:

[0053] The wearable oral monitoring device consists of a flexible substrate, embedded micro - sensors, a micro - battery, a data interaction interface, and a protective coating.

[0054] Flexible Substrate: It is made of medical silicone material through a molding process, with good biocompatibility and flexibility. The substrate is designed in a dental brace - type structure, similar in shape to a clear dental aligner, which can cover the maxillary or mandibular dentition of the patient. It has a thickness of about 1 mm and weighs no more than 10 g, ensuring a light and comfortable wearing experience.

[0055] Embedded Micro - Sensors: Include a temperature sensor and a humidity sensor, which are used to monitor the temperature and humidity changes in the oral environment respectively. The temperature sensor uses a thermistor material, with a measurement range of 30°C to 40°C and an accuracy of ±0.1°C; the humidity sensor uses a capacitive humidity - sensitive element, with a measurement range of 20% to 90% relative humidity and an accuracy of ±2%. The sensors are fixed to the inner side of the flexible substrate through an adhesive process, in contact with the oral soft tissues to collect environmental data in real - time.

[0056] Micro - Battery: It uses a rechargeable lithium - polymer battery with a capacity of 50 mAh, providing a battery life of more than 7 days. The battery is integrated into the edge of the flexible substrate through an embedding process, equipped with a wireless charging module. The charging coil is located on one side of the substrate, with a charging power of 5 mW, and it takes 2 hours to fully charge.

[0057] Data Interaction Interface: It consists of a Bluetooth transmission module and an encryption chip. The Bluetooth transmission module uses the Bluetooth 5.0 protocol, supporting low - power transmission, with a single - transmission power consumption of less than 10 mW and a transmission distance of up to 10 m, which is used to receive and send data. The encryption chip uses the AES - 128 encryption algorithm to encrypt the data to ensure secure transmission. Both are fixed to the micro - circuit board inside the substrate through surface - mount technology and pin soldering.

[0058] Protective Coating: It uses a transparent medical silicone coating, which is covered on the surface of the flexible substrate through a spraying process, with a thickness of about 0.1 mm. It protects the internal sensors and circuits from saliva erosion and improves the wearing comfort at the same time.

[0059] Monitoring Methods:

[0060] The wearable oral monitoring device monitors through the following two methods:

[0061] Directly monitor the oral environment: The temperature sensor and humidity sensor embedded in the device are in direct contact with the oral soft tissues to continuously monitor the temperature and humidity changes in the oral environment. The temperature measurement range is from 30°C to 40°C, the humidity measurement range is from 20% to 90% relative humidity, and the monitoring frequency is once per minute. The temperature and humidity data can reflect the tissue repair status. For example, an increase in temperature may indicate an inflammatory response, and abnormal humidity may reflect changes in saliva secretion, thereby indirectly evaluating the repair process.

[0062] Indirectly receive biosignal data: The wearable oral monitoring device is connected to the self-learning computing core in the intelligent implantation and regulation device through a Bluetooth 5.0 transmission module to receive the biosignal data collected by the flexible biosensor network, including the cell proliferation rate (detected by polymer conductive fibers), tissue oxygenation level (detected by an embedded oxygen sensor), and inflammatory factor concentration (detected by a chemical sensor). After the received biosignal data is integrated with the temperature and humidity data, it is encrypted by an encryption chip (AES-128 encryption algorithm) and transmitted to the doctor's device at a frequency of once per minute. The doctor's device is a dedicated mobile application that runs on a smartphone or tablet, enabling the doctor to view the data in real time and adjust the treatment plan. For example, the anti-inflammatory factor release strategy can be adjusted according to the inflammatory factor concentration.

[0063] Wearing method:

[0064] The wearable oral monitoring device is designed in a dental brace structure, and the wearing method is similar to that of a clear aligner. The patient can wear and remove it by themselves. The specific wearing steps are as follows:

[0065] Wearing preparation: Before use, the patient needs to rinse the device with clean water to ensure the surface is clean, and then wipe the teeth and gum areas with a medical alcohol swab to maintain oral hygiene.

[0066] Wearing process: Align the device with the upper or lower dental arch (select the wearing position according to the patient's defect site, for example, wear it on the lower jaw when repairing the lower gum), and gently press to make the flexible base fit the teeth and gums. The medical silicone material on the inner side of the device has a certain elasticity, which can closely fit the dental arch and soft tissues, and there is no obvious foreign body sensation after wearing.

[0067] Wearing time: It is recommended that the patient wear the device for no less than 20 hours a day and can continue to wear it during night sleep, only removing it during eating or brushing teeth. When removing, gently pull the edge of the device with your finger and gradually remove it from one side of the dental arch to avoid excessive force.

[0068] Wearing adaptability: The device is light in weight (not exceeding 10 grams), thin in thickness (1 mm), with a protective coating on the surface, resistant to saliva corrosion, and will not cause discomfort or allergic reactions due to long-term wearing. The dental brace design with a flexible substrate ensures stability and will not slip during wearing, making it suitable for long-term use.

[0069] Interaction with the system:

[0070] The wearable oral monitoring device is wirelessly connected to the self-learning computing core in the intelligent implantation and regulation device through a Bluetooth 5.0 transmission module. The biological signal data (cell proliferation rate, tissue oxygenation level, concentration of inflammatory factors) collected by the flexible biosensor network is first transmitted to the self-learning computing core. After being processed by the core, the data is sent to the wearable oral monitoring device. The device integrates the biological signal data with the temperature and humidity data it monitors itself, encrypts it through an encryption chip, and then transmits it to the doctor's device at a frequency of once per minute. Doctors can view the real-time data through a mobile application, analyze the repair process, and adjust the treatment plan when necessary, such as increasing the release of anti-inflammatory factors or shortening the release cycle of gene regulatory molecules.

[0071] Beneficial effects

[0072] The present invention provides an oral soft tissue repair device and method based on a biological cell scaffold, having the following beneficial effects:

[0073] 1. By integrating a bionic adaptive biological cell scaffold, a multi-dimensional precision three-dimensional printing system, and an intelligent implantation and regulation device, and combining finite element analysis and particle swarm optimization algorithm, the present invention can accurately simulate the chewing dynamics and saliva secretion characteristics of patients, effectively solving the problem of the lack of dynamic adaptability in the static design of traditional scaffolds. The optimized pore network of the scaffold not only matches the mechanical properties of the patient's gums and mucosa but also adapts to the chemical properties of the oral saliva environment, greatly improving the repair effect and the long-term stability of the scaffold.

[0074] 2. Using the fuzzy logic control technology of the self-learning computing core, the present invention can collect and analyze multi-dimensional biological signals (such as cell proliferation rate, tissue oxygenation level, concentration of inflammatory factors) in real time, and dynamically regulate the release strategy of self-assembled micro-biochips, overcoming the deficiencies of passive growth factor release and lack of real-time regulation in traditional tissue engineering technologies. The system preferentially releases growth factors, anti-inflammatory factors, or gene regulatory molecules according to the repair process (early, middle, late), significantly improving the repair efficiency and effectively reducing the risk of excessive inflammatory reactions.

[0075] 3. The present invention uses a multi-dimensional precision three-dimensional printing system to generate a personalized stent model based on the patient's oral biomechanics and metabolic data, solving the defect of the existing three-dimensional printing technology lacking precise analysis. The system is equipped with an adaptive air flow stabilization module and an in-situ infrared spectroscopy detector to ensure the stability of the printing process and real-time monitoring of the chemical composition. The printed stent can fully adapt to the patient's chewing dynamics and saliva secretion characteristics, thus improving the accuracy and success rate of the repair.

[0076] 4. The present invention combines a wearable oral monitoring device and a data interaction interface to achieve real-time transmission of postoperative biological signals and remote monitoring at the doctor's end, making up for the shortcoming of the traditional offline detection lacking continuous feedback. Doctors can adjust the treatment plan in a timely manner according to the real-time data to ensure the controllability and safety of the repair process. At the same time, by analyzing the data of multiple patients to optimize the release strategy, the treatment effect of subsequent patients is further improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Figure 1 is the specific flow chart of the present invention;

[0078] Figure 2 is the overall device framework diagram of the present invention;

[0079] Figure 3 is the stress distribution simulation diagram of the present invention;

[0080] Figure 4 is the electron microscope diagram of the biological cell stent of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0081] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to 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. Specific Embodiment 1:

[0083] As Figures 1 - 4 shown, an oral soft tissue repair device based on a biological cell stent includes a bionic adaptive biological cell stent, a multi-dimensional precision 3D printing system, and an intelligent implantation and regulation device.

[0084] The bionic adaptive biological cell scaffold is made of a composite of collagen, hyaluronic acid, and biodegradable poly(lactic-co-glycolic acid). The bionic adaptive biological cell scaffold has a multi-layer helical pore network, and the inner wall of the pore network is embedded with microfluidic channels. A chemically sensitive microvalve is arranged in the microfluidic channels. The chemically sensitive microvalve is composed of a composite material of polyacrylate and diatomite. The chemically sensitive microvalve is formed by mixing polyacrylate and diatomite particles through a high-temperature sintering process and embedding them into the inner wall of the microfluidic channels. The bionic adaptive biological cell scaffold internally integrates a self-assembled micro-biochip. The self-assembled micro-biochip is composed of a porous silicon substrate and a temperature-sensitive polymer coating. The porous silicon substrate forms a porous structure through chemical vapor deposition. The temperature-sensitive polymer coating is formed by a composite of poly(N-isopropylacrylamide) and an enzyme-responsive hydrogel. The temperature-sensitive polymer coating is formed by spin-coating a mixture of poly(N-isopropylacrylamide) and an enzyme-responsive hydrogel on the surface of the porous silicon substrate. Growth factors, anti-inflammatory factors, and gene regulatory molecules are encapsulated in the self-assembled micro-biochip. A bionic microcapsule array is embedded in the bionic adaptive biological cell scaffold. The bionic microcapsule array is composed of a chitosan outer shell and a temperature-sensitive gel core. The chitosan outer shell is made through an emulsification cross-linking process. The temperature-sensitive gel core is composed of a composite material of polyethylene glycol and hyaluronic acid ester. The temperature-sensitive gel core is filled inside the chitosan outer shell through a solution mixing process.

[0085] The multi-dimensional precision three-dimensional printing system is configured to generate a scaffold model based on patient oral biomechanics and tissue metabolism data. The structure of the multi-dimensional precision three-dimensional printing system includes a multi-material print head, an adaptive air flow stabilization module, and an in-situ infrared spectroscopy detector. The multi-material print head is composed of a ceramic-based nozzle and a microfluidic dispenser. The ceramic-based nozzle is made through a high-temperature sintering process and is fixed to the front end of the microfluidic dispenser through a threaded connection. The microfluidic dispenser is composed of a multi-channel silicon substrate and a micro-valve. The multi-channel silicon substrate forms a channel structure through an etching process. The micro-valve is embedded in the multi-channel silicon substrate through an assembly process. The adaptive air flow stabilization module is composed of an annular air flow generator and a temperature compensation unit. The annular air flow generator is fixed to the outer shell of the multi-dimensional precision three-dimensional printing system through a welding process. The temperature compensation unit is assembled by a heating wire and a temperature control sensor and is embedded inside the annular air flow generator. The in-situ infrared spectroscopy detector is composed of an infrared emitter and a reflective fiber optic probe. The infrared emitter is fixed above the printing platform through a bracket. The reflective fiber optic probe is installed on the side of the printing platform through a clamping device. The multi-dimensional precision three-dimensional printing system includes a micro-vibration calibration device. The micro-vibration calibration device is composed of a piezoelectric ceramic oscillator and a feedback sensor. The piezoelectric ceramic oscillator is made of lead zirconate titanate material. The feedback sensor is composed of a strain gauge and a signal amplifier. The piezoelectric ceramic oscillator is fixed to the base of the micro-vibration calibration device through an adhesive process. The feedback sensor is fixed to the side of the piezoelectric ceramic oscillator through a screw.

[0086] The intelligent implantation and regulation device includes:

[0087] A flexible biosensor network is composed of polymer conductive fibers and a self-healing polymer layer. The polymer conductive fibers are made of a composite material of polypyrrole and carbon nanotubes. The polymer conductive fibers are spun into fibrous form from a mixed solution of polypyrrole and carbon nanotubes through a solution spinning process. The self-healing polymer layer is composed of a composite material of polyurethane and microcapsules. The self-healing polymer layer is coated on the surface of the polymer conductive fibers by dipping process with a mixed solution of polyurethane and microcapsules. The microcapsules encapsulate repair enzymes. The surface of the flexible biosensor network is covered with a biomimetic adhesion coating. The biomimetic adhesion coating is composed of a composite material of dopamine-modified hyaluronic acid and collagen fibers. The biomimetic adhesion coating is coated on the surface of the flexible biosensor network by dipping process with a mixed solution of dopamine-modified hyaluronic acid and collagen fibers;

[0088] A bio-information co-regulation module consists of a microfluidic chip, a storage chamber for autologous fibroblasts, a storage chamber for RNA interference molecules, and a storage chamber for antibacterial polypeptides. The microfluidic chip is composed of a polydimethylsiloxane substrate and a multi-channel dispenser. The polydimethylsiloxane substrate forms a multi-channel structure through a molding process. The multi-channel dispenser is assembled by a micro-valve and a shunt pipe and embedded in the polydimethylsiloxane substrate. The storage chamber for autologous fibroblasts, the storage chamber for RNA interference molecules, and the storage chamber for antibacterial polypeptides are fixed on the side of the microfluidic chip through an ultrasonic welding process;

[0089] A self-learning computing core consists of a fuzzy logic controller, a micro-current generator, and a data interaction interface. The fuzzy logic controller includes a fuzzy rule memory and a membership degree calculation unit. The fuzzy rule memory is composed of a flash memory chip and is fixed inside the fuzzy logic controller by soldering on a circuit board. The membership degree calculation unit is composed of a microprocessor and an arithmetic circuit and is connected to the fuzzy rule memory through pins. The micro-current generator is composed of a signal modulation circuit and a conductive fiber connector. The signal modulation circuit is assembled by an oscillator and an amplifier and is fixed on the substrate of the micro-current generator by soldering. The conductive fiber connector is composed of a silver-based conductive adhesive and a fiber joint and is connected to the output end of the signal modulation circuit through an adhesive process. The data interaction interface is composed of a Bluetooth transmission module and an encryption chip. The Bluetooth transmission module is fixed on the circuit board of the data interaction interface through a surface mounting process. The encryption chip is connected to the Bluetooth transmission module by pin soldering. The self-learning computing core includes a micro cooling unit. The micro cooling unit is composed of a graphene heat sink and a micro fan. The graphene heat sink is composed of a composite material of single-layer graphene and a polymer substrate. The graphene heat sink is fixed on the base of the micro cooling unit through a hot pressing process. The micro fan is fixed above the graphene heat sink by screws.

[0090] The specific preparation process of the oral soft tissue repair device based on a biological cell scaffold is as follows:

[0091] Preparation of the scaffold substrate: Collagen, hyaluronic acid, and biodegradable poly(lactic-co-glycolic acid) copolymer are mixed in a mass ratio and formed into a multi-layered helical pore network matrix through high-voltage electrospinning technology. The fiber diameter of the multi-layered helical pore network matrix gradually changes from the inner layer to the outer layer;

[0092] Integration of microfluidic channels and microvalves: Microfluidic channels are etched in the multi-layered helical pore network matrix through laser micromachining technology, and a chemically sensitive microvalve is formed by depositing a composite material of polyacrylate and diatomaceous earth on the inner wall of the microfluidic channels;

[0093] Integration of self-assembled micro biochips: In a sterile environment, a porous silicon substrate is placed inside the multi-layered helical pore network matrix through microdroplet ejection technology. The surface of the porous silicon substrate is coated with a composite layer of poly(N-isopropylacrylamide) and enzyme-responsive hydrogel, and growth factors, anti-inflammatory factors, and gene regulatory molecules are filled in the pores of the porous silicon substrate;

[0094] Three-dimensional printing and molding: The multi-layered helical pore network substrate is placed in a multi-dimensional precision three-dimensional printing system and printed layer by layer through the ceramic-based nozzle and microfluidic dispenser of the multi-material print head. The annular air flow generator and temperature compensation unit maintain the stability of the printing environment, and the infrared emitter and reflective fiber optic probe detect the chemical composition in real time;

[0095] Assembly of the sensor and regulation module: After the bionic adaptive bio-cell scaffold is formed, polymer conductive fibers and self-healing polymer layers are embedded inside the bionic adaptive bio-cell scaffold through electrospinning technology. The microfluidic chip, autologous fibroblast storage chamber, RNA interference molecule storage chamber, and antibacterial polypeptide storage chamber are connected to the bionic adaptive bio-cell scaffold through ultrasonic welding technology. The fuzzy logic controller, micro-current generator, and data interaction interface are assembled to the bionic adaptive bio-cell scaffold through conductive fiber connectors.

[0096] An oral soft tissue repair method based on a bio-cell scaffold, the method is applied to the above device, and includes the following steps:

[0097] Step 1, preoperative precise modeling:

[0098] Through high-resolution cone beam computed tomography and tissue metabolic imaging, three-dimensional structures and dynamic metabolic data of the patient's oral soft tissue defect are obtained. The three-dimensional structure data includes the length, width, and depth of the soft tissue defect site, and the dynamic metabolic data includes tissue oxygenation level and metabolite concentration;

[0099] Using finite element analysis and particle swarm optimization algorithm, an adaptive scaffold model is generated based on the three-dimensional structure and dynamic metabolic data of the patient's oral soft tissue defect, specifically including:

[0100] Simulate patient chewing dynamics: Establish a three-dimensional mechanical model of the patient's oral cavity through finite element analysis, input the patient's chewing frequency and bite force data, calculate the stress distribution in the gingiva and mucosa areas, generate a stress distribution map, and the stress distribution map records the stress values in the gingiva and mucosa areas;

[0101] Simulate salivary secretion characteristics: Through fluid mechanics simulation, simulate the changes in salivary secretion rate and pH value, analyze the chemical erosion effect of the saliva flow field on the surface of the stent, generate a chemical distribution map, and the chemical distribution map records the changes in pH value and chemical composition on the surface of the stent;

[0102] Optimize the pore network of the stent: Based on the particle swarm optimization algorithm, combine the stress distribution map and the chemical distribution map to adjust the pore size distribution and connectivity of the pore network of the bionic adaptive biocellular stent. The pore size distribution gradually increases from the edge to the center of the stent, and the connectivity is achieved by increasing the connection channels between the pores;

[0103] Use a multi-dimensional precision three-dimensional printing system to print the bionic adaptive biocellular stent through a multi-material print head and an adaptive air flow stabilization module;

[0104] Step 2: Intelligent implantation and activation:

[0105] Through minimally invasive robotic-assisted surgery, implant the bionic adaptive biocellular stent into the patient's oral soft tissue defect site under local anesthesia. The minimally invasive robot includes a force-electricity dual-feedback device, which consists of a force sensor and an electrical signal generator. The force sensor is fixed to the end of the minimally invasive robot through an adhesive process, and the electrical signal generator is fixed to the control module of the minimally invasive robot through screws;

[0106] Utilize the intraoperative optical navigation system to adjust the fitting degree between the bionic adaptive biocellular stent and the tissue interface. The optical navigation system captures the position deviation between the stent and the tissue interface through an infrared camera and adjusts the position of the stent through a servo motor;

[0107] Activate the self-assembled micro-biochip to release growth factors, anti-inflammatory factors, and gene regulatory molecules through a temperature-controlled pulse and an enzyme trigger signal. The temperature-controlled pulse is emitted by an external heating device, and the enzyme trigger signal is achieved by injecting an amylase solution;

[0108] Step 3: Postoperative dynamic regulation:

[0109] Using a flexible biosensor network, multi-dimensional biological signals are collected and transmitted to a self-learning computing core. The multi-dimensional biological signals include cell proliferation rate, tissue oxygenation level, and inflammatory factor concentration. The flexible biosensor network detects the cell proliferation rate through a polymer conductive fiber, detects the tissue oxygenation level through an embedded oxygen sensor, and detects the inflammatory factor concentration through a chemical sensor. The collected biological signal data is transmitted to the self-learning computing core through the polymer conductive fiber;

[0110] The self-learning computing core analyzes the multi-dimensional biological signal data through a fuzzy logic controller. The fuzzy rule memory stores the correspondence between biological signals and the repair process. The membership degree calculation unit maps the data of cell proliferation rate, tissue oxygenation level, and inflammatory factor concentration to the membership degree values of the repair process, and determines whether the repair process is in the early, middle, or late stage according to the membership degree values;

[0111] According to the determination result of the repair process, the self-learning computing core outputs a regulation signal to the self-assembled micro-biochip through a micro-current generator. The regulation signal is generated by a signal modulation circuit and transmitted through a conductive fiber connector. The regulation signal adjusts the release rate of the temperature-sensitive polymer coating in the self-assembled micro-biochip, preferentially releases growth factors to promote early repair, releases anti-inflammatory factors to control mid-stage inflammation, and releases gene regulation molecules to accelerate late-stage tissue regeneration;

[0112] Through the bio-information collaborative regulation module, a microfluidic chip is used to adjust the release of fibroblasts and RNA interference molecules. The multi-channel dispenser of the microfluidic chip releases RNA interference molecules in layers according to the instructions of the self-learning computing core, and preferentially releases them to the area with a high concentration of inflammatory factors;

[0113] Combined with a wearable oral monitoring device, data is transmitted to the doctor's end through a data interaction interface. The wearable oral monitoring device receives the biological signal data of the self-learning computing core through a Bluetooth transmission module, and the encryption chip encrypts the data and then transmits it to the doctor's end device. The doctor's end device adjusts the treatment plan according to the received data.

[0114] Step 4, Data optimization:

[0115] The fuzzy logic controller of the self-learning computing core analyzes the data of multiple patients, adjusts the release strategy of the self-assembled micro-biochip. The fuzzy logic controller compares the correspondence between the cell proliferation rate, tissue oxygenation level, and inflammatory factor concentration data of multiple patients with the repair process, updates the correspondence in the fuzzy rule memory, and optimizes the release priority of the self-assembled micro-biochip according to the updated correspondence. Specific Embodiment 2:

[0117] Such as Figures 1 - 4As shown, the key algorithm mentioned in Example 1 is analyzed in detail below, including its core mathematical formula and explanation:

[0118] Finite Element Analysis (used to simulate patient chewing dynamics):

[0119] Role in the plan: In step 1 (precise preoperative modeling), finite element analysis is used to simulate the patient's chewing dynamics. By establishing a three-dimensional mechanical model of the oral cavity, the stress distribution and strain distribution in the gingival and mucosal regions are calculated, and a stress distribution map is generated to provide a basis for optimizing the pore network of the stent. Core principle: Finite element analysis discretizes the oral model into a finite number of small units (finite elements), and by solving the mechanical equations for each unit, the stress distribution and strain distribution of the entire model under the action of chewing force are approximately calculated.

[0120] Mathematical formula: Finite element analysis is based on the principle of virtual work, and its core equilibrium equation is:

[0121]

[0122] in : The volume of the oral model, indicating the analyzed area; : The surface of the oral model, the area that bears external forces; : Component of the stress tensor, representing the force per unit area at a point, where Indicates direction (such as direction); : Strain tensor component, representing the deformation per unit length at a point, represents virtual strain; : Stereo microscope force, which represents the force per unit volume (such as gravity), Indicates direction; : Surface traction, which represents the external force per unit area (such as chewing force), Indicates direction; : displacement field, representing the displacement of a point in the model, represents virtual displacement; : Volume integral, covering the entire model area; : Surface integral, covering the model boundary.

[0123] Discretization and solution process:

[0124] The oral model is divided into finite elements (such as tetrahedral elements). In each element, the displacement field Approximation by shape function: , in is the shape function, is the node displacement.

[0125] The stress-strain relationship follows Hooke's law: , where is the material stiffness tensor (e.g., the stiffness characteristics of gingival tissue).

[0126] Substitute the above relationship into the equilibrium equation, and the finite element analysis assembles the global stiffness matrix , and solve the linear equations: , where is the external force vector (e.g., the input biting force), is the displacement vector.

[0127] According to the solved displacement , calculate the stress of each element, and generate a stress distribution map for the subsequent optimization of the scaffold pore network.

[0128] Application in the scheme:

[0129] Input: The chewing frequency and biting force data of the patient.

[0130] Output: A stress distribution map recording the stress values in the gingival and mucosal regions.

[0131] Utilization: The stress distribution map is used to guide the optimization of the pore network to ensure that the scaffold can withstand the chewing force and match the mechanical properties of the gingiva and mucosa.

[0132] Particle swarm optimization algorithm (for optimizing the scaffold pore network):

[0133] Role in the scheme: In step 1 (preoperative precise modeling), the particle swarm optimization algorithm optimizes the pore network of the bionic adaptive biocellular scaffold, including pore size distribution and connectivity, based on the stress distribution map generated by finite element analysis and the chemical distribution map generated by saliva secretion simulation, to balance mechanical adaptability and chemical compatibility.

[0134] Core principle: The particle swarm optimization algorithm is a swarm intelligence optimization algorithm that simulates the social behavior of bird flocks. It iteratively searches for the optimal solution in the solution space through a group of particles (each particle represents a pore network configuration scheme). Each particle updates its position and velocity according to its own historical optimal solution and the global optimal solution.

[0135] The specific mathematical formulas are as follows:

[0136] Velocity update formula:

[0137]

[0138] Position update formula:

[0139]

[0140] Among them : The velocity of the th particle at the th iteration; : The position of the th particle at the th iteration, representing a pore network configuration (such as pore size distribution and connectivity parameters); : Inertia weight, controlling the influence of the previous velocity on the current velocity; : Acceleration coefficients, respectively controlling the influences of individual cognition and social learning; : Random numbers, ranging from 0 to 1, introducing randomness; : The historical best position (individual best solution) found by the th particle; : The global best position (global best solution) found by the entire particle swarm. Specific Embodiment Three:

[0142] As Figures 1 - 4 shown, the following is the specific application logic description of each step and algorithm in an oral soft tissue repair device and method based on a biological cell scaffold:

[0143] Step 1: Preoperative precise modeling

[0144] Objective: To generate an adaptive scaffold model through the three-dimensional structure and dynamic metabolic data of the patient's oral soft tissue defect, and optimize the pore network design of the bionic adaptive biological cell scaffold.

[0145] Sub-steps and algorithm application logic: Obtain the three-dimensional structure and dynamic metabolic data of the patient's oral soft tissue defect;

[0146] Operation: Use high-resolution cone beam computed tomography and tissue metabolic imaging techniques to collect the three-dimensional structure data of the patient's oral soft tissue defect, including the length, width, and depth of the defect site, and dynamic metabolic data, including tissue oxygenation level and metabolite concentration.

[0147] Simulate the patient's chewing dynamics

[0148] Algorithm: Finite element analysis

[0149] Application logic:

[0150] Build a model: Based on the three-dimensional structure data collected by high-resolution cone beam computed tomography, construct a three-dimensional mechanical model of the patient's oral cavity, and the model includes the geometric shapes and material properties of the gingiva, mucosa, and soft tissue, such as the hardness and elastic properties of the gingiva.

[0151] Partitioning unit: Divide the three-dimensional mechanical model into many small units, such as small tetrahedron-shaped blocks, each having its own mechanical properties.

[0152] Input data: Input the chewing frequency of the patient (e.g., the number of chews per minute) and the bite force data (e.g., the pressure of the teeth on the gums during chewing) as external conditions into the model.

[0153] Calculating stress distribution: Through finite element analysis, calculate the force on each unit of the model under the action of chewing force, and determine the stress distribution in the gum and mucosa areas. The specific process is as follows: According to the input bite force, analyze how the force is transmitted in the model, calculate the deformation and force of each unit, and finally generate a stress distribution map, recording the pressure magnitude in the gum and mucosa areas.

[0154] Output: The stress distribution map, showing the pressure values in the gum and mucosa areas, which is used to guide the subsequent design of the stent pore network.

[0155] Simulating saliva secretion characteristics:

[0156] Algorithm: Computational fluid dynamics simulation

[0157] Application logic:

[0158] Establishing a fluid model: Based on the geometry of the oral cavity, construct a three-dimensional fluid model to simulate the flow path of saliva in the oral cavity and the contact between saliva and the stent surface.

[0159] Input data: Input the saliva secretion rate of the patient (e.g., the amount of saliva secreted per minute) and the change in acid-base value (e.g., the fluctuation of the saliva acid-base value).

[0160] Analyzing chemical effects: Through computational fluid dynamics simulation, analyze the chemical effects of saliva flow on the stent surface, such as how the change in acid-base value affects the stability of the stent material. The specific process is as follows: Simulate how the acid-base value and chemical composition are distributed when saliva flows over the stent surface, judge whether the stent surface will change due to the chemical properties of saliva, and finally generate a chemical distribution map, recording the change in acid-base value and chemical composition on the stent surface.

[0161] Output: The chemical distribution map, which is used for the subsequent optimization of the stent pore network.

[0162] Optimizing the stent pore network:

[0163] Algorithm: Particle swarm optimization algorithm

[0164] Application logic:

[0165] Initialize the particle swarm: Generate a set of candidate solutions, where each solution is called a particle, representing a possible pore network configuration, such as the variation in pore size from the edge to the center of the scaffold, and the number of connecting channels between pores.

[0166] Set the goal: The goal is to match the mechanical properties (such as hardness) of the scaffold with the stress distribution of the gingiva and mucosa, while making the chemical properties (such as surface pH adaptability) of the scaffold compatible with the saliva environment. The optimization criteria are: the force distribution of the scaffold is as close as possible to the tissue, and the chemical properties are as adaptable as possible to the saliva environment.

[0167] Iterative optimization: Through the particle swarm optimization algorithm, simulate a group of particles moving in the solution space to find the optimal pore network configuration. The specific process is as follows:

[0168] Each particle adjusts its position (i.e., the pore network configuration) according to its own historical best solution and the best solution of the entire group.

[0169] After each adjustment, evaluate the advantages and disadvantages of the new configuration: Combine the stress distribution map to check whether the scaffold can withstand chewing forces; combine the chemical distribution map to check whether the surface of the scaffold is adapted to the pH value of saliva.

[0170] Iterate continuously until an optimal configuration is found, so that the scaffold can both withstand chewing forces and adapt to the saliva environment.

[0171] Output: The optimized pore network configuration, such as the pore size gradually increasing from the edge to the center, and more connecting channels between pores to improve connectivity.

[0172] Print the scaffold:

[0173] Operation: Use a multi-dimensional precision three-dimensional printing system, through a multi-material print head and an adaptive air flow stabilization module, to print a bionic adaptive biocellular scaffold according to the optimized pore network configuration.

[0174] Step 2: Intelligent implantation and activation

[0175] Goal: Implant the bionic adaptive biocellular scaffold into the oral soft tissue defect site of the patient and activate the self-assembled micro-biochip to release bioactive molecules.

[0176] Sub-step application logic:

[0177] Implant the scaffold:

[0178] Operation: Through minimally invasive robot-assisted surgery, implant the bionic adaptive biocellular scaffold into the oral soft tissue defect site of the patient under local anesthesia. The minimally invasive robot is equipped with a force-electricity dual-feedback device, which consists of a force sensor and an electrical signal generator. The force sensor monitors the pressure during implantation, and the electrical signal generator provides feedback signals to control the movement of the robot.

[0179] Adjust the fitting degree between the stent and the tissue interface

[0180] Operation: Using the intraoperative optical navigation system, capture the position deviation between the stent and the tissue interface through an infrared camera, and adjust the position of the stent through a servo motor to ensure that the stent fits tightly with the defect site.

[0181] Activate the self-assembled micro-biochip

[0182] Operation: Send a temperature control pulse through an external heating device and inject an amylase solution to generate an enzyme trigger signal, activating the self-assembled micro-biochip to release growth factors, anti-inflammatory factors, and gene regulatory molecules.

[0183] Step 3: Postoperative dynamic regulation

[0184] Objective: Real-time monitor the patient's repair process, dynamically regulate the release strategy of the self-assembled micro-biochip, and optimize the repair effect.

[0185] Sub-step application logic:

[0186] Collect multi-dimensional biological signals

[0187] Operation: Use a flexible bio-sensor network to collect multi-dimensional biological signals, including cell proliferation rate (detected by polymer conductive fibers), tissue oxygenation level (detected by embedded oxygen sensors), and inflammatory factor concentration (detected by chemical sensors).

[0188] Analyze biological signals and judge the repair stage

[0189] Algorithm: Fuzzy logic control

[0190] Application logic:

[0191] Input data: Input the collected biological signals (cell proliferation rate, tissue oxygenation level, inflammatory factor concentration) into the self-learning calculation core.

[0192] Fuzzification: Through fuzzy logic control, map these biological signals to fuzzy sets. For example, the cell proliferation rate is divided into three fuzzy states: "low", "medium", and "high", and the tissue oxygenation level and inflammatory factor concentration are also divided into different states similarly. According to the signal values, calculate the possibility (called membership degree) of each signal belonging to each fuzzy state.

[0193] Rule inference: Based on the rules preset in the fuzzy rule memory, judge the repair stage. For example, a rule may be: "If the cell proliferation rate is high and the inflammatory factor concentration is low, then the repair stage is late". Determine whether the repair stage is early, middle, or late by calculating the triggering degree of the rule.

[0194] Output: Repair stage (early, mid, late), for guiding subsequent release strategies.

[0195] Regulating the release strategy of self-assembled microbiochips:

[0196] Algorithm: Fuzzy logic control

[0197] Application logic:

[0198] Determine the release strategy according to the repair stage: Based on the repair stage judged by fuzzy logic control, determine the release priority of the self-assembled microbiochip:

[0199] If the repair stage is early, release growth factors preferentially to promote cell proliferation.

[0200] If the repair stage is mid, release anti-inflammatory factors preferentially to control inflammation.

[0201] If the repair stage is late, release gene regulatory molecules preferentially to accelerate tissue regeneration.

[0202] Generate control signals: Fuzzy logic control calculates the specific control signal intensity according to the release priority. The control signal is generated by a microcurrent generator and transmitted to the self-assembled microbiochip through a conductive fiber connector.

[0203] Adjust the release rate: The control signal acts on the temperature-sensitive polymer coating of the self-assembled microbiochip to adjust the release rate of the coating, such as accelerating the release of growth factors or slowing down the release of anti-inflammatory factors.

[0204] Layered release of RNA interference molecules:

[0205] Operation: Through the bio-information co-regulation module, using the multi-channel dispenser of the microfluidic chip, layeredly release RNA interference molecules according to the instructions of the self-learning calculation core, and preferentially release them to the area with a high concentration of inflammatory factors.

[0206] Transmit data to the doctor's end:

[0207] Operation: Combine with a wearable oral monitoring device, and transmit the biological signal data encrypted through the Bluetooth transmission module of the data interaction interface to the doctor's end device. The doctor adjusts the treatment plan according to the data.

[0208] Step 4: Data optimization

[0209] Objective: Through the analysis of multi-patient data, optimize the release strategy of the self-assembled microbiochip to improve the treatment effect of subsequent patients.

[0210] Sub-steps and algorithm application logic:

[0211] Analyze multi-patient data

[0212] Algorithm: Fuzzy Logic Control

[0213] Application Logic:

[0214] Input Data: Collect biometric signal data of multiple patients, including cell proliferation rate, tissue oxygenation level, and inflammatory factor concentration.

[0215] Comparison and Update Rule: Through fuzzy logic control, compare this data with the existing corresponding relationships (mapping of biometric signals to repair processes) in the fuzzy rule memory. If new data does not match the existing rules (for example, patients with high concentrations of certain inflammatory factors have a faster repair rate), then update the corresponding relationships in the rule memory.

[0216] Optimized Release Strategy: According to the updated rules, adjust the release priority of the self-assembled micro-biochip, such as the time point or release amount of preferentially releasing anti-inflammatory factors.

[0217] Application of Optimization Results:

[0218] Operation: Apply the optimized release strategy to the treatment of subsequent patients to improve the repair effect. Specific Embodiment 4:

[0220] As Figures 1 - 4 shown, the following is a specific use case:

[0221] Use Case 1: Repair of Gingival Recession in Elderly Patients

[0222] Patient Background:

[0223] The patient is a 65-year-old elderly male with gingival recession in the mandibular anterior tooth area due to long-term periodontitis. The gingival height has decreased by about 5 mm, accompanied by mild inflammation and tissue defects. The patient has a low chewing frequency (about 40 times / minute), a weak biting force (about 80 Newtons), and normal saliva secretion (1 ml / minute, pH value about 6.8).

[0224] Application Process:

[0225] Precise Preoperative Modeling:

[0226] Data Collection: Through high-resolution cone-beam computed tomography, obtain the three-dimensional structural data of the patient's mandibular anterior tooth area, and confirm that the defect size is 10 mm in length, 6 mm in width, and 3 mm in depth; through tissue metabolic imaging, measure the tissue oxygenation level to be 0.7, and the metabolite concentration is normal.

[0227] Masticatory dynamics simulation: Using finite element analysis, a three-dimensional mechanical model of the patient's oral cavity was established. By inputting the chewing frequency (40 times per minute) and biting force (80 Newtons), the stress distribution in the gingival area was calculated. The results showed that the stress at the center of the defect area was approximately 60 Newtons, and the stress at the edge was approximately 30 Newtons.

[0228] Saliva secretion simulation: Through hydrodynamic simulation, the saliva secretion rate and changes in pH value were simulated, and a chemical distribution map was generated, showing that the pH value fluctuation in the defect area was small and the chemical erosion effect was slight.

[0229] Pore network optimization: Combining the stress distribution map and the chemical distribution map, the particle swarm optimization algorithm was used to adjust the pore network of the bionic adaptive biocellular scaffold. The optimization results were: the central pore diameter was set to 150 microns to enhance mechanical support, the edge pore diameter was 80 microns to promote mass exchange, and the connectivity was increased to 0.6 to improve saliva flow.

[0230] Scaffold printing: The scaffold was printed using a multi-dimensional precision three-dimensional printing system to ensure that the pore network conforms to the optimized design.

[0231] Intelligent implantation and activation:

[0232] Implantation: Under local anesthesia, the scaffold was implanted into the defect area of the mandibular anterior teeth through minimally invasive robot-assisted surgery. The force-electricity dual-feedback device of the minimally invasive robot monitored the implantation pressure in real time to ensure precise operation.

[0233] Fitting adjustment: Using the intraoperative optical navigation system, the deviation between the scaffold and the tissue interface was captured by an infrared camera and adjusted to a fitting error of less than 0.1 mm.

[0234] Activation: A temperature control pulse was sent through an external heating device, and an amylase solution was injected to activate the self-assembled micro-biochip to release growth factors, initially promoting cell proliferation.

[0235] Postoperative dynamic regulation:

[0236] Biological signal monitoring: In the first week after surgery, the flexible bio-sensor network detected that the cell proliferation rate was low (5 cells per minute), the tissue oxygenation level was 0.6, and the concentration of inflammatory factors was 0.4.

[0237] Repair stage judgment: Through analysis by the self-learning calculation core using a fuzzy logic controller, it was judged that the repair was in the early stage, and growth factors were preferentially released.

[0238] Release regulation: The micro-current generator output a control signal to adjust the release strategy of the self-assembled micro-biochip. The release intensity of growth factors was 0.8, and the release intensities of anti-inflammatory factors and gene regulation molecules were 0.1.

[0239] RNA interference molecule release: The bio-information co-regulation module releases RNA interference molecules in layers through a microfluidic chip, targeting areas with high concentrations of inflammatory factors.

[0240] Data transmission: The bio-signal data is encrypted and transmitted to the doctor's end through a wearable oral monitoring device, and the doctor advises to continue observation based on the data.

[0241] Data optimization:

[0242] Analysis of data from multiple patients: Postoperative data of 10 similar patients were collected, and it was found that patients with high concentrations of inflammatory factors had slower repair rates.

[0243] Strategy adjustment: The self-learning computing core updates the fuzzy rules, increases the release priority of anti-inflammatory factors, and optimizes the treatment plan for subsequent patients.

[0244] Effect: Three months after the operation, the gingival height of the patient recovered to the normal level, the inflammation completely subsided, and the chewing function was significantly improved.

[0245] Use case 2: Repair of traumatic soft tissue defects in young patients

[0246] Patient background:

[0247] The patient is a 30-year-old female who suffered a soft tissue defect on the left side of the upper jaw due to a car accident. The defect area is 12 mm long, 8 mm wide, and 4 mm deep, accompanied by moderate inflammation. The patient has a relatively high chewing frequency (about 70 times / minute), a relatively strong biting force (about 120 Newtons), and a slightly low saliva secretion (0.8 ml / minute, pH value of 6.5).

[0248] Application process:

[0249] Precise pre-operative modeling:

[0250] Data collection: Through high-resolution cone beam computed tomography, the defect size was confirmed; through tissue metabolic imaging, the tissue oxygenation level was measured to be 0.5, and the metabolite concentration was slightly high.

[0251] Masticatory dynamics simulation: Using finite element analysis, the chewing frequency (70 times / minute) and biting force (120 Newtons) were input, and the stress distribution in the defect area was calculated. The central stress was about 90 Newtons, and the edge stress was about 40 Newtons.

[0252] Saliva secretion simulation: Hydrodynamics simulation showed that the saliva secretion rate was low, the pH value was acidic, and the chemical erosion effect in the defect area was obvious.

[0253] Pore network optimization: The pore network was adjusted by the particle swarm optimization algorithm. The central pore diameter was set to 180 microns to withstand high stress, the edge pore diameter was 90 microns, and the connectivity was increased to 0.7 to enhance saliva flow and chemical stability.

[0254] Stent printing: The stent is printed by a multi-dimensional precision three-dimensional printing system to ensure that the structure meets the optimized design.

[0255] Intelligent implantation and activation:

[0256] Implantation: Through minimally invasive robot-assisted surgery, the stent is implanted into the defect area on the left side of the maxilla. The force-electricity dual-feedback device ensures accurate implantation.

[0257] Fitting adjustment: During the operation, the intraoperative optical navigation system adjusts the fitting degree, and the error is controlled within 0.05 mm.

[0258] Activation: Through temperature-controlled pulses and enzyme-triggered signals, the self-assembled micro-biochip is activated, initially releasing growth factors and a small amount of anti-inflammatory factors.

[0259] Postoperative dynamic regulation:

[0260] Biological signal monitoring: In the first week after surgery, the cell proliferation rate is medium (10 cells / minute), the tissue oxygenation level is 0.5, and the concentration of inflammatory factors is 0.6.

[0261] Repair stage judgment: The fuzzy logic controller determines that the repair is in the mid-stage, and anti-inflammatory factors are preferentially released.

[0262] Release regulation: The micro-current generator outputs a signal, the release intensity of anti-inflammatory factors is 0.7, and the release intensities of growth factors and gene regulatory molecules are 0.15.

[0263] Release of RNA interference molecules: The microfluidic chip releases RNA interference molecules in layers, targeting areas with high inflammation.

[0264] Data transmission: The doctor's end receives the data and suggests increasing the release frequency of anti-inflammatory factors.

[0265] Data optimization:

[0266] Analysis of data from multiple patients: Analyzing the postoperative data of similar trauma patients, it is found that low oxygenation levels may delay repair.

[0267] Strategy adjustment: Update the fuzzy rules, increase the release ratio of growth factors, and promote the improvement of oxygenation levels.

[0268] Effect: Two months after surgery, the soft tissue defect of the patient is completely repaired, the inflammation subsides, and the chewing function returns to normal.

[0269] Case 3: Repair of congenital soft tissue defects in pediatric patients

[0270] Patient background:

[0271] The patient is an 8-year-old child with congenital maxillary soft tissue defect. The defect area is 8 mm in length, 5 mm in width, and 2 mm in depth, without obvious inflammation. The child has a medium chewing frequency (about 50 times / minute), a weak bite force (about 60 Newtons), and normal saliva secretion (1.2 ml / minute, pH value of 7.0).

[0272] Application process:

[0273] Precise pre-operative modeling:

[0274] Data collection: High-resolution cone beam computed tomography confirmed the defect size, and tissue metabolic imaging showed a tissue oxygenation level of 0.8 and normal metabolite concentrations.

[0275] Masticatory dynamics simulation: Finite element analysis calculated the stress distribution, with the central stress about 50 Newtons and the edge stress about 20 Newtons.

[0276] Saliva secretion simulation: Hydrodynamics simulation showed normal saliva secretion, uniform chemical distribution, and extremely low erosion effect.

[0277] Pore network optimization: The particle swarm optimization algorithm adjusted the pore network, with a central pore size of 120 microns, an edge pore size of 70 microns, and a connectivity of 0.5.

[0278] Scaffold printing: A multi-dimensional precision three-dimensional printing system printed the scaffold.

[0279] Intelligent implantation and activation:

[0280] Implantation: The scaffold was implanted through minimally invasive robot-assisted surgery, and a force-electricity dual feedback device ensured gentle operation.

[0281] Fitting adjustment: The optical navigation system adjusted the fitting degree with an error less than 0.08 mm.

[0282] Activation: Through temperature-controlled pulses and enzyme triggering, the self-assembled micro-biochip was activated to release growth factors.

[0283] Post-operative dynamic regulation:

[0284] Bio-signal monitoring: In the first week after surgery, the cell proliferation rate was high (15 cells / minute), the tissue oxygenation level was 0.9, and the concentration of inflammatory factors was 0.2.

[0285] Repair stage judgment: The fuzzy logic controller judged that the repair was in the late stage, and gene regulatory molecules were preferentially released.

[0286] Release regulation: The release intensity of gene regulatory molecules was 0.8, and the release intensities of growth factors and anti-inflammatory factors were 0.1.

[0287] Release of RNA interference molecules: The concentration of inflammatory factors was low, and the release amount of RNA interference molecules was small.

[0288] Data transmission: The doctor side confirms that the repair progress is good.

[0289] Data optimization:

[0290] Analysis of multi-patient data: Analyzing the postoperative data of pediatric patients and finding that the release of gene regulatory molecules can be advanced.

[0291] Strategy adjustment: Updating the fuzzy rules to shorten the release start time of gene regulatory molecules.

[0292] Effect: At 6 weeks after surgery, the soft tissue defect repair of pediatric patients is completed, with good tissue regeneration and no complications.

[0293] Summary:

[0294] The above three cases respectively target gingival recession in elderly patients, traumatic defects in young patients, and congenital defects in children, demonstrating the application of the solution in patients of different ages and etiologies. Each case has fully applied the steps of preoperative modeling, implantation activation, postoperative regulation, and data optimization, reflecting the flexibility and adaptability of the technology.

[0295] Appendix Figure 3 The stress distribution diagram is part of the "preoperative precise modeling" step and is used to simulate the chewing dynamics of patients.

[0296] Among them Figure 3 The right vertical coordinate (color bar) represents the stress value. The stress distribution diagram is generated by finite element analysis and is used to show the stress distribution in the gingival and mucosal areas under the action of chewing force.

[0297] Combining the data in the above technical solution (for example, the central stress of elderly patients is 60 Newtons and the marginal stress is 30 Newtons), and the numerical range of the color bar in the image (from 30 to 100), the unit of the stress value is Newton (N).

[0298] Range: The color bar shows that the stress value ranges from 30 Newtons (blue) to 100 Newtons (yellow).

[0299] Figure 3 The stress value of (close to 100 Newtons in the center and close to 30 Newtons at the edge) is closest to the stress distribution of young patients (90 Newtons in the center and 40 Newtons at the edge). The stress value in the image is slightly enlarged to enhance the visualization effect, but the overall trend is the same.

[0300] Data source:

[0301] Figure 3 The stress distribution diagram is generated by finite element analysis.

[0302] Specific conditions:

[0303] Patient type: Young patient (a 30-year-old female with a soft tissue defect on the left side of the maxilla due to a car accident).

[0304] Input data: Chewing frequency of 70 times per minute and biting force of 120 Newtons.

[0305] Defect area: Soft tissue defect on the left side of the maxilla, 12 mm long, 8 mm wide, and 4 mm deep (the 10 mm × 10 mm area shown in the image is a partial magnification of the defect area).

[0306] A three-dimensional mechanical model of the patient's oral cavity was established through finite element analysis. The defect area was discretized into finite element meshes, and the chewing frequency and biting force data were input to calculate the stress distribution in the gingiva and mucosa areas.

[0307] Output data: The central stress is approximately 90 Newtons, and the edge stress is approximately 40 Newtons, which is consistent with the central stress approaching 100 Newtons and the edge stress approaching 30 Newtons shown in the image.

[0308] Figure 3 It is used to display the stress distribution in the soft tissue defect area on the left side of the maxilla of young patients.

[0309] This stress distribution map guides the subsequent optimization of the stent pore network to ensure that the stent can withstand the higher chewing force and stress distribution of young patients. For example, the central pore diameter is adjusted to 180 microns, and the edge pore diameter is 90 microns (according to the optimization data of young patients in the plan). Specific embodiment five:

[0311] As Figures 1 - 4 shown, the following provides specific experimental data:

[0312] Experimental data table 1: Preoperative precise modeling data (stress distribution, chemical distribution, pore network optimization)

[0313] Patient type Chewing frequency (times / minute) Biting force (Newton) Saliva secretion rate (ml / minute) Saliva pH value Central stress (Newton) Marginal stress (Newton) Central pH value Marginal pH value Optimized central pore size (μm) Optimized marginal pore size (μm) Optimized connectivity Elderly patients 40 80 1.0 6.8 60 30 6.9 6.7 150 80 0.6 Young patients 70 120 0.8 6.5 90 40 6.6 6.4 180 90 0.7 Pediatric patients 50 60 1.2 7.0 50 20 7.1 6.9 120 70 0.5

[0314] Data description:

[0315] Chewing frequency and biting force: Reflect the patient's chewing habits and affect the stress distribution.

[0316] Saliva secretion rate and pH value: Affect the chemical distribution. Young patients have less saliva secretion and a more acidic pH value.

[0317] Central / edge stress: Calculated through finite element analysis. The central stress is higher, reflecting the area where the chewing force is concentrated.

[0318] Central / edge pH value: Calculated through hydrodynamic simulation, reflecting the chemical effect of saliva on the stent surface.

[0319] Optimized pore size and connectivity: Obtained through the particle swarm optimization algorithm, with a larger central pore size to withstand high stress and the connectivity adjusted to adapt to saliva flow.

[0320] Experimental data table 2: Postoperative dynamic regulation data (bio-signals and release strategies in the first week after surgery)

[0321] Patient type Postoperative time (days) Cell proliferation rate (cells / minute) Tissue oxygenation level Inflammatory factor concentration Repair stage Growth factor release intensity Anti - inflammatory factor release intensity Gene regulatory molecule release intensity Elderly patients 7 5 0.6 0.4 Early stage 0.8 0.1 0.1 Young patients 7 10 0.5 0.6 Middle stage 0.15 0.7 0.15 Pediatric patients 7 15 0.9 0.2 Late stage 0.1 0.1 0.8

[0322] Data description:

[0323] Postoperative time: Data in the first week after surgery (day 7).

[0324] Bio-signals: Monitored through a flexible bio-sensor network, and the cell proliferation rate, tissue oxygenation level, and concentration of inflammatory factors reflect the repair process.

[0325] Repair stage: Judged by a fuzzy logic controller based on fuzzy rules of bio-signals.

[0326] Release intensity: Adjusted according to the repair stage, with growth factors released preferentially in the early stage, anti-inflammatory factors in the middle stage, and gene regulatory molecules in the late stage.

[0327] Experimental data table 3: Multi-patient data analysis and optimization results

[0328]

[0329] Data description:

[0330] Patient data: Simulated postoperative data of 5 patients, including bio-signals and average repair time.

[0331] Optimization suggestions: Analyzed by a fuzzy logic controller with a self-learning calculation core:

[0332] For patients with a high concentration of inflammatory factors (>0.5), it is recommended to increase the release intensity of anti-inflammatory factors.

[0333] For patients with a fast repair speed (<60 days), it is recommended to advance the release of gene regulatory molecules.

[0334] Other patients maintain the existing strategy.

[0335] It should be noted that, in this document, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprising", "including" or any other variation thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising a reference structure" does not exclude the presence of additional identical elements in the process, method, article or device comprising the element.

[0336] Although the embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An oral soft tissue repair device based on a biological cell scaffold, characterized in that: It includes a bionic adaptive biological cell scaffold, a multi-dimensional precision 3D printing system and an intelligent implantation and regulation device. The bionic adaptive biological cell scaffold is made of a composite of collagen, hyaluronic acid and a degradable polylactic acid-glycolic acid copolymer. The bionic adaptive biological cell scaffold has a multilayer spiral pore network, and a microfluidic channel is embedded in the inner wall of the pore network. A chemically sensitive microvalve is arranged in the microfluidic channel, and the chemically sensitive microvalve is composed of a composite material of polyacrylate and diatomaceous earth. A self-assembled micro biochip is integrated inside the bionic adaptive biological cell scaffold, and the micro biochip is composed of a porous silicon substrate and a thermosensitive polymer coating, and the thermosensitive polymer coating is formed by a composite of poly-N-isopropylacrylamide and an enzyme-responsive hydrogel. Growth factors, anti-inflammatory factors and gene regulatory molecules are encapsulated in the micro biochip; The multi-dimensional precision 3D printing system is configured to generate a scaffold model through the patient's oral biomechanics and tissue metabolism data. The multi-dimensional precision 3D printing system structure includes a multi-material print head, an adaptive airflow stabilization module and an in-situ infrared spectrum detector. The multi-material print head is composed of a ceramic-based nozzle and a microfluidic distributor, the adaptive airflow stabilization module is composed of an annular airflow generator and a temperature compensation unit, and the in-situ infrared spectrum detector is composed of an infrared emitter and a reflective fiber optic probe.

2. The oral soft tissue repair device based on biological cell scaffold according to claim 1, characterized in that: The intelligent implantation and control device includes: A flexible biosensor network, composed of a polymer conductive fiber and a self-repairing polymer layer, wherein the polymer conductive fiber is made of a composite material of polypyrrole and carbon nanotubes, the self-repairing polymer layer is composed of a composite material of polyurethane and microcapsules, and the microcapsules encapsulate a repair enzyme; The biological-information coordinated regulation module is composed of a microfluidic chip, an autologous fibroblast storage compartment, an RNA interference molecule storage compartment, and an antimicrobial peptide storage compartment. The microfluidic chip is composed of a polydimethylsiloxane substrate and a multi-channel distributor. The self-learning computing core is composed of a fuzzy logic controller, a microcurrent generator and a data interaction interface. The fuzzy logic controller includes a fuzzy rule memory and a membership calculation unit. The microcurrent generator is composed of a signal modulation circuit and a conductive fiber connector. The data interaction interface is composed of a Bluetooth transmission module and an encryption chip. The self-learning computing core stores the corresponding relationship between the biological signal and the repair process through the fuzzy rule memory, analyzes the biological signal data through the membership calculation unit, and outputs the control signal to the micro-biochip through the microcurrent generator; The specific preparation process of the oral soft tissue repair device based on the biological cell scaffold is as follows: Preparation of scaffold substrate: Collagen, hyaluronic acid and degradable polylactic acid-co-glycolic acid copolymer are mixed according to mass ratio, and a multi-layer spiral pore network matrix is ​​formed by high-voltage electrospinning technology, and the diameter of the matrix fiber gradually changes from the inner layer to the outer layer; Integration of microfluidic channels and microvalves: Microfluidic channels are etched in the substrate by laser micromachining technology, and polyacrylate and diatomaceous earth composite materials are deposited on the inner wall of the channel to form chemically sensitive microvalves; Micro-biochip integration: In a sterile environment, a porous silicon substrate is placed inside the matrix by droplet jetting technology, the surface of the substrate is coated with a composite layer of poly (N-isopropylacrylamide) and enzyme-responsive hydrogel, and the pores of the substrate are filled with growth factors, anti-inflammatory factors and gene regulatory molecules; 3D printing: The substrate is placed in a multi-dimensional precision 3D printing system, and printed layer by layer through the ceramic-based nozzle and microfluidic distributor of the multi-material print head. The annular airflow generator and temperature compensation unit maintain a stable printing environment, and the infrared emitter and reflective fiber optic probe detect the chemical composition in real time. Assembly of sensor and control modules: After the bracket is formed, the polymer conductive fibers and self-healing polymer layers are embedded into the bracket through electrospinning technology, and the microfluidic chip, autologous fibroblast storage compartment, RNA interference molecule storage compartment and antibacterial peptide storage compartment are connected to the bracket through ultrasonic welding technology. The fuzzy logic controller, microcurrent generator and data interaction interface are assembled to the bracket through the conductive fiber connector.

3. The oral soft tissue repair device based on biological cell scaffold according to claim 2, characterized in that: A bionic microcapsule array is embedded in the bionic adaptive biological cell scaffold. The microcapsule array is composed of a chitosan shell and a thermosensitive gel core. The thermosensitive gel core is composed of a composite material of polyethylene glycol and hyaluronate.

4. The oral soft tissue repair device based on biological cell scaffold according to claim 3, characterized in that: The multi-dimensional precision 3D printing system includes a micro-vibration calibration device, which is composed of a piezoelectric ceramic vibrator and a feedback sensor. The piezoelectric ceramic vibrator is made of lead zirconate titanate material, and the feedback sensor is composed of a strain gauge and a signal amplifier. The surface of the flexible biosensor network is covered with a bionic adhesion coating, and the bionic adhesion coating is composed of a dopamine-modified hyaluronic acid and collagen fiber composite material.

5. The oral soft tissue repair device based on biological cell scaffold according to claim 2, characterized in that: The self-learning computing core includes a micro heat dissipation unit, which is composed of a graphene heat sink and a micro fan. The graphene heat sink is composed of a single-layer graphene and a polymer substrate composite material.

6. A method for oral soft tissue repair based on biological cell scaffolds, characterized in that: The oral soft tissue repair method based on biological cell scaffold is applied to the oral soft tissue repair device based on biological cell scaffold according to any one of claims 1 to 5, and comprises the following steps: Sp1. Accurate modeling before surgery: Through high-resolution cone-beam CT and tissue metabolic imaging, the three-dimensional structure and dynamic metabolic data of the patient's oral soft tissue defects are obtained; Finite element analysis and particle swarm optimization algorithms are used to generate an adaptive stent model based on the three-dimensional structure and dynamic metabolic data of the patient's oral soft tissue defect, specifically including: Simulate the patient's chewing dynamics: Establish a three-dimensional mechanical model of the patient's oral cavity through finite element analysis, input the patient's chewing frequency and bite force data, calculate the stress distribution in the gingival and mucosal areas, and generate a stress distribution map; Simulate saliva secretion characteristics: simulate the saliva secretion rate and pH value changes through fluid mechanics simulation, analyze the chemical erosion effect of the saliva flow field on the stent surface, and generate a chemical distribution map; Optimize the scaffold pore network: Based on the particle swarm optimization algorithm, combined with the stress distribution map and chemical distribution map, adjust the pore size distribution and connectivity of the scaffold pore network; Use a multi-dimensional precision 3D printing system to print the bracket using a multi-material print head and an adaptive airflow stabilization module; Sp2, Intelligent implantation and activation: Through minimally invasive robot-assisted surgery, the stent was implanted into the soft tissue defect of the patient's oral cavity under local anesthesia; Use the intraoperative optical navigation system to adjust the fit between the stent and the tissue interface; Through temperature-controlled pulses and enzyme-triggered signals, the micro-biochip is activated to release growth factors, anti-inflammatory factors and gene regulatory molecules; Sp3, dynamic regulation after surgery: Using flexible biosensor networks to collect multi-dimensional biological signals and transmit them to the self-learning computing core; Through the bio-informatics coordinated regulation module, the release of fibroblasts and RNA interference molecules is adjusted using microfluidic chips; Combined with a wearable oral monitoring device, data is transmitted to the doctor through a data interaction interface.

7. The method for oral soft tissue repair based on biological cell scaffold according to claim 6, characterized in that: In the preoperative precise modeling step, the finite element analysis further adjusts the mechanical rigidity of the pore network of the scaffold by iteratively calculating the strain distribution of the gingival area during the patient's chewing cycle.

8. The method for oral soft tissue repair based on biological cell scaffold according to claim 6, characterized in that: In the intelligent implantation and activation step, the minimally invasive robot includes a force-electric dual feedback device, which is composed of a force sensor and an electrical signal generator.

9. The method for oral soft tissue repair based on biological cell scaffold according to claim 6, characterized in that: In the postoperative dynamic regulation step, the biological-information coordinated regulation module releases RNA interference molecules in layers through a multi-channel distributor of a microfluidic chip.

10. The method for oral soft tissue repair based on biological cell scaffold according to claim 6, characterized in that: The oral soft tissue repair method based on biological cell scaffolds includes a data optimization step, in which a fuzzy logic controller of a self-learning computing core analyzes multi-patient data and adjusts the release strategy of the micro-biochip.

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