Oral soft tissue repair equipment and method based on biological cell scaffold

Abstract

The invention provides oral cavity soft tissue repair equipment and method based on a biological cell scaffold, and relates to the technical field of biological oral cavities, the oral cavity soft tissue repair equipment comprises a bionic self-adaptive biological cell scaffold, a multi-dimensional precise 3D printing system and an intelligent implantation and regulation device, the biomimetic self-adaptive biological cell scaffold is prepared by compounding collagen, hyaluronic acid and a degradable polylactic acid-glycolic acid copolymer, the biomimetic self-adaptive biological cell scaffold is provided with a multi-layer spiral pore network, a microfluid channel is embedded in the inner wall of the pore network, and a chemical sensitive micro valve is arranged in the microfluid channel. By integrating the bionic self-adaptive biological cell scaffold, the multi-dimensional precise three-dimensional printing system and the intelligent implantation and regulation device and combining finite element analysis and a particle swarm optimization algorithm, chewing dynamics and salivary secretion characteristics of a patient can be precisely simulated, and the problem that static design of a traditional scaffold lacks dynamic adaptability is effectively solved.

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] Currently, oral soft tissue repair mainly relies on the following technical means: Traditional surgical repair: Through surgical transplantation of autologous or allogeneic tissues (such as gingival tissue) to the defect site to restore the function and morphology of soft tissues.

[0004] Biomaterial scaffolds: Using biomaterials such as collagen and polylactic acid to make scaffolds and implant them into the defect site to promote tissue regeneration.

[0005] Tissue engineering technology: Combining stem cells and growth factors and using in vitro cultured tissues or cell-scaffold composites for repair.

[0006] Three-dimensional printing technology: Manufacturing personalized scaffolds through three-dimensional printing technology to adapt to the anatomical structure of the patient's oral cavity.

[0007] Although the existing technologies and methods have achieved certain results in oral soft tissue repair, there are still the following deficiencies: 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; 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 risks of mechanical mismatch or chemical erosion; Problem 3: Although current tissue engineering technology introduces 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; 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 masticatory dynamics and salivary secretion characteristics. In addition, postoperative monitoring methods are mostly offline detections, lacking real-time and continuous bio-signal feedback and unable to adjust treatment plans in a timely manner.

[0008] 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

[0009] Technical Problems to be Solved 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 solve the problems in the above background technology.

[0010] Technical Solutions

[0011] 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 the inner wall of the pore network is embedded with microfluidic channels. A chemical-sensitive microvalve is arranged in the microfluidic channels, and the chemical-sensitive microvalve is composed of a composite material of polyacrylate and diatomite. A self-assembled micro-biochip is integrated inside the bionic adaptive biological cell scaffold. 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 inside the micro-biochip. The multi-dimensional precision 3D printing system is configured to generate a scaffold model based on the oral biomechanics and tissue metabolism data of a patient. 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.

[0012] Preferably, the intelligent implantation and regulation device includes: A flexible bio-sensor network, composed of a polymer conductive fiber and a self-healing polymer layer. The polymer conductive fiber is 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. Repair enzymes are encapsulated inside the microcapsules. The bio-information collaborative regulation module is composed of a microfluidic chip, an autologous fibroblast storage bin, an RNA interference molecule storage bin, and an antibacterial polypeptide storage bin. The microfluidic chip is composed of a polydimethylsiloxane substrate and a multi-channel dispenser; The self-learning computing core is composed 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 micro-current 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 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-biological chip through the micro-current generator; The specific preparation process of the oral soft tissue repair device based on the biological cell scaffold is as follows: 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 fiber diameter of the matrix gradually changes from the inner layer to the outer layer; Integrate microfluidic channels and microvalves: Etch microfluidic channels in the matrix through laser micromachining technology, and deposit a polyacrylate and diatomite composite material on the inner wall of the channels to form a chemically sensitive microvalve; Integrate the micro-biological chip: In a sterile environment, place a porous silicon substrate inside the matrix through microdroplet ejection technology. Coat the surface of the substrate with a composite layer of poly(N-isopropylacrylamide) and an enzyme-responsive hydrogel, and fill growth factors, anti-inflammatory factors, and gene regulation molecules in the pores of the substrate; 3D printing and forming: Place the substrate in a multi-dimensional precision 3D printing system, and layer by layer print through the ceramic-based nozzle and microfluidic dispenser of the multi-material print head. The annular air flow generator and the temperature compensation unit maintain the stability of the printing environment, and the infrared emitter and the reflective fiber optic probe detect the chemical composition in real time; Assemble the sensor and regulation module: After the scaffold is formed, embed polymer conductive fibers and a self-healing polymer layer into the scaffold through electrospinning technology, connect the microfluidic chip, the autologous fibroblast storage bin, the RNA interference molecule storage bin, and the antibacterial polypeptide storage bin to the scaffold through ultrasonic welding technology, and assemble the fuzzy logic controller, the micro-current generator, and the data interaction interface to the scaffold through the conductive fiber connector.

[0013] Preferably, the bionic adaptive biological cell scaffold is internally embedded with a bionic microcapsule array. The microcapsule array is composed of a chitosan outer shell and a temperature-sensitive gel inner core. The temperature-sensitive gel inner core is composed of a composite material of polyethylene glycol and hyaluronic acid ester.

[0014] Preferably, the multi-dimensional precision 3D printing system includes a micro-vibration calibration device, which consists of a piezoelectric ceramic oscillator and a feedback sensor. The piezoelectric ceramic oscillator is made of lead zirconate titanate material, and the feedback sensor consists of a strain gauge and a signal amplifier. The surface of the flexible biosensor network is covered with a biomimetic adhesion coating, which is composed of a dopamine-modified hyaluronic acid and collagen fiber composite material.

[0015] Preferably, the self-learning computing core includes a micro heat dissipation unit, which consists of a graphene heat sink and a micro fan. The graphene heat sink is composed of a single-layer graphene and polymer substrate composite material.

[0016] A method for repairing oral soft tissues based on a biological cell scaffold includes the following steps: Sp1. Precise preoperative modeling: Obtain the three-dimensional structure and dynamic metabolic data of the patient's oral soft tissue defect through high-resolution cone beam CT and tissue metabolic imaging; Generate an adaptive scaffold model based on the three-dimensional structure and dynamic metabolic data of the patient's oral soft tissue defect by using finite element analysis and particle swarm optimization algorithm, 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 gingiva and mucosa areas, and generate a stress distribution map; Simulate the characteristics of saliva secretion: 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 scaffold surface, and generate a chemical distribution map; Optimize the pore network of the scaffold: 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 scaffold pore network; Use a multi-dimensional precision 3D printing system to print the scaffold through a multi-material print head and an adaptive air flow stabilization module; Sp2. Intelligent implantation and activation: Through minimally invasive robot-assisted surgery, implant the scaffold into the patient's oral soft tissue defect site under local anesthesia; Use the intraoperative optical navigation system to adjust the fit degree between the scaffold and the tissue interface; Activate the micro-biochip to release growth factors, anti-inflammatory factors, and gene regulatory molecules through temperature-controlled pulses and enzyme-triggered signals; Sp3. Postoperative dynamic regulation: Use the flexible biosensor network to collect multi-dimensional biological signals and transmit them to the self-learning computing core; Through the bio-information co-regulation module, use the microfluidic chip to adjust the release of fibroblasts and RNA interference molecules; In combination with a wearable oral monitoring device, data is transmitted to the doctor's end through a data interaction interface.

[0017] Preferably, in the preoperative precise modeling step, the finite element analysis calculates the strain distribution in the gingival area during the patient's chewing cycle through iterative calculations, and further adjusts the mechanical rigidity of the stent pore network.

[0018] 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 electrical signal generator.

[0019] Preferably, in the postoperative dynamic regulation step, the bio-information collaborative regulation module releases RNA interference molecules in layers through a multi-channel dispenser of a microfluidic chip.

[0020] Preferably, the method includes a data optimization step, and a fuzzy logic controller of a self-learning calculation core analyzes multi-patient data to adjust the release strategy of the micro-biochip.

[0021] The wearable oral monitoring device is a dental brace-type device for postoperative dynamic monitoring, specially designed to collect the patient's 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 the patient's oral soft tissues, helping doctors to timely understand the repair status and optimize the treatment strategy.

[0022] Device structure: The wearable oral monitoring device is composed of a flexible substrate, an embedded micro-sensor, a micro-battery, a data interaction interface, and a protective coating.

[0023] Flexible substrate: Made of medical silicone material through a molding process, it has good biocompatibility and flexibility. The substrate is designed in a dental brace structure, similar in shape to a clear dental brace, which can cover the maxillary or mandibular dentition of the patient, with a thickness of about 1 mm and a weight of no more than 10 g, ensuring a light and comfortable wearing experience.

[0024] Embedded micro-sensor: Includes 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, and collect environmental data in real time.

[0025] 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 and is 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.

[0026] Data Interaction Interface: It consists of a Bluetooth transmission module and an encryption chip. The Bluetooth transmission module uses the Bluetooth 5.0 protocol, supports low-power transmission, with a single transmission power consumption of less than 10 mW and a transmission distance of up to 10 meters, and 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 microcircuit board inside the substrate through surface mount technology and pin soldering.

[0027] Protection 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, protecting the internal sensors and circuits from saliva erosion, and at the same time improving the wearing comfort.

[0028] Monitoring Method: The wearable oral monitoring device monitors through the following two methods: Directly monitor the oral environment: The temperature sensor and humidity sensor embedded in the device are in direct contact with the oral soft tissue to continuously monitor the temperature and humidity changes in the oral environment. The temperature measurement range is 30°C to 40°C, the humidity measurement range is 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.

[0029] 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 the Bluetooth 5.0 transmission module, and receives the biosignal data collected by the flexible biosensor network, including the cell proliferation rate (detected by polymer conductive fibers), tissue oxygenation level (detected by embedded oxygen sensors), and inflammatory factor concentration (detected by chemical sensors). After the received biosignal data is integrated with the temperature and humidity data, it is encrypted by the 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, supporting doctors to view the data in real time and adjust the treatment plan. For example, adjust the anti-inflammatory factor release strategy according to the inflammatory factor concentration.

[0030] Wearing Method: The wearable oral monitoring device is designed in a dental brace structure, and the wearing method is similar to that of a clear aligner, and the patient can wear and remove it by themselves. The specific wearing steps are as follows: Preparation for wearing: 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 area with a medical alcohol cotton pad to maintain oral hygiene.

[0031] Wearing process: Align the device to the maxillary or mandibular dentition (select the wearing position according to the defective part of the patient, for example, wear it on the mandible when repairing the mandibular gums), and gently press to make the flexible base fit the teeth and gums. The medical silicone material on the inside of the device has a certain elasticity and can fit the dentition and soft tissues tightly, and there is no obvious foreign body sensation after wearing.

[0032] Wearing time: It is recommended that patients wear it for no less than 20 hours a day. They can continue to wear it when sleeping at night and only take it off when eating or brushing their teeth. When removing it, gently pull the edge of the device with your fingers and gradually remove it from one side of the dentition, avoiding excessive force.

[0033] Wearing adaptability: The device is light (no more than 10 grams), thin (1 mm), and covered with a protective coating that is resistant to saliva corrosion and will not cause discomfort or allergic reactions due to long-term wear. The braces-like design of the flexible base ensures stability and will not slip during wearing, making it suitable for long-term use.

[0034] Interaction with the system: The wearable oral monitoring device is wirelessly connected to the self-learning computing core in the intelligent implant and control device through the Bluetooth 5.0 transmission module. The biological signal data (cell proliferation rate, tissue oxygenation level, inflammatory factor concentration) collected by the flexible biosensor network is first transmitted to the self-learning computing core, which processes the data and sends it to the wearable oral monitoring device. The device integrates the biological signal data with the temperature and humidity data monitored by itself, encrypts it through the encryption chip, and transmits it to the doctor's end device once a minute. Doctors can view real-time data through mobile applications, 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.

[0035] Beneficial Effects

[0036] The present invention provides an oral soft tissue repair device and method based on a biological cell scaffold, which has the following beneficial effects: 1. The present invention integrates bionic adaptive biological cell scaffolds, multi-dimensional precision 3D printing systems and intelligent implantation and control devices, combined with finite element analysis and particle swarm optimization algorithms, to accurately simulate the chewing dynamics and saliva secretion characteristics of patients, effectively solving the problem of lack of dynamic adaptability of traditional scaffold static design. The optimized scaffold pore network 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 long-term stability of the scaffold.

[0037] 2. The present invention utilizes the fuzzy logic control technology of the self-learning computing core to collect and analyze multi-dimensional biological signals (such as cell proliferation rate, tissue oxygenation level, inflammatory factor concentration) in real time, and dynamically regulates the release strategy of the self-assembled micro-biochip, 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 response at the same time.

[0038] 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 accurate 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.

[0039] 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 lack of continuous feedback in traditional offline detection. 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, the release strategy is optimized through multi-patient data analysis, further improving the treatment effect of subsequent patients. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 is the specific flow chart of the present invention; Figure 2 is the overall device framework diagram of the present invention; Figure 3 is the stress distribution simulation diagram of the present invention; Figure 4 is the electron microscope diagram of the biological cell stent of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0041] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention. Specific Embodiment 1:

[0043] 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. A 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-layered helical pore network, and microfluidic channels are embedded in the inner wall of the pore network. A chemically sensitive microvalve is arranged in the microfluidic channel. The chemically sensitive microvalve is composed of a composite material of polyacrylate and diatomaceous earth. The chemically sensitive microvalve is formed by mixing polyacrylate and diatomaceous earth particles through a high-temperature sintering process and embedding them in the inner wall of the microfluidic channel. An autonomously assembling microbiochip is integrated inside the bionic adaptive biological cell scaffold. The autonomously assembling microbiochip 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 inside the autonomously assembling microbiochip. Bionic microcapsule arrays are embedded inside the bionic adaptive biological cell scaffold. The bionic microcapsule arrays are 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.

[0044] A 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 optical fiber probe. The infrared emitter is fixed above the printing platform through a bracket. The reflective optical fiber 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.

[0045] The intelligent implantation and regulation device includes: 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 by a solution spinning process using a mixture of polypyrrole and carbon nanotubes. 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 a dip coating process using a mixture 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 an impregnation process using a mixture of dopamine-modified hyaluronic acid and collagen fibers; A bio-information co-regulation module is composed 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 by a molding process. The multi-channel dispenser is assembled from micro valves and shunt pipes 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 to the side of the microfluidic chip by an ultrasonic welding process; 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 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 by pins. The microcurrent generator is composed of a signal modulation circuit and a conductive fiber connector. The signal modulation circuit is assembled from an oscillator and an amplifier and is fixed to the substrate of the microcurrent 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 by an adhesive process. The data interaction interface is composed of a Bluetooth transmission module and an encryption chip. The Bluetooth transmission module is fixed to the circuit board of the data interaction interface by a surface mount process. The encryption chip is connected to the Bluetooth transmission module by pin soldering. The self-learning computing core includes a micro heat dissipation unit. The micro heat dissipation 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 to the base of the micro heat dissipation unit by a hot pressing process. The micro fan is fixed above the graphene heat sink by screws.

[0046] The specific preparation process of the oral soft tissue repair device based on a biological cell scaffold is as follows: 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 electrospinning technology. The fiber diameter of the multi-layered helical pore network matrix gradually changes from the inner layer to the outer layer; 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; 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 micro-droplet 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; 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; 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 bin, RNA interference molecule storage bin, and antibacterial polypeptide storage bin 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.

[0047] A method for oral soft tissue repair based on a bio-cell scaffold, the method is applied to the above device, and includes the following steps: Step 1, preoperative precise modeling: 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; 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: Simulating 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 biting 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; Simulate salivary secretion characteristics: Through hydrodynamic simulation, simulate the salivary secretion rate and the change of pH value, analyze the chemical erosion effect of the saliva flow field on the surface of the scaffold, generate a chemical distribution map, and the chemical distribution map records the change of pH value and chemical composition on the surface of the scaffold; Optimize the pore network of the scaffold: 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 biological cell scaffold. The pore size distribution gradually increases from the edge of the scaffold to the center, and the connectivity is achieved by increasing the connection channels between the pores; Use a multi-dimensional precision three-dimensional printing system to print a bionic adaptive biological cell scaffold through a multi-material print head and an adaptive air flow stabilization module; Step 2: Intelligent implantation and activation: Through minimally invasive robotic-assisted surgery, implant the bionic adaptive biological cell scaffold into the oral soft tissue defect site of the patient under local anesthesia. The minimally invasive robot includes a force-electricity dual feedback device, which is composed 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; Use the intraoperative optical navigation system to adjust the fitting degree between the bionic adaptive biological cell scaffold and the tissue interface. The optical navigation system captures the position deviation between the scaffold and the tissue interface through an infrared camera and adjusts the position of the scaffold through a servo motor; 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; Step 3: Postoperative dynamic regulation: Use a flexible bio-sensor network to collect multi-dimensional biological signals and transmit them to the self-learning computing core. The multi-dimensional biological signals include cell proliferation rate, tissue oxygenation level and inflammatory factor concentration. The flexible bio-sensor 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 a polymer conductive fiber; The self-learning computing core analyzes the multi-dimensional biological signal data through a fuzzy logic controller. The fuzzy rule memory stores the corresponding relationship between the biological signals and the repair process. The membership degree calculation unit maps the data of the cell proliferation rate, tissue oxygenation level and inflammatory factor concentration into 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; According to the determination result of the repair process, the self-learning calculation core outputs a regulation signal to the self-assembled micro-biochip through the micro-current generator. The regulation signal is generated by the signal modulation circuit and transmitted through the conductive fiber connector. The regulation signal adjusts the release rate of the temperature-sensitive polymer coating in the self-assembled micro-biochip, preferentially releasing growth factors to promote early repair, releasing anti-inflammatory factors to control mid-term inflammation, and releasing gene regulation molecules to accelerate late-stage tissue regeneration; Through the bio-information collaborative regulation module, the 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 layer by layer according to the instructions of the self-learning calculation core, preferentially releasing them to the area with a high concentration of inflammatory factors; Combined with the wearable oral monitoring device, data is transmitted to the doctor's end through the data interaction interface. The wearable oral monitoring device receives the bio-signal data of the self-learning calculation core through the Bluetooth transmission module. 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.

[0048] Step 4, Data optimization: The fuzzy logic controller of the self-learning calculation core analyzes the data of multiple patients, adjusts the release strategy of the self-assembled micro-biochip. The fuzzy logic controller compares the corresponding relationships between the cell proliferation rate, tissue oxygenation level, and inflammatory factor concentration data of multiple patients with the repair process, updates the corresponding relationships in the fuzzy rule memory, and optimizes the release priority of the self-assembled micro-biochip according to the updated corresponding relationships. Specific embodiment two: As Figures 1 - 4 shown, the key algorithms mentioned in embodiment one are analyzed in detail below, including their core mathematical formulas and explanations: Finite element analysis (used to simulate patient chewing dynamics): Role in the solution: In step 1 (preoperative precise modeling), finite element analysis is used to simulate the chewing dynamics of patients. 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, providing a basis for the optimization of the stent pore network. Core principle: Finite element analysis discretizes the oral cavity model into a finite number of small elements (finite elements), and approximately calculates the stress distribution and strain distribution of the entire model under the action of chewing force by solving the mechanical equations for each element.

[0050] Mathematical formula: Finite element analysis is based on the principle of virtual work, and its core equilibrium equation is:

[0051] where : The volume of the oral cavity model, representing the analysis area; : The surface of the oral cavity model, the area that bears external forces; : Stress tensor components, representing the force per unit area at a certain point, where represents the direction (such as direction); : Strain tensor components, representing the deformation per unit length at a certain point, representing the virtual strain; : Stereomicroscope force, representing the force per unit volume (such as gravity), representing the direction; : Surface traction force, representing the external force per unit area (such as chewing bite force), representing the direction; : Displacement field, representing the displacement of a certain point in the model, representing the virtual displacement; : Volume integral, covering the entire model area; : Surface integral, covering the model boundary.

[0052] Discretization and solution process: Divide the oral cavity model into finite elements (such as tetrahedral elements). Within each element, the displacement field is approximated by the shape function: , where is the shape function, is the nodal displacement.

[0053] The stress-strain relationship follows Hooke's law: , where is the material stiffness tensor (such as the stiffness characteristics of gingival tissue).

[0054] 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 (such as the input bite force), is the displacement vector.

[0055] 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.

[0056] Application in the scheme: Input: The chewing frequency and bite force data of the patient.

[0057] Output: Stress distribution map, recording the stress values in the gingival and mucosal areas.

[0058] Utilization: The stress distribution map is used to guide the optimization of the pore network, ensuring that the scaffold can withstand chewing forces while matching the mechanical properties of the gingiva and mucosa.

[0059] Particle Swarm Optimization Algorithm (for optimizing the pore network of the scaffold): Role in the solution: 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, in order to balance mechanical adaptability and chemical compatibility.

[0060] 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 solution). Each particle updates its position and velocity based on its own historical optimal solution and the global optimal solution of the group.

[0061] The specific mathematical formulas are as follows: Velocity update formula:

[0062] Position update formula:

[0063] Where : 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 influence of individual cognition and social learning; : Random number, ranging from 0 to 1, introducing randomness; : The historical optimal position (individual optimal solution) found by the -th particle; : The global optimal position (global optimal solution) found by the entire particle swarm. Specific Example 3: As Figures 1 - 4 shown, the following is a specific application logic description of each step and algorithm in an oral soft tissue repair device and method based on a biocellular scaffold: Step 1: Preoperative precise modeling Objective: Generate an adaptive scaffold model based on 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 biocellular scaffold.

[0065] Sub-steps and algorithm application logic: Obtain the three-dimensional structure and dynamic metabolic data of the patient's oral soft tissue defect; 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, as well as the dynamic metabolic data, including tissue oxygenation level and metabolite concentration.

[0066] Simulate the patient's chewing dynamics Algorithm: Finite element analysis Application logic: Establish 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, which includes the geometric shapes and material properties of the gingiva, mucosa, and soft tissues, such as the hardness and elastic properties of the gingiva.

[0067] Divide the elements: Divide the three-dimensional mechanical model into many small elements, such as small tetrahedral blocks, each with its own mechanical properties.

[0068] Input data: Input the patient's chewing frequency (e.g., number of chews per minute) and bite force data (e.g., pressure of the teeth on the gingiva during chewing) as external conditions into the model.

[0069] Calculate the stress distribution: Through finite element analysis, calculate the force on each element of the model under the action of chewing force, and determine the stress distribution in the gingiva and mucosa regions. The specific process is: According to the input bite force, analyze how the force is transmitted in the model, calculate the deformation and force of each element, and finally generate a stress distribution map to record the pressure magnitude in the gingiva and mucosa regions.

[0070] Output: A stress distribution map showing the pressure values in the gingiva and mucosa regions, which is used to guide the subsequent design of the scaffold pore network.

[0071] Simulate the characteristics of saliva secretion: Algorithm: Computational fluid dynamics simulation Application logic: Establish 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 scaffold surface.

[0072] Input data: Input the patient's saliva secretion rate (e.g., volume of saliva secreted per minute) and changes in acid-base value (e.g., fluctuations in saliva pH).

[0073] Analytical Chemistry Impact: Through hydrodynamic simulation, analyze the chemical impact of saliva flow on the surface of the stent, such as how changes in pH value affect the stability of the stent material. The specific process is as follows: When simulating saliva flowing over the stent surface, determine how the pH value and chemical composition are distributed, and judge whether the surface of the stent will change due to the chemical properties of saliva. Finally, generate a chemical distribution map to record the changes in pH value and chemical composition on the stent surface.

[0074] Output: Chemical distribution map, which is used for the subsequent optimization of the stent pore network.

[0075] Optimize the Stent Pore Network: Algorithm: Particle Swarm Optimization Algorithm Application Logic: Initialize the particle swarm: Generate a set of candidate solutions, each solution is called a particle, representing a possible pore network configuration, such as the change in pore size from the edge to the center of the stent, and the number of connection channels between pores.

[0076] Set the goal: The goal is to make the mechanical properties (such as hardness) of the stent match the stress distribution of the gum and mucosa, and at the same time make the chemical properties (such as surface pH adaptability) of the stent compatible with the saliva environment. The optimization criteria are: the stress distribution of the stent is as close as possible to the tissue, and the chemical properties are as adaptable as possible to the saliva environment.

[0077] 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: 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.

[0078] After each adjustment, evaluate the advantages and disadvantages of the new configuration: Combine the stress distribution map to check whether the stent can withstand the chewing force; Combine the chemical distribution map to check whether the surface of the stent adapts to the pH value of saliva.

[0079] Iterate continuously until an optimal configuration is found, so that the stent can both withstand the chewing force and adapt to the saliva environment.

[0080] Output: Optimized pore network configuration, such as the pore size gradually increasing from the edge to the center, and more connection channels between pores to improve connectivity.

[0081] Print the Stent: 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 biological cell stent according to the optimized pore network configuration.

[0082] Step 2: Intelligent Implantation and Activation Objective: Implant a bionic adaptive biological cell scaffold into the oral soft tissue defect site of the patient and activate the self-assembling micro-biochip to release bioactive molecules.

[0083] Sub-step application logic: Implant the scaffold: Operation: Through minimally invasive robotic-assisted surgery, implant the bionic adaptive biological cell 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 robot's actions.

[0084] Adjust the fit between the scaffold and the tissue interface Operation: Use the intraoperative optical navigation system to capture the position deviation between the scaffold and the tissue interface through an infrared camera, and adjust the position of the scaffold through a servo motor to ensure that the scaffold fits tightly with the defect site.

[0085] Activate the self-assembling micro-biochip Operation: Send a temperature control pulse through an external heating device and inject an amylase solution to generate an enzyme trigger signal to activate the self-assembling micro-biochip to release growth factors, anti-inflammatory factors, and gene regulatory molecules.

[0086] Step 3: Postoperative dynamic regulation Objective: Monitor the patient's repair process in real time, dynamically regulate the release strategy of the self-assembling micro-biochip, and optimize the repair effect.

[0087] Sub-step application logic: Collect multi-dimensional biological signals Operation: Use a flexible biological 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).

[0088] Analyze biological signals and determine the repair stage Algorithm: Fuzzy logic control Application logic: Input data: Input the collected biological signals (cell proliferation rate, tissue oxygenation level, inflammatory factor concentration) into the self-learning calculation core.

[0089] 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.

[0090] Rule - based reasoning: Based on the rules preset in the fuzzy rule memory, determine the repair stage. For example, a rule might be: "If the cell proliferation rate is high and the concentration of inflammatory factors is low, then the repair stage is late." By calculating the degree of rule triggering, determine whether the repair stage is early, middle, or late.

[0091] Output: The repair stage (early, middle, late), which is used to guide the subsequent release strategy.

[0092] Regulating the release strategy of self - assembled micro - biochips: Algorithm: Fuzzy logic control Application logic: Determine the release strategy according to the repair stage: According to the repair stage judged by fuzzy logic control, determine the release priority of the self - assembled micro - biochips: If the repair stage is early, preferentially release growth factors to promote cell proliferation.

[0093] If the repair stage is middle, preferentially release anti - inflammatory factors to control inflammation.

[0094] If the repair stage is late, preferentially release gene regulatory molecules to accelerate tissue regeneration.

[0095] Generate control signals: Fuzzy logic control calculates the specific intensity of the control signals according to the release priority. The control signals are generated by a micro - current generator and transmitted to the self - assembled micro - biochips through a conductive fiber connector.

[0096] Adjust the release rate: The control signals act on the temperature - sensitive polymer coating of the self - assembled micro - biochips 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.

[0097] Layered release of RNA interference molecules: Operation: Through the bio - information collaborative regulation module, use the multi - channel dispenser of the microfluidic chip to layer - release RNA interference molecules according to the instructions of the self - learning calculation core, and preferentially release them to the regions with high concentrations of inflammatory factors.

[0098] Transmit data to the doctor's end: Operation: Combine with a wearable oral monitoring device, and through the Bluetooth transmission module of the data interaction interface, encrypt and transmit the biological signal data to the doctor's end device, and the doctor adjusts the treatment plan according to the data.

[0099] Step 4: Data optimization Objective: Through the analysis of multi - patient data, optimize the release strategy of the self - assembled micro - biochips to improve the treatment effect of subsequent patients.

[0100] Sub - steps and algorithm application logic: Analysis of multi-patient data Algorithm: Fuzzy logic control Application logic: Input data: Collect biometric data of multiple patients, including cell proliferation rate, tissue oxygenation level, and inflammatory factor concentration.

[0101] Comparison and update rules: 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 it is found that the new data does not match the existing rules (for example, patients with higher concentrations of certain inflammatory factors have a faster repair rate), then update the corresponding relationships in the rule memory.

[0102] Optimized release strategy: According to the updated rules, adjust the release priority of the self-assembled micro-biochips, such as the time point or release amount for preferentially releasing anti-inflammatory factors.

[0103] Application of optimization results: Operation: Apply the optimized release strategy to the treatment of subsequent patients to improve the repair effect. Specific embodiment four: As Figures 1 - 4 shown, the following is a specific use case: Use case 1: Repair of gingival recession in elderly patients Patient background: The patient is a 65-year-old elderly male who has developed gingival recession in the mandibular anterior tooth area due to long-term periodontitis. The gingival height has decreased by approximately 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).

[0105] Application process: Precise preoperative modeling: 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 long, 6 mm wide, and 3 mm deep; through tissue metabolic imaging, measure the tissue oxygenation level to be 0.7, and the metabolite concentration is normal.

[0106] Masticatory dynamics simulation: Using finite element analysis, establish a three-dimensional mechanical model of the patient's oral cavity, input the chewing frequency (40 times / minute) and biting force (80 Newtons), and calculate the stress distribution in the gingival area. The results show that the stress at the center of the defect area is about 60 Newtons, and the stress at the edge is about 30 Newtons.

[0107] Saliva secretion simulation: Through hydrodynamic simulation, simulate the changes in saliva secretion rate and pH value, and generate a chemical distribution map, showing that the pH value in the defect area fluctuates slightly and the chemical erosion effect is mild.

[0108] Pore network optimization: Combining the stress distribution map and the chemical distribution map, the particle swarm optimization algorithm is used to adjust the pore network of the bionic adaptive biological cell scaffold. The optimization results are as follows: the central pore diameter is set to 150 microns to enhance mechanical support, the edge pore diameter is 80 microns to promote mass exchange, and the connectivity is increased to 0.6 to improve saliva flow.

[0109] Scaffold printing: Use a multi-dimensional precision three-dimensional printing system to print the scaffold to ensure that the pore network conforms to the optimized design.

[0110] Intelligent implantation and activation: Implantation: Under local anesthesia, the scaffold is implanted into the defect area of the anterior mandibular teeth through minimally invasive robot-assisted surgery. The force-electricity dual-feedback device of the minimally invasive robot monitors the implantation pressure in real time to ensure precise operation.

[0111] Fitting adjustment: Using the intraoperative optical navigation system, the deviation between the scaffold and the tissue interface is captured by an infrared camera and adjusted to a fitting error of less than 0.1 mm.

[0112] Activation: A temperature control pulse is emitted through an external heating device, and an amylase solution is injected to activate the self-assembled micro-biochip to release growth factors, initially promoting cell proliferation.

[0113] Postoperative dynamic regulation: Bio-signal monitoring: In the first week after surgery, the flexible bio-sensor network detects that the cell proliferation rate is low (5 cells / minute), the tissue oxygenation level is 0.6, and the concentration of inflammatory factors is 0.4.

[0114] Repair stage judgment: The self-learning calculation core analyzes through a fuzzy logic controller and judges that the repair is in the early stage, and preferentially releases growth factors.

[0115] Release regulation: The micro-current generator outputs a control signal to adjust the release strategy of the self-assembled micro-biochip. The release intensity of growth factors is 0.8, and the release intensities of anti-inflammatory factors and gene regulation molecules are 0.1.

[0116] RNA interference molecule release: The bio-information co-regulation module releases RNA interference molecules in layers through a microfluidic chip, targeting the areas with a high concentration of inflammatory factors.

[0117] Data transmission: The bio-signal data is encrypted and transmitted to the doctor's end through a wearable oral monitoring device, and the doctor suggests continued observation based on the data.

[0118] Data optimization: Multi-patient data analysis: The postoperative data of 10 similar patients are collected, and it is found that patients with a high concentration of inflammatory factors have a slower repair speed.

[0119] 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.

[0120] Effect: Three months after the operation, the gingival height of the patient has recovered to the normal level, the inflammation has completely subsided, and the chewing function has been significantly improved.

[0121] Use case 2: Repair of traumatic soft tissue defect in a young patient Patient background: 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).

[0122] Application process: Precise pre-operative modeling: Data collection: Through high-resolution cone beam computed tomography, the defect size is confirmed; through tissue metabolic imaging, the tissue oxygenation level is measured to be 0.5, and the metabolite concentration is slightly high.

[0123] Masticatory dynamics simulation: Using finite element analysis, inputting the chewing frequency (70 times / minute) and biting force (120 Newtons), the stress distribution in the defect area is calculated. The central stress is about 90 Newtons, and the edge stress is about 40 Newtons.

[0124] Saliva secretion simulation: Fluid mechanics simulation shows that the saliva secretion rate is low, the pH value is acidic, and the chemical erosion effect in the defect area is obvious.

[0125] Pore network optimization: The particle swarm optimization algorithm adjusts the pore network. The central pore diameter is set to 180 microns to withstand high stress, the edge pore diameter is 90 microns, and the connectivity is increased to 0.7 to enhance saliva flow and chemical stability.

[0126] Scaffold printing: The multi-dimensional precision three-dimensional printing system prints the scaffold to ensure that the structure meets the optimized design.

[0127] Intelligent implantation and activation: Implantation: Through minimally invasive robot-assisted surgery, the scaffold is implanted into the defect area on the left side of the upper jaw. The force-electricity dual-feedback device ensures precise implantation.

[0128] Fitting adjustment: The intraoperative optical navigation system adjusts the fitting degree, and the error is controlled within 0.05 mm.

[0129] 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.

[0130] Postoperative dynamic regulation: Biological signal monitoring: In the first week after surgery, the cell proliferation rate is moderate (10 cells / minute), the tissue oxygenation level is 0.5, and the concentration of inflammatory factors is 0.6.

[0131] Repair stage judgment: The fuzzy logic controller determines that the repair is in the mid-stage, and anti-inflammatory factors are preferentially released.

[0132] Release regulation: The microcurrent 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.

[0133] Release of RNA interference molecules: The microfluidic chip releases RNA interference molecules in layers, targeting areas with high inflammation.

[0134] Data transmission: The doctor's end receives the data and recommends increasing the release frequency of anti-inflammatory factors.

[0135] Data optimization: Analysis of data from multiple patients: Analyzing the postoperative data of similar trauma patients, it is found that low oxygenation levels may delay repair.

[0136] Strategy adjustment: Update the fuzzy rules, increase the release ratio of growth factors, and promote the improvement of oxygenation levels.

[0137] Effect: Two months after surgery, the soft tissue defect of the patient is completely repaired, inflammation subsides, and masticatory function returns to normal.

[0138] Case 3: Repair of congenital soft tissue defects in pediatric patients Patient background: The patient is an 8-year-old child with congenital maxillary soft tissue defects. The defect area is 8 mm long, 5 mm wide, and 2 mm deep, with no obvious inflammation. The child's chewing frequency is moderate (about 50 times / minute), the biting force is weak (about 60 Newtons), and the saliva secretion is normal (1.2 ml / minute, pH value is 7.0).

[0139] Application process: Precise preoperative modeling: Data collection: High-resolution cone beam computed tomography confirms the defect size, and tissue metabolic imaging shows that the tissue oxygenation level is 0.8 and the metabolite concentration is normal.

[0140] Masticatory dynamics simulation: Finite element analysis calculates the stress distribution, with the central stress being about 50 Newtons and the edge stress being about 20 Newtons.

[0141] Saliva secretion simulation: Hydrodynamic simulation shows normal saliva secretion, uniform chemical distribution, and extremely low erosion effects.

[0142] Pore Network Optimization: The particle swarm optimization algorithm is used to adjust the pore network. The central pore diameter is 120 microns, the edge pore diameter is 70 microns, and the connectivity is 0.5.

[0143] Scaffold Printing: The scaffold is printed by a multi-dimensional precision three-dimensional printing system.

[0144] Intelligent Implantation and Activation: Implantation: The scaffold is implanted through minimally invasive robot-assisted surgery. The force-electricity dual-feedback device ensures gentle operation.

[0145] Fitting Adjustment: The optical navigation system adjusts the fitting degree with an error less than 0.08 mm.

[0146] Activation: The self-assembled micro-biochip is activated through temperature-controlled pulses and enzyme triggers to release growth factors.

[0147] Postoperative Dynamic Regulation: Bio-signal Monitoring: In the first week after surgery, the cell proliferation rate is relatively high (15 cells / minute), the tissue oxygenation level is 0.9, and the concentration of inflammatory factors is 0.2.

[0148] Repair Stage Judgment: The fuzzy logic controller determines that the repair is in the late stage, and gene regulatory molecules are preferentially released.

[0149] Release Regulation: The release intensity of gene regulatory molecules is 0.8, and the release intensities of growth factors and anti-inflammatory factors are 0.1.

[0150] RNA Interference Molecule Release: When the concentration of inflammatory factors is low, the release amount of RNA interference molecules is relatively small.

[0151] Data Transmission: The doctor confirms that the repair progress is good.

[0152] Data Optimization: Multi-patient Data Analysis: Analyzing the postoperative data of pediatric patients, it is found that the release of gene regulatory molecules can be advanced.

[0153] Strategy Adjustment: Update the fuzzy rules to shorten the release start time of gene regulatory molecules.

[0154] Effect: At 6 weeks after surgery, the soft tissue defect repair of pediatric patients is completed, with good tissue regeneration and no complications.

[0155] Summary: 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.

[0156] Appendix Figure 3The stress distribution diagram is part of the "preoperative precise modeling" step and is used to simulate the patient's chewing dynamics.

[0157] Among them Figure 3 The right vertical coordinate (color bar) represents the stress value. The stress distribution diagram is generated by finite element analysis to show the stress distribution in the gingiva and mucosa areas under the action of chewing force.

[0158] 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).

[0159] Range: The color bar shows that the stress value ranges from 30 Newtons (blue) to 100 Newtons (yellow).

[0160] 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.

[0161] Data source: Figure 3 It is the stress distribution diagram generated by finite element analysis.

[0162] Specific conditions: Patient type: Young patient (a 30-year-old female with a soft tissue defect on the left side of the upper jaw due to a car accident).

[0163] Input data: Chewing frequency is 70 times per minute, and biting force is 120 Newtons.

[0164] Defect area: Soft tissue defect on the left side of the upper jaw, 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).

[0165] A three-dimensional mechanical model of the patient's oral cavity is established through finite element analysis. The defect area is discretized into finite element meshes, and the chewing frequency and biting force data are input to calculate the stress distribution in the gingiva and mucosa areas.

[0166] Output data: The central stress is approximately 90 Newtons, and the marginal stress is approximately 40 Newtons, which is consistent with the central value of nearly 100 Newtons and the marginal value of nearly 30 Newtons shown in the image.

[0167] Figure 3 It is used to show the stress distribution in the soft tissue defect area on the left side of the upper jaw of young patients.

[0168] This stress distribution map guides the subsequent optimization of the stent pore network to ensure that the stent can withstand the higher chewing forces and stress distributions of young patients. For example, the central pore diameter is adjusted to 180 microns, and the edge pore diameter is 90 microns (based on the optimized data for young patients in the plan). Specific Embodiment Five: As Figures 1 - 4 shown, the following provides specific experimental data: Experimental Data Table 1: Preoperative Precise Modeling Data (Stress Distribution, Chemical Distribution, Pore Network Optimization) Patient type Mastication 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 Data Description: Chewing frequency and biting force: Reflect the patient's chewing habits and affect stress distribution.

[0170] Saliva secretion rate and pH value: Affect chemical distribution. Young patients have less saliva secretion and a more acidic pH value.

[0171] Central / edge stress: Calculated by finite element analysis. The central stress is higher, reflecting the area where chewing forces are concentrated.

[0172] Central / edge pH value: Calculated by hydrodynamic simulation, reflecting the chemical effect of saliva on the stent surface.

[0173] Optimized pore diameter and connectivity: Obtained through the particle swarm optimization algorithm. The central pore diameter is larger to withstand high stress, and the connectivity is adjusted to adapt to saliva flow.

[0174] Experimental Data Table 2: Postoperative Dynamic Regulation Data (Biological Signals and Release Strategies in the First Week after Surgery) 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 Data Description: Postoperative time: Data for the first week (day 7) after surgery.

[0175] Biological signals: Monitored through a flexible biosensor network. The cell proliferation rate, tissue oxygenation level, and inflammatory factor concentration reflect the repair process.

[0176] Repair stage: Judged by a fuzzy logic controller based on fuzzy rules of biological signals.

[0177] Release intensity: Adjusted according to the repair stage. Growth factors are preferentially released in the early stage, anti-inflammatory factors in the middle stage, and gene regulatory molecules in the late stage.

[0178] Experimental Data Table 3: Multi-Patient Data Analysis and Optimization Results

[0179] Data Description: Patient data: Simulate the postoperative data of 5 patients, including biological signals and average repair time.

[0180] Optimization suggestions: Analyze through the fuzzy logic controller of the self-learning calculation core: For patients with high inflammatory factor concentration (>0.5), it is recommended to increase the release intensity of anti-inflammatory factors.

[0181] For patients with fast repair speed (<60 days), it is recommended to advance the release of gene regulatory molecules.

[0182] Other patients maintain the existing strategy.

[0183] It should be noted that in this article, 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 term "comprising", "including" or any other variant thereof is 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 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.

[0184] 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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