Real-time monitoring and analyzing system for orthodontic correction of teeth

Abstract

The invention provides a real-time monitoring and analyzing system for tooth orthodontic correction, and relates to the technical field of oral treatment, the real-time monitoring and analyzing system comprises an information capturing system, an intelligent operation system and an appliance dynamic regulation and control system, the information capturing system comprises a three-dimensional oral cavity imager, a health condition evaluator, a multifunctional induction network and a portable monitoring device, the three-dimensional oral cavity imager generates a three-dimensional oral cavity image through triangulation positioning, extracts oral cavity mucous membrane features by combining an improved local binary pattern algorithm and adding an edge enhancement filter, and confirms the identity when the Euclidean distance is calculated to be smaller than a preset threshold value. According to the system, mucous membrane texture features are extracted through combination of the three-dimensional oral cavity imager and the improved LBP algorithm for identity verification, the health condition assessment instrument collects temperature, humidity and pH value every 6 minutes, the portable monitoring device collects pressure and biological signal data, and comprehensive collection of multi-dimensional data is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of oral treatment, and in particular to a real-time monitoring and analysis system for orthodontic treatment of teeth. Background Art

[0002] In the field of orthodontics, the development of treatment technology has evolved from simple manual operations to modern digital technology. In the early days, orthodontic treatment mainly relied on the doctor's clinical experience and basic tools. The doctor observed the patient's tooth arrangement with the naked eye, used simple tools such as calipers to measure the distance and angle between the teeth, and judged the type and severity of the deformity based on experience, and then manually designed the correction plan. Although this method laid the foundation for orthodontics, it lacked precision, and the treatment effect varied greatly due to individual differences among doctors.

[0003] According to a directional orthodontic correction method and a system having the same with Chinese patent number CN115770115A, the three-dimensional model of the first orthodontic tooth group and the second tooth group is obtained, wherein the second tooth group represents a tooth group adjacent to the first orthodontic tooth group in the correction direction, and the first orthodontic tooth group and the second tooth group each include at least one tooth: according to the correction direction of the first orthodontic tooth group, the force surface of the second tooth group away from the first orthodontic tooth group is determined; according to the correction direction and the force surface of the first orthodontic tooth group, the deformation of the three-dimensional model after correction is determined; based on the deformation of the three-dimensional model, the tooth model after orthodontic is established, and the translation thickening of the force surface of the teeth in the second tooth group is increased; the shapes of each tooth are fused and connected to form an output dentition model; the dentition model is output to the manufacturing device. The present invention not only takes into account the relative position relationship of the tooth arrangement, but also takes into account the force and movement direction of the teeth in orthodontics, so as to improve the correction efficiency and treatment effect of the invisible braces.

[0004] Although the existing technology can play the role of orthodontic correction, there are still the following problems in actual use: Problem 1: Existing technologies mainly rely on intraoral scanners or in vitro silicone rubber and plaster model scanning for data collection. The generated three-dimensional models are limited to the geometric shape of the teeth, ignoring other key information in the oral cavity, such as mucosal texture, environmental parameters (temperature, humidity, pH), and the patient's subjective comfort feedback. Mucosal texture can be used for identity recognition to prevent data confusion; environmental parameters such as low pH in the oral cavity may accelerate enamel demineralization and directly affect the treatment effect. Without these data, the comprehensiveness and accuracy of the treatment plan are limited; Problem 2: Although the existing technology takes into account the force relationship between teeth, the deformation is calculated based on a static mechanical model only, and the dynamic factors in the patient's daily life, such as chewing habits, oral activity intensity, etc., are not fully incorporated. These factors will cause the actual force field to deviate from the preset model, affecting the adaptability and treatment effect of the orthodontic appliance; Problem three: Existing technical solutions lack the ability to adjust dynamically after they are formulated. The tooth movement trajectory during the orthodontic process may deviate from expectations due to individual differences. Traditional methods cannot monitor the tooth status and force field distribution in real time, which may lead to excessive or uneven force values, causing periodontal damage or prolonging treatment time. Question 4: Existing technologies do not integrate patient comfort and pain feedback, and lack remote monitoring capabilities. The orthodontic treatment cycle is long, and the patient's subjective feelings directly affect compliance. If the discomfort is ignored, treatment participation may be reduced. In addition, patients need to return for frequent follow-up visits to adjust the orthodontic appliances, which increases time and financial costs. It is also difficult for doctors to grasp the progress of treatment in real time, delaying problem handling.

[0005] Therefore, a real-time monitoring and analysis system for orthodontic treatment is needed to solve the above problems. Summary of the invention

[0006] Technical issues solved In view of the deficiencies of the prior art, the present invention provides a real-time monitoring and analysis system for orthodontic correction, which solves the problems mentioned in the above background technology.

[0007] Technical Solution

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions: a real-time monitoring and analysis system for orthodontic treatment, including an information capture system, an intelligent computing system and a dynamic control system for an appliance; The information capture system includes a stereoscopic oral imager, a health status assessor, a multifunctional sensing network and a portable monitoring device. The stereoscopic oral imager generates a three-dimensional oral image through triangulation, combines an improved local binary pattern algorithm and adds an edge enhancement filter to extract oral mucosal features, and confirms the identity when the calculated Euclidean distance is less than a preset threshold; the health status assessor collects oral temperature, humidity and pH values, and records them every 6 minutes. The multifunctional sensing network contains 12 piezoelectric force sensors, 6 micro motion sensors and 10 strain detection sheets, which are respectively installed in the contact area, active point and deformation sensitive area of ​​the orthodontic appliance. The portable monitoring device contains an oral guard, an orthodontic pad and a lip sensor, and has built-in pressure, temperature and biosignal sensors to collect user oral data; The intelligent computing system includes a core computing unit and an edge reasoning unit. The core computing unit removes data noise and extracts tooth displacement and appliance force distribution data. The edge reasoning unit uses a group collaborative optimization method to adjust the appliance force field. 120 individuals are initialized, the force range is 0.4-2.2N, the inertia factor is reduced from 0.8 to 0.3, and the cycle is repeated 250 times. The dynamic control system of the orthodontic appliance includes an intelligent alloy drive unit, a micro power actuator unit and an embedded sensing network. The intelligent alloy drive unit adjusts the shape of the orthodontic appliance, the micro power actuator unit outputs speed, angle and duration signals, and the embedded sensing network contains 12 force sensors to monitor the status of the orthodontic appliance.

[0009] Preferably, the specific steps of the information capture system include: SpA1, data initialization acquisition: collect three-dimensional images through stereo oral imaging device, collect temperature, humidity and pH value through health status assessment instrument, collect mechanical data using multifunctional sensing network, and collect pressure and temperature data through portable monitoring device; SpA2, periodic dynamic update: temperature, humidity and pH data are collected repeatedly every 10 days; SpA3, outcome evaluation: Compare 3D images and mechanical data after treatment; The intelligent computing system optimizes the correction plan through multi-level analysis, and the specific steps include: SpB1, data preprocessing: the core computing unit removes the noise of the collected data and extracts the tooth displacement and appliance force distribution data; SpB2, in-depth analysis and optimization: The edge reasoning unit executes the group collaborative optimization method, initializes a preset number of individuals, the force range is based on the physiological tolerance of the teeth, the inertia factor is adjusted in a decreasing manner, the preset number of cycles are repeated, and the force field adjustment parameters are generated. 120 individuals are initialized, the force value is 0.4-2.2N, the inertia factor is reduced from 0.8 to 0.3, and the cycle is repeated 250 times; 28 days of tooth displacement and mechanical data are processed through multi-layer recursive neural calculations, 12 nodes are input, through 60-30-15 hidden layers, through Sigmoid activation, 4 nodes are output to predict the displacement trend, and the conditional autoencoding and adversarial optimization framework is used to generate the appliance adjustment strategy according to the prediction results; SpB3, advanced cloud computing: The edge inference unit uploads data to the cloud every 12 minutes, extracts similar case features through the cloud, and adjusts the parameters of the correction plan.

[0010] Preferably, the dynamic control system of the appliance realizes precise adjustment of the appliance, and the specific steps include: SpC1, morphology adaptive adjustment: the intelligent alloy drive unit receives the force field adjustment parameters of the intelligent computing system, adjusts the shape of the appliance through a multi-level collaborative optimization method, and the embedded sensing network contains 12 force sensors to detect the response of the teeth to the appliance; SpC2, dynamic optimization of force field: The micro-power execution unit generates control signals through multi-layer convolution prediction technology based on the prediction results of the edge reasoning unit. The first layer has 4×4 kernels, 4 layers of convolution, and 2 layers of 3×3 pooling. The output speed, angle and duration control the force field of the appliance. SpC3, Environmental response regulation: Biochemical sensors detect the concentration of periodontitis-related bacteria in the oral cavity, and adjust the force frequency to a preset range according to changes in concentration to ensure that the correction process adapts to the oral environment.

[0011] Preferably, the information capture system collects multi-dimensional orthodontic data to support correction analysis, the stereoscopic oral imager generates a three-dimensional model through point cloud optimization and surface reshaping, and records the initial position of the teeth; the health status assessor contains photoelectric and chemical sensors, which collect plaque concentration, gum health and tooth stability data to monitor oral health changes; the multifunctional sensing network is distributed in the contact area of ​​the orthodontic appliance to collect real-time mechanical data; the portable monitoring device records brushing behavior data through a motion tracker to analyze the user's oral hygiene habits.

[0012] Preferably, the information capture system and the intelligent computing system work together through efficient data transmission, and their composition includes: the information capture system transmits collected data via Bluetooth 5.1 at a rate of 2.5Mbps and a range of 12 meters. The data is encrypted with 192-bit AES to ensure security. After receiving the data, the intelligent computing system pushes the analysis results to the cloud every 12 minutes to adjust the parameters of the correction plan. The system contains an adaptive force field partition control component, which divides the orthodontic appliance into buccal, lingual and occlusal areas, applies independent force values ​​to each area, and the partition force field data is calculated by the edge reasoning unit.

[0013] Preferably, the system includes a real-time observation and interaction system, providing visual orthodontic process monitoring, and its specific composition includes a Wi-Fi6+ transmission component with a range of 35 meters; the virtual reality terminal displays 2560×1440 and also includes a vibration feedback device; the visualization terminal is a 32-inch screen with a brightness of 600cd / ㎡ and a contrast ratio of 1200:1; and the virtual scene is generated using two-way generation optimization technology.

[0014] Preferably, the system includes a treatment strategy optimization system to improve the long-term accuracy of the correction plan, specifically including: combining quantum search optimization, initially 1200K, rate 0.95, generating an optimization path, and using kernel regression analysis with a Gaussian kernel, γ=0.15, to analyze the correlation between historical data and current data, and then using time series convolution prediction to process data with a 144-hour window, predicting 2-4 month trends, and generating adjustment suggestions.

[0015] Preferably, the system includes a health protection and early warning system to monitor oral health risks in real time, including a health status assessor, a force sensor, a vibration indicator and an analysis core. Tooth stability data is collected by ultrasound every 50 minutes. The analysis core performs multi-objective collaborative optimization analysis and then calculates the risk level. The system integrates a multimodal health index assessment component, integrates temperature, humidity and pH data to generate a health index, and triggers a vibration indicator alarm.

[0016] Preferably, the system is provided with a remote coordination and data integration system to support doctors in remotely managing the orthodontic process, and its composition includes: cloud storage to save the full-cycle data collected by the information capture system, including three-dimensional images and mechanical data; the edge analysis device receives the analysis results of the intelligent computing system, optimizes the correction path through matrix decomposition and dispatches cloud resources, and generates remote adjustment instructions.

[0017] Preferably, the system contains a multidimensional information fusion and visualization system and a user interaction and experience improvement system to improve the transparency and comfort of the orthodontic process. Its composition includes: the multidimensional information fusion system processes three-dimensional images, mechanical data and health indexes through multi-level feature convolution technology to generate orthodontic reports; the user interaction system collects user feedback data through a touch platform, including pressure and temperature changes, and adjusts the correction plan in combination with dynamic Gaussian optimization and feature convolution learning.

[0018] Beneficial Effects

[0019] The present invention provides a real-time monitoring and analysis system for orthodontic treatment of teeth. It has the following beneficial effects: 1. This system uses a stereoscopic oral imaging device combined with an improved LBP algorithm to extract mucosal texture features for identity verification, a health status assessment device to collect temperature, humidity and pH values ​​every 6 minutes, and a portable monitoring device to collect pressure and biosignal data, thus achieving comprehensive collection of multi-dimensional data. Compared with technologies that rely solely on three-dimensional models, this system avoids data confusion through mucosal texture, monitors environmental parameters to promptly detect demineralization risks, and provides comfort feedback to optimize personalized solutions, making treatment more accurate and significantly improving correction results and patient compliance.

[0020] 2. The present invention realizes dynamic force field optimization and improves the adaptability of correction, solving the problem of force field deviation caused by the existing technology based on static mechanical models and not considering dynamic factors such as chewing habits. This system collects mechanical data in real time through a multifunctional sensing network, uses a group collaborative optimization algorithm to adjust the force field by the edge reasoning unit, and applies independent force values ​​to the adaptive force field partition control components respectively, breaking through the limitations of the static model, dynamically adapting to the influence of the patient's oral activities, improving the force field accuracy, reducing deviations, ensuring the matching degree of the orthodontic appliance with actual needs, and optimizing the treatment effect.

[0021] 3. The present invention enhances the real-time monitoring and dynamic adjustment capabilities, shortens the treatment cycle, improves the patient experience, reduces the burden of follow-up visits, and thus improves treatment compliance. In view of the shortcomings of the existing technology, such as lack of dynamic adjustment, inability to monitor in real time, and lack of integration of comfort feedback and remote monitoring, this system updates data every 10 days, pushes cloud results every 12 minutes, and adjusts mechanical parameters through intelligent alloys and micro-power actuators to form a closed-loop feedback to ensure force field balance, and the treatment cycle can be shortened. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a system framework diagram of the present invention; Figure 2 It is a diagram of the system operation steps of the present invention; Figure 3 It is a simulation diagram of the correction force change of the present invention; Figure 4 This is a simulation diagram of the change of the partition force value applied by the appliance over time during the orthodontic process of the present invention. DETAILED DESCRIPTION

[0023] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. Specific embodiment one:

[0025] like Figure 1-4 As shown, a real-time monitoring and analysis system for orthodontic treatment realizes accurate orthodontic treatment and real-time monitoring through the coordinated work of information capture system, intelligent computing system and appliance dynamic control system, and combines real-time observation and interaction system, treatment strategy optimization system, health protection and early warning system, remote coordination and data integration system, multi-dimensional information fusion and visualization system and user interaction and experience improvement system to provide comprehensive orthodontic management and user experience optimization. The following is a detailed working principle, covering the functions, equipment composition, parameter settings and their mutual connections of each system to ensure that every detail is clear and unambiguous.

[0026] The operation of the system starts with the multi-dimensional data collection of the information capture system. The information capture system includes a stereo oral imager, a health status assessor, a multifunctional sensing network and a portable monitoring device. The stereo oral imager is an optical three-dimensional scanning device, which consists of a blue LED light source, a high-resolution CCD camera and an optical projection lens group. The wavelength range of the light source is preferably 460-480 nanometers to ensure the penetration and safety of oral tissues. The resolution of the CCD camera is preferably more than 2 million pixels, and the projection lens group is responsible for generating structured light. When working, the imager emits structured light in a grid or stripe pattern to illuminate the inside of the mouth, and uses triangulation positioning technology to calculate the distance between the light reflection point and the camera, generate three-dimensional point cloud data, and form a three-dimensional image of the patient's teeth and oral mucosa. To ensure the accuracy of data attribution and avoid confusion of patient information, the imager has a built-in identity authentication module and uses an improved local binary pattern algorithm to extract oral mucosal texture features. The improvement is that an edge enhancement filter is added to enhance the details of the mucosal edge through high-pass filtering to generate a high-resolution feature vector, which is then compared with the patient features stored in the database and the Euclidean distance is calculated. The identity is confirmed when the distance is less than the preset threshold of 0.5-0.7. This threshold range balances the recognition accuracy and the false positive rate. The health status assessor is an integrated oral environment sensor device consisting of a temperature sensor, a humidity sensor, and a pH sensor. It is installed in a small probe and the probe is placed in the mouth near the gums. The temperature sensor is a thermistor type with a measurement range of 35-42 degrees Celsius and an accuracy of ±0.2 degrees Celsius. The humidity sensor is a capacitive type with a measurement range of 20-90% relative humidity and an accuracy of ±1.5% relative humidity. The pH sensor is a glass electrode type with a measurement range of 5-8pH and an accuracy of ±0.03pH. Data is recorded every 6 minutes. This frequency is based on the dynamic characteristics of oral environmental changes to ensure timely capture of health status fluctuations. The multifunctional sensing network consists of 12 piezoelectric force sensors, 6 micro motion sensors and 10 strain detection sheets. The piezoelectric force sensor uses piezoelectric ceramic material, has a measuring range of 0-5 Newtons and a sensitivity of 0.01 Newtons. The micro motion sensor is a MEMS accelerometer with a measuring range of ±2g and a sensitivity of 0.001g. The strain detection sheet is a metal foil strain gauge with a measuring range of 0-1000 microstrain and a sensitivity of 1 microstrain. These sensors are installed respectively in the contact area of ​​the orthodontic appliance, i.e. the buccal, lingual and occlusal surfaces where the teeth are in direct contact with the orthodontic appliance, the active point, i.e. the joint or driving part, and the deformation sensitive area, i.e. the beam structure that is easily deformed by force, to measure the force, motion acceleration and strain value of the orthodontic appliance, and provide real-time mechanical data.The portable monitoring device includes an oral belt, an orthodontic pad and a lip sensor. The oral belt is a ring-shaped silicone device worn near the gums, the orthodontic pad is a thin film pad attached to the inside of the orthodontic appliance, and the lip sensor is a small patch placed on the inside of the lip. It has built-in pressure sensors, temperature sensors and biosignal sensors. The pressure sensor is a thin film piezoresistive type with a measurement range of 0-100 kPa and a sensitivity of 0.1 kPa. The temperature sensor is a thermistor type consistent with the health status assessment instrument. The biosignal sensor is an electrode type that measures saliva conductivity with a range of 0.1-10 millisiemens per centimeter. It collects pressure, temperature and biosignal data when the user wears it to reflect comfort and physiological status. These devices transmit data to the intelligent computing system through the built-in Bluetooth 5.1 module. The Bluetooth 5.1 rate is 2.5 megabits per second and the range is 12 meters. 192-bit AES encryption is used to ensure transmission security.

[0027] After receiving the data, the intelligent computing system processes it through the core computing unit and the edge reasoning unit. The core computing unit is an embedded microprocessor, preferably an ARMCortex-A series, with a main frequency of 1.5 GHz or above, including a digital signal processing module, which is responsible for data preprocessing, using a median filter algorithm, with a window size of 5 points, to remove random noise from the collected data, extract the tooth displacement, i.e. the three-dimensional coordinate difference, in millimeters, and the force distribution data of the appliance, i.e. the force value matrix of each sensor point, in Newtons. The edge reasoning unit is an edge computing device, the model is preferably NVIDIA Jetson Nano, with GPU acceleration, and the group collaborative optimization method is used to adjust the force field of the orthodontic appliance. 120 individuals are initialized. This number is based on computing resources and convergence optimization. The literature supports the orthodontic optimization of 100-150 individuals, with a force range of 0.4-2.2 Newtons. According to the physiological tolerance range of the teeth, the inertia factor decreases from 0.8 to 0.3, and the linear decrease ensures the balance between global search and local convergence. The cycle is 250 times to ensure the optimization accuracy, and the force field adjustment parameters, i.e., the target force values ​​of each region, are generated. Subsequently, the edge reasoning unit processes the tooth displacement and mechanical data for 28 days through multi-layer recursive neural calculations. The network structure is an input of 12 nodes, corresponding to multi-dimensional input data such as displacement, force value, and angle. After three hidden layers of 60-30-15, the number of nodes decreases step by step to extract high-order features. The Sigmoid activation function is used, and the output range of 0-1 is suitable for normalized prediction. The output of 4 nodes represents the x, y, and z displacement and rotation angles to predict future displacement trends. Next, the conditional autoencoder and adversarial optimization framework is used. The conditional autoencoder maps the input data into latent variables, and the dimension is preferably 8 dimensions. The generator is a multi-layer fully connected network that generates adjustment strategies. The discriminator is a convolutional network that optimizes the authenticity of the strategy, and finally generates the appliance adjustment instructions. Every 12 minutes, based on the real-time requirements of orthodontics, the edge reasoning unit uploads data to the cloud through the Wi-Fi module. The Wi-Fi module supports Wi-Fi6+ with a rate of 1.5 gigabits per second. The cloud storage is a distributed server with a storage capacity of more than 1 terabyte. The data is saved. The edge analysis device is a high-performance server with a multi-core CPU. It extracts similar case features such as the displacement patterns of historical patients, adjusts the program parameters and provides feedback.

[0028] The dynamic control system of the appliance is adjusted according to the instructions of the intelligent computing system. The intelligent alloy drive unit is composed of a nickel-titanium shape memory alloy sheet and a micro heating circuit. The thickness of the alloy sheet is 0.5 mm, the deformation temperature is 50-70 degrees Celsius, the heating circuit power is 5 watts, the receiving force field adjustment parameters, the alloy morphology is changed by current heating, and the shape of the appliance is adjusted. The micro power execution unit contains a micro stepper motor and a drive circuit. The motor diameter is 5 mm and the torque is 0.1 Newton meters. The drive circuit adopts PWM control. According to the prediction results of the edge reasoning unit, the control signal is generated through multi-layer convolution prediction technology. The convolution network structure is the first layer of 4×4 kernel to capture local features, 4 layers of convolution to extract multi-scale features, 2 layers of 3×3 pooling to reduce dimensions and retain key information, GPU accelerated calculation, output speed 50-200 rpm, angle 0-90 degrees and duration 1-4 seconds, to control the appliance force field. The embedded sensing network is consistent with the multi-functional sensing network, containing 12 piezoelectric force sensors distributed in the key areas of the appliance to monitor the state of the appliance and feedback to the intelligent computing system. The biochemical sensor is an electrochemical microelectrode, which contains a reference electrode and a sensitive membrane. - CFU per milliliter, detects the concentration of periodontitis-related bacteria such as Porphyromonas gingivalis, and changes according to the concentration above Each milliliter of CFU is considered a risk, and the force application frequency is adjusted to 5-10 times per minute, and the motor PWM signal control is used to ensure the safety of the correction process.

[0029] The system supports analysis through multi-dimensional data. The stereoscopic oral imaging device uses point cloud optimization to remove discrete points and reshape the surface, that is, triangular mesh fitting to generate a three-dimensional model, record the initial position of the teeth, and the photoelectric sensor of the health status assessor is a photodiode with a wavelength of 650 nanometers, and the chemical sensor is based on the potential difference. It collects plaque concentration in milligrams per square centimeter, gum health, that is, bleeding index 0-3, and tooth stability data, that is, vibration amplitude in micrometers. The multi-functional sensing network provides real-time mechanical data. The motion tracker of the portable monitoring device is a combination of a three-axis accelerometer and a gyroscope with a sampling rate of 100 Hz, which records the frequency and duration of brushing behavior. The intelligent computing system contains an adaptive force field partition control component, which is a software module running on the edge reasoning unit. It divides the appliance into buccal, lingual and occlusal areas, calculates partition force field data such as 0.8 Newton on the buccal side and 1.2 Newton on the lingual side, and applies force separately through the micro-power execution unit. The real-time observation and interaction system includes a Wi-Fi6+ transmission component as a router module, a virtual reality terminal as a head-mounted display device with a 2560×1440 OLED screen and an eccentric motor vibration feedback device, and a visualization terminal as a 32-inch LCD screen with a brightness of 600cd per square meter and a contrast ratio of 1200:1. It displays the displacement trend and simulates the touch. The two-way generation optimization technology includes a generator and a discriminator network to generate virtual scenes. The quantum search optimization of the treatment strategy optimization system is a software algorithm with an initial temperature of 1200K and a rate of 0.95. It searches for the optimal path based on quantum fluctuations. The kernel regression analysis uses a Gaussian kernel with γ=0.15 to calculate data correlation. The time series convolution prediction predicts 2-4 months trends with a 144-hour window. The health protection and early warning system uses ultrasonic detection including a transmitter and a receiver with a frequency of 20-40 kHz, a multimodal health index assessment component as an algorithm module to fuse data to generate a 0-100 index, and a vibration reminder as a micro vibration motor to monitor risks. The remote coordination and data integration system optimizes the path and generates instructions. The multi-dimensional information fusion and visualization system processes data through multi-level feature convolution technology to generate reports, and the touch platform of the user interaction and experience improvement system collects feedback and adjusts the plan for the capacitive screen. Specific embodiment 2:

[0031] like Figure 1-4 As shown, the following are specific use cases of the above solution:

[0032] In order to protect the privacy of patients, pseudonyms are used. At the same time, three groups of cases are provided in the table, and the three groups of cases are for different situations. The specific descriptions are as follows: Use Case 1: Precision Correction of Teenagers’ Malaligned Teeth Xiao Ming, 15 years old, went to see an orthodontist because of uneven teeth (such as protruding upper front teeth). The doctor decided to use the system for correction, with the goal of gradually adjusting the position of the teeth while monitoring the correction effect and oral health in real time.

[0033] Operation process: Initial data collection: The doctor scanned Xiao Ming's oral cavity using a stereoscopic oral imaging device of the information capture system. The blue LED light source (wavelength 460-480 nanometers) emitted structured light, and the CCD camera (2 million pixels) captured the reflected light, generating a three-dimensional image through triangulation. The improved LBP algorithm (including edge enhancement filter) extracted mucosal features, calculated the Euclidean distance (less than 0.5-0.7) to confirm the identity, and the data was stored in the cloud storage.

[0034] The small probe of the health status assessor was placed near Xiao Ming's gums. The temperature sensor (accuracy ±0.2 degrees Celsius), humidity sensor (accuracy ±1.5% relative humidity) and pH sensor (accuracy ±0.03pH) collected initial oral environment data, recording a temperature of 36.8 degrees Celsius, a humidity of 85% and a pH of 6.8.

[0035] A multifunctional sensing network of 12 piezoelectric force sensors (measuring range 0-5 Newtons), 6 MEMS accelerometers, and 10 strain gauges were installed on the custom appliance to collect initial mechanical data, such as a buccal force of 0.5 Newtons.

[0036] Xiao Ming wears the oral guard and lip sensor of the portable monitoring device. The pressure sensor (range 0-100 kPa) records the wearing pressure of 10 kPa, and the temperature sensor and biosignal sensor (saliva conductivity 0.5 millisiemens per centimeter) collect comfort data.

[0037] Intelligent calculation and correction plan generation: The data is transmitted to the intelligent computing system via Bluetooth 5.1. The core computing unit uses a median filter algorithm to remove noise and extract the amount of tooth displacement and force field distribution.

[0038] The edge inference unit (NVIDIA Jetson Nano) runs the swarm collaborative optimization method, initializes 120 individuals, with a force range of 0.4-2.2 Newtons, and loops 250 times to generate initial force field adjustment parameters (such as 1.0 Newton in the upper incisor area). Multi-layer recursive neural computing (12 input nodes, 60-30-15 hidden layers, sigmoid activation) processes 28 days of data samples and predicts the displacement trend (0.2 mm on the x-axis). The conditional autoencoder and adversarial optimization framework generates adjustment strategies and outputs the orthodontic appliance parameters.

[0039] Dynamic adjustment of the appliance: The nickel-titanium alloy sheet (deformation temperature 50-70 degrees Celsius) of the intelligent alloy drive unit adjusts its shape according to the parameters, and the stepper motor (torque 0.1 Newton-meter) of the micro-power actuator outputs a speed of 100 revolutions per minute, an angle of 30 degrees, and a duration of 2 seconds through multi-layer convolution prediction technology (4 layers of convolution, 4×4 cores in the first layer) to apply a force field. The 12 force sensors of the embedded sensing network provide real-time feedback.

[0040] Periodic monitoring and updating: Temperature, humidity, and pH data are collected repeatedly every 10 days, the stereoscopic oral imager updates the 3D image, and the edge inference unit uploads data to the cloud every 12 minutes to adjust the plan to ensure that the displacement does not exceed 0.6 mm per month.

[0041] Expected results: After 6 months of treatment, Xiao Ming's upper incisors moved back 1.2 mm and were arranged neatly. The health status assessment device showed that the oral environment was stable (pH 6.9), and the visual terminal (32-inch LCD screen) showed the shift trend. The doctor confirmed that the treatment was successful.

[0042] Use Case 2: Health Monitoring and Treatment of Adult Periodontitis Patients Ms. Li, 35 years old, suffers from mild periodontitis, with slightly loose and misaligned teeth. The doctor uses the system to correct her teeth and monitor her periodontal health in real time to prevent her condition from worsening.

[0043] Operation process: Initial Assessment and Data Collection: The stereoscopic oral imaging device generates a three-dimensional oral image of Ms. Li, and the identity verification module confirms her identity. The health status assessment device records the initial data: temperature 37.0 degrees Celsius, humidity 80%, pH 6.5 (slightly acidic, indicating the risk of periodontitis).

[0044] A multifunctional sensor network measured mechanical data from the appliance, with force sensors showing 0.3 Newtons of force on loose areas of teeth and accelerometers detecting slight vibrations. A portable monitoring device recorded a pressure of 8 kilopascals and a saliva conductivity of 0.8 milliSiemens per centimeter, indicating an inflammatory response.

[0045] Health risk analysis and program adjustment: The core computing unit extracts the displacement and force field distribution, and the edge reasoning unit runs group collaborative optimization (force value 0.4-2.2 Newton). Due to periodontitis, the force value is reduced to 0.6 Newton. The multimodal health index assessment component integrates temperature, humidity, and pH data to generate a health index of 55 (below the threshold of 60), triggering the vibration reminder (vibration motor, frequency 150 Hz) alarm.

[0046] Biochemical sensors (electrochemical microelectrodes, detection range - CFU per milliliter) to detect the concentration of periodontitis-related bacteria CFU per milliliter, the edge reasoning unit generates a strategy to adjust the force application frequency to 5 times per minute.

[0047] Correction and real-time monitoring: The intelligent alloy drive unit adjusts the shape of the appliance, and the micro-power actuator outputs a speed of 80 revolutions per minute for 1 second, applying a low force field. The ultrasonic detection module (frequency 20-40 kHz) of the health protection and early warning system collects tooth stability data every 50 minutes, analyzes the core and calculates the risk level, prompting doctors to pay attention.

[0048] Data is uploaded to the cloud via Wi-Fi6+, and doctors adjust plans through remote coordination and data integration systems.

[0049] Expected results: After 3 months, Ms. Li's teeth shifted 0.4 mm, the health index rose to 70, and the bacterial concentration dropped to CFU per milliliter, periodontitis improvement, appliance force field stability, and virtual reality terminal (2560×1440) display of health trends.

[0050] Use Case 3: Remote Orthodontic Management and User Experience Optimization Mr. Zhang, 28 years old, is busy with work and cannot go to the hospital for follow-up visits frequently. The doctor remotely manages his orthodontic process through this system. Mr. Zhang is concerned about wearing comfort.

[0051] Operation process: Initial setup and feedback collection: Initial data collection was completed in the clinic, with a stereo oral imager generating three-dimensional images, a multifunctional sensing network recording force fields, a health status assessor collecting environmental data, and a portable monitoring device recording a pressure of 12 kPa and a temperature of 37.1 degrees Celsius.

[0052] After returning home, Mr. Zhang input feedback through the touch platform, reporting that the braces were slightly tight, and the pressure sensor data confirmed that the local pressure was 15 kPa.

[0053] Remote analysis and adjustment: Data is transmitted to the edge inference unit via Bluetooth 5.1, and group collaborative optimization generates a partitioned force field (0.8 Newton on the buccal side and 1.0 Newton on the lingual side). The user interaction and experience improvement system runs dynamic Gaussian optimization and feature convolution learning to analyze pressure changes and recommends reducing the buccal force value to 0.6 Newton.

[0054] The edge analysis device (32-core CPU) optimizes the path through matrix decomposition, the cloud storage (1 terabyte) saves the data, generates remote adjustment instructions, and updates every 12 minutes.

[0055] Real-time interaction and verification: The doctor uses the virtual reality terminal of the real-time observation and interaction system to view the 3D model, and the vibration feedback device simulates the touch to confirm the adjustment effect. The micro-power execution unit executes the new parameters, with a speed of 50 revolutions per minute and an angle of 20 degrees.

[0056] Mr. Zhang checked the displacement trend through the visual terminal and confirmed the improvement in comfort through the touch platform.

[0057] Expected results: After 2 months, Mr. Zhang's teeth shifted 0.3 mm and the pressure dropped to 10 kPa. The doctor remotely confirmed the improvement in bite, and the system records showed that the comfort score increased from 6 to 8 (out of 10).

[0058] Case 1 demonstrates precise correction, emphasizing the coordination of algorithms and hardware; Case 2 highlights health monitoring and reflects the ability to manage periodontitis; Case 3 focuses on remote management and user experience, demonstrating the flexibility of the system. The application of these cases fully utilizes the hardware in this technical solution, including stereo oral imagers, biochemical sensors, group collaborative optimization, and recursive neural computing algorithms to achieve orthodontic goals.

[0059] The following is a display of the experimental data of remotely managed comfortable orthodontic optimization:

[0060] It can be seen from the experimental data that the patient's tooth gap is 1.8 mm. The system uses the portable monitoring device and touch platform feedback, and the edge reasoning unit optimizes the partition force field (0.6 Newton on the buccal side). The task was completed in 2 months, and the comfort level was significantly improved.

[0061] Attached Figure 4 The graph shows how the force applied by the appliance in the buccal, lingual and occlusal areas changes over time during orthodontic treatment. The graph shows the dynamic changes in the force in the three areas within 28 days: the buccal side (blue line) decreases from 1.0 Newton to 0.5 Newton, the lingual side (red line) decreases from 0.8 Newton to 0.4 Newton, and the occlusal area (yellow line) decreases from 0.6 Newton to 0.3 Newton. The three curves show a linear downward trend, reflecting that the system gradually reduces the force through dynamic regulation, embodying the ability of partitioned force field control and real-time adjustment. Specific embodiment three:

[0063] like Figure 1-4 As shown, the key algorithms mentioned in the above embodiments are analyzed in detail below, including their core mathematical formulas and explanations: Group collaborative optimization method: The force field adjustment of the edge reasoning unit in the intelligent computing system and the morphology adjustment of the intelligent alloy drive unit in the dynamic control system of the orthodontic appliance can optimize the force field distribution and morphology adjustment parameters of the orthodontic appliance.

[0064] In the edge reasoning unit, the group collaborative optimization method adjusts the force field of the orthodontic appliance and initializes 120 individuals with a force range of 0.4-2.2 Newtons. Here, the inertia factor is reduced from 0.8 to 0.3 according to the physiological tolerance of the teeth. The cycle is repeated 250 times to ensure accuracy and generate force field adjustment parameters.

[0065] In the dynamic control system of the orthodontic appliance, the intelligent alloy drive unit adjusts the shape of the orthodontic appliance through a multi-level collaborative optimization method, receives force field adjustment parameters, and optimizes the alloy deformation path.

[0066] The specific mathematical formula is as follows: Group collaborative optimization method: Speed ​​update formula:

[0067] Position update formula:

[0068] Objective function (for correction force optimization):

[0069] :No. The velocity vector of the particle in The value at the iteration, in Newtons per second, indicates the speed and direction of particle movement. :No. The velocity vector of the particle in The updated value at the iteration, Inertia factor, controls the historical influence of speed, range 0.8-0.3, linearly decreasing (e.g. ), in this scheme, 0.8 is reduced to 0.3, : Individual learning factor, which controls the weight of the particle learning to its own optimal position, : Global learning factor, which controls the weight of the particle learning to the global optimal position, : Random number, range [0,1], introduces randomness to avoid local convergence. No. The historical best position of a particle, in Newtons, represents the optimal force value searched by the individual. The global best position, in Newtons, represents the optimal force value searched by all particles. :No. The current position of the particle in Newtons, representing the current force field parameters, :No. The updated position of each particle, :No. The target force value for each region, in Newtons, : Maximum allowable force value, in Newtons, set to 2.2 Newtons. : indicator function, if If yes, it is 1, otherwise it is 0. .

[0070] Multi-layer convolution prediction technology: It is used to dynamically optimize the force field of the micro-power actuator in the dynamic control system of the orthodontic appliance to generate the control signal of the orthodontic appliance force field.

[0071] The micro-power execution unit uses multi-layer convolution prediction technology to process data based on the prediction results of the edge inference unit. The network structure is that the first layer 4×4 kernel captures local features, 4 layers of convolution extract multi-scale features, 2 layers of 3×3 pooling reduces dimension and retains key information, GPU accelerates calculation, outputs speed (50-200 revolutions per minute), angle (0-90 degrees) and duration (1-6 seconds), and controls the force field of the orthodontic appliance.

[0072] The specific formula is as follows: Convolutional layer calculation:

[0073] Pooling layer calculation (max pooling):

[0074] Output layer calculation:

[0075] in :No. Layer feature map at position The activation value of Activation function, :No. Layer convolution kernel weights, first layer 4×4 kernel, dimension 4×4, :No. The value of the layer input feature map, No. Layer bias value, Pooling area, The pooling stride is set to 1, Output vector, dimension 3, representing speed, angle, and duration. The output layer weight matrix, the dimension depends on the size of the feature map of the previous layer, The last layer of convolution or pooling output, Output layer bias vector. Specific embodiment four: like Figure 1-4 As shown, the following are the detailed hardware components and hardware descriptions in each system: The information capture system is responsible for the collection of multi-dimensional data. Its hardware components include a stereoscopic oral imager, a health status assessor, a multifunctional sensing network and a portable monitoring device. The stereoscopic oral imager is an optical three-dimensional scanning device consisting of a blue LED light source, a high-resolution CCD camera and an optical projection lens group. The blue LED light source emits structured light in the wavelength range of 460-480 nanometers, and this range is preferably selected to ensure the penetration and safety of oral tissues. The power is about 5 watts and provides uniform illumination. The resolution of the high-resolution CCD camera is preferably above 2 million pixels, and the frame rate is 30 frames per second. It captures reflected light to generate high-precision image data. The optical projection lens group consists of micro lenses and prisms, which are responsible for generating structured light in a grid or stripe pattern, calculating the distance between the light reflection point and the camera through triangulation positioning technology, generating three-dimensional point cloud data, and forming a three-dimensional image of the teeth and oral mucosa. To achieve identity authentication, the imager has a built-in identity authentication module, which includes a small storage chip to store the patient feature database and a microprocessor to run an improved local binary pattern algorithm. The health status assessor is an integrated oral environment sensor device, which consists of a temperature sensor, a humidity sensor and a pH sensor. It is installed in a small probe. The probe shell is made of medical silicone, with a diameter of about 5 mm and a length of 10 mm. It is placed in the mouth near the gums. The temperature sensor is a thermistor type with a measurement range of 35-42 degrees Celsius, an accuracy of ±0.2 degrees Celsius, and a response time of less than 1 second. It collects oral temperature data. The humidity sensor is a capacitive type with a measurement range of 20-90% relative humidity and an accuracy of ±1.5% relative humidity. It detects changes in oral humidity. The pH sensor is a glass electrode type, containing a reference electrode and a sensitive membrane, with a measurement range of 5-8pH and an accuracy of ±0.03pH. It detects saliva pH and records every 6 minutes. The frequency is based on the rate of change of the oral environment. The multifunctional sensing network consists of 12 piezoelectric force sensors, 6 micro motion sensors and 10 strain detection sheets. The piezoelectric force sensor uses piezoelectric ceramic material, with a diameter of 3 mm, a thickness of 1 mm, a measurement range of 0-5 Newtons, and a sensitivity of 0.01 Newtons. It is installed in the contact area of ​​the appliance (buccal, lingual, and occlusal surfaces) to measure the force. The micro motion sensor is a MEMS accelerometer with a size of 2×2×1 mm, a measurement range of ±2g, and a sensitivity of 0.001g. It is installed at the active point of the appliance (joints and drive parts) to detect motion acceleration. The strain detection sheet is a metal foil strain gauge with a size of 5×2 mm, a measurement range of 0-1000 microstrain, and a sensitivity of 1 microstrain. It is installed in the deformation sensitive area to monitor the strain value. These sensors are connected to the micro data acquisition module through a flexible circuit board. The portable monitoring device includes an oral guard, an orthodontic pad, and a lip sensor.The oral guard is a ring-shaped silicone device with an inner diameter of about 30 mm, which is worn near the gums. The orthodontic pad is a thin film pad with a thickness of 0.5 mm, which is attached to the inside of the orthodontic appliance. The lip sensor is a small patch with a size of 10×5 mm, which is placed on the inside of the lip. All three have built-in pressure sensors, temperature sensors and biosignal sensors. The pressure sensor is a thin film piezoresistive type with a measurement range of 0-100 kPa and a sensitivity of 0.1 kPa, which detects the wearing pressure. The temperature sensor is a thermistor type, which is consistent with the health status assessor. The biosignal sensor is an electrode type, containing silver chloride electrodes, which measures the conductivity of saliva, with a range of 0.1-10 millisiemens per centimeter, reflecting the physiological state. All devices are integrated with Bluetooth 5.1 modules, and the chip model is preferably nRF52832, with a rate of 2.5 megabits per second, a range of 12 meters, support for 192-bit AES encryption, and transmit data to the intelligent computing system.

[0077] The intelligent computing system is responsible for data processing and solution optimization. Its hardware components include a core computing unit and an edge reasoning unit. The core computing unit is an embedded microprocessor, model ARMCortex-A series, with a main frequency of more than 1.5 GHz, including a digital signal processing module, 512MB of memory, 4GB of flash memory, running the median filter algorithm to remove noise, extract the amount of tooth displacement and the force distribution data of the orthodontic appliance. The edge reasoning unit is an edge computing device, the model is preferably NVIDIAJetsonNano, with a 4-core ARMCortex-A57CPU and a 128-core MaxwellGPU, a main frequency of 1.43 GHz, 4GB of memory, 16GBeMMC storage, running group collaborative optimization methods, multi-layer recursive neural computing and conditional autoencoding and adversarial optimization framework, optimizing force fields and generating adjustment strategies. The edge reasoning unit uploads data to the cloud through the Wi-Fi module. The Wi-Fi module supports the Wi-Fi6+ protocol, with a rate of 1.5 Gbps, a built-in antenna gain of 5dBi, and a coverage range of 35 meters.

[0078] The dynamic control system of the appliance realizes the adjustment of the appliance. Its hardware components include an intelligent alloy drive unit, a micro-power actuator unit and an embedded sensing network. The intelligent alloy drive unit consists of a nickel-titanium shape memory alloy sheet and a micro-heating circuit. The alloy sheet has a size of 10×5 mm, a thickness of 0.5 mm, a deformation temperature of 50-70 degrees Celsius, and has thermal deformation characteristics. The micro-heating circuit contains a resistor wire and a current controller with a power of 5 watts and a current range of 0-1 ampere. The heating alloy sheet adjusts the shape of the appliance. The micro-power actuator contains a micro-stepping motor and a drive circuit. The motor has a diameter of 5 mm, a torque of 0.1 Newton-meter, and a speed range of 50-200 revolutions per minute. The drive circuit uses a PWM controller. The chip model is preferably DRV8833. The output speed, angle between 0-90 degrees and a time period of 1-6 seconds are used to control the force field of the appliance. The embedded sensing network is consistent with the multi-functional sensing network, containing 12 piezoelectric force sensors, which are integrated into the appliance to monitor the status and provide feedback. The biochemical sensor is an electrochemical microelectrode with a size of 3×2 mm, including a reference electrode and a sensitive membrane. The detection range is - CFU per milliliter, detects the concentration of periodontitis-related bacteria, and outputs electrical signals to the drive circuit to adjust the force application frequency.

[0079] The real-time observation and interaction system provides visual monitoring, and its hardware components include Wi-Fi6+ transmission components, virtual reality terminals, and visualization terminals. The Wi-Fi6+ transmission component is a router module, and the model preferably supports the 802.11ax protocol, with a rate of 1.5 gigabits per second, a range of 35 meters, and a dual-band antenna (2.4GHz and 5GHz) to transmit data to the doctor's end. The virtual reality terminal is a head-mounted display device with a 2560×1440 OLED screen, a refresh rate of 90Hz, a field of view of 110 degrees, a built-in eccentric motor vibration feedback device, a power of 0.5 watts, and simulates touch according to force field data. The visualization terminal is a 32-inch LCD screen with a brightness of 600cd per square meter, a contrast ratio of 1200:1, a resolution of 1920×1080, and displays displacement trends.

[0080] The treatment strategy optimization system improves the accuracy of the plan. Its hardware relies on edge inference units and cloud devices to run algorithms. There is no independent hardware. The GPU of the edge inference unit accelerates quantum search optimization, kernel regression analysis, and time series convolution prediction.

[0081] The health protection and early warning system monitors health risks. Its hardware includes a health status assessor, force sensors, vibration reminders, and analysis cores. The health status assessor is consistent with the information capture system. The force sensors are 12 piezoelectric force sensors, which are shared with a multifunctional sensing network. The vibration reminder is a micro vibration motor with a diameter of 4 mm, a power of 0.3 watts, and a vibration frequency of 100-200 Hz. It alarms according to the health index. The analysis core is a microcontroller, preferably STM32F4, with a main frequency of 168MHz, including a floating-point unit, running multi-objective collaborative optimization analysis and health index evaluation. The ultrasonic detection module contains a transmitter and a receiver. The transmitter is a piezoelectric transducer with a frequency of 20-40 kHz and a power of 1 watt. The receiver is a high-sensitivity microphone that collects tooth stability data.

[0082] The remote coordination and data integration system supports remote management, and its hardware includes cloud storage and edge analysis devices. Cloud storage is a distributed server cluster, including multiple high-performance servers, each with a 16-core CPU, 64GB memory, and 1 terabyte SSD to store full-cycle data. The edge analysis device is a high-performance server with a 32-core CPU, 128GB memory, and 4TB hard disk, running a matrix decomposition algorithm to optimize paths and schedule resources.

[0083] The multi-dimensional information fusion and visualization system processes multi-source data. Its hardware relies on the GPU of the edge inference unit to run multi-level feature convolution technology and outputs reports to the 32-inch LCD screen of the visualization terminal.

[0084] The user interaction and experience improvement system optimizes the user experience. Its hardware includes a touch platform with a 10-inch capacitive screen with a resolution of 1280×800 and a multi-touch sensor to collect feedback data. The edge inference unit runs dynamic Gaussian optimization and feature convolution learning adjustment schemes.

[0085] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "comprising a reference structure" do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0086] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A real-time monitoring and analysis system for orthodontic treatment, characterized in that: Including information capture system, intelligent computing system and appliance dynamic control system; The information capture system includes a stereoscopic oral imager, a health status assessor, a multifunctional sensing network and a portable monitoring device. The stereoscopic oral imager generates a three-dimensional oral image through triangulation, combines an improved local binary pattern algorithm and adds an edge enhancement filter to extract oral mucosal features, and confirms the identity when the calculated Euclidean distance is less than a preset threshold; the health status assessor collects oral temperature, humidity and pH value, the multifunctional sensing network contains 12 piezoelectric force sensors, 6 micro motion sensors and 10 strain detection sheets, which are respectively installed in the contact area, active point and deformation sensitive area of ​​the orthodontic appliance; the portable monitoring device contains an oral protective band, an orthodontic pad and a lip sensor, and has built-in pressure, temperature and biosignal sensors to collect user oral data; The intelligent computing system includes a core computing unit and an edge reasoning unit. The core computing unit removes data noise and extracts tooth displacement and appliance force distribution data. The edge reasoning unit uses a group collaborative optimization method to adjust the appliance force field. The dynamic control system of the orthodontic appliance includes an intelligent alloy drive unit, a micro power actuator unit and an embedded sensing network. The intelligent alloy drive unit adjusts the shape of the orthodontic appliance, the micro power actuator unit outputs speed, angle and duration signals, and the embedded sensing network contains 12 force sensors to monitor the status of the orthodontic appliance.

2. A real-time monitoring and analysis system for orthodontic correction according to claim 1, characterized in that: The specific steps of the information capture system include: SpA1, data initialization acquisition: collect three-dimensional images through stereo oral imaging device, collect temperature, humidity and pH value through health status assessment instrument, collect mechanical data using multifunctional sensing network, and collect pressure and temperature data through portable monitoring device; SpA2, periodic dynamic update: repeat the collection of temperature, humidity and pH data every 10 days; SpA3, outcome evaluation: Compare 3D images and mechanical data after treatment; The intelligent computing system optimizes the correction plan through multi-level analysis, and the specific steps include: SpB1, data preprocessing: the core computing unit removes the noise of the collected data and extracts the tooth displacement and appliance force distribution data; SpB2, in-depth analysis and optimization: The edge reasoning unit executes the group collaborative optimization method, initializes a preset number of individuals, the force range is based on the physiological tolerance of the teeth, the inertia factor is adjusted in a decreasing manner, the preset number of cycles are repeated, and the force field adjustment parameters are generated. The 28-day tooth displacement and mechanical data are processed through multi-layer recursive neural calculations, 12 nodes are input, through 60-30-15 hidden layers, through Sigmoid activation, 4 nodes are output to predict the displacement trend, and the conditional autoencoding and adversarial optimization framework is used to generate the appliance adjustment strategy according to the prediction results; SpB3, advanced cloud computing: The edge inference unit uploads data to the cloud every 12 minutes, extracts similar case features through the cloud, and adjusts the parameters of the correction plan.

3. A real-time monitoring and analysis system for orthodontic correction according to claim 1, characterized in that: The dynamic control system of the appliance realizes precise adjustment of the appliance, and the specific steps include: SpC1, morphology adaptive adjustment: the intelligent alloy drive unit receives the force field adjustment parameters of the intelligent computing system, adjusts the shape of the appliance through a multi-level collaborative optimization method, and the embedded sensing network contains 12 force sensors to detect the response of the teeth to the appliance; SpC2, dynamic optimization of force field: The micro-power execution unit generates control signals through multi-layer convolution prediction technology based on the prediction results of the edge reasoning unit. The first layer has 4×4 kernels, 4 layers of convolution, and 2 layers of 3×3 pooling. The output speed, angle and duration control the force field of the appliance. SpC3, Environmental response regulation: Biochemical sensors detect the concentration of periodontitis-related bacteria in the oral cavity, and adjust the force frequency to a preset range according to changes in concentration to ensure that the correction process adapts to the oral environment.

4. A real-time monitoring and analysis system for orthodontic correction according to claim 1, characterized in that: The information capture system collects multi-dimensional orthodontic data to support correction analysis, and the stereoscopic oral imager generates a three-dimensional model through point cloud optimization and surface reshaping to record the initial position of the teeth; The health status assessor contains photoelectric and chemical sensors to collect data on plaque concentration, gum health and tooth stability to monitor changes in oral health; a multifunctional sensing network is distributed in the contact area of ​​the orthodontic appliance to collect real-time mechanical data; a portable monitoring device records brushing behavior data through a motion tracker to analyze the user's oral hygiene habits.

5. The real-time monitoring and analysis system for orthodontic correction according to claim 1, characterized in that: The information capture system and the intelligent computing system work together through efficient data transmission, and their composition includes: the information capture system collects data through Bluetooth 5.1 transmission, with a rate of 2.5Mbps and a range of 12 meters. The data is encrypted with 192-bit AES to ensure security. After receiving the data, the intelligent computing system pushes the analysis results to the cloud every 12 minutes to adjust the parameters of the correction plan. The system contains an adaptive force field partition control component, which divides the orthodontic appliance into buccal, lingual and occlusal areas, applies independent force values ​​to each area, and the edge reasoning unit calculates the partition force field data.

6. A real-time monitoring and analysis system for orthodontic correction according to claim 1, characterized in that: The system includes a real-time observation and interaction system, providing visual orthodontic process monitoring. The specific components include a Wi-Fi6+ transmission component with a range of 35 meters; the virtual reality terminal displays 2560×1440 and also includes a vibration feedback device; the visualization terminal is a 32-inch screen with a brightness of 600cd / ㎡ and a contrast ratio of 1200:1; and two-way generation optimization technology is used to generate virtual scenes.

7. A real-time monitoring and analysis system for orthodontic correction according to claim 1, characterized in that: The system includes a treatment strategy optimization system to improve the long-term accuracy of the correction plan, specifically including: combining quantum search optimization, initially 1200K, rate 0.95, to generate an optimization path, and at the same time using kernel regression analysis with a Gaussian kernel, γ=0.15, to analyze the correlation between historical data and current data, and then using time series convolution prediction to process data with a 144-hour window, predict 2-4 month trends, and generate adjustment suggestions.

8. A real-time monitoring and analysis system for orthodontic correction according to claim 1, characterized in that: The system includes a health protection and early warning system to monitor oral health risks in real time, including a health status assessor, a force sensor, a vibration indicator and an analysis core. The tooth stability data is collected through ultrasound every 50 minutes. The analysis core performs multi-objective collaborative optimization analysis and then calculates the risk level. The system integrates a multimodal health index assessment component, integrates temperature, humidity and pH data to generate a health index, and triggers the vibration indicator to alarm.

9. A real-time monitoring and analysis system for orthodontic correction according to claim 1, characterized in that: The system is equipped with a remote coordination and data integration system to support doctors in remotely managing the orthodontic process. Its composition includes: cloud storage to save the full-cycle data collected by the information capture system, including three-dimensional images and mechanical data; the edge analysis device receives the analysis results of the intelligent computing system, optimizes the correction path through matrix decomposition and dispatches cloud resources to generate remote adjustment instructions.

10. A real-time monitoring and analysis system for orthodontic correction according to claim 1, characterized in that: The system includes a multi-dimensional information fusion and visualization system and a user interaction and experience improvement system to improve the transparency and comfort of the orthodontic process. Its composition includes: the multi-dimensional information fusion system processes three-dimensional images, mechanical data and health indexes through multi-level feature convolution technology to generate orthodontic reports; the user interaction system collects user feedback data through a touch platform, including pressure and temperature changes, and adjusts the correction plan in combination with dynamic Gaussian optimization and feature convolution learning.

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