A real-time monitoring and analysis system for orthodontic treatment

Abstract

The present invention provides a real-time monitoring and analysis system for orthodontic treatment of teeth, which relates to the technical field of oral treatment. It includes an information capture system, an intelligent computing system, and an orthodontic appliance dynamic regulation system. The information capture system includes a three-dimensional oral imager, a health status evaluator, a multi-functional sensing network, and a portable monitoring device. The three-dimensional oral imager generates a three-dimensional oral image through triangulation positioning, combines the improved local binary pattern algorithm and adds an edge enhancement filter to extract the oral mucosa features, and confirms the identity when the Euclidean distance is less than a preset threshold. This system extracts the mucosal texture features through the three-dimensional oral imager combined with the improved LBP algorithm for identity verification. The health status evaluator collects temperature, humidity, and pH value every 6 minutes, and the portable monitoring device collects pressure and biometric signal data, realizing the comprehensive collection of multi-dimensional data.

Description

Technical Field

[0001] The present invention relates to the technical field of oral treatment, and particularly 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 techniques has evolved from simple manual operations to modern digital technologies. In the early days, orthodontic treatment mainly relied on doctors' clinical experience and basic tools. Doctors observed the arrangement of patients' teeth with the naked eye, measured the tooth spacing and angles using simple tools such as calipers, and judged the type and severity of deformities based on experience, and then manually designed the treatment 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 treatment method and a system having the same with Chinese patent number CN115770115A, it includes: obtaining three-dimensional models of a first orthodontic tooth group and a second tooth group, wherein the second tooth group represents a tooth group adjacent to the first orthodontic tooth group in the orthodontic direction, and the first orthodontic tooth group and the second tooth group each include at least one tooth; determining a force-receiving surface of the second tooth group deviating from the first orthodontic tooth group according to the orthodontic direction of the first orthodontic tooth group; determining the deformation amount of the three-dimensional model after orthodontic treatment according to the orthodontic direction and the force-receiving surface of the first orthodontic tooth group; establishing a tooth model after orthodontics based on the deformation amount of the three-dimensional model, and increasing the translational thickening of the force-receiving surface of the teeth in the second tooth group; fusing and connecting the outer shapes of each tooth to form an output dentition model; outputting the dentition model to a manufacturing device. The present invention not only considers the mutual positional relationship of tooth arrangement, but also considers the force and movement direction of teeth during orthodontics, improving the treatment efficiency and treatment effect of invisible orthodontic appliances.

[0004] Although the prior art can play a role in orthodontic treatment, there are still the following problems in actual use:

[0005] Problem 1: The prior art mainly relies on intraoral scanners or extraoral silicone rubber and plaster model scanning during data collection. The generated three-dimensional model is 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 subjective comfort feedback of the patient. Mucosal texture can be used for identity recognition to prevent data confusion; environmental parameters such as a low pH in the oral cavity may accelerate enamel demineralization, directly affecting the treatment effect. The lack of these data limits the comprehensiveness and accuracy of the treatment plan;

[0006] Problem 2: Although the existing technology takes into account the force relationship between teeth, the deformation is calculated based only on the static mechanical model, 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;

[0007] 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.

[0008] 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.

[0009] Therefore, a real-time monitoring and analysis system for orthodontic treatment is needed to solve the above problems. Summary of the invention

[0010] Technical issues solved

[0011] 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.

[0012] Technical Solution

[0013] 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;

[0014] 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;

[0015] The intelligent operation system includes a core operation unit and an edge inference unit. The core operation unit removes data noise and extracts tooth displacement and orthodontic appliance force distribution data. The edge inference unit adjusts the orthodontic appliance force field using a group collaborative optimization method; initialize 120 individuals, with a force value range of 0.4 - 2.2 N, the inertia factor decreasing from 0.8 to 0.3, and loop 250 times;

[0016] The orthodontic appliance dynamic regulation system includes a smart alloy drive unit, a micro power execution unit, and an embedded induction network. The smart alloy drive unit adjusts the shape of the orthodontic appliance. The micro power execution unit outputs rotation speed, angle, and duration signals. The embedded induction network contains 12 force sensors to monitor the state of the orthodontic appliance.

[0017] Preferably, the specific steps of the information capture system include:

[0018] SpA1. Data initialization and collection: Collect three-dimensional images through a stereoscopic oral imager, collect temperature, humidity, and pH values through a health status evaluator, collect mechanical data using a multi-functional induction network, and collect pressure and temperature data through a portable monitoring device;

[0019] SpA2. Periodic dynamic update: Repeat collecting temperature, humidity, and pH value data every 10 days;

[0020] SpA3. Result effectiveness evaluation: Compare the three-dimensional images and mechanical data after treatment;

[0021] The intelligent operation system optimizes the orthodontic treatment plan through multi-level analysis. The specific steps include:

[0022] SpB1. Data preprocessing: The core operation unit removes the noise of the collected data and extracts tooth displacement and orthodontic appliance force distribution data;

[0023] SpB2. In-depth analysis and optimization: The edge inference unit executes the group collaborative optimization method, initializes a preset number of individuals, the force value range is based on the physiological tolerance of the teeth, the inertia factor is adjusted decreasingly, and loops a preset number of times to generate force field adjustment parameters. Initialize 120 individuals, with a force value of 0.4 - 2.2 N, the inertia factor decreasing from 0.8 to 0.3, and loop 250 times; Process the tooth displacement and mechanical data for 28 days through multi-layer recursive neural computing, input 12 nodes, pass through 60 - 30 - 15 hidden layers, activate through Sigmoid, output 4-node predicted displacement trends, and adopt a conditional auto-encoding and adversarial optimization framework to generate an orthodontic appliance adjustment strategy according to the prediction results;

[0024] SpB3. Cloud advanced operation: The edge inference unit uploads data to the cloud every 12 minutes, and extracts similar case features through the cloud and adjusts the orthodontic treatment plan parameters.

[0025] Preferably, the orthodontic appliance dynamic regulation system realizes precise adjustment of the orthodontic appliance. The specific steps include:

[0026] SpC1, Morphological Adaptive Adjustment: The intelligent alloy drive unit receives the force field adjustment parameters of the intelligent operation system, adjusts the morphology of the orthodontic appliance through a multi-level collaborative optimization method, and the embedded induction network containing 12 force sensors detects the response of the teeth to the orthodontic appliance;

[0027] SpC2, Force Field Dynamic Optimization: The micro power execution unit generates a control signal according to the prediction result of the edge reasoning unit through a multi-layer convolution prediction technology. The first layer has a 4×4 kernel, 4 layers of convolution, and 2 layers of 3×3 pooling, and outputs the rotation speed, angle, and duration to control the force field of the orthodontic appliance;

[0028] SpC3, Environmental Response Regulation: The biochemical sensor detects the concentration of periodontitis-related bacteria in the oral cavity, and adjusts the force application frequency to the preset range according to the concentration change to ensure that the orthodontic process adapts to the oral environment.

[0029] Preferably, the information capture system collects multi-dimensional orthodontic data to support orthodontic analysis. The three-dimensional 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 assessment instrument contains optoelectronic and chemical sensors, collects plaque concentration, gum health, and tooth stability data, and monitors changes in oral health; the multi-functional induction 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.

[0030] Preferably, the information capture system and the intelligent operation system work together through high-efficiency data transmission. Its composition includes: the information capture system transmits the collected data through Bluetooth 5.1, with a rate of 2.5 Mbps and a range of 12 meters. The data is encrypted by 192-bit AES to ensure security. After receiving the data, the intelligent operation system pushes the analysis results to the cloud every 12 minutes to adjust the orthodontic treatment plan parameters. The system contains an adaptive force field partition control component, which divides the orthodontic appliance into the buccal side, lingual side, and occlusal area, and applies independent force values respectively. The edge reasoning unit calculates the partition force field data.

[0031] Preferably, the system includes a real-time observation and interaction system, which provides visual monitoring of the orthodontic process. The specific composition includes a Wi-Fi6+ transmission component with a range of 35 meters; the virtual reality terminal has a display of 2560×1440 and also includes a vibration feedback device; the visualization terminal is a 32-inch screen with a brightness of 600 cd / ㎡ and a contrast ratio of 1200:1; a two-way generation optimization technology is used to generate a virtual scene.

[0032] Preferably, the system includes a treatment strategy optimization system to improve the long-term accuracy of the orthodontic treatment plan, specifically including: combining quantum search optimization, starting at 1200K initially with a rate of 0.95 to generate an optimized path. At the same time, through kernel regression analysis using a Gaussian kernel with γ = 0.15, analyze the correlation between historical data and current data. Then, use temporal convolutional prediction to process the data with a 144-hour window, predict the trend for 2 - 4 months, and generate adjustment suggestions.

[0033] Preferably, the system includes a health guarantee and early warning system to monitor oral health risks in real time, including a health status assessment instrument, a force sensor, a vibration reminder, and an analysis core. The tooth stability data is collected by ultrasound every 50 minutes, and the analysis core performs multi-objective collaborative optimization analysis. Then, calculate the risk level. The system integrates a multi-modal health index assessment component, which integrates temperature, humidity, and pH value data to generate a health index and trigger the vibration reminder to alarm.

[0034] Preferably, a remote coordination and data integration system is set in the system to support doctors to remotely manage the orthodontic process. Its composition includes: cloud storage saves 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 operation system, optimizes the orthodontic path through matrix decomposition and schedules cloud resources to generate remote adjustment instructions.

[0035] Preferably, 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 an orthodontic report. The user interaction system collects user feedback data through a touch platform, including pressure and temperature changes, and combines dynamic Gaussian optimization and feature convolution learning to adjust the orthodontic treatment plan.

[0036] Beneficial effects

[0037] The present invention provides a real-time monitoring and analysis system for dental orthodontic treatment. It has the following beneficial effects:

[0038] 1. This system realizes the comprehensive collection of multi-dimensional data through a three-dimensional oral imager combined with an improved LBP algorithm to extract mucosal texture features for identity verification, a health status assessment instrument to collect temperature, humidity, and pH value every 6 minutes, and a portable monitoring device to collect pressure and biological signal data. Compared with the technology that only relies on three-dimensional models, this system avoids data confusion through mucosal texture, timely discovers the risk of demineralization through environmental parameter monitoring, and optimizes personalized plans through comfort feedback, making the treatment more accurate and significantly improving the orthodontic effect and patient compliance.

[0039] 2. The present invention realizes the optimization of the dynamic force field and improves the orthodontic adaptability, solving the problem of force field deviation in the prior art based on static mechanical models without considering dynamic factors such as chewing habits. Through the multi-functional induction network, the system collects mechanical data in real time. The edge inference unit adjusts the force field using the group collaborative optimization algorithm, and the adaptive force field partition control component applies independent force values respectively, breaking through the limitation of the static model, dynamically adapting to the influence of the patient's oral activities, improving the force field accuracy, reducing the deviation, ensuring the matching degree between the orthodontic appliance and the actual needs, and optimizing the treatment effect.

[0040] 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 the treatment compliance. Aiming at the deficiencies of the prior art in lacking dynamic adjustment, being unable to monitor in real time, and not integrating comfort feedback and remote monitoring, the system updates data every 10 days, pushes cloud results every 12 minutes, and the smart alloy and micro power execution unit adjust the mechanical parameters to form a closed-loop feedback, ensuring the balance of the force field and shortening the treatment cycle. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 is the system framework diagram of the present invention;

[0042] Figure 2 is the system operation step diagram of the present invention;

[0043] Figure 3 is the simulation diagram of the change of the orthodontic force of the present invention;

[0044] Figure 4 is the simulation diagram of the change of the partition force value applied by the orthodontic appliance over time during the orthodontic treatment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0045] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of 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:

[0047] As Figures 1-4As shown in the figure, a real-time monitoring and analysis system for orthodontic treatment realizes precise orthodontic treatment and real-time monitoring through the collaborative work of an information capture system, an intelligent operation system, and an orthodontic appliance dynamic regulation system. It also combines a real-time observation and interaction system, a treatment strategy optimization system, a health guarantee and early warning system, a remote coordination and data integration system, a multi-dimensional information fusion and visualization system, and a user interaction and experience improvement system to provide comprehensive orthodontic management and optimized user experience. The following is the detailed working principle, covering the functions, equipment composition, parameter settings, and their interrelationships of each system, ensuring that every detail is clear and explicit.

[0048] The system operation starts with multi-dimensional data collection from the information capture system. The information capture system includes a three-dimensional oral imager, a health status assessment device, a multi-functional sensing network, and a portable monitoring device. The three-dimensional 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 wavelength range of the light source is preferably 460 - 480 nanometers to ensure the penetrability and safety for oral tissues. The resolution of the CCD camera is preferably above 2 million pixels. The projection lens group is responsible for generating structured light. During operation, the imager emits structured light in the form of a grid or stripe pattern to irradiate the interior of the oral cavity, and uses triangulation technology to calculate the distance between the light reflection point and the camera, generating three-dimensional point cloud data and forming 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 is built-in with an authentication module, which uses an improved local binary pattern algorithm to extract the texture features of the oral mucosa. The improvement lies in adding an edge enhancement filter, which enhances the edge details of the mucosa through high-pass filtering, generating a high-resolution feature vector. Subsequently, it is compared with the patient features stored in the database, and the Euclidean distance is calculated. When the distance is less than the preset threshold of 0.5 - 0.7, the identity is confirmed. This threshold range balances the recognition accuracy and the false positive rate. The health status assessment device is an integrated oral environment sensor device, consisting of a temperature sensor, a humidity sensor, and a pH sensor, which are installed in a small probe. The probe is placed in the oral cavity near the gums. The temperature sensor is of the thermistor type, with a measurement range of 35 - 42 degrees Celsius and an accuracy of ±0.2 degrees Celsius. The humidity sensor is of the capacitive type, with a measurement range of 20 - 90% relative humidity and an accuracy of ±1.5% relative humidity. The pH sensor is of the glass electrode type, with a measurement range of 5 - 8 pH and an accuracy of ±0.03 pH. Data is recorded every 6 minutes, and this frequency is based on the dynamic characteristics of the oral environment changes to ensure timely capture of fluctuations in the health status. The multi-functional sensing network consists of 12 piezoelectric force sensors, 6 micro motion sensors, and 10 strain gauges. The piezoelectric force sensors use piezoelectric ceramic materials, with a measurement range of 0 - 5 Newtons and a sensitivity of 0.01 Newton. The micro motion sensors are MEMS accelerometers, with a measurement range of ±2g and a sensitivity of 0.001g. The strain gauges are metal foil strain gauges, with a measurement range of 0 - 1000 microstrain and a sensitivity of 1 microstrain. These sensors are respectively installed in the contact areas of the orthodontic appliance, namely the buccal, lingual, and occlusal surfaces where the teeth are in direct contact with the orthodontic appliance, the moving points, i.e., the joints or driving parts, and the deformation-sensitive areas, i.e., the beam structures that are easily deformed by force, to respectively measure the force magnitude, motion acceleration, and strain value of the orthodontic appliance, providing real-time mechanical data.The portable monitoring device includes an oral band, an orthodontic pad, and a lip sensor. The oral band 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. The lip sensor is a small patch placed inside the lip, with a built-in pressure sensor, temperature sensor, and bio-signal sensor. 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 bio-signal sensor is an electrode type, measuring the saliva conductivity in the range of 0.1 - 10 mS / cm, collecting the pressure, temperature, and bio-signal data when the user wears it, and reflecting the comfort level and physiological state. These devices transmit data to the intelligent computing system through the built-in Bluetooth 5.1 module. The Bluetooth 5.1 has a rate of 2.5 Mbps and a range of 12 meters, and uses 192-bit AES encryption to ensure the security of the transmission.

[0049] After receiving the data, the intelligent computing system processes it through the core computing unit and the edge inference unit. The core computing unit is an embedded microprocessor, preferably of the ARM Cortex-A series, with a main frequency of more than 1.5 GHz, including a digital signal processing module, responsible for data preprocessing. The median filtering algorithm is used, and the window size is preferably 5 points to remove the random noise of the collected data, extract the tooth displacement amount, that is, the three-dimensional coordinate difference, in millimeters, and the orthodontic appliance force distribution data, that is, the force value matrix of each sensor point, in Newtons. The edge inference unit is an edge computing device, preferably of the NVIDIA Jetson Nano model, with GPU acceleration. The swarm collaborative optimization method is used to adjust the orthodontic appliance force field. 120 individuals are initialized, and this quantity is optimized based on computing resources and convergence. The literature supports that the number of orthodontic optimization individuals is 100 - 150, and the force value range is 0.4 - 2.2 Newtons, which is set according to the physiological tolerance range of teeth. The inertia factor decreases linearly from 0.8 to 0.3 to ensure the balance between global search and local convergence. It loops 250 times to ensure the optimization accuracy and generates the force field adjustment parameters, that is, the target force values of each region. Subsequently, the edge inference unit processes the tooth displacement amount and mechanical data for 28 days through multi-layer recursive neural computing. The network structure has 12 input nodes, corresponding to multi-dimensional input data such as displacement, force value, and angle. It passes through three hidden layers of 60 - 30 - 15, and the number of nodes decreases gradually to extract high-order features. The Sigmoid activation function is used, and the output range of 0 - 1 is suitable for normalized prediction. The 4 output nodes represent the x, y, z displacements and the rotation angle to predict the future displacement trend. Then, a conditional auto-encoding and adversarial optimization framework is adopted. The conditional auto-encoding maps the input data to latent variables, preferably with a dimension of 8. The generator is a multi-layer fully connected network to generate adjustment strategies, and the discriminator is a convolutional network to optimize the authenticity of the strategies. Finally, the orthodontic appliance adjustment instructions are generated. Every 12 minutes, based on the real-time requirements of orthodontics, the edge inference unit uploads the 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, and the storage capacity is preferably more than 1 terabyte to save the data. The edge analysis device is a high-performance server with a multi-core CPU, which extracts the characteristics of similar cases, such as the displacement patterns of historical patients, adjusts the parameters of the treatment plan, and gives feedback.

[0050] The orthodontic appliance dynamic regulation system makes adjustments according to the instructions of the intelligent computing system. The intelligent alloy drive unit consists of a nickel-titanium shape memory alloy sheet and a micro heating circuit. The alloy sheet has a thickness of 0.5 mm, a deformation temperature of 50 - 70 °C, and the heating circuit has a power of 5 W. It receives the force field adjustment parameters, changes the alloy shape by heating with current, and adjusts the shape of the orthodontic appliance. The micro power execution unit contains a micro stepping motor and a drive circuit. The motor has a diameter of 5 mm and a torque of 0.1 N·m. The drive circuit uses PWM control. According to the prediction results of the edge reasoning unit, it generates control signals through multi-layer convolution prediction technology. The convolution network structure has a 4×4 kernel in the first layer to capture local features, 4 layers of convolution to extract multi-scale features, 2 layers of 3×3 pooling to reduce the dimension and retain key information, and GPU-accelerated calculation. The output speed is 50 - 200 revolutions per minute, the angle is 0 - 90 degrees, and the duration is 1 - 4 seconds to control the force field of the orthodontic appliance. The embedded induction network is consistent with the multi-functional induction network and contains 12 piezoelectric force sensors distributed in the key areas of the orthodontic appliance to monitor the state of the orthodontic appliance and feedback it to the intelligent computing system. The biochemical sensor is an electrochemical microelectrode containing a reference electrode and a sensitive membrane, with a detection range - CFU per milliliter, detecting the concentration of periodontitis-related bacteria such as Porphyromonas gingivalis. If the concentration change is higher than CFU per milliliter, it is regarded as a risk, and the force application frequency is adjusted to 5 - 10 times per minute, controlled by the motor PWM signal, to ensure the safety of the orthodontic treatment process.

[0051] 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:

[0053] like Figures 1-4 As shown, the following are specific use cases of the above solution:

[0054]

[0055] 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:

[0056] Use Case 1: Precision Correction of Teenagers’ Malaligned Teeth

[0057] Xiaoming, 15 years old, visited an orthodontist due to misaligned teeth (such as protruding upper front teeth). The doctor decided to use this system for treatment, with the goal of gradually adjusting the tooth position while monitoring the treatment effect and oral health in real time.

[0058] Operation process:

[0059] Initial data collection:

[0060] The doctor used a three-dimensional oral imager in the information capture system to scan Xiaoming's oral cavity. The blue LED light source (wavelength 460 - 480 nm) emits structured light, and the CCD camera (2 million pixels) captures the reflected light to generate a three-dimensional image through triangulation. The improved LBP algorithm (including an edge enhancement filter) is used to extract mucosal features, calculate the Euclidean distance (less than 0.5 - 0.7) to confirm identity, and the data is stored in cloud storage.

[0061] A small probe of the health status assessment instrument was placed near Xiaoming's gums. The temperature sensor (accuracy ±0.2 °C), humidity sensor (accuracy ±1.5% relative humidity), and pH sensor (accuracy ±0.03 pH) collected the initial oral environment data, recording a temperature of 36.8 °C, humidity of 85%, and pH value of 6.8.

[0062] Twelve piezoelectric force sensors (measurement range 0 - 5 N), six MEMS accelerometers, and ten strain gauges of the multifunctional sensing network were installed on the customized orthodontic appliance to collect initial mechanical data, such as a buccal force of 0.5 N.

[0063] Xiaoming wore the oral band and lip sensor of the portable monitoring device. The pressure sensor (range 0 - 100 kPa) recorded a wearing pressure of 10 kPa, and the temperature sensor and bio-signal sensor (saliva conductivity 0.5 mS / cm) collected comfort data.

[0064] Intelligent operation and orthodontic treatment plan generation:

[0065] The data was transmitted to the intelligent operation system via Bluetooth 5.1. The core operation unit used the median filtering algorithm to remove noise and extract the tooth displacement and force field distribution.

[0066] The edge inference unit (NVIDIA Jetson Nano) ran the population collaborative optimization method, initialized 120 individuals, with a force value range of 0.4 - 2.2 N, looped 250 times, and generated initial force field adjustment parameters (such as 1.0 N in the upper front tooth area). The multi-layer recursive neural computation (input 12 nodes, hidden layers 60 - 30 - 15, Sigmoid activation) processed 28-day data samples to predict the displacement trend (0.2 mm on the x-axis). The conditional auto-encoding and adversarial optimization framework generated adjustment strategies and output orthodontic appliance parameters.

[0067] Dynamic adjustment of the appliance:

[0068] 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.

[0069] 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.

[0070] 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.

[0071] Use Case 2: Health Monitoring and Treatment of Adult Periodontitis Patients

[0072] 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.

[0073] Operation process:

[0074] Initial Assessment and Data Collection:

[0075] 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).

[0076] 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.

[0077] Health risk analysis and program adjustment:

[0078] 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.

[0079] Biochemical sensor (electrochemical microelectrode, 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.

[0080] Orthodontics and real-time monitoring:

[0081] The intelligent alloy drive unit adjusts the shape of the orthodontic appliance. The micro power execution unit outputs a rotational speed of 80 revolutions per minute for 1 second and applies a low force field. The ultrasonic detection module (frequency 20 - 40 kHz) of the health guarantee and early warning system collects tooth stability data every 50 minutes, analyzes the core calculation risk level, and prompts the doctor to pay attention.

[0082] Data is uploaded to the cloud via Wi-Fi6+, and the doctor adjusts the plan through the remote coordination and data integration system.

[0083] Expected results: After 3 months, Ms. Li's tooth displacement is 0.4 mm, the health index rises to 70, the bacterial concentration drops to CFU per milliliter, periodontitis improves, the force field of the orthodontic appliance is stable, and the virtual reality terminal (2560×1440) shows the health trend.

[0084] Use case three: Remote orthodontic management and user experience optimization

[0085] Mr. Zhang, 28 years old, is busy at work and cannot visit the hospital for regular check-ups frequently. The doctor remotely manages his orthodontic process through this system, and Mr. Zhang pays attention to the wearing comfort.

[0086] Operation process:

[0087] Initial setup and feedback collection:

[0088] Initial data collection is completed at the clinic. The three-dimensional oral imager generates a three-dimensional image, the multi-functional sensing network records the force field, the health status evaluator collects environmental data, and the portable monitoring device records a pressure of 12 kPa and a temperature of 37.1 °C.

[0089] After Mr. Zhang goes home, he inputs feedback through the touch platform, reporting that the orthodontic appliance is slightly tight, and the pressure sensor data confirms a local pressure of 15 kPa.

[0090] Remote analysis and adjustment: Data is transmitted to the edge reasoning unit via Bluetooth 5.1, and the group collaborative optimization generates a partitioned force field (0.8 N on the buccal side, 1.0 N on the lingual side). The user interaction and experience improvement system runs dynamic Gaussian optimization and feature convolution learning, analyzes the pressure change, and suggests reducing the buccal side force value to 0.6 N.

[0091] The edge analysis device (32-core CPU) optimizes the path through matrix decomposition. The cloud storage (1 terabyte) saves data, generates remote adjustment instructions, and updates every 12 minutes.

[0092] Real-time interaction and verification:

[0093] The doctor views the three-dimensional model through the virtual reality terminal of the real-time observation and interaction system. The vibration feedback device simulates the touch sensation to confirm the adjustment effect. The micro power execution unit executes the new parameters, with a rotational speed of 50 revolutions per minute and an angle of 20 degrees.

[0094] Mr. Zhang views the displacement trend through the visualization terminal and confirms the improvement in comfort through the touch platform.

[0095] Expected results: After 2 months, Mr. Zhang's tooth displacement is 0.3 mm, the pressure drops to 10 kPa, the doctor remotely confirms the improvement in occlusion, and the system record shows that the comfort score increases from 6 to 8 (out of 10).

[0096] Among them, Case 1 demonstrates precise orthodontics, emphasizing the collaboration between algorithms and hardware. Case 2 highlights health monitoring, reflecting the ability to manage periodontitis. Case 3 focuses on remote management and user experience, demonstrating the flexibility of the system. The applications of these cases make full use of the hardware in this technical solution, including a three-dimensional oral imager, biochemical sensors, and group collaborative optimization and recursive neural computing algorithms, to achieve orthodontic goals.

[0097] The following is a display of the experimental data of the comfortable orthodontic optimization for remote management:

[0098]

[0099] It can be seen from the experimental data that the patient's tooth gap is 1.8 mm. The system feeds back through the portable monitoring device and the touch platform. The edge inference unit optimizes the zonal force field (buccal side: 0.6 N), and it is completed in 2 months, with a significant improvement in comfort.

[0100] Appendix Figure 4 It shows the variation of the force values applied by the orthodontic appliance in the buccal, lingual, and occlusal regions during the tooth orthodontic process over time. The chart shows the dynamic variation of the force values in the three regions within 28 days: the buccal side (blue line) drops from 1.0 N to 0.5 N, the lingual side (red line) drops from 0.8 N to 0.4 N, and the occlusal region (yellow line) drops from 0.6 N to 0.3 N. The three curves show a linear downward trend, reflecting that the system gradually reduces the force value through dynamic regulation, demonstrating the zonal force field control and real-time adjustment capabilities. Specific Embodiment 3:

[0102] As Figures 1-4 shown, the key algorithms mentioned in the above embodiments are analyzed in detail below, including their core mathematical formulas and explanations:

[0103] Group collaborative optimization method:

[0104] Applied to the force field adjustment of the edge inference unit in the intelligent computing system and the shape adjustment of the intelligent alloy drive unit in the orthodontic appliance dynamic regulation system, it can optimize the force field distribution and shape adjustment parameters of the orthodontic appliance.

[0105] In the edge inference unit, the group collaborative optimization method adjusts the force field of the orthodontic appliance, initializes 120 individuals, with the force value range of 0.4 - 2.2 Newtons, which is set according to the physiological tolerance of teeth. The inertia factor decreases from 0.8 to 0.3, and loops 250 times to ensure accuracy, generating force field adjustment parameters.

[0106] In the orthodontic appliance dynamic regulation system, the intelligent alloy drive unit adjusts the shape of the orthodontic appliance through a multi-level collaborative optimization method, receives the force field adjustment parameters, and optimizes the alloy deformation path.

[0107] The specific mathematical formulas are as follows:

[0108] Group collaborative optimization method:

[0109] Velocity update formula:

[0110]

[0111] Position update formula:

[0112]

[0113] Objective function (for orthodontic force optimization):

[0114]

[0115] : The velocity vector of the th particle at the th iteration, with the unit of Newton per second, representing the rate and direction of particle movement, : The updated value of the velocity vector of the th particle at the th iteration, Inertia factor, controlling the historical influence of velocity, with the range of 0.8 - 0.3, linearly decreasing (such as ), and 0.8 to 0.3 is used in this scheme, : Individual learning factor, controlling the weight of the particle learning towards its own best position, : Global learning factor, controlling the weight of the particle learning towards the global best position, : Random number, with the range [0, 1], introducing randomness to avoid local convergence. The The historical best position of a particle, in Newtons, representing the optimal force value found by the individual. The global best position, in Newtons, representing the optimal force value found by all particles. : The current position of the : The updated position of the : The target force value of the : The maximum allowable force value, in Newtons, set to 2.2 Newtons. : The indicator function, which is 1 if and 0 otherwise. .

[0116] Multi-layer convolutional prediction technology:

[0117] Applied to the dynamic optimization of the force field of the micro-power execution unit in the orthodontic appliance dynamic regulation system to generate the control signal of the orthodontic appliance force field.

[0118] The micro-power execution unit processes the data using multi-layer convolutional prediction technology according to the prediction results of the edge inference unit. The network structure is that the first layer has a 4×4 kernel to capture local features, 4 layers of convolution to extract multi-scale features, 2 layers of 3×3 pooling to reduce the dimension and retain key information, and GPU acceleration for calculation, outputting the rotational speed (50 - 200 revolutions per minute), angle (0 - 90 degrees), and duration (1 - 6 seconds) to control the orthodontic appliance force field.

[0119] The specific formulas are as follows:

[0120] Convolution layer calculation:

[0121] Pooling layer calculation (max pooling):

[0122]

[0123] Output layer calculation:

[0124]

[0125] Where : The activation value of the th layer feature map at position , Activation function, : The weight of the th layer convolutional kernel, the first layer has a 4×4 kernel, dimension 4×4, : The value of the th layer input feature map, The th layer bias value, Pooling area The pooling stride is set to 1 Output vector, with a dimension of 3, representing rotational speed, angle, and duration Output layer weight matrix, with a dimension depending on the size of the previous layer's feature map Output of the last convolutional or pooling layer Output layer bias vector Specific Embodiment 4:

[0127] As Figures 1-4 shown, the following are the detailed hardware compositions and hardware descriptions in each system:

[0128] The information capture system is responsible for the acquisition of multi-dimensional data. Its hardware components include a three-dimensional oral imager, a health status assessment device, a multi-functional sensing network, and a portable monitoring device. The three-dimensional 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 blue LED light source emits structured light with a wavelength range of 460 - 480 nanometers. This range is preferably selected to ensure the penetrability and safety of oral tissues. The power is about 5 watts, providing uniform illumination. The high-resolution CCD camera preferably has a resolution of more than 2 million pixels and a frame rate of 30 frames per second, capturing reflected light to generate high-precision image data. The optical projection lens group is composed of a micro lens and a prism, responsible for generating structured light in the form of a grid or stripe pattern, calculating the distance between the light reflection point and the camera through triangulation technology, generating three-dimensional point cloud data, and forming a three-dimensional image of the teeth and oral mucosa. To achieve identity verification, the imager is built-in with an identity verification module, which includes a small storage chip for storing the patient feature database and a microprocessor for running the improved local binary pattern algorithm. The health status assessment device is a set of integrated oral environment sensor devices, consisting of a temperature sensor, a humidity sensor, and a pH sensor, installed in a small probe. The probe housing is made of medical-grade silicone, with a diameter of about 5 mm and a length of 10 mm, placed near the gum in the oral cavity. The temperature sensor is of the 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, collecting oral temperature data. The humidity sensor is of the capacitive type, with a measurement range of 20 - 90% relative humidity, an accuracy of ±1.5% relative humidity, detecting changes in oral humidity. The pH sensor is of the glass electrode type, containing a reference electrode and a sensitive membrane, with a measurement range of 5 - 8 pH and an accuracy of ±0.03 pH, detecting the pH value of saliva and recording it every 6 minutes, with the frequency based on the change rate of the oral environment. The multi-functional sensing network consists of 12 piezoelectric force sensors, 6 micro motion sensors, and 10 strain detection chips. The piezoelectric force sensors use piezoelectric ceramic materials, with a diameter of 3 mm and a thickness of 1 mm, a measurement range of 0 - 5 Newtons, and a sensitivity of 0.01 Newton, installed in the contact areas (buccal side, lingual side, occlusal surface) of the orthodontic appliance to measure the applied force. The micro motion sensors are MEMS accelerometers, with a size of 2×2×1 mm, a measurement range of ±2g, and a sensitivity of 0.001g, installed at the moving points (joints and driving parts) of the orthodontic appliance to detect motion acceleration. The strain detection chips are metal foil strain gauges, with a size of 5×2 mm, a measurement range of 0 - 1000 microstrain, and a sensitivity of 1 microstrain, installed in the deformation-sensitive areas to monitor the strain value. These sensors are connected to a micro data acquisition module through a flexible circuit board. The portable monitoring device includes an oral band, 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.

[0129] 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.

[0130] The orthodontic appliance dynamic regulation system realizes the adjustment of the orthodontic appliance. Its hardware components include a smart alloy drive unit, a micro power execution unit, and an embedded sensing network. The smart alloy drive unit consists of a nickel-titanium shape memory alloy sheet and a micro heating circuit. The alloy sheet has dimensions of 10×5 mm, a thickness of 0.5 mm, a deformation temperature of 50 - 70 °C, and has the characteristic of thermally induced deformation. The micro heating circuit includes a resistance wire and a current controller, with a power of 5 W and a current range of 0 - 1 A, heating the alloy sheet to adjust the shape of the orthodontic appliance. The micro power execution unit includes a micro stepping motor and a drive circuit. The motor has a diameter of 5 mm, a torque of 0.1 N·m, and a rotational speed range of 50 - 200 revolutions per minute. The drive circuit uses a PWM controller, and the preferred chip model is DRV8833, outputting signals of rotational speed, angle between 0 - 90 degrees, and duration within the time period of 1 - 6 seconds to control the force field of the orthodontic appliance. The embedded sensing network is the same as the multi-functional sensing network, including 12 piezoelectric force sensors integrated in the orthodontic appliance to monitor the state and provide feedback. The biochemical sensor is an electrochemical microelectrode with dimensions of 3×2 mm, including a reference electrode and a sensitive membrane, and its detection range - CFU per milliliter, detecting the concentration of periodontitis-related bacteria and outputting an electrical signal to the drive circuit to adjust the force application frequency.

[0131] The real-time observation and interaction system provides visual monitoring. Its hardware components include a Wi-Fi6+ transmission component, a virtual reality terminal, and a visualization terminal. The Wi-Fi6+ transmission component is a router module, preferably supporting the 802.11ax protocol, with a rate of 1.5 Gbps, a range of 35 m, and including dual-band antennas (2.4 GHz and 5 GHz) to transmit data to the doctor's end. The virtual reality terminal is a head-mounted display device, including a 2560×1440 OLED screen, a refresh rate of 90 Hz, a field of view of 110 degrees, and an internal eccentric motor vibration feedback device with a power of 0.5 W to simulate the tactile sensation according to the force field data. The visualization terminal is a 32-inch LCD screen with a brightness of 600 cd per square meter, a contrast ratio of 1200:1, and a resolution of 1920×1080, displaying the displacement trend.

[0132] The treatment strategy optimization system improves the accuracy of the plan. Its hardware relies on the edge inference unit and the cloud device to run algorithms, without independent hardware. The GPU of the edge inference unit accelerates quantum search optimization, kernel regression analysis, and temporal convolutional prediction.

[0133] The health guarantee and early warning system monitors health risks. Its hardware includes a health status assessment device, force sensors, vibration indicators, and an analysis core. The health status assessment device is consistent with the information capture system. The force sensors are 12 piezoelectric force sensors, shared with the multi-functional induction network. The vibration indicator is a micro vibration motor with a diameter of 4 mm, a power of 0.3 W, and a vibration frequency of 100 - 200 Hz, which alarms according to the health index. The analysis core is a microcontroller, preferably of the STM32F4 model, with a main frequency of 168 MHz, including a floating-point operation unit, and running multi-objective collaborative optimization analysis and health index assessment. The ultrasonic detection module includes a transmitter and a receiver. The transmitter is a piezoelectric transducer with a frequency of 20 - 40 kHz and a power of 1 W. The receiver is a highly sensitive microphone, which collects tooth stability data.

[0134] The remote coordination and data integration system supports remote management. Its hardware includes cloud storage and an edge analysis device. The cloud storage is a distributed server cluster, including multiple high-performance servers, with a single machine configured with a 16-core CPU, 64 GB of memory, and 1 terabyte of SSD, which stores full-cycle data. The edge analysis device is a high-performance server, including a 32-core CPU, 128 GB of memory, and 4 TB of hard disk, running matrix decomposition algorithms to optimize paths and schedule resources.

[0135] 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 a 32-inch LCD screen of the visualization terminal for display.

[0136] The user interaction and experience improvement system optimizes the user experience. Its hardware includes a touch platform, which is a 10-inch capacitive screen with a resolution of 1280×800, including a multi-touch sensor, which collects feedback data. The edge inference unit runs dynamic Gaussian optimization and feature convolution learning to adjust the scheme.

[0137] 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 "including", "comprising" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "including a reference structure" does not exclude the existence of additional identical elements in the process, method, article or device including the element.

[0138] Although 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. A real-time monitoring and analysis system for orthodontic treatment, characterized in that: It includes an information capture system, an intelligent operation system, and an orthodontic appliance dynamic regulation system; The information capture system includes a three-dimensional oral imager, a health status evaluator, a multi-functional sensing network, and a portable monitoring device. The three-dimensional oral imager generates a three-dimensional oral image through triangulation positioning, combines the improved local binary pattern algorithm and adds an edge enhancement filter to extract oral mucosa features, and confirms the identity when the Euclidean distance is less than a preset threshold. The health status evaluator collects oral temperature, humidity, and pH value. The multi-functional sensing network includes 12 piezoelectric force sensors, 6 micro motion sensors, and 10 strain detection chips, which are respectively installed in the orthodontic appliance contact area, the movable point, and the deformation sensitive area. The portable monitoring device includes an oral band, an orthodontic appliance liner, and a lip sensor, and is built-in with pressure, temperature, and biological signal sensors to collect user oral data; The intelligent operation system includes a core operation unit and an edge reasoning unit. The core operation unit removes data noise and extracts tooth displacement and orthodontic appliance force distribution data. The edge reasoning unit uses the group collaborative optimization method to adjust the orthodontic appliance force field; The orthodontic appliance dynamic regulation system includes an intelligent alloy drive unit, a micro power execution unit, and an embedded sensing network. The intelligent alloy drive unit adjusts the shape of the orthodontic appliance. The micro power execution unit outputs rotation speed, angle, and duration signals. The embedded sensing network includes 12 force sensors to monitor the state of the orthodontic appliance; The specific steps of the information capture system include: SpA1. Data initialization collection: Collect three-dimensional images through a three-dimensional oral imager, collect temperature, humidity, and pH value through a health status evaluator, collect mechanical data using a multi-functional sensing network, and collect pressure and temperature data through a portable monitoring device; SpA2. Periodic dynamic update: Repeat collecting temperature, humidity, and pH value data every 10 days; SpA3. Result effectiveness evaluation: Compare the three-dimensional image and mechanical data after treatment; The intelligent operation system optimizes the orthodontic treatment plan through multi-level analysis. The specific steps include: SpB1. Data preprocessing: The core operation unit removes the noise of the collected data and extracts tooth displacement and orthodontic appliance force distribution data; SpB2. Deep analysis and optimization: The edge reasoning unit executes the group collaborative optimization method, initializes a preset number of individuals, the force value range is based on the physiological tolerance of teeth, the inertia factor is adjusted decreasingly, loops a preset number of times, generates force field adjustment parameters, processes the tooth displacement and mechanical data for 28 days through multi-layer recursive neural calculation, inputs 12 nodes, passes through 60-30-15 hidden layers, activates through Sigmoid, outputs 4-node predicted displacement trends, and uses the conditional auto-encoding and adversarial optimization framework to generate an orthodontic appliance adjustment strategy according to the prediction results; SpB3. Cloud advanced operation: The edge reasoning unit uploads data to the cloud every 12 minutes, and the cloud extracts the characteristics of similar cases and adjusts the parameters of the orthodontic treatment plan.

2. The real-time monitoring and analysis system for orthodontic treatment according to claim 1, wherein: The orthodontic appliance dynamic regulation system realizes the precise adjustment of the orthodontic appliance. The specific steps include: SpC1. Morphological Adaptive Adjustment: The intelligent alloy drive unit receives the force field adjustment parameters of the intelligent computing system, adjusts the orthodontic appliance morphology through a multi-level collaborative optimization method, and the embedded induction network contains 12 force sensors to detect the response of teeth to the orthodontic appliance; SpC2. Force Field Dynamic Optimization: The micro power execution unit generates control signals according to the prediction results of the edge reasoning unit through a multi-layer convolution prediction technology, with a 4×4 kernel in the first layer, 4 layers of convolution, and 2 layers of 3×3 pooling, and outputs the rotation speed, angle, and duration to control the force field of the orthodontic appliance; SpC3. Environmental Response Regulation: The biochemical sensor detects the concentration of periodontitis-related bacteria in the oral cavity and adjusts the force application frequency to the preset range according to the concentration change to ensure that the orthodontic process adapts to the oral environment.

3. The real-time monitoring and analysis system for orthodontic treatment according to claim 1, characterized in that: The information capture system collects multi-dimensional orthodontic data to support orthodontic analysis. The three-dimensional 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 assessment instrument contains optoelectronic and chemical sensors, collects data on plaque concentration, gum health, and tooth stability, and monitors changes in oral health; the multi-functional induction 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 and analyzes the user's oral hygiene habits.

4. The real-time monitoring and analysis system for orthodontic treatment according to claim 1, characterized in that: The information capture system and the intelligent computing system work together through high-efficiency data transmission. Its composition includes: the information capture system transmits the collected data through Bluetooth 5.1, with a rate of 2.5 Mbps and a range of 12 meters. The data is encrypted by 192-bit AES to ensure security. After the intelligent computing system receives the data, it pushes the analysis results to the cloud every 12 minutes to adjust the orthodontic treatment plan parameters. The system includes an adaptive force field partition control component, which divides the orthodontic appliance into the buccal side, lingual side, and occlusal area, and applies independent force values respectively. The edge reasoning unit calculates the partition force field data.

5. The real-time monitoring and analysis system for orthodontic treatment according to claim 1, wherein: The system includes a real-time observation and interaction system, which provides visual monitoring of the orthodontic process. Its specific composition includes a Wi-Fi6+ transmission component with a range of 35 meters; the virtual reality terminal has a display of 2560×1440 and also includes a vibration feedback device; the visualization terminal is a 32-inch screen with a brightness of 600 cd / ㎡ and a contrast ratio of 1200:1; a two-way generation optimization technology is used to generate a virtual scene.

6. The real-time monitoring and analysis system for orthodontic treatment according to claim 1, characterized in that: The system includes a treatment strategy optimization system to improve the long-term accuracy of the orthodontic treatment plan. Specifically, it includes: combining quantum search optimization, with an initial temperature of 1200K and a rate of 0.95, to generate an optimization path. At the same time, through kernel regression analysis with a Gaussian kernel and γ = 0.15, analyze the correlation between historical data and current data. Then, use temporal convolutional prediction to process the data with a 144-hour window, predict the trend for 2 - 4 months, and generate adjustment suggestions.

7. A real-time monitoring and analysis system for orthodontic treatment of teeth according to claim 1, characterized in that: The system includes a health guarantee and early warning system to monitor oral health risks in real-time, including a health status assessment instrument, force sensors, vibration prompt devices, and an analysis core. The 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 multi-modal health index assessment component, which integrates temperature, humidity, and pH value data to generate a health index and triggers the vibration prompt device to alarm.

8. The real-time monitoring and analysis system for orthodontic treatment according to claim 1, wherein: A remote coordination and data integration system is set up in the said system to support doctors in remotely managing the orthodontic process. Its components include: cloud storage for saving the full-cycle data collected by the information capture system, including three-dimensional images and mechanical data; an edge analysis device that receives the analysis results of the intelligent computing system, optimizes the treatment path through matrix decomposition, schedules cloud resources, and generates remote adjustment instructions.

9. The real-time monitoring and analysis system for orthodontic treatment according to claim 1, characterized in that: The said system includes a multi-dimensional information fusion and visualization system and a user interaction and experience enhancement system to improve the transparency and comfort of the orthodontic process. Its components include: the multi-dimensional information fusion system processes three-dimensional images, mechanical data, and health indices through multi-level feature convolution technology to generate an orthodontic report; the user interaction system collects user feedback data, including pressure and temperature changes, through a touch platform, and adjusts the treatment plan by combining dynamic Gaussian optimization and feature convolution learning.

Images