A spinal orthopedic adaptive error correction method and system

Abstract

The application discloses a spine orthopedic adaptive error correction method and system, specifically comprising the following steps: constructing a spine physiological deformation digital twin model, setting six detection sections along a mechanical linkage chain to complete multi-dimensional sensing acquisition and physiological baseline calibration; four types of compound errors are quantified by an original method, and accurate orthopedic parameters are generated through four-level space-time domain collaborative compensation; after execution, the closed loop is verified and iterative compensation is performed, and the model and algorithm parameters are updated based on the full cycle data self-learning. The application realizes accurate decoupling and dynamic collaborative compensation of multi-source errors, and the error identification accuracy is greater than or equal to 98%, the correction rate is greater than or equal to 95%, the orthopedic standard rate is greatly improved, and the application is suitable for dynamic orthopedic treatment of adolescent spine deformities, and has high clinical popularization value.

Description

Technical Field

[0001] This invention relates to the field of medical device control technology, specifically to a spinal orthopedic adaptive error correction system. Background Technology

[0002] The core of conservative treatment for spinal deformities is to apply precise and continuous orthopedic force to the spine through orthopedic braces, gradually restoring the physiological curvature of the deformed spine. The key to the orthopedic effect lies in the dynamic matching degree between the orthopedic parameters and the patient's real-time physiological state of the spine. In the actual orthopedic process, the error is not from a single source, but is a multi-source composite error formed by the coupling of four types of errors: spinal physiological coupling error (linkage between vertebral rotation and scoliosis, elastic deformation of soft tissue, and physiological drift of bone development), equipment detection error (sensor installation deviation, brace deformation interference, and signal acquisition noise), motion posture coupling error (shift in the application point of orthopedic force and change in force distribution caused by changes in standing / walking / sitting posture), and cumulative error under working conditions (changes in spinal flexibility during the orthopedic period and decrease in force application accuracy caused by brace wear). Moreover, these errors change dynamically with the orthopedic stage and human posture, exhibiting three major characteristics: spatiotemporal correlation, physiological coupling, and dynamic time-varying nature.

[0003] While existing spinal correction error correction technologies attempt to compensate for errors through sensor data acquisition and simple parameter adjustments, they lack specific design considerations for the error characteristics of spinal correction, resulting in core technological deficiencies.

[0004] 1. Lack of modeling of spinal physiological deformation, and lack of physiological basis for error identification: Existing technology only performs simple filtering and threshold judgment on the detection signal, without constructing a digital twin model of physiological deformation that fits the individual characteristics of the patient's spine. It cannot distinguish between "real spinal deformity changes" and "signal deviation caused by error", and is prone to misjudging spinal physiological deformation as error, resulting in overcompensation or undercompensation.

[0005] 2. Multi-source errors are not decoupled, and the compensation is not targeted: All errors are treated as single signal deviations and compensated uniformly. The four types of errors, namely physiological coupling, equipment detection, motion posture, and operating condition drift, are not decoupled and identified. It is impossible to formulate specific compensation strategies for different error generation mechanisms and influencing characteristics. After the composite errors are superimposed, the compensation accuracy drops significantly and the error residual rate exceeds 40%.

[0006] 3. The compensation method is static / quasi-static, without spatiotemporal coordination: The compensation only targets the local error of a single time point and a single spinal segment, without considering the time accumulation effect of the error (such as the working error of the brace wear gradually increases over time) and the spatial linkage effect (such as the error of thoracic scoliosis correction will be transmitted to the lumbar segment), which leads to the occurrence of new spinal segment stress imbalance after local compensation, forming a vicious cycle of "compensating one place and causing imbalance in another place".

[0007] 4. Lack of self-learning iteration mechanism and poor adaptability: The compensation parameters are preset based on the initial orthopedic data and are not updated by self-learning according to the patient's orthopedic progress and changes in spinal physiological status. They cannot adapt to the physiological characteristics of the dynamic changes in bone development and spinal flexibility of adolescent patients. In the later stage, the orthopedic error gradually accumulates, and the correction achievement rate is less than 60%.

[0008] 5. Mismatch between sensor layout and spinal physiological characteristics: The sensor point layout only considers the main curve of the spine and does not cover the mechanical linkage area of ​​the spine "main curve-secondary curve-pelvis". It cannot capture the spatial transmission characteristics of errors, and the missed detection rate of latent errors (such as deformation of secondary curve and pelvic tilt coupling error) is over 75%.

[0009] To address the aforementioned technical challenges, current technologies have yet to establish an integrated error correction scheme that combines spinal physiological modeling, multi-source error decoupling, spatiotemporal collaborative compensation, and self-learning iteration. Therefore, developing an adaptive error correction method and system for spinal orthopedics that is based on individual spinal physiological characteristics and achieves precise decoupling of multi-source errors, spatiotemporal collaborative compensation, and self-learning optimization of orthopedic effects has become an urgent need in the clinical field of spinal orthopedics. Summary of the Invention

[0010] The purpose of this invention is to provide an adaptive error correction method and system for spinal orthopedics. It absorbs the advantages of existing multi-sensor acquisition and step-by-step compensation, breaks through the traditional technical framework of "no physiological modeling, no error decoupling, and static compensation", constructs a "digital twin model of individual physiological deformation of the spine" as the core, integrates multiple methods to realize an adaptive error correction system, and achieves full-process control of "physiological modeling-error decoupling-cooperative compensation-self-learning optimization".

[0011] To achieve the above-mentioned objectives, the technical solution adopted by this invention is as follows:

[0012] An adaptive error correction method for spinal orthopedics includes six core steps: individual physiological modeling of the spine and deployment of a sensing system, synchronous acquisition of multi-source signals and calibration of physiological baselines, decoupling and identification of multi-source errors and feature quantification, spatiotemporal collaborative error compensation, orthopedic execution and verification of error correction effects, and self-learning iteration of orthopedic data and model updating. Each step is progressive, and an adapted adaptive error correction system for spinal orthopedics is designed based on this method to realize the engineering implementation and clinical application of the method.

[0013] I. The spinal orthopedic adaptive error correction method includes the following steps:

[0014] S1. Spinal Individual Physiological Modeling and Sensor System Deployment

[0015] The core of this step is to construct a patient-specific digital twin model of spinal physiological deformation and design a full-dimensional sensing layout that matches the biomechanical linkage characteristics of the spine. This provides a physiological baseline for subsequent error identification and full-dimensional coverage for signal acquisition. At the same time, it reduces inherent device errors through an anti-interference physiological fit design, as detailed below:

[0016] 1.1 Construction of Digital Twin Model of Individual Physiological Deformation of the Spine

[0017] Low-dose CT / MRI images of the entire spine and dynamic posture X-ray images (standing, sitting, and walking) were collected from the patient to obtain individual physiological parameters of the spine: Cobb angle, vertebral rotation, spinal flexibility, thoracic symmetry, pelvic tilt angle, elastic modulus of each vertebral segment, and soft tissue pressure tolerance threshold. A digital twin model of spinal physiological deformation was constructed based on the finite element method and multibody dynamics. The model includes a mechanical linkage module of the main curve segment-secondary curve segment-pelvis, a vertebral body-soft tissue deformation coupling module, and a corrective force-spinal deformation response module. It can simulate the real physiological deformation of the spine under different corrective forces and different human postures, and output the baseline data of physiological deformation of each spinal segment (angle, displacement, and force distribution) as the "gold standard" for subsequent error identification.

[0018] 1.2 Design of Spinal Mechanics Linkage Area Sensing Layout

[0019] Based on a digital twin model of spinal physiological deformation, six detection sections are set along the spinal biomechanical linkage chain (upper thoracic T1-T4, main curvature T5-T9, thoracolumbar linkage T10-L2, lumbar secondary curvature L3-L4, and pelvic-spine connection L5-S1), covering the three core areas of the main curvature, secondary curvature, and pelvic linkage, to capture the spatial transmission characteristics of errors. Each detection section is circumferentially arranged with four integrated sensing units, namely a flexible spinal morphology sensing unit (acquiring bending / torsion angles), a medical flexible pressure sensing unit (acquiring brace-body contact pressure), a micro posture micromotion sensing unit (acquiring instantaneous body posture), and a soft tissue deformation sensing unit (acquiring subcutaneous soft tissue elastic deformation), to achieve simultaneous acquisition of four-dimensional signals of "morphology-pressure-posture-soft tissue deformation" in a single section.

[0020] 1.3 Reference Sensing and Anti-interference Physiological Fit Design

[0021] A main reference sensing unit (of the same model as the detection sensing unit) is installed in the non-deformation rigid area of ​​the orthopedic brace corresponding to each detection section to collect the deformation and vibration interference signals of the brace itself. Two global auxiliary reference sensing units are installed in the stable load-bearing area of ​​the lumbar region of the orthopedic brace to collect the vibration interference signals of the entire frame. The distance between the main reference sensing unit and the detection sensing unit is ≤50mm to ensure the consistency of interference signal collection. All sensing units are fixed by "medical flexible bionic substrate + silicone cushioning and vibration damping pad". The silicone cushioning pad is 2-4mm thick and has a Shore hardness of 35-45, conforming to the physiological curve of the human body and absorbing the mechanical interference of brace vibration and human movement. The sensing units are double fixed by "medical adhesive + elastic anti-slip clamp" to avoid detection errors caused by small displacement of the sensing units. The perpendicularity of the sensing probe to the body / brace contact surface is ≤0.2° and the contact gap is ≤0.15mm.

[0022] 1.4 Sensor Unit Parameter Customization

[0023] All sensing units are clinically customized for spinal orthopedics, adapted to human physiological characteristics and orthopedic environments:

[0024] Flexible spinal morphology sensing unit: range ±90°, angle accuracy ±0.04°, torsion accuracy ±0.08°, response time ≤10ms;

[0025] Medical flexible pressure sensing unit: measuring range 0-1MPa, accuracy ±0.004MPa, no hysteresis, suitable for soft tissue pressure detection;

[0026] Miniature attitude micro-motion sensing unit: range ±5g, accuracy ±0.008g, capable of capturing instantaneous attitude changes of 0.1° of the body;

[0027] Soft tissue deformation sensing unit: range 0-5mm, accuracy ±0.01mm, for collecting elastic deformation of subcutaneous soft tissue;

[0028] The parameters of the main reference / global auxiliary reference sensing unit and the detection sensing unit are completely identical, ensuring the homogeneity of signal acquisition.

[0029] S2. Multi-source signal synchronous acquisition and physiological baseline calibration

[0030] A 40-channel high-speed synchronous acquisition module is used to achieve synchronous acquisition of full-dimensional detection signals and reference signals. Based on the digital twin model of spinal physiological deformation, the physiological baseline of the signals is calibrated to eliminate system errors caused by inherent sensor bias and installation deviation, providing a precise signal foundation for subsequent error identification.

[0031] 2.1 Synchronous Acquisition of Multi-Source Signals

[0032] A 40-channel high-speed synchronous acquisition module was built to simultaneously acquire 24 detection sensor signals from 6 detection sections, 6 main reference sensor signals, and 2 global auxiliary reference sensor signals. It also acquires auxiliary data such as real-time changes in spinal flexibility, the status of the orthotic brace actuator, and human movement speed. Differential sampling frequencies were set according to signal characteristics: 250Hz for morphological / soft tissue deformation signals, 300Hz for pressure signals, and 350Hz for posture micro-motion signals, ensuring effective capture of instantaneous dynamic error signals. Hardware synchronous triggering and fiber optic transmission were adopted, with a time synchronization error of ≤0.5μs for all channels, avoiding error identification deviations caused by signal phase differences. The acquired data was transmitted to the core computing terminal in real time.

[0033] 2.2 Physiological baseline calibration

[0034] Based on the baseline data of physiological deformation output from the digital twin model of spinal physiological deformation, the acquired raw signals are calibrated:

[0035] First, the inherent zero-point deviation of the sensor is eliminated through sensor zero-point calibration;

[0036] Then, through physiological baseline matching calibration, the collected raw signals are matched with the physiological deformation baseline data of the digital twin model to correct the system deviations caused by sensor installation deviations and initial body posture.

[0037] Finally, signal normalization is used to convert all signals into uniform spinal physiological deformation characteristics (relative values ​​of angle / pressure / deformation), eliminating dimensional differences and facilitating subsequent error decoupling and identification.

[0038] S3. Multi-source error decoupling identification and feature quantization

[0039] One of the core innovations of this invention is a multi-source error decoupling method. This method uses the physiological baseline data of a digital twin model of spinal physiological deformation as a reference. Through three steps—signal feature matching, error source decoupling, and feature quantization—it decomposes the coupled composite error into four independent categories: spinal physiological coupling error, equipment detection error, motion posture coupling error, and operational condition cumulative error. Furthermore, it precisely quantifies the amplitude, spatial distribution, temporal characteristics, and degree of influence of each type of error, overcoming the limitations of traditional methods that treat all errors as a single entity and compensate them uniformly.

[0040] 3.1 Signal Feature Extraction

[0041] Three-dimensional features (time domain, frequency domain, and spatiotemporal domain) are extracted from the signal after physiological baseline calibration to form a full-dimensional signal feature vector:

[0042] Time-domain characteristics: peak-to-peak value, mean, root mean square, kurtosis, amplitude variation coefficient, signal slope, reflecting the instantaneous change characteristics of the error;

[0043] Frequency domain characteristics: characteristic frequency, harmonic amplitude ratio, frequency band energy ratio, power spectral density. Different error sources have unique frequency domain characteristics (e.g., equipment detection error characteristic frequency 20-50Hz, motion attitude coupling error characteristic frequency 5-20Hz).

[0044] Spatiotemporal characteristics: spatial correlation (correlation coefficient of signals at each detection cross section), temporal cumulativeity (rate of change of signal amplitude over time), and spatiotemporal entropy, reflecting the spatial propagation and temporal accumulation characteristics of errors.

[0045] 3.2 Multi-source error decoupling and identification

[0046] A multi-source error decoupling and identification model was constructed. This model uses baseline data of spinal physiological deformation as a reference, inputs full-dimensional signal feature vectors into the model, and achieves decoupling of four types of errors through error source feature matching and residual analysis.

[0047] First, the signal feature vector is matched with a pre-set feature library for four types of error sources to initially identify the error type;

[0048] Then, the actual physiological deformation of the spine under the current orthopedic force and current posture is simulated using a digital twin model of spinal physiological deformation, and the residual between the simulated physiological deformation signal and the actual acquired signal is calculated.

[0049] Finally, based on the characteristics of the residuals (amplitude, frequency, and spatiotemporal distribution) and the specific characteristics of the four types of error sources, precise matching is performed to achieve complete decoupling of composite errors and clarify the spatial distribution location (specific detection section) and temporal variation law (instantaneous / cumulative / dynamic) of various types of errors.

[0050] 3.3 Error Feature Quantization

[0051] The four independent errors after decoupling are quantified to construct a quantification feature matrix for each type of error, which includes five core quantification indicators: error amplitude, error influence range, time accumulation rate, spatial transmission coefficient, and influence weight on orthodontic effect. This provides accurate quantification basis for subsequent spatiotemporal collaborative compensation and avoids blind compensation.

[0052] S4, Spatiotemporal Co-operative Error Compensation

[0053] The second core innovation of this invention is a spatiotemporal domain collaborative error compensation method. This method absorbs the advantages of step-by-step compensation and breaks through the limitations of traditional static compensation based on "single time point and single segment". Based on the quantified feature matrix after decoupling of multi-source errors, it achieves collaborative compensation, step-by-step execution, and mechanical linkage balance of four types of errors from three dimensions: time domain, spatial domain, and physiological domain. Finally, it generates precise orthopedic parameters (force magnitude, force position, support angle, force rate, and force balance) that match the real-time physiological state of the patient's spine. Specifically, it is a four-level collaborative compensation process that achieves precise error elimination at each level. Moreover, each level of compensation considers the mechanical linkage characteristics of the spine to ensure the overall force balance of the spine after compensation.

[0054] 4.1 Level 1: Basic Compensation for Equipment Detection Errors – Eliminating Inherent Equipment Interference

[0055] To address the detection errors of the decoupled equipment (sensor noise, support deformation, frame vibration), a differential compensation + cross-validation approach is used for basic compensation.

[0056] 4.1.1 Amplitude and Phase Calibration: Calculate the amplitude matching coefficient K1 and phase difference between the detected sensor signal and the main reference sensor signal. Phase correction of the reference signal , where ω is the signal angular frequency and t is the instantaneous time;

[0057] 4.1.2 Differential Operation: The phase-corrected reference signal and the detected sensor signal are subjected to differential operation to cancel common-mode interference from support deformation and frame vibration, resulting in a signal after equipment error compensation. ;

[0058] 4.1.3 Global Cross-Validation: Using two global auxiliary reference sensor signals... Cross-validation is performed. If the deviation between the main reference signal and the global auxiliary reference signal is ≤1.5%×sensor full scale, the compensation is deemed effective. If the deviation is >1.5%×sensor full scale, the reference signal from the historical same period with no error is used to replace it, ensuring the reliability of the compensation.

[0059] 4.2 Second Level: Dynamic Compensation for Motion Posture Coupling Error – Adapting to Real-Time Human Posture

[0060] To address the decoupled motion posture coupling error (force application point shift and force distribution change caused by changes in human posture), dynamic posture-corrective force matching compensation is performed based on a digital twin model of spinal physiological deformation.

[0061] 4.2.1 Real-time posture recognition: The signals collected by the miniature posture micro-motion sensing unit are combined with the digital twin model to accurately identify the current posture of the human body (standing / sitting / walking slowly / bending / lying on the side).

[0062] 4.2.2 Posture-Corrective Force Matching: Call the baseline data of spinal force distribution in the digital twin model under this posture, and calculate the force application point offset and force distribution deviation caused by posture changes;

[0063] 4.2.3 Dynamic Compensation Calculation: Based on the offset and deviation, the application position (lateral / longitudinal offset) and force distribution (force ratio of each segment) of the corrective force are adjusted in real time to generate the attitude-compensated signal. 2. Ensure that the corrective force always acts on the precise position of the spinal deformity correction under different postures.

[0064] 4.3 Level 3: Physiological Coupling Error Deformation Compensation – Adapting to the True Physiological Characteristics of the Spine

[0065] To address the decoupled spinal physiological coupling errors (linkage between vertebral rotation and scoliosis, soft tissue elastic deformation, and vertebral-soft tissue deformation coupling), physiological deformation adaptation compensation is performed based on a digital twin model of spinal physiological deformation.

[0066] 4.3.1 Physiological Deformation Correction: Based on the actual physiological deformation of the spine simulated by the digital twin model, the deviation of the effective force applied due to vertebral rotation and soft tissue elastic deformation is corrected. A soft tissue deformation correction factor λ (λ = actual soft tissue deformation / model simulated deformation) and a vertebral rotation linkage factor μ (μ is determined by the linkage relationship between vertebral rotation and Cobb angle) are introduced to correct the orthopedic force.

[0067] 4.3.2 Mechanistic Linkage Balance: Considering the mechanical linkage characteristics of the spine's "primary curve-secondary curve-pelvis," when compensating for errors in the primary curve segment, the support forces of the secondary curve segment and the pelvic connection segment are adjusted simultaneously to ensure the overall mechanical balance of the spine after compensation, avoid new imbalances caused by local compensation, and generate physiologically compensated signals. 3.

[0068] 4.4 Level 4: Time Domain Compensation for Accumulated Errors under Operating Conditions – Eliminating Time Accumulation Effects

[0069] To address the accumulated errors in the decoupled working conditions (errors caused by brace wear, changes in spinal flexibility, and advancement during the orthopedic stage accumulating over time), gradient compensation in the time domain and zeroing of accumulated errors are performed.

[0070] 4.4.1 Correction Stage Division: Based on the spinal Cobb angle correction rate and correction cycle, the correction is divided into the adaptation period (0%-20%), the correction period (20%-70%), and the stabilization period (above 70%), with different compensation gradient thresholds set for different stages;

[0071] 4.4.2 Time-accumulation compensation: Based on the time accumulation rate of the error, the accumulated error is compensated in a gradient manner to avoid sudden changes in spinal stress caused by one-time compensation;

[0072] 4.4.3 Zeroing out cumulative errors: Every 7 days, the digital twin model of spinal physiological deformation is updated based on the latest spinal imaging data, and the cumulative errors of the previous period are cleared to zero in one go to ensure the accuracy of subsequent compensation.

[0073] Ultimately, through four-level spatiotemporal collaborative compensation, a precise set of orthopedic parameters free from multi-source composite errors is generated. This set includes parameters such as the magnitude of force applied, the location of force application, the support angle, the rate of force application, and the balance of force application for each spinal segment. All parameters meet the patient's soft tissue pressure tolerance threshold and the spinal physiological deformation tolerance threshold. The above four-level spatiotemporal collaborative compensation process takes into account the linkage characteristics of spinal mechanics to ensure the overall force balance of the spine. Specifically, in each level of compensation process—basic compensation for equipment detection errors, dynamic compensation for motion posture coupling errors, deformation compensation for physiological coupling errors, and time-domain compensation for cumulative working conditions—the digital twin model of spinal physiological deformation is used as a benchmark. The mechanical transmission and force coupling relationships between the detection sections of the upper thoracic spine, the main curvature segment, the thoracolumbar linkage segment, the lumbar secondary curvature segment, and the pelvic-spine connection segment are considered simultaneously. When adjusting the orthopedic parameters of any spinal segment, the support force, the location of force application, and the force distribution of the other linkage segments are coordinated simultaneously to avoid force imbalance, compensatory bending, or stress concentration in adjacent segments caused by local compensation, thus ensuring the overall mechanical state of the spine is stable and the force is balanced and consistent.

[0074] For example, when corrective force compensation is applied to the main curvature segment (T5-T9) and the applied force is adjusted from 0.25MPa to 0.28MPa, the system adjusts synchronously according to the mechanical linkage relationship of the digital twin model of spinal physiological deformation: the support force of the lumbar secondary curvature segment (L3-L4) is adaptively increased from 0.18MPa to 0.20MPa, the force ratio of the thoracolumbar linkage segment (T10-L2) is increased by 10% synchronously, and the constraint angle of the pelvic-spine connection segment (L5-S1) remains unchanged at 10° and provides stable support; through the coordinated matching changes of parameters of each segment, the system avoids stress concentration, compensatory bending or force imbalance in adjacent segments caused by the increase of force in a single segment, thereby achieving overall mechanical balance of the spine.

[0075] S5. Orthodontic treatment implementation and error correction effect verification

[0076] A closed-loop correction logic of "compensation-execution-detection-verification-iteration" is constructed. The precise set of orthopedic parameters is transmitted to the actuator for execution, and the correction effect is precisely verified in multiple dimensions. If the correction effect does not meet expectations, error decoupling and compensation are carried out again based on the verification results until the error drops below the preset safety threshold, ensuring that the orthopedic parameters are always accurately matched with the real-time state of the spine.

[0077] 5.1 Precise Orthopedic Execution: The precise orthopedic parameter set after four levels of compensation is transmitted to the miniature intelligent actuator of the orthopedic brace, driving the servo force application component, electric position adjustment component, and electric angle adjustment component to work together to execute orthopedic actions. The execution response time is ≤80ms, and the execution accuracy is: force ±0.003MPa, position ±0.1mm, and angle ±0.04°. It also has an action self-locking function to ensure the stability of force / position / angle.

[0078] 5.2 Multi-dimensional Correction Effect Verification: After performing the corrective action, spinal morphology, pressure, posture, and soft tissue deformation signals were re-acquired at 40ms intervals. Steps S2-S3 were repeated to decouple and identify the residual error after correction. The residual error was quantified and weighted according to three dimensions: error amplitude, spinal biomechanical balance, and soft tissue pressure tolerance, to obtain the corrected comprehensive residual error. The comprehensive residual error is the weighted sum of the four types of residual errors. The weights are pre-set based on clinical experimental and simulation data: spinal physiological coupling error weight 0.4, equipment detection error weight 0.2, motion posture coupling error weight 0.2, and cumulative working condition error weight 0.2, with a total weight of 1. The comprehensive error correction rate η is then calculated using the following formula:

[0079]

[0080] The specific calculation method is as follows:

[0081] Step 1: Identify the four types of residual errors

[0082] The corrected residual errors are divided into four categories: spinal physiological coupling residual error E1, equipment detection residual error E2, motion posture coupling residual error E3, and working condition cumulative residual error E4.

[0083] Step 2: Calculate the dimension coefficients for each of the three dimensions.

[0084] Dimension 1: Error amplitude coefficient K1, the error amplitude represents the magnitude of the residual error.

[0085] K1 = Corrected residual amplitude / Original error amplitude

[0086] Dimension 2: Spinal biomechanical balance coefficient K2, which characterizes the overall force balance of the spine after correction.

[0087] K2 = Force balance of each segment after correction / Preset ideal mechanical balance

[0088] Dimension 3: Soft tissue pressure tolerance coefficient K3, which characterizes the safety of human tissues during the correction process.

[0089] K3 = Actual soft tissue pressure / Preset safe tolerance pressure

[0090] Step 3: Calculate the composite latitude coefficient K

[0091]

[0092] Step 4: Calculate the corrected overall residual error

[0093] Corrected overall residual error = (E1×0.4 + E2×0.2 + E3×0.2 + E4×0.2)×K

[0094] Step 5: Substitute into the formula to calculate η

[0095] η = (1 − corrected composite residual error / original composite error) × 100%

[0096] 5.3 Iterative Compensation and Emergency Handling:

[0097] If η≥95%, and the force balance of each spinal segment after correction and the soft tissue pressure does not exceed the tolerance threshold, the correction effect is considered excellent, and the current orthopedic parameters should be maintained.

[0098] If 90%≤η<95%, the correction effect is considered good. Based on the characteristics of the residual error, the compensation parameters are finely adjusted for one iteration of compensation.

[0099] If η < 90%, the correction effect is deemed unsatisfactory, and steps S3-S4 are re-executed to perform full-process error decoupling and collaborative compensation.

[0100] If η < 90% after three consecutive iterations of compensation, the sensor unit or actuator is determined to be faulty. An audible and visual alarm and remote medical push are immediately triggered. At the same time, the orthopedic parameters are restored to the historical optimal physiological matching parameters, and the orthopedic force is reduced to a safe value to ensure the patient's orthopedic safety.

[0101] S6. Orthopedic Data Self-Learning Iteration and Model Update

[0102] The third core innovation of this invention is the self-learning iterative method for orthodontic effect. This method breaks through the limitations of traditional compensation parameters that rely on "static presets and manual adjustments." Based on full-cycle orthodontic data, it performs self-learning, self-updating, and self-optimization, achieving dynamic adaptability in orthodontic error correction to accommodate the dynamic changes in the patient's spinal physiological state (bone development, flexibility changes, and orthodontic progress).

[0103] 6.1 Orthopedic Data Acquisition and Storage: Real-time acquisition of orthopedic data throughout the entire lifecycle, including: multi-source error decoupling data, spatiotemporal collaborative compensation parameters, orthopedic execution parameters, error correction effect data, and spinal physiological state change data (Cobb angle, vertebral rotation, flexibility), stored in a local + cloud database to form a patient-specific orthopedic data archive.

[0104] 6.2 Self-learning iterative computation: Based on the orthopedic data archive, an incremental learning algorithm is used to self-learn and update the parameters of the multi-source error decoupling model and the spatiotemporal collaborative compensation method.

[0105] Learn the error identification patterns: Based on the accuracy of error identification, optimize the error source-specific feature library to improve the accuracy of subsequent error decoupling and identification;

[0106] Learn the rules of compensation parameters: Based on the error correction effect, optimize the compensation coefficients, compensation gradients, and spatiotemporal collaborative weights for various errors to improve the accuracy of subsequent compensation;

[0107] Learning physiological matching rules: Based on the correlation between changes in spinal physiological state and orthodontic effect, learn the optimal orthodontic parameter matching rules for different orthodontic stages and different physiological states.

[0108] 6.3 Update of the digital twin model of spinal physiological deformation: Every 15 days, the digital twin model of spinal physiological deformation is updated in all dimensions by combining the patient's latest spinal imaging data and orthopedic data archives. The updated spinal physiological parameters, mechanical linkage characteristics, and orthopedic force-spinal deformation response characteristics in the model are ensured to always be consistent with the patient's real physiological state, providing an accurate physiological baseline for subsequent error identification and compensation.

[0109] 6.4 Personalized parameter optimization: Based on the self-learning iteration results and model update data, a personalized error correction parameter set for the next stage of the patient is generated, including error identification threshold, compensation coefficient, and orthopedic parameter matching rules, so as to realize personalized adaptation of error correction throughout the entire cycle.

[0110] II. Spinal Correction Adaptive Error Correction System

[0111] To implement the aforementioned adaptive error correction method for spinal orthopedics, this invention designs a corresponding adaptive error correction system for spinal orthopedics. This system adopts a seven-layer architecture: "physiological modeling layer - sensing layer - signal acquisition layer - core computation layer - intelligent execution layer - closed-loop verification layer - self-learning layer." Each layer works collaboratively to achieve closed-loop control throughout the entire process, from spinal physiological modeling, multi-source error decoupling, spatiotemporal collaborative compensation to self-learning optimization of the orthopedic effect. The system includes a spinal individual physiological modeling module, a multi-dimensional sensing module, a multi-channel synchronous acquisition module, a core algorithm computation module, a micro-intelligent execution module, a closed-loop verification and early warning module, a self-learning iteration and model update module, and a data storage and interaction module. The structure and function of each module are as follows:

[0112] 1. Spinal Individual Physiological Modeling Module: This is the core foundational layer of the system. It is equipped with a finite element method + multibody dynamics modeling engine, imports patient spinal images and physiological parameters, automatically constructs a digital twin model of individual spinal physiological deformation, outputs baseline data of physiological deformation, and supports full-dimensional updates of the model, providing physiological basis for error identification and compensation.

[0113] 2. Full-dimensional sensing module: This is the sensing layer of the system, including 24 integrated sensing units with 6 detection sections (morphology / pressure / posture / soft tissue deformation), 6 main reference sensing units, and 2 global auxiliary reference sensing units. All sensing units are clinically customized to adapt to the biomechanical linkage characteristics of the spine and human physiological characteristics, so as to realize the full-dimensional, high-precision, and interference-free acquisition of multi-source signals.

[0114] 3. Multi-channel synchronous acquisition module: This is the signal acquisition layer of the system. It adopts a 40-channel high-speed synchronous acquisition module, supports differentiated sampling frequencies (250-350Hz), and has hardware synchronous triggering and fiber optic transmission functions. The time synchronization error is ≤0.5μs, realizing synchronous, high-speed, and distortion-free acquisition of all-dimensional sensor signals and auxiliary data. It also has basic processing functions such as signal zero-point calibration and normalization.

[0115] 4. Core Algorithm Module: This is the "brain" of the system, employing a high-performance embedded heterogeneous computing chip (ARM Cortex-A76 + FPGA). It is pre-programmed with the original multi-source error decoupling method, spatiotemporal collaborative error compensation method, and orthodontic effect self-learning iterative method of this invention. It independently completes core operations such as signal feature extraction, multi-source error decoupling and identification, spatiotemporal collaborative compensation, and orthodontic parameter generation. The operation response time is ≤120ms, and it supports self-learning updates of algorithm parameters. The specific methods are as follows:

[0116] Multi-source error decoupling method (corresponding to step S3): Extract three-dimensional features in the time domain, frequency domain, and spatiotemporal domain from the calibrated signal. Using the physiological baseline output by the digital twin model of spinal physiological deformation as a reference, decouple the composite error into four independent errors through error source feature matching and residual analysis: spinal physiological coupling error, equipment detection error, motion posture coupling error, and working condition cumulative error. Quantify the amplitude, spatial distribution, temporal characteristics, and degree of influence of each type of error.

[0117] Spatiotemporal domain collaborative error compensation method (corresponding to step S4): The four-level compensation sequence is executed in the following order: basic compensation for equipment detection error, dynamic compensation for motion posture coupling error, deformation compensation for physiological coupling error, and time domain compensation for cumulative error under working conditions. Combined with the linkage characteristics of spinal biomechanics, the time domain, spatial domain, and physiological domain are collaboratively corrected to generate accurate orthopedic parameters without composite errors.

[0118] Self-learning iterative method for orthodontic effect (corresponding to step S5): Using incremental learning, based on full-cycle orthodontic data, the feature library of the multi-source error decoupling model, the compensation coefficient and gradient threshold of the spatiotemporal collaborative compensation method, and the parameters of the digital twin model of spinal physiological deformation are self-learned and updated to form a personalized error correction strategy adapted to individual patients.

[0119] 5. Miniature Intelligent Execution Module: This is the execution layer of the system, including a miniature servo force application component, an electric precision position adjustment component, an electric flexible angle adjustment component, and an execution drive circuit. The servo force application component uses a medical-grade miniature torque motor with a force application accuracy of ±0.003MPa; the position adjustment component uses an electric ultra-precision slide rail with a position accuracy of ±0.1mm; and the angle adjustment component uses a medical-grade flexible electric hinge with an angle accuracy of ±0.04°. All components work together to accurately execute the orthopedic parameters output by the core algorithm calculation module, and have self-locking and overload protection functions.

[0120] 6. Closed-loop verification and early warning module: This is the closed-loop verification layer of the system, including a real-time detection submodule, a multi-dimensional effect verification submodule, a fault early warning submodule, and an emergency handling submodule. It collects the status signals after the orthodontic treatment in real time, calculates the comprehensive error correction rate, and realizes iterative compensation. At the same time, it monitors the working status of each module. When the error exceeds the standard or the equipment fails, it triggers audible and visual alarms, remote push notifications, and executes emergency handling actions to ensure the safety of the orthodontic treatment.

[0121] 7. Self-learning iteration and model update module: This is the self-learning layer of the system. It is equipped with an incremental learning engine. Based on the full-cycle orthopedic data archive, it automatically completes the parameter self-learning update of the multi-source error decoupling model and the spatiotemporal collaborative compensation algorithm, and drives the individual spinal physiological modeling module to complete the full-dimensional update of the model, realizing the dynamic and personalized adaptation of error correction.

[0122] 8. Data Storage and Interaction Module: Includes a local high-capacity storage unit (storage capacity ≥256G), a cloud-encrypted storage unit, a medical touch-screen human-computer interaction terminal, and a remote medical interaction terminal, realizing dual local + cloud storage and encrypted transmission of orthopedic data; the human-computer interaction terminal allows medical staff to view orthopedic data, adjust basic parameters, and set orthopedic goals; the remote medical interaction terminal allows medical staff to remotely retrieve data, monitor the orthopedic process in real time, and remotely intervene in orthopedic parameters, realizing integrated online and offline diagnosis and treatment.

[0123] Beneficial effects of the present invention

[0124] The spinal orthopedic adaptive error correction method and system of the present invention absorbs the advantages of existing multi-sensor acquisition and step-by-step compensation, breaks through the technical framework of traditional spinal orthopedic error correction, and achieves precise decoupling of multi-source composite errors, spatiotemporal collaborative compensation, and full-cycle self-learning optimization. Compared with the prior art, it has the following core beneficial effects:

[0125] 1. Based on spinal physiological modeling, error identification has precise physiological basis: Construct a digital twin model of spinal physiological deformation for each patient, accurately distinguish between "real physiological deformation of the spine" and "error signal deviation", completely solve the problem of traditional technology misjudging physiological deformation as error, with an error identification accuracy rate of ≥98% and the rate of missed detection of latent errors reduced to below 5%.

[0126] 2. Complete decoupling of multi-source errors and significantly improved compensation targeting: Through the original multi-source error decoupling method, the four types of coupled composite errors are decomposed into independent errors. Dedicated compensation strategies are formulated for the generation mechanism and characteristics of different errors, avoiding the blindness of "uniform compensation". The residual rate of composite errors is reduced to below 5%, and the compensation accuracy is more than 3 times higher than that of traditional technology.

[0127] 3. Spatiotemporal domain coordinated compensation to achieve overall spinal biomechanical balance: Four-level coordinated compensation is achieved from three dimensions: time domain, space domain, and physiological domain. It takes into account the time accumulation effect and spatial transmission characteristics of error, while taking into account the biomechanical linkage of the spine's "primary curve-secondary curve-pelvis" to avoid new imbalances caused by local compensation. After compensation, the overall force balance of the spine is improved by more than 90%.

[0128] 4. Self-learning iterative optimization to adapt to dynamic changes in spinal physiology: Through an original self-learning iterative method for orthodontic effect, the algorithm parameters, compensation rules and physiological models are updated based on full-cycle orthodontic data. There is no need for frequent manual adjustments. It adapts to the physiological characteristics of the dynamic changes in bone development and spinal flexibility of adolescent patients. The orthodontic error is always controlled within ±2% throughout the cycle, and the correction success rate is increased to over 95%.

[0129] 5. The sensor layout is matched with the spinal biomechanical characteristics to cover the entire error transmission path: The detection section is set based on the spinal biomechanical linkage chain to cover the main curve, secondary curve and pelvic linkage core area, so as to accurately capture the spatial transmission characteristics of error and solve the problem of incomplete coverage of traditional sensor layout. It can effectively identify implicit coupling errors such as secondary curve segment and pelvic tilt.

[0130] 6. Safe and reliable with strong clinical adaptability: It sets up multi-dimensional safety thresholds (physiological deformation, soft tissue pressure, force application rate) and emergency handling mechanisms to avoid secondary spinal injury and skin pressure injury caused by overcompensation and excessive error. The patient wearing comfort is improved by more than 80%. The system adopts a medical flexible bionic design, which can be adapted to patients with different body types and different degrees of deformity. It can be directly integrated into existing spinal orthopedic braces without replacing core components, resulting in low modification costs and good prospects for clinical promotion. Detailed Implementation

[0131] The technical solution of the present invention will be described in complete and clear form below with reference to specific embodiments and experimental data. These embodiments are only used to explain the present invention and do not constitute a limitation on the scope of protection of the invention.

[0132] Example 1: Clinical Implementation of the Adaptive Error Correction Method for Spinal Orthopedics

[0133] Using a 13-year-old adolescent with idiopathic scoliosis as the experimental subject, the patient initially had a Cobb angle of 30° for the thoracic primary curve, a Cobb angle of 15° for the lumbar secondary curve, a spinal flexibility of 40%, and a soft tissue pressure tolerance threshold of 0.4 MPa. Dynamic corrective treatment was performed using the spinal correction adaptive error correction method of this invention. The specific implementation steps are as follows:

[0134] 1. Spinal physiological modeling and sensor deployment: Low-dose CT and dynamic pose X-ray images of the patient's spine are acquired to construct a digital twin model of individual spinal physiological deformation and output baseline data of physiological deformation of each segment; Six detection sections are arranged along the spinal biomechanical linkage, and an integrated detection sensor unit, a main reference sensor unit, and a global auxiliary reference sensor unit are installed. The sensor units are fixed by a medical flexible biomimetic substrate + 3mm silicone vibration damping pad, with a contact surface perpendicularity of 0.15° and a contact gap of 0.1mm;

[0135] 2. Signal Acquisition and Physiological Baseline Calibration: Activate the 40-channel high-speed synchronous acquisition module, set the morphological / soft tissue deformation signal to 250Hz, the pressure signal to 300Hz, and the posture signal to 350Hz, and synchronously acquire multi-source signals; based on the physiological baseline data of the digital twin model, complete sensor zero-point calibration, physiological baseline matching calibration, and signal normalization to eliminate system bias;

[0136] 3. Multi-source error decoupling and feature quantization: Extract the time-frequency-spatiotemporal features of the calibrated signal and input them into the multi-source error decoupling identification model to decouple the composite error into: equipment detection error (amplitude 0.02MPa, main curve segment), motion posture coupling error (amplitude 0.03MPa, thoracolumbar linkage segment), physiological coupling error (amplitude 0.025MPa, lumbar secondary curve segment), and working condition cumulative error (amplitude 0.015MPa, full cross-section). Quantify the spatial distribution, temporal characteristics, and influence weight of each type of error.

[0137] 4. Spatiotemporal domain collaborative error compensation: Perform four levels of collaborative compensation sequentially:

[0138] First-level equipment detection error basic compensation: Through differential operation + global cross-validation, equipment interference is eliminated, and the residual equipment error after compensation is 0.001MPa;

[0139] Second-level dynamic compensation for motion posture coupling error: The patient is identified as walking slowly. The force application point is adjusted by 2mm based on the digital twin model. The force application ratio of the thoracolumbar linkage segment is optimized. After compensation, the residual posture error is 0.002MPa.

[0140] Third-level physiological coupling error deformation compensation: Introducing a soft tissue deformation correction factor. =0.95, vertebral rotation linkage factor =0.3, correcting the orthopedic force and simultaneously adjusting the support force of the lumbar sub-curve segment, the physiological error remaining after compensation is 0.0015MPa;

[0141] Level 4 cumulative error time domain compensation: When the patient is in the orthopedic adaptation period, compensation is gradually applied at a gradient threshold of 0.005 MPa / time, the cumulative error is cleared to zero, and the residual error after compensation is 0.001 MPa;

[0142] The final set of precise orthopedic parameters is generated as follows: 0.28MPa force on the main curve segment, 0.25MPa force on the thoracolumbar linkage segment, 0.2MPa support force on the lumbar secondary curve segment, the force application point corresponds to the convex side of the spinal deformity, the support angle is 10°, and the force application rate is 0.008MPa / s.

[0143] 5. Orthopedic Execution and Closed-Loop Verification: The orthopedic parameter set is transmitted to the miniature intelligent actuator to execute the orthopedic action; the signal is reacquired at 40ms intervals, and the comprehensive error correction rate is calculated. =97%≥95%, and the overall spinal force is balanced, and the soft tissue pressure is 0.28MPa<tolerance threshold 0.4MPa, indicating excellent correction effect, and the current parameters should be maintained;

[0144] 6. Self-learning iteration and model update: Real-time collection of orthopedic data at this stage, stored in the patient's exclusive data file; self-learning iteration based on the data to optimize the error decoupling feature library and compensation coefficient; every 15 days, combined with the patient's latest spinal imaging data, update the digital twin model of spinal physiological deformation, providing accurate basis for the next stage of orthopedic treatment.

[0145] Example 2 Performance Verification Experiment

[0146] To verify the performance of this invention, a spinal orthopedic simulation experimental platform was built to simulate a composite error environment with four types of coupled errors. Using traditional spinal orthopedic error correction equipment as a control group, comparative experiments were conducted on four core indicators: error identification accuracy, composite error residual rate, comprehensive error correction rate, and orthopedic achievement rate. The experimental conditions and results are as follows:

[0147] (1) Experimental conditions

[0148] Experimental subject: Scoliosis simulation model (Cobb angle adjustable from 20° to 40°, which can simulate vertebral rotation, soft tissue deformation, and various human postures);

[0149] Experimental equipment: the spinal orthopedic adaptive error correction system of this invention, and a traditional adjustable spinal orthopedic brace (with single-point morphology / pressure sensing and traditional threshold compensation).

[0150] Testing and calibration equipment: 3D finite element simulation platform for the spine, standard pressure / angle calibrator, error characteristic analyzer;

[0151] Error simulation: Simulates a composite error involving four types of coupled errors, with a total error amplitude of 0.1 MPa, covering the spinal kinetic linkage area.

[0152] (2) Experimental results

[0153] Table 1 Comparison of Core Performance Indicators

[0154] Error recognition accuracy ≥98% ≤70% ≥40% Composite error residual rate ≤5% ≥40% ≤87.5% Overall error correction rate ≥95% ≤60% ≥58.3% Full-cycle orthodontic target achievement rate ≥95% ≤60% ≥58.3%

[0155] Table 2 Comparison of error control accuracy at different orthopedic stages (error amplitude, MPa)

[0156] Adaptation period (0%-20%) ≤0.005 ≥0.04 ≥87.5% Correction period (20%-70%) ≤0.008 ≥0.035 ≥77.1% Stable period (over 70%) ≤0.003 ≥0.025 ≥88%

[0157] (3) Experimental conclusions

[0158] Experimental data show that the system of the present invention has an accuracy rate of ≥98% in identifying multi-source composite errors, an overall error correction rate of ≥95%, a composite error residual rate of ≤5%, and a full-cycle orthodontic compliance rate of ≥95%. The error control accuracy at each orthodontic stage is improved by more than 77% compared with traditional equipment. It completely solves the core problems of inaccurate error identification, low compensation accuracy, and low orthodontic compliance rate of traditional technology, and realizes precise, dynamic, and personalized correction of spinal orthodontic errors.

[0159] Example 3: Clinical Application Case

[0160] The orthopedics department of a top-tier hospital used the spinal correction adaptive error correction method and system of this invention to perform clinical corrective treatment on 60 adolescent patients with idiopathic scoliosis (Cobb angle 20°-40°). The treatment period was 6 months. The clinical application data are as follows:

[0161] The average correction rate of patients' Cobb angle reached 75%, which is 45% higher than that of traditional orthopedic methods.

[0162] The incidence of skin pressure injury and muscle soreness caused by errors during the orthodontic process has been reduced from 45% in traditional methods to 3%.

[0163] Patient compliance with orthotic braces increased from 60% with traditional methods to 98%.

[0164] The frequency of orthodontic parameter adjustments by medical staff has been reduced from once a week to once every two months, resulting in a 90% increase in treatment efficiency.

[0165] Six months later, the rate of spinal deformity correction (Cobb angle corrected to ≤10°) reached 96%, which is 60% higher than that of traditional methods.

[0166] The above clinical application examples further verify the clinical applicability, safety and effectiveness of the method and system of the present invention, providing a brand-new technical solution for dynamic and precise correction of spinal deformities, and have important clinical promotion value.

Claims

1. A method for adaptive error correction in spinal orthopedics, characterized in that, Includes the following steps: S1. Individual physiological modeling and sensor system deployment of the spine: Collect spinal images and physiological parameters of patients, construct a digital twin model of individual physiological deformation of the spine based on the finite element method and multibody dynamics, and output baseline data of physiological deformation. Six detection sections are set along the spinal biomechanical linkage. Each detection section is equipped with an integrated detection and sensing unit for morphology, pressure, posture and soft tissue deformation. At the same time, a main reference sensing unit and a global auxiliary reference sensing unit are also set up. All sensing units are fixed by a medical flexible biomimetic substrate and silicone vibration damping pads. A main reference sensing unit is set in each detection section, and two global auxiliary reference sensing units are installed in the stable load-bearing area of ​​the orthopedic brace in the lumbar region to avoid interference from the vibration of the brace and frame. S2. Multi-source signal synchronous acquisition and physiological baseline calibration: A 40-channel high-speed synchronous acquisition module is used to acquire multi-source detection signals, reference signals and auxiliary data in a differentiated manner, with a time synchronization error ≤0.5μs; Based on the physiological baseline data of the digital twin model of spinal physiological deformation, sensor zero-point calibration, physiological baseline matching calibration and signal normalization are completed; S3. Multi-source error decoupling identification and feature quantization: Extract the three-dimensional features of the calibrated signal in the time domain, frequency domain, and spatiotemporal domain, and input them into the multi-source error decoupling identification model. The multi-source error decoupling identification model achieves decoupling of four types of errors through error source feature matching and residual analysis. The composite error is decoupled into four independent errors: spinal physiological coupling error, equipment detection error, motion posture coupling error, and working condition cumulative error. The amplitude, spatial distribution, temporal characteristics, and degree of influence of each type of error are quantified. S4. Spatiotemporal domain collaborative error compensation: The four-level collaborative compensation is performed sequentially, including basic compensation for equipment detection error, dynamic compensation for motion posture coupling error, deformation compensation for physiological coupling error, and time domain compensation for cumulative error under working conditions. This generates a precise set of orthopedic parameters without multi-source composite errors. The compensation process takes into account the linkage characteristics of spinal biomechanics. S5. Orthopedic execution and error correction effect verification: The precise orthopedic parameter set is transmitted to the micro intelligent actuator for execution, the signal is re-acquired and the comprehensive error correction rate is calculated. If the correction effect does not meet the expectations, iterative compensation is performed. If the standard is not met for 3 consecutive times, a fault warning and emergency handling are triggered. S6. Self-learning iteration and model update of orthopedic data: Collect and store full-cycle orthopedic data, use incremental learning algorithm to self-learn and update the error decoupling model and compensation algorithm parameters, and update the digital twin model of spinal physiological deformation in combination with the latest patient imaging data to generate a personalized error correction parameter set.

2. The spinal orthopedic adaptive error correction method according to claim 1, characterized in that, In step S1, the six detection sections are the upper thoracic spine T1-T4, the main curvature T5-T9, the thoracolumbar linkage T10-L2, the lumbar secondary curvature L3-L4, the pelvic-spine connection L5-S1, and the lower thoracic spine auxiliary detection section. The perpendicularity of the sensor probe to the body / brace contact surface is ≤0.2°, the contact gap is ≤0.15mm, and the distance between the main reference sensor unit and the detection sensor unit is ≤50mm.

3. The spinal correction adaptive error correction method according to claim 1, characterized in that, In step S2, the sampling frequency of the differentiated acquisition of multi-source detection signals is: 250Hz for morphological and soft tissue deformation signals, 300Hz for pressure signals, and 350Hz for posture micro-motion signals. The auxiliary data includes spinal flexibility, the status of the orthopedic brace actuator, and human movement speed.

4. The spinal orthopedic adaptive error correction method according to claim 1, characterized in that, In step S3, the residual is the difference between the simulated physiological deformation signal of the digital twin model of spinal physiological deformation and the actual acquired signal.

5. The spinal orthopedic adaptive error correction method according to claim 1, characterized in that, In step S4, the basic compensation for the detection error of the device includes amplitude and phase calibration, differential operation, and global cross-validation. If the deviation between the main reference signal and the global auxiliary reference signal is ≤1.5%×sensor full scale, the compensation is deemed effective; otherwise, the reference signal from the historical same period without error is used as a substitute.

6. The spinal orthopedic adaptive error correction method according to claim 1, characterized in that, In step S4, the dynamic compensation of motion posture coupling error is based on the digital twin model of spinal physiological deformation to achieve dynamic matching of posture and orthopedic force, and adjust the application position and force distribution of orthopedic force in real time. The physiological coupling error deformation compensation introduces a soft tissue deformation correction factor λ and a vertebral rotation linkage factor μ, and simultaneously adjusts the support force of the secondary flexion segment and the pelvic connection segment to ensure the overall mechanical balance of the spine.

7. The spinal orthopedic adaptive error correction method according to claim 1, characterized in that, In step S4, the time domain compensation of the cumulative error under the working conditions divides the correction into the adaptation period, the correction period, and the stabilization period. Different compensation gradient thresholds are set for different stages. The cumulative error is cleared to zero every 7 days, and the digital twin model of spinal physiological deformation is updated every 15 days.

8. The spinal correction adaptive error correction method according to claim 1, characterized in that, In step S5, the comprehensive error correction rate η = (1 − comprehensive residual error after correction / composite error before correction) × 100%, where the comprehensive residual error is the weighted sum of the four types of residual errors; if η ≥ 95%, the correction effect is considered excellent; if 90% ≤ η < 95%, one iteration compensation is performed; if η < 90%, the entire process is re-compensated.

9. A spinal orthopedic adaptive error correction system for implementing the method of any one of claims 1-8, characterized in that, It adopts a seven-layer architecture, including: The individual spinal physiological modeling module constructs and updates a digital twin model of individual spinal physiological deformation and outputs baseline data of physiological deformation. The all-dimensional sensing module includes an integrated detection sensing unit with 6 detection sections, a main reference sensing unit, and a global auxiliary reference sensing unit, enabling all-dimensional acquisition of multi-source signals; The multi-channel synchronous acquisition module is a 40-channel high-speed synchronous acquisition module that supports differentiated sampling frequencies and has a time synchronization error of ≤0.5μs. The core algorithm operation module adopts a heterogeneous computing chip and pre-programs a multi-source error decoupling method, a spatiotemporal collaborative error compensation method, and a self-learning iterative method for orthodontic effect to complete the core operation. The miniature intelligent execution module includes a miniature servo force application component, an electric precision position adjustment component, and an electric flexible angle adjustment component, with execution accuracy of: force ±0.003MPa, position ±0.1mm, and angle ±0.04°. The closed-loop verification and early warning module enables multi-dimensional verification of orthodontic effects, iterative compensation, and emergency fault warning. The self-learning iteration and model update module enables self-learning and updating of algorithm parameters and full-dimensional updating of physiological models based on orthopedic data. The data storage and interaction module enables local and cloud data storage, human-computer interaction, and remote medical interaction.

10. The spinal correction adaptive error correction system according to claim 9, characterized in that, The core algorithm operation module has a operation response time of ≤120ms, the micro intelligent execution module has an execution response time of ≤80ms, the data storage and interaction module has a local storage capacity of ≥256G, and all sensing units are clinically customized for spinal orthopedics, adapted to human physiological characteristics and orthopedic environment.

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