NFC sensor-based oral implant status monitoring system and method

Abstract

The application relates to an oral implant state monitoring system and method based on an NFC sensing, relates to the technical field of state monitoring, and comprises the following: a radio frequency coupling state parameter acquisition module, which is used for acquiring radio frequency coupling state parameters through NFC; a sensing signal acquisition module, which is used for acquiring first, second and third sensing signals; a radio frequency power supply fluctuation compensation analysis module, which is used for acquiring input load parameters, overall load transmission parameters and local connection state parameters; and a risk coupling analysis module, which is used for establishing an oral implant state risk coupling graph. The technical problems that, in the prior art, active sensor power supply is limited, single physical quantity sensing is difficult to comprehensively evaluate risks, and passive sensing signals are easily distorted by power supply fluctuations and medium interference in a complex oral dynamic environment are solved, and the technical effects that the real-time performance of oral implant state monitoring, the quantitative precision of multidimensional mechanical parameters and the reliability of early risk early warning are improved are achieved.

Description

Technical Field

[0001] This invention relates to the field of condition monitoring technology, specifically to a dental implant condition monitoring system and method based on NFC sensing. Background Technology

[0002] Currently, clinical assessment of dental implant status mainly relies on regular imaging examinations, such as periapical X-rays and cone-beam CT scans, and clinical manual examinations, such as percussion, palpation, modified plaque index, and patient subjective symptom feedback. However, this approach has many significant limitations. It cannot achieve real-time, continuous monitoring of implant stress, micromovement, and interface stability; it is highly dependent on physician experience, highly subjective, and cannot accurately quantify the actual dynamic load parameters borne by the implant during occlusion and changes in the overall load transmission path; attempts have been made to integrate active sensors within the implant, such as strain gauges and accelerometers, but these solutions are limited by the implant's limited space, battery power challenges, and bio-sealing requirements. These active solutions cannot achieve long-term, stable, and non-invasive in vivo monitoring and cannot comprehensively reflect the complex biomechanical coupling state of the implant-bone interface and the risk of loosening of local connectors. In recent years, near field communication (NFC) has utilized radio frequency field energy transmission and data interaction to enable battery-free operation of sensors. Its radio frequency field coupling characteristics are highly sensitive to changes in the electromagnetic parameters of the surrounding medium. However, most of these applications focus on monitoring environmental temperature and humidity or simple binary switch quantity detection. There is no solution that uses its radio frequency coupling state itself as a basis for dynamic compensation and systematically combines it with multi-dimensional physical sensing signals to solve the state decoupling and risk assessment of dental implants in complex oral dynamic environments such as temperature fluctuations, changes in salivary dielectrics, and transient loads.

[0003] Therefore, current technologies face several challenges, including limited power supply for active sensors, difficulty in comprehensively assessing risks from sensing a single physical quantity, and susceptibility of passive sensors to signal distortion due to power supply fluctuations and media interference in complex oral dynamic environments. Summary of the Invention

[0004] This application provides an NFC-based dental implant status monitoring system and method, which solves the technical problems existing in the prior art, such as limited power supply of active sensors, difficulty in comprehensively assessing risks by sensing a single physical quantity, and the susceptibility of passive sensors to signal distortion due to power supply fluctuations and media interference in complex oral dynamic environments. It achieves the technical effects of improving the real-time performance of dental implant status monitoring, the accuracy of multi-dimensional mechanical parameter quantification, and the reliability of early risk warning.

[0005] This application provides an NFC-based dental implant status monitoring system, comprising: a radio frequency coupling status parameter acquisition module, used to acquire radio frequency coupling status parameters by transmitting near-field radio frequency signals to a passive NFC sensing device disposed within the dental implant assembly via an NFC reader disposed outside the oral cavity; a sensing signal acquisition module, used to control a first sensor, a second sensor, and a third sensor to acquire a first sensing signal, a second sensing signal, and a third sensing signal, respectively, based on the radio frequency coupling status parameters; a radio frequency power supply fluctuation compensation analysis module, used to perform radio frequency power supply fluctuation compensation analysis on the first sensing signal, the second sensing signal, and the third sensing signal according to the radio frequency coupling status parameters, to acquire input load parameters, overall load transfer parameters, and local connectivity status parameters; and a risk coupling analysis module, used to perform dental implant status risk coupling analysis based on the input load parameters, the overall load transfer parameters, and the local connectivity status parameters, to establish a dental implant status risk coupling map.

[0006] In a possible implementation, the NFC-based dental implant status monitoring system includes: performing multi-dimensional evaluation of the radio frequency coupling status parameters to obtain voltage effectiveness, power supply stability, and resonance matching degree; weightedly fusing the voltage effectiveness, power supply stability, and resonance matching degree to obtain radio frequency coupling stability; determining whether the radio frequency coupling stability meets a preset stability threshold; if the radio frequency coupling stability meets the preset stability threshold, locking the impedance matching state and sensing signal conditioning gain of the passive NFC sensing device, and controlling the first sensor, the second sensor, and the third sensor to perform synchronous sampling to obtain the first sensing signal, the second sensing signal, and the third sensing signal.

[0007] In a possible implementation, the NFC-based dental implant status monitoring system includes: if the radio frequency coupling stability does not meet the preset stability threshold, identifying the radio frequency coupling anomaly type based on the radio frequency coupling status parameters; generating a radio frequency coupling adjustment strategy based on the radio frequency coupling anomaly type; performing radio frequency coupling adjustment based on the radio frequency coupling adjustment strategy, obtaining updated radio frequency coupling status parameters, and obtaining the first sensing signal, the second sensing signal, and the third sensing signal based on the updated radio frequency coupling status parameters.

[0008] In a possible implementation, the NFC-based dental implant status monitoring system includes: performing radio frequency power supply fluctuation compensation on the first, second, and third sensing signals according to the radio frequency coupling status parameters to obtain the compensated first, second, and third sensing signals; extracting time-domain features from the compensated first sensing signal to obtain the input load parameters; determining the overall load transfer parameters based on the amplitude ratio, phase difference, response delay, frequency response characteristics, and load / unload hysteresis characteristics between the compensated first and second sensing signals; and extracting the local connection status parameters based on the local frequency response characteristics of the compensated third sensing signal relative to the compensated first sensing signal, and the local response difference of the compensated third sensing signal relative to the compensated second sensing signal.

[0009] In a possible implementation, the NFC-based dental implant status monitoring system includes: constructing a current radio frequency coupling state vector based on the radio frequency coupling state parameters; obtaining a reference radio frequency coupling state vector corresponding to the passive NFC sensing device, and calculating a coupling state deviation vector of the current radio frequency coupling state vector relative to the reference radio frequency coupling state vector; constructing a first-channel radio frequency sensitive model; inputting the coupling state deviation vector into the first-channel radio frequency sensitive model to obtain a first zero-point drift estimate and a first sensitivity drift estimate; performing drift compensation on the first sensing signal based on the first zero-point drift estimate and the first sensitivity drift estimate to generate the compensated first sensing signal; and continuing to perform drift compensation on the second sensing signal and the third sensing signal based on the coupling state deviation vector to obtain the compensated second sensing signal and the compensated third sensing signal.

[0010] In a possible implementation, the NFC-based dental implant status monitoring system includes: when the first sensor is not subjected to an external load, controlling the NFC reading device to transmit signals to the passive NFC sensing device according to multiple preset radio frequency excitation states to obtain a first radio frequency excitation test dataset; calculating the zero-point drift and fitting the coupling state deviation mapping based on the first radio frequency excitation test dataset to obtain a zero-point drift calibration relationship layer; when the first sensor is subjected to a preset reference load, controlling the NFC reading device to transmit signals to the passive NFC sensing device according to the multiple preset radio frequency excitation states to obtain a second radio frequency excitation test dataset; calculating the sensitivity drift and fitting the coupling state deviation mapping based on the second radio frequency excitation test dataset to obtain a sensitivity drift calibration relationship layer; connecting the zero-point drift calibration relationship layer and the sensitivity drift calibration relationship layer as parallel nodes to generate the first channel radio frequency sensitivity model.

[0011] In a possible implementation, the NFC-based dental implant status monitoring system includes: comparing the input load parameters with an input load benchmark to obtain input load deviation characteristics, and predicting the risk of abnormal loading status based on the input load deviation characteristics to obtain abnormal loading status risk characteristics; comparing the overall load transfer parameters with an overall load transfer benchmark to obtain overall load transfer deviation characteristics, and predicting the risk of overall support status change based on the overall load transfer deviation characteristics to obtain overall support status change risk characteristics; predicting the abutment connection status change risk characteristics based on the local connection status parameters, and constructing the dental implant status risk coupling map by combining the abnormal loading status risk characteristics and the overall support status change risk characteristics.

[0012] In possible implementations, the radio frequency coupling state parameters include rectified voltage, NFC antenna terminal voltage, phase difference between voltage and current in the NFC antenna, received signal strength, modulation depth, communication bit error rate, and radio frequency coupling coefficient.

[0013] In a possible implementation, the first sensor is located at the coronal load input path of the dental implant assembly to acquire a first sensing signal characterizing the input load applied to the dental implant assembly; the second sensor is located at the position where the dental implant assembly is mechanically coupled with the surrounding support structure to acquire a second sensing signal characterizing the overall support state of the dental implant assembly; and the third sensor is located at the connection position between the implant abutment and the dental implant assembly to acquire a third sensing signal characterizing the local contact state of the abutment connection structure.

[0014] This application also provides a method for monitoring the status of dental implants based on NFC sensing. The method includes: transmitting near-field radio frequency signals to a passive NFC sensing device disposed within a dental implant assembly via an NFC reader disposed outside the oral cavity to obtain radio frequency coupling status parameters; controlling a first sensor, a second sensor, and a third sensor to collect a first sensing signal, a second sensing signal, and a third sensing signal, respectively, based on the radio frequency coupling status parameters; performing radio frequency power supply fluctuation compensation analysis on the first sensing signal, the second sensing signal, and the third sensing signal according to the radio frequency coupling status parameters to obtain input load parameters, overall load transfer parameters, and local connectivity status parameters; and performing dental implant status risk coupling analysis based on the input load parameters, the overall load transfer parameters, and the local connectivity status parameters to establish a dental implant status risk coupling map.

[0015] This application proposes an NFC-based dental implant status monitoring system and method, comprising: a radio frequency coupling status parameter acquisition module for acquiring radio frequency coupling status parameters via NFC; a sensor signal acquisition module for acquiring first, second, and third sensor signals; a radio frequency power supply fluctuation compensation analysis module for acquiring input load parameters, overall load transfer parameters, and local connection status parameters; and a risk coupling analysis module for establishing a risk coupling map of dental implant status. This system addresses the technical problems of limited power supply for active sensors, the difficulty in comprehensively assessing risks through single-physical-quantity sensing, and the susceptibility of passive sensors to signal distortion due to power supply fluctuations and media interference in complex oral dynamic environments. It achieves improved real-time performance of dental implant status monitoring, increased accuracy of multi-dimensional mechanical parameter quantification, and enhanced reliability of early risk warning. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the accompanying drawings of the embodiments of this disclosure will be briefly described below. Flowcharts are used in this application to illustrate the operations performed by the system according to the embodiments of this application. It should be understood that the preceding or following operations are not necessarily performed precisely in sequence. Instead, various steps can be processed in reverse order or simultaneously as needed. Furthermore, other operations can be added to these processes, or one or more steps can be removed from these processes.

[0017] Figure 1 A schematic diagram of the structure of an NFC-based dental implant status monitoring system provided in an embodiment of this application.

[0018] Figure 2 This is a schematic diagram of time-domain feature extraction of sensor signals in an NFC-based dental implant status monitoring system provided in an embodiment of this application.

[0019] Figure 3This is a schematic diagram of the NFC-based dental implant status monitoring method provided in the embodiments of this application.

[0020] Explanation of reference numerals in the attached diagram: 10 for RF coupling status parameter acquisition module, 20 for sensor signal acquisition module, 30 for RF power supply fluctuation compensation analysis module, and 40 for risk coupling analysis module. Detailed Implementation

[0021] To further illustrate the technical means and effects adopted by the present invention in order to achieve the intended purpose, the following detailed description is provided in conjunction with the accompanying drawings and preferred embodiments, based on the specific implementation methods, structures, features and effects of the present invention.

[0022] This application provides an NFC-based dental implant status monitoring system, such as... Figure 1 As shown, the system includes: The radio frequency coupling status parameter acquisition module 10 is used to acquire radio frequency coupling status parameters by transmitting near-field radio frequency signals to a passive NFC sensing device located inside the dental implant assembly through an NFC reading device located outside the oral cavity.

[0023] Preferably, the radio frequency coupling state parameter acquisition module is a functional unit responsible for performing initial radio frequency signal interaction and state information extraction. Specifically, an NFC reading device such as a handheld reader or a coil embedded in a denture outside the oral cavity generates a near-field radio frequency electromagnetic field and emits a near-field radio frequency signal, which passes through the oral mucosa and soft tissue to reach the implant component implanted in the alveolar bone. After the passive NFC sensing device encapsulated in the implant component enters the electromagnetic field, its internal LC resonant circuit electromagnetically couples with the external radio frequency field to acquire the radio frequency coupling state parameters.

[0024] Furthermore, the specific configuration of the radio frequency coupling state parameter acquisition module 10 also includes that the radio frequency coupling state parameters include rectified voltage, NFC antenna terminal voltage, phase difference between voltage and current in the NFC antenna, received signal strength, modulation depth, communication bit error rate, and radio frequency coupling coefficient.

[0025] Preferably, during this coupling process, the transmit power, antenna voltage, and current of the external NFC reader will fluctuate measurably due to changes in the state of the internal sensor of the implant and load changes such as changes in the dielectric properties of the surrounding medium, such as bone tissue and mucosa. Multiple quantifiable electrical indicators are obtained to determine the radio frequency coupling state parameters, specifically including the rectified voltage at the antenna end of the external NFC reader, the antenna voltage amplitude, the phase difference between voltage and current in the antenna circuit, the received signal strength, the modulation depth, the communication bit error rate, and the radio frequency coupling coefficient. The rectified voltage is the DC voltage value received at the antenna end of the external NFC reader and output after conversion by the backscattered signal of the passive NFC sensor through the internal rectifier circuit. It is generated by the rectification and filtering of the alternating voltage induced by the internal LC resonant circuit of the passive NFC sensor. Its amplitude directly depends on the energy intensity provided by the external radio frequency field and the load impedance state of the passive NFC sensor, reflecting the coupling energy transmission efficiency.

[0026] Preferably, the antenna terminal voltage amplitude refers to the peak-to-peak or effective value of the alternating voltage induced across the transmitting antenna coil of the external NFC reader. This value is determined by the reader's transmitting power and the antenna drive circuit, and is also affected by the modulation of the passive NFC sensor's reflection impedance. When the passive NFC sensor within the implant assembly enters the antenna's near-field region, its equivalent impedance is reflected back to the reader's antenna terminal through mutual inductance coupling, causing a measurable change in the antenna terminal voltage amplitude. The phase difference between voltage and current in the antenna circuit refers to the phase angle difference between the antenna terminal voltage waveform and the current waveform flowing through the antenna coil in the transmitting antenna circuit of the external NFC reader. This phase difference is determined by the total impedance of the antenna circuit. When the resonant frequency of the passive NFC sensor matches the external carrier frequency, the phase difference approaches zero; when changes in the surrounding environment cause a shift in the sensor's resonant frequency, the phase difference changes accordingly. Received signal strength refers to the signal amplitude or power level of the radio frequency signal power backscattered from the passive NFC sensor in the external NFC reader receiving link after being amplified by low noise. It reflects the transmission loss of the backscatter link, including spatial path loss, oral tissue medium absorption loss, and the reflection efficiency of the passive NFC sensor itself.

[0027] Preferably, the modulation depth refers to the ratio of the change in the amplitude of the backscattered signal envelope to the amplitude of the unmodulated carrier when the passive NFC sensing device superimposes the sensing data onto the backscattered carrier using load modulation. It is calculated as (maximum carrier amplitude - minimum carrier amplitude) / (maximum carrier amplitude + minimum carrier amplitude), reflecting the voltage change at the antenna end of the reading device caused by the impedance change of the sensor's load modulation switch. The communication bit error rate (BER) refers to the proportion of erroneous bits decoded by the receiving end to the total number of transmitted bits during the interaction of a specific length data frame between the external NFC reading device and the passive NFC sensing device. It is obtained by statistically analyzing the received data frames after performing cyclic redundancy check (CRC) or parity check on the reading device, and is used to quantify the digital communication integrity of the RF link. The RF coupling coefficient refers to the magnetic flux linkage efficiency between the transmitting antenna coil of the external NFC reading device and the internal receiving coil of the passive NFC sensing device. Its value is between 0 and 1, and is determined by the physical dimensions, number of turns, relative spatial distance, axial alignment angle of the two coils, and the permeability of the dielectric material between the two coils. It reflects the upper limit of the efficiency of RF energy transfer from the external reading device to the internal sensing device.

[0028] The sensor signal acquisition module 20 is used to control the first sensor, the second sensor, and the third sensor to acquire the first sensor signal, the second sensor signal, and the third sensor signal respectively based on the radio frequency coupling state parameters.

[0029] Preferably, the sensor signal acquisition module is a functional unit responsible for making decisions and executing synchronous data acquisition of multiple sensors based on the current state of the RF link. This module does not directly power the sensors, nor does it directly read the original physical quantities of the sensors. The input quantity is the RF coupling state parameter output by the RF coupling state parameter acquisition module. After the RF coupling state parameter enters the module, it is used to evaluate whether the RF energy received by the current passive NFC sensing device is sufficient to support effective sampling by the three internal sensors, and whether the RF link is in a stable resonant matching state.

[0030] Furthermore, the specific configuration of the sensor signal acquisition module 20 also includes: performing multi-dimensional evaluation of the radio frequency coupling state parameters to obtain voltage effectiveness, power supply stability, and resonance matching degree; performing weighted fusion of the voltage effectiveness, power supply stability, and resonance matching degree to obtain radio frequency coupling stability; determining whether the radio frequency coupling stability meets a preset stability threshold; if the radio frequency coupling stability meets the preset stability threshold, locking the impedance matching state and sensor signal conditioning gain of the passive NFC sensing device, and controlling the first sensor, the second sensor, and the third sensor to perform synchronous sampling to acquire the first sensor signal, the second sensor signal, and the third sensor signal.

[0031] Preferably, a multi-dimensional evaluation of the RF coupling state parameters is performed, extracting three evaluation indicators: voltage effectiveness, power supply stability, and resonance matching degree. Specifically, the rectified voltage value in the RF coupling state parameters is compared with the minimum operating voltage value of the internal circuitry of the passive NFC sensor. The proportion or magnitude by which the rectified voltage exceeds the minimum operating voltage is calculated to obtain the voltage effectiveness, reflecting whether the electrical energy provided by the current RF field is sufficient to drive the internal circuitry of the sensor. Within a set time window (e.g., N consecutive sampling periods), the fluctuation variance of the received signal strength and the frequency of change of the communication bit error rate are statistically analyzed. The statistical results are mapped to power supply stability, reflecting the stability of RF energy in the time dimension; the smaller the fluctuation, the higher the power supply stability. The resonance matching degree is calculated based on the phase difference between voltage and current in the antenna circuit. The smaller the absolute value of the phase difference (i.e., the closer to zero), the closer the resonant frequency of the passive NFC sensor is to the transmission carrier frequency of the external NFC reader, and the higher the resonance matching degree.

[0032] Preferably, the effective voltage, power supply stability, and resonance matching are weighted and fused: RF coupling stability = α × effective voltage + β × power supply stability + γ × resonance matching, where α, β, and γ are pre-calibrated weighting coefficients based on the influence of each indicator on the sensor signal quality, and the sum of the three is 1. Typically, effective voltage and resonance matching have higher weights. The overall RF coupling stability value is compared with a pre-stored preset stability threshold, which is a value determined experimentally during the system calibration phase and represents the minimum RF link quality requirement for allowing effective sampling. When the RF coupling stability reaches or exceeds a preset stability threshold, the impedance matching state and sensing signal conditioning gain of the passive NFC sensing device are locked. Specifically, the passive NFC sensing device contains an adjustable impedance matching network, such as a variable capacitor array or a switched capacitor network, to dynamically adjust the antenna resonant frequency to adapt to changes in the RF environment. The current control value of this matching network is fixed so that the matching network parameters will not be adjusted with fluctuations in the RF environment during subsequent sampling. The raw signals output by the three sensors need to be conditioned by an amplification circuit. The amplification factor is controlled by a programmable gain amplifier. The current gain control code of this amplifier is fixed so that the amplification factor of the three signals will not be automatically adjusted with changes in energy during subsequent sampling.

[0033] Preferably, under the condition that impedance matching and signal conditioning gain are both locked, sampling trigger pulse signals are sent simultaneously to the first sensor, the second sensor and the third sensor, so that the three sensors start to collect and convert their respective physical quantities at the same time to obtain the first sensing signal, the second sensing signal and the third sensing signal. The first sensing signal is an analog voltage or resistance change, representing the magnitude of the external load acting on the coronal load input path of the implant; the second sensing signal is an analog voltage or resistance change, representing the force state at the coupling interface between the implant and the surrounding support structure; the third sensing signal is an analog voltage or inductance change, representing the local relative displacement or contact force change at the connection position between the abutment and the implant.

[0034] Furthermore, the specific configuration of the sensor signal acquisition module 20 also includes: if the radio frequency coupling stability does not meet the preset stability threshold, identifying the radio frequency coupling anomaly type according to the radio frequency coupling state parameters; generating a radio frequency coupling adjustment strategy based on the radio frequency coupling anomaly type; performing radio frequency coupling adjustment based on the radio frequency coupling adjustment strategy, obtaining updated radio frequency coupling state parameters, and obtaining the first sensor signal, the second sensor signal, and the third sensor signal according to the updated radio frequency coupling state parameters.

[0035] Preferably, if the RF coupling stability does not meet the preset stability threshold, it indicates that the quality of the current NFC RF link has not met the minimum requirements for effective sampling. If the sensor signal is directly acquired in this state, the signals output by the three sensors will contain large errors due to insufficient power supply or resonance detuning. Therefore, anomaly pattern identification is performed on the RF coupling state parameter set. Specifically, if the rectified voltage is lower than the minimum operating voltage of the passive NFC sensing device, and the received signal strength is lower than the set threshold, but the phase difference remains within a reasonable range (±5°), it indicates that the transmission power of the external NFC reader is insufficient to drive the internal sensing device, and this is determined to be a power supply insufficiency type anomaly. If the antenna... If the absolute value of the phase difference between the voltage and current in the circuit exceeds the set threshold (e.g., greater than ±10°), and the RF coupling coefficient is lower than the calibrated value, but the rectified voltage is not lower than the minimum operating voltage, it indicates that the resonant frequency of the LC resonant circuit inside the sensing device deviates from the external carrier frequency, resulting in a decrease in energy transmission efficiency, and is judged as a resonant detuning anomaly; if the communication bit error rate exceeds the set threshold, and the modulation depth is lower than the set threshold, but the rectified voltage and resonant matching degree are within the normal range, it indicates that there is external electromagnetic interference in the RF link or the backscattered signal is submerged by noise, and is judged as a communication interference anomaly; if two or three anomaly characteristics occur simultaneously, it is judged as a composite anomaly.

[0036] Preferably, an RF coupling adjustment strategy is generated based on the type of RF coupling anomaly. This strategy is used to adjust the position, distance, and angle of the NFC reader relative to the passive NFC sensor. Specifically, for power supply insufficiency anomalies, a transmit power increase command is generated and sent to the external NFC reader via the NFC communication link, requesting it to increase the transmit power in a set step (e.g., 3dBm) until the rectified voltage rises above the minimum operating voltage. For resonance detuning anomalies, a carrier frequency fine-tuning command or internal tuning command is generated. Prioritizing the use of the NFC communication link, the external NFC reader is instructed to scan and adjust the transmit carrier frequency within its allowed frequency range in a set step (e.g., ±100kHz) to find a frequency point where the phase difference approaches zero. If external frequency adjustment is limited, the control code of the adjustable capacitor array inside the passive NFC sensor is adjusted to change its resonant frequency. For communication interference anomalies, a communication parameter adjustment command is generated, including reducing the data communication rate to improve anti-interference capability or increasing the forward error correction coding strength. For complex anomalies, adjustment strategies are executed sequentially according to the priority order of insufficient power supply → resonance detuning → communication interference. After each step, the RF coupling status parameters are re-evaluated. If the stability still does not meet the standard, the next adjustment step is continued.

[0037] Preferably, the RF coupling adjustment strategy is sent to the passive NFC sensing device via an external NFC reader in the form of digital commands, or to the external NFC reader itself via a wired / wireless interface. Specifically, after receiving a power boost command, the external NFC reader increases the drive voltage or changes the power amplifier bias state through its internal RF power amplifier circuit, thereby increasing the RF output power at the transmitting antenna end. After receiving a frequency adjustment command, the external NFC reader changes the carrier frequency output value through its internal frequency synthesizer (PLL). If the adjustment is internal tuning, the passive NFC sensing device, upon receiving a tuning command, changes the digital control code of its internal adjustable capacitor array, altering the equivalent capacitance value of the LC resonant circuit, thereby changing the resonant frequency. After receiving a communication parameter adjustment command, the external NFC reader or sensing device modifies the data rate in its internal protocol stack. The system first obtains the rate register or encoding mode register; then, under the new transmit power, carrier frequency, or communication parameters, it re-acquires the rectified voltage, antenna voltage amplitude, phase difference, received signal strength, modulation depth, communication bit error rate, and RF coupling coefficient to obtain updated RF coupling state parameters. It then recalculates the voltage validity, power supply stability, and resonance matching degree, weights and fuses them into a new RF coupling stability, and compares it with a preset stability threshold. If the updated RF coupling stability meets the preset stability threshold, it locks the impedance matching state and sensor signal conditioning gain, controls the three sensors to perform synchronous sampling, and acquires the first, second, and third sensor signals. If the updated RF coupling stability still does not meet the preset stability threshold, it repeats the adjustment until the RF coupling stability meets the threshold or reaches the preset maximum adjustment count limit. At this point, an RF coupling abnormality alarm is output.

[0038] Furthermore, the specific configuration of the sensing signal acquisition module 20 also includes: the first sensor is disposed at the coronal load input path of the oral implant assembly, and is used to acquire a first sensing signal characterizing the input load applied to the oral implant assembly; the second sensor is disposed at the position where the oral implant assembly is mechanically coupled with the surrounding support structure, and is used to acquire a second sensing signal characterizing the overall support state of the oral implant assembly; the third sensor is disposed at the connection position between the implant abutment and the oral implant assembly, and is used to acquire a third sensing signal characterizing the local contact state of the abutment connection structure.

[0039] The radio frequency power supply fluctuation compensation analysis module 30 is used to perform radio frequency power supply fluctuation compensation analysis on the first sensing signal, the second sensing signal and the third sensing signal according to the radio frequency coupling state parameters, and obtain input load parameters, overall load transfer parameters and local connection state parameters.

[0040] Preferably, the radio frequency power supply fluctuation compensation and analysis module is a functional unit responsible for correcting and compensating the three original sensor signals and extracting mechanical characteristic parameters from them. The passive NFC sensing device itself does not have an independent power supply. All its working power comes from the radio frequency field emitted by the external NFC reading device. In the oral environment, the relative position between the external NFC reading device and the implant will change in real time due to factors such as slight movements of the patient's head, differences in oral mucosal thickness, and changes in the saliva layer. This causes fluctuations in the radio frequency energy received by the passive NFC sensing device, which may produce zero-point drift and sensitivity drift. As a result, the original electrical signals output by the three sensors contain error components introduced by power supply fluctuations. This error is superimposed on the load signal and cannot be distinguished by directly reading the original signal.

[0041] Furthermore, the specific configuration of the RF power supply fluctuation compensation analysis module 30 also includes: performing RF power supply fluctuation compensation on the first sensing signal, the second sensing signal, and the third sensing signal according to the RF coupling state parameters, respectively, to obtain the compensated first sensing signal, the compensated second sensing signal, and the compensated third sensing signal; performing time-domain feature extraction on the compensated first sensing signal to obtain the input load parameters; determining the overall load transfer parameters based on the amplitude ratio, phase difference, response delay, frequency response characteristics, and loading / unloading hysteresis characteristics between the compensated first sensing signal and the compensated second sensing signal; and extracting the local connection state parameters based on the local frequency response characteristics of the compensated third sensing signal relative to the compensated first sensing signal, and the local response difference of the compensated third sensing signal relative to the compensated second sensing signal.

[0042] Preferably, the information in the radio frequency coupling state parameters that can quantify the current power supply state, including rectified voltage, antenna terminal voltage amplitude, radio frequency coupling coefficient, etc., is used to perform reverse correction calculation on the three original sensing signals. The zero-point offset and sensitivity offset caused by the current power supply state are subtracted from the original signals to restore the real signal components caused only by the actual load. These are respectively denoted as the compensated first sensing signal, the compensated second sensing signal, and the compensated third sensing signal.

[0043] Preferably, time-domain feature extraction is performed on the compensated first sensor signal, that is, quantitative feature calculation is performed on the signal sequence along the time axis. Specifically, based on the calibrated voltage-force curve of the sensor, the peak voltage is mapped to the peak value of the external occlusal force to obtain the maximum peak voltage value of the signal waveform during the occlusal loading phase; the voltage change per unit time during the rise of the signal waveform from the loading start point to the peak value is calculated to determine the signal rise rate, reflecting the speed of the occlusal force application, i.e., the loading speed of the patient's occlusal action; the total time length during which the signal waveform rises from the loading start point to the peak value and then falls back to the loading start point level is extracted to obtain the signal duration, reflecting the total duration of one occlusal action; the integral value of the signal waveform over the time axis during the loading duration, i.e., the area under the voltage-time curve, reflects the impulse of the occlusal force, such as... Figure 2 As shown, this illustrates the cumulative effect of the load on the implant over time.

[0044] Preferably, the compensated first sensing signal and the compensated second sensing signal are paired and calculated to determine the amplitude ratio, phase difference, response delay, frequency response characteristics, and loading / unloading hysteresis characteristics. Specifically, the peak voltage value of the second sensing signal waveform and the peak voltage value of the first sensing signal waveform are extracted, and the ratio of the former to the latter is calculated. When the ratio is less than 1, it indicates that the load is partially dissipated or attenuated during transmission; when the ratio is close to 1, it indicates that the load is almost completely transmitted to the bone interface; when the ratio gradually decreases over time, it indicates that the support stiffness of the implant-bone interface is decreasing. Using the waveform of the first sensing signal as a reference waveform, the phase angle of the second sensing signal waveform relative to the reference waveform on the time axis is calculated to obtain the phase difference value. This indicates that there is elastic deformation or viscoelastic hysteresis at the implant-bone interface during load transmission. An increase in the phase difference indicates a change in the elastic modulus of the interface or the appearance of micro-damage at the interface. Calculate the time difference between the moment when the first sensor signal waveform rises from the starting point to a certain set threshold (e.g., 10% of the peak value) and the moment when the second sensor signal waveform rises from the starting point to the same set threshold. Determine the response delay, which is the time required for the load to be transferred from the coronal side of the implant to the implant-bone interface. An increase in response delay indicates a decrease in the stiffness of the implant-bone interface or a change in its damping characteristics.

[0045] Preferably, multiple sets of first and second sensor signals collected by an external NFC reader under different occlusal frequencies are subjected to Fourier transforms to obtain the amplitude and phase spectra of each frequency component. The amplitude ratio and phase difference between the second and first sensor signals are calculated at different excitation frequencies to determine the frequency response characteristics, reflecting the dynamic transmission characteristics of the implant-bone interface under different frequency occlusal excitations. Within a complete occlusal loading-unloading cycle, a Lissajous figure is plotted with the instantaneous amplitude of the first sensor signal on the x-axis and the instantaneous amplitude of the second sensor signal on the y-axis. The curve formed during the loading phase does not coincide with the curve formed during the unloading phase. The area enclosed by the two curves is the hysteresis loop area. The size of the hysteresis loop area reflects the energy dissipated by the implant-bone interface during each occlusal process. The hysteresis loop area increases with the increase of the number of consecutive occlusal cycles, indicating the existence of cumulative micro-damage at the interface. This allows for the determination of overall load transfer parameters.

[0046] Preferably, the compensated third sensing signal is compared and analyzed with the compensated first sensing signal and the compensated second sensing signal, respectively. The third sensor is set at the connection position between the implant abutment and the implant assembly, and its signal directly reflects the local mechanical state of the abutment-implant connection interface. Specifically, Fourier transforms are performed on the two signals within the same occlusal loading cycle to obtain their respective amplitude-frequency characteristic curves. The ratio of the amplitude of the third signal to the amplitude of the first signal at each frequency point is calculated to obtain the relative frequency response curve and obtain the local frequency response characteristics. The compensated third sensing signal and the compensated second sensing signal are subjected to difference analysis. That is, within the same occlusal loading cycle, the difference between the third signal value and the second signal value is calculated at each sampling point to obtain the difference signal sequence and the peak value, phase shift, and mean drift of the difference signal are extracted. Finally, the local frequency response characteristics and the quantification results of the local response differences are combined into a local connection state parameter. The degree to which this parameter value deviates from the reference value is used to characterize the looseness of the abutment connection structure or the wear state of the connection interface.

[0047] Furthermore, the specific configuration of the RF power supply fluctuation compensation analysis module 30 also includes: constructing a current RF coupling state vector based on the RF coupling state parameters; obtaining a reference RF coupling state vector corresponding to the passive NFC sensing device, and calculating a coupling state deviation vector of the current RF coupling state vector relative to the reference RF coupling state vector; constructing a first channel RF sensitivity model; inputting the coupling state deviation vector into the first channel RF sensitivity model to obtain a first zero-point drift estimate and a first sensitivity drift estimate; performing drift compensation on the first sensing signal based on the first zero-point drift estimate and the first sensitivity drift estimate to generate the compensated first sensing signal; and continuing to perform drift compensation on the second sensing signal and the third sensing signal based on the coupling state deviation vector to obtain the compensated second sensing signal and the compensated third sensing signal.

[0048] Preferably, multiple RF coupling state parameters are arranged in a fixed dimensional order to form the current RF coupling state vector, which together constitutes a complete description of the RF link state at the current moment. The reference RF coupling state vector is a pre-stored reference vector, which is obtained during the calibration phase. The specific calibration conditions are that the external NFC reader and the passive NFC sensor are in the standard coupling position, the RF transmit power is set to the nominal value, and the entire system is in a stable and undisturbed environment. The RF coupling state parameters collected under these conditions are the reference RF coupling state vector, representing the standard values ​​when the RF link is in an ideal working state. Then, the difference between the current RF coupling state vector and the reference RF coupling state vector is calculated to determine the coupling state deviation vector. Each dimension element represents the amount and direction of deviation of the current RF coupling state relative to the reference state in that dimension. For example, a negative value in the rectified voltage dimension indicates that the current power supply is lower than the reference level, and a positive value in the phase difference dimension indicates that the current resonant frequency has shifted in the positive direction relative to the reference.

[0049] Preferably, when the first sensor is not subjected to an external load, the NFC reading device is controlled to transmit signals to the passive NFC sensing device according to multiple preset radio frequency excitation states to obtain a first radio frequency excitation test dataset; zero-point drift is calculated and coupling state deviation mapping is fitted based on the first radio frequency excitation test dataset to obtain a zero-point drift calibration relationship layer; when the first sensor is subjected to a preset reference load, the NFC reading device is controlled to transmit signals to the passive NFC sensing device according to the multiple preset radio frequency excitation states to obtain a second radio frequency excitation test dataset; sensitivity drift is calculated and coupling state deviation mapping is fitted based on the second radio frequency excitation test dataset to obtain a sensitivity drift calibration relationship layer; the zero-point drift calibration relationship layer and the sensitivity drift calibration relationship layer are connected as parallel nodes to generate the first channel radio frequency sensitive model.

[0050] Preferably, when the first sensor is not subjected to an external load, the NFC reading device is controlled to transmit near-field radio frequency signals to the passive NFC sensing device according to multiple preset radio frequency excitation states, and the radio frequency coupling state parameters and the output value of the first sensor corresponding to each preset radio frequency excitation state are obtained to form a first radio frequency excitation test dataset; a test radio frequency coupling state vector is constructed according to the radio frequency coupling state parameters corresponding to each preset radio frequency excitation state, and the test coupling state deviation vector of each test radio frequency coupling state vector relative to the reference radio frequency coupling state vector is calculated; then the difference between the first sensor output value corresponding to each preset radio frequency excitation state and the first sensor output value under the reference radio frequency excitation state is calculated to obtain the zero-point drift calibration quantity corresponding to each test coupling state deviation vector; then the correspondence between the test coupling state deviation vector and the zero-point drift calibration quantity is fitted to obtain a zero-point drift calibration relationship layer with the coupling state deviation vector as input and the zero-point drift estimate as output.

[0051] Preferably, when the first sensor is subjected to a preset reference load, the NFC reading device is controlled to transmit near-field radio frequency signals to the passive NFC sensing device according to multiple preset radio frequency excitation states, and the radio frequency coupling state parameters and the output value of the first sensor corresponding to each preset radio frequency excitation state are obtained to form a second radio frequency excitation test dataset; for each preset radio frequency excitation state, the difference between the first sensor output value in the second radio frequency excitation test dataset and the corresponding first sensor output value in the first radio frequency excitation test dataset is calculated, and the sensing sensitivity under the corresponding radio frequency excitation state is determined according to the difference and the preset reference load. The change in sensing sensitivity under each radio frequency excitation state relative to the sensing sensitivity under the reference radio frequency excitation state is calculated to obtain the sensitivity drift calibration amount corresponding to the deviation vector of each test coupling state; then the correspondence between the test coupling state deviation vector and the sensitivity drift calibration amount is fitted to obtain a sensitivity drift calibration relationship layer with the coupling state deviation vector as input and the sensitivity drift estimate as output. Finally, the zero-point drift calibration layer and the sensitivity drift calibration layer are integrated to construct the first channel RF sensitive model, which is used to describe the mapping relationship between the zero-point drift and sensitivity drift and the RF coupling state deviation vector. When the RF coupling state deviates in a specific direction and at a specific magnitude, the zero-point output and sensitivity output of the first sensor will correspondingly drift by a specific magnitude.

[0052] Preferably, the coupling state deviation vector is used as an input variable and substituted into the first channel RF sensitive model for calculation. The output is a first zero-point drift estimate, which is the estimated amplitude of the first sensor's output signal deviating from its theoretical zero value under zero load conditions in the current RF coupling state; and a first sensitivity drift estimate, which is the offset of the first sensor's sensitivity relative to its calibrated sensitivity in the current RF coupling state. Then, drift compensation is performed on the first sensing signal based on the first zero-point drift estimate and the first sensitivity drift estimate, including subtracting the first zero-point drift estimate from the original first sensing signal value. This operation eliminates the signal baseline offset caused by power supply fluctuations. The zero-point compensated signal value is multiplied by a sensitivity correction coefficient, which is the ratio of the calibrated sensitivity to the actual sensitivity (calibrated sensitivity plus sensitivity drift estimate), to eliminate the signal amplification gain change caused by power supply fluctuations. The compensated first sensing signal = (original first sensing signal - first zero-point drift estimate) × (calibrated sensitivity / (calibrated sensitivity + first sensitivity drift estimate)). Similarly, a second-channel RF sensitive model and a third-channel RF sensitive model are constructed respectively, and the coupling state deviation vector is input into them respectively to obtain the second zero-point drift estimate, the second sensitivity drift estimate, and the third zero-point drift estimate and the third sensitivity drift estimate. Then, zero-point compensation and sensitivity compensation are performed on the original second and third sensing signals respectively to generate the compensated second and third sensing signals, thereby ensuring the consistency of the three compensated signals in terms of time reference and compensation reference.

[0053] The risk coupling analysis module 40 is used to perform risk coupling analysis of oral implant status based on the input load parameters, the overall load transfer parameters and the local connection state parameters, and to establish an oral implant status risk coupling map.

[0054] Preferably, the risk coupling analysis module is a functional unit responsible for comprehensively analyzing three types of mechanical characteristic parameters and outputting the analysis results in the form of a visual atlas. Specifically, the mechanical integrity of the dental implant system depends on the coronal load input link, the implant-bone load transmission link, and the abutment-implant connection link. These three links form a series relationship in the mechanical transmission chain. The input load is transmitted to the implant body through the abutment connection interface, and then to the surrounding bone tissue through the implant-bone interface. This allows for the identification of correlation change patterns and the determination of risk sources and risk levels.

[0055] Furthermore, the specific configuration of the risk coupling analysis module 40 also includes: comparing the input load parameters with the input load benchmark to obtain input load deviation characteristics, and predicting the risk of abnormal loading state based on the input load deviation characteristics to obtain abnormal loading state risk characteristics; comparing the overall load transfer parameters with the overall load transfer benchmark to obtain overall load transfer deviation characteristics, and predicting the risk of overall support state change based on the overall load transfer deviation characteristics to obtain overall support state change risk characteristics; predicting the abutment connection state change risk characteristics based on the local connection state parameters, and constructing the oral implant state risk coupling map by combining the abnormal loading state risk characteristics and the overall support state change risk characteristics.

[0056] Preferably, after implantation and osseointegration (usually 3 to 6 months post-implantation), and while the patient is in a normal physiological occlusion state, multiple sets of input load parameters are continuously collected. The average value and standard deviation of each characteristic quantity are calculated to form an input load baseline vector. The input load parameters acquired in the current monitoring period are compared with the corresponding characteristic quantities in the baseline vector, and the differences and ratios are calculated item by item to obtain input load deviation characteristics and predict the risk of abnormal loading states. Specifically, the peak amplitude of the current input load parameter is compared with the baseline peak amplitude. If the current peak amplitude exceeds the baseline peak amplitude by a certain proportion (e.g., more than 120%), it is determined to be a momentary overload state; if the current peak amplitude consistently exceeds the baseline value by a proportion below a certain threshold (e.g., consistently below 60%), it is determined to be an abnormal light load state. The cumulative deviation of the waveform integral area in the current input load parameter relative to the baseline waveform integral area is calculated. The difference between the waveform integral area of ​​each occlusion and the baseline integral area is summed over time. When the cumulative deviation exceeds a set cumulative damage threshold, the risk of abnormal loading leading to micro-damage to the bone tissue around the implant is predicted. Finally, the comprehensive evaluation results output the risk characteristics of abnormal load conditions, including the instantaneous overload risk level, the long-term overload cumulative risk level, and the abnormal light load risk level.

[0057] Preferably, after the implant-bone integration is stable, multiple sets of overall load transfer parameters are collected, including amplitude ratio, phase difference, response delay, frequency response eigenvalue, and hysteresis loop area. An overall load transfer benchmark is calculated. The differences between each characteristic quantity in the overall load transfer parameters acquired in the current monitoring period and the corresponding characteristic quantity in the benchmark vector are calculated item by item. The direction and magnitude of the difference together constitute the overall load transfer deviation characteristics. Among them, a decrease in amplitude ratio indicates a decrease in transfer efficiency, which may be due to a decrease in the stiffness of the implant-bone interface or the appearance of fibrous tissue hyperplasia at the interface; an increase in phase difference indicates an aggravated response hysteresis, which may be due to a decrease in the elastic modulus of the implant-bone interface; a prolonged response delay indicates a longer force transfer time, which may be due to the degradation of the trabecular bone structure or a decrease in the bone density around the implant; a shift in the concave frequency band of the frequency response curve indicates a change in dynamic characteristics, which may be due to a drift in the natural frequency of the implant-bone interface, reflecting a change in the interface support stiffness; an increase in hysteresis loop area indicates an increase in energy dissipation, which may be due to the presence of micro-friction or microcrack propagation at the implant-bone interface. Then, the decrease in amplitude ratio, the increase in phase difference, the extension of response delay, the frequency response characteristic offset, and the increase in hysteresis loop area are multiplied by preset weighting coefficients and summed to obtain the overall support degradation index. The weighting coefficients are determined based on the sensitivity of each characteristic quantity to support degradation and its early warning value. Typically, the amplitude ratio and hysteresis loop area are given higher weights. The overall support degradation index is mapped to the overall support status change risk characteristics, including the support degradation degree score and the support degradation trend direction.

[0058] Preferably, the local frequency response amplitude ratio, indentation depth, peak value of the difference signal, and mean value drift of the difference signal in the local connection status parameters are compared with their respective threshold values. If the indentation depth exceeds a certain proportion of the calibrated value (e.g., exceeding 150%), it indicates a decrease in the contact stiffness of the abutment-implant connection interface and a loss of screw preload. If the peak value of the difference signal exceeds the calibrated peak value threshold, it indicates a relative micro-displacement between the abutment and the implant. If the deviation from zero exceeds the set threshold and fails to recover after multiple consecutive monitoring, it indicates that the abutment connection interface has undergone irreversible plastic deformation or the screw has undergone permanent elongation. When any two of the abnormal judgment conditions are met, the risk of change in the abutment connection status is predicted, and the risk characteristics of the change in the abutment connection status are output, including a preload reduction indicator, a micro-displacement exceeding the limit indicator, and a permanent deformation indicator.

[0059] Preferably, the risk level of abnormal loading status, the score of overall support degradation, and the combination of abutment connection risk indicators are used as three coordinate axes to locate the current implant status point in three-dimensional space. Then, the three-dimensional status points monitored in previous times are connected to form a trajectory curve. The extension direction of this curve in space reflects the development path of the risk. For example, the trajectory moving from the origin along the abnormal loading axis indicates that the risk source is mainly occlusal overload. The comprehensive risk value is decomposed into the contribution percentage of the three dimensions of abnormal loading, support degradation, and abutment connection. According to the number and degree of exceeding the standard of the three risk characteristics, the comprehensive risk is divided into four levels: safe, low risk, medium risk, and high risk. Finally, a dental implant status risk coupling map is generated to ensure the identification of chain risks that cannot be detected by a single parameter judgment, so as to take targeted clinical intervention measures.

[0060] In the above text, refer to Figure 1 A dental implant status monitoring system based on NFC sensing according to an embodiment of the present invention is described in detail. Next, reference will be made to... Figure 3 This invention describes a method for monitoring the status of dental implants based on NFC sensing, according to embodiments of the present invention. The method for monitoring the status of dental implants based on NFC sensing, such as... Figure 3 As shown, the method includes: transmitting near-field radio frequency signals to a passive NFC sensing device located within a dental implant assembly via an NFC reading device located outside the oral cavity to obtain radio frequency coupling state parameters; based on the radio frequency coupling state parameters, controlling a first sensor, a second sensor, and a third sensor to collect a first sensing signal, a second sensing signal, and a third sensing signal, respectively; performing radio frequency power supply fluctuation compensation analysis on the first sensing signal, the second sensing signal, and the third sensing signal according to the radio frequency coupling state parameters to obtain input load parameters, overall load transfer parameters, and local connection state parameters; and performing dental implant state risk coupling analysis based on the input load parameters, the overall load transfer parameters, and the local connection state parameters to establish a dental implant state risk coupling map.

[0061] In one possible implementation, based on the radio frequency coupling state parameters, the first sensor, the second sensor, and the third sensor are controlled to acquire the first sensing signal, the second sensing signal, and the third sensing signal, respectively. This includes: performing a multi-dimensional evaluation of the radio frequency coupling state parameters to obtain voltage effectiveness, power supply stability, and resonance matching degree; weighting and fusing the voltage effectiveness, the power supply stability, and the resonance matching degree to obtain radio frequency coupling stability; determining whether the radio frequency coupling stability meets a preset stability threshold; if the radio frequency coupling stability meets the preset stability threshold, locking the impedance matching state and sensing signal conditioning gain of the passive NFC sensing device, and controlling the first sensor, the second sensor, and the third sensor to perform synchronous sampling to acquire the first sensing signal, the second sensing signal, and the third sensing signal.

[0062] In one possible implementation, determining whether the radio frequency coupling stability meets a preset stability threshold includes: if the radio frequency coupling stability does not meet the preset stability threshold, identifying the radio frequency coupling anomaly type based on the radio frequency coupling state parameters; generating a radio frequency coupling adjustment strategy based on the radio frequency coupling anomaly type; performing radio frequency coupling adjustment based on the radio frequency coupling adjustment strategy, obtaining updated radio frequency coupling state parameters, and obtaining the first sensing signal, the second sensing signal, and the third sensing signal based on the updated radio frequency coupling state parameters.

[0063] In one possible implementation, RF power supply fluctuation compensation analysis is performed on the first sensing signal, the second sensing signal, and the third sensing signal according to the RF coupling state parameters to obtain input load parameters, overall load transfer parameters, and local connection state parameters. This includes: performing RF power supply fluctuation compensation on the first sensing signal, the second sensing signal, and the third sensing signal according to the RF coupling state parameters to obtain compensated first sensing signal, compensated second sensing signal, and compensated third sensing signal; extracting time-domain features from the compensated first sensing signal to obtain the input load parameters; determining the overall load transfer parameters based on the amplitude ratio, phase difference, response delay, frequency response characteristics, and load / unload hysteresis characteristics between the compensated first sensing signal and the compensated second sensing signal; and extracting the local connection state parameters based on the local frequency response characteristics of the compensated third sensing signal relative to the compensated first sensing signal, and the local response difference of the compensated third sensing signal relative to the compensated second sensing signal.

[0064] In one possible implementation, RF power supply fluctuation compensation is performed on the first sensing signal, the second sensing signal, and the third sensing signal according to the RF coupling state parameters to obtain the compensated first sensing signal, the compensated second sensing signal, and the compensated third sensing signal, including: constructing a current RF coupling state vector according to the RF coupling state parameters; obtaining a reference RF coupling state vector corresponding to the passive NFC sensing device, and calculating a coupling state deviation vector of the current RF coupling state vector relative to the reference RF coupling state vector; constructing a first channel RF sensitivity model; inputting the coupling state deviation vector into the first channel RF sensitivity model to obtain a first zero-point drift estimate and a first sensitivity drift estimate; performing drift compensation on the first sensing signal according to the first zero-point drift estimate and the first sensitivity drift estimate to generate the compensated first sensing signal; and continuing to perform drift compensation on the second sensing signal and the third sensing signal based on the coupling state deviation vector to obtain the compensated second sensing signal and the compensated third sensing signal.

[0065] In one possible implementation, constructing a first-channel radio frequency (RF) sensitive model includes: when the first sensor is not subjected to an external load, controlling the NFC reader to transmit signals to the passive NFC sensor according to multiple preset RF excitation states to obtain a first RF excitation test dataset; calculating zero-point drift and fitting coupling state deviation mapping based on the first RF excitation test dataset to obtain a zero-point drift calibration relationship layer; when the first sensor is subjected to a preset reference load, controlling the NFC reader to transmit signals to the passive NFC sensor according to the multiple preset RF excitation states to obtain a second RF excitation test dataset; calculating sensitivity drift and fitting coupling state deviation mapping based on the second RF excitation test dataset to obtain a sensitivity drift calibration relationship layer; and connecting the zero-point drift calibration relationship layer and the sensitivity drift calibration relationship layer as parallel nodes to generate the first-channel RF sensitive model.

[0066] In one possible implementation, a coupling analysis of the state risk of dental implants is performed based on the input load parameters, the overall load transfer parameters, and the local connection state parameters to establish a coupling map of the state risk of dental implants. This includes: comparing the input load parameters with an input load benchmark to obtain input load deviation characteristics, and predicting the risk of abnormal loading state based on the input load deviation characteristics to obtain abnormal loading state risk characteristics; comparing the overall load transfer parameters with an overall load transfer benchmark to obtain overall load transfer deviation characteristics, and predicting the risk of overall support state change based on the overall load transfer deviation characteristics to obtain overall support state change risk characteristics; predicting the abutment connection state change risk characteristics based on the local connection state parameters, and constructing the coupling map of the state risk of dental implants by combining the abnormal loading state risk characteristics and the overall support state change risk characteristics.

[0067] In one possible implementation, the radio frequency coupling state parameters include rectified voltage, NFC antenna terminal voltage, phase difference between voltage and current in the NFC antenna, received signal strength, modulation depth, communication bit error rate, and radio frequency coupling coefficient.

[0068] In one possible implementation, the first sensor is located at the coronal load input path of the dental implant assembly to acquire a first sensing signal characterizing the input load applied to the dental implant assembly; the second sensor is located at the mechanical coupling position between the dental implant assembly and the surrounding support structure to acquire a second sensing signal characterizing the overall support state of the dental implant assembly; and the third sensor is located at the connection position between the implant abutment and the dental implant assembly to acquire a third sensing signal characterizing the local contact state of the abutment connection structure.

[0069] The NFC-based dental implant status monitoring system provided in this embodiment of the invention can execute the NFC-based dental implant status monitoring method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0070] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. An NFC-based dental implant status monitoring system, characterized in that, The system includes: The radio frequency coupling status parameter acquisition module is used to transmit near-field radio frequency signals to a passive NFC sensing device located inside the dental implant assembly through an NFC reading device located outside the oral cavity, thereby acquiring radio frequency coupling status parameters. The sensor signal acquisition module is used to control the first sensor, the second sensor, and the third sensor to acquire the first sensor signal, the second sensor signal, and the third sensor signal respectively, based on the radio frequency coupling state parameters. The radio frequency power supply fluctuation compensation analysis module is used to perform radio frequency power supply fluctuation compensation analysis on the first sensing signal, the second sensing signal and the third sensing signal according to the radio frequency coupling state parameters, and obtain input load parameters, overall load transfer parameters and local connection state parameters. The risk coupling analysis module is used to perform risk coupling analysis of oral implant status based on the input load parameters, the overall load transfer parameters and the local connection state parameters, and to establish an oral implant status risk coupling map.

2. The NFC-based dental implant status monitoring system as described in claim 1, characterized in that, The steps performed by the sensor signal acquisition module include: The radio frequency coupling state parameters are evaluated in multiple dimensions to obtain voltage effectiveness, power supply stability, and resonance matching degree. The radio frequency coupling stability is obtained by weighted fusion of the voltage effectiveness, the power supply stability, and the resonance matching degree. Determine whether the radio frequency coupling stability meets the preset stability threshold; If the radio frequency coupling stability meets the preset stability threshold, the impedance matching state and sensing signal conditioning gain of the passive NFC sensing device are locked, and the first sensor, the second sensor and the third sensor are controlled to perform synchronous sampling to acquire the first sensing signal, the second sensing signal and the third sensing signal.

3. The NFC-based dental implant status monitoring system as described in claim 2, characterized in that, The steps performed by the sensor signal acquisition module include: If the radio frequency coupling stability does not meet the preset stability threshold, the radio frequency coupling anomaly type is identified based on the radio frequency coupling state parameters; Based on the aforementioned radio frequency coupling anomaly type, a radio frequency coupling adjustment strategy is generated; RF coupling adjustment is performed based on the RF coupling adjustment strategy to obtain updated RF coupling state parameters, and the first sensing signal, the second sensing signal, and the third sensing signal are obtained based on the updated RF coupling state parameters.

4. The NFC-based dental implant status monitoring system as described in claim 1, characterized in that, The steps performed by the radio frequency power supply fluctuation compensation analysis module include: According to the radio frequency coupling state parameters, the first sensing signal, the second sensing signal and the third sensing signal are respectively compensated for radio frequency power supply fluctuations to obtain the compensated first sensing signal, the compensated second sensing signal and the compensated third sensing signal. The input load parameters are obtained by performing time-domain feature extraction on the compensated first sensing signal. The overall load transfer parameters are determined based on the amplitude ratio, phase difference, response delay, frequency response characteristics, and loading / unloading hysteresis characteristics between the compensated first sensing signal and the compensated second sensing signal. The local connection state parameters are extracted based on the local frequency response characteristics of the compensated third sensing signal relative to the compensated first sensing signal, and the local response difference of the compensated third sensing signal relative to the compensated second sensing signal.

5. The NFC-based dental implant status monitoring system as described in claim 4, characterized in that, The steps performed by the radio frequency power supply fluctuation compensation analysis module include: Based on the radio frequency coupling state parameters, construct the current radio frequency coupling state vector; Obtain the reference radio frequency coupling state vector corresponding to the passive NFC sensing device, and calculate the coupling state deviation vector of the current radio frequency coupling state vector relative to the reference radio frequency coupling state vector. Construct the first channel radio frequency sensitive model; The coupling state deviation vector is input into the first channel radio frequency sensitive model to obtain the first zero-point drift estimate and the first sensitivity drift estimate; The first sensing signal is drift compensated based on the first zero-point drift estimate and the first sensitivity drift estimate to generate the compensated first sensing signal. Based on the coupling state deviation vector, drift compensation is further performed on the second sensing signal and the third sensing signal to obtain the compensated second sensing signal and the compensated third sensing signal.

6. The NFC-based dental implant status monitoring system as described in claim 5, characterized in that, The steps performed by the radio frequency power supply fluctuation compensation analysis module include: When the first sensor is not subjected to an external load, the NFC reading device is controlled to transmit signals to the passive NFC sensing device according to multiple preset radio frequency excitation states to obtain the first radio frequency excitation test dataset. Based on the first RF excitation test dataset, zero-point drift is calculated and coupling state deviation mapping is fitted to obtain the zero-point drift calibration relationship layer. When the first sensor is subjected to a preset reference load, the NFC reading device is controlled to transmit signals to the passive NFC sensing device according to the multiple preset radio frequency excitation states to obtain the second radio frequency excitation test dataset. Based on the second RF excitation test dataset, the sensitivity drift is calculated and the coupling state deviation mapping is fitted to obtain the sensitivity drift calibration relationship layer. The zero-point drift calibration layer and the sensitivity drift calibration layer are connected as parallel nodes to generate the first channel radio frequency sensitive model.

7. The NFC-based dental implant status monitoring system as described in claim 1, characterized in that, The steps performed by the risk coupling analysis module include: The input load parameters are compared with the input load benchmark to obtain the input load deviation characteristics, and the abnormal load state risk is predicted based on the input load deviation characteristics to obtain the abnormal load state risk characteristics. The overall load transfer parameters are compared with the overall load transfer benchmark to obtain the overall load transfer deviation characteristics. Based on the overall load transfer deviation characteristics, the overall support state change risk is predicted to obtain the overall support state change risk characteristics. Based on the local connection state parameters, the risk characteristics of abutment connection state changes are predicted. Combining the risk characteristics of abnormal loading state and the risk characteristics of overall support state changes, a coupling map of the state risk of the oral implant is constructed.

8. The NFC-based dental implant status monitoring system as described in claim 1, characterized in that, The radio frequency coupling state parameters include rectified voltage, NFC antenna terminal voltage, phase difference between voltage and current in the NFC antenna, received signal strength, modulation depth, communication bit error rate, and radio frequency coupling coefficient.

9. The NFC-based dental implant status monitoring system as described in claim 1, characterized in that, The first sensor is located in the coronal load input path of the oral implant assembly and is used to collect a first sensing signal characterizing the input load applied to the oral implant assembly. The second sensor is located at the position where the oral implant assembly is mechanically coupled with the surrounding support structure, and is used to collect a second sensing signal that characterizes the overall support state of the oral implant assembly. The third sensor is located at the connection point between the implant abutment and the oral implant assembly, and is used to collect third sensing signals that characterize the local contact state of the abutment connection structure.

10. A method for monitoring the status of dental implants based on NFC sensing, characterized in that, The method is applied to the NFC-based dental implant status monitoring system according to any one of claims 1-9, and the method includes: The NFC reader located outside the oral cavity transmits near-field radio frequency signals to a passive NFC sensor located inside the dental implant assembly to obtain radio frequency coupling status parameters. Based on the radio frequency coupling state parameters, the first sensor, the second sensor, and the third sensor are controlled to collect the first sensing signal, the second sensing signal, and the third sensing signal, respectively. Based on the radio frequency coupling state parameters, radio frequency power supply fluctuation compensation analysis is performed on the first sensing signal, the second sensing signal, and the third sensing signal to obtain input load parameters, overall load transfer parameters, and local connection state parameters. Based on the input load parameters, the overall load transfer parameters, and the local connection state parameters, a state risk coupling analysis of oral implants is performed to establish a state risk coupling map of oral implants.

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