Device and method for measuring occlusal force of dental crowns

Abstract

This invention discloses a device and method for measuring occlusal force of a dental crown, belonging to the field of oral restoration technology. It includes an installation cavity on the inner side of the crown body adapted for clinical implant abutments or natural tooth preparations to accommodate a pressure sensing module, with the cavity layout avoiding stress concentration areas on the inner side of the crown. Occlusal force distribution and peak force are collected through a multi-array layout of the inner pressure sensing modules. The pressure sensing modules employ multi-layer biocompatible encapsulation and integrate a temperature drift compensation unit. The pressure sensing modules are connected to a transmission unit, which transmits the collected occlusal force data to a data processing terminal. The data processing terminal integrates an anomaly warning module and an intelligent analysis module. This invention has the advantages of in-situ monitoring, high precision, durability, and ease of maintenance.

Description

Technical Field

[0001] This invention relates to the field of oral restoration technology, and more specifically, to a device and method for measuring the occlusal force of a dental crown. Background Technology

[0002] Dental restoration is a core treatment for tooth defects and edentulism. The long-term stability of implant-supported restorations (crowns) and conventional full-crown restorations directly depends on the distribution and magnitude of occlusal forces. Abnormal occlusal forces can easily lead to complications such as implant crown chipping, abutment loosening, peri-implant bone resorption, and crown loss, fracture of prepared teeth, and root resorption. Therefore, achieving accurate and long-term occlusal force monitoring without interfering with normal occlusal function is a key technical requirement for improving the clinical success rate and lifespan of restorations. Summary of the Invention

[0003] The purpose of this invention is to overcome the shortcomings of the prior art and provide a device and method for measuring the occlusal force of dental crowns. This invention solves the defects of the prior art and meets the monitoring needs of implanted dental crowns and ordinary full crowns. It has the advantages of in-situ monitoring, high precision, durability and easy maintenance.

[0004] The objective of this invention is achieved through the following solution: A device for measuring the occlusal force of a tooth crown, comprising: An installation cavity for accommodating the pressure sensing module is provided on the inner side of the crown body that is adapted to the clinical implant abutment or natural tooth preparation, and the layout of the installation cavity avoids the stress concentration area on the inner side of the crown. The bite force distribution and peak force are collected by a multi-array layout of the inner pressure sensing module. The pressure sensing module uses multi-layer biocompatible packaging and integrates a temperature drift compensation unit. The pressure sensing module is connected to a transmission unit, which transmits the collected occlusal force data to a data processing terminal. The data processing terminal integrates an anomaly warning module and an intelligent analysis module. The anomaly warning module receives non-invasive real-time transmitted data and performs scenario-specific anomaly warnings. The intelligent analysis module utilizes the multi-array pressure sensing structure built into the inner side of the crown and the corresponding data processing logic to form a collaborative technology unit. During the long-term operation of the sensors built into the inner side of the crown, the data model is updated, migrated, or inherited through the accumulation of historical data to achieve stable acquisition, analysis, or prediction of occlusal force.

[0005] Furthermore, the multi-array layout of the inner pressure sensing module specifically includes: arranging at least two pressure sensors in the force transmission area inside the crown to form a multi-array sensing unit.

[0006] Furthermore, the force transmission area on the inner side of the crown includes the area adjacent to the inner tapered connection of the implant crown and / or the inner axial wall cladding area of ​​a normal full crown.

[0007] Furthermore, the data processing terminal also integrates a data calibration module, which compares the collected data with the standard force value.

[0008] Furthermore, the pressure sensing module is fixed to the inner cavity of the crown using medical-grade adhesive or mechanical clamping structure, and the crown can be removed and replaced independently without removing the implant or damaging the natural tooth.

[0009] Furthermore, based on the pressure sensing module, an oral local environment parameter monitoring unit is integrated for the linkage acquisition of occlusal force and microenvironment data around implants or natural teeth.

[0010] Furthermore, it also includes a standardized verification module for sensors built into the inner side of the crown, which is used to perform in vitro verification of crowns with sensors built into the inner side using a simulated oral environment combined with dynamic load cycling. The standardized data obtained in the verification process is used for the training, testing and performance verification of artificial intelligence learning models.

[0011] A method for measuring occlusal force of a tooth crown, based on the tooth crown occlusal force measuring device as described above, wherein the method utilizes a multi-array pressure sensing structure built into the inner side of the tooth crown and corresponding data processing logic to form a collaborative technology unit, and updates, migrates, or inherits the data model through historical data accumulation during the long-term operation of the sensors built into the inner side of the tooth crown, so as to achieve stable acquisition, analysis, or prediction of occlusal force, specifically including the following sub-steps: Step S1, Spatiotemporal Synchronous Acquisition and Preprocessing of Multi-channel Signals: Using an integrated circuit module, the signals distributed within the inner cavity of the tooth crown are... N Each pressure sensor collects data synchronously at a specific time. t The original pressure signal vector obtained from the acquisition is represented as follows:

[0012] in, T The transpose is used to represent the signal as a column vector. Preprocessing is performed on the original signal to suppress high-frequency electronic noise and occasional spike interference. Let the preprocessed pressure signal be... ; Step S2, multidimensional feature extraction with occlusal pattern invariance: based on preprocessed pressure signal Construct a multidimensional feature vector to characterize the occlusal state. ; In the spatial dimension, in the coordinate system of the unfolded inner wall of the crown, calculate the equivalent two-dimensional coordinates of the pressure center point COP:

[0013] in, For the first The preset spatial position parameters of each pressure sensor on the inside of the crown, where N is the total number of pressure sensors installed on the inside of the crown; In the time dimension, peak load, average load and loading rate features are extracted from the pressure time series, where the loading rate is used to characterize the rate of change of the rising edge of the bite force. By combining spatial and temporal features, different bite patterns can be distinguished, providing input features with bite pattern invariance for subsequent prediction models; Step S3, predicting the bite force by introducing a physical constraint correction term: based on feature vectors. Construct a supervised regression prediction model This is used to output the corresponding predicted bite force value; Step S4, Full Lifecycle Adaptive Calibration: During long-term use, by identifying natural benchmark events, the prediction results are zero-point drift calibrated, and the calibrated output is:

[0014] in, The measured value corresponding to the baseline event. The reference value set at the factory. For adaptive update rate.

[0015] Further, in step S1, the preprocessing operation includes smoothing the signal using sliding window filtering, median filtering, weighted average filtering, or adaptive low-pass filtering; wherein, if sliding window filtering is used to smooth the signal, the filtered signal is represented as follows:

[0016] in, This is the size of the sliding window.

[0017] Further, in step S3, the supervised regression prediction model The model employs neural networks, support vector regression models, or combinations thereof. When zirconia is selected as the material for the crown body, to compensate for measurement biases introduced by zirconia under long-term loading and oral temperature changes, a temperature drift compensation term and a material hysteresis correction term are introduced into the model's prediction function. The predicted output is expressed as follows:

[0018] in, For model parameters, For real-time monitoring of temperature changes, This is the temperature drift compensation coefficient. For material hysteresis compensation function, These are the corresponding weighting coefficients.

[0019] The beneficial effects of this invention include: (1) This invention enables in-situ dynamic monitoring of occlusal force across different scenarios, solving the problem that existing in vitro measurement methods can only acquire static / single occlusal data and cannot cover both implant and natural tooth restoration scenarios. Specifically, the sensor is built into the inner side of the implant crown and the inner side of the ordinary tooth crown, adapting to the standard size of all-ceramic crowns, and can directly collect dynamic force data in real scenarios such as daily eating and speaking, filling the dual gap in monitoring the real occlusal force in implant restoration and natural tooth crown restoration. The machine learning model learns the relative relationship between multiple array signals and their changes over time, and can correct the sensor signals from the perspective of the overall force pattern, rather than relying on the output of a single sensor, so that the prediction results are more consistent with the overall distribution characteristics of the real occlusal force.

[0020] (2) This invention significantly improves the accuracy and stability of occlusal force measurement, solving the problems of long force transmission paths in existing invasive sensors and signal fluctuations caused by changes in contact angle in surface-attached sensors. Specifically, the pressure sensor is distributed in the core force transmission area on the inner side of the crown (next to the inner tapered connection of the implant crown and the inner axial wall contact area of ​​the full crown of ordinary teeth). The occlusal force is directly collected when it is transmitted to the inner side through the crown, shortening the force transmission path. This is combined with a multi-array layout and a temperature drift compensation module. During long-term use, the model can progressively update the signal baseline and mapping relationship based on continuous historical data, enabling the system to automatically adapt to the effects of slight wear of the restoration, changes in material properties, or adjustments in occlusal habits, avoiding cumulative errors caused by fixed parameters.

[0021] (3) This invention further improves the authenticity and consistency of measurement results based on hardware, solving the problem that even under the same sensing structure conditions, differences in occlusal habits, prosthesis materials, and assembly states among different individuals may still lead to systematic deviations in measurement results. Specifically, a machine learning-based occlusal force modeling and prediction module is introduced to learn and model the original signals output by the multi-array sensors, automatically correcting the systematic offset caused by individual and structural differences, making the output results closer to the true occlusal force value, and improving the consistency and comparability of monitoring results among different individuals and different prostheses. The machine learning model learns the relative relationships between multi-array signals and their changes over time, and can correct the sensing signals from the perspective of the overall force pattern, rather than relying on the output of a single sensor, so that the prediction results are more consistent with the overall distribution characteristics of the true occlusal force.

[0022] (4) This invention enables adaptive correction of data under long-term dynamic monitoring conditions, solving the problem that existing technologies often rely on fixed thresholds or static calibration parameters, making it difficult to adapt to slight wear, material aging, or changes in occlusal habits that occur after long-term wear of the prosthesis. Specifically, by continuously learning from historical occlusal force data through a machine learning model, the occlusal force mapping relationship can be dynamically updated without changing the hardware structure, thereby achieving adaptive maintenance of measurement accuracy under long-term use and avoiding systematic drift over time. During long-term use, the model can progressively update the signal baseline and mapping relationship based on continuous historical data, enabling the system to automatically adapt to the effects of slight wear of the prosthesis, changes in material properties, or adjustments in occlusal habits, avoiding cumulative errors caused by fixed parameters.

[0023] (5) This invention ensures the compatibility of the mechanical properties of the restoration, solving the problem that existing restorations with built-in sensors are prone to weakening mechanical properties due to unreasonable cavity structure, leading to porcelain chipping of the all-ceramic crown and fracture of the prepared body. Specifically, in some embodiments, the inner cavity of the crown is optimized by finite element method (cavity volume ≤ 10% of the total crown volume, avoiding the stress concentration area on the inner side), and combined with high-strength all-ceramic materials such as zirconia and lithium disilicate, so that the decrease in the mechanical strength of the crown is < 5%, which meets the mechanical performance standard of all-ceramic crown restorations (ISO 6872), taking into account both the safety of built-in sensors and long-term use. By coordinating the design of the cavity structure and the force path, the sensor installation area is separated from the main load-bearing path, which not only meets the requirements of the sensing function, but also avoids local stress concentration caused by structural weakening, thereby ensuring that the introduction of the sensing function will not have an adverse effect on the overall mechanical properties of the restoration.

[0024] (6) This invention improves the durability and adaptability of the sensor to the oral environment, solving the problem of insufficient failure period of existing surface-attached sensors due to exposure to the oral environment. Specifically, in some embodiments, the sensor is completely embedded in the inner side of the crown, with a multi-layer encapsulation of a Ti-6Al-4V titanium alloy substrate, a wear-resistant and corrosion-resistant coating, and a special adhesive for fixation. The sensor does not directly contact saliva, food, or opposing teeth, greatly improving corrosion resistance and wear resistance, and extending service life. Since the sensor works in a closed and stable inner interface environment for a long time, its signal output is not affected by direct friction and media erosion, which helps maintain the consistency of sensing characteristics and provides a reliable premise for modeling and prediction based on historical data.

[0025] (7) This invention completely avoids interference with occlusal function and aesthetics, and solves the problem that existing surface-attached sensors protrude from the surface of the restoration, which can easily lead to occlusal interference, food impaction, and affect aesthetics. Specifically, the sensor and integrated circuit are located inside the crown (not exposed on the occlusal surface or labial / buccal surface), and the outer surface of the crown maintains the same flatness and aesthetic shape as a conventional full crown, without any risk of occlusal interference or food impaction, and fully meets the functional and aesthetic requirements of oral restoration. This structural design allows the algorithm analysis to be based entirely on the original data obtained without interfering with the natural occlusal state, avoiding non-physiological forces introduced by human intervention or the measuring device itself, and improving the clinical reliability of the data from the source.

[0026] (8) This invention reduces clinical maintenance costs and patient trauma, solving the problem that existing invasive sensors require surgical removal of implants or damage to natural teeth after failure, increasing patient trauma and treatment costs. Specifically, the sensor is integrated into the detachable and replaceable crown body. In case of failure, only the crown needs to be replaced, without surgical operation, reducing patient trauma risk and maintenance costs. With the sensing module replaceable along with the crown, historical monitoring data can still serve as an important reference for model learning, enabling the system to quickly restore monitoring capabilities after hardware updates, reducing the time required for recalibration and adaptation.

[0027] (9) This invention achieves functional integration and clinical data linkage, solving the problem that existing solutions can only collect occlusal force individually, and the data is difficult to directly serve clinical diagnosis and treatment. Specifically, the integrated circuit on the inner side of the crown can simultaneously process signals from multiple array sensors. With the help of a wireless transmission module, the data can be transmitted to the clinical terminal in real time. Subsequently, it can be linked with CBCT images and medical record data to build a closed loop for diagnosis and treatment, providing accurate data support for implant occlusal adjustment, natural tooth crown occlusal adjustment, and complication early warning. Based on the long-term accumulation and analysis of multidimensional data, the result of a single occlusal force measurement can be transformed into clinical decision-making information with trend judgment and risk warning significance, thus elevating this invention from a data acquisition device to an auxiliary diagnostic and treatment tool. Attached Figure Description

[0028] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0029] Figure 1 This is a schematic diagram of the structure of the device according to an embodiment of the present invention. Figure 1 ; Figure 2 This is a schematic diagram of the structure of the device according to an embodiment of the present invention. Figure 2 . Detailed Implementation

[0030] All features disclosed in all embodiments of this specification, or steps in all methods or processes implied in the disclosure, may be combined and / or extended or replaced in any way, except for mutually exclusive features and / or steps.

[0031] It should be noted that relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0032] Given the current situation, the inventors of this invention, after further creative thinking and analysis, believe that the closest technology currently available is a sensor embedded in the implant body or natural tooth root. The structure and working principle of this type of solution involves integrating the sensor into the implant body (the part implanted within the bone) or implanting it invasively into the natural tooth root. Occlusal force data is indirectly obtained through stress transmission. The sensor is encapsulated in a titanium alloy cavity, integrally formed with the implant or root. The problems and causes of this type of solution are: ① Low measurement accuracy: the occlusal force is severely attenuated after being transmitted through multiple layers of the restoration, abutment (implant side), or dentin (natural tooth side), resulting in data deviations exceeding 25%; ② High trauma and maintenance costs: the sensor embedded in the implant body or natural tooth root cannot be removed, requiring surgical removal of the implant or damage to the natural tooth in case of failure, increasing patient suffering and treatment costs; ③ Poor mechanical compatibility: the implant body cavity weakens resistance to torsion and vertical loads, and implanting the sensor into the natural tooth root can damage the integrity of the tooth's hard tissues, increasing the risk of fracture.

[0033] In summary, existing occlusal force monitoring technologies struggle to simultaneously meet the comprehensive requirements of in-situ dynamic monitoring, long-term stable operation, high measurement accuracy, non-interference with normal occlusal function, and no damage to the mechanical properties of the restoration and natural teeth in both implant-supported crowns and conventional full-crown restorations. The fundamental reason is that existing solutions largely rely on external interference measurements or sensors directly placed at the force-bearing site, making it difficult to achieve a balance between restoration miniaturization, biocompatibility, aesthetic integrity, and long-term mechanical reliability. Meanwhile, the internal region of the restoration (i.e., the interface area that mates with the implant abutment or natural tooth preparation) has not been systematically developed in current technologies. This region offers advantages such as a stable force transmission path, relative isolation from the oral environment, and non-involvement in direct occlusal contact, providing a new structural basis for achieving concealed, long-term occlusal force monitoring.

[0034] Furthermore, relying solely on the raw mechanical signals output by sensors is insufficient to fully characterize the true distribution of occlusal forces within the complex oral environment. During chewing, occlusal forces exhibit significant individual variability, temporal fluctuations, and spatial non-uniformity, and their measurement results are easily influenced by multiple factors, including restoration morphology, material elastic modulus, temperature changes, and minute displacements. Introducing machine learning-based signal modeling and prediction strategies promises to overcome the dependence of traditional mechanical measurements on "single-point, instantaneous, and linear mapping." By jointly learning multi-point sensor signals, historical occlusal patterns, and individual parameters, accurate inversion and stable prediction of the magnitude, direction, and distribution of true occlusal forces can be achieved. Such models can automatically correct for systematic biases introduced by environmental noise and structural differences based on the raw sensor output, and through continuous learning, achieve adaptive model updates between different restoration types and individuals.

[0035] Therefore, the inventors of this invention ultimately believe that combining miniaturized, concealed sensor structure design with machine learning prediction models to construct an intelligent occlusal force monitoring system is expected to fundamentally solve the stability and practicality bottlenecks of existing technologies in clinical in situ applications, and provide a new technical path for the accurate assessment and long-term follow-up of implant and natural tooth crown restorations.

[0036] In a preferred embodiment, such as Figure 1 and Figure 2As shown, this invention proposes a crown occlusal force measurement technology, specifically involving the improvement and optimization of an intelligent monitoring device for dental prostheses. In particular, it addresses the structural improvements of the inner surface of implant crowns and the inner surface of ordinary full crowns with built-in micro-pressure sensors, and their application in in-situ dynamic measurement of occlusal force in dental prostheses, covering two major clinical scenarios: implant restoration and natural tooth full crown restoration. As the first aspect of this invention, a crown occlusal force measurement device is proposed, comprising a crown body, a sensing module, a data transmission and processing module, and an artificial intelligence feature analysis module. Through precise inner surface structure optimization, material selection, and functional integration, it is adapted to both implant restoration and natural tooth full crown restoration scenarios, specifically including: Regarding improvements to the crown body and inner cavity structure, the inventive concept lies in the integrated inner structure of the implant crown and the ordinary full crown. An installation cavity for accommodating the pressure sensing module is created within the inner side of the crown body, which is adapted to the clinical implant abutment or natural tooth preparation. The cavity layout avoids stress concentration areas on the inner side of the crown (such as the tapered connection area and the shoulder area of ​​the preparation edge). Furthermore, the invention is not limited to the specific material of the crown (zirconia, lithium disilicate, porcelain, etc.) or the specific dimensions (diameter, height) of the inner cavity. As long as the core logic of integrating the sensor internally within the crown without significantly weakening the crown's mechanical properties is met, it falls within the scope of this invention. This integrated inner structure also serves as the physical basis for long-term, stable occlusal force data acquisition, supporting subsequent machine learning-based individualized modeling, calibration, or predictive analysis. This structural improvement directly solves the shortcomings of existing surface-attached sensors, which are prone to detachment, and invasive sensors, which cause significant trauma. It is the physical basis for the cross-scenario in-situ monitoring achieved in the embodiments of this invention. Meanwhile, it should be noted that the definition of the above-mentioned inner integrated structure as a functional space is not limited to a specific geometric shape or processing method. As long as the structure is located inside the crown and undertakes the function of bearing and positioning the sensing module in the service state of the restoration, it belongs to the equivalent variation of the inner integrated structure described in this invention.

[0037] In specific implementation examples, the crown body in the embodiments of this invention is adapted to clinically universal restorative standards. The implant crown matches the Morse taper connection interface of a universal implant abutment, and the ordinary full crown matches the axial wall convergence angle of the natural tooth preparation. The crown body material can be selected from commonly used clinical all-ceramic materials such as zirconia and lithium disilicate glass ceramic. A sensor mounting cavity is opened on the inner side of the crown, and the cavity layout is optimized by finite element simulation. The implant crown cavity is located on its inner side, specifically next to the Morse taper area that fits with the abutment, avoiding the stress concentration area of ​​the taper connection. The ordinary full crown cavity is located on its inner side, specifically on the inner surface of the axial wall that fits with the natural tooth preparation, avoiding the stress concentration area of ​​the preparation edge shoulder. It should be further noted that the existing technology does not optimize the layout of the inner cavity of the crown for sensor integration, which can easily lead to stress concentration or sensor loosening, and there is no cross-scenario adaptation design. Therefore, to address the issues of reduced restoration strength and compatibility caused by built-in sensors, this invention's embodiment adapts to both implant abutment interfaces and natural teeth, utilizes finite element analysis to optimize the position of the inner cavity, and incorporates a stepped anti-slip structure within the cavity. Specifically, the finite element optimization design of the inner cavity for sensor integration, the universal inner cavity structure accommodating both implant crowns and natural tooth crowns, and the stepped anti-slip cavity structure balance crown mechanical properties with sensor installation stability. Optionally, a nano-level lubricating coating on the cavity surface and personalized texture markings on the crown's outer surface can improve assembly convenience. It should be noted that the crown's basic dimensions, Morse taper connection interfaces, and all-ceramic material selection only need to meet the general compatibility requirements of clinical restoration systems and will not be elaborated upon here.

[0038] In more specific implementation cases, to achieve the optimal balance between performance and cost, a zirconia full crown (compatible with both implant crowns and standard dental crowns) is used. The crown body is prepared using high-strength zirconia as the material and precision-machined using CAD / CAM. Implant crowns are machined with a Mohs taper connection interface suitable for universal implants, while standard dental crowns are machined with a 6° axial wall convergence angle suitable for natural tooth preparations.

[0039] A stepped sensor mounting cavity is opened on the inner side of the crown. The implant crown cavity is located on the inner side (next to the area that fits with the Mohs taper of the abutment, 0.5 mm away from the taper interface); the cavity of the ordinary full crown is located on the inner side (inner surface of the lingual axial wall, 1.0 mm away from the shoulder of the prepared body). The inner wall of the cavity is processed with anti-slip texture to prevent the sensor assembly from loosening.

[0040] In terms of sensor module design, in order to solve the problems of existing surface and invasive sensors lacking inner array layout and temperature compensation, having a simple packaging structure, and being prone to signal fluctuations and short lifespan, the inventive concept of the embodiment of the present invention lies in the functional design of a multi-array pressure sensing module on the inner side of the crown. In the core force transmission area on the inner side of the crown (such as the area next to the tapered connection on the inner side of the implant crown, or the axial wall fusion area on the inner side of a standard full crown), at least two miniature pressure sensors are arranged to form a multi-array sensing unit. This design is not limited to the specific type (MEMS piezoresistive, capacitive, flexible film, etc.) or number (4 / 6 / 8, etc.) of the sensors. As long as the design achieves accurate acquisition of occlusal force distribution and peak force through the inner multi-array layout, and reduces the risk of signal fluctuation from a single sensor, it falls within the scope of this invention. The multi-array sensing signals can be used as multi-dimensional feature inputs for machine learning models to reconstruct occlusal force, identify trends, or analyze abnormal patterns, addressing the shortcomings of existing single-sensor measurements (low accuracy and unstable signals) and achieving the core of "dynamic and accurate monitoring." The protection of multi-array sensing is not limited to regular arrays or equidistant distributions, but also includes irregular distributions, functional zoning arrangements, or adaptive layouts based on force path optimization.

[0041] Furthermore, regarding the oral-adaptive packaging and signal optimization of the sensing module, the sensing module adopts a multi-layer biocompatible packaging (including an outer structure resistant to saliva corrosion and chewing friction) and integrates a temperature drift compensation unit. This approach is not limited to specific packaging materials (PEEK coating, titanium alloy substrate, polyimide insulation layer, etc.) or compensation algorithm details. As long as the requirement of "the sensor maintaining signal stability and no biological rejection in the intracavitary environment for a long period (≥6 months)" is met, it falls within the scope of this invention. The aforementioned stable packaging and signal compensation provide a continuous, repeatable, and trainable data foundation for machine learning models, preventing inaccurate predictions due to environmental drift and addressing the shortcomings of existing solutions such as poor sensor durability and susceptibility to oral environmental interference, enabling long-term clinical application. Further optionally, signal optimization includes not only hardware-level compensation and packaging design but also equivalent solutions for correcting environmental disturbances through software or data processing.

[0042] In specific implementation cases, multi-array MEMS miniature pressure sensor components are used, with an array layout in the inner core force-bearing area. This includes a pressure sensing unit and a corresponding signal conditioning unit. The signal conditioning unit can integrate a temperature drift compensation module and employs a multi-layer biocompatible encapsulation structure. Through the multi-array layout design on the inner side of the crown, the integration of the temperature drift compensation module, and the miniaturized multi-layer gradient encapsulation structure adapted to the inner space, accurate dynamic monitoring can be achieved, adapting to the corrosive and abrasive environment of the oral cavity, thus solving the problems of low measurement accuracy and poor durability. Furthermore, the integration of pH value and temperature auxiliary monitoring units, as well as a hydrophobic coating on the sensor surface, expands the monitoring dimensions without affecting the core occlusal force monitoring function, and can be implemented optionally according to actual conditions.

[0043] More specifically, the pressure sensor should be a miniature piezoresistive pressure sensor: the sensor's measurement range is 100-1000N, and its size is 1×1×0.5mm; the sensor is arranged so that it is evenly distributed along the circumference of the inner cavity of the tooth crown and directly attached to the inner wall of the tooth crown (shortening the force transmission path).

[0044] The integrated circuit uses a miniature flexible PCB board, and the integrated module includes a signal conditioning unit, a temperature drift compensation module, and a low-power transmission unit. Each pressure sensor is connected to the signal input port of the integrated circuit via gold wire bonding, enabling synchronous acquisition of multi-channel signals.

[0045] In terms of data transmission and processing modules, the sensing module connects to a low-power transmission unit, and the external data processing terminal integrates a calibration algorithm and a built-in occlusal force abnormality early warning module (the early warning threshold for implant crowns is set to >500N, and the early warning threshold for ordinary full crowns is set to >400N, adapting to the load-bearing characteristics of natural teeth). The terminal supports data storage and export, adapts to common clinical data formats, and can be connected to hospital HIS systems. Through the transmission unit, data calibration algorithm, and scenario-specific occlusal force abnormality early warning module, non-invasive real-time transmission is achieved, solving the problems of unreliable data accuracy, data distortion, and inability to serve clinical needs. More specifically, the transmission unit can integrate an NFC emergency reading module, and the terminal connects to CBCT imaging system functions, thus improving the convenience of data reading / management.

[0046] Existing solutions only collect data without providing early warnings, and the calibration algorithms are not optimized for the occlusal force characteristics of different restorations. The embodiments of this invention can provide an early warning module for abnormal occlusal force in different scenarios, as well as calibration algorithm parameters adapted to implant and natural tooth restoration scenarios.

[0047] Regarding assembly and application methods, the sensor is fixed inside the cavity using a medical-grade encapsulating adhesive. This material has low viscosity, excellent curing performance, and is suitable for the moist environment of the oral cavity. After assembly, in vitro simulation verification was performed (37℃ artificial saliva environment, 1-2Hz dynamic loading for 10 minutes).6 After verification, the implant is installed clinically. In clinical application, the implant crown is fixed to the abutment through the Mohs taper interface (the sensor is located in the inner contact area), and the ordinary full crown is bonded to the natural tooth preparation through resin cement (the sensor is located in the inner axial wall contact area). Through the external terminal paired with the transmission unit, the occlusal force data in daily occlusal scenarios are collected in real time.

[0048] In more specific implementation cases, a medical-grade encapsulation adhesive is used to fix the sensor and integrated circuit in the inner cavity. This adhesive is compatible with zirconia material and has no biotoxicity after curing. The outer surface of the crown is polished, and the appearance and flatness of the implant crown and ordinary full crown meet the clinical standards for all-ceramic crowns.

[0049] The embodiments of the present invention also provide a verification process: In vitro mechanical verification: Implant crowns were mounted on simulated implants, and standard full crowns were mounted on natural tooth preparation models. The models were placed in an artificial saliva environment at 37°C and dynamically loaded at a frequency of 1-2 Hz for 10 minutes. 6 The test was conducted once (load range 100 - 500N) to detect the mechanical strength of the crown (decline < 4%) and the stability of the sensor signal (fluctuation ≤ 1.5%). Clinical simulation verification: The crown and opposing tooth were installed in the oral simulation model, and the occlusal force data of chewing, speaking and other actions were collected and compared with the standard force sensor (HBM C16). The data correlation coefficient R² ≥ 0.99.

[0050] It should be noted that the use of medical-grade encapsulating adhesives for fixation and standardized in vitro simulation verification procedures can ensure sensor assembly stability, verify long-term reliability, and solve the problems of sensor detachment and data instability. Furthermore, plasma cleaning pretreatment of the cavity can improve assembly reliability and data interpretation. CAD / CAM processing techniques and resin cement bonding methods are mature technologies in dental prosthesis fabrication and will not be elaborated upon here. Existing solutions only perform static, single-use in vitro measurements, lacking standardized verification and long-term dynamic monitoring across various scenarios. This invention proposes a standardized in vitro simulation verification procedure adapted to internal sensors and a long-term dynamic monitoring method covering implant and natural tooth scenarios.

[0051] Furthermore, the data processing module also includes machine learning-based bite force modeling and prediction, individualized correction and adaptive optimization, and bite force anomaly identification and prediction functions.

[0052] In this invention, the wireless transmission and scenario-specific clinical adaptation of occlusal force data are implemented. A sensing module connects to a low-power wireless transmission unit (such as BLE, NFC, RFID, etc.) to transmit the collected occlusal force data to an external terminal. This terminal integrates data calibration (comparison with standard force values) and scenario-specific anomaly warnings (different thresholds for implants and natural teeth). The scope of this invention is not limited by transmission protocol version or warning threshold size; as long as it enables non-invasive real-time data transmission and the data can directly serve clinical diagnosis and treatment, it falls within the scope of this invention. Furthermore, the external terminal uses machine learning methods to perform individual baseline learning on historical occlusal force data, achieving dynamic threshold adjustment, risk trend prediction, and intelligent identification of abnormal patterns. This addresses the shortcomings of existing solutions where data is difficult to translate into clinical decision-making, enabling clinical implementation. The external terminal is not limited to standalone hardware devices but also includes mobile terminals, wearable devices, or functional modules within hospital information systems.

[0053] In the design of the sensing structure and data processing logic on the inner side of the crown, the multi-array pressure sensing structure built into the inner side of the crown and the corresponding data processing logic form a collaborative technical unit. The data processing logic includes at least the process of synchronously acquiring, extracting features, correcting or modeling the multi-array sensing signals. It is not limited to a specific algorithm or processing order. As long as the spatial layout of the sensing structure and the data processing logic are functionally compatible to achieve stable acquisition, analysis or prediction of occlusal force, it is within the protection scope of this invention. Any scheme that modifies this invention by changing the algorithm implementation, the order of data processing steps or the software deployment location (local or cloud) is also within the protection scope of this invention.

[0054] In more specific implementation cases, in the modeling and prediction of occlusal force based on machine learning, a machine learning model is constructed based on the raw signals collected by the multi-array pressure sensors on the inner side of the crown to model and predict the occlusal force. The model takes the pressure signals output by the multi-array sensors, time series features, and device structural parameters as inputs, performs regression prediction on the magnitude and distribution of the actual occlusal force, and outputs the denoised and corrected occlusal force results. The model is deployed on an external data processing terminal or cloud analysis system and is linked with the sensing module in real time or near real time for long-term occlusal force monitoring and identification of abnormal changes.

[0055] Specifically, this includes a feature input system built based on the output signals of multiple array sensors, a machine learning prediction model aimed at occlusal force inversion, and a data interface and online inference mechanism between the model and the hardware sensing system. The machine learning model directly serves the occlusal force inversion task of the multiple array sensors on the inner side of the crown; the model input signal comes from an in-situ dynamic monitoring structure that does not interfere with occlusal function; the model is used for continuous monitoring of restorations under long-term service conditions, rather than single experimental analysis, thus solving the problem that existing technologies mostly use simple thresholds or empirical formulas to process occlusal data and lack a learning-based modeling method for the signal characteristics of the built-in sensors in the restoration.

[0056] Furthermore, deep learning networks can be introduced to perform fine classification of chewing patterns, and the model output can be visualized and reconstructed in three dimensions to expand the dimensions of data interpretation or improve the user interaction experience.

[0057] In machine learning-based individualized correction and adaptive optimization, an individualized correction strategy is introduced based on a machine learning model to address the impact of differences in occlusal habits, prosthesis materials, and structures on measurement results. The model can adaptively adjust the mapping relationship between sensor signals and actual occlusal force by combining patient basic information, prosthesis type, and historical monitoring data, thereby improving the consistency and comparability of occlusal force measurement results among different individuals.

[0058] Specifically, the learning mechanism that incorporates individual parameters for model calibration supports dynamic updates of model parameters as monitoring data accumulates. The calibration mechanism specifically models the difference in occlusal force bearing between implant crowns and natural full-crown teeth. The calibration process is permanently linked to the prosthesis's built-in sensing structure, addressing the issue of significant deviations in monitoring results from the same device across different individuals or restoration scenarios, which hinders clinical follow-up. The model can also incorporate genetic information or other systemic physiological parameters as input variables for machine learning.

[0059] In the identification and prediction of abnormal occlusal force based on machine learning, the machine learning model can perform time-series analysis on continuous occlusal force data to identify and predict abnormal occlusal force change trends. When the model identifies that the occlusal force is long-term high, suddenly increases or fluctuates abnormally, it triggers an early warning prompt to help judge the risk of overload of the restoration, potential chipping, loosening or surrounding tissue risk.

[0060] Specifically, by using time series modeling based on historical data, an abnormal pattern recognition or prediction mechanism can be established. The abnormal prediction is based on in-situ dynamic monitoring data of the inner side of the crown, and the prediction results directly serve the clinical safety assessment of the restoration. This can solve the problem that the system can only passively record data and cannot achieve risk prediction.

[0061] In other embodiments, based on the above-described embodiments, in order to solve the problems of nonlinear response, temperature drift and long-term stability of pressure signals under oral humid and hot environment and complex biological load conditions, a hierarchical data processing and occlusal force prediction process with physical constraints is constructed, the specific steps of which are as follows.

[0062] Step S1: Spatiotemporal synchronous acquisition and preprocessing of multi-channel signals. This involves using an integrated circuit module to acquire and preprocess signals distributed within the cavity inside the tooth crown. N Multiple pressure sensors simultaneously acquire data, with a preferred sampling frequency of 100Hz to balance the ability to capture transient changes in bite force with system power consumption control. At any given time... t The original pressure signal vector obtained from the acquisition is represented as follows:

[0063] Preprocessing is performed on the original signal to suppress high-frequency electronic noise and occasional spike interference. Preferably, a sliding window filter is used to smooth the signal, and the filtered signal is represented as follows:

[0064] in, The size of the sliding window (preferably 3–7, for example, ...) Those skilled in the art should understand that the above preprocessing methods are not limited to moving average filtering, but can also be implemented using median filtering, weighted average filtering, or adaptive low-pass filtering.

[0065] Step S2: Multidimensional feature extraction with occlusal pattern invariance. Based on the preprocessed pressure signal. Construct a multidimensional feature vector to characterize the occlusal state. .

[0066] In the spatial dimension, within the coordinate system of the crown's inner wall, to quantify the distribution characteristics of forces at multiple points, the equivalent two-dimensional coordinates of the Center of Pressure (COP) are calculated:

[0067] in, For the first The preset spatial position parameters of a pressure sensor inside the crown.

[0068] In the time dimension, peak load, average load, and loading rate features are extracted from the pressure time series, where the loading rate is used to characterize the rate of change of the rising edge of the bite force. By combining the above spatial and temporal features, different bite modes such as axial and lateral forces and early contact can be distinguished, providing input features with bite mode invariance for subsequent prediction models.

[0069] Step S3 involves predicting the bite force by introducing a physical constraint correction term. This is based on the feature vector. Construct a supervised regression prediction model This is used to output the corresponding predicted bite force value. The prediction model can be implemented using a neural network, a support vector regression model, or a combination thereof.

[0070] To compensate for measurement biases introduced by zirconia materials under long-term loading and oral temperature variations, a temperature drift compensation term and a material hysteresis correction term are introduced into the prediction function. The prediction output is expressed as follows:

[0071] in, For model parameters, For real-time monitoring of temperature changes, This is the temperature drift compensation coefficient. For material hysteresis compensation function, These are the corresponding weighting coefficients.

[0072] Step S4, Lifecycle Adaptive Calibration. During long-term use, the prediction results are zero-point drift calibrated by identifying natural baseline events (such as swallowing, static closure, and other low-load states). The calibrated output is as follows:

[0073] in, The measured value corresponding to the baseline event. The reference value set at the factory. For adaptive update rate, the preferred value range is 0 < <0.1.

[0074] In summary, the technical solutions of the above embodiments of the present invention have the following advantages: (1) The present invention addresses the problem that existing external bite force measurement schemes cannot achieve in-situ dynamic monitoring. It adopts a technical measure of embedding a miniature pressure sensor inside the implant crown and inside the crown of a normal tooth to directly collect real force data in daily bite scenarios.

[0075] (2) The present invention solves the problem of insufficient durability of surface-attached sensors by using medical corrosion-resistant and wear-resistant materials to encapsulate the sensor in an integrated manner, which is suitable for the complex, humid and high-frequency friction environment of the oral cavity.

[0076] (3) The present invention solves the problem of surface-attached sensors interfering with occlusal function and aesthetics of restorations. It adopts the technical measure of completely embedding the sensor inside the crown without exposing it, maintaining the appearance of the crown and the flatness of the occlusal surface, which meets the aesthetic requirements of all-ceramic crowns.

[0077] (4) The present invention solves the problem of signal fluctuation of surface-attached sensor by adopting a technical measure of distributing multiple array sensors in the core force transmission area inside the crown to optimize the stability of force signal acquisition.

[0078] (5) The present invention solves the problem of low measurement accuracy of the implant body and natural tooth root built-in sensor by adopting the technical measure of arranging the sensor close to the inner side of the crown (to the abutment / preparation end) to shorten the force transmission path and reduce signal distortion.

[0079] (6) The present invention solves the problems of high maintenance cost and large trauma of built-in sensors by adopting the technical measure of integrating the sensor into the detachable restoration (crown). In case of failure, the crown can be replaced separately without damaging the implant or natural tooth body.

[0080] (7) The present invention addresses the problem of the built-in sensor weakening the mechanical properties of the restoration. It adopts the technical measure of optimizing the structure of the inner cavity of the crown by using finite element analysis, avoiding the stress concentration area on the inner side of the crown, and adapting to the strength requirements of materials such as all-ceramic crowns.

[0081] (8) The present invention solves the problem that the existing solution only collects occlusal force. It adopts the technical measure of integrating multiple parameter monitoring with sensor module to realize the linkage collection of occlusal force and oral local environmental parameters.

[0082] (9) The present invention solves the problem that existing data is difficult to directly serve clinical diagnosis and treatment. It adopts the technical measure of wirelessly transmitting sensor data to the clinical system to build a closed loop of linkage analysis between bite force and medical records and imaging data.

[0083] (10) The embodiments of the present invention solve the problem of limited adaptability of the prior art by adopting a universal inner cavity design and modular sensing unit, which can simultaneously adapt to implant crowns and ordinary full crowns (different materials such as zirconium oxide and lithium disilicate).

[0084] (11) The present invention solves the problem that the output signal of traditional sensors is difficult to accurately reflect the magnitude and distribution of the real bite force. By introducing a mechanical modeling method based on machine learning, the original signals collected by multiple array sensors are fused and feature learned to achieve accurate inversion of the real bite force.

[0085] (12) The present invention solves the problem of poor consistency of bite force measurement results under different individuals, different restoration structures and materials. By constructing a machine learning prediction model to jointly model sensor signals and individual parameters, the model can be adaptively corrected, thereby improving the universality and stability of bite force monitoring results.

[0086] (13) The present invention addresses the problem that existing bite force monitoring systems lack long-term prediction and risk assessment capabilities. By performing time-series analysis on historical bite data based on machine learning models, it enables the prediction of abnormal bite force change trends and provides data support for early warning of prosthesis complications.

[0087] In summary, the embodiments of this invention revolve around the innovative spatial layout of "internal crown embedding," and introduce machine learning as a technical means for interpreting, predicting, and supporting clinical decision-making regarding occlusal force data. By naturally embedding machine learning into the technology chain of "internal structure, multi-array sensing, long-term data, and clinical application," the problems of non-in-situ, low precision, poor durability, and difficult maintenance of existing technologies are solved. Based on the solution of this invention, the continuous upgrading and optimization of future intelligent dental restoration systems can be supported. This invention integrates the spatial layout of the sensing structure, the signal acquisition method, and the data interpretation logic at the system level to form an overall technical solution. Any solution that only replaces a single structure, algorithm, or transmission method without substantially changing the technical concept of "internal crown embedding sensing—multi-array force acquisition—long-term data-driven analysis" should be considered an equivalent implementation of this invention. Any circumvention technology solution that only changes the algorithm or software form is within the scope of protection claimed in the claims of this invention.

[0088] It should be noted that, within the scope of protection defined in the claims of this invention, the following embodiments can be combined and / or extended or replaced in any logical manner from the above specific embodiments, such as the disclosed technical principles, disclosed technical features or implicitly disclosed technical features.

[0089] Example 1 A device for measuring the occlusal force of a tooth crown, comprising: An installation cavity for accommodating the pressure sensing module is provided on the inner side of the crown body that is adapted to the clinical implant abutment or natural tooth preparation, and the layout of the installation cavity avoids the stress concentration area on the inner side of the crown. The bite force distribution and peak force are collected by a multi-array layout of the inner pressure sensing module. The pressure sensing module uses multi-layer biocompatible packaging and integrates a temperature drift compensation unit. The pressure sensing module is connected to a transmission unit, which transmits the collected occlusal force data to a data processing terminal. The data processing terminal integrates an anomaly warning module and an intelligent analysis module. The anomaly warning module receives non-invasive real-time transmitted data and performs scenario-specific anomaly warnings. The intelligent analysis module utilizes the multi-array pressure sensing structure built into the inner side of the crown and the corresponding data processing logic to form a collaborative technology unit. During the long-term operation of the sensors built into the inner side of the crown, the data model is updated, migrated, or inherited through the accumulation of historical data to achieve stable acquisition, analysis, or prediction of occlusal force.

[0090] Example 2 Based on Example 1, the multi-array layout of the inner pressure sensing module specifically includes: arranging at least two pressure sensors in the force transmission area inside the crown to form a multi-array sensing unit.

[0091] It should be noted that the number of sensors is adjustable. The number of pressure sensors can be adjusted by spacing them along the circumference of the inner cavity of the crown. This is suitable for clinical scenarios where the measurement accuracy requirements are slightly lower and cost control is more stringent. It can be used for both implant crowns and ordinary anterior full crowns.

[0092] Example 3 Based on Example 2, the force transmission area on the inner side of the crown includes the area next to the inner tapered connection of the implant crown and / or the inner axial wall cladding area of ​​a normal full crown.

[0093] It should be noted that the crown material and inner wall thickness can be adjusted. For example, the crown body material can be replaced with lithium disilicate glass ceramic, which is suitable for anterior implant crowns or ordinary anterior full crowns, taking into account both aesthetic effects and basic monitoring functions.

[0094] Example 4 Based on Example 1, the data processing terminal also integrates a data calibration module, which compares the collected data with the standard force value.

[0095] Example 5 Based on Example 1, the pressure sensing module is fixed to the inner cavity of the crown by medical-grade adhesive or mechanical clamping structure, and the crown can be removed and replaced separately without removing the implant or damaging the natural tooth.

[0096] Example 6 Based on Example 1, an oral local environment parameter monitoring unit is integrated into the pressure sensing module for the linkage acquisition of occlusal force and microenvironment data around implants or natural teeth.

[0097] It should be noted that, as an auxiliary monitoring function, a pH monitoring unit (measurement range 5.5 - 7.5) has been added to the integrated circuit, which can simultaneously collect local pH data in the oral cavity, making it suitable for patients at high risk of peri-implantitis and people susceptible to natural tooth caries.

[0098] Example 7 Based on Example 1, a standardized verification module for a sensor built into the inner side of the crown is also included. This module is used to perform in vitro verification of the crown with the sensor built into the inner side by simulating the oral environment and combining dynamic load cycles. The standardized data obtained from the verification process is used for training, testing and performance verification of the artificial intelligence learning model.

[0099] Example 8 A method for measuring occlusal force of a tooth crown, based on the occlusal force measuring device described in any one of Examples 1 to 7, wherein the method utilizes a multi-array pressure sensing structure built into the inner side of the tooth crown and corresponding data processing logic to form a collaborative technology unit, and updates, migrates, or inherits the data model through historical data accumulation during the long-term operation of the sensors built into the inner side of the tooth crown, so as to achieve stable acquisition, analysis, or prediction of occlusal force, specifically including the following sub-steps: Step S1, Spatiotemporal Synchronous Acquisition and Preprocessing of Multi-channel Signals: Using an integrated circuit module, the signals distributed within the inner cavity of the tooth crown are... N Each pressure sensor collects data synchronously at a specific time. t The original pressure signal vector obtained from the acquisition is represented as follows:

[0100] in, T The transpose is used to represent the signal as a column vector. Preprocessing is performed on the original signal to suppress high-frequency electronic noise and occasional spike interference. Let the preprocessed pressure signal be... ; Step S2, multidimensional feature extraction with occlusal pattern invariance: based on preprocessed pressure signal Construct a multidimensional feature vector to characterize the occlusal state. ; In the spatial dimension, in the coordinate system of the unfolded inner wall of the crown, calculate the equivalent two-dimensional coordinates of the pressure center point COP:

[0101] in, For the first The preset spatial position parameters of each pressure sensor on the inside of the crown, where N is the total number of pressure sensors installed on the inside of the crown; In the time dimension, peak load, average load and loading rate features are extracted from the pressure time series, where the loading rate is used to characterize the rate of change of the rising edge of the bite force. By combining spatial and temporal features, different bite patterns can be distinguished, providing input features with bite pattern invariance for subsequent prediction models; Step S3, predicting the bite force by introducing a physical constraint correction term: based on feature vectors. Construct a supervised regression prediction model This is used to output the corresponding predicted bite force value; Step S4, Full Lifecycle Adaptive Calibration: During long-term use, by identifying natural benchmark events, the prediction results are zero-point drift calibrated, and the calibrated output is:

[0102] in, The measured value corresponding to the baseline event. The reference value set at the factory. For adaptive update rate.

[0103] Example 9 Based on Example 8, in step S1, the preprocessing operation includes smoothing the signal using sliding window filtering, median filtering, weighted average filtering, or adaptive low-pass filtering; wherein, if sliding window filtering is used to smooth the signal, the filtered signal is represented as follows:

[0104] in, This is the size of the sliding window.

[0105] Example 10 Based on Example 8, in step S3, the supervised regression prediction model The model employs neural networks, support vector regression models, or combinations thereof. When zirconia is selected as the material for the crown body, to compensate for measurement biases introduced by zirconia under long-term loading and oral temperature changes, a temperature drift compensation term and a material hysteresis correction term are introduced into the model's prediction function. The predicted output is expressed as follows:

[0106] in, For model parameters, For real-time monitoring of temperature changes, This is the temperature drift compensation coefficient. For material hysteresis compensation function, These are the corresponding weighting coefficients.

[0107] Some of the units described in the embodiments of the present invention can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not, in certain circumstances, constitute a limitation on the unit itself.

[0108] According to one aspect of the present invention, a computer program product or computer program is provided, the computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, causing the computer device to perform the methods provided in the various optional implementations described above.

[0109] In another aspect, embodiments of the present invention also provide a computer-readable medium, which may be included in the electronic device described in the above embodiments; or it may exist independently and not assembled into the electronic device. The computer-readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to perform the methods described in the above embodiments.

Claims

1. A device for measuring the occlusal force of a tooth crown, characterized in that, include: An installation cavity for accommodating the pressure sensing module is provided on the inner side of the crown body that is adapted to the clinical implant abutment or natural tooth preparation, and the layout of the installation cavity avoids the stress concentration area on the inner side of the crown. The bite force distribution and peak force are collected by a multi-array layout of the inner pressure sensing module. The pressure sensing module uses multi-layer biocompatible packaging and integrates a temperature drift compensation unit. The pressure sensing module is connected to a transmission unit, which transmits the collected occlusal force data to a data processing terminal. The data processing terminal integrates an anomaly warning module and an intelligent analysis module. The anomaly warning module receives non-invasive real-time transmitted data and performs scenario-specific anomaly warnings. The intelligent analysis module utilizes the multi-array pressure sensing structure built into the inner side of the crown and the corresponding data processing logic to form a collaborative technology unit. During the long-term operation of the sensors built into the inner side of the crown, the data model is updated, migrated, or inherited through the accumulation of historical data to achieve stable acquisition, analysis, or prediction of occlusal force.

2. The crown occlusal force measuring device according to claim 1, characterized in that, The multi-array layout of the internal pressure sensing module specifically includes: arranging at least two pressure sensors in the force transmission area inside the crown to form a multi-array sensing unit.

3. The crown occlusal force measuring device according to claim 2, characterized in that, The force transmission area on the inner side of the crown includes the area next to the inner tapered connection of the implant crown and / or the inner axial wall cladding area of ​​a normal full crown.

4. The crown occlusal force measuring device according to claim 1, characterized in that, The data processing terminal also integrates a data calibration module, which compares the collected data with the standard force value.

5. The crown occlusal force measuring device according to claim 1, characterized in that, The pressure sensing module is fixed to the inner cavity of the crown using medical-grade adhesive or mechanical clamping structure, and the crown can be removed and replaced independently without removing the implant or damaging the natural tooth.

6. The crown occlusal force measuring device according to claim 1, characterized in that, Based on the pressure sensing module, an oral local environment parameter monitoring unit is integrated for the linkage acquisition of occlusal force and microenvironment data around implants or natural teeth.

7. The crown occlusal force measuring device according to claim 1, characterized in that, It also includes a standardized verification module for sensors built into the inner side of the crown, which is used to perform in vitro verification of crowns with built-in sensors by simulating the oral environment and combining dynamic load cycles. The standardized data obtained in the verification process is used for the training, testing and performance verification of artificial intelligence learning models.

8. A method for measuring the occlusal force of a tooth crown, characterized in that, The crown occlusal force measuring device according to any one of claims 1 to 7, wherein the multi-array pressure sensing structure built into the inner side of the crown and the corresponding data processing logic form a collaborative technology unit, and during the long-term operation of the sensor built into the inner side of the crown, the data model is updated, migrated, or inherited through historical data accumulation to achieve stable acquisition, analysis, or prediction of occlusal force, specifically includes the following sub-steps: Step S1, Spatiotemporal Synchronous Acquisition and Preprocessing of Multi-channel Signals: Using an integrated circuit module, the signals distributed within the inner cavity of the tooth crown are... N Each pressure sensor collects data synchronously at a specific time. t The original pressure signal vector obtained from the acquisition is represented as follows: in, T The transpose is used to represent the signal as a column vector. Preprocessing is performed on the original signal to suppress high-frequency electronic noise and occasional spike interference. Let the preprocessed pressure signal be... ; Step S2, multidimensional feature extraction with occlusal pattern invariance: based on preprocessed pressure signal Construct a multidimensional feature vector to characterize the occlusal state. ; In the spatial dimension, in the coordinate system of the unfolded inner wall of the crown, calculate the equivalent two-dimensional coordinates of the pressure center point COP: in, For the first The preset spatial position parameters of each pressure sensor on the inside of the crown, where N is the total number of pressure sensors installed on the inside of the crown; In the time dimension, peak load, average load and loading rate features are extracted from the pressure time series, where the loading rate is used to characterize the rate of change of the rising edge of the bite force. By combining spatial and temporal features, different bite patterns can be distinguished, providing input features with bite pattern invariance for subsequent prediction models; Step S3, predicting the bite force by introducing a physical constraint correction term: based on feature vectors. Construct a supervised regression prediction model This is used to output the corresponding predicted bite force value; Step S4, Full Lifecycle Adaptive Calibration: During long-term use, by identifying natural benchmark events, the prediction results are zero-point drift calibrated, and the calibrated output is: in, The measured value corresponding to the baseline event. The reference value set at the factory. For adaptive update rate.

9. The method for measuring the occlusal force of a tooth crown according to claim 8, characterized in that, In step S1, the preprocessing operation includes smoothing the signal using sliding window filtering, median filtering, weighted average filtering, or adaptive low-pass filtering; wherein, if sliding window filtering is used to smooth the signal, the filtered signal is represented as follows: in, This is the size of the sliding window.

10. The method for measuring the occlusal force of a tooth crown according to claim 8, characterized in that, In step S3, the supervised regression prediction model The model employs neural networks, support vector regression models, or combinations thereof. When zirconia is selected as the material for the crown body, to compensate for measurement biases introduced by zirconia under long-term loading and oral temperature changes, a temperature drift compensation term and a material hysteresis correction term are introduced into the model's prediction function. The predicted output is expressed as follows: in, For model parameters, For real-time monitoring of temperature changes, This is the temperature drift compensation coefficient. For material hysteresis compensation function, These are the corresponding weighting coefficients.

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