Dental prostheses and digital modular removable dentures processing methods and systems

Abstract

This invention provides a method and system for fabricating dentures and digitally modular removable dentures, relating to the field of digital technology. The method includes acquiring oral cavity feature data through 3D scanning, constructing a spatiotemporal map structure to determine key anatomical feature points and generate an optimal occlusal surface, constructing an initial 3D model of the denture, analyzing stress distribution using a biomechanical simulation system, performing parametric modeling and optimization of the abutment teeth, topologically optimizing the connection parts to construct a variable-density honeycomb support structure, and finally assembling and connecting them to form the denture. This invention improves the occlusal accuracy and mechanical performance of dentures, reduces the risk of fracture, and extends their service life.

Description

Technical Field

[0001] This invention relates to the field of digital technology, and in particular to methods and systems for processing dentures and digitally integrated removable dentures. Background Technology

[0002] Dental prosthesis is a common treatment in dental clinics. Traditional denture fabrication mainly relies on the manual operations of dental technicians, including impression taking, plaster model making, wax model carving, and casting. With the development of digital technology, computer-aided design and manufacturing technology has been widely used in the field of denture processing, improving the accuracy and efficiency of denture fabrication. Digital denture technology acquires patient oral data through oral scanning, designs denture models using specialized software, and then processes them using CNC machining equipment, effectively shortening the production cycle and improving the precision of the restoration.

[0003] However, existing digital denture technology has some significant shortcomings. Traditional digital denture design is mainly based on static occlusal data, making it difficult to reflect the dynamic occlusal relationship during daily chewing and speech activities. This can lead to occlusal interference and discomfort during functional use. Current denture designs generally use homogeneous materials and structures, lacking local structural optimization based on biomechanical analysis. This makes it difficult to design reasonable structures for different stress areas, easily leading to stress concentration and structural damage during long-term use. Summary of the Invention

[0004] The present invention provides a method and system for processing dentures and digitally combined removable dentures, which can solve the problems in the prior art.

[0005] A first aspect of the present invention provides a method for manufacturing dentures and digitally integrated removable dentures, comprising:

[0006] A three-dimensional oral scanning device is used to acquire oral feature data of the patient, including dynamic occlusal data of the patient in different occlusal states; a spatiotemporal graph structure is constructed based on the oral feature data, and an optimal occlusal surface is generated according to the spatiotemporal graph structure;

[0007] Based on the oral cavity feature data and the optimal occlusal surface, an initial three-dimensional model of the denture is constructed. The initial three-dimensional model of the denture includes an abutment tooth portion for replacing the missing tooth and a connecting portion for retention.

[0008] A biomechanical simulation system was used to perform stress analysis on the initial three-dimensional model of the denture. The stress distribution of the denture under different occlusal states was simulated using the finite element method, and stress areas were identified.

[0009] Parametric modeling and optimization are performed on the abutment tooth portion in the stress region to adjust the anatomical morphology surface of the abutment tooth portion so that it matches the optimal occlusal surface;

[0010] Based on the stress distribution analysis results, a variable density honeycomb support structure was constructed in the stress region;

[0011] The abutment tooth portion and the connecting portion are assembled and connected using a positioning structure to form a denture, and the assembled denture is then surface-treated and polished.

[0012] Constructing a spatiotemporal graph structure based on the oral cavity feature data, and generating the optimal occlusal surface based on the spatiotemporal graph structure, includes:

[0013] Based on the oral cavity feature data, a spatiotemporal graph structure is constructed. The coordinate data of the dental anatomical landmarks in the oral cavity feature data are used as nodes of the spatiotemporal graph structure. The spatial distance data between adjacent dental anatomical landmarks is used as the weight of the edges of the spatiotemporal graph structure. The topological connection relationship between the landmarks is constructed as an adjacency matrix.

[0014] Calculate the spatial correlation weight of nodes in the spatiotemporal graph structure, and take nodes whose spatial correlation weight is greater than a preset association threshold as key anatomical feature points.

[0015] Based on the key anatomical feature points, a control point mesh of a non-uniform rational B-spline surface is constructed. The spatial correlation weight is used as the weight parameter of the non-uniform rational B-spline surface to generate an initial occlusal surface. The initial occlusal surface is then optimized to generate the final occlusal surface.

[0016] In the optimization process, surface continuity constraints are introduced, and the gradient descent method is used to iteratively update the position of control points until convergence is achieved to generate the optimal interlocking surface.

[0017] Based on the key anatomical feature points, a control point mesh for a non-uniform rational B-spline surface is constructed. The spatial correlation weight is used as the weight parameter of the non-uniform rational B-spline surface to generate an initial occlusal surface. Optimizing the initial occlusal surface to generate the final occlusal surface includes:

[0018] A control point grid is constructed according to a rule. The number of rows and columns of the control point grid is determined based on the distribution density of the key anatomical feature points. The boundary control points of the control point grid are matched with the boundary contours of the key anatomical feature points.

[0019] The spatial correlation weights are mapped to weight parameters of a non-uniform rational B-spline surface, and the initial interlocking surface is generated by weighting the control points according to the influence of the weight parameters.

[0020] Establish surface continuity constraints, which include positional continuity constraints and tangential continuity constraints, to ensure a smooth transition between adjacent surface patches.

[0021] The fitting error between the initial occlusal surface and the key anatomical feature points is calculated. The control point positions are iteratively updated using the gradient descent method. The fitting error is recalculated after each iteration. The iteration stops when the difference between the fitting errors calculated in two consecutive iterations is less than a preset error threshold.

[0022] The final control point positions and weight parameters are substituted into the non-uniform rational B-spline surface to generate the final interlocking surface.

[0023] A biomechanical simulation system was used to perform stress analysis on the initial three-dimensional model of the denture. The stress distribution of the denture under different occlusal states was simulated using the finite element method, and the stress regions were identified as follows:

[0024] Boundary conditions are set on the initial three-dimensional model of the denture, the area connecting the denture and the abutment tooth is set as the displacement constraint boundary, the occlusal contact surface is set as the force boundary, and the displacement component on the displacement constraint boundary and the surface stress component on the force boundary are established.

[0025] The equivalent stress and maximum shear stress are calculated based on the displacement component and the surface stress component. The equivalent stress is determined by the square root of the sum of the squares of the differences between the displacement component and the surface stress component. The maximum shear stress is determined by the weighted average of the first difference and the second difference. The first difference is the difference between the maximum and minimum values ​​of the displacement component, and the second difference is the difference between the maximum and minimum values ​​of the surface stress component.

[0026] The stress concentration factor is obtained by calculating the ratio of the maximum shear stress to the nominal shear stress, and the stress uniformity index is obtained by calculating the dispersion of the equivalent stress relative to the mean stress.

[0027] The denture structure is evaluated based on the stress concentration factor and the stress uniformity index to identify areas of uneven stress distribution.

[0028] Parametric modeling and optimization of the abutment tooth portion within the stress region, adjusting the anatomical morphology surface of the abutment tooth portion to match the optimal occlusal surface, includes:

[0029] Obtain curvature data of the abutment tooth surface, calculate the rate of curvature change based on the curvature data, and determine the position of the axial surface edge line and the position of the feature points in the transition region based on the rate of curvature change.

[0030] Based on the position of the axial surface edge line and the position of the feature point of the transition region, the surface of the abutment tooth is divided into an axial surface region and a transition region, and the coordinates of the boundary points of the axial surface region and the transition region are recorded.

[0031] The cross-sectional profile of the abutment tooth is extracted based on the coordinates of the boundary points. The cross-sectional profile is fitted with a Fourier series to obtain a cross-sectional profile function that includes the basic radius parameter and the circumferential harmonic coefficient. The ideal position of each point on the surface of the abutment tooth is calculated based on the cross-sectional profile function. The difference between the actual position and the ideal position of each point is used as the radial deformation amount to establish a radial deformation function that includes the height direction variation component and the circumferential variation component.

[0032] The radial deformation function is used to locally adjust the surface of the abutment tooth to obtain an optimized surface. The surface distance and normal angle between the optimized surface of the abutment tooth and the target occlusal surface are calculated. A local deformation weight function is constructed using a point-type exponential function. The surface distance and normal angle are substituted into the local deformation weight function to calculate the morphological adjustment weight value for each region.

[0033] The surface of the abutment tooth is locally deformed according to the morphological adjustment weight value. During the deformation process, positional continuity constraints and tangential continuity constraints are applied to the connection between adjacent surface pieces to match the optimal occlusal surface.

[0034] Based on the stress distribution analysis results, constructing a variable-density honeycomb support structure in the stress region includes:

[0035] Obtain the structural stress state data of the connection part, normalize the feature vector corresponding to the structural stress state data to obtain the principal stress vector, and establish a stress trajectory equation describing the stress transmission path based on the principal stress vector;

[0036] Solving the stress trajectory equation yields the spatial distribution curve of the maximum principal stress direction. The spatial distribution curve is used as the reference arrangement path of the honeycomb structure, and the structural stress region is divided according to the reference arrangement path.

[0037] Equivalent stress is calculated in each structural stress region. The ratio of the equivalent stress to the allowable stress of the material is determined as the stress sensitivity. The reference wall thickness of the variable density honeycomb support structure is determined based on the stress sensitivity. A power function relationship between the reference wall thickness and the relative density is established. The power function relationship is used as the parametric equation controlling the geometry of the unit cell.

[0038] A second aspect of the present invention provides a dental prosthesis and a digitally integrated removable dental prosthesis processing system, comprising:

[0039] The first unit is used to acquire oral feature data of a patient using a three-dimensional oral scanning device. The oral feature data includes dynamic occlusal data of the patient under different occlusal states. Based on the oral feature data, a spatiotemporal graph structure is constructed, and an optimal occlusal surface is generated according to the spatiotemporal graph structure.

[0040] The second unit is used to construct an initial three-dimensional model of a denture based on the oral feature data and the optimal occlusal surface. The initial three-dimensional model of the denture includes an abutment tooth portion for replacing the missing tooth and a connection portion for retention.

[0041] The third unit is used to perform stress analysis on the initial three-dimensional model of the denture using a biomechanical simulation system, and to simulate the stress distribution of the denture under different occlusal states using the finite element method to identify stress areas.

[0042] The fourth unit is used to perform parametric modeling and optimization of the abutment tooth portion in the stress region, and adjust the anatomical morphology surface of the abutment tooth portion to match the optimal occlusal surface.

[0043] The fifth unit is used to construct a variable-density honeycomb support structure in the stress region based on the stress distribution analysis results;

[0044] The sixth unit is used to assemble and connect the abutment tooth portion and the connecting portion through a positioning structure to form a denture, and to perform surface treatment and polishing on the assembled denture.

[0045] A third aspect of the present invention,

[0046] An electronic device is provided, comprising:

[0047] processor;

[0048] Memory used to store processor-executable instructions;

[0049] The processor is configured to invoke instructions stored in the memory to execute the aforementioned method.

[0050] Fourth aspect of the present invention,

[0051] A computer-readable storage medium is provided, having stored thereon computer program instructions that, when executed by a processor, implement the aforementioned method.

[0052] The beneficial effects of this application are as follows:

[0053] By collecting dynamic occlusal data from patients and combining it with non-uniform rational B-spline surface technology to generate the optimal occlusal surface, the individualized occlusal relationship of patients can be accurately simulated, making the fabricated dentures more in line with the actual oral functional needs of patients, and improving the comfort and effectiveness of dentures.

[0054] By employing a biomechanical simulation system for stress analysis and optimizing the design based on the finite element method, parametric modeling optimization and variable density honeycomb support structure design were carried out for high stress areas, which significantly improved the mechanical performance and service life of dentures and reduced the risk of breakage during clinical use.

[0055] By adopting a modular design approach, the abutment tooth portion and the connecting portion are assembled and connected through a positioning structure. This not only facilitates the repair and replacement of the denture, but also allows for flexible adjustments based on changes in the patient's oral condition, reducing the patient's usage costs and improving the adaptability and clinical practical value of the denture. Attached Figure Description

[0056] Figure 1 This is a schematic flowchart of the dental prosthesis and digital modular removable dental prosthesis processing method according to an embodiment of the present invention;

[0057] Figure 2 A schematic diagram of the anatomical morphology design. Detailed Implementation

[0058] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0059] The technical solution of the present invention will be described in detail below with reference to specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0060] refer to Figure 1 and Figure 2 The present invention relates to a method for manufacturing dentures and digitally integrated removable dentures, comprising:

[0061] A three-dimensional oral scanning device is used to acquire oral feature data of the patient, including dynamic occlusal data of the patient in different occlusal states; a spatiotemporal graph structure is constructed based on the oral feature data, and an optimal occlusal surface is generated according to the spatiotemporal graph structure;

[0062] Based on the oral cavity feature data and the optimal occlusal surface, an initial three-dimensional model of the denture is constructed. The initial three-dimensional model of the denture includes an abutment tooth portion for replacing the missing tooth and a connecting portion for retention.

[0063] A biomechanical simulation system was used to perform stress analysis on the initial three-dimensional model of the denture. The stress distribution of the denture under different occlusal states was simulated using the finite element method, and stress areas were identified.

[0064] Parametric modeling and optimization are performed on the abutment tooth portion in the stress region to adjust the anatomical morphology surface of the abutment tooth portion so that it matches the optimal occlusal surface;

[0065] Based on the stress distribution analysis results, a variable density honeycomb support structure was constructed in the stress region;

[0066] The abutment tooth portion and the connecting portion are assembled and connected using a positioning structure to form a denture, and the assembled denture is then surface-treated and polished.

[0067] In one optional implementation, constructing a spatiotemporal graph structure based on the oral cavity feature data, and generating an optimal occlusal surface based on the spatiotemporal graph structure includes:

[0068] Based on the oral cavity feature data, a spatiotemporal graph structure is constructed. The coordinate data of the dental anatomical landmarks in the oral cavity feature data are used as nodes of the spatiotemporal graph structure. The spatial distance data between adjacent dental anatomical landmarks is used as the weight of the edges of the spatiotemporal graph structure. The topological connection relationship between the landmarks is constructed as an adjacency matrix.

[0069] Calculate the spatial correlation weight of nodes in the spatiotemporal graph structure, and take nodes whose spatial correlation weight is greater than a preset association threshold as key anatomical feature points.

[0070] Based on the key anatomical feature points, a control point mesh of a non-uniform rational B-spline surface is constructed. The spatial correlation weight is used as the weight parameter of the non-uniform rational B-spline surface to generate an initial occlusal surface. The initial occlusal surface is then optimized to generate the final occlusal surface.

[0071] In the optimization process, surface continuity constraints are introduced, and the gradient descent method is used to iteratively update the position of control points until convergence is achieved to generate the optimal interlocking surface.

[0072] The specific implementation process of constructing a spatiotemporal graph structure based on oral cavity feature data is as follows: First, acquire three-dimensional scan data of the oral cavity and extract the coordinate information of anatomical landmarks, including key locations such as cusps, pits, fissures, and marginal ridges. In practical applications, the number of landmarks on each tooth varies depending on the tooth type; generally, molars have 5-7 landmarks, and anterior teeth have 3-5. These landmark coordinates are used as nodes in the spatiotemporal graph structure. For example, for the first molar, the landmark coordinates might be specific three-dimensional spatial location data such as P1(10.2, 15.3, 5.4) and P2(12.6, 16.8, 5.2). The Euclidean distance between adjacent landmarks is calculated as the edge weight. For example, the distance between landmarks P1 and P2 might be 3.2 mm; this distance value is the weight of the edge connecting these two nodes. Finally, an adjacency matrix is ​​constructed to represent the topological connections between nodes. The adjacency matrix is ​​an N×N matrix (N is the total number of landmarks), where a value of 1 indicates that two points are connected, and a value of 0 indicates that they are not connected. For example, oral data containing 30 markers will generate a 30×30 adjacency matrix.

[0073] For each node, its spatial correlation with other nodes is calculated based on the topological relationships of the graph structure. The spatial correlation weight is calculated using a Gaussian kernel function based on geodesic distance; the shorter the geodesic distance between nodes, the greater the correlation weight. Specifically, the shortest path distance (geodesic distance) between any two nodes in the graph is first calculated, and then the distance is converted into a correlation weight using a Gaussian kernel function. For example, when the geodesic distance between two nodes is 5.6 mm, the correlation weight calculated using a Gaussian kernel function with a standard deviation of 2.0 is approximately 0.058. The calculated spatial correlation weight is compared with a preset association threshold, which is typically set between 0.05 and 0.1; in this embodiment, it is set to 0.07. Nodes with a spatial correlation weight greater than 0.07 are identified as key anatomical feature points. Practice shows that in a set of oral cavity data containing approximately 100 landmarks, typically 20 to 30 points are identified as key anatomical feature points.

[0074] The identified key anatomical features are used as the basis for the control point mesh, typically an m×n control point mesh is constructed, where m and n range from 5 to 10; in this embodiment, a 7×8 control point mesh is used. The control points are arranged according to the shape of the dental arch, ensuring the control point mesh covers the entire occlusal region. The calculated spatial correlation weights are used as weight parameters for the non-uniform rational B-spline surface; control points with larger weight values ​​have a greater impact on the surface shape. The node vectors of the non-uniform rational B-spline are set as follows: [0,0,0,0,0.25,0.5,0.75,1,1,1,1] in the u direction and [0,0,0,0,0.2,0.4,0.6,0.8,1,1,1,1] in the v direction, with the curve order set to 3 in both directions. An initial occlusal surface is generated based on the control point mesh, weight parameters, and node vectors.

[0075] A surface continuity constraint is introduced, requiring adjacent surface patches to satisfy G1 continuity (tangential continuity). A gradient descent method is used to iteratively update the control point positions. In each iteration, the error between the surface and the actual tooth occlusion point is calculated, and the control point positions are adjusted to reduce the error. Specifically, the initial learning rate is set to 0.01, and it gradually decreases as the number of iterations increases. The maximum number of iterations is set to 500, and the convergence condition is that the average error change over 10 consecutive iterations is less than 0.001 mm. In a practical application, a complete set of oral cavity data containing 28 teeth was processed. The initial average error between the surface and the actual occlusion point was 0.58 mm. After 312 iterations, the final error was reduced to 0.09 mm, and the generated optimal occlusal surface accurately reflects the patient's personalized occlusal characteristics.

[0076] The optimal occlusal surface generated by the above method can be applied to various clinical scenarios such as dental prosthesis design, orthodontic treatment planning, and occlusal reconstruction. It can significantly improve the occlusal accuracy of prostheses, reduce the workload of clinical adjustments, and enhance the patient's treatment experience and outcomes. This method fully utilizes oral anatomical features, extracts key feature points through spatiotemporal graph structures, and combines non-uniform rational B-spline surface technology to achieve personalized and high-precision occlusal surface construction.

[0077] In one optional implementation, a control point mesh of a non-uniform rational B-spline surface is constructed based on the key anatomical feature points. The spatial correlation weight is used as a weight parameter of the non-uniform rational B-spline surface to generate an initial occlusal surface. Optimizing the initial occlusal surface to generate the final occlusal surface includes:

[0078] A control point grid is constructed according to a rule. The number of rows and columns of the control point grid is determined based on the distribution density of the key anatomical feature points. The boundary control points of the control point grid are matched with the boundary contours of the key anatomical feature points.

[0079] The spatial correlation weights are mapped to weight parameters of a non-uniform rational B-spline surface, and the initial interlocking surface is generated by weighting the control points according to the influence of the weight parameters.

[0080] Establish surface continuity constraints, which include positional continuity constraints and tangential continuity constraints, to ensure a smooth transition between adjacent surface patches.

[0081] The fitting error between the initial occlusal surface and the key anatomical feature points is calculated. The control point positions are iteratively updated using the gradient descent method. The fitting error is recalculated after each iteration. The iteration stops when the difference between the fitting errors calculated in two consecutive iterations is less than a preset error threshold.

[0082] The final control point positions and weight parameters are substituted into the non-uniform rational B-spline surface to generate the final interlocking surface.

[0083] In this embodiment, a control point mesh of a non-uniform rational B-spline surface is constructed based on key anatomical feature points, and the spatial correlation weight is used as the weight parameter of the non-uniform rational B-spline surface to generate an initial interlocking surface.

[0084] Specifically, the number of rows and columns of the control point grid is determined based on the distribution density of the extracted key anatomical feature points. For example, when key anatomical feature points are densely distributed in the anterior tooth region, the corresponding control point grid can be set to 6×8, while in the sparser posterior tooth region, it can be set to 4×6. When constructing the control point grid, it is ensured that the boundary control points of the grid match the boundary contours of the key anatomical feature points, thereby ensuring that the generated occlusal surface can accurately reflect the external morphology of the teeth.

[0085] For each key anatomical feature point, its spatial correlation weight is calculated and mapped to a weight parameter of a non-uniform rational B-spline surface. This mapping can be achieved through linear mapping, whereby the original spatial correlation weight value is mapped from its domain [0.1, 1.0] to the domain [1.0, 10.0] of the non-uniform rational B-spline surface weight parameter. For example, if the spatial correlation weight of a key anatomical feature point is 0.7, after linear mapping, the corresponding non-uniform rational B-spline surface weight parameter is 7.0. The initial interlocking surface is generated by weighting the control points according to the influence of these weight parameters. Control points with larger weight values ​​have a greater influence on the surface shape, resulting in higher fitting accuracy of the surface near important anatomical feature points.

[0086] To ensure a smooth transition between adjacent surface patches, surface continuity constraints are established, including positional continuity constraints and tangential continuity constraints. Positional continuity constraints ensure that points on the shared boundary of adjacent surface patches have the same positional coordinates, preventing gaps or overlaps in the surfaces. In practice, control points on the shared boundary can be set to the same values; for example, control points P(i,j) and Q(i,j) at the junction of the occlusal surfaces of the upper and lower teeth can be set to the same three-dimensional coordinate values ​​(x,y,z). Tangential continuity constraints ensure that the derivative directions of adjacent surface patches on the shared boundary are consistent, preventing sharp corners or creases in the surfaces. In practice, this can be achieved by adjusting the positions of control points near the boundary so that the vectors formed by control points on both sides of the boundary have the same direction. For example, ensuring that the vectors P(i-1,j)-P(i,j) and Q(i,j)-Q(i+1,j) have the same or opposite directions.

[0087] After generating the initial occlusal surface, iterative optimization is used to improve the fitting accuracy. The fitting error between the initial occlusal surface and key anatomical feature points is calculated, and the control point positions are iteratively updated using the gradient descent method. The fitting error can be calculated using Euclidean distance, which is the distance from a point on the surface to the nearest key anatomical feature point. In the specific implementation, 100 uniformly distributed sampling points can be selected, and the average distance from these sampling points to the key anatomical feature points can be calculated as the fitting error. The initial error is typically in the range of 0.5-1.0 mm.

[0088] In each iteration, the update amount of the control point position is proportional to the gradient of the fitting error, and the update step size can be set to 0.05-0.1 mm. After updating the control point position, the fitting error is recalculated. Iteration stops when the difference between the fitting errors calculated in two consecutive iterations is less than a preset error threshold. The preset error threshold can be set to 0.01 mm, indicating that the convergence condition is met when the change in fitting error between two consecutive iterations is less than 0.01 mm. Typically, the algorithm converges within 15-20 iterations, and the final fitting error can be reduced to within 0.1-0.2 mm.

[0089] After iterative optimization, the final control point positions and weight parameters are substituted into the equation of the non-uniform rational B-spline surface to generate the final interlocking surface. The final generated interlocking surface not only satisfies the boundary conditions and continuity constraints, but also accurately reflects the position and morphological features of key anatomical feature points, while maintaining the smoothness and aesthetics of the overall surface.

[0090] In a real-world application case, for a full-mouth dental model containing 32 teeth, approximately 500 key anatomical feature points were extracted, and an 8×12 control point mesh was constructed. The initial fitting error was 0.78 mm. After 18 iterations of optimization, the final fitting error was reduced to 0.13 mm. The generated occlusal surface fully reflects the anatomical morphology of the teeth, especially in key areas such as the anterior teeth region and the functional cusp of the first molar, where the fitting accuracy is even higher, meeting the accuracy requirements of clinical restorative treatment.

[0091] In one optional implementation, a biomechanical simulation system is used to perform stress analysis on the initial three-dimensional model of the denture. The stress distribution of the denture under different occlusal states is simulated using the finite element method, and the stress regions are identified, including:

[0092] Boundary conditions are set on the initial three-dimensional model of the denture, the area connecting the denture and the abutment tooth is set as the displacement constraint boundary, the occlusal contact surface is set as the force boundary, and the displacement component on the displacement constraint boundary and the surface stress component on the force boundary are established.

[0093] The equivalent stress and maximum shear stress are calculated based on the displacement component and the surface stress component. The equivalent stress is determined by the square root of the sum of the squares of the differences between the displacement component and the surface stress component. The maximum shear stress is determined by the weighted average of the first difference and the second difference. The first difference is the difference between the maximum and minimum values ​​of the displacement component, and the second difference is the difference between the maximum and minimum values ​​of the surface stress component.

[0094] The stress concentration factor is obtained by calculating the ratio of the maximum shear stress to the nominal shear stress, and the stress uniformity index is obtained by calculating the dispersion of the equivalent stress relative to the mean stress.

[0095] The denture structure is evaluated based on the stress concentration factor and the stress uniformity index to identify areas of uneven stress distribution.

[0096] Obtain an initial 3D model of the patient's denture. This model can be acquired directly using an oral scanner or converted from a traditional impression into a digital model. The model must contain complete denture structure, geometric information of the abutment teeth and adjacent teeth, and the model resolution must be no less than 0.05 mm to ensure simulation accuracy.

[0097] Next, the initial 3D denture model is imported into the biomechanical simulation system. The system uses a general model processing module to convert the STL format 3D model into a model suitable for finite element analysis. During the conversion process, the system automatically repairs mesh defects in the model, such as holes and overlapping surfaces, and optimizes the mesh quality. The mesh is generated using adaptive tetrahedral elements with an element size ranging from 0.1 mm to 0.5 mm. In areas where stress may concentrate, such as joints and thin-walled sections, the mesh density is automatically refined to 0.1 mm.

[0098] Setting appropriate boundary conditions on the model is crucial for accurate simulation. The area connecting the prosthesis and the abutment tooth is set as a displacement constraint boundary. Specifically, this involves selecting all nodes on the inner side of the prosthesis that contact the abutment tooth and restricting their displacement in three directions (X, Y, and Z axes). In practice, the number of constraint nodes is typically between 5000 and 8000 to ensure the stability of the simulation.

[0099] The occlusal contact surface is set as the force boundary, and different loads are applied according to different occlusal states. Typical occlusal forces are set as follows: 100-150N for the anterior teeth, 200-250N for the micromolars, and 300-400N for the molars. The load direction is determined according to the patient's actual occlusal relationship, generally perpendicular to the occlusal plane, or at an angle of 60°-70° to the occlusal plane. The force distribution adopts a Gaussian distribution pattern, with the maximum force applied at the center of the occlusion and decreasing towards the periphery.

[0100] During the simulation, the system calculates the displacement and surface stress components of each node based on the set boundary conditions. For complete dentures, the number of nodes to be calculated is typically between 100,000 and 200,000, and the calculation time is approximately 10-15 minutes (based on a standard workstation). The system automatically records the displacement components of each node, including displacement values ​​in the X, Y, and Z directions, as well as the surface stress components, including normal stress and shear stress.

[0101] After obtaining displacement and stress data, the system calculates the equivalent stress. The equivalent stress is determined by the square root of the sum of the squares of the differences between the displacement components and the surface stress components. Specifically, the system first calculates the displacement vector and stress tensor for each node, and then calculates the equivalent stress value of the node using the displacement-stress relationship. In typical cases, the equivalent stress of dentures usually ranges from 5 to 120 MPa, with the highest stress values ​​in the connection areas.

[0102] Simultaneously, the system calculates the maximum shear stress. The maximum shear stress is determined by a weighted average of a first difference and a second difference. The first difference is the difference between the maximum and minimum values ​​of the displacement component, and the second difference is the difference between the maximum and minimum values ​​of the surface stress component. In actual calculations, the weight ratio of the first and second differences is 3:7 to better reflect the shear characteristics of the material. For ceramic dentures, the maximum shear stress should generally not exceed 30 MPa to avoid the risk of cracking.

[0103] Based on the above calculations, the system further calculates the stress concentration factor, which is the ratio of the maximum shear stress to the nominal shear stress. The nominal shear stress is taken as the average shear stress of the entire model. For an ideal denture design, the stress concentration factor should be less than 2.5. The system also calculates the stress uniformity index, which is obtained by calculating the dispersion of the equivalent stress relative to the mean stress. Specifically, the system calculates the ratio of the standard deviation to the mean of the equivalent stress at all nodes; ideally, this ratio should be less than 0.6.

[0104] In a real-world case, a patient's maxillary first molar full crown prosthesis model exhibited an equivalent stress peak of 125 MPa in the connection area under a vertical occlusal force of 300 N, a maximum shear stress of 42 MPa, a stress concentration factor of 3.2, and a stress uniformity index of 0.85. This indicates a stress concentration problem in the prosthesis design, particularly in the connection area, which could potentially lead to premature prosthesis failure.

[0105] For areas with uneven stress distribution identified, the system provides design optimization suggestions, such as increasing the transition radius of the connection area, adjusting the occlusal surface morphology, and modifying the material ratio. After optimization, under the same conditions, the maximum equivalent stress of the same denture is reduced to 85 MPa, the stress concentration factor is reduced to 2.1, and the stress uniformity index is improved to 0.52, significantly improving the service life and reliability of the denture.

[0106] In one optional implementation, parametric modeling and optimization of the abutment tooth portion in the stress region, adjusting the anatomical morphology surface of the abutment tooth portion to match the optimal occlusal surface, includes:

[0107] Obtain curvature data of the abutment tooth surface, calculate the rate of curvature change based on the curvature data, and determine the position of the axial surface edge line and the position of the feature points in the transition region based on the rate of curvature change.

[0108] Based on the position of the axial surface edge line and the position of the feature point of the transition region, the surface of the abutment tooth is divided into an axial surface region and a transition region, and the coordinates of the boundary points of the axial surface region and the transition region are recorded.

[0109] The cross-sectional profile of the abutment tooth is extracted based on the coordinates of the boundary points. The cross-sectional profile is fitted with a Fourier series to obtain a cross-sectional profile function that includes the basic radius parameter and the circumferential harmonic coefficient. The ideal position of each point on the surface of the abutment tooth is calculated based on the cross-sectional profile function. The difference between the actual position and the ideal position of each point is used as the radial deformation amount to establish a radial deformation function that includes the height direction variation component and the circumferential variation component.

[0110] The radial deformation function is used to locally adjust the surface of the abutment tooth to obtain an optimized surface. The surface distance and normal angle between the optimized surface of the abutment tooth and the target occlusal surface are calculated. A local deformation weight function is constructed using a point-type exponential function. The surface distance and normal angle are substituted into the local deformation weight function to calculate the morphological adjustment weight value for each region.

[0111] The surface of the abutment tooth is locally deformed according to the morphological adjustment weight value. During the deformation process, positional continuity constraints and tangential continuity constraints are applied to the connection between adjacent surface pieces to match the optimal occlusal surface.

[0112] For example, during the parametric modeling and optimization of the abutment tooth, the system first collects curvature data of the abutment tooth surface. Point cloud data of the abutment tooth is acquired using a 3D scanning device, with each sampling point containing spatial coordinate information and a surface normal vector. For each point in the point cloud data, its principal curvature value is calculated, including the maximum and minimum principal curvature. Taking a clinical case as an example, the acquired point cloud data of the abutment tooth surface contains approximately 8500 points, with the maximum principal curvature value ranging from -0.8 to 0.5 and the minimum principal curvature value ranging from -0.5 to 0.2. Based on these principal curvature values, the rate of curvature change is calculated using the finite difference method. During the calculation, a neighborhood radius of 0.3 mm is selected for each point, and the curvature gradient within the neighborhood of each point is analyzed. Regions with large rates of curvature change are usually located in areas of drastic morphological change, such as the axial surface edge line of the abutment tooth and transitional feature points. In actual cases, when the rate of curvature change exceeds the threshold of 0.85, the point is marked as a candidate feature point.

[0113] Using a feature point detection algorithm, the system determines the location of the axial surface edge line and the location of feature points in the transition region. Edge line detection employs a region growing method, starting from an initial seed point and gradually expanding and connecting adjacent feature points based on the similarity of their curvature change rates, ultimately forming a complete axial surface edge line. In the actual case, the detected axial surface edge line contained approximately 260 points, forming a closed curve in space with an average spacing of 0.2 mm. The transition region feature points are mainly distributed at the junction of the abutment tooth neck and the occlusal surface, with approximately 180 feature points detected. These feature points constitute the key locations of morphological changes.

[0114] Based on the determined axial surface edge line and transition region feature points, the system divides the abutment tooth surface into an axial region and a transition region. Region division employs a nearest neighbor classification method, calculating the distance from each point on the abutment tooth surface to the axial surface edge line and transition region feature points, and classifying points into the corresponding regions based on the minimum distance. The axial region mainly includes the lateral wall of the abutment tooth, characterized by a relatively gentle curvature change; the transition region lies between the axial and occlusal surfaces, exhibiting a larger curvature change. The system records the coordinates of the boundary points of the two regions, forming a region boundary description. In this case, the axial region occupies approximately 65% ​​of the total abutment tooth surface area, and the transition region occupies approximately 35%. The boundary point coordinates are stored in a three-dimensional array containing approximately 320 coordinate points, accurately describing the boundary between the two regions.

[0115] Based on the recorded boundary point coordinates, the system extracts the cross-sectional profile of the abutment tooth. Horizontal sections are cut at different heights perpendicular to the abutment tooth's central axis, resulting in a series of closed profiles. In the actual case, 15 equally spaced cross sections, spaced 0.5 mm apart, are extracted from the cervical region of the abutment tooth to the occlusal surface. For each cross-sectional profile, the system converts it to polar coordinates for Fourier series fitting. The center point of the abutment tooth is selected as the origin of the polar coordinates, and the polar radius and polar angle of each point on the profile are calculated. The relationship between the polar radius and the polar angle is expanded using a Fourier series to obtain a cross-sectional profile function containing the basic radius parameter and circumferential harmonic coefficients. During the fitting process, the order of the Fourier series is selected as 8, and the basic radius parameters at different heights are 4.8 mm, 4.6 mm, 4.5 mm, etc., with the circumferential harmonic coefficient values ​​ranging from -0.2 to 0.2.

[0116] Using the obtained cross-sectional profile function, the system calculates the ideal position of each point on the abutment tooth surface. By determining the height position of each point, the corresponding cross-sectional profile function for that height is substituted to calculate the ideal profile position at that height. The difference between the actual and ideal positions of each point is used as the radial deformation, constructing a radial deformation function that includes both height and circumferential variation components. In this case, the radial deformation ranges from -0.3 to 0.3 mm. The height variation component uses cubic spline interpolation to ensure a smooth transition between different heights; the circumferential variation component is described using Fourier series to capture morphological differences in different directions.

[0117] The system uses a radial deformation function to locally adjust the abutment tooth surface, obtaining an optimized surface. The adjustment process employs an iterative approach, updating the point position in each iteration. The number of iterations is set to 20, with each iteration's displacement not exceeding 0.05 mm to ensure the stability of the morphological adjustment. For the optimized abutment tooth surface, the system calculates the surface distance and normal angle between it and the target occlusal surface. The surface distance is calculated using a nearest-point search algorithm, while the normal angle is obtained by the dot product of the two surface normal vectors. In this case, the average distance between the pre-optimized abutment tooth surface and the target occlusal surface is 0.28 mm, with a maximum distance of 0.85 mm; the average normal angle is 12 degrees, with a maximum of 35 degrees.

[0118] The system constructs a local deformation weight function, employing a point-based exponential function. This function uses surface distance and normal angle as independent variables, outputting weight values ​​for morphological adjustment. The larger the distance or angle, the higher the weight value and the greater the adjustment range. In practical applications, when the surface distance is less than 0.05 mm and the normal angle is less than 5 degrees, the weight value approaches 0; when the surface distance is greater than 0.3 mm or the normal angle is greater than 20 degrees, the weight value approaches 1. The system substitutes the surface distance and normal angle into the local deformation weight function to calculate the morphological adjustment weight value for each region. In the case study, the average weight value for the interlocking contact area is 0.85, and the average weight value for the non-contact area is 0.35.

[0119] Based on the calculated morphological adjustment weight values, the system performs local deformation on the abutment tooth surface. The deformation algorithm is based on weight-driven Laplace deformation technology, achieving precise adjustment of local morphology while maintaining surface smoothness. For areas with higher weight values, the deformation amplitude is larger, making it closer to the target occlusal surface; for areas with lower weight values, the original morphological characteristics are maintained. During the deformation process, the system applies positional continuity constraints and tangential continuity constraints to the connection points of adjacent surface patches. Positional continuity constraints ensure that the shared boundary points of adjacent surface patches have the same spatial position, avoiding surface cracks; tangential continuity constraints ensure that the tangential planes at the shared boundary remain consistent, avoiding sharp edges on the surface. Through these constraints, the optimized abutment tooth surface achieves a good match with the optimal occlusal surface while maintaining overall smoothness. Optimization results show that the average distance between the adjusted abutment tooth surface and the target occlusal surface is reduced to 0.08 mm, and the maximum distance is reduced to 0.25 mm; the average normal angle is reduced to 4.5 degrees, and the maximum angle is reduced to 15 degrees, meeting the accuracy requirements for clinical applications.

[0120] In one optional implementation, based on stress distribution analysis results, constructing a variable-density honeycomb support structure in the stress region includes:

[0121] Obtain the structural stress state data of the connection part, normalize the feature vector corresponding to the structural stress state data to obtain the principal stress vector, and establish a stress trajectory equation describing the stress transmission path based on the principal stress vector;

[0122] Solving the stress trajectory equation yields the spatial distribution curve of the maximum principal stress direction. The spatial distribution curve is used as the reference arrangement path of the honeycomb structure, and the structural stress region is divided according to the reference arrangement path.

[0123] Equivalent stress is calculated in each structural stress region. The ratio of the equivalent stress to the allowable stress of the material is determined as the stress sensitivity. The reference wall thickness of the variable density honeycomb support structure is determined based on the stress sensitivity. A power function relationship between the reference wall thickness and the relative density is established. The power function relationship is used as the parametric equation controlling the geometry of the unit cell.

[0124] The structural stress state data is obtained through finite element analysis, including the three-dimensional stress tensor information of each node. The stress tensor is represented in 3×3 matrix form, where diagonal elements represent normal stress and off-diagonal elements represent shear stress. In a practical case, a certain connecting component yielded a total of 12,450 nodes after finite element analysis, each containing complete stress tensor information. For the stress tensor of each node, the system obtains three principal stress values ​​and their corresponding eigenvectors by solving an eigenvalue problem. The principal stress values ​​represent the normal stress components in three mutually perpendicular directions, with no shear stress components. Taking a certain node as an example, the calculated three principal stress values ​​are 18.5 MPa, 7.2 MPa, and -4.3 MPa, and the corresponding eigenvectors include direction cosines. The system normalizes these eigenvectors to obtain principal stress vectors of unit length, ensuring that the vector magnitude is 1 for subsequent analysis.

[0125] Based on the obtained principal stress vectors, the system establishes stress trajectory equations describing the stress transmission path. The stress trajectory is a series of curves in space, with the tangents of these curves aligned with the principal stress directions. The stress trajectory equations are described using a system of ordinary differential equations, with spatial coordinates as independent variables and the components of the principal stress directions as differential coefficients. By defining the starting point coordinates, the system can integrate along the principal stress directions to generate continuous stress trajectory curves. For the direction of maximum principal stress, the system sets the integration step size to 0.2 mm, and each stress trajectory contains 50 integration steps. In this case study, starting from 100 nodes with significant stress, the system calculates 100 stress trajectory curves, which form the stress flow field distribution in space.

[0126] The system solves the stress trajectory equation to obtain the spatial distribution curves along the direction of the maximum principal stress. The solution process employs a fourth-order Runge-Kutta integral method to ensure integration accuracy and stability. The new position of each integration point is determined by a weighted average of the current position and four intermediate points, with weighting coefficients of 1 / 6, 1 / 3, 1 / 3, and 1 / 6, respectively. A detection condition is set at the boundary; the calculation stops when the integration path reaches the structural boundary to prevent the trajectory curve from exceeding the design domain. The obtained spatial distribution curves exhibit the stress transfer path along the direction of the maximum principal stress, with denser curves in high-stress regions and sparser curves in low-stress regions. The system uses these spatial distribution curves as the baseline layout path for the cellular structure, providing a basis for subsequent structural optimization.

[0127] Based on the spatial distribution curves of the maximum principal stress directions, the system divides the structural stress regions. The division method employs spatial mesh generation technology, dividing the design domain into several mesh elements. The mesh generation uses hexahedral elements, with an element size of 2 mm × 2 mm × 2 mm. For each mesh element, the number of stress trajectory curves passing through that element is counted; regions with a high number of curves indicate locations of stress concentration. Based on the stress trajectory density, the system divides the design domain into eight distinct structural stress regions, numbered from 1 to 8, with higher numbers indicating more concentrated stress transmission. The divided structural stress region data is stored in a three-dimensional array, with the array size matching the mesh generation dimensions, and array element values ​​being the region numbers.

[0128] Within each defined structural stress region, the system calculates the equivalent stress. The equivalent stress is calculated using the von Mises stress criterion, transforming the three-dimensional stress state into equivalent uniaxial stress values. During the calculation, the nodal stress values ​​within each mesh element are averaged to obtain the representative stress state of that element. For example, in region 1, the average equivalent stress is 5.3 MPa; in region 8, the average equivalent stress reaches 42.7 MPa. The ratio of these equivalent stresses to the allowable stress of the material is determined as the stress sensitivity. The allowable stress of the material depends on the mechanical properties of the selected material. In this practical case, assuming the allowable stress of the material is 120 MPa, the stress sensitivity of region 1 is 0.044, and the stress sensitivity of region 8 is 0.356.

[0129] Based on calculated stress sensitivity, the system determines the baseline wall thickness of the variable-density honeycomb support structure. Wall thickness is directly proportional to stress sensitivity; regions with higher stress sensitivity require thicker honeycomb structure walls to provide sufficient load-bearing capacity. The system sets the baseline wall thickness range between 0.3 mm and 1.2 mm, converting stress sensitivity into specific wall thickness values ​​through linear mapping. For example, the calculated baseline wall thickness for region 1 is 0.35 mm; the baseline wall thickness for region 8 is 0.98 mm. Considering the process constraints of additive manufacturing, all wall thickness values ​​are rounded up to multiples of 0.05 mm to ensure manufacturing feasibility.

[0130] The system establishes a power-law relationship between the baseline wall thickness and relative density. Relative density, the ratio of the volume of the solid material in the honeycomb structure to its total volume, is a crucial parameter characterizing lightweight design. In the power-law expression, relative density equals the baseline wall thickness divided by the power of the honeycomb cell size, with the power exponent determined based on the honeycomb structure type. In this practical case, a hexagonal honeycomb structure is used, with a power exponent of 1.5. The baseline size of the honeycomb cell is set to 5 mm, resulting in a relative density of 0.031 for region 1 and 0.088 for region 8. This power-law relationship ensures a reasonable balance between structural mass and load-bearing capacity.

[0131] The established power function relationship is used as the parametric equation controlling the geometry of the unit cells, and the system generates a variable-density honeycomb support structure. The parametric equation controls the geometric characteristics of the honeycomb unit cells, including wall thickness, unit cell size, and arrangement orientation. In implementation, the system first determines the arrangement orientation of the honeycomb unit cells based on the direction of the maximum principal stress, ensuring that the two parallel sides of the hexagonal unit cells are perpendicular to the direction of the maximum principal stress, thus optimizing mechanical properties. Secondly, based on the relative density values ​​of each region, the geometric dimensions of the unit cells are adjusted, including the inscribed circle radius and wall thickness. Finally, the system generates a complete honeycomb structure model in three-dimensional space. The model uses a voxel representation method with a resolution of 0.1 mm to ensure accurate representation of geometric details.

[0132] To verify the effectiveness of the optimized design, the system performed finite element analysis on the generated variable-density honeycomb support structure. The analysis results show that the optimized structure, while maintaining the original load-bearing capacity, reduced its mass by 37.5%. The maximum stress value decreased from 72.3 MPa in the original structure to 65.8 MPa, and the stress distribution became more uniform. The structural stiffness decreased by only 8.2%, while the specific stiffness (stiffness to mass ratio) increased by 47.3%. These data fully demonstrate the superior performance of the variable-density honeycomb support structure. In practical applications, the optimized structure can be directly manufactured using additive manufacturing technology without additional assembly processes, significantly improving production efficiency. Simultaneously, the porous nature of the honeycomb structure provides space for functional integration, such as the placement of sensors, cables, or cooling channels, further expanding the structure's application scenarios.

[0133] The present invention relates to a dental prosthesis and a digitally integrated removable dental prosthesis processing system, comprising:

[0134] The first unit is used to acquire oral feature data of a patient using a three-dimensional oral scanning device. The oral feature data includes dynamic occlusal data of the patient under different occlusal states. Based on the oral feature data, a spatiotemporal graph structure is constructed, and an optimal occlusal surface is generated according to the spatiotemporal graph structure.

[0135] The second unit is used to construct an initial three-dimensional model of a denture based on the oral feature data and the optimal occlusal surface. The initial three-dimensional model of the denture includes an abutment tooth portion for replacing the missing tooth and a connection portion for retention.

[0136] The third unit is used to perform stress analysis on the initial three-dimensional model of the denture using a biomechanical simulation system, and to simulate the stress distribution of the denture under different occlusal states using the finite element method to identify stress areas.

[0137] The fourth unit is used to perform parametric modeling and optimization of the abutment tooth portion in the stress region, and adjust the anatomical morphology surface of the abutment tooth portion to match the optimal occlusal surface.

[0138] The fifth unit is used to construct a variable-density honeycomb support structure in the stress region based on the stress distribution analysis results;

[0139] The sixth unit is used to assemble and connect the abutment tooth portion and the connecting portion through a positioning structure to form a denture, and to perform surface treatment and polishing on the assembled denture.

[0140] A third aspect of the present invention,

[0141] An electronic device is provided, comprising:

[0142] processor;

[0143] Memory used to store processor-executable instructions;

[0144] The processor is configured to invoke instructions stored in the memory to execute the aforementioned method.

[0145] Fourth aspect of the embodiments of the present invention,

[0146] A computer-readable storage medium is provided, having stored thereon computer program instructions that, when executed by a processor, implement the aforementioned method.

[0147] This invention can be a method, apparatus, system, and / or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for performing various aspects of the invention.

[0148] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for manufacturing dentures and digitally integrated removable dentures, characterized in that, include: A three-dimensional oral scanning device is used to acquire oral feature data of the patient, including dynamic occlusal data of the patient in different occlusal states; a spatiotemporal graph structure is constructed based on the oral feature data, and an optimal occlusal surface is generated according to the spatiotemporal graph structure; Based on the oral cavity feature data and the optimal occlusal surface, an initial three-dimensional model of the denture is constructed. The initial three-dimensional model of the denture includes an abutment tooth portion for replacing the missing tooth and a connecting portion for retention. A biomechanical simulation system was used to perform stress analysis on the initial three-dimensional model of the denture. The stress distribution of the denture under different occlusal states was simulated using the finite element method, and stress areas were identified. Parametric modeling and optimization of the abutment tooth portion within the stress region is performed to adjust its anatomical morphology and surface to match the optimal occlusal surface. Specifically, this includes: acquiring curvature data of the abutment tooth surface; calculating the rate of curvature change based on the curvature data; determining the axial surface edge line position and transition region feature point positions based on the rate of curvature change; dividing the abutment tooth surface into axial and transition regions based on the axial surface edge line position and the transition region feature point positions; recording the boundary point coordinates of the axial and transition regions; extracting the abutment tooth cross-sectional contour line based on the boundary point coordinates; fitting the cross-sectional contour line using a Fourier series to obtain a cross-sectional contour function containing the basic radius parameter and circumferential harmonic coefficients; and calculating... The ideal positions of each point on the abutment tooth surface are determined, and the difference between the actual and ideal positions is used as the radial deformation amount. A radial deformation function containing height and circumferential variation components is established. The abutment tooth surface is locally adjusted using the radial deformation function to obtain an optimized abutment tooth surface. The surface distance and normal angle between the optimized abutment tooth surface and the target occlusal surface are calculated. A local deformation weight function is constructed using a point-position exponential function. The surface distance and normal angle are substituted into the local deformation weight function to calculate the morphological adjustment weight value for each region. The abutment tooth surface is locally deformed according to the morphological adjustment weight value. During the deformation process, positional continuity constraints and tangential continuity constraints are applied to the connection points of adjacent surface patches to match them with the optimal occlusal surface. Based on the stress distribution analysis results, a variable density honeycomb support structure was constructed in the stress region; The abutment tooth portion and the connecting portion are assembled and connected using a positioning structure to form a denture, and the assembled denture is then surface-treated and polished.

2. The method according to claim 1, characterized in that, Constructing a spatiotemporal graph structure based on the oral cavity feature data, and generating the optimal occlusal surface based on the spatiotemporal graph structure, includes: Based on the oral cavity feature data, a spatiotemporal graph structure is constructed. The coordinate data of the dental anatomical landmarks in the oral cavity feature data are used as nodes of the spatiotemporal graph structure. The spatial distance data between adjacent dental anatomical landmarks is used as the weight of the edges of the spatiotemporal graph structure. The topological connection relationship between the landmarks is constructed as an adjacency matrix. Calculate the spatial correlation weight of nodes in the spatiotemporal graph structure, and take nodes whose spatial correlation weight is greater than a preset association threshold as key anatomical feature points. Based on the key anatomical feature points, a control point mesh of a non-uniform rational B-spline surface is constructed. The spatial correlation weight is used as the weight parameter of the non-uniform rational B-spline surface to generate an initial occlusal surface. The initial occlusal surface is then optimized to generate the final occlusal surface. In the optimization process, surface continuity constraints are introduced, and the gradient descent method is used to iteratively update the position of control points until convergence is achieved to generate the optimal interlocking surface.

3. The method according to claim 2, characterized in that, Based on the key anatomical feature points, a control point mesh for a non-uniform rational B-spline surface is constructed, and the spatial correlation weight is used as the weight parameter of the non-uniform rational B-spline surface to generate an initial interlocking surface. Optimizing the initial occlusal surface to generate the final occlusal surface includes: A control point grid is constructed according to a rule. The number of rows and columns of the control point grid is determined based on the distribution density of the key anatomical feature points. The boundary control points of the control point grid are matched with the boundary contours of the key anatomical feature points. The spatial correlation weights are mapped to weight parameters of a non-uniform rational B-spline surface, and the initial interlocking surface is generated by weighting the control points according to the influence of the weight parameters. Establish surface continuity constraints, which include positional continuity constraints and tangential continuity constraints, to ensure a smooth transition between adjacent surface patches. The fitting error between the initial occlusal surface and the key anatomical feature points is calculated. The control point positions are iteratively updated using the gradient descent method. The fitting error is recalculated after each iteration. The iteration stops when the difference between the fitting errors calculated in two consecutive iterations is less than a preset error threshold. The final control point positions and weight parameters are substituted into the non-uniform rational B-spline surface to generate the final interlocking surface.

4. The method according to claim 1, characterized in that, A biomechanical simulation system was used to perform stress analysis on the initial three-dimensional model of the denture. The stress distribution of the denture under different occlusal states was simulated using the finite element method, and the stress regions were identified as follows: Boundary conditions are set on the initial three-dimensional model of the denture, the area connecting the denture and the abutment tooth is set as the displacement constraint boundary, the occlusal contact surface is set as the force boundary, and the displacement component on the displacement constraint boundary and the surface stress component on the force boundary are established. The equivalent stress and maximum shear stress are calculated based on the displacement component and the surface stress component. The equivalent stress is determined by the square root of the sum of the squares of the differences between the displacement component and the surface stress component. The maximum shear stress is determined by the weighted average of the first difference and the second difference. The first difference is the difference between the maximum and minimum values ​​of the displacement component, and the second difference is the difference between the maximum and minimum values ​​of the surface stress component. The stress concentration factor is obtained by calculating the ratio of the maximum shear stress to the nominal shear stress, and the stress uniformity index is obtained by calculating the dispersion of the equivalent stress relative to the mean stress. The denture structure is evaluated based on the stress concentration factor and the stress uniformity index to identify areas of uneven stress distribution.

5. The method according to claim 1, characterized in that, Based on the stress distribution analysis results, constructing a variable-density honeycomb support structure in the stress region includes: Obtain the structural stress state data of the connection part, normalize the feature vector corresponding to the structural stress state data to obtain the principal stress vector, and establish a stress trajectory equation describing the stress transmission path based on the principal stress vector; Solving the stress trajectory equation yields the spatial distribution curve of the maximum principal stress direction. The spatial distribution curve is used as the reference arrangement path of the honeycomb structure, and the structural stress region is divided according to the reference arrangement path. Equivalent stress is calculated in each structural stress region. The ratio of the equivalent stress to the allowable stress of the material is determined as the stress sensitivity. The reference wall thickness of the variable density honeycomb support structure is determined based on the stress sensitivity. A power function relationship between the reference wall thickness and the relative density is established. The power function relationship is used as the parametric equation controlling the geometry of the unit cell.

6. A denture and a digitally integrated removable denture processing system, for implementing the method as described in any one of claims 1-5, characterized in that, include: The first unit is used to acquire oral feature data of a patient using a three-dimensional oral scanning device. The oral feature data includes dynamic occlusal data of the patient under different occlusal states. Based on the oral feature data, a spatiotemporal graph structure is constructed, and an optimal occlusal surface is generated according to the spatiotemporal graph structure. The second unit is used to construct an initial three-dimensional model of a denture based on the oral feature data and the optimal occlusal surface. The initial three-dimensional model of the denture includes an abutment tooth portion for replacing the missing tooth and a connection portion for retention. The third unit is used to perform stress analysis on the initial three-dimensional model of the denture using a biomechanical simulation system, and to simulate the stress distribution of the denture under different occlusal states using the finite element method to identify stress areas. The fourth unit is used to perform parametric modeling and optimization of the abutment tooth portion in the stress region, and adjust the anatomical morphology surface of the abutment tooth portion to match the optimal occlusal surface. The fifth unit is used to construct a variable-density honeycomb support structure in the stress region based on the stress distribution analysis results; The sixth unit is used to assemble and connect the abutment tooth portion and the connecting portion through a positioning structure to form a denture, and to perform surface treatment and polishing on the assembled denture.

7. An electronic device, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured to invoke instructions stored in the memory to execute the method according to any one of claims 1 to 5.

8. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, When the computer program instructions are executed by the processor, they implement the method described in any one of claims 1 to 5.

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