False tooth and digital combined movable false tooth processing method and system

Abstract

The invention provides a false tooth and digital combined movable false tooth processing method and system, and relates to the technical field of digitization, and the method comprises the following steps: obtaining oral cavity feature data through three-dimensional scanning, constructing a space-time diagram structure, determining key anatomical feature points, generating an optimal occlusion curved surface, constructing an initial false tooth three-dimensional model, and obtaining an initial false tooth three-dimensional model; a biomechanical simulation system is adopted to analyze stress distribution, parametric modeling optimization is carried out on an abutment part, topological optimization design is carried out on a connecting part to construct a variable-density honeycomb-shaped supporting structure, and finally the denture is formed through assembling and connecting. According to the invention, the occlusion precision and mechanical property of the false tooth are improved, the fracture risk is reduced, and the service life is prolonged.

Description

Technical Field

[0001] The present invention relates to the field of digital technology, and in particular to a denture and a digital combined removable denture processing method and system. Background Art

[0002] Denture restoration is a common treatment in dental clinics. Traditional denture production relies primarily on manual labor by dental technicians, including impression taking, plaster model making, wax-up sculpting, and casting. With the advancement of digital technology, computer-aided design and manufacturing (CAD / CAM) has gained widespread application in denture fabrication, improving the accuracy and efficiency of denture fabrication. Digital denture technology uses oral scanning to obtain patient data, uses specialized software to design the denture model, and then processes it 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, which makes it difficult to reflect the patient's dynamic occlusal relationship during daily functional activities such as chewing and speaking. As a result, the denture may experience 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 carry out reasonable structural design for different stress areas, which can easily lead to stress concentration and structural damage during long-term use. Summary of the Invention

[0004] The embodiments of the present invention provide a denture and a digital combined removable denture processing method and system, which can solve the problems in the prior art.

[0005] A first aspect of an embodiment of the present invention provides a method for processing a denture and a digital combined removable denture, comprising: Acquiring oral feature data of a patient using an oral 3D scanning device, wherein the oral feature data includes dynamic occlusal data of the patient in different occlusal states; constructing a spatiotemporal graph structure based on the oral feature data, and generating an optimal occlusal surface according to the spatiotemporal graph structure; constructing an initial denture three-dimensional model based on the oral feature data and the optimal occlusal surface, wherein the initial denture three-dimensional model includes an abutment portion for replacing a missing tooth and a connection portion for retention; A biomechanical simulation system is used to perform a stress analysis on the initial denture three-dimensional model, and a finite element method is used to simulate the stress distribution of the denture under different occlusal states to identify stress areas; Performing parameterized modeling optimization on the abutment portion in the stress region, and adjusting the anatomical curved surface of the abutment portion to match the optimal occlusal curved surface; Based on the stress distribution analysis results, a variable density honeycomb support structure is constructed in the stress area; The abutment part and the connecting part are assembled and connected through a positioning structure to form a denture, and the assembled denture is surface treated and polished.

[0006] Constructing a spatiotemporal graph structure based on the oral feature data, and generating an optimal occlusal surface according to the spatiotemporal graph structure includes: Constructing a spatiotemporal graph structure based on the oral feature data, using the coordinate data of the dental anatomical landmark points of the oral feature data as nodes of the spatiotemporal graph structure, using the spatial distance data between adjacent dental anatomical landmark points as weights of the spatiotemporal graph structure edges, and constructing the topological connection relationship between the landmark points into an adjacency matrix; Calculating the spatial correlation weights of nodes in the spatiotemporal graph structure, and taking nodes whose spatial correlation weights are greater than a preset correlation threshold as key anatomical feature points; Constructing a control point grid of a non-uniform rational B-spline surface based on the key anatomical feature points, using the spatial correlation weight 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 a final occlusal surface; In the optimization process, surface continuity constraints are introduced, and the gradient descent method is used to iteratively update the control point positions until convergence to generate the optimal occlusal surface.

[0007] Constructing a control point grid of a non-uniform rational B-spline surface based on the key anatomical feature points, using the spatial correlation weight as a weight parameter of the non-uniform rational B-spline surface, and generating an initial occlusal surface; optimizing the initial occlusal surface to generate a final occlusal surface includes: Constructing a regular control point grid, wherein the number of rows and columns of the control point grid is determined according to the distribution density of the key anatomical feature points, and the boundary control points of the control point grid match the boundary contours of the key anatomical feature points; Mapping the spatial correlation weights into weight parameters of a non-uniform rational B-spline surface, weighting the control points according to the influence of the weight parameters, and generating an initial occlusal surface; Establishing surface continuity constraints, wherein the surface continuity constraints include position continuity constraints and tangential continuity constraints, and ensuring smooth transitions between adjacent surface patches through the continuity constraints; Calculating the fitting error between the initial occlusal surface and the key anatomical feature points, iteratively updating the control point positions using a gradient descent method, recalculating the fitting error after each iterative update, and stopping the iteration when the difference between the fitting errors calculated in two consecutive iterative calculations 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 occlusal surface.

[0008] A biomechanical simulation system is used to perform stress analysis on the initial denture three-dimensional model. The stress distribution of the denture under different occlusal states is simulated by the finite element method. The stress areas identified include: Setting boundary conditions on the initial denture three-dimensional model, setting the denture and abutment connection area as a displacement constraint boundary, setting the occlusal contact surface as a force boundary, and establishing a displacement component on the displacement constraint boundary and a surface stress component on the force boundary; Calculating an equivalent stress and a maximum shear stress 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, and the maximum shear stress is determined by a weighted average of a first difference and a second difference, the first difference being the difference between the maximum and minimum values ​​of the displacement component, and the second difference being the difference between the maximum and minimum values ​​of the surface stress component; The ratio of the maximum shear stress to the nominal shear stress is calculated to obtain the stress concentration factor, and the dispersion degree of the equivalent stress relative to the average stress is calculated to obtain the stress uniformity index; The denture structure is evaluated based on the stress concentration coefficient and the stress uniformity index to determine the area with uneven stress distribution.

[0009] Performing parameterized modeling optimization on the abutment portion in the stress region and adjusting the anatomical surface of the abutment portion to match the optimal occlusal surface includes: Acquiring curvature data of the abutment tooth surface, calculating a curvature change rate according to the curvature data, and determining a position of an axial edge line and a position of a characteristic point in a transition region based on the curvature change rate; Dividing the abutment tooth surface into an axial surface area and a transition area based on the position of the axial surface edge line and the position of the characteristic point of the transition area, and recording the coordinates of the boundary points of the axial surface area and the transition area; Extracting a cross-sectional contour line of the abutment tooth based on the coordinates of the boundary points, fitting the cross-sectional contour line using a Fourier series to obtain a cross-sectional contour function including a basic radius parameter and a circumferential harmonic coefficient; calculating the ideal position of each point on the abutment tooth surface based on the cross-sectional contour function, using the difference between the actual position of each point and the ideal position as the radial deformation amount, and establishing a radial deformation function including a height variation component and a circumferential variation component; Using the radial deformation function to locally adjust the abutment tooth surface to obtain an optimized abutment tooth surface, and calculating the surface distance and normal angle between the optimized abutment tooth surface and the target occlusal surface; constructing a local deformation weight function using a point-type exponential function, substituting the surface distance and the normal angle into the local deformation weight function, and calculating the morphological adjustment weight value of each region; The abutment tooth surface is locally deformed according to the morphological adjustment weight value, and position continuity constraints and tangential continuity constraints are implemented on the connection between adjacent curved surface pieces during the deformation process to make them match the optimal occlusal surface.

[0010] Based on the stress distribution analysis results, the variable density honeycomb support structure is constructed in the stress area, including: Acquiring structural stress state data of the connection portion, normalizing a characteristic vector corresponding to the structural stress state data to obtain a principal stress vector, and establishing a stress trajectory equation describing a stress transfer path based on the principal stress vector; Solving the stress locus equation to obtain a spatial distribution curve of the maximum principal stress direction, using the spatial distribution curve as a reference arrangement path of the honeycomb structure, and dividing the structural stress region according to the reference arrangement path; The equivalent stress is calculated in each structural stress region, and 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, and the power function relationship is used as a parametric equation to control the geometric dimensions of the unit cell.

[0011] A second aspect of the present invention provides a denture and a digital combined removable denture processing system, comprising: The first unit is configured to acquire oral feature data of a patient using an oral 3D scanning device, wherein the oral feature data includes dynamic occlusal data of the patient in different occlusal states; construct a spatiotemporal graph structure based on the oral feature data, and generate an optimal occlusal surface according to the spatiotemporal graph structure; The second unit is configured to construct an initial denture three-dimensional model based on the oral feature data and the optimal occlusal surface, wherein the initial denture three-dimensional model includes an abutment portion for replacing a missing tooth and a connection portion for retention; The third unit is used to perform stress analysis on the initial denture three-dimensional model using a biomechanical simulation system, simulate the stress distribution of the denture under different occlusal states using a finite element method, and identify stress areas; A fourth unit is configured to perform parameterized modeling optimization on the abutment portion in the stress region, and adjust the anatomical morphological surface of the abutment portion to match the optimal occlusal surface; The fifth unit is used to construct a variable-density honeycomb support structure in the stress area based on the stress distribution analysis results; The sixth unit is used to assemble and connect the abutment part and the connecting part through a positioning structure to form a denture, and to perform surface treatment and polishing on the assembled denture.

[0012] According to a third aspect of the embodiments of the present invention, An electronic device is provided, comprising: processor; a memory for storing processor-executable instructions; The processor is configured to call the instructions stored in the memory to execute the aforementioned method.

[0013] According to a fourth aspect of the embodiments of the present invention, A computer-readable storage medium is provided, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the method described above is implemented.

[0014] The beneficial effects of this application are as follows: By collecting the patient's dynamic occlusal data and combining it with non-uniform rational B-spline surface technology to generate the optimal occlusal surface, the patient's individualized occlusal relationship can be accurately simulated, making the manufactured denture more in line with the patient's actual oral function needs, and improving the comfort and use effect of the denture.

[0015] A biomechanical simulation system was used for stress analysis and the design was optimized based on the finite element method. Parametric modeling optimization and variable-density honeycomb support structure design were performed for high-stress areas, which significantly improved the mechanical properties and service life of the denture and reduced the risk of fracture during clinical use.

[0016] A modular design method is adopted to assemble and connect the abutment part and the connecting part through a positioning structure, which not only facilitates the repair and replacement of the denture, but also allows for flexible adjustment according to changes in the patient's oral condition, reducing the patient's use cost and improving the adaptability and clinical practical value of the denture. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 Schematic diagram of the process of processing dentures and digital combined removable dentures according to an embodiment of the present invention; Figure 2 Design a schematic diagram for anatomical morphology. DETAILED DESCRIPTION

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0019] The following specific embodiments are used to describe the technical solution of the present invention in detail. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0020] refer to Figure 1 and Figure 2 The embodiment of the present invention provides a method for processing a denture and a digital combined removable denture, comprising: Acquiring oral feature data of a patient using an oral 3D scanning device, wherein the oral feature data includes dynamic occlusal data of the patient in different occlusal states; constructing a spatiotemporal graph structure based on the oral feature data, and generating an optimal occlusal surface according to the spatiotemporal graph structure; constructing an initial denture three-dimensional model based on the oral feature data and the optimal occlusal surface, wherein the initial denture three-dimensional model includes an abutment portion for replacing a missing tooth and a connection portion for retention; A biomechanical simulation system is used to perform a stress analysis on the initial denture three-dimensional model, and a finite element method is used to simulate the stress distribution of the denture under different occlusal states to identify stress areas; Performing parameterized modeling optimization on the abutment portion in the stress region, and adjusting the anatomical curved surface of the abutment portion to match the optimal occlusal curved surface; Based on the stress distribution analysis results, a variable density honeycomb support structure is constructed in the stress area; The abutment part and the connecting part are assembled and connected through a positioning structure to form a denture, and the assembled denture is surface treated and polished.

[0021] In an optional embodiment, constructing a spatiotemporal graph structure based on the oral feature data, and generating an optimal occlusal surface according to the spatiotemporal graph structure includes: Constructing a spatiotemporal graph structure based on the oral feature data, using the coordinate data of the dental anatomical landmark points of the oral feature data as nodes of the spatiotemporal graph structure, using the spatial distance data between adjacent dental anatomical landmark points as weights of the spatiotemporal graph structure edges, and constructing the topological connection relationship between the landmark points into an adjacency matrix; Calculating the spatial correlation weights of nodes in the spatiotemporal graph structure, and taking nodes whose spatial correlation weights are greater than a preset correlation threshold as key anatomical feature points; Constructing a control point grid of a non-uniform rational B-spline surface based on the key anatomical feature points, using the spatial correlation weight 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 a final occlusal surface; In the optimization process, surface continuity constraints are introduced, and the gradient descent method is used to iteratively update the control point positions until convergence to generate the optimal occlusal surface.

[0022] The specific implementation process for constructing a spatiotemporal graph structure based on oral feature data is as follows: 3D oral scan data is obtained and the coordinates of anatomical dental landmarks are extracted. These landmarks include key locations such as cusps, pits and fissures, and marginal ridges. In practice, the number of landmarks on each tooth varies depending on the tooth type; molars typically have 5-7 landmarks, while 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 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, and this distance value is the weight of the edge connecting these two nodes. An adjacency matrix is ​​constructed to represent the topological connectivity between the nodes. The adjacency matrix is ​​an N×N matrix (N is the total number of landmarks), where a matrix element value of 1 indicates a connection between two points, and a value of 0 indicates a disconnection. For example, for oral data containing 30 landmarks, a 30×30 adjacency matrix is ​​generated.

[0023] For each node, its spatial correlation with other nodes is calculated based on the topological relationship 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. In specific implementation, 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 usually set to a value between 0.05 and 0.1, and is set to 0.07 in this embodiment. Nodes with spatial correlation weights greater than 0.07 are identified as key anatomical feature points. Practice has shown that in a set of oral data containing approximately 100 landmark points, 20 to 30 points are usually identified as key anatomical feature points.

[0024] The identified key anatomical feature points are used as the basis of the control point grid. Usually, an m×n control point grid is constructed, with the values ​​of m and n ranging from 5 to 10. In this embodiment, a 7×8 control point grid is used. The control points are arranged according to the shape of the dental arch to ensure that the control point grid covers the entire occlusal area. The calculated spatial correlation weight is used as the weight parameter of the non-uniform rational B-spline surface. The larger the weight value, the greater the influence of the control point on the surface shape. The node vectors of the non-uniform rational B-spline are set, and the node vectors in the u direction are [0,0,0,0,0.25,0.5,0.75,1,1,1,1], and the node vectors in the v direction are [0,0,0,0,0.2,0.4,0.6,0.8,1,1,1,1], and the curve order is set to 3 in both directions. The initial occlusal surface is generated based on the control point grid, weight parameters and node vectors.

[0025] The surface continuity constraint condition is introduced, requiring G1 continuity (tangential continuity) between adjacent surface patches. The gradient descent method is used to iteratively update the control point position. The error between the surface and the actual tooth occlusion point is calculated in each iteration, and the control point position is adjusted to reduce the error. In the specific optimization process, the initial value of the learning rate is set to 0.01, and the learning rate gradually decreases as the number of iterations increases. The maximum number of iterations is set to 500 times, and the convergence condition is that the average error change for 10 consecutive iterations is less than 0.001 mm. In an actual application case, a set of complete oral data containing 28 teeth was processed. The average error between the initial surface and the actual occlusion point was 0.58 mm. After 312 iterative optimizations, the final error was reduced to 0.09 mm. The generated optimal occlusal surface can accurately reflect the patient's personalized occlusal characteristics.

[0026] The optimal occlusal surface generated by this method can be applied to a variety of clinical scenarios, including oral prosthesis design, orthodontic treatment planning, and occlusal reconstruction. It can significantly improve the occlusal accuracy of restorations, reduce the workload of clinical adjustments, and enhance the patient's treatment experience and outcomes. This method fully utilizes oral anatomical information, extracts key feature points through a spatiotemporal graph structure, and combines it with non-uniform rational B-spline surface technology to achieve personalized, high-precision occlusal surface construction.

[0027] In an optional embodiment, constructing a control point grid of a non-uniform rational B-spline surface based on the key anatomical feature points, using the spatial correlation weight as a weight parameter of the non-uniform rational B-spline surface, and generating an initial occlusal surface; optimizing the initial occlusal surface to generate a final occlusal surface includes: Constructing a regular control point grid, wherein the number of rows and columns of the control point grid is determined according to the distribution density of the key anatomical feature points, and the boundary control points of the control point grid match the boundary contours of the key anatomical feature points; Mapping the spatial correlation weights into weight parameters of a non-uniform rational B-spline surface, weighting the control points according to the influence of the weight parameters, and generating an initial occlusal surface; Establishing surface continuity constraints, wherein the surface continuity constraints include position continuity constraints and tangential continuity constraints, and ensuring smooth transitions between adjacent surface patches through the continuity constraints; Calculating the fitting error between the initial occlusal surface and the key anatomical feature points, iteratively updating the control point positions using a gradient descent method, recalculating the fitting error after each iterative update, and stopping the iteration when the difference between the fitting errors calculated in two consecutive iterative calculations 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 occlusal surface.

[0028] In this embodiment, a control point grid of a non-uniform rational B-spline surface is constructed based on key anatomical feature points, and the spatial correlation weight is used as a weight parameter of the non-uniform rational B-spline surface to generate an initial occlusal surface.

[0029] 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 region, the control point grid in that area can be set to 6×8, while in the posterior region, where the distribution is more sparse, it can be set to 4×6. When constructing the control point grid, ensure that the boundary control points of the grid match the boundary contours of the key anatomical feature points to ensure that the generated occlusal surface accurately reflects the external morphology of the teeth.

[0030] For each key anatomical feature, its spatial correlation weight is calculated and mapped to the weight parameters of the non-uniform rational B-spline surface. This weight parameter mapping process can be achieved through linear mapping, mapping the original spatial correlation weight value from its domain [0.1, 1.0] to the range of the non-uniform rational B-spline surface weight parameters [1.0, 10.0]. For example, if the spatial correlation weight of a key anatomical feature is 0.7, after linear mapping, the corresponding non-uniform rational B-spline surface weight parameter is 7.0. These weight parameters are weighted according to the degree of influence of the control points to generate the initial occlusal surface. Control points with larger weights have a greater influence on the surface shape, resulting in higher surface fitting accuracy near key anatomical features.

[0031] 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 the points on the shared boundary of adjacent surface patches have the same position coordinates, avoiding gaps or overlaps in the surface. In specific implementation, the control points on the shared boundary can be set to the same value. For example, the control points P(i,j) and Q(i,j) at the junction of the upper and lower occlusal surfaces of the mandibular teeth are 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, avoiding sharp corners or creases in the surface. In implementation, the positions of the control points near the boundary can be adjusted so that the vectors formed by the control points on both sides of the boundary have the same direction. For example, ensure that the directions of the two vectors P(i-1,j)-P(i,j) and Q(i,j)-Q(i+1,j) are consistent or opposite.

[0032] After the initial occlusal surface is generated, the fitting accuracy is improved through iterative optimization. The fitting error between the initial occlusal surface and the 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 the Euclidean distance, which is the distance from the point on the surface to the nearest key anatomical feature point. In a specific implementation, 100 evenly distributed sampling points can be selected, and the average distance from these sampling points to the key anatomical feature points is calculated as the fitting error. The initial error is usually in the range of 0.5-1.0 mm.

[0033] In each iteration, the control point positions are updated proportionally to the gradient of the fitting error, with an update step size of 0.05-0.1 mm. After the control point positions are updated, the fitting error is recalculated, and iterations are terminated when the difference between the fitting errors calculated over two consecutive iterations is less than a preset error threshold. The preset error threshold can be set to 0.01 mm, meaning that convergence is considered achieved when the fitting error between two consecutive iterations changes by less than 0.01 mm. Typically, the algorithm converges within 15-20 iterations, reducing the final fitting error to within 0.1-0.2 mm.

[0034] After iterative optimization, the final control point positions and weight parameters are substituted into the non-uniform rational B-spline surface equation to generate the final occlusal surface. This resulting occlusal surface not only satisfies boundary conditions and continuity constraints but also accurately reflects the positions and morphological features of key anatomical features while maintaining overall smoothness and aesthetics.

[0035] In an actual application case, for a full-mouth dental model containing 32 teeth, about 500 key anatomical feature points were extracted and an 8×12 control point grid was constructed. The initial fitting error was 0.78 mm. After 18 iterative optimizations, 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 and the functional cusp of the first molar, where the fitting accuracy is higher, meeting the precision requirements of clinical restorative treatment.

[0036] In an optional embodiment, a biomechanical simulation system is used to perform a stress analysis on the initial denture three-dimensional model, and the stress distribution of the denture under different occlusal states is simulated by a finite element method. Identifying stress areas includes: Setting boundary conditions on the initial denture three-dimensional model, setting the denture and abutment connection area as a displacement constraint boundary, setting the occlusal contact surface as a force boundary, and establishing a displacement component on the displacement constraint boundary and a surface stress component on the force boundary; Calculating an equivalent stress and a maximum shear stress 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, and the maximum shear stress is determined by a weighted average of a first difference and a second difference, the first difference being the difference between the maximum and minimum values ​​of the displacement component, and the second difference being the difference between the maximum and minimum values ​​of the surface stress component; The ratio of the maximum shear stress to the nominal shear stress is calculated to obtain the stress concentration factor, and the dispersion degree of the equivalent stress relative to the average stress is calculated to obtain the stress uniformity index; The denture structure is evaluated based on the stress concentration coefficient and the stress uniformity index to determine the area with uneven stress distribution.

[0037] Obtain a preliminary 3D model of the patient's oral cavity. This model can be obtained directly using an oral scanner or converted to a digital model from a traditional impression. The model must include the complete denture structure, abutment teeth, and adjacent teeth. The model resolution must be at least 0.05mm to ensure simulation accuracy.

[0038] Next, the initial denture 3D model is imported into the biomechanical simulation system. The system uses a general model processing module to convert the 3D model in STL format 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. Meshing is performed using adaptive tetrahedral elements with a cell size range of 0.1mm to 0.5mm. In areas where stress may concentrate, such as joints and thin-walled sections, the mesh density is automatically increased to 0.1mm.

[0039] Setting appropriate boundary conditions on the model is key to accurate simulation. The area connecting the denture and abutment is set as a displacement constraint boundary. Specifically, all nodes where the inner side of the denture contacts the abutment are selected and their displacements are constrained in three directions (X, Y, and Z). In practice, the number of constraint nodes is typically between 5,000 and 8,000 to ensure simulation stability.

[0040] The occlusal contact surface is set as the force boundary, and different loads are applied according to different occlusal states. Typical occlusal force settings are: 100-150N for the anterior teeth, 200-250N for the premolars, and 300-400N for the molars. The load direction is determined by the patient's actual occlusal relationship and is generally perpendicular to the occlusal plane or at a 60°-70° angle 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 toward the periphery.

[0041] During the simulation, the system calculates the displacement and surface stress components for each node based on the specified boundary conditions. For a complete denture, the number of nodes typically ranges from 100,000 to 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 and shear stresses.

[0042] After obtaining the displacement and stress data, the system calculates the equivalent stress. This is determined by taking the square root of the sum of the squares of the differences between the displacement components and the surface stress components. The system first calculates the displacement vector and stress tensor for each node. Then, using the displacement-stress relationship, the equivalent stress value for that node is calculated. In typical cases, the equivalent stress range for dentures is typically 5-120 MPa, with the highest stress values ​​occurring in the joint area.

[0043] At the same time, the system calculates the maximum shear stress. This is determined by taking the weighted average of the first and second differences. The first difference is the difference between the maximum and minimum displacement components, while the second difference is the difference between the maximum and minimum surface stress components. In actual calculations, the weight ratio of the first and second differences is 3:7 to better reflect the shear properties of the material. For ceramic dentures, the maximum shear stress should generally not exceed 30 MPa to avoid the risk of cracking.

[0044] Based on these 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 of the equivalent stress at all nodes to the mean. Ideally, this ratio should be less than 0.6.

[0045] In one case, a full-crown denture model of a patient's maxillary first molar experienced a peak equivalent stress of 125 MPa in the connection area under a 300N vertical occlusal force. The maximum shear stress was 42 MPa, the stress concentration factor was 3.2, and the stress uniformity index was 0.85. This indicates that the denture design has stress concentration issues, particularly in the connection area, which could lead to premature denture failure.

[0046] For identified areas of uneven stress distribution, the system provides design optimization suggestions, such as increasing the transition fillet radius in the connection area, adjusting the occlusal surface morphology, and modifying the material ratio. After optimization, the maximum equivalent stress of the same denture under the same conditions was reduced to 85 MPa, the stress concentration factor was reduced to 2.1, and the stress uniformity index was improved to 0.52, significantly improving the lifespan and reliability of the denture.

[0047] In an optional embodiment, performing parametric modeling optimization on the abutment portion in the stress region and adjusting the anatomical morphological surface of the abutment portion to match the optimal occlusal surface includes: Acquiring curvature data of the abutment tooth surface, calculating a curvature change rate according to the curvature data, and determining a position of an axial edge line and a position of a characteristic point in a transition region based on the curvature change rate; Dividing the abutment tooth surface into an axial surface area and a transition area based on the position of the axial surface edge line and the position of the characteristic point of the transition area, and recording the coordinates of the boundary points of the axial surface area and the transition area; Extracting a cross-sectional contour line of the abutment tooth based on the coordinates of the boundary points, fitting the cross-sectional contour line using a Fourier series to obtain a cross-sectional contour function including a basic radius parameter and a circumferential harmonic coefficient; calculating the ideal position of each point on the abutment tooth surface based on the cross-sectional contour function, using the difference between the actual position of each point and the ideal position as the radial deformation amount, and establishing a radial deformation function including a height variation component and a circumferential variation component; Using the radial deformation function to locally adjust the abutment tooth surface to obtain an optimized abutment tooth surface, and calculating the surface distance and normal angle between the optimized abutment tooth surface and the target occlusal surface; constructing a local deformation weight function using a point-type exponential function, substituting the surface distance and the normal angle into the local deformation weight function, and calculating the morphological adjustment weight value of each region; The abutment tooth surface is locally deformed according to the morphological adjustment weight value, and position continuity constraints and tangential continuity constraints are implemented on the connection between adjacent curved surface pieces during the deformation process to make them match the optimal occlusal surface.

[0048] For example, during the parametric modeling and optimization of the abutment, the system first collects curvature data of the abutment surface. Point cloud data of the abutment is acquired using a 3D scanning device. Each sampled point contains spatial coordinate information and a surface normal. For each point in the point cloud, its principal curvature values ​​are calculated, including the maximum and minimum principal curvatures. For a clinical case, the acquired point cloud data for the abutment surface contains approximately 8,500 points, with the maximum principal curvature values ​​ranging from -0.8 to 0.5, and the minimum principal curvature values ​​ranging from -0.5 to 0.2. Based on these principal curvature values, the curvature gradient 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. Areas with large curvature gradient values ​​are typically located in areas of dramatic morphological change, such as the axial margin of the abutment and feature points in the transition region. In a real-world case, when the curvature gradient exceeds a threshold of 0.85, the point is marked as a candidate feature point.

[0049] Using the feature point detection algorithm, the system determines the position of the axial edge line and the position of the transition area feature points. Edge line detection uses the region growing method, starting from the initial seed point, and gradually expanding and connecting adjacent feature points based on the similarity of the curvature change rate, eventually forming a complete axial edge line. In actual cases, the detected axial edge line contains approximately 260 points, which form a closed curve in space with an average spacing of 0.2 mm. The transition area feature points are mainly distributed at the junction of the abutment neck and the occlusal surface. A total of approximately 180 feature points were detected, which constitute the key positions of morphological changes.

[0050] Based on the determined axial edge line and transition area feature points, the system divides the abutment tooth surface into an axial area and a transition area. The area division uses the nearest neighbor classification method to calculate the distance from each point on the abutment tooth surface to the axial edge line and transition area feature points, and classify the points into the corresponding area based on the minimum distance. The axial area mainly includes the lateral wall of the abutment tooth, which is characterized by a relatively gentle change in curvature; the transition area is located between the axial plane and the occlusal plane, and has a larger change in curvature. The system records the coordinates of the boundary points of the two areas to form a description of the area boundaries. In the case, the axial area accounts for approximately 65% ​​of the total area of ​​the abutment tooth surface, and the transition area accounts for approximately 35%. The boundary point coordinates are stored in the form of a three-dimensional array, containing approximately 320 coordinate points, which accurately describe the dividing line between the two areas.

[0051] Based on the recorded boundary point coordinates, the system extracts the cross-sectional contour line of the abutment tooth. Horizontal sections are cut at different heights perpendicular to the central axis of the abutment tooth to obtain a series of closed contour lines. In the actual case, a total of 15 equally spaced cross sections are extracted from the neck of the abutment tooth to the occlusal surface, with a spacing of 0.5 mm. For each cross-sectional contour line, the system converts it into polar coordinate representation for Fourier series fitting. The center point of the abutment tooth is selected as the polar coordinate origin, and the polar diameter and polar angle of each point on the contour line are calculated. The relationship between the polar diameter and the polar angle is expanded by Fourier series to obtain a cross-sectional contour function containing basic radius parameters and circumferential harmonic coefficients. During the fitting process, the order of the Fourier series is selected as 8, and the basic radius parameters are 4.8 mm, 4.6 mm, 4.5 mm, etc. at sections of different heights, and the numerical value of the circumferential harmonic coefficient ranges from -0.2 to 0.2.

[0052] Using the obtained cross-sectional profile function, the system calculates the ideal position of each point on the abutment surface. By determining the height position of each point and substituting it into the cross-sectional profile function of the corresponding height, the ideal profile position at that height is calculated. The difference between the actual position and the ideal position of each point is used as the radial deformation, and a radial deformation function containing a height change component and a circumferential change component is constructed. In this case, the distribution range of the radial deformation is -0.3 to 0.3 mm. The height change component uses cubic spline interpolation to ensure a smooth transition between different heights; the circumferential change component is described by Fourier series to capture the morphological differences in different directions.

[0053] The system uses a radial deformation function to make local adjustments to the abutment tooth surface to obtain an optimized abutment tooth surface. The adjustment process adopts an iterative method, and the position of the point is updated in each iteration. The number of iterations is set to 20 times, and the displacement in each iteration does not exceed 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 by the nearest point search algorithm, and the normal angle is obtained by the dot product of the two surface normal vectors. In the case, the average distance between the abutment tooth surface and the target occlusal surface before optimization was 0.28 mm, and the maximum distance was 0.85 mm; the average normal angle was 12 degrees, and the maximum value was 35 degrees.

[0054] The system constructs a local deformation weight function in the form of a point-type exponential function. This function uses the surface distance and the normal angle as independent variables and outputs the weight value of the morphological adjustment. The larger the distance or the larger the angle, the higher the weight value and the greater the adjustment. In actual 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 and calculates the morphological adjustment weight value for each area. In the case, the average weight value of the occlusal contact area is 0.85, and the average weight value of the non-contact area is 0.35.

[0055] Based on the calculated morphological adjustment weights, the system locally deforms the abutment tooth surface. The deformation algorithm, based on weight-driven Laplace deformation technology, achieves precise local morphological adjustments while maintaining surface smoothness. Regions with higher weights are deformed more significantly, bringing them closer to the target occlusal surface; regions with lower weights maintain their original morphological characteristics. During the deformation process, the system applies positional and tangential continuity constraints to the connections between adjacent surface patches. Positional continuity constraints ensure that shared boundary points of adjacent surface patches have identical spatial positions, preventing cracks in the surface; tangential continuity constraints ensure that the tangent planes at shared boundaries remain consistent, preventing sharp edges. With these constraints, the optimized abutment tooth surface maintains overall smoothness while achieving a good match with the optimal occlusal surface. The optimization results show that the average distance between the adjusted abutment tooth surface and the target occlusal surface is reduced to 0.08 mm, with a maximum distance of 0.25 mm. The average normal angle is reduced to 4.5 degrees, with a maximum of 15 degrees, meeting the accuracy requirements for clinical applications.

[0056] In an optional embodiment, based on the stress distribution analysis results, constructing a variable density honeycomb support structure in the stress area includes: Acquiring structural stress state data of the connection portion, normalizing a characteristic vector corresponding to the structural stress state data to obtain a principal stress vector, and establishing a stress trajectory equation describing a stress transfer path based on the principal stress vector; Solving the stress locus equation to obtain a spatial distribution curve of the maximum principal stress direction, using the spatial distribution curve as a reference arrangement path of the honeycomb structure, and dividing the structural stress region according to the reference arrangement path; The equivalent stress is calculated in each structural stress region, and 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, and the power function relationship is used as a parametric equation to control the geometric dimensions of the unit cell.

[0057] The structural stress state data is obtained through finite element analysis and includes three-dimensional stress tensor information for each node. The stress tensor is represented as a 3×3 matrix, where the diagonal elements represent normal stresses and the off-diagonal elements represent shear stresses. In a real-world case, the total number of nodes obtained after finite element analysis for a certain connection component is 12,450, and each node contains complete stress tensor information. For the stress tensor at each node, the system solves an eigenvalue problem to obtain three principal stress values ​​and their corresponding eigenvectors. The principal stress values ​​represent the normal stress components in three mutually perpendicular directions, with no shear stress components. For a specific node, the three principal stress values ​​calculated are 18.5 MPa, 7.2 MPa, and -4.3 MPa, respectively, and the corresponding eigenvectors contain direction cosines. The system normalizes these eigenvectors to obtain principal stress vectors of unit length, ensuring that the vector modulus is 1 to facilitate subsequent analysis.

[0058] Based on the obtained principal stress vector, the system establishes a stress trajectory equation that describes the stress transfer path. The stress trajectory is a series of curves in space, and the tangent direction of these curves is consistent with the principal stress direction. The stress trajectory equation is described by a set of ordinary differential equations, with spatial coordinates as independent variables and the components of the principal stress direction as differential coefficients. By defining the starting point coordinates, the system can integrate along the principal stress direction to generate a continuous stress trajectory curve. For the maximum principal stress direction, the system sets the integration step to 0.2 mm, and each stress trajectory contains 50 integration steps. In the case, starting from 100 nodes with greater stress, the system calculated 100 stress trajectory curves, which formed the stress flow field distribution in space.

[0059] The system solves the stress trajectory equation to obtain the spatial distribution curve in the direction of the maximum principal stress. The solution process uses the fourth-order Runge-Kutta integration method to ensure the accuracy and stability of the integration. The new position of each integration point is determined by the weighted average of the current position and the four intermediate points, with weight coefficients of 1 / 6, 1 / 3, 1 / 3 and 1 / 6 respectively. The detection conditions are set at the boundary, and the calculation is stopped when the integration path reaches the boundary of the structure to prevent the trajectory curve from exceeding the design domain. The obtained spatial distribution curve shows the stress transfer path along the direction of the maximum principal stress. The curve is dense in the high stress area and sparse in the low stress area. The system uses these spatial distribution curves as the benchmark arrangement path of the honeycomb structure to provide a basis for subsequent structural optimization.

[0060] Based on the spatial distribution curve of the maximum principal stress direction, the system divides the structural stress regions. This division method uses spatial meshing technology to divide the design domain into a number of grid cells. The meshing uses hexahedral cells with a cell size of 2 mm × 2 mm × 2 mm. For each grid cell, the number of stress trajectory curves passing through the cell is counted. Regions with a large number of curves indicate locations of concentrated stress transfer. Based on the stress trajectory density, the system divides the design domain into eight different structural stress regions, numbered from 1 to 8, with larger numbers indicating more concentrated stress transfer. The divided structural stress region data is stored in a three-dimensional array. The array size matches the mesh size, and the array element value is the region number.

[0061] Within each demarcated structural stress zone, the system calculates the equivalent stress. The equivalent stress is calculated using the von Mises stress criterion, converting the three-dimensional stress state into an equivalent uniaxial stress value. During the calculation, the node stress values ​​within each mesh element are averaged to obtain the representative stress state of the element. For example, in zone 1, the average equivalent stress is 5.3 MPa; in zone 8, the average equivalent stress reaches 42.7 MPa. The ratio of these equivalent stresses to the allowable material stress is determined as the stress sensitivity. The allowable material stress depends on the mechanical properties of the selected material. In the actual case, assuming the allowable material stress is 120 MPa, the stress sensitivity of zone 1 is 0.044, and the stress sensitivity of zone 8 is 0.356.

[0062] Based on the calculated stress sensitivity, the system determines the baseline wall thickness of the variable-density honeycomb support structure. Wall thickness is proportional to stress sensitivity. In areas with higher stress sensitivity, the honeycomb structure wall thickness increases to provide sufficient load-bearing capacity. The system sets the baseline wall thickness range between 0.3 mm and 1.2 mm and converts stress sensitivity into specific wall thickness values ​​through linear mapping. Taking area 1 as an example, the calculated baseline wall thickness is 0.35 mm; the baseline wall thickness of area 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.

[0063] The system establishes a power function relationship between the baseline wall thickness and the relative density. Relative density is the ratio of the volume of the honeycomb structure's solid material to the total volume, and is an important parameter that characterizes the degree of lightweighting. In the power function relationship expression, the relative density is equal to the baseline wall thickness divided by the power of the honeycomb unit cell size, and the power exponent is determined according to the type of honeycomb structure. In the actual case, a hexagonal honeycomb structure is adopted, and the power exponent is 1.5. The baseline size of the honeycomb unit cell is set to 5 mm. Based on this, the relative density of area 1 is calculated to be 0.031, and the relative density of area 8 is calculated to be 0.088. This power function relationship ensures a reasonable balance between structural quality and load-bearing capacity.

[0064] The established power function relationship is used as a parametric equation to control the geometric dimensions of the unit cell, and the system generates a variable-density honeycomb support structure. The parametric equation controls the geometric characteristics of the honeycomb unit cell, including wall thickness, unit cell size, and arrangement direction. During the implementation process, the system first determines the arrangement direction of the honeycomb unit cell according to the direction of the maximum principal stress, so that the two parallel sides of the hexagonal unit cell are perpendicular to the direction of the maximum principal stress to optimize the mechanical properties. Secondly, according to the relative density values ​​of each area, the geometric dimensions of the unit cell 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 expression of geometric details.

[0065] 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 reduced its mass by 37.5% while maintaining its original load-bearing capacity. The maximum stress value was reduced from 72.3 MPa of the original structure to 65.8 MPa, and the stress distribution was more uniform. The structural stiffness only decreased by 8.2%, while the structural specific stiffness (ratio of stiffness to mass) 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 the need for additional assembly processes, greatly improving production efficiency. At the same time, the porous characteristics of the honeycomb structure also provide space for functional integration, such as the arrangement of sensors, cables or cooling channels, further expanding the application scenarios of the structure.

[0066] The denture and digital combined removable denture processing system according to the embodiment of the present invention includes: The first unit is configured to acquire oral feature data of a patient using an oral 3D scanning device, wherein the oral feature data includes dynamic occlusal data of the patient in different occlusal states; construct a spatiotemporal graph structure based on the oral feature data, and generate an optimal occlusal surface according to the spatiotemporal graph structure; The second unit is configured to construct an initial denture three-dimensional model based on the oral feature data and the optimal occlusal surface, wherein the initial denture three-dimensional model includes an abutment portion for replacing a missing tooth and a connection portion for retention; The third unit is used to perform stress analysis on the initial denture three-dimensional model using a biomechanical simulation system, simulate the stress distribution of the denture under different occlusal states using a finite element method, and identify stress areas; A fourth unit is configured to perform parameterized modeling optimization on the abutment portion in the stress region, and adjust the anatomical morphological surface of the abutment portion to match the optimal occlusal surface; The fifth unit is used to construct a variable-density honeycomb support structure in the stress area based on the stress distribution analysis results; The sixth unit is used to assemble and connect the abutment part and the connecting part through a positioning structure to form a denture, and to perform surface treatment and polishing on the assembled denture.

[0067] According to a third aspect of the embodiments of the present invention, An electronic device is provided, comprising: processor; a memory for storing processor-executable instructions; The processor is configured to call the instructions stored in the memory to execute the aforementioned method.

[0068] According to a fourth aspect of the embodiments of the present invention, A computer-readable storage medium is provided, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the method described above is implemented.

[0069] The present invention may be a method, an apparatus, a system and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for executing various aspects of the present invention.

[0070] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements 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 processing dentures and digital combined removable dentures, characterized in that: include: Acquiring oral feature data of a patient using an oral 3D scanning device, wherein the oral feature data includes dynamic occlusal data of the patient in different occlusal states; constructing a spatiotemporal graph structure based on the oral feature data, and generating an optimal occlusal surface according to the spatiotemporal graph structure; constructing an initial denture three-dimensional model based on the oral feature data and the optimal occlusal surface, wherein the initial denture three-dimensional model includes an abutment portion for replacing a missing tooth and a connection portion for retention; A biomechanical simulation system is used to perform a stress analysis on the initial denture three-dimensional model, and a finite element method is used to simulate the stress distribution of the denture under different occlusal states to identify stress areas; Performing parameterized modeling optimization on the abutment portion in the stress region, and adjusting the anatomical curved surface of the abutment portion to match the optimal occlusal curved surface; Based on the stress distribution analysis results, a variable density honeycomb support structure is constructed in the stress area; The abutment part and the connecting part are assembled and connected through a positioning structure to form a denture, and the assembled denture is surface treated and polished.

2. The method according to claim 1, characterized in that Constructing a spatiotemporal graph structure based on the oral feature data, and generating an optimal occlusal surface according to the spatiotemporal graph structure includes: Constructing a spatiotemporal graph structure based on the oral feature data, using the coordinate data of the dental anatomical landmark points of the oral feature data as nodes of the spatiotemporal graph structure, using the spatial distance data between adjacent dental anatomical landmark points as weights of the spatiotemporal graph structure edges, and constructing the topological connection relationship between the landmark points into an adjacency matrix; Calculating the spatial correlation weights of nodes in the spatiotemporal graph structure, and taking nodes whose spatial correlation weights are greater than a preset correlation threshold as key anatomical feature points; Constructing a control point grid of a non-uniform rational B-spline surface based on the key anatomical feature points, using the spatial correlation weight 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 a final occlusal surface; In the optimization process, surface continuity constraints are introduced, and the gradient descent method is used to iteratively update the control point positions until convergence to generate the optimal occlusal surface.

3. The method according to claim 2, characterized in that Constructing a control point grid of a non-uniform rational B-spline surface based on the key anatomical feature points, using the spatial correlation weight as a weight parameter of the non-uniform rational B-spline surface, and generating an initial occlusal surface; Optimizing the initial occlusal surface to generate a final occlusal surface includes: Constructing a regular control point grid, wherein the number of rows and columns of the control point grid is determined according to the distribution density of the key anatomical feature points, and the boundary control points of the control point grid match the boundary contours of the key anatomical feature points; Mapping the spatial correlation weights into weight parameters of a non-uniform rational B-spline surface, weighting the control points according to the influence of the weight parameters, and generating an initial occlusal surface; Establishing surface continuity constraints, wherein the surface continuity constraints include position continuity constraints and tangential continuity constraints, and ensuring smooth transitions between adjacent surface patches through the continuity constraints; Calculating the fitting error between the initial occlusal surface and the key anatomical feature points, iteratively updating the control point positions using a gradient descent method, recalculating the fitting error after each iterative update, and stopping the iteration when the difference between the fitting errors calculated in two consecutive iterative calculations 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 occlusal surface.

4. The method according to claim 1, wherein A biomechanical simulation system is used to perform stress analysis on the initial denture three-dimensional model. The stress distribution of the denture under different occlusal states is simulated by the finite element method. The stress areas identified include: Setting boundary conditions on the initial denture three-dimensional model, setting the denture and abutment connection area as a displacement constraint boundary, setting the occlusal contact surface as a force boundary, and establishing a displacement component on the displacement constraint boundary and a surface stress component on the force boundary; Calculating an equivalent stress and a maximum shear stress 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, and the maximum shear stress is determined by a weighted average of a first difference and a second difference, the first difference being the difference between the maximum and minimum values ​​of the displacement component, and the second difference being the difference between the maximum and minimum values ​​of the surface stress component; The ratio of the maximum shear stress to the nominal shear stress is calculated to obtain the stress concentration factor, and the dispersion degree of the equivalent stress relative to the average stress is calculated to obtain the stress uniformity index; The denture structure is evaluated based on the stress concentration coefficient and the stress uniformity index to determine the area with uneven stress distribution.

5. The method according to claim 1, wherein Performing parameterized modeling optimization on the abutment portion in the stress region and adjusting the anatomical surface of the abutment portion to match the optimal occlusal surface includes: Acquiring curvature data of the abutment tooth surface, calculating a curvature change rate according to the curvature data, and determining a position of an axial edge line and a position of a characteristic point in a transition region based on the curvature change rate; Dividing the abutment tooth surface into an axial surface area and a transition area based on the position of the axial surface edge line and the position of the characteristic point of the transition area, and recording the coordinates of the boundary points of the axial surface area and the transition area; Extracting a cross-sectional contour line of the abutment tooth based on the coordinates of the boundary points, fitting the cross-sectional contour line using a Fourier series to obtain a cross-sectional contour function including a basic radius parameter and a circumferential harmonic coefficient; calculating the ideal position of each point on the abutment tooth surface based on the cross-sectional contour function, using the difference between the actual position of each point and the ideal position as the radial deformation amount, and establishing a radial deformation function including a height variation component and a circumferential variation component; Using the radial deformation function to locally adjust the abutment tooth surface to obtain an optimized abutment tooth surface, and calculating the surface distance and normal angle between the optimized abutment tooth surface and the target occlusal surface; constructing a local deformation weight function using a point-type exponential function, substituting the surface distance and the normal angle into the local deformation weight function, and calculating the morphological adjustment weight value of each region; The abutment tooth surface is locally deformed according to the morphological adjustment weight value, and position continuity constraints and tangential continuity constraints are implemented on the connection between adjacent curved surface pieces during the deformation process to make them match the optimal occlusal surface.

6. The method according to claim 1, characterized in that Based on the stress distribution analysis results, the variable density honeycomb support structure is constructed in the stress area, including: Acquiring structural stress state data of the connection portion, normalizing a characteristic vector corresponding to the structural stress state data to obtain a principal stress vector, and establishing a stress trajectory equation describing a stress transfer path based on the principal stress vector; Solving the stress locus equation to obtain a spatial distribution curve of the maximum principal stress direction, using the spatial distribution curve as a reference arrangement path of the honeycomb structure, and dividing the structural stress region according to the reference arrangement path; The equivalent stress is calculated in each structural stress region, and 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, and the power function relationship is used as a parametric equation to control the geometric dimensions of the unit cell.

7. A denture and a digital combined removable denture processing system, for implementing the method according to any one of claims 1 to 6, characterized in that: include: The first unit is configured to acquire oral feature data of a patient using an oral 3D scanning device, wherein the oral feature data includes dynamic occlusal data of the patient in different occlusal states; construct a spatiotemporal graph structure based on the oral feature data, and generate an optimal occlusal surface according to the spatiotemporal graph structure; The second unit is configured to construct an initial denture three-dimensional model based on the oral feature data and the optimal occlusal surface, wherein the initial denture three-dimensional model includes an abutment portion for replacing a missing tooth and a connection portion for retention; The third unit is used to perform stress analysis on the initial denture three-dimensional model using a biomechanical simulation system, simulate the stress distribution of the denture under different occlusal states using a finite element method, and identify stress areas; A fourth unit is configured to perform parameterized modeling optimization on the abutment portion in the stress region, and adjust the anatomical morphological surface of the abutment portion to match the optimal occlusal surface; The fifth unit is used to construct a variable-density honeycomb support structure in the stress area based on the stress distribution analysis results; The sixth unit is used to assemble and connect the abutment part and the connecting part through a positioning structure to form a denture, and to perform surface treatment and polishing on the assembled denture.

8. An electronic device, characterized in that: include: processor; a memory for storing processor-executable instructions; The processor is configured to call the instructions stored in the memory to execute the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having computer program instructions stored thereon, characterized in that: When the computer program instructions are executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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