Intelligent optimization method for denture occlusal surface morphology based on fusion occlusion dynamics simulation

Abstract

The present application relates to the field of oral implant restoration and digital medical technology, and particularly relates to a denture occlusal surface form intelligent optimization method fusing occlusal dynamics simulation. The method solves the technical problem of insufficient function stability of the denture form generated by the prior art. The method comprises: determining the sensitivity coefficient of the grid vertex of the denture based on the jawbone anatomical data and the implant positioning information; performing dynamic occlusal simulation on the denture and the opposite tooth model based on the virtual occlusal motion trajectory, and introducing random perturbation to the occlusal contact point during the simulation; generating a cumulative risk field of the denture occlusal surface according to the dynamic occlusal simulation; deforming and optimizing the initial grid model of the denture based on the cumulative risk field and combining the occlusal function constraint of the denture and the opposite tooth model, to determine the optimized denture grid model. The present application is used in the scene of denture form optimization.

Description

Technical Field

[0001] This invention relates to the fields of oral implant restoration and digital medical technology, specifically to an intelligent optimization method for the occlusal surface morphology of prostheses that integrates occlusal dynamics simulation. Background Technology

[0002] In the field of dental implant restoration, especially for high crown-to-root ratio restorations caused by insufficient alveolar bone height, the design of the occlusal surface morphology directly affects the long-term biomechanical stability of the implant. Traditional digital denture design mainly relies on the patient's static anatomical data and opposing tooth models, using computer-aided design and manufacturing techniques to generate the morphology. Existing occlusal surface morphology optimization methods are usually based on deterministic occlusal motion trajectories and homogenized bone tissue assumptions for mechanical analysis and geometric adjustments. These methods eliminate geometric interference on the motion path through Boolean operations and may combine finite element analysis to assess stress distribution under specific loads. However, because they do not fully consider the spatial heterogeneity of the individual patient's jaw structure and the inherent physiological variations and uncertainties in mastication, the design results have limitations in dealing with the real complex oral biomechanical environment, resulting in insufficient functional stability of the denture morphology generated by existing technologies. Summary of the Invention

[0003] To address the technical problem of insufficient functional stability in the morphology of prostheses produced by existing technologies due to their failure to fully consider the spatial heterogeneity of individual patient jaw structures and the inherent physiological variations and uncertainties in mastication, the present invention aims to provide an intelligent optimization method for the occlusal surface morphology of prostheses that integrates occlusal dynamics simulation. The specific technical solution adopted is as follows:

[0004] In a first aspect, this invention provides an intelligent optimization method for the occlusal surface morphology of dentures that integrates occlusal dynamics simulation. This method includes: acquiring the patient's jawbone anatomical data, implant positioning information, opposing tooth model, and an initial mesh model of the denture; determining the sensitivity coefficient of the mesh vertices of the denture based on the jawbone anatomical data and implant positioning information; the sensitivity coefficient is used to characterize the potential risk impact of the mesh vertices on the bone tissue surrounding the implant when under force; performing dynamic occlusal simulation on the denture and opposing tooth model based on a virtual occlusal motion trajectory, and introducing random perturbations of the occlusal contact points during the simulation; the random perturbations are used to assess the risk fluctuation rate of the occlusal surface of the denture at different contact points due to motion deviation based on the sensitivity coefficient; generating a cumulative risk field of the occlusal surface of the denture based on the dynamic occlusal simulation; the cumulative risk field is used to characterize the cumulative risk distribution of each region of the denture surface during long-term occlusion; based on the cumulative risk field and combined with the occlusal functional constraints of the denture and opposing tooth model, deforming and optimizing the initial mesh model of the denture to determine the optimized denture mesh model; the occlusal functional constraints are determined based on the occlusal relationship between the denture and opposing tooth model.

[0005] In conjunction with the first aspect mentioned above, in one possible implementation, the method specifically includes: determining the nominal contact point between the prosthesis and the opposing tooth model at each time step of the dynamic occlusion simulation; generating a set of spatial location sampling points simulating physiological motion deviations at each nominal contact point; and evaluating the risk volatility corresponding to the nominal contact point based on the sensitivity coefficient and geometric features at each sampling point.

[0006] In conjunction with the first aspect mentioned above, in one possible implementation, the method specifically includes: determining the lateral force influence parameter based on the relationship between the surface normal direction and the implant axis at each sampling point; determining the risk index at each sampling point based on the sensitivity coefficient and the lateral force influence parameter at each sampling point; and determining the risk volatility based on the statistical dispersion of the risk index at each sampling point.

[0007] In conjunction with the first aspect mentioned above, in one possible implementation, the method specifically includes: mapping the risk assessment results of at least one contact event obtained from dynamic occlusion simulation onto the mesh surface of the denture; and accumulating and smoothing the mapped risk values ​​based on a spatial diffusion algorithm to generate an accumulated risk field of the denture occlusal surface.

[0008] In conjunction with the first aspect mentioned above, in one possible implementation, the method further includes: weighting the risk values ​​mapped to the grid surface based on the risk volatility corresponding to at least one contact event to determine a comprehensive risk value; and performing spatial diffusion accumulation and smoothing processing on the comprehensive risk value.

[0009] In conjunction with the first aspect mentioned above, in one possible implementation, the method specifically includes: calculating the gradient information of the cumulative risk field at the grid vertices; determining the morphological correction vector of each grid vertex based on the gradient information; adjusting the grid vertex displacement based on the morphological correction vector and the occlusal function constraint to determine the optimized denture grid model.

[0010] In conjunction with the first aspect mentioned above, in one possible implementation, the method further includes: identifying a functional retention region on the occlusal surface of the denture based on the relative positional relationship between the denture and the opposing tooth model; and generating constraints on the displacement of mesh vertices based on the functional retention region to constitute occlusal functional constraints.

[0011] In conjunction with the first aspect mentioned above, in one possible implementation, the method specifically includes: detecting the static contact point between the denture and the opposing tooth model in centric occlusion; and determining the static contact point and its adjacent preset range area as the function retention area.

[0012] In conjunction with the first aspect mentioned above, in one possible implementation, the method specifically includes: determining bone support strength parameters in at least one circumferential orientation around the implant based on jawbone anatomical data; determining the lever arm length of the grid vertex relative to a preset position on the implant serving as a mechanical fulcrum; and determining a sensitivity coefficient based on the bone support strength parameters and the lever arm length.

[0013] In conjunction with the first aspect mentioned above, in one possible implementation, the method specifically includes: establishing a coordinate system based on the long axis direction of the implant; acquiring bone density information based on jawbone anatomical data within a predetermined spatial region surrounding the implant; and performing integral calculations on the bone density information at specific locations within the predetermined spatial region to determine bone support strength parameters.

[0014] The present invention has the following beneficial effects:

[0015] This invention provides a systematic digital design process that, for the first time, integrates the patient's specific jawbone anatomy, the motion uncertainties in virtual occlusal dynamics simulation, and key occlusal functional requirements into a unified optimization framework. By first calculating sensitivity coefficients reflecting anatomical weaknesses and geometric leverage effects, then combining this with random perturbation assessments of physiological fluctuations to evaluate risk volatility, a cumulative risk field guiding morphological modifications is generated, ultimately driving mesh deformation under functional constraints. This complete closed-loop process overcomes the limitations of static design or idealized dynamic simulation in existing technologies. The resulting prosthesis occlusal surface morphology not only theoretically conforms to individual anatomy but also adapts to the real, variable masticatory mechanical environment. This provides an intelligent solution for reducing biomechanical risks around implants and improving the long-term stability of prostheses at the source, thus addressing the technical problem of insufficient functional stability in existing prostheses due to insufficient consideration of the spatial heterogeneity of the patient's individual jawbone structure and the inherent physiological variations and uncertainties in masticatory movements. Attached Figure Description

[0016] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart illustrating an intelligent optimization method for denture occlusal surface morphology based on fused occlusal dynamics simulation, provided in one embodiment of the present invention.

[0018] Figure 2This is a schematic diagram of the structure of an intelligent optimization device for denture occlusal surface morphology that integrates occlusal dynamics simulation, provided as an embodiment of the present invention. Detailed Implementation

[0019] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of the intelligent optimization method for denture occlusal surface morphology integrating occlusal dynamics simulation proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0021] The specific scheme of the intelligent optimization method for denture occlusal surface morphology based on fusion occlusal dynamics simulation provided by the present invention will be described in detail below with reference to the accompanying drawings.

[0022] Please see Figure 1 The diagram shows a flowchart of an intelligent optimization method for denture occlusal surface morphology based on fusion occlusal dynamics simulation provided by an embodiment of the present invention. The method includes the following steps S101-S105, which will be described in detail below.

[0023] S101. Obtain the patient's jawbone anatomy data, implant positioning information, opposing tooth model, and initial mesh model of the denture.

[0024] In one possible implementation, the patient's jawbone anatomy data is obtained based on preoperative oral cone-beam computed tomography (CT) scans; implant positioning information is determined based on a digital implant surgery planning scheme, which at least defines the three-dimensional coordinates of the implant platform center within the jawbone and its major axis direction vector, providing a geometric reference for establishing a local analysis coordinate system centered on the implant and determining the mechanical fulcrum; the opposing tooth model is obtained through optical scanning of the patient's intraoral opposing dentition or three-dimensional digitization of a plaster model; and the initial mesh model of the denture is obtained through computer-aided design based on a standard crown morphology database, or through manual design by a technician based on preliminary occlusal relationships followed by scanning.

[0025] S102. Based on jawbone anatomical data and implant positioning information, determine the sensitivity coefficient of the denture's grid vertices.

[0026] The sensitivity coefficient is used to characterize the potential risk impact of grid vertices on the bone tissue around the implant when under stress.

[0027] In one possible implementation, for each vertex of the initial mesh model surface of the denture, a quantified risk impact weight value, i.e., a sensitivity coefficient, is calculated based on the bone support conditions and geometric leverage effect determined for it.

[0028] For example, an anatomical-geometric coupling-based mapping mechanism can be used to calculate coefficients by combining the spatial lever arm length of a vertex with the bone support strength parameter corresponding to its orientation; the bone support strength parameter is directly related to the bone tissue mechanical properties extracted from jawbone anatomical data. For vertices located on long lever arms and pointing towards weak bone walls, a higher sensitivity coefficient is calculated to mark them as high-risk; for vertices with shorter lever arms or pointing towards strong bone walls, a lower sensitivity coefficient is calculated to characterize their relative safety.

[0029] S103. Based on the virtual occlusal motion trajectory, dynamic occlusal simulation is performed on the denture and opposing tooth models, and random perturbation of the occlusal contact point is introduced during the simulation process.

[0030] Among them, random perturbation is used to assess the risk volatility of the denture occlusal surface at different contact points due to motion deviation based on the sensitivity coefficient.

[0031] In one possible implementation, for the occlusal cycle process defined by the virtual occlusal motion trajectory, when simulating the relative movement and contact between the denture and the opposing tooth model, a spatial random deviation is applied at the contact point position based on the physiological uncertainty characteristics of the chewing motion.

[0032] For example, a dynamic risk assessment mechanism based on trajectory perturbation can be used. This involves generating a set of spatial offset sampling points conforming to a preset probability distribution at the nominal contact points where geometric interference or proximity occurs with the opposing tooth model. The intensity of the random perturbation is related to the amplitude of the simulated physiological tremor or trajectory drift. For occlusal stages or regions requiring assessment of high tolerance, a larger amplitude random perturbation is used to fully explore risk mutations; for routine assessments, a random perturbation with a typical physiological amplitude is used to balance simulation efficiency and assessment realism.

[0033] S104. Based on dynamic occlusion simulation, generate the cumulative risk field of the denture occlusal surface.

[0034] Among them, the cumulative risk field is used to characterize the cumulative risk distribution of each region on the denture surface during long-term occlusion.

[0035] In one possible implementation, the discrete, time-series risk data output by the dynamic occlusal simulation are mapped and synthesized into a continuous spatial risk distribution covering the entire occlusal surface of the denture, i.e., a cumulative risk field.

[0036] For example, a field generation mechanism based on spatial diffusion and accumulation can be adopted. This involves diffusing the risk assessment results of each contact event (including risk index and risk volatility information) to its neighboring grid vertices, and accumulating and smoothing the contributions of all events to generate a cumulative risk field. The value of the cumulative risk field is directly related to the long-term cumulative effect and concentration of risk borne by each spatial location. For areas where high-risk contacts frequently occur in dynamic simulations or are extremely sensitive to motion deviations, higher cumulative risk values ​​are generated to form risk hotspots; for low-risk or non-contact areas, lower cumulative risk values ​​are generated to characterize their safety status.

[0037] S105. Based on the cumulative risk field and combined with the occlusal function constraints of the denture and opposing tooth models, the initial mesh model of the denture is deformed and optimized to determine the optimized denture mesh model.

[0038] Among them, the occlusal function constraint is determined based on the occlusal relationship between the denture and the opposing tooth model.

[0039] In one possible implementation, based on the risk distribution revealed by the cumulative risk location and the functional protection requirements defined by the occlusal function constraints, a constrained geometric deformation problem is solved to iteratively adjust the vertex coordinates of the initial mesh model, ultimately outputting a three-dimensional denture morphology that achieves an optimal balance between biomechanical safety and masticatory functionality.

[0040] For example, a gradient-driven and constraint-based mesh optimization mechanism can be adopted: First, the gradient information of the cumulative risk field at the mesh vertices is calculated to generate a morphological correction vector indicating the direction of risk reduction; simultaneously, based on the occlusal function constraints, the function-preserving regions are identified on the mesh surface and assigned corresponding position constraint weights. Subsequently, an optimization equation is constructed and solved with the objective of minimizing global deformation energy, including an objective guiding term (defined by the correction vector), a position-preserving term (defined by the function constraints), and a surface smoothing term. The deformed vertex coordinates are directly calculated, thereby determining the optimized denture mesh model.

[0041] The technical solution provided by the above embodiments can bring at least the following beneficial effects: This embodiment provides a systematic digital design process, which for the first time integrates the patient's specific jawbone anatomy, the motion uncertainty in virtual occlusal dynamics simulation, and key occlusal functional requirements into a unified optimization framework. By first calculating the sensitivity coefficient reflecting anatomical weak points and geometric leverage effects, and then combining it with random perturbation assessment of risk volatility to simulate physiological fluctuations, a cumulative risk field guiding morphological modification is generated, ultimately driving mesh deformation under functional constraints. This complete closed-loop process overcomes the limitations of static design or idealized dynamic simulation in existing technologies, making the final generated denture occlusal surface morphology not only theoretically fit the individual anatomy, but also adapt to the real, variable masticatory mechanical environment. This provides an intelligent solution to reduce the biomechanical risks around implants and improve the long-term stability of prostheses from the source, thus solving the technical problem that existing technologies do not fully consider the spatial heterogeneity of the patient's individual jawbone structure and the inherent physiological variations and uncertainties in masticatory movements, resulting in insufficient functional stability of the generated denture morphology.

[0042] In one possible implementation, the above-mentioned dynamic occlusion simulation of the denture and opposing tooth model is based on the virtual occlusal motion trajectory, and a random perturbation process of the occlusal contact point is introduced during the simulation process. Specifically, this can be achieved through the following S201-S203, which will be explained in detail below.

[0043] S201. At each time step of the dynamic occlusion simulation, determine the nominal contact point between the denture and the opposing tooth model.

[0044] In one possible implementation, for each calculation moment on the discretized virtual occlusal motion trajectory, the spatial positional relationship between the denture mesh model and the opposing tooth mesh model in the corresponding pose at that moment is detected to determine the spatial position point where actual contact or very close to contact occurs in the ideal motion state, i.e., the nominal contact point.

[0045] For example, a contact point extraction mechanism based on geometric interference detection or a minimum distance threshold can be employed: Boolean operations or precise distance calculations are performed on the denture model and the opposing tooth model after pose transformation to identify all local areas where geometric penetration (interference) occurs or where the distance between them is less than a preset small tolerance (e.g., 0.05 mm to 0.1 mm). Representative points defined in these areas as the points where contact occurs (such as the center point of the penetration area or the midpoint of the nearest point pair) are extracted as nominal contact points. These points represent the initial positions where the occlusal force is expected to be transmitted under ideal conditions without motion deviation.

[0046] S202. At each nominal contact point, generate a set of spatial location sampling points that simulate physiological motion deviation.

[0047] In one possible implementation, for each determined nominal contact point, in order to simulate the unavoidable physiological tremors or slight trajectory drift during chewing, multiple spatial points, i.e. spatial position sampling points, are generated in the local neighborhood of that point to explore possible variations in the contact position.

[0048] For example, a generation mechanism based on local tangent planes and probability distribution sampling can be adopted: First, a local tangent plane is constructed with the nominal contact point as the center and its normal direction on the denture mesh surface as the normal. Then, within this tangent plane, a set of two-dimensional points whose position coordinates follow a specific probability distribution (such as a Gaussian distribution centered on the projection of the nominal contact point with a preset standard deviation) are generated. Finally, these two-dimensional points are vertically projected back onto the surface of the denture mesh to obtain the final three-dimensional spatial position sampling points. The number of sampling points is preset according to statistical significance requirements (e.g., no less than 20), while the parameters of the probability distribution (such as the standard deviation of the Gaussian distribution) are configured based on the typical amplitude of the simulated physiological deviation.

[0049] S203. Based on the sensitivity coefficient and geometric characteristics at each sampling point, assess the risk volatility corresponding to the nominal contact point.

[0050] In one possible implementation, for a set of spatial location sampling points generated around the same nominal contact point, the system calculates the risk level of each sampling point based on its personalized risk weight (sensitivity coefficient) and local surface geometry information, and quantifies the sensitivity of the local area where the nominal contact point is located to the deviation of the motion position based on the risk level distribution of the set of sampling points, i.e., the risk volatility.

[0051] For example, an evaluation mechanism based on statistical dispersion measures can be used: First, for each sampling point, an index value characterizing the potential risk at a single point is calculated, combining its sensitivity coefficient and surface orientation features (such as the angle between the normal and the implant axis). Then, the statistical dispersion of the set of risk indices calculated for all sampling points corresponding to the nominal contact point is calculated (e.g., the sample standard deviation of this set of risk indices). The value of this statistical standard deviation is defined as the risk volatility of the nominal contact point. A higher risk volatility indicates that the geometry at that location makes the contact risk more sensitive to small changes in position, i.e., the lower the tolerance.

[0052] The technical solution provided by the above embodiments can bring at least the following beneficial effects: This embodiment introduces a key mechanism for simulating physiological uncertainties in dynamic occlusion simulation. By generating a set of spatial sampling points simulating motion deviations at the nominal contact point, this method incorporates real chewing vibrations and trajectory drift factors into the analysis, enabling the risk assessment basis to leap from an ideal deterministic trajectory to the simulation of a complex physiological environment, laying a data foundation for subsequent quantification of fault tolerance.

[0053] In one possible implementation, the process of assessing the risk volatility corresponding to the nominal contact point based on the sensitivity coefficient and geometric characteristics at each sampling point can be specifically implemented through the following S301-S303, which will be explained in detail below.

[0054] S301. Based on the relationship between the surface normal direction and the implant axis at each sampling point, determine the lateral force influence parameters.

[0055] In one possible implementation, for each spatial sampling point, in order to quantify the tendency of the local geometry of that point to generate harmful lateral force, the system calculates a factor characterizing the intensity of the lateral force, namely the lateral force influence parameter, based on the spatial angular relationship between the surface direction (normal direction) at that point and the preset mechanical principal axis direction (implant axis) of the implant system.

[0056] For example, a parameter determination mechanism based on spatial vector angle analysis can be used: calculate the angle between the surface unit normal vector and the implant axial unit vector at the sampling point; then, based on the magnitude of this angle, determine the lateral force influence parameter through a preset mathematical mapping function (e.g., taking the sine value of the angle). When the surface normal is perpendicular to the implant axial direction (angle close to 90 degrees), the parameter value is large, indicating that the contact force at this point is easily decomposed into a large lateral component; when the surface normal is parallel to the implant axial direction (angle close to 0 degrees), the parameter value is very small or zero, indicating that the contact force is mainly transmitted along the axial direction.

[0057] S302. Based on the sensitivity coefficient and lateral force influence parameters at each sampling point, determine the risk index for each sampling point.

[0058] In one possible implementation, for each spatial sampling point, in order to obtain a quantitative value characterizing the relative biomechanical risk level caused by occlusal contact at that specific location, the system will comprehensively calculate a sensitivity coefficient reflecting the combined anatomical and geometric risk potential of that point, and a lateral force influence parameter reflecting the tendency of the local surface direction of that point to generate lateral force, to obtain a normalized risk assessment value, namely the risk index of that sampling point.

[0059] For example, deviation sampling points risk index Satisfy the following formula 1:

[0060]

[0061] in, In the The risk index at each deviation sampling point is a dimensionless scalar that characterizes the relative risk level of a single-point contact event. For deviation sampling points Sensitivity coefficient at the location; Sampling points The angle between the surface normal direction and the long axis direction of the implant; It is a sine trigonometric function used to extract the proportion of the lateral component of the normal force.

[0062] The influence of the contact force direction is quantified as a factor between 0 and 1: when the contact surface is perpendicular to the implant axis. When the value is close to 0, the lateral force is very small, and the risk index is low; when the contact surface is a steep slope, A value close to 1 indicates a large lateral force and a high risk index. This comprehensively reflects the risks associated with contact in the wrong location (highly sensitive area) or in the wrong direction (large lateral angle); It is the basic unit constituting dynamic risk assessment. It quantifies the potential to generate harmful lateral torque when meshing contact occurs at a specific deviation position.

[0063] S303. Determine the risk volatility based on the statistical dispersion of the risk index at each sampling point.

[0064] In one possible implementation, for the risk indices corresponding to all spatial location sampling points belonging to the same nominal contact point, in order to quantify the fluctuation or dispersion of the risk values ​​caused by small changes in the location of the sampling points, a specific statistical dispersion measure of the set of risk indices is calculated to define the risk volatility corresponding to the nominal contact point.

[0065] For example, a statistical measurement mechanism based on sample standard deviation can be used: calculate the arithmetic mean of the risk indices for all sampled points; then, calculate the deviation of the risk index for each sampled point from this mean, average the sum of squares of these deviations (considering the sample degrees of freedom), and take the square root to obtain the sample standard deviation of the risk indices. The value of this standard deviation is determined as the risk volatility. The larger the value, the greater the difference in the assessed risk level around the nominal contact point at different possible contact locations, i.e., the more sensitive the shape at that location is to motion deviations; the smaller the value, the more stable the risk level, i.e., the better the fault tolerance.

[0066] The technical solution provided in the above embodiments can bring at least the following beneficial effects: This embodiment provides a specific method for accurately quantifying the tolerance of local geometric morphology. By calculating the statistical dispersion of a set of sampling point risk indices to define risk volatility, this method transforms the abstract clinical concept of sensitivity to motion deviations into a calculable and stable mathematical indicator, thereby accurately identifying those hidden high-risk geometric features that are highly susceptible to risk surges under minor perturbations.

[0067] In one possible implementation, the process of generating the cumulative risk field of the denture occlusal surface based on dynamic occlusal simulation can be specifically implemented through the following S401-S402, which will be explained in detail below.

[0068] S401. Map the risk assessment results of at least one contact event obtained from the dynamic occlusion simulation onto the mesh surface of the denture.

[0069] In one possible implementation, for a series of discrete occlusal contact events recorded during dynamic occlusion simulation, the risk assessment results (including risk index and risk volatility information) corresponding to these events are correlated from the original spatiotemporal coordinates to the three-dimensional geometric representation of the denture occlusal surface, thus preparing data for subsequent spatial accumulation processing.

[0070] For example, a mapping mechanism based on spatial projection and attribute interpolation can be adopted: for each contact event, its location (nominal contact point) is usually located inside a triangular facet of the denture mesh; first, the facet where the point is located is determined, and then, based on the local centroid coordinates of the point on the facet, the risk assessment result (e.g., comprehensive risk value) belonging to the event is assigned to the three vertices of the facet through linear interpolation.

[0071] S402. Based on the spatial diffusion algorithm, the mapped risk values ​​are accumulated and smoothed to generate the accumulated risk field of the denture occlusal surface.

[0072] In one possible implementation, for discrete risk values ​​mapped to the vertices of the denture mesh, in order to generate a continuous, smooth scalar field that reflects the long-term spatial cumulative effect of risk, a spatial algorithm that simulates the transmission of mechanical stress or the diffusion of influence is used to perform spatial accumulation, integration and smoothing calculations on all discrete risk contributions, ultimately forming a cumulative risk field covering the entire occlusal surface.

[0073] For example, a spatial diffusion accumulation mechanism based on Gaussian kernel function convolution can be adopted: taking each grid vertex or mapping point carrying a risk value as the center, its risk value is diffused to neighboring vertices within a certain radius according to a kernel function (such as a Gaussian function) that decays with distance; traversing all risk source points, all diffusion contributions received by each vertex are accumulated to obtain a preliminary accumulated value; subsequently, the accumulated value of the entire field can be normalized and additional smoothing filtering (such as Laplacian smoothing) can be applied to eliminate numerical noise, and finally output a scalar field that continuously varies on the grid surface and has a uniform numerical range, i.e., the accumulated risk field.

[0074] The technical solution provided by the above embodiments can bring at least the following beneficial effects: This embodiment realizes the transformation from discrete temporal risk events to continuous spatial risk distribution. By mapping and diffusing the discrete risk assessment results obtained from dynamic simulation to the entire interlocking surface, this method generates a heat map that intuitively displays the cumulative distribution of risk space, thereby elevating the optimization objective from dealing with isolated contact points to regulating the risk topography of the entire functional surface, providing a direct basis for global and continuous morphological optimization.

[0075] In one possible implementation, after determining the risk volatility, it is necessary to weight the risk values ​​mapped to the grid surface. This process can be specifically implemented through the following S501-S502, which will be explained in detail below.

[0076] S501. Based on the risk volatility corresponding to at least one contact event, weight the risk value mapped to the grid surface.

[0077] In one possible implementation, to more significantly reflect the higher long-term risks of contact events that are sensitive to motion deviations and have poor stability during the generation of the cumulative risk field, a corresponding weight adjustment is applied to the initial risk value mapped to the grid surface based on the magnitude of the risk volatility corresponding to each contact event, thereby amplifying the contribution of high volatility events.

[0078] For example, a volatility-based linear or non-linear weighting mechanism can be employed: before or after mapping the composite risk value of the contact event onto the grid surface, the risk value is multiplied by a weighting factor determined by the risk volatility of that event. For instance, the weighting factor could be set as a linear function of risk volatility or other monotonically increasing functions. This means that for events with high risk volatility, their risk value is enhanced before participating in subsequent spatial diffusion accumulation; for events with low risk volatility, their risk value may be maintained or only slightly enhanced.

[0079] For example, the first k Time step u Comprehensive risk value of a nominal contact event The following formula 2 is satisfied:

[0080]

[0081] in, The lateral risk index is the nominal contact point itself. This represents the risk volatility at that point of contact. The fluctuation penalty weighting coefficient is a constant greater than 0 (for example, The value is 2), which is used to adjust the degree of emphasis on volatility.

[0082] Formula 2 expresses the risk of a single contact event as a linear combination of nominal risk and uncertainty risk, with the first term... This is the basic risk item, the second item. It is a risk adjustment term, which multiplies volatility by a weight. Then add the total risk. The larger the value, the more severe the penalty for poor fault tolerance; the two types of risks are balanced by weighted summation. It is the basic input data for generating the cumulative risk field. It not only focuses on the static risk under the ideal trajectory, but also integrates the dynamic risk caused by motion uncertainty, so that the final risk field can more comprehensively reflect the long-term cumulative damage potential under the real chewing environment.

[0083] S502. Perform spatial diffusion accumulation and smoothing processing on the weighted comprehensive risk value.

[0084] In one possible implementation, for each risk value after risk volatility weighting adjustment, in order to finally generate a smooth and continuous cumulative risk field that can be used to drive pattern optimization, spatial diffusion accumulation operation and global smoothing filtering are performed.

[0085] For example, a kernel diffusion-based accumulation algorithm and a neighborhood averaging-based smoothing algorithm can be used sequentially: First, each weighted comprehensive risk value is taken as a source point, and a preset diffusion kernel function (e.g., Gaussian kernel function) is used to calculate its spatial influence on all vertices of the denture mesh. The influence of all source points is superimposed and integrated to obtain the initial accumulated risk value of each vertex. Then, the initial accumulated risk value is normalized to make it fall into a uniform numerical range (e.g., the interval [0, 1]). Finally, a mesh topology-based smoothing filter (e.g., Laplace smoothing iteration) is applied to the normalized risk value to eliminate local noise or non-physical abrupt changes that may be introduced by discrete sampling or numerical calculation, thereby outputting the final smooth and continuous accumulated risk field.

[0086] The technical solution provided by the above embodiments can bring at least the following beneficial effects: This embodiment enhances the accuracy of the cumulative risk field in representing motion uncertainty factors. By weighting the risk value according to the risk volatility, the contribution of high-risk events with poor fault tolerance and instability is amplified in the process of risk accumulation, so that the generated risk field can more realistically reflect the distribution of potential fatigue damage caused by motion variation in long-term use, and guide optimization resources to be more accurately allocated to the most vulnerable areas.

[0087] In one possible implementation, the process of deforming and optimizing the initial mesh model of the denture based on the cumulative risk field and combined with the occlusal function constraints of the denture and the opposing tooth model to determine the optimized denture mesh model can be specifically implemented through the following S601-S603, which will be explained in detail below.

[0088] S601. Calculate the gradient information of the cumulative risk field at the grid vertices.

[0089] In one possible implementation, for the generated cumulative risk field covering the denture mesh surface, in order to obtain mathematical guidance on how each vertex should move to effectively reduce local risk, at each mesh vertex, the spatial rate of change of the risk field value along the mesh surface direction, i.e., gradient information, is calculated.

[0090] For example, a numerical calculation method based on discrete differential geometry can be used: For each vertex of the denture mesh, based on the cumulative risk value and spatial relationship of each vertex in its ring neighborhood, a three-dimensional vector is calculated as the gradient at that vertex by solving a locally fitted surface or applying a preset discrete difference scheme; the direction of this gradient vector points to the direction in which the risk field increases most rapidly at that point, and its magnitude characterizes the drastic degree of risk change. The gradient information is directly related to the spatial rate of change of the cumulative risk field between adjacent vertices.

[0091] S602. Determine the shape correction vector of each mesh vertex based on gradient information.

[0092] In one possible implementation, for the gradient information at each grid vertex obtained from the calculation, in order to generate a geometric displacement command that can be directly used to drive the deformation of the grid, the gradient information is transformed into a spatial displacement vector with a clear direction and reasonable magnitude, namely the shape correction vector.

[0093] For example, a determination mechanism based on mapping the negative gradient direction to the magnitude of the risk value can be adopted: First, the opposite direction of the gradient direction is taken as the reference direction in which the vertex should move to reduce risk; then, based on the magnitude of the accumulated risk value at the vertex, the displacement magnitude along the reference direction is determined through a preset mapping function (e.g., setting a risk threshold, and only when the risk is higher than the threshold is a displacement linearly mapped proportionally to the excess, subject to a maximum step size limit); finally, the reference direction and the displacement magnitude are combined to form the final shape correction vector. This vector indicates the target displacement that the vertex should undergo in deformation optimization.

[0094] For example, the first i The required displacement of each grid vertex along the risk reduction direction The following formula 3 is satisfied:

[0095]

[0096] in, The normalized cumulative risk field value at this vertex is dimensionless and has a range of [0, 1]. To preset a risk threshold, the value range is [0, 1) (for example, (Value is 0.2); risks below this threshold are considered acceptable and will not trigger displacement. The maximum single-step displacement is a length constant (exemplary, 0.1 mm) used to control the optimization step size and ensure convergence stability.

[0097] When the risk of the peak Less than or equal to the threshold When the displacement amplitude is 0, the point remains unchanged; when the vertex risk Above the threshold At that time, displacement amplitude and excess risk They are linearly proportional; the proportionality coefficient is determined by... The decision ensured that At the highest risk level in the entire game, the displacement amplitude is exactly [value missing]. This formula will abstract the normalized risk value. Transformed into concrete, physical geometric shapes and variables Its beneficial effect lies in achieving the quantitative control logic of "the higher the risk, the greater the deformation." Simultaneously, by setting thresholds... This avoids over-optimization of low-risk areas and retains essential functionalities; by setting a maximum step size This ensures the numerical stability of the iterative optimization process and prevents oscillations or divergence.

[0098] S603. Based on the morphological correction vector and occlusal function constraints, adjust the mesh vertex displacement to determine the optimized denture mesh model.

[0099] In one possible implementation, for the morphological correction vector determined for each mesh vertex and the occlusal function constraints defined for the entire mesh surface, a new set of vertex coordinates that satisfies all conditions is calculated by solving a mathematical optimization problem that integrates the requirements of target guidance, position preservation and geometric smoothness, thereby determining the optimized denture mesh model.

[0100] For example, an optimization mechanism based on weighted least squares energy minimization can be adopted: An energy equation for the new vertex coordinates is constructed, containing three core terms: a target-guiding term, which drives the new coordinates of the vertex towards its target position (original coordinates plus a shape correction vector), the weight of which reflects the strength of following the risk reduction instruction; a position-preserving term, which penalizes the new coordinates of the vertex from their original position, given high weight in the function-preserving region defined by the interlocking function constraint to strongly anchor key function points; and low or zero weight in other regions; and a surface-smoothing term, which penalizes drastic changes in local surface curvature caused by deformation to maintain the smoothness and continuity of the optimized surface, avoiding unnatural wrinkles or spikes. By solving the linear or nonlinear equations that minimize this total energy, the new vertex coordinates that maximize the evolution of the overall shape along the risk reduction direction while satisfying the function constraints can be directly calculated, thus determining the final optimized mesh model.

[0101] For example, the new vertex coordinate set Total deformation energy Satisfy the following formula 4:

[0102]

[0103] Let be the original coordinates of the i-th vertex. The new coordinates to be determined; The shape correction vector for this vertex; The position of this vertex maintains the constraint weight; for vertices within the function-preserving region, this weight is large; for other regions, this weight is small or even 0. The target guide constraint weights for this vertex are typically complementary to the position-preserving weights, for example... + =1; The global surface smoothing constraint weight; This is the Laplacian operator (discrete form), used to calculate the difference between a vertex and the average position of its neighborhood, reflecting local curvature. The change in local curvature before and after deformation was measured.

[0104] For position preservation terms, penalize the new coordinates. Deviation from original coordinates The degree of. The larger the value, the more firmly the vertex is anchored in place; As the target guide, guide the new coordinates. To the target location move, The larger the value, the more the vertex follows the risk field-driven correction instructions; The smoothing term penalizes drastic changes in local surface curvature before and after deformation, ensuring the optimized mesh surface remains smooth and preventing wrinkles or jagged edges. Solving this equation involves finding a set of... This minimizes the weighted sum of squares of these three terms (i.e., the total energy E), which is equivalent to solving a large sparse linear system of equations.

[0105] It is understandable that the weights of each item in the formula ( , , The sum of these constraints is not equal to 1, and does not need to be equal to 1, because they control the relative strength of different types of constraints. For example, increasing... This will make the results smoother but may slightly deviate from the target in terms of risk reduction; increasing the vertex size of the functional area This will lock the point more firmly. The specific setting of the weights falls under the category of engineering optimization. The desired optimization effect is achieved by adjusting the proportional relationship between them.

[0106] The technical solution provided in the above embodiments can bring at least the following beneficial effects: This embodiment establishes a data-driven, automatically executed core driving force for morphological optimization. By calculating the gradient of the cumulative risk field to generate a morphological correction vector, this method can automatically indicate the geometric deformation direction with the fastest risk reduction for each region of the interlocking surface, realizing a fundamental transformation of the optimization process from manual trial and error relying on experience to intelligent navigation based on the gradient field, greatly improving the efficiency and scientific nature of the design.

[0107] In one possible implementation, it is also necessary to construct a biting function constraint, which can be specifically implemented through the following S701-S702, which will be described in detail below.

[0108] S701. Based on the relative positional relationship between the denture and the opposing tooth model, identify the functional retention area on the occlusal surface of the denture.

[0109] In one possible implementation, to delineate areas on the occlusal surface of the denture that require special protection to prevent excessive modification of its geometry during optimization and damage to occlusal function, the functional retention area is determined by analyzing the spatial contact relationship between the denture model and the opposing tooth model in specific functional positions.

[0110] For example, a region identification mechanism based on static occlusal contact detection can be adopted: the denture model and the opposing tooth model are placed in a standard position (such as centric occlusion) that represents the maximum cusp interlocking occlusion state; then, through precise geometric interference detection or distance calculation, all vertices or surfaces on the denture model that are in actual contact with or at very close distance (e.g., less than 0.1 mm) to the opposing tooth model are identified; all denture surface areas within a preset neighborhood range (e.g., a spherical area with a radius of 0.5 to 1 mm) centered on these initial contact points are uniformly defined as the function retention area.

[0111] S702. Based on the function retention region, generate constraint conditions for the displacement of mesh vertices to form interlocking function constraints.

[0112] In one possible implementation, for the identified functional preservation region, in order to achieve precise control over the vertex displacement behavior within the region in subsequent mesh deformation optimization, specific displacement restriction rules are generated for the mesh vertices within the region according to a preset constraint strategy. The sum of these rules constitutes the interlocking functional constraint.

[0113] For example, a weighting mechanism based on regional attributes and risk status classification can be used to generate constraints: traversing all grid vertices, vertices located within the function-preserving region are assigned a higher position-preserving weight; vertices located outside the region are assigned a lower position-preserving weight or zero weight. Furthermore, for vertices within the function-preserving region that are simultaneously identified as high-risk (e.g., whose cumulative risk value exceeds a certain threshold), a relaxation factor can be introduced to appropriately reduce their position-preserving weight and correspondingly increase their target guidance weight. This allows these vertices in conflicting positions to undergo controlled, minor displacements during optimization, seeking a balance between preserving core functionality and eliminating necessary risks.

[0114] For example, a classification and weighting mechanism based on dual criteria of vertex attributes can be adopted: the system traverses all mesh vertices, and determines whether their normalized cumulative risk value exceeds a preset action threshold, and whether they belong to the function-preserving region defined by the initial set of centering engagement points. The vertices are divided into three categories and weights are assigned: Pure risk zone: and It requires full deformation to eliminate the risk; pure functional area: and The original state must be strictly maintained; high-risk functional areas (conflict areas): and This is the key point of logical deadlock; setting a relaxation factor for each vertex. Assign a position constraint weight and a deformable target weight For pure risk areas: , Deformation is fully permitted; for purely functional areas: , Strong anchoring to prevent displacement; for high-risk functional areas: introduce relaxation factors. (In this embodiment, the value is set to 0.4). Setting , This means allowing the point to seek equilibrium between its original and target positions, resulting in controlled, minute displacements; relaxation factor. The value can be adjusted according to clinical needs. If bone integration safety is a priority, it can be lowered. (e.g., 0.2); if chewing efficiency is a priority, the value can be increased. (e.g., 0.6).

[0115] The technical solution provided by the above embodiments can bring at least the following beneficial effects: This embodiment embeds functional protection decision logic into the automated optimization process. By determining the functional retention area based on the occlusal relationship and generating constraints, it ensures that the optimization algorithm can actively identify and respect the key functional anatomy that needs to be retained while pursuing biomechanical safety, thereby pre-setting a balance point between safety and function at the algorithm level.

[0116] In one possible implementation, the process of identifying the functional retention area on the occlusal surface of the denture based on the relative positional relationship between the denture and the opposing tooth model can be specifically implemented through the following S801-S802, which will be described in detail below.

[0117] S801. In centric occlusion, detect the static contact point between the denture and the opposing tooth model.

[0118] In one possible implementation, in order to determine the key local areas on the occlusal surface of the denture that participate in the formation of basic occlusal function, the denture model and the opposing tooth model are placed in the centric occlusal position, which represents the maximum cusp interlocking state, and the geometric position point where the two make spatial contact in this static position is accurately detected, i.e., the static contact point.

[0119] For example, a geometric contact detection mechanism based on precise distance calculation can be employed: the opposing tooth model is spatially positioned according to the centric occlusion; subsequently, the minimum distance between each vertex or triangular facet on the denture mesh model and the surface of the opposing tooth model is calculated; the nearest points on all vertices or facets with distances less than a preset micro-contact threshold (e.g., 0.05 mm to 0.1 mm) are identified and recorded as static contact points. The set of these points constitutes the initial geometrical contact representation of occlusal function.

[0120] S802. Define the static contact point and its adjacent preset range area as the function retention area.

[0121] In one possible implementation, to construct a protective area with an appropriate spatial range that can reliably ensure the continuity and stability of the biting function, each detected static contact point is taken as the core, and together with the adjacent curved surface area within a certain range around it, they are defined as the function retention area.

[0122] For example, a region generation mechanism based on spatial neighborhood expansion can be used: for each static contact point, the system defines a spatial neighborhood around that point on the denture mesh surface. The extent of this neighborhood can be determined by a preset distance radius (e.g., 0.5 mm to 1.5 mm) or topological steps (e.g., all vertices within a one- or two-ring neighborhood). After merging these expanded neighborhoods of all static contact points and removing overlapping portions, the resulting set of continuous or discrete surface regions is ultimately determined as the function-preserving region.

[0123] The technical solution provided in the above embodiments can bring at least the following beneficial effects: This embodiment provides a clear, reliable, and clinically recognized standard for identifying functional retention areas. By determining the functional area with the static contact point in the centric occlusion position as the core, this method closely conforms to the basic functional principles of prosthodontics, ensuring that the setting of functional constraints is both clinically realistic and highly operable and repeatable.

[0124] In one possible implementation, the process of determining the sensitivity coefficient of the denture mesh vertices based on jawbone anatomical data and implant positioning information can be specifically implemented through the following S901-S903, which will be described in detail below.

[0125] S901. Based on jawbone anatomical data, determine the bone support strength parameters in at least one circumferential orientation around the implant.

[0126] In one possible implementation, in order to transform the information on the spatial heterogeneity of bone contained in the patient's jawbone anatomical images into an evaluation index that can be used to quantify mechanical support capacity, tissue density information in a specific spatial region is extracted from the jawbone anatomical data and integrated and calculated according to different circumferential orientations around the implant, thereby obtaining bone support strength parameters that characterize the relative support capacity in each orientation.

[0127] For example, a parameter determination mechanism based on coordinate transformation and density integral calculation can be adopted: First, a local cylindrical coordinate system with the long axis of the implant as the Z-axis is established; then, a three-dimensional analysis area is preset around the implant; next, based on the spatial gray-level distribution of jawbone anatomical data in this area, the relative bone density characterization value of each voxel point is obtained through normalization processing; finally, for each discretized circumferential orientation (e.g., discretizing 360 degrees into multiple sectors), the relative bone density values ​​of all voxel points are weighted and spatially integrated in the analysis sub-region corresponding to that orientation, and the integration result is used as the bone support strength parameter of that orientation. The weighting function is designed to decrease with increasing distance from the implant surface to reflect the biomechanical principle that the closer the bone tissue is to the implant-bone interface, the greater its contribution to initial stability.

[0128] S902. Determine the lever arm length of the grid vertex relative to the preset position on the implant that serves as the mechanical fulcrum.

[0129] In one possible implementation, to quantify the leverage amplification effect of each point on the denture surface due to its geometric position, the spatial distance from each grid vertex to the reference point is calculated according to a preset mechanical reference point definition rule, and used as its lever arm length relative to the implant system.

[0130] In one possible implementation, the preset position refers to the center point of the implant platform, whose three-dimensional coordinates are directly determined by the implant positioning information. This point is defined as the fixed fulcrum in lever mechanics analysis.

[0131] For example, a lever arm determination mechanism based on three-dimensional spatial distance calculation can be adopted: First, according to the implant positioning information and the preset clinical mechanics rules (for example, defining the center point of the implant top platform as the fulcrum for lever analysis), a fixed point is determined in three-dimensional space as the fulcrum; then, for each vertex of the denture mesh, the Euclidean distance between its three-dimensional coordinates and the coordinates of the fulcrum is calculated, and this distance value is determined as the lever arm length of the vertex.

[0132] S903. Determine the sensitivity coefficient based on the bone support strength parameters and lever arm length.

[0133] In one possible implementation, to obtain a comprehensive risk quantification index that integrates anatomical support conditions and geometric leverage effects, the bone support strength parameter, which characterizes the resistance of the bone wall in a specific orientation, and the lever arm length, which characterizes the degree of unfavorable geometric position at that location, are coupled and calculated using preset mathematical rules, thereby determining the corresponding sensitivity coefficient for each grid vertex.

[0134] For example, a coefficient determination mechanism based on parameter ratio calculation can be adopted: for each grid vertex, according to its spatial orientation, the pre-calculated bone support strength parameter corresponding to that orientation is obtained; at the same time, the lever arm length calculated for that vertex is obtained; then, the lever arm length is divided by the bone support strength parameter, and the resulting quotient is determined as the sensitivity coefficient of that vertex. To prevent abnormal calculation results due to the bone support strength parameter being too small, a minimum safety threshold (such as 0.05) can be introduced to protect the parameter.

[0135] The technical solution provided in the above embodiments can bring at least the following beneficial effects: This embodiment constructs a quantitative bridge connecting individual anatomical characteristics and geometric risk assessment. By integrating bone support strength parameters and geometric lever arm length to calculate the sensitivity coefficient, this method creates a key indicator that can simultaneously reflect anatomical weaknesses and leverage amplification effects, enabling all subsequent analyses to carry personalized biomechanical weights, laying the foundation for the entire scheme to achieve anatomically driven optimization.

[0136] In one possible implementation, the process of determining the bone support strength parameters in at least one circumferential orientation around the implant based on jawbone anatomical data can be specifically implemented through the following S1001-S1003, which will be described in detail below.

[0137] S1001. Establish a coordinate system with the long axis of the implant as the reference.

[0138] In one possible implementation, a dedicated local coordinate system is constructed based on the axial features defined by the implant positioning information for all subsequent spatial calculations.

[0139] For example, a coordinate system construction mechanism based on vector and point definition can be adopted: First, obtain the coordinates of the center point of the implant tip platform from the implant positioning information as the origin of the coordinate system, and obtain the long axis direction vector of the implant as the principal axis (Z-axis) of the coordinate system; then, according to the right-hand rule, select or calculate a reference direction perpendicular to the Z-axis to define the X-axis, and then determine the Y-axis by the cross product of the Z-axis and the X-axis, thereby establishing a complete three-dimensional local rectangular coordinate system or cylindrical coordinate system. The positive direction of the Z-axis of this coordinate system usually points to the crown direction or upward along the long axis of the implant.

[0140] S1002. Within a pre-defined spatial area surrounding the implant, obtain bone tissue density information based on jawbone anatomical data.

[0141] In one possible implementation, jawbone anatomical data is processed to obtain key input bone density information for quantitatively assessing bone support capacity.

[0142] In one possible implementation, the pre-defined spatial region is a three-dimensional analysis area for assessing bone support strength. This region spatially surrounds the implant, and its location and size are determined based on the geometric parameters of the implant system and the biomechanical range of clinical concern.

[0143] For example, a density information acquisition mechanism based on image grayscale value extraction and normalization can be adopted: within a preset three-dimensional analysis area (e.g., extending a certain distance outward from the implant surface and covering the key area of ​​the neck), the original grayscale value corresponding to each voxel position in the jawbone anatomical data is read; then, through a preset normalization algorithm (e.g., maximum-minimum value normalization), these absolute grayscale values ​​affected by device parameters are mapped to a dimensionless value that is proportional to the relative mineralization density of bone tissue, i.e., bone tissue density information. This value is usually calibrated in the range of 0 to 1, where a higher value represents a higher bone density.

[0144] S1003. Perform integral calculation on the bone tissue density information at a specific location within the preset spatial area to determine the bone support strength parameters.

[0145] In one possible implementation, within a preset spatial region, for each discrete circumferential orientation, a weighted spatial integral operation is performed on the bone tissue density information in the space corresponding to that orientation, and the integral result is defined as the bone support strength parameter of that orientation.

[0146] For example, an integral calculation mechanism based on the weighting of the sector-shaped cylindrical region and distance attenuation can be adopted: For each circumferential orientation (such as each sector obtained by dividing a 360-degree circle equally), a sector-shaped cylindrical sub-region belonging to the azimuth range is defined within a preset three-dimensional spatial region; then, for each voxel in the sub-region, its acquired bone tissue density information value is read, and the value is multiplied by a weighting coefficient related to the distance of the voxel from the implant surface (for example, the weighting coefficient decreases with increasing distance to reflect that the bone closer to the implant bone interface contributes more to mechanical support); finally, the weighted density values ​​of all voxels are integrally divided in the sub-region, and the integral result is used as the bone support strength parameter for that specific orientation.

[0147] For example, the radial bone support strength parameter of the implant in the j-th circumferential orientation (angular sector) The following formula 5 is satisfied:

[0148]

[0149] in, In terms of location Above, coordinates ( Bone tissue density at ( ); For reference distance (exemplary, The value is 1.5, a preset positive constant used to control the rate of weight decay; To analyze the Euclidean distance from the point to the center of the implant tip (origin); and For the radial range of the integral; and The range of integration depth; This is a distance decay weighting function. It indicates that the contribution of bone tissue at a certain point to implant stability decreases as the Euclidean distance from that point to the center point (origin) of the implant tip increases; The weighted density of all points is accumulated (integrated) over the entire ROI section. The weighting function ensures that the higher the density of bone is closer to the implant-bone interface, the greater its contribution to the final strength parameter. It is a dimensionless, quantitative indicator that comprehensively reflects the situation at the implantation site. j In each orientation, the quality (density), quantity (volume), and location (distance) of bone contribute to the overall supporting capacity, and their values ​​directly characterize the relative potential of the bone wall in that orientation to resist lateral forces.

[0150] The technical solution provided by the above embodiments can bring at least the following beneficial effects: it provides a reliable method for extracting key biomechanical parameters from medical images. By establishing a local coordinate system and performing weighted integration on bone density information at a specific orientation to obtain bone support strength parameters, it realizes the transformation of qualitative image grayscale information into quantitative, mechanically meaningful evaluation indicators, providing crucial initial data input for the personalized implementation of the entire solution.

[0151] Please see Figure 2This document illustrates a schematic diagram of a smart optimization device 200 for denture occlusal surface morphology based on occlusal dynamics simulation, according to an embodiment of the present invention. The device includes: a communication unit 201 and a processing unit 202. The communication unit 201 is used to acquire the patient's jawbone anatomical data, implant positioning information, opposing tooth model, and initial mesh model of the denture. The processing unit 202 is used to determine the sensitivity coefficient of the denture's mesh vertices based on the jawbone anatomical data and implant positioning information. The sensitivity coefficient characterizes the potential risk impact of mesh vertices on the surrounding bone tissue when subjected to force. Based on the virtual occlusal motion trajectory, the device optimizes the occlusal surface morphology of the denture and its surrounding bone tissue. Dynamic occlusion simulation was performed on the opposing tooth model, and random perturbations of the occlusal contact points were introduced during the simulation. The random perturbations were used to evaluate the risk fluctuation rate of the denture occlusal surface at different contact points due to motion deviation based on the sensitivity coefficient. Based on the dynamic occlusion simulation, a cumulative risk field of the denture occlusal surface was generated. The cumulative risk field was used to characterize the cumulative risk distribution of each region of the denture surface during long-term occlusion. Based on the cumulative risk field and combined with the occlusal functional constraints of the denture and the opposing tooth model, the initial mesh model of the denture was deformed and optimized to determine the optimized denture mesh model. The occlusal functional constraints were determined based on the occlusal relationship between the denture and the opposing tooth model.

[0152] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0153] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A method for intelligent optimization of denture occlusal surface morphology integrating occlusal dynamics simulation, characterized in that, The method includes: Obtain the patient's jawbone anatomy data, implant positioning information, opposing tooth model, and initial mesh model of the denture; Based on the jawbone anatomical data, determine the bone support strength parameters in at least one circumferential orientation around the implant. Determine the lever arm length of the grid vertex relative to a preset position on the implant that serves as a mechanical fulcrum; Based on the bone support strength parameters and the lever arm length, a sensitivity coefficient is determined; the sensitivity coefficient is used to characterize the degree of potential risk impact of the grid vertices on the bone tissue around the implant when under force. Based on the virtual occlusal motion trajectory, dynamic occlusal simulation is performed on the denture and the opposing tooth model, and random perturbation of the occlusal contact point is introduced during the simulation process; the random perturbation is used to evaluate the risk fluctuation rate of the denture occlusal surface at different contact points due to motion deviation based on the sensitivity coefficient. The risk assessment result of at least one contact event obtained from the dynamic occlusion simulation is mapped onto the mesh surface of the denture; The mapped risk values ​​are accumulated and smoothed using a spatial diffusion algorithm to generate a cumulative risk field for the denture occlusal surface. The cumulative risk field is used to characterize the cumulative risk distribution of each region of the denture surface during long-term occlusion. Based on the relative positional relationship between the denture and the opposing tooth model, a functional retention area is identified on the occlusal surface of the denture; Based on the function preservation region, constraints on the displacement of the mesh vertices are generated to form the occlusal function constraints of the opposing tooth model. Calculate the gradient information of the cumulative risk field at the grid vertices; The shape correction vector of each mesh vertex is determined based on the gradient information; Based on the morphological correction vector and the occlusal function constraint, the displacement of the mesh vertices is adjusted to determine the optimized denture mesh model.

2. The denture occlusal surface morphology intelligent optimization method of fusion occlusion dynamics simulation according to claim 1, characterized in that, The method involves performing dynamic occlusion simulation on the denture and the opposing tooth model based on a virtual occlusal motion trajectory, and introducing random perturbations of the occlusal contact points during the simulation, including: At each time step of the dynamic occlusion simulation, the nominal contact point between the denture and the opposing tooth model is determined; At each nominal contact point, a set of spatial location sampling points simulating physiological motion deviations are generated; The risk volatility corresponding to the nominal contact point is evaluated based on the sensitivity coefficient and geometric characteristics at each sampling point.

3. The denture occlusal surface morphology intelligent optimization method of fusion occlusion dynamics simulation according to claim 2, characterized in that, The assessment of the risk volatility corresponding to the nominal contact point based on the sensitivity coefficient and geometric characteristics at each sampling point includes: Based on the relationship between the surface normal direction and the implant axis at each sampling point, the lateral force influence parameters are determined; Based on the sensitivity coefficient and the lateral force influence parameter at each sampling point, the risk index of each sampling point is determined; The risk volatility is determined based on the statistical dispersion of the risk index at each sampling point.

4. The denture occlusal surface morphology intelligent optimization method of fusion occlusion dynamics simulation according to claim 1, characterized in that, The method further includes: Based on the risk volatility corresponding to the at least one contact event, the risk values ​​mapped to the grid surface are weighted to determine the comprehensive risk value; Spatial diffusion accumulation and smoothing processing are applied to the comprehensive risk value.

5. The intelligent optimization method for denture occlusal surface morphology based on occlusal dynamics simulation according to claim 1, characterized in that, The identification of a functional retention area on the occlusal surface of the denture, based on the relative positional relationship between the denture and the opposing tooth model, includes: In centric occlusion, the static contact point between the denture and the opposing tooth model is detected; The static contact point and its adjacent preset range area are defined as the function retention area.

6. The denture occlusal surface morphology intelligent optimization method of fusion occlusion dynamics simulation according to claim 1, characterized in that, The determination of bone support strength parameters in at least one circumferential orientation around the implant based on the jawbone anatomical data includes: Establish a coordinate system with the long axis of the implant as the reference; Within a predetermined spatial region surrounding the implant, bone tissue density information is obtained based on the jawbone anatomical data; The bone tissue density information at a specific location within the preset spatial area is integrated to determine the bone support strength parameter.

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