Intelligent design method and system for ultra-thin ceramic false tooth

Abstract

The invention discloses an intelligent design method and system for ultra-thin ceramic false teeth. The intelligent design method comprises the following steps: constructing a two-dimensional template library and a three-dimensional template library, and respectively registering two-dimensional and three-dimensional templates with a three-dimensional dental model of a patient by using a template registration algorithm; after the registration of the three-dimensional template and the three-dimensional dental model of the patient is completed, a veneering three-dimensional model is obtained through Boolean subtraction; calculating the thickness of a marked anatomical region of the veneering three-dimensional model by adopting an SDF (Software Development Function), and if the thicknesses of some regions do not meet clinical requirements, performing thickening and smoothing operation on the veneering three-dimensional model according to a thickness threshold value determined by material physical characteristics and aesthetic veneering types corresponding to the regions; a mark of an inner surface bonding force enhancing area is arranged on a veneering three-dimensional model meeting clinical requirements, and according to the precision required by the veneering three-dimensional model, a method of direct three-dimensional modeling presentation or process fine adjustment is selected, so that manufacturing of a micro-area structure of the marked area is achieved. The veneering three-dimensional model designed by the invention can improve the repair quality and the comfort of a patient.

Description

Technical Field

[0001] The present invention relates to the field of oral restoration, and in particular to an intelligent design method for ultra-thin ceramic dentures based on digital design and intelligent analysis. The method is suitable for digital modeling, template marking library, thickness detection, and bonding force enhancement design of ultra-thin ceramic dentures in dental aesthetic restoration, and is particularly suitable for the design and optimization of different types of veneers that meet the aesthetic tastes of Chinese people. Background Art

[0002] In the existing oral restoration technology, the design of ultra-thin ceramic dentures mainly relies on the experience of doctors and technicians, especially in three-dimensional modeling, veneer thickness detection and control, inner surface bonding force design, etc. The traditional methods have problems such as cumbersome operation, insufficient design accuracy, and lack of personalized adjustment methods. For example, some aesthetic design software on the market currently has defects such as low matching accuracy and complicated operation procedures when processing 2D images and 3D model registration, which affects the final restoration effect. At the same time, traditional software lacks intelligent assistance in denture thickness detection and inner surface bonding force design, and cannot provide targeted optimization design according to different veneer types, which may lead to poor mechanical properties or poor fitting effects after patients wear them. Summary of the invention

[0003] In view of the shortcomings in the prior art, the present invention provides an intelligent design method for ultra-thin ceramic dentures to improve the quality of restoration and patient comfort.

[0004] The present invention achieves the above technical objectives through the following technical means.

[0005] Intelligent design method for ultra-thin ceramic dentures:

[0006] A functional-aesthetic typing template library is constructed in a computer, including a two-dimensional template library and a three-dimensional template library; the two-dimensional template library stores pre-set temperament typing tooth images, and n two-dimensional tooth model feature points P that are not on a straight line are pre-calibrated on each temperament typing tooth image 2d (i)=(x i ,y i ), i∈{1,2,…,n}, where n≥3, x i ,y i It is the representation of the two-dimensional dental model feature points in the plane coordinate system; the three-dimensional template library stores the three-dimensional tooth data of the temperament classification corresponding to the tooth image of the temperament classification, and it is necessary to calibrate the three-dimensional dental model feature points P corresponding to the two-dimensional template library one by one 3d (i)=(x i ,y i ,z i ),i∈{1,2,…,n}, where n≥3, x i ,yi 、z i It is the representation of the characteristic points of the three-dimensional dental model in the Cartesian coordinate system;

[0007] The two-dimensional template and the three-dimensional template are registered with the patient's three-dimensional dental model respectively using the template registration algorithm;

[0008] After the three-dimensional template is registered with the patient's three-dimensional dental model, a three-dimensional veneer model is obtained by performing a Boolean subtraction operation with the patient's three-dimensional dental model; the thickness of the marked anatomical area of ​​the three-dimensional veneer model is calculated using the shape diameter function method SDF. If the thickness of certain areas does not meet clinical requirements, the veneer design software performs thickening and smoothing operations on the three-dimensional veneer model based on the material physical properties corresponding to the area and the thickness threshold determined by the aesthetic veneer type;

[0009] A mark for the inner surface adhesion enhancement area is set on the veneer three-dimensional model that meets clinical requirements; if the required accuracy of the veneer three-dimensional model that meets clinical requirements is within the range of the manufacturing equipment, it is directly presented through three-dimensional modeling; if the required accuracy exceeds the capability of the manufacturing equipment, the veneer software outputs a veneer three-dimensional model file with texture marks, and when the manufacturing equipment reads the file, a process fine-tuning method is used to achieve micro-area structure manufacturing in the marked area.

[0010] Furthermore, the template registration algorithm inputs n pairs of points, the n pairs of points come from point sets A and B respectively, A = a1, ..., a n is the set of feature points to be aligned, B = b1,…,b n It is the set of reference points with which the feature point set A needs to be aligned; then the arithmetic mean of each point in the two point sets is taken to find the center of mass, and then the optimal rotation matrix and scaling factor are solved through the covariance matrix to finally achieve registration.

[0011] Furthermore, for the feature points of the two-dimensional template, A=A1; for the feature points of the three-dimensional template, A=A2; and:

[0012]

[0013] Among them, the coordinates (l i ,j i ,k i ) comes from the feature point P' on the patient's 3D dental model 3d (i)=(l i ,j i ,k i ), i∈{1,2,…,n}, n≥3;

[0014] The centroid is calculated by:

[0015]

[0016] Among them, a i represents the coordinates of the i-th feature point to be aligned, b i represents the coordinates of the i-th reference point (l i ,j i ,k i );

[0017] The covariance matrix is ​​calculated by:

[0018]

[0019] Among them, the variance

[0020] The covariance matrix is ​​subjected to singular value decomposition:

[0021] H=UDV T

[0022] Among them, U is an m×n matrix; D is an n×n matrix, all elements except the elements on the main diagonal are 0, and each element on the main diagonal is called a singular value; V is an m×n matrix; in Cartesian three-dimensional space, m=3;

[0023] The optimal rotation matrix R and scaling factor c are calculated as follows:

[0024]

[0025] Among them, the product DS of D and S is a matrix, and the trace of the matrix tr(DS) is the sum of the diagonal elements of this matrix; and the matrix d is used to determine the sign of the product of the determinants of the matrices U and V, and d = sgn(det(U)det(V T ));

[0026] Calculate the aligned point set B'=(b'1,…,b' i ,…,b' n ),in:

[0027] b′ i =μ A +cR(b i -μ B )

[0028] Define a translation vector t:

[0029] t=μ A -cRμ B

[0030] and then:

[0031] b′i =t+cRb i .

[0032] Furthermore, for two-dimensional registration, the coordinates of all pixels on the temperament-typed tooth images in the two-dimensional template library are I i =(I ix ,I iy ,0), according to formula I i '=t+cRI i A new image is formed, which is registered to the patient's three-dimensional dental model; for three-dimensional registration, all three-dimensional point coordinates T in the three-dimensional tooth data of temperament classification in the three-dimensional template library are i =(T ix ,T iy ,T iz ), according to the formula T i '=t+cRT i Transfer and register to the patient's 3D dental model.

[0033] Furthermore, the shape diameter function method is used to calculate the thickness of the marked anatomical area of ​​the veneer three-dimensional model, specifically: first, a vertex is selected on the model surface, a cone centered on the vertex normal vector is constructed, and rays are emitted from the vertex, retaining rays with the same normal direction as the intersection point; then, the lengths of these rays are weighted statistics, and the SDF value of the vertex is calculated, thereby achieving accurate acquisition of the local thickness of the model.

[0034] Furthermore, whether the required accuracy of the veneer three-dimensional model that meets clinical requirements is within the range of the manufacturing equipment is determined by comparing the manufacturing process b of the manufacturing equipment and the minimum accuracy a required to be achieved by the texture, where a is the product of the minimum non-zero grayscale value of the texture pattern and the global adjustable parameter.

[0035] Furthermore, the global adjustable parameter h is used to control the overall protrusion height of the texture.

[0036] Furthermore, the direct presentation through three-dimensional modeling is to use the PNG pattern as the UV texture of the inner surface adhesion enhancement area, and use the grayscale value of the pattern and the global adjustable parameter h to offset the normal vector of the vertex in the area to complete the final three-dimensional modeling of the patient's three-dimensional dental model veneer.

[0037] An extremely thin ceramic denture intelligent design system, comprising:

[0038] Template creation and registration module, used to build a functional-aesthetic classification template library and realize registration with the patient's 3D dental model;

[0039] The thickness detection and optimization module of the veneer 3D model calculates the thickness of the marked anatomical area of ​​the veneer 3D model. When the thickness of certain areas does not meet the clinical requirements, the veneer 3D model is automatically thickened and smoothed.

[0040] The inner surface adhesion enhancement module sets a mark of the inner surface adhesion enhancement area on the veneer three-dimensional model that meets clinical requirements, and selects the implementation method of the veneer three-dimensional model according to the required accuracy of the veneer three-dimensional model.

[0041] A computer device comprises a processor and a memory, wherein the memory is used to store at least one computer program, and the at least one computer program is loaded by the processor and executes the above-mentioned ultra-thin ceramic denture intelligent design method.

[0042] The beneficial effects of the present invention are:

[0043] (1) Higher design accuracy: The present invention significantly improves the design accuracy and efficiency through precise two-dimensional / three-dimensional registration technology, ensuring that the denture is highly matched with the patient's oral cavity.

[0044] (2) Intelligent thickness control: The present invention can adjust the thickness of the veneer according to clinical requirements to achieve the best balance between aesthetics and functionality; this solution not only ensures the personalization and accuracy of the design, but also effectively eliminates the design inconsistency and stability problems caused by differences in technicians' levels.

[0045] (3) Significantly improved bonding strength: The present invention greatly enhances the stability and durability of the denture through optimized inner surface bonding strength design, thereby improving the patient's wearing comfort and user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 This is a flow chart of the intelligent design method for ultra-thin ceramic dentures according to the present invention;

[0047] Figure 2 A schematic diagram of thickness marking of the three-dimensional template of the present invention;

[0048] Figure 3 This is a schematic diagram of the three-dimensional modeling of the inner surface adhesion enhancement area based on template marking according to the present invention. DETAILED DESCRIPTION

[0049] The present invention is further described below in conjunction with the accompanying drawings and specific embodiments, but the protection scope of the present invention is not limited thereto.

[0050] like Figure 1 As shown, the present invention provides an intelligent design method for ultra-thin ceramic dentures, which specifically includes the following processes:

[0051] 1. Establishment and registration method of 2D and 3D template marker library

[0052] The key to successful dental restoration treatment is how to ensure personalized and diversified ceramic denture products to meet the needs of patients and ensure the thickness and precision requirements of different anatomical functional areas of dentures. Therefore, based on the physiological and anatomical characteristics and aesthetic characteristics of teeth, a functional-aesthetic classification template library (a database) is constructed in the computer. This template library consists of two parts. One is a two-dimensional template library, which stores pre-set temperament classification tooth images, and each temperament classification tooth image is pre-calibrated with n two-dimensional tooth model feature points P that are not on a straight line. 2d (i)=(x i ,y i ), i∈{1,2,…,n}, where n≥3, x i ,y i is the representation of the two-dimensional dental model feature points in the plane coordinate system; the other is a three-dimensional template library, which stores the three-dimensional dental data of the temperament classification corresponding to the above-mentioned temperament classification dental image, and needs to calibrate the three-dimensional dental model feature points P that correspond one to one with the two-dimensional template library 3d (i)=(x i ,y i ,z i ),i∈{1,2,…,n}, where n≥3, where x i ,y i 、z i It is the representation of the feature points of the 3D dental model in the Cartesian coordinate system. In specific applications, it is necessary to click n feature points on the patient's 3D dental model in sequence, P' 3d (i)=(l i ,j i ,k i ), i∈{1,2,…,n}, where n≥3. For these n feature points, a practical selection method is: 2 distal points of the gingival margin of the canine and 2 distal points of the incisor edge of the central incisor. Of course, new feature points can also be added or existing feature points can be deleted.

[0053] Temperament classification is based on the patient's personality and temperament characteristics, and divides the aesthetic morphology of the maxillary anterior teeth into 6 categories, namely beautiful type, dignified type, elegant type, handsome type, resolute type, and bionic type. Each type corresponds to the relationship between the different lip and facial morphology and the patient's appearance and temperament. These types can be used as the basic template for the aesthetic morphology design of the anterior teeth. Through this design method based on temperament classification, the aesthetic needs of patients can be met in a more personalized way, while providing efficient and accurate aesthetic morphology design of the anterior teeth. This technology has been applied to patent CN109615694A, and the temperament classification types can be further expanded according to clinical needs in the future.

[0054] The above-mentioned two-dimensional template library and three-dimensional template library can be dynamically updated according to the patient's personalized needs, material physical properties, manufacturing process progress and clinical requirements, so as to meet more diverse repair needs.

[0055] The template registration algorithm has two application scenarios: (1) registering the 2D template with the patient's 3D dental cast; (2) registering the 3D template with the patient's 3D dental cast.

[0056] The template registration algorithm inputs two sets of points in the form of three-dimensional vectors and performs registration through the following steps: first, the centroid is calculated, then the rotation matrix and scaling factor are solved through the covariance matrix, and finally alignment and registration are achieved. The specific description is as follows:

[0057] The input of the algorithm is a pair of n points, which come from point sets A and B respectively, and these points are represented as 3D vectors. Assume A = a1,…,a n is the set of feature points to be aligned. For the feature points of the two-dimensional template, A = A1; for the feature points of the three-dimensional template, A = A2; the point set B = b1,…,b n It is the reference point set that the feature point set A needs to align with, that is, the point set B remains unchanged. These points are represented as a matrix, and the specific parameters are as follows:

[0058] (In the case of a two-dimensional template, all coordinates (x i ,y i ) are from P 2d (i))

[0059] (In the case of a three-dimensional template, all coordinates (x i ,y i ,z i ) are from P 3d (i))

[0060] (All coordinates (l i ,j i ,k i ) are from P' 3d (i)

[0061] First, find the centroid of the two point sets by calculating the arithmetic mean of the points:

[0062]

[0063] Among them, a i Represents the coordinates of the i-th feature point to be aligned (for the two-dimensional case, the coordinate form is (x i ,y i,0); for three-dimensional case, the coordinate form is (x i ,y i ,z i )), b i represents the coordinates of the i-th reference point (l i ,j i ,k i ).

[0064] The following variance also needs to be calculated to determine the scaling factor:

[0065]

[0066] Next, the following covariance matrix is ​​calculated:

[0067]

[0068] Then perform singular value decomposition on the covariance matrix:

[0069] H=UDV T

[0070] Among them, U is an m×n matrix; D is an n×n matrix, all elements except the elements on the main diagonal are 0, and each element on the main diagonal is called a singular value; V is an m×n matrix; in Cartesian three-dimensional space, m=3, and n is the number of points in the point set as mentioned above; U and V are both unitary matrices, that is, they satisfy U T U=I and V T V = I, I is the identity matrix;

[0071] Since the registration method of the present invention only allows translation, rotation and scaling, the following calculations need to be performed:

[0072] d=sgn(det(U)det(V T ))

[0073]

[0074] Among them, d is used to determine the sign of the determinant product of matrices U and V, and the S matrix is ​​a diagonal matrix with the first m-1 elements being 1 and the last element being d;

[0075] Finally, the optimal rotation matrix R and scaling factor c can be calculated:

[0076]

[0077] Among them, the product DS of D and S is a matrix, and the trace tr(DS) of the matrix is ​​the sum of the diagonal elements of the matrix; then, calculate the aligned point set B'=(b'1,…,b' i ,…,b'n ),in:

[0078] b′ i =μ A +cR(b i -μ B )

[0079] Define a translation vector t:

[0080] t=μ A -cRμ B

[0081] and then:

[0082] b′ i =t+cRb i

[0083] After calculating t, c and R, for the two-dimensional registration case, the coordinates of all pixels on the temperament typing tooth image in the two-dimensional template library are I i =(I ix ,I iy ,0), according to formula I i '=t+cRI i , forming a new image, which is registered with the patient's three-dimensional dental model to guide the technician's design; for the three-dimensional registration, all the three-dimensional point coordinates T in the three-dimensional tooth data of the temperament classification in the three-dimensional template library are i =(T ix ,T iy ,T iz ), according to the formula T i '=t+cRT i The registration is transferred to the patient's 3D dental model, on which the technician can quickly make further designs.

[0084] 2. Thickness detection and optimization based on template marking

[0085] In addition, the 3D template library contains multiple tooth models. For the aesthetic design of anterior teeth, each tooth is marked with anatomical areas on the mesh surface of the tooth according to clinical needs. These areas include the neck, axial surface, proximal surface and incisal end (see Figure 2). After the three-dimensional template in the database is aligned with the patient's three-dimensional dental model, a veneer three-dimensional model is obtained by performing a Boolean subtraction operation with the patient's three-dimensional dental model. The veneer three-dimensional model still needs to be fine-tuned and carved by the technician, but the marking information of these anatomical areas will still be retained for subsequent local thickness calculation; this embodiment uses the Shape Diameter Function (SDF, shape diameter function) method to calculate the thickness of the anatomical area marked on the veneer three-dimensional model. The specific process includes: first, select a vertex on the model surface, construct a cone with the vertex normal vector as the center, and emit rays from the vertex, retaining the rays with the same normal direction as the intersection point (the intersection point of the ray and the veneer three-dimensional model); then, the lengths of these rays are weighted statistics, and the SDF value of the vertex is calculated, so as to achieve accurate acquisition of the local thickness of the model. This method is particularly suitable for the optimization design of three-dimensional models with complex shapes.

[0086] In order to ensure that the strength of the veneer three-dimensional model meets the requirements in clinical applications, the parameter-thickness needs to be set in advance. The physical properties of the material (such as feldspar porcelain, zirconia ceramics, etc.) will affect the thickness parameter setting. Different clinical types of aesthetic veneers will also have different requirements for these parameters. Common aesthetic veneer types include windowed, butt-jointed and wrap-around types. Different types of anatomical regions have different requirements for thickness. For example, in practical applications, when a butt-jointed veneer type is selected and zirconia ceramic materials are used, the thickness of the neck is usually set to 0.3mm, the thickness of the axial and proximal surfaces is 0.5mm, and the thickness of the incisal end is 1mm. If the mechanical properties of the material are particularly excellent, the thickness parameter can be further reduced to 0.04mm for the neck, axial, proximal and incisal ends.

[0087] If it is detected that the thickness of certain areas does not meet the clinical requirements (i.e., the thickness threshold determined by the physical properties of the material corresponding to the area and the type of aesthetic veneer), the veneer design software will automatically perform thickening and smoothing operations on the veneer three-dimensional model according to the threshold (the specific process is the existing technology) to ensure that the final design meets the clinical standards and avoids the problem of discomfort caused by excessive thickness or insufficient material strength due to too small thickness.

[0088] 3. Design for enhanced inner surface adhesion

[0089] The present invention proposes a specific texture design to enhance the adhesion of veneers by conducting an in-depth analysis of the inner surface morphology of different types of veneers. Specifically, the design generates a microscopic concave-convex structure in the bonding area, thereby effectively increasing the bonding area and significantly improving the stability and durability of the veneer. The texture is stored locally as a template in the form of a PNG pattern or other forms. Taking the PNG pattern as an example, the pattern is represented by a grayscale channel, where the grayscale value (0-1) represents the relative height of the protrusion. A global adjustable parameter h is set in the veneer software to control the overall protrusion height of the texture.

[0090] Marks for the inner surface adhesion enhancement area are set on the 3D veneer model that meets clinical requirements.

[0091] Without loss of generality, let us assume that the manufacturing process of the equipment is b = 50μm, and the minimum accuracy required for the texture is recorded as a = the minimum non-zero grayscale value of the texture pattern × h. In the area where the adhesion needs to be enhanced, if the required accuracy of the veneer three-dimensional model is within the range of ordinary manufacturing equipment, the veneer software directly presents it through three-dimensional modeling: that is, a>b. The specific implementation method is: use the PNG pattern as the UV texture of the inner surface adhesion enhancement area, and use the grayscale value of the pattern and the parameter h to offset the normal vector of the vertices in the area to complete the final three-dimensional modeling of the patient's three-dimensional dental model veneer. See the specific schematic diagram Figure 3 .

[0092] If the required accuracy exceeds the capabilities of ordinary manufacturing equipment (for example, extremely small three-dimensional structures), three-dimensional modeling is no longer applicable. The veneering software outputs a veneering three-dimensional model file with texture marks. The manufacturing equipment reads the file and uses the process fine-tuning method to realize the micro-area structure manufacturing in the marked area: that is, a≤b. In this case, the pre-defined inner surface adhesion enhancement mark is used for process fine-tuning. Taking the photocuring process parameters as an example (Exp-t (exposure time), Intst (exposure intensity), Dt1 (the time the platform stays after descending to the set distance), Pd (the distance the printing platform moves downward), Pu (the distance the printing platform moves upward after Dt1 ends), Dt2 (the time it takes for the printing platform to stay until the image is exposed again after it rises to the set position), etc.), when the subsequent dedicated manufacturing equipment or model preprocessing software reads the texture mark, the manufacturing equipment automatically adjusts the manufacturing process parameters of the area (newExp-t, newIntst, newDt1, newP-d, newP-u, newDt2) according to the set process model to ensure that the geometric features of the three-dimensional hierarchical structure are accurately realized.

[0093] In addition, the texture mark can assist post-processing equipment to automatically complete sandblasting, etching and laser engraving operations, and can also assist post-processing personnel to manually perform these operations through visual observation, thereby increasing the roughness of the inner surface and further improving the bonding performance of the ceramic surface.

[0094] Ultra-thin ceramic denture intelligent design system, including:

[0095] Template creation and registration module, used to build a functional-aesthetic classification template library and realize registration with the patient's 3D dental model;

[0096] The thickness detection and optimization module of the veneer 3D model calculates the thickness of the marked anatomical area of ​​the veneer 3D model. When the thickness of certain areas does not meet the clinical requirements, the veneer 3D model is automatically thickened and smoothed.

[0097] The inner surface adhesion enhancement module sets a mark of the inner surface adhesion enhancement area on the veneer three-dimensional model that meets clinical requirements, and selects the implementation method of the veneer three-dimensional model according to the required accuracy of the veneer three-dimensional model.

[0098] The embodiment of the present application also provides a computer-readable storage medium, in which at least one computer program is stored, and the at least one computer program is loaded and executed by a processor of a computer device to implement the operations performed by the computer device in the ultra-thin ceramic denture intelligent design method of the above embodiment. For example, the computer-readable storage medium can be a read-only memory (ROM), a random access memory (RAM), a compact disc read-only memory (CD-ROM), a magnetic tape, a floppy disk, and an optical data storage device.

[0099] The embodiments are preferred implementations of the present invention, but the present invention is not limited to the above-mentioned implementations. Any obvious improvements, substitutions or modifications that can be made by those skilled in the art without departing from the essential content of the present invention belong to the protection scope of the present invention.

Claims

1. An intelligent design method for ultra-thin ceramic dentures, characterized by: A functional-aesthetic typing template library is constructed in a computer, including a two-dimensional template library and a three-dimensional template library; the two-dimensional template library stores pre-set temperament typing tooth images, and n two-dimensional tooth model feature points P that are not on a straight line are pre-calibrated on each temperament typing tooth image 2d (i)=(x i ,y i ), i∈{1,2,…,n}, where n≥3, x i ,y i It is the representation of the characteristic points of the two-dimensional dental model in the plane coordinate system; The three-dimensional template library stores the three-dimensional tooth data of the temperament classification corresponding to the tooth image of the temperament classification, and needs to calibrate the three-dimensional tooth model feature points P corresponding to the two-dimensional template library one by one 3d (i)=(x i ,y i ,z i ),i∈{1,2,…,n}, where n≥3, x i ,y i 、z i It is the representation of the characteristic points of the three-dimensional dental model in the Cartesian coordinate system; The two-dimensional template and the three-dimensional template are registered with the patient's three-dimensional dental model respectively using the template registration algorithm; After the three-dimensional template is registered with the patient's three-dimensional dental model, a three-dimensional veneer model is obtained by performing a Boolean subtraction operation with the patient's three-dimensional dental model; the thickness of the marked anatomical area of ​​the three-dimensional veneer model is calculated using the shape diameter function method SDF. If the thickness of certain areas does not meet clinical requirements, the veneer design software performs thickening and smoothing operations on the three-dimensional veneer model based on the material physical properties corresponding to the area and the thickness threshold determined by the aesthetic veneer type; A mark for the inner surface adhesion enhancement area is set on the veneer three-dimensional model that meets clinical requirements; if the required accuracy of the veneer three-dimensional model that meets clinical requirements is within the range of the manufacturing equipment, it is directly presented through three-dimensional modeling; if the required accuracy exceeds the capability of the manufacturing equipment, the veneer software outputs a veneer three-dimensional model file with texture marks, and when the manufacturing equipment reads the file, a process fine-tuning method is used to achieve micro-area structure manufacturing in the marked area.

2. The method for intelligent design of ultra-thin ceramic dentures according to claim 1, characterized in that: The template registration algorithm inputs n pairs of points, which come from point sets A and B respectively, A = (a1, ..., a n ) is the set of feature points to be aligned, B = (b1,…,b n ) is the reference point set with which the feature point set A needs to be aligned; then the arithmetic mean of each point in the two point sets is calculated to find the center of mass, and then the optimal rotation matrix and scaling factor are solved through the covariance matrix to finally achieve alignment.

3. The method for intelligent design of ultra-thin ceramic dentures according to claim 2, characterized in that: For the feature points of the two-dimensional template, A=A1; for the feature points of the three-dimensional template, A=A2; and: Among them, the coordinates (l i ,j i ,k i ) comes from the feature point P' on the patient's 3D dental model 3d (i)=(l i ,j i ,k i ), i∈{1,2,…,n}, n≥3; The centroid is calculated by: Among them, a i represents the coordinates of the i-th feature point to be aligned, b i represents the coordinates of the i-th reference point (l i ,j i ,k i ); The covariance matrix is ​​calculated by: Among them, the variance The covariance matrix is ​​subjected to singular value decomposition: H=UDV T Among them, U is an m×n matrix; D is an n×n matrix, all elements except the elements on the main diagonal are 0, and each element on the main diagonal is called a singular value; V is an m×n matrix; in Cartesian three-dimensional space, m=3; The optimal rotation matrix R and scaling factor c are calculated as follows: Among them, the product DS of D and S is a matrix, and the trace of the matrix tr(DS) is the sum of the diagonal elements of this matrix; and the matrix d is used to determine the sign of the product of the determinants of the matrices U and V, and d = sgn(det(U)det(V T )); Calculate the aligned point set B'=(b'1,…,b' i ,…,b' n ),in: b′ i =μ A +cR(b i -μ B ) Define a translation vector t: t=μ A -cRμ B and then: b’ i =t+cRb i 。 4. The method for intelligent design of ultra-thin ceramic dentures according to claim 3, characterized in that: For two-dimensional registration, all pixel coordinates on the temperament-typed tooth image in the two-dimensional template library are I i =(I ix ,I iy ,0), according to formula I i '=t+cRI i A new image is formed, which is registered to the patient's three-dimensional dental model; for three-dimensional registration, all three-dimensional point coordinates T in the three-dimensional tooth data of temperament classification in the three-dimensional template library are i =(T ix ,T iy ,T iz ), according to the formula T i '=t+cRT i Transfer and register to the patient's 3D dental model.

5. The method for intelligent design of ultra-thin ceramic dentures according to claim 1, characterized in that: The shape diameter function method is used to calculate the thickness of the marked anatomical area of ​​the veneer three-dimensional model. Specifically, first, a vertex is selected on the model surface, a cone centered on the vertex normal vector is constructed, and rays are emitted from the vertex, retaining rays with the same normal direction as the intersection point; then, the lengths of these rays are weighted statistics, and the SDF value of the vertex is calculated, thereby achieving accurate acquisition of the local thickness of the model.

6. The method for intelligent design of ultra-thin ceramic dentures according to claim 1, characterized in that: Whether the required accuracy of the veneer three-dimensional model that meets clinical requirements is within the range of the manufacturing equipment is determined by comparing the manufacturing process b of the manufacturing equipment and the minimum accuracy a required to be achieved by the texture, where a is the product of the minimum non-zero grayscale value of the texture pattern and the global adjustable parameter.

7. The method for intelligent design of ultra-thin ceramic dentures according to claim 6, characterized in that: The global adjustable parameter h is used to control the overall protrusion height of the texture.

8. The method for intelligent design of ultra-thin ceramic dentures according to claim 7, characterized in that: The direct presentation through three-dimensional modeling is to use the PNG pattern as the UV texture of the inner surface adhesion enhancement area, and use the grayscale value of the pattern and the global adjustable parameter h to offset the normal vector of the vertices in the area to complete the final three-dimensional modeling of the patient's three-dimensional dental model veneer.

9. A system for implementing the intelligent design method of ultra-thin ceramic dentures according to any one of claims 1 to 8, characterized in that: include: Template creation and registration module, used to build a functional-aesthetic classification template library and realize registration with the patient's 3D dental model; The thickness detection and optimization module of the veneer 3D model calculates the thickness of the marked anatomical area of ​​the veneer 3D model. When the thickness of certain areas does not meet the clinical requirements, the veneer 3D model is automatically thickened and smoothed. The inner surface adhesion enhancement module sets a mark of the inner surface adhesion enhancement area on the veneer three-dimensional model that meets clinical requirements, and selects the implementation method of the veneer three-dimensional model according to the required accuracy of the veneer three-dimensional model.

10. A computer device, characterized in that: The computer device includes a processor and a memory, wherein the memory is used to store at least one computer program, and the at least one computer program is loaded by the processor and executes the ultra-thin ceramic denture intelligent design method described in any one of claims 1-8.

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