Diagnostic wax pattern digital manufacturing method and system, product and medium

Abstract

The invention discloses a diagnostic wax pattern digital manufacturing method and system, a product and a medium, and relates to the field of image processing.The method comprises the steps that oral cavity data of a target patient are obtained according to a three-dimensional oral cavity scanner, facial feature data of the patient are synchronously collected through a facial scanner, and the oral cavity data are converted into a first digital model; converting the facial feature data into a second digital model, extracting key parameters of teeth of the target patient based on the first digital model, marking a to-be-modified area on the first digital model based on the personalized demand information, extracting a key control point in the to-be-modified area, extracting a repair parameter standard value from a clinical case database, and repairing the target patient according to the repair parameter standard value. Constructing an initial digital veneering model based on the parameter standard values; and converting the personalized demand information into a target adjustment amount of the key control point, and modifying the initial digital veneering model according to the target adjustment amount to obtain a digital diagnosis wax pattern. By implementing the method, the manufacturing accuracy of the oral cavity repairing and diagnosing wax pattern can be improved.

Description

Technical Field

[0001] This application relates to the field of image processing, and particularly to a method, system, product and medium for digitally manufacturing a diagnostic wax pattern. Background Art

[0002] With the continuous development of medical aesthetic technologies, oral rehabilitation treatment has become an important means for people to improve their appearance and enhance the quality of life. During oral rehabilitation, as an important diagnostic and design tool, a diagnostic wax pattern can help doctors and patients preview the final effect before treatment, which is of great significance for formulating a reasonable treatment plan.

[0003] Currently, traditional diagnostic wax pattern manufacturing is mainly completed manually. The specific steps include: first, determining the shape and tooth preparation amount of the pre-restored tooth on a plaster model; then, gradually shaping key parts such as marginal ridges and incisal forms through wax dropping techniques; finally, completing the production of the wax pattern through processes such as grinding and polishing.

[0004] However, since the manufacturing process highly depends on the personal experience and manual skills of technicians, large errors are likely to occur between different operators, affecting the accuracy of the rehabilitation treatment. Summary of the Invention

[0005] This application provides a method, system, product and medium for digitally manufacturing a diagnostic wax pattern, which is used to improve the manufacturing accuracy of oral rehabilitation diagnostic wax patterns.

[0006] In a first aspect, this application provides a method for digitally manufacturing a diagnostic wax pattern, which is applied to a system for digitally manufacturing a diagnostic wax pattern. The method includes: obtaining oral data of a target patient by a three-dimensional oral scanner and synchronously collecting facial feature data of the patient by a facial scanner. The oral data includes maxillary dentition data, mandibular dentition data and occlusion relationship data, and the facial feature data includes dynamic data in the front, side and smiling states; converting the oral data into a first digital model, converting the facial feature data into a second digital model, and extracting key parameters of the teeth of the target patient based on the first digital model. The key parameters include anatomical shape parameters, contour parameters and spatial position parameters of the teeth; obtaining personalized requirement information of the target patient, marking the area to be modified on the first digital model based on the personalized requirement information, and extracting key control points in the area to be modified. The key control points include mesiodistal marginal points, labial contour points and incisogingival margin points; extracting standard values of restoration parameters from a clinical case database, and constructing an initial digital veneer model based on the parameter standard values; converting the personalized requirement information into a target adjustment amount of the key control points, and modifying the initial digital veneer model according to the target adjustment amount to obtain a digital diagnostic wax pattern.

[0007] In the above embodiments, oral data and facial feature data were collected and converted into digital models. Key tooth parameters and key control points were extracted, and a standardized data collection and processing process was established. Based on the personalized demand information, target adjustments were made to the key control points, and the veneer model was constructed and modified by combining the standard values of the restoration parameters in the clinical case database, forming a complete digital diagnostic wax pattern manufacturing method. This method eliminates the dependence on subjective experience in the manual manufacturing process, making the manufacturing process of the diagnostic wax pattern more accurate and controllable.

[0008] Combined with some embodiments of the first aspect, in some embodiments, the steps of obtaining the personalized demand information of the target patient, marking the area to be modified on the first digital model based on the personalized demand information, and extracting the key control points in the area to be modified specifically include: displaying the first digital model through a human-computer interaction interface, and collecting the personalized demand information including the requirements for tooth chroma, morphology, and position improvement; marking the teeth to be modified on the first digital model to generate a three-dimensional grid model of the area to be modified; extracting the spatial coordinates of the mesial and distal marginal points, labial contour points, and incisogingival margin points on the three-dimensional grid model to generate key control point data; establishing a local coordinate system based on the key control point data to determine the positional relationship between the control points.

[0009] In the above embodiments, the first digital model was displayed on the human-computer interaction interface and the personalized demand information was collected to generate a three-dimensional grid model of the area to be modified. By extracting the spatial coordinates of the key control points on the grid model and establishing a local coordinate system, the precise positioning and quantitative description of the modified area were realized, providing a reliable data basis for the subsequent modification of the wax pattern and effectively improving the accuracy of the modification process.

[0010] Combined with some embodiments of the first aspect, in some embodiments, the steps of converting the personalized demand information into the target adjustment amount of the key control points, modifying the initial digital veneer model according to the target adjustment amount, and obtaining the digital diagnostic wax pattern specifically include: setting the target positions of the mesial and distal marginal points, labial contour points, and incisogingival margin points according to the personalized demand information; calculating the spatial distance between the current position and the target position of each key control point, and determining this distance value as the target adjustment amount; adjusting the grid node positions of the initial digital veneer model according to the target adjustment amount to obtain the adjusted grid nodes, and performing surface smoothing processing on the adjusted grid nodes to generate the digital diagnostic wax pattern.

[0011] In the above embodiments, the target positions of the key control points were set according to the personalized demand information, the spatial distance was calculated to determine the target adjustment amount, and the grid node positions were adjusted and surface smoothing processing was performed. This method converts the subjective personalized demand into objective spatial position adjustment parameters, realizes the precise modification of the veneer model, and ensures the morphological accuracy and surface smoothness of the digital diagnostic wax pattern.

[0012] In some embodiments in combination with some embodiments of the first aspect, after the steps of converting personalized demand information into the target adjustment amount of key control points and modifying the initial digital veneer model according to the target adjustment amount to obtain a digital diagnostic wax-up, the method further includes: generating multi-perspective preview effect diagrams of the digital diagnostic wax-up, where the preview effect diagrams include the front smile, side, and oblique side effects; superimposing and displaying the preview effect diagrams with a second digital model to obtain a displayed difference area; marking the area where the morphological change exceeds a preset deformation threshold in the displayed difference area to obtain a marked area; and displaying the marked area in a preset display format.

[0013] In the above embodiments, multi-perspective preview effect diagrams of the digital diagnostic wax-up are generated and superimposed and displayed with the facial feature data model, and the areas where the morphological change exceeds the preset deformation threshold are marked and displayed. The generation of the preview effect diagrams realizes the all-round display of the effect of the diagnostic wax-up, and the superimposed display and difference marking intuitively present the influence degree of the wax-up modification on the patient's facial aesthetics, thereby effectively ensuring the coordination between the final restoration effect and the patient's facial features.

[0014] In some embodiments in combination with some embodiments of the first aspect, after the steps of converting personalized demand information into the target adjustment amount of key control points and modifying the initial digital veneer model according to the target adjustment amount to obtain a digital diagnostic wax-up, the method further includes: obtaining the contact point data of the upper and lower teeth in the first digital model and generating an occlusal contact area according to the contact point data; detecting the thickness value of the digital diagnostic wax-up in the occlusal contact area; and when the thickness value is less than a preset safe thickness threshold, marking the occlusal contact area and displaying it in a preset display format.

[0015] In the above embodiments, the contact point data of the upper and lower teeth are obtained and the occlusal contact area is generated, the thickness value of the digital diagnostic wax-up in the occlusal contact area is detected, and when the thickness value is less than the preset safe thickness threshold, it is marked and displayed. The detection of the occlusal contact area and the thickness monitoring ensure that the veneer design meets the mechanical strength requirements, and the marking and display promptly prompt potential safety hazards, effectively improving the safety and reliability of the diagnostic wax-up design.

[0016] In some embodiments in combination with some embodiments of the first aspect, after the steps of converting personalized demand information into the target adjustment amount of key control points and modifying the initial digital veneer model according to the target adjustment amount to obtain a digital diagnostic wax-up, the method further includes: retrieving the historical case with the highest similarity to the digital diagnostic wax-up from the clinical case database, where the similarity is calculated from the veneer contour curve, surface curvature distribution, and relative position relationship of key control points; extracting the key parameters in the historical case and comparing them with the digital diagnostic wax-up; and generating parameter optimization suggestions according to the comparison results.

[0017] In the above embodiments, the historical case with the highest similarity is retrieved from the clinical case database, and parameter comparison and optimization suggestion generation are performed based on the veneer contour curve, surface curvature distribution, and relative position relationship of key control points. The retrieval of historical cases and parameter comparison establish a reference standard based on clinical experience, and the generation of optimization suggestions provides data support for the design of diagnostic wax patterns, ensuring the scientific rationality of the design scheme.

[0018] In combination with some embodiments of the first aspect, in some embodiments, after the steps of converting personalized demand information into the target adjustment amount of key control points, modifying the initial digital veneer model according to the target adjustment amount to obtain a digital diagnostic wax pattern, the method further includes: generating 3D printing parameters based on the digital diagnostic wax pattern, where the 3D printing parameters include printing layer thickness, support structure position, and printing direction; performing slicing processing on the digital diagnostic wax pattern based on the 3D printing parameters to obtain layered data; generating a printing path according to the layered data, and converting the printing path into a device control instruction; and controlling a 3D printing device to perform veneer printing according to the device control instruction.

[0019] In the above embodiments, 3D printing parameters including printing layer thickness, support structure position, and printing direction are generated based on the digital diagnostic wax pattern, the digital model is sliced and a printing path is generated, and the printing path is converted into a device control instruction for veneer printing. The precise setting of printing parameters and slicing processing achieve the precise conversion from the digital model to the physical model, and the automatic generation of device control instructions ensures the stability of the printing process, thus ensuring a high degree of consistency between the 3D printed veneer and the digital diagnostic wax pattern.

[0020] In a second aspect, an embodiment of the present application provides a diagnostic wax pattern digital manufacturing system, which includes: one or more processors and a memory; the memory is coupled to the one or more processors, and the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the diagnostic wax pattern digital manufacturing system to execute the method described in the first aspect and any possible implementation manner in the first aspect.

[0021] In a third aspect, an embodiment of the present application provides a computer program product containing instructions, which, when the computer program product runs on a diagnostic wax pattern digital manufacturing system, enables the diagnostic wax pattern digital manufacturing system to execute the method described in the first aspect and any possible implementation manner in the first aspect.

[0022] Fourthly, an embodiment of the present application provides a computer-readable storage medium, including instructions, which, when running on a diagnostic wax pattern digital production system, cause the diagnostic wax pattern digital production system to execute the method described in the first aspect and any possible implementation manner in the first aspect.

[0023] It can be understood that the diagnostic wax pattern digital production system provided in the second aspect, the computer program product provided in the third aspect, and the computer storage medium provided in the fourth aspect are all used to execute the method provided in the embodiments of the present application. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding method, which will not be elaborated here.

[0024] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. In the present application, by collecting oral data and facial feature data and converting them into digital models, extracting key tooth parameters and key control points, a standardized data collection and processing process is established. Based on personalized demand information, target adjustment is made to the key control points, and combined with the standard values of restoration parameters in the clinical case database, the veneer model is constructed and modified to form a complete digital diagnostic wax pattern production method. This method eliminates the dependence on subjective experience in the manual production process, making the production process of the diagnostic wax pattern more accurate and controllable.

[0025] 2. In the present application, by displaying the first digital model on the human-computer interaction interface and collecting personalized demand information, a three-dimensional grid model of the area to be modified is generated. By extracting the spatial coordinates of the key control points on the grid model and establishing a local coordinate system, precise positioning and quantitative description of the modified area are realized, providing a reliable data basis for subsequent wax pattern modification and effectively improving the accuracy of the modification process.

[0026] 3. In the present application, by setting the target positions of the key control points according to the personalized demand information, calculating the spatial distance to determine the target adjustment amount, adjusting the positions of the grid nodes and performing surface smoothing processing. This method converts subjective personalized demands into objective spatial position adjustment parameters, realizes precise modification of the veneer model, and ensures the morphological accuracy and surface smoothness of the digital diagnostic wax pattern. Description of the Drawings

[0027] Figure 1 is a flowchart of a diagnostic wax pattern digital production method in an embodiment of the present application; Figure 2 is another flowchart of a diagnostic wax pattern digital production method in an embodiment of the present application; Figure 3 is a schematic structural diagram of a physical device of a diagnostic wax pattern digital production system in an embodiment of the present application. Detailed Embodiments

[0028] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the specification of the present application, the singular forms "a", "an", "above-mentioned", "the", and "this" are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used in the present application refers to any or all possible combinations including one or more of the listed items.

[0029] Hereinafter, the terms "first" and "second" are only for descriptive purposes and should not be construed as implying or suggesting relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the embodiments of the present application, unless otherwise specified, the meaning of "a plurality" is two or more.

[0030] For ease of understanding, the method provided in this embodiment is described in a process below. Please refer to Figure 1 , which is a schematic flowchart of a process for digitally manufacturing a diagnostic wax pattern in an embodiment of the present application.

[0031] S101. Obtain oral data of a target patient using a three-dimensional oral scanner and synchronously collect facial feature data of the patient using a facial scanner.

[0032] Among them, the three-dimensional oral scanner refers to an optical scanning device for obtaining three-dimensional data inside the oral cavity; the oral data refers to three-dimensional point cloud data of oral tissues such as teeth and gums obtained by scanning; the facial scanner refers to an optical scanning device for collecting three-dimensional data of the human face; the facial feature data refers to three-dimensional point cloud data reflecting features such as the facial contour and expression of the patient.

[0033] This step is performed before manufacturing the diagnostic wax pattern and is used to obtain the basic data of the patient's oral cavity and face. Specifically, the patient's oral cavity is scanned using a three-dimensional oral scanner to obtain three-dimensional data including information such as the upper and lower dental arches and occlusion relationship, and at the same time, the facial feature data of the patient in the frontal, lateral, and smiling states is collected using a facial scanner. The two types of data need to be collected synchronously to ensure the consistency and accuracy of the data.

[0034] In some embodiments, the acquisition of oral and facial data can be achieved in various ways: Optionally, a handheld oral scanner is used to sequentially scan along the dental arch. After obtaining the three-dimensional data of individual teeth, they are stitched and reconstructed, and at the same time, a fixed facial scanner is used to collect facial data; Optionally, an integrated scanning device is adopted to simultaneously obtain oral and facial data, and data registration and reconstruction are automatically completed through built-in algorithms. It can be understood that other scanning methods can also be adopted to achieve data acquisition, which is not limited here.

[0035] S102. Convert the oral data into a first digital model, convert the facial feature data into a second digital model, and extract the key parameters of the teeth of the target patient based on the first digital model.

[0036] Among them, the digital model refers to a three-dimensional mesh model obtained through computer processing; the key parameters include anatomical shape parameters (such as crown height, width, etc.), contour parameters (such as incisal edge shape, tooth surface curvature, etc.), and spatial position parameters (such as inclination angle, rotation angle, etc.).

[0037] This step is executed after obtaining the original scan data and is used to convert the point cloud data into a digital model for subsequent processing. Specifically, first, the original scan data is denoised and optimized, and then a regular three-dimensional mesh model is generated through a surface reconstruction algorithm, where the oral data generates the first digital model and the facial feature data generates the second digital model. On this basis, various key parameters of the teeth of the target patient are obtained through a feature extraction algorithm.

[0038] In some embodiments, data conversion and parameter extraction can be achieved in various ways: Optionally, the Poisson reconstruction algorithm is used to convert the point cloud data into a triangular mesh model, and the key parameters are extracted through morphological analysis; Optionally, the NURBS surface reconstruction method is adopted to generate the digital model, and the feature parameters are automatically identified and extracted in combination with machine learning algorithms. It can be understood that other algorithms can also be adopted to achieve data processing and feature extraction, which is not limited here.

[0039] S103. Obtain the personalized requirement information of the target patient, mark the area to be modified on the first digital model based on the personalized requirement information, and extract the key control points in the area to be modified.

[0040] Among them, the personalized requirement information refers to the specific requirements of the patient for dental aesthetic restoration, including improvement requirements in aspects such as chroma, shape, and position; the area to be modified represents the tooth area that needs to be aesthetically restored; the key control points refer to the feature points used to control the modification of the tooth shape, including the mesial and distal marginal points, the labial contour points, and the incisogingival margin points, etc.; the mesial and distal marginal points represent the marginal position points of the mesial and distal of the tooth; the labial contour points are the feature control points of the labial surface contour of the tooth; the incisogingival margin points represent the position points at the junction of the tooth and the gingiva.

[0041] This step is executed after obtaining the digital model and before making specific modifications, and is used to determine the modification range and control points. Specifically, first communicate with the patient through the human-computer interaction interface to understand in detail their specific requirements for dental aesthetic restoration, including information such as the tooth color, shape, and position they hope to improve. Then mark the teeth that need to be modified on the first digital model to generate a three-dimensional mesh model of the area to be modified. Finally, extract key control points within this area, and these points will be used for subsequent shape adjustment. The system will record the spatial coordinates of each control point and establish a local coordinate system to determine the relative positional relationship between the control points.

[0042] In some embodiments, personalized requirement acquisition and control point extraction can be achieved in various ways: Optionally, display the tooth model through a graphical interaction interface, allowing the patient to directly mark the area that needs to be improved on the interface. The system automatically records the marked information and converts it into specific modification parameters, then uses a geometric feature recognition algorithm to automatically extract key control points, and finally establishes a local coordinate system for spatial position calculation; Optionally, use a professional questionnaire to collect the patient's requirements, and the doctor manually marks the area to be modified on the three-dimensional model according to the questionnaire results, then uses a surface feature analysis method to locate the position of the key control points, and determines the exact coordinates of the control points by least squares fitting. It can be understood that other methods can also be used to achieve requirement acquisition and control point extraction, which are not limited here.

[0043] This step specifically includes: Display the first digital model through the human-computer interaction interface and collect personalized requirement information including requirements for tooth chroma, shape, and position improvement.

[0044] In this step, the human-computer interaction interface refers to a graphical operation interface used to display the three-dimensional tooth model and receive user input; the first digital model represents the original three-dimensional model obtained by scanning the patient's oral cavity; the chroma information adopts the VITA color comparison board standard; the requirements for shape improvement include parameters such as tooth length, width, and convexity; the requirements for position improvement include spatial position parameters such as tooth tilt angle and protrusion.

[0045] The system first loads the first digital model and displays it in the 3D window, with realistic display using professional dental rendering parameters. The interface is divided into multiple functional areas: on the left is the model operation toolbar, which includes basic operations such as rotation, scaling, and translation; on the right is the parameter setting panel for inputting specific improvement requirements. Chroma acquisition is achieved through the digital colorimetric function. The system displays the standard VITA color palette, and the operator determines the target chroma by clicking. Morphological requirement acquisition uses a parametric input method. The system provides a standardized tooth morphology parameter table, including 21 specific indicators, and the operator fills in the target values. Position requirements use an interactive marking method. The operator directly marks the target position on the 3D model, and the system automatically calculates the position parameters. All the collected requirement information forms structured data and is stored in the patient's personalized requirement database.

[0046] Mark the teeth to be modified on the first digital model to generate a 3D mesh model of the area to be modified.

[0047] In this step, the marking operation is a process of area selection on the surface of the 3D model; the area to be modified refers to the tooth surface that needs aesthetic restoration; the 3D mesh model consists of vertices, edges, and faces and is used to describe the geometric shape of the tooth surface; the mesh density is determined by the model accuracy requirements.

[0048] The system provides a variety of marking tools for the operator to select the area to be modified. The operator first uses the area selection tool to make a box selection on the model surface, and the system automatically identifies the boundaries of the selected area. Perform mesh optimization on the selected area: first execute the mesh subdivision algorithm to refine the original mesh to a precision of 0.1 mm; then perform mesh smoothing to eliminate noise and irregular shapes; finally, perform boundary optimization to ensure the continuity of the boundary curve. The system performs a topology check on the optimized mesh model to repair mesh defects, including problems such as duplicate vertices and intersecting faces. The generated high-quality mesh model serves as the basic data for subsequent modifications.

[0049] Extract the spatial coordinates of the mesial and distal marginal points, labial contour points, and incisal-gingival margin points on the 3D mesh model to generate key control point data.

[0050] In this step, the mesial and distal marginal points define the marginal positions of the tooth proximal surfaces; the labial contour points describe the characteristic contours of the tooth labial surfaces; the incisal-gingival margin points mark the junction line between the tooth and the gingiva; the spatial coordinates are represented by a 3D rectangular coordinate system; the control point data includes the position and type information of the points.

[0051] The system uses a feature recognition algorithm to automatically extract key control points. First, it identifies the feature lines on the tooth surface through curvature analysis, including the tooth margin line and the contour line. On the margin line, the system locates the mesial and distal margin points through an extreme point detection algorithm. On the labial surface, the system extracts feature points on the contour line through equidistant sampling to ensure the uniform distribution of points. For the incisal-gingival margin, the system identifies the gingival junction line through curvature mutation detection and extracts control points on the junction line. Each control point records its three-dimensional coordinates and point type identifier, forming a complete control point data set.

[0052] Based on the key control point data, a local coordinate system is established to determine the positional relationship between the control points.

[0053] In this step, the local coordinate system is a reference coordinate system established on the tooth surface; the axis directions correspond to the anatomical features of the tooth; the positional relationship includes the distance and angle parameters between the control points; the reference plane is determined by the main anatomical landmark points.

[0054] The system establishes the local coordinate system through the following steps: First, it selects the midpoint of the incisal-gingival margin as the origin to establish an initial coordinate system. It uses the principal component analysis method to process the set of control points and determines the principal axis direction of the tooth as the Z-axis. The X-axis is defined as the labio-lingual direction, and the Y-axis is defined as the mesio-distal direction, forming a right-handed coordinate system. In this coordinate system, the system calculates the relative coordinates of all control points and calculates the spatial relationship parameters between the control points: the Euclidean distance between point pairs, the angle between the connecting line and the coordinate axes, the distance from the point to the reference plane, etc. These parameters are used for subsequent shape modification and adjustment.

[0055] S104: Extract the standard values of the restoration parameters from the clinical case database and construct an initial digital veneer model based on the parameter standard values.

[0056] Among them, the clinical case database refers to a data storage system containing a large amount of information on completed restoration cases; the standard values of the restoration parameters represent a set of standardized parameters extracted from successful cases; the initial digital veneer model refers to an initial restoration plan model constructed based on the standard parameters; the veneer model refers to a thin restoration body model used for aesthetic restoration of the tooth surface.

[0057] This step is executed after determining the key control points and is used to generate an initial restoration plan. Specifically, the system first retrieves the clinical case database to find successful cases similar to the current patient's situation. It extracts the standard parameter values including tooth shape parameters, contour curve parameters, thickness parameters, etc. from these cases. Then, based on these parameter values and combined with the basic shape of the patient's original teeth, it constructs an initial digital veneer model through parametric modeling. This model will serve as the basis for subsequent personalized adjustment.

[0058] In some embodiments, parameter extraction and model construction can be achieved in various ways: Optionally, a machine learning algorithm is used to analyze the clinical case database to extract the most representative parameter standard values, and then a veneer model is automatically generated through a parametric modeling algorithm. Finally, mesh optimization and local adjustment are performed to ensure the smoothness of the model. Optionally, professional doctors screen similar cases from the database, manually extract key parameters, construct a veneer model using the spline surface interpolation method, and perform preliminary matching and adjustment through a deformation algorithm. It can be understood that other ways can also be adopted to achieve parameter extraction and model construction, which are not limited herein.

[0059] S105. Convert the personalized demand information into the target adjustment amount of the key control points, and modify the initial digital veneer model according to the target adjustment amount to obtain a digital diagnostic wax pattern.

[0060] Among them, the target adjustment amount refers to the specific value of the spatial position adjustment required for the key control points, including the moving distances in each direction; the spatial distance represents the three-dimensional spatial displacement amount from the current position to the target position of the control point; the mesh node refers to the vertex of the basic geometric unit constituting the digital model; the surface smoothing process represents a mathematical method for smoothing the model surface; the digital diagnostic wax pattern refers to the final digital restoration plan model after personalized adjustment.

[0061] This step is executed after obtaining the initial veneer model and is used to convert the patient's personalized needs into a specific model modification plan. Specifically, first, according to the patient's personalized demand information, determine the target positions of each key control point. These target positions need to meet both aesthetic requirements and functional needs. Then calculate the three-dimensional spatial distance from each control point's current position to the target position, and use this distance value as the target adjustment amount. Next, use the mesh deformation algorithm to deform the entire veneer model according to the adjustment amount of the control points. During the deformation process, it is necessary to maintain the continuity and smoothness of the model and avoid sharp corners or irregular shapes. Finally, perform surface smoothing processing on the deformed model to obtain the final digital diagnostic wax pattern.

[0062] In some embodiments, model adjustment and generation of the final diagnostic wax pattern can be achieved in various ways: Optionally, first establish a parametric deformation model based on anatomical features, convert the personalized requirements into a spatial transformation matrix of control points, then use the radial basis function interpolation algorithm to calculate the displacement field of each point of the model, then update the positions of the grid nodes, and finally use the Laplace smoothing algorithm to optimize the model surface; Optionally, use a physics-based deformation algorithm, first convert the personalized requirements into external force actions, then calculate the deformation response of the model through finite element analysis, then update the positions of the grid vertices, and finally use the NURBS surface reconstruction method to generate a smooth final model. It can be understood that other numerical calculation methods can also be used to achieve personalized adjustment of the model, which is not limited here.

[0063] This step specifically includes: Set the target positions of the mesial and distal marginal points, labial contour points, and incisal-gingival margin points according to the personalized requirement information.

[0064] In this step, the target position refers to the ideal spatial position of the control point determined according to the aesthetic restoration requirements; the adjustment of the mesial and distal marginal points is based on the tooth width and adjacent relationship; the adjustment of the labial contour points involves convexity and contour curve; the adjustment of the incisal-gingival margin points needs to consider the gingival contour and exposure; the spatial position is represented by the three-dimensional coordinates in the local coordinate system.

[0065] The system determines the target positions of the control points through parametric calculation. For the mesial and distal marginal points, the system calculates the target coordinates of the marginal points according to the standard crown width and adjacent tooth gap requirements. The calculation process considers the continuity of tooth arrangement to ensure a reasonable adjacent tooth contact relationship. For the labial contour points, the system calculates the target shape of the contour curve based on the facial aesthetic analysis results. Specifically, it includes: determining the labial convexity coefficient, calculating the Bezier control points of the contour curve, and interpolating to generate a smooth target curve. For the incisal-gingival margin points, the system calculates the target positions of the incisal-gingival margin points according to the gingival aesthetic parameters, including the gingival margin height and symmetry of the gingival margin contour. All target positions are stored in the form of three-dimensional coordinates and established a corresponding relationship with the original positions.

[0066] Calculate the spatial distance between the current positions and the target positions of each key control point, and determine this distance value as the target adjustment amount.

[0067] In this step, the spatial distance refers to the Euclidean distance of the control point in three-dimensional space; the target adjustment amount represents the distance and direction that the control point needs to move; the adjustment vector is represented by a directed line segment from the starting point to the ending point; the calculation of the adjustment amount needs to consider biomechanical limitations.

[0068] The system uses numerical calculation methods to determine the target adjustment amount. First, a unified calculation coordinate system is established, and the coordinates of the current position and the target position are transformed into this coordinate system. For each control point, the system calculates the displacement vector from its current position to the target position, and this vector contains three components: X, Y, and Z. The spatial distance is calculated through the modulus of the vector, and the adjustment direction is determined through the direction of the vector. The system conducts biomechanical verification on the calculated adjustment amount to ensure that the adjustment amount is within the range that the dental tissue can withstand. The adjustment amount that exceeds the limit is corrected to ensure the feasibility of the restoration plan. All adjustment amounts form structured data, including the displacement amount and direction information of each control point.

[0069] Adjust the grid node positions of the initial digital veneer model according to the target adjustment amount to obtain the adjusted grid nodes, and perform surface smoothing on the adjusted grid nodes to generate a digital diagnostic wax pattern.

[0070] In this step, the grid nodes are the basic geometric units that make up the digital model; the adjustment of node positions is based on the calculation of the deformation field; surface smoothing is used to optimize the surface quality of the model; the digital diagnostic wax pattern is the final restoration design model.

[0071] The system realizes model adjustment through a deformation algorithm. First, a physics-based deformation field is constructed, and the adjustment amount of the control points is converted into a continuous displacement field. The radial basis function interpolation method is used to calculate the displacement amount of each grid node in the deformation field. The displacement amount decays as the distance from the control point increases, ensuring the locality of the deformation. Perform a topological check on the deformed grid to repair the grid defects generated during the deformation process. Then execute the surface smoothing algorithm: first, apply the Laplace smoothing operator to eliminate local unevenness; then use the NURBS surface reconstruction technology to reconstruct the model surface; finally, perform local detail optimization to ensure the continuity and smoothness of the surface. The generated digital diagnostic wax pattern has an ideal shape and surface quality and serves as the final restoration plan model.

[0072] Next, a further and more specific process description of the method provided in this embodiment will be given. Please refer to Figure 2 , which is another process schematic diagram of the digital manufacturing method of the diagnostic wax pattern in the embodiment of the present application.

[0073] S201. Convert the personalized demand information into the target adjustment amount of the key control points, and modify the initial digital veneer model according to the target adjustment amount to obtain a digital diagnostic wax pattern.

[0074] Among them, the target adjustment amount represents the displacement vector of each key control point in three-dimensional space, including components in the x, y, and z directions; the spatial coordinate calculation is based on a rectangular coordinate system, with millimeters used as the distance unit and radians used for angles; the grid deformation uses a physics-based continuous deformation field calculation method to ensure the continuity and smoothness of the model deformation.

[0075] The system first reads the target tooth morphology parameters included in the personalized demand information and establishes a global coordinate system. For each key control point, its target positions in the labiolingual direction, mesiodistal direction, and vertical direction are calculated respectively. The spatial displacement vector is obtained by calculating the Euclidean distance between the target position and the current position, and this vector is the target adjustment amount. The system uses a physics-based deformation algorithm to deform the veneer model, specifically through the following steps: First, a local influence area centered on the control point is established, and the influence radius is determined according to the tooth anatomical characteristics; then, the displacement amount of each grid node in the area is calculated, and the displacement amount decays exponentially with the increase of the distance; finally, the deformed model is subjected to surface smoothing processing, and the iterative Laplacian operator is used to optimize the positions of the grid vertices until the preset smoothness threshold is reached.

[0076] S202. Generate multi-view preview effect diagrams of the digital diagnostic wax-up, and the preview effect diagrams include the front smile, side, and oblique side effects.

[0077] Among them, the preview effect diagram refers to a two-dimensional image rendered from different perspectives of the digital diagnostic wax-up; the rendering parameters include the lighting model, material properties, and environmental parameters; the perspective parameters define the camera position and viewing direction, and are used to generate effect diagrams at different angles.

[0078] The system generates the preview effect diagram through computer graphics rendering technology. First, a standardized viewing perspective is set. The front smile perspective uses a direction perpendicular to the occlusal surface, the side perspective is parallel to the sagittal plane, and the oblique side perspective is deflected by 45 degrees on the horizontal plane. For each perspective, the system sets a professional dental lighting model, including a main light source to simulate the oral examination lamp and an ambient light source to simulate the clinic ambient light. The material parameters are set according to the optical characteristics of real teeth, including the diffuse reflection coefficient, specular reflection coefficient, and subsurface scattering parameters. The rendering uses a ray tracing algorithm, considering multiple light reflections and scatterings, to generate a realistic preview effect. The finally output image uses a high-resolution format to ensure clear display of details.

[0079] S203. Superimpose and display the preview effect diagram with the second digital model to obtain the displayed difference area.

[0080] Among them, the superimposed display means registering and fusing two images or models in the same coordinate system; the difference area represents the parts where there are obvious morphological differences between the two models; the registration parameters include the spatial position, rotation angle, and scaling ratio.

[0081] The system uses image registration technology to achieve the precise overlay of the preview effect diagram and the facial feature model. First, common feature points in the two data sources are extracted, including tooth contour points, lip margin points, etc. Based on these feature points, the optimal rigid transformation matrix is calculated, including the translation vector and the rotation matrix. The transformation matrix is applied to spatially align the preview effect diagram so that it coincides precisely with the facial model. When overlaying and displaying, the system calculates the point-to-point distance between the surfaces of the two models. When the distance exceeds the preset threshold, it is marked as a difference area. The difference area is displayed in pseudo-color, and different colors represent different degrees of morphological changes, facilitating the intuitive evaluation of the modification effect.

[0082] S204. Mark the areas where the morphological changes exceed the preset deformation threshold within the displayed difference area to obtain the marked areas. Among them, the amount of morphological change refers to the degree of geometric difference between the surfaces of the model before and after modification, obtained by calculating the Euclidean distance between corresponding points; the preset deformation threshold represents the maximum allowable amount of morphological change, with the unit of millimeters; the marked area refers to the set of continuous areas where the amount of morphological change exceeds the threshold.

[0083] The system first establishes a uniform sampling grid within the displayed difference area, with the grid spacing set to 0.1 millimeter. For each sampling point, the normal distance and tangential displacement of the surface before and after modification are calculated, and the square root of the sum of the squares of the two components is used to obtain the total deformation amount. When the deformation amount of a certain point exceeds the preset threshold (usually set to 2 millimeters), this point is marked as an over-deformed point. The region growing algorithm is used to connect adjacent over-deformed points into continuous areas, and scattered areas with an area less than 1 square millimeter are removed. Finally, the marked areas that need to be focused on are obtained. For each marked area, the system calculates its area, average deformation amount, maximum deformation amount and other statistical features for subsequent analysis and display.

[0084] S205. Display the marked areas in a preset display format.

[0085] Among them, the preset display format includes the color mapping scheme, transparency setting and annotation style; the color mapping adopts the heat map color matching scheme, and different colors represent different degrees of deformation; the transparency setting is used to adjust the overlay effect of the marked area and the original model; the annotation style defines the display rules of the text description and marking symbols.

[0086] The system uses a hierarchical display technology to achieve the visualization of the marked area. The original tooth model is displayed at the bottom layer, using standard dental model rendering parameters. The distribution of the deformation amount is displayed in the middle layer, and a gradient color spectrum from blue to red is used to represent the degree of deformation. Blue indicates slight deformation (less than 1 mm), and red indicates severe deformation (greater than 2 mm). Statistical information and annotations are displayed at the top layer, including the regional contour line, area value, and the mark of the maximum deformation position. The system supports interactive display control, and the operator can dynamically adjust the transparency of each layer to highlight the information of interest. For key positions, text descriptions with leaders are automatically added to label the specific deformation values and position descriptions.

[0087] S206. Obtain the contact point data of the upper and lower jaw teeth in the first digital model, and generate an occlusal contact area based on the contact point data.

[0088] Among them, the contact point data represents the contact position information of the upper and lower jaw teeth in the occlusal state; the occlusal contact area refers to the surface area within a certain range around the contact point; the contact distance threshold defines the maximum gap distance determined as contact, usually set to 0.3 mm.

[0089] The system analyzes the spatial relationship between the upper and lower jaw teeth through numerical calculation methods to identify the occlusal contact area. First, high-density sampling points are generated on the surfaces of the upper and lower jaw teeth, with a sampling interval of 0.1 mm. For each pair of opposite sampling points, the shortest distance between them is calculated. When the distance is less than the preset contact threshold, this point pair is marked as a potential contact point. To improve the accuracy, the system calculates the surface normal vectors near the contact points and only retains the contact point pairs with normal vectors close to parallel. Cluster analysis is performed on the retained contact points, and the contact points with similar spatial positions are merged into one contact area. The system calculates the area, average contact pressure, and contact center position of each contact area. Finally, a closed curve describing the shape of each contact area is generated for subsequent analysis and display.

[0090] S207. Detect the thickness value of the digital diagnostic wax pattern in the occlusal contact area.

[0091] Among them, the thickness value represents the material thickness of the digital diagnostic wax pattern in the local area, with the unit of mm; the measurement direction is defined as the normal direction of the tooth surface; the sampling point interval is set to 0.1 mm to ensure the measurement accuracy; the local minimum thickness value is determined by the minimum value of multiple measurement points in the same area.

[0092] The system uses numerical analysis methods to measure the thickness distribution of the occlusal contact area. First, a regular sampling grid is generated within the identified occlusal contact area, and a local coordinate system is established for each sampling point, where the Z-axis direction is consistent with the surface normal vector. At each sampling point, a measurement ray is emitted in the normal direction, and the intersection distances of the ray with the inner and outer surfaces of the diagnostic wax pattern are calculated, and this distance is the local thickness value. The system samples at least 100 measurement points within each occlusal contact area and uses a triangular interpolation algorithm to generate a continuous thickness distribution field. For each contact area, the minimum thickness value, average thickness value, and thickness change gradient are recorded, and these data are used for subsequent safety assessments.

[0093] S208. When the thickness value is less than the preset safety thickness threshold, mark the occlusal contact area and display it in the preset display format.

[0094] Among them, the preset safety thickness threshold is determined based on the mechanical properties of the material and is usually set to 0.5 mm; the marking methods include color coding and warning symbols; the display format defines the visual representation method of the marked area, including color, transparency, and boundary style.

[0095] The system analyzes and evaluates the thickness distribution of each occlusal contact area. The measured local minimum thickness value is compared with the preset safety threshold. When an area with insufficient thickness is found, the system automatically marks it. The marking uses a multi-level visualization scheme: the bottom layer displays the original diagnostic wax pattern model; the middle layer uses a heat map to display the thickness distribution, with the color gradually changing from green (safe) to red (dangerous); the top layer adds special warning marks, including the area contour line, thickness value annotation, and notes. The system generates a detailed report of the area with insufficient thickness, including information such as the specific location, area size, and minimum thickness value, providing a basis for subsequent design optimization.

[0096] S209. Retrieve the historical case with the highest similarity to the digital diagnostic wax pattern from the clinical case database. The similarity is calculated based on the veneer contour curve, surface curvature distribution, and relative position relationship of key control points.

[0097] Among them, the similarity is a numerical index that measures the similarity degree of the geometric features of two models, with a value range of 0 to 1; the contour curve is a characteristic line that describes the tooth shape; the surface curvature distribution reflects the local morphological features; the relative position relationship of key control points represents the spatial configuration relationship between feature points.

[0098] The system retrieves similar cases in the database through a feature matching algorithm. First, the feature vectors of the current diagnostic wax pattern are extracted, including: the Fourier descriptors of the contour curve, the statistical distribution of the surface principal curvatures, and the shape descriptors of the feature grid formed by the key control points. The same feature extraction process is performed for each historical case in the database. The weighted Euclidean distance is used to calculate the similarity between the feature vectors, and the weight coefficients are determined through statistical analysis, reflecting the importance of different features. The weight of the contour curve similarity is 0.4, the weight of the surface curvature distribution is 0.3, and the weight of the positional relationship of the control points is 0.3. The system sorts the retrieval results in descending order of similarity and selects the cases with a similarity greater than 0.8 as reference cases.

[0099] S210. Extract the key parameters in the historical case and compare them with the digital diagnostic wax pattern.

[0100] Among them, the key parameters include morphological parameters, positional parameters, and structural parameters; the morphological parameters describe the geometric features of the teeth, such as the aspect ratio and convexity coefficient; the positional parameters represent the spatial position and orientation of the teeth in the oral cavity; the structural parameters include technical indicators such as the material thickness distribution and the curvature of the transition region; the parameter comparison uses a standardized numerical comparison method.

[0101] The system uses a parametric analysis method for case comparison. First, a standardized parameter set is extracted from the historical case, including: the inclination angle of the long axis of the tooth, the labial convexity coefficient, the incisal edge morphological parameters, the proximal surface transition curve parameters, etc. The difference value between the current diagnostic wax pattern and the historical case is calculated for each parameter, and the difference value is normalized by subtracting the reference value and dividing by the standard deviation. The system calculates the average difference values of the morphological parameter group, the positional parameter group, and the structural parameter group respectively to generate a parameter difference distribution map. The parameters with a difference value exceeding 1.5 standard deviations are marked as key points, and these parameters indicate design features with significant deviations. The system organizes the analysis results into a structured data table, including parameter names, current values, reference values, degrees of difference, and statistical significance.

[0102] S211. Generate parameter optimization suggestions based on the comparison results.

[0103] Among them, the parameter optimization suggestions are specific adjustment plans generated based on the comparison analysis results; the optimization goals include morphological harmony, functional reliability, and aesthetic effects; the adjustment range is determined according to clinical experience and biomechanical principles; the optimization suggestions include adjustment directions, adjustment amounts, and priorities.

[0104] The system generates specific optimization suggestions through decision analysis algorithms. First, it classifies significantly deviated parameters into morphological, positional, and structural deviation categories. For each type of deviation, the system calculates the recommended adjustment amount according to clinical specifications. The calculation of the adjustment amount takes into account multiple factors: the biological significance of the parameter, its relevance to adjacent parameters, and the technical feasibility of the adjustment. The system generates hierarchical optimization suggestions: first, it processes structural parameters that affect function, such as insufficient material thickness; second, it optimizes morphological parameters that affect aesthetics, such as uncoordinated contour curves; finally, it adjusts minor positional parameters. Each optimization suggestion includes specific numerical targets, adjustment steps, and descriptions of expected effects. The system also generates a visual optimization guide, marking the adjustment direction with arrows and the adjustment amount with numerical values.

[0105] S212. Generate 3D printing parameters based on the digital diagnostic wax pattern. The 3D printing parameters include printing layer thickness, support structure position, and printing direction.

[0106] Among them, the printing layer thickness defines the material thickness of a single print, usually set to 0.05 - 0.1 mm; the support structure is a temporary support added to prevent model deformation during printing; the printing direction refers to the orientation of the model on the printing platform, which affects printing quality and efficiency.

[0107] The system determines the optimal printing parameter configuration through an optimization algorithm. First, it analyzes the geometric features of the diagnostic wax pattern to identify overhanging structures, detailed features, and key functional surfaces. Based on these features, the system calculates the ideal printing direction: facing the largest plane towards the printing platform to reduce the support structure; avoiding key functional surfaces facing upwards to reduce surface roughness. The generation of the support structure uses an adaptive algorithm: adding tree-like supports below overhanging areas, with the support density increasing as the overhanging area increases; the positions of the support contact points avoid functional surfaces and aesthetic surfaces. The setting of the printing layer thickness is based on the accuracy requirements of the model: using a 0.05 mm layer thickness in detailed areas to ensure accuracy; using a 0.1 mm layer thickness in other areas to improve efficiency. The system generates a complete printing parameter file, including process parameters such as slicing settings, temperature parameters, and printing speed.

[0108] S213. Perform slicing processing on the digital diagnostic wax pattern based on the 3D printing parameters to obtain layered data.

[0109] Among them, slicing processing means decomposing a three-dimensional model into a series of two-dimensional cross-sections according to the set layer thickness; the layered data includes the contour information, filling path, and support structure data of each layer; the contour information describes the two-dimensional vector curves of the model boundary; the filling path defines the specific trajectory of material deposition; the slicing accuracy is determined by the layer thickness parameter.

[0110] The system executes an adaptive slicing algorithm for model layering. First, the digital diagnostic wax pattern is converted into a triangular mesh model to ensure that the mesh quality meets the slicing requirements. According to the preset layer thickness parameter, the system calculates the intersection line between the horizontal slicing plane and the triangular mesh to generate the contour trajectory of each layer. For complex curved surface areas, the system adopts an adaptive layer thickness strategy: reducing the layer thickness to 0.05 mm in areas with larger curvature to improve the detail restoration; appropriately increasing the layer thickness to 0.1 mm in flat areas to improve the printing efficiency. The system generates structured layering data for each layer, including outer contour lines, internal filling patterns, and cross-sectional information of the support structure. All layering data are arranged in ascending order from bottom to top to form a complete slice data set.

[0111] S214. Generate a printing path based on the layering data and convert the printing path into device control instructions.

[0112] Among them, the printing path is a spatial curve describing the movement trajectory of the printing nozzle; the device control instructions include process parameters such as movement parameters, temperature control, and material extrusion amount; the movement parameters define the position, speed, and acceleration of the printing nozzle; the process parameters control the process conditions during printing.

[0113] The system adopts an intelligent path planning algorithm to generate the optimal printing path. For the layering data of each layer, first generate the outer contour printing path and use the contour parallel offset algorithm to ensure the boundary accuracy. Then calculate the internal filling path, and the filling method is selected according to the regional functional requirements: high-density cross filling is used in load-bearing areas, and honeycomb filling is used in non-load-bearing areas to save materials. The system converts the printing path into G-code instructions recognizable by the device, including parameters such as the spatial coordinates of the nozzle, movement speed, and material extrusion rate. At the same time, generate auxiliary control instructions to control the temperature of the printing platform, the temperature of the nozzle, and the rotation speed of the cooling fan. All control instructions are arranged in chronological order to form a complete device control program.

[0114] S215. Control the 3D printing device to perform veneer printing according to the device control instructions.

[0115] Among them, the 3D printing device is a professional medical device with a multi-axis motion control system; the device control system includes a motion control module, a temperature control module, and a material delivery module; the printing accuracy is jointly determined by mechanical accuracy and control accuracy; the printing quality monitoring includes real-time parameter detection and a quality feedback system.

[0116] The system achieves high-precision printing through a multi-level control strategy. First, it executes a device self-check program to check the status and accuracy of each actuator. After entering the printing state, the system decomposes the control instructions into basic motion units and calculates the rotational speed and acceleration of each axis motor through an interpolation algorithm. The real-time control module maintains the stability of printing parameters: the material temperature is maintained within a specific range to ensure fluidity; the moving speed of the nozzle is automatically adjusted according to the track curvature to ensure deposition uniformity. The system monitors key parameters during the printing process in real time: it monitors the temperature distribution through an infrared sensor, monitors the printing layer thickness through a displacement sensor, and detects the forming quality through a vision system. When an abnormality is detected, the control system automatically performs parameter compensation or pauses printing. After printing is completed, the system generates a quality inspection report, recording the parameter changes and quality indicators of the entire printing process.

[0117] The diagnostic wax pattern digital manufacturing system in the embodiment of the present invention application will be described from the perspective of hardware processing. Please refer to Figure 3 , which is a schematic structural diagram of an entity device of the diagnostic wax pattern digital manufacturing system in the embodiment of the present application.

[0118] It should be noted that Figure 3 The structure of the diagnostic wax pattern digital manufacturing system shown is only an example and should not impose any limitations on the functions and usage scope of the embodiments of the present invention.

[0119] As Figure 3 shown, the diagnostic wax pattern digital manufacturing system includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 302 or the program loaded from the storage section 308 into the random access memory (RAM) 303, such as executing the method described in the above embodiments. In the RAM 303, various programs and data required for system operation are also stored. The CPU 301, ROM 302, and RAM 303 are connected to each other through a bus 304. The input / output (I / O) interface 305 is also connected to the bus 304.

[0120] The following components are connected to the I / O interface 305: an input section 306 including an audio input device, a button switch, etc.; an output section 307 including a liquid crystal display (LCD), an audio output device, an indicator light, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 309 performs communication processing via a network such as the Internet. The drive 310 is also connected to the I / O interface 305 as needed. A removable medium 311 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc. is installed on the drive 310 as needed so that a computer program read from it can be installed into the storage section 308 as needed.

[0121] Specifically, according to an embodiment of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, an embodiment of the present invention includes a computer program product that includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication section 309, and / or installed from the removable medium 311. When the computer program is executed by the central processing unit (CPU) 301, various functions defined in the present invention are executed.

[0122] It should be noted that specific examples of computer-readable storage media may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, a computer-readable storage medium can be any tangible medium that contains or stores a program, and the program can be used by or combined with an instruction execution system, apparatus, or device.

[0123] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present invention. Among them, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code, and the above-mentioned module, program segment, or part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order from that marked in the accompanying drawings.

[0124] Specifically, the diagnostic wax pattern digital manufacturing system of this embodiment includes a processor and a memory, and a computer program is stored on the memory. When the computer program is executed by the processor, the diagnostic wax pattern digital manufacturing method provided in the above embodiment is implemented.

[0125] On the other hand, the present invention also provides a computer-readable storage medium, which may be included in the diagnostic wax pattern digital manufacturing system described in the above embodiment; or it may exist alone and not be assembled into the diagnostic wax pattern digital manufacturing system. The above storage medium carries one or more computer programs. When the above one or more computer programs are executed by a processor of the diagnostic wax pattern digital manufacturing system, the diagnostic wax pattern digital manufacturing system is enabled to implement the diagnostic wax pattern digital manufacturing method provided in the above embodiment.

[0126] As mentioned above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the various embodiments of the present application.

[0127] As used in the above embodiments, depending on the context, the term "when..." may be interpreted to mean "if...", or "after...", or "in response to determining...", or "in response to detecting...". Similarly, depending on the context, the phrase "when determining..." or "if detecting (the stated condition or event)" may be interpreted to mean "if determining...", or "in response to determining...", or "when detecting (the stated condition or event)", or "in response to detecting (the stated condition or event)".

[0128] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by relevant hardware instructed by a computer program. This program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above method embodiments. The foregoing storage medium includes: various media such as ROM or random access memory RAM, magnetic disks, or optical disks that can store program codes.

Claims

1. A digital manufacturing method for diagnostic wax patterns, characterized in that Applied to a digital manufacturing system for diagnostic wax patterns, the method includes: Obtaining oral data of a target patient by a three-dimensional oral scanner and synchronously collecting facial feature data of the patient by a facial scanner. The oral data includes maxillary dentition data, mandibular dentition data, and occlusion relationship data. The facial feature data includes dynamic data in the frontal, lateral, and smiling states. Converting the oral data into a first digital model, converting the facial feature data into a second digital model, and extracting key parameters of the teeth of the target patient based on the first digital model. The key parameters include anatomical shape parameters, contour parameters, and spatial position parameters of the teeth. Obtaining the personalized requirement information of the target patient, marking the area to be modified on the first digital model based on the personalized requirement information, and extracting key control points in the area to be modified. The key control points include mesiodistal marginal points, labial contour points, and incisogingival margin points. Extracting the standard values of restoration parameters from a clinical case database and constructing an initial digital veneer model based on the parameter standard values. Converting the personalized requirement information into the target adjustment amount of the key control points, and modifying the initial digital veneer model according to the target adjustment amount to obtain a digital diagnostic wax pattern.

2. The method according to claim 1, characterized in that, The step of obtaining the personalized requirement information of the target patient, marking the area to be modified on the first digital model based on the personalized requirement information, and extracting key control points in the area to be modified specifically includes: Displaying the first digital model through a man-machine interaction interface and collecting personalized requirement information including requirements for tooth chroma, shape, and position improvement. Marking the teeth to be modified on the first digital model and generating a three-dimensional mesh model of the area to be modified. Extracting the spatial coordinates of the mesiodistal marginal points, labial contour points, and incisogingival margin points on the three-dimensional mesh model to generate key control point data. Establishing a local coordinate system based on the key control point data and determining the positional relationship between the control points.

3. The method according to claim 1, wherein The step of converting the personalized requirement information into the target adjustment amount of the key control points, modifying the initial digital veneer model according to the target adjustment amount to obtain a digital diagnostic wax pattern specifically includes: Setting the target positions of the mesiodistal marginal points, labial contour points, and incisogingival margin points according to the personalized requirement information. Calculating the spatial distance between the current position and the target position of each key control point, and determining this distance value as the target adjustment amount. Adjusting the grid node positions of the initial digital veneer model according to the target adjustment amount to obtain the adjusted grid nodes, and performing surface smoothing processing on the adjusted grid nodes to generate a digital diagnostic wax pattern.

4. The method according to claim 1, wherein After the step of converting the personalized requirement information into the target adjustment amount of the key control points, modifying the initial digital veneer model according to the target adjustment amount to obtain a digital diagnostic wax pattern, the method further includes: Generating multi-view preview effect diagrams of the digital diagnostic wax pattern. The preview effect diagrams include frontal smile, lateral, and oblique lateral effects. Overlay the preview effect diagram with the second digital model to obtain a display difference area; Mark the area within the display difference area where the morphological change exceeds a preset deformation threshold to obtain a marked area; Display the marked area in a preset display format.

5. The method according to claim 4, wherein After the step of converting the personalized demand information into the target adjustment amount of the key control points and modifying the initial digital veneer model according to the target adjustment amount to obtain a digital diagnostic wax pattern, the method further includes: Obtain the contact point data of the upper and lower jaw teeth in the first digital model, and generate an occlusal contact area according to the contact point data; Detect the thickness value of the digital diagnostic wax pattern in the occlusal contact area; When the thickness value is less than a preset safety thickness threshold, mark the occlusal contact area and display it in the preset display format.

6. The method according to claim 1, wherein After the step of converting the personalized demand information into the target adjustment amount of the key control points and modifying the initial digital veneer model according to the target adjustment amount to obtain a digital diagnostic wax pattern, the method further includes: Retrieve the historical case with the highest similarity to the digital diagnostic wax pattern from the clinical case database, where the similarity is calculated from the veneer contour curve, surface curvature distribution, and relative position relationship of the key control points; Extract the key parameters in the historical case and compare them with the digital diagnostic wax pattern; Generate parameter optimization suggestions according to the comparison results.

7. The method according to claim 1, wherein After the step of converting the personalized demand information into the target adjustment amount of the key control points and modifying the initial digital veneer model according to the target adjustment amount to obtain a digital diagnostic wax pattern, the method further includes: Generate 3D printing parameters according to the digital diagnostic wax pattern, where the 3D printing parameters include printing layer thickness, support structure position, and printing direction; Perform slicing processing on the digital diagnostic wax pattern based on the 3D printing parameters to obtain layered data; Generate a printing path according to the layered data and convert the printing path into a device control instruction; Control a 3D printing device to perform veneer printing according to the device control instruction.

8. A digital production system for diagnostic wax patterns, characterized in that, The diagnostic wax pattern digital manufacturing system includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the diagnostic wax pattern digital manufacturing system to execute the method according to any one of claims 1-7.

9. A computer-readable storage medium, comprising instructions, characterized in that, When the instruction runs on the diagnostic wax pattern digital manufacturing system, it enables the diagnostic wax pattern digital manufacturing system to execute the method according to any one of claims 1-7.

10. A computer program product, characterized in that, When the computer program product runs on the diagnostic wax pattern digital manufacturing system, it enables the diagnostic wax pattern digital manufacturing system to execute the method according to any one of claims 1-7.

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