CN114504397B - Intelligent design method for removable partial denture - Google Patents
Intelligent design method for removable partial denture Download PDFInfo
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- CN114504397B CN114504397B CN202210136829.9A CN202210136829A CN114504397B CN 114504397 B CN114504397 B CN 114504397B CN 202210136829 A CN202210136829 A CN 202210136829A CN 114504397 B CN114504397 B CN 114504397B
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- A61C13/00—Dental prostheses; Making same
- A61C13/0003—Making bridge-work, inlays, implants or the like
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Abstract
The invention discloses a removable partial denture intelligent design method, which belongs to the technical field of 3D printing, and comprises the steps of carrying out Boolean operation according to an edge-triangle intersection method, establishing a grid growth relation list, counting the coincidence degree of seed nodes, the number and total area of triangular patches, and obtaining the fitting degree of a denture template model and an oral cavity model, thereby recommending the denture template model, solving the technical problems of improving the operation speed, reducing the memory utilization rate and realizing intelligent recommendation of the denture template model, greatly improving the intersection calculation speed, reducing the redundancy of the intersection calculation, greatly improving the stability and efficiency of an algorithm, improving the quality of a Boolean operation result, rapidly judging the spatial position relation of the points and the model, being applicable to closed and open grid models, being stable and reliable in algorithm, having fewer non-popular or unstable triangular patches in the Boolean operation result, being capable of carrying out continuous Boolean operation, and being applicable to cutting simulation.
Description
Technical Field
The invention belongs to the technical field of 3D printing, and particularly relates to an intelligent design method of removable partial dentures.
Background
The false tooth is divided into two types of removable and fixed ones. Fixed dentures (commonly known as "fixed dentures") are not intended to be worn by the patient themselves, while removable dentures (commonly known as "removable dentures") are intended to be easily worn by the patient.
At present, denture production gradually tends to be digital, and denture printing through a 3D printer gradually becomes a mainstream method in the market.
The following disadvantages are present in denture fabrication using 3D printers:
1. most of the existing 3D model spline editing and clipping technologies are realized based on space spline projection, and the projection direction of a curve is a key for ensuring the quality of the spline. For complex models, the projection direction of the curve is difficult to accurately determine, so that the problems of distortion, discontinuity and the like of spline lines in the spline line drawing process are caused, and the subsequent cutting is unstable.
2. A rapid and intelligent modeling flow cannot be formed, and the traditional manual drawing mode is still adopted to carry out modeling drawing on each component of the false tooth.
3. The traditional triangle-triangle intersection method is adopted to carry out Boolean operation, so that the memory occupancy rate is high, and the calculation speed is low.
Disclosure of Invention
The invention aims to provide an intelligent design method for removable partial denture, which solves the technical problems of improving the operation speed, reducing the memory utilization rate and realizing intelligent recommendation of a denture template model.
In order to achieve the above purpose, the invention adopts the following technical scheme:
an intelligent design method for removable partial denture comprises the following steps:
step 1: establishing a client server, a modeling server and a 3D printing platform, wherein the client server, the modeling server and the 3D printing platform are communicated with each other through the Internet, and the modeling server and an intraoral scanner are communicated with each other through the Internet; a man-machine interaction interface is arranged on the 3D printing platform;
after the client side server acquires the electronic order, the electronic order is sent to the modeling server for confirmation;
step 2: after confirming the electronic order, the modeling server acquires initial oral modeling data through an intraoral scanner; the modeling server simultaneously sends the electronic order and the initial oral modeling data to the 3D printing platform;
step 3: after the 3D printing platform confirms the electronic order, an oral cavity model is established according to initial oral cavity modeling data;
the 3D printing platform selects a bracket co-locating channel according to the oral cavity model, simultaneously selects the position of the abutment, and generates modeling basic information according to the bracket co-locating channel and the position information of the abutment;
the 3D printing platform acquires tooth missing position information through an oral cavity model;
step 4: taking modeling basic information and tooth missing position information as conditions by the 3D printing platform, and calling a false tooth template model from a model database;
step 5: the 3D printing platform respectively carries out Boolean operation on the oral cavity model and the denture template model according to an edge-triangle intersection method, respectively carries out gridding treatment on the oral cavity model and the denture template model, establishes a world coordinate system, respectively records coordinate information and node numbers of each seed node selected in the oral cavity model and the denture template model in the world coordinate system when gridding treatment is carried out, simultaneously records areas and grid numbers of all sub-grids in gridding treatment, respectively establishes a grid growth relation list of the oral cavity model and the denture template model, and the grid growth relation list is used for representing the subordinate relation between the seed nodes and the sub-grids;
step 6: comparing the contact ratio between the seed nodes in the oral cavity model and the denture template model by taking the oral cavity model as a reference to generate a contact ratio report;
step 7: according to the coincidence report, comparing the number of triangular patches in the oral cavity model and the denture template model and the total area of all triangular patches to generate a grid comparison report;
step 8: determining the fit degree between the denture template model and the oral cavity model according to the coincidence degree report and the grid comparison report;
step 9: and (3) screening the denture template model with the highest degree of fit with the oral cavity model from a model database by the 3D printing platform according to the methods from the step 4 to the step 8 to serve as a reference denture model, and displaying the reference denture model to an operator through a human-computer interface.
Preferably, the method of intersecting the triangle with the edge is that when the triangle-triangle intersection operation is performed according to the boolean operation method, only the edge of one triangle is used as a basic element to perform the intersection operation with another triangle, and in the process of intersection operation, the coordinates, the attribute and the index information of the intersection surface or the edge are recorded.
Preferably, the principle of the method adopted in establishing the grid growth relation list between the seed nodes and the sub-grids of the oral cavity model or the denture template model is the same, and the method specifically comprises the following steps:
step S5-1: when Boolean operation is carried out on the oral cavity model or the false tooth template model, the internal and external properties of all the intersecting triangle endpoints are determined;
step S5-2: selecting an endpoint with an attribute being an inner attribute as a seed node, and performing grid growth by taking all intersecting line segments intersecting the seed node as boundaries until all communicated triangular patches are accessed;
step S5-3: forming a new sub-grid by using the triangular patches accessed in the step S5-2;
step S5-4: generating grids by using the methods from the step S5-2 to the step S5-3 until all endpoints of the internal attribute generate sub grids, and merging all generated sub grids to complete the whole Boolean operation;
step S5-5: and establishing a subordinate relation between the sub-grids and the seed nodes, and generating a grid growth relation list.
Preferably, in executing step S5-1, the method for determining the internal and external properties of the end points of the intersecting triangle is as follows:
step S5-1-1: let Δabc and Δefg intersect, the intersection line thereof be mn, n abc And n def The normal lines of Δabc and Δefg are (x n1 ，y n1 ，z n1 ) And (x) n2 ，y n2 ，z n2 ) The method comprises the steps of carrying out a first treatment on the surface of the The coordinates of a point on the plane of Δabc and Δefg are (x) 1 ，y 1 ，z 1 ) And (x) 2 ，y 2 ，z 2 ) The plane equations in which they lie are expressed by the following formulas, respectively:
F abc (x，y，z)＝(X-x 1 ).x n1 +(y-y 1 ).y n1 +(z-z 1 ).z n1 ＝0；
F def (x，y，z)＝(x-x 2 ).x n2 +(y-y 2 ).y n2 +(z-z 2 ).z n2 ＝0；
step S5-1-2: the intersection of the edge ac and the delta def is at a point n, and for a point a, the position relation of the point a and the model is judged, namely the coordinates of the point a are brought into a plane equation to solve: if F abc (x, y, z) < 0, point a is "inside" the model, its attribute is the inner attribute, and vice versa is the outer attribute.
Preferably, when executing step 6, the method specifically comprises the following steps:
step 6-1: presetting a radius threshold R, selecting any seed node j in the oral cavity model, and setting a search area by taking the seed node j as a circle center and the radius threshold R as a radius;
step 6-2: mapping the search area onto the denture template model to perform coincidence search, namely finding out seed nodes of all denture template models existing in the search area on the denture template model, if no seed node of the denture template model is found in the search area, marking the seed node j as an isolated node, and executing the step 6-4; if at least one seed node of the denture template model is found in the search area, establishing a denture template model seed node set, and executing the step 6-3;
step 6-3: screening out the seed nodes of the denture template model with the shortest distance to the seed node j from the seed node set of the denture template model, and marking the seed node as the seed node j 1 Seed node j 1 Regarding the node to coincide with the seed node j, marking the seed node j as a coincident node, and executing the step 6-4;
step 6-4: and (3) repeating the steps 6-1 to 6-3 until all the seed nodes in the oral cavity model are subjected to coincidence search, counting the number of coincident nodes and the number of isolated nodes, judging the coincidence degree according to the duty ratio of the coincident nodes and the isolated nodes in the seed nodes of the oral cavity model, and generating a coincidence degree report.
Preferably, when executing step 7, the method specifically comprises the following steps:
step S7-1: optionally selecting a coincidence node k from the coincidence list 1 And simultaneously find and coincide with the node k 1 Seed node k of false tooth template model regarded as coincidence 2 ；
Step S7-2: according to the coincident node k 1 Finding out all the corresponding grid growth relation lists belonging to the coincident node k 1 Is a sub-network k of (2) 1i I is a positive integer, and the sub-network k is counted 1i And calculates the number of all sub-networks k 1i Is of the total surface of (1)Accumulating;
step S7-3: according to the coincident node k 2 Finding out all the corresponding grid growth relation lists belonging to the coincident node k 2 Is a sub-network k of (2) 2i I is a positive integer, and the sub-network k is counted 2i And calculates the number of all sub-networks k 2i Is a total area of (2);
step S7-4: and repeating the steps from the step S7-1 to the step S7-3 until all the coincident nodes in the coincident ratio list are compared, and generating a grid comparison report according to the comparison result.
Preferably, when executing step 8, the method specifically comprises the following steps:
step S8-1: presetting a coincidence degree threshold, finding out all false tooth template models with coincidence node proportion meeting the coincidence degree threshold according to a coincidence degree report, and establishing an alternative model set;
step S8-2: presetting a triangular patch number threshold and a total area threshold, screening all denture template models of which the total number of subnetworks accords with the triangular patch number threshold or the total area of subnetworks accords with the total area threshold from an alternative model set, and establishing a reference denture template model set;
step S8-3: and displaying the models in the reference denture template model set one by one to a user as reference denture template models.
The intelligent design method for the removable partial denture solves the technical problems of improving the operation speed, reducing the memory utilization rate and realizing intelligent recommendation of the denture template model, only carries out intersection calculation on the effective intersection area of the model, greatly improves the speed of the intersection calculation, adopts an edge-triangle intersection method to replace the triangle-triangle intersection, ensures that the intersection is unique, reduces the redundancy of the intersection calculation, greatly improves the stability and the efficiency of an algorithm, simultaneously improves the quality of a Boolean operation result, can quickly judge the spatial position relation between a breakpoint and the model, is suitable for closed and open grid models, has stable and reliable algorithm, has fewer non-popular or unstable triangular patches in the Boolean operation result, can carry out continuous Boolean operation, and can be used for cutting simulation.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a diagram of a mathematical model of the present invention for "inside and outside" point recognition;
FIG. 3 is a mathematical model diagram of the present invention when performing step S5-1;
FIG. 4 is a schematic illustration of three cases of intersection of an edge with a triangular patch of the present invention;
FIG. 5 is a graph of a growth model of a subgrid of the present invention.
Detailed Description
An intelligent design method of removable partial denture shown in fig. 1-5 comprises the following steps:
step 1: establishing a client server, a modeling server and a 3D printing platform, wherein the client server, the modeling server and the 3D printing platform are communicated with each other through the Internet, and the modeling server and an intraoral scanner are communicated with each other through the Internet; a man-machine interaction interface is arranged on the 3D printing platform;
after the client side server acquires the electronic order, the electronic order is sent to the modeling server for confirmation;
step 2: after confirming the electronic order, the modeling server acquires initial oral modeling data through an intraoral scanner; the modeling server simultaneously sends the electronic order and the initial oral modeling data to the 3D printing platform;
step 3: after the 3D printing platform confirms the electronic order, an oral cavity model is established according to initial oral cavity modeling data;
the 3D printing platform selects a bracket co-locating channel according to the oral cavity model, simultaneously selects the position of the abutment, and generates modeling basic information according to the bracket co-locating channel and the position information of the abutment;
the 3D printing platform acquires tooth missing position information through an oral cavity model;
step 4: taking modeling basic information and tooth missing position information as conditions by the 3D printing platform, and calling a false tooth template model from a model database;
step 5: the 3D printing platform respectively carries out Boolean operation on the oral cavity model and the denture template model according to an edge-triangle intersection method, respectively carries out gridding treatment on the oral cavity model and the denture template model, establishes a world coordinate system, respectively records coordinate information and node numbers of each seed node selected in the oral cavity model and the denture template model in the world coordinate system when gridding treatment is carried out, simultaneously records areas and grid numbers of all sub-grids in gridding treatment, respectively establishes a grid growth relation list of the oral cavity model and the denture template model, and the grid growth relation list is used for representing the subordinate relation between the seed nodes and the sub-grids;
step 6: comparing the contact ratio between the seed nodes in the oral cavity model and the denture template model by taking the oral cavity model as a reference to generate a contact ratio report;
step 7: according to the coincidence report, comparing the number of triangular patches in the oral cavity model and the denture template model and the total area of all triangular patches to generate a grid comparison report;
step 8: determining the fit degree between the denture template model and the oral cavity model according to the coincidence degree report and the grid comparison report;
step 9: and (3) screening the denture template model with the highest degree of fit with the oral cavity model from a model database by the 3D printing platform according to the methods from the step 4 to the step 8 to serve as a reference denture model, and displaying the reference denture model to an operator through a human-computer interface.
The method for intersecting the triangle by the edge is that when the triangle-triangle intersection operation is carried out according to the Boolean operation method, only the edge of one triangle is used as a basic element to carry out the intersection operation with the other triangle, and the coordinate, the attribute and the index information of the intersection surface or the edge are recorded in the intersection operation process.
The intersection of the triangular mesh model is the basis of boolean operations. Particularly for complex models, fast and accurate intersection is the key to the success of boolean operations. Many methods are used to speed up the intersection, such as BSP, OBB trees, spatial mapping, octree, etc., and the present invention uses Octree to speed up the search for intersecting triangular patches. In addition, in the process of intersection of the triangular patches, each intersection edge of one model can be intersected with the other model to form at least one pair of two points at the same position, so that a large amount of redundant calculation is generated in the process of intersection, and due to the influence of numerical calculation errors, intersection ambiguity is caused, and therefore the efficiency and stability of an algorithm are reduced. The invention adopts the method of intersecting the side-triangle to replace the method of intersecting the triangle-triangle to effectively solve the problems.
The intersection of an edge with a triangular patch is generally three, as shown in fig. 4, where the distribution of corner points is denoted by Q and P, the intersection point Q is one end point of the edge, the intersection point Q is located on one edge of the triangular patch, and the intersection point Q and the intersection point P are located in the triangular patch. The properties of these three types of intersections are set here as: endPoint, edgePoint, facePoint, the weight of these attributes decreases in turn. In the intersection calculation, the "triangle-triangle" intersection is also calculated, but in the intersection calculation, the base element, which is an edge (such as an edge ab) of one Δabc, intersects with another Δefg. In the intersection process, the intersection condition of the edge ab and delta efg is recorded, including the intersection point coordinates, attributes and the indexes of the intersection surfaces or edges. Since each non-boundary edge is shared by two triangular patches, when another delta bcd containing the edge ab and delta efg are subjected to intersection calculation, the calculation is not repeated based on the previously recorded intersection point, so that the redundancy of calculation is reduced. When the ambiguity of the previous paragraph appears when one edge intersects with the model, the intersection point with higher attribute weight is taken as the unique intersection point of the edge and the model, so that the condition that the intersection point of the edge and the triangle generates ambiguity is avoided.
The principle of the method adopted in the process of establishing a grid growth relation list between seed nodes and sub grids of an oral cavity model or a denture template model is the same, and the method specifically comprises the following steps:
step S5-1: when Boolean operation is carried out on the oral cavity model or the false tooth template model, the internal and external properties of all the intersecting triangle endpoints are determined;
step S5-2: selecting an endpoint with an attribute being an inner attribute as a seed node, and performing grid growth by taking all intersecting line segments intersecting the seed node as boundaries until all communicated triangular patches are accessed;
step S5-3: forming a new sub-grid by using the triangular patches accessed in the step S5-2;
step S5-4: generating grids by using the methods from the step S5-2 to the step S5-3 until all endpoints of the internal attribute generate sub grids, and merging all generated sub grids to complete the whole Boolean operation;
step S5-5: and establishing a subordinate relation between the sub-grids and the seed nodes, and generating a grid growth relation list.
The position relation between the judgment point and the model is the basis of three operations of Boolean operation realization and combination, intersection and difference. The conventional method adopts a ray tracing method to judge the position relation between the points and the model, but because the inside and outside of the model are opposite (related to the normal direction of the model), as shown in fig. 2, the method is not suitable for being assembled on the open model based on the intersection triangular patch normal vector. The invention provides a method for judging the position relation between the upper end point of an intersecting triangle and a model, as shown in fig. 3, according to the type of boolean operation and by utilizing the topological relation of grids, the method for determining the internal and external properties of the end points of the intersecting triangle when executing step S5-1 is as follows:
step S5-1-1: let Δabc and Δefg intersect, the intersection line thereof be mn, n abc And n def The normal lines of Δabc and Δefg are (x n1 ，y n1 ，z n1 ) And (x) n2 ，y n2 ，z n2 ) The method comprises the steps of carrying out a first treatment on the surface of the The coordinates of a point on the plane of Δabc and Δefg are (x) 1 ，y 1 ，z 1 ) And (x) 2 ，y 2 ，z 2 ) The plane equations in which they lie are expressed by the following formulas, respectively:
F abc (x，y，z)＝(x-x 1 ).x n1 +(y-y 1 ).y n1 +(z-z 1 ).z n1 ＝0；
F def (x，y，z)＝(x-x 2 ).x n2 +(y-y 2 ).y n2 +(z-z 2 ).z n2 ＝0；
step (a)S5-1-2: the intersection of the edge ac and the delta def is at a point n, and for a point a, the position relation of the point a and the model is judged, namely the coordinates of the point a are brought into a plane equation to solve: if F abc (x, y, z) < 0, point a is "inside" the model, its attribute is the inner attribute, and vice versa is the outer attribute.
Boolean operations include three types of operations: and, difference and intersection. Model M A And M B ，M AinB Representation model M A Is positioned at M B Internal grid, M AoutB Representation model M A Is positioned at M B External grid, M BinA Representation model M B Is positioned at M A Internal grid, M BoutA Representation model M B Is positioned at M A External grid, three boolean operations are as follows (M A Boolean M B )：
Boolean sum: m is M AoutB +M BoutA
Boolean difference: m is M AoutB +M BinA
Boolean crossover: m is M AinB +M BinA
After determining the 'inside and outside' of all the intersecting triangle endpoints, corresponding sub-grids are generated according to the Boolean operation type. E.g., a sub-grid within the model needs to be generated, then the endpoint that has been marked as "in" is selected, point x as shown in fig. 5. And taking the point as a seed point x, and taking the intersecting line segment as a boundary to perform grid growth until all the communicated triangular patches are accessed. These accessed triangular patches form a new sub-grid. The same approach is used until all endpoints marked "in" generate a sub-grid. And merging all generated sub-grids to complete the whole Boolean operation.
When the model is complex and the data is large, the algorithm has obvious advantages, the Boolean operation of all the models can be completed, the speed is high, and the other algorithms have problems. Meanwhile, compared with other algorithms, the result of the algorithm provided by the invention has more stable model quality and fewer non-popular and unstable triangular patches.
When executing the step 6, the method specifically comprises the following steps:
step 6-1: presetting a radius threshold R, selecting any seed node j in the oral cavity model, and setting a search area by taking the seed node j as a circle center and the radius threshold R as a radius;
step 6-2: mapping the search area onto the denture template model to perform coincidence search, namely finding out seed nodes of all denture template models existing in the search area on the denture template model, if no seed node of the denture template model is found in the search area, marking the seed node j as an isolated node, and executing the step 6-4; if at least one seed node of the denture template model is found in the search area, establishing a denture template model seed node set, and executing the step 6-3;
step 6-3: screening out the seed nodes of the denture template model with the shortest distance to the seed node j from the seed node set of the denture template model, and marking the seed node as the seed node j 1 Seed node j 1 Regarding the node to coincide with the seed node j, marking the seed node j as a coincident node, and executing the step 6-4;
step 6-4: and (3) repeating the steps 6-1 to 6-3 until all the seed nodes in the oral cavity model are subjected to coincidence search, counting the number of coincident nodes and the number of isolated nodes, judging the coincidence degree according to the duty ratio of the coincident nodes and the isolated nodes in the seed nodes of the oral cavity model, and generating a coincidence degree report.
When executing the step 7, the method specifically comprises the following steps:
step S7-1: optionally selecting a coincidence node k from the coincidence list 1 And simultaneously find and coincide with the node k 1 Seed node k of false tooth template model regarded as coincidence 2 ；
Step S7-2: according to the coincident node k 1 Finding out all the corresponding grid growth relation lists belonging to the coincident node k 1 Is a sub-network k of (2) 1i I is a positive integer, and the sub-network k is counted 1i And calculates the number of all sub-networks k 1i Is a total area of (2);
step S7-3: according to the coincident node k 2 Corresponding netLattice growth relationship list, find all subordinated to coincidence node k 2 Is a sub-network k of (2) 2i I is a positive integer, and the sub-network k is counted 2i And calculates the number of all sub-networks k 2i Is a total area of (2);
step S7-4: and repeating the steps from the step S7-1 to the step S7-3 until all the coincident nodes in the coincident ratio list are compared, and generating a grid comparison report according to the comparison result.
When executing the step 8, the method specifically comprises the following steps:
step S8-1: presetting a coincidence degree threshold, finding out all false tooth template models with coincidence node proportion meeting the coincidence degree threshold according to a coincidence degree report, and establishing an alternative model set;
step S8-2: presetting a triangular patch number threshold and a total area threshold, screening all denture template models of which the total number of subnetworks accords with the triangular patch number threshold or the total area of subnetworks accords with the total area threshold from an alternative model set, and establishing a reference denture template model set;
step S8-3: and displaying the models in the reference denture template model set one by one to a user as reference denture template models.
The intelligent design method for the removable partial denture solves the technical problems of improving the operation speed, reducing the memory utilization rate and realizing intelligent recommendation of the denture template model, only carries out intersection calculation on the effective intersection area of the model, greatly improves the speed of the intersection calculation, adopts an edge-triangle intersection method to replace the triangle-triangle intersection, ensures that the intersection is unique, reduces the redundancy of the intersection calculation, greatly improves the stability and the efficiency of an algorithm, simultaneously improves the quality of a Boolean operation result, can quickly judge the spatial position relation between a breakpoint and the model, is suitable for closed and open grid models, has stable and reliable algorithm, has fewer non-popular or unstable triangular patches in the Boolean operation result, can carry out continuous Boolean operation, and can be used for cutting simulation.
Claims (7)
1. An intelligent design method for removable partial denture is characterized in that: the method comprises the following steps:
step 1: establishing a client server, a modeling server and a 3D printing platform, wherein the client server, the modeling server and the 3D printing platform are communicated with each other through the Internet, and the modeling server and an intraoral scanner are communicated with each other through the Internet; a man-machine interaction interface is arranged on the 3D printing platform;
after the client side server acquires the electronic order, the electronic order is sent to the modeling server for confirmation;
step 2: after confirming the electronic order, the modeling server acquires initial oral modeling data through an intraoral scanner; the modeling server simultaneously sends the electronic order and the initial oral modeling data to the 3D printing platform;
step 3: after the 3D printing platform confirms the electronic order, an oral cavity model is established according to initial oral cavity modeling data;
the 3D printing platform selects a bracket co-locating channel according to the oral cavity model, simultaneously selects the position of the abutment, and generates modeling basic information according to the bracket co-locating channel and the position information of the abutment;
the 3D printing platform acquires tooth missing position information through an oral cavity model;
step 4: taking modeling basic information and tooth missing position information as conditions by the 3D printing platform, and calling a false tooth template model from a model database;
step 5: the 3D printing platform respectively carries out Boolean operation on the oral cavity model and the denture template model according to an edge-triangle intersection method, respectively carries out gridding treatment on the oral cavity model and the denture template model, establishes a world coordinate system, respectively records coordinate information and node numbers of each seed node selected in the oral cavity model and the denture template model in the world coordinate system when gridding treatment is carried out, simultaneously records areas and grid numbers of all sub-grids in gridding treatment, respectively establishes a grid growth relation list of the oral cavity model and the denture template model, and the grid growth relation list is used for representing the subordinate relation between the seed nodes and the sub-grids;
step 6: comparing the contact ratio between the seed nodes in the oral cavity model and the denture template model by taking the oral cavity model as a reference to generate a contact ratio report;
step 7: according to the coincidence report, comparing the number of triangular patches in the oral cavity model and the denture template model and the total area of all triangular patches to generate a grid comparison report;
step 8: determining the fit degree between the denture template model and the oral cavity model according to the coincidence degree report and the grid comparison report;
step 9: and (3) screening the denture template model with the highest degree of fit with the oral cavity model from a model database by the 3D printing platform according to the methods from the step 4 to the step 8 to serve as a reference denture model, and displaying the reference denture model to an operator through a human-computer interface.
2. The removable partial denture intelligent design method according to claim 1, wherein: the method for intersecting the triangle by the edge is that when the triangle-triangle intersection operation is carried out according to the Boolean operation method, only the edge of one triangle is used as a basic element to carry out the intersection operation with the other triangle, and the coordinate, the attribute and the index information of the intersection surface or the edge are recorded in the intersection operation process.
3. The removable partial denture intelligent design method according to claim 2, wherein: the principle of the method adopted in the process of establishing a grid growth relation list between seed nodes and sub grids of an oral cavity model or a denture template model is the same, and the method specifically comprises the following steps:
step S5-1: when Boolean operation is carried out on the oral cavity model or the false tooth template model, the internal and external properties of all the intersecting triangle endpoints are determined;
step S5-2: selecting an endpoint with an attribute being an inner attribute as a seed node, and performing grid growth by taking all intersecting line segments intersecting the seed node as boundaries until all communicated triangular patches are accessed;
step S5-3: forming a new sub-grid by using the triangular patches accessed in the step S5-2;
step S5-4: generating grids by using the methods from the step S5-2 to the step S5-3 until all endpoints of the internal attribute generate sub grids, and merging all generated sub grids to complete the whole Boolean operation;
step S5-5: and establishing a subordinate relation between the sub-grids and the seed nodes, and generating a grid growth relation list.
4. A removable partial denture intelligent design method according to claim 3, wherein: in executing step S5-1, the method for determining the internal and external properties of the end points of the intersected triangle is as follows:
step S5-1-1: let Δabc and Δefg intersect, the intersection line thereof be mn, n abc And n def The normal lines of Δabc and Δefg are (x n1 ,y n1 ,z n1 ) And (x) n2 ,y n2 ,z n2 ) The method comprises the steps of carrying out a first treatment on the surface of the The coordinates of a point on the plane of Δabc and Δefg are (x) 1 ,y 1 ,z 1 ) And (x) 2 ,y 2 ,z 2 ) The plane equations in which they lie are expressed by the following formulas, respectively:
F abc (x,y,z)＝(x-x 1 ).x n1 +(y-y 1 ).y n1 +(z-z 1 ).z n1 ＝0；
F def (x,y,z)＝(x-x 2 ).x n2 +(y-y 2 ).y n2 +(z-z 2 ).z n2 ＝0；
step S5-1-2: the intersection of the edge ac and the delta def is at a point n, and for a point a, the position relation of the point a and the model is judged, namely the coordinates of the point a are brought into a plane equation to solve: if F abc (x,y,z)<0, point a is located "inside" the model, its attribute is the inner attribute, and vice versa is the outer attribute.
5. The intelligent removable partial denture design method according to claim 4, wherein: when executing the step 6, the method specifically comprises the following steps:
step 6-1: presetting a radius threshold R, selecting any seed node j in the oral cavity model, and setting a search area by taking the seed node j as a circle center and the radius threshold R as a radius;
step 6-2: mapping the search area onto the denture template model to perform coincidence search, namely finding out seed nodes of all denture template models existing in the search area on the denture template model, if no seed node of the denture template model is found in the search area, marking the seed node j as an isolated node, and executing the step 6-4; if at least one seed node of the denture template model is found in the search area, establishing a denture template model seed node set, and executing the step 6-3;
step 6-3: screening out the seed nodes of the denture template model with the shortest distance to the seed node j from the seed node set of the denture template model, and marking the seed node as the seed node j 1 Seed node j 1 Regarding the node to coincide with the seed node j, marking the seed node j as a coincident node, and executing the step 6-4;
step 6-4: and (3) repeating the steps 6-1 to 6-3 until all the seed nodes in the oral cavity model are subjected to coincidence search, counting the number of coincident nodes and the number of isolated nodes, judging the coincidence degree according to the duty ratio of the coincident nodes and the isolated nodes in the seed nodes of the oral cavity model, and generating a coincidence degree report.
6. The removable partial denture intelligent design method according to claim 5, wherein: when executing the step 7, the method specifically comprises the following steps:
step S7-1: optionally selecting a coincidence node k from the coincidence list 1 And simultaneously find and coincide with the node k 1 Seed node k of false tooth template model regarded as coincidence 2 ；
Step S7-2: according to the coincident node k 1 Finding out all the corresponding grid growth relation lists belonging to the coincident node k 1 Is a sub-network k of (2) 1i I is a positive integer, and the sub-network k is counted 1i And calculates the number of all sub-networks k 1i Is a total area of (2);
step S7-3: according to the coincident node k 2 Corresponding grid growth relationship columnA table for finding all the slave nodes k 2 Is a sub-network k of (2) 2i I is a positive integer, and the sub-network k is counted 2i And calculates the number of all sub-networks k 2i Is a total area of (2);
step S7-4: and repeating the steps S7-1 to S7-3 until all the coincident nodes in the coincident ratio list are compared, and generating a grid comparison report according to the comparison result.
7. The removable partial denture intelligent design method as claimed in claim 6, wherein: when executing the step 8, the method specifically comprises the following steps:
step S8-1: presetting a coincidence degree threshold, finding out all false tooth template models with coincidence node proportion meeting the coincidence degree threshold according to a coincidence degree report, and establishing an alternative model set;
step S8-2: presetting a triangular patch number threshold and a total area threshold, screening all denture template models of which the total number of subnetworks accords with the triangular patch number threshold or the total area of subnetworks accords with the total area threshold from an alternative model set, and establishing a reference denture template model set;
step S8-3: and displaying the models in the reference denture template model set one by one to a user as reference denture template models.
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