CN116486035A - CAD body grid model editing and optimizing system based on characteristics - Google Patents
CAD body grid model editing and optimizing system based on characteristics Download PDFInfo
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Abstract
The invention relates to the field of computer programs and systems, in particular to a CAD body grid model editing and optimizing system based on characteristics, which comprises a pattern recognition processing module, a processor and a computer program, wherein the pattern recognition processing module is used for acquiring object data in an original model to obtain the original object data; a display for displaying the interactive three-dimensional object model to an end user; the method effectively fuses the tetrahedral mesh model and the hexahedral mesh model, when complex geometric figures are processed, model boundary lines can be initially determined through the tetrahedral mesh model, stress convergence and precision are ensured by the self-adaptive meshes, after the solid meshes are divided for the complex assembly, the hexahedral mesh model is used for editing and optimizing a local target area, and the optimized assembly is recombined into the three-dimensional target model.
Description
Technical Field
The invention relates to the field of computer programs and systems, in particular to a CAD body grid model editing and optimizing system based on characteristics.
Background
Along with the development of the time industry, in order to accelerate the development efficiency of products and shorten the development period, it is necessary to combine two processes of CAD and CAE, in this combination process, initially, a corresponding finite element analysis function is introduced into CAD software, so that after design, commercial CAD software such as SolidWorks, UG/NX has been provided with a part of the function of finite element analysis, however, these commercial software also simply unifies CAD and CAE data. If the model is changed, the whole model still needs to be simplified, meshed and the like.
Therefore, researchers directly edit finite element grids for analysis, and finite element analysis is used as discretization of a model which is widely applied in the prior art, and the model discretization mainly converts the model into a tetrahedral grid model or a hexahedral grid model. However, there is a wide range of controversy regarding whether tetrahedral or semi-automatic hexahedral mesh is employed in finite element solid modeling.
For an assembled entity structure comprising local thin-shell characteristics, under the action of concentrated load, different material properties and different unit extensibility generated by automatic grid division can influence the calculation accuracy of units, and not only unit types can influence the unit extensibility, but also complex design often brings large-scale freedom degree problems, and the symptom of the problems is how to obtain accurate calculation results of complex areas;
for simple geometry, or a model that can be conveniently manually meshed, more hexahedral meshes that rely on 8 nodes are commonly referred to as "brickwork units"; while for complex geometric models, automatic or semi-automatic meshing is usually adopted, and algorithms for automatically generating meshes usually adopt tetrahedrons instead of hexahedrons, because a typical three-dimensional model cannot be accurately described by hexahedral tiling, but is always split into a set of tetrahedral units.
We also always compare tetrahedral and hexahedral partitioned models in structural study analysis and get a comparison of more reliable calculation results. For either cell type, a small number of nodes results in low accuracy. The 4-node tetrahedrons and 8-node hexahedrons are typically used in the approximate straight line boundary model, whereas for the curved boundary model, more nodes and cell numbers are required for more accurate solution, or 10-node secondary tetrahedrons and 20-node secondary hexahedrons are employed. It is worth noting that the use of semi-automatic is often the best choice with secondary tetrahedrons, due to the much limited division of automatic hexahedrons, which can take a lot of time.
Based on the problems, the method fuses the tetrahedral grid model and the hexahedral grid model, so that the entity can be subjected to boundary constraint through the tetrahedral grid model to obtain the integral structure of the model, and the hexahedral grid model is used for geometric optimization when the entity is realized.
Disclosure of Invention
The invention aims to provide a CAD body grid model editing and optimizing system based on characteristics, which aims to solve the problems in the background technology.
In order to achieve the above purpose, the present invention provides the following technical solutions: a CAD body grid model editing and optimizing system based on characteristics comprises
The image recognition processing module is used for acquiring object data in the original model to obtain original object data, and recognizing the original object data to identify a plurality of characteristics of different types and generate functional data representations of the plurality of characteristics, which are called functional data hereinafter;
the processor is used for analyzing the functional data, reading the functional data into a corresponding body grid model, constructing a body grid model, dividing the outer surface area of the body grid model, converting the divided outer surface area into a plurality of operation areas, editing and optimizing the operation areas, and combining the operation areas to construct the interactive three-dimensional target model;
a display for displaying the interactive three-dimensional object model to an end user.
The body grid model comprises a tetrahedral grid model and a hexahedral grid model; the volumetric mesh model of the optimization system is a dual option, setting the tetrahedral mesh model to a default mode in use, only if specifically selected, will a hexahedral mesh model be inserted.
The editing optimization method adopted by the editing optimization system comprises the following operation steps:
s1: reading in original object data, identifying functional data of a plurality of features,
s2: judging whether the functional data are simple geometric figures or not according to the actual demands of the users, if so, selecting to import a tetrahedral mesh model, and executing a step S3; if not, importing the hexahedral mesh model, and executing the step S4;
s3: importing original object data into a tetrahedral mesh model, comprising the steps of:
s31: reading original object data into a tetrahedral mesh model, constructing a first target model, dividing the outer surface area of the first target model, and extracting a corresponding outer surface area;
s32: the method comprises the steps that a user interactively selects an outer surface area as an operation area of a shape feature to be edited;
s33: repeatedly analyzing the operation area of each shape feature selected by the user, judging whether the shape feature is a simple geometric figure or not, if so, selecting to import a hexahedral mesh model, and executing the step S4; if not, continuing to execute the next step in the tetrahedral mesh model, and executing step S34;
s34: establishing a local coordinate system for an operation area which is selected by a user and is imported into the tetrahedral mesh model, wherein an operation point corresponding to the operation area is used as an origin of the current local coordinate system; coordinates of all vertexes of the grid in a current local coordinate system are calculated and recorded, so that the whole grid can be conveniently edited through an operation area and an operation point;
s35: constructing MATLAB optimization linear programming algorithm for each 'operation point', solving an optimization equation by using a linprog () function, and solving an optimal solution of each 'operation point' to each vertex on the grid, so that the coordinates of each vertex are updated, the initial editing operation of the model is completed, and then, importing the model into step S4;
s4: importing original object data into a hexahedral mesh model, comprising the steps of:
s41: according to the input operation area, whether the space and direction information of the determined 'target area' exists, if so, the step S45 is carried out; if not, go to step S42;
s42: constructing a coordinate system of a local 'target area' according to an operable area of input original object data or an operating area of a shape feature to be edited, wherein an 'operating point' corresponding to the 'target area' is a centroid, the operating point is taken as an origin of the coordinate system, n rays are emitted from the centroid, and an intersection point of the rays and a contour line is taken as a feature point;
s43: in registration based on feature points, n feature points are extracted from a 'target area', and U and V respectively represent feature point sets in an input and reference model in the 'target area', wherein U= { U 1 ，u 2 ，…，u n }，V＝{v 1 ，v 2 ，…，v n One should seek a transformation function T (u i )＝v i The method comprises the steps of carrying out a first treatment on the surface of the Thereby obtaining feature points;
s44: on the outer surface grid of the 'target area', obtaining a plurality of needed characteristic points in the whole target area, and obtaining symbiotic entropy by adopting a gray level symbiotic matrix for the characteristic points, thereby determining the space and direction information of the 'target area';
s45: editing the whole grid through the target area, the mass center and the characteristic points under the guidance of the space and the direction information of the target area; the initial hexahedral mesh model is moved to the entity boundary from inside to outside, and hexahedral mesh of the entity body domain is generated;
s5: and editing and adjusting the body grid model to obtain the three-dimensional target model.
Solving an optimization equation according to a linprog () function in the MATLAB optimization linear programming algorithm involved in step S35 is:
[x，fval，exitflag，output，lambda]＝linprog(f，A，b，Aeq，beq，lb)
wherein x: representing the optimal solution, fval: representing the optimal value of the objective function, exitflag: indicating whether the result of the solution is success or failure, output: various output information in the optimization process, lambda: clathrating the lagrangian multiplier at the optimal solution by the structure; wherein exitflag=1 represents that the result of the solution is successful, if other numbers represent failure;
f: objective function coefficient matrix, a: inequality constrained coefficient matrix, b: a constant vector of inequality constraints; aeq: equation constrained coefficient matrix, beq: equation constrained constant vector, lb: representing the upper and lower ranges of the argument.
According to the transformation function of the feature point set obtained in step S42, the specific function formula is:
wherein,,is an m-th order polynomial; x= (x, y) T ，β i For the pending registration parameters, taking into account that the image may have affine transformations, here m=1, r s (x) Consists of the sum of n radial basis functions R (R):
wherein: r is R s (x) The value of (2) depends on the point x to the feature point u i Distance alpha of (a) i For undetermined registration parameters, to ensure that the elastic transformation at infinity is 0, the constraint needs to be satisfied:
using a transform T (u) for n pairs of feature points i )＝v i I=1, … n, the following set of equations can be obtained in combination with the constraints:
wherein: k is n×n submatrix, element K ij ＝R(||u i -u j ||) is provided; p is n×m submatrices, elementsV k ＝(v k ，1，…，v k ，n) T K=1, 2, respectively representing coordinates of the two-dimensional image; alpha= (alpha) 1 ，…，α n )T，β＝(β 1 ，…，β n ) T ；
After the corresponding feature points are extracted, solving an equation to obtain registration parameters of the image along the coordinate direction of the two-dimensional image, thereby obtaining the feature points.
After extracting the corresponding feature points in step S43, a gray level co-occurrence matrix is used for the feature points to obtain co-occurrence entropy, wherein the co-occurrence entropy is obtained by the gray level co-occurrence matrix, and specifically comprises:
the gray value i of one pixel, the gray value j of the adjacent pixel, if the gray value i of the adjacent pixel is j, the probability of the two gray pixel pairs simultaneously appearing is recorded as p (i, j); h C0 (x) The gray level co-occurrence matrix is a gray level co-occurrence matrix, which not only contains gray level statistical information of an image, but also reflects space and direction information of gray level distribution, i.e., probability of occurrence of a pair of adjacent pixels in different directions, and entropy reaches a maximum value when all p (i, j) are equal.
According to the change of the position of the feature point extracted in step S43, the corresponding symbiotic entropy is changed, and the space and direction information can be rapidly acquired through the gray level symbiotic matrix, so that the space and direction information of the "target area" can be determined.
Compared with the prior art, the invention has the beneficial effects that:
1. the method effectively fuses the tetrahedral mesh model and the hexahedral mesh model, when complex geometric figures are processed, model boundary lines can be initially determined through the tetrahedral mesh model, stress convergence and precision are ensured by the self-adaptive meshes, after the solid meshes are divided for the complex assembly, the hexahedral mesh model is used for editing and optimizing a local target area, and the optimized assembly is recombined into the three-dimensional target model.
2. The algorithm can quickly and effectively edit the CAD model of the body grid, and maintain the semantic shape characteristics of the model. The algorithm utilizes the characteristics of the finite element grids, solves the optimization equation through the linprog () function to obtain the optimal solution, has higher solving efficiency for a huge number of volumetric grid models, and is suitable for users to edit local shape features in real time according to the needs.
3. The method and the device reconstruct the vertex of the body grid through affine transformation of the variable area, can be suitable for being applied to modifying the parameters of the shape characteristics on the grid model, and obtain the effect of locally modifying the body grid model.
4. The algorithm of the invention can directly carry out local editing on the finite element grid model, optimize the grid region with poor quality after editing, accurately maintain the original characteristics of the model in the whole process, effectively solve the problem of repeated re-gridding in actual engineering, accelerate the development speed of products and have higher practical significance
5. The algorithm of the invention respectively performs targeted topological optimization and geometric optimization operations on hexahedrons positioned on the outer surface and the inner part of the grid through concepts such as a target area, a characteristic point and the like which are defined on the surface of the CAD grid body and used for representing the shape characteristics, and can rapidly perform body grid optimization on the area after the CAD grid model is locally edited while maintaining the shape characteristics of the model, so that the optimized grid model can meet the requirement of finite element analysis.
6. The application of the mixed iteration and gray level co-occurrence matrix saves CPU time and storage space when solving the large-scale freedom degree problem, greatly reduces the number of units when guaranteeing the equivalent freedom degree level, and obtains more accurate results, namely, the solving speed is faster under the condition of the same solving precision.
Drawings
FIG. 1 is a flow chart of the present invention;
fig. 2 is a structural diagram of the system of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Examples: referring to FIG. 1, a feature-based CAD body mesh model editing optimization system includes
The image recognition processing module is used for acquiring object data in the original model to obtain original object data, and recognizing the original object data to identify a plurality of characteristics of different types and generate functional data representations of the plurality of characteristics, which are called functional data hereinafter;
the processor is used for analyzing the functional data, reading the functional data into a corresponding body grid model, constructing a body grid model, dividing the outer surface area of the body grid model, converting the divided outer surface area into a plurality of operation areas, editing and optimizing the operation areas, and combining the operation areas to construct the interactive three-dimensional target model;
a display for displaying the interactive three-dimensional object model to an end user.
The body grid model comprises a tetrahedral grid model and a hexahedral grid model; the volumetric mesh model of the optimization system is a dual option, setting the tetrahedral mesh model to a default mode in use, only if specifically selected, will a hexahedral mesh model be inserted.
The editing optimization method adopted by the editing optimization system comprises the following operation steps:
s1: reading in original object data, identifying functional data of a plurality of features,
s2: judging whether the functional data are simple geometric figures or not according to the actual demands of the users, if so, selecting to import a tetrahedral mesh model, and executing a step S3; if not, importing the hexahedral mesh model, and executing the step S4;
s3: importing original object data into a tetrahedral mesh model, comprising the steps of:
s31: reading original object data into a tetrahedral mesh model, constructing a first target model, dividing the outer surface area of the first target model, and extracting a corresponding outer surface area;
s32: the method comprises the steps that a user interactively selects an outer surface area as an operation area of a shape feature to be edited;
s33: repeatedly analyzing the operation area of each shape feature selected by the user, judging whether the shape feature is a simple geometric figure or not, if so, selecting to import a hexahedral mesh model, and executing the step S4; if not, continuing to execute the next step in the tetrahedral mesh model, and executing step S34;
s34: establishing a local coordinate system for an operation area which is selected by a user and is imported into the tetrahedral mesh model, wherein an operation point corresponding to the operation area is used as an origin of the current local coordinate system; coordinates of all vertexes of the grid in a current local coordinate system are calculated and recorded, so that the whole grid can be conveniently edited through an operation area and an operation point;
s35: constructing MATLAB optimization linear programming algorithm for each 'operation point', solving an optimization equation by using a linprog () function, and solving an optimal solution of each 'operation point' to each vertex on the grid, so that the coordinates of each vertex are updated, the initial editing operation of the model is completed, and then, importing the model into step S4;
s4: importing original object data into a hexahedral mesh model, comprising the steps of:
s41: according to the input operation area, whether the space and direction information of the determined 'target area' exists, if so, the step S45 is carried out; if not, go to step S42;
s42: constructing a coordinate system of a local 'target area' according to an operable area of input original object data or an operating area of a shape feature to be edited, wherein an 'operating point' corresponding to the 'target area' is a centroid, the operating point is taken as an origin of the coordinate system, n rays are emitted from the centroid, and an intersection point of the rays and a contour line is taken as a feature point;
s43: in registration based on feature points, n feature points are extracted from a 'target area', and U and V respectively represent feature point sets in an input and reference model in the 'target area', wherein U= { U 1 ，u 2 ，…，u n }，V＝{v 1 ，v 2 ，…，v n One should seek a transformation function T (u i )＝v i The method comprises the steps of carrying out a first treatment on the surface of the Thereby obtaining feature points;
s44: on the outer surface grid of the 'target area', obtaining a plurality of needed characteristic points in the whole target area, and obtaining symbiotic entropy by adopting a gray level symbiotic matrix for the characteristic points, thereby determining the space and direction information of the 'target area';
s45: editing the whole grid through the target area, the mass center and the characteristic points under the guidance of the space and the direction information of the target area; the initial hexahedral mesh model is moved to the entity boundary from inside to outside, and hexahedral mesh of the entity body domain is generated;
s5: and editing and adjusting the body grid model to obtain the three-dimensional target model.
Solving an optimization equation according to a linprog () function in the MATLAB optimization linear programming algorithm involved in step S35 is:
[x，fval，exitflag，output，lambda]＝linprog(f，A，b，Aeq，beq，lb)
wherein x: representing the optimal solution, fval: representing the optimal value of the objective function, exitflag: indicating whether the result of the solution is success or failure, output: various output information in the optimization process, lambda: clathrating the lagrangian multiplier at the optimal solution by the structure; wherein exitflag=1 represents that the result of the solution is successful, if other numbers represent failure;
f: objective function coefficient matrix, a: inequality constrained coefficient matrix, b: a constant vector of inequality constraints; aeq: equation constrained coefficient matrix, beq: equation constrained constant vector, lb: representing the upper and lower ranges of the argument.
According to the transformation function of the feature point set obtained in step S42, the specific function formula is:
wherein,,is an m-th order polynomial; x= (x, y) T ，β i For the pending registration parameters, taking into account that the image may have affine transformations, here m=1, r s (x) Consists of the sum of n radial basis functions R (R):
wherein: r is R s (x) The value of (2) depends on the point x to the feature point u i Distance alpha of (a) i For undetermined registration parameters, to ensure that the elastic transformation at infinity is 0, the constraint needs to be satisfied:
using transformations on n pairs of feature pointsT(u i )＝v i I=1, … n, the following set of equations can be obtained in combination with the constraints:
wherein: k is n×n submatrix, element K ij ＝R(||u i -u j ||) is provided; p is n×m submatrices, elementsV k ＝(v k ，1，…，v k ，n) T K=1, 2, respectively representing coordinates of the two-dimensional image; alpha= (alpha) 1 ，…，α n )T，β＝(β 1 ，…，β n ) T ；
After the corresponding feature points are extracted, solving an equation to obtain registration parameters of the image along the coordinate direction of the two-dimensional image, thereby obtaining the feature points.
After extracting the corresponding feature points in step S43, a gray level co-occurrence matrix is used for the feature points to obtain co-occurrence entropy, wherein the co-occurrence entropy is obtained by the gray level co-occurrence matrix, and specifically comprises:
the gray value i of one pixel, the gray value j of the adjacent pixel, if the gray value i of the adjacent pixel is j, the probability of the two gray pixel pairs simultaneously appearing is recorded as p (i, j); h C0 (x) The gray level co-occurrence matrix is a gray level co-occurrence matrix, which not only contains gray level statistical information of an image, but also reflects space and direction information of gray level distribution, i.e., probability of occurrence of a pair of adjacent pixels in different directions, and entropy reaches a maximum value when all p (i, j) are equal.
According to the change of the position of the feature point extracted in step S43, the corresponding symbiotic entropy is changed, and the space and direction information can be rapidly acquired through the gray level symbiotic matrix, so that the space and direction information of the "target area" can be determined.
Firstly, the invention carries out editing optimization based on a commonly-used volumetric grid model of the CAD existing at present, and the model of the simple geometric or conveniently manually divided grids is more dependent on a hexahedral grid model, which is commonly called a brick laying unit, so as to carry out optimization treatment on a simple geometric figure, wherein the part is a familiar technology in the current industrial design, and the hexahedral grid model is moved to a solid boundary from inside to outside to generate a hexahedral grid of a solid domain, thereby obtaining the three-dimensional target model.
The core point of the invention is how to process complex geometric models, generally, the complex geometric models are automatically generated into grids by adopting a tetrahedron grid model, thus a target model can be quickly constructed, the external surface area of a first target model is segmented, the corresponding external surface area is extracted, wherein the segmented part area is judged to be a complex geometric figure, firstly, the optimal solution of each operating point of each 'operating area' to each vertex on the grid is obtained by solving an optimization equation by utilizing a linprog () function, the coordinates of each vertex are updated, and the initial editing operation of the model is completed,
then, the area for obtaining the preliminary boundary model is imported into a hexahedral grid model, characteristic points are found through the target area and the centroid, the space and the direction information of the target area are determined by adopting a gray level co-occurrence matrix, and the whole grid is edited through the target area, the centroid and the characteristic points under the guidance of the space and the direction information of the target area; and (3) moving the initial hexahedral mesh model to the solid boundary from inside to outside to generate a hexahedral mesh of the solid body domain, thereby obtaining the three-dimensional target model.
In practical application, as the parameter information related in the hexahedral mesh model is excessive, the reaction time of hardware and software is too long, and the hexahedral mesh model is used on the basis of the tetrahedral mesh model, so that the time of the software and hardware reaction is saved to a certain extent, and the original object data division range is enlarged; the basic structure of the existing CAD body grid model is not changed, a degradation unit at the boundary can be eliminated through the existing basic operation, and the quality of the unit is improved through the hexahedral grid model, so that the final hexahedral grid model is obtained. Experimental results show that the method can be used for conveniently and efficiently interactively generating.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (7)
1. A CAD body grid model editing and optimizing system based on characteristics is characterized in that: comprising
The image recognition processing module is used for acquiring object data in the original model to obtain original object data, and recognizing the original object data to identify a plurality of characteristics of different types and generate functional data representations of the plurality of characteristics, which are called functional data hereinafter;
the processor is used for analyzing the functional data, reading the functional data into a corresponding body grid model, constructing a body grid model, dividing the outer surface area of the body grid model, converting the divided outer surface area into a plurality of operation areas, editing and optimizing the operation areas, and combining the operation areas to construct the interactive three-dimensional target model;
a display for displaying the interactive three-dimensional object model to an end user.
2. A feature-based CAD body mesh model editing optimization system as recited in claim 1, wherein: the body grid model comprises a tetrahedral grid model and a hexahedral grid model; the volumetric mesh model of the optimization system is a dual option, setting the tetrahedral mesh model to a default mode in use, only if specifically selected, will a hexahedral mesh model be inserted.
3. A feature-based CAD body mesh model editing optimization system as recited in claim 1, wherein: the editing optimization method adopted by the editing optimization system comprises the following operation steps:
s1: reading in original object data, identifying functional data of a plurality of features,
s2: judging whether the functional data are simple geometric figures or not according to the actual demands of the users, if so, selecting to import a tetrahedral mesh model, and executing a step S3; if not, importing the hexahedral mesh model, and executing the step S4;
s3: importing original object data into a tetrahedral mesh model, comprising the steps of:
s31: reading original object data into a tetrahedral mesh model, constructing a first target model, dividing the outer surface area of the first target model, and extracting a corresponding outer surface area;
s32: the method comprises the steps that a user interactively selects an outer surface area as an operation area of a shape feature to be edited;
s33: repeatedly analyzing the operation area of each shape feature selected by the user, judging whether the shape feature is a simple geometric figure or not, if so, selecting to import a hexahedral mesh model, and executing the step S4; if not, continuing to execute the next step in the tetrahedral mesh model, and executing step S34;
s34: establishing a local coordinate system for an operation area which is selected by a user and is imported into the tetrahedral mesh model, wherein an operation point corresponding to the operation area is used as an origin of the current local coordinate system; coordinates of all vertexes of the grid in a current local coordinate system are calculated and recorded, so that the whole grid can be conveniently edited through an operation area and an operation point;
s35: constructing MATLAB optimization linear programming algorithm for each 'operation point', solving an optimization equation by using a linprog () function, and solving an optimal solution of each 'operation point' to each vertex on the grid, so that the coordinates of each vertex are updated, the initial editing operation of the model is completed, and then, importing the model into step S4;
s4: importing original object data into a hexahedral mesh model, comprising the steps of:
s41: according to the input operation area, whether the space and direction information of the determined 'target area' exists, if so, the step S45 is carried out; if not, go to step S42;
s42: constructing a coordinate system of a local 'target area' according to an operable area of input original object data or an operating area of a shape feature to be edited, wherein an 'operating point' corresponding to the 'target area' is a centroid, the operating point is taken as an origin of the coordinate system, n rays are emitted from the centroid, and an intersection point of the rays and a contour line is taken as a feature point;
s43: in the registration based on the feature points, n feature points are extracted from the 'target region', so that U and V respectively represent feature point sets in the input and reference models in the 'target region', wherein U=u 1 ，u 2 ，…，u n }，V＝{v 1 ，v 2 ，…，v n One should seek a transformation function T (u i )＝v i The method comprises the steps of carrying out a first treatment on the surface of the Thereby obtaining feature points;
s44: on the outer surface grid of the 'target area', obtaining a plurality of needed characteristic points in the whole target area, and obtaining symbiotic entropy by adopting a gray level symbiotic matrix for the characteristic points, thereby determining the space and direction information of the 'target area';
s45: editing the whole grid through the target area, the mass center and the characteristic points under the guidance of the space and the direction information of the target area; the initial hexahedral mesh model is moved to the entity boundary from inside to outside, and hexahedral mesh of the entity body domain is generated;
s5: and editing and adjusting the body grid model to obtain the three-dimensional target model.
4. A feature-based CAD body mesh model editing optimization system as recited in claim 3, wherein: solving an optimization equation according to a linprog () function in the MATLAB optimization linear programming algorithm involved in step S35 is:
[x，fval，exitflag，output，lambda]＝linprog(f，A，b，Aeq，beq，lb)
wherein x: representing the optimal solution, fval: representing the optimal value of the objective function, exitflag: indicating whether the result of the solution is success or failure, output: various output information in the optimization process, lambda: clathrating the lagrangian multiplier at the optimal solution by the structure; wherein exitflag=1 represents that the result of the solution is successful, if other numbers represent failure;
f: objective function coefficient matrix, a: inequality constrained coefficient matrix, b: a constant vector of inequality constraints; aeq: equation constrained coefficient matrix, beq: equation constrained constant vector, lb: representing the upper and lower ranges of the argument.
5. A feature-based CAD body mesh model editing optimization system as recited in claim 3, wherein: according to the transformation function of the feature point set obtained in step S42, the specific function formula is:
wherein,,is an hierarchical polynomial; x= (x, y) T ，β i For the registration parameters to be determined, taking into account that the image may have affine transformations, here we take the information = 1, r s (x) Consists of the sum of n radial basis functions R (R):
wherein: r is R s (x) The value of (2) depends on the point x to the feature point u i Distance alpha of (a) i For undetermined registration parameters, to ensure that the elastic transformation at infinity is 0, the constraint needs to be satisfied:
using a transform T (u) for n pairs of feature points i )＝v i I=1, ×n, the following set of equations can be obtained in combination with the constraint:
wherein: k is an n=n submatrix, element K ij ＝R(||u i -u j ||) is provided; p is n×m submatrices, elementsV k ＝(v k ，1，…，v k ，n) T K=1, 2, respectively representing coordinates of the two-dimensional image; alpha= (alpha) 1 ，…，α n )T，β＝(β 1 ，…，β n ) T ；
After the corresponding feature points are extracted, solving an equation to obtain registration parameters of the image along the coordinate direction of the two-dimensional image, thereby obtaining the feature points.
6. A feature-based CAD body mesh model editing optimization system as recited in claim 3, wherein: after extracting the corresponding feature points in step S43, a gray level co-occurrence matrix is used for the feature points to obtain co-occurrence entropy, wherein the co-occurrence entropy is obtained by the gray level co-occurrence matrix, and specifically comprises:
the gray value i of one pixel, the gray value j of the adjacent pixel, if the gray value i of the adjacent pixel is j, the probability of the two gray pixel pairs simultaneously appearing is recorded as p (i, j);the gray level co-occurrence matrix is a gray level co-occurrence matrix, which not only contains gray level statistical information of an image, but also reflects space and direction information of gray level distribution, i.e., probability of occurrence of a pair of adjacent pixels in different directions, and entropy reaches a maximum value when all p (i, j) are equal.
7. The feature-based CAD body mesh model editing optimization system of claim 6, wherein: according to the change of the position of the feature point extracted in step S43, the corresponding symbiotic entropy is changed, and the space and direction information can be rapidly acquired through the gray level symbiotic matrix, so that the space and direction information of the "target area" can be determined.
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