CN110334108A - A kind of three-dimensional CAD model similarity calculation method based on discrete bat algorithm - Google Patents
A kind of three-dimensional CAD model similarity calculation method based on discrete bat algorithm Download PDFInfo
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Abstract
The present invention relates to a kind of three-dimensional CAD model similarity calculation methods based on discrete bat algorithm, the number of edges difference matched according to source model with each face of object module pair calculates the shape similarity in face, and the structural similarity of face matching pair is calculated by syntople of the face matching between.During iteration seeks optimal face matched sequence, bat position is updated using two element optimisation strategies, to keep bat group mobile to the higher direction of total similarity in face.Optimize by successive ignition, obtains optimal face matching pair, the comprehensive similarity of source model and object module is calculated finally by formula.The present invention is the three-dimensional CAD model search method of high efficient and reliable, the Given information according to present in three-dimensional CAD model excavates implicit information, and according to its feature and Search Requirement, the retrieval mode for more meeting user demand is provided, it can apply in large-scale CAD model library, more accurately be retrieved and improve recall precision.
Description
Technical field
The present invention relates to information retrievals；And its technical field of database structure, in particular to it is a kind of to be based on discrete bat
The three-dimensional CAD model similarity calculation method of algorithm.
Background technique
In recent years, with the development of computer software and 3-D technology, CAD modeling technique is widely applied to the life of people
In the every field such as production, life, the development of processing manufacturing industry is not only pushed, people's lives are also facilitated.Due to enterprise
Increasing, increasing and people's demand the increase of three-dimensional CAD model, demand of the people to product is also more and more diversified,
To show one's talent in fierce market competition it is necessary to consider the multiplexing to existing procucts, thus reduce it is duplicate, unnecessary
Design, and shorten the production time, reduce development cost.
Three-dimensional CAD model retrieval can go out reusable part or component according to the conditional information retrieval that designer needs, and reduce
Unnecessary in product design, development process repeats step, improves the exploitation design efficiency of product, to obtain larger
Income；And in huge reusable resource library, since many threedimensional models possess complicated structure and diversified mostly
Classification, how efficiently to inquire and retrieve reusable resource is the key that one of three-dimensional model search.So far, both at home and abroad
Expert and scholar a large amount of research and experiment have been carried out to CAD model retrieval technique, also produce many outstanding problem moulds
Type, retrieval and optimization method.
In the prior art, common method for searching three-dimension model includes based on content, based on geometric transformation and is based on
The search method of graphics Web publishing etc., but there is also more or less problems for these methods；In the search method based on figure, hold
Some profound levels, such as semantic feature information are easily omitted, cause to misread model；When using feature descriptive model, by
In there are the describing modes of a variety of pairs of features, without consistent standard, it is to a certain extent and unreliable to cause to retrieve；And it is based on
It is using cognition of the people to model that the retrieval of content, which is understood to, i.e. the high-level semantic of model measures the similitude between model,
To be retrieved, but due to the subjectivity of cognition, cause existing " semantic between high-level semantics information and bottom visual information
Wide gap ".
Summary of the invention
The present invention solves problems of the prior art, provides a kind of three based on discrete bat algorithm of optimization
Victoria C AD model similarity calculation method.
The technical scheme adopted by the invention is that a kind of three-dimensional CAD model Similarity measures based on discrete bat algorithm
Method the described method comprises the following steps:
Step 1: enabling source model there are n different faces, object module, there are m different faces, n < m；Calculate source model
Face and each face of object module between similarity, construct similarity matrix Sa；
Step 2: the adjacent corresponding relationship matrix S in the face of building source model and object moduleb；
Step 3: according to obverse similarity and adjacent corresponding relationship, optimal face matching sequence being obtained based on bat algorithm
Column；
Step 4: the similitude of source model and object module being calculated with optimal face matching sequence.
Preferably, in the step 1, with f1, f2Represent two different faces, the face of any source model and either objective mould
Similarity between the face of type isWherein, N (f) indicates the side that face f is possessed
Quantity.
Preferably, all faces of source model and object module constitute shape similarity matrix Sa,
Preferably, in the step 2, the attribute adjacency matrix A of source model is enabled1, the attribute adjacency matrix A of object module2,
Obtain the adjacent corresponding relationship matrix S in face of source model and object moduleb,Wherein, fiAnd fjI-th of face respectively in source model
With j-th of face, fpAnd fqP-th of face and q-th of face respectively in object module.
Preferably, the step 3 the following steps are included:
Step 3.1: generating the face similarity matrix A (n, m) of source model and object module；
Step 3.2: according to face similarity matrix A initialization population, determining that bat number is num；
Step 3.3: initialization maximum impulse loudness A0, emission maximum frequency R0, search pulse frequency range [fmin, fmax],
The attenuation coefficient α of volume, the enhancing coefficient gamma of search rate, maximum number of iterations iter_max；The fitness of initialization population
Function f (xi)；
Step 3.4: judging whether the number of iterations has reached maximum number of iterations iter_max, if satisfied, then going to step
3.10, otherwise, carry out in next step；
Step 3.5: updating speed and position of each bat at each moment, fi=fmin+(fmax-fmin) × β,Wherein, t is t moment, and i is the index of bat；
Step 3.6: generating number rand1If rand1> Ri, an optimal solution x is selected from current optimal solution set*Carry out with
Machine disturbance takes the smallest two faces matching pair of shape similarity, exchanges matching face, obtain new explanation xnewAnd calculate its fitness value；
Step 3.7: generating number rand2If rand2< AiAnd f (xnew) < f (x*), then receive new explanation, otherwise, returns
Step 3.4；
Step 3.8: updating loudness by formulaUpdate transmitting frequency
Step 3.9: the fitness value of all bats being ranked up, current globally optimal solution and optimal value are found out；It returns
Return step 3.4；
Step 3.10: output optimal solution is that optimal face matches the optimal face matching sequence of sequence are as follows: { f1/f′1, f2/f
′2..., fn/f′n}。
Preferably, in the step 3.3,
Wherein, u is the index in face in model.
Preferably, in the step 4, similitude Simwhole,
The present invention provides a kind of three-dimensional CAD model similarity calculation method based on discrete bat algorithm of optimization, roots
The number of edges difference matched according to source model with each face of object module pair calculates the shape similarity in face, through face matching between
Syntople come calculate face matching pair structural similarity.During iteration seeks optimal face matched sequence, utilize
Two element optimisation strategies are updated bat position, to keep bat group mobile to the higher direction of total similarity in face.
Optimize by successive ignition, obtain optimal face matching pair, it is similar to the synthesis of object module to calculate source model finally by formula
Degree.
The present invention is the three-dimensional CAD model search method of high efficient and reliable, the Given information according to present in three-dimensional CAD model
Implicit information is excavated, and according to its feature and Search Requirement, provides the retrieval mode for more meeting user demand, can be applied
In large-scale CAD model library, is more accurately retrieved and improve recall precision.
Detailed description of the invention
Fig. 1 is flow chart of the invention；
Fig. 2 is model schematic of the invention, wherein f1、f2、f3、f4、f5、f6For different faces；
Fig. 3 is the attribute adjacent map schematic diagram of the model of Fig. 2.
Specific embodiment
The present invention is described in further detail below with reference to embodiment, but protection scope of the present invention is not limited to
This.
The present invention relates to a kind of three-dimensional CAD model similarity calculation method based on discrete bat algorithm, the method packets
Include following steps.
Step 1: enabling source model there are n different faces, object module, there are m different faces, n < m；Calculate source model
Face and each face of object module between similarity, construct similarity matrix Sa。
In the step 1, with f1, f2Represent two different faces, the face of the face of any source model and either objective model it
Between similarity beWherein, N (f) indicates the number on the side that face f is possessed
Amount.
All faces of source model and object module constitute shape similarity matrix Sa,
In the present invention, in three-dimensional CAD model, a model includes multiple faces, each face includes multiple summits, therefore two
The similarity of model can be portrayed by the similarity in each face, e.g., be calculated according to the quantity variance on side, if the number of edges in two faces
Difference is smaller, then the shape in the two faces is more similar, and number of edges differs more, then shape difference is bigger, and then must take office
Similarity Sim (f between the face of one source model and the face of either objective model1, f2)。
Step 2: the adjacent corresponding relationship matrix S in the face of building source model and object moduleb。
In the step 2, the attribute adjacency matrix A of source model is enabled1, the attribute adjacency matrix A of object module2, obtain source mould
The face of type and object module abuts corresponding relationship matrix Sb,
Wherein, fiAnd fjI-th of face and j-th of face respectively in source model, fpAnd fqP-th face respectively in object module and
Q-th of face.
In the present invention, the attribute adjacent map of model and the number of edges in each face can be extracted, constructs source model and target mould
The adjacent corresponding relationship matrix S in the face of typeb。
In the present invention, in the product exchange standard model of international standard, CAD model often is indicated with BREP B reps
Internal structure, to model to CAD model inside, B reps are between a kind of using face, side and vertex
Correlation describes the representation method of shape and geometry.In B reps, the surface of entity is usually by face (FaceBound)
Union indicate, and each face by the definition of the geometric curved surfaces (Surface) where it plus its boundary (Loop) come table
Showing, the boundary in face is by the union of side (Edge), and side is by geometrical curve (Curve) and vertex (Vertex) come what is indicated,
Vertex is indicated by geometric point (Point) again；In addition, the position (coordinate of point) etc. of the shape on side, vertex in three dimensions
It is all geological information；In general, geological information describes size, size, position, shape of body etc..
In the present invention, attribute adjacent map is a kind of for describing the graph structure of CAD model internal feature and characteristic relation, often
A face in one vertex correspondence model, the relationship between face and face in the corresponding model of relationship between vertex and vertex.
In the present invention, during three-dimensional model search, if the attribute adjacency matrix A of source model1, the attribute of object module
Adjacency matrix A2, the adjoining corresponding relationship matrix S in two each faces of model can be obtained according to the attribute adjacent map of two modelsb。
In the present invention,Wherein, i, j=1,2 ..., n；
P, q=1,2 ..., m；
If i-th of face of source model and j-th of face intersection, and p-th of face and q-th of face intersection in object module, i.e. A1
(fi, fj)=1&&A2(fp, fq)=1, then Sb(fi, fj, fp, fq)=1；
If i-th of face of source model and j-th of face intersection, p-th of face and q-th of face are non-intersecting in object module, i.e. A1
(fi, fj)=1&&A2(fp, fq)=0, then Sb(fi, fj, fp, fq)=0.5；
If p-th of face and q-th of face intersection in object module, i-th of the face and j-th of face of source model are non-intersecting, i.e. A1
(fi, fj)=0&&A2(fp, fq)=1, then Sb(fi, fj, fp,fQ)=0.5；
If i-th of the face and j-th of face of source model are non-intersecting, and p-th of face and q-th of face also not phase in object module
It hands over, i.e. A1(fi, fj)=0&&A2(fp, fq)=0, then Sb(fi, fj, fp, fq)=0.
Step 3: according to obverse similarity and adjacent corresponding relationship, optimal face matching sequence being obtained based on bat algorithm
Column.
The step 3 the following steps are included:
Step 3.1: generating the face similarity matrix A (n, m) of source model and object module；
Step 3.2: according to face similarity matrix A initialization population, determining that bat number is numn；
Step 3.3: initialization maximum impulse loudness A0, emission maximum frequency R0, search pulse frequency range [fmin, fmax],
The attenuation coefficient α of volume, the enhancing coefficient gamma of search rate, maximum number of iterations iter_max；The fitness of initialization population
Function f (xi)；
In the step 3.3,Wherein, u is mould
The index in face in type.
Step 3.4: judging whether the number of iterations has reached maximum number of iterations iter_max, if satisfied, then going to step
3.10, otherwise, carry out in next step；
Step 3.5: updating speed and position of each bat at each moment, fi=fmin+(fmax-fmin) × β,Wherein, t is t moment, and i is the index of bat；
Step 3.6: generating number rand1If rand1> Ri, an optimal solution x is selected from current optimal solution set*Carry out with
Machine disturbance takes the smallest two faces matching pair of shape similarity, exchanges matching face, obtain new explanation xnewAnd calculate its fitness value；
Step 3.7: generating number rand2If nand2< AiAnd f (xnew) < f (x*), then receive new explanation, otherwise, returns
Step 3.4；
Step 3.8: updating loudness by formulaUpdate transmitting frequency
Step 3.9: the fitness value of all bats being ranked up, current globally optimal solution and optimal value are found out；It returns
Return step 3.4；
Step 3.10: output optimal solution is that optimal face matches the optimal face matching sequence of sequence are as follows: { f1/f′1, f2/f
′2..., fn/f′n}。
In the present invention, there are m different faces there are n different faces, object module for source model, with every a line corresponding source
One face of model, each face for arranging corresponding object module, match sequence problem for finding optimal face, if n < m,
It can be considered that needs find the path for possessing n node, the one face matching pair of each node on behalf, and n section in map
Point is located at different lines of not going together, so that the matching of face representated by whole path interior joint is to similarity maximum.
In the present invention, calculated using discrete bat algorithm；
Bat position x is the vector of n dimension, and i-th of bat is in the position of t moment
Wherein n is the total quantity in the face of source model, represents a face matching to f per one-dimensionali/fi′；
Bat speed v is the position in order to change position X, similar with x, and speed is also the vector of n dimension, i-th bat
Bat is expressed as in the speed of t moment:Its indicated whether by each dimension and other positions into
Row exchange, if not exchanging, value is 0, and exchange, then jth is tieed upValue be the position swapped dimension；
The transmitting frequency in t moment of i-th of bat isPulse loudness isAt the t+1 moment, emit frequency and arteries and veins
Rushing loudness more new formula is
The frequency of i-th of bat is defined as fi∈ [0,1]；
Use xiSimilitude summation (shape similarity and adjacent pass including face of each matching pair representated by every dimension
It is similitude) fitness of Lai Hengliang bat, the fitness function of i-th of batWherein, fiu, f 'iuIt is current time i-th respectively
The position x of a batiThe representative face matching pair of u dimension；Sa(fiu, f 'iu) be the matching pair of u-th face shape similarity；Sb
(fiu, f 'iu, fiu+1, f 'iu+1) it is the matching of u-th face to the adjoining similarity matched with its latter face pair；
The addition of Position And Velocity changes the position of bat,It indicates to change xiFace matching
Sequence, so that fitness value is higher；It is that the subtraction of position and position obtains the result is that a speed v, both indicate to pass through speed v
Exchange interaction；The number of speed multiplies: the number of speed multiplies with probability meaning, v2=v1× c, c ∈ [0,1], c are a constants,
Calculate v2When, to v1If rand > c the value of the dimension is set 0, otherwise per one random number rand ∈ [0,1] of one-dimensional generation
It is constant.
In the present invention, in step 3.2, initialization population is needed；Initialize taboo list setSet A, which appoints, to be rounded
Number j ∈ [1, m],And A (i, j)=1, in the i-th row fetch bit in the matching pair of the face of A [i, j], (i, j) is added as i≤n
Enter set A, taboo list set B is added in j, ultimately generates num A, as num bat individual.
In the present invention, rand1And rand2It is random to generate.
Step 4: the similitude of source model and object module being calculated with optimal face matching sequence.
In the step 4, similitude Simwhole,
In the present invention, the similarity of two model entirety is that the similarity accumulation of each optimal face matching pair sums and asks flat
Mean value.
The number of edges difference that the present invention matches pair according to source model with each face of object module calculates the shape similarity in face,
The structural similarity of face matching pair is calculated by syntople of the face matching between.Seek optimal face matching to sequence in iteration
During column, bat position is updated using two element optimisation strategies, to make total similarity of the bat group to face
Higher direction is mobile.Optimize by successive ignition, obtains optimal face matching pair, calculate source model and target finally by formula
The comprehensive similarity of model.
The present invention is the three-dimensional CAD model search method of high efficient and reliable, the Given information according to present in three-dimensional CAD model
Implicit information is excavated, and according to its feature and Search Requirement, provides the retrieval mode for more meeting user demand, can be applied
In large-scale CAD model library, is more accurately retrieved and improve recall precision.
Claims (7)
1. a kind of three-dimensional CAD model similarity calculation method based on discrete bat algorithm, it is characterised in that: the method includes
Following steps:
Step 1: enabling source model there are n different faces, object module, there are m different faces, n < m；Calculate the face of source model
With the similarity between each face of object module, similarity matrix S is constructeda；
Step 2: the adjacent corresponding relationship matrix S in the face of building source model and object moduleb；
Step 3: according to obverse similarity and adjacent corresponding relationship, optimal face matching sequence being obtained based on bat algorithm；
Step 4: the similitude of source model and object module being calculated with optimal face matching sequence.
2. a kind of three-dimensional CAD model similarity calculation method based on discrete bat algorithm according to claim 1, special
Sign is: in the step 1, with f1,f2Represent two different faces, the face of the face of any source model and either objective model it
Between similarity beWherein, N (f) indicates the number on the side that face f is possessed
Amount.
3. a kind of three-dimensional CAD model similarity calculation method based on discrete bat algorithm according to claim 2, special
Sign is: all faces of source model and object module constitute shape similarity matrix Sa,
4. a kind of three-dimensional CAD model similarity calculation method based on discrete bat algorithm according to claim 1, special
Sign is: in the step 2, enabling the attribute adjacency matrix A of source model1, the attribute adjacency matrix A of object module2, obtain source mould
The face of type and object module abuts corresponding relationship matrix Sb,Wherein, fiAnd fjRespectively source model
In i-th of face and j-th of face, fpAnd fqP-th of face and q-th of face respectively in object module.
5. a kind of three-dimensional CAD model similarity calculation method based on discrete bat algorithm according to claim 1, special
Sign is: the step 3 the following steps are included:
Step 3.1: generating the face similarity matrix A (n, m) of source model and object module；
Step 3.2: according to face similarity matrix A initialization population, determining that bat number is num；
Step 3.3: initialization maximum impulse loudness A0, emission maximum frequency R0, search pulse frequency range [fmin,fmax], volume
Attenuation coefficient α, the enhancing coefficient gamma of search rate, maximum number of iterations iter_max；The fitness function f of initialization population
(xi)；
Step 3.4: judge whether the number of iterations has reached maximum number of iterations iter_max, if satisfied, step 3.10 is then gone to,
Otherwise, it carries out in next step；
Step 3.5: updating speed and position of each bat at each moment, fi=fmin+(fmax-fmin) × β,
Wherein, t is t moment, and i is the index of bat；
Step 3.6: generating number rand1If rand1>Ri, an optimal solution x is selected from current optimal solution set*It is disturbed at random
It is dynamic, the smallest two faces matching pair of shape similarity is taken, matching face is exchanged, obtains new explanation；newAnd calculate its fitness value；
Step 3.7: generating number rand2If rand2<AiAnd f (xneF)<f(x*), then receive new explanation, otherwise, return step 3.4；
Step 3.8: updating loudness by formulaUpdate transmitting frequency
Step 3.9: the fitness value of all bats being ranked up, current globally optimal solution and optimal value are found out；Return to step
Rapid 3.4；
Step 3.10: output optimal solution is that optimal face matches the optimal face matching sequence of sequence are as follows: { f1/f′1,f2/f′2,…,fn/
f′n}。
6. a kind of three-dimensional CAD model similarity calculation method based on discrete bat algorithm according to claim 5, special
Sign is: in the step 3.3,Wherein, u
For the index in face in model.
7. a kind of three-dimensional CAD model similarity calculation method based on discrete bat algorithm according to claim 5, special
Sign is: in the step 4, similitude Simwhole,
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| CN114997278A (en) * | 2022-05-09 | 2022-09-02 | 浙江大学 | Engineering digital information analysis method based on computer algorithm model |
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| CN111079208A (en) * | 2019-11-20 | 2020-04-28 | 杭州电子科技大学 | Particle swarm optimization algorithm-based method for identifying surface correspondence between CAD models |
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| CN113901256A (en) * | 2021-09-22 | 2022-01-07 | 成都飞机工业（集团）有限责任公司 | Similarity detection method and device for CAD model, terminal device and storage medium |
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