CN116090109B - Spacecraft assembly diversified layout optimization method and system, equipment and storage medium - Google Patents
Spacecraft assembly diversified layout optimization method and system, equipment and storage medium Download PDFInfo
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Abstract
The application discloses a spacecraft assembly diversified layout optimization method, a system, equipment and a storage medium, which provides an optimization scheme considering diversity targets among layout schemes, and in the spacecraft instrument cabin assembly layout optimization design process, similarity measurement is used as a diversity measurement index among the layout schemes to guide optimization so as to ensure that the finally obtained spacecraft layout design scheme has better performance and stronger diversity, and a plurality of groups of spacecraft instrument cabin assembly layout schemes with better performance and larger diversity can be simultaneously generated, so that the limitation of trapping in local optimization is relieved to a great extent, and more references are provided for spacecraft engineering design.
Description
Technical Field
The application relates to the technical field of spacecraft design, in particular to a spacecraft component diversified layout optimization method and system, electronic equipment and a computer readable storage medium.
Background
The design of the spacecraft instrument cabin component layout scheme is a key ring of the overall design of the spacecraft, and refers to how to fully utilize the limited space of the spacecraft to optimally arrange various components or instruments and equipment on the premise of meeting the engineering technical conditions and various constraint requirements of the internal and surrounding environments.
It should be noted that the design problem of the spacecraft component layout scheme is regarded as a large-scale system problem, because of the strong constraint of geometric non-interference, the solution space for placing the components is discontinuous, strong non-linear and the like, if the optimization solution is carried out by adopting a common Monte Carlo random sampling method, most of the obtained samples are invalid solutions with interference. Therefore, the current optimization design of the layout of the spacecraft instrument cabin components is focused on researching how to solve the interference problem among the components, mainly by constructing a resolved geometric interference formula, and adopting a gradient optimization method to efficiently iteratively converge from a random initial value to a non-interference feasible solution. For example, in a spacecraft layout optimization design method proposed in document 1 [ Chen X, yao W, zhao Y, chen X, zheng X, A practical satellite layout optimization design approach based on enhanced finite-circle method [ J ]. Structural and Multidisciplinary Optimization, 2018, 58 (6): 2635-2653 ], an author adopts a finite envelope circle method to approximately describe components to be laid out, an interference calculation problem between components is approximately converted into a series of distance calculation problems between circles (or balls) and non-interference between components is achieved by controlling non-intersection between circles (or balls). Document 2 [ Chen X, yao W, zhao Y, chen X, liu W, A novel satellite layout optimization design method based on Phi-function [ J ]. Acts Astronautica, 2021, 180:560-574 ] solves the problem of complex interference calculation between satellite layout components by adopting a Phi function method, and the Phi function value is a measure capable of accurately reflecting the mutual distance between two geometric bodies, so that by adopting the method, whether interference occurs between the components can be accurately and efficiently judged, and the magnitude of the interference can be calculated, thereby guiding the satellite layout optimization process to search towards the direction of a layout feasible solution.
However, the optimization solution based on the gradient algorithm has serious initial value dependence, the result is easy to fall into local optimum, and the local optimum solutions are difficult to jump out of the current relative geometric relationship, so that the further improvement of the performance index of the local optimum solutions is greatly limited. How to get more and better layout is still an unsolved problem, but it is important for the designer. Therefore, by researching the problem of optimizing the layout design of the spacecraft assembly, an intelligent algorithm for solving the diversified layout scheme is formed, and the method has very important significance for development of the spacecraft design.
Disclosure of Invention
The application provides a spacecraft assembly diversified layout optimization method and system, electronic equipment and a computer-readable storage medium, and aims to solve the technical problem that the optimization solution is easy to fall into the limitation of local optimization based on a gradient algorithm in the design of the existing spacecraft assembly layout scheme.
According to one aspect of the application, there is provided a spacecraft assembly diversification layout optimization method, comprising:
randomly generating a plurality of initial layout schemes with geometrical interference;
constructing spacecraft instrument cabin component layout optimization problems considering diversity by taking the minimum similarity value between the initial layout schemes as an objective function and taking no geometric interference of the initial layout schemes as constraint conditions, and carrying out optimization solution to obtain a plurality of preliminary optimization layout schemes;
screening based on a plurality of preliminary optimized layout schemes by adopting a determinant point process sampling method to obtain a plurality of depth optimized layout schemes;
based on a plurality of depth optimization layout schemes, constructing an optimization problem taking the overall physical performance of the spacecraft structure as an objective function, and solving by adopting a gradient algorithm to obtain a plurality of final layout optimization schemes.
Further, the process of calculating the similarity value between the two initial layout schemes is specifically:
and calculating the similarity between the two initial layout schemes by adopting a Gaussian kernel function, wherein the calculation formula is as follows:
;
wherein,,representing a gaussian kernel function +.>Representing the modulus of the vector, σ represents the width function.
Further, the process of calculating the similarity value between the two initial layout schemes is specifically:
transferring the coordinates of the components into a coordinate system taking the mass center of the spacecraft system as an origin, rotating an initial layout scheme around the mass center by different angles to generate a plurality of rotation schemes, calculating cosine similarity between the rotation schemes and another fixed scheme, taking the maximum value in the calculated result of the cosine similarity as a similarity value between the two initial layout schemes, wherein the calculation formula is as follows:
;
wherein vector A represents a fixed scheme, vectorThe rotation scheme is represented, alpha represents the rotation angle, and the value is [0, pi ]]，/>Modulo representing the vector, ∈>And->Representing vector A and +.>Is included in the composition of the composition.
Further, the expression of the spacecraft cabin assembly layout optimization problem considering diversity is as follows:
;
wherein the design variablesRepresentation assemblyi center coordinates,/-, and>representing the deflection angle of component i, superscript p representing the p-th initial layout scheme, M representing the number of initial layout schemes selected in the optimization, < >>Representing a symmetric positive definite matrix, the elements in the matrix representing similarity values between the initial layout scheme p and the initial layout scheme q, ">The amount of overlap between component i and component j is represented, and N represents the total number of components.
Further, the process of obtaining a plurality of depth optimization layout schemes by adopting the determinant point process sampling method to screen based on a plurality of preliminary optimization layout schemes comprises the following steps:
establishing a kernel matrix based on similarity calculation results among a plurality of preliminary optimization layout schemes, wherein the dimension of the kernel matrix represents the number of the preliminary optimization layout schemes, the element represents the similarity between two preliminary optimization layout schemes corresponding to a matrix index, diagonal elements are 1, and the probability of each preliminary optimization layout scheme being selected is calculated based on the following formula:
;
wherein,,representing the probability that the initial optimized layout scheme A is selected, L representing a kernel matrix established based on similarity calculation results among a plurality of initial optimized layout schemes, det representing a determinant of the calculation matrix, I being an identity matrix, and->A new kernel matrix is represented that is formed of elements in the L kernel matrix indexed by the selected initial layout scheme a.
Further, the optimization problem of taking the overall physical performance of the spacecraft structure as an objective function is specifically as follows:
;
wherein the design variablesRepresenting the central coordinates of component i>Represents the deflection angle of component i, N represents the total number of components, +.>、/>And->Respectively represent moment of inertia in x, y and z directions,/->Representing the amount of overlap between component i and component j.
Further, when constructing the spacecraft instrument cabin assembly layout optimization problem and carrying out optimization solution, k preliminary optimization layout schemes are obtained through optimization solution each time, and kn preliminary optimization layout schemes are obtained after n times of optimization solution.
In addition, the application also provides a spacecraft assembly diversified layout optimization system, which comprises:
the random generation module is used for randomly generating a plurality of initial layout schemes with geometrical interference possibly;
the initial optimization module is used for constructing spacecraft instrument cabin assembly layout optimization problems considering diversity and carrying out optimization solution by taking the minimum similarity value between the initial layout schemes as an objective function and taking no geometric interference of the initial layout schemes as constraint conditions, so as to obtain a plurality of initial optimization layout schemes;
the depth optimization module is used for screening based on a plurality of preliminary optimization layout schemes by adopting a determinant point process sampling method to obtain a plurality of depth optimization layout schemes;
the overall performance optimization module is used for constructing an optimization problem taking the overall physical performance of the spacecraft structure as an objective function based on a plurality of depth optimization layout schemes, and solving the optimization problem by adopting a gradient algorithm to obtain a plurality of final layout optimization schemes.
In addition, the application also provides an electronic device comprising a processor and a memory, wherein the memory stores a computer program, and the processor is used for executing the steps of the method by calling the computer program stored in the memory.
In addition, the application also provides a computer readable storage medium for storing a computer program for performing spacecraft cabin assembly layout optimization, which when run on a computer performs the steps of the method as described above.
The application has the following effects:
the method for optimizing diversified layout of spacecraft components generates a plurality of initial layout schemes with possible geometric interference. Then, with the minimum similarity value between the initial layout schemes as an objective function and the initial layout schemes without geometric interference as constraint conditions, constructing a spacecraft instrument cabin assembly layout optimization problem considering diversity and carrying out optimization solution to obtain a plurality of preliminary optimization layout schemes, so that preliminary optimization screening of the layout schemes is realized, and representativeness and mutual difference of the layout schemes are improved. And then, a determinant point process sampling method is adopted to screen based on a plurality of preliminary optimization layout schemes, so that a plurality of most representative and various depth optimization layout schemes are obtained, the depth optimization screening of the layout schemes is realized, and the representativeness and the difference of the layout schemes are further improved. And finally, constructing an optimization problem taking the overall physical performance of the spacecraft structure as an objective function, and carrying out re-optimization solution on the plurality of depth optimization layout schemes, so as to obtain a plurality of final layout optimization schemes, and ensuring that the plurality of final layout optimization schemes can meet the overall performance index of the spacecraft. The optimization method for the diversified layout of the spacecraft components provides an optimization scheme considering diversity targets among layout schemes, can simultaneously generate a plurality of groups of spacecraft instrument cabin component layout schemes with good performance and larger difference, greatly relieves the limitation of being in local optimum, and provides more references for spacecraft engineering design.
In addition, the spacecraft assembly diversification layout optimization system of the application also has the advantages.
In addition to the objects, features and advantages described above, the present application has other objects, features and advantages. The present application will be described in further detail with reference to the drawings.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application. In the drawings:
FIG. 1 is a schematic diagram of a preferred embodiment of the present application for converting a three-dimensional component layout optimization design problem of an instrument pod into a two-dimensional in-plane layout optimization problem;
FIG. 2 is a flow chart of a spacecraft assembly diversification layout optimization method in accordance with a preferred embodiment of the application;
FIG. 3 is a schematic diagram of another layout plan based on a layout plan rotated 90 deg. in a preferred embodiment of the present application, wherein (a) represents the layout plan before rotation and (b) represents the layout plan obtained by rotating the layout plan in (a) 90 deg. around the centroid;
fig. 4 (a), (b), (c), (d), (e), and (f) are schematic diagrams of six layouts obtained by dpp sampling based on the layout obtained by improving cosine similarity in the preferred embodiment of the present application;
fig. 5 (a), (b), (c), (d), (e), and (f) are schematic diagrams of the six layout schemes in fig. 4 after the moment of inertia optimization;
fig. 6 (a), (b), (c), (d), (e) and (f) are schematic diagrams of six layouts obtained by dpp sampling based on the layout obtained by gaussian similarity in the preferred embodiment of the present application;
fig. 7 (a), (b), (c), (d), (e), and (f) are schematic diagrams of the six layout schemes in fig. 6 after the moment of inertia optimization;
FIG. 8 is a schematic block diagram of a spacecraft assembly diversification layout optimization system in accordance with another embodiment of the application.
Detailed Description
Embodiments of the application are described in detail below with reference to the attached drawing figures, but the application can be practiced in a number of different ways, as defined and covered below.
It will be appreciated that in optimizing the design of spacecraft cabin assemblies, as shown in fig. 1, the assemblies are generally all simplified into rectangular or cylindrical bodies and are considered to be of uniform mass distribution with the centroid coinciding with the centroid. Because the components can only be installed on the board, when the three-dimensional layout optimization design of the instrument cabin is carried out, only the two-dimensional plane layout optimization problem after projection along the Z-axis direction is needed to be researched, namely, the three-dimensional component layout optimization design problem of the instrument cabin is converted into the two-dimensional plane layout optimization problem.
As shown in fig. 2, a preferred embodiment of the present application provides a spacecraft assembly diversification layout optimization method, which includes the following steps:
step S1: randomly generating a plurality of initial layout schemes with geometrical interference;
step S2: constructing spacecraft instrument cabin component layout optimization problems considering diversity by taking the minimum similarity value between the initial layout schemes as an objective function and taking no geometric interference of the initial layout schemes as constraint conditions, and carrying out optimization solution to obtain a plurality of preliminary optimization layout schemes;
step S3: screening based on a plurality of preliminary optimized layout schemes by adopting a determinant point process sampling method to obtain a plurality of depth optimized layout schemes;
step S4: based on a plurality of depth optimization layout schemes, constructing an optimization problem taking the overall physical performance of the spacecraft structure as an objective function, and solving by adopting a gradient algorithm to obtain a plurality of final layout optimization schemes.
It can be understood that in the spacecraft assembly diversified layout optimization method of the embodiment, a plurality of initial layout schemes with geometrical interference possibly exist are firstly generated, then, the initial layout schemes do not generate geometrical interference with minimum similarity value as an objective function, and the spacecraft instrument cabin assembly layout optimization problem considering the diversity is constructed and optimized and solved, so that a plurality of preliminary optimized layout schemes are obtained, preliminary optimized screening of the layout schemes is realized, and the representativeness and the mutual difference of the layout schemes are improved. And then, a determinant point process sampling method is adopted to screen based on a plurality of preliminary optimization layout schemes, so that a plurality of most representative and various depth optimization layout schemes are obtained, the depth optimization screening of the layout schemes is realized, and the representativeness and the difference of the layout schemes are further improved. And finally, constructing an optimization problem taking the overall physical performance of the spacecraft structure as an objective function, and carrying out re-optimization solution on the plurality of depth optimization layout schemes, so as to obtain a plurality of final layout optimization schemes, and ensuring that the plurality of final layout optimization schemes can meet the overall performance index of the spacecraft. The optimization method for the diversified layout of the spacecraft components provides an optimization scheme considering diversity targets among layout schemes, can simultaneously generate a plurality of groups of spacecraft instrument cabin component layout schemes with good performance and larger difference, greatly relieves the limitation of being in local optimum, and provides more references for spacecraft engineering design.
It will be appreciated that in said step S1, a number of layout schemes where geometrical interferometry may exist are generated, in particular using existing random sampling methods (e.g. monte carlo random sampling).
It can be understood that in the step S2, with the initial layout scheme satisfying the geometric non-interference as a constraint condition and the minimum similarity between the initial layout schemes as an objective function, a spacecraft cabin component layout optimization problem considering diversity is constructed and solved by adopting a gradient algorithm, where the problem can be described as follows:
；
wherein the design variablesRepresenting the central coordinates of component i>Representing the deflection angle of the component i, the superscript p representing the p-th initial layout scheme, M representing the number of initial layout schemes selected in the optimization, the similarity objective between the layout schemes may be represented by a symmetric positive definite matrix +.>The elements in the matrix are given to represent the similarity values between the initial layout scheme p and the initial layout scheme q,/>The amount of overlap between component i and component j is represented, and N represents the total number of components.
Optionally, in the step S2, the process of calculating the similarity value between the two initial layout schemes is specifically:
and calculating the similarity between the two initial layout schemes by adopting a Gaussian kernel function, wherein the calculation formula is as follows:
；
wherein,,representing a gaussian kernel function +.>Representing the modulus of the vector, σ represents the width function.
Specifically, in the component layout scheme design, each layout scheme corresponds to a set of vectors describing the geometric position coordinates of the object, and the similarity of the two layout schemes can be evaluated by calculating the similarity between the two vectors. Gaussian kernel functions are generally defined as monotonic functions of euclidean distance between two points in space, whose effect tends to be local, i.e. the function takes a very large value when a is close to B and a very small value when a is far from B. The application extends the Gaussian kernel function as a function for measuring the similarity between two vectors, i.e
。
Alternatively, the process of calculating the similarity value between the two initial layout schemes in step S2 specifically includes:
transferring the coordinates of the components into a coordinate system taking the mass center of the spacecraft system as an origin, rotating an initial layout scheme around the mass center by different angles to generate a plurality of rotation schemes, calculating cosine similarity between the rotation schemes and another fixed scheme, taking the maximum value in the calculated result of the cosine similarity as a similarity value between the two initial layout schemes, wherein the calculation formula is as follows:
；
wherein vector A represents a fixed scheme, vectorThe rotation scheme is represented, alpha represents the rotation angle, and the value is [0, pi ]]，/>Modulo representing the vector, ∈>And->Representing vector A and +.>Is included in the composition of the composition.
It will be appreciated that cosine similarity is defined as the cosine value of the angle between two vectors, which can determine whether the two vectors are pointing in approximately the same direction. When the two vectors have the same direction, the cosine value is 1; when the included angle of the two vectors is 90 degrees, the cosine value is 0; when the two vectors point in diametrically opposite directions, the cosine value is-1. Cosine similarity can be used to evaluate similarity of layout schemes as shown in the following:
；
wherein,,and->Representing the respective components of vectors a and B, respectively.
However, the focus of cosine similarity is on whether the directional orientations of the two vectors are identical. In the design of the layout scheme of the aircraft cabin assembly, the schemes which can be obtained by non-interference simple transformation are highly similar, for example, as shown in fig. 3, the schemes which can be overlapped by rotating a certain angle around the centroid of the current layout scheme are only 0.5 in terms of cosine similarity. Therefore, the application further improves the traditional cosine similarity calculation method, when calculating the similarity value between schemes, the coordinates of the components are transferred to a coordinate system taking the mass center of the spacecraft system as the origin, one scheme is rotated around the mass center by a certain angle, the cosine similarity with other fixed schemes is calculated by using a plurality of rotation schemes generated by the rotation angles, and the maximum similarity value between the rotation schemes and the fixed schemes is taken as the similarity value between the two schemes, so that the representativeness and the diversity of the layout schemes can be ensured simultaneously.
It can be understood that in the step S2, a geometric non-interference Phi function calculation method is specifically used to determine whether the initial layout scheme satisfies the constraint condition that no geometric interference occurs. The Phi function method is a typical method for effectively describing the relative position relationship between two geometric objects, and the central idea of the method is that: and judging whether the two geometric bodies interfere or not by calculating corresponding Phi function values according to the position information and the geometric sizes of the two geometric bodies. The Phi function satisfies: when the Phi function value is greater than 0, two geometric bodies are separated from each other; when the Phi function value is 0, the boundaries of two geometric bodies are just contacted; when the Phi function value is less than 0, it represents that two geometries interfere. The expression of the Phi function is:
；
where inter () represents the inner region of the geometry and boun () represents the region boundary of the geometry.
When describing the position information of the geometric body, firstly, a reference point is selected, usually the center is taken as the position reference point, after the reference point of the geometric body is selected, the position parameter of the geometric body can be determined by a group of point coordinates and a rotation angle, the established coordinate system usually takes the geometric center of a spacecraft system as the origin, then the specific shape characteristics of the object can be described according to the geometric information of the object, and then the position relation Phi function among the objects is calculated. The Phi function between common two-dimensional geometries is defined as follows:
(1) Circle and Phi function of circle
For two radii r i Circle C of (2) i (i=1, 2), the center of which is denoted as (x) i ,y i ) (i=1, 2), then the Phi function between them can be defined as:
；
(2) Convex polygon and Phi function of convex polygon
For convex m-sided shape K 1 Its apex is marked asEach edge is marked asConvex n-sided polygon K 2 Its vertex is denoted as (x) 2j ,y 2j ) (j=1, 2, …, n), each edge being denoted +.>The Phi function between two convex polygons can be defined as:
；
wherein,,representing a polygon K 2 The j-th vertex (x 2j ,y 2j ) To polygon K 1 Is (A) 1i ,B 1i ,C 1i ) Distance of->Representing a polygon K 1 Is the ith vertex (x) 1i ,y 1i ) To polygon K 2 The j-th edge (A) 2j ,B 2j ,C 2j ) Is a distance of (2);
(3) The Phi function of convex polygon and circle is plotted as (x) for convex m-polygon K with its vertices i ,y i ) (i=1, 2, …, m), each edge is noted:. For circle C, the center coordinates are (x c ,y c ) The radius is r. The Phi function between them can be defined as:
；
wherein the method comprises the steps of
；
In addition, optimization of the geometric non-interfering local scheme in view of similarity-based can yield a large number of diverse solutions. However, since the initial layout schemes in the step S1 are randomly generated, and it is difficult to perform large-sample calculation each time similarity is evaluated in the step S2, the calculation is very time-consuming and the calculation result is inaccurate, so when the spacecraft instrument cabin assembly layout optimization problem is constructed and the optimization solution is performed in the step S2, k preliminary optimization layout schemes are obtained by each optimization solution, and kn preliminary optimization layout schemes are obtained after n optimization solutions. For example, the application considers the similarity among ten schemes, namely k=10, in each round of preliminary optimization, and then performs 20 repeated solutions, thereby obtaining 200 preliminary optimized layout schemes.
It can be understood that, in the step S3, the filtering is performed based on a plurality of preliminary optimized layout schemes by adopting a determinant point process sampling method, and the process of obtaining a plurality of depth optimized layout schemes includes the following steps:
establishing a kernel matrix based on similarity calculation results among a plurality of preliminary optimization layout schemes, wherein the dimension of the kernel matrix represents the number of the preliminary optimization layout schemes, the element represents the similarity between two preliminary optimization layout schemes corresponding to a matrix index, diagonal elements are 1, and the probability of each preliminary optimization layout scheme being selected is calculated based on the following formula:
；
wherein,,representing the probability that the initial optimized layout scheme A is selected, L representing a kernel matrix established based on similarity calculation results among a plurality of initial optimized layout schemes, det representing a determinant of the calculation matrix, I being an identity matrix, and->Representing the L kernel matrix indexed by the selected initial layout scheme AA new matrix of elements.
It will be appreciated that the determinant point process (Determinantal Point Process, DPP) is a model of the possibility to pick up a subset of diversity as a determinant of a kernel matrix, the probability that the subset is selected being equal to the ratio of the kernel matrix determinant of the subset to the kernel matrix determinant of the complete set, i.e.:
；
therefore, the application establishes the similarity calculation result among a plurality of preliminary optimization layout schemes as a kernel matrix, wherein the dimension of the kernel matrix represents the number of the preliminary optimization layout schemes, the element represents the similarity between two preliminary optimization layout schemes corresponding to the matrix index, the diagonal elements are all 1, the kernel matrix is a symmetrical positive definite matrix, and the expression is:
；
wherein the core matrix L i，j Is a relation table for describing relation between individuals in the whole set, and element L ij Representing the similarity between the i, j-th individuals in the whole set, and taking the value of 0,1]Wherein, the similarity of each scheme and the scheme is 1.Representing a new kernel matrix of elements in the L kernel matrix indexed by the selected initial layout scheme a, e.g., choose a= { a, b }, then:
；
when the similarity of a and b is higher, the probability that a and b are sampled simultaneously is small.
It can be understood that after obtaining a plurality of most representative and diverse depth-optimized layout schemes, the optimization problem of constructing the overall physical performance of the spacecraft structure as an objective function in the step S4 is specifically:
the goal is set to minimize moment of inertia:
；
wherein the design variablesRepresenting the central coordinates of component i>Represents the deflection angle of component i, N represents the total number of components, +.>、/>And->Respectively represent moment of inertia in x, y and z directions,/->Representing the amount of overlap between component i and component j.
It will be appreciated that in other embodiments of the application, other overall performance metrics may be employed as optimization objectives for solving, such as weight minimization, acceleration performance maximization, etc.
It can be understood that the application also adopts the spacecraft assembly diversified layout optimization method to carry out example optimization design. Specifically, the trapezoid at the bottom of the instrument cabin of the spacecraft is an isosceles trapezoid with the upper bottom of 460mm, the lower bottom of 500mm and the height of 560mm, and the instrument cabin is 120mm in height. The instrument pod has a triaxial moment of inertia about its geometric center of:
；
the instrument cabin comprises 4 cuboid assemblies and 6 cylinder assemblies, the geometric dimensions and the mass information of the cuboid assemblies are shown in table 1, and the first 4 cuboid assemblies are shown in the table.
TABLE 1 information of components in a spacecraft cabin
After multiple optimization iterations, several sets of non-interferometric layout schemes with diversity can be obtained. The solution obtained by sampling the results of the layout solution obtained according to the modified cosine similarity is shown in fig. 4, the optimized corresponding layout solution taking the rotational inertia of the spacecraft system as an optimization target is shown in fig. 5, the corresponding rotational inertia result of each layout solution is shown in table 2, and the difference between the maximum rotational inertia and the minimum rotational inertia is 11.1%. In addition, the obtained layout schemes obtained by sampling from the results of the layout schemes obtained from gaussian similarity are shown in fig. 6, the corresponding layout schemes after optimizing the moment of inertia are shown in fig. 7, the moment of inertia results corresponding to each layout scheme are shown in table 3, and the maximum moment of inertia differs from the minimum moment of inertia by 8.8%.
TABLE 2 moment of inertia values for solutions based on modified cosine similarity
TABLE 3 moment of inertia values for solutions based on Gaussian similarity
The result shows that for the layout design of the general spacecraft instrument shelter assembly, various constraint is set in the optimization process, various layout schemes with good performance and variability can be generated, and a larger reference is provided for the actual spacecraft engineering design. Therefore, in the spacecraft assembly diversified layout optimization method, in the spacecraft instrument cabin assembly layout optimization design process, similarity measurement is used as a diversity measurement index between layout schemes to guide optimization, so that the performance of the finally obtained spacecraft layout design scheme is better and the finally obtained spacecraft layout design scheme has stronger diversity.
In addition, as shown in fig. 8, another embodiment of the present application further provides a spacecraft assembly diversified layout optimization system, preferably adopting the above method, the system includes:
the random generation module is used for randomly generating a plurality of initial layout schemes with geometrical interference possibly;
the initial optimization module is used for constructing spacecraft instrument cabin assembly layout optimization problems considering diversity and carrying out optimization solution by taking the minimum similarity value between the initial layout schemes as an objective function and taking no geometric interference of the initial layout schemes as constraint conditions, so as to obtain a plurality of initial optimization layout schemes;
the depth optimization module is used for screening based on a plurality of preliminary optimization layout schemes by adopting a determinant point process sampling method to obtain a plurality of depth optimization layout schemes;
the overall performance optimization module is used for constructing an optimization problem taking the overall physical performance of the spacecraft structure as an objective function based on a plurality of depth optimization layout schemes, and solving the optimization problem by adopting a gradient algorithm to obtain a plurality of final layout optimization schemes.
It can be appreciated that the spacecraft assembly diversification layout optimization system of the present embodiment first generates several initial layout schemes where geometric interference may exist. Then, with the minimum similarity value between the initial layout schemes as an objective function and the initial layout schemes without geometric interference as constraint conditions, constructing a spacecraft instrument cabin assembly layout optimization problem considering diversity and carrying out optimization solution to obtain a plurality of preliminary optimization layout schemes, so that preliminary optimization screening of the layout schemes is realized, and representativeness and mutual difference of the layout schemes are improved. And then, a determinant point process sampling method is adopted to screen based on a plurality of preliminary optimization layout schemes, so that a plurality of most representative and various depth optimization layout schemes are obtained, the depth optimization screening of the layout schemes is realized, and the representativeness and the difference of the layout schemes are further improved. And finally, constructing an optimization problem taking the overall physical performance of the spacecraft structure as an objective function, and carrying out re-optimization solution on the plurality of depth optimization layout schemes, so as to obtain a plurality of final layout optimization schemes, and ensuring that the plurality of final layout optimization schemes can meet the overall performance index of the spacecraft. The optimization system for the diversified layout of the spacecraft components provides an optimization scheme considering diversity targets among layout schemes, can simultaneously generate a plurality of groups of spacecraft instrument cabin component layout schemes with good performance and larger variability, greatly relieves the limitation of being in local optimum, and provides more references for spacecraft engineering design.
In addition, another embodiment of the present application also provides an electronic device, including a processor and a memory, where the memory stores a computer program, and the processor is configured to execute the steps of the method described above by calling the computer program stored in the memory.
In addition, another embodiment of the application also provides a computer readable storage medium storing a computer program for spacecraft cabin assembly layout optimization, which when run on a computer performs the steps of the method as described above.
Forms of general computer-readable storage media include: a floppy disk (floppy disk), a flexible disk (flexible disk), hard disk, magnetic tape, any other magnetic medium, a CD-ROM, any other optical medium, punch cards, paper tape, any other physical medium with patterns of holes, a Random Access Memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a FLASH erasable programmable read-only memory (FLASH-EPROM), any other memory chip or cartridge, or any other medium from which a computer can read. The instructions may further be transmitted or received over a transmission medium. The term transmission medium may include any tangible or intangible medium that may be used to store, encode, or carry instructions for execution by a machine, and includes digital or analog communications signals or their communications with intangible medium that facilitate communication of such instructions. Transmission media includes coaxial cables, copper wire and fiber optics, including the wires that comprise a bus for transmitting a computer data signal.
The above description is only of the preferred embodiments of the present application and is not intended to limit the present application, but various modifications and variations can be made to the present application by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.
Claims (9)
1. The spacecraft assembly diversified layout optimization method is characterized by comprising the following steps of:
randomly generating a plurality of initial layout schemes with geometrical interference;
constructing spacecraft instrument cabin component layout optimization problems considering diversity by taking the minimum similarity value between the initial layout schemes as an objective function and taking no geometric interference of the initial layout schemes as constraint conditions, and carrying out optimization solution to obtain a plurality of preliminary optimization layout schemes;
screening based on a plurality of preliminary optimized layout schemes by adopting a determinant point process sampling method to obtain a plurality of depth optimized layout schemes;
based on a plurality of depth optimization layout schemes, constructing an optimization problem taking the overall physical performance of the spacecraft structure as an objective function, and solving by adopting a gradient algorithm to obtain a plurality of final layout optimization schemes;
the expression of the spacecraft instrument cabin assembly layout optimization problem considering diversity is as follows:
wherein the design variable (x i ，y i ) Representing the center coordinates, θ, of component i i Representing the deflection angle of the component i, the superscript p represents the p-th initial layout scheme, M represents the number of initial layout schemes selected in the optimization, L pq Representation ofA symmetric positive definite matrix, the elements in the matrix representing the similarity values between the initial layout scheme p and the initial layout scheme q, delta ij The amount of overlap between component i and component j is represented, and N represents the total number of components.
2. The spacecraft assembly diversification layout optimization method of claim 1, wherein the process of calculating the similarity value between two initial layout schemes is specifically:
and calculating the similarity between the two initial layout schemes by adopting a Gaussian kernel function, wherein the calculation formula is as follows:
where similarity represents the similarity between two initial layout schemes, a and B represent two initial layout schemes generated randomly, each initial layout scheme corresponds to a set of vectors describing the geometric position coordinates of the object, k () represents a gaussian kernel function, σ represents a modulus of the vector, and σ represents a width function.
3. The spacecraft assembly diversification layout optimization method of claim 1, wherein the process of calculating the similarity value between two initial layout schemes is specifically:
transferring the coordinates of the components into a coordinate system taking the mass center of the spacecraft system as an origin, rotating an initial layout scheme around the mass center by different angles to generate a plurality of rotation schemes, calculating cosine similarity between the rotation schemes and another fixed scheme, taking the maximum value in the calculated result of the cosine similarity as a similarity value between the two initial layout schemes, wherein the calculation formula is as follows:
wherein vector A represents the fixed scheme, vector Bα]Representing rotation scheme, alpha represents rotation angleDegree, value is [0, pi ]]The expression of the vector is represented by the modulus of the vector, A i And B i [α]Representing vectors A and Bα, respectively]Is included in the composition of the composition.
4. The spacecraft assembly diversification layout optimization method of claim 1, wherein the process of screening based on a plurality of preliminary optimization layout schemes to obtain a plurality of depth optimization layout schemes by adopting a determinant point process sampling method comprises the following steps:
establishing a kernel matrix based on similarity calculation results among a plurality of preliminary optimization layout schemes, wherein the dimension of the kernel matrix represents the number of the preliminary optimization layout schemes, the element represents the similarity between two preliminary optimization layout schemes corresponding to a matrix index, diagonal elements are 1, and the probability of each preliminary optimization layout scheme being selected is calculated based on the following formula:
wherein P is L (A) Representing the probability that the initial optimized layout scheme A is selected, L representing a kernel matrix established based on similarity calculation results among a plurality of initial optimized layout schemes, det representing a determinant of the calculation matrix, I being an identity matrix, L A A new kernel matrix is represented that is formed of elements in the L kernel matrix indexed by the selected initial layout scheme a.
5. The spacecraft assembly diversification layout optimization method according to claim 1, wherein the optimization problem of the structure taking the overall physical performance of the spacecraft structure as an objective function is specifically:
wherein the design variable (x i ，y i ) Representing the center coordinates, θ, of component i i Representing the bias of component iAngle of rotation, N represents the total number of components, J x 、J y And J z Respectively represent the moment of inertia in the x, y and z directions, delta ij Representing the amount of overlap between component i and component j.
6. The spacecraft assembly diversity layout optimization method according to claim 1, wherein when a spacecraft instrument cabin assembly layout optimization problem considering diversity is constructed and optimization solution is performed, k preliminary optimization layout schemes are obtained by each optimization solution, and kn preliminary optimization layout schemes are obtained after n times of optimization solution.
7. A spacecraft assembly diversification layout optimization system, comprising:
the random generation module is used for randomly generating a plurality of initial layout schemes with geometrical interference possibly;
the initial optimization module is used for constructing spacecraft instrument cabin assembly layout optimization problems considering diversity and carrying out optimization solution by taking the minimum similarity value between the initial layout schemes as an objective function and taking no geometric interference of the initial layout schemes as constraint conditions, so as to obtain a plurality of initial optimization layout schemes; the expression of the spacecraft instrument cabin assembly layout optimization problem considering diversity is as follows:
wherein the design variable (x i ，y i ) Representing the center coordinates, θ, of component i i Representing the deflection angle of the component i, the superscript p represents the p-th initial layout scheme, M represents the number of initial layout schemes selected in the optimization, L pq Representing a symmetric positive definite matrix, the elements in the matrix representing similarity values between the initial layout scheme p and the initial layout scheme q, delta ij Representing the amount of overlap between component i and component j, N representing the total number of components;
the depth optimization module is used for screening based on a plurality of preliminary optimization layout schemes by adopting a determinant point process sampling method to obtain a plurality of depth optimization layout schemes;
the overall performance optimization module is used for constructing an optimization problem taking the overall physical performance of the spacecraft structure as an objective function based on a plurality of depth optimization layout schemes, and solving the optimization problem by adopting a gradient algorithm to obtain a plurality of final layout optimization schemes.
8. An electronic device comprising a processor and a memory, said memory having stored therein a computer program for executing the steps of the method according to any of claims 1-6 by invoking said computer program stored in said memory.
9. A computer-readable storage medium storing a computer program for performing a spacecraft cabin assembly layout optimization, wherein the computer program performs the steps of the method according to any one of claims 1-6 when run on a computer.
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