Large space antenna assembly sequence planning method considering structure fundamental frequency maximization

Abstract

The invention specifically relates to a large-scale space antenna assembly sequence planning method considering structure fundamental frequency maximization, and the method comprises the steps: building a dynamic model of a large-scale space antenna assembly process based on the structural characteristic that a geometric configuration is gradually increased in the assembly process and in combination with a modeling thought of a life-death element method in a finite element method; determining optimization variables and constraint conditions of the assembly sequence in the large space antenna assembly process; based on the dynamic model, considering the time-varying characteristic of the dynamic characteristic of the space antenna in the assembly process, and constructing an evaluation index of the assembly sequence; based on constraint conditions and evaluation indexes of the assembly sequence, an improved self-adaptive discrete particle swarm algorithm is provided, an assembly sequence optimization problem with maximized structural fundamental frequency is solved, and a feasible and optimal assembly sequence is obtained. According to the method, the structural rigidity in the assembling process can be improved, structural vibration in the assembling process is reduced, and the design redundancy of the orbit attitude control system is improved.

Description

Technical Field

[0001] This invention relates to the field of on-orbit assembly technology for large space structures, and more specifically to a method for planning the assembly sequence of large space antennas that considers maximizing the fundamental frequency of the structure. Background Technology

[0002] With the increasing complexity and number of components in space structures, the demand for assembly sequence planning is growing, making it a crucial step in the research of on-orbit assembly of large space structures. Throughout the on-orbit assembly phase, the geometry of the space structure gradually changes as modules are assembled. During assembly, the natural frequencies of the overall structure continuously change, exhibiting different trends depending on the assembly sequence. A well-planned assembly sequence can, to some extent, improve the overall stiffness during assembly, thereby reducing structural vibration and attitude changes. Therefore, research on assembly sequence planning during the on-orbit assembly phase is of significant research importance and value.

[0003] Existing research mostly studies assembly sequence planning problems from the perspectives of assembly feasibility, shortest assembly time, and minimum energy loss, with few considering the dynamic characteristics of the assembly process during optimization.

[0004] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of the present invention, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] This invention provides a method for planning the assembly sequence of a large space antenna that considers maximizing the structural fundamental frequency, which can improve the structural stiffness during the assembly process, reduce structural vibration during the assembly process, and improve the redundancy of the orbital attitude control system design.

[0006] Other features and advantages of the invention will become apparent from the following detailed description, or may be learned in part by practice of the invention.

[0007] According to a first aspect of the present invention, a method for planning the assembly sequence of a large space antenna considering the maximization of the structural fundamental frequency is provided, the method comprising:

[0008] Based on the structural characteristics of gradually increasing structural geometry during assembly, and combined with the modeling approach of the birth and death element method in the finite element method, a dynamic model of the large space antenna assembly process is established, thereby obtaining the stiffness array and mass array of the space antenna during the assembly process.

[0009] Determine the optimization variables and constraints for the assembly sequence in the assembly process of a large space antenna;

[0010] Considering the time-varying characteristics of the dynamic properties of the space antenna during the assembly process, an evaluation index related to the fundamental frequency of the assembly sequence is constructed using the stiffness array and mass array of the space antenna during the assembly process.

[0011] Based on the constraints and evaluation metrics of the assembly sequence, an improved adaptive discrete particle swarm optimization algorithm is proposed to solve the assembly sequence optimization problem of maximizing the structural fundamental frequency, and obtain a feasible and optimal assembly sequence.

[0012] In some exemplary embodiments, establishing a dynamic model of the large space antenna assembly process and obtaining the stiffness array and mass array during the assembly process includes:

[0013] A finite element model of the assembled structure is created in one go, and a dynamic model of all assembled modules is created based on the finite element model.

[0014] Then, the node matrix of each assembly module is obtained by numbering, and the mass matrix and stiffness matrix of each assembly module are obtained; before assembly begins, all assembly modules are "killed", that is, a mass matrix and stiffness matrix are set to zero are established;

[0015] During the assembly process, each time a new module is assembled, the node number of the assembled module is obtained through the node matrix, and the mass matrix and stiffness matrix of the corresponding node number are "activated", thereby obtaining the updated stiffness matrix and mass matrix in the assembly process.

[0016] In some exemplary embodiments, the optimization variables for determining the assembly sequence in the large space antenna assembly process specifically include:

[0017] The assembly modules are numbered using an absolute numbering scheme. Each module has a unique number associated with its position in the space antenna. The optimization variables are represented in the following form:

[0018] S = [E1 E2 ... E n ]

[0019] Among them, [E1 E2 ... E n ] indicates the assembly module number, and E i ∈{1,2,...n}.

[0020] In some exemplary embodiments, the constraints for determining the assembly sequence include:

[0021] Integrity constraint: the resulting assembly sequence S must contain all module units;

[0022] Without repetition constraints, each module unit appears only once in the resulting assembly sequence S;

[0023] Continuity constraint: in the resulting assembly sequence S, the modules assembled later must be connected to the previously assembled parts;

[0024] No interference constraint: For hexagonal antenna modules, the number of connection sides between the later assembled modules and the previously assembled parts in the obtained assembly sequence S shall not exceed 4.

[0025] In some exemplary embodiments, the evaluation index for constructing an assembly sequence related to the fundamental frequency of the structure based on the stiffness matrix and mass matrix during the assembly process specifically includes:

[0026] The structural fundamental frequency of the space antenna is calculated based on the continuously updated mass array and stiffness array during different assembly processes;

[0027] The structural fundamental frequency weighted sum of the space antennas throughout the assembly process is selected as the sequence evaluation index, and the optimization objective function is designed as follows:

[0028]

[0029] Among them, c i ω is the weighting coefficient. i The fundamental frequency of the entire space antenna structure when the i-th assembly module is installed.

[0030] In some exemplary embodiments, the construction of the improved adaptive discrete particle swarm optimization algorithm includes:

[0031] Define the position and velocity of the particles, using the position of each particle to represent the assembly sequence S, and the velocity of each particle to represent the velocity of the assembly module;

[0032] The formulas for updating the particle's position and velocity are defined as follows:

[0033]

[0034] In the formula, λ is the parametric inertia weight, which controls the previous generation velocity V. i (t) with respect to the current velocity V i The influence of (t+1); c1 and c2 are non-negative learning factors, with c1 controlling the individual's historical optimal position P. i (t) with respect to the current velocity V i The effect of (t+1); c2 controls the global optimal position P g (t) with respect to the current velocity V i The effect of (t+1);

[0035] An adaptive update strategy for the inertia weight λ is set. During the optimization process, the particle velocity is adjusted using this adaptive update strategy for the inertia weight λ, specifically expressed as follows:

[0036] λ k=λ start -(λ start -λ end )*(k / k max ) 2

[0037] In the formula, λ start and λ end Let k and k represent the initial inertia weight and the inertia weight at the maximum number of iterations, respectively; max These represent the current iteration step and the maximum iteration step, respectively.

[0038] In some exemplary embodiments, solving the assembly sequence optimization problem of maximizing the fundamental frequency of the structure to obtain a feasible and optimal assembly sequence includes:

[0039] Under the constraints of the assembly sequence, a feasible assembly sequence is determined, and the dynamic model of the assembly process is updated according to the assembly sequence after each assembly is completed.

[0040] The new assembly module's mass matrix and stiffness matrix are obtained based on the updated assembly process dynamic model. The structural fundamental frequency of the current structure is calculated by combining the mass matrix and stiffness matrix.

[0041] The optimal objective function of the assembly sequence is evaluated. If it is better than the historical best, the current optimal particle position and the global optimal objective function are updated; otherwise, they are not updated.

[0042] An assembly sequence that satisfies the constraints and maximizes the fundamental frequency of the structure is obtained through iterative optimization.

[0043] According to a second aspect of the present invention, a storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the large space antenna assembly sequence planning method considering maximizing the structural fundamental frequency as described in the first aspect.

[0044] According to a third aspect of the present invention, a computer program product is provided, on which a computer program is stored, wherein when the computer program is executed by a processor, it implements the large space antenna assembly sequence planning method considering the maximization of structural fundamental frequency as described in the first aspect above.

[0045] According to a fourth aspect of the present invention, an electronic device is provided, comprising:

[0046] Processor; and

[0047] Memory for storing the executable instructions of the processor;

[0048] The processor is configured to implement the large space antenna assembly sequence planning method considering maximizing the structural fundamental frequency as described in the first aspect when executing the executable instructions.

[0049] Considering the time-varying characteristics of structural dynamics during assembly, and aiming to improve the stability of the assembly process, an evaluation index maximizing the structural fundamental frequency is constructed. By combining the time-varying structural dynamics model update method during the on-orbit assembly stage with an improved discrete particle swarm optimization algorithm, an assembly sequence planning method considering the maximization of the structural fundamental frequency is proposed. From the perspective of structural vibration control, the orbital attitude control system during the assembly process is designed based on the concept of frequency band isolation. The assembly sequence obtained by maximizing the structural fundamental frequency allows for a larger control bandwidth, which can improve control performance and increase design margin. Specifically, this is reflected in the following points:

[0050] First, combining the modeling concept of the "birth and death element method" in the finite element method, a dynamic modeling method suitable for the assembly process is proposed. It does not require secondary node numbering, the calculation is relatively simpler, and the memory usage is reduced.

[0051] Second, considering the changes in the structural characteristics of the spatial antenna during the assembly process, a structural fundamental frequency maximization evaluation index was established to measure the impact of the assembly sequence on structural vibration.

[0052] Third, an improved solution framework for the adaptive discrete particle swarm optimization algorithm is proposed. By designing an adaptive update strategy for the inertial weights, the algorithm avoids getting trapped in local optima.

[0053] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Attached Figure Description

[0054] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention. It is obvious that the drawings described below are merely some embodiments of the invention, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0055] Figure 1 An on-orbit assembly scenario of a large space antenna, which is an exemplary embodiment of the present invention;

[0056] Figure 2 A dynamic modeling method for the assembly process, which is an exemplary embodiment of the present invention;

[0057] Figure 3 The designation is for a large space antenna module in an exemplary embodiment of the present invention;

[0058] Figure 4 Diagrams illustrating interference-free constraints and continuity constraints as exemplary embodiments of the present invention;

[0059] Figure 5A flowchart of an assembly sequence optimization algorithm for maximizing the structural fundamental frequency, as exemplified in an exemplary embodiment of the present invention;

[0060] Figure 6 This is a graph showing the variation of the optimal individual fitness function under different population sizes, as an exemplary embodiment of the present invention.

[0061] Figure 7 The assembly sequence for maximizing the fundamental frequency of the structure is an exemplary embodiment of the present invention;

[0062] Figure 8 A diagram illustrating the fundamental frequency variation of the assembly process in an exemplary embodiment of the present invention;

[0063] Figure 9 The graph shows the variation of the optimal individual fitness function as an exemplary embodiment of the present invention. Detailed Implementation

[0064] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that the invention will be more comprehensive and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0065] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily have to correspond to physical or logical entities.

[0066] To address the shortcomings and deficiencies of existing technologies, this exemplary embodiment provides a method for planning the assembly sequence of a large space antenna that considers maximizing the structural fundamental frequency. This includes establishing a dynamic model of the large space antenna assembly process; clarifying the optimization variables, constraints, and evaluation indicators of the assembly sequence; obtaining a feasible and optimal assembly sequence by using an improved adaptive discrete particle swarm optimization algorithm to solve the assembly sequence problem; and finally verifying the effectiveness of the proposed method through examples.

[0067] Specifically, this may include the following steps:

[0068] Step S1: Establish a dynamic model of the large space antenna assembly process;

[0069] Step S2: Define the optimization variables of the assembly sequence and define the geometric constraints of the module assembly process;

[0070] Step S3: Based on the dynamic model in Step 1, and considering the time-varying characteristics of the spatial antenna dynamics during the assembly process, an evaluation index for the assembly sequence is proposed.

[0071] Step S4: Based on the constraints in step 2 and the evaluation index in step 3, an improved adaptive discrete particle swarm optimization algorithm is constructed to solve the assembly sequence optimization problem of maximizing the fundamental frequency of the structure, and a feasible and optimal assembly sequence is obtained.

[0072] The steps in this exemplary embodiment will now be described in more detail with reference to the accompanying drawings and embodiments.

[0073] In step S1, the on-orbit assembly scenario of the target large space antenna is as follows: Figure 1 As shown, considering the time-varying structural characteristics of the geometric configuration during assembly, and combining the modeling concept of the "birth and death element method" in the finite element method, a structural finite element modeling method suitable for the assembly process is proposed, and a time-varying structural dynamics model of the on-orbit assembly process is established; specifically, it includes the following sub-steps:

[0074] Step S11: Establish finite element models of all assembly modules. Then, assuming all assembly modules have been installed, establish a dynamic model of the antenna structure after assembly, and confirm the assembly sequence S = [E1 E2 ... E n ];

[0075] Step S12: Through numbering, the node matrix H of the overall structural model can be obtained, and the node numbers of all assembled module units can be stored.

[0076] Step S13: Construct two zero matrices M0 and K0 with dimensions equal to the overall structure's degrees of freedom, which are used to update the mass matrix and stiffness matrix during the assembly process.

[0077] Step S14: Assemble the modules according to the assembly order S, gradually "activating" the corresponding modules. After assembling a new module, obtain the node number of the module through the node matrix H, superimpose the mass matrix and stiffness matrix of the module onto K0 and M0 established in step 1.3, delete the zero rows and zero columns in the matrix, and then obtain the updated stiffness matrix K and mass matrix M.

[0078] Step S15, the dynamic model of the overall structure is obtained through Hamilton's principle as follows:

[0079]

[0080] Where C is the damping term, F is the applied external force, and q is the nodal displacement vector. Take the first and second derivatives of q, respectively.

[0081] Following the above process, a single numbering step is sufficient to establish and update the dynamic model of the entire assembly process. The dynamic models for different configurations during structural assembly can be represented as follows:

[0082]

[0083] in, Structural parameters, such as type, shape, and size, are used; q is the nodal displacement vector. The dynamic modeling method for the assembly process is as follows: Figure 2 As shown.

[0084] In step S2, the optimization variables and constraints of the assembly sequence are defined; specifically, this includes the following sub-steps:

[0085] Step 2.1: Define the optimization variables for the assembly sequence. Assume the large space antenna has n modules. Considering that the module types at different locations in the designed large parabolic space antenna are often different, the module numbering adopts an absolute numbering scheme. Each module has a unique number related to its position in the space antenna, such as... Figure 3 Numbered in a clockwise direction as shown, the optimization variables can be represented in the following form:

[0086] S = [E1 E2 ... E n (3)

[0087] In the formula, [E1 E2 ... E n ] indicates the assembly module number, and E i ∈{1,2,...n}.

[0088] Step 2.2: Define the constraints of the assembly sequence. To ensure the feasibility of the assembly sequence, the following constraints must be met:

[0089] (a) Integrity constraint: The resulting assembly sequence S must contain all module units;

[0090] (b) No repetition constraint: Each module unit appears only once in the resulting assembly sequence S;

[0091] (c) Continuity constraint: The modules assembled later in the resulting assembly sequence S must be connected to the previously assembled parts;

[0092] (d) No interference constraints: For hexagonal antenna modules, the number of connection sides between the later assembled modules and the previously assembled parts in the obtained assembly sequence S must not exceed 4, otherwise they will collide with the assembly parts and it will be difficult to complete the assembly.

[0093] Among them, non-interference constraints and continuity constraints are as follows: Figure 4 As shown, checkmarks indicate cases where the constraints are met, while crosses indicate cases where the constraints are not met.

[0094] In step S3, the mass and stiffness of the i-th completed assembly module are obtained based on the dynamic model. The structural fundamental frequency of the entire space antenna structure during the installation of the i-th assembly module is calculated based on its mass and stiffness. An evaluation index for the assembly sequence is constructed, selecting the weighted sum of the structural fundamental frequencies of the space antenna throughout the assembly process as the sequence evaluation index. The optimization objective is expressed as...

[0095]

[0096] In the formula, c i ω is the weighting coefficient. i The fundamental frequency of the entire space antenna structure when the i-th assembly module is installed.

[0097] In step S4, the assembly sequence planning problem is solved using an improved adaptive particle swarm optimization method. The specific computational flow of the improved adaptive discrete particle swarm algorithm is as follows: Figure 5 As shown. Specifically, it includes the following sub-steps:

[0098] Step S41: Define the position and velocity of the particles. In the assembly sequence planning, the assembly sequence is the term to be optimized. Therefore, the position of each particle represents the assembly sequence S, and the dimension of each particle is the number of assembly modules n.

[0099] The position of each particle is represented as follows:

[0100] X i =(x i1 ,x i2 ,...,x in ),x ij ∈{1,2,...,n} (5)

[0101] In the formula, n is the dimension of the particle, i.e., the number of assembly modules. ij Let j represent the j-dimensional component of particle i, and let j represent the j-th assembly module of the assembly sequence S.

[0102] The velocity of each particle is represented as follows:

[0103] V i =(v i1 ,v i1 ,...,v in ),v ij ∈{1,2,…,n} (6)

[0104] In the formula, v ij This represents the velocity of the j-th assembly module of particle i. The initial particle velocity is randomly generated.

[0105] Step S42: Define the particle position and velocity update formulas. Updating particle position and velocity involves inter-particle operations. In the standard particle swarm optimization algorithm, these can be calculated using normal arithmetic operations. Due to the special properties of the assembly sequence, constraints need to be placed on particle velocity and position. Elements in the particle position should contain all assembly module numbers and cannot be repeated. Therefore, the particle and operation criteria are redefined, as follows:

[0106]

[0107] In the formula, λ is the parametric inertia weight, which controls the previous generation velocity V. i (t) with respect to the current velocity V i The influence of (t+1); c1 and c2 are non-negative learning factors, with c1 controlling the individual's historical optimal position P. i (t) with respect to the current velocity V i The effect of (t+1); c2 controls the global optimal position P g (t) with respect to the current velocity V i The effect of (t+1).

[0108] Step S43: Set an adaptive update strategy for the inertia weight λ. During the optimization process, an adaptive update strategy for the inertia weight λ is used to adjust the particle velocity, specifically expressed as follows:

[0109] λ k =λ start -(λ start -λ end )*(k / k max ) 2 (8)

[0110] In the formula, λ start and λ end Let k and k represent the initial inertia weight and the inertia weight at the maximum number of iterations, respectively; max These represent the current iteration step and the maximum iteration step, respectively.

[0111] Example 1

[0112] This invention uses a large parabolic reflector antenna with a 100-meter aperture as an example, providing numerical simulation examples to verify the effectiveness of the proposed assembly sequence planning method. The parabolic reflector antenna is composed of hexagonal modules, containing a total of 61 modules.

[0113] To determine the optimal parameters of the proposed algorithm during simulation, three sets of experiments with population sizes of 10, 15, and 20 were set up for comparison. Figure 6It can be seen that when the population size is 20 and the maximum number of iterations is 500, the algorithm converges quickly, requiring only 250 iterations to find the optimal individual fitness. When the population size is 15 and the maximum number of iterations is 500, the algorithm performs the worst, getting trapped in a local optimum.

[0114] The parameter settings for the improved adaptive discrete particle swarm optimization algorithm in this invention are as follows:

[0115] Population size is 20, maximum number of iterations k max The value is 200, the learning factor c1 is 0.2, the learning factor c2 is 0.8, and the parameter inertia weight λ start and λ end The values ​​are 0.9 and 0.4 respectively.

[0116] An improved adaptive discrete particle swarm optimization algorithm can be used to obtain the assembly sequence result that maximizes the fundamental frequency of the structure, as shown below. Figure 7 As shown. By Figure 8 It can be seen that the fundamental frequency of the spatial structure generally decreases with the number of assembly iterations, with the rate of decrease being faster in the first 30 assemblies, reaching 25%. The fundamental frequency of the structure varies significantly in the early assembly stages under different assembly sequences. The assembly sequence that maximizes the fundamental frequency exhibits a significantly higher fundamental frequency throughout the assembly process than the assembly sequence that does not consider the fundamental frequency. From the perspective of structural vibration control, if the track attitude control system during the assembly process is designed based on the concept of frequency band isolation, then the assembly process that maximizes the fundamental frequency allows for a larger control bandwidth, which can improve control performance and increase design margin.

[0117] In the simulation, the ant colony algorithm and the improved adaptive particle swarm algorithm were selected for comparison. In order to ensure the rationality and accuracy of the comparison results, the population size of the ant colony algorithm was set to 20, the pheromone heuristic factor was 1, the expectation heuristic factor was 2, and the pheromone evaporation factor was 0.95.

[0118] Depend on Figure 9 It can be seen that after 200 iterations, the optimal fitness function of the improved adaptive particle swarm optimization algorithm finally converges to 3.166 × 10⁻⁶. 4 The optimal fitness function is about 40% higher than that of the ant colony algorithm. The convergence time of the entire algorithm is 1016.4s, which is significantly better than the ant colony algorithm and is less likely to get trapped in local optima.

[0119] It should be noted that, as another aspect, this application also provides a storage medium, which may be included in an electronic device or may exist independently without being assembled into the electronic device. The storage medium carries one or more programs, which, when executed by an electronic device, cause the electronic device to perform the methods described in the following embodiments.

[0120] In one embodiment, this application provides a computer program product including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0121] Furthermore, the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of the present invention, and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Additionally, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0122] Other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention herein. This application is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The specification and embodiments are to be considered exemplary only, and the true scope and spirit of the invention are indicated by the claims.

[0123] It should be understood that the present invention is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is defined only by the appended claims.

Claims

1. A method for planning the assembly sequence of a large space antenna considering the maximization of the structural fundamental frequency, characterized in that, The method includes: Based on the structural characteristics of gradually increasing structural geometry during assembly, and combined with the modeling approach of the birth and death element method in the finite element method, a dynamic model of the large space antenna assembly process is established, thereby obtaining the stiffness matrix and mass matrix of the space antenna during the assembly process. Determine the optimization variables and constraints for the assembly sequence in the assembly process of a large space antenna; Considering the time-varying characteristics of the dynamic properties of the space antenna during the assembly process, an evaluation index related to the fundamental frequency of the assembly sequence is constructed using the stiffness array and mass array of the space antenna during the assembly process. Based on the constraints and evaluation metrics of the assembly sequence, an improved adaptive discrete particle swarm optimization algorithm is proposed to solve the assembly sequence optimization problem of maximizing the structural fundamental frequency, and obtain a feasible and optimal assembly sequence.

2. The method according to claim 1, characterized in that, The process of establishing a dynamic model for assembling a large space antenna, and obtaining the stiffness array and mass array during assembly, includes: A finite element model of the assembled structure is created in one go, and a dynamic model of all assembled modules is created based on the finite element model. Then, the node matrix of each assembly module is obtained by numbering, and the mass matrix and stiffness matrix of each assembly module are obtained; before assembly begins, all assembly modules are "killed", that is, a mass matrix and stiffness matrix are set to zero are established. During the assembly process, each time a new module is assembled, the node number of the assembled module is obtained through the node matrix, and the mass matrix and stiffness matrix of the corresponding node number are "activated", thereby obtaining the updated stiffness matrix and mass matrix in the assembly process.

3. The method according to claim 1, characterized in that, The optimization variables for determining the assembly sequence in the large space antenna assembly process are specifically: The assembly modules are numbered using an absolute numbering scheme. Each module has a unique number associated with its position in the space antenna. The optimization variables are represented in the following form: S=[E1 E2 ... E n ] Among them, [E1 E2 ... E n ] indicates the assembly module number, and E i ∈{1,2,...n}.

4. The method according to claim 3, characterized in that, The constraints for determining the assembly sequence include: Integrity constraint: the resulting assembly sequence S must contain all module units; Without repetition constraints, each module unit appears only once in the resulting assembly sequence S; Continuity constraint: in the resulting assembly sequence S, the modules assembled later must be connected to the previously assembled parts; No interference constraint: For hexagonal antenna modules, the number of connection sides between the later assembled modules and the previously assembled parts in the obtained assembly sequence S shall not exceed 4.

5. The method according to claim 4, characterized in that, The evaluation index for constructing an assembly sequence related to the fundamental frequency of the structure based on the stiffness matrix and mass matrix during the assembly process is as follows: The structural fundamental frequency of the space antenna is calculated based on the continuously updated mass array and stiffness array during different assembly processes; The structural fundamental frequency weighted sum of the space antennas throughout the assembly process is selected as the sequence evaluation index, and the optimization objective function is designed as follows: Among them, c i ω is the weighting coefficient. i The fundamental frequency of the entire space antenna structure when the i-th assembly module is installed.

6. The method according to claim 3, characterized in that, The improved adaptive discrete particle swarm optimization algorithm includes: Define the position and velocity of the particles, using the position of each particle to represent the assembly sequence S, and the velocity of each particle to represent the velocity of the assembly module; The formulas for updating the particle's position and velocity are defined as follows: In the formula, λ is the parametric inertia weight, which controls the previous generation velocity V. i (t) with respect to the current velocity V i The influence of (t+1); c1 and c2 are non-negative learning factors, with c1 controlling the individual's historical optimal position P. i (t) with respect to the current velocity V i The effect of (t+1); c2 controls the global optimal position P g (t) with respect to the current velocity V i The effect of (t+1); An adaptive update strategy for the inertia weight λ is set. During the optimization process, the particle velocity is adjusted using this adaptive update strategy for the inertia weight λ, specifically expressed as follows: l k =λ start -(l start -l end )*(k / k max ) 2 In the formula, λ start and λ end Let k and k represent the initial inertia weight and the inertia weight at the maximum number of iterations, respectively; max These represent the current iteration step and the maximum iteration step, respectively.

7. The method according to claim 6, characterized in that, The assembly sequence optimization problem for maximizing the fundamental frequency of the structure yields a feasible and optimal assembly sequence, including: Under the constraints of the assembly sequence, a feasible assembly sequence is determined, and the dynamic model of the assembly process is updated according to the assembly sequence after each assembly is completed. The new assembly module's mass matrix and stiffness matrix are obtained based on the updated assembly process dynamic model. The structural fundamental frequency of the current structure is calculated by combining the mass matrix and stiffness matrix. The optimal objective function of the assembly sequence is evaluated. If it is better than the historical best, the current optimal particle position and the global optimal objective function are updated; otherwise, they are not updated. An assembly sequence that satisfies the constraints and maximizes the fundamental frequency of the structure is obtained through iterative optimization.

8. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the large space antenna assembly sequence planning method that considers maximizing the structural fundamental frequency as described in any one of claims 1 to 7.

9. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the large space antenna assembly sequence planning method considering the maximization of the structural fundamental frequency as described in any one of claims 1 to 7.

10. An electronic device, characterized in that, include: processor; as well as Memory for storing the executable instructions of the processor; The processor is configured to execute, via executing the executable instructions, the large space antenna assembly sequence planning method considering the maximization of the structural fundamental frequency as described in any one of claims 1 to 7.

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