Satellite antenna virtual splicing assembly method based on gravitational potential function collaborative optimization

Abstract

This invention discloses a virtual splicing and assembly method for satellite antennas based on the cooperative optimization of gravitational potential energy functions. The method includes: constructing a virtual assembly initialization scenario driven by multi-source data; constructing a global gravitational potential energy field based on theoretical surfaces, converting the geometric surface errors of the sub-lobes into global potential energy values; constructing a local cooperative constraint potential energy field based on boundary topological relationships, which includes an elastic tensile potential energy component characterizing the gap width and a torsional potential energy component characterizing the step difference and normal deflection; treating the sub-lobes to be assembled as rigid bodies, solving for the differential of pose parameters based on the total potential energy function, and obtaining the virtual net external force and virtual net torque acting on the centroid of the sub-lobes; based on the virtual net external force and virtual net torque, synchronously updating the spatial position and attitude angle of all sub-lobes along the opposite direction of the potential energy gradient, and recalculating the global gravitational potential energy function and the local cooperative constraint potential energy function until the total potential energy converges. This invention achieves efficient and accurate virtual splicing and assembly of satellite antennas.

Description

Technical Field

[0001] This invention relates to the fields of three-dimensional data processing and digital virtual assembly, and more specifically, to a method for virtual splicing and assembling satellite antennas by co-optimizing the gravitational potential energy function. Background Technology

[0002] The modular parabolic antenna is a primary structural solution to overcome the limitations of large-aperture satellite antenna launch capabilities. Since the antenna surface accuracy (RMS) directly determines key electrical performance indicators such as gain and pointing accuracy, high-precision pre-assembly on the ground is crucial. Traditional physical trial assembly suffers from drawbacks such as high risk, long cycle time, and difficulty in adjustment, making it unsuitable for the rapid development requirements of modern spacecraft.

[0003] In recent years, with the development of high-precision 3D laser scanning and photogrammetry technologies, engineers can quickly acquire high-density point cloud data of antenna sublobe surfaces. Utilizing this massive amount of measured data for digital virtual assembly allows for simulating the assembly process and predicting the final surface shape without physical contact, becoming an important means to improve antenna manufacturing quality and efficiency.

[0004] However, using point cloud data for virtual antenna stitching still faces technical challenges. First, existing point cloud registration algorithms primarily focus on the overall fit between a single sublobe and the theoretical model, making it difficult to simultaneously account for the strict gap and step difference constraints (i.e., local topological relationships) between adjacent sublobes. Second, when multiple sublobes need to be simultaneously adjusted for pose coordination, the computational dimensionality is high and it is prone to getting trapped in local optima, resulting in slow convergence and low accuracy in assembly calculations. Existing general-purpose algorithms are unable to directly meet the requirements of such multi-constraint collaborative optimization scenarios. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention proposes a satellite antenna virtual splicing assembly method based on gravitational potential energy function collaborative optimization. The aim is to solve the pose and attitude of each assembled antenna sublobe in the virtual splicing assembly of satellite antennas, thereby improving assembly efficiency. This method solves the problems in existing technologies, such as the difficulty in taking into account the strict gap and step difference constraints between adjacent sublobes in pose collaborative adjustment, the high computational dimensionality, and the tendency to get trapped in local optima, which leads to slow convergence and low accuracy in assembly calculations.

[0006] To achieve the above-mentioned technical objectives, the present invention proposes the following technical solution:

[0007] A virtual splicing and assembly method for satellite antennas with coordinated optimization of gravitational potential energy functions, specifically comprising:

[0008] Obtain point cloud data of the sublobe of the satellite antenna to be assembled and the point cloud data of the reference satellite antenna sublobe. Combine the parabolic digital model equations designed by satellite antenna theory to perform coarse registration initialization and establish the mapping relationship between the global coordinate system and the local connected coordinate system.

[0009] Define the global gravitational force acting on the sublobe of the satellite antenna to be assembled, pointing to the theoretical surface, and construct the global gravitational potential energy function to convert the geometric surface error into the global potential energy value;

[0010] Based on the adjacency topology between the sublobes of the satellite antenna to be assembled, the splicing boundary point pairs are searched, and a local cooperative constraint potential energy function is constructed. The local cooperative constraint potential energy function includes an elastic tensile potential energy component that characterizes the gap width and a torsional potential energy component that characterizes the splicing step and normal deflection.

[0011] Each satellite antenna sublobe to be assembled is considered as a rigid body. Based on the sum of the global gravitational potential energy function and the local cooperative constraint potential energy function, the pose parameters of the sublobe are solved by differentiation. The virtual resultant external force acting on the centroid of each sublobe and the virtual resultant torque acting on the rigid body of each sublobe are calculated respectively.

[0012] Based on the virtual net external force and virtual net torque, the spatial position and attitude angle of all satellite antenna sublobes to be assembled are updated synchronously along the opposite direction of the potential energy gradient. The global gravitational potential energy function and the local cooperative constraint potential energy function are recalculated until the total potential energy of the entire satellite antenna to be assembled converges, thus completing the virtual splicing assembly.

[0013] Furthermore, the definition of the satellite antenna sublobe to be assembled being subjected to global gravitational force pointing towards the theoretical surface, and the construction of a global gravitational potential energy function to convert geometric surface errors into global potential energy values ​​are specifically as follows:

[0014] Let the parabolic equation for the theoretical design of satellite antennas be: , obtain the Block of satellite antenna sublobes to be assembled sampling point set ;

[0015] for any point on Calculate its distance to the theoretical parabola The nearest projection point on The square of the Euclidean distance is taken as the potential energy at a single point;

[0016] Computing sublobes global gravitational potential energy function , it is The weighted sum of the potential energies at each sampling point is expressed by the formula:

[0017] ;

[0018] in, Let be the transformation matrix to be solved. The global gravitational coefficient, This represents the shortest distance from a point to a surface.

[0019] Furthermore, the step of searching for splicing boundary point pairs based on the adjacency topology relationship between the sublobes of the satellite antenna to be assembled, and constructing the local cooperative constraint potential function, specifically involves:

[0020] Establish a neighborhood topology graph to identify satellite antennas to be assembled. All adjacent sublobes ;

[0021] For each pair of adjacent sublobes The KD-Tree algorithm is used to search for the set of nearest neighbor pairs in the boundary region. ;

[0022] Constructing a local cooperative constraint potential function The formula is as follows:

[0023] ;

[0024] in, The gap shrinkage coefficient, is the step difference suppression coefficient, and is set as follows: ; For sub-lobes The m-th sampling point within the boundary area For sub-lobes Within the boundary area and The nearest sampling point; the boundary region is the edge region where two adjacent sub-lobes are spliced ​​together. and Sampling points and The normal vector at that location.

[0025] Furthermore, each satellite antenna sublobe to be assembled is considered a rigid body, and the pose parameters of the sublobe are solved by differentiation based on the sum of the global gravitational potential energy function and the local cooperative constraint potential energy function; the virtual resultant external force acting on the centroid of each sublobe and the virtual resultant torque acting on the rigid body of each sublobe are calculated as follows:

[0026] The total potential energy function with respect to the translation vector Find the negative value of the partial derivative, which is taken as the virtual resultant external force acting on the centroid of the i-th satellite antenna sublobe to be assembled. The formula is expressed as:

[0027] ;

[0028] in, , These are the global gravitational potential energy function and the local cooperative constraint potential energy function of the i-th satellite antenna sublobe to be assembled, respectively.

[0029] For the k-th sampling point on the i-th sublobe Calculate the discrete infinitesimal potential energy gradient force acting on it. Combined with sampling points Relative to the center of mass lever arm vector The virtual resultant moment acting on the rigid body of the i-th satellite antenna sublobe to be assembled is calculated using the vector cross product. :

[0030] ;

[0031] in, Sampling points Potential energy in a system composed of all sublobes.

[0032] Furthermore, the step of synchronously updating the spatial position and attitude angle of all satellite antenna sublobes to be assembled along the opposite direction of the potential energy gradient based on the virtual resultant external force and virtual resultant torque, and recalculating the global gravitational potential energy function and the local cooperative constraint potential energy function until the total potential energy of the entire satellite antenna to be assembled converges, thus completing the virtual splicing assembly, specifically involves:

[0033] The translation vector of the satellite antenna sublobe to be assembled is updated based on the virtual resultant external force and the translation step size learning rate. The formula is expressed as:

[0034] ;

[0035] in, The learning rate is the step size for translation. Let be the updated translation vector of the i-th satellite antenna sublobe to be assembled. Let i be the translation vector of the i-th satellite antenna sublobe to be assembled before the update;

[0036] The virtual resultant moment is transformed into an increment of the rotation matrix using Lie algebra mapping, and the rotation vector is calculated. Then, the Rodriguez formula is used to generate the incremental rotation matrix. The formula is expressed as:

[0037] ;

[0038] in, Let be the updated rotation matrix for the i-th satellite antenna sublobe to be assembled. Let be the rotation matrix of the i-th satellite antenna sublobe before it is updated. The learning rate is the value of the rotation matrix.

[0039] After the update, the global gravitational potential energy function and the local cooperative constraint potential energy function are recalculated to determine whether the rate of change of the total potential energy of the system is less than the preset change threshold. If it is less, the transformation matrix of the current iteration is output as the optimal assembly pose; otherwise, the iteration continues. The transformation matrix consists of a translation vector and a rotation matrix.

[0040] Furthermore, this application also discloses a satellite antenna virtual splicing and assembly system with coordinated optimization of gravitational potential energy function, which specifically includes:

[0041] The initialization module is used to acquire point cloud data of the satellite antenna sublobe and reference sublobe to be assembled, and to perform coarse registration initialization in combination with the theoretical parabolic equation;

[0042] The global gravitational potential energy field construction module is used to construct the global gravitational potential energy function of the sublobe to be assembled pointing to the theoretical surface, and to convert the geometric surface error into the global potential energy value.

[0043] The local cooperative constraint potential energy field construction module is used to search for splicing boundary point pairs based on the adjacency topology relationship between sublobes, and construct a local cooperative constraint potential energy function containing elastic tensile potential energy components and torsional potential energy components.

[0044] The virtual resultant force solution module is used to treat the sub-lobe to be assembled as a rigid body, and to perform differential solution of the total potential energy function with respect to the pose parameters to obtain the virtual resultant external force and virtual resultant torque.

[0045] The pose iteration module is used to synchronously update the spatial position and attitude angle of all sublobes to be assembled along the opposite direction of the potential energy gradient based on the virtual resultant external force and virtual resultant torque, and to recalculate the global gravitational potential energy function and the local cooperative constraint potential energy function until the total potential energy of the entire satellite antenna to be assembled converges.

[0046] An electronic device is also disclosed, comprising a memory and a processor, wherein:

[0047] Memory is used to store computer programs that can run on a processor;

[0048] The processor is configured to execute, while running the computer program, a satellite antenna virtual splicing assembly method with coordinated optimization of gravitational potential energy function as described above.

[0049] A computer-readable storage medium storing computer instructions for causing a processor to execute a satellite antenna virtual splicing assembly method with coordinated optimization of gravitational potential energy function as described above.

[0050] Based on the above technical solution, this application has at least the following beneficial effects:

[0051] This application constructs a dual potential energy field of "global gravity + local cooperation", transforming the multi-sublobe pose cooperation problem into potential energy-driven rigid body motion, breaking through the local optimal dilemma of block-by-block registration, and realizing global optimal assembly under multiple constraint coupling.

[0052] This application transforms high-dimensional pose optimization into gradient descent dynamics evolution, eliminating the need to explicitly solve nonlinear constraint equations, significantly improving computational efficiency, and supporting real-time simulation virtual assembly of multi-sublobe satellite antennas; the potential energy function explicitly separates gap and step difference constraints, and sets the step difference suppression coefficient to be higher than the gap contraction coefficient, prioritizing the elimination of step difference influence, and the output pose is more in line with assembly specifications.

[0053] This application only requires the input of the point cloud of the satellite antenna sublobe to be assembled, and completely eliminates the need for physical tooling, laser trackers and manual fitting. It compresses the physical adjustment of several days into fully automatic calculations at the minute level, which greatly shortens the development cycle and reduces costs. Attached Figure Description

[0054] Figure 1 This invention proposes a virtual splicing and assembly method for satellite antennas with coordinated optimization of gravitational potential energy function.

[0055] Figure 2 This is a schematic diagram of a virtual splicing and assembly scenario for a spliced ​​satellite antenna as described in an embodiment of the present invention;

[0056] Figure 3 This is a schematic diagram of the elastic stretching potential energy of the local cooperative constraint potential energy function in an embodiment of the present invention;

[0057] Figure 4 This is a schematic diagram of the torsional potential energy of the local cooperative constraint potential energy function in an embodiment of the present invention. Detailed Implementation

[0058] To make the objectives, technical solutions, and advantages of this invention clearer, the following is combined with... Figures 1-4 The present invention will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the invention.

[0059] Although the steps in this invention are arranged by reference numerals, this is not intended to limit the order of the steps. Unless the order of the steps is explicitly stated or the execution of a step requires other steps as a basis, the relative order of the steps can be adjusted. It is understood that the term "and / or" as used herein refers to and covers any and all possible combinations of one or more of the associated listed items.

[0060] like Figure 1 As shown, the present invention proposes a satellite antenna virtual splicing assembly method with coordinated optimization of gravitational potential energy function, which specifically includes:

[0061] A virtual splicing and assembly method for satellite antennas with coordinated optimization of gravitational potential energy functions, specifically comprising:

[0062] S1. Construct a multi-source data-driven virtual assembly initialization scenario: Obtain point cloud data of the sublobes of the satellite antenna to be assembled and the point cloud data of the reference satellite antenna sublobes. Combine the parabolic numerical model equations designed in satellite antenna theory to perform coarse registration initialization and establish the mapping relationship between the global coordinate system and the local connected coordinate system. In this embodiment, the coordinate system refers to the coordinate system with the entire satellite antenna as the reference, and the local connected coordinate system refers to the coordinate system with each sublobe as the reference. The satellite antenna to be assembled and its sublobes are as follows: Figure 2 As shown.

[0063] S2. Construct a global gravitational potential energy field based on the theoretical surface: Define the global gravitational force acting on the satellite antenna sublobe to be assembled, pointing towards the theoretical surface, and construct a global gravitational potential energy function to convert the geometric surface error into a global potential energy value; the geometric surface error is the spatial deviation between the actual geometric shape of the satellite antenna sublobe and the theoretical parabolic surface.

[0064] In a preferred embodiment, step S2 specifically comprises:

[0065] Let the parabolic equation for the theoretical design of satellite antennas be: , obtain the Block of satellite antenna sublobes to be assembled sampling point set ;

[0066] for any point on Calculate its distance to the theoretical parabola The nearest projection point on The square of the Euclidean distance is taken as the potential energy at a single point;

[0067] Computing sublobes global gravitational potential energy function , it is The weighted sum of the potential energies at each sampling point is expressed by the formula:

[0068] ;

[0069] in, The transformation matrix to be solved (here, the transformation matrix refers to the transformation matrix of each sublobe from its local coordinate system to the global coordinate system, that is, the matrix that characterizes the pose transformation when the sublobe is assembled). The global gravitational coefficient, This represents the shortest distance from a point to the surface. The global gravitational potential energy function is used to characterize the "adsorption strength" of a theoretical surface to a sublobe (the farther a sublobe is from the ideal surface, the more it will be pulled back by a force close to the ideal surface; this force is the adsorption strength).

[0070] S3. Construct a local cooperative constraint potential energy field based on boundary topology: Search for splicing boundary point pairs based on the adjacency topology between the sublobes of the satellite antenna to be assembled, and construct a local cooperative constraint potential energy function; the local cooperative constraint potential energy function includes an elastic tensile potential energy component characterizing the gap width and a torsional potential energy component characterizing the splicing step and normal deflection.

[0071] In a preferred embodiment, step S3 specifically comprises:

[0072] Establish a neighborhood topology graph to identify satellite antennas to be assembled. All adjacent sublobes ;

[0073] For each pair of adjacent sublobes The KD-Tree algorithm is used to search for the set of nearest neighbor pairs in the boundary region. ,in , ;

[0074] Constructing a local cooperative constraint potential function The formula is as follows:

[0075] ;

[0076] in, The gap shrinkage coefficient, This is the step difference suppression coefficient. For sub-lobes The m-th sampling point within the boundary area For sub-lobes Within the boundary area and The nearest sampling point, the boundary region is the edge region where two adjacent sublobes are spliced ​​together; and set ; and Sampling points and The normal vector at that location.

[0077] In this embodiment, as Figure 3 , Figure 4 As shown: The first term in the local cooperative constraint potential energy function The elastic tensile potential energy between adjacent sublobe boundary points was simulated, attempting to pull the separated boundary points closer together, thereby eliminating the splicing gap; the second term The torsional potential energy generated by the inconsistency of the normals at boundary points was simulated, attempting to force the normals of adjacent boundary points to be parallel, thereby eliminating the geometrical order difference. And set... > This is to make the system more inclined to prioritize solving the step difference problem and focus more on eliminating the negative impact of the step difference.

[0078] S4. Solve for the generalized gradient of virtual force and virtual moment: Treat each satellite antenna sublobe to be assembled as a rigid body, and solve for the pose parameters of the sublobe by differentiation based on the sum of the global gravitational potential energy function and the local cooperative constraint potential energy function; calculate the virtual resultant external force acting on the centroid of each sublobe and the virtual resultant moment acting on the rigid body of each sublobe respectively.

[0079] In a preferred embodiment, step S4 specifically comprises:

[0080] The total potential energy function with respect to the translation vector Find the negative value of the partial derivative, which is taken as the virtual resultant external force acting on the centroid of the i-th satellite antenna sublobe to be assembled. The formula is expressed as:

[0081] ;

[0082] in, , These are the global gravitational potential energy function and the local cooperative constraint potential energy function of the i-th satellite antenna sublobe to be assembled, respectively.

[0083] The virtual net external force vector directly indicates the movement trend of the sublobe's centroid in the X, Y, and Z directions, driving the entire sublobe to translate towards a lower potential energy region.

[0084] Because the sublobe has a spatial geometric volume, and the gravitational forces differ at different locations from the theoretical surface, as well as the cooperative constraint forces differ at different locations on the edge, the forces at each point on the sublobe are uneven. Therefore, for the k-th sampling point on the i-th sublobe... Calculate the discrete infinitesimal potential energy gradient force acting on it. Combined with sampling points Relative to the center of mass The lever arm vector of the centroid in the global coordinate system The virtual resultant moment acting on the rigid body of the i-th satellite antenna sublobe to be assembled is calculated using the vector cross product. :

[0085] ;

[0086] in, Sampling points Potential energy in the system (i.e., the whole composed of all sublobes). The virtual resultant moment vector indicates the rotational tendency of the sublobes about the X, Y, and Z axes. When one side of the sublobe tilts up or deviates from the normal direction, the gravitational or torsional restoring force on that side is larger, forming a moment through the lever arm, driving the sublobe to rotate to eliminate angular deviations and step differences.

[0087] S5. Based on the virtual net external force and virtual net torque, synchronously update the spatial position and attitude angle of all satellite antenna sublobes to be assembled along the opposite direction of the potential energy gradient, and recalculate the global gravitational potential energy function and the local cooperative constraint potential energy function until the total potential energy of the entire satellite antenna to be assembled converges, thus completing the virtual splicing assembly.

[0088] In a preferred embodiment, step S5 specifically includes:

[0089] The translation vector of the satellite antenna sublobe to be assembled is updated based on the virtual resultant external force and the translation step size learning rate. The formula is expressed as:

[0090] ;

[0091] in, The learning rate is the step size for translation. Let be the updated translation vector of the i-th satellite antenna sublobe to be assembled. Let i be the translation vector of the i-th satellite antenna sublobe to be assembled before the update;

[0092] The virtual resultant moment is transformed into an increment of the rotation matrix using Lie algebra mapping, and the rotation vector is calculated. Then, the Rodriguez formula is used to generate the incremental rotation matrix. The formula is expressed as:

[0093] ;

[0094] in, Let be the updated rotation matrix for the i-th satellite antenna sublobe to be assembled. Let be the rotation matrix of the i-th satellite antenna sublobe before it is updated. The learning rate is the value of the rotation matrix.

[0095] After the update, the global gravitational potential energy function and the local cooperative constraint potential energy function are recalculated to determine whether the rate of change of the total potential energy of the system is less than the preset change threshold. If it is less, the transformation matrix of the current iteration is output as the optimal assembly pose; otherwise, the iteration continues. The transformation matrix consists of a translation vector and a rotation matrix.

[0096] Furthermore, this application also discloses a satellite antenna virtual splicing and assembly system with coordinated optimization of gravitational potential energy function, which specifically includes:

[0097] The initialization module is used to acquire point cloud data of the satellite antenna sublobe and reference sublobe to be assembled, and to perform coarse registration initialization in combination with the theoretical parabolic equation;

[0098] The global gravitational potential energy field construction module is used to construct the global gravitational potential energy function of the sublobe to be assembled pointing to the theoretical surface, and to convert the geometric surface error into the global potential energy value.

[0099] The local cooperative constraint potential energy field construction module is used to search for splicing boundary point pairs based on the adjacency topology relationship between sublobes, and construct a local cooperative constraint potential energy function containing elastic tensile potential energy components and torsional potential energy components.

[0100] The virtual resultant force solution module is used to treat the sub-lobe to be assembled as a rigid body, and to perform differential solution of the total potential energy function with respect to the pose parameters to obtain the virtual resultant external force and virtual resultant torque.

[0101] The pose iteration module is used to synchronously update the spatial position and attitude angle of all sublobes to be assembled along the opposite direction of the potential energy gradient based on the virtual resultant external force and virtual resultant torque, and to recalculate the global gravitational potential energy function and the local cooperative constraint potential energy function until the total potential energy of the entire satellite antenna to be assembled converges.

[0102] An electronic device is also disclosed, comprising a memory and a processor, wherein:

[0103] Memory is used to store computer programs that can run on a processor;

[0104] The processor is configured to execute, while running the computer program, a satellite antenna virtual splicing assembly method with coordinated optimization of gravitational potential energy function as described above.

[0105] A computer-readable storage medium storing computer instructions for causing a processor to execute a satellite antenna virtual splicing assembly method with coordinated optimization of gravitational potential energy function as described above.

[0106] In summary, to address the challenge of pose coordination adjustment during virtual assembly of spliced ​​satellite antennas, a satellite antenna virtual splicing assembly method based on gravitational potential energy function optimization was designed. This method achieves globally optimal collaborative assembly with multiple constraints, output pose that better conforms to engineering process specifications, and improves assembly efficiency and splicing accuracy.

[0107] In this specification, the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to at least one embodiment or example described in connection with a specific feature, structure, material, or characteristic. These specific features, structures, materials, or characteristics may be combined in a suitable manner in one or more embodiments or examples. Furthermore, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples and their features described in this specification.

[0108] The logic and / or steps shown in the flowchart or otherwise described can be viewed as a sequence of executable instructions for implementing logical functions. These instructions may be implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device. Such systems, apparatus, or devices include processor systems or other systems capable of receiving and executing instructions.

[0109] The above embodiments detail the principles and implementation methods of the present invention, and illustrate its working principle using specific examples. These examples are only used to help understand the method and core ideas of the present invention. Furthermore, based on the ideas of the present invention, actual implementation methods and application scope may vary. Therefore, the content of this specification should not be construed as limiting the present invention.

Claims

1. A virtual splicing and assembly method for satellite antennas with coordinated optimization of gravitational potential energy function, characterized in that, Specifically, the following steps are included: Obtain point cloud data of the sublobe of the satellite antenna to be assembled and the point cloud data of the reference satellite antenna sublobe. Combine the parabolic digital model equations designed by satellite antenna theory to perform coarse registration initialization and establish the mapping relationship between the global coordinate system and the local connected coordinate system. Define the global gravitational force acting on the sublobe of the satellite antenna to be assembled, pointing to the theoretical surface, and construct the global gravitational potential energy function to convert the geometric surface error into the global potential energy value; Based on the adjacency topology between the sublobes of the satellite antenna to be assembled, the splicing boundary point pairs are searched, and a local cooperative constraint potential energy function is constructed. The local cooperative constraint potential energy function includes an elastic tensile potential energy component that characterizes the gap width and a torsional potential energy component that characterizes the splicing step and normal deflection. Each satellite antenna sublobe to be assembled is considered as a rigid body. Based on the sum of the global gravitational potential energy function and the local cooperative constraint potential energy function, the pose parameters of the sublobe are solved by differentiation. The virtual resultant external force acting on the centroid of each sublobe and the virtual resultant torque acting on the rigid body of each sublobe are calculated respectively. Based on the virtual net external force and virtual net torque, the spatial position and attitude angle of all satellite antenna sublobes to be assembled are updated synchronously in the opposite direction of the potential energy gradient, and the global gravitational potential energy function and the local cooperative constraint potential energy function are recalculated until the total potential energy of the entire satellite antenna to be assembled converges, thus completing the virtual splicing assembly.

2. The satellite antenna virtual splicing and assembly method with coordinated optimization of gravitational potential energy function according to claim 1, characterized in that, The definition of the sublobe of the satellite antenna to be assembled being subjected to the global gravitational force pointing to the theoretical surface, and the construction of the global gravitational potential energy function, specifically converting the geometric surface error into a global potential energy value, are as follows: Let the parabolic equation for the theoretical design of satellite antennas be: , obtain the Block of satellite antenna sublobes to be assembled sampling point set ; for any point on Calculate its distance to the theoretical parabola The nearest projection point on The square of the Euclidean distance is taken as the potential energy at a single point; Computing sublobes global gravitational potential energy function , it is The weighted sum of the potential energies at each sampling point is expressed by the formula: ； in, Let be the transformation matrix to be solved. The global gravitational coefficient, This represents the shortest distance from a point to a surface.

3. The satellite antenna virtual splicing and assembly method with coordinated optimization of gravitational potential energy function according to claim 1, characterized in that, The specific steps for searching and splicing boundary point pairs based on the adjacency topology between the sublobes of the satellite antenna to be assembled, and constructing the local cooperative constraint potential function, are as follows: Establish a neighborhood topology graph to identify satellite antennas to be assembled. All adjacent sublobes ; For each pair of adjacent sublobes The KD-Tree algorithm is used to search for the set of nearest neighbor pairs in the boundary region. ; Constructing a local cooperative constraint potential function The formula is as follows: ； in, The gap shrinkage coefficient, is the step difference suppression coefficient, and is set as follows: ; For sub-lobes The m-th sampling point within the boundary area For sub-lobes Within the boundary area and The nearest sampling point; the boundary region is the edge region where two adjacent sub-lobes are spliced ​​together. and Sampling points and The normal vector at that location.

4. The satellite antenna virtual splicing and assembly method with coordinated optimization of gravitational potential energy function according to claim 1, characterized in that, The process involves treating each satellite antenna sublobe to be assembled as a rigid body, and then differentially solving for the pose parameters of the sublobe based on the sum of the global gravitational potential energy function and the local cooperative constraint potential energy function. Specifically, the virtual net external force acting on the centroid of each sublobe and the virtual net torque acting on the rigid body of each sublobe are calculated as follows: The total potential energy function with respect to the translation vector Find the negative value of the partial derivative, which is taken as the virtual resultant external force acting on the centroid of the i-th satellite antenna sublobe to be assembled. The formula is expressed as: ； in, , These are the global gravitational potential energy function and the local cooperative constraint potential energy function of the i-th satellite antenna sublobe to be assembled, respectively. For the k-th sampling point on the i-th sublobe Calculate the discrete infinitesimal potential energy gradient force it experiences. Combined with sampling points Relative to the center of mass lever arm vector The virtual resultant moment acting on the rigid body of the i-th satellite antenna sublobe to be assembled is calculated using the vector cross product. : ； in, Sampling points Potential energy in a system composed of all sublobes.

5. The satellite antenna virtual splicing and assembly method with coordinated optimization of gravitational potential energy function according to claim 4, characterized in that, The process of synchronously updating the spatial position and attitude angle of all satellite antenna sublobes to be assembled along the opposite direction of the potential energy gradient based on the virtual resultant external force and virtual resultant torque, and recalculating the global gravitational potential energy function and the local cooperative constraint potential energy function until the total potential energy of the entire satellite antenna to be assembled converges, thus completing the virtual splicing assembly, specifically involves: The translation vector of the satellite antenna sublobe to be assembled is updated based on the virtual resultant external force and the translation step size learning rate. The formula is expressed as: ； in, The learning rate is the step size for translation. Let be the updated translation vector of the i-th satellite antenna sublobe to be assembled. Let i be the translation vector of the i-th satellite antenna sublobe to be assembled before the update; The virtual resultant moment is transformed into an increment of the rotation matrix using Lie algebra mapping, and the rotation vector is calculated. Then, the Rodriguez formula is used to generate the incremental rotation matrix. The formula is expressed as: ； in, Let be the updated rotation matrix for the i-th satellite antenna sublobe to be assembled. Let be the rotation matrix of the i-th satellite antenna sublobe before it is updated. The learning rate is the value of the rotation matrix. After the update, the global gravitational potential energy function and the local cooperative constraint potential energy function are recalculated to determine whether the rate of change of the total potential energy of the system is less than the preset change threshold. If it is less, the transformation matrix of the current iteration is output as the optimal assembly pose; otherwise, the iteration continues. The transformation matrix consists of a translation vector and a rotation matrix.

6. A system for a satellite antenna virtual splicing and assembly method based on the coordinated optimization of gravitational potential energy function according to any one of claims 1 to 5, characterized in that, Specifically, it includes: The initialization module is used to acquire point cloud data of the satellite antenna sublobe and reference sublobe to be assembled, and to perform coarse registration initialization in combination with the theoretical parabolic equation; The global gravitational potential energy field construction module is used to construct the global gravitational potential energy function of the sublobe to be assembled pointing to the theoretical surface, and to convert the geometric surface error into the global potential energy value. The local cooperative constraint potential energy field construction module is used to search for splicing boundary point pairs based on the adjacency topology relationship between sublobes, and construct a local cooperative constraint potential energy function containing elastic tensile potential energy components and torsional potential energy components. The virtual resultant force solution module is used to treat the sub-lobe to be assembled as a rigid body, and to perform differential solution of the total potential energy function with respect to the pose parameters to obtain the virtual resultant external force and virtual resultant torque. The pose iteration module is used to synchronously update the spatial position and attitude angle of all sublobes to be assembled along the opposite direction of the potential energy gradient based on the virtual resultant external force and virtual resultant torque, and to recalculate the global gravitational potential energy function and the local cooperative constraint potential energy function until the total potential energy of the entire satellite antenna to be assembled converges.

7. An electronic device, characterized in that, The electronic device includes a memory and a processor, wherein: Memory is used to store computer programs that can run on a processor; A processor is configured to, while running the computer program, execute a satellite antenna virtual splicing assembly method with coordinated optimization of gravitational potential energy function as described in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute a satellite antenna virtual splicing assembly method with coordinated optimization of gravitational potential energy function as described in any one of claims 1-5.

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