Assembly parameter updating method, device, medium and product

Abstract

The present invention discloses an assembly parameter updating method, device, medium and product, which relates to the field of assembly technology. The method comprises: solving the assembly parameters of the measured model of the array-type thermal protection component to obtain the assembly parameter results; obtaining the measured data of the load-bearing structure surface under different assembly states; establishing an assembly parameter optimization model according to the assembly parameter results and the measured data of the load-bearing structure surface under different assembly states; the assembly parameter optimization model comprises: an objective function and constraints; optimizing the assembly parameters of the assembly parameter optimization model based on the objective function and the constraints to obtain the optimal assembly parameters. The present invention combines multiple constraints and converts the multi-objective problem into a single-objective problem by a weighted summation method. Thereby, the assembly parameter optimization update for the array-type thermal protection component can be realized, which plays an auxiliary guiding role in the spacecraft thermal protection assembly process and improves the assembly accuracy and efficiency of the spacecraft thermal protection system.

Description

Technical Field

[0001] The present invention relates to the field of assembly technology, and in particular to an assembly parameter updating method, device, medium and product. Background Art

[0002] When a reusable spacecraft enters the atmosphere, the friction between high-speed gas and the outer surface of the spacecraft generates a large amount of heat, causing the surface temperature of the spacecraft to rise rapidly. In order to ensure the safety of the spacecraft and the internal load-bearing structure of the spacecraft, an array-type thermal protection structure is required to protect the spacecraft. The array-type thermal protection structure is a reusable thermal protection system applied to spacecraft. It is laid on the surface of the body in the form of an array and a certain gap is reserved. Its surface contour requirements are high and it is a non-direct contact matching form. The gap between components can not only reserve space for the thermal expansion deformation of a single component, but also coordinate the load deformation between components. At the same time, if the step difference between components is too large, local hot spots will be generated on the windward surface, thereby increasing the local temperature of the spacecraft surface. Therefore, the array-type thermal protection components need to be accurately positioned, equally spaced, and have continuous curvature when assembled and laid.

[0003] The traditional assembly and positioning of array thermal protection structures is done by manual marking and limiting the position by positioning pallets. However, due to the lack of sufficient positioning references for the assembly and positioning of array components, and the existence of manufacturing deviations of the spacecraft body and components, the traditional manual assembly process is difficult to meet the precise assembly and positioning requirements of the thermal protection structure, including the overall contour of the array thermal protection structure, the fit between the array thermal protection structure and the body, and the gap and step difference requirements between the array thermal protection structures. At the same time, due to the complex structure of the thermal protection system of the spacecraft and the large number of components, multiple trial assembly, adjustment, disassembly, and reassembly processes are often required during its assembly process. In addition, according to the assembly process and process requirements, the spacecraft has different assembly states, such as tooling replacement, parts assembly, tooling rotation, etc. The above situations often cause the spacecraft's external structure to deform to varying degrees, making the previous trial assembly process inapplicable, affecting the overall assembly cycle and assembly quality.

[0004] Therefore, how to design an assembly parameter updating method to avoid the situation where the assembly parameters are not applicable due to changes in the assembly state has become a technical problem that urgently needs to be solved in this field. Summary of the invention

[0005] The purpose of the present invention is to provide an assembly parameter updating method, device, medium and product, which can realize the optimization and updating of assembly parameters for array-type thermal protection components, play an auxiliary guiding role in the spacecraft thermal protection assembly process, and improve the assembly accuracy and efficiency of the spacecraft thermal protection system.

[0006] To achieve the above object, the present invention provides the following solutions:

[0007] In a first aspect, the present invention provides an assembly parameter updating method, the method comprising:

[0008] The assembly parameters of the measured model of the arrayed heat protection component are solved to obtain assembly parameter results; the assembly parameter results include gap step difference parameters of the arrayed heat protection component and fit parameters of the arrayed heat protection component; the gap step difference parameters of the arrayed heat protection component are the gap and step difference between the seam point clouds of two adjacent heat protection components; the fit parameter of the arrayed heat protection component is the average value of the distances from a number of feature points on the inner surface of the measured model of the arrayed heat protection component to the bearing structure surface; the measured model of the arrayed heat protection component is a virtual model determined according to the spacecraft to be assembled; the bearing structure surface is a surface of the spacecraft to be assembled for assembling the arrayed heat protection component.

[0009] Obtain measured data of the load-bearing structure surface under different assembly conditions.

[0010] An assembly parameter optimization model is determined according to the assembly parameter results and measured data of the load-bearing structure surface under different assembly states; the assembly parameter optimization model includes: an objective function and constraint conditions; the objective function is a function related to the gap of the arrayed heat protection components, the step difference of the arrayed heat protection components and the weight coefficient of the fit of the arrayed heat protection components; the constraint conditions include: boundary constraints of the arrayed heat protection components, cross-section-related constraints of the arrayed heat protection components and assembly parameter constraints of the arrayed heat protection components.

[0011] The assembly parameters of the assembly parameter optimization model are optimized to obtain optimal assembly parameters.

[0012] Optionally, the process of solving the gap step difference parameter of the array type heat protection component specifically includes:

[0013] Based on the measured model of array thermal protection components, a gap step difference solution model is constructed.

[0014] An insertion section operation is performed on the measured model of the heat protection component to obtain two-dimensional seam point data; the insertion section operation is used to convert 3D point cloud data into 2D point data.

[0015] Feature points are selected for the two-dimensional seam point data to obtain curvature mutation points of the two-dimensional seam point data.

[0016] Boundary points and critical points are determined according to the curvature mutation points of the two-dimensional seam point data.

[0017] Substituting the boundary points and critical points into the gap step difference solution model, the gap step difference parameters of the array type heat protection component are obtained.

[0018] Optionally, the process of solving the fit parameters of the arrayed heat protection component specifically includes:

[0019] A plurality of characteristic points are uniformly selected on the inner surface of the measured model of the array-type heat protection component.

[0020] The average value of the distances from several feature points to the surface of the load-bearing structure is calculated to obtain the fit parameters of the array-type thermal protection component.

[0021] Optionally, obtaining measured data of the load-bearing structure surface under different assembly states specifically includes:

[0022] The measured point cloud data and optical fiber monitoring data of the initial assembly of the load-bearing structure surface of the measured model of the array-type thermal protection component are obtained respectively; the measured point cloud data of the initial assembly of the load-bearing structure surface are data obtained by laser scanning; the optical fiber monitoring data are data measured by the optical fiber sensor pasted on the load-bearing structure surface.

[0023] The relative deformation of the optical fiber monitoring data is calculated based on the curvature conversion model.

[0024] The measured point cloud data of the initial assembly of the load-bearing structure surface and the relative deformation of the optical fiber monitoring data are merged according to the grid deformation optimization algorithm to obtain the measured data of the load-bearing structure surface under different assembly states.

[0025] Optionally, the objective function is expressed as:

[0026]

[0027] Among them, G k (t k ) is the sum of the deviations between all gap parameters and theoretical parameters of the array thermal protection component after the kth iteration, F k (t k ) is the sum of all step difference parameter deviations of the array thermal protection component after the kth iteration, D k (t k ) is the total deviation of the degree of fit of the heat protection component after the kth iteration, μ1 is the weight coefficient of the gap of the arrayed heat protection component, μ2 is the weight coefficient of the step difference of the arrayed heat protection component, and μ3 is the weight coefficient of the degree of fit of the arrayed heat protection component.

[0028] Optionally, the boundary constraint condition of the array-type heat protection component is expressed as:

[0029]

[0030] In the formula, is the position parameter of the i-th thermal protection component after the k-th iteration, LT boundaryis the minimum space range allowed for pasting the i-th thermal protection component, UT boundary The maximum space range allowed for pasting the i-th heat protection component.

[0031] The expression of the cross-section related constraint of the array type heat protection component is:

[0032]

[0033] In the formula, (x, y, z) is the center point position, is the normal direction, T boundary is the theoretical boundary of the array thermal protection component, L R The length of the cross section, W R is the width of the cross section, k' is the curvature, G t is the theoretical gap parameter, I s is the cross-sectional spacing, I smin is the minimum cross-sectional distance between two adjacent components, I smax It is the maximum cross-sectional distance between two adjacent components.

[0034] The expression of the assembly parameter constraint condition of the array thermal protection component is:

[0035]

[0036] In the formula, is the gap parameter deviation of the jth section of the i-th thermal protection component after the k-1th optimization iteration, LT gap is the lower limit of the gap parameter constraint tolerance, UT gap is the upper limit of the gap parameter constraint tolerance, is the step difference parameter deviation of the jth section of the i-th thermal protection component after the k-1th optimization iteration, LT flush is the lower limit of the tolerance of the step difference parameter constraint, UT flush is the upper limit of the tolerance for the order difference parameter constraint, is the fitting parameter deviation of the i-th thermal protection component after the k-1th optimization iteration, LT dist is the lower limit of the tolerance of the fit parameter constraint, UT dist Constrain the upper tolerance limit for the fit parameter.

[0037] Optionally, the assembly parameter optimization model is an improved quantum particle swarm optimization algorithm, and the improved quantum particle swarm optimization algorithm includes: improvement of population initialization using Logistic-Sine-Cosine chaotic mapping, improvement of adaptive inertia weight and improvement of global search capability of Lévy flight mechanism.

[0038] In a second aspect, the present invention provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of an assembly parameter updating method described in the first aspect.

[0039] In a third aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of an assembly parameter updating method described in the first aspect.

[0040] In a fourth aspect, the present invention provides a computer program product, comprising a computer program, which, when executed by a processor, implements the steps of an assembly parameter updating method described in the first aspect.

[0041] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0042] The present invention provides an assembly parameter updating method, device, medium and product. In the present invention, the assembly parameter results include the gap step difference parameters of the arrayed thermal protection components and the fit parameters of the arrayed thermal protection components. The parameter updating problem of the present invention is a typical multi-objective optimization problem, which requires coordination and compromise between the various objectives so that the overall goal can be optimized as much as possible. However, since multi-objective problems are difficult to solve in engineering and the calculations are too complicated, the present invention combines multiple constraints and converts the multi-objective problem into a single-objective problem through a weighted summation method. Thereby, the assembly parameter optimization update for arrayed thermal protection components can be realized, which plays an auxiliary guiding role in the spacecraft thermal protection assembly process and improves the assembly accuracy and efficiency of the spacecraft thermal protection system. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0044] Figure 1 A schematic diagram of an assembly parameter optimization and update solution oriented to assembly process state changes provided in Example 1 of the present invention.

[0045] Figure 2 A schematic flow chart of an assembly parameter updating method provided in Example 1 of the present invention.

[0046] Figure 3 Schematic diagram of a model for solving gap step differences of array-type thermal protection components provided in Example 1 of the present invention.

[0047] Figure 4 This is a schematic cross-sectional diagram of an array-type heat protection component provided in Example 1 of the present invention.

[0048] Figure 5 A schematic diagram of discrete point curvature calculation provided in Example 1 of the present invention.

[0049] Figure 6 Schematic diagram of bonding the array-type heat protection component and the bearing structure provided in Example 1 of the present invention.

[0050] Figure 7 A schematic diagram of coordinate value derivation and calculation provided in Example 1 of the present invention.

[0051] Figure 8 A schematic diagram of differential coordinates (local annular domain) provided in Example 1 of the present invention.

[0052] Fig. 9 This is a diagram of the internal structure of a computer device provided in Example 5 of the present invention. DETAILED DESCRIPTION

[0053] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0054] The purpose of the present invention is to provide an assembly parameter updating method, device, medium and product, which can realize the optimization and updating of assembly parameters for array-type thermal protection components, play an auxiliary guiding role in the spacecraft thermal protection assembly process, and improve the assembly accuracy and efficiency of the spacecraft thermal protection system.

[0055] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0056] Example 1

[0057] In the assembly process of spacecraft array thermal protection structures, there are often situations where assembly parameters are not applicable due to changes in assembly state. To address this problem, an assembly parameter optimization and update framework for assembly process state changes is proposed. The process is as follows: Figure 1 The specific implementation steps are as follows:

[0058] (1) Based on the measured model of array-type thermal protection components, a gap step difference solution model is proposed. By inserting cross sections, the 3D point cloud data is converted into 2D point data. The positions of boundary points and critical points are determined by identifying the curvature mutation points, and the gap step difference parameters are calculated according to the solution model.

[0059] (2) Aiming at the typical curved surface matching structure between the load-bearing structure and the thermal protection component, a fitting parameter solution model is proposed to calculate the average value of the distance from several characteristic points on the inner surface of the measured model of the array thermal protection component to the curved surface of the load-bearing structure.

[0060] (3) In order to solve the problem of assembly structure deformation caused by changes in assembly status, a method based on the fusion of laser scanning and sensor detection is proposed. First, the laser scanning method is used to obtain the measured data of the initial assembly of the load-bearing structure surface. Second, the optical fiber sensor is evenly pasted on the surface of the load-bearing structure to obtain the optical fiber monitoring data. Third, the curvature conversion model is used to calculate the relative deformation of the load-bearing structure at the optical fiber pasting point. Finally, the optical fiber monitoring point is regarded as the control point. The three-dimensional measured point cloud data and the control point deformation data are fused through the grid deformation optimization algorithm to obtain the measured data of the load-bearing structure surface under different assembly states.

[0061] (4) According to the assembly parameter solution model and the assembly process state point cloud prediction model, an assembly parameter optimization model based on constraint conditions is established. The multi-objective problem is converted into a single-objective problem through the weighted summation method to optimize the component posture. Based on the improved quantum particle swarm optimization algorithm, the assembly parameter update facing the assembly state change is realized.

[0062] Based on the above implementation steps, Figure 2 As shown, this embodiment provides an assembly parameter updating method, the method comprising:

[0063] S1: Solving the assembly parameters of the arrayed heat protection component measured model to obtain assembly parameter results; the assembly parameter results include gap step difference parameters of the arrayed heat protection components and fit parameters of the arrayed heat protection components; the gap step difference parameters of the arrayed heat protection components are the gap and step difference between the seam point clouds of two adjacent heat protection components; the fit parameter of the arrayed heat protection components is the average value of the distances from a number of feature points on the inner surface of the arrayed heat protection component measured model to the bearing structure surface; the arrayed heat protection component measured model is a virtual model determined according to the spacecraft to be assembled; the bearing structure surface is a surface of the spacecraft to be assembled for assembling the arrayed heat protection components.

[0064] S2: Obtain measured data of the load-bearing structure surface under different assembly conditions.

[0065] S3: Determine an assembly parameter optimization model according to the assembly parameter results and the measured data of the load-bearing structure surface under different assembly states; the assembly parameter optimization model includes: an objective function and constraints; the objective function is a function related to the gap of the arrayed heat protection components, the step difference of the arrayed heat protection components and the weight coefficient of the fit of the arrayed heat protection components; the constraints include: boundary constraints of the arrayed heat protection components, cross-section-related constraints of the arrayed heat protection components and assembly parameter constraints of the arrayed heat protection components.

[0066] S4: Optimizing the assembly parameters of the assembly parameter optimization model to obtain optimal assembly parameters.

[0067] As an optional implementation method provided by this embodiment, the process of solving the gap step difference parameter of the array type heat protection component specifically includes:

[0068] A1: Based on the measured model of array-type thermal protection components, a gap step difference solution model is constructed.

[0069] A2: Performing an insert section operation on the measured model of the thermal protection component to obtain two-dimensional seam point data; the insert section operation is used to convert 3D point cloud data into 2D point data.

[0070] A3: Selecting feature points from the two-dimensional seam point data to obtain curvature mutation points of the two-dimensional seam point data.

[0071] A4: Determine the boundary points and critical points according to the curvature mutation points of the two-dimensional seam point data.

[0072] A5: Substitute the boundary points and critical points into the gap step difference solution model to obtain the gap step difference parameters of the array type heat protection component.

[0073] The parameters that affect the assembly quality of array thermal protection components are mainly the gap and step difference between the point clouds of the seams of two adjacent components. Before extracting the gap step difference from the point cloud data of the seams, a gap step difference solution model based on the point cloud data is first established, and the gap step difference solution model is feature-divided and defined. Then, the subsequent corresponding algorithm is designed to extract features, and finally the gap step difference solution model is substituted for the solution. The main parameter solution process is as follows: inserting sections, selecting feature points, solving gap step difference parameters, and other steps.

[0074] The characteristics of the gap step difference solution model are: Figure 3The point cloud data of a certain section of the joint point cloud of adjacent components are taken. The curvature mutation points A, B, C, and D are taken as the characteristic points of the joint edge. The step difference characteristic points A and B are defined as critical points, and the gap characteristic points C and D are defined as boundary points. Line MN is the straight line fitted on the upper surface on the left side of the joint. M′N′ is the straight line generated by translating line MN to point B. The distance between line MN and line M′N′ is the step difference value of this joint point cloud. Feature points C and D are the second curvature mutation points of the joint. Points C and D are projected onto the extension line of line MN to obtain points C′ and D′, respectively. The length of line C′D′ is the gap value of this joint point cloud.

[0075] In step A2, it specifically includes:

[0076] In the process of solving the parameters of the thermal protection component, in order to simplify the parameter solving process, the cross-section projection operation is performed to convert the 3D problem into a 2D problem. In the process of cross-section selection, the selection of the cross-section center point and the definition of the normal direction are particularly important for the accuracy of the gap step difference parameter extraction. This embodiment proposes a cross-section positioning method based on theoretical features on the basis of the alignment and registration of the measured data of the thermal protection component with the theoretical model to obtain two-dimensional seam point data. Figure 4 As shown in the figure, the measured data of the thermal protection component and the boundary characteristic line of the theoretical model are shown. The center point of the cross section is selected according to the length of the adjacent theoretical characteristic line of the component, and the point Γ is selected on the boundary line of the theoretical model. P1 , Γ P2 and Γ P3 is the center point of the joint section, and the characteristic line direction is the normal direction of the section Ensure that every two adjacent components have at least 1 to 2 cross sections to ensure the accuracy and efficiency of subsequent parameter updates.

[0077] In step A3, it specifically includes:

[0078] Given a two-dimensional projection point, based on the gap step difference solution model, it is necessary to extract the feature points of the two-dimensional seam point data. Among them, due to the different definitions of the gap step difference between adjacent components, the feature points are divided into gap feature points-boundary points and step difference feature points-critical points. Due to manufacturing process limitations and component assembly requirements, the boundaries of the upper surface of a single thermal protection component are chamfered. Therefore, the two-dimensional cross-sectional point cloud of a single component contains feature points with sudden changes in curvature. According to the definition of the gap step difference solution model, the curvature mutation points in the two-dimensional seam projection point cloud are used as feature points. The feature points are shown in Fig. Figure 3 shown.

[0079] To find the curvature mutation point of a two-dimensional discrete point, we first need to calculate the curvature of the discrete point. In this solution, the inverse of the radius of the circumscribed circle of three adjacent discrete points is used as the curvature calculation method of the middle point. The specific method is as follows:

[0080] The cross-sectional projection points of a single component are represented as a set of ordered point sequences Q that do not overlap with each other. i (x i ,y i ), i=0,1,...,n, connect three adjacent points in sequence to form a triangle, Figure 5 Where a, b, and c are the lengths of the three sides of the triangle, α is the angle between sides a and c, and the discrete point Q i The curvature calculation method is:

[0081]

[0082] Wherein, R is the radius of the circumscribed circle of the triangle, and p=(a+b+c) / 2.

[0083] From the shape characteristics of the thermal protection component and the gap step difference solution model, it can be seen that a single thermal protection component contains two curvature mutation points. The first curvature mutation point is defined as the critical point for the definition of the step difference result, and the second curvature mutation point is the boundary point for the definition of the gap result. At the same time, the curvature mutation point divides the seam point cloud into three regions, namely, the component upper surface point set, the transition corner point set, and the component matching surface point set. The component upper surface point set is fitted into a straight line, that is, Figure 3 At this point, all the above operations are substituted into the gap step difference solution model to obtain the gap step difference parameters of the array type heat protection component.

[0084] As an optional implementation method provided by this embodiment, the process of solving the fitting degree parameter of the array-type heat protection component specifically includes:

[0085] B1: uniformly selecting a number of characteristic points on the inner surface of the measured model of the array-type heat protection component.

[0086] B2: Calculate the average value of the distances from several feature points to the surface of the load-bearing structure to obtain the fit parameters of the array-type thermal protection component.

[0087] The fit model is used to describe the coordination between the inner surface of the thermal protection component and the spacecraft load-bearing structure. According to the bonding method between the array thermal protection system and the spacecraft load-bearing structure, the fit parameter is also an important parameter indicator for the assembly of the thermal protection system.

[0088] According to the curved surface characteristics of the load-bearing structure, it and the thermal protection component can be regarded as a typical curved surface matching structure, such as Figure 6 As shown, for this structure, the distance from the inner surface point of a single heat protection component to the closest point of the load-bearing structure surface is usually used to evaluate the fit state. Considering the uneven fit spacing between the two fit surfaces, this embodiment evenly selects n feature points on the inner surface of the measured model of the array-type heat protection component, and characterizes its fit parameter by calculating the average distance from the feature point to the load-bearing structure surface:

[0089]

[0090] Among them, t k is the thermal protection component pose parameter after the kth iteration in the optimization process, is the point p on the inner surface of the heat protection component i The value in the assembly coordinate system after the kth iteration, q i is the distance p from the load-bearing structure i The nearest point.

[0091] As an optional implementation provided by this embodiment, in step S2, it specifically includes:

[0092] S21: respectively obtaining the initial measured point cloud data and optical fiber monitoring data of the load-bearing structure curved surface assembly of the measured model of the array-type thermal protection component; the initial measured point cloud data of the load-bearing structure curved surface assembly is data obtained by a laser scanning method; the optical fiber monitoring data is data measured by an optical fiber sensor attached to the load-bearing structure curved surface.

[0093] S22: Calculate the relative deformation of the optical fiber monitoring data according to the curvature conversion model.

[0094] S23: According to the mesh deformation optimization algorithm, the measured point cloud data of the initial assembly of the load-bearing structure surface and the relative deformation amount of the optical fiber monitoring data are merged to obtain the measured data of the load-bearing structure surface under different assembly states.

[0095] In order to solve the problem of structural deformation in the assembly process caused by changes in the assembly status of the spacecraft, a fusion of laser scanning and sensor detection methods is adopted to obtain the initial point cloud data of the spacecraft load-bearing structure surface in the early stage of assembly, and optical fiber sensors are evenly pasted on the load-bearing structure surface to obtain optical fiber monitoring data under different assembly states. Through the heterogeneous data fusion method, the load-bearing structure surface data under different assembly states can be quickly, accurately and efficiently acquired, thereby maximizing the advantages of each sensor.

[0096] This embodiment uses a fiber Bragg grating sensor (FBG) to monitor the deformation of complex curved surfaces. When light with a certain frequency width is incident on the FBG fiber channel, the frequency is in the range of λ when passing through the grating area. B The nearby light waves are reflected back, while the light waves of other frequencies will be transmitted through the FBG sensor. Assuming that the ambient temperature remains unchanged when the grating is subjected to axial stress ε, the change of the grating center wavelength Δλ caused by uniform axial strain can be obtained B for:

[0097] Δλ B =λ B (1-P ε)ε (3);

[0098] Where P ε represents the effective elastic-optical coefficient, λ B is the grating center wavelength.

[0099] It can be seen that the shift of the grating wavelength has a linear relationship with its axial strain. At the same time, at a constant temperature in the laboratory, the change of the FBG central wavelength is only affected by the strain.

[0100] After obtaining the wavelength change information at the FBG pasting point, the relationship between the central wavelength change and the curvature k' is as follows:

[0101]

[0102] Where h is the structural thickness at the optical fiber pasting position.

[0103] Given the curvature information of multiple points on the load-bearing structure surface, take the AB segment of the deformation curve to establish the coordinate system, such as Figure 7 The starting point A of the curve shown coincides with the origin of the coordinate system, where: and is the tangent vector between point A and point B, θ i and θ i+1 are the angles between the tangent vectors at point A and point B and the positive direction of the X axis, S n is the arc length of the curve, l ab represents the length of the chord corresponding to the arc, and Δθ is the central angle of the arc AB. For point A, the coordinate increment of point B is (Δx, Δy).

[0104] It can be seen that θ can be obtained by integrating the curvature:

[0105] θ(s) = ∫l(s)ds (5);

[0106] Figure 7 In the equation, Δθ is the center angle of the arc segment, which can be obtained based on its geometric relationship:

[0107] αθ=180-(90-θ i )-(180-90-θ i+1 )=θ i +θ i+1 (6);

[0108] Chord length l ab The length is:

[0109]

[0110] Among them, r AB is the radius of curvature of the arc, which can be expressed by the curvature of points A and B.

[0111]

[0112] In the formula, r A and r B are the curvature radius at points A and B, respectively, and k A is the curvature value of point A, k B is the curvature value at point B.

[0113] From this, we can get the curvature conversion model:

[0114]

[0115] From the above formula, we can get

[0116]

[0117] Based on the measured point cloud data at the initial stage of assembly and the deformation of the fiber optic monitoring points under different states, the fiber optic monitoring points are used as control points, and the surface data of the load-bearing structure under different assembly states is acquired based on the Laplace mesh deformation algorithm. The essence of the Laplace mesh deformation algorithm is the encoding and decoding process of the local detail features of the mesh model, which is often used to deform the mesh model while maintaining the local detail features. This algorithm can be described as, given a mesh model and the position change of the specified vertex, solving the position of other vertices to keep the Laplace distance of all vertices unchanged before and after the change.

[0118] In the Laplace grid processing technology framework, by applying the Laplace operator to the vertices of the discrete grid model, we can obtain a differential representation called Laplace coordinates, such as Figure 8 Compared with the traditional Cartesian coordinates that directly define the spatial position information of each vertex, the surface differential expression method can describe local information including size and direction. Therefore, the algorithm that tries to keep the Laplace coordinates unchanged keeps the local details of the surface as much as possible during the processing.

[0119] Usually, M = (V, E, F) is used to represent a curved 3D model, where V represents the vertex set in the model, E represents the edge set, and F represents the triangle set. The initial coordinates of the vertices in the model are the absolute coordinates v in the Cartesian coordinate system. i =(x i ,y i ,z i ), define vertex v i The Laplace coordinates of δ i :

[0120] δ i =(δ i (x),δ i (y),δ i(z)) = v i -∑ j∈N(i) ω ij v j (11);

[0121] Among them, N(i)={j(i,j)∈E} represents all the vertex v i The vertices that can form edges are also called neighborhood vertices. ij For vertex v j Relative to v i The weight of , and ∑ω ij =1.

[0122] After the surface is deformed, the new position w of the control point is measured i , it is necessary to move the corresponding points of the source model to the new position after deformation while ensuring that the relative positions of the vertices remain unchanged, using v i ' represents, and satisfies, i=1,2,3,...,m, based on the mesh deformation of Laplace coordinates, solving the deformed vertex V' can be reduced to an optimization problem with position constraints:

[0123]

[0124] Where L is the Laplace matrix, Δ is the Laplace coordinate of the vertex before deformation, ω is the weight of the constraint point, ||LV'-Δ|| is to ensure that the coordinates remain unchanged as much as possible after the model is deformed, ||v i '-w i ||Ensure that the constraint point can reach the target position.

[0125] It should be noted that: 1) V represents the result of the optimization function. The optimization process is to minimize this value. It is just a representation and has no specific meaning. 2) During the overall deformation, in order to keep the local information unchanged after deformation, ||LV'-Δ|| is calculated, that is, the change of local information of each vertex before and after deformation is minimized. 3) w i is the point obtained by optical fiber monitoring after deformation (target value, Cartesian coordinates), v i ' is the result of the corresponding vertex deformation after deformation using the Laplace algorithm of the present invention. The two are very close, but the values ​​are not exactly the same, so they are not zero. The goal of the present invention is to minimize the difference between the two, thereby completing the deformation.

[0126] Solving the minimum problem in the equation is equivalent to solving the following overdetermined linear equation (the number of equations is greater than the number of unknowns):

[0127]

[0128] H is an m×n matrix, and the H value is the corresponding constraint point pair weight. h is an m×3 matrix, that is, hi =ω i w i , i=1,2,3,...,m, each column represents a coordinate component. The unknown number V' is an n×3 matrix, whose i-th row represents the three-dimensional coordinate v of the deformed mesh vertex i '(x,y,z), to solve the linear system, we only need to solve the formula:

[0129] A'V' = b (14);

[0130] The three-dimensional coordinates of each vertex after deformation can be obtained:

[0131] V'=(A' T A') -1 ·A' T b (15);

[0132] A' T Indicates the transpose of A'. When solving a matrix, if A' is not a square matrix, it is usually necessary to multiply both sides of the equation by A' T , which is convenient for solving.

[0133] When the mesh model is deformed, only the b matrix needs to be updated to obtain the vertex coordinates of the new model.

[0134] As the external structure of the load-bearing structure changes, the assembly parameters of the thermal protection components also need to be updated accordingly. The key technology of this embodiment is to update the assembly parameters facing the changes in different assembly states. In the above scheme, the initial assembly parameter results and the external shape of the load-bearing structure under different assembly states can be obtained. On this basis, the assembly parameters are updated based on the improved quantum particle swarm optimization algorithm.

[0135] In this embodiment, the assembly parameters are the gap between arrayed heat protection components, the step difference parameters, and the fit parameters of the arrayed heat protection components, among which the gap and step difference parameters conflict with the fit parameter optimization and cannot reach the optimum at the same time. Therefore, the parameter update problem of this embodiment is a typical multi-objective optimization problem, which requires coordination and compromise between the various objectives so that the overall goal can be achieved as optimally as possible. However, since multi-objective problems are difficult to solve in engineering and the calculations are too complicated, this embodiment combines multiple constraints and converts the multi-objective problem into a single-objective problem through a weighted summation method. The specific process is as follows:

[0136] (1) Objective function establishment

[0137] The key content of the assembly parameter updating problem facing the change of assembly state is to update the position and posture parameters of each component related to the assembly coordinate system.

[0138] Through the above multi-objective parameter definitions, a weight-based fitness function is established.

[0139]

[0140] Among them, G k (t k ) is the sum of the deviations between all gap parameters and theoretical parameters of the array thermal protection component after the kth iteration, F k (t k ) is the sum of all step difference parameter deviations of the array thermal protection component after the kth iteration, D k (t k ) is the total deviation of the degree of fit of the heat protection component after the kth iteration, μ1 is the weight coefficient of the gap of the arrayed heat protection component, μ2 is the weight coefficient of the step difference of the arrayed heat protection component, and μ3 is the weight coefficient of the degree of fit of the arrayed heat protection component.

[0141] (2) Establishment of constraint model

[0142] Due to the characteristics of thermal protection components such as special assembly structure, complex constraint characteristics, large number of coordination objects, poor rigidity and brittleness, it is necessary to establish a constraint condition model based on the assembly optimization process.

[0143] During the assembly process of thermal protection components, they are usually laid out in different areas. Different assembly areas have their own assembly boundaries, and their boundary constraints are:

[0144]

[0145] In the formula, is the position parameter of the i-th thermal protection component after the k-th iteration, LT boundary is the minimum space range allowed for pasting the i-th thermal protection component, UT boundary The maximum space range allowed for pasting the i-th heat protection component.

[0146] The cross section defined in this embodiment has its center point position (x, y, z) and normal direction All have constraints, and their cross-section related constraints include the following:

[0147]

[0148] In the formula, (x, y, z) is the center point position, is the normal direction, T boundary is the theoretical boundary of the array thermal protection component, L R The length of the cross section, W R is the width of the cross section, k' is the curvature, in this embodiment, k' is 6, G t is the theoretical gap parameter, I s is the cross-sectional spacing, Ismin is the minimum cross-sectional distance between two adjacent components, I smax It is the maximum cross-sectional distance between two adjacent components.

[0149] In view of the thermal protection assembly requirements, it is necessary to constrain its assembly parameters. The assembly parameter constraints are as follows:

[0150]

[0151] In the formula, is the gap parameter deviation of the jth section of the i-th thermal protection component after the k-1th optimization iteration, LT gap is the lower limit of the gap parameter constraint tolerance, UT gap is the upper limit of the gap parameter constraint tolerance, is the step difference parameter deviation of the jth section of the i-th thermal protection component after the k-1th optimization iteration, LT flush is the lower limit of the tolerance of the step difference parameter constraint, UT flush is the upper limit of the tolerance for the order difference parameter constraint, is the fitting parameter deviation of the i-th thermal protection component after the k-1th optimization iteration, LT dist is the lower limit of the tolerance of the fit parameter constraint, UT dist Constrain the upper tolerance limit for the fit parameter.

[0152] The quantum particle swarm optimization algorithm (QPSO) is based on the particle swarm algorithm and combines quantum characteristics to introduce a local attraction factor. The location of this factor is the attraction potential field of the entire quantum system, and other particles perform global position search and update in a bound state. This algorithm increases the population diversity in the iterative process, enhances the global optimization ability of the algorithm, and is more stable than the original PSO algorithm. However, it has shortcomings in terms of imbalance between global exploration and local development, easy to fall into local optimal solutions, and insufficient global exploration ability. This scheme proposes an improved quantum particle swarm optimization algorithm (LQPBO), which is mainly improved in the following three aspects. First, the population is initialized using the Logistic-Sine-Cosine chaotic map to maintain the diversity of the population, thereby enhancing the algorithm's optimization ability. Secondly, the local and global optimization capabilities of the adaptive inertia threshold balance algorithm are utilized. Finally, the Levy flight mechanism is used to optimize the foraging behavior and improve the global search ability.

[0153] 1) Logistic-Sine-Cosine Chaotic Mapping

[0154] As a mapping method with true randomness and spatial ergodicity, the chaotic function is applied to the initialization process of the population position, which is beneficial to the initial particle group to reduce the particle accumulation phenomenon, improve the diversity of position distribution and get rid of the constraints of the local optimal solution. Logistic mapping describes the nonlinear dynamics in a one-dimensional dynamic system and has strong randomness and universality. The Logistic mapping equation is:

[0155] x i+1 =ax i (1-x i ) (20);

[0156] Among them, x i represents the size of the population at time i, x i ∈(0, 1), a is the control parameter, a∈(0,4), the larger a is, the higher the chaos is, and the chaotic orbit state value range is (0, 1).

[0157] Sine and Cosine mapping use the periodicity of sine and cosine functions to produce chaotic behavior. The Sine and Cosine mapping equations are:

[0158]

[0159] Among them, a is the control parameter, which is generally 4, and the chaotic orbit state value range is (0, 1)

[0160] Aiming at the problem of uneven particle distribution and easy falling into fixed points in traditional chaotic mapping functions, this scheme introduces Sine mapping and Cosine mapping based on Logistic chaotic mapping. Logistic-Sine-Cosine chaotic mapping combines the characteristics of Logistic, Sine and Cosine mapping to create a new chaotic system.

[0161] The expression of Logistic-Sine-Cosine chaotic mapping is:

[0162] x i+1 =cos(π(4rx i (1-x i )+(1-r)sin(πx i )-0.5)) (23);

[0163] Among them, r∈(0,1), the chaotic orbit state value range is (0, 1).

[0164] The population initialization mechanism established based on the Logistic-Sine-Cosine mapping algorithm proposed in this embodiment ensures that the initial solution of the population can be randomly traversed and distributed in the complete value space, while effectively reducing the constraints of the local optimal value on the initial particles, and can provide a high-quality initial particle group with rich diversity and significant particle characteristics for the subsequent particle group update iterative process.

[0165] 2) Adaptive inertia weight

[0166] Using a fixed inertia weight can easily cause the QPSO method to fall into the local optimal problem and may lead to the loss of the best solution. Appropriate inertia weights help adapt to the overall search, so that the QPSO method can complete the global and local search of the parameter space, thereby obtaining more optimal solutions. The introduction of the adaptive inertia weight method allows the inertia weight to increase adaptability as the QPSO evolves, avoiding the problem of the inertia weight decreasing as the number of iterations increases, and ensuring that the adaptability increases as the number of iterations increases through the evolution rate. The calculation formula for the adaptive inertia weight is as follows:

[0167]

[0168] Where: w max is the maximum value of the inertia weight, w min is the minimum value of inertia weight, t max is the maximum number of iterations, and δ is a positive real number parameter that controls the decay rate. When δ>1, the inertia weight decreases slowly in the early stages of the iteration, which helps the algorithm perform a wider global search; and the decrease speed accelerates in the later stages of the iteration, which is conducive to a more concentrated local search. When δ<1, the inertia weight decreases quickly in the early stages of the iteration, making the algorithm more focused on local search in the early stages; and the decrease slows in the later stages of the iteration, which helps the algorithm maintain the diversity of the search, thereby avoiding falling into the local optimal solution too early.

[0169] 3) Lévy flight mechanism

[0170] The Lévy flight mechanism is a random search strategy commonly used in optimization algorithms. It simulates the natural behavior of certain animals, such as the migration paths of birds, which exhibit a specific type of random walk whose step length follows the Lévy distribution. The Lévy distribution has a heavy-tailed characteristic, converges faster, and provides a wide range of global search capabilities in optimization. The formula for generating the random step length of the Lévy flight is:

[0171]

[0172] In the formula, σ ν =1, Γ is the gamma function, β is a constant, β∈(0,2).

[0173] The parameter updating method of the present invention realizes the optimization and updating of assembly parameters for thermal protection components, plays an auxiliary guiding role in the thermal protection assembly process of spacecraft, and improves the assembly accuracy and efficiency of the thermal protection system of the spacecraft.

[0174] Example 2

[0175] A computer device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of an assembly parameter updating method in embodiment 1.

[0176] Example 3

[0177] A computer-readable storage medium stores a computer program, which implements the steps of an assembly parameter updating method in embodiment 1 when executed by a processor.

[0178] Example 4

[0179] A computer program product includes a computer program, which implements the steps of an assembly parameter updating method in embodiment 1 when executed by a processor.

[0180] Example 5

[0181] A computer device, which may be a database, may have an internal structure as shown in Fig. 9 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store pending transactions. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the steps of an assembly parameter updating method in Example 1 are implemented.

[0182] It should be noted that the object information (including but not limited to object device information, object personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in the present invention are all information and data authorized by the object or fully authorized by all parties, and the collection, use and processing of relevant data need to comply with relevant laws, regulations and standards of relevant countries and regions.

[0183] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided by the present invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided by the present invention may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited thereto. The processor involved in each embodiment provided by the present invention may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited thereto.

[0184] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0185] The principles and implementation methods of the present invention are described in this article using specific examples. The description of the above embodiments is only used to help understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A method for updating assembly parameters, characterized in that: The method comprises: Solving the assembly parameters of the arrayed heat protection component measured model to obtain assembly parameter results; the assembly parameter results include gap step difference parameters of the arrayed heat protection component and fit parameters of the arrayed heat protection component; the gap step difference parameters of the arrayed heat protection component are the gap and step difference between the seam point clouds of two adjacent heat protection components; the fit parameter of the arrayed heat protection component is the average value of the distances from a number of feature points on the inner surface of the arrayed heat protection component measured model to the bearing structure curved surface; the arrayed heat protection component measured model is a virtual model determined according to the spacecraft to be assembled; the bearing structure curved surface is a curved surface of the spacecraft to be assembled for assembling the arrayed heat protection component; Obtain measured data of the load-bearing structure surface under different assembly states; Determine an assembly parameter optimization model according to the assembly parameter results and measured data of the load-bearing structure surface under different assembly states; the assembly parameter optimization model includes: an objective function and constraint conditions; the objective function is a function related to the gap of the arrayed heat protection components, the step difference of the arrayed heat protection components and the weight coefficient of the fit of the arrayed heat protection components; the constraint conditions include: boundary constraint conditions of the arrayed heat protection components, cross-section-related constraints of the arrayed heat protection components and assembly parameter constraint conditions of the arrayed heat protection components; Optimizing the assembly parameters of the assembly parameter optimization model to obtain optimal assembly parameters; The process of solving the gap step difference parameters of the array thermal protection component specifically includes: Based on the measured model of array thermal protection components, a gap step difference solution model is constructed; Performing an insert section operation on the measured model of the heat protection component to obtain two-dimensional seam point data; the insert section operation is used to convert 3D point cloud data into 2D point data; Selecting feature points from the two-dimensional seam point data to obtain curvature mutation points of the two-dimensional seam point data; Determine the boundary point and the critical point according to the curvature mutation point of the two-dimensional seam point data; Substituting the boundary points and critical points into the gap step difference solution model to obtain the gap step difference parameters of the array type heat protection component; Obtain measured data of the load-bearing structure surface under different assembly states, including: The measured point cloud data and optical fiber monitoring data of the initial stage of the curved surface assembly of the bearing structure of the measured model of the array type thermal protection component are obtained respectively; the measured point cloud data of the initial stage of the curved surface assembly of the bearing structure is data obtained by a laser scanning method; the optical fiber monitoring data is data measured by an optical fiber sensor attached to the curved surface of the bearing structure; Calculate the relative deformation of optical fiber monitoring data according to the curvature conversion model; The measured point cloud data of the initial assembly of the load-bearing structure surface and the relative deformation of the optical fiber monitoring data are merged according to the grid deformation optimization algorithm to obtain the measured data of the load-bearing structure surface under different assembly states.

2. The assembly parameter updating method according to claim 1, characterized in that: The process of solving the fitting parameters of the array thermal protection components specifically includes: uniformly selecting a number of characteristic points on the inner surface of the measured model of the array-type heat protection component; The average value of the distances from several feature points to the surface of the load-bearing structure is calculated to obtain the fit parameters of the arrayed thermal protection component.

3. The assembly parameter updating method according to claim 1, characterized in that: The expression of the objective function is: Among them, G k (t k ) is the sum of the deviations between all gap parameters and theoretical parameters of the array thermal protection component after the kth iteration, F k (t k ) is the sum of all step difference parameter deviations of the array thermal protection component after the kth iteration, D k (t k ) is the total deviation of the degree of fit of the heat protection component after the kth iteration, μ1 is the weight coefficient of the gap of the arrayed heat protection component, μ2 is the weight coefficient of the step difference of the arrayed heat protection component, and μ3 is the weight coefficient of the degree of fit of the arrayed heat protection component.

4. The assembly parameter updating method according to claim 1, characterized in that: The boundary constraint condition of the array type heat protection component is expressed as: In the formula, is the position parameter of the i-th thermal protection component after the k-th iteration, LT boundary is the minimum space range allowed for pasting the i-th thermal protection component, UT boundary is the maximum space range allowed for pasting the i-th heat protection component; The expression of the cross-section related constraint of the array type heat protection component is: In the formula, (x, y, z) is the center point position, is the normal direction, T boundary is the theoretical boundary of the array thermal protection component, L R The length of the cross section, W R is the width of the cross section, k' is the curvature, G t is the theoretical gap parameter, I s is the cross-sectional spacing, I smin is the minimum cross-sectional distance between two adjacent components, I smax is the maximum cross-sectional distance between two adjacent components; The expression of the assembly parameter constraint condition of the array thermal protection component is: In the formula, is the gap parameter deviation of the jth section of the i-th thermal protection component after the k-1th optimization iteration, LT gap is the lower limit of the gap parameter constraint tolerance, UT gap is the upper limit of the gap parameter constraint tolerance, is the step difference parameter deviation of the jth section of the i-th thermal protection component after the k-1th optimization iteration, LT flush is the lower limit of the tolerance of the step difference parameter constraint, UT flush is the upper limit of the tolerance for the order difference parameter constraint, is the fitting parameter deviation of the i-th thermal protection component after the k-1th optimization iteration, LT dist is the lower limit of the tolerance of the fit parameter constraint, UT dist Constrain the upper tolerance limit for the fit parameter.

5. The assembly parameter updating method according to claim 1, characterized in that: The assembly parameter optimization model is an improved quantum particle swarm optimization algorithm, which includes: improvement of population initialization using Logistic-Sine-Cosine chaotic mapping, improvement of adaptive inertia weight and improvement of global search capability of Lévy flight mechanism.

6. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of an assembly parameter updating method as described in any one of claims 1 to 5.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of an assembly parameter updating method described in any one of claims 1 to 5 are implemented.

8. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of an assembly parameter updating method described in any one of claims 1 to 5 are implemented.

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