Intelligent spaceflight equipment assembling method and system based on digital twinning

Abstract

The invention relates to the technical field of spaceflight equipment assembly, in particular to an intelligent spaceflight equipment assembly method and system based on digital twinning. The method comprises the following steps: acquiring an assembly process parameter set of a solar wing ground assembly stage, and constructing an assembly stress distribution information set reflecting a solar wing ground initial stress field based on the assembly process parameter set; based on the assembly stress distribution information set, analyzing a dynamic coupling relationship between a thermal load and a ground initial stress field in an in-orbit high and low temperature alternating environment to obtain an in-orbit profile evolution information set of the solar wing; based on the on-orbit profile evolution information set, the influence of on-orbit profile change of the solar wing on the power generation efficiency is analyzed, and a power generation efficiency change information set is obtained; and based on the power generation efficiency change information set, establishing a ground assembly process parameter reverse parameter adjustment mechanism taking the on-orbit power generation efficiency as an optimization target, generating an iterative optimization strategy of the assembly process parameters, and outputting a solar wing intelligent assembly decision log. And continuous optimization of the assembly equipment is realized.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of aerospace equipment assembly, in particular to an aerospace equipment intelligent assembly method and system based on digital twinning. BACKGROUND

[0002] In the ground assembly of a spacecraft solar wing, the prior art mainly relies on fixed process procedures and static mechanical analysis to set assembly parameters, and evaluates the ground quality of the assembly body based on the assembly parameters, which artificially separates the assembly process from the on-orbit operation environment, can only ensure that the static shape of the solar wing at the time of delivery meets the design requirements, and lacks the ability to predict and optimize the performance of the solar wing during on-orbit service.

[0003] Since a dynamic coupling model of the ground assembly stress and the on-orbit thermal load is not established, the prior method cannot preview the shape evolution process of the solar wing under long-term high-low temperature alternating environment, and cannot quantify the specific influence of such shape change on the power generation efficiency, which leads to the setting of the assembly process parameters to have a large degree of empiricism and blindness, so that the on-orbit actual performance of the solar wing has an unpredictable attenuation risk, which becomes a technical bottleneck for improving the long-life and high-reliability operation capability of the spacecraft. SUMMARY

[0004] The application provides an aerospace equipment intelligent assembly method and system based on digital twinning to solve the above problems.

[0005] In a first aspect, the application provides an aerospace equipment intelligent assembly method based on digital twinning, which comprises: An assembly process parameter set of a solar wing ground assembly stage is acquired, and based on the assembly process parameter set, an assembly stress distribution information set reflecting the ground initial stress field of the solar wing is constructed; based on the assembly stress distribution information set, a dynamic coupling relationship between a thermal load and the ground initial stress field under an on-orbit high-low temperature alternating environment is analyzed to obtain an on-orbit shape evolution information set of the solar wing; based on the on-orbit shape evolution information set, the influence of the on-orbit shape change of the solar wing on the power generation efficiency is analyzed to obtain a power generation efficiency change information set; based on the power generation efficiency change information set, a ground assembly process parameter reverse parameter adjustment mechanism with the on-orbit power generation efficiency as an optimization target is established, an iterative optimization strategy of the assembly process parameters is generated, and a solar wing intelligent assembly decision log is output.

[0006] By the technical solution, the solar wing ground assembly process parameter set is acquired, and the assembly stress distribution information set is constructed, which lays a foundation for in-orbit analysis. The dynamic coupling relationship between thermal load and initial stress field is analyzed to obtain an in-orbit profile evolution information set, the solar wing behavior is predicted, the influence of profile change on power generation efficiency is analyzed to obtain a power generation efficiency change information set, the performance influence is quantified, and a reverse parameter adjustment mechanism is established to generate an optimization strategy and a decision log, thereby realizing continuous optimization of the assembly process, improving the in-orbit power generation efficiency and reliability of the solar wing, and reducing the failure risk.

[0007] Optionally, the assembly process parameter set includes a pasting adhesive layer pressure, a bolt tightening torque, and a plate-to-plate wire layout tension; based on the pasting adhesive layer pressure, stress transmission and distribution changes caused by pressure application in the solar wing assembly process are analyzed to obtain adhesive layer stress distribution information; based on the bolt tightening torque, stress concentration effects and diffusion laws caused by torque in the solar wing assembly process are analyzed to obtain bolt stress distribution information; based on the plate-to-plate wire layout tension, additional stress influences and distribution characteristics introduced by tension in the plate-to-plate wire layout area in the solar wing assembly process are analyzed to obtain wire layout stress distribution information; based on the adhesive layer stress distribution information, the bolt stress distribution information, and the wire layout stress distribution information, the overall stress state of the solar wing ground assembly is comprehensively evaluated, and the assembly stress distribution information set is constructed.

[0008] Optionally, based on the adhesive layer stress distribution information, the bolt stress distribution information, and in combination with the wire layout stress distribution information, stress field simulation data simulation is adopted to analyze the direction characteristics and interaction of stress transmission between the adhesive layer area, the bolt connection area, and the wire layout area, to quantify the stress superposition effect and the stress cancellation effect between the areas, and to construct stress transmission information; the adhesive layer area is an adhesive interface layer formed by the pasting adhesive layer pressure; the bolt connection area is a mechanical fastening connection part formed by the bolt tightening torque; the wire layout area is a cable path attachment area formed by the plate-to-plate wire layout tension constraint; based on the superposition effect and the cancellation effect, the spatial distribution characteristics of the stress concentration area concentrated in the superposition effect set and the stress balance area concentrated in the cancellation effect set in the solar wing structure are analyzed to obtain the overall stress state.

[0009] Optionally, based on the overall stress state, a dynamic mapping mechanism of thermal load cycle data and ground initial stress field in time and space dimensions is established according to preset thermal load cycle data of high-low temperature alternating environment in orbit, and time-varying coupling stress information of the solar wing is obtained; based on the time-varying coupling stress information of the solar wing, a structural internal force rebalancing process of the stress concentration region and the stress balance region caused by temperature alternation is analyzed, and stress redistribution path information is obtained; based on the stress redistribution path information, a structural deformation trend is deduced, the influence of the stress redistribution path on the macroscopic geometric morphology of the solar wing is deduced, and surface dynamic response information is obtained; based on the surface dynamic response information, a surface sequence reconstruction mechanism of the solar wing from heating to cooling in a continuous orbit cycle is constructed, and the in-orbit surface evolution information set is obtained; the surface sequence reconstruction mechanism is used to integrate discrete surface dynamic response information into continuous evolution atlas.

[0010] Optionally, based on the overall stress state, a dynamic mapping mechanism of thermal load cycle data and ground initial stress field in time and space dimensions is established according to preset thermal load cycle data of high-low temperature alternating environment in orbit, and time-varying coupling stress information of the solar wing is obtained; based on the time-varying coupling stress information of the solar wing, a structural internal force rebalancing process of the stress concentration region and the stress balance region caused by temperature alternation is analyzed, and stress redistribution path information is obtained; based on the stress redistribution path information, a structural deformation trend is deduced, the influence of the stress redistribution path on the macroscopic geometric morphology of the solar wing is deduced, and surface dynamic response information is obtained; based on the surface dynamic response information, a surface sequence reconstruction mechanism of the solar wing from heating to cooling in a continuous orbit cycle is constructed, and the in-orbit surface evolution information set is obtained; the surface sequence reconstruction mechanism is used to integrate discrete surface dynamic response information into continuous evolution atlas.

[0011] Optionally, based on the spatial distribution characteristics, the stress redistribution path information is combined, a structural deformation coordination analysis is adopted, a confrontation and balance relationship between the high deformation driving trend of the stress concentration region and the deformation constraint trend of the stress balance region is analyzed, and regional deformation interaction information is obtained; based on the regional deformation interaction information, the time-varying coupling stress information of the solar wing is combined, a deformation transmission path analysis is adopted, a whole process that local deformation of a high deformation driving region is transmitted through a solar wing structural skeleton and inhibited by a deformation constraint region is tracked, and local dominant surface feature information is obtained; based on the local dominant surface feature information, a geometric morphology integration analysis is adopted, deformation contributions of each local region and mutual restraint relationships are comprehensively analyzed, and influences of stress redistribution on the geometric morphology of the solar wing are obtained.

[0012] Optionally, based on the profile dynamic response information, through space-time correlation, the evolution continuity and transition law between profile dynamic response information of adjacent time points are analyzed to obtain profile evolution transition information; based on the profile evolution transition information, combined with the thermal load cycle data, through sequence fitting, discrete profile dynamic response information is integrated into profile time sequence information reflecting continuous change process of profile in a single orbit period; based on the profile time sequence information, through period connection, the repeatability and difference of profile change sequence between continuous multiple orbit periods are analyzed to construct the on-orbit profile evolution information set of the solar wing from heating to cooling in continuous orbit periods.

[0013] Optionally, based on the profile time sequence information, through surface normal extraction analysis, the normal direction of each position on the surface of the solar wing at different time points is extracted to obtain surface normal distribution sequence information; based on the surface normal distribution sequence information, through angle calculation analysis, the angle change of the normal of each surface position and the designed light receiving direction of the solar wing is calculated to obtain light receiving angle distribution sequence information; based on the light receiving angle distribution sequence information, through projection area integral analysis, the integral calculation of the effective projection area of the solar wing in the light receiving direction with time is obtained to obtain effective light receiving area sequence information; based on the effective light receiving area sequence information, through photoelectric conversion efficiency mapping analysis, the change law of the solar wing power generation efficiency with time is mapped to obtain power generation efficiency change information set.

[0014] Optionally, based on the power generation efficiency change information set, the influence degree of each parameter in the ground assembly process parameter set on the on-orbit power generation efficiency change is analyzed to obtain parameter influence degree information; based on the parameter influence degree information, combined with the assembly stress distribution information set, the reverse correlation rule between the assembly process parameters and the on-orbit power generation efficiency is established to obtain parameter adjustment rule information; based on the parameter adjustment rule information, through iterative convergence analysis, the sequential adjustment scheme of the assembly process parameters is generated to obtain the iterative optimization strategy; based on the iterative optimization strategy, the solar wing intelligent assembly decision log including the assembly process parameter optimization suggestion and the expected on-orbit power generation efficiency improvement amplitude is output.

[0015] In a second aspect, the present application provides a spaceflight equipment intelligent assembly system based on digital twinning, the system comprising: The stress analysis module is configured to acquire an assembly process parameter set of the solar wing in a ground assembly stage, and construct an assembly stress distribution information set reflecting an initial stress field of the solar wing on the ground based on the assembly process parameter set; the surface evolution module is configured to analyze a dynamic coupling relationship between a thermal load and the initial stress field of the solar wing on the ground under a high-low temperature alternating environment in orbit based on the assembly stress distribution information set, and obtain an on-orbit surface evolution information set of the solar wing; the efficiency analysis module is configured to analyze an influence of the on-orbit surface change of the solar wing on the power generation efficiency based on the on-orbit surface evolution information set, and obtain a power generation efficiency change information set; and the decision generation module is configured to establish a ground assembly process parameter reverse parameter adjustment mechanism with the on-orbit power generation efficiency as an optimization target based on the power generation efficiency change information set, generate an iterative optimization strategy of the assembly process parameter, and output a solar wing intelligent assembly decision log. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0017] Figure 1 An application scenario schematic diagram provided by an embodiment of the present application; Figure 2 A flowchart of a spaceflight equipment intelligent assembly method based on digital twinning provided by an embodiment of the present application; Figure 3 A structure schematic diagram of a spaceflight equipment intelligent assembly system based on digital twinning provided by an embodiment of the present application. DETAILED DESCRIPTION

[0018] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the following will combine the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the present application.

[0019] In addition, the term "and / or" in this paper is only to describe the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B, which can represent the three cases of A alone, A and B together, and B alone. In addition, the character " / " in this paper generally represents an "or" relationship between the associated objects unless otherwise specified.

[0020] The embodiments of this application will now be described in further detail with reference to the accompanying drawings.

[0021] Digital twin technology is being gradually introduced into the field of aerospace equipment assembly. However, existing applications are mostly focused on 3D visualization and data management of the assembly process, with core models primarily being static representations of physical entities. In solar array assembly, such static models can reflect the stress distribution under a certain operating condition, but they fail to construct a closed-loop dynamic mapping relationship from "ground assembly stress" to "on-orbit surface evolution" and then to "system power generation efficiency." This prevents digital twin models from fully realizing their core value in performance prediction and process optimization.

[0022] Based on this, this application provides a method and system for intelligent assembly of aerospace equipment based on digital twins. First, the ground assembly process parameters are characterized and mapped to the initial assembly stress field. Then, in the on-orbit analysis, the thermal load and the initial stress field are coupled to simulate the dynamic evolution process of the solar airfoil, thereby predicting the decay law of power generation efficiency. Finally, the ground assembly process parameters are optimized in reverse based on the performance prediction results, generating optimization strategies and decision records to form a closed loop of continuous improvement.

[0023] Figure 1 This application provides an application scenario diagram. In the process of solar array ground assembly, the method provided in this application is applied to establish a quantitative relationship between solar array ground assembly parameters and on-orbit deformation and power generation efficiency, and to adjust parameters in reverse accordingly to achieve closed-loop optimization of the process and performance improvement.

[0024] Specifically, the method provided in this application can be applied to any server, which interacts with sensors at the ground assembly site to obtain a set of assembly process parameters provided by the sensors at the ground assembly site. It analyzes the dynamic coupling relationship between thermal load and initial stress field to obtain an on-orbit surface evolution information set, predicts solar array behavior, and outputs a solar array intelligent assembly decision log to aerospace equipment assembly personnel, thereby achieving continuous optimization of the assembly process, improving the on-orbit power generation efficiency and reliability of the solar array, and reducing the risk of failure.

[0025] For specific implementation details, please refer to the following examples.

[0026] Figure 2 This is a flowchart illustrating a digital twin-based intelligent assembly method for aerospace equipment, provided as an embodiment of this application. The method of this embodiment can be applied to servers in the above scenarios. For example... Figure 2 As shown, the method includes: S201. Obtain the assembly process parameter set for the solar array ground assembly stage, and based on the assembly process parameter set, construct an assembly stress distribution information set that reflects the initial stress field of the solar array ground.

[0027] The assembly process parameter set can be a collection of process parameters involved in the ground assembly of the solar array, which can be provided by sensors at the ground assembly site. The assembly stress distribution information set can be a collection of information used to characterize the initial stress distribution state inside the structure of the solar array after ground assembly.

[0028] Specifically, as a key component of spacecraft, the quality of the ground assembly of solar arrays directly affects their on-orbit power generation performance and lifespan. Existing assembly technologies rely heavily on experience and lack prediction of on-orbit behavior, leading to deformation or failure of solar arrays due to stress concentration during on-orbit operation. By acquiring a set of assembly process parameters and using numerical simulation methods such as finite element analysis, a high-precision set of assembly stress distribution information can be constructed, providing basic data for subsequent on-orbit performance analysis. This is crucial for realizing the digitalization and intelligentization of the assembly process.

[0029] S202. Based on the assembly stress distribution information set, the dynamic coupling relationship between thermal load and ground initial stress field under the alternating high and low temperature environment in orbit is analyzed to obtain the on-orbit profile evolution information set of the solar array.

[0030] The on-orbit profile evolution information set can be a set of information used to characterize the changes in the profile of a solar array over time during its on-orbit operation.

[0031] Specifically, solar arrays face extreme temperature variations (such as from -100°C to 100°C) while in orbit. The coupling of thermal loads with the initial stress field on the ground can lead to surface distortion, which in turn affects the accuracy of solar orientation. Existing methods are difficult to quantify this coupling effect. By using mathematical modeling methods such as thermal-structural coupling simulation, we can analyze the interaction between the assembly stress distribution information set and the thermal load, simulate the evolution process of the solar array's surface in orbit, and obtain the on-orbit surface evolution information set. This helps to predict long-term on-orbit behavior and identify risks in advance.

[0032] S203. Based on the on-orbit surface evolution information set, analyze the impact of on-orbit surface changes of the solar array on power generation efficiency, and obtain the power generation efficiency change information set.

[0033] The power generation efficiency variation information set can be a set of information used to characterize the fluctuation of the on-orbit power generation efficiency of the solar array as the surface changes.

[0034] Specifically, changes in the solar array profile alter the light incident angle (e.g., a 2-degree offset), reducing power generation efficiency. Existing technologies lack a comprehensive analysis of the profile-efficiency relationship. By using optical simulation to analyze the on-orbit profile evolution information set and calculate the power generation efficiency under different profile states (e.g., a 5% decrease in efficiency), a power generation efficiency change information set can be obtained. This can quantify the direct impact of assembly processes on on-orbit performance and provide a basis for optimization.

[0035] S204. Based on the power generation efficiency change information set, establish a reverse parameter adjustment mechanism for ground assembly process parameters with on-orbit power generation efficiency as the optimization objective, generate an iterative optimization strategy for assembly process parameters, and output the intelligent assembly decision log of the solar array.

[0036] The iterative optimization strategy for assembly process parameters can be used to guide the adjustment of ground assembly process parameters. The solar array intelligent assembly decision log can be a report containing optimized assembly process parameters and expected on-orbit power generation efficiency.

[0037] Specifically, existing assembly optimization relies on trial and error, which is inefficient. Based on the information set of power generation efficiency changes, optimization algorithms such as genetic algorithms are used to establish a mapping relationship between ground assembly process parameters (such as bolt preload) and on-orbit power generation efficiency. With the goal of maximizing on-orbit power generation efficiency, the assembly process parameters are adjusted in reverse to generate an iterative optimization strategy. Then, the optimization process is recorded through a log system (such as the number of parameter adjustments 10 times), and an intelligent assembly decision log is output for engineers' reference. This achieves closed-loop control from on-orbit performance to ground assembly, improving assembly accuracy.

[0038] The method provided in this embodiment obtains the set of process parameters for the ground assembly of the solar array and constructs an assembly stress distribution information set, laying the foundation for on-orbit analysis. By analyzing the dynamic coupling relationship between thermal load and initial stress field, an on-orbit profile evolution information set is obtained to predict solar array behavior. The impact of profile changes on power generation efficiency is analyzed to obtain a power generation efficiency change information set, quantifying the performance impact. Furthermore, a reverse parameter tuning mechanism is established to generate optimization strategies and decision logs, enabling continuous optimization of the assembly process. This improves the on-orbit power generation efficiency and reliability of the solar array and reduces the risk of failure.

[0039] In some embodiments, the assembly process parameter set includes adhesive layer pressure, bolt tightening torque, and inter-plate conductor layout tension. Based on adhesive layer pressure, the stress transmission and distribution changes in the adhesive layer bonding area due to pressure application during solar wing assembly are analyzed to obtain adhesive layer stress distribution information. Based on bolt tightening torque, the stress concentration effect and diffusion law generated by the torque in the bolt connection area during solar wing assembly are analyzed to obtain bolt stress distribution information. Based on inter-plate conductor layout tension, the influence and distribution characteristics of the additional stress introduced by tension in the inter-plate conductor layout area during solar wing assembly are analyzed to obtain conductor layout stress distribution information. Based on adhesive layer stress distribution information, bolt stress distribution information, and conductor layout stress distribution information, the overall stress state of the solar wing ground assembly is comprehensively evaluated to construct an assembly stress distribution information set.

[0040] Adhesive layer pressure refers to the pressure applied to the adhesive bonding area. Bolt tightening torque refers to the torque applied to the bolt connection area. Inter-plate conductor layout tension refers to the tension experienced by the inter-plate conductors during the layout process. Adhesive layer stress distribution information reflects the stress distribution state of the adhesive bonding area under pressure. Overall stress state refers to the stress distribution state within the overall structure after the solar panel ground assembly is completed. Bolt stress distribution information reflects the stress concentration and diffusion patterns in the bolt connection area under torque. Conductor layout stress distribution information reflects the additional stress distribution characteristics of the inter-plate conductor layout area under tension.

[0041] Specifically, during the ground assembly of the solar array, assembly process parameters such as adhesive layer pressure, bolt tightening torque, and tension of the inter-plate conductor layout directly affect the structural stress state of the solar array. Improper control of these parameters can lead to excessive local stress or uneven stress distribution, thus affecting the structural stability and reliability of the solar array during on-orbit operation. For example, insufficient adhesive layer pressure (e.g., below 0.3 MPa) may result in weak adhesion, making it prone to delamination under thermal loads; excessive pressure (e.g., above 0.7 MPa) may cause excessive compression of the adhesive layer, generating residual stress. Insufficient bolt tightening torque (e.g., below 8 Nm) may lead to loose connections; excessive torque (e.g., above 12 Nm) may cause bolt overload and stress concentration. Improper tension of the inter-plate conductor layout (e.g., insufficient tension leading to conductor slack, excessive tension leading to conductor over-tightening) may introduce additional stress, affecting the solar array profile. This step addresses the aforementioned issues using the following methods: First, digital image correlation is used to acquire the full-field strain distribution of the adhesive layer region under a specific pressure (e.g., 0.5 MPa). Combined with a viscoelastic constitutive model, the shear stress transmission law within the adhesive layer is calculated to generate quantitative information describing the stress gradient (e.g., a 60% decrease from the center to the edge). For the bolted connection region, a torque-stress mapping model is established based on Hertzian contact theory. By applying a stepped tightening torque (e.g., gradually increasing from 5 Nm to 15 Nm) and using a strain rose sensor network to monitor the strain around the hole, the key region where the torque stress concentration factor reaches its peak is identified, forming a model including the stress diffusion angle. The system obtains information on bolt stress distribution; for the conductor layout area, a tension optical effect monitoring system is used to track the deflection change of the conductor under a preset tension (e.g., 5N) in real time, and the additional bending moment distribution is calculated by combining beam bending theory to obtain the conductor layout stress distribution information; finally, the three types of stress fields are projected onto a unified coordinate system through a weighted superposition algorithm, and coupled analysis is performed using the von Mises equivalent stress criterion. When the stress concentration in the bolt area and the compressive stress in the conductor layout area are detected to exceed the material yield limit (e.g., 250MPa for aluminum alloy), an automatic reconstruction mechanism is triggered, thereby establishing a comprehensive stress distribution information set that fully reflects the interaction between adhesive bonding, mechanical fastening, and conductor constraint.

[0042] The method provided in this embodiment analyzes the stress distribution corresponding to the adhesive layer pressure, bolt tightening torque, and inter-plate wire layout tension, and comprehensively evaluates the overall stress state to construct an assembly stress distribution information set. This information set can fully reflect the initial stress field of the solar array on the ground, providing an accurate data foundation for subsequent on-orbit surface evolution analysis and power generation efficiency optimization. At the same time, it improves the assembly quality and on-orbit reliability of the solar array, ensuring the successful execution of space missions.

[0043] In some embodiments, based on the stress distribution information of the adhesive layer, the stress distribution information of the bolts, and the stress distribution information of the conductor layout, stress field simulation data is used to analyze the stress transmission direction characteristics and interactions between the adhesive layer region, the bolt connection region, and the conductor layout region along the structure, quantify the stress superposition and cancellation effects between each region, and construct stress transmission information. The adhesive layer region is an adhesive interface layer formed by the pressure control of the adhesive layer; the bolt connection region is a mechanical fastening connection formed by the bolt tightening torque; the conductor layout region is a cable path attachment area formed by the tension constraint of the conductor layout between plates; based on the superposition and cancellation effects, the spatial distribution characteristics of the stress concentration region where the superposition effect is concentrated and the stress equilibrium region where the cancellation effect is concentrated in the solar array structure are analyzed to obtain the overall stress state.

[0044] Stress field simulation data can be used to dynamically simulate the stress field of a solar array structure using numerical simulation methods such as finite element analysis, in order to simulate the stress transfer process. Stress transfer information can be a dataset describing the directional characteristics and interactions of stress transfer between the adhesive layer region, bolted connection region, and conductor layout region. Superposition effect can be the phenomenon where multiple stress sources act together, leading to an increase in stress. Cancellation effect can be the phenomenon where multiple stress sources interact, leading to a decrease in stress. Stress concentration region can be a region where superposition effects concentrate, resulting in higher stress. Stress equilibrium region can be a region where cancellation effects concentrate, resulting in lower stress.

[0045] Specifically, as a key component of aerospace equipment, the quality of the ground assembly of the solar array directly determines the structural stability and power generation efficiency during on-orbit operation. The solar array faces a high and low temperature alternating environment in orbit. The initial stress field formed during ground assembly will dynamically couple with the thermal load, causing structural deformation and thus affecting power generation performance. If the stress of the adhesive layer, bolts or wire layout is analyzed in isolation, while ignoring the interaction between them, it is impossible to accurately predict the evolution of the on-orbit profile, which may lead to unreasonable assembly process parameters and increase the risk of on-orbit failure. This step addresses the aforementioned issues using the following methods: Based on the stress distribution information of the adhesive layer, bolts, and conductor layout, stress field simulation data is employed. For example, a refined digital model of the solar panel structure is constructed using finite element analysis software. Parameters controlled by the adhesive layer pressure (e.g., a pressure value of 0.5 MPa) are input to simulate the stress distribution at the adhesive interface in the adhesive layer region. Bolt connection regions are simulated using bolt tightening torque parameters (e.g., a torque of 10 Nm) to simulate the stress concentration effect at mechanical fasteners. The conductor layout region is simulated using inter-plate conductor tension parameters (e.g., a tension of 5 N) to simulate the additional stress influence in the cable path attachment area. Numerical calculation methods, such as stress superposition algorithms, are used to analyze stress transmission along the structure. The directional characteristics and interactions are analyzed, such as calculating the stress superposition effect at the interface between the adhesive layer and the bolt area (e.g., the combined stress rises to 1.2 MPa), or quantifying the cancellation effect at the interaction between the conductor layout area and the bolt area (e.g., the stress drops to 0.1 MPa), thereby constructing stress transfer information containing stress vectors and interaction matrices. Based on this, visualization tools such as stress cloud maps are used to analyze the spatial distribution characteristics of stress concentration areas where superposition effects are concentrated (e.g., high stress areas around bolt holes exceeding 1.0 MPa) and stress equilibrium areas where cancellation effects are concentrated (e.g., low stress areas along the conductor path below 0.2 MPa). Finally, a map reflecting the overall stress state of the solar array is obtained through comprehensive data fusion, providing accurate input for assembly process optimization.

[0046] The method provided in this embodiment can comprehensively assess the overall stress state of the solar array ground assembly, identify stress concentration and equilibrium areas, provide accurate input for subsequent on-orbit surface evolution analysis, thereby optimizing the assembly process, improving the structural stability and on-orbit power generation efficiency of the solar array, reducing the risk of failure, and ensuring the long-term reliable operation of aerospace equipment.

[0047] In some embodiments, based on the overall stress state and according to the preset thermal load cycle data of the on-orbit high and low temperature alternating environment, a dynamic mapping mechanism between the thermal load cycle data and the initial stress field on the ground in the spatiotemporal dimension is established to obtain the time-varying coupled stress information of the solar array; based on the time-varying coupled stress information of the solar array, the process of rebalancing the internal forces of the structure in the stress concentration area and the stress equilibrium area due to temperature alternation is analyzed to obtain the stress redistribution path information; based on the stress redistribution path information, the structural deformation trend is deduced, and by deducing the influence of the stress redistribution path on the macroscopic geometry of the solar array, the dynamic response information of the surface is obtained; based on the dynamic response information of the surface, a surface sequence reconstruction mechanism for the entire process of the solar array from heating to cooling in a continuous orbital cycle is constructed to obtain the on-orbit surface evolution information set; the surface sequence reconstruction mechanism is used to integrate the discrete dynamic response information of the surface into a continuous evolution map.

[0048] Thermal load periodic data can be periodic data describing the temperature change over time in an alternating high and low temperature environment in orbit. A dynamic mapping mechanism can be a mathematical or physical model used to establish a dynamic temporal and spatial correlation between thermal load periodic data and the initial ground stress field. Solar wing time-varying coupled stress information can reflect the time-varying stress changes within the solar wing under the combined action of thermal load and the initial ground stress field. The structural internal force rebalancing process can be the process by which temperature changes cause the redistribution of stress within the solar wing to reach a new equilibrium state. Stress redistribution path information can describe the direction and path of stress redistribution within the solar wing structure. Structural deformation trend inference can be the process of predicting the trend of solar wing geometry changes based on stress redistribution. Surface dynamic response information can reflect the dynamic changes of the solar wing surface under thermal load. A surface sequence reconstruction mechanism can be a method applicable to integrating discrete surface dynamic response information into a continuous evolution sequence. An evolution map can be a graphical or data sequence visually displaying the evolution process of the solar wing surface.

[0049] Specifically, as a key component of spacecraft, the power generation efficiency of the solar array is directly affected by its surface accuracy. The alternating high and low temperatures in orbit cause dynamic coupling between the thermal load and the initial stress field of the ground assembly, leading to uncontrollable evolution of the surface profile. Existing methods lack in-depth analysis of this dynamic coupling relationship, making it impossible to accurately predict the trend of surface profile changes. This results in decreased on-orbit power generation efficiency, increased structural fatigue risk, and reduced mission reliability. This step addresses these issues by starting with the analysis of the overall stress state of the solar array, using a thermal-structural coupling simulation platform (such as ANSYS). A parametric model is constructed using a mechanical approach. Preset thermal load cycle data from alternating high and low temperatures in orbit (e.g., sawtooth wave cycles from -100°C to +100°C) is spatiotemporally aligned with the initial stress field on the ground. A dynamic mapping mechanism is established through a transient dynamics module. For example, stress field superposition calculations are performed at each temperature step point (e.g., every 10°C interval), outputting a time-varying coupled stress cloud map of the solar array. Based on this data, sub-modeling techniques are used to focus on stress concentration areas (e.g., stress over-limit areas around bolt holes) and stress equilibrium areas (e.g., low-stress areas in the center of the panel). A custom stress transfer algorithm is used to analyze the structural internal force rebalancing process caused by temperature alternation. Specifically, this manifests as the release of compressive stress in the adhesive layer area when the temperature rises sharply. The tensile stress in the bolt area is multiplied, which in turn generates a stress redistribution path streamline diagram with direction vectors. Then, the structural deformation trend inference module is imported, and the influence of stress path on macroscopic surface is inferred frame by frame using geometric nonlinear analysis method. For example, the millimeter-level wave deformation caused by uneven boundary constraints is calculated by inverting nodal displacement, and the dynamic response dataset of surface containing timestamps is output. Finally, the discrete surface data is integrated into a continuous evolution map based on the sequence reconstruction algorithm (such as cubic spline interpolation). Specifically, thousands of transient surface data in each orbital cycle are sorted according to the thermal load phase to generate a spatiotemporal evolution matrix that can trace the surface state at any time. The surface deformation animation is rendered by the visualization engine, and a complete on-orbit surface evolution information set is constructed.

[0050] The method provided in this embodiment can accurately predict the on-orbit surface evolution of the solar array, identify potential structural deformation risks, and provide data support for optimizing ground assembly process parameters, thereby improving the on-orbit power generation efficiency and structural reliability of the solar array and extending the life of the spacecraft.

[0051] In some embodiments, based on the overall stress state and according to the preset thermal load cycle data of the on-orbit high and low temperature alternating environment, the continuous thermal load cycle is decomposed into multiple discrete temperature stages to obtain staged thermal load information; based on the staged thermal load information, the spatiotemporal correlation between different temperature stages and stress response is established to obtain thermal load-stress dynamic correlation information; based on the thermal load-stress dynamic correlation information, the correlation results of multiple temperature stages are integrated to construct a dynamic mapping mechanism that reflects the dynamic change process of the stress field within the entire thermal load cycle.

[0052] Temperature stages can be discrete temperature ranges decomposed from continuous thermal load cycles. Stage-specific thermal load information can be the thermal load parameters corresponding to each temperature stage. Thermal load-stress dynamic correlation information can be the correlation between different temperature stages and the stress response of the solar array.

[0053] Specifically, during the on-orbit operation of solar arrays, they face alternating high and low temperature environments. The interaction between the thermal load and the initial stress field formed during the ground assembly stage leads to a redistribution of stress in the solar array structure, which in turn affects its profile accuracy and power generation efficiency. Specific methods often ignore the dynamic coupling relationship between the thermal load and the initial stress field, or only use static analysis, resulting in inaccurate on-orbit profile prediction, a lack of basis for assembly process optimization, and an inability to effectively address the long-term impact of on-orbit environmental changes on solar array performance. By establishing a dynamic mapping mechanism between thermal load periodic data and the ground initial stress field in the spatiotemporal dimension, it is possible to accurately describe the stress field response process under thermal load changes, reveal the evolution law of stress concentration areas (such as bolted connection areas) and stress equilibrium areas under temperature alternation, and provide key inputs for subsequent on-orbit profile evolution analysis and power generation efficiency optimization. This step addresses the aforementioned issues using the following methods: First, a thermal load cycle decomposition technique is employed to decompose a continuous thermal load cycle (e.g., a temperature cycle from -100°C to 100°C) into multiple discrete temperature stages (e.g., each stage is 20°C). This yields staged thermal load information, including the temperature value, duration, and heat flux density parameters for each stage. This step relies on environmental simulation data and numerical analysis tools to ensure that the decomposed stages accurately reflect the actual on-orbit temperature gradient. Next, spatiotemporal correlation modeling methods, such as finite element simulation software, are used to simulate the stress response of the solar array structure at different temperatures for each temperature stage. By setting boundary conditions and material parameters (e.g., the thermal expansion coefficient of aluminum alloy), stress concentration regions (e.g., bolted connection areas) and stress equilibrium regions are analyzed. The system analyzes the magnitude and direction of stress changes in the domain to establish a dynamic correlation between temperature stages and stress field changes, generating dynamic correlation information between thermal load and stress, such as outputting stress cloud maps and data sequences through simulation. Finally, through data integration and fusion techniques, the correlation results of multiple temperature stages are comprehensively processed, such as by applying time series analysis or machine learning algorithms (e.g., regression models), to construct a mathematical model or mapping mechanism that reflects the dynamic change process of the stress field throughout the entire thermal load cycle. This mechanism can describe the evolution trajectory of the stress field with temperature changes and output as a visual map or digital matrix for subsequent on-orbit profile prediction and process optimization. The entire implementation process relies on a high-performance computing platform and accurate environmental data input to ensure the accuracy and real-time performance of the mapping mechanism, thereby providing reliable support for the intelligent assembly of solar arrays.

[0054] By establishing a dynamic mapping mechanism between thermal load cycle data and the initial stress field on the ground through the method provided in this embodiment, the dynamic response of the stress field during the on-orbit operation of the solar array can be accurately captured, improving the accuracy of on-orbit profile evolution prediction, providing a reliable basis for optimizing ground assembly process parameters, thereby improving the on-orbit power generation efficiency and reliability of the solar array, and enhancing its adaptability to complex space environments.

[0055] In some embodiments, based on the spatial distribution characteristics and combined with the stress redistribution path information, structural deformation coordination analysis is used to analyze the antagonistic and balance relationship between the high deformation driving trend in the stress concentration region and the deformation constraint trend in the stress equilibrium region, thereby obtaining regional deformation interaction information; based on the regional deformation interaction information and combined with the time-varying coupled stress information of the solar array, deformation transmission path analysis is used to track the entire process of local deformation in the high deformation driving region being transmitted through the solar array structural frame and suppressed by the deformation constraint region, thereby obtaining local dominant surface feature information; based on the local dominant surface feature information, geometric morphology integration analysis is used to synthesize the deformation contribution and mutual constraint relationship of each local region, thereby obtaining the influence of stress redistribution on the geometry of the solar array.

[0056] Structural deformation coordination analysis can be an analytical tool used to study the interaction between deformation trends in different regions, ensuring the coordination of structural deformation. A high deformation driving trend can be the tendency of stress concentration areas to undergo larger deformations under thermal loads. A deformation constraint trend can be the tendency of stress equilibrium regions to inhibit deformation and limit overall deformation. The antagonistic and equilibrium relationship can be the interaction between high deformation driving trends and deformation constraint trends. Regional deformation interaction information can be quantitative information describing the interaction of deformation trends in different regions. Deformation transmission path analysis can be an analytical tool used to track how local deformation is transmitted to other regions through the structural frame. Local deformation can be the geometric deformation occurring in a local area of ​​the solar fin. The solar fin structural frame can be the supporting structure of the solar fin, including components such as beams and frames, used to transmit forces and deformations. Deformation constraint region inhibition can be the inhibitory effect of stress equilibrium regions on deformation transmission. Local dominant surface feature information can be the surface change characteristics dominated by local deformation. Geometric morphology integration analysis can be an analytical tool that integrates all local deformation contributions to obtain the overall geometric morphology change. Deformation contribution can be the degree of contribution of each local region to the overall surface change. Mutual constraints can refer to the mutual influence and constraints between deformations in different local regions.

[0057] Specifically, during the on-orbit operation of the solar array, the thermal load and the initial stress field of the ground assembly dynamically couple due to the influence of the alternating high and low temperature environment, resulting in stress redistribution and subsequent evolution of the solar array profile. This profile change directly determines the power generation efficiency of the solar array. Therefore, accurately predicting the impact of stress redistribution on the geometry is crucial. Existing methods often ignore the complex interaction between deformation trends in local areas or only consider a single factor, leading to inaccurate profile prediction and affecting subsequent power generation efficiency analysis and assembly process optimization. This step addresses the aforementioned issues using the following methods: First, based on spatial distribution characteristics (e.g., stress concentration zones formed by bolted connections exhibit a ring-like distribution, while stress equilibrium zones formed by conductor layout exhibit a mesh-like distribution), and combined with stress redistribution path information (e.g., the path direction of stress diffusion from the bolted area to the panel edge under thermal load), structural deformation coordination analysis is employed. By constructing a regional deformation coupling model, the strength and equilibrium point of the conflict between high deformation driving trends (e.g., a strain gradient of 0.5 mm / m generated in the bolted area at high temperatures) and deformation constraint trends (e.g., the strain in the conductor area is suppressed to 0.2 mm / m through tension constraint) are quantified, generating regional deformation interaction information containing deformation priority and suppression coefficients. Then, based on this information, time-varying coupled stress data of the solar array (e.g., the stress corresponding to the -100℃ to +80℃ temperature difference cycle caused by the alternation of the sun and shadow sides of the orbit) are integrated. The stress fluctuation curve employs a deformation transmission path analysis method. By establishing a structural skeleton node transmission network, it tracks how local deformation in high deformation-driven areas (such as a 1.2mm warping in a bolt area) is transmitted through the carbon fiber support beam of the solar wing. Deformation-constrained areas (such as a conductor attachment area) are rigidly suppressed by geometric boundary conditions, ultimately forming local dominant surface feature information centered on local concavity and convexity (such as wave deformation with a peak height of 3.5mm in a specific area). Finally, using geometric morphology integration analysis, by superimposing the contribution weights of each local deformation (such as the deformation contribution rate of a panel corner reaching 40% of the overall deformation) and calculating the mutual constraint relationship (such as the 15% cancellation effect of deformation in adjacent areas due to phase difference), it outputs a complete influence map of stress redistribution on the geometry of the solar wing (such as the overall surface exhibiting symmetrical torsional deformation with an amplitude of 8mm).

[0058] The method provided in this embodiment can accurately quantify the impact of stress redistribution on the geometry of the solar array, improve the accuracy and reliability of on-orbit surface evolution prediction, thereby providing a scientific basis for optimizing ground assembly process parameters, ultimately improving the on-orbit power generation efficiency and service life of the solar array, while enhancing the adaptability and stability of aerospace equipment in complex environments.

[0059] In some embodiments, based on the surface dynamic response information, the evolution continuity and transition law between the surface dynamic response information at adjacent time points are analyzed through spatiotemporal correlation to obtain surface evolution transition information; based on the surface evolution transition information, combined with the thermal load cycle data, the discrete surface dynamic response information is integrated into surface time series information reflecting the continuous change process of the surface within a single orbital cycle through sequence fitting; based on the surface time series information, the repeatability and differences of surface change sequences between multiple consecutive orbital cycles are analyzed through cycle connection to construct an on-orbit surface evolution information set of the solar array from heating to cooling in a continuous orbital cycle.

[0060] Solar wing profile evolution transition information can be data describing the consistency and smooth transition of solar wing profile changes between adjacent time points. Solar wing profile time series information can be a set of time-series data characterizing the continuous change process of the solar wing profile within a single orbital period. Spatiotemporal correlation can be a technique for analyzing the consistency of data in the temporal and spatial dimensions. Sequence fitting can be a mathematical modeling method that integrates discrete data points into a continuous sequence. Periodic continuum can be a statistical method for analyzing the repeatability and differences between multiple periodic sequences.

[0061] Specifically, solar arrays experience alternating high and low temperature environments during on-orbit operation. Their surface changes are affected by the coupling of thermal loads and ground assembly stress, exhibiting complex dynamic characteristics. If only discrete surface dynamic response information is relied upon, it is impossible to fully capture the continuous evolution of the surface over time, leading to deviations in on-orbit performance prediction. Changes in the solar array surface directly affect its power generation efficiency, and the lack of transition information between discrete data points may obscure the deformation trend of key surfaces. For example, during the stage of sudden thermal load changes, the surface may undergo nonlinear deformation. If the continuity of evolution is not analyzed, it is impossible to accurately predict the transition behavior of the surface during heating and cooling, thereby affecting the accuracy of power generation efficiency assessment. This step addresses the aforementioned issues using the following methods: First, spatiotemporal correlation analysis is employed. Based on the dynamic response information of the profile (such as profile displacement data obtained through finite element simulation), time series alignment methods (such as dynamic time warping algorithms) and spatial interpolation techniques (such as Kriging interpolation) are used to analyze the evolutionary continuity and transition patterns between dynamic response information of the profile at adjacent time points (such as 0.1-second intervals). Specifically, this includes identifying smooth transition regions of profile changes (such as gradually changing profiles under uniform heat load distribution) and abrupt change points (such as local warping caused by sudden temperature changes), thereby obtaining profile evolution transition information. Next, a sequence fitting method is applied. Based on the profile evolution transition information and combined with heat load period data (such as temperature change curves generated by an orbital thermal model, with a period of, for example, 90 minutes), polynomial fitting techniques (such as least squares fitting) or spline interpolation algorithms (such as cubic spline interpolation) are used to convert the discrete profile dynamic response information into a coherent and transitional data. The dynamic response information (such as surface sampling points every 0.1 seconds) is integrated into surface time series information reflecting the continuous change process of the surface within a single orbital cycle. For example, a smooth curve is generated by fitting to describe the entire process of the surface from thermal expansion to cooling contraction. Then, using cycle connection analysis, based on the surface time series information, the repeatability (such as the similarity of the surface recovery mode in each cycle) and differences (such as the intensification of surface deformation due to material aging) of the surface change sequence between multiple consecutive orbital cycles (such as 10 cycles) are analyzed through sliding window comparison technology (such as setting the window size to 10 cycles) and correlation analysis methods (such as calculating the Pearson correlation coefficient). This constructs an on-orbit surface evolution information set of the solar array from heating to cooling in a continuous orbital cycle, and finally forms a complete dataset covering spatiotemporal evolution, providing a seamless and high-fidelity input foundation for subsequent power generation efficiency analysis and assembly optimization.

[0062] The method provided in this embodiment constructs a surface sequence reconstruction mechanism, which can transform discrete surface data into a continuous evolution sequence, improve the coherence and accuracy of surface prediction, provide reliable data support for subsequent power generation efficiency analysis, enhance the real-time performance and adaptability of solar array on-orbit performance monitoring, help optimize ground assembly processes, and improve the overall power generation efficiency and reliability of the solar array.

[0063] In some embodiments, based on the surface time series information, surface normal extraction analysis is used to extract the normal directions of each position on the solar array surface at different time points to obtain surface normal distribution sequence information; based on the surface normal distribution sequence information, angle calculation analysis is used to calculate the angle change between the normal at each surface position and the designed light-receiving direction of the solar array to obtain light-receiving angle distribution sequence information; based on the light-receiving angle distribution sequence information, projection area integration analysis is used to calculate the change of the effective projection area of ​​the solar array in the light-receiving direction over time to obtain effective light-receiving area sequence information; based on the effective light-receiving area sequence information, photoelectric conversion efficiency mapping analysis is used to map the change law of the solar array power generation efficiency over time to obtain a power generation efficiency change information set.

[0064] Surface normal extraction analysis can be based on surface time-series information, using geometric analysis methods to extract the normal directions of various locations on the solar array surface at different time points. The surface normal distribution sequence information can be a set of normal directions at various locations on the solar array surface arranged chronologically at different time points. Angle calculation analysis can be based on the surface normal distribution sequence information, using vector angle calculation methods to calculate the angle change between the normal at each surface location and the designed light-receiving direction of the solar array. The light-receiving angle distribution sequence information can be a set of sequences showing the change of the angle between the normal at each location on the solar array surface and the designed light-receiving direction over time. Projected area integration analysis can be based on the light-receiving angle distribution sequence information, using area integration methods to calculate the change of the effective projected area of ​​the solar array in the light-receiving direction over time. The effective light-receiving area sequence information can be a sequence of data showing the change of the effective projected area of ​​the solar array in the light-receiving direction over time. Photovoltaic conversion efficiency mapping analysis can be based on the effective light-receiving area sequence information, using efficiency mapping relationships to map the effective light-receiving area to changes in power generation efficiency.

[0065] Specifically, during the operation of solar arrays in orbit, the thermal load of the alternating high and low temperature environment causes dynamic changes in their structural surface. These changes cause the normal direction of each position on the surface of the solar array to shift relative to the designed light-receiving direction, thereby changing the effective light-receiving area of ​​the solar array and affecting the power generation efficiency. Existing methods often ignore the indirect impact of surface changes on power generation efficiency, or only estimate it through simplified models, resulting in inaccurate prediction of on-orbit power generation efficiency and failing to provide accurate feedback for the optimization of ground assembly processes. To address the aforementioned issues, based on surface time-series information, a surface normal extraction and analysis method is employed. Using computer-aided design (CAD) software such as CATIA's surface normal calculation module, based on the three-dimensional finite element mesh model of the solar array, the normal vectors of each mesh node on the surface at each time point are automatically extracted and stored as time-series data. For example, during the high-temperature phase, the normal of a certain mesh point shifts from its initial direction to a new direction, yielding the surface normal distribution sequence information. Building upon this, an angle calculation and analysis method is used. Through spatial vector operations, the angle between the normal at each surface position and the preset designed light-receiving direction (e.g., representing the ideal solar array direction towards the sun using a unit vector) is calculated. The angle value is calculated using the dot product formula and the inverse cosine function, and its changes over time are tracked. For instance, during thermal cycling, the continuous shift of the normal in a certain region causes the angle to gradually increase from 0 degrees to 10 degrees, yielding the light-receiving... The process begins with analyzing the angle distribution sequence. Then, through projection area integration analysis, using numerical integration techniques such as Gaussian integration or Monte Carlo methods, the solar array surface is discretized into tiny triangular patches. The cosine value of the angle between each patch is combined to calculate its projection contribution in the light-receiving direction. The effective projection area is then obtained by area-weighted summation, showing how the effective light-receiving area periodically fluctuates from 1.5 square meters to 0.7 square meters during on-orbit operation, thus obtaining the effective light-receiving area sequence. Finally, through photoelectric conversion efficiency mapping analysis, using the photoelectric conversion characteristic curve calibrated by ground tests (obtained by fitting experimental data), an interpolation algorithm maps the effective light-receiving area sequence to changes in power generation efficiency. For example, a 30% reduction in area corresponds to a 12% decrease in efficiency, resulting in a set of power generation efficiency change information. This allows for a comprehensive quantification of the dynamic impact on on-orbit power generation efficiency.

[0066] The method provided in this embodiment can accurately quantify the impact of on-orbit surface changes of the solar array on power generation efficiency, provide a set of information on power generation efficiency changes, provide a basis for optimizing ground assembly processes, thereby improving the on-orbit power generation efficiency and service life of the solar array, and ensuring the stability of spacecraft energy supply.

[0067] In some embodiments, based on the power generation efficiency change information set, the influence of each parameter in the ground assembly process parameter set on the on-orbit power generation efficiency change is analyzed to obtain parameter influence information; based on the parameter influence information, combined with the assembly stress distribution information set, an inverse correlation rule between assembly process parameters and on-orbit power generation efficiency is established to obtain parameter adjustment rule information; based on the parameter adjustment rule information, an iterative convergence analysis is performed to generate a sequential adjustment scheme for assembly process parameters to obtain the iterative optimization strategy; based on the iterative optimization strategy, the solar array intelligent assembly decision log, including assembly process parameter optimization suggestions and the expected on-orbit power generation efficiency improvement, is output.

[0068] Reverse correlation rules can be a system of rules establishing a reverse mapping between ground assembly process parameters and the on-orbit power generation efficiency of the solar array. Parameter adjustment rule information can be specific rule data based on reverse correlation rules, used to guide the adjustment of ground assembly process parameters. Iterative convergence analysis can be a data analysis method used to gradually approximate the optimal solution, analyzing the adjustment process of assembly process parameters to ensure that parameter adjustments gradually converge towards improving on-orbit power generation efficiency, ultimately forming a feasible adjustment scheme. Iterative optimization strategies can be assembly process parameter sequential adjustment schemes generated through iterative convergence analysis based on parameter adjustment rule information, clarifying the order of parameter adjustments, adjustment magnitude, and number of iterations.

[0069] Specifically, as a key component of aerospace equipment, the on-orbit power generation efficiency of solar arrays directly affects the energy supply and lifespan of the entire space mission. Ground assembly process parameters (such as adhesive layer pressure and bolt tightening torque) affect the initial stress field, which in turn couples with the on-orbit thermal load, causing surface changes and ultimately affecting power generation efficiency. Without a reverse parameter tuning mechanism, the ground assembly process may not be able to adapt to changes in the on-orbit environment, leading to decreased power generation efficiency or unstable performance, thereby increasing mission risks. Through a reverse parameter tuning mechanism, the on-orbit performance of solar arrays can be systematically fed back to the ground assembly stage, enabling continuous optimization of process parameters, ensuring that solar arrays maintain efficient operation in complex space environments, while reducing the cost and time waste caused by relying on existing trial-and-error methods, and improving the intelligence and precision of aerospace equipment assembly. To address the aforementioned issues: First, sensitivity analysis techniques (such as ANOVA or principal component analysis) are employed to assess the weight of each parameter in the ground assembly process parameter set (such as adhesive layer pressure, bolt tightening torque, and tension of the inter-plate conductor layout) on changes in on-orbit power generation efficiency, thereby obtaining information on the degree of parameter influence. For example, by calculating the correlation coefficient between parameters and efficiency fluctuations, it is found that bolt tightening torque has the highest contribution rate to the decrease in power generation efficiency (e.g., reaching 40%), and is therefore identified as a key adjustment parameter. Subsequently, combining the assembly stress distribution information set (including adhesive layer stress, bolt stress, and conductor layout stress distribution), regression analysis methods (such as multiple linear regression or machine learning models) are applied to establish inverse correlation rules between assembly process parameters and on-orbit power generation efficiency, generating parameter adjustment rule information. For example, when on-orbit power generation efficiency decreases due to surface changes, the model inversely derives the need to adjust the adhesive layer pressure from the default value of 0.3 MPa to 0.2 MPa to alleviate stress concentration in the adhesive layer area, while simultaneously determining the optimization direction of bolt tightening torque based on stress simulation data. Furthermore, iterative convergence analysis methods (such as gradient descent or genetic algorithms) are used to optimize the parameter adjustment rules through multiple rounds, generating sequential adjustment schemes to ensure that the parameter combinations gradually converge to the optimal solution. For example, during the iteration process, high-impact parameters such as bolt tightening torque are prioritized for adjustment, increasing from 5 N·m to 5.5 N·m. After monitoring the power generation efficiency response, the conductor layout tension is then fine-tuned from 8 N to 10 N. Through multiple iterations, local optima are avoided and efficiency is steadily improved. Finally, based on the optimization strategy, a smart assembly decision log for the solar array is output, which includes specific parameter optimization suggestions (such as setting the bolt tightening torque to 5.5 N·m) and the expected on-orbit power generation efficiency improvement (such as an expected improvement of 5%). This provides data-driven, precise guidance for ground assembly, achieving adaptive optimization of process parameters and maximizing performance.

[0070] By establishing a reverse parameter tuning mechanism through the method provided in this embodiment, the ground assembly process can be dynamically optimized, the on-orbit power generation efficiency of the solar array can be improved, the environmental adaptability and reliability of aerospace equipment can be enhanced, the assembly and debugging costs can be reduced, and the application of intelligent assembly technology in the aerospace field can be promoted.

[0071] Figure 3 A schematic diagram of the structure of an intelligent assembly system for aerospace equipment based on digital twins provided in an embodiment of this application is shown below. Figure 3 As shown, the aerospace equipment intelligent assembly system 300 based on digital twin in this embodiment includes: stress analysis module 301, surface evolution module 302, efficiency analysis module 303 and decision generation module 304.

[0072] The stress analysis module 301 is used to acquire the assembly process parameter set during the ground assembly stage of the solar array, and based on the assembly process parameter set, construct an assembly stress distribution information set reflecting the initial stress field of the solar array on the ground; the profile evolution module 302 is used to analyze the dynamic coupling relationship between thermal load and the initial stress field of the ground under the alternating high and low temperature environment in orbit based on the assembly stress distribution information set, and obtain the on-orbit profile evolution information set of the solar array; the efficiency analysis module 303 is used to analyze the impact of the on-orbit profile change of the solar array on the power generation efficiency based on the on-orbit profile evolution information set, and obtain the power generation efficiency change information set; the decision generation module 304 is used to establish a reverse parameter adjustment mechanism for the ground assembly process parameters with the on-orbit power generation efficiency as the optimization objective based on the power generation efficiency change information set, generate an iterative optimization strategy for the assembly process parameters, and output the solar array intelligent assembly decision log.

[0073] Optionally, the stress analysis module 301, when constructing the assembly stress distribution information set reflecting the initial stress field of the solar array ground based on the assembly process parameter set, is specifically used for: the assembly process parameter set including adhesive layer pressure, bolt tightening torque, and inter-plate conductor layout tension; based on the adhesive layer pressure, analyzing the stress transmission and distribution changes in the adhesive layer bonding area due to pressure application during solar array assembly to obtain adhesive layer stress distribution information; based on the bolt tightening torque, analyzing the stress concentration effect and diffusion law generated in the bolt connection area due to torque during solar array assembly to obtain bolt stress distribution information; based on the inter-plate conductor layout tension, analyzing the additional stress influence and distribution characteristics introduced by tension in the inter-plate conductor layout area during solar array assembly to obtain conductor layout stress distribution information; and based on the adhesive layer stress distribution information, the bolt stress distribution information, and the conductor layout stress distribution information, comprehensively evaluating the overall stress state of the solar array ground assembly to construct the assembly stress distribution information set.

[0074] Optionally, the stress analysis module 301, when comprehensively evaluating the overall stress state of the solar array ground assembly, is specifically used for: based on the adhesive layer stress distribution information, the bolt stress distribution information, and combined with the conductor layout stress distribution information, using stress field simulation data to simulate and analyze the directional characteristics and interactions of stress transmission along the structure between the adhesive layer region, the bolt connection region, and the conductor layout region, quantifying the stress superposition and cancellation effects between each region, and constructing stress transmission information; the adhesive layer region is an adhesive interface layer formed by the pressure control of the adhesive layer; the bolt connection region is a mechanical fastening connection formed by the bolt tightening torque; the conductor layout region is a cable path attachment area formed by the tension constraint of the conductor layout between plates; based on the superposition effect and the cancellation effect, analyzing the spatial distribution characteristics of the stress concentration region where the superposition effect is concentrated and the stress equilibrium region where the cancellation effect is concentrated in the solar array structure, to obtain the overall stress state.

[0075] Optionally, the stress analysis module 301, when analyzing the dynamic coupling relationship between the thermal load and the initial stress field on the ground under the alternating high and low temperatures in orbit to obtain the on-orbit surface evolution information set of the solar array, is specifically used for: based on the overall stress state, establishing a dynamic mapping mechanism between the thermal load cycle data and the initial stress field on the ground in the spatiotemporal dimension according to the preset thermal load cycle data of the alternating high and low temperatures in orbit, to obtain the time-varying coupled stress information of the solar array; based on the time-varying coupled stress information of the solar array, analyzing the process of rebalancing the internal forces of the structure in the stress concentration area and the stress equilibrium area due to temperature alternation, to obtain the stress redistribution path information; based on the stress redistribution path information, performing structural deformation trend deduction, and obtaining surface dynamic response information by deducing the influence of the stress redistribution path on the macroscopic geometry of the solar array; based on the surface dynamic response information, constructing a surface sequence reconstruction mechanism for the entire process of the solar array from heating to cooling within a continuous orbital cycle, to obtain the on-orbit surface evolution information set; the surface sequence reconstruction mechanism is used to integrate the discrete surface dynamic response information into a continuous evolution map.

[0076] Optionally, the surface evolution module 302, when establishing the dynamic mapping mechanism between thermal load cycle data and the initial ground stress field in the spatiotemporal dimension, is specifically used for: based on the overall stress state, decomposing the continuous thermal load cycle into multiple discrete temperature stages according to the preset thermal load cycle data of the on-orbit high and low temperature alternating environment, to obtain staged thermal load information; based on the staged thermal load information, establishing the spatiotemporal correlation between different temperature stages and stress response, to obtain thermal load-stress dynamic correlation information; based on the thermal load-stress dynamic correlation information, integrating the correlation results of multiple temperature stages, and constructing the dynamic mapping mechanism that reflects the dynamic change process of the stress field within the entire thermal load cycle.

[0077] Optionally, the surface evolution module 302, when performing structural deformation trend deduction and inferring the impact of stress redistribution paths on the macroscopic geometry of the solar array, specifically performs the following: based on the spatial distribution characteristics and combined with the stress redistribution path information, it employs structural deformation coordination analysis to analyze the antagonistic and balance relationship between the high deformation driving trend in the stress concentration region and the deformation constraint trend in the stress equilibrium region, obtaining regional deformation interaction information; based on the regional deformation interaction information and combined with the time-varying coupled stress information of the solar array, it employs deformation transmission path analysis to track the entire process of local deformation in the high deformation driving region being transmitted through the solar array structural skeleton and suppressed by the deformation constraint region, obtaining local dominant surface feature information; based on the local dominant surface feature information, it employs geometric morphology integration analysis to synthesize the deformation contributions and mutual constraints of each local region, obtaining the impact of stress redistribution on the geometry of the solar array.

[0078] Optionally, the surface evolution module 302, when constructing the surface sequence reconstruction mechanism for the entire process of the solar array from heating to cooling within a continuous orbital period to obtain the on-orbit surface evolution information set, is specifically used for: based on the surface dynamic response information, analyzing the evolution coherence and transition law between the surface dynamic response information at adjacent time points through spatiotemporal correlation to obtain surface evolution transition information; based on the surface evolution transition information, combined with the thermal load cycle data, integrating the discrete surface dynamic response information into a surface time sequence information reflecting the continuous change process of the surface within a single orbital period through sequence fitting; based on the surface time sequence information, analyzing the repeatability and differences of the surface change sequence between multiple consecutive orbital periods through periodic connection to construct the on-orbit surface evolution information set for the entire process of the solar array from heating to cooling within a continuous orbital period.

[0079] Optionally, the efficiency analysis module 303, when analyzing the impact of the on-orbit profile changes of the solar array on power generation efficiency and obtaining a power generation efficiency change information set, specifically performs the following: based on the profile time series information, through surface normal extraction analysis, extracts the normal directions of each position on the solar array surface at different time points to obtain surface normal distribution sequence information; based on the surface normal distribution sequence information, through angle calculation analysis, calculates the angle changes between the normal at each surface position and the designed light-receiving direction of the solar array to obtain light-receiving angle distribution sequence information; based on the light-receiving angle distribution sequence information, through projection area integration analysis, integrates and calculates the change of the effective projected area of ​​the solar array in the light-receiving direction over time to obtain effective light-receiving area sequence information; based on the effective light-receiving area sequence information, through photoelectric conversion efficiency mapping analysis, maps the change law of solar array power generation efficiency over time to obtain a power generation efficiency change information set.

[0080] Optionally, the decision generation module 304, when establishing a reverse parameter adjustment mechanism for ground assembly process parameters with on-orbit power generation efficiency as the optimization objective, generating an iterative optimization strategy for assembly process parameters, and outputting a solar array intelligent assembly decision log, specifically performs the following: based on the power generation efficiency change information set, analyzes the degree of influence of each parameter in the ground assembly process parameter set on the change in on-orbit power generation efficiency, and obtains parameter influence degree information; based on the parameter influence degree information, combined with the assembly stress distribution information set, establishes a reverse correlation rule between assembly process parameters and on-orbit power generation efficiency, and obtains parameter adjustment rule information; based on the parameter adjustment rule information, through iterative convergence analysis, generates a sequential adjustment scheme for assembly process parameters, and obtains the iterative optimization strategy; based on the iterative optimization strategy, outputs the solar array intelligent assembly decision log, including assembly process parameter optimization suggestions and the expected increase in on-orbit power generation efficiency.

[0081] The system in this embodiment can be used to execute the methods of any of the above embodiments, and its implementation principle and technical effect are similar, so they will not be described again here.

Claims

1. A method for intelligent assembly of aerospace equipment based on digital twins, characterized in that, include: Obtain the assembly process parameter set for the ground assembly stage of the solar array, and based on the assembly process parameter set, construct an assembly stress distribution information set that reflects the initial stress field of the solar array ground. Based on the assembly stress distribution information set, the dynamic coupling relationship between thermal load and ground initial stress field under the alternating high and low temperature environment in orbit is analyzed to obtain the on-orbit surface evolution information set of the solar array. Based on the aforementioned on-orbit profile evolution information set, the impact of on-orbit profile changes of the solar array on power generation efficiency is analyzed, and a power generation efficiency change information set is obtained. Based on the power generation efficiency change information set, a reverse parameter adjustment mechanism for ground assembly process parameters with on-orbit power generation efficiency as the optimization objective is established, generating an iterative optimization strategy for assembly process parameters, and outputting the solar array intelligent assembly decision log.

2. The method according to claim 1, characterized in that, The process of constructing an assembly stress distribution information set reflecting the initial stress field on the ground of the solar array based on the assembly process parameter set includes: The assembly process parameter set includes adhesive layer pressure, bolt tightening torque, and tension of the inter-board wire layout. Based on the adhesive layer pressure, the stress transmission and distribution changes in the adhesive layer bonding area caused by pressure application during the solar wing assembly process are analyzed to obtain adhesive layer stress distribution information. Based on the bolt tightening torque, the stress concentration effect and diffusion law generated by the torque in the bolt connection area during the solar panel assembly process are analyzed to obtain bolt stress distribution information; Based on the tension of the inter-plate conductor layout, the influence and distribution characteristics of the additional stress introduced by the tension in the inter-plate conductor layout area during the solar array assembly process are analyzed to obtain the conductor layout stress distribution information. Based on the stress distribution information of the adhesive layer, the stress distribution information of the bolts, and the stress distribution information of the conductor layout, the overall stress state of the solar array ground assembly is comprehensively evaluated, and the assembly stress distribution information set is constructed.

3. The method according to claim 2, characterized in that, The comprehensive assessment of the overall stress state of the solar array ground assembly includes: Based on the stress distribution information of the adhesive layer, the stress distribution information of the bolts, and the stress distribution information of the conductor layout, stress field simulation data is used to analyze the directional characteristics and interactions of stress transmission along the structure between the adhesive layer region, the bolt connection region, and the conductor layout region, quantify the stress superposition and cancellation effects between each region, and construct stress transmission information. The adhesive layer area is an adhesive interface layer formed by controlling the pressure of the adhesive layer. The bolted connection area is a mechanically fastened connection formed by the bolt tightening torque. The conductor layout area is the cable path attachment area formed by the tension constraint of the conductor layout between boards. Based on the superposition effect and the cancellation effect, the spatial distribution characteristics of the stress concentration region where the superposition effect is concentrated and the stress equilibrium region where the cancellation effect is concentrated in the solar array structure are analyzed to obtain the overall stress state.

4. The method according to claim 3, characterized in that, The analysis of the dynamic coupling relationship between thermal load and initial ground stress field under alternating high and low temperatures in orbit yields a set of on-orbit surface evolution information for the solar array, including: Based on the overall stress state, and according to the preset thermal load cycle data of the high and low temperature alternating environment in orbit, a dynamic mapping mechanism between the thermal load cycle data and the initial stress field on the ground is established in the spatiotemporal dimension to obtain the time-varying coupled stress information of the solar array. Based on the time-varying coupled stress information of the solar array, the process of rebalancing the internal forces of the structure in the stress concentration region and the stress equilibrium region due to temperature alternation is analyzed to obtain stress redistribution path information. Based on the stress redistribution path information, the structural deformation trend is deduced, and the dynamic response information of the surface is obtained by deducing the influence of the stress redistribution path on the macroscopic geometry of the solar array. Based on the dynamic response information of the solar array, a mechanism for reconstructing the solar array's shape sequence from heating to cooling during a continuous orbital period is constructed to obtain the on-orbit shape evolution information set. The surface sequence reconstruction mechanism is used to integrate discrete surface dynamic response information into a continuous evolution map.

5. The method according to claim 4, characterized in that, The mechanism for establishing a dynamic mapping between thermal load period data and the initial ground stress field in the spatiotemporal dimension includes: Based on the overall stress state, and according to the preset thermal load cycle data of the high and low temperature alternating environment in orbit, the continuous thermal load cycle is decomposed into multiple discrete temperature stages to obtain staged thermal load information. Based on the aforementioned staged thermal load information, a spatiotemporal correlation between different temperature stages and stress response is established to obtain dynamic correlation information between thermal load and stress. Based on the aforementioned dynamic correlation information between thermal load and stress, the correlation results of multiple temperature stages are integrated to construct the dynamic mapping mechanism that reflects the dynamic change process of the stress field throughout the entire thermal load cycle.

6. The method according to claim 5, characterized in that, The process of extrapolating structural deformation trends, and by extrapolating the impact of stress redistribution paths on the macroscopic geometry of the solar array, includes: Based on the spatial distribution characteristics and the stress redistribution path information, structural deformation coordination analysis is used to analyze the antagonistic and balance relationship between the high deformation driving trend in the stress concentration area and the deformation constraint trend in the stress equilibrium area, so as to obtain regional deformation interaction information. Based on the aforementioned regional deformation interaction information, combined with the solar wing time-varying coupled stress information, deformation transmission path analysis is used to track the entire process of local deformation in the high deformation-driven region being transmitted through the solar wing structural skeleton and suppressed by the deformation-constrained region, thereby obtaining local dominant surface feature information. Based on the aforementioned local dominant surface feature information, geometric morphology integration analysis is adopted to integrate the deformation contribution and mutual constraint relationship of each local region, thereby obtaining the influence of stress redistribution on the geometry of the solar array.

7. The method according to claim 6, characterized in that, The mechanism for reconstructing the surface sequence of the solar array during its entire heating and cooling process over a continuous orbital period yields the on-orbit surface evolution information set, including: Based on the dynamic response information of the surface, the evolution continuity and transition law between the dynamic response information of the surface at adjacent time points are analyzed through spatiotemporal correlation to obtain the surface evolution transition information. Based on the surface evolution transition information and combined with the thermal load cycle data, the discrete surface dynamic response information is integrated into surface time series information reflecting the continuous change process of the surface within a single orbital cycle through sequence fitting. Based on the aforementioned surface time series information, the repeatability and differences of surface change sequences between multiple consecutive orbital cycles are analyzed through periodic connection, thereby constructing an on-orbit surface evolution information set of the solar array from heating to cooling throughout the continuous orbital cycle.

8. The method according to claim 7, characterized in that, The analysis of the impact of on-orbit profile changes of the solar array on power generation efficiency yields a set of power generation efficiency change information, including: Based on the surface time series information, the normal directions of each position on the solar array surface at different time points are extracted through surface normal extraction analysis to obtain surface normal distribution sequence information; Based on the surface normal distribution sequence information, the angle variation between the normal of each surface position and the designed light-receiving direction of the solar array is calculated through angle calculation and analysis, and the light-receiving angle distribution sequence information is obtained. Based on the light-receiving angle distribution sequence information, the effective projected area of ​​the solar array in the light-receiving direction is calculated over time by integral analysis of the projected area, and the effective light-receiving area sequence information is obtained. Based on the effective light-receiving area sequence information, the change law of solar panel power generation efficiency over time is obtained through photoelectric conversion efficiency mapping analysis, resulting in a power generation efficiency change information set.

9. The method according to claim 8, characterized in that, The aforementioned mechanism establishes a reverse parameter tuning mechanism for ground assembly processes with on-orbit power generation efficiency as the optimization objective, generates an iterative optimization strategy for assembly process parameters, and outputs a smart assembly decision log for the solar array, including: Based on the power generation efficiency change information set, the influence of each parameter in the ground assembly process parameter set on the on-orbit power generation efficiency change is analyzed to obtain parameter influence information. Based on the information on the degree of influence of the parameters, and combined with the information set on assembly stress distribution, a reverse correlation rule between assembly process parameters and on-orbit power generation efficiency is established to obtain parameter adjustment rule information. Based on the parameter adjustment rule information, an iterative convergence analysis is conducted to generate a sequential adjustment scheme for the assembly process parameters, thus obtaining the iterative optimization strategy. Based on the iterative optimization strategy, the output includes intelligent assembly decision logs for the solar array, which include suggestions for optimizing assembly process parameters and the expected improvement in on-orbit power generation efficiency.

10. A digital twin-based intelligent assembly system for aerospace equipment, characterized in that: The method applied to any one of claims 1-9 includes: The stress analysis module is used to obtain the assembly process parameter set during the solar wing ground assembly stage, and based on the assembly process parameter set, to construct an assembly stress distribution information set that reflects the initial stress field of the solar wing ground. The surface evolution module is used to analyze the dynamic coupling relationship between thermal load and initial stress field on the ground under the alternating high and low temperature environment in orbit, based on the assembly stress distribution information set, and to obtain the on-orbit surface evolution information set of the solar array. The efficiency analysis module is used to analyze the impact of the on-orbit profile changes of the solar array on power generation efficiency based on the on-orbit profile evolution information set, and to obtain the power generation efficiency change information set. The decision generation module is used to establish a reverse parameter adjustment mechanism for ground assembly process parameters with on-orbit power generation efficiency as the optimization objective based on the power generation efficiency change information set, generate an iterative optimization strategy for assembly process parameters, and output the solar array intelligent assembly decision log.

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