Spaceflight equipment intelligent assembly method and system based on digital twinning

Abstract

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. The method comprises the following steps: acquiring an assembly process parameter set of a solar wing ground assembly stage, constructing 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; analyzing a dynamic coupling relationship between a thermal load and the initial stress field on the ground under an on-orbit high-low temperature alternating environment based on the assembly stress distribution information set, and obtaining an on-orbit profile evolution information set of the solar wing; analyzing an influence of on-orbit profile change of the solar wing on power generation efficiency based on the on-orbit profile evolution information set, and obtaining a power generation efficiency change information set; establishing 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, generating an iterative optimization strategy of the assembly process parameter, and outputting a solar wing intelligent assembly decision log. Continuous optimization of 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:

[0006] An assembly process parameter set of a solar wing in a ground assembly stage is acquired, and based on the assembly process parameter set, an assembly stress distribution information set reflecting an initial stress field of the solar wing on the ground is constructed; based on the assembly stress distribution information set, a dynamic coupling relationship between a thermal load and the initial stress field on the ground 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, an 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 parameter is generated, and a solar wing intelligent assembly decision log is output.

[0007] 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 on-orbit analysis. The dynamic coupling relationship between thermal load and initial stress field is analyzed to obtain an on-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 on-orbit power generation efficiency and reliability of the solar wing, and reducing the failure risk.

[0008] 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.

[0009] 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.

[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 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.

[0012] Optionally, based on the spatial distribution characteristics, combined with the stress redistribution path information, a structural deformation coordination analysis is adopted to analyze the antagonism and balance relationship between the high deformation driving trend of the stress concentration region and the deformation constraint trend of the stress balance region, and regional deformation interaction information is obtained; based on the regional deformation interaction information, combined with the time-varying coupling stress information of the solar wing, a deformation transmission path analysis is adopted to track the whole process that the local deformation of the high deformation driving region is transmitted through the solar wing structural skeleton and inhibited by the deformation constraint region, and local dominant surface feature information is obtained; based on the local dominant surface feature information, a geometric morphology integrated analysis is adopted to comprehensively analyze the deformation contribution and mutual restriction relationship of each local region, and the influence of stress redistribution on the geometric morphology of the solar wing is obtained.

[0013] 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.

[0014] 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 power generation efficiency of the solar wing with time is mapped to obtain the power generation efficiency change information set.

[0015] 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 range is output.

[0016] In a second aspect, the present application provides a spaceflight equipment intelligent assembly system based on digital twinning, the system comprising:

[0017] 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

[0018] In order to more clearly illustrate the technical solutions of 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.

[0019] Figure 1 An application scenario schematic diagram provided by an embodiment of the present application;

[0020] Figure 2 A flowchart of a spaceflight equipment intelligent assembly method based on digital twinning provided by an embodiment of the present application;

[0021] 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

[0022] In order to make the purpose, 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 scope of protection of the present application.

[0023] 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 without special instructions.

[0024] The embodiments of this application will now be described in further detail with reference to the accompanying drawings.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] For specific implementation details, please refer to the following examples.

[0030] 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:

[0031] 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.

[0032] The assembly process parameter set can be a set of process parameters involved in the ground assembly process of the solar wing, which can be provided by sensors of a ground assembly site. The assembly stress distribution information set can be a set of information for characterizing the initial stress distribution state of the structure of the solar wing after the ground assembly is completed.

[0033] Specifically, as a key component of a spacecraft, the ground assembly quality of the solar wing directly affects the on-orbit power generation performance and service life. Existing assembly techniques rely on experience and lack prediction of on-orbit behavior, resulting in deformation or failure of the solar wing due to stress concentration in orbit. By obtaining the assembly process parameter set and using numerical simulation means such as finite element analysis, a high-precision assembly stress distribution information set is constructed to provide basic data for subsequent on-orbit performance analysis, which is crucial for realizing the digitization and intelligentization of the assembly process.

[0034] S202, based on the assembly stress distribution information set, analyzing the dynamic coupling relationship between thermal load and ground initial stress field under on-orbit high-low temperature alternating environment, obtaining the on-orbit type surface evolution information set of the solar wing.

[0035] The on-orbit type surface evolution information set can be a set of information for characterizing the change of the type surface of the solar wing over time during on-orbit operation.

[0036] Specifically, the solar wing faces extreme temperature changes (such as -100°C to 100°C) in orbit, and the coupling of thermal load and ground initial stress field will cause type surface distortion, which will further affect the sun pointing accuracy. Existing methods are difficult to quantify this coupling effect. By using mathematical modeling means such as thermal-structural coupling simulation, the interaction between the assembly stress distribution information set and the thermal load is analyzed, the evolution process of the on-orbit type surface of the solar wing is simulated, and the on-orbit type surface evolution information set is obtained, which helps to predict long-term on-orbit behavior and identify risks in advance.

[0037] S203, based on the on-orbit type surface evolution information set, analyzing the influence of the on-orbit type surface change of the solar wing on the power generation efficiency, obtaining the power generation efficiency change information set.

[0038] The power generation efficiency change information set can be a set of information for characterizing the fluctuation of the power generation efficiency of the solar wing in orbit with the change of the type surface.

[0039] Specifically, the type surface change of the solar wing will change the light incidence angle (such as a 2-degree shift angle) and reduce the power generation efficiency. Existing techniques lack comprehensive analysis of the type surface-efficiency relationship. By using optical simulation means, the on-orbit type surface evolution information set is analyzed, the power generation efficiency under different type surface states is calculated (such as a 5% decrease in efficiency), and the power generation efficiency change information set is obtained, which can quantify the direct impact of the assembly process on on-orbit performance and provide a basis for optimization.

[0040] S204, based on the power generation efficiency change information set, a ground assembly process parameter reverse parameter adjustment mechanism is established to optimize the in-orbit power generation efficiency, an iterative optimization strategy of the assembly process parameters is generated, and a solar wing intelligent assembly decision log is output.

[0041] The iterative optimization strategy of the assembly process parameters can be an optimization strategy for guiding the adjustment of the ground assembly process parameters. The solar wing intelligent assembly decision log can be report information containing the optimized assembly process parameters and the expected in-orbit power generation efficiency.

[0042] Specifically, the existing assembly optimization relies on trial and error, which is inefficient. Based on the power generation efficiency change information set, a mapping relationship between the ground assembly process parameters (such as bolt pre-tightening force) and the in-orbit power generation efficiency is established through an optimization algorithm such as a genetic algorithm, and the in-orbit power generation efficiency is maximized as the target. The assembly process parameters are adjusted in reverse to generate an iterative optimization strategy, and then the optimization process (such as 10 times of parameter adjustment) is recorded through a log system to output an intelligent assembly decision log for engineers to refer to. This realizes closed-loop control from in-orbit performance to ground assembly, improving assembly accuracy.

[0043] In the manner provided by the embodiment, the solar wing ground assembly process parameter set is obtained and the assembly stress distribution information set is constructed, laying a foundation for in-orbit analysis. The dynamic coupling relationship between thermal load and initial stress field is analyzed to obtain the in-orbit surface evolution information set, the behavior of the solar wing is predicted, the influence of surface change on power generation efficiency is analyzed to obtain the 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, realizing continuous optimization of the assembly process, thereby improving the in-orbit power generation efficiency and reliability of the solar wing and reducing the risk of failure.

[0044] In some embodiments, the assembly process parameter set includes a paste layer pressure, a bolt tightening torque, and a plate inter-wire layout tension; based on the paste layer pressure, stress transmission and distribution changes in the paste layer bonding area during the solar wing assembly process due to the application of pressure are analyzed to obtain paste layer stress distribution information; based on the bolt tightening torque, stress concentration effects and diffusion laws in the bolt connection area during the solar wing assembly process due to the torque are analyzed to obtain bolt stress distribution information; based on the plate inter-wire layout tension, additional stress influence and distribution characteristics introduced by the tension in the plate inter-wire layout area during the solar wing assembly process are analyzed to obtain inter-wire layout stress distribution information; based on the paste layer stress distribution information, the bolt stress distribution information, and the inter-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.

[0045] The adhesive layer pressure can be a pressure value applied to the adhesive layer bonding area. The bolt tightening torque can be a torque value applied to the bolt connection area. The inter-plate wire layout tension can be a tension value that the inter-plate wire receives during the layout process. The adhesive layer stress distribution information can be information reflecting the stress distribution state of the adhesive layer bonding area under the action of pressure. The overall stress state can be the stress distribution state inside the overall structure after the solar wing ground assembly is completed. The bolt stress distribution information can be information reflecting the stress concentration and diffusion law of the bolt connection area under the action of torque. The wire layout stress distribution information can be information reflecting the additional stress distribution characteristics of the inter-plate wire layout area under the action of tension.

[0046] Specifically, during the ground assembly of the solar wing, assembly process parameters such as adhesive layer pressure, bolt tightening torque, and inter-plate wire layout tension directly affect the structural stress state of the solar wing. If these parameters are not properly controlled, it may lead to excessive local stress or uneven stress distribution, thereby affecting the structural stability and reliability of the solar wing during on-orbit operation. For example, too small adhesive layer pressure (such as less than 0.3 MPa) may result in poor bonding of the adhesive layer, which is prone to delamination under thermal load; too large pressure (such as more than 0.7 MPa) may cause excessive compression of the adhesive layer, resulting in residual stress. Insufficient bolt tightening torque (such as less than 8 Nm) may cause loose connection, and too large torque (such as more than 12 Nm) may cause bolt overload and stress concentration. Improper inter-plate wire layout tension (such as too small tension leading to wire relaxation, and too large tension leading to wire tightness) may introduce additional stress, affecting the solar wing profile. This step solves the above problems by the following methods: first, the digital image correlation method is used to collect the full-field strain distribution of the adhesive layer area under a certain pressure (such as 0.5 MPa), and the viscoelastic constitutive model is used to inverse calculate the shear stress transmission law inside the adhesive layer, to generate adhesive layer stress distribution information quantitatively describing the stress gradient (such as decaying by 60% from the center to the edge); for the bolt connection area, a torque-stress mapping model is established based on the Hertz contact theory, the stepwise tightening torque (such as from 5 Nm to 15 Nm) is applied, and the hole circumference strain is monitored by a strain gauge sensor network to identify the key area where the torque stress concentration coefficient reaches the peak value, and the bolt stress distribution information containing the stress diffusion angle is formed; for the wire layout area, a tension optical effect monitoring system is used to real-time track the deflection change of the wire under a preset tension (such as 5 N), and the additional bending moment distribution is calculated based on the beam bending theory to obtain the wire layout stress distribution information; finally, the three types of stress fields are projected to a unified coordinate system by a weighted superposition algorithm, and coupled analysis is performed using the von Mises equivalent stress criterion, and when the superposition of the stress concentration in the bolt area and the compressive stress in the wire layout area exceeds the material yield limit (such as 250 MPa for aluminum alloy), the reconstruction mechanism is automatically triggered, thereby establishing a global stress distribution information set that comprehensively reflects the interaction of adhesive bonding, mechanical fastening, and wire constraint.

[0047] By the way provided by the present embodiment, the stress distribution corresponding to the adhesive layer pressure, the bolt tightening torque and the inter-plate wire layout tension is analyzed respectively, the overall stress state is comprehensively evaluated, the assembly stress distribution information set is constructed, the information set can comprehensively reflect the initial stress field of the solar wing on the ground, accurate data basis is provided for subsequent on-orbit surface evolution analysis and power generation efficiency optimization, the solar wing assembly quality and on-orbit reliability are improved, and the successful execution of the space mission is ensured.

[0048] In some embodiments, based on the adhesive layer stress distribution information, the bolt stress distribution information, and 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 region, the bolt connection region and the wire layout region, to quantify the stress superposition effect and cancellation effect between the regions, and to construct stress transmission information; the adhesive layer region is an adhesive interface layer formed by the adhesive layer pressure; the bolt connection region is a mechanical fastening connection part formed by the bolt tightening torque; the wire layout region is a cable path attachment area formed by the inter-plate wire layout tension constraint; based on the superposition effect and the cancellation effect, the spatial distribution characteristics of the stress concentration region with superposition effect and the stress balance region with cancellation effect in the solar wing structure are analyzed, and the overall stress state is obtained.

[0049] The stress field simulation data simulation can be dynamic simulation of the stress field of the solar wing structure by using numerical simulation methods such as finite element analysis, to simulate the stress transmission process. The stress transmission information can be a data set describing the direction characteristics and interaction of stress transmission between the adhesive layer region, the bolt connection region and the wire layout region. The superposition effect can be the phenomenon that multiple stress sources jointly cause stress increase. The cancellation effect can be the phenomenon that multiple stress sources interact to cause stress decrease. The stress concentration region can be a region with high stress caused by concentrated superposition effect. The stress balance region can be a region with low stress caused by concentrated cancellation effect.

[0050] Specifically, as a key component of space equipment, the ground assembly quality of the solar wing directly determines the structural stability and power generation efficiency in orbit. The solar wing faces high and low temperature alternating environment in orbit, and the initial stress field formed during ground assembly will dynamically couple with thermal load, causing structural deformation and affecting power generation performance. If only the stress of the adhesive layer, bolts or wire layout is analyzed without considering their interaction, the on-orbit shape evolution cannot be accurately predicted, which may lead to unreasonable assembly process parameters and increase the risk of on-orbit failure. The above problems are solved by the following methods in this step: based on the stress distribution information of the adhesive layer, the stress distribution information of the bolt and the stress distribution information of the wire layout, stress field simulation data simulation means are used, such as using finite element analysis software to construct a refined digital model of the solar wing structure, inputting the parameters controlled by the adhesive layer pressure in the adhesive layer area (such as the pressure value is 0.5MPa) to simulate the stress distribution of the adhesive interface layer, the bolt tightening torque parameters in the bolt connection area (such as the torque is 10Nm) to simulate the stress concentration effect of the mechanical fastening part, and the wire layout tension parameters in the wire layout area (such as the tension is 5N) to simulate the additional stress influence of the cable path attachment area; the direction characteristics and interaction of stress transmission along the structure are analyzed by numerical calculation means, such as stress superposition algorithm, for example, the stress superposition effect (such as the combined stress rises to 1.2MPa) is calculated at the junction between the adhesive layer and the bolt area, or the offset effect (such as the stress decreases to 0.1MPa) is quantified at the interaction between the wire layout area and the bolt area, so as to construct the stress transmission information including stress vector and interaction matrix; on this basis, the spatial distribution characteristics of the stress concentration area (such as the high stress area around the bolt hole is more than 1.0MPa) concentrated by the superposition effect and the stress balanced area (such as the low stress area of the wire path is lower than 0.2MPa) balanced by the offset effect are analyzed by using visualization tools such as stress nephogram, and finally the atlas reflecting the overall stress state of the solar wing is obtained by integrating the comprehensive data, which provides accurate input for assembly process optimization.

[0051] By the way provided by the embodiment, the overall stress state of the solar wing ground assembly can be comprehensively evaluated, the stress concentration and balance area can be identified, and accurate input can be provided for subsequent on-orbit shape evolution analysis, so as to optimize the assembly process, improve the structural stability and on-orbit power generation efficiency of the solar wing, reduce the risk of failure, and ensure the long-term reliable operation of space equipment.

[0052] In some embodiments, based on the overall stress state, a dynamic mapping mechanism of the thermal load cycle data and the ground initial stress field in time and space dimensions is established according to preset thermal load cycle data of the on-orbit high-low temperature alternating environment, to obtain solar wing time-varying coupling stress information; based on the solar wing time-varying coupling stress information, a structural internal force rebalancing process of stress concentration regions and stress balance regions caused by temperature alternation is analyzed to obtain stress redistribution path information; based on the stress redistribution path information, a structural deformation trend is deduced, the influence of the stress redistribution path on the macroscopic geometric shape 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 on-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.

[0053] The thermal load cycle data can be periodic data describing the change of temperature with time in the on-orbit high-low temperature alternating environment. The dynamic mapping mechanism can be a mathematical or physical model for establishing the dynamic correlation between the thermal load cycle data and the ground initial stress field in time and space. The solar wing time-varying coupling stress information can be information reflecting the change of the internal stress of the solar wing with time under the combined action of thermal load and ground initial stress field. The structural internal force rebalancing process can be the process of redistribution of the internal stress of the solar wing due to temperature change to achieve a new equilibrium state. The stress redistribution path information can be information describing the direction and path of stress redistribution in the solar wing structure. The structural deformation trend deduction can be a process of predicting the geometric shape change trend of the solar wing based on stress redistribution. The surface dynamic response information can be information reflecting the dynamic change of the solar wing surface under the action of thermal load. The surface sequence reconstruction mechanism can be applicable to a method of integrating discrete surface dynamic response information into continuous evolution sequence. The evolution atlas can be a graph or data sequence that visually displays the evolution process of the solar wing surface.

[0054] Specifically, the solar wing as a key component of a spacecraft, its power generation efficiency is directly affected by the type surface accuracy, and the on-orbit high-low temperature alternating environment will cause the dynamic coupling of thermal load and the initial stress field of ground assembly, causing uncontrollable evolution of the type surface. The existing method lacks in-depth analysis of the dynamic coupling relationship, and cannot accurately predict the trend of the type surface change, resulting in the decrease of on-orbit power generation efficiency, the increase of structural fatigue risk and the decrease of mission reliability. The above problems are solved by the following method in this step: starting from the analysis of the overall stress state of the solar wing, a parameterized model is constructed by using a thermal-structure coupling simulation platform (such as ANSYS Mechanical), the thermal load cycle data (such as sawtooth wave cycle of temperature from-100°C to +100°C) of the preset on-orbit high-low temperature alternating environment is spatiotemporally aligned with the initial stress field on the ground, and a dynamic mapping mechanism is established by the transient dynamics module, for example, stress field superposition calculation is performed at each temperature step point (such as every 10°C interval), and the solar wing time-varying coupling stress cloud picture is output. Based on this data, the submodel technology is used to focus on the stress concentration area (such as the stress overrun area around the bolt hole) and the stress balance area (such as the low stress area in the middle of the panel), and the stress redistribution path streamline diagram with direction vector is generated by analyzing the structural internal force rebalancing process caused by temperature variation through the self-defined stress transfer algorithm. Then, the structure deformation trend deduction module is introduced, and the influence of the stress path on the macro type surface is deduced frame by frame by using the geometric nonlinear analysis method, for example, the millimeter-level wavy deformation caused by uneven boundary constraints is calculated by node displacement inversion, and the time-stamped type surface dynamic response data set is output. Finally, the discrete type surface data is integrated into a continuous evolution graph based on the sequence reconstruction algorithm (such as cubic spline interpolation method), which specifically realizes the sorting of thousands of transient type surface data in each orbit period according to the phase of thermal load, generates a spatiotemporal evolution matrix that can trace the type surface state at any time, and renders the type surface deformation animation through the visualization engine to complete the construction of the on-orbit type surface evolution information set.

[0055] By the way provided by the embodiment, the on-orbit type surface evolution of the solar wing can be accurately predicted, the potential structural deformation risk can be identified, and data support can be provided for ground assembly process parameter optimization, so as to improve the on-orbit power generation efficiency and structural reliability of the solar wing and prolong the service life of the spacecraft.

[0056] In some embodiments, based on the overall stress state, the continuous thermal load cycle is divided into multiple discrete temperature stages according to the thermal load cycle data of the preset on-orbit high-low temperature alternating environment, and stage thermal load information is obtained; based on the stage thermal load information, a spatiotemporal correlation between different temperature stages and stress response is established, and thermal load-stress dynamic correlation information is obtained; based on the thermal load-stress dynamic correlation information, the correlation results of multiple temperature stages are integrated, and a dynamic mapping mechanism reflecting the dynamic change process of the stress field in the whole thermal load cycle is constructed.

[0057] The temperature stage can be a discrete temperature interval after decomposition of a continuous thermal load cycle. The stage thermal load information can be a thermal load parameter corresponding to each temperature stage. The thermal load-stress dynamic correlation information can be a correlation between different temperature stages and the solar wing stress response.

[0058] Specifically, during the on-orbit operation of the solar wing, it faces high and low temperature alternating environment, the thermal load interacts with the initial stress field formed in the ground assembly stage, leading to the redistribution of the solar wing structure stress, and then affecting the surface precision and power generation efficiency. The specific method often ignores the dynamic coupling relationship between thermal load and initial stress field, or only uses static analysis, leading to inaccurate on-orbit surface prediction, lack of basis for assembly process optimization, and inability to effectively respond to the long-term impact of on-orbit environmental changes on the performance of the solar wing. By establishing a dynamic mapping mechanism of thermal load cycle data and ground initial stress field in time and space dimensions, the response process of the stress field under thermal load changes can be accurately described, and the evolution law of stress concentration areas (such as bolt connection areas) and stress balance areas under temperature alternation can be revealed, providing key input for subsequent on-orbit surface evolution analysis and power generation efficiency optimization. This step solves the above problems by the following methods: first, the thermal load cycle decomposition technology is used to decompose the continuous thermal load cycle (for example, temperature cycle from -100°C to 100°C) into multiple discrete temperature stages (for example, every 20°C as an interval stage), so as to obtain stage thermal load information, including temperature value, duration and heat flux density parameters of each stage. This step relies on environmental simulation data and numerical analysis tools to ensure that the decomposed stages can accurately reflect the actual on-orbit temperature gradient; then, using a time-space correlation modeling method, such as a finite element simulation software, the stress response of the solar wing structure at different temperatures is simulated for each temperature stage. By setting boundary conditions and material parameters (such as the thermal expansion coefficient of aluminum alloy), the stress variation amplitude and direction of stress concentration areas (such as bolt connection areas) and stress balance areas are analyzed, and a dynamic correlation between temperature stages and stress field changes is established, generating thermal load-stress dynamic correlation information, such as stress cloud map and data sequence output by simulation; finally, through data integration and fusion technology, the correlation results of multiple temperature stages are comprehensively processed, such as applying time series analysis or machine learning algorithm (such as regression model) to build a mathematical model or mapping mechanism reflecting the dynamic change process of the stress field in the whole thermal load cycle. The mechanism can describe the evolution trajectory of the stress field with temperature change and output as a visual map or digital matrix for subsequent on-orbit surface prediction and process optimization. The whole 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 intelligent assembly of the solar wing.

[0059] By the manner provided by the embodiment, a dynamic mapping mechanism of thermal load cycle data and an initial stress field on the ground is established, which can accurately capture the dynamic response of the stress field of the solar wing in on-orbit operation, improve the accuracy of on-orbit type surface evolution prediction, provide a reliable basis for ground assembly process parameter optimization, thereby improving the on-orbit power generation efficiency and reliability of the solar wing, and enhancing the adaptability to complex space environment.

[0060] In some embodiments, based on the spatial distribution characteristics, in combination with the stress redistribution path information, a structural deformation coordination analysis is adopted to analyze the antagonism and balance relationship between the high deformation driving trend of the stress concentration area and the deformation constraint trend of the stress balance area, to obtain regional deformation interaction information; based on the regional deformation interaction information, in combination with the time-varying coupled stress information of the solar wing, a deformation transmission path analysis is adopted to track the whole process that the local deformation of the high deformation driving area is transmitted through the solar wing structure skeleton and inhibited by the deformation constraint area, to obtain local dominant type surface feature information; based on the local dominant type surface feature information, a geometric morphology integrated analysis is adopted to comprehensively analyze the deformation contribution and mutual constraint relationship of each local area, to obtain the influence of stress redistribution on the geometric morphology of the solar wing.

[0061] The structural deformation coordination analysis can be an analysis means for studying the interaction between the deformation trends of different areas to ensure the coordination of structural deformation. The high deformation driving trend can be the trend that the stress concentration area tends to have larger deformation under thermal load. The deformation constraint trend can be the trend that the stress balance area has an inhibitory effect on deformation and limits the overall deformation. The antagonism and balance relationship can be the interaction between the high deformation driving trend and the deformation constraint trend. The regional deformation interaction information can be quantitative information describing the interaction of deformation trends of different areas. The deformation transmission path analysis can be an analysis means for tracking how the local deformation is transmitted to other areas through the structure skeleton. The local deformation can be the geometric deformation occurring in the local area of the solar wing. The solar wing structure skeleton can be the supporting structure of the solar wing, including beams, frames and other components, for transmitting force and deformation. The deformation constraint area inhibition can be the inhibitory effect of the stress balance area on deformation transmission. The local dominant type surface feature information can be the type surface change feature dominated by the local deformation. The geometric morphology integrated analysis can be an analysis means for comprehensively analyzing the contribution of all local deformations to obtain the overall geometric morphology change. The deformation contribution can be the contribution degree of each local area to the overall type surface change. The mutual constraint relationship can be the mutual influence and constraint between the deformations of different local areas.

[0062] 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).

[0063] 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.

[0064] In some embodiments, based on the profile dynamic response information, through space-time correlation, 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 the profile in a single orbit period; based on the profile time sequence information, through period connection, repeatability and difference of profile change sequence between consecutive multiple orbit periods are analyzed to construct in-orbit profile evolution information set of the solar wing from heating to cooling in the whole process in consecutive orbit periods.

[0065] The profile evolution transition information can be data describing the change continuity and smooth transition law of the solar wing profile between adjacent time points. The profile time sequence information can be a time sequence data set representing the continuous change process of the solar wing profile in a single orbit period. The space-time correlation can be a technical means for analyzing the continuity of data in time and space dimensions. The sequence fitting can be a mathematical modeling method for integrating discrete data points into a continuous sequence. The period connection can be a statistical method for analyzing the repeatability and difference between multiple period sequences.

[0066] Specifically, the solar wing experiences high and low temperature alternating environment during on-orbit operation, the shape change is affected by the coupling of thermal load and ground assembly stress, and presents complex dynamic characteristics. If only relying on discrete shape dynamic response information, the continuous evolution law of the shape in the time dimension cannot be fully captured, leading to deviation in on-orbit performance prediction. The shape change of the solar wing directly affects its power generation efficiency, and the missing transition information between discrete data points may mask the key shape deformation trend. For example, during the thermal load mutation stage, the shape may deform nonlinearly. If the evolution continuity is not analyzed, the transition behavior of the shape during the heating and cooling process cannot be accurately predicted, thereby affecting the accuracy of the power generation efficiency evaluation. The step solves the above problems by the following methods: first, a space-time correlation analysis technique is used to analyze the evolution continuity and transition law between the shape dynamic response information (such as shape displacement data obtained by finite element simulation) of adjacent time points (such as 0.1 second interval) based on the shape dynamic response information (such as shape displacement data obtained by finite element simulation), through a time series alignment method (such as dynamic time warping algorithm) and a spatial interpolation method (such as Kriging interpolation), to obtain the shape evolution transition information, including identifying the smooth transition region of the shape change (such as the gradual change of the shape when the thermal load is uniformly distributed) and the mutation point (such as local warping caused by sudden temperature change); then, a sequence fitting method is applied to integrate the discrete shape dynamic response information (such as shape sampling points every 0.1 second) into shape time series information reflecting the continuous change process of the shape in a single orbit period, such as generating a smooth curve to describe the whole process of the shape from thermal expansion to cooling contraction, based on the shape evolution transition information and the thermal load periodic data (such as the temperature change curve generated by the orbit thermal model, with a period of 90 minutes); then, a periodic connection analysis means is used to analyze the repeatability (such as the similarity of the shape recovery mode in each period) and difference (such as the intensification of the shape deformation due to material aging) between the shape change sequences in consecutive orbit periods (such as 10 periods) based on the shape time series information, through a sliding window comparison technique (such as setting the window size to 10 periods) and a correlation analysis method (such as calculating the Pearson correlation coefficient), to construct the on-orbit shape evolution information set of the solar wing from heating to cooling in consecutive orbit periods, and finally to form a complete data set covering the space-time evolution, providing a seamless and high-fidelity input basis for subsequent power generation efficiency analysis and assembly optimization.

[0067] By the way provided by the embodiment, the shape sequence reconstruction mechanism is constructed, which can convert discrete shape data into continuous evolution sequence, improve the continuity and accuracy of shape prediction, provide reliable data support for subsequent power generation efficiency analysis, and enhance the real-time performance and adaptability of on-orbit performance monitoring of the solar wing, which helps to optimize the ground assembly process and improve the overall power generation efficiency and reliability of the solar wing.

[0068] 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.

[0069] 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.

[0070] Specifically, during the on-orbit operation of the solar wing, due to the thermal load effect of high and low temperature alternating environment, the structure profile of the solar wing will change dynamically. This change will cause the normal direction of each position on the surface of the solar wing to deviate relative to the designed light receiving direction, thereby changing the effective light receiving area of the solar wing, and further affecting the power generation efficiency. In the existing method, the indirect influence of the profile change on the power generation efficiency is often ignored, or is only estimated through a simplified model, resulting in inaccurate on-orbit power generation efficiency prediction, and unable to provide accurate feedback for ground assembly process optimization. In view of the above problems, based on the profile time sequence information, the surface normal extraction analysis means is adopted, the surface normal calculation module in the computer aided design (CAD) software such as CATIA is used, based on the three-dimensional finite element grid model of the solar wing, the normal vector of each grid node on the surface at each time point is automatically extracted and stored as time sequence data, for example, the normal of a certain grid node deviates from the initial direction to the new direction in the high temperature stage, and the surface normal distribution sequence information is obtained. On this basis, the included angle calculation and analysis method is used, the space vector operation technology is used to calculate the included angle between the normal of each surface position and the preset designed light receiving direction (such as the ideal sun direction of the solar wing represented by a unit vector), the dot product formula and the inverse cosine function are used to calculate the included angle value, and the time change is tracked, for example, the normal of a certain area continuously deviates in the thermal cycle, resulting in the included angle gradually increasing from 0 degree to 10 degrees, and the light receiving included angle distribution sequence information is obtained. Then, through the projection area integral analysis means, the numerical integral technology such as Gauss integral or Monte Carlo method is used to discretize the surface of the solar wing into small triangular patches, the projection contribution of each patch in the light receiving direction is calculated combined with the cosine value of the included angle of each patch, and the effective projection area is obtained by area weighted summation. For example, the effective light receiving area periodically fluctuates from 1.5 square meters to 0.7 square meters during the on-orbit operation, and the effective light receiving area sequence information is obtained. Finally, through the photoelectric conversion efficiency mapping analysis means, the photoelectric conversion characteristic curve calibrated on the ground (the curve is obtained by fitting experimental data) is used, and the interpolation algorithm is used to map the effective light receiving area sequence to the power generation efficiency change, for example, the area decreases by 30% and the efficiency decreases by 12%, and the power generation efficiency change information set is obtained, so as to realize the comprehensive quantification of the dynamic influence of the on-orbit power generation efficiency.

[0071] Through the method provided by the embodiment, the influence of the on-orbit profile change of the solar wing on the power generation efficiency can be accurately quantified, the power generation efficiency change information set is provided, and the basis for ground assembly process optimization is provided, so as to improve the on-orbit power generation efficiency and service life of the solar wing, and ensure the stability of the spacecraft energy supply.

[0072] In some embodiments, based on the set of power generation efficiency change information, the influence degree of each parameter in the set of ground assembly process parameters on the in-orbit power generation efficiency change is analyzed to obtain parameter influence degree information; based on the parameter influence degree information, in combination with the set of assembly stress distribution information, a reverse association rule between the assembly process parameters and the in-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 of the assembly process parameters to obtain the iterative optimization strategy; and based on the iterative optimization strategy, the solar wing intelligent assembly decision log including the assembly process parameter optimization suggestion and the expected in-orbit power generation efficiency improvement range is output.

[0073] The reverse association rule can be a rule system that reversely maps the ground assembly process parameters to the in-orbit power generation efficiency of the solar wing. The parameter adjustment rule information can be specific rule data formulated based on the reverse association rule for guiding the adjustment of the ground assembly process parameters. The iterative convergence analysis can be a data analysis method for gradually approaching an optimal solution, which is used to analyze the adjustment process of the assembly process parameters to ensure that the parameter adjustment gradually converges towards the direction of improving the in-orbit power generation efficiency, and finally forms a feasible adjustment scheme. The iterative optimization strategy can be a sequential adjustment scheme of the assembly process parameters generated based on the parameter adjustment rule information through the iterative convergence analysis, which clearly defines the sequence, adjustment range and iteration number of each parameter adjustment.

[0074] 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.

[0075] 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.

[0076] 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.

[0077] 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.

[0078] 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.

[0079] Optionally, the stress analysis module 301, in the overall stress state of the integrated evaluation of the solar wing ground assembly, is specifically used for: based on the glue layer stress distribution information, the bolt stress distribution information, combined with the wire layout stress distribution information, using stress field simulation data simulation, analyzing the stress transmission direction characteristics and interaction between the glue layer area, the bolt connection area and the wire layout area, quantifying the stress superposition effect and cancellation effect between the areas, constructing stress transmission information; the glue layer area is a bonding interface layer formed by the pressure control of the adhesive layer; the bolt connection area is a mechanical fastening connection part formed by the tightening torque of the bolt; the wire layout area is a cable path attachment area formed by the tension constraint of the inter-board wire layout; based on the superposition effect and the cancellation effect, analyze the spatial distribution characteristics of the stress concentration area concentrated in the superposition effect and the stress balance area concentrated in the cancellation effect in the solar wing structure, and obtain the overall stress state.

[0080] Optionally, the stress analysis module 301, in the analysis of the dynamic coupling relationship between the thermal load and the ground initial stress field under the on-orbit high-low temperature alternating environment, obtains the on-orbit type surface evolution information set of the solar wing, and is specifically used for: based on the overall stress state, according to the thermal load cycle data of the preset on-orbit high-low temperature alternating environment, establishing a dynamic mapping mechanism of the thermal load cycle data and the ground initial stress field in the time and space dimensions, obtaining the solar wing time-varying coupling stress information; based on the solar wing time-varying coupling stress information, analyzing the structure internal force rebalancing process of the stress concentration area and the stress balance area caused by temperature alternation, obtaining stress redistribution path information; based on the stress redistribution path information, the structure deformation trend is deduced, the influence of the stress redistribution path on the macroscopic geometric form of the solar wing is deduced, and the type surface dynamic response information is obtained; based on the type surface dynamic response information, a type surface sequence reconstruction mechanism for the whole process from heating to cooling in a continuous orbit cycle is constructed, and the on-orbit type surface evolution information set is obtained; the type surface sequence reconstruction mechanism is used to integrate discrete type surface dynamic response information into continuous evolution atlas.

[0081] Optionally, the type surface evolution module 302, in the establishment of the dynamic mapping mechanism of the thermal load cycle data and the ground initial stress field in the time and space dimensions, is specifically used for: based on the overall stress state, according to the thermal load cycle data of the preset on-orbit high-low temperature alternating environment, the continuous thermal load cycle is decomposed into multiple discrete temperature stages, and the stage thermal load information is obtained; based on the stage thermal load information, the time and space correlation between different temperature stages and stress response is established, and the thermal load-stress dynamic correlation information is obtained; based on the thermal load-stress dynamic correlation information, the correlation results of multiple temperature stages are integrated, and the dynamic mapping mechanism reflecting the dynamic change process of the stress field in the whole thermal load cycle is constructed.

[0082] Optionally, the profile evolution module 302, in the process of structural deformation trend deduction, when deducing the influence of stress redistribution path on the macro-geometric shape of the solar wing, is specifically used for: based on the spatial distribution characteristics, combining the stress redistribution path information, using structural deformation coordination analysis, analyzing the antagonism and balance relationship between the high deformation driving trend of the stress concentration area and the deformation constraint trend of the stress balance area, obtaining regional deformation interaction information; based on the regional deformation interaction information, combining the time-varying coupled stress information of the solar wing, using deformation transmission path analysis, tracking the whole process of local deformation of the high deformation driving area being transmitted through the solar wing structure skeleton and being inhibited by the deformation constraint area, obtaining local dominant profile feature information; based on the local dominant profile feature information, using geometric shape integrated analysis, comprehensively analyzing the deformation contribution and mutual restriction relationship of each local area, obtaining the influence of stress redistribution on the geometric shape of the solar wing.

[0083] Optionally, the profile evolution module 302, in the process of constructing the profile sequence reconstruction mechanism of the solar wing from heating to cooling in the whole process of continuous orbit period, when obtaining the in-orbit profile evolution information set, is specifically used for: based on the profile dynamic response information, through space-time correlation, analyzing the evolution continuity and transition law between the profile dynamic response information of adjacent time points, obtaining profile evolution transition information; based on the profile evolution transition information, combining the thermal load period data, through sequence fitting, integrating the discrete profile dynamic response information into profile time sequence information reflecting the continuous change process of the profile in a single orbit period; based on the profile time sequence information, through period connection, analyzing the repeatability and difference of the profile change sequence between continuous multiple orbit periods, constructing the in-orbit profile evolution information set of the solar wing from heating to cooling in the whole process of continuous orbit period.

[0084] Optionally, the efficiency analysis module 303, in the process of analyzing the influence of the in-orbit profile change of the solar wing on the power generation efficiency, when obtaining the power generation efficiency change information set, is specifically used for: based on the profile time sequence information, through surface normal extraction analysis, extracting the normal direction of each position on the surface of the solar wing at different time points, obtaining surface normal distribution sequence information; based on the surface normal distribution sequence information, through angle calculation analysis, calculating the angle change between the normal of each surface position and the design light receiving direction of the solar wing, obtaining light receiving angle distribution sequence information; based on the light receiving angle distribution sequence information, through projection area integration analysis, integrating and calculating the change of the effective projection area of the solar wing in the light receiving direction with time, obtaining effective light receiving area sequence information; based on the effective light receiving area sequence information, through photoelectric conversion efficiency mapping analysis, mapping to obtain the change law of the power generation efficiency of the solar wing with time, obtaining the power generation efficiency change information set.

[0085] Optionally, the decision generation module 304, when generating the iteration optimization strategy of the assembly process parameters and outputting the solar wing intelligent assembly decision log by establishing the reverse parameter adjustment mechanism of the ground assembly process parameters with the on-orbit power generation efficiency as the optimization target, is specifically configured to: based on the power generation efficiency change information set, analyze the influence degree of each parameter in the ground assembly process parameter set on the on-orbit power generation efficiency change to obtain parameter influence degree information; based on the parameter influence degree information, combined with the assembly stress distribution information set, establish the reverse correlation rule between the assembly process parameters and the on-orbit power generation efficiency to obtain parameter adjustment rule information; based on the parameter adjustment rule information, generate the sequential adjustment scheme of the assembly process parameters through iteration convergence analysis to obtain the iteration optimization strategy; and based on the iteration optimization strategy, output the solar wing intelligent assembly decision log including the assembly process parameter optimization suggestion and the expected on-orbit power generation efficiency improvement range.

[0086] The system of the embodiment can be used to execute the method of any of the above embodiments, and has similar implementation principles and technical effects, which will not be described here again.

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. The assembly process parameter set includes adhesive layer pressure, bolt tightening torque, and tension of the inter-board wire layout. Based on the assembly stress distribution information set, the dynamic coupling relationship between thermal load and initial ground stress field under alternating high and low temperatures in orbit is analyzed to obtain the on-orbit surface evolution information set of 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 structural internal forces in the stress concentration region and the stress equilibrium region are rebalanced by temperature alternation, and stress redistribution path information is obtained. 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; 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: 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 in the bolt connection area due to the torque 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 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 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 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.

5. The method according to claim 4, 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.

6. The method according to claim 5, 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 repetition 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 in a continuous orbital cycle.

7. The method according to claim 6, 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.

8. The method according to claim 7, 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.

9. A digital twin-based intelligent assembly system for aerospace equipment, characterized in that: The method applied to any one of claims 1-8 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 reflecting 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 alternating high and low temperatures 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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