Spacecraft full-period monitoring and operation and maintenance decision-making system based on digital twinning

Abstract

The invention provides a spacecraft full-cycle monitoring and operation and maintenance decision-making system based on digital twinning, and belongs to the field of spacecraft full-life-cycle intelligent operation and maintenance. Structural data and environmental data are monitored in real time through a multi-source sensor network deployed on a spacecraft structure; fusing physical entity monitoring data and damage evolution rule virtual data in a twin database, constructing a structure damage resistance and mechanical property evaluation model, and executing geometric modeling, physical attribute analysis, behavior response simulation and damage evolution rule modeling; based on a simulation result of the virtual model, life prediction and reusability evaluation are carried out, and an operation and maintenance decision instruction is generated; and feeding back the operation and maintenance decision instruction to the physical entity to execute control, and updating state data in the twin database to perform iterative optimization. According to the method, full-scene coverage from design demonstration to scrap recovery is supported, the problem of stage splitting in the traditional technology is solved, and a closed-loop optimization mechanism is formed through real-time interaction between the physical entity and the virtual model.

Description

Technical Field

[0001] The present invention belongs to the field of intelligent operation and maintenance of spacecraft throughout its life cycle, and specifically relates to a spacecraft full-cycle monitoring and operation and maintenance decision-making system based on digital twins. Background Art

[0002] As spacecraft become more complex and mission requirements increase, the health status of spacecraft throughout their life cycle needs to be monitored and managed. Existing technologies rely heavily on physical testing to verify design performance, resulting in long R&D cycles and high costs. The manufacturing and assembly stages lack dynamic quality monitoring, which can easily lead to cumulative assembly errors. During on-orbit operation, fault diagnosis relies on historical experience, making it difficult to cope with sudden problems in complex space environments. The scrapping and recycling stages lack scientific evaluation and resource optimization methods, resulting in resource waste and environmental pollution. In addition, existing systems have difficulty achieving cross-stage data integration and model iteration, resulting in disconnection between design, manufacturing, and operation and maintenance, and failing to meet the core requirements of high reliability, long life, and low cost for spacecraft. Therefore, there is an urgent need for a technical system that can integrate multi-source data, dynamically correct models, and provide full-cycle intelligent decision support. Summary of the Invention

[0003] In response to the shortcomings of the existing technology, the present invention proposes a spacecraft full-cycle monitoring and operation and maintenance decision-making system based on digital twins. It analyzes the physical entity characteristics according to the spacecraft operation needs, establishes a virtual model, builds connections to realize the interaction of virtual and real information data, and uses the fusion and analysis of twin data to ultimately provide users with various service applications.

[0004] The present invention is achieved through the following technical solutions: 1. A digital twin-based spacecraft full-cycle monitoring and operation and maintenance decision-making system, characterized by: The system includes a decision execution module, an operation and maintenance processing module and a system architecture module; The decision execution module consists of physical entities, virtual models, application services, twin data, and a connection system. It collects real-time monitoring data from spacecraft through the physical entity, transmits it to the virtual model through the connection system for dynamic simulation, and drives the application service to generate operation and maintenance decision instructions. The operation and maintenance processing module includes a design / research unit, a manufacturing / assembly unit, a test / flight test unit, a diagnosis / repair unit, and a scrapping / recycling unit; it calls on the resources of the system architecture module to execute full-cycle technical tasks and feeds back the execution results to the decision-making mechanism module; The system architecture module includes a platform layer, an expression layer, a model layer, and a data layer; the data layer stores multi-source heterogeneous data, the model layer constructs a damage evolution and reliability assessment model, the expression layer visualizes the operating status, and the platform layer provides a human-computer interaction interface to support decision-making and operation and maintenance functions.

[0005] Furthermore, in the decision execution module, The physical entity deploys various sensors on the spacecraft structure to form a multi-source sensor network to monitor the structure's body data and environmental data in real time; The virtual model includes four layers of models: geometry, physics, behavior, and rules. The geometry model describes the size and assembly relationship of the components, the physics model analyzes the stress, strain, and temperature properties of the materials, the behavior model simulates the impact response and vibration characteristics, and the rule model models the evolution of structural damage. The twin data stores a material constitutive relationship library and a manufacturing process library, and integrates sensor physical data and damage evolution virtual data; The application service performs simulation, monitoring, prediction, and evaluation functions, and realizes life prediction and reusability evaluation through structural load identification technology and multi-scale damage detection methods; The connection system connects physical entities, virtual models, application services and twin data through a data transmission interface.

[0006] Further, in the operation and maintenance processing module; The design / demonstration unit integrates multi-source data and historical mission data to generate design optimization suggestions through finite element analysis; The manufacturing / assembly unit collects process parameters and assembly gap data, analyzes assembly deviations and triggers early warnings, and displays production progress through a 3D visual dashboard; The test / flight test unit integrates environmental parameters and structural damage data to modify material degradation models and predict performance under extreme operating conditions; The diagnosis / repair unit performs fault diagnosis based on on-orbit monitoring data and expert experience database, and simulates repair solutions through a physical engine; The scrap / recycling unit predicts component life thresholds based on damage data, simulates recycling plans, and generates compliance audit reports.

[0007] Further, in the system architecture module; The data layer stores geometric parameters, material properties, online monitoring data and maintenance process library; The model layer includes static / dynamic load knowledge model, material constitutive and damage evolution model, and structural reliability assessment model; The expression layer displays the evolution process of the structural defect position and the repair strategy through a graphics engine; The platform layer integrates multi-physics field simulation tools and a visual human-computer interaction interface.

[0008] A control method for a digital twin spacecraft full-cycle monitoring and operation and maintenance decision-making system: The method specifically comprises the following steps: Step 1: Through the multi-source sensor network deployed on the spacecraft structure, the stress, strain, temperature, vibration body data, aerodynamic force, and aerodynamic thermal environment data of the structure are monitored in real time, and the data are transmitted to the twin database for storage; Step 2: The physical entity monitoring data and virtual data of damage evolution laws are integrated into the twin database to build a structural damage resistance and mechanical performance evaluation model. The virtual model calls the integrated data from the twin database to perform geometric modeling, physical property analysis, behavioral response simulation, and damage evolution law modeling. Step 3: Based on the simulation results of the virtual model, life prediction and reusability evaluation are performed through structural load identification technology and multi-scale damage detection methods to generate operation and maintenance decision instructions; Step 4: Feedback the operation and maintenance decision instructions to the physical entity for execution control, and update the status data in the twin database at the same time; drive the iterative optimization of the virtual model based on the updated data.

[0009] Furthermore, the operation and maintenance decision instructions include: Combine finite element analysis with dynamic simulation verification during the design / demonstration phase; Generate assembly deviation warning signals and 3D visual production progress reports during the manufacturing / assembly stage; Generate repair work orders and repeat service life predictions during the diagnosis / repair phase.

[0010] An electronic device includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0011] A computer-readable storage medium is used to store computer instructions, which implement the steps of the above method when executed by a processor.

[0012] Beneficial effects of the present invention Compared with the prior art, the present invention has the following advantages: 1. This invention builds a dynamically updateable digital twin by integrating geometric parameters, material constitutive knowledge base, multi-source monitoring data, etc., supporting full-scenario coverage from design demonstration to scrapping and recycling, solving the problem of stage separation in traditional technology, and forming a closed-loop optimization mechanism through real-time interaction between physical entities and virtual models.

[0013] 2. The present invention integrates environmental simulation parameters and online monitoring data to dynamically correct the material degradation model, achieve accurate fault location, predict performance degradation under extreme working conditions, reduce the risk of human intervention, and improve the scientific nature of decision-making.

[0014] 3. This invention balances safety and cost through remaining life prediction and multi-objective recycling scheme optimization, builds a reusability assessment model based on historical data, supports component reuse decisions, and reduces waste generation.

[0015] 4. The present invention realizes manufacturing quality traceability, maintenance case storage and retirement archive management, ensures data credibility and knowledge reuse, and realizes real-time status monitoring, damage evolution presentation and interactive decision-making through 3D visualization. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A schematic diagram of a decision-making mechanism for a spacecraft full-cycle monitoring and operation and maintenance decision-making system based on digital twins according to an embodiment of the present invention; Figure 2 This is a schematic diagram of an operation and maintenance scenario of a spacecraft full-cycle monitoring and operation and maintenance decision-making system based on digital twins according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the system architecture of a digital twin-based spacecraft full-cycle monitoring and operation and maintenance decision-making system described in an embodiment of the present invention. DETAILED DESCRIPTION

[0017] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0018] The experimental methods used in the following examples are conventional methods unless otherwise specified. The materials, reagents, methods, and instruments used are conventional in the art and can be obtained commercially by those skilled in the art unless otherwise specified.

[0019] Combine Figures 1 to 3 ,The present invention proposes a spacecraft full-cycle monitoring and operation and maintenance decision-making system based on digital twins,,the system includes a decision execution module, an operation and maintenance processing module, and a system architecture module; The decision execution module consists of physical entities, virtual models, application services, twin data, and a connection system. The physical entity collects real-time monitoring data of the spacecraft, which is then input into the data layer storage of the system architecture module. The data is then transmitted to the virtual model via the connection system for dynamic simulation, and the application service is driven to generate operation and maintenance decision instructions. The operation and maintenance processing module includes a design / theory unit, a manufacturing / assembly unit, a test / flight test unit, a diagnosis / repair unit, and a scrapping / recycling unit; it calls the resources of the system architecture module, executes full-cycle technical tasks, and feeds back the execution results to the decision-making mechanism module; the operation and maintenance scenario module sends task instructions to the platform layer of the system architecture module, and outputs the results at the expression layer after processing at the model layer; the output results of the operation and maintenance scenario module are fed back to the twin data of the decision-making mechanism module to drive the iterative optimization of the virtual model.

[0020] The system architecture module includes a platform layer, an expression layer, a model layer, and a data layer. The model layer of the system architecture module calls data from the data layer to build and update the virtual model. The data layer stores multi-source heterogeneous data, the model layer builds a damage evolution and reliability assessment model, the expression layer visualizes the operating status, and the platform layer provides a human-computer interaction interface to support decision-making and operation and maintenance functions.

[0021] The decision execution module (decision making mechanism) is composed of a physical entity unit, a virtual model unit, an application service unit, a twin data unit and a connection system unit.

[0022] The physical entity unit deploys various sensors on the spacecraft structure to form a multi-source sensor network, which monitors the structure's stress, strain, temperature, vibration and other body data and environmental data such as aerodynamic force and aerodynamic heat in real time, and stores and transmits the data to the system's database.

[0023] The virtual model unit is a faithful digital mirror of the spacecraft entity, integrating four layers of models: geometry, physics, behavior, and rules. The geometric model describes the dimensions, shape, and assembly relationships of components, parts, and the entire system. The physical model analyzes the physical properties of materials and components, such as stress, strain, fatigue, displacement, and temperature. The behavioral model describes the response to external forces and disturbances, such as impact response, static load characteristics, and vibration characteristics. The rule model models the laws or rules governing the evolution of structural damage, characterizing the functions, real-time status, and evolution trends of the physical entity corresponding to the virtual model, enabling the model to perform assessment, optimization, prediction, and evaluation.

[0024] The twin data unit comprises physical entities, virtual models, data related to application services, domain knowledge, and their fusion, and is continuously updated and optimized as real-time data is generated. The spacecraft's twin data inputs include raw data such as a library of material constitutive relationships, a library of composite structural component designs, and a library of manufacturing and processing techniques. Based on this raw data, an online and offline multi-source intelligent sensing system acquires physical data such as stress, strain, displacement, and temperature. This data is then integrated with virtual data such as damage evolution patterns and aging behavior to construct a library of structural damage resistance and comprehensive mechanical performance assessment models. Furthermore, a library of remanufacturing methods and process technologies for typical material structures, such as composites, metals, and ceramic tiles, is proposed.

[0025] The application service unit integrates simulation, monitoring, emulation, prediction, evaluation, control, and optimization information systems, and provides intelligent operation, precise control, and reliable operation and maintenance services based on physical entities and virtual models. This part relies on structural load identification technology, multi-scale damage detection methods, remanufacturing-based repair technology, and spacecraft structure reusability performance evaluation technologies to achieve real-time monitoring, status analysis, performance evaluation, life prediction, and reusability evaluation of spacecraft structures. Based on 3D visualization and UI technology, it provides real-time, visual information such as the operating status and damage status of physical entities, giving users intuitive and friendly human-computer interaction and supporting collaborative decision-making.

[0026] The connection system unit connects the above parts in pairs, connecting physical entities, virtual models, application services, and twin data into an organic whole, so that information and data can be dynamically and real-time interactively transmitted between the parts, thereby realizing real-time interaction to ensure the consistency of virtual and real, and heaven and earth, and assist in iterative optimization of the model.

[0027] The operation and maintenance processing module includes a design / demonstration unit, a manufacturing / assembly unit, a test / flight test unit, a diagnosis / repair unit, and a scrapping / recycling unit. Through data interaction and dynamic correction between virtual models such as geometry, physics, behavior, and rules and physical entities, a digital twin of the spacecraft structure is constructed. During the design / demonstration phase, based on the overall / component / system design requirements, a high-precision parametric geometric model is constructed by integrating multi-source data (such as CAD / CAE models, simulation analysis results, historical mission data, and expert experience libraries). Dynamic simulation verification is performed in combination with finite element analysis to predict design defects and generate optimization suggestions, forming a closed-loop feedback loop of design-simulation-iteration to support the feasibility verification and performance improvement of spacecraft structural design.

[0028] During the manufacturing / assembly stage, production equipment data and process parameters, dimensional tolerances and assembly clearances are collected in real time, assembly deviations are calculated and analyzed, and early warnings are triggered. Full-process data is recorded and quality traceability is achieved. Production progress and pass rates are dynamically displayed through 3D visual dashboards to ensure the controllability and consistency of the manufacturing process.

[0029] During the test / flight test phase, online data and environmental simulation parameters are integrated to identify load parameters and structural damage status, correct material degradation characteristics, dynamically update the digital twin model, predict performance degradation under extreme working conditions and generate emergency plans, guide the optimization of test plans and test parameters, output remaining life predictions and outline modification suggestions, and complete model confidence verification in virtual-reality hybrid scenarios.

[0030] The diagnosis / maintenance phase integrates on-orbit monitoring environmental data, operational data, and expert experience to perform fault diagnosis, root cause reasoning, and performance prediction. A physics engine simulates the feasibility of repair plans, optimizes maintenance strategies, generates repair work orders, and ultimately integrates repair cases into a knowledge base, forming a closed-loop decision-making system. Based on maintenance data, the system predicts the service life of the aircraft.

[0031] In the scrapping / recycling stage, the component life threshold is predicted based on damage detection data and degradation models, and the scrapping risk is assessed in combination with environmental monitoring data. Different recycling plans are simulated and safety and cost are optimized. Resource allocation is balanced through multi-objective algorithms. Finally, the recycling process is verified through virtual drills and a compliance audit report is generated to achieve closed-loop management and risk control throughout the entire life cycle.

[0032] The system architecture module consists of four parts, namely platform layer, expression layer, model layer and data layer; corresponding to five major functional modules, namely digital prototype construction module, payload environment perception module, fault status identification module, return maintenance strategy module and go-around reliability assessment module.

[0033] The data layer includes geometric parameters, material category attributes and solid model information, corresponding callable data information such as material properties and boundary conditions, corresponding multimodal result information of online monitoring and offline detection, maintenance process library maintenance process and structure performance information, component structure comprehensive performance, evaluation data information and history, etc.

[0034] The model layer includes data-driven updateable structural digital models and mechanism models, static / dynamic load knowledge models that support real-time multi-physics measurement, constitutive models and damage evolution models of component structures and materials, structural maintenance mechanism models and control strategies under multi-field coupling, structural comprehensive performance testing and verification, and reliability assessment models, etc.

[0035] The expression layer includes a callable and displayable whole machine model and its dynamic parameter expression, the working state load at each stage and the corresponding structural change display, the structural defect location, morphology information and its evolution process presentation, the structural maintenance strategy, process flow and post-repair status expression, and the comprehensive performance and reliability evaluation results of the component throughout its life cycle.

[0036] The platform layer applies a variety of complex physical system digital modeling, multi-physics field simulation and calculation, computer-aided manufacturing, database management and visualization platform tools to realize the integration of digital twin software platform and form a human-computer interaction interface, providing a friendly window for spacecraft operation and maintenance decision-making.

[0037] That is, the digital prototype building module stores data, the load environment perception module and the fault status identification module build and use analysis models, the return maintenance strategy module performs evaluation and decision-making, and the go-around reliability assessment module evaluates performance and performs visualization.

[0038] A control method for the digital twin spacecraft full-cycle monitoring and operation and maintenance decision-making system: The method specifically comprises the following steps: Step 1: Through the multi-source sensor network deployed on the spacecraft structure, the stress, strain, temperature, vibration body data, aerodynamic force, and aerodynamic thermal environment data of the structure are monitored in real time, and the data are transmitted to the twin database for storage; Step 2: The physical entity monitoring data and virtual data of damage evolution laws are integrated into the twin database to build a structural damage resistance and mechanical performance evaluation model. The virtual model calls the integrated data from the twin database to perform geometric modeling, physical property analysis, behavioral response simulation, and damage evolution law modeling. Step 3: Based on the simulation results of the virtual model, life prediction and reusability evaluation are performed using structural load identification technology and multi-scale damage detection methods to generate operation and maintenance decision instructions; the operation and maintenance decision instructions include: Combine finite element analysis with dynamic simulation verification during the design / demonstration phase; Generate assembly deviation warning signals and 3D visual production progress reports during the manufacturing / assembly stage; Generate repair work orders and repeat service life predictions during the diagnosis / repair phase.

[0039] Step 4: Feedback the operation and maintenance decision instructions to the physical entity for execution control, and update the status data in the twin database at the same time; drive the iterative optimization of the virtual model based on the updated data.

[0040] An electronic device includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0041] A computer-readable storage medium is used to store computer instructions, which implement the steps of the above method when executed by a processor.

[0042] The memory in the embodiments of the present application can be volatile memory or non-volatile memory, or can include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct RAM bus random access memory (DR RAM). It should be noted that memory of the methods described herein is intended to comprise, but not be limited to, these and any other suitable types of memory.

[0043] In the above embodiments, all or part of the embodiments can be implemented using software, hardware, firmware, or any combination thereof. When implemented using software, all or part of the embodiments can be implemented in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired connection such as a coaxial cable, optical fiber, digital subscriber line (DSL), or wireless connection such as infrared, wireless, or microwave. The computer-readable storage medium can be any available medium that can be accessed by a computer, or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium such as a floppy disk, hard disk, magnetic tape, an optical medium such as a high-density digital video disc (DVD), or a semiconductor medium such as a solid-state disc (SSD).

[0044] During implementation, each step of the above method can be completed by an integrated logic circuit of the hardware in the processor or by instructions in the form of software. The steps of the method disclosed in conjunction with the embodiments of the present application can be directly embodied as being executed by a hardware processor, or can be executed by a combination of hardware and software modules in the processor. The software module can be located in a storage medium mature in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in conjunction with its hardware. To avoid repetition, it will not be described in detail here.

[0045] It should be noted that the processor in the embodiments of the present application can be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above-described method embodiment can be completed by hardware integrated logic circuits in the processor or by software instructions. The above-described processor can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic device, discrete gate or transistor logic device, or discrete hardware components. The various methods, steps, and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of the present application can be directly implemented and executed by a hardware decoding processor, or by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium well-known in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, etc. The storage medium is located in the memory, and the processor reads the information in the memory and, in conjunction with its hardware, completes the steps of the above-described method.

[0046] The above is a detailed introduction to the spacecraft full-cycle monitoring and operation and maintenance decision-making system based on digital twins proposed in the present invention, and the principles and implementation methods of the present invention are explained. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for general technical personnel in this field, according to the ideas of the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A digital twin-based spacecraft full-cycle monitoring and operation and maintenance decision-making system, characterized by: The system includes a decision execution module, an operation and maintenance processing module and a system architecture module; The decision execution module consists of physical entities, virtual models, application services, twin data, and a connection system. It collects real-time monitoring data from spacecraft through the physical entity, transmits it to the virtual model through the connection system for dynamic simulation, and drives the application service to generate operation and maintenance decision instructions. The operation and maintenance processing module includes a design / theory unit, a manufacturing / assembly unit, a test / flight test unit, a diagnosis / repair unit and a scrapping / recycling unit; Call the resources of the system architecture module to execute full-cycle technical tasks and feed back the execution results to the decision-making mechanism module; The system architecture module includes a platform layer, an expression layer, a model layer, and a data layer; the data layer stores multi-source heterogeneous data, the model layer constructs a damage evolution and reliability assessment model, the expression layer visualizes the operating status, and the platform layer provides a human-computer interaction interface to support decision-making and operation and maintenance functions.

2. The system according to claim 1, characterized in that: In the decision execution module, The physical entity deploys various sensors on the spacecraft structure to form a multi-source sensor network to monitor the structure's body data and environmental data in real time; The virtual model includes four layers of models: geometry, physics, behavior, and rules. The geometry model describes the size and assembly relationship of the components, the physics model analyzes the stress, strain, and temperature properties of the materials, the behavior model simulates the impact response and vibration characteristics, and the rule model models the evolution of structural damage. The twin data stores a material constitutive relationship library and a manufacturing process library, and integrates sensor physical data and damage evolution virtual data; The application service performs simulation, monitoring, prediction, and evaluation functions, and realizes life prediction and reusability evaluation through structural load identification technology and multi-scale damage detection methods; The connection system connects physical entities, virtual models, application services and twin data through a data transmission interface.

3. The system according to claim 2, characterized in that: In the operation and maintenance processing module; The design / demonstration unit integrates multi-source data and historical mission data to generate design optimization suggestions through finite element analysis; The manufacturing / assembly unit collects process parameters and assembly gap data, analyzes assembly deviations and triggers early warnings, and displays production progress through a 3D visual dashboard; The test / flight test unit integrates environmental parameters and structural damage data to modify material degradation models and predict performance under extreme operating conditions; The diagnosis / repair unit performs fault diagnosis based on on-orbit monitoring data and expert experience database, and simulates repair solutions through a physical engine; The scrap / recycling unit predicts component life thresholds based on damage data, simulates recycling plans, and generates compliance audit reports.

4. The system according to claim 3, characterized in that: In the system architecture module; The data layer stores geometric parameters, material properties, online monitoring data and maintenance process library; The model layer includes static / dynamic load knowledge model, material constitutive and damage evolution model, and structural reliability assessment model; The expression layer displays the evolution process of the structural defect position and the repair strategy through a graphics engine; The platform layer integrates multi-physics field simulation tools and a visual human-computer interaction interface.

5. A control method for a spacecraft full-cycle monitoring and operation and maintenance decision system based on the digital twin according to any one of claims 1 to 4, characterized in that: The method specifically comprises the following steps: Step 1: Through the multi-source sensor network deployed on the spacecraft structure, the stress, strain, temperature, vibration body data, aerodynamic force, and aerodynamic thermal environment data of the structure are monitored in real time, and the data are transmitted to the twin database for storage; Step 2: The physical entity monitoring data and virtual data of damage evolution laws are integrated into the twin database to build a structural damage resistance and mechanical performance evaluation model. The virtual model calls the integrated data from the twin database to perform geometric modeling, physical property analysis, behavioral response simulation, and damage evolution law modeling. Step 3: Based on the simulation results of the virtual model, life prediction and reusability evaluation are performed through structural load identification technology and multi-scale damage detection methods to generate operation and maintenance decision instructions; Step 4: Feedback the operation and maintenance decision instructions to the physical entity for execution control, and update the status data in the twin database at the same time; drive the iterative optimization of the virtual model based on the updated data.

6. The control method according to claim 5, characterized in that: In step 3, the operation and maintenance decision instructions include: Combine finite element analysis with dynamic simulation verification during the design / demonstration phase; Generate assembly deviation warning signals and 3D visual production progress reports during the manufacturing / assembly stage; Generate repair work orders and repeat service life predictions during the diagnosis / repair phase.

7. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to claim 6 or 7 are implemented.

8. A computer-readable storage medium for storing computer instructions, characterized in that: When the computer instructions are executed by a processor, the steps of the method according to claim 6 or 7 are implemented.

Images