Model-based complex equipment system design and verification integration method

Abstract

The invention belongs to the field of complex equipment system design and verification, and discloses a model-based complex equipment system design and verification integrated method, which takes four layers of'demand-behavior-structure-parameter 'as a framework, combines task scene demands, and constructs a full-process design and analysis method of a reusable space launch vehicle system. The method comprises the following steps: decomposing a complex system from demand analysis, performing parameter calibration and performance verification on a model by combining theoretical and experimental data, constructing a reusable space launch vehicle system model, and performing comprehensive analysis and verification of the system; in combination with a three-dimensional virtual environment and a configuration optimization algorithm of the model, performing rapid optimization on various configurations of the aerospace vehicle, and evaluating feasibility and performance of a design scheme; through bidirectional interaction of design and verification models and communication of system-level design and professional-level design, the problems of design and verification separation, insufficient model collaboration and the like in the prior art are solved, and the efficiency and precision of design and verification of a complex equipment system are improved.

Description

Technical Field

[0001] The present invention relates to the field of complex equipment system design and verification, and in particular to a model-based integrated method for complex equipment system design and verification. Background Art

[0002] With the continuous advancement of science and technology, complex equipment systems are increasingly used in aerospace, energy, transportation and other fields. These systems are usually highly complex and multidisciplinary, involving multiple professional fields such as mechanics, electronics, software, and control. In the traditional R&D model, design and verification are often separate processes, lacking an effective coordination mechanism, resulting in long R&D cycles, high costs, and low efficiency.

[0003] The existing complex equipment system research and development mainly faces the following problems: 1) Lack of two-way interaction between design model and verification model: Design model and verification model are usually created by different teams or tools, and data and information are difficult to share. As a result, design modifications cannot be reflected in the verification model in a timely manner, and verification results cannot be effectively fed back to the design, affecting design optimization and improvement.

[0004] 2) System-level design is out of touch with professional-level design: System-level design focuses on the overall architecture and functional requirements, while professional-level design goes deep into specific components and subsystems. Due to the lack of a through-link mechanism, the intentions and requirements of system-level design are difficult to be delivered to professional-level design in a timely manner, and the results of professional-level design are also difficult to be effectively summarized into system-level design, resulting in insufficient overall performance optimization.

[0005] 3) Difficulty in multidisciplinary collaboration: Complex equipment systems involve multiple disciplines. The models and data formats among different disciplines are not unified, and there is a lack of a unified collaborative platform, which leads to information islands and communication barriers, affecting R&D efficiency and product quality.

[0006] In order to solve the above problems, the industry began to explore the model-based system engineering (MBSE) method, which establishes a unified system model throughout the entire life cycle of requirements analysis, design, verification and maintenance. However, the existing MBSE method still has some shortcomings in practice: 1) Low model integration: Although the system model has been established, the design model and the verification model are still separated, and real-time two-way interaction cannot be achieved, which limits the effectiveness and timeliness of the model.

[0007] 2) Lack of process coordination: The design and verification process does not form a closed loop. Verification after design modifications requires rebuilding the verification model, which consumes a lot of time and resources.

[0008] 3) Imperfect tool chain: The modeling and simulation tools used in different professional fields vary, making it difficult to achieve unified management and exchange of models and data.

[0009] Therefore, there is an urgent need for an innovative method and process to achieve two-way interaction between design models and verification models, as well as the integration of system-level design and professional-level design. Summary of the invention

[0010] In view of the above defects or improvement needs of the prior art, the present invention provides a.

[0011] To achieve the above object, according to one aspect of the present invention, a model-based integrated method for complex equipment system design verification is provided, comprising the following steps: S1 multi-scenario demand analysis and solution design: Based on the four-level framework of "demand-behavior-structure-parameter", combined with the mission scenario requirements, special design and analysis are carried out to build a full-process design and analysis method for reusable space launch vehicle systems; S2 multi-domain system modeling and comprehensive verification: Based on the full-process design and analysis method of step S1, starting from the demand analysis, the complex system is decomposed to clarify the granularity and technical focus of modeling; based on the system decomposition results, the component model, subsystem model and system model are developed from bottom to top, and the model parameters are calibrated and the performance is verified by combining theoretical and experimental data, a reusable space vehicle system model is constructed, and a comprehensive system analysis and verification is carried out; S3 multi-dimensional immersive experience and evaluation optimization: Based on the reusable space launch vehicle system model constructed in step S2, the various configurations of the space launch vehicle are quickly optimized in combination with the model's three-dimensional virtual environment and configuration optimization algorithm to evaluate the feasibility and performance of the design scheme.

[0012] Preferably, step S1 specifically includes the following steps: S11 Determine mission requirements and design goals: Based on the task scenario, extract key requirements and gradually decompose them to the system and subsystem levels to form a systematic design goal; use SysML use case diagrams to clarify the main functions in the task scenario and the interaction between the system and external objects, and use SysML requirement diagrams to hierarchically decompose global requirements, extract key system functions and performance requirements, and define the task division of each subsystem; S12 system function analysis and dynamic behavior modeling: Through dynamic behavior modeling, the key functions of the system extracted in step S11 are decomposed into multiple specific task activities. The SysML activity diagram is used to describe the dynamic behavior and interaction process between the functional modules, and the execution order and logical relationship of the tasks are clarified. For each subsystem, a state machine diagram is established to analyze its working mode and state transition in different task scenarios, and define the trigger conditions and transition paths between states. Through the combination of activity diagrams and state machine diagrams, the dynamic response and function implementation methods of the system in the time dimension are comprehensively analyzed, and the collaborative work of each functional module is verified to ensure that the system can operate efficiently under different task requirements. S13 system architecture design and model construction: Starting from the global functions, the modular composition of the rocket system is clarified, the functional division of labor and interface relationship between modules are defined through SysML BDD diagrams, and the connection relationship between the internal components of the subsystem is described in detail using SysML IBD diagrams. Through a unified data interface and simulation platform, the product topology is combined with the professional profile architecture to ensure consistency from top-level design to detailed implementation; S14 parameter definition and system verification: Through the definition and simulation verification of design parameters, it is ensured that the system performance meets the mission requirements.

[0013] Preferably, the verification method includes static indicator verification and dynamic performance verification, wherein the static indicator verification is to verify the static performance of the system using a SysML parameter diagram; and the dynamic performance verification is to verify the performance of the rocket in different mission scenarios in combination with a dynamic simulation model.

[0014] Preferably, step S2 specifically includes the following steps: S21 system breakdown: Based on the mission requirements, the system functions are decomposed layer by layer into several subsystems, components and assemblies until they are directly described through physical models and modeling languages. The reusable space vehicle is decomposed into several subsystems, and then further decomposed into fuel management modules, attitude adjustment modules and other components. In the decomposition process, the granularity of each model and its interface properties are clarified to ensure the consistency of parameter transfer and interaction between components. S22 model development and testing: According to the system decomposition results in step S21, the basic component model is developed using the Modelica language, and the verified component models are integrated into the subsystem model to ensure the accuracy of the logical and physical relationships of the components in the subsystem. The key parameters of the component and subsystem models are calibrated by combining theoretical analysis with experimental data, and the model accuracy is improved through iterative optimization. The test results should cover static and dynamic performance; S23 system model development and integration: According to the system requirements and topology, the drag-and-drop modeling tool is used to integrate the component and subsystem models into a complete system model. The MWORKS simulation platform is used to verify the complete system model in multiple scenarios, and the coupling performance between subsystems is verified through multi-domain simulation analysis.

[0015] Preferably, step S3 specifically includes the following steps: S31 3D modeling and virtual environment creation: By building a three-dimensional model of the space vehicle through a virtual simulation platform and simulating different launch and recovery scenarios, designers can observe the operation performance of the space vehicle in different scenarios and optimize the design scheme through virtual scenario simulation; S32 configuration optimization and evaluation: By combining the model's stage ratio optimization algorithm and the model-based program angle optimization algorithm, various configurations of the space vehicle can be quickly optimized to improve the performance of the spacecraft.

[0016] The interstage ratio optimization algorithm of the model specifically includes the following steps: (1) Cost model establishment The speed increment model is used to evaluate the speed increment required for the flight mission, thereby calculating the fuel consumption required to perform the mission. The calculation formula is: ; in: is the orbital velocity, The relative velocity correction introduced by the Earth's rotation, is the aerodynamic loss, is the thrust loss, is the gravity loss, is the safety margin; (2) Parameter definition and modeling The rocket system is divided into multiple stages, and the mass composition of each stage includes payload, fuel mass, structural mass, etc. In a layer rocket, the definition is the mass of propellant, which consists of fuel and oxidizer, used to produce thrust; definition is the structural mass, which includes all parts not included in other categories; Definition The fuel that is not completely consumed after the rocket burns; defines the mass ratio and structural coefficient The calculation formula is: ; ;

[0017] (3) Optimization objectives and constraints pass is associated with costs, where For cost estimation, is the cost coefficient, is the takeoff mass, and the optimization objective is defined as minimizing the total takeoff mass of the rocket , and its calculation formula is: ; in, is the total mass of the second stage rocket; is the mass of the first stage rocket structure; is the mass of the first-stage rocket propellant; Taking the Long March 5 as an example, for the two-stage recoverable launch vehicle, the constraints are further defined: , ; , ; in, is the number of first-stage engines, is the thrust-to-weight ratio; (4) Problem solving and optimization The optimization objectives and constraints are organized into a mixed integer nonlinear optimization problem. The velocity increment is calculated using the Tsiolkovsky equation to balance thrust loss and path deviation. The gravity loss during propulsion is calculated using integrals. and path correction : ; ;

[0018] in, is the starting time, For the end time, is the acceleration due to gravity, is the flight path angle, is the engine thrust, is the angle of attack, is the mass of the rocket; (5) Result output The mass distribution, structural coefficient and thrust-to-weight ratio of each stage are optimized to minimize the total mass of the rocket system while meeting the flight mission requirements. Requirements and performance indicators.

[0019] Preferably, for the two-stage recoverable launch vehicle, the constraints are further defined: , ; , ; in, is the mass ratio, is the number of first-stage engines, For thrust-to-weight ratio.

[0020] The program angle optimization algorithm of the model specifically includes the following steps: (1) Optimization goal The optimization objective is defined as minimizing rocket fuel consumption , and its calculation formula is: ; in, For fuel consumption; (2) State variables and control variables State variables , including position, angle, velocity and mass; Control variables ,in It's thrust. is the thrust direction; (3) System constraints set up is the mass of the rocket, is the rocket's moment of inertia, is the height of the rocket, As the fuel consumption coefficient per unit impulse, the dynamic differential equation constraint of the rocket is established to describe the motion of the rocket in the world coordinate system, including the changes in position, angle and speed. The formula is: ;

[0021] in, is the speed in the horizontal direction, is the velocity in the vertical direction, is the mass of the rocket, is the rocket's moment of inertia, is the height of the rocket, is the fuel consumption coefficient per unit impulse, For thrust; (4) Initial and terminal constraints Define the initial state and terminal state, where the initial state is the initial condition of the launch point ; Terminal state is orbital element or landing condition ; (5) Path and inequality constraints For a two-stage reusable launch vehicle: Thrust constraint: ; Angle Constraint: ; Dynamic load and axial overload: ; in, is the maximum dynamic pressure of the integrated vehicle, It is the maximum axial overload of the integrated carrier; (6) Problem discretization The Legendre-Gauss-Lobatto pseudo-spectral method is used to discretize the state variables and control variables into multiple time nodes. The state and control are fitted by the Lagrange interpolation function, and the differential equation is converted into an algebraic equation to simplify the solution process. Discrete form: ; ;

[0022] in, is the basis function of the Lagrange interpolation function; The dynamic equations are solved at discrete nodes: ; in, ; Differentiation Matrix Represents the differential value of each basis function of the Lagrange interpolation function at each LGL point; (7) Discretization of constraints The initial conditions, terminal conditions, path constraints and inequality constraints are transformed into algebraic constraint relations at discrete points; the initial and terminal conditions are: ; ;

[0023] in It is used to specify the horizontal displacement of the landing point from the initial point, which can be determined in combination with the location of the recycling station. is the fuel consumption during the process. The inequality constraint is discretized as follows, and the thrust limit is discretized as: ;

[0024] The angle limit discretization is: ;

[0025] Where N is the number of matching points; (8) Problem Solving The optimization problem is handed over to the solver for linearization and solution to obtain the optimal fuel consumption, thrust and program angle.

[0026] In general, the above technical solution conceived by the present invention has the following beneficial effects compared with the prior art: 1. The dual-architecture design concept proposed in this invention solves the problem of difficulty in coordinating global functions and professional performance in traditional design by combining the architecture design from the product topology perspective with the architecture design from the professional profile perspective. The product topology perspective focuses on the functional implementation and modular design at the system level to ensure the coordinated work of each subsystem, while the professional profile perspective deeply optimizes the technical details of each subsystem to ensure the depth and accuracy of technical implementation. Through the combination of the two, the global functions and professional technologies of the system are organically integrated, which improves the design efficiency and accuracy.

[0027] 2. By introducing two-way interaction between design models and verification models, the problem of separation between traditional design and verification is solved. The interaction between the model generated in the design phase and the verification model is smooth, and the verification results can be fed back to the design phase in real time to promote continuous optimization of the design. This closed-loop mechanism enables seamless connection between the design and verification processes, avoids information loss and repeated modeling, and greatly improves the efficiency and accuracy of design and verification.

[0028] 3. By integrating system-level design with professional-level design, the present invention ensures a high degree of consistency from requirement definition to subsystem design. The unified modeling language and interface protocol enable feedback from each subsystem design to be delivered to the system-level design in a timely manner, ensuring that all links in the design process work together. The real-time feedback mechanism enables the design solution to be optimized and adjusted during the verification phase, improving the flexibility and adaptability of the design and ensuring the optimal overall performance of the system.

[0029] In summary, through the above technical solutions, the present invention effectively solves the problems of separation between design and verification, insufficient interdisciplinary collaboration, slow optimization process, etc. in the prior art, promotes the digitalization and automation of complex equipment system design and verification, and improves the overall efficiency and accuracy of equipment research and development. The present invention is applied to the design and verification process of complex engineering systems, providing important technical support for the efficient development of complex equipment systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is the overall framework diagram of system design and verification integration.

[0031] Figure 2 It is the overall design idea of ​​multi-scenario demand analysis and solution design.

[0032] Figure 3 It is a decomposition flow chart of multi-domain system modeling and comprehensive verification system.

[0033] Figure 4 It is a flow chart for the development of multi-domain system modeling and comprehensive verification components.

[0034] Figure 5 It is an integrated process of multi-domain system modeling and comprehensive verification system model.

[0035] Figure 6 It is an overall framework for multi-dimensional immersive experience and evaluation optimization. DETAILED DESCRIPTION

[0036] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0037] See also Figure 1 and Figure 6 The model-based complex equipment system design verification integrated method provided by the present invention comprises the following steps: S1 multi-scenario demand analysis and solution design: Based on the concepts of MBSE and SysML, with the four levels of "requirements-behaviors-structures-parameters" as the framework, combined with the mission scenario requirements, special design and analysis are carried out to build a full-process design and analysis method for reusable space vehicle systems. The specific implementation methods are as follows: S11 Determine mission requirements and design goals: The design work is based on the mission scenario, extracting key requirements and gradually decomposing them to the system and subsystem levels to form a systematic design goal: Mission analysis and use case definition: Based on the typical requirements of space launch vehicles in launch, orbit adjustment, recovery and other tasks, SysML use case diagrams are used to clarify the main functions in the mission scenario and the interaction between the system and external objects. For example, the requirements for the recovery of the first-stage rocket include deceleration, stable landing, and post-recovery status evaluation; Requirements decomposition and association analysis: The SysML requirement diagram is used to hierarchically decompose the global requirements, extract the key functions and performance requirements of the system, and define the task division of each subsystem. For example, the propulsion system needs to meet the controllable thrust output, and the thermal control system needs to ensure stable temperature regulation.

[0038] S12 system function analysis and dynamic behavior modeling: Through dynamic behavior modeling, the dynamic response and function implementation of the system are analyzed to ensure that the system can meet the mission requirements: Functional decomposition and dynamic analysis: The system functions are decomposed into multiple task activities, and the dynamic behavior of each functional module is modeled using SysML activity diagrams. For example, the rocket recovery process includes activities such as trajectory adjustment, deceleration, and landing buffer, and the input and output parameters of each stage are defined; Subsystem state description: For each subsystem, a state machine diagram is established to clarify its working mode and state transition in different mission scenarios. For example, the state of the propulsion system may include ignition, thrust adjustment, and shutdown, and the state transition conditions are defined based on the mission requirements.

[0039] S13 system architecture design and model construction: The system architecture design is completed by combining the product topology perspective with the professional profile perspective: Product topology perspective architecture: Starting from the global function, clarify the modular composition of the rocket system, such as the core first-stage system, the core second-stage system, the thermal control system, etc. Define the functional division of labor and interface relationship between modules through the SysML BDD diagram to ensure that the modules work together; Professional profile perspective architecture: In-depth analysis of the technical requirements and implementation methods within the subsystem, using the SysML IBD diagram to describe in detail the connection relationship between the components within the subsystem. For example, the collaborative interface between the attitude adjustment module of the control system and the propulsion system realizes feedback control through data transmission; Comprehensive optimization of the architecture level: Through a unified data interface and simulation platform, the product topology is combined with the professional profile architecture to ensure consistency from top-level design to detailed implementation.

[0040] S14 parameter definition and system verification: Through the definition and simulation verification of design parameters, ensure that the system performance meets the mission requirements: Index design: Based on the mission requirements and behavior modeling results, formulate the key performance indicators of the system, such as total mass, thrust range, trajectory accuracy, etc.; Static indicator verification: Use SysML parameter diagrams to verify the static performance of the system, such as the strength analysis of the rocket body structure under different load conditions; Dynamic performance verification: Combined with dynamic simulation models, verify the performance of the rocket in different mission scenarios, such as the accuracy of trajectory adjustment and the response time of the propulsion system.

[0041] S2 multi-domain system modeling and comprehensive verification: Based on the full-process design and analysis method of step S1, starting from the demand analysis, the complex system is decomposed to clarify the granularity and technical focus of modeling; based on the system decomposition results, the component model, subsystem model and system model are developed from bottom to top, and the model parameters are calibrated and the performance is verified by combining theoretical and experimental data, a reusable space vehicle system model is constructed, and a comprehensive system analysis and verification is carried out; This method follows the model-based system engineering concept and is divided into three stages: System decomposition stage: Starting from the demand analysis, the complex system is decomposed into several subsystems, and further refined into components and assemblies, clarifying the granularity and technical focus of modeling, see Figure 2 Model development and testing phase: Based on the system decomposition results, develop component models, subsystem models and system models from bottom to top, and calibrate the parameters and verify the performance of the models by combining theoretical and experimental data. Figure 3 , Figure 5 System model development phase: Based on the requirements and business scenarios, component models and subsystem models are integrated based on the system topology structure to build a complete system model and conduct comprehensive performance verification. Figure 4 .

[0042] The specific steps include: S21 system breakdown: System decomposition is the basis for modeling complex systems. Based on mission requirements, the system functions are decomposed layer by layer into several subsystems, components and assemblies until they are directly described by physical models and modeling languages. Top-down functional decomposition: The reusable space vehicle is decomposed into subsystems such as propulsion system, control system, thermal control system, and further decomposed into components such as fuel management module and attitude adjustment module. Model granularity and interface specification: In the decomposition process, the granularity of each model and its interface properties are clarified to ensure the consistency of parameter transfer and interaction between components.

[0043] S22 model development and testing: Component model development: Based on the results of system function decomposition, use the Modelica language to develop basic component models. For example, for the engine in the propulsion system, develop core components including thrust calculation model and fuel consumption model. Subsystem model development: Integrate the verified component models into subsystem models to ensure the accuracy of the logical and physical relationships of each component in the subsystem. For example, the control subsystem model needs to integrate attitude control and timing control modules to realize closed-loop control functions. Parameter calibration and model testing: Combine theoretical analysis with experimental data to calibrate the key parameters of component and subsystem models, and improve model accuracy through iterative optimization. The test results should cover static and dynamic performance.

[0044] S23 system model development and integration: System topology modeling: Based on system requirements and topology, use drag-and-drop modeling tools to integrate component and subsystem models into a complete system model. For example, build a space launch vehicle model that includes multiple subsystems such as propulsion, thermal control, and control. System-level simulation and verification: Perform multi-scenario verification of the complete system model through simulation platforms such as MWORKS. For example, verify thrust output, thermal management efficiency and other performance in mission scenarios such as launch and recovery. Cross-domain collaborative verification: Verify the coupling performance between subsystems through multi-domain simulation analysis. For example, optimize fuel efficiency and temperature control stability in the joint simulation of propulsion and thermal control subsystems.

[0045] S3 multi-dimensional immersive experience and evaluation optimization: Based on the reusable space launch vehicle system model constructed in step 32, the various configurations of the space launch vehicle are rapidly optimized in combination with the model's three-dimensional virtual environment and configuration optimization algorithm to evaluate the feasibility and performance of the design solution.

[0046] The specific steps are as follows: S31 3D modeling and virtual environment creation: 3D model construction: Build a 3D model of the space vehicle through the virtual simulation platform, covering the main components such as the fairing, the first stage of the rocket, and the second stage of the rocket. Based on the 3D model, simulate different launch and recovery scenarios, such as ignition, separation, and landing, and intuitively present the shape, structure, and movement process of the space vehicle.

[0047] Virtual environment simulation: In a virtual environment, designers observe the operating performance of space vehicles in different scenarios and optimize the design plan through virtual scene simulation.

[0048] S32 configuration optimization and evaluation: Configuration optimization algorithm: Combine the model's interstage ratio optimization algorithm and the model-based program angle optimization algorithm to quickly optimize the various configurations of the space vehicle (such as the configuration of the propulsion system, parameter optimization of the control system, etc.), thereby improving the performance of the spacecraft. For example, adjust the dynamic characteristics and structural parameters of the rocket according to the mission requirements, and use the activity diagram model in the design model to achieve dynamic closed-loop verification. After the model starts simulating, the design parameters are passed to the simulation model and the simulation model is driven to run, and the feedback results are obtained to achieve a closed-loop design simulation verification, so that it can achieve optimal performance in the shortest time.

[0049] The interstage ratio optimization algorithm of the model specifically includes the following steps: (1) Cost model establishment The speed increment model is used to evaluate the speed increment required for the flight mission, thereby calculating the fuel consumption required to perform the mission. The calculation formula is:

[0050] in: is the orbital velocity, The relative velocity correction introduced by the Earth's rotation, is the aerodynamic loss, is the thrust loss, is the gravity loss, is the safety margin; (2) Parameter definition and modeling The rocket system is divided into multiple stages, and the mass composition of each stage includes payload, fuel mass, structural mass, etc. In a layer rocket, the definition is the mass of propellant, which consists of fuel and oxidizer, used to produce thrust; definition is the structural mass, which includes all parts not included in other categories; Definition The fuel that is not completely consumed after the rocket burns; defines the mass ratio and structural coefficient The calculation formula is: ; ;

[0051] (3) Optimization objectives and constraints pass is associated with costs, where For cost estimation, is the cost coefficient, is the takeoff mass, and the optimization objective is defined as minimizing the total takeoff mass of the rocket , and its calculation formula is: ; in, is the total mass of the second stage rocket; is the mass of the first stage rocket structure; is the mass of the first-stage rocket propellant; Taking the Long March 5 as an example, for the two-stage recoverable launch vehicle, the constraints are further defined: , ; , ; in, is the number of first-stage engines, is the thrust-to-weight ratio; (4) Problem solving and optimization The optimization objectives and constraints are organized into a mixed integer nonlinear optimization problem. The velocity increment is calculated using the Tsiolkovsky equation to balance thrust loss and path deviation. The gravity loss during propulsion is calculated using integrals. and path correction : ; ;

[0052] in, is the starting time, For the end time, is the acceleration due to gravity, is the flight path angle, is the engine thrust, is the angle of attack, is the rocket mass; (5) Result output The mass distribution, structural coefficient and thrust-to-weight ratio of each stage are optimized to minimize the total mass of the rocket system while meeting the flight mission requirements. Requirements and performance indicators.

[0053] The program angle optimization algorithm of the model specifically includes the following steps: (1) Optimization goal The optimization objective is defined as minimizing rocket fuel consumption , and its calculation formula is: ;

[0054] in, For fuel consumption; (2) State variables and control variables State variables , including position, angle, velocity and mass; Control variables ,in It's thrust. is the thrust direction; (3) System constraints set up is the mass of the rocket, is the rocket's moment of inertia, is the height of the rocket, As the fuel consumption coefficient per unit impulse, the dynamic differential equation constraint of the rocket is established to describe the motion of the rocket in the world coordinate system, including the changes in position, angle and speed: ;

[0055] in, is the speed in the horizontal direction, is the velocity in the vertical direction, is the mass of the rocket, is the rocket's moment of inertia, is the height of the rocket, is the fuel consumption coefficient per unit impulse, For thrust; (4) Initial and terminal constraints Define the initial state and terminal state, where the initial state is the initial condition of the launch point ; Terminal state is orbital element or landing condition ; (5) Path and inequality constraints Taking the Long March 5 as an example, for a two-stage reusable launch vehicle, the thrust constraints are: ; Angle constraint: ; Dynamic load and axial overload: ; Thrust Constraint: ; Angle Constraint: ; Dynamic load and axial overload: ; in, is the maximum dynamic pressure of the integrated vehicle, It is the maximum axial overload of the integrated carrier; (6) Problem discretization The Legendre-Gauss-Lobatto pseudo-spectral method is used to discretize the state variables and control variables into multiple time nodes. The state and control are fitted by the Lagrange interpolation function, and the differential equation is converted into an algebraic equation to simplify the solution process. Discrete form: ; ;

[0056] in, is the basis function of the Lagrange interpolation function; The dynamic equations are solved at discrete nodes: ;

[0057] in, ; Differentiation Matrix Represents the differential value of each basis function of the Lagrange interpolation function at each LGL point; (7) Discretization of constraints The initial conditions, terminal conditions, path constraints and inequality constraints are transformed into algebraic constraint relations at discrete points; the initial and terminal conditions are: ; ;

[0058] in It is used to specify the horizontal displacement of the landing point from the initial point, which can be determined in combination with the location of the recycling station. is the fuel consumption during the process. The inequality constraint is discretized as follows, and the thrust limit is discretized as: ;

[0059] The angle limit discretization is: ;

[0060] Among them, N is the number of matching points.

[0061] (8) Problem Solving The optimization problem is handed over to the solver for linearization and solution to obtain the optimal fuel consumption, thrust and program angle.

[0062] Through the above technical solutions, the present invention effectively solves the problems of separation between design and verification, insufficient interdisciplinary collaboration, slow optimization process, etc. in the prior art, promotes the digitalization and automation of complex equipment system design and verification, and improves the overall efficiency and accuracy of equipment research and development. The present invention is applied to the design and verification process of complex engineering systems, providing important technical support for the efficient development of complex equipment systems.

[0063] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A model-based integrated method for complex equipment system design and verification, characterized by: The following steps are involved: S1 multi-scenario demand analysis and solution design: Based on the four-level framework of "demand-behavior-structure-parameter", combined with the mission scenario requirements, special design and analysis are carried out to build a full-process design and analysis method for reusable space launch vehicle systems; S2 multi-domain system modeling and comprehensive verification: Based on the full-process design and analysis method of step S1, starting from the demand analysis, the complex system is decomposed to clarify the granularity and technical focus of modeling; Based on the system decomposition results, the component model, subsystem model and system model are developed from bottom to top, and the model parameters are calibrated and performance verified by combining theoretical and experimental data. A reusable space launch vehicle system model is constructed, and comprehensive system analysis and verification is carried out. S3 multi-dimensional immersive experience and evaluation optimization: Based on the reusable space launch vehicle system model constructed in step S2, the various configurations of the space launch vehicle are quickly optimized in combination with the model's three-dimensional virtual environment and configuration optimization algorithm to evaluate the feasibility and performance of the design scheme.

2. The model-based integrated design and verification method for complex equipment systems according to claim 1, characterized in that: Step S1 specifically includes the following steps: S11 Determine mission requirements and design goals: Based on the task scenario, extract key requirements and gradually decompose them to the system and subsystem levels to form a systematic design goal; use SysML use case diagrams to clarify the main functions in the task scenario and the interaction between the system and external objects, and use SysML requirement diagrams to hierarchically decompose global requirements, extract key system functions and performance requirements, and define the task division of each subsystem; S12 system function analysis and dynamic behavior modeling: Through dynamic behavior modeling, the key functions of the system extracted in step S11 are decomposed into multiple specific task activities. The SysML activity diagram is used to describe the dynamic behavior and interaction process between the functional modules, and the execution order and logical relationship of the tasks are clarified. For each subsystem, a state machine diagram is established to analyze its working mode and state transition in different task scenarios, and define the trigger conditions and transition paths between states. Through the combination of activity diagrams and state machine diagrams, the dynamic response and function implementation methods of the system in the time dimension are comprehensively analyzed, and the collaborative work of each functional module is verified to ensure that the system can operate efficiently under different task requirements. S13 system architecture design and model construction: Starting from the global functions, the modular composition of the rocket system is clarified, the functional division of labor and interface relationship between modules are defined through SysML BDD diagrams, and the connection relationship between the internal components of the subsystem is described in detail using SysML IBD diagrams. Through a unified data interface and simulation platform, the product topology is combined with the professional profile architecture to ensure consistency from top-level design to detailed implementation; S14 parameter definition and system verification: Through the definition and simulation verification of design parameters, it is ensured that the system performance meets the mission requirements.

3. The model-based integrated design and verification method for complex equipment systems according to claim 2, characterized in that: The verification method in step S14 includes static indicator verification and dynamic performance verification. The static indicator verification is to verify the static performance of the system using the SysML parameter diagram; the dynamic performance verification is to verify the performance of the rocket in different mission scenarios in combination with the dynamic simulation model.

4. The model-based complex equipment system design verification integrated method according to claim 1, characterized in that: Step S2 specifically includes the following steps: S21 system breakdown: Based on the mission requirements, the system functions are decomposed layer by layer into several subsystems, components and assemblies until they are directly described through physical models and modeling languages. The reusable space vehicle is decomposed into several subsystems, and then further decomposed into fuel management modules, attitude adjustment modules and other components. In the decomposition process, the granularity of each model and its interface properties are clarified to ensure the consistency of parameter transfer and interaction between components. S22 model development and testing: According to the system decomposition results in step S21, the basic component model is developed using the Modelica language, and the verified component models are integrated into the subsystem model to ensure the accuracy of the logical and physical relationships of the components in the subsystem. The key parameters of the component and subsystem models are calibrated by combining theoretical analysis with experimental data, and the model accuracy is improved through iterative optimization. The test results should cover static and dynamic performance; S23 system model development and integration: According to the system requirements and topology, the drag-and-drop modeling tool is used to integrate the component and subsystem models into a complete system model. The MWORKS simulation platform is used to verify the complete system model in multiple scenarios, and the coupling performance between subsystems is verified through multi-domain simulation analysis.

5. The model-based integrated design and verification method for complex equipment systems according to claim 1, characterized in that: Step S3 specifically includes the following steps: S31 3D modeling and virtual environment creation: By building a three-dimensional model of the space vehicle through a virtual simulation platform and simulating different launch and recovery scenarios, designers can observe the operation performance of the space vehicle in different scenarios and optimize the design scheme through virtual scenario simulation; S32 configuration optimization and evaluation: By combining the model's stage ratio optimization algorithm and the model-based program angle optimization algorithm, various configurations of the space vehicle can be quickly optimized to improve the performance of the spacecraft.

6. The model-based integrated design and verification method for complex equipment systems according to claim 5, characterized in that: The interstage ratio optimization algorithm of the model specifically includes the following steps: (1) Cost model establishment The speed increment model is used to evaluate the speed increment required for the flight mission, thereby calculating the fuel consumption required to perform the mission. The calculation formula is: ; in: is the orbital velocity, The relative velocity correction introduced by the Earth's rotation, is the aerodynamic loss, is the thrust loss, is the gravity loss, is the safety margin; (2) Parameter definition and modeling The rocket system is divided into multiple stages. In a layer rocket, the definition is the mass of propellant, which consists of fuel and oxidizer, used to produce thrust; definition is the structural mass, which includes all parts not included in other categories; Definition The fuel that is not completely consumed after the rocket burns; define the mass ratio The formula for calculating the structural coefficient is: ； (3) Optimization objectives and constraints pass is associated with costs, where For cost estimation, is the cost coefficient, is the takeoff mass, and the optimization objective is defined as minimizing the total takeoff mass of the rocket , and its calculation formula is: ； in, is the total mass of the second stage rocket; is the mass of the first stage rocket structure; is the mass of the first-stage rocket propellant; (4) Problem solving and optimization The optimization objectives and constraints are organized into a mixed integer nonlinear optimization problem. The velocity increment is calculated by the Tsiolkovsky equation to balance the thrust loss and path deviation. The gravity loss during propulsion is calculated using integrals. and path correction : ； in, is the starting time, For the end time, is the acceleration due to gravity, is the flight path angle, is the engine thrust, is the angle of attack, is the mass of the rocket; (5) Result output The mass distribution, structural coefficient and thrust-to-weight ratio of each stage are optimized to minimize the total mass of the rocket system while meeting the flight mission requirements. Requirements and performance indicators.

7. The model-based integrated design and verification method for complex equipment systems according to claim 6, characterized in that: For the two-stage reusable launch vehicle, the constraints are further defined: , ; ； ； in, is the mass ratio, is the number of first-stage engines, For thrust-to-weight ratio.

8. The model-based integrated design and verification method for complex equipment systems according to claim 5, characterized in that: The program angle optimization algorithm of the model specifically includes the following steps: (1) Optimization goal The optimization objective is defined as minimizing rocket fuel consumption , and its calculation formula is: ； in, For fuel consumption; (2) State variables and control variables State variables , including position, angle, velocity and mass; Control variables ,in It's thrust. is the thrust direction; (3) System constraints set up is the mass of the rocket, is the rocket's moment of inertia, is the height of the rocket, As the fuel consumption coefficient per unit impulse, the dynamic differential equation constraint of the rocket is established to describe the motion of the rocket in the world coordinate system, including the changes in position, angle and speed. The formula is: ； in, is the speed in the horizontal direction, is the velocity in the vertical direction, is the mass of the rocket, is the rocket's moment of inertia, is the height of the rocket, is the fuel consumption coefficient per unit impulse, For thrust; (4) Initial and terminal constraints Define the initial state and terminal state, where the initial state is the initial condition of the launch point ; Terminal state is orbital element or landing condition ; (5) Path and inequality constraints For a two-stage reusable launch vehicle: Thrust constraint: ; Angle constraint: Dynamic load and axial overload: ;in, is the maximum dynamic pressure of the integrated vehicle, It is the maximum axial overload of the integrated carrier; (6) Problem discretization The Legendre-Gauss-Lobatto pseudo-spectral method is used to discretize the state variables and control variables into multiple time nodes. The state and control are fitted by the Lagrange interpolation function, and the differential equation is converted into an algebraic equation to simplify the solution process. Discrete form: ； in, is the basis function of the Lagrange interpolation function; The dynamic equations are solved at discrete nodes: ； in, ; Differential matrix Represents the differential value of each basis function of the Lagrange interpolation function at each LGL point; (7) Discretization of constraints Convert initial conditions, terminal conditions, path constraints and inequality constraints into algebraic constraint relations at discrete points; (8) Problem Solving The optimization problem is handed over to the solver for linearization and solution to obtain the optimal fuel consumption, thrust and program angle.

9. The model-based integrated design and verification method for complex equipment systems according to claim 8, characterized in that: The initial and terminal conditions are: ； in, It is used to specify the horizontal displacement of the landing point from the initial point, which can be determined in combination with the location of the recycling station. is the fuel consumption during the process; The inequality constraints are discretized as follows, and the thrust limit is discretized as: ； The angle limit discretization is: ； Among them, N is the number of matching points.

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