A margin analysis method based on MBSE aerospace equipment model

Abstract

The present invention discloses a margin analysis method based on the MBSE aerospace equipment model, comprising the following steps: S1. Establishing an MBSE model for aerospace equipment, converting design parameters, functional requirements, and system constraints into a unified system model; S2. Analyzing and determining coupled design parameters through the system model; S3. Generating margin characterization data based on the coupled design parameters; S4. Generating a margin allocation result based on the margin characterization data; S5. Generating a visualized margin allocation result based on the margin allocation result; S6. Calculating a safety margin boundary based on the visualized margin allocation result; S7. Generating an over-limit alarm and verification report based on the safety margin boundary; S8. Based on the over-limit alarm and verification report, monitoring changes in design parameters in real time and recording any over-limit situations of design parameters. This invention introduces the MBSE concept and, by establishing a formalized model of the system, provides a more systematic and accurate perspective for margin analysis.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of MBSE, and particularly relates to a margin analysis method based on an MBSE aerospace equipment model. BACKGROUND

[0002] With the rapid development of China's aerospace industry, it also faces great challenges. Systems in the field of aerospace contain millions of components and involve the close integration of software, hardware, and physical subsystems. The reliability requirements of systems under different environments and conditions are very high, and margin analysis is therefore critical. Margin analysis of aerospace equipment plays a crucial role in ensuring the safety and reliability of space missions. Margin analysis aims to assess the performance resilience of aerospace equipment under various working conditions and environments to ensure that it can still function normally and complete tasks when faced with different challenges.

[0003] Traditional margin analysis methods usually rely on empirical rules and manual calculations, and the analysis process is tedious, time-consuming and prone to errors. This method is less efficient in modern complex system design and is difficult to adapt to the needs of rapid iteration; it cannot effectively handle the mutual influence of various fields, especially when parameters and design goals from different disciplines conflict with each other, traditional margin analysis is difficult to find a global optimal solution; in modern complex systems, various subsystems are interconnected, and traditional methods are difficult to perform margin analysis at the system level, and can only analyze in components or individual subsystems.

[0004] MBSE refers to a formal way of applying modeling methods to support system requirements, design, analysis, testing and verification activities, which start from the conceptual design stage and run through the entire development process and subsequent life cycle stages. Its powerful modeling and analysis capabilities provide strong support for margin analysis. Based on the above characteristics of MBSE and the fact that traditional margin analysis methods in the field of aerospace equipment cannot meet the needs,

[0005] Therefore, how to provide a margin analysis method based on an MBSE aerospace equipment model is a problem that those skilled in the art need to solve. SUMMARY

[0006] One purpose of the present application is to provide a margin analysis method based on MBSE aerospace equipment model. The present application provides a new perspective for margin analysis by introducing the concept of MBSE. MBSE can accurately represent the margin range and type of design parameters and identify and handle complex coupling relationships in the system by establishing a unified system formalized model, which can convert design parameters, functional requirements and system constraints into quantifiable analysis basis. With the simulation function of the MBSE model, the present application can simulate system performance, optimize margin allocation and reduce dependence on actual tests at the design stage, effectively improving the systematicness and accuracy of analysis. In addition, MBSE supports interdisciplinary collaborative design and life cycle management, which can realize margin monitoring and dynamic optimization from design to production, testing, maintenance and other stages, and timely discover potential risks through automatic checking and real-time alarm mechanism, and generate checking reports. Compared with the traditional method, the present application significantly improves the efficiency and accuracy of margin analysis, reduces time and cost, and ensures that the system meets the performance and safety requirements throughout the life cycle.

[0007] According to the margin analysis method based on the MBSE aerospace equipment model of the embodiment of the present application, the following steps are included:

[0008] S1, establishing an MBSE model of aerospace equipment, converting design parameters, functional requirements and system constraints into a unified system model;

[0009] S2, identifying the coupling relationship between different subsystems in the system, analyzing and determining the coupling design parameters through the system model;

[0010] S3, constructing a margin set according to the coupling design parameters, clearly defining the margin range and type of each design parameter, and forming margin representation data;

[0011] S4, based on the margin representation data, performing margin allocation of different design parameters based on the functional architecture of the MBSE model, generating margin allocation results, and reasonably adjusting the margin in combination with the functional requirements of each level of the system;

[0012] S5, based on the margin allocation results, visualizing the margin allocation results using matrix charts, displaying the margin relationship between design parameters, and generating visual margin allocation results;

[0013] S6, based on the visual margin allocation results, further optimizing the margin of different design parameters using a linear programming model, and calculating the safety margin boundary of each design parameter;

[0014] S7, based on the generated safety margin boundary, performing automatic checking using OCL language, checking whether the margin meets the requirements in real time, and generating overrun alarm and checking report;

[0015] S8, according to the overrun alarm and the check report, real-time monitoring the change of the design parameter, triggering the safety event response, recording the event and tracing the overrun of the design parameter.

[0016] Optionally, the S1 specifically comprises:

[0017] S11, determining the design requirement, the function requirement and the system constraint of the spaceflight equipment, and clarifying the mutual relationship and the dependency relationship between the subsystems;

[0018] S12, constructing the function model of each subsystem by using the UML modeling language, and describing the function structure, the physical parameter, the performance index and the system constraint condition of the subsystem;

[0019] S13, connecting the function models of the subsystems into a whole system model by defining the unified interface and the data flow;

[0020] S14, in the whole system model, calibrating the constraint condition of each design parameter and its physical quantity, and clarifying the margin range of each design parameter;

[0021] S15, based on the calibrated design parameter and the constraint condition, adopting the multidisciplinary design optimization method to adjust and optimize the design parameter in the system model;

[0022] S16, based on the optimized design parameter, combining the design requirement and the system constraint, constructing the system constraint condition and the margin boundary, and generating the symbolized margin representation data;

[0023] S17, verifying the generated symbolized margin representation data by the simulation tool, generating the simulation result and evaluating whether the system satisfies the safety margin boundary of the design, and adjusting the system model according to the evaluation result.

[0024] Optionally, the S4 specifically comprises:

[0025] S41, based on the margin representation data, defining the margin boundary, the reasonable range and the margin type of each design parameter, and clarifying the margin requirement of each design parameter;

[0026] S42, according to the function architecture in the MBSE model, identifying the mapping relationship between the design parameter and each function level, analyzing the influence of the design parameter on the system function, and quantifying the system performance requirement;

[0027] S43, adopting the system architecture analysis method to perform the margin allocation of different design parameters on the function level, and adjusting the margin allocation strategy of each design parameter according to the priority and the contribution degree of the design parameter to the overall performance of the system;

[0028] S44, optimizing the margin allocation between the design parameters by the coupling relationship analysis and the matrix analysis method;

[0029]

[0030] wherein, L is the total margin of the system, x i and x j are the margin values of the design parameters; A ij is the coupling relationship coefficient between the design parameters, and N represents the design parameters.

[0031] Optionally, the S5 specifically includes:

[0032] S51, on the basis of the margin allocation result, a margin relationship matrix between the design parameters is constructed, and the margin influence degree between each pair of design parameters x i and x j is defined:

[0033] M ij = f(x i , x j );

[0034] wherein, x i is the value of the design parameter i, x j is the value of the design parameter j, f(x i , x j ) is the margin influence function between the design parameter i and the design parameter j, and the value of M ij indicates the strength of the margin influence;

[0035] S52, according to the constructed margin relationship matrix, a visual method is used to display the margin allocation result between the design parameters in the form of a matrix graph, and the value of each element in the matrix graph represents the margin relationship between the design parameters;

[0036] S53, on the basis of the visual margin relationship matrix, the influence relationship between each design parameter is analyzed, the design parameters are checked whether to meet the margin allocation condition in combination with the overall function requirement of the system, and a preliminary margin allocation scheme is generated;

[0037] S54, based on the preliminary margin allocation scheme, the margin allocation of the design parameters is optimized through linear programming to balance the margins of the design parameters, and an optimized margin allocation scheme is obtained:

[0038]

[0039] wherein, O(x) is an optimization objective function, x i is the margin of the design parameter i, α i is the weighted coefficient of each design parameter, β i is the square weighted coefficient of the design parameter, and n is the total number of the design parameters;

[0040] S55, final verification is performed on the optimized margin allocation scheme to check whether the optimization result meets the safety and function requirements of the system design.

[0041] Optionally, the S6 specifically includes:

[0042] S61, based on the generated visual margin allocation result, an initial margin allocation value of each design parameter is input into a linear programming model;

[0043] S62, a target function of the linear programming model is set, wherein the target function is a margin optimization target of all design parameters, and is expressed as:

[0044]

[0045] wherein f(U) is the margin optimization target of all design parameters, U i is a margin value of the design parameter i, c i is a corresponding weight coefficient;

[0046] S63, a constraint condition of the linear programming is determined, including a safety margin boundary and a margin range of the design parameter:

[0047] U min,i ≤U i ≤U max,i and

[0048] wherein U safe,i is the safety margin boundary of the design parameter, U min,i and U max,i are the margin range;

[0049] S64, by solving the linear programming model, an optimized margin value of each design parameter is obtained;

[0050] S65, according to the optimized margin value, a safety margin boundary of each design parameter is calculated, and is compared with the initial margin allocation result, to obtain an optimized margin adjustment result;

[0051] S66, according to the optimized margin result, a system model is updated.

[0052] Optionally, the S7 specifically includes:

[0053] S71, according to the safety margin boundary, an OCL language expression model is constructed to define a check condition of each design parameter;

[0054] S72, according to the generated check condition, an actual margin of each design parameter is compared, an out-of-limit result is recorded, an alarm is triggered, and alarm information is recorded;

[0055] S73, integrate the alarm information with the actual margin of the design parameter and the corresponding safety margin boundary to generate a verification report;

[0056] S74, in the verification report, list all the out-of-limit parameters in detail, provide the number, out-of-limit value and alarm time of the out-of-limit design parameter, and highlight the out-of-limit design parameter;

[0057] S75, integrate the verification report with the real-time monitoring system, track the change of the design parameter in real time, if an out-of-limit event occurs, update the alarm information and trigger the corresponding safety response process;

[0058] S76, generate a safety event response scheme according to the out-of-limit alarm and the verification report, record the detailed information of the out-of-limit event, including the event occurrence time, out-of-limit parameter and out-of-limit value, and trigger the design review process.

[0059] The beneficial effects of the present application are:

[0060] The present application breaks the limitation of traditional single component analysis by introducing a MBSE-based aerospace equipment margin analysis method, which can comprehensively and comprehensively analyze the mutual dependence and influence between each subsystem and component at the system level. By using the characteristics of the MBSE model, the present application is no longer limited to the margin analysis of a single design parameter or component, but can more comprehensively evaluate the performance margin of the entire system through the system level model, ensuring reasonable margin allocation and optimization of each design parameter at multiple levels, thereby improving the overall attention and control ability of the entire system.

[0061] In addition, with the help of the simulation function of the MBSE model, the present application can continuously monitor and optimize the margin throughout the entire equipment life cycle. This dynamic monitoring and optimization capability not only accurately calculates and adjusts the margin during the design phase, but also continuously evaluates the performance and safety of the system during actual use. By obtaining data in real time and analyzing it, potential design problems can be discovered and solved in a timely manner, effectively avoiding safety hazards caused by insufficient or unreasonable allocation of design margin.

[0062] The present application also integrates knowledge from different disciplines and uses a unified view for comprehensive margin analysis, which can comprehensively evaluate the system across disciplines. This method can effectively identify and handle the coupling effect between disciplines, further improving the reliability and resilience of the system. Comprehensive consideration of margin analysis of various disciplines not only enhances the system performance of aerospace equipment, but also improves the overall safety and reliability, providing a strong guarantee for the successful implementation of complex aerospace missions. BRIEF DESCRIPTION OF DRAWINGS

[0063] The accompanying drawings are included to provide a further understanding of the application, and are incorporated in and constitute a part of this specification, illustrate embodiments of the application, and together with the description serve to explain the application, and do not limit the application. In the drawings:

[0064] Fig. 1 A flow chart of a margin analysis method based on an MBSE aerospace equipment model is proposed for the application;

[0065] Fig. 2 A system architecture schematic diagram of a margin analysis method based on an MBSE aerospace equipment model is proposed for the application. DETAILED DESCRIPTION

[0066] The application will now be described in further detail with reference to the drawings. These drawings are simplified schematic diagrams, which only schematically show the basic structure of the application, and thus only show the components relevant to the application.

[0067] REFERENCE Figs. 1-2 A margin analysis method based on an MBSE aerospace equipment model, comprising the following steps:

[0068] S1, establishing an MBSE model of aerospace equipment, converting design parameters, functional requirements, and system constraints into a unified system model;

[0069] S2, identifying the coupling relationship between different subsystems in the system, analyzing and determining the coupling design parameters through the system model;

[0070] S3, constructing a margin set according to the coupling design parameters, clearly defining the margin range and type of each design parameter, and forming margin representation data;

[0071] S4, based on the margin representation data, performing margin allocation of different design parameters on the basis of the functional architecture of the MBSE model, generating margin allocation results, and combining the functional requirements of each level of the system to perform reasonable margin adjustment;

[0072] S5, based on the margin allocation results, visualizing the margin allocation results using a matrix chart, displaying the margin relationship between the design parameters, and generating visualized margin allocation results;

[0073] S6, based on the visualized margin allocation results, further optimizing the margin of different design parameters using a linear programming model, and calculating the safety margin boundary of each design parameter;

[0074] S7, based on the generated safety margin boundary, performing automatic checking using OCL language, checking whether the margin meets the requirements in real time, and generating an overrun alarm and a checking report;

[0075] S8, according to the overrun alarm and the check report, real-time monitoring the change of the design parameter, triggering the safety event response, recording the event and tracing the overrun of the design parameter.

[0076] In the embodiment, the S1 specifically comprises:

[0077] S11, determining the design requirement, the function requirement and the system constraint of the aerospace equipment, and determining the mutual relationship and the dependency relationship between the subsystems;

[0078] S12, using the UML modeling language to construct the function model of each subsystem, and describing the function structure, the physical parameter, the performance index and the system constraint condition of the subsystem;

[0079] S13, connecting the function models of the subsystems into a whole system model by defining the unified interface and the data flow;

[0080] S14, in the whole system model, calibrating the constraint condition of each design parameter and its physical quantity, and determining the margin range of each design parameter;

[0081] S15, based on the calibrated design parameter and the constraint condition, using the multidisciplinary design optimization method to adjust and optimize the design parameter in the system model;

[0082] S16, based on the optimized design parameter, combining the design requirement and the system constraint, constructing the system constraint condition and the margin boundary, and generating the symbolic margin representation data;

[0083] S17, verifying the generated symbolic margin representation data by the simulation tool, generating the simulation result and evaluating whether the system meets the safety margin boundary of the design, and adjusting the system model according to the evaluation result.

[0084] In the embodiment, the S4 specifically comprises:

[0085] S41, based on the margin representation data, defining the margin boundary, the reasonable range and the margin type of each design parameter, and determining the margin requirement of each design parameter;

[0086] S42, according to the function architecture in the MBSE model, identifying the mapping relationship between the design parameter and each function level, analyzing the influence of the design parameter on the system function, and quantifying the system performance requirement;

[0087] S43, using the system architecture analysis method to perform the margin allocation of different design parameters on the function level, and adjusting the margin allocation strategy according to the priority of each design parameter and the contribution degree to the overall performance of the system;

[0088] S44, optimizing the margin allocation between the design parameters by the coupling relationship analysis and the matrix analysis method.

[0089]

[0090] wherein, L is the total margin of the system, x i and x j are the margin values of the design parameters; A ij is the coupling relationship coefficient between the design parameters, and N represents the design parameters.

[0091] In the embodiment, the S5 specifically includes:

[0092] S51, on the basis of the margin allocation result, a margin relationship matrix between the design parameters is constructed, and the margin influence degree between each pair of design parameters x i and x j is defined:

[0093] M ij = f(x i , x j );

[0094] wherein, x i is the value of the design parameter i, x j is the value of the design parameter j, f(x i , x j ) is the margin influence function between the design parameter i and the design parameter j, and the value of M ij indicates the strength of the margin influence;

[0095] S52, according to the constructed margin relationship matrix, the margin allocation result between the design parameters is displayed in the form of a matrix graph by using a visualization method, and the value of each element in the matrix graph represents the margin relationship between the design parameters;

[0096] S53, on the basis of the visualized margin relationship matrix, the influence relationship between each design parameter is analyzed, the design parameters are checked whether they meet the margin allocation conditions in combination with the overall function requirement of the system, and a preliminary margin allocation scheme is generated;

[0097] S54, based on the preliminary margin allocation scheme, the margin allocation of the design parameters is optimized by linear programming to balance the margins of the design parameters, and an optimized margin allocation scheme is obtained:

[0098]

[0099] wherein, O(x) is an optimization objective function, x i is the margin of the design parameter i, a i is the weighted coefficient of each design parameter, b i is the square weighted coefficient of the design parameter, and n is the total number of the design parameters;

[0100] S55, final verification is performed on the optimized margin allocation scheme to check whether the optimization result meets the safety and function requirements of the system design.

[0101] In this embodiment, S6 specifically includes:

[0102] S61, based on the generated visual margin allocation result, the initial margin allocation value of each design parameter is input into the linear programming model;

[0103] S62, the objective function of the linear programming model is set, wherein the objective function is the margin optimization objective of all design parameters, and is expressed as:

[0104]

[0105] wherein f(U) is the margin optimization objective of all design parameters, U i is the margin value of the design parameter i, c i is the corresponding weight coefficient;

[0106] S63, the constraint condition of the linear programming is determined, including the safety margin boundary and the margin range of the design parameter:

[0107] U min,i ≤U i ≤U max,i and

[0108] wherein U safe,i is the safety margin boundary of the design parameter, U min,i and U max,i are the margin range;

[0109] S64, by solving the linear programming model, the optimized margin value of each design parameter is obtained;

[0110] S65, according to the optimized margin value, the safety margin boundary of each design parameter is calculated, and compared with the initial margin allocation result, to obtain the optimized margin adjustment result;

[0111] S66, according to the optimized margin result, the system model is updated.

[0112] In this embodiment, S7 specifically includes:

[0113] S71, according to the safety margin boundary, an OCL language expression model is constructed to define the check condition of each design parameter;

[0114] S72, according to the generated check condition, the actual margin of each design parameter is compared, the out-of-limit result is recorded, an alarm is triggered and alarm information is recorded;

[0115] S73, integrate the alarm information with the actual margin of the design parameter and the corresponding safety margin boundary to generate a verification report;

[0116] S74, in the verification report, list all the out-of-limit parameters in detail, provide the number, out-of-limit value and alarm time of the out-of-limit design parameter, and highlight the out-of-limit design parameter;

[0117] S75, integrate the verification report with the real-time monitoring system, track the changes of the design parameters in real time, if an out-of-limit event occurs, update the alarm information and trigger the corresponding safety response process;

[0118] S76, generate a safety event response scheme according to the out-of-limit alarm and the verification report, record the detailed information of the out-of-limit event, including the event occurrence time, out-of-limit parameter and out-of-limit value, and trigger the design review process.

[0119] Embodiment 1:

[0120] In order to verify the feasibility of the application in implementation, a system design stage of a certain space mission is carried out, in which the spacecraft is a multifunctional space equipment mainly used for performing high-precision flight tasks. In the design process of the spacecraft, multiple systems and subsystems are involved, including power system, electrical system, communication system, thermal control system, structural system and guidance control system, etc. Due to the operation of the spacecraft in complex environment, there is a strong coupling relationship between the subsystems, and the influence between the systems and how they work together must be considered in the design process to ensure the overall safety, reliability and performance of the spacecraft.

[0121] Firstly, a system-level model of the spacecraft is established by the MBSE method. In this model, each subsystem of the spacecraft is modeled one by one, and the interfaces and mutual influences between them are clearly defined.

[0122] After completing the construction of the system model, the team identifies the strong coupling design parameters of the spacecraft. These design parameters involve the core performance of the system, such as aerodynamic load, structural stiffness, heat transfer, power demand, etc. Through the identification of these parameters, the team can accurately master the performance margin of the spacecraft under different environmental and working conditions.

[0123] Subsequently, the team takes the identified strong coupling design parameters as input and performs margin characterization based on the MBSE model. By defining margin indicators and model parameter ranges, the team can accurately define and control each design parameter. These design parameters and margin indicators include aerodynamic deviation, load coefficient, structural strength, control accuracy, etc.

[0124] Next, the team conducted margin allocation and optimization based on the established system model. In this phase, the team used three methods to allocate margins to design parameters: model-based digital modeling language multiplicity, matrix allocation method, and mathematical data structure method.

[0125] After the margin allocation was completed, the team conducted automatic checking of design parameters. Based on OCL (Object Constraint Language) and constraint elements in the MBSE model, the team built a checking model to check the design parameters. The checking model can automatically identify whether the design parameters are out of limits and provide real-time alarm information to help designers adjust the design in a timely manner to avoid potential risks.

[0126] Table 1 Comparison of MBSE method and traditional design method in aircraft design

[0127]

[0128] This table shows the comparison of test times, costs, and time in different design stages (design parameter analysis, margin allocation and optimization, checking and monitoring) between the MBSE (Model-Based Systems Engineering) method and the traditional method, highlighting the significant advantages brought by the MBSE method. The MBSE method greatly reduces the need for physical tests through simulation and modeling optimization. Especially in the design parameter analysis and margin allocation stages, the number of tests is reduced to 1 / 5 and 1 / 5 of the traditional method. The MBSE method saves a lot of costs, saving 75% in the design parameter analysis stage, 80% in the margin allocation and optimization stage, and 83.3% in the checking and monitoring stage. These cost savings come from reducing physical tests, reducing manual intervention, and improving design stage efficiency. The MBSE method accelerates the work process in each stage, especially in the design parameter analysis and checking and monitoring stages, saving 50% of the time. By reducing redundant work and accelerating the verification process, the total project cycle is significantly shortened, enabling faster practical application.

[0129] Through data analysis, it can be seen that the MBSE method not only improves the accuracy of the design, but also plays a key role in significantly saving test costs and accelerating the design process. In particular, in the field of aerospace, reducing the number of tests and optimizing the design process time and cost can effectively improve the market competitiveness of products and shorten the mission implementation cycle. Therefore, the MBSE method has very important application value in the design, optimization, and verification of aerospace equipment, providing strong support for the smooth implementation of aerospace missions.

[0130] The above merely describes preferred specific embodiments of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art, according to the technical solution and inventive concept of the present application, makes equivalent replacement or change within the technical range disclosed by the present application, which should be covered within the protection scope of the present application.

Claims

1. A margin analysis method based on MBSE aerospace equipment model, characterized by: The steps include: S1. Establish an MBSE model for aerospace equipment and transform design parameters, functional requirements, and system constraints into a unified system model. S2. Identify the coupling relationship between different subsystems in the system, analyze and determine the coupling design parameters through system model; S3. Based on the coupled design parameters, a margin set is constructed to clarify the margin range and type of each design parameter and form margin characterization data; S4. Based on the margin characterization data and the functional architecture of the MBSE model, margin allocation is performed for different design parameters. Margin allocation results are generated and reasonable margin adjustments are made in combination with the functional requirements of each level of the system. S5. Based on the margin allocation results, a matrix chart is used to visualize the margin allocation results, display the margin relationship between the design parameters, and generate a visualized margin allocation result; S6. Based on the visualized margin allocation results, a linear programming model is used to further optimize the margins of different design parameters and calculate the safety margin boundaries of each design parameter; S7. Based on the generated safety margin boundary, the OCL language is used for automatic verification to check whether the margin meets the requirements in real time and generate an over-limit alarm and verification report; S8. Based on the over-limit alarm and verification report, monitor the changes in design parameters in real time and trigger the safety event response. At the same time, record the event and trace the over-limit situation of the design parameters.

2. The margin analysis method based on the MBSE aerospace equipment model according to claim 1 is characterized in that: Said S1 specifically includes: S11. Determine the design requirements, functional requirements, and system constraints of aerospace equipment, and clarify the relationships and dependencies between subsystems; S12. Use the UML modeling language to construct the functional model of each subsystem, describing the functional structure, physical parameters, performance indicators and system constraints of the subsystem; S13. Connect the functional models of each subsystem into an overall system model by defining unified interfaces and data flows; S14. In the overall system model, calibrate the constraints of each design parameter and its physical quantity, and clarify the margin range of each design parameter; S15. Based on the calibrated design parameters and constraints, use multidisciplinary design optimization methods to adjust and optimize the design parameters in the system model; S16. Based on the optimized design parameters, combined with the design requirements and system constraints, construct system constraints and margin boundaries, and generate symbolic margin representation data; S17. Use simulation tools to verify the generated symbolic margin characterization data, generate simulation results and evaluate whether the system meets the designed safety margin boundary, and adjust the system model based on the evaluation results.

3. The margin analysis method based on the MBSE aerospace equipment model according to claim 1 is characterized in that: The S4 specifically includes: S41. Based on the margin characterization data, define the margin boundary, reasonable range, and margin type of each design parameter, and clarify the margin requirements for each design parameter; S42. Based on the functional architecture in the MBSE model, identify the mapping relationship between design parameters and each functional level, analyze the impact of design parameters on system functions, and quantify system performance requirements; S43. Use system architecture analysis methods to allocate margins for different design parameters at the functional level, and adjust the margin allocation strategy based on the priority of each design parameter and its contribution to the overall system performance; S44. Optimize margin allocation between design parameters through coupling relationship analysis and matrix analysis methods; Where L is the total system margin, x i and x j is the margin value of the design parameter; A ij is the coupling coefficient between the design parameters, and N represents the design parameters.

4. The margin analysis method based on the MBSE aerospace equipment model according to claim 1 is characterized in that: The S5 specifically includes: S51. Based on the margin allocation results, construct the margin relationship matrix between the design parameters and define each pair of design parameters x i and x j The degree of influence of the margin between: M ij =f(x i ,x j ); Among them, x i is the value of the design parameter i, x j is the value of the design parameter j, f(x i ,x j ) is the margin influence function between design parameters i and j, M ij The value of indicates the strength of the margin effect; S52. Based on the constructed margin relationship matrix, a visualization method is used to present the margin allocation results between the design parameters in the form of a matrix diagram, where the value of each element in the matrix diagram represents the margin relationship between the design parameters; S53. Based on the visualized margin relationship matrix, analyze the influence relationship between each design parameter, combine the overall functional requirements of the system, check whether the design parameters meet the margin allocation conditions, and generate a preliminary margin allocation plan; S54. Based on the preliminary margin allocation scheme, the margin allocation of the design parameters is optimized through linear programming, the margins of the design parameters are balanced, and an optimized margin allocation scheme is obtained: Among them, O(x) is the optimization objective function, x i is the margin of design parameter i, α i is the weighting coefficient of each design parameter, β i is the square weighting coefficient of the design parameters, and n is the total number of design parameters; S55. Perform final verification on the optimized margin allocation scheme to check whether the optimization results meet the safety and functional requirements of the system design.

5. The margin analysis method based on MBSE aerospace equipment model according to claim 1 is characterized in that: The S6 specifically includes: S61. Based on the generated visualized margin allocation result, input the initial margin allocation value of each design parameter into the linear programming model; S62. Set the objective function of the linear programming model, where the objective function is the margin optimization target of all design parameters, expressed as: Among them, f(U) is the margin optimization target of all design parameters, U i is the margin value of design parameter i, c i is the corresponding weight coefficient; S63. Determine the constraints of the linear programming, including the safety margin boundaries and margin ranges of the design parameters: U min,i ≤U i ≤U max,i And Among them, U safe,i is the safety margin of the design parameters, U min,i and U max,i is the margin range; S64. Obtaining an optimized margin value for each design parameter by solving a linear programming model; S65. Calculate the safety margin boundary of each design parameter based on the optimized margin value, and compare it with the initial margin allocation result to obtain an optimized margin adjustment result; S66. Update the system model according to the optimized margin result.

6. The margin analysis method based on the MBSE aerospace equipment model according to claim 1, characterized in that: The S7 specifically includes: S71. Construct an OCL language expression model based on the safety margin boundary and define the verification condition of each design parameter; S72. Compare the actual margin of each design parameter according to the generated verification conditions, record the exceeding limit results, trigger an alarm and record the alarm information; S73, integrating the alarm information with the actual margin of the design parameters and the corresponding safety margin boundary to generate a verification report; S74. In the verification report, all out-of-limit parameters are listed in detail, including the number, value, and alarm time of the out-of-limit design parameter, and the out-of-limit design parameter is highlighted; S75. Integrate the verification report with the real-time monitoring system to track changes in design parameters in real time. If an out-of-limit event occurs, update the alarm information and trigger the corresponding safety response process; S76. Generate a safety incident response plan based on the over-limit alarm and verification report, record detailed information of the over-limit event, including the time of occurrence of the event, over-limit parameters and over-limit values, and trigger the design review process.

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