MBSE spaceflight equipment model-based margin analysis method

Abstract

The invention discloses a margin analysis method based on an MBSE spaceflight equipment model, and the method comprises the following steps: S1, building an MBSE model of spaceflight equipment, and converting design parameters, function demands and system constraints into a unified system model; s2, analyzing and determining coupling design parameters through a system model; s3, margin representation data are formed according to the coupling design parameters; s4, generating a margin distribution result based on the margin representation data; s5, based on the margin distribution result, generating a visual margin distribution result; s6, calculating a safety margin boundary based on a visual margin distribution result; s7, generating an over-limit alarm and verification report based on the safety margin boundary; and S8, monitoring the change of the design parameters in real time according to the over-limit alarm and verification report, and recording the over-limit condition of the design parameters. According to the method, the MBSE concept is introduced, and a more systematic and accurate view angle is provided for margin analysis by establishing a formalized model of the system.

Description

Technical Field

[0001] The present invention relates to the field of MBSE technology, and in particular to a margin analysis method based on an MBSE aerospace equipment model. Background Art

[0002] With the rapid development of my country's aerospace industry, its development is also facing huge challenges. Systems in the aerospace field contain millions of components and involve the tight integration of software, hardware, and physical subsystems. Ensuring the reliability of the system in different environments and conditions requires very high standards, so margin analysis becomes the key. Margin analysis of aerospace equipment plays a vital role in ensuring the safety and reliability of aerospace missions. Margin analysis aims to evaluate the performance resilience of aerospace equipment under various working conditions and environments to ensure that it can still operate normally and complete its mission in the face of different challenges.

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

[0004] MBSE is a formal way of applying modeling methods to support system requirements, design, analysis, inspection 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 the MBSE aerospace equipment model is an urgent problem that technicians in this field need to solve. Summary of the invention

[0006] One purpose of the present invention is to propose a margin analysis method based on the MBSE aerospace equipment model. The present invention provides a new perspective for margin analysis by introducing the concept of MBSE. MBSE transforms design parameters, functional requirements and system constraints into a quantifiable analysis basis by establishing a unified system formalization model, which can accurately characterize the margin range and type of design parameters, and identify and process complex coupling relationships within the system. With the help of the simulation function of the MBSE model, the present invention can simulate system performance in the design stage, optimize margin allocation, reduce dependence on actual experiments, and effectively improve the systematicity and accuracy of analysis. In addition, MBSE supports interdisciplinary collaborative design and full life cycle management, and can realize margin monitoring and dynamic optimization from design to production, testing, maintenance and other stages, and timely discover potential risks through automated verification and real-time alarm mechanisms, and generate verification reports. Compared with traditional methods, the present invention significantly improves the efficiency and accuracy of margin analysis, reduces time and cost, and ensures that the system always meets performance and safety requirements throughout its life cycle.

[0007] A margin analysis method based on an MBSE aerospace equipment model according to an embodiment of the present invention includes the following steps:

[0008] S1. Establish the MBSE model of aerospace equipment and transform the design parameters, functional requirements and system constraints into a unified system model;

[0009] S2. Identify the coupling relationship between different subsystems in the system, and determine the coupling design parameters through system model analysis;

[0010] S3. According to 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;

[0011] S4. Based on the margin characterization data and the functional architecture of the MBSE model, margin allocation of different design parameters is performed to generate margin allocation results, and reasonable margin adjustment is performed in combination with the functional requirements of each level of the system;

[0012] S5. Based on the margin allocation result, a matrix chart is used to visualize the margin allocation result, display the margin relationship between the design parameters, and generate a visualized margin allocation result;

[0013] S6. Based on the visualized margin allocation results, the linear programming model is used to further optimize the margins of different design parameters and calculate the safety margin boundaries of each design parameter;

[0014] 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 an over-limit alarm and verification report are generated;

[0015] S8. Based on the over-limit alarm and verification report, monitor the changes in design parameters in real time and trigger safety event response. At the same time, record the event and trace the over-limit situation of design parameters.

[0016] Optionally, the S1 specifically includes:

[0017] S11. Determine the design requirements, functional requirements and system constraints of aerospace equipment, and clarify the relationships and dependencies between subsystems;

[0018] S12. Use 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;

[0019] S13. Connect the functional models of each subsystem into an overall system model by defining a unified interface and data flow;

[0020] 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;

[0021] S15. Based on the calibrated design parameters and constraints, a multidisciplinary design optimization method is used to adjust and optimize the design parameters in the system model;

[0022] S16. Based on the optimized design parameters, combined with the design requirements and system constraints, the system constraints and margin boundaries are constructed to generate symbolic margin characterization data;

[0023] 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 according to the evaluation results.

[0024] Optionally, the S4 specifically includes:

[0025] 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 of each design parameter;

[0026] S42. According to 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;

[0027] S43. Using the system architecture analysis method, 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;

[0028] S44. Optimize the margin allocation between design parameters through coupling relationship analysis and matrix analysis methods;

[0029]

[0030] 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.

[0031] Optionally, the S5 specifically includes:

[0032] 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 margin influence degree between:

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

[0034] 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 parameter i and design parameter j, M ij The value of indicates the strength of the margin effect;

[0035] 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;

[0036] S53. On the basis of 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;

[0037] S54. Based on the preliminary margin allocation scheme, the margin allocation of the design parameters is optimized by linear programming, the margins of the design parameters are balanced, and an optimized margin allocation scheme is obtained:

[0038]

[0039] Among them, O(x) is the optimization objective function, x i is the margin of design parameter i, α i is the weighting coefficient for each design parameter, β i is the square weighting coefficient of the design parameters, n is the total number of design parameters;

[0040] 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.

[0041] Optionally, the S6 specifically includes:

[0042] S61, based on the generated visualized margin allocation result, inputting the initial margin allocation value of each design parameter into the linear programming model;

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

[0044]

[0045] 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;

[0046] S63. Determine the constraints of the linear programming, including the safety margin boundary and margin range of the design parameters:

[0047] U min,i ≤U i ≤U max,i and

[0048] Among them, U safe,i is the safety margin of the design parameters, U min,i and U max,i is the margin range;

[0049] S64, obtaining an optimization margin value of each design parameter by solving a linear programming model;

[0050] S65. Calculate the safety margin boundary of each design parameter according to the optimized margin value, and compare it with the initial margin allocation result to obtain an optimized margin adjustment result;

[0051] S66. Update the system model according to the optimized margin result.

[0052] Optionally, the S7 specifically includes:

[0053] S71. Construct an OCL language expression model according to the safety margin boundary and define the verification condition of each design parameter;

[0054] S72. According to the generated verification conditions, the actual margin of each design parameter is compared, the over-limit result is recorded, an alarm is triggered and the alarm information is recorded;

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

[0056] S74. In the verification report, all out-of-limit parameters are listed in detail, the number of the out-of-limit design parameter, the out-of-limit value and the alarm time are provided, and the out-of-limit design parameter is highlighted;

[0057] S75. Integrate the verification report with the real-time monitoring system to track the changes in design parameters in real time. If an over-limit event occurs, update the alarm information and trigger the corresponding safety response process;

[0058] 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.

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

[0060] The present invention breaks the limitation of traditional single component analysis by introducing the margin analysis method of aerospace equipment based on MBSE, and can comprehensively and comprehensively analyze the interdependence and influence between various subsystems and components at the system level. Utilizing the characteristics of the MBSE model, the present invention is no longer limited to the margin analysis of a single design parameter or component, but through the system-level model, it can more comprehensively evaluate the performance margin of the entire system, ensure reasonable margin allocation and optimization of various design parameters at multiple levels, thereby improving the global attention and control capabilities of the entire system.

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

[0062] The present invention also integrates knowledge from different disciplines and uses a unified view to conduct comprehensive margin analysis, which can comprehensively evaluate the system across disciplines. This method can effectively identify and process the coupling effects between disciplines, further improving the reliability and resilience of the system. The margin analysis that comprehensively considers factors from 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 THE DRAWINGS

[0063] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0064] Figure 1 This is a flow chart of a margin analysis method based on the MBSE aerospace equipment model proposed by the present invention;

[0065] Figure 2 This is a schematic diagram of the system architecture of a margin analysis method based on the MBSE aerospace equipment model proposed in the present invention. DETAILED DESCRIPTION

[0066] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0067] refer to Figure 1-2 , a margin analysis method based on MBSE aerospace equipment model, including the following steps:

[0068] S1. Establish the MBSE model of aerospace equipment and transform the design parameters, functional requirements and system constraints into a unified system model;

[0069] S2. Identify the coupling relationship between different subsystems in the system, and determine the coupling design parameters through system model analysis;

[0070] S3. According to 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;

[0071] S4. Based on the margin characterization data and the functional architecture of the MBSE model, margin allocation of different design parameters is performed to generate margin allocation results, and reasonable margin adjustment is performed in combination with the functional requirements of each level of the system;

[0072] S5. Based on the margin allocation result, a matrix chart is used to visualize the margin allocation result, display the margin relationship between the design parameters, and generate a visualized margin allocation result;

[0073] S6. Based on the visualized margin allocation results, the linear programming model is used to further optimize the margins of different design parameters and calculate the safety margin boundaries of each design parameter;

[0074] 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 an over-limit alarm and verification report are generated;

[0075] S8. Based on the over-limit alarm and verification report, monitor the changes in design parameters in real time and trigger safety event response. At the same time, record the event and trace the over-limit situation of design parameters.

[0076] In this implementation, S1 specifically includes:

[0077] S11. Determine the design requirements, functional requirements and system constraints of aerospace equipment, and clarify the relationships and dependencies between subsystems;

[0078] S12. Use 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;

[0079] S13. Connect the functional models of each subsystem into an overall system model by defining a unified interface and data flow;

[0080] 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;

[0081] S15. Based on the calibrated design parameters and constraints, a multidisciplinary design optimization method is used to adjust and optimize the design parameters in the system model;

[0082] S16. Based on the optimized design parameters, combined with the design requirements and system constraints, the system constraints and margin boundaries are constructed to generate symbolic margin characterization data;

[0083] 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 according to the evaluation results.

[0084] In this implementation manner, the S4 specifically includes:

[0085] 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 of each design parameter;

[0086] S42. According to 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;

[0087] S43. Using the system architecture analysis method, 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;

[0088] S44. Optimize the margin allocation between design parameters through coupling relationship analysis and matrix analysis methods;

[0089]

[0090] 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.

[0091] In this implementation manner, S5 specifically includes:

[0092] 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 margin influence degree between:

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

[0094] 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 parameter i and design parameter j, M ij The value of indicates the strength of the margin effect;

[0095] 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;

[0096] S53. On the basis of 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;

[0097] S54. Based on the preliminary margin allocation scheme, the margin allocation of the design parameters is optimized by linear programming, the margins of the design parameters are balanced, and an optimized margin allocation scheme is obtained:

[0098]

[0099] Among them, O(x) is the optimization objective function, x i is the margin of design parameter i, α i is the weighting coefficient for each design parameter, β i is the square weighting coefficient of the design parameters, n is the total number of design parameters;

[0100] 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.

[0101] In this implementation manner, S6 specifically includes:

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

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

[0104]

[0105] 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;

[0106] S63. Determine the constraints of the linear programming, including the safety margin boundary and margin range of the design parameters:

[0107] U min,i ≤U i ≤U max,i and

[0108] Among them, U safe,i is the safety margin boundary of the design parameters, U min,i and U max,i is the margin range;

[0109] S64, obtaining an optimization margin value of each design parameter by solving a linear programming model;

[0110] S65. Calculate the safety margin boundary of each design parameter according to the optimized margin value, and compare it with the initial margin allocation result to obtain an optimized margin adjustment result;

[0111] S66. Update the system model according to the optimized margin result.

[0112] In this implementation manner, the S7 specifically includes:

[0113] S71. Construct an OCL language expression model according to the safety margin boundary and define the verification condition of each design parameter;

[0114] S72. According to the generated verification conditions, the actual margin of each design parameter is compared, the over-limit result is recorded, an alarm is triggered and the alarm information is recorded;

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

[0116] S74. In the verification report, all out-of-limit parameters are listed in detail, the number of the out-of-limit design parameter, the out-of-limit value and the alarm time are provided, and the out-of-limit design parameter is highlighted;

[0117] S75. Integrate the verification report with the real-time monitoring system to track the changes in design parameters in real time. If an over-limit event occurs, update the alarm information and trigger the corresponding safety response process;

[0118] 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.

[0119] Embodiment 1:

[0120] In order to verify the feasibility of the present invention in implementation, the system design stage of a certain space mission, in which the aircraft is a multifunctional aerospace equipment, mainly used to perform high-precision flight missions. During the design process of the aircraft, multiple systems and subsystems are involved, including power systems, power systems, communication systems, thermal control systems, structural systems, and guidance and control systems. Since the aircraft operates in a complex environment and there is a strong coupling relationship between the subsystems, the impact between the various systems and how they work together must be considered during the design process to ensure the overall safety, reliability and performance of the aircraft.

[0121] First, the system-level model of the aircraft was established using the MBSE method. In this model, each subsystem of the aircraft was modeled one by one, and the interfaces and interactions between them were clarified.

[0122] After completing the construction of the system model, the team identified the strongly coupled design parameters of the aircraft. These design parameters involve the core performance of the system, such as aerodynamic loads, structural stiffness, heat transfer, power requirements, etc. By identifying these parameters, the team can accurately grasp the performance margin of the aircraft under different environments and working conditions.

[0123] The team then used the identified strongly coupled design parameters as input and performed margin characterization based on the MBSE model. By defining margin indicators and model parameter ranges, the team was able to accurately define and control various design parameters. These design parameters and margin indicators include aerodynamic deviation, load factor, structural strength, control accuracy, etc.

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

[0125] After the margin allocation was completed, the team automatically verified the design parameters. Based on OCL (Object Constraint Language) and the constraint elements in the MBSE model, the team built a verification model to verify the design parameters. The verification model can automatically identify whether the design parameters are out of limit and give real-time alarm information to help designers adjust the design in time to avoid potential risks.

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

[0127]

[0128] This table shows the comparison of the number of tests, cost and time in different design stages (design parameter analysis, margin allocation and optimization, verification and monitoring) when applying the MBSE (Model Driven System Engineering) method compared with 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% of the cost in the design parameter analysis stage, 80% in the margin allocation and optimization stage, and 83.3% in the verification and monitoring stage. These cost savings come from reducing physical tests, reducing manual intervention and improving the efficiency of the design stage. The MBSE method speeds up the work process at each stage, especially in the design parameter analysis and verification and monitoring stages, saving 50% of time respectively. By reducing redundant work and accelerating the verification process, the total project cycle is significantly shortened and can be put into practical application more quickly.

[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. Especially in the field of aerospace, reducing the number of tests and optimizing the time and cost of the design process 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, and provides a strong guarantee for the smooth implementation of aerospace missions.

[0130] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A margin analysis method based on MBSE aerospace equipment model, characterized in that: The steps include: S1. Establish the MBSE model of aerospace equipment and transform the design parameters, functional requirements and system constraints into a unified system model; S2. Identify the coupling relationship between different subsystems in the system, and determine the coupling design parameters through system model analysis; S3. According to 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 of different design parameters is performed to generate margin allocation results, and reasonable margin adjustment is performed in combination with the functional requirements of each level of the system; S5. Based on the margin allocation result, a matrix chart is used to visualize the margin allocation result, display the margin relationship between the design parameters, and generate a visualized margin allocation result; S6. Based on the visualized margin allocation results, the 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 an over-limit alarm and verification report are generated; S8. Based on the over-limit alarm and verification report, monitor the changes in design parameters in real time and trigger safety event response. At the same time, record the event and trace the over-limit situation of design parameters.

2. The margin analysis method based on the MBSE aerospace equipment model according to claim 1 is characterized in that: The 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 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 a unified interface and data flow; 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, a multidisciplinary design optimization method is used 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, the system constraints and margin boundaries are constructed to generate symbolic margin characterization 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 according to 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 of each design parameter; S42. According to 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. Using the system architecture analysis method, 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 the 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 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 margin influence degree 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 parameter i and design parameter 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. On the basis of 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 by 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 for each design parameter, β i is the square weighting coefficient of the design parameters, 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, inputting the initial margin allocation value of each design parameter into the linear programming model; S62, setting the objective function of the linear programming model, wherein 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 boundary and margin range 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 optimization margin value of each design parameter by solving a linear programming model; S65, calculating the safety margin boundary of each design parameter according to the optimized margin value, and comparing 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 MBSE aerospace equipment model according to claim 1 is characterized in that: The S7 specifically includes: S71. Construct an OCL language expression model according to the safety margin boundary and define the verification condition of each design parameter; S72. According to the generated verification conditions, the actual margin of each design parameter is compared, the over-limit result is recorded, an alarm is triggered and the alarm information is recorded; 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, the number of the out-of-limit design parameter, the out-of-limit value and the alarm time are provided, and the out-of-limit design parameter is highlighted; S75. Integrate the verification report with the real-time monitoring system to track the changes in design parameters in real time. If an over-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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