MBSE-based unmanned aircraft energy power system design method and system
By defining the Sysml model and fault tree network of the energy and propulsion system of the unmanned vehicle using the MBSE method, the problems of design complexity and information silos are solved, the system design is unified and efficiently integrated, and the design efficiency and reliability prediction are improved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-28
- Publication Date
- 2026-03-13
AI Technical Summary
Existing technologies for unmanned aerial vehicle (UAV) power system design suffer from problems such as information silos, interface mismatches, high design complexity, and reliance on experience for reliability and cost estimation, making it difficult to achieve unified and efficient system design integration.
We adopt a MBSE-based design approach, using Sysml models to define system requirements, functional activities, architecture, and interface relationships. We combine fault trees and Bayesian networks to calculate reliability and cost, and establish a unified system model to guide the design.
It achieves information consistency and efficient integration in energy and power system design, improves design efficiency, ensures the reliability of design schemes and the accuracy of cost prediction, and optimizes the design iteration process.
Smart Images
Figure CN121659449A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of ship digital model construction technology, specifically relating to the design method and system of energy and power system for unmanned underwater vehicles based on MBSE. Background Technology
[0002] Currently, most ship design institutes and shipyards use the traditional "document-based" design method. Each specialized team uses its own professional tools for design, generating and transmitting numerous documents (such as requirements documents, design specifications, and interface control documents) for information exchange and integration. This leads to omissions and untimely information synchronization issues when changes occur. Furthermore, the separate development of subsystems easily causes interface incompatibility and performance deficiencies during later integration. Finally, design knowledge and experience are scattered across various documents, creating difficulties for subsequent upgrades, modifications, and maintenance. Moreover, the reliability calculations and cost estimates of traditional system solutions heavily rely on experience and past databases, exacerbating the information silo effect and failing to reflect the implicit non-linear relationship between reliability and cost.
[0003] In the design phase of unmanned aerial vehicle (UAV) power systems, obtaining complete system requirements, functional activity analysis, and system composition is necessary to design a rigorous and feasible system solution. However, power systems are structurally complex and functionally highly coupled, making system design challenging. Therefore, in the context of model-based systems engineering, there is an urgent need to propose a model-based design method for power systems. Summary of the Invention
[0004] The technical problem to be solved by the present invention is to provide a design method and system for the energy and power system of unmanned aerial vehicles based on MBSE, for designing energy and power systems based on models.
[0005] The technical solution adopted by this invention to solve the above-mentioned technical problems is: a design method for the energy and propulsion system of an unmanned aerial vehicle based on MBSE, comprising the following steps: S1: System requirements diagram defining the Sysml model; S2: Define the functional activity model of the Sysml model according to system requirements; S3: Based on the decomposition of functional activities, define the system architecture and composition relationships of the energy and power system model; S4: Define the internal interface relationships of each level of the energy and power system model based on the interfaces of functional activities and the system architecture; S5: Define the parameters of the energy and power system model based on system requirements, as well as parameters related to reliability and cost, and establish a relationship between them and system requirements; S6: Generate fault tree structure information based on the topology and reliability parameters of the energy and power system model, and predict system reliability; S7: Establish a cost estimation model based on the MBSE model that takes reliability into account, and predict the cost considering reliability factors.
[0006] According to the above scheme, the specific steps in step S1 are as follows: Input the design requirements of each level of the energy and power system and establish the traceability relationship between each level.
[0007] According to the above scheme, the specific steps in step S2 are as follows: Analyze the functional activities at each level and establish the implementation relationships of the functional activities at each level; Identify external interaction interfaces and establish the relationship between functions and requirements; The functions are decomposed from the subsystem layer to the device layer to obtain the functional activities of specific devices.
[0008] According to the above scheme, in step S3, the energy and power system includes a protection subsystem, a power subsystem, an electric power subsystem, and an intelligent unmanned subsystem.
[0009] According to the above scheme, the specific steps in step S4 are as follows: Define the interface relationships between subsystems and the material flow transferred between interfaces; Define the internal relationships of the subsystems based on the interface relationships between the subsystems, and implement the interfaces between the subsystems in the devices within the subsystems; Define device-level internal interfaces based on the system's internal relationships.
[0010] According to the above scheme, the specific steps in step S6 are as follows: Through a third-party interface, editable fault tree structure information is output in JSON format; By setting parameters including task time, number of time slices and top event, the events of the fault tree are transformed into nodes in a Bayesian network; according to the static structure of the fault tree logic gates, directed edges are constructed between nodes within the time slice, and inter-slice edges between nodes are also constructed, and a conditional probability table is generated according to the corresponding logic gates. Transform the fault tree into a Bayesian network; By using forward inference with Bayesian networks to calculate the conditional probability of the top event occurring when each bottom event in the original fault tree model occurs individually, the system reliability prediction results are obtained.
[0011] According to the above scheme, the specific steps in step S7 are as follows: Cost items related to failure rate λ=1 / MTBF and MTTR include: Research funding C1 is positively correlated with increased MTBF; Purchase cost C2, including equipment purchase cost C21 General assembly and construction costs C 22 and warranty fee C 23 Warranty fee C 23 Related to reliability; Repair fee C3 includes grade repair fee C 31 Emergency repair fee C 32 ; The usage guarantee fee C4, which is indirectly related to reliability, includes the model training fee C. 41 Personnel protection fee C 42 And fuel consumption cost C 43 ; By integrating reliability and cost factors through the MBSE model, with equipment purchase cost C... 21 Based on this, the relationship between research and development expenses C1, maintenance expenses C3, operating and maintenance expenses C4, and purchase expenses C2 is established under the reliability factor: , , , ; The parameters passed to the functional model are: Equipment purchase cost C 21 , Reliability parameter MTBF, Maintainability parameter MTTR; The learning parameters for historical and online data are: Research input factor α Equipment construction factor p 21 p 22 p 23 , Maintenance sensitivity factors β1, β2, Use protection factors γ1, γ2, and γ3. MTBF sensitivity coefficients k1, k 23 k 42 k 43 .
[0012] According to the above plan, the following steps are also included: S8: If the energy and power system model changes, then execute step S6 to regenerate the fault tree structure information and iteratively calculate reliability and cost based on reliability parameters, cost parameters, and the topological relationship of the energy and power system model.
[0013] Design system for energy and propulsion system of unmanned aerial vehicle based on MBSE The requirements submodule is used to define the system requirements diagram for the Sysml model; Functional submodules are used to define functional activity models of the Sysml model according to system requirements; The architecture submodule is used to define the system architecture and composition relationships of the energy and power system model based on the decomposition of functional activities; The interface submodule is used to define the internal interface relationships of each level of the energy and power system model based on the interfaces of functional activities and the system architecture. The parameter submodule is used to define the parameters of the energy and power system model according to system requirements, as well as parameters related to reliability and cost, and to establish a satisfying relationship with system requirements; The fault tree submodule is used to generate fault tree structure information based on the topology and reliability parameters of the energy and power system model, and to predict system reliability. The model prediction submodule is used to build a reliability-considered cost estimation model based on the MBSE model and predict the cost taking reliability factors into account.
[0014] A computer memory storing a computer program executable by a computer processor, the computer program executing a design method for an unmanned aerial vehicle energy propulsion system based on MBSE.
[0015] The beneficial effects of this invention are as follows: 1. The MBSE-based design method and system for unmanned aerial vehicle power systems of the present invention uses a unified and logically coherent system model as the "sole source of trust" throughout the entire design process. The entire power model runs through all design stages of the power system, including requirements analysis, overall design, and verification, to solve the problem of "information consistency" between various subsystems and devices, including the transmission of design requirements, decomposition of overall performance, determination of system composition, design of internal interfaces, reliability elements, cost element attributes, etc. It replaces scattered documents as the center of information interaction, improves design efficiency, and realizes the function of designing power systems based on models.
[0016] 2. This invention allows for adjustments to the design scheme by calculating the reliability and cost of the model calculation system in the early stages of the design process, thereby improving the design iteration rate.
[0017] 3. This invention relies on a system model as the basic representation method, combined with practical design experience, to identify complex interfaces, ensure the consistency of technical requirements, functional activities, and architecture in the design, verify the reliability and cost of the design scheme, and open up a positive design path for the entire chain.
[0018] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description
[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0020] Figure 1 This is a flowchart of an embodiment of the present invention.
[0021] Figure 2 This is a diagram showing the relationship and transmission path between the functional model, reliability model, and cost model in this embodiment of the invention.
[0022] Figure 3 This is a diagram showing the requirements definition and traceability relationship at each level in an embodiment of the present invention.
[0023] Figure 4 This is a system-level functional activity analysis and requirement traceability relationship diagram according to an embodiment of the present invention.
[0024] Figure 5 This is a hierarchical functional activity analysis diagram of the power subsystem according to an embodiment of the present invention.
[0025] Figure 6 This is a tree diagram illustrating the energy and power system architecture analysis of an embodiment of the present invention.
[0026] Figure 7 This is a diagram showing the internal interface relationships of the energy and power system in an embodiment of the present invention.
[0027] Figure 8 This is a diagram showing the internal interface relationships of the power subsystem in an embodiment of the present invention.
[0028] Figure 9 This is an energy and power system performance chart according to an embodiment of the present invention.
[0029] Figure 10 This is a system fault tree diagram according to an embodiment of the present invention.
[0030] Figure 11 This is a system Bayesian network diagram according to an embodiment of the present invention.
[0031] Figure 12 This is a code diagram of the cost estimation function based on the MBSE model considering reliability in an embodiment of the present invention. Detailed Implementation
[0032] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.
[0033] Example 1 See Figure 1 The specific steps of the MBSE-based unmanned aerial vehicle power system design method are as follows: S1: See Figure 3 Define the system requirements diagram of the Sysml model, establish the design requirements of each level of the energy and power system as design input, and establish the traceability relationship between each level.
[0034] S2: See also Figure 4 Based on the requirements, a functional activity model of the Sysml model is defined, the main functional activities at each level are analyzed, the implementation relationships of activities at each level are established, external interaction interfaces are identified, and the satisfaction relationships between functions and requirements are established. This section describes the main functions and processes of the energy and power system and identifies relevant internal and external interaction items.
[0035] At the subsystem level, functions are further decomposed to the equipment level. Taking the main function of the electrical subsystem, "generating shipwide power," as an example, the inputs for this function are fuel oil and cooling water, and the output is electricity. The specific functional flow is as follows: Figure 5 As shown.
[0036] The concept of "generating power for the entire ship" is further broken down into the functional activities of specific equipment, identifying more detailed equipment functional processes. The diagram illustrates the main functions and workflows of the major power equipment, including the diesel generator set, switchboard, and emergency power supply device.
[0037] The activity decomposition steps at the device level are the same as those at the subsystem level, and will not be elaborated on here.
[0038] S3: Based on the decomposition of functional activities, define the overall architecture and composition relationships of the energy and power system model. The tree-like composition relationship of the energy and power system is as follows: Figure 6 As shown.
[0039] The energy and power system comprises four parts: a security subsystem, a power subsystem, an electrical subsystem, and an intelligent unmanned subsystem. Each subsystem consists of its own equipment. By constructing a model architecture diagram, the components of the system can be clearly designed.
[0040] S4: Based on the interface analysis of the functional activities in step S2 and the system architecture design scheme in step S3, define the internal interface relationships of each level of the energy and power system model. Only the internal interface diagrams of the top-level energy and power system and the power subsystem are described here.
[0041] like Figure 7 As shown, the interface relationships between the three major subsystems of the energy and power system and the material flow transmitted between the interfaces are described.
[0042] The internal relationship diagram of the power subsystem inherits the external interfaces of the power subsystem in the diagram above, allowing for more detailed design, such as... Figure 8 As shown.
[0043] The internal interface diagram of the power subsystem shows a more detailed implementation process of the three external interfaces—cooling water, fuel oil, and electricity—on the internal equipment of the power subsystem.
[0044] The internal interface diagram at the device level is similar and will not be elaborated here.
[0045] S5: Based on the requirements analysis in step S1, define the main parameter indicators and reliability and cost-related parameters of the energy and power system design scheme, and establish a satisfaction relationship with the requirements in step S1, such as... Figure 9 As shown.
[0046] S6: Through a third-party interface, the system model's topology and reliability parameters are exported as a fault tree structure in JSON format and input into the reliability calculation software. This information is then displayed during reliability analysis and allows modification of the fault tree's logical structure. The fault tree is shown as follows: Figure 10 As shown.
[0047] To subsequently calculate the reliability of system tasks, it is necessary to convert the events of the fault tree into nodes in a Bayesian network by setting parameters such as task time, number of time slices, and top event. Based on the static structure of the fault tree logic gates, directed edges are constructed between nodes within a time slice, and inter-slice edges between nodes are also constructed. Finally, a conditional probability table is generated based on the corresponding logic gates.
[0048] The fault tree was then transformed into a Bayesian network, with the Bayesian network architecture as follows: Figure 11 As shown.
[0049] By using forward inference with Bayesian networks, the conditional probability of the top event occurring when each bottom event in the original fault tree model occurs individually is calculated, thereby obtaining the predicted results of system reliability.
[0050] S7: By integrating reliability and cost factors through the MBSE model, and taking equipment purchase cost as the basis, this model considers the relationship between reliability factors such as R&D costs, maintenance costs, and operating support costs and purchase cost. It establishes a reliability-considered cost estimation model based on the MBSE model, enabling cost prediction that takes reliability factors into account. (See also...) Figure 12 .
[0051] Define the cost item: C1: Research funding, which is positively correlated with increasing MTBF.
[0052] C2: Purchase cost, including equipment purchase cost C 21 General assembly construction cost C 22 ; C23 Warranty fees are related to reliability.
[0053] C3: Repair fee, including C 31 Grade C repair fee; 32 Emergency repair costs.
[0054] It is related to the failure rate λ = 1 / MTBF and MTTR.
[0055] C4: Coverage fee, including model training fee C 41 C 42 Personnel support expenses; C 43 Fuel consumption costs. Indirectly related to reliability.
[0056] Based on the functional model, with equipment purchase cost as C 21 The basic cost, taking into account reliability factors, includes research and development expenses (C1), maintainability expenses (C3), maintenance and upkeep expenses (C4), and procurement expenses (C). 21 Based on the relationship, cost prediction considering reliability factors is achieved, and the formulas for calculating various costs are as follows: , , , ; The parameters passed to the functional model are: Equipment purchase cost C 21 , Reliability parameter MTBF, Maintainability parameter MTTR.
[0057] The learning parameters for historical and online data are: Research input factor α Equipment construction factor p 21 p 22 p 23 , Maintenance sensitivity factors β1, β2, Use protection factors γ1, γ2, and γ3. MTBF sensitivity coefficients k1, k 23 k 42 k 43 .
[0058] S8: If the design model of the energy and power system changes, proceed to step S6, re-export and import the JSON containing reliability, cost parameters and the topology relationship of the energy and power system model, and conduct an iterative calculation of reliability and cost.
[0059] This embodiment uses a unified, logically coherent system model as the "sole source of trust" throughout the entire design process. The entire energy and power model runs through all design stages of the energy and power system, including requirements analysis, overall design, and verification. It is used to solve the problem of "information consistency" between various subsystems and devices, including the transmission of design requirements, decomposition of overall performance, determination of system composition, design of internal interfaces, reliability elements, cost element attributes, etc. It replaces scattered documents and becomes the center of information exchange, improving design efficiency and realizing the function of designing energy and power systems based on models. In the early stages of design, the reliability and cost of the system can be calculated through the model to adjust the design scheme, thereby improving the design iteration rate.
[0060] Example 2 The steps in this embodiment are the same as in Embodiment 1, the difference being that each step is applied to a specific instance. See also Figure 2 Taking the energy and propulsion system of a certain ship as an example, the energy and propulsion system design scheme E based on MBSE mainly includes the requirements analysis, functional analysis, system composition, and internal connection relationships of the energy and propulsion system, subsystems, and equipment. Subsequently, the reliability parameters of the equipment are defined, and the system functional model is exported in JSON format through a third-party interface into a fault tree form, which is then input into the reliability-cost analysis model for system reliability and cost analysis.
[0061] E = {Q, F, C, I} The model element types represented by the quadruples in the above formula are as follows: Q: Demand model; F: Functional Activity Model; C: Architecture model; I: Internal connection model; As can be seen from the above definitions, the three levels of energy and power systems, subsystems, and equipment all include these four types of models.
[0062] E 系统 ={Q 系统 F 系统 C 系统 I 系统}; E 分系统 ={Q 分系统 F 分系统 C 分系统 I 分系统}; E 设备 ={Q 设备 F 设备 C 设备 I 设备}; The requirement model is used to describe functional and performance requirements, Q 系统 Q分系统 Q 设备 Establish a top-down traceability relationship, Q 系统 As the top-level requirement of the energy and power system, it should be decomposed into subsystem requirements Q. 分系统 Subsystem Requirements Q 分系统 The equipment requirement Q should be derived by decomposing it. 设备 The derivation relationship is shown by the corresponding requirement traceability diagram or traceability table.
[0063] Functional activity models are used to describe the functional activities that need to be achieved in an energy and power system, for example, F. 系统 F 分系统 F 设备 Establish a progressive relationship, F 系统 As a top-level functional activity. System-level functional activity F 系统 Functional activities F at the subsystem layer are required. 分系统 Specifically, the subsystem layer functional activities F 分系统 Functional activities F required at the device layer 设备 The specific implementation details are shown in the activity diagram.
[0064] An architecture model is used to describe the composition of the entire system and the system's hierarchical relationships. 系统 C 分系统 C 设备 The tree-like structure of the entire energy and power system architecture is established. This is achieved through a module definition diagram.
[0065] The internal connectivity model is used to describe the signal interface relationships at each level. 系统 Describe its C 分系统 Signal and interface relationships, I 分系统 Describe its C 设备 Signal and interface relationships, I 设备 Describe the signal and interface relationships of its internal hardware. Implemented using an internal module diagram.
[0066] Finally, define C 设备 The system's reliability attributes (MTTR, MTBF) and cost attributes are obtained. Through a third-party interface, the system topology and related parameters are exported in JSON format as a fault tree and input into the reliability and cost analysis model to analyze the system's reliability and cost.
[0067] This embodiment relies on the system model as the basic representation method, combined with practical design experience, to identify complex interfaces, ensure the consistency of technical requirements, functional activities, and architecture in the design, verify the reliability and cost of the design scheme, and open up the entire chain of positive design path.
[0068] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.
[0069] Example 3 This embodiment is used to implement the principle of the above method embodiment to build an unmanned vehicle energy and power system design system based on MBSE, including a requirement submodule, a functional submodule, an architecture submodule, an interface submodule, a parameter submodule, a fault tree submodule, and a model prediction submodule.
[0070] The requirements submodule is used to define the system requirements diagram for the Sysml model; Functional submodules are used to define functional activity models of the Sysml model according to system requirements; The architecture submodule is used to define the system architecture and composition relationships of the energy and power system model based on the decomposition of functional activities; The interface submodule is used to define the internal interface relationships of each level of the energy and power system model based on the interfaces of functional activities and the system architecture. The parameter submodule is used to define the parameters of the energy and power system model according to system requirements, as well as parameters related to reliability and cost, and to establish a satisfying relationship with system requirements; The fault tree submodule is used to generate fault tree structure information based on the topology and reliability parameters of the energy and power system model, and to predict system reliability. The model prediction submodule is used to build a reliability-considered cost estimation model based on the MBSE model and predict the cost taking reliability factors into account.
[0071] Each submodule is mainly used to implement the various steps of the method implementation, which will not be elaborated here.
[0072] It should be noted that, depending on the implementation needs, the various steps / components described in this application can be broken down into more steps / components, or two or more steps / components or parts of the operation of steps / components can be combined into new steps / components to achieve the purpose of this invention.
[0073] This embodiment also includes a processor, a communication interface, a memory, and a communication bus; wherein the processor, communication interface, and memory communicate with each other through the communication bus; the memory stores a computer program, and when the program is executed by the processor, the processor performs the steps of the MBSE-based unmanned vehicle energy and propulsion system design method.
[0074] This embodiment also provides a computer-readable storage medium storing executable instructions that, when executed by a processor, enable the processor to implement an MBSE-based design method for an unmanned aerial vehicle's energy and propulsion system.
[0075] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects.
[0076] Furthermore, this application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0077] This application is described with reference to the flowchart of the method and computer program product according to Embodiment 1 and the block diagram of the device (system) according to Embodiment 3. It should be understood that each step or block in the flowchart or block diagram, as well as combinations of steps or blocks in the flowchart or block diagram, can be implemented by computer program instructions.
[0078] These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing device to produce a machine, such that the instructions, which are executable by the processor of the computer or other programmable data processing device, produce instructions for implementing the process. Figure 1 One or more processes or boxes Figure 1 MBSE-based design system for unmanned aerial vehicle power systems, specifying functions within one or more boxes.
[0079] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes or boxes Figure 1 The function specified in one or more boxes.
[0080] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes or boxes Figure 1 The steps of the MBSE-based design method for unmanned aerial vehicle power systems are specified in one or more boxes.
[0081] The above embodiments are only used to illustrate the design concept and features of the present invention, and their purpose is to enable those skilled in the art to understand the content of the present invention and implement it accordingly. The protection scope of the present invention is not limited to the above embodiments. Therefore, all equivalent changes or modifications made based on the principles and design ideas disclosed in the present invention are within the protection scope of the present invention.
Claims
1. A design method for the energy and propulsion system of an unmanned aerial vehicle based on MBSE, characterized in that: Includes the following steps: S1: System requirements diagram defining the Sysml model; S2: Define the functional activity model of the Sysml model according to system requirements; S3: Based on the decomposition of functional activities, define the system architecture and composition relationships of the energy and power system model; S4: Define the internal interface relationships of each level of the energy and power system model based on the interfaces of functional activities and the system architecture; S5: Define the parameters of the energy and power system model based on system requirements, as well as parameters related to reliability and cost, and establish a relationship between them and system requirements; S6: Generate fault tree structure information based on the topology and reliability parameters of the energy and power system model, and predict system reliability; S7: Establish a cost estimation model based on the MBSE model that takes reliability into account, and predict the cost considering reliability factors.
2. The design method for the energy and propulsion system of an unmanned aerial vehicle based on MBSE according to claim 1, characterized in that: The specific steps in step S1 are as follows: Input the design requirements of each level of the energy and power system and establish the traceability relationship between each level.
3. The design method for the energy and propulsion system of an unmanned aerial vehicle based on MBSE according to claim 1, characterized in that: The specific steps in step S2 are as follows: Analyze the functional activities at each level and establish the implementation relationships of the functional activities at each level; Identify external interaction interfaces and establish the relationship between functions and requirements; The functions are decomposed from the subsystem layer to the device layer to obtain the functional activities of specific devices.
4. The design method for the energy and propulsion system of an unmanned aerial vehicle based on MBSE according to claim 1, characterized in that: In step S3, the energy and power system includes a protection subsystem, a power subsystem, an electric power subsystem, and an intelligent unmanned subsystem.
5. The design method for the energy and propulsion system of an unmanned aerial vehicle based on MBSE according to claim 1, characterized in that: The specific steps in step S4 are as follows: Define the interface relationships between subsystems and the material flow transferred between interfaces; Define the internal relationships of the subsystems based on the interface relationships between the subsystems, and implement the interfaces between the subsystems in the devices within the subsystems; Define device-level internal interfaces based on the system's internal relationships.
6. The design method for the energy and propulsion system of an unmanned aerial vehicle based on MBSE according to claim 1, characterized in that: The specific steps in step S6 are as follows: Through a third-party interface, editable fault tree structure information is output in JSON format; By setting parameters including task time, number of time slices and top event, the events of the fault tree are transformed into nodes in a Bayesian network; according to the static structure of the fault tree logic gates, directed edges are constructed between nodes within the time slice, and inter-slice edges between nodes are also constructed, and a conditional probability table is generated according to the corresponding logic gates. Transform the fault tree into a Bayesian network; By using forward inference with Bayesian networks to calculate the conditional probability of the top event occurring when each bottom event in the original fault tree model occurs individually, the system reliability prediction results are obtained.
7. The design method for the energy and propulsion system of an unmanned aerial vehicle based on MBSE according to claim 1, characterized in that: The specific steps in step S7 are as follows: Cost items related to failure rate λ=1 / MTBF and MTTR include: Research funding C1 is positively correlated with increased MTBF; Purchase cost C2, including equipment purchase cost C 21 General assembly construction cost C 22 and warranty fee C 23 Warranty fee C 23 Related to reliability; Repair fee C3 includes grade repair fee C 31 Emergency repair fee C 32 ; The usage guarantee fee C4, which is indirectly related to reliability, includes the model training fee C. 41 Personnel protection fee C 42 And fuel consumption cost C 43 ; By integrating reliability and cost factors through the MBSE model, with equipment purchase cost C... 21 Based on this, the relationship between research and development expenses C1, maintenance expenses C3, operating and maintenance expenses C4, and purchase expenses C2 is established under the reliability factor: ， ， ， ； The parameters passed to the functional model are: Equipment purchase cost C 21 , Reliability parameter MTBF, Maintainability parameter MTTR; The learning parameters for historical and online data are: Research input factor α Equipment construction factor p 21 p 22 p 23 , Maintenance sensitivity factors β1, β2, Use protection factors γ1, γ2, and γ3. MTBF sensitivity coefficients k1, k 23 k 42 k 43 .
8. The design method for the energy and propulsion system of an unmanned aerial vehicle based on MBSE according to claim 1, characterized in that: The steps also include: S8: If the energy and power system model changes, then execute step S6 to regenerate the fault tree structure information and iteratively calculate reliability and cost based on reliability parameters, cost parameters, and the topological relationship of the energy and power system model.
9. A design system for the energy and propulsion system of an unmanned aerial vehicle based on MBSE, characterized in that: The requirements submodule is used to define the system requirements diagram for the Sysml model; Functional submodules are used to define functional activity models of the Sysml model according to system requirements; The architecture submodule is used to define the system architecture and composition relationships of the energy and power system model based on the decomposition of functional activities; The interface submodule is used to define the internal interface relationships of each level of the energy and power system model based on the interfaces of functional activities and the system architecture. The parameter submodule is used to define the parameters of the energy and power system model according to system requirements, as well as parameters related to reliability and cost, and to establish a relationship between the parameters and system requirements. The fault tree submodule is used to generate fault tree structure information based on the topology and reliability parameters of the energy and power system model, and to predict system reliability. The model prediction submodule is used to build a reliability-considered cost estimation model based on the MBSE model and predict the cost taking reliability factors into account.
10. A computer memory, characterized in that: It contains a computer program that can be executed by a computer processor, which executes the MBSE-based unmanned vehicle power system design method as described in any one of claims 1 to 8.