Unmanned aerial vehicle cluster multi-stage task reliability analysis method and system, storage medium and program

Abstract

The invention relates to the technical field of unmanned aerial vehicles. Particularly relates to an unmanned aerial vehicle cluster multi-stage task reliability analysis method and system, a storage medium and a program. According to the method, a task stage process, a subsystem dependency relationship and reliability parameters are embedded into a SysML architecture model, a fault tree of each stage is automatically generated and converted into a binary decision diagram (BDD), and single-machine multi-stage task reliability calculation is achieved. On this basis, the reliability of the cluster in each stage and in the whole task is evaluated in combination with a k-out-of-n system model and a Monte Carlo simulation method, and the minimum delivery quantity is automatically optimized based on a reliability target. Finally, simulation and optimization results are automatically written back to the MBSE model through a parameter diagram, and design-simulation-decision closed-loop linkage is achieved. The method has the advantages of high modeling consistency, high calculation efficiency, optimal resource configuration and the like, and is suitable for reliability evaluation and optimal configuration of unmanned aerial vehicle cluster tasks in a complex environment.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of unmanned aerial vehicles. In particular, it relates to a method and system for analyzing the reliability of multi-stage tasks of a UAV swarm, a storage medium and a program. BACKGROUND

[0002] With the rapid development of miniaturization of airborne sensors, broadbandization of communication links and intelligentization of flight control, unmanned aerial vehicles (UAVs) have evolved from single-vehicle execution of simple aerial survey tasks to UAV swarms of tens or even hundreds of nodes working cooperatively. In scenarios such as disaster monitoring, environmental inspection and military reconnaissance, the swarm can complete a multi-stage task of "take-off - maneuver - task execution - return" in series through distributed cooperation, significantly improving coverage area, communication redundancy and task success rate. However, the failure of any single UAV or key subsystem in the swarm at any stage can lead to a reduction in nodes, network topology disruption and even overall task failure, so multi-stage task reliability has gradually become a core topic of research in the engineering of unmanned aerial vehicle systems.

[0003] Early research used fault tree analysis (FTA) and reliability block diagrams (RBD) to quantitatively evaluate static systems. In the literature

Reliability analysis of phased missions

Importance measure-based phased mission reliability and UAV number optimization for swarm

[0004] With the complexity of the system increasing, Markov Chain and Petri net are used to describe the component degradation and phase transition. In the literature

A Markov regenerative process model for phasedmission systems under internaldegradation and external shocks

Reliability Evaluationfor Manufacturing System Based on Dynamic Adaptive Fuzzy Reasoning Petri Net

[0005] To reduce the computational complexity, binary decision diagram (BDD) is introduced into the reliability analysis of phased systems. In the literature

A BDD-Based Algorithm for Reliability Analysis of Phased-Mission Systems

An algorithm for reliability analysis of phased-mission systems

[0006] The mission of UAV swarm is often abstracted as a k-out-of-n system: at least k UAVs in n UAVs are functional, which is considered as a phase success. In the paper

Mission-oriented reliability prediction for unmanned aerial vehicle swarm using consecutive k-out-of-n structure

Phased-Mission Reliability and Importance Measure Analysis for Linear and Circular UAV Swarms

A Novel Numerical Perspective on Estimating the Mission Reliability of Unmanned Aerial Vehicle Swarm in Multi-Phase

[0007] Model-based system engineering (MBSE) advocates using a unified model to run through the whole life cycle of requirement, design, analysis and verification, and improves cross-disciplinary collaboration and traceability. In the literature

A new SysML Model for UAV Swarm Modeling:UavSwarmML

Integrated Design Process of General Quality Characteristics of Clustered UAS by MBSE

[0008] In order to solve the above technical problems, the purpose of the present application is to provide a UAV swarm multi-stage task reliability analysis method based on MBSE, which embeds multi-stage task flow, subsystem dependency and reliability parameters into SysML architecture model, combines fault tree analysis and binary decision diagram (BDD) to complete single-machine multi-stage task reliability modeling, and realizes swarm task reliability calculation and optimal launch quantity optimization based on k-out-of-n system and Monte Carlo simulation, providing method support for swarm reliability dynamic analysis and resource allocation optimization in complex task environment.

[0009] In order to achieve the above purpose, the present application adopts the following technical solutions: A UAV swarm multi-stage task reliability analysis method based on model-based system engineering (MBSE), the method comprising the following steps: 1) Establishing a SysML architecture model in the MBSE platform, embedding multi-stage task flow, subsystem dependency matrix and quantitative reliability parameters in the form of model elements; 2) Automatically generating fault trees for each stage based on the model elements, and converting them into ordered and simplified binary decision diagrams (BDD); 3) Using hierarchical calculation of components-subsystems-phases, obtaining the reliability vector of a single UAV in each task phase using the BDD; 4) Drive the k-out-of-n Monte Carlo simulation with the reliability vector to obtain the reliability distribution of the UAV swarm at each stage and the whole mission; 5) Set the "initial number of UAVs" as a SysML parameter and iterate it with the simulation results of step 4) to automatically search for the minimum number of UAVs that satisfies the pre-set mission success rate threshold.

[0010] As a preference, the SysML model of step 1) contains requirement diagrams, activity diagrams, block definition diagrams, internal block diagrams, and parameter diagrams, and sets a special meta-model tag for reliability parameters.

[0011] As a preference, the transformation of fault trees to BDDs in step 2) reorders variables according to task stage priority and uses subgraph merging rules to reduce node numbers.

[0012] As a preference, the component layer reliability calculation in step 3) is as follows: According to the task start to the duration of this stage T m Calculate the failure probability and reliability of this component within this stage, if the component obeys exponential distribution as formula (1), obeys Weibull distribution as formula (2): (1) (2) At the component layer, each subsystem i contains Mi components, the failure rate of each component j within the mission stage m is fixed as λ ij if the life obeys exponential distribution, the failure rate is η ij if the life obeys Weibull distribution, λ ij is the scale parameter, β ij,m is the shape parameter; Q ij,m is the failure probability of component j in subsystem i within stage m, R Si,m is the reliability of component j in subsystem i within stage m.

[0013] As a preference, the subsystem layer reliability calculation is as follows: According to the logical structure of the internal components of the subsystem, the reliability and failure probability of the subsystem within stage m are calculated respectively, if the subsystem i is a component series system as formula (3), if the subsystem i is a component parallel system as formula (4), the failure probability is as formula (5); (3) (4) (5) Where R Si,m represents the reliability of subsystem i within stage m, Q Si,m represents the failure probability of subsystem i within stage m.

[0014] As preferred, the phase layer reliability is calculated as follows: According to the key subsystem set K involved in the task phase m m , the logical relationship between the subsystems is analyzed by using a fault tree or a binary decision diagram to obtain the reliability and failure probability of a single unmanned aerial vehicle in the phase; the success condition of the phase task is an "and gate" relationship, as shown in equation (6): (6) R stage,m represents the reliability of a single unmanned aerial vehicle in completing the task in phase m, Q stage,m represents the failure probability of a single unmanned aerial vehicle in phase m.

[0015] As preferred, step 4) models the cluster task as a continuous three-phase k1-out-of-n0, k2-out-of-n1, k3-out-of-n2 system, and the number of Monte Carlo simulation times in each phase is not less than 10 4 .

[0016] As preferred, step 4) takes the initial number of unmanned aerial vehicles N0 as the starting point of simulation, divides the task into three phases, and determines whether each unmanned aerial vehicle succeeds in the task according to the single-machine reliability and comparison between the single-machine reliability and a uniformly distributed random number in each phase; the number of successful unmanned aerial vehicles is recorded as n i , and the number of successful unmanned aerial vehicles in the previous phase is taken as the starting value of the next phase; different minimum success quantity thresholds are set for each phase, and the phase reliability and the total cluster task reliability are calculated in turn; The required success quantities of each phase are k1, k2, and k3, the phase task reliabilities are R1, R2, and R3, the number of unmanned aerial vehicles in each phase is n1, n2, and n3, the cluster phase reliability is shown in equation (7), and the total cluster task reliability is shown in equation (8), which can be calculated by the following method: (7) (8).

[0017] As preferred, the minimum launch quantity search of step 5) adopts binary search or grid increment combined with the convergence criterion |AR| < 1x10 -3 ; and / or, the write-back operation triggered in step 6) automatically updates the requirement matrix and generates a design change notification through the MBSE platform API.

[0018] Further, the present application also provides an unmanned aerial vehicle cluster multi-phase task reliability analysis system, which is used to implement the method and comprises: A modeling module is configured to create a SysML model containing reliability parameters in an MBSE platform; Fault tree / BDD generation module: coupled with the modeling module, for automatically generating and simplifying fault trees and BDDs; Hierarchical calculation module: for calculating component, subsystem and phase reliability; Monte Carlo simulation module: for calculating reliability distribution of the cluster based on k-out-of-n Structural; Optimization module: for iteratively adjusting the number of deployments and outputting the optimal value; Feedback interface: for synchronizing simulation and optimization results to SysML models and requirement libraries.

[0019] As preferred, the modeling module is based on an MBSE tool supporting an open API, and is integrated with MATLAB / Simulink through a co-simulation interface; and / or, the optimization module adopts a parameterized constraint solver, and supports simultaneous optimization of the number of deployments and the phase threshold k i ; and / or, the feedback interface stores reliability results in the form of requirement satisfaction, design changes and version control metadata in the model repository.

[0020] Further, the present application also provides a computer readable storage medium having a computer program or instructions stored thereon, which, when executed by a processor, implements the method.

[0021] Further, the present application also provides a computer program product comprising a computer program or instructions, which, when executed by a processor, implements the method.

[0022] The present application, due to the adoption of the above technical solutions, constructs a closed-loop modeling system integrating requirement definition, architecture modeling, failure modeling, simulation calculation and parameter optimization. Under this system, parameter diagram modeling and simulation analysis are completed, and the reliability evaluation capability of the method in a multi-phase task environment is verified. The results show that the method has good scalability and engineering applicability, and can realize the whole-process reliability analysis from requirement to simulation verification, task planning and resource configuration optimization, providing effective support for task decision and reliability design of complex unmanned cluster systems. This is embodied in the following aspects: 1. Realizing closed-loop consistency of requirement-design-simulation-optimization By pre-embedding multi-phase task flow, subsystem dependency and reliability parameters in the MBSE platform (SysML), the "one-source" management of model elements and engineering requirements is realized. Design scheme changes can trigger fault tree / BDD reconstruction and reliability recalculation in real time, ensuring that the analysis results are highly consistent with the latest design, and significantly shortening the iteration cycle of the traditional "design -> export -> offline simulation -> backfill" link.

[0023] 2. High efficiency of large-scale cluster phase reliability calculation Automatically convert the phased fault tree into an ordered and simplified binary decision diagram (BDD), which still maintains a polynomial level of computational complexity even with hundreds of nodes. Compared with the exponential state explosion of traditional Markov / Petri-Net, the average calculation time is reduced by more than 70%. The hierarchical 'component-subsystem-phase' solving strategy effectively isolates the impact of local changes on the global model, supporting single-point update and local recalculation.

[0024] 3. Dynamic k-out-of-n Monte Carlo framework improves simulation accuracy Set different threshold values k for take-off, task execution, and return phases i , and update the remaining node number n in real time during simulation i , to achieve fine description of the 'inter-phase coupling failure chain'. With a sample size of 10 4 , the confidence interval of the cluster task success rate can be converged to ±0.3%, improving the accuracy by about 25% compared with simple analysis or coarse-grained simulation.

[0025] 4. Cooperative optimization of launch quantity and reliability target With the goal of'minimum launch quantity to meet a given reliability threshold', parameterized binary / grid search is used for automatic optimization, avoiding manual repeated trial calculations. Actual cases show that under the premise of maintaining a 0.98 task success rate, the optimal launch quantity is reduced by 15-20% compared with the experience redundancy scheme, saving equipment and operation and maintenance costs.

[0026] 5. Parameterized modeling mechanism with easy expansion and traceability Use parameter graphs to explicitly associate key variables such as task time, component failure rate, and phase threshold. Quickly complete'scene parameter change' without changing the model structure; reliability results are automatically written back to the model repository in the form of requirement satisfaction degree and version metadata, forming a complete change history and responsibility chain, improving audit and compliance efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0027] Figure 1 Composition diagram of unmanned aerial vehicle single machine system.

[0028] Figure 2 Phased task process diagram.

[0029] Figure 3 Phase reliability block diagram.

[0030] Figure 4 Fault tree diagram of each phase.

[0031] Figure 5 Fault tree diagram of key sub-components.

[0032] Figure 6 Binary decision diagram for each stage.

[0033] Figure 7 Multi-level reliability calculation flowchart.

[0034] Figure 8 Closed-loop MBSE reliability analysis modeling diagram.

[0035] Figure 9 Module definition diagram internal constraint and parameter setting diagram.

[0036] Figure 10 Module definition diagram parameter setting diagram.

[0037] Figure 11 Parameter modeling calculation diagram.

[0038] Figure 12 Simulation result diagram. DETAILED DESCRIPTION

[0039] The technical solutions in the embodiments will be described clearly and completely below. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the protection scope of the present application.

[0040] I. Analysis of unmanned aerial vehicle system composition 1.1 Unmanned aerial vehicle system composition An unmanned aerial vehicle system (UAS) is generally composed of a flight platform, a task load, a ground control station, a data link and a support system. To systematically develop cluster task reliability modeling, functional decomposition of the internal structure of a single machine is required. According to a typical task flow, the present application divides a single unmanned aerial vehicle into seven core subsystems of a flight platform, power, electrical, flight control, navigation, link and task load, and constructs a block definition diagram of the composition of the unmanned aerial vehicle system device as shown in Figure 1 .

[0041] The present application divides the whole process of the unmanned aerial vehicle cluster performing a task into three stages of takeoff, task execution and return. Since the failure of any unmanned aerial vehicle in any stage leads to the failure of the whole unmanned aerial vehicle task, the multi-stage task process has the typical characteristics of a series system. On this basis, the task process is decomposed into multiple stages, as shown in Figure 2 .

[0042] During the execution of the task by the UAV cluster, the task completion capability depends on the health status of each single UAV during the execution of the task. The single UAV is usually designed for a specific task stage, and any failure of a subsystem can cause the task to be interrupted or failed. However, it should be noted that different task stages have different degrees of dependence on subsystems, and some subsystems only play a key role in a specific stage.

[0043] 1.2 Definition and selection of core subsystems In the multi-stage task of the UAV, the core subsystem refers to the subsystem that plays a decisive role in the success of the task in a specific stage, and the selection is based on the following principles: (1) functional dependence: directly supporting the core goal of the stage (such as the power device providing lift in the take-off stage); (2) failure criticality: failure will directly cause the stage task to fail (such as navigation failure in the task execution stage causing the target to be lost); (3) resource occupancy rate: occupying the most consumption or calculation resources in the stage (such as high-frequency attitude adjustment of the flight control system in the return stage).

[0044] In addition, although the basic support subsystem (such as electrical and flight platform) does not directly participate in the core operation of the stage, it provides power and structural support for the whole task, and failure will cause a cascade risk. In view of its importance and irreplaceability, the present application assumes that such subsystems always operate normally, and focuses on analyzing the influence of the reliability degradation of other functional subsystems in each stage on the success rate of the task, and accordingly establishes a phased task reliability model.

[0045] Based on the division of the multi-stage task, the present application defines the core subsystem and the determination principle, and completes the allocation scheme of the core subsystem in each stage. According to the reliability block diagram corresponding to the participation of each stage subsystem as shown in Figure 3

[0046] II. Reliability modeling and calculation of single-stage UAV Based on the multi-stage task flow of the UAV cluster and the division result of the core subsystem, a "bottom-up" hierarchical reliability modeling framework is proposed to complete the stage-by-stage calculation of reliability. This framework gradually quantifies from the component layer to the subsystem layer to the stage layer, laying a modeling foundation for cluster-level reliability analysis.

[0047] 2.1 Construction of single-machine fault tree model Our assumption now is that the basic support subsystem (aircraft platform, electrical subsystem) is always normal and is not included in the fault tree. First, define the top event as Ti, which represents the failure of the UAV in the ith stage task (i=1.2.3), and the failure events of the UAV subsystems are represented as power device subsystem Ma, flight control subsystem Mb, navigation subsystem Mc, link subsystem Md, and task load subsystem Me. The failure of any key subsystem will cause the UAV to fail to complete the corresponding task in the current stage. According to Figure 3 ,​4 The reliability block diagram constructs the fault tree of each stage of 5, as shown in Figure 4 .

[0048] After completing the UAV multi-stage task subsystem-level fault tree modeling of each stage, to further improve the accuracy of task reliability analysis and system fault tracing ability, the failure modes of key sub-components in each core subsystem are analyzed in detail. Xj represents the failure of the jth component of the subsystem (j = 1, 2,..., 22), as shown in Figure 5 .

[0049] 2.2 Stage-based BDD description After completing the single UAV multi-stage task fault tree model construction, the fault tree structure needs to be calculated efficiently and reliably. Based on the binary decision diagram (BDD) method, the multi-stage task fault tree model is converted into a BDD structure. The ordered and simplified characteristics of BDD are used to quickly and accurately calculate the failure probability of single machine task. According to the binary decision diagram of each stage converted from the fault tree as shown in Figure 6 , 1 represents failure, and 0 represents normal.

[0050] Figure 6 The binary decision diagram (BDD) structure of the single UAV multi-stage task execution process is shown. The diagram arranges the failure event nodes of the core subsystems of the take-off, task execution, and return stages in order according to the multi-stage task sequence. If the previous stage task is successful, the next stage failure judgment is performed, and if it fails, the task flow is directly terminated.

[0051] 2.3 Single machine stage reliability calculation To evaluate the stage reliability of UAV cluster in multi-stage task, this application calculates the stage reliability from the component layer based on the failure parameters of each subsystem component and the task duration, and derives the subsystem layer indicators based on the subsystem structure logic. Then, combined with the key subsystem configuration of the task stage, the single machine stage task reliability model is constructed to calculate the single machine stage reliability. The calculation process is shown in Figure 7 .

[0052] 2.3.1 Component layer reliability calculation According to the task start time to the duration of this stage T m , the failure probability and reliability of this component in this stage are calculated. If the component obeys the exponential distribution as formula (1), and obeys the Weibull distribution as formula (2): (1) (2) At component layer, each subsystem i contains Mi components, the failure rate of each component j in mission phase m is fixed as λ ij , the failure rate of which is η ij , the failure rate of which is η ij is the scale parameter, β ij,m is the shape parameter; Q ij,m is the failure probability of component j in phase m in subsystem i, R Si,m is the reliability of component j in phase m in subsystem i.

[0053] 2.3.2 Subsystem layer reliability calculation According to the logical structure between components in the subsystem, the reliability and failure probability of the subsystem in phase m are calculated respectively. If subsystem i is a component series system, as formula (3), if subsystem i is a component parallel system, as formula (4), the failure probability is as formula (5); (3) (4) (5) Where R Si,m represents the reliability of subsystem i in phase m, Q Si,m represents the failure probability of subsystem i in phase m.

[0054] 2.3.3 Phase layer reliability calculation According to the key subsystem set K m involved in mission phase m, the logical relationship between subsystems is analyzed by fault tree or binary decision diagram, and the reliability and failure probability of the single unmanned aerial vehicle in the phase are obtained; the success condition of the phase task is the "and gate" relationship, as formula (6): (6) R stage,m represents the reliability of the single unmanned aerial vehicle in phase m to complete the task, Q stage,m represents the failure probability of the single unmanned aerial vehicle in phase m. Finally, the phase layer results are taken as input to complete the cluster multi-machine cooperative simulation and task success rate evaluation.

[0055] Three, cluster task reliability calculation framework based on MBSE The application further constructs a closed-loop unmanned aerial vehicle cluster task reliability analysis architecture based on MBSE. The architecture models the multi-phase task process of the unmanned aerial vehicle cluster in combination with MBSE, and at the same time, through parameterized modeling, embeds design parameters, task strategies and reliability indicators into the model, realizes dynamic tracing and synchronous updating of the indicators to parameter changes.

[0056] 3.1 Closed-loop MBSE reliability analysis modeling architecture (SysML) To support the reliability analysis of multi-stage tasks of UAV clusters, a closed-loop MBSE reliability modeling process (as shown in FIG. 1) is designed, which covers the whole process of requirement definition, system function architecture decomposition, failure modeling, single machine and cluster simulation analysis and parameter optimization. The application focuses on the parameter modeling and simulation analysis part, and realizes the single machine and cluster task reliability simulation based on the integration of MagicDraw and MATLAB, and verifies the effectiveness of the method. Figure 8

[0057] 3.2 Monte Carlo simulation method of k-out-of-n system After completing the multi-stage task architecture model of the UAV cluster, since the UAVs are limited by tasks and communication during task execution, there are minimum requirements for the number of UAVs in each stage of the cluster during task execution. For example, in the launch stage, there must be enough UAVs to start executing tasks to achieve the networking task allocation and communication requirements within the cluster. In the task execution stage, the number of UAVs must at least meet the requirements of covering the task area, appropriate task quantity and communication within the network. In the return stage, the number of UAVs that can be recovered must be no less than a percentage of the initial number of UAVs in the cluster to ensure the cost of the cluster executing the task. This process can be regarded as a typical k-out-of-n system, that is, in n UAVs, any k or more successful UAVs can complete the task, and if the number of successful UAVs is less than k, the task fails.

[0058] A multi-stage reliability Monte Carlo simulation method is designed. The initial number of UAVs N0 is taken as the starting point of simulation, the task is divided into three stages, and the reliability of each UAV is compared with the uniform distribution random number and the reliability of the single machine to determine whether each UAV task is successful. The number of successful UAVs is denoted as n i , and the number of successful UAVs in the previous stage is taken as the starting value of the next stage. Different minimum success quantity thresholds are set for each stage, and the stage reliability and the total reliability of the cluster task are calculated in turn.

[0059] The required success quantity of each stage is k1, k2, and k3, the stage task reliability is R1, R2, and R3, the number of UAVs in each stage is n1, n2, and n3, the cluster stage reliability is shown in formula (7), and the total task reliability of the cluster is shown in formula (8), which can be calculated by the following method: (7) (8) 3.3 Parameterized modeling ​To realize the dynamic configuration and automatic simulation of reliability model, the parameter graph calculation model is constructed based on MagicDraw. By embedding the task reliability parameters, subsystem reliability indicators and task policy limit variables into the parameter graph model, the parameter design and analysis integrated reliability analysis framework is constructed, the model scalability and simulation analysis real-time performance are improved, the dynamic response and traceability of task reliability results to parameter changes are realized, the internal constraints and parameter settings of module definition graph are as shown in Figure 9 and Figure 10 , and the parameter graph design is as shown in Figure 11 .

[0060] In each module definition graph, internal constraints (Constraint) are set for key parameter calculation and constraint requirements, and MATLAB language is selected for constraint expression writing. Then, the related parameters and constraints are associated through the parameter graph to realize the dynamic calling and automatic solving of model internal parameter calculation and task reliability simulation process.

[0061] IV. CASE VERIFICATION 4.1 Task scenario and parameter setting A certain type of six-rotor unmanned aerial vehicle cluster undertakes all-weather tasks on the border line, and the task profile includes: take-off stage, task execution stage and return stage. The take-off stage is stable in the morning weather, the ground support is complete, and the failure rate is low. In the task execution stage, it is subjected to strong electromagnetic interference, temperature changes, and sudden sandstorms, etc., which cause the failure rate to rise to the highest. In the return stage, it is easy to encounter insufficient battery power, fatigue damage, etc., which cause the failure rate to rise slightly.

[0062] Under this condition, the initial cluster number is set as N0=10, and the minimum number of unmanned aerial vehicle cluster in each stage to ensure communication and task execution is shown in Table 1.

[0063] Table 1 Stage task data

[0064] Assuming that the same type of unmanned aerial vehicle has the same life cycle, the unmanned aerial vehicle failure probability in each stage is shown in Table 2.

[0065] Table 2 Stage unmanned aerial vehicle failure probability

[0066] 4.2 Multi-stage reliability simulation results According to the task profile set phase task duration, the minimum number of unmanned aerial vehicle requirements and stage failure probability parameters, a large number of random simulation is carried out on the initial unmanned aerial vehicle cluster with N0=10, the success rate of each stage and the whole task is counted, and the reliability level of the unmanned aerial vehicle cluster in the complex task environment is evaluated. The Monte Carlo simulation results are shown in Table 3.

[0067] Table 3 Monte Carlo simulation results

[0068] The simulation results show that the multi-stage task reliability Monte Carlo simulation method constructed can effectively calculate the success rate of the unmanned aerial vehicle cluster task, and as the number of simulations increases, the simulation deviation decreases, the result stability improves, and the applicability of the method is verified.

[0069] 4.3 Task success rate and input resource number (N0) optimization analysis Based on the multi-stage Monte Carlo simulation, in the simulation process, the stage failure probability and the minimum number of unmanned aerial vehicles are fixed, N0 is set from the minimum requirement value to K1+10, multiple simulations are carried out, and the average task success rate is recorded. By drawing the curve of the task success rate with N0, the minimum N0 reaching the 0.98 task success rate threshold is determined as the optimal input scheme.

[0070] The results show that as N0 increases to the 10th, the task success rate improves significantly, reaches the set threshold 0.98, and the optimal N0 input quantity is finally determined, which can ensure the reliability of the multi-stage task while avoiding excessive resource redundancy and improving the efficiency of the cluster operation.

[0071] The above is the description of the embodiments of the present application. Through the above description of the disclosed embodiments, those skilled in the art can implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art. The general principles defined in this application can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to these embodiments shown in the application, but will conform to the widest scope consistent with the principles and novel features disclosed in the application.

[0072] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can adopt a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer usable storage media containing computer usable program code (including but not limited to disk storage, CD-ROM, optical storage, etc.).

[0073] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flow or blocks Figure 1 means for functionally implementing the steps listed in the flowchart block or blocks.

[0074] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the function specified in the flowchart block or blocks. Figure 1 one or more flow or blocks Figure 1 means for functionally implementing the steps listed in the flowchart block or blocks.

[0075] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flow or blocks Figure 1 means for functionally implementing the steps listed in the flowchart block or blocks.

[0076] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0077] The memory can include non-persistent memory and / or volatile memory, such as a random access memory (RAM) including a cache area for the temporary storage of data. The memory can also include non-volatile memory, such as read only memory (ROM), electrically programmable read only memory (EPROM), or electrically erasable programmable read only memory (EEPROM), for the storage of software that is read during runtime. The memory is an example of computer readable media.

[0078] Computer-readable media includes permanent and non-permanent, movable and non-movable media that can be implemented by any method or technology for storage of information. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible to a computing device. According to the definition herein, computer-readable media does not include transitory media, such as modulated data signals and carriers.

Claims

1. A multi-stage mission reliability analysis method for UAV swarms based on model-based systems engineering (MBSE), characterized in that, The method includes the following steps: 1) Establish a SysML architecture model within the MBSE platform, embedding multi-stage task flows, subsystem dependency matrices, and quantitative reliability parameters as model elements; 2) Based on the model elements, automatically generate fault trees for each stage and transform them into ordered, simplified binary decision diagrams (BDDs); 3) A hierarchical calculation based on components, subsystems, and stages is adopted, and the reliability vector of a single UAV in each mission stage is obtained using the BDD. 4) Drive the k-out-of-n Monte Carlo simulation with the reliability vector to obtain the reliability distribution of the UAV swarm at each stage and throughout the entire mission; 5) Set the "initial deployment quantity" as a SysML parameter and iterate in conjunction with the simulation results of step 4) to automatically search for the minimum deployment quantity that meets the preset task success rate threshold.

2. The method according to claim 1, characterized in that, The SysML model in step 1) includes a requirement diagram, activity diagram, block definition diagram, internal block diagram, and parameter diagram, and sets a dedicated meta-model label for reliability parameters; and / or, in step 2), the transformation from fault tree to BDD reorders variables according to task phase priority and uses subgraph merging rules to reduce the number of nodes.

3. The method according to claim 1, characterized in that, The component layer reliability calculation in step 3) is as follows: Based on the duration T from the start of the task to this phase m Calculate the failure probability and reliability of the component during this stage. If the component follows an exponential distribution as shown in equation (1) or a Weibull distribution as shown in equation (2): （1） （2） At the component level, each subsystem i contains Mi components, such as components whose lifetimes follow an exponential distribution. The failure rate of each component j within mission phase m is fixed at λ. ij The lifetime follows a Weibull distribution with a failure rate of η. ij It is a scale parameter, β ij It is a shape parameter; Q ij,m R refers to the failure probability of component j in subsystem i within stage m. ij,m This refers to the reliability of component j in subsystem i within stage m; And / or, the subsystem layer reliability is calculated as follows: Based on the logical structure between the internal components of the subsystem, the reliability and failure probability of the subsystem in stage m are calculated respectively. If the subsystem i is a series system of components as shown in Equation (3), and if the subsystem i is a parallel system of components as shown in Equation (4), the failure probability is shown in Equation (5). （3） （4） （5） Where R Si,m Q represents the reliability of subsystem i within stage m. Si,m This represents the failure probability of subsystem i within stage m; And / or, the stage-level reliability calculation is as follows: Based on the set of key subsystems K involved in task phase m m By analyzing the logical relationships between subsystems using fault trees or binary decision graphs, the reliability and failure probability of a single UAV in this stage can be obtained; the success condition of the stage task is an AND gate relationship, as shown in equation (6): （6） R stage,m Q represents the reliability of a single UAV in completing a mission within phase m. stage,m This represents the failure probability of a single drone within stage m.

4. The method according to claim 3, characterized in that, Step 4) Model the cluster task as a three-stage system of k1-out-of-n0, k2-out-of-n1, and k3-out-of-n2, with at least 10 Monte Carlo simulations performed for each stage. 4 Second-rate.

5. The method according to claim 4, characterized in that, Step 4) Starting with the initial number of drones N0, the task is divided into 3 stages. For each stage, the success of the mission is determined by comparing the reliability of a single drone with that of a uniformly distributed random number. The number of successful drones is recorded as n. i The number of successes in the previous stage is used as the starting value for the next stage; different minimum success thresholds are set for each stage, and the stage reliability and the total reliability of the cluster tasks are calculated in turn. The required number of successful attempts in each stage are k1, k2, and k3, respectively, and the reliability of each stage task is R1, R2, and R3. The number of drones in each stage is n1, n2, and n3. The cluster stage reliability is shown in Equation (7), and the total cluster task reliability is shown in Equation (8). It can be calculated by the following method: （7） （8）。 6. The method according to claim 1, characterized in that, Step 5) searches for the minimum number of deliveries using a binary search or grid-incremental search, combined with the convergence criterion |ΔR| < 1 × 10⁻⁶. -3 ; and / or, the write-back operation triggered in step 6) automatically updates the requirements matrix and generates a design change notification via the MBSE platform API.

7. A multi-stage mission reliability analysis system for unmanned aerial vehicle (UAV) swarms, the system being used to implement the method described in any one of claims 1-6, comprising: Modeling module: Used to create SysML models with reliability parameters within the MBSE platform; Fault Tree / BDD Generation Module: Coupled with the modeling module, it is used to automatically generate and simplify fault trees and BDDs; Hierarchical computing module: used to calculate the reliability of components, subsystems, and stages; Monte Carlo simulation module: used for simulation based on k-out-of-n The cluster reliability distribution is obtained by structural analysis; Optimization module: Used to iteratively adjust the number of deployments and output the optimal value; Feedback interface: Used to synchronize simulation and optimization results to SysML models and requirements libraries.

8. The system according to claim 7, characterized in that, The modeling module is based on the MBSE tool, which supports open APIs, and is integrated with MATLAB / Simulink through a co-simulation interface; and / or, the optimization module employs a parametric constraint solver and supports simultaneous optimization of the deployment quantity and the stage threshold k. i ; and / or, the feedback interface stores reliability results in the model repository in the form of requirement satisfaction, design changes, and version control metadata.

9. A computer-readable storage medium having a computer program or instructions stored thereon, characterized in that, When the computer program or instructions are executed by a processor, they implement the method of any one of claims 1-6.

10. A computer program product, comprising a computer program or instructions, characterized in that, When the computer program or instructions are executed by a processor, they implement the method of any one of claims 1-6.

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