CN110991044B - Agent modeling-based aircraft system task reliability assessment method - Google Patents
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Abstract
The invention relates to an Agent modeling-based aircraft system task reliability assessment method, and belongs to the technical field of reliability engineering. The method comprises the following steps: step 1, establishing an Agent mixed type structure model comprising a control layer and an execution layer; step 2, performing Agent state design, agent parameter design, agent task profile design and Agent interaction protocol design on the Agent mixed type structure model; step 3, establishing a system task disturbance Agent model according to equipment faults, human errors and environmental factors in the task operation process of the aircraft system; step 4, the execution layer carries out Monte Carlo simulation on the system task disturbance Agent model according to the input parameters; step 5, setting a single task success criterion, counting the task success times and the total simulation times in the Monte Carlo simulation process according to the R = N 2 /N 1 And solving the reliability of the system task. The invention solves the problems that the accuracy of the existing system evaluation model is low and the reliability and performance of the task cannot be accurately evaluated.
Description
Technical Field
The invention relates to the technical field of reliability engineering, in particular to an Agent modeling-based aircraft system task reliability assessment method.
Background
The complex system taking the aircraft as the core is widely applied to various industry fields and is taken as a system set taking the aircraft as the core, and the complex system structure of the complex system comprises a plurality of subsystems, including the aircraft, a guarantee system, a control system and the like. The objects in the system are various in types, a large number of nonlinear behaviors exist, and under the task driving, the objects have wide interaction. In the task process, a system object can generate a state of performance degradation or failure due to various reasons, so that the task capability and indexes of the whole system are influenced, and the evaluation of the task reliability and the performance indexes is obviously interfered. The guarantee system has the capability of recovering and repairing the system, and can repair the aircraft system when a fault occurs or the performance is reduced, the complexity of the whole system is increased by the repairing action, and the accuracy of the result of the evaluation index is influenced.
In the comprehensive evaluation research direction of task reliability and performance at a complex system level, most methods cannot effectively reflect the complexity of the system, especially cannot effectively describe the complex interaction process and the nonlinear association relationship between system objects, the accuracy of an evaluation model is low, and the task reliability and performance cannot be accurately evaluated. On the other hand, the system has various disturbances including equipment faults, human errors and environmental fluctuations, and the randomness and the evaluation difficulty of the system are increased.
Disclosure of Invention
In view of the above analysis, the present invention aims to provide an Agent modeling-based aircraft system task reliability assessment method to solve some or all of the above problems existing in the current assessment of aircraft system task reliability and performance.
The purpose of the invention is mainly realized by the following technical scheme:
the invention provides an Agent modeling-based aircraft system task reliability assessment method, which comprises the following steps: step 1, establishing an Agent mixed type structure model comprising a control layer and an execution layer according to an object and an operation process of an aircraft system; step 2, performing Agent state design, agent parameter design, agent task profile design and Agent interaction protocol design on the Agent mixed type structure model according to the object state of the aircraft system, the behavior of the operation process and the task requirements; step 3, controlling the Agent state, the Agent parameters and the interaction process of the Agent task profile to generate input parameters through the Agent interaction protocol, and establishing a system task disturbance Agent model according to equipment faults, human errors and environmental factors in the task operation process of the aircraft system; step 4, selecting input parameters of a system task disturbance Agent model by a control layer of the Agent mixed type structure model, and carrying out Monte Carlo simulation on the system task disturbance Agent model by an execution layer according to the input parameters; step 5, setting a single task success criterion of the system task disturbance Agent model simulation, and counting the task success times and the total number in the Monte Carlo simulation processNumber of simulations, according to R = N 2 /N 1 Solving for system task reliability, where N 2 Number of simulations for successful task, N 1 Is the total number of simulations.
Further, the step 1 of establishing an Agent hybrid type structural model including a control layer and an execution layer according to the object and the operation process of the aircraft system includes: firstly, establishing a system Agent, establishing a system object Agent according to actual composition in the system, establishing an environment Agent aiming at a space environment, and jointly forming a simulation execution layer; and establishing a process control Agent to form a control layer aiming at the running process of the system, and managing and controlling the behavior of the simulation execution layer Agent by the control layer Agent.
Furthermore, in the step 2, the Agent state design conforms to the task section and the actual using process of the aircraft, the system object is in continuous dynamic change, the Agent states are continuous in the continuous time state section, and the Agent internal state change process is modeled according to the continuous state change process of the object, so that the simulation of the system object state is realized.
Furthermore, the Agent parameters are designed into the spatial position and time attribute of the object Agent and the input and output indexes of the system, and specifically comprise a spatial position parameter, a time characteristic parameter and a performance index parameter.
Furthermore, the Agent task profile design determines different Agent task profiles to define the behavior of the Agent according to task requirements, wherein the behavior comprises an environment layer profile, a disturbance layer profile and an event layer profile.
Further, the step 3 of establishing a system task disturbance Agent model according to equipment faults, human errors and environmental factors in the task operation process of the aircraft system comprises the following steps: the equipment failure is established by adopting a random sampling mode, the human error is modeled by adopting a quantitative method combining random sampling and human factor engineering, and the environmental factors are modeled by adopting a random event mode.
Furthermore, the core of the equipment fault random sampling mode is fault occurrence time sampling modeling, and a continuous random variable direct sampling value xi = F is adopted -1 (Z), wherein Z is [0,1]Are uniformly distributedThe random variable, F (x), is a distribution function of the random variable ξ, and F (x) is a monotonically increasing function.
Furthermore, the core of the random sampling mode adopted by the human error is the sampling mode of error occurrence time sampling modeling and the equipment fault being the same, and from the human factor engineering perspective, the mean value of the initial sampling error correction time is obtained through three factors of learning ability, error correction ability and group deviation correction ability of operators.
Further, the core of the random event adopted by the environmental factors is an extreme environmental disturbance event, the heterogeneous poisson distribution is adopted, and the probability of the extreme environmental disturbance occurring during the system operation is represented as:
where N (t) is the number of times of occurrence of extreme disturbance, λ (t) is a frequency function of occurrence of extreme disturbance within t time, and k is a natural number.
Further, the step 5 of adopting a plurality of performance indexes as the single task success criterion of the system task perturbation Agent model simulation comprises: the method comprises the following steps that a specified region is reached within a specified time, a starting task is completed, and task preparation time meets preset time; after the aircraft arrives at the task area as required, the task is successfully executed according to the task planning; the task execution process is not attacked by fatality and loses the task capability; further reliability evaluation is performed by averaging the output results of one or more performance indicators, the average of the output results being
Wherein the value of the performance index is N 1 And (5) performing secondary simulation.
The technical scheme of the invention has the beneficial effects that: the invention discloses an Agent modeling-based aircraft system task reliability assessment method, which comprises the following steps of: step 1, establishing an Agent mixed type structure model comprising a control layer and an execution layer according to an object and an operation process of an aircraft system; step 2, according to the flightCarrying out Agent state design, agent parameter design, agent task profile design and Agent interaction protocol design on the Agent mixed type structure model according to the object state, the operation process behavior and the task requirement of the device system; step 3, controlling the Agent state, the Agent parameters and the interaction process of the Agent task profile to generate input parameters through the Agent interaction protocol, and establishing a system task disturbance Agent model according to equipment faults, human errors and environmental factors in the task operation process of the aircraft system; step 4, the control layer of the Agent mixed type structure model selects input parameters of a system task disturbance Agent model, and the execution layer carries out Monte Carlo simulation on the system task disturbance Agent model according to the input parameters; step 5, setting a single task success criterion of system task disturbance Agent model simulation, counting the task success times and the total simulation times in the Monte Carlo simulation process according to the R = N 2 /N 1 Solving for system task reliability, wherein N 2 Number of simulations for successful task, N 1 Is the total simulation times. The method solves the problem that the accuracy of an evaluation model is low due to a complex interaction process, a nonlinear incidence relation and disturbance diversity among system objects, so that the reliability and performance of a task cannot be accurately evaluated.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
The drawings are only for purposes of illustrating particular embodiments and are not to be construed as limiting the invention, wherein like reference numerals are used to designate like parts throughout.
FIG. 1 is a flowchart of an Agent modeling-based aircraft system task reliability assessment method according to an embodiment of the present invention;
FIG. 2 is a block diagram of an Agent modeling-based aircraft system task reliability assessment method according to an embodiment of the invention;
FIG. 3 is a two-layer structure diagram of an Agent hybrid type structural model according to an embodiment of the present invention;
FIG. 4 is a schematic representation of a state change of an aircraft system object of an embodiment of the invention;
FIG. 5 is a diagram illustrating a state transition when the system uses the guaranteed state according to an embodiment of the present invention;
FIG. 6 is a schematic diagram of a cross-sectional structure of an Agent task according to an embodiment of the present invention;
fig. 7 is a schematic diagram of an Agent interaction process flow according to an embodiment of the present invention.
Detailed Description
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate preferred embodiments of the invention and together with the description, serve to explain the principles of the invention and not to limit the scope of the invention.
One specific embodiment of the invention, as shown in fig. 1, discloses an Agent modeling-based aircraft system task reliability assessment method, which comprises the following steps:
s1, establishing an Agent mixed type structure model comprising a control layer and an execution layer according to an object and an operation process of an aircraft system;
specifically, according to a subsystem, an object and a system operation process contained in an aircraft system, a double-layer Agent mixed type structure is provided, and the double-layer Agent mixed type structure mainly comprises a control layer and an execution layer.
S2, performing Agent state design, agent parameter design, agent task profile design and Agent interaction protocol design on the Agent mixed type structure model according to the object state of the aircraft system, the behavior of the operation process and the task requirements;
specifically, agent state design: in the running process of an aircraft system, all subsystems and objects are in dynamic change, the states of the subsystems and the objects can be changed due to discrete events under a continuous time state section, and in order to accurately describe the continuous states and the change process thereof, a state transition diagram method is adopted to realize the simulation of the continuous state change of the system objects.
Designing an Agent parameter: in order to accurately describe the state and the behavior of the Agent and more reasonably couple system data, different types of Agent parameters need to be established. For the entire system, relevant input and output parameters are determined in order to reasonably evaluate task reliability and performance indicators.
Designing an Agent task profile: in the operation process of the system, each system object generates a corresponding task profile according to a system task target. In the modeling process, a task profile of each Agent is designed according to different task modes and system task targets, and the behavior logic of the Agent model is determined.
Designing an Agent interaction protocol: the Agent modeling method is adopted for modeling, the complex interaction relation among system objects can be reflected, and the accuracy and flexibility of the model can be improved by reasonably describing the interaction process among the agents. And designing an interaction protocol between the agents by adopting a coloring Petri network-based method according to the modeling requirement and the effectiveness of the interaction protocol.
S3, controlling the Agent state, the Agent parameters and the interaction process of the Agent task profile to generate input parameters through the Agent interaction protocol, and establishing a system task disturbance Agent model according to equipment faults, human errors and environmental factors in the task operation process of the aircraft system;
specifically, three types of disturbances, namely equipment failures, human errors, and environmental factor fluctuations, are encountered during the mission operation of the aircraft system. And aiming at the three types of system disturbance, modeling is carried out in a reasonable mode. The equipment fault is established by adopting a random sampling method of discrete events, the artificial fault is modeled by adopting a method combining random sampling and human factor engineering, and the environmental factors are modeled by adopting a discrete event sampling method.
S4, the control layer of the Agent mixed type structure model selects input parameters of a system task disturbance Agent model, and the execution layer conducts Monte Carlo simulation on the system task disturbance Agent model according to the input parameters;
specifically, an Agent mixed type structure model facing to a system task process is established according to the modeling steps, and the simulation of the model is realized. The model itself has high randomness because the model inputs and outputs a plurality of random variables.
S5, setting a single task success criterion of system task disturbance Agent model simulation, counting the task success times and the total simulation times in the Monte Carlo simulation process, and performing R = N 2 /N 1 Solving for system task reliability, where N 2 Number of simulations for successful task, N 1 Is the total number of simulations.
Specifically, the Agent technology-based simulation is realized as follows: and (3) adopting a Monte Carlo simulation method to realize evaluation calculation of task reliability and related performance indexes in analog software. The calculation process is as follows: let the total simulation number of times be N 1 The number of successful tasks is N 2 The task reliability is R = N 2 /N 1 (ii) a The value of a certain performance index is N 1 Average value of output results of sub-simulation
FIG. 2 is a block diagram of an Agent modeling-based aircraft system task reliability assessment method.
In an embodiment of the present invention, with reference to fig. 2, the step 1 of building an Agent hybrid structure model including a control layer and an execution layer according to the object and the operation process of the aircraft system includes: firstly, establishing a system Agent, establishing a system object Agent according to actual composition in the system, establishing an environment Agent aiming at a space environment, and jointly forming a simulation execution layer; and establishing a process control Agent to form a control layer aiming at the running process of the system, and managing and controlling the behavior of the simulation execution layer Agent by the control layer Agent.
In a specific example, two basic task units of the aircraft execute a maneuver task, and the task process comprises basic processes of transition transportation, pre-use guarantee, task planning, task movement and the like. After the task process is interfered, the guarantee subsystem can carry out recovery activities. Each task basic unit is provided with 1 command vehicle (including a task planning system and a command system), 6 task launching vehicles (including a launching device and an aircraft, and the like), 1 support and guarantee vehicle (including support and guarantee equipment and tools), and the like. According to the Agent abstraction method and system objects and behaviors, as shown in fig. 3, the abstract establishment Agent types include: object class Agent: the system comprises an aircraft Agent, a task planning Agent, a command Agent, a monitoring Agent, a task transmitting vehicle Agent, a support and guarantee Agent, a personnel Agent and the like;
environment Agent: storing an environment Agent, a transportation environment Agent, a task operation environment and other agents;
process control Agent: the system comprises a transportation process Agent, a guarantee process Agent before use, a preparation process Agent, a planning process Agent, a decision process Agent and the like.
According to a specific embodiment of the invention, with reference to fig. 2, in step 2, the Agent state design conforms to the mission profile and the actual use process of the aircraft, the system object is in continuous dynamic change, the Agent states are continuous in the continuous time state profile, and the Agent internal state change process is modeled according to the continuous state change process of the object, so as to realize the simulation of the system object state.
Specifically, the state of the aircraft Agent basically includes: the state of the aircraft Agent changes in a transition mode under different conditions, such as a production state, a transportation and transfer state, a storage state, a mission state, a guarantee state, a complete failure state, a fault state, a maintenance state, a decommissioning state and the like, as shown in fig. 4.
In a specific embodiment of the present invention, with reference to fig. 2, the Agent parameters are designed as spatial position, time attribute of the object Agent, and input/output index of the system, specifically including spatial position parameter, time characteristic parameter, and performance index parameter.
Specifically, for the spatial position, the time attribute and the input and output index of the system of the object Agent, the following types of parameters are designed:
spatial position parameters: velocity v, acceleration a, distance s, length l, width w, high, etc.
Time characteristic parameters: time of transport t y Time of flight t f Time t for task planning g And the like.
Performance index parameters: mean time failureInterval time T TF Failure rate λ, failure detection rate R FD Spare part satisfaction rate R SF And so on.
In order to evaluate the average usage guarantee time, the time parameter may set the average usage guarantee time, the refueling time, the detection time, the appearance inspection time, and the like, wherein the average usage guarantee time is used as an output parameter, others are used as input parameters, and the relationship between each parameter and the state transition is as shown in fig. 5.
According to a specific embodiment of the invention, in combination with fig. 2, the Agent task profile design determines different Agent task profiles to define the behavior of the Agent according to task requirements, including an environment layer profile, a disturbance layer profile and an event layer profile.
Specifically, in the task profile design of the Agent, different Agent typical task profiles are determined according to task requirements, the behavior of the Agent is further clarified, and the design core of the task profile is to describe the task process of the Agent in continuous time, including description of typical events, interaction with other agents and the like. As shown in fig. 6, the environmental layer includes temperature and height; the disturbance layer comprises a detection fault, a launching vehicle fault, environmental disturbance and human error; the event layer comprises pre-mission safeguards, transfers and transports, aircraft moves, and the like.
In the process of executing tasks, the carrier launching vehicle breaks down and needs to ensure the system to carry out maintenance and guarantee operation, and interactive cooperation among the carrier Agent, the guarantee personnel and the guarantee Agent is shown in fig. 7.
In an embodiment of the present invention, with reference to fig. 2, the establishing, in step 3, a system task perturbation Agent model according to equipment faults, human errors and environmental factors during the task operation process of the aircraft system includes: the equipment failure is established by adopting a random sampling mode, the human error is modeled by adopting a quantitative method combining random sampling and human factor engineering, and the environmental factors are modeled by adopting a random event mode.
In a specific embodiment of the invention, the core of the equipment fault adopting the random sampling mode is fault occurrence time sampling modeling, and a direct sampling value xi = adopting a continuous random variableF -1 (Z), wherein Z is [0,1]F (x) is a distribution function of the random variable ξ, and F (x) is a monotonically increasing function.
In particular, fault triggering belongs to discrete events, the occurrence time of the discrete events is random, and the core of modeling equipment faults in the text is sampling modeling of the occurrence time of the faults. Therefore, a continuous random variable direct sampling method is adopted, and Z is set as [0,1 ]]F (x) is a distribution function of the random variable ξ, and F (x) is a monotonically increasing function, then the sample value of ξ is ξ = F -1 (Z)。
In a specific embodiment of the invention, the core of the random sampling mode adopted by the artificial fault is that the time sampling modeling of the fault occurrence is in the same sampling mode as the equipment fault, and the mean value of the initial sampling correction fault time occurrence is calculated by three factors of learning ability, error correction ability and group deviation correction ability of an operator from the human factor engineering perspective.
Specifically, under continuous operation conditions, personnel can make mistakes during the operation process, and the mistakes belong to discrete events, so the time of the mistakes is also distributed correspondingly, and the sampling mode adopts the same sampling method as the equipment faults. Meanwhile, from the human factor engineering perspective, the learning ability, the error correction ability and the group deviation correction ability of people need to be considered, so after initial sampling, correction is carried out. The operator has self-learning and error correction capabilities, the error occurrence probability can be effectively reduced, members in the group learn and help each other, so the group also has error correction capability and can reduce the error occurrence probability, and a specific mode of embodying three factors in the model is to correct the average error time after initial sampling.
In a specific embodiment of the present invention, the core of the random event adopted by the environmental factors is an extreme environmental disturbance event, and the heterogeneous poisson distribution is adopted, and the probability of the extreme environmental disturbance occurring during the system operation is represented as:
where N (t) is the number of times of occurrence of extreme disturbance, λ (t) is a frequency function of occurrence of extreme disturbance within t time, and k is a natural number.
It should be noted that, for non-extreme environmental disturbances, the change of the state of the environmental Agent is used to interact with other agents. If the temperature changes at different time nodes, other agents in the system can be influenced, and the environment agents interact with the other agents to realize the temperature change.
Particularly, extreme environmental changes can have great influence on tasks, and extreme environmental disturbance events in the running process of the model are simulated in a random probability event sampling mode.
The service life of part of equipment in the system is designed to obey exponential distribution, and the failure rate of the equipment is lambda i In the sampling mode, the life time of the equipment is TTF i ＝-ln(η)/λ ij (η∈[0,1])。
Setting the time of misoperation of the personnel in the system operation to obey the lognormal distribution, and setting the TTM of the time of misoperation of the personnel under the continuous operation condition i Is composed of
Wherein eta is ∈ [0,1]σ is the error time root mean square, μ t Is the error time mean; wherein mu t ＝μ 0 ×(1+k l +k a +k e ) In the formula, mu 0 Is the error time mean, k, when the operator is not affected at all l For self-learning of correction factors, k e For self-correcting influence correction factor, k a Is a group deviation rectifying correction factor.
The starting of the extreme environmental event can adopt non-homogeneous poisson distribution in general, namely, the number of times of the extreme environmental disturbance occurring during the operation of the system can be expressed as:
in a specific embodiment of the present invention, with reference to fig. 2, the step 5 of using multiple performance indexes as the criterion for the success of a single task of the system task perturbation Agent model simulation includes: the method comprises the following steps that a specified region is reached within a specified time, a starting task is completed, and task preparation time meets preset time; after the aircraft arrives at the task area as required, the task is successfully executed according to the task planning; the task execution process is not attacked by fatality and loses the task capability;
specifically, when performing index evaluation, first, a criterion (or a threshold value) should be determined for an evaluation index to determine whether the evaluation index meets a requirement. And evaluating whether the system task is successful or not can adopt a plurality of performance index combinations as a task success criterion of single model simulation, and when the performance output indexes of the simulation model are all in a specified range, judging that the system task is successful in the model simulation.
In a specific example, two basic task units execute tasks according to task requirements, and a task success criterion is defined as:
(1) a task preparation time t for reaching a predetermined area within a predetermined time and completing a task of action Preparation of ≤120min；
(2) After the aircraft arrives at the task area as required, the tasks can be successfully executed according to the task planning;
(3) the task execution process is not attacked by fatality and the task capability is lost.
Further reliability evaluation is performed by averaging the output results of one or more performance indicators, the average of the output results being
Wherein the value of the performance index is N 1 And (5) performing secondary simulation.
In a specific example, after a model is established in simulation software Anylogic, a Monte Carlo method is adopted for simulation, the simulation times are set to be 5000 times, the task success times in a simulation result are 3756 times, and the task reliability is 0.7512.
When the use guarantee state is refined, the state transition of the use guarantee state in more detail in fig. 5 is obtained, probability-distributed input processing is performed on the time of each sub-state of the use guarantee state, so that the output use guarantee time has randomness and discreteness, and the average use guarantee time of the performance index is 65.8min when the processing is performed in the simulation process.
In summary, the invention discloses an Agent modeling-based aircraft system task reliability assessment method, which comprises the following steps: step 1, establishing an Agent mixed type structure model comprising a control layer and an execution layer according to an object and an operation process of an aircraft system; 2, performing Agent state design, agent parameter design, agent task profile design and Agent interaction protocol design on the Agent mixed type structure model according to the object state of the aircraft system, the behavior of the operation process and the task requirement; step 3, controlling the Agent state, the Agent parameters and the interaction process of the Agent task profile to generate input parameters through the Agent interaction protocol, and establishing a system task disturbance Agent model according to equipment faults, human errors and environmental factors in the task operation process of the aircraft system; step 4, the control layer of the Agent mixed type structure model selects input parameters of a system task disturbance Agent model, and the execution layer carries out Monte Carlo simulation on the system task disturbance Agent model according to the input parameters; step 5, setting a single task success criterion of system task disturbance Agent model simulation, counting the task success times and the total simulation times in the Monte Carlo simulation process according to the R = N 2 /N 1 Solving for system task reliability, where N 2 Number of simulations for successful task, N 1 Is the total simulation times. The method solves the problem that the accuracy of an evaluation model is low due to a complex interaction process, a nonlinear incidence relation and disturbance diversity among system objects, so that the reliability and performance of a task cannot be accurately evaluated.
Those skilled in the art will appreciate that all or part of the processes for implementing the methods in the above embodiments may be implemented by a computer program, which is stored in a computer-readable storage medium, to instruct associated hardware. The computer readable storage medium is a magnetic disk, an optical disk, a read-only memory or a random access memory.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention.
Claims (10)
1. An Agent modeling-based aircraft system task reliability assessment method is characterized by comprising the following steps:
step 1, establishing an Agent mixed type structure model comprising a control layer and an execution layer according to an object and an operation process of an aircraft system;
2, performing Agent state design, agent parameter design, agent task profile design and Agent interaction protocol design on the Agent mixed type structure model according to the object state of the aircraft system, the behavior of the operation process and the task requirement;
step 3, controlling the Agent state, the Agent parameters and the interaction process of the Agent task profile to generate input parameters through the Agent interaction protocol, and establishing a system task disturbance Agent model according to equipment faults, human errors and environmental factors in the task operation process of the aircraft system;
step 4, the control layer of the Agent mixed type structure model selects input parameters of a system task disturbance Agent model, and the execution layer carries out Monte Carlo simulation on the system task disturbance Agent model according to the input parameters;
step 5, setting a single task success criterion of system task disturbance Agent model simulation, counting the task success times and the total simulation times in the Monte Carlo simulation process according to the R = N 2 /N 1 Solving for system task reliability, wherein N 2 Number of simulations for successful task, N 1 Is the total simulation times.
2. The method of claim 1, wherein the step 1 of building an Agent hybrid type structure model comprising a control layer and an execution layer according to the object and the operation process of the aircraft system comprises: firstly, establishing a system Agent, establishing a system object Agent according to actual composition in the system, establishing an environment Agent aiming at a space environment, and jointly forming a simulation execution layer; and establishing a process control Agent to form a control layer aiming at the running process of the system, and managing and controlling the behavior of the simulation execution layer Agent by the control layer Agent.
3. The method according to claim 1, wherein in step 2 the Agent state design conforms to the mission profile and actual usage of the aircraft, the system object is in continuous dynamic change, the Agent state is continuous in the continuous time state profile, and the Agent internal state change process is modeled according to the continuous state change process of the object, so as to realize the simulation of the system object state.
4. The method according to claim 1, wherein the Agent parameters are designed as spatial position, time attribute and input/output index of the system of the object Agent, and specifically include spatial position parameters, time characteristic parameters and performance index parameters.
5. The method according to claim 4, wherein the Agent task profile design determines the behavior of different Agent task profile explicit agents according to task requirements, wherein the Agent task profile explicit agents comprise an environment layer profile, a disturbance layer profile and an event layer profile.
6. The method according to claim 4 or 5, wherein the step 3 of establishing a system task disturbance Agent model according to equipment faults, human errors and environmental factors during the task operation process of the aircraft system comprises the following steps: the equipment failure is established by adopting a random sampling mode, the human error is modeled by adopting a quantitative method combining random sampling and human factor engineering, and the environmental factors are modeled by adopting a random event mode.
7. The method of claim 6, wherein the step of determining the target position is performed by a computerThe core of the equipment fault adopting the random sampling mode is fault occurrence time sampling modeling, and a direct sampling value xi = F adopting a continuous random variable -1 (Z), wherein Z is [0,1]F (x) is a distribution function of the random variable ξ, and F (x) is a monotonically increasing function.
8. The method of claim 6, wherein the core of random sampling for human error is that the time sampling of error occurrence models the same sampling mode as the equipment failure, and from the human engineering perspective, the initial sampling correction error time is averaged by three factors of learning ability, error correction ability and group correction ability of the operator.
9. The method of claim 6, wherein the core of the random event adopted by the environmental factors is an extreme environmental disturbance event, and the probability of the extreme environmental disturbance occurring during the operation of the system is expressed by adopting a non-homogeneous Poisson distribution:
where N (t) is the number of times of occurrence of extreme disturbance, λ (t) is a frequency function of occurrence of extreme disturbance within t time, and k is a natural number.
10. The method according to claim 1, wherein the step 5 of adopting a plurality of performance indexes for the single task success criterion of the system task disturbance Agent model simulation comprises the following steps: the method comprises the following steps that a specified region is reached within a specified time, a starting task is completed, and task preparation time meets preset time; after the aircraft arrives at the task area as required, the task is successfully executed according to the task planning; the task execution process is not attacked by fatality and loses the task capability;
further reliability evaluation is performed by averaging the output results of one or more performance indicators, the average of the output results being
Wherein the value of the performance index is N 1 And (5) performing secondary simulation.
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