CN108491250B - Self-adaptive intelligent agent communication method and system for reliability simulation of complex system - Google Patents
Self-adaptive intelligent agent communication method and system for reliability simulation of complex system Download PDFInfo
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Abstract
The invention discloses a self-adaptive intelligent agent communication method for simulating the reliability of a complex system. The environment simulation self-adaptive intelligent bodies carry out conversion among the subtask intelligent bodies according to the advancing of the simulation clock, the task information, the environment information and the system reliability model information are transmitted to the system simulation self-adaptive intelligent bodies in real time and are issued to the work unit simulation self-adaptive intelligent bodies, and the work unit simulation self-adaptive intelligent bodies update the self environment influence factors and the system reliability relation among the work unit simulation self-adaptive intelligent bodies according to the received information to form a dynamic system reliability model. The invention not only defines the communication interaction relation among the intelligent agents in the reliability simulation of the complex system based on the self-adaptive intelligent agent, but also clearly shows the transmission process of the message mechanism in the simulation operation process, and can effectively improve the design and development efficiency of the simulation model.
Description
Technical Field
The disclosure relates to the technical field of system reliability simulation, in particular to a self-adaptive intelligent agent communication method and system for complex system reliability simulation.
Background
The reliability simulation technology is an effective method which is developed along with the maturity and popularization of computer software and hardware technologies and can be used for the reliability prediction and evaluation of a complex system, and the reliability simulation technology is successfully applied to the field of reliability engineering. The method has the advantages of good universality, wide application, high analysis precision, high calculation efficiency and lower requirements on software and hardware conditions, can play an obvious effect on improving the reliability of products, and is increasingly widely applied to the field of reliability engineering.
The current methods for the reliability simulation of complex systems are mainly system dynamics simulation methods, process-oriented discrete event simulation methods, object-oriented simulation methods and agent-based simulation methods. The simulation method based on the intelligent agent has the advantages of flexibility, strong adaptability, good expansibility and the like, and is an effective means for solving the problem of the reliability simulation of a complex system.
The system reliability simulation based on the self-adaptive intelligent agent generally represents a system entity in an intelligent agent form, and sets a processing logic with independent decision rules and response environments, so that the system entity can operate independently or in a mutual connection manner, and the simulation function of a complex system is completed through relatively simple behaviors of the intelligent agents and a group cooperation manner. Therefore, in order to meet the requirement of the reliability simulation of the complex system, not only the composition structure and the operation relationship of the system need to be accurately described, but also the external environment and the task change need to be accurately reflected to the system, so that the structure and the association relationship of the system can be timely adjusted by combining the relevant requirement change. Therefore, in order to meet the above simulation requirements, a set of communication mechanism between adaptive intelligent agents is urgently needed, and through the mechanism, not only can the interaction between the adaptive intelligent agents be realized, the information interaction between a unit and a system and a unit be met, but also the communication and the information interaction between the unit and the system and the external environment and task conditions can be realized, so that on one hand, the system responds to the task requirements of the external environment to adjust the self structure and the association relation of the system, on the other hand, the system and each component unit feed back the running level under the current task and the environmental conditions to realize the purpose of statistical analysis of the reliability of the system and the unit, and further, the simulation and evaluation requirements of the task reliability of the complex system under various complex task conditions and environmental influence conditions are met.
Disclosure of Invention
In view of the above, the present disclosure is proposed to provide an adaptive smart body communication method and system for complex system reliability simulation that overcomes or at least partially solves the above mentioned problems.
A self-adaptive intelligent agent communication method for reliability simulation of a complex system comprises the following steps:
in the simulation operation process, the environment simulation self-adaptive agent EA converts among the subtask agents MA according to the propulsion of a simulation clock, and transmits task information, environment information and system reliability model information to the system simulation self-adaptive agent SA in real time in the conversion process;
the system simulation self-adaptive intelligent agent SA issues information to each working unit simulation self-adaptive intelligent agent UA;
and each working unit simulation self-adaptive intelligent agent UA updates the self environmental influence factor according to the received information and updates the system reliability relation among the working units, thereby forming a dynamic system reliability model.
Each working unit simulation self-adaptive agent UA detects whether the agent UA has a fault or not in each simulation clock interval according to a random fault occurrence algorithm;
if the fault occurs, the working unit simulation self-adaptive intelligent agent UA informs the system simulation self-adaptive intelligent agent SA of the information;
the system simulation self-adaptive agent SA starts a system reliability state detection process according to the information; if the system is detected to have a fault, the system simulation self-adaptive agent SA informs an environment simulation self-adaptive agent EA of a fault message;
and the environment simulation self-adaptive agent EA decides to suspend the execution of the task and wait for the system to repair or directly terminate the execution of the task according to the current task condition.
The communication between the subtask agent MA and the environment simulation adaptive agent EA comprises the following steps:
the MA sends task starting notice to the EA;
when the task profile is composed of subtasks smn of the task profileyGo to subtask smnzWhen the task is updated, the MA sends a task update notification to the EA;
current task profile msnxAt the end, the MA sends a task stop notification to the EA.
The communication between the subtask agent MA and the system emulation adaptive agent SA comprises the following steps:
current task profile msnxStarting, the task requires the system m to start executing the task, and the MA sends a system starting instruction message to the SA with the number m;
when the task profile msn is the task profile msnxSmn, sub-tasks ofyGo to subtask smnzWhen the task needs to be updated, the MA sends a task update notification to the SA;
SA on-task profile msnxWhen the system running state changes, the running state condition of the system is reported to the MA in real time;
current task profile msnxAt the end, the MA sends a task stop notification to all SAs that perform the task.
The communication between the environment simulation adaptive agent EA and the system simulation adaptive agent SA comprises:
when the system task context is from exConversion to eyThe EA sends a context switch notification message to all SAs performing the task.
The communication between the system emulation adaptive agent SA and the working unit emulation adaptive agent UA includes:
after receiving the notification of task execution, the SA sequentially sends starting instructions to all UAs according to the currently executed task profile and subtasks;
the SA informs all or some working units to execute a stop instruction under the conditions of task switching, task completion and failure;
when the SA executes a specific task and generates subtask change, the SA updates the reliability model of the SA to reflect the task change, and the SA requests each UA to carry out reliability relation transformation according to a new reliability model by sending a reliability model updating instruction to each UA;
the SA inquires the working state of each UA when needed and sends inquiry unit state information to the UA;
the UA reports the state condition of the UA to the SA under the requirement of the SA or when the state of the UA changes, and the UA sends a unit state report message to the SA at the moment;
after some units are failed, the SA starts a maintenance process, and after the units are repaired, the SA notifies the units that the units have been repaired, and at this time, a unit repair notification message may be sent to the UA.
An adaptive agent communication system for reliability simulation of a complex system, comprising: an environment simulation self-adaptive agent EA, a subtask agent MA, a system simulation self-adaptive agent SA and a working unit simulation self-adaptive agent UA;
in the simulation operation process, the environment simulation self-adaptive agent EA converts among the subtask agents MA according to the propulsion of a simulation clock, and transmits task information, environment information and system reliability model information to the system simulation self-adaptive agent SA in real time in the conversion process;
the system simulation self-adaptive intelligent agent SA issues information to each working unit simulation self-adaptive intelligent agent UA;
and each working unit simulation self-adaptive intelligent agent UA updates the self environmental influence factor according to the received information and updates the system reliability relation among the working units, thereby forming a dynamic system reliability model.
Each working unit simulation self-adaptive agent UA detects whether the agent UA has a fault or not in each simulation clock interval according to a random fault occurrence algorithm;
if the fault occurs, the working unit simulation self-adaptive intelligent agent UA informs the system simulation self-adaptive intelligent agent SA of the information;
the system simulation self-adaptive agent SA starts a system reliability state detection process according to the information; if the system is detected to have a fault, the system simulation self-adaptive agent SA informs an environment simulation self-adaptive agent EA of a fault message;
and the environment simulation self-adaptive agent EA decides to suspend the execution of the task and wait for the system to repair or directly terminate the execution of the task according to the current task condition.
The MA sends a task starting notice to the EA; when the task profile is composed of subtasks smn of the task profileyGo to subtask smnzWhen the task is updated, the MA sends a task update notification to the EA; current task profile msnxWhen the task is finished, the MA sends a task stop notice to the EA;
current task profile msnxStarting, the task requires the system m to start executing the task, and the MA sends a system starting instruction message to the SA with the number m; when the task profile msn is the task profile msnxSmn, sub-tasks ofyGo to subtask smnzWhen the task needs to be updated, the MA sends a task update notification to the SA; SA on-task profile msnxWhen the system running state changes, the running state condition of the system is reported to the MA in real time; current task profile msnxAt the end, the MA sends a task stop notification to all SAs that perform the task.
When the system task context is from exConversion to eyWhen the task is executed, the EA sends a context conversion notification message to all the SAs executing the task;
after receiving the notification of task execution, the SA sequentially sends starting instructions to all UAs according to the currently executed task profile and subtasks; the SA informs all or some working units to execute a stop instruction under the conditions of task switching, task completion and failure; when the SA executes a specific task and generates subtask change, the SA updates the reliability model of the SA to reflect the task change, and the SA requests each UA to carry out reliability relation transformation according to a new reliability model by sending a reliability model updating instruction to each UA; the SA inquires the working state of each UA when needed and sends inquiry unit state information to the UA; the UA reports the state condition of the UA to the SA under the requirement of the SA or when the state of the UA changes, and the UA sends a unit state report message to the SA at the moment; after some units are failed, the SA starts a maintenance process, and after the units are repaired, the SA notifies the units that the units have been repaired, and at this time, a unit repair notification message may be sent to the UA.
Compared with the prior art, the main beneficial effects of the present disclosure are as follows:
a complex system reliability simulation intelligent agent communication framework based on an adaptive intelligent agent is provided. The invention provides a self-adaptive intelligent agent-based heterogeneous system reliability simulation method, which is used for solving the problem that the reliability of a heterogeneous system is high, and can meet the requirement of the reliability simulation method. The communication mechanism clearly defines the simulation function and the mutual communication relation of various intelligent agents in reliability simulation.
The invention not only defines the communication interaction relation among the intelligent agents in the reliability simulation of the complex system based on the self-adaptive intelligent agent, but also clearly shows the transmission process of the message mechanism in the simulation operation process, defines the message transmission format and the specific content information transmission process among the intelligent agents, and can effectively improve the design and development efficiency of the simulation model. The communication mechanism has good expansibility and flexibility and high efficiency.
The invention can meet the simulation requirement of the complex system based on the variable structure reliability model, so that the model is easier to realize and has better reusability, can support the evaluation and analysis of the inherent reliability of the system, can also support the accurate calculation of the task reliability of the system, and is particularly suitable for the reliability analysis and research of a multi-stage complex system and a network system.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the disclosure. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
FIG. 1 illustrates a flow diagram of an adaptive agent communication method for reliability simulation of a complex system according to one embodiment of the present disclosure;
FIG. 2 illustrates a diagram of a system reliability simulation runtime communication interaction process based on adaptive agent simulation according to one embodiment of the present disclosure;
FIG. 3 illustrates an adaptive agent communication system schematic for a reliability simulation of a complex system according to one embodiment of the present disclosure.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
The invention provides a self-adaptive intelligent agent communication mechanism to be used for the reliability simulation of a complex system. In the simulation operation process, the environment simulation self-adaptive intelligent agent (EA) carries out conversion among the subtask intelligent agents (MA) according to the propulsion of a simulation clock, and transmits task information, environment information and system reliability model information to the system simulation self-adaptive intelligent agent (SA) in real time in the conversion process, the system simulation self-adaptive intelligent agent distributes the information to each working unit simulation self-adaptive intelligent agent (UA), and each working unit simulation self-adaptive intelligent agent updates the self environment influence factor according to the received information and updates the system reliability relation among the working units to form a dynamic system reliability model. In the simulation operation process, each working unit simulation self-adaptive intelligent agent detects whether the self has a fault or not through a random fault occurrence algorithm in each simulation clock interval, if the self has the fault, the unit informs the system simulation self-adaptive intelligent agent of information, the system simulation self-adaptive intelligent agent starts a system reliability state detection process according to the information, whether the system has the fault or not is judged according to the detected structure, if the system has the fault through detection, the system simulation self-adaptive intelligent agent informs an environment simulation self-adaptive intelligent agent of the fault information, and the environment simulation self-adaptive intelligent agent determines to suspend task execution to wait for system repair or directly terminate the execution of the task according to the current task condition.
In order to achieve the purpose, the requirement based on the reliability simulation of the self-adaptive intelligent bodies is met, based on a communication network established among the self-adaptive intelligent bodies, the cooperative response mechanism of the self-adaptive intelligent bodies under different task conditions and events is achieved by defining message processing modules of the self-adaptive intelligent bodies, and the occurrence and the system response process of various events of a system in work are simulated through the cooperative activity among the self-adaptive intelligent bodies. The communication between the self-adaptive intelligent agents adopts a ternary extensible semantic message format, and the general coding form is as follows:
message type:: message keyword:: message body
Message body ═ parameter value > [ < parameter ═ parameter value > ]
Wherein the message type mainly represents the type or property of the message, for example, "INFO" represents information type message, and "CTR" represents instruction type message; message keywords are used to indicate the core semantics of the message, e.g., "MSN _ START" means "task profile START" message, "MSN _ UPDATE" means "task UPDATE," "SRM _ UPDATE" means "reliability relationship model UPDATE of system," etc.; the message body mainly contains additional parameter names and parameter values of semantics to be expressed by the message, and the message body can be a plurality of parameter name and parameter value pairs, but at least comprises a pair of parameter names and parameter values and the like.
Example one
Fig. 1 is a flow chart of an embodiment of an adaptive agent communication method for reliability simulation of a complex system according to the present embodiment, wherein,
and step 13, each working unit simulation self-adaptive agent UA updates the self environmental influence factor according to the received information and updates the system reliability relation among the working units UA and UA to form a dynamic system reliability model.
Specifically, the network communication mechanism principle between the adaptive agents mainly includes the following four communication mechanisms:
1, communication mechanism between MA and EA.
(1) And (4) task starting notification. Mission Profile msnxInitially, the MA sends a task start notification to the EA in the format:
INFO::MSN_START::<MSN＝msnx>
the message represents a task profile msnxAnd starting, and after receiving the message by the EA, autonomously executing task environment initialization operation to prepare for executing environment self-adaptive action.
(2) And (5) notification of task update. When the task profile msn is the task profile msnxSmn, sub-tasks ofyGo to subtask smnzWhen, the MA sends a task update notification to the EA:
INFO::MSN_UPDATE::<MSN＝msnx><SMN＝smnz>
the message indicates that the task executed by the system is composed of subtasks smnyConversion to subtask smnzAnd after receiving the message, the EA executes environment self-adaption action according to the task updating condition and autonomously updates the environment parameters.
(3) Task stop notification. Current task profile msnxWhen finished, the MA sends a task stop notification to the EA:
INFO::MSN_STOP::<MSN＝msnx>
this message represents the task profile msn executed by the systemxHaving ended, the EA, upon receiving the message, will perform the operation of stopping the context update.
2, communication mechanism between MA and SA.
(1) And (5) starting control of the system. Current task profile msnxWhen the task starts, the system m is required to start executing the task by the task, the MA sends a system start instruction message to the SA with the number m:
CTR::SYS_START::<SYS＝m><MSN＝msnx>
this message indicates that the system numbered m starts and starts executing the task profile msnxAfter receiving the message, the SA with the number m autonomously executes task initialization operation and starts to execute a task profile msnx。
(2) And (5) notification of task update. When the task profile msn is the task profile msnxSmn, sub-tasks ofyGo to subtask smnzWhen the MA sends a task update notification to the SA:
INFO::MSN_UPDATE::<MSN＝msnx><SMN＝smnz>
the message indicates that the task executed by the system is composed of subtasks smnyConversion to subtask smnzAnd after receiving the message, the SA executes the task self-adaptive action according to the task updating condition and updates the working state.
(3) And reporting the system state. SA on-task profile msnxWhen the system operation state changes, the system operation state condition is reported to the MA in real time, for example, a report message:
REPORT::SYS_STATE::<SYS＝m><SYS_STATE＝FAIL><UNIT＝k>
the message indicates that the system m is currently out of order, and the number of the failure unit is k. After receiving the message, the MA determines whether the task is terminated or continued according to the current state of all systems executing the task. The SYS _ STATE parameter is a parameter expressing the STATE of the system, and the common values thereof are shown in table 1:
TABLE 1 common values for SYS _ STATE parameters of systems
(4) Task stop notification. Current task profile msnxAt the end, the MA sends a task stop notification to all SA executing tasks:
INFO::MSN_STOP::<SYS＝m><MSN＝msnx>
this message represents the task profile msn executed by the systemxAfter stopping or stopping, the SA numbered m receives the message and executes the task stopping action.
3, communication mechanism between EA and SA.
The communication between the EA and the SA is mainly such that the EA notifies the SA of a change in task environment so that the SA can perform environment adaptive operation according to the environmental change. When the system task context is from exConversion to eyAnd when the task is executed, the EA sends a context conversion notification message to all the SAs executing the task:
INFO::EVM_UPDATE::<E＝ey>
wherein eyIs an environmental impact factor vector, and the specific expression mode thereof can be a vector, an array, a matrix or a list, etc., for example, the e is expressed by the vectoryWhen e is presenty(par1, par2, par3 …, parQ), the notification message can be expressed as follows:
INFO::EVM_UPDATE::<E＝(par1,par2,par3…,parQ)>
after receiving the message, the SA automatically executes reliability self-adapting operation according to the environment parameters.
4, communication mechanism between SA and UA.
Interactive communication between the UA and the SA is a key to implementing system reliability simulation. For SA, it needs to notify each UA of the system in real time according to changes in task and environment to perform adaptive operation of response, and also needs to query the real-time status of each UA according to task needs; for UA, it is necessary to perform adaptive operation in time according to the change of system task and environment notified by SA, and report the state of itself to the system. The following communication mechanisms are mainly used between the SA and the UA:
(1) the unit initiates an instruction. After receiving the notification of task execution, the SA sequentially sends starting instructions to all UAs according to the currently executed task profile and subtasks:
CTR::START::<UNIT＝a>
and after receiving the instruction, the working unit a updates the self state to the starting state.
(2) The unit stalls the instruction. The SA can inform all or some working units to execute a stop instruction under the conditions of task switching, task completion, failure and the like, and the instruction format is as follows:
CTR::STOP::<UNIT＝a>
and after receiving the instruction, the working unit a updates the state of the working unit a to the shutdown state.
(3) And updating the reliability model instruction. When executing a specific task and generating a subtask change, the SA updates its reliability model to reflect the task change, and at this time, the SA sends a reliability model update instruction to each UA to request each UA to perform a transformation of a reliability relationship according to the new reliability model, where the instruction format is as follows:
CTR::SRM_UPDATE::<UNIT＝a><SRM＝srmx>
this message represents a reliability model update instruction sent by the system SA to UA numbered a, where the value SRM of the SRM parameterxA specific system reliability model is expressed, which may be in various flexible formats, such as a binary relation set, a relation matrix, a reliability block diagram, etc., for example, the binary relation set is used to express the system reliability model as srmx1-2,2-3,2-4,3-5,4-5, in which case the instruction may be expressed as follows:
CTR::SRM_UPDATE::<UNIT＝a><SRM＝{1-2,2-3,2-4,3-5,4-5}>
after receiving the command, the working unit a reorganizes the reliability relationship with other working units according to the given model structure.
(4) The unit state is queried. The SA may query the working status of each UA when needed, and at this time, may send the following format message to the UA:
QUERY::UNIT_STATE::<UNIT＝a>
this message indicates that the SA requires the UA numbered a to report its current status.
(5) And reporting the state of the unit. The UA reports the state condition of the UA to the SA when the UA requests the SA or the state of the UA changes, and at the moment, the UA sends a report message to the SA:
REPORT::UNIT_STATE::<UNIT＝a><UNIT_STATE＝FAIL>
this message indicates that unit a reports its current failure to the system. After receiving the message, the SA judges whether the system has a fault according to the reliability model of the current system, updates the state of the SA and reports the state condition to the MA. The UNIT _ STATE in the report message is a parameter expressing the STATE of the UNIT UA, and the common values thereof are shown in table 2:
TABLE 2 UNIT _ STATE parameter common values for work cells
(6) A cell repair notification. After some units are failed, the SA starts a maintenance process, and after the units are repaired, the SA notifies the units that the units are repaired, and at this time, the following format message can be sent to the UA:
INFO::UNIT_FIXED::<UNIT＝a>
the message indicates that the SA notifies the failed UA with number a that the unit has repaired, and after receiving the message, the unit a updates its state to a good state.
Example two
In the following, referring to the drawings and the embodiments, a certain type of equipment is taken as an example, the task association relationship between the equipment and each functional system in the shooting task process is shown in table 3, and it is assumed that each system failure rate F of the equipmenti(t) the distribution type and distribution function are shown in Table 4.
TABLE 3 Multi-stage task System Association information Table
TABLE 4 Equipment failure rates F of the systemsi(t) distribution type and distribution function
Based on the adaptive reliability simulation system, the invention is further described in detail by equipping each component system design component intelligent agent UA, correspondingly equipping the system as a system entity, and adopting analog 7.0 as a simulation model development environment. The simulation runtime communication process is shown in fig. 2.
In the marching task stage, the MA sends a sending environment starting notice to the EA:
INFO:: MSN _ START:: MSN ═ march >
The EA performs a read context information action, sends a context impact to the SA to initiate communication:
in INFO::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
the SA then sends a Unit Start instruction to the UA. After receiving the notification of task execution, the SA sends a starting instruction to each UA according to the currently executed task profile and subtasks:
CTR::START::<UNIT＝1>
after receiving the instruction, the working unit 1 updates the self state to the starting state.
When the task phase is changed from marching to an expansion phase, the MA sends a task update notification to the EA:
INFO:, MSN _ UPDATE:, < MSN ═ march > < SMN >
The message indicates that the task executed by the system is converted from subtask marching to subtask expansion, after receiving the message, the EA executes environment self-adaptive action according to the task updating condition, autonomously updates environment parameters, and sends environment conversion notification messages to all SAs executing the task:
INFO:::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
when the task executed by the SA changes, the SA updates its own reliability model, and at this time, the SA requests each UA to perform a transformation of the reliability relationship according to the new reliability model by sending a reliability model update instruction to each UA, where the instruction format is as follows:
CTR: (SRM _ UPDATE: < UNIT ═ 1> < UNIT ═ 5> < SRM >
The message represents a reliability model update instruction sent by the system SA to the UAs numbered 1 and 2, where the SRM parameter expands the specific system reliability model at the task stage, e.g., a binary relation set is used to express the system reliability model as SRMx1-5, the task phase system reliability model is mainly composed of two-part entities S1 and S2, and the instruction can be expressed as follows:
CTR::SRM_UPDATE::<UNIT＝1><UNIT＝5><SRM＝{1-5}>
after the chassis system and the fire control system receive the instruction, the reliability relation between the chassis system and other working units is reorganized according to a given model structure.
In the task stage operation process, the UA reports the operation state of the UA to the SA, and when the maintenance function is not considered, the UA sends a report message to the SA:
REPORT::UNIT_STATE::<UNIT＝1><UNIT_STATE＝FAIL>
this message indicates that the chassis system is reporting its current failure to the system. After receiving the message, the SA judges whether the system has a fault according to the reliability model of the current system, updates the state of the SA and reports the state condition to the MA. For example, report messages:
REPORT::SYS_STATE::<SYS＝1><SYS_STATE＝FAIL><UNIT＝1>
the message indicates that the equipment system 1 is currently out of order, the failure unit is numbered 1, i.e., the equipment numbered 1 fails while performing the deployment task, and the failure unit is the chassis system. After receiving the message, the MA determines whether the task is terminated or continued according to the current state of all systems executing the task.
If the maintenance is considered, the SA starts the maintenance process after the unit fails, and after the unit is repaired, the SA notifies the unit that the unit has been repaired, and at this time, the following format message may be sent to the UA:
INFO::UNIT_FIXED::<UNIT＝1>
the message indicates that the SA notifies the failed UA with number 1 that the unit has been repaired, i.e. the chassis fault occurred in the equipment system with number 1 during the deployment task has been cleared, and after receiving the message, the unit 1 updates its status to the sound status.
The SA feeds back the report system operation status to the MA in real time, for example, report message:
REPORT::SYS_STATE::<SYS＝1><SYS_STATE＝SUCESS>
after receiving the message, the MA determines that the task is successfully executed.
And in the task withdrawing stage, the MA sends a task stop notice to the EA:
INFO:: MSN _ STOP:: MSN ═ withdraw >
The message indicates that the task profile retraction performed by the system has ended, and the EA, upon receiving the message, will perform the operation of stopping the context update. Meanwhile, the MA sends a task stop notification to all SAs that perform the task:
INFO:: MSN _ STOP: < SYS ═ 1> < MSN >
The message indicates that the task profile withdrawal executed by the system is stopped or stopped, after receiving the message, the SA with the number 1 executes the task stopping action, and then knows all or some working units to execute the stopping instruction to the SA, wherein the instruction format is as follows:
CTR::STOP::<UNIT＝1>
after receiving the instruction, the working unit 1, i.e. the chassis system, updates the self state to the shutdown state.
EXAMPLE III
As shown in fig. 3, an embodiment of the present disclosure further provides an adaptive intelligent agent communication system for reliability simulation of a complex system, including: the system comprises an environment simulation self-adaptive agent EA31, a subtask agent MA32, a system simulation self-adaptive agent SA33 and a working unit simulation self-adaptive agent UA 34;
in the simulation operation process, the environment simulation self-adaptive agent EA31 converts among the subtask agents MA32 according to the propulsion of a simulation clock, and transmits task information, environment information and system reliability model information to the system simulation self-adaptive agent SA33 in real time in the conversion process;
the system simulation self-adaptive intelligent agent SA33 issues the information to each working unit simulation self-adaptive intelligent agent UA 34;
and each working unit simulation self-adaptive agent UA34 updates the self environmental influence factor according to the received information and updates the system reliability relation between each working unit simulation self-adaptive agent UA34 to form a dynamic system reliability model.
Each working unit simulation self-adaptive agent UA34 detects whether the self has a fault or not in each simulation clock interval according to a random fault generation algorithm;
if a fault occurs, the working unit simulation adaptive agent UA34 notifies the system simulation adaptive agent SA33 of information;
the system simulation self-adaptive agent SA33 starts a system reliability state detection process according to the information; if the system is detected to be in fault, the system simulation self-adaptive agent SA33 informs an environment simulation self-adaptive agent EA31 of a fault message;
and the environment simulation self-adaptive agent EA31 decides to suspend task execution and wait for system repair or directly terminate execution of the task according to the current task condition.
Specifically, the MA32 sends a task start notification to the EA 31; when the task profile is composed of subtasks smn of the task profileyGo to subtask smnzWhen so, MA32 sends a task update notification to EA 31; current task profile msnxAt the end, MA32 sends a task stop notification to EA 31;
current task profile msnxWhen the task requests the system m to start executing the task, the MA32 sends a system starting instruction message to the SA33 with the number of m; when the task profile msn is the task profile msnxSmn, sub-tasks ofyGo to subtask smnzWhen, the MA32 will send task update notification to the SA 33; SA33 in performing task profile msnxWhen the system operation state changes, the system operation state condition is fed back and reported to the MA32 in real time; current task profile msnxAt the end, the MA32 sends a task stop notification to all SA33 executing tasks.
When the system task context is from exConversion to eyWhen it is, EA31 sends a context switch notification message to all SA33 that perform the task;
after receiving the notification of task execution, the SA33 sequentially sends start instructions to the UAs 34 according to the currently executed task profile and subtasks; the SA33 informs all or some of the working units to execute a stop instruction under the conditions of task switching, task completion and failure; when a specific task is executed and subtasks are changed, the SA33 updates the reliability model of the SA33 to reflect the task change, and the SA33 requests each UA34 to carry out the transformation of the reliability relation according to the new reliability model by sending a reliability model updating instruction to each UA 34; the SA33 inquires the working state of each UA34 when needed and sends inquiry unit state information to the UA 34; the UA34 reports the state of the user to SA33 when the user requests SA33 or the state of the user changes, and at this time, the UA34 sends a unit state report message to SA 33; SA33 initiates a repair process after some units fail, and after the units are repaired, SA33 notifies the units that they have been repaired, at which point a unit repair notification message may be sent to UA 34.
In the embodiments of the present disclosure, a heterogeneous system reliability simulation requirement based on a self-adaptive intelligent agent is provided, which not only requires to accurately depict the system composition structure and the operation relationship under the current task and environmental conditions, but also needs to accurately reflect the operation state level of the system itself, thereby facilitating the statistics and calculation of the system reliability. The communication mechanism clearly defines the simulation function and the mutual communication relation of various intelligent agents in reliability simulation.
The invention not only defines the communication interaction relation among the intelligent agents in the reliability simulation of the complex system based on the self-adaptive intelligent agent, but also clearly shows the transmission process of the message mechanism in the simulation operation process, defines the message transmission format and the specific content information transmission process among the intelligent agents, and can effectively improve the design and development efficiency of the simulation model. The communication mechanism has good expansibility and flexibility and high efficiency.
The invention can meet the simulation requirement of the complex system based on the variable structure reliability model, so that the model is easier to realize and has better reusability, can support the evaluation and analysis of the inherent reliability of the system, can also support the accurate calculation of the task reliability of the system, and is particularly suitable for the reliability analysis and research of a multi-stage complex system and a network system.
In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the disclosure may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the disclosure, various features of the disclosure are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be interpreted as reflecting an intention that: that is, the claimed disclosure requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this disclosure.
Those skilled in the art will appreciate that the modules in the device in an embodiment may be adaptively changed and disposed in one or more devices different from the embodiment. The modules or units or components of the embodiments may be combined into one module or unit or component, and furthermore they may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Moreover, those skilled in the art will appreciate that while some embodiments described herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the disclosure and form different embodiments. For example, any of the embodiments claimed in the claims can be used in any combination.
Various component embodiments of the disclosure may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. The present disclosure may also be embodied as device or system programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing the present disclosure may be stored on a computer-readable medium or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.
It should be noted that the above-mentioned embodiments illustrate rather than limit the disclosure, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The disclosure may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several systems, several of these systems may be embodied by one and the same item of hardware.
The foregoing is directed to embodiments of the present disclosure, and it is noted that numerous improvements, modifications, and variations may be made by those skilled in the art without departing from the spirit of the disclosure, and that such improvements, modifications, and variations are considered to be within the scope of the present disclosure.
Claims (7)
1. An adaptive intelligent agent communication method for simulating the reliability of a complex system is characterized by comprising the following steps:
in the simulation operation process, the environment simulation self-adaptive agent EA converts among the subtask agents MA according to the propulsion of a simulation clock, and transmits task information, environment information and system reliability model information to the system simulation self-adaptive agent SA in real time in the conversion process;
the system simulation self-adaptive intelligent agent SA issues information to each working unit simulation self-adaptive intelligent agent UA;
each working unit simulation self-adaptive agent UA updates the self environmental influence factor according to the received information and updates the system reliability relation among the working units simulation self-adaptive agent UA and the self environmental influence factor, so as to form a dynamic system reliability model;
each working unit simulation self-adaptive agent UA detects whether the agent UA has a fault or not in each simulation clock interval according to a random fault occurrence algorithm;
if the fault occurs, the working unit simulation self-adaptive intelligent agent UA informs the system simulation self-adaptive intelligent agent SA of the information;
the system simulation self-adaptive agent SA starts a system reliability state detection process according to the information; if the system is detected to have a fault, the system simulation self-adaptive agent SA informs an environment simulation self-adaptive agent EA of a fault message;
the environment simulation self-adaptive agent EA decides to suspend task execution and wait for system repair or directly terminate the execution of the task according to the current task condition;
the communication between the subtask agent MA and the environment simulation adaptive agent EA comprises the following steps:
the MA sends task starting notice to the EA;
when the task profile is composed of subtasks smn of the task profileyGo to subtask smnzWhen the task is updated, the MA sends a task update notification to the EA;
current task profile msnxAt the end, the MA sends a task stop notification to the EA.
2. The method of claim 1, wherein the communication between the subtask agent MA and the system emulation adaptive agent SA comprises:
current task profile msnxStarting, the task requires the system m to start executing the task, and the MA sends a system starting instruction message to the SA with the number m;
when the task profile is transferred from the subtask smny to the subtask smnz of the task profile msnx, the MA sends a task update notification to the SA;
SA on-task profile msnxWhen the system running state changes, the running state condition of the system is reported to the MA in real time;
current task profile msnxAt the end, the MA sends a task stop notification to all SAs that perform the task.
3. The method of claim 1, wherein the communication between the environment emulation adaptive agent EA and the system emulation adaptive agent SA comprises:
when the system task context is from exConversion to eyThe EA sends a context switch notification message to all SAs performing the task.
4. The method of claim 1, wherein the system emulating communication between an adaptive agent SA and a work unit emulating adaptive agent UA comprises:
after receiving the notification of task execution, the SA sequentially sends starting instructions to all UAs according to the currently executed task profile and subtasks;
the SA informs all or some working units to execute a stop instruction under the conditions of task switching, task completion and failure;
when the SA executes a specific task and generates subtask change, the SA updates the reliability model of the SA to reflect the task change, and the SA requests each UA to carry out reliability relation transformation according to a new reliability model by sending a reliability model updating instruction to each UA;
the SA inquires the working state of each UA when needed and sends inquiry unit state information to the UA;
the UA reports the state condition of the UA to the SA under the requirement of the SA or when the state of the UA changes, and the UA sends a unit state report message to the SA at the moment;
after some units are failed, the SA starts a maintenance process, and after the units are repaired, the SA informs the units that the units are repaired, and sends a unit repair notice message to the UA.
5. An adaptive agent communication system for reliability simulation of a complex system, comprising: an environment simulation self-adaptive agent EA, a subtask agent MA, a system simulation self-adaptive agent SA and a working unit simulation self-adaptive agent UA;
in the simulation operation process, the environment simulation self-adaptive agent EA converts among the subtask agents MA according to the propulsion of a simulation clock, and transmits task information, environment information and system reliability model information to the system simulation self-adaptive agent SA in real time in the conversion process;
the system simulation self-adaptive intelligent agent SA issues information to each working unit simulation self-adaptive intelligent agent UA;
each working unit simulation self-adaptive agent UA updates the self environmental influence factor according to the received information and updates the system reliability relation among the working units simulation self-adaptive agent UA and the self environmental influence factor, so as to form a dynamic system reliability model;
each working unit simulation self-adaptive agent UA detects whether the agent UA has a fault or not in each simulation clock interval according to a random fault occurrence algorithm;
if the fault occurs, the working unit simulation self-adaptive intelligent agent UA informs the system simulation self-adaptive intelligent agent SA of the information;
the system simulation self-adaptive agent SA starts a system reliability state detection process according to the information; if the system is detected to have a fault, the system simulation self-adaptive agent SA informs an environment simulation self-adaptive agent EA of a fault message;
the environment simulation self-adaptive agent EA decides to suspend task execution and wait for system repair or directly terminate the execution of the task according to the current task condition;
the MA sends a task starting notice to the EA; when the task profile is composed of subtasks smn of the task profileyGo to subtask smnzWhen the task is updated, the MA sends a task update notification to the EA; current task profile msnxAt the end, the MA sends a task stop notification to the EA.
6. The system of claim 5, wherein a current task profile msnxStarting, the task requires the system m to start executing the task, and the MA sends a system starting instruction message to the SA with the number m; when the task profile msn is the task profile msnxSmn, sub-tasks ofyGo to subtask smnzWhen the task needs to be updated, the MA sends a task update notification to the SA; SA on-task profile msnxWhen the system running state changes, the running state condition of the system is reported to the MA in real time; current task profile msnxAt the end, the MA sends a task stop notification to all SAs that perform the task.
7. A system according to claim 5 or 6, wherein when the system task context is from exConversion to eyWhen the task is executed, the EA sends a context conversion notification message to all the SAs executing the task;
after receiving the notification of task execution, the SA sequentially sends starting instructions to all UAs according to the currently executed task profile and subtasks; the SA informs all or some working units to execute a stop instruction under the conditions of task switching, task completion and failure; when the SA executes a specific task and generates subtask change, the SA updates the reliability model of the SA to reflect the task change, and the SA requests each UA to carry out reliability relation transformation according to a new reliability model by sending a reliability model updating instruction to each UA; the SA inquires the working state of each UA when needed and sends inquiry unit state information to the UA; the UA reports the state condition of the UA to the SA under the requirement of the SA or when the state of the UA changes, and the UA sends a unit state report message to the SA at the moment; after some units are failed, the SA starts a maintenance process, and after the units are repaired, the SA informs the units that the units are repaired, and sends a unit repair notice message to the UA.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104715087A (en) * | 2013-12-14 | 2015-06-17 | 上海莞东拿信息科技有限公司 | Novel interactive simulation state traveling operating method synthetic system |
| CN105159746A (en) * | 2015-08-26 | 2015-12-16 | 哈尔滨工业大学 | Reliability simulation tool for fault-tolerant combined web service |
| CN106599454A (en) * | 2016-12-12 | 2017-04-26 | 北京航空航天大学 | Fault behavior modeling method of multi-phased task system based on Petri network |
Family Cites Families (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10367764B2 (en) * | 2009-12-22 | 2019-07-30 | Cyara Solutions Pty Ltd | System and method for automated contact center agent workstation testing |
| CN102841582B (en) * | 2012-08-08 | 2015-11-18 | 中国电力科学研究院 | A kind of power distribution network self-healing control system and its implementation |
| CN104679920B (en) * | 2013-11-27 | 2017-12-12 | 南京理工大学 | Waveguide device microwave gas discharge method for numerical simulation |
| US9753767B2 (en) * | 2014-03-11 | 2017-09-05 | Sas Institute Inc. | Distributed data set task selection |
| CN104656633B (en) * | 2014-12-23 | 2017-04-26 | 重庆大学 | Detecting method for early failure of logistics trolley of flexible manufacturing system |
| CN104571042B (en) * | 2014-12-31 | 2018-04-20 | 深圳市进林科技有限公司 | The control method of finished and entire car controller of intelligent automobile |
| CN105138770B (en) * | 2015-08-24 | 2018-04-24 | 中国航天标准化研究所 | Space product Reliablility simulation appraisal procedure based on indirect characteristic quantities |
| CN106844877A (en) * | 2016-12-28 | 2017-06-13 | 中国人民解放军军械工程学院 | The analysis method of multimode phased mission systems dependability parameter |
| CN107229499B (en) * | 2017-06-01 | 2020-06-30 | 安徽中鑫继远信息技术股份有限公司 | Master station simulation system and detection method for detecting fault terminal of power acquisition system |
| CN107462425B (en) * | 2017-08-04 | 2019-10-11 | 北京航天发射技术研究所 | A kind of failure prediction method and failure predication platform of car kits system |
-
2018
- 2018-04-13 CN CN201810330435.0A patent/CN108491250B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104715087A (en) * | 2013-12-14 | 2015-06-17 | 上海莞东拿信息科技有限公司 | Novel interactive simulation state traveling operating method synthetic system |
| CN105159746A (en) * | 2015-08-26 | 2015-12-16 | 哈尔滨工业大学 | Reliability simulation tool for fault-tolerant combined web service |
| CN106599454A (en) * | 2016-12-12 | 2017-04-26 | 北京航空航天大学 | Fault behavior modeling method of multi-phased task system based on Petri network |
Non-Patent Citations (2)
| Title |
|---|
| "基于自适应Agent仿真的系统可靠性预计方法";申莹;《装甲兵工程学院学报》;20130215;第27卷(第1期);第17、18页 * |
| "面向任务可靠性仿真的复杂系统任务剖面建模方法研究";曹军海;《第14届中国系统仿真技术及其应用学术年会》;20120811;第182页 * |
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