CN107395411A - The end connected sets modeling of one kind commander's control network two and analysis method - Google Patents

The end connected sets modeling of one kind commander's control network two and analysis method Download PDF

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CN107395411A
CN107395411A CN201710584301.7A CN201710584301A CN107395411A CN 107395411 A CN107395411 A CN 107395411A CN 201710584301 A CN201710584301 A CN 201710584301A CN 107395411 A CN107395411 A CN 107395411A
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network
mrow
commander
lambda
probability
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黄洪钟
刘俊
彭卫文
李彦锋
李懿凡
李享
任彬
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University of Electronic Science and Technology of China
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/14—Network analysis or design
    • H04L41/145—Network analysis or design involving simulating, designing, planning or modelling of a network
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/10—Geometric CAD
    • G06F30/18—Network design, e.g. design based on topological or interconnect aspects of utility systems, piping, heating ventilation air conditioning [HVAC] or cabling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/06—Management of faults, events, alarms or notifications
    • H04L41/0677—Localisation of faults
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L43/00—Arrangements for monitoring or testing data switching networks
    • H04L43/08—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters
    • H04L43/0805—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters by checking availability
    • H04L43/0811—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters by checking availability by checking connectivity

Abstract

The invention discloses one kind to command the end connected sets modeling of control network two and analysis method, applied to reliability field, by the various failure modes for having considered network, the probability of happening that fuzzy language describes is converted into Triangular Fuzzy Number using expertise, and Triangular Fuzzy Number is obtained into event occurrence rate with method of integral values ambiguity solution, the Solve problems for solving failure probability ambiguity and crash rate in the case of uncertainty in large-scale complicated system；And the dynamic fault tree model of control network two end communication is also commanded by establishing and is translated into Bayesian network, its task time discretization is obtained into discrete-time Bayesian network, System failure probability is obtained with joint tree reasoning algorithm according to each node condition probability tables；The solution that expression is simple, computational efficiency is high is provided when analyzing large-scale, complicated, dynamic structure for analysis method for reliability.

Description

The end connected sets modeling of one kind commander's control network two and analysis method
Technical field
The invention belongs to reliability field, more particularly to a kind of the end connected sets modeling of commander's control network two and analysis Method.
Background technology
Commander control be Military Command and Control department in warfare for wars demand come decision-making its administrative money Source and equipment using and allocating, and coordinates commander to issue an order, to dispose, mobile and control linchpin pipe soldiers and weaponry, So as to ensure that the military mission that higher level assigns efficiently can be completed smoothly.Command and control system, i.e. C3I (Command, Control, Communication, Intelligence) system is a kind of intelligence for modernization operational implementation commander's control System can be changed, it has complex network characteristic.Commander controls network using computer technology as core, communication network, communication equipment Deng based on, commander's key-course is formed with commandings at different levels, full-time universe is implemented to task army and main battle weapons equipment Commander's control of dynamic high-efficiency.Due to the fast development of modern science and technology, information technology has obtained continuous progress, and information is in army Irreplaceable effect is played in thing field, many new military combats based on information technology are theoretical and fighting technique obtains More and more important application, it has also become the important research direction of various countries' militarization construction.Certainly, information-based fight will As occupying a kind of mode of operation of leading role in future war.
And command and control system plays more and more important strategy function as the operation instrument in IT-based warfare. Due to the diversity of war environment, such as geographical environment change, the influence of atmospheric climate, the interference of electromagnetic environment, and commander's control It is huge that the randomness failure, the change of communication distance etc. of network communication equipment processed all controls the reliability of network to cause commander Challenge.And not retrievable central nervous system in modernized war is used as, commander's control network also will be primary as enemy Strike target.In network reliability problem by the current of severe challenge, how to ensure and improve to command this spy of control network The system reliability of different network, problem encountered and challenge are self-evident, and its corresponding military significance and strategic importance are even more Significantly.
The connectivity reliability of network refers to that network can realize the probability of connectivity capabilities, reflects the connection of each node in network Situation, it is to describe the reliability of network from the angle of network topology structure.Two end connected sets, i.e., appoint in network G Two nodes of meaning can realize the probability of connection.Communication node and communication link in network is controlled to exist for the commander of complexity The situation of certain failure probability, the assessment of two end connected sets will be carried out to it, need to consider the network equipment and communication link Influence to commander's control network.Equipment in network such as Radio Station, interchanger, the failure of router etc. all connect to network It is logical to produce material impact, and consider the dynamic time sequence process such as device backup, link priority communication in commander's control network, it is based on Graph theory and the conventional method of probability theory will no longer be suitable for the modeling and assessment of commander's control network.Further, since battlefield surroundings Changeable, experimental data lacks, equipment and link failure mode diversification, causes equipment and link failure rate to be difficult to accurate number Value expression.
Dynamic fault tree analysis method is a kind of systems reliability analysis side that can handle dynamic and static logic relation Method, this method are modeled to the dynamic characteristic in fault tree by being introduced into one group of dynamic logic gate, are then divided into fault tree Dynamic module and static module, respectively with Markov model and binary decision diagrams (bdds) (BDD, binary decision Diagram) model is solved.Markov model can solve the modeling problem of dynamic inactive logic and can quantitative analysis As a result, but the shortcomings that Markov model existence multiple shot array, the reliability applied to large-scale complicated system is not suitable for Analysis.And Dynamic fault tree analysis method can not carry out bidirection reasoning can not handle the feelings that crash rate is not exact numerical Condition.
The content of the invention
It is of the invention to model and divide in order to solve the above technical problems, proposing a kind of end connected sets of commander's control network two Analysis method.
The technical solution adopted by the present invention is：The end connected sets modeling of one kind commander's control network two and analysis method, Including：
S1, control the data in network system to be collected and analyzed commander, obtain commander's control network failure pattern, And the stale event rate for describing fuzzy language is converted into Triangular Fuzzy Number, obtains the crash rate defuzzified value of elementary event；
S2, the commander obtained according to step S1 control network failure pattern, and commander's control end communication system of network two is entered Row logic analysis and structure obtain the reliability block diagram of commander's control end communication system of network two with specificity analysis is connected；
S3, the reliability block diagram obtained to step S2, consider that logical relation and sequential relationship occur for elementary event, establish with The end communication of commander's control network two does not connect the dynamic fault tree model for top event；And dynamic fault tree model is converted into shellfish This network model of leaf；
S4, by task time it is discrete be several timeslices, discrete-time Bayesian network is obtained, according to discrete Bayes Network obtains each node condition probability tables in Bayesian network model corresponding to each gate of Dynamic fault tree；
S5, the discrete time for obtaining the crash rate defuzzified value input step S4 of the event of failure obtained in step S1 In Bayesian network, according to the conditional probability table that step S4 is obtained using joint tree reasoning algorithm, top event probability is obtained.
Further, the calculating process of the crash rate defuzzified value of elementary event described in step S1 is：Using crash rate The λ cut sets of fuzzy number represent the integrated value of fuzzy number or so membership function inverse function：
Wherein, μRAnd μ (A)L(A) be respectively fuzzy number A or so membership function inverse functions integrated value, mλ、nλRespectively mould The Lower and upper bounds of number A λ cut sets are pasted, Δ λ represents λ value width.
According to μRAnd μ (A)L(A) crash rate defuzzified value, is calculated：
I=0.5 μR(A)+0.5μL(A)
Wherein, I is the defuzzified value of elementary event crash rate corresponding to fuzzy number A.
Further, the step S5 also includes：Obtained by calculating the probability of the system failure when each elementary event failure To the posterior probability of each elementary event.
Further, the step S5 also includes：The probability of each elementary event failure during computing system failure, obtain each The importance of elementary event.
Beneficial effects of the present invention：A kind of end connected sets modeling of commander's control network two of the present invention and analysis side Method, the various failure modes of network are considered, the probability of happening that fuzzy language describes are converted into three using expertise Angle fuzzy number, and Triangular Fuzzy Number is obtained into event occurrence rate with method of integral values ambiguity solution, so as to solve large complicated system The solution of failure probability ambiguity and crash rate in the case of uncertainty in system；The present invention also commands control network two by establishing Hold the dynamic fault tree model of communication and be translated into Bayesian network, task time discretization is obtained into discrete time pattra leaves This network, and System failure probability is obtained with joint tree reasoning algorithm according to each node condition probability tables, it is different by adding Evidence carries out bidirection reasoning and obtains bottom event posterior probability and importance etc.；The method of the present invention overcomes convectional reliability analysis Method expresses the problems such as complicated, computational efficiency is low, multiple shot array when analyzing large-scale, complicated, dynamic structure.
Brief description of the drawings
Fig. 1 is the solution of the present invention flow chart；
Fig. 2 is two end communication system principle figures of foundation provided in an embodiment of the present invention；
Fig. 3 is two end reliability of communication system block diagrams of foundation provided in an embodiment of the present invention；
Fig. 4 is two end communication system dynamic fault tree model figures of foundation provided in an embodiment of the present invention；
Fig. 5 is two end communication system Bayesian network model figures of foundation provided in an embodiment of the present invention；
Fig. 6 is the interior system dependability change curve provided in an embodiment of the present invention when 1000 is small as n=2 being calculated Figure；
Fig. 7 is that the interior system dependability provided in an embodiment of the present invention when 1000 is small when n=2,3,4,5 being calculated becomes Change curve map.
Embodiment
For ease of skilled artisan understands that the technology contents of the present invention, enter one to present invention below in conjunction with the accompanying drawings Step explaination.
As shown in figure 1, the technical scheme is that：The end connected sets modeling of one kind commander's control network two and analysis Method, comprise the following steps：
S1：Various communication equipments, communication mode and experimental data in network system is controlled according to commander, consults standard All kinds of fault modes of commander's control network two end communication are entered with the experience of pertinent literature synthetic operation personnel and technical staff Row collects and analyzed.Obtained fault mode corresponds to elementary event and code name is as shown in table 1；Step S1 also refers to including collection simultaneously Wave control network failure pattern.
The commander's control end communication system failure pattern of network two of table 1 corresponds to elementary event and code name
Event Code name Event Code name
J1 router failures X1 P2 exchange faults X17
The failure of J1 ultrashort wave radio sets 1 X2 J1 ultrashort wave radio set failures A1
The failure of J1 ultrashort wave radio sets 2 X3 P1 ultrashort wave radio set failures A2
The failure of P1 ultrashort wave radio sets 1 X4 P2 ultrashort wave radio set failures A3
The failure of P1 ultrashort wave radio sets 2 X5 The ultrashort wave radio set failure of J1 to P1 communications B1
J1 to P1 ultrashort waves link transmission is interrupted X6 The high-speed data radio station failure of J1 to P1 communications B2
J1 high-speed radio station failures X7 J1 to P1 ultra short wave communication failures C1
P1 high-speed radio station failures X8 J1 to P1 high-speed data communication failures C2
J1 to P1 high speed data link Transmissions X9 J1 to P1 command control information transmission faults D1
P1 exchange faults X10 P1 to P2 command control information transmission faults D2
P1 router failures X11 P2 to P2 command control information transmission faults D3
The failure of P2 ultrashort wave radio sets 1 X12 J1 to P1 communications do not connect E1
The failure of P2 ultrashort wave radio sets 2 X13 P1 to P2 communications do not connect E2
P1 to P2 ultrashort waves link transmission is interrupted X14 P2 to P3 communications do not connect E3
P2 to P3 ultrashort waves link transmission is interrupted X15 " commander-operation " 2 points of communications do not connect T
P3 ultrashort wave radio set failures X16
Expertise is made full use of for elementary event crash rate that is uncertain and lacking, using the semanteme of expert judging Variable is represented, the elementary event crash rate that fuzzy language describes is converted into Triangular Fuzzy Number, base is obtained after ambiguity solution The defuzzified value of the crash rate of present event.The λ cut sets of the Triangular Fuzzy Number of event occurrence rate are as shown in table 2.
The elementary event crash rate fuzzy number λ cut sets (× 10 of table 2-4h-1)
Code name Elementary event crash rate fuzzy number λ cut sets Code name Elementary event crash rate fuzzy number λ cut sets
X1 (0.02λ+0.06,-0.02λ+0.10) X10 (0.02λ+0.07,-0.02λ+0.11)
X2 (0.02λ+0.08,-0.02λ+0.12) X11 (0.02λ+0.06,-0.02λ+0.10)
X3 (0.02λ+0.08,-0.02λ+0.12) X12 (0.02λ+0.08,-0.02λ+0.12)
X4 (0.02λ+0.08,-0.02λ+0.12) X13 (0.02λ+0.08,-0.02λ+0.12)
X5 (0.02λ+0.08,-0.02λ+0.12) X14 (0.02λ+0.13,-0.02λ+0.17)
X6 (0.02λ+0.13,-0.02λ+0.17) X15 (0.02λ+0.08,-0.02λ+0.12)
X7 (0.02λ+0.10,-0.02λ+0.14) X16 (0.02λ+0.08,-0.02λ+0.12)
X8 (0.02λ+0.10,-0.02λ+0.14) X17 (0.02λ+0.13,-0.02λ+0.17)
X9 (0.02λ+0.13,-0.02λ+0.17)
The method of integral values proposed using Lion.Fuzzy number is handled using the computing of λ cut sets, calculation formula is：
I=α μR(A)+(1-α)μL(A)
Wherein, I is fuzzy number defuzzified value, and α is optimistic coefficient, α ∈ [0,1], as α=0 or α=1, is corresponded to respectively Fuzzy number A defuzzifications are worth bound；It is the defuzzified value of elementary event crash rate corresponding to fuzzy number A during α=0.5 Typical value；μRAnd μ (A)L(A) be respectively fuzzy number or so membership function inverse function integrated value.
For Triangular Fuzzy Number, μRAnd μ (A)L(A) it is expressed as with λ cut sets：
Wherein, mλ、nλThe respectively Lower and upper bounds of fuzzy number A λ cut sets, the λ cut sets in the present embodiment are specially：λ=0, X1 λ cut sets are (λ+0.10 of 0.02 λ+0.06, -0.02) in 0.1,0.2 ..., 1, such as table 2, then fuzzy number X1 λ cut sets Upper bound mλThe lower bound n of=0.02 λ+0.06, λ cut setλ=-0.02 λ+0.10；Δ λ represents λ value width, in the present embodiment Δ λ=0.1.
Therefore the λ cut sets of each elementary event crash rate fuzzy number in table 2 are brought into formula (1)-(2), takes α=0.5 to calculate To the defuzzified value of each elementary event crash rate, as shown in table 3.
The elementary event crash rate defuzzified value (× 10 of table 3-4h-1)
Code name Elementary event crash rate defuzzified value Code name Elementary event crash rate defuzzified value
X1 0.08 X10 0.09
X2 0.10 X11 0.08
X3 0.10 X12 0.10
X4 0.10 X13 0.10
X5 0.10 X14 0.15
X6 0.15 X15 0.10
X7 0.12 X16 0.10
X8 0.12 X17 0.15
X9 0.15
S2：Commander's control end communication system principle figure of network two is established according to command and control system topological structure.
As shown in Fig. 2 system forms by command cars at different levels and as battlebus, respectively trip's command car J1, command car P1 is sought, even Command car P2 and step battlebus P3.Traffic guidance is realized by the vehicular communication equipment of command cars at different levels and step battlebus.Trip's command car In be equipped with router R1, ultrashort wave radio set V1 and V2, and high-speed data radio station U1, after command control information enters router Selection ultrashort wave radio set or high-speed radio station are communicated, wherein preferential use ultrashort wave radio set, two ultrashort wave radio set difference For the radio station V1 and cold standby part radio station V2 that works.Equipped in same battalion command car P1 priority communication ultrashort wave radio set V3 and V4 and High-speed data radio station U2, wherein V3 are work radio station, and V4 is cold standby part radio station.In addition P1 is also equipped with router R2 and interchanger S1, when the command control information transmitted from J1 reaches P1, command control information is transferred to by even command car P2 by R2 and S1. Two ultrashort wave radio set V5 and V6 and interchanger S2 are equipped with P2, likewise, V5 is work radio station, V6 is cold standby part radio station. The command control information that ultrashort wave receives is transmitted to step battlebus P3 by interchanger and commanded troops by P2, and P3 equips one and surpassed Short-wave radio set V7.The commander collected according to step S1 controls network failure pattern, and commander's control end communication system of network two is entered Row logic analysis and structure obtain the reliability block diagram of commander's control end communication system of network two with specificity analysis is connected, and such as scheme Shown in 3.
S3：The commander obtained to step S2 controls the end reliability of communication system block diagram of network two, considers elementary event Logical relation and sequential relationship, establish with " the end communication of commander's control network two does not connect " as the Dynamic fault tree mould of top event Type, as shown in Figure 4；Dynamic fault tree is converted into by Bayesian network to the rule that Bayesian network converts according to dynamic logic gate Model, as shown in Figure 5.
S4：Assuming that task time is T, by task time it is discrete be n timeslice, i.e. n failure state, be respectively [0, Δ], [Δ, 2 Δs] ..., [(n-1) Δ, n Δs], [n Δs ,+∞], each time interval width is Δ=T/n, so as to obtain from Temporal bayesian network is dissipated, and it is general to obtain each node condition in Bayesian network model corresponding to each gate of Dynamic fault tree Rate：
If the incoming event of gate has n, its state space is X=[X1,X2,…,Xn], each event Xi, i=1, 2 ..., n state space is all { 1,2 ..., m+1 }.Make k=max (X1,X2,…,Xn), r=min (X1,X2,…,Xn), if Y For the output state variable of gate, then the lower state with the output state variable Y of door is combined for incoming event free position It is for j conditional probability distribution：
Wherein, j=1,2 ... ..., m+1.
The conditional probability distribution that state for the output state variable Y of OR gate is j is：
If being preferentially followed successively by A, B, outgoing event Y with the incoming event of door, value is respectively a, b and i, then can be true The conditional probability distribution for determining outgoing event Y is as follows：
As a ＜ b≤n+1,
As a >=b,
Assuming that when cold standby part door has input A and backup input B, export as T, when it is state x to input A, it can be deduced that The conditional probability distribution that backup B is in state y is：(it is assumed herein that spare part A and B obey exponential distribution, due in x ＜ y ＜ n+1 In the case of A first fail, its state is x, then in discrete time section [(x-1) Δ, x Δs], can calculate the mistake of A in this section Imitate probability；Cold standby part B A failure before crash rate be 0, do not run, and A failure after run up to state y fail, its from It is [(y-1) Δ, y Δs] to dissipate time interval.If the A out-of-service times are τ, the B out-of-service times are t, then B actual run times are t- τ, because This, integral operation can be made of exponential distribution probability density and obtains the probability under x ＜ y ＜ n+1)；
Wherein, λ represents the element in λ cut sets, λ=0 in the embodiment of the present application, 0.1,0.2 ..., 1.
S5：In the elementary event failure probability value input discrete-time Bayesian network that ambiguity solution in step S1 is obtained, Each node condition probability distribution is obtained using joint tree reasoning algorithm according to formula (3)-(7), if T=100 hours, n=2, is utilized MATLAB Bayesian Networks Toolbox BNT, which is programmed, to be calculated top event probability P (commander's control network two end leads to Letter does not connect)=0.024, i.e., it is 0.9276 that commander, which controls the end connectivity reliability of network two,.
Can will extend to 1000 hours task time with same method, you can obtain as n=2 commander's control network and End connectivity reliability changes over time curve, as shown in Figure 6；In order to improve the accuracy of fail-safe analysis, timeslice is increased, So that more accurately portraying dynamic characteristic and temporal characteristicses within task time, more accurate system fault probability value is obtained.Work as n It is as shown in Figure 7 to change over time curve for system dependability when=2,3,4,5.
System dependability max value of error is 0.15% when here due to n=4 and n=5, it is contemplated that other uncertain factors Presence, it is believed that meet during n=5 system reliability requirement, can now obtain being in each shape probability of state in 100 hours systems As shown in table 4.
During 4 n=5 of table, in 1000 hours systems in each shape probability of state
T=i 1 2 3 4 5 6
P (T=i) 0.1393 0.1218 0.1061 0.0921 0.0796 0.4611
It is non-failure state when system is in state 6, i.e., the reliability of system is 0.4611.
Using the ability of Bayesian network bidirection reasoning each bottom event can be obtained by adding different evidential reasonings Posterior probability and importance.Using the forward reasoning of Bayesian network, evidence X is added respectivelyi=0, calculated using BNT tool boxes As each bottom event failure (Xi=0) probability of the system failure is as shown in table 5 when.
The probability of the system failure when each bottom event failure of table 5
Event code name P (T=0/Xi=0) Event code name P (T=0/Xi=0)
X1 1.0000 X10 1.0000
X2 0.5418 X11 1.0000
X3 0.5418 X12 0.5553
X4 0.5418 X13 0.5553
X5 0.5418 X14 1.0000
X6 0.5930 X15 1.0000
X7 0.5599 X16 1.0000
X8 0.5599 X17 1.0000
X9 0.5598
The reverse diagnosis reasoning of Bayesian network is recycled, adds evidence T=0 respectively, being calculated using BNT tool boxes to be The probability of each bottom event failure is as shown in table 6 during system failure (T=0).
The probability of each bottom event failure when the system failure of table 6
Event code name P(Xi=0/T=0) Event code name P(Xi=0/T=0)
X1 0.1427 X10 0.1597
X2 0.0957 X11 0.1427
X3 0.1740 X12 0.0981
X4 0.0957 X13 0.1766
X5 0.1740 X14 0.2585
X6 0.1533 X15 0.1766
X7 0.1175 X16 0.1766
X8 0.1175 X17 0.2585
X9 0.1447
It can be seen that X2、X4Failure probability it is minimum therefore reliable.X14、X17Failure probability it is maximum, be in system Most weak part.
One of ordinary skill in the art will be appreciated that embodiment described here is to aid in reader and understands this hair Bright principle, it should be understood that protection scope of the present invention is not limited to such especially statement and embodiment.For ability For the technical staff in domain, the present invention can have various modifications and variations.Within the spirit and principles of the invention, made Any modification, equivalent substitution and improvements etc., should be included within scope of the presently claimed invention.

Claims (4)

1. the end connected sets modeling of one kind commander's control network two and analysis method, it is characterised in that including：
S1, control the data in network system to be collected and analyzed commander, obtain commander's control network failure pattern, and will The stale event rate of fuzzy language description is converted into Triangular Fuzzy Number, obtains the crash rate defuzzified value of elementary event；
S2, the commander obtained according to step S1 control network failure pattern, and commander's control end communication system of network two is patrolled Collect analysis and structure and obtain the reliability block diagram of commander's control end communication system of network two with specificity analysis is connected；
S3, the reliability block diagram obtained to step S2, consider that logical relation and sequential relationship occur for elementary event, establish to command The communication of control network two end does not connect the dynamic fault tree model for top event；And dynamic fault tree model is converted into Bayes Network model；
S4, by task time it is discrete be several timeslices, discrete-time Bayesian network is obtained, according to discrete Bayesian network Obtain each node condition probability tables in Bayesian network model corresponding to each gate of Dynamic fault tree；
S5, the discrete time pattra leaves for obtaining the crash rate defuzzified value input step S4 of the event of failure obtained in step S1 In this network, according to the conditional probability table that step S4 is obtained using joint tree reasoning algorithm, top event probability is obtained.
2. the end connected sets modeling of a kind of commander's control network two according to claim 1 and analysis method, its feature It is, the calculating process of the crash rate defuzzified value of elementary event described in step S1 is：Using the λ cut sets of crash rate fuzzy number Represent the integrated value of fuzzy number or so membership function inverse function：
<mrow> <msub> <mi>&amp;mu;</mi> <mi>R</mi> </msub> <mrow> <mo>(</mo> <mi>A</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <mn>1</mn> <mn>2</mn> </mfrac> <mo>&amp;lsqb;</mo> <msubsup> <mi>&amp;Sigma;</mi> <mrow> <mi>&amp;lambda;</mi> <mo>=</mo> <mn>0.1</mn> </mrow> <mn>1</mn> </msubsup> <msup> <mi>m</mi> <mi>&amp;lambda;</mi> </msup> <mi>&amp;Delta;</mi> <mi>&amp;lambda;</mi> <mo>+</mo> <msubsup> <mi>&amp;Sigma;</mi> <mrow> <mi>&amp;lambda;</mi> <mo>=</mo> <mn>0</mn> </mrow> <mn>0.9</mn> </msubsup> <msup> <mi>m</mi> <mi>&amp;lambda;</mi> </msup> <mi>&amp;Delta;</mi> <mi>&amp;lambda;</mi> <mo>&amp;rsqb;</mo> </mrow>
<mrow> <msub> <mi>&amp;mu;</mi> <mi>L</mi> </msub> <mrow> <mo>(</mo> <mi>A</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <mn>1</mn> <mn>2</mn> </mfrac> <mo>&amp;lsqb;</mo> <msubsup> <mi>&amp;Sigma;</mi> <mrow> <mi>&amp;lambda;</mi> <mo>=</mo> <mn>0.1</mn> </mrow> <mn>1</mn> </msubsup> <msup> <mi>n</mi> <mi>&amp;lambda;</mi> </msup> <mi>&amp;Delta;</mi> <mi>&amp;lambda;</mi> <mo>+</mo> <msubsup> <mi>&amp;Sigma;</mi> <mrow> <mi>&amp;lambda;</mi> <mo>=</mo> <mn>0</mn> </mrow> <mn>0.9</mn> </msubsup> <msup> <mi>n</mi> <mi>&amp;lambda;</mi> </msup> <mi>&amp;Delta;</mi> <mi>&amp;lambda;</mi> <mo>&amp;rsqb;</mo> </mrow>
Wherein, μRAnd μ (A)L(A) be respectively fuzzy number A or so membership function inverse functions integrated value, mλ、nλRespectively fuzzy number A λ cut sets Lower and upper bounds, Δ λ represent λ value width；
According to μRAnd μ (A)L(A) crash rate defuzzified value, is calculated：
I=0.5 μR(A)+0.5μL(A)
Wherein, I is the defuzzified value of elementary event crash rate corresponding to fuzzy number A.
3. the end connected sets modeling of a kind of commander's control network two according to claim 1 and analysis method, its feature It is, the step S5 also includes：Each elementary event is obtained by calculating the probability of the system failure when each elementary event failure Posterior probability.
4. the end connected sets modeling of a kind of commander's control network two according to claim 3 and analysis method, its feature It is, the step S5 also includes：The probability of each elementary event failure, obtains the important of each elementary event during computing system failure Degree.
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Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108521344A (en) * 2018-03-27 2018-09-11 电子科技大学 Based on two end reliability estimation method of side expander graphs command and control network
CN108901053A (en) * 2018-06-28 2018-11-27 广东工业大学 A kind of wireless industrial Mesh router dispositions method, apparatus and system
CN108956107A (en) * 2018-05-18 2018-12-07 合肥通用机械研究院有限公司 Couple the Fault tree diagnosis method of the reciprocating compressor typical fault of Triangular Fuzzy Number
CN110362939A (en) * 2019-07-19 2019-10-22 北京计算机技术及应用研究所 Network target range equipment architecture Reliability modeling system and method based on Monte carlo
CN110955227A (en) * 2019-11-22 2020-04-03 西安电子科技大学 System fuzzy reliability analysis method based on fuzzy dynamic Bayesian network
CN111126750A (en) * 2019-11-04 2020-05-08 中国电子科技集团公司电子科学研究院 Key unit determination method and device and storage medium
CN112380159A (en) * 2020-11-17 2021-02-19 南京航空航天大学 IMA core processing system structure configuration method

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20130024417A1 (en) * 2011-07-18 2013-01-24 Andreas Joanni Method, system and computer program product for automatic generation of bayesian networks from system reliability models
CN103488886A (en) * 2013-09-13 2014-01-01 清华大学 State threat assessment method based on fuzzy dynamic Bayesian network
CN106401597A (en) * 2016-10-27 2017-02-15 华中科技大学 Failure prediction and diagnosis control method applicable to shield tunneling machine

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20130024417A1 (en) * 2011-07-18 2013-01-24 Andreas Joanni Method, system and computer program product for automatic generation of bayesian networks from system reliability models
CN103488886A (en) * 2013-09-13 2014-01-01 清华大学 State threat assessment method based on fuzzy dynamic Bayesian network
CN106401597A (en) * 2016-10-27 2017-02-15 华中科技大学 Failure prediction and diagnosis control method applicable to shield tunneling machine

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
JINHUA MI等: "Reliability analysis of multi-state systems with common cause failure based on Bayesian Networks", 《2012 INTERNATIONAL CONFERENCE ON QUALITY, RELIABILITY, RISK, MAINTENANCE, AND SAFETY ENGINEERING》 *
李彦锋: "复杂系统动态故障树分析的新方法及其应用研究", 《博士学位论文》 *
谢朝杰: "隶属度函数自调整模糊控制器的研究", 《硕士学位论文》 *

Cited By (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108521344A (en) * 2018-03-27 2018-09-11 电子科技大学 Based on two end reliability estimation method of side expander graphs command and control network
CN108521344B (en) * 2018-03-27 2020-04-07 电子科技大学 Method for evaluating reliability of two ends of command control network based on edge expansion diagram
CN108956107A (en) * 2018-05-18 2018-12-07 合肥通用机械研究院有限公司 Couple the Fault tree diagnosis method of the reciprocating compressor typical fault of Triangular Fuzzy Number
CN108901053A (en) * 2018-06-28 2018-11-27 广东工业大学 A kind of wireless industrial Mesh router dispositions method, apparatus and system
CN108901053B (en) * 2018-06-28 2021-07-13 广东工业大学 Industrial wireless Mesh router deployment method, device and system
CN110362939A (en) * 2019-07-19 2019-10-22 北京计算机技术及应用研究所 Network target range equipment architecture Reliability modeling system and method based on Monte carlo
CN111126750A (en) * 2019-11-04 2020-05-08 中国电子科技集团公司电子科学研究院 Key unit determination method and device and storage medium
CN111126750B (en) * 2019-11-04 2022-10-28 中国电子科技集团公司电子科学研究院 Key unit determination method and device and storage medium
CN110955227A (en) * 2019-11-22 2020-04-03 西安电子科技大学 System fuzzy reliability analysis method based on fuzzy dynamic Bayesian network
CN110955227B (en) * 2019-11-22 2021-10-22 西安电子科技大学 Radar lifting system reliability analysis method based on fuzzy dynamic Bayesian network
CN112380159A (en) * 2020-11-17 2021-02-19 南京航空航天大学 IMA core processing system structure configuration method

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