CN110490433A - A kind of train control system methods of risk assessment - Google Patents
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Abstract
The present invention relates to a kind of train control system methods of risk assessment, comprising: the data of acquisition train control system risk assessment object establish fault tree；Building includes the risk Metrics of risk occurrence frequency and severity degree；Calculation risk source occurrence frequency；Calculation risk event occurrence frequency；The severity degree of calculation risk；It according to risk case occurrence frequency and effect of risk severity, is compared respectively with risk Metrics, to evaluate risk class；Bayesian network is converted by fault tree, in conjunction with risk source occurrence frequency, determines the weak link of train control system；According to risk class, risk control is carried out to the weak link of train control system, to reduce the risk occurrence frequency of train control system, reduce severity degree.Compared with prior art, the present invention is based on the integrated applications of a variety of methods, guarantee risk evaluation result accurate and effective, and the weak link by determining train control system risk, provide reliable basis for train control system risk control.
Description
Technical field
The present invention relates to train control system risk assessment technology fields, more particularly, to a kind of train control system risk assessment side
Method.
Background technique
Train operation control system, i.e. train control system are one and are integrated with the safety_critical system of the technologies such as control, communication
System, be mainly used for control train running speed, ensure traffic safety and improve conevying efficiency, can real time control on train operation between
Every, prevent train overspeed, be ensure train high speed, safe and reliable operation core technology equipment and key safety system.
Compared with classical signal system, train control system has harsher safety requirements, and the security risk of its own will seriously jeopardize
The operational safety and efficiency of train.Either theoretically still in actual production, risk existing for train control system is carried out
Its meaning of the assessment of science is all very great.
Currently, the method for risk assessment includes failure mode effect and HAZAN method, Field Using Fuzzy Comprehensive Assessment, risk
Matrix method, Fault Tree Analysis etc..Wherein, failure mode effect and HAZAN method are a kind of qualitatively analyses, for multiple
Miscellaneous system, assessment result will lose accuracy；Field Using Fuzzy Comprehensive Assessment is used most in risk assessment, is generally adopted
Semi-quantitative assessment carried out to risk with analytic hierarchy process (AHP) and expert graded, principle is simple, strong operability, but by expert's
Subjective factor influences very big；Risk matrix method defines the principle of risk receiving, and specifically, risk Metrics are a two-dimentional moulds
Type evaluates risk class only according to risk occurrence frequency and severity degree, does not account for the more attributes of risk, cause
Risk evaluation results are unreliable；And Fault Tree Analysis not only can be shown that the logical relation between risk case and risk source, but also energy
The different degree of risk source is analyzed, but only when grasping exact fault data, the best effect of this method competence exertion.
The above methods of risk assessment has respective deficiency, for complicated train control system, if the single use above method
Carry out risk assessment, will be unable to guarantee risk evaluation result it is reliable with it is accurate, in addition, existing train control system risk assessment lead to
It is often to evaluate risk class according to risk Metrics, is not connected with the risk node in train control system, therefore in practice
It can not determine the weak link of train control system.
Summary of the invention
It is an object of the present invention to overcome the above-mentioned drawbacks of the prior art and provide a kind of train control system risks
Appraisal procedure.
The purpose of the present invention can be achieved through the following technical solutions: a kind of train control system methods of risk assessment, including
Following steps:
S1, the data for acquiring train control system risk assessment object, determine risk case and risk source, establish fault tree,
In, top event is risk case, and the bottom event of fault tree is risk source；
S2, building include the risk Metrics of risk occurrence frequency and severity degree, and risk occurrence frequency is divided into not
Same grade, and determine the different corresponding Trapezoid Fuzzy Numbers of grade, to establish trapezoidal membership function；
S3, using expert graded and FUZZY SET APPROACH TO ENVIRONMENTAL, and be based on trapezoidal membership function, be calculated risk source occur frequency
Rate；
S4, it is based on fault tree and risk source occurrence frequency, using Monte Carlo simulation algorithm, risk case hair is calculated
Raw frequency；
S5, using expert graded, Fuzzy AHP and Method of Evidence Theory, it is tight that effect of risk is calculated
Weight degree；
S6, according to the severity degree of step S4 risk event occurrence frequency and step S5 risk, respectively with step
Rapid S2 risk matrix is compared, to evaluate risk class；
S7, Bayesian network is converted by fault tree, in conjunction with step S3 risk source occurrence frequency, determines train control system
Weak link；
S8, according to risk class, risk control is carried out to the weak link of train control system, to reduce the risk of train control system
Occurrence frequency reduces severity degree.
Preferably, the step S1 specifically includes the following steps:
S11, the data for acquiring train control system risk assessment object, wherein the data for assessing object include assessment object
Function and assessment object normally execute the information of function；
S12, according to assessment object function, determine the risk case of the assessment object；
S13, the information that function is normally executed according to assessment object, determine the node for causing risk, from the section for causing risk
Risk source is determined in point；
S14, using risk case as top event, using risk source as bottom event, establish fault tree.
Preferably, trapezoidal membership function in the step S2 are as follows:
A=(a, b, c, d)
Wherein, μA(x) indicate that the trapezoidal membership function of risk source, x indicate the grade of risk occurrence frequency, A indicates risk
The Trapezoid Fuzzy Number in source, a, b, c and d are the level value in Trapezoid Fuzzy Number.
Preferably, the step S3 specifically includes the following steps:
S31, using expert graded, obtain expert's comment fuzzy number of risk source；
S32, assemble expert's comment fuzzy number, obtain the Trapezoid Fuzzy Number of risk source:
In formula, i indicates that i-th of risk source, j indicate j-th of expert's comment, AiIndicate the trapezoidal fuzzy of i-th of risk source
Number, BjIndicate the fuzzy number of j-th of expert's comment；
S33, risk source occurrence frequency is obtained by de-fuzzy according to trapezoidal membership function:
In formula, λiIndicate the occurrence frequency of i-th of risk source,Indicate the trapezoidal membership function of i-th of risk source.
Preferably, the step S4 specifically includes the following steps:
S41, the simulation times for initializing Monte Carlo simulation algorithm are α and the bottom event number of fault tree is β；
Corresponding bottom event is calculated by corresponding random number combination risk source occurrence frequency in β S42, generation random number
Time of origin interval:
In formula, t indicates the time of origin interval of bottom event, and F (t) indicates that normal working hours is less than the frequency of t, and λ is indicated
The occurrence frequency of risk source, F-1(t) inverse function for indicating F (t), solving the equation can determine bottom event time of origin interval；
S43, the time of origin interval for calculating top event, and the time of origin interval of top event in α emulation is recorded,
In, for or door, the time of origin interval of top event are as follows:
tT=min (tA+tB)
In formula, tTIndicate the time of origin interval of top event, tAIndicate the time of origin interval of bottom event A, tBIndicate bottom thing
The time of origin interval of part B；
For preferentially with door, the time of origin interval of top event are as follows:
In formula, λAIndicate the occurrence frequency of bottom event A, λBIndicate the occurrence frequency of bottom event B；
S44, seek α top event time of origin interval average value, the inverse of the average value is that frequency occurs for top event
Rate, i.e. risk case occurrence frequency.
Preferably, the step S5 specifically includes the following steps:
S51, building severity degree assessment indicator system and interpretational criteria, are calculated using Fuzzy AHP and are assessed
The weight of index；
S52, using expert graded and evidence theory, calculate the consequence menace level degree of membership of evaluation index；
S53, the weight of evaluation index is multiplied with consequence menace level degree of membership, is based on maximum subjection principle, obtains wind
The severity degree of danger.
Preferably, the step S51 specifically includes the following steps:
S511, the interpretational criteria of severity degree assessment indicator system and building based on triangular membership functions is obtained,
Wherein, severity degree assessment indicator system includes τ evaluation index:
In formula, v indicates the number of level-one evaluation index, nσIndicate the secondary evaluation index under a level-one evaluation index
Number；
S512, Fuzzy comparisons are carried out to the secondary evaluation index under the same level-one evaluation index, obtains Fuzzy comparisons square
Battle array:
mr,u=(ar,u,br,u,cr,u)
In formula, M is Fuzzy comparisons matrix, and n indicates the number of the secondary evaluation index under the level-one evaluation index, and r and u divide
It is not r-th of the evaluation index and u-th of evaluation index under the level-one evaluation index, mr,uIndicate expert to r-th of evaluation index
Assemble fuzzy number, a with the comment of u-th of evaluation indexr,u,br,u,cr,uIndicate r-th of evaluation index and u-th evaluation index
Triangular Fuzzy Number；
S513, r-th of evaluation index fuzzy weighted values first pass through geometric mean calculating, to obtain the arithmetic of judgment matrix
Mean matrix:
In formula,Indicate that expert assembles the arithmetic average of fuzzy number to the comment of r-th of evaluation index,Table
Show the arithmetic average of the Triangular Fuzzy Number of r-th of evaluation index；
The then fuzzy weighted values of r-th of evaluation index are as follows:
In formula, FWrIndicate the fuzzy weighted values of r-th of evaluation index, ar,br,crIndicate the triangle of r-th of evaluation index
Fuzzy number,Indicate the arithmetic average of the Triangular Fuzzy Number of u-th of evaluation index；
S514, de-fuzzy and normalized are carried out to the fuzzy weighted values of r-th of evaluation index, obtains r-th of assessment
The weight of index are as follows:
In formula, wrIndicate the accurate weight of r-th of evaluation index, wr' indicate r-th of evaluation index fuzzy weighted values；
Finally, the weight sets for obtaining τ evaluation index is W=[w1,w2,...,wτ]。
Preferably, the step S52 specifically includes the following steps:
S521, using expert graded, it is { cs that the menace level fuzzy set for obtaining evaluation index, which judges collection,1,cs2,cs3,
cs4,cs5,cs6}={ disaster, it is very serious, it is serious, it is critical, light, it is inessential }；
S522, it is directed to each evaluation index, constructs probability allocation matrix:
0≤pq,z≤ 1, z=1,2 ... 6
In formula, q indicates the total number of expert, pq,zIt is tight to indicate that q-th of expert according to its evidence is in z to evaluation index
The probability of weight grade is judged, pqIndicate that q-th of expert judges set to the probability of evaluation index；
S523, the corresponding discount factor of the different evidences of calculating: firstly, the judge distance between expert are as follows:
In formula, ekAnd eyRespectively indicate the evidence of k-th of expert and the evidence of y-th of expert, < pk,py> indicate that probability is commented
Sentence set pkAnd pyInner product of vectors, obtain the mutual similarity of evidence are as follows:
The then similarity matrix of two evidences are as follows:
Later, it sums to every row of similarity matrix, obtains the degree that each evidence is supported by other evidences are as follows:
In formula, sup (ek) indicate k-th of expert evidence ekThe degree supported by other evidences；
The then evidence e that k-th of expert provideskConfidence level are as follows:
Finally, discount factor matrix is obtained are as follows:
S524, according to discount factor matrix, update probability allocation matrix are as follows:
In formula, P ' indicates updated probability assignments matrix, p 'q,zIndicate that updated q-th of expert is in the index
The probability of z menace level is judged；
S525, with evidence theory, it is assumed that there are a problem needs to differentiate, all possible collection for differentiating result composition
Θ expression is shared, all elements in Θ are all mutual exclusions two-by-two, then Θ is referred to as identification framework:
Θ={ θ1,θ2,…,θl,…,θG}
In formula: θlIndicate that first of element in identification framework Θ, G indicate the element number in identification framework Θ；
Later, support of each evidence to each element in identification framework Θ is indicated using Basic probability assignment function ε
Degree, ε are from set 2ΘTo the mapping of [0,1],It indicates any subset of Θ, and meets:
In formula, ε (H) is the Basic probability assignment function of identification framework subset H, indicates evidence to identification framework subset H's
The original allocation of trusting degree；
Finally, updated probability assignments matrix is carried out fusion calculation, it is serious etc. to obtain consequence locating for evaluation index
Grade degree of membership:
In formula, tk(csz) indicate in k-th of evidence provided by experts, evaluation index is in the degree of membership of each menace level,Indicate the conflict spectrum between each evidence；
Form evaluation index consequence menace level subordinated-degree matrix:
μ=[t (cs1),t(cs2),...,t(cs6)]
τ evaluation index is shared, then the membership grade sets of whole evaluation indexes are as follows:
μ '=[μ1,μ2,...,μτ]T
In formula, μ1And μ2The consequence menace level subordinated-degree matrix of the 1st and the 2nd evaluation index is respectively indicated, with such
It pushes away, μτIndicate the consequence menace level subordinated-degree matrix of the τ evaluation index.
Preferably, the step S7 specifically includes the following steps:
S71, the structure based on fault tree, establish Bayesian network, and the conversion of Bayesian network is converted into according to fault tree
Criterion inputs the conditional probability of intermediate node, and using risk source occurrence frequency as the input of Bayesian network leaf node；
Top event occurs for S72, setting, the posterior probability of Bayesian network each intermediate node and leaf node is calculated, with determination
The weak link of train control system.
Compared with prior art, the invention has the following advantages that
One, the present invention is based on the risk assessment thinkings of risk Metrics, propose a kind of combination expert estimation and a variety of mathematics reason
The methods of risk assessment of opinion, can give full play to the experience effect of expert, while convert computable mathematics for expertise
Model greatly reduces influence of expert's subjective factor to risk evaluation result.
Two, the present invention carries out risk source occurrence frequency using fault tree, fuzzy mathematics, evidence theory and Monte Carlo simulation
With the calculating of risk case occurrence frequency, the accuracy of fault data is improved, is conducive to that risk Metrics is combined to assess reliably
Risk class, thus guarantee risk evaluation result it is reliable with it is accurate.
Three, for the present invention by the quantitative analysis of Bayesian network, can further determine that in train control system leads to risk thing
The weak link that part occurs provides reliable data supporting to reduce risk occurrence frequency and reducing severity degree,
Be conducive to carry out targetedly risk control to train control system, to ensure the operational safety and efficiency of train control system.
Detailed description of the invention
Fig. 1 is method flow schematic diagram of the invention；
Fig. 2 is the unshielded fault tree synthesis of train overspeed in embodiment；
Fig. 3 is the fault tree synthesis schematic diagram of ground installation failure in embodiment；
Fig. 4 is the fault tree synthesis schematic diagram of mobile unit failure in embodiment；
Fig. 5 is the fault tree synthesis schematic diagram of train positioning mistake in embodiment；
Fig. 6 is the fault tree synthesis schematic diagram of section cleared information errors in embodiment；
Fig. 7 is the structural schematic diagram of Bayesian network in embodiment；
Fig. 8 a be in embodiment fault tree or door be converted into the schematic diagram of Bayesian network；
Fig. 8 b is that the preferential and door of fault tree in embodiment is converted into the schematic diagram of Bayesian network；
Fig. 9 is the intermediate node posterior probability schematic diagram of train control system in embodiment；
Figure 10 is the leaf node posterior probability schematic diagram of train control system in embodiment.
Specific embodiment
The present invention is described in detail with specific embodiment below in conjunction with the accompanying drawings.
As shown in Figure 1, a kind of train control system methods of risk assessment, comprising the following steps:
S1, the data for acquiring train control system risk assessment object, determine risk case and risk source, establish fault tree,
In, top event is risk case, and the bottom event of fault tree is risk source；
S2, building include the risk Metrics of risk occurrence frequency and severity degree, and risk occurrence frequency is divided into not
Same grade, and determine the different corresponding Trapezoid Fuzzy Numbers of grade, to establish trapezoidal membership function；
S3, using expert graded and FUZZY SET APPROACH TO ENVIRONMENTAL, and be based on trapezoidal membership function, be calculated risk source occur frequency
Rate；
S4, it is based on fault tree and risk source occurrence frequency, using Monte Carlo simulation algorithm, risk case hair is calculated
Raw frequency；
S5, using expert graded, Fuzzy AHP and Method of Evidence Theory, it is tight that effect of risk is calculated
Weight degree；
S6, according to the severity degree of step S4 risk event occurrence frequency and step S5 risk, respectively with step
Rapid S2 risk matrix is compared, to evaluate risk class；
S7, Bayesian network is converted by fault tree, in conjunction with step S3 risk source occurrence frequency, determines train control system
Weak link；
S8, according to risk class, risk control is carried out to the weak link of train control system, to reduce the risk of train control system
Occurrence frequency reduces severity degree.
The present embodiment chooses CTCS-2 grades of train control systems and carries out risk assessment, concrete application process are as follows:
A, with " train overspeed is not protected " for risk assessment object, occur from train control system angle analysis risk case
Mechanism, establish fault tree as shown in Fig. 2~Fig. 6；
B, the feature low according to train control system component faults incidence establishes risk Metrics, and determines each occurrence frequency
Trapezoid Fuzzy Number corresponding to grade, wherein refer to GB/T21562 " rail traffic reliability, availability, and maintainability and peace
Full property specification and example " regulation, occurrence frequency is divided into 6 grades, be frequently respectively, be likely to, once in a while, seldom, can not,
It is not believed that；Severity degree is divided into 4 grades, is disaster respectively, critical, critical and inessential；It is corresponding
Risk class be intolerable (R1), undesirable (R2), tolerance (R3), insignificant (R4), it is contemplated that CTCS-2
Grade train control system is to the more demanding of safety, based on the integrity demands and each module of train control system to safety critical system
The low feature of crash rate, is further refined as 9 grades for occurrence frequency, severity degree is refined as 6 grades, with this
Obtain risk Metrics shown in table 1:
Table 1
C, using expert graded and fuzzy set theory founding mathematical models, calculation risk source (bottom event of fault tree) occurs
Frequency；With Monte Carlo simulation, calculation risk event (fault tree top event) occurrence frequency:
C1, by taking the TSR of mistake " C4-2:LEU send " in Fig. 3 as an example, calculate the risk source occurrence frequency, embodiment it is special
Family's scoring is given a mark by 5 experts, wherein 5 expert's comments be respectively it is impossible, impossible, rare, rare, can not
Can, then assemble expert's comment and obtain the fuzzy number of C4-2 risk source are as follows:
Corresponding trapezoidal membership function are as follows:
C2, trapezoidal membership function is determined according to obtained fuzzy number, obtaining risk source occurrence frequency by de-fuzzy is
λ4-2=6.536 × 10-9Secondary/h similarly obtains other risk source occurrence frequencies, and each risk source occurrence frequency calculates in the present embodiment
The results are shown in Table 2:
Table 2
The present embodiment reference is also made to the partial risks source occurrence frequency of current train control system, and specific data are as shown in table 3:
Table 3
C3, according to fault tree synthesis, with Monte Carlo simulation, using step C2 risk source occurrence frequency as input,
Calculating fault tree top event (risk case) occurrence frequency is
D, using Fuzzy AHP, evidence theory and expert graded founding mathematical models, risk case is determined
Severity degree:
D1, using the weight of Fuzzy AHP calculation risk evaluation index, calculate that steps are as follows:
D11, the severity degree evaluation index system of the present embodiment are as shown in table 4, and evaluation index different degree criterion is such as
Shown in table 5:
Table 4
Table 5
D12, Fuzzy comparisons are carried out to the evaluation index of same layer, obtains Fuzzy comparisons matrix, the present embodiment is referred to level-one
It is designated as example to be calculated: by 5 experts to first class index collection V=(v1,v2,v3) in index compared two-by-two, as a result such as table
Shown in 6, the Triangular Fuzzy Number after being assembled according to expert's comment is as shown in table 7:
Table 6
Table 7
| Element comparison | Triangular Fuzzy Number after assembly | Language judgement |
| v1vs.v2(m1,2) | (3.6,4,6,5,6) | Between it is slightly important and it is obvious it is important between |
| v2vs.v3(m2,3) | (3.2,4.2,5.2) | Between it is slightly important and it is obvious it is important between |
Construct Fuzzy comparisons matrix are as follows:
Index weights are obtained by calculating fuzzy weighted values, the de-fuzzy, normalization of three indexs according to above-mentioned matrix
For
Weight of the two-level index relative to first class index is similarly obtained, finally obtains two-level index with first class index weight
Final weight are as follows:
D2, severity degree is calculated with expert graded and evidence theory, steps are as follows for calculating:
D21, fuzzy set judge collection be { cs1, cs2 ... cs6 }=disaster, it is very serious, it is serious, it is critical, gently
, it is inessential }, probability allocation matrix is constructed for each evaluation index:
D22, the discount factor for calculating different evidences:
Firstly, the similarity matrix and Certainty Factor that are calculated are respectively as follows:
D23, update probability allocation matrix, carry out evidence fusion later, obtain v11Sequence severity membership function are as follows:Similarly corresponding sequence severity grade is calculated according to other two-level index
Degree of membership is respectively as follows:
D3, step D1 is multiplied to obtain with the result of step D2:
According to maximum subjection principle, it is known that the severity degree of " train overspeed is not protected " is " cs2: very tight
Weight "；
E, according to the calculated result of step C and step D, it is known that the occurrence frequency of " train overspeed is not protected " isIn " F7: rare " rank, consequence menace level is " cs2: very serious "；Correspond to table 1
Risk Metrics, it can be seen that the risk is in " R2: undesirable ", i.e., in train operation, for risk case, " train exceeds the speed limit
Operation is not protected " it needs that certain measure is taken to reduce risk；
F, according to fault tree synthesis, Bayesian network is converted by fault tree, calculates each node with Bayes' theorem
Posterior probability analyzes the weak link of train control system:
F1, it is based on Fig. 2~fault tree synthesis shown in fig. 6, the conversion criterion of Bayesian network is converted into according to fault tree,
It is as shown in Figure 7 to convert obtained Bayesian network, wherein or (conversion schematic diagram is corresponding as shown in table 8 for the corresponding conditional probability of door
In Fig. 8 a), preferentially conditional probability corresponding with door (converts schematic diagram and corresponds to Fig. 8 b) as shown in table 9, shown in table 8 and table 9
Data in, state=0 indicates that the event does not occur, state=1 indicate the event occur:
Table 8
Table 9
F2, using table 2 in step C2 and the resulting risk source occurrence frequency of table 3 as the priori of Bayesian network leaf node
Probability input, it is 1.377 × 10 that root node occurrence frequency, which is calculated,-9Secondary/h, the result calculated with step C3 is very close, says
Bright method proposed by the present invention and constructed Bayesian network are correct；
The state=1 of A0 in F3, setting Fig. 7, i.e. setting train control system equipment fails, after obtaining shown in table 10
Test probability:
Table 10
| Node serial number | Nodename | Posterior probability |
| A0 | Apparatus factor | 1 |
| A1 | ATP speed is lower than actual speed | 0.018 |
| A2 | Controlling curve mistake | 0.982 |
As shown in Table 10, the direct factor for causing risk case to occur is " controlling curve calculates mistake ", and is led
The reason of causing A2 to occur is present among ground and mobile unit, and therefore, it is necessary to further determine that in ground and mobile unit
Weak link:
Vehicle-mounted and ground installation module includes C0~C11, and the state=1 of even higher level of node A2 is arranged, that is, assumes column control
System occurs " controlling curve calculates mistake ", and the posterior probability of each module is as shown in table 11 in obtained vehicle-mounted and ground installation,
The posterior probability data drafting pattern of intermediate node each in table 11 is shown to get the intermediate node posterior probability to Bayesian network
It is intended to, as shown in Figure 9:
Table 11
| Node serial number | Nodename | Nodal community | Posterior probability |
| A2 | Controlling curve mistake | First nodes | 1 |
| C0 | SDP calculates mistake | Pyatyi node | 0.002 |
| C1 | The distance measuring unit that tests the speed failure | Pyatyi node | 0.006 |
| C2 | The TSR system failure | Pyatyi node | 0.082 |
| C3 | TCC hostdown | Pyatyi node | 0.137 |
| C4 | LEU failure | Pyatyi node | 0.087 |
| C5 | Active balise failure | Pyatyi node | 0.092 |
| C6 | BTM antenna failure | Pyatyi node | 0.081 |
| C7 | BTM failure | Pyatyi node | 0.159 |
| C8 | Passive balise failure | Pyatyi node | 0.091 |
| C9 | Track circuit failure | Pyatyi node | 0.208 |
| C10 | TCR module failure | Pyatyi node | 0.041 |
| C11 | Fail-safe computer calculates mistake | Pyatyi node | 0.013 |
Shown in table 11 and Fig. 9, it is known that track circuit failure, BTM failure and TCC hostdown are to lead to " control song
An important factor for line computation mistake " occurs, fault data of the CTCS-2 grades of train control systems of comparison the first half of the year in 2017 count report
In announcement, the frequency that this three breaks down be also it is highest, among these, the posterior probability data value of track circuit failure is maximum,
Show that track circuit failure is the most important factor for causing " controlling curve calculates mistake " to occur, therefore further determines C9
The reason of (leading to track circuit hazardous side failure) occurs, as shown in Figure 7, the downstream site of C9 includes C9-1~C9-7, setting
The state=1 of C9 calculates the posterior probability of each leaf node of C9, as shown in table 12:
Table 12
| Number | Event description | Event attribute | Posterior probability |
| C9 | Track circuit failure | Pyatyi node | 1 |
| C9-1 | Track relay mistake picks up | Leaf node | 0.067 |
| C9-2 | It is improper that attenuation disk adjusts terminal connection | Leaf node | 0.007 |
| C9-3 | Receiver reports the track condition of mistake outward | Leaf node | 0.340 |
| C9-4 | The adjustment of receiving end lightning protection simulation network disk is improper | Leaf node | 0.064 |
| C9-5 | The adjustment of sending end lightning protection simulation network disk is improper | Leaf node | 0.067 |
| C9-6 | Transmitter failure | Leaf node | 0.422 |
| C9-7 | Transmission level adjustment is improper | Leaf node | 0.034 |
The posterior probability data drafting pattern of leaf node each in table 12 is general to get the leaf node posteriority to Bayesian network
Rate schematic diagram, as shown in Figure 10, it is known that most important two factors for causing C9 to occur are that transmitter failure and receiver are outside
Report the track condition of mistake.
The train control system weak link that risk class evaluation result based on step E and step F are determined, it is known that need from
Lower two aspect carries out the risk control of train control system:
1, it controls risk occurrence frequency
The occurrence frequency of risk case is " rare ", but if only from the angle analysis of equipment, A0 (equipment) endangers
The frequency of dangerous side failure is 4.589 × 10-7 times/h, belong to " few ", it to be such as further reduced the generation of A0, according to step F
Analysis result from the point of view of, Ying Shouxian sets about taking measures on customs clearance from track circuit, and transmitter therein and recipient lead to rail
Road circuit cause danger side failure a possibility that it is maximum, therefore either in the design phase still in maintenance phase, all Ying Chongdian
Pay close attention to the two components；
Followed by BTM and TCC, the former is one of the entrance for receiving terrestrial information in mobile unit, and the latter is then that ground is set
Standby center, for the safety and reliability of the two should have the various board components of higher requirement, especially TCC compared with
It is more, it requires to be able to carry out faulty components to accurately judge that in maintenance phase, replaces in time, reduce rate of breakdown；
On the other hand, consider from driver's angle, since train not yet realizes automatic Pilot, by train operator in operational process
Manipulation, therefore great care is answered to reinforce training and supervision of the driver in terms of traffic safety and train manipulation.
2, severity degree is reduced
Step D's the result shows that consequence of risk case " train overspeed is not protected " is " very serious ", as a result,
It should take relevant measure, make great efforts the seriousness for reducing consequence, for example, the characteristics of being run according to bullet train and after the accident
The characteristics of, corresponding emergency preplan is established, after risk case occurs, by the training and rehearsal that early period is skilled, use is shortest
Time handles accident, and the degree of security implication, economic loss, service disruption is preferably minimized；
In another example professional training need to be reinforced from the angle of plant maintenance personnel, according to the survey report of official, river in Zhejiang Province temperature thing
One of reason that part occurs is exactly that the maintenance personal of Ningbo Train Dept is having found the case where track circuit occupied state is not inconsistent
Afterwards, station-locomotive joint control is executed not in time, therefore, it is necessary to greatly reduce unnecessary risk by reinforcing the training to maintenance personal
The generation of event.
In conclusion the present embodiment is primarily based on the characteristics of train control system, risk Metrics are established；Then, by dividing
After the function and risk case genesis mechanism of analysing train control system, fault tree is established；Then, expert graded, fuzzy is utilized
Collection theory and Monte Carlo simulation founding mathematical models, calculate the occurrence frequency of risk source and risk case；Expert is used again
Scoring and evidence theory founding mathematical models, calculate the severity degree of risk case；Later by with risk Metrics
Comparison, evaluate risk class, complete assessment to risk case；Finally, according to the corresponding Bayesian network of Construction of Fault Tree,
According to Bayes' theorem, quantitatively analyze cause in train control system risk case occur weak link, and from reduce risk
Occurrence frequency and the suggestion for reducing by two angles proposition risk controls of severity degree.
Claims (9)
1. a kind of train control system methods of risk assessment, which comprises the following steps:
S1, the data for acquiring train control system risk assessment object, determine risk case and risk source, establish fault tree, wherein therefore
The top event of barrier tree is risk case, and the bottom event of fault tree is risk source；
S2, building include the risk Metrics of risk occurrence frequency and severity degree, risk occurrence frequency are divided into different
Grade, and determine the different corresponding Trapezoid Fuzzy Numbers of grade, to establish trapezoidal membership function；
S3, using expert graded and FUZZY SET APPROACH TO ENVIRONMENTAL, and be based on trapezoidal membership function, risk source occurrence frequency is calculated；
S4, it is based on fault tree and risk source occurrence frequency, using Monte Carlo simulation algorithm, risk case is calculated, frequency occurs
Rate；
S5, using expert graded, Fuzzy AHP and Method of Evidence Theory, the serious journey of effect of risk is calculated
Degree；
S6, according to the severity degree of step S4 risk event occurrence frequency and step S5 risk, respectively with step S2
Risk matrix is compared, to evaluate risk class；
S7, Bayesian network is converted by fault tree, in conjunction with step S3 risk source occurrence frequency, determines the weakness of train control system
Link；
S8, according to risk class, risk control is carried out to the weak link of train control system, is occurred with reducing the risk of train control system
Frequency reduces severity degree.
2. a kind of train control system methods of risk assessment according to claim 1, which is characterized in that the step S1 is specifically wrapped
Include following steps:
S11, the data for acquiring train control system risk assessment object, wherein the data for assessing object include assessing the function of object
And assessment object normally executes the information of function；
S12, according to assessment object function, determine the risk case of the assessment object；
S13, the information that function is normally executed according to assessment object, determine the node for causing risk, from the node for causing risk
Determine risk source；
S14, using risk case as top event, using risk source as bottom event, establish fault tree.
3. a kind of train control system methods of risk assessment according to claim 2, which is characterized in that trapezoidal in the step S2
Membership function are as follows:
A=(a, b, c, d)
Wherein, μA(x) indicate that the trapezoidal membership function of risk source, x indicate the grade of risk occurrence frequency, A indicates the ladder of risk source
Shape fuzzy number, a, b, c and d are the level value in Trapezoid Fuzzy Number.
4. a kind of train control system methods of risk assessment according to claim 3, which is characterized in that the step S3 is specifically wrapped
Include following steps:
S31, using expert graded, obtain expert's comment fuzzy number of risk source；
S32, assemble expert's comment fuzzy number, obtain the Trapezoid Fuzzy Number of risk source:
In formula, i indicates that i-th of risk source, j indicate j-th of expert's comment, AiIndicate the Trapezoid Fuzzy Number of i-th of risk source, Bj
Indicate the fuzzy number of j-th of expert's comment；
S33, risk source occurrence frequency is obtained by de-fuzzy according to trapezoidal membership function:
In formula, λiIndicate the occurrence frequency of i-th of risk source,Indicate the trapezoidal membership function of i-th of risk source.
5. a kind of train control system methods of risk assessment according to claim 4, which is characterized in that the step S4 is specifically wrapped
Include following steps:
S41, the simulation times for initializing Monte Carlo simulation algorithm are α and the bottom event number of fault tree is β；
The hair of corresponding bottom event is calculated by corresponding random number combination risk source occurrence frequency in β S42, generation random number
Raw time interval:
In formula, t indicates the time of origin interval of bottom event, and F (t) indicates that normal working hours is less than the frequency of t, and λ indicates risk
The occurrence frequency in source, F-1(t) inverse function for indicating F (t), solving the equation can determine bottom event time of origin interval；
S43, the time of origin interval for calculating top event, and record the time of origin interval of top event in α emulation, wherein it is right
In or door, the time of origin interval of top event are as follows:
tT=min (tA+tB)
In formula, tTIndicate the time of origin interval of top event, tAIndicate the time of origin interval of bottom event A, tBIndicate bottom event B
Time of origin interval；
For preferentially with door, the time of origin interval of top event are as follows:
In formula, λAIndicate the occurrence frequency of bottom event A, λBIndicate the occurrence frequency of bottom event B；
S44, seek α top event time of origin interval average value, the inverse of the average value is top event occurrence frequency, i.e.,
Risk case occurrence frequency.
6. a kind of train control system methods of risk assessment according to claim 5, which is characterized in that the step S5 is specifically wrapped
Include following steps:
S51, building severity degree assessment indicator system and interpretational criteria calculate evaluation index using Fuzzy AHP
Weight；
S52, using expert graded and evidence theory, calculate the consequence menace level degree of membership of evaluation index；
S53, the weight of evaluation index is multiplied with consequence menace level degree of membership, is based on maximum subjection principle, obtains risk
Severity degree.
7. a kind of train control system methods of risk assessment according to claim 6, which is characterized in that the step S51 is specific
The following steps are included:
S511, the interpretational criteria of severity degree assessment indicator system and building based on triangular membership functions is obtained, wherein
Severity degree assessment indicator system includes τ evaluation index:
In formula, v indicates the number of level-one evaluation index, nσIndicate the number of the secondary evaluation index under a level-one evaluation index；
S512, Fuzzy comparisons are carried out to the secondary evaluation index under the same level-one evaluation index, obtain Fuzzy comparisons matrix:
mr,u=(ar,u,br,u,cr,u)
In formula, M is Fuzzy comparisons matrix, and n indicates the number of the secondary evaluation index under the level-one evaluation index, and r and u are respectively
R-th of evaluation index and u-th of evaluation index under the level-one evaluation index, mr,uIndicate expert to r-th of evaluation index and the
The comment of u evaluation index assembles fuzzy number, ar,u,br,u,cr,uIndicate the triangle of r-th of evaluation index and u-th of evaluation index
Shape fuzzy number；
S513, r-th of evaluation index fuzzy weighted values first pass through geometric mean calculating, to obtain the arithmetic average of judgment matrix
Matrix:
In formula,Indicate that expert assembles the arithmetic average of fuzzy number to the comment of r-th of evaluation index,Indicate r
The arithmetic average of the Triangular Fuzzy Number of a evaluation index；
The then fuzzy weighted values of r-th of evaluation index are as follows:
In formula, FWrIndicate the fuzzy weighted values of r-th of evaluation index, ar,br,crIndicate that the triangle of r-th of evaluation index is fuzzy
Number,Indicate the arithmetic average of the Triangular Fuzzy Number of u-th of evaluation index；
S514, de-fuzzy and normalized are carried out to the fuzzy weighted values of r-th of evaluation index, obtains r-th of evaluation index
Weight are as follows:
In formula, wrIndicate the accurate weight of r-th of evaluation index, wr' indicate r-th of evaluation index fuzzy weighted values；
Finally, the weight sets for obtaining τ evaluation index is W=[w1,w2,...,wτ]。
8. a kind of train control system methods of risk assessment according to claim 7, which is characterized in that the step S52 is specific
The following steps are included:
S521, using expert graded, it is { cs that the menace level fuzzy set for obtaining evaluation index, which judges collection,1,cs2,cs3,cs4,
cs5,cs6}={ disaster, it is very serious, it is serious, it is critical, light, it is inessential }；
S522, it is directed to each evaluation index, constructs probability allocation matrix:
0≤pq,z≤ 1, z=1,2 ... 6
In formula, q indicates the total number of expert, pq,zIt is serious etc. to indicate that q-th of expert according to its evidence is in z to evaluation index
The probability of grade is judged, pqIndicate that q-th of expert judges set to the probability of evaluation index；
S523, the corresponding discount factor of the different evidences of calculating: firstly, the judge distance between expert are as follows:
In formula, ekAnd eyRespectively indicate the evidence of k-th of expert and the evidence of y-th of expert, < pk,py> indicate that probability judges set
pkAnd pyInner product of vectors, obtain the mutual similarity of evidence are as follows:
The then similarity matrix of two evidences are as follows:
Later, it sums to every row of similarity matrix, obtains the degree that each evidence is supported by other evidences are as follows:
In formula, sup (ek) indicate k-th of expert evidence ekThe degree supported by other evidences；
The then evidence e that k-th of expert provideskConfidence level are as follows:
Finally, discount factor matrix is obtained are as follows:
S524, according to discount factor matrix, update probability allocation matrix are as follows:
In formula, P ' indicates updated probability assignments matrix, p 'q,zIndicate that updated q-th of expert is in z to the index
The probability of menace level is judged；
S525, with evidence theory, it is assumed that there are a problem needs to differentiate, all possible collection for differentiating result composition shares
Θ indicates that all elements in Θ are all mutual exclusions two-by-two, then Θ is referred to as identification framework:
Θ={ θ1,θ2,…,θl,…,θG}
In formula: θlIndicate that first of element in identification framework Θ, G indicate the element number in identification framework Θ；
Later, indicated using Basic probability assignment function ε each evidence to the degree of support of each element in identification framework Θ,
ε is from set 2ΘTo the mapping of [0,1],It indicates any subset of Θ, and meets:
In formula, ε (H) is the Basic probability assignment function of identification framework subset H, indicates evidence to the trust of identification framework subset H
The original allocation of degree；
Finally, updated probability assignments matrix is carried out fusion calculation, the person in servitude of consequence menace level locating for evaluation index is obtained
Category degree:
In formula, tk(csz) indicate in k-th of evidence provided by experts, evaluation index is in the degree of membership of each menace level,Indicate the conflict spectrum between each evidence；
Form evaluation index consequence menace level subordinated-degree matrix:
μ=[t (cs1),t(cs2),...,t(cs6)]
τ evaluation index is shared, then the membership grade sets of whole evaluation indexes are as follows:
μ '=[μ1,μ2,...,μτ]T
In formula, μ1And μ2The consequence menace level subordinated-degree matrix of the 1st and the 2nd evaluation index is respectively indicated, and so on,
μτIndicate the consequence menace level subordinated-degree matrix of the τ evaluation index.
9. a kind of train control system methods of risk assessment according to claim 4, which is characterized in that the step S7 is specifically wrapped
Include following steps:
S71, the structure based on fault tree, establish Bayesian network, and the conversion criterion of Bayesian network is converted into according to fault tree,
The conditional probability of intermediate node is inputted, and using risk source occurrence frequency as the input of Bayesian network leaf node；
Top event occurs for S72, setting, calculates the posterior probability of Bayesian network each intermediate node and leaf node, to determine column control
The weak link of system.
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