CN104965978A - Diagnosis failure probability calculation method and device - Google Patents

Abstract

The invention discloses a diagnosis failure probability calculation method and device. The method comprises: determining a diagnosis failure probability pdiag, RO of an operator; determining a diagnosis failure probability pdiag, US of a coordinator; determining a diagnosis failure probability prec, diag, which cannot be successively recovered by the coordinator, of the operator according to the diagnosis failure probability pdiag, US of the coordinator; and calculating pdiag according to the team or group diagnosis failure probability pdiag=pdiag, and RO'prec, diag. According to the calculation method and the calculation device for diagnosis failure probability disclosed by the invention, the team or group diagnosis failure probability is divided into the diagnosis failure probability of the operator and the diagnosis failure probability, which cannot be successively recovered by the coordinator, of the operator; then the diagnosis failure probability of the operator and the diagnosis failure probability, which cannot be successively recovered by the coordinator, of the operator are calculated respectively, and the sum of the two probabilities is used as the team or group diagnosis failure probability, so that the purpose of effectively evaluating the team or group diagnosis failure probability is realized.

Description

A kind of false diagnosis method for calculating probability and device

Technical field

The present invention relates to facing Information Science and Human Engineering subject field, especially, relate to a kind of false diagnosis method for calculating probability and device.

Background technology

Digitizing And Control Unit (Digital control system is called for short DCS) has been widely used in nuclear power, aviation, field of petrochemical industry at present, and the importance of man-machine interaction also receives general concern day by day.Effective man-machine interaction can promote reliability and the security of system.But man-machine interaction is simultaneously again the direct sources causing human-equation error, once man-machine interaction goes wrong, then task may be caused to lose efficacy or catastrophic failure.

Along with the widespread use of DCS, accident treatment code is also many becomes station guide code (State-oriented Procedure is called for short SOP) by event guiding code (Event-orientedProcedure is called for short EOP) in the past.On the one hand, in new digitizing man-machine interface, the cognitive process of people and traditional simulation system are distinguished very large, the new error mode likely introduced after needing the digitizing of analysis and Control chamber system.On the other hand, the use of SOP changes the logic that operator carries out accident treatment greatly.Under DCS and SOP background, pulpit accident treatment personnel generally not only only include operator.For nuclear power plant, also comprise expeditor, safety engineer and value long, they constitute operation teams and groups.Master-control room personnel depaly is divided into two levels: first level is execution level, comprises expeditor 1, primary Ioops operator 1, secondary circuit operator 1, and the accident treatment of expeditor to operator exercises supervision, but does not perform concrete operations.Second level is monitor layer, comprises long 1 of value, safety engineer 1.Instruction and the requirement of value length and safety engineer can only send to expeditor, directly can not send to operator.

In sum, the analysis background of human-initiated accident there occurs and has changed significantly, after accident, event handling completes according to SOP code jointly by operating teams and groups, for the behavioral characteristic operating teams and groups in DCS, needs a kind of new method badly and carries out Efficient Evaluation to teams and groups' false diagnosis probability.

Summary of the invention

The invention provides a kind of false diagnosis method for calculating probability and device, with solve human-initiated accident analysis background change when, need new method to carry out the technical matters of Efficient Evaluation to teams and groups' false diagnosis probability.

The technical solution used in the present invention is as follows:

A kind of false diagnosis method for calculating probability, comprising:

Determine operator's false diagnosis Probability p _{diag, RO};

Determine expeditor's false diagnosis Probability p _{diag, US};

According to expeditor's false diagnosis Probability p _{diag, US}, determine that expeditor fails and recover the Probability p of operator's false diagnosis _{rec, diag};

According to teams and groups' false diagnosis Probability p _diag=p _{diag, RO}× p _{rec, diag}calculate p _diag.

Further, operator's false diagnosis probability $p_{diag,RO}=\[1-\underset{i=1}{\overset{n}{\Π}}(1-p_{mon,i})\]K_1{K}_2,$

Wherein, K ₁> 0, K ₂> 0,

N is the number monitoring node;

K ₁for psychological pressure modifying factor;

K ₂for pot life modifying factor;

P _{mon, i}be i-th basic fault rate monitoring node.

Further, expeditor's false diagnosis Probability p _{diag, US}=p _{b, diag}× k ₁× k ₂× k ₃× k ₄× k ₅× k ₆,

Wherein, p _{b, diag}for expeditor diagnoses basic probability of failure, k ₁for time pressure modifying factor, k ₂for psychological pressure modifying factor, k ₃for task complexity modifying factor, k ₄train horizontal modifying factor, k ₅for code complicacy modifying factor, k ₆for man-machine interface complexity modifying factor.

Further, determine that expeditor fails and recover the Probability p of operator's false diagnosis _{rec, diag}for,

$p_{rec,diag}=\frac{1+19\×p_{diag,US}}{20}.$

According to a further aspect in the invention, additionally provide a kind of false diagnosis probability calculation device, comprising:

Operator's false diagnosis probability evaluation entity, for determining operator's false diagnosis Probability p _{diag, RO};

Expeditor's false diagnosis probability evaluation entity, for determining expeditor's false diagnosis Probability p _{diag, US};

Expeditor fails and recovers operator's false diagnosis probability evaluation entity, according to expeditor's false diagnosis Probability p _{diag, US}, recover the Probability p of operator's false diagnosis for determining that expeditor fails _{rec, diag};

Teams and groups' false diagnosis probability evaluation entity, for according to teams and groups' false diagnosis Probability p _diag=p _{diag, RO}× p _{rec, diag}calculate p _diag.

Further, operator's false diagnosis probability evaluation entity, specifically for calculating operator's false diagnosis probability

$p_{diag,RO}=\[1-\underset{i=1}{\overset{n}{\Π}}(1-p_{mon,i})\]K_1{K}_2,$

Wherein, K ₁> 0, K ₂> 0,

N is the number monitoring node;

K ₁for psychological pressure modifying factor;

K ₂for pot life modifying factor;

P _{mon, i}be i-th basic fault rate monitoring node.

Further, expeditor's false diagnosis probability evaluation entity, specifically for calculating expeditor's false diagnosis probability

p _diag，US＝p _b,diag×k ₁×k ₂×k ₃×k ₄×k ₅×k ₆,

Wherein, p _{b, diag}for expeditor diagnoses basic probability of failure, k ₁for time pressure modifying factor, k ₂for psychological pressure modifying factor, k ₃for task complexity modifying factor, k ₄train horizontal modifying factor, k ₅for code complicacy modifying factor, k ₆for man-machine interface complexity modifying factor.

Further, expeditor fails and recovers operator's false diagnosis probability evaluation entity, specifically for calculating p _{rec, diag}, $p_{rec,diag}=\frac{1+19\×p_{diag,US}}{20}.$

The present invention has following beneficial effect:

Teams and groups' false diagnosis probability after operator's accident is divided into operator's false diagnosis probability and expeditor to fail recovering the probability of operator false diagnosis by the present invention, then respectively operator's false diagnosis probability and the expeditor probability recovering operator false diagnosis that fails is calculated, and by both sum as teams and groups' false diagnosis probability, thus realize the object of teams and groups' false diagnosis probability being carried out to Efficient Evaluation.

Except object described above, feature and advantage, the present invention also has other object, feature and advantage.Below with reference to figure, the present invention is further detailed explanation.

Accompanying drawing explanation

The accompanying drawing forming a application's part is used to provide a further understanding of the present invention, and schematic description and description of the present invention, for explaining the present invention, does not form inappropriate limitation of the present invention.In the accompanying drawings:

Fig. 1 is the false diagnosis method for calculating probability process flow diagram one of the preferred embodiments of the present invention;

Fig. 2 is the false diagnosis method for calculating probability flowchart 2 of the preferred embodiments of the present invention;

Fig. 3 is the false diagnosis probability calculation device structural drawing one of the preferred embodiments of the present invention;

Fig. 4 is the false diagnosis probability calculation device structural drawing two of the preferred embodiments of the present invention.

Embodiment

Below in conjunction with accompanying drawing, embodiments of the invention are described in detail, but the multitude of different ways that the present invention can be defined by the claims and cover is implemented.

False diagnosis method for calculating probability in following examples mainly for each field of Applied Digital networked control systems, as nuclear power, aviation, field of petrochemical industry.

Embodiment one

With reference to Fig. 1, the preferred embodiments of the present invention provide a kind of false diagnosis method for calculating probability, comprising:

Step 101, determines operator's false diagnosis Probability p _{diag, RO}.

Diagnosis behavior for operator: when the state of DCS system changes, operator need determines the current residing status level of DCS system by the change/state of observing/monitoring some DCS system parameter.Suppose in t, DCS system provides n parameter to characterize the state/change of DCS system, operator observes/monitors these parameters sequentially according to SOP code, and operator often observes a parameter, all can upgrade the state model of oneself according to the meaning of parameter.As: operator observes parameter 1 in the t1 moment, obtain state model 1, state model 1 drives operator to be transferred to parameter 2 in the t2 moment, state model 2 is obtained by observing, state model 2 drives operator to be transferred to parameter 3 in the t3 moment, state model 3 is obtained by observing, parameter i is observed in the ti moment, obtain state model i, along with this observes parameter, upgrade the carrying out of the process of state model, operator is to the time of day of understanding Step wise approximation (operator thinks) system of DCS system state, final in the tn moment, make the final judgement to DCS system state.Operator's false diagnosis probability is the final wrongheaded probability of operator to DCS system state.

Step 102, determines expeditor's false diagnosis Probability p _{diag, US}.

The difference of expeditor and operator is, is that comprehensive cognition judges to the requirement of expeditor.Under these circumstances, be that a series of unit action analyzes by the knowledge type behavior job partitioning of expeditor be difficult.Therefore overall thinking is answered to the analysis of expeditor's diagnosis, basic probability of failure is arranged for expeditor's diagnosis of partial, then basic probability of failure is diagnosed to revise by behavior formation factor (performance shaping factor is called for short PSF) to expeditor.Expeditor's false diagnosis probability is the probability of expeditor to the understanding mistake of DCS system state.

Step 103, according to described expeditor's false diagnosis Probability p _{diag, US}, determine that expeditor fails and recover the Probability p of operator's false diagnosis _{rec, diag}.

Expeditor is the field director of accident treatment, operator only accepts the instruction of expeditor, the structure of expeditor's code is consistent with the structure of operator's code, it is conveniently expeditor's correction that operator is slipped up, namely whenever expeditor must remain in same code sequence with operator, can not jump in next code sequence prior to operator.Expeditor fail recover operator false diagnosis probability namely for the false diagnosis of operator, expeditor fails and corrects the probability of operator's false diagnosis.

Step 104, according to teams and groups' false diagnosis Probability p _diag=p _{diag, RO}× p _{rec, diag}calculate p _diag.

Teams and groups' false diagnosis probability, namely after operator perceives and has an accident, teams and groups are to the false diagnosis probability of accident, and wherein, it is long that teams and groups comprise operator, expeditor and value.

For the long recovery to operator's false diagnosis of value, due to the teams and groups' design feature in digitizing master-control room, first it need to recover expeditor's false diagnosis, by expeditor, operator's false diagnosis is recovered again, through transmitting, value is long very little on the impact of expeditor's false diagnosis, and therefore, no longer consideration value length is to the recovery of expeditor's false diagnosis in the calculation.So final teams and groups' false diagnosis probability is only considered that operator's false diagnosis probability and expeditor fail and is recovered the probability of operator's false diagnosis, and both are teams and groups' false diagnosis probability at amassing.

The present embodiment recovers the probability of operator's false diagnosis by being divided into by teams and groups' false diagnosis probability operator's false diagnosis probability and expeditor to fail, then respectively operator's false diagnosis probability and the expeditor probability recovering operator false diagnosis that fails is calculated, amassing both as teams and groups' false diagnosis probability, thus realize the object of teams and groups' false diagnosis probability being carried out to Efficient Evaluation.

Embodiment two

With reference to Fig. 2, embodiment two is the supplementary notes of carrying out on the basis of embodiment one.

Step 101, determines operator's false diagnosis Probability p _{diag, RO}.

Stochastic process is at moment t ₀residing state is under known condition, and stochastic process is at moment t > t ₀the condition distribution of residing state and process are at t ₀the characteristic that state before moment has nothing to do is called Markov property or markov property.The stochastic process with Markov property is called Markov process.State Markov process with distribution function, have:

If I is that { state space of X (t), t ∈ T}, if any number t to time t for stochastic process _n, X (t _n) at condition X (t _i)=x _iunder conditional distribution function be

P{X(t _n)≤x _n|X(t ₁)＝x ₁,X(t ₂)＝x ₂,…,X(t _n-1)＝x _n-1}＝P{X(t _n)≤x _n|X(t _n-1)＝x _n-1},x _n∈R

At this moment { X (t), t ∈ T} has Markov property or markov property, and claims this process to be Markov process to claim process.

Operator patrol dish or in an abnormal situation to the supervision of DCS device parameter, often follow following process: operator often observes a parameter, capital upgrades the state model of oneself according to the meaning of parameter, as: operator observes parameter 1 in the t1 moment, by its mental model, parameter meaning is made an explanation, obtain state model 1, state model 1 drives operator to be transferred to parameter 2 in the t2 moment, same process, obtain state model 2, state model 2 drives operator to be transferred to parameter 3 in the t3 moment, obtain state model 3, parameter i is observed in the ti moment, obtain state model i, along with observation parameter, upgrade the carrying out of the process of state model, the time of day of the system that the understanding Step wise approximation operator of operator to system state thinks, final in the tn moment, make the final judgement to system state.Operator's false diagnosis probability is the final wrongheaded probability of operator to DCS state.As known from the above, operator ti can observe which parameter concrete at any time, depend primarily on the meaning of the parameter (i-1) that t (i-1) moment is observed, therefore above-mentioned " supervision+state estimation " process of operator meets Markov property, is Markov process.

The transfer monitored is time and state is all discrete random series { X _t=X (t), t=0,1,2 ..., suppose that the parameter that in current time, all operators of needs observe has n, then the parameter set I={a of this random series ₁, a ₂, a ₃a _n, namely I is the state space of this Markov process, operator in t by parameter a _ibe transferred to arbitrary parameter a _jbe obey certain probability distribution, this distribution is determined jointly by the mental model of operator and state model, builds the transition matrix describing and monitor transfering probability distribution thus:

$p\left(1\right)=\left[\begin{array}{ccccccc}{p}_{11}& \mathrm{...}& p_{1i}& \mathrm{...}& p_{1j}& \mathrm{...}& p_{1n}\\ .& & .& & .& & .\\ .& & .& & .& & .\\ .& & .& & .& & .\\ p_{i1}& \mathrm{...}& p_{ii}& \mathrm{...}& p_{ij}& \mathrm{...}& p_{in}\\ .& & .& & .& & .\\ .& & .& & .& & .\\ .& & .& & .& & .\\ p_{j1}& \mathrm{...}& p_{j1}& \mathrm{...}& p_{jj}& \mathrm{...}& p_{jn}\\ .& & .& & .& & .\\ .& & .& & .& & .\\ .& & .& & .& & .\\ p_{n1}& \mathrm{...}& p_{ni}& \mathrm{...}& p_{nj}& \mathrm{...}& p_{nn}\end{array}\right]$

P _ijrepresent that state model and mental model drive lower-pilot person by parameter a _ibe transferred to parameter a _jprobability.

Wherein, n > 1, n ∈ Z, 1≤i≤n, 1≤j≤n,

I,j∈Z，i≠j，

0≤p _ij≤1， $\underset{j}{\Σ}{p}_{ij}=1.$

For any state X _t, it is transferred to X _t+1probability all can express as follows:

p _ij＝P(X _t+1＝a _j|X _t＝a _i} (1)

If need parameter a _isuccessfully be transferred to parameter a _j, prerequisite is to parameter a _isupervision also need successfully.If therefore operator's execution monitoring shifts successfully, then need parameter a _imonitor successfully and be transferred to parameter a at mental model, state model under combining driving _jthe two sets up simultaneously, namely has:

p _diag'＝p _mon'×p _SA'＝p _mon'×p _ij(2)

P _diag': to parameter a _imonitor successfully and be transferred to parameter a at mental model, state model under combining driving _jthe two probability simultaneously set up;

P _mon': to parameter a _imonitor successful probability;

P _sA': by parameter a under mental model and state model drive _ibe transferred to a _jprobability.

For data-driven, if need parameter a _isuccessfully be transferred to parameter a _j, demand fulfillment two point: to parameter a _imonitor successfully; Parameter a _inumerical value correct with mating of the value set in code.The coupling behavior based on code like this, its probability of failure is little of ignoring, i.e. p _ijbe infinitely close to 1, therefore can simplify approximate for formula (2) in HRA:

p _diag'＝p _mon'×p _ij≈p _mon' (3)

Supposing that the name of all processes monitors that probability of failure is all identical, is p _mon", then have

p _mon”＝1-p _mon' (4)

Regular pattern composite is slipped up, in monitoring process, the conspicuousness of monitored object becomes the key factor that impact monitors reliability, and different target conspicuousnesses may be inconsistent, therefore, in HRA, the impact of target conspicuousness on it is all considered on the supervision reliability of each supervision node, if i-th factor of influence monitoring node is k' _i, then i-th monitors that the basic fault rate of node is:

p _mon,i＝p _mon”×k' _i，k' _i＞0 (5)

For diagnostic phases, psychological pressure (K ₁) and pot life (K ₂) all can its reliability of appreciable impact on the whole, consider this two factors, if having n in the diagnostic procedure of an accident to monitor node, then operator's false diagnosis probability is:

$p_{diag,RO}=\[1-\underset{i=1}{\overset{n}{\Π}}(1-p_{mon,i})\]K_1{K}_2---\left(6\right)$

Wherein, K ₁> 0, K ₂> 0, K ₁, K ₂value can carry out value according to actual conditions with reference to " THERP handbook ",

N is the number monitoring node;

K ₁for psychological pressure modifying factor;

K ₂for pot life modifying factor;

P _{mon, i}be i-th basic fault rate monitoring node.

Step 102, determines expeditor's false diagnosis Probability p _{diag, US}.

Expeditor's false diagnosis probability is:

p _diag,US＝p _b,diag×k ₁×k ₂×k ₃×k ₄×k ₅×k ₆(7)

Wherein, p _{b, diag}for expeditor diagnoses basic probability of failure, p _{b, diag}value can with reference to related data in SPAR-H and CREAM method, when being applied to Digitizing And Control Unit, through with analog machine teacher, operator discussion repeatedly, observe Simulator Training/experiment and determine.K ₁for time pressure modifying factor, k ₂for psychological pressure modifying factor, k ₃for task complexity modifying factor, k ₄train horizontal modifying factor, k ₅for code complicacy modifying factor, k ₆for man-machine interface complexity modifying factor, k ₁-k ₆value can with reference to " THERP handbook ".

Step 103, according to expeditor's false diagnosis Probability p _{diag, US}, determine that expeditor fails and recover the Probability p of operator's false diagnosis _{rec, diag}.

The fail probability that recovers operator false diagnosis of expeditor adopts correlation level formula in " THERP handbook ", can be expressed as:

$p_{rec,diag}=\frac{1+19\×p_{diag,US}}{20}---\left(8\right)$

Step 104, according to teams and groups' false diagnosis Probability p _diag=p _{diag, RO}× p _{rec, diag}calculate p _diag.

The present embodiment recovers the probability of operator's false diagnosis by being divided into by teams and groups' false diagnosis probability operator's false diagnosis probability and expeditor to fail, then respectively operator's false diagnosis probability and the expeditor probability recovering operator false diagnosis that fails is calculated, amassing both as teams and groups' false diagnosis probability, thus realize the object of teams and groups' false diagnosis probability being carried out to Efficient Evaluation.

Enumerate specific embodiment to be below described the present embodiment.

Diagnostic phases operator monitors that node is as shown in table 1:

Table 1

Experimentally obtain name and monitor probability of failure p " _monapproximate 0.003.According to formula (5), i-th can be obtained and monitor that the basic fault rate of node is:

p _mon,i＝0.003k' _i

Then according to operator's false diagnosis probability calculation formula:

$p_{diag,RO}=\[1-\underset{i=1}{\overset{n}{\Π}}(1-p_{mon,i})\]K_1{K}_2$

K ₁=2, represent that pressure is large at heart, K ₂=0.1, represent a lot of time, above formula obtains p _{diag, RO}=2.4 × 10 ^-3.

For expeditor, expeditor's false diagnosis probability is according to false diagnosis Probability p _{diag, US}=p _{b, diag}× k ₁× k ₂× k ₃× k ₄× k ₅× k ₆, calculate: p _{diag, US}=2 × 10 ^-3.Wherein, by inquiry " THERP handbook ", determine that expeditor diagnoses basic probability of failure p _{b, diag}=0.01, k ₁=0.1, represent a lot of time, k ₂=2, represent that psychological pressure is high, k ₃=2, represent intermediate complexity, k ₄=1, represent general training level, k ₅=0.5, represent station guide code, k ₆=1, represent average man-machine interface.

Expeditor fails and recovers the probability of operator's false diagnosis:

$P_{rec,diag}=\frac{1+19\×P_{diag,US}}{20}=5.2\×{10}^{-2}$

Teams and groups' false diagnosis probability:

P _diag＝P _diag,RO×P _rec,diag＝1.2×10 ^-4

Embodiment three

The present embodiment is the supplementary notes of carrying out on the basis of above-described embodiment.

With reference to Fig. 2, determining operator's false diagnosis Probability p _{diag, RO}before, also comprise step 105, screening monitors node, and screening technique is:

The supervision node that removal system can process automatically;

For being provided with the auxiliary supervision node of alerting signal, if monitor node failure, then get rid of and monitor node;

Get rid of the supervision node not forming appreciable impact;

The supervision node chosen can provide necessary and sufficient information for diagnosing out current accident, namely accidents happened can provide not unnecessary also indispensable information to diagnosis to refer to the supervision node chosen, and get rid of the accident even more serious than current accident by information, the accident that the accident even more serious than current accident is namely higher than current accident condition function degradation degree.

The supervision content performed due to operator need after accident is very many, and not all node all needs at human reliability analysis (Human reliability analysis, be called for short HRA) quantitatively calculate in consider, the quantity of the supervision node filtered out is by the result of calculation of false diagnosis probability in appreciable impact HRA, therefore after by above-mentioned screening technique, to supervision, node screens, the reliability of the false diagnosis probability calculated is higher, thus is more conducive to Efficient Evaluation false diagnosis probability.

Embodiment four

With reference to Fig. 3, the preferred embodiments of the present invention provide a kind of false diagnosis probability calculation device, comprise: operator's false diagnosis probability evaluation entity 201, expeditor's false diagnosis probability evaluation entity 202, expeditor fails and recovers operator's false diagnosis probability evaluation entity 203, teams and groups' false diagnosis probability evaluation entity 204.

Wherein, operator's false diagnosis probability evaluation entity 201, for determining operator's false diagnosis Probability p _{diag, RO}; Expeditor's false diagnosis probability evaluation entity 202, for determining expeditor's false diagnosis Probability p _{diag, US}; Expeditor fails and recovers operator's false diagnosis probability evaluation entity 203, according to expeditor's false diagnosis Probability p _{diag, US}, recover the Probability p of operator's false diagnosis for determining that expeditor fails _{rec, diag}; Teams and groups' false diagnosis probability evaluation entity 204, for according to teams and groups' false diagnosis Probability p _diag=p _{diag, RO}× p _{rec, diag}calculate p _diag.

The present embodiment is the device embodiment corresponding with embodiment of the method one, specifically see the record in embodiment one, can not repeat them here.

The present embodiment is by operator's false diagnosis probability evaluation entity 201, expeditor fails and recovers operator's false diagnosis probability 203, respectively to operator's false diagnosis probability, expeditor fails and recovers operator false diagnosis probability and calculate, amassing both as teams and groups' false diagnosis probability, thus Efficient Evaluation is carried out to the teams and groups' false diagnosis probability after accident.

Embodiment five

The present embodiment is the supplementary notes of carrying out on the basis of embodiment four.

Operator's false diagnosis probability evaluation entity 201, specifically for calculating described operator's false diagnosis probability

$p_{diag,RO}=\[1-\underset{i=1}{\overset{n}{\Π}}(1-p_{mon,i})\]K_1{K}_2,$

Wherein, K ₁> 0, K ₂> 0, K ₁, K ₂value can carry out value according to actual conditions with reference to " THERP handbook ",

N is the number monitoring node;

K ₁for psychological pressure modifying factor;

K ₂for pot life modifying factor;

P _{mon, i}it is the basic fault rate of i-th described supervision node.

Expeditor's false diagnosis probability evaluation entity 202, specifically for calculating described expeditor's false diagnosis probability

p _diag，US＝p _b,diag×k ₁×k ₂×k ₃×k ₄×k ₅×k ₆,

Wherein, p _{b, diag}for expeditor diagnoses basic probability of failure, k ₁for time pressure modifying factor, k ₂for psychological pressure modifying factor, k ₃for task complexity modifying factor, k ₄train horizontal modifying factor, k ₅for code complicacy modifying factor, k ₆for man-machine interface complexity modifying factor.

Expeditor fails and recovers operator's false diagnosis probability evaluation entity 203, specifically for calculating p _{rec, diag},

$p_{rec,diag}=\frac{1+19\×p_{diag,US}}{20}.$

The present embodiment is the device embodiment corresponding with embodiment of the method two, specifically see the record in embodiment two, can not repeat them here.

Embodiment six

The present embodiment is the supplementary notes of carrying out on the basis of above-described embodiment.

With reference to Fig. 4, false diagnosis probability calculation device also comprises supervision node screening module 205.Monitor that node screening module 205 is for screening supervision node, specifically for:

The described supervision node that removal system can process automatically;

For being provided with the auxiliary described supervision node of alerting signal, if described supervision node failure, then get rid of described supervision node;

Get rid of the described supervision node not forming appreciable impact;

The supervision node chosen can provide necessary and sufficient information for diagnosing out current accident, namely accidents happened can provide not unnecessary also indispensable information to diagnosis to refer to the supervision node chosen, and get rid of the accident even more serious than current accident by information, the accident that the accident even more serious than current accident is namely higher than current accident condition function degradation degree.

The present embodiment is the device embodiment corresponding with embodiment of the method three, specifically see the record in embodiment three, can not repeat them here.

These are only the preferred embodiments of the present invention, be not limited to the present invention, for a person skilled in the art, the present invention can have various modifications and variations.Within the spirit and principles in the present invention all, any amendment done, equivalent replacement, improvement etc., all should be included within protection scope of the present invention.

Claims (8)

1. a false diagnosis method for calculating probability, is characterized in that, comprising:

Determine operator's false diagnosis Probability p _{diag, RO};

Determine expeditor's false diagnosis Probability p _{diag, US};

According to described expeditor's false diagnosis Probability p _{diag, US}, determine that expeditor fails and recover the Probability p of operator's false diagnosis _{rec, diag};

According to teams and groups' false diagnosis Probability p _diag=p _{diag, RO}× p _{rec, diag}calculate described p _diag.

2. false diagnosis method for calculating probability according to claim 1, is characterized in that,

Described operator's false diagnosis probability $p_{diag,RO}=\[1-\underset{i=1}{\overset{n}{\Π}}(1-p_{mon,i})\]K_1{K}_2,$

Wherein, K ₁> 0, K ₂> 0,

N is the number monitoring node;

K ₁for psychological pressure modifying factor;

K ₂for pot life modifying factor;

P _{mon, i}it is the basic fault rate of i-th described supervision node.

3. false diagnosis method for calculating probability according to claim 1, is characterized in that,

Described expeditor's false diagnosis Probability p _{diag, US}=p _{b, diag}× k ₁× k ₂× k ₃× k ₄× k ₅× k ₆,

Wherein, p _{b, diag}for expeditor diagnoses basic probability of failure, k ₁for time pressure modifying factor, k ₂for psychological pressure modifying factor, k ₃for task complexity modifying factor, k ₄train horizontal modifying factor, k ₅for code complicacy modifying factor, k ₆for man-machine interface complexity modifying factor.

4. according to the arbitrary described false diagnosis method for calculating probability of claim 1-3, it is characterized in that, the described expeditor of determination fails and recovers the Probability p of operator's false diagnosis _{rec, diag}for,

$p_{rec,diag}=\frac{1+19\×p_{diag,US}}{20}.$

5. a false diagnosis probability calculation device, is characterized in that, comprising:

Operator's false diagnosis probability evaluation entity, for determining operator's false diagnosis Probability p _{diag, RO};

Expeditor's false diagnosis probability evaluation entity, for determining expeditor's false diagnosis Probability p _{diag, US};

Expeditor fails and recovers operator's false diagnosis probability evaluation entity, according to described expeditor's false diagnosis Probability p _{diag, US}, recover the Probability p of operator's false diagnosis for determining that expeditor fails _{rec, diag};

Teams and groups' false diagnosis probability evaluation entity, for according to teams and groups' false diagnosis Probability p _diag=p _{diag, RO}× p _{rec, diag}calculate described p _diag.

6. false diagnosis probability calculation device according to claim 5, is characterized in that,

Described operator's false diagnosis probability evaluation entity, specifically for calculating described operator's false diagnosis probability

$p_{diag,RO}=\[1-\underset{i=1}{\overset{n}{\Π}}(1-p_{mon,i})\]K_1{K}_2,$

Wherein, K ₁> 0, K ₂> 0,

N is the number monitoring node;

K ₁for psychological pressure modifying factor;

K ₂for pot life modifying factor;

P _{mon, i}it is the basic fault rate of i-th described supervision node.

7. false diagnosis probability calculation device according to claim 5, is characterized in that,

Described expeditor's false diagnosis probability evaluation entity, specifically for calculating described expeditor's false diagnosis probability

p _diag，US＝p _b,diag×k ₁×k ₂×k ₃×k ₄×k ₅×k ₆,

Wherein, p _{b, diag}for expeditor diagnoses basic probability of failure, k ₁for time pressure modifying factor, k ₂for psychological pressure modifying factor, k ₃for task complexity modifying factor, k ₄train horizontal modifying factor, k ₅for code complicacy modifying factor, k ₆for man-machine interface complexity modifying factor.

8. according to the arbitrary described false diagnosis probability calculation device of claim 5-7, it is characterized in that, described expeditor fails and recovers operator's false diagnosis probability evaluation entity, specifically for calculating p _{rec, diag},

$p_{rec,diag}=\frac{1+19\×p_{diag,US}}{20}.$