CN103198232B - The determination method and device of digitized master control room staff's human factors analysis - Google Patents
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Abstract
The invention discloses the determination method and device of a kind of digitized master control room staff's human factors analysis, wherein, the method comprises determining that staff's failure probability to each stage that task responds, or determines the failure probability of dissimilar staff；Total failure probability is determined according to above-mentioned failure probability.Pass through the present invention, it is achieved that the analysis to digitized master control room staff's human factors analysis.
Description
Technical field
The present invention relates to human reliability's analysis field, in particular to the determination method and device of a kind of digitized master control room staff's human factors analysis.
Background technology
Due to master-control room of nuclear power plant (maincontrolroom, referred to as MCR) after digitized, man machine interface (man-machineinterface, referred to as MMI) there occurs great variety, information shows to be pointed out be transformed into large screen display (plantdisplaysystem from alarm window, alarm, referred to as PDS) and terminal show (videodisplayunit, referred to as VDU)；Operator controls to become to use the mouse manipulation of terminal from the control key manipulation transforms of traditional control Pan Tai with maneuverability pattern；The code that operator uses is transferred to the electronics code being guiding (State-OrientedProcedure, referred to as SOP) with power plant's state shown on the computer screen by traditional papery code.The digitized of power plant system will certainly cause all many changes of human factors.For the consequence probing into the internal mechanism of this change and Effect Mode and this change brings, need to set up the behavior model of the new people adapted therewith and human reliability analyzes (HumanReliabilityAnalysis, referred to as HRA) method.
Owing to the physiology of people and psychological factor are complicated, in addition with system and the interactivity of surrounding and dependency, cause the behavior of people to a certain extent to have definitiveness unlike machinery, electronic equipment, and be difficult to quantitative description.Human reliability's quantitative analysis method in correlation technique is the rarest, and the most representative human reliability's quantitative analysis method is CREAM quantitative analysis method and THERP+HCR quantitative analysis method.But the human reliability that these methods are only applicable in tradition master control room analyzes, and does not considers the feature after the digitized of master control room.
Summary of the invention
It is desirable to provide the determination method and device of a kind of digitized master control room staff's human factors analysis, with the problem solving to realize the analysis to digitized master control room staff's human factors analysis in prior art.
To achieve these goals, according to an aspect of the present invention, provide the determination method of a kind of digitized master control room staff's human factors analysis, comprise determining that staff's failure probability to each stage that task responds, or determine the failure probability of dissimilar staff；Total failure probability is determined according to described failure probability.
Preferably, described staff includes: first kind staff, Equations of The Second Kind staff and the 3rd class staff, wherein, described first kind staff performs accident treatment, described Equations of The Second Kind staff monitors the change of unit state parameter, monitors the implementation status of described first kind staff, and implementation status described in individual authentication, described 3rd class staff's independent check set state, judge nature of occurence, evaluate unit safe condition.
Preferably, each stage described includes: monitor stage, state estimation stage, response programming phase, response execution stage, wherein, the described supervision stage includes the transfer of described staff's monitoring system state, the described state estimation stage includes that described staff assesses the state monitored, described response programming phase includes that described staff determines that the response policy being used the state monitored, described response execution stage include that described staff performs described response policy.
Preferably, described total failure probability is total failure probability in each stage of each described first kind staff；Determining total failure probability in each stage of described first kind staff in such a way: use two event trees to carry out integrated the failure probability in each stage described, total failure probability in each stage obtaining described first kind staff according to below equation is: Ftotal=P(A)+a·P(B)+ab·P(C)+abc·P(D)；Wherein, P(A) it is the failure probability in supervision stage, a is the probability of success in supervision stage, P(B) it is the failure probability in state estimation stage, b is the probability of success in state estimation stage, P(C) for the failure probability of response programming phase, c is the probability of success of response programming phase, P(D) for responding the failure probability in execution stage.
Preferably, determine total failure probability in each stage of described first kind staff according to described failure probability, also include: adjust total failure probability F in each stage of described first kind staff according to equation belowtotal, obtain final total failure probability F of described first kind staffT: FT=Ftotal/ (1-T), wherein, T is the two class management roles factors of influence to human factors analysis, and T is more than or equal to 0 and less than 1.
Preferably, described total failure probability is total failure probability of the teams and groups that described first kind staff, described Equations of The Second Kind staff and described 3rd class staff are constituted；Determine that the failure probability of dissimilar staff includes: obtain first failure probability of described first kind staff；Obtain second failure probability of described Equations of The Second Kind staff；Medium relevant MD is used to determine the 3rd failure probability of described 3rd class staff according to described second failure probability；Determine that according to described failure probability total failure probability of described teams and groups includes: determine total failure probability of described teams and groups according to described first failure probability, described second failure probability and the 3rd failure probability.
Preferably, described 3rd failure probability is determined in such a way:Wherein, P (B/A) is described 3rd failure probability, and P (B) is described second failure probability.
Preferably, total failure probability of described teams and groups: F is determined in such a waycrew=PA×P(B/A)×PB, wherein, FcrewFor total failure probability of described teams and groups, PAFor described first failure probability.
According to another aspect of the present invention, provide the determination device of a kind of digitized master control room staff's human factors analysis, including: first determines module, for obtaining staff's failure probability to each stage that task responds, or determines the failure probability of dissimilar staff；Second determines module, for determining total failure probability according to described failure probability.
Preferably, described second determines module, and for using two event trees to carry out integrated the failure probability in each stage described, total failure probability in each stage obtaining described staff according to below equation is: Ftotal=P (A)+a P (B)+ab P (C)+abc P (D), wherein, P(A) it is the failure probability in supervision stage, a is the probability of success in supervision stage, P(B) being the failure probability in state estimation stage, b is the probability of success in state estimation stage, P(C) for the failure probability of response programming phase, c is the probability of success of response programming phase, P(D) for responding the failure probability in execution stage；Wherein, the described supervision stage includes the transfer of described staff's monitoring system state, the described state estimation stage includes that described staff assesses the state monitored, described response programming phase includes that described staff determines that the response policy being used the state monitored, described response execution stage include that described staff performs described response policy.
Preferably, described second determines module, is additionally operable to adjust total failure probability F in each stage of described staff according to equation belowtotal, obtain final total failure probability F of described staffT: FT=Ftotal/ (1-T), wherein, T is the two class management roles factors of influence to human factors analysis, and T is more than or equal to 0 and less than 1.
Preferably, described first determines that module includes: the first acquiring unit, for obtaining first failure probability of first kind staff；Second acquisition unit, for obtaining second failure probability of Equations of The Second Kind staff；Determine unit, for using medium relevant MD to determine the 3rd failure probability of the 3rd class staff according to described second failure probability；Described second determines module, for determining total failure probability of the teams and groups being made up of described first kind staff, described Equations of The Second Kind staff and described 3rd class staff according to described first failure probability, described second failure probability and the 3rd failure probability；Wherein, described first kind staff performs accident treatment, described Equations of The Second Kind staff monitors the change of unit state parameter, monitors the implementation status of described first kind staff, and implementation status described in individual authentication, described 3rd class staff's independent check set state, judge nature of occurence, evaluate unit and safe condition.
Preferably, described determine unit, be used for determining that described 3rd failure probability isWherein, P (B/A) is described 3rd failure probability, and P (B) is described second failure probability.
Preferably, described second determines module, for determining that total failure probability of described teams and groups is: Fcrew=PA×P(B/A)×PB, wherein, FcrewFor described total failure probability, PAFor described first failure probability.
Application technical scheme, determine staff's failure probability to each stage that task responds, or determine the failure probability of dissimilar staff, and determine total failure probability according to above-mentioned failure probability, it is achieved that the analysis to digitized master control room staff's human factors analysis.
Accompanying drawing explanation
The Figure of description of the part constituting the application is used for providing a further understanding of the present invention, and the schematic description and description of the present invention is used for explaining the present invention, is not intended that inappropriate limitation of the present invention.In the accompanying drawings:
Fig. 1 is the flow chart of the determination method of digitized master control room staff's human factors analysis according to embodiments of the present invention；
Fig. 2 is the flow chart of the determination method of the failure probability monitoring the stage according to embodiments of the present invention；
Fig. 3 is the schematic diagram of state estimation stage model according to embodiments of the present invention；
Fig. 4 is the schematic diagram of response programming phase model according to embodiments of the present invention；
Fig. 5 is the schematic diagram in human-equation error path according to embodiments of the present invention；
Fig. 6 is the schematic diagram of total failure probability of operator according to embodiments of the present invention；
Fig. 7 is the schematic diagram of total failure probability of teams and groups according to embodiments of the present invention；
Fig. 8 is the structured flowchart of the determination device of digitized master control room staff's human factors analysis according to embodiments of the present invention；And
Fig. 9 is the schematic diagram of system structural framework according to embodiments of the present invention.
Detailed description of the invention
It should be noted that in the case of not conflicting, the embodiment in the application and the feature in embodiment can be mutually combined.Describe the present invention below with reference to the accompanying drawings and in conjunction with the embodiments in detail.
According to embodiments of the present invention, it is provided that the determination method of a kind of digitized master control room staff's human factors analysis.
Fig. 1 is the flow chart of the determination method of digitized master control room staff's human factors analysis according to embodiments of the present invention, as it is shown in figure 1, the method comprising the steps of S102 is to step S104.
Step S102, determines staff's failure probability to each stage that task responds, or determines the failure probability of dissimilar staff.
Step S104, determines total failure probability according to above-mentioned failure probability.
The technical scheme of the application embodiment of the present invention, determine staff's failure probability to each stage that task responds, or determine the failure probability of dissimilar staff, and determine total failure probability according to above-mentioned failure probability, it is achieved that the analysis to digitized master control room staff's human factors analysis.
In embodiments of the present invention, said method may determine that total failure probability of a type of staff, it is also possible to determines total failure probability of the teams and groups that different types of staff forms.Separately below above-mentioned two aspect is described.
One, total failure probability of a type of staff
In embodiments of the present invention, illustrating as a example by first kind staff, first kind staff performs the reliability of the staff of accident treatment, and the staff performing above-mentioned process in nuclear power plant can be first and second loop operator.The response of task can be divided into by such staff: monitors stage, state estimation stage, response programming phase, response execution stage.
Wherein, the supervision stage includes the transfer of staff's monitoring system state, the state estimation stage includes that staff assesses the state monitored, response programming phase includes that described staff determines that the response policy being used the state monitored, response execution stage include that staff performs above-mentioned response policy.
In a preferred implementation of the embodiment of the present invention, total failure probability of a class staff can be determined in such a way: using two event trees to carry out integrated the failure probability in each stage, obtaining total failure probability according to below equation is:
Ftotal=P(A)+a·P(B)+ab·P(C)+abc·P(D)
Wherein, P(A) it is the failure probability in supervision stage, a is the probability of success in supervision stage, P(B) it is the failure probability in state estimation stage, b is the probability of success in state estimation stage, P(C) for the failure probability of response programming phase, c is the probability of success of response programming phase, P(D) for responding the failure probability in execution stage.
Further, determine that total failure probability of a class staff also includes according to the failure probability in each stage: adjust total failure probability F of such staff according to equation belowtotal, obtain final total failure probability FT: FT=Ftotal/ (1-T), wherein, T is the two class management roles factors of influence to human factors analysis, and T is more than or equal to 0 and less than 1.T can be 10% in actual applications.
In embodiments of the present invention, and being not concerned with how the failure probability in each stage that task responds is determined by a class staff, the failure probability according to each stage that focuses on of the embodiment of the present invention determines total failure probability.Below as a example by master-control room of nuclear power plant operator, a kind of embodiment of the failure probability determining each stage is described.
The important response of operator can be analyzed to supervision, state estimation, response plan, response 4 main tasks of execution, and assesses inefficacy (successfully) probability of these responses.
Each phased mission above-mentioned is built respectively the event tree quantitative evalution model that the Markov model of corresponding monitoring activity, the Bayesian network model of state estimation, the Bayesian network model of response plan and response perform, calculates the failure probability of every main task.The ultimate principle of 4 models, and the determination method of failure probability are briefly described below.
(1) Markov model of monitoring activity
The monitoring activity of Nuclear Power Plant Operators is exactly to obtain the behavior of information from the dynamic working environment of complexity.From the point of view of monitoring activity activity itself, in general, operator's transfer to the supervision of system mode, it is common that according to the current state of system, and unrelated with the state before system.Although monitoring that target has certain expection (particularly under accident/state-event), but monitoring path and transfer process it is not anticipated that property and obvious rule, having obvious randomness, therefore, supervision process can approximate and see stochastic process as.This class process is without the version (Changing Pattern without inevitable) determined, thus can not represent by accurate relationship, but can describe with random function.Markov model is typically for describing dynamic, continuous random process function, according to aforesaid analysis, assume that and monitor that the whereabouts of transfer only monitors to this that state of point and factor are relevant, it can be assumed that this transfer process is the continuous transfer process in time series of markov property, there is Markov property, can simulate with Markov model.Fig. 2 gives calculating and monitors success and the algorithm flow chart of probability of failure, as in figure 2 it is shown, analyze requirement, and the logical relation of each node of monitoring activity based on operator's monitoring activity, formulates monitoring activity quantitative analysis flow process as follows:
Step S1: judge power plant's running status, i.e. determines supervision task starting, carry out monitoring analyze before it needs to be determined that the current operating conditions of power plant, only take " normally " and "abnormal" two kinds.
Step S2: monitor procedure decomposition, i.e. based on supervision task, operator is monitored that process is decomposed with logic according to certain rule, mark (transfer) node (N) of supervision process.
When power plant's state is " normally ", monitor this generic task experience based on supervision task, dependent surveillance parameter, rule of operation with operator, take expert judgments method reasonably to determine supervision node；When power plant's state is "abnormal", task is monitored for the HRA under PSA framework, divide supervision node according to the SOP code of analysis event/accident.Node division is also based on realizing the knowledge token method of operating process with event handling code.
Step S3: determine monitoring activity time window, i.e. divides starting point T0 and the terminal TE of surveillance operation, determines the time period (window) of surveillance operation.In PSA-HRA analyzes, monitor that starting point may be configured as entering the SOP moment (remembering for 0 moment)；Rationally to determine the supervision starting point moment based on practical situation in other are analyzed.
Step S4: determine supervision transfering node, i.e. based on previous step, marks supervision node (transfering node) logical schematic of supervision process, determines supervision transfering node.
Step S5: calculate node i monitor the probability of success, the most respectively calculate " operator's node i discover the probability of success" transfer to node i operator from node i-1 monitor the transfer probability of success with operator, and take both probability products and obtain node i operator and monitor the probability of success.
Step S6: calculate node i and monitor the probability of success
Step S7: calculate and monitor the probability of success
Step S8: calculate and monitor probability of failure
(2) Bayesian network model of state estimation
State estimation relates to two relevant models, i.e. state model and mental model.State model is exactly operator's understanding to particular state, and when collecting fresh information when, state model can be often updated.Mental model is to be built by formal education, concrete training and operator's experience, and stores in the brain.State estimation process develops a state model the most exactly to assess current power plant's state.The assessment of state is mainly affected by parameter, and different parameters and the various combination of state thereof are likely to be obtained different states.To above-mentioned state estimation Bayesian network model, it illustrates further below.
First, the state estimation behavior of digitized master-control room of nuclear power plant operator is introduced.
When nuclear power plant occurs abnormality, the state parameter situation according to nuclear power plant is built a rational and logical explanation, assesses power plant's state in which by operator, performs the foundation of decision-making as follow-up response plan and response.This serial procedures is referred to as state estimation, and relates to two relevant models, i.e. state model and mental model.State model is exactly operator's understanding to the state that specific power plant system is presented, and when collecting fresh information when, state model can be often updated.Mental model is to be built by formal education, concrete training and operator's experience, and stores in the brain.State estimation process develops a state model the most exactly to describe current power plant's state.
If an event (as reported to the police) is very simple, operator need not any reasoning to the identification of power plant's state, then it is assumed that is the state estimation of Skill and method.If an anomalous event belongs to so-called " problem ", require that this problem Producing reason and impact are illustrated and build state model by operator, and the state model built carries out with the mental model of operator mating (i.e. similarity coupling), then this process is referred to as the state estimation of regular pattern composite.Equally, for unfamiliar state model, require operator's assessment and predict possible power plant's state, then more abstract between the 26S Proteasome Structure and Function in problem analysis space logical relation, carry out the analysis of profound level, gradually forming a state model and verify, finally determining power plant's state, this process is considered as the state estimation of knowledge type.
Secondly, the Bayesian network model of the state estimation of operator is introduced.
Influence factor and their cause effect relation affecting operator's state estimation reliability is identified by setting up expert group's (including that Nuclear Power Plant Operators teams and groups and people are because of expert), in general, after there is anomalous event in nuclear power plant, the state estimation of operator relates to two relevant models, i.e. state model and mental model.State model be exactly operator to system or the understanding of the particular state of assembly, and when collecting fresh information when, state model can be often updated.Mental model is to be built by formal education, concrete training and operator's experience, and stores in the brain.State estimation process develops a state model the most exactly to assess current power plant's state.If operator to evaluate real power plant current state well, then operator need utilizes the mental model of himself to go to pick out the state that power plant is current, and property easy to identify, the intelligence level/mental model of operator and mental pressure that this process is presented by power plant's state are affected.Intelligence level/mental model derives from the knowledge and experience of operator, knowledge and experience is mainly affected by organizing training and the affecting of exchanges and cooperation of teams and groups, if lack of training, then the knowledge and experience of operator can be impacted, and the exchanges and cooperation of teams and groups can supplement the deficiency of the individual knowledge and experience of operator.
The property easy to identify (the another kind of explanation of state model) of the state that power plant is presented mainly is affected by the automatization level of digitized man machine interface and system, if digitized Human Machine Interface is good, then information is eye-catching, easily collection information and identify system state in which, if system automation level is high, during then operator is not participating in concrete task, then easily lose the understanding of the system mode relevant to task.Additionally, operator's coupling between state model and mental model is had a great impact by stress level, stress level is mainly by the severity of event, the complexity of task and the impact of pot life, the complexity of same task is mainly affected with the quality of digitized Human Machine Interface by the quality of digitized rolling schedule design, the complicated task that then operator need completes of task in code is complicated, code or program are conducive to well instructing operator to respond plan, man machine interface bad (the interface management task as many) then operator is difficult to obtain the useful information being conducive to task to complete.Furthermore, event is the most serious, and the mental pressure of operator is the biggest, and the pot life completing task is the shortest, then the mental pressure of operator is the biggest.By above-mentioned analysis, state estimation is affected by exchanges and cooperation level, training level, digitized code, digitized man machine interface, the severity of event, the pot life of accident handling and the Automated water equality factor with system of teams and groups, these PSF factors affect graph of a relation (or claiming the Bayesian network model of state estimation) as shown in Figure 3, for the Bayesian network model (this figure can also increase corresponding node equally) of general state model with state estimation., wherein, undermost state estimation is exactly newly a kind of state estimation reliability node.
(3) Bayesian network model of response plan
In general, the reliability of response plan mainly by a line operator mental status, memory in information and individual character build-in attribute affected.The knowledge and experience of operator enriches, then will appreciate that specific power plant state is to taking which kind of response policy or plan.Knowledge and experience is mainly affected by organizing training and the affecting of exchanges and cooperation of teams and groups, if lack of training, then the knowledge and experience of operator can impacted, and the exchanges and cooperation of teams and groups can supplement the deficiency of the knowledge and experience of operator's individuality.Additionally, the formulation of response plan is had a great impact by stress level, stress level is mainly affected by the severity of event, the complexity of task and pot life, the complexity of same task mainly fine or not is affected by what quality and the man-machine interface of rolling schedule design designed, the complicated task that then operator need completes of task in code is complicated, code or program are conducive to well instructing operator to respond plan, and man-machine interface is bad, and operator is difficult to obtain the useful information being conducive to responding plan.Furthermore, response plan is also affected by the attitude of operator, the attitude of operator and responsibility are good, then it is difficult in violation of rules and regulations, attention is concentrated, the attitude of operator is mainly affected by the quality of the safety culture organized and management, and as safety culture is bad, then sense of risk and the safety attitude of operator are the most bad.
Fig. 4 shows the schematic diagram of response programming phase model, and operator responds the Bayesian network model of plan.By above-mentioned analysis and Fig. 4 it can be seen that response plan can be affected with factors such as organization and administration levels by the exchanges and cooperation level of teams and groups, training level, digitized code, digitized man machine interface, the severity of event, the pot life of accident handling, safety culture.
(4) the event tree quantitative evalution model that response performs
After Power Plant Accident, operator monitors for plant information, power plant's state is estimated and is responded plan, and after above cognitive process completes, operator need to carry out responding execution behavior for response plan.In DCS, respond execution behavior after accident and refer to that operator utilizes mouse to configure VDU picture, and click on the execution of SOP code.
The successful path of personel accident is as shown in Figure 5, the successful path of personel accident can include two processes, operator's cognitive process and course of action, at cognitive process, plant information is monitored by operator, and power plant's state is estimated, formulating response plan according to assessment result, at course of action, operator performs the response plan formulated.
Operator's reliability model
Utilize above-mentioned 4 models, calculate operator respectively and perform supervision, state estimation, response plan, respond probability of success when performing 4 main tasks.For the reliability model of power plant operator, two branch event trees are used to carry out comprehensive integration.After accident occurs, operator need to accurately monitor, effectively assesses power plant's state and responds plan, performing response action.
In embodiments of the present invention, said method is determined for performing the reliability of the staff of accident treatment, and the staff performing above-mentioned process in nuclear power plant is first and second loop operator.
Fig. 6 is the schematic diagram of total failure probability of operator according to embodiments of the present invention, and as shown in Figure 6, operator has 4 inefficacy branches, i.e. F1, F2, F3, F4.Operator's behavior failure probability uses equation below to calculate: Ftotal=P(A)+a·P(B)+ab·P(C)+abc·P(D)。
Two class management roles cause task Whole Performance to decline 10%, and the most final one, secondary circuit operator single PSA origination event people is because of failure probability: FT=Ftotal/90%。
Two, total failure probability of different types of staff
In embodiments of the present invention, staff may include that first kind staff, Equations of The Second Kind staff and the 3rd class staff, and wherein, first kind staff performs accident treatment, in nuclear power plant, first kind staff can be first and second loop operator；Equations of The Second Kind staff monitors the change of unit state parameter, monitors the implementation status of described first kind staff, and implementation status described in individual authentication, and in nuclear power plant, Equations of The Second Kind staff can be machine group leader/expeditor；3rd class staff's independent check set state, judge nature of occurence, evaluate unit and safe condition, in nuclear power plant the 3rd class staff can be peace work.
Determine that the failure probability of dissimilar staff includes: obtain first failure probability of first kind staff；Obtain second failure probability of Equations of The Second Kind staff；Use medium relevant the 3rd failure probability determining the 3rd class staff according to the second failure probability.Determine that total failure probability includes according to failure probability: determine total failure probability according to the first failure probability, the second failure probability and the 3rd failure probability.
In a preferred implementation of the embodiment of the present invention, the 3rd failure probability can be determined in such a way:Wherein, P (B/A) is the 3rd failure probability, and P (B) is the second failure probability.
Further, total failure probability: F can be determined in such a waycrew=PA×P(B/A)×PB, wherein, FcrewFor total failure probability, PAIt it is the first failure probability.Wherein, the first failure probability can be determined by the said method that the embodiment of the present invention provides, and does not repeats them here.
With an example, the determination method of total failure probability of dissimilar staff is described below.
In Ling Dong nuclear power plant DCS under accident condition teams and groups by pacifying work, machine group leader/expeditor and one, secondary circuit operator is constituted.One, secondary circuit operator performs DOS program and related accidents processing routine.Machine group leader/expeditor monitors unit principal states parameter change, monitoring one, secondary circuit operator's SOP program or the execution of corresponding accident treatment program, its key criterion of individual authentication and crucial manipulation.Peace work independent check Power Plant state, it is judged that nature of occurence, evaluates the nuclear safety state of unit.
The independent agendum of expeditor, monitors unit principal states parameter change, and expeditor monitors one, secondary circuit operator, and its key criterion of individual authentication and crucial handle.Expeditor with one, secondary circuit operator there is dependency, owing to considering the effect of its " individual authentication " in organizational structure design, this method uses the most conservative strategy, uses medium relevant (MD:moderatedependence) to calculate.Computing formula is:
Peace work independent check Power Plant state, it is judged that nature of occurence, evaluates the nuclear safety state of unit, and for last line of defense in organization, this method uses the method recovering the factor to calculate.
Expeditor uses papery code, and peace work judges for power plant's state, is not required to perform manipulation.Do not consider that the performance of the said two devices that two class management roles cause declines.
Analyze according to above, teams and groups' model as shown in Figure 7.The total failure probability of operator teams and groups is: Fcrew=PA×P(B/A)×P(B)。
According to embodiments of the present invention, corresponding to said method, the determination device of a kind of digitized master control room staff's human factors analysis is additionally provided.
Fig. 8 is the structured flowchart of the determination device of digitized master control room staff's human factors analysis according to embodiments of the present invention, and as shown in Figure 8, this device includes: first determines that module 10 and second determines module 20.Wherein, first determines module 10, for obtaining staff's failure probability to each stage that task responds, or determines the failure probability of dissimilar staff；Second determines module 20, determines that module 10 is connected with first, for determining total failure probability according to failure probability.
In embodiments of the present invention, said apparatus may determine that total failure probability of a type of staff, it is also possible to determines total failure probability of the teams and groups that different types of staff forms.Separately below above-mentioned two aspect is described.
One, total failure probability of a type of staff
In an embodiment of the embodiment of the present invention, second determines module 20, and the failure probability for each stage uses two event trees to carry out integrated, and obtaining described total failure probability according to below equation is:
Ftotal=P(A)+a·P(B)+ab·P(C)+abc·P(D)
Wherein, P(A) it is the failure probability in supervision stage, a is the probability of success in supervision stage, P(B) it is the failure probability in state estimation stage, b is the probability of success in state estimation stage, P(C) for the failure probability of response programming phase, c is the probability of success of response programming phase, P(D) for responding the failure probability in execution stage.
Wherein, the supervision stage includes the transfer of staff's monitoring system state, the state estimation stage includes that staff assesses the state monitored, response programming phase includes that staff determines that the response policy being used the state monitored, response execution stage include that staff performs response policy.
Further, second determines module 20, is additionally operable to adjust total failure probability F according to equation belowtotal, obtain final total failure probability FT: FT=Ftotal/ (1-T), wherein, T is the two class management roles factors of influence to human factors analysis, and T is more than or equal to 0 and less than 1.
Two, total failure probability of different types of staff
In an embodiment of the embodiment of the present invention, first determines that module 10 may include that the first acquiring unit, for obtaining first failure probability of first kind staff；Second acquisition unit, for obtaining second failure probability of Equations of The Second Kind staff；Determine unit, for using medium relevant MD to determine the 3rd failure probability of the 3rd class staff according to the second failure probability.
Second determines module 20, for determining total failure probability according to the first failure probability, the second failure probability and the 3rd failure probability.
Wherein, first kind staff performs accident treatment, and in nuclear power plant, first kind staff can be first and second loop operator；Equations of The Second Kind staff monitors the change of unit state parameter, monitors the implementation status of described first kind staff, and implementation status described in individual authentication, and in nuclear power plant, Equations of The Second Kind staff can be machine group leader/expeditor；3rd class staff's independent check set state, judge nature of occurence, evaluate unit and safe condition, in nuclear power plant the 3rd class staff can be peace work.
In an embodiment of the embodiment of the present invention, determine unit, be used for determining that the 3rd failure probability is
Wherein, P (B/A) is the 3rd failure probability, and P (B) is the second failure probability.
Further, described second determines module, is used for determining that described total failure probability is: Fcrew=PA×P(B/A)×PB, wherein, FcrewFor described total failure probability, PAFor described first failure probability.
In actual applications, can 4 known models of event tree quantitative evalution model that the Markov model of monitoring activity, the Bayesian network model of state estimation, the Bayesian network model of response plan and response perform be given integrated, Fig. 9 gives the structural framing schematic diagram of embodiment of the present invention system, the contents such as the main models of this inventive embodiments, data is connected.
From above description, can be seen that, the above embodiments of the present invention achieve following technique effect: computing technique being efficiently introduced in the human reliability's quantitative analysis under the background of digitized master control room, the quantitative analysis for the master-control room of nuclear power plant personnel after digitized and class's reliability provides a kind of new effective system.
The foregoing is 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.All within the spirit and principles in the present invention, any modification, equivalent substitution and improvement etc. made, should be included within the scope of the present invention.
Claims (12)
1. the determination method of digitized master control room staff's human factors analysis, it is characterised in that including:
Determine staff's failure probability to each stage that task responds, or determine the failure probability of dissimilar staff；
Total failure probability is determined according to described failure probability；
Described staff includes: first kind staff, Equations of The Second Kind staff and the 3rd class staff, wherein, described first kind staff performs accident treatment, described Equations of The Second Kind staff monitors the change of unit state parameter, monitors the implementation status of described first kind staff, and implementation status described in individual authentication, described 3rd class staff's independent check set state, judge nature of occurence, evaluate unit safe condition；
Each stage described includes: monitor stage, state estimation stage, response programming phase, response execution stage, wherein, the described supervision stage includes the transfer of described staff's monitoring system state, the described state estimation stage includes that described staff assesses the state monitored, described response programming phase includes that described staff determines that the response policy being used the state monitored, described response execution stage include that described staff performs described response policy；
Described method may determine that total failure probability of a type of staff, it is also possible to determines total failure probability of the teams and groups that different types of staff forms.
Method the most according to claim 1, it is characterised in that described total failure probability is total failure probability in each stage described of each described first kind staff；Determine total failure probability in each stage described of described first kind staff in such a way:
Using two event trees to carry out integrated the failure probability in each stage described, obtaining total failure probability in each stage described in described first kind staff according to below equation is: Ftotal=P (A)+a P (B)+ab P (C)+abc P (D)；
Wherein, P (A) is the failure probability in supervision stage, a is the probability of success in supervision stage, P (B) is the failure probability in state estimation stage, b is the probability of success in state estimation stage, P (C) is the failure probability of response programming phase, and c is the probability of success of response programming phase, and P (D) is the failure probability in response execution stage.
Method the most according to claim 2, it is characterised in that determine total failure probability in each stage described in described first kind staff according to described failure probability, also include:
Total failure probability F in each stage described in described first kind staff is adjusted according to equation belowtotal, obtain final total failure probability F of described first kind staffT: FT=Ftotal/ (1-T), wherein, T is the two class management roles factors of influence to human factors analysis, and T is more than or equal to 0 and less than 1.
Method the most according to claim 1, it is characterised in that described total failure probability is total failure probability of the teams and groups that described first kind staff, described Equations of The Second Kind staff and described 3rd class staff are constituted；
Determine that the failure probability of dissimilar staff includes: obtain first failure probability of described first kind staff；Obtain second failure probability of described Equations of The Second Kind staff；Medium relevant MD is used to determine the 3rd failure probability of described 3rd class staff according to described second failure probability；
Determine that according to described failure probability total failure probability of described teams and groups includes: determine total failure probability of described teams and groups according to described first failure probability, described second failure probability and the 3rd failure probability.
Method the most according to claim 4, it is characterised in that determine described 3rd failure probability in such a way:
Wherein, P (B/A) is described 3rd failure probability, and P (B) is described second failure probability.
Method the most according to claim 5, it is characterised in that determine total failure probability of described teams and groups in such a way:
Fcrew=PA×P(B/A)×PB, wherein, FcrewFor total failure probability of described teams and groups, PAFor described first failure probability.
7. the determination device of digitized master control room staff's human factors analysis, it is characterised in that including:
First determines module, for obtaining staff's failure probability to each stage that task responds, or determines the failure probability of dissimilar staff；Wherein, described staff includes: first kind staff, Equations of The Second Kind staff and the 3rd class staff, wherein, described first kind staff performs accident treatment, described Equations of The Second Kind staff monitors the change of unit state parameter, monitors the implementation status of described first kind staff, and implementation status described in individual authentication, described 3rd class staff's independent check set state, judge nature of occurence, evaluate unit safe condition；
Each stage described includes: monitor stage, state estimation stage, response programming phase, response execution stage, wherein, the described supervision stage includes the transfer of described staff's monitoring system state, the described state estimation stage includes that described staff assesses the state monitored, described response programming phase includes that described staff determines that the response policy being used the state monitored, described response execution stage include that described staff performs described response policy；
Second determines module, for determining total failure probability according to described failure probability；Described second determines that module may determine that total failure probability of a type of staff, it is also possible to determine total failure probability of the teams and groups that different types of staff forms.
Device the most according to claim 7, it is characterized in that, described second determines module, and for using two event trees to carry out integrated the failure probability in each stage described, obtaining total failure probability in each stage described in described staff according to below equation is: Ftotal=P (A)+a P (B)+ab P (C)+abc P (D), wherein,
P (A) is the failure probability in supervision stage, a is the probability of success in supervision stage, P (B) is the failure probability in state estimation stage, b is the probability of success in state estimation stage, P (C) is the failure probability of response programming phase, c is the probability of success of response programming phase, and P (D) is the failure probability in response execution stage；
Wherein, the described supervision stage includes the transfer of described staff's monitoring system state, the described state estimation stage includes that described staff assesses the state monitored, described response programming phase includes that described staff determines that the response policy being used the state monitored, described response execution stage include that described staff performs described response policy.
Device the most according to claim 8, it is characterised in that described second determines module, is additionally operable to adjust total failure probability F in each stage described in described staff according to equation belowtotal, obtain final total failure probability F of described staffT: FT=Ftotal/ (1-T), wherein, T is the two class management roles factors of influence to human factors analysis, and T is more than or equal to 0 and less than 1.
Device the most according to claim 7, it is characterised in that
Described first determines that module includes: the first acquiring unit, for obtaining first failure probability of first kind staff；Second acquisition unit, for obtaining second failure probability of Equations of The Second Kind staff；Determine unit, for using medium relevant MD to determine the 3rd failure probability of the 3rd class staff according to described second failure probability；
Described second determines module, for determining total failure probability of the teams and groups being made up of described first kind staff, described Equations of The Second Kind staff and described 3rd class staff according to described first failure probability, described second failure probability and the 3rd failure probability；
Wherein, described first kind staff performs accident treatment, described Equations of The Second Kind staff monitors the change of unit state parameter, monitors the implementation status of described first kind staff, and implementation status described in individual authentication, described 3rd class staff's independent check set state, judge nature of occurence, evaluate unit and safe condition.
11. devices according to claim 10, it is characterised in that described determine unit, are used for determining that described 3rd failure probability isWherein, P (B/A) is described 3rd failure probability, and P (B) is described second failure probability.
12. devices according to claim 11, it is characterised in that described second determines module, for determining that total failure probability of described teams and groups is: Fcrew=PA×P(B/A)×PB, wherein, FcrewFor described total failure probability, PAFor described first failure probability.
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| CN104915116A (en) * | 2015-06-15 | 2015-09-16 | 湖南工学院 | Human error probability calculating method and human error probability calculating device |
| CN104965652A (en) * | 2015-06-15 | 2015-10-07 | 湖南工学院 | Human error probability calculation method and device |
| CN104965978A (en) * | 2015-06-15 | 2015-10-07 | 湖南工学院 | Diagnosis failure probability calculation method and device |
| CN106950850A (en) * | 2017-02-20 | 2017-07-14 | 上海核工程研究设计院 | One kind digitlization instrument control System Dynamic Reliability integrated analysis method |
| CN109284925A (en) * | 2018-09-21 | 2019-01-29 | 南华大学 | A kind of measurement method, device, equipment and the storage medium of teams and groups' context-aware |
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