CN103235882B - Nuclear power plant's digitizing master-control room operator monitor behavior reliability decision method - Google Patents
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Abstract
The invention discloses a kind of nuclear power plant digitizing master-control room operator monitor behavior reliability decision method, comprise step: according to the monitoring process of operator in monitor task to digitizing master-control room, each monitor message source is divided into multiple supervision node; Determine the time window of the monitoring activity to multiple supervision node respectively; According to the time window of monitoring activity, determination operation person is to the transfer sequence of the monitoring activity of multiple supervision node; Calculate the supervision probability of success of multiple supervision node; According to the supervision probability of success, judge that whether the monitoring activity of nuclear power plant digitizing master-control room operator is reliable.The present invention is by monitoring to each in monitor task the division doubly ceasing source, thus construct monitoring activity and monitor the mathematical model of transfer, and to monitoring that the supervision probability of success of band point carries out quantum chemical method, finally can judge the monitoring activity reliability of nuclear power plant digitizing master-control room operator.
Description
Technical Field
The invention relates to the field of digital control of power plants, in particular to a method for judging the reliability of monitoring behaviors of operators in a digital main control room of a nuclear power plant.
Background
NUREG/RC (regulatory guidelines, technical documentation of the american nuclear regulatory commission)) series report that when the cognitive behavior of operators was studied since the beginning of the 80 th 20 th century, a series of experimental observations and studies were performed on the behavior of operators acquiring information, and the monitoring behavior of nuclear power plant operators was defined, i.e., the cognitive behavior of acquiring plant information from the environment of the nuclear power plant main control room. I.e., the operator observes the plant status to determine the operating status of the plant, including checking whether the system is operating properly and identifying and confirming certain changed conditions, such as observing the system operating parameters displayed by the display device, plant status parameters such as charts and alarms, communicating between operators, obtaining verbal reports from other operators in other areas of the plant, dispatching personnel to other areas of the plant to check equipment, etc. In a digital nuclear power plant, cognitive behaviors of plant-related information (such as state parameters, equipment states, system states, operation trends and the like) acquired by an operator from a DCS environment are monitored, a main control room of the nuclear power plant mainly displays target information to be monitored through a computer display unit (VDU), and the operator drives and guides the monitoring behaviors of the operator under a plant state model (normal state) and an SOP (health operation procedure) state (abnormal state).
The research on the operation and monitoring behaviors of the nuclear power plant originates from the 70 th 20 th century, and is based on the research on the monitoring behaviors of operators of the traditional nuclear power plant, and gradually develops to the research on the monitoring behaviors of semi-digitalized (namely digitalized modification of a control I & C system of a traditional nuclear power plant instrument) systems, and generally focuses on monitoring objects, monitoring failure modes, influencing factors, comparison (simulation and semi-digitalization) of the monitoring behaviors and the like of the operators.
The domestic research on visual behaviors mainly focuses on the fields of reading, information processing mechanisms, computer artificial intelligence and the like, and the research on the monitoring behaviors of operators in the nuclear power plant mainly focuses on the experience summary of effective modes of acquiring state information of the power plant by the operators, and mature theories and technologies are not formed.
The overseas research on monitoring behaviors of operators in a main control room mainly focuses on the research and experimental observation of basic phenomena (such as monitoring range, monitoring objects, monitoring task allocation and influence factors and the like) and characteristics of monitoring activities of the operators (mainly traditional nuclear power plants and semi-digital nuclear power plants), and only a few documents relate to cognitive modes and monitoring strategies for monitoring of the operators. The domestic research on the monitoring behaviors mainly focuses on the visual information processing mode in the fields of reading and artificial intelligence at present, and all the research also stays in qualitative description and summarization of the characteristics and rules of the monitoring behaviors. Moreover, none of the current studies on monitoring behaviors of operators in a master control room is subject to the digital nuclear power plant in operation, and does not have the essential background and characteristics of digitization.
Therefore, the monitoring research at home and abroad does not deeply research the monitoring behavior forming mechanism and the dynamics mechanism, a monitoring behavior reliability quantification model is not provided, the monitoring behavior research of operators in a digital master control room of the nuclear power plant is blank, the principle of the monitoring behavior of the operators and the process dynamics mechanism under the digital background are not established, and a monitoring reliability analysis program, a mathematical model and analysis software and a corresponding support database system are not established, so that the engineering application of the human factor reliability analysis of the nuclear power plant under the digital background is impossible at present, and the safety risk assessment of the nuclear power plant under the digital technical background is seriously influenced.
In addition, at present, the monitoring behavior research at home and abroad takes a traditional simulated nuclear power plant or semi-digital nuclear power as a research object, a complete digital main control room is not taken as an effective research object, related research means, technologies and conclusions have great limitations, and the related research conclusions and models lack pertinence. Meanwhile, the existing monitoring behavior research mainly focuses on experimental description and summarization of monitoring behavior characteristics, influencing factors, processes, rules and the like of operators in a traditional nuclear power plant master control room, does not relate to the research on the intrinsic rules such as dynamics mechanisms and principles of monitoring behaviors of operators, is difficult to deeply and objectively depict and describe the monitoring behaviors, is more difficult to find the intrinsic movement rules of the monitoring behaviors, and cannot be widely popularized and applied in a project; in addition, the existing monitoring behaviors are mainly qualitative description, corresponding monitoring reliability analysis programs and mathematical models are not established, and the foundation of engineering application is not provided. In addition, the existing monitoring behavior research does not establish a monitoring reliability calculation method, so that the dynamic, complex and multi-process monitoring behavior reliability engineering quantitative analysis lacks means, and a monitoring basic reliability data system is not established, so that the quantitative analysis lacks basic human error data.
Disclosure of Invention
The invention aims to provide a method for judging the reliability of the monitoring behavior of the operator in the digital master control room of the nuclear power plant, which can quantitatively analyze and judge the reliability of the monitoring behavior of the operator in the digital master control room of the nuclear power plant, and solves the technical problems that the principle and the process dynamics mechanism of the monitoring behavior of the operator in the digital background are not established, and a monitoring reliability analysis program, a mathematical model and a corresponding support database system are not established, so that the human factor reliability of the nuclear power plant in the digital background cannot be analyzed.
In order to achieve the purpose, the invention provides a method for judging the reliability of monitoring behaviors of operators in a digital main control room of a nuclear power plant, which comprises the following steps:
step S1: dividing each monitoring information source into a plurality of monitoring nodes according to the monitoring process of an operator to the digital main control room in the monitoring task;
step S2: determining time windows of monitoring behavior for the plurality of monitoring nodes, respectively;
step S3: according to the time window of the monitoring behaviors, determining the transfer sequence of the monitoring behaviors of the monitoring nodes by the operator;
step S4: calculating monitoring success probabilities of the plurality of monitoring nodes;
step S5: and judging whether the monitoring behavior of the operator of the digital main control room of the nuclear power plant is reliable or not according to the monitoring success probability.
As a further improvement of the invention:
the step S4 includes the steps of:
step S401: computing perceived success probability for operator node i
Step S402: calculating the success probability of the operator monitoring transition when the operator transitions from the monitoring behavior of the node i-1 to the monitoring behavior of the node i
Step S403: computing a monitoring success probability for node iIs calculated by the formula
Step S404: when the number of the monitoring nodes is calculated to be n, the monitoring success probability of the plurality of monitoring nodesIs calculated by the formulaWherein i is 1,2, …, n.
The step S402 includes the steps of:
when the monitoring behavior of the operator is isomorphic transition from the node i-1 to the node i, performing step S4021 and step S4022; when the monitoring behavior of the operator is transferred from the node i-1 to the node i to be heterogeneous, the steps S4023 and S4024 are performed;
step S4021: the probability of operator monitoring behavior isomorphic transition failure is calculated using the following formula:
wherein G isbIs a system state, HaHuman factor state, WcFor an alarm state, RzFor the operating protocol state and MqManaging task states for two types;
Gb(t) is the b-th system state, and at time t, b is (0, 1);
Ha(t) is the a-th state that the person is in, and at time t, a is (0, 1);
Wc(t) is the state of the c-th alarm, and at time t, c is (0, 1);
Rz(t) is the state of the z-th state operating protocol, and at time t, z is (0, 1);
Mq(t) is the status of the qth class ii management task, and at time t, q is (0, 1);
TRe fuin the e-th target unit, the operator monitoring activities are transferred from the area information f to the area information u in order to manage the task state q in a human factor state a, a system state b, a warning state c, an operation procedure state z and a second type;
Te urepresents the u-th part in the e-th functional block;
Te frepresents the f-th part of the e-th functional block;
for the operator at Ha、Gb、Wc、Rz、MqIn the state, monitoring the error rate of the f-th information transfer to the u-th information of the e module;
is at Ha、Gb、Wc、Rz、MqUnder the state, monitoring the u-th information transfer error rate of the e module;
p{Ha(t)|Ha(t-1) indicating a human factor system status error rate;
p{Gb(t)|Gb(t-1) is the fault rate of the state of the power plant system;
p{Wc(t)|Wc(t-1) } alarm status error rate;
p{Rz(t)|Rz(t-1) } is the operating protocol status error rate;
p{Mq(t)|Mq(t-1) is the status error rate of the second type management task;
step S4022: calculating the probability of isomorphic transition success of the monitoring behavior of the operator according to the formula
Step S4023: calculating the probability of operator monitoring behavior heterogeneous transfer failure by adopting the following formula:
wherein,
a second type management task state q, a comea component transfer process for transferring from the comea component of the aa block to the comeb component of the bb block at a time t, a system state b, a human factor state a, a warning state c, an operating procedure state z;
monitoring the comb component of the bb block in a system state b, a human factor state a, a warning state c, an operation procedure state z and a second type management task state q;
probability of failure to transfer from the comea-th component of the aa-th block to the comeb-th component of the bb-th block in a class ii management task state q at time t, system state b, human cause state a, warning state c, protocol state z;
Weightbb(t + Δ t) is at time (t + Δ t),monitoring the weight coefficient of a bb block in a system state b, a human factor state a, an alarm state c, an operation procedure state z and a second type management task state q;
monitoring the comb component failure probability of a bb block in a system state b, a human factor state a, a warning state c, an operation procedure state z and a second type management task state q;
step S4024: calculating the probability of isomorphic transition success of the monitoring behavior of the operator according to the formulaBefore performing the step S1, the method further includes the steps of:
step S0: and judging whether the operation state of the power plant is normal or abnormal.
When the determination result of the step S0 is normal, the division of each monitoring information source into a plurality of monitoring nodes in the step S1 is based on a monitoring task, relevant monitoring parameters, an operation procedure, and experience of an operator in monitoring the task;
when the determination result of the step S0 is abnormal, the division of each monitoring information source into a plurality of monitoring nodes is divided according to the operating rules of the state of the analysis event or accident in the step S1.
The invention has the following beneficial effects:
according to the method for judging the reliability of the monitoring behavior of the operator in the digital main control room of the nuclear power plant, the mathematical models of the monitoring behavior and the monitoring transfer are constructed by dividing each monitoring information source in the monitoring task, the monitoring success probability of the monitoring node can be quantitatively calculated, and the reliability of the monitoring behavior of the operator in the digital main control room of the nuclear power plant can be finally judged. The invention can also be popularized and applied to the field of monitoring activities and behavior reliability analysis of other operators in a similar digital industrial system master control room, and lays a foundation for monitoring failure prevention and control of the digital industrial system operators.
In addition to the objects, features and advantages described above, other objects, features and advantages of the present invention are also provided. The present invention will be described in further detail below with reference to the drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the invention and, together with the description, serve to explain the invention and not to limit the invention. In the drawings:
FIG. 1 is a schematic diagram of a nuclear power plant digital main control room operator monitoring behavior process according to a preferred embodiment of the present invention;
FIG. 2 is a schematic diagram of the logic process of the quantitative analysis of the monitoring behavior of the DCS main control room operator according to the preferred embodiment of the invention;
FIG. 3 is a schematic diagram of the transfer sequence of monitoring nodes of the monitoring process of the preferred embodiment of the present invention;
FIG. 4 is a diagram of a conditional hidden Markov model in accordance with a preferred embodiment of the present invention; and
FIG. 5 is a flow chart of a method for determining reliability of monitoring behavior of a nuclear power plant digital control room operator according to a preferred embodiment of the present invention;
fig. 6 is a flowchart illustrating a method for determining reliability of monitoring behavior of a digital main control room operator in a nuclear power plant according to a preferred embodiment 1 of the present invention.
Detailed Description
The embodiments of the invention will be described in detail below with reference to the drawings, but the invention can be implemented in many different ways as defined and covered by the claims.
Monitoring behavior is a source of information for plant operators as part of the operator's cognitive activities. Based on the characteristics and rules of monitoring behaviors of operators under the DCS, monitoring activities are cognitively divided into two stages based on the state of a power plant, namely, monitoring targets (information sources, namely monitoring units or information areas of a DCS main control room) for locking (unique monitoring identification objects at the current moment)i) Monitoring behavior (monitoring awareness), which is typically a static cognitive activity, including monitoring task confirmation (initiating event or initial activity), obtaining monitoring information (visual or auditory physical information, such as plant system status parameters, alarms, etc.), monitoring information identificationThe method comprises the steps of (including monitoring information selection, identification and evaluation), monitoring strategy selection (including making, monitoring strategy selection and monitoring path selection) and monitoring output (including information perception, monitoring cognitive result output, and result information transmission to a state evaluation cognitive link in a short-time memory form to trigger the state evaluation cognitive behavior of an operator); the second phase is monitoring transfer, i.e. the monitoring transfer between targets (information sources) by operators to complete the dynamic process of information search or monitoring path transfer (see fig. 1 and 2). Based on the current state and requirements of the power plant, the operator repeatedly circulates cognitive activities in the two stages in the monitoring process so as to realize continuous and dynamic monitoring and control of the power plant and provide parameters and information for state evaluation of the power plant.
As shown in fig. 1 and 2, the information sources displayed on the display device of the master control room of the nuclear power plant are: objecti (information source). Lock awareness of the ith information Object (for example, the second information source Object2) in the information sources Object (Object1, Object2 … Object n represent the 1 st information source, the 2 nd information source, and the … … nth information source) displayed on the display device of the nuclear power plant main control room by an operator, namely, a process of acquiring the lock information by the operator is recorded as monitoring awareness. The operator moves from the current information Object1 displayed on the nuclear plant main control room display device to the process of the next target information source Object2, i.e. the operator monitors the moving process. The monitoring process of the nuclear power plant master control room operator based on task-oriented power plant state information acquisition is a process that the operator repeatedly and repeatedly monitors, perceives and monitors and transfers among all monitoring information objects under the environment of the master control room until the monitoring task is completed and the monitoring activity is finished.
Monitoring behavior quantitative analysis principle:
monitoring behaviors (including normal operation states and abnormalities of a power plant) of an operator under a non-single (independent) task are dynamic and continuous, for a given monitoring task (such as monitoring a certain power plant event or accident, monitoring a certain specific system of the power plant and the like), based on the attached figure 2, the monitoring behaviors of the operator can be classified into corresponding monitoring points (called monitoring nodes or transfer nodes, and marked as (i, i ═ 1,2, …, n) (see the attached figure 3) according to the inherent sequential logic sequence (such as the presentation time of key information, the structural sequence, or the sequence of operation nodes specified by a regulation) of the monitoring activities of the monitoring process according to the monitoring targets in the process (specific information sources or targets in the monitoring task, such as the instant (Ti time) monitoring of a certain state/equipment temperature parameter in the operation of the power plant), and based on the following assumptions and logic rules:
(1) the monitoring activities are performed sequentially from left to right according to the logical relationship of the monitoring nodes as shown, and the operator cannot automatically skip the previous node to enter the next monitoring node during the monitoring process (i.e., the monitoring node condition is skipped without regard to the monitoring activities).
(2) The success or failure of the monitoring process, the awareness of the operator to each monitoring node in the graph, and the success or failure of the monitoring transfer between adjacent monitoring nodes, i.e., the failure of any one node to monitor results in a failure of the monitoring task.
(3) Repair factors that fail monitoring are not considered.
The monitoring activities of the operator are mainly affected by two types of influencing factors, namely the characteristics and the change of the environment, and in the DCS, the monitoring activities mainly refer to the display characteristics of information on a VDU and the change of displayed power plant system data/information (related to a human-computer interface of a digital control room); the second is knowledge and expectation of the operator, and a psychological plant state model which is inherently formed by the operator according to the understanding of the operator on the operation state of the nuclear plant. The former influencing factor results in a monitoring behavior referred to as a data-driven monitoring behavior, and the latter as a knowledge-driven or model-driven monitoring behavior. In industrial application, whether the monitoring action is successful or not is judged according to whether an operator makes a correct operation action of the next step according to data or information provided by the human-computer interface (the input information of the operator interacts with the digital human-computer interface), and if the operation action of the next step is correct, the monitoring step before the operation step is judged to be successful.
After an accident occurs in the DCS, main cognitive activities occur in the interaction process of an operator and a power plant interface in a monitoring stage. The power plant presents power plant information after an accident through the DCS, and an operator drives the sensing system to sense the power plant information through a human attention mechanism. The main cognitive load influencing the monitoring behavior of the operator comes from DCS information display perception change, work memory load change caused by VDU display and interface management tasks and change of attention mechanisms. The DCS main control room operator monitoring driving mechanism is a 'hybrid driving' that the data driving and the model driving work together, and the model driving is in the dominant position. Generally, when the power plant is in normal operation and transient state, the operator monitoring behavior is driven to be dominant by data, and when the power plant is in an event or accident operation state, the operator monitoring behavior is driven to be dominant by a model under the guidance of a state operation rule.
Isomorphic transfer: the heterogeneous transition is the transition between different digital screens.
Referring to fig. 5, the method for determining reliability of monitoring behaviors of operators in a digital main control room of a nuclear power plant comprises the following steps:
step S1: dividing each monitoring information source into N monitoring nodes, such as N, according to the monitoring process of operator to digital main control room in monitoring task1、N2…Nn(see FIG. 3). As can be seen in FIG. 3, the operator logically abstracts the monitoring node N based on some manipulation task1Starting to implement behaviors of monitoring awareness, monitoring transfer and the like until monitoring activities complete all monitoring logical nodes according to logical relations (rule guidance and the like) and reach the node N of which the monitoring is finishedn。
Step S2: the time windows of the monitoring activities of the plurality of monitoring nodes are respectively determined, namely, the starting point T0 and the end point TE of the monitoring activities are divided, and the time period (window) of the monitoring activities is determined. In the PSA-HRA (PSA: probabilistic safety analysis; HRA: humane reliability analysis), the monitoring start point may be set to the entering-state operation protocol time (note 0 time); in other analyses, the monitoring start point is determined appropriately on the basis of the actual situation.
Step S3: based on the time window of the monitoring behavior, the operator determines an order of transition of the monitoring behavior of the operator to the plurality of monitoring nodes (see fig. 3).
Step S4: and calculating the monitoring success probability of a plurality of monitoring nodes. In practice, the probability of perceived success of the operator node i is generally calculated by calculating the probability of perceived success of the operator node i respectively"operator monitor transition success probability with operator from node i-1 to node iAnd taking the product of the two probabilities to obtain the success probability of monitoring the operator of the node (i).
Step S5: and judging whether the monitoring behavior of the operator of the digital main control room of the nuclear power plant is reliable or not according to the monitoring success probability. In practical application, various international and domestic industry standards (different judging standards according to different application occasions and types of related human-computer interfaces and different judging standards according to practical application conditions or self-set standards of power plants) can be inquired according to the obtained monitoring success probability value, so that whether the monitoring success probability value is in an allowed range or not can be obtained (whether the monitoring success probability of an operator is reliable or not is determined according to items listed by the standards and the judging standards thereof) so as to judge whether the monitoring success probability value is reliable or not.
In the steps, by dividing each monitoring information source in the monitoring task, a mathematical model of monitoring behaviors and monitoring transfer is constructed, the monitoring success probability of the monitoring nodes can be quantitatively calculated, and finally the reliability of the monitoring behaviors of the operators in the digital main control room of the nuclear power plant can be judged. The invention can also be popularized and applied to the field of monitoring activities and behavior reliability analysis of other operators in a similar digital industrial system master control room, and lays a foundation for monitoring failure prevention and control of the digital industrial system operators.
Example 1:
referring to fig. 6, the method for determining the reliability of the monitoring transfer of the operator in the digital main control room of the nuclear power plant of the embodiment includes the following steps:
step S0: and judging the operation state of the power plant, namely determining the current operation state of the power plant before starting to determine a monitoring task and carrying out monitoring analysis, wherein only two types of normal and abnormal are adopted.
Step S1: according to the monitoring process of operator to digital main control room in monitoring task, dividing each monitoring information source into N monitoring nodes, such as N1、N2…Nn. In the present embodiment, when the determination result of step S0 is normal, the division of each monitoring information source into a plurality of monitoring nodes is performed based on the monitoring task, the relevant monitoring parameters, the operation procedure, and the experience of the operator in monitoring the aforementioned tasks; when the judgment result of step S0 is abnormal, the division of each monitoring information source into a plurality of monitoring nodes for the HRA monitoring task under the PSA framework is made according to the status operation rule of the analysis event or accident. In addition, the node division can be realized based on a knowledge characterization method of an operation flow and an event processing procedure.
Step S2: the time windows of the monitoring activities of the plurality of monitoring nodes are respectively determined, namely, the starting point T0 and the end point TE of the monitoring activities are divided, and the time period (window) of the monitoring activities is determined. In the PSA-HRA analysis, the monitoring start point may be set to the entering state operating protocol time (time 0); in other analyses, the monitoring start point is determined appropriately on the basis of the actual situation.
Step S3: the operator determines an order of transition of the monitoring activities of the operator to the plurality of monitoring nodes based on the time window of the monitoring activities.
Step S4: computing monitoring success for multiple monitoring nodesProbability. In practice, the probability of perceived success of the operator node i is generally calculated by calculating the probability of perceived success of the operator node i respectively"operator monitor transition success probability with operator from node i-1 to node iAnd taking the product of the two probabilities to obtain the success probability of monitoring the operator of the node (i). Probability of failure of the whole monitoring process based on Boolean algebraic logic operation ruleThe sum of the failure probabilities of all the monitoring nodes is obtained; the failure probability of each node can be monitored by monitoring the success probability of the nodeThe success probability of each node is determined by the success transition probability of two types of monitoring behaviors directly related to the node (namely, the perceived cognitive behavior of the node and the monitoring transition behavior of the node successfully transferred to the node from the last node), which are respectively marked asAnd
in this embodiment, step S4 is preferably implemented by the following steps:
step S401: computing perceived success probability for operator node i
The monitoring cognitive activity of the operator on the node i can be regarded as the monitoring cognitive activity of the operator on a fixed information resource, that is, the perception of the operator on information, and the failure probability of the monitoring cognitive activity can be obtained by two methods:
(1) firstly, based on classical probability value of monitoring fault of traditional nuclear power station, combining with digital information display characteristics, adopting extrapolation method to obtain interval of probability value of monitoring and perceiving failure, then obtaining perceiving success probability by supplementing
(2) Secondly, obtaining the average value and the range of the information perception error probability of the DCS operator based on a Signal Detection Theory (SDT) theory and combining the experimental statistical result of the analog machine, and then determining the perception success probability of the node i according to the corresponding node information characteristics and the influence factors
In the case where both signal and noise are in discrete states and are not readily distinguishable, signal detection theory may be applied. The signal must be detected by an operator, and two reaction modes of 'presence' (i detect the signal) and 'absence' (i do not detect the signal) can occur in the process of detection. The operator's reactions in the experiment were divided into four as shown in table 1: hit, false positive, false negative, and correct rejection.
TABLE 1 four reactions of the observer in the signal theory of detection experiment
In the signal detection theory, the values of the four types of events can be represented by probability values. The value of each type of event is equal to the number of occurrences of the event divided by the total number of occurrences of each column of events in the graph.
P(hit)+P(miss)＝1
P(fa)+P(cr)＝1
Based on signal detection theory (SDT, Signaldetect i)onteory) to compute a perceived success probability for operator node i
In the process of monitoring, the operator can accurately obtain the monitored object information from a large amount of background information (power plant state parameters, graphs, tables and the like), and if the process is processed as follows:
(1) assuming that the operator accurately obtains the node information is deemed to be perceiving successful, failure to obtain the node information correctly is deemed to be perceiving failure.
(2) Considering the background information of the node as noise in the SDT, the information that needs to be perceived by the operator during the monitoring process is what can be considered as an operator stimulus.
(3) The failure probability of the corresponding operator to perceive the node information is the probability of missing report of the operator in the SDT model, and the perception success probability of the operator to perceive the node iIs the probability P of an operator hit in the SDT model(hit)。
And the probability P of operator missing report(hit)Is based on the statistical probability value (P) of the response experiment of the operator failing to report stimulus (monitoring node information) in the noise background(miss)) Then the operator perceives the perceived success probability of node i
Step S402: calculating the success probability of the operator monitoring transition when the operator transitions from the monitoring behavior of the node i-1 to the monitoring behavior of the node iIn this embodiment, step S402 includes the following steps:
by influencing factors affecting operator monitoring behaviour, e.g. system state (G)b) Human factor state (H)a) Alarm state (W)c) Operating protocol status (R)z) And class II management tasks (M)q) States, etc., a conditional hidden markov model can be constructed, see fig. 4. In FIG. 4, HaIndicating the state of the human factor, GbRepresents the system state, wcIndicating an alarm state, RzIndicating the status of the operating protocol, MqIndicating a two-class management task state. The state values of the five influencing factors and the basic error rate and the weight of each main constituent factor of the five influencing factors can be obtained by monitoring eye movement experiments.
When the monitoring behavior of the operator is isomorphic transition from the node i-1 to the node i, the step S4021 and the step S4022 are performed, and a isomorphic Markov transition failure model of the DCS nuclear power plant under the condition of monitoring is established (namely, the probability of isomorphic transition failure of the monitoring behavior of the operator is calculated).
Step S4021: the probability of operator monitoring behavior isomorphic transition failure is calculated using the following formula:
wherein G isbIs a system state, HaHuman factor state, WcFor an alarm state, RzFor the operating protocol state and MqManaging task states for two types;
Gb(t) is the b-th system state, and at time t, b is (0, 1);
Ha(t) is the state that the a-th person is in, and at time t, a is (0, 1);
Wc(t) is the state of the c-th alarm,and at time t, c ═ 0, 1;
Rz(t) is the state of the z-th state operating protocol, and at time t, z is (0, 1);
Mq(t) is the status of the qth class ii management task, and at time t, q is (0, 1); where 1 corresponds to normal and 0 corresponds to abnormal.
TRe fuIn the e-th target unit, the operator monitoring activities are transferred from the area information f to the area information u in order to manage the task state q in a human factor state a, a system state b, a warning state c, an operation procedure state z and a second type;
Te urepresents the u-th part in the e-th functional block;
Te frepresents the f-th part of the e-th functional block;
for the operator at Ha、Gb、Wc、Rz、MqIn the state, monitoring the error rate of the f-th information transfer to the u-th information of the e module;
is at Ha、Gb、Wc、Rz、MqUnder the state, monitoring the u-th information transfer error rate of the e module;
p{Ha(t)|Ha(t-1) indicating a human factor system status error rate;
p{Gb(t)|Gb(t-1) is the fault rate of the state of the power plant system;
p{Wc(t)|Wc(t-1) } alarm status error rate;
p{Rz(t)|Rz(t-1) } is the operating protocol status error rate;
p{Mq(t)|Mq(t-1) is the status error rate of the second type management task;
step S4022: calculating the probability of isomorphic transition success of the monitoring behavior of the operator according to the formula
When the monitoring behavior of the operator is transferred from the node i-1 to the node i to be heterogeneous, the steps S4023 and S4024 are performed, and a heterogeneous Markov transfer failure model of the DCS nuclear power plant under the condition monitoring process is established (i.e., the probability of heterogeneous transfer failure of the monitoring behavior of the operator is calculated).
Step S4023: calculating the probability of operator monitoring behavior heterogeneous transfer failure by adopting the following formula:
wherein,
a second type management task state q, a comea component transfer process for transferring from the comea component of the aa block to the comeb component of the bb block at a time t, a system state b, a human factor state a, a warning state c, an operating procedure state z;
to be in a system state b, a human factor statea, a warning state c, an operation procedure state z, and a second type management task state q, monitoring a comb component of a bb block;
probability of failure to transfer from the comea-th component of the aa-th block to the comeb-th component of the bb-th block in a class ii management task state q at time t, system state b, human cause state a, warning state c, protocol state z;
Weightbb(t + Δ t) is at time (t + Δ t),monitoring the weight coefficient of a bb block in a system state b, a human factor state a, an alarm state c, an operation procedure state z and a second type management task state q;
monitoring the comb component failure probability of a bb block in a system state b, a human factor state a, a warning state c, an operation procedure state z and a second type management task state q;
step S4024: calculating the probability of isomorphic transition success of the monitoring behavior of the operator according to the formula
Another implementation of step S4:
when the monitoring task of step S1 is a single (independent) task, the operator monitoring failure quantitative analysis is as follows:
operator single task refers to the operator performing a relatively independent special handling task, such as valuing, testing or maintenance. The monitoring behavior of the operator is mainly to discover (perceive) abnormal information in the manipulation task. Assuming that the operator finds abnormal information in the task manipulationIf the monitoring is successful and if no abnormal information is found, the monitoring is failed, and then the signal inspection theory (SDT) method can be used to refer to the "perceived success probability of the operator node i"computing principles and methods to compute single (independent) task operator monitoring failure probability
Step S403: computing a monitoring success probability for node iIs calculated by the formula
Step S404: when the number of the monitoring nodes is n, the monitoring success probability of a plurality of monitoring nodes is calculatedIs calculated by the formulaWherein i is 1,2, …, n.
Step S5: and judging whether the monitoring behavior of the operator of the digital main control room of the nuclear power plant is reliable or not according to the monitoring success probability.
In summary, the present invention has the following advantages:
(1) the invention establishes the monitoring behavior process and the dynamics mechanism of the operator in the digital main control room of the nuclear power plant, and provides strategies and means for formulating the monitoring behavior specification of the operator, improving the monitoring performance and preventing the monitoring errors.
(2) The invention establishes a mathematical model for quantitative analysis of the operation and monitoring behaviors, provides a mathematical model for quantitative analysis and judgment of the operation task monitoring reliability executed by an operator, and provides an effective algorithm and a rule for quantitative analysis engineering of the engineering monitoring reliability.
(3) The invention can provide a means for monitoring reliability engineering application and provide support for human factor reliability analysis of operators in a digital main control room of a nuclear power plant based on the mathematical model for monitoring reliability analysis.
(4) The method can be popularized and applied to the field of monitoring activities and behavior reliability analysis of operators in a similar digital industrial system master control room in other fields, and lays a foundation for monitoring failure prevention and control of the operators of the digital industrial system.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (3)
1. A method for judging reliability of monitoring behaviors of operators in a digital main control room of a nuclear power plant is characterized by comprising the following steps:
step S1: dividing each monitoring information source into a plurality of monitoring nodes according to the monitoring process of an operator to the digital main control room in the monitoring task;
step S2: determining time windows of monitoring behavior for the plurality of monitoring nodes, respectively;
step S3: determining a transfer sequence of the monitoring behaviors of the plurality of monitoring nodes by the operator according to the time window of the monitoring behaviors;
step S4: calculating monitoring success probabilities of the plurality of monitoring nodes;
step S5: judging whether the monitoring behavior of the operator of the digital main control room of the nuclear power plant is reliable or not according to the monitoring success probability;
the step S4 includes the steps of:
step S401: computing perceived success probability for operator monitoring node i
Step S402: calculating the successful probability of the operator monitoring transition when the operator transfers from the monitoring behavior of the monitoring node i-1 to the monitoring behavior of the monitoring node i
Step S403: calculating the monitoring success probability of the monitoring node iIs calculated by the formula
Step S404: when the number of the monitoring nodes is calculated to be n, the monitoring success probability of the plurality of monitoring nodesIs calculated by the formula Wherein i is 1,2, …, n.
2. The method according to claim 1, wherein the step S402 comprises the steps of:
when the monitoring behavior of the operator is isomorphic transition from the monitoring node i-1 to the monitoring node i, performing step S4021 and step S4022; when the monitoring behavior of the operator is transferred from the monitoring node i-1 to the monitoring node i to be heterogeneous, performing step S4023 and step S4024; the isomorphic transfer refers to the transfer between human-computer interfaces of the same screen, and the heterogeneous transfer refers to the transfer between different digital screens;
step S4021: the probability of operator monitoring behavior isomorphic transition failure is calculated using the following formula:
wherein G isbIs a system state, HaHuman factor state, WcFor an alarm state, RzFor the operating protocol state, MqManaging task states for two types;
Gb(t) is the b-th system state, and at time t, b is (0, 1);
Ha(t) is the a-th state that the person is in, and at time t, a is (0, 1);
Wc(t) is the state of the c-th alarm, and at time t, c is (0, 1);
Rz(t) is the state of the z-th state operating protocol, and at time t, z is (0, 1);
Mq(t) is the status of the qth class ii management task, and at time t, q is (0, 1); wherein 1 corresponds to normal, and 0 corresponds to abnormal;
TRe fuis in a human factor state a, a system state b, an alarmInforming a state c, an operation procedure state z and a task management state q in a second type, wherein in the e-th target unit, the monitoring activity of an operator is transferred from the area information f to the area information u;
Te urepresents the u-th part in the e-th functional block;
Te frepresents the f-th part of the e-th functional block;
for the operator at Ha、Gb、Wc、Rz、MqIn the state, monitoring the error rate of the f-th information transfer to the u-th information of the e module;
is at Ha、Gb、Wc、Rz、MqUnder the state, monitoring the u-th information transfer error rate of the e module;
p{Ha(t)|Ha(t-1) indicating a human factor system status error rate;
p{Gb(t)|Gb(t-1) is the fault rate of the state of the power plant system;
p{Wc(t)|Wc(t-1) } alarm status error rate;
p{Rz(t)|Rz(t-1) } is the operating protocol status error rate;
p{Mq(t)|Mq(t-1) is the status error rate of the second type management task;
step S4022: calculating the probability of isomorphic transition success of the monitoring behavior of the operator according to the formula
Step S4023: calculating the probability of operator monitoring behavior heterogeneous transfer failure by adopting the following formula:
wherein,
a second type management task state q, a comea component transfer process for transferring from the comea component of the aa block to the comeb component of the bb block at a time t, a system state b, a human factor state a, a warning state c, an operating procedure state z;
monitoring the comb component of the bb block in a system state b, a human factor state a, a warning state c, an operation procedure state z and a second type management task state q;
probability of failure to transfer from the comea-th component of the aa-th block to the comeb-th component of the bb-th block in a class ii management task state q at time t, system state b, human cause state a, warning state c, protocol state z;
Weightbb(t + Δ t) is at time (t + Δ t),monitoring the weight coefficient of a bb block in a system state b, a human factor state a, an alarm state c, an operation procedure state z and a second type management task state q;
monitoring the comb component failure probability of a bb block in a system state b, a human factor state a, a warning state c, an operation procedure state z and a second type management task state q;
step S4024: calculating the probability of isomorphic transition success of the monitoring behavior of the operator according to the formula
3. The method according to claim 1 or 2, wherein before performing the step S1, the method further comprises the steps of:
step S0: judging whether the operation state of the power plant is normal or abnormal;
when the determination result of the step S0 is normal, the division of each monitoring information source into a plurality of monitoring nodes in the step S1 is performed based on monitoring tasks, relevant monitoring parameters, operating procedures, and experience of operator monitoring;
when the determination result of the step S0 is abnormal, the division of each monitoring information source into a plurality of monitoring nodes is divided according to the operating rules of the state of the analysis event or accident in the step S1.
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| 核电厂操纵员的状态评估模型研究;周勇;《中国优秀硕士学位论文全文数据库 经济与管理科学辑》;20111231;第2011年卷(第12期);J145-72 * |
| 考虑组织管理因素整体影响的人因可靠性分析方法研究;宋洪涛;《中国优秀硕士学位论文全文数据库 经济与管理科学辑》;20100228;第2010年卷(第02期);J145-69 * |
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