Nuclear power plant human factor reliability analysis method

Abstract

The invention relates to the field of nuclear power safety, and discloses a nuclear power plant human factor reliability analysis method, which can improve the accuracy of calculating the human factor reliability of a nuclear power plant by constructing a new evaluation model, allowing multiple analysts to participate in decision making and processing common conflict problems in evaluation suggestions. Comprising the following steps: analyzing a human factor event, identifying a plurality of behavior activities, identifying a behavior function corresponding to each behavior activity, and obtaining an error probability basic value of each error mode under the behavior function; and obtaining evaluation suggestions of the common performance condition description level given by each analyst, and generating basic reliability assignment of the evaluation suggestions based on a D-S evidence theory. And further fusing evaluation suggestions of all analysts to generate convergence basic reliability assignment. And calculating the probability of the common performance condition description level based on gambling probability conversion, correcting the weight factor under the behavior function failure, and obtaining the failure probability of each corrected behavior activity. And calculating a final human factor event failure probability.

Description

Technical Field

[0001] This application relates to the field of nuclear power safety, and particularly to a method for analyzing human factor reliability in nuclear power plants. Background Art

[0002] This section aims to provide background or context for the embodiments of the present application described in the claims. The description herein is not admitted to be prior art that has been publicly disclosed merely because it is included in this section.

[0003] With the in-depth promotion of the energy revolution in China and the optimization of the energy structure, nuclear energy is an important option. Therefore, the problem of how to ensure the safety of nuclear power needs to be solved urgently. Once an accident occurs in a nuclear power plant, it will not only cause heavy casualties and economic losses, but also have a huge negative social impact. Moreover, with the development of science and technology, the proportion of the human-machine interaction system in digital nuclear power plants is gradually increasing. According to statistics, due to the complexity of the industrial systems in nuclear power plants, more than 50% of the operating events are attributed to human factor failures, and "humans" are considered the dominant factor in nuclear power safety. Therefore, it is of great significance to analyze the human factor reliability of nuclear power plants.

[0004] In the daily operation of nuclear power plants, the human factor reliability of nuclear power plants can be analyzed through regular assessment and analysis, combined with the modular and integrated algorithms of computers, to evaluate the operating procedures, personnel arrangements, and organizational structures in nuclear power plants, so as to predict the probability of nuclear power dangerous accidents caused by "humans" (abbreviated as human factor failure probability), and thus rectify the key problems that are likely to cause human factor accidents. It can also conduct retrospective analysis on dangerous accidents after they actually occur, clarify the main causes of human factor events, so as to help managers formulate more effective safety strategies and related plans, and reduce the adverse effects of human factors on the operation of nuclear power plants.

[0005] As a representative second-generation human factor reliability analysis method, the Cognitive Reliability and Error Analysis Method (CREAM) emphasizes that the scenario environment has an important impact on human behavior, and believes that the occurrence of human events is often not only the wrong actions of personnel, but also the wrong cognition and judgment of the situation people are in, or specific environments forcing people to make wrong decisions. Therefore, this method takes environmental factors (also known as Common Performance Conditions (CPC)) as an important evaluation criterion for analyzing human factor reliability, and gives the description level of CPC and its influence effect on human factor reliability. In addition, CREAM proposes a unique retrospective analysis method, which has the dual analysis functions of retrospective and prediction. It can trace the root cause of human factor events, and can also qualitatively and quantitatively predict human factor probabilities. Therefore, the human factor reliability analysis method based on CREAM has received extensive attention in nuclear power plants.

[0006] In the classic CREAM for human reliability analysis in nuclear power plants, two types of methods are commonly used: the basic method and the extended method. In the basic method, analysts give evaluations of each CPC. By counting the number of certain evaluations that improve or reduce the expected effect on performance reliability, the control mode of human error events is determined, and finally an interval value of the human error probability is given. This method is simple and clear, easy to calculate, and can quickly locate the control mode of human error events, so that a general method can be formulated to reduce the losses caused by human error events. However, due to the overly simple evaluation structure, there is strong uncertainty in the calculation of the human error probability, so a more detailed and considerate plan cannot be formulated. Moreover, the operation of nuclear power plants highly depends on human factors, and the roughly estimated human failure probability will greatly affect the safety of the overall operation of nuclear power plants. Therefore, the basic method CREAM has not received strong attention in the actual operation of nuclear power plants.

[0007] The extended method further optimizes on the basis of the basic method. It deeply analyzes the impact of each CPC on human factors events and gives the basic value of the failure probability. Then, combined with the single evaluation of analysts, it calculates the influence degree of each CPC on the behavior activities in human factors events and gives the weight of the CPC. Finally, the basic value is corrected to obtain the final human error probability. This method fully considers the impact of different CPCs on the final human error probability. Compared with the classic method, the calculated human error probability is unique and determined, which can better help decision-makers make accurate and reasonable judgments. Therefore, the CREAM extended method is often used to analyze human factors events in the nuclear power field. However, there are still many problems to be solved in its CREAM evaluation framework, such as: multiple analysts cannot participate in the CREAM evaluation, there are differences in the evaluation opinions of analysts, and the evaluation lacks subjectivity, etc. Considering the importance of human reliability in nuclear power plants and the complexity of the analysis process of human factors events in nuclear power plants, it is essential to invite analysts with different identities and qualifications to participate in the evaluation. However, in the actual application of CREAM, the CPC description level is directly determined by the collective opinions of a single analyst or multiple analysts. If multiple analysts participate in the human reliability analysis process and there are certain differences among analysts, the classic CREAM cannot handle the above situations.

[0008] In view of the above problems, some methods have been gradually proposed. For example, the linear combination DEMATEL method and the fuzzy analytic hierarchy process are used to obtain the comprehensive weight of CPC, or the opinions of multiple analysts are expressed and fused using fuzzy numbers to obtain the CPC weight. To a certain extent, these methods have addressed the above problems existing in CREAM. However, in the process of analysts' given CPC evaluation and subsequent evaluation fusion, the special operating background of nuclear power engineering has not been considered, that is, the human factor reliability analysis of nuclear power plants based on the CREAM extension method needs to combine the weight factors corresponding to the behavioral function failures under engineering specifications to correct the CPC weight. In other words, there is currently no method that can simultaneously solve the above problems and well adapt to the human factor reliability analysis environment of nuclear power plants. Summary of the Invention

[0009] The purpose of this application is to provide a human factor reliability analysis method for nuclear power plants, which can process the common conflict problems in evaluation opinions by constructing a new evaluation model and improve the accuracy of calculating the human factor reliability of nuclear power plants.

[0010] This application discloses a human factor reliability analysis method for nuclear power plants, including the following steps:

[0011] Step 1: Analyze human factor events and identify multiple behavioral activities, identify the behavioral functions corresponding to each of the behavioral activities, and obtain the basic failure probability values of each failure mode under the behavioral functions;

[0012] Step 2: Obtain the evaluation opinions of the common performance condition description level given by each analyst, and generate the basic belief assignment of the evaluation opinions based on the D-S evidence theory;

[0013] Step 3: Further fuse the evaluation opinions of all analysts to generate a convergent basic belief assignment;

[0014] Step 4: Based on the gambling probability, convert the convergent basic belief assignment of the common performance condition description level into a probability value, correct the weight factor under the behavioral function failure, and obtain the failure probability of each of the behavioral activities after correction;

[0015] Step 5: Calculate the final failure probability of the human factor event.

[0016] · The step 2 further includes the following steps:

[0017] ◆ Step 2.1: Analysts give evaluation opinions according to the working condition standards.

[0018] ◆ Step 2.2: Based on the evaluation opinions, constitute the basic belief assignment:

[0019] The degree of trust of the evaluation opinion is represented by the basic belief assignment m(A) and satisfies the condition:

[0020]

[0021] Among them, represents an empty set;

[0022] Assign α as the belief to the evaluation opinion, and the non-belief part 1 - α is assigned to Θ, generating:

[0023]

[0024] Among them, m(Θ) represents the degree of trust in the entire set of evaluation opinions.

[0025] The evaluation opinions are composed of V subsets, and the proportion vector composed of the proportions of each subset is R = [r v (v = 1, 2,..., V), then the degree of trust in the v-th subset is:

[0026]

[0027] · Step 3 further includes the following steps:

[0028] Perform N - 1 times of self - fusion based on the Dempster combination rule to generate a convergent basic belief assignment, where N is the number of analysts:

[0029]

[0030] Among them,

[0031]

[0032] m1, m2,..., m n are n basic probability assignment functions on the identification framework Θ, m'(A) is the belief for fusing the evaluation opinions, k is the conflict coefficient, and A i is any subset in the identification framework Θ.

[0033] · Step 4 further includes the following steps:

[0034] ◆ Step 4.1: Obtain the probability of the common performance condition description level based on the gambling probability transformation. Specifically, among the evaluation opinions of the common performance condition, there are T common performance condition description levels of the opinions of the analysts, and the basic belief assignment of each common performance condition description level is m t '(A), and the gambling probability of the t - th common performance condition description level is obtained through the gambling probability transformation:

[0035]

[0036] Among them, P tLet \(P(A)\) be the probability of the \(t\) -th common performance condition description level in the evaluation opinion, where \(t = 1,2,\cdots,T\); \(|A|\) is the cardinality of the evaluation opinion, and \(T\) represents the number of common performance condition description levels in the evaluation opinion.

[0037] ◆ Step 4.2: Calculate the correction coefficient of the common performance condition description level.

[0038]

[0039] w t is the weight factor of the \(t\) -th common performance condition description level under one of the behavior function failures of the nuclear power plant.

[0040] ◆ Step 4.3: Determine the failure probability of each behavior activity:

[0041] where \(CFP\) is the corrected failure probability, \(CFP_0\) is the basic value of the error probability, \(l\) refers to the \(l\) -th evaluation opinion of the common performance condition in the evaluation opinion of the common performance condition, and \(w'\) l is the correction coefficient of the \(l\) -th evaluation opinion of the common performance condition.

[0042] · The said step 5 further includes the following steps:

[0043] Calculate the failure probability of the final human - induced event: \(HEP=CFP\) r \(\times F\) r

[0044] where there are a total of \(R\) behavior activities, \(r\) represents the \(r\) -th behavior activity among the \(R\) behavior activities, \(r = 1,2,\cdots,R\), and the influence on the final human - induced event is \(F=[F\) r .

[0045] This application also discloses a human - factor reliability analysis device for a nuclear power plant, including:

[0046] A memory for storing computer - executable instructions; and,

[0047] A processor coupled to the memory for implementing the steps in the method described above when executing the computer - executable instructions.

[0048] This application also discloses a computer - readable storage medium, in which computer - executable instructions are stored, and when the computer - executable instructions are executed by a processor, the steps in the method described above are implemented.

[0049] In the embodiments of the present application, multiple analysts are allowed to participate in the analysis process, and an evaluation framework for group participation in nuclear power plant human reliability analysis is constructed. By constructing an optimized method for nuclear power plant cognitive reliability and error analysis based on D-S evidence theory, the evaluation opinions on the description level of common performance conditions given by each analyst in the human reliability analysis of nuclear power plants are expressed based on D-S evidence theory, fully expressing the uncertainty of the evaluation opinions. Then, based on Dempster's combination rule and gambling probability conversion, the evaluation opinions are fused, which can handle the situation of multiple analysts participating in decision-making and can also handle the conflict problems existing in the evaluation opinions. By fusing the opinions of each analyst and combining the basic value of the error probability, the correction coefficient of the CPC description level is corrected, reducing the subjectivity existing in the evaluation opinions and obtaining a comprehensive opinion on the description level of common performance conditions. Combining the special background of nuclear power plant operation, the failure probability of human factor events is calculated, and more reasonable nuclear power plant human reliability is obtained. By inversely inferring the probability of accidents occurring in actual production events from the magnitude of the failure probability, it actually provides strong data support for the formulation of subsequent relevant plans, thereby further optimizing the nuclear power plant system and reducing the probability of accidents occurring in each link.

[0050] Furthermore, based on D-S evidence theory, the problems existing in classical CREAM can be well solved, and the constructed framework has downward compatibility and can be compatible with classical CREAM.

[0051] Each technical feature disclosed in the above invention content, each technical feature disclosed in the following embodiments and examples, and each technical feature disclosed in the drawings can be freely combined with each other to form various new technical solutions (these technical solutions should all be regarded as having been recorded in this specification), unless the combination of such technical features is technically infeasible. For example, in one example, features A + B + C are disclosed, and in another example, features A + B + D + E are disclosed, and features C and D are equivalent technical means that play the same role. Technically, only one of them can be selected and they cannot be used simultaneously. Feature E can be combined with feature C technically. Then, the solution of A + B + C + D should not be regarded as having been recorded because it is technically infeasible, while the solution of A + B + C + E should be regarded as having been recorded. Brief Description of the Drawings

[0052] Figure 1 It is a schematic flowchart of a nuclear power plant human reliability analysis method according to an embodiment of the present application.

[0053] Figure 2 It is a schematic diagram of a root cause traceability analysis framework of a nuclear power plant human reliability analysis method according to an embodiment of the present application.

[0054] Figure 3It is a schematic flow chart of a human reliability analysis method for nuclear power plants according to an embodiment of the present application. Detailed implementation manners

[0055] In the following description, many technical details are presented for the reader to better understand the present application. However, those of ordinary skill in the art can understand that even without these technical details and various changes and modifications based on the following embodiments, the technical solutions claimed in the present application can still be implemented.

[0056] Explanation of some concepts:

[0057] Human Reliability Analysis (HRA): A reliability analysis method for accidents caused by human errors.

[0058] Probabilistic Safety Assessment (PSA): A safety assessment and analysis method based on probability theory.

[0059] Cognitive Reliability and Error Analysis Method (CREAM): A human reliability analysis method

[0060] Common Performance Condition (CPC): Environmental factors that lead to human errors.

[0061] CPC description level: Describes the degree of influence of CPC on human events.

[0062] D-S evidence theory: A mathematical model for expressing, processing, and analyzing uncertainty.

[0063] Decision Making Trial and Evaluation Laboratory (DEMATEL): A method for calculating importance weights.

[0064] Basic belief assignment (BBA): A functional expression for measuring judgment reliability.

[0065] Human error probability (HEP): The probability of an accident caused by human error.

[0066] Pignistic Probability Transformation (PPT): A conversion formula that transforms a basic belief assignment function into a basic probability.

[0067] Belief: As used herein, "belief" and "degree of confidence" can be used interchangeably to indicate the degree of certainty of an analyst in an evaluation opinion.

[0068] To make the objectives, technical solutions, and advantages of this application clearer, the following will further describe the implementation manners of this application in detail with reference to the accompanying drawings.

[0069] The first implementation manner of this application relates to a method for analyzing human - factor reliability in a nuclear power plant, and its process is as Figure 1 shown. This method includes the following steps:

[0070] In step 1, analyze human - factor events and identify multiple behavioral activities, identify the behavioral functions corresponding to each of the behavioral activities, and obtain the basic failure probability values of each failure mode under the behavioral functions.

[0071] In step 2, obtain the evaluation opinions of the description level of common performance conditions given by each analyst, and generate the basic belief assignment of the evaluation opinions based on the D - S evidence theory.

[0072] In step 3, further fuse the evaluation opinions of all analysts to generate a convergent basic belief assignment.

[0073] In step 4, based on the pignistic probability, convert the convergent basic belief assignment of the description level of common performance conditions into a probability value, correct the weight factors under the failure of the behavioral functions, and obtain the failure probability of each of the behavioral activities after correction.

[0074] In step 5, calculate the final failure probability of the human - factor event.

[0075] In an optional embodiment, it further includes:

[0076] Based on the weight factors of the description level of common performance conditions, combine the evaluation opinions with the weight factors and correct the weight factors; calculate the failure probability of each behavioral activity after correction, and finally obtain the failure probability of the human - factor event.

[0077] In an optional embodiment, in step 101, CREAM classifies failure modes into 8 categories, including time (too early, too late, omission), process (too long, too short), force (too large, too small), speed (too fast, too slow), direction (wrong direction), distance (too far, too close), sequence (reversed, repeated, wrong action, inserted), target (wrong target). CREAM first analyzes human - factor events, starting from the failure modes, and constructs asFigure 2 The root cause tracing and analysis framework shown. That is, after listing the possible general antecedents and specific antecedents of 8 types of failure modes, select a certain antecedent as the consequence for analysis. In the corresponding "consequence - antecedent linked list" of the classification group containing this antecedent, analyze and find possible antecedents, which can be used as consequences to continue the analysis to find possible antecedents, and so on, until the root cause is finally analyzed and found. Then, identify the behavioral activities required for each step. The behavioral activities can include coordination, liaison, comparison, diagnosis, evaluation, identification, execution, maintenance, monitoring, observation, planning, recording, adjustment, scanning, inspection, etc.

[0078] In an alternative embodiment, CREAM believes that every behavioral activity occurring in a human factor event can always be attributed to one of the four basic cognitive functions: observation, interpretation, planning, or execution. And CREAM further analyzes and refines the failure modes and the corresponding basic values of failure probability CFP0 under each cognitive function. Table 1 shows the failure modes and basic values of failure probability in a nuclear power plant.

[0079]

Table 1

[0080]

[0081] In an alternative embodiment, in step 2, an evaluation given by an analyst according to the standard can be obtained in advance. Each analyst gives an assessment opinion of 9 CPCs for each behavioral activity based on their own experience and the operating conditions standard. The assessment opinion can be composed of a single or multiple CPC description levels. Table 2 shows the corresponding relationship between CPC and CPC description levels. When the assessment opinion is composed of a single CPC description level, for example, in a certain behavioral activity, an analyst's assessment opinion on the CPC of organizational management perfection is "{very effective}", which means that the analyst believes that the perfection of organizational management is very effective in influencing human factor events, and expects that in subsequent optimizations, by improving the perfection of organizational management, the occurrence of human factor events can be reduced, thereby improving human factor reliability.

[0082] When the assessment opinion is composed of multiple CPC description levels, for example, in a certain behavioral activity, an analyst's assessment opinion on the CPC of organizational management perfection is "{effective, very effective}", which means that the analyst believes that in the current situation, the perfection of organizational management may be effective or very effective for human factor events. And, the assessment opinion can also be expressed in the form of a ratio of CPC description levels. For example, an analyst's assessment opinion on the perfection of organizational management is "{effective}, {very effective} = 1:3", which indicates that the analyst believes that in the current situation, the description level of the perfection of organizational management for human factor events is effective or very effective, and the proportion is 1:3.

[0083] In addition, in the existing environment, each analyst can also provide a reliability α ∈ [0, 1] for their own evaluation opinion. The higher the reliability α, the stronger the analyst's confidence in the evaluation opinion, and the higher the confidence level of the evaluation opinion.

[0084]

Table 2

[0085]

[0086]

[0087] In an alternative embodiment, the identification framework Θ can be constructed in step 2. According to the D-S evidence theory, the identification framework Θ is a finite complete set composed of pairwise mutually exclusive elements. In the CREAM extension method for nuclear power plants, the identification framework Θ composed of evaluation opinions is constituted by the CPC description levels. As shown in Table 3, it shows the identification framework Θ of the evaluation opinions corresponding to each of the 9 CPCs.

[0088]

Table 3

[0089]

[0090]

[0091] In an alternative embodiment, step 2 may further include:

[0092] In each behavioral activity, the evaluation opinion of each CPC given by the analyst is a subset in the identification framework Θ. The degree of trust in the evaluation opinion is represented by the basic belief assignment m(A) and satisfies the condition:

[0093]

[0094] where, represents the empty set;

[0095] Assign α as the reliability to the evaluation opinion, and assign the non-reliability part 1 - α to Θ to generate:

[0096]

[0097] where m(Θ) represents the degree of trust in the entire set of evaluation opinions.

[0098] The evaluation opinion consists of V subsets, and the proportion vector composed of the proportions of each subset is R = [r v (v = 1, 2,..., V), then the degree of trust in the v-th subset is:

[0099]

[0100] In an alternative embodiment, step 3 may further include:

[0101] Performing N-1 times of self-fusion through the Dempster combination rule to generate a convergent basic belief assignment, where N is the number of analysts participating in the evaluation project:

[0102]

[0103] Wherein,

[0104]

[0105] m1, m2, …, m n are n basic probability assignment functions on the frame of discernment Θ, m'(A) is the degree of belief of the evaluation opinion that fuses the opinions of analysts, k is the conflict coefficient, and A i is an arbitrary subset in the frame of discernment Θ.

[0106] In an alternative embodiment, step 4 may further include:

[0107] It can be assumed that there are T levels of description of the common performance conditions in the evaluation opinions of analysts. The basic belief assignment for each level of description of the common performance conditions is m t '(A). Obtain the gambling probability of the t-th level of description of the common performance conditions through gambling probability conversion:

[0108]

[0109] Wherein, P t (A) is the probability of the t-th level of description of the common performance conditions in the evaluation opinion, |A| is the cardinality of the evaluation opinion, and T represents the number of levels of description of the common performance conditions in the evaluation opinion.

[0110] In an alternative embodiment, step 4 may further include:

[0111] Calculate the correction coefficient of the level of description of the common performance conditions corresponding to the failure of one of the behavioral functions of the nuclear power plant:

[0112]

[0113] w t is the weight factor of the t-th level of description of the common performance conditions under the failure of one of the behavioral functions of the nuclear power plant.

[0114] In an alternative embodiment, Table 4 shows the weight factors of the CPC description levels corresponding to the failures of four behavioral functions of the nuclear power plant.

[0115]

Table 4

[0116]

[0117] In an alternative embodiment, step 4 may further include: obtaining the corrected failure probability for each behavioral activity:

[0118]

[0119] where CFP is the corrected failure probability, CFP0 is the basic value of the error probability, and the basic value of the known error probability CFP0 is shown in Table 1. l refers to the l-th evaluation opinion of the common performance conditions in the evaluation opinion of the common performance conditions, and w′ l is the correction coefficient of the l-th evaluation opinion of the common performance conditions.

[0120] In an alternative embodiment, step 5 may further include:

[0121] Assume there are a total of R behavioral activities, r = 1, 2,..., R; r represents the r-th behavioral activity among the R behavioral activities, and the impact on the final human factor event is F = [F r . The failure probability of the final human factor event is:

[0122] HEP = CFP r × F r . (9)

[0123] To better understand the technical solution of the present application, the following will be described with a specific example. The details listed in this example are mainly for easy understanding and do not limit the protection scope of the present application.

[0124] Embodiment

[0125] In step 201: Analyze the human factor event and determine the behavioral activity.

[0126] Based on Figure 2 the CREAM root cause tracing analysis method shown, the behavioral activities of the human factor event and the occurred behavioral function failures are summarized as follows:

[0127] Behavioral activity 1: Observe the signal of the relief valve panel. The error is that after observing the signal, it is considered that the relief valve has been closed;

[0128] Behavioral activity 2: Observe the temperature of the relief valve outlet pipeline. The error is that although an abnormal high temperature is observed, it is not diagnosed as a relief valve seat leakage;

[0129] Behavioral activity 3: Observe the pressure in the relief tank passively. The error is that no observation is made;

[0130] Behavioral activity 4: Observe the rupture of the safety film and the rise of the containment sump water level. The error is the failure to observe.

[0131] In step 202: Identify the behavioral function and error mode.

[0132] Taking behavioral activity 2 as an example, the following is a detailed introduction. According to the error analysis of behavioral activity 2, it can be identified that the behavioral function at this time is "observation", and the failure mode is "misidentification". According to Table 2, the basic value of the error probability at this time is 0.07.

[0133] In step 203: Determine the failure probability of each behavioral activity.

[0134] During the evaluation process of behavioral activity 2, a total of three analysts participated in the evaluation, and the failure probability will be calculated by the following steps.

[0135] In step 2031: The analysts give evaluations according to the standards.

[0136] The three analysts give evaluation opinions of 9 CPCs according to their own experience and working conditions. The evaluation opinions are composed of the CPC description levels. In addition, the analysts give their own reliability for the evaluation opinions. Table 5 shows the evaluation opinions and reliabilities of the CPCs given by the analysts.

[0137]

Table 5

[0138]

[0139]

[0140] In step 2032: Construct a BBA based on the analysts' opinions.

[0141] Based on the D-S evidence theory, use Equation (2) to assign the reliability α of each analyst to the evaluation opinion itself, and the non-reliability part 1-α is assigned to the entire set Θ of the identification framework. If an evaluation opinion is composed of several subsets and there is a proportional relationship between them, reassign the reliability of each subset based on Equation (3). For example, Analyst 3 believes that the CPC description level of the adequacy of training and experience is "{Limited experience}:{Insufficient}=1:2", and the given confidence level is 0.3. Then, according to Equation (3), the basic reliability assignment function can be calculated as follows. Table 6 shows the BBA of the CPC generated based on the opinions of the three analysts.

[0142]

[0143]

[0144] m3(Θ) = 1 - 0.3 = 0.7

[0145]

Table 6

[0146]

[0147]

[0148] In step 2033: Fuse the BBA based on the Dempster combination rule.

[0149] Since a total of 3 analysts participated in the evaluation process, the BBA needs to be self-fused twice as shown in equations (4) and (5). Taking the CPC of "organizational perfection" as an example, the fusion process is introduced in detail as follows.

[0150] According to the Dempster combination rule, the first fusion process of organizational perfection is as follows:

[0151] k = 0.7 × 0.8 = 0.56

[0152]

[0153]

[0154]

[0155]

[0156]

[0157] On this basis, use the Dempster combination rule again. The specific fusion process is as follows:

[0158] k = 2 × (0.327 × 0.318 + 0.318 × 0.218) = 0.347

[0159]

[0160]

[0161]

[0162]

[0163]

[0164] Similarly, use equations (4) and (5) to perform the same operations on the remaining 8 CPCs. After sorting, the BBA integrating the opinions of each analyst can be obtained as shown in Table 7.

[0165]

Table 7

[0166]

[0167]

[0168] In step 2034: Calculate the probability of the CPC description level based on PPT.

[0169] According to the gambling probability conversion in formula (6), perform probability conversion on the BBA of all comprehensive analysis opinions in Table 7, and the probabilities of each CPC after comprehensive analyst opinions as shown in Table 8 can be obtained. Among them, taking "working conditions" as an example, the specific conversion process is as follows:

[0170]

Table 8

[0171]

[0172]

[0173]

[0174]

[0175] In step 2035: Calculate the correction coefficient of the CPC description level.

[0176] According to the common performance conditions of nuclear power plants and the weight factors corresponding to behavioral function failures in Table 4, the correction coefficients of each CPC description level as shown in Table 9 can be obtained according to formula (7).

[0177]

Table 9

[0178] CPC Correction factor Organizational perfection 1 Working conditions 0.998 Degree of perfection of the human-machine interface and operation support 0.524 Regulation / plan availability 1 Number of simultaneous objectives 1 Available time 1.142 Duty period (circadian rhythm) 1 Adequacy of training and experience 0.8 Quality of cooperation among crew members 0.978

[0179] In step 2036: Determine the failure probability of each behavioral activity.

[0180] Since in step 201, the failure mode of behavioral activity 2 has been determined as "misidentification", and its basic error probability is 0.07. Combining with the correction coefficients of each CPC description level in Table 9. Then the failure probability of this behavioral activity can be calculated by formula (8), and the failure probability of behavioral activity 2 is 0.03.

[0181] In step 204: Calculate the failure probability of the final human factor event.

[0182] According to the above steps, the failure probability of behavioral activity 2 is 0.03. Similarly, for the other four behavioral activities in the nuclear accident analysis, the same calculation process is carried out, and the failure probabilities of each behavioral activity are obtained as shown in Table 10. Finally, the failure probability of the final human factor event is obtained as 0.0065 using formula (9).

[0183]

Table 10

[0184]

[0185] After obtaining the failure probability of the final human factor event of behavioral activity 2, it can provide a data basis for subsequent optimization of the link of observing the temperature of the outlet pipeline of the pressure relief valve, and help reduce the probability of accidents.

[0186] In addition, the implementation manner of the present application also provides a nuclear power plant human factor reliability analysis device, which includes a memory for storing computer-executable instructions, and a processor; the processor is used to implement the steps in the above method implementation manners when executing the computer-executable instructions in the memory. Among them, the processor can be a central processing unit (Central Processing Unit, abbreviated as "CPU"), a graphic processing unit (Graphic Processing Unit, abbreviated as "GPU"), a digital signal processor (Digital Signal Processor, abbreviated as "DSP"), a microcontroller unit (Microcontroller Unit, abbreviated as "MCU"), a neural network processor (abbreviated as "NPU"), an application specific integrated circuit (Application Specific Integrated Circuit, abbreviated as "ASIC"), a field programmable gate array (FieldProgrammable Gate Array, abbreviated as "FPGA") or other programmable logic devices, etc. The aforementioned memory can be a read-only memory (read-only memory, abbreviated as "ROM"), a random access memory (random access memory, abbreviated as "RAM"), a flash memory (Flash), a hard disk or a solid state drive, etc. The steps of the methods disclosed in the embodiments of the present invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules in the processor.

[0187] Accordingly, an embodiment of the present application further provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the method embodiments of the present application. The computer-readable storage medium includes permanent and non-permanent, removable and non-removable media and can implement information storage by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of the computer's storage medium include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette tapes, disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible by a computing device. As defined herein, the computer-readable storage medium does not include transitory computer-readable media, such as modulated data signals and carrier waves.

[0188] It should be noted that in the present application, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising one" does not exclude the existence of another identical element in the process, method, article or device comprising the element. In the present application, if it is mentioned that an act is performed according to a certain element, it means performing the act according to at least the said element, including two cases: performing the act only according to the said element, and performing the act according to the said element and other elements. Expressions such as multiple, multiple times, multiple types, etc. include 2, 2 times, 2 types, and more than 2, more than 2 times, more than 2 types.

[0189] The serial numbers used to describe the steps of a method do not, by themselves, impose any limitation on the order of these steps. For example, steps with larger serial numbers are not necessarily to be executed after steps with smaller serial numbers. It is also possible to first execute steps with larger serial numbers and then execute steps with smaller serial numbers, or they can be executed in parallel, as long as such an execution order is reasonable to those skilled in the art. Another example is that multiple steps with consecutive serial numbers (such as step 101, step 102, step 103, etc.) do not restrict other steps from being executed therebetween. For example, there can be other steps between step 101 and step 102.

[0190] This specification includes combinations of various embodiments described herein. Separate mentions of embodiments (such as "an embodiment" or "some embodiments" or "preferred embodiments"); however, unless indicated to be mutually exclusive or clearly understood by those skilled in the art as being mutually exclusive, these embodiments are not mutually exclusive. It should be noted that, unless the context clearly indicates otherwise or requires, the word "or" is used in a non-exclusive sense in this specification.

[0191] All documents mentioned in this specification are considered to be integrally included in the disclosure of this application so that they can be used as a basis for modification when necessary. In addition, it should be understood that the above are only preferred embodiments of this specification and are not used to limit the protection scope of this specification. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of one or more embodiments of this specification shall be included within the protection scope of one or more embodiments of this specification.

[0192] In some cases, the actions or steps recited in the claims can be executed in an order different from that in the embodiments and still achieve the desired result. Additionally, the processes depicted in the drawings do not necessarily require the specific order or consecutive order shown to achieve the desired result. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A human factor reliability analysis method for nuclear power plants, characterized in that, It includes the following steps: Step 1: Analyze human factor events and identify multiple behavioral activities, identify the behavioral functions corresponding to each of the behavioral activities, and obtain the basic failure probability values of each failure mode under the behavioral functions; Step 2: Obtain the evaluation opinions on the description levels of common performance conditions given by each analyst, and generate the basic belief assignments of the evaluation opinions based on the D-S evidence theory; Step 3: Further fuse the evaluation opinions of all analysts to generate a convergent basic belief assignment; Step 4: Based on the gambling probability, convert the convergent basic belief assignment of the description level of the common performance conditions into a probability value, correct the weight factor under the failure of the behavioral function, and obtain the failure probability of each of the behavioral activities after correction; Step 5: Calculate the final failure probability of the human factor event.

2. The method for human factor reliability analysis of nuclear power plants according to claim 1, characterized in that, It further includes: Based on the weight factor of the description level of the common performance conditions, combine the evaluation opinion with the weight factor and correct the weight factor; Calculate the failure probability of each of the behavioral activities after correction, and finally obtain the failure probability of the human factor event.

3. The method for analyzing human reliability in a nuclear power plant according to claim 1, characterized in that, The generation of the basic belief assignment of the evaluation opinion further includes: The belief of the evaluation opinion is represented by the basic belief assignment m(A) and satisfies the condition: Among them, represents an empty set; Assign α as the belief to the evaluation opinion, and assign the non-belief part 1 - α to the identification framework Θ, generating: where m(Θ) represents the belief in the complete set of the evaluation opinions; The evaluation opinion consists of V subsets, and the proportion vector composed of the proportions of each subset is R = [r v (v = 1, 2,, V), then the reliability of the v-th subset is:

4. The method for human factor reliability analysis of nuclear power plants according to claim 3, wherein It further includes: Perform N - 1 times of self-fusion through the Dempster combination rule to generate a convergent basic belief assignment, where N is the number of the analysts: where, m1, m2,..., m n are n basic probability assignment functions on the identification framework Θ, m'(A) is the credibility of fusing the evaluation opinions, k is the conflict coefficient, and A i is any subset in the identification framework Θ.

5. The method for analyzing human factor reliability of nuclear power plants according to claim 1, characterized in that, It further includes: There are T levels of description of the common performance conditions in the evaluation opinions of the analysts' opinions on the common performance conditions, and the basic belief assignment for each level of description of the common performance conditions is m t '(A), obtaining the gambling probability of the t-th level of description of the common performance conditions through gambling probability conversion: Among them, P t (A) is the probability of the t-th common performance condition description level in the evaluation opinion, where t = 1, 2,..., T; |A| is the cardinality of the evaluation opinion, and T represents the number of common performance condition description levels in the evaluation opinion.

6. The method for human factor reliability analysis of nuclear power plants according to claim 1, wherein It further includes: Calculate the correction coefficient of the description level of the common performance conditions corresponding to the failure of one of the behavioral functions in the nuclear power plant; w t is the weight factor for the t-th common performance condition description level under one of the failure of the behavioral functions in the nuclear power plant.

7. The method for human factor reliability analysis of nuclear power plants according to claim 1, wherein The obtained failure probability of each of the behavioral activities after correction is: Wherein, CFP is the corrected failure probability, CFP0 is the basic value of the error probability, l refers to the evaluation opinion of the l-th common performance condition in the evaluation opinion of the common performance condition, and w′ l is the correction coefficient of the evaluation opinion of the l-th common performance condition.

8. The method for human factor reliability analysis of nuclear power plants according to claim 1, wherein The calculation of the final failure probability of the human factor event further includes: There are a total of R behavioral activities, where r represents the r-th behavioral activity among the R behavioral activities, r = 1, 2, ……, R, and the influence on the final human factor event is F = [F r . The failure probability of the final human factor event is: HEP = CFP r × F r 。 9. A human factor reliability analysis device for nuclear power plants, characterized in that, It includes: A memory for storing computer-executable instructions; and, A processor coupled to the memory for implementing the steps in the method according to any one of claims 1 to 8 when executing the computer-executable instructions.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by the processor, the steps in the method according to any one of claims 1 to 8 are implemented.

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