CN112288291A - Ship pilot human factor reliability prediction method based on improved CREAM - Google Patents
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Abstract
The invention discloses a ship pilot human factor reliability prediction method based on improved CREAM, which comprises the following steps: collecting relevant data of a pilot, improving a CREAM model, evaluating the performance effect of a CPC factor, adjusting the dependency of the CPC factor, constructing a multi-input multi-output rule base, modeling by a Bayesian network technology and actually calculating a HEP of the pilot; the method introduces fuzzy numbers into CPC factor performance effect evaluation, reduces the influence of subjective factors judged by experts on an evaluation result, establishes an improved CREAM model by utilizing a Bayesian network technology to determine a control mode, thereby quantifying the human error rate, and combining the multiple-input multiple-output rule base concept with evidence reasoning to rationalize the estimation of the human error rate; by analyzing the reliability of piloting operation of a pilot station pilot in a specific situation, the improved model has better reliability and sensitivity than a CREAM basic method, and can provide quantitative evaluation data for human reliability analysis of a ship pilot.
Description
Technical Field
The invention belongs to the technical field of ship pilot human factor reliability research, and particularly relates to a ship pilot human factor reliability prediction method based on improved CREAM.
Background
With the promotion of the globalization of trade, the trade volume between China and each participating country is greatly increased, and a large amount of goods transportation is completed by sea transportation, thereby further promoting the development and prosperity of the sea transportation market. The rapidly developing marine market has increasingly high requirements on the operating skills and comprehensive qualities of ship pilots, but the cases of ship safety accidents involving pilots are on the increasing trend, especially in the case of ship sea damage accidents during navigation in harbor channels. Such as the collision accident between the foreign ship and the domestic ship in the Yangtze river channel. The irregular operation behavior of the pilot is one of the important factors causing the accident, and the human reliability of the pilot is one of the decisive factors of the safety of the ship entering and leaving the port.
The CREAM method is a fault classification system that contains individual, organizational and environmental factors, and describes the relationship between cause and outcome through a series of CPC (common Performance condition) factors and tables. The core of the method is that the influence of the scene environment on the cognitive control mode of the human determines the probability of possible cognitive errors, and the CREAM method predicts by establishing 4 cognitive control modes and 9 CPC factors. Meanwhile, the existing CREAM basic method has the following defects: the performance evaluation of the CPC factor depends on human expert judgment and is easily influenced by subjectivity, and the lack of reliable data of HRA is always a main problem in maritime safety evaluation; the control mode human error probability interval is wide and overlapped, and the prediction precision is not high; the models consider the CPC factors to be completely independent, and actually the CPC factors have different influence degrees on the performance reliability of the human and can be mutually dependent according to the influence of the CPC factors on the performance reliability of the human; the relationship between the control mode and the CPC factor is too fuzzy and less sensitive.
The effective prediction of the reliability of the pilot man causes has important significance for guaranteeing the safety of the ship entering and exiting a port and improving the port competitiveness, however, the reliability of the pilot man causes is mostly qualitatively researched from unilateral influence factors in the existing research of the reliability of the pilot man, the quantitative prediction of the reliability of the pilot man causes in a specific scene environment is ignored, the influence of the scene environment where the pilot man performs a pilot task on the reliability of the pilot man is not fully considered, and obviously, the work of the pilot man can be influenced by various factors such as individuals, environment, society and the like. Therefore, in consideration of the influence of the situation environment on the human factor reliability, a pilot is taken as a research object, the individual factors and the situation environment of the pilot are comprehensively considered, and the ship pilot human factor reliability prediction method based on the improved CREAM is provided.
Disclosure of Invention
The invention aims to solve the defects in the prior art, a CREAM basic method is expanded to a fuzzy environment, and the dependence relationship between CPC factors is simulated by combining a Bayesian network technology, so that the human error rate is quantized, and the multiple-input multiple-output rule base concept is combined with evidence reasoning, so that the estimation of the human error rate is rationalized; by analyzing the reliability of piloting operation under the specific situation of a pilot at a piloting station, the improved model has better reliability and sensitivity compared with a CREAM basic method, and can provide quantitative evaluation data for the reliability analysis of the human factors of the ship pilot, so that the ship pilot human factor reliability prediction method based on the improved CREAM is provided.
In order to achieve the purpose, the invention provides the following technical scheme: a ship pilot human factor reliability prediction method based on improved CREAM comprises the following steps:
s1, collecting relevant data of a pilot, collecting relevant data of a CPC factor in a CREAM method through questionnaire, grading the data by utilizing a five-level Likter scale, and obtaining a performance effect evaluation table of the CPC factor through example description and combining with an expert questionnaire;
s2, constructing a CREAM basic model, and converting the numerical score obtained by the CPC factor into a probability value through a fuzzy number;
s3, evaluating the performance of the CPC factor, converting discrete definite values into continuous values by using fuzzy numbers, and converting the continuous values into clear values by defuzzification, so that the CPC factor can be included in HEP prediction and the subjectivity of experts is reduced;
and S4, adjusting the dependency of the CPC factors, wherein the CPC factors are not independent from each other, but are arbitrarily coupled or dependent. And determining the action mechanism and the mutual relation of the CPC factor influencing the human behavior performance according to the CPC factor adjusting rule. When the effect value of a certain CPC factor is not significant, correcting the effect value according to whether the joint direction of the adjusting rule and other related factor effect values reaches a threshold value or not;
s5, constructing a multi-input multi-output rule base, establishing an uncertain and simple If-Then rule base by adopting a subjective evaluation method based on If-Then rules in a fuzzy set theory, enabling the If-Then rule base to relate to all possible results related to confidence, and establishing a multi-input multi-output confidence rule base RKThe relation between the CPC factor and the control mode is expressed, so that the defect that the output result cannot completely reflect small change when the traditional rule base expresses the fuzziness is overcome;
s6, modeling by using a Bayesian network technology, modeling a rule base by using the Bayesian network technology, properly converting confidence in the rule base into conditional probability in a Bayesian mechanism and converting the conditional probability into a ten-node convergent graph, and calculating HEP according to a determined control mode to realize the prediction of the human factor reliability of a ship pilot;
s7, actually calculating the HEP of the pilot, calculating the human error rate HEP according to a formula through the control mode probability result obtained in the steps,
wherein j =1, 2, 3, 4, the greater the calculated value of HEP, the lower the level of human performance reliability.
Preferably, in step S1, the five-degree scoring criteria of the lectt scale are respectively very agreeable, not necessarily, not agreeable, and very disagreeable.
Preferably, in step S2, the evaluation value is converted into a fuzzy value by using the fuzzy number, and then the fuzzy resolving operation is performed by using the gravity center method to obtain a probability value. By the method, different important performance levels of the CPC can be included in the estimation of the HEP, and the uncertainty of the CPC performance assessment can be combined to reduce subjectivity.
Preferably, in step S4, the common orientation of the adjustment rule and the effect value of a CPC factor includes common improvement and common reduction.
Preferably, in step S5, the confidence rule base RKThe output control mode result of (2) is a combination of four control modes with different confidence degrees.
Preferably, in step S6, the confidence in the rule base is appropriately converted into a conditional probability in a bayesian mechanism and converted into a ten-node aggregated graph.
Preferably, in step S7, j =1, 2, 3, and 4 correspond to j = strategic, tactical, opportunistic, and chaotic, respectively.
The invention has the technical effects and advantages that: the invention provides a ship pilot human factor reliability prediction method based on improved CREAM.A CREAM basic method is expanded to a fuzzy environment, and the dependence relationship between CPC factors is simulated by combining a Bayesian network technology, so that the human factor error rate is quantized, and the multiple-input multiple-output rule base concept is combined with evidence reasoning, so that the estimation of the human factor error rate is rationalized; by analyzing the reliability of piloting operation of a pilot at a piloting station under a specific situation, the improved model can predict the reliability more accurately, and the result conforms to a basic method, so that an auxiliary decision can be provided for the pilot human factor reliability analysis.
Drawings
Fig. 1 is a flow chart of steps of a ship pilot human factor reliability prediction method based on improved CREAM.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Examples
A ship pilot human factor reliability prediction method based on improved CREAM comprises the following steps:
s1, collecting relevant data of a pilot, collecting relevant data of a CPC factor in a CREAM method through questionnaire, grading the data by utilizing a five-level Likter scale, and obtaining a performance effect evaluation table of the CPC factor through example description and combining with an expert questionnaire;
s2, constructing a CREAM basic model, and converting the numerical score obtained by the CPC factor into a probability value through a fuzzy number;
s3, evaluating the performance of the CPC factor, converting discrete definite values into continuous values by using fuzzy numbers, and converting the continuous values into clear values by defuzzification, so that the CPC factor can be included in HEP prediction and the subjectivity of experts is reduced;
and S4, adjusting the dependency of the CPC factors, wherein the CPC factors are not independent from each other, but are arbitrarily coupled or dependent. And determining the action mechanism and the mutual relation of the CPC factor influencing the human behavior performance according to the CPC factor adjusting rule. When the effect value of a certain CPC factor is not significant, correcting the effect value according to whether the joint direction of the adjusting rule and other related factor effect values reaches a threshold value or not;
s5, constructing a multi-input multi-output rule base, establishing an uncertain and simple If-Then rule base by adopting a subjective evaluation method based on If-Then rules in a fuzzy set theory, enabling the If-Then rule base to relate to all possible results related to confidence, and establishing a multi-input multi-output confidence rule base RKThe relation between the CPC factor and the control mode is expressed, so that the defect that the output result cannot completely reflect small change when the traditional rule base expresses the fuzziness is overcome;
s6, modeling by using a Bayesian network technology, modeling a rule base by using the Bayesian network technology, properly converting confidence in the rule base into conditional probability in a Bayesian mechanism and converting the conditional probability into a ten-node convergent graph, and calculating HEP according to a determined control mode to realize the prediction of the human factor reliability of a ship pilot;
s7, actually calculating the HEP of the pilot, calculating the human error rate HEP according to a formula through the control mode probability result obtained in the steps,
wherein j =1, 2, 3, 4, the greater the calculated value of HEP, the lower the level of human performance reliability.
In step S1, the five-level scoring criteria of the likert scale are respectively very agreeable, not necessarily agreeable, not agreeable, and very disagreeable.
In step S2, the evaluation value is converted into a fuzzy value by using the fuzzy number, and then the fuzzy resolving operation is performed by using the gravity center method to obtain a probability value. By the method, different important performance levels of the CPC can be included in the estimation of the HEP, and the uncertainty of the CPC performance assessment can be combined to reduce subjectivity.
In step S4, the common orientation of the adjustment rule and the effect value of a CPC factor includes common improvement and common reduction.
In step S5, confidence rule base RKThe output control mode result of (2) is a combination of four control modes with different confidence degrees.
In step S6, the confidence in the rule base is appropriately converted into a conditional probability in a bayesian mechanism and converted into a ten-node aggregated graph.
In step S7, j =1, 2, 3, and 4 correspond to j = strategic, tactical, opportunistic, and chaotic, respectively.
In summary, the following steps: the method introduces fuzzy numbers into CPC factor performance effect evaluation, reduces the influence of subjective factors judged by experts on an evaluation result, establishes an improved CREAM model by utilizing a Bayesian network technology to determine a control mode, thereby quantifying the human error rate, and combining the multiple-input multiple-output rule base concept with evidence reasoning to rationalize the estimation of the human error rate; by analyzing the reliability of piloting operation of a pilot station pilot in a specific situation, the improved model has better reliability and sensitivity than a CREAM basic method, and can provide quantitative evaluation data for human reliability analysis of a ship pilot. Meanwhile, some potential to be further researched is also discovered, including that the analysis of the weight of the CPC factor needs to properly consider the influence of CPC with neutral effect in the establishment of the confidence fuzzy rule base in combination with a specific situation.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments or portions thereof without departing from the spirit and scope of the invention.
Claims (7)
1. A ship pilot human factor reliability prediction method based on improved CREAM is characterized by comprising the following steps: the method comprises the following steps:
s1, collecting relevant data of a pilot, collecting relevant data of a CPC factor in a CREAM method through questionnaire, grading the data by utilizing a five-level Likter scale, and obtaining a performance effect evaluation table of the CPC factor through example description and combining with an expert questionnaire;
s2, constructing a CREAM basic model, and converting the numerical score obtained by the CPC factor into a probability value through a fuzzy number;
s3, evaluating the performance of the CPC factor, converting discrete definite values into continuous values by using fuzzy numbers, and converting the continuous values into clear values by defuzzification, so that the CPC factor can be included in HEP prediction and the subjectivity of experts is reduced;
and S4, adjusting the dependency of the CPC factors, wherein the CPC factors are not independent from each other, but are arbitrarily coupled or dependent. And determining the action mechanism and the mutual relation of the CPC factor influencing the human behavior performance according to the CPC factor adjusting rule. When the effect value of a certain CPC factor is not significant, correcting the effect value according to whether the joint direction of the adjusting rule and other related factor effect values reaches a threshold value or not;
s5 construction of multiple inputsA multi-output rule base, adopting a subjective evaluation method based on If-Then rules in a fuzzy set theory to establish an uncertain and simple If-Then rule base and make the same relate to all possible results related to confidence, and establishing a multi-input multi-output confidence rule base RKThe relation between the CPC factor and the control mode is expressed, so that the defect that the output result cannot completely reflect small change when the traditional rule base expresses the fuzziness is overcome;
s6, modeling by using a Bayesian network technology, modeling a rule base by using the Bayesian network technology, properly converting confidence in the rule base into conditional probability in a Bayesian mechanism and converting the conditional probability into a ten-node convergent graph, and calculating HEP according to a determined control mode to realize the prediction of the human factor reliability of a ship pilot;
s7, actually calculating the HEP of the pilot, calculating the human error rate HEP according to a formula through the control mode probability result obtained in the steps,
where j is 1, 2, 3, 4, the greater the calculated value of HEP, the lower the level of human performance reliability.
2. The ship pilot human factor reliability prediction method based on improved CREAM (credit rapid assessment and maintenance) as claimed in claim 1, characterized in that: in step S1, the five-level scoring criteria of the likert scale are respectively very agreeable, not necessarily agreeable, not agreeable, and very disagreeable.
3. The ship pilot human factor reliability prediction method based on improved CREAM (credit rapid assessment and maintenance) as claimed in claim 1, characterized in that: in step S2, the evaluation value is converted into a fuzzy value by using the fuzzy number, and then the fuzzy resolving operation is performed by using the gravity center method to obtain a probability value. By the method, different important performance levels of the CPC can be included in the estimation of the HEP, and the uncertainty of the CPC performance assessment can be combined to reduce subjectivity.
4. The ship pilot human factor reliability prediction method based on improved CREAM (credit rapid assessment and maintenance) as claimed in claim 1, characterized in that: in step S4, the common orientation of the adjustment rule and the effect value of a CPC factor includes common improvement and common reduction.
5. The ship pilot human factor reliability prediction method based on improved CREAM (credit rapid assessment and maintenance) as claimed in claim 1, characterized in that: in step S5, confidence rule base RKThe output control mode result of (2) is a combination of four control modes with different confidence degrees.
6. The ship pilot human factor reliability prediction method based on improved CREAM (credit rapid assessment and maintenance) as claimed in claim 1, characterized in that: in step S6, the confidence in the rule base is appropriately converted into a conditional probability in a bayesian mechanism and converted into a ten-node aggregated graph.
7. The ship pilot human factor reliability prediction method based on improved CREAM (credit rapid assessment and maintenance) as claimed in claim 1, characterized in that: in step S7, j is 1, 2, 3, and 4, and j is strategic, tactical, opportunistic, and chaotic.
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| CN112989604A (en) * | 2021-03-12 | 2021-06-18 | 北京航空航天大学 | Bayesian network-based cause scene safety quantitative evaluation method |
| CN113050595A (en) * | 2021-03-12 | 2021-06-29 | 北京航空航天大学 | Potential fault analysis method based on PFMEA and HRA method |
| CN112989604B (en) * | 2021-03-12 | 2022-07-05 | 北京航空航天大学 | Bayesian network-based cause scene safety quantitative evaluation method |
| CN113050595B (en) * | 2021-03-12 | 2022-07-05 | 北京航空航天大学 | Potential fault analysis method based on PFMEA and HRA method |
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