EFA-BBN-based method and system for quantitatively predicting personnel error probability by using computer

Abstract

An EFA-BBN-based computer quantitative prediction method and system for personnel error probability relates to the technical field of data analysis and computer prediction, and is characterized in that a father node PSF in a general BBN model is analyzed by using an EFA method in combination with a current situation condition, PSF correlation is clustered into an intermediate factor, the father node and a child node of the BBN model are connected, and the probability of personnel error is predicted. According to the prediction method, the number of the PSFs is not limited, n PSFs can be clustered into a small number of intermediate factors, the integrity of original information of the PSFs cannot be lost, and the generated EFA-BBN model can make up for the defect that an existing HRA method does not consider the relation between the PSFs. In addition, clustering factor nodes and child nodes in the model acquire a conditional probability table based on double truncated normal distribution (TN), then a success likelihood index (SLIM) method is utilized to estimate a personnel error probability value, and compared with an existing HRA method, the method can reduce subjectivity of expert judgment and more accurately estimate the personnel error probability.

Description

technical field [0001] The invention relates to the technical field of data analysis and computer prediction, in particular to a method and system for quantifying and predicting personnel error probability based on EFA-BBN. Background technique [0002] Determining the behavioral forming factors (PSFs) that influence human error is one of the key steps in human reliability analysis (HRA). A structural hierarchical taxonomy for PSFs has been proposed in existing research results, with the aim of reducing the overlap between PSFs and further exploring their causal relationships through modeling, observation, and expert judgment. These studies have successfully advanced research into the association of PSFs, however, they have relied heavily on expert judgment and are limited to establishing causal relationships between two or three factors. Furthermore, the overlap and causality of PSFs are investigated by traditional techniques that do not take into account the uncertainty o...

AI Technical Summary

Problems solved by technology

Considering only a few PSFs can simplify the analysis workload, but may not fully describe the reasons for human errors, while considering a large number of PSFs will introduce uncertainty in the correlation of PSFs and increase the workload

Since the existing methods usually ignore the relationship between behavioral formation factors (PSFs), there are certain errors in the prediction results of human error probability (HEP).

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