龍門核電廠運轉員之情境察覺預測模型之探討

2011年日本發生核電廠核熔毀事故之後，對於核能供電量高達20%的台灣，核電廠的正常營運是非常值得重視的議題。除了不可避免的天然災害以外，人為上的疏忽常是核能安全中主要的研究方向。本論文主要使用SEEV模型作為預測運轉員在異常情況時的視覺注意力配置，透過由上而下(top down)及由下而上(bottom up)兩個面向之感知程序，可以在模擬情境底下預測運轉員將如何關注警報畫面。本研究以40位受測者之數據分別依不同觀測人員和警報畫面進行建模，結果顯示此兩種方式均能建立相似的SEEV迴歸模型，其解釋能力係數分別為77.9%以及82.6%，而預測能力係數分別為76.6%以及71.8%。由SEEV迴歸模型發現在該情境裡，價值性是影響視覺注意力最大的因子，而期望性是最弱的影響因子。實驗的成果可用於核電廠運轉員對於警報畫面情境察覺的預測與主控室儀表板畫面的改善，例如將重要性高卻預測出較低關注程度的警報畫面區域進行設計改善或改變其警報呈現的方式，以提高核能警報的安全。

關鍵字

龍門核電廠；視覺監控；情境察覺； SEEV模型

並列摘要

With the nuclear leakage in Japan at 2011, the operation of nuclear power plants (NPPs) draws everyone's attention in Taiwan where about 20% of the electric power is supplied by NPPs. Besides natural disasters, human operation error is an important factor that affects the safety of NPPs operation. This study applied SEEV model to predict operators' visual attention during abnormal situations. Through the top-down and the bottom-up perceptual processing, the model could predict operators' gaze distribution on alarm displays under different simulation scenarios. Two regression models were built based on the data from 40 participants considering different groups of participants and of alarm displays. Results showed that these two SEEV models were consistent to each other. While the coefficient of determination were 77.9% and 82.6%, respectively, the coefficient of prediction were 76.6% and 71.8%. According to the model, the Value parameter was the most influential factor, whereas, the Expectancy parameter was the least, The results can be used for the prediction of operators' situation awareness on alarm displays in NPPs and for the improvement of alarm display design, and ultimately to enhance the safety of operation.

並列關鍵字

Luman Nuclear power plant ； Visual monitoring ； Situation awareness ； SEEV model

參考文獻

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龍門核電廠運轉員之情境察覺預測模型之探討

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