本文選題：知識(shí)自動(dòng)化 + 操作控制水平��； 參考：《自動(dòng)化學(xué)報(bào)》2017年11期

【摘要】：面向知識(shí)型工作自動(dòng)化,研究了流程工業(yè)生產(chǎn)過(guò)程中操作人員的腦認(rèn)知特征與操作控制水平之間的關(guān)鍵,建立了一種基于操作員腦網(wǎng)絡(luò)特征的操作熟練程度隱性知識(shí)的顯性化模型.采用關(guān)注信號(hào)瞬時(shí)相位、基于希爾伯特變換的相位鎖方法,構(gòu)建了腦功能網(wǎng)絡(luò)(Functional brain network,FBN).基于磨礦系統(tǒng)操作員腦功能網(wǎng)絡(luò)的圖論參數(shù)與社區(qū)連接強(qiáng)度,建立了特征空間,采用支持向量機(jī)與神經(jīng)網(wǎng)絡(luò)進(jìn)行特征分類(lèi).結(jié)果表明,在高頻區(qū),熟練操作員(熟手)的腦功能網(wǎng)絡(luò)連接強(qiáng)度明顯高于不熟練操作員(生手):在低頻部分則生手的腦功能網(wǎng)絡(luò)連接強(qiáng)度略高,其特征分類(lèi)準(zhǔn)確率為87.24%.磨礦系統(tǒng)操作過(guò)程中形成的溢流粒度(Grinding particle size,GPS)曲線(xiàn)可以初略地反映操作人員的熟練程度,本文在深入分析了其溢流粒度曲線(xiàn)與操作員腦網(wǎng)絡(luò)特征的基礎(chǔ)上,發(fā)現(xiàn)相對(duì)于溢流粒度曲線(xiàn)操作員的腦網(wǎng)絡(luò)特征可以更全面地描述操作控制水平(特別在操作開(kāi)始時(shí)間段),采用腦網(wǎng)絡(luò)特征識(shí)別操作控制水平在時(shí)間上超前于溢流粒度曲線(xiàn)識(shí)別方法.本研究對(duì)于將知識(shí)工作者的認(rèn)知特征引入到流程工業(yè)控制中,具有一定的借鑒意義.
[Abstract]:In this paper, the key factors between the cognitive characteristics of the operator and the level of operation control are studied in the process of process industry production, which is oriented to the knowledge type work automation. A dominant model of tacit knowledge of operational proficiency based on operator brain network features is established. Based on the phase locking method of Hilbert transform, the functional brain network is constructed by using the instantaneous phase of the concerned signal. Based on the graph theory parameters of the brain functional network of grinding system operator and the intensity of community connection, the feature space is established and the feature classification is carried out by using support vector machine and neural network. The results showed that in the high frequency region, the brain functional network connection intensity of the skilled operators (proficient hands) was significantly higher than that of the unskilled operators (unskilled operators: in the low frequency part, the brain functional network connection intensity was slightly higher, and the accuracy of the characteristic classification was 87.24%). The overflow granularity curve formed during the operation of the grinding system can reflect the proficiency of the operator at first. Based on the in-depth analysis of the overflow granularity curve and the characteristics of the operator's brain network, the overflow granularity curve and the characteristics of the operator's brain network are analyzed in this paper. It is found that the brain network features can describe the operation control level more comprehensively than the overflow granularity curve operator (especially at the beginning of the operation, the brain network feature is used to identify the operation control level ahead of the overflow level in time. Granularity curve recognition method. This study can be used as a reference for knowledge workers to introduce their cognitive characteristics into process industry control.
【作者單位】： 東北大學(xué)機(jī)械工程與自動(dòng)化學(xué)院;東北大學(xué)流程工業(yè)綜合自動(dòng)化國(guó)家重點(diǎn)實(shí)驗(yàn)室;曼徹斯特大學(xué)自動(dòng)化中心;
【基金】：國(guó)家自然科學(xué)基金(51505069,61621004) 遼寧省高等學(xué)校創(chuàng)新團(tuán)隊(duì)項(xiàng)目(LT2014006) 流程工業(yè)綜合自動(dòng)化國(guó)家重點(diǎn)實(shí)驗(yàn)室開(kāi)放基金(PAL-N201304)資助~~
【分類(lèi)號(hào)】：TP18

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