面向知识自动化的磨矿系统操作员脑认知特征与控制效果的相关分析

面向知识型工作自动化，研究了流程工业生产过程中操作人员的脑认知特征与操作控制水平之间的关键，建立了一种基于操作员脑网络特征的操作熟练程度隐性知识的显性化模型.采用关注信号瞬时相位、基于希尔伯特变换的相位锁方法，构建了脑功能网络（Functional brain network，FBN）.基于磨矿系统操作员脑功能网络的图论参数与社区连接强度，建立了特征空间，采用支持向量机与神经网络进行特征分类.结果表明，在高频区，熟练操作员（熟手）的脑功能网络连接强度明显高于不熟练操作员（生手）:在低频部分则生手的脑功能网络连接强度略高，其特征分类准确率为87.24%.磨矿系统操作过程中形成的溢流粒度（Grinding particle size，GPS）曲线可以初略地反映操作人员的熟练程度，本文在深入分析了其溢流粒度曲线与操作员脑网络特征的基础上，发现相对于溢流粒度曲线操作员的脑网络特征可以更全面地描述操作控制水平（特别在操作开始时间段），采用脑网络特征识别操作控制水平在时间上超前于溢流粒度曲线识别方法.本研究对于将知识工作者的认知特征引入到流程工业控制中，具有一定的借鉴意义.

Knowledge Automation-oriented Brain Cognitive Feature and Control Effect Analysis of Operator in Mineral Grinding Process

Towards knowledge work automation, the paper studies the key correlation between brain cognitive feature and operation level of operators in the process industrial production, and models the explication of tacit knowledge based on the functional brain network (FBN) feature of operators. Using phase locking value method based on the Hilbert transform focusing on instantaneous phase we construct FBN, and then apply parameters of graphic theory and link strength of community analysis of FBN of operators to the mineral grinding processing automated system, so as to obtain the feature space. The result of classification using SVM and ANN classifier suggests that the connection strength of FBNs of old hands is significantly higher than that of new learners in high frequency, while that of new learners is slightly higher in low frequency, and the accuracy of classification is 87.24%. The grinding particle size (GPS) represents the operation level initially and roughly. According to the deep analysis of GPS and FBN features, the paper suggests that the FBN features can describe the operation level more comprehensively (especially in the initial stage of operation) than GPS. The operation level detection based on FBN features is more look-ahead than based on GPS curves in time. The research provides a reference for introducing the cognitive features of knowledge worker into the process industry.