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.