The Realization of Intelligent Knowledge Adaptive Learning Method in the Field of Substation Operation and Maintenance

Abstract

In view of the lack of targeted and personalized knowledge learning in existing substation operators, this paper proposes an intelligent adaptive knowledge learning method in the field of substation operation and maintenance. First, an adaptive knowledge learning system is established including domain model, user model, teaching model and adaptive engine. In the user model, the item response theory (IRT) is used to estimate the learner's master degree of knowledge points contained in the domain model, and then their learning needs are obtained. Meanwhile, the Bayesian network method is used to speculate the cognitive style of learners. On the basis of a comprehensive consideration of learning needs and cognitive style, the particle swarm optimization algorithm (PSO) in the teaching model is used to recommend the optimal learning resource for learners to realize the high-efficiency personalized knowledge learning in substation operation and maintenance.