This paper presents an approach to solve the least-cost transition sequences estimation problem in labelled time Petri net (LTPN) systems with unobservable transitions. Particularly, the considered LTPN system is bounded, in which each transition has a nonnegative cost and is labelled as either observable or unobservable. We first introduce a graph, called a segmented modified state class graph (SMSCG), that is a compact representation of the partial state space of an LTPN system. Based on the SMSCG and an observed time-label sequence, a method to estimate logic consistent transition sequences is proposed. By exploiting the transitions-related timing constraints in SMSCGs, a procedure for determining which logic consistent transition sequences are timing consistent with the observed time-label sequence and a given time is reported. Finally, we develop an algorithm to perform the estimation of least-cost transition sequences.

Connecting the educational resources of specific disciplines to form a knowledge network is a key step in the intellectualization of education and research. Based on a knowledge graph of power grid technology education resources of 42510 literature and information in China National Knowledge Infrastructure (CNKI) in our former research, a recommendation algorithm is proposed in this paper. The algorithm aims at the problem of data sparsity and cold start of traditional recommendation algorithm, which adopts hybrid similarity fusion to quickly match core resources and improve the pertinence and efficiency of education and research. The work in this paper show that the integration of knowledge graph technology and academic literature database can provide scientific and intelligent data analysis methods for discipline education and research.

With the rapid advancement of technology, artificial intelligence (AI) has become an indispensable part of various industries. To keep pace with this trend, higher education institutions must cultivate AI professionals with interdisciplinary thinking and research capabilities. This paper, based on the “Double First-Class” program construction in Chinese universities, explores an AI course education model within the context of interdisciplinary integration. By analyzing existing issues in current AI course education, we propose a research-oriented perspective to build a comprehensive interdisciplinary knowledge integration system and establish a collaborative platform that bridges both in-class and external connections. This approach is intended to enhance students’ innovative and problem-solving abilities. By combining theory with practice, this paper advocates for deep reforms in AI courses for graduate students, aiming to improve their overall quality and cultivate “passionate, technology-driven, high-quality” talent for society.