Since the 19th National Congress, students’ ideological education has become more and more one of the national priorities, so the Civics course has become one of the essential compulsory courses for students at all stages of school and university, and the learning methods of Civics course have also become a hot issue of concern to students, which makes the learning process of supplementary learning methods very important. In this paper, a Markov model was developed to calculate the probability transfer matrix and predict the supplementary learning methods used by students. This paper also establishes a Markov model to predict the frequency of students’ online classroom learning at different stages, and it is found that, in the future, more and more students will use the Internet for their Civics course assisted learning; therefore, it is very important to establish a perfect Civics course online assisted learning platform, and this paper also puts forward some suggestions for establishing a Civics course online assisted learning system, which provides some methods for subsequent students’ Civics course learning. This paper also proposes some suggestions for establishing a web-assisted learning system for Civics courses and provides some methods for subsequent student learning in Civics courses.
The energy-saving of hydraulic excavators is evaluated by two significant indicators: operating efficiency (digging weight per unit time) and fuel consumption (volume of fuel consumed per unit time). These two indicators are only the final test results and cannot quantitatively evaluate the whole operation process of the excavator. In this paper, a complete set of experimental protocols is designed to quantify the energy-saving of the excavator by collecting its attitude data, fuel consumption data, and digging weight data in real-time. By restoring the excavator’s three-dimensional posture through the multi-body dynamics model, the posture corresponding to the energy efficiency of the excavator can be grasped very intuitively, which provides a research direction for optimizing the energy efficiency of the excavator.
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