Students are the backbone of national development and progress, and should stand at the forefront of the era of innovation and entrepreneurship. Cultivating the entrepreneurship of college students is not only a response to the national call, but also a basic requirement for implementing quality education and promoting the comprehensive development of college students. To better study the entrepreneurship of college students, cultivate a group of newcomers of the era who have patriotic feelings, dare to innovate, hardworking and sustainable struggle, and solve the problem of college students' employment difficulties from the root, the cultivation mode of college students' entrepreneurship is studied. Firstly, the impact of artificial intelligence (AI) technology on social ethics is analyzed. Secondly, it analyzes the current situation of the cultivation of science and engineering college students' entrepreneurship from three aspects: Chinese traditional cultural thoughts influence the concept of career choice, enterprises emphasize utilitarianism, and colleges and universities attach importance to knowledge education and despise spiritual education. Finally, the data statistics on the cultivation of entrepreneurship of science and engineering college students are carried out in the form of questionnaires. The results demonstrate that among the students surveyed, 21.31% have a strong willingness to start their own business, and 72.84% have the idea of starting their own business, which means that most students still want to start a business through their own efforts, not blindly looking for jobs. Simultaneously, among many majors, 87.5% of students majoring in agriculture and medicine are better at finding new ways to solve problems, while the proportion of students majoring in literature and history is the lowest. It also indicates that most students believe that schools should add more seminar courses, internship courses, design courses, experimental courses, etc., and allow students to choose learning courses across colleges and majors, to cultivate college students' entrepreneurship. The proposed method provides some ideas for the application of AI technology in the cultivation of students' entrepreneurship.

<abstract><p>Runtime verification (RV) is a lightweight approach to detecting temporal errors of system at runtime. It confines the verification on observed trajectory which avoids state explosion problem. To predict the future violation, some work proposed the predictive RV which uses the information from models or static analysis. But for software whose models and codes cannot be obtained, or systems running under uncertain environment, these predictive methods cannot take effect. Meanwhile, RV in general takes multi-valued logic as the specification languages, for example the $ true $, $ false $ and $ inconclusive $ in three-valued semantics. They cannot give accurate quantitative description of correctness when $ inconclusive $ is encountered. We in this paper present a RV method which learns probabilistic model of system and environment from history traces and then generates probabilistic runtime monitor to quantitatively predict the satisfaction of temporal property at each runtime state. In this approach, Hidden Markov Model (HMM) is firstly learned and then transformed to Discrete Time Markov Chain (DTMC). To construct incremental monitor, the monitored LTL property is translated into Deterministic Rabin Automaton (DRA). The final probabilistic monitor is obtained by generating the product of DTMC and DRA, and computing the probabilities for each state. With such a method, one can give early warning once the probability of correctness is lower than a pre-defined threshold, and have the chance to do adjustment in advance. The method has been implemented and experimented on real UAS (Unmanned Aerial Vehicle) simulation platform.</p></abstract>