An innovative approach to education and teaching, with a deeper integration of teaching and learning through a deeper mix of learning and study was proposed. The new organisational format combines independent learning in the form of microlessons and flipped classrooms with communication and cooperation in forums. In the context of the rapid development of Internet + education, big data information technology, and the accelerated promotion of education informatization by the Ministry of Education, this paper studies how to use the blended learning model to achieve the deep integration of information technology and classroom teaching through the innovative form of “microlesson and flipped classroom,” so as to improve students’ independent learning ability. Taking the university course of dynamic web design as an example, this course aims to achieve the teaching objectives of this course by using a deep learning model to guide the deep integration of information technology and classroom in a blended learning mode.
With the development of the world economy and the accelerating process of urbanization, cars have brought great convenience to people’s lives and activities, and have become an indispensable means of transportation. Intelligent vehicles have the important significance of reducing traffic accidents, improving transportation capacity and broad market prospects, and can lead the future development of the automotive industry, so they have received extensive attention. In the existing intelligent vehicle system, the laser radar is a well-deserved protagonist because of its excellent speed and precision. It is an indispensable part of achieving high-precision positioning, but to some extent, the price hindering its marketization is a major factor. Compared with lidar sensors, vision sensors have the advantages of fast sampling rate, light weight, low energy consumption and low price. Therefore, many domestic and foreign research institutions have listed them as the focus of research. However, the current vision-based intelligent vehicle environment sensing technology is also susceptible to factors such as illumination, climate and road type, resulting in insufficient accuracy and real-time performance of the algorithm. This paper takes the environment perception of intelligent vehicles as the research object, and conducts in-depth research on the existing problems in road recognition and obstacle detection algorithms, including road image vanishing point detection, road image segmentation problem, road scene based on binocular vision. Three-dimensional reconstruction and obstacle detection technology.
The ideological and political collaborative education mechanism is an important course teaching method that uses all courses as a carrier to cultivate students' all-round development in morality, intelligence, physique, and beauty. The purpose of this paper is to conduct a better research on the construction of the ideological and political collaborative education mechanism by building models based on edge computing and neural network algorithm. This paper first gave a general introduction to edge computing and neural network algorithm and then analyzed the current situation of ideological and political courses in a certain school. Then, edge computing and neural network algorithm were introduced into the analysis of an important course teaching method that used all courses as a carrier to cultivate students’ comprehensive development in morality, intelligence, physique, and beauty. The BP neural network model was established. Through analysis and comparison, the experimental results showed that 56.47% of the students believed that the impact of personal morality on the future development of college students was the first in the relationship between “virtue” and “talent.” More than half of the students believed that the “virtue” of building morality and cultivating people was mainly civic morality, and about 30% of the students thought that the main value was loving the party and patriotism, which meant that most students believed that the main value of building morality and cultivating people was to establish morality.
Smart cars are the result of the combination of the latest technological achievements in the fields of artificial intelligence, sensors, control science, computer, and network technology with the modern automobile industry. Intelligent cars usually have functions, such as automatic shifting, automatic driving, and automatic road condition recognition. The research of intelligent car technology involves many disciplines. This thesis focuses on the field of smart car visual navigation, focusing on image denoising, image information recognition, extraction, and pattern recognition control algorithms. The traditional trajectory tracking algorithm is mainly used in industrial computer or high-performance computer. The computational complexity leads to poor real-time control, and it is easily interfered by external complex terrain environment and internal disordered electromagnetic environment during vehicle driving. In general, on a regular basis, by the image analysis of the driver or the driver information, the image information is proposed using way trace processing technology, vehicle tracking control method and automatic driving rules. The simulation and experimental results show that the proposed control methods and rules used to carry out automatic driving vehicle are feasible. The algorithm reduces the complexity of the algorithm, improves the real-time and stability of the control and finally achieves a good trajectory tracking effect of the car on high-speed automatic driving.
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