To cultivate correct labour values and good labour quality of college students and effectively promote the development of their labour concept education, this work explores the impact of wireless network mobile devices on college students’ labour concept education under the environment of artificial intelligence. Firstly, a questionnaire survey is used to investigate the labour concept of 400 college students. Secondly, the impact of wireless network mobile devices on college students' labour education is obtained by comparing group A (using artificial intelligence APPs for wireless network devices to learn about labour concepts) and group B (using traditional classroom teaching methods for to learn about labour concepts). According to the statistical survey results, about 20% of college students agree that “if they have enough money to live, they do not have to work”; while less than 50% agree that “they cannot be admitted to civil servants and senior managers in the company and are willing to engage in ordinary labour in the future”. Meanwhile, about 50%–60% of college students think that “housework has nothing to do with me, and it’s all the work of adults”. In the question “What would you do when you find that the public area is dirty and poor, but it’s not your turn to be on duty?”, only about 50% of college students are willing to clean actively. Comparing the data of group A and group B suggests that the labour view expressed in group A is more biased in the cognition of labour purpose, and students in group A is more negative and lazier in labour attitude and labour habits, which shows that wireless network mobile devices have a great negative impact on the overall labour view of college students. Therefore, it is revealed in this work that the correct use of artificial intelligence technology in the education of labour concept has extremely important value for the intelligent development of education methods.
With the advancement of science and technology, robotics has made considerable progress. Robots can free humans from heavy repetitive labor. From the industrial field to the lives of the general public, robots are playing an increasingly important role. Path planning is one of the core contents of industrial wheeled robotics and has very important significance. Based on the reinforcement Q learning and BP network, this work studies the path planning of industrial wheeled robots. According to task requirements of path planning, design learning strategies, and control rules, use wireless communication to transmit environmental perception information and propose corresponding control strategies. The main researches are as follows: (1) Based on grid map environment, a path planning algorithm with Q-CM learning is designed. The algorithm first designs robot states and actions based on reinforcement Q learning and grid map and establishes Q matrix. Secondly, a coordinate matching (CM) obstacle avoidance control rule is designed to improve the efficiency of robot avoidance. Then, a reward function is designed for the evaluation problem of action execution. (2) Based on the map environment of free space and the generalization ability of BP neural network, a robot path planning with Q-BP learning is designed. The algorithm first designs the sensor detection mechanism and action selection strategy according to the state of the robot in the map environment. Secondly, a dynamic reward function is designed. Then, according to the obstacle avoidance requirements of special obstacles, the obstacle avoidance rules after three shocks were designed. The experimental results show robots can perform better path planning in a discrete and continuous free space map, and the obstacle avoidance effect is good.
The exploration expects to improve the teaching quality of the electronic technology distance course. The teaching management system of the electronic technology network course is constructed based on a flipped classroom. Firstly, the idea of the flipped classroom is analyzed, and the electronic technology course is designed. Secondly, the teaching management system of the electronic technology network course is established, and the remote experiment module is discussed. Finally, the questionnaire survey is made to investigate the use effect of the system. The results show that about 79% of the students are satisfied with the system. About 80% think it allows them to focus on the teaching process. Only 1.3% think that this system does not work for their learning. This shows that the system can help students improve their learning efficiency. However, the main defect is the lack of a real-time feedback module for users on the application effect of the system. In a word, the exploration provides a reference for developing and designing the follow-up network course teaching system.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.