Application of optical network transmission based on machine learning and wireless sensor networks in artificial intelligence online education system

Kefeng Li
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Abstract

The traditional network transmission mode faces challenges in the real-time and reliability of teaching resources, especially in the environment of Internet of Things and wireless network. With the rapid development of artificial intelligence technology, this paper aims to study the application of optical network transmission technology based on machine learning and wireless network in artificial intelligence online education system, so as to improve the transmission efficiency of educational information and user experience, and promote the learning effect. In this paper, machine learning algorithm is used to analyze and optimize data flow in wireless and mobile networks in real time. Meanwhile, high speed and low latency of optical networks are utilized for data transmission. By building experimental models and testing them in real educational Settings, we evaluate the performance of the system under various network conditions. The experimental results show that the online education system combined with machine learning and wireless optical network transmission is significantly better than the traditional methods in terms of data transmission speed, delay and stability. Especially in the high concurrent user environment, the system can effectively reduce the data packet loss rate and improve the learning experience of users.

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基于机器学习和无线传感器网络的光网络传输在人工智能在线教育系统中的应用
传统的网络传输模式在教学资源的实时性和可靠性方面面临挑战,尤其是在物联网和无线网络环境下。随着人工智能技术的飞速发展,本文旨在研究基于机器学习和无线网络的光网络传输技术在人工智能在线教育系统中的应用,从而提高教育信息的传输效率和用户体验,促进学习效果的提升。本文采用机器学习算法对无线网络和移动网络中的数据流进行实时分析和优化。同时,利用高速、低延迟的光网络进行数据传输。通过建立实验模型并在真实教育场景中进行测试,我们评估了系统在各种网络条件下的性能。实验结果表明,机器学习与无线光网络传输相结合的在线教育系统在数据传输速度、延迟和稳定性方面明显优于传统方法。特别是在高并发用户环境下,该系统能有效降低数据丢包率,改善用户的学习体验。
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