The simulation resources are now virtualized and deployed on cloud platform. It can provide on-demand simulation tests, improving the efficiency of simulation systems. Simulation resources are packaged into virtual machines or containers. Complex software are packaged into virtual machines, and simple simulation services are packaged into containers. However, how to deploy simulation systems under resource constraints is a problem worth studying. This paper studies a virtual machine and container based hybrid cloud simulation platform. An automatic deployment method is proposed to reduce labor costs and errors of manual deployment. A simulation case is applied to verify the usefulness and efficiency of our approach.

论文设计了一种能够自动检测网络媒体中的不良信息的模型。AlphaGo依靠深度学习中的评价网络与估值网络在人工智能方面取得了巨大的进步,文中结合AlphaGo算法中的设计思想,将检测网页文本中的不良词汇的过程分为"挑词阶段"和"判别阶段",统计文本中出现的不良信息对应的信息值,与设置的阈值规则进行比较,将超出阈值的判别为不良文本,对极端负面的文本进行标记。仿真实验表明,该模型能够较好地提高网络媒体中不良词汇的自动检出率。

As the carrier of information transmission, the internet inevitably contains much bad information. In view of this phenomenon, with the purpose of identifying the bad information in the network, we combine existing Chinese text mining technology for experimental research. In combination with the idea of AlphaGo double decision system, the experiment will deal with the text information identification and classification using two system models, so that more accurate results can be obtained. In the experiment, a system does text segmentation and feature selection. Another system uses this method based on rules and statistics to compare text to determine whether or not it is bad information based on the established bad information database. And finally the two system carry out the text classification work. In the meantime, the two-system model worked together to identify and classify the bad information. AlphaGo's strategy was used to combine the former decentralized methods to make the system as a whole. This enables the system to improve the execution efficiency without reducing the recall rate, and the identification and classification accuracy.

Recently, the popularity of mobile socialization has promoted the development of WeChat greatly, while the WeChat official account platform is one of the most significant functions of WeChat. A hybrid recommendation algorithm combined the content-based and collaborative filter has been proposed in this paper by analyzing the hot articles posted by WeChat official accounts and the characteristics of the official account’s name. This hybrid recommendation algorithm can be used to recommend articles that have higher quality and richer content for subscriber.

The convening of "The Belt and Road" summit makes the trade exchange between China and other countries more frequent. The language barrier becomes the greatest obstacle of popularizing China's high and new technology. The maturity of VR technology makes the ability of computer to create a real scene gradually improves, this passage introduces the design and implementation of a case about Chinese vocabulary learning based on mobile VR scene for people along "The Belt and Road" region. Using the related programming interfaces provided by Google VR SDK, this project program on the Unity 3D development platform. With the help of mobile terminals and low-cost VR devices, this project can make the people in "The Belt and Road" region learn high-tech Chinese vocabulary whenever and wherever possible and help them grasp the specific context of the use of specialized Chinese words quickly and efficiently.