Intelligent Network Traffic Control Based on Deep Reinforcement Learning

This paper studies the problems of load balancing and flow control in data center network, and analyzes several common flow control schemes in data center intelligent network and their existing problems. On this basis, the network traffic control problem is modeled with the goal of deep reinforcement learning strategy optimization, and an intelligent network traffic control method based on deep reinforcement learning is proposed. At the same time, for the flow control order problem in deep reinforcement learning algorithm, a flow scheduling priority algorithm is proposed innovatively. According to the decision output, the corresponding flow control and control are carried out, so as to realize the load balance of the network. Finally, experiments show, the network traffic bandwidth loss rate of the proposed intelligent network traffic control method is low. Under the condition of random 60 traffic density, the average bisection bandwidth obtained by the proposed intelligent network traffic control method is 4.0mbps and the control error rate is 2.25%. The intelligent network traffic control method based on deep reinforcement learning has high practicability in the practical application process, and fully meets the research requirements.

Searching for New Technology Acceptance Model under Social Context: Analyzing the Determinants of Acceptance of Intelligent Information Technology in Digital Transformation and Implications for the Requisites of Digital Sustainability

Intelligent information technology (IIT) based on AI and intelligent network communication technology is rapidly changing the social structure and the personal lives. However, IIT acceptancefrom various perspectives still requires extensive research. The research question in this paper examines how five factors—psychological, technological, resource, risk perception, and value factors—influence IIT acceptance. Based on an analysis of survey data, it was first found that the acceptance rate of IIT itself was generally very high. Second, in terms of IIT acceptance, among twenty-five predictors, voluntariness (+), positive image of technology (+), performance expectancy (+), relative advantage (+), radical innovation (+), and experience of use (+) were found to have significant effects on the IIT acceptance. Third, in addition to technological factors, psychological factors and risk perception factors also played an important role in individuals’ decisions regarding IIT acceptance.

Economic Market Fluctuation Model Based on Internet of Things Technology

In order to explore the impact of the Internet of Things technology on economic market fluctuations and the analysis effect of the Internet of Things technology on economic market fluctuations, this paper uses the Internet of Things algorithm to improve the economic fluctuation model. Moreover, this paper uses the Internet of Things algorithm to locate economic transactions and performs data processing to optimize the intelligent network system to improve the operating effect of the economic system. In addition, this paper improves the sensor node algorithm and proposes to use the weighted value of network node density to balance the positioning problem caused by the unbalanced distribution of network nodes in the detection area. Finally, this paper analyzes the market economy volatility model through the Internet of Things technology, combined with simulation experiments to explore the application of the Internet of Things technology in the economic market volatility model. Through experimental research, it can be known that economic market fluctuation models based on Internet of Things technology can play an important role in market economic analysis.

Application of Artificial Intelligence and Wireless Networks to Music Teaching

The study is aimed at improving student’s learning ability and constructing an intelligent, all-around, and three-dimensional innovative classroom by using intelligent technology. Middle school C is taken as the research object. First, the current situation of music teaching in middle school C is analyzed, and an intelligent music teaching mode suitable for middle school C is designed. Second, the intelligent music teaching mode is applied in the actual teaching process, and students’ feelings for intelligent music classrooms are explored by a questionnaire survey. Finally, the teaching effect is analyzed. The results show that 96.7% of the students hold a positive attitude towards the new teaching mode, indicating that contemporary students have relatively high adaptation to and recognition with the development of artificial intelligence (AI). 84% of the students are not particularly skilled in the operation of the intelligent network platform, and 9.3% of the students believe that the operation of the platform is relatively difficult. Therefore, teachers should teach students how to use tablets. 95% of the students hold that teachers can quickly find teaching resources related to the course content on the tablet and introduce them to students in the teaching process, indicating that teachers are familiar with the usage of new teaching equipment. On the whole, students have a good experience of the new teaching mode. This new teaching mode based on AI is a response to the new course concept proposed in China.

Intelligent Network Office System Based on Cloud Computing and Machine Learning

The traditional data automatic office system has limited mining and computing capabilities. Due to the iterative complexity of data mining algorithms, it is difficult to discover the relationships and rules existing in the Internet of Things data as well as impossible to advance the efficiency of the office system based on the existing Internet of Things data. This paper combines cloud computing and machine learning to construct an intelligent network office system, realizes large-scale IoT data processing through the combination of IoT data mining technology and cloud computing framework, and constructs the functional module structure of the intelligent network office system through demand analysis. On this basis, this paper conducts system performance verification and conducts experimental design based on network intelligent system demand. The experimental results show that the system constructed in this paper has certain practical effects, which can provide theoretical reference for subsequent related research.

A Data-Driven Control Strategy for Urban Express Ramp

Expressway, as the main artery of urban traffic, realizes the smooth operation of the whole urban road network through reasonably balancing the traffic flow. However, due to the lack of reasonable and effective traffic control, the safety and congestion of expressways are becoming more and more serious. The development of intelligent network technology provides a new idea to solve the control problem of expressways. In this paper, a data-driven ramp control model of urban expressway is constructed. The interaction of traffic information is realized through intelligent network connection technology. The cooperative control strategy of VSL and RM is adopted. The mutual feedback of VSL and RM is realized based on the improved METANET model. The simulation experiment based on VISSIM secondary development shows that the collaborative control strategy under the intelligent network environment could make the vehicle travel time reduced by 20.59% and the speed difference between adjacent sections of the expressway mainline by 34.07%, which realized the coordinated control of the mainline and the on-ramp under the intelligent network environment, alleviate the expressway traffic congestion, reduce the traffic pressure, and improve the efficiency of the road network.

Overview of Intelligent Vehicle Infrastructure Cooperative Simulation Technology for IoV and Automatic Driving

This paper reviews the development of vehicle road collaborative simulation in the new era, and summarizes the simulation characteristics of two core technologies in the field of transportation after entering the era of Intelligent Networking: Internet of Vehicles technology and automatic driving technology. This paper analyzes and compares the mainstream Internet of Vehicles (IoV) simulation and automatic driving simulation platforms on the market, deeply analyzes the model-based IoV simulation, and explores a new mode of IoV simulation in the era of big data. According to the latest classification standard of automatic driving in 2020, we summarize the simulation process of automatic driving. Finally, we offer suggestions on the development directions of intelligent network-connected vehicle simulation.