Anonymous communication can effectively protect personal privacy and data security, but it is also used by criminals to evade network censorship and engage in various illegal activities because of its concealment. The characteristics of anonymous traffic protocols are concealed, traffic characteristics are confused, and detection of anonymous traffic is difficult. In order to strengthen the supervision of anonymous traffic, this paper proposes an anonymous traffic detection method based on entropy estimation. Through the analysis of the packet entropy characteristics of the network session, the entropy value of the encrypted message sequence is used as the input feature of the classifier, and combined with the eXtreme Gradient Boosting Tree (XGBoost) algorithm, the detection of anonymous traffic is realized. Different from the existing anonymous traffic feature detection methods based on protocol features and traffic features, this method extracts the characteristics of the traffic itself for research. The experimental results show that the accuracy of this method can reach 98%.

Based on the operation of mediation and regulation effect in Python language, the analysis process of mediation and regulation effect is realized by using Pandas library and Statsmodels library of Python language, and the basic principles and analysis steps of mediation and regulation effect are clarified. It mainly demonstrates the bootstrap method instead of Sobel test in intermediary test, the realization of grouping regression by weighted least square method in adjustment test, as well as the commonly used two factor analysis of variance and interactive multiple regression analysis.

With the popularization of the internet and social media, the impact of network public opinion on the social level cannot be underestimated, and sentiment analysis in network public opinion is of utmost importance. This paper collects, organizes, and summarizes relevant literature on sentiment analysis in network public opinion research in China, adopting bibliometric methods to analyze the quantity of literature, co-occurrence of high-frequency keywords, and evolution of theme keywords. Following the pro‐ cess of sentiment analysis in network public opinion research, this study analyzes the main research content, ideas, and methods in data collection, data processing, sentiment recognition(classification/tendency),sentiment evolution. Finally, this study points out fu‐ ture research directions, and gives the working directions of next steps.

Network learning space is the product of the deep integration of information technology and learning science. The application efficiency of teachers' network learning space has a strong practical significance in promoting digital teaching. Based on the review of relevant literature, combined with expert interviews, this paper uses Interpretative Structural Modeling to build a hierarchical structure model that affects the effectiveness of online learning space application of primary and secondary school teachers. The research shows that space resources, self-efficacy, perceived usefulness are the direct factors that affect teachers' use of online learning space; School concept and the support of relevant policies are the basic influencing factors; Teacher related training, infrastructure conditions, space management, teacher incentive strategies and so on are indirect influencing factors. Based on the analysis of the structural characteristics, this paper gives some suggestions to promote the construction and high-quality development of online learning space for primary and secondary school teachers.

The course of Network Supervision and Control is one of the core courses of network security and law enforcement major in public security colleges. By analyzing the teaching status of this course, and on the basis of expounding the key contents of "Four self- confidence", this paper organically integrates "four self- confidence" into the classroom teaching of Network Supervision and Control, so as to realize the purpose of sublimating the teaching connotation of the course and building a new curriculum framework. This teaching mode can improve the overall design of the course, strengthen students' ideals and beliefs, ensure the teaching effect of the course, and lay a solid foundation for the training of network security professionals.

In response to the problem of weak correlation between related elements in current river pollution incidents, a river pollution knowledge graph is constructed and applied based on the advantages of knowledge representation in knowledge graphs. Firstly, the construction methods of the pattern layer and data layer of the knowledge graph are sorted out. Then, take the Mulanxi River Basin for example, the pattern layer of the river pollution knowledge graph is constructed in a top-down manner, the data layer is constructed using structured data and unstructured data extraction techniques, and the knowledge is stored and applied using the Neo4j graph database. Finally, rapid tracing of a pollution event in the Mulanxi River Basin is achieved using this knowledge graph. The experiment shows that the construction of this river pollution knowledge graph solves the problems of scattered data and weak correlation in water pollution events effectively, which can provide certain application value for rapid tracing of water pollution events.

This study designs an intelligent sleep massage pillow system, aiming to provide users with effective sleep protection. The system adopts TGAM brain wave detection, temperature and humidity control, massage control, Bluetooth, WIFI and other module technologies to control smart sleep products in real time. It is a highly intelligent and fully automated process. In addition, the Sleep Care APP also has a sleep staging algorithm, automatically generates sleep depth analysis reports, and provides users with personal‐ ized sleep plans. It also uses an AI smart assistant to analyze users’ sleep conditions in real time, helping users improve their sleep experience more scientifically.

In order to optimize reinforcement learning for the great errors causing by the unclear reward function, this paper deeply studies and implements the behavior cloning algorithm and data aggregation algorithm in the imitation learning algorithm. The algorithm flow is modeled by activity diagram, the relationship between classes is modeled by class diagram, and the core interaction process is modeled by sequence diagram. According to the experimental results, this paper compares the advantages and disadvantages of the behavior cloning algorithm and the data aggregation algorithm, and discovers that the behavior cloning algorithm offline training can avoid interaction with the real environment, but error accumulation will lead to error results; data aggregation algorithms must interact with the environment online, and select the corresponding state of the observation value according to the strategy to solve the problem of error accumulation.

To address the problem of poor localization performance caused by the small number of fingerprint points and the lack of representativeness of fingerprint features in the offline phase of fingerprint-based indoor localization, a CSI-based indoor localiza‐ tion method is proposed using Inverse Distance Weighted (IDW) interpolation and amplitude-phase fusion network. The IDW inter‐ polation algorithm is used to generate a large-capacity fingerprint library. Then, a parallel convolutional neural network (CNN) is used to process the amplitude and phase to obtain the positional fingerprint features. Finally, a novel integrated architecture fusing Random Forest (RF) and Multilayer Perceptron (MLP) is used for classification to obtain the estimated positions of the target loca‐ tion samples.

Aiming at the accidents caused by household gas leakage, such as human poisoning, ?re and explosion, this paper proposes the design and implementation of home kitchen environment remote monitoring system based on cloud platform technology. The system uses STM32 chip as the core processor, analyzes the real-time data collected by the sensor, compares the preset threshold for strategic control, and realizes the intelligent control of the gas electronic valve, alarm and intelligent window in the kitchen. And the data is regularly uploaded to the cloud platform for storage through the ESP8266 communication module, and the storage data of the cloud platform is invoked by the mobile terminal in real time to carry out remote dual control with human intervention on the kitchen state to realize reliable and safe management. 

Take Zhengzhou University of Light Industry for example, aiming at the problems existing in the traditional electronic training teaching mode, this paper proposes the teaching concept of "project driven+subject competition", and explores the open experimental mode after class to increase students' interest in learning, improves their autonomous learning and comprehensive analysis abilities, and cultivates students' unity and cooperation, hands-on ability, and innovation awareness. Based on our advantageous discipline competition experience, the teaching units of electricians and electronics are closely connected, thereby mobilizing students' enthusiasm to participate in discipline competitions. Teachers' teaching skills, teaching level, and comprehensive ability are improved in the teaching reform.

Addressing the issue of inadequate classification accuracy in named entity recognition via entity word-to-relationship modeling, we propose a method using rotational attention. Firstly, the text is encoded using the BERT model and Bi-LSTM, fol‐ lowed by extraction of features from input text using convolutional neural networks. Subsequently, the extracted feature sequence is inputted into the rotational attention model for output probability calculations, and the MLP layer is used for output classification. The study's outcomes affirm the efficacy and feasibility of the technique proposed in this paper, as it successfully yields superior re‐ sults on mainstream English databases including CADEC, GENIA and CoNLL2003, for named entity recognition exercise. 

Based on the core technology of the next generation Internet IPv6, which has the characteristics of large enough address space, higher security, better service quality and supporting mobile terminal equipment, the information development of universities is gradually promoting the IPv6 network deployment and IPv6 network application of information system. However, in the process of promoting IPv6 network deployment and access of application systems in universities, there are still some problems that some un‐ derlying network devices do not support IPv6 protocol, and security devices cannot achieve comprehensive control. In order not to change the current school network architecture, without affecting the current IPv4 network access, this paper puts forward the imple‐ mentation of IPv6 network deployment and access for college Web application system based on the intelligent DNS and reverse proxy. This technology has the characteristics of simple principle, strong stability, high security and easy maintenance. Through link‐ age deployment, IPv6 network access and IPv4 access security reinforcement of the school application system are realized, which provides reference practice for the deployment of IPv6 network application.