Network Management Information Research Articles

Design and implementation of marine information management network security system based on artificial intelligence embedded technology

The complexity of marine information types, data diversity, data collection difficulties and other aspects makes the network security of marine information management more and more prominent, and has become a major issue affecting the stability of the country and society, so it is urgent to establish a marine information management network security system. Traditional network security technology adopts a passive approach and cannot actively detect viruses, trojans, and other hidden objects in the network. Antivirus software would only be used when attacked. If the risk of network attack is too great, the consequences would be unimaginable. This paper designed a marine information management network security system based on artificial intelligence embedded technology, which improved the efficiency of marine information security management. This paper also applied the embedded technology of AI to the network security management, and proposed the k-means clustering algorithm (K-Means) of AI, which can greatly improve the network security. The experimental results in this paper showed that the intrusion detection rates of System 1 and System 2 were 56.3% and 78.3% respectively when the number of viruses was 50 at 30M, and 65.5% and 80.1% respectively when the number of viruses was 50 at 60M. It showed that the intrusion detection rate of System 2 was higher both at 30M and 60M.

Research on Networking of 10 kV Substation Management Information Area

At present, the substation network management information area, as an essential part of the substation network communication, is an important transmission channel to realize the intelligent auxiliary control of the substation network. Establishing a stable and reliable substation management information area network has become an essential part of upgrading the substation network. Firstly, this paper designs a Gigabit-Capable PON, and an industrial Ethernet networking scheme, based on certain city data. Secondly, based on the availability of single-device operation, a calculation model algorithm for reliability analysis is innovatively proposed to realize the comparison of networking solutions. Thirdly, regarding the economy, the concept of unit incremental investment reliability index is applied to two groups of networking solutions comparison for the first time. Finally, a networking scheme and suggestions for the substation network management information area are proposed.

Distribution network topology identification based on gradient boosting decision tree and attribute weighted naive Bayes

Topology identification is important to ensure a safe and stable distribution network operation, especially in the case of high proportion of new energy access to the distribution network. It could provide structural information for distribution network management and the foundation of distribution network system analysis. In consideration of the influence of different power flow parameters on topology identification results, this paper proposes a topology identification method based on gradient boosting decision tree (GBDT) and attribute weighted naive Bayes (AWNB). Firstly, a gradient boosting decision tree was used to calculate the importance of different power flow parameters to reduce the data dimension and computational complexity. Then each attribute is given a weight based on the result of the importance calculation. Secondly, the weighted set was used as input to train the AWNB classifier to make the model more realistic. Finally, the IEEE 30-node model was used to verify the performance of the proposed method. The results indicate that the proposed method has high accuracy and good robustness, which could maintain a high success rate of topology identification under different noise environments.

A research on data integration and application technology of urban comprehensive pipe gallery based on three‐dimensional geographic information system platform

AbstractThree‐dimensional Geographic Information System (3D GIS) technology is often used in geology, mining, hydrology and other fields. It is also used in intelligent management of urban comprehensive pipe gallery projects, because the real environment with the sky, ground, and underground can be intuitively simulated in the virtual reality environment by 3D GIS platform. Through the research on data integration and application technology of urban comprehensive pipe gallery, the 3D model of comprehensive pipe gallery project data can be created based on Building Information Modelling and GIS technologies, and a comprehensive underground pipelines network management information system has been developed to realise the integration of various data, including 3D data of comprehensive pipe gallery model and underground pipe network model, 2D or 3D GIS data of the ground and underground etc. On this basis, the functions of intelligent planning and approval management, the safety analysis and disaster prevention planning and decision‐making of urban comprehensive pipe gallery projects can be further realised. The research results have been applied to the planning and construction process management of the comprehensive pipe gallery project in the sub‐centre of Beijing.

Governance and sustainability of distributed continuum systems: a big data approach

Distributed computing continuum systems (DCCS) make use of a vast number of computing devices to process data generated by edge devices such as the Internet of Things and sensor nodes. Besides performing computations, these devices also produce data including, for example, event logs, configuration files, network management information. When these data are analyzed, we can learn more about the devices, such as their capabilities, processing efficiency, resource usage, and failure prediction. However, these data are available in different forms and have different attributes due to the highly heterogeneous nature of DCCS. The diversity of data poses various challenges which we discuss by relating them to big data, so that we can utilize the advantages of big data analytical tools. We enumerate several existing tools that can perform the monitoring task and also summarize their characteristics. Further, we provide a general governance and sustainable architecture for DCCS, which reflects the human body’s self-healing model. The proposed model has three stages: first, it analyzes system data to acquire knowledge; second, it can leverage the knowledge to monitor and predict future conditions; and third, it takes further actions to autonomously solve any issue or to alert administrators. Thus, the DCCS model is designed to minimize the system’s downtime while optimizing resource usage. A small set of data is used to illustrate the monitoring and prediction of the performance of a system through Bayesian network structure learning. Finally, we discuss the limitations of the governance and sustainability model, and we provide possible solutions to overcome them and make the system more efficient.

DISCO: Distributed computation offloading framework for fog computing networks

Fog computing networks have been widely integrated in IoT-based systems to improve the quality of services (QoS) such as low response service delay through efficient offloading algorithms. However, designing an efficient offloading solution is still facing many challenges including the complicated heterogeneity of fog computing devices and complex computation tasks. In addition, the need for a scalable and distributed algorithm with low computational complexity can be unachievable by global optimization approaches with centralized information management in the dense fog networks. In these regards, this paper proposes a distributed computation offloading framework (DISCO) for offloading the splittable tasks using matching theory. Through the extensive simulation analysis, the proposed approaches show potential advantages in reducing the average delay significantly in the systems compared to some related works.

Forecasting the trafic of telecommunication networks and their optimization

The article is devoted to methods for generating predictive estimates of the traffic of new segments of telecommunication systems before they are put into operation in order to optimize the structure of networks and improve their performance. Three types of traffic sources are distinguished — network management system, information protection and line integrity system, and users. Each source has its own approach to assessing the contribution to the total traffic. The resulting numerical estimates were used to find an array of optimal routes in terms of minimum load between a given initial communication node and a set of given end nodes. An original method for solving this problem is proposed.

Optimization of Intelligent Network Information Management System under Big Data and Cloud Computing

With the economy and society development, big data storage in management has become a problem which can’t be ignored. Task allocation management and optimization is a significant factor in the enterprise sustainable development. The enterprise information intelligent management has become management mode and presents information to the business managers in a more effective and lower cost. In order to solve the management problem under big data and cloud computing, a research on the optimization of intelligent network information management system is proposed. Firstly, build the overall model of big data diversion system under cloud computing platform, and design the architecture of Tiny OS operating system for big data diversion; Secondly, S3C2440 is used as the system control core to design the hardware structure of the shunting system; Finally, the software of the system is developed by using the dynamic loading method of functional subroutines in Linux operating system. The power supply voltage is DC 3.3 V and 1.25 v. DC 5 V is used as the overall power supply of the circuit board, and 0.1 is added at both ends of lm1117 chip F and 100 F capacitance for FIR filtering of power supply. Simulation experiments are carried out to verify the performance, which shows the superior performance of the big data shunting system designed in this paper.

Challenges and Strategies of Student Management in Universities in the Context of Big Data

As the most active and knowledge-intensive university, the forefront of the use of network information technology, the education, management, and service mode of the school, and the ideology, learning methods, and behavioural habits of teachers and students will be profoundly affected by the era of big data. The current form of student management in universities is too old and needs to be replaced by a new management system. With the promotion of the construction of smart campus, the rapid development of big data technology has realised the innovation of student management in colleges and universities. This paper takes students of Wuhan University as the research object. By collecting various application data in the campus information system, the K-means algorithm in cluster analysis is used to classify students’ campus behavioural characteristics, and then the Apriori algorithm is used to correlate students’ behavioural characteristics with their academic performance. The experimental results show that there is a close relationship between the consumption behaviour, work and rest behaviour, study behaviour characteristics of different student groups, and their academic performance. Using the results of these analyses, universities can adopt differentiated management measures for different categories of students, which can help improve students’ academic performance as well as further enhance the efficiency of student management.

The Application of Multimedia Visualization Platform in Business Management from the Perspective of Supply Chain Management

At present, in the process of enterprise operation and development, the implementation of enterprise operation modernization is a necessary condition and an effective measure to raise the management level. With the aim to achieve such enterprise modernization, it is necessary to improve the management’s modernization ability by improving the modernization level of enterprises. Therefore, corporate executives should pay more attention to the construction of informatization and implement informatization through effective methods and measures to further improve management efficiency. It will note the concept and connotation of business management, analyze the significance of the multimedia visualization platform at the level of enterprise business supply chain, analyze the current business management of enterprises and put forward scientific coping strategies, cater to the social development situation, and constantly innovate the development path of enterprise network information and chemical business management; this will help related majors.

A Random Matrix Network Model for the Network Teaching System of College Music Education Courses

How to improve the teaching management model has always been an important part of the research and exploration of university music teaching management. Based on the random matrix network theory, this paper builds a random matrix network model and uses the random matrix network of Internet/Intranet to realize the electronic and networked multimedia information management, which makes the data query more flexible and convenient. In the random matrix network environment, the security of the model network is greatly improved, which solves the expansion problem of the random matrix network. During the simulation process, the model integrates the relevant image, audio, video, animation, and other multimedia processing technologies and storage technologies. The system adopts object-oriented analysis and design ideas to analyze the requirements, adopts the random matrix network architecture, and uses tomcat as the server, and the SQL Server database launched by Microsoft is used as the back-end data support, and the Struts architecture is designed by using the MVC development mode to ensure the system has good maintainability and enhanced data processing capabilities. The experimental results show that the response time of the system login verification is less than 2 seconds, the response time of adding users is less than 2 seconds, the response time of downloading assignments is less than 2 seconds, the response time of uploading courseware is less than 3 seconds, and the response time of checking the results is less than 2 seconds. The separation of technology effectively improves the scalability and maintainability of the system.

Research on the Reality and Path of Resource-Based Enterprises’ Digital Transformation

With the rapid development of information and communication technology, big data, cloud computing, and other digital technologies have become the impetus for the transformation of resource-based enterprises. The digital transformation of resource-based enterprises mainly involves business process reengineering and business model innovation. At present, there are several problems, such as traditional ideas that are difficult to change, insufficient online participation, inconsistent data standardization, insecure database, and shortage of talents. Based on the case study of large coal mining enterprises in Ordos and the mechanism analysis from strategic planning, operation management, and marketing, this study puts forward three paths, which are information network management, process intelligence management, and service platform management. Finally, the study points out that data mining, marketing strategy, data standards, data security, and team construction should be improved.

Research on Logistics Information Management System Based on Blockchain Perspective

The current problems of information asymmetry, low process transparency and insecure information storage in logistics industry have restricted the development of logistics industry to a certain extent. Drawing on the advantages of blockchain technology in decentralization, data tamper-proof and data confidentiality, the article analyzes the typical application scenarios of blockchain in logistics, proposes a blockchain-based logistics information management framework, and gives a typical logistics blockchain network topology and information management process.

ADMINISTRATION AND MONITORING OF COMPUTER NETWORKS AS A METHOD OF SOLVING MODERN PROBLEMS IN FINANCIAL SYSTEMS

The article defines the role of administration and monitoring of computer networks in financial systems. The main objectives of monitoring are network activities. Research analysis and task setting are to monitor traffic as an important source of information for effective network management, bringing the main functions of network administrators in financial systems. The effectiveness of monitoring using network traffic analyzers depends on the topology of the network under study, its configuration, and the set of devices from which the network is built. The result of the study in the form of two main needs is monitoring and analysis. Effective network management in the field of finance and banking systems, as well as continuous improvement of software in financial systems. What are the requirements for software agent technology? The main functions and tasks of network administration - a description of monitoring, its tools and network analysis, which are divided into certain classes: protocol analyzers (Protocolanalyzers), system management tools (System Management), network management systems (Network Management Systems), built diagnostic and management systems (Embedded Systems), multifunctional devices for analysis and diagnostics, equipment for diagnostics and certification of cable systems, expert systems. The main functions of monitoring and analysis of network activity in financial systems are in the form of 4 items: performance analysis, network accounting, security management, error handling. The main aspects for the development of software for monitoring network activity, the needs that arise during the work with the network, the peculiarities of the administration of these networks, analyzed the existing software for monitoring network activity in financial systems. An example of the use of software for the administration and monitoring of computer networks is a description of the capabilities and highlighting the advantages and disadvantages of these programs in financial systems. This article discusses the following software: Total Network Monitor 2, Observium, Network Olympus, Zabbix.

Design of Network Information Management System Based on C/S Architecture

Due to the incomplete data preservation of traditional systems, the success rate of network information data update is reduced. Based on C/S architecture, a network information management system is designed. In terms of hardware design, the C/S architecture allows for the optimisation of the RFID circuitry to speed up the rapid conversion of data. In software design, information management model is established based on C/S architecture, protocol identification method deletes abnormal data in the transmission process, fuzzy method sets up data storage mode, centralizes control of network information, and completes the design of network information management system. The experimental results show that the new system has an average success rate of 98.96%, which is 9.16% and 10.46% higher than the traditional system, respectively, and can increase the update effect of network information.

Classification and Analysis of Malicious Traffic with Multi-layer Perceptron Model

Traffic classification is very important field of computer science as it provides network management information. Classification of traffic become complicated due to emerging technologies and applications. It is used for Quality of Service (QoS) provisioning, security and detecting intrusion in a system. In the past used of port, inspecting packet, and machine learning algorithms have been used widely, but due to the sudden changes in the traffic, their accuracy was diminished. In this paper a Multi-Layer Perceptron model with 2 hidden layers is proposed for traffic classification and target traffic classify into different categories. The experimental results indicate that proposed classifier efficiently classifies traffic and achieves 99.28% accuracy without feature engineering.

DEVELOPMENT OF INFORMATION SYSTEM BY MEANS OF ASP.NET

For most state institutions and enterprises, there is a problem of automation of the business process, which is distributed among branches throughout the republic. The article discusses the problem of designing a system for managing geographically distributed branches. The process of development of the business process management and automation system for the branch network management information system and minimization of the total operating cost of the system is described. An example of designing an information system for managing payments for administrative violations of traffic rules in branches of the Kyrgyz Post State Enterprise is given.

A network traffic forecasting method based on SA optimized ARIMA–BP neural network

Network traffic forecasting provides key information for network management, resource allocation, traffic attack detection. However, traditional linear and non-linear network traffic forecasting models cannot achieve enough prediction accuracy for future traffic prediction. In order to resolve this problem, a network traffic prediction method based on SA (Simulated Annealing) optimized ARIMA (Autoregressive Integrated Moving Average model)-BPNN (Back Propagation Neural Network) is proposed in this paper, which makes comprehensive use of linear model ARIMA, non-linear model BPNN and optimization algorithm SA. With enhancement of the BPNN global optimization ability, it can fully realize the potential of mining linear and non-linear laws of historical network traffic data, hence improving the prediction accuracy. This paper selects the historical network traffic data of two different sampling points in the WIDE project to predict, and utilizes the MAE(Mean Absolute Error), RMSE(Root Mean Square Error), and the MAPE(Mean Absolute Percentage Error) as the evaluation index of the prediction effect. Experimental results show that our proposed method outperformed traditional network traffic prediction model, with several improvements in network traffic prediction accuracy.

Design and Implementation of Hotel Network Management Information System in the Era of Big Data

The huge growth of social media and consumer generated content on the Internet has inspired the development of so-called big data analysis to understand and solve real-life problems. However, although a few studies have used new data sources to solve the important research problems in the hotel industry, there is no systematic application of big data analysis technology in these studies. The purpose of this study is to explore and demonstrate the utility of big data analysis to better understand important hotel industry issues. The software is used to analyze the big data generated in the hotel reservation module, customer check module and customer bill module in the hotel management system. This paper discusses the dimensions of hotel service quality, evaluates its relative importance, and evaluates the service quality of Furtherance hotel from the perspective of guests. Through the analysis of academic research and hotel operators’ current practice, this paper sorts out and clarifies the problems related to online reviews. Taking advantage of the advantages of user reviews, this paper aims to provide new insights into the determinants of hotel customer satisfaction by distinguishing customers from different linguistic groups. The results show that the return on net assets is 34%, the return on total assets is 26%, the profit margin on sales is 16%, and the asset liability ratio is 21%.

Decision support systems based on scientific evidence: bibliometric networks of invasive Lantana camara

Extraction and analysis of useful knowledge from the vast amount of relevant published literature can add valuable insights to any research theme or area of interest. We introduce a simplified bibliometric data analysis protocol for gaining substantial insights into research thematics, which can also serve as a handy practical skill for researchers while working from home. In this paper, we provide ways of developing a holistic research strategy using bibliometric-data driven approaches that integrate network analysis and information management, without the need for full paper access. This protocol is a comprehensive multi-modular pathway for analysis of metadata obtained from major scientific publishing houses by the use of a Decision Support System (DSS). A simple case study on the invasive species Lantana camara has been presented as a proof-of-concept to show how one can implement this DSS based protocol. Some perspectives are also provided on how the outcomes can be used directly or scaled up for long term research interventions. We hope that this work will simplify exploratory literature review, and enable rational design of research objectives for scholars, as well as the development of comprehensive grant proposals that address gaps in research.