项目名称：国家自然基金申请人群体行为建模及其对网络稳定性影响机理研究

中文摘要：自然基金申请网络负荷严重超标导致瘫痪现象，已经成为制约国家战略发展的重大紧迫急需问题。本项目以改善登录失败率为服务目标，深入研究自然基金申请人的登录行为，借助心理学和社会群体学理论，建立自然基金申请人群体行为理论和数学模型。在此基础上，进一步发展基于大数据的机器理解和智能学习理论，实现一套基于云计算的网络流量和群体行为自动分析理论和关键算法，通过网络流量和数据包的内容分析，实现对自然基金申报时间段内海量群体行为的特征自动提取、分析和理解；最后通过理论与实际分析的对比，对自然基金申请人群体行为模型的有效性进行检验和修正。本课题的研究将促进网络信息学、心理学、社会行为学的跨学科交叉融合，将形成有重大的理论意义的研究成果，同时将对我国发展自然基金数据网、提高申请成功率、改善青年科学家睡眠质量提供关键理论支撑和技术保障，具有重要的战略应用价值。

中文关键词：数学建模；特征提取

The application of the Natural Resources Fund’s application for a serious overload of the network caused a paralysis phenomenon, which has become a major urgent and urgent problem that constrains the development of the national strategy. This project aims at improving the failure rate of registration and serves as an in-depth study of the registration behavior of natural fund applicants. With the help of psychology and social group theory, the theory and mathematical model of natural fund applicant group behavior are established. Based on this, we will further develop machine understanding and intelligent learning theory based on big data, and implement a set of cloud computing-based automatic analysis theories and key algorithms for network traffic and group behavior. Through the analysis of network traffic and packet content, we will achieve The characteristics of massive group behaviors are automatically extracted, analyzed and understood in the reporting period of the fund. Finally, through the comparison of theoretical and practical analysis, the effectiveness of the natural fund applicant group behavior model is tested and amended. The research of this topic will promote the interdisciplinary integration of network informatics, psychology, and social behavior. It will form a major theoretical significance of the research results. At the same time, it will develop a natural fund data network for China, improve the success rate of applications, and improve youth. The sleep quality of scientists provides key theoretical support and technical guarantees, and has important strategic application value.

The paralysis of the natural resources fund applying for serious overload of the network has become a pressing and urgent problem that restricts the development of the national strategy. In order to improve the login failure rate as the service target, this project studies the login behavior of the natural fund applicant, and establishes the theory and mathematical model of the group behavior of the natural fund applicant with the help of the theory of psychology and social group science. On this basis, we further develop the theory of machine understanding and intelligent learning based on large data, and realize a set of automatic analysis theory and key algorithm of network traffic and group behavior based on cloud computing. Through the analysis of network traffic and data packets, the characteristics of mass group behavior in the time period of natural fund declaration are realized. Dynamic extraction, analysis and understanding; finally, through the comparison of theoretical and practical analysis, the effectiveness of the group behavior model of the natural fund applicant is tested and amended. The research will promote the interdisciplinary integration of network Informatics, psychology and social behaviourology, and will form a significant theoretical research results. At the same time, it will provide the key theoretical support and technical support for the development of China natural fund data network, the improvement of application success rate, and the improvement of the quality of sleep of young scientists. It has important strategic application value.

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