|Place of Conferral||北京|
|Keyword||安全预警数据挖掘 科研生产安全 关联规则 时序模式|
|Other Abstract||; |
Crisis ’coming is uncertain. The best way to avoid crisis is to do early safety warning predict before the real crisis. So, effective safety warning measures are important to social stability and security. This paper is based on the project of the supervision platform of research and manufacture safety of Xinjiang Institute of Physics and Chemistry. This project gets a lot of safety information of research and manufacture of institutes. In order to improve safety of research institute, this paper will have a deep research on early safety warning methods based on the supervision platform of research and manufacture safety. Early safety warning theory is the guide principle and technical approach is association rules and time sequent patterns of data mining. The reason why data mining is the data base on application layer has a large number of data and we urgent to convert these data into useful information and knowledge that can be widely used all kinds of safety job, including security patrols, scientific equipment protection, hazardous chemicals and fire-fighting equipment supervision.By the research on early safety warning methods based on data mining, this paper introduces the theory and technology of early safety warning and data mining, including the overview、mathematic foundation、technology application of early safety warning and the overview、algorithm、application of association rules and time sequent patterns of data mining. On this basis, this paper has a research on how to use association rules and time sequent patterns on supervision platform of research and manufacture safety to realize early safety warning predict. This paper defines warning association rules、warning time sequent mode and designs the algorithms of association rules prediction and time sequent patterns prediction on the guide of the theory of data mining and early safety warning. Based on the two prediction algorithms, the association rules prediction model and time sequent patterns are structured. Final, this paper implements the warning of association rules, security malfeasance from frequent item sets and the time sequent mode based on the supervision platform of research and manufacture safety.
|周生伟. 科研安全平台安全预警方法研究[D]. 北京. 中国科学院大学,2013.|
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