摘要
针对传统面向数据流分析算法中计算机能耗过大,拖慢计算机运行速度的问题,提出一种面向数据流的结构化自然语言分析算法。算法分通过典型数据流计算模型,对数据流进行处理,使之变成适应自然语言形式的数据;结合树状结构对数据流进行结构化划分;运行自然语言算法分析,分离处理划分后的数据流,借助计算机完成数据流研究。仿真测试结果表明,与面向数据流的自动分类分析、决策树分析以及非结构化语言分析三种算法相比,在结构化自然语言分析算法运行下,计算机能耗大大降低,同时运行速度提高,完成了研究的预期目标。
In traditional data flow analysis algorithms, the energy consumption of computer is too large and the running speed is slow. Therefore, a structured natural language analysis algorithm for data flow was proposed. This algorithm used the typical computing model to process the data stream, so as to make it fit the natural language form. Moreover, the structure of data stream was classified by the tree structure. Then, the natural language algorithm was used to analyze and process the partitioned data stream, and thus to complete the research on data stream with the help of the computer. Simulation results show that, compared with data flow-oriented automatic classification analysis, decision tree analysis and unstructured language analysis, the structured natural language analysis algorithm reduces the energy consumption of computer greatly and increases the running speed. The expected goal is achieved.
作者
朱玉胜
ZHU Yu-sheng(Foreign Languages School,Nanjing Xiaozhuang University,Nanjing Jiangsu 211171,China)
出处
《计算机仿真》
北大核心
2020年第5期250-254,共5页
Computer Simulation
关键词
数据流
结构化
自然语言分析算法
Data stream
Structural
Natural language analysis algorithm
