工业环境下的人机交互状态数据仿真研究

传统人机交互状态数据模拟过程数据视域特征采集不准确、数据模拟不连续,导致人机交互状态数据检测能力偏低,提出基于模糊关联聚类分析的人机交互状态数据模拟及挖掘方法。采用定量递归分析法完成数据模糊关联聚类。利用关联特征分布序列调度法,得到人机交互状态数据的规则训练集。在模糊扰动约束下,得到人机交互状态数据的聚类目标函数。采用相关性检测方法进行数据的人机交互设计,采用替代数据法对工业环境下的人机交互状态数据进行复指数检测,提取人机交互状态数据的弱关联性指数特征,分离工业环境下人机交互状态数据的信息,实现对工业环境下的人机交互状态数据仿真。仿真结果表明,采用上述方法进行人机交互状态数据模拟数据视域特征采集准确,且具有连续性,提高了研究方法的可应用性。

Study on Simulation of Human-Computer Interaction State data in Industrial Environment

In the traditional simulation of human-computer interactive data,the detection ability of human-computer interactive data is low.Therefore,this paper focuses on a method of simulating and mining human-computer interactive data based on fuzzy association clustering analysis.At first,the quantitative recursive analysis was used to complete the fuzzy correlation clustering of data.Then the rule training set of human-computer interactive state data was obtained by the method of scheduling correlation feature distribution sequence.Under the constraint of fuzzy disturbance,the clustering objective function of human-computer interactive state data was obtained.Moreover,correlation detection method was adopted to design the human-computer interaction of data.In addition,the surrogate data method was adopted for complex exponential detection of human-computer interactive state data in industrial environment.Furthermore,weak correlation index feature of human-computer interaction state data was extracted and the human-computer interaction state data information industry environment was separated.Finally,the simulation of human-computer interactive state data in industrial environment was finished.Simulation results prove that the proposed method is accurate and continuous in visual feature collection of human-computer interactive simulation data,so that the applicability of method is improved.