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当全球导航卫星系统(global navigation satellite system,GNSS)分布式仿真环境中共享的模型数量非常多时,检索模型和配置仿真任务将成为一个比较复杂的工程.为提高仿真模型选取和仿真任务配置的效率,设计了一套针对GNSS分布式仿真环境中仿真模型的实时智能推荐方法,方法中首先定义了模型关联关系和接口形状的概念,然后提出了一种条件约束下的频繁模式树(FP-tree)结构,并从理论上分析了该结构在检索任务量方面的减少程度,设计并推导了模型关联关系度的计算方法,以及整套智能推荐方法的运行流程.推荐方法在GNSS分布式仿真环境中进行了仿真验证,仿真结果与传统智能推荐方法做对比分析,分析结果表明,该方法针对仿真模型推荐时运行时间短,推荐结果准确度高,能够实时为用户推荐合适的模型.
When a large number of models are shared in the GNSS distributed simulation environment, the retrieval model and the configuration simulation task will become a complicated project.In order to improve the efficiency of simulation model selection and simulation task configuration , A set of real-time intelligent recommendation method for the simulation model in GNSS distributed simulation environment is designed. Firstly, the concept of model association and interface shape is defined. Then a FP-tree ) Structure, and theoretically analyzed the degree of reduction of this structure in the retrieval task volume, designed and derived the calculation method of the relationship degree of the model and the operation flow of the whole set of intelligent recommendation method.Recommended method in the GNSS distributed simulation environment The simulation results are compared with the traditional intelligent recommendation methods. The analysis results show that the proposed method is suitable for real-time recommendation of suitable models in view of the short running time of the simulation model recommendation and the high accuracy of the recommended results.