基于关联规则的高速铁路信息服务组合方法研究

Research on High-speed Railway Information Service Combination Method Based on Association Rules

随着高速铁路信息化与智能化的发展,移动应用不断涌现,基于服务组合的移动应用开发技术已成为高铁信息服务研究的热点之一。目前,服务组合主要采用静态的服务功能分类组合方法,这种方式未利用大数据挖掘的优势,难以解决服务功能过载的问题。针对此问题,提出一种基于关联规则的探索式服务组合方法,在服务组合中加入上下文信息,利用FP-growth优化算法实现对二维上下文项的挖掘,根据关联规则向用户推荐符合当前应用场景下的服务集合。在高速铁路信息服务组合试验中进行测试和验证,结果表明此方法可有效提高准确率和命中率,对未来的高速铁路移动服务组合技术发展提供了新的解决思路。

With the development of high-speed railway information and intelligence,mobile applications continue to emerge.The development technology of mobile applications based on service composition has become one of the hot spots in the research of high-speed railway information service.At present,service composition mainly adopts static service function classification composition method,which does not take advantage of big data mining and is difficult to solve the problem of service function overload.To solve this problem,this paper proposed an exploratory service composition method based on association rules,which adds context information into the service composition.The FP-growth optimization algorithm was used to realize the mining of two-dimensional context items.According to the association rules,the service sets that met the current application scenarios were recommended to users.Finally,it was tested and verified in the high-speed railway information service composition experiment.The results show that this method can effectively improve the accuracy and hit rate,which provides a new solution for the future development of high-speed railway mobile service composition technology.