面向医学生情智健康教育的云服务方法研究

Cloud framework and self-adapting service methods for college student mental health education

针对当前医学生情智健康教育服务系统存在的自适应能力差、访问效率较低等问题,提出了一种基于通用即插即用技术的医学生情智健康数据云框架与自适应服务方法.针对医学生情智教育的特点与需求,构建面向医学生情智健康在线数据服务的云框架模型,提出了一种自主感知和定时调度相结合的数据收集算法,实现对不同查询用户、时间、历史案例数据的回放和比较分析;开发了医学生情智健康教育云服务软件系统,与基于客户机服务器（C/S）构架方法进行网络操作、事务处理和人机功效对比实验.实验结果表明,数据事务访问成功率提高80％,平均查询时间降低68％,每1000条记录处理出错概率降低84％,每1000次会话掉线、中断概率降低82％,数据查询过程中的事务处理丢包率降低91％,系统总体使用满意度提高28％,每工作日案例分析效率提高27％,且可以提供C/S系统5倍的并发用户数.用户测评结果表明：系统运行稳定、查询速度快、异常机制完善,可以提供移动在线服务,为医学生情智健康教育咨询、评估和对比分析提供了可靠手段.

To resolve the matters of poor self-adapting ability and low accessing efficiency of data service systems for college student mental health education,a universal plug and play （UPnP） based cloud framework and self-adapting service methods were proposed.A cloud framework model for college student mental health was set up,and the dynamical response for network architecture （NA） was performed.A data collecting algorithm with monitoring and scheduling was put forward to realize playback and comparative analysis for different users,time and conditions.A mental health cloud service system was developed,and the technical index and ergonomic performance comparative experiments with the C/S-based systems were performed.The comparative results showed that the access accuracy of data transaction process was improved 80％,the average query time was decreased 68％,the wrong processing probability per 1000 data records was decreased 84％,the off-line-interrupt probability per 1000 sessions was decreased 82％,the data package loss probability for data query process was decreased 91％,the general satisfy degree was increase 28％,the instance analysis efficiency per working day was improved 27,and the parallel users could increase 5 times of C/S systems.The college student mental health cloud service system runs stably,inquires quickly,has perfect exception mechanism,and can provide mobile on-line data service for college student mental health education.