摘要
随着工业互联网的不断发展,大数据和人工智能促成了人机物全面互联.用户使用服务时产生的任务数据量正呈指数级增长,在为线上用户推荐服务满足个性化需求的同时,对于需要通过人机物交互完成的服务,如何整合线上和线下资源,并分派合适的人快速、有效地完成任务,也已成为一个挑战性问题.为了保证服务分派的准确性,提出了一种综合考虑人机物各方面数据特征的跨域融合服务分派方法,分别对用户评价的情感倾向性和业务数据的相似性进行分析,然后加入对业务执行有影响的物理世界的属性特征,以获得更合理的分派.最后,以一个互联网在线诊疗平台的医患分派为例,结果表明,文中提出的分派方法具有较高的准确性,可以获得更好的用户体验.
With the continuous development of the industrial Internet,big data and artificial intelligence contribute to the comprehensive interconnection in human-cyber-physical system.The amount of task data generated by users using the service is growing exponentially.While recommending services for online users to meet personalized needs,and for services that need to be completed through human-cyber-physical interaction,it has become a challenging problem how to integrate the various offline and online resources to dispatch the right person to complete the task quickly and effectively.In order to ensure the accuracy of services dispatch,this study proposes a cross-domain collaborative service dispatch method that takes into account the data characteristics of all these factors in human-cyber-physical system.In order to get a more reasonable dispatch,the sentiment characteristics of user evaluation and the similarity of business data are analyzed respectively,and then the attributes inherent in the real world are added of which have an impact on business processes.Finally,taking the doctor-patient assignment of an online diagnosis and treatment platform on the Internet as an example,the results show that the method proposed in this study has high accuracy and can improve the efficiency of task execution.
作者
袁敏
陈卓
徐冰青
YUAN Min;CHEN Zhuo;XU Bing-Qing(School of Computer and Electronic Information/School of Artificial Intelligence,Nanjing Normal University,Nanjing 210023,China)
出处
《软件学报》
EI
CSCD
北大核心
2021年第11期3404-3422,共19页
Journal of Software
基金
国家自然科学基金(41771411)
江苏省教育科学“十三五”规划(C-b/2016/01/24)
江苏省研究生科研与实践创新计划(SJCX19_0201)。
关键词
跨域融合
智能服务
服务分派
用户偏好
情感倾向分析
cross-domain integration
intelligent service
service dispatch
user preference
sentiment analysis
