Personalized news suggestions are an important technology to enhance people’s online news reading experiences. How to better understand users and news representation is a major issue in news recommendation. The majority of cutting-edge news recommendation techniques mostly neglect the link between title and content, explicitly and implicitly. They neglect to take into account the effects of many prospective news preferences on people’s behavior when they click on various news items. We first build a user-news interaction graph and then present the weight learning and preference decomposition (WLPD) news recommendation model for graph neural networks, which is based on WLPD. This model not only takes into account the impact of the relationship between news titles and content, explicit and implicit, on the likelihood that users will click on the news, but also takes into account the various potential preferences between users and news interaction. Finally, using actual news databases, we run a number of experiments. We discover that our model significantly improved in terms of accuracy and performance compared with other cutting-edge news recommendation techniques.

供应链成员彼此间的信息可信共享是实现供应链"四流合一"发展模式的技术基础.通过对传统供应链管理存在的诸多痛点进行分析,将工业互联网标识解析体系和区块链技术进行集成应用到供应链管理领域,形成一种新型的供应链管理解决方案.方案采用标识解析体系将供应链各信息流的异构数据统一映射到资源池,依托区块链技术去中心化及数据不可篡改等特点,将资源池标识数据进行分配及管理,保证供应链标识数据的安全存储与解析,构建互信共赢的供应链生态体系,实现产业链上下游企业协同.

为实现流程工业制造全流程达到整体最优,以典型高能炸药黑索金(RDX)制造过程为代表,结合生产工艺特点构建数字化车间.从车间工艺流程、运行管控、安全管控、数据管理及应用分析等方面开展数字化车间构建技术研究.在已有技术、生产工艺的基础上,抽取出与高能炸药智能制造数字化车间相关的共性和专用技术,并对其应用到的关键技术实施方法进行阐述.结果表明:数字化车间的建立能实现车间生产全流程信息感知,快速响应制造过程内外部变化,在保障RDX产品的产量和质量的同时,提高制造过程中的安全程度.

为构建信息物理网络系统(Cyber-Physical System,CPS),数据孪生技术作作为新的数字化关键技术已经应用到了与工业4.0相关的智能制造领域中.通过将物理实体或流程转化为准实时的数字化镜像,实现对生产全流程的模拟分析.首先,根据数据孪生理论的发展过程及目前的应用现状,明确了数字孪生技术应用的两个关键方向.其次,在对不同应用情景的研究下,创建数字孪生的方法以及所需的功能特性.并且,提出了在数据孪生技术驱动下,将传统的预测性维护方法由被动响应转型为主动服务的转变过程,为中国制造业由“生产型制造”向“服务型制造”转变提供支撑.