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全球科研项目数据库2026版

智能互联装备网络协同制造/运维集成技术与平台研发

项目来源

国家重点研发计划(NKRD)

项目主持人

洪开荣

项目受资助机构

中铁工程装备集团有限公司

项目编号

2020YFB1712102

立项年度

2020

立项时间

未公开

项目级别

国家级

研究期限

未知 / 未知

受资助金额

324.00万元

学科

网络协同制造和智能工厂

学科代码

未公开

基金类别

“网络协同制造和智能工厂”重点专项

关键词

数字化数据库 ; 选型规则库 ; 一站式技术服务 ; 协同制造资源 ; Digital database ; Selection rule base ; One stop technical service ; Collaborative manufacturing resources

参与者

赵新合；马琳

参与机构

湖南大学；南京工业大学

项目标书摘要：针对跨域装备互通难、系统应用部署难的问题，分析了云平台业务需求和业务之间的交互关系，基于分布式分层集群理念，进行了平台架构研究，采用HADOOP分布式集群，搭建装备制造/运维数据中心。经过分析目前隧道掘进机制造运维的状况，梳理了隧道掘进机制造协同业务流程、成套装备运维需求及流程、成套运维/群组制造互馈信息及模式，进行了制造/运维集成平台服务应用系统应用场景设计。进行地下交通工程装备制造/运维协同标准大纲及内容的策划，为平台的建设提供了标准支撑。

Application Abstract: Aiming at the problems of cross domain equipment interoperability and system application deployment,this paper analyzes the business requirements of cloud platform and the interaction relationship between businesses,studies the platform architecture based on the concept of distributed hierarchical cluster,and uses Hadoop distributed cluster to build the equipment manufacturing/operation and maintenance data center.After analyzing the current situation of TBM manufacturing and operation and maintenance,this paper combs the TBM manufacturing collaborative business process,complete equipment operation and maintenance requirements and processes,complete equipment operation and maintenance/group manufacturing mutual feed information and mode,and designs the application scene of manufacturing/operation and maintenance integrated platform service application system.Plan the outline and contents of equipment manufacturing/operation and maintenance coordination standards for underground transportation engineering,which provides standard support for the construction of the platform.

项目受资助省

河南省

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  • 1.A Survey on Adversarial Attack in the Age of Artificial Intelligence

    • 关键词：
    • INTRUSION DETECTION; MALWARE; SYSTEMS

    With the rapid evolution of the Internet, the application of artificial intelligence fields is more and more extensive, and the era of AI has come. At the same time, adversarial attacks in the AI field are also frequent. Therefore, the research into adversarial attack security is extremely urgent. An increasing number of researchers are working in this field. We provide a comprehensive review of the theories and methods that enable researchers to enter the field of adversarial attack. This article is according to the "Why? -> What? -> How?" research line for elaboration. Firstly, we explain the significance of adversarial attack. Then, we introduce the concepts, types, and hazards of adversarial attack. Finally, we review the typical attack algorithms and defense techniques in each application area. Facing the increasingly complex neural network model, this paper focuses on the fields of image, text, and malicious code and focuses on the adversarial attack classifications and methods of these three data types, so that researchers can quickly find their own type of study. At the end of this review, we also raised some discussions and open issues and compared them with other similar reviews.

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