联邦控制: 面向信息安全和权益保护的分布式控制方法

朱静; 王飞跃; 王戈; 田永林; 袁勇; 王晓; 齐红威; 贾晓丰

doi:10.16383/j.aas.c210182

联邦控制: 面向信息安全和权益保护的分布式控制方法

doi: 10.16383/j.aas.c210182

朱静^{1, 2, 3,},
王飞跃^{2, 3, 4,},
王戈³,
田永林^2,,
袁勇^5,,
王晓^{2, 4,},
齐红威^6,,
贾晓丰^7,

1.
南京航空航天大学自动化学院南京 210006 中国
2.
中国科学院自动化研究所复杂系统管理与控制国家重点实验室北京 100190 中国
3.
澳门科技大学系统工程研究所澳门 999078 中国
4.
青岛智能产业技术研究院青岛 266109 中国
5.
中国人民大学数学学院北京 100872 中国
6.
数据堂(北京)科技股份有限公司北京 100192 中国
7.
北京市大数据中心北京 101100 中国

基金项目: 中国澳门特别行政区科学技术发展基金(0050/2020/A1)资助

详细信息

作者简介:
朱静：南京航空航天大学自动化学院讲师. 2015年获得中国香港城市大学电子工程专业博士学位. 主要研究方向为控制理论与应用. E-mail: drzhujing@nuaa.edu.cn

王飞跃：中国科学院自动化研究所复杂系统管理与控制国家重点实验室主任, 中国科学院大学中国经济与社会安全研究中心主任, 青岛智能产业技术研究院院长. 主要研究方向为平行系统的方法与应用, 社会计算, 平行智能以及知识自动化. 本文通信作者. E-mail: feiyue.wang@ia.ac.cn

王戈：中国澳门科技大学系统工程研究所博士研究生. 主要研究方向为联邦治理、DAO、平行智能系统

田永林：中国科学技术大学与中国科学院自动化研究所联合培养博士研究生. 主要研究方向为计算机视觉, 智能交通. E-mail: tyldyx@mail.ustc.edu.cn

袁勇：中国人民大学数学学院教授, 中国自动化学会区块链专委会主任. 2008年获得山东科技大学计算机软件与理论专业博士学位. 主要研究方向为区块链与计算经济学. E-mail: yong.yuan@ruc.edu.cn

王晓：中国科学院自动化研究所复杂系统管理与控制国家重点实验室副研究员. 2016 年获得中国科学院大学社会计算博士学位. 主要研究方向为社会交通, 社会计算, 动态网群组织, 人工智能和社交网络分析. E-mail: x.wang@ia.ac.cn

齐红威：数据堂(北京)科技股份有限公司董事长, 高级工程师. 2004年获得中国科学院自动化研究所博士学位. 主要研究方向为人工智能数据采集, 处理, 以及联邦数据. E-mail: Henry@datatang.com

贾晓丰：北京市大数据中心数据管理部负责人, 高级工程师. 2015年获得中国科学院大学博士学位. 主要研究方向为大数据, 区块链, 隐私计算等领域的研究和工程实践. E-mail: jiaxf@jxj.beijing.gov.cn

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出版历程
- 收稿日期: 2021-03-08
- 网络出版日期: 2021-06-15
- 刊出日期: 2021-08-20

Federated Control: A Distributed Control Approach Towards Information Security and Rights Protection

ZHU Jing^{1, 2, 3
,},
WANG Fei-Yue^{2, 3, 4
,},
WANG Ge³,
TIAN Yong-Lin^2
,,
YUAN Yong^5
,,
WANG Xiao^{2, 4
,},
QI Hong-Wei^6
,,
JIA Xiao-Feng^7
,

1.
College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210006, China
2.
State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
3.
Institute of Systems Engineering, Macau University of Science and Technology, Macau 999078, China
4.
Qingdao Academy of Intelligent Industries, Qingdao 266109, China
5.
School of Mathematics, Renmin University of China, Beijing 100872, China
6.
Datang (Beijing) Technology Co., Ltd., Beijing 100192, China
7.
Beijing Big Data Centre, Beijing 101100, China

Funds: Supported by The Science and Technology Development Fund, Macau SAR, China (0050/2020/A1)

More Information

Author Bio:
ZHU Jing　Lecturer at the College of Automation Engineering, Nanjing University of Aeronautics and Astronautics. She received her Ph. D. degree in electrical engineering from City University of Hong Kong. China in 2015. Her research interest covers control theories and applications

WANG Fei-Yue　State specially appointed expert and director of the State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences. Director of China Economic and Social Security Research Center in University of Chinese Academy of Sciences. President of Qingdao Academy of Intelligent Industries. His research interest covers methods and applications for parallel systems, social computing, parallel intelligence, and knowledge automation. Corresponding author of this paper

WANG Ge　Ph. D. candidate at the Institute of Systems Engineering, Macau University of Science and Technology, China. His research interest covers federated governance, smart contracts, DAO, parallel intelligence

TIAN Yong-Lin　Ph. D. candidate in the Department of Automation, University of Science and Technology of China and the Institute of Automation, Chinese Academy of Sciences. His research interest covers computer vision and intelligent transportation system

YUAN Yong　Professor at the School of Mathematics, Renmin University of China. He is also the director of Technical Committee on Blockchain, Chinese Association of Automation. He received his Ph. D. degree in computer software and theory from Shandong University of Science and Technology in 2008. His research interest covers blockchain and computational economics

WANG Xiao　Associate professor at the State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences. She received her Ph. D. degree in social computing from the University of Chinese Academy of Sciences in 2016. Her research interest covers social computing, cybermovement organizations, artificial intelligence and social network analysis

QI Hong-Wei　Chairman at Datatang (Beijing) Technology Co., Ltd., senior engineer. He received his Ph. D. degree from Institute of Automation, Chinese Academy of Sciences in 2004. His research interst covers artificial intelligence data acquisition, processing, federal data

JIA Xiao-Feng　Director at the Data Management Department, Beijing Big Data Centre, senior engineer. He received his Ph. D. degree from University of Chinese Academy of Sciences in 2015. His research interest covers the research and practice of big data, block chain, and privacy computing

摘要

摘要:
本文提出一种基于联邦智能的分布式控制方法—联邦控制. 作为联邦生态的核心环节, 联邦控制从联邦智能的需求响应出发, 以联邦数据的信息安全和权益保护为目标, 以区块链、平行系统为技术支撑, 为大型复杂系统提供高效、安全、可靠的控制与管理.
- 联邦控制 /
- 联邦生态 /
- 区块链 /
- 联邦数据
Abstract:
In this paper, we propose a new distributed control framework based on federated intelligence, that is federated control. As the core component of federated ecology, federated control sets out from the demand of federated intelligence, targets at the information security and rights protection of federated data, supported by blockchain techniques and parallel control theories, and eventually provides effective, secured and reliable control strategy for large complex systems.
- Federated control /
- federated ecology /
- blockchain /
- federated data

HTML全文

图 1 联邦控制搜索结果(2021-02-28)

Fig. 1 Search results on federated control (2021-02-28)

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图 2 平行系统理论框图

Fig. 2 Framework of the parallel system theory

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图 3 联邦控制的层级架构

Fig. 3 Hierarchical framework of federated control

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图 4 联邦控制基本流程

Fig. 4 Flow chart of federated control

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图 5 联邦控制的研究框架

Fig. 5 Research framework of federated control

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图 6 基于区块链的联邦链式网络

Fig. 6 Blockchain based federated-chain network

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图 7 车联网联邦控制框架

Fig. 7 Framework of federated control in internet of vehicles

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图 8 旅游市场联邦控制模型

Fig. 8 Federated control model of tourist marketing systems

下载: 全尺寸图片幻灯片

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