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Organization Model of Cross-domain Emergency Intelligence from the Perspective of Collaboration |
Guo Hua1,2, Jiang Xun3, Xu Rui1, Hou Baiyi1, Zhang Jiandong1 |
1.Business School of Hohai University, Nanjing 211100 2.World Water Valley Institute, Hohai University, Nanjing 211100 3.Key Laboratory of Data Engineering and Knowledge Service of Jiangsu Province, Nanjing 210023 |
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Abstract In the face of intricate cross-domain emergencies, constructing a dynamic self-organized emergency intelligence network is essential for breaking down barriers among intelligence resources, intelligence services, and intelligence agencies. Multi-dimensional knowledge correlation and hybrid data fusion provide organizational solutions for emergency intelligence resources. The idea of domain-driven design provides methods for the division and organization of fine-grained emergency intelligence services. Additionally, the design improves the traditional intelligent agent model to support the system's perception of emergency evolution and response to social constraints. Based on the comprehensive perspective of intelligence resources, intelligence services, and service agents, this study analyzes the working mechanism and implementation path of the vertical cross-domain emergency intelligence chain. Furthermore, the study proposes the “Resource-Service-Agent” triple collaboration model and provides a solution to the emergency intelligence network. Finally, this research supports cross-domain knowledge organization, micro-service composition, and multi-agent collaborative operations.
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Received: 27 July 2020
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1 李阳, 孙建军. 面向智慧应急的情报资源保障能力建构[J]. 情报学报, 2019, 38(12): 1310-1319. 2 Goldin I, Mariathasan M. The butter?y defect: how globalization creates systemic risks and what to do about it[M]. Princeton: Princeton University Press, 2014. 3 Unlu A, Kapucu N, Sahin B. Disaster and crisis management in Turkey: a need for a unified crisis management system[J]. Disaster Prevention and Management, 2010, 19(2): 155-174. 4 Koliba C J, Mills R M, Zia A. Accountability in governance networks: an assessment of public, private, and nonprofit emergency management practices following hurricane Katrina[J]. Public Administration Review, 2011, 71(2): 210-220. 5 Kringos D, Carinci F, Barbazza E, et al. Managing COVID-19 within and across health systems: why we need performance intelligence to coordinate a global response[J]. Health Research Policy and Systems, 2020, 18(1): 80. 6 Kim K, Jung K. Dynamics of interorganizational public health emergency management networks: following the 2015 MERS response in South Korea[J]. Asia Pacific Journal of Public Health, 2018, 30(3): 207-216. 7 Dorasamy M, Raman M, Kaliannan M. Integrated community emergency management and awareness system: a knowledge management system for disaster support[J]. Technological Forecasting and Social Change, 2017, 121: 139-167. 8 李阳, 孙建军, 裴雷. 科学大数据与社会计算: 情报服务的现代转型与创新发展[J]. 图书与情报, 2017(5): 27-32. 9 Sakellariou S, Tampekis S, Samara F, et al. Review of state-of-the-art decision support systems (DSSs) for prevention and suppression of forest fires[J]. Journal of Forestry Research, 2017, 28(6): 1107-1117. 10 Fan X M, Xu Q, Alonso-Rodriguez A, et al. Successive landsliding and damming of the Jinsha River in eastern Tibet, China: prime investigation, early warning, and emergency response[J]. Landslides, 2019, 16(5): 1003-1020. 11 Zhai Y M, Chen S L, Ouyang Q W. GIS-based seismic hazard prediction system for urban earthquake disaster prevention planning[J]. Sustainability, 2019, 11(9): 2620. 12 van Ackere S, Beullens J, Vanneuville W, et al. FLIAT, an object-relational GIS tool for flood impact assessment in Flanders, Belgium[J]. Water, 2019, 11(4): 711. 13 Lei Y, Zhou X Q, Xie L. Emergency monitoring and disposal decision support system for sudden pollution accidents based on multimedia information system[J]. Multimedia Tools and Applications, 2019, 78(8): 11047-11071. 14 Van de Walle B, Turoff M. Decision support for emergency situations[J]. Information Systems and e-Business Management, 2008, 6: 295-316. 15 Pilone E, Mussini P, Demichela M, et al. Municipal emergency plans in Italy: requirements and drawbacks[J]. Safety Science, 2016 ,85: 163-170. 16 Aedo I, Díaz P, Carroll J M, et al. End-user oriented strategies to facilitate multi-organizational adoption of emergency management information systems[J]. Information Processing & Management, 2010, 46(1): 11-21. 17 Ford D N, Wolf C M. Smart cities with digital twin systems for disaster management[J]. Journal of Management in Engineering, 2020, 36(4): 04020027. 18 赫尔曼?哈肯. 协同学: 大自然构成的奥秘[M]. 凌复华, 译. 上海: 上海译文出版社, 2005. 19 Leonard H B, Howitt A M. Organising response to extreme emergencies: the Victorian Bushfires of 2009[J]. Australian Journal of Public Administration, 2010, 69(4): 372-386. 20 苏新宁, 朱晓峰, 崔露方. 基于生命周期的应急情报体系理论模型构建[J]. 情报学报, 2017, 36(10): 989-997. 21 李纲, 李阳. 智慧城市应急决策情报体系构建研究[J]. 中国图书馆学报, 2016, 42(3): 39-54. 22 李广建, 罗立群. 计算型情报分析的进展[J]. 中国图书馆学报, 2019, 45(4): 29-43. 23 蒋勋, 苏新宁, 周鑫. 适应情景演化的应急响应知识库协同框架体系构建[J]. 图书情报工作, 2017, 61(15): 60-71. 24 郭骅, 屈芳, 战培志. 城市应急管理情报平台构建研究[J]. 图书情报工作, 2018, 62(6): 93-104. 25 肖花. 协同理论视角下的突发事件应急处置信息资源共享研究[J]. 现代情报, 2019, 39(3): 109-114. 26 刘细文, 虞惠达. 分布式科技战略情报研究与服务之工作模式研究[J]. 情报学报, 2007, 26(3): 430-434. 27 李荣, 李辉, 吴雨蓉, 等. 面向战略情报研究的协同情报服务体系构建——基于科技前沿跟踪与预测实践分析[J]. 情报理论与实践, 2018, 41(3): 16-19. 28 张政, 王林, 孙晨, 等. 基于服务的应急信息“一张图”共享框架研究[J]. 测绘工程, 2016, 25(2): 47-51. 29 曹高辉, 徐元, 梁梦丽, 等. 基于情境的信息融合模型研究[J]. 情报学报, 2017, 36(6): 537-546. 30 Puttinaovarat S, Horkaew P. Flood forecasting system based on integrated big and crowdsource data by using machine learning techniques[J]. IEEE Access, 2020, 8: 5885-5905. 31 Luino F, Belloni A, Turconi L, et al. A historical geomorphological approach to flood hazard management along the shore of an alpine lake (northern Italy)[J]. Natural Hazards, 2018, 94(1): 471-488. 32 Zhang W, Zhou J Z, Liu Y, et al. Emergency evacuation planning against dike-break flood: a GIS-based DSS for flood detention basin of Jingjiang in central China[J]. Natural Hazards, 2016, 81(2): 1283-1301. 33 Meng X H, Zhang M, Wen J H, et al. A simple GIS-based model for urban rainstorm inundation simulation[J]. Sustainability, 2019, 11(10): 2830. 34 Chaawa M, Thabet I, Hanachi C, et al. Modelling and simulating a crisis management system: an organisational perspective[J]. Enterprise Information Systems, 2017, 11(4): 534-550. 35 Watts J, Morss R E, Barton C M, et al. Conceptualizing and implementing an agent-based model of information flow and decision making during hurricane threats[J]. Environmental Modelling & Software, 2019, 122: 104524. 36 Chen W, Zhai G F, Ren C Q, et al. Urban resources selection and allocation for emergency shelters: in a multi-hazard environment[J]. International Journal of Environmental Research and Public Health, 2018, 15(6): 1261. 37 Hawe G I, Wilson D T, Coates G, et al. STORMI: an agent-based simulation environment for evaluating responses to major incidents in the UK[C]// Proceedings of 9th International Conference on Information Systems for Crisis Response and Management, Simon Fraser University, 2012. 38 Ramaswami A, Russell A G, Culligan P J, et al. Meta-principles for developing smart, sustainable, and healthy cities[J]. Science, 2016, 352(6288): 940-943. 39 杨巧云, 姚乐野. 基于协调理论的应急情报部门跨组织工作流程研究[J]. 情报理论与实践, 2015, 38(8): 75-78, 84. 40 张玉磊. 跨界公共危机与中国公共危机治理模式转型: 基于整体性治理的视角[J]. 华东理工大学学报(社会科学版), 2016, 31(5): 59-78. 41 李胜, 卢俊. 从“碎片化”困境看跨域性突发环境事件治理的目标取向[J]. 经济地理, 2018, 38(11): 191-195, 240. 42 佟泽华, 韩春花, 宋锴, 等. 基于知识集成的竞争情报分析模型运行模式研究[J]. 情报理论与实践, 2014, 37(9): 48-54. 43 肖希明, 唐义. 国外多领域数字资源整合研究进展[J]. 中国图书馆学报, 2013, 39(4): 26-35. 44 Preece A, Hui K, Gray A, et al. KRAFT: an agent architecture for knowledge fusion[J]. International Journal of Cooperative Information Systems, 2001, 10(1/2): 171-195. 45 操玉杰, 李纲, 毛进, 等. 大数据环境下面向决策全流程的应急信息融合研究[J]. 图书情报知识, 2018(5): 95-104. 46 Smirnov A, Levashova T. Knowledge fusion patterns: a survey[J]. Information Fusion, 2019, 52: 31-40. 47 罗立群, 李广建. 智慧情报服务与知识融合[J]. 情报资料工作, 2019, 40(2): 87-94. 48 许海云, 武华维, 罗瑞, 等. 基于多元关系融合的科技文本主题识别方法研究[J]. 中国图书馆学报, 2019, 45(1): 82-94. 49 杨峰, 姚乐野. WSR描述下的快速响应情报体系: 一个综合集成的框架[J]. 情报资料工作, 2017(3): 11-17. 50 牟冬梅, 黄丽丽. 数字资源语义互联工具的比较及SWOT分析[J]. 情报理论与实践, 2014, 37(2): 136-140. 51 毕强, 刘健. 基于领域本体的数字文献资源聚合及服务推荐方法研究[J]. 情报学报, 2017, 36(5): 452-460. 52 郭骅, 苏新宁, 邓三鸿. “智慧城市”背景下的城市应急管理情报体系研究[J]. 图书情报工作, 2016, 60(15): 28-36, 52. 53 赵又霖, 庞烁, 吴宗大. 社会感知数据驱动下突发事件应急管理的时空语义模型构建研究[J]. 情报科学, 2021, 39(2): 44-53. 54 祝振媛, 李广建. “数据—信息—知识”整体视角下的知识融合初探——数据融合、信息融合、知识融合的关联与比较[J]. 情报理论与实践, 2017, 40(2): 12-18. 55 高劲松, 梁艳琪. 关联数据环境下知识融合模型研究[J]. 情报科学, 2016, 34(2): 50-54. 56 Smirnov A, Levashova T, Shilov N. Patterns for context-based knowledge fusion in decision support systems[J]. Information Fusion, 2015, 21: 114-129. 57 White F. Data fusion lexicon[R]. Virginia: Defense Technical Information Center, 1991: 16. 58 Luo R C, Kay M G. Multisensor integration and fusion: issues and approaches[OL]. (1988-08-09). https://doi.org/10.1117/12.946646. 59 Dasarathy B V. Sensor fusion potential exploitation-innovative architectures and illustrative applications[J]. Proceedings of the IEEE, 1997, 85(1): 24-38. 60 Meng T, Jing X Y, Yan Z, et al. A survey on machine learning for data fusion[J]. Information Fusion, 2020, 57: 115-129. 61 Liu J, Li T R, Xie P, et al. Urban big data fusion based on deep learning: an overview[J]. Information Fusion, 2020, 53: 123-133. 62 Ni Z J, Rong L L, Wang N, et al. Knowledge model for emergency response based on contingency planning system of China[J]. International Journal of Information Management, 2019, 46: 10-22. 63 徐雷, 潘珺. 事件表示方式及其语义表示模型研究[J]. 情报杂志, 2019, 38(6): 159-167. 64 Tai C H, Chang C T, Chang Y S. Hybrid knowledge fusion and inference on cloud environment[J]. Future Generation Computer Systems, 2018, 87: 568-579. 65 Zadeh L A. Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic[J]. Fuzzy Sets and Systems, 1997, 90(2): 111-127. 66 Lewis J, Fowler M. Microservices: a definition of this new architectural term[OL]. (2014-03-25). https://martinfowler.com/articles/microservices.html. 67 Gribaudo M, Iacono M, Manini D. Performance evaluation of replication policies in microservice based architectures[J]. Electronic Notes in Theoretical Computer Science, 2018, 337: 45-65. 68 孙宇, 周纲. 基于微服务架构的资源发现系统平台构建研究[J]. 中国图书馆学报, 2020, 46(1): 114-124. 69 曹树金, 马翠嫦. 信息聚合概念的构成与聚合模式研究[J]. 中国图书馆学报, 2016, 42(3): 4-19. 70 Nadareishvili I, Mitra R, McLarty M, et al. Microservice architecture: aligning principles, practices, and culture[M]. Sebastopol: O’Reilly Media, 2016. 71 钟陈星, 李杉杉, 张贺, 等. 限界上下文视角下的微服务粒度评估[J]. 软件学报, 2019, 30(10): 3227-3241. 72 Newman S. Building microservices: designing fine-grained systems[M]. Sebastopol: O’Reilly Media, 2015. 73 杨宁. 微服务平台中服务划分和选择策略研究和应用[D]. 北京: 北京邮电大学, 2019: 27. 74 Eric E. Domain-driven design: tackling complexity in the heart of software[M]. Upper Saddle River: Addison-Wesley Professional, 2003. 75 Vernon V. Implementing domain-driven design[M]. Upper Saddle River: Addison-Wesley Professional, 2013. 76 Omicini A, Ricci A, Viroli M. Artifacts in the A&A meta-model for multi-agent systems[J]. Autonomous Agents and Multi-Agent Systems, 2008, 17(3): 432-456. 77 Castelfranchi C. Goals, the true center of cognition[M]// Paglieri F, Tummolini L, Falcone R, et al. The goals of cognition: essays in honor of Cristiano Castelfranchi (Tributes). London: College Publications, 2012: 837-882. 78 王越, 吴建光, 胡进, 等. 面向复杂多阶段任务的多活性代理系统活性度量化策划方法[J]. 北京理工大学学报, 2018, 38(7): 703-708. 79 姚乐野, 李明, 曹杰. 基于Multi-Agent System的应急管理多元主体信息互动机制初探[J]. 情报资料工作, 2018(3): 44-50. 80 冯治东, 王桃, 顾清华, 等. 矿井突水平行应急管理理论和方法基础研究[J]. 系统工程理论与实践, 2017, 37(12): 3289-3296. 81 周敏, 董海荣, 徐惠春, 等. 平行应急疏散系统: 基本概念、体系框架及其应用[J]. 自动化学报, 2019, 45(6): 1074-1086. 82 张鼎华, 李卫俊, 申世飞. 基于混合仿真的群体性事件演化机理建模分析研究[J]. 情报杂志, 2019, 38(7): 131-137, 130. 83 Yu J, Zhang C R, Wen J H, et al. Integrating multi-agent evacuation simulation and multi-criteria evaluation for spatial allocation of urban emergency shelters[J]. International Journal of Geographical Information Science, 2018, 32(9): 1884-1910. 84 Li Y, Hu B S, Zhang D, et al. Flood evacuation simulations using cellular automata and multiagent systems - a human-environment relationship perspective[J]. International Journal of Geographical Information Science, 2019, 33(11): 2241-2258. 85 Shaikh N, Kakosimos K E, Adia N, et al. Concept and demonstration of a fully coupled and dynamic exposure-response methodology for crowd evacuation numerical modelling in airborne-toxic environments[J]. Journal of Hazardous Materials, 2020, 399: 123093. 86 Sudeikat J, Stegh?fer J P, Seebach H, et al. On the combination of top-down and bottom-up methodologies for the design of coordination mechanisms in self-organising systems[J]. Information and Software Technology, 2012, 54(6): 593-607. 87 Boes J, Migeon F. Self-organizing multi-agent systems for the control of complex systems[J]. Journal of Systems and Software, 2017, 134: 12-28. 88 Rao A S, Georgeff M P. BDI agents: from theory to practice[C]// Proceedings of the 1st International Conference on Multi-Agent Systems. San Francisco: The MIT Press, 1995: 312-319. 89 Wooldridge M J. An introduction to multiagent systems[M]. New York: John Wiley & Sons, 2009. 90 郭骅, 蒋勋. 面向整合管理的情报体系分析框架——一种建构主义视角[J]. 情报理论与实践, 2020, 43(2): 1-8. 91 Mariani S, Omicini A. Coordinating activities and change: an event-driven architecture for situated MAS[J]. Engineering Applications of Artificial Intelligence, 2015, 41: 298-309. 92 Bratman M E, Israel D J, Pollack M E. Plans and resource-bounded practical reasoning[J]. Computational Intelligence, 1988, 4(3): 349-355. 93 Bratman M E. Intentions, plans, and practical reason[M]. Cambridge: Harvard University Press, 1987: 27. 94 Cohen P R, Levesque H J. Intention is choice with commitment[J]. Artificial Intelligence, 1990, 42(2/3): 213-261. 95 Buford J F, Jakobson G, Lewis L. Peer-to-peer coupled agent systems for distributed situation management[J]. Information Fusion, 2010, 11(3): 233-242. |
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