面向海洋的多模态智能计算:挑战、进展和展望  

Marine oriented multimodal intelligent computing:challenges,progress and prospects

作　　者：聂婕[1] 左子杰 黄磊[1] 王志刚[1] 孙正雅[2] 仲国强 王鑫 王玉成 刘安安 张弘[6] 董军宇[1] 魏志强[1,4] Nie Jie;Zuo Zijie;Huang Lei;Wang Zhigang;Sun Zhengya;Zhong Guoqiang;Wang Xin;Wang Yucheng;Liu An'an;Zhang Hong;Dong Junyu;Wei Zhiqiang(Ocean University of China,Qingdao 266100,China;Institute of Automation,Chinese Academy of Sciences,Beijing 100190,China;Department of Computer Science and Technology,Tsinghua University,Beijing 100084,China;Pilot National Laboratory for Marine Science and Technology(Qingdao),Qingdao 266061,China;School of Electrical and Information Engineering,Tianjin University,Tianjin 300072,China;School of Astronautics,Beihang University,Beijing 100083,China)

机构地区：[1]中国海洋大学,青岛266100 [2]中国科学院自动化研究所,北京100190 [3]清华大学计算机科学与技术系,北京100084 [4]青岛海洋科学与技术试点国家实验室,青岛266061 [5]天津大学电气自动化与信息工程学院,天津300072 [6]北京航空航天大学宇航学院,北京100083

出　　处：《中国图象图形学报》2022年第9期2589-2610,共22页Journal of Image and Graphics

基　　金：国家重点研发计划资助(2021YFF0704000);国家自然科学基金项目(62072418,62172376,61872326);中央高校基本科研业务费专项资金资助(202042008)。

摘　　要：海洋是高质量发展的要地,海洋科学大数据的发展为认知和经略海洋带来机遇的同时也引入了新的挑战。海洋科学大数据具有超多模态的显著特征,目前尚未形成面向海洋领域特色的多模态智能计算理论体系和技术框架。因此,本文首次从多模态数据技术的视角,系统性介绍面向海洋现象/过程的智能感知、认知和预知的交叉研究进展。首先,通过梳理海洋科学大数据全生命周期的阶段演进过程,明确海洋多模态智能计算的研究对象、科学问题和典型应用场景。其次,在海洋多模态大数据内容分析、推理预测和高性能计算3个典型应用场景中展开现有工作的系统性梳理和介绍。最后,针对海洋数据分布和计算模式的差异性,提出海洋多模态大数据表征建模、跨模态关联、推理预测以及高性能计算4个关键科学问题中的挑战,并提出未来展望。The marine-oriented research is essential to high-quality of human-based development.But,the current recognition of the ocean system is less than 5%.To understand the ocean,big marine data is acquired from observation,monitoring,investigation and statistics.Thanks to the development of the multi-scaled ocean observation system,the extensive of multi-modal marine oriented data has developed via remote sensing image,spatio-temporal analysis,simulation data,literature review and video&audio monitoring.To resilient the sustainable development of human society,current deep analysis and multimodal ocean data mining method has promoted the marine understanding on the aspects of ocean dynamic processes,energy and material cycles,the evolution of blue life,scientific discovery,healthy environment,and the quick response of extreme weather and climate change.Compared to traditional big data,the multi-modal big ocean data has its unique features,such as the super-giant system(covering 71%of the earth’s surface,daily increment(10 TB),super multi-perspectives(“land-sea-air-ice-earth based”coupling,“hydrometeorological-acoustical-optical and electromagnetic-based”polymorphism),super spatial scale(“centimeter to hundreds kilometer based”),and temporal scale(“micro-second to inter-decadal based”).These features-derived challenges of existing multi-modal intelligent computing technology have to deal with such problems as cross-scale and multi-modal fusion analyses,multi-disciplinary and multi-domain coordinated reasoning,large computing power based multi-architecture compatible applications.We systematically introduce the cross-cutting researches of intelligent perception,cognition,and prediction for marine phenomena/processes based on multimodal data technology.First,we clarify the research objects,scientific problems,and typical application scenarios of marine multimodal intelligent computing through the evolution analysis of the lifecycle of marine science big data.Next,we target the differences between ocean

关 键 词：海洋大数据 多模态 海洋多媒体内容分析 海洋知识图谱 海洋大数据预测 海洋高性能计算 海洋目标重识别 

分 类 号：TP37[自动化与计算机技术—计算机系统结构]