第三届人工智能海洋学论坛在海洋研究所召开-深圳海测数据官网

第三届人工智能海洋学论坛在海洋研究所召开

2025-05-02 02:01:21 阅读（256）

「导读」5月27日至29日，由中国海洋学会人工智能海洋学专业委员会、中国科学院海洋研究所、南京信息工程大学主办，中国科学院海洋研究所、海洋动力环境观测与预报重点实验室承办的第三届人工智能海洋学论坛在海洋所古镇口园区召开。中国科学院院士陈大可担任大会主席...

5月27日至29日，由中国海洋学会人工智能海洋学专业委员会、中国科学院海洋研究所、南京信息工程大学主办，中国科学院海洋研究所、海洋动力环境观测与预报重点实验室承办的第三届人工智能海洋学论坛在海洋所古镇口园区召开。中国科学院院士陈大可担任大会主席，海洋研究所所长王凡、南京信息工程大学教授董昌明担任大会科学委员会主席，百余所高校、科研机构和政府企业的近600名专家学者参会。

第三届人工智能海洋学论坛开幕式现场

大会主题为“人工智能助力海洋科学研究范式变革”，特别邀请了中国科学院院士吴立新、中国工程院院士宋君强、中国科学院院士陈大可、中国工程院院士蒋兴伟作大会主旨报告。中国海洋学会、中国科学院可持续发展研究局、山东省科学技术厅、山东省海洋局、青岛市科学技术局、青岛市海洋发展局、国家（青岛）融合创新示范区古镇口核心区、中国科学院海洋研究所、南京信息工程大学等单位相关负责人出席会议并致辞。

大会主席陈大可院士致辞

陈大可院士在开幕式致辞时表示，近年来，在国家海洋战略的正确指导及中国海洋学会的大力支持下，人工智能海洋学的发展已初具规模，人工智能为传统学科的研究注入了新的活力和创新思路，为科研人员开辟了更为广阔的研究视野和更多的可能性。在未来，人工智能技术将以创新之力帮助解决海洋领域面临的各种挑战，助力海洋学科跨越式发展。

海洋所所长王凡致辞

中国科学院海洋研究所所长王凡代表主办单位致辞。他表示，海洋大数据、人工智能与海洋科学深度交叉融合，成为新一轮科技革命和产业变革的重要驱动力量。海洋所将人工智能海洋学方向列为新兴前沿发展方向，组建高水平研究团队，积极推动人工智能海洋学学科发展，引领海洋科学研究范式变革，加速培育形成海洋领域新质生产力。

吴立新院士、宋君强院士、陈大可院士、蒋兴伟院士做大会主旨报告

吴立新院士以“海洋与人工智能”为题，通过介绍最新发布的“问海”预报大模型，强调了AI技术在处理和分析大规模海洋数据集中的关键作用，为海洋科学精准预测和决策支持提供了新的可能。宋君强院士作了“地球系统数字孪生的构建与挑战”的报告，从全球变暖、极端天气频发大背景下，以地球系统数字孪生对人类社会发展产生的重大影响为切入点，突出构建地球系统数字孪生的重要性。陈大可院士作了题为“人工智能海洋学的现状与愿景”的报告，回顾了人工智能海洋学在建立海洋大数据系统、开发适用于海洋大数据的人工智能算法，以及在人工智能海洋学中应用海洋大模型和海洋数字孪生等方面的最新进展，并指出人工智能与海洋科学交叉融合的广阔前景。蒋兴伟院士作了“海洋遥感大数据赋能海南自贸港建设”的报告，展示了海洋遥感大数据如何赋能区域经济发展，提升海洋遥感技术的实际应用价值。以及海南省“陆海空”科技专项在促进产融结合，推动产业聚集等诸多方面的探索和设想。

论坛主会场

大会设置海洋环境智能预报、海洋大数据构建、人工智能算法模型3个专题共46个专题报告，全面展示我国在人工智能海洋学这一交叉学科的前沿成果和创新探索，交流研讨基于深度学习和大数据驱动的人工智能技术在海洋科学研究中的应用，加快推动人工智能与海洋科学交叉融合，实现海洋信息智能感知、数据深度挖掘、产品智慧应用，助力人工智能和大数据驱动的海洋科研新范式变革。大会开幕式前一天举办了人工智能海洋学培训课程，IEEE Fellow、中国科学院海洋研究所李晓峰研究员担任授课教师。

本届论坛的成功举办为人工智能与海洋学领域的专家学者提供了交流互动的机会，有力推动了人工智能与海洋科学深度交叉融合。

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article------>{"id":17,"cid":14,"subCid":"","title":"第三届人工智能海洋学论坛在海洋研究所召开","shortTitle":"","tagId":"","attr":"4,1,2","articleView":"","source":"","author":"","description":"5月27日至29日，由中国海洋学会人工智能海洋学专业委员会、中国科学院海洋研究所、南京信息工程大学主办，中国科学院海洋研究所、海洋动力环境观测与预报重点实验室承办的第三届人工智能海洋学论坛在海洋所古镇口园区召开。中国科学院院士陈大可担任大会主席...","img":"https://file.ocean-data.com.cn/venus/1742046310383.png","content":"5月27日至29日，由中国海洋学会人工智能海洋学专业委员会、中国科学院海洋研究所、南京信息工程大学主办，中国科学院海洋研究所、海洋动力环境观测与预报重点实验室承办的第三届人工智能海洋学论坛在海洋所古镇口园区召开。中国科学院院士陈大可担任大会主席，海洋研究所所长王凡、南京信息工程大学教授董昌明担任大会科学委员会主席，百余所高校、科研机构和政府企业的近600名专家学者参会。\n \n<img src=\"https://qdio.cas.cn/2019Ver/News/zhdt/202405/W020240530714332907094.png\">\n第三届人工智能海洋学论坛开幕式现场\n \n大会主题为“人工智能助力海洋科学研究范式变革”，特别邀请了中国科学院院士吴立新、中国工程院院士宋君强、中国科学院院士陈大可、中国工程院院士蒋兴伟作大会主旨报告。中国海洋学会、中国科学院可持续发展研究局、山东省科学技术厅、山东省海洋局、青岛市科学技术局、青岛市海洋发展局、国家（青岛）融合创新示范区古镇口核心区、中国科学院海洋研究所、南京信息工程大学等单位相关负责人出席会议并致辞。\n \n<img src=\"https://qdio.cas.cn/2019Ver/News/zhdt/202405/W020240530714333327423.png\">\n大会主席陈大可院士致辞\n \n陈大可院士在开幕式致辞时表示，近年来，在国家海洋战略的正确指导及中国海洋学会的大力支持下，人工智能海洋学的发展已初具规模，人工智能为传统学科的研究注入了新的活力和创新思路，为科研人员开辟了更为广阔的研究视野和更多的可能性。在未来，人工智能技术将以创新之力帮助解决海洋领域面临的各种挑战，助力海洋学科跨越式发展。\n<img src=\"https://qdio.cas.cn/2019Ver/News/zhdt/202405/W020240531570953305876.png\">\n海洋所所长王凡致辞\n \n中国科学院海洋研究所所长王凡代表主办单位致辞。他表示，海洋大数据、人工智能与海洋科学深度交叉融合，成为新一轮科技革命和产业变革的重要驱动力量。海洋所将人工智能海洋学方向列为新兴前沿发展方向，组建高水平研究团队，积极推动人工智能海洋学学科发展，引领海洋科学研究范式变革，加速培育形成海洋领域新质生产力。\n \n<img src=\"https://qdio.cas.cn/2019Ver/News/zhdt/202405/W020240530714334632348.png\">\n吴立新院士、宋君强院士、陈大可院士、蒋兴伟院士做大会主旨报告\n \n吴立新院士以“海洋与人工智能”为题，通过介绍最新发布的“问海”预报大模型，强调了AI技术在处理和分析大规模海洋数据集中的关键作用，为海洋科学精准预测和决策支持提供了新的可能。宋君强院士作了“地球系统数字孪生的构建与挑战”的报告，从全球变暖、极端天气频发大背景下，以地球系统数字孪生对人类社会发展产生的重大影响为切入点，突出构建地球系统数字孪生的重要性。陈大可院士作了题为“人工智能海洋学的现状与愿景”的报告，回顾了人工智能海洋学在建立海洋大数据系统、开发适用于海洋大数据的人工智能算法，以及在人工智能海洋学中应用海洋大模型和海洋数字孪生等方面的最新进展，并指出人工智能与海洋科学交叉融合的广阔前景。蒋兴伟院士作了“海洋遥感大数据赋能海南自贸港建设”的报告，展示了海洋遥感大数据如何赋能区域经济发展，提升海洋遥感技术的实际应用价值。以及海南省“陆海空”科技专项在促进产融结合，推动产业聚集等诸多方面的探索和设想。\n \n<img src=\"https://qdio.cas.cn/2019Ver/News/zhdt/202405/W020240530714335034598.png\">\n论坛主会场\n \n大会设置海洋环境智能预报、海洋大数据构建、人工智能算法模型3个专题共46个专题报告，全面展示我国在人工智能海洋学这一交叉学科的前沿成果和创新探索，交流研讨基于深度学习和大数据驱动的人工智能技术在海洋科学研究中的应用，加快推动人工智能与海洋科学交叉融合，实现海洋信息智能感知、数据深度挖掘、产品智慧应用，助力人工智能和大数据驱动的海洋科研新范式变革。大会开幕式前一天举办了人工智能海洋学培训课程，IEEE Fellow、中国科学院海洋研究所李晓峰研究员担任授课教师。\n \n本届论坛的成功举办为人工智能与海洋学领域的专家学者提供了交流互动的机会，有力推动了人工智能与海洋科学深度交叉融合。\n ","status":0,"pv":256,"link":"","createdAt":"2024-10-09 14:46:22","updatedAt":"2025-05-02 02:01:21","field":{},"tags":[]}

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