受全球变化影响,原本湿润的西南地区自21世纪以来干旱事件频发,已对区域内植被生长造成了不同程度的抑制,威胁西南生态屏障安全。本研究采用标准化降水蒸散指数分析了西南地区2001-2016年极端干旱事件的频率和特征,选择了持续时间最长...受全球变化影响,原本湿润的西南地区自21世纪以来干旱事件频发,已对区域内植被生长造成了不同程度的抑制,威胁西南生态屏障安全。本研究采用标准化降水蒸散指数分析了西南地区2001-2016年极端干旱事件的频率和特征,选择了持续时间最长、影响范围最广的2009-2010年极端干旱事件,利用CLM5.0陆面过程模式(Community Land Model version 5.0)对2009-2010年极端干旱事件下植被生长进行数值模拟,并将模拟结果与三套遥感数据[Global Inventory Modeling and Mapping Studies(GIMMS),Global Land Surface Satellite(GLASS),Global Mapping(GLOBMAP)]进行对比验证CLM5.0对西南地区植被对干旱响应的模拟适用性。结果表明,2001-2016年,中国西南地区发生3例持续时间超过6个月的极端干旱事件,其中持续时间最长、最严重的干旱发生在2009-2010年。模拟发现在2009-2010年极端干旱期间,CLM5.0对植被与干旱的相关性、滞后响应、累积效应以及抵抗力和恢复力的模拟效果较好,植被对干旱的响应强度呈从东南向西北递减的特征,68.66%的区域植被对干旱表现出滞后响应,且滞后响应(78.02%)、累积效应(89.17%)与干旱均呈现较大面积的正相关,与多源遥感的描述有较高的一致性。在对不同植被类型的干旱抵抗力和恢复力的模拟方面,CLM5.0的模拟表现也较为出色,森林比灌木和草甸有更强的干旱抵抗力,且森林的干旱抵抗力和恢复力呈现明显的相反趋势。本研究使用CLM5.0模型模拟与多源遥感验证的方法,为理解西南地区植被对干旱的多方面响应提供了一个补充视角,有助于较全面地评估和预测西南干旱对植被活动的影响。展开更多
工业5.0引领制造业向以人为本的智能制造(人本智造)变革,制造系统中不同岗位和角色的人展现出更多样化和更全面的操作、智能和社会属性。针对生产调度这一典型场景,在人-信息-物理生产系统(Human-cyber-physical production system,HCP...工业5.0引领制造业向以人为本的智能制造(人本智造)变革,制造系统中不同岗位和角色的人展现出更多样化和更全面的操作、智能和社会属性。针对生产调度这一典型场景,在人-信息-物理生产系统(Human-cyber-physical production system,HCPPS)的语义下,定义感知层-认知层-决策层贯通的智造回路环,从环内操作人融合的适应性创新、环上决策人融合的智能化创新和环外社会人融合的可持续创新这三个角度,构建多层次人本融合框架;并分别提出面向操作人融合的适应性调度技术、面向决策人融合的人-机混合智能技术和面向社会人融合的可持续协同优化技术;最后,以飞机脉动总装线典型调度场景为案例,验证了所提技术的有效性,为实现工业5.0的人本智造提供理论和技术实践参考。展开更多
利用1979-2018年中国区域地面气象要素驱动数据集(0.1°×0.1°)作为大气强迫资料,驱动CLM5.0(Community Land Model version 5.0)模拟了青藏高原地区1979-2018年的土壤温湿度变化。将土壤冻融过程划分为冻结期和非冻结期,...利用1979-2018年中国区域地面气象要素驱动数据集(0.1°×0.1°)作为大气强迫资料,驱动CLM5.0(Community Land Model version 5.0)模拟了青藏高原地区1979-2018年的土壤温湿度变化。将土壤冻融过程划分为冻结期和非冻结期,通过两个阶段的CLM5.0模拟与站点观测资料、同化资料(GLDAS-Noah)、卫星遥感资料(MODIS土壤温度资料和ESA CCI-COMBINED土壤湿度资料)的对比验证,探讨CLM5.0模拟土壤温湿度在青藏高原的适用性。结果表明:(1)CLM5.0可较准确地描述站点土壤温湿度的动态变化,CLM5.0模拟的土壤温湿度与观测资料具有一致的变化特征且数值上较为接近。CLM5.0模拟的准确性高于GLDAS-Noah。CLM5.0对站点土壤温度的描述更为准确。(2)CLM5.0能够较准确地描述高原冻融过程中的土壤温湿度特征,CLM5.0模拟土壤温湿度与MODIS和ESA CCICOMBINED遥感资料在高原总体呈显著正相关,相关系数大多在0.9以上。CLM5.0对土壤温度的模拟能力相对较好,对非冻结期土壤湿度的模拟能力优于冻结期。CLM5.0整体高估了土壤温度,平均偏差大多在0~4℃之间。土壤湿度的平均偏差大多在-0.1~0.1 m^(3)·m^(-3)之间,非冻结期的平均偏差相对较小。(3)CLM5.0模拟、GLDAS-Noah、MODIS和ESA CCI-COMBINED遥感资料的土壤温湿度均具有相似的空间分布,其中土壤温度空间分布特征相似度更高。CLM5.0具有较高的空间分辨率和更为精细的土壤分层,对土壤温湿度细节的刻画更为完善。(4)CLM5.0模拟资料在高原整体呈增温变干趋势,MODIS和ESA CCI-COMBINED遥感资料整体呈增温增湿趋势。CLM5.0模拟的土壤温度变化趋势相对准确,土壤湿度的变化趋势则存在较大偏差。展开更多
As embodied intelligence(EI),large language models(LLMs),and cloud computing continue to advance,Industry5.0 facilitates the development of industrial artificial intelligence(Ind AI)through cyber-physical-social syste...As embodied intelligence(EI),large language models(LLMs),and cloud computing continue to advance,Industry5.0 facilitates the development of industrial artificial intelligence(Ind AI)through cyber-physical-social systems(CPSSs)with a human-centric focus.These technologies are organized by the system-wide approach of Industry 5.0,in order to empower the manufacturing industry to achieve broader societal goals of job creation,economic growth,and green production.This survey first provides a general framework of smart manufacturing in the context of Industry 5.0.Wherein,the embodied agents,like robots,sensors,and actuators,are the carriers for Ind AI,facilitating the development of the self-learning intelligence in individual entities,the collaborative intelligence in production lines and factories(smart systems),and the swarm intelligence within industrial clusters(systems of smart systems).Through the framework of CPSSs,the key technologies and their possible applications for supporting the single-agent,multi-agent and swarm-agent embodied Ind AI have been reviewed,such as the embodied perception,interaction,scheduling,multi-mode large language models,and collaborative training.Finally,to stimulate future research in this area,the open challenges and opportunities of applying Industry 5.0 to smart manufacturing are identified and discussed.The perspective of Industry 5.0-driven manufacturing industry aims to enhance operational productivity and efficiency by seamlessly integrating the virtual and physical worlds in a human-centered manner,thereby fostering an intelligent,sustainable,and resilient industrial landscape.展开更多
With the continuous advancement and maturation of technologies such as big data,artificial intelligence,virtual reality,robotics,human-machine collaboration,and augmented reality,many enterprises are finding new avenu...With the continuous advancement and maturation of technologies such as big data,artificial intelligence,virtual reality,robotics,human-machine collaboration,and augmented reality,many enterprises are finding new avenues for digital transformation and intelligent upgrading.Industry 5.0,a further extension and development of Industry 4.0,has become an important development trend in industry with more emphasis on human-centered sustainability and flexibility.Accordingly,both the industrial metaverse and digital twins have attracted much attention in this new era.However,the relationship between them is not clear enough.In this paper,a comparison between digital twins and the metaverse in industry is made firstly.Then,we propose the concept and framework of Digital Twin Systems Engineering(DTSE)to demonstrate how digital twins support the industrial metaverse in the era of Industry 5.0 by integrating systems engineering principles.Furthermore,we discuss the key technologies and challenges of DTSE,in particular how artificial intelligence enhances the application of DTSE.Finally,a specific application scenario in the aviation field is presented to illustrate the application prospects of DTSE.展开更多
文摘受全球变化影响,原本湿润的西南地区自21世纪以来干旱事件频发,已对区域内植被生长造成了不同程度的抑制,威胁西南生态屏障安全。本研究采用标准化降水蒸散指数分析了西南地区2001-2016年极端干旱事件的频率和特征,选择了持续时间最长、影响范围最广的2009-2010年极端干旱事件,利用CLM5.0陆面过程模式(Community Land Model version 5.0)对2009-2010年极端干旱事件下植被生长进行数值模拟,并将模拟结果与三套遥感数据[Global Inventory Modeling and Mapping Studies(GIMMS),Global Land Surface Satellite(GLASS),Global Mapping(GLOBMAP)]进行对比验证CLM5.0对西南地区植被对干旱响应的模拟适用性。结果表明,2001-2016年,中国西南地区发生3例持续时间超过6个月的极端干旱事件,其中持续时间最长、最严重的干旱发生在2009-2010年。模拟发现在2009-2010年极端干旱期间,CLM5.0对植被与干旱的相关性、滞后响应、累积效应以及抵抗力和恢复力的模拟效果较好,植被对干旱的响应强度呈从东南向西北递减的特征,68.66%的区域植被对干旱表现出滞后响应,且滞后响应(78.02%)、累积效应(89.17%)与干旱均呈现较大面积的正相关,与多源遥感的描述有较高的一致性。在对不同植被类型的干旱抵抗力和恢复力的模拟方面,CLM5.0的模拟表现也较为出色,森林比灌木和草甸有更强的干旱抵抗力,且森林的干旱抵抗力和恢复力呈现明显的相反趋势。本研究使用CLM5.0模型模拟与多源遥感验证的方法,为理解西南地区植被对干旱的多方面响应提供了一个补充视角,有助于较全面地评估和预测西南干旱对植被活动的影响。
文摘工业5.0引领制造业向以人为本的智能制造(人本智造)变革,制造系统中不同岗位和角色的人展现出更多样化和更全面的操作、智能和社会属性。针对生产调度这一典型场景,在人-信息-物理生产系统(Human-cyber-physical production system,HCPPS)的语义下,定义感知层-认知层-决策层贯通的智造回路环,从环内操作人融合的适应性创新、环上决策人融合的智能化创新和环外社会人融合的可持续创新这三个角度,构建多层次人本融合框架;并分别提出面向操作人融合的适应性调度技术、面向决策人融合的人-机混合智能技术和面向社会人融合的可持续协同优化技术;最后,以飞机脉动总装线典型调度场景为案例,验证了所提技术的有效性,为实现工业5.0的人本智造提供理论和技术实践参考。
文摘利用1979-2018年中国区域地面气象要素驱动数据集(0.1°×0.1°)作为大气强迫资料,驱动CLM5.0(Community Land Model version 5.0)模拟了青藏高原地区1979-2018年的土壤温湿度变化。将土壤冻融过程划分为冻结期和非冻结期,通过两个阶段的CLM5.0模拟与站点观测资料、同化资料(GLDAS-Noah)、卫星遥感资料(MODIS土壤温度资料和ESA CCI-COMBINED土壤湿度资料)的对比验证,探讨CLM5.0模拟土壤温湿度在青藏高原的适用性。结果表明:(1)CLM5.0可较准确地描述站点土壤温湿度的动态变化,CLM5.0模拟的土壤温湿度与观测资料具有一致的变化特征且数值上较为接近。CLM5.0模拟的准确性高于GLDAS-Noah。CLM5.0对站点土壤温度的描述更为准确。(2)CLM5.0能够较准确地描述高原冻融过程中的土壤温湿度特征,CLM5.0模拟土壤温湿度与MODIS和ESA CCICOMBINED遥感资料在高原总体呈显著正相关,相关系数大多在0.9以上。CLM5.0对土壤温度的模拟能力相对较好,对非冻结期土壤湿度的模拟能力优于冻结期。CLM5.0整体高估了土壤温度,平均偏差大多在0~4℃之间。土壤湿度的平均偏差大多在-0.1~0.1 m^(3)·m^(-3)之间,非冻结期的平均偏差相对较小。(3)CLM5.0模拟、GLDAS-Noah、MODIS和ESA CCI-COMBINED遥感资料的土壤温湿度均具有相似的空间分布,其中土壤温度空间分布特征相似度更高。CLM5.0具有较高的空间分辨率和更为精细的土壤分层,对土壤温湿度细节的刻画更为完善。(4)CLM5.0模拟资料在高原整体呈增温变干趋势,MODIS和ESA CCI-COMBINED遥感资料整体呈增温增湿趋势。CLM5.0模拟的土壤温度变化趋势相对准确,土壤湿度的变化趋势则存在较大偏差。
基金supported by the National Key Research and Development Program of China(2021YFB1714300)the National Natural Science Foundation of China(62233005,U2441245,62173141)+3 种基金CNPC Innovation Found(2024DQ02-0507)Shanghai Natural Science(24ZR1416400)Shanghai Baiyu Lan Talent Program Pujiang Project(24PJD020)the Programme of Introducing Talents of Discipline to Universities(the 111 Project)(B17017)
文摘As embodied intelligence(EI),large language models(LLMs),and cloud computing continue to advance,Industry5.0 facilitates the development of industrial artificial intelligence(Ind AI)through cyber-physical-social systems(CPSSs)with a human-centric focus.These technologies are organized by the system-wide approach of Industry 5.0,in order to empower the manufacturing industry to achieve broader societal goals of job creation,economic growth,and green production.This survey first provides a general framework of smart manufacturing in the context of Industry 5.0.Wherein,the embodied agents,like robots,sensors,and actuators,are the carriers for Ind AI,facilitating the development of the self-learning intelligence in individual entities,the collaborative intelligence in production lines and factories(smart systems),and the swarm intelligence within industrial clusters(systems of smart systems).Through the framework of CPSSs,the key technologies and their possible applications for supporting the single-agent,multi-agent and swarm-agent embodied Ind AI have been reviewed,such as the embodied perception,interaction,scheduling,multi-mode large language models,and collaborative training.Finally,to stimulate future research in this area,the open challenges and opportunities of applying Industry 5.0 to smart manufacturing are identified and discussed.The perspective of Industry 5.0-driven manufacturing industry aims to enhance operational productivity and efficiency by seamlessly integrating the virtual and physical worlds in a human-centered manner,thereby fostering an intelligent,sustainable,and resilient industrial landscape.
基金Supported by Beijing Municipal Natural Science Foundation of China(Grant No.24JL002)China Postdoctoral Science Foundation(Grant No.2024M754054)+2 种基金National Natural Science Foundation of China(Grant No.52120105008)Beijing Municipal Outstanding Young Scientis Program of Chinathe New Cornerstone Science Foundation through the XPLORER PRIZE。
文摘With the continuous advancement and maturation of technologies such as big data,artificial intelligence,virtual reality,robotics,human-machine collaboration,and augmented reality,many enterprises are finding new avenues for digital transformation and intelligent upgrading.Industry 5.0,a further extension and development of Industry 4.0,has become an important development trend in industry with more emphasis on human-centered sustainability and flexibility.Accordingly,both the industrial metaverse and digital twins have attracted much attention in this new era.However,the relationship between them is not clear enough.In this paper,a comparison between digital twins and the metaverse in industry is made firstly.Then,we propose the concept and framework of Digital Twin Systems Engineering(DTSE)to demonstrate how digital twins support the industrial metaverse in the era of Industry 5.0 by integrating systems engineering principles.Furthermore,we discuss the key technologies and challenges of DTSE,in particular how artificial intelligence enhances the application of DTSE.Finally,a specific application scenario in the aviation field is presented to illustrate the application prospects of DTSE.
