This article elucidates the concept of large model technology,summarizes the research status of large model technology both domestically and internationally,provides an overview of the application status of large mode...This article elucidates the concept of large model technology,summarizes the research status of large model technology both domestically and internationally,provides an overview of the application status of large models in vertical industries,outlines the challenges and issues confronted in applying large models in the oil and gas sector,and offers prospects for the application of large models in the oil and gas industry.The existing large models can be briefly divided into three categories:large language models,visual large models,and multimodal large models.The application of large models in the oil and gas industry is still in its infancy.Based on open-source large language models,some oil and gas enterprises have released large language model products using methods like fine-tuning and retrieval augmented generation.Scholars have attempted to develop scenario-specific models for oil and gas operations by using visual/multimodal foundation models.A few researchers have constructed pre-trained foundation models for seismic data processing and interpretation,as well as core analysis.The application of large models in the oil and gas industry faces challenges such as current data quantity and quality being difficult to support the training of large models,high research and development costs,and poor algorithm autonomy and control.The application of large models should be guided by the needs of oil and gas business,taking the application of large models as an opportunity to improve data lifecycle management,enhance data governance capabilities,promote the construction of computing power,strengthen the construction of“artificial intelligence+energy”composite teams,and boost the autonomy and control of large model technology.展开更多
A real-time monitoring and 3D visualization analysis system is proposed for dam foundation curtain grouting. Based on the real-time control technology, the optimization method and the set theory, a mathematical model ...A real-time monitoring and 3D visualization analysis system is proposed for dam foundation curtain grouting. Based on the real-time control technology, the optimization method and the set theory, a mathematical model of the system is established. The real-time collection and transmission technology of the grouting data provides a data foundation for the system. The real-time grouting monitoring and dynamic alarming method helps the system control the grouting quality during the grouting process, thus, the abnormalities of grouting, such as jacking and hydraulic uplift, can be effectively controlled. In addition, the 3D grouting visualization analysis technology is proposed to establish the grouting information model(GIM). The GIM provides a platform to visualize and analyze the grouting process and results. The system has been applied to a hydraulic project of China as a case study, and the application results indicate that the real-time grouting monitoring and 3D visualization analysis for the grouting process can help engineers control the grouting quality more efficiently.展开更多
This article analyzes creation methods of automated design system, presents design system of a house foundation from blocks. The creation methods of automated design system of a house foundation from blocks are discov...This article analyzes creation methods of automated design system, presents design system of a house foundation from blocks. The creation methods of automated design system of a house foundation from blocks are discovered with Unified Modeling Language. Analyzed objects-classes： block, specification, model. Graphical system can design foundation, form specification of objects and create 3D model of house foundation. There are several types and different dimensions of concrete blocks. The program optimally arranges selected blocks so that monolithic parts will be minimal volume. Program selects a house foundation blocks from database by using ActiveX Data Objects technology, which by programming method connects drawing and database. Drawing＇s graphical objects have additional data from which exchange of data between graphical system and database is executed. Visualization system and example of house foundation from blocks project with specifications is presented. Creation problems of automated design system are discussed and conclusions are made.展开更多
得益于近期具有世界知识的大规模预训练模型的迅速发展,基于大模型的具身智能在各类任务中取得了良好的效果,展现出强大的泛化能力与在各领域内广阔的应用前景.鉴于此,对基于大模型的具身智能的工作进行了综述,首先,介绍大模型在具身智...得益于近期具有世界知识的大规模预训练模型的迅速发展,基于大模型的具身智能在各类任务中取得了良好的效果,展现出强大的泛化能力与在各领域内广阔的应用前景.鉴于此,对基于大模型的具身智能的工作进行了综述,首先,介绍大模型在具身智能系统中起到的感知与理解作用;其次,对大模型在具身智能中参与的需求级、任务级、规划级和动作级的控制进行了较为全面的总结;然后,对不同具身智能系统架构进行介绍,并总结了目前具身智能模型的数据来源,包括模拟器、模仿学习以及视频学习;最后,对基于大语言模型(Large language model,LLM)的具身智能系统面临的挑战与发展方向进行讨论与总结.展开更多
现有的异常检测方法能在特定应用场景下实现高精度检测,然而这些方法难以适用于其他应用场景,且自动化程度有限。因此,提出一种视觉基础模型(VFM)驱动的像素级图像异常检测方法SSMOD-Net(State Space Model driven-Omni Dimensional Ne...现有的异常检测方法能在特定应用场景下实现高精度检测,然而这些方法难以适用于其他应用场景,且自动化程度有限。因此,提出一种视觉基础模型(VFM)驱动的像素级图像异常检测方法SSMOD-Net(State Space Model driven-Omni Dimensional Net),旨在实现更精确的工业缺陷检测。与现有方法不同,SSMOD-Net实现SAM(Segment Anything Model)的自动化提示且不需要微调SAM,因此特别适用于需要处理大规模工业视觉数据的场景。SSMOD-Net的核心是一个新颖的提示编码器,该编码器由状态空间模型驱动,能够根据SAM的输入图像动态地生成提示。这一设计允许模型在保持SAM架构不变的同时,通过提示编码器引入额外的指导信息,从而提高检测精度。提示编码器内部集成一个残差多尺度模块,该模块基于状态空间模型构建,能够综合利用多尺度信息和全局信息。这一模块通过迭代搜索,在提示空间中寻找最优的提示,并将这些提示以高维张量的形式提供给SAM,从而增强模型对工业异常的识别能力。而且所提方法不需要对SAM进行任何修改,从而避免复杂的对训练计划的微调需求。在多个数据集上的实验结果表明,所提方法展现出了卓越的性能,与AutoSAM和SAM-EG(SAM with Edge Guidance framework for efficient polyp segmentation)等方法相比,所提方法在mE(mean E-measure)和平均绝对误差(MAE)、Dice和交并比(IoU)上都取得了较好的结果。展开更多
Recent studies have indicated that foundation models, such as BERT and GPT, excel atadapting to various downstream tasks. This adaptability has made them a dominant force in buildingartificial intelligence (AI) system...Recent studies have indicated that foundation models, such as BERT and GPT, excel atadapting to various downstream tasks. This adaptability has made them a dominant force in buildingartificial intelligence (AI) systems. Moreover, a newresearch paradigm has emerged as visualizationtechniques are incorporated into these models. Thisstudy divides these intersections into two researchareas: visualization for foundation model (VIS4FM)and foundation model for visualization (FM4VIS).In terms of VIS4FM, we explore the primary roleof visualizations in understanding, refining, and evaluating these intricate foundation models. VIS4FMaddresses the pressing need for transparency, explainability, fairness, and robustness. Conversely, in termsof FM4VIS, we highlight how foundation models canbe used to advance the visualization field itself. Theintersection of foundation models with visualizations ispromising but also introduces a set of challenges. Byhighlighting these challenges and promising opportunities, this study aims to provide a starting point forthe continued exploration of this research avenue.展开更多
针对当前工业类基础(industry foundation classes,IFC)标准文件的开发应用存在文件格式转换繁杂、数据互操作性弱及信息有效利用率低等问题,提出一种IFC模型数据映射至数据库、建筑信息模型(building information modeling,BIM)在Web...针对当前工业类基础(industry foundation classes,IFC)标准文件的开发应用存在文件格式转换繁杂、数据互操作性弱及信息有效利用率低等问题,提出一种IFC模型数据映射至数据库、建筑信息模型(building information modeling,BIM)在Web端重构及信息在线可视化分析的实施方法。基于Web框架,搭建IFC标准文件映射至关系型数据库的应用框架。该应用框架利用JavaScript语言对IFC标准文件深度解析,采用Python语言将IFC模型数据和建筑全生命周期有效信息储存至关系型数据库,结合实际案例及分析,展现并验证该应用框架的实施效果。结果表明:该方法可实现项目数据交互的完整性和高效性,支持建筑全生命周期相关应用的二次开发;同时融合智能建造技术和科学管理,提升BIM信息管理的有效性,促进建筑业数字化发展和智能化应用拓展。展开更多
Visual knowledge is a new form of knowledge representation that can encapsulate visual concepts and their relations in a succinct,comprehensive,and interpretable manner,with a deep root in cognitive psychology.As the ...Visual knowledge is a new form of knowledge representation that can encapsulate visual concepts and their relations in a succinct,comprehensive,and interpretable manner,with a deep root in cognitive psychology.As the knowledge of the visual world has been identified as an indispensable component of human cognition and intelligence,visual knowledge is poised to have a pivotal role in establishing machine intelligence.With the recent advance of artificial intelligence(AI)techniques,large AI models(or foundation models)have emerged as a potent tool capable of extracting versatile patterns from broad data as implicit knowledge,and abstracting them into an outrageous amount of numeric parameters.To pave the way for creating visual knowledge empowered AI machines in this coming wave,we present a timely review that investigates the origins and development of visual knowledge in the pre-big-model era,and accentuates the opportunities and unique role of visual knowledge in the big model era.展开更多
基金Supported by the National Natural Science Foundation of China(72088101,42372175)PetroChina Science and Technology Innovation Fund Program(2021DQ02-0904)。
文摘This article elucidates the concept of large model technology,summarizes the research status of large model technology both domestically and internationally,provides an overview of the application status of large models in vertical industries,outlines the challenges and issues confronted in applying large models in the oil and gas sector,and offers prospects for the application of large models in the oil and gas industry.The existing large models can be briefly divided into three categories:large language models,visual large models,and multimodal large models.The application of large models in the oil and gas industry is still in its infancy.Based on open-source large language models,some oil and gas enterprises have released large language model products using methods like fine-tuning and retrieval augmented generation.Scholars have attempted to develop scenario-specific models for oil and gas operations by using visual/multimodal foundation models.A few researchers have constructed pre-trained foundation models for seismic data processing and interpretation,as well as core analysis.The application of large models in the oil and gas industry faces challenges such as current data quantity and quality being difficult to support the training of large models,high research and development costs,and poor algorithm autonomy and control.The application of large models should be guided by the needs of oil and gas business,taking the application of large models as an opportunity to improve data lifecycle management,enhance data governance capabilities,promote the construction of computing power,strengthen the construction of“artificial intelligence+energy”composite teams,and boost the autonomy and control of large model technology.
基金Supported by the Innovative Research Groups of the National Natural Science Foundation of China(No.51321065)the National Natural Science Foundation of China(No.51339003 and No.51439005)
文摘A real-time monitoring and 3D visualization analysis system is proposed for dam foundation curtain grouting. Based on the real-time control technology, the optimization method and the set theory, a mathematical model of the system is established. The real-time collection and transmission technology of the grouting data provides a data foundation for the system. The real-time grouting monitoring and dynamic alarming method helps the system control the grouting quality during the grouting process, thus, the abnormalities of grouting, such as jacking and hydraulic uplift, can be effectively controlled. In addition, the 3D grouting visualization analysis technology is proposed to establish the grouting information model(GIM). The GIM provides a platform to visualize and analyze the grouting process and results. The system has been applied to a hydraulic project of China as a case study, and the application results indicate that the real-time grouting monitoring and 3D visualization analysis for the grouting process can help engineers control the grouting quality more efficiently.
文摘This article analyzes creation methods of automated design system, presents design system of a house foundation from blocks. The creation methods of automated design system of a house foundation from blocks are discovered with Unified Modeling Language. Analyzed objects-classes： block, specification, model. Graphical system can design foundation, form specification of objects and create 3D model of house foundation. There are several types and different dimensions of concrete blocks. The program optimally arranges selected blocks so that monolithic parts will be minimal volume. Program selects a house foundation blocks from database by using ActiveX Data Objects technology, which by programming method connects drawing and database. Drawing＇s graphical objects have additional data from which exchange of data between graphical system and database is executed. Visualization system and example of house foundation from blocks project with specifications is presented. Creation problems of automated design system are discussed and conclusions are made.
文摘得益于近期具有世界知识的大规模预训练模型的迅速发展,基于大模型的具身智能在各类任务中取得了良好的效果,展现出强大的泛化能力与在各领域内广阔的应用前景.鉴于此,对基于大模型的具身智能的工作进行了综述,首先,介绍大模型在具身智能系统中起到的感知与理解作用;其次,对大模型在具身智能中参与的需求级、任务级、规划级和动作级的控制进行了较为全面的总结;然后,对不同具身智能系统架构进行介绍,并总结了目前具身智能模型的数据来源,包括模拟器、模仿学习以及视频学习;最后,对基于大语言模型(Large language model,LLM)的具身智能系统面临的挑战与发展方向进行讨论与总结.
文摘现有的异常检测方法能在特定应用场景下实现高精度检测,然而这些方法难以适用于其他应用场景,且自动化程度有限。因此,提出一种视觉基础模型(VFM)驱动的像素级图像异常检测方法SSMOD-Net(State Space Model driven-Omni Dimensional Net),旨在实现更精确的工业缺陷检测。与现有方法不同,SSMOD-Net实现SAM(Segment Anything Model)的自动化提示且不需要微调SAM,因此特别适用于需要处理大规模工业视觉数据的场景。SSMOD-Net的核心是一个新颖的提示编码器,该编码器由状态空间模型驱动,能够根据SAM的输入图像动态地生成提示。这一设计允许模型在保持SAM架构不变的同时,通过提示编码器引入额外的指导信息,从而提高检测精度。提示编码器内部集成一个残差多尺度模块,该模块基于状态空间模型构建,能够综合利用多尺度信息和全局信息。这一模块通过迭代搜索,在提示空间中寻找最优的提示,并将这些提示以高维张量的形式提供给SAM,从而增强模型对工业异常的识别能力。而且所提方法不需要对SAM进行任何修改,从而避免复杂的对训练计划的微调需求。在多个数据集上的实验结果表明,所提方法展现出了卓越的性能,与AutoSAM和SAM-EG(SAM with Edge Guidance framework for efficient polyp segmentation)等方法相比,所提方法在mE(mean E-measure)和平均绝对误差(MAE)、Dice和交并比(IoU)上都取得了较好的结果。
基金supported by the National Natural Science Foundation of China(Grant Nos.U21A20469 and 61936002)the National Key R&D Program of China(Grant No.2020YFB2104100)grants from the Institute Guo Qiang,THUIBCS,and BLBCI.
文摘Recent studies have indicated that foundation models, such as BERT and GPT, excel atadapting to various downstream tasks. This adaptability has made them a dominant force in buildingartificial intelligence (AI) systems. Moreover, a newresearch paradigm has emerged as visualizationtechniques are incorporated into these models. Thisstudy divides these intersections into two researchareas: visualization for foundation model (VIS4FM)and foundation model for visualization (FM4VIS).In terms of VIS4FM, we explore the primary roleof visualizations in understanding, refining, and evaluating these intricate foundation models. VIS4FMaddresses the pressing need for transparency, explainability, fairness, and robustness. Conversely, in termsof FM4VIS, we highlight how foundation models canbe used to advance the visualization field itself. Theintersection of foundation models with visualizations ispromising but also introduces a set of challenges. Byhighlighting these challenges and promising opportunities, this study aims to provide a starting point forthe continued exploration of this research avenue.
文摘针对当前工业类基础(industry foundation classes,IFC)标准文件的开发应用存在文件格式转换繁杂、数据互操作性弱及信息有效利用率低等问题,提出一种IFC模型数据映射至数据库、建筑信息模型(building information modeling,BIM)在Web端重构及信息在线可视化分析的实施方法。基于Web框架,搭建IFC标准文件映射至关系型数据库的应用框架。该应用框架利用JavaScript语言对IFC标准文件深度解析,采用Python语言将IFC模型数据和建筑全生命周期有效信息储存至关系型数据库,结合实际案例及分析,展现并验证该应用框架的实施效果。结果表明:该方法可实现项目数据交互的完整性和高效性,支持建筑全生命周期相关应用的二次开发;同时融合智能建造技术和科学管理,提升BIM信息管理的有效性,促进建筑业数字化发展和智能化应用拓展。
基金supported by“Pioneer”and“Leading Goose”R&D Program of Zhejiang Province,China(No.2024C01161)the National Science and Technology Major Project of China(No.2023ZD0121300)+1 种基金the National Natural Science Foundation of China(No.62372405)the Fundamental Research Funds for the Central Universities,China。
文摘Visual knowledge is a new form of knowledge representation that can encapsulate visual concepts and their relations in a succinct,comprehensive,and interpretable manner,with a deep root in cognitive psychology.As the knowledge of the visual world has been identified as an indispensable component of human cognition and intelligence,visual knowledge is poised to have a pivotal role in establishing machine intelligence.With the recent advance of artificial intelligence(AI)techniques,large AI models(or foundation models)have emerged as a potent tool capable of extracting versatile patterns from broad data as implicit knowledge,and abstracting them into an outrageous amount of numeric parameters.To pave the way for creating visual knowledge empowered AI machines in this coming wave,we present a timely review that investigates the origins and development of visual knowledge in the pre-big-model era,and accentuates the opportunities and unique role of visual knowledge in the big model era.
