Decision modeling is an essential part of the combat system effectiveness simulation (CoSES), which needs to cope with the cognitive quality, diversity, flexibility, and higher abstraction of decision making. In this ...Decision modeling is an essential part of the combat system effectiveness simulation (CoSES), which needs to cope with the cognitive quality, diversity, flexibility, and higher abstraction of decision making. In this paper, a multi-paradigm decision modeling framework is proposed to support decision modeling at three levels of abstraction based on domain-specific modeling (DSM). This framework designs a domain-specific modeling language (DSML) for decision modeling to raise the abstraction level of modeling, transforms the domain-specific models to formalism-based models to enable formal analysis and early verification and validation, and implements the semantics of the DSML based on a Python scripts framework which incorporates the decision model into the whole simulation system. The case study shows that the proposed approach incorporates domain expertise and facilitates domain modeler's participation in CoSES to formulate the problem using DSML in the problem domain, and enables formal analysis and automatic implementation of the decision model in the solution domain.展开更多
数字化与智能化技术正作为新质生产力助力中国高炉炼铁的智能化转型升级。目前,以通用大模型(universal large language models, U-LLMs)作为基础框架,利用领域语料库进行2次训练构建行业垂直大模型(vertical large language models, V-...数字化与智能化技术正作为新质生产力助力中国高炉炼铁的智能化转型升级。目前,以通用大模型(universal large language models, U-LLMs)作为基础框架,利用领域语料库进行2次训练构建行业垂直大模型(vertical large language models, V-LLMs)指导工业生产已成为新态势。尽管已涌现出面向钢铁生产全流程的V-LLMs用于生产,但面向高炉工序构建V-LLMs的针对性研究尚处于初步阶段。通过梳理高炉炼铁智能化技术在近年来的演进升级路线,提出了以大模型驱动其范式重构与融合的新思路。将高炉V-LLMs的任务场景分为调度与决策2类,提出并设计了“数据层→应用层→感知层”的高炉V-LLMs渗透与应用路径,同时针对其未来的性能评估与优化提出5维评价体系,即工艺理解、安全可靠、知识迁移、实时性能与持续学习。随后,探讨了高炉V-LLMs驱动的3种智能升级新范式,包括高炉工况表征、高炉工况元宇宙,以及多场景融合,提出以高炉V-LLMs为核心的“物理↔虚拟↔感知”三维协同深度表征架构与“高炉画像”新概念,对高炉工况元宇宙的构建路线及多场景融合方针进行了梳理与讨论。最后,分析了高炉V-LLMs在未来发展与应用过程中面临的主要问题及可参考的解决方案。重点在于梳理高炉V-LLMs在构建、应用、评价中的可行性路线,结合行业发展现状对高炉V-LLMs驱动的炼铁智能化范式重构进行讨论,旨在为V-LLMs在中国高炉炼铁领域未来的深度应用提供理论指导,进一步推动中国高炉炼铁智能化的转型升级与发展。展开更多
基金Project (Nos. 61273198, 91024015, 61074107, 60974073,60974074, and 71031007) supported by the National Natural Science Foundation of China
文摘Decision modeling is an essential part of the combat system effectiveness simulation (CoSES), which needs to cope with the cognitive quality, diversity, flexibility, and higher abstraction of decision making. In this paper, a multi-paradigm decision modeling framework is proposed to support decision modeling at three levels of abstraction based on domain-specific modeling (DSM). This framework designs a domain-specific modeling language (DSML) for decision modeling to raise the abstraction level of modeling, transforms the domain-specific models to formalism-based models to enable formal analysis and early verification and validation, and implements the semantics of the DSML based on a Python scripts framework which incorporates the decision model into the whole simulation system. The case study shows that the proposed approach incorporates domain expertise and facilitates domain modeler's participation in CoSES to formulate the problem using DSML in the problem domain, and enables formal analysis and automatic implementation of the decision model in the solution domain.
文摘数字化与智能化技术正作为新质生产力助力中国高炉炼铁的智能化转型升级。目前,以通用大模型(universal large language models, U-LLMs)作为基础框架,利用领域语料库进行2次训练构建行业垂直大模型(vertical large language models, V-LLMs)指导工业生产已成为新态势。尽管已涌现出面向钢铁生产全流程的V-LLMs用于生产,但面向高炉工序构建V-LLMs的针对性研究尚处于初步阶段。通过梳理高炉炼铁智能化技术在近年来的演进升级路线,提出了以大模型驱动其范式重构与融合的新思路。将高炉V-LLMs的任务场景分为调度与决策2类,提出并设计了“数据层→应用层→感知层”的高炉V-LLMs渗透与应用路径,同时针对其未来的性能评估与优化提出5维评价体系,即工艺理解、安全可靠、知识迁移、实时性能与持续学习。随后,探讨了高炉V-LLMs驱动的3种智能升级新范式,包括高炉工况表征、高炉工况元宇宙,以及多场景融合,提出以高炉V-LLMs为核心的“物理↔虚拟↔感知”三维协同深度表征架构与“高炉画像”新概念,对高炉工况元宇宙的构建路线及多场景融合方针进行了梳理与讨论。最后,分析了高炉V-LLMs在未来发展与应用过程中面临的主要问题及可参考的解决方案。重点在于梳理高炉V-LLMs在构建、应用、评价中的可行性路线,结合行业发展现状对高炉V-LLMs驱动的炼铁智能化范式重构进行讨论,旨在为V-LLMs在中国高炉炼铁领域未来的深度应用提供理论指导,进一步推动中国高炉炼铁智能化的转型升级与发展。
