本文从建造管理的材料控制环节探讨了应用SPOOLGEN软件和ICMS(Integrality Construction Management System)系统结合优化建造管理和提高建造水平的途径,通过SPOOLGEN软件和ICMS系统完成智能数据提取和材料状态的自动优化匹配可高效、...本文从建造管理的材料控制环节探讨了应用SPOOLGEN软件和ICMS(Integrality Construction Management System)系统结合优化建造管理和提高建造水平的途径,通过SPOOLGEN软件和ICMS系统完成智能数据提取和材料状态的自动优化匹配可高效、合理、精准地完成材料控制,有效优化了管线材料控制方式,做到图料匹配,优化施工进度,进一步提高管线建造材料控制管理水平,实现生产设计自动智能化高效办公,提高项目管控水准和国际竞争力。展开更多
El Niño-Southern Oscillation(ENSO)is the strongest interannual climate mode influencing the coupled ocean-atmosphere system in the tropical Pacific,and numerous dynamical and statistical models have been develope...El Niño-Southern Oscillation(ENSO)is the strongest interannual climate mode influencing the coupled ocean-atmosphere system in the tropical Pacific,and numerous dynamical and statistical models have been developed to simulate and predict it.In some simplified coupled ocean-atmosphere models,the relationship between sea surface temperature(SST)anomalies and wind stress(τ)anomalies can be constructed by statistical methods,such as singular value decomposition(SVD).In recent years,the applications of artificial intelligence(AI)to climate modeling have shown promising prospects,and the integrations of AI-based models with dynamical models are active areas of research.This study constructs U-Net models for representing the relationship between SSTAs andτanomalies in the tropical Pacific;the UNet-derivedτmodel,denoted asτUNet,is then used to replace the original SVD-basedτmodel of an intermediate coupled model(ICM),forming a newly AI-integrated ICM,referred to as ICM-UNet.The simulation results obtained from ICM-UNet demonstrate their ability to represent the spatiotemporal variability of oceanic and atmospheric anomaly fields in the equatorial Pacific.In the ocean-only case study,theτUNet-derived wind stress anomaly fields are used to force the ocean component of the ICM,the results of which also indicate reasonable simulations of typical ENSO events.These results demonstrate the feasibility of integrating an AI-derived model with a physics-based dynamical model for ENSO modeling studies.Furthermore,the successful integration of the dynamical ocean models with the AI-based atmospheric wind model provides a novel approach to ocean-atmosphere interaction modeling studies.展开更多
文摘本文从建造管理的材料控制环节探讨了应用SPOOLGEN软件和ICMS(Integrality Construction Management System)系统结合优化建造管理和提高建造水平的途径,通过SPOOLGEN软件和ICMS系统完成智能数据提取和材料状态的自动优化匹配可高效、合理、精准地完成材料控制,有效优化了管线材料控制方式,做到图料匹配,优化施工进度,进一步提高管线建造材料控制管理水平,实现生产设计自动智能化高效办公,提高项目管控水准和国际竞争力。
基金supported by the National Natural Science Foundation of China(NFSCGrant No.42030410)+2 种基金Laoshan Laboratory(No.LSKJ202202402)the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDB40000000)the Startup Foundation for Introducing Talent of NUIST.
文摘El Niño-Southern Oscillation(ENSO)is the strongest interannual climate mode influencing the coupled ocean-atmosphere system in the tropical Pacific,and numerous dynamical and statistical models have been developed to simulate and predict it.In some simplified coupled ocean-atmosphere models,the relationship between sea surface temperature(SST)anomalies and wind stress(τ)anomalies can be constructed by statistical methods,such as singular value decomposition(SVD).In recent years,the applications of artificial intelligence(AI)to climate modeling have shown promising prospects,and the integrations of AI-based models with dynamical models are active areas of research.This study constructs U-Net models for representing the relationship between SSTAs andτanomalies in the tropical Pacific;the UNet-derivedτmodel,denoted asτUNet,is then used to replace the original SVD-basedτmodel of an intermediate coupled model(ICM),forming a newly AI-integrated ICM,referred to as ICM-UNet.The simulation results obtained from ICM-UNet demonstrate their ability to represent the spatiotemporal variability of oceanic and atmospheric anomaly fields in the equatorial Pacific.In the ocean-only case study,theτUNet-derived wind stress anomaly fields are used to force the ocean component of the ICM,the results of which also indicate reasonable simulations of typical ENSO events.These results demonstrate the feasibility of integrating an AI-derived model with a physics-based dynamical model for ENSO modeling studies.Furthermore,the successful integration of the dynamical ocean models with the AI-based atmospheric wind model provides a novel approach to ocean-atmosphere interaction modeling studies.
