为解决传统“候鸟式”南繁在海南仅可实现材料加代环节、异地协同效率低、服务资源分散、数据管理粗放的长期难题,本研究围绕海南南繁育种主要环节,设计了一种基于大数据和物联网技术的种业南繁CRO(Contract research organization)服...为解决传统“候鸟式”南繁在海南仅可实现材料加代环节、异地协同效率低、服务资源分散、数据管理粗放的长期难题,本研究围绕海南南繁育种主要环节,设计了一种基于大数据和物联网技术的种业南繁CRO(Contract research organization)服务管理平台,实现了与物联网技术协同应用,覆盖海南南繁育种CRO服务全链条,从种质资源选择、育种试验设计、试验田管理、数据采集与分析到成果转化的育种全生命周期管理体系,结合精准环境监测、作物表型监测、人工智能、大数据分析、多层级权限管理、数据加密和区块链溯源等技术手段,实现南繁育种智能化、精细化和高效化。应用效果表明,CRO服务打通了作物科研育种产业上中下游全链条,实现服务资源整合,使育种效率提升30%,成果转化效率提升15%。南繁CRO服务管理平台的应用可有效拓展南繁功能边界,从加代扩展到种质资源引进、基因编辑、测试评价、知识产权保护和成果转化等科研育种全生命周期,推动南繁育种向数字化、集约化转型。展开更多
Objectives:In order to improve the prediction accuracy of forced-air pre-cooling for blueberries,a mathematical model of forced-air pre-cooling for blueberries based on the micro-cluster method was established.Materia...Objectives:In order to improve the prediction accuracy of forced-air pre-cooling for blueberries,a mathematical model of forced-air pre-cooling for blueberries based on the micro-cluster method was established.Materials and Methods:In order to determine the optimal micro-cluster model parameters suitable for forced air pre-cooling of blueberries,three factors controlling the micro-cluster geometry parameters were evaluated by 7/8 pre-cooling time,uniformity,and convective heat transfer coeffcient.Results:It was found that the optimal values of the number of micro-clusters(n3),the distance between individual units within a micro-cluster(a)and the distance between micro-clusters(c)were 3,0.75,and 0.2,respectively.Under these optimal values,the temperature error of the micro-cluster method remained below 1°C,achieving highly accurate temperature predictions during the blueberry pre-cooling process.The results showed that the micro-cluster method effectively solved the challenges of complex confguration,long simulation time,and low accuracy compared to the porous medium and equivalent sphere methods.Conclusion:Based on the above analysis,it can be concluded that the micro-cluster method provids a theoretical basis for optimizing forced-air pre-cooling processes and making informed control decisions.展开更多
文摘为解决传统“候鸟式”南繁在海南仅可实现材料加代环节、异地协同效率低、服务资源分散、数据管理粗放的长期难题,本研究围绕海南南繁育种主要环节,设计了一种基于大数据和物联网技术的种业南繁CRO(Contract research organization)服务管理平台,实现了与物联网技术协同应用,覆盖海南南繁育种CRO服务全链条,从种质资源选择、育种试验设计、试验田管理、数据采集与分析到成果转化的育种全生命周期管理体系,结合精准环境监测、作物表型监测、人工智能、大数据分析、多层级权限管理、数据加密和区块链溯源等技术手段,实现南繁育种智能化、精细化和高效化。应用效果表明,CRO服务打通了作物科研育种产业上中下游全链条,实现服务资源整合,使育种效率提升30%,成果转化效率提升15%。南繁CRO服务管理平台的应用可有效拓展南繁功能边界,从加代扩展到种质资源引进、基因编辑、测试评价、知识产权保护和成果转化等科研育种全生命周期,推动南繁育种向数字化、集约化转型。
基金supported by the Natural Science Foundation of Shandong Province,China(No.ZR2021QC186)the China Postdoctoral Science Foundation(No.2023M743923).
文摘Objectives:In order to improve the prediction accuracy of forced-air pre-cooling for blueberries,a mathematical model of forced-air pre-cooling for blueberries based on the micro-cluster method was established.Materials and Methods:In order to determine the optimal micro-cluster model parameters suitable for forced air pre-cooling of blueberries,three factors controlling the micro-cluster geometry parameters were evaluated by 7/8 pre-cooling time,uniformity,and convective heat transfer coeffcient.Results:It was found that the optimal values of the number of micro-clusters(n3),the distance between individual units within a micro-cluster(a)and the distance between micro-clusters(c)were 3,0.75,and 0.2,respectively.Under these optimal values,the temperature error of the micro-cluster method remained below 1°C,achieving highly accurate temperature predictions during the blueberry pre-cooling process.The results showed that the micro-cluster method effectively solved the challenges of complex confguration,long simulation time,and low accuracy compared to the porous medium and equivalent sphere methods.Conclusion:Based on the above analysis,it can be concluded that the micro-cluster method provids a theoretical basis for optimizing forced-air pre-cooling processes and making informed control decisions.
