针对研究机构业务系统普遍缺乏后台监测数据、难以直接应用行业通用模型的问题,提出一种基于历史数据的数字化基础设施资源需求估算模型。该模型结合研究机构的业务系统分类（通用综合类、运营管理类、研发试制类、知识管理类）,定义关键资源变量与系统结构,引入时间间隔、项目申报通过率、经费投入趋势及数字化转型发展阶段特征系数等参数,构建了适用于不同数据掌握程度的需求估算方法,包括均值估计法和就高法。模型旨在为研究机构在缺乏实时监测数据的条件下,提供科学、可操作的基础设施资源规划依据。
Abstract
In response to the common lack of backend monitoring data in research institutions and the difficulty in directly applying industry-standard estimation models, this paper proposes a historical data-based model for estimating digital infrastructure resource demand. The model categorizes business systems into four types (general-integrated, operating-management, R&D trial, and knowledge-management systems), defines key resource variables and system structures, and incorporates parameters such as time intervals, project approval rates, funding trends, and digital transformation phase coefficients. It further establishes estimation methods—mean estimation and maximum-value estimation—suitable for varying levels of data availability. The model aims to provide a scientific and actionable basis for infrastructure resource planning in research institutions lacking real-time monitoring capabilities.
关键词
数字化基础设施 /
资源估算 /
研究机构 /
历史数据 /
需求模型 /
数字化转型
Key words
digital infrastructure /
resource estimation /
research institutions /
historical data /
demand model /
digital transformation
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] 陈彦达,周开东,林强,等.系统处理能力需求估算方法探讨[J].广东科技,2015,24(6):41-46.
[2] Standard Performance Evaluation Corporation.SPECweb2005:The Next Generation Web Server Benchmark[EB/OL].(2011-9-8).https://www.spec.org/web2005/.
[3] 陈英达,钟苏生,周开东,等.信息系统资源估算方法研究[J].信息技术与信息化,2017(11):128-131.
[4] 刘迪,翟季冬,陈文光.基于回归模型的高端容错计算机TPC-C性能估算研究[J].计算机学报,2013,36(6):1267-1279.
[5] Pentelas A,Katsiros D,Paranou D,et al.Dynamic Sizing of Cloud-Native Telco Data Centers with Digital Twin and Reinforcement Learning[J].IEEE Access,2024,12:91462-91479.
[6] Paranou D,Pentelas A,Katsiros D,et al.Forecasting Resource Demand for Dynamic Datacenter Sizing in Telco Infrastructures[C]//2023 IEEE International Conference on Big Data (BigData).IEEE,2024:3813-3822.
[7] 何地,赵炫焯,齐琦.中国数字经济发展水平测度、时空格局与区域差异研究[J].工业技术经济,2023,42(3):54-62.
[8] 韩缘如. 中国数字基础设施建设水平的测度与评价研究[D].济南:山东财经大学,2024.
[9] 国家市场监督管理总局,国家标准化管理委员会.信息化和工业化融合数字化转型价值效益参考模型:GB/T 23011—2022[S].北京:中国标准出版社,2022.
[10] 国家市场监督管理总局,国家标准化管理委员会.数字化转型管理参考架构:GB/T 45341—2025[S].北京:中国标准出版社,2025.
