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数据分析与知识发现  2022, Vol. 6 Issue (2/3): 138-150     https://doi.org/10.11925/infotech.2096-3467.2021.0967
  专辑 本期目录 | 过刊浏览 | 高级检索 |
区块链资源协同配置系统动力学预测模拟研究——以粤港澳大湾区为例*
王晓庆1,2,3(),陈东4
1南京财经大学公共管理学院 南京 210023
2南京航空航天大学经济管理学院 南京 211106
3南京财经大学红山学院 南京 210003
4国家信息中心大数据发展部 北京 100045
Simulating Dynamics Prediction with Collaborative Allocation System for Blockchain Resources: Case Study of Guangdong-HongKong-Macao Greater Bay Area
Wang Xiaoqing1,2,3(),Chen Dong4
1School of Public Administration, Nanjing University of Finance & Economics, Nanjing 210023, China
2College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
3Nanjing University of Finance & Economics Hongshan College, Nanjing 210003, China
4Big Data Development, State Information Center, Beijing 100045, China
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摘要 

【目的】 分析各区块链内重点资源配置间的相互作用机理,强化区域经济区块链的建设与经济的协同发展。【方法】 基于区块链内资源配置要素分析和系统动力学研究理论与方法,通过理论研究分析区块链内各项重要资源要素间的因果关系,结合Vensim软件系统模拟,分析区块链内产业链、创新链、人才链、资金链的运行规律和相关性及敏感性。【结果】 (1)由敏感性分析可知,产业链=资金链>人才链>创新链;(2)产业链方面,2030年为关键节点;(3)资金链方面,2021年-2025年为关键时间段;(4)人才链方面,2025年-2035年为关键时间段;(5)创新链方面,全时段为关键节点。【局限】 对于“五链”的影响因素选择较为简单粗糙,对其作用机理研究尚不透彻,仅是从一些直观的影响因素着手,通过数据收集,建立系统动力学模型,实现有限程度的预测分析。【结论】 本文方法能够为资源协同配置结果预测提供方法论指导。

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王晓庆
陈东
关键词 粤港澳大湾区资源协同配置系统动力学Vensim模拟相关性敏感性    
Abstract

[Objective] This paper analyzes the interaction mechanism among key resource allocations in each blockchain, aiming to construct better regional economic blockchains and promote the coordinated economic development. [Methods] Based on the analysis of resource allocation elements in the blockchain and system dynamics theories and methods, we used the Vensim system to simulate and analyze the related blockchain industries. [Results] (Ⅰ) Sensitivity analysis showed: industry chain = capital chain> talent chain> innovation chain; (Ⅱ) In terms of industry chain, the year of 2030 is a key node; (Ⅲ) For capital chain, the years from 2021 to 2025 is the key time period; (Ⅳ) In the talent chain, the years from 2025 to 2035 is the key time period; (Ⅴ) For innovation chain, the whole time is the key node. [Limitations] More research is needed to improve the selection of influencing factors for the “five chains” and examine their internal mechanism thoroughly. [Conclusions] The proposed method provides some guidance for predicting results of resource collaborative allocation.

Key wordsGuangdong-HongKong-Macao Greater Bay Area    Collaborative Resource Allocation    System Dynamics    Vensim Simulation    Correlation    Sensitivity
收稿日期: 2021-08-31      出版日期: 2022-01-07
ZTFLH:  TP393  
基金资助:*国家社会科学基金青年项目的研究成果之一(18CSH018)
通讯作者: 王晓庆,ORCID:0000-0001-9383-0852     E-mail: wxq@nufe.edu.cn
引用本文:   
王晓庆, 陈东. 区块链资源协同配置系统动力学预测模拟研究——以粤港澳大湾区为例*[J]. 数据分析与知识发现, 2022, 6(2/3): 138-150.
Wang Xiaoqing, Chen Dong. Simulating Dynamics Prediction with Collaborative Allocation System for Blockchain Resources: Case Study of Guangdong-HongKong-Macao Greater Bay Area. Data Analysis and Knowledge Discovery, 2022, 6(2/3): 138-150.
链接本文:  
https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2021.0967      或      https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2022/V6/I2/3/138
Fig.1  区块链资源协同配置系统模型
Fig.2  综合成效系统模型
Fig.3  “五链协同”系统模拟综合成效
Fig.4  产业链集聚成效系统模型
Fig.5  产业链集聚成效模拟结果
Fig.6  制造业与服务业产业协同集聚测度
Fig.7  创新链联接成效系统模型
Fig.8  创新链联接成效模拟结果
Fig.9  专利、科研合作网络下降趋势
Fig.10  人才链培育成效系统模型
Fig.11  人才链培育成效模拟结果
Fig.12  人才供需发展趋势
Fig.13  资金链激活成效系统模型
Fig.14  资金链激活成效模拟结果
Fig.15  区域投资热度模拟结果
Fig.16  敏感性模拟结果
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