|
|
|
| The Evolution Path of Big Data Industrial Ecosystem Based on Logistic-entropy Model |
| Zhai Lili,Liu Xiaoshan,Yang Caixia |
| (School of Economics and Management, Harbin University of Science and Technology,Harbin 150040,China) |
|
|
|
|
Abstract With the continuous upgrading and popularization of the new generation of information technologies, such as cloud computing, mobile Internet, Internet of things and artificial intelligence, the world has stepped into the era of digital economy. As a strategic emerging industry, the big data industry has attracted extensive attention from all walks of life.The national big data strategy proposed at the Fifth Plenary Session of the 18th Central Committee of the Communist Party of China (CPC) has promoted China's transformation from a "data big country" to a "data powerhouse".The Outline of the 14th Five-Year Plan issued by the state emphasizes accelerating the promotion of digital industrialization, promoting the digital transformation of industries, and creating a good digital ecology.The potential of big data is immeasurable, and those countries that are the first to establish a complete big data industry ecosystemhave a head start in the international industry competition. Therefore, the construction of a complete big data industry ecosystem is of great significance for building a data power and improving national competitiveness. The research on the evolution path of the big data industrial ecosystem can timely and accurately judge the state of the system, clarify the development direction of the big data industrial ecosystem, and ensure the sustainable and healthy development of the big data industry ecosystem.#br#In the evolution process of big data industrial ecosystem, on the one hand, subject populations with different characteristics develop symbiosis under the influence of the change of inter-subject relationship, and the population growth law conforms to the Logistic growth function model in biology. The Logistic model is widely used in the study of inter-subject relationship change, population evolution law and other issues, and has extensive practicality in the study of industrial evolution and development. At present, the academic community mainly uses the Logistic model to study the inter-subject relationship of different categories of populations, such as populations with different corporate functional characteristics and populations with different data themes, and provides the evolutionary path of populations with different characteristics. On the other hand, as an important production factor, data resources are the core resources for the development of the big data industrial ecosystem, and the influence of resource flow on system evolution should be considered. But the Logistic model cannot reflect the influence of resources, while the entropy model can help to study the influence of resource change on system development and evolution and optimize system resource allocation, and it is often used to study the system evolution caused by the change of system entropy flow. Therefore, this paper divides the main body of the big data industry ecosystem in detail according to different data sources. On this basis, the Logistic model and the entropy model are combined to construct the Logistic-entropy model which is used to comprehensively analyze the competition and cooperation relationship of inter-subject, the flow state of system resources and the evolution state of different populations, and the system evolution should aim at the increase of negentropy.#br#Through the Logistic—entropy model analysis, different enterprise populations and system integration evolution paths of big data industrial ecosystem are obtained.The data production enterprise population follows the evolution path of business data agglomeration—multiple channel data expansion—intelligent data application; the data serviceevolution path of enterprise population is data cloud aggregation—high quality data fusion—data assets; the evolution path of the transformation enterprise population is business information networking—process reengineering digitalization—service application intelligentizing; the evolution path of the big data industry ecosystem is the establishment of big data cloud storage processing center-data fusion expansion— digital intelligent application. Finally, according to the results of the model analysis, different transformation directions and industrial policies are proposed for different enterprise populations in the big data industrial ecosystem, so as to ensure the sustainable and healthy development of the big data industrial ecosystem. At the same time, the studyprovides important theoretical support for the implementation of big data development strategy at the national, industry and enterprise levels.#br#
|
|
Received: 05 May 2022
|
|
|
|
|
|
| [1] 孟方琳, 田增瑞, 赵袁军, 等.创新生态系统视域下公司创业投资中企业种群间共生演化——基于Logistic扩展模型[J].系统管理学报,2022,31(1):37-52.[2] 赵晨阳, 张鹏, 夏一雪, 等.共生视角下网络舆情多元主体引导模型研究[J].现代情报,2021,41(12):107-120.[3] 李洪波, 史欢.基于扩展Logistic模型的创业生态系统共生演化研究[J].统计与决策,2019,35(21):40-45.[4] 朱晓峰,黄晓婷,吴志祥.基于种群演化的政府数据开放实证研究[J].情报科学,2020,38(7):123-131.[5] AN Y C. Analysis on the sustainable development capacity of China's marine ecosystem from entropy perspective[J]. Journal of Coastal Research,2020,115(sp1): 68-71.[6] LIANG X D, ZHANG R Y, LIU C M, et al. Quantitative measurement of the sustainable water resource development system in China inspired by dissipative structure theory[J]. Sustainability,2018,10(11): 3996.[7] OH C Y, LEE D S. Entropy of international trades[J]. Physical Review. E,2017,95(5)：052319.[8] ZHENG F F, SONG Q R. Entropy maximization based capability allocation of clothing production enterprises in sharing economy[J]. IFAC PapersOnLine,2019,52(13)：211-216.[9] GORODETSKY V I, LARYUKHIN V B, SKOBELEV P O. Conceptual model of a digital platform for cyber-physical management of a modern enterprises[J]. Mekhatronika, Avtomatizatsiya, Upravlenie, 2019, 20(6): 323-332.[10] CUI Y, KARA S, CHAN K C. Manufacturing big data ecosystem: a systematic literature review[J]. Robotics and Computer-integrated Manufacturing, 2020, 62: 101861.[11] 周钟, 熊焰, 张林刚. 新兴技术产业应用生态系统构建与发展评价: 以大数据为例[J]. 中国科技论坛, 2020, 36(4): 65-73.[12] 赵海东, 李桥兴. 基于创新驱动的大数据产业生态系统发展水平评估[J]. 科技与经济, 2021, 34(4): 46-50.[13] CASTRO G D R, FERNNDEZ M C G, COLSA U. Unleashing the convergence amid digitalization and sustainability towards pursuing the sustainable development goals (SDGs): a holistic review[J]. Journal of Cleaner Production, 2021, 280: 122204.[14] 胡登峰. 大数据资源管理与新型产业创新生态系统建设[J]. 中国高校社会科学, 2021, 34(6): 101-107,156.[15] TOLSTYKH T, SHMELEVA N, GAMIDULLAEVA L. Evaluation of circular and integration potentials of innovation ecosystems for industrial sustainability[J]. Sustainability, 2020, 12(11): 4574.[16] 程胜.基于Logistic模型产业集群演化稳定性研究[J].西北农林科技大学学报(社会科学版),2007,7(3):35-41.[17] 范太胜.基于Lotka-Volterra模型的区域低碳产业生态系统演化研究[J].科技管理研究,2014,34(15):219-223.[18] YANG Q, HE L, LIU X X, et al. Computer-aided industrial development path optimization: a case from "Optics Valley of China"[J]. Cluster Computing: The Journal of Networks, Software Tools and Applications,2019,22(2):69-78.[19] 孙丽文, 李跃.京津冀区域创新生态系统生态位适宜度评价[J].科技进步与对策,2017,34(4):47-53.[20] 孙冰.同一企业内软件产品间共生模式的模型建构与实证研究——基于质参量兼容的扩展Logistic模型[J].管理评论,2017,29(5):153-164.[21] MENG F L, TIAN Z R, CHANG B Q, et al.Research on the niche evolution game of ecological community innovation of corporate venture capital based on logistic extended complexity model[J]. Complexity,2020,2020(1):6327218.[22] 曾子明, 方正东.基于熵理论的突发事件舆情演化研究[J].情报科学,2019,37(9):3-8.[23] MOLDAVANOV A V. Energy infrastructure of evolution for system with infinite number of links with environment.[J]. Bio Systems,2022,213:104607.[24] 翟凯.廉政制度系统完善与发展策略的战略配置——基于熵理论复合SWOT-PEST矩阵的探讨与实证分析[J].管理评论,2019,31(3):265-278.[25] 陈丹.企业技术创新和商业模式创新的匹配与协同演化[J].商业经济研究,2019,38(9):110-113.[26] 左文明, 丘心心.工业互联网产业集群生态系统构建——基于文本挖掘的质性研究[J].科技进步与对策,2022,39(5):83-93.[27] FROSH R A, GALOPOULOS N E.Strategies for manufacturing[J].Scientific American,1989,216(3):144-150.[28] 刘则渊, 代锦.产业生态化与我国经济的可持续发展道路[J].自然辩证法研究,1994,10(12):38-42,57.[29] 白彦壮, 张莹, 薛杨.社会性企业成长过程及其自组织演化机理——创业生态系统视角的研究[J].科技进步与对策,2017,34(4):84-89.[30] 王玲俊, 王英.基于耗散结构的光伏产业链风险演化条件机制研究[J].软科学,2018,32(1):21-26.[31] 李煜华, 向子威, 胡瑶瑛, 等.路径依赖视角下先进制造业数字化转型组态路径研究[J].科技进步与对策,2022,39(11):74-83.[32] 于喜展, 隋映辉.基于城市创新的产业集群生态:系统关联对接与结构演化[J].科技进步与对策,2010,27(21):56-60. |
|
|
|
