基于Logistic-熵的大数据产业生态系统演化路径研究

随着全球步入大数据时代，大数据产业作为战略性新兴产业备受关注。大数据产业生态系统良性演化能够为经济社会健康发展提供全方位支撑，系统内种群主体间关系的复杂多变和数据资源的频繁流动共同影响系统演化进程和路径选择。将Logistic模型与熵模型相结合，完整刻画、分析主体间竞合关系和系统资源流动状态，并在此基础上得出不同企业种群和系统集成演化路径：数据生产型企业业务数据集聚—多渠道数据拓展—数据智能化应用演化路径、数据服务型企业数据云聚合—高质量数据融合—数据资产化演化路径、转型企业业务信息网络化—流程再造数字化—服务应用智能化演化路径以及大数据产业生态系统大数据云存储处理中心建立—数据融合扩展—数智化应用种群集成演化路径。

The Evolution Path of Big Data Industrial Ecosystem Based on Logistic-entropy Model

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#