The Application of Big Data in Enterprise Information Intelligent Decision-Making

Abstract

With the continuous increase of mass information in the process of enterprise operation, information redundancy interference poses a challenge to enterprise information decision-making. As the core of an enterprise, reliability decision-making has a direct impact on the development of human economy and the overall economic strength of a country. Therefore, this paper applies big data analysis technology to enterprise information intelligent decision-making, and builds an enterprise information intelligent decisionmaking model based on big data analysis. The key data of enterprise is mined by using the density weight Canopy to improve the K-Gmedoids algorithm. After sorting, filtering, and transforming, the big data model designed in this paper uses interactive genetic algorithms to obtain the optimal decision-making strategy of enterprise information through experimental tests, which has a significant impact on the decision-making management of the enterprise and the competitiveness of the enterprise. The interactive genetic algorithm can obtain the information decision strategy that best matches the actual decision-making problems of the enterprise through a small number of iterations.INDEX TERMS Big data, enterprise information, intelligent algorithm, reliable decision-making, interactive genetic.

Research and application of the sintering whole process intelligent manufacturing system

“…Take a sizeable domestic steel enterprise 360 m 2 sintering machine as an example, the thickness of the material layer is set at 0.7 m and the ideal sintering end point is 72.72-76.36 m, the distance between the head and tail of the penultimate bellows from the ignition position. The ideal range of the vertical sintering speed is 1.83-1.93 cm min -1 according to Equation (5).…”

“…For the traditional manufacturing industry of steel, reshaping the development concept and path of the steel industry, manufacturing technology and system to achieve intelligent manufacturing in the metallurgical industry has become the main direction and long-term goal for the future [3,4]. As a critical process in steel production, the sintering system needs to be optimized by deepening the integration with new generation artificial intelligence technologies to achieve technological innovation and intelligent optimization of the sintering process [5,6].…”

Empirical analysis of decision recommendation models for various processes from a pragmatic perspective

“…The weights of criteria may be estimated using tools such as the analytical hierarchy process (AHP) Guo et al, 2021), a method for ranking preferences in accordance with how close they are to the perfect answer, and entropy measures (Seo et al 2021;. When only some of the criteria weights are available or if decision makers wish to demonstrate inequality across criteria, they turn to mathematical programming methodologies (Yu et al, 2021,Ying et al, 2021. By choosing preferences close to the positive ideal solution or distant from the negative ideal solution, DMs may more readily arrange the weights (relative importance) of each criterion.…”

Big data in the business environment: an analysis of its contributions to Competitiveness. A Literature Review

“…Several researchers have shown interest in big data, since the rapid development of information and communication technologies (IoT, cloud computing, blockchain, Artificial intelligent, etc.) will continue to generate a significant amount of data (6)(7)(8)(9)(10)(11). This has given rise to different points of view about the importance of the collection, storage and application of such data that organizations can do (1).…”