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引用次数: 0
摘要
摘 要 信息管理系统的建设是考量一个企业管理水平的基础,因此企业管理信息系统的建设是企业亟待解决的重要问题。利用先进的软件技术是提高企业管理水平的重要途径。同时,创新管理形式也是企业制度创新的重要体现。云平台和物联网技术的发展给企业管理模式、方法和手段带来了革命性的影响。本文基于信息重构模型和物联网技术,构建了企业综合管理系统,以期为企业发展提供参考。本文的新颖之处在于:(1)基础设施层面。综合管理信息化对企业管理决策非常重要,是提高企业管理水平的关键,因此我们采用新颖的管理信息系统模型,使系统高效运行。我们对传统的管理信息系统模型进行改进,使其适合企业分析流程。(2)算法设计层面。传统遗传算法在迭代求解过程中会收敛到某一点,导致近亲繁殖,破坏种群的多样性。因此,该算法只能得到内部最优值,无法得到全局最优解。因此,我们考虑采用新颖的数据分析模型来提高系统的效率。我们对传统 GA 进行了优化,使其在复杂数据场景下具有鲁棒性。(3) 应用层面。商业智能场景被视为应用。我们将提出的模型与最新的模型进行比较,并在回归性能、系统平均响应时间、系统每秒点击数和整体比较分析等方面测试其性能。
Construction of enterprise comprehensive management system based on information reconstruction and IoT
Abstract
The construction of information management system is the basis to consider the management level of an enterprise, so the construction of enterprise management information system is an important problem to be solved by enterprises. Using advanced software technology is an important way to improve the level of enterprise management. At the same time, innovative management form is also an important embodiment of enterprise system innovation. The development of cloud platform and Internet of things technology has brought revolutionary impact on enterprise management mode, methods and means. Based on information reconstruction model and Internet of things technology, this paper constructs an enterprise integrated management system, in order to provide reference for the development of enterprises. Novelty of the paper is: (1) Infrastructure level. The comprehensive management informatization is very important to the enterprise management decision-making, which is the key to improve the management level of the enterprise, hence, we use the novel MIS model to make the system efficient. We improve the traditional MIS model to make it fit for the business analytic process. (2) Algorithm design level. The traditional genetic algorithm will converge to a point in the iterative solution, resulting in inbreeding and destroying the diversity of the population. Therefore, the algorithm can only get the internal optimal value and cannot get the global optimal solution. Hence, we consider the novel data analytic model to make the system efficient. We optimize the traditional GA to make it robust for the complex data scenarios. (3) Application level. The business intelligence scenario is considered as the applications. The performance of the proposed pipeline is verified through the experimental analysis, we compare the proposed model with the latest ones and test the performance on regression performance, average response time of the system, number of hits per second of the system and the overall comparison analysis.
期刊介绍:
This Journal is established with a view to cater to increased awareness for high quality research in the seamless integration of heterogeneous technologies to formulate bankable solutions to the emergent complex engineering problems.
Assurance engineering could be thought of as relating to the provision of higher confidence in the reliable and secure implementation of a system’s critical characteristic features through the espousal of a holistic approach by using a wide variety of cross disciplinary tools and techniques. Successful realization of sustainable and dependable products, systems and services involves an extensive adoption of Reliability, Quality, Safety and Risk related procedures for achieving high assurancelevels of performance; also pivotal are the management issues related to risk and uncertainty that govern the practical constraints encountered in their deployment. It is our intention to provide a platform for the modeling and analysis of large engineering systems, among the other aforementioned allied goals of systems assurance engineering, leading to the enforcement of performance enhancement measures. Achieving a fine balance between theory and practice is the primary focus. The Journal only publishes high quality papers that have passed the rigorous peer review procedure of an archival scientific Journal. The aim is an increasing number of submissions, wide circulation and a high impact factor.