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Intelligent manufacturing management system based on data mining in artificial intelligence energy-saving resources

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At present, the old production management mode has become a stumbling block to the development of enterprises, and the high-end manufacturing technology is still not mature enough. This research mainly discusses the intelligent manufacturing management system based on data mining in artificial intelligence energy-saving resources. The enterprise business management system cannot accurately and timely grasp the actual situation of the production site, and the accuracy and feasibility of the upper-level planning cannot be guaranteed. At the same time, on-site personnel and equipment cannot get practical production plans and production instructions in time, resulting in product backlogs and excessive inventory. On the other hand, equipment is idle and resources are wasted, and the workshop scheduling system loses the corresponding scheduling role. The development of this system is mainly composed of front-end technology, back-end technology and front-end and back-end interaction technology. The interface design of the front end is mainly completed by the windows form application in c#. The interaction between the front and back ends is mainly realized by programming in each control of the form application. Back-end technology is the core content of the system, mainly including two key technologies: mixed programming of C #. Net and MATLAB and C # connecting SQL Server database. The system mainly includes five sub-functional modules: order management, material management, mixed model assembly line balance, assembly line logistics scheduling and system management. Order management and material management are the basis of the system, which provides parameter input for the balance of assembly line and logistics scheduling. The balance of mixed model assembly line is the core function of the system. The balance of mixed model assembly line is carried out by calling the intelligent algorithm written in MATLAB, and the optimal assembly scheme of workstation is displayed to the front end of the system, which reflects the intelligent characteristics of production control system for intelligent manufacturing. The logistics scheduling of assembly line takes the balance result of mixed model assembly line as the premise, takes the balance result as the task sequence input of logistics scheduling, and optimizes the operation efficiency of logistics system (driving path and running time of AGV). The operation results show that the comprehensive energy consumption of 10,000 yuan industrial output value is 401.19 kg standard coal/10,000 yuan, a year-on-year decrease of 6.96%. This study is helpful to the fine management of manufacturing industry.

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This research is supported by Talent Research Fund Project of Hefei University in 2018–2019(18-19RC40), Major scientific and technological projects of Anhui Province (201903a05020033), Anhui Provincial Natural Science Foundation (1908085QF270), and the Support Program Project for Excellent Youth Talent in Higher Education of Anhui Province (gxyq2020065).

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YG and WZ were involved in writing. QQ was involved in editing. KC and YW were involved in data analysis.

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Correspondence to Weitang Zhang.

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Communicated by Deepak kumar Jain.

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Guo, Y., Zhang, W., Qin, Q. et al. Intelligent manufacturing management system based on data mining in artificial intelligence energy-saving resources. Soft Comput 27, 4061–4076 (2023).

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