Informatization of Intelligent Manufacturing to Green Innovation Reform of Tobacco Manufacturing Enterprises in the Era of Big Data

Abstract

Objectives: Based on intelligent manufacturing technology, this paper studies the informatization method of green innovation reform of tobacco manufacturing enterprises in the era of big data. Methods: By combing the literature at home and abroad, this paper expounds the technologies of data mining and data visualization based on the idea of lean management. On this basis, the basis of data management in the silk workshop of X tobacco enterprise is investigated and analyzed in detail from the aspects of human resources, equipment and management environment. Taking the blade feeder in the silk workshop of X cigarette factory as the research object, this paper establishes the self inspection model of equipment health state by using the weighted sum method of overall linearity and local nonlinearity. Results: The system can self check the health status of equipment and visually display the results. This method realizes timely and effective inspection and early warning of equipment health status. At the same time, the visual Kanban of workshop site management can provide production and equipment information to relevant personnel in time. This meets its demand for data visualization and changes the current situation of workshop data table display. Conclusion: This method reengineers the information business process of tobacco enterprises and establishes a new performance evaluation system. This effectively improves the existing management system of the workshop.

A Deep Learning-Based Framework for Human Resource Recommendation

“…The management and analysis of such huge data in realtime is a very challenging task that is indeed an impossible task for a human being. Therefore, IMS (information management system) came into existence which is a management service and provides the capability of data gathering, database making, and management [7]. The management of data includes data extraction and storage; hence, IMS offers the analysis of a large amount of data.…”