Design And Implementation Of Prediction System For Power Coal Shipping

This paper is from an actual project which was developed by Dalian Maritime University and the Shenhua Group together. The author took part in it. The project began in the spring of 2010, and got the acceptance of experts in Beijing in the end of the year.The company's fuel management center is responsible for the deployment of power coal among. In order to ensure that the power coal storage is of security, economic and reasonable, the center need to develop a system for prediction of power coal shipping to replace the manual operation and provide strong information support to make decisions for the coal shipping.In this paper, the author designs and implements the Prediction System for Power Coal Shipping. The system is used to predict, store and analysis of shipping and storage data, and according to forecast, to arrange shipping of coal-fired to meet the coal-fired supply with the lowest cost. And the statistical data of coal shipping, supply, storage and shipping information helps leaders to make right decisions.First, the author analyses the requirement of the system and introduces the UML and Struts framework and Hibernate persistence technology. Then, the author presents the overall structure of the system which is divided into subsystems include prediction subsystem and query subsystem, and displays the system's functional design, including the core of the system prediction subsystem, then introduces the database design and GUI design of the system. The author takes the module of adding information for example to introduce the implementation mechanism of the system, followed by the implementation of the prediction module for inventory. Finally, the author provides the system running results, and on this basis summarizes the research and looks into deeper research in the future.The system has been running online, completely replaced the original manual operation, avoiding the dispersion of Excel data storage and the unity of query angle for statistics, greatly improved the work efficiency of business operations staff, and brought good benefit for enterprises.