Simulation Analysis Of Iron Ore Grade Based On Intelligent Technology

China is the largest steel producer country in the world and leads the global steel industry value chain largely. Iron ore which is the important raw material of steel industry, was paid extensive attention by the steel industry. Iron ore grade which is one of the key factors of the mine, runs through the whole process of mining process and affects mine's future planning. Iron ore grade analysis is one of the key techniques in the iron ore mining process. The correct Iron ore grade value contributes to maximum utilization of resources as well as the mining benefit maximization. With the great development of the steel industry, the equipment and technology of the iron ore mining are improving and the mine scale is growing. But the iron ore grade analysis technology has not been improved, the artificially sampling and testing method is widely used which influences the production efficiency and restricts the electromechanical integration process of the mines.Through on-the-spot investigation, it was discovered that Iron ore grade has close relations with the quality, volume, color, water content, block ore content and the producing area of the Iron ore. The relevant information and data of mineral grade during mining were collected. How to measure the importance of these factors to iron ore grade and use them to evaluate the Iron ore grade are two key problems needed to resolve. There is a lot of inconsistent information and too many attributes in these data. It is very difficult for the traditional linear analysis method to do this analysis. The BP neural network has the ability to approximate any nonlinear function, and it is simple and efficient, so it can be used for complex system modeling. Rough set theory is able to deal with incomplete or inaccurate information effectively, and can find potential rules. The combination of Rough sets and Neural Network techniques provides a new method for iron ore grade analysis.The relevant technology of the iron ore grade analysis has been introduced, and the advantages and disadvantages of the Rough sets and Neural Network theory have been analyzed. A new attribute reduction algorithm of inconsistent system base trie tree was proposed. Using the storage structure and operation efficiency of trie tree to optimizes the attribute reduction process. And it can reduce the inconsistent system effectively. An iron ore grade analysis model based on rough sets and the neural network technology has been established. With this model a simulation system based on Rough sets and Neural Network technology has been realized. In addition, the actual iron ore grade data was analyzed. The application provided decision-making basis of the iron ore screening for the mine and theoretical guidance of installation corresponding information collection mechanical device for the mine.