关键词: 智能矿山, 矿工定位, 无线射频识别技术, K-Means 聚类

Abstract: With the development of smart mining technology,more and more mines have begun to use automated equipment to replace human operations,thereby improving production efficiency and safety. In order to reduce the accuracy and increase the error caused by fluctuating signals when miners work underground in the mine,a mine location algorithm based on radio frequency identification (RFID) technology is proposed. Firstly,a location model based on the received signal strength index is established,and considering the influence of random noise in signal propagation,Log-Normal signal propagation model and Kalman filter algorithm are used to process and optimize the original signal. Secondly,a reference point optimization method based on K-Means clustering algorithm is designed to select appropriate reference points and improve the accuracy and stability of positioning. Experiments show that the proposed algorithm has a significant improvement in positioning accuracy and robustness compared with existing algorithms. This method can not only improve the working safety and efficiency of miners,but also provide more accurate and effective means for mine safety supervision and management.

Key words: intelligent mine,miner location,radio frequency identification technology,K-Means clustering