Research And Implementation Of Coal Mine Dynamic Disaster Monitoring System Based On Hadoop Cloud Platform

The mine environmental change, caused by the increasing mining depth and intensity, could lead to coal and gas outburst, tunnel collapse or other dynamic accidents, which may bring enormous threat to the underground miners and facilities. The causative factors of accidents are complicated, of which the occurrence is of burstiness and wide range. As a sequel, the predictions of accidents location and time are very difficult, which impede the accident prevention and reduction. Consequently, the production safety situation is becoming more and more serious. Because of the scarce of coal mine dynamic disaster early warning and forecasting expert resources, data volume of coal mine dynamic disaster monitoring system are complex and diverse, storage efficiency is low, information is isolated. Problems introduced above are disadvantageous to the data fusion, the deep data mining and effective decisions making. In this paper, the Hadoop cloud platform is applied into coal mine dynamic disaster monitoring system, which provides an information platform for effective solution to the above problems, as well as the possibility for coal mine dynamic disaster monitoring and early warning.The main work of this paper is as follows:(1) Review and analysis on the research status of coal mine dynamic disaster monitoring system.(2) The overall design of coal mine dynamic disaster monitoring system based on Hadoop cloud platform. The analysis of key technological problems needed to be solved.(3) For to solve the un-displayable problems caused by the multiplicity of file types generated by mine micro seismic and stress monitoring system on the cloud platform, the data collection technology of coal mine dynamic disaster monitoring cloud platform is studied and unified transformation and classification method is applied to different types of data files, which brings much convenience for coal mine disaster prediction experts and users to timely and accurately get the microseism and stress monitoring information.(4) Aiming at the burstiness of the monitoring data as well as the randomness of data accessing by users and disaster prediction experts, the data storage technology of coal mine dynamic disaster monitoring cloud platform is studied in this paper and replica replacement strategy for cloud storage is improved. The experimental results proved that the optimized strategy can make the distribution of the replica balanced on each node, therefore the load balancing of the cluster is achieved, which improves the utilization of storage space.(5) According to the key technologies mentioned above, a high performance data acquisition, storage and processing of coal mine dynamic disaster monitoring system is built.The system has been put into use in the mining center of China University of Mining and Technology. Practice has proved that coal mine dynamic disaster monitoring system based on Hadoop cloud platform realizes the microseism and stress information aggregation of monitoring results. It is convenient for professional analysis and early warning, which provides technical and application support for the scientific solution of coal mine safety problems in China.