Research On GSM-R Daea Mining Platform With Cloud Computing

GSM-R wireless communication system has been selected as the dedicated communication system of China Train Control System. Several railway lines has been using GSM-R since June2003. CTCS-3is China’s current train signal safety control system, which requires the reliability of the GSM-R network. So optimization of GSM-R network has become one of the main job of the network maintenance, which includes network data acquisition, data analysis and network parameter adjustment. But the data storage format is always not unified, which increase the difficulty of data fusion and decrease the query performance. And single server storage limits the storage capacity. So the paper deploys a cloud storage system to solve these storage problems. At the same time, one server based data processing has great limitations, it can only deal with simple, small data, and the algorithm will run slowly. It’s difficult to carry on deep level data mining. Deployment and maintenance of software is also very tedious and usage of software and hardware is low. So this paper design the GSM-R data mining platform based on cloud computing to solve these problems, which will improve the efficiency of data mining algorithm and greatly increase the processing scale. This paper also puts forward several optimization solutions for cloud computing system, which greatly improves the calculation efficiency. At last, this paper successfully applies the field strength coverage prediction model to GSM-R data mining platform, extracts the factors of field strength coverage according to the existing field strength coverage theories, and takes high speed into consideration, and takes measured data as training sample and trained an field strength coverage prediction neural network using parallel BP algorithm. The test result shows that the prediction model is better than theory model.