To solve the problems of low collection performance, poor wear leveling effect, and high memory overhead in the existing NAND flash memory garbage collection algorithms, a new garbage collection algorithm based on logical region heat was proposed. The heat calculation formula was redefined, the NAND memory of continuous logical address was defined as a heat range which was used to replace the heat of logical page, then the data with different heat was separated into the corresponding flash blocks with different erase counts. The cold and hot data were effectively separated,and the memory space was also saved. Meanwhile, a new collection cost function was constructed to improve the collection efficiency and wear leveling effect. The experimental results showed that compared with the excellent File-aware Garbage Collection (FaGC) algorithm, the total number of erase operations was reduced by 11%, the total number of copy operations was reduced by 13%, the maximum difference of erase counts was reduced by 42%, and the memory consumption was reduced by 75%. Therefore, the available flash memory space can be increased, the read and write performance of flash memory can be improved, and the flash memory life can be also extended by using the proposed algorithm.

Concerning the shortcomings of the methods which forecast railway freight volume, the paper proposed Grey Neural Network (GNN) based on the Improved Particle Swarm Optimization algorithm (IPSO-GNN). To make up for the shortfall of the conventional GNN and guarantee the prediction accuracy, it optimized the GNN whitening parameters through the IPSO. And it computed the railway freight volume and the correlation degree of influential factors. It built a railway freight volume model based on IPSO-GNN with six relating factors. The simulation results show that the prediction method is effective and feasible. The prediction precision of the given model in the railway freight volume forecast is better than those of the conventional GNN prediction method and other prediction methods.

Taking comprehensive use of 2.45GHz active Radio Frequency Identification (RFID) technique, information processing technology, wireless communication technology of General Packet Radio Service (GPRS), Global Positioning System (GPS) technique, mobile computing and network technology, this paper designed the software and hardware structure of the taxi anti-counterfeiting management system, developed the 2.45GHz active tags and information terminals with the functions of recognition, orientation navigation and mobile communication. Meanwhile, on the basis of the analysis of the main application models, it developed the upper application software of the system. By setting up the application system and testing, the results show that the system can work with ultra-low power, the peak current is only 2mA; And the data transmit in real-time while delaying less than 4 seconds; Also the identifiable distance of the RFID terminal is about 110m and it can read no less than 150 tags at the same time.