It is difficult to realize on-line measurement for the large dimension range of China Railway High-speed (CRH) body, the complexity of testing items and the variety of vehicles. Firstly, a measurement scheme of key dimensions for a large-scale bullet train was proposed, where binocular Charge Coupled Device (CCD) stereo vision was used to set up the measuring sub stations of each key dimension, and the laser tracker and coordinate transformation algorithm were used to complete the global calibration of each CCD camera's measuring sub station. In each measuring sub station, the stereo spatial ball detection technology was used to measure local key dimensions. At the same time, a neural network temperature error compensation model based on wavelet analysis was constructed, and the precision of space distance compensation reached 0.05 mm. The comparison between the proposed method and three-coordinate measuring machine, shows that the proposed method is simple in operation, high in flexibility and high in precision, which can effectively solve the key dimension detection problem of CRH body.

Focusing on the shortcoming of low storage efficiency and high recovery cost after copy failure of the current Hadoop, Hopfield Neural Network (HNN) was used to improve the overall performance. Firstly, the resource characteristics that affect the storage efficiency were analyzed. Secondly, the resource constraint model was established, the Hopfield energy function was designed and simplified. Finally, the average utilization rate of 8 nodes was analyzed by using the standard test case Wordcount, and the performance and resource utilization of the proposed strategy were compared with three typical algorithms including dynamic resource allocation algorithm, energy-efficient algorithm and Hadoop default storage strategy, and the comparison results showed that the average efficiency of the storage strategy based on HNN was promoted by 15.63%, 32.92% and 55.92% respectively. The results indicate that the proposed algorithm can realize the resource load balancing, help to improve the storage capacity of Hadoop, and speed up the retrieval.