In the view of the current support process of spare parts is not comprehensive enough, all aspects without linking with each other, the resources without being shared, spare parts with slow turnaround and high inventory levels, which results in low economic efficiency. Therefore, considering the modern information technology and network technology, the document research on the spare parts support process optimization, which is to make the process more scientific and reasonable, support resources more economical, and respond more sensitive, thereby increasing military and economic benefit of the spare parts support. By means of analyzing three major elements of the spare parts support process modeling including perception task, spare parts resources, business processes, and analysis of several major process modeling method, the document raise the modeling method of petri nets for the task-oriented spare parts support process.

Due to the short investment time of the new equipment, the materiel consumption and maintenance data is not much. As a result, its demand prediction belongs to the prediction of small sample data. Since general demand prediction methods are difficult to predict the materiel demand of new equipment, an applicable and efficient prediction method should be explored to solve the problem. Therefore, combining grey prediction theory and least square support vector machine and operating accumulative generation on the original data sequence to extract its deep law characteristic, the new equipment materiel demand prediction model based on Grey Least Square Support Vector Machine (GLSSVM) was established, and the model's parameters was optimized by SIWPSO. Finally an example was set using Neural Network, traditional LSVSM and GLSSVM to predict the materiel demand of new equipment X to verify the accuracy and effectiveness of GLSSVM. The result shows that the prediction precision of GLSSVM is superior to the other two methods.

The Internet of Things (IOT) plays an important role in improving the equipment support capability. Using the Internet of Things, cloud computing and other new advanced information technologies, the equipment material support system is constructed. In this paper, the architecture, the network structure and building steps of the equipment material support internet of things system are designed to promote the development of equipment material support.

The paper studied the effect on the performance of organic polymer solar cells at the active layer based on P3HT/PCBM from the addition of a solid derivative of nitrobenzene, 1-Bromo-4-Nitrobenzene, carried out experiment on 1-Bromo-4-Nitrobenzene doped with different doses in P3HT/PCBM active layer, and comparatively analyzed the performance parameters, absorption intensity, external quantum efficiency, fluorescence intensity, morphology characterization, and transient absorption of devices under different doses. Results showed that the organic polymer solar cells reached the maximum energy conversion efficiency, the energy conversion efficiency was improved from 2.2% to 3.4%, with an increase of 54 %, when the additive volume of 1-Bromo-4-Nitrobenzene was 25wt%. Addition of 1-Bromo-4-Nitrobenzene helped to improve the photoelectric conversion efficiency of organic polymer solar cells.

With the wide use of HDFS and increasing scale of small files, problems of HDFS in small files storage gradually exposed. Thus the article put forward a storage strategy of unstructured small file based on the type of file, and optimized the architecture of cluster to save memory and improve the efficiency of file access. Through experiment, the strategy is proved to be effective and reliable.