The optimized processing system for sci-tech novelty retrieval based on queuing theory

Sci-tech novelty retrieval (STNR) plays an important role in novelty retrieval of literature information in the colleges and universities. It can help experts to confirm novelty of sci-tech projects which are required for application and approval. In this study, we give the basic framework of the system, and build the mathematical models of the optimized processing system for STNR based on queuing theory, and to explore the optimized mode between distribution of STNR processing and the task assignment for STNR staffs. We also simulate the model above with real data, and give a discussion of it. The result shows that, the customers' queue in system model will be blocked and stopped from working when the number of STNR staffs convert from three into five at busy periods, while rapidly lowering when the number of STNR staffs reach six. The customers' queue in system model will be unimpeded and unencumbered as long as the number of STNR staffs is more than three at free periods. The model can be used to build an intelligent and coordinating processing system for sci-tech novelty retrieval.