When designing the underground logistics system, it is necessary to consider the uncertainty of logistics nodes, high cost, and high risk. This paper employed the theories of uncertain graph and dynamic programming to solve the network planning problem of underground logistics system. Firstly, we proposed the concepts of uncertainty measure matrix and vertices structure uncertainty graph by using uncertainty measure and uncertainty graph. Secondly, vertices structure uncertainty graph of the underground logistics system was constructed based on our proposed vertices structure uncertainty graph and the uncertainty of logistics nodes. Thirdly, the dynamic programming model of the underground logistics system was established, and its solution algorithm was also designed by improving simulated annealing. Finally, the correctness and feasibility of the method was validated by using a numerical example of the underground logistics system in Xianlin district, Nanjing City in China.

<abstract> <p>When the network optimization problem is discussed, in the actual situation, it is necessary to consider the uncertain factors in the network. This paper employs the theories of uncertainty, uncertain programming and network optimization to solve the uncertain network optimization problem. First, based on uncertainty theory and uncertainty graph, we redefine the concept of an uncertain network system, and propose a unified identification method for an uncertain network system based on a conditional uncertain measure matrix. Second, we establish the network optimization model for the shortest path problem based on a conditional uncertain measure matrix. Third, according to the measure simulation technology, a hybrid intelligent algorithm is designed to solve the model. Finally, the correctness and feasibility of the approach is illustrated by a numerical example of an underground logistics system.</p> </abstract>

In order to realize Digital Oil Field, some key problems need to be improved, esp. accurate and automatic prediction of oilfield development indexes which may be resolved by designing of intelligent prediction system. With the shortcoming of inference of system designed by us, automatic inference problem for a complicated intelligent prediction system was improved using pattern recognition method. First, intelligent prediction system and the methods as well as principles of pattern recognition were introduced. Then the framework of intelligent prediction system based on pattern recognition was formulated by using technologies and methods of human-computer interface, fuzzy processing and pattern recognition. Secondly, the knowledge base was extended as augmented knowledge base with introducing credibility to measure uncertainty of knowledge. Particularly, the methods and principles of pattern recognition were used to design two recognizers and one inferring machine. Moreover, the method of selecting predictive model based on reasoning of pattern recognition was presented by coupling them and intelligent prediction system. Finally, the design of improving intelligent prediction system of oilfield development indexes was simulated. Simulation result shows that improved system may automatically realize to select optimal prediction model by computer according to different reservoirs and different development stages. The results obtained in this thesis will helpful to design for intelligent prediction system.