Oil Pipeline Network Evaluation Based on Multi-Channel Convolution and H-Markov Model with Co-Evolution Mechanism

被引:0
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作者
Qianqian Li
Min Chen
机构
[1] Wuhan University,State Key Laboratory of Software Engineering, School of Computer Science
关键词
oil pipeline network layout evaluation model; co-evolution mechanism; coevoludonary behavior recognition; convolution kernel of different sizes; HMM;
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学科分类号
摘要
With the development of oil pipeline transportation domains, the layout of regional oil pipeline transportation network is doubly influenced by regional development and its own evolution, more than that, it is a dynamic concept. In order to get more reasonable evaluation results, it is necessary to improve the traditional evaluation method by regarding the network layout behavior and regional space expansion behavior as interactive objects. In this paper, on the basis of analyzing regional development characteristics and pipeline transportation network layout demands from the spatiotemporal perspective, the co-evolution mechanism between them is dissected. Based on this, the multi-dimensional layout evaluation index system is constructed from four dimensions, and a layout evaluation model of oil pipeline transportation network based on multi-channel convolution and the Hidden-Markov model with co-evolution mechanism (i.e., CEM-MCNN-HMM) is proposed, which serves as a framework of co-evolutionary behavior recognition to use convolution kernel of Afferent sizes to extract feature information of different granularity from data in Afferent channels, effectively obtain the property features, behavior features, and interactive features of the behavior objects, and then convert the behavior recognition problem into a classification problem. The Hidden-Markov model is used to excavate the status dependent relations with a certain span of time and main, the classification results to improve the robustness of the model Finally, taking real data set as training data, performance testing of the proposed model is carried out from three aspects: the rationality test of using fractal dimension metrics and resetting fractal measured unit, and the model evaluation criteria based on confusion matrix analysis. The result shows that the performance of CEM-MCNN-HMM is best among all models and can improve the judgment level of the transport network layout.
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页码:665 / 681
页数:16
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