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Intelligent integrated data processing model for oceanic warning system

机译：海洋预警系统智能集成数据处理模型

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摘要

There are a number of dirty data in observation data set derived from ocean observing network. These data should be carefully and reasonably processed before they are used for forecasting or analysis in oceanic warning system (OWS). Due to high-dimensional and dynamic oceanic data, we propose an intelligent integrated data processing model for the OWS. Firstly, we design an integrated framework of the oceanic data processing and present its processing model. The function of each module of this model is analyzed in details. Then, we propose several intelligent data processing methods, such as an intelligent data cleaning method based on the fuzzy c-means algorithm, a data filtering and clustering method based on the greedy clustering algorithm, and a data processing method based on the maximum entropy for the OWS. The efficiency and accuracy of the proposed model is proved by experimental results of observation data of the Red Tide. The proposed model can automatically find the new clustering center with the updated sample data, and outperforms several algorithms in data processing for the OWS.
机译：来自海洋观测网络的观测数据集中有许多脏数据。在将这些数据用于海洋预警系统（OWS）的预测或分析之前，应仔细，合理地处理这些数据。由于高维和动态海洋数据，我们为OWS提出了一种智能的集成数据处理模型。首先，我们设计了海洋数据处理的集成框架，并提出了其处理模型。详细分析了该模型的每个模块的功能。然后，我们提出了几种智能数据处理方法，例如基于模糊c均值算法的智能数据清除方法，基于贪婪聚类算法的数据过滤和聚类方法以及基于最大熵的数据处理方法。 OWS。赤潮观测资料的实验结果证明了该模型的有效性和准确性。提出的模型可以使用更新的样本数据自动找到新的聚类中心，并且在OWS的数据处理中胜过几种算法。

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