关键词: 模式挖掘, 时间序列, CCM-ECM模型, P-L（普朗克-洛伦兹）模型, 自适应

Abstract: In the process of pattern mining applied in intelligent method, in order to improve accuracy and reliability in data change model, this paper makes logic transformation in field impact factor which is defined by experts, and it is based on FC closure model. It uses mean square error algorithm based on time series of processing the original data, and uses huge intensity to judge the function then reject invalid element, and reduces the dimension of data to complete data preparation. Appropriate and practical mathematics model is selected to do the temporal data fitting. The thinking of classification analysis method is drawed on, bringing in CCM-ECM model to describe the final mining results, finishing pattern mining model（TODM） based on sequential design. At the same time, this paper proposes a set of more scientific calculation methods for the confidence level calculation and adaptive adjustment of the model, to achieve the purpose that excavates potential law in the data deeply, and increases the degree of high precision detailed description on the data changed model. Finally, in combination with the well construction process, the paper utilizes TODM model to accomplish the pattern mining system design after the well construction work.

Key words: pattern mining, time series, CCM-ECM model, P-L（Planck-Lorentz） model, self-adaption