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

摘要： 在模式挖掘应用于智能化方法过程中，为了提高数据变化模式的准确性和可用性，以FC闭包模型为基础，对专家界定的领域影响因子进行逻辑转化，采用距离均方差算法以时间序列为基础处理原始数据，并利用激巨判定函数摒弃无效元素，降低数据维度，完成数据准备。选定恰当可行的数学模型进行时序数据拟合，借鉴分类分析法的思想，引入CCM-ECM模型表达最终挖掘结果，完成时序下模式挖掘模型（TODM）设计，同时为该模型的置信度计算和自适应调整提出一套较为科学的计算方法，以此达到深度挖掘数据内部潜在规律，提高数据变化模式的高精细化描述程度的目的。最后结合油井施工作业过程，利用TODM模型实现了油井施工作业后模式挖掘系统的设计。

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