基于数据挖掘的地下流体观测井温度数据超差异常监测方法

Abnormal Monitoring Method of Temperature Data out of Tolerance in Underground Fluid Observation Wells Based on Data Mining

为了提高地下流体观测井温度监测结果的精度,设计一种针对地下流体观测井温度数据超差异常的全新监测方法。引进数据挖掘技术,通过对仓库中历史温度数据的选择、温度数据的处理与数据格式的标准化转换,实现对数据集合的高效率、频繁检索,获得更加高质量的温度数据集合。引进模糊ARHMM算法,建立温度数据马尔科夫链,对处理后的数据超差异常现象进行监控与预警。通过实验结果证明,设计方法可以实现对温度的高精度监测,从而提高监测结果与真实结果的一致性。

In order to improve the accuracy of temperature monitoring results of underground fluid observation wells,a new monitoring method for temperature data out of tolerance anomaly of underground fluid observation wells is designed.The introduction of data mining technology,through the selection of historical temperature data in the warehouse,the processing of temperature data and the standardized conversion of data format,realizes the efficient and frequent retrieval of data sets,and obtains higher quality temperature data sets.The fuzzy arhmm algorithm is introduced to establish the Markov chain of temperature data to monitor and warn the abnormal phenomenon of data out of tolerance after processing.The experimental results show that the design method can realize the high-precision monitoring of temperature,so as to improve the consistency between the monitoring results and the real results.