嵌入式实时系统中劣质数据动态清理方法研究

当前常用的劣质数据动态清理方法规模大,需对其进行剪枝处理后才可应用于劣质数据动态清理中,不仅效率低,且清理结果不准确。因此,提出一种新的嵌入式实时系统中劣质数据动态清理方法,劣质数据主要包括错误数据、重复数据和不完整数据,利用统计学求期望方法对错误进行清理,计算得到一个可信区间的基准范围,依据该基准范围对错误数据进行清理。利用编辑距离获取两个字符串之间的相似度,通过得到的相似度对重复数据进行动态清理。对嵌入式实时系统数据库中所有记录的不完整性进行评估,依据评估结果决定是否清除相应数据。实验结果表明,所提方法针对劣质数据有很高的清理准确性。

Bad data dynamic cleaning method in embedded real-time system research

The current size of the inferior data dynamic commonly used cleaning method, need to prune its treatment before they can be used in poor dynamic data cleaning, not only the efficiency is low, and the cleaning results are not accurate. Therefore, the poor dynamic data cleaning method is a new embedded real-time system, the inferior data mainly includes error data, repeated data and incomplete data, using statistical methods to clean up the expectation error, calculated on the basis of a range of confidence intervals, on the basis of the reference range of error data cleaning. Using the edit distance to obtain the similarity between the two strings, the data obtained through the similarity of dynamic cleaning. The integrity of all records in the embedded real-time system database is evaluated, and the corresponding data are determined according to the evaluation results. The experimental results show that the proposed method has a high accuracy for poor quality data.