基于深度挖掘技术的ERP系统数据录入误差检测方法

ERP system data entry error detection method based on deep mining technology

目前设计的ERP系统数据量大,录入误差检测方法有限,导致检测的误差数值较低。为了解决上述问题,基于深度挖掘技术研究了一种新的ERP系统数据录入误差检测方法。分析决策者和执行者之间的关系并进行深度挖掘,通过信息处理和数据录入集中控制数据参数;根据数据的特征性对环境中的障碍物划分类簇,多边的点集聚类满足ERP系统的分割要求。最后确定样本阈值,分析误差总值。实验结果表明,基于深度挖掘技术的ERP系统数据录入误差检测方法能够有效检测大量数据,在提高检测效率的同时,提高了检测的误差数值。

Currently designed ERP system has a large amount of data and limited detection methods of input error,which leads to a low value of detection error.In order to solve the above problems,a new method of data entry error detection in ERP system is researched based on deep mining technology.Analyze the relationship between the decision-maker and the executive and conduct in-depth mining,and centrally control the data parameters through information processing and data entry.The obstacles in the environment were divided into clusters according to the characteristics of the data,and the multi-lateral point cluster categories met the segmentation requirements of the ERP system.Finally,the sample threshold is determined and the total error value is analyzed.The experimental results show that the data entry error detection method of ERP system based on deep mining technology can effectively detect a large number of data,improve the detection efficiency,and improve the detection error value.