摘要
为了解决现有电力造价异常数据检测算法无法识别清单详情及清单与施工细节不符的问题,提出了一种基于规则匹配的电力造价异常数据辨识算法.利用K-means聚类算法实现了清单的初步分类和特征清单的提取,将特征清单的特征词作为清单类别特征.采用规则库对清单详情进行分词,并提取清单特征词,采用多项式贝叶斯算法计算出清单位于当前类别的概率.实验结果表明,所提出算法较传统异常数据检测算法的准确率提高了约10%.
In order to solve the problem that the existing abnormal data detection algorithms for electricity cost cannot identify list details and lists not inconsistent with construction details,a recognition algorithm based on rule matching for abnormal data of electricity cost was proposed.A K-means clustering algorithm was used to realize the preliminary classification of lists and the extraction of feature lists,and the feature words of feature lists were used as list category features.According to the rule base,the list details were segmented,and the list feature words were extracted.A polynomial Bayes algorithm was used to calculate the probability of a list in the current category.The experimental results show that the accuracy of as-proposed algorithm is about 10%higher than that of traditional detection algorithms for abnormal data.
作者
程津
周鲲
徐志强
伍家耀
CHENG Jin;ZHOU Kun;XU Zhi-qiang;WU Jia-yao(Economic and Technological Research Institute,State Grid Hunan Electric Power Co.Ltd.,Changsha 410000,China;Technical and Economic Department,Hunan Economic Research Electric Power Design Co.Ltd.,Changsha 410000,China)
出处
《沈阳工业大学学报》
CAS
北大核心
2023年第4期387-391,共5页
Journal of Shenyang University of Technology
基金
江西省科技厅项目(S2018CXCPB0484).
