新能源汽车监测平台数据异常识别及质量评价研究

Identification and Quality Evaluation of Abnormal Data in the New Energy Vehicle Monitoring Platform

为了提高新能源汽车监控数据的质量,提出了一种系统性的异常数据识别与质量评估方案。针对监控数据中的多种异常情况,设计了从数据收集到解析后的全面评估流程。该流程涵盖了数据规范、完整性、准确性、一致性和时效性等关键维度,并采用层次分析法与熵权法相结合的方式计算各维度权重。通过模糊综合评价方法,量化数据质量评分,避免了单一主观或客观因素对评估结果的影响。实证分析表明,该方案能够全面识别新能源汽车数据中的异常类型,并提供合理的质量评价结果。

To improve the quality of new energy vehicle monitoring data,a systematic abnormal data identification and quality evaluation scheme is proposed.The scheme addresses various abnormal situations in the monitoring data and designs a comprehensive evaluation process from data collection to data analysis.This process covers key dimensions such as data standardization,completeness,accuracy,consistency,and timeliness.The weights of each dimension are calculated using a combination of the Analytic Hierarchy Process(AHP)and the entropy weight method.Through the fuzzy comprehensive evaluation method,the data quality score is quantified,avoiding the influence of single subjective or objective factors on the evaluation results.Empirical analysis shows that this scheme can comprehensively identify abnormal data types in new energy vehicle monitoring and provide reasonable quality evaluation results.