基于数据关联图谱的电网调控数据自动修复方法研究OA

With the advancement of the new power system,high-quality data has become a crucial support for enhancing the coordinated control of large power grids and the lean management of dispatching.Aiming at the challenge of repairing abnormal massive,multi-source and heterogeneous data in cross-level collaborative power grid dispatching,an intelligent data repair method based on a data association graph is proposed.By integrating dispatching object relationships,spatio-temporal correlations,and data lineage,a panoramic power grid regulation and control data association graph is constructed,achieving the,fine-grained depiction of data throughout its entire life cycle.On this basis,a bidirectional tracking algorithm for abnormal data is proposed to enable efficient identification and accurate localization of anomalies.Furthermore,a multi-strategy collaborative data repair method integrating rules,models,and knowledge reasoning is developed,which combines repair impact assessment algorithm to generate the optimal repair strategy.Experimental results demonstrate that this method offers significant advantages in both repair efficiency and accuracy,effectively improving the quality of power grid regulation and control data and providing reliable data support for the safe and stable operation of smart grids.