为提高电力市场结算的效率和准确性，针对结算电量数据提出了一种基于图论及混合卷积神经网络的异常检测方法。首先，使用混合重采样技术对输入数据进行预处理，解决数据集的类别不平衡问题。其次，基于图论将电量数据从一维序列结构转换为二维图结构，并通过图卷积网络和卷积神经网络挖掘图结构的周期性规律和时间相关性特征，提高对异常电量的检测精度，并进一步在卷积神经网络中引入空间注意力机制以提高模型的检测性能。最后，在实际电量数据集上进行异常数据检测，结果表明所提方法在准确率和曲线下面积（area under curve，AUC）值等综合性能上具有优越性。

In order to improve the efficiency and accuracy of electricity market settlement, an anomaly detection method is proposed for power settlement electricity data based on graph theory and hybrid convolutional neural network. Firstly, the input data is preprocessed using a hybrid resampling technique to solve the class imbalance problem in the data sets. Secondly, based on graph theory, the electricity data is transformed from a one-dimensional sequence structure to a two-dimensional graph structure, and the periodicity and temporal correlation characteristics of the graph structure are mined through graph convolutional network and convolutional neural network to improve the detection accuracy of abnormal electricity. Furthermore, a spatial attention mechanism is introduced into convolutional neural network to improve the detection performance of the model. Finally, abnormal data detection is performed on the actual power data sets. And the results show that the proposed method is superior in comprehensive performance such as accuracy and area under curve (AUC) value.