针对目前负荷分解模型的深层负荷特征提取不充分，分解精度低以及训练成本高等问题，提出了一种多尺度特征融合模型。模型由负荷分解子网络及负荷识别子网络两部分构成，两个子网络均利用一维卷积和批量归一化等组成的卷积块进行负荷特征初提取，然后采用金字塔池化模块从多个维度精确提取深层负荷特征信息，并与特征初提取部分进行融合。金字塔池化模块使网络参数大大减少且降低了训练成本。同时与以往模型中的注意力机制不同的是，网络引入多头自注意力机制，每个注意力关注负荷特征的不同部分，从多个角度实现对重要负荷特征的筛选，进一步提高分解性能。最后，在UK-DALE和REDD数据集上进行实验，结果表明所提模型与4个基准模型相比，无论是负荷分解性能还是电器运行状态识别能力都有明显提升。

In order to address the current issues of insufficient extraction of deep load features, low decomposition accuracy, and high training costs in the load decomposition model, a multi-scale feature fusion model was proposed. The model was composed of two parts: the load decomposition subnetwork and the load recognition subnetwork, both of which were employed with convolutional blocks composed of one-dimensional convolution and batch normalization for the initial extraction of load features. Subsequently, a pyramid pooling module was incorporated to precisely extract deep load features from multiple dimensions and fused them with the initial feature extraction part. Network parameters and training costs were significantly reduced by the pyramid pooling module. At the same time, in contrast to previous models with attention mechanisms, a multi-head self-attention mechanism was incorporated by the network. Different segments of load features were focused on by each attention head, achieving the selection of crucial load characteristics from multiple perspectives and further enhancing the performance of load disaggregation. Finally, experiments on the UK-DALE and REDD datasets show that the proposed model outperforms four benchmark models in both load disaggregation performance and appliance operation state recognition ability.