飞机机翼的应力监测对于保障飞行安全具有重要意义。由于实际工程中传感器必须采用稀疏固定位置的布置方式，传感器无法覆盖整个机翼结构，获得的机翼应力数据不能覆盖整个机翼。为此，针对飞机机翼传感器的空间位置布置不完备问题，提出了一种基于多尺度核构造的深度特征融合网络。首先，按照机翼应力场的相似程度将其归为不同的应力场簇，遴选出距离各应力场簇中心最近的应力场作为基准应力场集；其次，通过从各基准应力场中抽取不同规格分量构造多尺度卷积核，对应力场中的缺失点进行多方位特征感知，从而捕获有利于信息填充的多尺度特征；最后，采用并行通道注意力模块自适应选择构造卷积核所捕获的重要特征，并将其映射到统一的特征空间中进行特征融合，进而获取应力场中缺失点的填补值。此外，为了验证所提出方法的准确性和泛化性，利用仿真获得的应力场数据随机生成不同缺失比例的缺失应力场数据集，进行机翼应力场的数据填补，与主流的方法相比，所提出的方法在MAE、RMSE以及MAPE等评价指标上均取得了最佳的填补效果。

Stress monitoring of aircraft wings is crucial for ensuring flight safety and maintaining the structural reliability of aircraft. In practical engineering applications， sensors must be installed at sparse and fixed locations， which limits their ability to capture stress data across the entire wing structure. As a result， comprehensive monitoring of the wing’s structural health cannot be ensured. To address this issue， this paper proposes a deep feature fusion network based on multi-scale kernel construction to reconstruct the wing stress field in cases where sensor placement is spatially sparse or incomplete. First， based on the similarity among stress fields， the data is clustered into several stress field groups， and the field closest to the center of each cluster is selected as the reference set. Then， by extracting components of varying scales from each reference stress field， multi-scale convolution kernels are constructed to perceive the missing data points from different directions， enabling the capture of multi-scale features conducive to information completion. Finally， a parallel channel attention module is employed to adaptively select salient features captured by the convolutional kernels and map them into a unified feature space for fusion， thereby generating the imputed values for the missing points. Additionally， comparative experiments were conducted on wing stress field datasets with different missing ratios. The proposed method outperforms mainstream approaches in terms of MAE， RMSE， and MAPE， achieving the best overall filling performance.