飞行器智能蒙皮通过在飞行器复合材料蒙皮上集成分布式传感器、驱动器和控制器，使其具有监测其本身状态和损伤的能力，其中物理场反演算法是智能蒙皮信号处理中的重要一环。但是由于传感器布置密度小等原因，传统的反演算法精度不高。为了提高智能蒙皮的监测精度，提出一种将反向传播（back propagation, BP）神经网络与改进的灰狼优化算法（improved grey wolf optimizer, IGWO）优化克里金模型融合的BP-IGWO反演算法。制作智能蒙皮原理样件，通过风洞试验对该算法进行验证。结果表明：BP-IGWO反演算法较之传统反演算法具有更高的精度和细节呈现能力，能更好地监测智能蒙皮的状态。

The smart skin of an aircraft is realized by integrating distributed sensors, actuators, and controllers into the composite skin, thereby enabling it to monitor its own state and detect damages. The physical field inversion algorithm plays a key role in the signal processing of the smart skin. However, due to factors such as the low sensor density, traditional inversion algorithms exhibit limited accuracy. In order to enhance the monitoring precision of the smart skin, a back propagation(BP) neural network-improved grey wolf optimizer(IGWO) inversion algorithm, which combined a BP neural network with an IGWO-optimized Kriging model, was proposed. A prototype of the smart skin was subsequently fabricated, and wind tunnel tests were conducted to validate the proposed algorithm. The results demonstrate that the BP-IGWO inversion algorithm achieves higher accuracy and superior detail representation compared to traditional inversion algorithms, and can better monitor the state of smart skin.