基于作战管理的有人-无人协同作战体系结构建模

航空发动机精确的剩余使用寿命预测是确保发动机安全服役必须开展的环节。针对复杂工况环境下涡扇发动机的剩余使用寿命预测问题，提出了一种基于极端梯度提升和卡尔曼滤波的寿命预测模型。首先，采用高斯分布对原始振动数据进行分析，提取具有时间序列和退化趋势的特征数据，并为其设置剩余使用寿命（RUL）标签；其次，利用随机搜索算法对融合参数范围进行寻优，在极端梯度提升(XGBoost)中加入卡尔曼滤波器解决预测值不平滑和噪声干扰的问题；最后在商用模块化航空推进系统仿真数据集（C-MAPSSC）上进行了验证和分析，实验结果证明：与其他模型相比，文中采用的寿命预测方法准确度更高。

Modeling of Cooperative Combat System Architecture of Manned-Unmanned Based on Battle Management

Accurate residual service life prediction of aero-engine is a necessary step to ensure the safe service of engine. Aiming at the problem of residual service life prediction of turbofan engine under complex working conditions, a life prediction model based on extreme gradient lifting and Kalman filter was proposed. Firstly, Gaussian distribution was used to analyze the original vibration data, extract the characteristic data with time series and degradation trend, and set the RUL label for it. Secondly, the random search algorithm is used to optimize the fusion parameter range, and Kalman filter is added to XGBoost to solve the problem of uneven predicted values and noise interference. Finally,validation and analysis were performed on the commercial modular aerospace propulsion system simulation (C-MAPSS)dataset.Experimental results show that compared with other models, the life prediction method used in this paper is more accurate.