中文说明：
基于GA-GM(1,1)模型的航空发电机状态趋势分析结果较GM(1,1)模型所得结果平均相对误差降低0.14个百分点，分析结果更加精确，为灰色分析模型及其优化模型在航空发电机剩余使用寿命预测技术研究中的应用奠定基础。同时也说明遗传优化算法在航空发电机状态趋势分析中的应用是可行的且具有实际科学意义。这也为GA-GM(1,1)模型在其他领域中的应用指明了道路。
English Description：
Compared with GM (1,1) model, the average relative error of ga-gm (1,1) model is reduced by 0.14 percentage points, and the analysis result is more accurate, which lays a foundation for the application of grey analysis model and its optimization model in the research of aero generator residual service life prediction technology. At the same time, it also shows that the application of genetic optimization algorithm in aerogenerator state trend analysis is feasible and has practical scientific significance. It also points out the way for the application of ga-gm (1,1) model in other fields.