## 6 Conclusions  
This study focuses on the flight parameters obtained from a certain type of aircraft, combined with data processing methods (pre-processing interpolation, flight maneuver recognition), and wing virtual ground calibration experiments. The study obtained complete flight data including strain based on this, and established a high-precision flight parameter-strain prediction model. Considering the large volume and high dimensionality of the flight data, the study (1) adopted flight maneuver recognition (FMR) to segment aircraft maneuver actions, segmenting different maneuver actions for targeted deep learning training, thus improving prediction accuracy; (2) Autoencoder (AE) neural network models were used for data dimensionality reduction, reducing the dimensions of subsequent learning models to improve accuracy while increasing efficiency. The study draws several conclusions as follows:  
1. Compared to methods such as locally linear embedding (LLE), principal component analysis (PCA), etc., the AE model has the smallest maximum MSE (the maximum MSE does not exceed 0.068, meeting the requirements), and the clustering effect is also obvious. Under equal conditions, the priority should be given to the AE model for flight data dimensionality reduction.
2. The long short-term memory (LSTM) model has temporal characteristics. Combined with the AE model and FMR, this model can accurately estimate the load or strain of key parts. Particularly, the accuracy of predicting strain amplitudes is higher than that of RNN and other neural network models.
3. Compared to recurrent neural network (RNN) model, the LSTM model has a smaller area of uncertainty within the 95% confidence interval, indicating better stability. Therefore, the LSTM model is more suitable for learning and training the flight data model, and can be also used for subsequent preparation of the aircraft load and stress spectrum.  
In conclusion, future research can utilize specific flight data for a particular aircraft type as the main dataset. Based on the methods proposed in this paper, including flight data preprocessing, FMR, and data obtained from virtual ground calibration tests, combined with the AE dimensionality reduction and LSTM method, a flight parameter-strain prediction model can be established to analyze aircraft loading and damage conditions.  
Figure 13: Comparison results by LSTM: (**a**) with FMR at maneuver state 1; (**b**) without FMR at maneuver state 1; (**c**) with FMR at maneuver state 3; (**d**) without FMR at maneuver state 3.  
**Author Contributions:** Conceptualization, W.H.; Methodology, W.H. and R.W.; Software, R.W.; Validation, W.H. and R.W.; Formal analysis, W.H.; Investigation, R.W.; Resources, W.H.; Data curation, W.H.; Writing--original draft preparation, W.H. and R.W.; Writing--review and editing, W.H. and M.Z.; Supervision, W.H. and Z.Y.; Project administration, M.Z. and Z.Y.; Funding acquisition, M.Z. and Z.Y. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the National Natural Science Foundation of China (Grant Nos. 12302156 and 52305273), China Postdoctoral Science Foundation (Grant No. 2023M742572), Natural Science Basic Research Program of Shaanxi (Program No. 2023-JC-QN-0012), Guangdong Basic and Applied Basic Research Foundation (Grant 2022A1515110636).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.  
**Conflicts of Interest:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.