Evaluation of Flight Training Quality for the Landing Flare Phase Using SD Card Data

To enhance the objectivity and precision of quality evaluation in flight training, this study proposes an assessment method for the landing flare phase based on time-series flight parameter data from Secure Digital (SD) card. By analyzing landing flare data from flight instructors and trainees, a standard sequence model was established, and the Dynamic Time Warping (DTW) algorithm was employed to calculate the similarity between individual trainee sequences and the standard sequence. Using K-means clustering, the landing flare quality was categorized into four distinct levels: Excellent (22.5%), Good (25.5%), Qualified (23.5%), and Improvement Needed (28.5%). The results demonstrated significant consistency with instructor evaluations (Spearman correlation coefficient 0.71). Furthermore, through the identification of weak parameters, specific technical deficiencies in areas such as airspeed control and pitch attitude maintenance could be accurately pinpointed. This approach not only effectively validates and supplements instructor assessments but also provides data-driven support for developing personalized training programs, thereby improving the quality and efficiency of flight training.