## 1 Introduction  
Flight data is playing an increasingly important role in data-driven civil aviation safety management, especially in the application of flight operation quality assurance (FOQA) [1], aircraft fault diagnosis [2], runway safety analysis [3], airline safety management [4], flight performance analysis [5], which strongly promotes the construction of smart civil aviation, the big data applications play an increasingly important role [6; 7; 8; 9]. During the flight, the aircraft QAR (quick access recorder) and FDR (flight data recorder) record all the performance and status parameters of various equipment and related components, such as operating parameters, flight real-time communication data, and other data. QAR database system is a comprehensive monitoring system that records and processes the operating state parameters of aircraft, engines, and electronic and electrical equipment. Therefore, flight data provides an applicable and convenient way to obtain flight information and has become the main approach of capturing flight safety information for pilot crew and aircraft health monitor. In the process of collecting and recording flight data, some data are lost at a certain point due to various reasons, which will result in a breakpoint in the data curve. This lost flight number is named the missing data; mechanical failures and random disturbances often result in some missing flight data, which always occur in general aviation. Especially, airplane vibration or shock severely often leads to the data logging sensor failing in general aviation. There are many other unclear reasons leading to the loss of flight data, which will make the analysis of aircraft operating performance and accident diagnosis more difficult. Therefore, it is necessary and meaningful to conclude an effective and practical method for correction and fitting of the missing flight data. Many scholars have carried out a series of studies on flight data analysis and applied research. [PERSON] and colleagues [10] had applied machine learning technology and other technologies to flight data analysis in general aviation, applied flight data to identify and isolate abnormal operations, implemented an important driving force for aviation safety based on data-driven. [PERSON] and [PERSON] [11] used neural networks and other methods in engine fault diagnosis and aircraft health management, and excellent results have been achieved. [PERSON] and colleagues [12] had applied a data mining technique for the generation of an information system's data model, which helps regulate the flight time and pilot scheduling. [PERSON] and colleagues [13] had made the effective management for air traffic control and pilots via flight data analysis. [PERSON] [14] provided a novel approach for non-linear aerodynamic modeling by GPR-based flight data. Combined with high altitude QAR flight data, the pilot's maneuvering behavior was analyzed [15]. [PERSON] [16] offered fault-tolerant control (FTC) systems for aircraft safety in case there are malfunctions in the IMU sensors monitoring flight data. [PERSON] [17] used the Bayesian network for flight landing data analysis. [PERSON] [18] presents an algorithm that automatically identifies and extracts steady-state engine operating points from engine flight data. It calculates the mean and standard deviation of select parameters contained in the incoming flight data stream. [PERSON] [19] make the four landing states divided by three flight parameter variables, including a touchdown, vertical acceleration, and distance to go, and establish pattern recognition based on a BP neural network is used to establish the landing state prediction model and used the genetic algorithm to initialize the model parameter. Revised the Markov chain to improve the model for higher prediction precision establish the landing safety prediction model by integrating pattern recognition and Markov chain with flight data.