Aviation Safety Risk Analysis and Flight Technology Assessment Issues

TL;DR

This paper discusses methods to improve flight safety in China's civil aviation by analyzing exceedance events, evaluating non-exceedance data, and developing real-time warning systems using machine learning and neural networks.

Contribution

It introduces integrated data analysis and machine learning techniques to enhance safety assessments, personnel evaluation, and automated warning systems in civil aviation.

Findings

Improved safety assessment through neural network-based control quantification.

Enhanced personnel skill evaluation using machine learning.

Development of real-time automated warning mechanisms.

Abstract

This text highlights the significance of flight safety in China's civil aviation industry and emphasizes the need for comprehensive research. It focuses on two main areas: analyzing exceedance events and statistically evaluating non-exceedance data. The challenges of current approaches lie in insufficient cause analysis for exceedances. The proposed solutions involve data preprocessing, reliability assessment, quantifying flight control using neural networks, exploratory data analysis, flight personnel skill evaluation with machine learning, and establishing real-time automated warnings. These endeavors aim to enhance flight safety, personnel assessment, and warning mechanisms, contributing to a safer and more efficient civil aviation sector.