Research On Critical Risk Factors Analysis Method For Airspace Sector Safety Assessment

With the rapid development of the civil aviation industry,air traffic flow has also increased continuously but available airspace resources is limited which has made impact of flight conflicts increasingly prominent.According to the requirements of the safety management system,safety assessment must be performed when airspace sector changes.For the existing risk factors,the risk analysis methods include fault tree analysis,Reason model,etc.These methods mainly focus on linear reasoning process.Cause finding process is related to workers experience and insufficient consideration is given to the system risk elements and system components interaction.Based on the previous words,Systematic Theoretical Process Analysis is used to conduct airspace sector safety assessment.airspace sector safety control structure are constructed established and potential unsafe control actions(speed modification,altitude modification,and vector clearance)in the operation of airspace sector are analyzed and evaluation matrix methods are used and priority rankings are made for risk factors through expert scoring.Linear Temporal Logic was Introduced and security constraints were converted into logical expressions,and STPA-verifier software was used to verify system security.The unsafe events reports related to controllers in the airspace sector were taken as the research object.Python was used to write the LDA model code and LDA risk models of airspace sector was trained through sample data learning,topics and keywords related to air traffic controller was obtained.Test samples was used to test the accuracy of the model.The results of the sample analysis was based on to draw Sankey diagram.Compared with the STPA analysis results,the results of the two methods are consistent.STPA and LDA were used to analyze potential unsafe behaviors in reports and provided important information for security managers.Researching the risk factors in the system based on historical data which has significant meaning for the intelligent analysis of civil aviation insecurity events in the future.