Air traffic flow feature analysis method based on critical model

Abstract

The invention relates to an air traffic flow evolution characteristic analysis method. The method comprises the following steps: step 1, implementing spatial domain gridding; 2, defining a non-local interaction rule for each lattice point; step 3, acquiring grid point state and airspace operation state change according to a simulation result; 4, predicting future grid point state and airspace operation state change based on a machine learning model; 5, phase change judgment results are given according to the grid point state and the airspace operation state; and step 6, inputting a phase change judgment result into an air traffic control automation system to realize flight state data updating. By fusing the spatial domain grid point state data, the spatial domain operation state evolution characteristics can be quickly judged, and the air traffic control operation efficiency is further improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of air traffic management, and in particular provides a method based on complex system theory and critical model for simulating, predicting and optimizing air traffic flow, thereby improving airspace utilization and reducing delays. BACKGROUND

[0002] With the growth of global air transportation demand, air traffic management systems are facing increasingly complex challenges. Traditional air traffic congestion judgment methods involve a large number of indicators, and it is difficult to effectively set the weights of each indicator. How to handle the dynamic behavior of air traffic management systems composed of densely interconnected subsystems is a difficult problem faced in the process of civil aviation operation. In particular, existing solutions still have room for improvement in practical application, especially in how to quantify and control these non-local effects. In order to solve this problem, the present application introduces non-local interaction to study airspace congestion phenomena and proposes a critical model that comprehensively considers structural and dynamic complexity. Through discretizing the airspace, air traffic flow simulation can be quickly implemented, and airspace congestion judgment results can be given according to the simulation results. SUMMARY

[0003] The present application aims to integrate the following elements to improve the efficiency of air traffic management congestion feature judgment: (1) Non-local interaction modeling: Considering the mutual influence between control sectors that are geographically far apart but functionally related, a mathematical model is established to express the influence of such non-local interaction on airspace flowability. (2) Critical model application: Adopting critical model theory as the analysis framework, the phenomenon that when a certain key parameter (such as congestion sector density or non-locality parameter) exceeds a certain threshold, the entire system may undergo a state transition from order to disorder is discussed. (3) Time dependence consideration: Introducing the time dimension, the influence of non-local interaction effects on system dynamic characteristics in different time periods is evaluated to ensure that the model can capture short-term fluctuations and long-term trend changes.

[0004] The technical solution to achieve the purpose of the present application is to provide a rapid judgment scheme for air traffic congestion features, including the following steps:

[0005] Step one, initialization setting, divide the civil aviation airspace into multiple regularly distributed grid points, each grid point represents a control sector, determine the traffic capacity of each grid point according to historical data, consider random variables to reflect the influence of uncertain factors, calculate the actual traffic capacity , where represents uniform distribution;

[0006] Step two, non-local interaction rule definition, define the connection rules of direct accessibility and indirect accessibility between grid points, for any two grid points​ and , set non-local interaction weight to describe the connection strength between each grid point, introduce probability function to measure the possibility of non-local interaction, where represents the distance between two grid points;

[0007] Step three, simulation running, simulate the process of aircraft crossing different grid points according to the established route, record the state change of all grid points at each time step , let be the number of aircraft in grid at time , then the occupancy rate of grid is defined as:

[0008]

[0009] When a grid reaches its maximum capacity, it is considered that a local congestion event has occurred, when multiple grids approach full load at the same time, it indicates that the overall traffic flow is in a critical state, the global occupancy rate is defined as:

[0010]

[0011] where is the total number of grid points;

[0012] Step four, identify the phase transition point by monitoring the change of global occupancy rate , extract key parameters that help to optimize air traffic management and scheduling strategies, such as average flight time and waiting time, flight time for each aircraft in grid can be defined as:

[0013] where represents the set of all grid points adjacent to grid , is the distance between and , is the speed of the aircraft between and , use machine learning model in the following form to predict the occupancy rate of specific grid in the future period of time, where represents the time step:

[0014] ​​​​

[0015] wherein is a function, denotes the model parameters;

[0016] Step five, critical process analysis, based on the above grid and its traffic capacity information and simulation results, based on the non-local interaction value , the critical model is applied to describe the movement path of the aircraft in the entire airspace and the interaction, when , then the grid occurs a phase transition, if , then the air traffic system occurs a phase transition, wherein and are the phase transition critical threshold of the grid and the air traffic system, respectively;

[0017] Step six, the state of each grid in the airspace and the operation state of the airspace are respectively entered into the air traffic control automation system, and the flight state data is updated.

Claims

1. A method for analyzing air traffic flow characteristics based on a critical model, characterized in that Comprising the following steps: Step one, initialization setting, divide the civil aviation area into multiple regular distributed grid points, each grid point represents a control sector, according to historical data to determine the traffic capacity of each grid point , considering random variables to reflect the influence of uncertain factors, calculating the actual traffic capacity , wherein represents uniform distribution;​ Step two, non-local interaction rule definition, define the connection rule of direct and indirect accessibility between grid points, for any two grid points and , set the non-local action weight to describe the connection strength between grid points, introduce the probability function to measure the possibility of non-local interaction, where represents the distance between two grid points; Step three, simulation running, simulate the process of aircraft crossing different grid according to the established route, record the state change of all grid in each time step, set the number of aircraft in grid as time, then the definition of grid occupancy rate is: When a certain grid reaches its maximum capacity, it is considered that a local congestion event has occurred, when multiple grids approach full load at the same time, it indicates that the overall traffic flow is in a critical state, the definition of global occupancy rate is: Wherein is the total number of grids; Step four, identify the phase transition point by monitoring the change of global occupancy rate, extract key parameters that contribute to the optimization of air traffic management and scheduling strategy, such as average flight time and waiting time, etc., flight time for each aircraft on the grid can be defined as: where denotes the set of all grid points adjacent to grid point, is the distance between and, is the speed of the aircraft between and, a machine learning model of the following form is used to predict the occupancy of a particular grid point for a future period of time where denotes the time step: Wherein is a function, indicating model parameters; Step five, critical process analysis, based on the above grid and its traffic capacity information and simulation results, based on non-local interaction value, application of critical model to describe the movement of aircraft in the entire airspace and interaction, when, then the grid phase transition, if, then the air traffic system phase transition, wherein and are the phase transition critical threshold of the grid and the air traffic system respectively; Step six, record the state of each grid in airspace and the running state of airspace into the air traffic control automation system, realize flight state data update.

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