CN118762560B - Method and system for evaluating airspace capacity of airway network - Google Patents

Abstract

The invention discloses a method and a system for evaluating airspace capacity of an air route network, which belong to the technical field of air transportation, and are characterized by establishing an air route network airspace topological network taking airports and sectors as nodes, determining the number of aircrafts entering the sectors in unit time through a discrete time difference equation from the nodes to the node traffic flow, classifying the sectors, determining the controller load of each sector, regressing the controller load in each sector and the number of aircrafts entering the sector in unit time, correcting the controller load of each sector to determine the sector capacity of each sector, acquiring the airport capacity in a stable running state, taking the maximum traffic flow of the topological network of the air route network as a target, establishing a single-target optimization model with the maximum traffic flow, and iteratively obtaining the airspace capacity of the air route network when the traffic flow in unit time is maximum. By the method, the airspace flow of the airway network can be accurately estimated.

Description

A method and system for evaluating airspace capacity of route network

Technical Field

The present invention relates to the field of aviation transportation technology, and more particularly to a method and system for quickly evaluating the airspace capacity of a large-scale route network based on sector nodes.

Background Art

The strong demand for aviation brought about by the strengthening of comprehensive national strength requires the capacity of airports and air route networks to provide support. The level of national traffic volume that the national air route network can meet is an important basis for formulating development plans and purchasing aircraft, and is of great concern to the government and aircraft manufacturers. The coordination of flight schedules before the change of seasons pays close attention to the possible level of delays and normal changes. Whether the air route network planning plan can achieve the expected goals needs to provide capacity performance as a criterion.

Route network planning aims to analyze the structural flaws of existing routes and airways exposed by the diversification of air transport demand and the rapid growth of flight traffic. Based on the distribution characteristics and development trends of traffic flows, combined with air traffic control support requirements, and utilizing transportation network design and optimization technologies, it provides a comprehensive layout and coordinated planning approach to address the strategic issues of airspace resources, ground facilities, and the construction and development of the civil aviation industry over the long term. By scientifically allocating and utilizing airspace resources, route network planning can improve air transport efficiency, reduce airline operating costs, guide the rational layout of ground communication, navigation, and surveillance facilities, and provide a reference for airport renovation and expansion.

At present, traditional capacity assessment methods for airspace capacity assessment mostly focus on regional sectors and terminal areas. Due to the complex route network structure and numerous nodes in large-scale airspace, it is difficult to directly model and solve the airspace capacity.

However, in the existing technology, the capacity assessment of the route network in a large airspace mainly takes the capacity of some elements in the route network as the capacity of the entire route network, such as taking the capacity of all airports in the route network airspace as the route network capacity or only considering the backbone route network in the route network. Compared with the route network in a general airspace, the route airspace network in a large airspace has more elements, and not considering the impact of other elements on it will lead to inaccurate assessment results.

Summary of the Invention

In response to the problems existing in the above-mentioned fields, the present invention proposes a route network airspace capacity assessment method and system, which can solve the technical problem that the route airspace network in a large airspace has many elements and does not consider the impact of other elements on it, which will lead to inaccurate assessment results.

To solve the above technical problems, the present invention discloses a method for evaluating airspace capacity of a route network, comprising the following steps:

Determine the traffic flow direction between sectors through the routes and traffic flows between sectors of the airport, and establish an airspace topology network with airports and sectors as nodes;

According to the airspace topology of the airway network, the number of aircraft entering the sector per unit time is determined by the discrete time difference equation of the node-to-node traffic flow;

Sectors are classified and the controller load for each sector is determined by calculating the static coupling parameters between sectors. The controller load in each sector is regressed against the number of aircraft entering the sector per unit time, and the controller load of each sector is corrected to determine the sector capacity of each sector.

The airport capacity under stable operation state is obtained. With the goal of maximizing the traffic flow of the topological network of the airway network airspace, the airport capacity and sector capacity are used as constraints. A single-objective optimization model with maximum traffic flow is established, and the airspace capacity of the airway network is iteratively obtained when the traffic flow per unit time is maximized.

Preferably, the establishment of an airspace topology network with airports and sectors as nodes comprises the following steps:

The airports and sectors in the large-scale airway network airspace are taken as nodes, and the internal structure of the sectors in the airway network airspace is reduced to nodes. The edges between the nodes are established according to whether there are airways and traffic flows between the nodes, and the airway network airspace topology network with airports and sectors as nodes is obtained.

Preferably, determining the number of aircraft entering the sector per unit time comprises the following steps:

Based on the obtained airway network airspace topology, the traffic flow direction between sectors is determined according to the routes and traffic flows between sectors. The Euler model is improved and the discrete time difference equation of the traffic flow from node s to node j is obtained as follows:

in, is the total traffic flow from node s to node j at time t;

i is the upstream sector or airport of the sector, and its value is [1, m]. j is the downstream sector or airport of the sector, and its value is [m+1, m+n]. _τs is the travel time of sector s.

β _isj is the flow divergence parameter, which represents the ratio of the flow from node i to node s to the flow from node i to node s. It is obtained based on the flow distribution ratio of busy sectors and airports in historical flight track data, and has

q _sj (t) is the traffic flow from node s to node j at time t;

The traffic flow from node s to node j at time t is the traffic flow that cannot enter node j due to traffic control or capacity limitation and remains in node s;

The number of aircraft in node s at time t is expressed as:

in, is the traffic flow that cannot flow out of node s at time t; is the traffic flow that does not flow out of node s due to the capacity limitation of downstream nodes at time t-1; is the traffic flow out of node s at time t, and Δt is the time step.

Preferably, determining the controller load for each type of sector comprises the following steps:

The complexity index of each sector is calculated using historical data, and the sectors are classified using the Gaussian mixture model. The CH index is used to judge the quality of the Gaussian mixture model's clustering results for the sample data. The higher the CH index, the better the clustering effect.

Determine the controller load for each sector type, divide the controller load into monitoring control load, conflict control load, and coordination control load, calculate the controller load, use the sector complexity index to calculate the controller load, and calculate the sector node capacity;

The sector capacity is evaluated based on traffic complexity. The controller load in the sector is corrected by calculating the sector static coupling degree. The calculation formula of the sector static coupling degree is:

Where OH is the sector static coupling parameter;

H is the number of sectors adjacent to the sector and connected with traffic;

_Ch is the positional relationship between the sector and the adjacent sectors, which can be divided into high and low sectors, east and west sectors, and inner and outer sectors;

V is the product of the area of the horizontal range used by aircraft in the sector and the number of altitude levels used by aircraft in the sector;

L is the boundary area of the sector, that is, the product of the horizontal connection length between the sector and all connected sectors and the number of available altitude layers;

J is the number of routes available for use within the sector;

L _h is the sector boundary area between the sector and the adjacent sector h;

J _h is the number of routes connecting this sector with the adjacent sector h.

Preferably, the correction of the controller load of each sector comprises the following steps:

The quantitative model for monitoring and control load is:

The quantitative model of conflict control load is:

The quantitative model of coordinated regulatory load is:

W _co = β × (F _in + F _out )

The quantitative model of the total control load is obtained as follows:

W＝aW _mo +bW _cf +c(1+OH)W _co

Where W is the total control load; a, b, and c are the coordination coefficients of the three control loads: surveillance control load, conflict control load, and coordination control load, respectively; OH is the sector static coupling degree; W _co is the coordination control load; β is the average coordination control load of a single aircraft; F _in and F _out are the number of flights entering and leaving the sector; W _cf is the conflict control load; γ _al , γ _sp , γ _hd , and γ _{al_keep} are the impact coefficients of flights with altitude change, speed change, heading change, and altitude stability on the conflict control load, respectively; W _mo is the surveillance control load; F is the number of flights served per unit time within the sector; is the average dwell time of each flight in the sector; λ _al , λ _sp , λ _hd , and λ _{al_keep} are the impact coefficients of flights with altitude change, speed change, heading change, and altitude unchanged on the surveillance control load, respectively; The proportions of flights with altitude change, speed change, heading change, and altitude unchanged, respectively; The average number of flight adjustments for altitude change, speed change, and heading change, respectively;

Using linear, quadratic, and cubic polynomials, the total number of aircraft entering a sector per unit time and the controller load per unit time in that sector were regressed, and the ^R2 was used to evaluate the fitting effect. The polynomial expression with the ^R2 closest to 1 was selected as the correction result of the controller load.

According to the capacity assessment rules, the number of aircraft entering the sector per hour when the controller load is 70% of one hour is the hourly capacity of the sector, and the number of aircraft entering the sector per fifteen minutes when the controller load is 80% of 15 minutes is the fifteen-minute capacity of the sector.

Preferably, obtaining the airport capacity in a stable operating state comprises the following steps:

Determine the duration of sample collection based on capacity assessment and flight schedules;

According to the selected sample statistics, the number of airport takeoffs, landings, and the frequency of airport takeoff and landing pairs in the historical data are calculated, and the number of takeoffs and landings is used as the horizontal and vertical coordinates, and the frequency is used as the bubble radius to draw a bubble chart;

Select a data point with a confidence level of 95% from the historical data of sample statistical duration, draw an envelope line to enclose the data point, and determine the envelope line of the historical peak service flight frequency;

Based on the historical peak service flight envelope, the sample points where the sum of the take-off and landing flights contained in the envelope is the maximum are determined to obtain the airport capacity, which provides constraints for solving the traffic flow in the topological network of the route network airspace.

Preferably, the establishment of a single-objective optimization model for maximizing traffic flow comprises the following steps:

The number of aircraft is related to the traffic flow in each time step. When the traffic flow in each time step is the largest, the number of aircraft handled by the sector is also the largest. This problem is transformed into a single-objective optimization problem, where the objective function is to maximize the traffic flow between all connected nodes in each time step:

max:

The decision variable is the flow rate q _sj (t) between all connected nodes, and the range of the decision variable is:

in, is the total traffic flow from node s to node j at time t; is the traffic flow that did not flow out in the previous time step, where the regulated flow at time t-1 is the maximum outflow flow at time t-1 minus the actual outflow flow:

The number of aircraft processed by each sector node per unit time does not exceed the sector node capacity, and the constraints are:

in, is the hourly capacity of node s, is the fifteen-minute capacity of node s;

For an airport node, when subject to the airport's arrival and departure capacity constraints, the number of aircraft flowing into and out of the airport node per unit time must not exceed the airport's node capacity per unit time. The constraints are:

The capacity of the route network is the maximum number of flights that the route network can handle within the airspace per unit time. The number of flights handled by the route network is the number of aircraft entering the sector per unit time, that is, the total number of aircraft entering the route network airspace from external sector nodes and airport nodes per unit time. The expression of the route network capacity is:

Where D(t) is the number of flights processed by the airspace route network at time t, TR is the flow set from airport nodes in the airspace and external sector nodes to sector nodes, and _qTR (k) is the traffic flow entering the airspace.

Preferably, the iterative process of obtaining the airspace capacity of the route network when the traffic flow per unit time is maximum comprises the following steps:

Taking the capacity of airport nodes and sector nodes in the topological network as constraints, an elite-preserving genetic algorithm is used to solve the single-objective optimization model for maximizing traffic flow.

When the number of aircraft entering the airspace of the route network reaches a stable state, the airspace capacity of the route network is obtained. Taking the maximum traffic flow as the goal, when the traffic flow in the network does not change much over time, the airspace capacity of the route network when the traffic flow is maximum is obtained.

Preferably, the airspace capacity of the route network is the number of flights entering the airspace of the route network per unit time, including the number of flights from external sector nodes entering the airspace and the number of flights from airports within the airspace entering the airspace per unit time;

At t = 0, the airport node and the external sector node provide traffic to the sector nodes in the airway network airspace. Traffic flows into the sector nodes directly connected to the airport node and the external sector node. As time goes by, the traffic enters other sector nodes. When the traffic in each sector tends to be saturated, the total number of flights entering the airspace also tends to be stable.

When the total number of aircraft entering the airspace does not change much over time, the number of aircraft entering the airspace route network per unit time is obtained, that is, the airspace capacity of the airspace route network with the largest traffic flow per unit time.

Preferably, a route network airspace capacity assessment system includes:

The airspace topology network building module is used to determine the traffic flow direction between sectors through the routes and traffic flows between sectors of the airport, and to establish an airspace topology network with airports and sectors as nodes;

The sector aircraft quantity determination module is used to determine the number of aircraft entering the sector per unit time based on the airspace topology of the route network and the discrete time difference equation of the node-to-node traffic flow;

The sector capacity acquisition module is used to classify sectors and determine the controller load for each sector by calculating the static coupling parameters between sectors. The controller load in each sector is regressed with the number of aircraft entering the sector per unit time, and the controller load of each sector is corrected to determine the sector capacity of each sector.

The traffic flow optimization module is used to obtain the airport capacity under stable operating conditions. With the goal of maximizing the traffic flow of the topological network of the airway network airspace, the airport capacity and sector capacity are used as constraints. A single-objective optimization model for maximizing traffic flow is established, and the airspace capacity of the airway network is iteratively obtained when the traffic flow per unit time is maximized.

Compared with the prior art, the present invention has the following beneficial effects:

This paper studies the topology of large-scale airway network airspace. By reducing the internal structure of sectors and replacing the impact of internal sector subnetworks on airway network airspace capacity with sector capacity, the paper then determines the edges between nodes based on whether there are airways between sectors and between sectors and airports, thereby obtaining a topological network for large-scale airway networks. The paper also studies sector and airport capacity, taking into account the complexity of traffic flow within sectors and the physical structure within sectors and between sectors and adjacent sectors. The paper then calculates the workload of air traffic controllers within sectors, regressing the workload against the number of aircraft entering the sector per unit time to obtain the sector capacity. The paper also calculates the sector coupling degree to correct for the workload. The improved Euler model is used to model the traffic flow in the topological network. With the goal of maximizing the traffic flow in the topological network, a single-objective optimization model is established with the capacity of sector nodes and airport nodes as constraints. The maximum flow in each time step is solved. When the traffic flow in the topological network is stable, the airspace capacity of the route network is obtained. This method improves the utilization of airspace resources and ensures the safe, efficient and smooth operation of air traffic. It is necessary to scientifically and accurately evaluate the capacity of the airspace of a large-scale route network.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a schematic flow chart of the overall method of the present invention;

FIG2 is a route airspace network diagram of the present invention;

FIG3 is a topological network diagram of the airspace of the route network of the present invention;

FIG4 is an improved Euler model of the present invention;

FIG5 is a flow chart of solving the airspace capacity of the route network according to the present invention.

DETAILED DESCRIPTION

The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with Figures 1 to 5 of the embodiments of the present invention. It should be understood that the terms used in the present invention are only used to describe specific implementation methods and are not intended to limit the present invention.

Traditional machine learning-based airspace capacity assessment methods are highly dependent on historical data. If the route network contains a large amount of planning content and lacks historical data support, such methods are difficult to apply to large-scale route network airspace capacity assessment.

Based on previous research, this application evaluates the airspace capacity of a large-scale route network, taking into account the entire airspace system, including the impact of airport capacity, sector capacity, route structure, and traffic flow in the airspace, and not just taking the capacity of a certain element in the airspace system as the capacity of the entire airspace. The large-scale route network capacity evaluation method based on sector nodes proposed in this application reduces the complexity of large-scale route network capacity evaluation, and uses sectors as control units in the Euler model to improve the Euler model and enrich the method of air traffic flow modeling. Through this method, it can be extended to the capacity modeling and evaluation of the national airspace network, and the bottleneck sector can be quickly identified through the saturation index, providing theoretical support for large-scale airspace planning. By classifying sectors according to sector complexity, a controller load calculation model for each type of sector is obtained. Even if there is a planning scheme without real-time control, the sector controller load can be quickly obtained, which makes up for the technical defect that airspace planning has no controller load verification support.

As shown in FIG1 , the present application proposes a method for evaluating airspace capacity of a route network, which is characterized by comprising the following steps:

S1: Determine the traffic flow direction between sectors through the routes and traffic flows between sectors of the airport, and establish an airspace topology network with airports and sectors as nodes;

S2: Based on the airspace topology of the airway network, the number of aircraft entering the sector per unit time is determined by the discrete time difference equation of the node-to-node traffic flow;

S3: Classify sectors and determine the controller load for each sector by calculating the static coupling parameters between sectors. Regress the controller load in each sector with the number of aircraft entering the sector per unit time, and adjust the controller load for each sector to determine the sector capacity of each sector.

S4: Obtain the airport capacity under stable operating conditions. Taking the maximum traffic flow of the topological network of the airway network airspace as the goal, using airport capacity and sector capacity as constraints, establish a single-objective optimization model for maximum traffic flow, and iteratively obtain the airspace capacity of the airway network when the traffic flow per unit time is maximized.

In step S1, an airspace topology network with airports and sectors as nodes is established, including the following steps:

The airports and sectors in the large-scale airway network airspace are taken as nodes, and the internal structure of the sectors in the airway network airspace is reduced to nodes. The edges between the nodes are established according to whether there are airways and traffic flows between the nodes, and the airway network airspace topology network with airports and sectors as nodes is obtained.

In step S2, the number of aircraft entering the sector per unit time is determined, including the following steps:

Based on the obtained airway network airspace topology, the traffic flow direction between sectors is determined according to the routes and traffic flows between sectors. The Euler model is improved and the discrete time difference equation of the traffic flow from node s to node j is obtained as follows:

in, is the total traffic flow from node s to node j at time t;

i is the upstream sector or airport of the sector, and its value is [1, m]. j is the downstream sector or airport of the sector, and its value is [m+1, m+n].

_τs is the travel time of sector s. The sample mean is calculated based on the historical flight path data of aircraft in the sector and rounded to the nearest minute. Traffic entering sector s at time k can only leave the sector at time k + _τs .

β _isj is the flow divergence parameter, which represents the ratio of the flow from node i to node s to the flow from node i to node s. It is obtained based on the flow distribution ratio of busy sectors and airports in historical flight track data, and has

q _sj (t) is the traffic flow from node s to node j at time t;

The traffic flow from node s to node j at time t is the traffic flow that cannot enter node j due to traffic control or capacity limitation and remains in node s;

The number of aircraft in node s at time t is expressed as:

in, is the traffic flow that cannot flow out of node s at time t; is the traffic flow that does not flow out of node s due to the capacity limitation of downstream nodes at time t-1; is the traffic flow out of node s at time t;

Δt is the time step, which is less than or equal to the minimum sector travel time within the modeling range to ensure that no traffic flow directly skips a sector within this time step. The time step used in this invention is 1 minute.

Load is a measure of the response level of controllers in implementing stable command of air traffic within a sector using communication, navigation and surveillance infrastructure under the combined influence of factors such as route structure, traffic layout, airspace restrictions, and training level within the sector. It is a decisive parameter for determining sector capacity.

In step S3, the controller load of each type of sector is determined, including the following steps:

The sector capacity is evaluated based on traffic complexity. The static coupling degree of the sector is calculated to correct the controller load in the sector. The calculation formula is:

Where OH is the sector static coupling parameter;

H is the number of sectors adjacent to the sector and connected with traffic;

_Ch is the positional relationship between the sector and the adjacent sectors, which can be divided into high and low sectors, east and west sectors, and inner and outer sectors;

V is the product of the area of the horizontal range available for use by aircraft in the sector and the number of altitude levels available for use by aircraft in the sector;

L is the boundary area of the sector, that is, the product of the horizontal connection length between the sector and all connected sectors and the number of available altitude layers;

J is the number of routes available for use within the sector;

L _h is the sector boundary area between the sector and the adjacent sector h;

J _h is the number of routes connecting the sector to the adjacent sector h;

The complexity index of each sector is calculated using historical data, and the sectors are classified using the Gaussian mixture model. The CH index is used to judge the quality of the Gaussian mixture model's clustering results for the sample data. The higher the CH index, the better the clustering effect.

Determine the controller load for each sector type, divide the controller load into monitoring control load, conflict control load, and coordination control load, calculate the controller load, use the sector complexity index to calculate the controller load, and calculate the sector node capacity;

The controller load in each sector is regressed against the number of aircraft entering the sector per unit time, and the sector capacity is obtained using the load threshold.

Correcting the regulated load includes the following steps:

The quantitative model for monitoring and control load is:

The quantitative model of conflict control load is:

The quantitative model of coordinated control load is:

W _co = β × (F _in + F _out )

The quantitative model of the total control load is obtained as follows:

W＝aW _mo +bW _cf +c(1+OH)W _co

Where W is the total control load; a, b, and c are the coordination coefficients of the three control loads, namely, surveillance control load, conflict control load, and coordination control load; OH is the sector static coupling degree; W _co is the coordination control load; β is the average coordination control load of a single aircraft; _Fin and F _out are the number of flights entering and leaving the sector; W _cf is the conflict control load; γ _al , γ _sp , γ _hd , and γ _{al_keep} are the impact coefficients of flights with altitude change, speed change, heading change, and altitude unchanged on the conflict control load, respectively; W _mo is the surveillance control load; F is the number of flights served per unit time in the sector; is the average dwell time of each flight in the sector; λ _al , λ _sp , λ _hd , and λ _{al_keep} are the impact coefficients of flights with altitude change, speed change, heading change, and altitude unchanged on the surveillance control load, respectively; The proportions of flights with altitude change, speed change, heading change, and altitude unchanged, respectively; The average number of flight adjustments for altitude change, speed change, and heading change, respectively;

The total number of aircraft entering a sector per unit time and the controller load per unit time in the sector were regressed using linear, quadratic, and cubic polynomials. The ^R² was used to evaluate the fitting effect. The polynomial expression with the ^R² closest to 1 was selected as the correction result of the controller load to determine the sector capacity of each sector, including:

According to the capacity assessment rules, the number of aircraft entering the sector per hour when the controller load is 70% of one hour is the hourly capacity of the sector, and the number of aircraft entering the sector per fifteen minutes when the controller load is 80% of 15 minutes is the fifteen-minute capacity of the sector.

In step S4, the airport capacity in a stable operating state is obtained, including the following steps:

Determine the duration of sample collection based on capacity assessment and flight schedules;

According to the sample duration, the frequency of airport takeoffs, landings, and airport takeoff and landing pairs in the historical data is counted, and a bubble chart is drawn with the number of takeoffs and landings as the horizontal and vertical coordinates and the frequency as the bubble radius.

Select a data point with a confidence level of 95% from the sample data, draw an envelope to enclose the data point, and determine the envelope of the historical peak service flights;

Based on the historical peak service flight envelope, the sample point where the sum of the take-off and landing flights contained in the envelope is the maximum is determined to obtain the airport capacity, which provides constraints for solving the maximum traffic flow in the route network airspace topology network.

Taking sector capacity and airport capacity as constraints, a single-objective optimization model for maximizing traffic flow is established, which includes the following steps:

The number of aircraft is related to the traffic flow in each time step. When the traffic flow in each time step is the largest, the number of aircraft handled by the sector is also the largest. This problem is transformed into a single-objective optimization problem, where the objective function is to maximize the traffic flow between all connected nodes in each time step:

max:

The decision variable is the flow rate q _sj (t) between all connected nodes, and the range of the decision variable is:

in, is the total traffic flow from node s to node j at time t; is the traffic flow that did not flow out in the previous time step, where the regulated flow at time t-1 is the maximum outflow flow at time t-1 minus the actual outflow flow:

The number of aircraft processed by each sector node per unit time does not exceed the sector node capacity, and the constraints are:

in, is the hourly capacity of node s, is the fifteen-minute capacity of node s;

For an airport node, when subject to the airport's arrival and departure capacity constraints, the number of aircraft flowing into and out of the airport node per unit time must not exceed the airport's node capacity per unit time. The constraints are:

The capacity of the route network is the maximum number of flights that the route network can handle within the airspace per unit time. The number of flights handled by the route network is the total number of aircraft entering the route network per unit time, that is, the total number of aircraft entering the route network airspace from external sector nodes and airport nodes per unit time. The expression of the route network capacity is:

Where D(t) is the number of flights processed by the airspace route network at time t, TR is the flow set from airport nodes in the airspace and external sector nodes to sector nodes, and _qTR (k) is the flow entering the airspace.

Iteratively obtaining the airspace capacity of the route network when the traffic flow per unit time is maximum includes the following steps:

Taking the capacity of airport nodes and sector nodes in the topological network as a constraint, the elite-preserving genetic algorithm is used to solve the maximum flow, and the airspace capacity topological network traffic flow modeling and maximum flow solution are obtained.

When the number of aircraft entering the airspace of the route network reaches a stable state, the capacity of the airspace of the route network is obtained and the maximum traffic flow is taken as the goal. When the traffic flow in the network does not change much over time, the capacity of the airspace of the route network is obtained.

The capacity of the airway network airspace is the number of flights entering the airway network airspace per unit time, including the number of flights from external sector nodes entering the airspace and the number of flights from airports within the airspace entering the airspace per unit time.

At t = 0, the airport node and the external sector node provide traffic to the sector nodes in the airway network airspace. Traffic flows into the sector nodes directly connected to the airport node and the external sector node. As time goes by, the traffic enters other sector nodes. When the traffic in each sector tends to saturation, the total number of flights entering the airspace also tends to be stable.

When the total number of aircraft entering the airspace does not change much over time, the number of aircraft entering the airspace route network per unit time is obtained, which is the route network capacity.

Based on the route network airspace capacity assessment method, this application also proposes a route network airspace capacity assessment system, including:

The airspace topology network building module is used to determine the traffic flow direction between sectors through the routes and traffic flows between sectors of the airport, and to establish an airspace topology network with airports and sectors as nodes;

The sector aircraft quantity determination module is used to determine the number of aircraft entering the sector per unit time based on the airspace topology of the route network and the discrete time difference equation of the node-to-node traffic flow;

The sector capacity acquisition module is used to classify sectors and determine the controller load for each sector by calculating the static coupling parameters between sectors. The controller load in each sector is regressed with the number of aircraft entering the sector per unit time, and the controller load of each sector is corrected to determine the sector capacity of each sector.

The traffic flow optimization module is used to obtain the airport capacity under stable operating conditions. With the goal of maximizing the traffic flow of the topological network of the airway network airspace, the airport capacity and sector capacity are used as constraints. A single-objective optimization model for maximizing traffic flow is established, and the airspace capacity of the airway network is iteratively obtained when the traffic flow per unit time is maximized.

The route network airspace capacity assessment method proposed in this application provides a basis for the macro-planning of air transportation and the national airspace system, rationally plans the route network airspace structure, optimizes fleet capacity and fleet structure, improves airspace resource utilization, ensures the safety, efficiency and smoothness of air traffic, and conducts a scientific and accurate assessment of the airspace capacity of a large-scale route network.

Example

The embodiment provided in this application takes the basic situation of the Shenyang Information Region as an example, obtains the topological network of the Shenyang Information Region, uses the operation data of TAAM simulation to count the complexity indicators in the sector, uses the Gaussian mixture model to classify the sectors, determines the optimal number of clusters according to the CH index, divides the sectors in the Shenyang Information Region into three categories, determines the controller load of each sector according to the complex characteristics of each sector, and then calculates the controller load of each sector, and regresses to obtain the capacity of each sector; counts the airport take-off and landing flights and draws the envelope of the airport's historical peak service flights to obtain the airport capacity; models the traffic flow in the topological network, solves the traffic flow in the topological network, and obtains the airspace capacity of the Shenyang Information Region when the traffic flow in the topological network is stable, and uses the saturation of the sector nodes to obtain the sector that limits the capacity of the Shenyang Information Region, and uses TAAM for simulation to prove the accuracy of this method.

The present invention addresses the complexity of large-scale airspace capacity assessment based on sector nodes. Airports and sectors in the airway network airspace are used as nodes. The edges between nodes are determined by whether there are routes and traffic flows between the nodes to obtain a topological network of the airway network airspace. The traffic flow in the topological network is modeled using an improved Euler model. The maximum traffic flow in each time step is solved using an elite-preserving genetic algorithm to obtain the airway network airspace capacity. The method includes the following steps:

First, the route network airspace topology structure with sectors as nodes takes airports and sectors in the large-scale route network airspace as nodes, without considering the internal route structure of the sector, and uses the sector capacity to replace the impact of the internal route subnet of the sector on the route network airspace capacity. The edges between nodes are determined according to whether there are routes and traffic flows between sectors and between airports and sectors, thereby obtaining the topological network of the route network airspace: In order to reduce the complexity of modeling the large-scale route network airspace, the internal structure of the sectors in the route network airspace is first reduced to nodes, and then the edges between nodes are established according to whether there are routes and traffic flows between nodes, thereby obtaining the route network airspace topology network with airports and sectors as nodes.

Aggregate multiple airports in the same control sector or terminal area into one airport node. For example, T2 and T3 in Figure 2 are two airports in the same terminal area. Reduce these two airports to one, which is T23 in Figure 3. Reduce the airports that are not in the approach control sector according to the area control sector range. Aggregate all airports in an area control sector into one airport node, such as T1 in Figure 3. N2 and N3 in Figure 2 are sectors with direct traffic connections with sectors within the modeling range. Reduce this sector to the external sector node N in Figure 3. Reduce multiple approach sectors belonging to the same airport or the same terminal area into one sector. If the same airport sector and The sectors of the same terminal area overlap and are reduced according to the approach sectors of the same airport. For example, P1, P2, and P3 in Figure 2 are the approach sectors of the same terminal area, which are reduced to sector node P in Figure 3. Sectors in other areas are not reduced, such as R1, R2, and R3 in Figure 3. If there is an airway between two nodes and there is traffic flow on the airway, a line is connected between the two nodes, and all traffic in the graph is concentrated on the node. There is no traffic flow on the line. The line only indicates whether the two nodes can be connected and determines the connection matrix between the nodes in the graph. Finally, based on the connection matrix between the airport node and the sector node, the topological network of the airway network airspace is determined.

The sector capacity is evaluated based on traffic complexity. The influence of physical structures such as the number of connected routes and borders between a sector and its adjacent sectors is taken into account. The static coupling degree of the sector is calculated to correct the controller load in the sector.

The calculation formula of the sector static coupling is specifically shown in the above step S3, where:

OH is the sector static coupling parameter, H is the number of sectors adjacent to the sector, and H is the number of sectors adjacent to the sector and connected with traffic;

C _h is the positional relationship between the sector and its adjacent sectors, which can be categorized as high-low, east-west, and inside-outside sectors. A high-low sector is one where two adjacent sectors have overlapping or intersecting horizontal ranges, but different vertical ranges that are connected. An east-west sector is one where two adjacent sectors have different horizontal ranges but border each other, but have overlapping or identical vertical ranges. An inside-outside sector is one where two adjacent sectors overlap or intersect both horizontally and vertically, and is often found in terminal control areas.

V is the range of the sector, which is the airspace range available for aircraft use within the sector, that is, the product of the area of the horizontal range available for aircraft use within the sector and the number of altitude layers available for aircraft use within the sector;

L is the boundary area of the sector, which is the product of the horizontal connection length between the sector and all connected sectors and the number of available altitude layers;

J is the number of routes available in the sector;

_Lh is the sector boundary area between the sector and the adjacent sector h. When the two sectors are east-west sectors, it is calculated as the vertical contact area between the two sectors, that is, the product of the horizontal connection length between the sector and sector h and the number of available altitude layers between the two sectors. When the two sectors are high-low sectors, it is the horizontal contact area between the two sectors. When the two sectors are inner-outer sectors, it is the sum of the horizontal contact area and the vertical contact area between the two sectors.

J _h is the number of routes connecting this sector to sector h.

Historical data is used to count the complexity indicators in each sector, and the Gaussian mixture model is used to classify the sectors. Then, the Calinski-Harabasz index (CH index) is used to judge the quality of the Gaussian mixture model's clustering results for sample data. The higher the CH index, the better the clustering effect.

Determine the controller load for each type of sector, divide the controller load into surveillance control load, conflict control load and coordination control load, and calculate the controller load. Use the sector complexity index to calculate the controller load and thus the sector node capacity. Finally, regress the controller load in each sector with the number of aircraft entering the sector per unit time, and use the load threshold to obtain the sector capacity. Use a method based on historical data statistics to calculate the airport capacity. According to the airport's historical operation data, the airport's take-off and landing flights and frequency are counted, and the airport's historical peak service flight envelope is drawn to obtain the airport capacity.

The quantitative model of the monitoring control load, the quantitative model of the conflicting control load, the quantitative model of the coordinated control load, and the quantitative model of the total control load are specifically shown in the corresponding formulas in step S3.

The capacity envelope is obtained by using the airport's historical operation data, and the process of obtaining the airport node capacity is as follows:

1) Select the statistical sample duration. The sample statistical duration is selected based on capacity assessment and flight schedule planning. In this paper, 1 hour and 15 minutes are selected as the statistical duration.

2) Count the number of airport takeoffs, landings, and the frequency of airport takeoff and landing pairs in the historical data based on the sample duration, and draw a bubble chart with the number of takeoffs and landings as the horizontal and vertical coordinates and the frequency as the bubble radius;

3) Draw the envelope of the historical peak service flights, select a data point with a confidence level of 95% in the sample data, draw an envelope to enclose the data point, and thus determine the envelope of the historical peak service flights;

4) Determine the airport capacity. Based on the historical peak service flight envelope, determine the sample point on the envelope where the sum of the take-off and landing flights is the maximum, thereby obtaining the airport capacity and providing constraints for solving the maximum traffic flow in the route network airspace topology network.

Second, based on the airspace capacity assessment of the route network using the improved Euler model, on the basis of the obtained topological network, the traffic flow direction between sectors is determined according to the routes and traffic flows between sectors, and the traffic flow in the topological network is modeled using the improved Euler model.

As shown in Figure 4, for sector node s, based on the direction of traffic flow, its upstream sector or airport is i∈[1,m], and its downstream sector or airport is j∈[m+1,m+n]. Furthermore, a node can be both upstream and downstream of another node. By improving the Euler model, we obtain the discrete time difference equation for the traffic flow from node s to node j, as shown in the formula in step S2.

As As shown, the number of aircraft is related to the traffic flow within each time step. Therefore, when the traffic flow at each time step is maximized, the number of aircraft that a sector can handle is also maximized. This problem is a single-objective optimization problem. The single-objective function for maximizing traffic flow is specifically shown in step S4 above.

Third, the capacity of airport nodes and sector nodes in the topological network is used as a restriction, and the elite-preserving genetic algorithm is used to solve the maximum flow, thereby obtaining the airspace capacity topological network traffic flow modeling and maximum flow solution. That is, when the number of aircraft entering the airspace of the route network reaches a stable state, the airspace capacity of the route network is obtained, and then the maximum traffic flow is taken as the goal. When the traffic flow in the network does not change much over time, the capacity of the route network airspace is obtained.

As shown in Figure 5, the calculated capacity of the large-scale route network airspace is the number of aircraft entering the route network airspace per unit time, including the number of aircraft entering the airspace from external sector nodes and the number of aircraft entering the airspace from airports within the airspace. At t = 0, airport nodes and external sector nodes provide traffic to sector nodes within the route network airspace. Traffic flows into sector nodes directly connected to airport nodes and external sector nodes. Over time, traffic flows into other sector nodes. When traffic within each sector approaches saturation, the total number of aircraft entering the airspace also stabilizes. When the total number of aircraft entering the airspace does not change much over time, the number of aircraft entering the airspace route network per unit time is obtained, which is the route network capacity.

The elite-preserving genetic algorithm in Python's genetic algorithm toolbox geatpy is used to solve the maximum traffic flow in each time step.

As the basis for the macro-planning of the national air transport and airspace system, it is necessary to scientifically and accurately assess the capacity of the airspace of a large-scale route network in order to optimize the airspace structure, fleet capacity and structure, improve the utilization rate of airspace resources, and ensure the safe, efficient and smooth operation of air traffic.

In order to reduce the complexity of evaluating the airspace capacity of a large-scale route network, this application proposes a method for evaluating the capacity of a large-scale route network using sectors as nodes. The topological network of the route network airspace is obtained with airports and sectors as nodes, and the capacity of the airport nodes and sector nodes in the topological network is then calculated as constraints for finding the maximum flow in the network. The airspace capacity of the route network is solved using a static modeling and dynamic solution method. The traffic flow in the topological network is modeled using the improved Euler model. A single-objective optimization model is established with the maximum flow in the network as the goal and the capacity of the sector nodes and airport nodes as constraints. The elite-retention genetic algorithm is used to solve the maximum flow within the time step. When the traffic flow in the topological network is stable, the airspace capacity of the route network is obtained.

This application studies the topological network of the airspace of a large-scale route network, reduces the internal structure of the sector, replaces the impact of the internal subnet of the sector on the airspace capacity of the route network with the sector capacity, and determines the edges between nodes based on whether there are routes between sectors and between sectors and airports, and whether there are routes to obtain the topological network of the large-scale route network.

This application studies the sector and airport capacity. Taking into account the complexity of traffic flow within the sector and the physical structure within the sector and between the sector and adjacent sectors, the controller load in the sector is calculated, and the controller load is regressed with the number of aircraft entering the sector per unit time to obtain the sector capacity. The sector coupling degree is calculated to correct the controller load. The airport's historical operating data is used to count the hourly take-off and landing aircraft pairs at the airport and their frequency of occurrence. The data with a confidence interval of 95% is taken to draw the airport capacity envelope to obtain the airport capacity.

This application studies a method for modeling traffic flow in a topological network, improves the Euler model by taking sectors as control units, and uses the improved Euler model to model traffic flow in a topological network. Then, the maximum traffic flow in the topological network is taken as the goal, and a single-objective optimization model is established by taking the hourly capacity and fifteen-minute capacity of sector nodes and airport nodes as constraints. The elite-retaining genetic algorithm is used to solve the maximum flow in each time step, and the airspace capacity of the route network is obtained when the traffic flow in the topological network is stable. Taking the Shenyang Information Region as an example, the capacity of the Shenyang Information Region is calculated, and the sector nodes that limit the capacity of the Shenyang Information Region are obtained according to the saturation of the sector nodes. The TAAM simulation is used to verify the effectiveness of this method.

The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

In addition, unless otherwise specified, all technical and scientific terms used in the present invention have the same meaning as commonly understood by those skilled in the art to which the present invention belongs. All documents mentioned in this specification are incorporated by reference to disclose and describe the methods related to the documents. In the event of any conflict with any incorporated document, the content of this specification shall prevail.

Claims (8)

1. The method for evaluating the airspace capacity of the airway network is characterized by comprising the following steps of:

Determining traffic flow direction between sectors through the air route and traffic flow between the sectors of the airport, and establishing an air route network airspace topology network taking the airport and the sectors as nodes;

The method comprises the steps of taking airports and sectors in a large-range air-network space domain as nodes, reducing the internal structure of the sectors in the air-network space domain into nodes, and establishing edges between the nodes according to whether air ways exist between the nodes and traffic flows, so as to obtain the air-network space domain topology network taking the airports and the sectors as nodes, wherein the determination of the number of the aircrafts entering the sectors in the unit time comprises the following steps of determining traffic flow directions between the sectors according to the air ways and the traffic flows between the sectors on the basis of the obtained air-network space topology network, improving an Euler model, and obtaining the discrete time difference equation of the traffic flows from the nodes s to the nodes j, wherein the number of the aircrafts entering the sectors in the unit time is:

Wherein, the I is the upstream sector or airport of the sector, j is the downstream sector or airport of the sector, j is [ m+1, m+n ], τ _s is the transit time of sector s, β _isj is the flow divergence parameter, which represents the ratio of the flow from node i to node s to the flow of node j, and is obtained by solving the flow distribution ratio of busy period sector and airport in the historical track data, wherein the flow is the flow of node i to node sQ _sj (t) is the traffic flow from node s into node j at t; To preserve traffic flow in node s at t that cannot enter node j due to traffic control or capacity restriction in the traffic flowing from node s to node j, the number of aircraft in node s at t is expressed as:

Wherein, the Traffic flow which cannot flow out of the node s at the moment t; Limiting the traffic flow of the non-outflow node s for the time t-1 due to the capacity of the downstream node; The traffic flow of the outflow node s at the moment t is the time step, and deltat is the time step;

classifying the sectors, determining the controller load of each sector by calculating the static coupling parameters among the sectors, regressing the controller load in each sector and the number of aircrafts entering the sector in unit time, and correcting the controller load of each sector to determine the sector capacity of each sector;

And obtaining airport capacity in a stable running state, taking the maximum traffic flow of a topological network of an air space network as a target, taking the airport capacity and the sector capacity as constraint conditions, establishing a single-target optimization model with the maximum traffic flow, and iteratively obtaining the air space capacity of the air space network when the traffic flow in unit time is maximum.

2. The method of claim 1, wherein said determining the controller load for each type of sector comprises the steps of:

The complexity index in each sector is counted by utilizing historical data, and the sectors are classified by utilizing a Gaussian mixture model, and the quality of the sample data clustering result by the Gaussian mixture model is judged according to CH indexes, wherein the higher the CH index is, the better the clustering effect is;

determining the controller load of each type of sector, dividing the controller load into a monitoring control load, a conflict control load and a coordination control load, calculating the controller load by using the complexity index of the sector, and calculating the node capacity of the sector;

The sector capacity is evaluated based on traffic complexity, the static coupling degree of the sector is calculated, the controller load in the sector is corrected, and the calculation formula of the static coupling degree of the sector is as follows:

wherein OH is the static coupling degree of the sector;

h is the number of sectors adjacent to the sector and having traffic connections;

c _h is the position relation between the sector and the adjacent sector, and is divided into a high fan, a low fan, a east-west fan and an inner fan and an outer fan;

V is the product of the area of the horizontal range for aircraft use in the sector and the number of altitude layers for aircraft use in the sector;

l is the boundary area of the sector, namely the product of the horizontal connection length of the sector and all connected sectors and the number of height layers for use;

j is the number of routes available in the sector;

l _h is the sector boundary region between the sector and the adjacent sector _h;

J _h is the number of lanes connected between that sector and the adjacent sector h.

3. The method for estimating airspace capacity of an air network according to claim 2, wherein the correcting the controller load of each sector includes the steps of:

the quantization model for monitoring the control load is:

the quantization model of the conflict control load is as follows:

the quantitative model for coordinating the control load is as follows:

W_co＝β×(F_in+F_out)

the quantization model for obtaining the total pipe load is as follows:

W=aW_mo+bW_cf+c(1+OH)W_co

Wherein W is total management load, a, b and c are coordination coefficients of three management loads of monitoring management load, conflict management load and coordination management load respectively, O _H is sector static coupling degree, W _co is coordination management load, beta is average coordination management load of a single-frame aircraft, F _in、F_out is number of flights entering and exiting a sector, W _cf is conflict management load, gamma _al、γ_sp、γ_hd、γ_{al_keep} is influence coefficient of flights with unchanged altitude change, speed change, heading change and altitude on conflict management load respectively, W _mo is monitoring management load, F is number of flights served in a unit time in a sector, Lambda _al、λ_sp、λ_hd、λ_{al_keep} is the influence coefficient of flights with altitude change, speed change, heading change and unchanged altitude on the monitoring control load; respectively, altitude change, speed change, heading change and altitude-unchanged flight proportion; average adjustment times of flights with altitude change, speed change and course change respectively;

Regression is carried out on the total entering frame times of a sector in unit time and the controller load in the unit time of the sector by using primary, secondary and tertiary polynomials, the fitting effect is evaluated by using R ², and the expression of the polynomial with the R ² closest to 1 is selected as the correction result of the controller load;

According to the capacity evaluation rule, the number of times of entering the sector hour corresponding to the controller load of 70% of one hour is calculated as the capacity of the sector hour, and the number of times of entering the sector fifteen minutes corresponding to the controller load of 80% of 15 minutes is calculated as the capacity of the sector fifteen minutes.

4. A method of estimating airspace capacity of an air network according to claim 3, wherein the step of acquiring the airport capacity in the steady operation state includes the steps of:

according to the capacity evaluation and the flight time, formulating a sample statistical time length to be selected;

According to the frequency of airport take-off and landing frames and airport take-off and landing frame pairs in the selected sample statistical duration historical data, taking the take-off and landing frames as an abscissa and an ordinate respectively, taking the frequency as a bubble radius, and drawing a bubble graph;

Selecting a data point with 95% confidence level in the historical data of the sample statistical time length, drawing an envelope curve, wrapping the data point, and determining a historical peak service rack envelope curve;

and determining a sample point with the maximum sum of the take-off and landing frames contained on the envelope according to the historical peak service frame envelope, so as to obtain airport capacity, and providing constraint for solving traffic flow in the topological network of the air space of the road network.

5. The method for estimating airspace capacity of a road network according to claim 4, wherein the establishing a single-target optimization model with the maximum traffic flow comprises the following steps:

The number of aircraft is related to the traffic flow per time step, and when the traffic flow per time step is maximum, the number of aircraft handled by the sector is also maximum, i.e. the problem is converted into a single objective optimization problem, the objective function being to maximize the traffic flow between all the connected nodes per time step:

the decision variable is the flow q _sj (t) among all the connected nodes, and the range of the decision variable is as follows:

Wherein, the All traffic flows flowing to node j at node s at time t; The traffic flow which does not flow in the last time step is controlled at the time t-1, wherein the flow which is controlled at the time t-1 is the maximum flow which flows out at the time t-1 minus the actual flow which flows out:

The number of aircraft processed by each sector node in unit time does not exceed the capacity of the sector node, and the constraint conditions are that:

Wherein, the As the hour capacity of the node s,Fifteen minutes capacity for node s;

for an airport node, when the airport node is limited by the airport entrance and exit capacity, the number of aircrafts which flow in and out in the unit time of the airport node does not exceed the node capacity of the unit time of the airport, and the constraint conditions are as follows:

the capacity of the road network is the maximum number of frames which can be processed by the road network in the airspace in unit time, the number of times of processing the road network is the number of aircrafts entering a sector in unit time, namely the total number of aircrafts entering the airspace of the road network from an external sector node and an airport node in unit time, and the capacity expression of the road network is as follows:

wherein D (t) is the processing frame number of the airspace route network at the moment t, TR is the flow set from the airport node in the airspace to the sector node from the external sector node, and q _TR (k) is the traffic flow entering the airspace.

6. The method for estimating spatial capacity of a road network according to claim 5, wherein the iterative obtaining of the spatial capacity of the road network when the traffic flow per unit time is maximum comprises the steps of:

Taking the capacities of airport nodes and sector nodes in a topological network as limits, and solving a single-target optimization model with the maximum traffic flow by using elite reservation genetic algorithm;

When the number of aircrafts entering the airspace of the airway network reaches a stable value, the airspace capacity of the airway network is obtained, the maximum traffic flow is taken as a target, and when the traffic flow in the network does not change with time, the airspace capacity of the airway network is obtained when the traffic flow is maximum.

7. The method for estimating an airspace capacity of an airliner according to claim 6, wherein the airspace capacity of the airliner is an number of frames of an airspace entering the airliner in a unit time, including the number of frames of an external sector node entering the airspace in a unit time and the number of frames of an airport entering the airspace in the airspace;

When t=0, the airport node and the external sector node provide flow to the sector nodes in the airspace of the airway network, the sector nodes directly connected with the airport node and the external sector node have flow in, the flow enters other sector nodes along with the time, and when the flow in each sector tends to be saturated, the total entering frame of the airspace also tends to be stable;

when the total number of times of entering the airspace is not changed greatly along with time, the number of aircrafts entering the airspace road network in unit time is obtained, namely the airspace capacity of the airspace road network with the maximum traffic flow in unit time.

8. A road network airspace capacity assessment system, comprising:

The airspace topological network building module is used for determining the traffic flow direction between the sectors through the air paths and the traffic flow between the sectors of the airport and building an air path network airspace topological network taking the airport and the sectors as nodes;

The method comprises the steps of taking airports and sectors in the air space domain of the air network as nodes, reducing the internal structure of the sectors in the air space domain of the air network into nodes, establishing edges between the nodes according to whether air exists between the nodes and traffic flows, and obtaining the air space domain topological network of the air network taking the airports and the sectors as nodes, wherein the determination of the number of the air vehicles entering the sectors in the unit time comprises the following steps of determining the traffic flow direction between the sectors according to the air paths and the traffic flows between the sectors on the basis of the obtained air space domain topological network of the air network, improving the Euler model, and obtaining the discrete time difference equation of the traffic flow between the nodes s and the nodes j, wherein the air space topological network of the air network is obtained by the steps of:

Wherein, the I is the upstream sector or airport of the sector, j is the downstream sector or airport of the sector, j is [ m+1, m+n ], τ _s is the transit time of sector s, β _isj is the flow divergence parameter, which represents the ratio of the flow from node i to node s to the flow of node j, and is obtained by solving the flow distribution ratio of busy period sector and airport in the historical track data, wherein the flow is the flow of node i to node sQ _sj (t) is the traffic flow from node s into node j at t; To preserve traffic flow in node s at t that cannot enter node j due to traffic control or capacity restriction in the traffic flowing from node s to node j, the number of aircraft in node s at t is expressed as:

Wherein, the Traffic flow which cannot flow out of the node s at the moment t;

Limiting the traffic flow of the non-outflow node s for the time t-1 due to the capacity of the downstream node; The traffic flow of the outflow node s at the moment t is the time step, and deltat is the time step;

The sector capacity acquisition module is used for classifying the sectors, determining the controller load of each type of sector by calculating the static coupling parameters of the sectors, regressing the controller load of each sector and the number of aircrafts entering the sector in unit time, and correcting the controller load of each sector to determine the sector capacity of each sector;

The traffic flow optimizing module is used for acquiring the airport capacity in a stable running state, taking the maximum traffic flow of the topological network of the air space of the air route network as a target, taking the airport capacity and the sector capacity as constraint conditions, establishing a single-target optimizing model with the maximum traffic flow, and iteratively acquiring the air space capacity of the air route network when the traffic flow in unit time is maximum.