WO2019192615A1

WO2019192615A1 - Air traffic flow management method and system based on big data

Info

Publication number: WO2019192615A1
Application number: PCT/CN2019/081631
Authority: WO
Inventors: 郑强; 黎时针; 王秋毕; 苗英俊
Original assignee: 杭州坚果壳科技开发有限公司
Priority date: 2018-04-06
Filing date: 2019-04-06
Publication date: 2019-10-10

Abstract

An air traffic flow management method based on big data, used for at least one flight in a controlled airspace, comprising the following steps: S1 collecting aerial data and acquiring a flow threshold value; S2 acquiring an optimal flight departure time corresponding to the flight; S3 acquiring flight operating variables corresponding to the flight; S4 on the basis of the flight operating variables, acquiring an air traffic flow value corresponding to the flight operating variables; and S5 comparing the air traffic flow value with the flow threshold value, and when the two match, selecting the optimal flight departure time as a designated flight departure time. By means of this method, usage of a controlled airspace is estimated in advance, and existing flight departure times are thus adjusted, optimizing air traffic operational efficiency. An air traffic flow management system based on big data, comprising: a flow threshold value unit, which acquires and obtains a flow threshold value; an optimal flight departure time unit, which acquires an optimal flight departure time corresponding to a flight; a flight operation variable unit, which acquires flight operation variables corresponding to the optimal flight departure time; an air traffic flow unit, which acquires an air traffic flow value; and an analysis unit, which analyzes and compares the air traffic flow value with the flow threshold value to obtain analysis results. The present system analyzes aerial data, estimates usage of a controlled airspace in advance, and thus adjusts the departure times of each flight, optimizing air traffic operational efficiency.

Description

Air traffic flow management method and system based on big data

Technical field

The invention belongs to the field of air traffic management, and particularly relates to a method and system for managing air traffic flow based on big data.

Background technique

Air traffic flow management refers to the timely adjustment of air traffic flow when air traffic flow reaches or is close to an air traffic control capability, thereby ensuring that air traffic flights flow optimally through or through specific areas, thereby improving airports and airspace. Available capabilities.

However, the current method for air traffic flow management is the interval control method. The interval control method is to control the number of flights entering and exiting each point/line by controlling the flight through a specific point/line interval, and finally obtaining the number of routes in the corresponding air area.

However, this approach has the following drawbacks:

1. The flow of air traffic optimization in the air field cannot be effectively realized, and the interval of the point/line is often manually determined or estimated based on simple experience. The use of such point/line spacing is not optimally applicable to many Changing aerial field

2. The point/line interval control method cannot consider the influence of realistic elements. In the air field, each flight has different navigation elements (location, altitude, ascending and descending trend, route, speed, etc.) and intentional navigation elements (intentional height, intended position, intended route, flight path, etc.). Affect the capacity required for air traffic flow management, which in turn affects the number of flights that can be accommodated in the air area

3. Can't change flexibly. Once the distance between points/lines is determined, it is difficult to change flexibly.

4. Under the interval control method, the flight can only take off in strict accordance with the specified time, which makes the flight time exchange inflexible. That is to say, the interval control method requires the flight to operate strictly according to the guarantee time. Once a flight misses the guarantee time, it may cause the subsequent flight to be retired or the flight is sorted and queued directly. This will cause the flight time resources to be wasted, and some of the flight waiting time is long, especially like sorting the normal flight after the delayed flight.

Summary of the invention

The purpose of the present disclosure is to provide a method and system for managing air traffic flow based on big data, wherein the air traffic flow management method and system pre-estimate the use of airspace airports by analyzing air data, thereby rationally arranging flight times and optimizing air traffic. operating efficiency.

The purpose of the present disclosure is to provide a big data-based air traffic flow management method and system, wherein the air traffic flow management method and system optimizes allocation of air resources by adjusting flight times, fully utilizes air resources, and improves air traffic flow management efficiency. .

The purpose of the present disclosure is to provide a method and system for managing air traffic flow based on big data, wherein the air traffic flow management method and system arrange flight time according to a prediction model, and reduce the occurrence of flight delays and wasted time.

The purpose of the present disclosure is to provide a method and system for managing air traffic flow based on big data, wherein the air traffic flow management method and system combines big data analysis and flexibly adjust flight time in real time, so that the flight time can cope with the actual emergency situation in the air. .

The purpose of the present disclosure is to provide a method and system for managing air traffic flow based on big data, the method and system for managing air traffic flow to solve the problem of flight time generation in existing flight control, and to better implement airborne Traffic management has a great practical guiding significance.

To achieve the above objective, the main technical solution of the present disclosure is to provide a big data-based air traffic flow management method for controlling at least one flight in an airspace, including the following steps:

S1: collecting air data and obtaining a traffic related threshold, where the traffic related threshold indicates traffic tolerance in the airspace;

S2: obtaining a flight preference time of the corresponding flight;

S3: Acquire flight operation elements of the corresponding flight according to the flight preference time, where the flight operation elements indicate flight flight information corresponding to the flight preference time;

S4: acquiring, according to the flight running elements, an air traffic flow related value corresponding to the flight running elements;

S5: Align the air traffic flow related value with the traffic related threshold, and when matching, select the flight preferred time as the flight specific time.

In some embodiments, in the step S5, when the air traffic flow related value does not match the traffic related threshold, the flight preference time is changed, and the steps S3-S5 are continued until the flight preferred time is The flight is at a specific time.

In some embodiments, the step S1 includes:

S11: acquiring at least one piece of information of conventional air traffic capacity information, traffic complexity tolerance information, meteorological environment information, military activity information, and infrastructure information, and the acquired information is defined as the air data;

S12: The airspace traffic estimation analyzes the air data, and obtains the traffic related threshold.

In some embodiments, the step S3 includes:

S31: Obtain flight flight plan data and relevant wind direction wind speed information in the flight route according to the flight preferred moment;

S32: Analyze the flight flight plan data and the relevant wind direction wind speed in the flight route, and obtain the flight operation elements.

In some embodiments, after the step S32,

S33: Acquire bad weather/military activity information that affects flight flight in the flight route, and correct the flight operation elements.

In some embodiments, after the step S32,

S34: Acquire the other flight history flight four-dimensional route of the same flight route, and correct the flight operation elements.

In some embodiments, the step S4 includes:

S41: Obtain at least one of a flight number in a specified airspace, a flight density in a specified airspace, a traffic complexity correlation value, and probability statistical data according to the flight running component.

S42: Convert the acquired data into the air traffic flow related value.

In some embodiments, the probabilistic statistical data includes one or a combination of a conventional number of instructions to be transmitted, an air traffic focus/conflict point, and an airport operational focus/conflict point.

In some embodiments, the step S2 includes:

S21: obtaining at least one of a flight plan departure time, an earliest executable departure time, and a flight operator application time;

S22: Select the flight preference time.

It is an object of the present disclosure to provide a big data based air traffic flow management system characterized by comprising the following:

a traffic correlation threshold unit, wherein the traffic correlation threshold unit acquires air data and obtains a traffic correlation threshold;

a flight preference time unit, wherein the flight preference time unit acquires a flight preference time of the corresponding flight;

a flight running unit, wherein the flight running unit communicatively connects to the flight preferred time unit to obtain flight running elements corresponding to the flight preferred time;

An air traffic flow unit, wherein the air traffic flow unit is communicatively coupled to the flight operating unit, and the air traffic flow related value is obtained based on the flight operating unit;

An analysis unit, wherein the analysis unit is communicatively coupled to the air traffic flow unit and the flow related threshold to analyze and compare the air flow related value and the flow related threshold to obtain an analysis result.

In some embodiments, the analysis unit is communicatively coupled to the flight preference time unit, and when the air traffic correlation value does not match the traffic related threshold, adjusting the flight preference time until the air flow related The value matches the traffic related threshold, and the flight preferred moment is selected as the flight time of flight.

In some embodiments, the traffic related threshold unit further includes a traffic capacity module, a traffic complexity module, an environment module, a facility information module, and a threshold analysis module, wherein the traffic capacity module, the traffic complexity module, and the environment module The facility information module is configured to obtain conventional air traffic capacity information, traffic complexity tolerance information, meteorological environment information, military activity information, and infrastructure information, and the threshold analysis module analyzes the conventional air traffic capacity information, and traffic Complexity tolerance information, meteorological environment information, military activity information, and infrastructure information to obtain the traffic related threshold.

In some embodiments, the threshold analysis module is an airspace traffic estimation module.

In some embodiments, the flight operation unit includes a flight data acquisition module and a flight data analysis module, wherein the flight data acquisition module acquires flight flight plan data and related wind direction wind speed information in the flight route, the flight data analysis The module obtains the flight running elements based on the data.

In some embodiments, the flight data acquisition module acquires other flight history flight four-dimensional routes of the same flight route, and the flight data analysis module corrects the flight operation elements based on the information.

In some embodiments, the flight data acquisition module acquires severe weather/military activity information affecting flight flight in the flight route, and the flight number analysis module modifies the flight operation elements based on the information.

In some embodiments, the air traffic flow unit acquires at least one of a number of flights within a designated airspace, a specified flight density within the airspace, a traffic complexity correlation value, and probabilistic statistical data, and converts the acquired data into the air Traffic flow related value.

In some embodiments, the flight preference time unit includes a time acquisition module and a time adjustment module, wherein the time acquisition module is communicatively coupled to the external device to obtain a flight plan departure time, a flight first executable takeoff time, and a flight operation. At least one of a party application time, the time adjustment module is communicatively coupled to the analysis unit to adjust a flight time of flight based on the analysis result.

DRAWINGS

Figure 1 is a schematic diagram of the route of the aircraft in the flight space.

2 is a data flow diagram of the big data based air traffic flow management method and the air traffic flow management system according to the present disclosure.

3 is a flow diagram of the big data based air traffic flow management method and the air traffic flow management system according to the present disclosure.

4 is a block diagram of the big data based air traffic flow management system in accordance with the present disclosure.

detailed description

The technical solutions in the embodiments of the present disclosure are clearly and completely described in the following with reference to the accompanying drawings in the embodiments of the present disclosure. It is obvious that the described embodiments are only a part of the embodiments of the present disclosure, and not all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present disclosure are within the scope of the disclosure.

It should be understood by those skilled in the art that in the disclosure of the present disclosure, the terms "vertical", "transverse", "upper", "lower", "front", "back", "left", "right", " The orientation or positional relationship of the indications of "upright", "horizontal", "top", "bottom", "inside", "outside", etc. is based on the orientation or positional relationship shown in the drawings, which is merely for convenience of description of the present disclosure and The above description is not to be construed as a limitation of the present disclosure.

It will be understood that the term "a" is understood to mean "at least one" or "one or more", that is, in one embodiment, the number of one element may be one, and in other embodiments, the element The number can be multiple, and the term "a" cannot be construed as limiting the quantity.

As shown in Figure 1, a flight route diagram of the flight equipment in the airspace is shown. In general, the flight equipment flies in the airspace according to the specified flight route. Of course, the flight equipment may change the flight route slightly due to the actual situation during the flight, forming the actual flight route, and the actual flight route is also known to facilitate the air traffic controller. Get the status of each flight device in time and perform corresponding air traffic flow management.

Air traffic flow management refers to the timely adjustment of air traffic flow by air traffic control units when air traffic flow reaches or is close to the available air traffic control capacity, thus ensuring that air traffic flights flow optimally through or through specific areas, thereby improving the airport. And the available capabilities in the air field. It is specifically mentioned herein that the airspace described in the present disclosure includes the field of flight of the flight equipment in the air and the field of flight on the ground.

The present disclosure provides a big data-based air traffic flow management method, wherein the big data-based air traffic flow management method utilizes big data information collection and analysis technology and related analysis calculations to pre-estimate air traffic when a particular flight is flying at a specific time. The traffic correlation value is determined, and whether the air traffic flow related value satisfies the flight requirement, and if yes, the specific time is determined as the flight recommendation time, and if not, the specific time is changed until the flight recommendation time is selected.

Specifically, the present disclosure provides a big data-based air traffic flow management method for controlling at least one flight in an airspace, including the following steps:

S2: obtaining a flight preference time of the corresponding flight;

Of course, in the step S5, when the air traffic flow related value does not match the traffic related threshold, the flight preference time is changed, and the steps S3-S5 are continued until the flight preferred time is the flight. Specific moments.

The big data-based air traffic flow management method will be described in detail below, wherein the traffic related threshold includes, but is not limited to, conventional air traffic capacity information, traffic complexity tolerance information, meteorological environment information, military activity information, and infrastructure information. And the traffic correlation threshold may be obtained by estimating the airspace capacity, that is, the step S1 further includes the following steps:

It is worth noting that the conventional air traffic capacity information includes, but is not limited to, conventional available inbound and outbound route, conventional available flight airspace, conventional available flight level, and conventional available pilot airspace, number of available runways at the airport, and runway declination (For example, in the case of multiple runways, only one runway is scheduled to take off, the other runway is only scheduled to land), the cross-aggregation of routes in the conventional airspace, the conflicts between the inbound and outbound routes in the conventional airspace, and the use spacing criteria, and then estimated by the airspace capacity. The conventional available traffic capacity information is derived.

The traffic complexity tolerance information includes, but is not limited to, a single seat capability value, a sector amount in the air domain, and a seat setting manner of each sector.

The meteorological environment information includes, but is not limited to, the location and development trend of the bad weather, so that according to the location and development trend of the bad weather in the space, the weather can be obtained for the conventional inbound and outbound route, the available flight space, the available altitude layer, The impact of the ability to direct airspace can be used.

For example, when the bad weather covers part of the available guidance airspace, the covered part of the guided airspace is in an unavailable state, and then according to the related algorithm of the airspace capacity assessment of the prior art, the traffic capacity and traffic complexity are obtained. influences.

The military activity environment includes, but is not limited to, the effects of military activities on conventional airspace restrictions and military activity areas on airport flights. For example, when military activities occur, certain designated airspaces, altitudes, route routes, etc., are not available for a specified period of time, thereby obtaining military activity restrictions on traffic capacity and traffic complexity based on airspace capacity assessment analysis. Impact. For example, when military activities occur, the number of flights taking off and landing within a specified time period may be limited. According to the airspace capacity assessment, the impact of military activity restrictions on traffic capacity and traffic complexity is obtained.

The infrastructure information includes, but is not limited to, the availability status of the infrastructure and the impact on traffic capacity and traffic complexity in the event of a failure. For example, runway, taxiway availability, and subsequent impact on traffic capacity, such as when it fails.

In this way, in an embodiment of the present disclosure, at least one type of information of conventional air traffic capacity information, traffic complexity tolerance information, meteorological environment information, military activity information, and infrastructure information is obtained through airspace capacity assessment, and Obtain the traffic related threshold.

In addition, the flight preference moment refers to a time when the flight operator desires to perform the reference of the flight. Generally, the flight preference moment is selected from the flight plan departure time, the flight first executable takeoff time, and the flight operator application time. In one case, the flight plan departure time can be obtained by collecting a flight flight plan, and the earliest executable departure time of the flight can be calculated by the operator/guarantee by analyzing the real-time situation of the flight, or calculated by the flight support system. The flight operator application time is obtained by acquiring the application time data submitted by the flight operator.

That is to say, the step S2 further comprises the following steps:

S22: Select the flight preference time.

The flight operations include, but are not limited to, information such as flight location, heading, speed, altitude, ascending and descending trend, and flight path, flight altitude, and target altitude at a subsequent time. In an embodiment of the present disclosure, the flight running unit acquires the flight preference time as a preset time, thereby obtaining an estimated route of a specific flight.

Specifically, the flight operation elements may obtain flight flight plan data, relevant wind direction wind speed information in the flight route, bad weather/military activity information affecting the flight flight in the flight route, and other flight historical flight four-dimensional routes of the same flight route. Obtain.

The flight flight plan data can be obtained by obtaining the FPL Message (Filed Flight Plan Message) from the civil aviation message system. In the pilot message, the flight schedule, the flight path of each aircraft, the cruising speed, the applied cruising altitude, and the altitude information of each estimated flight segment are included. Thus, in combination with the relevant wind direction wind speed information in the flight line, it is possible to calculate a relatively accurate flight operation element that takes off at the preferred time.

The relevant wind direction wind speed information in the flight route, by collecting the wind direction wind speed information observed or estimated at each level in the flight path, combined with the flight flight plan data information, can preliminarily calculate the more accurate flight operation elements at the specified time and the intended operation Meta information.

In addition, in the event of military activity information/severe weather information, the flight will take special operations (such as flying around bad weather, changing the route, changing altitude, waiting in the air, etc.), and correcting the flight calculations of the above preliminary calculations from these prediction data. Zhu Yuan.

Also, the other flight history flight four-dimensional route of the same flight route can be learned by radar data and ADS-B (a flight position and motion monitoring system in civil aviation operation). Based on these data, you can learn about the flight altitude, speed, military activities encountered during the flight routes (routes, destinations, etc.), flight voyages during bad weather, and changes in altitude. The flight calculation elements of the above preliminary calculation are corrected by these prediction data.

That is to say, the step S3 further comprises the following steps:

S31: Obtain flight flight plan data and relevant wind direction wind speed information in the flight route according to the flight preference time;

In addition, the step S3 further includes the following steps:

Or the step S4 further includes the following steps:

Moreover, the air traffic flow related value is mainly obtained by collecting and analyzing the flight running items of each flight at each designated time. The air traffic flow value mainly includes the following types of information: the number of flights in the designated airspace, the specified flight density in the airspace, the traffic complexity correlation value, and the probability statistical data. The probabilistic statistical data includes the number of conventional instructions to be sent (outbound guidance, departure altitude command, inbound guidance, inbound drop height command, etc., air traffic focus/conflict points (cross-crossing altitude flight, same-direction crossing altitude flight) , departure and departure conflict deployment, overflight flight deployment, etc.) and airport operation concerns/conflict points (passing the runway, taking off and landing, taxiing conflicts, etc.).

The number of flights in the designated airspace and the flight density in the designated airspace can be learned according to the location of the estimated flight at the preferred time. The number of regular instructions to be sent can be learned by knowing the flight route, altitude, etc. according to the flight plan.

The information of the traffic focus point/conflict point needs to be informed according to each flight operation element at the specified time, location information, the time height and the subsequent estimated flight altitude, the estimated flight line and the like. According to the set rules, it is analyzed whether there are cross-crossing high-altitude flights, same-way crossing altitude flight, inbound and outbound conflict deployment, and overflight flight deployment. (Generally learned by analyzing whether its flight path and altitude are intersected in four-dimensional space).

The airport operation line focus/conflict point is based on the runway information expected by each flight, the taxi route information (calculated based on the parking space information and the expected runway information), and it is estimated that each flight is taxiing at the airport operation. Information such as conflicts, crossing runway conflicts, etc.

In addition, the traffic correlation value comparison analysis processing method is as follows: from the above, the data related to the flow correlation value is converted into a quantifiable "air traffic flow related value", such as the number of flights, the flight density, and the quantifiable traffic. Complexity related values. At the same time, the dynamic data related to the flow-related threshold is converted into a quantifiable “flow-related threshold”, such as capacity (ie, the number of lines available for travel) and quantifiable (traffic complexity tolerance), thereby The above two quantized flow-related values and the flow-related value thresholds are compared and analyzed to obtain a comparison result.

That is to say, the step S4 further comprises the following steps:

S41: acquiring, according to the flight running elements, the number of flights in the designated airspace, specifying at least one of a flight density, a traffic complexity correlation value, and probability statistical data in the airspace;

S42: Convert the acquired data into the air traffic flow related value.

The probabilistic statistical data includes the number of conventional instructions to be sent (outbound guidance, departure altitude command, inbound guidance, inbound drop height command, etc., air traffic focus/conflict points (cross-crossing altitude flight, same-direction crossing altitude flight) One or a combination of airport operational concerns/conflict points (crossing the runway, takeoff and landing, taxiing conflicts, etc.)

The number of flights in the designated airspace and the flight density in the designated airspace are obtained according to the location where the flight is estimated to be at the preferred moment. The number of conventional instructions to be sent can be learned according to the flight plan and its subsequent expected flight path, altitude and other information. The information of the traffic concern/conflict point needs to be informed according to each flight operation element at the specified time, the location information, the time height and the subsequent estimated flight altitude, the estimated flight route and the like. According to the set rules, it is analyzed whether there are cross-crossing high-altitude flights, same-way crossing altitude flight, inbound and outbound conflict deployment, and overflight flight deployment. The airport operation line focus/conflict point is based on the runway information expected for each flight, the taxi route information, and the information about the taxi collision and the runway conflict when the flight is running at the airport is calculated.

The present disclosure provides a big data based air traffic flow management system, wherein the big data based air traffic flow management system comprises the following:

An analysis unit, wherein the analysis unit is communicatively coupled to the air traffic flow unit and the flow related threshold to analyze the air flow related value and the flow related threshold.

Of course, the analysis unit is communicatively coupled to the flight preference time unit, and when the air traffic correlation value does not match the traffic related threshold, adjusting the flight preference time until the air traffic correlation value matches the The traffic related threshold is selected as the flight execution time.

The traffic related threshold unit further includes a traffic capacity module, a traffic complexity module, an environment module, a facility information module, and a threshold analysis module, wherein the traffic capacity module, the traffic complexity module, the environment module, and the facility information module are respectively used. Obtaining conventional air traffic capacity information, traffic complexity tolerance information, meteorological environment information, military activity information, and infrastructure information, the threshold analysis module is implemented as an airspace traffic estimate, and the threshold analysis module analyzes the conventional air Traffic capacity information, traffic complexity tolerance information, meteorological environment information, military activity information, and infrastructure information to obtain the traffic related threshold.

The flight preference time unit further includes a time acquisition module and a time adjustment module, wherein the time acquisition module is communicatively coupled to the external device to obtain at least a flight plan departure time, a flight first executable takeoff time, and a flight operator application time In one aspect, the time adjustment module is communicatively coupled to the analysis unit to adjust a flight time of flight according to the analysis result.

The flight operation unit includes a flight data acquisition module and a flight data analysis module, wherein the flight data acquisition module acquires flight flight plan data and relevant wind direction wind speed information in the flight route, and the flight data analysis module acquires the data according to the data. The flight runs the yuan.

Of course, in some cases, the flight data acquisition module acquires other flight history flight four-dimensional routes of the same flight route, and the flight data analysis module corrects the flight operation elements according to the information.

Alternatively, in some cases, the flight data acquisition module acquires severe weather/military activity information affecting flight flight in the flight route, and the flight data analysis module corrects the flight operation elements based on the information.

The flight flight plan data can be obtained by obtaining the FPL Message (Filed Flight Plan Message) from the civil aviation text system. In the pilot message, the flight schedule, the flight path of each aircraft, the cruising speed, the applied cruising altitude, and the altitude information of each estimated flight segment are included. In combination with the relevant wind direction wind speed information in the flight line, it is possible to calculate a relatively accurate flight operation of the flight that takes off at the preferred time.

The relevant wind direction wind speed information in the flight route, by collecting the wind direction wind speed information observed or estimated at each altitude level in the flight route, combined with the flight flight plan data information, can preliminarily calculate the more accurate flight operation elements at the set time and the intended operation. Meta information.

Also, the other flight history flight four-dimensional route of the same flight route can be learned by radar data and ADS-B (a flight position and motion monitoring system in civil aviation operation). Based on these data, information such as the altitude, speed, military activities, bad weather, flight detours, and altitude levels of other flights (air routes, destinations, etc.) can be obtained. The flight calculation elements of the above preliminary calculation are corrected by these prediction data.

The air traffic flow unit acquires at least one of a number of flights within a specified airspace, a specified flight density within the airspace, a traffic complexity correlation value, and probability statistical data, and converts the acquired data into the air traffic flow related value.

The flight flight plan data can be obtained by obtaining the FPL Message (Filed Flight Plan Message) from the civil aviation text system. In the neighboring text, the flight schedule, the flight path of each aircraft, the cruising speed, the applied cruising altitude, and the altitude information of each expected flight section are included. In combination with the relevant wind direction wind speed information in the flight line, it is possible to calculate a relatively accurate flight operation of the flight that takes off at the preferred time.

In addition, the present disclosure is not limited to the above-described preferred embodiments, and any other form of product can be derived by anyone of the present disclosure, but no matter what shape or structure is changed, It is to be understood that the same or similar technical solutions are disclosed within the scope of the present disclosure.

Claims

A big data based air traffic flow management method for controlling at least one flight in an airspace, comprising the steps of:

S1: collecting air data and obtaining a traffic related threshold, where the traffic related threshold indicates traffic tolerance in the airspace;

S2: obtaining a flight preference time of the corresponding flight;

S3: Acquire flight operation elements of the corresponding flight according to the flight preference time, where the flight operation elements indicate flight flight information corresponding to the flight preference time;

S4: acquiring, according to the flight running elements, an air traffic flow related value corresponding to the flight running elements;

S5: Align the air traffic flow related value with the traffic related threshold, and when matching, select the flight preferred time as the flight specific time.
The big data-based air traffic flow management method according to claim 1, wherein in the step S5, when the air traffic flow related value does not match the traffic related threshold, the flight optimization time is changed, and the flow continues. Steps S3-S5 are performed until the flight preference time is the flight specific time.
The big data-based air traffic flow management method according to any one of claims 1 or 2, wherein the step S1 comprises:

S11: acquiring at least one piece of information of conventional air traffic capacity information, traffic complexity tolerance information, meteorological environment information, military activity information, and infrastructure information, and the acquired information is defined as the air data;

S12: The airspace traffic estimation analyzes the air data, and obtains the traffic related threshold.
The big data-based air traffic flow management method according to any one of claims 1 or 2, wherein the step S3 comprises:

S31: Obtain flight flight plan data and relevant wind direction wind speed information in the flight route according to the flight preferred moment;

S32: Analyze the flight flight plan data and the relevant wind direction wind speed in the flight route, and obtain the flight operation elements.
The big data-based air traffic flow management method according to any one of claims 1 or 2, wherein said step S4 comprises:

S41: acquiring, according to the flight running elements, the number of flights in the designated airspace, specifying at least one of a flight density, a traffic complexity correlation value, and probability statistical data in the airspace;

S42: Convert the acquired data into the air traffic flow related value.
The big data-based air traffic flow management method according to claim 5, wherein the probability statistical data includes a conventional number of instructions to be transmitted, an air traffic focus/conflict point, and an airport operation focus/conflict point number. Or a combination thereof.
An air traffic flow management system based on big data, including the following:

a traffic correlation threshold unit, wherein the traffic correlation threshold unit acquires air data and obtains a traffic correlation threshold;

a flight preference time unit, wherein the flight preference time unit acquires a flight preference time of the corresponding flight;

a flight running unit, wherein the flight running unit communicatively connects to the flight preferred time unit to obtain flight running elements corresponding to the flight preferred time;

An air traffic flow unit, wherein the air traffic flow unit is communicatively coupled to the flight operating unit, and the air traffic flow related value is obtained based on the flight operating unit;

An analysis unit, wherein the analysis unit is communicatively coupled to the air traffic flow unit and the flow related threshold to analyze and compare the air flow related value and the flow related threshold to obtain an analysis result.
The big data-based air traffic flow management system of claim 7, wherein the analysis unit is communicatively coupled to the flight preference time unit, when the air traffic correlation value does not match the traffic related threshold, The flight preference time is adjusted until the air traffic related value matches the traffic related threshold, and the flight preferred time is selected as the flight time of flight.
The big data-based air traffic flow management system according to any one of claims 7 or 8, wherein the traffic related threshold unit further comprises a traffic capacity module, a traffic complexity module, an environment module, a facility information module, and a threshold analysis module. The traffic capacity module, the traffic complexity module, the environment module, and the facility information module are respectively used to obtain conventional air traffic capacity information, traffic complexity tolerance information, meteorological environment information, military activity information, and infrastructure information, The threshold analysis module analyzes the conventional air traffic capacity information, traffic complexity tolerance information, meteorological environment information, military activity information, and infrastructure information to obtain the traffic related threshold.
The big data-based air traffic flow management system according to any one of claims 7 or 8, wherein the flight data acquisition module acquires flight flight plan data and relevant wind direction wind speed information in the flight route, and the flight data analysis module is based on These data capture the flight run items.
The big data-based air traffic flow management system according to claim 10, wherein the flight data acquisition module acquires other flight history flight four-dimensional routes of the same flight route, and the flight data analysis module corrects the information according to the information. The flight runs the yuan.
The big data-based air traffic flow management system according to claim 10, wherein said flight data acquisition module acquires bad weather/military activity information affecting flight flight in said flight route, said flight data analysis module according to The information corrects the flight operations.
The big data-based air traffic flow management system according to any one of claims 7 or 8, wherein the air traffic flow unit acquires the number of flights in the designated airspace, specifies the flight density in the airspace, the traffic complexity correlation value, and the probability statistics. At least one of the data, and converting the acquired data into the air traffic flow related value.
The big data-based air traffic flow management system according to any one of claims 7 or 8, wherein the flight preference time unit comprises a time acquisition module and a time adjustment module, wherein the time acquisition module is communicatively connected to the external device To obtain at least one of a flight plan departure time, a flight first executable takeoff time, and a flight operator application time, the time adjustment module is communicably connected to the analysis unit to adjust a flight flight time according to the analysis result.