A flight operation situation analysis method, device and medium

Abstract

The application provides a flight operation situation analysis method, comprising the following steps: determining an abnormal airport according to a preset normal rate threshold and normal rate index data corresponding to each airport in a monitoring area; querying real-time weather condition information corresponding to the abnormal airport from a weather query platform, judging whether the abnormal airport is affected by weather according to the real-time weather condition information corresponding to the abnormal airport, if not, extracting all pre-stored abnormal flights in the abnormal airport, and screening corresponding abnormal flow control points in a flow control point database according to the pre-stored abnormal flights; taking the abnormal flow control point with the largest number of pre-stored abnormal flights as a maximum impact flow control point, and taking flow control data associated with the maximum impact flow control point as an impact factor output. The flight operation situation analysis method provided by the application accurately analyzes the operation situation of airport flights, finds out abnormal flights, and obtains a relatively accurate impact factor that affects flight abnormalities.

Description

Technical Field

[0001] This invention relates to the field of aviation management, and in particular to a method, equipment and medium for analyzing flight operation status. Background Technology

[0002] With the development of science and technology, my country's civil aviation is developing faster and faster, more and more cities are building airports, and more and more flights are opening. Therefore, air traffic control is becoming more and more important. Air traffic control requires real-time monitoring of whether there are any abnormalities in the operation of flights at each airport and analysis of the specific reasons for such abnormalities.

[0003] Currently, the system only monitors whether there are any abnormalities in airport flights, but lacks detailed analysis of the specific reasons for these abnormalities. As a result, it cannot achieve accurate statistics and analysis of flight operation status, which is not conducive to the centralized management of flights by air traffic control units. Summary of the Invention

[0004] To overcome the shortcomings of existing technologies, one of the objectives of this invention is to provide a flight operation status analysis method that can solve the problem that the current method only monitors whether there are abnormalities in airport flights, but lacks detailed analysis of the specific causes of abnormalities. As a result, it cannot achieve accurate statistics and analysis of flight operation status, which is not conducive to the centralized management of flights by air traffic control units.

[0005] The second objective of this invention is to provide an electronic device that can solve the problem that current methods only monitor whether airport flights are abnormal, but lack detailed analysis of the specific causes of the abnormalities. As a result, they cannot achieve accurate statistics and analysis of flight operation status, which is not conducive to the centralized management of flights by air traffic control units.

[0006] The third objective of this invention is to provide a computer-readable storage medium that can solve the problem that current methods only monitor whether airport flights are abnormal, but lack detailed analysis of the specific causes of the abnormalities. As a result, they cannot achieve accurate statistics and analysis of flight operation status, which is not conducive to the centralized management of flights by air traffic control units.

[0007] One of the objectives of this invention is achieved through the following technical solution:

[0008] A method for analyzing flight operation status includes the following steps:

[0009] Locate abnormal airports by determining them based on preset normality rate thresholds and normality rate index data for each airport within the monitoring area.

[0010] Analyze the weather, query the real-time weather information corresponding to the abnormal airport from the meteorological query platform, and determine whether the abnormal airport is affected by the weather based on the real-time weather information corresponding to the abnormal airport. If yes, return to the step of locating the abnormal airport; otherwise, execute the step of filtering flow control points.

[0011] Filter flow control points, extract all pre-stored abnormal flights in abnormal airports, and filter out the corresponding abnormal flow control points in the flow control point database based on the pre-stored abnormal flights;

[0012] Output the impact factor, taking the abnormal flow control point with the most pre-stored abnormal flights as the flow control point with the greatest impact, and outputting the flow control data associated with the flow control point with the greatest impact as the impact factor.

[0013] Furthermore, the pre-stored abnormal flights include pre-stored abnormal inbound flights and pre-stored abnormal outbound flights, the abnormal flow control points include inbound flow control points and outbound flow control points, the flow control points with the greatest impact include the flow control points with the greatest impact on inbound flights and the flow control points with the greatest impact on outbound flights, and the impact factors include inbound impact factors and outbound impact factors.

[0014] Furthermore, the screening of flow control points specifically includes the following steps:

[0015] Extract abnormal flights, and extract the pre-stored abnormal inbound flights and pre-stored abnormal outbound flights corresponding to the abnormal airport;

[0016] The departure points are identified, and the arrival on-time rate for each arrival point in the abnormal airport is calculated based on the pre-stored abnormal arrival flights. The departure on-time rate for each departure point in the abnormal airport is calculated based on the pre-stored abnormal departure flights. The arrival point with the lowest arrival on-time rate is identified as the abnormal arrival point, and the departure point with the lowest departure on-time rate is identified as the abnormal departure point.

[0017] Associate flow control points: Based on the pre-stored abnormal arrival flights corresponding to the abnormal arrival points, use several flow control points in the flow control point database as arrival flow control points and associate them with the abnormal arrival points; based on the pre-stored abnormal departure flights corresponding to the abnormal departure points, use several flow control points in the flow control point database as departure flow control points and associate them with the abnormal departure points.

[0018] Furthermore, if the arrival normalization rate for each arrival point in an abnormal airport is 100%, then there are no abnormal arrival points in the abnormal airport; if the departure normalization rate for each departure point in an abnormal airport is 100%, then there are no abnormal departure points in the abnormal airport.

[0019] Furthermore, the output influence factor includes the following steps:

[0020] Analyze the flow control points and count the number of pre-stored abnormal inbound flights corresponding to each inbound flow control point and the number of pre-stored abnormal outbound flights corresponding to each outbound flow control point.

[0021] The influencing factors are determined by identifying the inbound flow control point with the highest number of pre-stored abnormal inbound flights as the inbound flow control point with the highest number of pre-stored abnormal outbound flights as the outbound flow control point with the highest number of pre-stored abnormal outbound flights. The flow control data corresponding to the inbound flow control point with the highest influence is output as the inbound influencing factor, and the flow control data corresponding to the outbound flow control point with the highest influence is output as the outbound influencing factor.

[0022] Furthermore, the flow control data includes the flow control source unit, flow control reason, restriction type, flow control description, start time, and end time.

[0023] Furthermore, it also includes a normality rate display, which uses different colored charts to show the normality rate of arrivals for each arrival point and the normality rate of departures for each departure point.

[0024] Furthermore, the charts corresponding to the departure on-time rate and the arrival on-time rate being greater than 75% are green, the charts corresponding to the departure on-time rate and the arrival on-time rate being greater than 50% and less than or equal to 75% are yellow, the charts corresponding to the departure on-time rate and the arrival on-time rate being greater than 25% and less than or equal to 50% are orange, and the charts corresponding to the departure on-time rate and the arrival on-time rate being less than or equal to 25% are red.

[0025] The second objective of this invention is achieved by the following technical solution:

[0026] An electronic device, comprising: a processor;

[0027] The program includes a memory and a program, wherein the program is stored in the memory and configured to be executed by a processor, the program including a method for performing a flight operation status analysis method as described in this application.

[0028] The third objective of this invention is achieved by the following technical solution:

[0029] A computer-readable storage medium having a computer program stored thereon, the computer program being executed by a processor as a method for analyzing flight operation status according to this application.

[0030] Compared with the prior art, the beneficial effects of the present invention are as follows: The flight operation status analysis method in this application determines abnormal airports based on a preset normality rate threshold and the normality rate index data corresponding to each airport in the monitoring area. After excluding the influence of weather, all pre-stored abnormal flights in the abnormal airport are extracted. Based on the pre-stored abnormal flights, the corresponding abnormal flow control points are selected from the flow control point database. The flow control point with the most pre-stored abnormal flights is taken as the flow control point with the greatest influence. The flow control data associated with the flow control point with the greatest influence is output as an influence factor. The operation status of airport flights is accurately analyzed, abnormal flights are found, and the influence factors that affect flight abnormalities are obtained more accurately.

[0031] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it according to the contents of the specification, the preferred embodiments of the present invention are described in detail below with reference to the accompanying drawings. Specific embodiments of the present invention are given in detail below with reference to the accompanying drawings. Attached Figure Description

[0032] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0033] Figure 1 This is a flowchart illustrating a flight operation status analysis method according to the present invention;

[0034] Figure 2 This is a schematic diagram of the regularity rate display interface in a flight operation status analysis method of the present invention;

[0035] Figure 3 This is a schematic diagram of the flight operation status interface map in a flight operation status analysis method of the present invention;

[0036] Figure 4 This is a schematic diagram of the abnormal departure point flow control analysis interface in a flight operation status analysis method of the present invention;

[0037] Figure 5 This is a schematic diagram of the flow control list in a flight operation status analysis method of the present invention;

[0038] Figure 6 This is a schematic diagram of the flight list in a flight operation status analysis method according to the present invention. Detailed Implementation

[0039] The present invention will now be further described in conjunction with the accompanying drawings and specific embodiments. It should be noted that, without conflict, the various embodiments or technical features described below can be arbitrarily combined to form new embodiments.

[0040] like Figure 1 As shown, a flight operation status analysis method in this application specifically includes the following steps:

[0041] In this embodiment, based on commonly used monitoring standards for civil aviation airports, three on-time performance indicators are set for each airport to reflect its operational status: on-time departure rate, on-time departure rate, and on-time arrival rate. The on-time performance alarm thresholds are set as follows: Green, on-time performance rate greater than 75%; Yellow, on-time performance rate greater than 50% and less than or equal to 75%; Orange, on-time performance rate greater than 25% and less than or equal to 50%; Red, on-time performance rate less than or equal to 25%. In this embodiment, the on-time departure rate reflects the comprehensive support capabilities of various units when flights are transiting through the airport. The airport's on-time departure rate is equal to the ratio of on-time departures to the total number of departures, expressed as a percentage.

[0042] The system locates abnormal airports based on a preset on-time rate threshold and on-time rate indicator data for each airport within the monitored area. In this embodiment, the on-time rate indicator data includes departure on-time rate, departure on-time rate, and arrival on-time rate, such as... Figure 2 As shown, the above-mentioned normal operation rates for each airport are represented by different colored charts as percentages: green, normal operation rate greater than 75%; yellow, normal operation rate greater than 50% and less than or equal to 75%; orange, normal operation rate greater than 25% and less than or equal to 50%; and red, normal operation rate less than or equal to 25%. In this embodiment, the main measurement is of the impact of external factors on the airport; therefore, if at least one of the departure normal operation rate, departure normal operation rate, or arrival normal operation rate is less than 75%, the corresponding airport is considered an abnormal airport. Figure 2 The chart within the rectangle represents the normality rate of less than 75%. Due to formatting requirements, its color is not displayed in the chart.

[0043] In this embodiment, the analysis first checks whether the abnormal flight schedules at airports with abnormal flight schedules are affected by the weather. Therefore, the real-time weather information corresponding to the abnormal airport is queried from the meteorological query platform. Based on the real-time weather information corresponding to the abnormal airport, it is determined whether the abnormal airport is affected by the weather. If so, the process returns to the step of locating the abnormal airport; otherwise, the process of filtering flow control points is executed.

[0044] The process involves filtering flow control points, extracting all pre-stored abnormal flights from the abnormal airport, and then filtering the corresponding abnormal flow control points from the flow control point database based on these pre-stored abnormal flights. In this embodiment, considering that the on-time departure rate can be quickly determined from the airport's internal system and resolved rapidly, and that the departure on-time rate is caused by flow control issues from external flow control points and cannot be detected and statistically analyzed in a timely manner, the departure on-time rate is primarily used as the criterion for judging the degree of abnormality of abnormal departure points, and the flow control points corresponding to the departure on-time rate are the main objects of anomaly analysis. In this implementation, the pre-stored abnormal flights include pre-stored abnormal inbound flights and pre-stored abnormal outbound flights, the abnormal flow control points include inbound flow control points and outbound flow control points, the flow control points with the greatest impact include the flow control points with the greatest impact on inbound flights and the flow control points with the greatest impact on outbound flights, and the impact factors include inbound impact factors and outbound impact factors.

[0045] This step specifically includes the following steps:

[0046] Extract abnormal flights, and extract the pre-stored abnormal inbound flights and pre-stored abnormal outbound flights corresponding to the abnormal airport;

[0047] Investigate arrival and departure points. Calculate the on-time arrival rate for each arrival point in the abnormal airport based on pre-stored abnormal arrival flights, and calculate the on-time departure rate for each departure point in the abnormal airport based on pre-stored abnormal departure flights. Identify the arrival point with the lowest on-time arrival rate as the abnormal arrival point, and the departure point with the lowest on-time departure rate as the abnormal departure point. If the on-time arrival rate for each arrival point in the abnormal airport is 100%, then there are no abnormal arrival points in the abnormal airport, and subsequent steps do not require analysis of arrival points. Similarly, if the on-time departure rate for each departure point in the abnormal airport is 100%, then there are no abnormal departure points in the abnormal airport, and subsequent steps do not require analysis of departure points. The following example illustrates this: Suppose all flights at Guangzhou Airport depart from three departure points: YIN, VIBOS, and LMN. If some departing flights experience abnormalities at each of these three departure points, calculate the departure on-time rate for each of the three departure points (YIN, VIBOS, and LMN). Then, analyze the departure point with the lowest departure on-time rate as the primary departure point affecting the airport's abnormalities.

[0048] The system associates flow control points. Based on the pre-stored abnormal inbound flights corresponding to abnormal inbound points, several flow control points in the flow control point database are used as inbound flow control points and associated with the abnormal inbound points. Similarly, based on the pre-stored abnormal outbound flights corresponding to abnormal outbound points, several flow control points in the flow control point database are used as outbound flow control points and associated with the abnormal outbound points. In this embodiment, all pre-stored abnormal inbound and outbound flights include specific flight times and routes. Based on the aircraft's flight trajectory along the route, the flow control points passed by the flight are determined. The flow control points passed by the pre-stored abnormal inbound flights corresponding to the abnormal inbound points are used as inbound flow control points, and the number of these inbound flow control points can be one or more. The flow control points passed by the pre-stored abnormal outbound flights corresponding to the abnormal outbound points are used as outbound flow control points, and the number of these outbound flow control points can be one or more. Whether a route has been passed can be better displayed on a map to show the overall operational status, such as... Figure 3 The image shows a map of flight operations at Guangzhou Airport. The map elements include the airport, flow control points, departure points, arrival points, waypoints, and flight routes. Airports are represented by two parallel line segments, with the angle of inclination consistent with the direction of the local airport runways. Flow control points (waypoints used to describe flow control between control units and sectors) are represented by pentagrams, with colors indicating the on-time departure rate of flights taking off or landing at Guangzhou Central Airport and passing through that point. Departure points (departure points at the boundary of the approach control area) are represented by hollow equilateral triangles, with colors indicating the on-time departure rate of the airport at that point. Arrival points (arrival points at the boundary of the approach control area) are represented by solid inverted triangles, with colors indicating the on-time arrival rate of flights passing through that point to their respective airports. Figure 3 Based on the route and the location of flow control points, it can quickly determine the flow control points corresponding to abnormal arrival and departure points.

[0049] The output impact factor is determined by identifying the flow control point with the highest number of pre-stored abnormal flights as the most influential flow control point, and outputting the flow control data associated with the most influential flow control point as the impact factor. This step specifically includes the following steps:

[0050] Analyze the flow control points and count the number of pre-stored abnormal inbound flights corresponding to each inbound flow control point and the number of pre-stored abnormal outbound flights corresponding to each outbound flow control point.

[0051] The influencing factors are determined by identifying the inbound flow control point with the highest number of pre-stored abnormal inbound flights as the inbound flow control point with the highest number of pre-stored abnormal outbound flights as the outbound flow control point with the highest number of pre-stored abnormal outbound flights. The flow control data corresponding to the inbound flow control point with the highest influence is output as the inbound influencing factor, and the flow control data corresponding to the outbound flow control point with the highest influence is output as the outbound influencing factor. In this embodiment, an abnormal arrival point corresponds to one or more arrival flow control points, meaning that pre-stored abnormal arrival flights arriving from this abnormal arrival point are affected by flow control information from one or more arrival flow control points; an abnormal departure point corresponds to one or more departure flow control points, meaning that pre-stored abnormal departure flights departing from this abnormal departure point are affected by flow control information from one or more departure flow control points; therefore, the departure flow control point with the greatest impact on the abnormal departure point and the arrival flow control point with the greatest impact on the abnormal arrival point are calculated respectively. The flow control data in this embodiment includes the flow control reason, which is the flow control information sent by the control unit to the flow control point for managing flight operations, generally related to military activities or flight schedule arrangements. Figure 4 The image shows the flow control analysis interface for abnormal departure points. Figure 4 The example of abnormal departure points LMN at Guangzhou Airport is used to illustrate this. The arrival flow control points corresponding to the abnormal departure points LMN and the number of affected flights are statistically analyzed. They are the arrival flow control points PLT, LMN, NUVGA, SAGON, ODOPI, PANBO, and DABER. PLT (12) indicates that 12 flights in the departure point LMN are affected by the flow control point PLT; LMN (1) indicates that 1 flight in the departure point LMN is affected by the flow control point LMN; NUVGA (2) indicates that 2 flights in the departure point LMN are affected by the flow control point NUVGA; SAGON (4) indicates that 4 flights in the departure point LMN are affected by the flow control point NUVGA; ODOPI (2) indicates that 2 flights in the departure point LMN are affected by the flow control point ODOPI; PANBO (7) indicates that 7 flights in the departure point LMN are affected by the flow control point PANBO; and DABER (1) indicates that 1 flight in the departure point LMN is affected by the flow control point DABER. Figure 4 The pie chart shows the number of flights affected by different arrival flow control points on the departure LMN. These influencing factors represent the output of flow control data.

[0052] In this embodiment, the flow control data is essentially a flow control list containing several flow control-related information, such as... Figure 5 As shown, Figure 5 This represents the flow control list corresponding to a single abnormal departure point (PANBO). Figure 5This includes the flow control source unit, flow control reason, restriction type, start time, end time, and flow control description. The flow control source unit is the original sending unit of the flow control data. The flow control reason explains why real-time flight control is needed. The restriction type is active restriction, forwarding restriction, or passive restriction. Each flow control reason in the flow control data corresponds to a unique start time, and its flow control description is the specific flow control measure. In this embodiment, flight information is mainly displayed by a flight list, such as... Figure 6 The flight list shown in this application includes date, flight number, departure airport, arrival airport, mission type, aircraft number, and current status. It can display the affected flights in the form of a flight list, which facilitates the overall scheduling and management of flights.

[0053] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any way. Those skilled in the art can readily implement the present invention based on the accompanying drawings and the description above. However, any modifications, alterations, or variations made by those skilled in the art without departing from the scope of the present invention, utilizing the disclosed technical content, are equivalent embodiments of the present invention. Furthermore, any equivalent changes, alterations, or variations made to the above embodiments based on the essential technology of the present invention are still within the protection scope of the present invention.

Claims

1. A method for analyzing flight operation status, characterized in that: Includes the following steps: Locate abnormal airports by determining them based on preset normality rate thresholds and normality rate index data for each airport within the monitoring area. Analyze the weather, query the real-time weather information corresponding to the abnormal airport from the meteorological query platform, and determine whether the abnormal airport is affected by the weather based on the real-time weather information corresponding to the abnormal airport. If yes, return to the step of locating the abnormal airport; otherwise, execute the step of filtering flow control points. Flow control points are filtered, and all pre-stored abnormal flights at abnormal airports are extracted. These pre-stored abnormal flights include pre-stored abnormal inbound flights and pre-stored abnormal outbound flights. Based on these pre-stored abnormal flights, corresponding abnormal flow control points are selected from the flow control point database. These abnormal flow control points include inbound flow control points and outbound flow control points. The screening of flow control points specifically includes the following steps: Extract abnormal flights, and extract the pre-stored abnormal inbound flights and pre-stored abnormal outbound flights corresponding to the abnormal airport; Investigate arrival and departure points, calculate the arrival on-time rate for each arrival point in the abnormal airport based on the pre-stored abnormal arrival flights, calculate the departure on-time rate for each departure point in the abnormal airport based on the pre-stored abnormal departure flights, and identify the arrival point with the lowest arrival on-time rate as the abnormal arrival point and the departure point with the lowest departure on-time rate as the abnormal departure point. Associate flow control points: Based on the pre-stored abnormal arrival flights corresponding to the abnormal arrival points, use several flow control points in the flow control point database as arrival flow control points and associate them with the abnormal arrival points; based on the pre-stored abnormal departure flights corresponding to the abnormal departure points, use several flow control points in the flow control point database as departure flow control points and associate them with the abnormal departure points. Output impact factors, including inbound and outbound impact factors. The abnormal flow control point with the highest number of pre-stored abnormal flights is designated as the maximum impact flow control point. The maximum impact flow control point includes the maximum impact inbound flow control point and the maximum impact outbound flow control point. The flow control data associated with the maximum impact flow control point is then output as the impact factor. The output impact factor includes the following steps: Analyze the flow control points and count the number of pre-stored abnormal inbound flights corresponding to each inbound flow control point and the number of pre-stored abnormal outbound flights corresponding to each outbound flow control point. The influencing factors are determined by identifying the inbound flow control point with the highest number of pre-stored abnormal inbound flights as the inbound flow control point with the highest number of pre-stored abnormal outbound flights as the outbound flow control point with the highest number of pre-stored abnormal outbound flights. The flow control data corresponding to the inbound flow control point with the highest influence is output as the inbound influencing factor, and the flow control data corresponding to the outbound flow control point with the highest influence is output as the outbound influencing factor.

2. The flight operation status analysis method as described in claim 1, characterized in that: When the arrival on-time rate for each arrival point in an abnormal airport is 100%, there are no abnormal arrival points in the abnormal airport. When the departure on-time rate for each departure point in an abnormal airport is 100%, there are no abnormal departure points in the abnormal airport.

3. The flight operation status analysis method as described in claim 1, characterized in that: The flow control data includes the flow control source unit, flow control reason, restriction type, flow control description, start time, and end time.

4. The flight operation status analysis method as described in claim 1, characterized in that: It also includes a normality rate display, which uses different colored charts to show the normality rate of arrivals for each arrival point and the normality rate of departures for each departure point.

5. The flight operation status analysis method as described in claim 4, characterized in that: The charts corresponding to departure and arrival on-time rates greater than 75% are green; the charts corresponding to departure and arrival on-time rates greater than 50% and less than or equal to 75% are yellow; the charts corresponding to departure and arrival on-time rates greater than 25% and less than or equal to 50% are orange; and the charts corresponding to departure and arrival on-time rates less than or equal to 25% are red.

6. An electronic device, characterized in that... include: processor; Memory; And a computer program, wherein the computer program is stored in the memory and configured to be executed by a processor, wherein when the program is executed by the processor, it implements a flight operation status analysis method as described in any one of claims 1-5.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements a flight operation status analysis method as described in any one of claims 1-5.

Images