Flight delay response grade evaluation method and central control equipment in airport

Abstract

The invention relates to the technical field of air transportation management, in particular to a flight delay response grade evaluation method and central control equipment in an airport, and the method comprises the steps: obtaining a delay feature evaluation index value based on flight actual operation data and flight plan data in a central control system in a specified airport; according to the response level interval information to which the delay feature evaluation indexes belong and the delay feature evaluation index values, an improved matter-element model is adopted for processing, and an association degree matrix and a weight sequence of the delay feature evaluation indexes and response levels are obtained; according to the correlation degree matrix and the weight sequence, obtaining an evaluation value of flight delay; and obtaining an evaluation result of the flight delay response level according to the evaluation value and the evaluation rule of the flight delay. According to the method, factors comprehensively considered by a large-area flight delay index system in different time periods are fully considered, optimization of a future development trend on a current early warning level judgment result is considered, and the current early warning level judgment result is dynamically corrected, so that an early warning decision has perspectiveness.

Description

Technical Field

[0001] This application relates to the field of air transport management technology, and in particular to a method for assessing flight delay response levels and a central control device within a designated airport. Background Technology

[0002] With the rapid development of the air transport industry and the continuous growth of flight volume, large-scale flight delays caused by factors such as severe weather, airspace flow control, and emergencies are becoming increasingly frequent. As a key node in the air transport network, the operational efficiency of large hub airports directly affects the normal operation of the entire air transport network. Failure to provide timely and accurate early warnings and responses to large-scale flight delays will not only cause huge economic losses but may also lead to passenger congestion, service complaints, and seriously affect airport operational order and service quality.

[0003] Currently, domestic and international research on flight delay early warning mainly focuses on flight delay prediction, airport flight delay risk warning, and traffic congestion identification. However, research on the assessment of large-scale flight delay response levels at major hub airports is relatively limited. Existing flight delay response level assessments often do not consider airport operating time periods. Airports typically set fixed warning thresholds based on experience, lacking a scientific and dynamic assessment mechanism that cannot adapt to changes in delay trends at different times and under different operating environments. The traditional criteria for evaluating large-scale flight delays are subjective, relying mainly on daily experience and lacking time-specificity, failing to consider the characteristics of different airport operating periods. The determination of delay response levels is also relatively simple, lacking a systematic approach and a clear, operational method for determining criteria. Furthermore, there is a lack of a dynamic correction mechanism; once the criteria are established, they cannot be adjusted and updated according to changes in actual conditions. Unreasonable criteria can lead to inaccurate delay response level assessments, resulting in untimely delay handling or unnecessary occupation of handling resources.

[0004] Meanwhile, delay response level assessment is essentially a multi-indicator comprehensive assessment problem. Traditional matter-element models, when the measured data for one or more indicators of the matter being assessed are outside the segment interval, may fail to obtain the correlation degree of the indicators or result in inconsistent representation scales of the correlation degree with different segment intervals after substituting the measured data into the proximity function. Furthermore, a defined boundary for the delay indicator judgment interval is beneficial for measuring and calculating the correlation degree and weight of delay indicators. Although there is a theoretical upper limit to the right boundary of the segment interval in actual production operations, due to the objective requirements of safe operation and the existence of large-scale flight delay handling mechanisms, it is impossible to allow delay indicators to approach this theoretical upper limit in actual operation. Therefore, the segment intervals in the matter-element model are often countable sets whose right boundaries cannot be completely determined. Traditional methods do not handle this situation well, often ignoring this lack of interval closure and forcibly giving a defined right boundary for the segment interval. This approach does not adequately adapt to the actual operational situation.

[0005] Therefore, there is an urgent need for an early warning technology solution that can combine airport time-slot characteristics, dynamically adjust judgment criteria, adapt to complex data scenarios, and incorporate future trend analysis to improve the scientificity and accuracy of decision-making in response to large-scale flight delays. Summary of the Invention

[0006] (a) Technical problems to be solved

[0007] In view of the above-mentioned shortcomings and deficiencies of the existing technology, this application provides a flight delay response level assessment method and airport central control equipment based on the improvement of the traditional matter-element model. It solves the technical problems of the existing technology, such as the strong subjectivity of the flight delay response level judgment standard, the lack of scientific evaluation mechanism and dynamic correction mechanism, and the inability to adapt to the changes in delay situation at different times and under different operating environments.

[0008] (II) Technical Solution

[0009] To achieve the above objectives, the main technical solutions adopted in this application include:

[0010] In a first aspect, embodiments of this application provide a method for assessing flight delay response levels, including:

[0011] S100. Based on the actual flight operation data and flight plan data in the central control system of the designated airport, obtain the values ​​of multiple delay characteristic evaluation indicators corresponding to the actual operation data and plan data.

[0012] S200. Based on the response level range information of each delay feature evaluation index, the values ​​of the delay feature evaluation index are processed using an improved matter-element model to obtain the correlation matrix between all delay feature evaluation indices and all response levels, and the weight sequence of all delay feature evaluation indices corresponding to each response level.

[0013] S300. Based on the correlation matrix and weight sequence, obtain the assessment value of flight delay;

[0014] S400. Based on the assessment value of flight delay and the given assessment rules, obtain the assessment result of the flight delay response level.

[0015] Optionally, in some embodiments of this application, step S100 includes:

[0016] Based on the flight operation status of the central control system within the designated airport, centered on the current time within a specified duration, obtain the corrected flight quantity for correcting at least one indicator;

[0017] The value of at least one delay characteristic evaluation index related to flight volume is adjusted based on the adjusted flight volume.

[0018] Optionally, in some embodiments of this application, step S100 further includes:

[0019] The first indicator for evaluating delay characteristics: the number of passenger flights that have not yet taken off and whose expected irregularity time exceeds 1 hour.

[0020] Based on the actual departure time, planned departure time, scheduled airport taxiing time, calculated departure time, and current time of departing flights, a first delay characteristic evaluation index value is obtained using predetermined calculation rules; this first delay characteristic evaluation index is an indicator related to flight volume.

[0021] The second delay characteristic evaluation indicator: the number of passenger flights that have not taken off due to irregularities exceeding 1 hour;

[0022] Based on the actual departure time, scheduled departure time, prescribed airport taxiing time, and current time of departing flights, a second delay characteristic evaluation index value is obtained using predetermined calculation rules; this second delay characteristic evaluation index is an indicator related to flight volume.

[0023] The third delay characteristic evaluation indicator: the number of departing passenger flights with irregularities exceeding 1 hour and no passengers boarding.

[0024] Based on the departure flight's start boarding time, scheduled departure time, scheduled airport ground taxiing time, and current time, the value of the third delay characteristic evaluation index is obtained using predetermined calculation rules.

[0025] The fourth delay characteristic evaluation indicator: the number of departing passenger flights that have been waiting on board for more than 1 hour without taking off;

[0026] Based on the actual departure time, boarding end time, cabin door closing time, prescribed airport ground taxiing time, and current time of departing flights, the fourth delay characteristic evaluation index value is obtained using predetermined calculation rules.

[0027] The fifth delay characteristic evaluation indicator: the number of departing flights temporarily cancelled within 2 hours;

[0028] Based on the flight status, scheduled departure time, cancellation time, and current time of departing flights, the value of the fifth delay characteristic evaluation index is obtained using predetermined calculation rules.

[0029] The sixth delay characteristic evaluation indicator: the total number of canceled inbound and outbound flights during the late arrival phase, i.e., from 22:00 to the end of the night flight;

[0030] Based on the flight status of departing flights, cancellation time and current time, estimated arrival time of arriving flights, total number of arriving flights, total number of departing flights, and number of departing cancellations, the sixth delay characteristic evaluation index value is obtained using predetermined calculation rules.

[0031] The seventh delay characteristic evaluation indicator: the number of passengers in the restricted area within the terminal building;

[0032] Based on the number of planned departing flights and the current time, future duration, planned departure time, and actual cabin door closing time, the value of the seventh delay characteristic evaluation index is obtained using predetermined calculation rules.

[0033] And / or,

[0034] Based on the flight operation status within a specified time period centered on the current time in the central control system of a designated airport, obtain the corrected flight quantity for correcting at least one indicator, including:

[0035] Based on the flight operation status, the departure capacity of flights departing in the next 30 minutes, the planned number of flights departing in the next 30 minutes, the actual departure time, the planned departure time, the estimated processing time for delayed flights, and the number of delayed flights, the corrected flight volume is obtained using preset calculation rules.

[0036] The calculation rules for all delay characteristic evaluation indicators are different.

[0037] Optionally, in some embodiments of this application, step S200 includes:

[0038] Obtain the correlation matrix according to the following formula (1);

[0039] Formula (1) is an expression for the correlation between the numerical values ​​of the delay characteristic evaluation index and the response level judgment interval:

[0040]

[0041] in, For the elements of the classical domain matter-element model in the improved matter-element model, the actual value sequence of the matter-element to be evaluated. , This represents the actual value of the i-th feature evaluation index in the object element to be evaluated; This represents the correlation value between the actual value of the i-th indicator in the object to be evaluated and the j-th delay response level determination interval. This represents the range of values ​​for the i-th index in the j-th classical domain matter element. ; This represents the maximum value of the left endpoint of the value range of the i-th index in the classical domain matter element; This represents the minimum value of the left endpoint of the range of values ​​for the i-th index in the classical domain matter element.

[0042] The correlation matrix is ​​as follows: .

[0043] Optionally, in some embodiments of this application, the weight sequence of each flight delay warning indicator corresponding to the response level j is obtained according to the following formula (2). ;

[0044] Formula (2) represents the weight of the correlation between the i-th delay feature evaluation index and different response levels:

[0045] ;

[0046] in:

[0047] ;

[0048] n represents the number of flight delay warning indicators;

[0049] This represents the maximum value of the left endpoint of the value range of the i-th index in the classical domain matter element;

[0050] and These represent the two endpoints of the value range of the i-th index in the segment element.

[0051] Optionally, in some embodiments of this application, the assessment value of flight delay is obtained according to the following formula (3);

[0052] Formula (3) is used to finally determine the degree of correlation between the object to be evaluated and the response level: ;

[0053] This represents the overall correlation of the j-th delay response level; Represents the object element to be evaluated; This represents the correlation value between the i-th indicator and the j-th delay response level in the object to be evaluated.

[0054] Optionally, in some embodiments of this application, step S400 includes:

[0055] A certain object to be evaluated The comprehensive correlation matrix of its evaluation value as a flight delay is as follows:

[0056] ;

[0057] but, The corresponding value is the final assessment result of the flight delay response level. The highest value indicates that the current delay situation is in a state of widespread yellow response level. The highest value indicates that the current delay situation is in a state of widespread red response.

[0058] Optionally, in some embodiments of this application, the method described in the above embodiments includes: updating the response level interval information to which each delay feature evaluation index belongs;

[0059] Specifically, historical operational data of designated airports and historical operational data of other airports, as well as flight delay warning duration and frequency information, are obtained. All data are statistically analyzed, and the probability distribution function of large-scale flight delay warning durations at airports nationwide is obtained.

[0060] Statistical iterative optimization is performed using probability distribution functions and historical operating data to obtain the response level range information for each updated delay feature evaluation index.

[0061] Secondly, embodiments of this application provide a central control device within a designated airport, comprising:

[0062] A memory and a processor, wherein a computer program is stored in the memory, and the processor executes the computer program in the memory and performs the method described in the above embodiments.

[0063] (III) Beneficial Effects

[0064] This application provides a method for assessing flight delay response levels and a central control device within an airport. Considering the characteristics of overnight flights and associated public transportation at large hub airports, it constructs a time-segmented method for assessing large-area flight delay response levels. Compared to existing technologies, this method optimizes the current warning response level judgment by considering airport operating time periods and future flight delay trends. The improved correlation and weight calculation methods effectively solve the engineering bottleneck problems of missing closure in response level judgment intervals and dimensional differences during the normalization of different indicator data leading to inaccurate representation of real-world significance. This significantly improves the engineering applicability and situational awareness accuracy of the large-area flight delay response level assessment method. Attached Figure Description

[0065] Figure 1 This is a flowchart of a flight delay response level assessment method according to this application;

[0066] Figure 2 This is the basic logical flow of a flight delay response level assessment method in one embodiment of this application;

[0067] Figure 3 This is a flowchart of a flight delay response level assessment method in one embodiment of this application;

[0068] Figure 4 This is a schematic diagram of a flight delay trend prediction model in one embodiment of this application;

[0069] Figure 5 This is a schematic diagram illustrating the correlation between the actual values ​​of the feature evaluation index and the delay response level determination interval in one embodiment of this application;

[0070] Figure 6 This is a schematic diagram illustrating the frequency distribution of large-scale flight delay warning durations at airports nationwide, as shown in one embodiment of this application.

[0071] Figure 7 This is a schematic diagram of the APP system interface in one embodiment of this application;

[0072] Figure 8 This is a schematic diagram of the PC-side system interface in one embodiment of this application. Detailed Implementation

[0073] To better explain and facilitate understanding of this application, a detailed description of its embodiments is provided below in conjunction with the accompanying drawings. While exemplary embodiments of this application are shown in the drawings, it should be understood that this application can be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a clearer and more thorough understanding of this application and to fully convey the scope of this application to those skilled in the art.

[0074] Example 1

[0075] Figure 1 This is a flowchart illustrating a method for assessing flight delay response levels according to this application. Figure 2 The basic logical process for issuing early warnings of large-scale flight delays at hub airports. Figure 3 This is a logic diagram of a flight delay response level assessment system according to this application. In this embodiment, the executing entity of the flight delay response level assessment method is the central control equipment within a designated airport, such as the central control equipment of Daxing Airport (i.e., the backend server) or any computing device running Daxing Airport data (e.g.,...). Figure 3 (e.g., China Airport Flight Management System (FOMS)). Figure 1 As shown, the method for assessing the flight delay response level includes:

[0076] S100, based on actual flight operation data and flight plan data from the central control system within the designated airport (i.e. Figure 2 (Based on airport operation data and related information), obtain the values ​​of multiple delay characteristic evaluation indicators corresponding to actual operation data and planned data.

[0077] Specifically, actual flight operation data and flight plan data refer to: flight number, arrival / departure flight identifier, planned arrival / departure time, estimated arrival / departure time, actual arrival / departure time, and origin / destination station.

[0078] Based on the flight operation status within a specified time period centered on the current time in the central control system of the designated airport, obtain the corrected flight volume for correcting at least one indicator.

[0079] The value of at least one delay characteristic evaluation index related to flight volume is adjusted based on the adjusted flight volume.

[0080] For example, the actual flight operation data and flight plan data in this embodiment can be the operation data for the current 24 hours and the flight plan data for the next 24 hours.

[0081] correspond Figure 2 In this embodiment, the selection of indicators can be based on pre-given indicator information.

[0082] S200. Based on the response level range information of each delay feature evaluation index, the values ​​of the delay feature evaluation index are processed using an improved matter-element model to obtain the correlation matrix between all delay feature evaluation indices and all response levels, and the weight sequence of all delay feature evaluation indices corresponding to each response level.

[0083] For example, the delay characteristic evaluation indicators in this embodiment can be five or seven. When there are five indicators in this embodiment, the actual operation data and flight plan data of night flights may not be included. When there are seven indicators, the actual operation data and flight plan data of night flights may be included. Thus, the method of this embodiment can solve the technical problem that it is difficult to disperse stranded passengers caused by flight delays or cancellations after the suspension of comprehensive transportation at night, which can easily lead to passenger conflicts.

[0084] For ease of expression, all delay characteristic evaluation indicators will be referred to simply as indicators.

[0085] The matter-element model can be an existing model. In this embodiment, in order to better associate it with the delay feature index and interval information, an improved matter-element model is adopted. For details, please refer to the description in Embodiment 4 below.

[0086] The criteria for determining the delay response level of each indicator are shown in Table 1 below. The determination intervals for each delay response level can be pre-defined or manually assigned when used for the first time. In subsequent uses, the pre-defined intervals can be dynamically corrected based on delay data from multiple airports.

[0087] S300. Based on the correlation matrix and weight sequence, obtain the assessment value of flight delay.

[0088] S400. Based on the assessment value of flight delay and the given assessment rules, obtain the assessment result of the flight delay response level.

[0089] Based on the flight delay response level assessment method in the above embodiments, this method takes into account the characteristics of nighttime flights and supporting public transportation at large hub airports, and considers the optimization effect of future operation trends on the current warning level, thereby improving the foresight of decision-making, ensuring the accuracy of the assessment results, and is applicable to different airports.

[0090] Example 2

[0091] To better understand the delay characteristic evaluation indicators in step S100 of Embodiment 1, the following detailed examples are provided using flight information from Daxing Airport. This embodiment uses seven delay characteristic evaluation indicators as examples, but it is not limited to only seven; the indicators can be set according to actual needs.

[0092] The first delay characteristic evaluation index (referred to as index 1): the number of passenger flights that have not taken off and whose expected irregularity time exceeds 1 hour;

[0093] Based on the actual departure time, planned departure time, scheduled airport taxiing time, calculated departure time, and current time of departing flights, a first delay characteristic evaluation index value is obtained using predetermined calculation rules; this first delay characteristic evaluation index is an indicator related to flight volume.

[0094] For Indicator 1, in this embodiment, flights that meet the following conditions are considered as flight information in Indicator 1:

[0095] (1) The flight identification number (FID) is “Departure (D)”;

[0096] (2) Actual departure time (ATOT) is empty;

[0097] (3) Calculate the takeoff time (CTOT) - scheduled departure time (STD) - scheduled airport taxi time (STT) > 60 min;

[0098] (4) When the calculated takeoff time (CTOT) is invalid, i.e., CTOT < current time, use: current time - scheduled departure time (STD) - scheduled airport ground taxi time (STT) > 60 min;

[0099] Calculation rules for the first delay characteristic evaluation index:

[0100] The expression for calculating the first delay characteristic evaluation index is as follows:

[0101] ;

[0102] Wherein, FID is the flight identifier ('D' indicates departure, 'A' indicates arrival), ATOT is the actual departure time (null if empty), CTOT is the calculated departure time, STD is the scheduled departure time, STT is the prescribed airport ground taxiing time, and T is the current time.

[0103] The second delay characteristic evaluation index (referred to as index 2): the number of passenger flights that have not taken off due to irregularity exceeding 1 hour;

[0104] Based on the actual departure time, scheduled departure time, prescribed airport taxiing time, and current time of departing flights, a second delay characteristic evaluation index value is obtained using predetermined calculation rules; this second delay characteristic evaluation index is an indicator related to flight volume.

[0105] For Indicator 2, in this embodiment, flights that meet the following conditions are considered as flight information in Indicator 2:

[0106] (1) The flight is identified as “Departure (D)”;

[0107] (2) Actual departure time (ATOT) is empty;

[0108] (3) Current time - Scheduled departure time (STD) - Scheduled airport ground taxiing time (STT) > 60 min;

[0109] Calculation rules for the second delay characteristic evaluation index:

[0110] The expression for calculating the second delay characteristic evaluation index is as follows:

[0111] ;

[0112] Where: FID is the flight identifier ('D' indicates departure, 'A' indicates arrival), ATOT is the actual departure time (null if empty), STD is the scheduled departure time, STT is the scheduled airport taxiing time, and T is the current time.

[0113] The above-mentioned indicators 1 and 2 belong to the corresponding flight information. In practice, given the dynamic diversion capacity of each airport, the above-mentioned indicators 1 and 2 can be dynamically modified to ensure the accuracy and timeliness of the evaluation results in the method of this embodiment.

[0114] For example, based on the flight operation status within a specified time period centered on the current time in the central control system of a designated airport, the corrected flight volume can be obtained to correct at least one indicator, including:

[0115] Based on the flight operation status, the departure capacity of flights departing in the next 30 minutes, the planned number of flights departing in the next 30 minutes, the actual departure time, the planned departure time, the estimated processing time for delayed flights, and the number of delayed flights, the corrected flight volume is obtained using preset calculation rules.

[0116] The third delay characteristic evaluation index (referred to as index 3): the number of departing passenger flights with irregularities exceeding 1 hour and no passengers boarding.

[0117] Based on the departure flight's start boarding time, scheduled departure time, scheduled airport ground taxiing time, and current time, the value of the third delay characteristic evaluation index is obtained using predetermined calculation rules.

[0118] For indicator 3, in this embodiment, flights that meet the following conditions are included as flight information in indicator 3:

[0119] (1) The flight is identified as “Departure (D)”;

[0120] (2) Start boarding time (ASBT) is empty;

[0121] (3) Current time - Scheduled departure time (STD) - Scheduled airport ground taxiing time (STT) > 60 min;

[0122] Calculation rules for the first delay characteristic evaluation index:

[0123] The expression for calculating the third delay characteristic evaluation index is as follows:

[0124] ;

[0125] Where: FID is the flight identifier ('D' indicates departure, 'A' indicates arrival), ASBT is the start boarding time (null if empty), STD is the scheduled departure time, STT is the scheduled airport ground taxiing time, and T is the current time.

[0126] The fourth delay characteristic evaluation index (referred to as index 4): the number of departing passenger flights that have been waiting on board for more than 1 hour without taking off;

[0127] Based on the actual departure time, boarding end time, cabin door closing time, prescribed airport ground taxiing time, and current time of departing flights, the fourth delay characteristic evaluation index value is obtained using predetermined calculation rules.

[0128] For indicator 4, in this embodiment, flights that meet the following conditions are considered as flight information in indicator 4: that is, flights that meet the following conditions:

[0129] (1) The flight is identified as “Departure (D)”;

[0130] (2) Actual departure time (ATOT) is empty;

[0131] (3) The boarding end time (AEBT) is not empty;

[0132] (4) Current time - door closing time (ATDC) - scheduled airport ground taxiing time (STT) > 60 min;

[0133] Fourth Delay Characteristic Evaluation Index Calculation Rules:

[0134] The expression for calculating the fourth delay characteristic evaluation index is as follows:

[0135] ;

[0136] Where: FID is the flight identifier ('D' indicates departure, 'A' indicates arrival), ATOT is the actual departure time (null if empty), AEBT is the boarding end time, ATDC is the door closing time, STT is the prescribed airport ground taxiing time, and T is the current time.

[0137] The fifth delay characteristic evaluation index (referred to as index 5): the number of departing flights temporarily cancelled within 2 hours;

[0138] Based on the flight status, scheduled departure time, cancellation time, and current time of departing flights, the value of the fifth delay characteristic evaluation index is obtained using predetermined calculation rules.

[0139] For indicator 5, in this embodiment, flights that meet the following conditions are considered as flight information in indicator 5:

[0140] (1) The flight is identified as “Departure (D)”;

[0141] (2) The flight status is "cancelled";

[0142] (3) Scheduled departure time (STD) - cancellation time < 120 min;

[0143] (4) Current time - cancellation time < 240 min;

[0144] Fifth Delay Feature Evaluation Index Calculation Rules:

[0145] The expression for calculating the fifth delay characteristic evaluation index is as follows:

[0146] ;

[0147] Where: FID is the flight identifier ('D' indicates departure, 'A' indicates arrival), STU is the flight status ('cancel' indicates cancellation), STD is the scheduled departure time, T is the current time, and Tc is the cancellation time.

[0148] The sixth delay characteristic evaluation index (hereinafter referred to as index 6): the total number of canceled inbound and outbound flights during the late arrival phase, i.e., from 22:00 to the end of the night flight;

[0149] Based on the flight status of departing flights, cancellation time and current time, estimated arrival time of arriving flights, total number of arriving flights, total number of departing flights, and number of departing cancellations, the sixth delay characteristic evaluation index value is obtained using predetermined calculation rules.

[0150] For indicator 6, in this embodiment, flights that meet the following conditions are included as flight information in indicator 6:

[0151] (1) Departure cancellation = Flight identification is “Departure (D)”, and flight status is “Cancelled (cancel)”, and 22:00 < cancellation time < 06:00 the next day;

[0152] (2) The inbound flight is identified as “Inbound (A)”, with an estimated arrival time (ELDT);

[0153] (3) Indicator 6 = number of departing flights cancelled + number of inbound flights that meet the criteria of {current time < estimated arrival time (ELDT) < current time + 60 min};

[0154] The calculation rules for the sixth delay characteristic evaluation index are as follows:

[0155] The expression for calculating the sixth delay characteristic evaluation index is as follows:

[0156] ;

[0157] ;

[0158] Where: FID is the flight identifier ('D' indicates departure, 'A' indicates arrival), STU is the flight status ('cancel' indicates cancellation), CT is the cancellation time, ELDT is the estimated arrival time, T is the current time, m is the total number of arrival flights, n is the total number of departure flights, and N is the total number of arrival flights. c Flights departing from the port were cancelled.

[0159] The seventh delay characteristic evaluation indicator (referred to as indicator 7): the number of passengers in the restricted area within the terminal building;

[0160] Based on the number of planned departing flights and the current time, future duration, planned departure time, and actual cabin door closing time, the value of the seventh delay characteristic evaluation index is obtained using predetermined calculation rules.

[0161] The calculation rules for the seventh delay characteristic evaluation index are as follows:

[0162] The expression for calculating the seventh delay characteristic evaluation index is as follows:

[0163] ;

[0164] ;

[0165] Where: SDF represents the planned number of departing flights, and T represents the current time. For future duration, T actal FR represents the actual door closing time, STD represents the scheduled departure time, and ATDC represents the actual door closing time.

[0166] Based on the seven delay characteristic evaluation indicators obtained above, response levels are classified.

[0167] Based on the number of flight delays and their impact on operations, this embodiment classifies responses into yellow, orange, and red responses, from lowest to highest severity. Simply put, a yellow response indicates a significant impact on airport operations; an orange response indicates a serious impact; and a red response indicates extremely serious flight delays. For each response level, the airport, airlines, and relevant support units should take corresponding measures to minimize the impact of flight delays on passengers. Based on airport support capabilities and past experience, the judgment ranges for each delay response level are shown in Table 1.

[0168] Table 1. Judgment Intervals for Each Delay Response Level

[0169]

[0170] Currently, the criteria for determining flight delay response levels mainly rely on daily experience, and the judgment criteria are highly subjective. The aforementioned delay characteristic evaluation index system takes into account the operational characteristics of large hub airports. The calculation rules for all delay characteristic evaluation indicators are different. By combining airport support capabilities and past support experience, delay response level standards are set to meet the needs of stable airport operation.

[0171] Example 3

[0172] To better understand the corrected flight volume in S100 of Embodiment 1, this embodiment, in conjunction with the accompanying drawings, explains the specific steps for correcting the value of at least one delay feature evaluation index associated with the flight volume among multiple delay feature evaluation indicators based on the corrected flight volume.

[0173] In some embodiments, based on the flight operation status within a specified time period centered on the current time in the central control system of a designated airport, the corrected flight quantity for correcting at least one indicator is obtained, including:

[0174] Based on the flight operation status, the departure capacity of flights departing in the next 30 minutes, the planned number of flights departing in the next 30 minutes, the actual departure time, the planned departure time, the estimated processing time for delayed flights, and the number of delayed flights, the following calculation rules are used to obtain the corrected flight volume.

[0175] Obtaining the corrected flight volume is based on the analysis of future trends to correct the current delay situation, ensuring that the assessment results are more scientific and reasonable. This application mainly uses the corrected flight volume as a correction parameter for the large-area flight delay characteristic evaluation index, to correct large-area flight delay characteristic evaluation index 1 and index 2.

[0176] The number of delayed flights includes all scheduled but not yet departed flights as of the current time, which will need to be processed and executed later. A flight delay trend prediction chart based on corrected parameters is shown below. Figure 4As shown, with the middle horizontal line as the boundary, the upper half represents departures and the lower half represents arrivals. Using the current timeline (virtual vertical axis) as the boundary, the flight operation status is projected backwards and forwards within a certain time range, such as the planned and actual flight volume for the previous hour, and the planned and projected flight volume for the next hour. Figure 4 In the diagram, the number of delayed flights is the area difference between the planned departure flights and the actual departure flights shown on the line graph before the current time.

[0177] The estimated time to clear delayed flights is the time required to completely clear all delayed flights based on the current flight capacity.

[0178] ;

[0179] ;

[0180] ;

[0181] in: To correct for flight volume, NN represents the number of delayed flights, TT represents the estimated processing time for delayed flights, STD represents the scheduled departure time, and ATOT represents the actual departure time (null if empty). To ensure the departure capacity of flights in the next 30 minutes, This represents the planned number of departing flights in the next 30 minutes.

[0182] In this embodiment, based on future trends, the delay indicator parameters are adjusted using modified flight volume, enhancing the foresight of the assessment decision and ensuring a more scientific and reasonable assessment result. Compared to existing technologies, this approach optimizes the current early warning response level judgment by considering airport operating time periods and future flight delay trends. It improves the engineering applicability and situational awareness accuracy of large-scale flight delay response level assessment methods, enabling dynamic adjustments based on the current situation to adapt to changes in delay trends under different time periods and operating environments.

[0183] Example 4

[0184] In the implementation of this embodiment, the problem of flight delay response level assessment is reduced to a multi-index evaluation and analysis problem. In this embodiment, the basic model of traditional matter-element theory is used, and the improved correlation and weight calculation method of this application is applied to process the various indicators and the judgment intervals of each delay response level obtained in the aforementioned embodiment 2; the matter-element model corresponding to the seven indicators is used for illustration.

[0185] S201, based on the aforementioned seven indicators, construct the classical domain, the section domain, and the object element to be judged;

[0186] The classical domain is determined based on the characteristics of the matter-element being evaluated and its value range. Assuming the evaluation level is divided into m levels, then... To represent the j-th level, use Let represent the evaluation index for the i-th feature. In this embodiment, there are evaluation indices for 7 features. This represents the range of values ​​for the i-th feature evaluation index at the j-th level. The range of values ​​for this range is represented by the interval [interval name missing]. This indicates that a classical domain matter element is formed by combining N, c, and v in ordered triplet form. :

[0187] ;

[0188] definition Let the range of values ​​for the domain element with respect to the i-th feature evaluation index be: ;

[0189] Among them, the range of values It is obtained by taking the union of all value intervals of the i-th feature evaluation index, that is... .

[0190] Then the domain element The expression is:

[0191] ;

[0192] The object to be evaluated is defined as:

[0193] ;

[0194] in, Let be the actual value of the evaluation index for the i-th feature of the object to be evaluated.

[0195] S202, Obtain the correlation matrix according to the following formula;

[0196] This formula expresses the correlation between the numerical values ​​of delay characteristic evaluation indicators and the response level determination interval:

[0197] ;

[0198] in, For the elements of the classical domain matter-element model in the improved matter-element model, the actual value sequence of the matter-element to be evaluated. ; This represents the actual value of the i-th feature evaluation index in the object element to be evaluated; This represents the correlation value between the actual value of the i-th indicator in the object to be evaluated and the j-th delay response level determination interval. This represents the range of values ​​for the i-th index in the j-th classical domain matter element. ; This represents the maximum value of the left endpoint of the value range of the i-th index in the classical domain matter element; This represents the minimum value of the left endpoint of the range of values ​​for the i-th index in the classical domain matter element.

[0199] The correlation matrix is ​​as follows: .

[0200] S203, obtain the weight sequence of each flight delay warning indicator corresponding to response level j according to the following formula. ;

[0201] This formula represents the weight of the correlation between the i-th delay characteristic evaluation index and different response levels:

[0202] ;

[0203] in:

[0204] ;

[0205] n represents the number of flight delay warning indicators;

[0206] This represents the maximum value of the left endpoint of the value range of the i-th index in the classical domain matter element;

[0207] This represents the left endpoint value of the value range of the i-th index in the domain element;

[0208] This represents the right endpoint value of the range of values ​​for the i-th index in the domain element.

[0209] Based on the description of steps 201 to 203 above, in step S300, the assessment value of flight delay is obtained according to the correlation matrix and weight sequence, which can be specifically explained as follows:

[0210] The assessment value of flight delay is obtained using the following formula;

[0211] This formula is used to ultimately determine the degree of correlation between the object element to be evaluated and the response level: ;

[0212] in: This represents the overall correlation of the j-th delay response level; Represents the object element to be evaluated; This represents the correlation value between the i-th indicator and the j-th delay response level in the object to be evaluated.

[0213] Optionally, taking indicator 1 as an example, the method for determining the correlation between the actual value of a certain feature evaluation indicator and the interval for determining the delay response level is as follows: Figure 5 As shown:

[0214] Closed interval: The closer to the midpoint of the interval, the greater the correlation with the interval;

[0215] Non-closed intervals: The farther away from the left boundary of the interval, the greater the correlation with the interval;

[0216] In the table of judgment intervals for each delay response level, closed intervals are yellow and orange responses, and open intervals are red responses.

[0217] The judgment is based on the position of the indicator value relative to the entire range, to avoid the indicator being too large or abnormal trends being overweighted, which would lead to imbalance.

[0218] This embodiment addresses the bottlenecks in traditional assessment models, namely, the distortion of correlation and weight calculations after normalization due to differences in the dimensions of characteristic indicators, and the non-closure problem caused by the ambiguity of the right boundary of the judgment interval. It improves the traditional matter-element model and refines the calculation method of the comprehensive correlation between flight delay degree and various delay response levels. At the same time, it uses variable weight theory to construct an indicator weight calculation method, which improves the applicability of the assessment model and the accuracy of situational awareness.

[0219] To better understand the evaluation results in step S400 above, the evaluation results will be explained in detail below with reference to the weighting formula given above.

[0220] A certain object to be evaluated The comprehensive correlation matrix of its evaluation value as a flight delay can be:

[0221] ;

[0222] In practice That is, the final evaluation result, The corresponding value is the final assessment result of the flight delay response level. The highest value indicates that the current delay situation is in a state of widespread yellow response level. The highest value indicates that the current delay situation is in a state of widespread red response.

[0223] To further ensure accuracy and robustness of results, the method in this embodiment may further include updating the aforementioned manually given interval information.

[0224] For example, by updating the response level range information to which each delay feature evaluation indicator belongs, the criteria for judging the delay response level indicator can be optimized.

[0225] Specifically, historical operational data of designated airports (such as Daxing Airport) and historical operational data of other airports, as well as information on the frequency of flight delay warnings, are obtained. All data are statistically analyzed, and the probability distribution function of the duration of large-scale flight delay warnings at airports across the country is obtained.

[0226] In this embodiment, a probability distribution function and historical operational data can be used to dynamically fit the delay distribution pattern, and the flight delay response level judgment criteria can be statistically iterated and optimized to obtain the response level interval information of each delayed feature evaluation index, thereby reducing human influence.

[0227] The optimization approach and functional logic for the delay response level assessment criteria in this embodiment are as follows:

[0228] Data information including specific airports issuing delay warnings, the month of the warning, the duration of the warning, and the reason for the warning can be obtained from the Civil Aviation Administration's air traffic network. Taking large-scale flight delay warning data from all major airports in China over two years as an example, after comprehensive processing, the results show that the frequency distribution of the duration of large-scale flight delay warnings conforms to a log-normal distribution. Figure 6 As shown.

[0229] The probability distribution function of the duration of large-scale flight delay warnings at airports nationwide was obtained through fitting:

[0230] ;

[0231] in, It is the probability density function of the log-normal distribution model for a warning delay duration of t; These are the undetermined parameters for the log-normal distribution model.

[0232] Similarly, in this embodiment, historical operational data is used as data for review and analysis. Subsequently, the review and analysis data is periodically input into the delay response level assessment model to obtain early warning delay duration data. The statistical analysis output results are then fitted to obtain the corresponding probability distribution model parameters. The deviation value between the two parameters is defined as:

[0233] , ;

[0234] The above formula is used to continuously iterate and calculate until the deviation between the final fitted parameter result and the distribution parameter of the national historical delay data is within a given threshold range. Then the iteration optimization stops, thereby realizing the update of the judgment standard for delay response level index.

[0235] This embodiment dynamically fits the delay distribution pattern based on historical data and iteratively optimizes the judgment criteria, reducing human influence and significantly improving the scientific, accurate, and timely nature of large hub airports in dealing with large-scale flight delays. It has high practical value and promising prospects for promotion.

[0236] Example 5

[0237] This embodiment also provides a central control device within a designated airport, comprising: a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program in the memory and performs any of the methods described in the above embodiments.

[0238] Specifically, the central control device is deployed on both a mobile app and a PC. Based on the solution implemented in this embodiment, it can display various evaluation results on the display screen, thereby providing further response suggestions, such as... Figure 7 , Figure 8 As shown, the system displays interface content that provides decision-making reference suggestions for airport operation and management personnel. At the same time, the display terminal in this embodiment can also issue delay response level instructions through the system, visualize the current airport delay situation, and record process data for easy review and analysis later.

[0239] In the description of this application, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0240] In this application, unless otherwise expressly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0241] In this application, unless otherwise expressly specified and limited, "above" or "below" the second feature can mean that the first and second features are in direct contact, or that they are in indirect contact through an intermediate medium. Furthermore, "above," "on top of," and "over" the second feature can mean that the first feature is directly above or diagonally above the second feature, or simply that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" the second feature can mean that the first feature is directly below or diagonally below the second feature, or simply that the first feature is at a lower horizontal level than the second feature.

[0242] In the description of this specification, the terms "one embodiment," "some embodiments," "embodiment," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0243] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make modifications, alterations, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A method for assessing flight delay response levels, characterized in that, include: S100. Based on the actual flight operation data and flight plan data in the central control system of the designated airport, obtain the values ​​of multiple delay characteristic evaluation indicators corresponding to the actual operation data and plan data. S200. Based on the response level range information of each delay feature evaluation index, the values ​​of the delay feature evaluation index are processed using an improved matter-element model to obtain the correlation matrix between all delay feature evaluation indices and all response levels, and the weight sequence of all delay feature evaluation indices corresponding to each response level. S300. Obtain the assessment value of flight delay based on the correlation matrix and the weight sequence; S400. Based on the assessment value of the flight delay and the given assessment rules, obtain the assessment result of the flight delay response level.

2. The method according to claim 1, characterized in that, The S100 further includes: Based on the flight operation status of the central control system within the designated airport, centered on the current time within a specified duration, obtain the corrected flight quantity for correcting at least one indicator; The value of at least one delay feature evaluation index associated with flight volume is corrected based on the corrected flight volume.

3. The method according to claim 2, characterized in that, S100 includes: The first indicator for evaluating delay characteristics: the number of passenger flights that have not yet taken off and whose expected irregularity time exceeds 1 hour. Based on the actual departure time, planned departure time, scheduled airport taxiing time, calculated departure time, and current time of departing flights, a first delay characteristic evaluation index value is obtained using predetermined calculation rules; this first delay characteristic evaluation index is an indicator related to flight volume. The second delay characteristic evaluation indicator: the number of passenger flights that have not taken off due to irregularities exceeding 1 hour; Based on the actual departure time, scheduled departure time, prescribed airport taxiing time, and current time of departing flights, a second delay characteristic evaluation index value is obtained using predetermined calculation rules; this second delay characteristic evaluation index is an indicator related to flight volume. The third delay characteristic evaluation indicator: the number of departing passenger flights with irregularities exceeding 1 hour and no passengers boarding. Based on the departure flight's start boarding time, scheduled departure time, scheduled airport ground taxiing time, and current time, the value of the third delay characteristic evaluation index is obtained using predetermined calculation rules. The fourth delay characteristic evaluation indicator: the number of departing passenger flights that have been waiting on board for more than 1 hour without taking off; Based on the actual departure time, boarding end time, cabin door closing time, prescribed airport ground taxiing time, and current time of departing flights, the fourth delay characteristic evaluation index value is obtained using predetermined calculation rules. The fifth delay characteristic evaluation indicator: the number of departing flights temporarily cancelled within 2 hours; Based on the flight status, scheduled departure time, cancellation time, and current time of departing flights, the value of the fifth delay characteristic evaluation index is obtained using predetermined calculation rules.

4. The method according to claim 3, characterized in that, The S100 further includes: The sixth delay characteristic evaluation indicator: the total number of canceled inbound and outbound flights during the late arrival phase, i.e., from 22:00 to the end of the night flight; Based on the flight status of departing flights, cancellation time and current time, estimated arrival time of arriving flights, total number of arriving flights, total number of departing flights, and number of departing cancellations, the sixth delay characteristic evaluation index value is obtained using predetermined calculation rules. The seventh delay characteristic evaluation indicator: the number of passengers in the restricted area within the terminal building; Based on the number of planned departing flights and the current time, future duration, planned departure time, and actual cabin door closing time, the value of the seventh delay characteristic evaluation index is obtained using predetermined calculation rules. And / or, Based on the flight operation status within a specified time period centered on the current time in the central control system of a designated airport, obtain the corrected flight quantity for correcting at least one indicator, including: Based on the flight operation status, the departure capacity of flights departing in the next 30 minutes, the planned number of flights departing in the next 30 minutes, the actual departure time, the planned departure time, the estimated processing time for delayed flights, and the number of delayed flights, the corrected flight volume is obtained using preset calculation rules. The calculation rules for all delay characteristic evaluation indicators are different.

5. The method according to claim 1, characterized in that, Obtain the correlation matrix according to the following formula (1); Formula (1) is an expression for the correlation between the numerical values ​​of the delay characteristic evaluation index and the response level judgment interval: Among them, v ji For the elements of the classical domain matter-element model in the improved matter-element model, the actual value sequence of the matter-element to be evaluated is: , This represents the actual value of the i-th feature evaluation index in the object element to be evaluated; This represents the correlation value between the actual value of the i-th indicator in the object to be evaluated and the j-th delay response level determination interval. This represents the range of values ​​for the i-th index in the j-th classical domain matter element. ; This represents the maximum value of the left endpoint of the value range of the i-th index in the classical domain matter element; This represents the minimum value of the left endpoint of the range of values ​​for the i-th index in the classical domain matter element. The correlation matrix is ​​as follows: .

6. The method according to claim 5, characterized in that, The weight sequence of each flight delay warning indicator corresponding to response level j is obtained according to the following formula (2). ; Formula (2) represents the weight of the correlation between the i-th delay feature evaluation index and different response levels: ； in: ； n represents the number of flight delay warning indicators; This represents the maximum value of the left endpoint of the value range of the i-th index in the classical domain matter element; and These represent the two endpoints of the value range of the i-th index in the segment element.

7. The method according to claim 6, characterized in that, The assessment value of flight delay is obtained according to the following formula (3); Formula (3) is used to finally determine the degree of correlation between the object to be evaluated and the response level: ; in, This represents the overall correlation of the j-th delay response level; Represents the object element to be evaluated; This represents the correlation value between the i-th indicator and the j-th delay response level in the object to be evaluated.

8. The method according to claim 7, characterized in that, The S400 includes: A certain object to be evaluated The comprehensive correlation matrix of its evaluation value as a flight delay is as follows: but, The corresponding value is the final assessment result of the flight delay response level. The highest value indicates that the current delay situation is in a state of widespread yellow response level. The highest value indicates that the current delay situation is in a state of widespread red response.

9. The method according to any one of claims 1 to 8, characterized in that, The method includes: updating the response level range information to which each delay feature evaluation index belongs; Specifically, historical operational data of designated airports and historical operational data of other airports, as well as flight delay warning duration and frequency information, are obtained. All data are statistically analyzed, and the probability distribution function of large-scale flight delay warning durations at airports nationwide is obtained. Statistical iterative optimization is performed using probability distribution functions and historical operating data to obtain the response level range information for each updated delay feature evaluation index.

10. A central control device within a designated airport, characterized in that, include: A memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program in the memory and performs the method according to any one of claims 1 to 9.

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