Navigation notice sorting method and device
By extracting and processing flight and announcement data from historical data, and analyzing the correlation and timeliness of announcement characteristics and statistical indicators using the ANOVA method, the problem of difficulty in dealing with disorderly classified data in the existing technology is solved, the accuracy and efficiency of navigation announcement sorting is improved, and the actual operational requirements of the sorting results and the safety of aviation operation are ensured.
Patent Information
- Application Number
- CN202411658909.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-20
- Publication Date
- 2025-03-21
AI Technical Summary
The existing navigation notice sorting method is difficult to deal with disorderly classification data, causing the sorting results to deviate from actual operational needs, affecting the operational efficiency, decision-making accuracy and flight safety of the airline.
By selecting flight data and valid announcement data affected by announcements from the historical data set, excluding the delay impact of previous flight abnormalities, recalculate the effective time window for cancelled reports and substituted reports, extract the announcement characteristics, and match the flight and announcement data according to the association rules. The ANOVA method is used to analyze the correlation and timeliness of announcement characteristics and statistical indicators, and sort them according to the announcement priority and analysis results.
It improves the accuracy and efficiency of navigation announcement sorting, makes the sorting results more in line with actual operational needs, helps business personnel to better decide on the order of notification processing under the accumulation of multiple announcements, and improves the safety of aviation operations.
Smart Images
Figure CN119671112A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of aviation data processing, and in particular to a method and device for sorting NOTAMs. Background Art
[0002] The existing NOTAM sorting method usually uses the grey correlation analysis method, which requires that both the parent sequence and the child sequence are quantitative data. However, in the characteristics of NOTAM, there are disordered categorical data that are difficult to process using the grey correlation analysis method, such as the sending unit, notification series, and important notification type, which causes the NOTAM sorting results to deviate from the actual operational needs, thereby having a negative impact on the airline's operational efficiency, decision-making accuracy, and flight safety. Summary of the invention
[0003] The present invention provides a method and device for sorting navigation notices, so as to solve the problem that the prior art is difficult to process disordered classified data, improve the accuracy and efficiency of sorting navigation notices, and make the sorting results more in line with actual operation needs.
[0004] To achieve the above object, an embodiment of the present invention provides a method for sorting navigation notices, comprising:
[0005] Select the first flight data that is affected by the notice and causes flight abnormality from the historical data set, eliminate the delay effect caused by the previous flight abnormality for the first flight data, and perform delay processing on the canceled flights and alternate flights to obtain the second flight data;
[0006] Selecting valid notification data from the historical data set as first notification data, recalculating the notification valid time window of the cancellation report and the replacement report in the first notification data, obtaining second notification data and extracting notification features;
[0007] The second flight data and the second announcement data are matched according to the association rules, the matching results are grouped, and the correlation and timeliness of the announcement characteristics and statistical indicators of each group are analyzed based on the ANOVA method, and the announcements are sorted according to the announcement priority and the analysis results.
[0008] As an improvement of the above scheme, the factors affected by the notification include military activity factors, airport factors, air traffic control factors and weather factors.
[0009] As an improvement to the above solution, the elimination of the delay caused by the abnormality of the preceding flight includes:
[0010] For flights affected by abnormalities of previous flights, the scheduled take-off time of the flight will be adjusted according to the original scheduled take-off time and the previous delay time, and the scheduled landing time of the flight will be adjusted according to the actual take-off time and the planned flight time.
[0011] As an improvement to the above solution, the delay processing of canceled flights and alternate flights includes:
[0012] For the cancelled flights, the scheduled departure time is the original scheduled departure time plus the number of minutes of advance cancellation, and the flight delay is calculated based on the advance cancellation time, that is, if the flight is cancelled on the day of departure and within 6 hours in advance, the flight delay is 12 hours; if the flight is cancelled on the day of departure but more than 6 hours before departure, the flight delay is 6 hours; if the flight is cancelled one day before departure, the flight delay is 3 hours; if the flight is cancelled two days before departure, the flight delay is 2 hours; if the flight is cancelled three days or more before departure, the flight delay is 1 hour;
[0013] The flight delay for the alternate flight was 12 hours.
[0014] As an improvement of the above solution, the recalculating the notification valid time window of the cancellation report and the replacement report in the first notification data includes:
[0015] For the replacement report, the replacement report effective time is used as the start time of the notification effective time window, and the replacement report expiration time is used as the end time of the notification effective time window;
[0016] For the cancellation report, the minimum value between the original notification effective time and the cancellation report effective time is selected as the start time of the notification effective time window, and the expiration of the original notification is used as the end time of the notification effective time window.
[0017] As an improvement of the above scheme, the notification characteristics include flight information region determination characteristics, notification subject, notification effective time difference, messaging unit, notification sequence code, flight type and issuance purpose.
[0018] As an improvement of the above solution, matching the second flight data with the second announcement data according to the association rule includes:
[0019] If the affected flight area announced in the second notification data is a flight information region, first match the airports in the flight information region, and then match the flights with the airport as the departure airport or arrival airport in the second flight data;
[0020] If the affected flight area announced in the second announcement data is an airport, matching flights in the second flight data that use the airport as a departure airport or an arrival airport;
[0021] If the scheduled departure time or the scheduled landing time of the flight is within the effective time window of the announcement, the flight is matched with the announcement to obtain a matching result.
[0022] As an improvement to the above solution, the matching results are grouped and the correlation and timeliness of the notification features and statistical indicators of each group are analyzed based on the ANOVA method, and the notifications are sorted according to the notification priority and the analysis results, including:
[0023] Calculate the departure time difference between the start time of the notified effective time window and the scheduled departure time of the flight that matches it, and calculate the arrival time difference between the scheduled landing time of the flight and the end time of the notified effective time window that matches it;
[0024] Divide 24 hours into increasing time intervals, and group the matching results according to which time interval their departure time difference or arrival time difference belongs to;
[0025] Based on the ANOVA method, the correlation and timeliness of the notification characteristics and statistical indicators of each notification within each time interval are analyzed, and the notifications are first sorted from high to low according to their priority, and then the notifications of the same priority are sorted from high to low according to the correlation and timeliness to obtain the notification sorting result.
[0026] As an improvement of the above solution, the statistical indicators include the average delay duration of flights affected by each notice and the number of delayed flights per unit time.
[0027] To achieve the above-mentioned purpose, an embodiment of the present invention further provides a navigation notice sorting device, comprising:
[0028] A flight data processing module is used to select the first flight data that is affected by the notice and causes flight abnormality from the historical data set, eliminate the delay effect caused by the previous flight abnormality for the first flight data, and perform delay processing on the canceled flights and alternate flights to obtain the second flight data;
[0029] A notification data processing module, used to select valid notification data from the historical data set as first notification data, recalculate the notification effective time window of the cancellation report and the replacement report in the first notification data, obtain the second notification data and extract notification features;
[0030] The data association and grouping sorting module is used to match the second flight data and the second announcement data according to the association rules, group the matching results and analyze the correlation and timeliness of the announcement characteristics and statistical indicators of each group based on the ANOVA method, and sort the announcements according to the announcement priority and analysis results.
[0031] To achieve the above objectives, an embodiment of the present invention further provides a computer device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor implements the above-mentioned navigation notice sorting method when executing the computer program.
[0032] To achieve the above-mentioned purpose, an embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium includes a stored computer program; wherein, when the computer program is running, the device where the computer-readable storage medium is located controls to execute the above-mentioned navigation notice sorting method.
[0033] To achieve the above-mentioned purpose, an embodiment of the present invention further provides a computer program product, which includes a computer program; wherein, when the computer program is running, it controls the device where the program product is located to execute the above-mentioned navigation notice sorting method.
[0034] Implementing the embodiments of the present invention has the following beneficial effects:
[0035] The method and device for sorting navigation notices provided in the embodiment of the present invention selects the first flight data which is affected by the notice and causes flight abnormality from the historical data set, eliminates the delay effect caused by the abnormality of the preceding flight for the first flight data, and performs delay processing on the canceled flights and the alternate flights to obtain the second flight data; selects the valid notification data from the historical data set as the first notification data, recalculates the notification effective time window of the cancellation report and the replacement report in the first notification data, obtains the second notification data and extracts the notification features; matches the second flight data with the second notification data according to the association rule, groups the matching results and sorts them based on AN The OVA method analyzes the correlation and timeliness of the notification characteristics of each group with the statistical indicators, and sorts the notifications according to the notification priority and analysis results. It can not only process unordered classified data, improve the accuracy and efficiency of navigation notification sorting, and make the sorting results more in line with actual operational needs, but also locate the notification characteristics with high correlation and high timeliness, which helps business personnel to better decide the order of notification processing when facing multiple notifications, thereby improving the safety of aviation operations. BRIEF DESCRIPTION OF THE DRAWINGS
[0036] Figure 1 is a flow chart of a method for sorting NOTAMs provided by an embodiment of the present invention;
[0037] Figure 2 is a structural block diagram of a navigation notice sorting device provided by an embodiment of the present invention;
[0038] Figure 3 It is a structural block diagram of a computer device provided by an embodiment of the present invention. DETAILED DESCRIPTION
[0039] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.
[0040] In the description of the present invention, it should be noted that the step numbers in the text are only for the convenience of explaining the specific embodiments and do not serve to limit the order in which the steps are executed. The terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features.
[0041] As one of the important dynamic information in the aviation field, NOTAM has the characteristics of high concurrency, large-scale reporting, complex processing and high real-time requirements. How to handle NOTAM in a timely and accurate manner is a major challenge facing airlines. With the continuous development of the aviation industry, the increase in the number of NOTAMs and the increase in the complexity of their content have brought great pressure to the operations of airlines, especially when dealing with peak demand, which may affect the safety of flights.
[0042] See also Figure 1 , Figure 1 : is a flow chart of a method for sorting NOTAMs provided by an embodiment of the present invention. The method for sorting NOTAMs includes steps S1 to S3, which are specifically as follows:
[0043] S1. Select the first flight data that is affected by the notice and causes flight abnormality from the historical data set, eliminate the delay effect caused by the previous flight abnormality from the first flight data, and perform delay processing on the canceled flights and alternate flights to obtain the second flight data;
[0044] S2, selecting valid notification data from the historical data set as the first notification data, recalculating the notification valid time window of the cancellation report and the replacement report in the first notification data, obtaining the second notification data and extracting the notification features;
[0045] S3. Match the second flight data and the second announcement data according to the association rules, group the matching results, and analyze the correlation and timeliness of the announcement characteristics and statistical indicators of each group based on the ANOVA method, and sort the announcements according to the announcement priority and the analysis results.
[0046] It can be understood that the notification data that is replaced, cancelled, or has no data in the historical data set is deleted, and the valid notification data is selected as the first notification data.
[0047] The present invention is based on the assumption that "the earlier or later notification processing time will positively affect the overall delay situation of its associated flights".
[0048] In an embodiment of the present invention, valid flight data and notice data are first extracted from a historical data set, and data processing is performed separately. Then, the processed notice data and the flight data affected by them are associated and grouped. Finally, the correlation between notice features and statistical indicators is analyzed using the ANOVA method, and notice sorting is implemented in combination with notice priority. This not only can process unordered classified data, improve the accuracy and efficiency of sorting of navigation notices, and make the sorting results more in line with actual operational needs, but also can locate notice features with high relevance and high timeliness. This not only can process unordered classified data, improve the accuracy and efficiency of sorting of navigation notices, and make the sorting results more in line with actual operational needs, but also can locate notice features with high relevance and high timeliness, which helps business personnel to better decide the order of notice processing when facing multiple notices, thereby improving the safety of aviation operations.
[0049] The present invention fully considers the types (categorized, disordered) of most features of the notification data, and uses the ANOVA method to analyze the correlation between various types of independent variables and continuous dependent variables, thus making up for the deficiency that the prior art can only analyze continuous independent variables.
[0050] In an optional embodiment, the factors affected by the announcement include military activity factors, airport factors, air traffic control factors and weather factors.
[0051] It is understandable that according to the “Classification of Abnormal Causes Defined by the General Administration”, flights whose influencing factors are not related to the announcement are deleted, and the data of the first flight that was affected by the announcement and caused flight abnormalities are selected.
[0052] In the embodiment of the present invention, only the flight data that is actually affected by the announcement is selected, and normal flight data and abnormal flights that are not related to the announcement are excluded, so as to provide a data basis for subsequent data association and announcement sorting.
[0053] In an optional embodiment, eliminating the delay impact caused by the abnormality of the preceding flight includes:
[0054] For flights affected by abnormalities of previous flights, the scheduled take-off time of the flight will be adjusted according to the original scheduled take-off time and the previous delay time, and the scheduled landing time of the flight will be adjusted according to the actual take-off time and the planned flight time.
[0055] It is understandable that the mutual influence between flights will lead to the accumulation of delay time, resulting in deviation in statistical results. Therefore, it is necessary to exclude the impact of delays caused by abnormalities of previous flights.
[0056] In an embodiment of the present invention, by adjusting the scheduled flight departure time and the scheduled flight landing time to exclude the impact of the abnormality of the preceding flight, it is possible to avoid the delay time in the statistical data being "contaminated" by irrelevant factors, thereby ensuring that the statistical results more accurately reflect the direct impact of the announcement on the flight, and providing a more reliable data basis for the sorting of the announcements.
[0057] In an optional embodiment, the delay processing of the canceled flights and alternate flights includes:
[0058] For the cancelled flights, the scheduled departure time is the original scheduled departure time plus the number of minutes of advance cancellation, and the flight delay is calculated based on the advance cancellation time, that is, if the flight is cancelled on the day of departure and within 6 hours in advance, the flight delay is 12 hours; if the flight is cancelled on the day of departure but more than 6 hours before departure, the flight delay is 6 hours; if the flight is cancelled one day before departure, the flight delay is 3 hours; if the flight is cancelled two days before departure, the flight delay is 2 hours; if the flight is cancelled three days or more before departure, the flight delay is 1 hour;
[0059] The flight delay for the alternate flight was 12 hours.
[0060] It is understandable that since the actual delay time cannot be calculated for cancelled flights, the advance cancellation time is converted into the flight delay time to reflect the degree of impact caused by the announcement; since the actual landing time of the alternate flight may be early or late, the flight delay penalty is uniformly set at 12 hours.
[0061] In an embodiment of the present invention, for canceled flights, the conversion of the advance cancellation time provides a quantitative method to measure the impact of the announcement; for alternate flights, unified delay time processing simplifies the data analysis process and avoids calculation difficulties caused by the complexity of the actual situation; through the standardized delay time calculation method, abnormal flights whose delay duration is difficult to judge in practice can be included in the delay statistics to ensure the comparability of the data.
[0062] In an optional embodiment, the recalculating the notification valid time window of the cancellation report and the replacement report in the first notification data includes:
[0063] For the replacement report, the replacement report effective time is used as the start time of the notification effective time window, and the replacement report expiration time is used as the end time of the notification effective time window;
[0064] For the cancellation report, the minimum value between the original notification effective time and the cancellation report effective time is selected as the start time of the notification effective time window, and the expiration of the original notification is used as the end time of the notification effective time window.
[0065] In an embodiment of the present invention, by recalculating the effective time window of the cancellation and replacement announcements, it is ensured that the impact of these announcements on flights during the actual effective period is accurately evaluated, providing a data basis for subsequent grouping and sorting, making the announcement sorting results more accurate.
[0066] In an optional embodiment, the notification characteristics include flight information region determination characteristics, notification subject, notification effective time difference, messaging unit, notification sequence code, flight type and issuance purpose.
[0067] It can be understood that the flight information region determination characteristics, notification subject and notification effective time difference are new features generated for notification data by the present invention, and the reporting unit and notification sequence code are disordered categorical data that are difficult to process using grey correlation analysis.
[0068] Among them, the flight information region determination feature is_FIR is used to determine whether a_item is a flight information region; among them, the notified flight affected area a_item is "item a)" in the notification data, which is used to fill in the ICAO (International Civil Aviation Organization) airport code of the airport or flight information region where the facility, airspace or situation to be reported is located.
[0069] The notice subject qcode_2 indicates the subject of the navigation notice content; wherein qcode is the navigation notice content code, which is represented by a five-character code, the first character of which is the identification code, the second and third characters are the subject of the navigation notice content; the fourth and fifth characters are the status of the subject. Since the first character is ignored during data processing, the notice subject feature qcode_2 is taken from the first two characters of qcode, that is, the second and third characters of the five-character code are extracted to obtain the subject of the navigation notice content.
[0070] The announcement effective time difference time_before_start = announcement effective time - announcement receiving time. Since each announcement has three times, namely the announcement receiving time (i.e. the time when the announcement is received), the announcement effective time and the announcement expiration time, the announcement effective time difference is the time difference between the announcement effective time and the announcement receiving time, indicating how long after the announcement is received that the announcement becomes effective.
[0071] In an optional embodiment, matching the second flight data with the second announcement data according to an association rule includes:
[0072] If the affected flight area announced in the second notification data is a flight information region, first match the airports in the flight information region, and then match the flights with the airport as the departure airport or arrival airport in the second flight data;
[0073] If the affected flight area announced in the second announcement data is an airport, matching flights in the second flight data that use the airport as a departure airport or an arrival airport;
[0074] If the scheduled departure time or the scheduled landing time of the flight is within the effective time window of the announcement, the flight is matched with the announcement to obtain a matching result.
[0075] It will be appreciated that the matching of airports within a flight information region can be achieved by comparing the first two digits of the flight information region code and the airport code.
[0076] In an embodiment of the present invention, the matching range is gradually narrowed according to the flight area affected by the announcement from the flight information region to the airport, and the announcement data and the flight data affected by it are associated to ensure that the planned take-off time or the planned landing time of the flight is within the effective time window of the announcement. This can more accurately reflect the actual impact of the announcement on the flight and avoid analysis deviations caused by time errors.
[0077] In an optional embodiment, grouping the matching results and analyzing the correlation and timeliness of the notification features and statistical indicators of each group based on the ANOVA method, and sorting the notifications according to the notification priority and the analysis results, includes:
[0078] Calculate the departure time difference between the start time of the notified effective time window and the scheduled departure time of the flight that matches it, and calculate the arrival time difference between the scheduled landing time of the flight and the end time of the notified effective time window that matches it;
[0079] Divide 24 hours into increasing time intervals, and group the matching results according to which time interval their departure time difference or arrival time difference belongs to;
[0080] Based on the ANOVA method, the correlation and timeliness of the notification characteristics and statistical indicators of each notification within each time interval are analyzed, and the notifications are first sorted from high to low according to their priority, and then the notifications of the same priority are sorted from high to low according to the correlation and timeliness to obtain the notification sorting result.
[0081] It can be understood that the notification priority important_level is a new feature generated by the present invention for notification data, which is the level mapping of important notification types in the "navigation notification graded notification mechanism", and the important notification types are disordered classified data that are difficult to process using grey correlation analysis.
[0082] In an embodiment of the present invention, the associated announcements and the flights affected by them are grouped according to the "difference between the scheduled take-off and landing time of the flight and the effective time window of the announcement", and the ANOVA method is used to compare the announcements of each group, and the correlation between each feature of the announcement and the statistical indicators is calculated. This can process various types of data more comprehensively and avoid deviations in the sorting results. Then, the announcements are sorted according to their priority and correlation results to obtain announcement sorting results that are more in line with actual operational needs, which helps airlines better optimize operational processes, reduce potential risks, and improve overall service quality.
[0083] For example, 24 hours are divided into time intervals of 0-4 hours, 4-12 hours or 12-24 hours. For takeoff, the grouping is performed by calculating whether the "start time of the notification effective time window - the scheduled flight departure time" belongs to the time interval of 0-4 hours, 4-12 hours or 12-24 hours;
[0084] For associated arrival, the grouping is done by calculating whether the "scheduled flight landing time - the end time of the notified effective time window" belongs to the time interval of 0-4 hours, 4-12 hours, or 12-24 hours;
[0085] The grouping results are shown in the following table:
[0086] 0-4 hours 4-12 hours 12-24 hours Announcement 001 Flight 001, Flight 002 Flight 003 Flight 004 Notice 002 Flight 005 Flight 006, Flight 007 - Announcement 003 Flight 001, Flight 003 Flight 004 Flight 008
[0087] In an optional embodiment, the statistical indicators include the average delay duration of flights affected by each notification and the number of delayed flights per unit time.
[0088] It is understandable that the average delay duration and the number of delayed flights per unit time are two perspectives for analyzing flight delays. The individual selection or weighted combination of statistical indicators should be determined according to the needs of the actual business scenario.
[0089] In an embodiment of the present invention, the average delay duration of flights affected by each notice and the number of delayed flights per unit time are used as statistical indicators, and the relationship between notice characteristics and statistical indicators is quantitatively analyzed. By locating notice characteristics with high relevance and high timeliness, it is not only possible to process disordered classified data and improve the accuracy and efficiency of navigation notice sorting, so that the sorting results are more in line with actual operational needs, but also by locating notice characteristics with high relevance and high timeliness, it is helpful for business personnel to better decide the order of notice processing when facing multiple notices, thereby improving the safety of aviation operations.
[0090] Exemplarily, the number of delayed flights per unit time is selected as a statistical indicator. First, the average delay time t of the flights affected by each notification in the time interval of 0-4 hours is calculated to obtain a feature-label matrix with multiple notification features s as independent variables and the average delay time t as the dependent variable; then, ANOVA variance analysis is performed between each notification feature s and the average delay time t in turn to measure the correlation between s and t. After traversing all notification features, it can be obtained which notification features have a high correlation with the average delay time. Similarly, the same calculation is performed for the time intervals of 4-12 hours and 12-24 hours to analyze the correlation between the same notification feature and the average delay time in different time intervals. If a notification feature has a high correlation with the average delay time in all three time intervals, it means that the timeliness of the notification feature is not high; if the correlation for the three time intervals decreases, it means that its timeliness is high.
[0091] Similarly, the number of delayed flights per unit time is selected as the statistical indicator for analysis. It is found that qcode shows high correlation in each time period grouping. Therefore, qcode is a high-correlation and low-timeliness attribute. Therefore, the priority classification based on the important announcement type adopted by the current business is not necessarily the only classification method. Qcode can also be considered as the classification basis. It is also found that some features (such as a_item and is_FIR) have obvious timeliness, that is, these indicators show more significant correlation in certain time periods, but weaker in other time periods. Combined with the ranking of the number of delayed flights per unit time in each grouping of is_FIR and a_item, the top rankings are all flight information regions. Therefore, more attention needs to be paid to route announcements other than airport and runway announcements.
[0092] That is to say, after selecting the statistical indicators, the notification features with a high correlation with the statistical indicators are found based on ANOVA variance analysis, and then the notifications are sorted from high to low according to their priority, and then the notifications of the same priority are sorted from high to low according to the relevance and timeliness. The timeliness can also be considered for further refinement of the sorting.
[0093] The method for sorting navigation notices provided in an embodiment of the present invention selects first flight data that is affected by the notice and causes flight abnormality from a historical data set, eliminates the delay effect caused by the abnormality of the preceding flight for the first flight data, and performs delay processing on the canceled flights and the alternate flights to obtain second flight data; selects valid notification data from the historical data set as the first notification data, recalculates the notification effective time window of the cancellation report and the replacement report in the first notification data, obtains the second notification data and extracts notification features; matches the second flight data with the second notification data according to the association rule, groups the matching results and performs a matching operation based on ANOV Method A analyzes the correlation and timeliness between the notification characteristics of each group and the statistical indicators, and sorts the notifications according to the notification priority and analysis results. It can not only process unordered classified data, improve the accuracy and efficiency of navigation notification sorting, and make the sorting results more in line with actual operational needs, but also locate the notification characteristics with high correlation and high timeliness, which helps business personnel to better decide the order of notification processing when facing multiple notifications, thereby improving the safety of aviation operations.
[0094] See also Figure 2 , Figure 2 1 is a structural block diagram of a navigation notice sorting device 10 provided in an embodiment of the present invention. The navigation notice sorting device 10 comprises:
[0095] The flight data processing module 11 is used to select the first flight data that is affected by the notice and causes flight abnormality from the historical data set, eliminate the delay effect caused by the previous flight abnormality for the first flight data, and perform delay processing on the canceled flights and alternate flights to obtain the second flight data;
[0096] The notification data processing module 12 is used to select valid notification data from the historical data set as the first notification data, recalculate the notification effective time window of the cancellation report and the replacement report in the first notification data, obtain the second notification data and extract the notification features;
[0097] The data association and grouping sorting module 13 is used to match the second flight data and the second announcement data according to the association rules, group the matching results and analyze the correlation and timeliness of the announcement characteristics and statistical indicators of each group based on the ANOVA method, and sort the announcements according to the announcement priority and analysis results.
[0098] Optionally, the factors affected by the announcement include military activity factors, airport factors, air traffic control factors and weather factors.
[0099] Optionally, the flight data processing module 11 is further used for:
[0100] For flights affected by abnormalities of previous flights, the scheduled take-off time of the flight will be adjusted according to the original scheduled take-off time and the previous delay time, and the scheduled landing time of the flight will be adjusted according to the actual take-off time and the planned flight time.
[0101] Optionally, the flight data processing module 11 is further used for:
[0102] For the cancelled flights, the scheduled departure time is the original scheduled departure time plus the number of minutes of advance cancellation, and the flight delay is calculated based on the advance cancellation time, that is, if the flight is cancelled on the day of departure and within 6 hours in advance, the flight delay is 12 hours; if the flight is cancelled on the day of departure but more than 6 hours before departure, the flight delay is 6 hours; if the flight is cancelled one day before departure, the flight delay is 3 hours; if the flight is cancelled two days before departure, the flight delay is 2 hours; if the flight is cancelled three days or more before departure, the flight delay is 1 hour;
[0103] The flight delay for the alternate flight was 12 hours.
[0104] Optionally, the notification data processing module 12 is further used to:
[0105] For the replacement report, the replacement report effective time is used as the start time of the notification effective time window, and the replacement report expiration time is used as the end time of the notification effective time window;
[0106] For the cancellation report, the minimum value between the original notification effective time and the cancellation report effective time is selected as the start time of the notification effective time window, and the expiration of the original notification is used as the end time of the notification effective time window.
[0107] Optionally, the notification characteristics include flight information region determination characteristics, notification subject, notification effective time difference, messaging unit, notification sequence code, flight type and issuance purpose.
[0108] Optionally, the data association and grouping ordering module 13 is further used to:
[0109] If the affected flight area announced in the second notification data is a flight information region, first match the airports in the flight information region, and then match the flights with the airport as the departure airport or arrival airport in the second flight data;
[0110] If the affected flight area announced in the second announcement data is an airport, matching flights in the second flight data that use the airport as a departure airport or an arrival airport;
[0111] If the scheduled departure time or the scheduled landing time of the flight is within the effective time window of the announcement, the flight is matched with the announcement to obtain a matching result.
[0112] Optionally, the data association and grouping ordering module 13 is further used to:
[0113] Calculate the departure time difference between the start time of the notified effective time window and the scheduled departure time of the flight that matches it, and calculate the arrival time difference between the scheduled landing time of the flight and the end time of the notified effective time window that matches it;
[0114] Divide 24 hours into increasing time intervals, and group the matching results according to which time interval their departure time difference or arrival time difference belongs to;
[0115] Based on the ANOVA method, the correlation and timeliness of the notification characteristics and statistical indicators of each notification within each time interval are analyzed, and the notifications are first sorted from high to low according to their priority, and then the notifications of the same priority are sorted from high to low according to the correlation and timeliness to obtain the notification sorting result.
[0116] Optionally, the statistical indicators include the average delay duration of flights affected by each notice and the number of delayed flights per unit time.
[0117] It is worth noting that the working process of each module in the navigation notice sorting device 10 described in the embodiment of the present invention can refer to the working process of the navigation notice sorting method described in the above embodiment, and will not be repeated here.
[0118] The navigation notice sorting device provided in the embodiment of the present invention selects the first flight data which is affected by the notice and causes the flight abnormality from the historical data set, eliminates the delay influence caused by the abnormality of the preceding flight for the first flight data, and performs delay processing on the canceled flights and the alternate flights to obtain the second flight data; selects the valid notice data from the historical data set as the first notice data, recalculates the notice effective time window of the cancellation report and the replacement report in the first notice data, obtains the second notice data and extracts the notice features; matches the second flight data with the second notice data according to the association rule, groups the matching results and performs the matching based on ANOV Method A analyzes the correlation and timeliness between the notification characteristics of each group and the statistical indicators, and sorts the notifications according to the notification priority and analysis results. It can not only process unordered classified data, improve the accuracy and efficiency of navigation notification sorting, and make the sorting results more in line with actual operational needs, but also locate the notification characteristics with high correlation and high timeliness, which helps business personnel to better decide the order of notification processing when facing multiple notifications, thereby improving the safety of aviation operations.
[0119] In addition, an embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium includes a stored computer program; wherein, when the computer program is running, the device where the computer-readable storage medium is located controls the device to execute the above-mentioned navigation notice sorting method.
[0120] An embodiment of the present invention further provides a computer program product, which includes a computer program; wherein, when the computer program is running, the device where the computer program product is located is controlled to execute the above-mentioned navigation notice sorting method.
[0121] See also Figure 3 , Figure 3 : is a structural block diagram of a computer device 20 provided in an embodiment of the present invention, wherein the computer device 20 comprises: a processor 21, a memory 22, and a computer program stored in the memory 22 and executable on the processor 21. When the processor 21 executes the computer program, the steps in the above-mentioned NOTAM sorting method embodiment are implemented. Alternatively, when the processor 21 executes the computer program, the functions of each module / unit in the above-mentioned device embodiments are implemented.
[0122] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory 22 and executed by the processor 21 to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program in the computer device 20.
[0123] The computer device 20 may include, but is not limited to, a processor 21 and a memory 22. Those skilled in the art will appreciate that the schematic diagram is merely an example of the computer device 20 and does not limit the computer device 20. The computer device 20 may include more or fewer components than shown in the figure, or may combine certain components, or different components. For example, the computer device 20 may also include input and output devices, network access devices, buses, etc.
[0124] The processor 21 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor 21 is the control center of the computer device 20, and uses various interfaces and lines to connect various parts of the entire computer device 20.
[0125] The memory 22 can be used to store the computer program and / or module. The processor 21 implements various functions of the computer device 20 by running or executing the computer program and / or module stored in the memory 22 and calling the data stored in the memory 22. The memory 22 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory 22 can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.
[0126] Wherein, if the module / unit integrated in the computer device 20 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor 21. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc.
[0127] The above is a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several improvements and modifications without departing from the principle of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.
Claims
1. A method for sorting NOTAMs, characterized in that: include: Select the first flight data that is affected by the notice and causes flight abnormality from the historical data set, eliminate the delay effect caused by the previous flight abnormality for the first flight data, and perform delay processing on the canceled flights and alternate flights to obtain the second flight data; Selecting valid notification data from the historical data set as first notification data, recalculating the notification valid time window of the cancellation report and the replacement report in the first notification data, obtaining second notification data and extracting notification features; The second flight data and the second announcement data are matched according to the association rules, the matching results are grouped, and the correlation and timeliness of the announcement characteristics and statistical indicators of each group are analyzed based on the ANOVA method, and the announcements are sorted according to the announcement priority and the analysis results.
2. The method for sorting NOTAMs according to claim 1, wherein: The factors affected by the notice include military activity factors, airport factors, air traffic control factors and weather factors.
3. The method for sorting NOTAMs according to claim 1, wherein: The exclusion of delays caused by abnormalities of previous flights includes: For flights affected by abnormalities of previous flights, the scheduled take-off time of the flight will be adjusted according to the original scheduled take-off time and the previous delay time, and the scheduled landing time of the flight will be adjusted according to the actual take-off time and the planned flight time.
4. The method for sorting NOTAMs according to claim 1, wherein: The delay treatment of cancelled flights and alternate flights includes: For the cancelled flights, the scheduled departure time is the original scheduled departure time plus the number of minutes of advance cancellation, and the flight delay is calculated based on the advance cancellation time, that is, if the flight is cancelled on the day of departure and within 6 hours in advance, the flight delay is 12 hours; if the flight is cancelled on the day of departure but more than 6 hours before departure, the flight delay is 6 hours; if the flight is cancelled one day before departure, the flight delay is 3 hours; if the flight is cancelled two days before departure, the flight delay is 2 hours; if the flight is cancelled three days or more before departure, the flight delay is 1 hour; The flight delay for the alternate flight was 12 hours.
5. The method for sorting NOTAMs according to claim 1, wherein: The recalculating the notification valid time window of the cancellation report and the replacement report in the first notification data includes: For the replacement report, the replacement report effective time is used as the start time of the notification effective time window, and the replacement report expiration time is used as the end time of the notification effective time window; For the cancellation report, the minimum value between the original notification effective time and the cancellation report effective time is selected as the start time of the notification effective time window, and the expiration of the original notification is used as the end time of the notification effective time window.
6. The method for sorting NOTAMs according to claim 1, wherein: The notification features include flight information region determination features, notification subject, notification effective time difference, messaging unit, notification sequence code, flight type and issuance purpose.
7. The method for sorting NOTAMs according to claim 1, wherein: The matching the second flight data with the second notification data according to the association rule includes: If the affected flight area announced in the second notification data is a flight information region, first match the airports in the flight information region, and then match the flights with the airport as the departure airport or arrival airport in the second flight data; If the affected flight area announced in the second announcement data is an airport, matching flights in the second flight data that use the airport as a departure airport or an arrival airport; If the scheduled departure time or the scheduled landing time of the flight is within the effective time window of the announcement, the flight is matched with the announcement to obtain a matching result.
8. The method for sorting NOTAMs according to claim 1, wherein: The matching results are grouped and the correlation and timeliness of the notification characteristics and statistical indicators of each group are analyzed based on the ANOVA method, and the notifications are sorted according to the notification priority and the analysis results, including: Calculate the departure time difference between the start time of the notified effective time window and the scheduled departure time of the flight that matches it, and calculate the arrival time difference between the scheduled landing time of the flight and the end time of the notified effective time window that matches it; Divide 24 hours into increasing time intervals, and group the matching results according to which time interval their departure time difference or arrival time difference belongs to; Based on the ANOVA method, the correlation and timeliness of the notification characteristics and statistical indicators of each notification within each time interval are analyzed, and the notifications are first sorted from high to low according to their priority, and then the notifications of the same priority are sorted from high to low according to the correlation and timeliness to obtain the notification sorting result.
9. The method for sorting NOTAMs according to claim 8, characterized in that: The statistical indicators include the average delay duration of flights affected by each notice and the number of delayed flights per unit time.
10. A NOTAM sorting device, characterized in that: include: A flight data processing module is used to select the first flight data that is affected by the notice and causes flight abnormality from the historical data set, eliminate the delay effect caused by the previous flight abnormality for the first flight data, and perform delay processing on the canceled flights and alternate flights to obtain the second flight data; A notification data processing module, used to select valid notification data from the historical data set as first notification data, recalculate the notification effective time window of the cancellation report and the replacement report in the first notification data, obtain the second notification data and extract notification features; The data association and grouping sorting module is used to match the second flight data and the second announcement data according to the association rules, group the matching results and analyze the correlation and timeliness of the announcement characteristics and statistical indicators of each group based on the ANOVA method, and sort the announcements according to the announcement priority and analysis results.