CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims priority to Chinese Patent Application No. 201910301274.7 filed on Apr. 15, 2019, the content of which is hereby incorporated by reference in its entirety.

TECHNICAL FIELD

The disclosure relates to the field of flight delay prediction technologies and, in particular, to a method and an apparatus for predicting flight delay, a device and a storage medium.

BACKGROUND

With the rapid development of air-transport industry, the airport throughput of a country increases year by year under the driving by both of airport and regional economy, and accordingly the normal departure rate of flights decreases year by year. Predictive analysis of flight delay is significantly important for an air traffic flow management department and airline companies to make decisions, which can not only assist the air traffic flow management department to take measures to reduce economic losses but also remind the passengers to change the schedule in advance by issuing delay warning information. Therefore, it is an effective method for solving the problem of delay to accurately predict flight departures and landings.

Flight execution is affected by multiple factors together, such as weather, airline companies, airports and the like, and these factors are highly random. Therefore, in the prior art, regarding the method for predicting delay in departures and landings of flights, a flight delay spread model and a data mining method based on a statistical learning theory are commonly used to predict short-time delay rate or average delay time of flights. For example, an aircrew needs to execute multiple flights, and there exists a time connection between the multiple flights, therefore the delay of one flight may influence downstream flights, and the delay in the downstream flights may be continuously spread, which would result in a widespread delay. In the prior art, the spread delay rate and the average delay time for the downstream flights are generally studied to carry out a prediction of departures and landings of the flights.

However, the attention is merely focused on the short-time delay rate and the average delay time in the prior art, without a prediction of the flight operation situation of an airport in terms of time period.

SUMMARY

The present disclosure provides a method and an apparatus for predicting flight delay, a device and a storage medium, to realize the prediction of flight delay during each of a plurality of time periods.

In a first aspect, an embodiment of the present disclosure provides a method for predicting flight delay, including:

acquiring flight historical data, where the flight historical data includes take-off amount and delay amount of flights during each of a plurality of time periods;

determining prior knowledge of each of the plurality of time periods according to the take-off amount and the delay amount of the flights during each of the plurality of time periods;

constructing a support-vector-machine (SVM) prediction model according to the prior knowledge and a standard SVM model; and

predicting a flight delay situation according to the SVM prediction model.

In this solution, by determining the prior knowledge of each of the plurality of time periods according to the take-off amount and the delay amount of flights during each of the plurality of time periods in the flight historical data and constructing the SVM prediction model according to the prior knowledge and the standard SVM model, the accuracy of the SVM prediction model is improved; and further by predicting the flight delay situation according to the SVM prediction model, the prediction of the flight delay situation for each of the plurality of time periods is achieved and the reliability of the flight delay prediction is improved.

Optionally, the method for predicting flight delay provided in the embodiment of the present application further includes:

acquiring massive delay response system (MDRS) information; and

verifying the SVM prediction model according to the MDRS information.

In this solution, by verifying the SVM prediction model according to the MDRS information, the SVM prediction model is made closer to the actual flight operation and the reliability of the model is improved.

Optionally, the verifying the SVM prediction model according to the MDRS information includes:

acquiring a first flight delay prediction result according to the SVM prediction model; comparing a second flight delay prediction result in the MDRS information with the first flight delay prediction result; and updating the SVM prediction model according to the MDRS information, if an error between the second flight delay prediction result and the first flight delay prediction result is larger than a preset error threshold.

In this solution, by comparing the flight delay prediction result acquired according to the SVM prediction model and the flight delay prediction result in the MDRS information, and updating the SVM prediction model according to the MDRS information when the error between two kinds of flight delay prediction results is larger than the preset error threshold, the accuracy of the SVM prediction model is improved.

Optionally, the flight historical data further includes at least one of the following:

flight historical delay time, planned flight time, an airline company, a destination airport, a departure direction, a flight model, a planned take-off time period and a planned take-off date.

Optionally, the predicting the flight delay situation according to the SVM prediction model includes:

selecting first flight historical data as a training set according to the flight historical data;

training the SVM prediction model according to the first flight historical data, and acquiring a trained SVM prediction model;

selecting second flight historical data as a test set according to the flight historical data;

testing the trained SVM prediction model according to the second flight historical data; and

predicting the flight delay situation according the SVM prediction model that passes the test.

Optionally, the method for predicting flight delay provided in the embodiment of the present application further includes:

performing dimension reduction processing on the flight historical data according to the flight historical data.

The following are an apparatus for predicting flight delay, a device, a storage medium and a computer program product provided in embodiments of the present application, and the method for predicting flight delay in the first aspect or the optional implementations of the first aspect can be referred to for the content and effects thereof.

In a second aspect, an embodiment of the present application provides an apparatus for predicting flight delay, including:

a first acquisition module, configured to acquire flight historical data, where the flight historical data includes take-off amount and delay amount of flights during each of a plurality of time periods;

a determination module, configured to determine prior knowledge of each of the plurality of time periods according to the take-off amount and the delay amount of the flights during each of the plurality of time periods;

a construction module, configured to construct a SVM prediction model according to the prior knowledge and a standard SVM model; and

a prediction module, configured to predict a flight delay situation according to the SVM prediction model.

Optionally, the apparatus for predicting flight delay further includes:

a second acquisition module, configured to acquire massive delay response system (MDRS) information; and

a verification module, configured to verify the SVM prediction model according to the MDRS information.

Optionally, the verification module includes:

an acquisition submodule, configured to acquire a first flight delay prediction result according to the SVM prediction model;

a comparison submodule, configured to compare a second flight delay prediction result in the MDRS information and the first flight delay prediction result; and

an updating submodule, configured to update the SVM prediction model according to the MDRS information, if an error between the second flight delay prediction result and the first flight delay prediction result is larger than a preset error threshold.

Optionally, the flight historical data further includes at least one of the following:

flight historical delay time, planned flight time, an airline company, a destination airport, a departure direction, a flight model, a planned take-off time period and a planned take-off date.

Optionally, the prediction module is specifically configured to:

select first flight historical data as a training set according to the flight historical data;

train the SVM prediction model according to the first flight historical data, and acquire a trained SVM prediction model;

select second flight historical data as a test set according to the flight historical data;

test the trained SVM prediction model according to the second flight historical data; and

predict the flight delay situation according to the SVM prediction model that passes the test.

Optionally, the apparatus for predicting flight delay provided in the embodiment of the present application further includes:

a processing module, configured to perform dimension reduction processing on the flight historical data according to the flight historical data.

In a third aspect, an embodiment of the present application provides a device, including:

a processor; a memory; and a computer program; where the computer program is stored in the memory and configured to be executed by the processor, and the computer program includes instructions for implementing the method for predicting flight delay according to the first aspect or optional implementations of the first aspect.

In a fourth aspect, an embodiment of the present application provides a computer readable storage medium, where the computer readable storage medium has a computer program stored thereon, and the computer program causes a server to implement the method for predicting flight delay according to the first aspect or optional implementations of the first aspect.

In a fifth aspect, an embodiment of the present disclosure provides a computer program product, including: executable instructions for implementing the method for predicting flight delay according to the first aspect or optional implementations of the first aspect.

Using the method and the apparatus for predicting flight delay, the device and the storage medium provided in the present disclosure, the flight historical data which includes the take-off amount and the delay amount of the flights during each of a plurality of time periods is acquired, and the prior knowledge of each of the plurality of time periods is determined according to the take-off amount and the delay amount of the flights during each of the plurality of time periods; the SVM prediction model is constructed according to the prior knowledge and the standard SVM model; and finally the flight delay situation is predicted according to the SVM prediction model. By determining the prior knowledge of each of the plurality of time periods according to the take-off amount and the delay amount of flights during each of the plurality of time periods in the flight historical data and constructing the SVM prediction model according to the prior knowledge and the standard SVM model, the accuracy of the SVM prediction model is improved; and further by predicting the flight delay situation according to the SVM prediction model, the prediction of the flight delay situation for each of the plurality of time periods is achieved and the reliability of the flight delay prediction is improved.

