CN115310732A - Flight delay prediction method and system - Google Patents
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Abstract
The invention provides a flight delay prediction method and a flight delay prediction system, wherein the method comprises the steps of obtaining flight operation information of a target flight, weather information of a take-off and landing city of the target flight in preset operation time and associated delay information of an airport associated with the target flight; after the flight operation information, the weather information and the associated delay information are converted into feature vectors, predicting whether the target flight will be delayed or not through a pre-constructed flight delay prediction model; and if the delay of the target flight is predicted, further determining the delay level of the target flight, and determining a processing scheme corresponding to the delay level according to the corresponding relation acquired in advance. The method disclosed by the invention can comprehensively influence the factors of flight delay to predict the flight delay and provide a corresponding processing scheme according to the prediction result.
Description
Technical Field
The disclosure relates to the technical field of big data analysis, in particular to a flight delay prediction method and a flight delay prediction system.
Background
Due to the weather, the flow, passengers and the like, part of flights are delayed or cancelled frequently, and a large number of flight delays are easily formed under the influence of time and space correlation of a flight network.
The application number is 202110436375.2, the invention is named as an intelligent flight delay prediction method based on deep learning, and discloses the following contents:
acquiring flight data of a target flight and weather data of a take-off and landing city within the running time of the target flight, calculating delay time of the target flight, and making a delay label according to the delay time; performing digital processing on the flight data and the weather data; preprocessing the flight data and the weather data after digital processing, dividing a plurality of delay categories according to delay labels, and setting a deep learning model; training a deep learning algorithm model by using the preprocessed flight data and weather data, and predicting the delay condition of the target flight by using a weather forecast and deep learning algorithm;
although the flight delay analysis is carried out through deep learning in the comparison file, the result prediction is carried out only through a single model, flight data are formed by summarizing various types of data, and the prediction result of the single model is often unsatisfactory; and as the delay time becomes longer, the probability that the predicted value deviates from the standard value is greatly increased, and the local optimal solution is easily trapped.
The invention discloses an departure flight delay prediction method based on aging information and deep learning, which has the application number of 201911066077.8 and discloses the following contents:
acquiring flight information data and meteorological information data; performing feature conversion according to the flight information data and the meteorological information data, establishing an departing flight delay prediction model based on deep learning, and predicting flight delay by adopting the departing flight delay prediction model based on the deep learning;
although the time-efficiency information is added to the comparison file on the basis of deep learning, the comparison file still adopts a single model to carry out delay prediction, and the prediction result of the single model is easy to fall into the difficulty of local optimal solution.
The information disclosed in this background section is only for enhancement of understanding of the general background of the application and should not be taken as an acknowledgement or any form of suggestion that this information forms the prior art already known to a person skilled in the art.
Disclosure of Invention
The embodiment of the disclosure provides a flight delay prediction method and a flight delay prediction system, and the method can at least solve part of problems in the prior art.
In a first aspect of an embodiment of the present disclosure,
a flight delay prediction method is provided, the method comprises
Acquiring flight operation information of a target flight, weather information of a take-off and landing city of the target flight within preset operation time, and associated delay information of an airport associated with the target flight;
after the flight operation information, the weather information and the associated delay information are converted into feature vectors, whether the target flight will be delayed or not is predicted through a pre-constructed flight delay prediction model,
if the target flight will be delayed, further determining the delay level of the target flight, and determining a processing scheme corresponding to the delay level according to a pre-acquired corresponding relation, wherein,
the flight delay prediction model is constructed based on a plurality of neural network models, and the output of the prior neural network model is used as the input of the subsequent neural network model and is used for performing delay prediction on the target flight.
In an alternative embodiment of the method according to the invention,
the method further comprises training a flight delay prediction model, and the method for training the flight delay prediction model comprises the following steps:
constructing a model training data set, wherein the model training data set comprises historical flight operation information, historical weather information and historical associated delay information in a preset time period;
converting the model training data set into training characteristic vectors, inputting a flight delay prediction model to be trained, updating the model training data set based on a training output result of the flight delay prediction model to be trained until a preset training convergence condition is reached to finish training the flight delay prediction model to be trained, wherein,
the flight delay prediction model to be trained comprises a preceding neural network model and a succeeding neural network model, the preceding neural network model performs preceding training learning according to the training feature vector and outputs a first output result to the succeeding neural network model, and the succeeding neural network performs succeeding training learning according to the first output result and outputs a training prediction value.
In an alternative embodiment of the method according to the invention,
before inputting the training feature vector into a flight delay prediction model to be trained, the method further comprises:
determining a data preprocessing method corresponding to the training data characteristics according to the training data characteristics of the model training data set, and screening out dirty data in the model training data set to obtain a standard model training data set, wherein the standard model training data set comprises standard flight operation information, standard weather information and standard association delay information;
respectively converting the standard flight operation information, the standard weather information and the standard associated delay information of the standard model training data set into corresponding standard data characteristics;
and scoring the standard flight operation information, the standard weather information and the standard association delay information according to the data type corresponding to the standard data characteristic, and converting the information with the score exceeding a preset threshold value into a training characteristic vector.
In an alternative embodiment of the method according to the invention,
the method for training the flight delay prediction model further comprises the step of constructing a prior neural network model for training the flight delay prediction model based on the deep neural network model, wherein the prior neural network model comprises an input layer, a convolutional layer, a hidden layer and an output layer,
the convolutional layer performs batch normalization processing on the training feature vectors and determines output features corresponding to all hidden layers;
determining a predicted value corresponding to the output feature based on a first loss function of the preceding neural network model, and judging whether the preceding neural network model converges according to the predicted value and a loss value of a preset target value,
if not, determining the gradient value of the hidden layer through a back propagation algorithm according to the loss value, and updating the weight value of each layer of the prior neural network model according to the gradient value until the prior neural network model converges, wherein,
the judgment basis of the convergence of the prior neural network model comprises the judgment of whether the time difference value between the estimated arrival time of the flight and the actual arrival time of the flight output by the prior neural network model is within a preset time range.
In an alternative embodiment of the method according to the invention,
the first loss function of the prior neural network model is shown in the following equation:
wherein the content of the first and second substances,
、
respectively representing the number of hidden neurons and output neurons,
、
respectively representing the weight values corresponding to the hidden neuron and the output neuron,
、
representing values input to hidden neurons and output neurons, respectively;
the weight value of each layer of the prior neural network model is shown as the following formula:
wherein, the first and the second end of the pipe are connected with each other,
the duration of the period is indicated and,
a loss value representing the ith predicted value and a preset target value at time t, L representing an error function,
indicating the ith predicted value at time t.
In an alternative embodiment of the method according to the invention,
the method for training the flight delay prediction model further comprises the step of training a post-neural network model of the flight delay prediction model, wherein the post-neural network model is constructed on the basis of a convolutional neural network model, the post-neural network model comprises a convolutional layer, a pooling layer and a classification layer,
after local connection and weight sharing extraction are carried out on input characteristic information through the convolutional layer of the posterior neural network, the weight value of the input characteristic information is calibrated in a self-adaptive mode, wherein the input characteristic information is a predicted value of flight arrival time output by the prior neural network model;
the pooling layer fuses the input characteristic information and the corresponding weight values into a comprehensive characteristic value;
outputting the probability of the corresponding category of the input characteristic information according to the comprehensive characteristic value through a softmax classifier of the classification layer;
and (3) carrying out loop iteration training on the post-neural network model until the probability of the corresponding category of the output and input characteristic information meets a preset condition, wherein,
the input characteristic information corresponding category comprises a delay level.
In an alternative embodiment of the method according to the invention,
the method for adaptively calibrating the weight value of the input characteristic information is shown by the following formula:
wherein the content of the first and second substances,
、
the sigmod function and ReLu function are represented separately,
indicates the number of the input characteristic information,
represents the weight value corresponding to the kth input characteristic information,
representing the convolved values of the input feature information.
In a second aspect of an embodiment of the present disclosure,
there is provided a flight delay prediction system, the system comprising:
the system comprises a first unit, a second unit and a third unit, wherein the first unit is used for acquiring flight operation information of a target flight, weather information of a take-off and landing city of the target flight in a preset operation time and associated delay information of an airport associated with the target flight;
a second unit, configured to convert the flight operation information, the weather information, and the associated delay information into feature vectors, predict whether the target flight will be delayed through a pre-constructed flight delay prediction model,
a third unit configured to further determine a delay level of the target flight if it is predicted that the target flight will be delayed, and determine a processing scenario corresponding to the delay level according to a correspondence relationship obtained in advance, wherein,
the flight delay prediction model is constructed based on a plurality of neural network models, and the output of the preceding neural network model is used as the input of the following neural network model and is used for performing delay prediction on the target flight.
In a third aspect of the embodiments of the present disclosure,
there is provided a flight delay prediction apparatus comprising:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to invoke the memory-stored instructions to perform the method of any of the preceding.
In a fourth aspect of an embodiment of the present disclosure,
there is provided a computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the method of any of the preceding.
The present disclosure provides a flight delay prediction method, comprising
Acquiring flight operation information of a target flight, weather information of a take-off and landing city of the target flight within preset operation time, and associated delay information of an airport associated with the target flight;
through flight operation information, weather information and associated delay information, the most main factors influencing flight delay can be summarized, the accuracy of flight delay prediction is improved, and the fact that data to be processed by a flight delay prediction model is necessary and small in quantity is guaranteed.
After the flight operation information, the weather information and the associated delay information are converted into the characteristic vectors, whether the target flight is delayed or not is predicted through a pre-constructed flight delay prediction model,
the flight delay prediction model is constructed based on a plurality of neural network models, and the output of the preceding neural network model is used as the input of the following neural network model and is used for performing delay prediction on the target flight.
The flight delay model is constructed based on the neural network models, compared with the flight delay prediction of the existing single model, the flight delay prediction model is higher in accuracy and higher in stability, prediction accuracy is exponentially improved after the multiple models are fused compared with that of the single model, and generalization capability is strong.
If the target flight is predicted to be delayed, further determining the delay level of the target flight, and determining a processing scheme corresponding to the delay level according to a pre-acquired corresponding relation, wherein,
the method disclosed by the embodiment of the disclosure can not only predict whether the flight is delayed, but also further determine the flight delay level, and set a corresponding scheme according to the flight delay level, thereby providing a solution of the system.
Drawings
FIG. 1 is a schematic flow chart illustrating a flight delay prediction method according to an embodiment of the disclosure;
FIG. 2 is a schematic flow diagram of a prior neural network model for training a flight delay prediction model according to an embodiment of the present disclosure;
FIG. 3 is a schematic flow chart of a post neural network model for training a flight delay prediction model according to an embodiment of the present disclosure;
FIG. 4 is a schematic diagram illustrating a probability of a classification layer outputting a class corresponding to feature information according to an embodiment of the present disclosure;
fig. 5 is a schematic structural diagram of a flight delay prediction system according to an embodiment of the disclosure.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present disclosure more clear, the technical solutions of the embodiments of the present disclosure will be described clearly and completely with reference to the drawings in the embodiments of the present disclosure, and it is obvious that the described embodiments are only a part of the embodiments of the present disclosure, not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments disclosed herein without making any creative effort, shall fall within the protection scope of the present disclosure.
The terms "first," "second," "third," "fourth," and the like in the description and claims of this disclosure and in the above-described drawings, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the disclosure described herein are capable of operation in other sequences than those illustrated or described herein.
It should be understood that, in various embodiments of the present disclosure, the sequence numbers of the processes do not mean the execution sequence, and the execution sequence of the processes should be determined by the functions and the inherent logic of the processes, and should not constitute any limitation on the implementation process of the embodiments of the present disclosure.
It should be understood that in the present disclosure, "including" and "having" and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be understood that in the present disclosure, "plurality" means two or more. "and/or" is merely an association describing an associated object, meaning that three relationships may exist, for example, and/or B, may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. "comprising a, B and C", "comprising a, B, C" means that all three of a, B, C are comprised, "comprising a, B or C" means comprising one of three of a, B, C, "comprising a, B and/or C" means comprising any 1 or any 2 or 3 of three of a, B, C.
It should be understood that in this disclosure, "B corresponding to a", "a corresponds to B", or "B corresponds to a" means that B is associated with a, from which B can be determined. Determining B from a does not mean determining B from a alone, but may also be determined from a and/or other information. And the matching of A and B means that the similarity of A and B is greater than or equal to a preset threshold value.
As used herein, the term "if" may be interpreted as "at \8230; …" or "in response to a determination" or "in response to a detection" depending on the context.
The technical solution of the present disclosure is explained in detail below with specific examples. These several specific embodiments may be combined with each other below, and details of the same or similar concepts or processes may not be repeated in some embodiments.
Fig. 1 is a schematic flow chart of a flight delay prediction method according to an embodiment of the present disclosure, and as shown in fig. 1, the method includes:
s101, acquiring flight operation information of a target flight, weather information of a take-off and landing city of the target flight in a preset operation time and associated delay information of an airport associated with the target flight;
in practical application, weather reasons, air traffic management reasons and airline reasons are main factors causing flight delay, wherein the associated delay information of the target flight associated airport in the embodiment of the disclosure is used for indicating the delay information related to the target flight departure and landing airport, and the associated delay information can intuitively display direct factors of the target flight delay, thereby being beneficial to improving the accuracy of final flight delay prediction. For example, if there is a flight delay at the transit airport or the landing airport of the target flight, there is a large probability that the target flight will also be delayed.
Illustratively, the flight operation information of the embodiment of the disclosure may include historical delayed flight information of the target flight, historical departure and landing airports of the target flight, historical departure and landing time of the target flight, flight number of the target flight, and the like;
the weather information of the disclosed embodiment may include historical weather information of the take-off and landing airport, such as minimum temperature, maximum temperature, observation station air pressure, sea level air pressure, height of the observation station, relative humidity, wind direction, wind speed, wind power size, visibility, and the like;
the associated delay information of the embodiment of the present disclosure may include a number of the associated airport, a flight operation condition of the associated airport, whether the associated airport is delayed, and in the case that there is a delay, a flight number, a stop position, and air flow information corresponding to the delayed flight, etc.
It should be noted that, the flight operation information, the weather information, and the associated delay information in the embodiments of the present disclosure are only exemplary illustrations, and do not limit the types and the numbers of the flight operation information, the weather information, and the associated delay information.
Through flight operation information, weather information and associated delay information, the most main factors influencing flight delay can be gathered, the accuracy of flight delay prediction is improved, and the fact that data to be processed by a flight delay prediction model is necessary and small in quantity is guaranteed.
S102, after the flight operation information, the weather information and the associated delay information are converted into feature vectors, predicting whether the target flight will be delayed or not through a pre-constructed flight delay prediction model;
illustratively, the flight delay prediction model of the embodiment of the disclosure is constructed based on a plurality of neural network models, and the output of the preceding neural network model is used as the input of the following neural network model for performing delay prediction on the target flight.
The flight delay model is constructed based on the neural network models, compared with the existing single model for flight delay prediction, the flight delay prediction model is higher in accuracy and higher in stability, and the prediction accuracy after the multiple models are fused is exponentially improved compared with that of the single model, so that the generalization capability is strong.
Before converting flight operation information, weather information and associated delay information into feature vectors, the method further comprises data preprocessing, before inputting the data into the network model, discrete variable data cannot directly enter the model for training, unordered discrete variables can mislead a network content learning mechanism and confuse the importance among feature variables, so that the data preprocessing is required, and the method can comprise data cleaning and missing value processing, wherein,
the data cleansing includes cleansing dirty data in the data, where the dirty data is error or invalid data, and in the embodiment of the present disclosure, the data cleansing may include missing value cleansing, format content cleansing repeated data cleansing, non-required data, and association verification, and the like. Data can be standardized through data cleaning, and extra calculation expenses are reduced.
The missing value processing may be implemented by one or more of homogeneous mean interpolation, modeling prediction, mean interpolation, high-dimensional mapping, and multiple interpolation, which is not limited in the embodiment of the present disclosure.
In an alternative embodiment of the method according to the invention,
before inputting the training feature vectors into the flight delay prediction model to be trained, the method further comprises:
determining a data preprocessing method corresponding to the training data characteristics according to the training data characteristics of the model training data set, and screening out dirty data in the model training data set to obtain a standard model training data set, wherein the standard model training data set comprises standard flight operation information, standard weather information and standard association delay information;
respectively converting standard flight operation information, standard weather information and standard associated delay information of the standard model training data set into corresponding standard data characteristics;
and scoring the standard flight operation information, the standard weather information and the standard association delay information according to the data type corresponding to the standard data characteristic, and converting the information with the score exceeding a preset threshold value into a training characteristic vector.
Optionally, the method for scoring the data of the standard model training data set according to the data type in the embodiment of the present disclosure may be shown as follows:
wherein, the first and the second end of the pipe are connected with each other,
the amount of data representing the training data set,
、
coordinate values corresponding to data features representing a standard model training data set,
、
the coordinate value corresponding to the standard data characteristic is represented,
、
and coordinate values corresponding to standard deviations of the standard data features are respectively represented.
Illustratively, even if after data preprocessing, not all data in the training set are suitable for being converted into feature vectors, the embodiment of the present disclosure scores the data in the standard model training data set according to the data features in the standard model training data set, scores the data in the standard model training data set according to the data types, converts the data with the score exceeding a preset threshold into the training feature vectors, screens out the features with the best classification effect from a large amount of data in the training data set, compresses the dimensions of a feature space, and generates the features with high precision and error rate.
In an alternative embodiment of the method according to the invention,
the method further comprises training a flight delay prediction model, and the method for training the flight delay prediction model comprises the following steps:
constructing a model training data set, wherein the model training data set comprises historical flight operation information, historical weather information and historical associated delay information in a preset time period;
converting the model training data set into training characteristic vectors, inputting a flight delay prediction model to be trained, updating the model training data set based on a training output result of the flight delay prediction model to be trained until a preset training convergence condition is reached to finish training the flight delay prediction model to be trained, wherein,
the flight delay prediction model to be trained comprises a preceding neural network model and a succeeding neural network model, the preceding neural network model performs preceding training learning according to the training feature vector and outputs a first output result to the succeeding neural network model, and the succeeding neural network performs succeeding training learning according to the first output result and outputs a training prediction value.
Illustratively, the output value of the preceding neural network model can be used as the input value of the following neural network model, so that the flight delay prediction model can perform secondary learning, the overfitting capability of the model is improved, and the accuracy of the model is ensured.
Optionally, the flight delay prediction model according to the embodiment of the present disclosure may allocate a corresponding weight value to an output value of a preceding neural network model, and allocate a corresponding weight value to an output value of a following neural network model, so as to implement reasonable adjustment on data of different dimensions and different types, and update a model training data set with the output value of the following neural network model after the weight value is allocated, thereby further improving the prediction accuracy of the flight delay prediction model.
In an alternative embodiment of the method according to the invention,
fig. 2 is a schematic flow chart of a prior neural network model for training a flight delay prediction model according to an embodiment of the present disclosure, the prior neural network model including an input layer, a convolutional layer, a hidden layer, and an output layer, as shown in fig. 2,
s201, the convolution layer performs batch normalization processing on output data of the input layer, and determines output characteristics of all hidden layers;
s202, determining a predicted value corresponding to the output feature based on a first loss function of the prior neural network model, judging whether the prior neural network model converges according to the predicted value and a loss value of a preset target value,
s203, if the hidden layer is not converged, determining the gradient value of the hidden layer through a back propagation algorithm according to the loss value, and updating the weight value of each layer of the prior neural network model according to the gradient value until the prior neural network model is converged.
Illustratively, the preceding neural network model of the embodiment of the present disclosure includes an input layer, a convolutional layer, a hidden layer, and an output layer, the preceding neural network model and the following neural network model of the embodiment of the present disclosure may include a deep neural network, a convolutional neural network, a long-short term memory network, and the like, and the embodiment of the present disclosure does not limit the type of the preceding neural network model. The prior neural network model and the posterior neural network model of the present disclosure structurally maintain an output layer of the prior neural network model connected with an input layer of the posterior neural network model.
Illustratively, the embodiment of the present disclosure may train a preceding neural network model through two directions of forward propagation and backward propagation, and optionally, the convolutional layer performs batch normalization processing on the output data of the input layer, so as to reduce covariate transfer inside the network and improve the generalization capability of the network. Further, a predicted value corresponding to the output feature may be determined according to a first loss function, and whether the preceding neural network model converges may be determined according to the predicted value and a loss value of a preset target value.
In this disclosure, the criterion for determining whether the previous neural network model converges may include whether a time difference between the estimated flight arrival time output by the previous neural network model and the actual flight arrival time is within a preset time range, if yes, the previous neural network may be determined to converge, and if not, the previous neural network may be determined not to converge.
In an alternative embodiment of the method according to the invention,
the first loss function of the prior neural network model is shown in the following equation:
wherein the content of the first and second substances,
、
respectively representing the number of hidden neurons and output neurons,
、
respectively representing the weight values corresponding to the hidden neuron and the output neuron,
、
representing values input to hidden neurons and output neurons, respectively.
If not, determining the gradient value of the hidden layer through a back propagation algorithm according to the loss value, and updating the weight value of each layer of the prior neural network model according to the gradient value until the prior neural network model converges.
The weight value of each layer of the prior neural network model is shown in the following formula:
wherein the content of the first and second substances,
the duration of the period is indicated,
a loss value representing the ith predicted value and a preset target value at time t, L representing an error function,
indicating the ith predicted value at time t.
Fig. 3 is a schematic flow chart of a post-neural network model for training a flight delay prediction model according to an embodiment of the present disclosure, where the post-neural network model includes a convolution layer, a pooling layer, and a classification layer, as shown in fig. 3,
s301, after local connection and weight sharing extraction are carried out on input feature information through the convolution layer of the posterior neural network, the weight value of the input feature information is calibrated in a self-adaptive mode;
and the input characteristic information is the predicted value of the flight arrival time output by the prior neural network model. Illustratively, by adaptively calibrating the weight values of the input feature information, the importance degree of each feature channel can be automatically learned, and features which contribute more strongly to the overall model can be selectively emphasized, and features which contribute more weakly or even do not contribute are suppressed.
In an alternative embodiment of the method according to the invention,
the method for adaptively calibrating the weight value of the input characteristic information is shown in the following formula:
wherein the content of the first and second substances,
、
the sigmod function and ReLu function are represented separately,
indicates the number of input characteristic information,
represents the weight value corresponding to the kth input characteristic information,
representing the convolved values of the input feature information.
S302, the pooling layer fuses the input feature information and the corresponding weight values into a comprehensive feature value;
the input characteristic information and the corresponding weight value are fused into the comprehensive characteristic value, so that a large number of redundant parameters caused by dense connection of the convolutional layer can be reduced, the stability of the model in the operation process is enhanced, the global distribution of the whole characteristic response can be expressed, and the global information of the network can be utilized by the lower layer.
S303, outputting the probability of the corresponding category of the input characteristic information according to the comprehensive characteristic value through a softmax classifier of the classification layer;
fig. 4 is a schematic diagram illustrating a probability of a classification layer outputting a class corresponding to feature information according to an embodiment of the disclosure. As shown in fig. 4, taking the parameter dimension of the input classification layer as 9 × 30 as an example, the original parameter dimension is adjusted to 2430 × 1 and 2430 × 5 × 1, and 30 × 1 and 30 × 5 through two fully-connected layers, respectively, and the value after the final adjustment of the dimension is input to the softmax classifier to output the probability of the corresponding class of the input feature information.
And S304, carrying out loop iteration training on the posterior neural network model until the probability of outputting and inputting the corresponding category of the characteristic information meets the preset condition.
The input characteristic information corresponding category comprises delay levels. The flight delay model is constructed based on the neural network models, compared with the flight delay prediction of the existing single model, the flight delay prediction model is higher in accuracy and higher in stability, prediction accuracy is exponentially improved after the multiple models are fused compared with that of the single model, and generalization capability is strong.
S103, if the delay of the target flight is predicted, further determining the delay level of the target flight, and determining a processing scheme corresponding to the delay level according to the corresponding relation acquired in advance;
for example, in the case of predicting the delay of the target flight, the disclosed embodiment may further determine the expected delay time of the target flight, set a corresponding delay level according to the flight delay time,
in this embodiment of the present disclosure, setting a corresponding delay level according to the flight delay time may include:
if the delay time is within the time floating range acceptable by the preset arrival time, determining that the delay is not delayed;
if the delay time is within a range of a first delay time and a second delay time, the delay time can be regarded as a first delay level, wherein the first delay time can comprise 30 minutes, and the second delay time can comprise 60 minutes;
if the delay time is within the range of the second delay time and a third delay time, the delay time can be regarded as a second delay level, wherein the third delay time can comprise 120 minutes;
if the delay time exceeds a third delay time, a third delay level may be identified.
And determining a processing scheme corresponding to the delay level according to the delay level and a pre-acquired corresponding relationship, wherein the pre-acquired corresponding relationship can be used for indicating the corresponding relationship between the delay level and the processing scheme. In an exemplary manner, the first and second electrodes are,
if the delay level is the first delay level, the mileage or the member points corresponding to the airline company can be given to the passenger;
if the delay level is the second delay level, corresponding rest environments, catering services for passengers and the like can be arranged for the passengers;
if the delay level is the third delay level, the passenger can be arranged with a lodging room, and the passenger who has other flights in the following can be carried out with the flight refund and the like.
The method disclosed by the embodiment of the disclosure can not only predict whether the flight is delayed, but also further determine the flight delay level, and set a corresponding scheme according to the flight delay level, thereby providing a solution of the system.
The present disclosure provides a flight delay prediction method, comprising
Acquiring flight operation information of a target flight, weather information of a take-off and landing city of the target flight within preset operation time, and associated delay information of an airport associated with the target flight;
through flight operation information, weather information and associated delay information, the most main factors influencing flight delay can be summarized, the accuracy of flight delay prediction is improved, and the fact that data to be processed by a flight delay prediction model is necessary and small in quantity is guaranteed.
After the flight operation information, the weather information and the associated delay information are converted into the characteristic vectors, whether the target flight is delayed or not is predicted through a pre-constructed flight delay prediction model,
the flight delay prediction model is constructed based on a plurality of neural network models, and the output of the prior neural network model is used as the input of the subsequent neural network model and is used for performing delay prediction on the target flight.
The flight delay model is constructed based on the neural network models, compared with the existing single model for flight delay prediction, the flight delay prediction model is higher in accuracy and higher in stability, and the prediction accuracy after the multiple models are fused is exponentially improved compared with that of the single model, so that the generalization capability is strong.
If the target flight will be delayed, further determining the delay level of the target flight, and determining a processing scheme corresponding to the delay level according to a pre-acquired corresponding relation, wherein,
the method disclosed by the embodiment of the disclosure can not only predict whether the flight is delayed, but also further determine the flight delay level, and set a corresponding scheme according to the flight delay level, thereby providing a solution of the system.
Fig. 5 is a schematic structural diagram of a flight delay prediction system according to an embodiment of the disclosure, as shown in fig. 5,
there is provided a flight delay prediction system, the system comprising:
a first unit 51, configured to obtain flight operation information of a target flight, weather information of a departure/landing city of the target flight within a preset operation time, and association delay information of an airport associated with the target flight;
a second unit 52, configured to convert the flight operation information, the weather information, and the associated delay information into a feature vector, predict whether the target flight will be delayed through a flight delay prediction model that is constructed in advance,
a third unit 53, configured to, if it is predicted that the target flight will be delayed, further determine a delay level of the target flight, and determine, according to a correspondence relationship obtained in advance, a processing scheme corresponding to the delay level, where,
the flight delay prediction model is constructed based on a plurality of neural network models, and the output of the preceding neural network model is used as the input of the following neural network model and is used for performing delay prediction on the target flight.
In a third aspect of the embodiments of the present disclosure,
there is provided a flight delay prediction apparatus comprising:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to invoke the memory-stored instructions to perform the method of any of the preceding.
In a fourth aspect of an embodiment of the present disclosure,
there is provided a computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the method of any one of the preceding claims.
The present invention may be methods, apparatus, systems and/or computer program products. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied therewith for carrying out aspects of the invention.
The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as a punch card or an in-groove protruding structure with instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.
The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device via a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.
Computer program instructions for carrying out operations of the present invention may be assembler instructions, instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, aspects of the present invention are implemented by personalizing an electronic circuit, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA), with state information of computer-readable program instructions, which can execute the computer-readable program instructions.
Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.
These computer-readable program instructions may be provided to a processing unit of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processing unit of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
It is noted that, unless expressly stated otherwise, all features disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose. Thus, unless expressly stated otherwise, each feature disclosed is one example only of a generic series of equivalent or similar features. Where used, it is further preferred, even further and more preferred that the brief introduction of the further embodiment is made on the basis of the preceding embodiment, the contents of which further, preferably, even further or more preferred the rear band is combined with the preceding embodiment as a complete constituent of the further embodiment. Several further, preferred, still further or more preferred arrangements of the belt after the same embodiment may be combined in any combination to form a further embodiment.
It will be appreciated by persons skilled in the art that the embodiments of the invention described above and shown in the drawings are given by way of example only and are not limiting of the invention. The objects of the invention have been fully and effectively accomplished. The functional and structural principles of the present invention have been shown and described in the embodiments, and any variations or modifications may be made to the embodiments of the present invention without departing from the principles described.
Finally, it should be noted that: the above embodiments are only used for illustrating the technical solutions of the present disclosure, and not for limiting the same; although the present disclosure has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art will understand that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present disclosure.
Claims (10)
1. A flight delay prediction method, the method comprising:
acquiring flight operation information of a target flight, weather information of a take-off and landing city of the target flight within preset operation time, and associated delay information of an airport associated with the target flight;
after the flight operation information, the weather information and the associated delay information are converted into the characteristic vectors, whether the target flight is delayed or not is predicted through a pre-constructed flight delay prediction model,
if the target flight will be delayed, further determining the delay level of the target flight, and determining a processing scheme corresponding to the delay level according to a pre-acquired corresponding relation, wherein,
the flight delay prediction model is constructed based on a plurality of neural network models, and the output of the prior neural network model is used as the input of the subsequent neural network model and is used for performing delay prediction on the target flight.
2. The method of claim 1, further comprising training a flight delay prediction model, the method of training a flight delay prediction model comprising:
constructing a model training data set, wherein the model training data set comprises historical flight operation information, historical weather information and historical associated delay information in a preset time period;
converting the model training data set into training characteristic vectors, inputting a flight delay prediction model to be trained, updating the model training data set based on a training output result of the flight delay prediction model to be trained until a preset training convergence condition is reached to finish training the flight delay prediction model to be trained, wherein,
the flight delay prediction model to be trained comprises a preceding neural network model and a succeeding neural network model, the preceding neural network model performs preceding training learning according to the training feature vector and outputs a first output result to the succeeding neural network model, and the succeeding neural network performs succeeding training learning according to the first output result and outputs a training prediction value.
3. The method of claim 2, wherein prior to inputting the training feature vectors into a flight delay prediction model to be trained, the method further comprises:
determining a data preprocessing method corresponding to the training data characteristics according to the training data characteristics of the model training data set, and screening out dirty data in the model training data set to obtain a standard model training data set, wherein the standard model training data set comprises standard flight operation information, standard weather information and standard association delay information;
respectively converting standard flight operation information, standard weather information and standard associated delay information of the standard model training data set into corresponding standard data characteristics;
and scoring the standard flight operation information, the standard weather information and the standard association delay information according to the data type corresponding to the standard data characteristic, and converting the information with the score exceeding a preset threshold value into a training characteristic vector.
4. The method of claim 3, wherein the method of training the flight delay prediction model further comprises training a prior neural network model of the flight delay prediction model, wherein the prior neural network model is constructed based on a deep neural network model and comprises an input layer, a convolutional layer, a hidden layer, and an output layer,
the convolution layer performs batch normalization processing on the training feature vectors and determines output features corresponding to all hidden layers;
determining a predicted value corresponding to the output feature based on a first loss function of the preceding neural network model, and judging whether the preceding neural network model converges according to the predicted value and a loss value of a preset target value,
if not, determining the gradient value of the hidden layer through a back propagation algorithm according to the loss value, and updating the weight value of each layer of the prior neural network model according to the gradient value until the prior neural network model converges, wherein,
the judgment basis of the convergence of the prior neural network model comprises the judgment of whether the time difference value between the estimated arrival time of the flight and the actual arrival time of the flight output by the prior neural network model is within a preset time range.
5. The method of claim 4,
the first loss function of the prior neural network model is shown in the following equation:
wherein, the first and the second end of the pipe are connected with each other,
、
respectively representing the number of hidden neurons and output neurons,
、
respectively representing the weight values corresponding to the hidden neuron and the output neuron,
、
representing values input to hidden neurons and output neurons, respectively;
the weight value of each layer of the prior neural network model is shown in the following formula:
wherein, the first and the second end of the pipe are connected with each other,
the duration of the period is indicated and,
a loss value representing the ith predicted value and a preset target value at time t, L representing an error function,
indicating the ith predicted value at time t.
6. The method of claim 4, wherein the method for training the flight delay prediction model further comprises training a post-neural network model of the flight delay prediction model, wherein the post-neural network model is constructed based on a convolutional neural network model, the post-neural network model comprises a convolutional layer, a pooling layer and a classification layer,
after local connection and weight sharing extraction are carried out on input characteristic information through the convolutional layer of the posterior neural network, the weight value of the input characteristic information is calibrated in a self-adaptive mode, wherein the input characteristic information is a predicted value of flight arrival time output by the prior neural network model;
the pooling layer fuses the input characteristic information and the corresponding weight values into a comprehensive characteristic value;
outputting the probability of the corresponding category of the input characteristic information according to the comprehensive characteristic value through a softmax classifier of the classification layer;
and (3) carrying out loop iteration training on the post-neural network model until the probability of the corresponding category of the output and input characteristic information meets a preset condition, wherein,
the input characteristic information corresponding category comprises delay levels.
7. The method of claim 6,
the method for adaptively calibrating the weight value of the input characteristic information is shown by the following formula:
wherein the content of the first and second substances,
、
the sigmod function and ReLu function are represented separately,
indicates the number of input characteristic information,
represents the weight value corresponding to the kth input characteristic information,
representing the convolved values of the input feature information.
8. A flight delay prediction system, the system comprising:
the system comprises a first unit, a second unit and a third unit, wherein the first unit is used for acquiring flight operation information of a target flight, weather information of a take-off and landing city of the target flight in a preset operation time and association delay information of an airport associated with the target flight;
a second unit, configured to convert the flight operation information, the weather information, and the associated delay information into feature vectors, predict whether the target flight will be delayed through a pre-constructed flight delay prediction model,
a third unit configured to further determine a delay level of the target flight if it is predicted that the target flight will be delayed, and determine a processing scenario corresponding to the delay level according to a correspondence relationship obtained in advance, wherein,
the flight delay prediction model is constructed based on a plurality of neural network models, and the output of the prior neural network model is used as the input of the subsequent neural network model and is used for performing delay prediction on the target flight.
9. A flight delay prediction apparatus, comprising:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to invoke the memory-stored instructions to perform the method of any one of claims 1 to 7.
10. A computer readable storage medium having computer program instructions stored thereon, which when executed by a processor implement the method of any one of claims 1 to 7.
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